Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Cdsclaudy/awesome-papers
In simple words, this repo is just a list of lists for Research Papers.
https://github.com/Cdsclaudy/awesome-papers
List: awesome-papers
Last synced: 3 months ago
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In simple words, this repo is just a list of lists for Research Papers.
- Host: GitHub
- URL: https://github.com/Cdsclaudy/awesome-papers
- Owner: Cdsclaudy
- Created: 2023-03-16T19:12:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-06-01T16:54:11.000Z (over 1 year ago)
- Last Synced: 2024-04-10T10:14:35.253Z (7 months ago)
- Size: 4.09 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-papers - In simple words, this repo is just a list of lists for Research Papers. (Other Lists / PowerShell Lists)
README
# AWESOME PAPERS
The following document consists of the list of research papers from established and licensed organizations.
## Latest Update: 1st June 2023
## NOTE:
- None of these papers are owned by me. Please refer to the links for the original source and related information.
- This repository is not a store for any research work, it will be purely a list.
- The paper list will be updated on a weekly basis, which will include older/newer papers/conferences.
- The necessary tagging is temporary and may change over revisions.
- For any further details please contact me at [email protected]
## **Contents**
- [Organizations](#organizations)
- [IEEE (Institute of Electrical and Electronics Engineers)](#ieee-institute-of-electrical-and-electronics-engineers)
- [Conferences](#conferences)
- [2023 17th European Conference on Antennas and Propagation (EuCAP)](#2023-17th-european-conference-on-antennas-and-propagation-eucap)
- [2023 Systems and Information Engineering Design Symposium (SIEDS)](#2023-systems-and-information-engineering-design-symposium-sieds)
- [2023 IEEE Applied Power Electronics Conference and Exposition (APEC)](#2023-ieee-applied-power-electronics-conference-and-exposition-apec)
- [2023 8th Asia Conference on Power and Electrical Engineering (ACPEE)](#2023-8th-asia-conference-on-power-and-electrical-engineering-acpee)
- [2023 International VLSI Symposium on Technology, Systems and Applications (VLSI-TSA/VLSI-DAT)](#2023-international-vlsi-symposium-on-technology-systems-and-applications-vlsi-tsavlsi-dat)
- [2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)](#2023-ieee-10th-jubilee-workshop-on-advances-in-information-electronic-and-electrical-engineering-aieee)
- [2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)](#2023-international-conference-on-wireless-communications-signal-processing-and-networking-wispnet)
- [2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT)](#2023-ieee-12th-international-conference-on-communication-systems-and-network-technologies-csnt)
- [2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)](#2023-international-conference-on-recent-advances-in-electrical-electronics-ubiquitous-communication-and-computational-intelligence-raeeucci)
- [2023 Winter Summit on Smart Computing and Networks (WiSSCoN)](#2023-winter-summit-on-smart-computing-and-networks-wisscon)
- [2023 IEEE Rural Electric Power Conference (REPC)](#2023-ieee-rural-electric-power-conference-repc)
- [2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)](#2023-ieee-open-conference-of-electrical-electronic-and-information-sciences-estream)
- [2023 IEEE Devices for Integrated Circuit (DevIC)](#2023-ieee-devices-for-integrated-circuit-devic)
- [2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)](#2023-ieee-international-conference-on-software-testing-verification-and-validation-workshops-icstw)
- [2023 IEEE Conference on Software Testing, Verification and Validation (ICST)](#2023-ieee-conference-on-software-testing-verification-and-validation-icst)
- [2023 3rd International Conference on Smart Data Intelligence (ICSMDI)](#2023-3rd-international-conference-on-smart-data-intelligence-icsmdi)
- [2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)](#2023-ieee-9th-intl-conference-on-big-data-security-on-cloud-bigdatasecurity-ieee-intl-conference-on-high-performance-and-smart-computing-hpsc-and-ieee-intl-conference-on-intelligent-data-and-security-ids)
- [2023 11th International Symposium on Digital Forensics and Security (ISDFS)](#2023-11th-international-symposium-on-digital-forensics-and-security-isdfs)
- [2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)](#2023-international-conference-on-computational-intelligence-and-knowledge-economy-iccike)
- [2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP)](#2023-1st-international-conference-on-innovations-in-high-speed-communication-and-signal-processing-ihcsp)
- [2023 International Conference on Networking and Communications (ICNWC)](#2023-international-conference-on-networking-and-communications-icnwc)
- [2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)](#2023-ieee-international-symposium-on-hardware-oriented-security-and-trust-host)
- [2023 Communication Strategies in Digital Society Seminar (ComSDS)](#2023-communication-strategies-in-digital-society-seminar-comsds)
- [2023 IEEE 16th Dallas Circuits and Systems Conference (DCAS)](#2023-ieee-16th-dallas-circuits-and-systems-conference-dcas)
- [2023 IEEE International Systems Conference (SysCon)](#2023-ieee-international-systems-conference-syscon)
- [2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)](#2023-ieee-international-conference-on-autonomous-robot-systems-and-competitions-icarsc)
- [19th International Conference on AC and DC Power Transmission (ACDC 2023)](#19th-international-conference-on-ac-and-dc-power-transmission-acdc-2023)
- [2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)](#2023-7th-international-conference-on-trends-in-electronics-and-informatics-icoei)
- [2023 International Conference on Computer Communication and Informatics (ICCCI)](#2023-international-conference-on-computer-communication-and-informatics-iccci)
- [2023 Wireless Telecommunications Symposium (WTS)](#2023-wireless-telecommunications-symposium-wts)
- [2023 2nd International Conference on Mechatronics and Electrical Engineering (MEEE)](#2023-2nd-international-conference-on-mechatronics-and-electrical-engineering-meee)
- [2023 5th International Conference on Recent Advances in Information Technology (RAIT)](#2023-5th-international-conference-on-recent-advances-in-information-technology-rait)
- [2023 4th International Conference on Smart Grid Metrology (SMAGRIMET)](#2023-4th-international-conference-on-smart-grid-metrology-smagrimet)
- [2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)](#2023-ieee-6th-international-conference-on-industrial-cyber-physical-systems-icps)
- [2023 International Electrical Engineering Congress (iEECON)](#2023-international-electrical-engineering-congress-ieecon)
- [2023 24th International Symposium on Quality Electronic Design (ISQED)](#2023-24th-international-symposium-on-quality-electronic-design-isqed)
- [2023 76th Annual Conference for Protective Relay Engineers (CFPR)](#2023-76th-annual-conference-for-protective-relay-engineers-cfpr)
- [2023 IEEE 8th International Conference for Convergence in Technology (I2CT)](#2023-ieee-8th-international-conference-for-convergence-in-technology-i2ct)
- [2023 International Conference on Electronics Packaging (ICEP)](#2023-international-conference-on-electronics-packaging-icep)
- [2023 9th International Conference on Automation, Robotics and Applications (ICARA)](#2023-9th-international-conference-on-automation-robotics-and-applications-icara)
- [2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)](#2023-international-conference-on-computer-science-information-technology-and-engineering-iccosite)
- [2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)](#2023-2nd-edition-of-ieee-delhi-section-flagship-conference-delcon)
- [2023 IEEE Conference on Technologies for Sustainability (SusTech)](#2023-ieee-conference-on-technologies-for-sustainability-sustech)
- [2023 7th International Conference on Green Energy and Applications (ICGEA)](#2023-7th-international-conference-on-green-energy-and-applications-icgea)
- [2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG)](#2023-international-conference-on-science-engineering-and-business-for-sustainable-development-goals-seb-sdg)
- [2023 4th International Conference on Signal Processing and Communication (ICSPC)](#2023-4th-international-conference-on-signal-processing-and-communication-icspc)
- [2023 IEEE Global Engineering Education Conference (EDUCON)](#2023-ieee-global-engineering-education-conference-educon)
- [2023 4th International Conference on Computing and Communication Systems (I3CS)](#2023-4th-international-conference-on-computing-and-communication-systems-i3cs)
- [2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)](#2023-11th-international-ieeeembs-conference-on-neural-engineering-ner)
- [2023 Optical Fiber Communications Conference and Exhibition (OFC)](#2023-optical-fiber-communications-conference-and-exhibition-ofc)
- [2023 IEEE Wireless and Microwave Technology Conference (WAMICON)](#2023-ieee-wireless-and-microwave-technology-conference-wamicon)
- [2023 International Interdisciplinary PhD Workshop (IIPhDW)](#2023-international-interdisciplinary-phd-workshop-iiphdw)
- [2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)](#2023-ieee-international-conference-on-software-analysis-evolution-and-reengineering-saner)
- [2023 International Conference on Business Analytics for Technology and Security (ICBATS)](#2023-international-conference-on-business-analytics-for-technology-and-security-icbats)
- [2023 6th International Conference on Communication Engineering and Technology (ICCET)](#2023-6th-international-conference-on-communication-engineering-and-technology-iccet)
- [2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)](#2023-third-international-conference-on-advances-in-electrical-computing-communication-and-sustainable-technologies-icaect)
- [2023 IEEE International Conference on Soft Robotics (RoboSoft)](#2023-ieee-international-conference-on-soft-robotics-robosoft)
- [2023 11th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES)](#2023-11th-workshop-on-modelling-and-simulation-of-cyber-physical-energy-systems-mscpes)
- [2023 IEEE Aerospace Conference](#2023-ieee-aerospace-conference)
- [2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)](#2023-ieee-symposium-in-low-power-and-high-speed-chips-cool-chips)
- [2023 Ninth International Conference on eDemocracy & eGovernment (ICEDEG)](#2023-ninth-international-conference-on-edemocracy--egovernment-icedeg)
- [2023 IEEE International Reliability Physics Symposium (IRPS)](#2023-ieee-international-reliability-physics-symposium-irps)
- [2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)](#2023-integrated-communication-navigation-and-surveillance-conference-icns)
- [2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)](#2023-34th-annual-semi-advanced-semiconductor-manufacturing-conference-asmc)
- [2023 IEEE Wireless Communications and Networking Conference (WCNC)](#2023-ieee-wireless-communications-and-networking-conference-wcnc)
- [2023 International Conference on Protection and Automation of Power Systems (IPAPS)](#2023-international-conference-on-protection-and-automation-of-power-systems-ipaps)
- [2023 IEEE Custom Integrated Circuits Conference (CICC)](#2023-ieee-custom-integrated-circuits-conference-cicc)
- [2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)](#2023-ieee-international-conference-on-electrical-systems-for-aircraft-railway-ship-propulsion-and-road-vehicles--international-transportation-electrification-conference-esars-itec)
- [2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE)](#2023-ieee-conference-on-power-electronics-and-renewable-energy-cpere)
- [2023 7th International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS)](#2023-7th-international-conference-on-management-engineering-software-engineering-and-service-sciences-icmss)
- [2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)](#2023-3rd-international-conference-on-innovative-practices-in-technology-and-management-iciptm)
- [2023 International Conference on Code Quality (ICCQ)](#2023-international-conference-on-code-quality-iccq)
- [2023 International Applied Computational Electromagnetics Society Symposium (ACES)](#2023-international-applied-computational-electromagnetics-society-symposium-aces)
- [2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)](#2023-10th-international-conference-on-signal-processing-and-integrated-networks-spin)
- [2023 5th Asia Energy and Electrical Engineering Symposium (AEEES)](#2023-5th-asia-energy-and-electrical-engineering-symposium-aeees)
- [2023 25th International Conference on Digital Signal Processing and its Applications (DSPA)](#2023-25th-international-conference-on-digital-signal-processing-and-its-applications-dspa)
- [2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON)](#2022-opju-international-technology-conference-on-emerging-technologies-for-sustainable-development-otcon)
- [2022 IEEE International Conference on Cyborg and Bionic Systems (CBS)](#2022-ieee-international-conference-on-cyborg-and-bionic-systems-cbs)
- [SoutheastCon 2023](#southeastcon-2023)
- [2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)](#2023-9th-international-conference-on-advanced-computing-and-communication-systems-icaccs)
- [ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)](#icassp-2023---2023-ieee-international-conference-on-acoustics-speech-and-signal-processing-icassp)
- [2023 6th International Conference on Information Systems and Computer Networks (ISCON)](#2023-6th-international-conference-on-information-systems-and-computer-networks-iscon)
- [2023 10th International Conference on Computing for Sustainable Global Development (INDIACom)](#2023-10th-international-conference-on-computing-for-sustainable-global-development-indiacom)
- [2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC)](#2023-international-conference-on-recent-trends-in-electronics-and-communication-icrtec)
- [2023 15th International Conference on Computer and Automation Engineering (ICCAE)](#2023-15th-international-conference-on-computer-and-automation-engineering-iccae)
- [2023 IEEE International Conference on Smart Mobility (SM)](#2023-ieee-international-conference-on-smart-mobility-sm)
- [2023 11th International Conference on Information and Education Technology (ICIET)](#2023-11th-international-conference-on-information-and-education-technology-iciet)
- [2023 IEEE International Conference on Cybernetics and Innovations (ICCI)](#2023-ieee-international-conference-on-cybernetics-and-innovations-icci)
- [2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)](#2023-ieee-conference-on-virtual-reality-and-3d-user-interfaces-abstracts-and-workshops-vrw)
- [2023 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)](#2023-ieee-workshop-on-electrical-machines-design-control-and-diagnosis-wemdcd)
- [2023 IEEE 14th Latin America Symposium on Circuits and Systems (LASCAS)](#2023-ieee-14th-latin-america-symposium-on-circuits-and-systems-lascas)
- [2023 9th International Conference on Electrical Energy Systems (ICEES)](#2023-9th-international-conference-on-electrical-energy-systems-icees)
- [2023 International Russian Smart Industry Conference (SmartIndustryCon)](#2023-international-russian-smart-industry-conference-smartindustrycon)
- [2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)](#2023-ieee-conference-virtual)
- [2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT)](#2023-international-conference-on-device-intelligence-computing-and-communication-technologies-dicct)
- [2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE)](#2023-13th-international-symposium-on-advanced-topics-in-electrical-engineering-atee)
- [2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT)](#2023-ieee-12th-international-conference-on-educational-and-information-technology-iceit)
- [2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)](#2023-2nd-international-conference-on-big-data-information-and-computer-network-bdicn)
- [2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA)](#2023-33rd-international-conference-radioelektronika-radioelektronika)
- [2023 28th International Computer Conference, Computer Society of Iran (CSICC)](#2023-28th-international-computer-conference-computer-society-of-iran-csicc)
- [2023 IEEE 3rd International Symposium on Joint Communications & Sensing (JC&S)](#2023-ieee-3rd-international-symposium-on-joint-communications--sensing-jcs)
- [2023 9th International Conference on Mechatronics and Robotics Engineering (ICMRE)](#2023-9th-international-conference-on-mechatronics-and-robotics-engineering-icmre)
- [2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)](#2023-19th-international-conference-on-the-design-of-reliable-communication-networks-drcn)
- [2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)](#2023-7th-ieee-electron-devices-technology--manufacturing-conference-edtm)
- [2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)](#2023-3rd-international-conference-on-neural-networks-information-and-communication-engineering-nnice)
- [2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)](#2023-international-conference-on-sustainable-computing-and-data-communication-systems-icscds)
- [2023 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL)](#2023-ieee-international-symposium-on-inertial-sensors-and-systems-inertial)
- [2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)](#2023-international-conference-on-advances-in-electronics-control-and-communication-systems-icaeccs)
- [2023 IEEE Underwater Technology (UT)](#2023-ieee-underwater-technology-ut)
- [2023 IEEE 20th International Conference on Software Architecture (ICSA)](#2023-ieee-20th-international-conference-on-software-architecture-icsa)
- [2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)](#2023-ieee-6th-eurasian-conference-on-educational-innovation-ecei)
- [2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)](#2023-ieee-20th-international-conference-on-software-architecture-companion-icsa-c)
- [2023 IEEE World Engineering Education Conference (EDUNINE)](#2023-ieee-world-engineering-education-conference-edunine)
- [2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)](#2023-ieee-8th-international-conference-on-big-data-analytics-icbda)
- [2023 Future of Educational Innovation-Workshop Series Data in Action](#2023-future-of-educational-innovation-workshop-series-data-in-action)
- [2023 Somaiya International Conference on Technology and Information Management (SICTIM)](#2023-somaiya-international-conference-on-technology-and-information-management-sictim)
- [2023 IEEE International Symposium on Power Line Communications and its Applications (ISPLC)](#2023-ieee-international-symposium-on-power-line-communications-and-its-applications-isplc)
- [2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)](#2023-international-conference-on-innovative-data-communication-technologies-and-application-icidca)
- [2023 International Conference on Communication System, Computing and IT Applications (CSCITA)](#2023-international-conference-on-communication-system-computing-and-it-applications-cscita)
- [2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)](#2023-4th-international-conference-on-computing-mathematics-and-engineering-technologies-icomet)
- [WSA & SCC 2023; 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding](#wsa--scc-2023-26th-international-itg-workshop-on-smart-antennas-and-13th-conference-on-systems-communications-and-coding)
- [2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)](#2023-international-conference-on-it-innovation-and-knowledge-discovery-itikd)
- [2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)](#2023-ieee-international-conference-on-integrated-circuits-and-communication-systems-icicacs)
- [2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)](#2023-international-conference-on-electrical-computer-and-communication-engineering-ecce)
- [2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)](#2023-international-conference-on-intelligent-systems-for-communication-iot-and-security-iciscois)
- [2023 2nd International Conference for Innovation in Technology (INOCON)](#2023-2nd-international-conference-for-innovation-in-technology-inocon)
- [2023 International Conference on Emerging Smart Computing and Informatics (ESCI)](#2023-international-conference-on-emerging-smart-computing-and-informatics-esci)
- [2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)](#2023-ieee-pes-grid-edge-technologies-conference--exposition-grid-edge)
- [2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)](#2023-ieee-international-conference-on-pervasive-computing-and-communications-percom)
- [2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)](#2023-ieee-13th-annual-computing-and-communication-workshop-and-conference-ccwc)
- [2023 IEEE International Conference on Mechatronics (ICM)](#2023-ieee-international-conference-on-mechatronics-icm)
- [2023 15th International Conference on Developments in eSystems Engineering (DeSE)](#2023-15th-international-conference-on-developments-in-esystems-engineering-dese)
- [2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)](#2023-24th-international-conference-on-thermal-mechanical-and-multi-physics-simulation-and-experiments-in-microelectronics-and-microsystems-eurosime)
- [2023 IEEE Workshop on Microelectronics and Electron Devices (WMED)](#2023-ieee-workshop-on-microelectronics-and-electron-devices-wmed)
- [2023 IEEE Applied Sensing Conference (APSCON)](#2023-ieee-applied-sensing-conference-apscon)
- [2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)](#2023-5th-international-youth-conference-on-radio-electronics-electrical-and-power-engineering-reepe)
- [2023 6th International Conference on Energy Conservation and Efficiency (ICECE)](#2023-6th-international-conference-on-energy-conservation-and-efficiency-icece)
- [2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)](#2023-11th-international-conference-on-internet-of-everything-microwave-engineering-communication-and-networks-iemecon)
- [2023 20th Learning and Technology Conference (L&T)](#2023-20th-learning-and-technology-conference-lt)
- [2023 Systems of Signals Generating and Processing in the Field of on Board Communications](#2023-systems-of-signals-generating-and-processing-in-the-field-of-on-board-communications)
- [2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH)](#2023-22nd-international-symposium-infoteh-jahorina-infoteh)
- [2023 IEEE 3rd International Conference in Power Engineering Applications (ICPEA)](#2023-ieee-3rd-international-conference-in-power-engineering-applications-icpea)
- [2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)](#2023-international-conference-on-intelligent-and-innovative-technologies-in-computing-electrical-and-electronics-iitcee)
- [2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)](#2023-ieee-2nd-international-conference-on-electrical-engineering-big-data-and-algorithms-eebda)
- [2023 57th Annual Conference on Information Sciences and Systems (CISS)](#2023-57th-annual-conference-on-information-sciences-and-systems-ciss)
- [2023 35th International Conference on Microelectronic Test Structure (ICMTS)](#2023-35th-international-conference-on-microelectronic-test-structure-icmts)
- [2023 36th International Conference on VLSI Design and 2023 22nd International Conference on Embedded Systems (VLSID)](#2023-36th-international-conference-on-vlsi-design-and-2023-22nd-international-conference-on-embedded-systems-vlsid)
- [2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)](#2023-ieee-ias-global-conference-on-renewable-energy-and-hydrogen-technologies-globconht)
- [2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS)](#2023-ieee-15th-international-symposium-on-autonomous-decentralized-system-isads)
- [2023 International Conference on Robotics and Automation in Industry (ICRAI)](#2023-international-conference-on-robotics-and-automation-in-industry-icrai)
- [2023 10th Iranian Conference on Renewable Energy & Distributed Generation (ICREDG)](#2023-10th-iranian-conference-on-renewable-energy--distributed-generation-icredg)
- [2023 7th International Conference on Robotics, Control and Automation (ICRCA)](#2023-7th-international-conference-on-robotics-control-and-automation-icrca)
- [2023 International Conference on Smart Computing and Application (ICSCA)](#2023-international-conference-on-smart-computing-and-application-icsca)
- [2023 Second International Conference on Electronics and Renewable Systems (ICEARS)](#2023-second-international-conference-on-electronics-and-renewable-systems-icears)
- [2023 4th International Conference on Advancements in Computational Sciences (ICACS)](#2023-4th-international-conference-on-advancements-in-computational-sciences-icacs)
- [2023 14th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)](#2023-14th-power-electronics-drive-systems-and-technologies-conference-pedstc)
- [2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)](#2023-19th-ieee-international-colloquium-on-signal-processing--its-applications-cspa)
- [2023 Annual Reliability and Maintainability Symposium (RAMS)](#2023-annual-reliability-and-maintainability-symposium-rams)
- [2023 8th International Conference on Technology and Energy Management (ICTEM)](#2023-8th-international-conference-on-technology-and-energy-management-ictem)
- [2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)](#2023-open-source-modelling-and-simulation-of-energy-systems-osmses)
- [2023 International Conference on Power, Instrumentation, Energy and Control (PIECON)](#2023-international-conference-on-power-instrumentation-energy-and-control-piecon)
- [2023 Argentine Conference on Electronics (CAE)](#2023-argentine-conference-on-electronics-cae)
- [2023 7th International Conference on Computing Methodologies and Communication (ICCMC)](#2023-7th-international-conference-on-computing-methodologies-and-communication-iccmc)
- [2023 6th Conference on Cloud and Internet of Things (CIoT)](#2023-6th-conference-on-cloud-and-internet-of-things-ciot)
- [2023 15th International Conference on Knowledge and Smart Technology (KST)2023 15th International Conference on Knowledge and Smart Technology (KST)](#2023-15th-international-conference-on-knowledge-and-smart-technology-kst)
- [2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)](#2023-international-conference-on-artificial-intelligence-and-knowledge-discovery-in-concurrent-engineering-iceconf)
- [2023 International Conference on Artificial Intelligence and Smart Communication (AISC)](#2023-international-conference-on-artificial-intelligence-and-smart-communication-aisc)
- [2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)](#2023-1st-international-conference-on-advanced-innovations-in-smart-cities-icaisc)
- [2023 International Conference for Advancement in Technology (ICONAT)](#2023-international-conference-for-advancement-in-technology-iconat)
- [2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)](#2023-international-conference-on-intelligent-systems-advanced-computing-and-communication-isacc)
- [2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)](#2023-advanced-computing-and-communication-technologies-for-high-performance-applications-accthpa)
- [2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)](#2023-international-conference-on-computer-electrical--communication-engineering-iccece)
- [2023 Conference on Information Communications Technology and Society (ICTAS)](#2023-conference-on-information-communications-technology-and-society-ictas)
- [2023 IEEE Texas Power and Energy Conference (TPEC)](#2023-ieee-texas-power-and-energy-conference-tpec)
- [2023 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD)](#2023-ieee-multi-conference-on-natural-and-engineering-sciences-for-sahels-sustainable-development-mne3sd)
- [2023 15th International Conference on Computer Research and Development (ICCRD)](#2023-15th-international-conference-on-computer-research-and-development-iccrd)
- [2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)](#2023-ieee-6th-information-technologynetworkingelectronic-and-automation-control-conference-itnec)
- [2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)](#2023-ieee-3rd-international-conference-on-power-electronics-and-computer-applications-icpeca)
- [2023 25th International Conference on Advanced Communication Technology (ICACT)](#2023-25th-international-conference-on-advanced-communication-technology-icact)
- [2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)](#2023-ieee-pes-conference-on-innovative-smart-grid-technologies---middle-east-isgt-middle-east)
- [2023 27th International Conference on Information Technology (IT)](#2023-27th-international-conference-on-information-technology-it)
- [2023 IEEE 7th Global Electromagnetic Compatibility Conference (GEMCCON)](#2023-ieee-7th-global-electromagnetic-compatibility-conference-gemccon)
- [2023 SICE International Symposium on Control Systems (SICE ISCS)](#2023-sice-international-symposium-on-control-systems-sice-iscs)
- [2023 17th International Conference on the Experience of Designing and Application of CAD Systems (CADSM)](#2023-17th-international-conference-on-the-experience-of-designing-and-application-of-cad-systems-cadsm)
- [2023 11th International Winter Conference on Brain-Computer Interface (BCI)](#2023-11th-international-winter-conference-on-brain-computer-interface-bci)
- [2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)](#2023-international-conference-on-advances-in-intelligent-computing-and-applications-aicaps)
- [2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)](#2023-3rd-international-conference-on-intelligent-communication-and-computational-techniques-icct)
- [2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)](#2023-third-international-conference-on-artificial-intelligence-and-smart-energy-icais)
- [2023 5th International Conference on Power, Control & Embedded Systems (ICPCES)](#2023-5th-international-conference-on-power-control--embedded-systems-icpces)
- [2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)](#2022-ieee-28th-international-conference-on-parallel-and-distributed-systems-icpads)
- [2023 International Multi-disciplinary Conference in Emerging Research Trends (IMCERT)](#2023-international-multi-disciplinary-conference-in-emerging-research-trends-imcert)
- [2022 19th China International Forum on Solid State Lighting & 2022 8th International Forum on Wide Bandgap Semiconductors (SSLCHINA: IFWS)](#2022-19th-china-international-forum-on-solid-state-lighting--2022-8th-international-forum-on-wide-bandgap-semiconductors-sslchina-ifws)
- [2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)](#2023-ieee-international-symposium-on-high-performance-computer-architecture-hpca)
- [2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)](#2023-international-conference-on-artificial-intelligence-in-information-and-communication-icaiic)
- [2023 International Conference on Computing, Networking and Communications (ICNC)](#2023-international-conference-on-computing-networking-and-communications-icnc)
- [2023 IEEE International Solid- State Circuits Conference (ISSCC)](#2023-ieee-international-solid--state-circuits-conference-isscc)
- [2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)](#2023-ieee-power--energy-society-innovative-smart-grid-technologies-conference-isgt)
- [2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)](#2023-26th-conference-on-innovation-in-clouds-internet-and-networks-and-workshops-icin)
- [2023 National Conference on Communications (NCC)](#2023-national-conference-on-communications-ncc)
- [2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)](#2023-3rd-international-conference-on-robotics-electrical-and-signal-processing-techniques-icrest)
- [2023 IEEE 17th International Conference on Semantic Computing (ICSC)](#2023-ieee-17th-international-conference-on-semantic-computing-icsc)
- [2023 International Microwave and Antenna Symposium (IMAS)](#2023-international-microwave-and-antenna-symposium-imas)
- [2023 Global Conference on Wireless and Optical Technologies (GCWOT)](#2023-global-conference-on-wireless-and-optical-technologies-gcwot)
- [2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)](#2023-4th-international-conference-on-innovative-trends-in-information-technology-icitiit)
- [2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)](#2023-ieee-international-students-conference-on-electrical-electronics-and-computer-science-sceecs)
- [2023 IEEE International Conference on Big Data and Smart Computing (BigComp)](#2023-ieee-international-conference-on-big-data-and-smart-computing-bigcomp)
- [2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)](#2023-ieee-20th-consumer-communications--networking-conference-ccnc)
- [2023 5th Australian Microwave Symposium (AMS)](#2023-5th-australian-microwave-symposium-ams)
- [2023 International Conference on Electronics, Information, and Communication (ICEIC)](#2023-international-conference-on-electronics-information-and-communication-iceic)
- [2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)](#2023-international-conference-on-machine-intelligence-for-geoanalytics-and-remote-sensing-migars)
- [2023 31st Southern African Universities Power Engineering Conference (SAUPEC)](#2023-31st-southern-african-universities-power-engineering-conference-saupec)
- [2023 International Conference On Cyber Management And Engineering (CyMaEn)](#2023-international-conference-on-cyber-management-and-engineering-cymaen)
- [2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)](#2023-5th-international-conference-on-smart-systems-and-inventive-technology-icssit)
- [2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS)](#2023-ieee-36th-international-conference-on-micro-electro-mechanical-systems-mems)
- [2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)](#2023-international-conference-on-intelligent-supercomputing-and-biopharma-isbp)
- [2023 18th Wireless On-Demand Network Systems and Services Conference (WONS)](#2023-18th-wireless-on-demand-network-systems-and-services-conference-wons)
- [2023 International Conference on Power Electronics and Energy (ICPEE)](#2023-international-conference-on-power-electronics-and-energy-icpee)
- [2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)](#2023-international-conference-on-intelligent-data-communication-technologies-and-internet-of-things-idciot)
- [2023 Fourth International Symposium on 3D Power Electronics Integration and Manufacturing (3D-PEIM)](#2023-fourth-international-symposium-on-3d-power-electronics-integration-and-manufacturing-3d-peim)
- [2023 6th World Conference on Computing and Communication Technologies (WCCCT)](#2023-6th-world-conference-on-computing-and-communication-technologies-wccct)
- [2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC)](#2023-28th-asia-and-south-pacific-design-automation-conference-asp-dac)
- [2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC)](#2023-ieee-2nd-international-conference-on-ai-in-cybersecurity-icaic)
- [2023 International Conference on Information Networking (ICOIN)](#2023-international-conference-on-information-networking-icoin)
## **Organizations**
### **IEEE (Institute of Electrical and Electronics Engineers)**
### **Conferences**
#### **2023 17th European Conference on Antennas and Propagation (EuCAP)**
- DOI: 10.23919/EuCAP57121.2023
- DATE: 26-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Results in Adaptive MoM Analysis with Goal-Oriented Error Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133649)|R. J. McDougall; P. I. Cilliers; M. M. Botha|10.23919/EuCAP57121.2023.10133649|adjoint problem;dual solution;error indicator;goal functional;adjoint problem;dual solution;error indicator;goal functional|
|[Min-Path-Tracing: A Diffraction Aware Alternative to Image Method in Ray Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132934)|J. Eertmans; C. Oestges; L. Jacques|10.23919/EuCAP57121.2023.10132934|Ray Tracing;Image Method;Diffraction;Telecommunications;Optimization;Ray Tracing;Image Method;Diffraction;Telecommunications;Optimization|
|[Sparse Hemispherical Arrays with Hierarchical Low Discrepancy Sequence Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133565)|P. Nayeri; R. Haupt|10.23919/EuCAP57121.2023.10133565|aperiodic array;conformal array;hemispherical array;phased array;sparse array.;aperiodic array;conformal array;hemispherical array;phased array;sparse array.|
|[Outdoor-to-Indoor Loss Measurement for Rural/Suburban Residential Scenario at 6 and 37 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132924)|R. Sun; D. Viorel; W. Keusgen|10.23919/EuCAP57121.2023.10132924|fixed wireless access;channel modeling;propagation;measurements;unlicensed spectrum;fixed wireless access;channel modeling;propagation;measurements;unlicensed spectrum|
|[Folding the Feeding Network of a Millimeter-Wave Circularly Polarized Printed Antenna Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133737)|A. Jabri; Y. Tawk; J. Costantine|10.23919/EuCAP57121.2023.10133737|Circular Polarization;Foldable Feeding Network;Millimeter Wave Arrays;Circular Polarization;Foldable Feeding Network;Millimeter Wave Arrays|
|[Wideband Phased Array System at K-Band for Satellite Down-link Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133466)|B. Shi; Nasimuddin; F. Chin; X. Qing|10.23919/EuCAP57121.2023.10133466|antenna;circularly polarized;phased array;beamforming;K-band;wideband antenna;satellite downlink;antenna;circularly polarized;phased array;beamforming;K-band;wideband antenna;satellite downlink|
|[Dispersion Characteristics of Additive-Manufactured Metasurfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133597)|K. Hecht; D. González-Ovejero; M. J. Mencagli|10.23919/EuCAP57121.2023.10133597|surface waves;metasurfaces (MTSs);planar lenses;homogenization;Acrylonitrile Butadiene Styrene(ABS) posts;additive manufacturing;surface waves;metasurfaces (MTSs);planar lenses;homogenization;Acrylonitrile Butadiene Styrene(ABS) posts;additive manufacturing|
|[Microwave Spectroscopy of Melanoma Progression Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133712)|R. Tchinov; J. Boparai; O. Miller; Y. Jallouli; M. Popović|10.23919/EuCAP57121.2023.10133712|skin;melanoma;microwave;spectroscopy;skin;melanoma;microwave;spectroscopy|
|[Active Non-Foster-based Magneto-Electric Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133677)|D. Nozina; J. Bartolic; S. Hrabar|10.23919/EuCAP57121.2023.10133677|Huygens source antenna;magneto-electric antenna;negative inductance;non-Foster;self-oscillations;Huygens source antenna;magneto-electric antenna;negative inductance;non-Foster;self-oscillations|
|[A Planar Distributed Receiver Coil Antenna Array to Encapsulate Vertical and Lateral H-Fields for Drone Wireless Charging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133370)|V. K. Srivastava; A. Bharadwaj; A. Sharma|10.23919/EuCAP57121.2023.10133370|Distributed receiver coil antenna;anti-parallel turn coils;wireless power transfer;magnetic resonance coupling;near-field;drone charging;Distributed receiver coil antenna;anti-parallel turn coils;wireless power transfer;magnetic resonance coupling;near-field;drone charging|
|[Design Strategy of Microwave Resonant Sensors with Stable Response for Blood Glucose Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133631)|A. Cuccaro; A. Dell’Aversano; G. Buonanno; S. Costanzo; R. Solimene|10.23919/EuCAP57121.2023.10133631|antennas;electromagnetics;propagation;measurements;antennas;electromagnetics;propagation;measurements|
|[Multi-Probe Array Design for Partially-Coherent Phase Retrieval in Near-Field Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133730)|J. Kornprobst; A. Paulus; J. Knapp; T. F. Eibert|10.23919/EuCAP57121.2023.10133730|Magnitude-only antenna measurements;phaseless;antenna arrays;field transformations;source reconstruction;Magnitude-only antenna measurements;phaseless;antenna arrays;field transformations;source reconstruction|
#### **2023 Systems and Information Engineering Design Symposium (SIEDS)**
- DOI: 10.1109/SIEDS58326.2023
- DATE: 27-28 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Energy Trading Market Simulator Blockchain-based](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137796)|A. Boumaiza|10.1109/SIEDS58326.2023.10137796|Blockchain;Energy Trading;GIS;Agent-based modeling;Social Simulation;Real-time mapping;Spatial process;Blockchain;Energy Trading;GIS;Agent-based modeling;Social Simulation;Real-time mapping;Spatial process|
|[Finding a Needle in the Haystack: Predicting the Location of Lost People Using Agent-Based Modeling and Behavioral Inertia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137885)|J. Nguyen; C. Joseph; B. Richardson; R. Hayes; R. Pakula; R. Koester|10.1109/SIEDS58326.2023.10137885|;|
|[A Systems Approach to Improving the Spectator Experience at Collegiate Football Games](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137886)|H. Austin; A. Freed; A. Labus; B. Lynch; J. Mastrullo; J. Sharff; R. J. Riggs|10.1109/SIEDS58326.2023.10137886|Systems Approach;Process Improvement;Transportation;Traffic Analysis;Modeling;Customer Experience;Systems Approach;Process Improvement;Transportation;Traffic Analysis;Modeling;Customer Experience|
|[Embodied AI for Financial Literacy Social Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137791)|Q. Jiang; L. Zak; S. Leshem; P. Rampa; S. Howle; H. N. Green; T. Iqbal|10.1109/SIEDS58326.2023.10137791|;|
|[Designing a Dashboard to Streamline Pediatric Heart Transplant Decision Making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137887)|C. Hyldahl; O. Kaczmarskyj; J. Laruffa; A. Miller; L. Snavely; A. Wan; S. L. Riggs|10.1109/SIEDS58326.2023.10137887|;|
|[Deterring Adversarial Learning in Penetration Testing by Exploiting Domain Adaptation Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137792)|S. Bera; L. Glenn; A. Raghavan; E. Meno; T. Cody; P. A. Beling|10.1109/SIEDS58326.2023.10137792|adversarial learning;penetration testing;cybersecurity;reinforcement learning;adversarial learning;penetration testing;cybersecurity;reinforcement learning|
|[Investigating the Impact of Temporal Labeling of Emergency Department Visits for COVID-19: Comparing Healthcare Disparities Analyses Using Comprehensive, Single-Site Data with National COVID Cohort Collaborative (N3C) Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137801)|M. Jones; A. Winger; C. Wernz; J. Michel; S. Jiang; A. Zhou; E. J. Hilton; M. Zemmel; S. Sengupta; K. Barnes; J. Loomba; D. E. Brown|10.1109/SIEDS58326.2023.10137801|;|
|[The Current State and Future Needs of Systems Engineering Education: A Proposed Curriculum *](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137808)|T. J. Gwilliam; S. T. Keleta; M. D. Salomonsky; V. V. Vangala; M. E. Varghese; M. W. Burkett; W. T. Scherer|10.1109/SIEDS58326.2023.10137808|;|
|[Methods for the Spatial Analysis of Invasive Species and Ecosystem Fragmentation at Conservation Sites in Malta Using Drones *](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137865)|E. C. McConville; A. I. Oancea; E. M. Enright; T. C. Brown; J. J. Henriques|10.1109/SIEDS58326.2023.10137865|;|
|[Car Wash Deterrent System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137799)|R. De Souza; H. Saleem; E. Nelson; J. Bender|10.1109/SIEDS58326.2023.10137799|;|
|[Capacity Planning and Investment for Electrification of Maritime Container Ports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137908)|E. M. Forkin; C. A. Paulen; M. J. Swierczewski; T. Roy; T. D. Costello; D. C. Loose; J. Y. Williams; D. L. Slutzky; T. L. Polmateer; K. R. Jackson; D. C. Hendrickson; J. H. Lambert|10.1109/SIEDS58326.2023.10137908|;|
|[Degradations of Trust in Automation Associated with Repeated Monitoring Checks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10137809)|A. B. Bright; J. E. Cotter; N. L. Tenhundfeld|10.1109/SIEDS58326.2023.10137809|Automation;Trust;Reliability;Monitoring Checks;Situational Awareness;Autonomous Vehicles;Automation;Trust;Reliability;Monitoring Checks;Situational Awareness;Autonomous Vehicles|
#### **2023 IEEE Applied Power Electronics Conference and Exposition (APEC)**
- DOI: 10.1109/APEC43580.2023
- DATE: 19-23 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Digital Energy Management Control Strategy of a Fully Active Hybrid Converter for Unmanned Aerial Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131571)|X. Zhang; Y. Jeong; R. A. L. Rorrer|10.1109/APEC43580.2023.10131571|Multi-input;DC/DC converter;digital control;Multi-input;DC/DC converter;digital control|
|[A Converter Based Switching Loss Measurement Method for WBG Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131509)|Q. Yang; A. Nabih; R. Zhang; Q. Li; Y. Zhang|10.1109/APEC43580.2023.10131509|WBG;GaN;Switching loss;Soft switching;Turn-off loss;WBG;GaN;Switching loss;Soft switching;Turn-off loss|
|[ZVS Clamp-Switch Quasi Z-Source dc/dc Boost Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131659)|B. Ulrich|10.1109/APEC43580.2023.10131659|ZVS;clamp-switch;boost;quasi Z-source;ZVS;clamp-switch;boost;quasi Z-source|
|[Coupled Inductor Based Non-Isolated High Conversion Ratio Boost Extender](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131533)|V. K. Rathore; M. Evzelman; M. M. Peretz|10.1109/APEC43580.2023.10131533|High density;coupled inductor;boost extender;Capacitor stacking;High voltage gain;Single-switch;Switched capacitor;High density;coupled inductor;boost extender;Capacitor stacking;High voltage gain;Single-switch;Switched capacitor|
|[Design and Implementation of a Multiport System for Solar EV Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131342)|R. Rezaii; M. Nilian; S. Ghosh; M. T. Elrais; M. Safayatullah; I. Batarseh; F. AlaqI|10.1109/APEC43580.2023.10131342|Solar electrical vehicle;dual-input LLC;hybrid dc-dc converter;Solar electrical vehicle;dual-input LLC;hybrid dc-dc converter|
|[Wide Output Range High Efficiency MHz DC/DC for PD3.1 Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131422)|G. Fan; Y. Qi; W. Chen; C. Zhao; K. Yao|10.1109/APEC43580.2023.10131422|PD 3.1;Wide range regulation;MHz;Multilevel;Multilevel natural voltage balance;PD 3.1;Wide range regulation;MHz;Multilevel;Multilevel natural voltage balance|
|[A 40W Dual-Inductor Hybrid Converter with Flying-Capacitor-Tapped Auxiliary Stage for Fast Transient Response in 48V PoL Automotive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131256)|N. Khan; K. Liang; T. Robitaille; G. V. Piqué; J. Pigott; H. J. Bergveld; O. Trescases|10.1109/APEC43580.2023.10131256|;|
|[A Power Boost Technique for the Isolated Modular Multilevel DC-DC Converter Based on Sub-module Capacitor Voltages Ripple](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131286)|S. Yin; M. Mehrabankhomartash; D. Divan; M. Saeedifard|10.1109/APEC43580.2023.10131286|DC-DC converter;power boost;modeling;DC-DC converter;power boost;modeling|
|[Modeling and Validation of Common-mode Emissions of SiC MOSFET-based Voltage Source Inverter Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131302)|T. Li; J. Gudex; R. Olson; H. Abdallah; R. M. Cuzner; J. Katcha|10.1109/APEC43580.2023.10131302|wide-bandgap semiconductors;electromagnetic interference;common-mode modeling;common-core inductor;wide-bandgap semiconductors;electromagnetic interference;common-mode modeling;common-core inductor|
|[Simple Radiated Noise Estimation Based on Datasets of SiC and Si IPMs for Inverter Use](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131141)|T. Tadakuma; M. Rogers; M. Joko|10.1109/APEC43580.2023.10131141|Power device;SiC;Si;MOSFET;IGBT;Diode;IPMs;Radiated noise;EMC;Rapid prototyping;Power device;SiC;Si;MOSFET;IGBT;Diode;IPMs;Radiated noise;EMC;Rapid prototyping|
|[Frequency Response Characterization of High-Bandwidth Current Viewing Resistors Used in Dynamic Testing of Power Semiconductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131165)|S. J. Jimenez; B. W. Nelson; A. Curbow; A. N. Lemmon; C. D. New|10.1109/APEC43580.2023.10131165|current viewing resistor;shunt resistor;dynamic characterization;double pulse test;wide band-gap semiconductors;current viewing resistor;shunt resistor;dynamic characterization;double pulse test;wide band-gap semiconductors|
|[Inductance and Parasitic Capacitance Modeling of Spiral Air-core Inductor in MHz Inductive Power Transfer System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131527)|J. Huang; Y. Dou; P. Wang; Z. Ouyang; M. A. E. Andersen|10.1109/APEC43580.2023.10131527|Air-core inductor;parasitic capacitance;modeling;inductive power transfer (IPT);Air-core inductor;parasitic capacitance;modeling;inductive power transfer (IPT)|
#### **2023 8th Asia Conference on Power and Electrical Engineering (ACPEE)**
- DOI: 10.1109/ACPEE56931.2023
- DATE: 14-16 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Multi-terminal VSC-HVDC Fault Current Limiting Method Based on Virtual Resistor Hybrid Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135801)|Y. Huang; C. Yuan; Y. Zhang; Y. Liao; J. Li; Q. Xu|10.1109/ACPEE56931.2023.10135801|Multi-terminal VSC-HVDC;DC line;Fault current limiting;MOA energy absorption;Multi-terminal VSC-HVDC;DC line;Fault current limiting;MOA energy absorption|
|[Research on hybrid energy storage capacity configuration based on double-layer cluster division](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135781)|W. Handi; W. Ruixing; L. Kun; L. Lijun|10.1109/ACPEE56931.2023.10135781|power flow propagation index;double-layer cluster division;hybrid energy storage;capacity optimization configuration;net load fluctuation;power flow propagation index;double-layer cluster division;hybrid energy storage;capacity optimization configuration;net load fluctuation|
|[Forecast of Electric Vehicle Ownership Based on GRA-GM(1.1) Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135697)|G. Liu; H. Yu; Z. Lv; K. Kang; H. Li; J. Zhang|10.1109/ACPEE56931.2023.10135697|electric vehicles;ownership forecast;multi-factor grey prediction;linear regression;electric vehicles;ownership forecast;multi-factor grey prediction;linear regression|
|[Integrated Optimal Scheduling Strategy for VSG Microgrids Based on Improved Multi-objective Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135847)|J. Sun; M. Zhang; H. Cao; J. Huang|10.1109/ACPEE56931.2023.10135847|VSG;microgrid;carbon trading;multi-objective optimisation;INSGA2-DS;integrated dispatch;VSG;microgrid;carbon trading;multi-objective optimisation;INSGA2-DS;integrated dispatch|
|[Control Strategy of Bidirectional Power Converter for Mobile Energy Storage Vehicles based on Wide-Gain Variable-Mode Multi-Control Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135616)|L. Song; C. Lu; J. Wu; Y. Zhou; G. Pan; W. Wu|10.1109/ACPEE56931.2023.10135616|mobile energy storage vehicle;LLC converter;reverse gain;extended gain;bidirectional converter;mobile energy storage vehicle;LLC converter;reverse gain;extended gain;bidirectional converter|
|[Distribution network parameter estimation method based on interval harmonic state estimation and prediction-correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135816)|Y. Chen; Z. Shao; Y. Zhang|10.1109/ACPEE56931.2023.10135816|harmonic state estimation;uncertainty;parameter estimation;alternate iteration method;harmonic state estimation;uncertainty;parameter estimation;alternate iteration method|
|[Analysis on Mechanism of Low Frequency Mode Small Disturbance Stability Improved Strategies Based on a PLL-equivalent Model of Weak Grid Tied VSC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135857)|F. Miao; Y. Zhao; Z. Dong|10.1109/ACPEE56931.2023.10135857|voltage source converter;low frequency mode;small disturbance stability;improved control;mechanism analysis;voltage source converter;low frequency mode;small disturbance stability;improved control;mechanism analysis|
|[Power System Resilience Analysis Considering Static Network Topology and Dynamic Operational Adjustments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135796)|J. Li; J. Wu; X. He; K. Song|10.1109/ACPEE56931.2023.10135796|Resilience analysis;complex networks;operational adjustment;weaknesses identification;Resilience analysis;complex networks;operational adjustment;weaknesses identification|
|[Research on Control Strategy of Permanent Magnet Motor Drive System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135880)|J. Zhang|10.1109/ACPEE56931.2023.10135880|vector control;field weakening control;current inverter;vector control;field weakening control;current inverter|
|[Multi-factor Reliability Evaluation and Analysis Method of HVDC Transmission Line Considering the Influence of External Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135864)|D. Feng; G. Xie; K. Zhang; Q. Xin; L. Li; Y. Ji; T. Hou|10.1109/ACPEE56931.2023.10135864|HVDC transmission;transmission lines;reliability assessment;environmental factors;failure probability model;HVDC transmission;transmission lines;reliability assessment;environmental factors;failure probability model|
|[Operation Mode and Reliability Analysis of Hybrid HVDC System with Multiple Sending Terminals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135605)|X. Gao; Q. Chen; X. Cao; L. Li; Q. Xin; T. Hou; Y. Ji|10.1109/ACPEE56931.2023.10135605|multiple sending terminals;HVDC system;reliability evaluation;subsystem division;state enumeration method;multiple sending terminals;HVDC system;reliability evaluation;subsystem division;state enumeration method|
|[Harmonic Suppression Analysis of PR and PI Controllers in Single Phase Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135620)|B. Feng; Y. Liu; C. Wang; Y. Zhao; H. Yang|10.1109/ACPEE56931.2023.10135620|Harmonic Suppression;single-phase inverter;PI control;PR control;Harmonic Suppression;single-phase inverter;PI control;PR control|
#### **2023 International VLSI Symposium on Technology, Systems and Applications (VLSI-TSA/VLSI-DAT)**
- DOI: 10.1109/VLSI-TSA/VLSI-DAT57221.2023
- DATE: 17-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A 7∼10.5-Gb/s Reference-Less Linear Half-rate CDR Circuit Using Automatic Band Selector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133980)|Y. -E. Hsu; S. -I. Liu|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10133980|Clock and data recovery;reference-less;half-rate;phase detector;frequency acquisition;Clock and data recovery;reference-less;half-rate;phase detector;frequency acquisition|
|[A Sub-Sampling Phase-Locked Loop with a TDC-Based Frequency-Locked Loop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134118)|Y. -M. Hong; T. -H. Lin|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134118|Phase-locked loop;sub-sampling;fast-locking;frequency-locked loop (FLL);time-to-digital converter (TDC);Phase-locked loop;sub-sampling;fast-locking;frequency-locked loop (FLL);time-to-digital converter (TDC)|
|[A Sub-Sampling Phase-Locked Loop With a Robust Agile-Locking Frequency-Locked Loop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134200)|C. -M. Chen; Y. -M. Hong; T. -H. Lin|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134200|Phase-locked loop (PLL);integer-N PLL;sub-sampling;phase noise;frequency-locked loop (FLL);Phase-locked loop (PLL);integer-N PLL;sub-sampling;phase noise;frequency-locked loop (FLL)|
|[A 0.02mm2Sub-Sampling PLL with Spur Reduction Technique in 90nm CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134274)|S. -J. Cheng; Y. -R. Qiu; C. -H. Hong; W. -Y. Liu; C. H. Li; C. -P. Chen|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134274|Phase-locked loop;current-controlled oscillator;small area;sub-sampling;spur reduction;Phase-locked loop;current-controlled oscillator;small area;sub-sampling;spur reduction|
|[High Swing VCO with Current-Reused Frequency Doubler and Darlington Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134455)|S. -J. Cheng; S. -L. Jang; H. Chen; C. -H. Hong; C. -P. Chen|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134455|CMOS;gm-boosted;voltage-controlled oscillator (VCO);current-reused;push-push pair;frequency doubler (FD);Darlington amplifier (DA);CMOS;gm-boosted;voltage-controlled oscillator (VCO);current-reused;push-push pair;frequency doubler (FD);Darlington amplifier (DA)|
|[Designing the Most Energy-Efficient Recommendation Inference Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134071)|Y. -L. Lin; J. Kao; K. Chen|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134071|Recommendation;Inference Accelerator;DLRM;Recommendation;Inference Accelerator;DLRM|
|[Achieving Uniform Memory Read Distribution in Storage Class Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133945)|Y. -M. Chang; T. -C. Kuo; C. -S. B. Shung|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10133945|Storage Class Memory;System Architecture;Memory Management;Endurance;Storage Class Memory;System Architecture;Memory Management;Endurance|
|[Exploring the Optimized AI SoC Design Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133942)|K. Wei|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10133942|;|
|[A 0.0072-mm2 10-bit 100-MS/s Calibration-free SAR ADC Using Digital Place-and-Route Tools in 40-nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134074)|Y. -H. Tsai; S. -I. Liu|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134074|Analog-to-digital converter (ADC);successive approximation register (SAR);digital place-and-route (DPR);bootstrapped switch;double-tail comparator;Analog-to-digital converter (ADC);successive approximation register (SAR);digital place-and-route (DPR);bootstrapped switch;double-tail comparator|
|[A 411nA Quiescent Current Hysteretic Buck Converter with Self-Control Biasing and Dynamic Voltage Scaling (DVS) for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134413)|W. -T. Yeh; G. -S. Yao; C. -Y. Chen; C. -H. Tsai|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134413|ultra-low power;hysteretic buck converter;self-control biasing;dynamic voltage scaling;ultra-low power;hysteretic buck converter;self-control biasing;dynamic voltage scaling|
|[A CMOS Buffer Amplifier with Slew-Rate Enhancement and Power Saving Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134266)|L. -J. Lu; P. -H. Chu; W. -C. Huang; Y. -T. Liao|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134266|CMOS;driver;slew rate;power-efficient;CMOS;driver;slew rate;power-efficient|
|[A Low Power Readout Circuit for a Tri-axial Piezoelectric MEMS Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134216)|S. -Y. Ciou; S. -J. Chang|10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134216|Tri-axial readout circuit;TIA;SAR ADC;simultaneous sampling circuit;low power;low area overhead;Tri-axial readout circuit;TIA;SAR ADC;simultaneous sampling circuit;low power;low area overhead|
#### **2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)**
- DOI: 10.1109/AIEEE58915.2023
- DATE: 27-29 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Noisy Phoneme Recognition Using 2D Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134866)|J. Ramonaitė; G. Korvel|10.1109/AIEEE58915.2023.10134866|speech recognition;convolutional neural network;spectrograms;mel spectrograms;speech recognition;convolutional neural network;spectrograms;mel spectrograms|
|[The Synthetic Data Application in the UAV Recognition Systems Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134962)|D. Duplevska; V. Medvedevs; D. Surmacs; A. Aboltins|10.1109/AIEEE58915.2023.10134962|Neural networks;Convolutional neural networks;Artificial neural networks;Synthetic data;Generative adversarial networks;Neural networks;Convolutional neural networks;Artificial neural networks;Synthetic data;Generative adversarial networks|
|[Demonstration of 50 ps Per-Position Differential Pulse Position Modulation Data Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134802)|O. Selis; S. Migla; P. E. Sics; M. Zeltins; S. Spolitis; A. Aboltins|10.1109/AIEEE58915.2023.10134802|Pulse position modulation;ultra-wideband;timing;energy efficiency;communication;Pulse position modulation;ultra-wideband;timing;energy efficiency;communication|
|[Supercapacitor Balancing Circuit Design Considerations for High Current Charge-Discharge Cycles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134865)|K. Gaspersons; K. Kroics|10.1109/AIEEE58915.2023.10134865|Supercapacitor balancing;supercapacitors;DC microgrid;passive balancing;Supercapacitor balancing;supercapacitors;DC microgrid;passive balancing|
|[Diagnostics of Pre-Fault Conditions Using The Impact of Electrolytic Capacitor Aging on Power Supply Dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135051)|P. Suskis; J. Zakis; A. Suzdalenko; H. Van Khang; R. Pomarnacki|10.1109/AIEEE58915.2023.10135051|capacitor;power electronics;reliability;capacitor;power electronics;reliability|
|[Toward Bee Behavioral Pattern Recognition on Hive Entrance using YOLOv8](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134563)|T. Sledević; D. Plonis|10.1109/AIEEE58915.2023.10134563|Convolutional neural network;bee detection;object tracking;Convolutional neural network;bee detection;object tracking|
|[The Latest in Natural Language Generation: Trends, Tools and Applications in Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134841)|H. K. Skrodelis; A. Romanovs; N. Zenina; H. Gorskis|10.1109/AIEEE58915.2023.10134841|Natural Language Generation;Artificial Intelligence;Natural Language Understanding;Pre-trained Language Models;Natural Language Generation;Artificial Intelligence;Natural Language Understanding;Pre-trained Language Models|
|[Brain-Computer Interfacing: Developments, Perspectives and Limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134795)|J. Simanovics; D. Kolosovs; A. Litvinenko|10.1109/AIEEE58915.2023.10134795|brain-computer interface;neuroscience;neurotechnology;fMRI;fNIRS;electroencephalography;magnetoencephalography;positron emission tomography;deep learning;brain-computer interface;neuroscience;neurotechnology;fMRI;fNIRS;electroencephalography;magnetoencephalography;positron emission tomography;deep learning|
|[Analysis on RGB Dataset Requirements for Remote Inspection of Power Grid Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134938)|D. Gauce; D. Kolosovs; A. Litvinenko|10.1109/AIEEE58915.2023.10134938|Remote Sensing;digital transformation;power grid infrastructure;Digital Twin;data analytics;Remote Sensing;digital transformation;power grid infrastructure;Digital Twin;data analytics|
|[Machine Learning-based Sensor Data Forecasting for Precision Evaluation of Environmental Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135031)|A. Kempelis; M. Narigina; E. Osadcijs; A. Patlins; A. Romanovs|10.1109/AIEEE58915.2023.10135031|Forecasting;Sensor Data;Machine Learning;Deep Learning;Neural Networks;Forecasting;Sensor Data;Machine Learning;Deep Learning;Neural Networks|
|[Control Unit for Thermoelectrically Configurable Medical Lighting Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134913)|A. Bubovich; A. Pumpurs; R. Saltanovs|10.1109/AIEEE58915.2023.10134913|Thermoelectric devices;lighting;lighting control;medical devices;medical control systems;Thermoelectric devices;lighting;lighting control;medical devices;medical control systems|
|[Managing Information System Security in Higher Education Organizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134911)|V. Minkevics; J. Kampars; J. Grabis|10.1109/AIEEE58915.2023.10134911|IS security management;security operation center;big data;threats;capability maturity model;IS security management;security operation center;big data;threats;capability maturity model|
#### **2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)**
- DOI: 10.1109/WiSPNET57748.2023
- DATE: 29-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Web 3.0 and NFTs enabled eWaste Management System for Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134167)|R. M. Samuel; J. M R; G. M R; S. S. N B; S. N. Rao|10.1109/WiSPNET57748.2023.10134167|e-waste management;Blockchain;AWS;Sustainability;NFT;Web 3.0;e-waste management;Blockchain;AWS;Sustainability;NFT;Web 3.0|
|[An Explorative Study on Extractive Text Summarization through k-means, LSA, and TextRank](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134303)|K. Ramani; K. Bhavana; A. Akshaya; K. S. Harshita; C. R. Thoran Kumar; M. Srikanth|10.1109/WiSPNET57748.2023.10134303|Text Summarization;Extractive Summarization;Text Rank;LSA;k-means;ROUGE Score;Text Summarization;Extractive Summarization;Text Rank;LSA;k-means;ROUGE Score|
|[DDoS Vulnerabilities Analysis in SDN Controllers: Understanding the Attacking Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134518)|M. Sinha; P. Bera; M. Satpathy|10.1109/WiSPNET57748.2023.10134518|SDN controllers;DDoS attacks;controller's resource utilization;switch's resource utilization;SDN controllers;DDoS attacks;controller's resource utilization;switch's resource utilization|
|[Realization of Electronically Controllable, Wide bandwidth Instrumentation Amplifier using single DVCCTA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134111)|H. Pamu; K. K. Puli; K. K. Gurrala|10.1109/WiSPNET57748.2023.10134111|Instrumentation Amplifier (IA);DVCCTA;electronically controllable;wide bandwidth;Voltage mode IA;Transadmittance mode IA;Instrumentation Amplifier (IA);DVCCTA;electronically controllable;wide bandwidth;Voltage mode IA;Transadmittance mode IA|
|[Study of Characteristics and Applications of Microwave Photonic Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134140)|A. Sahu; G. S. Chandrabhan; V. Akkaraju; R. R. T.|10.1109/WiSPNET57748.2023.10134140|;|
|[Disaster-news datasets for multi-label document classification, sentence classification, and abstractive document summarization tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134469)|S. Banerjee; S. Mukherjee; S. Bandyopadhyay|10.1109/WiSPNET57748.2023.10134469|Sentence classification;Document classification;Document summarization;Dataset;Sentence classification;Document classification;Document summarization;Dataset|
|[Design and Simulation of a Novel Cell Interaction Based Square Calculator in Quantum-Dot Cellular Automata](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134190)|D. Thomas; V. S. Solomi|10.1109/WiSPNET57748.2023.10134190|QCA (Quantum-dot Cellular Automata);QCA circuits;square calculator;Vedic mathematics;QCA (Quantum-dot Cellular Automata);QCA circuits;square calculator;Vedic mathematics|
|[Design of Noise Transfer Function for Delta Sigma Modulator based on Modified Jacobi Polynomial Approximations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134293)|J. A. Twinkle; P. V. Chandramani; R. Srinivasan|10.1109/WiSPNET57748.2023.10134293|Noise Transfer Function;Optimization;Modified Jacobi Polynomial Functions;Delta Sigma Modulator;Noise Transfer Function;Optimization;Modified Jacobi Polynomial Functions;Delta Sigma Modulator|
|[Customized CNN with Adam and Nadam Optimizers for Emotion Recognition using Facial Expressions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134002)|K. P; R. S. P; H. P; D. M; C. Iwendi|10.1109/WiSPNET57748.2023.10134002|human emotion;facial expression;convolutional neural network;human behavior;face micro-expressions;human emotion;facial expression;convolutional neural network;human behavior;face micro-expressions|
|[Maximising Highway Safety through AI-enabled Detection of Pedestrians and Animals in V2X Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134307)|R. Maity; J. M R; P. S. Maitra; S. G; G. M R; S. S. N B; K. U. Menon|10.1109/WiSPNET57748.2023.10134307|Animal Crossing Detection;Pedestrian Detection;V2X;Machine Learning;Animal Crossing Detection;Pedestrian Detection;V2X;Machine Learning|
|[Efficient FPGA Implementation of RSA Algorithm Using Vedic Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134210)|J. K. K J; K. P; G. V; S. K|10.1109/WiSPNET57748.2023.10134210|Vedic multiplier;Booth multiplier;RSA algorithm;Public Key Cryptography Introduction (Heading 1);Vedic multiplier;Booth multiplier;RSA algorithm;Public Key Cryptography Introduction (Heading 1)|
|[A Supervised Machine Learning Model based Spectrum Sensing using NI USRP-2922 SDR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134276)|R. F. Singh Russells P; M. G. Raj S|10.1109/WiSPNET57748.2023.10134276|Spectrum Sensing;NI USRP 2922;Machine Learning;Statistical features;SVM;Spectrum Sensing;NI USRP 2922;Machine Learning;Statistical features;SVM|
#### **2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT)**
- DOI: 10.1109/CSNT57126.2023
- DATE: 8-9 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Digital Storage Oscilloscope (DSO) Design and VLSI Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134676)|S. Paul|10.1109/CSNT57126.2023.10134676|oscilloscope;basys-3 board;xadc;vivado;digilent;oscilloscope;basys-3 board;xadc;vivado;digilent|
|[Analysis of 6T Full Adder using 2T XOR and 2T XNOR Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134726)|K. Rahimunnisa; M. Poovarasan; V. Meiyarasu|10.1109/CSNT57126.2023.10134726|ALU;Cadence Virtuoso;GPDK 90 nm;CPTL;ALU;Cadence Virtuoso;GPDK 90 nm;CPTL|
|[Area and Delay Efficient Hybrid Prefix Adders for Residue Number System Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134666)|C. Kavya; S. Varadarajan|10.1109/CSNT57126.2023.10134666|HybridAdder;Ling Adder;Kogge StoneAdder;Prefix Adders;Residue Number System;HybridAdder;Ling Adder;Kogge StoneAdder;Prefix Adders;Residue Number System|
|[Comparative Validation of SRAM Cells Based on Decoupled Read Technique using 45nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134659)|B. Saranga; L. Gupta; A. Sachdeva; T. Sharma|10.1109/CSNT57126.2023.10134659|Read-delay;Write-delay;Static noise margin(SNM);Leakage Power;Electrical Quality Metric(EQM);Read-delay;Write-delay;Static noise margin(SNM);Leakage Power;Electrical Quality Metric(EQM)|
|[Compressor using Cadence 180nm for Image Processing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134733)|P. T. K. Reddy; R. Subramanyam; M. L. Kartheek; C. Melvin; P. Nagabushanam; P. S. Kiran|10.1109/CSNT57126.2023.10134733|compressor;XOR*= XOR-XNOR;compound complex logic gates;compressor;XOR*= XOR-XNOR;compound complex logic gates|
|[Design and Verification of an Adder-Subtractor Using UVM Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134642)|M. Dharani; M. Bharathi; B. M. Rajeswari; A. M. Yadav; D. Niranjan; A. C. D. Reddy|10.1109/CSNT57126.2023.10134642|Adder-subtractor;UVM;EDA playground;Adder-subtractor;UVM;EDA playground|
|[Designing 64-Bit LUT based FFT Structure for High-Speed DSP Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134710)|M. Bharathi; G. A. Sai; B. D. Sree; K. Bharadwaj Karthik; B. U. K. Naik; Y. J. Shirur|10.1109/CSNT57126.2023.10134710|Distributed Arithmetic (DA);Power Spectral Density (PSD);Fast Fourier Transform (FFT);Look-up Table (LUT);Distributed Arithmetic (DA);Power Spectral Density (PSD);Fast Fourier Transform (FFT);Look-up Table (LUT)|
|[FPGA Accelerated Automotive ADAS Sensor Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134612)|B. G. Swamy; B. Pardhasaradhi; U. S. Acharya; P. Srihari; S. Reddy; R. Annavajjala|10.1109/CSNT57126.2023.10134612|Track-to-track fusion;hardware accelerator;field programmable gate array (FPGA);advanced driver assistance and safety (ADAS);automotive sensor fusion;Track-to-track fusion;hardware accelerator;field programmable gate array (FPGA);advanced driver assistance and safety (ADAS);automotive sensor fusion|
|[Improved Shape Memory Alloy-based MEMS Perforated Switch for RF Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134651)|A. S. Bale; N. Ghorpade; R. Hamsalekha; J. Pushpanjali; W. Deepan; J. Herial|10.1109/CSNT57126.2023.10134651|Actuation;SMA;deflection;switch;perforations;Actuation;SMA;deflection;switch;perforations|
|[Ultra-Low Power and High Speed CNTFETs Based Magnitude Comparator Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134746)|D. Harika; B. Hari Chandana; N. Divya; K. Neelima|10.1109/CSNT57126.2023.10134746|;|
|[Write Driver for Low Power Consumption in Magnetic Tunnel Junction (MTJ) Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134617)|M. Francis Aswin; R. Subramanyam; N. Aswathy; N. M. S. Mangai; P. Nagabushanam|10.1109/CSNT57126.2023.10134617|Magnetic tunnel junction;spin transfer;write driver;memory applications;Magnetic tunnel junction;spin transfer;write driver;memory applications|
|[C-slot Flexible Microstrip Antenna with Triple Band Characteristics for WLAN/WiMAX/UWB Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134753)|Satyam; K. Neelima|10.1109/CSNT57126.2023.10134753|FM;WLAN;WiMAX;UWB;Felt;and HFSS;FM;WLAN;WiMAX;UWB;Felt;and HFSS|
#### **2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)**
- DOI: 10.1109/RAEEUCCI57140.2023
- DATE: 19-21 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Approach to Multi-Disease Detection in Healthcare using CNN, Random Forest & XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134394)|M. Mohammed; R. Mongia; M. Anand|10.1109/RAEEUCCI57140.2023.10134394|covid-19 pandemic;deep learning;disease detection;diagnostics;digital transformation;healthcare;covid-19 pandemic;deep learning;disease detection;diagnostics;digital transformation;healthcare|
|[Content recommendation based on recognised Emotion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134505)|S. Challa; M. V. Gowri; R. M. S; P. Nandhini; D. D. C. C. A|10.1109/RAEEUCCI57140.2023.10134505|Recommendation systems;Long-short Term;Neural Network;Unstructured multimodal data;User-based ensemble model;Recommendation systems;Long-short Term;Neural Network;Unstructured multimodal data;User-based ensemble model|
|[Smart Waste Segregation and Collection System with IoT-enabled Monitoring and Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134423)|S. B. Suthar; R. Chithra; G. K. Abhinaya; H. Harikumar|10.1109/RAEEUCCI57140.2023.10134423|Environmental balance;IoT technology;Plastic waste management;Real-time data monitoring;Sensors;Sustainable environment;Environmental balance;IoT technology;Plastic waste management;Real-time data monitoring;Sensors;Sustainable environment|
|[Study of Low Power Techniques in Analog Circuit and Its Application to Design Second Generation Current Conveyors and Voltage Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134520)|S. K. Dash; A. Bakshi; J. R. Panda; S. N. Mishra|10.1109/RAEEUCCI57140.2023.10134520|Bulk driven;low power;CCII;Self cascode;Bulk driven;low power;CCII;Self cascode|
|[Automatic Traffic Sign Board Detection from Camera Images Using Deep learning and Binarization Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134376)|A. Ashwini; K. E. Purushothaman; B. P. Prathaban; M. Jenath; R. Prasanna|10.1109/RAEEUCCI57140.2023.10134376|Autonomous Recognition System;Bit mapping;Binarization Geometrical recognition;Deep Learning based Edge Detection;Autonomous Recognition System;Bit mapping;Binarization Geometrical recognition;Deep Learning based Edge Detection|
|[De-noising of impaired Speech Signal using Optimized Adaptive Filtering Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134065)|G. A. S. P. Korrapati; A. Rane; B. Roy; S. H. Pauline; S. Dhanalakshmi|10.1109/RAEEUCCI57140.2023.10134065|;|
|[Task based Approach for Smart Secure Data Transmission using TinyOS RTOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134384)|R. Seetharaman; S. Gayathri; M. V. T. Prasad; H. T. Selvan; S. Peruvazhuthi; C. Jayakumar; A. Sivanantham|10.1109/RAEEUCCI57140.2023.10134384|Wireless sensor networks;Data Security;Primary encryption;RTOS;TinyOS Tasks;Wireless sensor networks;Data Security;Primary encryption;RTOS;TinyOS Tasks|
|[Design of Planar Antenna for UWB Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134107)|S. Arumugam; S. Manoharan|10.1109/RAEEUCCI57140.2023.10134107|Circular radiator;Defective Ground Structure;High-speed communication;Monopole antenna;Ultrawide Band;Circular radiator;Defective Ground Structure;High-speed communication;Monopole antenna;Ultrawide Band|
|[A miniaturized dual narrow-band bandpass filter using a hairpin resonator and coupled lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134052)|E. Shankar; K. V. P. Kumar; V. K. Velidi|10.1109/RAEEUCCI57140.2023.10134052|dual-band;narrow bandwidth;bandpass filter;hairpin resonator;coupled line;dual-band;narrow bandwidth;bandpass filter;hairpin resonator;coupled line|
|[Design and Analysis of Dielectric Materials on Gate All Around Tunnel Field Effect Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133950)|N. N. Kumar; A. S. Bale; J. S; K. A. Kumar; K. Shivakumar; K. V. Hari Raghavendra|10.1109/RAEEUCCI57140.2023.10133950|GAATFET;On Current;OFF current;Subthreshold Swing;GAATFET;On Current;OFF current;Subthreshold Swing|
|[Detection and Prevention of Distributed Denial of Service in Mobile ADHOC Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134098)|A. Sadhu; S. P. Kumar; S. Ojha; Z. Qureshi|10.1109/RAEEUCCI57140.2023.10134098|Computer networks;Cluster formation;DDoS attack;network security;algorithm;Computer networks;Cluster formation;DDoS attack;network security;algorithm|
|[Performance Comparison of Deep Learning Techniques for Classification of Fruits as Fresh and Rotten](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134242)|J. N. V. D. T. Nerella; V. K. Nippulapalli; S. Nancharla; L. P. Vellanki; P. S. Suhasini|10.1109/RAEEUCCI57140.2023.10134242|Agricultural industry;CNN;Inception v3;Agricultural industry;CNN;Inception v3|
#### **2023 Winter Summit on Smart Computing and Networks (WiSSCoN)**
- DOI: 10.1109/WiSSCoN56857.2023
- DATE: 15-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[AI Model for Bird Species Prediction with Detection of Rare, Migratory and Extinction Birds using ELM Boosted by OBS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133844)|J. N M; H. Tabassum; M. Sharmila; N. R. Chilakapati; A. Pavani; C. D. Souza|10.1109/WiSSCoN56857.2023.10133844|Extreme Learning Machine (ELM);Swarm Intelligence;Bird Swarm Algorithm (BSA);Optimized BSA (OBSA);Spectrogram;bird sound;Extreme Learning Machine (ELM);Swarm Intelligence;Bird Swarm Algorithm (BSA);Optimized BSA (OBSA);Spectrogram;bird sound|
|[Map Building of Indoor Environment with Sensors using Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133850)|S. A. Latha Mary; K. Ulagapriya; A. Poonguzhali; R. Menaha; B. David; T. R. Priyadharshini|10.1109/WiSSCoN56857.2023.10133850|OGM processing and building;Neural Network;OGM processing and building;Neural Network|
|[Medical Insurance Premium Prediction Using Linear Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133866)|K. P. Linija; G. S; D. Mavaluru; S. Ravali; S. A. L. Mary|10.1109/WiSSCoN56857.2023.10133866|Artificial intelligence;machine learning;Medical Insurance premium;LinearSupport vector machine;Healthcare;Artificial intelligence;machine learning;Medical Insurance premium;LinearSupport vector machine;Healthcare|
|[Zynq SoC-based IPs for Deep Learning Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133848)|P. J. M; M. K. B; S. S. S; S. P. J. Vasantha Rani|10.1109/WiSSCoN56857.2023.10133848|VGG-16;Object Detection;DNN;CNN;Xilinx FPGA;Vitis;HLS;IP Core;VGG-16;Object Detection;DNN;CNN;Xilinx FPGA;Vitis;HLS;IP Core|
|[Hybrid Artificial Neural Network with Advanced Noctiluca Optimization for Activity Monitoring and Prognostic Study of Signals Using EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133860)|K. K; R. Kala; A. R; B. K; E. B. Thirumangaialwar|10.1109/WiSSCoN56857.2023.10133860|Human Behavioral Data;EEG brain activity;Optimized Artificial Neural Network;Emotional Analysis;Advanced Noctiluca Optimization;Human Behavioral Data;EEG brain activity;Optimized Artificial Neural Network;Emotional Analysis;Advanced Noctiluca Optimization|
|[An Efficient Approach to Detect Diabetes using XGBoost Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133854)|K. Laxmikant; R. Bhuvaneswari; B. Natarajan|10.1109/WiSSCoN56857.2023.10133854|Diabetic;XGBoost;Classification;Ensemble Machine Learning;Diabetic;XGBoost;Classification;Ensemble Machine Learning|
|[Intelligent Traffic Light Control System using Caffe Model: A Deep Learning Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133847)|K. Jaspin; E. Ajitha; J. D. Rose; K. Sherin|10.1109/WiSSCoN56857.2023.10133847|Traffic control;Image processing;Neural networks;Caffe model;Arduino;Traffic control;Image processing;Neural networks;Caffe model;Arduino|
|[Lorawan Prototype for Smart Home Vulnerabilities and Threats Investigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133862)|S. Nagaraju|10.1109/WiSSCoN56857.2023.10133862|Smart city;Smart home;Vulnerabilities;Threats;Security;Privacy;Smart city;Smart home;Vulnerabilities;Threats;Security;Privacy|
|[Brain Tumor Detection and Classification Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133851)|G. N; V. Pushpalatha; R. C; S. L; S. S|10.1109/WiSSCoN56857.2023.10133851|Brain tumor;MRI images;deep learning;convolutional neural networks (CNN);EfficientNetB1;Resnet50;DenseNet;Brain tumor;MRI images;deep learning;convolutional neural networks (CNN);EfficientNetB1;Resnet50;DenseNet|
|[Estimation Of Air Quality Index In Delhi By Merging Neural Networks And Multiple Regression Techniques with Principal Components Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133846)|S. Kulkarni; H. S. Bali; R. Krishna|10.1109/WiSSCoN56857.2023.10133846|Air Quality Index;Neural Networks;Multiple Regression;Principal Component Analysis (PCA);Air Quality Index;Neural Networks;Multiple Regression;Principal Component Analysis (PCA)|
|[An Efficient Down Sampling Scheme Based on a Matching Factor for Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133849)|R. Zhang; Y. Ye; Y. Zhu; S. Song|10.1109/WiSSCoN56857.2023.10133849|Edge computing;Internet of Things;Machine Learning;Supervised Learning;Edge computing;Internet of Things;Machine Learning;Supervised Learning|
|[Recommendation system for Twitch Social Network graph using Link Prediction Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133857)|S. A. Sadhana; K. Selvakumar; S. Sabena; L. SaiRamesh|10.1109/WiSSCoN56857.2023.10133857|Twitch;Link Prediction;Graph;Social Network;Machine learning;recommendations;classifier;Supervised learning;Twitch;Link Prediction;Graph;Social Network;Machine learning;recommendations;classifier;Supervised learning|
#### **2023 IEEE Rural Electric Power Conference (REPC)**
- DOI: 10.1109/REPC49397.2023
- DATE: 25-28 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Thermal Modeling of Distribution Lines for Power Systems Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136857)|B. Carnovale; S. Nguyen; R. Kerestes; G. Shirek|10.1109/REPC49397.2023.00010|conductor modeling;distribution lines;power flow;resistance;thermal modeling;conductor modeling;distribution lines;power flow;resistance;thermal modeling|
|[Descriptive Example of a Hybrid Hosting Capacity Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136862)|P. Radatz; K. Montano-Martinez; M. Rylander; R. Dugan|10.1109/REPC49397.2023.00011|Distribution Systems;Hosting Capacity;Hybrid Hosting Capacity assessment;Distribution Systems;Hosting Capacity;Hybrid Hosting Capacity assessment|
|[Demand Management Using Dynamic Voltage Reduction at Rural Electric Cooperatives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136869)|M. F. Watson; F. Eldali; D. Farr; J. Smith|10.1109/REPC49397.2023.00012|Conservation voltage reduction (CVR);demand management;demand reduction;dynamic voltage reduction (DVR);volt-var optimization (VVO);rural feeders;Conservation voltage reduction (CVR);demand management;demand reduction;dynamic voltage reduction (DVR);volt-var optimization (VVO);rural feeders|
|[Voltage Bypassing Operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136855)|A. Hannah|10.1109/REPC49397.2023.00013|Single phase regulators;medium voltage;Substation regulators;bypass switching of voltage regulators;Single phase regulators;medium voltage;Substation regulators;bypass switching of voltage regulators|
|[Wildfire Risk Reduction Through Wildlife Risk Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136863)|D. T. Eccleston; J. F. Dwyer; R. E. Harness; T. A. Barnes; J. Downie|10.1109/REPC49397.2023.00014|wildlife;reliability;avian-protection-plans;wildfire-mitigation-plan;compliance;avian-electrocution;wildlife-electrocution;system-hardening;wildlife;reliability;avian-protection-plans;wildfire-mitigation-plan;compliance;avian-electrocution;wildlife-electrocution;system-hardening|
|[Understanding Impacts of Frequency Calculations on Underfrequency Load Shedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136850)|K. Prabakar; Y. N. Velaga; A. Hoke; R. Jain; J. Sawant; D. Vaidhynathan; L. Yu; K. Aramaki; L. Unruh; A. Isaacs|10.1109/REPC49397.2023.00015|COMTRADE playback;controller evaluation;frequency measurement;phase-locked loop;relay evaluation;under-frequency load shedding;COMTRADE playback;controller evaluation;frequency measurement;phase-locked loop;relay evaluation;under-frequency load shedding|
|[Modern Planning Framework Based on Non-wires Alternatives for Advancing Distribution Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136856)|D. Montenegro; C. McEntee; M. Hernandez; R. Dugan|10.1109/REPC49397.2023.00016|Distribution planning;economics;parallel computing;power system modeling;power system simulation;Distribution planning;economics;parallel computing;power system modeling;power system simulation|
|[Taking the Concept of the Rural Electric Power Conference to India and Beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136852)|C. L. Brooks; P. K. Sen|10.1109/REPC49397.2023.00017|international;electrification;India;REPC;rural power;energy sustainability;renewable energy;developing nations;international;electrification;India;REPC;rural power;energy sustainability;renewable energy;developing nations|
|[Capacity Utilization of a Transformer at Different Dynamic Loading Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136854)|A. B. M. S. Azam; W. H. Schmidt; K. Vicklund; M. Dymond|10.1109/REPC49397.2023.00018|Transformer;Dynamic loading;fault current;transmission;X/R values;Transformer;Dynamic loading;fault current;transmission;X/R values|
|[PMU-Based Controller for Optimum Real and Reactive Power Flow in Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136851)|J. Joseph; I. Grinberg|10.1109/REPC49397.2023.00019|battery energy storage;geothermal heat pump;distributed energy resource;distribution system;synchrophasor;inverter-based resource;distribution feeder;battery energy storage;geothermal heat pump;distributed energy resource;distribution system;synchrophasor;inverter-based resource;distribution feeder|
|[Advancing Rural Electrification through Community-Based EV Charging Stations: Opportunities and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136865)|Y. Badiei; J. C. do Prado|10.1109/REPC49397.2023.00020|charging stations;electric vehicles (EVs);rural electrification;charging stations;electric vehicles (EVs);rural electrification|
|[Challenges to Rural Service Transformers on Increased Electric Vehicle Charging Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136860)|S. Mukherjee|10.1109/REPC49397.2023.00021|Service transformer;resilience;pole mounted;underground;electric vehicle;fast charger;infrastructure;aging;Service transformer;resilience;pole mounted;underground;electric vehicle;fast charger;infrastructure;aging|
#### **2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)**
- DOI: 10.1109/eStream59056.2023
- DATE: 27-27 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Proceedings of the 2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream’ 2023): Organizers Foreword](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134800)|D. Navakauskas; Š. Paulikas; T. Sledevič; D. Udris|10.1109/eStream59056.2023.10134800|;|
|[Electromagnetic Interferences Resistant PI/I Controller for Electrical Energy Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135030)|A. Baskys; M. Sapurov; V. Bleizgys; R. Pomarnacki; N. Paulauskas; V. K. Huynh|10.1109/eStream59056.2023.10135030|feedback control system;electromagnetic interferences;electrical energy converters;load disturbance;robustness;feedback control system;electromagnetic interferences;electrical energy converters;load disturbance;robustness|
|[Dielectric Resonator Loaded Two Port Circularly Polarized MIMO Antenna for Sub 6 GHz Band Coverage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134770)|S. Gedageri; V. Ghali; V. Krishnareddy; S. B. Siddappa; V. Singh; A. K. Dwivedi|10.1109/eStream59056.2023.10134770|Dielectric Resonator Antenna;MIMO;Circular Polarization;5 G;Dielectric Resonator Antenna;MIMO;Circular Polarization;5 G|
|[Opportunities for Automated E-learning Path Generation in Adaptive E-learning Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134844)|O. Ovtšarenko|10.1109/eStream59056.2023.10134844|e-learning;competency tree;personalisation;adaptation;e-assessment;e-learning;competency tree;personalisation;adaptation;e-assessment|
|[Development and Research of Miniature High Precision Modular Rotary Encoder Kit Based on Dual Optical Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134846)|D. Gurauskis; A. Kilikevičius; D. Mažeika|10.1109/eStream59056.2023.10134846|Modular encoder;optical sensor;position error;Modular encoder;optical sensor;position error|
|[Dynamic Characteristics Study of a Newly Developed Suppression System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134953)|V. Giedraitis; K. Kilikevičiene; D. Vainorius; A. Kilikevičius|10.1109/eStream59056.2023.10134953|suppression system;sound pressure;recoil forces;suppression system;sound pressure;recoil forces|
|[Methodology of Low Orbit Satellites Signal Processing in the Electromagnetically Noisy Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135033)|V. Masalskyi; V. Bučinskas; A. Dzedzickis; A. Tyshchenko; O. Masalskyi|10.1109/eStream59056.2023.10135033|signal acquisition;satellites;filtering;sensors;control system;signal acquisition;satellites;filtering;sensors;control system|
|[Theoretical and Experimental Research of Device for Ultrafine Particulate Matter Agglomeration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134973)|D. Vainorius; J. Matijošius; S. Borodinas; A. Džiugys|10.1109/eStream59056.2023.10134973|ultrafine solid particles;agglomeration;sound pressure;acoustics;ultrafine solid particles;agglomeration;sound pressure;acoustics|
|[Comparative Analysis of the Applicability of Five Clustering Algorithms for Market Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134796)|D. Teslenko; A. Sorokina; K. Smelyakov; O. Filipov|10.1109/eStream59056.2023.10134796|clustering algorithms;clustering quality evaluation;distance metrics;market segmentation;clustering algorithms;clustering quality evaluation;distance metrics;market segmentation|
|[Research and Development of Information Technology for Determining Shoe Size by Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135036)|I. Sakharov; K. Smelyakov; O. Bohomolov|10.1109/eStream59056.2023.10135036|information technology;shoe size determination;two-dimensional image;scanner of a sheet of paper;k-means clustering;object detection;information technology;shoe size determination;two-dimensional image;scanner of a sheet of paper;k-means clustering;object detection|
|[Deposition of Polydisperse Ultrafine Particles in a Gas Flow Using an Advanced Electro-Cyclone Apparatus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134775)|A. Chlebnikovas; A. Kilikevićius|10.1109/eStream59056.2023.10134775|gas flow;ultrafine particulate matter;electrostatic;cyclone;pollution;gas flow;ultrafine particulate matter;electrostatic;cyclone;pollution|
|[Toward Bee Motion Pattern Identification on Hive Landing Board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134852)|T. Sledevič; V. Abromavičius|10.1109/eStream59056.2023.10134852|convolutional neural network;bee detection;object tracking;convolutional neural network;bee detection;object tracking|
|[Explainable AI (XAI): Explained](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134984)|G. P. Reddy; Y. V. P. Kumar|10.1109/eStream59056.2023.10134984|Black box models;Explainable AI (XAI);Interpretability;Local Interpretable Model-Agnostic Explanations (LIME);SHapley Additive exPlanations (SHAP);Transparency;Trust in AI;Black box models;Explainable AI (XAI);Interpretability;Local Interpretable Model-Agnostic Explanations (LIME);SHapley Additive exPlanations (SHAP);Transparency;Trust in AI|
#### **2023 IEEE Devices for Integrated Circuit (DevIC)**
- DOI: 10.1109/DevIC57758.2023
- DATE: 7-8 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Enhancing the Performance of Electrostatically Doped Double POLO PERC Solar Cell through Metal Silicides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134982)|S. Kashyap; R. Pandey; J. Madan; R. Sharma|10.1109/DevIC57758.2023.10134982|contact passivation;double-POLO;PERC;Silvaco;silicide;contact passivation;double-POLO;PERC;Silvaco;silicide|
|[Design of Low power and High-Performance Decoder Using Carbon Nanotube Field Effect Transistor (CNTFET)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134883)|P. Kumar; N. Gupta; L. Rai; R. Gupta|10.1109/DevIC57758.2023.10134883|decoder;carbon nanotube field effect transistor (CNTFET);power;delay;DVL;decoder;carbon nanotube field effect transistor (CNTFET);power;delay;DVL|
|[1-bit Magnitude Comparator based on ReversibleLogic using QCA Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134942)|V. Dhare; D. Modi|10.1109/DevIC57758.2023.10134942|majority voter;reversible gate;comparator;qca;majority voter;reversible gate;comparator;qca|
|[Gate Engineered IGBT with Improved Device Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134985)|S. K. Sahu; O. Parmar; S. T. Chacko; A. S. Rajput|10.1109/DevIC57758.2023.10134985|trench gate technology;insulated gate bipolar transistor;on-state voltage;power devices;trench gate technology;insulated gate bipolar transistor;on-state voltage;power devices|
|[Performance Analysis of Low Power Multiplexer for Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135043)|L. Rai; P. Kumar; N. Gupta; R. Gupta|10.1109/DevIC57758.2023.10135043|power dissipation;PTL;TG;GDI;CMOS;power dissipation;PTL;TG;GDI;CMOS|
|[Input Variable Bypass or IVB Technique for Logic Functions Simplification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135020)|H. Maity; P. Kundu; A. Bhowmik; A. K. Barik|10.1109/DevIC57758.2023.10135020|digital circuits;Boolean algebra;combinational circuits;scientific research;innovation;research development;energy efficiency;digital circuits;Boolean algebra;combinational circuits;scientific research;innovation;research development;energy efficiency|
|[Implementation of the Quantum BCD-to-Excess-3 Code Converter using New Quantum Reversible Circuit Block](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134807)|P. Kundu; H. Maity; A. Bhowmik; B. Roy|10.1109/DevIC57758.2023.10134807|quantum computing;reversible gate;code converter;quantum cost;garbage output;quantum computing;reversible gate;code converter;quantum cost;garbage output|
|[Comparative Analysis of EEPL and PPL Techniques in 18nm FinFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134850)|G. Rana; K. Sharma; A. Sharma; L. Gupta; A. Sachdeva|10.1109/DevIC57758.2023.10134850|FinFET;push-pull pass transistor logic;energy economized pass transistor logic;FinFET;push-pull pass transistor logic;energy economized pass transistor logic|
|[Design and Simulation of Cascode Current reuse low power Operational transconductance amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134781)|R. R. Kumar; K. Sharma; A. Sachdeva; L. Gupta|10.1109/DevIC57758.2023.10134781|Biological acquisition systems;cascode MOS;Figure of merit;oparational transconductance amplifier;low-power low-noise;Biological acquisition systems;cascode MOS;Figure of merit;oparational transconductance amplifier;low-power low-noise|
|[Optimizing Photovoltaic Performance of MASnPbI3 Perovskite Solar Cells through Layer Thickness Variations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134949)|S. Rawat; N. Shrivastav; R. Pandey; J. Madan|10.1109/DevIC57758.2023.10134949|solar cell;perovskite;SCAPS-1d;MASnPbI3;efficiency;solar cell;perovskite;SCAPS-1d;MASnPbI3;efficiency|
|[Slotted Waveguide Antenna for Microwave Remote Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134877)|M. Chakrabarti; M. Gangopadhyaya; A. Chattopadhyay|10.1109/DevIC57758.2023.10134877|airborne radar;microwave remote sensing;radar antenna;slotted waveguide array antenna;synthetic aperture radar;airborne radar;microwave remote sensing;radar antenna;slotted waveguide array antenna;synthetic aperture radar|
|[One Dimensional Chaotic Map Implementation in FPGA Board for Image Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10134854)|P. Sarkar; A. K. Das; A. Saha|10.1109/DevIC57758.2023.10134854|chaos;FPGA;encryption;decryption;chaos;FPGA;encryption;decryption|
|[Impact of Doping Variation on the Performance of Sb2S3 based Solar Cell using Device Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10135055)|V. Yadav; S. Kashyap; R. Pandey; J. Madan|10.1109/DevIC57758.2023.10135055|acceptor;doping;Sb2 S3;solar cell;variation;acceptor;doping;Sb2 S3;solar cell;variation|
#### **2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)**
- DOI: 10.1109/ICSTW58534.2023
- DATE: 16-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[We Tried and Failed: An Experience Report on a Collaborative Workflow for GUI-based Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132215)|A. Bauer; E. Alégroth|10.1109/ICSTW58534.2023.00015|model-based testing;automated testing;GUI testing;collaborative testing;collaborative workflow;model-based testing;automated testing;GUI testing;collaborative testing;collaborative workflow|
|[An Experiment in Requirements Engineering and Testing using EARS Notation for PLC Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132248)|M. E. Salari; E. Paul Enoiu; W. Afzal; C. Seceleanu|10.1109/ICSTW58534.2023.00016|EARS;PLC;Requirement Engineering;Testing;EARS;PLC;Requirement Engineering;Testing|
|[Model-Based Policy Synthesis and Test-Case Generation for Autonomous Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132202)|R. Gu; E. Enoiu|10.1109/ICSTW58534.2023.00017|autonomous systems;model checking;testing;test-case generation;autonomous systems;model checking;testing;test-case generation|
|[ADAS Verification in Co-Simulation: Towards a Meta-Model for Defining Test Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132236)|F. Basciani; V. Cortellessa; S. DiMartino; D. Di Nucci; D. DiPompeo; C. Gravino; L. L. Lucio Starace|10.1109/ICSTW58534.2023.00018|ADAS Testing;Co-simulation;Model-based Testing;Visual Editor;ADAS Testing;Co-simulation;Model-based Testing;Visual Editor|
|[From BDD Scenarios to Test Case Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132208)|T. Zameni; P. van Den Bos; J. Tretmans; J. Foederer; A. Rensink|10.1109/ICSTW58534.2023.00019|Behavior-Driven Development;Model-Based testing;Compositional testing;Behavior-Driven Development;Model-Based testing;Compositional testing|
|[Improving Model Learning by Inferring Separating Sequences from Traces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132200)|R. Braz; A. Simao; R. Groz; C. Oriat|10.1109/ICSTW58534.2023.00020|model learning;FSM;characterization sets;and separating sequences;model learning;FSM;characterization sets;and separating sequences|
|[MUPPAAL: Reducing and Removing Equivalent and Duplicate Mutants in UPPAAL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132193)|J. Cuartas; J. Aranda; M. Cordy; J. Ortiz; G. Perrouin; P. -Y. Schobbens|10.1109/ICSTW58534.2023.00021|Model-Based Testing;Timed Automata;Mutation Testing;UPPAAL;Model-Based Testing;Timed Automata;Mutation Testing;UPPAAL|
|[Automating GUI-based Software Testing with GPT-3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132233)|D. Zimmermann; A. Koziolek|10.1109/ICSTW58534.2023.00022|UI Testing;Test Automation;Deep Learning;Language Models;UI Testing;Test Automation;Deep Learning;Language Models|
|[Towards Explainable Test Case Prioritisation with Learning-to-Rank Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132275)|A. Ramírez; M. Berrios; J. R. Romero; R. Feldt|10.1109/ICSTW58534.2023.00023|test case prioritisation;explainable artificial intelligence;machine learning;learning-to-rank;test case prioritisation;explainable artificial intelligence;machine learning;learning-to-rank|
|[Generating concrete test cases from vehicle data using models obtained from clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132253)|N. Chetouane; F. Wotawa|10.1109/ICSTW58534.2023.00024|model-based testing;model extraction;clustering;model-based testing;model extraction;clustering|
|[Similarities of Testing Programmed and Learnt Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132221)|F. Dobslaw; R. Feldt|10.1109/ICSTW58534.2023.00025|Software Testing;Software Engineering;Machine Learning;Non-Duality;Software Boundaries;Software Testing;Software Engineering;Machine Learning;Non-Duality;Software Boundaries|
|[Regression Test Generation by Usage Coverage Driven Clustering on User Traces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132240)|F. Tamagnan; F. Bouquet; A. Vernotte; B. Legeard|10.1109/ICSTW58534.2023.00026|machine learning;clustering algorithms;software testing;test generation;machine learning;clustering algorithms;software testing;test generation|
|[Evaluating the Effectiveness of Attacks and Defenses on Machine Learning Through Adversarial Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132207)|V. R. Gala; M. A. Schneider|10.1109/ICSTW58534.2023.00027|adversarial machine learning;adversarial at-tacks;CW attack;adversarial defense;KDE defense;adaptive CW attack;artificial intelligence;testing;adversarial machine learning;adversarial at-tacks;CW attack;adversarial defense;KDE defense;adaptive CW attack;artificial intelligence;testing|
#### **2023 IEEE Conference on Software Testing, Verification and Validation (ICST)**
- DOI: 10.1109/ICST57152.2023
- DATE: 16-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Software Testing Research Challenges: An Industrial Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132192)|N. Alshahwan; M. Harman; A. Marginean|10.1109/ICST57152.2023.00008|Automated Software Engineering;Software Testing;Automated Program Repair;Artificial Intelligence;Genetic Improvement;Automated Remediation;Automated Software Engineering;Software Testing;Automated Program Repair;Artificial Intelligence;Genetic Improvement;Automated Remediation|
|[Test Automation: From Slow & Weak to Fast, Flaky, & Blind to Smart & Effective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132201)|J. Offutt|10.1109/ICST57152.2023.00009|;|
|[AI is a game-changing technology: how to test and robustify Machine-Learning software?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132242)|Y. Le Traon|10.1109/ICST57152.2023.00010|;|
|[A Case Against Coverage-Based Program Spectra](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132176)|P. A. Soha; T. Gergely; F. Horváth; B. Vancsics; Á. Beszédes|10.1109/ICST57152.2023.00011|Spectrum-Based Fault Localization;Automated Debugging;Assertions;Backward Dynamic Program Slice;Spectrum-Based Fault Localization;Automated Debugging;Assertions;Backward Dynamic Program Slice|
|[A Coverage-Driven Systematic Test Approach for Simultaneous Localization and Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132224)|P. Tasche; P. Herber|10.1109/ICST57152.2023.00012|Autonomous Vehicles;Simultaneous Localization and Mapping;Test Automation;Coverage-driven Testing;Input Space Partitioning;Autonomous Vehicles;Simultaneous Localization and Mapping;Test Automation;Coverage-driven Testing;Input Space Partitioning|
|[Android Fuzzing: Balancing User-Inputs and Intents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132258)|M. Auer; A. Stahlbauer; G. Fraser|10.1109/ICST57152.2023.00013|Test Generation;Fuzzing;Intents;Android;Test Generation;Fuzzing;Intents;Android|
|[Batching Non-Conflicting Mutations for Efficient, Safe, Parallel Mutation Analysis in Rust](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132214)|Z. Lévai; P. McMinn|10.1109/ICST57152.2023.00014|mutation analysis;mutation testing;mutation batching;rust;static analysis;mutation analysis;mutation testing;mutation batching;rust;static analysis|
|[Constraint-Guided Automatic Side Object Placement for Steering Control Testing in Virtual Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132249)|B. Kim|10.1109/ICST57152.2023.00015|;|
|[CorCA: An Automatic Program Repair Tool for Checking and Removing Effectively C Flaws](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132244)|J. Inácio; I. Medeiros|10.1109/ICST57152.2023.00016|Code Repair;Buffer Overflow Vulnerabilities;Static Analysis;Fuzzing;Software Security;Code Repair;Buffer Overflow Vulnerabilities;Static Analysis;Fuzzing;Software Security|
|[Distributed Repair of Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132206)|D. L. Calsi; M. Duran; X. -Y. Zhang; P. Arcaini; F. Ishikawa|10.1109/ICST57152.2023.00017|DNNs;automated repair;risk levels;DNNs;automated repair;risk levels|
|[Embedding Context as Code Dependencies for Neural Program Repair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132257)|N. Nashid; M. Sintaha; A. Mesbah|10.1109/ICST57152.2023.00018|deep learning;program repair;program slicing;control flow;data flow;graph neural networks;deep learning;program repair;program slicing;control flow;data flow;graph neural networks|
|[Heap Fuzzing: Automatic Garbage Collection Testing with Expert-Guided Random Events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132213)|G. Polito; P. Tesone; N. Palumbo; S. Ducasse; J. Privat|10.1109/ICST57152.2023.00019|garbage collection;testing;fuzzing;virtual machines;garbage collection;testing;fuzzing;virtual machines|
|[Homo in Machina: Improving Fuzz Testing Coverage via Compartment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132267)|J. Bundt; A. Fasano; B. Dolan-Gavitt; W. Robertson; T. Leek|10.1109/ICST57152.2023.00020|fuzz testing;fuzzing;fuzz testing;fuzzing|
#### **2023 3rd International Conference on Smart Data Intelligence (ICSMDI)**
- DOI: 10.1109/ICSMDI57622.2023
- DATE: 30-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Automated Shopping Cart: Reducing Long Queues One Cart At A Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127663)|K. Gorai; S. V S C Santosh; S. S; V. Murugan A S; P. TR|10.1109/ICSMDI57622.2023.00010|Deep Neural Networks;Databases;Automation;Weight Sensors;Shopping;IOT;Android;Deep Neural Networks;Databases;Automation;Weight Sensors;Shopping;IOT;Android|
|[A Guide Towards Implementing the Effective Algorithm for Optimum Topology in Complex Terrains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127911)|A. Salaria; A. Singh|10.1109/ICSMDI57622.2023.00011|Optimum topology network algorithms;Wireless sensor networks;Evaluation criteria;Optimization algorithms;Optimum topology network algorithms;Wireless sensor networks;Evaluation criteria;Optimization algorithms|
|[Sarcasm Detection: A Systematic Review of Methods and Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127869)|Y. Salini; J. HariKiran|10.1109/ICSMDI57622.2023.00012|Sarcasm Detection;Natural Language Processing (NLP);Irony Detection;Sentiment Analysis;Sarcasm Detection;Natural Language Processing (NLP);Irony Detection;Sentiment Analysis|
|[Decentralized E-Commerce Platform Implemented using Smart Contracts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127664)|B. S. Liya; P. S; R. K. S; N. K|10.1109/ICSMDI57622.2023.00013|Blockchain;DApp;Smart Contracts;E-Commerce;Ethereum;Solidity;IPFS;Web3.0;Decentralized data storage;Blockchain;DApp;Smart Contracts;E-Commerce;Ethereum;Solidity;IPFS;Web3.0;Decentralized data storage|
|[News text Analysis using Text Summarization and Sentiment Analysis based on NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127895)|A. Mishra; A. Sahay; M. a. Pandey; S. S. Routaray|10.1109/ICSMDI57622.2023.00014|Natural Language Processing;Text Summarization;Sentiment Analysis;NLTK;Python;Natural Language Processing;Text Summarization;Sentiment Analysis;NLTK;Python|
|[Twitter Sentiment Analysis for Bitcoin Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127661)|A. Jagini; K. Mahajan; N. Aluvathingal; V. Mohan; P. TR|10.1109/ICSMDI57622.2023.00015|Bitcoin;Sentiment Analysis;Valence Aware Dictionary and Sentiment Reasoner;Twitter;Linear regression;Bitcoin;Sentiment Analysis;Valence Aware Dictionary and Sentiment Reasoner;Twitter;Linear regression|
|[A Secure and Privacy Preserving Telehealth Solution in Fog Based Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127893)|S. Gopalan; R. Verma; S. Jaswal|10.1109/ICSMDI57622.2023.00016|Fog Computing;Privacy Preservation;Telehealth System;Stacking;Machine Learning;Real-time;Advanced Encryption Standard (AES) Encryption;Fog Computing;Privacy Preservation;Telehealth System;Stacking;Machine Learning;Real-time;Advanced Encryption Standard (AES) Encryption|
|[Sustainable Farming Community using Green Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127658)|M. Sobhana; M. K. Chandra; K. Rakesh; K. Vivek|10.1109/ICSMDI57622.2023.00017|Farm-to-community;Farm-to-door;User-friendly;Green-Marketing;Farm-to-community;Farm-to-door;User-friendly;Green-Marketing|
|[Predicting Credit Card Churn: Application of XGBoost Tuned by Modified Sine Cosine Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127871)|L. Jovanovic; M. Kljajic; V. Mizdrakovic; V. Marevic; M. Zivkovic; N. Bacanin|10.1109/ICSMDI57622.2023.00018|Credit Card Churn Prediction;Sine Cosine Algorithm;XGBoost;Metaheuristics;Optimization;Shapley Additive Explanations;Credit Card Churn Prediction;Sine Cosine Algorithm;XGBoost;Metaheuristics;Optimization;Shapley Additive Explanations|
|[Exploring Innovative Methods for Enhancing Data Security in Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127906)|A. Prakash; S. Chauhan|10.1109/ICSMDI57622.2023.00019|Cryptography;Steganography;Cloud Computing;Encryption;Decryption;Cryptography;Steganography;Cloud Computing;Encryption;Decryption|
|[Multimodal Efficient Bioscrypt Authentication using MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127923)|N. B. J; D. P. E L; K. Sivasankari; A. S. Kumar; K. R. Priya Dharshini|10.1109/ICSMDI57622.2023.00020|Multimodal Biometrics;Fingerprint Recognition using 2-Dimensioanl Short Term Fourier Transform (2D STFT);Pre Processing Image Restoration method;Image Enhancement using Hybrid Transformation;Multimodal Biometrics;Fingerprint Recognition using 2-Dimensioanl Short Term Fourier Transform (2D STFT);Pre Processing Image Restoration method;Image Enhancement using Hybrid Transformation|
|[The Social Media Implications on the Sales of Business Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127947)|E. Jacia; G. Dyan; A. Ifan; F. L. Gaol; T. Matsuo|10.1109/ICSMDI57622.2023.00021|Social Media;Business;Promotion;Technology;Influence;Social Media;Business;Promotion;Technology;Influence|
|[A Review on the Capability and Smart Contract Potential of Block chain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127896)|D. Singh; M. V. Malhotra|10.1109/ICSMDI57622.2023.00022|Blockchains;Bitcoin;Ethereum;Smart Contracts;Transactions;Blockchains;Bitcoin;Ethereum;Smart Contracts;Transactions|
#### **2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)**
- DOI: 10.1109/BigDataSecurity/HPSC58521.2023
- DATE: 6-8 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Semantically Rich Differential Access to Secure Cloud EHR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132149)|R. Walid; K. P. Joshi; S. Geol Choi|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00012|Attribute-Based Encryption (ABE);Attribute-Based Access Control (ABAC);Searchable Encryption (SE);Attribute Revocation;Electronic Health Record (EHR);Knowledge Graph (Ontology);Cloud Security;Cloud Computing;Attribute-Based Encryption (ABE);Attribute-Based Access Control (ABAC);Searchable Encryption (SE);Attribute Revocation;Electronic Health Record (EHR);Knowledge Graph (Ontology);Cloud Security;Cloud Computing|
|[A Survey of Weakly-supervised Semantic Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132119)|K. Zhu; N. N. Xiong; M. Lu|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00013|Semantic segmentation;Sparse annotations;Weakly-supervised;Semantic segmentation;Sparse annotations;Weakly-supervised|
|[PriFR: Privacy-preserving Large-scale File Retrieval System via Blockchain for Encrypted Cloud Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132128)|H. Ren; G. Xu; H. Qi; T. Zhang|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00014|Blockchain;big data;private query;searchable encryption;cloud computing;Blockchain;big data;private query;searchable encryption;cloud computing|
|[Privacy Analysis of Federated Learning via Dishonest Servers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132138)|T. R. Jeter; M. T. Thai|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00015|Federated Learning;Data Privacy;Data Leakage;Dishonest Servers;Reconstruction Attacks;Federated Learning;Data Privacy;Data Leakage;Dishonest Servers;Reconstruction Attacks|
|[2MiCo: A Contrastive Semi-Supervised Method with Double Mixup for Smart Meter Modbus RS-485 Communication Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132148)|X. Li; M. D. Hossain; H. Ochiai; L. Khan|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00016|contrastive learning;semi-supervised method;attack detection;multi-class classification;industrial control system;cybersecurity;contrastive learning;semi-supervised method;attack detection;multi-class classification;industrial control system;cybersecurity|
|[A New Method of Construction of Permutation Trinomials with Coefficients 1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132126)|H. Guo; S. Wang; H. Song; Y. Han; X. Zhang; J. Liu|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00017|finite field;permutation trinomial;fractional reciprocal polynomial;polar decomposition;exponential sum;finite field;permutation trinomial;fractional reciprocal polynomial;polar decomposition;exponential sum|
|[Cross-Consensus Measurement of Individual-level Decentralization in Blockchains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132154)|C. Li; B. Palanisamy; R. Xu; L. Duan|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00018|Blockchain;Decentralization;PoW;DPoS;Measurement Study;Blockchain;Decentralization;PoW;DPoS;Measurement Study|
|[An Expert Knowledge Generation Model in Smart Contract Vulnerability Fuzzing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132143)|X. Li|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00019|smart contracts;vulnerability detection;fuzzing;classification model;taint analysis;smart contracts;vulnerability detection;fuzzing;classification model;taint analysis|
|[Cyber Attack Detection using Secret Sharing Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132110)|C. S. Chum; X. Wei; X. Zhang|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00020|cyber attack detection;secret sharing schemes;multifactor authentication;implementation algorithm;cyber attack detection;secret sharing schemes;multifactor authentication;implementation algorithm|
|[Research and Application of Blind Watermark Based on DCT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132136)|R. Zhang; S. Yuan|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00021|blind watermark;digital image watermark;DCT transform;robustness;information hiding;blind watermark;digital image watermark;DCT transform;robustness;information hiding|
|[Exploring Downvoting in Blockchain-based Online Social Media Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132127)|R. Sun; C. Li; J. Liu; X. Sun|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00022|blockchain;online social media;Steemit;down-vote;bot account;blockchain;online social media;Steemit;down-vote;bot account|
|[Malicious ADS-B data Generation Based on Improved GAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132125)|J. Lei; R. Jiang; Z. Wu|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00023|ADS-B;Generative adversarial network;machine learning;malicious data;security;ADS-B;Generative adversarial network;machine learning;malicious data;security|
|[Inventory Big Data Management for Internet of Things based on Privacy Preserving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132117)|C. Li; H. Zhou; Y. Liu; H. Huang; S. Liu|10.1109/BigDataSecurity-HPSC-IDS58521.2023.00024|IoT;Integrity Detection;Bilinear Pairings;Privacy;IoT;Integrity Detection;Bilinear Pairings;Privacy|
#### **2023 11th International Symposium on Digital Forensics and Security (ISDFS)**
- DOI: 10.1109/ISDFS58141.2023
- DATE: 11-12 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[IEWS: a Free Open Source Intelligent Early Warning System Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131709)|A. A. El Tawil; K. Samrouth|10.1109/ISDFS58141.2023.10131709|malware detection;machine Learning;early detection;open source;malware detection;machine Learning;early detection;open source|
|[AI-Based Network Security Anomaly Prediction and Detection in Future Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131845)|G. Abdiyeva-Aliyeva|10.1109/ISDFS58141.2023.10131845|Anomaly detection;IDS;KNN;Naive Bayes;NIDS;Anomaly detection;IDS;KNN;Naive Bayes;NIDS|
|[Survey on IoT Multi-Factor Authentication Protocols: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131870)|Z. A. -A. Mohammad Fneish; M. El-Hajj; K. Samrouth|10.1109/ISDFS58141.2023.10131870|survey;Multi-Factor Authentication;IoT;Internet of Things;authentication;survey;Multi-Factor Authentication;IoT;Internet of Things;authentication|
|[Strategies for a Startup Software-as-a-Service Organizations with Minimal Budget to Achieve Security and Compliance Goals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131124)|P. Lanka; C. Varol; N. Shashidhar|10.1109/ISDFS58141.2023.10131124|Data Security;Information Security;Risk Management;Risk Mitigation;Software as a Service;Data Security;Information Security;Risk Management;Risk Mitigation;Software as a Service|
|[A Review on Latest Developments in Post-Quantum Based Secure Blockchain Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131840)|A. Karakaya; A. Ulu|10.1109/ISDFS58141.2023.10131840|blockchain;post-quantum;information security;cryptography;blockchain;post-quantum;information security;cryptography|
|[Fuzzy Logic-based Steganographic Scheme for high Payload Capacity with high Imperceptibility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131727)|I. Théophile; N. J. De La Croix; T. Ahmad|10.1109/ISDFS58141.2023.10131727|Cybersecurity;Data hiding;Steganography;Fuzzy logic;Infrastructure;Cybersecurity;Data hiding;Steganography;Fuzzy logic;Infrastructure|
|[Virtualization and Validation of Emulated STM-32 Blue Pill Using the QEMU Open-Source Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131693)|D. Gutierrez; W. Ocampo; A. Perez-Pons; H. Upadhyay; S. Joshi|10.1109/ISDFS58141.2023.10131693|QEMU;STM-32 Blue Pill;Emulation;Type-2 Hypervisor;QEMU;STM-32 Blue Pill;Emulation;Type-2 Hypervisor|
|[Wiegand vs. OSDP in Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131733)|S. Saadat|10.1109/ISDFS58141.2023.10131733|OSDP;Wiegand;access control;cybersecurity;vulnerabilities;compatibility;OSDP;Wiegand;access control;cybersecurity;vulnerabilities;compatibility|
|[Mechatronics Education- Current and Future Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131706)|K. Tantawi; A. Nasab; L. Potter; O. Tantawi; N. Wilson; A. Henrie; A. Sirinterlikci; E. Kaplanoglu|10.1109/ISDFS58141.2023.10131706|Mechatronics;Education;Engineering Technology;Mechatronics;Education;Engineering Technology|
|[Less is More: Deep Learning Framework for Digital Forensics in Resource-Constrained Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131803)|S. Rath; T. Das; I. Astaburuaga; S. Sengupta|10.1109/ISDFS58141.2023.10131803|Digital Forensics;Machine Learning;Knowledge Distillation;Transfer Learning;Digital Forensics;Machine Learning;Knowledge Distillation;Transfer Learning|
|[Encryption process of Blockchain based online course curriculum education system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131770)|M. R. Ahmmed; M. Mamun Sakib; M. U. Rahman; R. Bibi; M. M. Hasan Talukder; M. Johora Akter Polin|10.1109/ISDFS58141.2023.10131770|Blockchain;Transaction;Encryption;Bitcoin;Education;Machine Learning;Blockchain;Transaction;Encryption;Bitcoin;Education;Machine Learning|
|[A Lightweight Medical Image Encryption Scheme Using Chaotic Maps and Image Scrambling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131882)|S. Sudevan; K. Jain|10.1109/ISDFS58141.2023.10131882|telehealth;image encryption;chaotic maps;image scrambling;telehealth;image encryption;chaotic maps;image scrambling|
|[Surveillance, Reconnaissance and Detection Services for Disaster Operations of IoT-Based eVTOL UAVs with Swarm Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131702)|M. Bakirci; M. M. Ozer|10.1109/ISDFS58141.2023.10131702|Swarm UAV systems;secure communication;disaster operations;surveillance;reconnaissance;Swarm UAV systems;secure communication;disaster operations;surveillance;reconnaissance|
#### **2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)**
- DOI: 10.1109/ICCIKE58312.2023
- DATE: 9-10 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[IoT based Air Pollution Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131745)|N. Thakur; V. P. Mishra; S. M. N. Islam; Z. Neyaz; R. Jain; S. Roy|10.1109/ICCIKE58312.2023.10131745|Healthcare computing;low-cost sensing technologies;Pollution analysis;Air pollution monitoring system;GPS;Sensors and Internet Of Things;Healthcare computing;low-cost sensing technologies;Pollution analysis;Air pollution monitoring system;GPS;Sensors and Internet Of Things|
|[Review On Contemporary Constraints And Resolutions Regarding Electric Vehicles And Battery Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131737)|K. M. Shaji; R. Dudhe; A. F. Alex|10.1109/ICCIKE58312.2023.10131737|battery;electric vehicle;lithium-ion;cost;recycle;charge;renewable energy;battery;electric vehicle;lithium-ion;cost;recycle;charge;renewable energy|
|[Role of Artificial Intelligence in the Indian Judicial System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131795)|L. P. Gorlamudiveti; S. G. Sethu|10.1109/ICCIKE58312.2023.10131795|Artificial Intelligence;Judicial System;India;Legal research and Outcome Predictions;Artificial Intelligence;Judicial System;India;Legal research and Outcome Predictions|
|[Experimental Investigation of Incremental Sheet Metal Forming to Manufacture Complex Parts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131743)|M. A. Shaarawy; A. Mahrous; H. M. A. Hussein; M. Shazly; V. Naranje|10.1109/ICCIKE58312.2023.10131743|Incremental sheet metal forming;Complex part;Car hood;Aluminum alloy 1050;Incremental sheet metal forming;Complex part;Car hood;Aluminum alloy 1050|
|[Computer System for Detection and Classification of Welding Defects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131694)|A. A. A. Ramadan; H. M. A. Hussein; A. G. Mazloum; S. S. Sakr; V. Naranje|10.1109/ICCIKE58312.2023.10131694|Ultrasonic techniques;Phased array;welding discontinuities;convolutional neural network;Ultrasonic techniques;Phased array;welding discontinuities;convolutional neural network|
|[Academic Credential Authentication on Blockchain: DeLone and McLean model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131763)|S. Alvi; M. Iqbal|10.1109/ICCIKE58312.2023.10131763|Digital Certificates;Blockchain;IS success model;Kingdom Saudi Arabia;GCC;Digital Certificates;Blockchain;IS success model;Kingdom Saudi Arabia;GCC|
|[Energy based Machine Learning Spectrum Sensing in 5G Cognitive Radios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131872)|M. U. Muzaffar; R. Sharqi|10.1109/ICCIKE58312.2023.10131872|cognitive radio;spectrum sensing;machine learning;energy vector;cognitive radio;spectrum sensing;machine learning;energy vector|
|[Energy Storage System Analysis for Hybrid Wind-Solar Lighting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131817)|S. Sriprasanna; R. Dudhe|10.1109/ICCIKE58312.2023.10131817|Energy system;Fuel cell;PV system;Wind energy;Home appliance;Heat demand;Renewable energies;Simulink;Energy system;Fuel cell;PV system;Wind energy;Home appliance;Heat demand;Renewable energies;Simulink|
|[Application of Smart Systems and Innovative Solutions for Harmony Home “A Sustainable Net Zero Energy House”](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131848)|D. Tachouali; D. Sallam; K. Shakhshir; S. Al Zahabi; T. Wriekat; V. Ks|10.1109/ICCIKE58312.2023.10131848|Solar Decathlon Middle East;Innovation;Automation;Smart System;VRF System;BMS;Residential;Thermodynamic System;BIPV;Sustainable;Net Zero House;Solar Decathlon Middle East;Innovation;Automation;Smart System;VRF System;BMS;Residential;Thermodynamic System;BIPV;Sustainable;Net Zero House|
|[Digital Gender Divide In India: An Investigation of Policies Bridging The Gap In This New Age of Inequality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131833)|R. Raghuvanshi; R. Mishra|10.1109/ICCIKE58312.2023.10131833|Digital divide;gender disparity;inequality;digital literacy;inclusiveness;Digital divide;gender disparity;inequality;digital literacy;inclusiveness|
|[Influence of Anonymity on Online Counseling Platforms amongst Young Adults](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131883)|S. A. Hamdulay; A. Balodi Bhardwaj; R. Gupta; S. Riyaz|10.1109/ICCIKE58312.2023.10131883|Anonymity;Online counseling mental health;counselors;Anonymity;Online counseling mental health;counselors|
|[Analysis of Flexible Aircraft Design Using Reduced Order Modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131750)|S. Choudhary; V. H. Gaidhane; V. Naranje|10.1109/ICCIKE58312.2023.10131750|Reduced order model;Balanced truncation;Transfer functions;Stability analysis;Gain margin;Reduced order model;Balanced truncation;Transfer functions;Stability analysis;Gain margin|
|[Blockchain in Emission Management: Opportunities and Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131681)|T. Ali; P. Maheshwari|10.1109/ICCIKE58312.2023.10131681|blockchain;emission management;motivation;challenge;blockchain;emission management;motivation;challenge|
#### **2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP)**
- DOI: 10.1109/IHCSP56702.2023
- DATE: 4-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Suspicious Activity Detection Using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127155)|K. Barsagade; S. Tabhane; V. Satpute; V. Kamble|10.1109/IHCSP56702.2023.10127155|suspicious activity;deep learning;convolutional neural network;suspicious activity;deep learning;convolutional neural network|
|[Efficient Diffie Hellman Two Round Secret Key Agreement Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127113)|M. Pundir; A. Kumar; S. Choudhary|10.1109/IHCSP56702.2023.10127113|Asymmetric Encryption;Secret Key;Modular Arithmetic;Public Key Cryptosystem;Prime Number;Asymmetric Encryption;Secret Key;Modular Arithmetic;Public Key Cryptosystem;Prime Number|
|[Comparison of State Vector Machine and Decision Tree - Content Based Image Retrieval Algorithms to Perceive Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127132)|G. Kaur; S. Saini|10.1109/IHCSP56702.2023.10127132|QBIC;CBIR;Image Retrieval;SVM;DT;QBIC;CBIR;Image Retrieval;SVM;DT|
|[Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127182)|G. K. Arora; S. Taj Mahaboob; S. Adilakshmi; S. K. Rani; A. Kasthuri; U. Mamodiya|10.1109/IHCSP56702.2023.10127182|DWT;LWT;MRI;CT;DT-CWT;DWT;LWT;MRI;CT;DT-CWT|
|[Internet of Medical Things (Iotm) Based Sustainable Architecture For Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127209)|M. Dighriri|10.1109/IHCSP56702.2023.10127209|IoMT;ML;HRV;SVM;ECG;IoMT;ML;HRV;SVM;ECG|
|[Investigation of Seismic Signal Processing for Detection and Classification of Moving Unarmoured Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127162)|N. Nathani; U. N. Bera; S. Bhalerao|10.1109/IHCSP56702.2023.10127162|Seismic Signal;STFT;IA;IF;Seismic Signal;STFT;IA;IF|
|[An Effective Identification of Flavor Complaint By Adaptive Analysis of Electroencephalogram (EEG) Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127108)|V. Roy; S. Khaparkar; P. Tripathi|10.1109/IHCSP56702.2023.10127108|EEG;HICA;HPCA;ICA;PCA;EEG;HICA;HPCA;ICA;PCA|
|[Effective Multi-Data-Set Kernel Culture System Development in Data Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127157)|A. Chouksey; S. Pareyani; A. Rajak|10.1109/IHCSP56702.2023.10127157|SVM;WMKL;data mining;AI;Kernel;SVM;WMKL;data mining;AI;Kernel|
|[An Effective Analysis of Secured Power Aware Routing Protocol For Power and Energy Fortifying In Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127145)|R. V. Kshirsagar; S. Shah; A. Vishwakarma|10.1109/IHCSP56702.2023.10127145|WSN;SPARP;nodes;AES;AODV;WSN;SPARP;nodes;AES;AODV|
|[Analyzing Encrypted Cloud Data With Multiple Keywords Productively For Factories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127110)|S. Sahu; S. Bazal; V. Anand|10.1109/IHCSP56702.2023.10127110|SSE;SHS;E-TFIDF;SSE;SHS;E-TFIDF|
|[Realization of SWAP Gate using Electro-Optic Phenomenon-based Mach-Zehnder Interferometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127135)|M. Prajapat; K. K. Choure; P. P. Devi; G. Singh|10.1109/IHCSP56702.2023.10127135|Mach-Zehnder Interferometer (MZI);SWAP Gate;Beam Propagation Method (BPM);Mach-Zehnder Interferometer (MZI);SWAP Gate;Beam Propagation Method (BPM)|
|[Discrete and Continuous Time Chaos Based on True Random Number Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127186)|R. Patel; R. Gupta|10.1109/IHCSP56702.2023.10127186|RNG;TRNG;PRN;Chaos;LFSR;ADC;RNG;TRNG;PRN;Chaos;LFSR;ADC|
|[Design and Implementation of Pothole Detection & Instance Detection Based on A Unified Approach For Globally Roads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127163)|M. Pagale; S. Mulik; R. Purohit; A. Thakare; S. Jadhav|10.1109/IHCSP56702.2023.10127163|CNN;DNN;Instance detection;Pothole Detection;Accuracy;CNN;DNN;Instance detection;Pothole Detection;Accuracy|
#### **2023 International Conference on Networking and Communications (ICNWC)**
- DOI: 10.1109/ICNWC57852.2023
- DATE: 5-6 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Federated Learning with Internet of Things for Data Privacy and Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127281)|S. Cherukuvada; N. Krishnaraj; K. Bellam|10.1109/ICNWC57852.2023.10127281|Machine Learning;Internet of Things;Data Privacy;Security;Facebook Data;Privacy preserving;Federated Learning.;Machine Learning;Internet of Things;Data Privacy;Security;Facebook Data;Privacy preserving;Federated Learning.|
|[An Early Prediction Model for Chronic Kidney Disease Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127500)|R. Deepa; R. Priscilla; A. Pandi; B. Renukadevi|10.1109/ICNWC57852.2023.10127500|CKD;pre-processing data;extra tree classifier;decision tree;ML models;CKD;pre-processing data;extra tree classifier;decision tree;ML models|
|[Spam Detection in Text Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127456)|S. Kusumanjali; K. P. Kumar; U. V. Krishna; T. Anurag; K. Vignesh|10.1109/ICNWC57852.2023.10127456|;|
|[The Revelation of Tamil Cryptographic Lexicon –Connecting Tamil Characteristics and Cubic Curve](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127454)|J. Josepha Menandas; M. S. Christo|10.1109/ICNWC57852.2023.10127454|Tamil cryptography;translation;confidentiality;cubic curve;Hardy- Ramanujan number;points on infinity;Galois ffield;Tamil cryptography;translation;confidentiality;cubic curve;Hardy- Ramanujan number;points on infinity;Galois ffield|
|[Resource Prudent CNN Models for Disease Identification of Rice Crops](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127388)|T. P. P; B. Baranidharan|10.1109/ICNWC57852.2023.10127388|Convolutional Neural Networks;Image Recognition;Feature Extraction;Deep Learning;Rice Disease Identification.;Convolutional Neural Networks;Image Recognition;Feature Extraction;Deep Learning;Rice Disease Identification.|
|[Emotion Recognition in Speech Signals using MFCC and Mel-Spectrogram Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127355)|P. Muthuvel; T. Jaswanth; S. Firoz; S. N. Sri; N. Mukhesh|10.1109/ICNWC57852.2023.10127355|;|
|[FOOTHOLDER: Collection of tools and scripts used to learn and experiment the exploitation of vulnerable machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127486)|V. Nair; K. Yashwin; A. P. K|10.1109/ICNWC57852.2023.10127486|Vulnerability assessment and penetration testing;CLI- command line interface;enumeration;GUI - Graphical user interface;Nmap - Network Mapper;SSH - secure shell;FTP;Hydra-parallelized login cracker tool.;Vulnerability assessment and penetration testing;CLI- command line interface;enumeration;GUI - Graphical user interface;Nmap - Network Mapper;SSH - secure shell;FTP;Hydra-parallelized login cracker tool.|
|[IoT-Edge Computing for Efficient and Effective Information Process on Industrial Automation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127492)|M. R. Kumar; B. R. Devi; K. Rangaswamy; M. Sangeetha; K. V. R. Kumar|10.1109/ICNWC57852.2023.10127492|;|
|[Improving Reliability of Embedded RISC-V SoC for Low-cost Space Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127244)|A. S. Rao; R. Anilkumar; K. Padmapriya; K. Sudeendra Kumar|10.1109/ICNWC57852.2023.10127244|Reliability;SoC;RISC-V;Radiation;Fault Detection;Reliability;SoC;RISC-V;Radiation;Fault Detection|
|[Detection Of DDOS Attack using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127537)|S. Santhosh; M. Sambath; J. Thangakumar|10.1109/ICNWC57852.2023.10127537|Cyber Attack;Attack Detection Mechanisms;DDOS Attacks;Machine Learning;Random Forest Algorithm;XGboost Algorithm;Cyber Attack;Attack Detection Mechanisms;DDOS Attacks;Machine Learning;Random Forest Algorithm;XGboost Algorithm|
|[Product Authentication System using Blockchain*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127447)|R. Nagar; N. Chaturvedi; J. Prabakaran|10.1109/ICNWC57852.2023.10127447|Blockchain;Counterfeit;Supply Chain.;Blockchain;Counterfeit;Supply Chain.|
|[Decentralized Portfolio Tracking Application On A Polygon Layer 2 Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127419)|E. Sujatha; G. N. Kumar; N. V. Vikas; M. Y. Reddy|10.1109/ICNWC57852.2023.10127419|Html;Tailwind;NodeJS;Byzantine fault;tolerance;Merkel tree;Html;Tailwind;NodeJS;Byzantine fault;tolerance;Merkel tree|
|[Regression Analysis-Based Predictive Model for E-Commerce Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127390)|S. G; S. M; D. N|10.1109/ICNWC57852.2023.10127390|Sales prediction;Regression;Random Forest Regressor;Decision Tree Regressor.;Sales prediction;Regression;Random Forest Regressor;Decision Tree Regressor.|
#### **2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)**
- DOI: 10.1109/HOST55118.2023
- DATE: 1-4 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[ProcessorFuzz: Processor Fuzzing with Control and Status Registers Guidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133714)|S. Canakci; C. Rajapaksha; L. Delshadtehrani; A. Nataraja; M. B. Taylor; M. Egele; A. Joshi|10.1109/HOST55118.2023.10133714|greybox fuzzing;processor;coverage;RTL verification;RISC-V;greybox fuzzing;processor;coverage;RTL verification;RISC-V|
|[Targeted Bitstream Fault Fuzzing Accelerating BiFI on Large Designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133494)|S. Engels; M. Ender; C. Paar|10.1109/HOST55118.2023.10133494|FPGA security;fault injection;hardware reverse-engineering;FPGA security;fault injection;hardware reverse-engineering|
|[EC-CFI: Control-Flow Integrity via Code Encryption Counteracting Fault Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132915)|P. Nasahl; S. Sultana; H. Liljestrand; K. Grewal; M. LeMay; D. M. Durham; D. Schrammel; S. Mangard|10.1109/HOST55118.2023.10132915|fault attacks;control-flow integrity;encryption;fault attacks;control-flow integrity;encryption|
|[Low-Latency Masking with Arbitrary Protection Order Based on Click Elements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133813)|M. Simões; L. Bossuet; N. Bruneau; V. Grosso; P. Haddad; T. Sarno|10.1109/HOST55118.2023.10133813|Side-channel attacks;hardware masking;asynchronous circuits;low-latency;leakage assessment;Side-channel attacks;hardware masking;asynchronous circuits;low-latency;leakage assessment|
|[A Low-Randomness First-Order Masked Xoodyak](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133290)|S. Peng; B. Yang; S. Yin; H. Zhao; C. Zhao; S. Wei; L. Liu|10.1109/HOST55118.2023.10133290|Lightweight Cryptography;Xoodyak;DomainOriented Masking;Lightweight Cryptography;Xoodyak;DomainOriented Masking|
|[Security Order of Gate-Level Masking Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133208)|S. Takarabt; J. Bahrami; M. Ebrahimabadi; S. Guilley; N. Karimi|10.1109/HOST55118.2023.10133208|Gate-level masking;number of shares;threshold schemes/threshold implementation (TI);incompleteness order;high-order monovariate attacks;statistical moments of a distribution;Hamming weight least significant bit leakage;2nd-order leakage of threshold implementation style.;Gate-level masking;number of shares;threshold schemes/threshold implementation (TI);incompleteness order;high-order monovariate attacks;statistical moments of a distribution;Hamming weight least significant bit leakage;2nd-order leakage of threshold implementation style.|
|[SCALE: Secure and Scalable Cache Partitioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133713)|N. R. Holtryd; M. Manivannan; P. Stenström|10.1109/HOST55118.2023.10133713|;|
|[Advanced Covert-Channels in Modern SoCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133626)|L. Bossuet; C. A. Lara-Nino|10.1109/HOST55118.2023.10133626|covert channels;frequency modulation;multicluster;SoC-FPGAs;zynq ultrascale+;covert channels;frequency modulation;multicluster;SoC-FPGAs;zynq ultrascale+|
|[Lightweight Countermeasures Against Original Linear Code Extraction Attacks on a RISC-V Core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133316)|T. Gousselot; O. Thomas; J. -M. Dutertre; O. Potin; J. -B. Rigaud|10.1109/HOST55118.2023.10133316|linear code extraction;reverse engineering;hardware security;countermeasure;RISC-V;linear code extraction;reverse engineering;hardware security;countermeasure;RISC-V|
|[CIFER: Code Integrity and control Flow verification for programs Executed on a RISC-V core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133542)|A. Zgheib; O. Potin; J. -B. Rigaud; J. -M. Dutertre|10.1109/HOST55118.2023.10133542|CFG;CFI;CFEI;RISC-V;Trace Encoder;FIA;CFG;CFI;CFEI;RISC-V;Trace Encoder;FIA|
|[Improving Single-Trace Attacks on the Number-Theoretic Transform for Cortex-M4](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133270)|G. Assael; P. Elbaz-Vincent; G. Reymond|10.1109/HOST55118.2023.10133270|single-trace side-channel attacks;NTT;postquantum cryptography;CRYSTALS-Kyber;belief propagation;single-trace side-channel attacks;NTT;postquantum cryptography;CRYSTALS-Kyber;belief propagation|
|[Detour: Layout-aware Reroute Attack Vulnerability Assessment and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132919)|M. Gao; D. Forte|10.1109/HOST55118.2023.10132919|;|
|[Dual Channel EM/Power Attack Using Mutual Information and its Real-time Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10133261)|Y. Bai; J. Park; M. Tehranipoor; D. Forte|10.1109/HOST55118.2023.10133261|Side-channel attack;linear combination;RDCP;real-time;Side-channel attack;linear combination;RDCP;real-time|
#### **2023 Communication Strategies in Digital Society Seminar (ComSDS)**
- DOI: 10.1109/ComSDS58064.2023
- DATE: 12-12 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Automated Analysis of Communication Strategies of Telegram Channels in the Area of Psychology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130418)|I. Bogdanovskaya; B. Nizomutdinov; A. Uglova|10.1109/ComSDS58064.2023.10130418|communication strategies;information space;automated data collection;communication tactics;telegram;communication strategies;information space;automated data collection;communication tactics;telegram|
|[Factors of the City Inhabitants' Involvement in Housing Resident Chat Rooms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130412)|A. V. Mikhaylov; S. V. Uskova; E. A. Chizhova; A. A. Maximov|10.1109/ComSDS58064.2023.10130412|network society;online groups on social networks;virtual community;personal values;network society;online groups on social networks;virtual community;personal values|
|[Communication Strategies for Involving the Digital Generation in Scientific Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130421)|E. V. Strogetskaya; I. B. Betiger; M. P. Zamotin|10.1109/ComSDS58064.2023.10130421|student engagement;scientific communication;digital generation in science;undergraduate and graduate students of engineering specialties;professional identity;student engagement;scientific communication;digital generation in science;undergraduate and graduate students of engineering specialties;professional identity|
|[Communication Strategies for Reproducing Representations of the Past in Digital Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130413)|D. S. Artamonov; I. V. Suslov; V. A. Syusyukin|10.1109/ComSDS58064.2023.10130413|quantitative content analysis;digital media;traditional media;digitalization of media;communication strategies;quantitative content analysis;digital media;traditional media;digitalization of media;communication strategies|
|[Communication Strategies in the Digital Environment as a Tool to Form the Communicator's Personal Brand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130424)|A. S. Shimichev; M. B. Rotanova|10.1109/ComSDS58064.2023.10130424|communication strategies;digital environment;personal brand of the communicator;professional communication culture;professional training of the future communicator;communication strategies;digital environment;personal brand of the communicator;professional communication culture;professional training of the future communicator|
|[Communication Strategies of Wide Mediatization of a Sports Incident in Pluralistic Media Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130415)|D. P. Gavra; E. V. Akimovich; L. V. Balakhonskaya; L. A. Vitkova; V. V. Balakhonsky|10.1109/ComSDS58064.2023.10130415|communicative strategy;wide mediatization;value-based mediatization;mediatization of sports;image reduction tactics;sports incident;digital media;public opinion formation;communicative strategy;wide mediatization;value-based mediatization;mediatization of sports;image reduction tactics;sports incident;digital media;public opinion formation|
|[Criteria for Successful Socialization in the Digital Transformation of Society](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130410)|A. A. Shcherbina; E. A. Pashkovsky|10.1109/ComSDS58064.2023.10130410|socialization;personality;criteria of success;socialization standard;social capital;personal capital;social modeling;higher education;socialization;personality;criteria of success;socialization standard;social capital;personal capital;social modeling;higher education|
|[Demediatization 2.0. as a Communication Strategy: Definition, Basic Characteristics & Key Aspects of the Analysis Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130428)|D. P. Gavra; D. P. Shishkin; E. V. Bykova; Y. V. Taranova|10.1109/ComSDS58064.2023.10130428|mediatization;demediatization;demediatization 2.0;digital society;datafication;mediatized worlds;medialogics;event drmediatization;mediatization;demediatization;demediatization 2.0;digital society;datafication;mediatized worlds;medialogics;event drmediatization|
|[Demonstrativeness as a Communication Strategy in the Digital Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130360)|N. V. Shashkova; M. E. Kudryavtseva; M. S. Sigaeva|10.1109/ComSDS58064.2023.10130360|strategy;demonstrative behavior;imitation;social and digital status;monetization;strategy;demonstrative behavior;imitation;social and digital status;monetization|
|[Digital Influencer Factory: The Price of Word Authority](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130358)|A. V. Gorodishchev; A. N. Gorodishcheva; G. P. Kovalev|10.1109/ComSDS58064.2023.10130358|Computer-generated imagery;digital influence;media literacy;information authority;PR;digital ethic;Computer-generated imagery;digital influence;media literacy;information authority;PR;digital ethic|
|[Digital Media Communication Strategy Model of Russian Corporate Citizens](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130435)|L. V. Sharakhina|10.1109/ComSDS58064.2023.10130435|digital media;communication strategy;corporate citizen;digital media;communication strategy;corporate citizen|
|[Digital Tools in Conducting a Linguo-semiotic Study of Tactile Communication: the Potential of the Russian National Corpus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130429)|V. V. Boguslavskaya; A. G. Ratnikova; L. V. Sharakhina|10.1109/ComSDS58064.2023.10130429|the Russian National Corpus;digital technologies;digital tools;tactile communication;methods of verbal presentation;the Russian National Corpus;digital technologies;digital tools;tactile communication;methods of verbal presentation|
|[Gender as a Communication Issue in Mass Media and Politics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130364)|I. A. Bykov; A. N. Chernova|10.1109/ComSDS58064.2023.10130364|Gender;feminist theory of communication;communication strategies;mass media;politics;Gender;feminist theory of communication;communication strategies;mass media;politics|
#### **2023 IEEE 16th Dallas Circuits and Systems Conference (DCAS)**
- DOI: 10.1109/DCAS57389.2023
- DATE: 14-16 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Filter Assessment for Zeta Converter topologies in Photovoltaic applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130275)|Y. Zahid; X. Song|10.1109/DCAS57389.2023.10130275|Photovoltaic module;Dc-Dc converter;Zeta converter;MPPT;Photovoltaic module;Dc-Dc converter;Zeta converter;MPPT|
|[Virtualization for Automotive Safety and Security Exploration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130221)|M. R. Kabir; S. Ray|10.1109/DCAS57389.2023.10130221|;|
|[Efficient Bounding Box Selection for Object Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130252)|E. R. Adams; A. C. Depoian; A. G. Kurz; G. I. Cayce; J. C. Riley; C. P. Bailey; P. Guturu|10.1109/DCAS57389.2023.10130252|Efficient Machine Learning;Object Localization;Target Acquisition;Vision Transformer;Efficient Machine Learning;Object Localization;Target Acquisition;Vision Transformer|
|[MIMO Antenna Optimization: From Configuring Structure to Sizing with the aid of Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130253)|F. Mir; L. Kouhalvandi; F. Ebrahimi; L. Matekovits|10.1109/DCAS57389.2023.10130253|Multiple input;multiple output (MIMO);bottom-up optimization (BUO);artificial neural network (ANN);Multiple input;multiple output (MIMO);bottom-up optimization (BUO);artificial neural network (ANN)|
|[Solar Energy Modeling and Assessment in Texas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130234)|K. K. M. Siu; K. R. Adapala; D. Wu|10.1109/DCAS57389.2023.10130234|Solar Energy;Renewable Energy System;Photovoltaic Model;Texas;Solar Energy;Renewable Energy System;Photovoltaic Model;Texas|
|[A Filter-less RMS-based S-FSK demodulation technique for SunSpec rapid shutdown receiver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130177)|B. Yaseen; A. Akour; F. R. Shahroury; H. H. Ahmad|10.1109/DCAS57389.2023.10130177|MATLAB;Root-Mean-Square (RMS) demodulation;S-FSK;SunSpec;Rapid Shutdown(RSD);Filter-less;MATLAB;Root-Mean-Square (RMS) demodulation;S-FSK;SunSpec;Rapid Shutdown(RSD);Filter-less|
|[Understanding the Innovations Required for a Green & Secure Artificial Intelligence Paradigm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130257)|M. M. Rizvee; M. H. Rahman; P. Chakraborty; S. Shomaji|10.1109/DCAS57389.2023.10130257|Artificial Intelligence;Artificial Intelligence Security;Green Artificial Intelligence;Artificial Intelligence;Artificial Intelligence Security;Green Artificial Intelligence|
|[Potential and Pitfalls of Multi-valued Logic Circuits for Hardware Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130261)|T. Hossain; S. M. M. Ahsan; T. Hoque|10.1109/DCAS57389.2023.10130261|Multi-valued logic(MVL);ternary logic;2D materials;hardware security;hardware attacks;side-channel attacks;Multi-valued logic(MVL);ternary logic;2D materials;hardware security;hardware attacks;side-channel attacks|
|[Error Control Codes Based Modified Orthogonal Latin Squares for High-Speed Cache Memories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130241)|R. A. Ahmed|10.1109/DCAS57389.2023.10130241|Error control codes;Orthogonal Latin square codes;SECDED;cache memory;Error control codes;Orthogonal Latin square codes;SECDED;cache memory|
|[Fabrication and Characterization of Flexible Solid-State MIM Supercapacitor with Inkjet-Printing of Stacked Ag NP and Polymer Dielectric Layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130228)|M. R. Momota; T. Ferdous; T. Fujiwara; B. I. Morshed|10.1109/DCAS57389.2023.10130228|Energy storage;Inkjet-printing;Solid-state supercapacitor;Stacked MIM capacitor;Energy storage;Inkjet-printing;Solid-state supercapacitor;Stacked MIM capacitor|
|[Single Photon Detectors for Quantum Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130206)|M. S. Ara Shawkat; S. Hasan; N. McFarlane|10.1109/DCAS57389.2023.10130206|;|
|[Blending Entrepreneurial Education in Senior and Graduate-level Electrical Engineering Courses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130181)|S. Tabassum; P. Sundaravadivel|10.1109/DCAS57389.2023.10130181|Entrepreneurship;STAR cycle;Sensors;Semiconductor Devices;Internet-of-Things;Computational thinking;Engineering courses;Entrepreneurship;STAR cycle;Sensors;Semiconductor Devices;Internet-of-Things;Computational thinking;Engineering courses|
|[Advancement of PMCW Radar and Its Board-Level Prototyping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130242)|M. Brown; C. Li|10.1109/DCAS57389.2023.10130242|Automotive radar;noncontact measurement;orthogonal waveform encoding;PCB technology;phase modulated continuous wave (PMCW) radar;radar interference mitigation;system prototyping;Automotive radar;noncontact measurement;orthogonal waveform encoding;PCB technology;phase modulated continuous wave (PMCW) radar;radar interference mitigation;system prototyping|
#### **2023 International Symposium on Medical Robotics (ISMR)**
- DOI: 10.1109/ISMR57123.2023
- DATE: 19-21 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Development of a Preliminary Use Case for Socially Assistive Robot-Augmented Early Intervention with Clinical Stakeholders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130222)|M. M. Blankenship; C. Bodine|10.1109/ISMR57123.2023.10130222|;|
|[Smart Room with AI Capabilities for Efficient and Safe Doctor Checkup in the COVID era](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130225)|L. A. Mateos|10.1109/ISMR57123.2023.10130225|;|
|[Preliminary Theoretical Considerations of a Hand Orthosis Based on a Prestressed, Compliant Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130230)|L. Schaeffer; D. Herrmann; V. Boehm|10.1109/ISMR57123.2023.10130230|;|
|[In Situ Flexible Needle Adjustment Towards MRI-Guided Spinal Injections Based on Finite Element Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130218)|Y. Wang; Y. Xu; K. -W. Kwok; I. Iordachita|10.1109/ISMR57123.2023.10130218|;|
|[Open Source MR-Safe Pneumatic Radial Inflow Motor and Encoder (PRIME): Design and Manufacturing Guidelines*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130240)|A. L. Gunderman; M. Azizkhani; S. Sengupta; K. Cleary; Y. Chen|10.1109/ISMR57123.2023.10130240|;|
|[Closed-form Kinematic Model and Workspace Characterization for Magnetic Ball Chain Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130219)|G. Pittiglio; M. Mencattelli; P. E. Dupont|10.1109/ISMR57123.2023.10130219|Steerable Catheters/Needles;Surgical Robotics;Magnetic Actuation;Steerable Catheters/Needles;Surgical Robotics;Magnetic Actuation|
|[N-mirror Robot System for Laser Surgery: A Simulation Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130180)|G. Ma; W. Ross; P. J. Codd|10.1109/ISMR57123.2023.10130180|;|
|[A Radial Folding Mechanism to Enable Surgical Continuum Manipulators to Fit Through Smaller Ports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130276)|M. E. Smith; D. S. Esser; M. Rox; A. Kuntz; R. J. Webster|10.1109/ISMR57123.2023.10130276|;|
|[What Happens When Pneu-Net Soft Robotic Actuators Get Fatigued?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130227)|J. Libby; A. A. Somwanshi; F. Stancati; G. Tyagi; A. Patel; N. Bhatt; J. Rizzo; S. F. Atashzar|10.1109/ISMR57123.2023.10130227|;|
|[Physiological Motion Compensation in Patch Clamping using Electrical Bio-impedance Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130269)|K. Van Assche; Y. Zhang; M. Ourak; E. Verschooten; P. X. Joris; E. V. Poorten|10.1109/ISMR57123.2023.10130269|;|
|[Development of Robot-assisted Ultrasound System for Fetoscopic Tracking in Twin to Twin Transfusion Syndrome Surgery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130208)|Y. Cai; A. Davoodi; R. Li; M. Ourak; K. Niu; J. Deprest; E. V. Poorten|10.1109/ISMR57123.2023.10130208|ultrasound tracking;optical navigation;robot-assisted surgery;TTTS fetoscopic;ultrasound tracking;optical navigation;robot-assisted surgery;TTTS fetoscopic|
|[Towards Safe and Efficient Reinforcement Learning for Surgical Robots Using Real-Time Human Supervision and Demonstration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130214)|Y. Ou; M. Tavakoli|10.1109/ISMR57123.2023.10130214|;|
|[Concentric Tube Robot Optimization and Path Planning for Epilepsy Surgeries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10130244)|Z. Zou; J. Burgner-Kahrs; T. Looi; J. Drake|10.1109/ISMR57123.2023.10130244|;|
#### **2023 IEEE International Systems Conference (SysCon)**
- DOI: 10.1109/SysCon53073.2023
- DATE: 17-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Human View Dynamics Model for Regaining and Relinquishing Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131108)|H. A. H. Handley|10.1109/SysCon53073.2023.10131108|Human Viewpoint;Manned-Unmanned Teaming;Organizational Adaptation;Transition Graph;Human Viewpoint;Manned-Unmanned Teaming;Organizational Adaptation;Transition Graph|
|[Design parameter exploration for community-based flood early warning system with dynamic probabilistic performance assessment approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131274)|Y. Tomita; N. Kohtake|10.1109/SysCon53073.2023.10131274|community-based flood early warning system (CBFEWS);dynamic probabilistic performance assessment;sociotechnical systems;engineered system;sensitivity analysis;community-based flood early warning system (CBFEWS);dynamic probabilistic performance assessment;sociotechnical systems;engineered system;sensitivity analysis|
|[Architecting an Enterprise-of-Enterprises with the 10-Layer Rubric: Transforming EoE Decision-Making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131052)|J. S. Chavis; J. G. Osborn; D. P. Syed; R. Terrell|10.1109/SysCon53073.2023.10131052|Enterprise;Architecture;Framework;Enterprise;Architecture;Framework|
|[Thirteen concepts to play it safe with the cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131180)|T. Hendriks; B. Akesson; J. Voeten; M. Hendriks; J. C. Parada; M. García-Gordillo; S. Sáez; J. J. Valls|10.1109/SysCon53073.2023.10131180|Safety-critical systems;cloud computing;edge computing;safety and performance concepts;performance management;service continuity;operational mode management;Safety-critical systems;cloud computing;edge computing;safety and performance concepts;performance management;service continuity;operational mode management|
|[Adaptive Backstepping Control of Electro Hydrostatic Actuator with Improved Parameter Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131065)|Y. Huang; J. Liu; Z. Chen; J. Gu|10.1109/SysCon53073.2023.10131065|electro hydrostatic actuator;adaptive control;parameter estimation;electro hydrostatic actuator;adaptive control;parameter estimation|
|[Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131138)|M. H. Bin Shahid; A. Azim|10.1109/SysCon53073.2023.10131138|Fault Detection;Fault Classification;Machine Learning (ML);Transmission Lines (TL) Fault;Ensemble Method;Fault Detection;Fault Classification;Machine Learning (ML);Transmission Lines (TL) Fault;Ensemble Method|
|[Stakeholder Needs in Systems Engineering: A Proposal for a Formal Definition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131072)|A. Kubin; S. Wagenmann; F. Reichert; C. Mandel; A. Albers|10.1109/SysCon53073.2023.10131072|Stakeholder Needs;Requirements Engineering;ASE - Advanced Systems Engineering;SGE - System Generation Engineering;Systematic Literature Review;Stakeholder Needs;Requirements Engineering;ASE - Advanced Systems Engineering;SGE - System Generation Engineering;Systematic Literature Review|
|[Monkeypox Detection and Classification Using Deep Learning Based Features Selection and Fusion Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131067)|S. Maqsood; R. Damaševičius|10.1109/SysCon53073.2023.10131067|deep learning;monkeypox;transfer learning;deep features;classification;deep learning;monkeypox;transfer learning;deep features;classification|
|[Reference Architecture in Relation to Business Reasoning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131128)|S. Acur; T. Hendriks|10.1109/SysCon53073.2023.10131128|Reference Architecture;business strategy;differentiators;core and non-core elements;e-bike companies;Reference Architecture;business strategy;differentiators;core and non-core elements;e-bike companies|
|[Modeling A UAV Surveillance Scenario- An Applied MBSE Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131074)|V. Lopez; A. Akundi|10.1109/SysCon53073.2023.10131074|MBSE;SysML;MBSE framework;UA V;Surveillance;MBSE;SysML;MBSE framework;UA V;Surveillance|
|[Optimization of Key Levers of Influence In Knowledge and Ease-of-Change Management and Addressing Variability in Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131111)|R. K. Jonkers|10.1109/SysCon53073.2023.10131111|systems engineering;optimization;decision-making;set- based design;robust design;knowledge management;system dynamics;Monte Carlo simulation;chaos theory;systems engineering;optimization;decision-making;set- based design;robust design;knowledge management;system dynamics;Monte Carlo simulation;chaos theory|
|[Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131100)|S. Mandapaka; C. Diaz; H. Irisson; A. Akundi; V. Lopez; D. Timmer|10.1109/SysCon53073.2023.10131100|deep learning;industry 4.0;smart factory;machine vision system;quality control automation;product defect detection;CNN;object detection;object recognition;image classification;deep learning;industry 4.0;smart factory;machine vision system;quality control automation;product defect detection;CNN;object detection;object recognition;image classification|
|[Challenges and Opportunities in the Digital Engineering Simulation Curriculum Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131107)|H. Y. See Tao; N. Hutchison; M. Clifford; G. Kerr; P. Beling; T. Sherburne; P. Wach; D. Long; C. Arndt; D. Verma; T. A. McDermott|10.1109/SysCon53073.2023.10131107|digital engineering;digital transformation;modeling and simulation;architecture;curriculum development;simulation training environment;workforce development;digital engineering competencies;and cyber resilience;digital engineering;digital transformation;modeling and simulation;architecture;curriculum development;simulation training environment;workforce development;digital engineering competencies;and cyber resilience|
#### **2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)**
- DOI: 10.1109/ICARSC58346.2023
- DATE: 26-27 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Will robots beat the human world champion football in 2050?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129632)|R. van de Molengraft|10.1109/ICARSC58346.2023.10129632|;|
|[Human Action Understanding and Anticipation: From humanoid robots to brain function and cognitive systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129547)|J. Santos-Victor|10.1109/ICARSC58346.2023.10129547|;|
|[Applying 3D Object Detection from Self-Driving Cars to Mobile Robots: A Survey and Experiments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129637)|M. K. Wozniak; V. Kårefjärd; M. Hansson; M. Thiel; P. Jensfelt|10.1109/ICARSC58346.2023.10129637|perception;mobile robots;object detection;perception;mobile robots;object detection|
|[Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129564)|K. Saunders; G. Vogiatzis; L. J. Manso|10.1109/ICARSC58346.2023.10129564|Computer vision;Autonomous vehicles;3D/stereo scene analysis;Vision and Scene Understanding;Computer vision;Autonomous vehicles;3D/stereo scene analysis;Vision and Scene Understanding|
|[Double Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129640)|J. Kiefer; K. Dorer|10.1109/ICARSC58346.2023.10129640|Deep Reinforcement Learning;Curriculum Learning;Proximal Policy Optimization (PPO);kick learning;Deep Reinforcement Learning;Curriculum Learning;Proximal Policy Optimization (PPO);kick learning|
|[LiDAR-Based Augmented Reality for the Development of Test Scenarios on Safety for Autonomous Operation of a Shunting Locomotive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129540)|N. Kohlisch; P. Koch; S. May|10.1109/ICARSC58346.2023.10129540|LiDAR;augmented reality;test scenario generation;LiDAR;augmented reality;test scenario generation|
|[Sensor Placement Optimization using Random Sample Consensus for Best Views Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129597)|C. M. Costa; G. Veiga; A. Sousa; U. Thomas; L. Rocha|10.1109/ICARSC58346.2023.10129597|Best views estimation;sensor placement optimization;random sample consensus;bin picking;Best views estimation;sensor placement optimization;random sample consensus;bin picking|
|[A data acquisition system to capture extreme human driving behaviour](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129538)|I. R. Lima; J. Santos-Victor|10.1109/ICARSC58346.2023.10129538|Autonomous Vehicles;Driving Safety;Expert Driving;Behavioural Engineering;Sensing System;Autonomous Vehicles;Driving Safety;Expert Driving;Behavioural Engineering;Sensing System|
|[People Re-Identification in Service Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129612)|V. Pinto; R. Bettencourt; R. Ventura|10.1109/ICARSC58346.2023.10129612|Human-Robot Interaction;People Re-ID;People Tracking;Multiple Kalman-filter;RGB-D dataset;Human-Robot Interaction;People Re-ID;People Tracking;Multiple Kalman-filter;RGB-D dataset|
|[ECE-based Deep Ensemble for Neural Network Calibration in Satellite Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129562)|P. Conde; T. Barros; C. Premebida; U. J. Nunes|10.1109/ICARSC58346.2023.10129562|satellite imagery;deep learning;uncertainty calibration;deep ensemble;satellite imagery;deep learning;uncertainty calibration;deep ensemble|
|[Exploring tactile sensing to perform the power grasp of a human-robot handshake](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129544)|A. R. C. Pagaimo; P. Moreno|10.1109/ICARSC58346.2023.10129544|Human-Robot Handshaking;Tactile Sensing;Robotic Hand Design;Physical Human-Robot Interaction;Human-Robot Handshaking;Tactile Sensing;Robotic Hand Design;Physical Human-Robot Interaction|
|[Harvesting tomatoes with a Robot: an evaluation of Computer-Vision capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129601)|A. Stepanova; H. Pham; N. Panthi; A. Zourmand; F. Christophe; M. -P. Laakkonen; M. Gautam; M. Järvinen; V. Tuomela|10.1109/ICARSC58346.2023.10129601|Agriculture;Computer Vision;Robotics;Agriculture;Computer Vision;Robotics|
|[Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129619)|L. C. Klein; J. Braun; F. N. Martins; H. Wörtche; A. S. de Oliveira; J. Mendes; V. H. Pinto; P. Costa; J. Lima|10.1109/ICARSC58346.2023.10129619|Indoor Localization;Machine Learning;RobotAtFactory 4.0;Robotics Competitions;Embedded systems;Indoor Localization;Machine Learning;RobotAtFactory 4.0;Robotics Competitions;Embedded systems|
#### **19th International Conference on AC and DC Power Transmission (ACDC 2023)**
DATE: 1-3 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Coordinated control of VSC-HVDC synchronous grid forming and grid following stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136518)|C. Barker; J. Fradley; A. Adamczyk|10.1049/icp.2023.1300|;|
|[Onshore and offshore AC grids short-circuit analysis of VSC-HVDC integrated offshore wind power plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136512)|J. Song; M. Cheah-Mane; E. Prieto-Araujo; O. Gomis-Bellmunt|10.1049/icp.2023.1301|;|
|[AC power system stability with voltage source converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136504)|R. H. Renner|10.1049/icp.2023.1302|;|
|[Managing harmonics and resonances in HVDC connected 66 kV offshore windfarms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136477)|C. Karlsson|10.1049/icp.2023.1303|;|
|[Preventive DC-side decoupling: a control and operation concept to limit the impact of DC faults in offshore multi-terminal HVDC systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136511)|P. Düllmann; C. Klein; P. Winter; H. Köhler; M. Steglich; J. Teuwsen; A. Moser|10.1049/icp.2023.1304|;|
|[Protection study for SST-integrated LVDC networks with multiple feeders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136498)|V. Psaras; R. Peña-Alzola; I. Abdulhadi; M. Syed; G. Burt; A. Kazerooni; F. Shillitoe|10.1049/icp.2023.1305|;|
|[Comparative research on DC braking choppers for VSC-HVDC with offshore wind farms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136473)|C. Wu; X. P. Zhang; X. Zhou; D. Kong|10.1049/icp.2023.1306|;|
|[Analysis and extension of switched inductor and capacitor based high gain DC-DC boost converter with flexible control at higher gains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136479)|A. Iqbal; M. Samiullah; M. Al-Hitmi; A. M. Al-Wahedi|10.1049/icp.2023.1307|;|
|[Optimal hybrid multiplexed AC-DC-AC power converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136502)|M. Deakin|10.1049/icp.2023.1308|;|
|[Grid following converters stability study and control enhancements using an improved test setup](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136474)|Y. Lamrani; L. Huang; F. Colas; X. Guillaud; F. Blaajberg; C. Cardozo; T. Prevost|10.1049/icp.2023.1309|;|
|[A unified fault analysis framework for MMC VSC-HVDC scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136466)|L. Zou|10.1049/icp.2023.1310|;|
|[An impedance-based fault detection and classification technique for DC microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136506)|P. Chauhan; C. P. Gupta; M. Tripathy|10.1049/icp.2023.1311|;|
|[Localized fault detection and classification technique for low voltage DC microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10136478)|S. Sharma; M. Tripathy|10.1049/icp.2023.1312|;|
#### **2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)**
- DOI: 10.1109/ICOEI56765.2023
- DATE: 11-13 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Independent PV-Battery Systems by Combining Autonomous Incremental Conductance Particle Swarm Technique with a Power Management Circuitry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125591)|P. Anitha; B. Selvakumar; A. S. Kamaraja; K. K. Kumar|10.1109/ICOEI56765.2023.10125591|SPV;Lithium Batteries;IC Particle Swarm algorithms;State of Charging;Power managing circuitry;SPV;Lithium Batteries;IC Particle Swarm algorithms;State of Charging;Power managing circuitry|
|[Development of an E-Commerce System using MEAN Stack with NX Monorepo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125948)|S. L. J. Shabu; S. P. Kumar; R. Pranav; S. Refonaa; Maheswari|10.1109/ICOEI56765.2023.10125948|Distributed task execution;Computation;Flexibility;Distributed task execution;Computation;Flexibility|
|[Utilizing Machine Learning Models to Determine the Security Level of Different Cryptosystems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125757)|K. Pranathi; B. L. Priya; A. Y. Felix|10.1109/ICOEI56765.2023.10125757|Cryptosystem;Image encryption;Security analysis;SVM (Support vector machine);Cryptosystem;Image encryption;Security analysis;SVM (Support vector machine)|
|[Face Detection based Secured ATM System with Two Step Verification using Fisher Face Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125744)|V. Praveena; A. S; A. S. S; G. K; K. M|10.1109/ICOEI56765.2023.10125744|ATM (Automated Teller Machine);Security;Face recognition;Mobile Application;OTP (One Time Password);ATM (Automated Teller Machine);Security;Face recognition;Mobile Application;OTP (One Time Password)|
|[An Enhanced Decentralized Social Network based on Web3 and IPFS using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125612)|D. Palanikkumar; G. Arun; R. Arunadevi; S. R. Gayathri; P. Dharun|10.1109/ICOEI56765.2023.10125612|Blockchain;Social Networks;Decentralized System;Privacy;Inter Planetary File System (IPFS);Ethereum;Blockchain;Social Networks;Decentralized System;Privacy;Inter Planetary File System (IPFS);Ethereum|
|[Docker Networking: A Security Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126070)|S. Chamoli; V. Mittal|10.1109/ICOEI56765.2023.10126070|Docker;Network;Security;Container;Bridge;Capability;OpenSSL;Docker;Network;Security;Container;Bridge;Capability;OpenSSL|
|[Mitigating Cyber-Security Risks using Cyber-Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126001)|A. Anand; A. Chirputkar; P. Ashok|10.1109/ICOEI56765.2023.10126001|Cyber-Security;Cyber-analytics;Machine Learning;Artificial Intelligence;Cyber-Security;Cyber-analytics;Machine Learning;Artificial Intelligence|
|[A Comprehensive and Secure Trustless Blockchain Framework for Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125789)|S. Rajendar; U. Thangavel; S. Devendran; V. Selvi; S. S. Muthumanickam|10.1109/ICOEI56765.2023.10125789|Autonomous Vehicles;Blockchain;Electronic control unit;Roadside unit;Vehicle Contact Address;Autonomous Vehicles;Blockchain;Electronic control unit;Roadside unit;Vehicle Contact Address|
|[A Novel Decentralized Product Verification using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125833)|S. S. D; M. F. M. P; S. B. S. M; S. K; R. Reshma; S. P. Sasirekha|10.1109/ICOEI56765.2023.10125833|Supply chain;Blockchain. Hyperledger Fabric;Hyperledger Composer;Hyperledger playground;IPFS;Supply chain;Blockchain. Hyperledger Fabric;Hyperledger Composer;Hyperledger playground;IPFS|
|[A Data Security-based Efficient Compression and Encryption for Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125686)|S. K. P; S. P; L. R; M. N|10.1109/ICOEI56765.2023.10125686|Cloud technology;cyber security;cryptography;quantization;two-factor authentication;public-key infrastructure (PKI);Cloud technology;cyber security;cryptography;quantization;two-factor authentication;public-key infrastructure (PKI)|
|[RSA Cryptography and GZIP Steganography Techniques for Information Hiding and Security using Java](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125906)|P. GURUNATHAN; R. S. DEVI|10.1109/ICOEI56765.2023.10125906|Cryptography;Information Security;Intruders;Rivest;Shamir;Adleman (RSA) algorithm;Steganography;Cryptography;Information Security;Intruders;Rivest;Shamir;Adleman (RSA) algorithm;Steganography|
|[An End to End Blockchain based Non- Fungible Token Platform for Buying and Selling Digital Arts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126017)|D. A. Patil; V. Nagwekar; M. Arunkumar; S. Datta; A. Angelin Florence|10.1109/ICOEI56765.2023.10126017|Non-fungible tokens;Blockchain;Cryptocurrency;Decentralized;Non-fungible tokens;Blockchain;Cryptocurrency;Decentralized|
|[Generation of Hilarious Animated Characters using GAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125904)|D. E. V; V. V. H. Kumar; R. D. Kumar; V. M. Sahithi|10.1109/ICOEI56765.2023.10125904|Generative Adversarial Networks;Animation;Neural Network;Zero-Sum game;Generative Adversarial Networks;Animation;Neural Network;Zero-Sum game|
#### **2023 International Conference on Computer Communication and Informatics (ICCCI)**
- DOI: 10.1109/ICCCI56745.2023
- DATE: 23-25 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Blockchain Based Certificate Authentication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128289)|G. Balamurugan; K. K. A. Sahayaraj|10.1109/ICCCI56745.2023.10128289|Blockchain;Digital Certificate;Certificate Verification;Hyper ledger;Hashing;Blockchain;Digital Certificate;Certificate Verification;Hyper ledger;Hashing|
|[Secure and Proficient Provable Data Procurity With Privacy Protection in Cloud Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128477)|D. C. S. Kumar; L. Vasantha; P. Siroshini; P. Jagruthi; N. Pavithra; T. Supriya|10.1109/ICCCI56745.2023.10128477|SEPDP;Data owners;User;Storage in the cloud;SEPDP;Data owners;User;Storage in the cloud|
|[Identification of Pneumonia Symptoms in Covid19 patients using Transfer Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128630)|P. Ebin; B. K. Athira|10.1109/ICCCI56745.2023.10128630|Covid19;Deep Learning;Transfer Learning;VGG16;X-Ray Images;Covid19;Deep Learning;Transfer Learning;VGG16;X-Ray Images|
|[Detection and Conversion of Co2 Into Moist](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128611)|S. S. S. V. Suryasri; M. S. S. Reddy; E. L. Reddy; M. N. Kumar|10.1109/ICCCI56745.2023.10128611|MQ-135;LCD (Liquid-Crystal Display);CO2 (Carbon-dioxide);Arduino;LED (Light Emitting Diode);MQ-135;LCD (Liquid-Crystal Display);CO2 (Carbon-dioxide);Arduino;LED (Light Emitting Diode)|
|[Vehicle Lane Detection for Accident Prevention and Smart Autodrive Using OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128394)|M. Garg; A. Sehrawat; P. Savaridassan.|10.1109/ICCCI56745.2023.10128394|Lane Detection;Artificial Intelligence;Computer Vision;OpenCV;Gamma Correction;Lane Detection;Artificial Intelligence;Computer Vision;OpenCV;Gamma Correction|
|[An IOT Framework based Automated Wireless Meter System for Monitoring, Billing and Controlling Power Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128510)|K. Harini.; V. Anbarasu|10.1109/ICCCI56745.2023.10128510|Meters;Monitoring;Relays;GSM;Smart Energy Meter;Meters;Monitoring;Relays;GSM;Smart Energy Meter|
|[An Autonomous Crop-Cutting Mechanism Using A Drone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128584)|T. S. Sree; M. Srinivas; K. J. Swaroop; B. S. V. S. Pavan; P. Abhishek|10.1109/ICCCI56745.2023.10128584|Autonomous vehicle;Agriculture;Drone;Tractor;Hexacopter;Sensors;Autonomous vehicle;Agriculture;Drone;Tractor;Hexacopter;Sensors|
|[IoT Based Identification And Attendance Monitoring System Using Design Thinking Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128367)|K. Sangeetha; M. Shobana; V. S. Nagul Pranav; S. Darunya; K. P. Madhumitha; M. Nidharshini|10.1109/ICCCI56745.2023.10128367|RFID;attendance monitoring system;attendance;detect fraud;save time.;RFID;attendance monitoring system;attendance;detect fraud;save time.|
|[Unmanned Plant Irrigation System Using Iot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128295)|R. S. Ram; E. G; T. Karuppasamy; A. Jayapalan|10.1109/ICCCI56745.2023.10128295|IOT;Farm;Sensors;Soil Moisture;Irrigation;LDR;IOT;Farm;Sensors;Soil Moisture;Irrigation;LDR|
|[The Role of Artificial Intelligence in Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128180)|S. Ganesan; N. Somasiri; S. Pokhrel|10.1109/ICCCI56745.2023.10128180|Computer Assisted Instruction;iTutor;E-TCL expert tutoring system;meta model MDE stage;ontology;semantic language;Computer Assisted Instruction;iTutor;E-TCL expert tutoring system;meta model MDE stage;ontology;semantic language|
|[Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128231)|S. Ganesan; N. Somasiri; C. Colombage|10.1109/ICCCI56745.2023.10128231|Sentiment;LSTM;CBOW;Custom Embedding Model;Word2Vec Model;Averaging Model;GloVe Model;Sentiment;LSTM;CBOW;Custom Embedding Model;Word2Vec Model;Averaging Model;GloVe Model|
|[Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128281)|H. Ashok; S. Ganesan; N. Somasiri|10.1109/ICCCI56745.2023.10128281|IoT;security;Blockchain;IoT;security;Blockchain|
|[Detecting And Mitigating Selfish Secondary Users In Cognitive Radio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128483)|S. Thennavan; T. Karuppasamy; P. Savarinathan; A. Jayapalan|10.1109/ICCCI56745.2023.10128483|Cognitive Radio;PUEA;helper node;Hash Function;Rijndael algorithm;Two layer authentication tag.;Cognitive Radio;PUEA;helper node;Hash Function;Rijndael algorithm;Two layer authentication tag.|
#### **2023 Wireless Telecommunications Symposium (WTS)**
- DOI: 10.1109/WTS202356685.2023
- DATE: 19-21 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Towards Generating True Random Numbers using Magnetoresistive RAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131866)|J. Dreyer; R. Tönjes; N. Aschenbruck|10.1109/WTS202356685.2023.10131866|;|
|[A comparison of power allocation mechanisms for 5G D2D mobile communication networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131761)|D. Georgiadis; E. Politi; G. Dimitrakopoulos|10.1109/WTS202356685.2023.10131761|D2D communications;Energy efficiency;D2D communications;Energy efficiency|
|[BER Analysis of Cross-QAM Uncoded Space-Time Labeling Diversity with Three Transmit Antennas in Nakagami-m Fading Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131720)|D. Ayanda; M. Costa; B. S. Da Silva; D. Mate; S. Solwa|10.1109/WTS202356685.2023.10131720|Cross QAM;heuristic algorithm;labeling mapper;Nakagami-m fading channels;wireless communications;Cross QAM;heuristic algorithm;labeling mapper;Nakagami-m fading channels;wireless communications|
|[Concurrent Transmitting LiDAR Sensor with Bipolar Optical Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131707)|G. Kim; J. Eom; Y. Park|10.1109/WTS202356685.2023.10131707|LiDAR sensor;bipolar opitcal code;code-inversion keying;LiDAR sensor;bipolar opitcal code;code-inversion keying|
|[Encryption-Aware PHY Security for Wiretap Channels with Multiple Independent Jammers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131796)|T. Sadig; M. Maleki; N. H. Tran; H. R. Bahrami|10.1109/WTS202356685.2023.10131796|Physical layer security;encryption;Gaussian wiretap channel;rate-equivocation region;secrecy capacity;cooperative jamming;Physical layer security;encryption;Gaussian wiretap channel;rate-equivocation region;secrecy capacity;cooperative jamming|
|[Implementation and Investigation of High Endurance UAVs in a 4G LTE Network for Monitoring Power Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131813)|S. Ahmed; D. G. Lee; J. Vong; J. Kurone-Ghariban; C. J. Matchinsky; T. Kester; M. Cha; S. Dobbs; Z. Yu|10.1109/WTS202356685.2023.10131813|;|
|[VLC Indoor Positioning Using RFR and SVM Reduced Features Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131742)|A. Affan; H. M. Asif; N. Tarhuni|10.1109/WTS202356685.2023.10131742|Random Forest Regression;Support Vector Machine;Visible Light Communication;Simulation;Channel Model;Random Forest Regression;Support Vector Machine;Visible Light Communication;Simulation;Channel Model|
|[Elbow estimation -based source enumeration method for LPI/LPD signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131679)|R. Sarjonen; M. Höyhtyä|10.1109/WTS202356685.2023.10131679|source enumeration;antenna arrays;low probability of detection;electronic warfare;source enumeration;antenna arrays;low probability of detection;electronic warfare|
|[Demonstration of Open Radio Access Network Intelligent Controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131759)|P. R. B. da Silva; J. P. S. H. Lima; V. Afanasiev; L. H. M. S. Eduardo Melão; M. S. P. Facina|10.1109/WTS202356685.2023.10131759|Open RAN;RIC;demo;Machine Learning;xApps;Open RAN;RIC;demo;Machine Learning;xApps|
|[Methods of Automating Power Swapping Mechanisms for Extending UAV Flight Missions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131699)|C. Lai; M. T. Chau; R. Thai; S. Souvannakoumane; C. Watagoda; K. Oda; S. Robinson; G. G. Razungles; M. Hoang; S. Dobbs; Z. Yu|10.1109/WTS202356685.2023.10131699|Index Terms—UAV;power management;power system;endurance;drones;ground station;lipo battery;battery swapping;wireless induction charging;robotic arm;battery vending machine;Index Terms—UAV;power management;power system;endurance;drones;ground station;lipo battery;battery swapping;wireless induction charging;robotic arm;battery vending machine|
|[Enhanced Pseudonym Changing in VANETs: How Privacy is Impacted Using factitious Beacons](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131712)|J. Wang; Y. Sun; C. Phillips|10.1109/WTS202356685.2023.10131712|factitious beacon;mix-zone;privacy;pseudonym;VANET simulation;factitious beacon;mix-zone;privacy;pseudonym;VANET simulation|
|[Quantifying and Improving Resilience in the Informal Social Networks of Organizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131676)|J. D. Caddell; R. Nilchiani|10.1109/WTS202356685.2023.10131676|social network analysis;resilience;complex systems;social network analysis;resilience;complex systems|
|[Federated/Deep Learning in UAV Networks for Wildfire Surveillance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10131685)|A. E. Hoffy; S. Seok-Chul Kwon; H. -G. Yeh|10.1109/WTS202356685.2023.10131685|;|
#### **2023 2nd International Conference on Mechatronics and Electrical Engineering (MEEE)**
- DOI: 10.1109/MEEE57080.2023
- DATE: 10-12 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Rapid Trials-and-Errors Approach Based on Time-domain EMI Testing–a New Way to Speed up Product EMC Compliance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127024)|W. Wu; Y. Wu|10.1109/MEEE57080.2023.10127024|Trial-and-error;EMC;pre-compliance;EMI;measurement;Trial-and-error;EMC;pre-compliance;EMI;measurement|
|[Analysis of Fluid Field and Temperature Field of Permanent Magnet Synchronous Motor for Special Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126592)|J. Li; J. Gao; Z. He; S. Huang; C. Hu; Y. Wu|10.1109/MEEE57080.2023.10126592|PMSM;flow field;temperature field;cooling system;wind friction loss;PMSM;flow field;temperature field;cooling system;wind friction loss|
|[Underwater Target Motion Analysis with Dynamic Sensor Selection in Multi-Sensor Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126794)|V. P. Dubey; R. K. Singh; S. Bhaumik|10.1109/MEEE57080.2023.10126794|Fisher information matrix;sensor selection;underwater tracking;Fisher information matrix;sensor selection;underwater tracking|
|[Capacitor Allocation Optimization for Improved Distribution Network Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126738)|A. Jimoh; S. O. Ayanlade; F. K. Ariyo; A. Aremu; B. A. Jimoh; M. A. Jimoh|10.1109/MEEE57080.2023.10126738|capacitor allocation;dingo optimization algorithm;voltage profile;real power loss;reactive power loss;capacitor allocation;dingo optimization algorithm;voltage profile;real power loss;reactive power loss|
|[Intelligent Power Management Control System Modelling for Battery/Supercapacitor Electric Vehicles Using MBSE and SysML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126676)|M. -A. Mossadak; A. Chebak; A. A. Elmahjoub|10.1109/MEEE57080.2023.10126676|Electric vehicle;Battery;Supercapacitor;SysML;MBSE;Intelligent PMCS;Electric vehicle;Battery;Supercapacitor;SysML;MBSE;Intelligent PMCS|
|[Highly Sensitive MIM-Based Semi-circular Refractive Index Sensor for Detection of Glucose Concentration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126507)|S. Saima; I. A. Pranti; U. K. Saifa; S. A. Mumu; R. H. Sagor|10.1109/MEEE57080.2023.10126507|Refractive Index Sensor;Biosensor;Surface Plasmon Polaritons (SPPs);Metal-Insulator-Metal waveguide;Finite Element Method (FEM);Refractive Index Sensor;Biosensor;Surface Plasmon Polaritons (SPPs);Metal-Insulator-Metal waveguide;Finite Element Method (FEM)|
|[Numerical Simulation and Experimental Study on Focusing of Magnetostrictive Guided Wave Phased Array in Pipeline](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126523)|P. Ma; Y. Yang; J. Wang; W. Jiang; G. Zhao; P. Liang; B. Li; J. Li; Y. He|10.1109/MEEE57080.2023.10126523|magnetostriction;guided waves;phased array;focus;defect determination;magnetostriction;guided waves;phased array;focus;defect determination|
|[Proof of Concept of a Caterpillar-type Robot Explorer for Seismic Events in Areas of the Peruvian Fire Belt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126887)|K. Cuellar-Canales; J. Salas-Vega; C. Belito-Orihuela; A. Garcia-Romero; J. Huaripata-Leon; A. Aquino-Fernandez|10.1109/MEEE57080.2023.10126887|Proof of concept;robot explorer;earthquakes;fire belt;Proof of concept;robot explorer;earthquakes;fire belt|
|[Structural Design and Simulation Analysis of Two-Dimensional Space Turntable](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126682)|Y. Wang; J. Tian; W. Feng; S. Chen; D. Gu; X. Mou|10.1109/MEEE57080.2023.10126682|high precision and rigidity;the resistance torque;the shaft structure;structural design;simulation analysis;high precision and rigidity;the resistance torque;the shaft structure;structural design;simulation analysis|
|[Design of Transcranial Electromagnetic Therapy Device Based on Kano](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126737)|M. Chang; D. Yan; Y. Wang; S. Chen|10.1109/MEEE57080.2023.10126737|perceptual engineering;kano model;cervicocranial electromagnetic therapeutic apparatus;autism;spss analysis;perceptual engineering;kano model;cervicocranial electromagnetic therapeutic apparatus;autism;spss analysis|
|[Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126810)|J. Kwak; W. Jo; S. Lee; H. Kim; J. Koo; D. Kim|10.1109/MEEE57080.2023.10126810|similarity;cutting force;Siamese neural network;dynamic time warping;Manhattan distance;long short-term memory;similarity;cutting force;Siamese neural network;dynamic time warping;Manhattan distance;long short-term memory|
|[Stabilization and Setpoint Tracking for a Class of Systems with Matched and Unmatched Perturbations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126852)|L. Dritsas|10.1109/MEEE57080.2023.10126852|Control;Automation and Control Systems;Robotics And Mechatronics;Control;Automation and Control Systems;Robotics And Mechatronics|
|[A New Architecture of Augmented Reality Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127071)|Z. Xu; S. Wu; L. Zhang|10.1109/MEEE57080.2023.10127071|augmented reality;system architecture;date management;multi-platform support;virtual object tracking;augmented reality;system architecture;date management;multi-platform support;virtual object tracking|
|[Recent Advances in Assistive Systems for Blind and Visually Impaired Persons: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127086)|I. Soliman; A. H. Ahmed; A. Nassr; M. AbdelRaheem|10.1109/MEEE57080.2023.10127086|Visual Aid;Embedded Systems;Smart Walking Assistant;Audio Guide;Visual Aid;Embedded Systems;Smart Walking Assistant;Audio Guide|
|[Robust Assessment of Dysarthrophonic Voice with RASTA-PLP Features: A Nonlinear Spectral Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126695)|R. Islam; M. Tarique|10.1109/MEEE57080.2023.10126695|Accuracy;ANN;classifier;dysarthria;dysarthophonia;deep learning;machine learning;pathology;PLP;RASTA-PLP;speech;vocal disorder;Accuracy;ANN;classifier;dysarthria;dysarthophonia;deep learning;machine learning;pathology;PLP;RASTA-PLP;speech;vocal disorder|
#### **2023 5th International Conference on Recent Advances in Information Technology (RAIT)**
- DOI: 10.1109/RAIT57693.2023
- DATE: 3-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Deep Ensemble Architecture for Knee Osteoarthritis Severity Prediction and Report Generation*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126826)|T. Saini; A. Ajad; N. K. M|10.1109/RAIT57693.2023.10126826|Classification;Localisation;CNN;YOLO;Medical Images;BERT;Knee X-ray;Transformer;Classification;Localisation;CNN;YOLO;Medical Images;BERT;Knee X-ray;Transformer|
|[UAVs-assisted Multi-Hop D2D Communication using Hybrid PTS for disaster management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127102)|S. Ghosh; A. Bhowmick; S. D. Roy; S. Kundu|10.1109/RAIT57693.2023.10127102|VAV;D2D;Hybrid PTS;Outage Probability;Throughput;VAV;D2D;Hybrid PTS;Outage Probability;Throughput|
|[Sweep Coverage with Faults](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126982)|A. K. Dhar; B. Gorain; M. Mahawar|10.1109/RAIT57693.2023.10126982|sweep coverage;mobile sensors;fault tolerance;distributed algorithms;sweep coverage;mobile sensors;fault tolerance;distributed algorithms|
|[Microarray Data Analysis for Diagnosis of Cancer Diseases by Machine Learning algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127091)|S. Begum; S. Samanta; S. Ahmed; D. Chakraborty|10.1109/RAIT57693.2023.10127091|PCC;RF;DT;mRNA and ReliefF;PCC;RF;DT;mRNA and ReliefF|
|[Blockchain-based Secure Storage and Management of Electronic Health Record using a Smart Card](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126943)|I. Srivastava; A. Raj; D. S. Gupta|10.1109/RAIT57693.2023.10126943|Blockchain;Smart Cards;Electronic Health Records;Access Control;Proof-of-Authority;Blockchain;Smart Cards;Electronic Health Records;Access Control;Proof-of-Authority|
|[High Throughput Circuit Design of Flash Type Analog to Digital Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126785)|S. Chinthala; M. A. B. M|10.1109/RAIT57693.2023.10126785|Analog to Digital Converter;Digital Signal Processing;Flash Type ADC;Analog to Digital Converter;Digital Signal Processing;Flash Type ADC|
|[Rad-Former: Structuring Radiology Reports using Transformers*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127096)|A. Ajad; T. Saini; K. M. Niranjan|10.1109/RAIT57693.2023.10127096|Deep Learning;Radiology;Structuring;Summarization;Transformer;Healthcare;Deep Learning;Radiology;Structuring;Summarization;Transformer;Healthcare|
|[P4 based Switch Centric Flow table Overflow Detection and Mitigation in Data Plane Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126579)|L. Jain; V. U.|10.1109/RAIT57693.2023.10126579|Software Defined Networking;P4 language;Flow table Overflow attack;Software Defined Networking;P4 language;Flow table Overflow attack|
|[Realistic Benchmark Datasets for Team Formation Problem in Social Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127014)|B. R. Addanki; B. S. Durga|10.1109/RAIT57693.2023.10127014|social networks;power law;team formation problem;popularity of skills;bench-mark data sets;social networks;power law;team formation problem;popularity of skills;bench-mark data sets|
|[An Efficient Speaker Identification Approach for Biometric Access Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127101)|K. Jha; A. Jain; S. Srivastava|10.1109/RAIT57693.2023.10127101|Speaker identification;Power Normalized Cepstral Coefficients;Temporal masking;Formant;Convolutional Neural Network;Speaker identification;Power Normalized Cepstral Coefficients;Temporal masking;Formant;Convolutional Neural Network|
|[A Lightweight Blockchain Framework for secure transaction in resource constrained IoT devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126898)|R. Raj; M. Ghosh|10.1109/RAIT57693.2023.10126898|Blockchain;Resource constrained IoT devices;PBFT;CoAP;IPFS;Blockchain;Resource constrained IoT devices;PBFT;CoAP;IPFS|
|[Emotion Recognition from Masked Faces using Inception-v3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126777)|A. Agarwal; S. Susan|10.1109/RAIT57693.2023.10126777|Facial expression analysis;Emotion recognition;Masked faces;Deep learning;Facial expression analysis;Emotion recognition;Masked faces;Deep learning|
|[Secrecy Outage Analysis of Energy Harvesting Enabled Two User Cooperative NOMA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126578)|A. Baranwal; S. D. Roy; S. Kundu|10.1109/RAIT57693.2023.10126578|Secrecy outage probability;Energy harvesting;Successive interference cancellation;NOMA;Secrecy outage probability;Energy harvesting;Successive interference cancellation;NOMA|
|[Outage Analysis of a D2D Network for MIMO-NOMA-based Downlink Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126590)|A. Das; S. Ghosh; A. Bhowmick; S. D. Roy; S. Kundu|10.1109/RAIT57693.2023.10126590|D2D communication;Kernelized Energy Detection;MIMO;NOMA;MRC;outage;D2D communication;Kernelized Energy Detection;MIMO;NOMA;MRC;outage|
|[CV-CXR: A Method for Classification and Visualisation of Covid-19 virus using CNN and Heatmap*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127066)|A. Ajad; T. Saini; K. M. Niranjan|10.1109/RAIT57693.2023.10127066|Chest X-ray;Covid-19;Grad-CAM;CNN;Visualisation;Classification;Medical Images;Healthcare;Chest X-ray;Covid-19;Grad-CAM;CNN;Visualisation;Classification;Medical Images;Healthcare|
|[High Throughput Circuit Designs of Digital to Analog Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127011)|M. V. D. B. Anjaneya; M. A. B. M|10.1109/RAIT57693.2023.10127011|Digital to Analog Converter;Digital Signal Processing;Sampling;Digital to Analog Converter;Digital Signal Processing;Sampling|
#### **2023 4th International Conference on Smart Grid Metrology (SMAGRIMET)**
- DOI: 10.1109/SMAGRIMET58412.2023
- DATE: 24-28 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Development of a LabVIEW-Based Data Acquisition and Monitoring System for Demand Response Laboratory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128687)|M. Čuljak; J. Havelka; H. Pandžić|10.1109/SMAGRIMET58412.2023.10128687|data acquiring and monitoring;demand response;LabView;Modbus TCP/IP;living laboratory;data acquiring and monitoring;demand response;LabView;Modbus TCP/IP;living laboratory|
|[Particle swarm optimization trained neural network for overhead line conductor temperature prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128652)|T. Sterc; B. Filipovic-Grcic; B. Franc; K. Mesic; A. Zupan; B. Jurisic|10.1109/SMAGRIMET58412.2023.10128652|STR (Static Thermal Rating);DTR (Dynamic Thermal Rating);OTLM (Overhead Transmission Line Monitoring);OHL (Overhead Line);ANN (Artificial Neural Network);PSO (Particle Swarm Optimization);STR (Static Thermal Rating);DTR (Dynamic Thermal Rating);OTLM (Overhead Transmission Line Monitoring);OHL (Overhead Line);ANN (Artificial Neural Network);PSO (Particle Swarm Optimization)|
|[Vector control design and analysis for PMSM using real-time HIL simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128660)|K. Knol; T. Benšić; M. Barukčić|10.1109/SMAGRIMET58412.2023.10128660|PMSM;vector control;MBD;HIL;co-simulation;PMSM;vector control;MBD;HIL;co-simulation|
|[Integrating Vehicular Clouds For Supporting Smart Transportation Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128665)|J. Balen; M. Matijašević; L. Pejčić; J. Kundid; M. M. Azeem|10.1109/SMAGRIMET58412.2023.10128665|Cloud computing;Cloud services;Smart city;Smart Transportation Infrastructure;Vehicular clouds;VANET;Cloud computing;Cloud services;Smart city;Smart Transportation Infrastructure;Vehicular clouds;VANET|
|[Smart Devices Applicable in Transformer Core Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128689)|A. Dorer; F. Razum; T. Župan; Ž. Janić; J. Konjevod|10.1109/SMAGRIMET58412.2023.10128689|transformer core;smart measurement devices;Raspberry Pi;transformer core;smart measurement devices;Raspberry Pi|
|[Precision high power wide bandwidth AC/DC current shunt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128653)|A. Šala; J. Konjevod; P. Mostarac; S. Gašparini|10.1109/SMAGRIMET58412.2023.10128653|;|
|[A Shielded PCB Probe Optimized for Magnetic Near Field Measurements below 3 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128658)|M. Filipašić; M. Dadić|10.1109/SMAGRIMET58412.2023.10128658|near field probe;optimization;magnetic field measurements;FEM;electromagnetic compatibility;near field probe;optimization;magnetic field measurements;FEM;electromagnetic compatibility|
|[DC Power Metering in Low-Voltage Microgrids: Definitional and Methodological Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128690)|G. Frigo; F. Costa|10.1109/SMAGRIMET58412.2023.10128690|DC Grids;DC Power;Power Quality;Harmonics;Converter-Interfaced Generation (CIG);DC Grids;DC Power;Power Quality;Harmonics;Converter-Interfaced Generation (CIG)|
|[Local Aggregation of PMU Measurements in a Low-Inertia Distribution Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128654)|F. Costa; G. Frigo|10.1109/SMAGRIMET58412.2023.10128654|Phasor Measurement Unit;Power System Inertia;Local Control;Reliability Index;Measurement Aggregation;Phasor Measurement Unit;Power System Inertia;Local Control;Reliability Index;Measurement Aggregation|
|[Estimating grounding resistance of medium voltage cables using measured sheaths current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128688)|A. Ghaderi Baayeh; M. Kleemann|10.1109/SMAGRIMET58412.2023.10128688|power system protection;medium voltage cables;sheaths grounding;distribution system;online parameter estimation;power system protection;medium voltage cables;sheaths grounding;distribution system;online parameter estimation|
|[Pulse Pattern Optimization for Medium Voltage 3-Level NPC Converter Using Open Source Optimization Tools in Julia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128661)|M. Varga; N. Turk; D. Cikač; N. Bulić|10.1109/SMAGRIMET58412.2023.10128661|Selective harmonic elimination;Pulse-width modulation;Julia Programming Language;3-Level NPC converter;Selective harmonic elimination;Pulse-width modulation;Julia Programming Language;3-Level NPC converter|
|[Advantages of Using Diagnostic and Monitoring Data for Intelligent Condition Monitoring of Power Network Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128655)|B. Jurišić; T. Župan; M. Schönberger; L. Teklić; G. Levačić|10.1109/SMAGRIMET58412.2023.10128655|intelligent condition monitoring;diagnostic measurements;monitoring systems;transmission system operator;intelligent condition monitoring;diagnostic measurements;monitoring systems;transmission system operator|
|[The impact of different prosumer configurations on achieving savings due to the increase in electricity prices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128650)|Z. Šimić; N. Mišljenović; M. Dubravac; D. Topić|10.1109/SMAGRIMET58412.2023.10128650|prosumer;electricity price;PV power plant;battery energy storage;load shifting;prosumer;electricity price;PV power plant;battery energy storage;load shifting|
|[Microwave Radiation Protective Suit with Silver Threads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128659)|K. Malarić; B. Šaravanja; T. Pušić|10.1109/SMAGRIMET58412.2023.10128659|conductive textiles;shielding effectiveness (SE);protective suit;microwave radiation;conductive textiles;shielding effectiveness (SE);protective suit;microwave radiation|
|[Investigation of the low-cost sampling devices for the purpose of high-precision electric power measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128664)|I. Štambuk; J. Konjevod; R. Malarić|10.1109/SMAGRIMET58412.2023.10128664|electrical power;sampling devices;low-cost metrology;synchronization;electrical power;sampling devices;low-cost metrology;synchronization|
|[Smart marina: concept of stereovision based berthing aid system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128656)|P. Mostarac; L. Kahlina; J. Janković; Ž. Ilić; G. Šišul; A. Šala|10.1109/SMAGRIMET58412.2023.10128656|Stereovision;smart marina;berthing aid system;Stereovision;smart marina;berthing aid system|
|[High resistance measurement based on industrial datalogger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128657)|H. Hegeduš; M. Marković|10.1109/SMAGRIMET58412.2023.10128657|High electrical resistance;pull-up configuration;pull-down configuration;half bridge circuit;operational amplifiers;settling time;High electrical resistance;pull-up configuration;pull-down configuration;half bridge circuit;operational amplifiers;settling time|
|[Synchrophasor Logger Based on Laboratory Oscilloscope and Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128663)|K. N. Vujević; M. Despalatović; A. Sunjerga; G. Petrović|10.1109/SMAGRIMET58412.2023.10128663|Synchrophasors;Remote monitoring;Remote control;Raspberry Pi;Global Positioning System;Synchrophasors;Remote monitoring;Remote control;Raspberry Pi;Global Positioning System|
#### **2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)**
- DOI: 10.1109/ICPS58381.2023
- DATE: 8-11 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Deep Reinforcement Learning based Demand Response for Domestic Variable Volume Water Heater](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128065)|L. Chen; Y. Su; T. Zhang|10.1109/ICPS58381.2023.10128065|demand response;variable volume water heater;uncertainty;deep reinforcement learning;demand response;variable volume water heater;uncertainty;deep reinforcement learning|
|[Tomato disease degree recognition based on RGB and Lab color space conversion method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128053)|H. He; C. Ning; M. Liu; J. Zhu|10.1109/ICPS58381.2023.10128053|classification;attention module;infection severity;color space;classification;attention module;infection severity;color space|
|[Adaptive Neural Network Asymptotic Tracking Control for Autonomous Surface Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128025)|Y. Liu; Q. Wang; B. Fu|10.1109/ICPS58381.2023.10128025|Autonomous surface vehicles;Asymptotic tracking;backstepping;neural networks;Autonomous surface vehicles;Asymptotic tracking;backstepping;neural networks|
|[Quantized Finite-Time $\mathcal{H}_{\infty}$ Control of Fuzzy Distributed Parameter CPSs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128039)|T. -F. Li; J. H. Park|10.1109/ICPS58381.2023.10128039|Distributed parameter CPSs;T-S fuzzy model;Quantization;Finite-time;Distributed parameter CPSs;T-S fuzzy model;Quantization;Finite-time|
|[Intelligent Vehicle Trajectory Tracking Control Based on $H_{\infty}$ Proportional-differential Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128098)|G. Pan; Z. Feng; H. Guo|10.1109/ICPS58381.2023.10128098|trajectory tracking;H∞ PD control;coordinate transformation matrix;trajectory tracking;H∞ PD control;coordinate transformation matrix|
|[Exergy-related Operating Performance Assessment for Hot Rolling Process Based on Multiple imputation and Multi-class Support Vector Data Description](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128059)|C. Zhang; K. Peng; J. Dong; L. Ma; Y. Wang; D. Hua|10.1109/ICPS58381.2023.10128059|Operating performance assessment;exergy;multiple imputation;multi-class support vector data description;hot rolling process;Operating performance assessment;exergy;multiple imputation;multi-class support vector data description;hot rolling process|
|[Digital Twin Development: Mathematical Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128007)|D. Krummacker; M. Reichardt; C. Fischer; H. D. Schotten|10.1109/ICPS58381.2023.10128007|Digital Twin;Mathematical Modeling;Simulation Design;Digital Twin;Mathematical Modeling;Simulation Design|
|[A Survey of Few-shot Learning-based Compound Fault Diagnosis Methods for Industrial Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128105)|L. Ma; F. Shi; Z. Wu; K. Peng|10.1109/ICPS58381.2023.10128105|fault diagnosis;compound faults;few-shot learning;industrial processes;system reliability;fault diagnosis;compound faults;few-shot learning;industrial processes;system reliability|
|[Backstepping-based Anti-disturbance Flight Control for Attitude and Altitude Unmanned Helicopters with State Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128011)|Y. Li; Y. Huang; H. Liu; J. Li|10.1109/ICPS58381.2023.10128011|Helicopter system;Backstepping control;Non-linear disturbance observer;State constraints;Helicopter system;Backstepping control;Non-linear disturbance observer;State constraints|
|[Integrating Worker Assistance Systems and Enterprise Resource Planning in Industry 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128096)|M. Brünninghaus; M. Redeker|10.1109/ICPS58381.2023.10128096|Industrial Worker Assistance System;Enterprise Resource Planning;Industry 4.0 Integration;Industrial Worker Assistance System;Enterprise Resource Planning;Industry 4.0 Integration|
|[Multimode DALSTM model for anomaly detection of nuclear reactor core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128088)|Y. Wang; X. Wang; Y. Wang; X. Li; C. Zhao; Z. Lv|10.1109/ICPS58381.2023.10128088|nuclear reactor core;anomaly detection;multimode;nuclear reactor core;anomaly detection;multimode|
|[An efficient condition monitoring and fault diagnosis method for bearings under multiple working conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128051)|Q. Zeng; Q. Zhu; Y. Feng; Y. Wang|10.1109/ICPS58381.2023.10128051|data-driven fault diagnosis;multiple working conditions;linear discriminant analysis;data-driven fault diagnosis;multiple working conditions;linear discriminant analysis|
|[Sliding window-based real-time remaining useful life prediction for milling tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128005)|C. Tong; Q. Zhu; Y. Feng; Y. Wang|10.1109/ICPS58381.2023.10128005|remaining useful life prediction;time-series characteristics;real-time prediction;remaining useful life prediction;time-series characteristics;real-time prediction|
|[Research on Dynamic Labels in Network Pruning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128021)|L. Zhang; Y. Luo; S. Xie; X. Wu|10.1109/ICPS58381.2023.10128021|model compression;dynamic pruning;mask prediction;dynamic labels;model compression;dynamic pruning;mask prediction;dynamic labels|
|[Research on testing and evaluation of USV formation control based on nonlinear dynamic inversion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128071)|S. Li; W. Xue; H. Ye; H. Zhang|10.1109/ICPS58381.2023.10128071|formation heading;multi-USV systems;nonlinear dynamic inversion (NLDI);formation control;formation heading;multi-USV systems;nonlinear dynamic inversion (NLDI);formation control|
|[Mobile Charging Platform Improves Distribution System Resilience and Electric Vehicles Charging Service](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128001)|J. Zhong; Y. Qian; X. Wang; Y. Zhao; P. Wu; C. Chen; M. Cai|10.1109/ICPS58381.2023.10128001|mobile charging;electric vehicles;load regulation;distribution system resilience;mobile charging;electric vehicles;load regulation;distribution system resilience|
|[A Short-term Wind Power Forecasting Method Based on NWP Wind Speed Fluctuation Division and Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128032)|Q. Li; J. Lv; M. Ding; D. Li; Z. Fang|10.1109/ICPS58381.2023.10128032|wind power forecasting;fuzzy c-means clustering;deep learning;wind power forecasting;fuzzy c-means clustering;deep learning|
|[Incipient Gradual Fault Detection via Transformed Component and Dissimilarity Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10128091)|L. Mu; W. Sun; Y. Zhang; N. Feng|10.1109/ICPS58381.2023.10128091|incipient gradual fault detection;transformed components;dissimilarity analysis;incipient gradual fault detection;transformed components;dissimilarity analysis|
#### **2023 International Electrical Engineering Congress (iEECON)**
- DOI: 10.1109/iEECON56657.2023
- DATE: 8-10 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Design of a Robust Control for a Single-Phase AC-DC Converter Using LMI Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126791)|H. Tang; S. In; P. Soth; S. Soeng; H. Cheng; V. Huy; S. Srang; C. Choeung|10.1109/iEECON56657.2023.10126791|robust control;model uncertainty;linear matrix inequality;single-phase AC-DC converter;all-pass filter;robust control;model uncertainty;linear matrix inequality;single-phase AC-DC converter;all-pass filter|
|[Robust Dual-Current Control of a Three-Phase Grid-Tied Inverter under Unbalanced Grid Voltage Using LMI Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126574)|P. Soth; H. Tang; B. So; S. San; H. Cheng; C. Choeung; S. Srang; C. Or|10.1109/iEECON56657.2023.10126574|robust control;three-phase grid-tied inverter;linear matrix inequality;negative sequence compensator;symmetrical component;robust control;three-phase grid-tied inverter;linear matrix inequality;negative sequence compensator;symmetrical component|
|[Smart e-Public Bus Station Module via on Cloud-based PLC plus HMI Control System for New Nannal Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126730)|S. Kantawong; P. Pinnate|10.1109/iEECON56657.2023.10126730|Smart e-public bus station module;Cloud-based PLC control;HMI;MAC;Arduino;IR sensor;PV;MPPT;Proteus8;Smart e-public bus station module;Cloud-based PLC control;HMI;MAC;Arduino;IR sensor;PV;MPPT;Proteus8|
|[Smart e-Public Pharmacy Machine Assistants via on Cloud-based and AI Diagnostic System for New Normal Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126724)|S. Kantawong; S. Sriyookaen|10.1109/iEECON56657.2023.10126724|Pharmacy machine module;F-PI controller;FLCs;NETPIE;QR;AI diagnostic;ANN;BPN;Raspberry-Pi;Pharmacy machine module;F-PI controller;FLCs;NETPIE;QR;AI diagnostic;ANN;BPN;Raspberry-Pi|
|[Investigating the applicable position of established cloud servers using the hybrid partitional clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126551)|A. Kantasa–ard; N. Phakdee; N. Wattanamongkhol|10.1109/iEECON56657.2023.10126551|Partitional clustering;K-Means;K-Medoid;Cloud server allocation;Center-of-Gravity;Partitional clustering;K-Means;K-Medoid;Cloud server allocation;Center-of-Gravity|
|[Steady-State Operating-Point Calculation of Variable Frequency Induction-Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126763)|P. Aree|10.1109/iEECON56657.2023.10126763|Induction motor;variable frequency drive;steady-state operating point;Induction motor;variable frequency drive;steady-state operating point|
|[Improved Partial Retransmission Hard-Decision Message-Passing ARQ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126878)|U. Tuntoolavest; T. Julnipitawong; V. Manthamkarn|10.1109/iEECON56657.2023.10126878|partial retransmission;hMP-ARQ;hard-decision message-passing;group common;false unverified symbols;partial retransmission;hMP-ARQ;hard-decision message-passing;group common;false unverified symbols|
|[Low Complexity Nonbinary Decoder: Correct Only Data part with Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127045)|U. Tuntoolavest; V. Manthamkarn; P. Sawaengtham; A. Duriyaphan|10.1109/iEECON56657.2023.10127045|systematic nonbinary codes;systematic nonbinary decoder;correct only data;low complexity;VSD;systematic nonbinary codes;systematic nonbinary decoder;correct only data;low complexity;VSD|
|[Estimation of Vertical Jump Height using Capacitive Sensing Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126670)|Y. Kaewjumras; B. Klongratog; W. Inboonma; S. Sukprasertchai; N. Somdock|10.1109/iEECON56657.2023.10126670|Capacitive sensor;Sport science;Measure vertical jump;Capacitive sensor;Sport science;Measure vertical jump|
|[Four-Element Wideband Circularly Polarized Sequentially-Rotated Slot Antenna Array for Small Spacecraft Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126954)|N. Supreeyatitikul; T. Lertwiriyaprapa; C. Phongcharoenpanich|10.1109/iEECON56657.2023.10126954|Antenna array;bi-directional;circularly polarized;monopole;sequentially-rotated feed network;Antenna array;bi-directional;circularly polarized;monopole;sequentially-rotated feed network|
|[Metasurface-Based Wideband CP Antenna Array Using Hybrid Coupler for 5G Mid-band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127032)|N. Supreeyatitikul; T. Lertwiriyaprapa; C. Phongcharoenpanich|10.1109/iEECON56657.2023.10127032|Antenna array;circularly polarized antenna;hybrid coupler;metasurface;polarization conversion;Antenna array;circularly polarized antenna;hybrid coupler;metasurface;polarization conversion|
|[Comparison of Feature Extraction Methods for Classifying Energy Theft and Defective Meters in Automatic Meter Reading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127065)|S. Janthong; K. Chalermyanont; R. Duangsoithong|10.1109/iEECON56657.2023.10127065|Non-Technical Losses (NTL);Automatic Meter Reading (AMR);Feature Extraction;Supervised Learning;Energy theft;Defective meters;Non-Technical Losses (NTL);Automatic Meter Reading (AMR);Feature Extraction;Supervised Learning;Energy theft;Defective meters|
|[Ischemic Stroke Post-treatment Prediction using Machine Learning to develop Web Application for Healthcare Centers in Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126975)|K. Worraviseat; P. Danvirutai; S. Anutrakulchai; N. Kasemsap; C. Srichan|10.1109/iEECON56657.2023.10126975|stroke;artificial intelligence;AI-assisted diagnosis;precision medicine;random forest algorithm;web application;stroke;artificial intelligence;AI-assisted diagnosis;precision medicine;random forest algorithm;web application|
|[Performance Evaluation of Infrastructure as a Service across Cloud Service Providers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127100)|S. Sithiyopasakul; T. Archevapanich; B. Purahong; P. Sithiyopasakul; A. Lasakul; C. Benjangkaprasert|10.1109/iEECON56657.2023.10127100|Auto Scaling;Cloud Computing;Virtual Machine;Web Server;Performance Testing;IaaS;CSP;Auto Scaling;Cloud Computing;Virtual Machine;Web Server;Performance Testing;IaaS;CSP|
|[Development of monitoring PM2.5 based on IoT and Google Data Studio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126631)|P. Saeng-On; N. Thaitae; S. Sonasang|10.1109/iEECON56657.2023.10126631|PM2.5;Monitoring;IoT;PM2.5;Monitoring;IoT|
|[Electromagnetic Field Simulation of Pantograph for Electric Train Using 3D Finite Element Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126550)|P. Saikham; P. Pao-La-Or; T. Yuangkaew; A. Bunmat|10.1109/iEECON56657.2023.10126550|3-D Finite Element Method;Magnetic Field;Pantograph;3-D Finite Element Method;Magnetic Field;Pantograph|
|[Dual-band UHF and HF-RFID Tag Antenna for Tracking and Energy Harvesting Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126836)|T. Mhunkaew; S. Kawdungta; D. Torrungrueng|10.1109/iEECON56657.2023.10126836|Dual-band;HF-RFID;UHF-RFID;Energy Harvesting;Dual-band;HF-RFID;UHF-RFID;Energy Harvesting|
|[Compact High-Gain Dual-band Patch Antenna With Dielectric Superstrate for Wi-Fi 6 Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126809)|B. Muangrung; A. Lang; D. Torrungrueng; C. Phongcharoenpanich; S. Kawdungta|10.1109/iEECON56657.2023.10126809|Dielectric superstrate;Dual-band;Patch Antenna;Wi-Fi 6;Dielectric superstrate;Dual-band;Patch Antenna;Wi-Fi 6|
#### **2023 24th International Symposium on Quality Electronic Design (ISQED)**
- DOI: 10.1109/ISQED57927.2023
- DATE: 5-7 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A SPICE-based Framework to Emulate Quantum Circuits with classical LC Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129351)|M. M. Islam; S. Hossain; A. Aziz|10.1109/ISQED57927.2023.10129351|Quantum circuit;LC oscillator;Classical emulator;Quantum computing;Universal gates;Quantum circuit;LC oscillator;Classical emulator;Quantum computing;Universal gates|
|[Metal Inter-layer Via Keep-out-zone in M3D IC: A Critical Process-aware Design Consideration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129285)|M. S. Vemuri; U. R. Tida|10.1109/ISQED57927.2023.10129285|;|
|[AGNI: In-Situ, Iso-Latency Stochastic-to-Binary Number Conversion for In-DRAM Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129301)|S. M. Shivanandamurthy; S. S. Vatsavai; I. Thakkar; S. A. Salehi|10.1109/ISQED57927.2023.10129301|convolutional neural networks;processing-in-memory;stochastic to binary conversion;convolutional neural networks;processing-in-memory;stochastic to binary conversion|
|[Design and Evaluation of multipliers for hardware accelerated on-chip EdDSA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129381)|H. Gupta; M. Kabra; N. D. Patwari; P. H. C; M. Rao|10.1109/ISQED57927.2023.10129381|Encryption;hardware accelerator;hardware security;multipliers;polynomial multiplication;hardware cryptosystems;Encryption;hardware accelerator;hardware security;multipliers;polynomial multiplication;hardware cryptosystems|
|[Self-Checking Performance Verification Methodology for Complex SoCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129396)|P. Ghosh; V. N. Dwaraka Mai; A. Chopra; B. Sood|10.1109/ISQED57927.2023.10129396|Performance Verification;RTL Parameter;SoC Design;Bus Functional Model (BFM);Verification IP (VIP);Network on chip (NoC);SoC;UVM;Performance Verification;RTL Parameter;SoC Design;Bus Functional Model (BFM);Verification IP (VIP);Network on chip (NoC);SoC;UVM|
|[Polynomial Formal Verification of a Processor: A RISC-V Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129397)|L. Weingarten; A. Mahzoon; M. Goli; R. Drechsler|10.1109/ISQED57927.2023.10129397|Polynomial Formal Verification;PFV;BDD;BDD-based verification;Partial Simulation;RISC-V;Polynomial Formal Verification;PFV;BDD;BDD-based verification;Partial Simulation;RISC-V|
|[Application of Machine Learning for Quality Risk Factor Analysis of Electronic Assemblies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129339)|B. Reidy; D. Duggan; B. Glasauer; P. Su; R. Zand|10.1109/ISQED57927.2023.10129339|Component analysis;neural network;machine learning;printed circuit board;random forest;support vector machine;Component analysis;neural network;machine learning;printed circuit board;random forest;support vector machine|
|[Quality-driven Design Methodology for PUFs in FPGAs for Secure IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129405)|X. Wang; Y. Song; K. Prakash; Z. Zilic; T. Langsetmo|10.1109/ISQED57927.2023.10129405|Physical Unclonable Function (PUF);Internetof-Things (IoT);Field-Programmable Gate Array (FPGA);Hardware Security;Design Methodology;Physical Unclonable Function (PUF);Internetof-Things (IoT);Field-Programmable Gate Array (FPGA);Hardware Security;Design Methodology|
|[HD2FPGA: Automated Framework for Accelerating Hyperdimensional Computing on FPGAs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129332)|T. Zhang; S. Salamat; B. Khaleghi; J. Morris; B. Aksanli; T. S. Rosing|10.1109/ISQED57927.2023.10129332|Hyperdimensional (HD) computing;Automation;FPGA;Hyperdimensional (HD) computing;Automation;FPGA|
|[XOR-CiM: An Efficient Computing-in-SOT-MRAM Design for Binary Neural Network Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129322)|M. Morsali; R. Zhou; S. Tabrizchi; A. Roohi; S. Angizi|10.1109/ISQED57927.2023.10129322|SOT-MRAM;computing-in-memory;binary neural networks;SOT-MRAM;computing-in-memory;binary neural networks|
|[Security and Reliability Challenges in Machine Learning for EDA: Latest Advances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129359)|Z. Xie; T. Zhang; Y. Peng|10.1109/ISQED57927.2023.10129359|;|
|[Image-Based Zero-Day Malware Detection in IoMT Devices: A Hybrid AI-Enabled Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129348)|Z. He; H. Sayadi|10.1109/ISQED57927.2023.10129348|Hardware Performance Counters;IoT/IoMT;Deep Learning;Reinforcement Learning;Zero-Day Malware Detection;Hardware Performance Counters;IoT/IoMT;Deep Learning;Reinforcement Learning;Zero-Day Malware Detection|
|[Routability-aware Placement Guidance Generation for Mixed-size Designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129328)|C. -Y. Cheng; T. -C. Wang|10.1109/ISQED57927.2023.10129328|;|
|[MC-MCF: A Multi-Capacity Model for Ordered Escape Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129354)|Z. Gao; S. Dong; Z. Tang; W. Yu|10.1109/ISQED57927.2023.10129354|Ordered escape routing;multi-capacity;multi-commodity flow;Ordered escape routing;multi-capacity;multi-commodity flow|
|[DC-Model: A New Method for Assisting the Analog Circuit Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129366)|Y. Wang; J. Xin; H. Liu; Q. Qin; C. Chai; Y. Lu; J. Hao; J. Xiao; Z. Ye; Y. Wang|10.1109/ISQED57927.2023.10129366|DC parameters;circuit metrics;neural networks;analog optimizer;DC parameters;circuit metrics;neural networks;analog optimizer|
|[Accounting for Floorplan Irregularity and Configuration Dependence in FPGA Routing Delay Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129290)|G. Barajas; J. W. Greene; F. Li; J. Tandon|10.1109/ISQED57927.2023.10129290|Field-programmable gate arrays;FPGAs;delay estimation;Elmore delay;common path resistance;Field-programmable gate arrays;FPGAs;delay estimation;Elmore delay;common path resistance|
|[An Effective Cost-Skew Tradeoff Heuristic for VLSI Global Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129350)|A. B. Kahng; S. Thumathy; M. Woo|10.1109/ISQED57927.2023.10129350|;|
|[Automated Supervised Topic Modeling Framework for Hardware Weaknesses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129378)|R. Hassan; C. Bandi; M. -T. Tsai; S. Golchin; S. M. P D; S. Rafatirad; S. Salehi|10.1109/ISQED57927.2023.10129378|National Vulnerability Database (NVD);Common Vulnerability and Exposure (CVE);Common Weakness Enumeration (CWE);Internet of Things (IoT);Hardware Weakness;Ontology Learning;Natural Language Processing (NLP);National Vulnerability Database (NVD);Common Vulnerability and Exposure (CVE);Common Weakness Enumeration (CWE);Internet of Things (IoT);Hardware Weakness;Ontology Learning;Natural Language Processing (NLP)|
#### **2023 76th Annual Conference for Protective Relay Engineers (CFPR)**
- DOI: 10.1109/CFPR57837.2023
- DATE: 27-30 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Algorithm to Mitigate Protection Challenges in a Distribution System Integrated with Inverter-Based Distributed Energy Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126703)|A. Chanda; V. Chhibbar; C. Arbona; P. Dongale|10.1109/CFPR57837.2023.10126703|IBDER;RBFNN;Protection;Recloser;Fault;IBDER;RBFNN;Protection;Recloser;Fault|
|[Communication bandwidth considerations for digital substation applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126969)|J. Groat; G. S. A. B. Vandiver; B. Vasudevan|10.1109/CFPR57837.2023.10126969|;|
|[Effective Use of Incipient Failure Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126768)|C. L. Benner; B. Don Russell; J. Wischkaemper; K. Muthu-Manivannan|10.1109/CFPR57837.2023.10126768|;|
|[Electromechanical Differential Relays Misoperation and Investigation. Part 2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126675)|A. Rangel|10.1109/CFPR57837.2023.10126675|;|
|[Export Cable Protection for Offshore Wind Farms Using Type-IV Wind Turbine Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127019)|A. Mohammadhassani; N. Skoff; T. Nguyen; A. Mehrizi-Sani|10.1109/CFPR57837.2023.10127019|DC-AC power converters;differential protection;distance protection;overcurrent protection;power system protection;wind energy integration;DC-AC power converters;differential protection;distance protection;overcurrent protection;power system protection;wind energy integration|
|[Fast Communication-Based Protection and Isolation Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126535)|Q. Guo; F. Harirchi; Y. Sharon|10.1109/CFPR57837.2023.10126535|distribution protection;power system;communication;distribution protection;power system;communication|
|[Fault location for multi-terminal lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127004)|J. Blumschein; C. Dzienis; J. Hauschild|10.1109/CFPR57837.2023.10127004|Fault Location;Impedance Calculation;Travelling Wave;Multi-terminal lines;Fault Location;Impedance Calculation;Travelling Wave;Multi-terminal lines|
|[How much measurement error can someone expect from various degrees of CT saturation?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127052)|D. Millner|10.1109/CFPR57837.2023.10127052|current transformer;relaying;transformer saturation;current transformer;relaying;transformer saturation|
|[IDA Restoration- The Transmission Engineering Efforts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126811)|M. Hossain; W. A. Rice; T. H. Nguyen; S. C. Brown; S. Pigford|10.1109/CFPR57837.2023.10126811|;|
|[Implementation and Analysis of a Digital Model of the Incremental-Quantity Distance Element Using EMTP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126709)|T. Russell; P. Bhattarai; M. Cox; M. Quinteros; T. Field|10.1109/CFPR57837.2023.10126709|traveling waves;incremental quantity;distance element;line-protection relay;electromagnetic transients;traveling waves;incremental quantity;distance element;line-protection relay;electromagnetic transients|
|[Inverter-Based Generation Integration Protection Challenges: Real-life Experiences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126885)|M. Hossain; S. M. Boyer; S. C. Brown; T. H. Nguyen|10.1109/CFPR57837.2023.10126885|Inverter-Based Resources(IBR) Resources that are asynchronously connected to the electric grid and are either completely or partially interfaced with the bulk power system through power electronics;Bulk Electric System (BES) The electricity power generation facilities combined with the high-voltage transmission system which together create and transport electricity;Source Impedance Ratio (SIR) the ratio of the source impedance behind the relay location;ZS to the line impedance protected by the distance relay;ZL;Inverter-Based Resources(IBR) Resources that are asynchronously connected to the electric grid and are either completely or partially interfaced with the bulk power system through power electronics;Bulk Electric System (BES) The electricity power generation facilities combined with the high-voltage transmission system which together create and transport electricity;Source Impedance Ratio (SIR) the ratio of the source impedance behind the relay location;ZS to the line impedance protected by the distance relay;ZL|
|[Modeling and Simulating Single Points of Failure for TPL-001-5.1 Compliance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126572)|M. Chapariha; I. Anand; G. Webster; M. Rahmatian; S. Alaeddini; W. Winters; B. Varughese; S. Hayes; D. Erwin|10.1109/CFPR57837.2023.10126572|Protection;Planning;NERC TPL-001;Modeling;Simulation;Protection;Planning;NERC TPL-001;Modeling;Simulation|
|[Protection and Control Challenges of Low-Voltage Networks with High Distributed Energy Resources Penetration - Part 1: Utility Workshop and Low-Voltage Network Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126827)|Z. Cheng; E. Udren; J. Holbach; M. J. Reno; M. E. Ropp|10.1109/CFPR57837.2023.10126827|Distributed Energy Resources;Hardware-in-the-loop Simulation;Low-voltage Networks;Protective Relaying;Real-Time Digital Simulation;Distributed Energy Resources;Hardware-in-the-loop Simulation;Low-voltage Networks;Protective Relaying;Real-Time Digital Simulation|
|[A Tale of Two Out-of-Phase Synchronizing Events at BC Hydro](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127009)|M. Nagpal; L. Gu; R. Barone; R. Chowdhury; M. Thompson|10.1109/CFPR57837.2023.10127009|;|
|[Testing Challenges of a Complete PAC Digital Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126765)|B. Vasudevan; J. Groat; B. Vandiver|10.1109/CFPR57837.2023.10126765|;|
|[Testing of Phasor Measurement Units as per IEC/IEEE Standards - The Whats and the Hows?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126514)|S. Kuber; A. Gonzalez|10.1109/CFPR57837.2023.10126514|;|
|[Unintended Consequences of Extra Sensitive Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126857)|B. Don Russell; C. L. Benner; K. Muthu-Manivannan; J. Wischkaemper|10.1109/CFPR57837.2023.10126857|;|
|[Use of DFR's for Distribution Substation Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126750)|J. Ayala|10.1109/CFPR57837.2023.10126750|DFR;Grid;BES;Distribution;Disturbances;DFR;Grid;BES;Distribution;Disturbances|
#### **2023 IEEE 8th International Conference for Convergence in Technology (I2CT)**
- DOI: 10.1109/I2CT57861.2023
- DATE: 7-9 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Flutter Based Mobile Application for Electric Vehicle Charging Reservation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126435)|H. K. S M; G. R; D. M. Naik; H. N; B. Bhabya|10.1109/I2CT57861.2023.10126435|Electric Vehicle;Mobile Application;Feedback;Vehicle Identification Number;Electric Vehicle;Mobile Application;Feedback;Vehicle Identification Number|
|[Embedded System Software Reliability Estimation During New Product Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126401)|A. Biswal; R. S; R. K. Sreenilayam|10.1109/I2CT57861.2023.10126401|software;reliability;AUTOSAR;reliability growth;software;reliability;AUTOSAR;reliability growth|
|[Static Code Analyser to Enhance Developer Productivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126395)|D. R. I. Peiris; N. Kodagoda|10.1109/I2CT57861.2023.10126395|static analysis;productivity;code quality;read-ability;static analysis;productivity;code quality;read-ability|
|["Talking Books" : A Sinhala Abstractive Text Summarization Approach for Sinhala Textbooks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126205)|B. R. M. S. R. B. Rathnayake; K. Manathunga; D. Kasthurirathna|10.1109/I2CT57861.2023.10126205|Summarization;Google-AI;GPT3;Abstractive Summary;Summarization;Google-AI;GPT3;Abstractive Summary|
|[Performance Evaluation of Medium Voltage Cascaded H-Bridge converter for Low frequency High power variable power factor Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126436)|A. K. Wankhede; A. Sharma; B. G. Fernandes|10.1109/I2CT57861.2023.10126436|Cascaded H-Bridge;MV drives;harmonic performance;Cascaded H-Bridge;MV drives;harmonic performance|
|[Sentiment Analysis of Hotel Reviews - a Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126445)|G. Sreenivas; K. M. Murthy; K. Prit Gopali; N. Eedula; M. H R|10.1109/I2CT57861.2023.10126445|Sentiment Analysis;Hotel Review Data;SVM;Decision Trees;Multinomial Naïve Bayes;Stochastic Gradient Descent;XGBoost;K-means;LDA;VADER;TextBlob;TF-IDF;Word2Vec;Sentiment Analysis;Hotel Review Data;SVM;Decision Trees;Multinomial Naïve Bayes;Stochastic Gradient Descent;XGBoost;K-means;LDA;VADER;TextBlob;TF-IDF;Word2Vec|
|[Scalable Fault Handling Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126345)|J. K. Sharma; P. Jaisal; M. Gupta|10.1109/I2CT57861.2023.10126345|EMS;fault;notification;scalable;load balancing;parallel;isolate;sequential;FCAPS;EMS;fault;notification;scalable;load balancing;parallel;isolate;sequential;FCAPS|
|[Responsive Transcutaneous Electrical Stimulation for Management of Diabetic Foot Neuropathy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126247)|D. Bhor; T. K; M. Gowri; B. K. Mishra; D. Kalita; A. Thirugnanam; K. B. Mirza|10.1109/I2CT57861.2023.10126247|Diabetic Peripheral Neuropathy (DPN);Diabetic Foot Ulcers;electrical stimulation;Force-sensing resistor (FSR);Diabetic Peripheral Neuropathy (DPN);Diabetic Foot Ulcers;electrical stimulation;Force-sensing resistor (FSR)|
|[Machine Learning-based Prediction of pH and Temperature using Macromodel of Si3N4-gated Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126184)|M. Doshi; R. Datar; S. Deshpande; G. Bacher|10.1109/I2CT57861.2023.10126184|Machine Learning Algorithm;Classification;Decision Tree;Neural Network;Si3N4-gated Transistor;pH sensor;Machine Learning Algorithm;Classification;Decision Tree;Neural Network;Si3N4-gated Transistor;pH sensor|
|[Performance Study of 84.5 kWp Grid Linked SPV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126325)|S. Kumar; M. S. Ballal; P. S. Kulkarni; P. K. Sharma|10.1109/I2CT57861.2023.10126325|performance ratio;PVsyst;CUF;PV module;string;PPA;Yield;performance ratio;PVsyst;CUF;PV module;string;PPA;Yield|
|[In-field Chilli Crop Disease Detection Using YOLOv5 Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126468)|M. K M; A. Ranjan; R. Machavaram|10.1109/I2CT57861.2023.10126468|Chilli disease detection;object detection;YOLOv5;deep learning model;Chilli disease detection;object detection;YOLOv5;deep learning model|
|["Electrically Small Wearable Tunable Antenna that fits into Smartwatch Dial"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126237)|P. Mhatre; M. Joshi|10.1109/I2CT57861.2023.10126237|wearable antenna;human body model;wearable antenna;human body model|
|[Predicting the Number of Fatalities in an Air Crash](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126440)|P. T.R; N. Manoj; P. A. Kumar; R. Harshita|10.1109/I2CT57861.2023.10126440|Xgboost;RMSE;pearson correlation coefficient;air crash fatality rate;Xgboost;RMSE;pearson correlation coefficient;air crash fatality rate|
|[Smart Health App for Identifying Brain Tumors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126477)|W. E. Madapatha; S. V. S. Gunasekara; P. M. Kumarage|10.1109/I2CT57861.2023.10126477|brain tumor;CNN;thresholding;superpixel;MRI scan;brain tumor;CNN;thresholding;superpixel;MRI scan|
|[A Novel Comprehensive Solution to Preserve Environment by Optimum Tuning of Electrostatic Precipitator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126422)|S. S. Patil; H. Naidu|10.1109/I2CT57861.2023.10126422|ESP;Flue gas;ash;precipitator;ESP;Flue gas;ash;precipitator|
|[Lifecare Management system using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126217)|A. K. Chikaraddi; S. G. Kanakaraddi; P. Kamat; S. V. Budani; K. Gull|10.1109/I2CT57861.2023.10126217|Keywords- CLIA;Insurance Company;Random Forest;Agent Proposal System;Logistic Regression;Naive Bayes(NB);Decision Tree;Policy approval Evaluation;Policy Master;Keywords- CLIA;Insurance Company;Random Forest;Agent Proposal System;Logistic Regression;Naive Bayes(NB);Decision Tree;Policy approval Evaluation;Policy Master|
|[Modelling of High Speed Long-Reach LiWi-OCDMA System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126156)|M. Kumari|10.1109/I2CT57861.2023.10126156|Light fidelity (LiFi);visible light communication (VLC);shift zero cross correlation (SZCC);optical code division multiple access (OCDMA);Light fidelity (LiFi);visible light communication (VLC);shift zero cross correlation (SZCC);optical code division multiple access (OCDMA)|
|[Research Approaches for Building Analytics in Social Network towards Crowdsourcing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126479)|N. Kasturi; S. G. Totad; G. Ghosh|10.1109/I2CT57861.2023.10126479|Crowdsourcing;Social Network;Text Mining;Natural Language;Machine Learning;Crowdsourcing;Social Network;Text Mining;Natural Language;Machine Learning|
|[Stress Analysis in Online Examination using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126298)|P. Prabhakar; S. Palaniswamy; P. B. Pati|10.1109/I2CT57861.2023.10126298|deep learning;stress analysis;support vector machine;mobilenet;resnet;neural network and facial features;deep learning;stress analysis;support vector machine;mobilenet;resnet;neural network and facial features|
|[Weighted Pooling RoBERTa for Effective Text Emotion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126396)|M. Mathew; J. Prakash|10.1109/I2CT57861.2023.10126396|Emotion Detection;Text classification;RoBERTa transformer;Attention layer;Weighted pooling;Emotion Detection;Text classification;RoBERTa transformer;Attention layer;Weighted pooling|
|[Prediction of Air Quality Index in Kolkata city using an Advanced Learned Interval Type-3 Fuzzy Logic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126430)|A. Tarafdar; P. Majumder; U. K. Bera|10.1109/I2CT57861.2023.10126430|Air Quality Index (AQI);Air quality;Type-3 Fuzzy Logic System;Prediction model;Air Quality Index (AQI);Air quality;Type-3 Fuzzy Logic System;Prediction model|
|[Auto-labelling of Bug Report using Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126470)|A. Patil; A. Jadon|10.1109/I2CT57861.2023.10126470|Defect Reports;Bug Reports;Duplicate Detection;Similarity Search;Information Retrieval;Machine Learning;Natural Language Processing;Text Analysis;Defect Reports;Bug Reports;Duplicate Detection;Similarity Search;Information Retrieval;Machine Learning;Natural Language Processing;Text Analysis|
|[Optimum Coordination and Settings of Overcurrent Relays using Teaching Learning based Optimization in a Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126242)|E. Gairola; M. S. Rawat|10.1109/I2CT57861.2023.10126242|Overcurrent relay;teaching learning based algorithm;genetic algorithm;plug setting multiplier;coordination time interval;time multiplier setting;Firefly algorithm;Overcurrent relay;teaching learning based algorithm;genetic algorithm;plug setting multiplier;coordination time interval;time multiplier setting;Firefly algorithm|
|[Detection of Diabetic Retinopathy using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126384)|P. Bidwai; S. Gite; K. Patwa; K. Maheshwari; T. S. Bais; K. Batavia|10.1109/I2CT57861.2023.10126384|diabetic retinopathy (DR);convolutional neural network (CNN);fundus images;transfer learning;deep learning (DL);diabetic retinopathy (DR);convolutional neural network (CNN);fundus images;transfer learning;deep learning (DL)|
|[Detecting Fire in Color Images using Convolutional Neural Network Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126490)|D. Tanmayi; E. Udaya; P. B. Pati|10.1109/I2CT57861.2023.10126490|fire accidents;early detection;environmental sensors;visual signals;deep learning methods;CNN architecture;AlexNet;fire accidents;early detection;environmental sensors;visual signals;deep learning methods;CNN architecture;AlexNet|
|[Automated Handwriting Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126330)|K. Deekshitha; C. Patange; M. Harshitha; P. Y.J|10.1109/I2CT57861.2023.10126330|Automated writing machine;Raspberry Pi;Arduino Uno;Computer Numerical Control;Automated writing machine;Raspberry Pi;Arduino Uno;Computer Numerical Control|
|[Regression based Machine Learning approach to predict Flight Price between Bangalore and Kolkata](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126456)|A. P. Reddy; A. Tarafdar; U. K. Bera|10.1109/I2CT57861.2023.10126456|Flight rate prediction;Flight rate;Machine learning;prediction;Flight rate prediction;Flight rate;Machine learning;prediction|
|[Low Latency Requirement and Video Usage in Sri Lanka](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126169)|L. K. Pulasthi Dhananjaya Gunawardhana|10.1109/I2CT57861.2023.10126169|Video Compression;Video Compiling;Video Decompiling;Random Frame Dropping;H.265;Video Compression;Video Compiling;Video Decompiling;Random Frame Dropping;H.265|
|[Knowledge-Based Medical Tourism Recommender System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126286)|A. Harinivas; B. R; C. A. Gowda; P. Mohata; R. Sharmila|10.1109/I2CT57861.2023.10126286|Medical Tourism;Data Curation;Knowledge-Based Recommender System;Aggregated Score;Medical Tourism;Data Curation;Knowledge-Based Recommender System;Aggregated Score|
|[AI Based Smart Cleaner with IOT Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126417)|P. Ambadkar; P. Kumbhare; C. Bonde; H. Shukla; K. D. B. Vidhale|10.1109/I2CT57861.2023.10126417|Arduino microcontroller;Ultrasonic sensors;Vacuum pull machine;Arduino microcontroller;Ultrasonic sensors;Vacuum pull machine|
|[An Architecture for Microprocessor-Executable Skin Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126432)|C. V. N. Rondón; D. A. C. Carvajal; B. M. Delgado; S. A. C. Casadiego; D. G. Ibarra|10.1109/I2CT57861.2023.10126432|Skin Cancer;Computer Aided Diagnosis;Embedded System;Open-Source;Skin Cancer;Computer Aided Diagnosis;Embedded System;Open-Source|
|[Power Transformer FRA Studies Using Ladder, Multi-Conductor Transmission Lines (MTL), Circular MTL, and Hybrid Winding Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126461)|M. Samal; M. Mondal|10.1109/I2CT57861.2023.10126461|Transformer winding;Ladder network;MTL;CMTL;Hybrid model;Frequency response;DPI;Transformer winding;Ladder network;MTL;CMTL;Hybrid model;Frequency response;DPI|
|[Model Predictive Control (MPC) Based BLDC Drive for Indian Drive Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126284)|P. Ketaki; M. R. Sindhu|10.1109/I2CT57861.2023.10126284|Brushless DC (BLDC) motor;MPC;Hysteresis current control;Torque ripple;PWM control;Speed controller;closed loop control;Brushless DC (BLDC) motor;MPC;Hysteresis current control;Torque ripple;PWM control;Speed controller;closed loop control|
|[Simulation and Validation of Permanent Magnet Synchronous Motor Drives Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126378)|J. Jegan; I. Karuppasamy|10.1109/I2CT57861.2023.10126378|electrical machines;power converters;electrical drives;reinforcement learning;actor-critic;electrical machines;power converters;electrical drives;reinforcement learning;actor-critic|
|[Relative Study of Intelligent Control Techniques to Maintain Variable Pitch-Angle of the Wind Turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126335)|V. Khatavkar; S. Andhale; P. Pillewar; U. Alset|10.1109/I2CT57861.2023.10126335|Wind turbine;pitch angle;fuzzy;fuzzy-PID;adaptive fuzzy-PID;Wind turbine;pitch angle;fuzzy;fuzzy-PID;adaptive fuzzy-PID|
|[Biometric-based Smart Security System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126356)|R. Shah; S. Punjabi; S. Kamath; Y. Dange; Y. Bandi|10.1109/I2CT57861.2023.10126356|Deep Learning;Librosa;Multilayer perceptron;Neural Network;NumPy;Speech Processing;Open CV;Face Recognition;Arduino UNO;Fingerprint Recognition module R307;Deep Learning;Librosa;Multilayer perceptron;Neural Network;NumPy;Speech Processing;Open CV;Face Recognition;Arduino UNO;Fingerprint Recognition module R307|
|[Survey on Smartphone Sensors and User Intent in Smartphone Usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126192)|P. Bhatele; M. Bedekar|10.1109/I2CT57861.2023.10126192|Smartphone sensors;Gyroscope;Accelerometer;Clipboard activity;Online learning platform;Smartphone sensors;Gyroscope;Accelerometer;Clipboard activity;Online learning platform|
|[Intrusion Detection System using Ensemble Learning Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126368)|A. K. Dasari; S. K. Biswas; S. Sanyal; B. Purkayastha|10.1109/I2CT57861.2023.10126368|Intrusion Detection;Ensemble Learning;SMOTE oversampling;Minimum Redundancy Maximum Relevance;Extra Trees;Intrusion Detection;Ensemble Learning;SMOTE oversampling;Minimum Redundancy Maximum Relevance;Extra Trees|
|[Personalized Algorithms and Techniques for Development of a Smart Infusion Pump for ICU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126150)|V. S. Nair; R. K. Pathinarupothi; R. P; T. Madathil|10.1109/I2CT57861.2023.10126150|Personalized infusion;Smart infusion pump;Vital parameters;Overdose and underdose of drug;Machine learning algorithms;Personalized infusion;Smart infusion pump;Vital parameters;Overdose and underdose of drug;Machine learning algorithms|
|[Identifying Image Modifications using DCT and JPEG Quantization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126200)|P. Kubal; N. Pulgam; V. Mane|10.1109/I2CT57861.2023.10126200|Social Network;Discrete Cosine Transform;Image Identification;JPEG Quantization;Social Network;Discrete Cosine Transform;Image Identification;JPEG Quantization|
|[Investigation on Impact of Partial Shading on Solar PV Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126444)|S. S. Choudhary; T. N. Gupta; I. Hussain|10.1109/I2CT57861.2023.10126444|Partial Shading;String;Bypass diodes;mismatch;Hot spot;PV modules;Partial Shading;String;Bypass diodes;mismatch;Hot spot;PV modules|
|[Analytical Performance of Traditional Feature Selection Methods on High Dimensionality Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126303)|D. S; B. M. Gera; K. N|10.1109/I2CT57861.2023.10126303|Dimensionality Reduction;Feature Selection;Sequential Feature Selection;Computational time and Accuracy;Dimensionality Reduction;Feature Selection;Sequential Feature Selection;Computational time and Accuracy|
|[Lifetime Improvement of Non Volatile Memory Cache by Write Restriction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126275)|P. Y. J; J. S; M. J; N. C. Reddy N|10.1109/I2CT57861.2023.10126275|Cache;Lifetime;Non-Volatile Memory;Write Variation;Cache;Lifetime;Non-Volatile Memory;Write Variation|
|[Framework for Improving the Accuracy of the Machine Learning Model in Predicting Future Values](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126236)|V. K; G. Dayalan|10.1109/I2CT57861.2023.10126236|Machine Learning;Accuracy;Quality of features;Quality of algorithms;Prediction;Machine Learning;Accuracy;Quality of features;Quality of algorithms;Prediction|
#### **2023 International Conference on Electronics Packaging (ICEP)**
- DOI: 10.23919/ICEP58572.2023
- DATE: 19-22 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Preparing for 6G: Developing best practices and standards for industrial measurements of low-loss dielectrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129693)|L. Enright; M. Olszewska-Placha; M. Hill; S. Phommakesone; D. Kato; C. A. Hill; H. Kähäri; C. Lee; C. -S. Chen; N. D. Orloff; M. Celuch; U. Ray|10.23919/ICEP58572.2023.10129693|Dielectrics;measurements;mmWave;standard reference material;Dielectrics;measurements;mmWave;standard reference material|
|[AOI Pattern Detection Study for Fine Pitch Advanced Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129720)|F. Xue; Z. Zou; C. Reynolds; T. Wassick; G. Pomerantz; N. Tang; C. Cheng; S. Martell; M. Tsuriya|10.23919/ICEP58572.2023.10129720|AOI;Advanced Substrate;Defect Detection;Inspection;Fine Pitch Patterns;AOI;Advanced Substrate;Defect Detection;Inspection;Fine Pitch Patterns|
|[Copper Trace Adhesion Measurement Study for Advanced Substrate Circuitry Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129699)|L. Y. Chen; T. -H. Wang; Y. Deng; I. Mokhtar; S. R. Martell; M. Tsuriya|10.23919/ICEP58572.2023.10129699|Copper Adhesion Strength;Measurement Methodology;Fine Line Pattern;Advanced Substrates;RDL;Copper Adhesion Strength;Measurement Methodology;Fine Line Pattern;Advanced Substrates;RDL|
|[An In-containing Lower-Temperature Lead-Free Solder Paste for Wafer-Level Package Application that Outperforms SAC305](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129775)|H. Zhang; S. P. Lim|10.23919/ICEP58572.2023.10129775|lead-free;temperature cycle;low-temperature solder;reliability;joint morphology;lead-free;temperature cycle;low-temperature solder;reliability;joint morphology|
|[Selective Cu Surface Activation for Cu-Sn Thermocompression Bonding without Flux Deposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129677)|R. Negishi; S. Saito; I. Tomatsu|10.23919/ICEP58572.2023.10129677|Cu surface treatment;Selective deposition;Sn based bonding;Thermocompression bonding;Cu surface treatment;Selective deposition;Sn based bonding;Thermocompression bonding|
|[Hybrid Surface Treatment for Copper-to-Copper Thermal Compression Bonding Thermal Compression Bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129664)|L. -H. Shih; W. -T. Chen; V. Lin; J. -M. Song|10.23919/ICEP58572.2023.10129664|3D-IC;Plasma surface modification;Cu/Cu bonding;Thermocompression bonding;3D-IC;Plasma surface modification;Cu/Cu bonding;Thermocompression bonding|
|[Fabrication of Highly (111)-oriented Nanotwinned Cu in Fine-pitch Vias for Cu/SiO2 Hybrid Bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129678)|S. -C. Yang; J. -J. Ong.; D. -P. Tran; W. -L. Chiu; O. -H. Lee; C. -W. Chiang; H. -H. Chang; C. -H. Wang; C. Chen|10.23919/ICEP58572.2023.10129678|Highly (111)-oriented nanotwinned Cu;Cu hybrid bonding;Cu/SiO2 damascene process;ultra-fine pitch packaging technology;three-dimensional integrated circuit;Highly (111)-oriented nanotwinned Cu;Cu hybrid bonding;Cu/SiO2 damascene process;ultra-fine pitch packaging technology;three-dimensional integrated circuit|
|[Fine-pitch <111>-oriented NT-Cu/SiO2 hybrid joints with high thermal fatigue resistance and low contact resistivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129702)|J. -J. Ong; W. -L. Chiu; H. -H. Chang; D. -P. Tran; C. Chen|10.23919/ICEP58572.2023.10129702|Fine-pitch Cu/SiO2 hybrid bonding;Reliability analysis;High thermal fatigue;low contact resistivity;low temperature bonding;Fine-pitch Cu/SiO2 hybrid bonding;Reliability analysis;High thermal fatigue;low contact resistivity;low temperature bonding|
|[Interconnect Table Description for Efficient 2.5/3D Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129688)|O. Vikinski; A. Waizman|10.23919/ICEP58572.2023.10129688|Interconnect;bumps;TSV;co-design;productivity;Interconnect;bumps;TSV;co-design;productivity|
|[Measurement of Thermal Strain of Metallized Silicon Nitride Substrate in Thermal Cycling Test by Digital Image Correlation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129650)|M. C. Ngo; H. Miyazaki; K. Hirao; M. Fukushima|10.23919/ICEP58572.2023.10129650|Silicon nitride;Metallized ceramic substrate;Thermal cycling test;DIC;Power module;Silicon nitride;Metallized ceramic substrate;Thermal cycling test;DIC;Power module|
|[Semiconductor Package Design Flow and Platform Applied on Fan-out Chip on Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129673)|Y. Lin; K. -T. Chang; H. -C. Kuo; C. -Y. Huang; C. -C. Wang|10.23919/ICEP58572.2023.10129673|2.5D package;fan-out chip on substrate (FOCoS);flip-chip ball grid array (FCBGA) package;SiP-id;2.5D package;fan-out chip on substrate (FOCoS);flip-chip ball grid array (FCBGA) package;SiP-id|
|[Failure Analysis of Joints Bonded by Ag-In Transient Liquid Phase Process during Shear Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129705)|X. Liu; H. Tatsumi; Z. Jin; H. Nishikawa|10.23919/ICEP58572.2023.10129705|Die-attachment;transient liquid phase bonding;intermetallic compounds;failure analysis;FEM analysis;Die-attachment;transient liquid phase bonding;intermetallic compounds;failure analysis;FEM analysis|
|[Controlling porosity during transient liquid phase bonding for high-temperature soldering processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129722)|N. R. Abdul Razak; X. F. Tan; S. D. McDonald; M. J. Bermingham; J. Venezuela; T. Nishimura; K. Nogita|10.23919/ICEP58572.2023.10129722|Electronics packaging;interconnection;high-temperature solder;transient liquid phase bonding;Electronics packaging;interconnection;high-temperature solder;transient liquid phase bonding|
|[Enhanced Cu-to-Cu Bonding by Using Sn Passivation Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129779)|P. Y. Kung; W. L. Huang; C. L. Kao; Y. C. Hung; C. R. Kao|10.23919/ICEP58572.2023.10129779|3D IC;Cu-to-Cu bonding;Sn passivation layer;Compensate for the height difference;3D IC;Cu-to-Cu bonding;Sn passivation layer;Compensate for the height difference|
|[Effects of Surface Contaminants on Bonding Strength for Direct Cu-Cu Bonding With Passivation Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129767)|A. -Y. K. Pétillot; S. Shoji; H. Kawarada; J. Mizuno|10.23919/ICEP58572.2023.10129767|VUV;Plasma treatment;Surface contaminants;Cu bonding;VUV;Plasma treatment;Surface contaminants;Cu bonding|
|[Reactive Ion Etching Challenges and Technology for Memory Device Fabrication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129785)|S. Tahara|10.23919/ICEP58572.2023.10129785|3D-NAND;DRAM;HAR etch;patterning;3D-NAND;DRAM;HAR etch;patterning|
|[Research and Development Facilities for Fast Manufacturing IoT Device Prototyping in AIST Kyushu](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129788)|H. Hirai; M. Ozono; T. Ishikawa; E. Maeda|10.23919/ICEP58572.2023.10129788|Heterogeneous integration;system in package;proof of concept;through mold via;minimal fab;Heterogeneous integration;system in package;proof of concept;through mold via;minimal fab|
|[Battery-Less Environmental Sensor Platform for Enclosures in a Zoo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129659)|H. Kanaya; M. Kawano; S. -I. Shinohara; O. Takiguchi; H. Nogami|10.23919/ICEP58572.2023.10129659|Battery-less;Environmental sensor;Captive animals;Bluetooth low energy;Battery-less;Environmental sensor;Captive animals;Bluetooth low energy|
|[A Temporary Bonding De-Bonding Tape with High Thermal Resistance and Excellent TTV for 3DIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129768)|Y. Sakamoto; R. Watanabe; I. Daido; T. Takahashi|10.23919/ICEP58572.2023.10129768|Temporary bonding de-bonding;Hybrid bonding;3DIC;Thermal resistance;TTV;Temporary bonding de-bonding;Hybrid bonding;3DIC;Thermal resistance;TTV|
|[Copper contamination control in Hybrid Copper Bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129740)|W. Kim; S. H. Han; Y. Lee; D. Shin; W. Choi; J. Moon; K. Lim; B. Moon; M. D. Rhee|10.23919/ICEP58572.2023.10129740|Copper contamination;Contamination free;In-situ clean;Plasma;Hybrid copper bonding;Copper contamination;Contamination free;In-situ clean;Plasma;Hybrid copper bonding|
|[Physical Properties of Large Cu Grain and Application to Cu-SiO2 Hybrid Bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129755)|R. Kobayashi; E. Sone; M. Sawa; M. Murugesan; T. Fukushima|10.23919/ICEP58572.2023.10129755|Hybrid Bonding;Direct Bonding;Acid Copper Plating;Grain Growth;3D-IC;Hybrid Bonding;Direct Bonding;Acid Copper Plating;Grain Growth;3D-IC|
|[Time Evolution Study of Two-Step Plasma-Treated Copper-Copper Direct Bonding in Ambient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129764)|L. Hu; Y. D. Lim; P. Zhao; M. J. Zhong Lim; W. Miao; V. Q. Dinh; X. Ju; C. S. Tan|10.23919/ICEP58572.2023.10129764|Time evolution study;plasma treatment;die-die bonding;copper-copper direct bonding;ambient condition;Time evolution study;plasma treatment;die-die bonding;copper-copper direct bonding;ambient condition|
|[A New Authentication Method Using The Smart Individuality Printing to Improve The Traceability of Semiconductor Packages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129667)|K. Takano; A. Miyazaki; M. Saito; M. Sugimoto; T. Yamada; S. Takyu|10.23919/ICEP58572.2023.10129667|Individuality printing;traceability;semiconductor;anti-counterfeiting;Backside coating tape;Individuality printing;traceability;semiconductor;anti-counterfeiting;Backside coating tape|
|[Assembly and packaging technology on Silicon-Ceramic-based composite substrates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129654)|C. Kleinholz; M. Fischer; J. Müller|10.23919/ICEP58572.2023.10129654|LTCC;SiCer;metallization paste;soldering;wire bonding;LTCC;SiCer;metallization paste;soldering;wire bonding|
|[Thin Die Flip Chip Process Enablement for Stacked IC Memory Packages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129716)|C. -Y. Huang; J. Lee; T. -H. Chiang; K. Lin; C. L. Gan; T. Jensen|10.23919/ICEP58572.2023.10129716|Flip Chip;Warpage;Chip Package Interaction;IC Memory Stacking;Flip Chip;Warpage;Chip Package Interaction;IC Memory Stacking|
|[How to enhance Sn-Bi low-temperature solder by alloying?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129679)|S. -K. Lin; C. -H. Yang; Y. -C. Liu; Y. Hirata; H. Nishikawa|10.23919/ICEP58572.2023.10129679|Low-temperature Pb-free solder;Sn-Bi-Ag;CALculation of PHase Diagram (CALPHAD);Low-temperature Pb-free solder;Sn-Bi-Ag;CALculation of PHase Diagram (CALPHAD)|
|[High-productivity patterning for advanced package by combination of the optical engine equipped with unique spatial light modulator and the high-speed/accuracy stage control technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129682)|Y. Hanada; M. Mizubata; Y. Fujisawa; T. Kawashima|10.23919/ICEP58572.2023.10129682|FCCSP;FCBGA;FOPLP;direct imaging;high-throughput;FCCSP;FCBGA;FOPLP;direct imaging;high-throughput|
|[In-situ Observation of Underfill Dispensing Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129718)|D. C. Hu; E. Hao Chen; J. C. Lee; C. Peng Sun; Y. E. Liang|10.23919/ICEP58572.2023.10129718|Heterogeneous integration;underfill;real-time observation;underfill dispensing;2.2D;chiplets;Heterogeneous integration;underfill;real-time observation;underfill dispensing;2.2D;chiplets|
|[Double layer wiring on fabric using soft polyurethane film substrate and its application to motion capture devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129690)|H. Takahashi; N. Tomita; S. Takamatsu; M. Yamamoto; T. Itoh|10.23919/ICEP58572.2023.10129690|wearable device;screen printing;e-textile;IMU sensor;motion capture;wearable device;screen printing;e-textile;IMU sensor;motion capture|
|[High Density RDL Interconnection of Die to Die using Chip-First Process for Heterogeneous Integration (HI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129737)|Y. Han; T. Kanayama; H. Mitsutomi; K. Nogita; T. Suetsugu|10.23919/ICEP58572.2023.10129737|Chip-first process;Fine pattern RDL;Die-to-Die interconnection;Cavity in Si carrier;Chip-first process;Fine pattern RDL;Die-to-Die interconnection;Cavity in Si carrier|
|[Electroplating Uniformity Enhancement for High Performance Fan-Out Panel Level Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129762)|Y. -F. Chiang; B. Wu; M. Kuo; J. Yang; J. -K. Fang|10.23919/ICEP58572.2023.10129762|Electroplating;Panel;Uniformity;Fan-out;Electroplating;Panel;Uniformity;Fan-out|
|[Micro Ball Mount Process for High Performance Fan-Out Large Panel Level Packaging Back-end Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129684)|P. Lu; J. S. Weng; H. H. Chen; J. Yang; J. -K. Fang|10.23919/ICEP58572.2023.10129684|ball mount;panel;back-end;fan-out;ball mount;panel;back-end;fan-out|
|[Materials Technology Correlation Between Front-end and Back-end Processes in Advanced Semiconductor Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129760)|K. Watahiki; Y. Midoh; K. Okamoto|10.23919/ICEP58572.2023.10129760|Front-end process;Back-end process;Photoresist;Correlation;Natural Language Processing (NLP);Front-end process;Back-end process;Photoresist;Correlation;Natural Language Processing (NLP)|
|[A Deep Learning Reconstruction Technique and Workflow to Enhance 3D X-ray Imaging Resolution and Speed for Electronics Package Failure Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129701)|A. Gu; A. Andreyev; M. Terada; T. Rodgers; V. Viswanathan|10.23919/ICEP58572.2023.10129701|X-ray;Deep-learning;Packages;Failure Analysis;X-ray;Deep-learning;Packages;Failure Analysis|
|[A machine learning approach to explore tensile properties of low-temperature solders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129657)|Y. -C. Liu; A. Kholik; S. -K. Lin|10.23919/ICEP58572.2023.10129657|low-temperature solder;machine learning;low-temperature solder;machine learning|
|[Low cycle fatigue of an interface between a substrate and molding resin in a power module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129750)|T. Ikeda; S. Nakagawa; M. Koganemaru; T. Kakara|10.23919/ICEP58572.2023.10129750|Power module;Interface;Fatigue crack extension;Thermal cycle test;Power module;Interface;Fatigue crack extension;Thermal cycle test|
|[An Accurate Estimate of Effective Thermal-Mechanical Properties of Coreless Circuit Substrate for Advanced Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129698)|W. Y. Jhu; H. C. Cheng|10.23919/ICEP58572.2023.10129698|Coreless circuit substrate;Effective property;Trace mapping and modeling;Flip-chip chip scale packaging;Process simulation;Warpage;Coreless circuit substrate;Effective property;Trace mapping and modeling;Flip-chip chip scale packaging;Process simulation;Warpage|
|[FOPOP Warpage Analysis for Package Design Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129706)|K. Zhang; V. Lin; T. Shih; A. Kang; Y. Wang|10.23919/ICEP58572.2023.10129706|Warpage;Package Design Optimization;Warpage;Package Design Optimization|
|[Highly Reliable Laser-sintered Copper FilmsTransformed from Cu2O Nanoparticles by Plasma Modification of the Polymeric Substrate Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129735)|W. -H. Cheng; W. -R. Yang; J. -M. Song|10.23919/ICEP58572.2023.10129735|laser sintering;cuprous oxide;plasma modification;bending fatigue;laser sintering;cuprous oxide;plasma modification;bending fatigue|
|[Significant consumption of Ni-P layer in Ni-P/Sn-0.7Cu solder joints during thermomigration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129736)|S. Oya; H. Tatsumi; H. Nishikawa|10.23919/ICEP58572.2023.10129736|thermomigration;temperature gradient;Ni-P;Sn-0.7Cu solder;crystal orientation;thermomigration;temperature gradient;Ni-P;Sn-0.7Cu solder;crystal orientation|
|[Effect of Bonding Position between Cu Wire and Al pad in THB Reliability Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129749)|K. Shinohara; T. Kobayashi; A. Miyota|10.23919/ICEP58572.2023.10129749|Cu wire;Al pad;bonding position;Corrosion;THB;Cu wire;Al pad;bonding position;Corrosion;THB|
|[Robust bonding at 175°C of pressureless Ag nanoparticle sinter joint on Ni/Au finished Cu substrates in air](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129758)|S. Kim; M. -S. Kim; S. Mhin; D. Kim|10.23919/ICEP58572.2023.10129758|Ag nanoparticles;power electronics;pressureless sintering;low-temperature sintering;die-attach;Ag nanoparticles;power electronics;pressureless sintering;low-temperature sintering;die-attach|
|[Copper and silver sintered die-attach compared in HV-H3TRB and thermal shock cycling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129660)|F. Steiner; D. Ishikawa; H. Nakako; T. Blank|10.23919/ICEP58572.2023.10129660|copper;silver;sintering;H3TRB;thermal shock cycling;copper;silver;sintering;H3TRB;thermal shock cycling|
|[Influence of interfacial interaction on the reliability of the bond between encapsulation epoxy and copper substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129766)|S. Zhao; C. Chen; M. Ueshima; M. Haga; K. Suganuma|10.23919/ICEP58572.2023.10129766|power module;epoxy encapsulation;reliability;power module;epoxy encapsulation;reliability|
#### **2023 9th International Conference on Automation, Robotics and Applications (ICARA)**
- DOI: 10.1109/ICARA56516.2023
- DATE: 10-12 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Model-Based Approach for Robotics Education with Emphasis on Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125977)|R. Beneder; P. Schmitt; C. Kornyefalvy|10.1109/ICARA56516.2023.10125977|Robotics Education;Embedded Systems Education;Control Systems;Embedded Software;Robotics Education;Embedded Systems Education;Control Systems;Embedded Software|
|[Preoperative Personalized Vascular 3D Simulator of the Intelligent Robotic Ecosystem LevshAI for Endovascular Neurosurgery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125981)|I. Menshikov; K. Orlov; V. Berestov; A. Bernadotte|10.1109/ICARA56516.2023.10125981|3D simulator;vascular simulator;Ecosystem LevshAI;remote neurosurgery;remote endovascular surgery;robotics;telesurgery;AI;screening;algorithms;3D simulator;vascular simulator;Ecosystem LevshAI;remote neurosurgery;remote endovascular surgery;robotics;telesurgery;AI;screening;algorithms|
|[Measurement Pose Optimization for Joint Offset Calibration with a Hand-Eye Camera](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125953)|X. Zhang; H. Goto; S. Shirafuji; K. Okuhara; N. Takamura; N. Kagawa; H. Baba; J. Ota|10.1109/ICARA56516.2023.10125953|measurement pose optimization;joint offset;robot calibration;hand-eye camera;measurement pose optimization;joint offset;robot calibration;hand-eye camera|
|[CART-II: Development of Collision Avoidance Robotic Tether with Soft Sensing Capabilities for Underwater Nuclear Inspection Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125773)|A. K. Al Mhdawi; N. Wright; S. Benson; M. Haroutunian|10.1109/ICARA56516.2023.10125773|Tether;force sensing;conductive;Remotely Operated Vehicles (ROV);marine environment;thrust;nuclear inspection;3D printing;Tether;force sensing;conductive;Remotely Operated Vehicles (ROV);marine environment;thrust;nuclear inspection;3D printing|
|[Towards Self-Configuring Plug & Produce Robot Systems Based on Ontologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126075)|C. Eymüller; J. Hanke; A. Poeppel; W. Reif|10.1109/ICARA56516.2023.10126075|Plug & Produce;Ontologies;Self-Configuring Robots;Skills;CPPS;Plug & Produce;Ontologies;Self-Configuring Robots;Skills;CPPS|
|[MQTT Enabled Simulation Interface for Motion Execution of Industrial Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125673)|A. Sartori; R. Waspe; C. Schlette|10.1109/ICARA56516.2023.10125673|Industrial Robots;On-Demand Simulation;MQTT;Industrial Robots;On-Demand Simulation;MQTT|
|[Extending the Target-Search Algorithm for Kilobots by Adding Random Walk Behavior](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125694)|M. Troxler; R. Dornberger; T. Hanne|10.1109/ICARA56516.2023.10125694|collision avoidance;Kilobot;random walk;target-search;Lévy distribution;V-REP;collision avoidance;Kilobot;random walk;target-search;Lévy distribution;V-REP|
|[NU-Biped-4 a Lightweight and Low-Power Consumption Full-Size Bipedal Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126025)|M. Folgheraiter; S. Yessirkepov; T. Umurzakov; R. Korabay|10.1109/ICARA56516.2023.10126025|Bipedal Robot;Humanoid Robot;Low-Power Consumption Biped;Elastic Foot;Bipedal Robot;Humanoid Robot;Low-Power Consumption Biped;Elastic Foot|
|[Toward Computationally Efficient Path Generation and Push Planning for Robotic Nonprehensile Manipulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125726)|A. Elibol; N. Y. Chong|10.1109/ICARA56516.2023.10125726|robotic arm;planar pushing;path generation;push planning;robotic arm;planar pushing;path generation;push planning|
|[Collision Avoidance of Multiple Moving Agents by Adapting the A* Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125730)|K. Neuschwander; R. Dornberger; T. Hanne|10.1109/ICARA56516.2023.10125730|pathfinding;path planning;A* algorithm;multi-agent;collision-detection;shortest path;fastest path;pathfinding;path planning;A* algorithm;multi-agent;collision-detection;shortest path;fastest path|
|[Experience-based Problem Solver for Robot System Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125871)|J. Lu; R. Takamido; J. Ota|10.1109/ICARA56516.2023.10125871|Experience-based;environment arrangement;case-injected genetic algorithm;robotic arm;motion planning;Experience-based;environment arrangement;case-injected genetic algorithm;robotic arm;motion planning|
|[Cooperative Collision Avoidance in Mobile Robots using Dynamic Vortex Potential Fields](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125851)|W. P. Martis; S. Rao|10.1109/ICARA56516.2023.10125851|Vortex Potential Fields;Mobile Robots;Collision Avoidance;Reciprocity;Vortex Potential Fields;Mobile Robots;Collision Avoidance;Reciprocity|
|[Cyber Security for Surgical Remote Intelligent Robotic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126050)|A. Bernadotte|10.1109/ICARA56516.2023.10126050|Cybersecurity;AI agent;motion copying systems;Surgical Remote Intelligent Robotic System;LevshAI;teleoperating;telesurgery;Cybersecurity;AI agent;motion copying systems;Surgical Remote Intelligent Robotic System;LevshAI;teleoperating;telesurgery|
|[Autonomous Navigation of Quadrotors Using Tactile Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125600)|N. Borkar; P. Krishnamurthy; A. Tzes; F. Khorrami|10.1109/ICARA56516.2023.10125600|Autonomous navigation;quadrotor;force/con-tact sensors;unknown environments;Autonomous navigation;quadrotor;force/con-tact sensors;unknown environments|
|[Software Model for Robot Programming and Example of Implementation for Navigation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125856)|S. Chaychi; D. Zampunieris; S. Reis|10.1109/ICARA56516.2023.10125856|software design;separation of concerns;proac- tive computing;navigation;software design;separation of concerns;proac- tive computing;navigation|
|[Modified Bug Algorithm with Proximity Sensors to Reduce Human-Cobot Collisions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126056)|A. Li; H. Gurocak|10.1109/ICARA56516.2023.10126056|cobot;robot;collision avoidance;bug algorithm;cobot;robot;collision avoidance;bug algorithm|
|[RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125630)|A. Eirale; M. Martini; M. Chiaberge|10.1109/ICARA56516.2023.10125630|Person Following;Robot Assistant;Deep Rein-forcement Learning;Human-Centered Navigation;Person Following;Robot Assistant;Deep Rein-forcement Learning;Human-Centered Navigation|
|[Photogrammetry-based Dynamic Path Tracking of Industrial Robots Using Adaptive Neuro-PID Control Method and Robust Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125681)|J. Tang; T. Zhou; E. Zakeri; T. Shu; W. -F. Xie|10.1109/ICARA56516.2023.10125681|Industrial robots;dynamic path tracking;robust Kalman filter;photogrammetry sensors;Neuro-PID control method;Industrial robots;dynamic path tracking;robust Kalman filter;photogrammetry sensors;Neuro-PID control method|
|[Correlation Analysis of Factors Influencing the Motion Planning Accuracy of Articulated Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125613)|O. Kedilioglu; M. Nikol; J. Walter; J. Franke|10.1109/ICARA56516.2023.10125613|Visual servoing;motion planning;accuracy;Visual servoing;motion planning;accuracy|
|[Robot Locomotion Control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125666)|V. S. Vivekanand; S. Hashemkhani; S. Venkatachalam; R. Kubendran|10.1109/ICARA56516.2023.10125666|central pattern generators;non-linear neurons;spike-based control;neuromorphic computing;neuromodulation;central pattern generators;non-linear neurons;spike-based control;neuromorphic computing;neuromodulation|
|[Introduction of A Row-Skip Pattern in Complete Coverage Path Planning for Agricultural Fields](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125619)|D. P. Arab; M. Spisser; C. Essert|10.1109/ICARA56516.2023.10125619|Complete Coverage Path Planning;Precision Agriculture;Autonomous Agriculture;Vehicle Routing Problem;Wheeled robots;Path Planning;Route Planning;Complete Coverage Path Planning;Precision Agriculture;Autonomous Agriculture;Vehicle Routing Problem;Wheeled robots;Path Planning;Route Planning|
|[Impact Force Location and Intensity Identification Using Joint-Position Sensors in Humanoids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125728)|S. Choueiri; H. Diab; M. Owayjan; R. Achkar|10.1109/ICARA56516.2023.10125728|humanoid stabilization;force detection;force localization;ensemble learning;humanoid stabilization;force detection;force localization;ensemble learning|
|[An Elastic Shoulder Joint for Humanoid Robotics Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125796)|S. Yessirkepov; T. Umurzakov; R. Shaimerdenov; M. Folgheraiter|10.1109/ICARA56516.2023.10125796|Humanoid Shoulder Joint;Parallel Manipulator;Tendon-driven Mechanism;Humanoid Shoulder Joint;Parallel Manipulator;Tendon-driven Mechanism|
|[Design and Development of a Prototype Upper-limb Rehabilitation Robot Based on Multi-body Dynamics Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125669)|H. -H. Sung; C. -K. Hsiao; C. -K. Lee; C. -Y. Wang|10.1109/ICARA56516.2023.10125669|upper-limb rehabilitation robot;multi-axis robot with synchronous motion;RecurDyn multi-body dynamic simulation software;upper-limb rehabilitation robot;multi-axis robot with synchronous motion;RecurDyn multi-body dynamic simulation software|
|[A Cable-Driven Robotic Eye for Understanding Eye-Movement Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126021)|A. John; J. van Opstal; A. Bernardino|10.1109/ICARA56516.2023.10126021|Cable-driven robots;Bio-inspired robots;Eye-movements;Oculomotor system;Saccades;Cable-driven robots;Bio-inspired robots;Eye-movements;Oculomotor system;Saccades|
|[Comparative Modeling Study of Pneumatic Artificial Muscle Using Neural Networks, ANFIS and Curve Fitting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125812)|M. Abu Mallouh; W. Sameer Araydah; B. Jouda; M. A. Al-Khawaldeh|10.1109/ICARA56516.2023.10125812|Pneumatic Artificial Muscle;modeling;Neural Network;ANFIS;NARX;Pneumatic Artificial Muscle;modeling;Neural Network;ANFIS;NARX|
|[JUST TELL ME: A Robot-assisted E-health Solution for People with Lower-extremity Disability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125947)|A. Coutras; O. Obidat; M. Zhu; W. Wang|10.1109/ICARA56516.2023.10125947|Robotics;human-robot interaction;E-health;mobility impairments;computer vision;healthcare informatics systems;Robotics;human-robot interaction;E-health;mobility impairments;computer vision;healthcare informatics systems|
|[A Discrete-time Distributed Optimization Algorithm for Multi-robot Coordination Target Monitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125641)|Y. Zheng; Q. Liu; G. Chi|10.1109/ICARA56516.2023.10125641|distributed algorithm;constrained optimization;multi-robot coordination;convergence;distributed algorithm;constrained optimization;multi-robot coordination;convergence|
|[Emergency Stop System of Computer Vision Workstation Based on GMM-HMM and LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125926)|M. Wu; F. Guo; J. Wu; Y. Xiao; M. Jin; Q. Zhang|10.1109/ICARA56516.2023.10125926|SSD algorithm;Cartesian robot workstation;LSTM algorithm;HMM algorithm;SSD algorithm;Cartesian robot workstation;LSTM algorithm;HMM algorithm|
|[Automatic Generation of Robot Actions for Collaborative Tasks from Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125800)|M. Zand; K. Kodur; M. Kyrarini|10.1109/ICARA56516.2023.10125800|Human-robot collaboration;Robot Action Generation;Natural Language Processing;Assistive Cooking;Speech;Human-robot collaboration;Robot Action Generation;Natural Language Processing;Assistive Cooking;Speech|
|[Hierarchical Reinforcement Learning for In-hand Robotic Manipulation Using Davenport Chained Rotations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125281)|F. R. Sanchez; Q. Wang; D. C. Bulens; K. McGuinness; S. J. Redmond; N. E. O'Connor|10.1109/ICARA56516.2023.10125281|Robotic manipulation;deep reinforcement learning;hierarchical reinforcement learning;Robotic manipulation;deep reinforcement learning;hierarchical reinforcement learning|
|[Design and Feasibility Test of an Automatic Scraping Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125772)|Z. Cui; L. Han; G. Dong; Y. Lin; Y. Gao; S. Fan|10.1109/ICARA56516.2023.10125772|automatic scraping;vision recognition;robot;measurement system;automatic scraping;vision recognition;robot;measurement system|
|[Reinforcement-Learning Based Robotic Assembly of Fractured Objects Using Visual and Tactile Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125938)|X. Song; N. Lamb; S. Banerjee; N. K. Banerjee|10.1109/ICARA56516.2023.10125938|reinforcement learning;fractured shape repair;machine learning;robotic assembly;sim-to-real;reinforcement learning;fractured shape repair;machine learning;robotic assembly;sim-to-real|
|[Inflatable Robotic Manipulator for Space Debris Mitigation by Visual Servoing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125753)|P. Palmieri; M. Troise; M. Gaidano; M. Melchiorre; S. Mauro|10.1109/ICARA56516.2023.10125753|soft robotics;space robotics;flexible robotics;inflatable structures;space debris;soft robotics;space robotics;flexible robotics;inflatable structures;space debris|
|[Oblivious Robots Performing Different Tasks on Grid Without Knowing Their Team Members](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125816)|S. Ghosh; A. Sharma; P. Goswami; B. Sau|10.1109/ICARA56516.2023.10125816|Robots;Gathering;Arbitrary pattern formation;Infinite grid;Robots;Gathering;Arbitrary pattern formation;Infinite grid|
|[Studying Worker Perceptions on Safety, Autonomy, and Job Security in Human-Robot Collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125842)|G. Kaur; S. Banerjee; N. K. Banerjee|10.1109/ICARA56516.2023.10125842|Robot acceptance;human-robot interaction;human-robot collaboration;corobots;Robot acceptance;human-robot interaction;human-robot collaboration;corobots|
|[An Effective Method for Creating Virtual Doors and Borders to Prevent Autonomous Mobile Robots from Entering Restricted Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125943)|K. Omer; A. Monteriú|10.1109/ICARA56516.2023.10125943|virtual walls;virtual doors;virtual borders;human-centered environments;no-go zones;AAL robotics;ware-house robotics;logistics robots;virtual walls;virtual doors;virtual borders;human-centered environments;no-go zones;AAL robotics;ware-house robotics;logistics robots|
|[Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125781)|R. V. Wicaksana Putra; M. Shafique|10.1109/ICARA56516.2023.10125781|Autonomous mobile agents;robots;UAVs;spiking neural networks;energy efficiency;online learning;Autonomous mobile agents;robots;UAVs;spiking neural networks;energy efficiency;online learning|
|[Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125597)|R. Donald; P. Gavriel; A. Norton; S. R. Ahmadzadeh|10.1109/ICARA56516.2023.10125597|Contextual Autonomy;Unmanned Aerial Vehicles;Fuzzy Systems;Contextual Autonomy;Unmanned Aerial Vehicles;Fuzzy Systems|
|[UAV- Navigation Using Map Slicing and Safe Path Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125893)|H. U. Unlu; D. Chaikalis; A. Tsoukalas; A. Tzes|10.1109/ICARA56516.2023.10125893|path planning;navigation;indoor exploration;search and rescue;simultaneous localization and mapping;path planning;navigation;indoor exploration;search and rescue;simultaneous localization and mapping|
|[Design of an Energy-Efficient Self-Heterogeneous Aerial-Ground Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125819)|A. A. Alajami; L. D. Santa Cruz; R. Pous|10.1109/ICARA56516.2023.10125819|Hybrid robot;heterogeneous robot;mobile robots;energy-efficient UAV;taxiing;Hybrid robot;heterogeneous robot;mobile robots;energy-efficient UAV;taxiing|
|[A Deep Reinforcement Learning Visual Servoing Control Strategy for Target Tracking Using a Multirotor UAV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125971)|A. Mitakidis; S. N. Aspragkathos; F. Panetsos; G. C. Karras; K. J. Kyriakopoulos|10.1109/ICARA56516.2023.10125971|target tracking;visual servoing;reinforcement learning;target tracking;visual servoing;reinforcement learning|
|[Densifying SLAM for UAV Navigation by Fusion of Monocular Depth Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125712)|Y. Habib; P. Papadakis; C. Le Barz; A. Fagette; T. Gonçalves; C. Buche|10.1109/ICARA56516.2023.10125712|dense SLAM;monocular depth prediction;drone navigation;dense SLAM;monocular depth prediction;drone navigation|
|[Mechatronic Design of an Amphibious Drone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125643)|N. Evangeliou; D. Chaikalis; N. Giakoumidis; A. Tzes|10.1109/ICARA56516.2023.10125643|Unmanned Aerial Vehicle;Amphibious drone;Hybrid drone;Unmanned Surface Vessel;Waterproof drone;Unmanned Aerial Vehicle;Amphibious drone;Hybrid drone;Unmanned Surface Vessel;Waterproof drone|
#### **2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)**
- DOI: 10.1109/ICCoSITE57641.2023
- DATE: 16-16 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Classification of Emotions on Song Lyrics using Naïve Bayes Algorithm and Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127851)|G. S. Ramadhan; B. Irawan; C. Setianingsih; F. P. Dwigantara|10.1109/ICCoSITE57641.2023.10127851|Naïve Bayes;text classification;PSO;Naïve Bayes;text classification;PSO|
|[Airport Runway Foreign Object Debris (FOD) Detection Based on YOLOX Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127676)|J. Taupik; T. Alamsyah; A. Wulandari; E. U. Armin; A. Hikmaturokhman|10.1109/ICCoSITE57641.2023.10127676|Computer Vision;Object Detection;FOD;YOLOX;Computer Vision;Object Detection;FOD;YOLOX|
|[Comparison of Feature Extraction to Test Dryness and Moisture Levels in Burned Restoration Areas Using Linear Discriminant Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127793)|Y. S. Sari; F. Aina Rizky; I. Ranggadara; N. R. Kurnianda; I. Prihandi; Suhendra|10.1109/ICCoSITE57641.2023.10127793|Algorithm;Dryness;Forest Fire;Moisture;Restoration;Algorithm;Dryness;Forest Fire;Moisture;Restoration|
|[Hepatitis Cluster Model With K-Means Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127719)|A. Chusyairi; O. Nurdiawan; K. Sambath; R. N. Hayat; Y. Arie Wijaya|10.1109/ICCoSITE57641.2023.10127719|K-Means;Hepatitis;Clustering;Ministry of Health;K-Means;Hepatitis;Clustering;Ministry of Health|
|[WebGIS Development to Integrate, Visualize, Map, and Disseminate Population Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127778)|G. W. Sasmito; R. Ratono|10.1109/ICCoSITE57641.2023.10127778|WebGIS;Population;Data;Rapid Application Development;WebGIS;Population;Data;Rapid Application Development|
|[Educational Data Mining Patterns K-anonymity for The Analytics of Student Privacy Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127762)|A. Triayudi; I. Fitri; S. R. Yahya; S. Sumiati|10.1109/ICCoSITE57641.2023.10127762|Learning analytics;Privacy;Data publishing;Anonymization;Distance learning;Learning analytics;Privacy;Data publishing;Anonymization;Distance learning|
|[Implementation of Convolutional Neural Network Algorithm to Pest Detection in Caisim](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127792)|C. L. Nazalia; P. Palupiningsih; B. Prayitno; Y. S. Purwanto|10.1109/ICCoSITE57641.2023.10127792|Deep Learning;CNN;Classification;Caisim Pest;Deep Learning;CNN;Classification;Caisim Pest|
|[Detection of Kidney Cysts of Kidney Ultrasound Image using Hybrid Method: KNN, GLCM, and ANN Backpropagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127703)|Mardison; Yuhandri|10.1109/ICCoSITE57641.2023.10127703|ultrasound (USG) 2D Image;kidney cysts;KNN method;GLCM method;ANN type Backpropagation method;ultrasound (USG) 2D Image;kidney cysts;KNN method;GLCM method;ANN type Backpropagation method|
|[Mandalika Modeling Topic on Social Media Using Latent Dirichlet Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127821)|V. C. Hardita; R. Hammad; A. Z. Amrullah|10.1109/ICCoSITE57641.2023.10127821|Mandalika Circuit;Topic Modelling;LDA;Twitter;Mandalika Circuit;Topic Modelling;LDA;Twitter|
|[Improved VGG-16 for Classifying Thyroid Nodule on Thyroid Ultrasound Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127750)|T. Anissa; H. A. Nugroho; I. Soesanti|10.1109/ICCoSITE57641.2023.10127750|deep learning;thyroid nodule;composition;characteristic;classification;deep learning;thyroid nodule;composition;characteristic;classification|
|[Acute Respiratory Infections Diagnosis Using Learning Vector Quantization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127811)|Abdurrasyid; M. N. Indah Susanti; I. Indrianto; K. Zhafira|10.1109/ICCoSITE57641.2023.10127811|Acute Respiratory Infections;Learning Vector Quantization;Machine Learning;Classification Accuracy;ARI Symptoms;Acute Respiratory Infections;Learning Vector Quantization;Machine Learning;Classification Accuracy;ARI Symptoms|
|[Comparison of Heart Disease Symptom Algorithm Using Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127740)|E. J. Wahyu; C. Fauzi|10.1109/ICCoSITE57641.2023.10127740|Heart Disease;Data Mining;Naïve Bayes and Particle Swarm Optimization;Heart Disease;Data Mining;Naïve Bayes and Particle Swarm Optimization|
|[Similarity Measurement on Logo Image Using CBIR (Content Base Image Retrieval) and CNN ResNet-18 Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127711)|L. N. Rani; Y. Yuhandri|10.1109/ICCoSITE57641.2023.10127711|similarity measurement;logo image;CBIR Method;CNN Method;ResNet-18 Architecture;similarity measurement;logo image;CBIR Method;CNN Method;ResNet-18 Architecture|
|[Synthesis of IoT Sensor Telemetry Data for Smart Home Edge-IDS Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127781)|S. GVK; A. Bangari; M. Rao; J. Bapat; D. Das|10.1109/ICCoSITE57641.2023.10127781|Intrusion Detection System (IDS);IoT Sensor Telemetry;anomaly detection;machine learning;XGBoost Classification;Data Synthesis;Intrusion Detection System (IDS);IoT Sensor Telemetry;anomaly detection;machine learning;XGBoost Classification;Data Synthesis|
|[Reconstruction of simulated VLBI data using the SARA method and random raw patches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127816)|R. D. Atmaja; A. B. Suksmono; D. Danudirdjo; T. Hidayat|10.1109/ICCoSITE57641.2023.10127816|reconstruction;simulated VLBI data;random raw patches;SARA;reconstruction;simulated VLBI data;random raw patches;SARA|
|[Predicting the Success of Garment Sales on Transaction Data using the Classification Method with the Naïve Bayes Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127693)|A. Sani; Samuel; D. Suryadi; F. N. Hasan; A. Davy Wiranata; S. Aisyah|10.1109/ICCoSITE57641.2023.10127693|Data Mining;Prediction;Classification Method;Naïve Bayes Algorithm;Data Mining;Prediction;Classification Method;Naïve Bayes Algorithm|
|[Malware Clustering System using Moth-Flame Optimization as IoT Security Strengthening](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127768)|R. Adrian; T. Widiasari; M. A. R. Somardani; A. J. Okke|10.1109/ICCoSITE57641.2023.10127768|firewall;IoT;network;mfo;security;firewall;IoT;network;mfo;security|
|[Optimization of Data Warehouse Architecture to Improve Information System Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127721)|S. B; A. A. Ilham; A. W. Paundu|10.1109/ICCoSITE57641.2023.10127721|Data Warehouse;Information System;ETL;Optimization;Scheduling;Data Warehouse;Information System;ETL;Optimization;Scheduling|
|[A Systematic Literature Review of Generative Adversarial Network Potential In AI Artwork](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127706)|F. Rasyad; H. A. Kongguasa; N. C. Onggususilo; Anderies; A. Kurniawan; A. A. S. Gunawan|10.1109/ICCoSITE57641.2023.10127706|AI;Art;Creativity;GAN;Image;AI;Art;Creativity;GAN;Image|
|[Gesture-Controlled Robotic Arm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127689)|M. M. U. Saleheen; M. R. Fahad; R. Khan|10.1109/ICCoSITE57641.2023.10127689|Arduino Mega;Hand glove;Servomotor;Flex sensors;Gyroscope;Hand-gesture-controlled robotic arm;Arduino Mega;Hand glove;Servomotor;Flex sensors;Gyroscope;Hand-gesture-controlled robotic arm|
|[Chi-Square Features Selection in Unsupervised Learning Algorithm for Measuring Key Performance Indicators in Riau Province](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127849)|U. R. Gurning; Mustakim; I. Permana; I. Maita|10.1109/ICCoSITE57641.2023.10127849|Chi-Square;Clustering;Feature Selection;Key Performance Indicator;Sistem Informasi Administrasi Kependudukan (SIAK);Unsupervised Learning;Chi-Square;Clustering;Feature Selection;Key Performance Indicator;Sistem Informasi Administrasi Kependudukan (SIAK);Unsupervised Learning|
|[Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127736)|R. S. Oetama; Y. Yanfi; M. M. Ikhsan Assiddiq U.P; S. Muhamad Isa|10.1109/ICCoSITE57641.2023.10127736|Binomo;KNIME;sentiment analysis;Support Vector Machine;lexicon;Binomo;KNIME;sentiment analysis;Support Vector Machine;lexicon|
|[Visualizing Texture of Fractal Arts in Unity3D Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127738)|B. H. Lifindra; R. Sarno|10.1109/ICCoSITE57641.2023.10127738|Fractal;Art;Image;Unity3D;Shader;Fractal;Art;Image;Unity3D;Shader|
|[Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online During the Covid-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127704)|Y. Lie; A. S. Perbangsa; B. Pardamean|10.1109/ICCoSITE57641.2023.10127704|Online Purchasing Decision;Airline Ticket;Covid-19 Pandemic;Online Transaction;Online Purchasing Decision;Airline Ticket;Covid-19 Pandemic;Online Transaction|
|[Design of Liveness-Enforcing Supervisors for FMSs Modeled by S4PR with Controllable Places and Transitions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127786)|M. H. Abdul-Hussin|10.1109/ICCoSITE57641.2023.10127786|Siphons;S4PR;Petri Nets (PNs);FMSs;Deadlocks;Simulation;Maximally permissive;Siphons;S4PR;Petri Nets (PNs);FMSs;Deadlocks;Simulation;Maximally permissive|
|[Confirmatory Factor Analysis for The Impact of Students’ Social Medial on University Digital Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127744)|W. Erpurini; A. G. Putrada; N. Alamsyah; S. F. Pane; M. Nurkamal Fauzan|10.1109/ICCoSITE57641.2023.10127744|confirmatory factor analysis;social media;digital marketing;university;Kaiser-Meyer-Olkin (KMO) test;Bartlett’s test;path analysis;confirmatory factor analysis;social media;digital marketing;university;Kaiser-Meyer-Olkin (KMO) test;Bartlett’s test;path analysis|
|[Multivariate time series with Prophet Facebook and LSTM algorithm to predict the energy consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127735)|S. R. Riady; R. Apriani|10.1109/ICCoSITE57641.2023.10127735|Multivariate time series;Prophet Facebook;LSTM;Deep Learning;Energy consumption;Multivariate time series;Prophet Facebook;LSTM;Deep Learning;Energy consumption|
|[Human Resources Management Information System Requirement Analysis and Development in Humanitarian Foundation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127769)|M. Maryani; H. Alianto; A. S. Perbangsa; Yulius|10.1109/ICCoSITE57641.2023.10127769|Human Resources;Information Systems;Foundation;OOAD;UML;Human Resources;Information Systems;Foundation;OOAD;UML|
|[Application of Machine Learning Algorithm for Mental State Attention Classification Based on Electroencephalogram Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127825)|K. Suwida; S. C. Hidayati; R. Sarno|10.1109/ICCoSITE57641.2023.10127825|Electroencephalogram (EEG);Brain-computer interface (BCI);Discrete Wavelet Transform (DWT);Machine Learning;Xtreme Gradient Boost (XGBoost);Electroencephalogram (EEG);Brain-computer interface (BCI);Discrete Wavelet Transform (DWT);Machine Learning;Xtreme Gradient Boost (XGBoost)|
|[Effect Difference Size of Tetrahedron Sun Tracker Based on Sensor for Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127761)|H. A. Lastya; Y. Away; T. Tarmizi; I. D. Sara; M. Ikhsan|10.1109/ICCoSITE57641.2023.10127761|sun tracker;dual-axis;tetrahedron;light sensor;solar cell;sun tracker;dual-axis;tetrahedron;light sensor;solar cell|
|[Determining The Delivery Of Goods Using The K-Nearest Neighbor Algorithm And The Saving Matrix Method To Obtain The Optimal Route And Save Costs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127714)|Lidiawati; H. Setiawan; A. I. Ramdhani; Satria; Sulistyowati; K. Mukiman|10.1109/ICCoSITE57641.2023.10127714|Route;K-Nearest Neighbor Algorithm;Saving Matrix Method;Cost Savings;Route;K-Nearest Neighbor Algorithm;Saving Matrix Method;Cost Savings|
|[Indoor Positioning System Based on BSSID on Office Wi-Fi Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127734)|R. Aisuwarya; R. Ferdian; I. H. Yulianti|10.1109/ICCoSITE57641.2023.10127734|Wi-Fi;BSSID;Fingerprinting;KNN;monitoring;Wi-Fi;BSSID;Fingerprinting;KNN;monitoring|
|[Comparison of Human Emotion Classification on Single-Channel and Multi-Channel EEG using Gate Recurrent Unit Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127686)|Y. Pamungkas; U. W. Astuti|10.1109/ICCoSITE57641.2023.10127686|EEG-based emotion recognition;feature extraction;band decomposition;emotion classification;Gate Recurrent Unit algorithm;EEG-based emotion recognition;feature extraction;band decomposition;emotion classification;Gate Recurrent Unit algorithm|
|[Factor Affecting Behavior Intention To Use Mobile Payment Adoption: An Analysis Of Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127839)|M. K. Fadhil; A. P. Subriadi|10.1109/ICCoSITE57641.2023.10127839|Mobile Payment;Consumer Behavior;Behavior Intention;Technology Adoption;Financial Technology;Systematic Literature Review;Mobile Payment;Consumer Behavior;Behavior Intention;Technology Adoption;Financial Technology;Systematic Literature Review|
|[Model Reference Adaptive Control Design for CubeSat with Magnetorquer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127853)|A. T. Santoso; M. R. Rosa; Edwar|10.1109/ICCoSITE57641.2023.10127853|CubeSat;magnetorquer;attitude control;MRAC;CubeSat;magnetorquer;attitude control;MRAC|
|[Techno-Economics Analysis Of RAN-Spectrum Sharing Scheme Use Sensitivity Analysis Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127697)|W. W. Priambodo; H. Wijanto; N. M. Adriansyah|10.1109/ICCoSITE57641.2023.10127697|sharing infrastructure;techno-economy;cost efficiency;mobile network operators;5G;core network sharing;sharing infrastructure;techno-economy;cost efficiency;mobile network operators;5G;core network sharing|
|[Imbalanced Classes Treatment in Deep Learning Multi-label Aspect Classification using Oversampling and Under-sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127671)|D. I. Af’Idah; P. D. Anggraeni; S. F. Handayani; Dairoh|10.1109/ICCoSITE57641.2023.10127671|Imbalanced text classification;Multi-label Aspect Classification;ADSYN;SMOTE;Imbalanced text classification;Multi-label Aspect Classification;ADSYN;SMOTE|
|[Public Sentiment Analysis of KOMINFO Data Leaking by Bjorka using Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127745)|R. Sabian; A. Supriyanto; Sulistiowati|10.1109/ICCoSITE57641.2023.10127745|sentiment analysis;data leak;support vector machine;sentiment analysis;data leak;support vector machine|
|[Optimization of the Use of Artificial Neural Network Models for Accuracy Data Measurement Palm Oil Production Prediction Rate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127854)|I. R. Setiawan; A. Z. Fanani; G. N. Surname; P. Purwanto|10.1109/ICCoSITE57641.2023.10127854|MSE;ANN;prediction;palm oil;ARIMA;Accuracy;MSE;ANN;prediction;palm oil;ARIMA;Accuracy|
|[Object Size Recognition as Intra-class Variations using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127785)|R. Rahmania; H. L. Hendric Spits Warnars; B. Soewito; F. L. Gaol|10.1109/ICCoSITE57641.2023.10127785|deep-learning;intra-class variations;object recognition;retail products;transfer-learning;deep-learning;intra-class variations;object recognition;retail products;transfer-learning|
|[Analysis of Netflix New Policy to Intention to Subscribe after Bubble Burst Phenomenon](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127841)|R. Gustavo; A. Wijaya; Andrianus; E. Halim; M. Hebrard|10.1109/ICCoSITE57641.2023.10127841|Bubble Burst;Intention to Subscribe;Video on Demand;Netflix New Policy;Bubble Burst;Intention to Subscribe;Video on Demand;Netflix New Policy|
|[Optimizing Big Data Implementation to Create Business Value and Architecture Proposed in the Banking Industry: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127701)|I. Kristiana; M. Meyliana; A. N. Hidayanto; H. Prabowo|10.1109/ICCoSITE57641.2023.10127701|data-driven;big data;data governance;big data architecture;business value;bank;data-driven;big data;data governance;big data architecture;business value;bank|
|[The Influence of Video Advertising Content and Trending Video Advertising Usage on Hedonism and Customer Loyalty Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127815)|S. Surjandy; E. Herlambang; R. A. Harum; A. Yusuf; J. Gultom|10.1109/ICCoSITE57641.2023.10127815|Customer Loyalty;Hedonism;Trending Video Advertising;Video Advertising;Customer Loyalty;Hedonism;Trending Video Advertising;Video Advertising|
|[Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127847)|B. Sulistio; H. L. H. S. Warnars; F. L. Gaol; B. Soewito|10.1109/ICCoSITE57641.2023.10127847|Stock prediction;Convolutional Neural Network;Long Short Term Memory;Gated Recurrent Neural Networks;Deep Learning;Stock prediction;Convolutional Neural Network;Long Short Term Memory;Gated Recurrent Neural Networks;Deep Learning|
#### **2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)**
- DOI: 10.1109/DELCON57910.2023
- DATE: 24-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Chitkara University Organized IEEE Delhi Section Conference – DELCON 2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127463)|S. Juneja; R. Chhabra; R. Sharma|10.1109/DELCON57910.2023.10127463|Delhi section owned conference;IEEE;IEEE conference;academic conference;paper presentations;invited talks;keynote talk;Delhi section owned conference;IEEE;IEEE conference;academic conference;paper presentations;invited talks;keynote talk|
|[MOS Current Mode Logic (MCML) based techniques for D-Flip Flop in 180 nm Technology using LTspice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127334)|A. Dhull; C. S. Vinitha; A. Mittal|10.1109/DELCON57910.2023.10127334|MOS current mode logic (MCML);D-flip- flops (DFF);RMS noise;power dissipation;MOS current mode logic (MCML);D-flip- flops (DFF);RMS noise;power dissipation|
|[Real Time Human Assisted Path Planning for Autonomous Agent using VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127333)|V. Khemchandani; M. A. Khan; M. U. Barkaa; S. Chandra; N. M. Wadalkar|10.1109/DELCON57910.2023.10127333|AI;autonomous agent;navigation algorithm;path planning;virtual reality;AI;autonomous agent;navigation algorithm;path planning;virtual reality|
|[Virtual Reality Based Attention Simulator using EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127358)|V. Khemchandani; K. Goswani; M. P. Teotia; S. Chandra; N. M. Wadalkar|10.1109/DELCON57910.2023.10127358|attention;brain computer interface;EEG Signal;virtual reality;attention;brain computer interface;EEG Signal;virtual reality|
|[Classification of Potholes using Convolutional Neural Network Model: A Transfer Learning Approach using Inception ResnetV2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127302)|S. Singh; R. Chhabra; A. Moudgil|10.1109/DELCON57910.2023.10127302|road infrastructure;economic growth;land ecosystem;road accident;image dataset;road infrastructure;economic growth;land ecosystem;road accident;image dataset|
|[Fault Detection and Localization Technique in The Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127395)|T. Halder|10.1109/DELCON57910.2023.10127395|Fault detection and diagnosis;Modeling & Simulation and Performance evaluations;Fault detection and diagnosis;Modeling & Simulation and Performance evaluations|
|[Fragmentation-Aware RCSA Algorithm for Fair Spectrum Allocation in SDM-EON](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127427)|B. S. Heera; A. Sharma; V. Lohani; Y. N. Singh|10.1109/DELCON57910.2023.10127427|routing core and spectrum assignment;fragmentation-aware;fair bandwidth allocation;space division multiplexing;elastic optical networks;routing core and spectrum assignment;fragmentation-aware;fair bandwidth allocation;space division multiplexing;elastic optical networks|
|[Impact of Technology Scaling in DSM Region on Performance of Intercalation-doped MLGNR as VLSI Interconnects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127300)|T. Kaur; M. K. Rai; R. Khanna|10.1109/DELCON57910.2023.10127300|graphene nano-ribbons;undoped;intercalation-doped;technology scaling;frequency spectrum;graphene nano-ribbons;undoped;intercalation-doped;technology scaling;frequency spectrum|
|[Modelling and Simulation of a Bi-Directional DC to DC Converter System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127485)|T. Halder|10.1109/DELCON57910.2023.10127485|Bi-directional converter;Buck-Boost operational modes;Power transfer;Modeling & Simulation;Bi-directional converter;Buck-Boost operational modes;Power transfer;Modeling & Simulation|
|[Numerical Simulations of FASnI3 based Solar Cell with the Variation of Absorber Layer Thickness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127484)|S. Rawat; S. Kashyap; J. Madan; R. Pandey|10.1109/DELCON57910.2023.10127484|lead-free;SCAPS;simulations;solar cell;lead-free;SCAPS;simulations;solar cell|
|[Modeling of Organizational Influencing Factors for Smart Manufacturing in the Indian Context by Using the DEMATEL Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127381)|T. S. Gaur; V. Yadav|10.1109/DELCON57910.2023.10127381|mcdm;dematel;smart manufacturing;influencing factors;mcdm;dematel;smart manufacturing;influencing factors|
|[Back Radiation Reduction in a UWB Antenna using Metamaterial Surface for Wireless Communication Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127229)|D. Negi; K. Kaur; A. Bansal; B. Dua; N. Singh; S. Dhyani|10.1109/DELCON57910.2023.10127229|artificial magnetic conductor (AMC);uwb antenna;reflector plane;metamaterial;back radiation reduction;artificial magnetic conductor (AMC);uwb antenna;reflector plane;metamaterial;back radiation reduction|
|[Solar Radiation Forecasting using Customized Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127529)|M. Chauhan; M. Sandhu; A. Singh|10.1109/DELCON57910.2023.10127529|solar radiation forecasting;solar power generation prediction;artificial neural network;solar radiation forecasting;solar power generation prediction;artificial neural network|
|[Acid-Base Experimentation for A Swallowable Medical Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127305)|K. Tomar; S. Tiwari; E. Dhakate; K. Thakur; B. M. Hardas; N. Mahajan|10.1109/DELCON57910.2023.10127305|implantable biomedical system;GERD;real-time pH-monitoring;pH sensor;implantable biomedical system;GERD;real-time pH-monitoring;pH sensor|
|[Opportunistic Interference Alignment for Cognitive MIMO-NOMA Downlink Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127479)|S. Soni; N. M. Bankar; R. Makkar; D. Rawal; N. Sharma; K. G. Maradia|10.1109/DELCON57910.2023.10127479|Interference alignment;cognitive radio;non-orthogonal multiple access;multiple-input multiple-output;Interference alignment;cognitive radio;non-orthogonal multiple access;multiple-input multiple-output|
|[Driver Inattentiveness Detection Techniques for Intelligent Transportation Systems: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127504)|A. Patel; R. Chhabra; C. R. Krishna|10.1109/DELCON57910.2023.10127504|driver inattentiveness;ITS;sensors;machine learning;deep learning;driver inattentiveness;ITS;sensors;machine learning;deep learning|
|[Comparative Analysis of TCAD augmented ML Algorithms in modeling of AlGaN/GaN HEMTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127296)|S. Awasthi; P. K. Kaushik; V. Kumar; A. Gupta|10.1109/DELCON57910.2023.10127296|ann;gan hemt;gradient boost;machine learning;tcad;threshold voltage;transconductance;ann;gan hemt;gradient boost;machine learning;tcad;threshold voltage;transconductance|
|[Understanding the Influence of Student’s Emotions in Academic Success](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127402)|S. Koppad; J. Gadad; P. Patil; V. M|10.1109/DELCON57910.2023.10127402|emotional intelligence;ability;capacity;moods;academic performance;engineering;emotional intelligence;ability;capacity;moods;academic performance;engineering|
|[Dataset for Face-mask Recognition in Poor Visibility Conditions based upon IoT enabled Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127304)|N. Sharma; A. Khera; D. Sayal; A. Singh; I. Kansal|10.1109/DELCON57910.2023.10127304|dataset for facemask detection;deep learning;robotics;hazy environment;image dehazing;dataset for facemask detection;deep learning;robotics;hazy environment;image dehazing|
|[Study of Relation of Human Factors on Operational Modes and Display Attributes on See-through Displays under Low Visibility Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127384)|J. Kumar; S. S. Saini; D. Agrawal; A. Kataria; V. Karar|10.1109/DELCON57910.2023.10127384|head-up display;human factor;situational awareness;information;avionics;see-through display;head-up display;human factor;situational awareness;information;avionics;see-through display|
|[A Comprehensive Review on Deep Learning Approaches for Question Answering and Machine Reading Comprehension in NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127327)|R. R; G. B; J. J S|10.1109/DELCON57910.2023.10127327|deep learning;text comprehension;artificial intelligence;question answering;natural language processing;deep learning;text comprehension;artificial intelligence;question answering;natural language processing|
|[Heterogenous Public Safety Wireless Networks with mmWave Small Cells: An IAB-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127474)|P. Gandotra; V. Bhatia; B. Lall|10.1109/DELCON57910.2023.10127474|public safety;HetNet;broadcasting;mmWave;IAB;wireless backhaul;public safety;HetNet;broadcasting;mmWave;IAB;wireless backhaul|
|[Indian Railways in PAT Scheme: Achievements, Outcome and Way Forward for Enhancing Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127432)|A. Bakre; M. Deore; N. Prasad; V. K. Dutt|10.1109/DELCON57910.2023.10127432|perform achieve and trade (PAT) scheme;energy conservation (EC) act;bureau of energy efficiency (BEE);nationally determined contributions (NDCs);specific energy consumption (SEC);designated consumers (DCs);energy efficiency targets and issuance of energy saving certificates (ESCERTs);perform achieve and trade (PAT) scheme;energy conservation (EC) act;bureau of energy efficiency (BEE);nationally determined contributions (NDCs);specific energy consumption (SEC);designated consumers (DCs);energy efficiency targets and issuance of energy saving certificates (ESCERTs)|
|[Face Spoofing Detection System using Local Invariant Feature Set](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127478)|B. Kaur|10.1109/DELCON57910.2023.10127478|biometrics;dual-hahn moments;face spoofing;polar harmonic transform;local binary pattern;scale invariant feature transform;tchebichef moments;zernike moments;biometrics;dual-hahn moments;face spoofing;polar harmonic transform;local binary pattern;scale invariant feature transform;tchebichef moments;zernike moments|
|[OptSpec: Optimization of Specifications Data for Engine Anatomy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127318)|P. Hegade; K. Tiwari; A. Godikar|10.1109/DELCON57910.2023.10127318|name-value;processing;relation;search;specifications;name-value;processing;relation;search;specifications|
|[Effectiveness of Abstraction in Problem Based Learning Knowledge Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127438)|P. Hegade; M. Netekal; A. Shettar|10.1109/DELCON57910.2023.10127438|abstraction;knowledge construction;problem based learning;problem solving;abstraction;knowledge construction;problem based learning;problem solving|
|[Impactful Study of a Counter-doped Pocket on a Charge Plasma Tunnel FET Biosensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127287)|P. Goma; A. K. Rana|10.1109/DELCON57910.2023.10127287|biosensor;counter-doped pocket;Si(1-x) Ge(x) material;sensitivity;tunnel FET;biosensor;counter-doped pocket;Si(1-x) Ge(x) material;sensitivity;tunnel FET|
|[Design of Error Tolerant Subtractor using Truncation Approximation Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127573)|Z. Bhat; S. A. Loan; N. Afzal|10.1109/DELCON57910.2023.10127573|low power;VLSI design;approximate;truncation;subtractor;low power;VLSI design;approximate;truncation;subtractor|
|[Effect of Cubic And Quintic Nonlinearities on Spatially Generated Rogue Waves In CQDNLSE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127239)|M. Gupta; S. Malhotra; A. Kumar; R. Gupta|10.1109/DELCON57910.2023.10127239|rogue waves;DNLSE;cubic and quintic nonlinearity;rogue waves;DNLSE;cubic and quintic nonlinearity|
|[Role of Artificial Intelligence in Brain Stroke Management: A survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127366)|S. K. Bhatia; S. Goyal; T. S. Arora; R. Chhabra|10.1109/DELCON57910.2023.10127366|brain stroke;deep learning;ischemic stroke;hemorrhagic stroke;machine learning;brain stroke;deep learning;ischemic stroke;hemorrhagic stroke;machine learning|
|[Commercial Solar PV Off-Grid Battery Charging/Swapping Station: Opportunity and Solution for E-rickshaw](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127418)|A. D. Verma; V. Shishodia; A. Tomar; P. Gaur|10.1109/DELCON57910.2023.10127418|battery charging & swapping station;e-rickshaw;lithium-ion battery;off-grid solar PV system;return on investment;battery charging & swapping station;e-rickshaw;lithium-ion battery;off-grid solar PV system;return on investment|
|[Crop Recommendation Application using Ensemble Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127576)|B. Kusumasri; S. V; S. Satyavada; G. U. Kiran|10.1109/DELCON57910.2023.10127576|Gaussian naive bayes;logistic regression;stacking classifier;smart agriculture;precision agriculture;crop recommendation;Gaussian naive bayes;logistic regression;stacking classifier;smart agriculture;precision agriculture;crop recommendation|
|[Graphene-based Materials for Advanced Electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127297)|A. Ajay; J. Sharma; S. Sharma; L. K. Sharma|10.1109/DELCON57910.2023.10127297|graphene;battery;fuel cells;photonic devices;electronics;graphene;battery;fuel cells;photonic devices;electronics|
|[Influence of Absorber Thickness on the Performance of Sb2S3 based Solar Cell using Numerical Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127526)|V. Yadav; S. Kashyap; R. Pandey; J. Madan|10.1109/DELCON57910.2023.10127526|Sb2S3;SCAPS;simulation;solar cell;thickness;Sb2S3;SCAPS;simulation;solar cell;thickness|
|[PD-Box: A People Place Data Box for Processing Engine Anatomy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127379)|P. Hegade; D. Kalburgi|10.1109/DELCON57910.2023.10127379|engine;people;place;recommendations;search;specifications;wikipedia;engine;people;place;recommendations;search;specifications;wikipedia|
|[Effectiveness of Game Based Learning in Problem Based Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127288)|P. Hegade; O. Patil; A. Shettar|10.1109/DELCON57910.2023.10127288|decomposition;game based learning;reflections;problem based learning;decomposition;game based learning;reflections;problem based learning|
|[VIhanceD: Efficient Video Super Resolution for Edge Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127275)|A. Papanai; S. Babbar; A. Pandey; H. Kathuria; A. K. Sharma; N. Gupta|10.1109/DELCON57910.2023.10127275|super resolution;video super resolution;video upscaling;optical flow;super resolution;video super resolution;video upscaling;optical flow|
|[Implementation of Logic Gates using Charge Plasma Based Tunnel FET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127417)|N. Mahoviya; P. Singh; D. S. Yadav|10.1109/DELCON57910.2023.10127417|logic gate;OR gate;AND gate;logic gate;OR gate;AND gate|
|[Examining the Impact of Teaching Electronics Fundamentals in different Learning Environments on Student’s Conceptual Knowledge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127276)|R. Kaur; A. Mantri; P. Nagabhushan; G. Singh|10.1109/DELCON57910.2023.10127276|conceptual understanding;engineering education;experiential learning;interactive engagement;learning environment;learning motivation;conceptual understanding;engineering education;experiential learning;interactive engagement;learning environment;learning motivation|
|[Iris and Periocular Recognition using Shape Descriptors and Local Invariant Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127462)|B. Kaur|10.1109/DELCON57910.2023.10127462|biometrics;krawtchouk moments;local binary pattern;periocular;scale invariant feature transform;tchebichef moments;zernike moments;dual-hahn moments;biometrics;krawtchouk moments;local binary pattern;periocular;scale invariant feature transform;tchebichef moments;zernike moments;dual-hahn moments|
|[Static Method to Locate Risky Features in Android Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127577)|V. Khullar; T. Gera; T. Mehta|10.1109/DELCON57910.2023.10127577|applications;benign;feature;malware;static;applications;benign;feature;malware;static|
|[Data Analytics of Online Education during Pandemic Health Crisis: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127423)|I. Ahmad; S. Sharma; R. Kumar; S. Dhyani; A. Dumka|10.1109/DELCON57910.2023.10127423|higher education;educational technology;COVID-19;hybrid learning;AI chatbots;higher education;educational technology;COVID-19;hybrid learning;AI chatbots|
|[Effectiveness of Metaphors in Problem Based Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127310)|P. Hegade; Aryan; A. Shettar|10.1109/DELCON57910.2023.10127310|design;metaphors;pattern recognition;problem based learning;design;metaphors;pattern recognition;problem based learning|
|[Identifying Factors that Influence Student’s Participation in an Online Discussion Forum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127471)|M. T. Shaikh; U. Koppikar; V. M|10.1109/DELCON57910.2023.10127471|discussion forums;social network analysis;gender;stream;grades;first-year engineering;discussion forums;social network analysis;gender;stream;grades;first-year engineering|
#### **2023 IEEE Conference on Technologies for Sustainability (SusTech)**
- DOI: 10.1109/SusTech57309.2023
- DATE: 19-22 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[SHIFTing to sustainable behavior: An ethical-persuasive approach for mobile application development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129581)|A. Mehellou; M. S. M. Saleh; B. Omar|10.1109/SusTech57309.2023.10129581|sustainable behavior;persuasive technology;SHIFT framework;mobile application;Design Science Research;sustainable behavior;persuasive technology;SHIFT framework;mobile application;Design Science Research|
|[Ethical Leadership and Turnover Intentions: A systematic literature review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129599)|C. Athanasiadou; D. Chatzoudes; G. Theriou|10.1109/SusTech57309.2023.10129599|Ethical leadership;Turnover intentions;Employee retention;Systematic literature review;Future research;Ethical leadership;Turnover intentions;Employee retention;Systematic literature review;Future research|
|[Photovoltaic Panel and Battery Design for Solar-Powered Charging Devices in Public Spaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129622)|M. Yancey; J. Roberts; J. Salmon|10.1109/SusTech57309.2023.10129622|Optimization;Public Charging Devices;Photovoltaic Systems;Partial Shading;Solar Energy;Optimization;Public Charging Devices;Photovoltaic Systems;Partial Shading;Solar Energy|
|[A UAV and Deep Transfer Learning Based Environmental Monitoring: Application to Native and Invasive Species classification in Southern regions of the USA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129545)|S. Sarkar; R. Kelley|10.1109/SusTech57309.2023.10129545|UAV;Drone;Feature Extraction;Transfer learning;Deep Learning;Image Classification;Native plant species;Invasive plant species;UAV;Drone;Feature Extraction;Transfer learning;Deep Learning;Image Classification;Native plant species;Invasive plant species|
|[Design and Techno-Economic Analysis of a 150-MW Hybrid CSP-PV Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129553)|K. Liaqat; J. C. Ordonez; L. Schaefer; A. J. Zolan|10.1109/SusTech57309.2023.10129553|Concentrated Solar Power;Photovoltaics;Thermal Storage;Hybrid Power Plant;Levelized Cost of Electricity;Concentrated Solar Power;Photovoltaics;Thermal Storage;Hybrid Power Plant;Levelized Cost of Electricity|
|[E-Waste Recycling Gets Smarter with Digitalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129536)|N. A. Baker; J. Stehr; U. Handmann|10.1109/SusTech57309.2023.10129536|E-waste;artificial intelligence;transfer learning;web crawler;web scrapper;multi-sensors;E-waste;artificial intelligence;transfer learning;web crawler;web scrapper;multi-sensors|
|[Electric Bus Charge/Discharge Scheduling Optimization Method for Power Flow Smoothing in a Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129541)|N. Kato; Y. Ihara; Y. Kodama; Y. Iino; Y. Hayashi; R. Maeda; K. Oishi; K. Mori|10.1109/SusTech57309.2023.10129541|electric bus;renewable energy;photovoltaic;charging/discharging schedule;V2G;mixed integer linear programming;electric bus;renewable energy;photovoltaic;charging/discharging schedule;V2G;mixed integer linear programming|
|[Environment Sensor Node Design for Building Energy Management Systems (BEMS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129579)|D. F. Espejel-Blanco; J. A. Hoyo-Montano; J. M. Chavez; F. A. Hernandez-Aguirre|10.1109/SusTech57309.2023.10129579|Sensors;HVAC;BEMS;Energy Savings;Sensors;HVAC;BEMS;Energy Savings|
|[The Challenges of Transition from Traditional to Net Zero Energy Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129611)|E. Salem; E. Elwakil|10.1109/SusTech57309.2023.10129611|Zero Energy Building;Nearly Zero Energy Building;Low Energy Building;Sustainable Building;Renewable Energy;Building performance;ZEB Challenges;Zero Energy Building;Nearly Zero Energy Building;Low Energy Building;Sustainable Building;Renewable Energy;Building performance;ZEB Challenges|
|[Hyperspectral Sensing for Soil Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129629)|K. Fleming; A. Gardner; P. Nagel; Y. Miao; K. Mizuta|10.1109/SusTech57309.2023.10129629|Proximal spectroscopic reference;precision agriculture;spatial data density;Proximal spectroscopic reference;precision agriculture;spatial data density|
|[SINCHDrone Technology Demonstration UAV Hybrid Incorporating Power Regeneration Technologies & Weight Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129572)|K. Kudebeh; J. Baez; L. Austin; Z. Yu; A. Lo; S. K. Dobbs; J. Rico|10.1109/SusTech57309.2023.10129572|UAV;VTOL;airplane;quadcopter;hybrid;battery;solar;induction;regeneration;autonomous;power;endurance;weight;UAV;VTOL;airplane;quadcopter;hybrid;battery;solar;induction;regeneration;autonomous;power;endurance;weight|
|[Minimizing the Cost Gap between Net Zero Energy and Conventional Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129570)|E. Salem; E. Elwakil|10.1109/SusTech57309.2023.10129570|Building Cost;Energy Cost;Zero Energy Building;nearly Zero Energy Building;Low Energy Building;Sustainable Building;Renewable Energy;Building performance;ZEB Challenges;Building Cost;Energy Cost;Zero Energy Building;nearly Zero Energy Building;Low Energy Building;Sustainable Building;Renewable Energy;Building performance;ZEB Challenges|
|[Trust Model System for the Energy Grid of Things Network Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129594)|N. S. Fernando; Z. Zeng; J. M. Acken; R. B. Bass|10.1109/SusTech57309.2023.10129594|Trust Model;Security;Energy Services Interface;Distributed Energy Resources;Energy Grid of Things;Distributed Control Module;Trust;Trust Model;Security;Energy Services Interface;Distributed Energy Resources;Energy Grid of Things;Distributed Control Module;Trust|
|[Impacts of Freight Fleet Electrification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129590)|N. Zuniga-Garcia; V. Freyermuth; M. Stinson; O. Sahin|10.1109/SusTech57309.2023.10129590|freight;electric vehicles;greenhouse gases;freight;electric vehicles;greenhouse gases|
|[Class 2 transformers: ubiquitous, hidden, and inefficient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129539)|A. T. Nguyen; C. R. Sullivan|10.1109/SusTech57309.2023.10129539|transformers;power supplies;HVAC systems;power electronics;magnetics;transformers;power supplies;HVAC systems;power electronics;magnetics|
|[Detecting Fast Frequency Events in Power System: Development and Comparison of Two Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129580)|H. A. Alghamdi; M. A. Adham; U. Farooq; R. B. Bass|10.1109/SusTech57309.2023.10129580|Frequency event;Frequency response;Frequency event detection algorithm;Least-squares linear regression;Discrete wavelet transform;Frequency event;Frequency response;Frequency event detection algorithm;Least-squares linear regression;Discrete wavelet transform|
|[A Precision Public Health Study on the Divergence of Life Expectancies Over Time in United States Counties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129534)|S. Sheth; L. Bettencourt|10.1109/SusTech57309.2023.10129534|Community Human Development Index;CHDI;Sustainable Development;Risk Management;Precision Public Health;WHO;Life Expectancy;Healthcare;Health Policy;Precision Medicine;United Nations Sustainable Development Goals;UNSDG;Sustainability;Community Human Development Index;CHDI;Sustainable Development;Risk Management;Precision Public Health;WHO;Life Expectancy;Healthcare;Health Policy;Precision Medicine;United Nations Sustainable Development Goals;UNSDG;Sustainability|
|[The Community Human Development Index (CHDI) as a Precision Public Health Vulnerability Metric and Risk Indicator for Predictive Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129592)|S. Sheth; L. Bettencourt|10.1109/SusTech57309.2023.10129592|Community Human Development Index;CHDI;Social Vulnerability Index;Sustainable Development;Risk Management;Precision Public Health;WHO;Life Expectancy;Healthcare;Health Policy;Precision Medicine;United Nations Sustainable Development Goals;UNSDG;Sustainability;Community Human Development Index;CHDI;Social Vulnerability Index;Sustainable Development;Risk Management;Precision Public Health;WHO;Life Expectancy;Healthcare;Health Policy;Precision Medicine;United Nations Sustainable Development Goals;UNSDG;Sustainability|
|[Sustainable Fleet Operation Strategies to Minimize the Economic and Societal Emission Costs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129606)|H. Mozafari; A. Soltanpour; F. Jazlan; M. Ghamami; A. Zockaie|10.1109/SusTech57309.2023.10129606|Fleet management;Electrification;Sustainability;Fleet management;Electrification;Sustainability|
|[Creation of an FPGA-WSN-based Forest Fire Alert System using Data-Driven Attribute Relationship Determination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129589)|S. Dutta; R. D. Khamkar|10.1109/SusTech57309.2023.10129589|forest fire;fpga;wireless sensor network;data analysis;attribute;relationship;forest fire;fpga;wireless sensor network;data analysis;attribute;relationship|
|[Sustainable Fleet Management Strategies Considering Environmental Concerns, Covid-19 Pandemic, and Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129645)|H. Mozafari; A. Soltanpour; F. Jazlan; M. Ghamami; A. Zockaie|10.1109/SusTech57309.2023.10129645|Fleet management;Electrification;Sustainability;COVID-19 pandemic;Optimization model;Fleet management;Electrification;Sustainability;COVID-19 pandemic;Optimization model|
|[Bio-inspired Multiobjective Optimization Approach for Total Harmonic Distortion Reduction in a DC-AC Power Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129638)|J. Águila-León; M. Lucero-Tenorio; D. Díaz-Bello; C. Vargas-Salgado; C. Vega-Gómez|10.1109/SusTech57309.2023.10129638|THD;multi-objective optimization;GA;power converter;energy quality;THD;multi-objective optimization;GA;power converter;energy quality|
|[Improving the sustainability of printed circuit boards through additive printing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129587)|K. Grant; S. Zhang; J. Kettle|10.1109/SusTech57309.2023.10129587|Printed circuit boards;PCB;sustainability;life cycle assessment;printed electronics;large area electronics;additive printing;Printed circuit boards;PCB;sustainability;life cycle assessment;printed electronics;large area electronics;additive printing|
|[Optimizing emissions for machine learning training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129569)|S. P. Ekanayake; T. Shah; S. Evans|10.1109/SusTech57309.2023.10129569|;|
|[Using ML training computations for grid stability in 2050](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129635)|S. Evans; T. Shah|10.1109/SusTech57309.2023.10129635|;|
|[Quantum Software Architecture Blueprints for the Cloud: Overview and Application to Peer-2-Peer Energy Trading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129617)|C. O’Meara; M. Fernández-Campoamor; G. Cortiana; J. Bernabé-Moreno|10.1109/SusTech57309.2023.10129617|Hyrbid quantum-classical application;software architecture;cloud environment;quantum algorithm;QUBO;Hyrbid quantum-classical application;software architecture;cloud environment;quantum algorithm;QUBO|
|[Using Historical Activity Data for RTU Fault Prediction with Machine Learning and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129641)|A. Z. Lin; M. U. Nyugen|10.1109/SusTech57309.2023.10129641|RTU;Fault Prediction;Machine Learning;Deep Learning;Feature Generation;Ensemble Method;K-means clustering;RTU;Fault Prediction;Machine Learning;Deep Learning;Feature Generation;Ensemble Method;K-means clustering|
|[A Readiness Model for Facilitating the Implementation of Metal Additive Manufacturing at SMEs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129602)|M. Sæterbø; W. D. Solvang|10.1109/SusTech57309.2023.10129602|Metal additive manufacturing;sustainability;Kolarctic region;SMEs;readiness model;Metal additive manufacturing;sustainability;Kolarctic region;SMEs;readiness model|
|[Towards Renewable Energy Systems: A Design Framework for a Low Grid-Dependent Residential District](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129565)|M. Farrokhifar; L. Havinga; P. -J. Hoes|10.1109/SusTech57309.2023.10129565|renewable generation;energy management;building performance;energy storage;heating demand;renewable generation;energy management;building performance;energy storage;heating demand|
|[Classification framework for vehicle routing problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129561)|A. Belmabrouk; A. Lahmar; H. Chouikhi; H. Bentaher|10.1109/SusTech57309.2023.10129561|Vehicle routing problem;Bibliometric analysis;Systematic review;Classification Framework;Original variants;Vehicle routing problem;Bibliometric analysis;Systematic review;Classification Framework;Original variants|
|[A Protection Scheme in RTDS Model of an IEEE 16-Bus System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129583)|S. Monemi; A. Boghzian|10.1109/SusTech57309.2023.10129583|AcSELerator Quickset;Circuit Breakers;Distance Protection;POTT Scheme;Power System Fault;Power System Protection;Relays;RSCAD;RTDS;SEL-311L;AcSELerator Quickset;Circuit Breakers;Distance Protection;POTT Scheme;Power System Fault;Power System Protection;Relays;RSCAD;RTDS;SEL-311L|
|[Towards Implementation of a Small-Scale Prototype Model of a Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129571)|S. Monemi; J. Aviles; E. Oliver; D. Prajapati; M. Nava; J. -M. Brown; S. Robinson|10.1109/SusTech57309.2023.10129571|Bus;Control;Distribution;Generation;Load;Power;Power factor;Smart Grid;Transformers;Transmission;Relay;Bus;Control;Distribution;Generation;Load;Power;Power factor;Smart Grid;Transformers;Transmission;Relay|
|[Customizing Smart Warehouse Management for Large Scale Production Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129595)|N. Khan; W. D. Solvang; H. Yu|10.1109/SusTech57309.2023.10129595|smart warehouse management (SWM);Industry 4.0;energy industry;sustainability;smart warehouse management (SWM);Industry 4.0;energy industry;sustainability|
|[Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129614)|A. Busheska; N. Sabella; N. Almeida; E. Rocha|10.1109/SusTech57309.2023.10129614|Machine Learning;Adaptive Reuse;Pathologies;Cracks;Thermography;Machine Learning;Adaptive Reuse;Pathologies;Cracks;Thermography|
|[An efficient urban water management practice based on optimum LPCD estimated using the MLR-GA optimization approach- A case study for Jaipur, Rajasthan (India)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129533)|D. Patil; S. Kar; R. Gupta|10.1109/SusTech57309.2023.10129533|Water scarcity;Urban water management Multiple Regression Analysis;Genetic Algorithm;Water scarcity;Urban water management Multiple Regression Analysis;Genetic Algorithm|
|[A Review on Civil Applications of Vertical Take-Off and Landing Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129574)|A. Alsalem; M. Zohdy|10.1109/SusTech57309.2023.10129574|Multi-copter;VTOL;Vertical;landing;take off;Quadcopter;Dual Copter;Hex-copter;Helicopter;Tri Copter;aerodynamic;Multi-copter;VTOL;Vertical;landing;take off;Quadcopter;Dual Copter;Hex-copter;Helicopter;Tri Copter;aerodynamic|
|[Model Predictive Voltage Control of Large-Scale PV or Hybrid PV-BESS Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129604)|O. Abu-Rub; S. Debnath; P. Marthi; M. Saeedifard|10.1109/SusTech57309.2023.10129604|MPC;PV plant;EMT simulations;IBR;MPC;PV plant;EMT simulations;IBR|
|[Greenhouse smart irrigation based on soil moisture and vegetation index measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129559)|M. Cervera-Díaz; M. F. León-Chávez; C. Santiesteban-Toca; C. Lozoya|10.1109/SusTech57309.2023.10129559|smart irrigation;greenhouse agriculture;water stress;soil moisture;NDVI;educational innovation;higher education;smart irrigation;greenhouse agriculture;water stress;soil moisture;NDVI;educational innovation;higher education|
|[Utilizing IoT Technological Innovation by Startup Businesses for Sustainable Smart Transportation in Developing Countries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129642)|M. Tondro; M. Jahanbakht|10.1109/SusTech57309.2023.10129642|Internet of Things (IoT);Emerging Technology;Disruptive Technology;Smart Transportation;Innovation for Sustainable Development;Industry 4.0 (I4.0);Internet of Things (IoT);Emerging Technology;Disruptive Technology;Smart Transportation;Innovation for Sustainable Development;Industry 4.0 (I4.0)|
|[Identifying deforested areas through convolutional neural network for drone reforesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129558)|J. Villalobos-Montiel; A. Aguilar-Gonzalez; L. Orona; C. Lozoya|10.1109/SusTech57309.2023.10129558|convolutional neural network;machine learning;drone reforesting;educational innovation;higher education;convolutional neural network;machine learning;drone reforesting;educational innovation;higher education|
|[Computer Vision-based Method to Energy Saving Retrofit: A Study of Improving Energy Efficiency in Existing Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129552)|Q. Amarkhil|10.1109/SusTech57309.2023.10129552|computer vision;energy efficiency;building retrofit;machine learning;computer vision;energy efficiency;building retrofit;machine learning|
|[Day Ahead Load Forecasting using Random Forest method with meteorological variables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129542)|J. Vaish; K. M. Siddiqui; Z. Maheshwari; A. Kumar; S. Shrivastava|10.1109/SusTech57309.2023.10129542|;|
|[Framework for Dual Transformation: A Systematic Literature Review on the Interplays between Digitalization and Sustainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129630)|C. Kürpick; A. Kühn; L. Olszewski; R. Dumitrescu|10.1109/SusTech57309.2023.10129630|Dual Transformation;Manufacturing Industry;Digitalization;Sustainability;Strategic Management;Framework;Dual Transformation;Manufacturing Industry;Digitalization;Sustainability;Strategic Management;Framework|
|[Sustainability on a University Campus Considering Recent Energy Efficiency Initiative in Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10129598)|A. Alhaqbani; S. Albadaily; W. Alfraidi|10.1109/SusTech57309.2023.10129598|Energy Efficiency;Sustainability;University Campus;Photovoltaic Generation;Mathematical Model;Energy Efficiency;Sustainability;University Campus;Photovoltaic Generation;Mathematical Model|
#### **2023 7th International Conference on Green Energy and Applications (ICGEA)**
- DOI: 10.1109/ICGEA57077.2023
- DATE: 10-12 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[An Efficient Scalogram Generator for Partial Shading Analysis of Photovoltaic Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125923)|T. N. Duc; D. N. Dang; T. L. Viet; H. Takano|10.1109/ICGEA57077.2023.10125923|photovoltaic;partial shading diagnosis;convolutional neural network;wavelet transform;scalogram;photovoltaic;partial shading diagnosis;convolutional neural network;wavelet transform;scalogram|
|[A Hybrid Model for Solar Radiation Forecasting towards Energy Efficient Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125987)|Q. Peng; X. Zhou; R. Zhu; R. Liang; X. He; S. Gao|10.1109/ICGEA57077.2023.10125987|solar irradiation;random forest;Informer;short-term prediction;solar irradiation;random forest;Informer;short-term prediction|
|[Virtual Synchronous Generator Control Implementation in Single-stage Solar Energy Conversion System for Transient Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125631)|H. Jiang; Y. S. Tan; F. Wei; S. Cao; C. B. Soh; K. T. Tan; S. B. Krishnan|10.1109/ICGEA57077.2023.10125631|PV;Solar energy conversion system;Renewable;VSG;Virtual inertia;PV;Solar energy conversion system;Renewable;VSG;Virtual inertia|
|[Effects of Particulate Matter Pollution in Predicting Solar Radiation: A Case Study of Eastern Cape, South Africa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125976)|Y. T. Olorunfemi; O. B. Wojuola; J. A. Adesina|10.1109/ICGEA57077.2023.10125976|Artificial neural network;meteorological parameters;particulate matters;solar radiation;support vector regression;Artificial neural network;meteorological parameters;particulate matters;solar radiation;support vector regression|
|[Pilot Evaluation of a Low Cost Fully Mechanical Solar Tracker Using Swiss Lever Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125950)|A. K. T. Shahriar; S. Akamphon|10.1109/ICGEA57077.2023.10125950|solar tracker;passive tracking;open source hardware;in-situ;rural development;solar tracker;passive tracking;open source hardware;in-situ;rural development|
|[Development of an IoT Based Photovoltaic Monitoring System Using Hybrid Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125779)|K. Li|10.1109/ICGEA57077.2023.10125779|Internet of Things (IoT);solar;PV system;hybrid modeling;monitoring;anomaly detection;Internet of Things (IoT);solar;PV system;hybrid modeling;monitoring;anomaly detection|
|[The Application Study on Solar Photovoltaic Technology in the Ocean Park Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125657)|J. YUAN; L. ZHANG; C. S. Kim|10.1109/ICGEA57077.2023.10125657|Solar Photovoltaic Technology;Conventional Energy;Ocean Park Design;Solar Photovoltaic Technology;Conventional Energy;Ocean Park Design|
|[Deep Learning-Based Real-Time Solar Irradiation Monitoring and Forecasting Application for PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125683)|V. X. Son Huu; D. D. Hieu; N. H. Minh Giang; H. Takano; N. D. Tuyen|10.1109/ICGEA57077.2023.10125683|solar irradiance forecast;deep learning;forecast aggregation;real-time;solar irradiance forecast;deep learning;forecast aggregation;real-time|
|[Fuzzy Optimal Scheduling of Integrated Electro-Heating Energy with the Participation of an Energy Hub](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126004)|T. Wei; R. Cheng|10.1109/ICGEA57077.2023.10126004|integrated electro-heating system;uncertainty of source and load;fuzzy opportunity constraints;energy hub;integrated electro-heating system;uncertainty of source and load;fuzzy opportunity constraints;energy hub|
|[Collaborative Optimal Configuration Model of RIES with Biogas, Photovoltaic, and Wind](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125832)|Q. Cai; K. Xiang; W. Chen; Z. Li; P. Hu|10.1109/ICGEA57077.2023.10125832|RIES with biogas;photovoltaic and wind;economy;environmental protection;collaborative optimal configuration;RIES with biogas;photovoltaic and wind;economy;environmental protection;collaborative optimal configuration|
|[Robust Optimization Based Multi-level Coordinated Scheduling Strategy for Energy Hub in Spot Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126058)|J. Chen; C. Mao; D. Wang; S. Qiu; C. Ma; Z. Liu|10.1109/ICGEA57077.2023.10126058|Energy hub;Multi-stage robust optimization;Integrated energy system;Energy management;Spot market;Energy hub;Multi-stage robust optimization;Integrated energy system;Energy management;Spot market|
|[Low Carbon Economic Dispatch of Integrated Energy Systems Considering Stepped Carbon Trading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125763)|X. Qi; J. Zheng; F. Mei; J. Wu; Y. Zhang|10.1109/ICGEA57077.2023.10125763|integrated energy system;stepped carbon trading mechanism;P2G;low carbon economy;integrated energy system;stepped carbon trading mechanism;P2G;low carbon economy|
|[Hybrid Renewable Energy System Design and Optimization for Developing Countries Using HOMER Pro: Case of Rwanda](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125739)|E. Niringiyimana; S. Wanquan; G. Dushimimana; J. b. Niyigena|10.1109/ICGEA57077.2023.10125739|HOMER pro;wind power;PV modules;NCP and hybrid systems;HOMER pro;wind power;PV modules;NCP and hybrid systems|
|[Comprehensive Control Method of Three-Phase Load Imbalance Considering Distributed Photovoltaic Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125941)|Y. Li; H. Tang; F. Chen; S. Zhou; Y. Zhao; X. Zeng; P. Hu|10.1109/ICGEA57077.2023.10125941|consumer-phase relationship identification;three-phase load imbalance;UMAP;genetic algorithm;consumer-phase relationship identification;three-phase load imbalance;UMAP;genetic algorithm|
|[Decentralized Droop Control Strategies for Parallel-Connected Distributed Generators in an AC Islanded Microgrid: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126012)|S. G. Ndeh; B. J. Ebot; A. F. Akawung; N. D. Khan; T. Emmanuel|10.1109/ICGEA57077.2023.10126012|droop control;distributed generators;stability;active power;reactive power;droop control;distributed generators;stability;active power;reactive power|
|[Minimum Capacity of Wind and Solar Energy Considering the Expansion of Hydro Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126054)|Y. Wang; X. Luo; H. Su; Y. Lv; L. Huang; Y. Xu; X. Sui|10.1109/ICGEA57077.2023.10126054|hydro plant;wind turbine;photovoltaic unit;stochastic programming;trial-and-error method;hydro plant;wind turbine;photovoltaic unit;stochastic programming;trial-and-error method|
|[Making Power Systems Sustainable with Natural Ester Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125989)|K. Y. Lam; H. Too; J. Tan|10.1109/ICGEA57077.2023.10125989|transformers;natural ester dielectric fluid;power systems;economics;sustainability;transformers;natural ester dielectric fluid;power systems;economics;sustainability|
|[Shut-down Strategy of PEMFC Power System Based on Pulse Current Control Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125813)|R. Wang; L. Wu; J. Cao; H. Yang; H. Tang|10.1109/ICGEA57077.2023.10125813|PEMFC power system;Shut-down strategy;Pulse current control method;Cell voltage characteristics;Pressure characteristics;PEMFC power system;Shut-down strategy;Pulse current control method;Cell voltage characteristics;Pressure characteristics|
|[Fuzzy Logic Control and Battery Energy Storage System Based Power System Secondary Frequency Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125827)|X. Long; D. Chena|10.1109/ICGEA57077.2023.10125827|Fuzzy Logic Control;BESS;Frequency Regulation;SOC;Fuzzy Logic Control;BESS;Frequency Regulation;SOC|
|[Research on Operation and Maintenance Mode of Digital Substation Based on Fuzzy Analytic Hierarchy Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125650)|Y. Sun; X. Xie; C. Wu; W. Liu; Q. Fan|10.1109/ICGEA57077.2023.10125650|Digital Substation;Operation and maintenance management mode;Fuzzy analytic hierarchy process;Digital Substation;Operation and maintenance management mode;Fuzzy analytic hierarchy process|
|[Extract Power-line Key Points From LiDAR Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125984)|Y. Niu; H. Wu; J. Zhu; L. Huang; Y. Liang; J. Qian|10.1109/ICGEA57077.2023.10125984|power line;point cloud;projection;key point;extract;power line;point cloud;projection;key point;extract|
|[Efficiency Comparison of 2-Level and 3-Level Si IGBT Based Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125768)|M. Aydin; E. Beşer; H. Kelebek|10.1109/ICGEA57077.2023.10125768|2-level inverter;IGBT;3-level inverter;neutral point inverter;diode clamped inverter;2-level inverter;IGBT;3-level inverter;neutral point inverter;diode clamped inverter|
|[Fault Diagnosis of Interturn Short Circuit Using Repetitive Surge Oscillograph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125595)|G. Li; Q. Deng; C. Luo; D. Xie; D. Zhu; Y. Wu; S. Yang; X. Yang|10.1109/ICGEA57077.2023.10125595|interturn short circuit;repetitive surge oscillograph;transformers;fault diagnosis;artificial neural network;interturn short circuit;repetitive surge oscillograph;transformers;fault diagnosis;artificial neural network|
|[A Study on Damping Controller for Grid-Forming Inverter Power Synchronization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125844)|J. Park; M. Yoon|10.1109/ICGEA57077.2023.10125844|GFM;Power Synchronization;Droop Control;Damping Control;SCR;SISO;Instability Mechanism;GFM;Power Synchronization;Droop Control;Damping Control;SCR;SISO;Instability Mechanism|
|[A Rapid Model Prediction Control Method for T- Type Three-Level Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125746)|G. Wang; L. Zhang; Z. Wang; X. Yin|10.1109/ICGEA57077.2023.10125746|Three Levels;Inverter;Model Prediction Control;Calculation Quantity;Three Levels;Inverter;Model Prediction Control;Calculation Quantity|
|[Simulation of Transient Overvoltage Under Different Operation Modes of Disconnectors in 500 kV Pumped Storage Power Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125835)|H. Ren; Y. Zhang|10.1109/ICGEA57077.2023.10125835|Isolating switch;VFTO;Equipment insulation;Isolating switch;VFTO;Equipment insulation|
|[WPT Resonant Frequency Design Considerations for Electrical Vehicle Dynamic Charging Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125831)|J. Y. Lee; S. Cao; J. J. Chong; R. T. Naayagi; S. S. Lee; T. K. Jet|10.1109/ICGEA57077.2023.10125831|Dynamic Charging;Electric Vehicle (EV);Wireless Power Transfer (WPT);Resonant DC/DC converter;Dynamic Charging;Electric Vehicle (EV);Wireless Power Transfer (WPT);Resonant DC/DC converter|
|[Statistical Analysis of Multidimensional Components for the Diagnosis of Faults in Electric Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125924)|D. Torres; W. Oñate; G. Caiza; C. Guerrero|10.1109/ICGEA57077.2023.10125924|Induction Motor;Statistical Tools;Incipient Failures;Diagnosis;Induction Motor;Statistical Tools;Incipient Failures;Diagnosis|
|[Application of Park's Vector and Current Envelope Method for Diagnosis of Stator Winding Inter Turn Fault of Induction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125848)|R. Kumar; R. Anand|10.1109/ICGEA57077.2023.10125848|induction motor stator fault;park's vector magnitude;current envelope;statistical parameters;induction motor stator fault;park's vector magnitude;current envelope;statistical parameters|
|[Enhanced Integrated Inductor and Transformer Design for WPT LCC Resonant Converter System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125685)|S. Cao; N. Eriyanie; R. T. Naayagi; S. S. Lee; H. Jiang; T. K. Jet|10.1109/ICGEA57077.2023.10125685|wireless charging;magnetic coupling;finite element analysis (FEA);LCC resonant circuit;circuit simulation;wireless charging;magnetic coupling;finite element analysis (FEA);LCC resonant circuit;circuit simulation|
|[Design, Modeling of a Green Building Energy Optimized Efficient System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125752)|B. Gebreslassie; A. Kalam; A. Zayegh|10.1109/ICGEA57077.2023.10125752|energy saving building;optimum energy saving;smart building and sustainable materials;energy saving building;optimum energy saving;smart building and sustainable materials|
|[Design of Fuzzy Controller for a Hybrid Active/Passive Cooling System in Smart Homes with a Windcatcher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125762)|S. Banjar; A. I. Hussein; M. Mohamad; R. H. M. Aly|10.1109/ICGEA57077.2023.10125762|Active/passive;smart system;cooling;wind catcher;fuzzy logic;neural network;Active/passive;smart system;cooling;wind catcher;fuzzy logic;neural network|
|[Forecasting of Electricity Consumption Using Neural Networks in Public Hospital Buildings with Installed Smart Meters in Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125911)|D. Damrongsak; W. Wongsapai; N. Lekgamheng; G. Wattakawigran; N. Promsuk; N. Suetrong|10.1109/ICGEA57077.2023.10125911|Electricity Consumption;Forecasting;Neural Networks;Smart Meters;Electricity Consumption;Forecasting;Neural Networks;Smart Meters|
|[Passive Cooling with Finned Roof Tiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126040)|H. Salem; A. Sedaghat; M. A. Malayer|10.1109/ICGEA57077.2023.10126040|Cool roof;passive cooling;power consumption;air-conditioning;solar heat gain;Cool roof;passive cooling;power consumption;air-conditioning;solar heat gain|
|[Shallow Geothermal and Solar Energy-Driven Adsorption-Compression Hybrid Air Conditioning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125690)|C. -H. Chen; J. -H. Lu; J. -C. Perng; J. -J. Chen|10.1109/ICGEA57077.2023.10125690|adsorption chillers;shallow geothermal;solar energy;adsorption chillers;shallow geothermal;solar energy|
|[A Comparison on Energy Benchmarks of Designated Public University Buildings in Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125797)|D. Damrongsak; W. Wongsapai; G. Wattakawigran; N. Lekgamheng|10.1109/ICGEA57077.2023.10125797|Energy benchmark;Universities;Public buildings;Thailand;Specific Energy Consumption;Energy Utilization Index;Energy Performance Indicator;Energy benchmark;Universities;Public buildings;Thailand;Specific Energy Consumption;Energy Utilization Index;Energy Performance Indicator|
|[Mitigating the Challenge of Energy Crisis via Energy Audit and Efficiency Measures: A Case of a Household in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125637)|O. A. Lawal; A. A. Jimoh; Z. A. Abdulkadir; A. B. Bello; M. O. Balogun; A. R. Atanda|10.1109/ICGEA57077.2023.10125637|energy audit;lighting;HVAC;building envelop;electrical system;energy efficiency;energy audit;lighting;HVAC;building envelop;electrical system;energy efficiency|
|[Performance Evaluation and Energy Management System for Parallel Hybrid Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125733)|M. L. Swarupa|10.1109/ICGEA57077.2023.10125733|performance evaluation;energy management system;Indian driving cycles;performance evaluation;energy management system;Indian driving cycles|
|[Simulation Research on Active Equalization Control of Dynamical Systems in Hybrid Aircraft](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125820)|R. Han|10.1109/ICGEA57077.2023.10125820|Hybrid propulsion system;Electric aviation;Resistance model;Modular powertrain;Power equalization control;Hybrid propulsion system;Electric aviation;Resistance model;Modular powertrain;Power equalization control|
|[Feasibility Study and Cost Analysis of Electrification for a Non-electrified Railway Line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125725)|M. Molteni; M. Bubici; A. Di Martino; M. Longo; D. Zaninelli|10.1109/ICGEA57077.2023.10125725|electrification;railways;infrastructure;energy;decarbonisation;transportation;electrification;railways;infrastructure;energy;decarbonisation;transportation|
|[EDLC Modeling Based on Charge Redistribution Phenomenon](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125990)|H. Guo; Z. Yang; F. Lin; H. Zhang|10.1109/ICGEA57077.2023.10125990|EDLC;model building;parameter identification;Charge redistribution;EDLC;model building;parameter identification;Charge redistribution|
|[Research on the Development Status of Electric Energy Storage at Home and Abroad from the Perspective of Standardization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126066)|N. Niu; D. Che|10.1109/ICGEA57077.2023.10126066|electric energy storage;energy storage industry;standardization;electric energy storage;energy storage industry;standardization|
|[Impact of Corrosion in Carbon Steel Pipeline of Marine Wastewater Outfall during Electric Power Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125917)|S. P. Koko; K. Kusakana|10.1109/ICGEA57077.2023.10125917|wastewater;absolute roughness;carbon steel pipe;Hydrokinetic;permanent magnet synchronous generator;wastewater;absolute roughness;carbon steel pipe;Hydrokinetic;permanent magnet synchronous generator|
|[A real-time experimental investigation of thin cambered tubercle blades for performance improvement of Darrieus vertical axis wind turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125996)|P. Prakash; L. K. Pendyala; R. C. Teladevalapalli; S. Mitra|10.1109/ICGEA57077.2023.10125996|Vertical axis wind turbine;Bio-inspiration;Aerodynamic design;Dynamic rotating body;Vertical axis wind turbine;Bio-inspiration;Aerodynamic design;Dynamic rotating body|
#### **2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG)**
- DOI: 10.1109/SEB-SDG57117.2023
- DATE: 5-7 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Improved Kibria-Lukman Type Estimator:Application and Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124637)|B. B. Aladeitan; O. Adebimpe; K. Ayinde; A. F. Lukman|10.1109/SEB-SDG57117.2023.10124637|Kibria-Lukman estimator;Linear regression model;Modified Kibria-Lukman estimator;Multicollinearity;Ridge estimator;Kibria-Lukman estimator;Linear regression model;Modified Kibria-Lukman estimator;Multicollinearity;Ridge estimator|
|[A Modified KL Estimator for the Binary Logistic Regression Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124573)|B. B. Aladeitan; O. Adebimpe; K. Ayinde; A. Lukman; O. Oludoun; E. Abiodun|10.1109/SEB-SDG57117.2023.10124573|Logistic Regression;Multicollinearity;Modified Kibria Lukman;KL estimator;Logistic Regression;Multicollinearity;Modified Kibria Lukman;KL estimator|
|[A comparative study of selected machine learning algorithms for cyber threat detection in open source data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124615)|M. O. Adebiyi; M. O. Ajayi; F. B. Osang; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124615|Machine Learning;Cyber Threat Intelligence;Algorithms;Machine Learning;Cyber Threat Intelligence;Algorithms|
|[A Comparative of Classification Models for Predicting of Heart Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124478)|M. O. Arowolo; R. O. Adesina; M. O. Adebiyi; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124478|Heart disease;Machine learning;Classification Health;Prediction;Heart disease;Machine learning;Classification Health;Prediction|
|[Mathematical Modeling and Optimal Control Strategies of HIV/AIDS with HAART](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124496)|O. Abiodun; A. Olukayode; J. Ndako|10.1109/SEB-SDG57117.2023.10124496|HIV/AIDS;Optimal control;reproduction number;HAART;HIV/AIDS;Optimal control;reproduction number;HAART|
|[A Smote-Based Churn Prediction System Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124631)|A. O. Akinrotimi; R. O. Ogundokun; M. A. Mabayoje; R. A. Oyekunle; M. O. Adebiyi|10.1109/SEB-SDG57117.2023.10124631|Churn prediction;Oversampling technique;Dimensionality reduction;Classification algorithm;Churn prediction;Oversampling technique;Dimensionality reduction;Classification algorithm|
|[Perception and Experience of Nigerian Private University Undergraduates on Virtual Learning during COVID-19 Lockdown](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124593)|M. Ake; H. Nweke-Love; B. Omitola; J. Iseolorunkanmi; J. Agada; J. Oladapo|10.1109/SEB-SDG57117.2023.10124593|Nigeria;Perception;Experience;Virtual Learning;COVID-19 Lockdown;Nigeria;Perception;Experience;Virtual Learning;COVID-19 Lockdown|
|[Analysis of Quantile Regression as an Alternative to Multiple Linear Regression: (A Case Study of Birth Weight Data)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124638)|S. Z. Geoffrey; S. E. Obamiyi; B. Badeji-Ajisafe; B. Ayogu; K. Abiola; O. B. Abiola|10.1109/SEB-SDG57117.2023.10124638|Gestation;Quantile Regression;Ordinary Least Square (OLS);multiple regression model;Gestation;Quantile Regression;Ordinary Least Square (OLS);multiple regression model|
|[Overview of Green Tribology in Recent World: Fundamentals and Future Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124520)|J. F. Kayode; S. L. Lawal; S. A. Afolalu|10.1109/SEB-SDG57117.2023.10124520|tribology;green tribology;friction;wear;lubrication;tribology effect;tribology;green tribology;friction;wear;lubrication;tribology effect|
|[A Computation Investigation of the Impact of Convex Hull subtour on the Nearest Neighbour Heuristic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124469)|E. O. Asani; A. E. Okeyinka; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124469|Travelling Salesman Problem;Nearest Neighbour Heuristic;Convex Hull;Node-based heuristics;Travelling Salesman Problem;Nearest Neighbour Heuristic;Convex Hull;Node-based heuristics|
|[Keylogger Detection: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124477)|E. V. C.; A. A. A.; A. A. A.|10.1109/SEB-SDG57117.2023.10124477|Keylogger;Hooks;Memory Forensic;Phishing;Keylogger;Hooks;Memory Forensic;Phishing|
|[The Institutional Diffusion of Digital Identity IT Artifact among Marginalized Communities in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124605)|H. J. Onawola; C. Ononiwu|10.1109/SEB-SDG57117.2023.10124605|Institutional diffusion;Digital identity;Marginalized communities;phenomenon;philosophical;Institutional diffusion;Digital identity;Marginalized communities;phenomenon;philosophical|
|[Development of an Image Processing Techniques for Vehicle Classification Using OCR and SVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124622)|I. O. Joshua; M. O. Arowolo; M. O. Adebiyi; O. R. Oluwaseun; K. A. Gbolagade|10.1109/SEB-SDG57117.2023.10124622|Support Vector Machine;Object Character Recognition;Image Process Techniques;Vehicle;Support Vector Machine;Object Character Recognition;Image Process Techniques;Vehicle|
|[Ethics of therapeutic foods consumption for Neurodegenerative Diseases and Hormesis-Based Anti-Aging Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124581)|A. F. Olaniran; A. E. Taiwo; M. E. Alike-Peter; C. E. Okonkwo; A. Sanusi; O. D. Olaniran; O. C. Erinle|10.1109/SEB-SDG57117.2023.10124581|Neurodegenerative diseases;Ethics;Responsible consumption;therapeutic foods;Hormesis;Anti-Aging;Neurodegenerative diseases;Ethics;Responsible consumption;therapeutic foods;Hormesis;Anti-Aging|
|[Comparing Core Consensus mechanisms for Education Blockchains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124612)|E. C. Onuoha; A. Adebiyi; K. T. Akindeji; M. Adebiyi|10.1109/SEB-SDG57117.2023.10124612|blockchain;consensus;validation;mining;decentralized;network;transactions;blockchain;consensus;validation;mining;decentralized;network;transactions|
|[Effects of contraceptives non-use on sexual engagement among secondary school students in North Central, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124641)|A. Odesanmi; F. Asamu; A. Affiong; I. Olusegun; O. O. Oye; B. Rasak; C. Igbolekwu; I. Oyekola|10.1109/SEB-SDG57117.2023.10124641|Contraceptive;Sexual engagement;Secondary School Students;Contraceptive;Sexual engagement;Secondary School Students|
|[Detection and Prevention of Data Leakage in Transit Using LSTM Recurrent Neural Network with Encryption Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124503)|M. K. Abiodun; A. E. Adeniyi; A. O. Victor; J. B. Awotunde; O. G. Atanda; J. K. Adeniyi|10.1109/SEB-SDG57117.2023.10124503|Deep learning;Encryption algorithm;Data leakage;Detection Introduction;Deep learning;Encryption algorithm;Data leakage;Detection Introduction|
|[VulScan: A Web-Based Vulnerability Multi-Scanner for Web Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124601)|T. O. Odion; I. O. Ebo; R. M. Imam; A. I. Ahmed; U. N. Musa|10.1109/SEB-SDG57117.2023.10124601|Web application;vulnerability scanners;web attacks;authentication;Web application;vulnerability scanners;web attacks;authentication|
|[Effects of Dispersion of Graphene Nanoplatelets on the Improvement of Thermal Properties and Morphology of Polymer Nano-Composites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124543)|A. Adeodu; I. A. Daniyan; K. A. Bello; A. D. Funmilayo; O. Adelowo; P. Ikubanni|10.1109/SEB-SDG57117.2023.10124543|Differential Scanning Calorimetry;Epoxy resin;Graphene nanoplatelets;Thermal property;X-ray Diffraction;Differential Scanning Calorimetry;Epoxy resin;Graphene nanoplatelets;Thermal property;X-ray Diffraction|
|[Physicomechanical Properties of Al6063 Metal Matrix Composite Reinforceed with Incinerated Waste Cardboard Paper Ash](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124505)|A. Adeleke; J. Odusote; P. Ikubanni; A. Lawal|10.1109/SEB-SDG57117.2023.10124505|aluminium matrix composite;mechanical properties;cardboard paper ash;physical properties;materials engineering;aluminium matrix composite;mechanical properties;cardboard paper ash;physical properties;materials engineering|
|[A Comparative Performance Study of Support Vector Machine, KNN, and Ensemble Classifiers on through-wall human detection Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124476)|J. A. Enoch; I. B. Oluwafemi; O. K. Paul; F. A. Ibikunle; O. Emmanuel; A. F. Olamide|10.1109/SEB-SDG57117.2023.10124476|Disasters;Search and Rescue (SAR);Prediction;Classification;Heterogenous;Structures;Disasters;Search and Rescue (SAR);Prediction;Classification;Heterogenous;Structures|
|[Fractional Reduced Differential Transform Method for Solving Fractional Order Measles Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124484)|S. E. Fadugba; M. O. Oluwayemi; A. O. Ajayi; O. Faweya; J. T. Okunlola; M. Kekana|10.1109/SEB-SDG57117.2023.10124484|Caputo fractional derivative;measles model;non-integer order;power series solution;Caputo fractional derivative;measles model;non-integer order;power series solution|
|[Towards Numerical Investigation of Velocity Variation on Thin Ellipsoidal Aerofoil (NACA 3520) Using Surface Vorticity Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124382)|O. A. Adeaga|10.1109/SEB-SDG57117.2023.10124382|Inviscid;Aerofoil;Vorticity;vortex element;Inviscid;Aerofoil;Vorticity;vortex element|
|[A Comparative Study of the Performances of Single-mode, Two-mode, and Three-mode Biometric Security Systems Using Deep Structured Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124544)|O. G. Atanda; M. K. Abiodun; J. B. Awotunde; J. K. Adeniyi; A. E. Adeniyi|10.1109/SEB-SDG57117.2023.10124544|biometrics;convolution neural network;genetic algorithm;receiver operating characteristics;biometrics;convolution neural network;genetic algorithm;receiver operating characteristics|
|[Development of a Fermentation Vat for Value Chain Addition in Locust Bean Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124577)|A. M. Olaniyan; B. K. Abdulkareem; M. M. Odewole; E. O. Ariyo; A. I. Oyebanji; E. A. Alhassan|10.1109/SEB-SDG57117.2023.10124577|African locust bean;fermentation vat;proximate composition;food condiment;quality food;value addition;African locust bean;fermentation vat;proximate composition;food condiment;quality food;value addition|
|[Product Verification using Blockchain Technology: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124602)|E. O. Igbekele; J. Aideloje; A. A. Adebiyi; A. Adebiyi|10.1109/SEB-SDG57117.2023.10124602|Blockchain;Supply Chain;Product Verification;Blockchain;Supply Chain;Product Verification|
|[Prediction of Diabetes Mellitus in Developing Countries: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124482)|L. O. O.; A. A. A.; I. E. O.|10.1109/SEB-SDG57117.2023.10124482|Machine Learning;Diabetes Prediction;Support Vector Machine;K-Nearest Neighbor;Logistic Regression;Machine Learning;Diabetes Prediction;Support Vector Machine;K-Nearest Neighbor;Logistic Regression|
|[Optimization of Corn steep Liquor as a Sole Substrate for Bioethanol Production using Saccharomyces Cerevisiae](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124599)|O. A. Falowo; A. E. Taiwo; I. Afolabi; B. S. Fakinle; J. O. Ojediran|10.1109/SEB-SDG57117.2023.10124599|Bioethanol;Corn steep liquor;Nitrogen source;Carbon Source;Fermentation;Optimization;Bioethanol;Corn steep liquor;Nitrogen source;Carbon Source;Fermentation;Optimization|
|[Weibull Distribution Modelling of Wind Energy System and Voltage Total Harmonic Distortion Prediction of Ado-Ekiti Feeder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124532)|O. Adeoye; P. Olulope|10.1109/SEB-SDG57117.2023.10124532|Distortion;Feeder;Harmonic;Model;Prediction;Weibull;Distortion;Feeder;Harmonic;Model;Prediction;Weibull|
|[Factors Influencing Celerity of Punishment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124551)|O. E. Joseph; T. -A. Timilehin; A. T. Lanre; A. Festus; O. J. Joseph; O. Daniel Ilesanmi; A. Ogadimma; O. I. Akintoyese; A. B. Adeyinka|10.1109/SEB-SDG57117.2023.10124551|Punishment;criminal Justice;Deterrence and Order;Punishment;criminal Justice;Deterrence and Order|
|[The Trend of Financial Inclusion: A Comparative Analysis of Selected African Countries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124479)|A. O. Oladipo; J. N. Taiwo; B. -C. Egbide; J. U. Madugba; S. A. Fakile; I. N. Erondu; I. A. -L. A.; O. Oyetola|10.1109/SEB-SDG57117.2023.10124479|Financial Inclusion;Financial products;Savings;Credits;public finance;Financial Inclusion;Financial products;Savings;Credits;public finance|
|[Comparison between the performance of artificial neural network and adaptive neuro-fuzzy inference system in modelling crop evapotranspiration of a maize crop in soil amended with biochar and inorganic fertilizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124578)|O. T. Faloye; A. E. Ajayi; T. Babalola; B. Adabembe; O. E. Adeyeri; A. T. Ogunrinde; A. Okunola; A. Fashina|10.1109/SEB-SDG57117.2023.10124578|Crop water use;maize;modelling;Artificial intelligence;Crop water use;maize;modelling;Artificial intelligence|
|[Agrolend: A Blockchain implementation approach of smart contract farming platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124467)|J. K. Adeniyi; T. T. Adeniyi; J. B. Awotunde; M. K. Abiodun; O. G. Atanda|10.1109/SEB-SDG57117.2023.10124467|blockchain;contract farming;smart contract;smart farming;blockchain;contract farming;smart contract;smart farming|
|[Sustainable Business Model for Long-Range Wireless Wide Area Based Internet of Things Network: The Heterogeneous approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124648)|O. A. Agbolade; S. A. Oyetunji; F. M. Dahunsi|10.1109/SEB-SDG57117.2023.10124648|LoRaWAN;Heterogeneous Network;IoT;LPWAN;LoRaWAN;Heterogeneous Network;IoT;LPWAN|
|[Deep Learning-Based Digital Assistant for Farmers in South Africa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124564)|P. Mkhwanazi; A. Adegun; M. Adigun|10.1109/SEB-SDG57117.2023.10124564|Digital assistant;Deep Learning (DL);Natural Language Processing (NLP);Artificial Intelligence (AI);Machine Learning (ML);Digital assistant;Deep Learning (DL);Natural Language Processing (NLP);Artificial Intelligence (AI);Machine Learning (ML)|
|[Self-Assessment Chatbot for COVID-19 prognosis using Deep Learning-based Natural Language Processing (NLP)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124619)|E. Thwala; A. Adegun; M. Adigun|10.1109/SEB-SDG57117.2023.10124619|Deep learning;Chatbot;Recurrent Neural Network;Natural Language Processing;Machine learning;Deep learning;Chatbot;Recurrent Neural Network;Natural Language Processing;Machine learning|
|[Demand for Weather-Index Insurance among Selected Arable Crop Farmers in Guinea Savannah, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124507)|B. O. Ajiboye; T. T. Amos; C. O. Aremu; G. A. Adeyonu; W. Ayojimi|10.1109/SEB-SDG57117.2023.10124507|Demand;Weather;Arable crop;Insurance;Index;Demand;Weather;Arable crop;Insurance;Index|
|[Strength and Durability Assessment of Low Water Absorption Glasscrete Blocks for Zero-Spalling Effect in Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124497)|A. O. D; O. S. O; N. B. O|10.1109/SEB-SDG57117.2023.10124497|Spalling;Glasscrete Blocks;Sustainability;Durability;Construction;Civil Engineering;Spalling;Glasscrete Blocks;Sustainability;Durability;Construction;Civil Engineering|
|[Statistical Analysis of Rebound Hammer Assessment on Reinforced Concrete Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124607)|A. O. D.; A. J. O.; A. C.; A. S. A.|10.1109/SEB-SDG57117.2023.10124607|Rebound hammer;Compressive strength;Reinforced Concrete Building;Failure;Structural Health;Rebound hammer;Compressive strength;Reinforced Concrete Building;Failure;Structural Health|
|[Geospatial and Statistical Evidence of Non-Engineered Landfill Leachate Effects on Groundwater Quality in an Urbanised Area of Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124533)|D. A. Olasehinde; O. M. Abioye; E. A. Alhassan; I. D. Olasehinde; T. A. Ogunrinde; R. B. Olasehinde|10.1109/SEB-SDG57117.2023.10124533|Dumpsite;Leachate;Geospatial analysis;Groundwater Pollution;Linear regression;Dumpsite;Leachate;Geospatial analysis;Groundwater Pollution;Linear regression|
|[Factors Affecting the Adoption of Multiple Climate-Smart Agricultural Practice in North-Central, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124526)|T. Awe; O. Bamiro; A. Shittu; M. Kehinde; O. Olugbenga; A. Abigail|10.1109/SEB-SDG57117.2023.10124526|Climate change adaptation;ordered probit;minimal tillage;organic compost;agroforestry;crop rotation and green manure;Climate change adaptation;ordered probit;minimal tillage;organic compost;agroforestry;crop rotation and green manure|
|[Evaluation of a Sustainable Automated Hand Washing System Equipped with a Water Recycling Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124609)|K. Bello; M. Kanakana-Katumba; R. Mahadhzi; T. O. Ayeye; O. Adeniran; B. A. Bolaji|10.1109/SEB-SDG57117.2023.10124609|Hand washing;sustainable;recycle water;Sensors;Hand washing;sustainable;recycle water;Sensors|
|[Development of an android-based reminder system for the covid-19 Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124645)|A. J. Awokola; B. P. Falola; A. E. Adeniyi; M. K. Abiodun; A. O. Madamidola; J. B. Awotunde; M. Olagunju; O. G. Atanda; P. Omolehin|10.1109/SEB-SDG57117.2023.10124645|Covid-19;Pandemic;Management;Android;Reminder;Covid-19;Pandemic;Management;Android;Reminder|
|[A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124569)|E. Adetiba; O. O. Oluleye; A. H. Ifijeh; V. Oguntosin; O. M. Olaniyan; O. A. Akinola; G. Afolabi; J. O. Odetola.; A. Abayomi|10.1109/SEB-SDG57117.2023.10124569|Solar Irradiance;ANN;Solar Photovoltaic Panel;Power Prediction;Solar Irradiance;ANN;Solar Photovoltaic Panel;Power Prediction|
|[Bacteriological Qualities Of Ikogosi Warm Water Spring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124643)|D. O. Olasehinde; E. D. Wilkie; O. V. Fatoye; M. A. Acho; R. B. Olasehinde|10.1109/SEB-SDG57117.2023.10124643|Bacteriological qualities;Ikogosi warm water spring;Bacteriological qualities;Ikogosi warm water spring|
|[Towards the Integration of Iris Biometrics in Automated Teller Machines(ATM)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124646)|O. Fowora; O. Okesola; A. Adebiyi; M. Adebiyi|10.1109/SEB-SDG57117.2023.10124646|ATM;Biometrics;Iris;Verification;Bank;PIN;ATM;Biometrics;Iris;Verification;Bank;PIN|
|[Hybrid Cloud Storage Techniques Using Rsa And Ecc](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124559)|U. Musa; M. O. Adebiyi; O. Adigun; A. A. Adebiyi; C. O. Aremu|10.1109/SEB-SDG57117.2023.10124559|RSA algorithm;ECC algorithm;Cloud Storage;RSA algorithm;ECC algorithm;Cloud Storage|
|[An Empirical Diagnosis of the Maladies and Therapies of Public Budgeting in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124625)|B. -C. Egbide; M. Joseph; F. Samuel; B. -C. Jane Ogochukwu; O. Adenike Omowumi; I. A. -L. Ayomide; A. A. Deborah|10.1109/SEB-SDG57117.2023.10124625|Budget Management;Budget Discipline;Budgeting Problems;Budgeting Remedies;Nigeria;Budget Management;Budget Discipline;Budgeting Problems;Budgeting Remedies;Nigeria|
|[Systematic Review on the Recent Trends of Cybersecurity in Automobile Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124561)|C. P. Eze; J. Emmanuel; C. I. -O. Onietan; I. Isewon; J. Oyelade|10.1109/SEB-SDG57117.2023.10124561|cyber-security;automobile industry;in-vehicle network;cyber-security;automobile industry;in-vehicle network|
|[Fault Estimation Scheme Considering the Integration of Renewable Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124589)|K. Moloi; K. Reddy|10.1109/SEB-SDG57117.2023.10124589|Discrete wavelet transform;Distributed generation;Fault estimation;Support vector regression;Discrete wavelet transform;Distributed generation;Fault estimation;Support vector regression|
|[A Preliminary Study on the Application of Hybrid Machine Learning Techniques in Network Intrusion Detection Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124596)|C. Iyanu-Oluwa Onietan; I. Martins; T. Owoseni; E. C. Omonedo; C. P. Eze|10.1109/SEB-SDG57117.2023.10124596|cyber-security;hybrid machine learning;network intrusion detection;cyber-security;hybrid machine learning;network intrusion detection|
|[A Cost Effective Groundnut Oil Producing Machine for Rural Development in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124630)|S. J. Aliyu; O. J. O; A. E. A. S; S. A. S; O. S. S; S. K. O; O. O. J; A. A; I. I. G; A. A. Banji; O. T.O; O. O.J; B. J. Ajewole; E. R.R|10.1109/SEB-SDG57117.2023.10124630|design;moisture content;Groundnut;machine efficiency;oil producing component;temperature;design;moisture content;Groundnut;machine efficiency;oil producing component;temperature|
|[Importance of hybrid organic carburizers on the Mechanical properties of mild steel: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124621)|C. Ehi-Okoebor; E. Y. Salawu; S. A. Afolalu; O. O. Ajayi; J. F. Kayode; S. L. Lawal|10.1109/SEB-SDG57117.2023.10124621|mild steel;carburization;hardness;strength;mild steel;carburization;hardness;strength|
|[Factors Influencing Career Choices in Agriculture-Related Fields Amongst Secondary School Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124545)|B. Rasak; F. Asamu; O. Arisukwu; O. Iwelumor; I. Oyekola; E. Oyeyipo; M. Ake; C. Igbolekwu; T. Aremu|10.1109/SEB-SDG57117.2023.10124545|Agriculture-Career guidance;social factors influence Career choice;secondary School;Agriculture-Career guidance;social factors influence Career choice;secondary School|
|[Evaluation of the marshal properties of crumb rubber-modified warm mix asphalt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124542)|S. O. Braimoh; O. M. Ogundipe; A. J. Gana; A. Special; O. J. Uwagboe; O. J. Aladegboye|10.1109/SEB-SDG57117.2023.10124542|warm mix asphalt;crumb rubber;bitumen;fatigue tests;evaluation;properties;warm mix asphalt;crumb rubber;bitumen;fatigue tests;evaluation;properties|
|[High Impedance Fault Detection Scheme with the Penetration of Distributed Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124563)|K. Moloi; H. M. Langa|10.1109/SEB-SDG57117.2023.10124563|Discrete wavelet transform;Distributed generation;High impedance fault;Support vector machine;Discrete wavelet transform;Distributed generation;High impedance fault;Support vector machine|
|[Investigation of the Energy Capabilities of Selected Agricultural Wastes in a Fluidized-Bed Combustor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124555)|O. P. Abolusoro; A. M. Orah; A. S. Yusuf; W. B. Ayandotun; W. Ibrahim|10.1109/SEB-SDG57117.2023.10124555|Biomass;Combustion;Fluidized bed;Energy;Groundnut shell;Sugarcane bagasse;Biomass;Combustion;Fluidized bed;Energy;Groundnut shell;Sugarcane bagasse|
|[Evaluating the Efficacy of Hypermedia Utilization Modes on Students' Gender Gap in Biology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124560)|F. E. Dada; M. A. Ahmed|10.1109/SEB-SDG57117.2023.10124560|Gender studies;Multimedia /Hypermedia software;secondary education;students' interest Introduction;Gender studies;Multimedia /Hypermedia software;secondary education;students' interest Introduction|
|[Framework on Enhancing Biometric Template Protection Transformation Scheme Using Residue Number System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124538)|O. F. Segun; B. R. Seyi; G. K. Alagbe|10.1109/SEB-SDG57117.2023.10124538|Biometric;Traits;Template;Transformed;Residue Number System (RNS);Biometric;Traits;Template;Transformed;Residue Number System (RNS)|
|[Isotherm and Statistical Validity Modelings of Adsorption of Endocrine Disruptive Cr(VI) onto Calcinated Earthworm Cast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124501)|A. O. Dada; G. O. Adediran; E. D. Fehintoluwa; O. A. Charity; O. D. Adewumi; J. Ojediran; B. E. Tokula; S. O. Olusanya|10.1109/SEB-SDG57117.2023.10124501|Adsorption;Heavy metal;Endocrine Disruptive;Isotherm;Statistical Modeling;Adsorption;Heavy metal;Endocrine Disruptive;Isotherm;Statistical Modeling|
|[Observations of Ionospheric Propagation Factor at Two African Equatorial Ionization Anomaly Stations Using Ionosonde Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124537)|N. O. Bakare; B. O. Adebesin|10.1109/SEB-SDG57117.2023.10124537|Ionospheric propagation factor M(3000)F2;Ionosonde;Equatorial station;Radio propagation;Space weather;Ionospheric propagation factor M(3000)F2;Ionosonde;Equatorial station;Radio propagation;Space weather|
|[Generating Realistic African Fashion Designs for Men using Deep Convolutional Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124486)|E. Ogbuju; G. Yashim; F. Oladipo|10.1109/SEB-SDG57117.2023.10124486|image synthesis;convolutional neural networks;neural style transfer;image segmentation;deep convolutional generative adversarial network;African fashion;Nigerian fashion;image synthesis;convolutional neural networks;neural style transfer;image segmentation;deep convolutional generative adversarial network;African fashion;Nigerian fashion|
|[Electronic Health Referral (EHR) System: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124588)|I. E. O.; O. O. A; A. A. A.|10.1109/SEB-SDG57117.2023.10124588|EHR;Health;Hospital;Referrals;Diseases;EHR;Health;Hospital;Referrals;Diseases|
|[Development of an Internet of Things Based Fire Detection and Automatic Extinguishing System for Smart Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124381)|A. Akinwumi; F. Folaranmi|10.1109/SEB-SDG57117.2023.10124381|Internet of Things (IOT) WI-FI module;appliction;software;board;fire detection;extinguishing;API system;Internet of Things (IOT) WI-FI module;appliction;software;board;fire detection;extinguishing;API system|
|[Investigation of Diesel Blends with Edible Vegetable Oils for Combustion in Swirl Burners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124515)|A. S. Akinwonmi; O. A. Adeaga; T. A. Orhadahwe|10.1109/SEB-SDG57117.2023.10124515|palm kernel oil;liquid fuels;swirlers;vegetable oils;palm kernel oil;liquid fuels;swirlers;vegetable oils|
|[Leveraging Modelling and Simulation to address Manufacturing Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124595)|A. A. Noiki; E. Y. Salawu; S. A. Afolalu; F. Joseph Kayode; S. L. Lawal|10.1109/SEB-SDG57117.2023.10124595|leveraging;simulation;modelling;intelligent manufacturing;digitalisation;technologies;leveraging;simulation;modelling;intelligent manufacturing;digitalisation;technologies|
|[Preparation, Physicochemical and Spectroscopic Characterization of Low-Cost Acid Functionalised Rice Husk Activated Carbon (AF-RHAC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124611)|B. E. Tokula; A. O. Dada; A. A. Inyinbor; C. O. Aremu; K. S. Obayomi; O. D. Adewumi; F. A. Adekola; J. Ojediran|10.1109/SEB-SDG57117.2023.10124611|Rice husk;activated carbon;spectroscopic characterization;physicochemical characterization;Rice husk;activated carbon;spectroscopic characterization;physicochemical characterization|
|[Optimizing Antibacterial Activity of Psidium guajava Extracts using Solvent Fractionation method and its Efficacy against Foodborne Pathogens](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124522)|J. A. Ndako; E. O. Oludipe; V. T. Dojumo; V. O. Fajobi; R. F. Echemita; P. J. Ndako; S. A. Junaid; A. O. Omole|10.1109/SEB-SDG57117.2023.10124522|Antibacterial activity;Psidium guajava;phytochemical screening;liquid-liquid fractionation;Antibacterial activity;Psidium guajava;phytochemical screening;liquid-liquid fractionation|
|[The Multi-risk Factors Promoting Uterine Fibroids in Women](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124571)|M. A. Okesola; O. O. Ogunlana; B. E. Adegboye; I. S. Afolabi; F. A. Bello; A. J. Lasisi|10.1109/SEB-SDG57117.2023.10124571|Benign;Body Mass Index;Glutathione peroxidase Reduced glutathione;Uterine fibroid;Benign;Body Mass Index;Glutathione peroxidase Reduced glutathione;Uterine fibroid|
|[Green Supply Chain Management: Impacts, Challenges, Opportunities, and Future Perspectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124618)|A. A. Noiki; E. Y. Salawu; S. A. Afolalu; J. F. Kayode; S. L. Lawal|10.1109/SEB-SDG57117.2023.10124618|green;supply chain;impact;challenges;components sustainability;green;supply chain;impact;challenges;components sustainability|
|[Re-Appraising Women to Women Discrimination Towards Attaining Gender Equality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124553)|A. O. Alaiyemola; O. Akanmode; O. Iwelumor; M. Ake|10.1109/SEB-SDG57117.2023.10124553|challenges;gender issues;humanities;national development;re-appraisal;challenges;gender issues;humanities;national development;re-appraisal|
|[Prospects and Constraints of Cattle Farming Business in Kwara State, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124580)|B. Rasak; F. Asamu; O. Arisukwu; O. Iwelumor; I. Oyekola; E. Oyeyipo; M. Ake; C. Igbolekwu; F. A. Joseph|10.1109/SEB-SDG57117.2023.10124580|Cattle farming business;youth unemployment;Employment;Livestock;Farming;Cattle farming business;youth unemployment;Employment;Livestock;Farming|
|[Performance Evaluation of Corrosion Inspection Robot (CIR) for Pipelines and Tunnels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124498)|S. J. Aliyu; T. J. Erinle; B. J. Ajewole; E. A. S. Ajisegiri; D. H. Oladebeye; K. E. Ojaomo; O. J. Ogunniyi; O. J. Adeniran; R. R. Elewa|10.1109/SEB-SDG57117.2023.10124498|Corrosion Inspection Robot (CIR);Monitoring;Petrochemical;Pipeline;Semi-autonomous;Corrosion Inspection Robot (CIR);Monitoring;Petrochemical;Pipeline;Semi-autonomous|
|[Development of Neural Network-Based Spectrum Prediction Schemes for Cognitive Wireless Communication: A Case Study of Ilorin, North Central, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124518)|F. O. Ehiagwina; N. T. Surajudeen-Bakinde; A. S. Afolabi; A. M. Usman|10.1109/SEB-SDG57117.2023.10124518|Cognitive radio network;artificial neural network;long-short term memory;spectrum prediction;SDG 9;SDG 11;Cognitive radio network;artificial neural network;long-short term memory;spectrum prediction;SDG 9;SDG 11|
|[Climate Smart Agriculture Strategies among Crop Farmers in North Central Nigeria: Implication on farm Productivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124616)|M. D. Ayeni; A. Owolabi; O. T. Ayeni; Y. J. Alhassan|10.1109/SEB-SDG57117.2023.10124616|Arable crop;Climate Smart;Determinants;Nigeria;Productivity;Arable crop;Climate Smart;Determinants;Nigeria;Productivity|
|[Physicochemical, Functional and Viscosity Profile of Starches from Three Banana Cultivars as Affected by Oxidation and Acetylation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124550)|E. M. Ogunbusola; O. O. Alabi; T. A. Sanni; H. O. Adubiaro; K. T. Araoye; C. N. Jaiyeoba; K. Oni|10.1109/SEB-SDG57117.2023.10124550|Acetylation;Banana;Functional properties;Pasting properties;Oxidation;Acetylation;Banana;Functional properties;Pasting properties;Oxidation|
|[Effects of Immunisation on Child Mortality in West Africa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124485)|B. Obasaju; I. Adama; O. P. Ishola; F. Oloyede; R. Osabohien; A. Aregbesola; A. Onabote|10.1109/SEB-SDG57117.2023.10124485|child mortality;global vaccine action plan;component;immunisation;Vaccine-Preventable Disease;formatting;child mortality;global vaccine action plan;component;immunisation;Vaccine-Preventable Disease;formatting|
|[Free Radical-Scavenging and Oxidative Hepatic Injury-Alleviating Properties of Azadirachta Indica Seed Protein Isolate and Hydrolysates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124554)|M. A. Acho; R. O. Olsehinde; A. G. Oluwafemi; C. O. Nwonuma; R. O. Arise|10.1109/SEB-SDG57117.2023.10124554|Antioxidant;oxidative stress;hepatoprotective;neem;bioactive peptides;Antioxidant;oxidative stress;hepatoprotective;neem;bioactive peptides|
|[Transformation of Waste to Wealth: Acquisition of Fuel from Polymers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124614)|T. O. Oni; S. O. Ayodeji; S. J. Aliyu; E. S. Ajisegiri; R. A. Ibikunle; O. J. Ogunniyi|10.1109/SEB-SDG57117.2023.10124614|synthetic polymers;pyrolysis;fuel;diesel;physicochemical properties;synthetic polymers;pyrolysis;fuel;diesel;physicochemical properties|
|[A Multi-Criteria Decision Making Approach to Journal Selection and Ranking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124480)|O. Oladipupo; O. Makpokpomi; S. Adubi|10.1109/SEB-SDG57117.2023.10124480|;|
|[A Bibliometric Analysis and Science Mapping of Recommendation Systems Research from 1987 to 2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124525)|O. Olufunke; O. Okuoyo|10.1109/SEB-SDG57117.2023.10124525|bibliometric analysis;Bibliometrix;Biblioshiny;science mapping;recommender system;bibliometric analysis;Bibliometrix;Biblioshiny;science mapping;recommender system|
|[Numerical Modeling and Investigation of Flow of Incompressible Non-Newtonian Fluids through Uniform Slightly Deformable Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124471)|O. O. Alabi; O. A. Adeaga; S. A. Akintola|10.1109/SEB-SDG57117.2023.10124471|flow rate non-Newtonian fluid;gut;numerical;simulation;peristaltic flow Introduction;flow rate non-Newtonian fluid;gut;numerical;simulation;peristaltic flow Introduction|
|[Suppression of Alkali- Silica Reactions in Concrete by Partially Replacing Cement with Cassava Peel Ash](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124626)|J. A. Ajayi; A. J. Gana; G. Ayinnuola; A. Adanikin; E. Faleye; O. Popoola|10.1109/SEB-SDG57117.2023.10124626|Alkali-Silica Reaction (ASR);Cassava peel ash;Concrete;Cement;Elemental makeup;Pozzolans;Water and acid solubility;Alkali-Silica Reaction (ASR);Cassava peel ash;Concrete;Cement;Elemental makeup;Pozzolans;Water and acid solubility|
|[Prevalence of Malaria Parasitaemia and Associated Risk Factors Among Febrile Children Attending a Secondary Health Care Facility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124594)|J. A. Ndako; O. Y. Ozoadibe; V. T. Dojumo; I. J. Owolabi; C. J. Ndako; O. P. Tomisin; M. R. Omoniyi|10.1109/SEB-SDG57117.2023.10124594|Malaria parasitaemia;Risk factors;Malaria Diagnosis;infectious mode in children;Malaria parasitaemia;Risk factors;Malaria Diagnosis;infectious mode in children|
|[Plant Disease Diagnosis and Detection using Type-2 Fuzzy Logic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124608)|J. B. Awotunde; G. J. Ajamu; M. Abdulraheem; A. E. Adeniyi; R. G. Jimoh; I. D. Oladipo; M. K. ABIODUN; J. K. ADENIYI|10.1109/SEB-SDG57117.2023.10124608|Plant diseases;Type-2 fuzzy logic;Diagnosis;Detection;Farm management;Food crops;Food security;Plant diseases;Type-2 fuzzy logic;Diagnosis;Detection;Farm management;Food crops;Food security|
|[Technoturity: Exploring the Paradox of Technology and Effect on Workplace Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124534)|S. E. Owolabi; A. Aregbesola; F. Yusuf; J. Omale|10.1109/SEB-SDG57117.2023.10124534|technoturity;social media behavior;social media use;innovative communication technologies;workplace performance;technoturity;social media behavior;social media use;innovative communication technologies;workplace performance|
|[Growth performance and blood profile of weaned rabbits fed graded levels of Hevea brasiliensis seed meal as a protein source](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124513)|R. Animashahun; S. Omoikhoje; J. Agbede; E. Onibi; O. Alabi|10.1109/SEB-SDG57117.2023.10124513|Haematology;Performance;Proximate components;Rabbit;Rubber seed;Serology;Haematology;Performance;Proximate components;Rabbit;Rubber seed;Serology|
|[Effects of Annickia Chlorantha Bark Ethanolic Extract on Testicular Function Indices and the HPLC Fingerprint on Spermatogenic Type 1 & 2 Hexokinase Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124386)|C. Nwonuma; U. Inemesit; F. A. O.; B. Omoniwa; A. Akinduko; O. Osemwegie; D. Omodiagbe; I. Matthew; O. Alejolowo|10.1109/SEB-SDG57117.2023.10124386|Plant Bark;Testis;Hexokinase;Ligands;Quercetin;Resorcinols;Plant Bark;Testis;Hexokinase;Ligands;Quercetin;Resorcinols|
|[Application of programmable electronic flow meter for enhanced data capturing: A case study of the beverage industry in Ogun state, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124623)|O. O. Olamide; O. E. Olakunle; O. O. Anthony; E. B. Omoniyi; A. J. Olakunle|10.1109/SEB-SDG57117.2023.10124623|Configuration;Connectivity;Conservation;Digitization;Flow meters;Configuration;Connectivity;Conservation;Digitization;Flow meters|
|[Auspicious and Adverse Effects of Curcuma longa L Supplemented Diet on Total Protein and Kidney Injury Molecule-1 in Indomethacin Treated Rats](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124489)|A. G. Oluwafemi; O. B. Ajayi; A. M. Amarachi; D. R. Emmanuel; B. P. Omoniwa|10.1109/SEB-SDG57117.2023.10124489|protein;curcuma longa rhizome;supplemented diet;indomethacin;concentration;protein;curcuma longa rhizome;supplemented diet;indomethacin;concentration|
|[Prospects for Nigerian Electricity Production from Renewable Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124504)|O. J. Ogunniyi; T. O. Oni; P. P. Ikubanni; S. J. Aliyu; E. A. Ajisegiri; R. A. Ibikunle; T. A. Adekanye; A. A. Adeleke; J. B. Ajewole; O. L. Ogundipe; O. J. Adeniran; R. R. Elewa|10.1109/SEB-SDG57117.2023.10124504|renewable energy;electricity;sources;fuel;power generation;renewable energy;electricity;sources;fuel;power generation|
|[The Crustal Structure of Landmark University and OMU-ARAN Environment, Nigeria, using High-Resolution Aeromagnetic Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124524)|D. K. Oladele; F. S. Cornelius; A. L. Sunday; A. E. Adetola; E. Gracia|10.1109/SEB-SDG57117.2023.10124524|Landmark University;Aeromagnetic;Geological Structure;High-resolution;Omu-Aran;Landmark University;Aeromagnetic;Geological Structure;High-resolution;Omu-Aran|
|[Aqueous extract of Anacardium occidentale leaf (AEAOL) alleviates cadmium chloride-associated alterations in the testes of Wistar rats](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124591)|T. Olaolu; O. Ajibili; D. Rotimi|10.1109/SEB-SDG57117.2023.10124591|Anacardium occidentale;Heavy metals;Testicular function;Testosterone;Anacardium occidentale;Heavy metals;Testicular function;Testosterone|
|[Optimization of process parameters for intermediate pyrolysis of sugarcane bagasse for biochar production using response surface methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124642)|O. O. Anthony; I. A. Rotimi; A. S. A. Emmanuel; O. Ejiroghene; E. B. Omoniyi; A. E. Benjamin; A. J. Samuel; I. U. Kingsley|10.1109/SEB-SDG57117.2023.10124642|Renewable energy;Biochar;Biomass;Characterization;Modelling;Optimization;Renewable energy;Biochar;Biomass;Characterization;Modelling;Optimization|
|[AI-PaaS: Towards the Development of an AI-Powered Accident Alert System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124541)|E. O. Asani; O. D. Akande; E. E. Okosun; O. T. Olowe; R. O. Ogundokun; A. E. Okeyinka|10.1109/SEB-SDG57117.2023.10124541|Hidden Markov Model;Accident detection and alert;IoT;Machine Learning;Artificial Intelligence;Sensors;Hidden Markov Model;Accident detection and alert;IoT;Machine Learning;Artificial Intelligence;Sensors|
|[Towards the Development of a Smart Parking System for Highly Populated Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124575)|R. O. Ogundokun; E. I. Soladoye; E. O. Asani; O. Akande; O. J. Ishola; O. Akinwumi; C. O. Aremu; A. N. Babatunde|10.1109/SEB-SDG57117.2023.10124575|Churn prediction;Oversampling technique;Dimensionality reduction;Classification algorithm;Churn prediction;Oversampling technique;Dimensionality reduction;Classification algorithm|
|[Blockchain Technologies & SDG 3:Imperatives For Revamping Health Management Information Systems in Developing Countries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124597)|R. O. Ogundokun; E. S. Akpabio; O. J. Ishola; C. K. Ayo; A. Falade; C. O. Aremu|10.1109/SEB-SDG57117.2023.10124597|Sustainable development goal;Blockchain;Blockchain technologies;Health management information system;Health sector;Sustainable development goal;Blockchain;Blockchain technologies;Health management information system;Health sector|
|[A Review of Failure Analyses in Engineering: Causes, Effects and Possible Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124556)|O. T. Samson; I. O. Michael; A. A. Akanni; I. P. Pelumi; A. S. Avidime|10.1109/SEB-SDG57117.2023.10124556|Failure Analysis;Causes of failure;types of failure;Engineer;Reflexibility;Failure Analysis;Causes of failure;types of failure;Engineer;Reflexibility|
|[Multi-structural Mapping of Subsurface Geological Features in Omu-Aran and Environs using 3D Euler Deconvolution of Aeromagnetic Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124509)|S. C. Falade; A. B. Arogundade; L. S. Adebiyi; K. O. Dopamu; E. A. Alejolowo|10.1109/SEB-SDG57117.2023.10124509|Structural Mapping;Geological Structures;Fault Zone;Euler Deconvolution;Magnetic Method;Structural Mapping;Geological Structures;Fault Zone;Euler Deconvolution;Magnetic Method|
|[Intrusion Detection Systems Based on Machine Learning Approaches: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124506)|R. O. Ogundokun; U. Basil; A. N. Babatunde; A. T. Abdulahi; A. R. Adenike; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124506|Deep learning;Machine learning;Cyber-attack detection;Network intrusion detection;Network security;Intrusion detection;Deep learning;Machine learning;Cyber-attack detection;Network intrusion detection;Network security;Intrusion detection|
|[Application of Modular Algorithm for Payment Card Number Validation on Mobile Devices Using LUHN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124487)|I. O. Joshua; A. O. Idris; A. A. Adebiyi; A. F. Kadri; A. E. Precious|10.1109/SEB-SDG57117.2023.10124487|Security;Luhn algorithm;Credit Card Number Validation;Visa card Validation;Security;Luhn algorithm;Credit Card Number Validation;Visa card Validation|
|[Advances in Nano-coating and Nanoparticles - A Brief Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124529)|O. M. Ikumapayi; S. A. Afolalu; T. S. Ogedengbe; J. F. Kayode|10.1109/SEB-SDG57117.2023.10124529|Coating;Nano;Particles;Synthesis;Corrosion;Coating;Nano;Particles;Synthesis;Corrosion|
|[Effects of Microstructural Variables on Corrosion Protective Layer of Steel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124514)|S. L. Lawal; S. A. Afolalu; T. -C. Jen; E. T. Akinlabi|10.1109/SEB-SDG57117.2023.10124514|corrosion;protection;coating layers;microstructures;corrosion;protection;coating layers;microstructures|
|[Modeling and Simulation of Parameters for Pipe Welding Applications-an Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124572)|S. L. Lawal; S. A. Afolalu; T. -C. Jen; E. T. Akinlabi|10.1109/SEB-SDG57117.2023.10124572|modelling;simulation;pipes;welding;microstructures;modelling;simulation;pipes;welding;microstructures|
|[Impact of Environmental Variables on Corrosion Rate of Steel- An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124511)|S. L. Lawal; S. A. Afolalu; T. -C. Jen; E. T. Akinlabi|10.1109/SEB-SDG57117.2023.10124511|green;corrosion protection;coating;pitting;variables;corrosion rate;green;corrosion protection;coating;pitting;variables;corrosion rate|
|[Mathematical Modeling and Its Methodological Approach: Application to Infectious Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124470)|O. Abiodun; A. Olukayode; J. Ndako|10.1109/SEB-SDG57117.2023.10124470|Mathematical modeling;Epidemiology;SIR;SIS;SEIR;Mathematical modeling;Epidemiology;SIR;SIS;SEIR|
|[Managing Uncertainty in Production Planning for Fast-Moving Consumer Goods: A Linear Programming and Monte Carlo Simulation Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124567)|O. O. Olanrele; S. O. Ismaila; O. A. Adeaga; O. A. Adeyemi; A. S. Akintaro|10.1109/SEB-SDG57117.2023.10124567|uncertainty modelling;fast-moving consumer goods (FMCG);algorithm;linear programming;Monte Carlo simulation;uncertainty modelling;fast-moving consumer goods (FMCG);algorithm;linear programming;Monte Carlo simulation|
|[Metastatic Breast Cancer Detection Using Deep Learning Algorithms: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124547)|V. O. Adedayo-Ajayi; R. O. Ogundokun; A. E. Tunbosun; M. O. Adebiyi; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124547|Deep learning;Machine learning;Metastatic breast cancer;systematic review;Deep learning;Machine learning;Metastatic breast cancer;systematic review|
|[Climate Smart Agriculture and Nigeria's SDG 2 Prospects: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124557)|E. S. Akpabio; K. F. Akeju; K. O. Omotoso; F. Ohunakin; R. O. Ogundokun|10.1109/SEB-SDG57117.2023.10124557|Sustainable development goal;Smart agriculture;Climate change;Climate-smart agricultural;Sustainable development goal;Smart agriculture;Climate change;Climate-smart agricultural|
|[Injection of Reactive Power Support for Enhanced Performance of Radial Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124493)|O. Olabode; T. Ajewole; I. Okakwu; D. Akinyele; F. Ariyo|10.1109/SEB-SDG57117.2023.10124493|Optimal sites;optimal sizes;voltage stability index;Cuckoo search algorithm;loss minimization;Optimal sites;optimal sizes;voltage stability index;Cuckoo search algorithm;loss minimization|
|[Advanced Joining Methods in Manufacturing in the 21st century: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124535)|O. T. Samson; I. O. Michael; J. O. Ijiwo|10.1109/SEB-SDG57117.2023.10124535|brazing;joining;welding;soldering;adhesive;challenges;solution;brazing;joining;welding;soldering;adhesive;challenges;solution|
|[A Review of the Mechanical and Physical Properties of Gear Manufactured by Powder Metallurgy Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124566)|S. O. Ongbali; F. F. Koleola; S. A. Afolalu; S. Oladipupo; T. E. Shomefun; E. Y. Salawu|10.1109/SEB-SDG57117.2023.10124566|Mechanical and physical properties;gear;manufacturing;powder metallurgy;technique;Mechanical and physical properties;gear;manufacturing;powder metallurgy;technique|
|[Review of Project Monitoring and Evaluation Technique as Index for Sustainable Manufacturing Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124516)|S. O. Ongbali; S. A. Afolalu; E. Y. Salawu; S. A. Omotehinse|10.1109/SEB-SDG57117.2023.10124516|Project;monitoring and evaluation;sustainable;manufacturing;performance;Project;monitoring and evaluation;sustainable;manufacturing;performance|
|[Interaction effect of green sand mixtures parameters on tensile strength mechanical property of cast aluminum 6351 using a statistical approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124598)|I. P. Udeorah; E. Elvis; O. O. Anthony; I. C. Osuizugbo; M. K. Onifade; H. I. Owamah|10.1109/SEB-SDG57117.2023.10124598|Aluminium alloys;bentonite clay;dextrin additive;green sand mixtures;mechanical property;Aluminium alloys;bentonite clay;dextrin additive;green sand mixtures;mechanical property|
|[Police Brutality, Human Rights Violations and the 2020 #EndSARS Protests in Lagos, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124500)|O. J. Iseolorunkanmi; O. M. Awolesi; N. -L. Henry; O. Gbenga; I. C. Akinojo; O. A. Olanrewaju|10.1109/SEB-SDG57117.2023.10124500|Police Brutality;Human Rights;Violations;EndSARS Protests;Nigeria;Police Brutality;Human Rights;Violations;EndSARS Protests;Nigeria|
|[Computational Techniques for Cardiovascular Diseases Prediction: Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124568)|I. D. Olusoji; R. M. Alade; O. A. Abiodun; O. O. Olabisi|10.1109/SEB-SDG57117.2023.10124568|Computational models;Systematic review;Heart;Prediction;Vessel;Blood;Computational models;Systematic review;Heart;Prediction;Vessel;Blood|
|[Rotatory Inertia Influence on Dynamic Behavior of Non-Winkler Timoshenko Beam Traversed by Pair Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124565)|A. Adedowole; S. N. Ogunyebi; S. A. Jimoh|10.1109/SEB-SDG57117.2023.10124565|Concentrated Loads;Galerkin's Method;Non-Winkler Foundation;Rotatory Inertia;Transverse Vibration;Concentrated Loads;Galerkin's Method;Non-Winkler Foundation;Rotatory Inertia;Transverse Vibration|
|[Border Control via Passport Verification using Fingerprint Authentication Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124584)|O. T. Omolewa; E. J. Adeioke; O. O. Titilope; A. K. Sakarivan; A. J. Kehinde|10.1109/SEB-SDG57117.2023.10124584|Border Control;Passport Verification;FingerprintAuthentication;Biometric;Border Control;Passport Verification;FingerprintAuthentication;Biometric|
|[Comparative Assessment of Radioactivity Concentration in Sweet Potatoes from Different Geopolitical Zones of Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124519)|A. S. Ajani; G. B. Egbeyale; O. E. Shogo; C. S. Odeyemi; A. N. Galadima; P. O. Oyero|10.1109/SEB-SDG57117.2023.10124519|Natural radionuclides;potatoes;effective dose;ECLR;Natural radionuclides;potatoes;effective dose;ECLR|
|[Nigerian Steel-slag for Road Works: Physical, Mineralogy and Micro-structural Characterization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124590)|D. Oguntayo; M. Ogundipe; O. Aluko; B. Oguntayo; R. Rahmon; O. Ogundipe|10.1109/SEB-SDG57117.2023.10124590|steel slag;aggregates;road works;characterization;steel slag;aggregates;road works;characterization|
|[Cloud Intrution Detection System Using Antlion Optimization Algorithm and Support Vector Machine (SVM) Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124606)|H. A. Christopher; J. A. Ojeniyi; S. A. Adepoju; O. A. Abisoye|10.1109/SEB-SDG57117.2023.10124606|Ant Lion Optimization;Support Vector Machine (SVM);CIDS;Feature Selection;Cloud Computing;Ant Lion Optimization;Support Vector Machine (SVM);CIDS;Feature Selection;Cloud Computing|
|[Statistical Analysis of a Deep Learning Based Trimodal Biometric System Using Paired Sampling T-Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124624)|O. G. Atanda; W. Ismaila; A. O. Afolabi; O. A. Awodoye; A. S. Falohun; J. P. Oguntoye|10.1109/SEB-SDG57117.2023.10124624|Biometrics;Convolutional Neural Network;Mayfly Algorithm;t-test;Biometrics;Convolutional Neural Network;Mayfly Algorithm;t-test|
|[Homotopy Perturbation Technique for Fractional Volterra and Fredholm Integro Differential Equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124633)|T. Oyedepo Ayinde; M. O. Oluwayemi; M. Abdullahi; J. A. Osilagun; L. O. Ahmed|10.1109/SEB-SDG57117.2023.10124633|Caputo derivative;Volterra-Fredhoolm;Fractional integro-differential equations;Homotopy perturbation technique;Approximate solution;Caputo derivative;Volterra-Fredhoolm;Fractional integro-differential equations;Homotopy perturbation technique;Approximate solution|
|[Solution of Volterra-Fredholm Integro-Differential Equations Using the Chebyshev Computational Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124647)|T. Oyedepo; M. O. Oluwayemi; A. A. Ayoade; R. Pandurangan|10.1109/SEB-SDG57117.2023.10124647|First kind Chebyshev polynomials;Volterra-Fredholm;Integro-differential equations;Approximate solutions;First kind Chebyshev polynomials;Volterra-Fredholm;Integro-differential equations;Approximate solutions|
|[Iterative Decomposition Method for solving Singular differential, Singular integral and Singular integro-differential equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124603)|O. A. TAIWO; J. O. OKORO|10.1109/SEB-SDG57117.2023.10124603|Singular differential;Singular integro differen- tial;Iterative Decomposition Method;Errors;Discretization;Singular differential;Singular integro differen- tial;Iterative Decomposition Method;Errors;Discretization|
|[Predictive Risk Assessment of Heart Failure using HAZOP and Qualitative Risk Analyses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124508)|T. A. Oshin; A. J. Tawose|10.1109/SEB-SDG57117.2023.10124508|heart failure;risk analysis;HAZOP;blood flow;cardiovascular system;SDG 3;good health and wellbeing;heart failure;risk analysis;HAZOP;blood flow;cardiovascular system;SDG 3;good health and wellbeing|
|[Enhancing the Performance of SMEs Post Covid-19: The Role of Strategic Agility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124521)|F. O. Peter; A. A. Motunrayo; A. Sajuyigbe; A. Peter; T. Asiyanbola|10.1109/SEB-SDG57117.2023.10124521|Nigeria;Strategic Agility;Innovation;SMEs;COVID-19;Nigeria;Strategic Agility;Innovation;SMEs;COVID-19|
|[Data Logging Model for Metropolitan Vehicle Movement Monitoring and Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124383)|S. Adeniyi; K. E. Jack; T. H. Salami; P. O. Ojekunle; L. J. Olatomiwa; A. O. Oyelami|10.1109/SEB-SDG57117.2023.10124383|Artificial Intelligence;Computer Vision;Data Logging Model;Metropolitan Cities;Vehicle Movement Monitoring;Artificial Intelligence;Computer Vision;Data Logging Model;Metropolitan Cities;Vehicle Movement Monitoring|
|[Inception V3 Based Approach for the Recognition of Age-related macular degeneration Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124539)|R. O. Ogundokun; A. T. Abdulahi; A. R. Adenike; A. N. Babatunde; R. S. Babatunde|10.1109/SEB-SDG57117.2023.10124539|Age-related macular degeneration;Deep learning;Deep transfer learning;Automated system;Retinal images;Age-related macular degeneration;Deep learning;Deep transfer learning;Automated system;Retinal images|
|[Application of Pre-Trained CNN Methods to Identify COVID-19 Pneumonia from Chest X-Ray](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124492)|R. O. Ogundokun; A. B. Adelodun; A. A. Adebiyi; M. Daniyal; M. Odusami|10.1109/SEB-SDG57117.2023.10124492|COVID-19;Pretrained CNN;Image processing;Medical image;COVID-19;Pretrained CNN;Image processing;Medical image|
|[Indigenous Crime Control Mechanisms in Kabba, Kogi State, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124604)|J. A. Oye; O. Arisukwu; F. F. Asamu; P. B. Ogunlade; J. I. Oye; G. O. Oye|10.1109/SEB-SDG57117.2023.10124604|Crime Control;Indigenous Mechanisms;Kabba;Owe;Province;Nigeria;Crime Control;Indigenous Mechanisms;Kabba;Owe;Province;Nigeria|
|[Development and Performance Evaluation of Bone Crushing Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124562)|P. Ikubanni; A. Adeleke; A. Aluko; B. JollyJames; O. Agboola; S. Olajide|10.1109/SEB-SDG57117.2023.10124562|bone crushing;cow bones;crushing rate;crushing efficiency;mechanical design;bone crushing;cow bones;crushing rate;crushing efficiency;mechanical design|
|[Construction of a Low-Power Rating FM Transmitter Radio with Audio Console for Community Broadcast and Security Alert](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124617)|A. S. Ajani; O. A. Ogunbode; G. B. Egbeyale; C. S. Odeyemi; A. F. Babalola; P. O. Oyero|10.1109/SEB-SDG57117.2023.10124617|Transmitter;audio console;modulator;Transmitter;audio console;modulator|
|[Detection and Extraction of Colours in Digital Images and Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124468)|I. C. Obagbuwa; K. Klaaste; A. J. Jasmin|10.1109/SEB-SDG57117.2023.10124468|Colour detection;colour extraction;digital image processing;MATLAB;Python;computer vision;human vision;animal vision;electromagnetic spectrum;Colour detection;colour extraction;digital image processing;MATLAB;Python;computer vision;human vision;animal vision;electromagnetic spectrum|
|[Securing web applications against SQL injection attacks - A Parameterised Query perspective)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124613)|J. O. Okesola; A. S. Ogunbanwo; A. Owoade; E. O. Olorunnisola; K. Okokpuji|10.1109/SEB-SDG57117.2023.10124613|Ecommerce;Electronic Commerce;Parameterized Queries;SQL injection attacks;Web applications;Ecommerce;Electronic Commerce;Parameterized Queries;SQL injection attacks;Web applications|
|[Malvertisements Detection using urlscan.io, Pulsedive, and SucuriSiteCheck](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124634)|J. O. Okesola; A. S. Ogunbanwo; A. Owoade; E. O. Olorunnisola; K. Okokpujie|10.1109/SEB-SDG57117.2023.10124634|Intrusion Detection System;Malicious advertisement;Malvertisements;Pulsedive;SucuriSiteCheck;Urlscan.io;Intrusion Detection System;Malicious advertisement;Malvertisements;Pulsedive;SucuriSiteCheck;Urlscan.io|
|[Speech Refinement Using Custom Filter for Developing Robust S2S Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124474)|O. Julius; I. C. Obagbuwa; A. A. Adebiyi; E. B. Michael|10.1109/SEB-SDG57117.2023.10124474|Automatic Translations;Audio Signals;Neural Network;Short-Time Fourier Transform;Signal-to-noise ratio;Automatic Translations;Audio Signals;Neural Network;Short-Time Fourier Transform;Signal-to-noise ratio|
|[Quantum Theory Approach to Performance Enhancement in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124582)|M. O. Adebiyi; D. Fatinikun-Olaniyan; F. Osang; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124582|Quantum computing;Machine learning algorithms;Support vector machine;Digital bits;Qubits;Quantum computing;Machine learning algorithms;Support vector machine;Digital bits;Qubits|
|[Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124385)|O. Julius; I. C. Obagbuwa; A. A. Adebiyi; E. B. Michael|10.1109/SEB-SDG57117.2023.10124385|Deep Learning;Fourier Transform;Machine Translation;Principal Component Analysis;Wavelet;Deep Learning;Fourier Transform;Machine Translation;Principal Component Analysis;Wavelet|
|[Impact of some essential plant oils on viability of stored cowpea and maize seeds for food security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124546)|A. Okunola; C. Okunola|10.1109/SEB-SDG57117.2023.10124546|cowpea;maize;food security;viability and essential oils;cowpea;maize;food security;viability and essential oils|
|[Fuzzy-Based Prediction of Spread of Covid-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124510)|B. O. Adegoke; O. F. Adegoke|10.1109/SEB-SDG57117.2023.10124510|fuzzy inference system;factors of COVID-19 spread;membership function;fuzzification;defuzzification;aggregation;fuzzy inference system;factors of COVID-19 spread;membership function;fuzzification;defuzzification;aggregation|
|[A Systematic Review of the Factors Affecting the Sustainability of SMEs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124502)|O. A. Odegbesan; C. K. Ayo; O. Salau|10.1109/SEB-SDG57117.2023.10124502|Sustainability Factors;SMEs;Systematic Literature Review;Sustainability Factors;SMEs;Systematic Literature Review|
|[Tensile property and microstructural characterization of fractured joints of rotary frictional welded similar and dissimilar metals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124574)|P. Ikubanni; O. Agboola; A. Adeleke; C. -S. Odogwu; D. Chukwumati; T. Adekanye|10.1109/SEB-SDG57117.2023.10124574|Rotary friction welding;similar metal;dissimilar metal;tensile test;materials engineering;Rotary friction welding;similar metal;dissimilar metal;tensile test;materials engineering|
|[Survey on Current Trend in Emotion Recognition Techniques Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124548)|M. O. Adebiyi; D. Fatinikun-Olaniyan; A. A. Adebiyi; A. A. Okunola|10.1109/SEB-SDG57117.2023.10124548|Human-Machine Interaction;Emotion Recognition;Deep Learning;Speech;Electroencephalogram;Human-Machine Interaction;Emotion Recognition;Deep Learning;Speech;Electroencephalogram|
|[Implementation of Multi-modal Speech Emotion Recognition Using Text Data and Audio Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124472)|F. Adesola; O. Adeyinka; A. Kayode; A. Ayodele|10.1109/SEB-SDG57117.2023.10124472|Machine learning;RNN;CSS;CNN;CRNN;Machine learning;RNN;CSS;CNN;CRNN|
|[Evolution of Computer Network from Inception to the Internet of Everything: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124570)|A. Falade; C. Ayo; K. Akindeji; A. Adebiyi|10.1109/SEB-SDG57117.2023.10124570|Computer Networks;Internet of Things (IoT);Internet of Everything;(IoE);Information and Communication Technologies (ICT);Computer Networks;Internet of Things (IoT);Internet of Everything;(IoE);Information and Communication Technologies (ICT)|
|[Analysis of Soil Nutrients and Water Levels Using Internet of Things (IoT) for Different Land Use Options](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124635)|T. E. Babalola; A. D. Babalola; O. T. Faloye; B. A. Adabembe; A. T. Ogunrinde|10.1109/SEB-SDG57117.2023.10124635|Smart soil analysis;Laboratory soil analysis;Land use patterns;soil depth;soil properties;Smart soil analysis;Laboratory soil analysis;Land use patterns;soil depth;soil properties|
|[Implementation of University Course Time-tabling system using Iterative Forward Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124610)|F. Adesola; O. Adeyinka; A. Kayode; A. Ayodele|10.1109/SEB-SDG57117.2023.10124610|Timetabling;methodologies;constraints;Heuristic;Courses;Timetable;Assignment;Timetabling;methodologies;constraints;Heuristic;Courses;Timetable;Assignment|
|[SEM-ANN-based approach to understanding ICT adoption for SME Sustainability in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124585)|O. A. Odegbesan; C. K. Ayo; O. Salau|10.1109/SEB-SDG57117.2023.10124585|ICT adoption;technology;organization and environment framework;structural equation modelling;artificial neural network;small and medium enterprises;ICT adoption;technology;organization and environment framework;structural equation modelling;artificial neural network;small and medium enterprises|
|[Mitigating Interference and Improving the SINR in a Discrete Time Frame of a Downlink MU-MIMO Transmission in 5G and Beyond Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124579)|A. A. Masud; O. D. Uchechukwu; A. O. Adikpe; F. Ibikunle|10.1109/SEB-SDG57117.2023.10124579|MIMO;MU-MIMO;Outage Probability;5G;B5G;MIMO;MU-MIMO;Outage Probability;5G;B5G|
|[A Review on Discontinuous Reception Mechanism as a Power Saving Approach for 5G User Equipments at Millimetre-Wave Frequencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124494)|F. C. Njoku; F. Ibikunle; A. O. Adikpe|10.1109/SEB-SDG57117.2023.10124494|5G;Discontinuous reception;Energy efficiency;RRC_INACTIVE;Power saving;Paging;5G;Discontinuous reception;Energy efficiency;RRC_INACTIVE;Power saving;Paging|
|[Its Based Demand Management Model for Congestion Mitigation on an Urban Traffic Corridor in Ilorin, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124558)|G. Ogunkunbi; D. Oguntayo; O. Adeleke; L. Salami; F. Ariba|10.1109/SEB-SDG57117.2023.10124558|Intelligent transportation system;traffic congestion;traffic demand management;traffic studies;Ilorin;Intelligent transportation system;traffic congestion;traffic demand management;traffic studies;Ilorin|
|[Experimental Evaluation of an Automatic Cabinet Dryer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124523)|T. Adekanye; O. Abiodun|10.1109/SEB-SDG57117.2023.10124523|agriculture;crop processing;okra;drying;moisture content;agriculture;crop processing;okra;drying;moisture content|
|[Atmospheric Air Temperature Observation from the Landmark University Weather Station, Omu-Aran, Nigeria for Long Term Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124628)|B. O. Adebesin; C. O. Aremu; R. O. Ajibade; S. O. Ikubanni; S. J. Adebiyi; A. A. Gbadamosi|10.1109/SEB-SDG57117.2023.10124628|Atmospheric weather station;Landmark University;Equatorial station;Air temperature;Meteorology;Atmospheric weather station;Landmark University;Equatorial station;Air temperature;Meteorology|
|[Adsorptive removal of cadmium (II) and Asensic (III) ion from aqueous solution using zeolite Y synthesized from Kaolin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124495)|J. B. Adeoye; J. O. Bello; A. I. Okoji; N. P. Ezeh; E. Chukwunedum; K. S. Obayomi; O. T. Abayomi|10.1109/SEB-SDG57117.2023.10124495|Adsorption;Waste water Treatment;Zeolite Y;Kinetics;Thermodynamic;Adsorption;Waste water Treatment;Zeolite Y;Kinetics;Thermodynamic|
|[Construction and Evaluation of a Galvanized Steel Flat-Plate Collector for Water Heating Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124627)|S. O. Ikubanni; I. I. Shittu; B. D. Olanrewaju; S. J. Adebiyi; B. O. Adebesin; K. O. Dopamu; S. C. Falade; L. S. Adebiyi; E. A. Alejolowo|10.1109/SEB-SDG57117.2023.10124627|Renewable energy;Non-concentrating;Solar thermal collector;Serpentine flat-plate;Renewable energy;Non-concentrating;Solar thermal collector;Serpentine flat-plate|
|[Institutional Shareholders' Monitoring Intensity and Mergers and Acquisitions(M&A) Decisions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124527)|O. Obagbuwa; F. Kwenda|10.1109/SEB-SDG57117.2023.10124527|Institutional shareholder;Mergers and Acquisitions;Monitoring Intensity;Shareholder distraction;Institutional shareholder;Mergers and Acquisitions;Monitoring Intensity;Shareholder distraction|
|[Development of a Machine Learning Model For Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124592)|U. Musa; M. O. Adebiyi; A. A. Adebiyi; A. A. Adebiyi|10.1109/SEB-SDG57117.2023.10124592|Machine learning;Predictive model;Java Virtual Machine (JVM);Netbeans Integrated Software Development Environment (IDE);Weka Tool;Weka Plugin;Machine learning;Predictive model;Java Virtual Machine (JVM);Netbeans Integrated Software Development Environment (IDE);Weka Tool;Weka Plugin|
|[Improving the food security status of sweet potato-based farm households in the face of post-harvest losses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124583)|O. M. Bamiro; C. O. Aremu; W. Ayojimi; S. O. Solaja; B. A. Tijani|10.1109/SEB-SDG57117.2023.10124583|post-harvest;sweet potato;food security;households;post-harvest;sweet potato;food security;households|
|[Assessment of the Effects of Bathroom Effluents on the Soil in Halls of Residence, Landmark University, OMU-ARAN, Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124481)|O. O. Elemile; O. P. Ejigboye; E. M. Ibitogbe; O. P. Folorunso; E. O. Ajayi; O. Adeniyi; C. Akogwu; E. Dogara; Z. Geo-needam; D. Okoli|10.1109/SEB-SDG57117.2023.10124481|Bathroom effluents;Soil pollution;Hostels;Omu-Aran;Landmark;Bathroom effluents;Soil pollution;Hostels;Omu-Aran;Landmark|
|[Anti-Cattle Rustling Device for Local Community](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124528)|E. Ajisegiri; O. Aderoba; J. Audu; K. Osanyinpeju; D. Jerugba; V. Akhamere; S. Aliyu|10.1109/SEB-SDG57117.2023.10124528|Cattle Rustling;IoT;GPS;microcontroller;tracker;Cattle Rustling;IoT;GPS;microcontroller;tracker|
|[Green Synthesis, Optimization and Extraction of Silica from Rice Straw Ash for Solar Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124600)|O. Oyewole; J. B. Adeoye; T. A. Oshin; A. Paul; T. S. Abayomi; O. D. Kolawole|10.1109/SEB-SDG57117.2023.10124600|Silica;XRF;XRD;SEM;Rice straw;Silica;XRF;XRD;SEM;Rice straw|
|[Solar-wind and IMF-Bz inclined Precusor of Geomagnetic Induced Currents and their Significance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124640)|B. O. Adebesin|10.1109/SEB-SDG57117.2023.10124640|Geomagnetic induced current;Geomagnetic storm;equatorial latitude;Perturbation;magnetosphere;Geomagnetic induced current;Geomagnetic storm;equatorial latitude;Perturbation;magnetosphere|
|[Flood Areas Prediction in Nigeria using Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124629)|O. J. Adetunji; I. A. Adeyanju; A. O. Esan|10.1109/SEB-SDG57117.2023.10124629|Artificial Neural Network;Flood;Python;Artificial Neural Network;Flood;Python|
|[An Ontology-Based Diabetes Prediction Algorithm Using Naïve Bayes Classifier and Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124491)|F. M. Okikiola; O. S. Adewale; O. O. Obe|10.1109/SEB-SDG57117.2023.10124491|Ontology;Naïve bayes;Decision tree;Diabetes;Prediction;Ontology;Naïve bayes;Decision tree;Diabetes;Prediction|
|[Design and Fabrication of a Prototype Automated Railway Gate Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124384)|B. Adaramola; A. Adetunla; J. Kayode; C. Okoronkwo|10.1109/SEB-SDG57117.2023.10124384|Automation;Railway Controller;Sensors;Embedded System;Automation;Railway Controller;Sensors;Embedded System|
|[Electro-pneumatic Actuation of a Six-mould compartment Mini-sized Briquetting Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124499)|A. Adetunla; S. Afolabi; E. O. Ige; S. Afolalu|10.1109/SEB-SDG57117.2023.10124499|Waste Management;Electro-Pneumatic;Solenoid Valve;Energy Generation;Waste Management;Electro-Pneumatic;Solenoid Valve;Energy Generation|
|[A cost Effective Ceramic Filters for Water Purification for Developing Nations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124576)|O. Ogundipe; A. Adetunla; O. Igbinosa; O. Ikumapayi|10.1109/SEB-SDG57117.2023.10124576|Ceramics;Clay;Turbidity;Waste Management;Water Filter;Ceramics;Clay;Turbidity;Waste Management;Water Filter|
|[Evaluation of the nutritive values of the leaves of three common trees in Landmark University as potential feed resources in poultry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124636)|R. Animashahun; S. Omoikhoje; O. Alabi; S. Aro; M. Falana; J. Agbede|10.1109/SEB-SDG57117.2023.10124636|Agriculture;feed resources;proximate components;minerals;amino acids;anti-nutritive factors;poultry;Agriculture;feed resources;proximate components;minerals;amino acids;anti-nutritive factors;poultry|
|[Evaluating the Mechanical Integrity of Multi-Pass Friction Stir Processed Aluminium Alloy Impregnated with Titanium Particles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124517)|A. Adetunla; O. Rominiyi; S. Akande; T. Azeez|10.1109/SEB-SDG57117.2023.10124517|Automation;Railway Controller;Sensors;Embedded System;Automation;Railway Controller;Sensors;Embedded System|
|[An IoT Controlled Smart Grid System for Theft Detection and Remote Power Redirection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124530)|A. Adedotun; E. Emmanuel; A. Adeoye; I. P. Okokpujie|10.1109/SEB-SDG57117.2023.10124530|IoT;Smart Grid;Embedded System;Power Monitoring;IoT;Smart Grid;Embedded System;Power Monitoring|
|[Serodiagnosis of Samonella Infection using a Logistic Regression Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124490)|J. A. Ndako; A. O. Owolabi; V. T. Dojumo; V. O. Fajobi; I. J. Owolabi; S. A. Junaid|10.1109/SEB-SDG57117.2023.10124490|;|
|[Overview of Recent Cyberattacks: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124473)|I. Emmanuel O.; E. V. C.; O. E. I.; N. P. C.|10.1109/SEB-SDG57117.2023.10124473|Cybersecurity;Risks;Threats;Vulnerability;Cyberattacks;Cybersecurity;Risks;Threats;Vulnerability;Cyberattacks|
|[Effect of Spectrogram Preprocessing and Enhancement on Speaker Recognition Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124644)|A. A. Akinrinmade; E. Adetiba; J. A. Badejo; S. I. Popoola|10.1109/SEB-SDG57117.2023.10124644|convolutional neural network;preprocessing;post-processing;speaker recognition;convolutional neural network;preprocessing;post-processing;speaker recognition|
|[An Active Speaker Detection Method in Videos using Standard Deviations of Color Histogram](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124488)|A. A. Akinrinmade; E. Adetiba; J. A. Badejo; O. Oshin|10.1109/SEB-SDG57117.2023.10124488|Active Speaker Detection (ASD);Color Histograms (CHs);Standard Deviations;Active Speaker Detection (ASD);Color Histograms (CHs);Standard Deviations|
|[Exploring Fusion of the Face and Voice Modalities Using CNN Features for a Better Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124540)|A. A. Akinrinmade; E. Adetiba; J. A. Badejo; C. O. Lawal|10.1109/SEB-SDG57117.2023.10124540|convolutional neural network;face recognition;fusion;speaker recognition;convolutional neural network;face recognition;fusion;speaker recognition|
|[Research Implication for Infectious Skin Disease and Phytotherapy in Developing Countries Based on 21st Century Bibliometric Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124639)|E. O. Oludipe; A. O. Owolabi; A. O. Ajayi; P. A. Obateru; S. O. Adebudo; J. A. Ndako; S. O. Owa|10.1109/SEB-SDG57117.2023.10124639|skin disease;dermatology;phytomedicine;VOSviewer;bibliometric analysis;infectious disease;skin disease;dermatology;phytomedicine;VOSviewer;bibliometric analysis;infectious disease|
|[Web Page Prediction Model using Machine Learning Approaches: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124586)|P. A. Omosebi; A. P. Adewole; O. Sennaike|10.1109/SEB-SDG57117.2023.10124586|webpage;machine learning;prediction;classification;webpage;machine learning;prediction;classification|
|[Evaluation of the energy potential of biogas produced from the anaerobic co-digestion of animal and poultry dung](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124620)|R. A. Ibikunle; J. L. Barizaa; U. F. Nmadubuko; E. A. Mojiminiyi; S. T. Alao; B. G. Waziri; O. T. Aluko|10.1109/SEB-SDG57117.2023.10124620|Renewable energy;Substrates;Anaerobic digestion;Mesophilic process;Biogas;Energy potential;Renewable energy;Substrates;Anaerobic digestion;Mesophilic process;Biogas;Energy potential|
|[SHA-AES based on Chaining Block Code and Galois Counter Mode Encryptions for the Privacy of Smart Farming Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124512)|A. A. Alfa; T. O. Aro; O. O. Adunni; D. Z. Iliya; O. Awe; A. Owonipa|10.1109/SEB-SDG57117.2023.10124512|CBC;GCM;cryptography;lightweight;privacy;memory consumption;speed;IoT;security status;encryption mode;CBC;GCM;cryptography;lightweight;privacy;memory consumption;speed;IoT;security status;encryption mode|
|[Mobile-Based Deep Learning for Yam Disease Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124483)|A. O. Akinlolu; O. A. Odejobi; F. O. Ajayi; E. R. Jimoh|10.1109/SEB-SDG57117.2023.10124483|Deep Learning;Yam Disease;Mobile-Based;Convolution Neural Networks (CNN);Deep Learning;Yam Disease;Mobile-Based;Convolution Neural Networks (CNN)|
|[Effects of Crumb Rubber as Coarse Aggregate Replacement on the Fatigue Property of Warm Mix Asphalt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124549)|B. Solomon; O. M. Ogundipe; A. J. Gana; I. Fredrick|10.1109/SEB-SDG57117.2023.10124549|warm mix asphalt;crumb rubber;bitumen;fatigue tests;evaluation;warm mix asphalt;crumb rubber;bitumen;fatigue tests;evaluation|
|[Interference Mitigation Using Particle Swarm Optimization Algorithm in Television White Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124552)|J. Notcker; E. Adetiba; A. Abayomi; K. K. Ronoh; O. Oshin; K. A. Greyson|10.1109/SEB-SDG57117.2023.10124552|Television White Space;Particle Swarm Optimization;Artificial Bee Colony;Interference;Television White Space;Particle Swarm Optimization;Artificial Bee Colony;Interference|
|[On the Application of Modelling Forest Fire in the Environment: A Bayesian Model Averaging Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124475)|O. M. Oladoja; A. G. Folorunso; T. M. Adegoke; S. O. Bashiru; K. C. Arum; A. A. Mustapha|10.1109/SEB-SDG57117.2023.10124475|Posterior Inclusion Probability;Duff Moisture Code;Model Uncertainty;Parameter Prior;Posterior Inclusion Probability;Duff Moisture Code;Model Uncertainty;Parameter Prior|
|[An Overview of Tribology and its Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124632)|J. F. Kayode; S. L. Lawal; S. A. Afolalu|10.1109/SEB-SDG57117.2023.10124632|tribology;friction;wear;lubricants;applications;tribology;friction;wear;lubricants;applications|
#### **2023 4th International Conference on Signal Processing and Communication (ICSPC)**
- DOI: 10.1109/ICSPC57692.2023
- DATE: 23-24 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Design of a Smart Embedded Vision System Based on FPGA for Medical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125679)|A. B. Yandrapati; V. K. P. R. Singam; B. Namburu; V. Paladugu; T. K. Karakula|10.1109/ICSPC57692.2023.10125679|Embedded vision system;ZYNQ SOC;Ethernet;OV7670 camera module;Embedded vision system;ZYNQ SOC;Ethernet;OV7670 camera module|
|[A Novel Deep Learning Approach for Detection of Sleep Apnea from ECG Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125944)|T. Anbalagan; M. K. Nath; D. S. Keerthi; K. Pranathi; K. Satyanarayana|10.1109/ICSPC57692.2023.10125944|Obstructive sleep apnea;ECG;LSTM;DNN;BiLSTM;Obstructive sleep apnea;ECG;LSTM;DNN;BiLSTM|
|[Analysis of Gall Bladder & Kidney Stone Using Spectroscopic Technique: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125670)|A. Kumar; L. Phagna; S. Rawat; N. Gupta|10.1109/ICSPC57692.2023.10125670|Gallbladder;Kidney;LIBS;Renal Stone;Gallbladder;Kidney;LIBS;Renal Stone|
|[Ensemble Learning for the Survivability Prediction of Breast Cancer Patients Using METABRIC and SEER Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125945)|E. J. Sweetlin; S. Saudia|10.1109/ICSPC57692.2023.10125945|Bagging;Boosting;Ensemble;Filter-Wrapper;Stacking;Bagging;Boosting;Ensemble;Filter-Wrapper;Stacking|
|[Revolutionizing Poultry Farming with IoT: An Automated Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125606)|R. Murugeswari; P. Jegadeesh; G. N. Kumar; S. N. Babu; B. Samar|10.1109/ICSPC57692.2023.10125606|Flock’s health;remote monitoring device;automatic watering;automatic feeding;Flock’s health;remote monitoring device;automatic watering;automatic feeding|
|[Skin Cancer Prediction Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10126035)|R. R. Sekar; Y. J. M. Reddy; K. Nani; C. S. P. Reddy; K. Chiranjeevi; K. Vikram|10.1109/ICSPC57692.2023.10126035|Skin Cancer;Convolutional Neural Network;Neural Network;Skin Cancer;Convolutional Neural Network;Neural Network|
|[Security and Privacy Threats of IoT Devices: A & Short Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125863)|R. Veluvarthi; A. Rameswarapu; K. V. Sai Kalyan; J. Piri; B. Acharya|10.1109/ICSPC57692.2023.10125863|Internet of Things(IoT);IoT-Security;Privacy;Internet of Things(IoT);IoT-Security;Privacy|
|[Hybrid Power Management Using Interleaved Landsman Converter Implemented Through Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125590)|G. Mahalakshmi; S. Arun Kumar; K. Abhisek; J. Arthi; T. Ellanchikkumar|10.1109/ICSPC57692.2023.10125590|Micro grid;Interleaved Landsman converter;KNN algorithm;NodeMCU;MPPT;MATLAB;Simulink;Micro grid;Interleaved Landsman converter;KNN algorithm;NodeMCU;MPPT;MATLAB;Simulink|
|[A Novel Framework for Network Intrusion Detection in Healthcare Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125636)|S. W. Betsy; A. Murugesan; N. B. S. Ganapathy; N. Pughazendi|10.1109/ICSPC57692.2023.10125636|Active Learning;Cybersecurity;Intrusion Detection;Healthcare;Active Learning;Cybersecurity;Intrusion Detection;Healthcare|
|[Online Sales Prediction in E-Commerce Market Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125855)|S. Ajaykrishna; T. S. Suganya; B. Rao; N. Pughazendi|10.1109/ICSPC57692.2023.10125855|Deep Learning;Sales prediction;LSTM;IARIMA;Neural Networks;Deep Learning;Sales prediction;LSTM;IARIMA;Neural Networks|
|[A Survey on IOT Based Air Pollution Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125940)|P. James Vijay; A. Madhuri; G. Madhu Sri; Y. Sri Venkata Siva Naga Prasad|10.1109/ICSPC57692.2023.10125940|Air quality monitoring;IOT;Gas sensors;Raspberry pi;Node MCU ESP8266;ESP32.;Air quality monitoring;IOT;Gas sensors;Raspberry pi;Node MCU ESP8266;ESP32.|
#### **2023 IEEE Global Engineering Education Conference (EDUCON)**
- DOI: 10.1109/EDUCON54358.2023
- DATE: 1-4 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Framework for Infusing Cybersecurity Programs With Real-World Artificial Intelligence Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125138)|J. E. DeBello; E. Troja; L. M. Truong|10.1109/EDUCON54358.2023.10125138|Student-centered Learning Environments;Engaging Undergraduate Students in Research;Game-based Learning and Gamification;Student-centered Learning Environments;Engaging Undergraduate Students in Research;Game-based Learning and Gamification|
|[Digital Makerspace – a Tool with Multipurpose Use and Potential for Teaching and Others](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125279)|I. Rothe; A. Kless|10.1109/EDUCON54358.2023.10125279|web-based tool;web components;learning material;digital makerspace;quiz;online exercise;eLearning;web-based tool;web components;learning material;digital makerspace;quiz;online exercise;eLearning|
|[Human Capital as the Basis for the Professional Expectations Formation of Technical University Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125131)|N. Opletina; M. Kunyaeva|10.1109/EDUCON54358.2023.10125131|in-demand competence profile;human capital;labor market;social and personal qualities;professional expectations;accumulation factors of human capital;in-demand competence profile;human capital;labor market;social and personal qualities;professional expectations;accumulation factors of human capital|
|[Using Kolb's Experiential Learning in Agile Software Development Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125125)|S. Datta|10.1109/EDUCON54358.2023.10125125|Experiential learning;communication;Teamwork;Understanding client requirements;Agile Software development;Experiential learning;communication;Teamwork;Understanding client requirements;Agile Software development|
|[Impact of Remote Labs in Preparing Students for Work 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125216)|A. Al-Zoubi; M. Castro; F. R. Shahroury; E. Sancristobal|10.1109/EDUCON54358.2023.10125216|Remote Labs;Work 4.0;New Jobs;Fourth Industrial Revolution;Remote Labs;Work 4.0;New Jobs;Fourth Industrial Revolution|
|[Blockchain for Management of Virtual Internationalization in Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125236)|A. Al-Zoubi; M. Aldmour|10.1109/EDUCON54358.2023.10125236|blockchain;virtual internationalization;higher education;management model;blockchain;virtual internationalization;higher education;management model|
|[Sharing our experience of the ASSETs+ European Defence Challenge from the design to the implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125211)|E. Grivel; M. Burgos; D. Stadnicka; G. Fantoni|10.1109/EDUCON54358.2023.10125211|Challenge;Defence;Webinars;Challenge;Defence;Webinars|
|[Artificial Intelligence for Defence in an EQF6 Training and Education Program, from the Design to the Execution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125126)|E. Grivel; B. Pesquet; T. . -B. Airimitoaie; A. Zemmari|10.1109/EDUCON54358.2023.10125126|Artificial intelligence;Defence;seasonal schools;invited speakers;project based learning;Artificial intelligence;Defence;seasonal schools;invited speakers;project based learning|
|[From accessibility to participation: Broadening diversity and inclusion in higher engineering and computing education through an OOICCI model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125233)|Y. Xu; J. Liu; Y. Wen|10.1109/EDUCON54358.2023.10125233|higher engineering and computer education;diversity;inclusion;China;higher engineering and computer education;diversity;inclusion;China|
|[A Student-Centered Learning Methodology in Power Electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125249)|J. Avaritsiotis|10.1109/EDUCON54358.2023.10125249|power electronics education;student-centered learning methodology;laboratory projects;power electronics education;student-centered learning methodology;laboratory projects|
|[Augmented Reality for Programming Teaching: An Exploratory Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125256)|J. Branco; N. Pombo|10.1109/EDUCON54358.2023.10125256|Augmented reality;Python;programming;software engineering education;Augmented reality;Python;programming;software engineering education|
#### **2023 4th International Conference on Computing and Communication Systems (I3CS)**
- DOI: 10.1109/I3CS58314.2023
- DATE: 16-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Random Forest Regression based Water Quality Prediction for Smart Aquaculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127488)|P. Swetha; A. H. K. P. Rasheed; V. P. Harigovindan|10.1109/I3CS58314.2023.10127488|Aquaculture;Machine learning;Random forest regression;Water quality prediction.;Aquaculture;Machine learning;Random forest regression;Water quality prediction.|
|[Image Representation of Numerical Data-points for Classification Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127482)|R. K. Deka; K. P. Kalita; S. K. Chettri|10.1109/I3CS58314.2023.10127482|Feature Engineering;Classification;Convolution Neural Network;Feature Engineering;Classification;Convolution Neural Network|
|[Homomorphic Encryption based Federated Learning for Financial Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127502)|S. Dhiman; S. Nayak; G. K. Mahato; A. Ram; S. K. Chakraborty|10.1109/I3CS58314.2023.10127502|Homomorphic Encryption;Federated Learning;Privacy-preserving;Homomorphic Encryption;Federated Learning;Privacy-preserving|
|[A Transfer Learning Based Approach for Human Foot Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127506)|R. Kushwaha; G. Singal; N. Nain|10.1109/I3CS58314.2023.10127506|Footprint;Singularity;Transfer learning;Level 1 features;Minutiae.;Footprint;Singularity;Transfer learning;Level 1 features;Minutiae.|
|[BiLSTM with CRF Part-of-Speech Tagging for Khasi language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127278)|R. Hoojon; D. A. Nath|10.1109/I3CS58314.2023.10127278|Natural language processing;Khasi POS tagger;Bidirectional Long Short Term Memory;Conditional Random Field.;Natural language processing;Khasi POS tagger;Bidirectional Long Short Term Memory;Conditional Random Field.|
|[Design and Development of a Wearable Wristband for Measuring Pulse Transit Time (PTT) using Pressure Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127534)|M. Sukesh Rao; S. C. Bangera|10.1109/I3CS58314.2023.10127534|wrist pulse;pulse transit time;pressure sensor;pulse wave velocity;wrist pulse;pulse transit time;pressure sensor;pulse wave velocity|
|[Landslide susceptibility mapping using support vector machine for Meghalaya, India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127361)|M. Ado; K. Amitab|10.1109/I3CS58314.2023.10127361|landslide;susceptibility mapping;machine learning;support vector machine;Northeast India;landslide;susceptibility mapping;machine learning;support vector machine;Northeast India|
|[A lightweight semantic model for IoT architecture: Smart water meter usecase](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127428)|S. Z. Marshoodulla; G. Saha|10.1109/I3CS58314.2023.10127428|Internet of Things;Semantic Annotation;Ontologies;Smart Water Meters;Internet of Things;Semantic Annotation;Ontologies;Smart Water Meters|
|[Validating User-Centric Business Process based Service Composition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127225)|S. K. Mishra; A. Sarkar|10.1109/I3CS58314.2023.10127225|Business Process;Service Composition;Look-up Table;TGG Rule;Clinical Decision Support System;Validation;Business Process;Service Composition;Look-up Table;TGG Rule;Clinical Decision Support System;Validation|
|[A Blockchain-Assisted Authentication for SDN-IoT Network Using Smart Contract](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127386)|B. Bargayary; N. Medhi|10.1109/I3CS58314.2023.10127386|Authentication;Blockchain;Internet-of-Thing;Smart Contract;Software-Defined Networking;Token;Authentication;Blockchain;Internet-of-Thing;Smart Contract;Software-Defined Networking;Token|
|[Identification and Analysis of Assamese vowel speech signal using Formant Feature-Fusion and Feed-Forward Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10127265)|A. Sarmah; R. Rehman; P. Mahanta; K. Dutta|10.1109/I3CS58314.2023.10127265|Assamese vowel;Formant;Feature-Fusion;Feed-Forward Neural Network.;Assamese vowel;Formant;Feature-Fusion;Feed-Forward Neural Network.|
#### **2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)**
- DOI: 10.1109/NER52421.2023
- DATE: 24-27 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Screening of Mild Cognitive Impairment in Patients with Parkinson's Disease Using a Variational Mode Decomposition Based Deep-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123759)|M. Parajuli; A. W. Amara; M. Shaban|10.1109/NER52421.2023.10123759|Parkinson's Disease;Mild Cognitive Impairment;Variational Mode Decomposition;Deep Learning;Parkinson's Disease;Mild Cognitive Impairment;Variational Mode Decomposition;Deep Learning|
|[The Influence of Spatial Smoothing Kernel Size on ICA Model Order and Spatial Maps of Intrinsic Connectivity Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123835)|B. Jarrahi|10.1109/NER52421.2023.10123835|;|
|[Two-Photon Targeted, Quad Whole-Cell Patch-Clamping Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123815)|G. I. Vera Gonzalez; P. O. Kgwarae; S. R. Schultz|10.1109/NER52421.2023.10123815|patch-clamp;automation;two-photon imaging;neuroscience;neurotechnology;patch-clamp;automation;two-photon imaging;neuroscience;neurotechnology|
|[Long-Term Stable Electromyography Classification Using Canonical Correlation Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123768)|E. Donati; S. Benatti; E. Ceolini; G. Indiveri|10.1109/NER52421.2023.10123768|;|
|[Linear Feedback Control of Spreading Dynamics in Stochastic Nonlinear Network Models: Epileptic Seizures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123896)|M. SA; T. W|10.1109/NER52421.2023.10123896|;|
|[Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123881)|H. Agarwal; H. Rathore|10.1109/NER52421.2023.10123881|deep brain stimulators;neural oscillations;synchronization’ reinforcement learning;actor-critic;machine learning;deep brain stimulators;neural oscillations;synchronization’ reinforcement learning;actor-critic;machine learning|
|[Impacts of imagined lexical tone on Mandarin speech imagery BCI performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123911)|Z. Guo; H. Zhang; F. Chen|10.1109/NER52421.2023.10123911|brain computer interface;functional near-infrared spectroscopy;speech imagery;tone;brain computer interface;functional near-infrared spectroscopy;speech imagery;tone|
|[TUDAMatch: Time-Series Unsupervised Domain Adaptation for Automatic Sleep Staging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123742)|Y. Luo; Y. Zheng; H. Shao; L. Zhang; L. Li|10.1109/NER52421.2023.10123742|Automatic sleep staging;Unsupervised Domain Adaptation;Time-series Classification;Automatic sleep staging;Unsupervised Domain Adaptation;Time-series Classification|
|[Primate Motor Cortical Activity Displays Hallmarks of a Temporal Difference Reinforcement Learning Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123827)|V. S. A. Tarigoppula; J. S. Choi; J. P. Hessburg; D. B. McNiel; B. T. Marsh; J. T. Francis|10.1109/NER52421.2023.10123827|reinforcement learning;motor cortex;BMI;reward;mirror neuron;reinforcement learning;motor cortex;BMI;reward;mirror neuron|
|[Modulation of Intracortical S1 Responses Following Peripheral Nerve High-Frequency Electrical Stimulation in Danish Landrace Pigs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123841)|T. Al Muhammadee Janjua; T. G. N. dos Santos Nielsen; F. R. Andreis; S. Meijs; W. Jensen|10.1109/NER52421.2023.10123841|Long-term potentiation;LTP-like neuroplasticity;primary somatosensory cortex;animal model;Long-term potentiation;LTP-like neuroplasticity;primary somatosensory cortex;animal model|
|[Novel Neural Microprobe with Adjustable Stiffness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123721)|N. Sharafkhani; J. M. Long; S. D. Adams; A. Z. Kouzani|10.1109/NER52421.2023.10123721|neural microprobe;adjustable stiffness;insertion;neural tissue;brain micromotion;FEM;strain;two-photon polymerization;fabrication;neural microprobe;adjustable stiffness;insertion;neural tissue;brain micromotion;FEM;strain;two-photon polymerization;fabrication|
#### **2023 Optical Fiber Communications Conference and Exhibition (OFC)**
- DOI: 10.23919/OFC49934.2023
- DATE: 5-9 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Modal Gain Equalization of Few-mode Erbium-doped Fiber Amplifiers Enabled by Mirrored Mode Exchanges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117228)|T. Xu; Z. Yang; Y. Liu; Q. Guo; R. Zhou; X. Xiao; W. Li; W. Li; C. Du; Z. Huang; L. Zhang|10.23919/OFC49934.2023.10117228|;|
|[Power Efficient Core Pumped Multicore Erbium Doped Optical Fiber Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117250)|T. Ohtsuka; T. Kikuchi; T. Suganuma; T. Hasegawa; H. Tazawa|10.23919/OFC49934.2023.10117250|;|
|[Photonic-Lantern-Based MDM Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116116)|L. Grüner-Nielsen; N. M. Mathew; M. Galili; L. S. Rishøj; K. Rottwitt|10.23919/OFC49934.2023.10116116|;|
|[Polarization-Insensitive Isolators and Circulators on InP Photonics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117340)|Y. Jiao; R. Ma; S. Reniers|10.23919/OFC49934.2023.10117340|;|
|[Fully Passive Integrated-optic Chromatic Dispersion Compensator and its Use to PAM4 Signal Compensation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117357)|K. Takiguchi|10.23919/OFC49934.2023.10117357|;|
|[Free-standing, microscale, mode-selective photonic lantern supported by a truss structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116566)|Y. Dana; Y. Garcia; D. M. Marom|10.23919/OFC49934.2023.10116566|;|
|[Fabrication-tolerant, 2-mode, 4λ multiplexer based on Si waveguides for beyond Tbit/s optical Ethernet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117174)|T. Fujisawa; K. Saitoh|10.23919/OFC49934.2023.10117174|;|
|[Athermal Silicon Photonic Wavemeter with Wide Temperature Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116501)|B. Stern; K. Kim; H. Gariah; D. Bitauld|10.23919/OFC49934.2023.10116501|;|
|[64-channel fiber-optic ultrasound detector array with high sensitivity for photoacoustic imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117239)|A. Wang; L. Yang; D. Xu; G. Chen; C. Dai; Q. Sun|10.23919/OFC49934.2023.10117239|;|
|[Photonic Micro-Ring Tensor Core for Parallel and Shared Batch Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116494)|Y. Jiang; W. Zhang; X. Liu; Z. He|10.23919/OFC49934.2023.10116494|;|
|[Power Efficient Coherent Detection for Short-Reach System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117276)|H. Zhang|10.23919/OFC49934.2023.10117276|;|
#### **2023 IEEE Wireless and Microwave Technology Conference (WAMICON)**
- DOI: 10.1109/WAMICON57636.2023
- DATE: 17-18 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Co-Design of Doherty Power Amplifier and Post-Matching Bandpass Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124924)|H. Lyu; R. Lovato; S. P. Gowri; X. Gong; K. Chen|10.1109/WAMICON57636.2023.10124924|Co-design;Doherty;filter;high efficiency;load modulation;matching;power amplifier;Co-design;Doherty;filter;high efficiency;load modulation;matching;power amplifier|
|[Load-Modulated Double-Balanced Amplifier with Quasi-Isolation to Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124922)|J. Guo; K. Chen|10.1109/WAMICON57636.2023.10124922|Balanced amplifier;high efficiency;linear;load modulation;mismatch;power amplifier;PAPR;wideband;Balanced amplifier;high efficiency;linear;load modulation;mismatch;power amplifier;PAPR;wideband|
|[A High-IP3 Harmonic Tuned Wideband 40 dBm RF Power Amplifier for 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124915)|H. Song; T. Rashid|10.1109/WAMICON57636.2023.10124915|class-AB;OIP3;RF power amplifier;5G communication;broadband;high efficiency;gallium nitride device;high electron mobility transistor;class-AB;OIP3;RF power amplifier;5G communication;broadband;high efficiency;gallium nitride device;high electron mobility transistor|
|[Dynamic Supply Modulation of a 6 – 12 GHz Transmit Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124905)|C. Nogales; L. Marzall; G. Lasser; Z. Popović|10.1109/WAMICON57636.2023.10124905|Array;Envelope Tracking;GaN;Power Amplifiers;Supply Modulation;Array;Envelope Tracking;GaN;Power Amplifiers;Supply Modulation|
|[A Dual-Channel 15 Gb/s PRBS Generator for a D-Band PMCW Radar Transmitter in 22 nm FDSOI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124888)|F. Probst; A. Engelmann; M. Koch; R. Weigel|10.1109/WAMICON57636.2023.10124888|Fully-depleted silicon on insulator (FDSOI);MIMO radar;phase-modulated continuous-wave (PMCW);joint radar-communication (RadCom);true single-phase-clock (TSPC);Fully-depleted silicon on insulator (FDSOI);MIMO radar;phase-modulated continuous-wave (PMCW);joint radar-communication (RadCom);true single-phase-clock (TSPC)|
|[A Fully-Integrated Band-Switchable CMOS Low Noise Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124921)|S. B. Hamidi; D. Dawn|10.1109/WAMICON57636.2023.10124921|fully-integrated;CMOS low noise amplifier;frequency band;impedance matching;band-switchable;and noise figure;fully-integrated;CMOS low noise amplifier;frequency band;impedance matching;band-switchable;and noise figure|
|[Sub-6 GHz GaAs pHEMT SPDT Switch with Low Insertion Loss and High Power Handling Capability Using Dual-Gate Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124891)|J. Kwon; J. Yoo; J. Lee; T. Kim; C. Park|10.1109/WAMICON57636.2023.10124891|Dual-gate;GaAs;pseudo high electron mobility transistor (pHEMT);RF switch;single-pole double-throw (SPDT);Dual-gate;GaAs;pseudo high electron mobility transistor (pHEMT);RF switch;single-pole double-throw (SPDT)|
|[A Miniaturized UHF RFID Tag Antenna Attached to a Container of Filled Liquid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124920)|M. -T. Nguyen; H. -M. Chen; C. -H. Chen; Y. -F. Lin|10.1109/WAMICON57636.2023.10124920|miniaturized antenna;UHF RFID Tag;container of full liquid;read range;equivalent circuit model;miniaturized antenna;UHF RFID Tag;container of full liquid;read range;equivalent circuit model|
|[Computation of Input Impedance of Rectangular Waveguide-backed Metasurface Arrays with Feed Networks using Coupled Dipole Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124892)|I. Yoo; D. Smith|10.1109/WAMICON57636.2023.10124892|Metasurfaces;Leaky wave antennas;Aperture antenna;Metasurfaces;Leaky wave antennas;Aperture antenna|
|[Advanced Cascaded Filter Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124917)|W. Fathelbab|10.1109/WAMICON57636.2023.10124917|cascaded trisections;cascaded quadruplets;cascaded fivetuplets;cascaded sixptuplets;finite frequency transmission zeros;filter synthesis;lowpass networks;mixed coupling;single coupling;cascaded trisections;cascaded quadruplets;cascaded fivetuplets;cascaded sixptuplets;finite frequency transmission zeros;filter synthesis;lowpass networks;mixed coupling;single coupling|
|[A New Axial Mode Helix Antenna: The Archimedean Screw Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124885)|F. E. Parsche|10.1109/WAMICON57636.2023.10124885|Archimedean screw;screw;auger;axial mode helix antenna;wire helix;beam;circular polarization;screen compliment;Babinet’s Principle;slot equivalent;Archimedean screw;screw;auger;axial mode helix antenna;wire helix;beam;circular polarization;screen compliment;Babinet’s Principle;slot equivalent|
#### **2023 Data Compression Conference (DCC)**
- DOI: 10.1109/DCC55655.2023
- DATE: 21-24 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Video Transformer based Video Quality Assessment with Spatiotemporally adaptive Token Selection and Assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125315)|S. Zhao; H. Yin; H. Wang; Y. Zhou|10.1109/DCC55655.2023.00008|;|
|[Gradient Linear Model for Chroma Intra Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125351)|C. -W. Kuo; X. Li; X. Xiu; H. -J. Jhu; N. Yan; X. Wang; Y. Ye; J. Chen; R. -L. Liao|10.1109/DCC55655.2023.00009|video coding;intra prediction;Versatile Video Coding (VVC);enhanced compression model (ECM);cross-component linear model (CCLM);video coding;intra prediction;Versatile Video Coding (VVC);enhanced compression model (ECM);cross-component linear model (CCLM)|
|[Decoder-side Chroma Intra Mode Derivation in Video Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125421)|X. Li; R. -L. Liao; J. Chen; Y. Ye|10.1109/DCC55655.2023.00010|;|
|[An Improvement to Merge Mode in ECM With Template Matching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125377)|R. -L. Liao; Y. Ye; J. Chen; X. Li|10.1109/DCC55655.2023.00011|Enhanced compression model;Template matching;temporal motion vector predictor;bi prediction with CU level weight;geometric partition mode;Enhanced compression model;Template matching;temporal motion vector predictor;bi prediction with CU level weight;geometric partition mode|
|[Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125297)|E. Özyılkan; M. Ulhaq; H. Choi; F. Racapé|10.1109/DCC55655.2023.00012|;|
|[Model Compression for Data Compression: Neural Network Based Lossless Compressor Made Practical](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125489)|L. Qin; J. Sun|10.1109/DCC55655.2023.00013|lossless compression;model compression;lasso method;lossless compression;model compression;lasso method|
|[Recursive Prefix-Free Parsing for Building Big BWTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125303)|M. Oliva; T. Gagie; C. Boucher|10.1109/DCC55655.2023.00014|;|
|[Computing the optimal BWT of very large string collections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125457)|D. Cenzato; V. Guerrini; Z. Lipták; G. Rosone|10.1109/DCC55655.2023.00015|Burrows Wheeler Transform;optimal BWT;string collections;number of runs;data compression;repetitiveness measures;Burrows Wheeler Transform;optimal BWT;string collections;number of runs;data compression;repetitiveness measures|
|[Bit-Parallel (Compressed) Wavelet Tree Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125471)|P. Dinklage; J. Fischer; F. Kurpicz; J. -P. Tarnowski|10.1109/DCC55655.2023.00016|wavelet tree;huffman;data parallelism;wavelet tree;huffman;data parallelism|
|[A Spatial-Focal Error Concealment Scheme for Corrupted Focal Stack Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125525)|K. Wu; Y. Wang; W. Liu; K. -H. Yap; L. -P. Chau|10.1109/DCC55655.2023.00017|;|
|[An Adaptive Intra-frame Quantization Parameter Derivation Model Jointing with Inter-frame Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10125355)|H. Ren; S. Wang; S. Ma; W. Gao|10.1109/DCC55655.2023.00018|Video Compression;Rate Control;Quantization;Video Compression;Rate Control;Quantization|
#### **2023 International Interdisciplinary PhD Workshop (IIPhDW)**
- DOI: 10.1109/IIPhDW54739.2023
- DATE: 3-5 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[SVD-Assisted Joint Pre- and Post-Equalization in Optical MIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124406)|J. Singh; A. Ahrens; S. Lochmann; C. B. Peces|10.1109/IIPhDW54739.2023.10124406|MIMO system;multi-mode fiber communication;SVD;PPE;MIMO system;multi-mode fiber communication;SVD;PPE|
|[Exploratory Study of the Gender Equity in the North African AEC Industry and Academia: Ratios, Causes and Remedial Actions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124425)|H. Bouhmoud; D. Loudyi; A. Giordano; S. Azhar; M. Farah|10.1109/IIPhDW54739.2023.10124425|gender equality;women in construction;female workforce;women inclusion;women in the AEC industry;SDG5;Sustainable Development Goal;Africa;gender equality;women in construction;female workforce;women inclusion;women in the AEC industry;SDG5;Sustainable Development Goal;Africa|
|[Improvement of Electrical Tomographic Imaging of Moisture by Mixing Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124422)|G. Kłsowski; T. Rymarczyk; K. Niderla|10.1109/IIPhDW54739.2023.10124422|moisture inspection;electrical tomography;machine learning;neural networks;linear regression;model optimization;moisture inspection;electrical tomography;machine learning;neural networks;linear regression;model optimization|
|[Learning Experience Platforms in German and Lithuanian K12 Schools: Case Study Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124407)|J. Melnikova; A. Batuchina; J. Zascerinska; A. Ahrens|10.1109/IIPhDW54739.2023.10124407|education digitalization;learning experience platforms;case study in K12 schools in Germany and Lithuania;education digitalization;learning experience platforms;case study in K12 schools in Germany and Lithuania|
|[Field Test on Energy Flows in Residential Buildings with PV Systems, Heat Pump Based Heating and Battery Electric Car Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124432)|A. Wego|10.1109/IIPhDW54739.2023.10124432|energy flow;photovoltaic;heat pump;electric vehicle;energy flow;photovoltaic;heat pump;electric vehicle|
|[Field Test on Seasonal Behavior of an Electric Vehicle in Everyday Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124430)|A. Wego|10.1109/IIPhDW54739.2023.10124430|electric vehicle;battery degradation;seasonal consumption;charging losses;e-mobility;electric vehicle;battery degradation;seasonal consumption;charging losses;e-mobility|
|[STEM Education: A Comparative Study of Platforms in Selected Countries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124402)|J. Zascerinska; S. Emet; S. Usca; A. Bikova|10.1109/IIPhDW54739.2023.10124402|STEM (Science, Technology, Engineering, Mathematics) education;STEM platform;STEM educational content;learning data analytics;OER (Open Educational Resources);STEM (Science, Technology, Engineering, Mathematics) education;STEM platform;STEM educational content;learning data analytics;OER (Open Educational Resources)|
|[Evaluation of Traffic Burstiness Using Gap-Based Microscopic Modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124418)|A. Ahrens; C. Benavente-Peces; J. Zaščerinska; J. Melnikova; O. Purvinis|10.1109/IIPhDW54739.2023.10124418|car traffic flow burstiness;car traffic flow bottlenecks;customers' waiting time;gap distribution function;car traffic flow burstiness;car traffic flow bottlenecks;customers' waiting time;gap distribution function|
|[Enhancement of PCA-Based Dimensionality Reduction Using BB-BC Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124414)|S. Kaur; C. R. Krishna; S. Kumar; J. Singh|10.1109/IIPhDW54739.2023.10124414|PCA;BB-BC evolution theory;soft computing;PCA;BB-BC evolution theory;soft computing|
|[Functional Architecture for Solution Independent Realizations of Digital Continuity in System Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124410)|M. Manoury|10.1109/IIPhDW54739.2023.10124410|mechanical engineering;system creation;digital thread;digital continuity;ARCADIA;mechanical engineering;system creation;digital thread;digital continuity;ARCADIA|
|[Applying Logistic Regression with Elastic Net and PCA to Determine the Objects Location in EIT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124419)|K. Król; T. Rymarczyk; E. Kozłowski; K. Niderla|10.1109/IIPhDW54739.2023.10124419|logistic regression;tomography;elastic net;logistic regression;tomography;elastic net|
#### **2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)**
- DOI: 10.1109/SANER56733.2023
- DATE: 21-24 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[An Empirical Study of Smart Contract Decompilers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123564)|X. Liu; B. Hua; Y. Wang; Z. Pan|10.1109/SANER56733.2023.00011|Empirical study;Smart contracts;Decompilation;Empirical study;Smart contracts;Decompilation|
|[Understanding the Archived Projects on GitHub](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123670)|X. Xia; S. Zhao; X. Zhang; Z. Lou; W. Wang; F. Bi|10.1109/SANER56733.2023.00012|Open Source Governance;Software Evolution;Software Deprecation;Open Source Governance;Software Evolution;Software Deprecation|
|[Towards Understanding the Impacts of Textual Dissimilarity on Duplicate Bug Report Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123457)|S. Jahan; M. M. Rahman|10.1109/SANER56733.2023.00013|Software bug;duplicate bug detection;textual dissimilarity;word embedding;t-SNE;Software bug;duplicate bug detection;textual dissimilarity;word embedding;t-SNE|
|[A Hierarchical Topical Modeling Approach for Recommending Repair of Quality Bugs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123610)|R. Krasniqi; H. Do|10.1109/SANER56733.2023.00014|Quality Concerns;Hierarchical Topic Modeling;Quality Bugs;Quality Concerns;Hierarchical Topic Modeling;Quality Bugs|
|[Solder: Retrofitting Legacy Code with Cross-Language Patches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123487)|R. Williams; A. Gavazzi; E. Kirda|10.1109/SANER56733.2023.00015|;|
|[Pseudocode to Code Based on Adaptive Global and Local Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123466)|Q. Yu; Z. Huang; N. Gu|10.1109/SANER56733.2023.00016|Code generation;Program repair;Machine learning;Software engineering;Program comprehension;Code generation;Program repair;Machine learning;Software engineering;Program comprehension|
|[Identifying Emergent Leadership in Open Source Software Projects Based on Communication Styles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123616)|Y. Huang; Y. Yang; J. Wang; W. Zheng; Q. Wang|10.1109/SANER56733.2023.00017|Leadership;Communication style;Linguistic pattern;Open source software;Leadership;Communication style;Linguistic pattern;Open source software|
|[NeuralCCD: Integrating Multiple Features for Neural Coincidental Correctness Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123558)|Z. Tao; Y. Lei; H. Xie; J. Hu|10.1109/SANER56733.2023.00018|fault localization;coincidental correctness;deep learning;multiple features;fault localization;coincidental correctness;deep learning;multiple features|
|[Studying and Complementing the Use of Identifiers in Logs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123657)|J. Zhao; Y. Tang; S. Sunil; W. Shang|10.1109/SANER56733.2023.00019|Log Mining;Software Artifacts;Log Mining;Software Artifacts|
|[Automatic Identification of Crash-inducing Smart Contracts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123502)|C. Ni; C. Tian; K. Yang; D. Lo; J. Chen; X. Yang|10.1109/SANER56733.2023.00020|Crash-inducing Smart Contract;Static Source Code Metric;Quality Assurance;Ethereum;Machine Learning;Crash-inducing Smart Contract;Static Source Code Metric;Quality Assurance;Ethereum;Machine Learning|
|[MulCS: Towards a Unified Deep Representation for Multilingual Code Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123512)|Y. Ma; Y. Yu; S. Li; Z. Jia; J. Ma; R. Xu; W. Dong; X. Liao|10.1109/SANER56733.2023.00021|Code search;multi-language;contrastive learning;intermediate representation;Code search;multi-language;contrastive learning;intermediate representation|
#### **2023 International Conference on Business Analytics for Technology and Security (ICBATS)**
- DOI: 10.1109/ICBATS57792.2023
- DATE: 7-8 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Rock-Paper-Scissors Image Classification Using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111433)|F. Ahmed; W. A. Khan; M. Iqbal; A. R. Ahmad Abazeed; H. Alrababah; M. F. Khan|10.1109/ICBATS57792.2023.10111433|Transfer learning (TL);AlexNet;Rock-Paper-Scissors;Transfer learning (TL);AlexNet;Rock-Paper-Scissors|
|[Risk-Informed Target Set Analysis of Nuclear Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111450)|A. Alkaabi; C. Yeun|10.1109/ICBATS57792.2023.10111450|nuclear;safety;risk;plant;target set analysis;surveillance;nuclear;safety;risk;plant;target set analysis;surveillance|
|[Deep Learning and Industrial Internet of Things to Improve Smart City Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111164)|U. Asad; A. S. Mohammed|10.1109/ICBATS57792.2023.10111164|Industrial Internet of Things;deep learning;Smart City;Cyber Attack;Convolution Neural Network;Industrial Internet of Things;deep learning;Smart City;Cyber Attack;Convolution Neural Network|
|[Analysis of Query Processing on Different Databases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111417)|M. Salahat; M. A. Raza; Z. Khawar; J. A. Malik; H. Raza; H. K. G. Nair; M. Jarrah|10.1109/ICBATS57792.2023.10111417|Database Management System;Query processing;style;Database Management System;Query processing;style|
|[Goldonomics: Cryptocurrency vs. Gold; Which is a Better Store of Value in the Global](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111385)|P. Jain; A. Haddad; C. Paramaiah|10.1109/ICBATS57792.2023.10111385|Goldonomics;Bitcoin;Gold;Safe haven;Investment;Store of Value;Hedge;Goldonomics;Bitcoin;Gold;Safe haven;Investment;Store of Value;Hedge|
|[The Impact of Implementing Mind Mapping Technology on Raising the Achievement of Curriculum Processing Unit in Teaching Methodologies & Styles Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111107)|M. Altarawneh; T. Al-Momani; M. Al-Dlalah; T. Kaddumi|10.1109/ICBATS57792.2023.10111107|Achievement;Curricula;Mind Mapping;Teaching Methods;Achievement;Curricula;Mind Mapping;Teaching Methods|
|[Wormhole Attack Detection Technques In MANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111269)|M. Bashir; S. Tahir; M. F. Almufareh; B. Hamid; F. Qamar|10.1109/ICBATS57792.2023.10111269|MANET;Wormhole;detection techniques;dynamic;infrastructure;SVM;GA;MANET;Wormhole;detection techniques;dynamic;infrastructure;SVM;GA|
|[The Nexus between Digital Innovation and Digital Entrepreneurship in the Strategic Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111410)|K. Tangri; D. Kalra; P. Katuse; T. A. Masaeid; M. A. Afifi|10.1109/ICBATS57792.2023.10111410|digital innovation;digital entrepreneurship;digital transformation;digital innovation;digital entrepreneurship;digital transformation|
|[Agent-Based Modelling and Simulation of Crowd Evacuation: Case Study for Electric Train Cabin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111239)|S. Maghaydah; P. Maheshwari; K. M. Alomari|10.1109/ICBATS57792.2023.10111239|agent-based simulation;Netlogo;High-speed trains;Evacuation procedures;Egress modelling;agent-based simulation;Netlogo;High-speed trains;Evacuation procedures;Egress modelling|
|[Digital Platforms’ Influence on Project Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111200)|M. E. Khatib; M. AlQurashi; S. AlHashemi; M. AlKetbi; S. AlHarmoodi|10.1109/ICBATS57792.2023.10111200|Digital Platforms;UAE;Governmental Entities;Project Management;Strengths;Weaknesses;Performance;Digital Platforms;UAE;Governmental Entities;Project Management;Strengths;Weaknesses;Performance|
|[Impact of Open Big Data and Insurtech on Business Digitalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111209)|Z. Saidat; H. J. Abdelrahim; D. Ahmad Alkhodary; G. Qasaimeh|10.1109/ICBATS57792.2023.10111209|big data;insurtech;business digitalization;big data;insurtech;business digitalization|
#### **2023 6th International Conference on Communication Engineering and Technology (ICCET)**
- DOI: 10.1109/ICCET58756.2023
- DATE: 24-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Low-Overhead Frame Structure Design for Short Frame Burst OFDM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123669)|W. Dang; L. Zhu; Y. Han; H. Hu; Y. Cai; M. Yang|10.1109/ICCET58756.2023.00007|low-overhead;short frame burst OFDM;frame structure;AGC;timing synchronization;frequency offset estimation;average data transmission rate;low-overhead;short frame burst OFDM;frame structure;AGC;timing synchronization;frequency offset estimation;average data transmission rate|
|[An Improved Interleaver Identification Algorithm Based on Conformity of Parity-check Equation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123581)|M. Liu; Z. Chen; Z. Sun; H. Yi|10.1109/ICCET58756.2023.00008|low signal-to-noise ratio;interleaver identification;verification equation;low signal-to-noise ratio;interleaver identification;verification equation|
|[Low Redundancy Two-Dimensional Matrix-Based HVDB Code for Double Error Correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123509)|Y. Hou; X. Liu; L. Tang; S. Zhong; H. Luo|10.1109/ICCET58756.2023.00009|multiple cell upset (MCU);error correcting code (ECC);horizontal-vertical-diagonal-block (HVDB) code;matrix-based code;multiple cell upset (MCU);error correcting code (ECC);horizontal-vertical-diagonal-block (HVDB) code;matrix-based code|
|[Distribution-Matching Stack Object Counting Based on Depth Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123666)|Y. Zhao; X. Gong; Y. Wang; T. Liang|10.1109/ICCET58756.2023.00010|deep learning;stacked object counting;monocular depth matrix;density map;deep learning;stacked object counting;monocular depth matrix;density map|
|[UVM-Based Verification of OFDM Baseband Processing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123464)|C. Zhao; J. Yang; J. Tian; X. Deng; Y. Cai; M. Yang|10.1109/ICCET58756.2023.00011|UVM;functional coverage;OFDM;verification;UVM;functional coverage;OFDM;verification|
|[Low-Power Heterogeneous Networking Method Based on NB-IoT and WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123592)|M. Guan; L. Qi; X. Jin; S. Guan; C. Xia; C. Xu|10.1109/ICCET58756.2023.00012|narrowband internet of things (NB-IoT);wireless sensor network (WSN);low-power;heterogeneous networking;narrowband internet of things (NB-IoT);wireless sensor network (WSN);low-power;heterogeneous networking|
|[Design of multi-Sensor Cooperative Detection Architecture Based on Analytic Hierarchy Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123498)|K. Li; X. Cai; J. Liu; S. Huang; Y. Chen; W. Wang; Y. Yu; Q. Zhu; Z. Xu; D. Wang|10.1109/ICCET58756.2023.00013|multiple sensors;collaborative detection;analytic hierarchy process;multiple sensors;collaborative detection;analytic hierarchy process|
|[Intelligent Situation Sensing and Multi-dimensional Presentation for Power Emergency Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123659)|R. Li; X. Yang; H. Yang; Y. Wang; S. Che; F. Liu; Z. He; H. Liu; S. Wang|10.1109/ICCET58756.2023.00014|Power emergency communications;situation sensing;multi-dimensional presentation;federated learning;multi-head attention mechanism;tensor theory.;Power emergency communications;situation sensing;multi-dimensional presentation;federated learning;multi-head attention mechanism;tensor theory.|
|[Achievable Rate Performance Analysis for Massive MIMO Relay Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123458)|L. Guo; M. Guan; C. Sun; Y. Wang|10.1109/ICCET58756.2023.00015|massive MIMO relay transmission;zero-forcing (ZF);achievable rate performance;massive MIMO relay transmission;zero-forcing (ZF);achievable rate performance|
|[Pe-OTN/OSU Based multi-Service Bearing Scheme and Evolution Strategy for Electric Power Backbone Transmission Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123682)|X. Zhao; Y. Xu; R. Tang; F. Pan|10.1109/ICCET58756.2023.00016|OSU;Pe-OTN;power backbone transmission network;power communication network;network evolution;OSU;Pe-OTN;power backbone transmission network;power communication network;network evolution|
|[Optimization of Resource Allocation Algorithm for Visible Light and WiFi Heterogeneous Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123595)|L. Yang; X. Zheng; X. Ma; Y. Tian|10.1109/ICCET58756.2023.00017|VLC;WiFi;resource allocation;scheduling algorithm;VLC;WiFi;resource allocation;scheduling algorithm|
#### **2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)**
- DOI: 10.1109/ICAECT57570.2023
- DATE: 5-6 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Bengali Word Identification and Verification Using Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117745)|S. Hasan; M. A. Nadif; N. Bin Rahman; M. Rana|10.1109/ICAECT57570.2023.10117745|Image classification;Machine learning;Artificial intelligence;Neural Network;Image classification;Machine learning;Artificial intelligence;Neural Network|
|[A framework for big data adoption and sustainable institutional performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117717)|S. Jayashree; M. N. Hassan Reza; C. A. Malarvizhi; M. B. Alias; M. Mohiuddin|10.1109/ICAECT57570.2023.10117717|big data adoption;sustainable education;TOE framework;Technological infrastructure;Management support;Financial resources;External support;big data adoption;sustainable education;TOE framework;Technological infrastructure;Management support;Financial resources;External support|
|[Conceptual Design of an Automated Irrigation and Fixation System for Long-Term Crop Care of Cherry Tomatoes for Applications in Microgravity Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117929)|R. Barreto; J. Cornejo; J. C. Suárez Quispe; C. N. Ochoa; D. O. Tacuri|10.1109/ICAECT57570.2023.10117929|microgravity;automation;irrigation;greenhouse;cherry tomatoes;clinostat;microgravity;automation;irrigation;greenhouse;cherry tomatoes;clinostat|
|[Comparative study of Deep Learning Models for Covid 19 Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118260)|J. V; S. N|10.1109/ICAECT57570.2023.10118260|Covid 19;CT scan;Chest X-rays Deep Neural Networks;Covid 19;CT scan;Chest X-rays Deep Neural Networks|
|[Random Forest Regressor Approach for Predicting Resale Value of used Vehicles (RFRVP)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118285)|M. Rane; M. Patil; N. Rane; A. Amune|10.1109/ICAECT57570.2023.10118285|Machine Learning;Supervised Learning;Old car Prediction;Linear Regression;Random Forest;Machine Learning;Supervised Learning;Old car Prediction;Linear Regression;Random Forest|
|[IMPROVED DOUBLE-LEVEL BLENDING MODEL (IDLBM): CUSTOMER CHURN ESTIMATING IN INSURANCE INDUSTRY](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118051)|N. Jajam; N. P. Challa; K. Prasanna|10.1109/ICAECT57570.2023.10118051|Insurance sector customers;loss prediction;Blending model;Customer Churn;IDLBM;Insurance sector customers;loss prediction;Blending model;Customer Churn;IDLBM|
|[A Comparative Survey of Maize Leaf Diseases using Pre-Trained Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117735)|K. Kanchanadevi; G. K. Sandhia|10.1109/ICAECT57570.2023.10117735|Maize leaf disease detection;Transfer learning;VGG19Net;ResNet152;InceptionV3Net;MobileNetV2;DenseNet201;NASNetLarge;Maize leaf disease detection;Transfer learning;VGG19Net;ResNet152;InceptionV3Net;MobileNetV2;DenseNet201;NASNetLarge|
|[A New Hybrid Model ARFIMA-LSTM Combined with News Sentiment Analysis Model for Stock Market Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118349)|Satyaveer; P. Patel; H. Chandra; P. Pal; S. K. Singh|10.1109/ICAECT57570.2023.10118349|ARFIMA-LSTM;NLP;STOCK MARKET FORECASTING;RNN;RMSE;MSE;MAPE;AI;ANN;ARFIMA-LSTM;NLP;STOCK MARKET FORECASTING;RNN;RMSE;MSE;MAPE;AI;ANN|
|[Chromatic Partitioning of a Network and an Optimum Scheduling Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117620)|M. Sony; R. Manjusha|10.1109/ICAECT57570.2023.10117620|path graph;vertex coloring;chromatic polynomial;portioning;scheduling;path graph;vertex coloring;chromatic polynomial;portioning;scheduling|
|[Comparison of Performance of Machine Learning Algorithms for Diabetes Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118315)|P. Dalve; D. Bobby; A. Marathe; A. Dusane; S. Daga|10.1109/ICAECT57570.2023.10118315|Diabetes detection;KNN;Machine learning;Random Forest;Support Vector Machine;XGBoost;Diabetes detection;KNN;Machine learning;Random Forest;Support Vector Machine;XGBoost|
|[Bi-LSTM-LDA— A Topic Modelling Technique to Identify the Relevant Law for Indian Legal Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118275)|N. Sivaranjani; J. Jayabharathy|10.1109/ICAECT57570.2023.10118275|Latent Dirichlet Allocation (LDA);Bi-LSTM;Neural topic models;probabilistic topic models;Indian legal cases;Latent Dirichlet Allocation (LDA);Bi-LSTM;Neural topic models;probabilistic topic models;Indian legal cases|
#### **2023 IEEE International Conference on Soft Robotics (RoboSoft)**
- DOI: 10.1109/RoboSoft55895.2023
- DATE: 3-7 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Mechanical Modeling and Optimal Model-based Design of a Soft Pneumatic Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122083)|W. -T. Yang; H. S. Stuart; M. Tomizuka|10.1109/RoboSoft55895.2023.10122083|;|
|[Design of 3D-Printed Continuum Robots Using Topology Optimized Compliant Joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121935)|Y. Sun; T. C. Lueth|10.1109/RoboSoft55895.2023.10121935|;|
|[Effects of Compliance on Path-Tracking Performance of a Miniature Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122013)|M. Uğur; B. Arslan; A. Özzeybek; O. Özcan|10.1109/RoboSoft55895.2023.10122013|Modeling;Control;Learning for Soft Robots;Cellular and Modular Robots;Legged Robots;Modeling;Control;Learning for Soft Robots;Cellular and Modular Robots;Legged Robots|
|[A Vacuum-Powered Soft Mesh Gripper for Compliant and Effective Grasping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122056)|L. Xinquan; W. Yuzhe; X. Zhen; S. M. Ocak|10.1109/RoboSoft55895.2023.10122056|;|
|[One-Piece 3D-Printed Legs Using Compliant Mechanisms That Produce Effective Propulsive Force for Hexapod Robot Locomotion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121937)|A. Kaneko; D. Owaki; M. Shimizu; T. Umedachi|10.1109/RoboSoft55895.2023.10121937|;|
|[Kinematic-Model-Free Tip Position Control of Reconfigurable and Growing Soft Continuum Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121994)|A. AlAttar; I. B. Hmida; F. Renda; P. Kormushev|10.1109/RoboSoft55895.2023.10121994|;|
|[DragonClaw: A low-cost pneumatic gripper with integrated magnetic sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122024)|V. H. Sundaram; R. Bhirangi; M. E. Rentschler; A. Gupta; T. Hellebrekers|10.1109/RoboSoft55895.2023.10122024|;|
|[A Sodium Azide-Powered Free-Piston Gas Compressor for Mobile Pneumatic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122095)|R. H. Heisser; T. Sribhibhadh; S. Adelmund; K. Shimasaki; N. Usevitch; A. H. Memar; A. Amini; A. A. Stanley|10.1109/RoboSoft55895.2023.10122095|;|
|[Design of a Soft Bio-Inspired Tissue Transport Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121942)|V. G. Kortman; J. Jovanova; A. Sakes|10.1109/RoboSoft55895.2023.10121942|;|
|[EDAMS: An Encoder-Decoder Architecture for Multi-grasp Soft Sensing Object Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121962)|O. Shorthose; A. Albini; L. Scimeca; L. He; P. Maiolino|10.1109/RoboSoft55895.2023.10121962|Soft robotic hands;object identification;tactile sensing;Soft robotic hands;object identification;tactile sensing|
|[Measuring a Soft Resistive Strain Sensor Array by Solving the Resistor Network Inverse Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121960)|Y. Zhao; C. K. Khaw; Y. Wang|10.1109/RoboSoft55895.2023.10121960|;|
#### **2023 11th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES)**
- DOI: 10.1109/MSCPES58582.2023
- DATE: 9-9 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[SynTiSeD – Synthetic Time Series Data Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123429)|M. Meiser; B. Duppe; I. Zinnikus|10.1109/MSCPES58582.2023.10123429|Smart Living;Smart Meter;Synthetic Sensor Data;Energy Data;Simulation;Agents;NILM;Seq2Point;win-dowGRU;Machine Learning;Smart Living;Smart Meter;Synthetic Sensor Data;Energy Data;Simulation;Agents;NILM;Seq2Point;win-dowGRU;Machine Learning|
|[CPGrid-OT: Cyber-Power Data Generation Using Real-Time Reconfigurable Testbed for Resiliency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123420)|H. M. Mustafa; S. Basumallik; S. Kidder; A. Srivastava|10.1109/MSCPES58582.2023.10123420|C37.118;cyber attack;hardware-in-the-loop;real-time cyber-power data generation;C37.118;cyber attack;hardware-in-the-loop;real-time cyber-power data generation|
|[Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123428)|B. Hyder; A. Ahmed; P. Mana; T. Edgar; S. Niddodi|10.1109/MSCPES58582.2023.10123428|Machine Learning;Anomaly Detection System;Datasets;Co-simulation;Machine Learning;Anomaly Detection System;Datasets;Co-simulation|
|[Developing a Campus Microgrid Model utilizing Modelica and the OpenIPSL Library](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123421)|F. Fachini; A. Pigott; G. Laera; T. Bogodorova; L. Vanfretti; K. Baker|10.1109/MSCPES58582.2023.10123421|Microgrid;Phasor Modeling;Simulation;Modelica;OpenIPSL;Power Systems;Microgrid;Phasor Modeling;Simulation;Modelica;OpenIPSL;Power Systems|
|[Multi-domain Modeling of a Steam Power Plant with Power Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123433)|K. Warns; A. Ullah Amin Shah; M. Aguilera; J. Kim; L. Vanfretti; H. Gook Kang|10.1109/MSCPES58582.2023.10123433|steam turbine modeling;Modelica;multi-domain modeling;OpenIPSL;Thermal Power;nuclear power plant;steam turbine modeling;Modelica;multi-domain modeling;OpenIPSL;Thermal Power;nuclear power plant|
|[Comparing Thermal Library Modeling Suites for Integrated Modeling of Nuclear Power Plant and Power Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123419)|A. Ullah Amin Shah; K. Warns; J. Kim; M. Aguilera; L. Vanfretti; H. Gook Kang|10.1109/MSCPES58582.2023.10123419|flexible operation;balance of plant;electrical grid;Modelica;ThermalPower;TRANSFORM;ThermoPower;OpenIPSL;flexible operation;balance of plant;electrical grid;Modelica;ThermalPower;TRANSFORM;ThermoPower;OpenIPSL|
|[Towards a more comprehensive open-source model for interdisciplinary smart integrated energy systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123432)|B. Wiegel; T. Steffen; D. Babazadeh; C. Becker|10.1109/MSCPES58582.2023.10123432|energy system modeling;integrated energy system;multi-modal;open-source;database;Modelica;energy system modeling;integrated energy system;multi-modal;open-source;database;Modelica|
|[Distributed algorithm for simulating dynamic interactions within a general cyber-physical system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123434)|M. Ilić; M. Kosanić|10.1109/MSCPES58582.2023.10123434|Energy dynamics;structure of cyber-physical systems (CPS);distributed algorithms;DC microgrids with constant power load;generalised reactive power in time domain;composition of complex algorithms;Energy dynamics;structure of cyber-physical systems (CPS);distributed algorithms;DC microgrids with constant power load;generalised reactive power in time domain;composition of complex algorithms|
|[Modelling and Eigenanalysis of Sub-synchronous Oscillations Excited by Large Wind Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123424)|C. van Vledder; J. R. Torres; A. Stefanov; P. Palensky; O. Anaya-Lara; B. Kruimer; F. Gonzalez-Longatt|10.1109/MSCPES58582.2023.10123424|Sub-synchronous oscillations;observability;phasor measurement units;control interaction;wide area monitoring and control;Sub-synchronous oscillations;observability;phasor measurement units;control interaction;wide area monitoring and control|
|[EVs and ERCOT: Foundations for Modeling Future Adoption Scenarios and Grid Implications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123430)|K. Nelson; P. Moura; J. Mohammadi|10.1109/MSCPES58582.2023.10123430|Electric Vehicles;GHG Emissions;Charging Profiles;Electrical Grid Impact;EV Tax Credits;EV modeling;Electric Vehicles;GHG Emissions;Charging Profiles;Electrical Grid Impact;EV Tax Credits;EV modeling|
|[Graph Theoretic Approach for Decentralized Control Architecture of Cyber Physical Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123423)|H. K. R. M; V. V|10.1109/MSCPES58582.2023.10123423|Centralized control;cyber physical systems;decentralized control architecture;electricity network;cyber network;smart grid;Centralized control;cyber physical systems;decentralized control architecture;electricity network;cyber network;smart grid|
#### **2023 IEEE Aerospace Conference**
- DOI: 10.1109/AERO55745.2023
- DATE: 4-11 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Aircraft Conceptualization and Analysis by Flight Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115814)|S. -S. Olzem; B. Salamat; T. Kienast; T. A. Drouven; E. Gerhard|10.1109/AERO55745.2023.10115814|;|
|[SPHEREx Preliminary Mission Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115793)|F. Alibay; O. V. Sindiy; P. A. T. Jansma; C. M. Reynerson; E. B. Rice; J. Rocca; S. Susca; S. C. Unwin; R. L. Akeson; S. E. Mihaly; M. S. Werner|10.1109/AERO55745.2023.10115793|;|
|[PILOT: Using a Small Satellite Constellation to Understand Cold Plasma in the Inner Magnetosphere](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115934)|C. Spittler; D. Malaspina; R. Ergun; J. Link; B. Unruh; M. Danowski; R. Rohrschneider; J. Goldstein; L. DeMoudt; J. Parker|10.1109/AERO55745.2023.10115934|;|
|[Towards Mining Rare Earth Elements on the Moon](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116027)|G. Hedrick|10.1109/AERO55745.2023.10116027|;|
|[Sample Recovery Helicopter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115951)|F. Mier-Hicks; H. F. Grip; A. Kalantari; S. Moreland; B. Pipenberg; M. Keennon; T. K. Canham; M. Pauken; E. Decrossas; T. Tzanetos; J. B. Balaram|10.1109/AERO55745.2023.10115951|;|
|[On-Orbit Demonstrations of Proactive Tasking of Glint Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115608)|R. t. Nallapu; B. Jagatia; P. Linden; L. M. Donahue; A. Ayasse|10.1109/AERO55745.2023.10115608|;|
|[MMX Locomotion Subsystem: mechanics for extraterrestrial low gravity drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115807)|V. Langofer; R. Bayer; A. Kolb; K. Sasaki|10.1109/AERO55745.2023.10115807|;|
|[Non-Cooperative Space Object Capture and Manipulation with Soft Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115860)|A. Carambia; C. G. Frazelle; A. D. Kapadia; I. D. Walker|10.1109/AERO55745.2023.10115860|;|
|[HALE UAV: Output Stabilization with the Minimum Time Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115706)|A. Ajami; I. Pranoto; J. -P. Gauthier|10.1109/AERO55745.2023.10115706|;|
|[Minimally Intrusive Single-Chip Recession/Temperature Sensors for Spacecraft Thermal Protection Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115771)|R. Okojie; G. Go; C. Petrov; N. Tiliakos|10.1109/AERO55745.2023.10115771|;|
|[Heliophysics Environmental and Radiation Measurement Experiment Suite Integration and Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115641)|S. Petro; T. Null|10.1109/AERO55745.2023.10115641|;|
#### **2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)**
- DOI: 10.1109/COOLCHIPS57690.2023
- DATE: 19-21 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Lookup Table Modular Reduction: A Low-Latency Modular Reduction for Fast ECC Processor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122002)|A. Opasatian; M. Ikeda|10.1109/COOLCHIPS57690.2023.10122002|Modular Multiplier;field-programmable gate array (FPGA);Elliptic Curve Cryptography;Modular Multiplier;field-programmable gate array (FPGA);Elliptic Curve Cryptography|
|[Dual Vector Load for Improved Pipelining in Vector Processors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121996)|V. Razilov; J. Zhong; E. Matúš; G. Fettweis|10.1109/COOLCHIPS57690.2023.10121996|RISC-V;dual load;vector processor;DSP;RISC-V;dual load;vector processor;DSP|
|[Cachet: A High-Performance Joint-Subtree Integrity Verification for Secure Non-Volatile Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122117)|T. Kubo; S. Takamaeda-Yamazaki|10.1109/COOLCHIPS57690.2023.10122117|secure architecture;non-volatile memory;crash consistency;secure architecture;non-volatile memory;crash consistency|
|[COOL-NPU: Complementary Online Learning Neural Processing Unit with CNN-SNN Heterogeneous Core and Event-driven Backpropagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121940)|S. Kim; S. Kim; S. Hong; S. Kim; D. Han; J. Choi; H. -J. Yoo|10.1109/COOLCHIPS57690.2023.10121940|;|
|[A Low-power Neural 3D Rendering Processor with Bio-inspired Visual Perception Core and Hybrid DNN Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122036)|D. Han; J. Ryu; S. Kim; S. Kim; J. Park; H. -J. Yoo|10.1109/COOLCHIPS57690.2023.10122036|;|
|[Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121954)|Z. Qin; K. Wei; H. Amano; K. Nakadai|10.1109/COOLCHIPS57690.2023.10121954|HARK;FPGA;Audio processing;Sound source separation;HLS;HARK;FPGA;Audio processing;Sound source separation;HLS|
|[FPGA Emulation of Through-Silicon-Via (TSV) Dataflow Network for 3D Standard Chip Stacking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122025)|T. Ohkawa; M. Aoyagi|10.1109/COOLCHIPS57690.2023.10122025|;|
|[A Real-Time Keyword Spotting System Based on an End-To-End Binary Convolutional Neural Network in FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121981)|J. Yoon; D. Lee; N. Kim; S. -J. Lee; G. -H. Kwak; T. -H. Kim|10.1109/COOLCHIPS57690.2023.10121981|keyword spotting;convolutional neural networks;binarization;inference;FPGA;keyword spotting;convolutional neural networks;binarization;inference;FPGA|
|[Flexibly Controllable Dynamic Cooling Methods for Solid-State Annealing Processors to Improve Combinatorial Optimization Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121990)|G. Inoue; D. Okonogi; T. Van Chu; J. Yu; M. Motomura; K. Kawamura|10.1109/COOLCHIPS57690.2023.10121990|Annealing processor;Ising model;Pseudo-temperature control;Dynamic cooling method;Annealing processor;Ising model;Pseudo-temperature control;Dynamic cooling method|
|[A 2.41-μW/MHz, 437-PE/mm2 CGRA in 22 nm FD-SOI With RISC-Like Code Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121985)|T. Kaiser; F. Gerfers|10.1109/COOLCHIPS57690.2023.10121985|;|
|[MazeCov-Q: An Efficient Maze-Based Reinforcement Learning Accelerator for Coverage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122120)|I. Syafalni; M. I. Firdaus; A. M. Riyadhus Ilmy; N. Sutisna; T. Adiono|10.1109/COOLCHIPS57690.2023.10122120|;|
#### **2023 Ninth International Conference on eDemocracy & eGovernment (ICEDEG)**
- DOI: 10.1109/ICEDEG58167.2023
- DATE: 3-5 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Keynote - Trust-based Public Policy Platform in the Age of Uber-Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121963)|S. Ae Chun|10.1109/ICEDEG58167.2023.10121963|;|
|[Keynote - AI for the Public Sector and the Case of Legal NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122084)|M. Stürmer|10.1109/ICEDEG58167.2023.10122084|;|
|[Tutorial - Overseeing Government With AI: Automated Ranking and Filtering of Legal Notices in the Government Gazette](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122044)|H. S. Xavier|10.1109/ICEDEG58167.2023.10122044|;|
|[Tutorial: Health Sector Online Services, an Assessment Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122103)|D. Sarantis; A. Barbosa; P. Acosta; M. F. T. Anticona|10.1109/ICEDEG58167.2023.10122103|;|
|[Sentiment Analysis Techniques for Peer Feedback: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122085)|M. Pinargote-Ortega; L. Bowen-Mendoza; J. Meza; S. Ventura|10.1109/ICEDEG58167.2023.10122085|sentiment analysis;artificial intelligence;higher education;peer assessment;sentiment analysis;artificial intelligence;higher education;peer assessment|
|[Automotive Aftermarket Goes Smart: A System Dynamics Approach to Innovation the Business Model in the Ecuadorian Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121953)|A. Robalino-López; H. N. Molina; Z. Aniscenko|10.1109/ICEDEG58167.2023.10121953|Smart-business;Business-innovation;Complex-systems;System Dynamics;Automotive aftermarket;Smart-business;Business-innovation;Complex-systems;System Dynamics;Automotive aftermarket|
|[Digitalized Co-Production of Emergency Response: To Make Local Initiatives National](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122074)|S. Pilemalm; W. Alkusaibati|10.1109/ICEDEG58167.2023.10122074|digitalization;co-production;emergency response;digitalization;co-production;emergency response|
|[Platform Economy Employment Opportunities for Youth in the Global South](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122096)|R. Musvosve; M. Turpin; J. -P. Van Belle|10.1109/ICEDEG58167.2023.10122096|platform economy;gig economy;digital labour markets;youth unemployment;employment opportunities;Global South employment;systematic literature review;platform economy;gig economy;digital labour markets;youth unemployment;employment opportunities;Global South employment;systematic literature review|
|[How Trust in Authority and Trust in Networks Relates to Verification and Sharing Behaviours: COVID-19 Health Information Sharing on WhatsApp](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121951)|B. Vermeulen; J. -P. Van Belle|10.1109/ICEDEG58167.2023.10121951|fake information;information verification behaviour;social media;government information;trust in authority;fake information;information verification behaviour;social media;government information;trust in authority|
|[Detection of Educational Influencers and Communities on TikTok](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122011)|A. Fiallos; S. Figueroa|10.1109/ICEDEG58167.2023.10122011|Social Network Analysis;topic modelling;TikTok;Social Network Analysis;topic modelling;TikTok|
|[Digital Transformation of the Public Sector in the Americas: An Empirical Analysis to Identify Leap-froggers and Trends since 2003](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121923)|J. Martins; M. M. Nielsen|10.1109/ICEDEG58167.2023.10121923|digital transformation;public sector;trends;leap-froggers;Americas;digital transformation;public sector;trends;leap-froggers;Americas|
|[Design and Implementation of Hospital Online Services: In-Depth Assessment of Greece](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121970)|D. Sarantis; S. Papadopoulos; D. Soares; J. Carvalho|10.1109/ICEDEG58167.2023.10121970|e-Health;Assessment;Evaluation;Website;Hospital;Greece;e-Health;Assessment;Evaluation;Website;Hospital;Greece|
|[Could an ISMS Model (ISO/IEC 27001:2013 Standard) Implementation Really Protect Public Data?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122109)|R. Tintin; M. Hidalgo|10.1109/ICEDEG58167.2023.10122109|Public data security;ISO/IEC 27001;2013 Standard;Information Security;Information Security Management System;ISMS;Public data security;ISO/IEC 27001;2013 Standard;Information Security;Information Security Management System;ISMS|
|[Towards Smart Citizen Control in Public Procurement: Ecuador's Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121991)|M. Fortuny; E. Guerrero; D. Riofrío; F. Simon|10.1109/ICEDEG58167.2023.10121991|e-Procurement;Citizen Control;Public Procure-ment;Corruption Risk Indicators;e-Procurement;Citizen Control;Public Procure-ment;Corruption Risk Indicators|
|[Enhancing Dynamic Profiles on Voting Advice Applications Using Social Media Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122118)|C. Nigon; J. Mancera; L. Terán|10.1109/ICEDEG58167.2023.10122118|VAA;Question Answering;NLP;Social Media;VAA;Question Answering;NLP;Social Media|
|[Designing a Framework for Explainable Health Recommender System Based on the Ecuadorian Data Protection Regulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122066)|B. Jaramillo; E. Loza-Aguirre; L. Terán|10.1109/ICEDEG58167.2023.10122066|data management framework;health recommender system;explainable artificial intelligence;Ecuadorian legal framework;data management framework;health recommender system;explainable artificial intelligence;Ecuadorian legal framework|
|[A Systematic Literature Review on South African Government to Harness Software as a Service for Enhanced E-Government](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121964)|S. M. Maluleka; A. Budree; J. -P. Van Belle|10.1109/ICEDEG58167.2023.10121964|e-Government;Software as a Service (SaaS);Information and Communication Technology (ICT);South Africa;e-Government;Software as a Service (SaaS);Information and Communication Technology (ICT);South Africa|
|[Building Cognitive Cities in Developing Countries: Ecuador Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121914)|D. Martinez-Mosquera; L. Recalde; C. Tipantuna|10.1109/ICEDEG58167.2023.10121914|cognitive cities;data;developing countries;IoT networks;open data;social media;cognitive cities;data;developing countries;IoT networks;open data;social media|
|[Grateful Chatbots: Public Sensemaking through Individual Gratitude Interventions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122089)|T. Schuler; E. Portmann|10.1109/ICEDEG58167.2023.10122089|conversational agents;conversation theory;conversational design;personality adapted conversational agents;design science research;human centred interaction science and technology;sensemaking;collective intelligence;self-actualization;digital learning;computing with words;conversational agents;conversation theory;conversational design;personality adapted conversational agents;design science research;human centred interaction science and technology;sensemaking;collective intelligence;self-actualization;digital learning;computing with words|
|[Citizen's Digital Profile. Legal Aspects and Current Practice in Russia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121979)|M. Bundin; A. Martynov; E. Shireeva|10.1109/ICEDEG58167.2023.10121979|digital profile;digital government;public services digital consent;personal data;digital profile;digital government;public services digital consent;personal data|
|[Framework for Training a VA that Supports Territorial Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122094)|V. Morocho; R. Achig; L. Vivanco; N. Pacurucu; J. Bustamante|10.1109/ICEDEG58167.2023.10122094|Public participation;Territorial Planning;Virtual Asistant;Spatial Data Infrastructure (SDI);Public participation;Territorial Planning;Virtual Asistant;Spatial Data Infrastructure (SDI)|
|[Cognitive Mobility Model for Women Farmers Marketing their Products in the City: Allpa Warmi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122043)|P. Jiménez-Pacheco; J. Meza|10.1109/ICEDEG58167.2023.10122043|cognitive model;women farmers;mobile application;mobility;agro-ecological market;cognitive model;women farmers;mobile application;mobility;agro-ecological market|
|[Using Design-Based Research for an Academic Dropout and Retention Dashboard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10122065)|V. Heredia-Jimenez; J. Yaguana; A. Jiménez-Macías; M. Ortiz-Rojas|10.1109/ICEDEG58167.2023.10122065|Learning dashboards;Analytical dashboard;Student retention;Data visualization;Academic advising;Design-Based Research;Learning dashboards;Analytical dashboard;Student retention;Data visualization;Academic advising;Design-Based Research|
|[Zero Rating Effects in South American Countries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121948)|R. D. Triviño; A. A. Franco; R. L. Ochoa|10.1109/ICEDEG58167.2023.10121948|zero-rating;Internet;net neutrality;mobile broadband;zero-rating;Internet;net neutrality;mobile broadband|
#### **2023 IEEE International Reliability Physics Symposium (IRPS)**
- DOI: 10.1109/IRPS48203.2023
- DATE: 26-30 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Investigating Nanowire, Nanosheet and Forksheet FET Hot-Carrier Reliability via TCAD Simulations: Invited Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118211)|M. Vandemaele; B. Kaczer; E. Bury; J. Franco; A. Chasin; A. Makarov; H. Mertens; G. Hellings; G. Groeseneken|10.1109/IRPS48203.2023.10118211|Border traps;carrier energy distribution function;forksheet FETs;gate-all-around FETs;hot-carrier degra-dation;interface defects;nanosheet FETs;nanowire FETs;non-equilibrium BTI;non-radiative multiphonon model;recovery;Si-R bond;simulations;TCAD;Border traps;carrier energy distribution function;forksheet FETs;gate-all-around FETs;hot-carrier degra-dation;interface defects;nanosheet FETs;nanowire FETs;non-equilibrium BTI;non-radiative multiphonon model;recovery;Si-R bond;simulations;TCAD|
|[Towards a Universal Model of Dielectric Breakdown](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117846)|A. Padovani; P. L. Torraca; J. Strand; A. Shluger; V. Milo; L. Larcher|10.1109/IRPS48203.2023.10117846|Dielectric Breakdown;Ginestra®;bond-breakage;precursors;carriers' injection;Dielectric Breakdown;Ginestra®;bond-breakage;precursors;carriers' injection|
|[Analysis of TDDB lifetime projection in low thermal budget HfO2/SiO2 stacks for sequential 3D integrations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117955)|A. Vici; R. Degraeve; P. J. Roussel; J. Franco; B. Kaczer; I. De Wolf|10.1109/IRPS48203.2023.10117955|TDDB;low thermal budget;defect density;leakage;soft breakdown;High-K/Metal Gate stack;TDDB;low thermal budget;defect density;leakage;soft breakdown;High-K/Metal Gate stack|
|[Signal duration sensitive degradation in scaled devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118314)|G. Bersuker; E. Tang; D. Veksler|10.1109/IRPS48203.2023.10118314|oxide degradation;operation duration;defect generation;oxide degradation;operation duration;defect generation|
|[Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117874)|M. M. Frank; N. Li; M. J. Rasch; S. Jain; C. -T. Chen; R. Muralidhar; J. -P. Han; V. Narayanan; T. M. Philip; K. Brew; A. Simon; I. Saraf; N. Saulnier; I. Boybat; S. Woźniak; A. Sebastian; P. Narayanan; C. Mackin; A. Chen; H. Tsai; G. W. Burr|10.1109/IRPS48203.2023.10117874|Analog memory;in-memory computing;neural networks;phase change memory;semiconductor device reliability;Analog memory;in-memory computing;neural networks;phase change memory;semiconductor device reliability|
|[ReRAM CiM Fluctuation Pattern Classification by CNN Trained on Artificially Created Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118305)|A. Yamada; N. Misawa; C. Matsui; K. Takeuchi|10.1109/IRPS48203.2023.10118305|CiM;ReRAM;Fluctuation;RTN;Oxygen Vacancy;CiM;ReRAM;Fluctuation;RTN;Oxygen Vacancy|
|[Neuromorphic Computation-in-Memory System (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117704)|K. Takeuchi|10.1109/IRPS48203.2023.10117704|CiM;Computation-in-Memory;Neuromorphic Computing;Spiking Neural Network;Reservoir Computing;Event-based Vision Sensor;CiM;Computation-in-Memory;Neuromorphic Computing;Spiking Neural Network;Reservoir Computing;Event-based Vision Sensor|
|[Thermal Induced Retention Degradation of RRAM-based Neuromorphic Computing Chips](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118164)|A. Ma; B. Gao; X. Mou; P. Yao; Y. Du; J. Tang; H. Qian; H. Wu|10.1109/IRPS48203.2023.10118164|RRAM-based chip;thermal;reliability;retention;neuromorphic computing;RRAM-based chip;thermal;reliability;retention;neuromorphic computing|
|[Reliability issues of gate oxides and $p-n$ junctions for vertical GaN metal–oxide–semiconductor field-effect transistors (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118047)|T. Narita; D. Kikuta; K. Ito; T. Shoji; T. Mori; S. Yamaguchi; Y. Kimoto; K. Tomita; M. Kanechika; T. Kondo; T. Uesugi; J. Kojima; J. Suda; Y. Nagasato; S. Ikeda; H. Watanabe; M. Kosaki; T. Oka|10.1109/IRPS48203.2023.10118047|MOSFET power amplifiers;Dielectric breakdown;Semiconductor defects;Stress measurement;Semiconductor device reliability;MOSFET power amplifiers;Dielectric breakdown;Semiconductor defects;Stress measurement;Semiconductor device reliability|
|[A common hard-failure mechanism in GaN HEMTs in accelerated switching and single-pulse short-circuit tests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117943)|D. Wieland; S. Ofner; M. Stabentheiner; B. Butej; C. Koller; J. Sun; A. Minetto; K. Reiser; O. Häberlen; M. Nelhiebel; M. Glavanovics; D. Pogany; C. Ostermaier|10.1109/IRPS48203.2023.10117943|GaN high electron mobility transistors (HEMTs);short-circuit robustness;FBSOA;single pulse short circuit (SPSC);switching accelerated life-time test (SALT);GaN high electron mobility transistors (HEMTs);short-circuit robustness;FBSOA;single pulse short circuit (SPSC);switching accelerated life-time test (SALT)|
|[High- Temperature PBTI in Trench-Gate Vertical GaN Power MOSFETs: Role of Border and Semiconductor Traps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117667)|D. Favero; A. Cavaliere; C. De Santi; M. Borga; W. G. Filho; K. Geens; B. Bakeroot; S. Decoutere; G. Meneghesso; E. Zanoni; M. Meneghini|10.1109/IRPS48203.2023.10117667|GaN-on-Si;trench gate MOSFET;vertical GaN;Reliability;PBTI;GaN-on-Si;trench gate MOSFET;vertical GaN;Reliability;PBTI|
|[Trapping in $\text{Al}_{2}\mathrm{O}_{3}/\text{GaN}$ MOScaps investigated by fast capacitive techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117719)|M. Fregolent; A. Marcuzzi; C. De Santi; E. B. Treidel; G. Meneghesso; E. Zanoni; M. Meneghini|10.1109/IRPS48203.2023.10117719|MOS capacitors;trapping;vertical GaN;MOS capacitors;trapping;vertical GaN|
|[Write-error-rate of Spin-Transfer-Torque MRAM (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117666)|D. C. Worledge|10.1109/IRPS48203.2023.10117666|MRAM;spin-transfer torque;STT-MRAM;write-error-rate;MRAM;spin-transfer torque;STT-MRAM;write-error-rate|
|[Double-sided Row Hammer Effect in Sub-20 nm DRAM: Physical Mechanism, Key Features and Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117677)|L. Zhou; J. Li; Z. Qiao; P. Ren; Z. Sun; J. Wang; B. Wu; Z. Ji; R. Wang; K. Cao; R. Huang|10.1109/IRPS48203.2023.10117677|Capacitive crosstalk;DRAM;process dependence;row hammer effect;trap-assisted electron migration;Capacitive crosstalk;DRAM;process dependence;row hammer effect;trap-assisted electron migration|
|[A New Ramp Stress Reliability Assessment on Pulse Energy Based OTS Switching Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118352)|P. C. Chang; P. J. Liao; C. H. Wu; Y. C. Chang; D. H. Hou; E. Ambrosi; H. Y. Lee; J. H. Lee; X. Y. Bao Taiwan|10.1109/IRPS48203.2023.10118352|Selector;GeCTe;Endurance;Lifetime;Ramp stress method;Time-saving reliability test;Polarity dependence effect;Selector;GeCTe;Endurance;Lifetime;Ramp stress method;Time-saving reliability test;Polarity dependence effect|
|[Comprehensive Analysis of Hole-Trapping in SiN Films with a Wide Range of Time Constants Based on Dynamic C-V](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118083)|H. Seki; R. Ichihara; Y. Higashi; Y. Nakasaki; M. Saitoh; M. Suzuki|10.1109/IRPS48203.2023.10118083|Silicon nitride;hole trap;trap density;activation energy;charge centroid;Silicon nitride;hole trap;trap density;activation energy;charge centroid|
|[Towards Understanding the Physics of Gate Switching Instability in Silicon Carbide MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117740)|M. W. Feil; K. Waschneck; H. Reisinger; J. Berens; T. Aichinger; P. Salmen; G. Rescher; W. Gustin; T. Grasser|10.1109/IRPS48203.2023.10117740|silicon carbide;wide-bandgap;MOSFET;threshold voltage;drift;bias temperature instability;gate switching instability;gate switching stress;silicon carbide;wide-bandgap;MOSFET;threshold voltage;drift;bias temperature instability;gate switching instability;gate switching stress|
|[Extended Analysis of Power Cycling Behavior of TO-Packaged SiC Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117650)|I. Kovacevic-Badstuebner; S. Race; U. Grossner; E. Mengotti; C. Kenel; E. Bianda; J. Jormanainen|10.1109/IRPS48203.2023.10117650|power cycling;SiC power MOSFETs;FEM modeling;solder;TO-package;bond wires;power cycling;SiC power MOSFETs;FEM modeling;solder;TO-package;bond wires|
|[V-Ramp test and gate oxide screening under the “lucky” defect model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118184)|K. P. Cheung|10.1109/IRPS48203.2023.10118184|;|
|[Investigation of different screening methods on threshold voltage and gate oxide lifetime of SiC Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118276)|L. Shi; S. Zhu; J. Qian; M. Jin; M. Bhattacharya; M. H. White; A. K. Agarwal; A. Shimbori; T. Liu|10.1109/IRPS48203.2023.10118276|Screening;Gate oxide lifetime;Threshold voltage;Burn-in;Extrinsic failure;Electron and hole trapping;Pulse;Screening;Gate oxide lifetime;Threshold voltage;Burn-in;Extrinsic failure;Electron and hole trapping;Pulse|
|[Transient Leakage Current as a Non-destructive Probe of Wire-bond Electrochemical Failures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117658)|M. A. Z. Mamun; A. Mavinkurve; M. van Soestbergen; M. A. Alam|10.1109/IRPS48203.2023.10117658|Bond wire corrosion;Thermally stimulated charging current;Highly accelerated stress tests (HAST);Peck's equation;Epoxy Molding compound (EMC);Bond wire corrosion;Thermally stimulated charging current;Highly accelerated stress tests (HAST);Peck's equation;Epoxy Molding compound (EMC)|
|[Wafer Level Chip Scale Package Failure Mode Prediction using Finite Element Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117636)|V. Dudash; K. V. Machani; B. Boehme; S. Capecchi; J. Ok; K. Meier; F. Kuechenmeister; M. Wieland; K. Bock|10.1109/IRPS48203.2023.10117636|failure mode;finite element modeling;lead-free solder;plastic strain;WLCSP;failure mode;finite element modeling;lead-free solder;plastic strain;WLCSP|
|[Knowledge Based Qualification for Thermal Interface Material Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117756)|E. Armagan; A. Saha; K. C. Liu; B. Gebrehiwot; M. Cartas; A. Das; T. Rawlings; P. Raghavan|10.1109/IRPS48203.2023.10117756|Thermo-mechanical reliability;temperature cycling;power cycling;thermal interface materials;knowledge-based qualification;reliability models;accelerated stress testing;Thermo-mechanical reliability;temperature cycling;power cycling;thermal interface materials;knowledge-based qualification;reliability models;accelerated stress testing|
|[Recent Advances on Electromigration in Cu/SiO2 to Cu/SiO2 Hybrid Bonds for 3D Integrated Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118173)|S. Moreau; D. Bouchu; J. Jourdon; B. Ayoub; S. Lhostis; H. Frémont; P. Lamontagne|10.1109/IRPS48203.2023.10118173|3D integration;hybrid bonding;interconnects;reliability;failure mode;electromigration;3D integration;hybrid bonding;interconnects;reliability;failure mode;electromigration|
|[Material instabilities in the TaOx-based resistive switching devices (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117796)|M. Skowronski|10.1109/IRPS48203.2023.10117796|non-volatile memory devices;reliability;interdiffusion;transmission electron microscopy;non-volatile memory devices;reliability;interdiffusion;transmission electron microscopy|
|[Investigation of resistance fluctuations in ReRAM: physical origin, temporal dependence and impact on memory reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117882)|L. Reganaz; D. Deleruyelle; Q. Rafhay; J. Minguet Lopez; N. Castellani; J. F. Nodin; A. Bricalli; G. Piccolboni; G. Molas; F. Andrieu|10.1109/IRPS48203.2023.10117882|Non-Volatile memory;ReRAM;RTN;Kinetic-Monte-Carlo simulations;Current fluctuations;Non-Volatile memory;ReRAM;RTN;Kinetic-Monte-Carlo simulations;Current fluctuations|
|[Unveiling Retention Physical Mechanism of Ge-rich GST ePCM Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118155)|L. Laurin; M. Baldo; E. Petroni; G. Samanni; L. Turconi; A. Motta; M. Borghi; A. Serafini; D. Codegoni; M. Scuderi; S. Ran; A. Claverie; D. Ielmini; R. Annunziata; A. Redaelli|10.1109/IRPS48203.2023.10118155|Drift;Ge-rich GST;Phase Change Memory;Drift;Ge-rich GST;Phase Change Memory|
|[Challenges and solutions to the defect-centric modeling and circuit simulation of time-dependent variability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118334)|J. Martin-Martinez; J. Diaz-Fortuny; P. Saraza-Canflanca; R. Rodriguez; R. Castro-Lopez; E. Roca; F. V. Fernandez; M. Nafria|10.1109/IRPS48203.2023.10118334|Bias Temperature Instability (BTI);Random Telegraph Noise (RTN);Hot Carrier Injection (HCI);variability;aging;degradation;characterization;CMOS;reliability;array;Reliability-Aware Design (RAD);Bias Temperature Instability (BTI);Random Telegraph Noise (RTN);Hot Carrier Injection (HCI);variability;aging;degradation;characterization;CMOS;reliability;array;Reliability-Aware Design (RAD)|
|[A Unified Aging Model Framework Capturing Device to Circuit Degradation for Advance Technology Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117914)|S. Mukhopadhyay; C. Chen; M. Jamil; J. Standfest; I. Meric; B. Gill; S. Ramey|10.1109/IRPS48203.2023.10117914|transistor;circuit;reliability;aging model;transistor;circuit;reliability;aging model|
|[Improving the Tamper-Aware Odometer Concept by Enhancing Dynamic Stress Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118108)|J. Diaz-Fortuny; D. Sangani; P. Saraza-Canflanca; E. Bury; R. Degraeve; B. Kaczer|10.1109/IRPS48203.2023.10118108|annealing;array chip;Bias Temperature Instabilities (BTI);Hot Carrier Degradation (HCD);integrated circuit reliability;ring-oscillator;tamper detection;annealing;array chip;Bias Temperature Instabilities (BTI);Hot Carrier Degradation (HCD);integrated circuit reliability;ring-oscillator;tamper detection|
|[Integrated Test Circuit for Off-State Dynamic Drain Stress Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117885)|J. Hai; F. Cacho; X. Federspiel; T. Garba-Seybou; A. Divay; E. Lauga-Larroze; J. . -D. Arnould|10.1109/IRPS48203.2023.10117885|Off-state reliability;Integrated test circuit;Dynamic stress;TDDB;Degradation modes;Off-state reliability;Integrated test circuit;Dynamic stress;TDDB;Degradation modes|
|[Reliability Modeling of Middle-Of-Line Interconnect Dielectrics in Advanced process nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117619)|R. Kasim; C. Lin; C. Perini; J. Palmer; N. Gilda; S. Imam; J. R. Weber; C. Wallace; J. Hicks|10.1109/IRPS48203.2023.10117619|component;Interconnect dielectric Reliability;Middle of Line Reliability;low-k reliability;low-k TDDB;component;Interconnect dielectric Reliability;Middle of Line Reliability;low-k reliability;low-k TDDB|
|[Location of Oxide Breakdown Events under Off-state TDDB in 28nm N-MOSFETs dedicated to RF applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117725)|T. Garba-Seybou; X. Federspiel; F. Monsieur; M. Sicre; F. Cacho; J. Hai; A. Bravaix|10.1109/IRPS48203.2023.10117725|CMOS;charge trapping;hard breakdown;interface traps;Off-state damage;soft breakdown;CMOS;charge trapping;hard breakdown;interface traps;Off-state damage;soft breakdown|
|[GHz AC to DC TDDB Modeling with Defect Accumulation Efficiency Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117582)|X. Yu; C. Yan; Y. Ding; Y. Qu; Y. Zhao|10.1109/IRPS48203.2023.10117582|AC TDDB;defect accumulation efficiency;failure mechanism;lifetime model;AC TDDB;defect accumulation efficiency;failure mechanism;lifetime model|
|[Semantic Autoencoder for Modeling BEOL and MOL Dielectric Lifetime Distributions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117878)|W. Yan; E. Wu; A. G. Schwing; E. Rosenbaum|10.1109/IRPS48203.2023.10117878|Time-dependent dielectric breakdown;TDDB;machine learning;lifetime distribution;BEOL;MOL;Time-dependent dielectric breakdown;TDDB;machine learning;lifetime distribution;BEOL;MOL|
|[Optimization of SCR for High-Speed Digital and RF Applications in 45-nm SOI CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118266)|S. Huang; S. Parthasarathy; Y. P. Zhou; J. -J. Hajjar; E. Rosenbaum|10.1109/IRPS48203.2023.10118266|SCR;SOI;electrostatic discharge;CMOS;SCR;SOI;electrostatic discharge;CMOS|
|[Collector Engineering of ESD PNP in BCD Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117930)|Y. Zhou; D. LaFonteese; E. Rosenbaum|10.1109/IRPS48203.2023.10117930|Electrostatic discharge;bipolar transistors;semiconductor device breakdown;semiconductor device reliability;TCAD;Electrostatic discharge;bipolar transistors;semiconductor device breakdown;semiconductor device reliability;TCAD|
|[Impact of Thin-oxide Gate on the On-Resistance of HV-PNP Under ESD Stress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117638)|M. M; N. K. Kranthi; G. Boselli; M. Shrivastava|10.1109/IRPS48203.2023.10117638|High Voltage PNP;ESD;On Resistance;Gate Field Effect;High Voltage PNP;ESD;On Resistance;Gate Field Effect|
|[Reliability Characterization of HBM featuring $\text{HK}+\text{MG}$ Logic Chip with Multi-stacked DRAMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118277)|S. Ha; S. Lee; G. Bae; D. Lee; S. H. Kim; B. Woo; N. -H. Lee; Y. Lee; S. Pae|10.1109/IRPS48203.2023.10118277|HBM;NBTI;HTOL;NCF;Reliabillity;HBM;NBTI;HTOL;NCF;Reliabillity|
|[Impact of Gate Stack Thermal Budget on NBTI Reliability in Gate-All-Around Nanosheet P-type Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117828)|H. Zhou; M. Wang; N. Loubet; A. Gaul; Y. Sulehria|10.1109/IRPS48203.2023.10117828|GAA;Nanosheet (NS);NBTI;GAA;Nanosheet (NS);NBTI|
|[Characterization and modeling of DCR and DCR drift variability in SPADs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117921)|M. Sicre; X. Federspiel; B. Mamdy; D. Roy; F. Calmon|10.1109/IRPS48203.2023.10117921|Single-Photon Avalanche Diode (SPAD);Dark Count Rate (DCR);device-to-device variability;reliability;device modeling;Monte-Carlo approach;defects;thermal activation energy;Single-Photon Avalanche Diode (SPAD);Dark Count Rate (DCR);device-to-device variability;reliability;device modeling;Monte-Carlo approach;defects;thermal activation energy|
|[Risk Management Informed by an Uncertain Bathtub Curve (Invited)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117694)|J. Jopling|10.1109/IRPS48203.2023.10117694|reliability;bathtub curve;infant mortality;early life fail;aging;wearout;endurance;risk management;knowledge-based qualification;standards-based qualification;reliability;bathtub curve;infant mortality;early life fail;aging;wearout;endurance;risk management;knowledge-based qualification;standards-based qualification|
|[A pragmatic network-aware paradigm for system-level electromigration predictions at scale](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117698)|H. Zahedmanesh; P. Roussel; I. Ciofi; K. Croes|10.1109/IRPS48203.2023.10117698|Electromigration;Interconnect;Power Delivery Network (PDN);Redundancy;System-Level;Electromigration;Interconnect;Power Delivery Network (PDN);Redundancy;System-Level|
|[Estimation of SOH Degradation of Coin Cells Subjected to Accelerated Life Cycling with Randomized Cycling Depths and C-Rates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117727)|P. Lall; V. Soni; G. Sethi; K. Yiang|10.1109/IRPS48203.2023.10117727|capacity degradation;SOH modeling;SOH estimation;li-ion battery;randomized battery testing;capacity degradation;SOH modeling;SOH estimation;li-ion battery;randomized battery testing|
|[Novel Operation Scheme for Suppressing Disturb in HfO2-based FeFET Considering Charge- Trapping-Coupled Polarization Dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118125)|T. Hamai; K. Suzuki; R. Ichihara; Y. Higashi; Y. Yoshimura; K. Sakuma; K. Ota; K. Takahashi; K. Matsuo; S. Fujii; M. Saitoh|10.1109/IRPS48203.2023.10118125|FeFET;memory;polarization;charge-trap;disturb;FeFET;memory;polarization;charge-trap;disturb|
|[The Role of Defects and Interface Degradation on Ferroelectric HZO Capacitors Aging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118229)|L. Benatti; S. Vecchi; M. Pesic; F. M. Puglisi|10.1109/IRPS48203.2023.10118229|Ferroelectric Capacitors;Ferroelectric Modeling;Small signal model;Neuromorphic;Ferroelectric Capacitors;Ferroelectric Modeling;Small signal model;Neuromorphic|
|[MTJ degradation in multi-pillar SOT-MRAM with selective writing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117917)|S. Van Beek; K. Cai; K. Fan; G. Talmelli; A. Trovato; N. Jossart; S. Rao; A. Chasin; S. Couet|10.1109/IRPS48203.2023.10117917|SOT-MRAM;failure;self-heating;MTJ;MgO;multi - pillar;SOT-MRAM;failure;self-heating;MTJ;MgO;multi - pillar|
|[Comprehensive study on prediction of endurance properties from breakdown voltage in high-reliable STT-MRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118152)|H. Sato; H. M. Shin; H. Jung; S. W. Lee; H. Bae; H. Kwon; K. H. Ryu; W. C. Lim; Y. S. Han; J. H. Jeong; J. M. Lee; D. S. Kim; K. Lee; J. H. Lee; J. H. Park; Y. J. Song; Y. Ji; B. I. Seo; J. W. Kim; H. H. Kim|10.1109/IRPS48203.2023.10118152|STT-MRAM;endurance;breakdown voltage;STT-MRAM;endurance;breakdown voltage|
|[Cross-coupled Self-Heating and Consequent Reliability Issues in GaN-Si Hetero-integration: Thermal Keep-Out-Zone Quantified](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118187)|M. P. Sruthi; M. A. Zaman Mamun; D. R. Nair; A. Chakravorty; N. DasGupta; A. DasGupta; M. A. Alam|10.1109/IRPS48203.2023.10118187|Hetero-integration;Self-heating;Reliability;Keep-out-zone;Hetero-integration;Self-heating;Reliability;Keep-out-zone|
|[Reliability Challenges from 2.5D to 3DIC in Advanced Package Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117647)|R. Lu; Y. -C. Chuang; J. -L. Wu; J. He|10.1109/IRPS48203.2023.10117647|3DIC;Advanced Packaging;Relaibility;4 Point Bending Tests;Nano-Indentation;3DIC;Advanced Packaging;Relaibility;4 Point Bending Tests;Nano-Indentation|
|[Drain voltage impact on charge redistribution in GaN-on-Si E-mode MOSc-HEMTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117813)|C. Leurquin; W. Vandendaele; R. Gwoziecki; B. Mohamad; G. Despesse; F. Iucolano; R. Modica; A. Constant|10.1109/IRPS48203.2023.10117813|Dynamic reliability;GaN-on-Si MOSc-HEMT;HV-BTI;VTH instability;Carbon in GaN;Dynamic reliability;GaN-on-Si MOSc-HEMT;HV-BTI;VTH instability;Carbon in GaN|
|[Dielectric Thickness and Fin Width Dependent OFF-State Degradation in AlGaN/GaN SLCFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118346)|A. S. Kumar; M. J. Uren; M. D. Smith; M. Kuball; J. Parke; H. G. Henry; R. S. Howell|10.1109/IRPS48203.2023.10118346|Gallium Nitride;SLCFET;TDDB;lifetime;reliability;degradation;step-stressing;noise-analysis;3D- TCAD;percolation theory;traps;Gallium Nitride;SLCFET;TDDB;lifetime;reliability;degradation;step-stressing;noise-analysis;3D- TCAD;percolation theory;traps|
|[Unique Lattice Temperature Dependent Evolution of Hot Electron Distribution in GaN HEMTs on C-doped GaN Buffer and its Reliability Consequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118255)|R. R. Chaudhuri; V. Joshi; A. Gupta; T. Joshi; R. R. Malik; M. A. Mir; S. D. Gupta; M. Shrivastava|10.1109/IRPS48203.2023.10118255|Carbon doped GaN Buffer;Electroluminescence;GaN HEMT Reliability;Hot Electrons;High Temperature Reliability;Carbon doped GaN Buffer;Electroluminescence;GaN HEMT Reliability;Hot Electrons;High Temperature Reliability|
|[Dynamic Interplay of Surface and Buffer Traps in Determining Drain Current Injection induced Device Instability in OFF-state of AlGaN/GaN HEMTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117664)|M. A. Mir; V. Joshi; R. R. Chaudhuri; M. A. Munshi; R. R. Malik; M. Shrivastava|10.1109/IRPS48203.2023.10117664|Dynamic ON Resistance (RON);AlGaN/GaN HEMTs;Current injection;GaN buffer traps;GaN surface traps;Dynamic ON Resistance (RON);AlGaN/GaN HEMTs;Current injection;GaN buffer traps;GaN surface traps|
|[Ultra Long-term Measurement Results of BTI-induced Aging Degradation on 7-nm Ring Oscillators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117873)|K. Kobayashi; T. Kishita; H. Nakano; J. Furuta; M. Igarashi; S. Kumashiro; M. Yabuuchi; H. Sakamoto|10.1109/IRPS48203.2023.10117873|BTI;long-term;recovery;ring oscillator;FinFET;BTI;long-term;recovery;ring oscillator;FinFET|
|[Modeling of NBTI Induced Threshold Voltage Shift Based on Activation Energy Maps Under Consideration of Variability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117818)|C. Bogner; C. Schlunder; M. Waltl; H. Reisinger; T. Grasser|10.1109/IRPS48203.2023.10117818|NBTI;variability;ultra fast measurements;transistor array;activation energy maps;NBTI;variability;ultra fast measurements;transistor array;activation energy maps|
|[The Role of Mobility Degradation in the BTI-Induced RO Aging in a 28-nm Bulk CMOS Technology: (Student paper)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118026)|D. Sangani; J. Diaz-Fortuny; E. Bury; B. Kaczer; G. Gielen|10.1109/IRPS48203.2023.10118026|BTI;HCI;Circuit Simulations;Compact Modelling;BTI;HCI;Circuit Simulations;Compact Modelling|
#### **2023 Integrated Communication, Navigation and Surveillance Conference (ICNS)**
- DOI: 10.1109/ICNS58246.2023
- DATE: 18-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Autonomous Forecast Trend Monitoring in Support of Air Traffic Management Efficacy Improvements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124300)|A. Klein|10.1109/ICNS58246.2023.10124300|Autonomous Forecast Trend Monitoring;weather translation;traffic management initiative impact reduction;Autonomous Forecast Trend Monitoring;weather translation;traffic management initiative impact reduction|
|[ACPS: Design of an Integrated Architecture for Airborne System and Cyber-Physical System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124298)|Z. Wu; R. Dong|10.1109/ICNS58246.2023.10124298|airborne system;cyber-physical system;system model;security analysis;airborne system;cyber-physical system;system model;security analysis|
|[Multi-objective Collaborative Trajectory Deconfliction Incorporating Equity and Airline Priorities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124326)|Y. Zhou; M. Hu; Y. Zhang|10.1109/ICNS58246.2023.10124326|Air traffic;strategic deconfliction;multi-objective optimization;equity;Air traffic;strategic deconfliction;multi-objective optimization;equity|
|[An Air-Ground Channel Modeling Approach for Multiple Antenna Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124263)|A. Gürbüz; M. Walter|10.1109/ICNS58246.2023.10124263|MIMO;air-ground channel;channel models;propagation;MIMO;air-ground channel;channel models;propagation|
|[Dual-band Air-Ground Radio Performance: Example Flight Test Results](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124253)|D. W. Matolak; Z. Afroze|10.1109/ICNS58246.2023.10124253|air-ground communications;radio;air-ground communications;radio|
|[Using trusted responders in constrained aviation environments to reduce authentication overhead](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124308)|J. Graefe; L. Leonardon; M. Esmael|10.1109/ICNS58246.2023.10124308|OSCP;OSCP Stapling;OSCP Trusted Responder;OSCP Aviation;OSCP;OSCP Stapling;OSCP Trusted Responder;OSCP Aviation|
|[On the Impact of UAS Contingencies on ATC Operations in Shared Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124297)|J. Teutsch; C. Petersen; G. Schwoch; T. J. Lieb; T. Bos; R. Zon|10.1109/ICNS58246.2023.10124297|UAS;U-space;air traffic management;dynamic airspace re-configuration;contingencies;NARSIM;U-FLY;SESAR;UAS;U-space;air traffic management;dynamic airspace re-configuration;contingencies;NARSIM;U-FLY;SESAR|
|[Image-based Conflict Detection with Convolutional Neural Network under Weather Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124287)|P. H. Dang; M. A. Mohamed; S. Alam|10.1109/ICNS58246.2023.10124287|conflict detection;air traffic management;image classification;convolutional neural network;conflict detection;air traffic management;image classification;convolutional neural network|
|[Roadmap towards an ECAC-wide Flight Centric ATC implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124327)|C. S. Kluenker; T. Finck|10.1109/ICNS58246.2023.10124327|Flight Centric ATC;Sectorless;ECAC;Air Traffic Management;Flight Centric ATC;Sectorless;ECAC;Air Traffic Management|
|[Conceptual Analysis of Allocation Strategies for Air Traffic Control Concepts without Conventional Sector Boundaries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124289)|T. Finck; C. S. Kluenker; M. Weber|10.1109/ICNS58246.2023.10124289|Flight Centric ATC;Dynamic Airspace;Allocation;Human-in-the-Loop Simulation;Flight Centric ATC;Dynamic Airspace;Allocation;Human-in-the-Loop Simulation|
|[Method for Formal Analysis of the Type and Content of Airline Standard Operating Procedures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124313)|J. Bashatah; L. Sherry|10.1109/ICNS58246.2023.10124313|Standard Operating Procedures;Time to Proficiency;Operational Reliability;Standard Operating Procedures;Time to Proficiency;Operational Reliability|
|[Adaptable Graph Networks for Air Traffic Analysis Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124325)|S. S. Holdren; M. Z. Li; J. Hoffman|10.1109/ICNS58246.2023.10124325|Graph analysis;Network models;Air traffic flow management;Time-Based Flow Management;Graph analysis;Network models;Air traffic flow management;Time-Based Flow Management|
|[Agile or V-Model – Can Modern it Frameworks and Tools Deliver Software Assurance for ATM-Grade Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124255)|J. Surlan; D. Mittermair; S. Galler|10.1109/ICNS58246.2023.10124255|;|
|[Dynamic and Cooperative Optimization of Entry and Exit Points for Multiple Sectors in Free Route Airspace Considering Wind Forecasts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124312)|J. Huang; M. Hu; L. Yang; Y. Zhou; Q. Gao; X. Jiang|10.1109/ICNS58246.2023.10124312|Free Route Airspace;Entry and Exit Points;Wind Forecasts;Dijkstra algorithm;NSGA-II algorithm;Free Route Airspace;Entry and Exit Points;Wind Forecasts;Dijkstra algorithm;NSGA-II algorithm|
|[On the effect of uncompensated latencies on trajectory reconstruction for surveillance performance monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124264)|C. A. Chuquitarco Jiménez; J. Vico Navarro; C. C. Puchol; J. V. Balbastre|10.1109/ICNS58246.2023.10124264|ADS-B;uncompensated latency;bias estimation and correction;Kalman filter;SAAS-C;OTR;ADS-B;uncompensated latency;bias estimation and correction;Kalman filter;SAAS-C;OTR|
|[Noise Measurements of Unmanned Aircraft Vehicles: Experiences, Challenges and Recommendations for Standards taken from Flight Trials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124257)|T. J. Lieb; J. Treichel; A. Volkert|10.1109/ICNS58246.2023.10124257|Unmanned Aircraft Vehicles;Drone Noise;Noise Measurements;Social Acceptance;Challenges;Recommendations;Regulations;Unmanned Aircraft Vehicles;Drone Noise;Noise Measurements;Social Acceptance;Challenges;Recommendations;Regulations|
|[Flight Testing Drone Contingencies during Runway Inspection in U-space Shared Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124322)|G. Schwoch; T. J. Lieb; H. Lenz|10.1109/ICNS58246.2023.10124322|Drones;UTM;U-space;Contingencies;Flight Testing;Runway Inspection;Scaled Coordinate Transformation;Dynamic Airspace Reconfiguration;Drones;UTM;U-space;Contingencies;Flight Testing;Runway Inspection;Scaled Coordinate Transformation;Dynamic Airspace Reconfiguration|
|[A Software Framework for Synthetic Aeronautical Data Traffic Generation in Support of LDACS Evaluation Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124317)|L. J. A. Jansen; T. Gräupl; N. Mäurer; K. Morioka; C. Schmitt|10.1109/ICNS58246.2023.10124317|LDACS;digital aeronautical communications;communications link performance;flight tests;LDACS;digital aeronautical communications;communications link performance;flight tests|
|[LDACS Flight Trials: Demonstration of ATS-B2, IPS, and Seamless Mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124329)|T. Gräupl; D. M. Mielke; M. A. Bellido-Manganell; L. J. A. Jansen; N. Mäurer; A. Gürbüz; A. Filip-Dhaubhadel; L. Schalk; D. Becker; M. Skorepa; F. Wrobel; K. Morioka; S. Kurz; J. Meser|10.1109/ICNS58246.2023.10124329|LDACS;wireless aeronautical communication;LDACS;wireless aeronautical communication|
|[International LDACS Security Validation Activities -A Cooperation Effort between DLR and ENRI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124307)|N. Mäurer; T. Ewert; L. J. A. Jansen; T. Gräupl; K. Morioka; C. Schmitt|10.1109/ICNS58246.2023.10124307|LDACS;Cybersecurity;Communications Performance;ENRI;DLR;LDACS;Cybersecurity;Communications Performance;ENRI;DLR|
|[Performance Based Determination of Detect-and-Avoid Ranges in a Constrained Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124304)|N. Peinecke|10.1109/ICNS58246.2023.10124304|Detect and Avoid;Geovectoring;Sensor Ranges;State-Based DAA;Detect and Avoid;Geovectoring;Sensor Ranges;State-Based DAA|
|[Differences between URClearED Remain Well Clear and DO-365](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124330)|E. Theunissen; G. Corraro; F. Corraro; U. Ciniglio; E. Filippone; N. Peinecke|10.1109/ICNS58246.2023.10124330|DAA;DWC;RWC;UAS;RPAS;DAA;DWC;RWC;UAS;RPAS|
|[Current Challenges in Mission Planning Systems for UAVs: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124299)|J. -P. Huttner; M. Friedrich|10.1109/ICNS58246.2023.10124299|UAV;Drones;HMI;HCI;human factors;UAV;Drones;HMI;HCI;human factors|
|[Development and Evaluation of a U-space Route Structure for the City of Frankfurt Connecting Airport and Trade Fair Via Fast-Time Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124254)|C. Kallies; K. Schweiger; R. Karásek; F. Morscheck; D. Lambers|10.1109/ICNS58246.2023.10124254|Air Traffic Management;Unmanned Aircraft System Traffic Management;UTM;U-space;Urban Air Mobility;UAM;Air Traffic Management;Unmanned Aircraft System Traffic Management;UTM;U-space;Urban Air Mobility;UAM|
|[Air-to-Air Collision Risk Models (CRM) in the Terminal Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124323)|A. K. Thapa; J. Shortle; L. Sherry|10.1109/ICNS58246.2023.10124323|Collision Risk;Models;Simulation;Safety;Collision Risk;Models;Simulation;Safety|
|[Resolution of Potential Conflicts caused by Contingency Events in an AAM Traffic Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124251)|A. T. Altun; B. Baspinar; Y. Xu; G. Inalhan; M. W. Hardt|10.1109/ICNS58246.2023.10124251|AAM;UTM;pre-flight replanning;potential conflict resolution;demand capacity balancing;contingency management;optimization;AAM;UTM;pre-flight replanning;potential conflict resolution;demand capacity balancing;contingency management;optimization|
|[Network Communications Evolution’s Effects on Air Traffic Systems – Nuisance or National Risk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124331)|K. Duncan; D. Hoenigmann; T. Guyah|10.1109/ICNS58246.2023.10124331|air traffic navigation;asynchronous;jitter;packet loss;latency;synchronous;pseudowire;TDM;critical infrastructure;air traffic navigation;asynchronous;jitter;packet loss;latency;synchronous;pseudowire;TDM;critical infrastructure|
|[Towards Trajectory Conflict Prediction Using AI/ML For V&V Test Case Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124252)|W. Mingus; L. Sherry; J. Shortle|10.1109/ICNS58246.2023.10124252|system validation;deep learning neural networks;system validation;deep learning neural networks|
|[A Machine Learning GNSS Interference Detection Method based on ADS-B Multi-index Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124266)|D. Zuo; C. Shi; K. Jin; P. Zhao; W. Zou; K. Cai|10.1109/ICNS58246.2023.10124266|ADS-B;GNSS interference;detection;Recurrent Neural Network;Long Short Term Memory;ADS-B;GNSS interference;detection;Recurrent Neural Network;Long Short Term Memory|
|[Mode N - A promising Approach for future Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124309)|S. Marquard; V. Görldt; M. Grandt; F. Madritsch; F. Butsch; F. Adam|10.1109/ICNS58246.2023.10124309|Mode N;Performance Based Navigation (PBN);Alternative Positioning;Navigation and Timing (A-PNT);Global Navigation Satellite Systems (GNSS);Spectrum efficiency;Mode N;Performance Based Navigation (PBN);Alternative Positioning;Navigation and Timing (A-PNT);Global Navigation Satellite Systems (GNSS);Spectrum efficiency|
|[Trust Framework for Data Sharing between Industry and Government](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124290)|M. Metcalfe; J. Nager; C. S. Hacker|10.1109/ICNS58246.2023.10124290|;|
|[Software-Defined Architecture and Front-End Optimization for DO-178B Compliant Distance Measuring Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124320)|F. Farhangian; B. Shakibafar; B. Cedric; R. Landry|10.1109/ICNS58246.2023.10124320|software defined radio;DME;distance measuring equipment;avionics;DO-178;software defined radio;DME;distance measuring equipment;avionics;DO-178|
|[Using Surveillance Flight Track Data and Terrestrial Sky Imaging to Record Airspace Contrail Statistics: the Washington D.C. Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124321)|B. R. Lopez; L. Sherry|10.1109/ICNS58246.2023.10124321|aircraft induced clouds;contrails;navigational avoidance;global heating;climate change;aircraft induced clouds;contrails;navigational avoidance;global heating;climate change|
|[Privacy-Preserving Implementation of an Auction Mechanism for ATFM Slot Swapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124262)|P. Feichtenschlager; K. Schuetz; S. Jaburek; C. G. Schuetz; E. Gringinger|10.1109/ICNS58246.2023.10124262|air traffic flow management;ATFM regulation;flight prioritization;combinatorial auction;genetic algorithm;multi-party computation;air traffic flow management;ATFM regulation;flight prioritization;combinatorial auction;genetic algorithm;multi-party computation|
|[System of Unmanned Aerial Vehicles for road safety improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124306)|S. Bouassida; N. Neji; L. Nouveliere; J. Mohamed; J. Neji|10.1109/ICNS58246.2023.10124306|UAV;U2U;U2V and U2I communications;driver behavior;road safety;cooperative vehicles;UAV;U2U;U2V and U2I communications;driver behavior;road safety;cooperative vehicles|
|[Air Traffic Control System Cyber Security Using Humans and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124305)|G. Atkins; K. Sampigethaya|10.1109/ICNS58246.2023.10124305|ADS-B;air traffic control;crew;cyber security;machine learning;ADS-B;air traffic control;crew;cyber security;machine learning|
|[A Predictive Control Framework for UAS Trajectory Planning Considering 4G/5G Communication Link Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124315)|R. Zuo; Z. Wang; C. E. Caicedo Bastidas; M. Cenk Gursoy; A. Solomon; Q. Qiu|10.1109/ICNS58246.2023.10124315|UAS;UAV Trajectory Planning;A*;Communication Quality;UAS;UAV Trajectory Planning;A*;Communication Quality|
|[RODAD: Resilience Oriented Decentralized Anomaly Detection for Urban Air Mobility Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124294)|S. Wei; H. Huang; G. Chen; E. Blasch; Y. Chen; R. Xu; K. Pham|10.1109/ICNS58246.2023.10124294|Decentralized;Container-Based Microservices;Edge-Fog-Cloud Computing;Machine Learning;Anomaly Detection;Decentralized;Container-Based Microservices;Edge-Fog-Cloud Computing;Machine Learning;Anomaly Detection|
|[Studying the impact of COVID-19 on the European Air Transportation Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124296)|J. Vossen; E. Spinielli; Q. Goens; R. Koelle|10.1109/ICNS58246.2023.10124296|component;network analysis;graph representation learning;change dynamics;component;network analysis;graph representation learning;change dynamics|
|[A Digital Twin Mixed-reality System for Testing Future Advanced Air Mobility Concepts: A Prototype](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124310)|J. Zhao; C. Conrad; Q. Delezenne; Y. Xu; A. Tsourdos|10.1109/ICNS58246.2023.10124310|Future Flight Vision and Roadmap;HADO;Unmanned Aircraft System;Digital Twin;Mixed-reality;Future Flight Vision and Roadmap;HADO;Unmanned Aircraft System;Digital Twin;Mixed-reality|
|[BB-Planner: an urban traffic dataset generation tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124291)|J. V. Navarro; P. M. Pallarés; C. C. Puchol; C. A. Chuquitarco-Jimenez; J. V. B. Tejedor; J. A. V. Carbó|10.1109/ICNS58246.2023.10124291|UAV;drone;dataset;simulation;surveillance;UTM;UAV;drone;dataset;simulation;surveillance;UTM|
|[A Framework for Uncertainty Assessment in Event Tree Safety Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124292)|S. Nikdel; J. Shortle|10.1109/ICNS58246.2023.10124292|Risk assessment;aircraft accidents;safety analysis;uncertainty distributions;event sequence diagram;Risk assessment;aircraft accidents;safety analysis;uncertainty distributions;event sequence diagram|
|[Dimensional Role Analysis: The Role of Humans and Automation for Increasingly Autonomous Aviation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124260)|A. R. Lacher; L. Ren; D. R. Maroney; C. Schulenberg; J. Daniels|10.1109/ICNS58246.2023.10124260|Autonomy;aviation systems;contextual framework;human-machine teaming;directed graph;Autonomy;aviation systems;contextual framework;human-machine teaming;directed graph|
|[Visual Navigation of UAVs in Indoor Corridor Environments using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124261)|M. S. Akremi; N. Neji; H. Tabia|10.1109/ICNS58246.2023.10124261|Unmanned aerial vehicles (UAVs);Visual navigation;Convolutional neural network (CNN);ResNet;DenseNet;autonomous navigation;Unmanned aerial vehicles (UAVs);Visual navigation;Convolutional neural network (CNN);ResNet;DenseNet;autonomous navigation|
|[Path-Based Statistical Modeling of Multipath Components in Propagation Channels for Wireless Communications in Unmanned Aviation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124256)|D. M. Mielke; D. Becker; M. Walter|10.1109/ICNS58246.2023.10124256|channel modeling;channel sounding;multipath tracking;aeronautical communications;channel modeling;channel sounding;multipath tracking;aeronautical communications|
|[Safety and Security Considerations on the Airbus Wake Energy Retrieval Program "fello'fly"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124302)|T. Ewert; N. Mäurer|10.1109/ICNS58246.2023.10124302|Wake Energy Retrieval;Air Traffic Management;Aeronautical Communications Security;Aircraft Separation;Wake Energy Retrieval;Air Traffic Management;Aeronautical Communications Security;Aircraft Separation|
|[Testing Operating Procedures for Large UAS with Detect and Avoid Capabilities in Civil Air Traffic Management Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124301)|T. Bleakley; E. Sunil|10.1109/ICNS58246.2023.10124301|UAS;Detect and Avoid;DAA;self-separation;collision avoidance;airspace integration;air traffic control;UAS;Detect and Avoid;DAA;self-separation;collision avoidance;airspace integration;air traffic control|
|[Noise Footprint of Electric Aviation at Regional Airports: A Case Study of VNY](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124303)|A. Datey; B. Kao; R. Francisco; M. Moradpour; R. Kim; N. Bronicki; J. Rakas|10.1109/ICNS58246.2023.10124303|airport;aviation;electric aircraft;noise;regional air mobility;airport;aviation;electric aircraft;noise;regional air mobility|
|[Benefits of Satellite Navigation to U.S. Airports Using Ground Based Augmentation System (GBAS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124318)|S. Qui; J. Rakas|10.1109/ICNS58246.2023.10124318|airline;airport;ground based augmentation system;instrument landing system;performance analysis;reliability;airline;airport;ground based augmentation system;instrument landing system;performance analysis;reliability|
|[BUBBLES Separation Management Environment: architecture and validation of a separation management tool for UTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124311)|C. C. Puchol; J. Vico Navarro; C. A. Chuquitarco-Jiménez; J. Vicente Balbastre Tejedor; P. Y. Pérez|10.1109/ICNS58246.2023.10124311|Separation management;U-space;UTM;separation minima;separation provision;BUBBLES;UAS (Unmanned Aircraft System);UAM;Separation management;U-space;UTM;separation minima;separation provision;BUBBLES;UAS (Unmanned Aircraft System);UAM|
|[Development of a Weather Capability for the Urban Air Mobility Airspace Research Roadmap](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124295)|T. Bonin; J. Jones; G. Enea; I. Levitt; N. Phojanamongkolkij|10.1109/ICNS58246.2023.10124295|urban air mobility;weather;research roadmap;requirements;urban air mobility;weather;research roadmap;requirements|
|[Towards Full Integration of Manned and Unmanned Air Traffic: A Test Case Study – Performance Results of Future All Aviation CNS Technology (FACT) Project](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124293)|R. Yeniçeri; E. Koyuncu; İ. Ösken; P. Cásek; M. Pálenská; İ. Orhan; H. Yapıcıoğlu; B. Açıkel; U. Turhan; M. O. Diken; K. -P. Sternemann; J. Pouzet; U. Doetsch|10.1109/ICNS58246.2023.10124293|CNS;airspace;integration;ATM;UTM;CNS;airspace;integration;ATM;UTM|
|[Tracking Performance Analysis of Ground-Based Radar Networks for Urban Air Mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124316)|R. Aievola; F. Causa; G. Fasano; L. Manica; G. Gentile; M. Dubois|10.1109/ICNS58246.2023.10124316|Urban Air Mobility;Radar network;Ground Based Surveillance System;Sense and Avoidance;Confliction detection;Urban Air Mobility;Radar network;Ground Based Surveillance System;Sense and Avoidance;Confliction detection|
|[Coarse Grained FLS-based Processor with Prognostic Malfunction Feature for UAM Drones using FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124332)|H. O. Ahmed|10.1109/ICNS58246.2023.10124332|distributed systems;fault tolerant systems;parallel circuits;prognostics;register transfer level implementation;urban air mobility;FPGA;flight rules;artificial intelligence;fuzzy-neuro;neuromorphic;robotics;automation;distributed systems;fault tolerant systems;parallel circuits;prognostics;register transfer level implementation;urban air mobility;FPGA;flight rules;artificial intelligence;fuzzy-neuro;neuromorphic;robotics;automation|
|[A Survey of Physical Layer-Aided UAV Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124288)|R. Dhakal; L. N. Kandel|10.1109/ICNS58246.2023.10124288|UAV security;Physical Layer Security;UAV security;Physical Layer Security|
|[UAS Air-Risk Assessment In and Around Airports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124319)|P. Pothana; J. Joy; P. Snyder; S. Vidhyadharan|10.1109/ICNS58246.2023.10124319|Unmanned Aircraft;Risk assessment;Airport operations;Unmanned Aircraft;Risk assessment;Airport operations|
|[Quantifying AAM Communications Quality using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10124258)|F. Wieland; D. Matolak; Z. Drescher|10.1109/ICNS58246.2023.10124258|Air-Ground Communications;Advanced Air Mobility (AAM);Machine Learning;Wireless Transmission;Air-Ground Communications;Advanced Air Mobility (AAM);Machine Learning;Wireless Transmission|
#### **2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)**
- DOI: 10.1109/ASMC57536.2023
- DATE: 1-4 May 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Reduction of Defects with Single Piece Upper Electrode (SPUE) in Oxide Etch Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121082)|F. Y. Chai; P. Hoon Tan; B. Tay; H. L. Chang; K. Yeow Pang|10.1109/ASMC57536.2023.10121082|;|
|[Reducing CFx residue from Etching Process by Optimizing Post Plamsa Treatment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121107)|K. D. Yang; H. Yang; E. Han; Y. Kim; T. Han; Y. J. Kim; N. Lim; J. Lim; J. J. Kim|10.1109/ASMC57536.2023.10121107|dry etching process;reaction byproducts;in-situ cleaning;CF residue;dry etching process;reaction byproducts;in-situ cleaning;CF residue|
|[Flux Real-Time Imaging System: High-Speed, High-Throughput Automated Visual Inspection and Defect Detection for Monitoring Flux Dispense](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121071)|C. Edwards; R. Wilcox; D. Vance; M. Swamy; N. Xu|10.1109/ASMC57536.2023.10121071|defect detection;machine learning;imaging system;machine vision;image processing;visual inspection;defect detection;machine learning;imaging system;machine vision;image processing;visual inspection|
|[Semiconductor Engineering Workforce Challenges, Industry and Academia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121139)|R. Pearson; K. Hirschman; S. Bolster; D. Gross|10.1109/ASMC57536.2023.10121139|semiconductor;workforce;education;microelectronics;manufacturing;semiconductor;workforce;education;microelectronics;manufacturing|
#### **2023 IEEE Wireless Communications and Networking Conference (WCNC)**
- DOI: 10.1109/WCNC55385.2023
- DATE: 26-29 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Measurements and Modeling of Narrow-Beam Channel Dispersion Characteristics in Vehicle-to-Infrastructure Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118782)|W. Zhang; T. Zhou; L. Liu|10.1109/WCNC55385.2023.10118782|Narrow-beam;channel measurements;channel modeling;dispersion characteristics;vehicle-to-infrastructure (V2I);Narrow-beam;channel measurements;channel modeling;dispersion characteristics;vehicle-to-infrastructure (V2I)|
|[A Novel GPU Acceleration Algorithm Based on CUDA and MPI for Ray Tracing Wireless Channel Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118847)|J. Chen; Y. Wang; J. Huang; C. -X. Wang|10.1109/WCNC55385.2023.10118847|RT;GPU;CUDA;MGTree;6G wireless communications;RT;GPU;CUDA;MGTree;6G wireless communications|
|[Adaptive Non-Stationary Vehicle-to-Vehicle MIMO Channel Simulator and Emulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10119045)|D. Huang; L. Xin; J. Huang; C. -X. Wang|10.1109/WCNC55385.2023.10119045|Non-stationary V2V channel;birth-death process;GBSM;SoFM;channel emulator;Non-stationary V2V channel;birth-death process;GBSM;SoFM;channel emulator|
|[Massive MIMO Channel Measurements for a Railway Station Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118778)|M. Hofer; D. Löschenbrand; S. Zelenbaba; G. Humer; B. Rainer; T. Zemen|10.1109/WCNC55385.2023.10118778|multiband;massive MIMO;railway;channel hardening;multiband;massive MIMO;railway;channel hardening|
#### **2023 International Conference on Protection and Automation of Power Systems (IPAPS)**
- DOI: 10.1109/IPAPS58344.2023
- DATE: 24-25 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Approach for Detection and Protection of Fault Excitation in Synchronous Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123313)|E. Zahmatkeshan|10.1109/IPAPS58344.2023.10123313|Synchronous machine;Fault excitation;Identification;Protection;Synchronous machine;Fault excitation;Identification;Protection|
|[A Protection Scheme for Multi-Terminal HVDC Grids Using Local Transient Voltage Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123317)|A. Khaleghi-Abbasabadi; M. Mehrabi-Kooshki; S. Jamali|10.1109/IPAPS58344.2023.10123317|component;Multi-Terminal HVDC system;MMC-HVDC protection;Transient Voltage;Non-unit protection;High-impedance faults;Hilbert transform;component;Multi-Terminal HVDC system;MMC-HVDC protection;Transient Voltage;Non-unit protection;High-impedance faults;Hilbert transform|
|[A Reliability Oriented Method for Optimal Placement Of Sectionalizing Switches Using BPSO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123307)|S. Fayyazi; E. Azad-Farsani|10.1109/IPAPS58344.2023.10123307|switch;Binary Particle Swarm Algorithm;Placement;switch;Binary Particle Swarm Algorithm;Placement|
|[Accurate Evaluation of Dynamic Impedance Characteristics in Distance Protection Relays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10123328)|S. A. Rashidaee; A. Raeisi; V. Samadian|10.1109/IPAPS58344.2023.10123328|Protection relay;Distance;Relay setting;Quadrilateral characteristic;Dynamic;Static;Protection relay;Distance;Relay setting;Quadrilateral characteristic;Dynamic;Static|
#### **2023 IEEE Custom Integrated Circuits Conference (CICC)**
- DOI: 10.1109/CICC57935.2023
- DATE: 23-26 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A 28nm All-Digital, 1.92-7.32mV/LSB, 0.5-2GS/s sample rate, 0-latency Voltage Sensor with Dynamic PVT Calibration for Wide-range Adaptive Voltage Scaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121271)|Y. Du; H. Ge; Z. Chen; K. Zhou; Z. Shen; W. Shan|10.1109/CICC57935.2023.10121271|;|
|[Synchronous Die-to-Die Signaling Using Aeonic Connect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121280)|M. v. Ierssel; F. Buhler; D. Moore; J. Fredenburg|10.1109/CICC57935.2023.10121280|;|
|[A 65 nm 2.02 mW 50 Mbps Direct Analog to MJPEG Converter for Video Sensor Nodes using low-noise Switched Capacitor MAC-Quantizer with automatic calibration and Sparsity-aware ADC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121318)|K. Gaurav Kumar; G. Barik; B. Chatterjee; S. Bose; S. Maity; S. Sen|10.1109/CICC57935.2023.10121318|;|
|[A 40nm 0.35V 25MHz Half-Select Disturb-Free Bitinterleaving 10T SRAM With Data-Aware Write-Path](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10121235)|Y. Li; J. Chen; Y. Wang; Z. Yin; H. Chen; Y. Ha|10.1109/CICC57935.2023.10121235|;|
#### **2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)**
- DOI: 10.1109/ESARS-ITEC57127.2023
- DATE: 29-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Intelligent Driver Monitoring System for Safe Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114838)|A. Vijay|10.1109/ESARS-ITEC57127.2023.10114838|autonomous driving;convolutional neural net-work;driver monitoring system;MobileNetV2;autonomous driving;convolutional neural net-work;driver monitoring system;MobileNetV2|
|[Fuel Cell/Battery Hybrid Electric System for UAV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114892)|J. -I. Corcau; L. Dinca|10.1109/ESARS-ITEC57127.2023.10114892|fuel cell;battery;hybrid;fuzzy logic;simulation;load;fuel cell;battery;hybrid;fuzzy logic;simulation;load|
|[On the dynamic behavior of an aged Lithium-iron phosphate battery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114902)|D. Iannuzzi; B. Fahimi; C. Napolitano|10.1109/ESARS-ITEC57127.2023.10114902|Dynamic modelling;SOH index;Testing;Dynamic modelling;SOH index;Testing|
|[New Model-Based Algorithm for Fault Detection and Identification in DC Railway Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114876)|D. Lanzarotto; F. Wallart; G. Blaszczyk; P. Verrax; A. Bertinato; L. Leclere|10.1109/ESARS-ITEC57127.2023.10114876|Railway DC;Model-Based Algorithm;Fault Detection;Railway;Railway DC;Model-Based Algorithm;Fault Detection;Railway|
#### **2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE)**
- DOI: 10.1109/CPERE56564.2023
- DATE: 19-21 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Optimized Photovoltaic Energy System for Tier 2 Electricity Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10119597)|S. Gullu; I. Batarseh; M. Salameh; S. Al-Hallaj|10.1109/CPERE56564.2023.10119597|Battery Management System;Battery Sizing;Battery Model;Photovoltaic;Thermal Management System;Battery Management System;Battery Sizing;Battery Model;Photovoltaic;Thermal Management System|
|[Ecosystem based Opportunity Identification and Feasibility Evaluation for Demand Side Management Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10119582)|Z. G. Ma; K. Christensen; M. Vaerbak; N. Fatras; D. A. Howard; B. N. Jørgensen|10.1109/CPERE56564.2023.10119582|demand side management;regional need;domain analysis;business ecosystem;CSTEP ecosystem analysis;demand side management;regional need;domain analysis;business ecosystem;CSTEP ecosystem analysis|
|[Stand-alone PV System For Remote Area Refrigeration: Techno-economic Case Study in Egypt.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10119596)|N. A. El-Din; W. R. Anis; A. A. M. S. Ahmed; S. O. Abdellatif|10.1109/CPERE56564.2023.10119596|Renewable energy resources;PV system;Reliability;Cost of kTrh generated;System economics;State of charge (SOC).;Renewable energy resources;PV system;Reliability;Cost of kTrh generated;System economics;State of charge (SOC).|
|[Design and optimization of DC-grids power exchange](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10119639)|K. Liu; H. Yamada; K. Iwatsuki; T. Otsuji|10.1109/CPERE56564.2023.10119639|power exchange;microgrid;optimization;power exchange;microgrid;optimization|
#### **2023 7th International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS)**
- DOI: 10.1109/ICMSS56787.2023
- DATE: 6-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Java EE Data Persistence Layer Model Based on Design Pattern and DbUtils](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117975)|H. Ouyang; T. Palaoag|10.1109/ICMSS56787.2023.10117975|Java EE;JDBC;Data Persistence Layer;DbUtils;Reflection;Desing Pattern;Annotation;Java EE;JDBC;Data Persistence Layer;DbUtils;Reflection;Desing Pattern;Annotation|
|[Leveraging Best Industry Practices to Developing Software for Academic Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117776)|L. T. Connelly; M. L. Hammel; L. Lin|10.1109/ICMSS56787.2023.10117776|source code management;version control;issue tracking;branching strategies;pull requests;coding standards;clean code;automated testing;unit testing;CI/CD;specifications;software quality;source code management;version control;issue tracking;branching strategies;pull requests;coding standards;clean code;automated testing;unit testing;CI/CD;specifications;software quality|
|[Application and Improvement of Agile Development in Intelligent Health Hut Software Project](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118316)|H. Wang; Z. Ma|10.1109/ICMSS56787.2023.10118316|Intelligent Health Hut;software project management;agile development;Scrum;Intelligent Health Hut;software project management;agile development;Scrum|
|[Architecture Design of Enterprise Information System Based on Docker and DevOps Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117715)|T. Chen; H. Suo|10.1109/ICMSS56787.2023.10117715|Docker;Virtual machine;Container technology;Enterprise information system;DevOps;Distributed architecture;Docker;Virtual machine;Container technology;Enterprise information system;DevOps;Distributed architecture|
#### **2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)**
- DOI: 10.1109/ICIPTM57143.2023
- DATE: 22-24 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Revealing AI-Based Ed-Tech Tools Using Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118162)|A. Raj; V. Sharma; S. Rani; B. Balusamy; A. K. Shanu; A. Alkhayyat|10.1109/ICIPTM57143.2023.10118162|Big Data;Technology Enabled Learning (TEL);Outcome Based Education (OBE) RapidMiner;MicroStrategy;Alteryx;Ed-Tech;Big Data;Technology Enabled Learning (TEL);Outcome Based Education (OBE) RapidMiner;MicroStrategy;Alteryx;Ed-Tech|
|[NutriFACT: An Android based Mobile Application for Intake of Food Nutrition during Rare Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118023)|M. Parmar; H. Rajgor; S. Khant|10.1109/ICIPTM57143.2023.10118023|Nutrition;Food;Rare Disease;Mobile application;Health diet;Nutrition;Food;Rare Disease;Mobile application;Health diet|
|[Covid19 Disease Assessment Using CNN Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118086)|M. S. C; S. K. Mishra; S. Sandhya; K. Vidhya; J. R; G. Manjula|10.1109/ICIPTM57143.2023.10118086|COVID-19;Deep Learning;Neural Network;Accuracy;Scaling;Noise Removal;Website;COVID-19;Deep Learning;Neural Network;Accuracy;Scaling;Noise Removal;Website|
|[Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10118171)|G. Sujatha; M. Ayyannan; S. G. Priya; V. Arun; A. N. Arularasan; M. J. Kumar|10.1109/ICIPTM57143.2023.10118171|Artificial Neural Network;Phishing URL;Legitimate URL;Jupyter;Google Colab;Datasets;Artificial Neural Network;Phishing URL;Legitimate URL;Jupyter;Google Colab;Datasets|
#### **2023 International Conference on Code Quality (ICCQ)**
- DOI: 10.1109/ICCQ57276.2023
- DATE: 22-22 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Mutant Selection Strategies in Mutation Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114663)|R. Pitts|10.1109/ICCQ57276.2023.10114663|mutation testing;mutation analysis;random selection;mutant selection;equivalent mutants;mutation testing;mutation analysis;random selection;mutant selection;equivalent mutants|
|[Understanding Software Performance Challenges an Empirical Study on Stack Overflow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114662)|D. A. Al Shoaibi; M. W. Mkaouer|10.1109/ICCQ57276.2023.10114662|performance challenges;performance regression;software quality;performance challenges;performance regression;software quality|
|[Applying Machine Learning Analysis for Software Quality Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114664)|A. Khan; R. R. Mekuria; R. Isaev|10.1109/ICCQ57276.2023.10114664|Machine learning;correlation method;metrics;defect prediction;cumulative failure prediction;Machine learning;correlation method;metrics;defect prediction;cumulative failure prediction|
|[Test-based and metric-based evaluation of code generation models for practical question answering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114665)|S. Kovalchuk; D. Fedrushkov; V. Lomshakov; A. Aliev|10.1109/ICCQ57276.2023.10114665|code generation;test generation;natural language generation;evaluation;metrics;natural language processing;code generation;test generation;natural language generation;evaluation;metrics;natural language processing|
#### **2023 International Applied Computational Electromagnetics Society Symposium (ACES)**
- DOI: 10.23919/ACES57841.2023
- DATE: 26-30 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Partial Shielding to Improve Sensitivity of a Fully Passive Bio-Magnetic Signal Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114779)|K. Zhu; A. Kiourti|10.23919/ACES57841.2023.10114779|Bio-magnetic fields;bio-sensors;shielding;noise;Bio-magnetic fields;bio-sensors;shielding;noise|
|[Noise Coil For Improving Sensitivity in a Fully Passive Bio-Magnetic Signal Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114693)|K. Zhu; A. Kiourti|10.23919/ACES57841.2023.10114693|Bio-magnetic fields;biosensors;coils;noise;Bio-magnetic fields;biosensors;coils;noise|
|[In Vitro Validation of Partial Shielding to Detect an Extremely Weak and Wideband Bio-Magnetic Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114791)|K. Zhu; A. Kiourti|10.23919/ACES57841.2023.10114791|Bio-magnetic fields;coils;noise;shielding;Bio-magnetic fields;coils;noise;shielding|
|[Impact Evaluation of an External Point Source to a Generalized Model of the Human Neck](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114759)|A. A. Varvari; D. I. Karatzidis; T. Ohtani; Y. Kanai; N. V. Kantartzis|10.23919/ACES57841.2023.10114759|biomedical engineering;body area networks;computational electromagnetics;Green's function methods;biomedical engineering;body area networks;computational electromagnetics;Green's function methods|
|[Fast Computational Dosimetry of Transcranial Electric Stimulation Using Probabilistic Matrix Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114783)|D. Wang; N. I. Hasan; L. J. Gomez|10.23919/ACES57841.2023.10114783|Probabilistic matrix decomposition;brain stimulation;computational dosimetry;reciprocity;Probabilistic matrix decomposition;brain stimulation;computational dosimetry;reciprocity|
|[Integral Equation for Analyzing Neuron Response to Non-invasive Electromagnetic Brain Stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114742)|D. M. Czerwonky; L. J. Gomez|10.23919/ACES57841.2023.10114742|E-field dosimetry;ephaptic coupling;non-invasive brain stimulation;E-field dosimetry;ephaptic coupling;non-invasive brain stimulation|
|[Transmission Improvement of Windshield with Periodic Structure at 5G Frequency Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114709)|J. Kim; C. J. Reddy|10.23919/ACES57841.2023.10114709|Windshield;IR absorption;transmission loss;periodic structure;5G frequency;Windshield;IR absorption;transmission loss;periodic structure;5G frequency|
|[Latest Features in Altair Feko 2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114763)|J. van Tonder; R. Marchand; D. Le Roux; U. Jakobus; M. Helwig; I. Zhabitskiy; F. Cátedra; L. Lozano; M. Vogel|10.23919/ACES57841.2023.10114763|Coatings;Feko;FSS;hybrid MoM;MLFMM;radomes;SBR;SRT;UTD;WinProp;Coatings;Feko;FSS;hybrid MoM;MLFMM;radomes;SBR;SRT;UTD;WinProp|
|[Circularly Polarized Series Fed Antenna Array for Automotive 5G mmWave Communcations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114794)|A. Yacoub; D. N. Aloi|10.23919/ACES57841.2023.10114794|5G mmWave;FEKO;Microstrip patch arrays;Automotive antennas;5G mmWave;FEKO;Microstrip patch arrays;Automotive antennas|
|[Annular Scan Volume Phased Array Fed Reflectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114737)|T. J. Prince; T. H. Hand; E. Lier; D. S. Filipovic|10.23919/ACES57841.2023.10114737|;|
|[Uncertain Material Parameter Extraction using FEKO Optimizations for Space Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114736)|J. A. McVay; S. Rai; I. Avina; H. Tran|10.23919/ACES57841.2023.10114736|conformal antenna;laser ablation;leaky-wave antenna;multilayer insulation;parameter extraction;space-based capabilities;technical embroidery;conformal antenna;laser ablation;leaky-wave antenna;multilayer insulation;parameter extraction;space-based capabilities;technical embroidery|
|[Case Study on Deployable Origami Antennas for Terahertz CubeSat Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114788)|A. J. Alqaraghuli; A. Singh; J. M. Jornet|10.23919/ACES57841.2023.10114788|CubeSat antennas;Origami antennas;Terahertz communication and sensing;CubeSat swarms;CubeSat antennas;Origami antennas;Terahertz communication and sensing;CubeSat swarms|
|[Simulation of Metallic Quantum Gate Structures with Advanced Volume Integral Equation Solver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114744)|Y. Wang; W. Sun; Z. Jacob; D. Jiao|10.23919/ACES57841.2023.10114744|;|
|[Low-Loss Wireless Implant Telemetry Using Magnetoinductive Waveguides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114778)|C. Jenkins; A. Kiourti|10.23919/ACES57841.2023.10114778|Electrically small resonant loops;magnetoinductive waveguide (MIW);magnetoinductive waves (MI waves);wireless body area networks (WBANs);implant telemetry;Electrically small resonant loops;magnetoinductive waveguide (MIW);magnetoinductive waves (MI waves);wireless body area networks (WBANs);implant telemetry|
|[Integral Equation for Analyzing Neuron Response to Non-invasive Electromagnetic Brain Stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114766)|D. M. Czerwonky; L. J. Gomez|10.23919/ACES57841.2023.10114766|E-field dosimetry;ephaptic coupling;non-invasive brain stimulation;E-field dosimetry;ephaptic coupling;non-invasive brain stimulation|
|[Fast Multi-source Electromagnetic Simulations using Augmented Partial Factorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114774)|H. -C. Lin; Z. Wang; C. W. Hsu|10.23919/ACES57841.2023.10114774|augmented partial factorization;frequency-domain method;multi-source;scattering matrix;augmented partial factorization;frequency-domain method;multi-source;scattering matrix|
|[Active Transmission-Type Metasurface for Linear-to-Circular Polarization Conversion at a Certain Frequency Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114708)|J. Lin; D. Li; L. Lin; W. Yu; J. Sheng|10.23919/ACES57841.2023.10114708|Active metasurface;Linear to circular(LTC);Polarization conversion;Transmission-Type;Active metasurface;Linear to circular(LTC);Polarization conversion;Transmission-Type|
|[Noise Coil For Improving Sensitivity in a Fully Passive Bio-Magnetic Signal Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114790)|K. Zhu; A. Kiourti|10.23919/ACES57841.2023.10114790|Bio-magnetic fields;biosensors;coils;noise;Bio-magnetic fields;biosensors;coils;noise|
|[Effect of Thermal Design Considerations of Implanted Antenna on Tissue Heating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114712)|A. Alemaryeen; S. Noghanian|10.23919/ACES57841.2023.10114712|electromagnetic-heating;implanted antennas;multi-physics simulation;optimization;thermal analysis;electromagnetic-heating;implanted antennas;multi-physics simulation;optimization;thermal analysis|
|[Low-Loss Wireless Implant Telemetry Using Magnetoinductive Waveguides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114755)|C. Jenkins; A. Kiourti|10.23919/ACES57841.2023.10114755|Electrically small resonant loops;magnetoinductive waveguide (MIW);magnetoinductive waves (MI waves);wireless body area networks (WBANs);implant telemetry;Electrically small resonant loops;magnetoinductive waveguide (MIW);magnetoinductive waves (MI waves);wireless body area networks (WBANs);implant telemetry|
|[Microwave Detection of Cancer-Cell Using FDTD Half-Space*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114715)|A. A. B. Shahid; N. R. Roy|10.23919/ACES57841.2023.10114715|API;Cancer;EM imaging;FDTD;Scattering;API;Cancer;EM imaging;FDTD;Scattering|
|[Estimation of Dielectric Property for Biological Tissues by Multi-Frequency Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114706)|C. Aydinalp; I. Dilman; S. Joof; T. Yilmaz; M. Çayören; M. N. Akinci|10.23919/ACES57841.2023.10114706|microwave material characterization;open-ended coaxial probes;microwave material characterization;open-ended coaxial probes|
|[Performance Evaluation of Two Particle Swarm Optimization Adaptations for Microwave Breast Hyperthermia Focusing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114722)|G. Yildiz; I. Farhat; C. Aydinalp; L. Farrugia; K. Z. Adami; T. Yilmaz; I. Akduman|10.23919/ACES57841.2023.10114722|;|
|[Surface Propagation for the Mars Helicopter Mission using Parabolic Equation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114733)|N. Chahat; G. Gupta; M. D. Chase; C. Duncan|10.23919/ACES57841.2023.10114733|communication;dominant path model;deterministic two ray model;knife edge diffraction;Mars;helicopter;propagation;parabolic equation;surface;telecommunication;UHF;communication;dominant path model;deterministic two ray model;knife edge diffraction;Mars;helicopter;propagation;parabolic equation;surface;telecommunication;UHF|
|[FEKO Modeling for an Alternative Feed Method for Arecibo Incoherent Scatter Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114683)|M. Alkhatib; J. K. Breakall; U. L. Rohde; A. K. Poddar; O. Alzaabi|10.23919/ACES57841.2023.10114683|Gregorian sub-reflector;Circle of Least Confusion;numerical methods;Gregorian sub-reflector;Circle of Least Confusion;numerical methods|
|[A look at a novel curved antenna design with FEKO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114703)|J. K. Breakall; U. L. Rohde; A. K. Poddar|10.23919/ACES57841.2023.10114703|Curved antenna;integral equation;numerical optimization;Curved antenna;integral equation;numerical optimization|
|[Fast Multi-source Electromagnetic Simulations using Augmented Partial Factorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114786)|H. -C. Lin; Z. Wang; C. W. Hsu|10.23919/ACES57841.2023.10114786|augmented partial factorization;frequency-domain method;multi-source;scattering matrix;augmented partial factorization;frequency-domain method;multi-source;scattering matrix|
|[Non-Uniform Time-Stepping and Windowing for Fast Simulation of Photodetectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114796)|E. Simsek; I. M. Anjum; T. F. Carruthers; C. R. Menyuk|10.23919/ACES57841.2023.10114796|drift-diffusion;photodetector;photodiode;drift-diffusion;photodetector;photodiode|
|[One to Twenty Million MOM Unknowns: Direct Solve on a PC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114696)|J. Shaeffer|10.23919/ACES57841.2023.10114696|Direct Factor Method of Moments;low rank matrix algebra and electromagnetic scattering;Direct Factor Method of Moments;low rank matrix algebra and electromagnetic scattering|
|[An Augmented Stochastic Green's Function Method with the Short-orbit Contribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114787)|S. Luo; Q. J. Lim; S. Lin; Z. Peng|10.23919/ACES57841.2023.10114787|Chaos;electromagnetic coupling;Green's function;ray tracing;statistical analysis;Chaos;electromagnetic coupling;Green's function;ray tracing;statistical analysis|
|[Simulation of Metallic Quantum Gate Structures with Advanced Volume Integral Equation Solver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114704)|Y. Wang; W. Sun; Z. Jacob; D. Jiao|10.23919/ACES57841.2023.10114704|;|
|[Discrete Taylor Transform and Inverse Transform in 2D and 3D](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114785)|A. Baghai-Wadji|10.23919/ACES57841.2023.10114785|;|
|[Generalization and Acceleration of the Trotter's Product Rule by Symmetrization and Other Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114752)|A. Baghai-Wadji|10.23919/ACES57841.2023.10114752|;|
|[Advanced hp-Refinement Methodology for Singular Solutions in Frequency-Domain Computational Electromagnetics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114762)|B. M. Notaroš; J. J. Harmon; J. Corrado|10.23919/ACES57841.2023.10114762|anisotropic refinement;exponential convergence;finite element method;higher order modeling;hp-refinement;multi-level refinement;refinement-by-superposition;singular solutions;anisotropic refinement;exponential convergence;finite element method;higher order modeling;hp-refinement;multi-level refinement;refinement-by-superposition;singular solutions|
|[Visualization of Electric and Magnetic Fields Inside Circular and Baffle Waveguides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114743)|Z. Hasan; A. Elsherbeni|10.23919/ACES57841.2023.10114743|Electromagnetic Fields;TE Mode;TM Mode;Software;Waveguides;Cavities;Electromagnetic Fields;TE Mode;TM Mode;Software;Waveguides;Cavities|
|[Electromagnetic Visualization Tools for Effective Undergraduate Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114720)|R. Lumnitzer; A. Z. Elsherbeni|10.23919/ACES57841.2023.10114720|coaxial cable;FDFD;parallel-plate waveguide;two-wire transmission line;coaxial cable;FDFD;parallel-plate waveguide;two-wire transmission line|
|[Scalable Snap Together Array Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114682)|G. Mitchell; T. Anthony; Z. Larimore; A. Good; P. Parsons|10.23919/ACES57841.2023.10114682|Lego;electrically steered array;multi-band;scalable;reconfigurable;additive manufacturing;Lego;electrically steered array;multi-band;scalable;reconfigurable;additive manufacturing|
|[Techniques to Improve TCDA Bandwidth Beyond 46:1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114728)|S. B. Venkatakrishnan; J. Volakis|10.23919/ACES57841.2023.10114728|Baluns;FSS Superstrates;High-isolation;Low-angle scanning;R-Card;TCDA;UWB Apertures;Baluns;FSS Superstrates;High-isolation;Low-angle scanning;R-Card;TCDA;UWB Apertures|
|[Three-Dimensional Radar Cross Section Patterns of Volumetrically-Distributed Random Antenna Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114738)|K. Buchanan; S. Wheeland|10.23919/ACES57841.2023.10114738|;|
|[Doppler Superpulse Processing for Improved Tomographic Characterization of Space Objects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114771)|A. Serrano; R. L. Morrison|10.23919/ACES57841.2023.10114771|Doppler superpulses;Doppler Tomography;DTI;RTI;Doppler superpulses;Doppler Tomography;DTI;RTI|
|[X-Band Radar for Monitoring Space Debris](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114710)|M. Schoenfeld; A. Swochak|10.23919/ACES57841.2023.10114710|Radar;X-band;Space Debris;NASA;Radar;X-band;Space Debris;NASA|
|[On-Orbit Test Range Visualization Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114718)|C. D. Gonzalez-Huertas; R. Mata; J. R. Birge|10.23919/ACES57841.2023.10114718|radar;space test range;control software;radar;space test range;control software|
|[Enabling Deep Space Radar with EM Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114692)|D. L. Grimes; M. E. MacDonald|10.23919/ACES57841.2023.10114692|gyrotron;HUSIR;MAGY;radar;Simulation;Surf3D;gyrotron;HUSIR;MAGY;radar;Simulation;Surf3D|
|[Millstone Hill Radar L-Band Sensitivity Upgrade](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114702)|D. F. Beals; M. Abouzhara; M. Schoenfeld; P. MacGibbon|10.23919/ACES57841.2023.10114702|Millstone Hill Radar;transmitter;klystron;L-Band;Millstone Hill Radar;transmitter;klystron;L-Band|
|[Radar-Vision Fusion for Vehicle Detection and Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114745)|Y. Wu; D. Li; Y. Zhao; W. Yu; W. Li|10.23919/ACES57841.2023.10114745|deep learning;radar-vision fusion;vehicle detection;deep learning;radar-vision fusion;vehicle detection|
|[Non-destructive testing on length of steel pipelines partially embedded in land surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114768)|X. Liu; L. Gong; B. Chen; W. Guan; Z. Luo; Z. Liu|10.23919/ACES57841.2023.10114768|partially embedded steel pipeline;non-destructive testing;waveguide transitions;return loss;partially embedded steel pipeline;non-destructive testing;waveguide transitions;return loss|
|[Efficient Model Assisted Probability of Detection Estimations in Eddy Current NDT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114740)|J. Qiu; M. Xu; Y. Bao; J. Song|10.23919/ACES57841.2023.10114740|adaptive cross approximation (ACA) algorithm;boundary element method (BEM);eddy current testing (ECT);model assisted probability of detection (MAPOD);singular value decomposition (SVD);adaptive cross approximation (ACA) algorithm;boundary element method (BEM);eddy current testing (ECT);model assisted probability of detection (MAPOD);singular value decomposition (SVD)|
|[Learning-Based Predictive Uncertainty Estimation of Magnetic Flux Leakage Data for Parametric Defect Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114705)|Z. Li; X. Huang; S. Mukherjee; L. Peng; Y. Xu; Y. Deng|10.23919/ACES57841.2023.10114705|Uncertainty Estimation;Magnetic flux leakage;Deep Ensemble;Autoencoder;Uncertainty Estimation;Magnetic flux leakage;Deep Ensemble;Autoencoder|
|[Enhanced Defect Detection in NDE Using Pixel Level Data Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114697)|S. Mukherjee; L. Udpa; Y. Deng|10.23919/ACES57841.2023.10114697|Complementary sparse representation;Eddy current;Magnetic flux leakage;Nondestructive evaluation;Complementary sparse representation;Eddy current;Magnetic flux leakage;Nondestructive evaluation|
|[Fast Reconstruction of Optimal Pupil Apodization for Telescope Coronagraph Design Based on the Method of Moments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114707)|S. Yan; P. Chen|10.23919/ACES57841.2023.10114707|Coronagraph;eigenvalue problem;method of moments;mode reconstruction;Coronagraph;eigenvalue problem;method of moments;mode reconstruction|
|[Detection and Remediation of Internal Resonance Problems in Integral Equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114730)|M. M. Bibby; A. F. Peterson|10.23919/ACES57841.2023.10114730|computational electromagnetics;integral equations;internal resonance problems;numerical solutions;residuals;computational electromagnetics;integral equations;internal resonance problems;numerical solutions;residuals|
|[An Automated Adaptive h-Refinement Technique for Solving SIEs with Nonconformal Meshes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114793)|C. Díaz-Cáez; S. Yan|10.23919/ACES57841.2023.10114793|;|
|[Solving Combined Field Integral Equations with Physics-informed Residual Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114780)|M. LI; T. SHAN; F. YANG; S. XU|10.23919/ACES57841.2023.10114780|machine learning;physics-informed residual learning;graph neural networks;integral equation;electromagnetic modeling;machine learning;physics-informed residual learning;graph neural networks;integral equation;electromagnetic modeling|
|[Compressing a Fast Multipole Method Representation of an Integral Equation Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114747)|R. J. Adams; J. C. Young; S. D. Gedney|10.23919/ACES57841.2023.10114747|fast multipole method;integral equation;fast multipole method;integral equation|
|[Improvement of Continuity Condition on the PEC-Dielectric Interfaces for the Solution of Volume-Surface Integral Equation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114769)|Z. Xia; J. Liu; Z. Li; J. Song|10.23919/ACES57841.2023.10114769|Continuity condition (CC);method of moments (MoM);volume-surface integral equation (VSIE);Continuity condition (CC);method of moments (MoM);volume-surface integral equation (VSIE)|
|[Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchronous Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114719)|S. M. Sajjad Hossain Rafin; Q. Ali; O. A. Mohammed|10.23919/ACES57841.2023.10114719|Sub-Harmonic Synchronous Machines;Brushless Machines;Sub-Harmonic Excitation;Hybrid Excitation;Sub-Harmonic Synchronous Machines;Brushless Machines;Sub-Harmonic Excitation;Hybrid Excitation|
|[Design of a Multilayer Microstrip Delay Line on a Water Based Composite Dielectric Medium](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114724)|R. Agaram; K. Sathish; N. H. N; A. A. Deshpande; S. Sethi|10.23919/ACES57841.2023.10114724|Delay;Dielectric constant;Delay;Dielectric constant|
|[Enhanced EMI models for Underwater targets detection and classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114760)|F. Shubitidze; I. Shamata; B. E. Barrowes|10.23919/ACES57841.2023.10114760|Underwater sensors;UXO;Inversion;Classification;EMI;Sensing;orthogonal models;Underwater sensors;UXO;Inversion;Classification;EMI;Sensing;orthogonal models|
|[Modeling and Measurement of the Isolation Effectiveness of Inductive Metal Screens](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114764)|R. Carroll; L. S. Riggs; M. L. Waller; M. Hartline; A. N. Beal|10.23919/ACES57841.2023.10114764|Inductive screens;isolation effectiveness;scattering from periodic structures;Inductive screens;isolation effectiveness;scattering from periodic structures|
|[Informed Deep Learning in Metamaterials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114734)|O. Khatib; S. Ren; J. Malof; W. J. Padilla|10.23919/ACES57841.2023.10114734|metamaterials;metasurfaces;deep learning;physics informed;inverse design;metamaterials;metasurfaces;deep learning;physics informed;inverse design|
|[Generative Neural Network Enables Reconfigurable Metasurface on Real-Time Free-Form Targets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114792)|E. Wen; X. Yang; D. F. Sievenpiper|10.23919/ACES57841.2023.10114792|neural network;deep learning;reconfigurable;conformal;metasurfaces;inverse design;neural network;deep learning;reconfigurable;conformal;metasurfaces;inverse design|
|[Simultaneous Beam-Steering and Polarization Conversion Using a Varactor-Integrated Metasurface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114765)|X. Yang; D. Bharadia; D. F. Sievenpiper|10.23919/ACES57841.2023.10114765|metasurface;beam steering;polarization conversion;metasurface;beam steering;polarization conversion|
|[Hybrid Electrocapillary Actuation of Liquid Metal for an Intelligent Reflecting Surface Unit Cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114741)|T. Tahmid; M. T. Kouchi; S. J. Dacuycuy; G. A. V. Manio; W. A. Shiroma; A. T. Ohta|10.23919/ACES57841.2023.10114741|intelligent reflective surface;liquid metal;intelligent reflective surface;liquid metal|
|[A 2-Bit Reconfigurable Reflect-Array Antenna Element at Ka-Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114729)|E. Wang; G. Peng; Z. H. Jiang|10.23919/ACES57841.2023.10114729|2-bit quantization;beam scanning;millimeter-wave;reconfigurable reflect-array (RRA);2-bit quantization;beam scanning;millimeter-wave;reconfigurable reflect-array (RRA)|
|[Ruggedized Reconfigurable Antennas through the Implementation of Innovative Mechanical Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114782)|G. Mackertich-Sengerdy; S. D. Campbell; P. L. Werner; D. H. Werner|10.23919/ACES57841.2023.10114782|compliant mechanism electromagnetics;electromechanical reconfiguration;reconfigurable antennas;ruggedized antennas;compliant mechanism electromagnetics;electromechanical reconfiguration;reconfigurable antennas;ruggedized antennas|
|[Active Transmission-Type Metasurface for Linear-to-Circular Polarization Conversion at a Certain Frequency Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114684)|J. Lin; D. Li; L. Lin; W. Yu; J. Sheng|10.23919/ACES57841.2023.10114684|Active metasurface;Linear to circular(LTC);Polarization conversion;Transmission-Type;Active metasurface;Linear to circular(LTC);Polarization conversion;Transmission-Type|
|[Observations on Adaptive Refinement Using the Locally-corrected Nyström Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114687)|A. F. Peterson|10.23919/ACES57841.2023.10114687|adaptive refinement;computational electromagnetics;dipole antennas;integral equations;numerical solutions;adaptive refinement;computational electromagnetics;dipole antennas;integral equations;numerical solutions|
|[Anomalous Current Spikes in the Locally Corrected Nystrom Discretization of Volume Integral Equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114739)|J. C. Young; R. J. Adams; S. D. Gedney|10.23919/ACES57841.2023.10114739|integral equation methods;locally corrected Nystrom method;moment method;integral equation methods;locally corrected Nystrom method;moment method|
|[p-Adaptive Quadrature for the Chebyshev-based Boundary Integral Equation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114725)|D. Aslanyan; C. Sideris|10.23919/ACES57841.2023.10114725|;|
|[A Preliminary Comparison of MLFMA and $\mathcal{H}$-matrix Acceleration of Locally Corrected Nyström Solutions to Scattering Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114754)|V. Okhmatovski; I. Jeffrey|10.23919/ACES57841.2023.10114754|Integral equations;Nystrom method;hierarchi-cal matrices;fast direct solution;Integral equations;Nystrom method;hierarchi-cal matrices;fast direct solution|
|[Toward Photonic Nanojet Imaging for Microscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114700)|N. M. Bøgely Rehn; P. -E. Hansen; M. Karamehmedović|10.23919/ACES57841.2023.10114700|optical microscopy;photonic jets;scattering;numerical field computation;optical microscopy;photonic jets;scattering;numerical field computation|
|[Simulation of Two-dimensional Propagation Problems involving Metasurfaces using a Surface Integral Equation Solver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114775)|S. C. Sierra; R. Zhao; R. Chen; H. Bagci|10.23919/ACES57841.2023.10114775|Electromagnetic analysis;generalized sheet transition conditions;metasurfaces;surface integral equations;Electromagnetic analysis;generalized sheet transition conditions;metasurfaces;surface integral equations|
|[Electromagnetic Scattering Analysis using a Hybridizable Discontinuous Galerkin-Boundary Integral Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114732)|R. Zhao; M. Dong; L. Chen; H. Bagci; J. Hu|10.23919/ACES57841.2023.10114732|Hybridizable discontinuous Galerkin;boundary integral equation;electromagnetic scattering;Hybridizable discontinuous Galerkin;boundary integral equation;electromagnetic scattering|
|[New Implementations of Complete Radiation Boundary Conditions for Maxwell's Equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114713)|T. Hagstrom|10.23919/ACES57841.2023.10114713|radiation conditions;time-domain methods;radiation conditions;time-domain methods|
|[On the Spiral Resonator Arrays Size Analysis for Misalignment Compensation in Wireless Power Transfer Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114797)|N. Fontana; S. Barmada; M. Raugi|10.23919/ACES57841.2023.10114797|Wireless Power Transfer;Metamaterials;Metasurfaces;Misalignment Compensation;Wireless Power Transfer;Metamaterials;Metasurfaces;Misalignment Compensation|
|[Design of Chiral 4-Tiered Wireless Power Transfer (WPT) Systems for Vertiports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114746)|S. M. Khan; C. Bailey|10.23919/ACES57841.2023.10114746|Vertiports;eVTOL;Helical antennas;parasitic elements;wireless power transfer (WPT);near-field;chirality;Vertiports;eVTOL;Helical antennas;parasitic elements;wireless power transfer (WPT);near-field;chirality|
|[End-to-end Nanophotonic Inverse Design for Computational Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114758)|Z. Lin|10.23919/ACES57841.2023.10114758|;|
|[Inverse Design of Nonlocal Metasurfaces Using Augmented Partial Factorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114694)|S. Li; H. -C. Lin; C. W. Hsu|10.23919/ACES57841.2023.10114694|metasurfaces;nanophotonics;topology optimization;metasurfaces;nanophotonics;topology optimization|
|[Adaptive Generation of Passive Rational Function Approximations for Electromagnetic Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114688)|A. Lemus; F. Coronado; A. E. Engin|10.23919/ACES57841.2023.10114688|macromodeling;rational functions;scattering parameters;stability;vector fitting;macromodeling;rational functions;scattering parameters;stability;vector fitting|
#### **2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)**
- DOI: 10.1109/SPIN57001.2023
- DATE: 23-24 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Regional analysis of ESM models using Bias Corrected spatial disaggregated superresolution convolutional neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116770)|A. Awasthi|10.1109/SPIN57001.2023.10116770|;|
|[Hybrid multi-objective constrained optimization based on feasibility segregation, non-dominated sorting and crowding distance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116769)|A. B. Kalita; S. C. Rajbongshi; G. Trivedi|10.1109/SPIN57001.2023.10116769|NSFPA-FS;NSFA-FS;NSBA-FS;Constraint Handling;NSFPA-FS;NSFA-FS;NSBA-FS;Constraint Handling|
|[Analysis of Crop Leaf Image Classification using Deep Learning Models over Novel Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116960)|W. K. Varma; V. Kumar|10.1109/SPIN57001.2023.10116960|Crop plants leaf;Deep learning;Machine learning;Image processing;RGB image;Image classification.;Crop plants leaf;Deep learning;Machine learning;Image processing;RGB image;Image classification.|
|[Design of High Gain Metasurface Antennas using Hybrid Atomic Orbital Search and Human Mental Search Algorithm for IoT Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116841)|P. Rohit; A. Datta; M. Satyanarayana|10.1109/SPIN57001.2023.10116841|Atomic Orbital Search;Gain;Human mental search optimization;Metasurface antenna;Atomic Orbital Search;Gain;Human mental search optimization;Metasurface antenna|
|[Investigation of Deep Learning Models for Vehicle Damage Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116703)|A. R. Rababaah|10.1109/SPIN57001.2023.10116703|deep learning;convolution neural networks;pattern recognition;machine intelligence;vehicle damage;deep learning;convolution neural networks;pattern recognition;machine intelligence;vehicle damage|
|[DQPSK based Hybrid VCSEL-SMF-FSO Link for Gigabit PON](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116245)|K. Dalal; S. Kukreti|10.1109/SPIN57001.2023.10116245|Free Space Optics;VCSEL;Passive Optical Network;DQPSK;Hybrid VCSEL-SMF-FSO;Free Space Optics;VCSEL;Passive Optical Network;DQPSK;Hybrid VCSEL-SMF-FSO|
|[Tunable LSPR in Asymmetric Plasmonic Bowtie Nanostructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116564)|K. Dalal; Y. Sharma|10.1109/SPIN57001.2023.10116564|Nanostructures;Plasmonics;Finite Difference Time Domain;Asymmetric nano-bowtie;Nanostructures;Plasmonics;Finite Difference Time Domain;Asymmetric nano-bowtie|
|[A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116360)|A. Khtun; S. G. S. Hossain|10.1109/SPIN57001.2023.10116360|Fall Detection;Fourier Coefficients;Activities of Daily Living;KNN Classifier;SVM Classifier;Signal Processing;Fall Detection;Fourier Coefficients;Activities of Daily Living;KNN Classifier;SVM Classifier;Signal Processing|
|[Study of thermocouple degradation through accelerated aging under corrosive environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116553)|V. D. Kumar; A. Bhattacharyya; R. P. Behera; K. Prabakar|10.1109/SPIN57001.2023.10116553|Thermocouple;Accelerated Aging;Mean time to failure (MTTF);Weibuu distribution.;Thermocouple;Accelerated Aging;Mean time to failure (MTTF);Weibuu distribution.|
|[Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116860)|S. Sharan; J. J. Domínguez-Jiménez; P. Nauth|10.1109/SPIN57001.2023.10116860|Mobile robots;Ant Colony Optimization;Path Planning;Bio-inspired Algorithm;Mobile robots;Ant Colony Optimization;Path Planning;Bio-inspired Algorithm|
|[Cross-terms Reduction in WVD using Sliding Window-based Variational Mode Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116192)|K. N. Faisal; R. R. Sharma|10.1109/SPIN57001.2023.10116192|Non-stationary signal analysis;Time-frequency analysis;Wigner-Ville distribution (WVD);Cross-term reduction;Variational mode decomposition (VMD).;Non-stationary signal analysis;Time-frequency analysis;Wigner-Ville distribution (WVD);Cross-term reduction;Variational mode decomposition (VMD).|
|[Semantic Communications enabled Intelligent Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117354)|S. Iyer; R. Pandya; R. Khanai; D. Torse; K. Pai; R. Kallimani|10.1109/SPIN57001.2023.10117354|Semantic communications;Deep learning;Intelligent wireless networks;Semantic communications;Deep learning;Intelligent wireless networks|
|[Fog computing based Distributed Denial of Service Attack Detection Method for Large-Scale Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116991)|S. Prabavathy; I. R. P. Reddy|10.1109/SPIN57001.2023.10116991|Internet of things;Fog computing;DDoS;Partial rank correlation;Pearson correlation;Internet of things;Fog computing;DDoS;Partial rank correlation;Pearson correlation|
|[A deep hybrid GNN based on edge-conditioned and graph isomorphism network convolutions for PC-3 anticancer screening](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116850)|A. S. Remigio; J. Aborot; J. Hong|10.1109/SPIN57001.2023.10116850|anticancer screening;graph neural networks;graph convolution;deep hybrid network;anticancer screening;graph neural networks;graph convolution;deep hybrid network|
|[Examining R Peak Changes in Ischemic Condition using Random Forest Classifier under Stress Test ECG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117129)|R. Bandyopadhyay; B. C. Neelapu; K. Pal; J. Sivaraman|10.1109/SPIN57001.2023.10117129|exercise electrocardiogram;ischemic heart disease;random forest model;R wave amplitude;ST-segment;exercise electrocardiogram;ischemic heart disease;random forest model;R wave amplitude;ST-segment|
|[A Comparative Analysis of Speech Enhancement Techniques Based on Sparsity Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116123)|R. Kumar; M. Tripathy; R. S. Anand|10.1109/SPIN57001.2023.10116123|Speech Enhancement;Sparsity;InteUigibility;Compressive Sensing Theory;Speech Enhancement;Sparsity;InteUigibility;Compressive Sensing Theory|
|[Study of Novel Small-Signal JFET Amplifiers in Sziklai pair Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117320)|S. Shukla; S. S. Arshad; G. Srivastava; A. K. Sharma; S. K. Pandey; S. N. Singh; P. Soni|10.1109/SPIN57001.2023.10117320|Sziklai pair;Darlington pair;Small-signal amplifiers;JFETs small-signal amplifiers;Sziklai pair;Darlington pair;Small-signal amplifiers;JFETs small-signal amplifiers|
|[Correlation study of service order reports and quality control tests in a MRI scanner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116600)|C. H. Murata; T. M. B. Farias; K. A. C. Daros; O. S. Fontoura; H. Carrete|10.1109/SPIN57001.2023.10116600|quality control;MRI;service order;maintenance;quality control;MRI;service order;maintenance|
|[Quality Evaluation of Laparoscopic Videos Using the Interdependency of Luminance and Texture Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116086)|S. Biswas; R. Palanisamy|10.1109/SPIN57001.2023.10116086|Laparoscopic video;Motion blur distortion;Luminance;Texture;Gray-level co-occurrence matrix;Adaptive neuro fuzzy inference system;Video quality assessment;Laparoscopic video;Motion blur distortion;Luminance;Texture;Gray-level co-occurrence matrix;Adaptive neuro fuzzy inference system;Video quality assessment|
|[SentiMatch: Sentiment Analysis of Multi-modal Social Media Posts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116315)|A. Yadav; N. Jain; L. Sharma; P. Singh|10.1109/SPIN57001.2023.10116315|social media;sentiment analysis;image processing;deep learning;social media;sentiment analysis;image processing;deep learning|
|[DeepNet-Gait: Human Identification by Gait Using Convolutional Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117067)|V. Rani; M. Kumar|10.1109/SPIN57001.2023.10117067|Activation function;CNN;HGR;Stance;Swing;Activation function;CNN;HGR;Stance;Swing|
|[Spider Monkey Optimization based Resource Provisioning in Cloud Computing Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116420)|Archana; N. Kumar|10.1109/SPIN57001.2023.10116420|Particle swarm optimization;spider monkey optimization;resource provisioning;meta-heuristics;cloud computing;Particle swarm optimization;spider monkey optimization;resource provisioning;meta-heuristics;cloud computing|
|[Handwritten Devanagari Word Detection and Localization using Morphological Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116577)|M. Bisht; R. Gupta|10.1109/SPIN57001.2023.10116577|Handwritten text;Devanagari script;text detection and localization;morphological image processing;Handwritten text;Devanagari script;text detection and localization;morphological image processing|
|[Automatic Seizure Detection using Rhythmicity Spectrograms and Inception-v3 Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117093)|E. Gupta; M. Gupta; R. A. Sachdeva; P. Handa; N. Goel|10.1109/SPIN57001.2023.10117093|seizure;spectrograms;EEG;transfer learning;deep learning;open dataset;seizure;spectrograms;EEG;transfer learning;deep learning;open dataset|
|[Improving the Classification of Network Slice Using SMOTETomek and Machine Learning Models in 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117118)|A. Rajak; R. Tripathi|10.1109/SPIN57001.2023.10117118|5G;Network Slicing;Machine Learning;SMOTETomek Method;5G;Network Slicing;Machine Learning;SMOTETomek Method|
|[Novel Method for Detection of Alzheimer’s Disease using Gini Impurity based Decision Tree Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117331)|A. Chandra; R. Chakraborty; S. Nandi; B. Porel|10.1109/SPIN57001.2023.10117331|Alzheimer’s disease;cerebro spinal fluid;decision tree;gini impurity;gray matter;white matter;Alzheimer’s disease;cerebro spinal fluid;decision tree;gini impurity;gray matter;white matter|
|[Analysis of i-PPG signals acquired using smartphones for the calculation of pulse transit time and oxygen saturation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117035)|D. A. S. Kaviya; J. B. Jeeva|10.1109/SPIN57001.2023.10117035|smartphone;saturated oxygen;pulse transit time;i-PPG;smartphone;saturated oxygen;pulse transit time;i-PPG|
|[Integration of Fiber Optics and Free Space Optics: A Hybrid Approach for Last Mile Connectivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116742)|H. Singh|10.1109/SPIN57001.2023.10116742|Hybrid;Passive Optical Network;FSO;WDM;Fog;Bit Error Rate;Hybrid;Passive Optical Network;FSO;WDM;Fog;Bit Error Rate|
|[Paper-based Two-electrode Sensor fabricated by Pencil Drawing for Glucose Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117031)|A. Singh; M. Bhuyan|10.1109/SPIN57001.2023.10117031|Paper-based;pencil-drawn;two-electrode;graphite;glucose sensing;Paper-based;pencil-drawn;two-electrode;graphite;glucose sensing|
|[Hexagonal lattice twin core photonic crystal fiber based chemical sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117365)|V. Chaudhary; S. Singh|10.1109/SPIN57001.2023.10117365|Twin core PCF;Chemical sensor;Mode coupling;Birefringence;Transmission signal;Twin core PCF;Chemical sensor;Mode coupling;Birefringence;Transmission signal|
|[Performance of Load Frequency Control of Two-Area Power System by Using Proportional Integral Derivative Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117171)|A. K. Maurya; H. Khan; H. Ahuja|10.1109/SPIN57001.2023.10117171|PI;PID controller;LFC;PID tuner;PI;PID controller;LFC;PID tuner|
|[Pulmonary Nodules Binary Classification using CNN and LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116430)|S. Gupta; A. Garg; V. Bishnoi; N. Goel|10.1109/SPIN57001.2023.10116430|Pulmonary Nodules;CNN;LSTM;Pulmonary Nodules;CNN;LSTM|
|[A vision-based litter detection and classification using SSD MobileNetv2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116919)|A. Balmik; S. Barik; M. Jha; A. Nandy|10.1109/SPIN57001.2023.10116919|litter;litter detection;trash;SSD MobileNetv2;litter;litter detection;trash;SSD MobileNetv2|
|[Multi Spectral Feature Extraction to Improve Lung Sound Classification using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116295)|Rishabh; D. Kumar|10.1109/SPIN57001.2023.10116295|Audio Signal Processing;MFCC;Chroma STFT;Mel-Spectrogram;Spectral Contrast and Tonnetz;Audio Signal Processing;MFCC;Chroma STFT;Mel-Spectrogram;Spectral Contrast and Tonnetz|
|[Multiple Image Watermarking in YCbCr Color Space Using Schur-SVD-DCT in Wavelet Domain and its authentication using SURF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116129)|D. Awasthi; P. Khare; V. K. Srivastava|10.1109/SPIN57001.2023.10116129|Lifting wavelet transform;SVD;Schur decomposition;SURF;YCbCr;Lifting wavelet transform;SVD;Schur decomposition;SURF;YCbCr|
|[A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117110)|M. I. Mahmud; M. Mamun; A. Abdelgawad|10.1109/SPIN57001.2023.10117110|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116075)|M. Mamun; M. I. Mahmud; M. Meherin; A. Abdelgawad|10.1109/SPIN57001.2023.10116075|Lung cancer;CT scan imaging;Deep Learning;CNN;ResNet-50;Inception V3;Xception.;Lung cancer;CT scan imaging;Deep Learning;CNN;ResNet-50;Inception V3;Xception.|
|[MobileNetV3 based Classification Model for Diabetic Retinopathy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117341)|M. Prajapati; S. K. Baliarsingh; J. Hota|10.1109/SPIN57001.2023.10117341|Diabetic Retinopathy;Classification;Deep Learning;CNN;MobileNetV3;Medical Image Processing.;Diabetic Retinopathy;Classification;Deep Learning;CNN;MobileNetV3;Medical Image Processing.|
|[Miniaturized balanced bandpass filter with three transmission zeros](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116147)|M. Tian; Z. Long; T. Zhang|10.1109/SPIN57001.2023.10116147|balanced bandpass filter;common-mode suppression;transmission zero;balanced bandpass filter;common-mode suppression;transmission zero|
|[Application of Deep CNN for image-based identification and classification of plant diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116967)|R. Khanam; G. Mehta|10.1109/SPIN57001.2023.10116967|Plant Disease identification;Image processing;Machine Learning;Image Classification;Deep Convolution Neural Network;Plant Disease identification;Image processing;Machine Learning;Image Classification;Deep Convolution Neural Network|
|[Design and Implementation of Online Legal Forum to Complain and Track UGC Cases using NextJs and GraphQL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117400)|O. Bhamare; P. Gite; A. Lohani; K. Choudhary; J. Choudhary|10.1109/SPIN57001.2023.10117400|Keywords: NextJs;GraphQL;MERN stack;Web based UGC Cases Tracking System;Keywords: NextJs;GraphQL;MERN stack;Web based UGC Cases Tracking System|
|[Using deep learning to analyse behaviour in video surveillances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116489)|A. Makwana|10.1109/SPIN57001.2023.10116489|Anomaly Detection;Crowd Behavior;CNN;Deep Learning;RNN;Video Survemance;Anomaly Detection;Crowd Behavior;CNN;Deep Learning;RNN;Video Survemance|
|[Whitening Transformation of i-vectors in Closed-Set Speaker Verification of Children](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116604)|K. Radha; M. Bansal; R. Sharma|10.1109/SPIN57001.2023.10116604|Automatic speaker verification;Gaussian mixture model;Probabilistic linear discriminant analysis;Whitening transformation;Zero-phase component analysis.;Automatic speaker verification;Gaussian mixture model;Probabilistic linear discriminant analysis;Whitening transformation;Zero-phase component analysis.|
|[Analyzing the Fuel Economy of Hybrid Electric Vehicle for Different Road and Traffic Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116839)|A. Mukhopadhyay; H. Ahuja; B. Moulik|10.1109/SPIN57001.2023.10116839|Hybrid Electric Vehicles;Fuel economy;Driving cycle;Hybrid Electric Vehicles;Fuel economy;Driving cycle|
|[Automated Static Malware Analysis Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116580)|B. Balodi; S. Sharma; A. K. Shukla; B. Singh|10.1109/SPIN57001.2023.10116580|malware detection;machine learning;python;static analysis;deep learning;random forest classifier.;malware detection;machine learning;python;static analysis;deep learning;random forest classifier.|
|[Estimation of Respiratory Rate from a Corrupted PPG Signal using Time-Frequency Spectrogram](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117416)|Pankaj; A. Kumar; R. Komaragiri; M. Kumar|10.1109/SPIN57001.2023.10117416|Morlet wavelet;Photoplethysmography (PPG);Pulse oximeter;Respiratory-induced intensity variation;Respiratory rate (RR);Wearable device;Morlet wavelet;Photoplethysmography (PPG);Pulse oximeter;Respiratory-induced intensity variation;Respiratory rate (RR);Wearable device|
|[Cyberbullying and Suicide Ideation Detection via Hybrid Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117371)|A. S. Mishra; A. Srivastava; A. R. Modi; A. Pandey|10.1109/SPIN57001.2023.10117371|Cyberbullying;Suicide;Natural Language Processing;BDRNN GRU;BDRNN LSTM;GloVe Embedding layer;Bi-GRU.;Cyberbullying;Suicide;Natural Language Processing;BDRNN GRU;BDRNN LSTM;GloVe Embedding layer;Bi-GRU.|
|[A Novel Metaheuristic Approach for Lifetime Enhancement of 3-Tier Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116837)|A. Yadav; A. Pandey; A. Singh; B. D. Kumar; A. Pandey|10.1109/SPIN57001.2023.10116837|Wireless sensor networks (WSNs);Routing;Clustering;Gateways;Sensor;Particle Swarm Optimization (PSO);Wireless sensor networks (WSNs);Routing;Clustering;Gateways;Sensor;Particle Swarm Optimization (PSO)|
|[Multiresolution based neutrosophic framework for building footprint identification using remote sensing images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117227)|A. Ramachandran; R. A. Ansari|10.1109/SPIN57001.2023.10117227|Building identification;unsupervised segmentation;neutrosophic sets;multiresolution analysis;Building identification;unsupervised segmentation;neutrosophic sets;multiresolution analysis|
|[Approaches towards Fake News Detection using Machine Learning and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117154)|N. Kumar; N. Kar|10.1109/SPIN57001.2023.10117154|Machine learning;Deep learning;natural language processing;Naive Bayes;Support Vector Machine;KNearest Neighbour;Long short-term memory;Machine learning;Deep learning;natural language processing;Naive Bayes;Support Vector Machine;KNearest Neighbour;Long short-term memory|
|[Monte Carlo Simulation of the Non-Systematic and Systematic Polar Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116987)|N. Kumar; D. Kedia; G. Purohit|10.1109/SPIN57001.2023.10116987|5G;non-systematic and systematic polar codes;successive cancellation decoder;bit error rate;MATLAB;5G;non-systematic and systematic polar codes;successive cancellation decoder;bit error rate;MATLAB|
|[A Novel Attention Heat Map-Based Video Anomaly Detection Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116189)|P. Sharma; M. Gangadharappa|10.1109/SPIN57001.2023.10116189|Heat map;anomalous event;pre-trained model;image segmentation;supervised learning;Heat map;anomalous event;pre-trained model;image segmentation;supervised learning|
|[GUI Based Machine Learning Algorithms to Predict Alzheimer’s Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116616)|S. Ravi; V. R. P. K; Y. Satuluri; S. M. Ali; C. Nekkanti; P. Ramesh|10.1109/SPIN57001.2023.10116616|Alzheimer’s disease;OASIS;random forest;SVM;logistic regression;decision tree;Alzheimer’s disease;OASIS;random forest;SVM;logistic regression;decision tree|
|[Transfer Learning-based Rich Feature Analysis on Leather Images for Species Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117459)|A. Varghese; M. Jawahar; A. A. Prince|10.1109/SPIN57001.2023.10117459|Generative Adversarial Network (GAN);leather image;rich features;species prediction;support vector machine (SVM);transfer learning;Generative Adversarial Network (GAN);leather image;rich features;species prediction;support vector machine (SVM);transfer learning|
|[Amended feeding for Deep Knowledge Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116150)|J. Naranjo; C. Zhu; V. Stoffova|10.1109/SPIN57001.2023.10116150|Knowledge Tracing;Deep Learning;Question Embedding;Smart Education;Knowledge Tracing;Deep Learning;Question Embedding;Smart Education|
|[Design and Development of Electronic Power Conditioner for Rubidium Atomic Frequency Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116487)|K. Shukla; T. Katiyar; J. Thakkar; K. K. Parikh|10.1109/SPIN57001.2023.10116487|Electronic Power Conditioner (EPC);resonant reset forward converter and Multi-stage EMI filter;Electronic Power Conditioner (EPC);resonant reset forward converter and Multi-stage EMI filter|
|[Swarm Intelligence Algorithms for Profit Maximization and Delay Minimization in Internet Data Centers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116375)|H. Paul; S. Mini|10.1109/SPIN57001.2023.10116375|Internet Data Centers;Particle Swarm Optimization;Profit Maximization;Service Delay Minimization;Grey Wolf optimization;Whale optimization;Firefly optimization.;Internet Data Centers;Particle Swarm Optimization;Profit Maximization;Service Delay Minimization;Grey Wolf optimization;Whale optimization;Firefly optimization.|
|[Designing and Performance Analysis of Low Insertion Loss with Polarization-Insensitive FSS-antenna-radome system for Airbome Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117350)|M. Okramcha; M. R. Tripathy|10.1109/SPIN57001.2023.10117350|FSS-antenna-radome system;X-band;insertion loss;polarization-insensitive;bandpass;gain;FSS-antenna-radome system;X-band;insertion loss;polarization-insensitive;bandpass;gain|
|[Discrimination of Ventricular Arrhythmias using Machine Learning models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116579)|M. F. K; S. K; B. S. Kumar; L. Kavya; Y. Karuna; S. Saritha|10.1109/SPIN57001.2023.10116579|Ventricular Arrhythmia;Feature extraction;Distance metric;Machine learning;Two-fold classification;Ventricular Arrhythmia;Feature extraction;Distance metric;Machine learning;Two-fold classification|
|[Semi-Autonomous Robust Control Design for a Wall Climbing-type Micro Aerial Robot in Outdoor Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115465)|S. Gupta; T. Sandhan; S. Samanta|10.1109/SPIN57001.2023.10115465|Robust control;Sliding Mode Control;Wall climbing robot;Micro Aerial Robot.;Robust control;Sliding Mode Control;Wall climbing robot;Micro Aerial Robot.|
|[Design of Network Rejoin Policy in Multi-UAVs Flying Ad-hoc Network and Robust Tracking Control Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116722)|S. Gupta; S. Samanta; S. Dutta|10.1109/SPIN57001.2023.10116722|Multi-UAV system;FANET;Self-recovery;sliding mode control;path tracking control;Multi-UAV system;FANET;Self-recovery;sliding mode control;path tracking control|
|[Real-time deployment and test set analysis of automatic colonoscopy polyp identification architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116179)|N. Garg; Palak; N. Goel|10.1109/SPIN57001.2023.10116179|Polyp detector;flutter;deployment;deep learning;Polyp detector;flutter;deployment;deep learning|
|[Performance Analysis of IIC techniques for Brain MR-images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116089)|P. Thakur; N. Syamala; Y. Karuna; S. Saritha|10.1109/SPIN57001.2023.10116089|Intensity Inhomogeneity;LSGAN;Brain MRimages;Image Denoising;Intensity Inhomogeneity;LSGAN;Brain MRimages;Image Denoising|
|[Water Quality Prediction Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117415)|N. S. Pagadala; M. Marri; A. Myla; B. Abburi; K. S. Ramtej|10.1109/SPIN57001.2023.10117415|Conductivity;Hardness;Machine Learning;PH;Turbidity;Water Quality;Conductivity;Hardness;Machine Learning;PH;Turbidity;Water Quality|
|[ISL2022: A novel dataset creation on Indian sign language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116636)|P. B. V; R. R; S. S; R. S|10.1109/SPIN57001.2023.10116636|Indian sign language (ISL);Sign language recognition (SLR);Static & Dynamic gestures;Simple & Complex background;Indian sign language (ISL);Sign language recognition (SLR);Static & Dynamic gestures;Simple & Complex background|
|[FPGA Implementation Of a High Throughput Low Power Advanced Encryption Standard (AES-128) Cipher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116674)|J. Kala; J. Panda; L. Tanwar|10.1109/SPIN57001.2023.10116674|AES;Encryption;FPGA;Low Power;Secure;Simulation;Throughput;AES;Encryption;FPGA;Low Power;Secure;Simulation;Throughput|
|[Quantitative Analysis of EEG Neurofeedback using optimized 1-DPSO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116248)|M. K. Waghmare; M. Malhotra; R. Sr|10.1109/SPIN57001.2023.10116248|Brain-computer Interface;electroencephalogram;neurofeedback;particle swarm intelligence.;Brain-computer Interface;electroencephalogram;neurofeedback;particle swarm intelligence.|
|[Kannada Word Detection in Heterogeneous Scene Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117096)|N. Shikha; R. Pranav; N. R. Singh; V. Umadevi; M. Hussain|10.1109/SPIN57001.2023.10117096|scene text detection;kannada;word detection;neural network;deep learning;scene text detection;kannada;word detection;neural network;deep learning|
|[DoS Attack Detection in Wireless Sensor Networks (WSN) Using Hybrid Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117098)|G. S. Rao; M. Harshitha; V. R. Joshitha; S. S. Sravya; M. V. Priya|10.1109/SPIN57001.2023.10117098|Wireless Sensor Networks (WSN);DoS attack;Machine Learning;Hybrid Models;Network Security;Wireless Sensor Networks (WSN);DoS attack;Machine Learning;Hybrid Models;Network Security|
|[Handwritten OCR for word in Indic Language using Deep Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117106)|M. K. Gupta; S. Vikram; S. Dhawan; A. Kumar|10.1109/SPIN57001.2023.10117106|Offline Handwritten Character Recognition;OHCR;Convolution Neural Networks;CNN;Transformers;encoder-decoder;Offline Handwritten Character Recognition;OHCR;Convolution Neural Networks;CNN;Transformers;encoder-decoder|
|[Tender Coconut Classification using Decision Tree and Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117353)|I. Bhat; V. Umadevi; N. Jagadeesh; S. Bhat; R. S. Shenoy|10.1109/SPIN57001.2023.10117353|Classification;Coconut;Decision Tree;Deep Learning;Classification;Coconut;Decision Tree;Deep Learning|
|[Production prediction using machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116936)|R. Batra; P. Abbi; R. Sharma; H. Agarwal; P. Bhulania|10.1109/SPIN57001.2023.10116936|MSME;VIF;Pearson Correlation;Supervised Learning.;MSME;VIF;Pearson Correlation;Supervised Learning.|
|[Skin Lesion Segmentation using Residual U-NET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116287)|S. Manivannan; N. Venkateswaran|10.1109/SPIN57001.2023.10116287|Skin Lesion Segmentation;U-NET;Semantic Segmentation;Skin Lesion Segmentation;U-NET;Semantic Segmentation|
|[Versatile VM Universal Biquad Filter Employing OTAs and Its Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116384)|D. Saxena; M. Nizamuddin|10.1109/SPIN57001.2023.10116384|OTA;Filters;Oscillators;Voltage Mode;TOQSO;OTA;Filters;Oscillators;Voltage Mode;TOQSO|
|[Image and Video D’encryption using Gray Pattern based algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116933)|R. Malkuchi; P. Peri; U. Kamle|10.1109/SPIN57001.2023.10116933|Gray pattern;Pixel permutation;Shuffle key;Graphical User Interface;Gray pattern;Pixel permutation;Shuffle key;Graphical User Interface|
|[Deep CNN and LSTM Architecture-Based Approach for COVID-19 Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117454)|S. Mehta; U. Rastogi; P. Dwivedi|10.1109/SPIN57001.2023.10117454|Covid-19;Convolution Neural Network;Long short-term memory;VGG-16;Automation;Deep Learning;Covid-19;Convolution Neural Network;Long short-term memory;VGG-16;Automation;Deep Learning|
|[Dynamic Performance Enhancement of Fractional-Order PID Controller using a High-Level Ensemble Swarm Intelligence Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116786)|M. D. Mahapatra; S. Mahata; R. R. De Maity; R. K. Mudi; C. Dey|10.1109/SPIN57001.2023.10116786|ensemble particle swarm optimization;fractional calculus;fractional order PID controller;optimization;ensemble particle swarm optimization;fractional calculus;fractional order PID controller;optimization|
|[Coordinated Design of Damping Controllers using Grey Wolf Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10116198)|M. K. Kar; P. A. Krishna; A. A. Godbole; S. V. Patil; R. S. Meshram; R. S. Sonawane|10.1109/SPIN57001.2023.10116198|FACTS;GWO;PSS;SMIB;SSSC;FACTS;GWO;PSS;SMIB;SSSC|
|[Application of Machine Learning Algorithms in Medical Image Analysis: A case study for Breast Cancer detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10117210)|A. Tripathi; S. Anwar|10.1109/SPIN57001.2023.10117210|Medical Imaging;Breast cancer;machine learning;Accuracy;Medical Imaging;Breast cancer;machine learning;Accuracy|
#### **2023 5th Asia Energy and Electrical Engineering Symposium (AEEES)**
- DOI: 10.1109/AEEES56888.2023
- DATE: 23-26 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Optimal Day-Ahead Dispatch of Air-Conditioning Load under Dynamic Carbon Emission Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114285)|X. Zeng; Y. Zhang; B. Guo; L. Huang; C. Li|10.1109/AEEES56888.2023.10114285|air-conditioning load;electricity carbon emission factors;optimal day-ahead dispatch;meta-heuristic optimization;air-conditioning load;electricity carbon emission factors;optimal day-ahead dispatch;meta-heuristic optimization|
|[Diagnosis and Analysis of a 220kV Transformer Winding Deformation Defect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114215)|Z. Liu; J. Wang; X. Tian; X. Liu; X. Li|10.1109/AEEES56888.2023.10114215|transformer;winding deformation;anti-short circuit ability;frequency response analysis;transformer;winding deformation;anti-short circuit ability;frequency response analysis|
|[Advanced Thermal Modeling and Experimental Performance of Foil-Winding Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114241)|H. Zhan; Y. Duan|10.1109/AEEES56888.2023.10114241|Foil-winding transformer;hot-spot temperature;winding temperature profile;Foil-winding transformer;hot-spot temperature;winding temperature profile|
|[Research on the Control of Unidirectional Power Flow DC Transformer for Photovoltaic DC High-Voltage Convergence Based on Soft Switching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114088)|Y. Yao; J. Chen; W. Xu; P. He; Y. Chen; J. Li|10.1109/AEEES56888.2023.10114088|unidirectional power flow;dc transformer;LLC converter;voltage gain;control;unidirectional power flow;dc transformer;LLC converter;voltage gain;control|
|[Adaptive Differential Protection Method for Transformer Based on Positive Sequence Steady-State Quantity in Photovoltaic Station Access Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114303)|X. Chen; Y. Zhu; Z. Li; H. Zhang|10.1109/AEEES56888.2023.10114303|photovoltaic (PV) station;ratio differential protection;transformer;adaptive differential protection;photovoltaic (PV) station;ratio differential protection;transformer;adaptive differential protection|
|[Optimization of Inter-Regional Flexible Resources for Renewable Accommodation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114311)|W. Kong; H. Ye; N. Wei; D. Xing; S. Liu; W. Chen|10.1109/AEEES56888.2023.10114311|HVDC tie-line;renewable accommodation;flexibility;storage;HVDC tie-line;renewable accommodation;flexibility;storage|
|[Stability Analysis of Multi-microgrid Dominated by Grid-forming Converter Based on Bifurcation Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114210)|B. Zhu; Z. Liu; H. Lei; B. Liu; D. Wang; C. Mao|10.1109/AEEES56888.2023.10114210|Bifurcation theory;grid-forming converter;multi-microgrid (MMG);stability;Bifurcation theory;grid-forming converter;multi-microgrid (MMG);stability|
|[Design Procedure of a 30kW Single-Phase Three-Level I-Type Neutral Point Clamped Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114300)|Y. Fu; Y. Cai; H. Bai; Y. Ye; R. Yao; Y. Li|10.1109/AEEES56888.2023.10114300|High Power Inverter;Neutral Point Clamped;Parameter;High Power Inverter;Neutral Point Clamped;Parameter|
|[Coupling Mechanism and Stability Analysis of Parallel Grid-Forming Inverters in High-Frequency Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114257)|S. Zheng; R. Zhu; X. Qi|10.1109/AEEES56888.2023.10114257|grid-forming inverter;equivalent impedance model;coupling mechanism;high-frequency band;impedance stability;grid-forming inverter;equivalent impedance model;coupling mechanism;high-frequency band;impedance stability|
|[Improved DROOP Control Strategy for Parallel Operation of Multiple Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114370)|Z. Y. Ning; Z. Chen; Y. Teng; Y. Hu|10.1109/AEEES56888.2023.10114370|Microgrid;inverter;parallel connection;droop control;virtual impedance;Microgrid;inverter;parallel connection;droop control;virtual impedance|
|[Energy Efficiency Analysis of Data Center Based on All-Inverter Coupled Air-Side Free Cooling Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114148)|C. Ai; Y. Chen; L. Zhang|10.1109/AEEES56888.2023.10114148|all-inverter;air-side free cooling;energy efficiency;PUE;data center;all-inverter;air-side free cooling;energy efficiency;PUE;data center|
|[Research on Improving Voltage Support Stability of VSG Control System for Wind Power Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114350)|S. Liu; K. He; P. Yang|10.1109/AEEES56888.2023.10114350|Air storage system;Virtual synchronizer;Low voltage ride through capability;Virtual impedance;Reactive power voltage control;Air storage system;Virtual synchronizer;Low voltage ride through capability;Virtual impedance;Reactive power voltage control|
|[Fault Current Analysis of DFIG Considering Multi-time Scale Protection and Control under Asymmetrical Voltage Dips](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114190)|Z. Yang; Y. Li; J. Hu|10.1109/AEEES56888.2023.10114190|doubly fed induction generator (DFIG);asymmetrical fault current;multi-time scales;analytic expressio;doubly fed induction generator (DFIG);asymmetrical fault current;multi-time scales;analytic expressio|
|[Coordination Control Strategy of Distribution Grid Voltage Based on Adjustable Resources in Substation Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114098)|S. Sun; M. Shi; Y. Qu; B. Li; J. Fei; X. Yang|10.1109/AEEES56888.2023.10114098|PV power generation;output prediction;distribution grid;reactive compensation;voltage deviation;coordination control;PV power generation;output prediction;distribution grid;reactive compensation;voltage deviation;coordination control|
|[Research on Very Fast Transient Voltage of 330kV GIS Disconnector Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114157)|T. Yue; W. Qi; Z. Jin; L. Xiang|10.1109/AEEES56888.2023.10114157|GIS isolating switch;VFTO;test;waveform parameters;statistical analysis;GIS isolating switch;VFTO;test;waveform parameters;statistical analysis|
|[High Order Inertia Response Analysis of Wind Power Generation Based on Power-Internal Voltage Relationship](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114194)|W. He; X. Yuan; Y. Li; Y. Zhang|10.1109/AEEES56888.2023.10114194|inertia control;inertial response;wind power generation;inertia control;inertial response;wind power generation|
|[Comparative Analysis of Dynamic Tracking Ability of Phase-Locked Loop in Case of Sudden Change of Power Grid Voltage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114111)|J. Li; B. Li; Q. Zhong; B. Li; X. Chen; L. Ji; Q. Hong|10.1109/AEEES56888.2023.10114111|voltage mutation;phase-locked loop;second-order generalized integrator;dual-synchronous decoupling coordinate system;transient duration;overshoot;voltage mutation;phase-locked loop;second-order generalized integrator;dual-synchronous decoupling coordinate system;transient duration;overshoot|
|[Risk Analysis of Transmission Line Sag Based on Energization Time and Elastic Modulus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114131)|Q. Chen; X. Zhao; W. Han; P. Zhang; Z. Tan; Z. Wang; J. Wei; J. Han|10.1109/AEEES56888.2023.10114131|transmission conductor;conductor sag;power-on time;temperature variation;transmission conductor;conductor sag;power-on time;temperature variation|
|[Torque Ripple Suppression Strategy for PMSM Based on High Frequency Square Wave Injection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114167)|J. Song; Q. Zhang; C. Liu; Q. Ma|10.1109/AEEES56888.2023.10114167|permanent magnet synchronous moto;harmonic current suppression;torque ripple;permanent magnet synchronous moto;harmonic current suppression;torque ripple|
|[Innovative Design and Simulation of a Downhole Turbine Generator for the Rotary Steerable System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114281)|Z. Li; D. Li; B. Guo; T. Zhao|10.1109/AEEES56888.2023.10114281|triz;contradiction matrix;generator;retarder;simulation;triz;contradiction matrix;generator;retarder;simulation|
|[Research on Air Gap Monitoring Technology of Hydro-Generator Based on Infrared Monocular Visual Image Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114318)|C. Li; J. Yan; R. Rong; X. Wang; L. Hao; Y. Wang; H. Wang; M. Wang|10.1109/AEEES56888.2023.10114318|Hydroturbine;Generator air gap;Image processing;Monocular vision;Hydroturbine;Generator air gap;Image processing;Monocular vision|
|[Influence of Inrush Current on Second Harmonic Characteristics of Direct-driven Wind Turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114308)|X. Jia; Z. Xiang; P. Zhou|10.1109/AEEES56888.2023.10114308|PMSG;excitation inrush current;harmonic current;restraint measures;PMSG;excitation inrush current;harmonic current;restraint measures|
|[SHEPWM-Based Dead-Zone Control for PMSM under Low Switching Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114326)|C. Wang; Q. Zhang; K. Yang|10.1109/AEEES56888.2023.10114326|PMSM;SHE;dead-zone-based control;low switching frequency;PMSM;SHE;dead-zone-based control;low switching frequency|
|[Deep Learning Based Structural Reliability Finite Element Analysis Surrogate for Hydro-Generator Lower Frame](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114264)|J. Song; T. Fu|10.1109/AEEES56888.2023.10114264|deep learning;finite element analysis;surrogate model;hydro-generator lower frame;deep learning;finite element analysis;surrogate model;hydro-generator lower frame|
|[Anti-disturbance Hamiltonian Model of Diesel Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114259)|J. Yan; F. Yang; P. Wang; X. Liu; Z. Zheng|10.1109/AEEES56888.2023.10114259|Independent power system;Pulsed load;Incremental negative damping characteristics;Diesel generator;Hamilton implementation;Energy structure;Independent power system;Pulsed load;Incremental negative damping characteristics;Diesel generator;Hamilton implementation;Energy structure|
|[An Improved Decreased Torque Gain MPPT Algorithm of Wind Turbine with Consideration of Pitch Angle Adjustment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114232)|Y. Zhang; L. Zhou|10.1109/AEEES56888.2023.10114232|wind power;MPPT;decreased torque gain;pitch angle adjustment;angle of attack;wind power;MPPT;decreased torque gain;pitch angle adjustment;angle of attack|
|[A Novel Method for Directly Calculating Dynamic Equivalent Generator Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114159)|Y. Hou; W. Zhao; W. Li; L. Zhu|10.1109/AEEES56888.2023.10114159|EEAC;CCCOI-RM;unit-grid phasor diagram;dynamic equivalence;coherent generators group;phasor equation;EEAC;CCCOI-RM;unit-grid phasor diagram;dynamic equivalence;coherent generators group;phasor equation|
|[Study on a New Modeling Method for Primary Frequency Regulation of Ultra-Supercritical Thermal Power Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114357)|Y. Gao; M. He; Q. Yu; Z. Xu; Q. Che; C. He; Y. Dong|10.1109/AEEES56888.2023.10114357|primary frequency modulation;nonlinear servo system;variable parameter turbine model;primary frequency modulation;nonlinear servo system;variable parameter turbine model|
|[Maintenance Scheduling Optimization for AC-DC Grid Based on Component Status under N-1 Contingencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114332)|Z. Cai; J. Zhang; B. Han; Y. Bao; X. Liu; H. Zheng|10.1109/AEEES56888.2023.10114332|maintenance scheduling;unit commitment;N-1 security;component status;maintenance scheduling;unit commitment;N-1 security;component status|
|[Design of a DC Miniature Solid-State Circuit Breaker and Impact Analysis of Stray Inductance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114189)|W. Yang; C. Tao; X. Wu; W. Zhuang; Y. Wu; Y. Wu|10.1109/AEEES56888.2023.10114189|miniature DC solid state circuit breaker;stray inductance;power electronic;miniature DC solid state circuit breaker;stray inductance;power electronic|
|[Research on Partial Discharge Location Method of DC Wall Bushing Based on Partial Discharge Transfer Ratio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114317)|Z. Pan; J. Zhang; Z. Xie; J. Deng|10.1109/AEEES56888.2023.10114317|DC wall Bushing;partial discharge transfer ratio;partial discharge;location method;DC wall Bushing;partial discharge transfer ratio;partial discharge;location method|
|[Protection Scheme for Hybrid Cascade HVDC Transmission Line Based on Current Control Adjustment Deviation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114133)|J. Shen; H. Cheng; Z. Liu; H. Ye; H. Liu; Q. Gui; X. Zheng; N. Tai|10.1109/AEEES56888.2023.10114133|hybrid cascade HVDC transmission system;fault response characteristics of control signal;DC line protection;integration of control and protection;hybrid cascade HVDC transmission system;fault response characteristics of control signal;DC line protection;integration of control and protection|
|[Effect Analysis of DC Bias on the Transfer Characteristics of Current Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114371)|Y. Pu; H. Fan; S. Lin|10.1109/AEEES56888.2023.10114371|DC bias;Current transformer;Finite element method;Magnetic flux density;Secondary side induced current;DC bias;Current transformer;Finite element method;Magnetic flux density;Secondary side induced current|
|[Development of a Grid Adaptability Evaluation Method for Systems with Renewable Energy Connected to Weakly-Synchronized Sending-End DC Power Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114145)|Y. Liu; N. Chen; L. Zhu; L. Zhang|10.1109/AEEES56888.2023.10114145|grid adaptability;power grid strength;short-circuit current;fault ride through;grid integration;renewable energy;grid adaptability;power grid strength;short-circuit current;fault ride through;grid integration;renewable energy|
|[A Grid Forming/Following Sequence Switching Control Strategy for Supporting Frequency Stability of Isolated Power Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114155)|W. Qiu; Q. Yang; T. Yang; X. Ma; X. Xiao; H. Shi; Y. Zhao; S. Liang|10.1109/AEEES56888.2023.10114155|frequency dynamic;grid forming control;grid following control;sequence switching control strategy;isolated power grids;frequency dynamic;grid forming control;grid following control;sequence switching control strategy;isolated power grids|
|[Research on Bird Nest Image Recognition and Detection Technology of Transmission Lines Based on Improved Faster-RCNN Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114337)|Z. Zhang; H. Ni; M. Liu; Z. Zhang; G. Liu; S. Cheng; M. Wang; C. Li|10.1109/AEEES56888.2023.10114337|Image recognition;Neural network;Bird's Nest;Inception-v3;Attention mechanism;lightweight feature extraction;Image recognition;Neural network;Bird's Nest;Inception-v3;Attention mechanism;lightweight feature extraction|
|[Research on Data Interpolation of Energy Storage Power Station Based on DC-GAN Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114244)|J. Zheng; Y. Li|10.1109/AEEES56888.2023.10114244|deep convolutional generative adversarial network(DCGAN);energy storage power station;large-capacity battery safety;safety monitoring;data interpolation;deep convolutional generative adversarial network(DCGAN);energy storage power station;large-capacity battery safety;safety monitoring;data interpolation|
|[Study on Galloping of Transmission Lines Based on Multi-scale Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114107)|X. Yu; X. Liu; F. Li; H. Li|10.1109/AEEES56888.2023.10114107|component;transmission line;galloping;dynamic response;multi-scale method;iced conductor;component;transmission line;galloping;dynamic response;multi-scale method;iced conductor|
|[Study on Factors Influencing Lightning Withstand Level of Transmission Line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114160)|L. Wei; L. Donghui; L. Zhizhong; W. Sen; H. Panfeng; S. Wei|10.1109/AEEES56888.2023.10114160|ATP-EMTP;110kV;grounding resistance;insulator;arrester;lightning wire;ATP-EMTP;110kV;grounding resistance;insulator;arrester;lightning wire|
|[Stressing Analysis of Fittings under Random Wind Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114240)|X. Yu; X. Liu; S. Huang; K. Yuan|10.1109/AEEES56888.2023.10114240|component;transmission line;random wind;dynamic response;power fittings;fracture failure;component;transmission line;random wind;dynamic response;power fittings;fracture failure|
|[Research on the Optimization of Return Line Laying Mode of 500 kV Pumped Storage Power Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114293)|J. Chen; J. Cao; Z. Ye; J. Lian; C. Li; W. Zhang|10.1109/AEEES56888.2023.10114293|high-voltage cable;circulating current;return wire;metal sheath;cross interconnection;high-voltage cable;circulating current;return wire;metal sheath;cross interconnection|
|[Analysis of Galloping Amplitude of Iced Conductor with Three-Degree of Freedom](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114097)|X. Yu; X. H. Liu; J. Sun|10.1109/AEEES56888.2023.10114097|iced conductor;amplitude;aerodynamic coefficient;iced conductor;amplitude;aerodynamic coefficient|
|[Form Finding and Ice-Shedding Analysis of Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114186)|J. Yang; X. Liu; W. Hu; Z. Lin; J. Dai|10.1109/AEEES56888.2023.10114186|component;transmission line;ice-shedding;dynamic response;form finding;catenary;component;transmission line;ice-shedding;dynamic response;form finding;catenary|
|[Research on Protection Scheme of Offshore Wind Power Transmission Configured with Offshore High-Voltage Reactor Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114267)|H. Zhang; C. Wang; J. Wang; Y. Yu|10.1109/AEEES56888.2023.10114267|offshore wind power;offshore high-voltage reactor station;protection configuration scheme;protection action logic;setting calculation principle;offshore wind power;offshore high-voltage reactor station;protection configuration scheme;protection action logic;setting calculation principle|
|[Research on Optimization of High Voltage Cable Circulating Current Suppression Based on Short Circuit Current Guidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114368)|J. Cao; J. Chen; Z. Ye; J. Lian; X. Tan; W. Zhang|10.1109/AEEES56888.2023.10114368|high-voltage cable;circulating current;cross-bonded;induced current;ATM-EMTP;high-voltage cable;circulating current;cross-bonded;induced current;ATM-EMTP|
|[Risk Analysis of Wind-Induced Vibration of the Conductor with Small Span and Large Height Difference Based on Numerical Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114114)|Q. Chen; H. Zhao; F. Fu; G. Yu; Z. Tan; Z. Wang; J. Wei; J. Han|10.1109/AEEES56888.2023.10114114|small span large height difference conductors;wind vibration response;finite element models;aerodynamic damping;clearance safety;small span large height difference conductors;wind vibration response;finite element models;aerodynamic damping;clearance safety|
|[Study on Dynamic Aerodynamic Coefficients Based on Wind Tunnel Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114280)|D. An; X. H. Liu; J. Sun|10.1109/AEEES56888.2023.10114280|quasi-static hypothesis;galloping;iced conductor;aerodynamic coefficient;wind tunnel test;quasi-static hypothesis;galloping;iced conductor;aerodynamic coefficient;wind tunnel test|
|[Design and Application of an Ironless Halbach PMLSM Propulsion System for Medium Speed Maglev Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114333)|K. Wang; L. Zhao; B. Zhang; J. Jin|10.1109/AEEES56888.2023.10114333|halbach array;ironless linear synchronous motor;Maglev vehicle;propulsion system;permanent magnet;halbach array;ironless linear synchronous motor;Maglev vehicle;propulsion system;permanent magnet|
|[Parameter Identification of Electro-Hydraulic Servo System of Steam Turbine Based on Improved Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114279)|Q. Che; C. He; Z. Xu; Q. Yu; Y. Gao; Y. Dong|10.1109/AEEES56888.2023.10114279|improved particle swarm optimization;parameter identification;electro-hydraulic servo system;steam turbine;improved particle swarm optimization;parameter identification;electro-hydraulic servo system;steam turbine|
|[Monitoring System for Cabin Temperature via Thermal Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114117)|R. Yan; K. Zhang; X. Yao; S. Fang; T. Wang; H. Wei|10.1109/AEEES56888.2023.10114117|infrared thermal imaging;temperature distribution;condition monitoring;infrared thermal imaging;temperature distribution;condition monitoring|
|[Design and Performance Analysis of A New Axially Distributed Lightweight Rotor Electromagnetic Shock Absorber for Armored Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114366)|T. Zhao; D. Li; B. Guo; Z. Li|10.1109/AEEES56888.2023.10114366|armored vehicles;electromagnetic shock absorber;electromagnetic characteristics;damping characteristics;armored vehicles;electromagnetic shock absorber;electromagnetic characteristics;damping characteristics|
|[A Mechanism Study of Sub-microsecond High Voltage Pulse Fragmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114248)|Y. Li; J. Luo; Z. Wang; S. Fang; H. Xia; M. He|10.1109/AEEES56888.2023.10114248|high voltage fragmentation;mechanism study;pulse rise time;electric field;high voltage fragmentation;mechanism study;pulse rise time;electric field|
|[A Load Side Multi-type Interference Generating Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114242)|H. Min; W. Hu; F. Yang; Z. Yang; Y. Lei; S. Luo|10.1109/AEEES56888.2023.10114242|generation device;key parameters;energy saving;generation device;key parameters;energy saving|
|[A Dual-Band Direction Changeable Wireless Power Transfer Device with High Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114149)|Y. Lv; Y. Zheng; Q. Yang|10.1109/AEEES56888.2023.10114149|magnetically coupled resonance (MCR);wireless power transfer (WPT);dual-band;high efficiency;direction changeable wireless power transfer;magnetically coupled resonance (MCR);wireless power transfer (WPT);dual-band;high efficiency;direction changeable wireless power transfer|
|[Theoretical Study on Predicting the Performance of Steam Ejector by Analogy Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114205)|Y. Mei; K. Qian; Y. Hou; W. Chen|10.1109/AEEES56888.2023.10114205|ejector;performance prediction;entrainment ratio;simulation;ejector;performance prediction;entrainment ratio;simulation|
|[Research on Self-Learning and Self-Correcting Technology of Sampling Deviation of Substation Secondary Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114344)|L. Cai; Z. Zhang; S. Gong; S. Li; B. Tang|10.1109/AEEES56888.2023.10114344|secondary equipment;climate change;sampling accuracy;accuracy deviation curve;secondary equipment;climate change;sampling accuracy;accuracy deviation curve|
|[Performance Evaluation of Primary Frequency Control for Flexible Grid Connected Coal-Fired Units Based on Self-Organizing Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114351)|Y. Dong; Z. Zhao; W. Zhang; Y. Guo; R. Li|10.1109/AEEES56888.2023.10114351|primary frequency control;performance evaluation;k-means clustering;self-organizing map;coal-fired unit;primary frequency control;performance evaluation;k-means clustering;self-organizing map;coal-fired unit|
|[Evaluation Method of Three-Dimensional and Multi-Dimensional All-Electric Intelligent Fishing Ground Operation Effect Based on Improved AHP Entropy Weight in Cloud Computing Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114219)|J. Cao; L. Chen; W. Wu; Y. Xu; X. Yu|10.1109/AEEES56888.2023.10114219|all-electric intelligent fishing ground;cloud computing environment;IAHP;EW method;IAHP-EW method;fuzzy comprehensive evaluation;multidimensional comprehensive evaluation index;all-electric intelligent fishing ground;cloud computing environment;IAHP;EW method;IAHP-EW method;fuzzy comprehensive evaluation;multidimensional comprehensive evaluation index|
|[Research on Anti-Icing Mechanism of Insulators Super-Hydrophobic Surface Coating Under Freezing Rain Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114196)|C. Li; R. Rong; J. Guo; Y. Zhang; T. Zhao; M. Wang|10.1109/AEEES56888.2023.10114196|Composite insulator;Anti-icing;Water droplet;Collision efficiency;Super-hydrophobic;Composite insulator;Anti-icing;Water droplet;Collision efficiency;Super-hydrophobic|
|[Reliability Study of Fluorosilicone Rubber Composite Insulators under Strong Radiation and Low Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114154)|C. Sun; K. Shi; H. Wang; Y. Sun; B. Chen; J. Liu|10.1109/AEEES56888.2023.10114154|reliability;fluorosilicone rubber;strong radiation;low temperature;composite insulators;reliability;fluorosilicone rubber;strong radiation;low temperature;composite insulators|
|[Simulation of the Diffusion Process of SO2 Molecules from SF6 Insulated Electrical Equipment to SO2 Detection Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114315)|L. Kang; C. Tunan; Q. Zongjia; Z. Guoqiang; W. Qian|10.1109/AEEES56888.2023.10114315|SO2 detection device;SO2 diffusion process;gas transfer acceleration measure;external flow disturbance;SO2 detection device;SO2 diffusion process;gas transfer acceleration measure;external flow disturbance|
|[Study on Electromagnetic-Thermal Simulation of 10kV Air Insulated Switchgear](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114076)|G. Sun; S. Chen; B. Chen; Y. Liu; B. Chen; H. Li; R. Deng; A. Fu|10.1109/AEEES56888.2023.10114076|air insulated switchgear;electromagnetic-thermal field coupling;temperature rise characteristic;contact failure;air insulated switchgear;electromagnetic-thermal field coupling;temperature rise characteristic;contact failure|
|[Risk Assessment of Tower Transmission Based on Insulator Online Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114165)|D. Ma; J. Peng; B. Zhang; H. Ding; S. Yang; Y. Liu|10.1109/AEEES56888.2023.10114165|insulator;tower;life prediction;regression neural network;insulator;tower;life prediction;regression neural network|
|[Variable-Inductor Based Tuning Method for Multiple-Relay Wireless Power Transfer System in Composite Insulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114074)|C. Li; H. Huang; D. Ju; J. Xiong; S. Zuo; F. Li; W. Liu|10.1109/AEEES56888.2023.10114074|wireless power transfer;variable inductor;dynamic tuning;wireless power transfer;variable inductor;dynamic tuning|
|[Preparation and Properties of Surface Modified TiO2 / PTFE Fluorocarbon Anti-pollution Flashover Coating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114095)|Z. Jie; Z. Junwen; L. Xueguang; L. Xiaoying|10.1109/AEEES56888.2023.10114095|coupling agent modification;nano-TiO2;fluorocarbon resin;anti-pollution flashover;self-cleaning;coupling agent modification;nano-TiO2;fluorocarbon resin;anti-pollution flashover;self-cleaning|
|[Aging Failure Analysis of IGBT Module Solder Layer Based on Multi-physical Field Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114170)|Q. Zhu; Q. Chen; L. Zhu; H. Zhang|10.1109/AEEES56888.2023.10114170|insulated gate bipolar transistor (IGBT);solder layer;reliability;multi-physical field coupling;insulated gate bipolar transistor (IGBT);solder layer;reliability;multi-physical field coupling|
|[Study on Super Hydrophobic Modification of RTV Anti Pollution Flashover Coatings Doped with Nano-SiC and CW](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114106)|Z. Jie; Z. Junwen; L. Xueguang; L. Xiaoying|10.1109/AEEES56888.2023.10114106|RTV;anti pollution flashover;super hydrophobic;RTV;anti pollution flashover;super hydrophobic|
|[Research on the Application of Oil Film Condition Monitoring of Unit Bearing Bush Based on Stress Wave Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114176)|L. Hongyuan; G. Fan|10.1109/AEEES56888.2023.10114176|generator set;bearing bush;oil film;condition monitoring;stress wave analysis technology;generator set;bearing bush;oil film;condition monitoring;stress wave analysis technology|
|[Analysis of Permeability Growth Mode of Regional Distributed Power Supply Based on Improved Bass Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114314)|M. Yin; L. Li; H. Yan; M. Ren; J. Wang; J. Kan|10.1109/AEEES56888.2023.10114314|improving bass model;grey theory;distributed power supply permeability;growth pattern;improving bass model;grey theory;distributed power supply permeability;growth pattern|
|[Modelling for Active Power Characteristic of Electrolytic Magnesium Industry Based on Industrial Process Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114122)|D. Cao; S. Liao; L. Yao; R. Wang; Z. Li; J. Yan; Y. Li|10.1109/AEEES56888.2023.10114122|industrial load;load modeling;electrolytic magnesium;active power characteristics;industrial load;load modeling;electrolytic magnesium;active power characteristics|
|[Non-negative Matrix Decomposition-Based Power Package Recommendation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114199)|Y. Ma; X. Jiang; Z. Wang; X. Wei; Y. Wang; Z. Lin|10.1109/AEEES56888.2023.10114199|Recommended electricity package;Non-negative matrix decomposition-based;User characteristic matrix;Package feature matrix;Element iteration;Recommended electricity package;Non-negative matrix decomposition-based;User characteristic matrix;Package feature matrix;Element iteration|
|[Research on 1100kV GIS Bus Thermal Network Model with Spring Contacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114330)|Y. Liu; Y. Yao; J. Zhong; Z. Wang; H. Zhang; M. Li|10.1109/AEEES56888.2023.10114330|Keywords- GIS bus;Spring contact;Thermoelectric analogy;Thermal network method;Temperature rise calculation;Keywords- GIS bus;Spring contact;Thermoelectric analogy;Thermal network method;Temperature rise calculation|
|[Inertia Identification of Power System Based on ARMAX Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114228)|D. Wu; H. Liu; W. Peng; L. Yu; L. Shi; Y. Yu|10.1109/AEEES56888.2023.10114228|inertia identification;ARMAX model;step disturbance;impulse disturbance;inertia identification;ARMAX model;step disturbance;impulse disturbance|
|[Segmentation Modeling Method of Load-variable Structure Characteristics Based on Real-time Data Interactive Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114127)|C. Wen; M. Wang; D. Qin; R. Pan; J. Chen; L. Ma; C. Yang|10.1109/AEEES56888.2023.10114127|static load;parameter identification;variable structure;piecewise load model;static load;parameter identification;variable structure;piecewise load model|
|[An Early Warning Model of Substation Over-Limit Based on Dynamic Multi-objective Intelligent Detection and Tracking Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114321)|A. Xing; Y. Shunfu; S. Bo; Z. Xiaohua; S. Meng; G. Zhang; C. Li|10.1109/AEEES56888.2023.10114321|substation;target detection;out-of-limit warning;regional out-of-bounds;substation;target detection;out-of-limit warning;regional out-of-bounds|
|[Optimization of Secondary Iron of Homopolar Linear Synchronous Motor for Traction Application Based on Finite Element Method and Regression Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114261)|K. Wang; Y. Hu; Q. Wang; Q. Ge|10.1109/AEEES56888.2023.10114261|homopolar linear synchronous motor;motor modeling;regression model;multi-objective optimization;homopolar linear synchronous motor;motor modeling;regression model;multi-objective optimization|
|[Optimization of High Speed Permanent Magnet Synchronous Motor Based on Co-simulation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114116)|D. Zhou; L. Lu; C. Yang|10.1109/AEEES56888.2023.10114116|high speed permanent magnet synchronous motor;genetic algorithm;topsis method;finite element simulation;high speed permanent magnet synchronous motor;genetic algorithm;topsis method;finite element simulation|
|[Simulation Analysis of Temperature Rise of 10kV Atmospheric Pressure Sealed Air Insulated Switch Cabinet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114120)|H. Li; Z. He; S. Chen; G. Guo; J. Huang; G. Sun; J. Chen; X. Min|10.1109/AEEES56888.2023.10114120|air insulated switch cabinet;multi-physical field coupling simulation;static temperature rise;transient temperature rise;air insulated switch cabinet;multi-physical field coupling simulation;static temperature rise;transient temperature rise|
|[Thinking of Common Information Model of Digital Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114104)|L. Zhijun; H. Kewei; Z. Yuguang; S. Ning; S. Yuanliang|10.1109/AEEES56888.2023.10114104|component;common information model;region model;monitoring patrol;measurement type;data fusion;component;common information model;region model;monitoring patrol;measurement type;data fusion|
#### **2023 25th International Conference on Digital Signal Processing and its Applications (DSPA)**
- DOI: 10.1109/DSPA57594.2023
- DATE: 29-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Symbol Sequence Recognition Using Viterbi Algorithm and Probability Approximation Table](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113407)|K. Alimagadov; A. Khamukhin; S. Umnyashkin|10.1109/DSPA57594.2023.10113407|sequence recognition;Viterbi algorithm;object detection;machine learning;computer vision;sequence recognition;Viterbi algorithm;object detection;machine learning;computer vision|
|[RNN-Based Method for Classifying Natural Human Emotional States from Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113437)|A. K. Alimuradov; A. Y. Tychkov; D. S. Dudnikov; M. A. Tyurin; M. I. Yuskaev; M. G. Myasnikova; D. S. Moiseev|10.1109/DSPA57594.2023.10113437|speech signal processing;emotional speech;classification of emotional states;artificial neural network;recurrent neural network;speech signal processing;emotional speech;classification of emotional states;artificial neural network;recurrent neural network|
|[Combining Computer Vision and Word Processing to Classify Film Genres](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113362)|N. Andriyanov; V. Dementev|10.1109/DSPA57594.2023.10113362|multimodal data;computer vision;natural language processing;feature aggregation;multimodal data;computer vision;natural language processing;feature aggregation|
|[Piecewise Constant Signal Nonlinear Filtering based on Local Center / Surround Shrinkage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113454)|V. Antsiperov|10.1109/DSPA57594.2023.10113454|nonlinear filtering;piecewise constant signal;Poisson noise;receptive fields;surround suppression;shrinkage;nonlinear filtering;piecewise constant signal;Poisson noise;receptive fields;surround suppression;shrinkage|
|[Efficient and Low Complexity Frequency Synchronization in NR-5G Downlink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113363)|M. Assaf; O. G. Ponomarev|10.1109/DSPA57594.2023.10113363|5G;PSS;initial access;SSS;Carrier frequency offset;5G;PSS;initial access;SSS;Carrier frequency offset|
|[Indoor Positioning by CSI Amplitude and Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113417)|A. Astafiev; O. Astafieva; I. Kondrushin|10.1109/DSPA57594.2023.10113417|Indoor positioning;channel state information;amplitude;neural networks;Indoor positioning;channel state information;amplitude;neural networks|
|[Analysis of Characteristics of High-Frequency Path with Parallel Connections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113397)|A. Bakulin; A. Kaverin|10.1109/DSPA57594.2023.10113397|S-parameters;group delay;attenuation;gain;parallel connection of quadripoles;S-parameters;group delay;attenuation;gain;parallel connection of quadripoles|
|[Implementation of Time Scales Comparison over a Fiber-optic Line using DPN Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113410)|A. N. Malimon; R. I. Balaev; A. V. Naumov|10.1109/DSPA57594.2023.10113410|Time and frequency metrology;pseudo-noise coding and additional tone modulation;fiber-optic lines;time scales comparison methods;Time and frequency metrology;pseudo-noise coding and additional tone modulation;fiber-optic lines;time scales comparison methods|
|[Pulse Mask for Processing Parameters of Digital Signals E4, STM-1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113428)|A. V. Kleopin; A. A. Beloborodov; K. O. Suslova|10.1109/DSPA57594.2023.10113428|pulse mask;oscilloscope;Butterworth filter;slew rate;signal distortion;pulse mask;oscilloscope;Butterworth filter;slew rate;signal distortion|
|[Signal Processing with Spiking Neuron Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113406)|V. Bondarev|10.1109/DSPA57594.2023.10113406|adaptive signal processing;spiking neuron;integrate and fire neuron;adaptive signal processing;spiking neuron;integrate and fire neuron|
|[Algorithms for Calculating the Position of the Pupil in Tasks of the Optical-Oculographic Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113388)|Y. A. Turovsky; V. Y. Alekseev; S. V. Borzunov|10.1109/DSPA57594.2023.10113388|oculography;infrared oculography;gaze tracking;human-machine interface;linear regression;regression model construction;oculography;infrared oculography;gaze tracking;human-machine interface;linear regression;regression model construction|
|[Spatial Suppression of Interference Complex using Phase Adaptation Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113399)|Y. Parshin; B. Q. Vuong|10.1109/DSPA57594.2023.10113399|Wideband interference;narrowband interference;spatial suppression;interference complex;phase adaptation algorithm;Wideband interference;narrowband interference;spatial suppression;interference complex;phase adaptation algorithm|
|[Palimpsest Research Based on Hyperspectral Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113438)|N. A. Obukhova; P. S. Baranov; A. A. Motyko; A. A. Chirkunova; A. A. Pozdeev|10.1109/DSPA57594.2023.10113438|hyperspectral images;hidden text revealing;PCA;ICA;contrast enhancement;hyperspectral images;hidden text revealing;PCA;ICA;contrast enhancement|
|[Covert Detection of Air Targets with a Passive Radar System Using External Signals from Broadcast TV Stations of the DVB-T2 Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113448)|C. D. S.; C. O. G.; R. K. Y.|10.1109/DSPA57594.2023.10113448|passive radar;airborne target detection;TV broadcast signal;DVB-T2;UAV;passive radar;airborne target detection;TV broadcast signal;DVB-T2;UAV|
|[Multichannel Acoustic Echo Canceller Based on Fast Affine Projection Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113387)|V. Djigan|10.1109/DSPA57594.2023.10113387|acoustic echo canceller;affine projection algorithm;FAP;RLS;NLMS;LMS;room impulse response;acoustic echo canceller;affine projection algorithm;FAP;RLS;NLMS;LMS;room impulse response|
|[Room Response Equalizer Based on Simplified RLS Adaptive Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113366)|V. Djigan|10.1109/DSPA57594.2023.10113366|inverse adaptive filtering;modified x-filtered algorithm;Recursive Least Squares;RLS;room response equalizer;inverse adaptive filtering;modified x-filtered algorithm;Recursive Least Squares;RLS;room response equalizer|
|[Analysis of Methods for Estimation of Signal Delay for Optical Precision Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113400)|S. Donchenko; O. Kolmogorov; D. Prokhorov; D. Lubchenko; I. Novikova|10.1109/DSPA57594.2023.10113400|time delay;digital processing;optical fiber;time scales synchronization;laser rangefinder;heterodyne interferometer;time delay;digital processing;optical fiber;time scales synchronization;laser rangefinder;heterodyne interferometer|
|[Adaptation of Hemodynamics to extreme Exercise according to Impedance Cardiography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113392)|V. F. Fedorov; D. V. Nikolaev; V. L. Stolyar|10.1109/DSPA57594.2023.10113392|hemodynamics;bororeflex;impedance cardiography;extreme exercise;marathon;heart rate;stroke volume;total peripheral vascular resistance;total peripheral vascular resistance. cardiac output;central blood volume;hemodynamics;bororeflex;impedance cardiography;extreme exercise;marathon;heart rate;stroke volume;total peripheral vascular resistance;total peripheral vascular resistance. cardiac output;central blood volume|
|[Comparison of the Parameters Dynamics of two Heart Rate Analysis Methods before and after extreme Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113444)|V. F. Fedorov; D. V. Nikolaev; V. L. Stolyar|10.1109/DSPA57594.2023.10113444|heart rate;heart rate variability (HRV);analysis of heart rate variations;baroreflex;extreme loads;marathon;super marathon;orthoclinostatic test;heart rate;heart rate variability (HRV);analysis of heart rate variations;baroreflex;extreme loads;marathon;super marathon;orthoclinostatic test|
|[The Method of Creating Digital Signal Processing Systems for Complexes of a Nuclear Energy Objects' Decommission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113365)|K. Lavrentev; L. Filimonyuk|10.1109/DSPA57594.2023.10113365|decommission;digital signal processing;nuclear energy objects;model;drones;characteristics;cause-effect link;decommission;digital signal processing;nuclear energy objects;model;drones;characteristics;cause-effect link|
|[Development of a Simplified Mathematical Model of the Radiation Pattern of a Dual-Reflector Antenna System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113416)|D. Filippov; I. Silvestrov|10.1109/DSPA57594.2023.10113416|global navigation satellite system (GNSS);dual-reflector antenna;radiation pattern;far zone;amplitude distribution of the field;global navigation satellite system (GNSS);dual-reflector antenna;radiation pattern;far zone;amplitude distribution of the field|
|[Digital Neural Network Model of the Detector of Anomalous Changes in the Signals Reflecting the Operation of Automated Process Control Systems of Enterprises under the Influence of Cyber Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113395)|A. L. Glebets; M. O. Golovlev; A. N. Ragozin|10.1109/DSPA57594.2023.10113395|neural network;machine learning;information systems diagnostics;process anomaly detection;neural network;machine learning;information systems diagnostics;process anomaly detection|
|[Route Planning for Unmanned Aerial Vehicles During Monitoring Mobile Objects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113421)|V. I. Goncharenko; G. N. Lebedev; N. G. Zhuravleva|10.1109/DSPA57594.2023.10113421|unmanned aerial vehicle;organization of group flight of unmanned aerial vehicles;mobile objects monitoring;genetic algorithm;flight planning;unmanned aerial vehicle;organization of group flight of unmanned aerial vehicles;mobile objects monitoring;genetic algorithm;flight planning|
|[Application of Artificial Neural Networks to Improve BER performance of SEFDM signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113396)|V. Pavlov; I. Gorbunov; S. Zavjalov|10.1109/DSPA57594.2023.10113396|SEFDM;Demodulation;Artificial Neural Networks;modulation schemes;BER performance;SEFDM;Demodulation;Artificial Neural Networks;modulation schemes;BER performance|
|[Channel Capacity of Interconnected IoT Sensors System with Antennas' Mutual Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113408)|Y. Parshin; M. Grachev|10.1109/DSPA57594.2023.10113408|channel capacity;mutual coupling;scaling factor method;IoT;wireless sensor;spatial encoding;multipath propagation;channel capacity;mutual coupling;scaling factor method;IoT;wireless sensor;spatial encoding;multipath propagation|
|[Special Properties of Fourier Transforms of Pulse-Amplitude Modulated Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113423)|K. A. Grebenyuk|10.1109/DSPA57594.2023.10113423|types of pulse-amplitude modulation;properties of Fourier transform;Fourier transforms of pulse-amplitude modulated signals;types of pulse-amplitude modulation;properties of Fourier transform;Fourier transforms of pulse-amplitude modulated signals|
|[Low-Bit Noise-Immune Analog-to-Digital Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113445)|Y. Bekhtin; Y. Filatov; A. Ilyin|10.1109/DSPA57594.2023.10113445|analog-to-digital converse (ADC);low-bit ADC;noise-immune ADC;probabilistic relay;non-linear filtering;analog-to-digital converse (ADC);low-bit ADC;noise-immune ADC;probabilistic relay;non-linear filtering|
|[Coding Matrix of Polyphase Probing Signal with Zero Autocorrelation Zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113455)|R. N. Ipanov; A. A. Komarov|10.1109/DSPA57594.2023.10113455|autocorrelation function;complementary sequences;orthogonal sequences;pulse train;probing signal;zero autocorrelation zone;autocorrelation function;complementary sequences;orthogonal sequences;pulse train;probing signal;zero autocorrelation zone|
|[Clustering and Fitting to Reduce PAPR in Multi-User OFDM Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113449)|S. Krikunov; R. Bychkov; A. Blagodarnyi; A. Ivanov|10.1109/DSPA57594.2023.10113449|PAPR;OFDM;Machine Learning;PAPR;OFDM;Machine Learning|
|[Development of an Algorithm for Constructing a Route Based on the Data of GPX-Files](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113405)|K. D.A.; B. V.V.|10.1109/DSPA57594.2023.10113405|shortest path;graph theory;building routes;gpx-tracks;Dijkstra's algorithm;shortest path;graph theory;building routes;gpx-tracks;Dijkstra's algorithm|
|[Investigation of the Influence of the Quality of Load Matching on the Group Delay Time in the Line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113441)|V. Y. Kaspin; V. L. Voronov|10.1109/DSPA57594.2023.10113441|S parameters;group delay;long lines;matching;two port;S parameters;group delay;long lines;matching;two port|
|[Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113394)|A. Kents; A. Zotin; K. Simonov|10.1109/DSPA57594.2023.10113394|CT imaging;COVID-19 lung pathology;pulmonary fibrosis;outcome prediction;texture image analysis;color coding;CT imaging;COVID-19 lung pathology;pulmonary fibrosis;outcome prediction;texture image analysis;color coding|
|[Design of Feedback Controller for Path Tracking of Mobile Robot with Differential Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113453)|D. Khablov|10.1109/DSPA57594.2023.10113453|mobile robot;microwave sensor;Doppler Effect;differential drive;occupancy grid;probabilistic roadmap;mobile robot;microwave sensor;Doppler Effect;differential drive;occupancy grid;probabilistic roadmap|
|[An Approach to Routes Enumeration in QC LDPC Base Matrices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113447)|A. Kharin; K. Zavertkin; S. Ganin|10.1109/DSPA57594.2023.10113447|Communication systems;Quasi-Cyclic codes;Low-Density Parity-Check codes;error correction codes;cycles;EMD-spectre;Communication systems;Quasi-Cyclic codes;Low-Density Parity-Check codes;error correction codes;cycles;EMD-spectre|
|[Algorithm of Palm Vein Image Selection for Biometric Authentication Systems Based on Multidimensional Markov Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113443)|N. Kharina; A. Zemtsov; S. Chernyadyev|10.1109/DSPA57594.2023.10113443|biometric palm vein authentication;biometric template processing;object thinning algorithm;topological skeletonization algorithm;multidimensional Markov chain;biometric palm vein authentication;biometric template processing;object thinning algorithm;topological skeletonization algorithm;multidimensional Markov chain|
|[Signals Processing in Doppler Medical Measurement System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113435)|V. K. Klochko; I. V. Andreeva|10.1109/DSPA57594.2023.10113435|multi-channel system;blood flow velocity measurement;Doppler effect;error estimate;multi-channel system;blood flow velocity measurement;Doppler effect;error estimate|
|[A Study of the Automatic Object Extraction Algorithm on the Image Sequence Under Scaling Ttransformations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113422)|P. V. Babayan; E. S. Kozhina|10.1109/DSPA57594.2023.10113422|motion detection;background subtraction method;estimation of geometric transformations;scale estimation;multi-template algorithm;motion detection;background subtraction method;estimation of geometric transformations;scale estimation;multi-template algorithm|
|[Multiclass Recognition of Marine Vessels Based on Polarization Decomposition of SAR Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113456)|A. V. Kvasnov|10.1109/DSPA57594.2023.10113456|remote sensing;object recognition;multilayer perceptron;synthetic aperture radar;radar target;polarization decomposition;remote sensing;object recognition;multilayer perceptron;synthetic aperture radar;radar target;polarization decomposition|
|[Sub-Nyquist Bandpass Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113451)|V. Lenikov; T. Naumovich; A. Chastikov; D. Dubovcev|10.1109/DSPA57594.2023.10113451|bandpass sampling;sub-Nyquist sampling;aliasing;order of aliasing;alias clustering;intracluster aliasing;intercluster aliasing;multi-channel multi-rate samping;bandpass sampling;sub-Nyquist sampling;aliasing;order of aliasing;alias clustering;intracluster aliasing;intercluster aliasing;multi-channel multi-rate samping|
|[Measurement of Pulse Signal Amplitude Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113401)|A. V. Kleopin; V. V. Makarov; L. N. Selin|10.1109/DSPA57594.2023.10113401|pulse signal;compensation method;measurement error;reference voltage;digital storage oscilloscope;voltage calibrator;pulse signal;compensation method;measurement error;reference voltage;digital storage oscilloscope;voltage calibrator|
|[Localization of Railway Wheel Markings Based on Preliminary Localization of Symbols](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113361)|A. Mareev; A. Orlov; R. Rybkin|10.1109/DSPA57594.2023.10113361|video stream;localization;marking;video stream;localization;marking|
|[Algorithm for Detecting Biological Objects and Assessment of their Parameters for Automated Bioassay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113457)|E. V. Medvedeva; A. S. Olkova; M. A. Tolstoukhov|10.1109/DSPA57594.2023.10113457|biodiagnostic methods;bioassay;object detection algorithms;Gaussian mixture model;video images;assessment of the nature of object movement;biodiagnostic methods;bioassay;object detection algorithms;Gaussian mixture model;video images;assessment of the nature of object movement|
|[Rotation Invariant 2D Ear Recognition Using Gabor Filters and Ensemble of Pre-trained Deep Convolutional Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113436)|R. Mehta; G. Ujjwal; S. SJ; S. Vityazev; K. K. Singh|10.1109/DSPA57594.2023.10113436|Gabor filter;Feature extraction;CNN;Recognition;Rotation invariant;Gabor filter;Feature extraction;CNN;Recognition;Rotation invariant|
|[The Features of Interference Signal Processing when Measuring the Complex Dielectric Permittivity of Planar-Layered Media of Natural Origin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113418)|G. Linets; A. Bazhenov; S. Malygin; N. Grivennaya; S. Melnikov; V. Goncharov|10.1109/DSPA57594.2023.10113418|total internal refraction;UAV;Brewster's angle;complex permittivity;interference signal;phase shift;total internal refraction;UAV;Brewster's angle;complex permittivity;interference signal;phase shift|
|[Physiological Objectification of the Spread of Phase Trajectories of the Heart's Electrical Activity in a Digital ECG Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113434)|M. Elena N.|10.1109/DSPA57594.2023.10113434|Phaseography;electrical activity of the heart;Phaseography;electrical activity of the heart|
|[Metrology Characteristics Estimation for Digitally Modulated Signals Measurement Instruments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113430)|I. V. Mogilev; N. R. Bazhenov|10.1109/DSPA57594.2023.10113430|modulation;EVM;traceability;quadrature amplitude modulation (QAM);the 5th generation mobile network (5G);amplitude depth;phase deviation;modulation;EVM;traceability;quadrature amplitude modulation (QAM);the 5th generation mobile network (5G);amplitude depth;phase deviation|
|[Hardware Implementation of the Precision Time Base Technique for Digital Sampling Oscilloscopes on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113364)|B. Moussa; K. Chaccour; R. Bouyekhf|10.1109/DSPA57594.2023.10113364|Digital Sampling Oscilloscopes;time uncertainties;measurement jitter;PTB;sampling technique;eye diagram;Digital Sampling Oscilloscopes;time uncertainties;measurement jitter;PTB;sampling technique;eye diagram|
|[Adaptive Interference Cancellation in MIMO Information Transmission Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113413)|Y. Parshin; T. Nguyen|10.1109/DSPA57594.2023.10113413|MIMO systems;adaptive algorithm;interference cancellation;MMSE criterion;MIMO systems;adaptive algorithm;interference cancellation;MMSE criterion|
|[Quick Lookup Codes as a New Area of Optimization Theory Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113459)|Z. V. V.; O. G. V.|10.1109/DSPA57594.2023.10113459|Optimization Theory;Quick-Lookup Codes;Viterbi Algorithm;Multithreshold Algorithm;Main Theorem of Multithreshold Decoding;Optimal Decoder;codes with multiple rates;Optimization Theory;Quick-Lookup Codes;Viterbi Algorithm;Multithreshold Algorithm;Main Theorem of Multithreshold Decoding;Optimal Decoder;codes with multiple rates|
|[The Network Output Background Subtraction (NOBS) Algorithm for Unattended Luggage Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113420)|I. Ovodov|10.1109/DSPA57594.2023.10113420|abandoned luggage detection;unattended luggage detection;abandoned luggage detection;unattended luggage detection|
|[Information Transmission Efficiency of MIMO System in Presence of Noise Complex](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113440)|P. A.Yu.; P. Yu.N.|10.1109/DSPA57594.2023.10113440|phase noise;flicker noise;fractal Brownian motion;MIMO system;outage probability;matrix of channel coefficients;phase noise;flicker noise;fractal Brownian motion;MIMO system;outage probability;matrix of channel coefficients|
|[Choice of Antenna Array Geometry for Digital Spatial Filtering in Azimuth and Elevation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113389)|I. Peshkov; N. Fortunova; I. Zaitseva|10.1109/DSPA57594.2023.10113389|antenna;circular array;cylindrical array;direction-finding;direction-of-arrival;directivity;MUSIC;antenna;circular array;cylindrical array;direction-finding;direction-of-arrival;directivity;MUSIC|
|[On the Segmentation of Sunflower Plants in UAV Photos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113424)|D. Poleshchenko; I. Mikhailov; V. Petrov|10.1109/DSPA57594.2023.10113424|leaf area index (LAI);degree of plant development;segmentation;sunflower;leaf area index (LAI);degree of plant development;segmentation;sunflower|
|[Fast Method of Diagonal Sliding Spatial-Frequency Fourier Processing of Discrete Finite Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113439)|P. Alexey; P. Olga|10.1109/DSPA57594.2023.10113439|direct two-dimensional discrete Fourier transform;two-dimensional discrete finite signal;reference region;spatial-frequency spectrum;spatial-frequency Fourier processing;direct two-dimensional discrete Fourier transform;two-dimensional discrete finite signal;reference region;spatial-frequency spectrum;spatial-frequency Fourier processing|
|[Methods and Algorithms for Horizontal Sliding Spatial-Frequency Fourier Processing of Two-Dimensional Discrete Finite Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113429)|P. Alexey; P. Olga|10.1109/DSPA57594.2023.10113429|two-dimensional discrete signal;two-dimensional discrete Fourier transform;spatial domain;spatial-frequency spectrum;spatial-frequency processing;two-dimensional discrete signal;two-dimensional discrete Fourier transform;spatial domain;spatial-frequency spectrum;spatial-frequency processing|
|[Methods and Algorithms for Vertical Sliding Spatial-Frequency Fourier Processing of Two-Dimensional Discrete Finite Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113419)|P. Alexey; P. Olga|10.1109/DSPA57594.2023.10113419|spatial-frequency domain;discrete signal;signal processing;direct two-dimensional discrete Fourier transform;spectrum;fast Fourier transform;spatial-frequency domain;discrete signal;signal processing;direct two-dimensional discrete Fourier transform;spectrum;fast Fourier transform|
|[Pilot Synthesis for Distributed Synchronization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113432)|V. Ryzhov; A. Laktyushkin|10.1109/DSPA57594.2023.10113432|pilot design;time synchronization;correlation properties;distributed system;mesh;pilot design;time synchronization;correlation properties;distributed system;mesh|
|[Effect of Time-Frequency Representations for Fault Classification of Rolling Bearing in Noisy Conditions Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113425)|P. K. Sahu; R. N. Rai|10.1109/DSPA57594.2023.10113425|Bearing;Noise;Spectrogram;Scalogram;Q-constant Transform;Deep Learning;CNN-VGG16;Bearing;Noise;Spectrogram;Scalogram;Q-constant Transform;Deep Learning;CNN-VGG16|
|[Increase the Speed of Data Transmission over a Linear Channels with Memory by Suppressing Transient Distortions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113414)|V. Sannikov; V. Volchkov|10.1109/DSPA57594.2023.10113414|Data transmission system;transient distortions;linear communication channel with memory;bipolar signals without aftereffect;adjustable time points;data transfer rate;Data transmission system;transient distortions;linear communication channel with memory;bipolar signals without aftereffect;adjustable time points;data transfer rate|
|[Combining Index Modulation with Codebooks for Noncoherent Reception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113452)|A. B. Sergienko; P. V. Apalina|10.1109/DSPA57594.2023.10113452|noncoherent reception;energy-based receiver;index modulation;AWGN channels;phase shift keying;noncoherent reception;energy-based receiver;index modulation;AWGN channels;phase shift keying|
|[Optimization of Space-Time Block Codes for Noncoherent Reception in Fast Fading Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113398)|A. B. Sergienko|10.1109/DSPA57594.2023.10113398|space-time block code;noncoherent reception;Rayleigh channel;fast fading;generalized likelihood ratio test;error rate performance;autoencoder network;codebook optimization;joint coding and modulation;space-time block code;noncoherent reception;Rayleigh channel;fast fading;generalized likelihood ratio test;error rate performance;autoencoder network;codebook optimization;joint coding and modulation|
|[Two and More Dimensional Representations of Human Body Composition Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113411)|D. V. Nikolaev; S. P. Shchelykalina; M. V. A. Kolesnikov; O. A. Starunova; V. S. Romanova|10.1109/DSPA57594.2023.10113411|bioimpedance vector analysis;two-dimensional representation;bioimpedance vector analysis of human body composition;BIVA;BIVA HBC;bioimpedance analysis of human body composition;2D BIA;bioimpedance vector analysis;two-dimensional representation;bioimpedance vector analysis of human body composition;BIVA;BIVA HBC;bioimpedance analysis of human body composition;2D BIA|
|[Epileptic Seizure Prediction Using 1D-MobileNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113426)|S. SJ; R. Mehta; S. Vityazev; K. K. Singh|10.1109/DSPA57594.2023.10113426|Epilepsy;1D MobileNet;EEG signals;seizure prediction;binary classification;deep learning;Epilepsy;1D MobileNet;EEG signals;seizure prediction;binary classification;deep learning|
|[A Comparative Characteristics of Discrete Frequency Transform Variants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113450)|A. Shoberg; P. Babich; G. Shoberg|10.1109/DSPA57594.2023.10113450|digital signal processing;integral transform;discrete cosine transform;rotation invariance;digital signal processing;integral transform;discrete cosine transform;rotation invariance|
|[Method for Improving the Accuracy of the Theoretical Evaluation of the Effectiveness of Space Debris Monitoring by an Orbital Television System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113391)|R. S. Siryi; P. S. Baranov|10.1109/DSPA57594.2023.10113391|computer vision system;space debris;signal-to-noise ratio;object detection;spacecraft;computer vision system;space debris;signal-to-noise ratio;object detection;spacecraft|
|[Digital Data Processing when Reproducing a Unit of Length in the Range up to 60 m](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113431)|D. A. Sokolov; A. V. Mazurkevich; S. A. Kozachenkov; I. S. Silvestrov|10.1109/DSPA57594.2023.10113431|fast Fourier transform;digital filtering;digital signal processing;unit length reproduction;femtosecond laser;interference;cross-correlation function;fast Fourier transform;digital filtering;digital signal processing;unit length reproduction;femtosecond laser;interference;cross-correlation function|
|[A Stable Algorithm for Differentiating Noisy Signals in the Problem of Nonparametric Identification of Non-stationary Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113390)|Y. Voskoboynikov; S. Solodusha|10.1109/DSPA57594.2023.10113390|identification;differentiation of noisy signal;selection of smoothing parameter;identification;differentiation of noisy signal;selection of smoothing parameter|
|[Digital SDR-based Modem Using Optimal Multi-Frequency Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113433)|S. A. Suhotskiy; S. V. Zavjalov|10.1109/DSPA57594.2023.10113433|Soft-Defined Radio;SDR;Multi-frequency signals;OFDM;SEFDM;Soft-Defined Radio;SDR;Multi-frequency signals;OFDM;SEFDM|
|[2D/3D ResNet Deep Neural Network for 4G and 5G NR Wireless Channel Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113403)|V. S. Usatyuk; S. I. Egorov|10.1109/DSPA57594.2023.10113403|Wireless Channel Estimation;Deep Neural Network;4G;5G New radio;ResNet;ReLU;Wireless Channel Estimation;Deep Neural Network;4G;5G New radio;ResNet;ReLU|
|[Low Error Floor QC-LDPC Codes Construction Using Modified Cole's Trapping Sets Enumerating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113442)|V. S. Usatyuk|10.1109/DSPA57594.2023.10113442|Extrinsic Message Degree;EMD;Importance Sampling;QC-LDPC;Trapping Sets;Pseudo-codeword;Extrinsic Message Degree;EMD;Importance Sampling;QC-LDPC;Trapping Sets;Pseudo-codeword|
|[Digital Processing of Time Scales Shift Measurements for Synchronization of Navigation Equipment with an External Time Scale](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113458)|N. N. Vasilyuk; O. V. Denisenko; A. V. Veitsel|10.1109/DSPA57594.2023.10113458|GNSS timing receiver;time to digital converter;external time scale;time transfer;GNSS timing receiver;time to digital converter;external time scale;time transfer|
|[Formation of Discrete Signals on a Two-Sided Infinite Interval Using Recursive Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113404)|V. V. P.; S. V. G.|10.1109/DSPA57594.2023.10113404|signal approximation;recursive filter;recursive model;difference equations;discrete signals;causal signals;non-causal signals;least squares method;signal approximation;recursive filter;recursive model;difference equations;discrete signals;causal signals;non-causal signals;least squares method|
|[Determining the Accuracy of the Measurement of Arrival Time of the Useful Signal by the Meter for Non-Energy Parameters of the Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113402)|V. M. Artyushenko; V. I. Volovach|10.1109/DSPA57594.2023.10113402|multiplicative (modulating) noise;tracking meter;arrival time of the signal;measurement variance;coherent signal;incoherent signal;spectral density;automatic gain control;multiplicative (modulating) noise;tracking meter;arrival time of the signal;measurement variance;coherent signal;incoherent signal;spectral density;automatic gain control|
|[The Accuracy of the Joint Measurement of Two Information Parameters of the Useful Signal under the Influence of Additive and Multiplicative Noises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113427)|V. M. Artyushenko; V. I. Volovach|10.1109/DSPA57594.2023.10113427|multiplicative (modulating) noise;“low” level of noise;joint measurement of parameters;error variance;correlation coefficient;slow multiplicative noise;frequency-modulated signal;multiplicative (modulating) noise;“low” level of noise;joint measurement of parameters;error variance;correlation coefficient;slow multiplicative noise;frequency-modulated signal|
|[A Multispectral Imaging Algorithm Conforming to the Visual Perception of Human Eyes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113412)|X. Yang; N. A. Obukhova|10.1109/DSPA57594.2023.10113412|image segmentation;image fusion;CIEDE 2000;near-infrared fluorescence endoscope;image segmentation;image fusion;CIEDE 2000;near-infrared fluorescence endoscope|
|[Detecting Impulse Signals and Interference Cancelling by Adaptive Filters with Controlled Adaptation Intervals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113409)|V. A. Zasov; M. V. Romkin|10.1109/DSPA57594.2023.10113409|algorithm;adaptive;interference;detection;prediction;interval;flow;algorithm;adaptive;interference;detection;prediction;interval;flow|
|[Possibilities of Population Algorithm for Searching by Swarm of Fireflies in Multiextremal Optimization Problems under Interference Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113393)|V. A. Zasov; K. A. Busargina|10.1109/DSPA57594.2023.10113393|swarm;algorithm;optimization;multimodal;fireflies;interference;transparency;illumination;swarm;algorithm;optimization;multimodal;fireflies;interference;transparency;illumination|
|[Application of Optimal Signals Based on Eigenfunctions of Bandlimited Cores in Multipath Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113446)|A. V. Zhila; S. V. Zavjalov|10.1109/DSPA57594.2023.10113446|spectral efficiency;single frequency signals;optimization;correlation;restrictions;BER performance;multipath;spectral efficiency;single frequency signals;optimization;correlation;restrictions;BER performance;multipath|
|[A Family of High-Speed Voltage Repeaters and Output Stages of Analog Chips Based on Radiation-Resistant Analog Master Slice Array Crystals MH2XA030/031](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113415)|N. N. Prokopenko; O. V. Dvornikov; A. A. Zhuk|10.1109/DSPA57594.2023.10113415|ADC;anti-aliasing filter;buffer amplifier;operational amplifier;amplitude response;slew rate;analog master slice array crystal;ADC;anti-aliasing filter;buffer amplifier;operational amplifier;amplitude response;slew rate;analog master slice array crystal|
#### **2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON)**
- DOI: 10.1109/OTCON56053.2023
- DATE: 8-10 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Detection of Malicious Traffic in IoMT Environment Using Intelligent XGboost Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113978)|Y. Manchala; J. Nayak; H. S. Behera|10.1109/OTCON56053.2023.10113978|IoMT;XGboost;Classification;Malicious Trafftc;Health care;IoMT;XGboost;Classification;Malicious Trafftc;Health care|
|[Data–Driven Techniques in Logistics & Supply Chain Management: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114034)|P. K. Detwal; G. Soni; D. Kumar; B. Ramtiyal|10.1109/OTCON56053.2023.10114034|data-driven techniques;machine learning;logistics;supply chain management;data-driven techniques;machine learning;logistics;supply chain management|
|[Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance Data Streams Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113972)|M. Arya; B. K. Dewangan; M. Verma; M. Rohini; A. Motwani; S. K. Sar|10.1109/OTCON56053.2023.10113972|Big Data;Data Stream;Data Mining;Deep Learning;Feature Selection;SMOTE Algorithm;Honey-Bee Algorithm;Ensemble;Classification.;Big Data;Data Stream;Data Mining;Deep Learning;Feature Selection;SMOTE Algorithm;Honey-Bee Algorithm;Ensemble;Classification.|
|[Dark Web: A Review on the deeper side of the Web](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113989)|C. Prabha; A. Mittal|10.1109/OTCON56053.2023.10113989|Dark Web;Internet;Anonymity;TOR;Onion Routing;Dark Web;Internet;Anonymity;TOR;Onion Routing|
|[Extreme Contextual Multi-Anomaly Identification And Tracking From Aerial Down Shot View](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113943)|I. Kar; A. Chatterjee; J. Vishal; S. Tyagi|10.1109/OTCON56053.2023.10113943|Extremely Rare Visual Anomaly;Multiple Anomaly tracking;Supervised Learning;Unsupervised Learning;Aerial Field of View;contextual anomaly;surveillance;Extremely Rare Visual Anomaly;Multiple Anomaly tracking;Supervised Learning;Unsupervised Learning;Aerial Field of View;contextual anomaly;surveillance|
|[Detection of Brain Tumor using Machine Learning Classifiers – A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113960)|G. Anitha; A. S. Usman; S. Ahmed; H. Nasser|10.1109/OTCON56053.2023.10113960|Brain Tumor;Machine learning;Deep learning;Hybrid deep learning;Brain Tumor;Machine learning;Deep learning;Hybrid deep learning|
|[Hybrid Recommendation System with Enhanced Generalized Sequential Pattern Algorithm for ELearning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114040)|B. Krishna; C. Ravinder; R. Nayak; F. Parvez|10.1109/OTCON56053.2023.10114040|E-Learning Generalized Sequential Pattern;Learning Management System;Recommendation system;Sequential Pattern Mining.;E-Learning Generalized Sequential Pattern;Learning Management System;Recommendation system;Sequential Pattern Mining.|
|[Value Chain Benefits Estimation Due To Supply Chain Finance Adoption-A Simulation Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113938)|H. Ambadapudi; R. Matai|10.1109/OTCON56053.2023.10113938|India;Supply Chain Finance;AtmaNirbhar Bharat;Auto industry;Value Chain benefits;India;Supply Chain Finance;AtmaNirbhar Bharat;Auto industry;Value Chain benefits|
|[Application of microphone array in ADAS using Model Based Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113905)|A. Oktey; S. K. Mohaney|10.1109/OTCON56053.2023.10113905|microphone;ADAS;Model Based Development;Cross-correlation;MIL;SIL;microphone;ADAS;Model Based Development;Cross-correlation;MIL;SIL|
|[Implementation of ESG Index on Long-term Value and Performance of Oganizations Using AI and ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114037)|D. Jagyasi; A. R. Raut|10.1109/OTCON56053.2023.10114037|Artificial intelligence;Machine learning;ESG;pretrained Bert models;Artificial intelligence;Machine learning;ESG;pretrained Bert models|
|[Comparative Analysis of Machine Learning Models for Short-Term Load Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114042)|G. K. Patel; V. Kale; S. Khond|10.1109/OTCON56053.2023.10114042|Short-term load forecasting;regression model;day-ahead load forecasting;smart grid;machine learning;predictive models.;Short-term load forecasting;regression model;day-ahead load forecasting;smart grid;machine learning;predictive models.|
|[Tools and Techniques for Annotating the Plant Leaf Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113950)|J. Praveen Gujjar; V. Naveen Kumar; M. S. Guru Prasad|10.1109/OTCON56053.2023.10113950|Brown spot;Machine learning;Image processing;Paddy leaf disease detection;Classification;Brown spot;Machine learning;Image processing;Paddy leaf disease detection;Classification|
|[Recent Trends in Remote Healthcare Applications and Futuristic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113949)|S. Wagholikar; O. Wagholikar|10.1109/OTCON56053.2023.10113949|Artificial intelligence;Healthcare;Internet of things;Patents;Remote;Telehealth;Telemedicine;VOSViewer;Wearable;Artificial intelligence;Healthcare;Internet of things;Patents;Remote;Telehealth;Telemedicine;VOSViewer;Wearable|
|[Evolution of Low - Power Blockchain Technology by using Iterative Sharding for IoT Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113981)|S. Purbey; A. K. Choudhary; B. K. Dewangan|10.1109/OTCON56053.2023.10113981|Blockchain;Sharding;Low Energy;Mining;Delay;EHO;Fitness;Throughput;Jitter;Scenarios;Blockchain;Sharding;Low Energy;Mining;Delay;EHO;Fitness;Throughput;Jitter;Scenarios|
|[Bengali language-based Disease diagnosis system for rural people using Bidirectional Encoder Representations from Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113944)|K. P. Mandal; P. Mukherjee; B. Chakraborty; S. Ganguly|10.1109/OTCON56053.2023.10113944|BERT;NLP;disease;Bengali language;Machine learning;Medical;BERT;NLP;disease;Bengali language;Machine learning;Medical|
|[Diabetic Retinopathy Prediction and Analysis Using Ensemble Classifier in Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113997)|S. V. Hemanth; A. Saravanan|10.1109/OTCON56053.2023.10113997|Deep Learning;Image Processing;Ensemble;Random forest.;Deep Learning;Image Processing;Ensemble;Random forest.|
|[Automatic Bottle Filling and Capping Machine using SCADA with the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114011)|B. L. Prasanna; G. MadhusudhanaRao; S. Kaushaley; S. Nakka; P. K. Jena|10.1109/OTCON56053.2023.10114011|Bottle Filling;IoT;SCADA;Energy Efficiency;Speed Control of VFD;Manufacturing sector;Industrial Competency;Bottle Filling;IoT;SCADA;Energy Efficiency;Speed Control of VFD;Manufacturing sector;Industrial Competency|
|[Reinforcement Learning (RL) for optimal power allocation in 6G Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113929)|A. Dogra; R. K. Jha; K. R. Jha|10.1109/OTCON56053.2023.10113929|6G network;Reinforcement learning;RL;Optimal power allocation;6G network;Reinforcement learning;RL;Optimal power allocation|
|[Machine Learning Security Algorithms and Framework for IOT System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114014)|M. Mishra; V. K. Mishra; S. Tekale; T. NagaPraveena; K. Parijatha; B. Dewangan; S. Hadimani|10.1109/OTCON56053.2023.10114014|IoT Applications;(IoT) Internet of Things;Attacks;Security Privacy;Deep Learning;Machine Learning;IoT Applications;(IoT) Internet of Things;Attacks;Security Privacy;Deep Learning;Machine Learning|
|[A Short Term Recursive Matrix Pencil Based Distribution System Protection Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113999)|B. Deshmukh; D. K. Lal; S. Biswal|10.1109/OTCON56053.2023.10113999|Single line to ground (SLG);high impedance fault (HIF);feeder fault;distribution generations (DGs);recursive matrix pencil method (RMPM);Single line to ground (SLG);high impedance fault (HIF);feeder fault;distribution generations (DGs);recursive matrix pencil method (RMPM)|
|[ARIMA time Series Model vs. K-Means Clustering for Cloud Workloads Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113979)|V. K. Mishra; M. Mishra; S. Tekale; T. N. Praveena; R. Venkatesh; B. K. Dewangan|10.1109/OTCON56053.2023.10113979|Cloud computing;Time series Model;Data Analytics;Google cluster;Cloud computing;Time series Model;Data Analytics;Google cluster|
|[Security analysis of Three-Factor Authentication Protocol Based on Extended Chaotic-Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113994)|S. Devanapalli; K. Phaneendra|10.1109/OTCON56053.2023.10113994|Mutual Authentication;Key Agreement;Cryptanalysis;Chebyshev Chaotic Map;Mutual Authentication;Key Agreement;Cryptanalysis;Chebyshev Chaotic Map|
|[Routing Protocols and Their Performance in Mobile Ad hoc Networks: A Quality of Service Optimization Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113987)|R. Hari Sing; V. B. Narsimha|10.1109/OTCON56053.2023.10113987|MANET;Routing Protocols;Quality of Service (QoS);Reactive andProactive Routing;Routing optimization;MANET;Routing Protocols;Quality of Service (QoS);Reactive andProactive Routing;Routing optimization|
|[A Review on Clinical Named Entity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113977)|P. Kashtriya; P. Singh; P. Bansal|10.1109/OTCON56053.2023.10113977|Clinical Entity Recognition;Boundary Detection;CNN;UNet;NLP;Deep Learning;Clinical Entity Recognition;Boundary Detection;CNN;UNet;NLP;Deep Learning|
|[Study and Implementation of Location-based Access control Mechanism in Cloud services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113996)|S. Panchbudhe; M. Dave|10.1109/OTCON56053.2023.10113996|authentication;access control;cloud services;deep learning;static location;authentication;access control;cloud services;deep learning;static location|
|[Generation of power system using renewable resources for sustainable development leading upcoming technologies: an analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114001)|P. Singh; S. Shokeen; S. Garg|10.1109/OTCON56053.2023.10114001|power generation;renewable resources;analysis;non-renewable resources;electricity;progressive growth;development in current scenario;upcoming technologies;power generation;renewable resources;analysis;non-renewable resources;electricity;progressive growth;development in current scenario;upcoming technologies|
|[TIDF + Second derivative Order controller for Load Frequency Control of a two-area thermal power system by DE method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114044)|A. Biswal; P. Dwivedi; S. Bose|10.1109/OTCON56053.2023.10114044|Tilted Integral Derivative Controller with Filter (TIDF);Tilt Integral Derivative with Filter plus Second Derivative Order (TIDFD²);Differential Evolution (DE);Tilted Integral Derivative Controller with Filter (TIDF);Tilt Integral Derivative with Filter plus Second Derivative Order (TIDFD²);Differential Evolution (DE)|
|[A Proposed study to improvise sorting and management of E-waste](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113926)|K. Sharma; N. Jain; Jyotsna|10.1109/OTCON56053.2023.10113926|Sorting;Sensors;E-Waste;Technology.;Sorting;Sensors;E-Waste;Technology.|
|[Medical Image Diagnosis Using Deep Learning Classifiers for COVID-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114013)|B. B. Jayasingh; T. Jyothi|10.1109/OTCON56053.2023.10114013|COVID-19;Normal;Pneumonia;Deep Learning Techniques;illness categorization;Medical Images;performance validation;COVID-19;Normal;Pneumonia;Deep Learning Techniques;illness categorization;Medical Images;performance validation|
|[Success and failure rate prediction of Android Application using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113988)|S. J. Sankar; U. Singh; I. Ali; M. N. Naskar; M. K. Gourisaria|10.1109/OTCON56053.2023.10113988|Imbalance data-set;outlier handling;missing value;SMOTE;ADASYN;Android Authenticity Predictions.;Imbalance data-set;outlier handling;missing value;SMOTE;ADASYN;Android Authenticity Predictions.|
|[Farmer’s Assistant in Agricultural Sector by using Machine Learning and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113908)|R. Jadhav; P. Bhaladhare|10.1109/OTCON56053.2023.10113908|Crop Recommendation;Fertilizer Recommendation;Plant Disease Classification;Machine Learning;Deep Learning;Image pre-processing.;Crop Recommendation;Fertilizer Recommendation;Plant Disease Classification;Machine Learning;Deep Learning;Image pre-processing.|
|[PEM Fuel Cell based PV/Wind Hybrid Energy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114038)|R. K. Parida|10.1109/OTCON56053.2023.10114038|PV array;DFIG;Fuel Cell;DC to DC converter;DC to AC converter;Wind power;PV array;DFIG;Fuel Cell;DC to DC converter;DC to AC converter;Wind power|
|[A Meticulous Presaging of Heart Disease Optimized by Boruta feature Selection and RFE over Gradient Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113902)|R. Aggarwal; S. Kumar|10.1109/OTCON56053.2023.10113902|Correlation;GB;RFE;Boruta;FIB;LR;Random Forest etc;Correlation;GB;RFE;Boruta;FIB;LR;Random Forest etc|
|[Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114045)|S. Kumar; S. K. Chandra; R. N. Shukla; L. Panigrahi|10.1109/OTCON56053.2023.10114045|Industry 4.0 Internet of Things (IoT) Machine Learning Artificial intelligence Industrial Internet of Things (IIoT);Industry 4.0 Internet of Things (IoT) Machine Learning Artificial intelligence Industrial Internet of Things (IIoT)|
|[The Cocoon of Anthropoid on Trauma Alleviation using Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113970)|C. Rajan; M. Bhavatharani; R. Abarna; M. Dhanasri|10.1109/OTCON56053.2023.10113970|PPG sensor;Stress;HRV analysis;Virtual Reality;Healthy life;PPG sensor;Stress;HRV analysis;Virtual Reality;Healthy life|
|[Management of IT Operations and IT Infrastructure with Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114008)|S. Saurav; K. S. Sudeep|10.1109/OTCON56053.2023.10114008|Virtual Reality (VR);Industrial maintenance;IT maintenance;IT operation;Virtual Reality (VR);Industrial maintenance;IT maintenance;IT operation|
|[Automated Vehicle Braking System and Driver Health Monitoring System Using IOT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113976)|B. S. Manoj; Y. Puneeth; S. Yuvaraj|10.1109/OTCON56053.2023.10113976|Autonomous Braking System (ABS);Internet of Things (IoT);UV Sensor;GPS.;Autonomous Braking System (ABS);Internet of Things (IoT);UV Sensor;GPS.|
|[A Review on Recent Work On OCT Image Classification for Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114003)|J. Subhedar; A. Mahajan|10.1109/OTCON56053.2023.10114003|Optical coherence tomography(OCT);OCT classification;Deep learning;CNN;Medical image analysis.;Optical coherence tomography(OCT);OCT classification;Deep learning;CNN;Medical image analysis.|
|[Analysis of Machine Learning algorithms used in Face Recognition Attendance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113925)|G. Goel; V. Bansal; D. Bura|10.1109/OTCON56053.2023.10113925|Face Recognition;Local Binary Pattern (LBP);Principal Component Analysis (PCA).;Face Recognition;Local Binary Pattern (LBP);Principal Component Analysis (PCA).|
|[Digital Payments Revolution: A Study of Awareness, Acceptance, and Usage of Unified Payments Interface Technology Among Selected Women in India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114004)|J. Thakkar; P. Thakkar|10.1109/OTCON56053.2023.10114004|Unified Payment Inteface;Technology;Digital payments;Quick Response and FinTech;Unified Payment Inteface;Technology;Digital payments;Quick Response and FinTech|
|[Deep-learning models for Covid-19 Detection Using Chest X-Ray Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113965)|P. Semwal; R. Saini|10.1109/OTCON56053.2023.10113965|Deep learning;COVID-19;VGG16;ResNet50;InceptionV3;InceptionResNetV2;Chest X-Ray (CXR) images;Deep learning;COVID-19;VGG16;ResNet50;InceptionV3;InceptionResNetV2;Chest X-Ray (CXR) images|
|[Deep learning model for speech emotion classification based on GCI and GOI detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114027)|O. Vernekar; S. R. Nirmala; K. Chachadi|10.1109/OTCON56053.2023.10114027|Emotion;Speech;GCI&GOI;CNN;Emotion;Speech;GCI&GOI;CNN|
|[A Review On Collaborative Filtering Using Knn Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113985)|A. Sharma; J. N. K. S; D. Rana; S. Setia|10.1109/OTCON56053.2023.10113985|Recommendation system;collaborative filtering;k-nearest neighbor algorithm;Recommendation system;collaborative filtering;k-nearest neighbor algorithm|
|[A Bibliometric Analysis of Network Security on Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113954)|P. Diwan; B. Khandelwal; B. K. Dewangan; P. Shriwas|10.1109/OTCON56053.2023.10113954|Blockchain;Smart Contract;Bibliometric Analysis;Network Security;IoT;Overlay Visualization;Network Visualization;Density Visualization;Scopus;VOSviewer;Blockchain;Smart Contract;Bibliometric Analysis;Network Security;IoT;Overlay Visualization;Network Visualization;Density Visualization;Scopus;VOSviewer|
|[Group Emotion Detection using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113900)|K. Kousalya; R. S. Mohana; B. K. Kumar; E. K. Jithendiran; R. C. Kanishk; T. Logesh; E. R. Kumar; B. A. Sahithya|10.1109/OTCON56053.2023.10113900|Convolutional Neural Network;Deep Learning;Group Emotion Detection;Convolutional Neural Network;Deep Learning;Group Emotion Detection|
|[Review on Artificial Intelligence and Human Computer Interaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114002)|S. Kushwaha|10.1109/OTCON56053.2023.10114002|AI;Deep Learning;Healthcare;HCI;Computing;Machine Learning;etc;AI;Deep Learning;Healthcare;HCI;Computing;Machine Learning;etc|
|[Seasonal Variation of Corrosion Rate in the Water Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113975)|S. Kumar; R. Singh; N. S. Maurya|10.1109/OTCON56053.2023.10113975|Corrosion rate;Distribution network;Summer and Winter;Patna;Bihar;Corrosion rate;Distribution network;Summer and Winter;Patna;Bihar|
|[Evolution for a secured path using NexGen firewalls](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113935)|B. Rajkumar; G. Arunakranthi|10.1109/OTCON56053.2023.10113935|Cyber Security;Firewalls;NGFW;IPS;Deep Packet Inspection;Network Security;firewall;Cyber Security;Firewalls;NGFW;IPS;Deep Packet Inspection;Network Security;firewall|
|[Performance Comparison of Dysgraphia Detection Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114036)|G. Goyal; S. Karwal; R. Garg|10.1109/OTCON56053.2023.10114036|Dysgraphia detection;writing disease;machine learning;Dysgraphia detection;writing disease;machine learning|
|[Design and development of a fuzzy-based singlephase STATCOM operator for non-linear loads in standalone PV systems to improve power quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114000)|M. Rajesh; A. L. Devi|10.1109/OTCON56053.2023.10114000|LC filter;Non Linear Load;Fuzzy controller;Power quality;STATCOM;Photovoltaic Cell;PV Inverter;Total Harmonic Distortion (THD);Passive Harmonic Filter;LC filter;Non Linear Load;Fuzzy controller;Power quality;STATCOM;Photovoltaic Cell;PV Inverter;Total Harmonic Distortion (THD);Passive Harmonic Filter|
|[Web Accessibility Supporting Diversity Inclusion and Effective Internet Communication in e-commerce, for Sustainable Social Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113942)|N. Patvardhan; M. Ranade; K. Patvardhan|10.1109/OTCON56053.2023.10113942|Economic Independence;E-Commerce websites;Information and Communications Technology (ICT);Persons with Disability;Web Content Accessibility Guidelines (WCAG);Web accessibility;Economic Independence;E-Commerce websites;Information and Communications Technology (ICT);Persons with Disability;Web Content Accessibility Guidelines (WCAG);Web accessibility|
|[A Comparative Study in Pedestrian Detection for Autonomous Driving Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113992)|M. Ramchandani; S. P. Sahu; D. K. Dewangan|10.1109/OTCON56053.2023.10113992|LiDAR;Radar;vision sensor;intelligent vehicle system;Pedestrian detection;autonomous driving;LiDAR;Radar;vision sensor;intelligent vehicle system;Pedestrian detection;autonomous driving|
|[Energy Management In Hybrid Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113958)|S. P; R. D. P; S. G; S. S. Pa; N. D; M. B. Y|10.1109/OTCON56053.2023.10113958|Energy Management;State of Charge (SOC);Solar radiation;Maximum Power Point Tracking (MPPT);Energy Management;State of Charge (SOC);Solar radiation;Maximum Power Point Tracking (MPPT)|
|[Microarray Data Classification using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113990)|S. Prajapati; H. Das; M. K. Gourisaria|10.1109/OTCON56053.2023.10113990|Support Vector Machine;Machine Learning;Random Forest;K-Nearest Neighbor;Decision Tree;Microarray Data.;Support Vector Machine;Machine Learning;Random Forest;K-Nearest Neighbor;Decision Tree;Microarray Data.|
|[A Comparative Study of Clustering Humans Based on Their Stress Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114012)|J. G. Jayawickrama; R. A. H. M. Rupasingha|10.1109/OTCON56053.2023.10114012|Clustering;Human stress;Machine learning;Sleeping habits;Clustering;Human stress;Machine learning;Sleeping habits|
|[A Hybrid Method for Anomaly Detection Using Distance Deviation and Firefly Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114024)|A. Shrivastava; P. R. Vamsi|10.1109/OTCON56053.2023.10114024|Anomaly Detection;Metaheuristic Algorithm;Outlier Detection;Swarm Intelligence.;Anomaly Detection;Metaheuristic Algorithm;Outlier Detection;Swarm Intelligence.|
|[A Decision-making Framework Model for Replacement of Track Switch-point Changing Box using a Multiple Attribute Decision Making Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114035)|A. Chaudhuri; A. K. Chaudhuri; S. Malani|10.1109/OTCON56053.2023.10114035|Locomotive;Railway track;Multi Attribute Decision Making;switch point changing boxes;Derailment;Locomotive;Railway track;Multi Attribute Decision Making;switch point changing boxes;Derailment|
|[Garbage Management System Using Arduino Device to support Swachh Bharath Mission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113921)|S. M. Metagar; F. R. Sayyed; P. Singhvi|10.1109/OTCON56053.2023.10113921|Ardiuno;Sensors dustbins;wires;Servo motor GSM900A Module.;Ardiuno;Sensors dustbins;wires;Servo motor GSM900A Module.|
|[Rabin and ElGamal Cryptosystem for a secured communication: A Detailed Cryptanalysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113914)|R. Chakraborty; M. Das|10.1109/OTCON56053.2023.10113914|Rabin;ElGamal;assymetric cryptography;crypTool;Cryptanalysis;Rabin;ElGamal;assymetric cryptography;crypTool;Cryptanalysis|
|[Methodology for SNR Improvement on SatCom Network From orbiting Satellites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113993)|D. Upadhyay; N. Mohd; P. Tiwari; A. Singh|10.1109/OTCON56053.2023.10113993|Satellite communication;Inteference;Simulation;MEO;LEO;Capacity;Satellite communication;Inteference;Simulation;MEO;LEO;Capacity|
|[Frequency Estimation using KLS Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113955)|T. Nizampatnam; P. Kumar|10.1109/OTCON56053.2023.10113955|Sinusoidal signal;frequency estimation;Least-Squares method;Pre-filtering;Kalman Filter;Mean Square Error (MSE);Sinusoidal signal;frequency estimation;Least-Squares method;Pre-filtering;Kalman Filter;Mean Square Error (MSE)|
|[A Novel Design and Development model for people counting in a Closed Environment with Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114007)|P. Gupta; U. Ghugar; C. V. Vardhan; R. Nayak; C. S. Rajpoot|10.1109/OTCON56053.2023.10114007|Machine Learning;YOLO;People Count;Object detection;Machine Learning;YOLO;People Count;Object detection|
|[A Futuristic Perspective on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113980)|S. Kushwaha|10.1109/OTCON56053.2023.10113980|AI;ANN;CNN;NLP;Deep Learning;AI;ANN;CNN;NLP;Deep Learning|
|[Quantification of Power System Flexibility: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113939)|A. Harit; P. Jain; S. Yamujala; R. Bhakar|10.1109/OTCON56053.2023.10113939|Flexibility assessment;flexibility metrics;flexibility quantification;renewable energy sources;reliability;Flexibility assessment;flexibility metrics;flexibility quantification;renewable energy sources;reliability|
|[Prediction of Liver Abnormality using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113966)|L. Gottemukkala; Y. Jeevan Nagendra Kumar; U. Sai Manikanta Phani Teja; N. Tanishq Dhanraj; Y. Nitish|10.1109/OTCON56053.2023.10113966|Liver Disease;Machine Learning;Liver Disease;Machine Learning|
|[Hyperspectral Image Classification for Remote Sensing: Comparative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113916)|D. Adlakha; T. Singh; R. Garg|10.1109/OTCON56053.2023.10113916|HSIC;Support vector machine;Principal Component Analysis;Remote Sensing Hybrid Spectral Network;Convolutional Neural Networks;HSIC;Support vector machine;Principal Component Analysis;Remote Sensing Hybrid Spectral Network;Convolutional Neural Networks|
|[Investigation of Superpixel Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113948)|A. K. Godishala; H. Yassin; D. T. C. Lai; R. Veena|10.1109/OTCON56053.2023.10113948|images;deep learning;image processing;segmentation;denoising;Felzenszwalbs;SLIC;Quick shift;Compact watershed;non-local means;NLM;superpixels;oversegmentation;images;deep learning;image processing;segmentation;denoising;Felzenszwalbs;SLIC;Quick shift;Compact watershed;non-local means;NLM;superpixels;oversegmentation|
|[Gesture Controlled Bot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114047)|J. Kaur; P. Mishra; P. Singh; P. K. Singh|10.1109/OTCON56053.2023.10114047|Gesture;Automation;Arduino;Interaction;Sensors;Accelerometer;BOT;Gesture;Automation;Arduino;Interaction;Sensors;Accelerometer;BOT|
|[Design and Analysis of Oil Pipeline Leakage Detection Model using WDM FBG Sensors through Simulation of Temperature and Strain Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113984)|S. Kumar; N. Kumar; J. Singh|10.1109/OTCON56053.2023.10113984|Underwater Oil Pipeline;Fiber Bragg Grating (FBG) Sensor;Apodization;Index Modulation;Reflectivity;Underwater Oil Pipeline;Fiber Bragg Grating (FBG) Sensor;Apodization;Index Modulation;Reflectivity|
|[An Approach to Evaluate Load Balancing and Crucial Data Analysis Through Hadoop Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114022)|P. K. Shriwas; S. Kuraiya; P. Diwan; V. Kumar; B. Dewangan|10.1109/OTCON56053.2023.10114022|Map-Reduce;Cloud Computing;Load-Balancing;Data Analytics;Map-Reduce;Cloud Computing;Load-Balancing;Data Analytics|
|[Machine Learning based Workload Prediction for Auto-scaling Cloud Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114033)|S. T. Singh; M. Tiwari; A. S. Dhar|10.1109/OTCON56053.2023.10114033|cloud computing;service level agreement;proactive auto-scaling;workload prediction;machine learning;multi-class support vector machine;cloud computing;service level agreement;proactive auto-scaling;workload prediction;machine learning;multi-class support vector machine|
|[A Recommendation System for Decentralized Autonomous Organization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113941)|E. Gras; R. George; K. Churchill; M. Kiruthika|10.1109/OTCON56053.2023.10113941|youtube;recommendation;analysis;machine learning;DAO;youtube;recommendation;analysis;machine learning;DAO|
|[A Comparison of Deep Learning Algorithms Dealing With Limited Samples in Hyperspectral Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114005)|P. Ranjan; A. Girdhar|10.1109/OTCON56053.2023.10114005|Hyperspectral;Deep Learning;Classification;Limited Samples;Spectral;Spatial;Accuracy;Hyperspectral;Deep Learning;Classification;Limited Samples;Spectral;Spatial;Accuracy|
|[PBL an Inductive Approach: Case study in India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113982)|S. Lavania; A. Giri|10.1109/OTCON56053.2023.10113982|Project Based Learning (PBL);Traditional classroom teaching;Bloom’s taxonomy;Project Based Learning (PBL);Traditional classroom teaching;Bloom’s taxonomy|
|[Impact of digital media marketing on purchasing behavior in the low hill urban areas of Himachal Pradesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113971)|Bharti; C. K. Upadhyay; S. K. Garg; M. Ghai|10.1109/OTCON56053.2023.10113971|customer;consumer;buying;advertisement;online advertisement;online buying;online groups;digital media celebrity;FMCG;customer;consumer;buying;advertisement;online advertisement;online buying;online groups;digital media celebrity;FMCG|
|[PDE based Diffusion Filters for Image Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114016)|R. Yamini; R. R. Jeyalakshmi; R. Sowmini|10.1109/OTCON56053.2023.10114016|Linear diffusion;Perona malik model;edge enhancing and filter;Linear diffusion;Perona malik model;edge enhancing and filter|
|[Hybrid Image Captioning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113957)|L. Panigrahi; R. R. Panigrahi; S. K. Chandra|10.1109/OTCON56053.2023.10113957|Convolutional Neural Network;image captions;Recurrent Neural Network;LSTM;attention moel;encoder;decoder;Convolutional Neural Network;image captions;Recurrent Neural Network;LSTM;attention moel;encoder;decoder|
|[Non-Coherent Optical OFDM Transceiver based Machine learning : Regression Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113956)|A. Ibrahim; A. Elsheikh; A. M. Abdelsalam; J. Prat|10.1109/OTCON56053.2023.10113956|Optical modulation;Regression tree;Neural network;ACO-OFDM;Machine learning;Optical modulation;Regression tree;Neural network;ACO-OFDM;Machine learning|
|[Precision Agriculture: Influencing factors and challenges faced by farmers in delta districts of Tamil Nadu](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113906)|S. Arjune.; V. S. Kumar|10.1109/OTCON56053.2023.10113906|Precision Agriculture;Behavioural factors;Agro ecological factors;Technical factors;Perception factors;Precision Agriculture;Behavioural factors;Agro ecological factors;Technical factors;Perception factors|
#### **2022 IEEE International Conference on Cyborg and Bionic Systems (CBS)**
- DOI: 10.1109/CBS55922.2023
- DATE: 24-26 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Development of a Compact Autonomous Propeller-driven Capsule Robot for Noninvasive Gastric Endoscopic Examination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115374)|Y. Zhang; Z. Li; W. Ke; C. Hu|10.1109/CBS55922.2023.10115374|;|
|[Emotion Prediction in Conversation Based on Relationship Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115361)|L. Yingjian; W. Xiaoping; L. Shanglin|10.1109/CBS55922.2023.10115361|;|
|[Flexible stretchable sensor garments that monitor human movement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115386)|M. Bi; Y. Zhao; C. Chen; Y. Liu; S. Chen; Y. Ding; G. Li|10.1109/CBS55922.2023.10115386|;|
|[Improve the Performance of Active Knee Prosthesis During Stairs Ascent by Using the Leading Reference Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115345)|Q. Huang; W. Wei; B. Huang; H. Wang; P. Huang; G. Li|10.1109/CBS55922.2023.10115345|;|
|[An intelligent prosthetic system for EMG pattern recognition based prosthesis control*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115334)|L. Tian; Y. Zheng; N. Jiang; H. Zhang; Y. Liu; X. Li; G. Li|10.1109/CBS55922.2023.10115334|;|
|[Prescribed performance following control of mobile rehabilitation robot based on human walking intention estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115351)|M. Zhang; Y. Guo; Q. Yan|10.1109/CBS55922.2023.10115351|Mobile Rehabilitation Robot;Set Membership Filtering;Walking Intention Estimation;Following Control;Prescribed Performance;Mobile Rehabilitation Robot;Set Membership Filtering;Walking Intention Estimation;Following Control;Prescribed Performance|
|[Enveloping grasp planning of a three-fingered deployable metamorphic robotic grasper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115393)|C. Gao; Y. Zhang; H. Lei; X. Kang; P. Xu; B. Li|10.1109/CBS55922.2023.10115393|robotic grasper;metamorphic mechanism;enveloping grasp planning;grasping simulation;robotic grasper;metamorphic mechanism;enveloping grasp planning;grasping simulation|
|[Development of an intelligent bionic robot rat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115377)|X. Dong; J. Wu; Z. Yin; X. Li|10.1109/CBS55922.2023.10115377|;|
|[Vision-based method for precise manipulation of magnetic spiral microrobots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115341)|C. Gao; D. Gong; X. Deng; L. Feng; W. Zhang|10.1109/CBS55922.2023.10115341|;|
|[Posture Estimation and Trajectory Tracking for SQuRo: a Small-sized Quadruped Robotic Rat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115373)|J. Gao; R. Du; X. Quan; G. Jia; Q. Huang; T. Fukuda; Q. Shi|10.1109/CBS55922.2023.10115373|;|
|[Bionic Multi-legged Robot Based on End-to-end Artificial Neural Network Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115331)|D. Yang; Y. Liu; F. Ding; Y. Yu|10.1109/CBS55922.2023.10115331|;|
|[Modeling and Experimental Characterization of Propeller-driven Capsule Endoscope Robot for Gastrointestinal Minimally Invasive Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115353)|W. Wang; Y. Zhang; C. Hu|10.1109/CBS55922.2023.10115353|;|
|[Design, fabrication and characterization of gecko inspired micro-fibrillar adhesive materials for wall climbing robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115372)|X. Yu; W. Tan; R. Wang; Y. Zhang; L. Yang; Y. Gu; C. Zhang|10.1109/CBS55922.2023.10115372|;|
|[Extraction of Key Foreground Information from Visual Feedback Images for Contact Micromanipulation in Liquid Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115354)|J. Chen; H. Wang; K. Bai; K. Lin; Q. Shi; T. Sun; Q. Huang; T. Fukuda|10.1109/CBS55922.2023.10115354|;|
|[Development of an in vitro perfusable neural interface model for sequential connection of nerve cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115337)|M. Liu; H. Wang; Q. Shi; Y. Hou; J. Liu; T. Sun; Q. Huang; T. Fukuda|10.1109/CBS55922.2023.10115337|neural interface;microfluidics;perfusion culture;SH-SY5Y cell;sequential connection of nerve cells;neural interface;microfluidics;perfusion culture;SH-SY5Y cell;sequential connection of nerve cells|
|[Rail-Guided Multi-Robot System for the Cooperative Manipulation of Cross-Scale Targets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115406)|H. Tao; H. Wang; Y. Hou; S. Guo; K. Lin; M. Wang; Q. Huang; T. Fukuda|10.1109/CBS55922.2023.10115406|;|
|[An Automated Scheme for Dielectrophoretic Cell Stretching Manipulation*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115379)|T. Chen; Y. Sun; M. Zhu; W. Yu; M. G. L. Sun; H. Yang|10.1109/CBS55922.2023.10115379|;|
|[Peripheral Nerve Block and Stimulation for Controlling Rat Ankle Joint Angle using Visual Feedback System*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115376)|M. Takeuchi; K. Tokutake; T. Miyamoto; N. Ito; T. Aoyama; S. Kurimoto; H. Hirata; Y. Hasegawa|10.1109/CBS55922.2023.10115376|;|
|[The influence of structural parameters on the design function of the tendon driven prosthetic hand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115347)|Y. Zheng; X. Li; L. Tian; Y. Liu; Y. Tan; Xugang; Jiang; G. Li|10.1109/CBS55922.2023.10115347|;|
|[The effects of a passive ankle exoskeleton on biomechanical performance during overground walking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115364)|Z. Song; H. Dong; R. Xu; L. Meng; D. Ming|10.1109/CBS55922.2023.10115364|;|
|[Trace Finger Kinematics from Surface Electromyography by Using Kalman Decoding Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115330)|H. Zhang; X. Zhou; Z. Yang; L. Tian; Y. Zheng; G. Li|10.1109/CBS55922.2023.10115330|;|
|[A Novel Metric based on Bootstrapping Approach for sEMG Signal Quality Assessment Towards Robust Decoding of Lower Limb Locomotion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115305)|F. Tan; W. Wei; Y. Dong; Z. Sun; X. Yong; O. W. Samuel; G. Li|10.1109/CBS55922.2023.10115305|;|
|[Scaled-down of high-voltage circuits for dielectric elastomer actuators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115362)|A. Minaminosono; R. Onuki; Y. Ohsugi; N. Hosoya; S. Maeda|10.1109/CBS55922.2023.10115362|;|
|[Development of Separate Exoskeleton Socket of Wrist Joint on Myoelectric Prosthetic Hand for Congenital Defects with Symbrachydactyly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115392)|Y. Inoue; Y. Kuroda; Y. Yamanoi; Y. Yabuki; H. Yokoi|10.1109/CBS55922.2023.10115392|;|
|[DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115360)|J. Gao; H. Ruan; B. Xu; Z. Zeng|10.1109/CBS55922.2023.10115360|;|
|[Improving Transferability of Adversarial Point Clouds with Model Commonalities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115395)|J. Lv; L. Liu; Y. Zhang; D. Li; Z. Zeng|10.1109/CBS55922.2023.10115395|;|
|[Automated Construction of the biomimetic module under holographic imaging feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115365)|X. Li; Y. Zhang; J. Cui; X. Wang; D. Wang; W. Hao|10.1109/CBS55922.2023.10115365|3D bioprinting technology;Artificial tissue module;3D morphology;Mechanical stiffness;3D bioprinting technology;Artificial tissue module;3D morphology;Mechanical stiffness|
|[GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115327)|H. Ruan; B. Xu; J. Gao; L. Liu; J. Lv; Y. Sheng; Z. Zeng|10.1109/CBS55922.2023.10115327|;|
|[Distributed Multiple Line-Outages Detection in Power Grid With Finite Time Observer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115367)|Y. Chen; Z. -W. Liu; Y. Yu; B. Wang; W. Yao; H. Liu; R. Chen; D. Li|10.1109/CBS55922.2023.10115367|Power system;multiple line-outages detection;complex network;finite time observer;Power system;multiple line-outages detection;complex network;finite time observer|
|[Power Plant Combustion Control System Based on Digital Twin Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115336)|Z. Hong; G. Shiqi; Z. Yiwei; S. Jingen|10.1109/CBS55922.2023.10115336|Digital twins;Networked Control System Laboratory (NCSLab);Combustion process control system;3D Model;Digital twins;Networked Control System Laboratory (NCSLab);Combustion process control system;3D Model|
|[QuWheeleg: Quadruped Wheeled-leg Robot Based on Electromagnetic Clutch Pulse Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115391)|Y. He; C. Sun; X. Quan; Y. Jin; R. Wang; Q. Shi|10.1109/CBS55922.2023.10115391|;|
|[Azimuth Estimation of Swimming Fish by Artificial Lateral Line System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115306)|X. Guo; S. Zhang; K. Zhou; J. Zheng; C. Wang; G. Xie|10.1109/CBS55922.2023.10115306|;|
|[The Tumbling Motion Planning of Humanoid Robot with Rolling-Stone Dynamics Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115304)|J. Cao; J. Gao; W. Zuo; J. Liu; X. Xin; M. Jin|10.1109/CBS55922.2023.10115304|humanoid robot;motion planning;trajectory optimization;collision analysis;humanoid robot;motion planning;trajectory optimization;collision analysis|
|[Modeling and Analysis of Micro-plasma Bubbles with Electric Field Concentration*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115333)|Y. Yamashita; S. Sakuma; Y. Yamanishi|10.1109/CBS55922.2023.10115333|;|
|[Human-Riding Inspired Acceleration Control of a Wheel-Legged Humanoid Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115358)|X. Zhang; Z. Yu; X. Chen; G. Huang; M. Zhu; L. Zhao; X. Qiu; Q. Huang|10.1109/CBS55922.2023.10115358|;|
|[A Sequential Design Framework of Robot Leg for High Locomotion Performance*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115396)|B. Qi; S. Zhang; Y. Fu|10.1109/CBS55922.2023.10115396|;|
|[Information Express of Soft Bionic Finger Based on Visual Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115340)|S. Li; J. Wang; X. Li; Z. Huang|10.1109/CBS55922.2023.10115340|;|
|[The hypothesis that the Wide-Field Neurons perform dynamic programming and its encoding model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115375)|L. Qian; Q. Yan; L. Shi; S. Wang|10.1109/CBS55922.2023.10115375|Wide-Field Neurons;dendrite field;dynamic programming;small moving objects;Wide-Field Neurons;dendrite field;dynamic programming;small moving objects|
|[Creating Virtual Fear to Control the Locomotion Behavior of Pigeon Robots Using Micro-Stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115397)|L. Yang; Z. Ma; M. Li; L. Yang; Z. Shang|10.1109/CBS55922.2023.10115397|;|
|[Consecutive jumping control of a locust biobot via thoracic nerve stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115371)|P. Liu; H. Wang; Y. Li|10.1109/CBS55922.2023.10115371|;|
|[The Effects of Adaptive Kalman Filtering on the Morphology of Auditory Brainstem Response in Real Active Behavior: A Pilot Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115394)|X. Wang; H. Zhang; Y. Xu; J. Tan; Y. He; Y. Qiu; Z. Huang; Y. Tao; M. Wang; M. Zhu; S. Chen; G. Li|10.1109/CBS55922.2023.10115394|Auditory Brainstem Response;Adaptive Kalman Filtering;Active Behavior;Auditory Brainstem Response;Adaptive Kalman Filtering;Active Behavior|
|[Power optimization in Battery-Powered Micro-Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115384)|H. -W. Huang; N. Khandelwal; T. Kerssemakers; I. Ballinger; G. Traverso|10.1109/CBS55922.2023.10115384|;|
|[Research on Change Detection Technology of Equipment Appearance Defect in Substation Scene](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115350)|H. Ji; S. Yang; L. Zhuang; Y. Ning; W. Li-Fen; X. Kai-Yi|10.1109/CBS55922.2023.10115350|;|
|[Development of a focusing device for the improvement of needle-free injector using electrically induced microbubbles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115380)|Y. Ma; W. Huang; K. Ichikawa; Y. Yamanishi|10.1109/CBS55922.2023.10115380|;|
|[Research on the human-following method, fall gesture recognition, and protection method for the walking-aid cane robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115357)|N. Chen; X. Chen; C. Chen; Y. Leng; C. Fu|10.1109/CBS55922.2023.10115357|;|
|[Multi-UAV Formation Control Based on Distributed Model Predictive Control*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115368)|S. -S. Liu; M. -F. Ge; Z. -W. Liu|10.1109/CBS55922.2023.10115368|multiple unmanned aerial vehicles;formation control;model predictive control;rolling optimization;multiple unmanned aerial vehicles;formation control;model predictive control;rolling optimization|
|[Smart paddleboard and other assistive veyances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115303)|S. Mann; J. Bhimani; S. Khaki; C. Leaver-Preyra|10.1109/CBS55922.2023.10115303|Humanistic intelligence;wearable technology;electric machines;WaterHCI;Humanistic Intelligence;HIntelligence;HIntel;HInt;HI;BOC-plane;BOC-space;Humanistic intelligence;wearable technology;electric machines;WaterHCI;Humanistic Intelligence;HIntelligence;HIntel;HInt;HI;BOC-plane;BOC-space|
|[Probing Local Cellular Mechanics by Atomic Force Microscopy with Modified Spherical Tip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115335)|Z. Zuo; X. Liu; X. Tang; F. Liu; D. Liu; Y. Li; Q. Huang; T. Arai|10.1109/CBS55922.2023.10115335|single cell analysis;mechanical characterization;micromanipulation;Atomic Force Microscopy;single cell analysis;mechanical characterization;micromanipulation;Atomic Force Microscopy|
|[A FPGA-based integrated DAQ system towards to BCI signal transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115403)|F. Qiu; H. Zhang; X. Fan; Z. Yang|10.1109/CBS55922.2023.10115403|;|
|[Cross-modal Task Understanding and Execution of Voice-fingertip Reading Instruction by Using Small Family Service Robotic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115355)|Z. Zhou; S. Zhu; K. Zhu; C. Cheng; J. Gu|10.1109/CBS55922.2023.10115355|Task understanding;Cross-modal instruction;Missing Reference Object of Pronoun;Multi-modal information;Task understanding;Cross-modal instruction;Missing Reference Object of Pronoun;Multi-modal information|
|[Movement of bipedal robot based on Whole Body Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115385)|Z. Wang; Y. Chen; Y. Bai; C. Hu; W. Ke|10.1109/CBS55922.2023.10115385|Linear Quadratic Regulator;Whole Body Control;Quadratic Programming;biped robot;Linear Quadratic Regulator;Whole Body Control;Quadratic Programming;biped robot|
|[An active pursuit strategy for autonomous mobile robots based on deep reinforcement learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115383)|Y. Gao; L. Cheng; Y. He; D. Wang|10.1109/CBS55922.2023.10115383|;|
|[Multi-sensory Olfactory Quadruped Robot for Odor Source Localization*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115389)|Y. He; L. Cheng; Y. Pan; Y. Li; D. Wang; H. Zheng|10.1109/CBS55922.2023.10115389|;|
|[Robotic pressure sensing sensor based on triboelectric nanogenerator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115400)|Z. Wang; J. Cui; M. Luan; C. Hao; Y. Zheng; C. Xue|10.1109/CBS55922.2023.10115400|;|
|[Cellular Wettability Assessment by Air-Injection-mediated Liquid Exclusion methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115348)|A. Han; N. Tanaka; J. Takahara; A. Awazu; H. Nasu; Y. Haruzono; Y. Tanaka|10.1109/CBS55922.2023.10115348|;|
|[An improved method for reasonable segmentation based on Post-DAE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115369)|Y. Zhou; Y. Dai; J. Zhang|10.1109/CBS55922.2023.10115369|;|
|[An Approach for EEG Data Augmentation Based on Deep Convolutional Generative Adversarial Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115388)|Y. Dong; X. Tang; F. Tan; Q. Li; Y. Wang; H. Zhang; J. Xie; W. Liang; G. Li; P. Fang|10.1109/CBS55922.2023.10115388|Data Augmentation;EEG;Brain-computer interface (BCI);Generative Adversarial Network (GAN);Data Augmentation;EEG;Brain-computer interface (BCI);Generative Adversarial Network (GAN)|
|[Asynchronous resonance caused by phase delay on adaptive networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115363)|C. Ren; H. Zhang; Z. Huang; X. Zhu; Z. Zhan|10.1109/CBS55922.2023.10115363|;|
|[Soft Prescribed Performance Control of Mobile Wheeled Inverted Pendulum Systems Subjected to Unknown Disturbances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115343)|M. Zhang; Y. Cao; J. Huang|10.1109/CBS55922.2023.10115343|prescribed performance control;soft prescribed function;mobile wheeled inverted pendulum;underactuated mechanical system;prescribed performance control;soft prescribed function;mobile wheeled inverted pendulum;underactuated mechanical system|
|[An optimized multi-label TSK fuzzy system for emotion recognition of multimodal physiological signals*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115349)|Y. Li; Z. Fu; X. He; J. Huang|10.1109/CBS55922.2023.10115349|;|
|[Sliding Mode Control for Quadrotor-Slung Load Transportation System with State Constraints*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115402)|F. Ding; C. Sun; Y. Ai; J. Huang|10.1109/CBS55922.2023.10115402|;|
|[Research on Manipulation of Soft Tissue Based on 3D Vision*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115332)|C. Sun; P. Li; X. Wu; X. Gao; Y. Liu|10.1109/CBS55922.2023.10115332|;|
|[Path-Following Guidance for Powered Wheelchair Users Using Mixed-Reality Technology with Shared Control Policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115329)|Z. Liao; J. Salazar; A. Ravankar; S. Chinchilla; E. Monacelli; Y. Hirata|10.1109/CBS55922.2023.10115329|;|
|[Intelligent Robotic Motion Copying System LevshAi for Neurosurgical Endovascular Operations with Haptic Feedback and Preoperative Personalized Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115398)|I. Menshikov; A. Bernadotte|10.1109/CBS55922.2023.10115398|;|
|[An IMU-Based Auditory Early Warning Method for the Intracranial Hematoma Puncture Surgery Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115370)|Y. Zhang; Y. Wang; C. Wu; K. Hu; G. Li; S. Du; L. Wang|10.1109/CBS55922.2023.10115370|;|
|[Probe-Type Artificial Cell Membranes Formed with Nanopore-Modified Gold Needles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115352)|K. Shoji; S. Ikarashi|10.1109/CBS55922.2023.10115352|;|
|[EEG Patterns and Classification of Different Sequential Finger Movements*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115359)|K. Wang; C. Liu; S. Zhang; Y. Huang; M. Xu; D. Ming|10.1109/CBS55922.2023.10115359|Electroencephalography;Sequential Finger Movements;Movement Related Cortical Potential;Event Related Desynchronization;Spatial Filters;Electroencephalography;Sequential Finger Movements;Movement Related Cortical Potential;Event Related Desynchronization;Spatial Filters|
|[Demand Response optimization of Cement Manufacturing Industry Based on Reinforcement Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115387)|X. -Y. Ye; Z. -W. Liu; M. Chi; M. -F. Ge; Z. Xi|10.1109/CBS55922.2023.10115387|;|
|[Distributed Online Optimization for Charging and Discharging of Plug-in Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115405)|H. Xiong; M. Chi; Z. -W. Liu; D. Hu; L. -L. Zhu; C. Jiang; J. -P. Gui; Y. Rao|10.1109/CBS55922.2023.10115405|;|
|[Sample-based Event-triggered Control of Thermostatically Controlled Loads for Demand Side Management*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115381)|C. Xu; J. Xu; K. Wang; L. Zhu; M. Yu; C. Jiang; Y. Rao|10.1109/CBS55922.2023.10115381|Demand side management;distributed control;event-triggered communication;thermostatically controlled load- s.;Demand side management;distributed control;event-triggered communication;thermostatically controlled load- s.|
|[Architecture and design of smart coal-fired power plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115404)|X. Bao; X. Mao; X. Liu; H. Chen; Z. Wang; X. Zheng|10.1109/CBS55922.2023.10115404|Smart power plants;fault diagnosis;intelligent coordinated control;coal-fired power plants;Smart power plants;fault diagnosis;intelligent coordinated control;coal-fired power plants|
|[A Distributed Large-scale Electric Vehicle Charge and Discharge Scheduling for Power Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115302)|R. Tian; L. Fan; C. Fu; M. Yu; B. Qi|10.1109/CBS55922.2023.10115302|;|
|[A Baseline Based User Invitation Aggregator Operation Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115346)|L. Fan; R. Tian; C. Fu; D. Xie; W. Wang; L. Sun; J. Wang; Y. Zhao; R. Ye|10.1109/CBS55922.2023.10115346|aggregator;user invitation;demand response;baseline-based;aggregator;user invitation;demand response;baseline-based|
|[Deep Regression on Quaternion: A Forward Kinematics Neural Network for Study Six-DoF Pose](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115356)|H. Zhu; W. Xu; J. Wu; J. Huang; Y. Li; B. Yu; J. Li; C. Lv|10.1109/CBS55922.2023.10115356|;|
|[Methods for Performance Improvement in Recurrent Rehabilitative Brain Computer Interface Applications*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115382)|X. Zhang; C. Menon|10.1109/CBS55922.2023.10115382|;|
|[Conditional Generative Adversarial Network-based Finger Position Estimation for Controlling Multi-Degrees-of-Freedom Myoelectric Prosthetic Hands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115366)|H. Jiang; Y. Yamanoi; Y. Kuroda; P. Chen; S. Togo; Y. Jiang; H. Yokoi|10.1109/CBS55922.2023.10115366|;|
|[Motion Mode Conversion Drive Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115328)|R. Wang; Z. Lu; A. Liu; C. Liu; G. Liu; W. Li|10.1109/CBS55922.2023.10115328|;|
|[Underwater Image Enhancement based on Improved Water-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115390)|Y. Chen; H. Li; Q. Yuan; Z. Wang; C. Hu; W. Ke|10.1109/CBS55922.2023.10115390|neural network;image enhancement;enhancement unit;neural network;image enhancement;enhancement unit|
|[Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115342)|Y. Zhu; S. Nazirjonov; B. Jiang; J. Colan; T. Aoyama; Y. Hasegawa; B. Belousov; K. Hansel; J. Peters|10.1109/CBS55922.2023.10115342|;|
#### **SoutheastCon 2023**
- DOI: 10.1109/SoutheastCon51012.2023
- DATE: 1-16 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Towards an Integrated Framework for Managing Software-Defined Networking Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115203)|S. Barlowe; M. Stanley; N. Lowry; C. Tipton; G. Cruz|10.1109/SoutheastCon51012.2023.10115203|software defined networking;computer networks;visualization;network topology;database systems;software defined networking;computer networks;visualization;network topology;database systems|
|[Adaptive Visibility Graph Initialization on Edge Computing to Accelerate Hybrid Path Planning for Mobile Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115186)|J. Ou; S. H. Hong; Y. Wang|10.1109/SoutheastCon51012.2023.10115186|mobile robot;GPU;visibility graph;genetic algorithm;Dijkstra’s algorithm;path planning;mobile robot;GPU;visibility graph;genetic algorithm;Dijkstra’s algorithm;path planning|
|[Mobile Augmented Reality Shopping System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115069)|C. Roche; A. Hamam|10.1109/SoutheastCon51012.2023.10115069|augmented reality;webAR;object recognition;mobile computing;mobile augmented reality;progressive web app;augmented reality;webAR;object recognition;mobile computing;mobile augmented reality;progressive web app|
|[Alabama A&M Symmetric Overloaded Minimal Instruction Set Architecture (SOMA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115149)|P. Jungwirth; A. Scott; Z. Xiao|10.1109/SoutheastCon51012.2023.10115149|Minimal instruction set computer (MISC);symmetric overloaded MISC architecture;SOMA;RISC.;Minimal instruction set computer (MISC);symmetric overloaded MISC architecture;SOMA;RISC.|
|[Baby Physical Safety Monitoring in Smart Home Using Action Recognition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115146)|V. Adewopo; N. Elsayed; K. Anderson|10.1109/SoutheastCon51012.2023.10115146|Deep Learning;ConvLSTM-I3D;Transfer Learning;Action Recognition;Computer Vision;Smart Baby Care;Deep Learning;ConvLSTM-I3D;Transfer Learning;Action Recognition;Computer Vision;Smart Baby Care|
|[Learn to Trace Odors: Robotic Odor Source Localization via Deep Learning Methods with Real-world Experiments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115175)|L. Wang; Z. Yin; S. Pang|10.1109/SoutheastCon51012.2023.10115175|;|
|[Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115128)|M. Nawar; M. Shomer; S. Faddel; H. Gong|10.1109/SoutheastCon51012.2023.10115128|Deep Learning;Transfer Learning;Load Forecasting;Transformer;Sequential Models;Deep Learning;Transfer Learning;Load Forecasting;Transformer;Sequential Models|
|[Analyzing Collaborative Navigational Maze Behavior in a Multiagent Systems Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115084)|B. K. Morse; R. A. Flores|10.1109/SoutheastCon51012.2023.10115084|multi-agent systems;multi-agent programming;navigation strategies;multi-agent systems;multi-agent programming;navigation strategies|
|[PredNet⊕GAN: A Higher-Level Induction of Predictive Coding into Adversarial Setting Leading to a Semi/Pseudo-GAN Perspective at a Lower Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115204)|S. R. Mikkilineni; T. D. Privat; M. W. Totaro|10.1109/SoutheastCon51012.2023.10115204|Unsupervised learning;Wasserstein loss;Conditional-Wasserstein loss;L1-loss;Gradient Difference Loss;GDL;Predictive Coding;Generative Adversarial Networks;Future frame prediction;Videos;GAN;PredNet;KITTI dataset;Something-Something dataset;Combinatorial Experimentation;Unsupervised learning;Wasserstein loss;Conditional-Wasserstein loss;L1-loss;Gradient Difference Loss;GDL;Predictive Coding;Generative Adversarial Networks;Future frame prediction;Videos;GAN;PredNet;KITTI dataset;Something-Something dataset;Combinatorial Experimentation|
|[VRFlex: Towards the Design of a Virtual Reality Hyflex Class Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115164)|S. Noor; P. Hayes; B. Thaman; L. Admasu; M. Hossain|10.1109/SoutheastCon51012.2023.10115164|Virtual Reality;Smart Class;IoT-based Classroom;Hybrid Class;Virtual Reality;Smart Class;IoT-based Classroom;Hybrid Class|
|[Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115070)|Z. Xu|10.1109/SoutheastCon51012.2023.10115070|FDA premarket regulation;BERT;pre-trained models;Natural Language Processing;FDA premarket regulation;BERT;pre-trained models;Natural Language Processing|
|[Combined CRC and Bit Framing for Enhanced Error Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115195)|C. A. Corral; H. Thornquist|10.1109/SoutheastCon51012.2023.10115195|error detection;cyclic redundancy check;bit framing;generator polynomials;primitive polynomials;error detection;cyclic redundancy check;bit framing;generator polynomials;primitive polynomials|
|[PINN-IT: A Python Framework for Automatically Creating Neural Network Based Solvers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115101)|C. Lew|10.1109/SoutheastCon51012.2023.10115101|Physics informed neural networks;partial differential equations;artificial intelligence;solvers;simulations;Physics informed neural networks;partial differential equations;artificial intelligence;solvers;simulations|
|[Phishing Web Page Detection using Web Scraping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115148)|M. Boyapati; R. Aygun|10.1109/SoutheastCon51012.2023.10115148|Phishing webpage detection;machine learning models;web scraping;hybrid feature extraction;Phishing webpage detection;machine learning models;web scraping;hybrid feature extraction|
|[Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115209)|G. Bellio; R. Russell; O. Kursun|10.1109/SoutheastCon51012.2023.10115209|Hyperspectral Imaging;Cloud Segmentation;Categorical Boosting;Ensemble Learning;Clustering for Feature Extraction;Hyperspectral Imaging;Cloud Segmentation;Categorical Boosting;Ensemble Learning;Clustering for Feature Extraction|
|[Using Ethereum Platform to Securely Register Students' Extracurricular Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115089)|C. W. Bou-Saba; A. Guillen|10.1109/SoutheastCon51012.2023.10115089|Ethereum blockchain;smart contract;Metamask crypto wallet;decentralized Web3 App;Etherscan;React JS;Solidity;ERC20 Token;Ethereum blockchain;smart contract;Metamask crypto wallet;decentralized Web3 App;Etherscan;React JS;Solidity;ERC20 Token|
|[Decoding the Encoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115100)|A. Ray; X. Li; L. Barisoni; K. Chakrabarty; K. Lafata|10.1109/SoutheastCon51012.2023.10115100|Autoencoders;Perturbation;Latent Space;Outlier;Uncertainty quantification;Autoencoders;Perturbation;Latent Space;Outlier;Uncertainty quantification|
|[Robustness Study of Polarization-Insensitive Spatial-Filtering Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115129)|J. N. Spitzmiller|10.1109/SoutheastCon51012.2023.10115129|antenna arrays;beam forming;null steering;spatial filters;spatial processing;array polarization;antenna arrays;beam forming;null steering;spatial filters;spatial processing;array polarization|
|[Cellular Network Deployment from Fixed Wing Uncrewed System for Network Denied Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115117)|B. Wilkinson; J. Sims; C. White; C. Thomas; T. Williams; C. Teer; R. B. Green|10.1109/SoutheastCon51012.2023.10115117|5G;Autonomous aerial vehicles;Communication systems;5G;Autonomous aerial vehicles;Communication systems|
|[Impedance Characterization of a Return-to-Zero (RZ) Current Steering Digital-to-Analog Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115094)|A. Naguib|10.1109/SoutheastCon51012.2023.10115094|Return to zero;current steering DAC;high-speed;linearity;spurious free dynamic range (SFDR);Return to zero;current steering DAC;high-speed;linearity;spurious free dynamic range (SFDR)|
|[Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115137)|M. Emaduddin; T. Halic; D. Demirel; C. Bayrak; V. S. Arikatla; S. De|10.1109/SoutheastCon51012.2023.10115137|Endoscopy;3D reconstruction;3D mapping;specular reflection removal;tissue;GI tract;Endoscopy;3D reconstruction;3D mapping;specular reflection removal;tissue;GI tract|
|[A Systematic Mapping Study of the Advancement in Software Vulnerability Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115111)|A. Gautier; C. Whitehead; D. Dzielski; T. Devine; J. Hernandez|10.1109/SoutheastCon51012.2023.10115111|Cybersecurity;Vulnerabilities;Forecasting;Risk;Modeling;Cybersecurity;Vulnerabilities;Forecasting;Risk;Modeling|
|[Augmented Reality Shopping System and Experience: Overview of the Literature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115104)|C. Roche; A. Hamam|10.1109/SoutheastCon51012.2023.10115104|augmented reality;webAR;object recognition;mobile computing;mobile augmented reality;progressive web app;augmented reality;webAR;object recognition;mobile computing;mobile augmented reality;progressive web app|
|[Multi-objective and Finite Element Based Optimization of High-Frequency Solid State Transformer For Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114961)|A. Olatunji; I. Bhattacharya; W. Adepoju; E. N. Esfahani; T. Banik|10.1109/SoutheastCon51012.2023.10114961|FEMM;Artificial Intelligence;multi-objective optimization;electric vehicle;genetic algorithm;solid state transformer;FEMM;Artificial Intelligence;multi-objective optimization;electric vehicle;genetic algorithm;solid state transformer|
|[Trans-Barrier Communication Device For High Data Rate Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115144)|C. A. Corral; C. M. Reinke; C. L. Gibson; I. F. El-Kady; G. B. Haschke|10.1109/SoutheastCon51012.2023.10115144|channel characterization;vector fitting;impulse response;mechanical transduction;powerline communication;channel characterization;vector fitting;impulse response;mechanical transduction;powerline communication|
|[Applying Display Design Principles to Organizational Cybersecurity to Create Effective Visualizations and Dashboards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115132)|D. J. Lalena; D. M. Feinauer|10.1109/SoutheastCon51012.2023.10115132|;|
|[Taxonomy of Consumer and Industrial IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115217)|S. Alahmadi; P. Rojas; H. Idriss; M. Bayoumi|10.1109/SoutheastCon51012.2023.10115217|Consumer Internet of Things(CIoT);Industrial Internet of Things (IIoT);Internet of Things(IoT);Consumer Internet of Things(CIoT);Industrial Internet of Things (IIoT);Internet of Things(IoT)|
|[Consensus-based Communication-aware Formation Control for a Mobile Multi-agent System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115199)|S. Xing; T. Yang; H. Song|10.1109/SoutheastCon51012.2023.10115199|Unmanned Aerial Vehicles;Multi-agent Systems;Communication-aware;Decentralized;Distributed;Consensus-based;Formation Control;Unmanned Aerial Vehicles;Multi-agent Systems;Communication-aware;Decentralized;Distributed;Consensus-based;Formation Control|
|[Analyses of Automated Malicious Internet Traffic Using Open-Source Honeypots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115073)|W. Bythwood; J. Bentley; I. Vakilinia|10.1109/SoutheastCon51012.2023.10115073|Index Terms—Honeypot;Network Attack;Botnet;Index Terms—Honeypot;Network Attack;Botnet|
|[A Unified Model for Non-linear Distortion Mitigation Using a Volterra Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115214)|S. Sud|10.1109/SoutheastCon51012.2023.10115214|;|
|[Towards Forecasting Engagement in Children with Autism Spectrum Disorder using Social Robots and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115150)|R. Mishra; K. C. Welch|10.1109/SoutheastCon51012.2023.10115150|autism spectrum disorder;robotics;engagement forecast;Deep Learning;CNN;LSTM;affective computing;autism spectrum disorder;robotics;engagement forecast;Deep Learning;CNN;LSTM;affective computing|
|[CO2 Emission Forecasting for Living Standards in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115183)|I. Parvez; L. Leon; N. Contreras; F. Polanco; E. Fentry; S. Sajal|10.1109/SoutheastCon51012.2023.10115183|Smart cities;CO2;Internet of Things;IoT;greenhouse effect;gases emissions;Smart cities;CO2;Internet of Things;IoT;greenhouse effect;gases emissions|
|[A Skin Sensor for Epileptic Seizure Detection and Notification Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114958)|L. Leon; N. Contreras; F. Polanco; E. Fentry; S. Sajal; I. Parvez|10.1109/SoutheastCon51012.2023.10114958|Skin capacitance;body-centric communication;epilepsy;GSR sensor;arduino;SUDEP;Skin capacitance;body-centric communication;epilepsy;GSR sensor;arduino;SUDEP|
|[An Enhanced Tensor Factorization Approach for Monostatic MIMO Radar with Unknown Mutual Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115106)|E. Baidoo; T. Q. Asenso; B. Danso Kwakye|10.1109/SoutheastCon51012.2023.10115106|Direction finding;Tensor;Mutual coupling;Bayesian;PARAFAC decomposition;Direction finding;Tensor;Mutual coupling;Bayesian;PARAFAC decomposition|
|[CNN-based real-time prediction of growth stage in soybeans cultivated in hydroponic set-ups](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115131)|S. B. Dhal; S. Mahanta; K. C. Gadepally; S. He; M. Hughes; J. Moore; K. J. Nowka; S. Kalafatis|10.1109/SoutheastCon51012.2023.10115131|hydroponic;CVAT;CNN;Flask;GUI;hydroponic;CVAT;CNN;Flask;GUI|
|[Fingerprinting Bots in a Hybrid Honeypot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115143)|W. Bythwood; A. Kien; I. Vakilinia|10.1109/SoutheastCon51012.2023.10115143|Hybrid Honeypot;Bot detection;Threat Intelligence;Hybrid Honeypot;Bot detection;Threat Intelligence|
|[Gesture control of a robotic arm via LoRa technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115133)|A. S. Okoro; Y. Sufian|10.1109/SoutheastCon51012.2023.10115133|LoRa;Sensors;MPU6050;Hand gesture;ESP32;Robotics;LoRa;Sensors;MPU6050;Hand gesture;ESP32;Robotics|
|[Reducing Structured Query Language Injection Vulnerabilities Through Functional Programming Principles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114959)|M. Piscatello|10.1109/SoutheastCon51012.2023.10114959|web security;functional programming;injection vulnerabilities;software design;web security;functional programming;injection vulnerabilities;software design|
|[C# Frequency Sampling-Based FIR Filter Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114965)|B. Q. Tran; C. Bowyer; J. C. Squire|10.1109/SoutheastCon51012.2023.10114965|FIR;filter;frequency sampling;FIR;filter;frequency sampling|
|[Signal conditioning of a novel ultrasonic transducer with integrated temperature and amplitude sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115076)|B. Karbouj; J. Krüger|10.1109/SoutheastCon51012.2023.10115076|Ultrasonic transducer;piezoelectric effect;pyroelectric effect;signal processing;Ultrasonic transducer;piezoelectric effect;pyroelectric effect;signal processing|
|[A 30 GHz Steerable Patch Array Antenna for Software-Defined Radio Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115182)|M. Jean; E. Velazquez; X. Gong; M. Yuksel|10.1109/SoutheastCon51012.2023.10115182|beamforming;mmWave;phase shifters;SDR;beamforming;mmWave;phase shifters;SDR|
|[Application of Equivalent Negative Sequence Current Calculations in Transmission Planning Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115190)|J. E. Walker; R. Ramos|10.1109/SoutheastCon51012.2023.10115190|generator current imbalance;harmonic currents;negative sequence currents;transmission planning studies;generator current imbalance;harmonic currents;negative sequence currents;transmission planning studies|
|[Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115113)|J. Rogers; R. Aygun; L. Etzkorn|10.1109/SoutheastCon51012.2023.10115113|temporal data;record deduplication;record linkage;entity matching;directed acyclic graphs;temporal data;record deduplication;record linkage;entity matching;directed acyclic graphs|
|[Developing an automation process for documenting results in ABET review process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115105)|H. Qiu; X. Wang; J. Shen; W. Chen|10.1109/SoutheastCon51012.2023.10115105|Jupyter notebook;python;ABET;data analysis;Jupyter notebook;python;ABET;data analysis|
|[A Synergistic Learning Based Electric Vehicle Charging Demand Prediction Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115078)|A. Garrison; M. Rashid; N. Chen|10.1109/SoutheastCon51012.2023.10115078|;|
|[Autonomous Navigation of Underwater Vehicle with Static and Dynamic Obstacles using PTEM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115077)|B. Brown; H. E. Sevil|10.1109/SoutheastCon51012.2023.10115077|Autonomous Underwater Vehicle (AUV);Autonomous navigation;2D path planning;Obstacle detection and avoidance;Environment mapping;Autonomous Underwater Vehicle (AUV);Autonomous navigation;2D path planning;Obstacle detection and avoidance;Environment mapping|
|[A Multi-Layer Perceptron Neural Network for Fault Type Identification for Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115074)|A. B. Bhadra; R. Jalilzadeh Hamidi|10.1109/SoutheastCon51012.2023.10115074|Deep learning (DL);Fault type;TL;Multi-Layer Perceptron Artificial Neural Network (MLP-ANN);Deep learning (DL);Fault type;TL;Multi-Layer Perceptron Artificial Neural Network (MLP-ANN)|
|[Implementation of Directional Incremental-Quantity Relay in Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114960)|J. Rodriguez; R. J. Hamidi|10.1109/SoutheastCon51012.2023.10114960|Incremental quantities;directional relay;TD32;time-domain.;Incremental quantities;directional relay;TD32;time-domain.|
|[Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115189)|M. Maydanchi; A. Ziaei; M. Basiri; A. N. Azad; S. Pouya; M. Ziaei; F. Haji; S. Sargolzaei|10.1109/SoutheastCon51012.2023.10115189|Cardiovascular diseases;Classification models;AdaBoost;Random Forest;Decision Tree;KNN;Naïve Bayes;Perceptron;Cardiovascular diseases;Classification models;AdaBoost;Random Forest;Decision Tree;KNN;Naïve Bayes;Perceptron|
|[Rapid Analysis of Mechanisms Incorporating Compliant and Soft Links in MATLAB Simscape](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115153)|K. Tran; T. Nguyen; A. C. Esquen; L. Schwenck; D. H. KIm; C. Tekes; A. Tekes|10.1109/SoutheastCon51012.2023.10115153|compliant mechanisms;soft robots;modeling;MATLAB Simscape;compliant mechanisms;soft robots;modeling;MATLAB Simscape|
|[A Novel Thermal Tactile Stimulator Device for Quantitative Sensory Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115168)|U. Kursun; O. Kursun; O. V. Favorov|10.1109/SoutheastCon51012.2023.10115168|Neuropathic assessment and monitoring tools;embedded systems for telehealth and telediagnosis;Cortical Metrics;quantitative sensory testing device;Neuropathic assessment and monitoring tools;embedded systems for telehealth and telediagnosis;Cortical Metrics;quantitative sensory testing device|
|[Fusion of LiDAR and Computer Vision for Autonomous Navigation in Gazebo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115172)|C. Riordan; C. O’Donnell|10.1109/SoutheastCon51012.2023.10115172|Gazebo;Autonomous Navigation;Turtlebot;Li-DAR;YOLO;SLAM;Gazebo;Autonomous Navigation;Turtlebot;Li-DAR;YOLO;SLAM|
|[Culvert Condition Prediction via Artificial Neural Network Machine Learning-Based Models using SMOTE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115085)|C. Schultz; C. McNinch; J. Qi; M. Smith; N. Barclay|10.1109/SoutheastCon51012.2023.10115085|artificial neural network (ANN);machine learning (ML);imbalanced dataset;synthetic minority oversampling technique (SMOTE);culverts;prediction;artificial neural network (ANN);machine learning (ML);imbalanced dataset;synthetic minority oversampling technique (SMOTE);culverts;prediction|
|[Design and Analysis of a Pediatric Prosthetic Knee with Compliant Hinges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115096)|B. Estrada; A. Delaughter; M. Allah; C. Wilson; A. Contreras Esquen; A. Tekes|10.1109/SoutheastCon51012.2023.10115096|compliant hinges;prosthetic knee joint;MATLAB Simscape;compliant hinges;prosthetic knee joint;MATLAB Simscape|
|[Prediction of Gait Intention by Utilizing Pre-Movement EEG Signals: A Pilot Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115079)|M. Y. M. Naser; C. Mcclary; I. A. Cheruku; B. P. R. Dendi; S. Bhattacharya|10.1109/SoutheastCon51012.2023.10115079|Electroencephalography (EEG);gait intention;gait prediction;Brain-Computer Interaction (BCI);Oculus VR;Electroencephalography (EEG);gait intention;gait prediction;Brain-Computer Interaction (BCI);Oculus VR|
|[A Deep Generative Adversarial Network (GAN)-enabled Abnormal Pedestrian Behavior Detection at Grade Crossings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114963)|G. Song; Y. Qian; Y. Wang|10.1109/SoutheastCon51012.2023.10114963|anomaly detection;generative adversarial network;skeleton detection;pedestrian behavior;anomaly detection;generative adversarial network;skeleton detection;pedestrian behavior|
|[Prediction Model of Breast Cancer Survival Months: A Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115220)|M. Y. M. Naser; D. Chambers; S. Bhattacharya|10.1109/SoutheastCon51012.2023.10115220|Breast cancer;cancer survival prediction;Random Forest (RF);SEER NIH;Breast cancer;cancer survival prediction;Random Forest (RF);SEER NIH|
|[A Fully Integrated VLSI Circulator for 915-MHz ISM Band RFID and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115213)|O. J. Hernandez|10.1109/SoutheastCon51012.2023.10115213|circulator;directivity;isolations;RFID;IoT;circulator;directivity;isolations;RFID;IoT|
|[Discovering Command and Control Channels Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115173)|C. Wang; A. Kakkar; C. Redino; A. Rahman; A. S; R. Clark; D. Radke; T. Cody; L. Huang; E. Bowen|10.1109/SoutheastCon51012.2023.10115173|attack graphs;reinforcement learning;RL;command and control;C2;cyber defense;cyber network operations;cyber terrain;attack graphs;reinforcement learning;RL;command and control;C2;cyber defense;cyber network operations;cyber terrain|
|[Comparative Analysis of RF Circuits for Interconnecting Wireless Sensor Nodes in Noisy Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115218)|I. Atawodi; Z. Zhou|10.1109/SoutheastCon51012.2023.10115218|Wireless Sensor Networks;Nodes;Internet of Things;BLE(Bluetooth Low Energy);Zigbee, Micro Controller Unit;RSSI;Wireless Sensor Networks;Nodes;Internet of Things;BLE(Bluetooth Low Energy);Zigbee, Micro Controller Unit;RSSI|
|[Enhancement of Relative Permittivity of Material with Metallic Inclusions – Experimental Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115156)|A. K. Saha; W. Pendleton|10.1109/SoutheastCon51012.2023.10115156|relative material permittivity;dielectric constant enhancement;free space material measurement;periodic metal patch in dielectric substrate;vector network analyzer;relative material permittivity;dielectric constant enhancement;free space material measurement;periodic metal patch in dielectric substrate;vector network analyzer|
|[Integration of EEG and Eye Tracking Technology: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115167)|S. Jamal; M. V. Cruz; S. Chakravarthy; C. Wahl; H. Wimmer|10.1109/SoutheastCon51012.2023.10115167|Electroencephalography;EEG;eye tracking;cognitive processing;attention;machine learning;Electroencephalography;EEG;eye tracking;cognitive processing;attention;machine learning|
|[Grid Code Enablement and C-HIL Validation of Distributed Energy Resources with OpenFMB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115179)|R. Sarup; J. Hambrick; C. Brown; I. A. Alassane; M. Burck; D. Bradley; F. A. Jajeh; É. Grégoire; C. Fallaha; S. Seal; F. Mumtaz; S. Laval|10.1109/SoutheastCon51012.2023.10115179|Grid codes;C-HIL;OpenFMB;1547;DER;distributed intelligence;Grid codes;C-HIL;OpenFMB;1547;DER;distributed intelligence|
|[Water Quality Predictions for Urban Streams Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115154)|L. Jalagam; N. Shepherd; J. Qi; N. Barclay; M. Smith|10.1109/SoutheastCon51012.2023.10115154|machine learning (ML);stormwater infrastructure;prediction;data analytics;decision making;water quality;machine learning (ML);stormwater infrastructure;prediction;data analytics;decision making;water quality|
|[BlockPlace: A Novel Blockchain-based Physical Marketplace System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115212)|B. Brooks-Patton; S. Noor|10.1109/SoutheastCon51012.2023.10115212|Blockchain Markets;IoT Markets;Secure Markets;Blockchain Commerce;Peer-to-peer Commerce;Blockchain Markets;IoT Markets;Secure Markets;Blockchain Commerce;Peer-to-peer Commerce|
|[Understanding Open Charge Point Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115127)|W. Terrance; K. Kouadio; T. Youssef|10.1109/SoutheastCon51012.2023.10115127|;|
|[DYSCO Vis.: DYnamic Self-Correction for Obstructed Visualizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115155)|J. Cappi; A. Lear; J. Hauenstein|10.1109/SoutheastCon51012.2023.10115155|visualization;display failure;safety-critical visualization;visualization;display failure;safety-critical visualization|
|[Neural Network Modeling of the Transient Heating of Icy Mountains due to Foehn Winds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115080)|S. Wang|10.1109/SoutheastCon51012.2023.10115080|neural networks;Foehn wind;ice;snow;melting;mountain;glacier;iceberg;transient heating;neural networks;Foehn wind;ice;snow;melting;mountain;glacier;iceberg;transient heating|
|[Detection of Ethereum Eclipse Attack based on Hybrid Method and Dynamic Weighted Entropy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115068)|D. Bhumichai; R. Benton|10.1109/SoutheastCon51012.2023.10115068|Blockchain;Eclipse Attack;Imbalanced and Overlapped data;Ethereum Blockchain;Anomaly Detection Model;Blockchain;Eclipse Attack;Imbalanced and Overlapped data;Ethereum Blockchain;Anomaly Detection Model|
|[IDARE – Intelligently Denying Authorization of Rogue-drone Entry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115125)|B. Biswal|10.1109/SoutheastCon51012.2023.10115125|Drone Security;Rogue drone;Cryptography;Secret Sharing;Drone Security;Rogue drone;Cryptography;Secret Sharing|
|[Carrier Generation Ability of CNTs to Harvest Maximum Energy from Solar Spectrum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114964)|A. Shabir; B. Zaidi; M. Ullah|10.1109/SoutheastCon51012.2023.10114964|CNTs;bandgap;spectra;continuum;carrier generation;CNTs;bandgap;spectra;continuum;carrier generation|
|[Reducing the Climate Crisis by Decreasing Technology & Computing eWaste: An Examination of User Actions and Behaviors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115222)|J. Squillace; Z. Hozella; J. Cappella|10.1109/SoutheastCon51012.2023.10115222|Privacy;information systems climate crisis;user behavior theory;carbon footprint;information security;green computing;electronic cookies;eWaste;case study methodology;Privacy;information systems climate crisis;user behavior theory;carbon footprint;information security;green computing;electronic cookies;eWaste;case study methodology|
|[Finite Control Set Model Predictive Control for Compensating Harmonics and Reactive Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115086)|M. Useche; R. J. Hamidi; A. Garces|10.1109/SoutheastCon51012.2023.10115086|Finite set model predictive control;harmonics;predictive control;power quality;Finite set model predictive control;harmonics;predictive control;power quality|
|[Programmed PWM of 5- and 7-Level Multilevel Inverters to Meet 5% Voltage Distortion Limit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115147)|B. Diong; B. Wilson|10.1109/SoutheastCon51012.2023.10115147|Multilevel inverter;Switching losses;Total harmonic distortion;Programmed PWM;Multilevel inverter;Switching losses;Total harmonic distortion;Programmed PWM|
|[WasteMiner: An Efficient Waste Collection System for Smart Cities Leveraging IoT and Data Mining Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115200)|T. S. Zaman; T. Islam; S. R. Vadla; U. K. Rangu|10.1109/SoutheastCon51012.2023.10115200|IOT;Data Mining;Waste collection;Shortest Distance;Frequent Itemsets;IOT;Data Mining;Waste collection;Shortest Distance;Frequent Itemsets|
|[Explainable AI In Education : Current Trends, Challenges, And Opportunities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115140)|A. Rachha; M. Seyam|10.1109/SoutheastCon51012.2023.10115140|Explainable AI (XAI);Education;Educational Data Mining;Learning Analytics;Explainable AI (XAI);Education;Educational Data Mining;Learning Analytics|
|[A Local Machine Learning Approach for Fingerprint-based Indoor Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115169)|N. Agah; B. Evans; X. Meng; H. Xu|10.1109/SoutheastCon51012.2023.10115169|Indoor localization;WiFi fingerprinting;binary classification;convolutional neural network;Indoor localization;WiFi fingerprinting;binary classification;convolutional neural network|
|[A Gradient Descent Strategy for Improved Synthetic Discriminant Function Fringe-Adjusted Joint Transform Correlation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115134)|B. Airehenbuwa; M. Ndoye; J. Khan|10.1109/SoutheastCon51012.2023.10115134|ATR;correlation;filters;MSTAR;clutter;fringe-adjusted joint transform correlation filter;synthetic discriminant function;ATR;correlation;filters;MSTAR;clutter;fringe-adjusted joint transform correlation filter;synthetic discriminant function|
|[Non-Invasive Spectral-Based Swarm Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115171)|C. Campell; R. M. Parry; R. Tashakkori|10.1109/SoutheastCon51012.2023.10115171|apiology;informatics;IoT;digital signal processing;swarm detection;precision apiculture;non-negative matrix factorization;unsupervised anomaly detection;minimum covariance determinant estimator;apiology;informatics;IoT;digital signal processing;swarm detection;precision apiculture;non-negative matrix factorization;unsupervised anomaly detection;minimum covariance determinant estimator|
|[Development of a Custom Wrist Wearable for Monitoring Motion, Temperature, SPO2, and Heart Rate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115216)|J. Frech; J. Naber; D. Jackson|10.1109/SoutheastCon51012.2023.10115216|Biometric sensing;wearable technology;Biometric sensing;wearable technology|
|[Communicability - A Software Quality Attribute Proposal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115145)|C. Allen; I. Ildephonce|10.1109/SoutheastCon51012.2023.10115145|quality attribute;communicability;communication;quality attribute scenario;quality attribute;communicability;communication;quality attribute scenario|
|[Component-Level Shielding with Assembled Copper Trenches in Package Slots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115102)|G. A. Duhni; M. Khasgiwala; J. L. Volakis; P. Markondeya Raj|10.1109/SoutheastCon51012.2023.10115102|Electromagnetic interference;MIMO;and Shielding effectiveness.;Electromagnetic interference;MIMO;and Shielding effectiveness.|
|[EMG-Based Hand Gestures Classification Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115158)|N. Ghaffar Nia; E. Kaplanoglu; A. Nasab|10.1109/SoutheastCon51012.2023.10115158|Electromyography (EMG);Machine Learning;Hand Gestures;Classification;Electromyography (EMG);Machine Learning;Hand Gestures;Classification|
|[Deep zero-inflated negative binomial model and its application in scRNA-seq data integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115099)|M. Wei; R. Liu; Y. J. Wang; C. Huang|10.1109/SoutheastCon51012.2023.10115099|single cell RNA sequencing;batch effect;drop out effect;zero-inflated negative binomial distribution;single cell RNA sequencing;batch effect;drop out effect;zero-inflated negative binomial distribution|
|[Prediction of Diabetes at Early Stage using Interpretable Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115152)|M. S. Islam; M. Minul Alam; A. Ahamed; S. I. Ali Meerza|10.1109/SoutheastCon51012.2023.10115152|Diabetes;Machine Learning;Interpretable Machine Learning;Prediction;Diabetes;Machine Learning;Interpretable Machine Learning;Prediction|
|[Video Normalization in Identifying Fake Videos Using a Long Short-Term Memory Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115139)|K. Thakkar; D. Lo|10.1109/SoutheastCon51012.2023.10115139|Deepfake Detection;Long Short-term Memory (LSTM);Video Normalization;Deepfake Detection;Long Short-term Memory (LSTM);Video Normalization|
|[Security Analysis of Cardiovascular Implantable Electronic Device (CIED) using a Threat Model-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115206)|N. M. Istiak Chowdhury; R. Hasan|10.1109/SoutheastCon51012.2023.10115206|CIED;threat model;attacker;security.;CIED;threat model;attacker;security.|
|[Security Analysis of a Smart City Traffic Control System using a Threat Model-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115120)|S. Alshamrani; R. Hasan|10.1109/SoutheastCon51012.2023.10115120|threat model;smart cities;traffic control systems;threat model;smart cities;traffic control systems|
|[Online Threats vs. Mitigation Efforts: Keeping Children Safe in the Era of Online Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115142)|T. O’Dell; A. K. Ghosh|10.1109/SoutheastCon51012.2023.10115142|online safety;kid;child;teenager;adolescent;K12;remote learning;online learning;e-learning;distance learning;cybersecurity awareness;EdTech;videoconferencing;online safety;kid;child;teenager;adolescent;K12;remote learning;online learning;e-learning;distance learning;cybersecurity awareness;EdTech;videoconferencing|
|[Effect of Localized Electrode/Pulp Tissue Conditions on Bipolar Measurements of Banana Samples Electrical Impedance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115192)|J. Slay; R. Sotner; T. J. Freeborn|10.1109/SoutheastCon51012.2023.10115192|;|
|[Effectiveness of Train-the-Trainer Workshops in Intelligent Industrial Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115184)|K. Tantawi; L. Potter; C. Silver; J. Roberts; N. Wilson; R. Raymond; O. Tantawi|10.1109/SoutheastCon51012.2023.10115184|Intelligent Industrial Robotics;Train-the-trainer;CoVid-19 Pandemic;Intelligent Industrial Robotics;Train-the-trainer;CoVid-19 Pandemic|
|[Intuitive Smart Creative Designing using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115138)|L. Valentino D’souza; M. Kumar Rajendran|10.1109/SoutheastCon51012.2023.10115138|Computer Vision;DB-Resnet;MobileNet;CRNN;YOLOR;Deep Learning;CNN;Feature Extraction;Programmatic;CTR Enhancement;Computer Vision;DB-Resnet;MobileNet;CRNN;YOLOR;Deep Learning;CNN;Feature Extraction;Programmatic;CTR Enhancement|
|[A Conformal Wideband Patch Antenna with an Integrated L-Strip Feed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115123)|S. Radavaram; M. Pour|10.1109/SoutheastCon51012.2023.10115123|Conformal antennas;integrated L-probe;wideband patch antennas;Conformal antennas;integrated L-probe;wideband patch antennas|
|[Bipedal Navigation Planning over Rough Terrain using Traversability Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115107)|S. McCrory; B. Mishra; R. Griffin; J. Pratt; H. E. Sevil|10.1109/SoutheastCon51012.2023.10115107|;|
|[Detection of Rayleigh Faded Signals using Two Distributed Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115098)|S. Saha; X. Sun; L. Cao; R. Viswanathan|10.1109/SoutheastCon51012.2023.10115098|Rayleigh Fading;Bivariate Exponential Distribution;Genetic Algorithm;Rayleigh Fading;Bivariate Exponential Distribution;Genetic Algorithm|
|[Towards A Secured SCADA Architecture: A DNP3 Test Case](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115208)|A. S. Soliman; R. Devine; D. Landi; S. M. S. H. Rafin; M. H. Cintuglu; O. A. Mohammed|10.1109/SoutheastCon51012.2023.10115208|SCADA;DNP3;Security;SCADA;DNP3;Security|
|[Design of an Eight-Wheeled Mobile Delivery Robot and Its Climbing Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115114)|O. M. T. Kaya; G. Erdemir|10.1109/SoutheastCon51012.2023.10115114|Delivery robot;8-wheeled robot;mobile robot;and climbing simulation;Delivery robot;8-wheeled robot;mobile robot;and climbing simulation|
|[Two New Open Source Devices for Project-Based Learning in Controls](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115159)|K. Tran; T. Nguyen; R. Ramirez; T. Utschig; C. Tekes; A. Tekes|10.1109/SoutheastCon51012.2023.10115159|3D printed laboratory equipment design;digital embedded control systems;engineering education;3D printed laboratory equipment design;digital embedded control systems;engineering education|
|[A performance evaluation of machine learning algorithms applied to multilevel converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115161)|A. Hindi; J. Ha; F. Filho; R. L. Smith; T. Henrique; E. Weaver|10.1109/SoutheastCon51012.2023.10115161|;|
|[Simplified Simulations and Experiments to Determine Impedance Trends for Parallel Circuits in Series and in Parallel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115178)|Y. Jin; R. A. Gerhardt|10.1109/SoutheastCon51012.2023.10115178|impedance;time constants;generalized equation;MATLAB APP;frequency dependence;impedance;time constants;generalized equation;MATLAB APP;frequency dependence|
|[Confidential Computing in the Post-Quantum Era](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115130)|S. C. Puckett; J. D. Crabtree|10.1109/SoutheastCon51012.2023.10115130|confidential computing;quantum;crypto coprocessor;QKD;QRNG;confidential computing;quantum;crypto coprocessor;QKD;QRNG|
|[(3 + α)-Order Transfer Functions for Approximating Butterworth-Type Flat Passband Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115219)|Z. Pautzke; D. Kubanek; T. J. Freeborn|10.1109/SoutheastCon51012.2023.10115219|Fractional-order circuits;fractional-filters;analog filters;Fractional-order circuits;fractional-filters;analog filters|
|[Advanced Biomedical Laboratory (ABL) Synergy With Communication, Robotics, and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115201)|R. Cristian Voicu; S. Steele; J. Diaz Rodriguez; Y. Chang; C. Ham|10.1109/SoutheastCon51012.2023.10115201|Machine Vision;Sensor Fusion;Temporal Tracking;Classification;Detection;Automation;Mechatronics;Machine Vision;Sensor Fusion;Temporal Tracking;Classification;Detection;Automation;Mechatronics|
|[Approximated Fractional-Order Capacitor "Seed" Impedance Characterization to Support Fully-Controllable Immittance Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115103)|S. M. Gale; J. Koton; T. J. Freeborn|10.1109/SoutheastCon51012.2023.10115103|;|
|[Electrical Properties of Degenerate Boron Doped Graphene](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115198)|M. F. Rabbe; V. Sheremet; S. Ganguly; A. K. Sood; J. W. Zeller; L. S. Chaudhary; V. Avrutin; Ü. Özgür; N. K. Dhar|10.1109/SoutheastCon51012.2023.10115198|Graphene;p-type graphene;boron dopant;Hall measurement;Hall mobility;Graphene;p-type graphene;boron dopant;Hall measurement;Hall mobility|
|[AI-Driven Management of Dynamic Multi-Tenant Cloud Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115110)|N. F. Mir|10.1109/SoutheastCon51012.2023.10115110|cloud computing;artificial intelligence (AI);neural networks;multi-tenancy;virtualization;software-defined networking (SDN);VLAN;VxLAN;network virtualization;cloud computing;artificial intelligence (AI);neural networks;multi-tenancy;virtualization;software-defined networking (SDN);VLAN;VxLAN;network virtualization|
|[A Vision for STEM Education at the University of Technology, Jamaica](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115207)|S. Thorpe|10.1109/SoutheastCon51012.2023.10115207|Engineering;Computing;Education;Engineering;Computing;Education|
|[Convolutional Neural Networks: An Analysis of Varying Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115075)|B. -J. G. Panlaqui; D. Deb|10.1109/SoutheastCon51012.2023.10115075|;|
|[Oscillating Surge Wave Energy Converter Geometry Analysis For Direct Seawater Desalination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115119)|J. McMorris; F. Filho|10.1109/SoutheastCon51012.2023.10115119|oscillating surge wave energy converter;geometry analysis;renewable energy;desalination;oscillating surge wave energy converter;geometry analysis;renewable energy;desalination|
|[An Effective Transfer Learning Based Landmark Detection Framework for UAV-Based Aerial Imagery of Urban Landscapes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115176)|B. Praveen; V. Menon; T. Mukherjee; B. Mesmer; S. Gholston; S. Corns|10.1109/SoutheastCon51012.2023.10115176|landmark detection;transfer learning;ResNet50;FasterRCNN;deep learning;aerial imagery;landmark detection;transfer learning;ResNet50;FasterRCNN;deep learning;aerial imagery|
|[Impact and Mitigation of Electromagnetic Interference Between HVTL and Pipelines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115180)|H. Hussein; A. E. -L. S. Ahmed; S. Mousa; E. -S. M. El-Refaie; O. A. Mohammed|10.1109/SoutheastCon51012.2023.10115180|AC Corrosion;Electromagnetic fields;High Voltage Transmission Lines;Pipelines;AC Corrosion;Electromagnetic fields;High Voltage Transmission Lines;Pipelines|
|[Improved Performance of a Two-Stage Converter Topology Using Incremental Conductance-Based MPPT Design for Grid-Tied PV Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115210)|M. S. Mollik; S. M. S. H. Rafin; A. A. Mamun; A. M. M. Irfeey; A. S. Soliman; O. A. Mohammed|10.1109/SoutheastCon51012.2023.10115210|Photovoltaic (PV) systems;Grid-Connected PV;DC–DC converter;MPPT;Photovoltaic (PV) systems;Grid-Connected PV;DC–DC converter;MPPT|
|[System Verilog versus UVM-based Verification of AXI4-Lite Arbitration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115141)|A. R. Babu; P. Anand; Y. Kim; S. Jadhav|10.1109/SoutheastCon51012.2023.10115141|AXI4-Lite;verification;arbitration;Universal Verification Methodology (UVM);System Verilog (SV);AXI4-Lite;verification;arbitration;Universal Verification Methodology (UVM);System Verilog (SV)|
|[Development of a Fish Robot Equipped with Novel 3D Printed Soft Bending Actuators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115181)|S. Steele; J. Diaz Rodriguez; S. Sripathy; T. Ashuri; S. Gharaie; Y. Chang; A. Ali Amiri Moghadam|10.1109/SoutheastCon51012.2023.10115181|Soft Actuator;Fish Robot;3D printing;Soft Actuator;Fish Robot;3D printing|
|[Analysis of HCD Effects for NMOS Transistor with Technology Scaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115193)|S. M. Shakil; M. Sana Ullah|10.1109/SoutheastCon51012.2023.10115193|MOSFETs;HCD;Reliability;Technology Scaling;Substrate Current;Threshold Voltage;MOSFETs;HCD;Reliability;Technology Scaling;Substrate Current;Threshold Voltage|
|[Fog Assisted Tiger Alarming Framework for Saving Endangered Wild Life](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115072)|M. Kumar Mondal; R. Mandal; S. Banerjee; M. Sanyal; U. Ghosh; U. Biswas|10.1109/SoutheastCon51012.2023.10115072|tiger alert framework;fog computing;latency;network usage;cloud computing;camera trapping technology;execution time;tiger alert framework;fog computing;latency;network usage;cloud computing;camera trapping technology;execution time|
|[EEG based BCI for Autonomous Control: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115194)|S. Biswas; W. D. Hairston; J. S. Metcalfe; S. Bhattacharya|10.1109/SoutheastCon51012.2023.10115194|Brain-computer interface;electroencephalogram (EEG);brain science and computer technology;signal acquisition technique;vehicles and quadcopters;Brain-computer interface;electroencephalogram (EEG);brain science and computer technology;signal acquisition technique;vehicles and quadcopters|
|[Portable Acoustic and Bioimpedance Sensing System to Support Vocal Pathology Assessments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115124)|J. Layton; M. Gosa; T. J. Freeborn|10.1109/SoutheastCon51012.2023.10115124|;|
|[Towards A Systematic Literature Review for Evaluating Smart Home Environments using light bulb sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115135)|R. Biggs; S. Thorpe|10.1109/SoutheastCon51012.2023.10115135|smart;sensors;assistive;technology;interaction;framework;smart;sensors;assistive;technology;interaction;framework|
|[Feedback Based Correction System for Adaptive Management in Smart Irrigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115071)|M. Nair; H. M. Jalajamony; E. Gibbs; R. E. Fernandez|10.1109/SoutheastCon51012.2023.10115071|irrigation efficiency;correction factor;feedback-based;smart sprinklers;irrigation efficiency;correction factor;feedback-based;smart sprinklers|
|[Aerial to Terrestrial Edge Communication Using LoRa in Drone-Aided Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115215)|H. M. Jalajamony; M. Nair; M. Jones-Whitehead; M. I. Abbas; N. Harris; R. E. Fernandez|10.1109/SoutheastCon51012.2023.10115215|precision agriculture;edge communication;drone;lora;precision agriculture;edge communication;drone;lora|
|[Data Analytics for Cybersecurity Based on Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115197)|L. Wang; R. L. Mosher; P. Duett; T. C. Falls|10.1109/SoutheastCon51012.2023.10115197|data analytics;network intrusion;cybersecurity;deep learning;radial basis function network;multi-layer perceptron;data analytics;network intrusion;cybersecurity;deep learning;radial basis function network;multi-layer perceptron|
|[Towards Developing a formal model for tracking Cyber-Security Investments in Jamaica](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115211)|C. Brown; S. Thorpe|10.1109/SoutheastCon51012.2023.10115211|Capabilities;Cyber-security;investments;financial;Capabilities;Cyber-security;investments;financial|
|[A Deep Learning and Hidden Hierarchical Markov Model based analysis of Polar Ice Depletions and effects on Movements of Vulnerable Polar Animals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115081)|E. Mukherjee; C. Barone|10.1109/SoutheastCon51012.2023.10115081|Convolutional Neural Networks;Long Short-Term Memory Models;Hidden Hierarchical Markov Models;Polar ice depletions;Vulnerable polar animals;Convolutional Neural Networks;Long Short-Term Memory Models;Hidden Hierarchical Markov Models;Polar ice depletions;Vulnerable polar animals|
|[Can Artificial Intelligence Pass a Sophomore Level Digital Design Laboratory?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115116)|C. Elder; G. Pozek; S. Horine; A. Tripaldelli; B. Butka|10.1109/SoutheastCon51012.2023.10115116|Artificial Intelligence;ChatGPT;Digital Design Laboratory;Artificial Intelligence;ChatGPT;Digital Design Laboratory|
|[Experimental Performance Analysis of a Self-Driving Vehicle Using High-Definition Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115093)|O. Toker|10.1109/SoutheastCon51012.2023.10115093|Self-driving vehicles;Sensor Fusion;Self-driving vehicles;Sensor Fusion|
|[Ethernet Device Authentication via Physical Layer Fingerprinting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115118)|W. Suski; C. Card; B. Few|10.1109/SoutheastCon51012.2023.10115118|authentication;intrusion detection;network access control;authentication;intrusion detection;network access control|
|[Online Availability of a Robot Therapy for Children with Autism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115087)|N. Ackovska; B. Ilijoski|10.1109/SoutheastCon51012.2023.10115087|autism spectrum disorder;human-robot interaction;human-computer interaction;application development;autism spectrum disorder;human-robot interaction;human-computer interaction;application development|
|[AppMAIS Simple Data Visualization App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114962)|L. Richardson; C. Campell; R. Tashakkori; W. O’Brien; S. E. Davis|10.1109/SoutheastCon51012.2023.10114962|Precision apiculture;Data visualization;Swarm detection;Robbery;Dash;Precision apiculture;Data visualization;Swarm detection;Robbery;Dash|
|[A Low-Cost Modified Energy Detection-Based Spectrum Sensing Algorithm with GNU Radio for Cognitive Radio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115163)|D. Baker; A. N. Beal; L. Joiner; T. M. Syed|10.1109/SoutheastCon51012.2023.10115163|GNU Radio;Software Defined Radio;Energy Detection;Spectrum Sensing;GNU Radio;Software Defined Radio;Energy Detection;Spectrum Sensing|
|[Development of a High-Speed High-Fidelity SCADA Simulation Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115177)|S. R. Wright; L. Cannan; T. H. Morris|10.1109/SoutheastCon51012.2023.10115177|SCADA;modeling;simulation;cybersecurity;SCADA;modeling;simulation;cybersecurity|
|[A Systematic Study to Determine 5G Baseline Performance for Scientific Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115108)|V. Kumar; X. Fan; E. S. Peterson; J. V. Cree|10.1109/SoutheastCon51012.2023.10115108|;|
|[A Collection of Datasets and Simulation Frameworks for Industrial Control System Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115122)|M. E. Alim; J. Smalligan; T. H. Morris|10.1109/SoutheastCon51012.2023.10115122|SCADA;testbeds;cybersecurity;datasets;OpenPLC;SCADA;testbeds;cybersecurity;datasets;OpenPLC|
|[A Study on the Potential of Hydrogen Fuel Cells for Maritime Transportation Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115221)|H. R. Chavan; J. Knollmeyer; S. Khan|10.1109/SoutheastCon51012.2023.10115221|Hydrogen;Maritime Transportation;Fuel Cells;Decarbonization;Hydrogen;Maritime Transportation;Fuel Cells;Decarbonization|
|[Last One Standing: Building a Wireless Link That Survives in Challenging Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115188)|A. Torabi; G. Sklivanitis; D. A. Pados; E. S. Bentley; J. Suprenant; M. Medley|10.1109/SoutheastCon51012.2023.10115188|SoC;FPGA;interference avoidance;cognitive radio;software-defined radio;waveform design;wireless testbed;SoC;FPGA;interference avoidance;cognitive radio;software-defined radio;waveform design;wireless testbed|
|[Feature Extraction of Network Traffic in Ethereum Blockchain Network Layer for Eclipse Attack Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115126)|D. Bhumichai; R. Benton|10.1109/SoutheastCon51012.2023.10115126|Ethereum blockchain;Eclipse attack;Feature Engineering;Network traffic layer;Ethereum blockchain;Eclipse attack;Feature Engineering;Network traffic layer|
|[Drive-By-Wire Conversion of an Electric Golf-Cart for Self-Driving Vehicles Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115090)|M. DeCicco; R. Khalghani; O. Toker|10.1109/SoutheastCon51012.2023.10115090|Drive-By-Wire conversion;Embedded Systems;Self-driving vehicles;Drive-By-Wire conversion;Embedded Systems;Self-driving vehicles|
|[Biomedical Sensing - A Sensor Fusion Approach For Improved Medical Detection & Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115191)|A. Ovando; R. Ramirez; S. Steele; A. A. Amiri Moghadam; Y. Chang; R. C. Voicu|10.1109/SoutheastCon51012.2023.10115191|Medical Diagnoses;Tissue Fingerprinting;Biomedical;Communication;Sensing;Medical Diagnoses;Tissue Fingerprinting;Biomedical;Communication;Sensing|
|[Effect of Surface Field on Breakdown for CMOS Single Photon Avalanche Diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115115)|S. Hasan; A. Nizam; N. McFarlane; M. S. Ara Shawkat|10.1109/SoutheastCon51012.2023.10115115|Device Simulation;device modeling;TCAD simulation;single photon avalanche diode (SPAD);avalanche photodiode;Device Simulation;device modeling;TCAD simulation;single photon avalanche diode (SPAD);avalanche photodiode|
|[Webcam Lighting Studio: A Framework for Real-Time Control of Webcam Lighting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115165)|K. Gonagur; S. Aggarwal; D. Fillmore; A. Nasipuri|10.1109/SoutheastCon51012.2023.10115165|webcam lighting;DMX;system design;webcam lighting;DMX;system design|
|[Extracting Ancient Maya Structures from Aerial LiDAR Data using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115095)|F. -E. Jannat; J. Zhang; A. Willis; W. Ringle|10.1109/SoutheastCon51012.2023.10115095|LiDAR;remote-sensing;segmentation;deep-learning;U-Net;LiDAR;remote-sensing;segmentation;deep-learning;U-Net|
|[Low-Frequency Shielding Effectiveness of Magnetic Alloys](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115097)|G. A. Duhni; M. Khasgiwala; J. L. Volakis; P. Markondeya Raj|10.1109/SoutheastCon51012.2023.10115097|Shielding Effectiveness;NSA 65-6;IEEE 299;Kovar;and Magnetic alloys.;Shielding Effectiveness;NSA 65-6;IEEE 299;Kovar;and Magnetic alloys.|
|[Controlling an Industrial Warehouse Stacker Crane Robot using a Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115092)|A. Bozinovski; S. Bozinovski|10.1109/SoutheastCon51012.2023.10115092|Flexible manufacturing systems;Industrial warehouse;Stacker crane robot;Raspberry Pi microcontroller;Flexible manufacturing systems;Industrial warehouse;Stacker crane robot;Raspberry Pi microcontroller|
|[Development of Course Modules in Python for Hardware Security Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115166)|B. Olney; M. A. F. Amador; R. Karam|10.1109/SoutheastCon51012.2023.10115166|;|
|[Towards A Map-Based Web Application for Prescribed Fire Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115091)|M. Yan; X. Hu|10.1109/SoutheastCon51012.2023.10115091|fire simulation;modeling and simulation;web-based system;fire simulation;modeling and simulation;web-based system|
|[A Relation between AND and ∀, and OR and ∃](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115151)|A. Bozinovski; S. Bozinovski|10.1109/SoutheastCon51012.2023.10115151|Artificial Intelligence;predicate reasoning;quantifiers;logic gates;Artificial Intelligence;predicate reasoning;quantifiers;logic gates|
|[UnIC-Net: Uncertainty Aware Involution-Convolution Hybrid Network for Two-level Disease Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115109)|M. F. Islam; S. Zabeen; F. B. Rahman; M. A. Islam; F. B. Kibria; M. A. Manab; D. Z. Karim; A. A. Rasel|10.1109/SoutheastCon51012.2023.10115109|involution;convolution;skin cancer;malaria parasite;image classification;involution;convolution;skin cancer;malaria parasite;image classification|
|[Performance Evaluation of CuBi2O4-based Thin Film Solar Cells with Non-toxic Oxide Electron and Hole Transport Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10114966)|S. Das|10.1109/SoutheastCon51012.2023.10114966|CuBi2O4;Copper Bismuth Oxide;Solar Cell;Heterojunction;Photovoltaic Device;Numerical Simulation;SCAPS-1D;CuBi2O4;Copper Bismuth Oxide;Solar Cell;Heterojunction;Photovoltaic Device;Numerical Simulation;SCAPS-1D|
|[An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115205)|M. Pudyel; M. Atay|10.1109/SoutheastCon51012.2023.10115205|masked face recognition;machine learning;ocular biometrics;synthesized mask;Covid-19 pandemic;masked face recognition;machine learning;ocular biometrics;synthesized mask;Covid-19 pandemic|
|[Modeling of Perimeter Gated SPAD-based Direct Time-of-Flight Sensor for Low Light LiDAR Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115088)|N. Irfan; S. Hasan; S. Ara Shawkat|10.1109/SoutheastCon51012.2023.10115088|Single Photon Avalanche Diode (SPAD);Light Detection and Ranging (LiDAR);direct time-of-flight (DTOF) sensor;flash LiDAR;Modeling;Single Photon Avalanche Diode (SPAD);Light Detection and Ranging (LiDAR);direct time-of-flight (DTOF) sensor;flash LiDAR;Modeling|
|[Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10115067)|C. J. Strickland; R. J. Haddad|10.1109/SoutheastCon51012.2023.10115067|Frequency Hopping;Artificial Intelligence;Jamming;Neural Network;Random Number Generators;Pseudo Noise;Maximum Length Sequences;Frequency Hopping;Artificial Intelligence;Jamming;Neural Network;Random Number Generators;Pseudo Noise;Maximum Length Sequences|
#### **2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)**
- DOI: 10.1109/ICACCS57279.2023
- DATE: 17-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Gesture Based Virtual Assistant For Deaf-Mutes Using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112986)|B. G. Jairam; D. Ponnappa|10.1109/ICACCS57279.2023.10112986|sign language;deep learning;deaf-mutes;virtual assistant;convolutional neural network;tensor flow;openCV;hand gesture;sign language;deep learning;deaf-mutes;virtual assistant;convolutional neural network;tensor flow;openCV;hand gesture|
|[The Utilization of ResNet Model in White Blood Cell Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112720)|C. Li; Z. Xiao; H. Wang; S. Jin|10.1109/ICACCS57279.2023.10112720|Blood Cell Classifier;CNN;Python;ResNet-50;Machine Learning;Blood Cell Classifier;CNN;Python;ResNet-50;Machine Learning|
|[IoT Based Smart Poultry Farm Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112870)|T. Malini; D. L. Aswath; R. Abhishek; R. Kirubhakaran; S. Anandhamurugan|10.1109/ICACCS57279.2023.10112870|IoT;warmness sensor;gasoline sensor;feeding;SMS;accuracy;IoT;warmness sensor;gasoline sensor;feeding;SMS;accuracy|
|[Vehicle to Vehicle Communication Using Cognitive Radio Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113011)|P. V. Kumar; R. Sri Sangeetha; P. S; S. Sivaranjani; M. R; P. N|10.1109/ICACCS57279.2023.10113011|V2V Communication;Cognitive radio;Power spectral density;data transfer among vehicles;cognitive waveform and Arduino Uno;V2V Communication;Cognitive radio;Power spectral density;data transfer among vehicles;cognitive waveform and Arduino Uno|
|[Application of Convolution Neural Network to Detect the Stages of Alzheimer Disease for Magnetic Resonance Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113131)|S. A. J; D. A. A; M. N. T; J. P. J|10.1109/ICACCS57279.2023.10113131|Deep learning;CNN;Dementia;magnetic resonance imaging;stages of alzheimer disease;Deep learning;CNN;Dementia;magnetic resonance imaging;stages of alzheimer disease|
|[Multimodal Wearable Sensors-based Stress and Affective States Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112973)|R. Gupta; A. Bhongade; T. K. Gandhi|10.1109/ICACCS57279.2023.10112973|Stress;stress monitoring;stress detection;physiological stress response;stress rehabilitation;machine learning;Stress;stress monitoring;stress detection;physiological stress response;stress rehabilitation;machine learning|
|[A Build and Deploy Ethereum Smart Contract for Food Supply Chain Management in Truffle - Ganache Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112889)|N. N. Ahamed; R. Vignesh|10.1109/ICACCS57279.2023.10112889|Blockchain;Supply Chain Management;Ethereum;Smart Contract;Truffle;Ganache;Blockchain;Supply Chain Management;Ethereum;Smart Contract;Truffle;Ganache|
|[Hybrid Blockchain and IPFS for Secure Industry 4.0 Framework of IoT-based Skin Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112751)|S. Bhadula; S. Sharma; A. Johri|10.1109/ICACCS57279.2023.10112751|Authentication;blockchain;Internet of Things (IoT);interplanetary file system (IPFS);Smart contract;Security;Authentication;blockchain;Internet of Things (IoT);interplanetary file system (IPFS);Smart contract;Security|
|[IoT Based Secured Smart Voting System Using Diffie Hellman Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113082)|V. S; R. R; R. P; M. S. S; P. R; J. S|10.1109/ICACCS57279.2023.10113082|aadhar card;database security and authentication;secret decipher key;one time password;vote casting;admin login and user login;aadhar card;database security and authentication;secret decipher key;one time password;vote casting;admin login and user login|
|[Design and Implementation of Solar based Day and Night Battery Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112976)|S. R; S. R; S. C. V; S. M; S. S. D|10.1109/ICACCS57279.2023.10112976|Energy Generation;IR sensor;Solar Panel;LDR;Energy Generation;IR sensor;Solar Panel;LDR|
|[Performance Evaluation Of Lifi-OCDMA System Using ZCC Code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112786)|M. Kumari|10.1109/ICACCS57279.2023.10112786|Light fidelity (LiFi);visible light communication (VLC);radio frequency (RF);zero cross correlation (ZCC);optical code division multiple access (OCDMA);OptiSystem;Bit error rate (BER);Light fidelity (LiFi);visible light communication (VLC);radio frequency (RF);zero cross correlation (ZCC);optical code division multiple access (OCDMA);OptiSystem;Bit error rate (BER)|
|[Fault Analysis and Security of Direct Current Deficiency of Modular Multilevel Converter System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113129)|M. Chandrashekhar; P. K. Dhal|10.1109/ICACCS57279.2023.10113129|Burden current;MMC;VSC;security;DC frame work;AC framework;Burden current;MMC;VSC;security;DC frame work;AC framework|
|[Video Classification Using CNN and Ensemble Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112975)|K. Duvvuri; H. Kanisettypalli; K. Jaswanth; M. K.|10.1109/ICACCS57279.2023.10112975|Ensemble Learning;CNN;MaxPooling;Video Classification;UCF11;Flatten;Frames;Ensemble Learning;CNN;MaxPooling;Video Classification;UCF11;Flatten;Frames|
|[Analyzing Data Compression Techniques for Biomedical Signals and Images Using Downsampling and Upsampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112725)|T. R. Reddy; S. Balaji; R. Ramya; K. R. A. Britto; P. Thanapal; V. Elamaran|10.1109/ICACCS57279.2023.10112725|Data compression;down-sampling;up-sampling;sub-band coding;wavelet filters;Data compression;down-sampling;up-sampling;sub-band coding;wavelet filters|
|[Survey on Water Quality Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113069)|D. Kavitha; G. T.R.; D. Devaraj; H. V|10.1109/ICACCS57279.2023.10113069|Water quality prediction;Naïve Bayes classifier;CatBoost classifier;Random Forest algorithm;Logistic Regression;SMLT;Machine learning algorithms;Accuracy;Water quality prediction;Naïve Bayes classifier;CatBoost classifier;Random Forest algorithm;Logistic Regression;SMLT;Machine learning algorithms;Accuracy|
|[A Study on Blood-Cell Segmentation Method for the Identification of Hematological Disorders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112715)|G. M; H. D; K. S; J. S S|10.1109/ICACCS57279.2023.10112715|CNN;Blood Cell Segmentation;U-Net;CNN;Blood Cell Segmentation;U-Net|
|[Cardiovascular Stroke Prediction System using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112727)|A. K M; M. R; S. F. F; M. R. B|10.1109/ICACCS57279.2023.10112727|Machine learning;Black Box;Logistic regression;Heart ailment;SVM;Machine learning;Black Box;Logistic regression;Heart ailment;SVM|
|[Grid-Connected 3L-NPC Inverter with PI Controller Based on Space Vector Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113092)|K. Chenchireddy; M. G. Basha; S. Dongari; P. Kumar; G. Karthik; G. Maruthi|10.1109/ICACCS57279.2023.10113092|SVM;PI Controller;Grid-Connected 3-Level 3-Phase NPC Inverter;SVM;PI Controller;Grid-Connected 3-Level 3-Phase NPC Inverter|
|[A Review on IoT-based Defensive Devices for Women Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113015)|K. K. Kommineni; S. J. Basha; M. Sandeep; P. S. Vadana; T. S. R. Sai; D. S. Kumar|10.1109/ICACCS57279.2023.10113015|Abuses;Crime;Defensive Devices;IoT;Women Safety;Women Security;Women Protection;Abuses;Crime;Defensive Devices;IoT;Women Safety;Women Security;Women Protection|
|[Artificial Synthesis of Single Person Videos through Motion Transfer using Cycle Generative Adversarial Networks and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112738)|A. K. Das; V. Patidar; R. Naskar|10.1109/ICACCS57279.2023.10112738|Artificial video synthesis;CycleGAN;Edge detection;Generative Adversarial Network (GAN);Pose detection;Pillow technique;Artificial video synthesis;CycleGAN;Edge detection;Generative Adversarial Network (GAN);Pose detection;Pillow technique|
|[Virtual Dressing Room Application Using GANs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113074)|B. A; S. N; A. S; J. V|10.1109/ICACCS57279.2023.10113074|Virtual Reality;Augmented Reality;Generative Adversial Networks;Image Processing;Warping;Semantic Generation;Virtual Clothing;Virtual Reality;Augmented Reality;Generative Adversial Networks;Image Processing;Warping;Semantic Generation;Virtual Clothing|
|[Weeds and Crop Image Classification using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112958)|H. S. Panati; G. P; D. A. A; M. N. T|10.1109/ICACCS57279.2023.10112958|Deep learning;CNN;weeds classification;soybean;Image Processing;Agriculture;Crop Analysis;Deep learning;CNN;weeds classification;soybean;Image Processing;Agriculture;Crop Analysis|
|[Alpha Gadget: A Comprehensive Review of Technologies for Improving Business Involvement and participation in E-Commerce](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113087)|S. Singhal; S. Poudel; S. Karki; R. Ghimire|10.1109/ICACCS57279.2023.10113087|user-friendly;gadgets;retailers;modern world technology;consumer;user-friendly;gadgets;retailers;modern world technology;consumer|
|[Shadow APR-ing for APR Poisoning Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112712)|D. Majumdar; R. R. Chintala|10.1109/ICACCS57279.2023.10112712|Address Resolution Protocol;Spoofing;MITM attack;DDoS attack;IP-MAC pair;ARP cache;Address Resolution Protocol;Spoofing;MITM attack;DDoS attack;IP-MAC pair;ARP cache|
|[A Study on Digital Pathology Image Segmentation Using Deep Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112997)|A. T. E; S. Vimala|10.1109/ICACCS57279.2023.10112997|Deep Learning;Image Pathology;WSI Images;classification;segmentation;Deep Learning;Image Pathology;WSI Images;classification;segmentation|
|[Logistics and Supply Chain Management in Leather Industry Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112883)|N. N. Ahamed; R. Vignesh|10.1109/ICACCS57279.2023.10112883|Blockchain;Supply Chain;Logistics;Smart Contract;Track/Trace;RFID;Blockchain;Supply Chain;Logistics;Smart Contract;Track/Trace;RFID|
|[Design and Simulation of Microstrip Patch Antenna Using Circular Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112900)|K. C; A. S; D. J; I. N. M|10.1109/ICACCS57279.2023.10112900|Microstrip patch antenna;RFID;WLAN;Circular structure;Microstrip patch antenna;RFID;WLAN;Circular structure|
|[Dynamic Wireless Charging and Cloud Based Metering of Electrical Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112858)|L. N; J. H; H. I. S; G. Kumar Ks; A. S|10.1109/ICACCS57279.2023.10112858|Innovation Charging Technology Electrified;Innovation Charging Technology Electrified|
|[Cloud Based Electric Bike Speed and Safety Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112816)|L. N; A. K; A. A; B. R. S; B. D; A. P. N|10.1109/ICACCS57279.2023.10112816|Battery efficiency;vehicle and battery safety;speed controlling;changing speed limits using IOT;datas stored in databases;Battery efficiency;vehicle and battery safety;speed controlling;changing speed limits using IOT;datas stored in databases|
|[NFT Club – A NFT Marketplace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112704)|R. J. Anandhi; A. Bhakta; A. Purswani; K. Karan; A. Mishra|10.1109/ICACCS57279.2023.10112704|NFT;Blockchain;Ethereum;MetaMask;Web3.js;NFT;Blockchain;Ethereum;MetaMask;Web3.js|
|[Cyber-Ego: The Digital Self](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112873)|R. Saravanan; R. Aishwarya|10.1109/ICACCS57279.2023.10112873|GPT-2;DCTTS;SDA;Image animation;FOM;Transfer learning;Flask.;GPT-2;DCTTS;SDA;Image animation;FOM;Transfer learning;Flask.|
|[Automated Induction Motor Monitoring System Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112942)|N. Subhashini; M. Mouli; J. Mugunthan; R. P. Kumar; M. Revanth; M. Tejaa|10.1109/ICACCS57279.2023.10112942|Induction motor;voltage;current;speed Arduino;monitoring;protection;IoT;relay.;Induction motor;voltage;current;speed Arduino;monitoring;protection;IoT;relay.|
|[Security of Cyber Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112935)|D. K V; J. S N; G. B; D. G. Chandu; A. Dinesh|10.1109/ICACCS57279.2023.10112935|Machine learning;Lasso;Ridge;detection.;Machine learning;Lasso;Ridge;detection.|
|[An Overview Of Interpretability Techniques For Explainable Artificial Intelligence (XAI) In Deep Learning-Based Medical Image Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113001)|P. D. S; R. Kumar K; V. S; N. K; A. K|10.1109/ICACCS57279.2023.10113001|Explainable Artificial Intelligence (XAI);XAI architecture;Medical Image Analysis;Interpretability method;XAI approaches;Explanation methods;Visual explanation;Deep neural network.;Explainable Artificial Intelligence (XAI);XAI architecture;Medical Image Analysis;Interpretability method;XAI approaches;Explanation methods;Visual explanation;Deep neural network.|
|[Crime Data Analysis and Safety Recommendation System Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113102)|R. M. Akil; S. Sarathambekai; T. Vairam; R. S. Krishnan; G. S. Dharaneesh; D. Janarthanan|10.1109/ICACCS57279.2023.10113102|Crime Analysis;KMeans Clustering;Visualization;NCRB;Machine Learning.;Crime Analysis;KMeans Clustering;Visualization;NCRB;Machine Learning.|
|[Wireless Voice Controlled Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113022)|S. Kuriakose; H. M M; D. G. Keerthana; S. Adarsh; H. K|10.1109/ICACCS57279.2023.10113022|Bluetooth;Voice;Speech to Text;Robot;Mobile device;Navigation;Arduino;Bluetooth;Voice;Speech to Text;Robot;Mobile device;Navigation;Arduino|
|[Potential Posture Analysis during Sleep State using Jenkins cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112755)|B. V; R. A; A. H|10.1109/ICACCS57279.2023.10112755|Sleep;posture analysis;OSA;quality sleep;sleep cycle;health monitoring.;Sleep;posture analysis;OSA;quality sleep;sleep cycle;health monitoring.|
|[An Overview of the Importance of Power Electronic Converters in Electric Vehicle Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113067)|C. M|10.1109/ICACCS57279.2023.10113067|electric vehicles;power electronic converter topologies;dc-dc bi-directional topologies;electric vehicles;power electronic converter topologies;dc-dc bi-directional topologies|
|[OMR Reader Info Scanner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113018)|S. Agarwal; M. Varun; S. Prabakeran|10.1109/ICACCS57279.2023.10113018|Optical Mark Recognition;Image Based Technique;Template Matching;OMR;Scanner;Recognition;Image processing;Multiple Choice Test;Python;OpenCV;Optical Mark Recognition;Image Based Technique;Template Matching;OMR;Scanner;Recognition;Image processing;Multiple Choice Test;Python;OpenCV|
|[Heart Attack Prediction using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113045)|J. S. Rose; P. Malin Bruntha; S. Selvadass; R. M. V; B. C. Mary M; M. J. D|10.1109/ICACCS57279.2023.10113045|Cardio-vascular disease;heart attack;machine learning;logistic regression;prediction;Cardio-vascular disease;heart attack;machine learning;logistic regression;prediction|
|[Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112922)|M. A. Kumar; V. Valli Mayil; T. R. Jeyalakshmi; A. Ghanshala; A. Gupta|10.1109/ICACCS57279.2023.10112922|Body sensors;Cloud;Deep Learning;Disease Diagnosis;Fog;Healthcare;IoT;WBAN;and Wearable;Body sensors;Cloud;Deep Learning;Disease Diagnosis;Fog;Healthcare;IoT;WBAN;and Wearable|
|[Gesture Based Computer Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113075)|A. Baheti; D. Patwa; S. Gajjar|10.1109/ICACCS57279.2023.10113075|Human-Computer Interaction (HCI);Gestures;Computer Control System;Artificial Intelligence;Occlusion;Segmentation;Single Shot detector and Python;Human-Computer Interaction (HCI);Gestures;Computer Control System;Artificial Intelligence;Occlusion;Segmentation;Single Shot detector and Python|
|[Effective Utilization of Plastic Garbage for Road Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112972)|M. L. Meghana; G. V. Rajya Lakshmi; G. Harika; N. C. Harshit|10.1109/ICACCS57279.2023.10112972|Waste Management;Plastic Garbage;Environment;Eco-friendly;Mobile Application;Android Studio;Firebase;Waste Management;Plastic Garbage;Environment;Eco-friendly;Mobile Application;Android Studio;Firebase|
|[RFA and RAA: Optimal Path finding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113007)|R. N. Kumar; D. P. G. Ramakrishna; R. Aishwarya; P. Amarnath|10.1109/ICACCS57279.2023.10113007|path planning;path tracking;Security;Landmark indicating;Accident analizing;Rout guidance;path planning;path tracking;Security;Landmark indicating;Accident analizing;Rout guidance|
|[Reconfigured Boost Converter for PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112714)|R. Tiwari; R. S. K; S. H. S M; S. G. V; S. S. C|10.1109/ICACCS57279.2023.10112714|DC/DC Converter;PV System;DC Load;Boost converter;Reconfigured Boost Converter;DC/DC Converter;PV System;DC Load;Boost converter;Reconfigured Boost Converter|
|[Survey on Prediction of Autism Spectrum Disorder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112672)|L. S. S; S. Shrey; S. Shrivastava; S. Singh; Y. N. Swamy|10.1109/ICACCS57279.2023.10112672|Machine learning;dataset;Autism;SVM algorithm;AdaBoost;ANN;Random Forest;Machine learning;dataset;Autism;SVM algorithm;AdaBoost;ANN;Random Forest|
|[A Case Study using Simple Moving Average Filters to accomplish ECG denoising on an FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112847)|S. Fairooz; S. Balaji; R. Ramya; M. S. Prakash Balaji; P. Thanapal; V. Elamaran|10.1109/ICACCS57279.2023.10112847|CIC technique;digital filters;ECG denoising;FPGA design;healthcare engineering;moving average filters;CIC technique;digital filters;ECG denoising;FPGA design;healthcare engineering;moving average filters|
|[Design and Development of an Intelligent and Smart Helmet for Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112926)|P. Jagadesh; P. V. Priya; T. Deepak; A. G. Anto; D. J. Jagannath|10.1109/ICACCS57279.2023.10112926|Blind People;Physically disabled;Assistive Device;HC-SR04;Pi camera;obstructions;Gyro-sensor;Piezoelectric crystals;Ultrasound;Voice Feedback;Blind People;Physically disabled;Assistive Device;HC-SR04;Pi camera;obstructions;Gyro-sensor;Piezoelectric crystals;Ultrasound;Voice Feedback|
|[Detecting Foliar Diseases in Potato Crops Through a Network of Convolutional Neurons](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113089)|A. Bajpai; M. Tyagi; B. Patro; S. Yadav|10.1109/ICACCS57279.2023.10113089|Leaf disease;Deep learning;VGG16;AlexNet;Smart Agriculture;Convolutional Neural Network;Leaf disease;Deep learning;VGG16;AlexNet;Smart Agriculture;Convolutional Neural Network|
|[Crow Detection In Peanut Field Using Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112813)|D. Harini; K. B. Sri; M. M. Durga; O. V. Brahmaiah|10.1109/ICACCS57279.2023.10112813|Field-view;Crow Detection;Convolutional Neural Network(CNN);MobileNet SSD;OpenCV;Raspberry Pi OS;Field-view;Crow Detection;Convolutional Neural Network(CNN);MobileNet SSD;OpenCV;Raspberry Pi OS|
|[Raise of Complaints on day-to-day issues by the public using Global Positioning System for finding the exact location of the problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112970)|L. P. Divvela; V. Sravanthi; P. Hemannth; T. K. Sai Avinash|10.1109/ICACCS57279.2023.10112970|Current GPS location;Image depicting the problem;regional languages;redirecting to google maps;categorization of complaints;Current GPS location;Image depicting the problem;regional languages;redirecting to google maps;categorization of complaints|
|[A Novel Approach for Data Encryption Based on Vector-Matrix Keys](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113061)|M. Maragatharajan; L. Sathishkumar; J. Manikandan; S. Suprakash; P. Naveen|10.1109/ICACCS57279.2023.10113061|Encryption;Data;Vectors;Matrices;Keys;Cipher text;Decryption;Plain text;Encryption;Data;Vectors;Matrices;Keys;Cipher text;Decryption;Plain text|
|[Machine Learning Based Currency Classification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112886)|C. S. Jyothi; S. Rajeswari; G. R. Prasad; S. Lowkya Gayathri|10.1109/ICACCS57279.2023.10112886|Data augmentation;TensorFlow Lite;Teachable Machine;Image classification;Currency recognition;Machine learning;Transfer learning;Data augmentation;TensorFlow Lite;Teachable Machine;Image classification;Currency recognition;Machine learning;Transfer learning|
|[Analysis of the Surface Water Quality in the State of Karnataka using Distributed Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112732)|S. Dasu; S. G; J. Shetty|10.1109/ICACCS57279.2023.10112732|basin;classification;cluster;hpcc systems;node;potability;prediction;surface water;visualization;basin;classification;cluster;hpcc systems;node;potability;prediction;surface water;visualization|
|[Joint Intent Classification and Slot Tagging on Agricultural Dataset for Indic Languages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113115)|A. Gupta; P. Immadisetty; P. Rajesh; S. G|10.1109/ICACCS57279.2023.10113115|Intent Classification;Speech Recognition;Slot Tagging;Agriculture Dataset;JointBERT;LSTM;Query-based system;Natural Language Processing;Information System;Voice Assistance;Telugu Corpus;Intent Classification;Speech Recognition;Slot Tagging;Agriculture Dataset;JointBERT;LSTM;Query-based system;Natural Language Processing;Information System;Voice Assistance;Telugu Corpus|
|[Classification of Vegetation using Sentinal-2 Images of Jodhpur Region](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112869)|K. S. Vani; E. T. N. Lakshmi; M. Susarla; M. A. Hussain; P. Enosh|10.1109/ICACCS57279.2023.10112869|Remote sensing;image classification;Vegetation;Google Earth Engine;SVM;CART;RandomForest;Remote sensing;image classification;Vegetation;Google Earth Engine;SVM;CART;RandomForest|
|[An Efficient Class Room Teaching Learning Method Using Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113096)|D. Valluru; M. A. Mustafa; H. Y. Jasim; K. Srikanth; M. V. L. N. RajaRao; P. S. S. Sreedhar|10.1109/ICACCS57279.2023.10113096|Class room learning;Machine learning;Augmented Reality;Collaborative learning practices;Teaching Learning method;Deep Learning;Class room learning;Machine learning;Augmented Reality;Collaborative learning practices;Teaching Learning method;Deep Learning|
|[Heart Attack Prediction Using Artificial Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112664)|N. N. Bharathi; M. F. Shaik; T. Poojita; T. Sravanthi; M. Rafi; I. R. Raja|10.1109/ICACCS57279.2023.10112664|ANN;Heart attack prediction;Jupiter Lab;ANN;Heart attack prediction;Jupiter Lab|
|[Comprehensive Analysis of Deep Learning-based Customer Feedback Classification Methodologies for Recommender Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112661)|N. Srinu; J. Paramesh; M. Sriram|10.1109/ICACCS57279.2023.10112661|Air Lines;Deep Learning;Recommender system;Sentiment;Social Media Posts;Tourism;Air Lines;Deep Learning;Recommender system;Sentiment;Social Media Posts;Tourism|
|[Land Development Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112760)|M. Madhavi; V. Kusumaniswari; G. S. H. Srinivas; T. P. K. Nayak; M. Shivaji|10.1109/ICACCS57279.2023.10112760|Land Development;Mobile Application development;Online platform;Database;Owners and developers;Land Development;Mobile Application development;Online platform;Database;Owners and developers|
|[Ant Colony Optimization with Simulated Annealing Algorithm for Google Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112798)|J. K. C. Revanna; N. Y. B. Al-Nakash|10.1109/ICACCS57279.2023.10112798|Ant Colong Optimzation;Heuristic;Particle Swarm optimziation;Simulated Anneling algorithm;localization;Cost Value;Hill Climbing;Global Minima;Ant Colong Optimzation;Heuristic;Particle Swarm optimziation;Simulated Anneling algorithm;localization;Cost Value;Hill Climbing;Global Minima|
|[Reading Aid and Translator with Raspberry Pi for Blind people](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113042)|V. Adusumilli; M. F. Shaik; N. Kolavennu; L. B. M. T. Adepu; P. A. V; I. R. Raja|10.1109/ICACCS57279.2023.10113042|Visually impaired;TTS;Raspberry pi;OCR;camera;local language;document text;Earphones;Visually impaired;TTS;Raspberry pi;OCR;camera;local language;document text;Earphones|
|[Design Aspects of Miniaturized Spiral Microstrip Patch Antenna with Inductive Loading Technique for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113023)|T. Kishor; T. M. Neebha; A. S. Chelladurai; S. P. Sharma; A. D. Andrusia; B. Bhaskar|10.1109/ICACCS57279.2023.10113023|spiral meander antenna;Miniaturization;Miniaturized antenna;Low frequency antenna;biomedical application;Inductive loading;ISM band;antenna design;spiral meander antenna;Miniaturization;Miniaturized antenna;Low frequency antenna;biomedical application;Inductive loading;ISM band;antenna design|
|[Optimizing Medical Healthcare Services Using Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112736)|K. Muthumanickam; A. Arthi; B. Umarani; T. Kumaravel|10.1109/ICACCS57279.2023.10112736|Big Data;COVID-19;Data Analytics;Healthcare Sector;Patient Data;Length of Stay;Big Data;COVID-19;Data Analytics;Healthcare Sector;Patient Data;Length of Stay|
|[Application of Haar Cascade Classifier for Kitchen Safety Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112721)|J. A. Bernabe; J. Dylan O. Catapang; L. D. Valiente|10.1109/ICACCS57279.2023.10112721|Haar Cascade;kitchen safety;Alert System;Blynk;Face Detection;Sensors;Haar Cascade;kitchen safety;Alert System;Blynk;Face Detection;Sensors|
|[Smart Reader for Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113122)|A. Charishma; A. A. Vaishnavi; D. Rajeswara Rao; T. T. Sri|10.1109/ICACCS57279.2023.10113122|OpenCV;Tesseract;Text-to-speech (TTS);Image-to-Text;Optical Character Recognition (OCR);Speech output;Raspberry pi.;OpenCV;Tesseract;Text-to-speech (TTS);Image-to-Text;Optical Character Recognition (OCR);Speech output;Raspberry pi.|
|[Leveraging ARMA and ARMAX Time-Series Forecasting Models for Rainfall Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113031)|G. L. Vara Prasad; B. Ravi Teja; S. Govathoti; S. Dhanikonda|10.1109/ICACCS57279.2023.10113031|Agriculture;Rainfall;Temperature;Climatic Changes;Time-Series Forecasting Model;Rainfall Prediction;Agriculture;Rainfall;Temperature;Climatic Changes;Time-Series Forecasting Model;Rainfall Prediction|
|[Analysis of Lithium-ion Battery Degradation in V2G Integrated System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112682)|T. Gunasekar; T. Mohanasundaram; K. S. Nishanthkumar; S. N. Sri Arvind; S. Srikanth; L. Sudharsan|10.1109/ICACCS57279.2023.10112682|Battery;Electric Vehicle;Depth of Discharge;Vehicle to Grid;Microgrid;Lithium-ion;Battery;Electric Vehicle;Depth of Discharge;Vehicle to Grid;Microgrid;Lithium-ion|
|[Enhancement of Network Lifetime in Wireless Sensor Networks Using Modified Cluster Head Selection Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112754)|P. Satyanarayana; G. S. Guptha Maremalla; G. Phanindra; A. C. R. R. Devi; K. Anantha Rajya Lakshmi; Y. S. S. Sriramam|10.1109/ICACCS57279.2023.10112754|Routing;Wireless sensor networks;Network lifetime;Energy efficiency;Cluster head;Routing;Wireless sensor networks;Network lifetime;Energy efficiency;Cluster head|
|[Three Dimensional Solar Tracking Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113091)|C. Pavithra; R. Rithan; R. Rohith; M. Santhosh; M. Vijayadharshini; R. Lokith|10.1109/ICACCS57279.2023.10113091|Atmega328P;Machine Learning;Micro Controller;Solar Tracker;Atmega328P;Machine Learning;Micro Controller;Solar Tracker|
|[Remote Controlled Unmanned Water Vehicle with Human Detection and GPS Using Yolov4 for Flood Search Operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112774)|L. A. C. Llanes; C. Rhod H. Ulbis; R. G. Garcia|10.1109/ICACCS57279.2023.10112774|YOLOv4;Hovercraft;Raspberry Pi;Search and Rescue Operation;Human Detection;Real-Time Video Feed;SMS and Email Notification;People Counter;Image Capture;Accuracy;Unmanned Vehicle;YOLOv4;Hovercraft;Raspberry Pi;Search and Rescue Operation;Human Detection;Real-Time Video Feed;SMS and Email Notification;People Counter;Image Capture;Accuracy;Unmanned Vehicle|
|[Implementation of Improved Energy Balanced Routing Protocol to Enlarge Energy Efficiency in MANET for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112716)|P. Satyanarayana; K. Bhoomika; D. Mukesh; P. Srujana; R. M. Bai; Y. S. S. Sriramam|10.1109/ICACCS57279.2023.10112716|Clustering;Routing;Wireless sensor networks (WSN’s);Network lifetime;Mobile Adhoc Networks;Energy Consumption;Clustering;Routing;Wireless sensor networks (WSN’s);Network lifetime;Mobile Adhoc Networks;Energy Consumption|
|[A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113026)|R. R. Dixit; G. R|10.1109/ICACCS57279.2023.10113026|blockchain;cybersecurity;privacy;blockchain applications;cryptocurrency;blockchain properties;blockchain limitations;blockchain future;blockchain;cybersecurity;privacy;blockchain applications;cryptocurrency;blockchain properties;blockchain limitations;blockchain future|
|[A Review on Evolution of Approximate Truncation Multipliers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112897)|V. B. E; K. Kalirajan; S. N; P. N; M. R; P. K; R. E|10.1109/ICACCS57279.2023.10112897|approximate multipliers;truncated multipliers;fixed-width;signal processing;quantization error;approximate multipliers;truncated multipliers;fixed-width;signal processing;quantization error|
|[Significant Challenges to espouse DevOps Culture in Software Organisations By AWS : A methodical Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113021)|S. S. Sravan; C. Sai Ganesh; K. V. D. Kiran; T. Aakash Chandra; K. Aparna; T. Vignesh|10.1109/ICACCS57279.2023.10113021|DevOps;Agile software development;continous integration;continous delivery;Automation;DevOps Culture;DevOps Adoption;Software Development Lifecycle(SDLC);DevOps;Agile software development;continous integration;continous delivery;Automation;DevOps Culture;DevOps Adoption;Software Development Lifecycle(SDLC)|
|[Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112939)|R. Gokulapriya; R. K. Sambandam; M. Balamurugan|10.1109/ICACCS57279.2023.10112939|Sentiment Analysis;LSTM;Twitter;Python;Google Colab;Sentiment Analysis;LSTM;Twitter;Python;Google Colab|
|[Innovative Usage Of Drones In Medical Emergency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112949)|K. G. Lakshmi; P. Kumar; M. Rishitha; G. N. Swamy; T. Aravind Kumar|10.1109/ICACCS57279.2023.10112949|Drone;Servo-Motor;Transmitter;Servomotor;Receiver;Flight-Controller;GPS;Battery;Medicine;Drone;Servo-Motor;Transmitter;Servomotor;Receiver;Flight-Controller;GPS;Battery;Medicine|
|[HSV Model based Skin Color Segmentation using Uncomplicated Threshold and Logical AND Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112992)|P. Ganesan; B. S. Sathish; L. M. I. Leo Joseph; G. Sajiv; R. Murugesan; A. Akilandeswari; S. Gomathi|10.1109/ICACCS57279.2023.10112992|HSV color space;segmentation;skin color;AND logic;threshold;HSV color space;segmentation;skin color;AND logic;threshold|
|[Investigation of Fuzzy and SOM based Segmentation of High-Resolution Satellite Images in HSL Color Space for Information Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112963)|P. Ganesan; B. S. Sathish; L. M. I. Leo Joseph; R. Murugesan; G. Sajiv; A. Akilandeswari; M. Gnanaprakash|10.1109/ICACCS57279.2023.10112963|FCM;color space;PFCM;soft computing;MFCM;segmentation;SOM;FCM;color space;PFCM;soft computing;MFCM;segmentation;SOM|
|[Aquatic Plant Disease Detection Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113002)|R. Aishwarya; P. A. Froila Stephanie; R. Yogitha; C. D. Srinivas|10.1109/ICACCS57279.2023.10113002|Aquatic;Plant disease;Detection;Features;Deep Learning;Agriculture and Prediction;Aquatic;Plant disease;Detection;Features;Deep Learning;Agriculture and Prediction|
|[Effective Solid Waste Segregation for Sustainable Disposal: Model for Coimbatore City, India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112674)|V. R. A; J. G|10.1109/ICACCS57279.2023.10112674|solid waste management;door-to-door collection;landfill;waste disposal;type of waste;recycled;trash;solid waste management;door-to-door collection;landfill;waste disposal;type of waste;recycled;trash|
|[Information Security Using Counter-Anti-Forensics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112678)|R. Singamsetty; K. S. Chandra Has; S. Kukkapalli; V. N. Mannipudi; K. V. D. Kiran; A. V. Praveen Krishna|10.1109/ICACCS57279.2023.10112678|Information Security;Digital forensics;Cyber security;anti-digital forensics;cyber attack;prevention;privacy.;Information Security;Digital forensics;Cyber security;anti-digital forensics;cyber attack;prevention;privacy.|
|[Big Data Analysis in Healthcare: A Comprehensive Overview : Exploring the Benefits of Big Data for Health Care Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113099)|S. V. Venna; S. Reddy Narra; T. P. Sai Tadisetti; S. S. R. Nandanampati; R. K. Tata; S. A|10.1109/ICACCS57279.2023.10113099|Big data;Computing power;Healthcare;clinical decision support systems;Data mining;predictive analytics;Big data;Computing power;Healthcare;clinical decision support systems;Data mining;predictive analytics|
|[MPPT-Based Charge Controller for Battery Fast Charging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112852)|M. Deepika; P. Karthikeyan; A. V. Keerthana; M. Lakshmanan; P. Gowtham; C. Kumar; S. Jaisiva|10.1109/ICACCS57279.2023.10112852|Solar PV System;MPPT Charge Controller;Perturb and Observe Algorithm;Maximum Power Point (MPP);Battery Fast Charging and H-Bridge Inverter;Solar PV System;MPPT Charge Controller;Perturb and Observe Algorithm;Maximum Power Point (MPP);Battery Fast Charging and H-Bridge Inverter|
|[Design of Novel topology for Volumetric Efficiency Analysis in Hydrogen Fuel Cell with Maximum Power Point Tracking Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112961)|N. Prakash; P. Pavithra; P. Sobika; P. Revan; S. Jaisiva; C. Kumar; M. Lakshmanan|10.1109/ICACCS57279.2023.10112961|Fuel Cell;MPPT;Multilevel Inverter;Boost Converter;Fuel Cell;MPPT;Multilevel Inverter;Boost Converter|
|[Security Auditing Methodology Using Crowdsourced Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113128)|K. N. Sasank; S. Revanth; J. Yadavalli; A. K. Panidepu; K. V. D. Kiran; V. V. Prasad Padyala|10.1109/ICACCS57279.2023.10113128|Vulnerabilities;Vulnerability Assessment;Cybersecurity;Information security;Intrusion detection;Intrusion prevention;Cyber Risk Assessment;Vulnerabilities;Vulnerability Assessment;Cybersecurity;Information security;Intrusion detection;Intrusion prevention;Cyber Risk Assessment|
|[Footprint Counting using Face Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112683)|Y. Dubey; Y. Rewatkar; S. Giratkar; S. Uplapwar; N. Parate; N. Mulmuley|10.1109/ICACCS57279.2023.10112683|Footprint Count;Face detection;Face Matching;Face Classification;Footprint Count;Face detection;Face Matching;Face Classification|
|[Empirical Analysis of Quantum Compution using Qiskit Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112866)|P. Palsodkar; A. Khandare; A. Raut; V. Giradkar; S. Dekate; R. Rokade|10.1109/ICACCS57279.2023.10112866|Quantum computing;Qiskit;Qubit;Classical computing;IBM;Quantum computing;Qiskit;Qubit;Classical computing;IBM|
|[Design and Analysis of Flying Capacitor Multilevel Matrix Converter for aDFIG Machine based Wind System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112752)|G. Pandu Ranga Reddy; V. Sowmyasree; M. Venkateshwarlu; Y. Indiara Priya Dartshini|10.1109/ICACCS57279.2023.10112752|DFIG Machine;Flying Capacitor Multilevel Matrix Converters;Grid;Matrix-Converter;SVPWM;Wind Turbine;DFIG Machine;Flying Capacitor Multilevel Matrix Converters;Grid;Matrix-Converter;SVPWM;Wind Turbine|
|[Dynamic Ranking of Personalized Recommendations using Cosine Similarity and User Ratings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113028)|Y. Yaswanth; D. Pragathi; D. Supraja|10.1109/ICACCS57279.2023.10113028|Cosine similarity;Profile vector;Dynamic Ranking;Android Mobile Application;Cosine similarity;Profile vector;Dynamic Ranking;Android Mobile Application|
|[Digital Watermarking Analysis Using Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113111)|M. A. Ur Rahaman; M. Safoora Begum; J. N. Sai Krishna; A. Teja Mani Raju; R. K. Tata; G. Swain|10.1109/ICACCS57279.2023.10113111|wavelet;embedded;authentically;reverberation (keywords);wavelet;embedded;authentically;reverberation (keywords)|
|[Evaluating the Performance of Supervised Machine Learning Algorithms for Predicting Multiple Diseases: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113100)|G. Angayarkanni; S. Hemalatha|10.1109/ICACCS57279.2023.10113100|Support Vector Machine (SVM);K-Nearest Neighbor (KNN);Logistic Regression (LR);Naïve Bayes (NB);Random Forest (RF);Decision Tree (DT);Voting Classifier;and Multidisease;Support Vector Machine (SVM);K-Nearest Neighbor (KNN);Logistic Regression (LR);Naïve Bayes (NB);Random Forest (RF);Decision Tree (DT);Voting Classifier;and Multidisease|
|[A Survey and Implementation on Using A Runtime Overhead To Enable Serverless Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113032)|N. Saravana Kumar; S. Selvakumara Samy|10.1109/ICACCS57279.2023.10113032|Applications;Issues;Techniques;Development;Paradigm;Performance;Challenging;Lambda;AWS;Cloud computing;Serverless computing;Applications;Issues;Techniques;Development;Paradigm;Performance;Challenging;Lambda;AWS;Cloud computing;Serverless computing|
|[Natural Language Processing Techniques in Diagnostic Digital Cytopathology for Cervical Intraepithelial Neoplasia Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113060)|A. Kapruwan; S. Sharma; H. Rai Goyal|10.1109/ICACCS57279.2023.10113060|Natural language processing (NLP);HPV vaccine;Tokens-to-Token Vision Transformers(T2T-ViT);Sign Language;Mixed Method Research (MMR);Cervical intraepithelial neoplasia (CIN);Natural language processing (NLP);HPV vaccine;Tokens-to-Token Vision Transformers(T2T-ViT);Sign Language;Mixed Method Research (MMR);Cervical intraepithelial neoplasia (CIN)|
|[Design and Analysis of Tomato Leaf Disease Identification System Using Improved Lightweight Customized Deep Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112920)|M. Gehlot; G. Chhabra Gandhi|10.1109/ICACCS57279.2023.10112920|Plant disease classification;Tomato disease;Convolutional Neural Network;Image Processing;Deep Learning;MobileNet;lightweight;Plant disease classification;Tomato disease;Convolutional Neural Network;Image Processing;Deep Learning;MobileNet;lightweight|
|[An Ample Approach for Rational Apothecary to Enhance Medication Adherence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113016)|T. Dharanika; M. Manojh; R. Hariharan; A. Sahith Anwar; H. Mohamed Hamdan|10.1109/ICACCS57279.2023.10113016|Prescription tablet dispenser system;Raspberry Pi;Adaptive Medicine;Pill Tray;Prescription tablet dispenser system;Raspberry Pi;Adaptive Medicine;Pill Tray|
|[Identifying a Range of Important Issues to Improve Crop Production](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112663)|C. Geetha; S. Thaiyalnayaki; D. Vetriveeran; P. Jeyanthi; B. Selvapriya|10.1109/ICACCS57279.2023.10112663|Crop;Machine Learning;Predictive Techniques;agriculture;classifiers;accuracy;Crop;Machine Learning;Predictive Techniques;agriculture;classifiers;accuracy|
|[Monitoring Indoor and Outdoor Air Quality Using Raspberry PI Processor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112842)|V. Sajjan; E. Srikanth Reddy; C. P. Pavan Kumar Hota; B. Kiran Kumar|10.1109/ICACCS57279.2023.10112842|IOT;Wireless sensor community;IAQ;Raspberry PI;GSM;Etc;IOT;Wireless sensor community;IAQ;Raspberry PI;GSM;Etc|
|[Machine Learning based Cardiac Disease Prediction- A Comparative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112914)|M. R. Kumar; D. A. A; T. M. G. Saran; R. J. R. Kumar; D. V. S. S. Subramanyam; M. N. T|10.1109/ICACCS57279.2023.10112914|cardiac disease prediction;machine learning;logistic regression;random forest;navie bayes;predictive analysis;cardiac disease prediction;machine learning;logistic regression;random forest;navie bayes;predictive analysis|
|[Revisiting the Utility of Spectral Measures on Spoken Letter Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112969)|J. Asokan; G. S. S. N. Dheeraj; R. V. S. S. M. Naidu; R. S. Reddy; G. Sasi; V. Elamaran|10.1109/ICACCS57279.2023.10112969|FFT;spectral measures;speech signal;unvoiced consonants;voiced consonants;FFT;spectral measures;speech signal;unvoiced consonants;voiced consonants|
|[Smart Farming for Agriculture Management Using IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112839)|G. B. N. Rao; K. V. Rao; R. Kamarajugadda; A. A. Reddy; P. P. Rani|10.1109/ICACCS57279.2023.10112839|Viability;Quality;Quantity;GSM;Provocation;AI and IoT;Viability;Quality;Quantity;GSM;Provocation;AI and IoT|
|[A Review on Quality Speech Recognition Under Noisy Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112783)|M. M; V. R S|10.1109/ICACCS57279.2023.10112783|Speech recognition;Human Machine Interaction;Feature extraction;Speech classifier;Word to Word recognition;Noisy environment;Speech recognition;Human Machine Interaction;Feature extraction;Speech classifier;Word to Word recognition;Noisy environment|
|[Augmented Reality and Waste Reduction: Enhancing the Recycling Process for Mobile E-Waste in Automotive Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112913)|S. Sureshkumar; P. K. Rani; A. C P; B. A. Kumar; K. R|10.1109/ICACCS57279.2023.10112913|Augmented Reality;Mobile E-Waste;Automobile Industry;Electronics;Motherboard and Smart phones;Augmented Reality;Mobile E-Waste;Automobile Industry;Electronics;Motherboard and Smart phones|
|[Spatiotemporal Anomaly Object Detection Using Edge Cloud Collaborative For Real-Time Surveillance Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112775)|A. R; V. D; R. S; R. S; D. S|10.1109/ICACCS57279.2023.10112775|Kalman filer;Hungarian algorithm;You Only Look Once;mean Average Precision;Intersection over Union;Kalman filer;Hungarian algorithm;You Only Look Once;mean Average Precision;Intersection over Union|
|[Fuzzy Logic-Based Pitch Angle Control for VariableSpeed Wind Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112927)|G. P. R. Reddy; T. U. Chnadrakanth; V. Sowmyasree; D. Vannurappa|10.1109/ICACCS57279.2023.10112927|Probabilistic Reasoning Controller;Pitch Angle Control;Perpetual Synchronous Machine;Windmill;Probabilistic Reasoning Controller;Pitch Angle Control;Perpetual Synchronous Machine;Windmill|
|[Photovoltaic based Battery Sizing for Domestic Prosumers Under TOU Tariff Environment using Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112891)|R. S. Raj; N. S. Bhalaji; R. Santhoshkumar; S. Surya; C. Kumar; S. Jaisiva; M. Lakshmanan|10.1109/ICACCS57279.2023.10112891|optimization;dynamic load;price demand;optimization;dynamic load;price demand|
|[Agricultural Crop Recommendation Based on Productivity and Season](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113047)|U. S; A. S; K. S; B. Y. J; S. R|10.1109/ICACCS57279.2023.10113047|Data Mining;Data base;Machine Learning;Agriculture;Crop Prediction;Datasets;Random Tree Algorithm;Data Mining;Data base;Machine Learning;Agriculture;Crop Prediction;Datasets;Random Tree Algorithm|
|[Performance Analysis of ResNet50 Architecture based Pest Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112802)|U. S; V. N. R; P. N. R; L. S|10.1109/ICACCS57279.2023.10112802|Artificial Intelligence;Machine Learning;Image Processing;Pest detection;Plant Disease;ResNet;Deep Learning;Convolution;Data Processing;Feature Extraction;Artificial Intelligence;Machine Learning;Image Processing;Pest detection;Plant Disease;ResNet;Deep Learning;Convolution;Data Processing;Feature Extraction|
|[E-Slot Microstrip Patch Antenna for WLAN Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112735)|U. S; V. S. Akshaya; V. D. R; R. R; F. A. M|10.1109/ICACCS57279.2023.10112735|Microstrip;E slot;Vehicle to vehicle;VSWR;Reflection coefficient;antenna;Microstrip;E slot;Vehicle to vehicle;VSWR;Reflection coefficient;antenna|
|[Secure EELB-AOMDV Protocol to Mitigate Blackhole Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112747)|B. S. Rani; K. Shyamala|10.1109/ICACCS57279.2023.10112747|AOMDV;Blackhole attack;multipath routing;trust based protocol;direct trust;security;AOMDV;Blackhole attack;multipath routing;trust based protocol;direct trust;security|
|[Renewable Energy Management System Using Three Phase Sinewave Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113085)|H. N; S. M; S. A|10.1109/ICACCS57279.2023.10113085|Renewable energy source;Three phase inverter;Solar pv;Battery;Power grid;Load Management;Renewable energy source;Three phase inverter;Solar pv;Battery;Power grid;Load Management|
|[Design and analysis of a Foldable Miniaturized Single Band Origami Antenna for Space Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112697)|A. A. A; S. M; I. B. J; E. P; T. M. Neebha; A. D. Andrushia|10.1109/ICACCS57279.2023.10112697|Finite element method;flexible and foldable;ISM band;meandered monopole;origami magic cube;reconfigurable;Finite element method;flexible and foldable;ISM band;meandered monopole;origami magic cube;reconfigurable|
|[Prediction of Stable Angina for Coronary Artery Diseases using Boosting Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112812)|Y. Dubey; G. Wath; A. Pantawane; S. Gawande; D. Dhopte; H. Bhagawatkar|10.1109/ICACCS57279.2023.10112812|Coronary Artery Disease;Stable Angina;Prediction;Boosting Algorithm;Coronary Artery Disease;Stable Angina;Prediction;Boosting Algorithm|
|[Predicting Academic Grades of Students in Computer Programming Using Classification Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112996)|C. P. Pavan Kumar Hota; V. Asanambigai; D. Lakshmi|10.1109/ICACCS57279.2023.10112996|computer programming;Classification Algorithms;computer programming;Classification Algorithms|
|[Effective Energy Storage for EV Using Battery Supercapacitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112898)|B. P. T; N. P; S. V. A; S. R; V. Mt|10.1109/ICACCS57279.2023.10112898|HESS;Adaptive Control;Achievable Performance;Robustness;λ-Tracking;HESS;Adaptive Control;Achievable Performance;Robustness;λ-Tracking|
|[Security Constrained Unit Commitment With Load Redistribution Cyber Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112893)|A. Sheela; S. M; J. T. K; V. Gowrishankar; N. R. Kumar|10.1109/ICACCS57279.2023.10112893|Unit commitment;cyber attack;load redistribution;network constrain;error detection;economic dispatch;security constrain;priority list method;dynamic programming and optimal unit;Unit commitment;cyber attack;load redistribution;network constrain;error detection;economic dispatch;security constrain;priority list method;dynamic programming and optimal unit|
|[Optimization Of Electric Vehicle (EV) Routing to Facilitate EV Hub](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112938)|P. S. Raghavendran; A. Sheela; J. A. Joseph; S. Ragul|10.1109/ICACCS57279.2023.10112938|Electric Vehicle;Battery Swapping;Battery Charging Hub;Battery and Energy Management;Routing Problem;Limited Driving Range;Optimization of EV Routing;Electric Vehicle;Battery Swapping;Battery Charging Hub;Battery and Energy Management;Routing Problem;Limited Driving Range;Optimization of EV Routing|
|[Smart Chair for Bio-Medical Applications Using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113070)|A. Kumari; H. R. Daksh; A. Abhishek; A. Utsav|10.1109/ICACCS57279.2023.10113070|Android Application;Bio-Medical Applications;Internet of Things (IoT);RFID Card;Smart Chair;Wi-Fi module;Android Application;Bio-Medical Applications;Internet of Things (IoT);RFID Card;Smart Chair;Wi-Fi module|
|[Week Ahead Wind Power Forecast Using A Time-Series Forecasting Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112685)|V. S R; P. R|10.1109/ICACCS57279.2023.10112685|Machine Learning;Regression;Ensemble Learning;Wind power;Wind speed;Time-Series Forecasting;Machine Learning;Regression;Ensemble Learning;Wind power;Wind speed;Time-Series Forecasting|
|[High-Performance Computing Based on Residue Number System: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112959)|J. R. J; S. S. C; B. V; B. L. R|10.1109/ICACCS57279.2023.10112959|Residue number system;Modular Arithmetic;Moduli selection;Chinese remainder theorem;Congruency operation;Mixed-Radix conversion;Residue number system;Modular Arithmetic;Moduli selection;Chinese remainder theorem;Congruency operation;Mixed-Radix conversion|
|[An Effecient Model for Plant Disease Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112990)|N. S. P; S. D; C. N. B; B. Lincy R|10.1109/ICACCS57279.2023.10112990|Plant diseases;Crop Monitorin;CNN;Deep;Learning;Image Handling;Plant diseases;Crop Monitorin;CNN;Deep;Learning;Image Handling|
|[Anti-Theft Alarm System and Tracking in Coal Mines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112875)|R. R; S. S. R; S. M; J. M; P. S|10.1109/ICACCS57279.2023.10112875|Coal;Coal Mines;Trucks;Radio Frequency Identification;Simple Mail Transfer Protocol;Optical Character Recognition;Levenshtein distance algorithm;Coal;Coal Mines;Trucks;Radio Frequency Identification;Simple Mail Transfer Protocol;Optical Character Recognition;Levenshtein distance algorithm|
|[Lung_RUNET: a Segmentation Framework for Lung Nodules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113093)|P. M. Bruntha; S. Dhanasekar; L. J. Ahmed; V. Govindaraj; S. I. A. Pandian; S. S. Abraham|10.1109/ICACCS57279.2023.10113093|Lung Cancer;Lung Nodule;Pulmonary Nodule;Nodule Segmentation;UNet;Lung Cancer;Lung Nodule;Pulmonary Nodule;Nodule Segmentation;UNet|
|[Design and Implementation of Methane Gas Detection and Estimation Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112797)|A. K; D. H. D; D. Rani P; D. D; S. P. Rajeev; B. Sreeja S D|10.1109/ICACCS57279.2023.10112797|MQ sensor;soil moisture sensor;methane detection;Arduino IDE;Tinkercad;firebase real-time database;telegram bot;MQ sensor;soil moisture sensor;methane detection;Arduino IDE;Tinkercad;firebase real-time database;telegram bot|
|[An Investigation on Attacks in Application Layer Protocols and Ransomeware Threats in Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112669)|P. K; B. Nataraj; P. Duraisamy|10.1109/ICACCS57279.2023.10112669|Internet of Things;Architecture layers;Application layer;Security threats;Ransomware Attacks;RFID Attacks;Application Layer Protocols;Internet of Things;Architecture layers;Application layer;Security threats;Ransomware Attacks;RFID Attacks;Application Layer Protocols|
|[Language Detection Using Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112773)|Y. Rajanak; R. Patil; Y. P. Singh|10.1109/ICACCS57279.2023.10112773|Natural Language Processing;Language Detection;Virtual Assistants;Text Analytics;Machine Learning;Natural Language Processing;Language Detection;Virtual Assistants;Text Analytics;Machine Learning|
|[A Concept-based Ontology Mapping Method for Effective Retrieval of Bio-Medical Documents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113073)|S. V; J. V; D. Srinivasan; P. M; S. D. Nivethika; A. N|10.1109/ICACCS57279.2023.10113073|Concept mapping;Stanford Parser;Parts-of-speech tagge;Context-free phrase structure grammar representation;WordNet Ontology;Cosine Similarity measure;Concept mapping;Stanford Parser;Parts-of-speech tagge;Context-free phrase structure grammar representation;WordNet Ontology;Cosine Similarity measure|
|[An IoT Based Gesture Recognition System for Vocally and Hearing Impaired Individuals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112835)|D. Ganeshkumar; T. Subasri; R. Kaleeshwari; R. Raksha; D. Muthukumar|10.1109/ICACCS57279.2023.10112835|deaf and dumb;smart glove;sign language;gestures;communication;flex sensors;IoT(Internet of Things);deaf and dumb;smart glove;sign language;gestures;communication;flex sensors;IoT(Internet of Things)|
|[A Compressive Overview of Facial Feeling Responses Utilizing AI and Profound learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112781)|V. Ch; K. A. Jyotsna; G. Jyothi; J. Anitha; N. Rajeswaran|10.1109/ICACCS57279.2023.10112781|Facial Expression Recognition;Natural Language Processing;AI and Learning algorithm;Facial Expression Recognition;Natural Language Processing;AI and Learning algorithm|
|[Deep Learning Technique to Detect and Classify Brain Tumor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113118)|N. V; G. V; G. Thilagavathi; H. P. R; N. Kumar K|10.1109/ICACCS57279.2023.10113118|Segmentation;Convolution Neural Networks;Probabilistic Neural Networks;Edge Detection;Masking;Res Net50;Segmentation;Convolution Neural Networks;Probabilistic Neural Networks;Edge Detection;Masking;Res Net50|
|[Density Map Based Estimation of Crowd Counting Using Vgg-16 Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113006)|A. G; N. K. S; S. S. V; S. S|10.1109/ICACCS57279.2023.10113006|Neural Networks (NN);Multi-Column Convolutional Neural Network (MCNN);Density Estimation;Convolutional Neural Network (CNN);Visual Geometry Group (VGG);Ground Truth;Neural Networks (NN);Multi-Column Convolutional Neural Network (MCNN);Density Estimation;Convolutional Neural Network (CNN);Visual Geometry Group (VGG);Ground Truth|
|[Cardiovascular Disease Prediction Using Machine Learning Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112809)|M. Ramu; M. Harshitha; R. Hanisha; P. Chandana; P. Meghana|10.1109/ICACCS57279.2023.10112809|Random Forest;Classification and Regression tree;K Nearest Neighbor;Naïve Bayes;Support Vector Machine;Ada boost;Logistic Regression;Feature Selection;Machine Learning;Random Forest;Classification and Regression tree;K Nearest Neighbor;Naïve Bayes;Support Vector Machine;Ada boost;Logistic Regression;Feature Selection;Machine Learning|
|[An Efficient Framework on Encoding/Decoding Scheme for CDMA Network on Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112740)|R. Pradeep; S. Vidhya; M. Jagadeeswari; B. Anuradha|10.1109/ICACCS57279.2023.10112740|Encoding;Decoding;CDMA;Network on Chip;Encoding;Decoding;CDMA;Network on Chip|
|[Smart Water Meter Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112764)|P. Sakthi; S. A. Nivedhithaa; A. Nivethikaa; M. Prathiksha|10.1109/ICACCS57279.2023.10112764|IoT SmartMeter;Flow Meter;Solenoid valve;Ethernet shield W5100;Motor;Arduino UNO;IoT SmartMeter;Flow Meter;Solenoid valve;Ethernet shield W5100;Motor;Arduino UNO|
|[Food Addressing and Consultation System Using Deep Learning By Applying Design Thinking Framework : An exploration of health recommendation system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112848)|I. A; R. B; L. S; M. S. D; M. K|10.1109/ICACCS57279.2023.10112848|Food Addressing;Exercise;Web Extraction;Object Detection;Health Suggestion;Design Thinking;Food Addressing;Exercise;Web Extraction;Object Detection;Health Suggestion;Design Thinking|
|[Gas Leakage Detection using Node Microcontroller Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112902)|P. Dorge; P. Awari; P. Jichkar; S. Shete; S. Mondhe|10.1109/ICACCS57279.2023.10112902|Monitoring;Security;Gas Sensor;Node MCU;Buzzer;LPG Gas Leakage;Monitoring;Security;Gas Sensor;Node MCU;Buzzer;LPG Gas Leakage|
|[Lora Based Renewable Energy Monitoring and Power Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112789)|A. S. P; D. M; S. K; R. SureshKumar|10.1109/ICACCS57279.2023.10112789|Solar panel;Wind mill;Battery;Inverter;LoRa;Power management system;Proteus;Field Station;Base Station;Solar panel;Wind mill;Battery;Inverter;LoRa;Power management system;Proteus;Field Station;Base Station|
|[Design And Development of Dust Detection and Filtering System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112892)|N. L. Datta; A. Tabassum; J. Bhaavani; K. Y. R. Kumar; R. V. H. Prasad; A. R. Raja|10.1109/ICACCS57279.2023.10112892|Dust sensor;GP2Y1010AU0F;Air filters;air purifier;dust detection;Dust sensor;GP2Y1010AU0F;Air filters;air purifier;dust detection|
|[IoT Based Smart Way of Watering Plants and Feeding Pets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112691)|V. Soniya; K. R. Shankar; S. Karishma; D. Vamsi; R. V. H. Prasad|10.1109/ICACCS57279.2023.10112691|Indoor plants;pets;IoT;ESP8266 NodeMCU;ESP32 CAM;soil moisture sensor;servomotor;food dispenser;Indoor plants;pets;IoT;ESP8266 NodeMCU;ESP32 CAM;soil moisture sensor;servomotor;food dispenser|
|[Design Of DC To AC Converter For Voltage Sag Compensation Application Using PV Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112945)|B. S; V. G; T. G. A; M. Mohanraj|10.1109/ICACCS57279.2023.10112945|Solar panel;DC-DC Boost Converter;Bidirectional DC-DC Converter;Single phase Inverter;MATLAB SIMULINK;Solar panel;DC-DC Boost Converter;Bidirectional DC-DC Converter;Single phase Inverter;MATLAB SIMULINK|
|[Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113108)|D. Singh; D. Joshi; D. C. Dobhal; J. Pant; H. Pant|10.1109/ICACCS57279.2023.10113108|Machine Learning;Diabetic Retinopathy;SVM;Logistic Regression;AdaBoost;Random Forest;Naïve Bayes;Machine Learning;Diabetic Retinopathy;SVM;Logistic Regression;AdaBoost;Random Forest;Naïve Bayes|
|[Malware Detection Using Xilinx Software and Adaptive Test Pattern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112911)|S. Meivel; S. K. Nagaharipriya; P. Priyankadevi; S. Sangavi|10.1109/ICACCS57279.2023.10112911|Adaptive Generator;VLSI Circuits;PODEM Algorithm;Unique Sensitization;BIST Architecture;Adaptive Generator;VLSI Circuits;PODEM Algorithm;Unique Sensitization;BIST Architecture|
|[Hand Gesture Recognition for Blind Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112770)|K. K. S; A. Asokan; V. Velmurugan; B. M|10.1109/ICACCS57279.2023.10112770|pattern recognition;gesture;Viola-Jones procedure;aspect ratio;machine learning;human-computer interaction;pattern recognition;gesture;Viola-Jones procedure;aspect ratio;machine learning;human-computer interaction|
|[A Survey on Lora and IoT based Auto Irrigation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112677)|R. R. Kumar; G. Dency Flora; S. Yuvaraj; S. Sanjay; S. R. Ashok Kumar; S. Karpakam|10.1109/ICACCS57279.2023.10112677|Conventionalagriculture;LoRaTechnology;wireless communication;Auto Irrigation;Transceiver unit;Mobile Application;Conventionalagriculture;LoRaTechnology;wireless communication;Auto Irrigation;Transceiver unit;Mobile Application|
|[Identify The Potential Instance of Copy Forgery Using Similar Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112853)|M. A; K. N; T. S|10.1109/ICACCS57279.2023.10112853|Copy-Submit Fake Detection;Digital Image Forensics;Key point Collection;Search for the same location search algorithm;CMFD;Scale-invariant feature transform (SIFT);Fuzzy c-means (FCM);Copy-Submit Fake Detection;Digital Image Forensics;Key point Collection;Search for the same location search algorithm;CMFD;Scale-invariant feature transform (SIFT);Fuzzy c-means (FCM)|
|[X-rays imaging analysis for early diagnosis of the thoracic disorders using Capsule Neural Network with Transfer learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112810)|H. Pant; M. C. Lohani; A. Kumar Bhatt|10.1109/ICACCS57279.2023.10112810|Chest X-ray;Classification;capsule Neural Network;Transfer Learning;Thoracic Disorders;Segmentaion;U-Net;convolutional neural network;Chest X-ray;Classification;capsule Neural Network;Transfer Learning;Thoracic Disorders;Segmentaion;U-Net;convolutional neural network|
|[IoT based Safety Gadget for Child Monitoring and Notification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112699)|T. Thamaraimanalan; R. Pathmavasan; T. R. Pradeep; N. Praveen; R. Srija|10.1109/ICACCS57279.2023.10112699|Child monitoring;Geofence;Safety gadget;Internet of things;Child monitoring;Geofence;Safety gadget;Internet of things|
|[Three Fold Classification Using Shift Invariant Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113056)|S. Patel; V. Srivastava; A. Bajpai|10.1109/ICACCS57279.2023.10113056|Age Estimation;Race And Gender Classification;SIDNN;Loss Function;UTK Face;Relu;Softmax;Sigmoid;Age Estimation;Race And Gender Classification;SIDNN;Loss Function;UTK Face;Relu;Softmax;Sigmoid|
|[Aadhar UID Mask Detecting Tool Using CNN With Verhoeff Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113112)|S. M. A; M. B; R. P; S. N; S. D. K R|10.1109/ICACCS57279.2023.10113112|UIDAI;Machine learning;CNN;UID;Biometric;PyTesseract;BPL;CIDR;OTP;SRGAN;UIDAI;Machine learning;CNN;UID;Biometric;PyTesseract;BPL;CIDR;OTP;SRGAN|
|[An Investigation of Task Offloading Latency in Edge-Cloud Environment Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112713)|J. H. P; P. K. S; N. R. R; K. N|10.1109/ICACCS57279.2023.10112713|Task Offloading;Edge-cloud computing;Resource management;Internet of Things (IoT);Task Offloading;Edge-cloud computing;Resource management;Internet of Things (IoT)|
|[Analysis of Computer Vision for Graphics and Animation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112718)|A. Singh; M. Kumar; A. Saxena|10.1109/ICACCS57279.2023.10112718|Computational camera;Computer vision 3D;mobile processing;video image quality;scanning and reconstruction component;component;Computational camera;Computer vision 3D;mobile processing;video image quality;scanning and reconstruction component;component|
|[Utilization Of Data Analytics in Analysing Audio and Crowd Video Content](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113080)|A. Singh; M. Kumar; A. Saxena|10.1109/ICACCS57279.2023.10113080|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[AI Agents at Different Data Centers to Minimize the Energy Spending](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113130)|R. Dharaniya; S. V S; S. B. N; S. A. Zaid|10.1109/ICACCS57279.2023.10113130|AI;Data centers;Predictive models;Energy savings;Machine learning;Network efficiency;Cloud-based service;Microgrids;Sustainability;Globalization;AI;Data centers;Predictive models;Energy savings;Machine learning;Network efficiency;Cloud-based service;Microgrids;Sustainability;Globalization|
|[Impact of Emotional Intelligence and Artificial Intelligence on Employee Retention: A Review of the Service Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113017)|P. Saxena; S. Sharma; R. B. Jora|10.1109/ICACCS57279.2023.10113017|Artificial Intelligence;Emotional Intelligence;and employee retention;Artificial Intelligence;Emotional Intelligence;and employee retention|
|[Comparative Study of Algorithms for Sentiment Analysis on IMDB Movie Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113113)|N. S. L. S. Charitha; K. Yasaswi; V. Rakesh; M. Varun; M. Yeswanth; J. S. Kiran|10.1109/ICACCS57279.2023.10113113|movie reviews;polarity detection;sentiments;NLP techniques;machine learning models;movie reviews;polarity detection;sentiments;NLP techniques;machine learning models|
|[Performance Evaluation of MIMO System with Different Receiver Structures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112838)|T. P. Varma; S. Naraganeni; K. R. Chandra; K. P. Rao; M. V. P. A; V. R. Ch|10.1109/ICACCS57279.2023.10112838|Capacity;Channel Model;Decorrelator;Fading channel;Bit Error Rate;Matched Filter;Throughput;Capacity;Channel Model;Decorrelator;Fading channel;Bit Error Rate;Matched Filter;Throughput|
|[Ensemble Filter-based Feature Selection Model for Cyber Attack Detection in Industrial Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112989)|L. Arya; G. P. Gupta|10.1109/ICACCS57279.2023.10112989|Feature Selection;Cyber Security;Industrial Internet of Things;Ensemble learning;Machine Learning;Network Attack;Feature Selection;Cyber Security;Industrial Internet of Things;Ensemble learning;Machine Learning;Network Attack|
|[Signs With Smart Connectivity for Better Road Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113120)|V. A. Kumar; N. V; S. K. K; S. Gp; P. M; S. J|10.1109/ICACCS57279.2023.10113120|Sensor;IOT;Road accident;Road safety;Vehicles;Weather;Traffic;Speed limit;Web application;Sensor;IOT;Road accident;Road safety;Vehicles;Weather;Traffic;Speed limit;Web application|
|[Mutual Coupling Reduction in MIMO Antenna for UWB Applications Using H Shaped Stubs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112999)|K. N. Sai; K. Vara Prasad; V. S. Brahmma; K. Venkata Sai Ganesh; C. Aasritha|10.1109/ICACCS57279.2023.10112999|Multiple Input Multiple Output;STUBS;Diversity Gain;Envelope Correlation Coefficient;Ultra-Wide Band;X-Band;Multiple Input Multiple Output;STUBS;Diversity Gain;Envelope Correlation Coefficient;Ultra-Wide Band;X-Band|
|[An Efficient Energy Management and Theft Alert System using IoT Enabled Smart Meter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113116)|M. Sentamilselvi; T. Pradeepa; S. Oviya; A. P. Shre|10.1109/ICACCS57279.2023.10113116|Energy meters;Internet of Things;Smart grid;Smart meters;Theft;Machine learning;Energy meters;Internet of Things;Smart grid;Smart meters;Theft;Machine learning|
|[Early Diagnosis and Detection of Vascular Dementia based on Mobile V Net Framework using DCNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112734)|S. K; S. A; S. B; S. S|10.1109/ICACCS57279.2023.10112734|Brain MRI;deep learning;Convolutional Neural Network;Mobile Net;early-stage detection;and diagnosis;Brain MRI;deep learning;Convolutional Neural Network;Mobile Net;early-stage detection;and diagnosis|
|[Wearable Sensors in Daily Life: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112956)|A. K; D. H. D; D. Rani P; D. D; S. P. Rajeev; B. Sreeja S D|10.1109/ICACCS57279.2023.10112956|Wearable sensors;IoT;IJP;PERS;Covid-19;Motion sensing;Parkinson's disease;Wearable sensors;IoT;IJP;PERS;Covid-19;Motion sensing;Parkinson's disease|
|[Determining Various Gait Metrics using IMU and the Geographical Location of the Patient Using GPS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112671)|K. Rajasekaran; K. S; A. W. M. A; N. V|10.1109/ICACCS57279.2023.10112671|Gait analysis;mobility;IoT;wearable sensors;gait metrics;IMU;gait parameters;Gait analysis;mobility;IoT;wearable sensors;gait metrics;IMU;gait parameters|
|[A Survey of Breast Cancer Detection Using Medical Imaging Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112759)|C. Ganesh; T. Jeevitha; A. Jayalakshmi; M. Shanthini; N. S. Kumar; P. Malini|10.1109/ICACCS57279.2023.10112759|Smart Farmhouse;Internet of Things;humidity;air qualityegg count;feeding of food;and Arduino;Smart Farmhouse;Internet of Things;humidity;air qualityegg count;feeding of food;and Arduino|
|[Hand Sign Recognition To Structured Sentences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112706)|R. Harchandani; P. N; J. P. George|10.1109/ICACCS57279.2023.10112706|Action recognition;Convolutional Neural Network;Depth data;video analytics;Deep learning;and Realtime classification;Action recognition;Convolutional Neural Network;Depth data;video analytics;Deep learning;and Realtime classification|
|[A DHCP Based Approach To IP Address Management And Allocation In A Network Using VLSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112844)|S. P. R; R. R; J. Narayanan; D. Balaji; K. S|10.1109/ICACCS57279.2023.10112844|Dynamic Host Configuration Protocol (DHCP);Variable Length Subnet Mask (VLSM);Internet Protocol (IP);Transmission Control Protocol (TCP);Local Area Network (LAN);Fixed Length Subnet Masking (FLSM);Media Access Control (MAC);Classless Inter-Domain Ranging (CIDR);Dynamic Host Configuration Protocol (DHCP);Variable Length Subnet Mask (VLSM);Internet Protocol (IP);Transmission Control Protocol (TCP);Local Area Network (LAN);Fixed Length Subnet Masking (FLSM);Media Access Control (MAC);Classless Inter-Domain Ranging (CIDR)|
|[Stock Market Prediction Using Sentiment Analysis and Incremental Clustering Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112768)|L. S. Parvatha; D. Naga Veera Tarun; M. Yeswanth; J. S. Kiran|10.1109/ICACCS57279.2023.10112768|Sentiment analysis;k-means clustering;LSTM;GRU;CNN;RMSE;MSE;MAE;Sentiment analysis;k-means clustering;LSTM;GRU;CNN;RMSE;MSE;MAE|
|[Bi-Directional DC-DC Flyback Converter using Zero Voltage Switching for Hybrid Electric Vehicle Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113030)|S. G; C. Kumar|10.1109/ICACCS57279.2023.10113030|DC to DC bi-directional Flyback converter;zero voltage switching;electric vehicle;DC to DC bi-directional Flyback converter;zero voltage switching;electric vehicle|
|[A Comparative Study on Emotion AI using Machine Learning and Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113020)|A. Pandiaraj; P. Nagaraj; B. V. Durga; A. Tejasri; M. T. Teja; M. Navi Venkata Pavan Kumar|10.1109/ICACCS57279.2023.10113020|Machine learning;Deep learning;Sentiment Analysis;Word Embedding;Hybrid model;Positive;Negative;and Neutral tweets.;Machine learning;Deep learning;Sentiment Analysis;Word Embedding;Hybrid model;Positive;Negative;and Neutral tweets.|
|[Obstacle Detection and Navigation for The Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112994)|D. J; M. A. P; S. Madhumita S S; P. N|10.1109/ICACCS57279.2023.10112994|IoT;Arduino;Ultrasonic Sensors;Distance Measurement;Arduino Uno;Power Supply;Voice Commands;Obstacle Detection.;IoT;Arduino;Ultrasonic Sensors;Distance Measurement;Arduino Uno;Power Supply;Voice Commands;Obstacle Detection.|
|[IoT Based Hydroponic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112778)|P. Anirudh; G. A. E. S. Kumar; R. Phani Vidyadhar; G. Pranav; B. A. Kumar|10.1109/ICACCS57279.2023.10112778|Hydroponics;Indoor agriculture;IoT;Automation;Artificial sunlight;LDR;DHT11.;Hydroponics;Indoor agriculture;IoT;Automation;Artificial sunlight;LDR;DHT11.|
|[IoT Based Supervisory and Diagnosis System for Solar Farm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112993)|S. P; K. G; J. M; D. M; A. K. L; D. N|10.1109/ICACCS57279.2023.10112993|PV system error detection;PV system data acquisition;IoT-based monitoring;solar power generation;advanced string connection module;Advanced String Combiner Unit;PV system error detection;PV system data acquisition;IoT-based monitoring;solar power generation;advanced string connection module;Advanced String Combiner Unit|
|[AutoML - Learning, Understanding and Applying Machine Learning to Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113008)|S. Patankar; H. Prajapati; J. Shah; A. Upadhyay|10.1109/ICACCS57279.2023.10113008|Classification;Regression;Dataset;Machine Learning;deep learning;Preprocessing;Recommendation system;Classification;Regression;Dataset;Machine Learning;deep learning;Preprocessing;Recommendation system|
|[A Survey On Smart Vacuum Leaning Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112909)|R. A; R. V; R. R. R. N; R. A; S. K; S. P|10.1109/ICACCS57279.2023.10112909|Smart vacuum cleaner;Manual;Automation;Bluetooth;Obstructions;Wheel design;Path planning;Smart vacuum cleaner;Manual;Automation;Bluetooth;Obstructions;Wheel design;Path planning|
|[Intelligent Door Lock System using Ensemble Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112702)|P. Ramadoss; T. A; S. S; S. P|10.1109/ICACCS57279.2023.10112702|Enhanced Result;Ensemble Learning;Integrating Algorithms;Motion Detection;Remote Access;Smart Door Lock;Enhanced Result;Ensemble Learning;Integrating Algorithms;Motion Detection;Remote Access;Smart Door Lock|
|[Securing the Confidential Data Using Blockchain and DT Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112904)|J. S R; M. A; N. R; K. G; N. C|10.1109/ICACCS57279.2023.10112904|password manager;Decentralized;Interplanetary file storage;Blockchain Network;P2P Network;Design Thinking (DT);password manager;Decentralized;Interplanetary file storage;Blockchain Network;P2P Network;Design Thinking (DT)|
|[Machine Learning Models for Analysis and Prediction of Chronic Kidney Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112872)|R. Bhowmick; A. X. V M|10.1109/ICACCS57279.2023.10112872|kidney disease;machine learning;random forest;logistic regression;Gradient boosting;Decision trees;kidney disease;machine learning;random forest;logistic regression;Gradient boosting;Decision trees|
|[Semantic Modelling of Multivariate Time-Series Data in Cognitive IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113010)|V. Jha; P. Tripathi|10.1109/ICACCS57279.2023.10113010|massive;heterogeneous;multivariate time-series;copula;semantic pattern extraction;probability;CIoT;massive;heterogeneous;multivariate time-series;copula;semantic pattern extraction;probability;CIoT|
|[IoT - Based ATM Pin Entry by Random Word Generator Using Design Thinking Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112662)|A. R; N. K; N. S. V; M. S; P. S|10.1109/ICACCS57279.2023.10112662|ATM theft;Password stealing;Design Thinking;Random words;Alphabetic letter;Transactions;ATM theft;Password stealing;Design Thinking;Random words;Alphabetic letter;Transactions|
|[Classification of Normal and Abnormal ECG Signals Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113038)|A. A. Gnanadas; P. V; J. N; Y. M|10.1109/ICACCS57279.2023.10113038|ECG training;ECG beat classification;Auto-matedECGanalysis;logistic regression;Radial basis Function;Random Forest;ECG training;ECG beat classification;Auto-matedECGanalysis;logistic regression;Radial basis Function;Random Forest|
|[Smart Farming System using NPK Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112795)|B. Cheruvu; S. B. Latha; M. Nikhil; H. Mahajan; K. Prashanth|10.1109/ICACCS57279.2023.10112795|Internet of Things;smart farming;IoT sensors;NPK sensor;soil measurement;machine learning;python API;Thingspeak server;Internet of Things;smart farming;IoT sensors;NPK sensor;soil measurement;machine learning;python API;Thingspeak server|
|[Design of Circular Ring Shaped UWB Antenna for BANs and MI Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113090)|P. M; M. A; A. S. R; N. S; S. R. R; D. Srinivasan|10.1109/ICACCS57279.2023.10113090|BAN;UWB Antenna;Fidelity factor;Return loss;BAN;UWB Antenna;Fidelity factor;Return loss|
|[Real Time Sales Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112946)|M. Preethi; A. R. Kanna|10.1109/ICACCS57279.2023.10112946|Realtime Data;Data Analysis;Machine Learning;Creating Business Analysis;Realtime Data;Data Analysis;Machine Learning;Creating Business Analysis|
|[Design of Power Delay Efficient Wallace Muliplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112916)|K. C; Dharani; Vanjipriya; I. N. M|10.1109/ICACCS57279.2023.10112916|Wallace Multiplier;Power;Delay;Wallace Multiplier;Power;Delay|
|[Prediction of Cardiovascular Disease Risk using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112763)|J. Kensarin; A. X. V M; U. J. V. Sai; S. S. Srujan; K. V. Prakash|10.1109/ICACCS57279.2023.10112763|Machine Learning;Cardiovascular;Early Diagnosis;Data Processing;Random Forest;Logistic Regression;Machine Learning;Cardiovascular;Early Diagnosis;Data Processing;Random Forest;Logistic Regression|
|[Communication using RedTacton Devices for Tourism development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112991)|R. Madasamy; P. Babu; S. L. Dhanabalan; K. Diderot|10.1109/ICACCS57279.2023.10112991|Human Area networks;RedTacton;Arduino UNO;WAN;LAN;communication and Body Coupled Communication (BCC);Human Area networks;RedTacton;Arduino UNO;WAN;LAN;communication and Body Coupled Communication (BCC)|
|[Continuous Health Monitoring System for Patients Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112921)|K. Rajasekaran; M. S. Chakaravarthi; P. Lokaswar|10.1109/ICACCS57279.2023.10112921|Blood pressure;Body temperature;oxygen saturation;pulse rate;rate of breathing;communication;healthcare;ESP32-S;Internet-of-things (IoT);Blood pressure;Body temperature;oxygen saturation;pulse rate;rate of breathing;communication;healthcare;ESP32-S;Internet-of-things (IoT)|
|[Smart Chatbot for College Information Enquiry Using Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112919)|Channabasamma; L. S. P; M. Indu; N. Swetha; C. Saritha|10.1109/ICACCS57279.2023.10112919|interactive website;chatbot;HTML;CSS;deep neural network;PyTorch;interactive website;chatbot;HTML;CSS;deep neural network;PyTorch|
|[Student Academic Performance Enhancement Model Using Web Based Learning Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112964)|K. S. Chandana; S. Niharika; P. Priyanka; Y. Vijayalakshmi; M. D. Lakshmi; S. Ruffia|10.1109/ICACCS57279.2023.10112964|Web development;E-learning;Online learning platform;Students;Coding platform;Graduation;Videos;learning resources;Web development;E-learning;Online learning platform;Students;Coding platform;Graduation;Videos;learning resources|
|[Traffic Infraction and Alert System for Two-wheelers using Deep Learning and YOLO v3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113048)|G. G. Sreeja; R. R; O. S; S. K|10.1109/ICACCS57279.2023.10113048|Deep Learning;Object Segmentation;Backdrop Subtraction;non-helmet detection;Traffic violation;Deep Learning;Object Segmentation;Backdrop Subtraction;non-helmet detection;Traffic violation|
|[Single Phase Advanced Multilevel Inverter with Reduced Devices and THD for Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113033)|V. M; P. R. G; S. Prasad. S|10.1109/ICACCS57279.2023.10113033|Multilevel inverter;modulation index;5-level;7-level;SPWM techniques;and THD;Multilevel inverter;modulation index;5-level;7-level;SPWM techniques;and THD|
|[Energy Hole Minimization in Wireless Mobile Ad Hoc Networks Using Enhanced Expectation-Maximization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112728)|J. V. Gripsy; A. Jayanthiladevi|10.1109/ICACCS57279.2023.10112728|Wireless Mobile Ad Hoc Networks (MANETs);Energy Efficiency (EE);Energy Hole (EH);Sensor Node;Cluster Head (CH);Routing Protocol;Heterogeneous Network;Coverage Area;Expectation Maximization (EM);Wireless Mobile Ad Hoc Networks (MANETs);Energy Efficiency (EE);Energy Hole (EH);Sensor Node;Cluster Head (CH);Routing Protocol;Heterogeneous Network;Coverage Area;Expectation Maximization (EM)|
|[Pseudo Binocular Stereo Vision Camera Realignment using NCC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112912)|A. J. C. Alveyra; Y. Edgar Foronda; G. V. Magwili|10.1109/ICACCS57279.2023.10112912|Stereo-Vision;Computer-Vision;Normalized Cross-Correlation;Vergence;Stereo Matching;Non-rectified Image;Fuzzy Control;Stereo-Vision;Computer-Vision;Normalized Cross-Correlation;Vergence;Stereo Matching;Non-rectified Image;Fuzzy Control|
|[Robust Transfer Learning Based Modelling for Accelerating the Learning of Ai in the Field of NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112829)|S. Ravimaram; J. N. Kumar S; A. Sathish; S. Vatchala; R. Rawat; M. R. T. F|10.1109/ICACCS57279.2023.10112829|Federated learning;natural language processing;transfer learning;Artificial Intelligence;Federated learning;natural language processing;transfer learning;Artificial Intelligence|
|[Hyperspectral Information in Urban Areas Using Big Data and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112929)|V. C; G. A; D. K S; S. T. P|10.1109/ICACCS57279.2023.10112929|Hyperspectral Data;Spatial data;Big Data;Distant recognition;Multispectral data;Hyperspectral Data;Spatial data;Big Data;Distant recognition;Multispectral data|
|[Developing an Automatic Evaluation of Exertion Using a Smart Phone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112834)|K. N. L; S. Abirami B|10.1109/ICACCS57279.2023.10112834|Neural Networks;Spectral Analysis;TinyML;EON tuner;Edge impulse;Neural Networks;Spectral Analysis;TinyML;EON tuner;Edge impulse|
|[Reduction In UWB - MIMO Antenna Mutual Coupling Using P - Stub](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112871)|D. S. Bhargav; K. Vara Prasad; V. U. Maheshwar Rao; G. Vikas; K. V. Karthik|10.1109/ICACCS57279.2023.10112871|MIMO antenna;Mutual Coupling (MC) Reduction;Diversity Gain (DG);Envelope Correlation Coefficient (ECC);Scattering Parameters;Ultra-wide band (UWB);Defected Ground Structure (DGS);MIMO antenna;Mutual Coupling (MC) Reduction;Diversity Gain (DG);Envelope Correlation Coefficient (ECC);Scattering Parameters;Ultra-wide band (UWB);Defected Ground Structure (DGS)|
|[Noise Cancellation Using Speech Separation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113126)|M. Kalaiarasu; S. J. Rani; M. Preethi; G. Siddharth|10.1109/ICACCS57279.2023.10113126|Speech separation;Sound source;Anti-noise signal;Noise Cancellation;Active Noise Cancellation;Neural Networks;audio;LSTM;CNN;signals;sensors;Speech separation;Sound source;Anti-noise signal;Noise Cancellation;Active Noise Cancellation;Neural Networks;audio;LSTM;CNN;signals;sensors|
|[Medicinal Herbs Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112700)|M. Preethi; S. J. Rani; K. S. Pradhiksha; J. R. Kumar; T. Vishal|10.1109/ICACCS57279.2023.10112700|Classification;CNN;ANN;Classification;CNN;ANN|
|[Design and Implementation of Smart Multipurpose Robot using Vex and Node MCU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112680)|J. S. Mohamed; K. Ramanathan Sulochanan; A. B. Jinnah; M. Kamatchi|10.1109/ICACCS57279.2023.10112680|Robot;ultrasonic sensor;Water level sensor;battery monitoring sensor;Vex and Node microcontroller;IOT;Robot;ultrasonic sensor;Water level sensor;battery monitoring sensor;Vex and Node microcontroller;IOT|
|[A Web-based Real-time Sales Data Analysis using LSTM Model for Better Insight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112837)|I. R. P; J. Anitha; S. I. Alex Pandian|10.1109/ICACCS57279.2023.10112837|LSTM;Time Series;Sequential Models;Business Analytics;Profitability;LSTM;Time Series;Sequential Models;Business Analytics;Profitability|
|[Climate Change Forecast for Forest Fire Risk Prediction using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112983)|R. S. Priya; K. Vani.|10.1109/ICACCS57279.2023.10112983|Machine learning;climate change;forest fire;greenhouse gas;temperature forecast;Machine learning;climate change;forest fire;greenhouse gas;temperature forecast|
|[Image Tamper Detection and Correction Based on Mean Pixel Value and Logistic Map](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112748)|A. R. Gottimukkala; A. Pradhan; S. N. V. J. D. Kosuru; G. Swain|10.1109/ICACCS57279.2023.10112748|watermarking;tamper detection;data hiding;logistic map;block average;image security;watermarking;tamper detection;data hiding;logistic map;block average;image security|
|[Efficiency Evaluation Parameters of Digital Image Watermarking Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113036)|S. N. V. J. D. Kosuru; A. Pradhan; A. R. Gottimukkala; G. Swain|10.1109/ICACCS57279.2023.10113036|watermarking;imperceptibility;robustness; security;complexity;attacks on watermarking;watermarking;imperceptibility;robustness; security;complexity;attacks on watermarking|
|[FPGA Implementation of Medical Image Fusion using PCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112690)|R. P. Vidyadhar; B. Mahalakshmi; G. S. Kethan; K. Deepak|10.1109/ICACCS57279.2023.10112690|DWT;PCA;CT;MRI;XPS;and VB;DWT;PCA;CT;MRI;XPS;and VB|
|[ANN-Based Model for Analysis and Determination of Crop Damage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112703)|J. Pant; P. Pant; H. Pant; S. Dhanik; D. Singh; A. Juyal|10.1109/ICACCS57279.2023.10112703|Crop Damage;classification;Artificial Neural Network (ANN);Accuracy;Machine Learning;outliers;Activation Function;Crop Damage;classification;Artificial Neural Network (ANN);Accuracy;Machine Learning;outliers;Activation Function|
|[IoT based Food Spoilage Detection Monitoring using Blynk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113034)|S. S; A. K. V; N. S. Kumar; P. G|10.1109/ICACCS57279.2023.10113034|Food Spoilage Detection;IoT;NODEMCU;Blynk App;Food Spoilage Detection;IoT;NODEMCU;Blynk App|
|[Smart Attendance Monitoring System Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112850)|R. G; P. G; P. P N; A. P. S; V. Sekhar; N. S. Kumar|10.1109/ICACCS57279.2023.10112850|IoT;Attendance Monitoring;IR Sensor;Arduino;RFID;IDE;IoT;Attendance Monitoring;IR Sensor;Arduino;RFID;IDE|
|[A Performance Analysis of Predicting Diabetics Disease Using Hybrid Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113086)|S. P. K; S. A; A. S; C. S|10.1109/ICACCS57279.2023.10113086|Classification;prediction;K-fold cross validation;Regression;Feature selection;Machine Learning;Classification;prediction;K-fold cross validation;Regression;Feature selection;Machine Learning|
|[Web Based Hospital Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112962)|A. C. Babu; V. N. C. S. Teja; A. D. Reddy; E. N. Kumar; V. Srinivas|10.1109/ICACCS57279.2023.10112962|Admin;Patient;Doctor;Hospital Management System;Web Application;PHP;MYSQL;Appointments;Admin;Patient;Doctor;Hospital Management System;Web Application;PHP;MYSQL;Appointments|
|[Bronchopneumonia Diagnosis Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112695)|M. Bindhulahari; T. L. Shriya; M. R. Chowdary; K. Mahitha; T. P. Kumar; S. K. Kannaiah|10.1109/ICACCS57279.2023.10112695|Bronchopneumonia;Deep Learning Algorithms;Convolutional Neural Networks;Image Classification;Supervised Classifier Algorithms;Chest X-rays;Bronchopneumonia;Deep Learning Algorithms;Convolutional Neural Networks;Image Classification;Supervised Classifier Algorithms;Chest X-rays|
|[Blood Donation Management System: A Novel Approach to Streamlining Blood Collection and Distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112950)|J. Amose; G. R. M; G. K. S; N. G|10.1109/ICACCS57279.2023.10112950|Blood;donor;recipient;management;webapp;Blood;donor;recipient;management;webapp|
|[Detection of Fuel Consumption and Measuring Distance with Remaining Fuel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112843)|A. L. Sankeerthana; N. Neelima; A. Yamuna; K. Lakshmika|10.1109/ICACCS57279.2023.10112843|Ultrasonic Sensor;Node MCU;Blynk Console;Blynk IOT;Arduino Genuino;Web Dashboard;Ultrasonic Sensor;Node MCU;Blynk Console;Blynk IOT;Arduino Genuino;Web Dashboard|
|[Video Based 3D Convolution Network for Air Quality Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112841)|S. K; P. B. M; B. A; A. K. V; D. Thangaraju|10.1109/ICACCS57279.2023.10112841|Measurement of air quality;Three-dimensional convolution;Classification of video;Measurement of air quality;Three-dimensional convolution;Classification of video|
|[Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113105)|A. K; M. Jayan; L. Jacob|10.1109/ICACCS57279.2023.10113105|Disaster Tweets;Machine Learning;Deep Learning;Natural Language Processing;Disaster Management;TF-IDF Vectorizer;Word2Vec Vectorizer;Support Vector Machine;Bi-LSTM;Disaster Tweets;Machine Learning;Deep Learning;Natural Language Processing;Disaster Management;TF-IDF Vectorizer;Word2Vec Vectorizer;Support Vector Machine;Bi-LSTM|
|[Prediction of Autism Spectrum Disorder Using Efficient Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112807)|M. S. Venkata Sai Krishna Narala; S. Vemuri; C. Kattula|10.1109/ICACCS57279.2023.10112807|Autism Spectrum Disorder;Efficient Net;Facial Images;Convolutional Neural Network.;Autism Spectrum Disorder;Efficient Net;Facial Images;Convolutional Neural Network.|
|[Closed Loop Temperature Control System for Paddy Dryers Using Node Microcontroller Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112701)|R. R; H. V. S; K. K; K. M|10.1109/ICACCS57279.2023.10112701|wireless monitoring;temperature control;sensor;paddy grain;paddy dryer;micro controller;wireless monitoring;temperature control;sensor;paddy grain;paddy dryer;micro controller|
|[Fungal Diseased Mango Leaves Segmentation Using Soft Computing methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112933)|R. B. Joseph; A. K. Prabavathy|10.1109/ICACCS57279.2023.10112933|Image Segmentation;Anthracnose Disease;Soft computing;Neural Network;Image Segmentation;Anthracnose Disease;Soft computing;Neural Network|
|[Data Analytics System for Digital Currency Price Prediction Using Regression Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112863)|J. P V; K. K; B. P; K. S; D. S|10.1109/ICACCS57279.2023.10112863|Cryptocurrency;Data analytics;Regression algorithm;Deep learning;Neural network;Price prediction;Cryptocurrency;Data analytics;Regression algorithm;Deep learning;Neural network;Price prediction|
|[Traffic Rules Violation Detection using YOLO and HAAR Cascade](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112954)|S. L. Avupati; A. Harshitha; S. P. Jeedigunta; D. Sai Chikitha Chowdary; B. Pushpa|10.1109/ICACCS57279.2023.10112954|Object Detection;Machine Learning;Computer Vision;You Only Look Once (YOLO);Haar Cascade;Dlib;Traffic Rule Violation;Object Detection;Machine Learning;Computer Vision;You Only Look Once (YOLO);Haar Cascade;Dlib;Traffic Rule Violation|
|[Dysphonia-based Parkinson's Detection using Deep Learning and Ensemble Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112746)|S. A. Varma Vegesna; S. T. Ginnegolla; R. R. Yeruva; V. R. Arimanda; S. Boda|10.1109/ICACCS57279.2023.10112746|Parkinson’s disease detection;Dysphonia;Hyperparameter tuning;Ensemble techniques;Stacking technique;GridSearchCV;Parkinson’s disease detection;Dysphonia;Hyperparameter tuning;Ensemble techniques;Stacking technique;GridSearchCV|
|[A Methodology for Energy Management System Using Bidirectional DC-DC Converter Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112782)|R. Senthilkumar; S. Indu; P. Leninpugalhanthi; M. Daniel; A. I. Bhadusha; T. K. Sundar|10.1109/ICACCS57279.2023.10112782|Solar panel;voltage;inverter;NodeMCU;electric vehicle;Li-ion battery;wind energy;Solar panel;voltage;inverter;NodeMCU;electric vehicle;Li-ion battery;wind energy|
|[Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical Recording Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112688)|N. Ashokkumar; C. K. Pappa; S. Kumar A; D. Srinivasan; M. M. Vijay|10.1109/ICACCS57279.2023.10112688|Human activity monitoring;Surface electromyography (sEMG);biomedical signal compression;turning angle;Human activity monitoring;Surface electromyography (sEMG);biomedical signal compression;turning angle|
|[A Concise Survey On Predicting Stock Prices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113004)|O. Kondiparthy; S. Nimmagadda; G. K. M. Venkata; J. R. Gera; V. K. Burugari; S. Kailasam|10.1109/ICACCS57279.2023.10113004|LSTM;CNN;RNN;Deep learning;Stock price prediction;Finance;Machine learning;Forecasting;LSTM;CNN;RNN;Deep learning;Stock price prediction;Finance;Machine learning;Forecasting|
|[Survey of Non-English Language Compilers : (Exploring the Diversity of Programming Languages)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112953)|B. Sunayana; K. Shaik; K. R. Vemula; S. Sahoo; R. K. Tata; A. Senthil|10.1109/ICACCS57279.2023.10112953|Compiler;non-English language;translation;application;specifications;Compiler;non-English language;translation;application;specifications|
|[IoT powered Smart Stroller : (A Novel Approach for infant monitoring and safety)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112742)|A. Sawant; N. Akhter; A. Bhatia; A. Shetty; S. Bhabal|10.1109/ICACCS57279.2023.10112742|IoT device;Baby Stroller;Security;Face recognition;Arduino;Surveillance;IoT device;Baby Stroller;Security;Face recognition;Arduino;Surveillance|
|[Monitoring and Warning of Flooding Conditions Using IoT Based System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112930)|N. Ashokkumar; V. Arun; S. Prabhu; V. Kalaimagal; D. Srinivasan; B. Shanthi|10.1109/ICACCS57279.2023.10112930|Arduino;NodeMCU;IoT;Flood warning system;ThingSpeak;Arduino;NodeMCU;IoT;Flood warning system;ThingSpeak|
|[A Remote Monitoring Greenhouse Agricultural Farming with Edge Computing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112819)|S. Vimalnath; K. Santhosh Kumar; K. Naveen; D. Saravana Kumar|10.1109/ICACCS57279.2023.10112819|Smart Agriculture;Farming Methods;Sensor Devices;Edge Computing;Irrigation System;Green Housing Farming;Wireless Network System;Smart Agriculture;Farming Methods;Sensor Devices;Edge Computing;Irrigation System;Green Housing Farming;Wireless Network System|
|[Design and Implementation of IoT based Energy Efficient Smart Metering System for Domestic Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113012)|N. Ashokkumar; V. Arun; S. Prabhu; V. Kalaimagal; D. Srinivasan; B. Shanthi|10.1109/ICACCS57279.2023.10113012|Smart meters;Energy consumption;Monitoring;IoT;ESP32;Sensors;Blynk application;Smart meters;Energy consumption;Monitoring;IoT;ESP32;Sensors;Blynk application|
|[A Comparative Analysis of Substrate Selection for RF Energy Harvesting Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112855)|H. I. Nivetha; T. Sathiyapriya; R. Sudhakar; S. Thilagavathi|10.1109/ICACCS57279.2023.10112855|Dielectric substrate;RF energy harvesting;Rectangular microstrip patch antenna;Wireless communication;Dielectric substrate;RF energy harvesting;Rectangular microstrip patch antenna;Wireless communication|
|[Enhancing PM2.5 Predictions Using Combination of Graph Convolutional Network and Bi-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112686)|P. Kunar; N. R; Sudha|10.1109/ICACCS57279.2023.10112686|Air Pollution;Particulate Matter (PM2.5);GCN;Bi-LSTM;Time Series Prediction;Data Augmentation;Visualization;Air Pollution;Particulate Matter (PM2.5);GCN;Bi-LSTM;Time Series Prediction;Data Augmentation;Visualization|
|[Disease Cluster Specific learning for Patient Treatment Prediction on High Dimensional EHR Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112707)|M. S. Kavitha; G. G. Sreeja; J. Shanthini; S. Karthik|10.1109/ICACCS57279.2023.10112707|Patient Trajectory Data;Disease Treatment Prediction;Deep Learning;Autoencoder;Electronic Health Record;Reconstruction Error;Patient Trajectory Data;Disease Treatment Prediction;Deep Learning;Autoencoder;Electronic Health Record;Reconstruction Error|
|[A Predictive Model for Road Traffic Data Analysis and Visualization to Detect Accident Zones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112862)|S. Subhashini; R. Maruthi|10.1109/ICACCS57279.2023.10112862|Road accident zone analysis;Road Accidents;Predictive model;Data analysis;Data visualization;Latent class clustering analysis;Cartogram;Road accident zone analysis;Road Accidents;Predictive model;Data analysis;Data visualization;Latent class clustering analysis;Cartogram|
|[Deep Learning Techniques in Digital Clinical Diagnostic System for Lung Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113055)|D. Rawat; S. Sharma; S. Bhadula|10.1109/ICACCS57279.2023.10113055|Deep learning;LC;DCDS;Artificial intelligence;Deep learning;LC;DCDS;Artificial intelligence|
|[A review on Machine Learning based IDS approaches in Wireless sensor networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112982)|M. M. T; K. Devika; R. Rajakumar; M. R; D. K; M. Sreedevi|10.1109/ICACCS57279.2023.10112982|Intrusion detection;Wireless sensor network;Security;KDD-CUP;NSL-KDD;Intrusion detection;Wireless sensor network;Security;KDD-CUP;NSL-KDD|
|[Crop Recommendation and Prediction System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113081)|C. Rakesh D; V. Vardhan; B. B. Vasantha; G. Sai Krishna|10.1109/ICACCS57279.2023.10113081|Agriculture;Machine Learning;Farming;Crop suggestion;k-Nearest Neighbor (KNN);Crop Recommendation;Agriculture;Machine Learning;Farming;Crop suggestion;k-Nearest Neighbor (KNN);Crop Recommendation|
|[SignBot: An Automated Robot with Efficient Net Model for Signature Creation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112679)|N. M; B. P; S. G M; T. Tamizhan R|10.1109/ICACCS57279.2023.10112679|Neural Network;EfficientNet;Machine Learning;Convolutional Neural Network;Neural Network;EfficientNet;Machine Learning;Convolutional Neural Network|
|[Design of Arithmetic Circuits Using an Approximation Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112859)|K. Arputharaj; A. S. Rajasekaran|10.1109/ICACCS57279.2023.10112859|Low power consumption;booth multiplier;encoding;2’s complement;and complex multiplication;Low power consumption;booth multiplier;encoding;2’s complement;and complex multiplication|
|[PLC Based Levelling and Positioning Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112978)|S. A; S. S. P; V. S; S. L. V; P. V; J. A|10.1109/ICACCS57279.2023.10112978|manual adjustment;efficiency;accuracy;automation Programmable logic controller;manual adjustment;efficiency;accuracy;automation Programmable logic controller|
|[Feature Fusion and Multi-Stream CNNs for ScaleAdaptive Multimodal Sign Language Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112915)|N. Singla; M. Taneja; N. Goyal; R. Jindal|10.1109/ICACCS57279.2023.10112915|Sign Language;Multi-stream CNNs;scale awareness;Feature Fusion;Gabor Filter;Local Binary Pattern;and Spatial Pyramidal Pooling;Sign Language;Multi-stream CNNs;scale awareness;Feature Fusion;Gabor Filter;Local Binary Pattern;and Spatial Pyramidal Pooling|
|[An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113066)|A. Kumari Sirivarshitha; K. Sravani; K. S. Priya; V. Bhavani|10.1109/ICACCS57279.2023.10113066|Face Detection;Face Recognition;Face Alignment;Feature Extraction;Python;OpenCV Library;face Recognition Library;Face Detection;Face Recognition;Face Alignment;Feature Extraction;Python;OpenCV Library;face Recognition Library|
|[Interview Bot with Automatic Question Generation and Answer Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112918)|R. Pandey; D. Chaudhari; S. Bhawani; O. Pawar; S. Barve|10.1109/ICACCS57279.2023.10112918|Chatbot;CNN (Convolutional Neural Networks);word2Vec;seq2seq model;NER (named entity recognition);BERT (Bidirectional Encoder Representations from Transformers);TF-IDF (Term Frequency-Inverse Document Frequency);cosine similarity;Chatbot;CNN (Convolutional Neural Networks);word2Vec;seq2seq model;NER (named entity recognition);BERT (Bidirectional Encoder Representations from Transformers);TF-IDF (Term Frequency-Inverse Document Frequency);cosine similarity|
|[Early Renaissance Art Generation Using Deep Convolutional Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113079)|S. T; G. J; S. A. G.; D. M|10.1109/ICACCS57279.2023.10113079|early-renaissance;generative art;neural networks;DCGAN;paintings;noise manipulation;brush paintings;early-renaissance;generative art;neural networks;DCGAN;paintings;noise manipulation;brush paintings|
|[Classification of Weeds Detection Control Management Using Artificial and Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113127)|N. P; S. K. S; M. Ganesh; G. B. Prakash Yadav; T. S. Suneeth; K. M. Kumar|10.1109/ICACCS57279.2023.10113127|Weed detection;Artificial Neural Networks;Classification;Image Recognition;Agriculture;Weed Control;Weed Management;Weed detection;Artificial Neural Networks;Classification;Image Recognition;Agriculture;Weed Control;Weed Management|
|[ATMEGA 328-based Gas Leakage Monitoring and Alerting IoT System with SMS Notification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112899)|P. Naveen; K. Ravi Teja; K. Sahithi Reddy; S. Melikis Sam; M. Dinesh Kumar; M. Saravanan|10.1109/ICACCS57279.2023.10112899|Gas Leakages;Gas Leakage Detector;Gas Sensor MQ6;GSM;LPG;microcontroller (ATMEGA328);Gas Leakages;Gas Leakage Detector;Gas Sensor MQ6;GSM;LPG;microcontroller (ATMEGA328)|
|[A Review Of Plant Disease Detection Methods Using Image Processing Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112981)|P. Maragathavalli; S. Jana|10.1109/ICACCS57279.2023.10112981|Agriculture;Automated;Detection;Plant diseases;Qualitative;Production;Agriculture;Automated;Detection;Plant diseases;Qualitative;Production|
#### **ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)**
- DOI: 10.1109/ICASSP49357.2023
- DATE: 4-10 June 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Counterfactual Two-Stage Debiasing For Video Corpus Moment Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095182)|S. Yoon; J. W. Hong; S. Eom; H. S. Yoon; E. Yoon; D. Kim; J. Kim; C. Kim; C. D. Yoo|10.1109/ICASSP49357.2023.10095182|Video Corpus Moment Retrieval;Video Moment Retrieval;Video Grounding;Video Corpus Moment Retrieval;Video Moment Retrieval;Video Grounding|
|[Test-Time Training-Free Domain Adaptation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096430)|Y. Feng; W. He; K. You; B. Liu; Z. Zhang; Y. Wang; M. Li; Y. Lou; J. Li; G. Li; J. Liao|10.1109/ICASSP49357.2023.10096430|Transfer Learning;Domain Adaption;Training-Free Model;Feature Statistics Transformation;Transfer Learning;Domain Adaption;Training-Free Model;Feature Statistics Transformation|
|[AdapITN: A Fast, Reliable, and Dynamic Adaptive Inverse Text Normalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094599)|T. -B. Nguyen; L. D. M. Nhat; Q. M. Nguyen; Q. T. Do; C. M. Luong; A. Waibel|10.1109/ICASSP49357.2023.10094599|ASR;inverse text normalization;semiotic pharse;phonetization phrase;ASR;inverse text normalization;semiotic pharse;phonetization phrase|
|[Compressed Distributed Regression over Adaptive Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097010)|M. Carpentiero; V. Matta; A. H. Sayed|10.1109/ICASSP49357.2023.10097010|Distributed optimization;diffusion strategy;randomized quantizers;differential quantization;Distributed optimization;diffusion strategy;randomized quantizers;differential quantization|
|[The Role of Memory in Social Learning When Sharing Partial Opinions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096186)|M. Cirillo; V. Bordignon; V. Matta; H. Sayed|10.1109/ICASSP49357.2023.10096186|Social learning;Bayesian update;information diffusion;partial information;Social learning;Bayesian update;information diffusion;partial information|
|[IoU-Aware Multi-Expert Cascade Network Via Dynamic Ensemble for Long-Tailed Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095439)|W. -C. Fan; C. -Y. Hong; Y. -C. Hsu; T. -L. Liu|10.1109/ICASSP49357.2023.10095439|Object Detection;long-tailed distribution;representation learning;Object Detection;long-tailed distribution;representation learning|
|[TransLink: Transformer-Based Embedding for Tracklets’ Global Link](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097136)|Y. Zhang; S. Wang; Y. Fan; G. Wang; C. Yan|10.1109/ICASSP49357.2023.10097136|Multi-object tracking (MOT);Transformer;self-attention;association;identity switch;Multi-object tracking (MOT);Transformer;self-attention;association;identity switch|
|[Burst Perception-Distortion Tradeoff: Analysis and Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096218)|D. Xue; L. Herranz; J. V. Corral; Y. Zhang|10.1109/ICASSP49357.2023.10096218|burst image restoration;perception-distortion tradeoff;inter-frame alignment;burst image restoration;perception-distortion tradeoff;inter-frame alignment|
|[Approximation Error Back-Propagation for Q-Function in Scalable Reinforcement Learning with Tree Dependence Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096433)|Y. Yan; Y. Dong; K. Ma; Y. Shen|10.1109/ICASSP49357.2023.10096433|exponential decay property;scalable reinforcement learning;tree;exponential decay property;scalable reinforcement learning;tree|
|[EMC2-Net: Joint Equalization and Modulation Classification Based on Constellation Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096687)|H. Ryu; J. Choi|10.1109/ICASSP49357.2023.10096687|Modulation classification;machine learning;constellation;multipath fading channels;equalizer;Modulation classification;machine learning;constellation;multipath fading channels;equalizer|
|[Deep Manifold Graph Auto-Encoder For Attributed Graph Embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095904)|B. Hu; Z. Zang; J. Xia; L. Wu; C. Tan; S. Z. Li|10.1109/ICASSP49357.2023.10095904|Manifold learning;structure information;graph embedding;crowding problem;Manifold learning;structure information;graph embedding;crowding problem|
|[Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095840)|C. Qiang; P. Yang; H. Che; Y. Zhang; X. Wang; Z. Wang|10.1109/ICASSP49357.2023.10095840|style transfer;semi-supervised;expressive and controllable speech synthesis;hierarchical prosody;style transfer;semi-supervised;expressive and controllable speech synthesis;hierarchical prosody|
|[CyFi-TTS: Cyclic Normalizing Flow with Fine-Grained Representation for End-to-End Text-to-Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095323)|I. -S. Hwang; Y. -S. Han; B. -K. Jeon|10.1109/ICASSP49357.2023.10095323|End-to-End Text-to-Speech Synthesis;Kullback–Leibler Divergence;Normalizing Flow;End-to-End Text-to-Speech Synthesis;Kullback–Leibler Divergence;Normalizing Flow|
|[Learning Graph Laplacian from Intrinsic Patterns via Gaussian Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095223)|K. Watanabe; K. Maeda; T. Ogawa; M. Haseyama|10.1109/ICASSP49357.2023.10095223|Graph learning;Gaussian process;latent variables;graph signal processing;Graph learning;Gaussian process;latent variables;graph signal processing|
|[Overcoming the Seesaw in Monocular 3D Object Detection Via Language Knowledge Transferring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095283)|W. Xu; T. Fu|10.1109/ICASSP49357.2023.10095283|Monocular 3D Detection;Multimodal language-Image learning;Monocular depth estimation;Monocular 3D Detection;Multimodal language-Image learning;Monocular depth estimation|
|[Improving Dropout in Graph Convolutional Networks for Recommendation via Contrastive Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096747)|H. Okamura; K. Maeda; R. Togo; T. Ogawa; M. Haseyama|10.1109/ICASSP49357.2023.10096747|Collaborative filtering;recommender systems;graph convolutional networks;Collaborative filtering;recommender systems;graph convolutional networks|
|[Estimation of Visual Contents from Human Brain Signals via VQA Based on Brain-Specific Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096890)|R. Shichida; R. Togo; K. Maeda; T. Ogawa; M. Haseyama|10.1109/ICASSP49357.2023.10096890|Brain decoding;functional magnetic resonance imaging;convolutional neural network;visual question answering;Brain decoding;functional magnetic resonance imaging;convolutional neural network;visual question answering|
|[Combining the Silhouette and Skeleton Data for Gait Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096986)|L. Wang; R. Han; W. Feng|10.1109/ICASSP49357.2023.10096986|Gait recognition;two-branch neural network;graph convolution;convolutional neural network;Gait recognition;two-branch neural network;graph convolution;convolutional neural network|
|[Unobtrusive Respiratory Monitoring System for Intensive Care](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095831)|X. Tan; M. Hu; G. Zhai; Y. Zhu; W. Li; X. Zhang|10.1109/ICASSP49357.2023.10095831|Respiratory rate measurements;Noncontact detection;Physiological signals;ICU application;Respiratory rate measurements;Noncontact detection;Physiological signals;ICU application|
|[End-to-End Amp Modeling: from Data to Controllable Guitar Amplifier Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094769)|L. Juvela; E. -P. Damskägg; A. Peussa; J. Mäkinen; T. Sherson; S. I. Mimilakis; K. Rauhanen; A. Gotsopoulos|10.1109/ICASSP49357.2023.10094769|Guitar Amplifiers;Virtual Analog Modeling;Neural Networks;LSTM;Guitar Amplifiers;Virtual Analog Modeling;Neural Networks;LSTM|
|[Provably Convergent Plug & Play Linearized ADMM, Applied to Deblurring Spatially Varying Kernels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096037)|C. Laroche; A. Almansa; E. Coupeté; M. Tassano|10.1109/ICASSP49357.2023.10096037|Plug & Play;Image resoration;Deblurring;Optimization;Plug & Play;Image resoration;Deblurring;Optimization|
|[Robust Video Anomaly Detection Framework via Prior Knowledge and Multi-Path Frame Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094566)|M. Zhang; J. Wang; J. Wang; Q. Qi; Z. Zhuang; H. Sun; N. Xiao|10.1109/ICASSP49357.2023.10094566|video anomaly detection;prior knowledge;frame prediction;multi-layer GCN;video anomaly detection;prior knowledge;frame prediction;multi-layer GCN|
|[Unrolled Fourier Disparity Layer Optimization for Scene Reconstruction from Few-Shots Focal Stacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095720)|B. L. Bon; M. Le Pendu; C. Guillemot|10.1109/ICASSP49357.2023.10095720|Unrolled optimization;Fourier Disparity Layers;Light field;Reconstruction;Refocusing;Unrolled optimization;Fourier Disparity Layers;Light field;Reconstruction;Refocusing|
|[Adaptive Axonal Delays in Feedforward Spiking Neural Networks for Accurate Spoken Word Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094768)|P. Sun; E. Eqlimi; Y. Chua; P. Devos; D. Botteldooren|10.1109/ICASSP49357.2023.10094768|adaptive axonal delay;spiking neural network;speech processing;auditory modeling;adaptive axonal delay;spiking neural network;speech processing;auditory modeling|
|[EEG2IMAGE: Image Reconstruction from EEG Brain Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096587)|P. Singh; P. Pandey; K. Miyapuram; S. Raman|10.1109/ICASSP49357.2023.10096587|Deep Learning;EEG;GAN;Deep Learning;EEG;GAN|
|[Multispectral Image Fusion based on Super Pixel Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095874)|N. Ofir|10.1109/ICASSP49357.2023.10095874|Multispectral Images;Image Fusion;Near-Infrared;Superpixel Segmentation;Multispectral Images;Image Fusion;Near-Infrared;Superpixel Segmentation|
|[NF-PCAC: Normalizing Flow Based Point Cloud Attribute Compression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096294)|R. B. Pinheiro; J. -E. Marvie; G. Valenzise; F. Dufaux|10.1109/ICASSP49357.2023.10096294|Point clouds;Learning-Based;Compression;Attributes;Normalizing Flow;Point clouds;Learning-Based;Compression;Attributes;Normalizing Flow|
|[An Adaptive DFE Using Light-Pattern-Protection Algorithm in 12 NM CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097007)|S. Xing; C. Lin; Y. Li; H. Wang|10.1109/ICASSP49357.2023.10097007|Adaptive DFE;SSLMS algorithm;robustness;scalability;light-pattern-protection algorithm;Adaptive DFE;SSLMS algorithm;robustness;scalability;light-pattern-protection algorithm|
|[Hierarchical Transformer for Multi-Label Trailer Genre Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095502)|Z. Cai; H. Ding; X. Wu; M. Xu; X. Cui|10.1109/ICASSP49357.2023.10095502|multi-label;trailer genre classification;self-attention;Hierarchical Transformer;transfer learning;multi-label;trailer genre classification;self-attention;Hierarchical Transformer;transfer learning|
|[A Contrastive Framework to Enhance Unsupervised Sentence Representation Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096240)|H. Ma; Z. Li; H. Guo|10.1109/ICASSP49357.2023.10096240|Contrastive learning;unsupervised learning;natural language processing;Contrastive learning;unsupervised learning;natural language processing|
|[Self-Attention Based Action Segmentation Using Intra-And Inter-Segment Representations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096960)|C. Patsch; E. Steinbach|10.1109/ICASSP49357.2023.10096960|Action Segmentation;Activity Recognition;Video Understanding;Action Segmentation;Activity Recognition;Video Understanding|
|[A Deep Disentangled Approach for Interpretable Hyperspectral Unmixing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095764)|R. A. Borsoi; T. Imbiriba; D. Erdoğmuş|10.1109/ICASSP49357.2023.10095764|Hyperspectral data;hyperspectral unmixing;neural networks;disentanglement learning;deep learning.;Hyperspectral data;hyperspectral unmixing;neural networks;disentanglement learning;deep learning.|
|[Residual Squeeze-and-Excitation U-Shaped Network for Minutia Extraction in Contactless Fingerprint Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095461)|A. N. Cotrim; H. Pedrini|10.1109/ICASSP49357.2023.10095461|Minutia extraction;contactless fingerprint;deep convolutional neural network;residuals;squeeze-and-excitation;Minutia extraction;contactless fingerprint;deep convolutional neural network;residuals;squeeze-and-excitation|
|[Learnable Flow Model Conditioned on Graph Representation Memory for Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096313)|Z. Zhu; W. Liu; Z. Deng|10.1109/ICASSP49357.2023.10096313|Invertible network;graph neural network;memory mechanism;anomaly detection;Invertible network;graph neural network;memory mechanism;anomaly detection|
|[Building Blocks for a Complex-Valued Transformer Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095349)|F. Eilers; X. Jiang|10.1109/ICASSP49357.2023.10095349|Deep learning techniques;Complex-valued neural networks;Transformer architecture;Deep learning techniques;Complex-valued neural networks;Transformer architecture|
|[PCSalmix: Gradient Saliency-Based Mix Augmentation for Point Cloud Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095576)|T. Hong; Z. Zhang; J. Ma|10.1109/ICASSP49357.2023.10095576|Point Cloud Classification;Mix Augmentation;Gradient Saliency;Point Cloud Classification;Mix Augmentation;Gradient Saliency|
|[Entropy Based Feature Regularization to Improve Transferability of Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095195)|R. Baena; L. Drumetz; V. Gripon|10.1109/ICASSP49357.2023.10095195|Deep Learning;Transfer Learning;Entropy;Regularization;Deep Learning;Transfer Learning;Entropy;Regularization|
|[Exploring Complementary Features in Multi-Modal Speech Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096709)|S. Wang; Y. Ma; Y. Ding|10.1109/ICASSP49357.2023.10096709|Speech Emotion Recognition;Multi-modal Fusion;Transformer;Speech Emotion Recognition;Multi-modal Fusion;Transformer|
|[Database-Aware ASR Error Correction for Speech-to-SQL Parsing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097246)|Y. Shao; A. Kumar; N. Nakashole|10.1109/ICASSP49357.2023.10097246|Speech-to-SQL;ASR error correction;database;natural language interface;Speech-to-SQL;ASR error correction;database;natural language interface|
|[Mixed Sample Augmentation for Online Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096043)|Y. Shen; L. Xu; Y. Yang; Y. Li; Y. Guo|10.1109/ICASSP49357.2023.10096043|Online knowledge distillation;data augmentation;CutMix;knowledge ensemble distillation;Online knowledge distillation;data augmentation;CutMix;knowledge ensemble distillation|
|[Super-Resolution Harmonic Retrieval of Non-Circular Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095946)|Y. Zhang; Y. Wang; Z. Tian; G. Leus; G. Zhang|10.1109/ICASSP49357.2023.10095946|low-rank Toeplitz-Hankel covariance reconstruction;harmonic retrieval;non-circularity;augmented covariance;low-rank Toeplitz-Hankel covariance reconstruction;harmonic retrieval;non-circularity;augmented covariance|
|[Semi-Swinderain: Semi-Supervised Image Deraining Network Using SWIN Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095214)|C. Ren; D. Yan; Y. Cai; Y. Li|10.1109/ICASSP49357.2023.10095214|Deraining;Swin Transformer;Memory;Semi-suprivised;Contrastive Loss;Deraining;Swin Transformer;Memory;Semi-suprivised;Contrastive Loss|
|[Learning Generalizable Light Field Networks from Few Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096979)|Q. Li; F. Multon; A. Boukhayma|10.1109/ICASSP49357.2023.10096979|Novel view synthesis;neural light field;volumetric rendering;Novel view synthesis;neural light field;volumetric rendering|
|[Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095775)|R. Chen; B. Hao; I. C. Paschalidis|10.1109/ICASSP49357.2023.10095775|Distributionally Robust Optimization;Multi-class Classification;Deep Learning.;Distributionally Robust Optimization;Multi-class Classification;Deep Learning.|
|[Adaptive Mask Co-Optimization for Modal Dependence in Multimodal Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096641)|Y. Zhou; X. Liang; S. Zheng; H. Xuan; T. Kumada|10.1109/ICASSP49357.2023.10096641|Emotion recognition;multimodal learning;modal dependence;adaptive mask;co-optimization;Emotion recognition;multimodal learning;modal dependence;adaptive mask;co-optimization|
|[Improving Occluded Human Pose Estimation Via Linked Joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097055)|S. Ye; Z. Hong; J. Zheng; S. Zhang|10.1109/ICASSP49357.2023.10097055|Occluded human pose estimation;key-point heatmaps;Occluded human pose estimation;key-point heatmaps|
|[DST: Deformable Speech Transformer for Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096966)|W. Chen; X. Xing; X. Xu; J. Pang; L. Du|10.1109/ICASSP49357.2023.10096966|speech emotion recognition;deformable network;Transformer;deformable attention mechanism;speech emotion recognition;deformable network;Transformer;deformable attention mechanism|
|[Permutation Invariant Training for Paraphrase Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094896)|J. Bai; C. Yin; H. Hong; J. Zhang; C. Li; Y. Wang; W. Rong|10.1109/ICASSP49357.2023.10094896|Paraphrase Identification;Permutation Invariance;Symmetry Regularization;Paraphrase Identification;Permutation Invariance;Symmetry Regularization|
|[Code-Enhanced Fine-Grained Semantic Matching For Tag Recommendation In Software Information Sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095822)|L. Li; P. Wang; X. Zheng; Q. Xie|10.1109/ICASSP49357.2023.10095822|Recommender Systems;Software Information Processing;Semantic Matching;Deep Learning;Recommender Systems;Software Information Processing;Semantic Matching;Deep Learning|
|[Audio Coding With Unified Noise Shaping And Phase Contrast Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096386)|B. Jo; S. Beack; T. Lee|10.1109/ICASSP49357.2023.10096386|Audio coding;quantization;DFT;noise shaping;TCX;Audio coding;quantization;DFT;noise shaping;TCX|
|[Binary Image Fast Perfect Recovery from Sparse 2D-DFT Coefficients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095387)|S. -C. Pei; K. -W. Chang|10.1109/ICASSP49357.2023.10095387|Perfect recover;2D-DFT;prime number;sampling;Perfect recover;2D-DFT;prime number;sampling|
|[Reducing the GAP Between Streaming and Non-Streaming Transducer-Based ASR by Adaptive Two-Stage Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095040)|H. Tang; Y. Fu; L. Sun; J. Xue; D. Liu; Y. Li; Z. Ma; M. Wu; J. Pan; G. Wan; M. Zhao|10.1109/ICASSP49357.2023.10095040|Speech Recognition;Conformer Transducer;Knowledge Distillation;Power Transformation;Speech Recognition;Conformer Transducer;Knowledge Distillation;Power Transformation|
|[Uncertainty-Aware Few-Shot Class-Incremental Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096589)|J. Zhu; J. Zhao; J. Zhou; L. He; J. Yang; Z. Zhang|10.1109/ICASSP49357.2023.10096589|Few-Shot Learning;Class-Incremental Learning;Uncertainty-Aware;Curriculum Learning;Few-Shot Learning;Class-Incremental Learning;Uncertainty-Aware;Curriculum Learning|
|[Flowreg: Latent Space Regularization Using Normalizing Flow For Limited Samples Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097230)|C. Wang; J. Gao; Y. Hua; H. Wang|10.1109/ICASSP49357.2023.10097230|regularization;normalizing flow;supervised learning;unsupervised learning;regularization;normalizing flow;supervised learning;unsupervised learning|
|[Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095800)|Y. Qiu; Y. Jin; L. Yu; J. Wang; X. Zhang|10.1109/ICASSP49357.2023.10095800|Multi-agent reinforcement learning;cooperation;mutual help;Multi-agent reinforcement learning;cooperation;mutual help|
|[Semi-Supervised Semantic Segmentation with Structured Output Space Adaption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095628)|W. Huang; F. Zhang|10.1109/ICASSP49357.2023.10095628|Semantic segmentation;semi-supervised learning;generative adversarial networks;Semantic segmentation;semi-supervised learning;generative adversarial networks|
|[Conditional LS-GAN Based Skylight Polarization Image Restoration and Application in Meridian Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096855)|T. Yang; H. Bo; X. Yang; J. Gao; Z. Shi|10.1109/ICASSP49357.2023.10096855|skylight polarization;generative adversarial network;image restoration;polarized light navigation;skylight polarization;generative adversarial network;image restoration;polarized light navigation|
|[Towards Trustworthy Multi-Label Sewer Defect Classification via Evidential Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096569)|C. Zhao; C. Hu; H. Shao; Z. Wang; Y. Wang|10.1109/ICASSP49357.2023.10096569|Trustworthy visual inspection;evidential deep Learning;multi-label sewer defect classification;sewer pipelines;Trustworthy visual inspection;evidential deep Learning;multi-label sewer defect classification;sewer pipelines|
|[Learning from the Raw Domain: Cross Modality Distillation for Compressed Video Action Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097076)|Y. Liu; J. Cao; W. Bai; B. Li; W. Hu|10.1109/ICASSP49357.2023.10097076|Cross modality;knowledge distillation;compressed domain;video action recognition;Cross modality;knowledge distillation;compressed domain;video action recognition|
|[Identifying Opinion Influencers over Social Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094722)|V. Shumovskaia; M. Kayaalp; A. H. Sayed|10.1109/ICASSP49357.2023.10094722|Social learning;social influence;explainability;inverse modeling;online learning;graph learning;Social learning;social influence;explainability;inverse modeling;online learning;graph learning|
|[Beamforming Optimization in RIS-Aided Mimo Systems Under Multiple-Reflection Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096563)|D. Wijekoon; A. Mezghani; E. Hossain|10.1109/ICASSP49357.2023.10096563|Reconfigurable Intelligent Surface (RIS);mutual coupling;multiple reflection effects;physically-consistent model;Reconfigurable Intelligent Surface (RIS);mutual coupling;multiple reflection effects;physically-consistent model|
|[MLP-GAN for Brain Vessel Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096997)|B. Xie; H. Tang; B. Duan; D. Cai; Y. Yan|10.1109/ICASSP49357.2023.10096997|MLP;Multi-View;GANs;Segmentation;MLP;Multi-View;GANs;Segmentation|
|[Image Sharing Chain Detection VIA Sequence-To-Sequence Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095000)|J. You; Y. Li; R. Liang; Y. Tan; J. Zhou; X. Li|10.1109/ICASSP49357.2023.10095000|Sharing chain detection;image forensics;Seq2Seq model;online social networks;Sharing chain detection;image forensics;Seq2Seq model;online social networks|
|[Input-Dependent Dynamical Channel Association For Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095107)|Q. Tang; Y. Zhang; X. Xu; J. Wang; Y. Guo|10.1109/ICASSP49357.2023.10095107|Dynamical Channel Association;Input- dependent;Knowledge Distillation;Dynamical Channel Association;Input- dependent;Knowledge Distillation|
|[An Adaptive Plug-and-Play Network for Few-Shot Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096887)|H. Li; L. Li; Y. Huang; N. Li; Y. Zhang|10.1109/ICASSP49357.2023.10096887|Few-shot learning;adaptive resizer;learnable metric;Few-shot learning;adaptive resizer;learnable metric|
|[Decontamination Transformer For Blind Image Inpainting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094950)|C. -Y. Li; Y. -Y. Lin; W. -C. Chiu|10.1109/ICASSP49357.2023.10094950|Blind image inpainting;Transformer;Blind image inpainting;Transformer|
|[Prefix Tuning for Automated Audio Captioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096877)|M. Kim; K. Sung-Bin; T. -H. Oh|10.1109/ICASSP49357.2023.10096877|Automated audio captioning;audio representation learning;multi-modal learning;prefix tuning;Automated audio captioning;audio representation learning;multi-modal learning;prefix tuning|
|[SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096410)|Q. Li; J. Xu; R. Yuan; Y. Zhang; R. Feng|10.1109/ICASSP49357.2023.10096410|Breast mass segmentation;Spatial correlation;Shape guidance;Breast mass segmentation;Spatial correlation;Shape guidance|
|[Hyperspectral Image Denoising Via Nonlocal Rank Residual Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096242)|Z. Zha; B. Wen; X. Yuan; J. Zhou; C. Zhu|10.1109/ICASSP49357.2023.10096242|HSI denoising;nonlocal rank residual;low-rank;nonlocal self-similarity;alternating minimization;HSI denoising;nonlocal rank residual;low-rank;nonlocal self-similarity;alternating minimization|
|[PRRD: Pixel-Region Relation Distillation For Efficient Semantic Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094967)|C. Wang; J. Zhong; Q. Dai; Y. Qi; R. Li; Q. Lei; B. Fang; X. Li|10.1109/ICASSP49357.2023.10094967|Knowledge Distillation;Semantic Segmentation;Multi-Scale Context;Pixel-Region Relation;Knowledge Distillation;Semantic Segmentation;Multi-Scale Context;Pixel-Region Relation|
|[Improved Indoor Localization With NLOS Signal Propagations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094695)|W. Huang; Y. Zhao; X. Wu; L. Yin|10.1109/ICASSP49357.2023.10094695|Indoor localization;NLOS signal propagation;IMU correction;data fusion;Indoor localization;NLOS signal propagation;IMU correction;data fusion|
|[HQP-MVS:High-Quality Plane Priors Assisted Multi-View Stereo for Low-Textured Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096871)|Z. Tian; R. Wang; Z. Wang; R. Wang|10.1109/ICASSP49357.2023.10096871|depth estimation;multi-view stereo;patchmatch;planar priors;low-textured areas;depth estimation;multi-view stereo;patchmatch;planar priors;low-textured areas|
|[MSN-net: Multi-Scale Normality Network for Video Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097052)|Y. Liu; D. Li; W. Zhu; D. Yang; J. Liu; L. Song|10.1109/ICASSP49357.2023.10097052|Video anomaly detection;memory network;deep auto-encoder;attention;unsupervised learning;Video anomaly detection;memory network;deep auto-encoder;attention;unsupervised learning|
|[High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096582)|S. Mulleti; Y. C. Eldar|10.1109/ICASSP49357.2023.10096582|Finite-rate-of-innovation (FRI) signals;modulo sampling;high-dynamic range ADCs;unlimited sampling;Finite-rate-of-innovation (FRI) signals;modulo sampling;high-dynamic range ADCs;unlimited sampling|
|[Lip-to-Speech Synthesis in the Wild with Multi-Task Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095582)|M. Kim; J. Hong; Y. M. Ro|10.1109/ICASSP49357.2023.10095582|Lip-to-speech synthesis;Multi-task learning;Multimodal learning;Speech reconstruction;Lip reading;Lip-to-speech synthesis;Multi-task learning;Multimodal learning;Speech reconstruction;Lip reading|
|[NC-WAMKD: Neighborhood Correction Weight-Adaptive Multi-Teacher Knowledge Distillation for Graph-Based Semi-Supervised Node Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095852)|J. Liu; P. Guo; Y. Song|10.1109/ICASSP49357.2023.10095852|knowledge distillation;semi-supervised node classification;graph neural network;knowledge distillation;semi-supervised node classification;graph neural network|
|[Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095812)|A. F. Ebihara; T. Miyagawa; K. Sakurai; H. Imaoka|10.1109/ICASSP49357.2023.10095812|Sequential Probability Ratio Test;Density ratio estimation;Early classification;Time series;Sequential Probability Ratio Test;Density ratio estimation;Early classification;Time series|
|[Improving Image Captioning with Control Signal of Sentence Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094586)|Z. Zhu; S. Wang; H. Qu|10.1109/ICASSP49357.2023.10094586|Image Captioning;Control Signal;Sentence Quality;Quality-oriented Self-Annotated Training;Image Captioning;Control Signal;Sentence Quality;Quality-oriented Self-Annotated Training|
|[Multiple Acoustic Features Speech Emotion Recognition Using Cross-Attention Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095777)|Y. He; N. Minematsu; D. Saito|10.1109/ICASSP49357.2023.10095777|speech emotion recognition;transformer;cross attention;multi features fusion;speech emotion recognition;transformer;cross attention;multi features fusion|
|[Graph Neural Networks for Object Type Classification Based on Automotive Radar Point Clouds and Spectra](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096657)|L. Saini; A. Acosta; G. Hakobyan|10.1109/ICASSP49357.2023.10096657|Radar;object type classification;graph neural networks;Radar;object type classification;graph neural networks|
|[Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094733)|D. Li; M. Che; W. Meng; Y. Wu; Y. Yu; F. Xia; W. Li|10.1109/ICASSP49357.2023.10094733|Playing technique detection;multi-scale net-work;self-attention;music information retrieval;Playing technique detection;multi-scale net-work;self-attention;music information retrieval|
|[FedPrompt: Communication-Efficient and Privacy-Preserving Prompt Tuning in Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095356)|H. Zhao; W. Du; F. Li; P. Li; G. Liu|10.1109/ICASSP49357.2023.10095356|FL;prompt;PLM;split learning;FL;prompt;PLM;split learning|
|[Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096540)|C. Huang; W. Tan; J. Shi; Z. Xing; B. Yan|10.1109/ICASSP49357.2023.10096540|Unsupervised image denoising;Aleatoric uncertainty;Unsupervised image denoising;Aleatoric uncertainty|
|[Unsupervised Extractive Summarization With Heterogeneous Graph Embeddings for Chinese Documents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097065)|C. Lin; Y. Liu; S. An; D. Yin|10.1109/ICASSP49357.2023.10097065|Extractive Text Summarization;Heterogeneous Graph Embeddings;Unsupervised Learning;Extractive Text Summarization;Heterogeneous Graph Embeddings;Unsupervised Learning|
|[Simple Pooling Front-Ends for Efficient Audio Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096211)|X. Liu; H. Liu; Q. Kong; X. Mei; M. D. Plumbley; W. Wang|10.1109/ICASSP49357.2023.10096211|Audio classification;audio front-ends;on-device;convolutional neural networks;deep learning;Audio classification;audio front-ends;on-device;convolutional neural networks;deep learning|
|[A Compensated Shrinkage Affine Projection Algorithm for Debiased Sparse Adaptive Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096735)|Y. Zhang; I. Yamada|10.1109/ICASSP49357.2023.10096735|adaptive filtering;affine projection algorithm;sparsity-aware processing;forward-backward splitting;adaptive filtering;affine projection algorithm;sparsity-aware processing;forward-backward splitting|
|[CTCBERT: Advancing Hidden-Unit Bert with CTC Objectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094922)|R. Fan; Y. Wang; Y. Gaur; J. Li|10.1109/ICASSP49357.2023.10094922|Self-supervised learning;HuBERT;CTC;CE;Self-supervised learning;HuBERT;CTC;CE|
|[Analysis and Re-Synthesis of Natural Cricket Sounds Assessing the Perceptual Relevance of Idiosyncratic Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096859)|M. Oliveira; V. Almeida; J. Silva; A. Ferreira|10.1109/ICASSP49357.2023.10096859|Cricket sounds;human auditory acuity;Cricket sounds;human auditory acuity|
|[Hierarchical Spatial-Temporal Transformer with Motion Trajectory for Individual Action and Group Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096109)|X. Zhu; D. Wang; Y. Zhou|10.1109/ICASSP49357.2023.10096109|Group activity recognition;motion trajectories;spatial-temporal Transformer;graph neural network;Group activity recognition;motion trajectories;spatial-temporal Transformer;graph neural network|
|[S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095473)|P. Kadam; H. Prajapati; M. Zhang; J. Xue; S. Liu; C. . -C. J. Kuo|10.1109/ICASSP49357.2023.10095473|point cloud classification;rotation invariance;PointHop;point cloud classification;rotation invariance;PointHop|
|[Salient Co-Speech Gesture Synthesizing with Discrete Motion Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095304)|Z. Ye; J. Jia; H. Wu; S. Huang; S. Sun; J. Xing|10.1109/ICASSP49357.2023.10095304|Co-speech gesture;Discrete motion representation;Temporal attention;Co-speech gesture;Discrete motion representation;Temporal attention|
|[Hierarchical Diffusion Models for Singing Voice Neural Vocoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095749)|N. Takahashi; M. Kumar; Singh; Y. Mitsufuji|10.1109/ICASSP49357.2023.10095749|neural vocoder;diffusion models;singing voice;neural vocoder;diffusion models;singing voice|
|[Period VITS: Variational Inference with Explicit Pitch Modeling for End-To-End Emotional Speech Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096480)|Y. Shirahata; R. Yamamoto; E. Song; R. Terashima; J. -M. Kim; K. Tachibana|10.1109/ICASSP49357.2023.10096480|Text-to-speech;end-to-end model;pitch modeling;variational inference;emotional speech;Text-to-speech;end-to-end model;pitch modeling;variational inference;emotional speech|
|[Sanet: Spatial Attention Network with Global Average Contrast Learning for Infrared Small Target Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096649)|J. Zhu; S. Chen; L. Li; L. Ji|10.1109/ICASSP49357.2023.10096649|Infrared small target detection;Global average contrast;Spatial attention;Spatial pyramid pooling;Infrared small target detection;Global average contrast;Spatial attention;Spatial pyramid pooling|
|[An ASR-Free Fluency Scoring Approach with Self-Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095311)|W. Liu; K. Fu; X. Tian; S. Shi; W. Li; Z. Ma; T. Lee|10.1109/ICASSP49357.2023.10095311|Fluency scoring;Automatic Speech Recognition;Non-native Speech;Self-Supervised Learning;Fluency scoring;Automatic Speech Recognition;Non-native Speech;Self-Supervised Learning|
|[Memory-Augmented U-Transformer For Multivariate Time Series Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096179)|S. Qin; Y. Luo; G. Tao|10.1109/ICASSP49357.2023.10096179|Transformer;Anomaly Detection;Multivariate Time Series;Memory Module;Transformer;Anomaly Detection;Multivariate Time Series;Memory Module|
|[Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based On Labeled Multi-Bernoulli Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095971)|H. Liu; J. Yang; Y. Xu; L. Yang|10.1109/ICASSP49357.2023.10095971|Distributed multi-sensor (DMS);multitarget tracking (MTT);generalized covariance intersection (GCI);labeled multi-Bernoulli (LMB);Distributed multi-sensor (DMS);multitarget tracking (MTT);generalized covariance intersection (GCI);labeled multi-Bernoulli (LMB)|
|[Rethink Pair-Wise Self-Supervised Cross-Modal Retrieval From A Contrastive Learning Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095914)|T. Gong; J. Wang; L. Zhang|10.1109/ICASSP49357.2023.10095914|Cross-modal retrieval;contrastive learning;self-supervised;instance learning;prototype learning;Cross-modal retrieval;contrastive learning;self-supervised;instance learning;prototype learning|
|[Grad-StyleSpeech: Any-Speaker Adaptive Text-to-Speech Synthesis with Diffusion Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095515)|M. Kang; D. Min; S. J. Hwang|10.1109/ICASSP49357.2023.10095515|speech synthesis;text-to-speech;any-speaker TTS;voice cloning;speech synthesis;text-to-speech;any-speaker TTS;voice cloning|
|[Classification via Subspace Learning Machine (SLM): Methodology and Performance Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096564)|H. Fu; Y. Yang; V. K. Mishra; C. . -C. Jay Kuo|10.1109/ICASSP49357.2023.10096564|Machine Learning;Classification;Subspace Learning;Machine Learning;Classification;Subspace Learning|
|[T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096172)|Y. Li; Z. Su; Y. Li; H. Zhang; S. Wang; W. Wu; Y. Zhang|10.1109/ICASSP49357.2023.10096172|Semantic Parsing;Text-to-SQL;Seq-to-Seq Decoding;Natural Language Processing;Semantic Parsing;Text-to-SQL;Seq-to-Seq Decoding;Natural Language Processing|
|[Two-Stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094881)|C. Park; M. Lee; S. Cho; D. Kim; S. Lee|10.1109/ICASSP49357.2023.10094881|Anomaly detection;industrial defect segmentation;autoencoder;Anomaly detection;industrial defect segmentation;autoencoder|
|[TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096642)|H. J. Park; S. Woo Yang; J. S. Kim; W. Shin; S. W. Han|10.1109/ICASSP49357.2023.10096642|adaptive attention normalization;any-to-any;siamese loss;voice conversion;adaptive attention normalization;any-to-any;siamese loss;voice conversion|
|[AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096460)|J. S. Kim; H. Joon Park; W. Shin; S. W. Han|10.1109/ICASSP49357.2023.10096460|sound event localization and detection;you only look once;angular distance;polyphony environment;sound event localization and detection;you only look once;angular distance;polyphony environment|
|[Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095386)|T. Kerepecky; J. Liu; X. W. Ng; D. W. Piston; U. S. Kamilov|10.1109/ICASSP49357.2023.10095386|Light-sheet fluorescence microscopy;Dual-view imaging;deep learning;image deconvolution;Light-sheet fluorescence microscopy;Dual-view imaging;deep learning;image deconvolution|
|[Global-Context Aware Generative Protein Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095229)|C. Tan; Z. Gao; J. Xia; B. Hu; S. Z. Li|10.1109/ICASSP49357.2023.10095229|Bio signal processing;compuatational biology;structural biology;protein design;deep learning;Bio signal processing;compuatational biology;structural biology;protein design;deep learning|
|[Bag of Tricks with Quantized Convolutional Neural Networks for Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095157)|J. Hu; M. Zeng; E. Wu|10.1109/ICASSP49357.2023.10095157|Model Quantization;Acceleration;Convolutional Neural Networks;Image Classification;Model Quantization;Acceleration;Convolutional Neural Networks;Image Classification|
|[Semi-Supervised Speech Enhancement Based On Speech Purity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096093)|Z. Cui; S. Zhang; Y. Chen; Y. Gao; C. Deng; J. Feng|10.1109/ICASSP49357.2023.10096093|speech enhancement;semi-supervised learning;speech purity;deep neural network;speech enhancement;semi-supervised learning;speech purity;deep neural network|
|[Modify: Model-Driven Face Stylization Without Style Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095222)|Y. Ding; J. Liang; J. Cao; A. Zheng; R. He|10.1109/ICASSP49357.2023.10095222|face stylization;test-time training;face stylization;test-time training|
|[Precognition in Contextual Spoken Language Understanding via Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095750)|N. Su; B. Du; Y. Zhang; C. Liu; Y. Wang; H. Chen; X. Lu|10.1109/ICASSP49357.2023.10095750|Spoken language understanding;Multi-turn dialog system;Knowledge distillation;Deep mutual learning;Variational Information Maximization;Posterior regularization;Spoken language understanding;Multi-turn dialog system;Knowledge distillation;Deep mutual learning;Variational Information Maximization;Posterior regularization|
|[GaPP: Multi-Target Tracking with Gaussian Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095337)|F. Goodyer; B. I. Ahmad; S. Godsill|10.1109/ICASSP49357.2023.10095337|Multi-target tracking;Gaussian process;Poisson process;online parameter learning;particle filter;Multi-target tracking;Gaussian process;Poisson process;online parameter learning;particle filter|
|[Adversarial Network Pruning by Filter Robustness Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095291)|X. Zhuang; Y. Ge; B. Zheng; Q. Wang|10.1109/ICASSP49357.2023.10095291|Neural network pruning;structured pruning;adversarial training;adversarial example;Neural network pruning;structured pruning;adversarial training;adversarial example|
|[Leveraging Phone-Level Linguistic-Acoustic Similarity For Utterance-Level Pronunciation Scoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096699)|W. Liu; K. Fu; X. Tian; S. Shi; W. Li; Z. Ma; T. Lee|10.1109/ICASSP49357.2023.10096699|Linguistic-Acoustic Similarity;Phone Embed-ding;Goodness of Pronunciation;Pronunciation Scoring;Linguistic-Acoustic Similarity;Phone Embed-ding;Goodness of Pronunciation;Pronunciation Scoring|
|[CTTSR: A Hybrid CNN-Transformer Network for Scene Text Image Super-Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097214)|K. Dai; N. Kang; L. Kuang|10.1109/ICASSP49357.2023.10097214|scene text super-resolution;transformer;multi-head attention mechanism;text recognition;text position;scene text super-resolution;transformer;multi-head attention mechanism;text recognition;text position|
|[CF-VTON: Multi-Pose Virtual Try-on with Cross-Domain Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095176)|C. Du; S. Xiong|10.1109/ICASSP49357.2023.10095176|virtual try-on;pose transfer;appearance flow;conditional image synthesis;human parsing and understanding;virtual try-on;pose transfer;appearance flow;conditional image synthesis;human parsing and understanding|
|[In Search of Strong Embedding Extractors for Speaker Diarisation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096449)|J. -W. Jung; H. -S. Heo; B. -J. Lee; J. Huh; A. Brown; Y. Kwon; S. Watanabe; J. S. Chung|10.1109/ICASSP49357.2023.10096449|speaker diarisation;speaker verification;data augmentation;evaluation protocol;speaker diarisation;speaker verification;data augmentation;evaluation protocol|
|[Towards Making a Trojan-Horse Attack on Text-to-Image Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096984)|F. Hu; A. Chen; X. Li|10.1109/ICASSP49357.2023.10096984|Text-to-image retrieval;Trojan-horse attack;adversarial patch generation;Text-to-image retrieval;Trojan-horse attack;adversarial patch generation|
|[Elastic Graph Transformer Networks for EEG-Based Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096511)|W. -B. Jiang; X. Yan; W. -L. Zheng; B. -L. Lu|10.1109/ICASSP49357.2023.10096511|EEG;eye movements;emotion recognition;graph transformer;EEG;eye movements;emotion recognition;graph transformer|
|[Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095156)|K. You; B. Liu; K. Xu; Y. Xiong; Q. Xu; M. Feng; T. G. Csapó; B. Zhu|10.1109/ICASSP49357.2023.10095156|Self-supervised learning;Mask modeling;Masked auto-encoder;Ultrasound tongue imaging.;Self-supervised learning;Mask modeling;Masked auto-encoder;Ultrasound tongue imaging.|
|[Content-Insensitive Dynamic Lip Feature Extraction for Visual Speaker Authentication Against Deepfake Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096249)|Z. Guo; S. Wang|10.1109/ICASSP49357.2023.10096249|Dynamic feature extraction;visual speaker authentication;deepfake attacks;contrastive learning;Dynamic feature extraction;visual speaker authentication;deepfake attacks;contrastive learning|
|[Sample-Aware Knowledge Distillation for Long-Tailed Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096016)|S. Zheng; Y. Zhang; H. Huang; Y. Qu|10.1109/ICASSP49357.2023.10096016|Long-tail;imbalance;knowledge distillation;sample-aware;Long-tail;imbalance;knowledge distillation;sample-aware|
|[A3S: Adversarial Learning of Semantic Representations for Scene-Text Spotting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096434)|M. Fujitake|10.1109/ICASSP49357.2023.10096434|Scene-text spotting;Document analysis;Deep learning;Scene-text spotting;Document analysis;Deep learning|
|[A Novel Cross-Component Context Model for End-to-End Wavelet Image Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096867)|A. Meyer; A. Kaup|10.1109/ICASSP49357.2023.10096867|lifting scheme;neural image compression;compressive autoencoder;wavelet image coding;cross-component;lifting scheme;neural image compression;compressive autoencoder;wavelet image coding;cross-component|
|[Multi-Head Uncertainty Inference for Adversarial Attack Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097229)|Y. Yang; S. Yang; J. Xie; Z. Si; K. Guo; K. Zhang; K. Liang|10.1109/ICASSP49357.2023.10097229|Uncertainty inference;adversarial attack detection;image recognition;Dirichlet distribution;Uncertainty inference;adversarial attack detection;image recognition;Dirichlet distribution|
|[ESCL: Equivariant Self-Contrastive Learning for Sentence Representations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096142)|J. Liu; Y. Liu; X. Han; C. Deng; J. Feng|10.1109/ICASSP49357.2023.10096142|Natural Language Processing;Representation Learning;Pre-trained Language Models;Contrastive Learning;Natural Language Processing;Representation Learning;Pre-trained Language Models;Contrastive Learning|
|[Vision Transformer-Based Feature Extraction for Generalized Zero-Shot Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095217)|J. Kim; K. Shim; J. Kim; B. Shim|10.1109/ICASSP49357.2023.10095217|Generalized zero-shot learning;Vision Transformer;Generalized zero-shot learning;Vision Transformer|
|[A Robust Kalman Filter Based Approach for Indoor Robot Positionning with Multi-Path Contaminated UWB Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096761)|J. Cano; Y. Ding; G. Pages; E. Chaumette; J. Le Ny|10.1109/ICASSP49357.2023.10096761|UWB;Robotics;Navigation;RKF;UWB;Robotics;Navigation;RKF|
|[Semi-Supervised Sound Event Detection with Pre-Trained Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095687)|L. Xu; L. Wang; S. Bi; H. Liu; J. Wang|10.1109/ICASSP49357.2023.10095687|sound event detection;mean-teacher;pre-trained;temporal contrastive loss;sound event detection;mean-teacher;pre-trained;temporal contrastive loss|
|[Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097176)|K. Wilkinghoff|10.1109/ICASSP49357.2023.10097176|anomalous sound detection;representation learning;domain generalization;machine listening;anomalous sound detection;representation learning;domain generalization;machine listening|
|[Tensor-based Complex-valued Graph Neural Network for Dynamic Coupling Multimodal brain Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095707)|Y. Yang; G. Cai; C. Ye; Y. Xiang; T. Ma|10.1109/ICASSP49357.2023.10095707|Multimodal;Tensor;Graph neural network;Neuroimage;Gating;Multimodal;Tensor;Graph neural network;Neuroimage;Gating|
|[Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095900)|J. Zeng; Y. Liu; G. Cheung; W. Hu|10.1109/ICASSP49357.2023.10095900|Sparse graph learning;graph convolutional networks;graph signal processing;Sparse graph learning;graph convolutional networks;graph signal processing|
|[Divcon: Learning Concept Sequences for Semantically Diverse Image Captioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094565)|Y. Zheng; Y. -L. Li; S. Wang|10.1109/ICASSP49357.2023.10094565|Image Captioning;Diverse Image Captions;Semantic Diversity;Semantic Concepts;Image Captioning;Diverse Image Captions;Semantic Diversity;Semantic Concepts|
|[EBEN: Extreme Bandwidth Extension Network Applied To Speech Signals Captured With Noise-Resilient Body-Conduction Microphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096301)|J. Hauret; T. Joubaud; V. Zimpfer; É. Bavu|10.1109/ICASSP49357.2023.10096301|Speech enhancement;PQMF-banks;Bandwidth extension;Frugal AI;Signal Processing;Speech enhancement;PQMF-banks;Bandwidth extension;Frugal AI;Signal Processing|
|[Long Range Imaging Using Multispectral Fusion of RGB and NIR Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095148)|H. Zhang; L. Mei; C. Jung|10.1109/ICASSP49357.2023.10095148|Image fusion;attention mechanism;convolutional neural network;hidden texture;long-range imaging;pyramid feature selection;Image fusion;attention mechanism;convolutional neural network;hidden texture;long-range imaging;pyramid feature selection|
|[FedEEG: Federated EEG Decoding Via inter-Subject Structure Matching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095564)|W. Hang; J. Li; S. Liang; Y. Wu; B. Lei; J. Qin; Y. Zhang; K. -S. Choi|10.1109/ICASSP49357.2023.10095564|Electroencephalogram (EEG);Federated learning;Deep learning;Discriminative feature;Class center;Electroencephalogram (EEG);Federated learning;Deep learning;Discriminative feature;Class center|
|[Visual Onoma-to-Wave: Environmental Sound Synthesis from Visual Onomatopoeias and Sound-Source Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096517)|H. Ohnaka; S. Takamichi; K. Imoto; Y. Okamoto; K. Fujii; H. Saruwatari|10.1109/ICASSP49357.2023.10096517|Environmental sound synthesis;onomatopoeia;visual text;deep neural network;Environmental sound synthesis;onomatopoeia;visual text;deep neural network|
|[Asynchronous Social Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096238)|M. Cemri; V. Bordignon; M. Kayaalp; V. Shumovskaia; A. H. Sayed|10.1109/ICASSP49357.2023.10096238|Social learning;asynchronous updates;adaptive social learning;graph learning;Social learning;asynchronous updates;adaptive social learning;graph learning|
|[Untargeted Backdoor Attack Against Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095980)|C. Luo; Y. Li; Y. Jiang; S. -T. Xia|10.1109/ICASSP49357.2023.10095980|Backdoor Attack;Object Detection;Physical Attack;Trustworthy ML;AI Security;Backdoor Attack;Object Detection;Physical Attack;Trustworthy ML;AI Security|
|[Improving Contextual Biasing with Text Injection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096287)|T. N. Sainath; R. Prabhavalkar; D. Caseiro; P. Rondon; C. Allauzen|10.1109/ICASSP49357.2023.10096287|end-to-end ASR;contextual biasing;end-to-end ASR;contextual biasing|
|[Noise PSD Insensitive RTF Estimation in a Reverberant and Noisy Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094840)|C. Li; R. C. Hendriks|10.1109/ICASSP49357.2023.10094840|RTF estimation;spatial filter;Eigenvalue Decomposition;RTF estimation;spatial filter;Eigenvalue Decomposition|
|[Error Analysis of Convolutional Beamspace Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095614)|P. -C. Chen; P. P. Vaidyanathan|10.1109/ICASSP49357.2023.10095614|Convolutional beamspace;DOA estimation;MUSIC;MSE;Convolutional beamspace;DOA estimation;MUSIC;MSE|
|[Unitary Esprit for Coprime Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096262)|P. -C. Chen; P. P. Vaidyanathan|10.1109/ICASSP49357.2023.10096262|Unitary ESPRIT;coprime arrays;multiple invariances;DOA estimation;pairing problem;Unitary ESPRIT;coprime arrays;multiple invariances;DOA estimation;pairing problem|
|[UX-Net: Filter-and-Process-Based Improved U-Net for real-time time-domain audio Separation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095038)|K. Patel; A. Kovalyov; I. Panahi|10.1109/ICASSP49357.2023.10095038|Speech separation;multi-channel processing;neural networks;recurrent networks;real-time processing;Speech separation;multi-channel processing;neural networks;recurrent networks;real-time processing|
|[SARdBScene: Dataset and Resnet Baseline for Audio Scene Source Counting and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097115)|M. Nigro; S. Krishnan|10.1109/ICASSP49357.2023.10097115|Audio scene analysis;audio source counting;acoustic scene classification;speaker counting;sound event detection;Audio scene analysis;audio source counting;acoustic scene classification;speaker counting;sound event detection|
|[CD-FSOD: A Benchmark For Cross-Domain Few-Shot Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096216)|W. Xiong|10.1109/ICASSP49357.2023.10096216|Few-shot Object Detection;Cross-domain;Few-shot Object Detection;Cross-domain|
|[Fast and Accurate Factorized Neural Transducer for Text Adaption of End-to-End Speech Recognition Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096895)|R. Zhao; J. Xue; P. Parthasarathy; V. Miljanic; J. Li|10.1109/ICASSP49357.2023.10096895|neural transducer model;factorized transducer model;KL divergence;n-gram;neural transducer model;factorized transducer model;KL divergence;n-gram|
|[Augmentation Robust Self-Supervised Learning for Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096151)|C. Xu; Y. Li; D. Lee; D. Hoon Park; H. Mao; H. Do; J. Chung; D. Nair|10.1109/ICASSP49357.2023.10096151|Human Activity Recognition;Self-supervised learning;Human Activity Recognition;Self-supervised learning|
|[SPADE: Self-Supervised Pretraining for Acoustic Disentanglement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096561)|J. Harvill; J. Barber; A. Nair; R. Pishehvar|10.1109/ICASSP49357.2023.10096561|keyword spotting;source localization;self-supervised pretraining;disentanglement;acoustics;keyword spotting;source localization;self-supervised pretraining;disentanglement;acoustics|
|[Unified Keyword Spotting and Audio Tagging on Mobile Devices with Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095534)|H. Dinkel; Y. Wang; Z. Yan; J. Zhang; Y. Wang|10.1109/ICASSP49357.2023.10095534|Keyword spotting;Audio tagging;Vision Transformers;weakly supervised learning;Keyword spotting;Audio tagging;Vision Transformers;weakly supervised learning|
|[Anomalous Sound Detection Using Audio Representation with Machine ID Based Contrastive Learning Pretraining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096054)|J. Guan; F. Xiao; Y. Liu; Q. Zhu; W. Wang|10.1109/ICASSP49357.2023.10096054|Anomalous sound detection;metadata information;contrastive learning;self-supervised learning;Anomalous sound detection;metadata information;contrastive learning;self-supervised learning|
|[Cross-Modality depth Estimation via Unsupervised Stereo RGB-to-infrared Translation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095982)|S. Tang; X. Ye; F. Xue; R. Xu|10.1109/ICASSP49357.2023.10095982|Depth estimation;Stereo;Cross-modality;Infrared;Image translation;Depth estimation;Stereo;Cross-modality;Infrared;Image translation|
|[Solving Jigsaw Puzzle of Large Eroded Gaps Using Puzzlet Discriminant Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096300)|X. Song; X. Yang; J. Ren; R. Bai; X. Jiang|10.1109/ICASSP49357.2023.10096300|Puzzle Reassembly;Puzzlet Discriminant Network;Genetic Aglorithm;Combinatorial Optimization;Puzzle Reassembly;Puzzlet Discriminant Network;Genetic Aglorithm;Combinatorial Optimization|
|[Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096683)|D. Shan; Z. Li; W. Chen; Q. Li; J. Tian; Q. Hong|10.1109/ICASSP49357.2023.10096683|Coarse-to-Fine;Vision-Language;Semantic Segmentation;Coarse-to-Fine;Vision-Language;Semantic Segmentation|
|[A New Semi-Supervised Classification Method Using a Supervised Autoencoder for Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094856)|C. Gille; F. Guyard; M. Barlaud|10.1109/ICASSP49357.2023.10094856|Semi-supervised learning;Autoencoder neural networks;Semi-supervised learning;Autoencoder neural networks|
|[A Frequency-Domain Recursive Least-Squares Adaptive Filtering Algorithm Based On A Kronecker Product Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095470)|H. He; J. Chen; J. Benesty; Y. Yu|10.1109/ICASSP49357.2023.10095470|Acoustic system identification;frequencydomain adaptive filter;Kronecker product decomposition;recursive least-squares (RLS) algorithm;Acoustic system identification;frequencydomain adaptive filter;Kronecker product decomposition;recursive least-squares (RLS) algorithm|
|[Asymptotic Bias and Variance of Kernel Ridge Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096774)|V. Solo|10.1109/ICASSP49357.2023.10096774|bias;variance;kernel trick;reproducing kernel Hilbert space;ridge regression;bias;variance;kernel trick;reproducing kernel Hilbert space;ridge regression|
|[Rethinking Implicit Neural Representations For Vision Learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094875)|Y. Song; Q. Zhou; L. Ma|10.1109/ICASSP49357.2023.10094875|Implicit Neural Representations;Computer Vision;Pattern Recognition;Representation Learning;Implicit Neural Representations;Computer Vision;Pattern Recognition;Representation Learning|
|[Streaming Voice Conversion via Intermediate Bottleneck Features and Non-Streaming Teacher Guidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096066)|Y. Chen; M. Tu; T. Li; X. Li; Q. Kong; J. Li; Z. Wang; Q. Tian; Y. Wang; Y. Wang|10.1109/ICASSP49357.2023.10096066|voice conversion (VC);streaming VC;intermediate bottleneck features (IBFs);teacher guidance (TG);voice conversion (VC);streaming VC;intermediate bottleneck features (IBFs);teacher guidance (TG)|
|[Learning Sparse auto-Encoders for Green AI image coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096638)|C. Gille; F. Guyard; M. Antonini; M. Barlaud|10.1109/ICASSP49357.2023.10096638|;|
|[Bias Identification with RankPix Saliency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097093)|S. Konate; L. Lebrat; R. S. Cruz; C. Fookes; A. Bradley; O. Salvado|10.1109/ICASSP49357.2023.10097093|Saliency method;Explainability;Bias identification;Saliency method;Explainability;Bias identification|
|[Efficient Multi-Scale Attention Module with Cross-Spatial Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096516)|D. Ouyang; S. He; G. Zhang; M. Luo; H. Guo; J. Zhan; Z. Huang|10.1109/ICASSP49357.2023.10096516|Attention mechanism;cross-dimension interaction;image classification;object detection;Attention mechanism;cross-dimension interaction;image classification;object detection|
|[Pseudo-Query Generation For Semi-Supervised Visual Grounding With Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095558)|J. Jin; J. Ye; X. Lin; L. He|10.1109/ICASSP49357.2023.10095558|Visual grounding;semi-supervised;pseudoquery;knowledge distillation;Visual grounding;semi-supervised;pseudoquery;knowledge distillation|
|[One-Shot Neural Band Selection for Spectral Recovery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096000)|H. -M. Hu; Z. Xu; W. Xu; Y. Song; Y. Zhang; L. Liu; Z. Han; A. Meng|10.1109/ICASSP49357.2023.10096000|Band Selection;Spectral Recovery;Hyperspectral Image Processing;Band Selection;Spectral Recovery;Hyperspectral Image Processing|
|[Neural Band-to-Piano Score Arrangement with Stepless Difficulty Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095462)|M. Terao; E. Nakamura; K. Yoshii|10.1109/ICASSP49357.2023.10095462|Automatic piano arrangement;score reduction;symbolic music processing;deep learning;Automatic piano arrangement;score reduction;symbolic music processing;deep learning|
|[Interference Leakage Minimization in RIS-Assisted MIMO Interference Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094656)|I. Santamaria; M. Soleymani; E. Jorswieck; J. Gutiérrez|10.1109/ICASSP49357.2023.10094656|Reconfigurable intelligent surface (RIS);interference channel;multiple-input multiple-output;interference leakage minimization;Reconfigurable intelligent surface (RIS);interference channel;multiple-input multiple-output;interference leakage minimization|
|[HTNet: Human Topology aware network for 3d Human pose estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095949)|J. Cai; H. Liu; R. Ding; W. Li; J. Wu; M. Ban|10.1109/ICASSP49357.2023.10095949|3D Human Pose Estimation;Human Topology;Error Accumulation;Hierarchical Structure;3D Human Pose Estimation;Human Topology;Error Accumulation;Hierarchical Structure|
|[Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094712)|B. D. Weerdt; Y. C. Eldar; N. Deligiannis|10.1109/ICASSP49357.2023.10094712|deep unfolding;Transformer networks;sparse recovery;compressed sensing;deep unfolding;Transformer networks;sparse recovery;compressed sensing|
|[Fast 3D Human Pose Estimation Using RF Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094778)|C. Yu; D. Zhang; Z. Wu; C. Xie; Z. Lu; Y. Hu; Y. Chen|10.1109/ICASSP49357.2023.10094778|Human Pose Estimation;RF Sensing;Lightweight Deep Learning Model;Human Pose Estimation;RF Sensing;Lightweight Deep Learning Model|
|[Knowledge-Aware Graph Convolutional Network with Utterance-Specific Window Search for Emotion Recognition In Conversations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095097)|X. Zhang; P. He; H. Liu; Z. Yin; X. Liu; X. Zhang|10.1109/ICASSP49357.2023.10095097|Conversational emotion recognition;graph neural networks;knowledge graph;Conversational emotion recognition;graph neural networks;knowledge graph|
|[BadRes: Reveal the Backdoors Through Residual Connection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094691)|M. He; T. Chen; H. Zhou; S. Zhang; J. Li|10.1109/ICASSP49357.2023.10094691|Backdoor attack;neural networks;residual connection;Backdoor attack;neural networks;residual connection|
|[Framewise Multiple Sound Source Localization and Counting Using Binaural Spatial Audio Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096463)|L. Wang; Z. Jiao; Q. Zhao; J. Zhu; Y. Fu|10.1109/ICASSP49357.2023.10096463|Binaural localization;multiple source localization;deep learning;multi-label classification;Binaural localization;multiple source localization;deep learning;multi-label classification|
|[Kernel Estimation and Deconvolution for Blind Image Super-Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094753)|J. Gong; H. Gao; J. Chao; Z. Zhou; Z. Yang; Z. Zeng|10.1109/ICASSP49357.2023.10094753|Super-resolution;blind super-resolution;blur kernel;kernel estimation;kernel deconvolution;Super-resolution;blind super-resolution;blur kernel;kernel estimation;kernel deconvolution|
|[Dual Meta Calibration Mix for Improving Generalization in Meta-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096594)|Z. -Y. Mi; Y. -B. Yang|10.1109/ICASSP49357.2023.10096594|Meta-Learning;Few-Shot Learning;Data Augmentation;Domain Generalization;Meta-Learning;Few-Shot Learning;Data Augmentation;Domain Generalization|
|[Tensorized LSSVMS For Multitask Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094580)|J. Liu; Q. Tao; C. Zhu; Y. Liu; J. A. K. Suykens|10.1109/ICASSP49357.2023.10094580|Multitask learning;tensor regression;CP decomposition;LSSVM;shared factor;Multitask learning;tensor regression;CP decomposition;LSSVM;shared factor|
|[An Improved Optimal Transport Kernel Embedding Method with Gating Mechanism for Singing Voice Separation and Speaker Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096651)|W. Yuan; Y. Bian; S. Wang; M. Unoki; W. Wang|10.1109/ICASSP49357.2023.10096651|Optimal transport;optimal transport kernel embedding;gating mechanism;singing voice separation;speaker identification;Optimal transport;optimal transport kernel embedding;gating mechanism;singing voice separation;speaker identification|
|[A Unitary Transform Based Generalized Approximate Message Passing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095346)|J. Zhu; X. Meng; X. Lei; Q. Guo|10.1109/ICASSP49357.2023.10095346|GLM;AMP;GAMP;message passing;quantized compressed sensing;GLM;AMP;GAMP;message passing;quantized compressed sensing|
|[Infrared and Visible Image Fusion by Using Multi-Scale Transformation and Fractional-Order Gradient Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096652)|S. Wu; K. Zhang; X. Yuan; C. Zhao|10.1109/ICASSP49357.2023.10096652|Image fusion;fractional-order gradient;MDLatLRR;norm optimization;Image fusion;fractional-order gradient;MDLatLRR;norm optimization|
|[Optimal Transport with a Diversified Memory Bank for Cross-Domain Speaker Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095876)|R. Zhang; J. Wei; X. Lu; W. Lu; D. Jin; L. Zhang; J. Xu|10.1109/ICASSP49357.2023.10095876|Speaker verification;domain adaptation;optimal transport;diversified memory bank;self-paced learning;Speaker verification;domain adaptation;optimal transport;diversified memory bank;self-paced learning|
|[BHE-DARTS: Bilevel Optimization Based on Hypergradient Estimation for Differentiable Architecture Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095940)|Z. Cai; L. Chen; H. -L. Liu|10.1109/ICASSP49357.2023.10095940|Neural Architecture Search;Bilevel Optimization;Jacobian- and Hessian-vector product;Neural Architecture Search;Bilevel Optimization;Jacobian- and Hessian-vector product|
|[Not All Classes are Equal: Adaptively Focus-Aware Confidence for Semi-Supervised Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095538)|H. Zhu; Y. Lu; H. Zhao; G. Zhao; X. Zhao|10.1109/ICASSP49357.2023.10095538|Semi-supervised object detection;Learning status;Adaptive confidence threshold;Memory dictionary;Semi-supervised object detection;Learning status;Adaptive confidence threshold;Memory dictionary|
|[Combining Dual-Tree Wavelet Analysis and Proximal Optimization for Anisotropic Scale-Free Texture Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096738)|L. Davy; N. Pustelnik; P. Abry|10.1109/ICASSP49357.2023.10096738|Anisotropy;scale-free;texture;dual-tree;total-variation;proximal algorithms;strong convexity;Anisotropy;scale-free;texture;dual-tree;total-variation;proximal algorithms;strong convexity|
|[Interaction-Assisted Multi-Modal Representation Learning for Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095080)|H. Wu; J. Wang; Z. Zu|10.1109/ICASSP49357.2023.10095080|;|
|[Transaudio: Towards the Transferable Adversarial Audio Attack Via Learning Contextualized Perturbations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096873)|G. Qi; Y. Chen; Y. Zhu; B. Hui; X. Li; X. Mao; R. Zhang; H. Xue|10.1109/ICASSP49357.2023.10096873|Automatic speech recognition system;transferable adversarial attack;contextualized perturbation;Automatic speech recognition system;transferable adversarial attack;contextualized perturbation|
|[Semi-Supervised Remote Sensing Image Change Detection Using Mean Teacher Model for Constructing Pseudo-Labels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097089)|Z. Mao; X. Tong; Z. Luo|10.1109/ICASSP49357.2023.10097089|change detection;semi-supervised;consistency;mean teacher;change detection;semi-supervised;consistency;mean teacher|
|[SAR Image Despeckling with Residual-in-Residual Dense Generative Adversarial Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096355)|Y. Bai; Y. Xiao; X. Hou; Y. Li; C. Shang; Q. Shen|10.1109/ICASSP49357.2023.10096355|SAR;despeckling;generative adversarial network;residual learning;dense connection;SAR;despeckling;generative adversarial network;residual learning;dense connection|
|[Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097031)|G. Xu; J. Yang; J. Guo; Z. Huang; B. Zhang|10.1109/ICASSP49357.2023.10097031|session based recommendation;user intention;number of item occurrences;GNN;session based recommendation;user intention;number of item occurrences;GNN|
|[Co-Design for Mimo Radar and Mimo Communication Aided by Reconfigurable Intelligent Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095160)|D. Li; B. Tang; L. Xue|10.1109/ICASSP49357.2023.10095160|MIMO;co-design;reconfigurable intelligent surface (RIS);ADMM;MIMO;co-design;reconfigurable intelligent surface (RIS);ADMM|
|[CLMAE: A Liter and Faster Masked Autoencoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096059)|Y. Song; L. Ma|10.1109/ICASSP49357.2023.10096059|Pre-training;masked autoencoders;Pre-training;masked autoencoders|
|[GCC-Speaker: Target Speaker Localization with Optimal Speaker-Dependent Weighting in Multi-Speaker Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095551)|G. Li; W. Xue; W. Liu; J. Yi; J. Tao|10.1109/ICASSP49357.2023.10095551|Target speaker localization;speaker-dependent weighting;generalized cross-correlation;Target speaker localization;speaker-dependent weighting;generalized cross-correlation|
|[MID-Attribute Speaker Generation Using Optimal-Transport-Based Interpolation of Gaussian Mixture Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097113)|A. Watanabe; S. Takamichi; Y. Saito; D. Xin; H. Saruwatari|10.1109/ICASSP49357.2023.10097113|speech synthesis;cross-lingual speech synthesis;multi-speaker speech synthesis;speaker generation;speech synthesis;cross-lingual speech synthesis;multi-speaker speech synthesis;speaker generation|
|[M2-CTTS: End-to-End Multi-Scale Multi-Modal Conversational Text-to-Speech Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096905)|J. Xue; Y. Deng; F. Wang; Y. Li; Y. Gao; J. Tao; J. Sun; J. Liang|10.1109/ICASSP49357.2023.10096905|speech synthesis;conversational TTS;prosody;multi-grained;multi-modal;speech synthesis;conversational TTS;prosody;multi-grained;multi-modal|
|[Nord: Non-Matching Reference Based Relative Depth Estimation from Binaural Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094615)|P. Manocha; I. D. Gebru; A. Kumar; D. Markovic; A. Richard|10.1109/ICASSP49357.2023.10094615|depth estimation;binaural audio depth;binaural metrics;differentiable metric;objective audio quality;depth perception assessment;depth estimation;binaural audio depth;binaural metrics;differentiable metric;objective audio quality;depth perception assessment|
|[ROI-Based Deep Image Compression with Swin Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094674)|B. Li; J. Liang; H. Fu; J. Han|10.1109/ICASSP49357.2023.10094674|Learned image compression;region-of-interest;Swin transformers;Learned image compression;region-of-interest;Swin transformers|
|[Interpretability in the Context of Sequential Cost-Sensitive Feature Acquisition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094701)|Y. W. Liyanage; D. Zois|10.1109/ICASSP49357.2023.10094701|model–based interpretability;instance– level sparsity;glass–box models;explainability;datum–wise decisions;model–based interpretability;instance– level sparsity;glass–box models;explainability;datum–wise decisions|
|[Neural Transducer Training: Reduced Memory Consumption with Sample-Wise Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095544)|S. Braun; E. McDermott; R. Hsiao|10.1109/ICASSP49357.2023.10095544|Neural transducer;memory;loss;benchmark;Neural transducer;memory;loss;benchmark|
|[Multi-Resolution Location-Based Training for Multi-Channel Continuous Speech Separation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096684)|H. Taherian; D. Wang|10.1109/ICASSP49357.2023.10096684|Continuous speech separation;complex spectral mapping;location-based training;Continuous speech separation;complex spectral mapping;location-based training|
|[CyPMLI: WISL-Minimized Unimodular Sequence Design via Power Method-Like Iterations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096061)|A. Eamaz; F. Yeganegi; M. Soltanalian|10.1109/ICASSP49357.2023.10096061|Auto-correlation;power method-like iterations;radar signals;unimodular sequences;weighted integrated sidelobe level;Auto-correlation;power method-like iterations;radar signals;unimodular sequences;weighted integrated sidelobe level|
|[Weight-Sharing Supernet for Searching Specialized Acoustic Event Classification Networks Across Device Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096117)|G. -T. Lin; Q. Tang; C. -C. Kao; V. Rozgic; C. Wang|10.1109/ICASSP49357.2023.10096117|Acoustic Event Classification;AudioSet;Neural Architecture Search;Weight-sharing Supernet;Knowledge Distillation;Acoustic Event Classification;AudioSet;Neural Architecture Search;Weight-sharing Supernet;Knowledge Distillation|
|[Fast Convolution Algorithm for Real-Valued Finite Length Sequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096394)|W. Wang; V. DeBrunner; L. S. DeBrunner|10.1109/ICASSP49357.2023.10096394|BF convolution;FFT-based convolution;DHT-based convolution;RV-based convolution;BF convolution;FFT-based convolution;DHT-based convolution;RV-based convolution|
|[Do Prosody Transfer Models Transfer Prosodyƒ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095131)|A. T. Sigurgeirsson; S. King|10.1109/ICASSP49357.2023.10095131|prosody modeling and generation;prosody transfer;speech synthesis;prosody modeling and generation;prosody transfer;speech synthesis|
|[Do Coarser Units Benefit Cluster Prediction-Based Speech Pre-Training?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096788)|A. Elkahky; W. -N. Hsu; P. Tomasello; T. -A. Nguyen; R. Algayres; Y. Adi; J. Copet; E. Dupoux; A. Mohamed|10.1109/ICASSP49357.2023.10096788|self-supervision;representation learning;unit discovery;self-supervision;representation learning;unit discovery|
|[Learned Video Coding with Motion Compensation Mixture Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094757)|K. Q. Dinh; K. Pyo Choi|10.1109/ICASSP49357.2023.10094757|motion compensation;mixture model;motion compensation;mixture model|
|[HEiMDaL: Highly Efficient Method for Detection and Localization of Wake-Words](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097018)|A. Kundu; M. Samragh; M. Cho; P. Padmanabhan; D. Naik|10.1109/ICASSP49357.2023.10097018|Keyword spotting;voice assistants;wake-word detection;detection;localization;BC-ResNet;Keyword spotting;voice assistants;wake-word detection;detection;localization;BC-ResNet|
|[Spherical Vector Quantization for Spatial Direction Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095626)|S. Ragot; A. Vasilache|10.1109/ICASSP49357.2023.10095626|Spherical codes;vector quantization;3D audio;direction of arrival (DoA);Spherical codes;vector quantization;3D audio;direction of arrival (DoA)|
|[Sequential Datum–Wise Joint Feature Selection and Classification in the Presence of External Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097057)|S. P. Ekanayake; D. Zois; C. Chelmis|10.1109/ICASSP49357.2023.10097057|supervised classification;instance–wise feature selection;dynamic programming;inaccurate oracle;high–performance classifier;supervised classification;instance–wise feature selection;dynamic programming;inaccurate oracle;high–performance classifier|
|[Multi-Scale Compositional Constraints for Representation Learning on Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096573)|G. Paraskevopoulos; C. Lavania; L. Chum; S. Sundaram|10.1109/ICASSP49357.2023.10096573|Multimodal;Contrastive Learning;Audiovisual processing;Action Recognition;Compositionality;Multimodal;Contrastive Learning;Audiovisual processing;Action Recognition;Compositionality|
|[Active Learning of non-Semantic Speech Tasks with Pretrained models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096465)|H. Lee; A. Saeed; A. L. Bertozzi|10.1109/ICASSP49357.2023.10096465|active learning;audio;non-semantic speech;self-supervised learning;transfer learning;active learning;audio;non-semantic speech;self-supervised learning;transfer learning|
|[A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097148)|L. Lin; J. Gao|10.1109/ICASSP49357.2023.10097148|Directed Graph;Graph Convolutional Neural Network;Magnetic Laplacian;Graph Framelets;Graph Framelet Transform;Directed Graph;Graph Convolutional Neural Network;Magnetic Laplacian;Graph Framelets;Graph Framelet Transform|
|[Multi-Output RNN-T Joint Networks for Multi-Task Learning of ASR and Auxiliary Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096273)|W. Wang; D. Zhao; S. Ding; H. Zhang; S. -Y. Chang; D. Rybach; T. N. Sainath; Y. He; I. McGraw; S. Kumar|10.1109/ICASSP49357.2023.10096273|End-to-end ASR;RNN-Transducer;joint network;capitalization;pause prediction;End-to-end ASR;RNN-Transducer;joint network;capitalization;pause prediction|
|[Visual-Aware Text-to-Speech*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095084)|M. Zhou; Y. Bai; W. Zhang; T. Yao; T. Zhao; T. Mei|10.1109/ICASSP49357.2023.10095084|Text to Speech;Face to Face Interaction;Digital Human;Text to Speech;Face to Face Interaction;Digital Human|
|[Output-Dependent Gaussian Process State-Space Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095784)|Z. Lin; L. Cheng; F. Yin; L. Xu; S. Cui|10.1109/ICASSP49357.2023.10095784|Gaussian process state-space model;linear model of coregionalization;variational inference;sparse Gaussian process;Gaussian process state-space model;linear model of coregionalization;variational inference;sparse Gaussian process|
|[A Mathematical Model for Neuronal Activity and Brain Information Processing Capacity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095567)|Y. Zheng; D. Zhu; J. Ren; T. Liu; K. Friston; T. Li|10.1109/ICASSP49357.2023.10095567|;|
|[Attention Based Relation Network for Facial Action Units Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095414)|Y. Wei; H. Wang; M. Sun; J. Liu|10.1109/ICASSP49357.2023.10095414|Facial action unit recognition;Attention mechanism;AU relation learning;Facial action unit recognition;Attention mechanism;AU relation learning|
|[Learning Hybrid Representations of Semantics and Distortion for Blind Image Quality Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096087)|X. Wang; J. Xiong; B. Li; J. Suo; H. Gao|10.1109/ICASSP49357.2023.10096087|Blind image quality assessment;knowledge distillation;distortion classification;multi-task learning;Blind image quality assessment;knowledge distillation;distortion classification;multi-task learning|
|[Affinity Learning With Blind-Spot Self-Supervision for Image Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095804)|Y. Zhou; L. Zhou; I. H. Laradji; T. Lun Lam; Y. Xu|10.1109/ICASSP49357.2023.10095804|image denoising;affinity learning;blindspot denoising;image denoising;affinity learning;blindspot denoising|
|[A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096834)|H. Pi; S. Tian; M. Lu; J. Liu; Y. Guo; S. Zhang|10.1109/ICASSP49357.2023.10096834|Super-Resolution;VR Video Projection;Omnidirectional SR;Deep Neural Network;Super-Resolution;VR Video Projection;Omnidirectional SR;Deep Neural Network|
|[APGP: Accuracy-Preserving Generative Perturbation for Defending Against Model Cloning Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094956)|A. Cheng; J. Cheng|10.1109/ICASSP49357.2023.10094956|Model cloning;Knowledge distillation;Intellectual property protection;Generative model;Model cloning;Knowledge distillation;Intellectual property protection;Generative model|
|[HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-wave radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096835)|Y. Huang; Y. Wang; K. Shi; C. Gu; Y. Fu; C. Zhuo; Z. Shi|10.1109/ICASSP49357.2023.10096835|Millimeter-wave radar;gait recognition;point flow;hierarchical neural network;dynamic sampling;Millimeter-wave radar;gait recognition;point flow;hierarchical neural network;dynamic sampling|
|[Liveness Score-Based Regression Neural Networks for Face Anti-Spoofing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095535)|Y. Kwak; M. Jung; H. Yoo; J. Shin; C. Kim|10.1109/ICASSP49357.2023.10095535|Face anti-spoofing;Regression neural network;Label encoding;Bio-metrics;Face anti-spoofing;Regression neural network;Label encoding;Bio-metrics|
|[Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096444)|F. G. Veshki; S. A. Vorobyov|10.1109/ICASSP49357.2023.10096444|;|
|[Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096995)|Z. You; Y. Yan; Z. Shi; M. Zhao; J. Yan; H. Liu; X. Hei; X. Ren|10.1109/ICASSP49357.2023.10096995|Laryngeal leukoplakia;classification;dense multiscale convolutional neural network;white light endoscope images;Laryngeal leukoplakia;classification;dense multiscale convolutional neural network;white light endoscope images|
|[String-Based Molecule Generation Via Multi-Decoder VAE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095212)|K. Kwon; K. Jeong; J. Park; H. Na; J. Shin|10.1109/ICASSP49357.2023.10095212|Ensemble;generative models;out-of-distribution;auto-regressive;novelty;Ensemble;generative models;out-of-distribution;auto-regressive;novelty|
|[Contrastive Domain Adaptation Via Delimitation Discriminator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095783)|X. Wei; B. Wen; L. Chen; Y. Liu; C. Zhao; Y. Lu|10.1109/ICASSP49357.2023.10095783|Domain adaptation;Contrastive learning;Adversarial learning;Image classification;Domain adaptation;Contrastive learning;Adversarial learning;Image classification|
|[Towards Robust Audio-Based Vehicle Detection Via Importance-Aware Audio-Visual Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096773)|J. U. Kim; S. Tae Kim|10.1109/ICASSP49357.2023.10096773|Audio-based vehicle detection;audiovisual integration;contrastive learning;deep learning;Audio-based vehicle detection;audiovisual integration;contrastive learning;deep learning|
|[Modulation-Based Center Alignment and Motion Mining for Spatial Temporal Action Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095853)|W. Zhao; K. Huang; C. Zhang|10.1109/ICASSP49357.2023.10095853|Action detection;Action Center Alignment;Sparse self-attention;Motion Mining;Action detection;Action Center Alignment;Sparse self-attention;Motion Mining|
|[Randmasking Augment: A Simple and Randomized Data Augmentation For Acoustic Scene Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095001)|J. Han; M. Matuszewski; O. Sikorski; H. Sung; H. Cho|10.1109/ICASSP49357.2023.10095001|Acoustic scene classification;data augmentation;randomized masking;masking augmentation;Acoustic scene classification;data augmentation;randomized masking;masking augmentation|
|[MHSCNET: A Multimodal Hierarchical Shot-Aware Convolutional Network for Video Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096265)|W. Xu; R. Wang; X. Guo; S. Li; Q. Ma; Y. Zhao; S. Guo; Z. Zhu; J. Yan|10.1109/ICASSP49357.2023.10096265|Video Summarization;Shot-aware Representation;Multimodal information;Signal Processing;Video Summarization;Shot-aware Representation;Multimodal information;Signal Processing|
|[Defending Against Universal Patch Attacks by Restricting Token Attention in Vision Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096862)|H. Yu; J. Chen; H. Ma; C. Yu; X. Ding|10.1109/ICASSP49357.2023.10096862|adversarial defense;universal adversarial patch;vision transformer;self attention;adversarial defense;universal adversarial patch;vision transformer;self attention|
|[Two-Stage Video De-Raining with Spatio-Temporal Fusion and Illumination-Invariant Detail Preservation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094963)|Y. Tan; Y. Xiang; L. Cai; P. Wang; Y. Zhang; Y. Fu|10.1109/ICASSP49357.2023.10094963|Video de-raining;spatio-temporal information;illumination variance;detail preservation;Video de-raining;spatio-temporal information;illumination variance;detail preservation|
|[AugTarget Data Augmentation for Infrared Small Target Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095060)|S. Chen; J. Zhu; L. Ji; H. Pan; Y. Xu|10.1109/ICASSP49357.2023.10095060|Infrared small target detection;Data augmentation;Target & batch augmentation;Infrared small target detection;Data augmentation;Target & batch augmentation|
|[MMATR: A Lightweight Approach for Multimodal Sentiment Analysis Based on Tensor Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097030)|P. Koromilas; M. A. Nicolaou; T. Giannakopoulos; Y. Panagakis|10.1109/ICASSP49357.2023.10097030|multimodal sentiment analysis;tensor methods;multimodal sentiment analysis;tensor methods|
|[Real-Time Speech Enhancement with Dynamic Attention Span](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095821)|C. Zheng; Y. Zhou; X. Peng; Y. Zhang; Y. Lu|10.1109/ICASSP49357.2023.10095821|speech enhancement;acoustic echo cancellation;noise suppression;time-variance;attention;speech enhancement;acoustic echo cancellation;noise suppression;time-variance;attention|
|[DQFORMER: Dynamic Query Transformer for Lane Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097047)|H. Yang; S. Lin; R. Jiang; Y. Lu; H. Wang|10.1109/ICASSP49357.2023.10097047|Lane detection;transformer;dynamic query;line voting;Lane detection;transformer;dynamic query;line voting|
|[CROSSSPEECH: Speaker-Independent Acoustic Representation for Cross-Lingual Speech Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096929)|J. -H. Kim; H. -S. Yang; Y. -C. Ju; I. -H. Kim; B. -Y. Kim|10.1109/ICASSP49357.2023.10096929|text-to-speech;speech synthesis;cross-lingual TTS;speaker generalization;text-to-speech;speech synthesis;cross-lingual TTS;speaker generalization|
|[Focusing on Targets for Improving Weakly Supervised Visual Grounding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096489)|V. -Q. Pham; N. Mishima|10.1109/ICASSP49357.2023.10096489|visual grounding;weakly supervised;cropping;dependency parsing;visual grounding;weakly supervised;cropping;dependency parsing|
|[Lightweight Portrait Segmentation Via Edge-Optimized Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096654)|X. Zhang; G. Wang; L. Yang; C. Chen|10.1109/ICASSP49357.2023.10096654|Portrait segmentation;Lightweight Edge-optimized Attention;Portrait segmentation;Lightweight Edge-optimized Attention|
|[Leveraging Sparsity with Spiking Recurrent Neural Networks for Energy-Efficient Keyword Spotting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097174)|M. Dampfhoffer; T. Mesquida; E. Hardy; A. Valentian; L. Anghel|10.1109/ICASSP49357.2023.10097174|Spiking neural networks;keyword spotting;speech commands;energy-efficiency;sparsity;Spiking neural networks;keyword spotting;speech commands;energy-efficiency;sparsity|
|[TSPTQ-ViT: Two-Scaled Post-Training Quantization for Vision Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096817)|Y. -S. Tai; M. -G. Lin; A. -Y. A. Wu|10.1109/ICASSP49357.2023.10096817|Model compression;vision transformer;post-training quantization;Model compression;vision transformer;post-training quantization|
|[Explanations for Automatic Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094635)|X. Wu; P. Bell; A. Rajan|10.1109/ICASSP49357.2023.10094635|Explanation;Automatic Speech Recognition;Explanation;Automatic Speech Recognition|
|[Contrast-PLC: Contrastive Learning for Packet Loss Concealment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096217)|H. Xue; X. Peng; Y. Lu|10.1109/ICASSP49357.2023.10096217|packet loss concealment;contrastive learning;speech synthesis;self-supervised learning;packet loss concealment;contrastive learning;speech synthesis;self-supervised learning|
|[Efficient Large-Scale Audio Tagging Via Transformer-to-CNN Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096110)|F. Schmid; K. Koutini; G. Widmer|10.1109/ICASSP49357.2023.10096110|Audio Tagging;AudioSet;Patchout Audio Transformer;MobileNetV3;Knowledge Distillation;Audio Tagging;AudioSet;Patchout Audio Transformer;MobileNetV3;Knowledge Distillation|
|[Masking Speech Contents by Random Splicing: is Emotional Expression Preserved?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097094)|F. Burkhardt; A. Derington; M. Kahlau; K. Scherer; F. Eyben; B. Schuller|10.1109/ICASSP49357.2023.10097094|speech;emotional;random splicing;anonymization;masking;speech;emotional;random splicing;anonymization;masking|
|[On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094846)|Z. Bukhsh; A. Saeed|10.1109/ICASSP49357.2023.10094846|out-of-distribution;audio;speech;uncertainty estimation;deep learning;nearest neighbors;out-of-distribution;audio;speech;uncertainty estimation;deep learning;nearest neighbors|
|[Improving Scheduled Sampling for Neural Transducer-Based ASR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095826)|T. Moriya; T. Ashihara; H. Sato; K. Matsuura; T. Tanaka; R. Masumura|10.1109/ICASSP49357.2023.10095826|speech recognition;neural network;end-to-end;neural transducer;scheduled sampling;speech recognition;neural network;end-to-end;neural transducer;scheduled sampling|
|[Estimating Inharmonic Signals with Optimal Transport Priors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095082)|F. Elvander|10.1109/ICASSP49357.2023.10095082|Inharmonicity;spectral estimation;frequency estimation;optimal mass transport;Inharmonicity;spectral estimation;frequency estimation;optimal mass transport|
|[Gluformer: Transformer-based Personalized glucose Forecasting with uncertainty quantification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10096419)|R. Sergazinov; M. Armandpour; I. Gaynanova|10.1109/ICASSP49357.2023.10096419|wearable devices;time series;calibration;probabilistic modeling;wearable devices;time series;calibration;probabilistic modeling|
#### **2023 6th International Conference on Information Systems and Computer Networks (ISCON)**
- DOI: 10.1109/ISCON57294.2023
- DATE: 3-4 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Estimation of Recall Values and Accuracy of Gender Identification for the Different Age Groups Based on Voice Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112070)|A. Singhal; D. K. Sharma|10.1109/ISCON57294.2023.10112070|Age;Mel Frequency Cepstral Coefficients;Recall Values;Recurrent Neural Network - Bidirectional Long Short-Term Memory;Gender Identification;Age;Mel Frequency Cepstral Coefficients;Recall Values;Recurrent Neural Network - Bidirectional Long Short-Term Memory;Gender Identification|
|[Deep Learning and Data Mining Techniques For Cardiovascular Disease Prediction: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112062)|S. Pandey; R. Kaur; B. Sharma|10.1109/ISCON57294.2023.10112062|Cardiovascular problems;Machine Learning;Deep Learning;Data Mining;Classification techniques and Prediction;Cardiovascular problems;Machine Learning;Deep Learning;Data Mining;Classification techniques and Prediction|
|[AI-Based Techniques for the Detection of Neuro-Developmental Disorders: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112000)|A. Gupta; D. Malhotra|10.1109/ISCON57294.2023.10112000|Neuro Developmental Disorder (NDD);Central Nervous System (CNS);Autism Spectrum Disorder (ASD);Attention Deficit Hyperactivity Disorder (ADHD);Childhood Disintegrative Disorder (CDD);Neuro Developmental Disorder (NDD);Central Nervous System (CNS);Autism Spectrum Disorder (ASD);Attention Deficit Hyperactivity Disorder (ADHD);Childhood Disintegrative Disorder (CDD)|
|[Investigating Challenges to Internet of Things (IoT)Applications and Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111967)|S. Khattar; R. Kaur; T. Verma; B. Sharma|10.1109/ISCON57294.2023.10111967|IoT;Technologies;RFID;Challenges;Applications;IoT;Technologies;RFID;Challenges;Applications|
|[URL based Phishing Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112184)|M. Shoaib; M. S. Umar|10.1109/ISCON57294.2023.10112184|Cyber Security;Phishing;Phishing Detection;URL;Machine Learning;Deep Learning;Cyber Security;Phishing;Phishing Detection;URL;Machine Learning;Deep Learning|
|[Impact of Telepresence of Hotel Websites on Behavioral Intention of Indian consumers: A Select Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112008)|U. Khandelwal; A. Sharma; A. Panicker|10.1109/ISCON57294.2023.10112008|Telepresence;Hotel Websites;Behavioral Intentions;Indian consumers;Telepresence;Hotel Websites;Behavioral Intentions;Indian consumers|
|[ML Algorithms that have been Utilised to Classify Neuro-Developmental Disorders: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112157)|S. Pandey; S. Sharma|10.1109/ISCON57294.2023.10112157|Artificial Intelligence;Autism;Autism Spectrum Disorder;Autism Diagnostic Interview-Revised;Neuro Developmental Disorder;Support Vector Machine;Artificial Intelligence;Autism;Autism Spectrum Disorder;Autism Diagnostic Interview-Revised;Neuro Developmental Disorder;Support Vector Machine|
|[Improving the quality of Monocular Depth Estimation using Ensemble learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112150)|A. Ali; R. Ali; M. F. Baig|10.1109/ISCON57294.2023.10112150|Convolutional neural networks;Densely connected networks;Residual networks;SLAM;MobileNet;Convolutional neural networks;Densely connected networks;Residual networks;SLAM;MobileNet|
|[Skin Segmentation and SVM for Identification and Spotlighting of Hand Gesture for ISLR System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112063)|U. Rastogi; A. Pandey; V. Kumar|10.1109/ISCON57294.2023.10112063|Indian Sign Language Recognition system (ISLR);Indian Sign Language (ISL);Skin Segmentation;Color Space;Support Vector Machine (SVM);Indian Sign Language Recognition system (ISLR);Indian Sign Language (ISL);Skin Segmentation;Color Space;Support Vector Machine (SVM)|
|[Activity Recognition System via Unification of CNN and SVM in Complex Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111982)|P. Choudhary; P. Pathak; A. Chaubey|10.1109/ISCON57294.2023.10111982|Cooking Activity Recognition;Cooking State Recognition;CNN;Human Action Recognition;Object Recognition;SVM;etc;Cooking Activity Recognition;Cooking State Recognition;CNN;Human Action Recognition;Object Recognition;SVM;etc|
|[Post-Processing Deblocking Technique for Reduction of Blocking Artifacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112084)|A. K. Sandhu|10.1109/ISCON57294.2023.10112084|Image compression;blocking artifacts;smooth;detailed;intermediate;blur;Image compression;blocking artifacts;smooth;detailed;intermediate;blur|
|[Cloud Base Intrusion Detection System using Convolutional and Supervised Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112007)|A. K. Shukla; A. Sharma|10.1109/ISCON57294.2023.10112007|IDS;Cloud Computing;Machine Learning;SVM;CNN;KNN;IDS;Cloud Computing;Machine Learning;SVM;CNN;KNN|
|[Classification and Mitigation of DDOS attacks Based on Self-Organizing Map and Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111988)|A. K. Shukla; A. Sharma|10.1109/ISCON57294.2023.10111988|DDos Attacks Classification;Machine Learning;Cloud computing;SVM;SOM;DDos Attacks Classification;Machine Learning;Cloud computing;SVM;SOM|
|[Implementation and Comparison of Artificial Intelligence Techniques in Software Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112041)|I. Verma; D. Kumar; R. Goel|10.1109/ISCON57294.2023.10112041|Artificial Intelligence;Machine Learning;Deep Learning;Software Testing;Test Automation;Artificial Intelligence;Machine Learning;Deep Learning;Software Testing;Test Automation|
|[An Overview of Bio-Inspired and Deep Learning Model for Extraction of Land Use Pattern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111962)|Chatrabhuj; K. Meshram|10.1109/ISCON57294.2023.10111962|Machine learning;Bio-inspired model;Land use pattern;Deep learning;Image processing;Machine learning;Bio-inspired model;Land use pattern;Deep learning;Image processing|
|[Impact of Social Media Like WhatsApp for Education Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111966)|S. Gawade; G. Hegde; P. S. Marulkar; A. Bhansali|10.1109/ISCON57294.2023.10111966|Education and e-learning;Heatmap Analysis;ICT-Information and Communication Technology;Regression Analysis;WhatsApp tool;Education and e-learning;Heatmap Analysis;ICT-Information and Communication Technology;Regression Analysis;WhatsApp tool|
|[A Comprehensive Survey on Learning Based Methods for Link Prediction Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112010)|S. U. Balvir; M. M. Raghuwanshi; K. R. Singh|10.1109/ISCON57294.2023.10112010|Link Prediction;Learning Based Methods;Prediction Methods;Social Networks;Network Analysis;Link Prediction;Learning Based Methods;Prediction Methods;Social Networks;Network Analysis|
|[Distributed Attacks Classification Based on Radical Basis Function and Particle Swarm Optimization In Hypervisor Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112162)|A. K. Shukla; A. Sharma|10.1109/ISCON57294.2023.10112162|Cloud;security;Attack;Machine learning;Detection;Cloud;security;Attack;Machine learning;Detection|
|[Data Preprocessing for Stock Price Prediction Using LSTM and Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112026)|A. S. Rajpurohit; H. Mhaske; P. S. Gaikwad; S. P. Ahirrao; N. B. Dhamale|10.1109/ISCON57294.2023.10112026|Data Wrangling;preprocessing;Stock Prediction;Sentiment Analysis;Long short term Memory;Recurrent Neural Network;Logistic Regression.;Data Wrangling;preprocessing;Stock Prediction;Sentiment Analysis;Long short term Memory;Recurrent Neural Network;Logistic Regression.|
|[An Ultra-Compact 4-Port MIMO Antenna for Multiband Applications Including Bluetooth and UWB with Integrated Band-Stop Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112155)|V. Kikan; G. Jaitly; A. Dagar; S. Singh; N. C. Deo; S. Singh; A. Kumar; M. Sharma|10.1109/ISCON57294.2023.10112155|Ultra-compact;Bluetooth-UWB;four-port MIMO;WiMAX;WLAN;Ultra-compact;Bluetooth-UWB;four-port MIMO;WiMAX;WLAN|
|[Enhanced Biometric Cryptosystem Using Ear & Iris Modality Based On Binary Robust Independent Elementary Feature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112197)|P. Kaur; N. Kumar|10.1109/ISCON57294.2023.10112197|Key handling;BRIEF;BCS;ear biometric;iris biometric;Key handling;BRIEF;BCS;ear biometric;iris biometric|
|[Auto Safety Technology With Enhanced Facial Recognition To Prevent Replay Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112087)|S. Khairnar; S. Dahake; R. Gaikwad; S. D. Thepade; B. Patil; A. Chaudhari|10.1109/ISCON57294.2023.10112087|Anti-spoofing;face recognition;security;image processing;binarization;TSBTC;face liveliness detection;replay attack dataset;Anti-spoofing;face recognition;security;image processing;binarization;TSBTC;face liveliness detection;replay attack dataset|
|[A Novel Metaheuristic Approach for Efficient Data Dissemination based on Ideal Decision in VANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112005)|D. Gupta; R. Rathi|10.1109/ISCON57294.2023.10112005|data dissemination;autonomous vehicular network;security;priority scheduling;metaheuristic;data dissemination;autonomous vehicular network;security;priority scheduling;metaheuristic|
|[Machine Learning based Food Demand Estimation for Restaurants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112059)|N. K. Pandey; A. K. Mishra; V. Kumar; A. Kumar; M. Diwakar; N. Tripathi|10.1109/ISCON57294.2023.10112059|Machine Learning;Random Forest;Gradient Regressor.;Machine Learning;Random Forest;Gradient Regressor.|
|[Modeling Reliability Growth among Different Issue Types for Multi-Version Open Source Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112061)|S. Singh; M. Mehrotra; T. S. Bharti|10.1109/ISCON57294.2023.10112061|OSS;Prediction models;Multi-release software;Software Reliability Modelling;Imperfect Debugging;OSS;Prediction models;Multi-release software;Software Reliability Modelling;Imperfect Debugging|
|[Mitigation and Comparison of Chromatic Dispersion at Distinct Frequencies Using Hybrid Model at A Bit Rate of 100Gbps over 120Km Distance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112148)|A. Verma; S. Kumar; I. Singh; A. Kumar; R. K. Saini; S. K. Moharana|10.1109/ISCON57294.2023.10112148|Fiber Bragg Grating;UFBG;Electronic Dispersion Compensation (EDC);Erbium Doped Fiber Amplifier (EDFA);Quality-factor;Bit error rate (BER);Chromatic Dispersion (CD);OptiSystem 7.0;Fiber Bragg Grating;UFBG;Electronic Dispersion Compensation (EDC);Erbium Doped Fiber Amplifier (EDFA);Quality-factor;Bit error rate (BER);Chromatic Dispersion (CD);OptiSystem 7.0|
|[Security and Privacy of Digital Data of Smart Cities: An Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112107)|S. Kumar; A. Jayanthiladevi|10.1109/ISCON57294.2023.10112107|Healthcare;Smart Cities;Privacy protection;Cyber-physical systems;Information security;Healthcare;Smart Cities;Privacy protection;Cyber-physical systems;Information security|
|[Devanagari Lipi Recognition Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111956)|P. Pathak; N. Pachauri; P. Tiwari|10.1109/ISCON57294.2023.10111956|Devanagari Lipi;Hindi letter recognition;classification;convolutional neural network;Devanagari Lipi;Hindi letter recognition;classification;convolutional neural network|
|[Identifying Anti-Social Activities in Surveillance Monitoring Applications using Deep-CNN based Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112113)|A. Jaggi; A. Aggarwal; A. Gupta|10.1109/ISCON57294.2023.10112113|Surveillance;machine learning;transfer learning;convolutional;YOLOv7;Surveillance;machine learning;transfer learning;convolutional;YOLOv7|
|[Efficacy of Femvertising: The novel Instrument for Digital Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112046)|U. Khandelwal; P. Arora|10.1109/ISCON57294.2023.10112046|Digital Marketing;Social Media Marketing;Advertising;Facebook;Instagram;YouTube;Gender;Femvertising.;Digital Marketing;Social Media Marketing;Advertising;Facebook;Instagram;YouTube;Gender;Femvertising.|
|[Software Fault Prediction using Wrapper based Ant Colony Optimization Algorithm for Feature Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111995)|S. Mondal; A. K. Sahu; H. Kumar; R. M. Pattanayak; M. K. Gourisaria; H. Das|10.1109/ISCON57294.2023.10111995|feature selection;software fault prediction;ant colony optimization;pheromone;fitness;feature selection;software fault prediction;ant colony optimization;pheromone;fitness|
|[Investigating the Decline in Vegetation Cover of Gautam Buddha Nagar District using GIS and Satellite Remote Sensing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112171)|D. Dinesh; K. Pandey; S. Shukla; V. Kumar; H. C. Karnatak|10.1109/ISCON57294.2023.10112171|NDVI;Remote Sensing;GIS;Change Detection;Radiometric correction;NDVI;Remote Sensing;GIS;Change Detection;Radiometric correction|
|[Analysis of Factors Influencing The Use of QRIS Through Mobile Banking in Jakarta and Tangerang](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111978)|J. Husin; S. Cordelia; V. D. Christophilus; N. Limantara|10.1109/ISCON57294.2023.10111978|TAM (Technology Acceptance Model);QRIS;Digital Payment;Mobile Banking Applications;Barcode;TAM (Technology Acceptance Model);QRIS;Digital Payment;Mobile Banking Applications;Barcode|
|[Hybrid Machine Learning Technique for Prediction of Phishing Websites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112166)|S. Kumar; G. P. Dubey; B. Gupta|10.1109/ISCON57294.2023.10112166|Artificial Intelligence;Machine Learning;Hybrid;and Phishing Websites;Artificial Intelligence;Machine Learning;Hybrid;and Phishing Websites|
|[A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111944)|P. Mandloi; G. P. Dubey; P. S. Chauhan|10.1109/ISCON57294.2023.10111944|Covid;Patient;Prediction;Dataset;Artificial Intelligence are all potential key terms here.;Covid;Patient;Prediction;Dataset;Artificial Intelligence are all potential key terms here.|
|[A LSTM based Deep Learning Model for Smart Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111964)|B. Mandloi; G. P. Dubey; K. Tahiliani|10.1109/ISCON57294.2023.10111964|LSTM;Smart Industry 4.0;AI;Machine Learning;IOT;Robotics;LSTM;Smart Industry 4.0;AI;Machine Learning;IOT;Robotics|
|[Effective Security Mechanisms against Distributed Denial of Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111999)|K. Gaur; K. Gaur; T. Sachdeva; M. Diwakar; P. Singh; N. K. Pandey|10.1109/ISCON57294.2023.10111999|Honeypot;SnowDeeP;Encrypted Marking based Detection and Filtering;IP address spoofing;Distributed Denial of Service attack;Honeypot;SnowDeeP;Encrypted Marking based Detection and Filtering;IP address spoofing;Distributed Denial of Service attack|
|[PSO based Web Documents Prioritization for Adaptive Websites using multi-Criteria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112161)|S. Kumar; A. Garg; M. Haider|10.1109/ISCON57294.2023.10112161|PSO;Genetic Algorithm;Metaheuristic Optimization;Website reorganization;PSO;Genetic Algorithm;Metaheuristic Optimization;Website reorganization|
|[A Comparative Study of Various Learning Models for Object Detection in Contextual Scene Interpretation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112043)|T. Singh; D. H. Shrivastava|10.1109/ISCON57294.2023.10112043|Convolution Neural Network;Recursive Neural Network;Deep Learning;Machine learning;Small Object;Salient object;Convolution Neural Network;Recursive Neural Network;Deep Learning;Machine learning;Small Object;Salient object|
|[Optimized Full Adder-Subtractor in QCA for nano-computing applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112074)|V. Jain; D. K. Sharma; H. M. Gaur|10.1109/ISCON57294.2023.10112074|QCA;Full Adder;Full Subtractor;Full Adder-Subtractor;Cost Function;QCA;Full Adder;Full Subtractor;Full Adder-Subtractor;Cost Function|
|[CNN based Leaves Disease Detection in Potato Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112080)|R. Verma; R. Mishra; P. Gupta; Pooja; S. Trivedi|10.1109/ISCON57294.2023.10112080|Convolutional Neural Network (CNN);TensorFlow;Machine learning;plant leaf;Image processing;VggNet;ResNet;AlexNet;Convolutional Neural Network (CNN);TensorFlow;Machine learning;plant leaf;Image processing;VggNet;ResNet;AlexNet|
|[Underwater Image Enhancement using Convolutional Block Attention Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111974)|N. Singh; A. Bhat|10.1109/ISCON57294.2023.10111974|Image processing;deep learning;Underwater Image Enhancement;Convolution Block Attention Module (CBAM;Underwater Image Enhancement Benchmark (UIEB);Image processing;deep learning;Underwater Image Enhancement;Convolution Block Attention Module (CBAM;Underwater Image Enhancement Benchmark (UIEB)|
|[A Retrospective: Sightseeing Excursion of Threatened Miscarriage Pertaining Ensemble Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111961)|S. Singh; S. Tiwari; P. Goel; D. Tiwari|10.1109/ISCON57294.2023.10111961|Machine Learning;Ensemble Learning;Voting Classifier;Bagging;Ada-Boost;Machine Learning;Ensemble Learning;Voting Classifier;Bagging;Ada-Boost|
|[Free Space and Lane Boundary Fault Recognition and Prediction for Independent Vehicles Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112006)|S. Dangi; D. Kumar|10.1109/ISCON57294.2023.10112006|Fault Prediction;Lane Boundary;Autonomous system Vehicles;Self-Driving Cars;Free space;Fault Prediction;Lane Boundary;Autonomous system Vehicles;Self-Driving Cars;Free space|
|[A Hybrid GA-FF based Ranking Method for Reorganizing Website Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112051)|S. Kumar; H. K. Singh; B. Tripathi|10.1109/ISCON57294.2023.10112051|Web Mining;Genetic Algorithm Metaheuristics optimization;Firefly algorithm;Web Mining;Genetic Algorithm Metaheuristics optimization;Firefly algorithm|
|[An Insight into Machine Learning Techniques to Detect Anomalous Users](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112179)|P. Kumar; A. Kumar; K. Banerjee; A. Paharia; A. Singh; A. Chaudhary|10.1109/ISCON57294.2023.10112179|Cyber security;machine learning;abnormal behaviour detection;user profiling;account classification;RF (Random Forest);DT (Decision Tree);anomaly detection;SVM (Support Vector Machine);Cyber security;machine learning;abnormal behaviour detection;user profiling;account classification;RF (Random Forest);DT (Decision Tree);anomaly detection;SVM (Support Vector Machine)|
|[Light-Weight Deep Learning Model for Human Action Recognition in Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111975)|R. Kumar; S. Kumar|10.1109/ISCON57294.2023.10111975|Transfer Learning;CNN;VGG19;UCF50;Transfer Learning;CNN;VGG19;UCF50|
|[A Novel Machine Learning and Deep Learning Driven Prediction for Pre-diabetic Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112131)|S. Shahakar; P. Chopde; N. Purohit; A. Vishwakarma; A. Nite; A. K. Sharma|10.1109/ISCON57294.2023.10112131|Prediction;Machine Learning;Deep Learning;Glycemic Index;Fruit;Pre-diabetic Patients;CNN;Prediction;Machine Learning;Deep Learning;Glycemic Index;Fruit;Pre-diabetic Patients;CNN|
|[User Adaptive Video Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112154)|V. Tiwari; C. Bhatnagar|10.1109/ISCON57294.2023.10112154|user adaptive;query;attention;interactive video summarization;perception based;user adaptive;query;attention;interactive video summarization;perception based|
|[A Survey of Software Defects Research Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112194)|F. Meng; R. Huang; J. Wang|10.1109/ISCON57294.2023.10112194|deep learning;defect prediction;defect identification;defect analysis;bug report assignment;deep learning;defect prediction;defect identification;defect analysis;bug report assignment|
|[Music Recommendation System through Hand Gestures and Facial Emotions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112159)|M. Chaudhry; S. Kumar; S. Q. Ganie|10.1109/ISCON57294.2023.10112159|Convolutional Neural Network;Facial Expression Recognizer;TensorFlow;Mediapipe;Hand Gesture Recognition;Pygame;Feature extraction;Emotion detection;Tkinter;Music Player;Webcam;Convolutional Neural Network;Facial Expression Recognizer;TensorFlow;Mediapipe;Hand Gesture Recognition;Pygame;Feature extraction;Emotion detection;Tkinter;Music Player;Webcam|
|[A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111989)|A. K. Yadav; R. K. Bharti; R. S. Raw|10.1109/ISCON57294.2023.10111989|Logical space;Public Cloud;Hardware-level Security;Port Zoning;Raw device mapping;Logical space;Public Cloud;Hardware-level Security;Port Zoning;Raw device mapping|
|[Early Prediction of Stampede In Assemblage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112152)|K. Kaur; R. Kumar; P. Kumar|10.1109/ISCON57294.2023.10112152|machine learning;sensor data;stampede.;machine learning;sensor data;stampede.|
|[DeepLBS: A deep Convolutional Neural Network-Based Ligand-Binding Site Prediction Tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112034)|R. Semwal; I. Aier; P. Tyagi; U. Raj; P. K. Varadwaj|10.1109/ISCON57294.2023.10112034|Convolutional neural network;deep neural network;ligand binding site;machine learning;proteomics;Convolutional neural network;deep neural network;ligand binding site;machine learning;proteomics|
|[A Comprehensive Study of Image Inpainting Techniques with Algorithmic approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112205)|T. Shanmukhaprasanthi; S. M. Rayavarapu; Y. L. Lavanya; G. S. Rao|10.1109/ISCON57294.2023.10112205|Image inpainting;GAN(Generative Adversarial Network);Diffusion based inpainting;Convolution based inpainting;Exemplar based inpainting;Image inpainting;GAN(Generative Adversarial Network);Diffusion based inpainting;Convolution based inpainting;Exemplar based inpainting|
|[Heart Disease Evaluation with Deep Learning and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112002)|S. Upadhyay; S. K. Singh; J. K. Goswami; S. S. Singh|10.1109/ISCON57294.2023.10112002|Heart Disease;DL;ML;SVM;RNN;LSTM;Heart Disease;DL;ML;SVM;RNN;LSTM|
|[An Improved HVC based blind watermarking algorithm using SVD and DWT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111993)|A. Toofani; H. Garg|10.1109/ISCON57294.2023.10111993|Watermarking;Discrete wavelet transform;Singular value decomposition;Human visual characteristics;Watermarking;Discrete wavelet transform;Singular value decomposition;Human visual characteristics|
|[Interpretation and Analysis of Machine Learning Models for Brain Stroke Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112188)|R. Kumari; H. Garg|10.1109/ISCON57294.2023.10112188|Brain stroke;LIME;ML models;Gradient Boost;Brain stroke;LIME;ML models;Gradient Boost|
|[Classification of Mental Health and Emotion of Human from Text using Machine Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111973)|T. Madhu Midhan; P. Selvaraj; M. Harshavardan Kumar Raju.; M. Bhanu Prakash Reddy.; T. Bhaskar.|10.1109/ISCON57294.2023.10111973|Machine Learning (ML);Natural Language Processing (NLP);Emotions;Mental Health;Text Sentences;Text-Based Emotions;Machine Learning (ML);Natural Language Processing (NLP);Emotions;Mental Health;Text Sentences;Text-Based Emotions|
|[Colon Cancer Tissue Classification Using ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112181)|A. Tripathi; K. Kumar; A. Misra; B. K. Chaurasia|10.1109/ISCON57294.2023.10112181|colorectal cancer;classification;ML;histopathological image classification;differential-box-count;colorectal cancer;classification;ML;histopathological image classification;differential-box-count|
|[Machine Learning based Efficient Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112048)|A. Pokhriyal; D. Jha; G. S. Srivastava; H. Jha|10.1109/ISCON57294.2023.10112048|Recommender systems;Decision Tree;Content Based Filtering;Collaborative Filtering;Hybrid Filtering;KNN;CNN;LSTM;Recommender systems;Decision Tree;Content Based Filtering;Collaborative Filtering;Hybrid Filtering;KNN;CNN;LSTM|
|[Alzheimer’s Diseases Detection by using Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112095)|H. Shetty; H. Surlekar; G. Nagare|10.1109/ISCON57294.2023.10112095|Alzheimer’s disease;Dementia;Deep Learning;Artificial Intelligence;Neurological Examination;Alzheimer’s disease;Dementia;Deep Learning;Artificial Intelligence;Neurological Examination|
|[Q-Learning Based Optimized Localization in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112130)|P. Yadav; S. C. Sharma|10.1109/ISCON57294.2023.10112130|WSN;Localization;Machine Learning;K-fold;Q-Learning;AOMDV;KNN;ANN;SVM.;WSN;Localization;Machine Learning;K-fold;Q-Learning;AOMDV;KNN;ANN;SVM.|
|[Handwritten Image Detection using DCGAN with SIFT and ORB Optical Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112139)|R. Dubey; I. Das|10.1109/ISCON57294.2023.10112139|Handwriting Detection;GAN;Dense SIFT;SIFT;ORB;Dense ORB;Handwriting Detection;GAN;Dense SIFT;SIFT;ORB;Dense ORB|
|[Analysis of the Use of Gamification Elements in E-commerce Applications as a Purchase Intention Factor Using the Structural Equation Model (SEM)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112151)|F. Claudia; V. Vierrini; Natalia; N. Limantara|10.1109/ISCON57294.2023.10112151|gamification;e-commerce;purchase intention;structural equation model;perceived value;perceived enjoyment;gamification;e-commerce;purchase intention;structural equation model;perceived value;perceived enjoyment|
|[Analysis and Visualization of Fake News Detection Dataset through Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112024)|D. Patel; S. Bansal; J. Arora; R. Rani; A. Dev; A. Sharma|10.1109/ISCON57294.2023.10112024|Fake News Prediction;K Means Clustering;Logistic Regression;Random Forest Classification;Fake News Prediction;K Means Clustering;Logistic Regression;Random Forest Classification|
|[Machine Learning-Based Sentiment Analysis for the Social Media Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112120)|P. Upadhyay; S. Saifi; R. Rani; A. Sharma; P. Bansal|10.1109/ISCON57294.2023.10112120|Sentiment Analysis;Machine Learning;Twitter;Python.;Sentiment Analysis;Machine Learning;Twitter;Python.|
|[Prevention of Security Attacks in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112020)|K. Gaur; K. Gaur; M. Diwakar; P. Singh; N. K. Pandey|10.1109/ISCON57294.2023.10112020|Cloud Computing;Cloud Security;SIEM;Hybrid Cloud Model;Fuzzy logic;Cloud Computing;Cloud Security;SIEM;Hybrid Cloud Model;Fuzzy logic|
|[Comparative Analysis of 5G Security Mechanisms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111963)|T. Sachdeva; S. Kumar; M. Diwakar; P. Singh; N. K. Pandey; S. Choudhary|10.1109/ISCON57294.2023.10111963|5G (fifth generation);security mechanisms;Contour stellar images;eight power amplifiers;End to End security approach.;5G (fifth generation);security mechanisms;Contour stellar images;eight power amplifiers;End to End security approach.|
|[SQL Injection Attacks and Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112156)|K. Gaur; M. Diwakar; K. Gaur; P. Singh; T. Sachdeva; N. K. Pandey|10.1109/ISCON57294.2023.10112156|SQL injection (SQLi) attack;deep learning;attacker;machine learning;Brute Force;SQL injection (SQLi) attack;deep learning;attacker;machine learning;Brute Force|
|[Multi-class & binary classification of Parkinson’s disease and SWEDD variants using SBR features derived from SPECT imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112104)|N. Aggarwal; B. S. Saini; S. Gupta|10.1109/ISCON57294.2023.10112104|Multiclass & Binary classifications;Parkinson’s disease;Deep and Machine learning;SBR features;SWEDD;Multiclass & Binary classifications;Parkinson’s disease;Deep and Machine learning;SBR features;SWEDD|
|[Cardiovascular Disease Prediction Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112052)|P. Divyasri; D. SreeLakshmi; P. Sathvika; P. Teja; T. V. Charan|10.1109/ISCON57294.2023.10112052|SVM;Decision tree;XGBoost;KNN;Random Forest;Adaptive voting classifier;Electrocardiogram (ECG);SVM;Decision tree;XGBoost;KNN;Random Forest;Adaptive voting classifier;Electrocardiogram (ECG)|
|[AI Value Alignment Problem: The Clear and Present Danger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112100)|S. Chaturvedi; C. Patvardhan; C. V. Lakshmi|10.1109/ISCON57294.2023.10112100|Artificial intelligence;Value alignment problem;AI ethics;ethical AI;Artificial Super Intelligence.;Artificial intelligence;Value alignment problem;AI ethics;ethical AI;Artificial Super Intelligence.|
|[Multi-Station Collaborative Analysis of Earthquake Precursors Considering Data Missing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112082)|F. Ge; Y. Huang; L. Chen; Y. Xie|10.1109/ISCON57294.2023.10112082|data missing;multi-station;impending seismic precursor;graph neural networks;data missing;multi-station;impending seismic precursor;graph neural networks|
|[Role of Artificial Intelligence-Enabled Recruitment Processes in Sourcing Talent](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112009)|S. Sen; S. Kadam; V. V. Ravi Kumar|10.1109/ISCON57294.2023.10112009|Artificial Intelligence;Business activity;Data-Driven;HR Processes;Recruitment;Talent;Artificial Intelligence;Business activity;Data-Driven;HR Processes;Recruitment;Talent|
|[Identification of Characteristics of Mewari Poems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112199)|S. Jain; K. Dutta|10.1109/ISCON57294.2023.10112199|Mewari Poetry;Mewari Corpus;Rhyme;Moods;Natural Language Processing;Mewari Poetry;Mewari Corpus;Rhyme;Moods;Natural Language Processing|
|[Mineable Fast Cryptocurrency KIITCOIN using SCRYPT Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112050)|A. Vikram; P. Aditya; A. Mukhopadhyay; S. Mitra; L. K. Sahu; C. Pradhan|10.1109/ISCON57294.2023.10112050|Bitcoin;Blockchain technology;Crypto-asset;Digital coin;KIIT;KIITCOIN;Litecoin;Mining;P2P Network.;Bitcoin;Blockchain technology;Crypto-asset;Digital coin;KIIT;KIITCOIN;Litecoin;Mining;P2P Network.|
|[Recent CNN Advancements For Stratification of Hyperspectral Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112174)|P. Ranjan; R. Kumar; A. Girdhar|10.1109/ISCON57294.2023.10112174|Hyperspectral;Classification Accuracy;CNN;Deep Learning;Spectral;Spatial.;Hyperspectral;Classification Accuracy;CNN;Deep Learning;Spectral;Spatial.|
|[Automatic Classification of Meter in Bangla Poems: A Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112039)|N. Ahmed; S. T. Aziz; M. A. N. Mojumder; M. A. Mridul|10.1109/ISCON57294.2023.10112039|Bangla Poetry;Poetic Meter;SVM;KNN classifier;NLP;TF-IDF;Bangla Poetry;Poetic Meter;SVM;KNN classifier;NLP;TF-IDF|
|[Radial Basis Function Regression (RBFR), ARRBFR models for Estimation of Particle Froude Number in Sewer Pipes Under Deposited Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112031)|S. Kumar; M. Agarwal; V. Deshpande|10.1109/ISCON57294.2023.10112031|Froude number;NDB;self-cleaning;AR;RBFR;Froude number;NDB;self-cleaning;AR;RBFR|
|[Leveraging Blockchain Technology for Improving the Quality of Corporate Governance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112178)|M. Mehta; A. Khurana; V. R. Kumar|10.1109/ISCON57294.2023.10112178|Blockchain;Corporate Governance;Fraud;Technology;Transparency;Board;Stakeholders;Blockchain;Corporate Governance;Fraud;Technology;Transparency;Board;Stakeholders|
|[Serverless Data Protection in Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112206)|A. Garde; S. Gandhale; R. Dharankar; B. S. Sangtani; N. Deshmukh; S. Deshpande; R. Rathi; A. Srivastava|10.1109/ISCON57294.2023.10112206|Amazon Web Services;Backup;Cloud Computing;Data Protection;Recovery;Serverless;Amazon Web Services;Backup;Cloud Computing;Data Protection;Recovery;Serverless|
|[How IoT-Enabled Smart Manufacturing Affect Firm Performance through Internationalization: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112164)|A. U. Putri; Noerlina; T. N. Mursitama; L. Y. Arnakim|10.1109/ISCON57294.2023.10112164|internet of things (IoT);smart manufacturing;internationalization;firm performance;systematic literature review;internet of things (IoT);smart manufacturing;internationalization;firm performance;systematic literature review|
|[Artificial Narrow Intelligence Techniques in Intelligent Digital Financial Inclusion System for Digital Society](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112133)|R. Rawat; H. R. Goyal; S. Sharma|10.1109/ISCON57294.2023.10112133|Artificial intelligence (AI);Digital financial inclusion system (DFIS);Artificial Narrow Intelligence (ANI);Expert system;Algorithms;Neural network;Machine learning;Artificial intelligence (AI);Digital financial inclusion system (DFIS);Artificial Narrow Intelligence (ANI);Expert system;Algorithms;Neural network;Machine learning|
|[CNN-LSTM Based Approach for Sleep Apnea Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112203)|N. Saroha; M. Aryan; M. Singh; A. Goel|10.1109/ISCON57294.2023.10112203|Sleep Apnea Detection;Electrocardiogram;Convolutional Neural Network;Long Short-Term Memory;Sleep Apnea Detection;Electrocardiogram;Convolutional Neural Network;Long Short-Term Memory|
|[Traditional Indian Textiles Classification using Deep feature fusion with Curvelet transforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112134)|S. Varshney; S. Singh; C. V. Lakshmi; C. Patvardhan|10.1109/ISCON57294.2023.10112134|Convolutional Neural Network;Deep learning;Curvelet Transform;Feature fusion;Classification.;Convolutional Neural Network;Deep learning;Curvelet Transform;Feature fusion;Classification.|
|[An In-Silico Comparison Of Ginsenoside’s Anticancer Activity Against The Son Of Sevenless Homolog 1.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112037)|A. Sinha; Bharmjeet; A. Das|10.1109/ISCON57294.2023.10112037|Ginsenoside;Molecular docking;Cancer;admetSAR;SOS1;Ginsenoside;Molecular docking;Cancer;admetSAR;SOS1|
|[Efficient Health Analyser and Monitoring System (E.H.A.M.S) for Periodic Medical Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111949)|K. S. Das; S. Sarkar; S. Malick; S. Bhattacharyya|10.1109/ISCON57294.2023.10111949|Internet of things;Embedded systems;Sensor applications;Smart sensors;Emerging technologies;Internet of things;Embedded systems;Sensor applications;Smart sensors;Emerging technologies|
|[Leader Election in Internet of Things Using Blockchain: Issues and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112140)|K. Kumari; M. K. Murmu|10.1109/ISCON57294.2023.10112140|Internet of Things;leader election;blockchain;Internet of Things;leader election;blockchain|
|[Video Crawling Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112177)|R. S. Khedgaonkar; A. A. Patil; P. M. Sonsare|10.1109/ISCON57294.2023.10112177|Deep Learning;CNN;video;ConvNet;Natural language Processing;Deep Learning;CNN;video;ConvNet;Natural language Processing|
|[Stilbostemin C as a Potential Candidate for Therapeutic Targeting of Rab3b Protein in Countering Alzheimers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112094)|S. Madaan; P. Kumar|10.1109/ISCON57294.2023.10112094|Alzheimer’s;Datasets;Differential expression genes;Biomarkers;RAB3B protein;Stilbostemin C;Alzheimer’s;Datasets;Differential expression genes;Biomarkers;RAB3B protein;Stilbostemin C|
|[Analysis of Lidar-Based Autonomous Vehicle Detection Technologies for Recognizing Objects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112089)|A. K. Srivastava; A. Singhal; A. Sharma|10.1109/ISCON57294.2023.10112089|Sensor fusion;Thermal infrared camera;LiDAR;Autonomous vehicles;Convolutional Neural Network;Deep Learning;LiDAR point cloud;Sensor fusion;Thermal infrared camera;LiDAR;Autonomous vehicles;Convolutional Neural Network;Deep Learning;LiDAR point cloud|
|[A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112042)|A. Khandelwal; A. Shukla; M. Sain|10.1109/ISCON57294.2023.10112042|Herbal plants;Image processing;Deep learning;convolutional neuron network;computer vision;Herbal plants;Image processing;Deep learning;convolutional neuron network;computer vision|
|[Sentiment Analysis of Audio Files Using Machine Learning and Textual Classification of Audio Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112195)|S. Saraswat; S. Bhardwaj; S. Vashistha; R. Kumar|10.1109/ISCON57294.2023.10112195|Machine Learning;Audio Transcripts;Textual Classification;Sentiment Analysis;Machine Learning;Audio Transcripts;Textual Classification;Sentiment Analysis|
|[An Architectural Framework for Word level Language Identification in Mixed Script Text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112136)|D. Singh; S. Shekhar|10.1109/ISCON57294.2023.10112136|Introduction;Related Work;Datasets;Methodology;Word2Vec;TF-IDF;skip-gram;A continuous bag of words;etc;Experiments and Evaluation;Introduction;Related Work;Datasets;Methodology;Word2Vec;TF-IDF;skip-gram;A continuous bag of words;etc;Experiments and Evaluation|
|[Enhanced DDoS Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112033)|R. Pandey; M. Pandey; A. Nazarov|10.1109/ISCON57294.2023.10112033|DDoS;Deep Learning;Random Forest;Logistic Regression;KNN;NSL KDD Dataset;DDoS;Deep Learning;Random Forest;Logistic Regression;KNN;NSL KDD Dataset|
|[LoRaWAN Gateway Architecture for Aquaculture Monitoring in Rural Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111936)|D. Singh; G. Sharma; I. Minhas; G. Singh; P. Mahajan; P. Verma; G. C. Manocha|10.1109/ISCON57294.2023.10111936|LoRaWAN;Gateway;Aquaculture;IoT;LoRaWAN;Gateway;Aquaculture;IoT|
|[BILMB: Design of a hybrid Bio inspired Incremental Learning-based model for analysing effects of Meditation on different Body-parts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112067)|A. A. Tayade; R. N. Khobragade; D. S. Datar|10.1109/ISCON57294.2023.10112067|Meditation;EEG;Features;Gabor;Wavelet;Fourier;Cosine;CNN;EHO;Apriori;Fuzzy;Clusters;Meditation;EEG;Features;Gabor;Wavelet;Fourier;Cosine;CNN;EHO;Apriori;Fuzzy;Clusters|
|[VISMA: A Machine Learning Approach to Image Manipulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112168)|M. Bende; M. Khandelwal; D. Borgaonkar; P. Khobragade|10.1109/ISCON57294.2023.10112168|Deep Neural network;CUDA;GANs;Machine Learning;Computer vision;Deep Neural network;CUDA;GANs;Machine Learning;Computer vision|
|[An Efficient Offloading Technique using DQN for MEC-IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112053)|P. Shukla; S. Pandey; D. Agarwal|10.1109/ISCON57294.2023.10112053|Internet of Things (IoT);Mobile-Edge Computing (MEC);Quality of Service (QoS);SVM;Random Forest;Logistic Regression;Deep Q Network (DQN).;Internet of Things (IoT);Mobile-Edge Computing (MEC);Quality of Service (QoS);SVM;Random Forest;Logistic Regression;Deep Q Network (DQN).|
|[Analysis of Various Routing Protocols based on Quality of Service for FANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111945)|D. Rai; S. S. Rajput; D. Rai|10.1109/ISCON57294.2023.10111945|FANET;Quality of Service;DSR;OLSR;AODV;FANET;Quality of Service;DSR;OLSR;AODV|
|[An Optimised Energy Efficient Task Scheduling Algorithm based on Deep Learning Technique for Energy Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112019)|J. Mahilraj; P. Sivaram; N. Lokesh; B. Sharma|10.1109/ISCON57294.2023.10112019|Cloud Computing;Deep Learning Technique;Energy Consumption;optimization;Resource Utilization;Task Scheduling Algorithm;Cloud Computing;Deep Learning Technique;Energy Consumption;optimization;Resource Utilization;Task Scheduling Algorithm|
|[Residual Steganography: Embedding Secret Data in Images using Residual Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112114)|V. P. R. Poluri; S. Gunnam; B. Maredi; M. K. Beeraboina|10.1109/ISCON57294.2023.10112114|Steganography;Convolutional neural network;Residual network;Conceal;Steganography;Convolutional neural network;Residual network;Conceal|
|[Speech Recognition of Vedic Sanskrit using Deep Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111941)|M. Pandey; R. Pandey; A. Nazarov|10.1109/ISCON57294.2023.10111941|Natural Language Processing;Deep Learning;Speech Recognition;Neural Networks;Vedic Sanskrit;Natural Language Processing;Deep Learning;Speech Recognition;Neural Networks;Vedic Sanskrit|
|[FedCER - Emotion Recognition Using 2D-CNN in Decentralized Federated Learning Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112028)|M. Agrawal; M. A. Anwar; R. Jindal|10.1109/ISCON57294.2023.10112028|emotion recognition;federated learning;convolutional neural networks;privacy preservation;electroencephalogram signal;emotion recognition;federated learning;convolutional neural networks;privacy preservation;electroencephalogram signal|
|[A task offloading scheme with Queue Dependent VM in fog Center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112106)|S. Behera; N. Panda; U. C. De; B. B. Dash; B. Dash; S. S. Patra|10.1109/ISCON57294.2023.10112106|Task offloading;Scale up and scale down;Queueing model;performance analysis;fog computing;Task offloading;Scale up and scale down;Queueing model;performance analysis;fog computing|
|[Deep U-Net Architecture for Semantic Segmentation of Dental Carries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111940)|P. K. Gorantla; S. Gunnam; R. Saripineni; M. Kaki; S. Dhanavath|10.1109/ISCON57294.2023.10111940|Dental caries;Semantic segmentation. Dental Caries U-Net (DCUN);Dental caries;Semantic segmentation. Dental Caries U-Net (DCUN)|
|[Analysing Satellite Images using Segmentation with U-net and Focal Tversky Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112138)|A. Sinha; K. K. Senapati|10.1109/ISCON57294.2023.10112138|Imbalanced classification;Segmentation;Urbanization;Spatial resolution;U-net;Imbalanced classification;Segmentation;Urbanization;Spatial resolution;U-net|
|[IoT-Based Digital LPG Gas Cylinder Trolley to Prevent Hazards with Voice-Controlled Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112147)|S. Chawla; H. Chawla|10.1109/ISCON57294.2023.10112147|HC-05;HX711;IoT;LCD;LPG;Load cell;NodeMCU;HC-05;HX711;IoT;LCD;LPG;Load cell;NodeMCU|
|[Comparative Examination of Fake News Recognition Using an Ensemble of Numerous Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111965)|S. Saraswat; N. Faujdar; S. Sharma|10.1109/ISCON57294.2023.10111965|Fake News;Random Forest;Support Vector;Ensemble Modelling;Fake News;Random Forest;Support Vector;Ensemble Modelling|
|[Performance Analysis of Machine Learning Algorithms Using Bagging Ensemble Technique for Software Fault Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111952)|R. Samantaray; H. Das|10.1109/ISCON57294.2023.10111952|Software Fault Prediction;Ensemble Learning;Machine Learning;Bagging;Classification;Decision Tree;Logistic Regression;Gaussian Naive Bayes;Support Vector Machine;K-Nearest Neighbors.;Software Fault Prediction;Ensemble Learning;Machine Learning;Bagging;Classification;Decision Tree;Logistic Regression;Gaussian Naive Bayes;Support Vector Machine;K-Nearest Neighbors.|
|[MECT-PSO Based Energy Efficient Routing in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112182)|P. T. Agarkar; M. D. Chawhan; P. R. Hajare; N. P. Giradkar; P. R. Patil|10.1109/ISCON57294.2023.10112182|On-Demand routing schemes;minimum hop paths;MECT-PSO scheme;average energy loss;swarm optimization;Dijkstra algorithm;and On Path Nodes;On-Demand routing schemes;minimum hop paths;MECT-PSO scheme;average energy loss;swarm optimization;Dijkstra algorithm;and On Path Nodes|
|[Prediction of Fruits and Vegetable Diseases Using Machine Learning and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112097)|D. Kumar; K. N. Kamlesh; A. Kumar; S. Banerjee; D. K. Vishal|10.1109/ISCON57294.2023.10112097|Internet of things;Machine learning;sensors;actuators;segmentation;Internet of things;Machine learning;sensors;actuators;segmentation|
|[3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112146)|R. Roy; B. Annappa; S. Dodia|10.1109/ISCON57294.2023.10112146|MRI;U-Net;Dice and focal Loss;Segmentation;Brain tumor;Central nervous system (CNS);MRI;U-Net;Dice and focal Loss;Segmentation;Brain tumor;Central nervous system (CNS)|
|[Broker Clustering Enabled Lightweight Communication in IoT using MQTT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112105)|S. Tripathi; B. K. Chaurasia|10.1109/ISCON57294.2023.10112105|Internet of Things (IoT);Message Queuing Telemetry Transport/MQ Telemetry Transport (MQTT);Machine-to-Machine Communication (M2M);Internet of Things (IoT);Message Queuing Telemetry Transport/MQ Telemetry Transport (MQTT);Machine-to-Machine Communication (M2M)|
|[Flyhigh:Machine Learning Based Airline Fare Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112014)|A. K. Choudhary; R. P. Jagadeesh; E. Girija; M. Madhuri; N. Shravani|10.1109/ISCON57294.2023.10112014|Machine learning technique;Regression;Random Forest algorithm;Feature Selection;Flask;Machine learning technique;Regression;Random Forest algorithm;Feature Selection;Flask|
|[An Optimal Feature Selection with Neural Network-Based Classification Model for Dengue Fever Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112011)|V. Ramasamy; S. Vadivel; S. Kothandapani; J. Mahilraj; P. Sivaram; B. Sharma|10.1109/ISCON57294.2023.10112011|Classification;Data mining;Deep Learning;Dengue Identification;Uncertain Neural Network;Accuracy;Specificity;Sensitivity;Classification;Data mining;Deep Learning;Dengue Identification;Uncertain Neural Network;Accuracy;Specificity;Sensitivity|
|[CNN Model Suitability Analysis for Prediction of Tomato Leaf Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111996)|C. Vengaiah; M. Priyadharshini|10.1109/ISCON57294.2023.10111996|Leaf Disease Detection;CNN;ResNet-101;VGGNet;Leaf Disease Detection;CNN;ResNet-101;VGGNet|
|[AVWAC based Gradual transitions detection in presence of high Object-Camera Motions and Uneven Illuminations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112141)|K. S. Chandwani; V. Namdeo; S. Malode; P. T. Agarkar; N. P. Giradkar; P. R. Patil|10.1109/ISCON57294.2023.10112141|object-camera motion;gradual transitions;fade out;fade in;AVWAC;global maxima;global minima;object-camera motion;gradual transitions;fade out;fade in;AVWAC;global maxima;global minima|
|[Lips and Tongue Cancer Classification Using Deep Learning Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112158)|S. Bansal; R. S. Jadon; S. K. Gupta|10.1109/ISCON57294.2023.10112158|Image Dataset;CNN;Deep Learning;Lips and Tongue Cancerous and Non-cancerous Images;Image Dataset;CNN;Deep Learning;Lips and Tongue Cancerous and Non-cancerous Images|
|[Data Sovereignty Provision Blockchain for Remote Healthcare Service](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112016)|H. Ryu; H. Kim; S. Agarwal; D. K. Sharma; B. Kapito; P. Ali|10.1109/ISCON57294.2023.10112016|remote healthcare;data sovereignty;privacy;blockchain;information security;remote healthcare;data sovereignty;privacy;blockchain;information security|
|[Human Pose Estimation using Artificial Intelligence with Virtual Gym Tracker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112064)|N. Faujdar; S. Saraswat; S. Sharma|10.1109/ISCON57294.2023.10112064|Human pose estimation;Media pipe;OpenCV;AI Gym Tracker;Human pose estimation;Media pipe;OpenCV;AI Gym Tracker|
|[Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112065)|S. Prakash; A. S. Jalal; P. Pathak|10.1109/ISCON57294.2023.10112065|Deep Learning;LSTM;CNN-LSTM;GRULSTM;LSTM with Attention;Bi-directional LSTM;Stacked LSTM;Prophet;COVID-19;Deep Learning;LSTM;CNN-LSTM;GRULSTM;LSTM with Attention;Bi-directional LSTM;Stacked LSTM;Prophet;COVID-19|
|[Comparative Analysis of Tomato Leaf Disease Detection Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112092)|S. Agnihotri; J. Gupta; N. Garg; P. Khatri|10.1109/ISCON57294.2023.10112092|CNN;KNN;VGG-19;Tomato Disease;CNN;KNN;VGG-19;Tomato Disease|
|[Study of under-water Sonar System for change in propagation speed, depth of water, bottom loss and estimating optimal PDFs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112071)|A. Kumar; R. Kumar; M. Chandra; K. Kishore|10.1109/ISCON57294.2023.10112071|Channel Modeling;Underwater Acoustic Communications;Sonar System;PDFs estimation;Channel Modeling;Underwater Acoustic Communications;Sonar System;PDFs estimation|
|[Balance Search Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112115)|M. K. Khandelwal; N. Sharma|10.1109/ISCON57294.2023.10112115|Optimization;fitness value;PSO;BS-PSO;Search Diversity;Optimization;fitness value;PSO;BS-PSO;Search Diversity|
|[Breast Cancer Histopathology Image Classification using ADAptive Moment (ADAM) optimization Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111983)|M. K. Jain; B. M. Singh; M. Singh|10.1109/ISCON57294.2023.10111983|Histopatholgy;BreakHis;Cohens Kappa;CovNet’s;ADAM;CNN, ALEX Net;ResNet;Histopatholgy;BreakHis;Cohens Kappa;CovNet’s;ADAM;CNN, ALEX Net;ResNet|
|[BPSA (Back Propagation Sleep Awake) Clustering Protocol for Energy Optimization of Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112167)|A. Bhardwaj; B. Gupta; S. Rana; S. K. Goyal; R. K. Gujral|10.1109/ISCON57294.2023.10112167|Back Propagation;Sleep-mode;Awake-mode;Clustering;Back Propagation;Sleep-mode;Awake-mode;Clustering|
|[Acknowledgement Verification of Stored Data in Shared Cloud Resource Pool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112204)|P. Goswami; V. Sharma; S. Debnath; A. K. Khan|10.1109/ISCON57294.2023.10112204|Shared cloud storage;Acknowledgement verification;Data security;Cloud User(CU);Cloud Service Provider(CSP);Shared cloud storage;Acknowledgement verification;Data security;Cloud User(CU);Cloud Service Provider(CSP)|
|[Sentiment Analysis Techniques and their Uses: An Analytical Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111939)|A. Sikarwar|10.1109/ISCON57294.2023.10111939|Sentiment Analysis;Numerical Scales;Polarity;Sentiment Analysis;Numerical Scales;Polarity|
|[Self-Attention Vision Transformer with Transfer Learning for Efficient Crops and Weeds Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112049)|S. Sharma; M. Vardhan|10.1109/ISCON57294.2023.10112049|computer vision;deep learning;convolutional neural network;vision transformer;self-attention;agriculture;computer vision;deep learning;convolutional neural network;vision transformer;self-attention;agriculture|
|[Deep Learning Based Methods in Image Analytics for Vehicle Detection: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112132)|P. K. Vishwakarma; N. Jain|10.1109/ISCON57294.2023.10112132|Deep learning;vehicle detection;vehicle classification;intelligent transport system;CNN;R-CNN;YOLO;Deep learning;vehicle detection;vehicle classification;intelligent transport system;CNN;R-CNN;YOLO|
|[A Contextual Query Expansion Model using BERT Based Deep Neural Embeddings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111984)|D. Vishwakarma; S. Kumar|10.1109/ISCON57294.2023.10111984|Word Embedding;BERT;Glove;Contextualised Query Expansion;Query Drift;Information Retrieval;Word Embedding;BERT;Glove;Contextualised Query Expansion;Query Drift;Information Retrieval|
|[COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112122)|A. Shinde; S. Shinde|10.1109/ISCON57294.2023.10112122|Feature extraction;classification;CT;machine learning;COVID-19;Support Vector Machine;Feature extraction;classification;CT;machine learning;COVID-19;Support Vector Machine|
|[Augmented Ensemble Learning Model for Biomarkers Prioritization to Enhance Disease Identification Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112109)|D. K. Vaishali; S. Shambharkar; R. K. Somkunwar; R. R. Kolte|10.1109/ISCON57294.2023.10112109|Biomarker;prioritization;machine;learning;convolutional;neural;GoogLeNet;Inception net;gene network;Biomarker;prioritization;machine;learning;convolutional;neural;GoogLeNet;Inception net;gene network|
|[An Intelligent and Adaptive Multipath Routing Scheme for Mobile Ad Hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112112)|P. Pandey; R. Singh|10.1109/ISCON57294.2023.10112112|MANET;Routing Protocols;AOMDV;RWP;Multipath;MANET;Routing Protocols;AOMDV;RWP;Multipath|
|[Application of Bayesian Theorem for Precise Analysis of Clinical Test Results](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112017)|S. C. Dimri; B. Kumar; K. C. Purohit|10.1109/ISCON57294.2023.10112017|Diagnosis;Bayes theorem;Disease detection;Positive;Sensitivity;Probability;Diagnosis;Bayes theorem;Disease detection;Positive;Sensitivity;Probability|
|[Comparative Analysis of Applications of Machine Learning in Credit Card Fraud Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112099)|S. Ghosh; S. Bilgaiyan; M. K. Gourisaria; A. Sharma|10.1109/ISCON57294.2023.10112099|Credit Card Fraud;Anomaly Detection;OCSVM;AutoEncoders;Binary Classification;Credit Card Fraud;Anomaly Detection;OCSVM;AutoEncoders;Binary Classification|
|[Weather Forecasting Based Shared Bike Demand Analysis using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112160)|S. Shashank; M. K. Gourisaria; S. Bilgaiyan|10.1109/ISCON57294.2023.10112160|Machine Learning;Bike Sharing;Automobiles and Vehicles;Regression;Prediction;R2 Score;PCAcomponent;Machine Learning;Bike Sharing;Automobiles and Vehicles;Regression;Prediction;R2 Score;PCAcomponent|
|[An Approach of Blockchain to Enhance Supply Chain Transparency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112036)|A. Kaushik; N. Jain|10.1109/ISCON57294.2023.10112036|Blockchain technology;supply chain;trust;Global value chain;Management strategy;Blockchain technology;supply chain;trust;Global value chain;Management strategy|
|[A Stacking Ensemble Learning Model for Rainfall Prediction based on Indian Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112077)|P. P. G. Jaiswal; A. Dhote; A. Dubey; H. Patil; N. Madavi; N. Rahangdale; S. Patil; S. Kale|10.1109/ISCON57294.2023.10112077|Rainfall Prediction;Machine Learning;Regression;Ensemble Stacking;Rainfall Prediction;Machine Learning;Regression;Ensemble Stacking|
|[Optimising Feature Selection: A Comparative Study of mRMR-Boruta/RFE Hybrid Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112125)|M. Sharma; D. K. Sharma|10.1109/ISCON57294.2023.10112125|data mining;feature selection;hybrid approach;mrmr;recursive feature elimination;boruta;classification;data mining;feature selection;hybrid approach;mrmr;recursive feature elimination;boruta;classification|
|[An Ensemble-Based Model of Detecting Plant Disease using CNN and Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112023)|S. Saxena; S. Rathor|10.1109/ISCON57294.2023.10112023|Plant Disease detection;CNN;Maxpooling;Random Forest;Batch normalization;Disease classification.;Plant Disease detection;CNN;Maxpooling;Random Forest;Batch normalization;Disease classification.|
|[Performance Evaluation of Speed Sensitive Handover in Two-Tier Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111957)|V. Solanki; R. K. Sachdeva; V. Lamba; U. Solanki; B. K. Sahoo|10.1109/ISCON57294.2023.10111957|Fuzzy Logic;Wireless Networks;Channel Management;Handoff Call optimization;Urban Development;Fuzzy Logic;Wireless Networks;Channel Management;Handoff Call optimization;Urban Development|
|[An Effective Deep Learning Model for Content-Based Gastric Image Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112189)|M. Singh; M. K. Singh|10.1109/ISCON57294.2023.10112189|CBMIR;Deep learning;Euclidean;KVASIR;CBGIR;CBMIR;Deep learning;Euclidean;KVASIR;CBGIR|
|[Power Control in Device-to-Device Communications using Deep Deterministic Policy Gradient Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112096)|R. Kumar; S. Majumder|10.1109/ISCON57294.2023.10112096|Actor Critic;D2D;DDPG;Gradient loss;Target network;Actor Critic;D2D;DDPG;Gradient loss;Target network|
|[Detection of Tomato leaf diseases using Attention Embedded Hyper-parameter Learning Optimization in CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111992)|J. Mahilraj; P. Sivaram; B. Sharma; N. Lokesh; B. Bobinath; R. Moriwal|10.1109/ISCON57294.2023.10111992|Agriculture;Attention Mechanism;Convolutional Neural Networks;Disease Detection methods;Tomato leaves;Agriculture;Attention Mechanism;Convolutional Neural Networks;Disease Detection methods;Tomato leaves|
|[Plant Leaf Diseases Severity Estimation using Fine-Tuned CNN Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111948)|R. Kumar; A. Chug; A. P. Singh|10.1109/ISCON57294.2023.10111948|Plant Disease Severity Assessment;Fine Tuning;Convolutional Neural Networks;Hyperparameter Tunning.;Plant Disease Severity Assessment;Fine Tuning;Convolutional Neural Networks;Hyperparameter Tunning.|
|[A Lightweight and Efficient Scheme for e-Health Care System using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111937)|S. Saxena; N. Arya; S. K. Bharti; V. Dwivedi|10.1109/ISCON57294.2023.10111937|Health Care;Blockchain technology;Symmetric encryption;Miners;Communication overhead;Health Care;Blockchain technology;Symmetric encryption;Miners;Communication overhead|
|[Establishing the Correlation of Powers Skills with Program success](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112066)|N. Akhter; B. Goswami; E. Rashid|10.1109/ISCON57294.2023.10112066|Power Skills;Soft Skills;Collaboration;Communication;Problem-solving;Strategic Thinking;Collaborative Leadership;Power Skills;Soft Skills;Collaboration;Communication;Problem-solving;Strategic Thinking;Collaborative Leadership|
|[Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112186)|M. P. Singh; Y. Karkhur|10.1109/ISCON57294.2023.10112186|Ransomware;Cryptocurrency;Portable Executable Header;Artificial Neural Network;Power Iteration;Ransomware;Cryptocurrency;Portable Executable Header;Artificial Neural Network;Power Iteration|
|[Comparative Analysis of Smart Cities based Architecture, Applications, Technologies, & Challenges in Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112172)|R. Kumar; B. Sharma|10.1109/ISCON57294.2023.10112172|Internet of Things (IoT);Smart cities;Environmental monitoring;Architecture;Applications;Technologies;Internet of Things (IoT);Smart cities;Environmental monitoring;Architecture;Applications;Technologies|
|[Risk Minimization Approach for Image Restoration Using L2 Penalty in EM Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112127)|R. P. Singh; M. K. Singh|10.1109/ISCON57294.2023.10112127|deconvolution;expectation-maximization;image restoration;L2 penalty;mean squared error;risk minimization;deconvolution;expectation-maximization;image restoration;L2 penalty;mean squared error;risk minimization|
|[Deep Learning-Assisted MRI Image Segmentation and Classification for Precise Brain Tumor Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111960)|S. C. Das; M. A. Sarder; S. Das; D. H. Tanvir; S. T. Aziz; A. Islam|10.1109/ISCON57294.2023.10111960|Brain tumor;Classification;Machine learning;Random Forest;U-Net;Semantic segmentation;neural network;VGG16;Resnet34;InceptionV3;SVM;YOLO.;Brain tumor;Classification;Machine learning;Random Forest;U-Net;Semantic segmentation;neural network;VGG16;Resnet34;InceptionV3;SVM;YOLO.|
|[Deep Learning-Based Classification of Indian Classical Music Based on Raga](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111985)|A. Singha; N. R. Rajalakshmi; J. Arun Pandian; S. Saravanan|10.1109/ISCON57294.2023.10111985|Music Genre classification;Musical features;Audio signal processing;Music information retrieval;Raga identification;Deep learning;Music Genre classification;Musical features;Audio signal processing;Music information retrieval;Raga identification;Deep learning|
|[Aspect Based Sentiment Analysis for Amazon Data Products using PAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112193)|Y. Prakash; D. K. Sharma|10.1109/ISCON57294.2023.10112193|Aspect based sentiment analysis (AbSA);Sentiment analysis (SA);NLP;Topic- Modeling;Machine Learning (ML).;Aspect based sentiment analysis (AbSA);Sentiment analysis (SA);NLP;Topic- Modeling;Machine Learning (ML).|
|[An Approach to Recognize Human Activities based on ConvLSTM and LRCN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112060)|S. Bhatia; T. Chauhan; S. Gupta; S. Gambhir; J. H. Panchal|10.1109/ISCON57294.2023.10112060|CNN;LSTM;LRCN;ConvLSTM;HAR;CNN;LSTM;LRCN;ConvLSTM;HAR|
|[Classification using Perceptron in Low Feature Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111977)|R. Indu; S. C. Dimri|10.1109/ISCON57294.2023.10111977|Perceptron;linearly separable;linearly non-separable;convex hull;Euclidean distance;Perceptron;linearly separable;linearly non-separable;convex hull;Euclidean distance|
|[Cyber Security of Smart Metering Infrastructure Using Hybrid Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112175)|P. Chandel; B. Sawle|10.1109/ISCON57294.2023.10112175|AI;Machine Learning;Accuracy;Hybrid;Cyber;Smart meter;AI;Machine Learning;Accuracy;Hybrid;Cyber;Smart meter|
|[Brain Tumor Detection from MRI Images Based on ResNet18](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112025)|M. C. S. Tang; S. S. Teoh|10.1109/ISCON57294.2023.10112025|brain tumor detection;deep learning;image processing;convolutional neural network;medical imaging;brain tumor detection;deep learning;image processing;convolutional neural network;medical imaging|
|[A Support Vector Machine Learning Technique for Detection of Phishing Websites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111968)|S. Jain; C. Gupta|10.1109/ISCON57294.2023.10111968|Phishing Websites;SVM;AI;Machine Learning;Deep.;Phishing Websites;SVM;AI;Machine Learning;Deep.|
|[Exploring the Impact of Big Data Analytics Capabilities on Indonesian Firm Performance - A Mediation Analysis of Business Process Agility and Process-oriented Dynamic Capability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112001)|N. Kusbianto; E. G. Sukoharsono; A. Darmawan|10.1109/ISCON57294.2023.10112001|Firm Performance;IT Capability Framework;Big Data Analytics Capabilities;Process-oriented Dynamic Capability;Business Process Agility;Firm Performance;IT Capability Framework;Big Data Analytics Capabilities;Process-oriented Dynamic Capability;Business Process Agility|
|[A Novel Hybrid BAT-PSO Approach for Task Scheduling and Workload Forecasting for Cloud Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112207)|A. Sharma; S. S. Chauhan|10.1109/ISCON57294.2023.10112207|PSO-BAT;cost;tasks;scheduling;PSO-BAT;cost;tasks;scheduling|
|[Cyber Attack Detection in IoT using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111990)|K. Tomar; K. Bisht; K. Joshi; R. Katarya|10.1109/ISCON57294.2023.10111990|Cyber attack;IoT;VGG-16;VGG-19;Attack Detection;Cyber attack;IoT;VGG-16;VGG-19;Attack Detection|
|[Machine Learning based URL Analysis for Phishing Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112057)|R. Jha; G. Kunwar|10.1109/ISCON57294.2023.10112057|Artificial Intelligence;Machine learning;Decision Tree;Phishing Attack Detection.;Artificial Intelligence;Machine learning;Decision Tree;Phishing Attack Detection.|
|[Predicting the Patient’s Severity Using Machine Learning Applied to Lungs MRI Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111986)|R. Jha; G. Kunwar|10.1109/ISCON57294.2023.10111986|Deep learning;ResNet50;DenseNet 169.;Deep learning;ResNet50;DenseNet 169.|
|[Machine Learning-Based Automated Medical Diagnosis for Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112144)|H. Khatter; A. Yadav; A. Srivastava|10.1109/ISCON57294.2023.10112144|Artificial Intelligence;Machine Learning;Health Analysis;Preventive systems;Artificial Intelligence;Machine Learning;Health Analysis;Preventive systems|
|[Supervised Machine Learning for Cloud Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112078)|S. Singhal; R. Srivastava; R. Shyam; D. Mangal|10.1109/ISCON57294.2023.10112078|Cloud Security;Machine Learning;Supervised Learning;UNSW dataset;ISOT dataset;Cloud Security;Machine Learning;Supervised Learning;UNSW dataset;ISOT dataset|
|[Attentional Deep Learning novel approach for Facial Expression Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112135)|N. Khan; A. V. Singh; R. Agrawal|10.1109/ISCON57294.2023.10112135|attentional neural network;convolutional neural network;deep learning network;facial expression recognition system;attentional neural network;convolutional neural network;deep learning network;facial expression recognition system|
|[Energy Optimization Algorithms Study On Routing Protocol In Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111997)|M. Singh; V. K. Garg; A. Bansal; A. Garg; D. Baresary|10.1109/ISCON57294.2023.10111997|DEEC;LEACH-C;Base Station;Wireless Sensor Network;ZECR;DEEC and LEACH;DEEC;LEACH-C;Base Station;Wireless Sensor Network;ZECR;DEEC and LEACH|
|[The Effect of Information System Success Model, Information Security, and Customer Satisfactions on Digital Bank Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111969)|Efendi; A. Gui; G. M. I. Santosa; A. A. Pitchay; C. Jourdan; Sudiana|10.1109/ISCON57294.2023.10111969|information system success;security;privacy;satisfaction;information system success;security;privacy;satisfaction|
|[Feature Selection using Ant Colony Optimization for Microarray Data Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112054)|S. Prajapati; H. Das; M. K. Gourisaria|10.1109/ISCON57294.2023.10112054|Microarray Data;Machine Learning;Ant Colony Optimization;Classification;Random Forest;Decision Tree;Logistic Regression;Microarray Data;Machine Learning;Ant Colony Optimization;Classification;Random Forest;Decision Tree;Logistic Regression|
|[XGBoost-based dynamic ride-sharing model for New York City.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112119)|N. Srivastava; S. Tanaje; A. Kulkarni; M. Navan; A. D. Chowdhary; B. N. Gohil|10.1109/ISCON57294.2023.10112119|Green transportation;spatio temporal;Dynamic Ride sharing;Grasshopper API;NYC trip records;travel time prediction;Green transportation;spatio temporal;Dynamic Ride sharing;Grasshopper API;NYC trip records;travel time prediction|
|[An Integrated Model for Text to Text, Image to Text and Audio to Text Linguistic Conversion using Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112123)|A. R. Singh; D. Bhardwaj; M. Dixit; L. Kumar|10.1109/ISCON57294.2023.10112123|image-to-text voice-to-text;voice-to-voice;computer vision; cross language communication);image-to-text voice-to-text;voice-to-voice;computer vision; cross language communication)|
|[An Integrated Approach of Fire Identification utilizing Web of Things and Aerial Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112198)|R. Mahajan; R. Priyadarshini|10.1109/ISCON57294.2023.10112198|Automated Aerial Vehicle;Picture Handling;Fire Recognition;Web of Things;Remote Sensor Organizations;Automated Aerial Vehicle;Picture Handling;Fire Recognition;Web of Things;Remote Sensor Organizations|
|[Mucormycosis Metabolic Network Modeling: A Constraint-Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111947)|T. Gupta; S. Vashistha; S. Kulshrestha; P. Narad; A. Sengupta|10.1109/ISCON57294.2023.10111947|Genome-Scale Metabolic Modeling;Flux Balance Analysis;Constraints;mucormycosis. COBRA Toolbox;Genome-Scale Metabolic Modeling;Flux Balance Analysis;Constraints;mucormycosis. COBRA Toolbox|
|[MCDM Computational Approaches for Green Supply Chain Management Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112124)|A. Jaiswal; P. Negi; N. Singh|10.1109/ISCON57294.2023.10112124|Green supply chain management (GSCCM);Multi-Criteria Decision Making;Environment-friendly green supply chain;Green supply chain management (GSCCM);Multi-Criteria Decision Making;Environment-friendly green supply chain|
|[MonkeyPox, Measles and ChickenPox Detection through Image-Processing using Residual Neural Network (ResNet)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112085)|K. Sharma; Kishlay; V. Kumar; M. Mittal|10.1109/ISCON57294.2023.10112085|pox classification;monkeypox;measles;chickenpox;Deep Learning;pox classification;monkeypox;measles;chickenpox;Deep Learning|
|[Towards Transliteration between Sindhi Scripts from Devanagari to Perso-Arabic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112098)|S. S. Rathore; B. Nathani; N. Joshi; P. Katyayan; C. P. Dadlani|10.1109/ISCON57294.2023.10112098|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111938)|R. Sharma; P. Katyayan; N. Joshi|10.1109/ISCON57294.2023.10111938|name entities;machine translation;hybrid;neural machine translation;name entities;machine translation;hybrid;neural machine translation|
|[A Model for Translation of Text from Indian Languages to Bharti Braille Characters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112021)|N. Joshi; P. Katyayan|10.1109/ISCON57294.2023.10112021|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112137)|N. Joshi; P. Katyayan|10.1109/ISCON57294.2023.10112137|multiwords;neural machine translation;bharti braille;multiwords;neural machine translation;bharti braille|
|[Brain Tumor Identification and Classification using a Novel Extraction Method based on Adapted Alexnet Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112075)|P. M. S. Guru; G. J. Praveen; R. Dodmane; T. H. Sardar; A. Ashwitha; A. N. Yeole|10.1109/ISCON57294.2023.10112075|Brain Tumour;Alexnet;Magnetic Resonance Imaging;Segmentation;Brain Tumour;Alexnet;Magnetic Resonance Imaging;Segmentation|
|[AttnHAR: Human Activity Recognition using Data Collected from Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112183)|M. Sethi; M. Yadav; M. Singh; P. G. Shambharkar|10.1109/ISCON57294.2023.10112183|Wearable sensing devices;Human Activity Recognition;healthcare;Self-attention;Time Series Data;Wearable sensing devices;Human Activity Recognition;healthcare;Self-attention;Time Series Data|
|[Artificial Intelligence based Model Using Various Machine Learning Techniques to detect DDoS Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112081)|R. Singh; M. Kumar; S. Sharma; Akashdeep|10.1109/ISCON57294.2023.10112081|Machine Learning;Artificial Intelligence;Knowledge Discovery in Databases;Denial-of-service attack;Machine Learning;Artificial Intelligence;Knowledge Discovery in Databases;Denial-of-service attack|
|[A/B Testing and Audience Creation for Effective Digital Marketing: Evidences from Facebook Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112086)|N. Singh; A. Jaiswal; P. Singh; D. K. Sharma|10.1109/ISCON57294.2023.10112086|A/B testing;Facebook;Digital Marketing;Facebook Analytics;A/B testing;Facebook;Digital Marketing;Facebook Analytics|
|[Role of AI, Big data in Smart Healthcare System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111971)|A. Yadav; N. Ahmad; I. R. Khan; P. Agarwal; H. Kaur|10.1109/ISCON57294.2023.10111971|AI;ML;Smart healthcare;EHR;Deep Learning;Artificial Neural Network(ANN);AI;ML;Smart healthcare;EHR;Deep Learning;Artificial Neural Network(ANN)|
|[Character Recognition Technique Implementation for Complicated Deteriorated Scene](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112185)|S. Sharma; B. K. Pandey; D. Pandey; R. Anand; A. Sharma; S. Saini|10.1109/ISCON57294.2023.10112185|Complex degraded image;Thresholding;Morphology;Edge detection;OCR;Complex degraded image;Thresholding;Morphology;Edge detection;OCR|
|[Ontology Based Agriculture Data Mining using IWO and RNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112187)|D. Saraswat|10.1109/ISCON57294.2023.10112187|Data Mining;Ontology;IWO;RNN;Agriculture 4.0;Agriculture 5.0;Data Mining;Ontology;IWO;RNN;Agriculture 4.0;Agriculture 5.0|
|[Implementation and Performance Evaluation of Load Balanced Routing in SDN based Fat Tree Data Center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112200)|R. P. Dhanya; V. S. Anitha|10.1109/ISCON57294.2023.10112200|Data Center;Multi path routing;Software DefinedNetworking;LoadBalancing;Fat Tree;Data Center;Multi path routing;Software DefinedNetworking;LoadBalancing;Fat Tree|
|[Advanced NLP Based Entity Key Phrase Extraction and Text-Based Similarity Measures in Hadoop Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112121)|A. Dash; A. Mohanty; S. Ghosh|10.1109/ISCON57294.2023.10112121|Text Analytics;Information Retrieval;semantic textual similarity;NLP;BOW;TD-IDF;VSM;word2Vec;mapReduce;Text Analytics;Information Retrieval;semantic textual similarity;NLP;BOW;TD-IDF;VSM;word2Vec;mapReduce|
|[An Intelligent Versatile Robot with Weather Monitoring System for Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112101)|S. Forhad; M. S. Hossen; I. A. Ahsan; S. Saifee; K. N. I. Nabeen; M. R. K. Shuvo|10.1109/ISCON57294.2023.10112101|Weather Monitoring System;Water and Pesticide Spraying;Seeding;Obstacle Detection;and Soil Analysis;Weather Monitoring System;Water and Pesticide Spraying;Seeding;Obstacle Detection;and Soil Analysis|
|[Optimal FIR Filter Design using Honey Badger Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112079)|R. Sharma; S. Yadav; S. K. Saha|10.1109/ISCON57294.2023.10112079|Honey Badger Algorithm;FIR;LPF;HPF;BPF;BSF;Honey Badger Algorithm;FIR;LPF;HPF;BPF;BSF|
|[Designing A Real-Time Bring your Own Device Security Awareness Model for Mobile Device Users within Namibian Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112191)|E. Shihepo; F. Bhunu-Shava; M. Chitauro|10.1109/ISCON57294.2023.10112191|BYOD security;awareness;cyber threats;enterprise;BYOD-SAM;BYOD security;awareness;cyber threats;enterprise;BYOD-SAM|
|[A Survey of Sarcasm Detection Techniques in Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112176)|B. Singh; D. K. Sharma|10.1109/ISCON57294.2023.10112176|Natural Language Processing;Sarcasm detection;Social media;Natural Language Processing;Sarcasm detection;Social media|
|[Advanced NLP Framework for Text Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112058)|J. Praveen Gujjar; H. R. Prasanna Kumar; M. S. Guru Prasad|10.1109/ISCON57294.2023.10112058|genism;nlpaug;nltk;Polygot;sklearn;texthero;genism;nlpaug;nltk;Polygot;sklearn;texthero|
|[Enhanced Smart Home Architecture using Deep Reinforcement Learning and Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112192)|Subhita; Divya; Kavita|10.1109/ISCON57294.2023.10112192|Machine Learning;Smart Home;Blockchain Deep Reinforcement Learning;SNR;BER;Machine Learning;Smart Home;Blockchain Deep Reinforcement Learning;SNR;BER|
|[Early Detection of Paralysis in Human Subjects from ECG Signal of Human Subjects using AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112091)|S. Gupta; V. Kumar|10.1109/ISCON57294.2023.10112091|Paralysis attack;human health;feature selection;neural network;ECG signal;Paralysis attack;human health;feature selection;neural network;ECG signal|
|[Smart Piscis Monitoring System Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112143)|M. Sathyamoorthy; C. N. Vanitha; K. Kaliswary; R. Kumar; B. Sharma; S. Chowdhury|10.1109/ISCON57294.2023.10112143|Node MCU;Temperature sensor;PH sensor;Servo motor;Fish tank;Alert generation;Node MCU;Temperature sensor;PH sensor;Servo motor;Fish tank;Alert generation|
|[An Efficient Integrated approach of Fuzzy C-Means Map Reduce for Weather Forecasting Data Collection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112170)|M. Sathyamoorthy; R. Kumar; C. N. Vanitha; B. Sharma; V. Syamraj; S. Chowdhury|10.1109/ISCON57294.2023.10112170|Wireless Sensor Networks;Fuzzy c-means algorithm;frequent single weather data generation;data splitting;map reduce;prediction.;Wireless Sensor Networks;Fuzzy c-means algorithm;frequent single weather data generation;data splitting;map reduce;prediction.|
|[Smart City Waste Management System using IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111953)|M. Sathyamoorthy; C. N. Vanitha; S. Praveen Raja; A. K. Sharma; B. Sharma; S. Chowdhury|10.1109/ISCON57294.2023.10111953|Smart waste management;Ultrasonic sensor;RFID tag;Arduino;Wi-Fi module;Blynk IoT.;Smart waste management;Ultrasonic sensor;RFID tag;Arduino;Wi-Fi module;Blynk IoT.|
|[Blockchain Interoperability for A Reputation-Based Drug Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112196)|D. H. Tanvir; R. Amin; A. Islam; M. S. Islam; M. M. Rashid|10.1109/ISCON57294.2023.10112196|Blockchain Interoperability;Drug supply chain;hyperledger fabric;reputation system;burn-to-claim;tracking and tracing;Blockchain Interoperability;Drug supply chain;hyperledger fabric;reputation system;burn-to-claim;tracking and tracing|
|[Load balancing in Cloud Computing Environment using Modified Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111981)|N. Verma; B. N. Gohil; A. S. Kansara|10.1109/ISCON57294.2023.10111981|Cloudlet Scheduling;Cloudsim;Dynamic Environment;Execution Time;Genetic Algorithm;Honey Bee Load Balancing;Heuristic-based Scheduling;Load Balancing;Response Time;Cloudlet Scheduling;Cloudsim;Dynamic Environment;Execution Time;Genetic Algorithm;Honey Bee Load Balancing;Heuristic-based Scheduling;Load Balancing;Response Time|
|[Task Scheduling in Cloud Computing using Mean Ant Colony Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112102)|A. S. Kansara; B. N. Gohil; N. Verma|10.1109/ISCON57294.2023.10112102|Cloud Computing;Task scheduling;Virtual machine placement;Cloudsim;ACO records;travel time prediction;Cloud Computing;Task scheduling;Virtual machine placement;Cloudsim;ACO records;travel time prediction|
|[Stock Market Forecasting using ANN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111976)|A. Kumar; R. K. Tripathi; S. C. Agarwal|10.1109/ISCON57294.2023.10111976|artificial neural network (ANN);forecasting;share market;stock market;artificial neural network (ANN);forecasting;share market;stock market|
|[A Comprehensive Survey of various Cyber Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111998)|A. K. Sharma; R. K. Galav; B. Sharma|10.1109/ISCON57294.2023.10111998|Cyber-attack;Cyber Security;Physical attack Cyber-attack prediction;Cyber-attack detection;Machine Learning;Deep Learning;Artificial Intelligence;Cyber-attack;Cyber Security;Physical attack Cyber-attack prediction;Cyber-attack detection;Machine Learning;Deep Learning;Artificial Intelligence|
|[Recommendation System for Movies Using Improved version of SOM with Hybrid Filtering Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111972)|S. Sharma; H. K. Shakya|10.1109/ISCON57294.2023.10111972|hybrid recommender system;content-based method;resource allocation;collaborative filtering;singular value decomposition;singular value decomposition ++.;hybrid recommender system;content-based method;resource allocation;collaborative filtering;singular value decomposition;singular value decomposition ++.|
|[A Hybrid Recommendation System of Upcoming Movies Using Improved version of SOM with Hybrid Filtering Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112003)|S. Sharma; H. K. Shakya|10.1109/ISCON57294.2023.10112003|hybrid recommender system;content-based method;resource allocation;collaborative filtering;singular value decomposition;singular value decomposition++;hybrid recommender system;content-based method;resource allocation;collaborative filtering;singular value decomposition;singular value decomposition++|
|[Image encryption using XOR-based continuous tone MSS and Cellular Automata](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112116)|Akanksha; H. Garg; S. Shivani|10.1109/ISCON57294.2023.10112116|image encryption;MSS;XOR-based continuous tone Visual Cryptography;cellular automata;image encryption;MSS;XOR-based continuous tone Visual Cryptography;cellular automata|
|[Emotions Classification Framework based on Facial Expressions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112163)|S. P. Tomar; H. Bhardwaj; S. Shekhar|10.1109/ISCON57294.2023.10112163|Mood;brain activity;visible color difference;and emotions;Mood;brain activity;visible color difference;and emotions|
|[Boosted Convolutional Neural Networks Based Hybrid Approach for Power Quality Disturbances Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112117)|Rahul; K. Dagar; M. Gangadharappa|10.1109/ISCON57294.2023.10112117|Power quality(PQ);Deep Learning;Machine Learning;Symbiotic Organisms Search (SOS);CNN;Power quality(PQ);Deep Learning;Machine Learning;Symbiotic Organisms Search (SOS);CNN|
|[Modeling of NHPP-Based SRGM with Two Types of Faults](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112073)|A. Tiwari; A. Sharma|10.1109/ISCON57294.2023.10112073|Fault detection process (FDP);Non-homogeneous Poisson-process (NHPP) Impefect debugging;Mean value function (MVF);Software reliability growth models (SRGMs);Fault detection process (FDP);Non-homogeneous Poisson-process (NHPP) Impefect debugging;Mean value function (MVF);Software reliability growth models (SRGMs)|
|[Computing Stock Market Price Behavior Using Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112128)|P. K. Sarangi; J. Mohanty; S. K. Mohapatra; P. Sahu|10.1109/ISCON57294.2023.10112128|Stock Market;Linear Regression;Polynomial Regression;Support Vector Regression;Stock Market Behaviour;Stock Market;Linear Regression;Polynomial Regression;Support Vector Regression;Stock Market Behaviour|
|[Design and Analysis of IoT based Automatic Smart Tea Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112072)|M. Kumari; A. Kumar|10.1109/ISCON57294.2023.10112072|IoT;Node MCU;Sensors;Thingspeak;IoT;Node MCU;Sensors;Thingspeak|
|[A Stock Market Trends Analysis of Reliance using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112145)|V. Kukreti; C. Bhatt; R. Dani|10.1109/ISCON57294.2023.10112145|Stock market;Machine learning;Long short-term memory;Random forest;Support-vector-regression;Stock market;Machine learning;Long short-term memory;Random forest;Support-vector-regression|
|[Design & Imlementation of Smart RO Purifier for Remote Monitoring using IoT Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112068)|A. Kumar; M. Kumari|10.1109/ISCON57294.2023.10112068|IoT;Purifier;Sensor;IoT;Purifier;Sensor|
|[A Ten Year bibliometric analysis of Internet of Things (IoT) from 2011 to 2020](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112032)|S. K. Yadav|10.1109/ISCON57294.2023.10112032|Internet of Things;IoT;Scopus;Bibliometric study;Internet of Things;IoT;Scopus;Bibliometric study|
|[Arrhythmia Detection from ECG Signals using CNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112173)|G. Kumar; S. K. Pandey; N. Varshney; R. R. Janghel; K. U. Singh; A. Kumar|10.1109/ISCON57294.2023.10112173|ECG;Arrhythmia;CNN;Softmax;classification;ECG;Arrhythmia;CNN;Softmax;classification|
|[Performance Assessment of DSDV and AODV Routing Algorithms in MANET under Active Black Hole Assault](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112190)|D. Shukla; R. Singh|10.1109/ISCON57294.2023.10112190|MANET;AODV;DSDV;Black hole assault;PDR;Throughput;Delay;NS2;Route Request;Route Reply;Route Error;MANET;AODV;DSDV;Black hole assault;PDR;Throughput;Delay;NS2;Route Request;Route Reply;Route Error|
|[Human Detection in Surveillance Video using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111951)|N. Mohod; P. Agrawal; V. Madan|10.1109/ISCON57294.2023.10111951|YOLOv4;CSP-DarkNet;MaskRCNN;ResNet50;YOLOv4;CSP-DarkNet;MaskRCNN;ResNet50|
|[Predicting the Success of Motion Pictures using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112202)|G. Kumar; S. K. Pandey; N. Varshney; P. Mishra; K. U. Singh; K. Verma|10.1109/ISCON57294.2023.10112202|CNN;Arrhythmia;ECG signal;class imbalance;classification;CNN;Arrhythmia;ECG signal;class imbalance;classification|
|[Deep Learning Framework for Banana Shelf Life Classification based on Ripening: BSLC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112153)|R. Agrawal; M. Kumar|10.1109/ISCON57294.2023.10112153|Banana life Classification;Machine Learning;Deep Learning;VGG-16;Inception;Feature extraction;Banana life Classification;Machine Learning;Deep Learning;VGG-16;Inception;Feature extraction|
|[IoT Based Smart Systems using Artificial Intelligence and Machine Learning: Accessible and Intelligent Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112093)|B. Ahmed; M. Shuja; H. M. Mishra; A. Qtaishat; M. Kumar|10.1109/ISCON57294.2023.10112093|Internet of Things;Machine Learning;Artificial Intelligence;Distributed Security Framework;Internet of Things;Machine Learning;Artificial Intelligence;Distributed Security Framework|
|[Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111959)|H. M. Mishra; B. Ahmed; M. Shuja; A. Qtaishat; M. Kumar|10.1109/ISCON57294.2023.10111959|Artificial Intelligence;Machine Learning;Healthcare;Artificial Intelligence;Machine Learning;Healthcare|
|[Optimize a novel Integrated Solutions to analyses Privacy persevering of the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111958)|A. K. Mishra; N. Tripathi; P. Bagla; N. K. Pandey; S. Mittal; D. S. Rana|10.1109/ISCON57294.2023.10111958|IoT network;privacy;Key Generator Centre;Security;Pseudo-Random Number Generators (PRNG);IoT network;privacy;Key Generator Centre;Security;Pseudo-Random Number Generators (PRNG)|
|[Machine Learning to Predict Cardiovascular Disease: Systematic Meta-Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112149)|M. Shuja; A. Qtaishat; H. M. Mishra; M. Kumar; B. Ahmed|10.1109/ISCON57294.2023.10112149|Machine Learning;Heart Diseases;Classification;Prediction;Machine Learning;Heart Diseases;Classification;Prediction|
|[Exploring the Techniques for Crowd Video Summarization: A Comprehensive Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112044)|A. Singh; M. Kumar|10.1109/ISCON57294.2023.10112044|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[Profit function Optimization for Growing Items Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111991)|A. Sharma; A. K. Saraswat|10.1109/ISCON57294.2023.10111991|Growing items;economic ordering quantity model;delay in payment;constant demand;mortality;Growing items;economic ordering quantity model;delay in payment;constant demand;mortality|
|[Security of Image Using Watermarking Techniques and Visual Cryptography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112018)|L. Srivastava; H. Garg|10.1109/ISCON57294.2023.10112018|Copyright protection;Watermarking technique;DCT method;Visual Cryptography;Copyright protection;Watermarking technique;DCT method;Visual Cryptography|
|[An Efficient Image Encryption Reversible Data Hiding Technique to Improve Payload and High Security in Cloud Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112201)|S. S. Neetha; J. Bhuvana; R. Suchithra|10.1109/ISCON57294.2023.10112201|Data Hiding Images;Histogram Shifting;Data hiding;Secret Message;Watermarked Images;Data embedding.;Data Hiding Images;Histogram Shifting;Data hiding;Secret Message;Watermarked Images;Data embedding.|
|[Automated Diagnosis of Breast Cancer using Combined Features and Random Forest Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112126)|D. Muduli; R. Priyadarshini; R. C. Barik; S. K. Nanda; R. K. Barik; D. S. Roy|10.1109/ISCON57294.2023.10112126|Computer-aided diagnosis (CAD);Principal Component Analysis (PCA);Linear Discriminant Analysis (LDA);Two-dimensional Discrete Wavelet Transform (2D DWT);Global Contrast Normalization(GCN);Computer-aided diagnosis (CAD);Principal Component Analysis (PCA);Linear Discriminant Analysis (LDA);Two-dimensional Discrete Wavelet Transform (2D DWT);Global Contrast Normalization(GCN)|
|[An Energy Balancing Clustering Based Routing Protocol For Wsn’s](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111950)|N. R. S. Jebaraj; Ganesh; D. Mangal|10.1109/ISCON57294.2023.10111950|Wireless Sensor Networks (WSNs);Energy Efficient Routing Protocol;Cluster Head Election;Network Life Time;Wireless Sensor Networks (WSNs);Energy Efficient Routing Protocol;Cluster Head Election;Network Life Time|
|[Cloud Scheduling Heuristic Approaches for Load Balancing in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112056)|R. Mishra; M. Gupta|10.1109/ISCON57294.2023.10112056|Cloud Computing;Data Centers;Virtual Machines;Load Balancing;Scheduling Heuristics;Task Scheduling;Cloud Computing;Data Centers;Virtual Machines;Load Balancing;Scheduling Heuristics;Task Scheduling|
|[The Role of IOT in Health/Patient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112013)|A. Kumar; P. Pathak; V. K. Dwivedi; D. K. Sharma|10.1109/ISCON57294.2023.10112013|IoT;healthcare;upgraded technology;medical devices;sensors;economical;time reduction.;IoT;healthcare;upgraded technology;medical devices;sensors;economical;time reduction.|
|[Study on Deep Learning Models for Human Pose Estimation and its Real Time Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112004)|J. Jangade; K. S. Babulal|10.1109/ISCON57294.2023.10112004|Human pose estimation;Two Dimensional HPE;Three Dimensional HPE;Human Body Models;Human pose estimation;Two Dimensional HPE;Three Dimensional HPE;Human Body Models|
|[An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112055)|Z. Wang; J. Wang; C. Yan|10.1109/ISCON57294.2023.10112055|SDN;Energy-efficient;Traffic Scheduling Method;Heuristic Algorithm;Slime Mould Algorithm;SDN;Energy-efficient;Traffic Scheduling Method;Heuristic Algorithm;Slime Mould Algorithm|
|[Analysis and Interpretation of Adolescent Multi Relationship and Privacy during COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112040)|S. Mukherjee; K. F. Rahman; U. P. Shukla; S. Gupta; K. Sharma; D. Jangid; N. Paharia|10.1109/ISCON57294.2023.10112040|Social demographic;Mental wellbeing;Adolescents;Peer connectivity;Depression;Anxiety;Extracurricular activities;Academic achievement;Social demographic;Mental wellbeing;Adolescents;Peer connectivity;Depression;Anxiety;Extracurricular activities;Academic achievement|
|[Accident Detection Using Fog Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111980)|M. Srivastava; P. Dixit; P. Ranjan|10.1109/ISCON57294.2023.10111980|Fog Computing;Yolov3;Accidental Detection;Fog Computing;Yolov3;Accidental Detection|
|[Security and Scalability of E-Commerce Website by OWASP threats.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111955)|M. Srivastava; A. Raghuvanshi; D. Khandelwal|10.1109/ISCON57294.2023.10111955|Phishing attack;SQL injection;XSS attack;XXE;Multi Factor authentication;OWASP;regex;broken authentication.;Phishing attack;SQL injection;XSS attack;XXE;Multi Factor authentication;OWASP;regex;broken authentication.|
|[Modified Caesar Cipher with Image Steganography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111954)|M. Srivastava; U. Srivastava; S. Srivastava|10.1109/ISCON57294.2023.10111954|Data Security;Cryptography;Encryption;Decryption;Steganography;Data Security;Cryptography;Encryption;Decryption;Steganography|
|[Data Hiding using Image Steganography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112069)|M. Srivastava; P. Dixit; S. Srivastava|10.1109/ISCON57294.2023.10112069|Steganography;stego image;LSB;R-Color channel;RSA;cipher text;Steganography;stego image;LSB;R-Color channel;RSA;cipher text|
|[A Neural Network based Concept to Improve Downscaling Accuracy of Coarse Resolution Satellite Imagery for Parameter Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112108)|A. Agarwal; B. K. Gupta; K. Kumar; R. Agrawal|10.1109/ISCON57294.2023.10112108|Curve fitting;Error minimization;Downscaling;LANDSAT;LST;MODIS;Neural Network;Curve fitting;Error minimization;Downscaling;LANDSAT;LST;MODIS;Neural Network|
|[A Study of Cloud Based Solution for Data Analytics in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112083)|U. Gupta; R. Sharma|10.1109/ISCON57294.2023.10112083|Big Data;Healthcare;Cloud Computing;Data Analytics;Hadoop;Big Data;Healthcare;Cloud Computing;Data Analytics;Hadoop|
|[Hospital Out Patient Visit Forecasting using Gated Recurrent Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112169)|K. Thapa; A. K. Timalsina|10.1109/ISCON57294.2023.10112169|Univariate;Time Series Data;Forecasting;Recurrent Neural Networks;Gated Recurrent Unit;Univariate;Time Series Data;Forecasting;Recurrent Neural Networks;Gated Recurrent Unit|
|[Factors Affecting Consumer Behaviour during COVID-19:A Case Study in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112165)|F. Shahriar; M. Mohammadian|10.1109/ISCON57294.2023.10112165|ICT;Social media;Privacy;Information science;Online fraud;ICT;Social media;Privacy;Information science;Online fraud|
|[Key Factors Related to Cyber Security Affecting Consumer Attitude in Online Shopping: A Study in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112129)|M. Sadab; M. Mohammadian; A. B. Ullah|10.1109/ISCON57294.2023.10112129|E-commerce;Online shopping;Cyber security;Consumer attitude;Influential factor;Covid-19;E-commerce;Online shopping;Cyber security;Consumer attitude;Influential factor;Covid-19|
#### **2023 10th International Conference on Computing for Sustainable Global Development (INDIACom)**
- DATE: 15-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Empirical Study on Real Time People Counting using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112438)|H. Nagolu; S. Sahu||Video surveillance;single shot detector;deep learning;counting;line of control;Video surveillance;single shot detector;deep learning;counting;line of control|
|[Peer-to-Peer Self-Driving Car Rental: A Case Study on the Development and Limitations of a Novel Transport System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112328)|Z. Jahan; N. Parween; M. Chauhan; M. Chhabra||Peer to Peer (P2P);Collaborative consumption;Economy;willingness;Business Model;Peer to Peer (P2P);Collaborative consumption;Economy;willingness;Business Model|
|[Effective Brain Tumor Segmentation for MRI Image Analysis using Dual Attention Network based YOLACT++](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112545)|N. Roy; S. K. Roy; P. Sharan||Segmentation;Brain Tumor;Computer Vision;Magnetic Resonance Imaging;YOLACT;CNN;Segmentation;Brain Tumor;Computer Vision;Magnetic Resonance Imaging;YOLACT;CNN|
|[Classification and Comparative Analysis of Earth's Nearest Objects using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112391)|D. Khajuria; A. Sharma; N. Sharma; M. Mangla||NASA - nearest earth objects;scatterplot;boxplot;distplot;pairplot;pie-chart;classification;decision tree;logistic regression;random forest;NASA - nearest earth objects;scatterplot;boxplot;distplot;pairplot;pie-chart;classification;decision tree;logistic regression;random forest|
|[Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112248)|V. Pabbi; V. Khullar; M. Angurala||Monkeypox;machine learning;deep learning;feature extraction;oversampling;Monkeypox;machine learning;deep learning;feature extraction;oversampling|
|[StudentCoin Price Prediction and Relation to Blockchain Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112393)|R. Kaur; Sonia; C. Iorio||Cryptocurrencies;Education;Blockchain;STC (StudentCoin);price prediction;Monte Carlo simulation;time-series data;Cryptocurrencies;Education;Blockchain;STC (StudentCoin);price prediction;Monte Carlo simulation;time-series data|
|[Exploring the Efficiency of Text-Similarity Measures in Automated Resume Screening for Recruitment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112537)|A. Alsharef; Sonia; H. Nassour; J. Sharma||Similarity measures;Cosine similarity;Sqrt-Cos similarity;ISC similarity;Resume-recommendation;Decision-making;Natural Language Processing;Similarity measures;Cosine similarity;Sqrt-Cos similarity;ISC similarity;Resume-recommendation;Decision-making;Natural Language Processing|
|[Text Summarization of Amazon Customer Reviews using NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112462)|H. Nainwal; A. Garg; A. Chakraborty; D. Bathla||Text Summarization;NLP;Machine learning;Deep learning;Word cloud;Text Summarization;NLP;Machine learning;Deep learning;Word cloud|
|[Performance Analysis of External Modulators for Use in Future Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112478)|D. Singh; N. Goel; P. Kumar||Radio over fiber;Mach Zehnder Modulator;Base station;Radio over fiber;Mach Zehnder Modulator;Base station|
|[5G Ultra-Reliable Low-Latency Communication: Use Cases, Concepts and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112312)|S. Akhila; Hemavathi||5G;eMBB;mMTC;URLLC;WIVE;5G;eMBB;mMTC;URLLC;WIVE|
|[A Nudity Detection Algorithm for Web-based Online Networking Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112443)|R. Dewan; R. Polishetty; N. Jagadam; K. K. Ravulakollu; M. K. Goyal; B. Sharan||Nudity Detection;Images Detection;Machine Learning;Deep Learning;CNN;Nudity Detection;Images Detection;Machine Learning;Deep Learning;CNN|
|[Weight Training Pose Estimations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112458)|S. K. Singh; W. R. Thakur; A. Raghuvanshi; A. I. Abidi||AI gym trainer;pose detections;pose estimations;OpenPose;Computer vision;AI gym trainer;pose detections;pose estimations;OpenPose;Computer vision|
|[Systematic Review of Dyslexia Detection Approaches based on Physiological Traits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112373)|S. Joshi; A. Shah; N. Chhetri; A. I. Abidi||Dyslexia;Physiological Traits;EEG;CNN;handwriting;eye movement;Dyslexia;Physiological Traits;EEG;CNN;handwriting;eye movement|
|[Cement Strength Prediction using Regression Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111840)|R. Dewan; R. Polishetty; B. Sharan; M. K. Goyal; A. M. Balde; A. M. Ahmad Greynoon||Concrete cement strength;Linear regression;Decision tree regression;Random Forest regression;Ada boosting;Gradient Boosting;Stacking method;Concrete cement strength;Linear regression;Decision tree regression;Random Forest regression;Ada boosting;Gradient Boosting;Stacking method|
|[Automatic Test Case Generation Framework for Changed Code using Modified AEO Algorithm in Regression Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112410)|A. S. Verma; A. Choudhary; S. Tiwari||Regression Testing;Test case generation;Statement coverage;MAEO;Optimization;Framework;Regression Testing;Test case generation;Statement coverage;MAEO;Optimization;Framework|
|[Human Activity Recognition with Smartphone using Classical Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112279)|R. Shekhar; D. S. Tomar; R. K. Pateriya; B. Sharan||Human Activity Recognition;Health care;Sensors;Machine Learning;Classification;Human Activity Recognition;Health care;Sensors;Machine Learning;Classification|
|[A Systematic Review of IoT Malware Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112581)|B. Sharma; R. Kumar; A. Kumar; M. Chhabra; S. Chaturvedi||IoT;Malware detection IoT;IoT Malware;Machine learning;Static Analysis;Dynamic Analysis;IoT;Malware detection IoT;IoT Malware;Machine learning;Static Analysis;Dynamic Analysis|
|[A Novel Approach for Customer Segmentation and Product Recommendation to Boost Sales using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112415)|A. Chakraborty; K. Dey; S. Ghosh; R. Chakraborty; I. Mitra; P. Nandy||Customer Segmentation;K-Means Clustering algorithm;Recommendation Systems;Ensemble Learning;Manifold learning;Customer Segmentation;K-Means Clustering algorithm;Recommendation Systems;Ensemble Learning;Manifold learning|
|[Harnessing Fault Tolerant Capabilities of USE Clocking Scheme for Designing QCA Flip- flops](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112527)|S. Husain; N. Gupta||Flip flops;Fault tolerance;QCA;Single cell omission defect;USE Clocking scheme;Flip flops;Fault tolerance;QCA;Single cell omission defect;USE Clocking scheme|
|[Anomaly Detection using Linear Neural Network and Multidimensional Reduction Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112560)|P. Mehra; M. S. Ahuja||Multi-dimensional reduction;Anomaly detection;Decentralized system;Unprecedented data;Linear Neural Network;Multi-dimensional reduction;Anomaly detection;Decentralized system;Unprecedented data;Linear Neural Network|
|[A Review on the use of Machine Learning Techniques in Music Recommendation System for Healthcare Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112289)|Y. P. Pingle; L. K. Ragha||Disease Diagnosis;Health care Management;Machine learning;Music Recommendation System;Music Therapy;Disease Diagnosis;Health care Management;Machine learning;Music Recommendation System;Music Therapy|
|[Printed Circuit Board Profiling for Assembly using Thermal Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112480)|L. Kumar; P. Kumar||PCB;Thermal Image;Image Processing;Reflow Process;Temperature;PCB;Thermal Image;Image Processing;Reflow Process;Temperature|
|[Smart Healthcare: Future Applications & Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112541)|S. Vermani||Covid-19;IoT;AI;Healthcare;Technological advancements;Covid-19;IoT;AI;Healthcare;Technological advancements|
|[A Survey of Punjabi Language Translation using OCR and ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112285)|S. Khepra; P. Kumari; R. Gupta; Abhishek; V. S. Bramhe||Machine Learning (ML);Artificial Intelligence (AI);Natural Language Processing (NLP);Gurmukhi Script;Machine translation (MT);Machine Learning (ML);Artificial Intelligence (AI);Natural Language Processing (NLP);Gurmukhi Script;Machine translation (MT)|
|[Hand Gesture Recognition using OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112341)|R. Gupta; A. Singh||OpenCV;Hand Recognition System;Shape Features;OpenCV;Hand Recognition System;Shape Features|
|[Image to Pencil Sketch Converter using Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112484)|R. Gupta; S. Subedi||Python;OpenCv;Computer Vision;IDLE;Grey-Scale;Photo Transformation;Python;OpenCv;Computer Vision;IDLE;Grey-Scale;Photo Transformation|
|[An Item-based Collaborative Filtering Approach for Movie Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112338)|P. Dwivedi; B. Islam||Recommendation System;Item Based Collaborative Filtering;Automation;Clustering;Machine Learning;Recommendation System;Item Based Collaborative Filtering;Automation;Clustering;Machine Learning|
|[Sales Forecasting: A Comparison of Traditional and Modern Times-Series Forecasting Models on Sales Data with Seasonality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112431)|Y. Ali; S. Nakti||Time-series Forecasting models;Sales forecasting;Deep learning;Prediction;Seasonal Time Series Data;LSTM;SARIMA;Fb-Prophet;Time-series Forecasting models;Sales forecasting;Deep learning;Prediction;Seasonal Time Series Data;LSTM;SARIMA;Fb-Prophet|
|[A Review on the Role of Artificial Intelligence in Tourism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112472)|A. Dangwal; M. Kukreti; M. Angurala; R. Sarangal; M. Mehta; P. Chauhan||AI in customer service;Tourism industry;Synergy between AI and human emotions;New AI technologies and techniques;Customer satisfaction in hospitality industry AI;The tourism sector;AI in customer service;Tourism industry;Synergy between AI and human emotions;New AI technologies and techniques;Customer satisfaction in hospitality industry AI;The tourism sector|
|[A Research Survey on Security Enhancement in WSN-based IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112434)|V. V. Bhandiwad; L. K. Ragha||Energy Efficiency;Internet of Things;Security;Sensors;Wireless Sensor Networks;Energy Efficiency;Internet of Things;Security;Sensors;Wireless Sensor Networks|
|[Need and Challenges in Quantum Computing in Fog Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112412)|S. Beniwal||Fog computing;workforce development;quantum computing;Fog computing;workforce development;quantum computing|
|[Enhancing Medical Domain Data Security using Inbuilt Data Encryption and Steganography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112482)|T. Modi; J. Patel; M. Paliwal; K. Shah; A. Shastri||Medical Domain;Encryption;Steganography;AES;Linked Pixel Steganography (LPS) Technique;Medical Domain;Encryption;Steganography;AES;Linked Pixel Steganography (LPS) Technique|
#### **2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC)**
- DOI: 10.1109/ICRTEC56977.2023
- DATE: 10-11 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Machine Learning based Routing approach and Resource Management in Vehicular Adhoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111882)|M. V. Gudur; S. M; P. P; N. L; M. B. Neelagar; S. B|10.1109/ICRTEC56977.2023.10111882|Machine Learning;VANET;Routing;Path selection;Road Side Unit (RSU);Machine Learning;VANET;Routing;Path selection;Road Side Unit (RSU)|
|[A novel optimization approach using multi-objective PSO incorporated with SEPIC and buck-boost converters for renewable energy sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111898)|P. P; H. N. S. Harsha; M. V. Gudur; N. L; M. B. Neelagar; S. B|10.1109/ICRTEC56977.2023.10111898|Maximum power point;Photovoltaic;Model predictive controller;Multiobjective;Traditional energy sources;Maximum power point;Photovoltaic;Model predictive controller;Multiobjective;Traditional energy sources|
|[Binary Classification of Human Emotion using EEG and LTSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111854)|S. Saleem; J. S. O; P. Vashishta|10.1109/ICRTEC56977.2023.10111854|Emotion Detection;Electroencephalography;Emotions;LTSM;Neural Network;Emotion Detection;Electroencephalography;Emotions;LTSM;Neural Network|
|[Comparative Analysis and Development of an Efficient Management System for a Photo-Voltaic Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111881)|S. S. Tippannavar; Y. S D; C. M. B N; M. S. M P; P. K M; V. P. M|10.1109/ICRTEC56977.2023.10111881|Concentrators;Energy;Multi-meter;Photovoltaic module;Solar Panel;Sustainable Future;Thermal Management system;Concentrators;Energy;Multi-meter;Photovoltaic module;Solar Panel;Sustainable Future;Thermal Management system|
|[EVAS - Emergency Vehicle Alert System using LoRa for automobiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111859)|S. S. Tippannavar; P. K M; Y. S D; M. S. M P; C. M. B N; V. P. M S|10.1109/ICRTEC56977.2023.10111859|Arduino;LCD;LoRa module;LoRa WAN;Automobiles;Alert system;Communication;Amplifier;Early Warning system;Arduino;LCD;LoRa module;LoRa WAN;Automobiles;Alert system;Communication;Amplifier;Early Warning system|
|[Smart Transformer - An Analysis of Recent Technologies for Monitoring Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111875)|S. S. Tippannavar; V. Mishra; Y. S D; R. R. Gowda; S. H R; A. M|10.1109/ICRTEC56977.2023.10111875|Transformer;Communication;Oil Monitoring;Electricity;Theft monitoring;LoRa;Arduino;Machine Learning;CNN;Efficiency;Embedded Systems;Transformer;Communication;Oil Monitoring;Electricity;Theft monitoring;LoRa;Arduino;Machine Learning;CNN;Efficiency;Embedded Systems|
|[An Analysis on the Techniques for Water Quality Prediction from Remotely Sensed data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111933)|A. Sarma; J. S. O|10.1109/ICRTEC56977.2023.10111933|Water Quality Prediction;Remote Sensing;GIS;Machine Learning;Water Quality Prediction;Remote Sensing;GIS;Machine Learning|
|[Artificial Neural Network Based Classification Of Motor Imagery EEG Signals For Efficient Brain Computer Interface System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111856)|S. G; S. K. J|10.1109/ICRTEC56977.2023.10111856|Electroencephalogram;Brain Computer Interface;Motor Imagery;Artificial Neural Network;Electroencephalogram;Brain Computer Interface;Motor Imagery;Artificial Neural Network|
|[NBA Game Prediction Using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111906)|A. Patrot; H. H; S. B; G. P L; Sahana|10.1109/ICRTEC56977.2023.10111906|Machine learning;Linear;SVM;Decision tree;Machine learning;Linear;SVM;Decision tree|
|[Brain Tumor Detection Using the Inception Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111883)|R. Singamshetty; S. Sruthi; K. Chandhana; S. Kollem; C. R. Prasad|10.1109/ICRTEC56977.2023.10111883|data augmentation;inception v3;k-fold;sensitivity;specificity;application page;data augmentation;inception v3;k-fold;sensitivity;specificity;application page|
|[Detection of Lung Tumor using an efficient Quadratic Discriminant Analysis Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111903)|S. K. Gupta; V. S. Kumar; A. Khang; B. Hazela; N. T; B. Haralayya|10.1109/ICRTEC56977.2023.10111903|Healthcare;Lung Tumor;Quadratic Discriminant Analysis;Deep Learning;Image Segmentation;Healthcare;Lung Tumor;Quadratic Discriminant Analysis;Deep Learning;Image Segmentation|
|[A Review on Animal Detection and Classification using Computer Vision Techniques: Scope for Future Enhancement to Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111888)|B. K; D. V|10.1109/ICRTEC56977.2023.10111888|Animal Detection;Artificial Intelligence;Computer Vision;Neural Network;Internet of Technology (IoT):;Animal Detection;Artificial Intelligence;Computer Vision;Neural Network;Internet of Technology (IoT):|
|[Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111862)|S. K. Gupta; S. Saxena; A. Khang; B. Hazela; C. K. Dixit; B. Haralayya|10.1109/ICRTEC56977.2023.10111862|Image Labeler;Vehicle Tracking;Number plate Detection;IoT;Deep Learning;Image Labeler;Vehicle Tracking;Number plate Detection;IoT;Deep Learning|
|[Image Segmentation on Gabor Filtered images using Projective Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111885)|S. K. Gupta; A. Alemran; P. Singh; A. Khang; C. K. Dixit; B. Haralayya|10.1109/ICRTEC56977.2023.10111885|Image Processing;Gabor filters;Projective Transformation;Phase correlation;Image Processing;Gabor filters;Projective Transformation;Phase correlation|
|[Solving Roulette Wheel Selection Method using Swarm Intelligence for Trajectory Planning of Intelligent Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111861)|S. K. Gupta; W. Ahmad; D. A. Karras; A. Khang; C. K. Dixit; B. Haralayya|10.1109/ICRTEC56977.2023.10111861|Intelligent Systems;Swarm Intelligence;Trajectory Planning;Roulette Wheel Selection;Intelligent Systems;Swarm Intelligence;Trajectory Planning;Roulette Wheel Selection|
|[MQTT Broker Performance Comparison between AWS, Microsoft Azure and Google Cloud Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111870)|A. S. Suwardi Ansyah; M. Arifin; M. B. Alfan; M. V. Suriawan; N. H. Farhansyah; A. M. Shiddiqi; H. Studiawan|10.1109/ICRTEC56977.2023.10111870|IoT;AWS;MQTT Broker;Azure;GCP;MQTTLoader;IoT;AWS;MQTT Broker;Azure;GCP;MQTTLoader|
|[Comparative Analysis and Implementation of High Current and Low Output Ripple Converters for BLDC Drive System - EV Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111853)|P. Chandran; V. KG; T. J. KS; S. V. V|10.1109/ICRTEC56977.2023.10111853|DC - DC converter;Boost;ZVZCS;Super-Lift Luo;SEPIC;BLDC and EV;DC - DC converter;Boost;ZVZCS;Super-Lift Luo;SEPIC;BLDC and EV|
|[Investigating and Checking the Javelin Athlete's Movement Parameters Using Smart WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111871)|S. D. P; R. Sabitha; J. Shirisha; A. Balaji|10.1109/ICRTEC56977.2023.10111871|Javelin athlete's movement parameters;Wireless Sensor Network;Smartphone app;Cloud Server;Sensor;Javelin athlete's movement parameters;Wireless Sensor Network;Smartphone app;Cloud Server;Sensor|
|[Misbehavior Node Detection using Hamming Residue Mechanism in Clustering WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111868)|T. Sivakumar; K. S. Rekha; N. Vikram; B. M. Kannan|10.1109/ICRTEC56977.2023.10111868|Misbehavior node detection;Wireless sensor network;Clustering;Hamming residue mechanism;Ant colony optimization;Energy efficiency;Misbehavior node detection;Wireless sensor network;Clustering;Hamming residue mechanism;Ant colony optimization;Energy efficiency|
|[Ecological Observing using Sensor and IoT to Protect the Global Warming in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111876)|K. S. V J K; S. G; B. T; S. A. Nisha A|10.1109/ICRTEC56977.2023.10111876|Internet of Things;Water Pollution Observing;Wastage Management;Air Pollution Observing;Radiation Observing;Weather Observing;Internet of Things;Water Pollution Observing;Wastage Management;Air Pollution Observing;Radiation Observing;Weather Observing|
|[SDR – Self Driving Car Implemented using Reinforcement Learning & Behavioural Cloning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111893)|S. S. Tippannavar; Y. S D; P. K M|10.1109/ICRTEC56977.2023.10111893|Machine Learning;CNN;RNN;Reinforcement Learning;Behavioural Cloning;Python;Canny Edge Detection;Road Traffic Signs;Communication;Radar;DNN;Perceptron;SDC;Artificial Intelligence;Car;Self-Drive;Machine Learning;CNN;RNN;Reinforcement Learning;Behavioural Cloning;Python;Canny Edge Detection;Road Traffic Signs;Communication;Radar;DNN;Perceptron;SDC;Artificial Intelligence;Car;Self-Drive|
|[Drive Cycle Based Speed Control of BLDC Motor Using Pulse Width Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111848)|B. Bairwa; M. Murari; M. Sahapur; K. M.R; M. F. Khan|10.1109/ICRTEC56977.2023.10111848|FPGA;BLDC;IGBT;Smart power Module;PWM;FPGA;BLDC;IGBT;Smart power Module;PWM|
|[Detection Of MRI Brain Tumor Using Customized Deep Learning Method Via Web App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111887)|A. S. Sri; B. V. Reddy; K. Balakrishna; V. Akshitha; S. Kollem; C. R. Prasad|10.1109/ICRTEC56977.2023.10111887|MRI scans;CNN model;Brain tumor;Deep learning;Pre-processing;Data augmentation;MRI scans;CNN model;Brain tumor;Deep learning;Pre-processing;Data augmentation|
|[Efficient Novel Binary to Gray Code Converter Using Coulombic Interaction on Quantum Dot Cellular Automata](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111872)|I. Y. Sheikh|10.1109/ICRTEC56977.2023.10111872|Quantum-dot cellular automata (QCA);Coulombic interaction;Latency;Binary to gray (B2G) code;Quantum-dot cellular automata (QCA);Coulombic interaction;Latency;Binary to gray (B2G) code|
|[Sliding Mode Controller with Disturbance Estimator for Fuzzy Logic Controller fed PMSM Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111899)|A. R. Mogili; S. J; P. S. Bojanapalli|10.1109/ICRTEC56977.2023.10111899|PMSM;Sliding Mode Control;Disturbance Estimator;Fuzzy Logic Controller;PMSM;Sliding Mode Control;Disturbance Estimator;Fuzzy Logic Controller|
|[Wind Turbine Integrated Generator Rectifier System with Fuzzy Logic Controller based on MPPT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111867)|N. K. Peelam; K. R; N. K; S. N|10.1109/ICRTEC56977.2023.10111867|Offshore wind turbines;MPPT;Rectifiers;Fuzzy logic controller;Offshore wind turbines;MPPT;Rectifiers;Fuzzy logic controller|
|[A Novel Fuzzy Logic Controller Topology for Grid Connected PV System by DC Voltage Droop Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111873)|M. V. Kumar; M. Kudadala|10.1109/ICRTEC56977.2023.10111873|Droop control;photovoltaic (pv);inertia analysis;damping analysis;synchronization analysis;fuzzy logic controller;Droop control;photovoltaic (pv);inertia analysis;damping analysis;synchronization analysis;fuzzy logic controller|
|[Temperature Dependent Capacity Fade Prediction of Electric Vehicles Batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111909)|B. Bairwa; S. P; S. B. S; A. K. P|10.1109/ICRTEC56977.2023.10111909|batteries;drive cycles;temperature;battery age;batteries;drive cycles;temperature;battery age|
|[Performance Analysis for LLC Resonant Converter in Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111915)|S. K; G. K. P|10.1109/ICRTEC56977.2023.10111915|Electric vehicle;LLC resonant converter;frequency control;small signal modeling;Electric vehicle;LLC resonant converter;frequency control;small signal modeling|
|[Enhanced Multimedia Broadcast Multicast service using virtualized 5G network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111931)|S. B. Rudraswamy; K. Marathe; S. Lasitha; T. Chethan; S. Anjali; V. Sinchana|10.1109/ICRTEC56977.2023.10111931|Software-Defined Virtual Mobile Network (SDVMN) architecture;Network Functions Virtualization (NFV);Software-Defined Networking (SDN);Commercial off-the-shelf (COTS) Hardware;virtualization;Software-Defined Virtual Mobile Network (SDVMN) architecture;Network Functions Virtualization (NFV);Software-Defined Networking (SDN);Commercial off-the-shelf (COTS) Hardware;virtualization|
|[Modeling of Low Cost Battery Charge Controller for Stationary to Mobile Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111855)|B. Bairwa; M. K. A; M. M. Magadum; K. S; R. Chitrashree|10.1109/ICRTEC56977.2023.10111855|Battery charge controllers;MATLAB Simulink;controllers;Pulse width Modulation;Load;Battery charge controllers;MATLAB Simulink;controllers;Pulse width Modulation;Load|
|[Performance Analysis of Half-Bridge LC Resonant Converter for UPS Battery Charging Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111860)|S. S. Borkar; G. K P|10.1109/ICRTEC56977.2023.10111860|Resonant;Converters;Controller;small-signal modeling;Average current mode control;Simulation;Resonant;Converters;Controller;small-signal modeling;Average current mode control;Simulation|
#### **2023 15th International Conference on Computer and Automation Engineering (ICCAE)**
- DOI: 10.1109/ICCAE56788.2023
- DATE: 3-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Proximity-Based Reward System and Reinforcement Learning for Path Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111485)|M. -A. Blais; M. A. Akhloufi|10.1109/ICCAE56788.2023.10111485|reinforcement learning;path planning;robotics;proximity;reward;reinforcement learning;path planning;robotics;proximity;reward|
|[Integration of Machine Learning in Agile Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111340)|V. Ghabak; A. Seetharaman|10.1109/ICCAE56788.2023.10111340|machine learning;agile supply chain management;industry 4.0;supply chain risk management;advanced technologies;machine learning;agile supply chain management;industry 4.0;supply chain risk management;advanced technologies|
|[Neural Network Technology for Choosing a Learning Path](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111183)|S. Kabdushev; D. Shaltykova; Y. Vitulyova; A. Bakirov; I. Suleimenov|10.1109/ICCAE56788.2023.10111183|neural networks;diagnostics of social groups;learning trajectory;psychological tests;digital image;electronic textbook;neural networks;diagnostics of social groups;learning trajectory;psychological tests;digital image;electronic textbook|
|[Investigating the Role of PTEN and P53 in Autism: Design of A Mutant Information Prediction System (MIPS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111310)|S. G. Jacob; B. Bennet; M. M. Ali Sulaiman|10.1109/ICCAE56788.2023.10111310|data mining;TP53;PTEN;Autism;machine learning;information systems;data mining;TP53;PTEN;Autism;machine learning;information systems|
|[Machine Learning Model for Sentiment Analysis on Mental Health Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111148)|B. Kaushik; A. Sharma; A. Chadha; R. Sharma|10.1109/ICCAE56788.2023.10111148|machine learning;word embedding;text analysis;TextBlob;machine learning;word embedding;text analysis;TextBlob|
|[Machine Learning Based Power Analysis for Simon with Secure Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111482)|S. Takemoto; Y. Ikezaki; Y. Nozaki; M. Yoshikawa|10.1109/ICCAE56788.2023.10111482|deep learning;security;lightweight block cipher;side-channel attack;deep learning;security;lightweight block cipher;side-channel attack|
|[A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111467)|S. S. Burrewar; M. Haque; T. U. Haider|10.1109/ICCAE56788.2023.10111467|urban green spaces;remote sensing data;machine learning;deep learning;mapping;urban green spaces;remote sensing data;machine learning;deep learning;mapping|
|[A Review of Deep Learning Methods for Automated Clinical Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111456)|S. S. Mahdi; N. Deligiannis; H. Sahli|10.1109/ICCAE56788.2023.10111456|Automated Clinical Coding;Electronic Health Records;Explainable and Interpretable AI;Multi-label Classification;Automated Clinical Coding;Electronic Health Records;Explainable and Interpretable AI;Multi-label Classification|
|[SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111438)|X. Q. Ong; K. Hui Lim|10.1109/ICCAE56788.2023.10111438|Recommendation Systems;Skill Recommendation;Occupational Analysis;Natural Language Processing;Neural Networks;Recommendation Systems;Skill Recommendation;Occupational Analysis;Natural Language Processing;Neural Networks|
|[Student Management Model Integrating E-Commerce Based on Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111175)|K. T. Vo; T. Nguyen; T. -T. Ta; T. -A. Nguyen-Hoang; N. -T. Dinh|10.1109/ICCAE56788.2023.10111175|Blockchain Technology;Smart City;Security;Service and Public Sector Systems;Blockchain Technology;Smart City;Security;Service and Public Sector Systems|
|[MGRU-M1dCNN: A Hybrid Model for Stock Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111233)|A. Kanwal; S. Chandrasekaran|10.1109/ICCAE56788.2023.10111233|MGRU-M1dCNN;GRU;CNN;stock price prediction;time series data;MGRU-M1dCNN;GRU;CNN;stock price prediction;time series data|
|[Model for Verifying the Reliability of Candidate Data Based on Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111120)|T. Nguyen; K. T. VO; T. -T. Ta; T. -A. Nguyen-Hoang; N. -T. Dinh|10.1109/ICCAE56788.2023.10111120|Blockchain Technology;Smart City;Security;Service and Public Sector Systems;Blockchain Technology;Smart City;Security;Service and Public Sector Systems|
|[Employing Thresholding and Template Rotation to Enhance the Effectiveness of Template Matching Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111421)|B. R. Gunawardana|10.1109/ICCAE56788.2023.10111421|OpenCV;image processing;object counting;template matching;OpenCV;image processing;object counting;template matching|
|[Route Prediction from GPS Trajectory and Road Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111441)|R. Chawuthai; K. Kawachakul; K. Boonrod; T. Threepak|10.1109/ICCAE56788.2023.10111441|GPS data analytics;machine learning;route prediction;GPS data analytics;machine learning;route prediction|
|[Computer Vision-Based Non-invasive Sweetness Assessment of Mangifera Indica L. Fruit Using K-means Clustering and CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111250)|A. N. Yumang; L. A. G. Francia; R. J. L. Romero|10.1109/ICCAE56788.2023.10111250|Carabao mango;computer vision;Brix;convolutional neural network;k-means clustering;Carabao mango;computer vision;Brix;convolutional neural network;k-means clustering|
|[Fault-Tolerant Broadcast Algorithm for Tree Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111474)|M. H. Karaata; A. Dabees|10.1109/ICCAE56788.2023.10111474|fault tolerance;fault containment;distributed computing;safety;self-stabilization;self-healing;wave algorithms;fault tolerance;fault containment;distributed computing;safety;self-stabilization;self-healing;wave algorithms|
|[Augmented Reality Campus Exploration Application Incorporating Equity, Diversity, and Inclusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111189)|P. Jindal; A. J. Park; E. Hwang|10.1109/ICCAE56788.2023.10111189|augmented reality (AR);equity;diversity;and inclusiveness (EDI);global positioning system (GPS);point of interest (POI);augmented reality (AR);equity;diversity;and inclusiveness (EDI);global positioning system (GPS);point of interest (POI)|
|[Verification of the Usefulness of the Virtual Reality Application for URM Assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111354)|J. Svetlík; J. Kováč; M. Pollák; M. Kočiško; T. Stejskal; M. Šašala|10.1109/ICCAE56788.2023.10111354|montage;virtual reality;virtual reality software;montage;virtual reality;virtual reality software|
|[Automated Detection of Biases within the Healthcare System Using Clustering and Logistic Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111425)|J. Prakhar; M. T. U. Haider|10.1109/ICCAE56788.2023.10111425|bias;big data;logistic regression;clustering;bias;big data;logistic regression;clustering|
|[Las Piñas Flood Monitoring System with Alternate Route Using Bayesian Network via Mobile Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111369)|R. U. Cartusiano; F. R. G. Cruz|10.1109/ICCAE56788.2023.10111369|flood;traffic;disaster risk reduction;IoT;Bayesian;Dijkstra;mobile app;e-WAS;e-Governance;flood;traffic;disaster risk reduction;IoT;Bayesian;Dijkstra;mobile app;e-WAS;e-Governance|
|[Artificial Intelligence-Based Smart Tele-Assisting Technology for First-Year Engineering Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111274)|M. Racha; S. Chandrasekaran; A. Stojcevski|10.1109/ICCAE56788.2023.10111274|augmented reality;artificial intelligence;machine learning;practical activity;augmented reality;artificial intelligence;machine learning;practical activity|
|[Design and Implementation of Modbus RTU/TCP to Profibus Gateway Using Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111395)|D. Zaheri; M. H. Refan|10.1109/ICCAE56788.2023.10111395|profibus;Modbus;gateway;VPC3+S;Raspberry Pi;profibus;Modbus;gateway;VPC3+S;Raspberry Pi|
|[IoT Based Water Consumption Monitoring System for Water Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111335)|A. E. Clemente; R. Miguel G. Samaniego; F. R. G. Cruz|10.1109/ICCAE56788.2023.10111335|IoT;water monitoring;mobile application;lavatory;touchless activation;ferry boat;IoT;water monitoring;mobile application;lavatory;touchless activation;ferry boat|
|[An RFID-Assisted Smart Livestock and Poultry Farming System on the Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111202)|B. Wu; J. Li; Q. Liu; K. . -L. Du|10.1109/ICCAE56788.2023.10111202|smart animal farming;RFID;Internet of Things;cloud computing;food safety;smart animal farming;RFID;Internet of Things;cloud computing;food safety|
|[Coverage Prediction and REM Construction for 5G Networks in Band n78](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111476)|C. E. Garcia; I. Koo|10.1109/ICCAE56788.2023.10111476|band n78;5G mid-band;coverage prediction;extra-trees;machine learning;band n78;5G mid-band;coverage prediction;extra-trees;machine learning|
|[Development of a Power Demand Meter for Residential Loads Using ZigBee Wireless Sensor Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111423)|A. R. Alzaga; K. R. Caguete; M. E. N. De Vera; K. Karl E. Villarino; G. V. Magwili; M. C. Pacis|10.1109/ICCAE56788.2023.10111423|power demand meter;automatic meter reading;ZigBee;one-third Simpson’s rule;LabView;power demand meter;automatic meter reading;ZigBee;one-third Simpson’s rule;LabView|
|[Musculoskeletal Modelling and Simulation for Upper Limb Muscle Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111291)|M. F. Ashari; A. Hanafusa; S. Mohamaddan|10.1109/ICCAE56788.2023.10111291|computer simulation;upper limb;muscle;OpenSim;activities of daily living;computer simulation;upper limb;muscle;OpenSim;activities of daily living|
|[PIR Sensor Activated Camera for Home Security System Using ATMega328p](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111419)|R. V. Pellegrino; J. Rhey C. Don; A. B. Lopez; A. John F. Tenorio|10.1109/ICCAE56788.2023.10111419|ATmega328p;sensors;efficient motion sensing closed circuit-television;ATmega328p;sensors;efficient motion sensing closed circuit-television|
|[Sensor Management for Localisation of an Evasive Target: The Benefits of Game Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111480)|B. Ristic; A. Skvortsov; S. Arulampalam; D. Y. Kim|10.1109/ICCAE56788.2023.10111480|Sensor management;Game theory;Autonomous decision making;Active surveillance;Bayesian estimation;Sensor management;Game theory;Autonomous decision making;Active surveillance;Bayesian estimation|
|[A Long-Term Wind Power Prediction using Support Vector Regression and Ensemble Boosted Tree Algorithm (SVR-EBTA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111136)|R. J. E. Jizmundo; R. J. F. Maltezo; F. J. G. Villanueva; M. C. Pacis|10.1109/ICCAE56788.2023.10111136|wind power prediction;support vector regression;ensemble boosted tree;paired t-test;estimated forecast error;regression;wind power prediction;support vector regression;ensemble boosted tree;paired t-test;estimated forecast error;regression|
|[A Lightweight Hybrid Framework for Real-Time Detection of Process Related Anomalies in Industrial Time Series Data Generated by Online Industrial IoT Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111201)|A. K. Bagchi; S. Chandrasekaran|10.1109/ICCAE56788.2023.10111201|anomaly detection;Industrial Time Series;Real- time sensor data;anomaly detection;Industrial Time Series;Real- time sensor data|
|[Design of Picoammeter Device Interface Boards for the CTS-5010 Automatic Test Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111117)|J. R. Diaz; K. John Fajutagana; M. J. Dalena; A. Flores; M. A. Latina|10.1109/ICCAE56788.2023.10111117|ammeter;current;noise;picoampere;feedback;shunt;logarithmic;settling time;ammeter;current;noise;picoampere;feedback;shunt;logarithmic;settling time|
#### **2023 IEEE International Conference on Smart Mobility (SM)**
- DOI: 10.1109/SM57895.2023
- DATE: 19-21 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[The Last Mile Problem: Determining the Commuters' Willingness and Demand Preferences towards Micromobility Solutions in Sri Lanka](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112461)|S. Suntharalingam; L. Gunawardena|10.1109/SM57895.2023.10112461|sustainable urban transportation;micromobility;shared mobility;mobility-as-a-service;electric vehicles;sustainable urban transportation;micromobility;shared mobility;mobility-as-a-service;electric vehicles|
|[Theory of Change for the Transformation Towards Open Smart and Sustainable Mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112508)|Z. Mahrez; E. Sabir; W. Saad; T. Nesh-Nash; M. Sadik|10.1109/SM57895.2023.10112508|mobility development model;open;smart;sustainable;mobility development model;open;smart;sustainable|
|[A Slow Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112492)|Z. Koh; Y. Qin; Y. L. Guan; C. Yuen|10.1109/SM57895.2023.10112492|Traffic flow forecasting;Long-short term memory;Empirical mode decomposition;Traffic flow forecasting;Long-short term memory;Empirical mode decomposition|
|[Optimal Placement of Bus Stops using Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112283)|C. Li; R. Ge; X. Wu; A. Khamis|10.1109/SM57895.2023.10112283|Bus Stop Placement;Metaheuristics;Particle Swarm Optimization (PSO);Adaptive PSO;Bus Stop Placement;Metaheuristics;Particle Swarm Optimization (PSO);Adaptive PSO|
|[Deep Reinforcement Learning-based Traffic Signal Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112300)|J. Ruan; J. Tang; G. Gao; T. Shi; A. Khamis|10.1109/SM57895.2023.10112300|Traffic Congestion;Traffic Signal Control;Reinforcement Leaning;Deep Learning;Traffic Congestion;Traffic Signal Control;Reinforcement Leaning;Deep Learning|
|[Cooperative Variable Speed Limit Control using Multi-agent Reinforcement Learning and Evolution Strategy for Improved Throughput in Mixed Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112494)|K. Lin; Z. Jia; P. Li; T. Shi; A. Khamis|10.1109/SM57895.2023.10112494|Variable Speed Limit;Connected and Automated Vehicles;Multi-agent Reinforcement Learning;Evolution Strategy;Graph Attention Networks;Variable Speed Limit;Connected and Automated Vehicles;Multi-agent Reinforcement Learning;Evolution Strategy;Graph Attention Networks|
|[School Bus Routing using Metaheuristics Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112473)|Z. Xue; X. Deng; B. Chen; A. Khamis|10.1109/SM57895.2023.10112473|school Bus Routing;Metaheuristics;Genetic Algorithm;Ant Colony System;Cluster-first route-second;school Bus Routing;Metaheuristics;Genetic Algorithm;Ant Colony System;Cluster-first route-second|
|[Optimal Placement of Drone Delivery Stations and Demand Allocation using Bio-inspired Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112569)|F. Elsaid; E. T. Sanchez; Y. Li; A. Khamis|10.1109/SM57895.2023.10112569|Last-mile delivery;optimal placement;delivery drones;optimization;bio-inspired algorithms;Last-mile delivery;optimal placement;delivery drones;optimization;bio-inspired algorithms|
|[Benchmark of Deep Learning Visual and Far-Infrared Videos Toward Weather-tolerant Pedestrian Traffic Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112301)|T. Fukuda; I. Arai; A. Endo; M. Kakiuchi; K. Fujikawa|10.1109/SM57895.2023.10112301|Smart cities;city planning;people flow;image processing;far-infrared video;Smart cities;city planning;people flow;image processing;far-infrared video|
|[A Constant Time Secure and Private Evaluation of Decision Trees in Smart Cities Enabled by Mobile IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112275)|A. Kjamilji|10.1109/SM57895.2023.10112275|secure IoT;machine learning;decision trees;classification;homomorphic encryption;privacy preserving algorithms;secure IoT;machine learning;decision trees;classification;homomorphic encryption;privacy preserving algorithms|
|[Smart Mobility for Sustainable Development Goals: Enablers and Barriers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112562)|A. Khamis; S. Malek|10.1109/SM57895.2023.10112562|smart mobility;sustainable development;accessibility;SDGs;smart mobility;sustainable development;accessibility;SDGs|
|[Automated Mobility: A Comparison between Aviation and Automotive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112503)|A. Khamis; P. Goswami|10.1109/SM57895.2023.10112503|automated mobility;self-flying vehicles;self-driving vehicles;operational design domain;automated mobility;self-flying vehicles;self-driving vehicles;operational design domain|
|[Scalable Planning of Garbage Collection in a Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112307)|G. Daoud; M. El-Darieby|10.1109/SM57895.2023.10112307|Garbage Collection;Route Planning;MILP;OpenStreetMap;Road Graph;Gurobi;Garbage Collection;Route Planning;MILP;OpenStreetMap;Road Graph;Gurobi|
|[SMAC-tuned Deep Q-learning for Ramp Metering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112246)|O. ElSamadisy; Y. Abdulhai; H. Xue; I. Smirnov; E. B. Khalil; B. Abdulhai|10.1109/SM57895.2023.10112246|Ramp metering - Reinforcement learning - Hyper-parameter tuning;Ramp metering - Reinforcement learning - Hyper-parameter tuning|
|[Analysis and Design of Hyperloop Communication Network Based on QoS Requirements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112433)|W. Hedhly; O. Amin; M. -S. Alouini; B. Shihada|10.1109/SM57895.2023.10112433|Hyperloop communications;vacuum tube communications;high-speed flying train;queuing theory;performance analysis;Hyperloop communications;vacuum tube communications;high-speed flying train;queuing theory;performance analysis|
|[Design of an Adaptive Traffic Light Network System through an AIoT-based Analytic Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112474)|J. D. C. Ayson; L. K. D. Bautista; M. A. B. Dionglay; M. C. V. Ditan; A. J. L. Jubac; D. J. D. Lopez; J. M. P. Araña|10.1109/SM57895.2023.10112474|gridlock;traffic intersection;adaptive traffic light;AIoT;gridlock;traffic intersection;adaptive traffic light;AIoT|
|[Mobile Aerial Base Stations for Ultra-Reliable and Energy-Efficient Downlink Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112375)|Y. Nabil; H. ElSawy; S. Al-Dharrab; H. Attia; H. Mostafa; A. Khalil; I. Qamar|10.1109/SM57895.2023.10112375|Mobile aerial networks;ultra-reliable communication;unmanned aerial vehicles (UAVs);stochastic geometry;Mobile aerial networks;ultra-reliable communication;unmanned aerial vehicles (UAVs);stochastic geometry|
|[ADAM: An Auction-based Datacenter Management in Vehicular Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112493)|S. R. Rizvi; S. Zehra; S. Olariu; S. El-Tawab|10.1109/SM57895.2023.10112493|Vehicular Cloud;Datacenter;Auction;Parking;Agents;Jobs;Scheduling;Allocation;Smart City;Vehicular Cloud;Datacenter;Auction;Parking;Agents;Jobs;Scheduling;Allocation;Smart City|
|[HD Maps for Connected and Automated Vehicles: Enabling Technologies and Future Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112453)|G. Abdelkader; T. Alghamdi; K. Elgazzar; A. Khamis|10.1109/SM57895.2023.10112453|HD maps;connected vehicles;assisted and automated driving vehicles;real-time updates;HD maps;connected vehicles;assisted and automated driving vehicles;real-time updates|
|[Cybersecurity Regulation of Smart Mobility Hardware Systems: Case Assessment for Spin-Based MTJ Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112292)|D. Divyanshu; R. Kumar; D. Khan; S. Amara; Y. Massoud|10.1109/SM57895.2023.10112292|Cybersecurity;smart mobility;secure hardware system;spintronics;magnetic tunnel junction (MTJ);Cybersecurity;smart mobility;secure hardware system;spintronics;magnetic tunnel junction (MTJ)|
|[Secure and Intelligent Video Surveillance using Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112347)|E. Simmers; A. Salman; E. Day; A. Oracevic|10.1109/SM57895.2023.10112347|Cryptography;Unmanned Aerial Vehicles;Security;Intelligent Transportation Systems;Internet of Things;Secure Video Surveillance;Artificial Intelligence;Cryptography;Unmanned Aerial Vehicles;Security;Intelligent Transportation Systems;Internet of Things;Secure Video Surveillance;Artificial Intelligence|
|[Solar-Powered Vehicle-to-Load (V2L) Plug-in Electric Vehicles: Alleviation of the Photovoltaic Power Decay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112517)|I. Pervez; C. Antoniadis; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112517|Electric vehicle;battery;grid-connected system;photovoltaic;metaheuristic algorithm;maximum power extraction (MPE);Electric vehicle;battery;grid-connected system;photovoltaic;metaheuristic algorithm;maximum power extraction (MPE)|
|[Image-based Automated Framework for Detecting and Classifying Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112531)|R. Hamadi; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112531|Clustering;computer vision;embedding;UAV;intrusion detection;Clustering;computer vision;embedding;UAV;intrusion detection|
|[Reinforcement Learning Based Intrusion Detection Systems for Drones: A Brief Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112557)|R. Hamadi; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112557|Reinforcement-Learning;Intrusion Detection Systems;Smart Cities;Unmanned Aerial Vehicles;Reinforcement-Learning;Intrusion Detection Systems;Smart Cities;Unmanned Aerial Vehicles|
|[Graph Neural Networks for Traffic Pattern Recognition: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112264)|E. Binshaflout; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112264|Graph neural networks;traffic pattern recognition;intelligent transportation systems;smart mobility;Graph neural networks;traffic pattern recognition;intelligent transportation systems;smart mobility|
|[Real-time Video Frame De-raining using Disentangled Generative Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112450)|A. Hamrouni; R. Hamadi; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112450|Generative adversarial network;autoencoder;video de-raining;deep learning;Generative adversarial network;autoencoder;video de-raining;deep learning|
|[V3Trans-Crowd: A Video-based Visual Transformer for Crowd Management Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112514)|Y. Zuo; A. Hamrouni; H. Ghazzai; Y. Massoud|10.1109/SM57895.2023.10112514|Crowd management;Crowd behavior analysis;computer vision;visual transformer;Crowd management;Crowd behavior analysis;computer vision;visual transformer|
|[Medium DC Voltage Power Converter for Electrified Railway with Fault Tolerant Control Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112309)|B. Rouabah; M. A. Mahboub; M. R. Kafi; H. Toubakh; M. Djemai; L. Ben-Brahim|10.1109/SM57895.2023.10112309|Electrified railway;Multicellular DC/DC power converter;Active fault tolerant control (FTC);Electrified railway;Multicellular DC/DC power converter;Active fault tolerant control (FTC)|
|[Practical Implementation of Electric Vehicle Integration into a Microgrid using V2G and G2V](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112437)|Z. Ullah; M. Zeeshan; S. Ahmed|10.1109/SM57895.2023.10112437|electric vehicle;V2G;V2X;microgrid;electric vehicle;V2G;V2X;microgrid|
|[Improving Bus Arrival Time Prediction Accuracy with Daily Periodic Based Transportation Data Imputation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112252)|T. Niwa; I. Arai; A. Endo; M. Kakiuchi; K. Fujikawa|10.1109/SM57895.2023.10112252|Intelligent Transport System;Bus Arrival Time Prediction;Imputation;Machine Learning;Intelligent Transport System;Bus Arrival Time Prediction;Imputation;Machine Learning|
|[Transportation Mode Recognition based on Cellular Network Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112273)|K. Zhagyparova; A. Bader; N. Kouzayha; H. ElSawy; T. Al-Naffouri|10.1109/SM57895.2023.10112273|transportation mode detection;channel state information;stochastic geometry;machine learning;random forest;transportation mode detection;channel state information;stochastic geometry;machine learning;random forest|
|[System Leadership: Self-Driving Vehicles Regulation and the Role of Government](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112457)|S. Iskandarova; S. El-Tawab|10.1109/SM57895.2023.10112457|System Leadership;Policy;Government Regulations;Self-Driving Vehicles;Autonomous Driving;Intelligent Transportation Systems (ITS);Data Analysis;System Leadership;Policy;Government Regulations;Self-Driving Vehicles;Autonomous Driving;Intelligent Transportation Systems (ITS);Data Analysis|
#### **2023 11th International Conference on Information and Education Technology (ICIET)**
- DOI: 10.1109/ICIET56899.2023
- DATE: 18-20 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Scoping Review of Empirical Studies in Gather.town](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111430)|C. K. Lo; Y. Song|10.1109/ICIET56899.2023.10111430|Gather.town;Metaverse;virtual world;literature review;research synthesis;Gather.town;Metaverse;virtual world;literature review;research synthesis|
|[Does Higher Education Need Virtual Reality? A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111293)|J. Maiero; C. D. Fehling; S. Müser|10.1109/ICIET56899.2023.10111293|Virtual Reality;Higher education;Survey;Virtual Reality;Higher education;Survey|
|[Research on the Influence of Embedding Mind Map in 360 Video on Learning Effect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111326)|T. Yahan; H. Guan; C. Chao|10.1109/ICIET56899.2023.10111326|Mind map;VR video;Learning effect;Eye-tracking;Mind map;VR video;Learning effect;Eye-tracking|
|[Using 360 Virtual Reality Video in History Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111196)|P. S. Cholifah; N. Luh Sakinah Nuraini; P. Mahanani|10.1109/ICIET56899.2023.10111196|virtual reality;teacher readiness;technology integration;history learning;virtual reality;teacher readiness;technology integration;history learning|
|[DOVAR: Data-on-Object Visualization with Virtual and Augmented Reality in Scientific Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111097)|X. Wang; M. Rivera; L. Isais; C. Styles|10.1109/ICIET56899.2023.10111097|visualization;data-on-object;scientific education;visualization;data-on-object;scientific education|
|[Digital Twins and Virtual Reality as Means for Teaching Industrial Robotics: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111361)|I. A. Nava-Téllez; M. Carlos Elias-Espinosa; E. B. Escamilla; A. Hernández Saavedra|10.1109/ICIET56899.2023.10111361|virtual reality;digital twin;robotics;interactive learning;higher education;education innovation;virtual reality;digital twin;robotics;interactive learning;higher education;education innovation|
|[The Explore of Virtual Learning Environment: A Study of Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111380)|G. Li; J. Xu|10.1109/ICIET56899.2023.10111380|learning management system;MOODLE;higher education;learning management system;MOODLE;higher education|
|[Using Virtual Reality in Education of Programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111156)|M. Konecki; M. Konecki; D. Vlahov|10.1109/ICIET56899.2023.10111156|programming;education;challenges;virtual reality;programming;education;challenges;virtual reality|
|[The Impact of Embodiment on Training Effectiveness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111344)|M. Sanaei; M. Machacek; S. Gilbert; C. Eubanks; P. Wu; J. Oliver|10.1109/ICIET56899.2023.10111344|training;communication;body ownership;agency;performance;virtual reality;training;communication;body ownership;agency;performance;virtual reality|
|[Using Deep Learning to Track Representational Flexibility Development of Children with Autism in a Virtual World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111218)|Z. Sokolikj; F. Ke; S. Chakraborty; J. Moon|10.1109/ICIET56899.2023.10111218|virtual/augmented reality;machine learning;artificial neural networks;computers for special needs;virtual/augmented reality;machine learning;artificial neural networks;computers for special needs|
|[Evaluating Success in the Digitalized Thesis Management Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111267)|R. Kauppinen; A. Lagstedt; J. P. Lindstedt|10.1109/ICIET56899.2023.10111267|digitalization;thesis management process;information system;adaptation;evaluation;survey;digitalization;thesis management process;information system;adaptation;evaluation;survey|
|[Quantitative Evaluation of Navigation Controllers on Mobile Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111317)|P. Leesutthipornchai; D. Pradubsuwun|10.1109/ICIET56899.2023.10111317|quantitative metric;navigation controllers;mobile applications;quantitative metric;navigation controllers;mobile applications|
|[The Use of Cloud Computing and its Security Risks in a Philippine Education System: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111146)|E. Blancaflor; B. Y. P. Saunar; T. Darrel C. Bilbao; I. H. B. Villarias; I. Paula V. Mapue|10.1109/ICIET56899.2023.10111146|cloud computing;cloud user;cloud service provider (CSP);cloud security;cloud computing;cloud user;cloud service provider (CSP);cloud security|
|[Vocational Students’ Perceptions on the VBee Diary Writing Application: Features and Writing Activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111171)|Y. D. Perdani; C. Sidupa; J. Y. Luke; F. Hanita Rusgowanto|10.1109/ICIET56899.2023.10111171|VBee;application features;diary writing activity;writing application;VBee;application features;diary writing activity;writing application|
|[Design of an Online Self-Study Platform Based on Campus Scenes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111270)|Y. Liu; M. Huang; C. Wu; R. Yang; M. Imparato|10.1109/ICIET56899.2023.10111270|student learning;self-study;online platform;campus scenes;student learning;self-study;online platform;campus scenes|
|[Digitalization of Professional Regulation Commission (PRC) Process of Accreditation of Nutritionist and Dietetics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111141)|A. M. C. Chua; C. E. M. Jaurigue; J. A. D. F. Torres; J. C. De Goma|10.1109/ICIET56899.2023.10111141|digitalization;agile Development lifecycle;RTM;PRC;digitalization;agile Development lifecycle;RTM;PRC|
|[The Effectiveness of Self-regulated Learning Management System for Cognitive Scores Improvements of Undergraduate Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111214)|P. M.; N. Prammanee|10.1109/ICIET56899.2023.10111214|learning management system;self-regulation learning concept;Chung-Teh Fan's 27% technique,;learning management system;self-regulation learning concept;Chung-Teh Fan's 27% technique,|
|[Usability Evaluation of Online Learning Management System: Blackboard, Google Classroom and Canvas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111426)|M. J. J. Gumasing; E. Kaye Endozo; J. C. Ceasar Laingo; W. Henry Tapucar|10.1109/ICIET56899.2023.10111426|Usability;Learning Management System;Blackboard;Canvas;Google Classroom;Usability;Learning Management System;Blackboard;Canvas;Google Classroom|
|[A Method of Extracting Difficult-to-Understand Video Intervals for Generating Assignments using Keywords in Online Lecture Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111432)|I. Sano; Y. Wang; Y. Kawai; K. Sumiya|10.1109/ICIET56899.2023.10111432|e-learning;on-demand lectures;assignment generation;lecture videos;difficult video intervals;e-learning;on-demand lectures;assignment generation;lecture videos;difficult video intervals|
|[Teaching Practice of Engineering Mechanics Based on Finite Element Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111408)|S. Wang; X. Wang; F. Peng|10.1109/ICIET56899.2023.10111408|teaching practice;finite element analysis;modeling;teaching;teaching practice;finite element analysis;modeling;teaching|
|[Anonymous OBE-Driven Formative Assessment Platform for Classroom in Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111399)|Y. Zhang; L. Wei; X. Fu; Y. Zheng|10.1109/ICIET56899.2023.10111399|anonymous system;classroom instruction;formative assessment;higher education;outcomes-based education;anonymous system;classroom instruction;formative assessment;higher education;outcomes-based education|
|[Experiences of a "G" Acceleration Generator through Angular Velocities in a Dynamic Design Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111254)|H. R. Morano Okuno; J. Enrique Chong-Quero; G. S. Benitez; R. C. Castillo; A. G. Garcia|10.1109/ICIET56899.2023.10111254|rigid body simulation;human centrifuge system;artificial gravity;angular velocities;accelerations;educational innovation;higher education;professional education;rigid body simulation;human centrifuge system;artificial gravity;angular velocities;accelerations;educational innovation;higher education;professional education|
#### **2023 IEEE International Conference on Cybernetics and Innovations (ICCI)**
- DOI: 10.1109/ICCI57424.2023
- DATE: 30-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Real-Time Credit Card Fraud Detection Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112320)|P. Thongthawonsuwan; T. Ganokratanaa; P. Pramkeaw; N. Chumuang; M. Ketcham|10.1109/ICCI57424.2023.10112320|Surveillance System;Credit Card Fraud Detection;Online Application;Surveillance System;Credit Card Fraud Detection;Online Application|
|[Smart Agricultural Greenhouses for Earthworm Farming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112360)|W. Yimyam; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; N. Chumuang|10.1109/ICCI57424.2023.10112360|Smart Agricultural;Greenhouses;Earthworm Farming;Smart Agricultural;Greenhouses;Earthworm Farming|
|[Globally Harmonized System Label detection using Color Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112447)|T. Pinthong; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; N. Chumuang|10.1109/ICCI57424.2023.10112447|service Support System;ITIL;geographic Information System;service Support System;ITIL;geographic Information System|
|[Data Imputation with Genetic Algorithm and Multiple Linear Regression for Improving Performance of Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112242)|S. Amphan; P. Songmuamg|10.1109/ICCI57424.2023.10112242|MISSING VALUES IMPUTATION;GENETIC ALGORITHM;MULTIPLE LINEAR REGRESSION;MISSING VALUES IMPUTATION;GENETIC ALGORITHM;MULTIPLE LINEAR REGRESSION|
|[Missing Values Imputation Framework for Mixed Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10111846)|K. Chungnoy; P. Songmuamg|10.1109/ICCI57424.2023.10111846|mixed datatype;imputation;bee algorithm;missing value;data preprocessing;mixed datatype;imputation;bee algorithm;missing value;data preprocessing|
|[Mobile Bot Application for Identification of Trypanosoma evansi Infection through Thin-Blood Film Examination Based on Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112327)|R. Jomtarak; V. Kittichai; M. Kaewthamasorn; S. Thanee; A. Arnuphapprasert; K. M. Naing; T. Tongloy; S. Boonsang; S. Chuwongin|10.1109/ICCI57424.2023.10112327|Trypanosoma evansi;a microscopic examination;YOLO algorithms;model performance;Mobile application;Trypanosoma evansi;a microscopic examination;YOLO algorithms;model performance;Mobile application|
|[Intelligent Forecasting of Energy Consumption using Temporal Fusion Transformer model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112297)|S. Jittanon; Y. Mensin; C. Termritthikun|10.1109/ICCI57424.2023.10112297|Temporal Fusion Transformer;Deep Learning Smart Grid;Energy Consumption;Forecasting;Temporal Fusion Transformer;Deep Learning Smart Grid;Energy Consumption;Forecasting|
|[Matching Thai Profession Descriptions from Different Sources Using English as Interlingual Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112230)|V. Luantangsrisuk; R. Kedtiwerasak; K. Saengthongpattana; T. Ruangrajitpakorn|10.1109/ICCI57424.2023.10112230|Document Matching;Similarity;Profession Description;Proxy Language;Thai to English Translation;Document Matching;Similarity;Profession Description;Proxy Language;Thai to English Translation|
|[Development of heat stroke detection system using image processing techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112513)|W. Yimyam; M. Ketcham|10.1109/ICCI57424.2023.10112513|heat stroke;heat stroke detection system;image processing;heat stroke;heat stroke detection system;image processing|
|[Smart Alarm Clock for Effective Sleep Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112383)|K. Kwansomkid; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; N. Chumuang|10.1109/ICCI57424.2023.10112383|Sleep Health;Smart Alarm Clock;Internet Of Thing;Sleep Health;Smart Alarm Clock;Internet Of Thing|
|[Home smart alarm system for the visually impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112265)|W. Yimyam; T. Ganokratanaa|10.1109/ICCI57424.2023.10112265|smart alarm system;visually impaired;Internet Of Thing;smart alarm system;visually impaired;Internet Of Thing|
|[Sorting Red and Green Chilies by Digital Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112254)|N. Chumuang; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; J. Chongsri; W. Yimyam|10.1109/ICCI57424.2023.10112254|Haar -like Feature;Face Detection;Image Processing;Chili;Sorting;Haar -like Feature;Face Detection;Image Processing;Chili;Sorting|
|[Poultry raising light control system for laying hens and broilers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112499)|T. Pinthong; T. Ganokratanaa|10.1109/ICCI57424.2023.10112499|Sleep Health;Smart Alarm Clock;Internet Of Thing;Sleep Health;Smart Alarm Clock;Internet Of Thing|
|[The application of data mining techniques for predicting education to new undergraduate students at Chiang Mai Rajabhat University](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112233)|S. Panpaeng; P. Phanphaeng; J. Kumnuanta; P. Yommakit; K. Kocento; P. Wongchompoo|10.1109/ICCI57424.2023.10112233|Data Mining;Prediction;Association Rule;Education;Data Mining;Prediction;Association Rule;Education|
|[Pillow for Detecting Snoring with Embedded Techniques for Elderly People with Snoring Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112489)|W. Yimyam; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; N. Chumuang|10.1109/ICCI57424.2023.10112489|detecting snoring;embedded technique;internet of thing;detecting snoring;embedded technique;internet of thing|
|[Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112422)|A. Farooq; S. Z. N. Zukhraf; H. Maryam; C. Attard; M. Kamal|10.1109/ICCI57424.2023.10112422|Educational Robotics;Learning Outcomes;Robotic Contests and Competitions;Women Empowerment;STEM Education;Creativity;Intelligent Systems;Technological Innovation;Educational Robotics;Learning Outcomes;Robotic Contests and Competitions;Women Empowerment;STEM Education;Creativity;Intelligent Systems;Technological Innovation|
|[A Lightweight Controller for Autonomous Following of a Target Platform for Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112481)|A. Farooq; S. Shafi; Z. Ullah; M. Quresh; N. Chumuang|10.1109/ICCI57424.2023.10112481|Robotics;Autonomous Systems;Target Tracking;Embedded Controller;Intelligent Systems;Technological Innovation;Robotics;Autonomous Systems;Target Tracking;Embedded Controller;Intelligent Systems;Technological Innovation|
|[Automatic Computer Shutdown with Image Processing via Webcam to Save Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112402)|N. Chumuang; M. Ketcham; T. Ganokratanaa; P. Pramkeaw; W. Yimyam; Chuamoo|10.1109/ICCI57424.2023.10112402|Haar-like Feature;Face Detection;Image Processing;Haar-like Feature;Face Detection;Image Processing|
|[Alert system with IoT Techniques for Stray Children](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112468)|P. Pramkeaw; T. Ganokratanaa; T. Pinthong; N. Kitprasert|10.1109/ICCI57424.2023.10112468|Stray Children;Embedded Technique;IoT;Stray Children;Embedded Technique;IoT|
|[Smart Bin for Covid19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112379)|M. Ketcham; T. Ganokratanaa; P. Pramkeaw; N. Chumuang|10.1109/ICCI57424.2023.10112379|Sleep Health;Smart Alarm Clock;Internet Of Thing;Sleep Health;Smart Alarm Clock;Internet Of Thing|
|[Prediction and Efficiency of Stroke Disease using data mining technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112495)|W. Suksangaram; W. Hemtong|10.1109/ICCI57424.2023.10112495|Stroke disease;Data Classification;Decision tree;Naïve Bayes;K-Nearest Neighbors;Stroke disease;Data Classification;Decision tree;Naïve Bayes;K-Nearest Neighbors|
|[Customer Service Support System by ITIL version 3 Operating Framework with the Geographic Information System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10112332)|T. Thanamatee; T. Ganokratanaa; P. Pramkeaw; N. Chumuang; M. Ketcham|10.1109/ICCI57424.2023.10112332|Service Support System;ITIL;Geographic Information System;Service Support System;ITIL;Geographic Information System|
#### **2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)**
- DOI: 10.1109/VRW58643.2023
- DATE: 25-29 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Co-building Viewer's Representation: Animated Documentary Extended by Virtual Reality from the Perspective of Anthropology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108702)|C. M. Guo; X. Fu|10.1109/VRW58643.2023.00008|virtual reality;animated documentary;anthropology;H.5.1. [Information Interfaces and Presentation]: Multimedia Information Systems—Artificial augmented and virtual realities;virtual reality;animated documentary;anthropology;H.5.1. [Information Interfaces and Presentation]: Multimedia Information Systems—Artificial augmented and virtual realities|
|[Artistic Exploration of Stop-Motion Animation in Virtual Reality: Spatializing the Analog Techniques of 2D Replacement and Object Animation by Using Digital Cutout and Realtime Rendering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108588)|F. Bruckner; J. Salhofer; C. Gürtler; M. Hattler; M. Husinsky|10.1109/VRW58643.2023.00009|Virtual Reality;Stop-Motion Animation;Art-Based Research;Multiplane Camera;Virtual Live Performance;Spatiality in Virtual Environments;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Applied computing-Arts and humanities-Media arts;Virtual Reality;Stop-Motion Animation;Art-Based Research;Multiplane Camera;Virtual Live Performance;Spatiality in Virtual Environments;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Applied computing-Arts and humanities-Media arts|
|[Spatial Considerations: Hybridizing Production Modes for an Immersive Adaptation of Shakespeare's The Merchant of Venice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108816)|H. Rall; E. Harper|10.1109/VRW58643.2023.00010|Shakespeare;Virtual Reality;Fulldome;Animation;Adaptation;Shakespeare;Virtual Reality;Fulldome;Animation;Adaptation|
|[Interactive Spatialized Animations in “The Wedding Chamber Project” as a Methodology to Produce Phenomenological Diegetic Renderings Inside an XR Immersive Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108799)|A. Montenegro|10.1109/VRW58643.2023.00011|Immersive;Usability;HUD;Fourth Wall;Immersive;Usability;HUD;Fourth Wall|
|[Adaptive Immersive VR Training Based on Performance and Self-Efficacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108645)|L. F. Lui; U. Radhakrishnan; F. Chinello; K. Koumaditis|10.1109/VRW58643.2023.00012|I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism;Virtual Reality;I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism;Virtual Reality|
|[Use of Eye Behavior With Visual Distraction for Attention Training in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108669)|Y. J. Li; P. A. Ramalakshmi; C. Mei; S. Jung|10.1109/VRW58643.2023.00013|virtual reality;embodiment;human-computer interaction;autism;attention training;Human-centered computing [Human computer interaction (HCI)]: Interaction paradigms—Virtual reality;Human-centered computing [Interaction design]: Interaction design process and methods—Scenario-based design;virtual reality;embodiment;human-computer interaction;autism;attention training;Human-centered computing [Human computer interaction (HCI)]: Interaction paradigms—Virtual reality;Human-centered computing [Interaction design]: Interaction design process and methods—Scenario-based design|
|[On The Effectiveness of Virtual Eye-Hand Coordination Training With Head Mounted Displays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108882)|M. H. Mughrabi; F. Kaya; A. U. Batmaz; A. Aliza; W. Stuerzlinger; B. Borazan; E. Tonyali; M. Sarac|10.1109/VRW58643.2023.00014|Human-centered computing-Human Computer Interaction (HCI);Human-centered computing-Virtual Reality;Human-centered computing-Pointing;Human-centered computing-Human Computer Interaction (HCI);Human-centered computing-Virtual Reality;Human-centered computing-Pointing|
|[Designing an Empathy Training for Depression Prevention Using Virtual Reality and a Preliminary Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108699)|Y. J. Li; A. Huang; B. S. Sanku; J. S. He|10.1109/VRW58643.2023.00015|virtual reality;embodiment;empathy training;human-computer interaction;depression;avatar design;Human-centered computing [Human computer interaction (HCI)]: Interaction paradigms—Virtual reality;Human-centered computing [Interaction design]: Interaction design process and methods—Scenario-based design;virtual reality;embodiment;empathy training;human-computer interaction;depression;avatar design;Human-centered computing [Human computer interaction (HCI)]: Interaction paradigms—Virtual reality;Human-centered computing [Interaction design]: Interaction design process and methods—Scenario-based design|
|[Creating Informal Learning and First Responder Training XR Experiences with the ImmersiveDeck](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108579)|C. Schönauer; M. Roussou; J. Rüggeberg; J. Rüggeberg; L. Katsikaris; S. Rogkas; D. Christopoulos|10.1109/VRW58643.2023.00016|Human-centered computing;Visualization;Human-centered computing;Visualization;Visualization design and evaluation methods;Human-centered computing;Visualization;Human-centered computing;Visualization;Visualization design and evaluation methods|
|[Does Adding Physical Realism to Virtual Reality Training Reduce Time Compression?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108720)|K. Lofca; J. Jerald; D. Costa; R. Kopper|10.1109/VRW58643.2023.00017|Human-centered computing-Time-perception-Virtual Reality;Human-centered computing-Human computer in-teraction (HCI)-Interaction devices-Haptic devices;Human-centered computing-Time-perception-Virtual Reality;Human-centered computing-Human computer in-teraction (HCI)-Interaction devices-Haptic devices|
|[IEEE VR 2023 Workshops](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108730)|Y. Wang; J. -R. Chardonnet; L. -H. Lee; P. Hui|10.1109/VRW58643.2023.00019|;|
|[Application of XR technology in stomatology education: Theoretical basis, application scenarios and future prospects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108721)|R. Chen; B. Liao|10.1109/VRW58643.2023.00020|Virtual Reality;Augmented Reality;Mix Reality;Stomatology education;Stomatology education-Augmented Reality-Virtual reality;Computing methodologies-Artificial intelligence-Knowledge representation and reasoning;Virtual Reality;Augmented Reality;Mix Reality;Stomatology education;Stomatology education-Augmented Reality-Virtual reality;Computing methodologies-Artificial intelligence-Knowledge representation and reasoning|
|[Replicability and Transparency for the Creation of Public Human User Video Game Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108697)|E. J. Pretty; R. Guarese; H. M. Fayek; F. Zambetta|10.1109/VRW58643.2023.00021|Open source software;Computer games;Replicability;Transparency;Human computer interaction;Human-centered computing—Collaborative and social computing—Collaborative and social computing systems and tools—Open source software;Applied computing—Computers in other domains—Personal computers and PC applications—Computer games;Open source software;Computer games;Replicability;Transparency;Human computer interaction;Human-centered computing—Collaborative and social computing—Collaborative and social computing systems and tools—Open source software;Applied computing—Computers in other domains—Personal computers and PC applications—Computer games|
|[VR/AR/MR in the Electricity Industry: Concepts, Techniques, and Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108819)|J. Xiao; Y. Qian; W. Du; Y. Wang; Y. Jiang; Y. Liu|10.1109/VRW58643.2023.00022|Immersive Techniques;Virtual Reality;Augmented Reality;Mixed Reality;Electricity Industry;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Computing methodologies-Artificial intelligence-Knowledge representation and reasoning;Immersive Techniques;Virtual Reality;Augmented Reality;Mixed Reality;Electricity Industry;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Computing methodologies-Artificial intelligence-Knowledge representation and reasoning|
|[Towards a 3D Evaluation Dataset for User Acceptance of Automated Shuttles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108643)|M. Yan; W. Geng; P. Hui|10.1109/VRW58643.2023.00023|Human-centered computing;Human computer interaction (HCI);HCI design and evaluation methods;User studies;Interaction design;Empirical studies in interaction design;Human-centered computing;Human computer interaction (HCI);HCI design and evaluation methods;User studies;Interaction design;Empirical studies in interaction design|
|[IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS 2023)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108626)|A. Cannavò; B. Kapralos; S. Seinfeld; F. G. Pratticò; C. Zhang|10.1109/VRW58643.2023.00024|;|
|[Reconstruction of Human Body Pose and Appearance Using Body-Worn IMUs and a Nearby Camera View for Collaborative Egocentric Telepresence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108850)|Q. Zhang; A. Paruchuri; Y. Cha; J. Huang; J. Kandel; H. Jiang; A. Ilie; A. State; D. A. Szafir; D. Szafir; H. Fuchs|10.1109/VRW58643.2023.00025|Computing methodologies-Artificial intelligence-Computer vision-Reconstruction;Computing methodologies-Machine learning-Machine learning approaches-Neural networks;Computing methodologies-Artificial intelligence-Computer vision-Reconstruction;Computing methodologies-Machine learning-Machine learning approaches-Neural networks|
|[Get a Variable Grip: A Comparison of Three Gripping Techniques for Controller-Based Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108761)|A. K. Pedersen; H. Isaksen; M. B. Riedel; M. Jϕrgensen; R. Paisa; N. C. Nilsson|10.1109/VRW58643.2023.00026|Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Interaction devices—Haptic devices;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Interaction devices—Haptic devices|
|[A Study on Affordable Manipulation in Virtual Reality Simulations: Hand-Tracking versus Controller-Based Interaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108791)|F. De Lorenzis; M. Nadalin; M. Migliorini; F. Scarrone; F. Lamberti; J. Fiorenza|10.1109/VRW58643.2023.00027|Human-machine interaction-Virtual Reality-Hand -tracking-Simulation;Human-machine interaction-Virtual Reality-Hand -tracking-Simulation|
|[Automated Multimodal Data Capture for Photorealistic Construction Progress Monitoring in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108857)|H. Stedman; Z. Lu; V. M. Pawar|10.1109/VRW58643.2023.00028|Autonomous mobile inspection;deep learning construction monitoring;laser scanning;human-robot decision making;virtual reality;Human—centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Computing methodologies—Artificial intelligence—Computer vision—Reconstruction;Computer systems organization—Embedded and cyber—physical systems—Robotics-Robotic autonomy;Autonomous mobile inspection;deep learning construction monitoring;laser scanning;human-robot decision making;virtual reality;Human—centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Computing methodologies—Artificial intelligence—Computer vision—Reconstruction;Computer systems organization—Embedded and cyber—physical systems—Robotics-Robotic autonomy|
|[Towards Outdoor Collaborative Mixed Reality: Lessons Learnt from a Prototype System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108714)|N. Numan; Z. Lu; B. Congdon; D. Giunchi; A. Rotsidis; A. Lernis; K. Larmos; T. Kourra; P. Charalambous; Y. Chrysanthou; S. Julier; A. Steed|10.1109/VRW58643.2023.00029|mixed reality;augmented reality;virtual reality;collab-oration;outdoor;city-scale;reconstruction;registration;Human-centered computing-Collaborative and social computing systems and tools;Human-centered computing-Ubiquitous and mobile computing systems and tools;mixed reality;augmented reality;virtual reality;collab-oration;outdoor;city-scale;reconstruction;registration;Human-centered computing-Collaborative and social computing systems and tools;Human-centered computing-Ubiquitous and mobile computing systems and tools|
|[Simulating Location-Based Experiences in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108618)|C. Hilton; X. Pan; R. Koeck; H. Yu|10.1109/VRW58643.2023.00030|Applied computing [Media Arts];[Human-centered computing]: Virtual reality;Computing Methodologies [Virtual Reality];Applied computing [Media Arts];[Human-centered computing]: Virtual reality;Computing Methodologies [Virtual Reality]|
|[Enhanced Surgeons: Understanding the Design of Augmented Reality Instructions for Keyhole Surgery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108577)|C. Davis; S. Yoo; M. J. Clarkson; S. Thompson|10.1109/VRW58643.2023.00031|Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods;Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods|
|[Unlocking Social Innovation in XR for Healthcare in Coastal Communities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108876)|A. V. Reyes; M. N. Varga; H. Bradwell; R. Baxter|10.1109/VRW58643.2023.00032|Applied Computing-Life;Medical Sciences-Health informatics;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Human-centered computing-Interaction design-Interaction design process and methods-Participatory design;Applied Computing-Life;Medical Sciences-Health informatics;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Human-centered computing-Interaction design-Interaction design process and methods-Participatory design|
|[SEPSIS COLLAB: A Virtual Reality Training Simulation For Sepsis Treatment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108833)|A. Williams-Bhatti; D. Carruthers; A. S. Wilson|10.1109/VRW58643.2023.00033|VR Medical Training Simulation;Sepsis;Sepsis Screening Tool and Sepsis Six Bundle;Collaborative Learning;Instructional Design;VR Medical Training Simulation;Sepsis;Sepsis Screening Tool and Sepsis Six Bundle;Collaborative Learning;Instructional Design|
|[Towards a Metaverse in Health Informatics: 3D Visualisation of Physical Activity from VR Gaming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108642)|S. Yoo; C. Parker|10.1109/VRW58643.2023.00034|Human-centered computing;Visualization;Visualization techniques;Treemaps;Visualization design and evaluation methods;Human-centered computing;Visualization;Visualization techniques;Treemaps;Visualization design and evaluation methods|
|[ElboVR: Iterative Development of a VR Application for Post-Surgery Elbow Rehabilitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108851)|A. Andresen; J. Valvik; M. Rosholm; T. Khumsan; N. C. Nilsson; A. Adjorlu|10.1109/VRW58643.2023.00035|Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality|
|[Interaction models for surgical planning in eXtended Reality. Challenges in radiologist-surgeon communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108775)|C. J. Latorre-Rojas; A. Rozo-Torres; W. J. Sarmiento|10.1109/VRW58643.2023.00036|Human-centered computing-Visualization-Visu-alization techniques-Treemaps;Human-centered computing-Visualization-Visualization design and evaluation methods;Human-centered computing-Visualization-Visu-alization techniques-Treemaps;Human-centered computing-Visualization-Visualization design and evaluation methods|
|[Exploring Affordances for AR in Laparoscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108683)|M. Negrão; J. Jorge; J. Vissoci; R. Kopper; A. Maciel|10.1109/VRW58643.2023.00037|Augmented Reality;Human computer Interaction;Laparoscopy;computer assisted interventions;Human-centered computing—Human computer interaction (HCI)—nteraction paradigms—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—nteraction paradigms—Collaborative interaction;Augmented Reality;Human computer Interaction;Laparoscopy;computer assisted interventions;Human-centered computing—Human computer interaction (HCI)—nteraction paradigms—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—nteraction paradigms—Collaborative interaction|
|[Development of an Immersive Virtual Colonoscopy Viewer for Colon Growths Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108840)|J. Serras; A. Maciel; S. Paulo; A. Duchowski; R. Kopper; C. Moreira; J. Jorge|10.1109/VRW58643.2023.00038|Human-centered computing-Human computer interaction (HCI)-nteraction paradigms-Virtual reality;Applied computing-Life and medical sciences-Computational biology-Imaging;Human-centered computing-Human computer interaction (HCI)-nteraction paradigms-Virtual reality;Applied computing-Life and medical sciences-Computational biology-Imaging|
|[An Online Therapeutic Intervention for Veterans Patients Suffering with Chronic Pain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108891)|E. D. Reilly; M. Volonte; T. Bickmore|10.1109/VRW58643.2023.00039|Human-centered computing-Human computer interaction (HCI)-Empirical studies in HCI;Human-centered computing-Human computer interaction (HCI)-Empirical studies in HCI|
|[Streamlining Epilepsy Surgery Planning Rounds with Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108772)|Z. Aminolroaya; S. Wiebe; W. Willett; C. B. Josephson; G. McLeod; F. Maurer|10.1109/VRW58643.2023.00040|Human-centered computing-Virtual reality;Human-centered computing-Scientific visualization;Human-centered computing- Visualization-Visualization techniques;Human-centered computing-Virtual reality;Human-centered computing-Scientific visualization;Human-centered computing- Visualization-Visualization techniques|
|[Virtual Resection Planning using Bezier Surface Interactions in Collaborative VR Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108900)|V. Chheang; R. Bruggernann; B. Preim; C. Hansen|10.1109/VRW58643.2023.00041|I.3.7 [COMPUTER GRAPHICS]: Three-Dimensional Graphics and Realism-Virtual reality;;I.3.7 [COMPUTER GRAPHICS]: Three-Dimensional Graphics and Realism-Virtual reality;|
|[Investigation of Thermal Perception and Emotional Response in Augmented Reality using Digital Biomarkers: A Pilot Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108744)|S. Eom; S. Kim; Y. Jiang; R. J. Chen; A. R. Roghanizad; M. Z. Rosenthal; J. Dunn; M. Gorlatova|10.1109/VRW58643.2023.00042|Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality|
|[Virtual Reality applied to medical education and training on Diabetic Foot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108611)|G. Riva; W. Dores; A. Damasio; D. G. Cacione; J. Jorge; E. Zorzal|10.1109/VRW58643.2023.00043|Computing methodologies-Virtual reality-;Computing methodologies-Virtual reality-|
#### **2023 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)**
- DOI: 10.1109/WEMDCD55819.2023
- DATE: 13-14 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Design Morphology for High-Speed Rotors in Electrical Machines Based on Analytical Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110935)|M. Lauerburg; P. Toraktrakul; K. Hameyer|10.1109/WEMDCD55819.2023.10110935|High-speed electrical machines;Design morphology;Equivalent ring method;High-speed electrical machines;Design morphology;Equivalent ring method|
|[Effect of Slot Leakage Flux on Winding Inductance and AC Resistance in an Axial Flux Machine with Distributed Windings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110898)|B. Srivastava; C. R. Lines|10.1109/WEMDCD55819.2023.10110898|Slot leakage flux;skin effect;proximity effect;slot inductance;AC inductance;winding AC resistance;copper loss;distributed winding;yokeless stator;axial flux PM machine;Slot leakage flux;skin effect;proximity effect;slot inductance;AC inductance;winding AC resistance;copper loss;distributed winding;yokeless stator;axial flux PM machine|
|[Investigating the Impact of Material Cost Fluctuations on the Total Manufacturing Cost of EV Traction Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110901)|V. Madonna; C. M. Meano; R. Cossu; M. Pensato; K. F. Hansen|10.1109/WEMDCD55819.2023.10110901|Electrical machines;cost analysis;PMSM;IM;SRM;Electrical machines;cost analysis;PMSM;IM;SRM|
|[Overview of Sensorless Control Strategies for Electric Vehicle Traction IPMSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110929)|A. Holczer; F. D. Freijedo; R. Bojoi|10.1109/WEMDCD55819.2023.10110929|electric vehicle;flux maps;magnetic saturation;observers;sensorless control;traction IPMSM;electric vehicle;flux maps;magnetic saturation;observers;sensorless control;traction IPMSM|
|[Design and Analysis of a 100kW Rotary Transformer for XROTOR Wind Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110930)|Y. Teng; O. Anaya-Lara; R. Yazdanpanah; D. Campos-Gaona; S. A. Mortazavizadeh|10.1109/WEMDCD55819.2023.10110930|high power;high efficiency;rotary transformer;sensitivity analysis;X-rotor;high power;high efficiency;rotary transformer;sensitivity analysis;X-rotor|
|[Comparison of Different Sampled PWM Strategies Applied to High-Speed Drives: A Predictive Time-Frequency Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110938)|A. De Andrade; L. Sadi-Haddad; R. Lateb; J. Da Silva|10.1109/WEMDCD55819.2023.10110938|Fourier Transform;Spectrogram;Time-Frequency Analysis;Permanent Magnet Machine;Pulse Width Modulation;Resonance;Space Vector Pulse Width Modulation;Variable Frequency Drive;Voltage Source Inverter;Fourier Transform;Spectrogram;Time-Frequency Analysis;Permanent Magnet Machine;Pulse Width Modulation;Resonance;Space Vector Pulse Width Modulation;Variable Frequency Drive;Voltage Source Inverter|
|[Advances in Frequency Response Techniques for Diagnosing Inter-Turn Faults in Salient Poles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110950)|A. Mugarra; C. A. Platero|10.1109/WEMDCD55819.2023.10110950|Frequency Response Analysis (FRA);Impedance measurement;Fault diagnosis;Fault detection;Rotating machines;Generators;Alternators;Field analysis;Testing;Frequency Response Analysis (FRA);Impedance measurement;Fault diagnosis;Fault detection;Rotating machines;Generators;Alternators;Field analysis;Testing|
|[Refined Structural Design and Thermal Analyses of a High-Speed Wound-Field Generator for the More Electrical Aircraft](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110937)|A. Guiducci; S. G. Barbieri; S. Nuzzo; D. Batater; F. Berni; G. Cicalese; S. Fontanesi; G. Franceschini|10.1109/WEMDCD55819.2023.10110937|;|
|[Assessment of Experimental Approaches for the Evaluation of Material Compatibility of E-fluids With the Insulation System of Low Voltage Rotating Electrical Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110932)|L. Yang; S. Zhang; X. Tong; K. Hameyer|10.1109/WEMDCD55819.2023.10110932|electric drive;direct oil cooling;material compatibility;insulation system;electric drive;direct oil cooling;material compatibility;insulation system|
|[Combination of 2D and 3D Finite Element Models in the Design of Axial Flux Permanent Magnet Machines for Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110931)|A. Abdelli; A. Gilson; B. Chareyron; G. Zito|10.1109/WEMDCD55819.2023.10110931|axial flux machines;eddy currents;finite element analysis;magnet segmentation;permanent magnet;axial flux machines;eddy currents;finite element analysis;magnet segmentation;permanent magnet|
|[A Tutorial on Double Pulse Test of Silicon and Silicon Carbide MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110895)|M. I. Masoud; W. Issa; W. Yates|10.1109/WEMDCD55819.2023.10110895|Double Pulse Test;Power electronics;Si- MOSFET;SiC-MOSFET;Switching Loss;Industry skills;electrical Engineering market;Double Pulse Test;Power electronics;Si- MOSFET;SiC-MOSFET;Switching Loss;Industry skills;electrical Engineering market|
|[Lifetime estimation of corona-resistance wire for electrical machines operating under the partial discharge regime](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110897)|Y. Ji; P. Giangrande; W. Zhao; V. Madonna; H. Zhang; M. Galea|10.1109/WEMDCD55819.2023.10110897|partial discharge;electrical endurance;corona-resistant wire;and electrical machines;partial discharge;electrical endurance;corona-resistant wire;and electrical machines|
|[Manufacturing-oriented magnetic analysis of segmented stator lamination stacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110943)|H. A. Weiss; T. Stäble; G. Pasquarella; G. Senn; N. Berger|10.1109/WEMDCD55819.2023.10110943|electrical steel;magnetic properties;iron loss;efficiency issues;manufacturing;high volume production;segmentation;electrical steel;magnetic properties;iron loss;efficiency issues;manufacturing;high volume production;segmentation|
|[Determination of an Asymmetrical Nine Phase Induction Machine Stator and Rotor Inductances Using Winding Function Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110904)|T. S. Ajayi; G. Adam; D. C. Gaona; O. Anaya-Lara|10.1109/WEMDCD55819.2023.10110904|Winding Function;Turn function;Inductance;Multiphase induction machine;Winding Function;Turn function;Inductance;Multiphase induction machine|
|[Winding Type Alternation of a Refurbished Old Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110941)|N. Tosun; G. Gülletutan; M. S. Yakut; D. Alp Yilmaz; Ö. Bayer; O. Keysan|10.1109/WEMDCD55819.2023.10110941|Hydroelectric generators;power generation;rotating machines;synchronous generators;Hydroelectric generators;power generation;rotating machines;synchronous generators|
|[An Integrated Strategy for the Real-Time Detection and Discrimination of Stator Inter-Turn Short-Circuits and Converter Faults in Asymmetrical Six-Phase Induction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110900)|K. Laadjal; F. Bento; J. Serra; A. J. Marques Cardoso|10.1109/WEMDCD55819.2023.10110900|Asymmetrical Six-Phase Induction Motors (ASPIMs);Stator Faults;Short Time Least Square Prony's (STLSP) Algorithm;Voltage and Current Symmetrical Components;Fault Tolerant Operating (FTO)Conditions;Open Circuit Faults (OCFs);Model Predictive Control (MPC);Asymmetrical Six-Phase Induction Motors (ASPIMs);Stator Faults;Short Time Least Square Prony's (STLSP) Algorithm;Voltage and Current Symmetrical Components;Fault Tolerant Operating (FTO)Conditions;Open Circuit Faults (OCFs);Model Predictive Control (MPC)|
|[On Winding Manufacturing Technologies for Coreless Axial-Flux Permanent-Magnet Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110905)|F. Marcolini; G. D. Donato; F. Giulii Capponi; M. Incurvati; F. Caricchi|10.1109/WEMDCD55819.2023.10110905|Axial Flux;Coreless;Litz-wire;Permanent Magnet;Printed Circuit Board;Axial Flux;Coreless;Litz-wire;Permanent Magnet;Printed Circuit Board|
|[Verifying Strand Transposition in Stator Windings via X-ray Computed Tomography derived Three-Dimensional Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110906)|J. Hoole; D. North; N. Simpson; P. H. Mellor|10.1109/WEMDCD55819.2023.10110906|computed tomography;strand transposition;computed tomography;strand transposition|
|[A Novel Two-Phase Permanent Magnet Rotor Machine for Automotive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110899)|M. F. Troncoso C.; F. Stella; G. Pellegrino|10.1109/WEMDCD55819.2023.10110899|Two-phase;AC Machines;Brushless machines;Electric Machines;Permanent magnet machines;Two-phase;AC Machines;Brushless machines;Electric Machines;Permanent magnet machines|
|[Design Procedure and Preliminary Analysis for the Introduction of Axial Asymmetry in the Synchronous Reluctance Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110903)|M. U. Naseer; A. Kallaste; B. Asad; T. Vaimann; A. Rassõlkin|10.1109/WEMDCD55819.2023.10110903|AC machines;design tools;additive manufacturing;three-dimensional printing;rotating machines;AC machines;design tools;additive manufacturing;three-dimensional printing;rotating machines|
|[On the Effect of Position Signal Error on the Performance of Single-Phase BLDC Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110902)|N. Saed; S. Asgari; A. Muetze|10.1109/WEMDCD55819.2023.10110902|single-phase BLDC machine;position signal error;Hall-effect sensor;vibration;single-phase BLDC machine;position signal error;Hall-effect sensor;vibration|
|[Optimal Sizing of Hairpin Conductors in highway operation with PWM power supply](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110896)|R. Notari; G. Devito; F. Bernardi; M. Pastura; D. Barater; S. Nuzzo|10.1109/WEMDCD55819.2023.10110896|PWM;IPMSM;AC Loss Minimization;ARTEMIS;Hairpin windings;PWM;IPMSM;AC Loss Minimization;ARTEMIS;Hairpin windings|
|[Analysis of Advanced Passive Heatsinks For Electrical Machines Enabled by Additive Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110940)|M. Sarap; A. Kallaste; P. S. Ghahfarokhi; T. Vaimann|10.1109/WEMDCD55819.2023.10110940|Additive manufacturing;Switched reluctance machine;Passive heatsinks;Additive manufacturing;Switched reluctance machine;Passive heatsinks|
|[Injectionless Full Range Speed Sensorless Control for Synchronous Reluctance Motors based on PWM Current Ripple](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110945)|L. Ortombina; F. Bernardi; L. Alberti; D. Barater|10.1109/WEMDCD55819.2023.10110945|ellipse fitting;current ripple;PWM;space vector modulation;sensorless drive;synchronous reluctance motor;ellipse fitting;current ripple;PWM;space vector modulation;sensorless drive;synchronous reluctance motor|
|[Performance Analysis of a Permanent Magnet Motor with Continuous Hairpin Winding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110948)|M. Soltani; S. Nuzzo; D. Barater; M. D. Nardo|10.1109/WEMDCD55819.2023.10110948|hairpin winding;magnetic wedge;continuous hairpin;hairpin winding;magnetic wedge;continuous hairpin|
|[High-speed IPM Motors with Rotor Sleeve: Structural Design and Performance Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110939)|J. Binder; M. Silvagni; S. Ferrari; B. Deusinger; A. Tonoli; G. Pellegrino|10.1109/WEMDCD55819.2023.10110939|interior permanent magnet (IPM) synchronous machine;rotor retaining sleeve;centrifugal stress;carbon fiber;electric motor design;high-speed electric motor;analytical method;finite element analysis (FEA);interior permanent magnet (IPM) synchronous machine;rotor retaining sleeve;centrifugal stress;carbon fiber;electric motor design;high-speed electric motor;analytical method;finite element analysis (FEA)|
|[Statistical Assessment of Core Loss Measurement Techniques for Laminated Steel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110947)|L. Colombo; S. Soltanipour; A. Tokat; A. Reinap; T. Thiringer; F. Marquéz Fernandez; J. Lindström; M. Alaküla|10.1109/WEMDCD55819.2023.10110947|Core loss;inferential statistics;standardized measurements;ANOVA;t-test;Core loss;inferential statistics;standardized measurements;ANOVA;t-test|
|[Scaling of Ferrite-assisted Synchronous Reluctance Machines for Lifting Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110927)|P. Ragazzo; S. Ferrari; G. Dilevrano; L. Beatrici; C. Girardi; G. Pellegrino|10.1109/WEMDCD55819.2023.10110927|AC machines;lifting systems;ferrite-assisted synchronous machines;scaling procedure;AC machines;lifting systems;ferrite-assisted synchronous machines;scaling procedure|
|[Stray flux and air gap flux experimental measurement and analysis in large hydro generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110944)|S. Bernier; A. Merkhouf; K. Al-Haddad|10.1109/WEMDCD55819.2023.10110944|stray flux;air gap flux;large hydro-generators;diagnostic;ellipticity;eccentricity;fault detection;stray flux;air gap flux;large hydro-generators;diagnostic;ellipticity;eccentricity;fault detection|
|[High Power Density Motor for Light Electric Aircraft – Design Study and Lab Tests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110934)|T. Wolnik; T. Jarek; J. Golec; R. Topolewski; D. Jastrzębski|10.1109/WEMDCD55819.2023.10110934|High power density motors;high specific power (HSP);PMSM motors;more electric aircraft (MEA);aircraft propulsion;electric motors;High power density motors;high specific power (HSP);PMSM motors;more electric aircraft (MEA);aircraft propulsion;electric motors|
|[An Investigation on DC-Link Voltage Influence on E-Drive Efficiency for E-mobility Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110942)|A. Carlsson; V. Josefsson; F. Furufors; S. Nategh; D. Ekholm; N. Kleen|10.1109/WEMDCD55819.2023.10110942|Battery Voltage;Electric drives;E-mobility;Field weakening;Iron loss;Switching loss;Battery Voltage;Electric drives;E-mobility;Field weakening;Iron loss;Switching loss|
|[Numerical Study of Oil Jet Cooling in Electric Traction Motors with Hairpin Windings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110928)|W. Maddumage; A. Cairns; A. Paykani|10.1109/WEMDCD55819.2023.10110928|Permanent magnet motor;oil cooling;hairpin end winding;numerical modelling;jet impingement;thermal design;Permanent magnet motor;oil cooling;hairpin end winding;numerical modelling;jet impingement;thermal design|
|[An Advanced Thermal Modeling Method for Directly Oil-cooled Traction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110946)|G. Puccio; M. Raimondo; S. Nategh; D. Barater; D. Ericsson; A. Carlsson|10.1109/WEMDCD55819.2023.10110946|Computational fluid dynamics;conjugate heat transfer;E-mobility;brushless permanent magnets machine;Computational fluid dynamics;conjugate heat transfer;E-mobility;brushless permanent magnets machine|
|[A Novel Modified Archimedes Spiral Antenna for Partial Discharge Detection in Inverter-Fed Electrical Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110949)|Y. L. Ogundiran; A. Griffo|10.1109/WEMDCD55819.2023.10110949|Electrical machine insulation;inverters;power electronics;machine end-winding;modified Archimedes spiral (MASA);partial discharge (PD);Electrical machine insulation;inverters;power electronics;machine end-winding;modified Archimedes spiral (MASA);partial discharge (PD)|
|[Insulation Monitoring in Ungrounded Electrical System for More Electric Aircrafts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110936)|Y. L. Ogundiran; A. Griffo; J. Wang; S. Sundeep; F. Bruder-Mandler; T. Groh|10.1109/WEMDCD55819.2023.10110936|more electric aircraft;power systems;insulation;condition monitoring;reliability;more electric aircraft;power systems;insulation;condition monitoring;reliability|
#### **2023 IEEE 14th Latin America Symposium on Circuits and Systems (LASCAS)**
- DOI: 10.1109/LASCAS56464.2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Voltage Regulation in a Buck Converter for an Unknown CPL: An Extended Feedback Control Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108291)|F. Serra; W. Gil-González; O. D. Montoya; J. C. Hernandez|10.1109/LASCAS56464.2023.10108291|Output voltage regulation;unknown constant power load;extended feedback linearization;nonlinear control law;Output voltage regulation;unknown constant power load;extended feedback linearization;nonlinear control law|
|[Active inductors modelling and trade-offs reexamined](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108182)|A. Seré; L. Barboni; S. Bourdel; F. Silveira|10.1109/LASCAS56464.2023.10108182|Active inductors;modelling;quality factor;noise;design;Active inductors;modelling;quality factor;noise;design|
|[A Radiation Hardened Smart Power Switch Based on SOI Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108134)|R. H. G. Chacón; E. G. de Carvalho; J. L. de Emeri; Â. A. dos Santos; P. C. Secheusk; J. A. Diniz; S. Finco|10.1109/LASCAS56464.2023.10108134|Current limiter;fault isolation;power supply protection;telemetry;radiation hardness;Current limiter;fault isolation;power supply protection;telemetry;radiation hardness|
|[Design of an ECG front-end considering ST segment distortion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108102)|B. Guénégo; C. Lelandais-Perrault; E. Avignon-Meseldzija; G. Sou; P. Bénabès|10.1109/LASCAS56464.2023.10108102|Electrocardiogram (ECG);Chopper Amplifier;My-ocardial Ischemia;Signal Distortion;ST segment;Embedded baseline wander elimination;Electrocardiogram (ECG);Chopper Amplifier;My-ocardial Ischemia;Signal Distortion;ST segment;Embedded baseline wander elimination|
|[DNAr-Analog: A Library With a Multiplexer to Easily Design, Program, and Simulate Dsd Analog Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108135)|P. A. C. Oliveira; R. A. Marks; J. V. C. Teixeira; M. V. Guterres; O. P. V. Neto|10.1109/LASCAS56464.2023.10108135|Circuit design;DSD analog circuits;DNA strand displacement;molecular computing;nanocomputing;Circuit design;DSD analog circuits;DNA strand displacement;molecular computing;nanocomputing|
|[Quantitative Information Flow for Hardware: Advancing the Attack Landscape](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108235)|L. M. Reimann; S. Erdönmez; D. Sisejkovic; R. Leupers|10.1109/LASCAS56464.2023.10108235|confidentiality;hardware security;quantitative information flow;confidentiality;hardware security;quantitative information flow|
|[High-Speed Sampler for UWB Breast Cancer Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108279)|L. M. M. De Almeida; B. Sanches; W. A. M. Van Noije|10.1109/LASCAS56464.2023.10108279|breast cancer detector;ultra-wideband;track and hold circuit;equivalent time sampling;front end;breast cancer detector;ultra-wideband;track and hold circuit;equivalent time sampling;front end|
|[Accelerated Hot-Carrier Aging Based on Ultrafast Laser for CMOS Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108173)|R. Ascázubi; B. Ajdari; C. Shirota|10.1109/LASCAS56464.2023.10108173|CMOS;ultrafast laser;hot-carrier;TPA;CMOS;ultrafast laser;hot-carrier;TPA|
|[A Compact Analogue Counter for Single Photon Imagers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108126)|S. Shatakshi Panda; B. Choubey|10.1109/LASCAS56464.2023.10108126|Analogue Counter;CMOS;Linearity;Photon;Single Photon Avalanche Diode (SPAD);Analogue Counter;CMOS;Linearity;Photon;Single Photon Avalanche Diode (SPAD)|
|[Efficient Architecture for VVC Angular Intra Prediction based on a Hardware-Friendly Heuristic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108393)|V. Borges; M. Perleberg; M. Porto; L. Agostini|10.1109/LASCAS56464.2023.10108393|Hardware design;intraframe prediction;angular modes;Versatile Video Coding;Hardware design;intraframe prediction;angular modes;Versatile Video Coding|
|[A Fast Cold-Start Integrated System for Ultra-Low Voltage SC Energy-Harvesting Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108216)|L. F. M. Dutra; P. C. C. De Aguirre; A. G. Girardi; L. Compassi-Severo|10.1109/LASCAS56464.2023.10108216|Energy harvesting;DC-DC converter;coldstart;Energy harvesting;DC-DC converter;coldstart|
|[Hardware Design for the Affine Motion Compensation of the VVC Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108350)|M. M. Muñoz; D. Maass; M. Perleberg; L. Agostini; M. Porto|10.1109/LASCAS56464.2023.10108350|Affine MC;VVC;Hardware;Interpolation Filters;Affine MC;VVC;Hardware;Interpolation Filters|
|[Performance Benchmarking of FinFET- and TFET-Based STT-MRAM Bitcells Operating at Ultra-Low Voltages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108163)|S. S. Pérez; A. Bedoya; L. M. Prócel; R. Taco|10.1109/LASCAS56464.2023.10108163|Spintronics;STT-MRAM;Tunnel FET;TFET;Fin-FET;Magnetic Tunnel Junction;MTJ;DMTJ;leakage current;Spintronics;STT-MRAM;Tunnel FET;TFET;Fin-FET;Magnetic Tunnel Junction;MTJ;DMTJ;leakage current|
|[Parkinson's Treatment Emulation Using Asynchronous Cellular Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108309)|I. K. Chatzipaschalis; K. -A. Tsakalos; G. C. Sirakoulis; A. Rubio|10.1109/LASCAS56464.2023.10108309|Parkinson;Asynchronous Cellular Automaton-based Neuron (ACAN);Asynchronous Cellular Neural Networks (ACNNs);Field Programmable Gate Arrays (FPGAs);Parkinson;Asynchronous Cellular Automaton-based Neuron (ACAN);Asynchronous Cellular Neural Networks (ACNNs);Field Programmable Gate Arrays (FPGAs)|
|[Design of Quasi Delay Insensitive Combinational Circuits Based on Optimized DIMS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108335)|D. L. Oliveira; M. H. Victor; L. C. Moreira; F. F. Nascimento|10.1109/LASCAS56464.2023.10108335|asynchronous logic;QDI class;dual-rail encode;gate orphan;indicability;asynchronous logic;QDI class;dual-rail encode;gate orphan;indicability|
|[Error Resilience Evaluation of Approximate Storage in the Motion Compensation of VVC Decoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108314)|R. Soares; M. Isquierdo; F. Sampaio; A. Rahmani; N. Dutt; G. Correa; D. Palomino; B. Zatt|10.1109/LASCAS56464.2023.10108314|Approximate storage;video decoding;motion compensation;error resilience;Approximate storage;video decoding;motion compensation;error resilience|
|[Multi-Size Inverse DCT-II Hardware Design for the VVC Decoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108348)|B. Garcia; B. Silveira; C. Diniz; D. Palomino; G. Correa|10.1109/LASCAS56464.2023.10108348|Versatile Video Coding;Inverse Transform;Hardware Architecture;ASIC;Versatile Video Coding;Inverse Transform;Hardware Architecture;ASIC|
|[High-Throughput and Multiplierless Hardware Design for the AV1 Fractional Motion Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108378)|R. Domanski; W. Kolodziejski; W. Penny; M. Porto; B. Zatt; L. Agostini|10.1109/LASCAS56464.2023.10108378|AV1;Fractional Motion Estimation;Hardware Design;Video Coding;AV1;Fractional Motion Estimation;Hardware Design;Video Coding|
|[Using Lyapunov Exponents to Estimate Sensitivity to Process Variability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108159)|E. de Almeida Ramos; R. Reis|10.1109/LASCAS56464.2023.10108159|Chaos Theory;Lyapunov Exponents;Variability;FinFETs;Microelectronics;Chaos Theory;Lyapunov Exponents;Variability;FinFETs;Microelectronics|
|[Analysis and Design of a Self-Powered VEH System Based on ULP Comparator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108276)|E. Holguín; L. M. Prócel; A. Brenes; A. Vladimirescu; L. Trojman|10.1109/LASCAS56464.2023.10108276|Acceleration;VEH;MEMS;Comparator;ULP;Acceleration;VEH;MEMS;Comparator;ULP|
|[Compact Time-Based Sensor-to-Digital Converters in Skywater 130nm Open-Source Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108228)|J. Marin; I. Vourkas; C. A. Rojas; J. Gak|10.1109/LASCAS56464.2023.10108228|CMOS integrated circuits;sensor interface;open source silicon;CMOS integrated circuits;sensor interface;open source silicon|
|[Design and evaluation of CNN hardware accelerator designs for in-seat passenger anomaly detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108325)|A. Bhatnagar; P. Nandi; M. Rao|10.1109/LASCAS56464.2023.10108325|Hardware CNN;accelerator;anomaly detection;Hardware CNN;accelerator;anomaly detection|
|[A Self Oscillating Current-Reuse Image Reject Mixer for Ultra Low Power Receivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108127)|A. Reddy Yedala; S. Aniruddhan|10.1109/LASCAS56464.2023.10108127|Narrowband Internet of Things (NB-IoT);User Equipment (UE);current-reuse (CR);Self-oscillating mixer (SOM);Image reject (IR);image rejection ratio (IRR);Narrowband Internet of Things (NB-IoT);User Equipment (UE);current-reuse (CR);Self-oscillating mixer (SOM);Image reject (IR);image rejection ratio (IRR)|
|[An Energy-Efficient StEFCal VLSI Design with Approximate Squarer and Divider Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108306)|M. M. A. Da Rosa; P. Da Costa; G. Paim; E. Da Costa; R. Soares; S. Bampi|10.1109/LASCAS56464.2023.10108306|StEFCal;AxRSU;SquASH;Newton-Raphson;Goldschmid;StEFCal;AxRSU;SquASH;Newton-Raphson;Goldschmid|
|[An RF-EH Employing Controlled-Impedance Matching for Ultra-Low Voltage Batteryless Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108099)|T. C. De-Oliveira; R. P. De Oliveira; A. G. Girardi; P. C. C. De Aguirre; L. Compassi-Severo|10.1109/LASCAS56464.2023.10108099|ultra-low voltage;impedance matching;RF energy harvesting;batteryless devices;ultra-low voltage;impedance matching;RF energy harvesting;batteryless devices|
|[The Fault-tolerant Single-FPGA Systems with a Self-repair Reconfiguration Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108372)|R. Pánek; J. Lojda|10.1109/LASCAS56464.2023.10108372|Fault Tolerance;Partial Dynamic Reconfiguration Controller;FPGA;Fault Tolerance Evaluation;Fault Tolerance;Partial Dynamic Reconfiguration Controller;FPGA;Fault Tolerance Evaluation|
|[Pico-Ampere Current Biasing Platform for on-chip Tuning of Analog Blocks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108384)|G. Fierro; F. Silveira|10.1109/LASCAS56464.2023.10108384|pico-Ampere;on-chip tuning;bias current generation;pico-Ampere;on-chip tuning;bias current generation|
|[Thermal Energy Harvesting to Power a Battery-Less Node of a Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108140)|S. Boselli; R. Gaudio; M. P. Grilli; F. Silveira; M. Siniscalchi|10.1109/LASCAS56464.2023.10108140|TEG;wireless sensor;DC-DC converter;harvesting circuit;TEG;wireless sensor;DC-DC converter;harvesting circuit|
|[Deep Learning-Based Receiver Energy Prediction in Energy Harvesting Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108110)|E. -h. Zazoua; W. Ajib; M. Boukadoum|10.1109/LASCAS56464.2023.10108110|wireless sensor networks;energy harvesting;deep learning;LSTM;Transformer;N-BEATS;wireless sensor networks;energy harvesting;deep learning;LSTM;Transformer;N-BEATS|
|[Stacked-Cascode Current Steering Architecture for Gallium Nitride Variable-Gain LNAs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108284)|J. L. González; D. Vázquez; R. Moreno; J. Duncan; O. K. Jensen; S. Chatzinotas; B. Ottersten|10.1109/LASCAS56464.2023.10108284|Current steering;VGLNA;LNA;GaN;HEMT;Current steering;VGLNA;LNA;GaN;HEMT|
|[Minimizing Power Consumption Through Brute Force Algorithm in Elastic Optical Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108288)|S. Y. M. Bandiri; S. Diakite; M. F. O. Sanya; F. A. B. S. Filho; H. S. S. L. Pereira; T. C. Pimenta|10.1109/LASCAS56464.2023.10108288|Elastic Optical Network;Energy Consumption;Network Topology;Elastic Optical Network;Energy Consumption;Network Topology|
|[Design and Evaluation of Inexact Computation based Systolic Array for Convolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108234)|S. K. Nandigama; H. C. Prashanth; M. Rao|10.1109/LASCAS56464.2023.10108234|Systolic Array;Convolution;Approximate Computing;Image Processing;Systolic Array;Convolution;Approximate Computing;Image Processing|
|[Analysis of AV1 Arithmetic Decoder Design Space with a Novel Multi-Boolean Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108168)|J. S. Gomes; T. P. Bitencourt; S. Bampi; F. L. Livi Ramos|10.1109/LASCAS56464.2023.10108168|video coding;arithmetic decoding;hardware de-sign;AV1;video coding;arithmetic decoding;hardware de-sign;AV1|
|[On the Bode Diagram Plot of Switched Converters Using MATLAB Simulink - a Tutorial Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108197)|D. M. B. Leguizamón; R. Urbina; C. Páez; A. Fajardo; G. Perilla|10.1109/LASCAS56464.2023.10108197|Bode Plot;Switched Converters;Middlebrook Method;Simulink®;MATLAB®;Bode Plot;Switched Converters;Middlebrook Method;Simulink®;MATLAB®|
|[Concise Memory Organization for a Customizable Hardware Design of a Quantum Coprocessor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108201)|N. Nedjah; S. Raposo; L. De Macedo Mourelle|10.1109/LASCAS56464.2023.10108201|Quantum computing;memory organization;Quantum computing;memory organization|
|[Programmable Seizure Detector Using a 32-bit RISC Processor for Implantable Medical Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108303)|K. F. Razi; A. Schmid|10.1109/LASCAS56464.2023.10108303|RISC processor;Epileptic seizure detection;Programmable medical implant;Feature ranking;RISC processor;Epileptic seizure detection;Programmable medical implant;Feature ranking|
|[Self-Calibrating Circuit for Implantable Current Stimulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108300)|N. Martínez; M. Miguez; J. Sapriza; J. Gak; A. Arnaud|10.1109/LASCAS56464.2023.10108300|Current Calibration;Implantable Medical Devices;Low-Power Design;Current Calibration;Implantable Medical Devices;Low-Power Design|
|[Complexity and Coding Efficiency Assessment of AOMedia Video 1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108213)|Í. Siqueira; G. Corrêa; M. Grellert|10.1109/LASCAS56464.2023.10108213|video coding;AV1;complexity;profiling;video coding;AV1;complexity;profiling|
|[Extended Feedback Linearization Control for Voltage Regulation in a Buck Converter with an Unknown Resistive Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108198)|O. D. Montoya; W. Gil-González; F. Serra; C. H. De Angelo; J. C. Hernandez|10.1109/LASCAS56464.2023.10108198|Extended feedback linearization control;unknown resistive load;inverse and invariance estimation method;general feedback control gains;output voltage regulation;Extended feedback linearization control;unknown resistive load;inverse and invariance estimation method;general feedback control gains;output voltage regulation|
|[A Wide-Band High-Speed Sample and Hold in 0.35µm CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108219)|M. B. Moreira; H. Lapuyade; F. Rivet; Y. Deval|10.1109/LASCAS56464.2023.10108219|analog to digital converters;CMOS;wide-band design;sample and hold;analog to digital converters;CMOS;wide-band design;sample and hold|
|[Neutron Irradiation of Si-PIN Diodes and Laser Injection Equivalence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108138)|R. Ascázubi; F. R. Palomo; V. Navarrete-Larive; J. M. Quesada; M. A. Cortés-Giraldo; J. A. Pavón-Rodriguez|10.1109/LASCAS56464.2023.10108138|n_TOF;ultrafast laser;neutron;SEE;SPA;TPA;n_TOF;ultrafast laser;neutron;SEE;SPA;TPA|
|[A low-complexity experimental characterization of the neodymium magnet grade](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108103)|L. Barón; R. Urbina; M. Pèrez; C. Páez; A. Fajardo|10.1109/LASCAS56464.2023.10108103|Permanent Magnets;Permanent Magnets Simulation;Permanent Magnets Characterization;Hall-sensors;Permanent Magnets;Permanent Magnets Simulation;Permanent Magnets Characterization;Hall-sensors|
|[Novel measurement method to estimate the main resonance of a Power Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108391)|M. Peyrard; G. Jacquemod; N. Froidevaux; M. Moign|10.1109/LASCAS56464.2023.10108391|Power Delivery Network;resonance;microcontroller;supply noise measurement;Power Delivery Network;resonance;microcontroller;supply noise measurement|
|[Design-oriented model for short-channel MOS transistors based on inversion charge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108277)|D. A. Pino-Monroy; P. Scheer; M. K. Bouchoucha; C. Galup-Montoro; M. J. Barragan; J. -M. Fournier; J. -M. Fournier; A. Cathelin; S. Bourdel|10.1109/LASCAS56464.2023.10108277|Analytical MOSFET modeling;charge-based MOSFET model;inversion coefficient;nonlinear distortion;short-channel effects;28nm FD-SOI;Analytical MOSFET modeling;charge-based MOSFET model;inversion coefficient;nonlinear distortion;short-channel effects;28nm FD-SOI|
|[STT-MRAM Technology For Energy-Efficient Cryogenic Memory Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108316)|E. Garzón; L. Yavits; A. Teman; M. Lanuzza|10.1109/LASCAS56464.2023.10108316|Magnetic tunnel junction (MTJ);single-barrier MTJ (SMTJ);double-barrier MTJ (DMTJ);STT-MRAM;embedded memory;energy-efficient;cryogenic;77 K;Magnetic tunnel junction (MTJ);single-barrier MTJ (SMTJ);double-barrier MTJ (DMTJ);STT-MRAM;embedded memory;energy-efficient;cryogenic;77 K|
|[Security Implications of Decoupling Capacitors on Leakage Reduction in Hardware Masking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108340)|S. Seçkiner; S. Köse|10.1109/LASCAS56464.2023.10108340|Side-channel analysis;Decoupling capacitor;Hardware masking;Side-channel analysis;Decoupling capacitor;Hardware masking|
|[Exploring Multi-Parameter Optimization of Automated HLS Tools and the Difficulty of Setting Complex Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108151)|N. Shalom; I. Levi|10.1109/LASCAS56464.2023.10108151|Design Automation;Design tools;FFT;High Level Synthesis;HLS;Multi parameter;Optimization;Design Automation;Design tools;FFT;High Level Synthesis;HLS;Multi parameter;Optimization|
|[Overview of Cryogenic Operation in Nanoscale Technology Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108221)|N. Roknian; Y. Shoshan; I. Stanger; A. Teman; E. Charbon; A. Fish|10.1109/LASCAS56464.2023.10108221|Cryogenic Operation;CMOS;Liquid Helium Temperature;Quantum computing;Cryogenic Operation;CMOS;Liquid Helium Temperature;Quantum computing|
#### **2023 9th International Conference on Electrical Energy Systems (ICEES)**
- DOI: 10.1109/ICEES57979.2023
- DATE: 23-25 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Digital Twin as an Effective and Versatile Tool for Modeling and Optimizing Hybrid Energy Complexes at All Stages of the Life Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110753)|M. G. Tyagunov; R. P. Sheverdiev|10.1109/SmartIndustryCon57312.2023.10110753|renewable energy;hybrid energy complex;energy storage system;wind power plant;photovoltaic plant;digital twin;3D model;renewable energy;hybrid energy complex;energy storage system;wind power plant;photovoltaic plant;digital twin;3D model|
|[Development of an Integrated Expert System for Distribution Network Diagnostics Based on Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110786)|I. F. Galiev; M. S. Garifullin; I. P. Alekseev; A. R. Gizatullin; A. M. Makletsov|10.1109/SmartIndustryCon57312.2023.10110786|expert system;artificial intelligence technology;retrospective offline information;database;online monitoring subsystem;problem-oriented modules;knowledge base;expert system;artificial intelligence technology;retrospective offline information;database;online monitoring subsystem;problem-oriented modules;knowledge base|
|[Development of a Methodology for the Identification of Ferrous Metal Products by Their Contactless Point Labeling Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110717)|A. V. Astafiev|10.1109/SmartIndustryCon57312.2023.10110717|identification;recognition;computer vision;neural networks;product labeling;RetinaNet;identification;recognition;computer vision;neural networks;product labeling;RetinaNet|
|[An Approach to Improving the Efficiency of the Database of a Large Industrial Enterprise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110728)|M. M. Bazhutin; V. S. Moshkin|10.1109/SmartIndustryCon57312.2023.10110728|SQL;databases;CTE;optimization;SQL;databases;CTE;optimization|
|[Characteristics of Angular Displacement Actuators Based on Magnetostrictive Plates under the Action of Prestressing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110789)|P. Grakhov; A. Fedin; V. Yasoveev|10.1109/SmartIndustryCon57312.2023.10110789|magnetostrictive bilayer plates;flexural deformations;magnetostriction;elastic prestresses;moments of external and internal forces;elastic stress distribution;linear expansion temperature coefficients;magnetostrictive bilayer plates;flexural deformations;magnetostriction;elastic prestresses;moments of external and internal forces;elastic stress distribution;linear expansion temperature coefficients|
|[Development of an Imitation-Resistant Satellite Authentication Protocol Using Modular Codes of Residue Number System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110825)|I. A. Kalmykov; D. V. Dukhovnyj; A. A. Olenev|10.1109/SmartIndustryCon57312.2023.10110825|authentication methods;zero-knowledge proof protocols;modular code of residue number system;authentication methods;zero-knowledge proof protocols;modular code of residue number system|
|[Modification of Schnorr Authentication Protocol Using Modular Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110780)|I. A. Kalmykov; N. K. Chistousov; N. I. Kalmykova|10.1109/SmartIndustryCon57312.2023.10110780|modular code of residue number system;zero-knowledge proof protocols;Schnorr authentication protocol;modular code of residue number system;zero-knowledge proof protocols;Schnorr authentication protocol|
|[Universal Route Search Module in Conditions of Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110758)|D. M. Beloborodov; A. I. Razumowsky|10.1109/SmartIndustryCon57312.2023.10110758|component;ant colony optimization;route search;component;ant colony optimization;route search|
|[Statistical Analysis Methods of the Data Obtained by Water Electro Conductivity Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110797)|A. I. Burumbaev; V. T. Kuanishev; N. M. Barbin|10.1109/SmartIndustryCon57312.2023.10110797|statistica;analysis;electro conductivity;normal distribution;abnormal distribution;correlation analysis;statistica;analysis;electro conductivity;normal distribution;abnormal distribution;correlation analysis|
|[Development of the Digital Twin for Flotation of Non-Ferrous Metal Ore Beneficiation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110750)|D. A. Shnayder; E. A. Kalinina|10.1109/SmartIndustryCon57312.2023.10110750|minerals industry;flotation;digital twin;Open Modelica;R;minerals industry;flotation;digital twin;Open Modelica;R|
|[Development of an Automated Diagnostic System of Lung Pathologies in Lymphoma](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110813)|O. N. Kuzyakov; S. A. Sorokina; E. A. Shutova|10.1109/SmartIndustryCon57312.2023.10110813|lung pathology;lymphoma;computed tomography;image segmentation;convolutional neural networks;DICOM;lung pathology;lymphoma;computed tomography;image segmentation;convolutional neural networks;DICOM|
|[Self-timed Fused Multiplier-Adder Pipeline Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110742)|I. Sokolov; Y. Stepchenkov; Y. Diachenko|10.1109/SmartIndustryCon57312.2023.10110742|self-timed circuit;multiply-add unit;pipeline;multiplexing;result periodicity;energy consumption;self-timed circuit;multiply-add unit;pipeline;multiplexing;result periodicity;energy consumption|
|[Method for Modeling and Visualization of Agricultural Crops Growth Based on Augmented Reality Technology in Terms of the Greenhouse Effect Dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110816)|D. V. Goncharov; O. A. Ivashchuk; N. G. Reznikov|10.1109/SmartIndustryCon57312.2023.10110816|augmented reality;visualization;crop productivity;greenhouse effect;assessment of adaptation scripts;modeling;augmented reality;visualization;crop productivity;greenhouse effect;assessment of adaptation scripts;modeling|
|[Determining the Positioning Accuracy of a Multifunctional Module Based on a SCARA Type Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110832)|O. Filipovich; N. Chalenkov; A. Vozhzhov|10.1109/SmartIndustryCon57312.2023.10110832|manipulator;SCARA;repeatability;accuracy;manipulator;SCARA;repeatability;accuracy|
|[Integration of Intelligent Industrial Systems into a Workshop-Level Information Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110771)|L. Martinova; S. Sokolov; R. Pushkov|10.1109/SmartIndustryCon57312.2023.10110771|CNC machine tool;smart manufacturing;digital cloud platform;automated manufacturing;equipment health monitoring;intelligent manufacturing technologists;CNC machine tool;smart manufacturing;digital cloud platform;automated manufacturing;equipment health monitoring;intelligent manufacturing technologists|
|[Optimization of Micro-Object Identification by Correcting Distorted Image Points](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110801)|I. I. Jumanov; R. A. Safarov; O. I. Djumanov|10.1109/SmartIndustryCon57312.2023.10110801|image;micro-objects;identification;recognition;redundant information structure;dynamic models;neural networks;image;micro-objects;identification;recognition;redundant information structure;dynamic models;neural networks|
|[Model of the Initial Section of RTD’s CVC for RFID Tags](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110720)|E. Kuimov; N. Vetrova; S. Meshkov|10.1109/SmartIndustryCon57312.2023.10110720|RFID;RTD;nonlinear elements;optimization;modeling;RFID;RTD;nonlinear elements;optimization;modeling|
|[Classification of Incoming Messages of the University Admission Campaign](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110769)|N. V. Smirnov; A. S. Trifonov|10.1109/SmartIndustryCon57312.2023.10110769|text classification;NLP;multiclass classification;multilabel classification;machine learning;text classification;NLP;multiclass classification;multilabel classification;machine learning|
|[Modular Technology of Definite Multiple Integrals Calculation: Analytical Analysis and Experimental Verification of Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110802)|V. O. Groppen; A. A. Berko|10.1109/SmartIndustryCon57312.2023.10110802|modular enumeration;multiple integrals;analyze of efficiency;experimental verification;modular enumeration;multiple integrals;analyze of efficiency;experimental verification|
|[Integrative Approach to Creation of Information Systems and Entropy Analysis of Linguistic Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110775)|E. Rusyaeva; A. Poltavsky; G. Akhobadze|10.1109/SmartIndustryCon57312.2023.10110775|entropy;probability;linguistic analysis;cognitive modeling;entropy;probability;linguistic analysis;cognitive modeling|
|[Analysis of Consumer Category Data in the Context of an Industrial Enterprise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110829)|Y. N. Kondrashova; A. M. Tretyakov; A. V. Shalimov|10.1109/SmartIndustryCon57312.2023.10110829|reliability;power supply reliability categories;reliability category selection factors;auxiliary degree of responsibility of the mechanism;reliability;power supply reliability categories;reliability category selection factors;auxiliary degree of responsibility of the mechanism|
|[Optimal Energy Consumption Control in a Multi-Zone Building Based on a Hybrid Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110760)|O. Y. Maryasin|10.1109/SmartIndustryCon57312.2023.10110760|energy optimization;energy modeling;artificial neural networks;openmodelica;genetic algorithm;energy optimization;energy modeling;artificial neural networks;openmodelica;genetic algorithm|
|[Digital Assistant to Operator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110835)|D. Antonov; P. Burankina; V. Dement’ev|10.1109/SmartIndustryCon57312.2023.10110835|object detection models;artificial neural networks;mobile development;cognitive service;YOLOv4;YOLOv4-tiny;object detection models;artificial neural networks;mobile development;cognitive service;YOLOv4;YOLOv4-tiny|
|[Uncertainty Estimation in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110722)|V. Arkov|10.1109/SmartIndustryCon57312.2023.10110722|parameter uncertainty;supervised learning;modeling;prediction methods;forecasting;parameter uncertainty;supervised learning;modeling;prediction methods;forecasting|
|[Reliability of a Cluster of Duplicated Computer Systems with the Criticality of Functional Requests to Waiting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110735)|V. A. Bogatyrev; S. V. Bogatyrev; V. V. Sivov|10.1109/SmartIndustryCon57312.2023.10110735|real-time;cluster;redundant computer system;permissible waiting time;real-time;cluster;redundant computer system;permissible waiting time|
|[Optimal Parameters Determination for Extreme Learning Machine in the Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110761)|E. S. Abramova; A. A. Orlov|10.1109/SmartIndustryCon57312.2023.10110761|neural network;extreme learning machine;human activity recognition;neural network;extreme learning machine;human activity recognition|
|[Optimization of Micro-object Identification Based on the Mellin Transform and the Use of Parallel Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110834)|I. I. Jumanov; S. M. Kholmonov|10.1109/SmartIndustryCon57312.2023.10110834|non-stationary object;image;identifications;recognition;clustering;property extraction;Mellin transform;non-stationary object;image;identifications;recognition;clustering;property extraction;Mellin transform|
|[Electronic Passport as the Basis of the Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110811)|D. Topolsky; A. Belyakov; V. Pochinskaia|10.1109/SmartIndustryCon57312.2023.10110811|electronic passport;digital twin;data lake;full-text search;big data;decision support system;electronic passport;digital twin;data lake;full-text search;big data;decision support system|
|[Digital Twins: Forecasting and Formation of Optimal Control Programs for NPP Power Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110766)|E. Jharko; K. Chernyshov|10.1109/SmartIndustryCon57312.2023.10110766|digital twin;forecasting;optimal control program;intelligent operator support system;digital twin;forecasting;optimal control program;intelligent operator support system|
|[Building an Attack Tree for Analysis of Information Security Risks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110738)|U. Kuzmina; O. Kazakov; B. Erushev|10.1109/SmartIndustryCon57312.2023.10110738|information risks;security model;information security threats;countermeasures;attack graph;information assets;information risks;security model;information security threats;countermeasures;attack graph;information assets|
|[Development of an Automated Method for Compiling a List of Protected Objects Based on a Special Classification of Information Assets in the Field of Information Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110804)|A. V. Manzhosov; I. P. Bolodurina; N. A. Dolgushev|10.1109/SmartIndustryCon57312.2023.10110804|special classification;information asset;automated means;information security;special classification;information asset;automated means;information security|
|[A Model for the Optimal Ordering of Weighted Vertices on a Graph for Local Integrated Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110732)|M. Knyazeva; A. Tselykh; E. Kotov|10.1109/SmartIndustryCon57312.2023.10110732|optimal allocation;local integrated energy systems;graph modelling;network scheduling;quasi-Hamiltonian path;optimal allocation;local integrated energy systems;graph modelling;network scheduling;quasi-Hamiltonian path|
|[Optimization of Prediction Results Based on Ensemble Methods of Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110726)|F. M. Nazarov; S. Yarmatov|10.1109/SmartIndustryCon57312.2023.10110726|voting ensemble regression;gradient boosting algorithms;ensemble method;random forest;voting ensemble regression;gradient boosting algorithms;ensemble method;random forest|
|[Solving the Inverse Kinematics Problem for a Seven-Link Robot-Manipulator by the Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110817)|V. A. Karapetyan; V. N. Miryanova|10.1109/SmartIndustryCon57312.2023.10110817|kinematics;manipulator;direct kinematics problem;inverse kinematics problem;optimization algorithm;particle swarm method;meta-optimization;kinematics;manipulator;direct kinematics problem;inverse kinematics problem;optimization algorithm;particle swarm method;meta-optimization|
|[Heuristic Techniques for Constructing Hidden Markov Models of Stochastic Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110792)|M. M. Gavrikov; A. Y. Mezentseva; R. M. Sinetsky|10.1109/SmartIndustryCon57312.2023.10110792|pattern recognition;hidden Markov models;Baum-Welch algorithm;stochastic processes;pattern recognition;hidden Markov models;Baum-Welch algorithm;stochastic processes|
|[Approach to Identifying Areas of Uncontrolled Oscillations in Human-Machine Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110762)|I. Zaitceva; N. V. Kuznetsov; B. Andrievsky|10.1109/SmartIndustryCon57312.2023.10110762|human operator;manual control;actuator saturation;human robot interaction;nonlinear;frequency response;sensitivity function;human operator;manual control;actuator saturation;human robot interaction;nonlinear;frequency response;sensitivity function|
|[Continuous Speaker Authentication when Using Network Administrator Virtual Assistant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110730)|S. Khanbekov; A. Zakharov|10.1109/SmartIndustryCon57312.2023.10110730|speaker authentication;continuous authentication;acoustic security and privacy;voice assistant;IoT security;speaker authentication;continuous authentication;acoustic security and privacy;voice assistant;IoT security|
|[Deep Learning in Automation of Checking Homework Assignments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110830)|E. V. Karmanova; I. V. Gavrilova; O. E. Maslennikova|10.1109/SmartIndustryCon57312.2023.10110830|handwritten text recognition;Russian language;control automation;neural network technologies;online service;handwritten text recognition;Russian language;control automation;neural network technologies;online service|
|[Image Preprocessing to Improve Object Recognition in Complex Weather Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110822)|Y. Schegolikhin; M. Mitrokhin; A. Eremin|10.1109/SmartIndustryCon57312.2023.10110822|image preprocessing;vehicle detection;neural network algorithms;image preprocessing;vehicle detection;neural network algorithms|
|[Organization of Wireless Sensor Network at Drilling Sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110754)|A. N. Krasnov; M. Y. Prakhova; Y. V. Kalashnik|10.1109/SmartIndustryCon57312.2023.10110754|drilling site;communication channel;wireless sensor network;routing;connectivity;drilling site;communication channel;wireless sensor network;routing;connectivity|
|[AI-Based Method for Frame Detection in Engineering Drawings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110731)|A. Kashevnik; A. Ali; A. Mayatin|10.1109/SmartIndustryCon57312.2023.10110731|computer vision;engineering drawings;frame detection;computer vision;engineering drawings;frame detection|
|[Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110736)|I. E. Shishkin; A. N. Grekov|10.1109/SmartIndustryCon57312.2023.10110736|artificial intelligence;machine learning;computer vision;marine environment;real-time recognition;pollution detection;artificial intelligence;machine learning;computer vision;marine environment;real-time recognition;pollution detection|
|[Blockchain Architecture for Secure Storage of IoT Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110839)|A. Zakharov; I. Zakharova; D. Korenev|10.1109/SmartIndustryCon57312.2023.10110839|security;Internet of Things;Blockchain;Blockchain of blockchains;security;Internet of Things;Blockchain;Blockchain of blockchains|
|[Digital Clones at the Adaptable Control in the Agricultural Biotechnology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110739)|O. A. Ivashchuk; V. I. Fedorov; V. A. Berezhnoy|10.1109/SmartIndustryCon57312.2023.10110739|digital clone;digital phenotyping;3D modeling;artificial neuron nets;agricultural biotechnology;adaptation scenarios;digital clone;digital phenotyping;3D modeling;artificial neuron nets;agricultural biotechnology;adaptation scenarios|
|[Research of Multipath Routing and Load Balancing Processes in Software Defined Networks Based on Bird Migration Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110788)|D. Perepelkin; M. Ivanchikova; T. Nguyen|10.1109/SmartIndustryCon57312.2023.10110788|software defined networks;multipath routing;load balancing;swarm intelligence;bird migration algorithm;software defined networks;multipath routing;load balancing;swarm intelligence;bird migration algorithm|
|[Enhanced User Authentication Algorithm Based on Behavioral Analytics in Web-Based Cyberphysical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110791)|A. Y. Iskhakov; M. V. Mamchenko; S. P. Khripunov|10.1109/SmartIndustryCon57312.2023.10110791|multi-factor authentication;browser fingerprint;outlier detection;anomaly detection;user behavior;standard audit log;web platform;multi-factor authentication;browser fingerprint;outlier detection;anomaly detection;user behavior;standard audit log;web platform|
|[Informational Image of a Person’s Gait According to Mobile Phone Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110718)|N. Dorofeev; A. Grecheneva; R. Sharapov|10.1109/SmartIndustryCon57312.2023.10110718|signals;accelerometer;movement;gait;mobile phone;wearable device;signals;accelerometer;movement;gait;mobile phone;wearable device|
|[Analysis of Theft of Housing and Communal Services Resources Based on Neural Network Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110782)|N. Dorofeev; M. Goryachev|10.1109/SmartIndustryCon57312.2023.10110782|theft of housing and communal services resources;neural network approach;intelligent metering devices;individual consumer model;theft of housing and communal services resources;neural network approach;intelligent metering devices;individual consumer model|
|[Decision Support System for Feasibility Study and Determination the Optimal Way to Increase the Throughput Capacity of Main Oil Pipelines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110838)|R. Tashbulatov; R. Karimov; A. Valeev|10.1109/SmartIndustryCon57312.2023.10110838|main oil pipeline;throughput capacity;feasibility study;hydraulic efficiency;economic effect;payback period;main oil pipeline;throughput capacity;feasibility study;hydraulic efficiency;economic effect;payback period|
|[Vulnerability of Biometric Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110772)|K. Yliana; A. Arina; P. Anastasia|10.1109/SmartIndustryCon57312.2023.10110772|authentication;information security;biometric systems;fingerprint;personal data;information leak;vulnerability;authentication;information security;biometric systems;fingerprint;personal data;information leak;vulnerability|
|[Development of the Concept and Architecture of an Automated System for Updating Physical Knowledge for Information Support of Search Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110764)|A. Bobunov; D. Korobkin; S. Fomenkov|10.1109/SmartIndustryCon57312.2023.10110764|architecture;physical knowledge;information support;search design;architecture;physical knowledge;information support;search design|
|[Development of a System for Remote Condition Monitoring of Industrial Machines and Defect Locating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110778)|A. Valeev; R. Tashbulatov; R. Karimov|10.1109/SmartIndustryCon57312.2023.10110778|diagnostics;condition monitoring;strain gauge sensor;industrial internet of things;automation;defect;diagnostics;condition monitoring;strain gauge sensor;industrial internet of things;automation;defect|
|[Neural Network Method for Solving Fractional Differential Equations α with the Dirichlet Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110785)|N. T. Duc; A. F. Galimyanov; I. Z. Akhmetov|10.1109/SmartIndustryCon57312.2023.10110785|fractional differential equations;Dirichlet’s problem;conformable fractional derivative;artificial neural network;fractional differential equations;Dirichlet’s problem;conformable fractional derivative;artificial neural network|
|[The Computational Schemes of a Noise-Resistant Coding for Autonomous Robotic Complexes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110831)|E. A. Titenko; O. G. Dobroserdov; D. P. Teterin|10.1109/SmartIndustryCon57312.2023.10110831|Galois field;polynomial;degree;tabular conversion;hardware complexity;Galois field;polynomial;degree;tabular conversion;hardware complexity|
|[On the Choice of a Model for Representing Data Flow Parameters in a Digital System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110741)|V. Promyslov; K. Semenkov|10.1109/SmartIndustryCon57312.2023.10110741|model;digital system;network calculus;multidimensional flow;model;digital system;network calculus;multidimensional flow|
|[Using Computer Vision to Analyze the Sequence of Vehicles Passing Through Regulated Intersections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110803)|V. Shepelev; A. Glushkov; A. Vorobyev|10.1109/SmartIndustryCon57312.2023.10110803|traffic light object;neural network;composition of the traffic flow;road network junction;traffic congestion;traffic capacity;traffic light object;neural network;composition of the traffic flow;road network junction;traffic congestion;traffic capacity|
|[Combined Method of Cognitive Assessment of the Specialist Professional Potential](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110755)|I. F. Yasinskiy; T. V. Gvozdeva; V. V. Tyutikov|10.1109/SmartIndustryCon57312.2023.10110755|neural network technology;mathematical modeling;computational hybrids;professional potential assessment;neural network technology;mathematical modeling;computational hybrids;professional potential assessment|
|[Designing a High-Performance Resource Management Module for Real-Time Multiprocessor Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110821)|R. A. Biktashev; A. I. Martyshkin|10.1109/SmartIndustryCon57312.2023.10110821|architecture;operating systems;control algorithms;processes and resources;formalization;finite non-deterministic automation;architecture;operating systems;control algorithms;processes and resources;formalization;finite non-deterministic automation|
|[Artificial Neural Network Method for Solving a Fractional Order Differential Equation with the Cauchy-Type Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110823)|N. T. Duc; A. F. Galimyanov; I. Z. Akhmetov|10.1109/SmartIndustryCon57312.2023.10110823|fractional differential equations;Cauchy problem;fractional integrals and Riemann-Liouville derivatives;neural architecture feedforward;fractional differential equations;Cauchy problem;fractional integrals and Riemann-Liouville derivatives;neural architecture feedforward|
|[The algorithm of Intelligent Control Adjustment of the Mode Map of Hot Blast Stove’s Unit Based on Fuzzy Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110747)|A. Prasolov; S. Andreev; I. Nazarov|10.1109/SmartIndustryCon57312.2023.10110747|hot-blast stove;heat exchange;linguistic variable;fuzzy logic;hot-blast stove;heat exchange;linguistic variable;fuzzy logic|
|[Identification of a Depressive State Among Users of the Vkontakte Social Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110824)|A. A. Zotkina; A. I. Martyshkin|10.1109/SmartIndustryCon57312.2023.10110824|social networks;VKontakte;SVM;NLTK;KNN;depression;social networks;VKontakte;SVM;NLTK;KNN;depression|
|[Information Security Incident Handling Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110721)|A. Iakovleva; M. Zhukova; T. Strekaleva|10.1109/SmartIndustryCon57312.2023.10110721|incident;investigation;cyber fatigue;algorithm;digital evidence;timeline;incident;investigation;cyber fatigue;algorithm;digital evidence;timeline|
|[Development and Use of OPC UA Tools for Data Collection and Monitoring of Technological Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110757)|G. Martinov; A. Al Khoury; A. Issa|10.1109/SmartIndustryCon57312.2023.10110757|CNC;CNC machine tool digital shadow;OPC UA server- client;MDC system;CNC;CNC machine tool digital shadow;OPC UA server- client;MDC system|
|[Multi-Class Classification Using Quantum Kernel Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110752)|M. Mokhles; I. Makarov|10.1109/SmartIndustryCon57312.2023.10110752|quantum computers;machine learning;kernel methods;quantum kernels;quantum machine learning;multi-class classification;quantum computers;machine learning;kernel methods;quantum kernels;quantum machine learning;multi-class classification|
|[Network Stegoinsider Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110748)|A. Salita; A. Krasov|10.1109/SmartIndustryCon57312.2023.10110748|steganography;network;machine learning;insider;steganography;network;machine learning;insider|
|[Evidential Markers of Virtual Reality: Linguistic Markup of the Navigation Route](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110806)|N. V. Khalina; N. N. Pivkina; T. S. Borzhikov|10.1109/SmartIndustryCon57312.2023.10110806|evidentiality;virtual reality;mental immersion;sensory perception;inner language;virtual reality mental scanning language (VRMSL);evidentiality;virtual reality;mental immersion;sensory perception;inner language;virtual reality mental scanning language (VRMSL)|
|[Ontological Framework for Constructing Hybrid Prognoses and Risk Assessment of Critical Conditions of Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110768)|V. Gribova; E. Shalfeeva|10.1109/SmartIndustryCon57312.2023.10110768|risk assessment;forecast;declarative knowledge;framework;explanation generation;decision support system;risk assessment;forecast;declarative knowledge;framework;explanation generation;decision support system|
|[Development and Application of an Application with Augmented Reality Technology for Training Future Aircraft Designers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110795)|N. M. Borgest; S. A. Vlasov; D. S. Glibotsky|10.1109/SmartIndustryCon57312.2023.10110795|application;augmented reality;aircraft construction;3D model;object recognition;application;augmented reality;aircraft construction;3D model;object recognition|
|[The Neural Network Controller for the Dry Low Emission Combustor of Gas-Turbine Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110733)|T. A. Kuznetsova; A. A. Sukharev|10.1109/SmartIndustryCon57312.2023.10110733|neural network;built-in mathematical model;automatic control system;gas turbine power plant;dry low emission combustor;harmful substances emission;neural network;built-in mathematical model;automatic control system;gas turbine power plant;dry low emission combustor;harmful substances emission|
|[Forensic Search for Traces of Unauthorized Access Using the Kerberos Authentication Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110781)|Y. A. Alexeevskaya; Y. V. Molodtsova; R. A. Alexeevsky|10.1109/SmartIndustryCon57312.2023.10110781|windows active directory;Kerberos protocol;authentication server;pass the hash;windows active directory;Kerberos protocol;authentication server;pass the hash|
|[Numerical Modeling of Rough Surfaces of Additive Manufacturing Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110763)|O. V. Zakharov; A. S. Yakovishin; F. D. Suleymanova|10.1109/SmartIndustryCon57312.2023.10110763|surface texture;additive manufacturing products;numerical modeling;height parameters;surface model;surface texture;additive manufacturing products;numerical modeling;height parameters;surface model|
|[Digital Twin of Scott-T Connection Special Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110837)|A. Stulov; A. Tikhonov; A. Karzhevin|10.1109/SmartIndustryCon57312.2023.10110837|digital twin;Scott-T connection;special transformer;simulation model;equivalent circuit;phase converter;digital twin;Scott-T connection;special transformer;simulation model;equivalent circuit;phase converter|
|[Calculating the Maximum Response Time of Protection Systems of Industrial Control System Network to the Impact of a DDoS Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110756)|A. M. Boger; A. N. Sokolov|10.1109/SmartIndustryCon57312.2023.10110756|DDoS attack;industrial control system (ICS);information protection;two-time approximation method;stochastic network topological transformation method;intruder model;DDoS attack;industrial control system (ICS);information protection;two-time approximation method;stochastic network topological transformation method;intruder model|
|[Algorithms for Robotic Intelligent Systems for Predicting Fire Hazardous Situations at an Early Stage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110799)|O. Emelyanova; S. Efimov; S. Jatsun|10.1109/SmartIndustryCon57312.2023.10110799|source of air pollution during ignition;analyzer sensors;carbon monoxide;mobile instrument platform;source of air pollution during ignition;analyzer sensors;carbon monoxide;mobile instrument platform|
|[An Approach to the Production of Prototype Printed Circuit Boards on Bench-Type Machine with the CNC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110810)|G. Martinov; N. Martemianova|10.1109/SmartIndustryCon57312.2023.10110810|printed circuit board;CNC system;end-to-end design;gerber file;part program;DipTrace;EDA/CAD;printed circuit board;CNC system;end-to-end design;gerber file;part program;DipTrace;EDA/CAD|
|[The Parallelization of Computations for Ensuring Information Security in Connected Vehicle Systems Using Q-Effective Programming: The Example of Dijkstra’s Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110745)|M. P. Sokolov; P. A. Manatin; N. D. Zyulyarkina|10.1109/SmartIndustryCon57312.2023.10110745|cybersecurity;connected vehicles;improving parallel computing efficiency;Q-effective program;high-performance computing;Dijkstra’s algorithm;computing on a supercomputer;cybersecurity;connected vehicles;improving parallel computing efficiency;Q-effective program;high-performance computing;Dijkstra’s algorithm;computing on a supercomputer|
|[Nature-Like Structural Node Components for Software and Hardware Complexes on the Basis of Single-Board Computers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110783)|A. Poletikin; N. Mengazetdinov; K. Semenkov|10.1109/SmartIndustryCon57312.2023.10110783|instrumentation and control systems;SBC;veurons;instrumentation and control systems;SBC;veurons|
|[Reliability of Multipath Networks with Optimization of the Location of Inter-Path Communication Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110818)|V. A. Bogatyrev; A. Tu Le; E. A. Abramova|10.1109/SmartIndustryCon57312.2023.10110818|reliability;optimization;multipath routing;connectivity probability;data transmission;reliability;optimization;multipath routing;connectivity probability;data transmission|
|[Segmenting Prostate Cancer on TRUS Images with a Small Dataset: A Comprehensive Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110773)|D. A. Lyutkin; A. Y. Romanov; N. D. Nasonov|10.1109/SmartIndustryCon57312.2023.10110773|diseases;mathematical algorithms;pathology detection;machine learning;neural networks;training algorithm;quality metrics;diseases;mathematical algorithms;pathology detection;machine learning;neural networks;training algorithm;quality metrics|
|[Segmentation of Prostate Cancer on TRUS Images Using ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110727)|R. I. Zaev; A. Y. Romanov; R. A. Solovyev|10.1109/SmartIndustryCon57312.2023.10110727|neural networks;machine learning;segmentation;diseases;pathology detection;image processing;neural networks;machine learning;segmentation;diseases;pathology detection;image processing|
|[A New Rectifier for Industrial Use](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110809)|E. Koptjaev; A. J. Marques Cardoso|10.1109/SmartIndustryCon57312.2023.10110809|rectifier;dimensions;mass;DC electrical network;innovation;direct current;rectifier;dimensions;mass;DC electrical network;innovation;direct current|
|[A New DC Source for Experimental Use](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110740)|E. Koptjaev; A. J. Marques Cardoso|10.1109/SmartIndustryCon57312.2023.10110740|power supply;dimensions;weight;tokamak;high voltage;innovation;power supply;dimensions;weight;tokamak;high voltage;innovation|
|[A Comparative Study of Brushless Non-salient Pole Monopolar Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110774)|E. Koptjaev; A. J. Marques Cardoso|10.1109/SmartIndustryCon57312.2023.10110774|efficiency;brushless generator;coils;reliability;monopolar;alternator;efficiency;brushless generator;coils;reliability;monopolar;alternator|
|[Development of Regulatory Documents for the Calculation of Electrical Loads of Residential Buildings Using Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110746)|Y. Soluyanov; A. Fedotov; A. Akhmetshin|10.1109/SmartIndustryCon57312.2023.10110746|smart grid;smart metering;specific design electrical load;electrical load schedule;multi-unit residential building;design of electrical networks;smart grid;smart metering;specific design electrical load;electrical load schedule;multi-unit residential building;design of electrical networks|
|[A Software Framework for Jetson Nano to Detect Anomalies in CAN Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110807)|S. Staroletov|10.1109/SmartIndustryCon57312.2023.10110807|anomaly detection;CAN bus;automotive;embedded software;anomaly detection;CAN bus;automotive;embedded software|
|[Development of an Application for Forecasting the Development of Technologies on the Example of Television and Radio Broadcasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110805)|D. Korobkin; G. Vereschak; S. Fomenkov|10.1109/SmartIndustryCon57312.2023.10110805|Parsing;patent;forecasting;television;radio technologies;Parsing;patent;forecasting;television;radio technologies|
|[Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote Data Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110777)|E. Jharko; M. Mamchenko; S. P. Khripunov|10.1109/SmartIndustryCon57312.2023.10110777|vSLAM;remote SLAM;edge;fog;cloud;smart city;UAV;vSLAM;remote SLAM;edge;fog;cloud;smart city;UAV|
|[Construction Smart System for Monitoring Technical Condition of Furnace Transformer Vacuum Circuit Breaker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110723)|I. R. Abdulveleev; A. S. Karandaev; E. A. Khramshina; I. V. Liubimov; S. A. Evdokimov; V. R. Gasiyarov|10.1109/SmartIndustryCon57312.2023.10110723|ladle-furnace unit;furnace transformer;high-voltage vacuum circuit breaker;technical condition;temperature;online monitoring system;experiment;ladle-furnace unit;furnace transformer;high-voltage vacuum circuit breaker;technical condition;temperature;online monitoring system;experiment|
|[Construction Principle for Object-Oriented Digital Twins of Mechatronic Complexes of Rolling Mills](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110770)|A. A. Radionov; P. A. Bovshik; B. M. Loginov; A. S. Karandaev; V. R. Khramshin|10.1109/SmartIndustryCon57312.2023.10110770|rolling mill;mechatronic complex;virtual commissioning;digital twin;construction;principle;experiment;rolling mill;mechatronic complex;virtual commissioning;digital twin;construction;principle;experiment|
|[Investigation of Frequency Controlled Electric Drive with Control Signal Limitation by the Frequency Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110828)|V. V. Shokhin; V. R. Khramshin; G. P. Kornilov; O. V. Permyakova|10.1109/SmartIndustryCon57312.2023.10110828|asynchronous motor;vector control;frequency converter;flux linkage;electric drive coordinate limitation;structural modeling;asynchronous motor;vector control;frequency converter;flux linkage;electric drive coordinate limitation;structural modeling|
|[Artificial Neural Network Predictive Autoencoder with Pre-Digital Signal Processing Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110779)|A. N. Ragozin; A. D. Pletenkova|10.1109/SmartIndustryCon57312.2023.10110779|prediction;artificial neural network;autoencoder;signals;digital filtering;anomalies;prediction;artificial neural network;autoencoder;signals;digital filtering;anomalies|
|[A method for Generating a Digital Twin Structure for a System for Organizing Preventive Maintenance in the Electricity Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110812)|V. Antonov; L. Kromina; A. Fakhrullina; L. Rodionova|10.1109/SmartIndustryCon57312.2023.10110812|digital twin;preventive maintenance;preventive maintenance schedule;intelligent systems;artificial neuron;formal digital twin model;digital twin;preventive maintenance;preventive maintenance schedule;intelligent systems;artificial neuron;formal digital twin model|
|[Automated Data Acquisition System for Cold Water Metering at Factory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110796)|S. Kalabanov; R. Shagiev; R. Ishmuratov|10.1109/SmartIndustryCon57312.2023.10110796|water meter;automated data collection system;wireless system;software;water meter;automated data collection system;wireless system;software|
|[The Development of the Technique for Automated Tracing of Printed Circuit Board Interconnections of Electronic Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110767)|A. A. Vagapov; R. O. Guzanov; I. V. Iakovlev|10.1109/SmartIndustryCon57312.2023.10110767|printed circuit boards;printed circuit boards interconnect tracing;ant algorithm;printed circuit boards;printed circuit boards interconnect tracing;ant algorithm|
|[Improving the Decision-Making System of ITS Based on Dynamic Modeling of the Intensity of Vehicle Traffic along the Lanes, Taking into Account Environmental Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110743)|V. Morozov; V. Shepelev; A. Vorobyev|10.1109/SmartIndustryCon57312.2023.10110743|traffic flow;lane occupancy;neural network;signal-controlled intersections;poluton;traffic flow;lane occupancy;neural network;signal-controlled intersections;poluton|
|[Building Deep Neural Networks for solving Machine Learning Problems in Agricultural Production](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110765)|A. Rogachev; E. Melikhova; N. Zolotykh|10.1109/SmartIndustryCon57312.2023.10110765|land use tasks;machine learning;deep neural networks;parallel architectures;convolutional layers of neurons;land use tasks;machine learning;deep neural networks;parallel architectures;convolutional layers of neurons|
|[Design Documentation Agreement Automated System with Approving Person Intelligent Support](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110833)|V. Latypova|10.1109/SmartIndustryCon57312.2023.10110833|design documentation;documentation agreement;intelligent support;approving person;remark text mining;algorithm Lingo;design documentation;documentation agreement;intelligent support;approving person;remark text mining;algorithm Lingo|
|[Approach to Efficient Task Allocation in a Collaborative Robotic System Using Modified Cost Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110787)|R. R. Galin; S. B. Galina|10.1109/SmartIndustryCon57312.2023.10110787|human-robot collaboration;task allocation;method;algorithm;efficiency;collaborative robotic system;human-robot collaboration;task allocation;method;algorithm;efficiency;collaborative robotic system|
|[Approach to Efficient Task Allocation and Cost Minimization in Collaborative Robotic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110790)|S. B. Galina; R. R. Galin|10.1109/SmartIndustryCon57312.2023.10110790|algorithm;task allocation;directed graph;technological process;algorithm;task allocation;directed graph;technological process|
|[The Holographic and Perceptron Neuron Networks Joint Application for the Dynamic Systems Behavior Forecast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110729)|V. Olonichev; B. Staroverov; S. Tarasov|10.1109/SmartIndustryCon57312.2023.10110729|dynamic system;temporal series;prognosis;holography;neural networks;local approximation;dynamic system;temporal series;prognosis;holography;neural networks;local approximation|
|[Neutral Point Voltage Balance Based on Space-Vector PWM with Five-Stage Sequence for Three-Level Voltage Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110815)|A. N. Shishkov; M. M. Dudkin; V. K. Le; N. A. Eremin|10.1109/SmartIndustryCon57312.2023.10110815|autonomous voltage inverter;three-level neutral point clamped voltage source inverter;space-vector PWM;five-stage switching sequence;neutral point voltage;autonomous voltage inverter;three-level neutral point clamped voltage source inverter;space-vector PWM;five-stage switching sequence;neutral point voltage|
|[Industrial Internet of Things Platform for Water Resource Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110776)|A. N. Ushkov; N. O. Strelkov; V. V. Krutskikh; A. I. Chernikov|10.1109/SmartIndustryCon57312.2023.10110776|industrial Internet of Things;water monitoring;wireless sensor network;radiowave propagation;MQTT;industrial Internet of Things;water monitoring;wireless sensor network;radiowave propagation;MQTT|
|[Computer Model of "Smart Grid" for Power Transmission Lines with Tree-Like Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110719)|A. Karpov; D. Sarychev; S. Kalabanov|10.1109/SmartIndustryCon57312.2023.10110719|power transmission line;computer model;topology;fault position;power transmission line;computer model;topology;fault position|
|[Power Quality Improvement in the Grid via High-power AC Regenerative Electric Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110800)|A. S. Maklakov; A. A. Nikolaev; V. R. Gasiyarov; A. A. Filimonova|10.1109/SmartIndustryCon57312.2023.10110800|power converters;harmonics;THD;selective harmonic elimination;power converters;harmonics;THD;selective harmonic elimination|
|[Investigation of the Temperature Condition of Electric Motors Using the Comsol Package](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110836)|S. V. Oskin; Z. Hamedovich Naguchev; D. M. Taranov|10.1109/SmartIndustryCon57312.2023.10110836|electric motor;aerodynamics;heat;heat dissipation;modeling;Comsol;electric motor;aerodynamics;heat;heat dissipation;modeling;Comsol|
|[Mathematical Model of a Micro Grid for a Hybrid Autonomous Station Based on Renewable Energy Sources for the Republic of Ingushetia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110814)|G. R. Titova; M. I. Malsagov|10.1109/SmartIndustryCon57312.2023.10110814|renewable energy sources;autonomous systems;micro grids;microgenerating hybrid systems;renewable energy sources;autonomous systems;micro grids;microgenerating hybrid systems|
|[Strategic Framework for Monitoring Systems for Territorial Administration Objects Using Data Warehouse Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110749)|V. Goryunova; T. Goryunova|10.1109/SmartIndustryCon57312.2023.10110749|strategic management;territorial object;digitalization;monitoring;indicators;data warehouses;ontologies;strategic management;territorial object;digitalization;monitoring;indicators;data warehouses;ontologies|
|[A Pipeline for Traffic Accident Dataset Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110794)|V. Stepanyants; M. Andzhusheva; A. Romanov|10.1109/SmartIndustryCon57312.2023.10110794|dataset development;detection;segmentation;traffic accident datasets;video annotation;dataset development;detection;segmentation;traffic accident datasets;video annotation|
|[Evaluation of the Influence of Priorities in Multichannel Data Transmission Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110826)|S. Lygdenov|10.1109/SmartIndustryCon57312.2023.10110826|local network;data transmission systems;multichannel;priorities;simulation;efficiency;reliability;local network;data transmission systems;multichannel;priorities;simulation;efficiency;reliability|
|[Development of a Means and Algorithm for Balancing the Load of Processors in a Reconfigurable Computing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110784)|A. I. Martyshkin; R. A. Biktashev; E. G. Bershadskaya|10.1109/SmartIndustryCon57312.2023.10110784|balancing;high-performance computing system;cluster;scalability;real-time operating system;reconfigurable computing system;balancing;high-performance computing system;cluster;scalability;real-time operating system;reconfigurable computing system|
|[Application of an Extreme Regulator to Control an Inertial Object](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110798)|V. Gusev|10.1109/SmartIndustryCon57312.2023.10110798|management in uncertain conditions;extreme regulator;target function;tax rate;management in uncertain conditions;extreme regulator;target function;tax rate|
|[Dependence of Current Inverter Critical Frequencies on its Load Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110751)|S. Umarov|10.1109/SmartIndustryCon57312.2023.10110751|autonomous current inverter;active-inductive load;commutating capacitor;load power factor;autonomous current inverter;active-inductive load;commutating capacitor;load power factor|
|[Application of a Fuzzy Logic Controller in a D-STATCOM in an Electrical Network with Distributed Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110819)|A. F. Akkad; N. Erdili; E. Sosnina|10.1109/SmartIndustryCon57312.2023.10110819|distributed generation;renewable energy sources;power quality;D-STATCOM;PI controller;artificial intelligence;fuzzy logic controller;distributed generation;renewable energy sources;power quality;D-STATCOM;PI controller;artificial intelligence;fuzzy logic controller|
|[Overview of Scene Graph Generation Approaches in Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110820)|A. T. Latipova; P. P. Kumar|10.1109/SmartIndustryCon57312.2023.10110820|computer vision;scene graph generation;hyperparameter tuning;object detection;deep learning;computer vision;scene graph generation;hyperparameter tuning;object detection;deep learning|
|[Signal Delay Study in the Adaptive Control Scheme with the Model in the Loop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110724)|G. F. Akhmedyanova; T. A. Pishchukhina; A. M. Pishchukhin|10.1109/SmartIndustryCon57312.2023.10110724|adaptive control;signal delay;additional control loop;phase detector;reference model;adaptive control;signal delay;additional control loop;phase detector;reference model|
|[Use of the Larionov Scheme as Negative Sequence Filter of a Three-Phase Voltage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110793)|A. V. Petrov; D. A. Kostiukov; M. V. Zhukov|10.1109/SmartIndustryCon57312.2023.10110793|three-phase rectifier;voltage unbalance;positive sequence;signal ripple;spectral analysis;higher harmonics;negative sequence;three-phase rectifier;voltage unbalance;positive sequence;signal ripple;spectral analysis;higher harmonics;negative sequence|
|[The Study of Error of Determining the Phase Shift](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110737)|A. V. Petrov; D. A. Kostiukov; P. A. Zvada|10.1109/SmartIndustryCon57312.2023.10110737|initial phase;zero detector;zero crossing;sampling frequency;higher harmonics;initial phase;zero detector;zero crossing;sampling frequency;higher harmonics|
|[Modification of the Risk Potential Predicting Algorithm for Monitoring the State of the NPP Power Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110744)|E. Abdulova|10.1109/SmartIndustryCon57312.2023.10110744|risk potential;technical and economic indicators;power unit;clustering;instant predictive model;risk potential;technical and economic indicators;power unit;clustering;instant predictive model|
|[Study of Electrical Loads of Individual Residential Buildings with the Subsequent Development of Regulatory Documents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110725)|Y. Soluyanov; A. Fedotov; A. Akhmetshin|10.1109/SmartIndustryCon57312.2023.10110725|smart grid;smart metering devices;specific design electrical load;electrical load schedule;individual residential building;design of electrical networks;smart grid;smart metering devices;specific design electrical load;electrical load schedule;individual residential building;design of electrical networks|
|[Influence of External Magnetic Fields on Measurement Errors by Induction Current Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110808)|V. V. Mikhaylov|10.1109/SmartIndustryCon57312.2023.10110808|Current transformers;induction converters;magnetic fields of influence of neighboring buses;Current transformers;induction converters;magnetic fields of influence of neighboring buses|
|[Designing an Educational Intelligent System with Natural Language Processing Based on Fuzzy Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110734)|K. Kulagin; M. Salikhov; R. Burnashev|10.1109/SmartIndustryCon57312.2023.10110734|expert system;django;intelligent system;knowledge base;geographic information system;fuzzy logic;folium;expert system;django;intelligent system;knowledge base;geographic information system;fuzzy logic;folium|
|[Research of High-Voltage Discharge in Oil on a Simulator with a Various Set of Defects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110759)|A. A. Kuznetsov; A. V. Gorlov; M. A. Volchanina|10.1109/SmartIndustryCon57312.2023.10110759|power transformers;insulation defects;partial discharges;acoustic diagnostics;defects simulator;power transformers;insulation defects;partial discharges;acoustic diagnostics;defects simulator|
#### **2023 International Russian Smart Industry Conference (SmartIndustryCon)**
- DOI: 10.1109/SmartIndustryCon57312.2023
- DATE: 27-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Digital Twin as an Effective and Versatile Tool for Modeling and Optimizing Hybrid Energy Complexes at All Stages of the Life Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110753)|M. G. Tyagunov; R. P. Sheverdiev|10.1109/SmartIndustryCon57312.2023.10110753|renewable energy;hybrid energy complex;energy storage system;wind power plant;photovoltaic plant;digital twin;3D model;renewable energy;hybrid energy complex;energy storage system;wind power plant;photovoltaic plant;digital twin;3D model|
|[Development of an Integrated Expert System for Distribution Network Diagnostics Based on Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110786)|I. F. Galiev; M. S. Garifullin; I. P. Alekseev; A. R. Gizatullin; A. M. Makletsov|10.1109/SmartIndustryCon57312.2023.10110786|expert system;artificial intelligence technology;retrospective offline information;database;online monitoring subsystem;problem-oriented modules;knowledge base;expert system;artificial intelligence technology;retrospective offline information;database;online monitoring subsystem;problem-oriented modules;knowledge base|
|[Development of a Methodology for the Identification of Ferrous Metal Products by Their Contactless Point Labeling Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110717)|A. V. Astafiev|10.1109/SmartIndustryCon57312.2023.10110717|identification;recognition;computer vision;neural networks;product labeling;RetinaNet;identification;recognition;computer vision;neural networks;product labeling;RetinaNet|
|[An Approach to Improving the Efficiency of the Database of a Large Industrial Enterprise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110728)|M. M. Bazhutin; V. S. Moshkin|10.1109/SmartIndustryCon57312.2023.10110728|SQL;databases;CTE;optimization;SQL;databases;CTE;optimization|
|[Characteristics of Angular Displacement Actuators Based on Magnetostrictive Plates under the Action of Prestressing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110789)|P. Grakhov; A. Fedin; V. Yasoveev|10.1109/SmartIndustryCon57312.2023.10110789|magnetostrictive bilayer plates;flexural deformations;magnetostriction;elastic prestresses;moments of external and internal forces;elastic stress distribution;linear expansion temperature coefficients;magnetostrictive bilayer plates;flexural deformations;magnetostriction;elastic prestresses;moments of external and internal forces;elastic stress distribution;linear expansion temperature coefficients|
|[Development of an Imitation-Resistant Satellite Authentication Protocol Using Modular Codes of Residue Number System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110825)|I. A. Kalmykov; D. V. Dukhovnyj; A. A. Olenev|10.1109/SmartIndustryCon57312.2023.10110825|authentication methods;zero-knowledge proof protocols;modular code of residue number system;authentication methods;zero-knowledge proof protocols;modular code of residue number system|
|[Modification of Schnorr Authentication Protocol Using Modular Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110780)|I. A. Kalmykov; N. K. Chistousov; N. I. Kalmykova|10.1109/SmartIndustryCon57312.2023.10110780|modular code of residue number system;zero-knowledge proof protocols;Schnorr authentication protocol;modular code of residue number system;zero-knowledge proof protocols;Schnorr authentication protocol|
|[Universal Route Search Module in Conditions of Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110758)|D. M. Beloborodov; A. I. Razumowsky|10.1109/SmartIndustryCon57312.2023.10110758|component;ant colony optimization;route search;component;ant colony optimization;route search|
|[Statistical Analysis Methods of the Data Obtained by Water Electro Conductivity Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110797)|A. I. Burumbaev; V. T. Kuanishev; N. M. Barbin|10.1109/SmartIndustryCon57312.2023.10110797|statistica;analysis;electro conductivity;normal distribution;abnormal distribution;correlation analysis;statistica;analysis;electro conductivity;normal distribution;abnormal distribution;correlation analysis|
|[Development of the Digital Twin for Flotation of Non-Ferrous Metal Ore Beneficiation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110750)|D. A. Shnayder; E. A. Kalinina|10.1109/SmartIndustryCon57312.2023.10110750|minerals industry;flotation;digital twin;Open Modelica;R;minerals industry;flotation;digital twin;Open Modelica;R|
|[Development of an Automated Diagnostic System of Lung Pathologies in Lymphoma](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110813)|O. N. Kuzyakov; S. A. Sorokina; E. A. Shutova|10.1109/SmartIndustryCon57312.2023.10110813|lung pathology;lymphoma;computed tomography;image segmentation;convolutional neural networks;DICOM;lung pathology;lymphoma;computed tomography;image segmentation;convolutional neural networks;DICOM|
|[Self-timed Fused Multiplier-Adder Pipeline Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110742)|I. Sokolov; Y. Stepchenkov; Y. Diachenko|10.1109/SmartIndustryCon57312.2023.10110742|self-timed circuit;multiply-add unit;pipeline;multiplexing;result periodicity;energy consumption;self-timed circuit;multiply-add unit;pipeline;multiplexing;result periodicity;energy consumption|
|[Method for Modeling and Visualization of Agricultural Crops Growth Based on Augmented Reality Technology in Terms of the Greenhouse Effect Dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110816)|D. V. Goncharov; O. A. Ivashchuk; N. G. Reznikov|10.1109/SmartIndustryCon57312.2023.10110816|augmented reality;visualization;crop productivity;greenhouse effect;assessment of adaptation scripts;modeling;augmented reality;visualization;crop productivity;greenhouse effect;assessment of adaptation scripts;modeling|
|[Determining the Positioning Accuracy of a Multifunctional Module Based on a SCARA Type Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110832)|O. Filipovich; N. Chalenkov; A. Vozhzhov|10.1109/SmartIndustryCon57312.2023.10110832|manipulator;SCARA;repeatability;accuracy;manipulator;SCARA;repeatability;accuracy|
|[Integration of Intelligent Industrial Systems into a Workshop-Level Information Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110771)|L. Martinova; S. Sokolov; R. Pushkov|10.1109/SmartIndustryCon57312.2023.10110771|CNC machine tool;smart manufacturing;digital cloud platform;automated manufacturing;equipment health monitoring;intelligent manufacturing technologists;CNC machine tool;smart manufacturing;digital cloud platform;automated manufacturing;equipment health monitoring;intelligent manufacturing technologists|
|[Optimization of Micro-Object Identification by Correcting Distorted Image Points](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110801)|I. I. Jumanov; R. A. Safarov; O. I. Djumanov|10.1109/SmartIndustryCon57312.2023.10110801|image;micro-objects;identification;recognition;redundant information structure;dynamic models;neural networks;image;micro-objects;identification;recognition;redundant information structure;dynamic models;neural networks|
|[Model of the Initial Section of RTD’s CVC for RFID Tags](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110720)|E. Kuimov; N. Vetrova; S. Meshkov|10.1109/SmartIndustryCon57312.2023.10110720|RFID;RTD;nonlinear elements;optimization;modeling;RFID;RTD;nonlinear elements;optimization;modeling|
|[Classification of Incoming Messages of the University Admission Campaign](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110769)|N. V. Smirnov; A. S. Trifonov|10.1109/SmartIndustryCon57312.2023.10110769|text classification;NLP;multiclass classification;multilabel classification;machine learning;text classification;NLP;multiclass classification;multilabel classification;machine learning|
|[Modular Technology of Definite Multiple Integrals Calculation: Analytical Analysis and Experimental Verification of Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110802)|V. O. Groppen; A. A. Berko|10.1109/SmartIndustryCon57312.2023.10110802|modular enumeration;multiple integrals;analyze of efficiency;experimental verification;modular enumeration;multiple integrals;analyze of efficiency;experimental verification|
|[Integrative Approach to Creation of Information Systems and Entropy Analysis of Linguistic Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110775)|E. Rusyaeva; A. Poltavsky; G. Akhobadze|10.1109/SmartIndustryCon57312.2023.10110775|entropy;probability;linguistic analysis;cognitive modeling;entropy;probability;linguistic analysis;cognitive modeling|
|[Analysis of Consumer Category Data in the Context of an Industrial Enterprise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110829)|Y. N. Kondrashova; A. M. Tretyakov; A. V. Shalimov|10.1109/SmartIndustryCon57312.2023.10110829|reliability;power supply reliability categories;reliability category selection factors;auxiliary degree of responsibility of the mechanism;reliability;power supply reliability categories;reliability category selection factors;auxiliary degree of responsibility of the mechanism|
|[Optimal Energy Consumption Control in a Multi-Zone Building Based on a Hybrid Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110760)|O. Y. Maryasin|10.1109/SmartIndustryCon57312.2023.10110760|energy optimization;energy modeling;artificial neural networks;openmodelica;genetic algorithm;energy optimization;energy modeling;artificial neural networks;openmodelica;genetic algorithm|
|[Digital Assistant to Operator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110835)|D. Antonov; P. Burankina; V. Dement’ev|10.1109/SmartIndustryCon57312.2023.10110835|object detection models;artificial neural networks;mobile development;cognitive service;YOLOv4;YOLOv4-tiny;object detection models;artificial neural networks;mobile development;cognitive service;YOLOv4;YOLOv4-tiny|
|[Uncertainty Estimation in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110722)|V. Arkov|10.1109/SmartIndustryCon57312.2023.10110722|parameter uncertainty;supervised learning;modeling;prediction methods;forecasting;parameter uncertainty;supervised learning;modeling;prediction methods;forecasting|
|[Reliability of a Cluster of Duplicated Computer Systems with the Criticality of Functional Requests to Waiting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110735)|V. A. Bogatyrev; S. V. Bogatyrev; V. V. Sivov|10.1109/SmartIndustryCon57312.2023.10110735|real-time;cluster;redundant computer system;permissible waiting time;real-time;cluster;redundant computer system;permissible waiting time|
|[Optimal Parameters Determination for Extreme Learning Machine in the Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110761)|E. S. Abramova; A. A. Orlov|10.1109/SmartIndustryCon57312.2023.10110761|neural network;extreme learning machine;human activity recognition;neural network;extreme learning machine;human activity recognition|
|[Optimization of Micro-object Identification Based on the Mellin Transform and the Use of Parallel Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110834)|I. I. Jumanov; S. M. Kholmonov|10.1109/SmartIndustryCon57312.2023.10110834|non-stationary object;image;identifications;recognition;clustering;property extraction;Mellin transform;non-stationary object;image;identifications;recognition;clustering;property extraction;Mellin transform|
|[Electronic Passport as the Basis of the Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110811)|D. Topolsky; A. Belyakov; V. Pochinskaia|10.1109/SmartIndustryCon57312.2023.10110811|electronic passport;digital twin;data lake;full-text search;big data;decision support system;electronic passport;digital twin;data lake;full-text search;big data;decision support system|
|[Digital Twins: Forecasting and Formation of Optimal Control Programs for NPP Power Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110766)|E. Jharko; K. Chernyshov|10.1109/SmartIndustryCon57312.2023.10110766|digital twin;forecasting;optimal control program;intelligent operator support system;digital twin;forecasting;optimal control program;intelligent operator support system|
|[Building an Attack Tree for Analysis of Information Security Risks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110738)|U. Kuzmina; O. Kazakov; B. Erushev|10.1109/SmartIndustryCon57312.2023.10110738|information risks;security model;information security threats;countermeasures;attack graph;information assets;information risks;security model;information security threats;countermeasures;attack graph;information assets|
|[Development of an Automated Method for Compiling a List of Protected Objects Based on a Special Classification of Information Assets in the Field of Information Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110804)|A. V. Manzhosov; I. P. Bolodurina; N. A. Dolgushev|10.1109/SmartIndustryCon57312.2023.10110804|special classification;information asset;automated means;information security;special classification;information asset;automated means;information security|
|[A Model for the Optimal Ordering of Weighted Vertices on a Graph for Local Integrated Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110732)|M. Knyazeva; A. Tselykh; E. Kotov|10.1109/SmartIndustryCon57312.2023.10110732|optimal allocation;local integrated energy systems;graph modelling;network scheduling;quasi-Hamiltonian path;optimal allocation;local integrated energy systems;graph modelling;network scheduling;quasi-Hamiltonian path|
|[Optimization of Prediction Results Based on Ensemble Methods of Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110726)|F. M. Nazarov; S. Yarmatov|10.1109/SmartIndustryCon57312.2023.10110726|voting ensemble regression;gradient boosting algorithms;ensemble method;random forest;voting ensemble regression;gradient boosting algorithms;ensemble method;random forest|
|[Solving the Inverse Kinematics Problem for a Seven-Link Robot-Manipulator by the Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110817)|V. A. Karapetyan; V. N. Miryanova|10.1109/SmartIndustryCon57312.2023.10110817|kinematics;manipulator;direct kinematics problem;inverse kinematics problem;optimization algorithm;particle swarm method;meta-optimization;kinematics;manipulator;direct kinematics problem;inverse kinematics problem;optimization algorithm;particle swarm method;meta-optimization|
|[Heuristic Techniques for Constructing Hidden Markov Models of Stochastic Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110792)|M. M. Gavrikov; A. Y. Mezentseva; R. M. Sinetsky|10.1109/SmartIndustryCon57312.2023.10110792|pattern recognition;hidden Markov models;Baum-Welch algorithm;stochastic processes;pattern recognition;hidden Markov models;Baum-Welch algorithm;stochastic processes|
|[Approach to Identifying Areas of Uncontrolled Oscillations in Human-Machine Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110762)|I. Zaitceva; N. V. Kuznetsov; B. Andrievsky|10.1109/SmartIndustryCon57312.2023.10110762|human operator;manual control;actuator saturation;human robot interaction;nonlinear;frequency response;sensitivity function;human operator;manual control;actuator saturation;human robot interaction;nonlinear;frequency response;sensitivity function|
|[Continuous Speaker Authentication when Using Network Administrator Virtual Assistant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110730)|S. Khanbekov; A. Zakharov|10.1109/SmartIndustryCon57312.2023.10110730|speaker authentication;continuous authentication;acoustic security and privacy;voice assistant;IoT security;speaker authentication;continuous authentication;acoustic security and privacy;voice assistant;IoT security|
|[Deep Learning in Automation of Checking Homework Assignments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110830)|E. V. Karmanova; I. V. Gavrilova; O. E. Maslennikova|10.1109/SmartIndustryCon57312.2023.10110830|handwritten text recognition;Russian language;control automation;neural network technologies;online service;handwritten text recognition;Russian language;control automation;neural network technologies;online service|
|[Image Preprocessing to Improve Object Recognition in Complex Weather Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110822)|Y. Schegolikhin; M. Mitrokhin; A. Eremin|10.1109/SmartIndustryCon57312.2023.10110822|image preprocessing;vehicle detection;neural network algorithms;image preprocessing;vehicle detection;neural network algorithms|
|[Organization of Wireless Sensor Network at Drilling Sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110754)|A. N. Krasnov; M. Y. Prakhova; Y. V. Kalashnik|10.1109/SmartIndustryCon57312.2023.10110754|drilling site;communication channel;wireless sensor network;routing;connectivity;drilling site;communication channel;wireless sensor network;routing;connectivity|
|[AI-Based Method for Frame Detection in Engineering Drawings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110731)|A. Kashevnik; A. Ali; A. Mayatin|10.1109/SmartIndustryCon57312.2023.10110731|computer vision;engineering drawings;frame detection;computer vision;engineering drawings;frame detection|
|[Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110736)|I. E. Shishkin; A. N. Grekov|10.1109/SmartIndustryCon57312.2023.10110736|artificial intelligence;machine learning;computer vision;marine environment;real-time recognition;pollution detection;artificial intelligence;machine learning;computer vision;marine environment;real-time recognition;pollution detection|
|[Blockchain Architecture for Secure Storage of IoT Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110839)|A. Zakharov; I. Zakharova; D. Korenev|10.1109/SmartIndustryCon57312.2023.10110839|security;Internet of Things;Blockchain;Blockchain of blockchains;security;Internet of Things;Blockchain;Blockchain of blockchains|
|[Digital Clones at the Adaptable Control in the Agricultural Biotechnology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110739)|O. A. Ivashchuk; V. I. Fedorov; V. A. Berezhnoy|10.1109/SmartIndustryCon57312.2023.10110739|digital clone;digital phenotyping;3D modeling;artificial neuron nets;agricultural biotechnology;adaptation scenarios;digital clone;digital phenotyping;3D modeling;artificial neuron nets;agricultural biotechnology;adaptation scenarios|
|[Research of Multipath Routing and Load Balancing Processes in Software Defined Networks Based on Bird Migration Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110788)|D. Perepelkin; M. Ivanchikova; T. Nguyen|10.1109/SmartIndustryCon57312.2023.10110788|software defined networks;multipath routing;load balancing;swarm intelligence;bird migration algorithm;software defined networks;multipath routing;load balancing;swarm intelligence;bird migration algorithm|
|[Enhanced User Authentication Algorithm Based on Behavioral Analytics in Web-Based Cyberphysical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110791)|A. Y. Iskhakov; M. V. Mamchenko; S. P. Khripunov|10.1109/SmartIndustryCon57312.2023.10110791|multi-factor authentication;browser fingerprint;outlier detection;anomaly detection;user behavior;standard audit log;web platform;multi-factor authentication;browser fingerprint;outlier detection;anomaly detection;user behavior;standard audit log;web platform|
|[Informational Image of a Person’s Gait According to Mobile Phone Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110718)|N. Dorofeev; A. Grecheneva; R. Sharapov|10.1109/SmartIndustryCon57312.2023.10110718|signals;accelerometer;movement;gait;mobile phone;wearable device;signals;accelerometer;movement;gait;mobile phone;wearable device|
|[Analysis of Theft of Housing and Communal Services Resources Based on Neural Network Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110782)|N. Dorofeev; M. Goryachev|10.1109/SmartIndustryCon57312.2023.10110782|theft of housing and communal services resources;neural network approach;intelligent metering devices;individual consumer model;theft of housing and communal services resources;neural network approach;intelligent metering devices;individual consumer model|
|[Decision Support System for Feasibility Study and Determination the Optimal Way to Increase the Throughput Capacity of Main Oil Pipelines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110838)|R. Tashbulatov; R. Karimov; A. Valeev|10.1109/SmartIndustryCon57312.2023.10110838|main oil pipeline;throughput capacity;feasibility study;hydraulic efficiency;economic effect;payback period;main oil pipeline;throughput capacity;feasibility study;hydraulic efficiency;economic effect;payback period|
|[Vulnerability of Biometric Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110772)|K. Yliana; A. Arina; P. Anastasia|10.1109/SmartIndustryCon57312.2023.10110772|authentication;information security;biometric systems;fingerprint;personal data;information leak;vulnerability;authentication;information security;biometric systems;fingerprint;personal data;information leak;vulnerability|
|[Development of the Concept and Architecture of an Automated System for Updating Physical Knowledge for Information Support of Search Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110764)|A. Bobunov; D. Korobkin; S. Fomenkov|10.1109/SmartIndustryCon57312.2023.10110764|architecture;physical knowledge;information support;search design;architecture;physical knowledge;information support;search design|
|[Development of a System for Remote Condition Monitoring of Industrial Machines and Defect Locating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110778)|A. Valeev; R. Tashbulatov; R. Karimov|10.1109/SmartIndustryCon57312.2023.10110778|diagnostics;condition monitoring;strain gauge sensor;industrial internet of things;automation;defect;diagnostics;condition monitoring;strain gauge sensor;industrial internet of things;automation;defect|
|[Neural Network Method for Solving Fractional Differential Equations α with the Dirichlet Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110785)|N. T. Duc; A. F. Galimyanov; I. Z. Akhmetov|10.1109/SmartIndustryCon57312.2023.10110785|fractional differential equations;Dirichlet’s problem;conformable fractional derivative;artificial neural network;fractional differential equations;Dirichlet’s problem;conformable fractional derivative;artificial neural network|
|[The Computational Schemes of a Noise-Resistant Coding for Autonomous Robotic Complexes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110831)|E. A. Titenko; O. G. Dobroserdov; D. P. Teterin|10.1109/SmartIndustryCon57312.2023.10110831|Galois field;polynomial;degree;tabular conversion;hardware complexity;Galois field;polynomial;degree;tabular conversion;hardware complexity|
|[On the Choice of a Model for Representing Data Flow Parameters in a Digital System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110741)|V. Promyslov; K. Semenkov|10.1109/SmartIndustryCon57312.2023.10110741|model;digital system;network calculus;multidimensional flow;model;digital system;network calculus;multidimensional flow|
|[Using Computer Vision to Analyze the Sequence of Vehicles Passing Through Regulated Intersections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110803)|V. Shepelev; A. Glushkov; A. Vorobyev|10.1109/SmartIndustryCon57312.2023.10110803|traffic light object;neural network;composition of the traffic flow;road network junction;traffic congestion;traffic capacity;traffic light object;neural network;composition of the traffic flow;road network junction;traffic congestion;traffic capacity|
|[Combined Method of Cognitive Assessment of the Specialist Professional Potential](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110755)|I. F. Yasinskiy; T. V. Gvozdeva; V. V. Tyutikov|10.1109/SmartIndustryCon57312.2023.10110755|neural network technology;mathematical modeling;computational hybrids;professional potential assessment;neural network technology;mathematical modeling;computational hybrids;professional potential assessment|
|[Designing a High-Performance Resource Management Module for Real-Time Multiprocessor Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110821)|R. A. Biktashev; A. I. Martyshkin|10.1109/SmartIndustryCon57312.2023.10110821|architecture;operating systems;control algorithms;processes and resources;formalization;finite non-deterministic automation;architecture;operating systems;control algorithms;processes and resources;formalization;finite non-deterministic automation|
|[Artificial Neural Network Method for Solving a Fractional Order Differential Equation with the Cauchy-Type Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110823)|N. T. Duc; A. F. Galimyanov; I. Z. Akhmetov|10.1109/SmartIndustryCon57312.2023.10110823|fractional differential equations;Cauchy problem;fractional integrals and Riemann-Liouville derivatives;neural architecture feedforward;fractional differential equations;Cauchy problem;fractional integrals and Riemann-Liouville derivatives;neural architecture feedforward|
|[The algorithm of Intelligent Control Adjustment of the Mode Map of Hot Blast Stove’s Unit Based on Fuzzy Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110747)|A. Prasolov; S. Andreev; I. Nazarov|10.1109/SmartIndustryCon57312.2023.10110747|hot-blast stove;heat exchange;linguistic variable;fuzzy logic;hot-blast stove;heat exchange;linguistic variable;fuzzy logic|
|[Identification of a Depressive State Among Users of the Vkontakte Social Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110824)|A. A. Zotkina; A. I. Martyshkin|10.1109/SmartIndustryCon57312.2023.10110824|social networks;VKontakte;SVM;NLTK;KNN;depression;social networks;VKontakte;SVM;NLTK;KNN;depression|
|[Information Security Incident Handling Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110721)|A. Iakovleva; M. Zhukova; T. Strekaleva|10.1109/SmartIndustryCon57312.2023.10110721|incident;investigation;cyber fatigue;algorithm;digital evidence;timeline;incident;investigation;cyber fatigue;algorithm;digital evidence;timeline|
|[Development and Use of OPC UA Tools for Data Collection and Monitoring of Technological Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110757)|G. Martinov; A. Al Khoury; A. Issa|10.1109/SmartIndustryCon57312.2023.10110757|CNC;CNC machine tool digital shadow;OPC UA server- client;MDC system;CNC;CNC machine tool digital shadow;OPC UA server- client;MDC system|
|[Multi-Class Classification Using Quantum Kernel Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110752)|M. Mokhles; I. Makarov|10.1109/SmartIndustryCon57312.2023.10110752|quantum computers;machine learning;kernel methods;quantum kernels;quantum machine learning;multi-class classification;quantum computers;machine learning;kernel methods;quantum kernels;quantum machine learning;multi-class classification|
|[Network Stegoinsider Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110748)|A. Salita; A. Krasov|10.1109/SmartIndustryCon57312.2023.10110748|steganography;network;machine learning;insider;steganography;network;machine learning;insider|
|[Evidential Markers of Virtual Reality: Linguistic Markup of the Navigation Route](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110806)|N. V. Khalina; N. N. Pivkina; T. S. Borzhikov|10.1109/SmartIndustryCon57312.2023.10110806|evidentiality;virtual reality;mental immersion;sensory perception;inner language;virtual reality mental scanning language (VRMSL);evidentiality;virtual reality;mental immersion;sensory perception;inner language;virtual reality mental scanning language (VRMSL)|
|[Ontological Framework for Constructing Hybrid Prognoses and Risk Assessment of Critical Conditions of Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110768)|V. Gribova; E. Shalfeeva|10.1109/SmartIndustryCon57312.2023.10110768|risk assessment;forecast;declarative knowledge;framework;explanation generation;decision support system;risk assessment;forecast;declarative knowledge;framework;explanation generation;decision support system|
|[Development and Application of an Application with Augmented Reality Technology for Training Future Aircraft Designers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110795)|N. M. Borgest; S. A. Vlasov; D. S. Glibotsky|10.1109/SmartIndustryCon57312.2023.10110795|application;augmented reality;aircraft construction;3D model;object recognition;application;augmented reality;aircraft construction;3D model;object recognition|
|[The Neural Network Controller for the Dry Low Emission Combustor of Gas-Turbine Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110733)|T. A. Kuznetsova; A. A. Sukharev|10.1109/SmartIndustryCon57312.2023.10110733|neural network;built-in mathematical model;automatic control system;gas turbine power plant;dry low emission combustor;harmful substances emission;neural network;built-in mathematical model;automatic control system;gas turbine power plant;dry low emission combustor;harmful substances emission|
|[Forensic Search for Traces of Unauthorized Access Using the Kerberos Authentication Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110781)|Y. A. Alexeevskaya; Y. V. Molodtsova; R. A. Alexeevsky|10.1109/SmartIndustryCon57312.2023.10110781|windows active directory;Kerberos protocol;authentication server;pass the hash;windows active directory;Kerberos protocol;authentication server;pass the hash|
|[Numerical Modeling of Rough Surfaces of Additive Manufacturing Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110763)|O. V. Zakharov; A. S. Yakovishin; F. D. Suleymanova|10.1109/SmartIndustryCon57312.2023.10110763|surface texture;additive manufacturing products;numerical modeling;height parameters;surface model;surface texture;additive manufacturing products;numerical modeling;height parameters;surface model|
|[Digital Twin of Scott-T Connection Special Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110837)|A. Stulov; A. Tikhonov; A. Karzhevin|10.1109/SmartIndustryCon57312.2023.10110837|digital twin;Scott-T connection;special transformer;simulation model;equivalent circuit;phase converter;digital twin;Scott-T connection;special transformer;simulation model;equivalent circuit;phase converter|
|[Calculating the Maximum Response Time of Protection Systems of Industrial Control System Network to the Impact of a DDoS Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110756)|A. M. Boger; A. N. Sokolov|10.1109/SmartIndustryCon57312.2023.10110756|DDoS attack;industrial control system (ICS);information protection;two-time approximation method;stochastic network topological transformation method;intruder model;DDoS attack;industrial control system (ICS);information protection;two-time approximation method;stochastic network topological transformation method;intruder model|
|[Algorithms for Robotic Intelligent Systems for Predicting Fire Hazardous Situations at an Early Stage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110799)|O. Emelyanova; S. Efimov; S. Jatsun|10.1109/SmartIndustryCon57312.2023.10110799|source of air pollution during ignition;analyzer sensors;carbon monoxide;mobile instrument platform;source of air pollution during ignition;analyzer sensors;carbon monoxide;mobile instrument platform|
|[An Approach to the Production of Prototype Printed Circuit Boards on Bench-Type Machine with the CNC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110810)|G. Martinov; N. Martemianova|10.1109/SmartIndustryCon57312.2023.10110810|printed circuit board;CNC system;end-to-end design;gerber file;part program;DipTrace;EDA/CAD;printed circuit board;CNC system;end-to-end design;gerber file;part program;DipTrace;EDA/CAD|
|[The Parallelization of Computations for Ensuring Information Security in Connected Vehicle Systems Using Q-Effective Programming: The Example of Dijkstra’s Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110745)|M. P. Sokolov; P. A. Manatin; N. D. Zyulyarkina|10.1109/SmartIndustryCon57312.2023.10110745|cybersecurity;connected vehicles;improving parallel computing efficiency;Q-effective program;high-performance computing;Dijkstra’s algorithm;computing on a supercomputer;cybersecurity;connected vehicles;improving parallel computing efficiency;Q-effective program;high-performance computing;Dijkstra’s algorithm;computing on a supercomputer|
|[Nature-Like Structural Node Components for Software and Hardware Complexes on the Basis of Single-Board Computers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110783)|A. Poletikin; N. Mengazetdinov; K. Semenkov|10.1109/SmartIndustryCon57312.2023.10110783|instrumentation and control systems;SBC;veurons;instrumentation and control systems;SBC;veurons|
#### **2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)**
- DOI: 10.1109/VR55154.2023
- DATE: 25-29 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[IEEE VR 2023 Steering Committee Message](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108442)|M. Billinghurst; J. Chen; S. Coquillart; K. Kiyokawa; G. Klinker; A. Lécuyer; B. Mohler; A. Sadagic; J. E. Swan; M. Whitton|10.1109/VR55154.2023.00086|;|
|[IEEE VR 2023 Message from the Program Chairs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108431)|B. Bodenheimer; V. Popescu; J. Quarles; L. Wang|10.1109/VR55154.2023.00006|;|
|[Keynote Speaker: Digital Humans in Virtual Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108408)|B. Guo|10.1109/VR55154.2023.00011|;|
|[Keynote Speaker: Immersive Video Reality—Technology, Standard and Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108477)|W. Gao|10.1109/VR55154.2023.00087|;|
|[Keynote Speaker: Reconstructing Reality: From Physical World to Virtual Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108452)|B. Guo|10.1109/VR55154.2023.00012|;|
|[Creating Virtual AI Beings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108416)|H. Shum|10.1109/VR55154.2023.00013|;|
|[VGTC Virtual Reality Lifetime Achievement Award](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108436)|A. S. Rizzo|10.1109/VR55154.2023.00092|;|
|[VGTC Virtual Reality Service Award](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108484)|J. E. Swan|10.1109/VR55154.2023.00093|;|
|[RemoteTouch: Enhancing Immersive 3D Video Communication with Hand Touch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108425)|Y. Zhang; Z. Li; S. Xu; C. Li; J. Yang; X. Tong; B. Guo|10.1109/VR55154.2023.00016|Human-centered computing-Collaborative and social computing;Computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality;Human-centered computing-Collaborative and social computing;Computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality|
|[iARVis: Mobile AR Based Declarative Information Visualization Authoring, Exploring and Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108433)|J. Chen; C. Li; S. Song; C. Wang|10.1109/VR55154.2023.00017|Visualization systems and tools;Augmented Reality;Visualization systems and tools;Augmented Reality|
|[Exploring 3D Interaction with Gaze Guidance in Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108465)|Y. Bao; J. Wang; Z. Wang; F. Lu|10.1109/VR55154.2023.00018|Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed/augmented reality;Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed/augmented reality|
|[Investigating Spatial Representation of Learning Content in Virtual Reality Learning Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108413)|M. Belani; H. V. Singh; A. Parnami; P. Singh|10.1109/VR55154.2023.00019|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Human-centered computing-Interaction design-Empirical studies in interaction design;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Human-centered computing-Interaction design-Empirical studies in interaction design|
|[Delta Path Tracing for Real-Time Global Illumination in Mixed Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108429)|Y. Xu; Y. Jiang; S. Wang; K. Li; G. Geng|10.1109/VR55154.2023.00020|Mixed / augmented reality;Ray tracing;Mixed / augmented reality;Ray tracing|
|[Redirected Walking Based on Historical User Walking Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108411)|C. -W. Fan; S. -Z. Xu; P. Yu; F. -L. Zhang; S. -H. Zhang|10.1109/VR55154.2023.00021|Computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality;Computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality|
|[Level-of-Detail AR: Dynamically Adjusting Augmented Reality Level of Detail Based on Visual Angle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108438)|A. Wysopal; V. Ross; J. Passananti; K. Yu; B. Huynh; T. Höllerer|10.1109/VR55154.2023.00022|Level of Detail;Augmented Reality;Automation;User Study;Task Performance;User Satisfaction;Level of Detail;Augmented Reality;Automation;User Study;Task Performance;User Satisfaction|
|[Volumetric Avatar Reconstruction with Spatio-Temporally Offset RGBD Cameras](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108476)|G. Rendle; A. Kreskowski; B. Froehlich|10.1109/VR55154.2023.00023|H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems—Artificial, augmented, and virtual realities;H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems—Artificial, augmented, and virtual realities|
|[I'm Transforming! Effects of Visual Transitions to Change of Avatar on the Sense of Embodiment in AR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108469)|R. Otono; A. Genay; M. Perusquía-Hernández; N. Isoyama; H. Uchiyama; M. Hachet; A. Lécuyer; K. Kiyokawa|10.1109/VR55154.2023.00024|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed/augmented reality;Human-centered computing-Human computer interaction (HCI)-Empirical studies in HCI;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed/augmented reality;Human-centered computing-Human computer interaction (HCI)-Empirical studies in HCI|
|[An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108449)|C. A. T. Cortes; C. -T. Lin; T. -T. N. Do; H. -T. Chen|10.1109/VR55154.2023.00025|VR Sickness, Postural Instability, Virtual Reality, Translational Gain, EEG;VR Sickness, Postural Instability, Virtual Reality, Translational Gain, EEG|
|[Real-Time Recognition of In-Place Body Actions and Head Gestures using Only a Head-Mounted Display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108456)|J. Zhao; M. Shao; Y. Wang; R. Xu|10.1109/VR55154.2023.00026|Body Action Recognition;Head Gesture Recognition;Virtual Locomotion;Body Action Recognition;Head Gesture Recognition;Virtual Locomotion|
|[How Do I Get There? Overcoming Reachability Limitations of Constrained Industrial Environments in Augmented Reality Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108446)|D. Bambušek; Z. Materna; M. Kapinus; V. Beran; P. Smrž|10.1109/VR55154.2023.00027|Augmented Reality;Virtual Reality;Transitional Interface;Human Robot Interaction;Constrained Industrial Environmnets;Augmented Reality;Virtual Reality;Transitional Interface;Human Robot Interaction;Constrained Industrial Environmnets|
|[Tell Me Where To Go: Voice-Controlled Hands-Free Locomotion for Virtual Reality Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108085)|J. Hombeck; H. Voigt; T. Heggemann; R. R. Datta; K. Lawonn|10.1109/VR55154.2023.00028|Human-centered computing-Human computer interaction (HCI);Computing methodologies-Artificial intelligence-Natural language processing Speech recognition;Human-centered computing-Human computer interaction (HCI);Computing methodologies-Artificial intelligence-Natural language processing Speech recognition|
|[CompenHR: Efficient Full Compensation for High-resolution Projector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108448)|Y. Wang; H. Ling; B. Huang|10.1109/VR55154.2023.00029|Projector compensation, Spatial augmented reality, Projector-camera system;Projector compensation, Spatial augmented reality, Projector-camera system|
|[Remapping Control in VR for Patients with AMD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108458)|M. Nitsche; B. Bosley; S. Primo; J. Park; D. Carr|10.1109/VR55154.2023.00030|AMD;VR;Image Remapping;medical application;H.5.2 [Information Systems]: Information Interfaces and Presentation - User Interfaces;J.3 [Computer Applications]: Life and Medical Sciences;AMD;VR;Image Remapping;medical application;H.5.2 [Information Systems]: Information Interfaces and Presentation - User Interfaces;J.3 [Computer Applications]: Life and Medical Sciences|
|[Comparing Visual Attention with Leading and Following Virtual Agents in a Collaborative Perception-Action Task in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108414)|S. -K. Wong; M. Volonte; K. -Y. Liu; E. Ebrahimi; S. V. Babu|10.1109/VR55154.2023.00031|Virtual Agents;Virtual Reality;Behavior Modeling;Human-Computer Interaction;Animation;H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems-Animations;Evaluation/methodology;I.3.3 [Computer Graphics]: Three-Dimensional Graphics and Realism-Virtual reality;;Virtual Agents;Virtual Reality;Behavior Modeling;Human-Computer Interaction;Animation;H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems-Animations;Evaluation/methodology;I.3.3 [Computer Graphics]: Three-Dimensional Graphics and Realism-Virtual reality;|
|[Style-aware Augmented Virtuality Embeddings (SAVE)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108488)|J. Hoster; D. Ritter; K. Hildebrand|10.1109/VR55154.2023.00032|Mixed reality-Virtual reality-Reconstruction;Mixed reality-Virtual reality-Reconstruction|
|[CaV3: Cache-assisted Viewport Adaptive Volumetric Video Streaming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108421)|J. Liu; B. Zhu; F. Wang; Y. Jin; W. Zhang; Z. Xu; S. Cui|10.1109/VR55154.2023.00033|;|
|[Evoking empathy with visually impaired people through an augmented reality embodiment experience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108470)|R. Guarese; E. Pretty; H. Fayek; F. Zambetta; R. van Schyndel|10.1109/VR55154.2023.00034|Human-centered computing-Accessibility-Accessibility technologies;Human-centered computing-Accessibility-Empirical studies in accessibility;Human-centered computing-Human computer interaction (HCI)-Interaction techniques-Auditory feedback;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality;Human-centered computing-Accessibility-Accessibility technologies;Human-centered computing-Accessibility-Empirical studies in accessibility;Human-centered computing-Human computer interaction (HCI)-Interaction techniques-Auditory feedback;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality|
|[Investigating Noticeable Hand Redirection in Virtual Reality using Physiological and Interaction Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108468)|M. Feick; K. P. Regitz; A. Tang; T. Jungbluth; M. Rekrut; A. Krüger|10.1109/VR55154.2023.00035|Virtual Reality;Hand Redirection;Physiological Data;Detection Thresholds;Virtual Reality;Hand Redirection;Physiological Data;Detection Thresholds|
|[Power, Performance, and Image Quality Tradeoffs in Foveated Rendering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108084)|R. Singh; M. Huzaifa; J. Liu; A. Patney; H. Sharif; Y. Zhao; S. Adve|10.1109/VR55154.2023.00036|Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods;Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods|
|[Wind comfort and emotion can be changed by the cross-modal presentation of audio-visual stimuli of indoor and outdoor environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108417)|K. Ito; J. Hosoi; Y. Ban; T. Kikuchi; K. Nakagawa; H. Kitagawa; C. Murakami; Y. Imai; S. Warisawa|10.1109/VR55154.2023.00037|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality|
|[Lightweight Scene-aware Rain Sound Simulation for Interactive Virtual Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108474)|H. Cheng; S. Liu; J. Zhang|10.1109/VR55154.2023.00038|Human-centered computing;Human computer interaction;Interaction techniques;Auditory feedback;Applied computing;Arts and humanities;Sound and music computing;Human-centered computing;Human computer interaction;Interaction techniques;Auditory feedback;Applied computing;Arts and humanities;Sound and music computing|
|[A Compact Photochromic Occlusion Capable See-through Display with Holographic Lenses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108491)|C. -W. Ooi; Y. Hiroi; Y. Itoh|10.1109/VR55154.2023.00039|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality;Human-centered computing-Communication hardware;interfaces and storage-Displays and imagers;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality;Human-centered computing-Communication hardware;interfaces and storage-Displays and imagers|
|[Continuous VR Weight Illusion by Combining Adaptive Trigger Resistance and Control-Display Ratio Manipulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108444)|C. Stellmacher; A. Zenner; O. J. A. Nunez; E. Kruijff; J. Schöning|10.1109/VR55154.2023.00040|virtual reality;adaptive trigger resistance;virtual weight;control display ratio;psychophysical experiment;virtual reality;adaptive trigger resistance;virtual weight;control display ratio;psychophysical experiment|
|[Simultaneous Scene-independent Camera Localization and Category-level Object Pose Estimation via Multi-level Feature Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108437)|J. Wang; Y. Qi|10.1109/VR55154.2023.00041|Scene-independent camera localization;Cagetory-level object pose estimation;Feature fusion;Multi-task learning;Geometry constraint;Scene-independent camera localization;Cagetory-level object pose estimation;Feature fusion;Multi-task learning;Geometry constraint|
|[Design and Development of a Mixed Reality Acupuncture Training System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108424)|Q. Sun; J. Huang; H. Zhang; P. Craig; L. Yu; E. G. Lim|10.1109/VR55154.2023.00042|Mixed reality—immersive technologies—virtual environments—;Chinese acupuncture—Medical education;Mixed reality—immersive technologies—virtual environments—;Chinese acupuncture—Medical education|
|[Providing 3D Guidance and Improving the Music-Listening Experience in Virtual Reality Shooting Games Using Musical Vibrotactile Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108419)|Y. Yamazaki; S. Hasegawa|10.1109/VR55154.2023.00043|Human-centered computing-Haptic devices;Human-centered computing-Virtual reality;Human-centered computing-Sound-based input / output;Human-centered computing-Haptic devices;Human-centered computing-Virtual reality;Human-centered computing-Sound-based input / output|
|[Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108435)|H. Yun; J. L. Ponton; C. Andujar; N. Pelechano|10.1109/VR55154.2023.00044|Virtual reality;Motion capture;Inverse kinematics;Embodiment;Avatar Animation;Virtual reality;Motion capture;Inverse kinematics;Embodiment;Avatar Animation|
|[CoboDeck: A Large-Scale Haptic VR System Using a Collaborative Mobile Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108410)|S. Mortezapoor; K. Vasylevska; E. Vonach; H. Kaufmann|10.1109/VR55154.2023.00045|Computer systems organization—Embedded and cyberphysical systems—Robotics;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Interaction devices—Haptic devices;;Computer systems organization—Embedded and cyberphysical systems—Robotics;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Interaction devices—Haptic devices;|
|[Empirically Evaluating the Effects of Eye Height and Self-Avatars on Dynamic Passability Affordances in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108439)|A. Bhargava; R. Venkatakrishnan; R. Venkatakrishnan; H. Solini; K. Lucaites; A. C. Robb; C. C. Pagano; S. V. Babu|10.1109/VR55154.2023.00046|Human-centered-computing;Empirical-studies-in-HCI;Human-centered-computing;Empirical-studies-in-HCI|
|[AR-MoCap: Using Augmented Reality to Support Motion Capture Acting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108475)|A. Cannavò; F. G. Pratticò; A. Bruno; F. Lamberti|10.1109/VR55154.2023.00047|Collaborative virtual production;acting rehearsal and performance;motion capture;body ownership;virtual characters;visual effects;augmented reality;Human-centered computing—Human computer interaction (HCI)—Mixed / augmented reality—;Human-centered computing—Human computer interaction (HCI)—Collaborative interaction—;Computing methodologies—Computer vision—;Image and video acquisition—Motion capture;Human-centered computing—Applied computing-Arts and humanities—Performing arts;Human-centered computing—Applied computing-Arts and humanities—Media arts;Collaborative virtual production;acting rehearsal and performance;motion capture;body ownership;virtual characters;visual effects;augmented reality;Human-centered computing—Human computer interaction (HCI)—Mixed / augmented reality—;Human-centered computing—Human computer interaction (HCI)—Collaborative interaction—;Computing methodologies—Computer vision—;Image and video acquisition—Motion capture;Human-centered computing—Applied computing-Arts and humanities—Performing arts;Human-centered computing—Applied computing-Arts and humanities—Media arts|
|[Exploring Neural Biomarkers in Young Adults Resistant to VR Motion Sickness: A Pilot Study of EEG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108472)|G. Li; K. Pohlmann; M. McGill; C. P. Chen; S. Brewster; F. Pollick|10.1109/VR55154.2023.00048|VR motion sickness;brain state problem;EEG;resistance to VR motion sickness;Neuro-ergonomics;vestibular system;VR motion sickness;cybersickness;EEG;VR motion sickness;brain state problem;EEG;resistance to VR motion sickness;Neuro-ergonomics;vestibular system;VR motion sickness;cybersickness;EEG|
|[Optimizing Product Placement for Virtual Stores](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108482)|W. Liang; L. Wang; X. Yu; C. Li; R. Alghofaili; Y. Lang; L. -F. Yu|10.1109/VR55154.2023.00049|Human-centered computing-Human computer interaction (HCI)-;-Computing methodologies-Virtual reality;Human-centered computing-Human computer interaction (HCI)-;-Computing methodologies-Virtual reality|
|[Fully Automatic Blendshape Generation for Stylized Characters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108478)|J. Wang; Y. Qiu; K. Chen; Y. Ding; Y. Pan|10.1109/VR55154.2023.00050|Avatars;Blendshapes;facial animation;stylized char-acters;Human-centered computing-Visualization-Visu-alization techniques-Treemaps;Human-centered computing-Visualization-Visualization design and evaluation methods;Avatars;Blendshapes;facial animation;stylized char-acters;Human-centered computing-Visualization-Visu-alization techniques-Treemaps;Human-centered computing-Visualization-Visualization design and evaluation methods|
|[Where to Render: Studying Renderability for IBR of Large-Scale Scenes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108479)|Z. Yi; K. Xie; J. Lyu; M. Gong; H. Huang|10.1109/VR55154.2023.00051|Computer graphics techniques-Image-based rendering-Scene rendering;Evaluation methods-Renderability-View selection-Rendering path planning;Computer graphics techniques-Image-based rendering-Scene rendering;Evaluation methods-Renderability-View selection-Rendering path planning|
|[WARPY: Sketching Environment-Aware 3D Curves in Mobile Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108496)|R. Alghofaili; C. Nguyen; V. Krs; N. Carr; R. Mĕch; L. -F. Yu|10.1109/VR55154.2023.00052|Computing methodologies-Computer graphics-Graphics systems and interfaces-Mixed / augmented reality;Computing methodologies-Computer graphics-Graphics systems and interfaces-Mixed / augmented reality|
|[Enhancing the Reading Experience on AR HMDs by Using Smartphones as Assistive Displays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108467)|S. Bang; W. Woo|10.1109/VR55154.2023.00053|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented re-ality;Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented re-ality;Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User studies|
|[Towards an Understanding of Distributed Asymmetric Collaborative Visualization on Problem-solving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108427)|W. Tong; M. Xia; K. K. Wong; D. A. Bowman; T. -C. Pong; H. Qu; Y. Yang|10.1109/VR55154.2023.00054|asymmetric collaborative visualization;virtual reality;data visualization;problem solving;asymmetric collaborative visualization;virtual reality;data visualization;problem solving|
|[Manipulation of Motion Parallax Gain Distorts Perceived Distance and Object Depth in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108494)|X. Teng; R. S. Allison; L. M. Wilcox|10.1109/VR55154.2023.00055|Depth Perception;Egocentric Distance;Motion Gain;Motion Parallax;Depth Perception;Egocentric Distance;Motion Gain;Motion Parallax|
|[A Large-Scale Study of Proxemics and Gaze in Groups](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108412)|M. R. Miller; C. DeVeaux; E. Han; N. Ram; J. N. Bailenson|10.1109/VR55154.2023.00056|Human-centered computing-Human computer interaction (HCI-Interaction paradigms-Virtual reality;Human-centered computing-Collaborative and social computing-Empirical studies in collaborative and social computing;Human-centered computing-Human computer interaction (HCI-Interaction paradigms-Virtual reality;Human-centered computing-Collaborative and social computing-Empirical studies in collaborative and social computing|
|[Realistic Defocus Blur for Multiplane Computer-Generated Holography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108460)|K. Kavaklı; Y. Itoh; H. Urey; K. Akşit|10.1109/VR55154.2023.00057|Hardware—Emerging Technologies—Emerging optical and photonic technology;Hardware—Communication hardware, interfaces and storage—Display and imagers;Hardware—Emerging Technologies—Emerging optical and photonic technology;Hardware—Communication hardware, interfaces and storage—Display and imagers|
|[Examining the Fine Motor Control Ability of Linear Hand Movement in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108481)|X. Yi; X. Wang; J. Li; H. Li|10.1109/VR55154.2023.00058|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User models;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality Human-centered computing-Human computer interaction (HCI)-HCI design and evaluation methods-User models|
|[MAGIC: Manipulating Avatars and Gestures to Improve Remote Collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108489)|C. G. Fidalgo; M. Sousa; D. Mendes; R. K. Dos Anjos; D. Medeiros; K. Singh; J. Jorge|10.1109/VR55154.2023.00059|Human-centered computing-Collaborative and social computing-Collaborative and social computing theory;con-cepts and paradigms-Computer supported cooperative work;Human-centered computing-Collaborative and social computing-Collaborative and social computing theory;con-cepts and paradigms-Computer supported cooperative work|
|[Extended Depth-of-Field Projector using Learned Diffractive Optics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108407)|Y. Li; Q. Fu; W. Heidrich|10.1109/VR55154.2023.00060|Computing methodologies-Computer graphics-Image manipulation-Image processing;Computing methodologies-Computer graphics-Image manipulation-Image processing|
|[Toward Intuitive Acquisition of Occluded VR Objects Through an Interactive Disocclusion Mini-map](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108428)|M. Maslych; Y. Hmaiti; R. Ghamandi; P. Leber; R. K. Kattoju; J. Belga; J. J. LaViola|10.1109/VR55154.2023.00061|visualization;object selection;virtual reality;occlusion;mini map;head mounted displays;user studies;visualization;object selection;virtual reality;occlusion;mini map;head mounted displays;user studies|
|[Locomotion-aware Foveated Rendering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108455)|X. Shi; L. Wang; J. Wu; W. Ke; C. -T. Lam|10.1109/VR55154.2023.00062|Virtual Reality;Foveated Rendering;Gaze-contingent Rendering;Perception;Virtual Reality;Foveated Rendering;Gaze-contingent Rendering;Perception|
|[Measuring the Effect of Stereo Deficiencies on Peripersonal Space Pointing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108466)|A. U. Batmaz; M. H. Mughribi; M. Sarac; M. B. Machuca; W. Stuerzlinger|10.1109/VR55154.2023.00063|Human-centered computing-Human Computer Interaction (HCI);Human-centered computing-Virtual Reality;Human-centered computing-Pointing;Human-centered computing-Human Computer Interaction (HCI);Human-centered computing-Virtual Reality;Human-centered computing-Pointing|
|[Cross-View Visual Geo-Localization for Outdoor Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108434)|N. C. Mithun; K. S. Minhas; H. -P. Chiu; T. Oskiper; M. Sizintsev; S. Samarasekera; R. Kumar|10.1109/VR55154.2023.00064|Cross View Visual Geo Localization;Ground Aerial Matching;Outdoor Augmented Reality;Transformer Neural Network;Visual Inertial Navigation;Cross View Visual Geo Localization;Ground Aerial Matching;Outdoor Augmented Reality;Transformer Neural Network;Visual Inertial Navigation|
|[Virtual Optical Bench: Teaching Spherical Lens Layout in VR with Real-Time Ray Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108451)|M. Bellgardt; S. Pape; D. Gilbert; M. Prochnau; G. König; T. W. Kuhlen|10.1109/VR55154.2023.00065|Applied computing—Education—Interactive learning environments;Applied computing—Education—Interactive learning environments|
|[SCP-SLAM: Accelerating DynaSLAM With Static Confidence Propagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108443)|M. -F. Yu; L. Zhang; W. -F. Wang; J. -H. Wang|10.1109/VR55154.2023.00066|Human-centered computing;Visualization;Visualization techniques;Human-centered computing;Visualization;Visualization techniques|
|[Exploring the Effects of Augmented Reality Notification Type and Placement in AR HMD while Walking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108490)|H. Lee; W. Woo|10.1109/VR55154.2023.00067|Human-centered computing-Human computer in-teraction (HCI)-HCI design and evaluation methods-User studies;Human-centered computing-Ubiquitous and mobile computing-Ubiquitous and mobile computing design and evaluation methods;Human-centered computing-Human computer in-teraction (HCI)-HCI design and evaluation methods-User studies;Human-centered computing-Ubiquitous and mobile computing-Ubiquitous and mobile computing design and evaluation methods|
|[AR Interfaces for Disocclusion—A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108461)|S. Liao; Y. Zhou; V. Popescu|10.1109/VR55154.2023.00068|Computing methodologies—Computer graphics—Graphics systems and interfaces—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality;Computing methodologies—Computer graphics—Graphics systems and interfaces—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality|
|[A study of the influence of AR on the perception, comprehension and projection levels of situation awareness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108457)|C. Truong-Allié; M. Herbeth; A. Paljic|10.1109/VR55154.2023.00069|Augmented Reality;Situation Awareness;Head Mounted Display;Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods;Augmented Reality;Situation Awareness;Head Mounted Display;Human-centered computing—Visualization—Visualization techniques—Treemaps;Human-centered computing—Visualization—Visualization design and evaluation methods|
|[Persuasive Vibrations: Effects of Speech-Based Vibrations on Persuasion, Leadership, and Co-Presence During Verbal Communication in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108422)|J. Saint-Aubert; F. Argelaguet; M. Macé; C. Pacchierotti; A. Amedi; A. Lécuyer|10.1109/VR55154.2023.00070|Audio;Haptic;Vibrotactile feedback;Speech;Co-Presence;Leadership;Persuasion;Audio;Haptic;Vibrotactile feedback;Speech;Co-Presence;Leadership;Persuasion|
|[You Make Me Sick! The Effect of Stairs on Presence, Cybersickness, and Perception of Embodied Conversational Agents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108409)|S. Ang; A. Fernandez; M. Rushforth; J. Quarles|10.1109/VR55154.2023.00071|Human-centered computing—Human computer interaction (HCI)—Empirical studies in HCI;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality;Human-centered computing—Human computer interaction (HCI)—Empirical studies in HCI;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality|
|[Exploring the Social Influence of Virtual Humans Unintentionally Conveying Conflicting Emotions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108086)|Z. Choudhary; N. Norouzi; A. Erickson; R. Schubert; G. Bruder; G. F. Welch|10.1109/VR55154.2023.00072|Human-centered computing—Human computer interaction (HCI)—HCI design and evaluation methods-User studies;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms-Virtual reality;Human-centered computing—Human computer interaction (HCI)—HCI design and evaluation methods-User studies;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms-Virtual reality|
|[HoloBeam: Paper-Thin Near-Eye Displays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108432)|K. Akşit; Y. Itoh|10.1109/VR55154.2023.00073|Holographic Displays;Computer Generated Holography;Computational Displays;Augmented Reality;Virtual Reality;Near Eye Displays;AR glasses;VR Headsets;Optics;Learned Methods;Optimization;Machine Learning;Deep Learning;Holographic Displays;Computer Generated Holography;Computational Displays;Augmented Reality;Virtual Reality;Near Eye Displays;AR glasses;VR Headsets;Optics;Learned Methods;Optimization;Machine Learning;Deep Learning|
|[A Haptic Stimulation-Based Training Method to Improve the Quality of Motor Imagery EEG Signal in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108447)|S. Cheng; J. Tian|10.1109/VR55154.2023.00074|Brain computer interface;Virtual reality;Haptic stimulation;Brain computer interface;Virtual reality;Haptic stimulation|
|[Proposal for an aerial display using dynamic projection mapping on a distant flying screen](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108426)|M. Iuchi; Y. Hirohashi; H. Oku|10.1109/VR55154.2023.00075|Computing methodologies-Computer graphics-Graphics systems and interfaces-Mixed / augmented reality;Hardware-Communication hardware;interfaces and storage-Displays and imagers;Computing methodologies-Computer graphics-Graphics systems and interfaces-Mixed / augmented reality;Hardware-Communication hardware;interfaces and storage-Displays and imagers|
|[LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108450)|R. K. Kundu; R. Islam; J. Quarles; K. A. Hoque|10.1109/VR55154.2023.00076|Virtual Reality;Cybersickness Detection;Explainable Artificial Intelligence;Deep Learning;Model Reduction;Virtual Reality;Cybersickness Detection;Explainable Artificial Intelligence;Deep Learning;Model Reduction|
|[Exploring Enhancements towards Gaze Oriented Parallel Views in Immersive Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108459)|T. Teo; K. Sakurada; M. Sugimoto|10.1109/VR55154.2023.00077|Human-centered computing—Visualization;Human-centered computing—Virtual reality;Human-centered computing—Visualization;Human-centered computing—Virtual reality|
|[Investigating Guardian Awareness Techniques to Promote Safety in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108418)|S. Wu; J. Li; M. Sousa; T. Grossman|10.1109/VR55154.2023.00078|Human-centered computing-Visualization-Visualization techniques-Treemaps;Human-centered computing-;Visualization-Visualization design and evaluation methods;Human-centered computing-Visualization-Visualization techniques-Treemaps;Human-centered computing-;Visualization-Visualization design and evaluation methods|
|[Virtual Reality in Supporting Charitable Giving: The Role of Vicarious Experience, Existential Guilt, and Need for Stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108495)|O. Li; H. Qiu|10.1109/VR55154.2023.00079|Virtual reality;charitable giving;vicarious experience;guilt;need for stimulation;J.4 [Social and Behavioral Sciences];K.4.4 [Computers and Society]: Electronic Commerce;Virtual reality;charitable giving;vicarious experience;guilt;need for stimulation;J.4 [Social and Behavioral Sciences];K.4.4 [Computers and Society]: Electronic Commerce|
|[Scaling VR Video Conferencing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108420)|M. Dasari; E. Lu; M. W. Farb; N. Pereira; I. Liang; A. Rowe|10.1109/VR55154.2023.00080|VR.;Video.;Conferencing.;VR.;Video.;Conferencing.|
|[Like a Rolling Stone: Effects of Space Deformation During Linear Acceleration on Slope Perception and Cybersickness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108083)|T. Nie; I. B. Adhanom; E. S. Rosenberg|10.1109/VR55154.2023.00081|Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Com-puting methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality;;Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality;Com-puting methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality;|
|[Comparing Scatterplot Variants for Temporal Trends Visualization in Immersive Virtual Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108445)|C. Quijano-Chavez; L. Nedel; C. M. D. S. Freitas|10.1109/VR55154.2023.00082|Evaluation;graphical perception;immersive analytics;trend visualization;virtual reality;Evaluation;graphical perception;immersive analytics;trend visualization;virtual reality|
|[Designing Viewpoint Transition Techniques in Multiscale Virtual Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108464)|J. -I. Lee; P. Asente; W. Stuerzlinger|10.1109/VR55154.2023.00083|Human-centered computing;Human computer interaction (HCI);Interaction techniques;Human-centered computing;Human computer interaction (HCI);Interaction techniques|
|[MoPeDT: A Modular Head-Mounted Display Toolkit to Conduct Peripheral Vision Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108453)|M. Albrecht; L. Assländer; H. Reiterer; S. Streuber|10.1109/VR55154.2023.00084|Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—Interactive systems and tools—User interface toolkits;Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Mixed / augmented reality;Human-centered computing—Human computer interaction (HCI)—Interactive systems and tools—User interface toolkits|
#### **2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT)**
- DOI: 10.1109/DICCT56244.2023
- DATE: 17-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Comparative Study of Various Breath Assist Devices and the Effect of Sleep Apnea (SA) Among the Indian Population](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110129)|D. J; P. Kokil; S. S; T. Jayanthi|10.1109/DICCT56244.2023.10110129|sleep-related disorder (SRD);Obstructive Sleep Apnea (OSA);Continuous Positive Airway Pressure (CPAP);Epworth Sleepiness Scale (ESS);Berlin Questionnaire;sleep-related disorder (SRD);Obstructive Sleep Apnea (OSA);Continuous Positive Airway Pressure (CPAP);Epworth Sleepiness Scale (ESS);Berlin Questionnaire|
|[Internet of Things (IoT) Based Cost Effective Weather Monitoring Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110037)|S. Chakraborty; T. Joshi; S. Agarwal|10.1109/DICCT56244.2023.10110037|Weather Station;IoT;Embedded Computing System;Arduino Software [1];ESP8266;Smart Environment;Weather Station;IoT;Embedded Computing System;Arduino Software [1];ESP8266;Smart Environment|
|[Machine Learning Based Techniques for Node Localization in WSN: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110235)|P. Yadav; K. Kumar; S. C. Sharma|10.1109/DICCT56244.2023.10110235|WSN;Localization;Machine Learning;Range-Based;Range-Free;WSN;Localization;Machine Learning;Range-Based;Range-Free|
|[Impact of Ge Mole Fraction on the Performance of Si1-xGex/InAs Charged Plasma-Based JLTFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110175)|K. Kumar; P. Yadav; S. C. Sharma|10.1109/DICCT56244.2023.10110175|Junctionless TFET;Charge plasma;Band gap engineering;Mole fraction;Band to band tunneling;Hetero junction;Junctionless TFET;Charge plasma;Band gap engineering;Mole fraction;Band to band tunneling;Hetero junction|
|[Impact of Parasitic Resistance on Modelling and Performance of Solar Photovoltaic Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110047)|T. A. Chandel; M. Y. Yasin; M. A. Mallick|10.1109/DICCT56244.2023.10110047|Conversion Efficiency;Parasitic Resistance;Power Degradation;Solar Energy;SPV Module;Conversion Efficiency;Parasitic Resistance;Power Degradation;Solar Energy;SPV Module|
|[FPGA Implementation of Chaotic Oscillators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110161)|A. Jauhari; M. Varshney; N. Alam|10.1109/DICCT56244.2023.10110161|FPGA;Chaotic System;Chaotic Oscillator;Pseudo Random Number Generator;FPGA;Chaotic System;Chaotic Oscillator;Pseudo Random Number Generator|
|[4-trit CNFET-based Arithmetic Logic Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110209)|A. Kumar; S. K. Gupta; P. Kota|10.1109/DICCT56244.2023.10110209|Ternary;CNFET;ALU;Nanoelectronics;Adder;Ternary;CNFET;ALU;Nanoelectronics;Adder|
|[10-bit SAR ADC for Biomedical Sensor Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110217)|S. Mehra; S. K. Gupta; P. Kota|10.1109/DICCT56244.2023.10110217|Sample and hold;Comparator;Digital-to-analog;SAR Logic;Sample and hold;Comparator;Digital-to-analog;SAR Logic|
|[In-Memory Half Adder Computation using CNTFET Transistors in 8T SRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110189)|N. Ahmad; A. Kumar; S. Kumar Gupta; P. Kota|10.1109/DICCT56244.2023.10110189|In-memory computation;Half adder;NAND/NOR logic;VS-CNTFET;In-memory computation;Half adder;NAND/NOR logic;VS-CNTFET|
|[Absorber Layer Doping Concentration Optimization of Lead-Free Cs2CuSbCl6 Perovskite Solar Cells for Improved Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110299)|A. Vaid; V. Kumar; A. Kumar|10.1109/DICCT56244.2023.10110299|Current Density;Efficiency;Fill-Factor;Quantum Efficiency;Perovskite;SCAPS-1D;Current Density;Efficiency;Fill-Factor;Quantum Efficiency;Perovskite;SCAPS-1D|
|[A Paradigm Shift towards Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110300)|C. N; J. Jha; A. Sayal; V. Gupta; A. Gupta|10.1109/DICCT56244.2023.10110300|Artificial Intelligence;Computer vision;automation;Artificial Intelligence;Computer vision;automation|
|[Performance evaluation of FiWi based OCDMA system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110289)|M. Kumari|10.1109/DICCT56244.2023.10110289|Fiber wireless (FiWi);free space optics (FSO);radio frequency (RF);modified new zero cross correlation (MNZCC);optical code division multiple access (OCDMA);Fiber wireless (FiWi);free space optics (FSO);radio frequency (RF);modified new zero cross correlation (MNZCC);optical code division multiple access (OCDMA)|
|[Unmanned Aerial Vehicles (UAVs): Evaluation of OLSR, DSDV, AODV, and DSR Dynamic Routing Protocols](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110279)|V. Gupta; D. Seth|10.1109/DICCT56244.2023.10110279|UAV;OLSR;AODV;DSDV;DSR;NS-3;UAV;OLSR;AODV;DSDV;DSR;NS-3|
|[The Enhancement in Road Safety using Different Image Detection and Recognition Techniques: - A State of Art](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110257)|M. Agarwal; D. Seth|10.1109/DICCT56244.2023.10110257|Intelligent Transport system;CNN;Object detection model;Intelligent Transport system;CNN;Object detection model|
|[Design of Frequency Domain Metric for Evaluation of Mode Decomposition Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110130)|G. Navalyal; A. Navalyal; S. Naik|10.1109/DICCT56244.2023.10110130|Mode decomposition;evaluation metric;signals;frequency domain;Mode decomposition;evaluation metric;signals;frequency domain|
|[High Gain, Compact Design Integrated With FSS As Superstrate In Patch Antenna For Sub6 Ghz 5G Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110241)|S. Ara; P. Kumari Nunna; Labiba|10.1109/DICCT56244.2023.10110241|FSS;Patch antenna,5G application, HFSS;FSS;Patch antenna,5G application, HFSS|
|[Applications of IoT in Retail: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110252)|O. Gupta; P. Joshi|10.1109/DICCT56244.2023.10110252|IoT;Retail sector;Bibliometric Analysis;IoT;Retail sector;Bibliometric Analysis|
|[Monitoring flash flood using Optical and Microwave Sensor dataset:- A case study of Cachar district, Assam (India)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110185)|A. Mehta; K. S. Rawat|10.1109/DICCT56244.2023.10110185|Flood;SAR;SNAP;Natural Disaster;Flood;SAR;SNAP;Natural Disaster|
|[Design and Investigation of Stacked Nanosheet Transistor Parameters for Analog Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110055)|V. K. Kakar; P. Kumar Pal|10.1109/DICCT56244.2023.10110055|nanosheet (NS);analog performance;RF application;stacked nanosheet;short channel effects;nanosheet field effect transistor.;nanosheet (NS);analog performance;RF application;stacked nanosheet;short channel effects;nanosheet field effect transistor.|
|[Effect of Wfin, Hfin and Lg on the performance of 14-nm FinFET for analog and RF applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110111)|P. Vijaya; R. Lorenzo|10.1109/DICCT56244.2023.10110111|FinFET;SCE;downscaling of gate-length;high dielectric gate oxide;analog RF parameters;SOI;TCAD simulation;FinFET;SCE;downscaling of gate-length;high dielectric gate oxide;analog RF parameters;SOI;TCAD simulation|
|[Analysis of Heart Disease Prediction using Various Machine Learning Techniques: A Review Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110139)|K. Joshi; G. A. Reddy; S. Kumar; H. Anandaram; A. Gupta; H. Gupta|10.1109/DICCT56244.2023.10110139|K- nearest neighbor;Decision tree;Coronary illness;Machine Learning;Random Forest;K- nearest neighbor;Decision tree;Coronary illness;Machine Learning;Random Forest|
|[Recent Advancements in Structural Health Monitoring using Optical Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110052)|S. Agarwal; G. Srivastava|10.1109/DICCT56244.2023.10110052|Advanced materials;optical sensors;fiber optics;industrial structure;Advanced materials;optical sensors;fiber optics;industrial structure|
|[Comparative Analysis of Static and Mobile Anchors in Sensor Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110135)|V. R. Kulkarni|10.1109/DICCT56244.2023.10110135|Wireless Sensor Networks;Localization;Beacons;Mobile anchor;Wireless Sensor Networks;Localization;Beacons;Mobile anchor|
|[Review and Performance Analysis of Inductorless Charge Pumps for Low-Power Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110203)|V. Pawar|10.1109/DICCT56244.2023.10110203|Charge Pumps;Switched-Capacitor Convertors;DC-DC Converters;Voltage Doublers;Boost Charge Pumps;Charge Pumps;Switched-Capacitor Convertors;DC-DC Converters;Voltage Doublers;Boost Charge Pumps|
|[Implementation of Digital Applications Using Efficient CML based designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110295)|B. Choudhary|10.1109/DICCT56244.2023.10110295|CML;Quad Cell;MTT Cell;4-Bit RCA and LFSR;CML;Quad Cell;MTT Cell;4-Bit RCA and LFSR|
|[Isolation Enhancement in a Two-Element MIMO Antenna Using Electromagnetic Metamaterial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110218)|G. Singh; A. Abrol; S. Kumar; B. K. Kanaujia; V. Kumar Pandey|10.1109/DICCT56244.2023.10110218|Antenna;compact;decoupling structure;metamaterial;MIMO;Antenna;compact;decoupling structure;metamaterial;MIMO|
|[A Low Power Feedback Cutting 8T SRAM Cell for Improved Stability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110163)|A. P. Kumar; R. Lorenzo; U. Prajvalitha; A. Singh|10.1109/DICCT56244.2023.10110163|Feedback cutting;Low power;SRAM;Stability;Static Noise Margin;Feedback cutting;Low power;SRAM;Stability;Static Noise Margin|
|[Analysis of AlGaN/GaN Interface Traps in the Enhancement-mode p-GaN HEMT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110108)|P. Nautiyal; P. Pande; V. Kundu; V. Joshi|10.1109/DICCT56244.2023.10110108|p-GaN HEMT;interface traps;current collapse;threshold voltage instability;activation energy;p-GaN HEMT;interface traps;current collapse;threshold voltage instability;activation energy|
|[Algorithm Trading through Application Programming Interface (API) in Indian Stock Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110105)|A. Mohan; N. S. Bohra; S. Bansal|10.1109/DICCT56244.2023.10110105|Algorithmic Trading (AT);Application Programming Interface (API);Algorithmic Trading (AT);Application Programming Interface (API)|
|[Simulation Study of a Compact UWB Antenna for Detection of Breast Tumors using Microwave Imaging Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110104)|P. Grover; H. S. Singh; S. Kumar Sahu|10.1109/DICCT56244.2023.10110104|Microwave Imaging;Tapered patch antenna;backscattered reflection coefficient;Specific Absorption Rate;Microwave Imaging;Tapered patch antenna;backscattered reflection coefficient;Specific Absorption Rate|
|[Enhancement of Security Posture in Smart Farming: Challenges and Proposed Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110208)|S. Sarowa; V. Kumar; B. Bhanot; M. Kumar|10.1109/DICCT56244.2023.10110208|Smart Farming;Cyber Attacks;Internet of Things (IoT);Blockchain;Robotics;Drone;Smart Farming;Cyber Attacks;Internet of Things (IoT);Blockchain;Robotics;Drone|
|[Analysis of Attack Patterns and Cyber Threats in Healthcare Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110141)|S. Sarowa; B. Bhanot; V. Kumar; M. Kumar|10.1109/DICCT56244.2023.10110141|ICT;ransomware;cyber threats;cyber security;ICT;ransomware;cyber threats;cyber security|
|[Performance Evaluation and Analysis of IoT Network using KNN and SVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110194)|H. Sivaraman; U. Garg; A. Gupta; N. Sharma; A. Sharma|10.1109/DICCT56244.2023.10110194|IoT;Machine Learning;SVM;KNN;DDOS;Attacks;Network;Algorithms;Public Key Infrastructure;Botnet;etc;IoT;Machine Learning;SVM;KNN;DDOS;Attacks;Network;Algorithms;Public Key Infrastructure;Botnet;etc|
|[A Characterization Study of Mutual Coupling between Rectangular Microstrip Patch Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110214)|S. R; K. P. Ray|10.1109/DICCT56244.2023.10110214|Mutual Coupling;Electromagnetic Interference;RMSA;Radiation Pattern;FEKO;MoM;Mutual Coupling;Electromagnetic Interference;RMSA;Radiation Pattern;FEKO;MoM|
|[Design Process of Wideband Power Amplifier In The L-Band Frequency Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110219)|Y. Kumar; K. P. Ray|10.1109/DICCT56244.2023.10110219|CLASS-AB;L-BAND;Load-Pull Analysis;Large Signal Gain;ADS;CLASS-AB;L-BAND;Load-Pull Analysis;Large Signal Gain;ADS|
|[Development of IoT Enabled Framework for LPG Gas Leakage Detection and Weight Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110294)|D. Gautam; S. Bhatia; N. Goel; B. Mallikaijuna; G. H S; B. Bhushan Naib|10.1109/DICCT56244.2023.10110294|Internet of things;Gas Leakage;Global System for Mobile Communication;LPG (Liquefied Petroleum Gas);Weight monitoring;Internet of things;Gas Leakage;Global System for Mobile Communication;LPG (Liquefied Petroleum Gas);Weight monitoring|
|[Ev-MDP : A Novel Metaheuristic Technique for Heart Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110296)|S. Goyal|10.1109/DICCT56244.2023.10110296|UCI Cleveland;Statlog;Genetic Evolution (GE);Mathematical Operator Algorithm;UCI Cleveland;Statlog;Genetic Evolution (GE);Mathematical Operator Algorithm|
|[Secrecy Outage Probability of NOMA Network under Amplify and Forward Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110247)|B. Jain; D. Yadav; R. K. Singh; R. Ranjan; S. P. Singh|10.1109/DICCT56244.2023.10110247|6G;NOMA;Secrecy Analysis;6G;NOMA;Secrecy Analysis|
|[Distribution of Nanomachines over Nano-networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110077)|N. Jain; J. Yadav; U. Chugh; R. Ranjan; S. Pratap Singh|10.1109/DICCT56244.2023.10110077|Molecular communication;nanonetworks;Fick’s laws;Levy Distribution;Inverse Gaussian Distribution;Brownian Motion;Molecular communication;nanonetworks;Fick’s laws;Levy Distribution;Inverse Gaussian Distribution;Brownian Motion|
|[Disorientation of Path of Birds Due to Electromagnetic Waves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110192)|R. Choudhary; V. Kumar|10.1109/DICCT56244.2023.10110192|Geomagnetic field;Component of Earth’s magnetic field;Direction of Magnetic field;Navigation;Magnetoreception;Geomagnetic field;Component of Earth’s magnetic field;Direction of Magnetic field;Navigation;Magnetoreception|
|[A Study on Intensity of Sound Waves for Variable Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110067)|V. Sharma; V. Kumar; P. Pathak|10.1109/DICCT56244.2023.10110067|Smartphones;Phyphox;Audio Amplitude;Sound Pressure;Tone Generator;Smartphones;Phyphox;Audio Amplitude;Sound Pressure;Tone Generator|
|[The Future IoT: The Current Generation 5G and Next Generation 6G and 7G Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110066)|S. Bhatia; B. Mallikarjuna; D. Gautam; U. Gupta; S. Kumar; S. Verma|10.1109/DICCT56244.2023.10110066|Future IoT;Smart Things;5G;6G and 7G technologies;Future IoT;Smart Things;5G;6G and 7G technologies|
|[Python And Opencv For Sign Language Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110225)|R. Srinivasan; R. Kavita; M. Kavitha; B. Mallikarjuna; S. Bhatia; B. Agarwal; V. Ahlawat; A. Goel|10.1109/DICCT56244.2023.10110225|Feature Extraction;CV;OpenCV;Sign Language;Computer Vision;Feature Extraction;CV;OpenCV;Sign Language;Computer Vision|
|[Dynamics of business and opportunities for employability using predictive modeling tools of Machine Learning and R programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110272)|L. Prasad; P. Mishra; S. Kumar Gupta; Ş. Aslan|10.1109/DICCT56244.2023.10110272|Employability;job seekers;data mining classification algorithms;employability prediction;institutions;Employability;job seekers;data mining classification algorithms;employability prediction;institutions|
|[Effect of channel Variation on DC Performance parameters of MOS and DTMOS technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110102)|K. Komal; N. Rup Prakash|10.1109/DICCT56244.2023.10110102|MOSFET;PMOS;NMOS;DTMOS;Channel width;Channel length;MOSFET;PMOS;NMOS;DTMOS;Channel width;Channel length|
|[Dimensionality Reduction of Multivariate Images Using the Linear & Nonlinear Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110258)|A. R. Pathare; A. S. Joshi|10.1109/DICCT56244.2023.10110258|Multivariate image analysis (MIA);hyperspectral image (HSI) dimensionality reduction (DR);principal component analysis (PCA);local linear embedding (LLE);support vector machine (SVM);Multivariate image analysis (MIA);hyperspectral image (HSI) dimensionality reduction (DR);principal component analysis (PCA);local linear embedding (LLE);support vector machine (SVM)|
|[Ring Oscillators based All Digital Phase Locked Loop: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110103)|L. Gupta; T. Sharma; B. Saranga|10.1109/DICCT56244.2023.10110103|All digital Phase-locked loop ADPLL;digital control oscillator;Phase detector;Multistage DCO;All digital Phase-locked loop ADPLL;digital control oscillator;Phase detector;Multistage DCO|
|[Security in Internet of Things: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110070)|B. B. Pannayagol; S. Deshpande|10.1109/DICCT56244.2023.10110070|Internet of Things;Security;Machine learning;Artificial intelligence;Blockchain technology;Internet of Things;Security;Machine learning;Artificial intelligence;Blockchain technology|
|[Controlling the Ambipolar current by using Graded drain doped TFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110171)|P. Dhiman; K. K. Kavi; R. K. Ratnesh; A. Kumar|10.1109/DICCT56244.2023.10110171|Ambipolariy;TFET;Subthreshold Swing;on current;off current;transconductance;Ambipolariy;TFET;Subthreshold Swing;on current;off current;transconductance|
|[IoT Enabled Low-Cost Real-Time Remote Air Quality Monitoring and Forecasting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110229)|S. Samadi; A. K. Kumawat|10.1109/DICCT56244.2023.10110229|air quality monitoring;internet of things;low-cost;machine learning;prediction;air quality monitoring;internet of things;low-cost;machine learning;prediction|
|[A Systematic Literature Review: Approach Toward Blockchain Future Research Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110088)|H. Nandanwar; R. Katarya|10.1109/DICCT56244.2023.10110088|Blockchain;Distributed ledger;Bitcoin;Security privacy;Blockchain application;Blockchain;Distributed ledger;Bitcoin;Security privacy;Blockchain application|
|[Based on RDWT and SVD Arnold Transform: A Strong and Secure Image Watermarking Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110042)|B. Shukla; K. Singh|10.1109/DICCT56244.2023.10110042|Arnold transform;Copyright protection;watermarking scheme;Singular value decomposition.;Arnold transform;Copyright protection;watermarking scheme;Singular value decomposition.|
|[Improving Pronunciation of the Students of Uttarakhand Region Using Pre-Defined Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110199)|S. Arora; K. Ajay; L. Mohan; R. Singh Koranga|10.1109/DICCT56244.2023.10110199|Mother Tongue Influence (MTI);Communication Skills;Algorithm;Kumauon;Regional;Mother Tongue Influence (MTI);Communication Skills;Algorithm;Kumauon;Regional|
|[Artificial Intelligence Based Handwriting Digit Recognition (HDR) - A Technical Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110186)|Y. Yogesh; G. S. P. Ghantasala; A. Priya|10.1109/DICCT56244.2023.10110186|research;handwriting digit recognition (HDR);research development;optical character recognition;artificial intelligence;scientific research;Innovation;research;handwriting digit recognition (HDR);research development;optical character recognition;artificial intelligence;scientific research;Innovation|
|[Performance Evaluation of Wireless Fading Channels for Indoor Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110138)|S. D L; R. Dilli; K. M|10.1109/DICCT56244.2023.10110138|AWGN;BER;BPSK;Modulation techniques;Rayleigh;Rician;SNR;AWGN;BER;BPSK;Modulation techniques;Rayleigh;Rician;SNR|
|[Performance Assessment of Multivariate Statistical and Bagging Ensembles in Landslide Susceptibility Mapping: Case Study of National Highway-10](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110089)|S. Dey; S. Das|10.1109/DICCT56244.2023.10110089|Multivariate Statistical Technique;Bagging Ensemble;Landslide Susceptibility;National Highway-10;Receiver Operating Characteristics Curve;Multivariate Statistical Technique;Bagging Ensemble;Landslide Susceptibility;National Highway-10;Receiver Operating Characteristics Curve|
|[Comparative study of Structures of Electrochromic Device for Flexible Electrochromic Display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110119)|J. Singh; S. Srivastava; S. Shrivas; R. Pandey; A. Dubey|10.1109/DICCT56244.2023.10110119|Display on Textile;Electrochromic Devices;PEDOT: PSS;Flexible Electronics;Smart Textile;Display on Textile;Electrochromic Devices;PEDOT: PSS;Flexible Electronics;Smart Textile|
|[Ensemble Approach to Solve Multiple Skin Disease Classification Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110278)|P. Ghadekar; A. Bongulwar; A. Jadhav; R. Ahire; A. Dumbre; S. Ali|10.1109/DICCT56244.2023.10110278|Classification;Deep learning;Dermatology;and skin lesion;Classification;Deep learning;Dermatology;and skin lesion|
|[Mammographic mass Classification using DL based ROI segmentation and ML based Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110098)|J. Rani; J. Singh; J. Virmani|10.1109/DICCT56244.2023.10110098|Mammographic images;screen film mammography;classification;Machine learning;Mammographic images;screen film mammography;classification;Machine learning|
|[Plant Leaf Disease Classification using Convolutional Neural Network on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110124)|P. Shah; G. Rathod; R. Gajjar; N. Gajjar; M. I. Patel|10.1109/DICCT56244.2023.10110124|Leaf Disease Classification;Field Programmable Gate Array (FPGA);Edge Computing;Machine Learning;Leaf Disease Classification;Field Programmable Gate Array (FPGA);Edge Computing;Machine Learning|
|[Fusion of Artificial Intelligence and 5G in Defining Future UAV Technologies - A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110231)|B. Dash; M. F. Ansari; S. Swayamsiddha|10.1109/DICCT56244.2023.10110231|AI;DL;5G;UAV;QoS;QoE;AI;DL;5G;UAV;QoS;QoE|
|[Bibliometric Analysis of White- Collar crimes- Concept and Development Using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110180)|K. Singh; R. Bahuguna; M. Memoria; R. Kumar|10.1109/DICCT56244.2023.10110180|White-Collar crime;criminology;artificial intelligence;machine learning;GPS;etc;White-Collar crime;criminology;artificial intelligence;machine learning;GPS;etc|
|[Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110149)|M. S; N. Bharathiraja; P. K; N. Ravindhar; M. V. Kumar; R. Marappan|10.1109/DICCT56244.2023.10110149|machine learning;online learning;knowledge acquisition;recommender system;clustering strategy;agent-based recommender;intelligent recommendation;machine learning;online learning;knowledge acquisition;recommender system;clustering strategy;agent-based recommender;intelligent recommendation|
|[Conformal UWB Antenna with enhanced gain characteristics using Mu-Negative Metamaterial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110156)|D. Negi; A. Bansal; K. Kaur|10.1109/DICCT56244.2023.10110156|UWB antenna;Broadband;metamaterial;and microwave absorber;UWB antenna;Broadband;metamaterial;and microwave absorber|
|[Classification and Prediction of Kashmiri Apple Plant by using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110222)|U. Garg; K. Jadli; R. S. Pundir; M. Manchanda; N. Gupta|10.1109/DICCT56244.2023.10110222|Convolutional Neural Network;Pooling;Apple Disease;Convolutional Neural Network;Pooling;Apple Disease|
|[Synthetic Image Generation for Visual Particle Size Distribution Estimation based on U-NET Convolutional Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110193)|A. Chavan; A. Schmidt; H. Klopries; D. Aufderheide|10.1109/DICCT56244.2023.10110193|Machine Learning;Alternative Fuels;Synthetic Image Generation;Deep Learning;Contour Detection;U-Net;Machine Learning;Alternative Fuels;Synthetic Image Generation;Deep Learning;Contour Detection;U-Net|
|[A Wideband Circularly Polarized CSRR Loaded Patch Antenna for ISM And C-Band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110091)|J. S. Varma; J. Ranjan Panda; G. Anjaneyulu; S. K. Dash; S. Sahu|10.1109/DICCT56244.2023.10110091|Wideband;Circular polarization;WiFi 6/6E;IMT band;5G;Wideband;Circular polarization;WiFi 6/6E;IMT band;5G|
|[Four Legged Parallel Manipulator for Autonomous Delivery Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110234)|M. Vikhe; N. Bothra; N. Malaviya; M. Kothari; R. Koshti|10.1109/DICCT56244.2023.10110234|Quadruped;Gyroscope;Autonomous robot;Quadruped;Gyroscope;Autonomous robot|
|[Performance of CNN for different facial expression images with varying input dataset sizes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110137)|N. Arora; P. Yadav; K. Tripathi; S. Sharma|10.1109/DICCT56244.2023.10110137|CNN;feature vector;classification;facial images;computer vision;deep learning;CNN;feature vector;classification;facial images;computer vision;deep learning|
|[Using the Indian MST Radar Noise-Level Observations of Stratosphere-Troposphere Replace were made over the tropical station Gadanki](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110079)|R. P. Rao; P. Yadav; A. Pandey; R. S. Yadav|10.1109/DICCT56244.2023.10110079|Tropical tropopause;Mesosphere Stratosphere Troposphere Radar;ST replace;Ozone;and Water steam;Tropical tropopause;Mesosphere Stratosphere Troposphere Radar;ST replace;Ozone;and Water steam|
|[A Comparative Study on the Effectiveness of Various Machine Learning Paradigms in Image Recognition and Predictive Modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110061)|D. Upadhyay; M. Gupta; A. Verma; S. Mittal|10.1109/DICCT56244.2023.10110061|Transfer Learning;Image Classifier;Confusion Matrix;Accuracy;Precision;Transfer Learning;Image Classifier;Confusion Matrix;Accuracy;Precision|
|[A Hybrid Mel Frequency Cepstral Coefficients and Bayesian Gaussian Mixure Model for Voice based Authentication Websites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110176)|V. UmaRani; M. P; S. M; S. Nischitha|10.1109/DICCT56244.2023.10110176|Bayesian Gaussian Mixure model;HMFCC;voice authentication;Bayesian Gaussian Mixure model;HMFCC;voice authentication|
|[Comprehensive approach of real time web-based face recognition system using Haar Cascade and LBPH algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110049)|A. Kumar; D. Singh|10.1109/DICCT56244.2023.10110049|Machine learning;Haar Cascade;LBPH Algorithm;Face Recognition;Database;Machine learning;Haar Cascade;LBPH Algorithm;Face Recognition;Database|
|[Prediction and detection of nutrition deficiency using machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110072)|A. Kumar Mishra; N. Tripathi; A. Gupta; D. Upadhyay; N. Kumar Pandey|10.1109/DICCT56244.2023.10110072|Classification;Convolutional Neural Network;Artificial neural network;Densenet-121;Detection;Image Analyzing;Machine Learning;Nutritional Deficiencies;Classification;Convolutional Neural Network;Artificial neural network;Densenet-121;Detection;Image Analyzing;Machine Learning;Nutritional Deficiencies|
|[Detection of Lung Disease using an Efficient Image Classification Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110084)|P. Das; D. C. Dobhal|10.1109/DICCT56244.2023.10110084|Classification strategy;UCI;reduced attribute set;embedded model;feature selection;Classification strategy;UCI;reduced attribute set;embedded model;feature selection|
|[Comparing Classifiers for Recognizing the Emotions by extracting the Spectral Features of Speech Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110282)|P. Mehra; S. Kant Verma|10.1109/DICCT56244.2023.10110282|MFCC;LPC;CatBoost;Artificial Intelligence;Spectral Features;MFCC;LPC;CatBoost;Artificial Intelligence;Spectral Features|
|[A Comprehensive Analysis of the Architecture of Static Random Access Memory Utilizing CMOS for Mobile Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110298)|K. C. Mishra; R. Kumar Singh|10.1109/DICCT56244.2023.10110298|Delay;Write;06 transistors;Read;6T SRAM unit;Power;new load-less 4T SRAM unit;SRAM;deep- submicron;Delay;Write;06 transistors;Read;6T SRAM unit;Power;new load-less 4T SRAM unit;SRAM;deep- submicron|
|[Vector Auto-Regression-Based Predictive model for Smart Meter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110196)|M. Jukaria; A. Upadhyay; A. Kumar|10.1109/DICCT56244.2023.10110196|HAN;VAR;Smart Meter;Machine Learning;AI;EDA;HAN;VAR;Smart Meter;Machine Learning;AI;EDA|
|[Efficient Centroid Based Distance Monitoring System Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110060)|A. Upadhyay; M. Kumar; M. Jukaria|10.1109/DICCT56244.2023.10110060|Covid-19;CNN;AI;Social Distancing;Centroid based;deep learning;Covid-19;CNN;AI;Social Distancing;Centroid based;deep learning|
|[Gain Enhancement in UWB Antenna using AMC Reflector for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110143)|D. Negi; K. Kaur|10.1109/DICCT56244.2023.10110143|artificial magnetic conductor;wideband antenna;wireless applications;metasurface;gain improvement;artificial magnetic conductor;wideband antenna;wireless applications;metasurface;gain improvement|
|[Design and Investigation of graphene based 2 × 2 CPW feed Vivaldi MIMO Antenna for THz Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110122)|R. K. Kushwaha; D. Shukla; Y. Tripathi; P. Karuppanan|10.1109/DICCT56244.2023.10110122|Vivaldi;MIMO;Graphene;Diversity Gain;ECC;Vivaldi;MIMO;Graphene;Diversity Gain;ECC|
|[A novel Compact RHCP Antenna with wide 3 dB AR bandwidth for S-band IRNSS application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110285)|Z. Patel; A. Saravaiya|10.1109/DICCT56244.2023.10110285|MPA;RL BW;RIS;AR BW;RHCP;MPA;RL BW;RIS;AR BW;RHCP|
|[Low-Cost Blind-Aid Stick to Prevent Accidents of Visually Impaired People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110195)|K. Gopal; J. Titoria; A. Uroj; B. Kumar|10.1109/DICCT56244.2023.10110195|Accident detection;Alert system;GPS;GSM;Blind Sensor Stick;Ultrasonic;Arduino Uno;Infrared Sensor;Shock Sensor;Accident detection;Alert system;GPS;GSM;Blind Sensor Stick;Ultrasonic;Arduino Uno;Infrared Sensor;Shock Sensor|
|[Anomaly Detection in Image Processing using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110120)|S. Jain; N. Choudhary|10.1109/DICCT56244.2023.10110120|Anomaly Detection;Autoencoder;Long Short-Term Memory;RNN;CNN;Anomaly Detection;Autoencoder;Long Short-Term Memory;RNN;CNN|
|[Field Effect Transistor (FET)-Sensor for Biological Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110155)|S. R. Kothiyal; R. Kumar Ratnesh; A. Kumar|10.1109/DICCT56244.2023.10110155|FET-Biosensor;Fabrication Process of FET-Biosensor;Immobilization;Antibody Detection;Intravascular Detection;DNA Detection;FET-Biosensor;Fabrication Process of FET-Biosensor;Immobilization;Antibody Detection;Intravascular Detection;DNA Detection|
|[Analysis of Performance Parameters and Gate Line Spacing Variation of Vertical Organic Light-Emitting Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110116)|S. Srishti; C. Tiwari; V. Mishra|10.1109/DICCT56244.2023.10110116|Emissive layer (EML);Hole Transport Layer (HTL);Organic thin film transistor (OTFT);Vertical organic light-emitting transistors (VOLETs);Emissive layer (EML);Hole Transport Layer (HTL);Organic thin film transistor (OTFT);Vertical organic light-emitting transistors (VOLETs)|
|[Leveraging the Power of ‘Modeling and Computer Simulation’ for Education: An Exploration of its Potential for Improved Learning Outcomes and Enhanced Student Engagement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110159)|A. Alam|10.1109/DICCT56244.2023.10110159|Modeling;Computer Simulation;Education;Learning;Teaching;Classroom;Pedagogy;Curriculum;Educational Technology;eLearning;ICT in Education;Modeling;Computer Simulation;Education;Learning;Teaching;Classroom;Pedagogy;Curriculum;Educational Technology;eLearning;ICT in Education|
|[Interpolative AMBTC based Reversible Data Hiding In Encrypted Images using Rhombus Mean](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110223)|S. Mittal; S. Goyal; S. Aggarwal; R. Kumar|10.1109/DICCT56244.2023.10110223|AMBTC;Image Compression;Interpolation;Huffman-encoding;PSNR;AMBTC;Image Compression;Interpolation;Huffman-encoding;PSNR|
|[Computerized Face Mask Detection System Using Deep CNN and Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110187)|A. Giri; D. S. Bisht; A. Chauhan; I. Kumar|10.1109/DICCT56244.2023.10110187|ResNet50;RestNet101;VGG19;VGG16;Transfer Learning;ResNet50;RestNet101;VGG19;VGG16;Transfer Learning|
|[Principal Component Analysis of Key Gases in Transformer Oil using DGA Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110290)|R. A. Raj; L. John Baptist Andrews; S. K. Venkatachary; S. D|10.1109/DICCT56244.2023.10110290|Principal component analysis;dissolved gases;power transformer;oil insulation;multi-colinearity.;Principal component analysis;dissolved gases;power transformer;oil insulation;multi-colinearity.|
|[A Unique Metaheuristic Algorithm for Human Urbanisation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110056)|V. K. Sharma; M. Iqbal; K. M. Pandey; V. K. Sharma|10.1109/DICCT56244.2023.10110056|Meta-heuristic Algorithms;Cytopathology;Optimization Algorithms;Meta-heuristic Algorithms;Cytopathology;Optimization Algorithms|
|[Evaluating the impact of smart city on land use efficiency in Dehradun district by using GIS and Remote Sensing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110184)|H. S. Pokhariya; D. Pratap Singh; V. Rathi|10.1109/DICCT56244.2023.10110184|Land use land cover;Google Earth Engine;Random Forest;Dehradun;Change Detection;Remote Sensing;Smart city;sustainable development goals;Land use land cover;Google Earth Engine;Random Forest;Dehradun;Change Detection;Remote Sensing;Smart city;sustainable development goals|
|[Federated learning: Applications, Security hazards and Defense measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110075)|S. Tyagi; I. S. Rajput; R. Pandey|10.1109/DICCT56244.2023.10110075|Federated learning;Horizontal FL;Vertical FL;Federated TL;Poisoning attack;Federated learning;Horizontal FL;Vertical FL;Federated TL;Poisoning attack|
|[An innovative wheelchair for quadreplegic patient using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110128)|R. Chauhan; J. Upadhyay; C. Bhatt|10.1109/DICCT56244.2023.10110128|Quadriplegia;paralysis;wheelchair;IoT;gestures;self-mobility;Artificial Intelligence (AI);Machine learning (ML);Quadriplegia;paralysis;wheelchair;IoT;gestures;self-mobility;Artificial Intelligence (AI);Machine learning (ML)|
|[VLSI Architectures for Video Salient Object Detection using 3D Dual Tree Complex Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110107)|S. B. D; K. B. Raja; K. R. Venugopal|10.1109/DICCT56244.2023.10110107|DTCWT;Video Saliency;Distributive Arithmetic;Systolic Array FPGA;DTCWT;Video Saliency;Distributive Arithmetic;Systolic Array FPGA|
|[Strengthening Data Security of India using a mixed approach of Cryptography and Steganography Techniques: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110086)|L. Negi; S. Kumar; M. Bharti|10.1109/DICCT56244.2023.10110086|Data Security;Cryptography;Steganography;Encryption;Decryption;Network;hackers;Data Security;Cryptography;Steganography;Encryption;Decryption;Network;hackers|
|[Extreme Gradient Boost Classifier based Credit Card Fraud Detection Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110188)|D. S. Nijwala; S. Maurya; M. P. Thapliyal; R. Verma|10.1109/DICCT56244.2023.10110188|Credit Card Fraud Detection;Extreme Gradient Boost;XGBOOST Classifiers;Machine-Learning;Credit Card Fraud Detection;Extreme Gradient Boost;XGBOOST Classifiers;Machine-Learning|
|[Community Detection in Networks: A Comparative study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110206)|A. Priya; S. Sharma; K. Sinha; Y. Yogesh|10.1109/DICCT56244.2023.10110206|networks;community;modularity;community detection;networks;community;modularity;community detection|
|[Adaptive-Optimal Control for Reconfigurable Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110043)|A. Maheshwari; A. Rautela; M. M. Rayguru; S. K. Valluru|10.1109/DICCT56244.2023.10110043|Reinforcement learning;reconfigurable robots;adaptive-optimal control;value iteration;single-link manipulator;Reinforcement learning;reconfigurable robots;adaptive-optimal control;value iteration;single-link manipulator|
|[Analysis of Organic Thin Film Transistor based ALU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110212)|T. Marium; S. N. Biswas|10.1109/DICCT56244.2023.10110212|Compact modeling;organic thin film transistors (OTFTs);SPICE modeling;ALU;Compact modeling;organic thin film transistors (OTFTs);SPICE modeling;ALU|
|[Study of Tsallis Distribution of Plasma inside Paul Trap using 3D Color-Map Plots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110197)|I. Ghosh; V. Saxena; A. Krishnamachari|10.1109/DICCT56244.2023.10110197|Plasma confinement;q-Gaussian distribution;Mathieu equation;entropy;Plasma confinement;q-Gaussian distribution;Mathieu equation;entropy|
|[Machine Learning Based Theoretical and Experimental Analysis of DDoS Attacks in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110201)|O. P. Suman; M. Kumar|10.1109/DICCT56244.2023.10110201|Machine learning;IDS;DDoS attacks;Cloud Network;Cloud security;Hybrid model;Machine learning;IDS;DDoS attacks;Cloud Network;Cloud security;Hybrid model|
|[Integrating Global and Local Features for Efficient Face Identification Using Deep CNN Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110170)|K. Jha; S. Srivastava; A. Jain|10.1109/DICCT56244.2023.10110170|Face Identification;Histogram of the Oriented Gradient;Dynamic Histogram Equalization;Global Feature;Local Feature;Deep Convolutional Neural Network;Face Identification;Histogram of the Oriented Gradient;Dynamic Histogram Equalization;Global Feature;Local Feature;Deep Convolutional Neural Network|
|[Spatial Channel Attention based Change Detection in Synthetic Aperture Radar Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110172)|L. Khandelwal; V. N. Sujit Vudattu; U. Chandra Pati|10.1109/DICCT56244.2023.10110172|Change detection;convolution neural network;spatial channel attention;synthetic aperture radar;Change detection;convolution neural network;spatial channel attention;synthetic aperture radar|
|[RGB-D Dataset: The Impact of Yoga and Gym Exercise for Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110291)|V. Sharma; B. Sharma; J. Panda|10.1109/DICCT56244.2023.10110291|Dataset creation;human activity recognition;pose estimation;machine learning;Dataset creation;human activity recognition;pose estimation;machine learning|
|[Standalone SEIG using Smart Meta-Heuristic Algorithm to Optimize its Performance and Loadability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110158)|S. Paliwal; S. Kumar Sinha; Y. Kumar Chauhan|10.1109/DICCT56244.2023.10110158|Induction generator;Newton-Raphson;wind energy conversion system;Induction generator;Newton-Raphson;wind energy conversion system|
|[Steerable Pyramid-based Multi-Scale Fusion Algorithm for Single Image Dehazing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110293)|P. Purkayastha; M. S. Choudhary; M. Kumar|10.1109/DICCT56244.2023.10110293|Laplacian Pyramid;Steerable Pyramid;Gaussian Pyramid;Multi-Scale Fusion;Single Image Dehazing;MATLAB;Laplacian Pyramid;Steerable Pyramid;Gaussian Pyramid;Multi-Scale Fusion;Single Image Dehazing;MATLAB|
|[Performance Analysis of Nature-Inspired Optimization Algorithms for Chronic Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110169)|P. Yadav; N. Arora; S. Chander Sharma|10.1109/DICCT56244.2023.10110169|Chronic Disease;Machine Learning;Nature-Inspired Optimization;Particle Swarm Optimization;Predictive Model;Chronic Disease;Machine Learning;Nature-Inspired Optimization;Particle Swarm Optimization;Predictive Model|
|[DDoS Attacks Analysis with Cyber Data Forensics using Weighted Logistic Regression and Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110133)|J. Tolanur; S. Chaudhari|10.1109/DICCT56244.2023.10110133|Cloud Computing;DDoS Attacks;Big Data;Cyber Data Forensic;Regression;Random forest;Cloud Computing;DDoS Attacks;Big Data;Cyber Data Forensic;Regression;Random forest|
|[Design and analysis of a source pocket dual material hetero dielectric double gate TFET for improved performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110167)|P. Dhiman; K. Kumar Kavi; R. A. Mishra; A. Kumar|10.1109/DICCT56244.2023.10110167|Tunnel Field Effect Transistor (TFET);Sub-threshold Swing (SS);Band-to-Band Tunneling (BTBT);Hetero-dielectric;Source Pocket;Tunnel Field Effect Transistor (TFET);Sub-threshold Swing (SS);Band-to-Band Tunneling (BTBT);Hetero-dielectric;Source Pocket|
|[Improved Channel Model of UAV to UAV Millimeter Wave Link Under Hovering Fluctuations and Doppler Effect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110142)|R. Tasnim Rupoma; W. Bin Ataur Rahman Akash; M. Golam Mostafa|10.1109/DICCT56244.2023.10110142|channel model;directivity gain;millimeter-wave;next-generation networks;outage probability;channel model;directivity gain;millimeter-wave;next-generation networks;outage probability|
|[Simulation Analysis of High-k Dielectric Junction less FET for Reduction of Subthreshold Leakage Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110191)|A. Chaudhary; A. Kumar Singh; M. Kumar Yadav|10.1109/DICCT56244.2023.10110191|Junction less-FET;High-k dielectric;Leakage Current;Junction less-FET;High-k dielectric;Leakage Current|
|[IEEE 802.11 DCF MAC Protocol for CR-Enabled WLAN in AdHoc and Infrastructure modes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110255)|N. Joshi; P. Yadav; S. C. Sharma|10.1109/DICCT56244.2023.10110255|802.11 (DCF) MAC protocol;CR-WLAN;Cognitive Radio Network;AdHoc network;Infrastructure network;802.11 (DCF) MAC protocol;CR-WLAN;Cognitive Radio Network;AdHoc network;Infrastructure network|
|[Localization in Precision Agriculture: A Machine Learning Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110233)|P. Yadav; N. Joshi; S. C. Sharma|10.1109/DICCT56244.2023.10110233|Localization;WSN;Machine Learning;Precision Agriculture;Localization;WSN;Machine Learning;Precision Agriculture|
|[VGG-16 based Gait Recognition using Skeleton Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110160)|I. Ali; I. Junaid; S. Ari|10.1109/DICCT56244.2023.10110160|Gait Recongnition;VGG-16;SkeGEI;Multilayer perceptron;Gait Recongnition;VGG-16;SkeGEI;Multilayer perceptron|
|[Machine Learning Thyroid Model for Prediction System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110065)|C. Nayak; D. Ajalkar; J. P. Shinde; S. S. Barik|10.1109/DICCT56244.2023.10110065|Convolutional Neural Network;Cytopathology;Optimization Algorithms;Convolutional Neural Network;Cytopathology;Optimization Algorithms|
|[An Improved Video Recommendation System for IoT Devices Using Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110183)|M. Kabir; R. Tasfia; M. G. Mostafa|10.1109/DICCT56244.2023.10110183|Federated learning;federated averaging;federated swapping;JointCloud computing;video recommendation scheme;Federated learning;federated averaging;federated swapping;JointCloud computing;video recommendation scheme|
|[Deep Learning Approach for Detection of Diabetic Retinopathy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110238)|K. Ratna; A. Shedage; R. Agal; B. Maheshwari; A. Aggarwal; S. R. Goyal|10.1109/DICCT56244.2023.10110238|Diabetic Retinopathy (DR);Convolutional Neural Networks (CNNs);ResNet;APTOS 2019 Blindness Detection dataset;Diabetic Retinopathy (DR);Convolutional Neural Networks (CNNs);ResNet;APTOS 2019 Blindness Detection dataset|
|[An Analysis of Crop Recommendation Systems Employing Diverse Machine Learning Methodologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110085)|Dolli; P. Rawat; M. Bajaj; S. Vats; V. Sharma|10.1109/DICCT56244.2023.10110085|Decision Tree;Naive Bayes;KNN;Random Forest;XG-Boost;Decision Tree;Naive Bayes;KNN;Random Forest;XG-Boost|
|[ASD Diagnosis in Children, Adults, and Adolescents using Various Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110166)|P. Rawat; M. Bajaj; S. Vats; V. Sharma|10.1109/DICCT56244.2023.10110166|Machine Learning;Autism;Disorder;Machine Learning;Autism;Disorder|
|[Conventional Current Reference Generation Strategies for Grid-Connected Distributed Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110095)|J. Joshi; V. P. Dubey; J. Kandpal; V. Chamoli|10.1109/DICCT56244.2023.10110095|GCPV Inverter;LVRT;Current Control;GCPV Inverter;LVRT;Current Control|
|[Development of Lane and Zebra Crossing Marker Detection Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110090)|V. Chamoli; R. Gowri; V. P. Dubey; J. Kandpal; J. Joshi; D. Kumar|10.1109/DICCT56244.2023.10110090|lane detection;zebre crossing detection;sobel;canny;image processing;video processing;lane detection;zebre crossing detection;sobel;canny;image processing;video processing|
|[A Hybrid FA for High Performance Arithmetic Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110263)|J. Kandpal; R. Gowari; V. P. Dubey; M. S. Hussain; J. Joshi; V. Chamoli|10.1109/DICCT56244.2023.10110263|FA;PDP;CPL;CMOS;XOR-XNOR;FA;PDP;CPL;CMOS;XOR-XNOR|
|[EfficientNet-based Image Captioning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110117)|P. Bansal; K. Malik; S. Kumar; C. Singh|10.1109/DICCT56244.2023.10110117|Attention Mechanism;CNN;EfficientNet;Image Caption;LSTM;RNN;Semantic Ontology;Attention Mechanism;CNN;EfficientNet;Image Caption;LSTM;RNN;Semantic Ontology|
|[Optimization of PV array with PID controller and MPPT(P&O)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110277)|S. Saxena; V. K. Tayal; H. P. Singh; V. K. Yadav|10.1109/DICCT56244.2023.10110277|Solar energy;Photovoltaic;Boost converter;PID controller;MPPT and P&O;Solar energy;Photovoltaic;Boost converter;PID controller;MPPT and P&O|
|[Metaheuristic Algorithm Implementation for PV Array Reconfiguration under Realistic Moving Cloud Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110264)|M. Kapur; A. S. Mahal; P. Kumar; D. Pathak; Manisha; P. Gaur|10.1109/DICCT56244.2023.10110264|PV array reconfiguration;TCT;PSO;GWO;PV array reconfiguration;TCT;PSO;GWO|
|[Time Series Anomaly Detection System with Linear Neural Network and Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10110220)|P. Mehra; M. S. Ahuja; M. Aeri|10.1109/DICCT56244.2023.10110220|Anomaly Detection;Autoencoder;decentralized machine learning;Linear Neural Network;Time Series;Anomaly Detection;Autoencoder;decentralized machine learning;Linear Neural Network;Time Series|
#### **2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE)**
- DOI: 10.1109/ATEE58038.2023
- DATE: 23-25 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Color Extraction Module for Unsupervised Image Classification Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108115)|A. -T. Andrei; O. Grigore|10.1109/ATEE58038.2023.10108115|histogram;color extraction;machine learning;unsupervised classification;aerial images;histogram;color extraction;machine learning;unsupervised classification;aerial images|
|[Initial Conditions Computation Approximation of Nonlinear Systems using the DFEHD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108302)|U. Vargas; G. -C. Lazaroiu|10.1109/ATEE58038.2023.10108302|harmonics;model simulation;transient simulation;harmonics;model simulation;transient simulation|
|[3D FEM Model of a Hybrid Stepper Using Scalar-Vector Potential Formulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108283)|O. Craiu; T. -I. Ichim; L. C. Popescu|10.1109/ATEE58038.2023.10108283|Finite Element Method;hybrid stepper motor;holding torque;magnetic vector potential;Finite Element Method;hybrid stepper motor;holding torque;magnetic vector potential|
|[Analyzing the Torque Transfer between Two In-Wheel Motors of an Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108365)|L. Popescu; O. Craiu; L. Melcescu|10.1109/ATEE58038.2023.10108365|electric vehicles;electric multi-motor powertrain;torque transfer;PWM control;simulation;testing bench;electric vehicles;electric multi-motor powertrain;torque transfer;PWM control;simulation;testing bench|
|[Producing Electricity with Photovoltaic Panels in Motion and Discharging Li-ion Batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108089)|N. -S. Popa; M. -O. Popescu; V. Mocanu|10.1109/ATEE58038.2023.10108089|Photovoltaic panels;li-ion batteries;USB 6008;LabView;data acquisitions;Photovoltaic panels;li-ion batteries;USB 6008;LabView;data acquisitions|
|[Experimental Determination of the Energy Provided by the Photovoltaic Panels Placed on the Roof of a Vehicle with an Imposed Route](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108293)|A. Turcanu|10.1109/ATEE58038.2023.10108293|electric vehicles;photovoltaic energy;imposed route;autonomy extension Introduction;electric vehicles;photovoltaic energy;imposed route;autonomy extension Introduction|
|[Monitoring Power Flow in End-user Consumption Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108265)|I. -M. Mindreanu; A. -M. Morega|10.1109/ATEE58038.2023.10108265|Electric power;reactive power;disturbances;waveforms;harmonics;power quality;Electric power;reactive power;disturbances;waveforms;harmonics;power quality|
|[A Practical Implementation Of A Digital Document Signature System Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108308)|A. Petcu; M. Frunzete; D. A. Stoichescu|10.1109/ATEE58038.2023.10108308|blockchain;document;signature;digital;solidity;blockchain;document;signature;digital;solidity|
|[Electrical Fault Diagnosis of Solar PV Array Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108218)|H. Al-Zubaidi; M. A. Shehab; A. Al-Gizi|10.1109/ATEE58038.2023.10108218|Photovoltaic array;Fault diagnosis;Machine learning;Classification techniques;Photovoltaic array;Fault diagnosis;Machine learning;Classification techniques|
|[Design and Construction of an H-bridge Inverter used in Wireless Power Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108150)|M. Vlad; P. M. Octavian; D. Vasile; P. Nicolae-Silviu|10.1109/ATEE58038.2023.10108150|H-bridge inverter;wireless transfer;MOSFET transistor driver;Schmitt-Trigger;bootstrap circuit;H-bridge inverter;wireless transfer;MOSFET transistor driver;Schmitt-Trigger;bootstrap circuit|
|[Low-Pass Filter Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108156)|G. Rezmeriță; A. Bordianu; S. Pușcașu|10.1109/ATEE58038.2023.10108156|LTspice;Chebyshev filter;Butterworth filter;Bessel filter;LTspice;Chebyshev filter;Butterworth filter;Bessel filter|
|[Five-phase Induction Motor Design using an Analytic Method and Finite Element Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108344)|I. Vasile; C. Dumitru; E. Tudor; A. -I. Constantin; I. -C. Sburlan; D. Lipcinski|10.1109/ATEE58038.2023.10108344|motor design;induction motor;traction motor;multiphase motor;optimal design;motor design;induction motor;traction motor;multiphase motor;optimal design|
|[Fully Differential Band-pass Filter for Biosignals Based on a DFVF and High CMRR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108139)|M. Yerena-Mora; F. Sandoval-Ibarra; J. M. Rocha-Pérez|10.1109/ATEE58038.2023.10108139|Band-pass preamplifier;biosignals;CMOS OTA;CMRR;Band-pass preamplifier;biosignals;CMOS OTA;CMRR|
|[Drop impact experiments on cylindrical pillars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108194)|P. V. Taranu; I. Rasuceanu; C. Patrascu; C. Balan|10.1109/ATEE58038.2023.10108194|drop impact;fluid sheet;biological fluids;drop impact;fluid sheet;biological fluids|
|[IoT Device Identification: A Machine Learning Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108170)|R. -A. Crăciun; R. Nicolae Pietraru; M. Alexandru Moisescu|10.1109/ATEE58038.2023.10108170|Internet of Things;Machine Learning;Cybersecurity;Devices;Identification;Platforms;Internet of Things;Machine Learning;Cybersecurity;Devices;Identification;Platforms|
|[Structural Analysis for a Limited-Contact Dynamic Compression Plate Used in Osteosynthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108251)|A. -M. Mitrofan; A. -M. Morega|10.1109/ATEE58038.2023.10108251|osteosynthesis plate;limited contact dynamic compression plate;bone fracture;numerical simulation;bone fixation;osteosynthesis plate;limited contact dynamic compression plate;bone fracture;numerical simulation;bone fixation|
|[First Steps Towards the Design of a multi-Chemistry, multi-Battery State of Health Screening System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108169)|T. -I. Voicilă; G. -C. Serițan; B. -A. Enache; R. Porumb; M. Stănculescu|10.1109/ATEE58038.2023.10108169|Cascaded Buck-Boost converter;Multi-chemistry;Multi-battery;State of Health screening system;Cascaded Buck-Boost converter;Multi-chemistry;Multi-battery;State of Health screening system|
|[YOLOv3-based Intracranial Hemorrhage Localization from CT Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108273)|A. Ferdi; S. Benierbah; Y. Ferdi|10.1109/ATEE58038.2023.10108273|Object detection;intracranial hemorrhage;YOLOv3;data augmentation;Object detection;intracranial hemorrhage;YOLOv3;data augmentation|
|[PV System Efficiency under Various Conditions by Monitoring the MPPT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108351)|M. A. Bandora; V. C. Ifrim; A. Moldovan|10.1109/ATEE58038.2023.10108351|PV system;Fractional Open Circuit Voltage (FOCV);Semi-Pilot Cell (SPC);Maximum Power Point Tracking (MPPT);Perturb and Observe (P&O);PV system;Fractional Open Circuit Voltage (FOCV);Semi-Pilot Cell (SPC);Maximum Power Point Tracking (MPPT);Perturb and Observe (P&O)|
|[Speed Control Application with PLC High Speed Outputs Port](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108371)|M. Rata; G. Rata|10.1109/ATEE58038.2023.10108371|PLC;PID controller;PWM Signal;industrial automation;PLC;PID controller;PWM Signal;industrial automation|
|[Comfort vs Health: A Winter Snapshot of Indoor Air Quality During the Energy Crisis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108245)|R. N. Pietraru; F. -C. Adochiei; I. M. Costea; C. K. Bănică|10.1109/ATEE58038.2023.10108245|population health;indoor air quality;thermal comfort;real time monitoring;energy crisis;population health;indoor air quality;thermal comfort;real time monitoring;energy crisis|
|[Performance Analysis of EKF-based Sensorless Induction Motor Drive using FPGA Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108274)|S. Y. Maddu; N. Ramesh Bhasme|10.1109/ATEE58038.2023.10108274|Sensorless;EKF;ELO;Snetly Controller;Induction Motor;Sensorless;EKF;ELO;Snetly Controller;Induction Motor|
|[MATLAB Library for Simulation of High Impedance Faults in Distribution Networks and Related Protective Relay Behaviour Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108111)|C. Şolea; D. Toader; M. Vinţan; M. Greconici; D. Vesa; I. Tatai|10.1109/ATEE58038.2023.10108111|high impedance fault;distribution network;fault simulation;fault detection;MATLAB/Simulink;high impedance fault;distribution network;fault simulation;fault detection;MATLAB/Simulink|
|[Detailed Investigation of the Residual and Non-Symmetry Active and Reactive Power Flow for No-Neutral Three-Phase Nonlinear Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108343)|I. V. Nemoianu; V. Manescu Paltanea; G. Paltanea; M. -I. Dascalu; R. M. Ciuceanu|10.1109/ATEE58038.2023.10108343|power grid;three-phase circuits;nonlinear circuit elements;residual (distorting) non-symmetry active power;non-symmetry reactive power;residual (distorting) active power;residual (distorting) reactive power;power grid;three-phase circuits;nonlinear circuit elements;residual (distorting) non-symmetry active power;non-symmetry reactive power;residual (distorting) active power;residual (distorting) reactive power|
|[Theoretical Assumptions in Conductivity and Dielectric Properties Assessment of Biological Tissues - Errors and Resulting Consequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108270)|D. A. Coman; B. Amuzescu; G. Nistor|10.1109/ATEE58038.2023.10108270|tissue permittivity;tissue conductivity;isotropy;brain tissue;first order approximation;tissue permittivity;tissue conductivity;isotropy;brain tissue;first order approximation|
|[Designing a Programmable Solar Simulator for the Study of Photovoltaic Panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108143)|C. -C. Pomazan|10.1109/ATEE58038.2023.10108143|solar simulator;solar irradiation;photovoltaic;solar simulator;solar irradiation;photovoltaic|
|[Human Behavior, Recognition, and Interpretation System using Physiological Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108220)|I. R. Adochiei; O. Stirbu; S. T. Nicolescu; R. Victoria Paraschiv; C. K. Bănică; F. -C. Adochiei|10.1109/ATEE58038.2023.10108220|Convolutional Neural Network;alpha waves;temperament test;Convolutional Neural Network;alpha waves;temperament test|
|[MIMO Antenna with Low Mutual Coupling and High Gain for n257-Band 5G mm-Wave](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108208)|S. Kumar; N. n. Tiwari; M. A. A; A. K. Dwivedi; V. Singh|10.1109/ATEE58038.2023.10108208|MIMO Antenna;2-Port;5G;mm-wave;ECC;CCL;MIMO Antenna;2-Port;5G;mm-wave;ECC;CCL|
|[Design of an Automated Control System for Soil Nutrient Deficiency of Yellow Corn](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108166)|A. L. Cordero; J. Bagaoisan; R. C. Caringal; S. Fontillas; R. R. Ogalesco; G. Marie Pelaez; A. G. Tuazon; R. Anacan; C. Hiwatig; M. Villanueva|10.1109/ATEE58038.2023.10108166|NPK;Sensor;treatment process;controller;control system;soil nutrient deficiency;soil nutrients;yellow corn;fuzzy logic;fertilizer applicator.;NPK;Sensor;treatment process;controller;control system;soil nutrient deficiency;soil nutrients;yellow corn;fuzzy logic;fertilizer applicator.|
|[Experimental Evaluation of Traction System Based on Induction Motor and Rotor Field Orientation Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108125)|A. Bitoleanu; M. Popescu; C. V. Suru|10.1109/ATEE58038.2023.10108125|electric traction;induction motor;rotor field oriented control;experimental evaluation;electric traction;induction motor;rotor field oriented control;experimental evaluation|
|[Influence of Locomotive Wheels Slipping on a Traction System with Rotor Flux-Oriented Control and Hysteresis Current Controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108203)|M. Popescu; A. Bitoleanu; C. V. Suru|10.1109/ATEE58038.2023.10108203|rotor field-oriented control;electric traction;induction motor;hysteresis controller;PI controller;wheels slipping;rotor field-oriented control;electric traction;induction motor;hysteresis controller;PI controller;wheels slipping|
|[Random number generation in hardware reconfigurable wireless sensor nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108373)|I. Rădoi; L. Dobrescu; C. Rusea|10.1109/ATEE58038.2023.10108373|FPGA;random number;wireless sensor node;entropy;FPGA;random number;wireless sensor node;entropy|
|[Hysteresis Current Controllers with Limited Switching Frequency for Electric Traction Systems with Asynchronous Motor and Rotor Field Orientation Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108246)|C. V. Suru; M. Popescu; A. Bitoleanu|10.1109/ATEE58038.2023.10108246|hysteresis controller;switching frequency;traction system.;hysteresis controller;switching frequency;traction system.|
|[Multi-Input Bidirectional DC-DC Converter for Energy Management in Hybrid Electrical Vehicles Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108261)|A. Benevieri; L. Carbone; S. Cosso; F. Gallione; S. Hussain|10.1109/ATEE58038.2023.10108261|DC-DC converter;multi-input converter;EVs;HEVs;Supercapacitors;Boost converter;DC-DC converter;multi-input converter;EVs;HEVs;Supercapacitors;Boost converter|
|[Determining the Parameters and Simulate Operation of the Synchronous Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108097)|I. -D. Ilina|10.1109/ATEE58038.2023.10108097|synchronous generators;parameters and simulation;static experimental methods;experimental validation;synchronous generators;parameters and simulation;static experimental methods;experimental validation|
|[A simplified Approach for Accurate Arrythmia Detection using Automated Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108192)|T. Zaharia; G. M. Danciu; I. Ilie; I. E. Nicolae; S. C. Nechifor|10.1109/ATEE58038.2023.10108192|AutoML;LigthtGBM;ML benchmark;classification;ECG;arrythmia;AutoML;LigthtGBM;ML benchmark;classification;ECG;arrythmia|
|[Displacement and Rupture of Viscous Interface in the Vicinity of the Wall under an Air Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108244)|B. Diana; I. C. Sorana; B. Corneliu|10.1109/ATEE58038.2023.10108244|interface;viscous fluids;air flow;CFD;VoF;interface;viscous fluids;air flow;CFD;VoF|
|[Field Oriented Control System Modeling for a New Flywheel Energy Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108380)|A. Benevieri; L. Carbone; S. Cosso; F. Gallione; D. Gnudi; S. Hussain|10.1109/ATEE58038.2023.10108380|Permanent Magnet Synchronous Motor;Field Oriented Control;SPWM;Flywheel Energy Storage System;Flywheel control system;Permanent Magnet Synchronous Motor;Field Oriented Control;SPWM;Flywheel Energy Storage System;Flywheel control system|
|[Numerical Analysis of Electric Field Interactions Associated with DNA Hybridization in A Microfluidic Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108386)|S. Gogoneață; Y. Veli; A. M. Morega|10.1109/ATEE58038.2023.10108386|DNA hybridization;microchannel;electro-osmotic flow;zeta potential;temperature gradient;DNA hybridization;microchannel;electro-osmotic flow;zeta potential;temperature gradient|
|[HDL simulation model for testing and verification of “system in package” sensor architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108271)|I. Rădoi; L. Dobrescu; C. Rusea|10.1109/ATEE58038.2023.10108271|FPGA;sensor;system in package;FPGA;sensor;system in package|
|[On-site Performances Evaluation of Photovoltaic Panels Affected by Bypass Activated Diodes Detected with Thermal Cameras](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108146)|A. -I. Constantin; D. Marin; P. Angheliţă; I. Tiberiu Şerban; C. Morari; G. Iosif|10.1109/ATEE58038.2023.10108146|PV panel;PV system;fault detection;thermal imaging;thermography;I-V curve;bypass diode;maintenance;PV panel;PV system;fault detection;thermal imaging;thermography;I-V curve;bypass diode;maintenance|
|[Practical Aspects Regarding the Propulsion of UAV Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108242)|D. Lipcinski; M. Popa; C. Ilie; N. Tănase; D. Ovezea; R. Marian Mihai|10.1109/ATEE58038.2023.10108242|UAV;UAS;dual excitation generator;hover;UAV;UAS;dual excitation generator;hover|
|[A New Strategy for Detection and Management of Faults in High Power NPC Converter Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108255)|A. Benevieri; L. Carbone; S. Cosso; F. Gallione; S. Hussain|10.1109/ATEE58038.2023.10108255|Fault tolerance;NPC converter;multilevel converter;space vector modulation (SVM);Fault tolerance;NPC converter;multilevel converter;space vector modulation (SVM)|
|[Performances Assessment of an Inverter after 8 Years of Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108100)|D. Marin; A. -I. Constantin; P. Angheliţă; I. Tiberiu Şerban|10.1109/ATEE58038.2023.10108100|photovoltaic inverter;efficiency;photovoltaic system;THD;harmonics;photovoltaic inverter;efficiency;photovoltaic system;THD;harmonics|
|[Torque Profile Enhancement of a Coaxial Transverse-Radial Flux Magnetic Gear Using Taguchi Optimization Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108345)|M. Abolghasemi; A. Ghaheri; A. Harooni; S. Ebrahim Afjei|10.1109/ATEE58038.2023.10108345|finite element method;magnetic gear;optimization;Taguchi method;transverse flux;finite element method;magnetic gear;optimization;Taguchi method;transverse flux|
|[A mathematical model of joint contracture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108321)|H. Kagami|10.1109/ATEE58038.2023.10108321|joint contracture;mathematical model;numerical simulation;onset mechanism;collagen;joint contracture;mathematical model;numerical simulation;onset mechanism;collagen|
|[Stochastic Generation and Transmission Expansion Planning using Sample Average Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108334)|O. Altun; E. Karatepe|10.1109/ATEE58038.2023.10108334|generation and transmission expansion planning;stochastic programming;sample average approximation;generation and transmission expansion planning;stochastic programming;sample average approximation|
|[Influence of Electrical Accelerated Aging on the Conductivity and Activation Energy of Free Ions in Mineral Oil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108223)|L. Marius Dumitran; A. Manea; T. Gorjanu|10.1109/ATEE58038.2023.10108223|Power transformers;mineral oil;accelerated aging;partial discharges;electric conductivity;activation energy;Power transformers;mineral oil;accelerated aging;partial discharges;electric conductivity;activation energy|
|[Numerical and Experimental Study of the Airflow in a Square Section Bifurcation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108180)|D. -D. Cristea; N. . -. O. Tanase; C. Balan|10.1109/ATEE58038.2023.10108180|airflow;bifurcation;CFD;respiratory system;airflow;bifurcation;CFD;respiratory system|
|[Mapping Sites for Retrofitting non-Powered Dams in Romania as Renewable Power Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108119)|G. Dunca; D. Maria Bucur; T. Haakon Bakken; A. Harby; E. Pummer; M. Istrate; C. Boariu; C. Roman|10.1109/ATEE58038.2023.10108119|hydropower potential;retrofitting;small dams;sills;hydropower potential;retrofitting;small dams;sills|
|[Considerations Regarding the Directivity of Antenna Arrays, Uniformly Distributed and Non-uniformly Illuminated](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108236)|R. Popa; G. Iubu|10.1109/ATEE58038.2023.10108236|linear phased array;planar phased array;directivity parameters;weighting functions;Taylor weighting;radiation pattern;array factor;electromagnetic compatibility;linear phased array;planar phased array;directivity parameters;weighting functions;Taylor weighting;radiation pattern;array factor;electromagnetic compatibility|
|[Specific Absorption Rate Variability in Long Term Exposure In Vivo Experiments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108118)|G. Rosu; L. Tuta; S. Spandole-Dinu; A. -M. Catrina; O. Calborean; A. Andone; L. Ole Fichte; O. Baltag|10.1109/ATEE58038.2023.10108118|biologic experiments;dosimetry;polarization;specific absorption rate;uncertainty;biologic experiments;dosimetry;polarization;specific absorption rate;uncertainty|
|[Dimensioning Aspects of Horizontal Earth Grounding, With Pipe Type Electrodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108263)|V. Maier; S. Pavel; H. Beleiu; D. Niste; C. Darab|10.1109/ATEE58038.2023.10108263|horizontal earth electrodes;dispersion resistance;calculation methodology;coefficient of use;horizontal earth electrodes;dispersion resistance;calculation methodology;coefficient of use|
|[Influence Analysis of the Geometry and Materials For the Electromagnetic Torque Calculation on a Stepper Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108186)|I. Ionică; M. Modreanu; A. Morega; C. Boboc|10.1109/ATEE58038.2023.10108186|hybrid stepper motor;finite element method;detent torque;holding torque;mathematical modeling;geometry;materials;numerical analysis;hybrid stepper motor;finite element method;detent torque;holding torque;mathematical modeling;geometry;materials;numerical analysis|
|[Photonic Crystal Fiber-Based Surface Plasmon Resonance Sensor for Sensing Broad Range of Refractive Indices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108191)|N. Hussain; M. R. Masuk; M. F. Hossain|10.1109/ATEE58038.2023.10108191|Microchannel;Surface Plasmon Resonance;Dual Core;Photonic Crystal Fiber;Microchannel;Surface Plasmon Resonance;Dual Core;Photonic Crystal Fiber|
|[An Indoor Air Quality Score Computation and System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108307)|I. Vîlciu; B. -A. Enache; G. -C. Serițan; T. -I. Voicilă|10.1109/ATEE58038.2023.10108307|Indoor Air Quality;particulate matter;quality indices;score;Indoor Air Quality;particulate matter;quality indices;score|
|[Distributed Wind Generation in Brazilian Sub-Transmission Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108363)|A. Yineth Montero Cruz; J. Aquiles Baesso Grimoni|10.1109/ATEE58038.2023.10108363|Wind energy;distributed generation;sub-transmission networks;test network;electrical models and simulation;Wind energy;distributed generation;sub-transmission networks;test network;electrical models and simulation|
|[Advanced Down-Counting Operation in SPICE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108172)|I. -C. Guran; A. Florescu; L. A. Perișoară; M. Ș. Teodorescu; I. B. Bacîș; A. Vasile|10.1109/ATEE58038.2023.10108172|simulation;SPICE;digital circuits;mixed-signal circuits;down-counter;analog-to-digital converter;phase-locked loop;battery management systems;simulation;SPICE;digital circuits;mixed-signal circuits;down-counter;analog-to-digital converter;phase-locked loop;battery management systems|
|[Design of a System for Experimental Study of the Temperature Field of Electronic Modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108158)|B. Evstatiev; N. Evstatieva|10.1109/ATEE58038.2023.10108158|temperature field;electronic modules;thermal camera;classification;electronic system;temperature field;electronic modules;thermal camera;classification;electronic system|
|[The Simulation and Implementation of a Solution for Compensation at Power Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108341)|P. -M. Nicolae; I. -D. Nicolae; M. -S. Nicolae|10.1109/ATEE58038.2023.10108341|power system simulation;active filters;energy efficiency;power quality;power system simulation;active filters;energy efficiency;power quality|
|[Space Charge Accumulation in Models of Inhomogeneous Insulations of DC Cable Joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108320)|C. Stancu; L. Taranu; P. Notingher|10.1109/ATEE58038.2023.10108320|DC cable joints;XLPE/EPR insulation;XLPE/EPDM insulation;thermal ageing;space charge;electric field;DC cable joints;XLPE/EPR insulation;XLPE/EPDM insulation;thermal ageing;space charge;electric field|
|[On Engaging Bioengineering Students in Hands-on Robotics Applied in Rehabilitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108387)|S. V. Paţurcă; O. A. Hociung; A. C. Oprea; A. Răduţă-Petrescu|10.1109/ATEE58038.2023.10108387|hands-on projects;bioengineering;prosthesis;robotic devices;hands-on projects;bioengineering;prosthesis;robotic devices|
|[Modelling the Temperature Field of Electronic Devices with the Use of Infrared Thermography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108375)|N. Evstatieva; B. Evstatiev|10.1109/ATEE58038.2023.10108375|electronic module;thermal modelling;infrared camera;temperature field;electronic module;thermal modelling;infrared camera;temperature field|
|[A Multi Agent-System Approach for the Optimal Placement of PMUs in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108137)|I. -A. Cojoacă|10.1109/ATEE58038.2023.10108137|multi agent-system;phasor measurement unit;optimal placement;multi agent-system;phasor measurement unit;optimal placement|
|[Detection of Melanomas Using Ensembles of Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108394)|L. Ichim; R. I. Mitrică; D. Popescu|10.1109/ATEE58038.2023.10108394|image processing;convolutional neural networks;ensembles of neural networks;skin lesion;melanoma detection;performance metrics;image processing;convolutional neural networks;ensembles of neural networks;skin lesion;melanoma detection;performance metrics|
|[Automated INL and DNL Testing System as a Didactic Laboratory Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108366)|C. Zet; C. Foşalău; A. Hariton|10.1109/ATEE58038.2023.10108366|ADC;nonlinearity errors;automated test system;didactic laboratory;ADC;nonlinearity errors;automated test system;didactic laboratory|
|[CT Image-Based Segmentation, 3D Reconstruction and SLA Printing of Human Trachea](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108383)|C. Mateescu; N. -O. Tanase; C. Balan|10.1109/ATEE58038.2023.10108383|Keywords: CT image;3D printing;trachea;Keywords: CT image;3D printing;trachea|
|[Validation of Microgrid Algorithms using At-Scale Simulation and Emulation Real-Time (ASSERT) Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108324)|S. Chanda; Y. Agalgaonkar; S. Pannala; R. Hovsapian|10.1109/ATEE58038.2023.10108324|microgrid;modeling;at-scale;power hardware-in-loop;microgrid;modeling;at-scale;power hardware-in-loop|
|[Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108354)|S. Chanda; M. Mohanpurkar; R. Hovsapian|10.1109/ATEE58038.2023.10108354|Digital real-time simulation;grid modernization;hardware-in-the-loop;optimization;quantum-in-the-loop;quantum computing;smart grid;resiliency;Digital real-time simulation;grid modernization;hardware-in-the-loop;optimization;quantum-in-the-loop;quantum computing;smart grid;resiliency|
|[Deep Learning Enhanced Spectrum Sensing for LoRa Spreading Factor Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108224)|P. M. Mutescu; A. Lavric; A. I. Petrariu; V. Popa|10.1109/ATEE58038.2023.10108224|Spectrum Sensing;Deep Learning;Image Classification;IoT;Modulation Classification;LoRa modulation;Spectrum Sensing;Deep Learning;Image Classification;IoT;Modulation Classification;LoRa modulation|
|[Design of a Miller Amplifier using gm/ID based on A First-order Building Block Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108237)|V. H. A. Palma; F. Sandoval-Ibarra|10.1109/ATEE58038.2023.10108237|Amplifiers;CMOS analog integrated circuits;Firstorder system;gm/ID methodology;OTA Miller;Amplifiers;CMOS analog integrated circuits;Firstorder system;gm/ID methodology;OTA Miller|
#### **2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT)**
- DOI: 10.1109/ICEIT57125.2023
- DATE: 16-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Construction of Comprehensive Online Learning Environment for English Major Undergraduates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107834)|X. Cai|10.1109/ICEIT57125.2023.10107834|online learning and teaching;blended learning;learning resources;online learning and teaching;blended learning;learning resources|
|[Research on Online Course Development and Construction Based on OBE Concept-Take "Inclusive Education" as an Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107896)|Y. Jing; Y. Yue|10.1109/ICEIT57125.2023.10107896|Outcome-based education;Online courses;Inclusive education;Outcome-based education;Online courses;Inclusive education|
|[Using UTAUT Model to Examine Acceptance of Online Interpreting Learning in China](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107837)|G. Yao|10.1109/ICEIT57125.2023.10107837|technology acceptance;online interpreting teaching;UTAUT model;technology acceptance;online interpreting teaching;UTAUT model|
|[Prediction and Intervention in the Effect of Online Learning in the Context of Learning Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107873)|Z. Wu; Y. Z. Bai|10.1109/ICEIT57125.2023.10107873|online learning behaviour;learning analytics;learning prediction;teaching interventions;online learning behaviour;learning analytics;learning prediction;teaching interventions|
|[Construction of Virtual Teaching and Research Section of Circuit Analysis Course in U-learning Era](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107786)|L. Shuguang; Z. Yunyan; W. Chengwei; Z. Long|10.1109/ICEIT57125.2023.10107786|U-learning era;virtual teaching and research section;circuit analysis;rain classroom;U-learning era;virtual teaching and research section;circuit analysis;rain classroom|
|[An Empirical Study on the Effect of Learning Input on College Students’ Online Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107860)|Z. Wu; Y. Bai|10.1109/ICEIT57125.2023.10107860|learning input;online learning;learning effect;influencing factors;structural equation model;learning input;online learning;learning effect;influencing factors;structural equation model|
|[Research on the Interaction Design of Online Learning Platforms for University Students Based on a User’s Mental Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107782)|Y. Jiang; Q. Ma; L. Zhang; S. Ma|10.1109/ICEIT57125.2023.10107782|mental model;online learning platform;interaction design;user experience;mental model;online learning platform;interaction design;user experience|
|[Construction and Application of the Teaching Cloud Platform for Engineering Drawing Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107900)|Y. Xue; H. Mu; L. Xue|10.1109/ICEIT57125.2023.10107900|engineering drawing;cloud platform;cloudsite;CADbro;independent learning;engineering drawing;cloud platform;cloudsite;CADbro;independent learning|
|[Fault Diagnosis Method for the Analog Circuit of Online Experiment Based on Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107899)|L. Zheng; W. Jiang; R. Hu; G. Du; H. Huang; J. Chen|10.1109/ICEIT57125.2023.10107899|knowledge graph;fault diagnosis;analog circuit;online experiment;knowledge graph;fault diagnosis;analog circuit;online experiment|
|[A Review of Factors Affecting Cognitive Load in Immersive Virtual Learning Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107848)|L. Zhang; G. Liu|10.1109/ICEIT57125.2023.10107848|Cognitive load;Immersive;Virtual reality;Factors;Cognitive load;Immersive;Virtual reality;Factors|
|[The Impact of Computer-Assisted Learning (CAL) in Distance Learning on Students’ Mathematics Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107890)|S. Xia|10.1109/ICEIT57125.2023.10107890|computer-assisted learning;distance education;mathematics education;computer-assisted learning;distance education;mathematics education|
|[Discussing the Effect of Computer-Based Instruction on College Students’ Learning Outcomes in Statistics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107772)|M. Feng; X. Xia|10.1109/ICEIT57125.2023.10107772|computer-based instruction;statistics course;learning outcomes;computer-based instruction;statistics course;learning outcomes|
|[Cognitive Learning Strategies with ICT: Case Study of Foreign Language Learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107778)|M. Müller; M. Višić|10.1109/ICEIT57125.2023.10107778|ICT;multimedia;cognitive strategy;e-tutorial;learners;ICT;multimedia;cognitive strategy;e-tutorial;learners|
|[A Prototype of Efficient Learning System for Objective-Driven Learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107889)|H. Wang; Q. Zhuge; E. H. . -M. Sha; R. Xu|10.1109/ICEIT57125.2023.10107889|personalized learning;learning path recommendation;efficient learning;dynamically programming;personalized learning;learning path recommendation;efficient learning;dynamically programming|
|[The Exploration and Practice of Blended Learning for “Preschool Child Development Psychology” Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107846)|H. Huang|10.1109/ICEIT57125.2023.10107846|blended learning;quality matters higher education rubric standards;dynamic classroom teaching model;preschool child development psychology;blended learning;quality matters higher education rubric standards;dynamic classroom teaching model;preschool child development psychology|
|[Design and Research of Blended Collaborative Learning Model for Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107857)|X. Liang; D. Zhao|10.1109/ICEIT57125.2023.10107857|deep learning;blended collaborative learning;teaching model;higher-order thinking;deep learning;blended collaborative learning;teaching model;higher-order thinking|
|[Exploring Autonomy in College Language Classes at a Private University in China](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107842)|Q. Tian|10.1109/ICEIT57125.2023.10107842|private undergraduate universities;college students’ language autonomous learning;private undergraduate universities;college students’ language autonomous learning|
|[Case-Based Learning for Better Understanding in Pharmacology: An Experience with Pharmacy Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107838)|T. Taesotikul; S. Watana; S. Nawanopparatsakul; N. Nuntharatanapong; C. Chinpaisal; P. Phuagphong|10.1109/ICEIT57125.2023.10107838|case-based learning;active learning;student-centered learning;pharmacology education;case-based learning;active learning;student-centered learning;pharmacology education|
|[Research on the Blended Teaching Mode Based on TRIZ Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107775)|L. Jiang|10.1109/ICEIT57125.2023.10107775|TRIZ theory;the blended teaching mode;multiple resources;dynamic integration;innovative thinking;TRIZ theory;the blended teaching mode;multiple resources;dynamic integration;innovative thinking|
|[The Construction of Intelligent Homework System in Schools under the Concept of Wisdom Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107898)|M. Zhou|10.1109/ICEIT57125.2023.10107898|"Double reduction" policy;wisdom education;online education;home-school collaboration;intelligent homework system;"Double reduction" policy;wisdom education;online education;home-school collaboration;intelligent homework system|
|[Interactive Teaching of College Physical Based on SPOC and Flipped Classroom-Exploration of Table Tennis Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107881)|L. Tingting; G. Jinyan; F. Yanxiang; Y. Zhenlong|10.1109/ICEIT57125.2023.10107881|SPOC;flipped classroom;college physical;interactive teaching;SPOC;flipped classroom;college physical;interactive teaching|
|[Exploration and Practice of Creative Writing Course Based on Personalized Learning Concept](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107864)|X. Li; W. Zhang; H. Tian; Y. Liu; C. Li; Z. Yang|10.1109/ICEIT57125.2023.10107864|creative writing teaching;literary creation talent training;personalized learning;artificial intelligence writing;creative writing teaching;literary creation talent training;personalized learning;artificial intelligence writing|
|[Exploring Online Teaching Modes of Practice Lessons in the Epidemic Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107776)|M. Zhang; M. Zhang|10.1109/ICEIT57125.2023.10107776|Covid-19 environment;practical lessons online;operating system;Linux;Covid-19 environment;practical lessons online;operating system;Linux|
|[A Study of Academic Achievement Attribution Analysis Based on Explainable Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107887)|T. Li; W. Ren; Z. Xia; F. Wu|10.1109/ICEIT57125.2023.10107887|academic achievement;machine learning;SHAP;attribution theory;academic achievement;machine learning;SHAP;attribution theory|
|[Synthesis of Digital Fabrication Laboratory for Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107847)|S. Sopapradit; P. Nilsook; P. Wannapiroon|10.1109/ICEIT57125.2023.10107847|digital fabrication laboratory;fab lab;higher education;digital fabrication laboratory;fab lab;higher education|
|[Research on Educational Cognitive Computing Model Based on Problem Solving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107861)|Z. Yang; G. Fan; W. Pan; Z. Ren|10.1109/ICEIT57125.2023.10107861|cognitive computing;model;problem solving skills;cognitive computing;model;problem solving skills|
|[Research on Information Teaching Mode of Higher Mathematics Based on Innovative Thinking Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107855)|Y. Zhang|10.1109/ICEIT57125.2023.10107855|participatory learning;information technology;visual teaching;innovative application ability;thinking training;participatory learning;information technology;visual teaching;innovative application ability;thinking training|
|[Research on Beyond Information Literacy Education Model of University Libraries under the Open Science Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107870)|N. Dong|10.1109/ICEIT57125.2023.10107870|beyond information literacy;open science;general literacy education;curriculum system;beyond information literacy;open science;general literacy education;curriculum system|
|[Research on the Application of Gamification Programming Teaching for High School Students’ Computational Thinking Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107843)|Z. Qu; J. Liu; L. Che; Y. Su; W. Zhang|10.1109/ICEIT57125.2023.10107843|game-based learning;gamification programming;computational thinking;high school information technology;game-based learning;gamification programming;computational thinking;high school information technology|
|[Exploration on Blended Teaching Mode of Data Mining Course with CDIO Concept](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107892)|Y. Zhu; M. Liu; L. Li; Y. Wang|10.1109/ICEIT57125.2023.10107892|data mining;blended teaching;CDIO concept;active learning;encouraging learning;data mining;blended teaching;CDIO concept;active learning;encouraging learning|
|[Teaching Innovation and Practice of Mind Mapping Applied to Engineering Drawing Course](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107785)|Y. Xue; H. Mu; L. Xue; X. Wang|10.1109/ICEIT57125.2023.10107785|engineering drawing;mind mapping;emissive thinking;teaching practice;engineering drawing;mind mapping;emissive thinking;teaching practice|
|[Research on the Integration of Computer Aided Instruction Methods in Dulcimer Teaching in College Music Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107871)|C. Xue; Z. Hou; R. Ma|10.1109/ICEIT57125.2023.10107871|computer aided network teaching;dulcimer teaching forum;Internet +;computer aided network teaching;dulcimer teaching forum;Internet +|
|[A Review of International Research on Artificial Intelligence in Teachers' Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107869)|Q. Duan; M. Xiao; Y. Bai|10.1109/ICEIT57125.2023.10107869|AI;Teachers' Teaching;style;Citespace;insert;AI;Teachers' Teaching;style;Citespace;insert|
|[Exploration of Mathematics Education by Metaverse Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107894)|X. Wu; Y. Chen; Y. Wu|10.1109/ICEIT57125.2023.10107894|mathematics education;metaverse technology;teaching mode;educational application;mathematics education;metaverse technology;teaching mode;educational application|
|[Visual Analysis of Precision Teaching Research Based on VOS Viewer in Data-Driven Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107845)|W. Xin; W. Bin|10.1109/ICEIT57125.2023.10107845|data-driven;precision teaching;visual analytics;VOS viewer;data-driven;precision teaching;visual analytics;VOS viewer|
|[Research and Development of Progressive Experimental System for Digital Electronic Technology with Fusion of Virtuality and Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107835)|Y. Wang; J. Li; D. Chen; G. Liu; Y. Zhao; C. Wang|10.1109/ICEIT57125.2023.10107835|digital electronic technology;experimental system;fusion of virtuality and reality;virtual simulation;FPGA;digital electronic technology;experimental system;fusion of virtuality and reality;virtual simulation;FPGA|
|[Analysis of the Current Situation and Characteristics of College Teaching Innovation Based on Video Analysis Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107886)|Y. Yang; B. Wan; D. Zeng|10.1109/ICEIT57125.2023.10107886|student-centered;teaching innovation;video analysis;university teaching;student-centered;teaching innovation;video analysis;university teaching|
|[Preliminary Study on Teaching System of Integrating Moral Education into Virtual Simulation Experiment of Urban Rail Transit Dispatching Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107784)|Z. Guangjian; Z. Lizhu|10.1109/ICEIT57125.2023.10107784|curriculum ideological and political education;project-based teaching;guiding text;jointly educate students;curriculum ideological and political education;project-based teaching;guiding text;jointly educate students|
|[Review of CiteSpace-Based Research on Artificial Intelligence for Teaching Management and Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107874)|H. Wei; P. Deng|10.1109/ICEIT57125.2023.10107874|artificial intelligence;teaching management;teaching evaluation;CiteSpace;knowledge graph;artificial intelligence;teaching management;teaching evaluation;CiteSpace;knowledge graph|
|[The Construction of "Management Accounting" MOOC and the Construction of Entropy Weight of Teaching Quality-TOPSIS Evaluation Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107840)|W. Chenxuan; Y. Tao; K. Ling|10.1109/ICEIT57125.2023.10107840|management accounting;MOOC online course;entropy weight-TOPSIS evaluation model;Index system weight;management accounting;MOOC online course;entropy weight-TOPSIS evaluation model;Index system weight|
|[Exploring the Factors Affecting Learners’ Adoption Intention of MOOC in Higher Education during the COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107853)|K. Suriyapaiboonwattan; K. Hone|10.1109/ICEIT57125.2023.10107853|MOOC;COVID-19;UTAUT2;MOOC adoption;SEM;ThaiMOOC;technology acceptance model;MOOC;COVID-19;UTAUT2;MOOC adoption;SEM;ThaiMOOC;technology acceptance model|
|[Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107897)|J. Liu; T. Gao; S. Liang; K. Chen; J. Zeng; F. Jing; J. Zhou|10.1109/ICEIT57125.2023.10107897|teaching performance;teaching ratings;data science in education;computational education;teachers’ professional development;teaching performance;teaching ratings;data science in education;computational education;teachers’ professional development|
|[Evaluation of Undergraduate Training Quality of Logistics Management Specialty Based on Improved Fuzzy Comprehensive Evaluation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107828)|T. Nie; N. Mo|10.1109/ICEIT57125.2023.10107828|major in logistics management;fuzzy comprehensive evaluation method;entropy value method;major in logistics management;fuzzy comprehensive evaluation method;entropy value method|
|[The State of IS Curricula for Digital Transformation: A Semantic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107830)|H. Shi; D. Hwang; D. Chong|10.1109/ICEIT57125.2023.10107830|digital transformation;IS curriculum;semantic analysis;knowledge pool;similarity;digital transformation;IS curriculum;semantic analysis;knowledge pool;similarity|
|[Measuring College Students’ Satisfaction with the Teaching Effectiveness of Online English Courses Based on the IPA Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107883)|X. Xia; M. Feng; S. Wang|10.1109/ICEIT57125.2023.10107883|online teaching;student satisfaction;importance-performance analysis;online teaching;student satisfaction;importance-performance analysis|
|[Evaluation of College Teachers’ Information Teaching Ability Based on Adaptive Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107779)|Y. Du; L. Niu|10.1109/ICEIT57125.2023.10107779|college teachers ability evaluation;BP neural network;adaptive genetic algorithm;educational technology ability;college teachers ability evaluation;BP neural network;adaptive genetic algorithm;educational technology ability|
|[Research and Practice on Discrete Mathematics Using Blended Learning Model and "Rain Classroom" Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107836)|Y. Wang; M. Liu; Y. Zhu; L. Li|10.1109/ICEIT57125.2023.10107836|students' comprehensive ability;teaching methods and reform;hybrid teaching model;rain classroom;students' comprehensive ability;teaching methods and reform;hybrid teaching model;rain classroom|
|[Research on the Strategies of Integrating Mathematics Culture into Higher Mathematics Classroom Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107888)|L. Jia; M. Zhang|10.1109/ICEIT57125.2023.10107888|mathematical culture;classroom teaching;history of mathematics;mathematical modeling;the beauty of mathematics;mathematical culture;classroom teaching;history of mathematics;mathematical modeling;the beauty of mathematics|
|[Practical Analyses on the Flipped Classroom Approach to Management Accounting Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107879)|Y. Zhao; A. Y. Dawod|10.1109/ICEIT57125.2023.10107879|Teaching Develop;Management accounting;Teaching model;Flipped classroom;Teaching Develop;Management accounting;Teaching model;Flipped classroom|
|[Teaching Practice of Engineering Mechanics Based on Finite Element Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107893)|S. Wang; X. Wang; F. Peng|10.1109/ICEIT57125.2023.10107893|teaching practice;finite element method;modeling;simulation;teaching;teaching practice;finite element method;modeling;simulation;teaching|
|[I-assistant: An Intelligent Teaching Assistant System for Classroom Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107774)|X. Zhang; J. Geng; Y. Chen; S. Hu; T. Huang|10.1109/ICEIT57125.2023.10107774|teaching assistant;intelligent education;learning situation analysis;teaching assistant;intelligent education;learning situation analysis|
|[A Study of Interactive Behavioral Characteristics of High School IT Classroom Teaching in a Smart Classroom Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107832)|K. Li; K. Huang; R. Shi|10.1109/ICEIT57125.2023.10107832|smart classroom;high school information technology;classroom interaction;evaluation;smart classroom;high school information technology;classroom interaction;evaluation|
|[Teaching Reform Practice for Introduction to Artificial Intelligence under the Background of New Finance and Economics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107895)|Y. Wang; X. Lin; J. Li; J. Yang|10.1109/ICEIT57125.2023.10107895|introduction to artificial intelligence;new finance and economics;characteristic training;introduction to artificial intelligence;new finance and economics;characteristic training|
|[The Scenario-Based Design of Practical Teaching in Training-Organization Course of Intelligence and Electronic Warfare Department](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107850)|S. Bai; S. Rao; J. Hong|10.1109/ICEIT57125.2023.10107850|training-organization course;practical teaching;scenario-based design;actual combat teaching;training-organization course;practical teaching;scenario-based design;actual combat teaching|
|[Experimental Design and Teaching Research of a MPLS VPN Network Based on BGP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107884)|J. Tao; H. Chen; H. Liu; X. She|10.1109/ICEIT57125.2023.10107884|MPLS;VPN;BGP;design;teaching;MPLS;VPN;BGP;design;teaching|
|[Construction and Application of an Information-Based Teaching Platform Based on VirtualBox Virtual Machine Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107829)|J. Pan; J. Han; K. Qin|10.1109/ICEIT57125.2023.10107829|informatization teaching;VirtualBox;virtual machine technology;platform construction;informatization teaching;VirtualBox;virtual machine technology;platform construction|
|[Research on Application Status and Deepening Strategy of Smart Campus in Primary and Secondary Schools: Case Study of a County in Western China](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107777)|L. Zhang; L. Wu; T. Wu; A. Alamin|10.1109/ICEIT57125.2023.10107777|smart campus;investigation and research;construction status;development strategy;smart campus;investigation and research;construction status;development strategy|
|[Research on the Construction of Think-Tank Type Service System in University Libraries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107872)|W. Huang; N. Dong; X. Sun; M. Li|10.1109/ICEIT57125.2023.10107872|think-tank type service system;according to the theory;information ecology;evidence-based library theory;think-tank type service system;according to the theory;information ecology;evidence-based library theory|
|[A Comparative Study of Educational Robots in China and the United States](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107877)|X. Lin|10.1109/ICEIT57125.2023.10107877|educational robots;China and the United States similarities and differences;comparative study method;inspirations and suggestions;educational robots;China and the United States similarities and differences;comparative study method;inspirations and suggestions|
|[The Construction of Subject Knowledge Mapping in the Background of Smart Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107844)|M. Wu; Z. Wang|10.1109/ICEIT57125.2023.10107844|knowledge map;smart education;educational resources;knowledge map;smart education;educational resources|
|[Energy-Efficiency Optimization-Based User Selection and Power Allocation for Uplink NOMA-Enabled IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107783)|Y. Wu; S. Liu; X. Lin; L. Sun|10.1109/ICEIT57125.2023.10107783|NOMA;energy efficiency;power allocation;user selection;NOMA;energy efficiency;power allocation;user selection|
|[A Computational Thinking Assessment Tool on Text- Based Programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107885)|J. Wang; W. Zhang; X. Zeng; P. Li|10.1109/ICEIT57125.2023.10107885|computational thinking;text-based programming;assessment tool;qualitative analysis;quantitative analysis;computational thinking;text-based programming;assessment tool;qualitative analysis;quantitative analysis|
|[When E-learning Meets Web 3.0: Applications and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107827)|X. Li; B. Qin|10.1109/ICEIT57125.2023.10107827|E-learning;web 3.0;systematic review;applications;challenges;E-learning;web 3.0;systematic review;applications;challenges|
|[Exploring the Mainstream Learning Approaches to Enhancing Learning Efficiency in the Background of Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107773)|G. Min; Z. Li; M. Wang; X. Ding|10.1109/ICEIT57125.2023.10107773|big data;learning styles;learning efficiency;visualized analysis;critical reading;big data;learning styles;learning efficiency;visualized analysis;critical reading|
|[Factit: A Fact-Checking Browser Extension](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107833)|A. T. Velasco; A. Roi C. Cortez; J. M. B. Camay; I. Michael C. Giba; M. A. Diloy|10.1109/ICEIT57125.2023.10107833|fake news detection;browser extension;machine learning;logistic regression;fake news detection;browser extension;machine learning;logistic regression|
|[Teaching Chinese as a Foreign Language in the Background of Internet Plus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107882)|M. Xu|10.1109/ICEIT57125.2023.10107882|internet plus;education;Chinese as a foreign language;teaching;internet plus;education;Chinese as a foreign language;teaching|
|[Automatic Classification of Instructional Video Based on Different Presentation Forms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107851)|M. Qiusha; L. Ziyi; L. Wenhao|10.1109/ICEIT57125.2023.10107851|instructional videos;video classification;presentation form;target detection;naive bayes algorithm;instructional videos;video classification;presentation form;target detection;naive bayes algorithm|
|[Hot Spot and Development Trend of Primary and Secondary School Teachers’ ICT Ability in China Based on CiteSpace6.1.R2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107862)|H. Yuan; Q. Xu; J. Jiang|10.1109/ICEIT57125.2023.10107862|teachers' information technology ability;research hot spot;development trend;knowledge map;visual analysis;teachers' information technology ability;research hot spot;development trend;knowledge map;visual analysis|
|[Reform and Practice of Digital Image Processing Case Teaching Based on Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107867)|G. Li; X. Jing|10.1109/ICEIT57125.2023.10107867|Attention mechanism;cultivation of innovative talents;teaching reform;experimental teaching;Attention mechanism;cultivation of innovative talents;teaching reform;experimental teaching|
|[Development of Civil Aviation Engine Virtual Simulation Teaching Platform Based on AR/VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107866)|C. Liu; J. Tian; B. Wang; X. Liu|10.1109/ICEIT57125.2023.10107866|VR/AR technology;virtual simulation teaching platform;civil aviation engine;VR/AR technology;virtual simulation teaching platform;civil aviation engine|
|[Research and Practice on Blended Collaborative Learning Mode of Programming Course Based on Smart Class](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107876)|L. Luo; S. Chen; Y. Wu; L. Lei|10.1109/ICEIT57125.2023.10107876|intelligent teaching platform;blended collaborative learning;collaborative community;teaching practice;intelligent teaching platform;blended collaborative learning;collaborative community;teaching practice|
#### **2023 2nd International Conference on Big Data, Information and Computer Network (BDICN)**
- DOI: 10.1109/BDICN58493.2023
- DATE: 6-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[MRI Reconstruction Based on Transfer Learning Dynamic Dictionary Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109898)|C. Cheng; D. Lin|10.1109/BDICN58493.2023.00007|Transfer learning;dictionary learning;MRI reconstruction;compressed sensing;Transfer learning;dictionary learning;MRI reconstruction;compressed sensing|
|[Research on improved algorithm of logistics optimization based on big data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109978)|M. Xin; H. Zhu|10.1109/BDICN58493.2023.00008|Logistics;big data;Value chain;Administration;Logistics;big data;Value chain;Administration|
|[Resource Recommendation of Agricultural Vocational Education Based on Demand Perception Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109929)|Q. Wang; G. Li; J. Song|10.1109/BDICN58493.2023.00009|Agricultural vocational education;Resource sharing;Personalized recommendation;Demand perception;Agricultural vocational education;Resource sharing;Personalized recommendation;Demand perception|
|[Research on the Construction of Innovation Literacy Index System of Higher Vocational Students Based on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109901)|T. Gao|10.1109/BDICN58493.2023.00010|innovation quality;data analysis;innovation quality;data analysis|
|[Anomaly Recognition Method for Massive Data of Power Internet of Things Based on Bayesian Belief Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109967)|X. Lu; W. Xie; J. Hu; H. Wang|10.1109/BDICN58493.2023.00011|Bayesian belief network;Clustering algorithm;Power IoT;Discrete wavelet transform;Massive data;Anomaly identification;Bayesian belief network;Clustering algorithm;Power IoT;Discrete wavelet transform;Massive data;Anomaly identification|
|[Optimization Algorithm of Learning Quality Evaluation Based on Cognition in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109911)|Y. Wang|10.1109/BDICN58493.2023.00012|Cloud Computing;Learning Quality;Evaluation optimization;Algorithm Research;Cloud Computing;Learning Quality;Evaluation optimization;Algorithm Research|
|[UAV video based vibration frequency detection for bridge stay cables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109933)|P. Yang; X. Yang; Z. Zang; S. Liang|10.1109/BDICN58493.2023.00013|stay cable;UVA;vibration measurement;spatial phase;singular spectrum analysis;stay cable;UVA;vibration measurement;spatial phase;singular spectrum analysis|
|[Research on 3D Point Cloud Object Detection Methods Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109944)|S. Lv; X. Li; B. Liu|10.1109/BDICN58493.2023.00014|Deep learning;3D point cloud;Object detection;Laser radar;Deep learning;3D point cloud;Object detection;Laser radar|
|[Toll Trajectory Repair Method Based on Electronic Toll Collection Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109900)|Y. Su; L. Liao; F. Zou|10.1109/BDICN58493.2023.00015|Trajectory repair;ETC;path recognition;Trajectory repair;ETC;path recognition|
|[Personalized Differential Privacy Preservation Method for Trajectory Based on Regional Density Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109947)|W. Zhi; X. Gong; Y. Wang|10.1109/BDICN58493.2023.00016|hotspots;sensitive location point;differential privacy;privacy score;privacy budget allocation;hotspots;sensitive location point;differential privacy;privacy score;privacy budget allocation|
|[Research on project mining optimization algorithm based on fuzzy evaluation of power big data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109939)|Z. Mao; Y. Jin; Y. Shi; M. Zhang|10.1109/BDICN58493.2023.00017|fuzzy clustering;Power big data;Energy Internet;fuzzy clustering;Power big data;Energy Internet|
|[Design and research of substation inspection mode based on digital twins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109922)|Z. Zhang; Y. Duan|10.1109/BDICN58493.2023.00018|Digital twin;Real-time synchronization;UE4;Substation patrol inspection;Digital twin;Real-time synchronization;UE4;Substation patrol inspection|
|[Construction And Management System Of University Laboratory Based On Channel Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109913)|L. Shao|10.1109/BDICN58493.2023.00019|channel technology;computer Lab;construction and management;channel technology;computer Lab;construction and management|
|[Operation Method of Orderly Charging and Switching of Port Shore Power Based on Improved Ant Colony Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109954)|A. Ge; L. Jia; C. Xu; W. Hu; T. Wan|10.1109/BDICN58493.2023.00020|Port shore power;Charging management;Path optimization;Ant colony algorithm;Port shore power;Charging management;Path optimization;Ant colony algorithm|
|[Based on Binary Logistic Regression Model Analysis of Factors of Farmers’ Willingness to Transfer Farmland](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109965)|M. -L. Duan|10.1109/BDICN58493.2023.00021|Binary logistic regression analysis;Factor analysis;Willingness of farmers to transfer farmland;Influencing factors;Binary logistic regression analysis;Factor analysis;Willingness of farmers to transfer farmland;Influencing factors|
|[Application of LSTM in Quantitative Trading with Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109979)|J. Wang; Y. Hu; Y. Zhao|10.1109/BDICN58493.2023.00022|quantitative trading;portfolio optimization;LSTM;Mean-variance Model;Big-Data for Finance;quantitative trading;portfolio optimization;LSTM;Mean-variance Model;Big-Data for Finance|
|[Research on Stand-alone Continuous Data Protection Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109966)|H. Chen; K. Wang; H. Dong; J. Wan; J. Xiao; Z. Yao|10.1109/BDICN58493.2023.00023|Stand-alone protection;Data protection;Data backup;Data recovery;System restore;File recovery;CDP;RTO;RPO;Stand-alone protection;Data protection;Data backup;Data recovery;System restore;File recovery;CDP;RTO;RPO|
|[A Method for Data Quality Validation Based on Shapes Constraint Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109948)|X. Yang; H. Zhang; J. Li; S. Li|10.1109/BDICN58493.2023.00024|SHACL;Data quality;Semantic technology;Ontology model;SHACL;Data quality;Semantic technology;Ontology model|
|[Research on the Mechanism Design of Generation Capacity Transfer Compensation in Electricity Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109961)|Y. Hu; L. Han; J. Zhang; F. Cui; N. Xu; H. Liu; J. Liu|10.1109/BDICN58493.2023.00025|electricity market;capacity compensation;medium-and tong-term market;electricity price;generation cost;electricity market;capacity compensation;medium-and tong-term market;electricity price;generation cost|
|[Based on feature reconstruction and feature intersection, the Deep CTR Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109924)|N. Bian; L. Wang|10.1109/BDICN58493.2023.00026|Recommended system;CTR prediction;Feature intersection;Feature reconstruction;Recommended system;CTR prediction;Feature intersection;Feature reconstruction|
|[Design and Management of Web Game Interactive Application Platform Based on RETAIN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109946)|K. Guo|10.1109/BDICN58493.2023.00027|RETAIN Model;Web Game Platform;Interactive Application;Modular Hosting;RETAIN Model;Web Game Platform;Interactive Application;Modular Hosting|
|[Testing Problems of Agglomeration Degree Algorithms Based on Shaanxi Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109916)|A. Gao; Y. Zhang; R. Liu|10.1109/BDICN58493.2023.00028|Industrial Agglomeration;EG Algorithm;MAUP;Correlation Analysis;Industrial Agglomeration;EG Algorithm;MAUP;Correlation Analysis|
|[Forecasting House Resale Prices using Ensemble learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109937)|Z. Li; Z. Li|10.1109/BDICN58493.2023.00029|Xgboost;GA-HL-Xg-Boost;MAPE;Xgboost;GA-HL-Xg-Boost;MAPE|
|[RscaNet:a novel attention mechanism for image classification model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109963)|J. Jia; Y. Gao|10.1109/BDICN58493.2023.00030|component;Image classification;Garbage sorting;Deep learning;component;Image classification;Garbage sorting;Deep learning|
|[Design of Memory FAT file System for Airborne Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109959)|X. Han; J. Zhang; H. Tian|10.1109/BDICN58493.2023.00031|onboard sensor;data storage;file management;onboard sensor;data storage;file management|
|[Research on Voiceprint Recognition Based on MFCC-PCA-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109969)|L. Xiong; Z. Liao; Y. Liang; X. Gong|10.1109/BDICN58493.2023.00032|Keywords-Deep Learning;Voiceprint Recognition;Principal Component Analysis;Long Short Term Memory Network;Keywords-Deep Learning;Voiceprint Recognition;Principal Component Analysis;Long Short Term Memory Network|
|[A Down-sampling Method Based on The Discrete Wavelet Transform for CNN Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109941)|Y. Li; Z. Liu; H. Wang; L. Song|10.1109/BDICN58493.2023.00033|deep learning;down-sampling;channels stack;discrete wavelet transform;deep learning;down-sampling;channels stack;discrete wavelet transform|
|[An Antenna Design of GNSS Terminal on B3 Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109974)|Q. Zhao; Q. Zhu; P. Yang; Z. Zhang; X. Luo; Z. Yan|10.1109/BDICN58493.2023.00034|antenna;microstrip;GNSS;HFSS.;antenna;microstrip;GNSS;HFSS.|
|[Bus passenger flow prediction using BO-optimized XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109905)|M. Yan; K. Zhou|10.1109/BDICN58493.2023.00035|Passengerflow;XGBoost;Bayesian optimization;Passengerflow;XGBoost;Bayesian optimization|
|[A Transformer-Based Network for Low-Light Image Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109936)|C. Gong; Z. Liu; X. Wang; X. Wang|10.1109/BDICN58493.2023.00036|self-attention;image enhancement;transformer;ISP;self-attention;image enhancement;transformer;ISP|
|[Modeling Method of Global Equity Based On Asteroid Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109926)|J. Mao|10.1109/BDICN58493.2023.00037|Modeling;GEEI;Global Equity;Asteroid Mining;Modeling;GEEI;Global Equity;Asteroid Mining|
|[Methods of Separable RGB-D 3D Human Pose Estimation for Different Scenes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109899)|H. Yu; X. Qian; F. Chen; H. Liu; K. Li; T. Cai|10.1109/BDICN58493.2023.00038|Deep learning;3D human pose estimation;RGB-D;3d convolutional networks;Deep learning;3D human pose estimation;RGB-D;3d convolutional networks|
|[Anomaly Detection of Power Information System Based on Attention Mechanism CNN-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109957)|S. Zhang; J. Duan; Y. Li; J. Chen; J. Zhao|10.1109/BDICN58493.2023.00039|anomaly detection;log;attention mechanism;electric power information system;anomaly detection;log;attention mechanism;electric power information system|
|[Knowledge Graph Construction for Risk Assessment Evidence of Special Items](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109950)|C. Yang|10.1109/BDICN58493.2023.00040|Special Items;Knowledge Graph;Neo4j (key words);Special Items;Knowledge Graph;Neo4j (key words)|
|[Research on an Improved QUIC Protocol MINQUIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109938)|L. Sun|10.1109/BDICN58493.2023.00041|MINQUIC;quick UDP internet connections;QUIC;BBR;congestion control;Tluroughput;MINQUIC;quick UDP internet connections;QUIC;BBR;congestion control;Tluroughput|
|[Improved Resnet18 Expression Recognition Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109927)|Y. Zhang; Y. Wei|10.1109/BDICN58493.2023.00042|Expression recognition;Attention mechanism;feature extraction;Activation function;Expression recognition;Attention mechanism;feature extraction;Activation function|
|[Research on real-time spectrum analysis technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109903)|J. Liu; T. Liu; J. Zheng; X. Feng|10.1109/BDICN58493.2023.00043|component;spectrum analysis;parallel processing;FFT;high-speed;component;spectrum analysis;parallel processing;FFT;high-speed|
|[A dermoscopic image segmentation algorithm based on U-shaped architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109940)|K. Ru; X. Li|10.1109/BDICN58493.2023.00044|component;medical image segmentation;multi-scale;deep learning;attention mechanism;component;medical image segmentation;multi-scale;deep learning;attention mechanism|
|[Generation of Chinese classical poetry based on pretrained model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109902)|Z. Wang; L. Guan; G. Liu; J. Ma|10.1109/BDICN58493.2023.00045|Deep Learning;Text Generation;Pre-trained model;BART;Classical Chinese poetry;Turing test;Deep Learning;Text Generation;Pre-trained model;BART;Classical Chinese poetry;Turing test|
|[EEG Signal Classification and Feature Extraction Methods Based on Deep Learning: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109904)|C. Xu; R. -Z. Xia|10.1109/BDICN58493.2023.00046|Electroencephalography;Feature extraction;Deep learning;Convolution neural network;Classification;Electroencephalography;Feature extraction;Deep learning;Convolution neural network;Classification|
|[Research on risk evaluation method of dynamic training of ideological and political education based on machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109912)|G. Wang|10.1109/BDICN58493.2023.00047|Artificial intelligence technology;education Intelligent supply;machine learning;Artificial intelligence technology;education Intelligent supply;machine learning|
|[Research on Screen Shooting Resilient Watermarking based on Dot-Matric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109960)|S. Kang; B. Jin; Y. Liu; Z. Wang|10.1109/BDICN58493.2023.00048|component;screen shooting watermarking;dotmatric;informationhiding;digital watermarking;component;screen shooting watermarking;dotmatric;informationhiding;digital watermarking|
|[Risk assessment of intelligent real estate development of super-large urban complex project based on data informatization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109909)|J. Bi; M. Zhang|10.1109/BDICN58493.2023.00049|Commercial complex;development;risk evaluation;Commercial complex;development;risk evaluation|
|[A Novel Effective Combinatorial Framework for Sign Language Translation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109931)|S. Lin; J. You; Z. He; H. Jia; L. Chen|10.1109/BDICN58493.2023.00050|component;CLIP4clip;MarianMT;Multimodal;Multitask;Sign Language Ttranslation;component;CLIP4clip;MarianMT;Multimodal;Multitask;Sign Language Ttranslation|
|[An improved smoothed L0 norm block sparse signal recovery algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109956)|J. Feng; Q. Zhao|10.1109/BDICN58493.2023.00051|Smoothed LO norm;Sparse signal;Reconstruction algorithm;Negative exponential function;Smoothed LO norm;Sparse signal;Reconstruction algorithm;Negative exponential function|
|[Multimodal Emotion Analysis Based on AttMISA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109962)|X. Wang; X. Mi|10.1109/BDICN58493.2023.00052|EmotionalAnalysis;Multimodal;AttMISA;EmotionalAnalysis;Multimodal;AttMISA|
|[Analysis and Classification of Glassware based on Decision Trees and K-Means Clustering Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109952)|M. Zhu; W. Chang; S. Kon|10.1109/BDICN58493.2023.00053|Pearson correlation coefficient;decision tree regression model;K-Means clustering analysis;BP neural network classification;glass;Pearson correlation coefficient;decision tree regression model;K-Means clustering analysis;BP neural network classification;glass|
|[How can entities improve the quality of medical dialogue generation?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109977)|L. Xiong; Y. Guo; Y. Chen; S. Liang|10.1109/BDICN58493.2023.00054|Entity prediction;Entity-aware Fusion Dialogue Generation;Entity prediction;Entity-aware Fusion Dialogue Generation|
|[Research on Computerized Automatic Word Segmentation of Chinese Stylistic Words](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109934)|M. Xu|10.1109/BDICN58493.2023.00055|natural language processing;Chinese;automatic word segmentation;natural language processing;Chinese;automatic word segmentation|
|[Research on Multimedia Ecological Aesthetic Education in universities based on TOPSIS algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109917)|J. Chen; J. Wang; C. Zhao|10.1109/BDICN58493.2023.00056|TOPSIS method;multimedia application;ecological aesthetic education;TOPSIS method;multimedia application;ecological aesthetic education|
|[Design of Music Art Teaching Quality Evaluation System Based on Intelligent Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109918)|B. Ye|10.1109/BDICN58493.2023.00057|Intelligent optimization Algorithm;Music Art;Teaching Quality;Evaluation System;Intelligent optimization Algorithm;Music Art;Teaching Quality;Evaluation System|
|[PRFT: A Fuzz Testing Method for Tire Pressure Monitoring System Based on Protocol Reverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109980)|M. Li; Y. Wang; H. Zhang; J. Wang|10.1109/BDICN58493.2023.00058|Tire Pressure Monitoring System;Fuzz Testing;Protocol Reverse;Cybersecurity;Tire Pressure Monitoring System;Fuzz Testing;Protocol Reverse;Cybersecurity|
|[Combining Event Segment Classification And Graph Self-Encoder For Event Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109964)|Y. Yuan; Y. Wang; X. Gong|10.1109/BDICN58493.2023.00059|event Forecast;event evolution graph;self-encoder;event segment classification;event Forecast;event evolution graph;self-encoder;event segment classification|
|[IMM-Net: Integrated Multi-stream Memory Network for real-time semantic segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109915)|Z. Huo; J. Chen; F. Luo; H. Jia; Y. Qiao|10.1109/BDICN58493.2023.00060|real-time semantic segmentation;IMM-Net;multistream;memory;inference speed;real-time semantic segmentation;IMM-Net;multistream;memory;inference speed|
|[Pose ResNet: A 3D human pose estimation network model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109958)|W. Bao; Z. Ma; D. Liang; X. Yang; T. Niu|10.1109/BDICN58493.2023.00061|3D human pose estimation;CBAM;WASP;synthetic occlusion;3D human pose estimation;CBAM;WASP;synthetic occlusion|
|[Computer Security Algorithm Based on Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109925)|J. Yin; B. He; J. Zhang|10.1109/BDICN58493.2023.00062|Convolutional Neural Network;Computer Security;Security Algorithm;Risk Assessment;Convolutional Neural Network;Computer Security;Security Algorithm;Risk Assessment|
|[Lip Recognition Based on 3D Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109906)|L. Zhong; C. Yan|10.1109/BDICN58493.2023.00063|deep learning;3D Convolutional Neural Network;lip recognition;attention mechanism;deep learning;3D Convolutional Neural Network;lip recognition;attention mechanism|
|[Application of Artificial Intelligence in the Construction of Intelligent Knowledge Base](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109930)|W. Zhu; D. Huang; Y. Zhang; Y. Yu; Q. Wu|10.1109/BDICN58493.2023.00064|artificial intelligence;Intelligent knowledge base;Construction of knowledge base;artificial intelligence;Intelligent knowledge base;Construction of knowledge base|
|[Research on the Intelligent Orchestration System of Cloud Network Based on ONAP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109907)|X. Zhu; Y. Liu|10.1109/BDICN58493.2023.00065|ONAP;cloud network;intelligent orchestration;ONAP;cloud network;intelligent orchestration|
|[On optimal routing and spectrum allocation in elastic optical networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109972)|J. Zhang; P. Miao; F. Zhang|10.1109/BDICN58493.2023.00066|elastic optical networks;routing and spectrum allocation;integer linear programming;elastic optical networks;routing and spectrum allocation;integer linear programming|
|[Abnormal Linux process detection based on CPU usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109919)|H. Wei; H. Yan|10.1109/BDICN58493.2023.00067|KVM;Linux;Abnormal Process;CPU usage;KVM;Linux;Abnormal Process;CPU usage|
|[DT-PBFT: A Double-Layer Group Consensus Algorithm of Credibility for IoT Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109970)|Y. Chen; Y. Jia|10.1109/BDICN58493.2023.00068|IoT;blockchain;PBFT;consensus;IoT;blockchain;PBFT;consensus|
|[Adversarial Multi-dimensional Distribution Alignment for Cross-domain Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109914)|C. Fan; Y. Wang; Y. Wu|10.1109/BDICN58493.2023.00069|Domain Adaptation;Sentiment Analysis;Transfer Learning;Adversarial Learning;Domain Adaptation;Sentiment Analysis;Transfer Learning;Adversarial Learning|
|[Restoration Algorithm of Lijiang Baisha Murals Based on Structure Tensor and Gradient Priority Calculation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109968)|L. Yang; Y. Yu|10.1109/BDICN58493.2023.00070|component;Baisha mural;mural digital restoration;structure tensor;priority;similarity measurement;component;Baisha mural;mural digital restoration;structure tensor;priority;similarity measurement|
|[SplitKV: Improve key-value separation store performance by splitting operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109942)|J. Ren; K. Qian; G. Chen|10.1109/BDICN58493.2023.00071|LSM-tree;Key-value separation;Big data storage;RocksDB;LSM-tree;Key-value separation;Big data storage;RocksDB|
|[3D reconstruction of PCB based on ICP point cloud optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109935)|Z. Fu; B. Qiu; J. Shen; N. Chen|10.1109/BDICN58493.2023.00072|component;PCB defect detection;optimal point cloud alignment;iterative nearest point algorithm;superimposed non-overlapping parts;component;PCB defect detection;optimal point cloud alignment;iterative nearest point algorithm;superimposed non-overlapping parts|
|[Research on Facility Layout of Manufacturing Workshop Based on Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109953)|L. Jiang|10.1109/BDICN58493.2023.00073|manufacturing workshop;facility layout;genetic algorithm;manufacturing workshop;facility layout;genetic algorithm|
|[Genetic Algorithm based Fire-fighting Path Planning Method for Multiple Spontaneous Combustion Events in Closed Coal Yard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109949)|F. Wu; Y. Ruan; H. Lei; Z. Zhou; X. Liu; Q. Lin; X. He; Z. Wang|10.1109/BDICN58493.2023.00074|spontaneous combustion point of coal pile;cooling path optimization;coal loss;genetic algorithm;spontaneous combustion point of coal pile;cooling path optimization;coal loss;genetic algorithm|
|[Fault Diagnosis for Rolling Bearings Based on Recurrence Plot and Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109955)|Y. Tian; J. Huang; Y. Sun|10.1109/BDICN58493.2023.00075|Fault diagnosis;Recurrence plot;Convolutional neural network;Nonlinear analysis;Fault diagnosis;Recurrence plot;Convolutional neural network;Nonlinear analysis|
|[An Unmanned Vehicle Location Algorithm Based on UWb Combined TOF and TDOA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109920)|W. Ruan; Y. Liu|10.1109/BDICN58493.2023.00076|UWB;TOF;TDOA;Taylor;Kalman;UWB;TOF;TDOA;Taylor;Kalman|
|[Application of Virtual Reality Technology in Electric Power Training System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109973)|P. Yang; H. Zhou|10.1109/BDICN58493.2023.00077|Virtual reality technology;Electric power;Training system;Power system operation;Company operation;Virtual reality technology;Electric power;Training system;Power system operation;Company operation|
|[Anonymous Password-Authenticated Key Exchange Protocol Based on Lattice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109908)|Y. Song; S. Guo; R. Ding|10.1109/BDICN58493.2023.00078|lattice;RL WE;SPHF;Anonymous PAKE;lattice;RL WE;SPHF;Anonymous PAKE|
|[Style Transfer with Subject-Object Separation using Masked Random Adaptive Instance Normalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109945)|Y. Kang; C. Zhu|10.1109/BDICN58493.2023.00079|Style transfer;Subject-object separation;The AdaIN network;Edge enhancement;Style transfer;Subject-object separation;The AdaIN network;Edge enhancement|
|[A Deep Semantic Retrieval Model for Business Line Construction Supervision Clauses by Contrastive Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109943)|Y. -F. Mei|10.1109/BDICN58493.2023.00080|contrastive learning;semantic retrieval;automated and intelligent platform;sentence embeddings;construction safety supervision;contrastive learning;semantic retrieval;automated and intelligent platform;sentence embeddings;construction safety supervision|
|[Visual Object Tracking Based on Double Siamese Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109981)|R. Yang; Y. Wang; W. Cai|10.1109/BDICN58493.2023.00081|Visual Tracking;Siamese Network;Attention Mechanism;Transformer;Visual Tracking;Siamese Network;Attention Mechanism;Transformer|
|[Security Analysis and Improvement of Proxy Signature Scheme with Certificateless and Blind Properties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109951)|X. Hu|10.1109/BDICN58493.2023.00082|computer security;cryptography;certificateless proxy blind signature;certificateless signature scheme;unforgeability;computer security;cryptography;certificateless proxy blind signature;certificateless signature scheme;unforgeability|
|[Research on a Crack Extraction Algorithm of Bridge Deck of Simple Supported Girder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109910)|S. Li; H. Yu|10.1109/BDICN58493.2023.00083|Crack Extraction;Semantic Segmentation;Residual Network;Multi-scale information fusion;Crack Extraction;Semantic Segmentation;Residual Network;Multi-scale information fusion|
#### **2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA)**
- DOI: 10.1109/RADIOELEKTRONIKA57919.2023
- DATE: 19-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Single Frequency Networks for DVB-T2: Analysis of Real Case Scenarios in Czech Republic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109047)|L. Polak; V. Barta; J. Kufa; M. Simka; R. Zedka; R. Sotner; A. Dhaka|10.1109/RADIOELEKTRONIKA57919.2023.10109047|DVB-T2;MER;OFDM;quasi-error free reception;SFN;RF measurement;DVB-T2;MER;OFDM;quasi-error free reception;SFN;RF measurement|
|[A Reconfigurable Leaky Wave Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109054)|J. Machac; M. Svanda; V. Kabourek|10.1109/RADIOELEKTRONIKA57919.2023.10109054|Reconfigurable antenna;leaky wave antenna;mushroom cell;varactor;Reconfigurable antenna;leaky wave antenna;mushroom cell;varactor|
|[The Creation Method of a Solar Tracker Using the Zybo SoC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109071)|R. Szabo; R. -S. Ricman|10.1109/RADIOELEKTRONIKA57919.2023.10109071|field programmable gate arrays;robotic assembly;robot control;robot programming;solar energy;solar panels;solar power generation;solar tracker;system-on-chip.;field programmable gate arrays;robotic assembly;robot control;robot programming;solar energy;solar panels;solar power generation;solar tracker;system-on-chip.|
|[An Adaptive Window Function based Memristor Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109029)|Y. R. Ananda; A. Hajra; G. Trivedi|10.1109/RADIOELEKTRONIKA57919.2023.10109029|Memristor;nonlinearity;adaptive parameter;memristor modeling;window function;Memristor;nonlinearity;adaptive parameter;memristor modeling;window function|
|[Threshold Degradation of Digital Receivers due to Interference in Fixed Wireless Access Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109030)|A. Hilt|10.1109/RADIOELEKTRONIKA57919.2023.10109030|mobile anyhaul;5G;4G;FWA;μ/mmW;radio link;QAM;interference;BER;threshold;topology;availability;mobile anyhaul;5G;4G;FWA;μ/mmW;radio link;QAM;interference;BER;threshold;topology;availability|
|[Improved RCS Chipless RFID Tag Array for Long Reading Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109004)|M. El-Hadidy; L. Lasantha; S. I. Khan; Y. E. Sayed; N. C. Karmakar|10.1109/RADIOELEKTRONIKA57919.2023.10109004|Tag array;Chipless Radio-Frequency Identification (RFID);long reading range;Radar Cross Section (RCS);Tag array;Chipless Radio-Frequency Identification (RFID);long reading range;Radar Cross Section (RCS)|
|[Some Variants Current Mode Filter via Commercial Integrated Circuits and its Proprieties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109073)|B. Brtník|10.1109/RADIOELEKTRONIKA57919.2023.10109073|current conveyor;transimpedance amplifier;current-mode network;current mode filter.;current conveyor;transimpedance amplifier;current-mode network;current mode filter.|
|[Highly Independent Dual-Band Permittivity Sensors for Simultaneous Measurement of Solid Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109074)|S. Alam; Z. Zakaria; I. Surjati; N. A. Shairi; M. Alaydrus; T. Firmansyah|10.1109/RADIOELEKTRONIKA57919.2023.10109074|microwave sensors;dual-band;independent performance;simultaneous measurements.;microwave sensors;dual-band;independent performance;simultaneous measurements.|
|[Application of Compressed Sensing to Radar Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109041)|J. Perďoch; M. Pacek; Z. Matoušek; S. Gažovová|10.1109/RADIOELEKTRONIKA57919.2023.10109041|compressed sensing;sparse sensing;radar signal processing;reconstruction algorithm;compressed sensing;sparse sensing;radar signal processing;reconstruction algorithm|
|[Comparative Exploration of Gate Count and Leakage Optimized D-Latch in Nanometer CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109063)|P. Bhattacharjee; G. N. Goud; V. K. Singh; V. P. Yadav; A. J. Mondal; A. Majumder|10.1109/RADIOELEKTRONIKA57919.2023.10109063|low power;VLSI;D-latch;LECTOR;smallsignal model;static & dynamic power;low power;VLSI;D-latch;LECTOR;smallsignal model;static & dynamic power|
|[Text Data Pre-Processing for Time-series Modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109034)|J. Poměnková; P. Koráb; D. Štrba|10.1109/RADIOELEKTRONIKA57919.2023.10109034|text mining;natural language processing;timeseries modeling;data pre-processing;text mining;natural language processing;timeseries modeling;data pre-processing|
|[Low Voltage FinFET Based OTA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109070)|E. Demirci; S. KeleŞ|10.1109/RADIOELEKTRONIKA57919.2023.10109070|low Voltage;FinFET (Fin Field Effect Transistor;OTA (Operational Transconductance Amplifier);low Voltage;FinFET (Fin Field Effect Transistor;OTA (Operational Transconductance Amplifier)|
|[Effects of the Multi-Beam Optical FBMC Technology with Applied Data Separations in Optical Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109036)|R. Róka; L. Hudcová; A. Koval’Ová; A. G. Armada|10.1109/RADIOELEKTRONIKA57919.2023.10109036|multi-beam optical FBMC model;atmospheric turbulences;optical wireless transmission path;numerical simulations;multi-beam optical FBMC model;atmospheric turbulences;optical wireless transmission path;numerical simulations|
|[Electronically Controlled Waveguide Phase Shifter for High Power Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109037)|M. Popela; O. Simon; J. Lacik|10.1109/RADIOELEKTRONIKA57919.2023.10109037|ferrite circulator;stepper motor;waveguide shunt;rectangular waveguide;ferrite circulator;stepper motor;waveguide shunt;rectangular waveguide|
|[Clustering Using Conditional Generative Adversarial Networks (cGANs)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109069)|M. Ružička; M. Dopiriak|10.1109/RADIOELEKTRONIKA57919.2023.10109069|clustering;conditional generative adversarial network;GAN;k-means;mean-shift;clustering;conditional generative adversarial network;GAN;k-means;mean-shift|
|[Development of an Intelligent Racking System for early Detection of Abnormal Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109003)|M. Dobrovolný; J. Fikejz; J. Roleček|10.1109/RADIOELEKTRONIKA57919.2023.10109003|steel racking systems;sensors;accelerometers;intelligent remote monitoring;steel racking systems;sensors;accelerometers;intelligent remote monitoring|
|[Omnidirectional Image Quality Assessment Database (OMNIQAD): Description and Examples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109005)|M. Simka; L. Polak; J. Kufa; M. Novotny; A. Zizien; K. Fliegel|10.1109/RADIOELEKTRONIKA57919.2023.10109005|Omnidirectional images (360°);VR database;quality assessment;immersive content;image compression;image distortion;JPEG;JPEG XL;HEIC;AVIF;Omnidirectional images (360°);VR database;quality assessment;immersive content;image compression;image distortion;JPEG;JPEG XL;HEIC;AVIF|
|[Performance Analysis of Cooperative NOMA Power Line Communication Networks with Imperfect SIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109086)|E. M. Shaheen; M. R. Soleymani|10.1109/RADIOELEKTRONIKA57919.2023.10109086|NOMA-PLC communication network;log-normal fading channel;outage probability;system throughput;imperfect successive interference cancellation;NOMA-PLC communication network;log-normal fading channel;outage probability;system throughput;imperfect successive interference cancellation|
|[Interaction of selected parts of the human body with radio frequency fields](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109067)|B. Dolnik; E. Dolníková; E. Lumnitzer; E. Jurgovská; P. Liptai; L. Šárpataky|10.1109/RADIOELEKTRONIKA57919.2023.10109067|electromagnetic waves;human body;absorption;impact;measurements;electromagnetic waves;human body;absorption;impact;measurements|
|[Comparison of Size Reduction Techniques in Spiral Antenna Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10109087)|R. N. Sağ; T. Tüylü; N. T. Tokan|10.1109/RADIOELEKTRONIKA57919.2023.10109087|archimedean;meander line;resistive loading;ring;spiral antenna;tapered line;gap loading;size reduction;archimedean;meander line;resistive loading;ring;spiral antenna;tapered line;gap loading;size reduction|
#### **2023 28th International Computer Conference, Computer Society of Iran (CSICC)**
- DOI: 10.1109/CSICC58665.2023
- DATE: 25-26 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Analyzing the Use of Auditory Filter Models for Making Interpretable Convolutional Neural Networks for Speaker Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105387)|H. Fayyazi; Y. Shekofteh|10.1109/CSICC58665.2023.10105387|Auditory Filter Models;eXplainable AI;Deep Learning;Speaker Identification;Auditory Filter Models;eXplainable AI;Deep Learning;Speaker Identification|
|[Deceptive review detection using GAN enhanced by GPT structure and score of reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105368)|M. Tamimi; M. Salehi; S. Najari|10.1109/CSICC58665.2023.10105368|Deceptive review detection;Generative Adversarial Network (GAN);Generative Pre-trained Transformer (GPT);text feature;behavioral feature;Deceptive review detection;Generative Adversarial Network (GAN);Generative Pre-trained Transformer (GPT);text feature;behavioral feature|
|[Leveraging Model Driven Techniques for Designing Web-GIS Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105349)|Z. Hashemian; B. Zamani; M. Adibi|10.1109/CSICC58665.2023.10105349|Geographic Information Systems;Web-GIS;Web-based GIS;Model Driven Engineering;Geographic Information Systems;Web-GIS;Web-based GIS;Model Driven Engineering|
|[Multi-Objective Optimization for Neural Network Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105405)|M. Shokoohi; M. Teshnehlab|10.1109/CSICC58665.2023.10105405|Neural Network;Optimization;Overfitting;Local minimum;Generalizability;Classification;Neural Network;Optimization;Overfitting;Local minimum;Generalizability;Classification|
|[Latency-aware SDN-based Mobile Edge Computation Offloading in Industrial IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105378)|S. S. Hoseini; T. Beyer; A. Ghaderi; Z. Movahedi|10.1109/CSICC58665.2023.10105378|MEC;Computation offloading;SDN;IIoT;MEC;Computation offloading;SDN;IIoT|
|[A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105351)|M. Azizmalayeri; A. Zarei; A. Isavand; M. T. Manzuri; M. H. Rohban|10.1109/CSICC58665.2023.10105351|Adversarial Training;Attack;Data-Centric;Out-of-Distribution Detection;Adversarial Training;Attack;Data-Centric;Out-of-Distribution Detection|
|[Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105400)|S. Hassanzadeh Mostafaei; J. Tanha; A. Sharafkhaneh; R. Agrawal; Z. Hassanzadeh Mostafaei|10.1109/CSICC58665.2023.10105400|Machine Learning;Sleep Staging;Imbalanced Data;Biological Signals;Polysomnography;Sleep Heart Health Study;Machine Learning;Sleep Staging;Imbalanced Data;Biological Signals;Polysomnography;Sleep Heart Health Study|
|[Balance Control of a Humanoid Robot Using DeepReinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105418)|E. Kouchaki; M. Palhang|10.1109/CSICC58665.2023.10105418|humanoid robot;deep reinforcement learning;actor-critic;balance control;humanoid robot;deep reinforcement learning;actor-critic;balance control|
|[SBSN: Harvesting Stable Body Sensor Node by Providing an Energy Efficient Adaptive Sampling Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105340)|R. Mohammadi; Z. Shirmohammadi|10.1109/CSICC58665.2023.10105340|Body Sensor Node;Energy Harvesting;Energy Efficiency;Adaptive sampling rate;Paint risk;Body Sensor Node;Energy Harvesting;Energy Efficiency;Adaptive sampling rate;Paint risk|
|[Quantizing YOLOv7: A Comprehensive Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105310)|M. Baghbanbashi; M. Raji; B. Ghavami|10.1109/CSICC58665.2023.10105310|deep neural network;DNN;object detection;YOLOv7;compression;quantization;deep neural network;DNN;object detection;YOLOv7;compression;quantization|
|[An Approximate Method for Spatial Task Allocation in Partially Observable Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105411)|S. Amini; M. Palhang; N. Mozayani|10.1109/CSICC58665.2023.10105411|task allocation;partially observable world;teamwork;multi-robot systems;Monte-Carlo planning;task allocation;partially observable world;teamwork;multi-robot systems;Monte-Carlo planning|
|[An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105364)|H. Barangi; S. K. Rahimi; B. Zamani; H. Moradi|10.1109/CSICC58665.2023.10105364|Ontology;Ontology as a Service;Semantic Interoperability;Context-Aware Systems;Ontology;Ontology as a Service;Semantic Interoperability;Context-Aware Systems|
|[A Hybrid Deep Learning Network for Sentiment Analysis on SemEval-2017 Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105312)|B. Sar-Saifee; J. Tanha; M. Aeini|10.1109/CSICC58665.2023.10105312|sentiment analysis;twitter social network;deep learning;natural language processing;sentiment analysis;twitter social network;deep learning;natural language processing|
|[Predicting the Load Capacity of 4G Cellular Networks With Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105423)|H. Azadegan; F. Esmaili|10.1109/CSICC58665.2023.10105423|Channel Quality Indicator;Deep Learning;Event;Cellular Communication;Channel Quality Indicator;Deep Learning;Event;Cellular Communication|
|[Predicting Users' Demographic Features Based on Searched Queries and Installed Apps and Games](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105350)|G. Kalhor; B. Bahrak|10.1109/CSICC58665.2023.10105350|online business;gender;age;installed app;search query;machine learning;online business;gender;age;installed app;search query;machine learning|
|[Real-time Steal Recognition on CCTV-Based Videos for Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105393)|S. Kerachi; A. Komaei Koma; H. Asharioun|10.1109/CSICC58665.2023.10105393|action recognition;recurrent neural networks;convolutional neural networks;deep learning;surveillance cameras;embedded systems;real-time;action recognition;recurrent neural networks;convolutional neural networks;deep learning;surveillance cameras;embedded systems;real-time|
|[Fashion Compatibility Learning Via Triplet-Swin Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105392)|H. Darvishi; R. Azmi; F. Moradian; M. Zarvani|10.1109/CSICC58665.2023.10105392|embedding method;fashion compatibility;outfit recommendation;swin transformer;triplet network;embedding method;fashion compatibility;outfit recommendation;swin transformer;triplet network|
|[OutCLIP, A New Multi-Outfit CLIP Based Triplet Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105384)|Z. Haghgu; R. Azmi; L. Zamani; F. Moradian|10.1109/CSICC58665.2023.10105384|Triplet Net;Vision Transformers;CLIP Transformer;Object Detection;Multi Outfit;Image Retrieval;Deep Learning;Triplet Net;Vision Transformers;CLIP Transformer;Object Detection;Multi Outfit;Image Retrieval;Deep Learning|
|[A Question Summarization Method based-on Deep Learning in Persian Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105324)|A. Ghafouri; I. Barati; M. H. Elahimanesh; H. Hasanpour|10.1109/CSICC58665.2023.10105324|question summarization;abstract summarization;pre-trained language model;transformer-based models;natural language understanding;question summarization;abstract summarization;pre-trained language model;transformer-based models;natural language understanding|
|[A Novel Convolutional-Transformer Neural Network Architecture for Diagnosis of Pneumothorax](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105407)|A. Sanati; M. A. Dashtestani; H. Rostami; S. T. Azad|10.1109/CSICC58665.2023.10105407|Pneumothorax;Convolutional Neural Network;Attention mechanism;Transformers;Classification;Pneumothorax;Convolutional Neural Network;Attention mechanism;Transformers;Classification|
#### **2023 IEEE 3rd International Symposium on Joint Communications & Sensing (JC&S)**
- DOI: 10.1109/JCS57290.2023
- DATE: 5-7 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Interference Mitigation in Joint Communications and Sensing-Part II: Coding and Spreading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107466)|H. Li|10.1109/JCS57290.2023.10107466|;|
|[Interference Mitigation in Joint Communications and Sensing-Part I: Correlation and Collision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107459)|H. Li|10.1109/JCS57290.2023.10107459|;|
|[Spectral Efficiency Analysis for a Spike-Based Pulse Repetition Frequency Modulation Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107510)|F. Roth; M. Dörpinghaus; G. Fettweis|10.1109/JCS57290.2023.10107510|IoT sensor node;A/D conversion;IoT sensor node;A/D conversion|
|[Analysis of V2X Sidelink Positioning in sub-6 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107458)|Y. Ge; M. Stark; M. F. Keskin; F. Hofmann; T. Hansen; H. Wymeersch|10.1109/JCS57290.2023.10107458|Sub-6 GHz;multipath;sidelink positioning;performance bound;ray-tracing;Sub-6 GHz;multipath;sidelink positioning;performance bound;ray-tracing|
|[Sensing-Assisted Receivers for Resilient-By-Design 6G MU-MIMO Uplink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107512)|V. C. Andrei; X. Li; U. J. Mönich; H. Boche|10.1109/JCS57290.2023.10107512|Worst-case jammer;Multiple-Input-Multiple-Output (MIMO);6G;Joint communication and sensing (JCAS);Beamforming;Worst-case jammer;Multiple-Input-Multiple-Output (MIMO);6G;Joint communication and sensing (JCAS);Beamforming|
|[A CSP's View on Opportunities and Challenges of Integrated Communications and Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107457)|A. Kadelka; G. Zimmermann; J. Plachý; O. Holschke|10.1109/JCS57290.2023.10107457|Integrated communications and sensing;communication services provider;5G;6G;Integrated communications and sensing;communication services provider;5G;6G|
|[Framework for Simulation Models and Algorithms in ISAC Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107507)|C. Smeenk; C. Schneider; R. S. Thoma|10.1109/JCS57290.2023.10107507|ISAC;Simulation;Signal Processing;ISAC;Simulation;Signal Processing|
|[6G Radio-Based Parking Lot Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107515)|M. Bauhofer; Y. Zhang; M. Arnold; S. Ten Brink|10.1109/JCS57290.2023.10107515|;|
|[26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107463)|B. van Berlo; Y. Miao; R. Hersyandika; B. Willetts; K. Mao; A. Zare; S. Pollin; N. Meratnia|10.1109/JCS57290.2023.10107463|Joint Communication and Sensing;Integrated Sensing and Communication;Human Blockage Prediction;Machine Learning;Domain Shift;Measurement Dataset;Joint Communication and Sensing;Integrated Sensing and Communication;Human Blockage Prediction;Machine Learning;Domain Shift;Measurement Dataset|
|[Crowd Counting Model Training with the Method of Moments in Electromagnetics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107513)|L. Storrer; H. C. Yildirim; M. Willame; J. Louveaux; P. De Doncker; S. Pollin; F. Horlin|10.1109/JCS57290.2023.10107513|Wi-Fi sensing;Method of Moments;crowd monitoring;people counting;Support Vector Machine;Wi-Fi sensing;Method of Moments;crowd monitoring;people counting;Support Vector Machine|
|[Improving Radio Environment Maps with Joint Communications and Sensing: An Outlook](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107465)|A. Krause; P. Schulz; F. Burmeister; G. Fettweis|10.1109/JCS57290.2023.10107465|Radio environment map (REM);spectrum sensing;machine learning (ML);joint communications and sensing (JCAS);Radio environment map (REM);spectrum sensing;machine learning (ML);joint communications and sensing (JCAS)|
|[Passive Wi-Fi-based Radars with 802.11ax MU-MIMO Signals: AoD Estimation with a Single Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107509)|H. C. Yildirim; L. Storrer; M. Willame; J. Louveaux; P. De Doncker; F. Horlin|10.1109/JCS57290.2023.10107509|Passive Wi-Fi Radar;MU-MIMO;precoding;angle-of-departure estimation;Passive Wi-Fi Radar;MU-MIMO;precoding;angle-of-departure estimation|
|[Defected Ground Structure-based Compact Wideband Monopole Antenna Design with Equivalent Circuit Modeling for Communication and Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107460)|M. R. Hossen; M. Ramzan; P. Sen|10.1109/JCS57290.2023.10107460|DGS;Wideband/UWB;Equivalent Circuit Model;Experimental Validation;JC&S;DGS;Wideband/UWB;Equivalent Circuit Model;Experimental Validation;JC&S|
|[Antenna-Duplexed Passive Beamforming Front-end for Joint Communication and Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107462)|M. Umar; P. Sen|10.1109/JCS57290.2023.10107462|Antenna duplexing;Beamforming;Butler matrix;Circulator;Full duplex;Multiplexer;RF switch;Antenna duplexing;Beamforming;Butler matrix;Circulator;Full duplex;Multiplexer;RF switch|
|[A Codebook Approach to Spillover Cancellation in Multi-Antenna Radar and Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107508)|A. Sakhnini; A. Bourdoux; S. Pollin|10.1109/JCS57290.2023.10107508|;|
|[A Novel Waveform Design for OFDM-Based Joint Sensing and Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107516)|Y. Geng|10.1109/JCS57290.2023.10107516|OFDM;6G;JCAS;radar;waveform;DFT;IDFT;OFDM;6G;JCAS;radar;waveform;DFT;IDFT|
|[OTFS Communications-assisted Environment Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107514)|B. Li; W. Yuan|10.1109/JCS57290.2023.10107514|OTFS;location estimation;Doppler-frequency-shift;time-delay;OTFS;location estimation;Doppler-frequency-shift;time-delay|
|[Dirichlet Process Clustering-based Radio SLAM with Arbitrarily-Shaped Reflectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107464)|J. Lee; H. Kim; H. Wymeersch; S. Kim|10.1109/JCS57290.2023.10107464|5G millimeter wave;6G Terahertz;simultaneous localization and mapping;Dirichlet process;vehicular networks;5G millimeter wave;6G Terahertz;simultaneous localization and mapping;Dirichlet process;vehicular networks|
|[Software-Defined MIMO OFDM Joint Radar-Communication Platform with Fully Digital mmWave Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107511)|C. D. Ozkaptan; H. Zhu; E. Ekici; O. Altintas|10.1109/JCS57290.2023.10107511|;|
|[Secure Integrated Sensing and Communication for Binary Input Additive White Gaussian Noise Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10107461)|O. Günlü; M. Bloch; R. F. Schaefer; A. Yener|10.1109/JCS57290.2023.10107461|;|
#### **2023 9th International Conference on Mechatronics and Robotics Engineering (ICMRE)**
- DOI: 10.1109/ICMRE56789.2023
- DATE: 10-12 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Model Predictive Control Design of a 3-DOF Robot Arm Based on Recognition of Spatial Coordinates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106581)|Z. Zhou; Y. Zhang; Y. Li|10.1109/ICMRE56789.2023.10106581|colour and depth recognition;inverse kinematics;dynamic model;model predictive control;robotic arm;colour and depth recognition;inverse kinematics;dynamic model;model predictive control;robotic arm|
|[Xiaotian-Hybrid: A Novel Wheeled-Quadruped Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106567)|T. Ren; Z. Cao; H. Chen; W. Zhang|10.1109/ICMRE56789.2023.10106567|Quadruped Robots;Wheeled Robots;Force Control;Quadruped Robots;Wheeled Robots;Force Control|
|[Research on Gesture Guidance and Teaching of Cooperative Robot Based on Nine-axis AirMouse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106524)|W. Chu; X. Juliang; X. Dong; Z. Heqiang; S. Terigen|10.1109/ICMRE56789.2023.10106524|collaborative robot;nine-axis AirMouse;inertial measurement unit;man-machine mapping;teaching repetition;collaborative robot;nine-axis AirMouse;inertial measurement unit;man-machine mapping;teaching repetition|
|[Developing of Automatic Gasket Gluing Robot for a Tractor Company](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106521)|S. Plengkham; N. Busyakanistha; N. Promsuwan; N. Santiudommongkol; R. Chancharoen|10.1109/ICMRE56789.2023.10106521|machine vision;image processing;automatic glue extrusion;G-code;vision sensor;cartesian robot;machine vision;image processing;automatic glue extrusion;G-code;vision sensor;cartesian robot|
|[A Light-Weight Quasi-Direct Drive Collaborative Robot Arm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106520)|Y. Zhao; B. Huang; S. Lin; Z. Zhu; Z. Jia|10.1109/ICMRE56789.2023.10106520|robot arm;manipulation;proprioceptive actuation;mechatronics;robot arm;manipulation;proprioceptive actuation;mechatronics|
|[Human Robot Interaction with Triboelectric Nanogenerator for Tactile Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106577)|C. Li; Y. Wu; Y. Liu; F. Schreier; Z. Bing; A. Knoll; S. Eivazi|10.1109/ICMRE56789.2023.10106577|human robot interaction;Triboelectric Nano-generator;tactile sensor;human robot interaction;Triboelectric Nano-generator;tactile sensor|
|[Development of BIM Semantic Robot Autonomous Inspection and Simulation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106602)|B. Huang; H. Liao; Y. Ge; W. Zhang; H. Kang; Z. Wang; J. Wu|10.1109/ICMRE56789.2023.10106602|building information modeling;crack inspection;construction robotics;robot autonomous inspection;building information modeling;crack inspection;construction robotics;robot autonomous inspection|
|[Tomato Crop Identification and Recognition for an Autonomous Agricultural Robotic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106586)|Q. Sahai; B. Gilliam; B. Chandrasekaran|10.1109/ICMRE56789.2023.10106586|crop recognition;tomato plant;agriculture;autonomous;detection;Turtlebot;crop recognition;tomato plant;agriculture;autonomous;detection;Turtlebot|
|[Experimental Study on the Effect of Singularity on the Stiffness Modelling of Industrial Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106517)|D. Gao; K. Wu; J. Li; J. Zhong; H. Zhao|10.1109/ICMRE56789.2023.10106517|industrial robot;stiffness modelling;singularity;condition number;industrial robot;stiffness modelling;singularity;condition number|
|[Multi-mode Planning for a Low-cost Robot Effective Exploration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106584)|R. Li; J. Xu; H. Fang|10.1109/ICMRE56789.2023.10106584|Autonomous;Exploration;SLAM;Decision-making;Path-planning;Autonomous;Exploration;SLAM;Decision-making;Path-planning|
|[Attitude Control of an All-Wheel-Drive Rover with Integrated Active Suspension System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106596)|S. Yin; W. Wang; L. Hu; Z. Zhu; Z. Jia|10.1109/ICMRE56789.2023.10106596|Mobile Robot;Omni-Directional Vehicle;Active Suspension;Attitude Control;Mobile Robot;Omni-Directional Vehicle;Active Suspension;Attitude Control|
|[A Positioning Method Based on Kalman Filter for FAST Feed Support Cable Inspection Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106594)|X. Sun; G. Liu; X. Zhang; C. Li; J. Zhao|10.1109/ICMRE56789.2023.10106594|positioning;Kalman filter;inspection robot;GNSS;odometer;positioning;Kalman filter;inspection robot;GNSS;odometer|
|[A Framework for Online and Offline Programming of Multi-Robot Cooperative Motion Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106607)|S. Mo; Y. Guan; Y. Li; X. Chen|10.1109/ICMRE56789.2023.10106607|Online and Offline programming;Multi-Robot;Cooperative path planning;SolidWorks;Online and Offline programming;Multi-Robot;Cooperative path planning;SolidWorks|
|[A Reliable Docking Mechanism and Close-Range Docking Algorithm for Modular Reconfigurable Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106592)|B. Huang; B. Zhou; H. Pang; J. Xu; H. Fang|10.1109/ICMRE56789.2023.10106592|docking mechanism;autonomous docking;self- reconfigurable robot;docking mechanism;autonomous docking;self- reconfigurable robot|
|[Similarity Contrastive Capsule Transformation for Image-Text Matching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106583)|B. Zhang; X. Sun; X. Li; S. Wang; D. Liu; J. Jia|10.1109/ICMRE56789.2023.10106583|image-text retrieval;score vector;capsule network;image-text retrieval;score vector;capsule network|
|[Detection for Tiny Screw and Screw Hole by Semantic Segmentation Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106576)|W. Niu; H. Wang; C. Zhuang|10.1109/ICMRE56789.2023.10106576|screw detection;semantic segmentation;BiseNetV2;vibration environment;screw detection;semantic segmentation;BiseNetV2;vibration environment|
|[Dumping Point Localization of Autonomous Excavation Based on Vision in Trenching Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106604)|J. Zhao; Y. Hu; P. Tan; X. Xia; Z. Feng; L. Zhang|10.1109/ICMRE56789.2023.10106604|autonomous excavation;camera marker system;dumping point localization;trenching tasks;autonomous excavation;camera marker system;dumping point localization;trenching tasks|
|[Robust UWB Navigation System for UAV Swarm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106570)|S. Chen; A. Xing; X. Li; Y. Wang; W. Liu|10.1109/ICMRE56789.2023.10106570|UWB;Localization;Navigation;UAV swarm;UWB;Localization;Navigation;UAV swarm|
|[Table-Top Platform of a Large Scale Underwater Swarm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106595)|R. Fu; Y. Ding|10.1109/ICMRE56789.2023.10106595|underwater swarm;experiment platform;collective motion;submarine;tracking-by-detection;underwater swarm;experiment platform;collective motion;submarine;tracking-by-detection|
|[Hybrid Feature Based 6D Pose Tracking under Binocular Vision for Automated Micro-assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106573)|T. Xie; X. Zhang; H. Li; J. Zhang|10.1109/ICMRE56789.2023.10106573|micro-assembly;6D pose tracking;hybrid feature;binocular vision;micro-assembly;6D pose tracking;hybrid feature;binocular vision|
|[Autonomous Driving Peripheral And Central Vision Region Selection For Semantic Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106575)|A. Abdelkader; M. Abdelwahab; F. Ibrahim; M. Abdelaziz|10.1109/ICMRE56789.2023.10106575|autonomous systems;autonomous driving transportation systems;machine vision;autonomous systems;autonomous driving transportation systems;machine vision|
|[Hierarchical Visual Localization and Measurement Method for Outdoor Large-Scale Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106606)|A. Xing; Y. Wang; X. Li; S. Chen; W. Liu|10.1109/ICMRE56789.2023.10106606|hierarchical;large-scale environment;binocular vision;container;localization;hierarchical;large-scale environment;binocular vision;container;localization|
|[A Sampling-based Next-Best-View Path Planner for Environment Exploration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106522)|Q. Liu; Y. Jiang; Y. Li|10.1109/ICMRE56789.2023.10106522|NBV;exploration;B-spline;NBV;exploration;B-spline|
|[A Retrofitted Dynamic Window Approach with Pivot Point Control for Maneuvering Inland Vessels on Constrained Surfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106525)|D. Wang; Y. Zhang; P. Slaets|10.1109/ICMRE56789.2023.10106525|dynamic window approach;pivot point;collision avoidance;confined surfaces;unmanned surface vehicles;dynamic window approach;pivot point;collision avoidance;confined surfaces;unmanned surface vehicles|
|[Structure Keeping Control for Heterogeneous Formations Based on Consistency Theory and Graph Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106518)|A. Wang; F. Jing; X. Huang; C. Gong; S. Xu|10.1109/ICMRE56789.2023.10106518|structure keeping control;consistency theory;graph theory;heterogeneous formation;structure keeping control;consistency theory;graph theory;heterogeneous formation|
|[Smart Trash Classification Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106603)|P. Wiriwithya; S. Rungnarongruck; S. Pongamphai; S. Puapattanakul; R. Chancharoen|10.1109/ICMRE56789.2023.10106603|trash classification;tensorflow;deep learning;Programmable Logic Control (PLC);python;Convolutional Neural Network (CNNs);ImageNet;trash classification;tensorflow;deep learning;Programmable Logic Control (PLC);python;Convolutional Neural Network (CNNs);ImageNet|
|[Guaranteed Gaussian Process Predictive Control for Lipschitz Nonlinear System with Input and State Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106582)|J. Zhang; H. Wang|10.1109/ICMRE56789.2023.10106582|Model predictive control;Gaussian Processes;Intelligent control;Robot control;Model predictive control;Gaussian Processes;Intelligent control;Robot control|
|[Coupling of an Automatic Landslide Warning System for Retaining Walls for Road Infrastructure Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106572)|D. S. Cuicapuza Curipaco; P. Luis Godoy Jerónimo; A. T. Diaz; G. Perez Campomanes; S. A. Chamorro Quijano; M. Mehdi Hadi Mohamed; D. C. Guerreros|10.1109/ICMRE56789.2023.10106572|sliding;automation;mechatronics;prevention;sliding;automation;mechatronics;prevention|
|[Design of Underwater Vehicle for Ship Draft Observation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106597)|J. Zhu; M. Han; H. Zhang; L. Qi; L. Zhao; C. Wang|10.1109/ICMRE56789.2023.10106597|underwater vehicle;structural design;strength calibration;hydrodynamic analysis;underwater vehicle;structural design;strength calibration;hydrodynamic analysis|
|[A Dual-Pump Energy-Efficient Electro-Hydraulic Drive with Extended Velocity Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106608)|J. Beale; D. Padovani|10.1109/ICMRE56789.2023.10106608|electro-hydraulic actuator;hydraulic pump;energy efficiency;press;electro-hydraulic actuator;hydraulic pump;energy efficiency;press|
|[Optimal Strategy of Disassembly Process in Electric Vehicle Battery Based on Human-Machine Collaboration Re-manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106599)|G. Wang; J. Gao; J. Xiao|10.1109/ICMRE56789.2023.10106599|electric vehicle battery;disassembly;machine vision;human-machine collaboration;electric vehicle battery;disassembly;machine vision;human-machine collaboration|
|[Design and Kinematics Analysis of 3-PUU Translational Parallel Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106587)|E. Marliana; L. Nurahmi; A. Wahjudi; I. M. Londen Batan; G. Wei|10.1109/ICMRE56789.2023.10106587|parallel mechanism;translational;robot;kinematics;singularity;workspace;parallel mechanism;translational;robot;kinematics;singularity;workspace|
|[Universal Five-Axis RTCP Adaptive Dispensing Interpolation System Based on Dual NURBS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10106585)|Y. Lin; D. Li; M. Cheng|10.1109/ICMRE56789.2023.10106585|RTCP;adaptive dispensing;five-axis interpolation;dual NURBS;RTCP;adaptive dispensing;five-axis interpolation;dual NURBS|
#### **2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)**
- DOI: 10.1109/DRCN57075.2023
- DATE: 17-20 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Dependability: Enablers in 5G Campus Networks for Industry 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108299)|A. Khalil; B. Becker; L. Wernet; R. Kundel; B. Richerzhagen; T. Meuser; R. Steinmetz|10.1109/DRCN57075.2023.10108299|Smart factory;Industry 4.0;5G;Dependability;Smart factory;Industry 4.0;5G;Dependability|
|[UAV-assisted Multiband WSN Coverage Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108189)|S. Pullwitt; J. Schlichter; L. Wolf|10.1109/DRCN57075.2023.10108189|WSN;real-world;UAV;WSN;real-world;UAV|
|[Distributed Management of Dynamic Reliability in Beyond 5G Industrial Environments for Extreme Low Latency Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108176)|D. Borsatti; G. Davoli; W. Cerroni; C. Raffaelli; W. Y. Poe; R. Trivisonno; R. Bolla|10.1109/DRCN57075.2023.10108176|Beyond 5G;adaptive reliability;low latency;Beyond 5G;adaptive reliability;low latency|
|[Risk Assessment on Hardware Offloading Architecture of Network Security Protocols with Linux-based Control Plane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108289)|Ó. G. Bermejo; D. Dik; M. S. Berger|10.1109/DRCN57075.2023.10108289|Linux;MACsec;risk assessment;security;Linux;MACsec;risk assessment;security|
|[Quantum Key Distribution with Trusted Relay using an ETSI-compliant Software-Defined Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108347)|R. Bassi; Q. Zhang; A. Gatto; M. Tornatore; G. Verticale|10.1109/DRCN57075.2023.10108347|;|
|[A distributed network-aware TSCH scheduling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108193)|I. F. V. Junior; J. Granjal; M. Curado|10.1109/DRCN57075.2023.10108193|IoT;IIoT;TSCH;RPL;IoT;IIoT;TSCH;RPL|
|[Automated Fault Detection Framework for Reliable Provision of IoT Applications in Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108238)|S. Attarha; S. Band; A. Förster|10.1109/DRCN57075.2023.10108238|Fault detection;Time series data analysis;Feature engineering;Internet-of-Things(IoT);Smart agriculture;Smart Farming;Fault detection;Time series data analysis;Feature engineering;Internet-of-Things(IoT);Smart agriculture;Smart Farming|
|[Sum Rate Maximization of Uplink Active RIS and UAV-assisted THz Mobile Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108256)|S. Farrag; E. A. Maher; A. El-Mahdy; F. Dressler|10.1109/DRCN57075.2023.10108256|Wireless communication;Reliable Communication;Sum Rate;UAV;THz;active RIS and D2D;Wireless communication;Reliable Communication;Sum Rate;UAV;THz;active RIS and D2D|
|[Safe Routing in Energy-aware IP networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108152)|Y. Magnouche; J. Leguay; F. Zeng|10.1109/DRCN57075.2023.10108152|Energy-aware routing;Reliability;Integer linear programming;Benders decomposition;Column generation;Energy-aware routing;Reliability;Integer linear programming;Benders decomposition;Column generation|
|[Routing protocols exploiting queue information for deterministic networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108382)|J. Miserez; G. P. Sharma; W. Tavernier|10.1109/DRCN57075.2023.10108382|Deterministic networking;routing protocols;Quality-of-Service;QoS;real-time applications;Deterministic networking;routing protocols;Quality-of-Service;QoS;real-time applications|
|[Uplink NOMA for UAV-Aided Maritime Internet-of-Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108290)|N. Nomikos; A. Giannopoulos; P. Trakadas; G. K. Karagiannidis|10.1109/DRCN57075.2023.10108290|Maritime Communication Networks;Maritime Internet-of-Things;Non-Orthogonal Multiple Access (NOMA);Opportunistic Selection;Unmanned Aerial Vehicle (UAV);Maritime Communication Networks;Maritime Internet-of-Things;Non-Orthogonal Multiple Access (NOMA);Opportunistic Selection;Unmanned Aerial Vehicle (UAV)|
|[Blockchain-based Self-Sovereign Identity Solution for Vehicular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108183)|E. Zeydan; J. Mangues; S. Arslan; Y. Turk|10.1109/DRCN57075.2023.10108183|self-sovereign;digital identity;blockchain;vehicular networks.;self-sovereign;digital identity;blockchain;vehicular networks.|
|[Age of Information Resilience With a Strategic Out-of-Band Relay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108388)|L. Badia; F. Chiariotti|10.1109/DRCN57075.2023.10108388|Age of Information;Data acquisition;Modeling;Robust communications;Relay;Age of Information;Data acquisition;Modeling;Robust communications;Relay|
|[Measuring and Modeling ICAO’s PBCS Performance Metrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108287)|M. Klügel; D. Schupke|10.1109/DRCN57075.2023.10108287|ICAO PBCS;Performance Based Communication;Reliability;Latency;ICAO PBCS;Performance Based Communication;Reliability;Latency|
|[An NIDS for Known and Zero-Day Anomalies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108319)|A. Hussain; F. Aguiló-Gost; E. Simó-Mezquita; E. Marín-Tordera; X. Massip|10.1109/DRCN57075.2023.10108319|Cyber Security;Network Intrusion Detection System;Machine Learning;One-Class SVM;Random Forest;Cyber Security;Network Intrusion Detection System;Machine Learning;One-Class SVM;Random Forest|
|[Towards Optimal Path Allocation for Unreliable Reconfigurable Intelligent Surfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108214)|M. Bensalem; A. Engelmann; A. Jukan|10.1109/DRCN57075.2023.10108214|SMDP;path allocation;virtual reality;reconfigurable intelligent surfaces;reliability;SMDP;path allocation;virtual reality;reconfigurable intelligent surfaces;reliability|
|[Optimum Network Slicing for Ultra-reliable Low Latency Communication (URLLC) Services in Campus Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108188)|I. Zacarias; F. Carpio; A. C. Drummond; A. Jukan|10.1109/DRCN57075.2023.10108188|;|
|[Machine-Learning-Assisted Failure Prediction in Microwave Networks based on Equipment Alarms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108226)|F. Lateano; O. Ayoub; F. Musumeci; M. Tornatore|10.1109/DRCN57075.2023.10108226|Machine learning;microwave networks;alarms forecasting;failure identification and prediction;root-cause analysis;Machine learning;microwave networks;alarms forecasting;failure identification and prediction;root-cause analysis|
|[Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108389)|A. Giannopoulos; S. Spantideas; N. Nomikos; A. Kalafatelis; P. Trakadas|10.1109/DRCN57075.2023.10108389|wireless networks;reinforcement learning;power control;Q-learning;resource allocation;radio resource management;wireless networks;reinforcement learning;power control;Q-learning;resource allocation;radio resource management|
|[Availability and Throughput Evaluation of Optical Based High Throughput Satellite Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108160)|M. Wenning; A. Autenrieth; J. -P. Elbers; C. Mas-Machuca|10.1109/DRCN57075.2023.10108160|satellite networks;availability analysis;non-terrestrial networks;satellite networks;availability analysis;non-terrestrial networks|
|[Performance Evaluation of TI-LFA in Traffic-Engineered Segment Routing-Based Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108280)|L. Roelens; Ó. G. d. Dios; I. d. Miguel; E. Echeverry; R. J. D. Barroso|10.1109/DRCN57075.2023.10108280|Segment Routing;Link Failures;IP Fast-ReRoute;TI-LFA;Discrete Event Simulation;Segment Routing;Link Failures;IP Fast-ReRoute;TI-LFA;Discrete Event Simulation|
|[A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108128)|K. Kioskli; S. Papastergiou|10.1109/DRCN57075.2023.10108128|swarm intelligence;artificial intelligence;cybersecurity;personalized healthcare;long-term conditions;swarm intelligence;artificial intelligence;cybersecurity;personalized healthcare;long-term conditions|
|[A Machine Learning-Driven Threat Hunting Architecture for Protecting Critical Infrastructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108333)|M. A. Lozano; I. P. Llopis; A. C. Alarcón; M. E. Domingo|10.1109/DRCN57075.2023.10108333|Critical Infrastructures Protection;Cyberat-tacks;Machine Learning;Threat Hunting;Architecture;Critical Infrastructures Protection;Cyberat-tacks;Machine Learning;Threat Hunting;Architecture|
|[McEliece Cryptosystem: Reducing the Key Size with QC-LDPC codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108339)|P. Pérez-Pacheco; P. Caballero-Gil|10.1109/DRCN57075.2023.10108339|cryptography;McEliece cryptosystem;key size;QC-LDPC codes;cryptography;McEliece cryptosystem;key size;QC-LDPC codes|
|[Theoretical Analysis and Software Implementation of a Quantum Encryption Proposal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108353)|D. Escanez-Exposito; P. Caballero-Gil|10.1109/DRCN57075.2023.10108353|public-key cryptography;quantum computing.;public-key cryptography;quantum computing.|
|[A Holistic Framework for Safeguarding of SMEs: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108247)|N. Bountouni; S. Koussouris; A. Vasileiou; S. A. Kazazis|10.1109/DRCN57075.2023.10108247|SMEs;cybersecurity framework;IoT;cloud;demonstrator;SMEs;cybersecurity framework;IoT;cloud;demonstrator|
|[Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108105)|J. Antić; J. P. Costa; A. Černivec; M. Cankar; T. Martinčič; A. Potočnik; G. B. Elguezabal; N. Leligou; I. T. Boigues|10.1109/DRCN57075.2023.10108105|runtime;security monitoring;supply chain resilience;smart logistics;deep learning;natural language processing;anomaly detection;masked language modelling;self learning;self healing;runtime;security monitoring;supply chain resilience;smart logistics;deep learning;natural language processing;anomaly detection;masked language modelling;self learning;self healing|
|[A Moving Target Defense Security Solution for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108190)|T. Kyriakakis; S. Ioannidis|10.1109/DRCN57075.2023.10108190|security;moving target defences;internet of things;security;moving target defences;internet of things|
|[Classical vs. Quantum Machine Learning for Breast Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108230)|S. Díaz-Santos; D. Escanez-Exposito|10.1109/DRCN57075.2023.10108230|Quantum Machine Learning;Quantum Classification;Variational Quantum Classifier;Support Vector Classification;Breast Cancer Detection;Quantum Machine Learning;Quantum Classification;Variational Quantum Classifier;Support Vector Classification;Breast Cancer Detection|
|[Information Sharing for Creating Awareness for Securing Healthcare Ecosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108266)|S. Islam; C. Grigoriadis; S. Papastergiou|10.1109/DRCN57075.2023.10108266|Information Sharing;STIX;Risk;Healthcare System;Primary Agent;Supervisor Agent formatting;Multi-agent System;Information Sharing;STIX;Risk;Healthcare System;Primary Agent;Supervisor Agent formatting;Multi-agent System|
|[Pseudonymisation in the context of GDPR-compliant medical research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108370)|I. Basdekis; C. Kloukinas; C. Agostinho; I. Vezakis; A. Pimenta; L. Gallo; G. Spanoudakis|10.1109/DRCN57075.2023.10108370|pseudonymisation;privacy;data minimisation;GDPR;observational studies;pseudonymisation;privacy;data minimisation;GDPR;observational studies|
|[Forensic Investigation Using RAM Analysis on the Hadoop Distributed File System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108330)|S. Laing; R. Ludwiniak; B. E. Boudani; C. Chrysoulas; G. Ubakanma; N. Pitropakis|10.1109/DRCN57075.2023.10108330|cloud systems;RAM Analysis;Java Heap Analysis;Hadoop;HDFS;forensic analysis;cloud systems;RAM Analysis;Java Heap Analysis;Hadoop;HDFS;forensic analysis|
|[Security shortcomings in healthcare: a preliminary investigation of Data Protection Authorities’ decisions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10108175)|C. Nanou; M. Kampyli; M. Crociani; V. Danilatou|10.1109/DRCN57075.2023.10108175|privacy by design;GDPR compliance;data breach;healthcare;privacy by design;GDPR compliance;data breach;healthcare|
#### **2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)**
- DOI: 10.1109/EDTM55494.2023
- DATE: 7-10 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Hafnia-based ferroelectric devices for lower power memory and AI applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102996)|S. Takagi; K. Toprasertpong; E. Nako; M. Takenaka; R. Nakane|10.1109/EDTM55494.2023.10102996|Ferroelectrics;Polarization;FETs;FeRAM;HfZrO2;Reservoir Computing;Ferroelectrics;Polarization;FETs;FeRAM;HfZrO2;Reservoir Computing|
|[Wurtzite-Type Ferroelectrics for Microelectronic Devices: Scalability and Integration to Silicon based Ferroelectric FETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103080)|S. Fichtner; G. Schönweger; F. Dietz; H. Hanssen; H. Züge; T. -N. Kreutzer; F. Lofink; H. Kohlstedt; H. Kapels; M. Mensing|10.1109/EDTM55494.2023.10103080|Ferroelectric;FeFET;Thin Film;Ferroelectric;FeFET;Thin Film|
|[Tuning indirect-to-direct bandgap of lonsdaleite Si0.5Ge0.5 alloy via compressive strain for optical gain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103070)|R. Mayengbam; S. Das; C. S. Tan; W. Fan|10.1109/EDTM55494.2023.10103070|Density Functional Theory;SiGe;Bandgap;Absorption;Density Functional Theory;SiGe;Bandgap;Absorption|
|[Bi2O2Se-Perovskite Heterostructure Based Bipolar Photosensors as Reconfigurable Logic-In-Sensor Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103057)|L. Xu; S. Liu; R. Huang; M. He|10.1109/EDTM55494.2023.10103057|Bi2O2Se-perovskite heterostructure;Bipolar photoresponse;Logic-in-sensor;Bi2O2Se-perovskite heterostructure;Bipolar photoresponse;Logic-in-sensor|
|[A Liquid Medium Coriolis Gyroscope based on Electrochemical Molecular Electronic Transducer for Low Angular Rate Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103074)|Y. K. Cheung; H. Yu|10.1109/EDTM55494.2023.10103074|Coriolis gyroscope;Fluid gyroscope and electrochemical;Coriolis gyroscope;Fluid gyroscope and electrochemical|
|[Process Control Challenges and Solutions for Advanced Semiconductor Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103045)|Y. J. Jee; S. H. Han; S. Wolfling|10.1109/EDTM55494.2023.10103045|process control;lab-to-fab;materials characterization;hybrid metrology;machine learning;process control;lab-to-fab;materials characterization;hybrid metrology;machine learning|
|[Budgeting and predicting pattern defects using edge placement error and machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102957)|T. Jee; J. You; H. -G. Lee; S. -H. Lee; S. Hong; J. Seo; R. Meir; N. Oved; J. Park; S. -I. Kim; B. -J. Lim; C. -H. Kwak; J. -H. Yeo|10.1109/EDTM55494.2023.10102957|EPE;Vision ML;ML;Pattern Defect;EPE;Vision ML;ML;Pattern Defect|
|[Vector-Matrix-Multiplication Acceleration with Multi-Input Pr0.7Ca0.3MnO3 based RRAM for Highly Parallel In-Memory Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103016)|J. Sakhuja; V. Saraswat; S. Lashkare; U. Ganguly|10.1109/EDTM55494.2023.10103016|IMC;VMM;RRAM (memristor;PCMO;IMC;VMM;RRAM (memristor;PCMO|
|[TaOx ReRAM Cell-level Unidirectional Neural Network Weight Control for Non-linearity & Variation Robust Transfer Learning of Low Cost Digital eCiM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103092)|A. Yamada; N. Misawa; S. Muraoka; K. Kawai; C. Matsui; K. Takeuchi|10.1109/EDTM55494.2023.10103092|ReRAM;eCiM;Manhattan rule training;ReRAM;eCiM;Manhattan rule training|
|[Variability Aware FET Model With Physics Knowledge Based Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103099)|K. Sheelvardhan; S. Guglani; M. Ehteshamuddin; S. Roy; A. Dasgupta|10.1109/EDTM55494.2023.10103099|;|
|[Graphical approach of equipment health monitoring using network analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102976)|W. Ki; M. Choi; Y. Lee; K. Yoon; H. Hwang; J. Park; T. Kang; J. Park; Y. Kim|10.1109/EDTM55494.2023.10102976|Equipment health monitoring;Network;WGCNA;Sensor correlation;Equipment health monitoring;Network;WGCNA;Sensor correlation|
|[A Closer Look to Fan-out Panel Level Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102969)|T. Braun; O. Hölck; M. Voitel; M. Obst; S. Voges; K. -F. Becker; R. Aschenbrenner; M. Schneider-Ramelow|10.1109/EDTM55494.2023.10102969|Fan-out Panel Level Packaging;Warpage and Compression Molding;Fan-out Panel Level Packaging;Warpage and Compression Molding|
|[A noble VFO(Vertical wire Fan Out) technology for small form factor and high performance memory applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102966)|K. -J. Sung; K. Eun; S. Lee; S. Yoon; H. -Y. Son; K. -W. Lee|10.1109/EDTM55494.2023.10102966|(Keywords;Vertical wire;VFO;FOWLP;Memory fan out and Mobile package);(Keywords;Vertical wire;VFO;FOWLP;Memory fan out and Mobile package)|
|[Broadband Graphene-Silicon Integrated Imagers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103093)|Y. Xu; L. Chen; L. Peng; W. Fang; Y. Chen; X. Wang; Y. Dong; S. C. Bodepudi; Y. Zhao; C. Gao; B. Yu|10.1109/EDTM55494.2023.10103093|Broadband;Graphene;Silicon;Imaging;Broadband;Graphene;Silicon;Imaging|
|[Highly responsive ultraviolet narrowband organic photodetector based on acceptor free photomultiplication: A smart approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102973)|S. R. Sridhar; M. J. Pandey; B. Kumar|10.1109/EDTM55494.2023.10102973|Photomultiplication;Photodetector;Responsivity;Photomultiplication;Photodetector;Responsivity|
|[A five-transistor active pixel sensor with a wide dynamic range and a high-speed pixel operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102934)|T. Ma; C. Gao; Q. Li; Y. Qi; S. Deng; K. Wang|10.1109/EDTM55494.2023.10102934|Active pixel sensor;Photodiode-body-biased MOSFET;Wide dynamic range;High speed);Active pixel sensor;Photodiode-body-biased MOSFET;Wide dynamic range;High speed)|
|[On the Dopant, Defect States, and Mobility in W Doped Amorphous In2O3 for BEOL Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103082)|Y. Hu; K. A. Aabrar; A. Palmieri; M. Bergschneider; M. Pešić; C. D. Young; S. Datta; K. Cho|10.1109/EDTM55494.2023.10103082|IWO;BEOL transistor;defects;IWO;BEOL transistor;defects|
|[Ru Stress Assessment by Membrane Wrinkling for Interconnect Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103023)|V. Founta; J. -P. Soulié; I. De Wolf; J. Van De Vondel; J. Swerts; Z. Tőkei; C. Adelmann|10.1109/EDTM55494.2023.10103023|Ruthenium;alternative metals;stress;stress relaxation;film bending;film wrinkling;Ruthenium;alternative metals;stress;stress relaxation;film bending;film wrinkling|
|[Low-resistance (NH4)xWO3 Nanowire Sensors for Acetone Recognition Operating at Low Voltage with Low Power Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102994)|Y. Narita; T. Tanaka; K. Uchida|10.1109/EDTM55494.2023.10102994|Gas sensor;nanowire;Joule heating;Gas sensor;nanowire;Joule heating|
|[Temperature Measurement Method Using FET Type Gas Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102965)|C. Lee; G. Jung; W. Shin; Y. Jeong; J. Park; D. Kim; J. -J. Kim; J. -H. Lee|10.1109/EDTM55494.2023.10102965|Temperature measurement;FET type gas sensor;CMOS process;MOSFET;Temperature measurement;FET type gas sensor;CMOS process;MOSFET|
|[ML-assisted IC Test Binning with Real-Time Prediction at the Edge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102972)|T. Honda; T. Haarhuis; J. D. David; H. Hannink; G. Prewitt; V. Rajan|10.1109/EDTM55494.2023.10102972|IC Test;Tester;Dynamic Test Controller;Machine Learning;Edge;Inference Engine;IC Test;Tester;Dynamic Test Controller;Machine Learning;Edge;Inference Engine|
|[An approach to represent time series by patterns and process dependent statistics to present properties of signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103083)|S. Kim; C. Park; S. Chae; M. Sohn; C. Lee; Y. Kim; J. -Y. Park|10.1109/EDTM55494.2023.10103083|pattern;time series;classification;statistic;pattern;time series;classification;statistic|
|[Flip Chip Joining with Injection Molded Solder Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103044)|T. Hisada; T. Aoki|10.1109/EDTM55494.2023.10103044|solder bump;injection molded solder;solder bump;injection molded solder|
|[Low temperature SiCN as dielectric for hybrid bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103036)|V. S. Kumar Channam; S. Iacovo; E. Walsby; I. Belov; A. Jourdain; A. Sepulveda; E. Beyne|10.1109/EDTM55494.2023.10103036|PECVD;Low temperature SiCN;Hybrid Bonding and Cu Diffusion barrier;PECVD;Low temperature SiCN;Hybrid Bonding and Cu Diffusion barrier|
|[Controllable Conductive Filament Formation in Resistive-RAM Using ZnO Nanoparticles and Its Mechanisms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103040)|J. -H. Byun; W. -S. Ko; K. -N. Kim; D. -Y. Lee; E. -G. Kim; E. -A. Koo; S. -Y. Kwon; G. -W. Lee|10.1109/EDTM55494.2023.10103040|ReRAM;ZnO NPs;formation energy;uniformity;ReRAM;ZnO NPs;formation energy;uniformity|
|[Crossbar Arrays based on “Wall” Phase-Change Memory (PCM) and Ovonic- Threshold Switching (OTS) Selector: a Device Integration Challenge Towards New Computing Paradigms in Embedded Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102961)|G. Bourgeois; V. Meli; R. Antonelli; C. Socquet-Clerc; T. Magis; F. Laulagnet; B. Hemard; M. Bernard; L. Fellouh; P. Dezest; J. Krawczyk; S. Dominguez; F. Baudin; J. Garrione; C. Pellissier; J. . -A. Dallery; N. Castellani; M. . -C. Cyrille; C. Charpin; F. Andrieu; G. Navarro|10.1109/EDTM55494.2023.10102961|Crossbar;“Wall” PCM;OTS selector;Crossbar;“Wall” PCM;OTS selector|
|[Five-Fold Reduction in RESET Energy Consumption by Nitrogen Doping in Phase Change Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102995)|W. Uddin; A. K. Agrawal; P. Meihar; A. Singh; T. Malviya; R. Ranjan; S. Lashkare; K. Priyadarshi; U. Ganguly|10.1109/EDTM55494.2023.10102995|;|
|[DC and Transient Microscopic Simulation of Nanowire NMOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102931)|C. Jungemann; T. Rippchen; M. Noei; T. Linn|10.1109/EDTM55494.2023.10102931|MOSFET;nanowire;subthreshold swing;cryogenic;Boltzmann equation);MOSFET;nanowire;subthreshold swing;cryogenic;Boltzmann equation)|
|[Interpolative Device Models for Hafnia-Based FeFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103103)|I. Hossen; A. L. Glasmann; S. Najmaei; G. C. Adam|10.1109/EDTM55494.2023.10103103|FeFET;device model;interpolation;FeFET;device model;interpolation|
|[New Understanding of Screen Radius and Re-evaluation of Memory Window in Cylindrical Ferroelectric Capacitor for High-density 1T1C FeRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102982)|M. Deng; C. Su; Z. Fu; K. Wang; R. Huang; Q. Huang|10.1109/EDTM55494.2023.10102982|3D cylindrical ferroelectric capacitor;hafnium oxide;FeRAM;3D cylindrical ferroelectric capacitor;hafnium oxide;FeRAM|
|[Advanced SiC Power Technology and Package](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103126)|B. Kim; A. Bolotnikov; H. Jeong; C. Kim; H. Das; G. Ponram|10.1109/EDTM55494.2023.10103126|SiC;MOSFET;JFET;Power efficiency;package;SiC;MOSFET;JFET;Power efficiency;package|
|[Analysis and Mitigation of Interference in a Multi-RADAR Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102949)|E. Onyejegbu; A. Lee; A. Ashimbayeva; B. Nakarmi; I. A. Ukaegbu|10.1109/EDTM55494.2023.10102949|Interference mitigation;RADAR;linear frequency modulation;random hopped LFM;beat frequency;Interference mitigation;RADAR;linear frequency modulation;random hopped LFM;beat frequency|
|[Robust Real-time 4K-UHD Videos Streaming Transmission through Photonic-based Terahertz Wireless Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103088)|X. -W. Miao; P. -C. Su; F. -K. Shih; P. Torkaman; K. -M. Feng; S. -H. Yang|10.1109/EDTM55494.2023.10103088|terahertz communication;radio-over-fiber;real-time 4K streaming;terahertz communication;radio-over-fiber;real-time 4K streaming|
|[Design and Optimization of a Double Cladding Octo-wing Segmented Cladding Fibe Using Response Surface Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103129)|M. Pournoury; D. Kim|10.1109/EDTM55494.2023.10103129|Fiber Optics;Fiber design;Fiber laser;Fiber Optics;Fiber design;Fiber laser|
|[Stochastic Resonance Effects of Floating Gate Technology-based Leaky Integrate-and-Fire (FG LIF) Neurons in Summing Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103007)|A. Goda; C. Matsui; K. Takeuchi|10.1109/EDTM55494.2023.10103007|Stochastic neuron;floating gate;population code;stochastic resonance;leaky integrate and fire;Stochastic neuron;floating gate;population code;stochastic resonance;leaky integrate and fire|
|[Ferroelectric FET based Signed Synapses of Excitatory and Inhibitory Connection fo Stochastic Spiking Neural Network based Optimizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102951)|J. Luo; T. Liu; Z. Fu; X. Wei; Q. Huang; R. Huang|10.1109/EDTM55494.2023.10102951|Ferroelectric FET (FeFET);synapse;combinatorial optimization problems;Ferroelectric FET (FeFET);synapse;combinatorial optimization problems|
|[IGZO Photonic-Synaptic Transistors with Outstanding Linearity by Controlling Oxygen Vacancy for Neuromorphic Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103091)|T. Seo; J. Yun; Y. Chung|10.1109/EDTM55494.2023.10103091|synaptic transistor;artificial intelligence;weight update;linearity;synaptic transistor;artificial intelligence;weight update;linearity|
|[Contacts for 2D-Material MOSFETs: Recent Advances and Outstanding Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103009)|S. J. Koester|10.1109/EDTM55494.2023.10103009|2D materials;TMDCs;contacts;2D materials;TMDCs;contacts|
|[Grain-Size Enlargement of MoS2 Film by Low-Rate Sputtering with Molybdenum Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103043)|S. Imai; R. Ono; I. Muneta; K. Kakushima; T. Tatsumi|10.1109/EDTM55494.2023.10103043|transition metal di-chalcogenide (TMDC;molybdenum disulfide;ultra-high vacuum (UHV) radio frequency (RF) magnetron sputtering;low-rate sputtering;grain size;transition metal di-chalcogenide (TMDC;molybdenum disulfide;ultra-high vacuum (UHV) radio frequency (RF) magnetron sputtering;low-rate sputtering;grain size|
|[Development of MFMIS Gatestack with Thick Hafnium Zirconium Oxide (HZO) for Nonvolatile Memory Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102929)|B. Kang; J. Park; J. Hwang; S. Lee; H. Shin; J. An; H. You; S. -M. Ahn; S. Jeon; R. -H. Baek|10.1109/EDTM55494.2023.10102929|thick HZO;gate;memory;integration;memory window;etching;thick HZO;gate;memory;integration;memory window;etching|
|[3D NAND Memory Operation of Oxide-Semiconductor Channel FeFETs and the Potential Impact of In-Plane Polarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103035)|J. Hao; X. Mei; T. Saraya; T. Hiramoto; M. Kobayashi|10.1109/EDTM55494.2023.10103035|FeFET;HfO2-based ferroelectric;Oxide Semiconductor;3D NAND;FeFET;HfO2-based ferroelectric;Oxide Semiconductor;3D NAND|
|[Guideline of Device Optimization for Ferroelectric InGaZnO Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102963)|Y. -H. Chen; I. -T. Wang; Y. -M. Zheng; T. -H. Hou|10.1109/EDTM55494.2023.10102963|HZO;FeFET;IGZO;channel floating;HZO;FeFET;IGZO;channel floating|
|[Back-End-of-Line-Compatible Anneal-Free Ferroelectric Field-Effect Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103049)|S. -H. Tsai; Z. Li; M. M. Mo Ei Phyu; Z. Fang; S. Hooda; C. -K. Chen; E. Zamburg; A. V. -Y. Thean|10.1109/EDTM55494.2023.10103049|Ferroelectric;growth temperature;seed layer;BEOL;monolithic three-dimensional integration;in-memory computing;Ferroelectric;growth temperature;seed layer;BEOL;monolithic three-dimensional integration;in-memory computing|
|[High-Endurance (>1011cycles) and Thermally-Stable Sub-100nm TiO2 Channel FeFET for Low-Power Memory Centric 3D-LSI Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103063)|T. Shiokawa; R. Ichihara; T. Hamai; K. Sakuma; K. Takahashi; K. Matsuo; M. Saitoh|10.1109/EDTM55494.2023.10103063|FeFET;Oxide semiconductor;FeFET;Oxide semiconductor|
|[An Improved Robust Infinitely Differentiable Drift Resistance Model for BSIM High Voltage Compact Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103122)|A. Singhal; G. Gill; G. Pahwa; C. Hu; H. Agarwal|10.1109/EDTM55494.2023.10103122|BSIM-BULK;Gummel Symmetry test;Harmonic Balance test;LDMOS;BSIM-BULK;Gummel Symmetry test;Harmonic Balance test;LDMOS|
|[A new back-to-back graded AlGaN barrier for complementary integration technique based on GaN/AlGaN/GaN platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103055)|J. Zhou; H. -B. Do; M. M. De Souza|10.1109/EDTM55494.2023.10103055|GaN/AlGaN/GaN;back-to-back graded AlGaN;complementary integration;n-channel;p-channel;breakdown voltage;GaN/AlGaN/GaN;back-to-back graded AlGaN;complementary integration;n-channel;p-channel;breakdown voltage|
|[Characterization and Modeling of I-V, C-V and Trapping behavior of SiC Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102944)|M. S. Nazir; A. Pampori; Y. H. Zarkob; A. Kar; Y. S. Chauhan|10.1109/EDTM55494.2023.10102944|Charge-Based Model;Drift Region;Kink;Power Mosfet;Silicon Carbide and Trapping;Charge-Based Model;Drift Region;Kink;Power Mosfet;Silicon Carbide and Trapping|
|[Enhance Gate Reliability and Threshold Voltage Stability of p-GaN Gate High-Electron-Mobility Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103019)|H. Chen; J. Chen; C. Wang; Z. Jiang; M. Hua|10.1109/EDTM55494.2023.10103019|GaN HEMT;p-GaN;gate reliability;VTH stability;GaN HEMT;p-GaN;gate reliability;VTH stability|
|[Suppressed Dynamic Avalanche and Enhanced Turn-off dV/dt Controllability in 3300V Scaled IGBTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103121)|X. Zhou; M. Fukui; K. Takeuchi; T. Saraya; T. Hiramoto|10.1109/EDTM55494.2023.10103121|IGBT;scaling;TCAD simulation;dynamic avalanche;IGBT;scaling;TCAD simulation;dynamic avalanche|
|[Effects of Oxide Species on the Reduction of Contact Resistivity of Al/oxide/n-GaN MIS Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102978)|J. Koba; J. Koike|10.1109/EDTM55494.2023.10102978|GaN;MIS contact;MIGS;GaN;MIS contact;MIGS|
#### **2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE)**
- DOI: 10.1109/NNICE58320.2023
- DATE: 24-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Traffic Track Dynamic Data Analysis Based on BP Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105670)|Y. Zhang; D. Dong; J. Sha|10.1109/NNICE58320.2023.10105670|Traffic track;Track quality;BP neural network;Dynamic correction data;Traffic track;Track quality;BP neural network;Dynamic correction data|
|[Study on MRI Slices-based Lightweight Neural Network in Alzheimer's Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105740)|Q. Zhang; Y. Long; H. Cai|10.1109/NNICE58320.2023.10105740|Alzheimer's disease;structural magnetic resonance imaging;lightweight;efficient channel attention;triplet loss;Alzheimer's disease;structural magnetic resonance imaging;lightweight;efficient channel attention;triplet loss|
|[Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105727)|X. Li; X. Wang; H. Zhang; J. Zhang|10.1109/NNICE58320.2023.10105727|session-based recommendation;graph Neural network;self-supervised learning;session-based recommendation;graph Neural network;self-supervised learning|
|[Polynomial Improved Convolution Kernel Graph Network For Fault Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105666)|H. Zhang; Z. Wang; L. Qiu; S. Zhang; G. Yang; F. Xiang|10.1109/NNICE58320.2023.10105666|Diagnosis;Graph Convolutional Neural Network (GNN);Polynomial approximation;Diagnosis;Graph Convolutional Neural Network (GNN);Polynomial approximation|
|[A Channel Attention based Large Kernel Convolutional Neural Network for Bearing Fault Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105739)|F. Li; L. Wang; D. Wang; J. Wu; Y. Wang; J. Sun|10.1109/NNICE58320.2023.10105739|fault diagnosis;deep learning;convolutional neural network;channel attention;fault diagnosis;deep learning;convolutional neural network;channel attention|
|[Self-attention Convolutional Neural Network-based SLA Decomposition for Network Slicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105804)|H. Ji; Y. Wang; L. Tian; Q. Sun; Z. Zhang|10.1109/NNICE58320.2023.10105804|network slicing;service level agreement;self-attention;convolutional neural network;network slicing;service level agreement;self-attention;convolutional neural network|
|[Fault Location of Cabled Seafloor Observation Network Based on Fault Feature and Fault Distance Relation Mining Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105701)|S. Luan; G. Li; W. Gan; W. Xing|10.1109/NNICE58320.2023.10105701|Cabled Seafloor Observation Network;Fault Location;Variational Mode Decomposition;Deep Learning;Cabled Seafloor Observation Network;Fault Location;Variational Mode Decomposition;Deep Learning|
|[Wind power prediction based on long short-term memory neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105768)|K. Song; X. Xiao; W. Ma; K. Liu|10.1109/NNICE58320.2023.10105768|component;wind power prediction;deep learning;time series;long short-term memory neural network;component;wind power prediction;deep learning;time series;long short-term memory neural network|
|[Research on Sentence Embeddings for Text Matching through Multiview Interactive Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105673)|X. Liu; F. Chen; Y. Hu; X. Li|10.1109/NNICE58320.2023.10105673|deep learning;text matching;sentence embedding;deep learning;text matching;sentence embedding|
|[Study on The Robustness of Cascade Failure and Recovery of Dependent Networks Under Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105798)|J. Meng; Y. Zhu; L. Wang|10.1109/NNICE58320.2023.10105798|Dependent network;Cascade failure;Recovery;Robustness;Dependent network;Cascade failure;Recovery;Robustness|
|[Neural Matrix Factorization Model Based On Latent Factor Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105775)|A. Chang; Q. Wang; X. Zhang|10.1109/NNICE58320.2023.10105775|Recommender Systems;Matrix Factorization;Deep Learning;Recommender Systems;Matrix Factorization;Deep Learning|
|[Towards Graph Contrastive Learning for Recommendation with Sampling Embedding Perturbation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105711)|G. Chen; J. Li; Y. Ma|10.1109/NNICE58320.2023.10105711|Recommendation system;Contrastive Learning;Graph Embedding Learning;Data Augmentation;Recommendation system;Contrastive Learning;Graph Embedding Learning;Data Augmentation|
|[Traffic sign defogging based on conditional adversarial neural network pix2pixHD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105664)|J. Zhang|10.1109/NNICE58320.2023.10105664|Foggy environment;image defogging;adversarial neural network;pix2pixHD;traffic sign recognition;Foggy environment;image defogging;adversarial neural network;pix2pixHD;traffic sign recognition|
|[Evaluation Model of Learner's Cognitive Level Based on RoBERTa Fused with CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105807)|Y. Cai; Y. Zhao; H. Luo; Z. Huang; Z. Huang; F. Zhang|10.1109/NNICE58320.2023.10105807|Cognitive level;RoBERTa;CNN;Bloom's Cognitive Target Classification Theory;Cognitive level;RoBERTa;CNN;Bloom's Cognitive Target Classification Theory|
|[Cascade Prediction with Recurrent Neural Networks and Diffusion Depth Distributions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105676)|S. Huang; W. Yu|10.1109/NNICE58320.2023.10105676|information cascade;cascade prediction;recurrent neural networks;information cascade;cascade prediction;recurrent neural networks|
|[MTL-JER: Meta-Transfer Learning for Low-Resource Joint Entity and Relation Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105766)|D. Peng; Z. Pei; D. Mo|10.1109/NNICE58320.2023.10105766|Joint entity and relation extraction;Meta-Learning;Low-resource;Joint entity and relation extraction;Meta-Learning;Low-resource|
|[Prediction Model of Ship Arrival Time using Neural Network and Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105708)|X. Zhang; X. Zhang; P. Li|10.1109/NNICE58320.2023.10105708|Estimate time of arrival;Long and short-term memory;Kalman filter;Estimate time of arrival;Long and short-term memory;Kalman filter|
|[Low earth orbit satellite admission control scheme based on deep Q-learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105799)|D. Wei; D. Zheng; L. Yang; R. Cai|10.1109/NNICE58320.2023.10105799|component;satellite communication;cache technology;Q-learning;admission control;component;satellite communication;cache technology;Q-learning;admission control|
|[Mobile Robot Path Planning Based on Improved Ant Colony Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105742)|L. Lu; X. Jiang|10.1109/NNICE58320.2023.10105742|ant colony algorithm;mobile robots;path planning;pheromone;ant colony algorithm;mobile robots;path planning;pheromone|
|[Defect Detection of Photovoltaic Panels Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105789)|H. Lu; X. Huang; H. Shi; H. He|10.1109/NNICE58320.2023.10105789|component;defect detection;convolution model;transformer;component;defect detection;convolution model;transformer|
|[A Novel Two-Stage Net for Human Body Region Segmentation via Centimeter-Wave Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105808)|J. Xiao; Z. Chen; B. Luo|10.1109/NNICE58320.2023.10105808|Human body segmentation;centimeter-wave radar sensor;two-stage neural network;cross-modal supervision;Human body segmentation;centimeter-wave radar sensor;two-stage neural network;cross-modal supervision|
|[Partial VGG16: reformed network for classification under partial occlusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105689)|B. Sun; B. Chen; Y. Deng|10.1109/NNICE58320.2023.10105689|partial occlusion;partial convolution;VGG16;classification;partial occlusion;partial convolution;VGG16;classification|
|[Layer-wise Top-$k$ Gradient Sparsification for Distributed Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105718)|G. Li|10.1109/NNICE58320.2023.10105718|component;deep learning;gradient sparsification;data parallelism;component;deep learning;gradient sparsification;data parallelism|
|[A hybrid prediction framework based on deep learning for wind power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105749)|Y. Jin; S. Wang; Z. Ling; D. Wang; G. Wang|10.1109/NNICE58320.2023.10105749|wind power;ensemble empirical mode decomposition;convolutional neural network;long and short and term memory network;wind power;ensemble empirical mode decomposition;convolutional neural network;long and short and term memory network|
|[Analysis of Radar Survival Probability against Front Door Coupling Attack based on Beam Pattern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105731)|X. Hu; J. Yang; C. Meng; Y. Xu; Y. Xie|10.1109/NNICE58320.2023.10105731|High power microwave;Front door coupling;Radar;Survival probability;Beam Pattern;High power microwave;Front door coupling;Radar;Survival probability;Beam Pattern|
|[Research on Robotic High-Precision Target Detection Technology for Transmission Lines Based on Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105809)|B. Xu; X. Gu; W. Liu; J. Zhang; D. Lin|10.1109/NNICE58320.2023.10105809|deep learning;detection algorithm;bolt detection;live working robot;deep learning;detection algorithm;bolt detection;live working robot|
|[Dam crack detection based on deep learning cascade detection algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105763)|Q. Wang; B. Li; Z. Deng; W. Zhao|10.1109/NNICE58320.2023.10105763|Cracks;deep learning;algorithms;Cracks;deep learning;algorithms|
|[FedOES: An Efficient Federated Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105791)|Y. Li; Z. Liu; Y. Huang; P. Xu|10.1109/NNICE58320.2023.10105791|federated learning;efficient communication;Non-IID;sparse gradient;federated learning;efficient communication;Non-IID;sparse gradient|
|[A Survey of Vehicle Trajectory Prediction Based on Deep-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105706)|H. Yin; Y. Wen; J. Li|10.1109/NNICE58320.2023.10105706|Autonomous driving;deep-learning models;vehicle trajectory prediction;Autonomous driving;deep-learning models;vehicle trajectory prediction|
|[Highly overlapping strawberry leaf detection based on YOLOv5s-MBLS deep learning method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105782)|T. Hu; J. Zhang|10.1109/NNICE58320.2023.10105782|formatting;YOLOv5s-MBLS;backbone;the MBL module;SIoU loss;formatting;YOLOv5s-MBLS;backbone;the MBL module;SIoU loss|
|[Soil Nutrient Evaluation System Based on Improved Recurrent Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105705)|Y. Bu; X. Yu; Y. Yang|10.1109/NNICE58320.2023.10105705|recurrent neural network;agricultural soil;nutrient analysis;grading evaluation;recurrent neural network;agricultural soil;nutrient analysis;grading evaluation|
|[An IoT Device Recognition Method based on Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105697)|M. Lu; L. Li; Y. Gao; X. Li|10.1109/NNICE58320.2023.10105697|IoT Security;Device Identification;Convolutional Neural Network;IoT Security;Device Identification;Convolutional Neural Network|
|[Deep Learning-based Viewpoint Prediction Model and Influencing Attention Factors for Design Drawings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105774)|B. Liu|10.1109/NNICE58320.2023.10105774|Attention Management;Eye Tracking;Viewpoint Prediction;Design;Deep Learning;Attention Management;Eye Tracking;Viewpoint Prediction;Design;Deep Learning|
|[PLA: Fast and Accurate Face Alignment Network based on Prior Landmarks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105788)|H. Li; Y. Ding; M. Shao; L. Lu; J. Huang|10.1109/NNICE58320.2023.10105788|component;face alignment;landmark analysis;object detection;prior knowledge;component;face alignment;landmark analysis;object detection;prior knowledge|
|[YOLO-Owl: An Occlusion Aware Detector for Low Illuminance Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105800)|J. Hu; Z. Cui|10.1109/NNICE58320.2023.10105800|Object Detection;Low illuminance;Occlusion;Object Detection;Low illuminance;Occlusion|
|[Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105760)|C. Ma; L. Yang; H. Hu; Y. Chen; A. Bu|10.1109/NNICE58320.2023.10105760|Underwater Image Enhancement;Conditional Generation Adversarial Networks;Channel Attention;Pixel Attention;Underwater Image Enhancement;Conditional Generation Adversarial Networks;Channel Attention;Pixel Attention|
|[Research on Gait Feature Fusion Method of OpenPose and GEI Based on Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105757)|J. H. Yuan|10.1109/NNICE58320.2023.10105757|OpenPose;GEI;Deep Learning;Convolutional Neural Network;OpenPose;GEI;Deep Learning;Convolutional Neural Network|
|[Salient Object Detection Based on Transformer and Multi-scale Feature Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105709)|L. Zhao; H. Wang|10.1109/NNICE58320.2023.10105709|salient object detection;boundary enhancement;Transformer;multi-scale features;salient object detection;boundary enhancement;Transformer;multi-scale features|
|[PENet: Pre-Enhanced Network for Object Detection and Instance Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105781)|Y. Shi; J. Hu; L. Li|10.1109/NNICE58320.2023.10105781|Multi-level feature fusion;instance segmentation;object detection;convolutional neural network;Multi-level feature fusion;instance segmentation;object detection;convolutional neural network|
|[Effect of BDS-2 Different Orbiting Satellites on BDS-3 Single-frequency Single Point Positioning Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105769)|H. Zhang; Q. Guan; C. Fan|10.1109/NNICE58320.2023.10105769|BDS-2;BDS-3;satellite orbit;single point positioning;BDS-2;BDS-3;satellite orbit;single point positioning|
|[Dynamic Decision Boundaries for Improved Robustness against Adversarial Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105771)|Z. Long|10.1109/NNICE58320.2023.10105771|component;adversarial training;robustness;uncertainty classifier;component;adversarial training;robustness;uncertainty classifier|
|[Face mask detection based on improved YOLOv3 algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105750)|J. Wu; L. Wang|10.1109/NNICE58320.2023.10105750|Face mask detection;YOLOv3;Structural Reparameterization;Attention mechanism;Focal Loss;Face mask detection;YOLOv3;Structural Reparameterization;Attention mechanism;Focal Loss|
|[EEG Source Imaging based on a Transformer Encoder Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105793)|T. Zheng; Z. Guan|10.1109/NNICE58320.2023.10105793|Electroencephalography(EEG);Source Imaging;Transformer encoder;Electroencephalography(EEG);Source Imaging;Transformer encoder|
|[Global optimization using a combination of differential evolution and modified Powell method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105732)|Z. Wang; F. Zhang; D. Liu; X. Chen; J. Yin|10.1109/NNICE58320.2023.10105732|Optimization;differential evolution algorithm;powell method;benchmark test;Optimization;differential evolution algorithm;powell method;benchmark test|
|[Application of adaptive beamforming technology in underwater acoustic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105792)|B. Yin; J. Chen|10.1109/NNICE58320.2023.10105792|array signal processing;adaptive beamforming;underwater acoustic signal;robustness;array signal processing;adaptive beamforming;underwater acoustic signal;robustness|
|[A Multi-scale Fusion Network with Transformer for Medical Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105758)|G. Lin; L. Chen|10.1109/NNICE58320.2023.10105758|deep learning;medical image segmentation;Transformer;deep learning;medical image segmentation;Transformer|
|[Subspace-based Optimization Method with Adaptive Total Variation for Microwave Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105668)|W. Zhu; W. Li; X. Jiang; F. Sun|10.1109/NNICE58320.2023.10105668|subspace-based optimization method;microwave imaging;adaptive total variation;multiplicative regularization;subspace-based optimization method;microwave imaging;adaptive total variation;multiplicative regularization|
|[Fine-grained Classification of Bone Scintigrams by Using Radiomics Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105690)|X. Ma; Y. He; Q. Lin; Y. Cao; Z. Man|10.1109/NNICE58320.2023.10105690|SPECT images;Radiomics;Machine Learning;SPECT images;Radiomics;Machine Learning|
|[Segmentation of Metastasized Lesions in Bone Scintigrams Using U-Net++ with Attention Gate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105681)|A. Xie; Y. He; Q. Lin; Y. Cao; Z. Man|10.1109/NNICE58320.2023.10105681|CNN;AG;Bone metastasis;Lesion segmentation;SPECT imaging;CNN;AG;Bone metastasis;Lesion segmentation;SPECT imaging|
|[Study on Applying Median Cancellation Adaptive Clutter Suppression Interference Algorithm to Airborne Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105703)|Z. Li; J. Zhao; S. Lu|10.1109/NNICE58320.2023.10105703|airborne radar;SAR-GMTI;adaptive clutter suppression interference;airborne radar;SAR-GMTI;adaptive clutter suppression interference|
#### **2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)**
- DOI: 10.1109/ICSCDS56580.2023
- DATE: 23-25 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Machine Learning based Deceptive Speech Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104721)|W. Wang; X. Shen; H. Feng; B. Wang; T. Yu|10.1109/ICSCDS56580.2023.10104721|Detection Deception;Machine Learning;Speech Analysis;Fundamental Frequency;Jitter;Mel-frequency Cepstral Coefficients;Detection Deception;Machine Learning;Speech Analysis;Fundamental Frequency;Jitter;Mel-frequency Cepstral Coefficients|
|[Transfer Learning-based Driver Distraction Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104662)|L. Goel; S. Chennamaneni; A. Golla; S. Puchhakayala; A. Rudraram|10.1109/ICSCDS56580.2023.10104662|Deep Learning;Distractions;Inattentiveness;Transfer learning;Safe Driving;Deep Learning;Distractions;Inattentiveness;Transfer learning;Safe Driving|
|[A Backward Business-Oriented Approach of Major Sectors in Emphasizing Sustainable Technological Pillars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105080)|Aryan; R. Kalra; P. L. Mehta|10.1109/ICSCDS56580.2023.10105080|Accessibility;Adaptability;Reliability;Cost-effectiveness;Scalability;Internet of Things (IoT);Privacy & Security;Sustainability;Business Canvas;Cloud Computing;Edge Computing;Accessibility;Adaptability;Reliability;Cost-effectiveness;Scalability;Internet of Things (IoT);Privacy & Security;Sustainability;Business Canvas;Cloud Computing;Edge Computing|
|[Prediction of Sufficient Accuracy for Human Activity Recognition using Novel Long Short Term Memory in Compared with Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104587)|S. Charan; M. S. Saravanan.; R. Surendran.|10.1109/ICSCDS56580.2023.10104587|Machine Learning;Human Activity Recognition;Long Short Term Memory (LSTM);Decision Tree;Postural;Local Invariant Methods;Recurrent Neural Network;Machine Learning;Human Activity Recognition;Long Short Term Memory (LSTM);Decision Tree;Postural;Local Invariant Methods;Recurrent Neural Network|
|[Automated Driver Drowsiness Detection System using Computer Vision and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104942)|T. Srilakshmi; H. Reddy; Y. Potluri; L. R. Burra; M. V. Thota; R. Gundimeda|10.1109/ICSCDS56580.2023.10104942|Road-Safety;Driver Drowsiness;Fatigue;Facial Expressions;Computer Vision;Image Processing;Alert.;Road-Safety;Driver Drowsiness;Fatigue;Facial Expressions;Computer Vision;Image Processing;Alert.|
|[NLP based Analysis and Detection of Unethical Text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104943)|A. Khandekar; C. D. Hema; A. Meghana; A. Mounika; V. S. Vaishnavi|10.1109/ICSCDS56580.2023.10104943|Natural Language Processing (NLP);Bidirectional Long Short Term Memory (Bi-LSTM);Deep Learning (DL);Hate speech detection;Unethical text;social media negativity;Natural Language Processing (NLP);Bidirectional Long Short Term Memory (Bi-LSTM);Deep Learning (DL);Hate speech detection;Unethical text;social media negativity|
|[Combining Multi-Features for Lung Cancer Detection in Computed Tomography Images: A Feature Fusion Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104741)|P. Tumuluru; L. R. Burra; J. Vamsinath; S. M. Kasturi; L. S. K. Jetti; Z. Fatima|10.1109/ICSCDS56580.2023.10104741|High dimensionality images;Feature fusion model;Feature Extraction;State-of-the-art method.;High dimensionality images;Feature fusion model;Feature Extraction;State-of-the-art method.|
|[Detection of Fruit from an Image Using a Single-Shot Detector for Accurate Calorie Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105073)|S. S. Jandhyala; A. Satapathy; S. H. Saitaj; L. Niharika|10.1109/ICSCDS56580.2023.10105073|Convolutional Neural Network;Single Shot Detector;Object Detection;Calorie Estimation;Calorie Tracking;Convolutional Neural Network;Single Shot Detector;Object Detection;Calorie Estimation;Calorie Tracking|
|[A Framework for Design and Development of Message sharing using Open-Source Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104679)|S. Khaleelullah; P. Marry; P. Naresh; P. Srilatha; G. Sirisha; C. Nagesh|10.1109/ICSCDS56580.2023.10104679|Open-Source Software;Message Passing;Open-Source Assessment;Open-Source Software;Message Passing;Open-Source Assessment|
|[Review of Application of Highly Interactive and Immersive Computing Technologies for Enhancement of Post-Stroke Survivor’s Rehabilitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104892)|S. Shankar; S. J. Syed Ali Fathima; M. Uma Priya; S. Shriram; S. Priyadarshini; A. Benazir Begum|10.1109/ICSCDS56580.2023.10104892|Augmented Reality;Virtual Reality;Stroke;Rehabilitation;Artificial Intelligence;Augmented Reality;Virtual Reality;Stroke;Rehabilitation;Artificial Intelligence|
|[Effective Surveillance using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105124)|A. Marwaha; A. Chirputkar; P. Ashok|10.1109/ICSCDS56580.2023.10105124|Surveillance;Night vision;Camera;Image Processing;Security;Computer Vision;Surveillance;Night vision;Camera;Image Processing;Security;Computer Vision|
|[Traffic Matrix Estimation Techniques- A Survey on Current Practices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104701)|K. Swetha.; U. Prabu.; G. Angel.; Y. Lahari.|10.1109/ICSCDS56580.2023.10104701|Traffic Matrix;Datacenter Networks;Optical Networks;Traffic Matrix Estimation;Road Networks;Deep Learning;Traffic Matrix;Datacenter Networks;Optical Networks;Traffic Matrix Estimation;Road Networks;Deep Learning|
|[An Improved Machine Learning based Crop Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105119)|B. S. Sri; G. Pavani; B. Y. S. Sindhuja; V. Swapna; P. L. Priyanka|10.1109/ICSCDS56580.2023.10105119|Agriculture;crop yield;soil;Crop;Machine Learning.;Agriculture;crop yield;soil;Crop;Machine Learning.|
|[Study of Image Forgery Detection using Scale Invariant Feature Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104934)|V. Jyothi; G. Sri Priya Reddy; M. Sree Kavya; K. Harshitha Reddy; P. Shvithi Reddy|10.1109/ICSCDS56580.2023.10104934|Image Forgery;Scale invariant feature transform;tampering;Image Forgery;Scale invariant feature transform;tampering|
|[Contagious Disease Prediction using Random Forest Algorithm Interpolated with Fuzzy Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104962)|S. Geeitha.; P. Karthikeyan.; S. Aravinth.; P. Nachiappan.; S. Jagadeesh.|10.1109/ICSCDS56580.2023.10104962|Machine Learning;Contagious disease;Predictive Analysis;Random Forest Algorithm;Feature Selection;Machine Learning;Contagious disease;Predictive Analysis;Random Forest Algorithm;Feature Selection|
|[Heart Disease Prediction using Ensemble ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104770)|M. Bajaj; P. Rawat; C. Bhatt; R. Chauhan; T. Singh|10.1109/ICSCDS56580.2023.10104770|heart disease prediction;ensemble machine learning;sensitivity;specificity;heart disease prediction;ensemble machine learning;sensitivity;specificity|
|[Design of Personal Protective Respirator in Healthcare: An Emergency Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104768)|D. Selvakarthi; D. Sivabalaselvamani; K. S. Aashish; P. Kanishka; M. Kavinesh; S. Jaishnavi|10.1109/ICSCDS56580.2023.10104768|Human lungs;respiration;ventilator bag;blood oxygen level;protective equipment;Human lungs;respiration;ventilator bag;blood oxygen level;protective equipment|
|[Briefing of Textual Information using Text Rank](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104894)|G. N. B. Bethel; B. Divyasree; V. Sandhya; K. A. Nimisha; A. S. Bodduluri|10.1109/ICSCDS56580.2023.10104894|Text Summarization;Single Document Summarization;Multi Document Summarization;Keywords Extraction;TFIDF;Text Rank;Text Summarization;Single Document Summarization;Multi Document Summarization;Keywords Extraction;TFIDF;Text Rank|
|[Forensic Speaker Recognition System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104687)|B. V. K. Babu; D. K. Bhargav; R. K. Sah; L. Regalla; N. Singh|10.1109/ICSCDS56580.2023.10104687|Speaker Recognition;GMM;SM;MFCC;x-vector models;Speaker Recognition;GMM;SM;MFCC;x-vector models|
|[A Brief Survey on Image Denoising based Feature Extraction and Classification Models for Oral Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104790)|P. S. Krisha; S. R. Peram|10.1109/ICSCDS56580.2023.10104790|Oral Cancer;Oropharyngeal Carcinoma;Histological Pictures;Epithelial Layer;Deep Learning;Image Denoising;Feature Extraction.;Oral Cancer;Oropharyngeal Carcinoma;Histological Pictures;Epithelial Layer;Deep Learning;Image Denoising;Feature Extraction.|
|[Real-Time Number Plate and Helmet Detection of Motorcyclists using YOLOv5 and ResNet-50](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105076)|P. Aashik Mathew; A. Jagarlamudi; N. Bharathwaj; K. Jaspin|10.1109/ICSCDS56580.2023.10105076|YOLO v5;ResNet50;Convolutional-Neural Network (CNN);Canny Edge Detection;Cross Stage Partial Network (CSPN);Common Objects in Context (COCO);YOLO v5;ResNet50;Convolutional-Neural Network (CNN);Canny Edge Detection;Cross Stage Partial Network (CSPN);Common Objects in Context (COCO)|
|[Virtual Mouse using Coordinate Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104598)|M. M. Sahiwala; S. R. Singh; D. Pawar; D. D. Jakasaniya; K. M. Patel|10.1109/ICSCDS56580.2023.10104598|Coordinate system;Hand landmarks;Visual Detection;Finger Points;Coordinate system;Hand landmarks;Visual Detection;Finger Points|
|[Comparison of Optimization Techniques for Detection of Fruit Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104842)|S. Tandon; H. K. Shah; M. K. Nallakaruppan; C. Akash|10.1109/ICSCDS56580.2023.10104842|Convolution Neural Network(CNN);Inception V3;Adam;Root Mean Squared Propagation;Convolution Neural Network(CNN);Inception V3;Adam;Root Mean Squared Propagation|
|[Spam based Email Identification and Detection using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104659)|S. C. Lanka; K. Akhila; K. Pujita; P. V. Sagar; S. Mondal; S. Bulla|10.1109/ICSCDS56580.2023.10104659|Machine Learning;spam;malicious;accuracy;communication;Machine Learning;spam;malicious;accuracy;communication|
|[Building an Intelligent Brain Tumor System using Magnetic Resonance Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105044)|B. V. Kumar; Y. Ayyappa; B. K. Aparna; B. R. Kiran; B. N. G. Krishna; E. Kavya|10.1109/ICSCDS56580.2023.10105044|Convolutional Neural Network;Deep Learning;Image Processing;Sequential Model;Transfer Learning;Convolutional Neural Network;Deep Learning;Image Processing;Sequential Model;Transfer Learning|
|[A Survey on Artificial Intelligence (AI) based Job Recommendation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104718)|A. Patil; D. Suwalka; A. Kumar; G. Rai; J. Saha|10.1109/ICSCDS56580.2023.10104718|Job Recommendation System;Content Based Filtering;Knowledge Graph;Machine Learning;Job Recommendation System;Content Based Filtering;Knowledge Graph;Machine Learning|
|[Disease Recognition of Crops using ResNet and MDFC-ResNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104714)|A. Y. Krishna; S. T. Sri; N. D. G; V. Sravya; P. S. Praneetha; B. V. Vardhan|10.1109/ICSCDS56580.2023.10104714|disease recognition;ResNet;multiple crops;feature compensation;singular value decomposition;web application;disease recognition;ResNet;multiple crops;feature compensation;singular value decomposition;web application|
|[A Review of Multidata Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104780)|A. Ravi Kumar.; D. Sumathi.|10.1109/ICSCDS56580.2023.10104780|Human Activity Recognition;Artificial Intelligence;Data Modality;Device Modality;Human Activity Recognition;Artificial Intelligence;Data Modality;Device Modality|
|[Enhanced Model for Automatic Tamil Text Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105055)|I. K. Pious; S. Girirajan|10.1109/ICSCDS56580.2023.10105055|Automatic Text Summarization;Keyword based scoring;Sentiment Scoring;Text Ranking based scoring;Text mining;Automatic Text Summarization;Keyword based scoring;Sentiment Scoring;Text Ranking based scoring;Text mining|
|[Analysis and Prediction of Lettuce Crop Yield in Aeroponic Vertical Farming using Logistic Regression Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104763)|R. Gowtham; R. Jebakumar|10.1109/ICSCDS56580.2023.10104763|Aeroponics;Machine Learning;Lettuce;Indoor Farming;Data Visualization;Data Science;Aeroponics;Machine Learning;Lettuce;Indoor Farming;Data Visualization;Data Science|
|[Green Energy Implementation based on Smart Grid Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104793)|M. Sujitha; G. Veeranna; R. Venkatesh; S. Srinidhi; J. Ranga; L. M. Rao|10.1109/ICSCDS56580.2023.10104793|Performance based-design;Renewable Energy Technology;Design criteria;Design procedures;Evaluation;Challenges;Future trends;Solar power;Electricity;Performance based-design;Renewable Energy Technology;Design criteria;Design procedures;Evaluation;Challenges;Future trends;Solar power;Electricity|
|[Emotion Detection of People Wearing Face Mask](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104665)|N. S. T. P. Pedaprolu; Y. Jayanth; S. Madhur; D. S. Sumanth; G. S. Vishruth; P. C. S. Reddy|10.1109/ICSCDS56580.2023.10104665|Facial Expressions;Emotions;Machine Learning;Computer Vision;Face Masks;Facial Expressions;Emotions;Machine Learning;Computer Vision;Face Masks|
|[LSTM with Bayesian Slide Optimization for Time Series Forecasting in Real Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104650)|A. Poornima; K. Archana; P. Dhivya; N. Sangavi|10.1109/ICSCDS56580.2023.10104650|Convolutional Neural Network;LSTM;Sliding Window Protocol;Mean Absolute Error Rate;Convolutional Neural Network;LSTM;Sliding Window Protocol;Mean Absolute Error Rate|
|[Visual Questions Answering Developments, Applications, Datasets and Opportunities: A State-of-the-Art Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104870)|H. J. Singh; G. Bathla; M. Mehta; G. Chhabra; P. Singh|10.1109/ICSCDS56580.2023.10104870|Visual Question Answering;Machine Learning;Deep Learning;Natural Language Processing;Visual Question Answering;Machine Learning;Deep Learning;Natural Language Processing|
|[Identification of Deepfakes using Strategic Models and Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104880)|S. R. Nallapati; D. Dommeti; S. Medhalavalasa; K. K. Bonku; P. V. V. S. Srinivas; D. Bhattacharyya|10.1109/ICSCDS56580.2023.10104880|Deepfake Detection;Deep Learning;Convolutional Neural Network;Gated Recurrent Unit;Image Noise Patterns;Deepfake Detection;Deep Learning;Convolutional Neural Network;Gated Recurrent Unit;Image Noise Patterns|
|[Multi-dimensional Optimization of a Visual Model for Measurement Assurance of Model-Specific Test Equipment that Integrates Data Flow Information Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105108)|Q. Ji|10.1109/ICSCDS56580.2023.10105108|Data flow information;multi-dimensional optimization;visual model;measurement assurance;model-specific test equipment;Data flow information;multi-dimensional optimization;visual model;measurement assurance;model-specific test equipment|
|[A Novel Segmentation-Registration (SR) based Gesture Recognition Algorithm for Equestrian Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104912)|H. Ma|10.1109/ICSCDS56580.2023.10104912|Segmentation-registration;gesture recognition;algorithm design;equestrian images;image processing;Segmentation-registration;gesture recognition;algorithm design;equestrian images;image processing|
|[A Novel Framework for Super-Resolution Reconstruction of Equestrian Videos Considering Compressed Sensing Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104841)|H. Ma|10.1109/ICSCDS56580.2023.10104841|Compressed sensing algorithm;video processing;novel framework;super-resolution reconstruction;image feature;Compressed sensing algorithm;video processing;novel framework;super-resolution reconstruction;image feature|
|[Brainwave Sensor based Smart Home Controller for Paralysed People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104682)|N. C; S. B; S. R; V. R|10.1109/ICSCDS56580.2023.10104682|Machine Learning (ML);Electroenceplogram (EEG);Automation;Brain computer interface (BCI);Random Forest algorithm;Machine Learning (ML);Electroenceplogram (EEG);Automation;Brain computer interface (BCI);Random Forest algorithm|
|[A Study on Cross-Lingual Speech Emotion Analysis using Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105094)|C. Suresh; M. Charan Sathvik; N. Deepthi; K. Mohana Sai Purnima; K. P. S. Chouhan|10.1109/ICSCDS56580.2023.10105094|Emotion;Physiological change;Natural language;Speech Emotion Recognition;Audio signals;Emotional well-being;Emotion;Physiological change;Natural language;Speech Emotion Recognition;Audio signals;Emotional well-being|
|[Apriori Enabled Super Market Rack Rearrangement as A Means of Making Recommendations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104751)|H. C. M. Perumal; B. S. Chokkalingam|10.1109/ICSCDS56580.2023.10104751|Apriori;Recommendation;Association;Apriori;Recommendation;Association|
|[Sentiment Analysis on User Feedback of a Social Media Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105082)|A. Singh; H. Srivastava; M. Aman; G. Dubey|10.1109/ICSCDS56580.2023.10105082|sentiment analysis;opinion mining;feedback;social media;sentiment analysis;opinion mining;feedback;social media|
|[Sign Language to Text Classification using One-Shot Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104608)|S. Pal; S. Ruhella; S. Sinha; A. Goel|10.1109/ICSCDS56580.2023.10104608|One-Shot Learning;Image Classification;Sign Language Recognition;Siamese Network;One-Shot Learning;Image Classification;Sign Language Recognition;Siamese Network|
|[Raspberry Pi based Nutritional Health Kits for Diabetic Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104654)|P. Mannepally; K. C. Janapati; B. Bhanavath; P. Ravula|10.1109/ICSCDS56580.2023.10104654|Raspberry Pi Pico;Diabetes;Nutrition;Raspberry Pi Pico;Diabetes;Nutrition|
|[Patient Health Monitoring using Foot Pressure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104849)|S. Janarthanan; S. Pavithra Devi; V. Satheesh; P. Ravi Varma; K. N. Baluprithviraj; M. Madhan Mohan|10.1109/ICSCDS56580.2023.10104849|Pressure;Sensor;Acupuncture;Pressure points;Foot pressure;Pressure;Sensor;Acupuncture;Pressure points;Foot pressure|
|[An Improved Real-Time Sign Language Recognition using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104708)|L. Goel; N. P. Karthik; M. J. Naidu; P. Sinha; A. Thota|10.1109/ICSCDS56580.2023.10104708|Sign Language Recognition;Transfer Learning;OpenCV;TensorFlow;Deep Learning;Sign Language Recognition;Transfer Learning;OpenCV;TensorFlow;Deep Learning|
|[Facial Landmark-based Cursor Control and Speechto-Text System for Paralyzed Individuals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104936)|L. R. Kalabarige; K. A. Abhilash; K. A. Trivedi; M. Dathatreya|10.1109/ICSCDS56580.2023.10104936|Shape Predictor;OpenCV;Human Computer Interface (HCI);Speech Recognition;Pyaudio;Shape Predictor;OpenCV;Human Computer Interface (HCI);Speech Recognition;Pyaudio|
|[Video Conferencing using WebRTC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104791)|R. Deshmukh; N. Nand; A. Pawar; D. Wagh; A. Kudale|10.1109/ICSCDS56580.2023.10104791|Video conferencing;WebRTC;Signaling;Peer-to-peer;Media stream;Protocols;Video conferencing;WebRTC;Signaling;Peer-to-peer;Media stream;Protocols|
|[NLP based Text Summarization of Fintech RFPs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104748)|K. Patil; M. Badamikar; S. Sonawane|10.1109/ICSCDS56580.2023.10104748|Extractive Summarization;Natural Language Processing;RFPs (Request for Proposal);Extractive Summarization;Natural Language Processing;RFPs (Request for Proposal)|
|[Game Automation System Based on Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105000)|P. Srihari; K. K. N. Venkat; B. S. Sirisha; B. V. Yaswanth; B. V. Krishna|10.1109/ICSCDS56580.2023.10105000|Computer Vision;Motion detection;Gesture detection;Media-Pipe;OpenCV;Computer Vision;Motion detection;Gesture detection;Media-Pipe;OpenCV|
#### **2023 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL)**
- DOI: 10.1109/INERTIAL56358.2023
- DATE: 28-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Practical Approaches to Allan Deviation Analysis of Low-Cost MEMS Inertial Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103985)|T. Hiller; M. Vujadinović; L. Blocher; W. Mayer; D. Radović; T. Balslink; T. Northemann; A. Buhmann|10.1109/INERTIAL56358.2023.10103985|MEMS inertial sensor;low-cost;Allan deviation;gyroscope;accelerometer;angle random walk;bias instability;MEMS inertial sensor;low-cost;Allan deviation;gyroscope;accelerometer;angle random walk;bias instability|
|[Compensation of Non-Orthogonality Changes in Low-Cost MEMS Gyroscopes Across Soldering, Temperature and Lifetime](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103996)|T. Hiller; P. Tritschler; L. Blocher; W. Mayer; M. Vujadinović; T. Balslink; M. Schöfthaler; T. Northemann|10.1109/INERTIAL56358.2023.10103996|MEMS gyroscope;low-cost inertial sensor;cross-axis sensitivity;non-orthogonality;quadrature;skewness;MEMS gyroscope;low-cost inertial sensor;cross-axis sensitivity;non-orthogonality;quadrature;skewness|
|[Highly Accurate Inertial Navigation that Compensates for the Earth's Rotation and Sensor BIAS Using Non-Holonomic Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103969)|M. Kimishima; T. Sawada; A. Sonoura; T. Amano; H. Kamata; K. Yamashita|10.1109/INERTIAL56358.2023.10103969|Earth rotation;North finding;Gyrocompass;Inertial navigation;Non holonomic;Maytagging;Inertial sensors;Multi-IMU;Multiple IMU;IMU array;Earth rotation;North finding;Gyrocompass;Inertial navigation;Non holonomic;Maytagging;Inertial sensors;Multi-IMU;Multiple IMU;IMU array|
|[Modeling and Experimental Analysis of Low-Cost MEMS Gyroscopes Under PCB Bending Stress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103800)|W. Mayer; A. Küster; P. Tritschler; T. Hiller; D. Radović; A. Zimmermann|10.1109/INERTIAL56358.2023.10103800|MEMS inertial sensor;gyroscope;Coriolis force;sensitivity;bending;PCB stress;gap distance;analytical model;MEMS inertial sensor;gyroscope;Coriolis force;sensitivity;bending;PCB stress;gap distance;analytical model|
|[Scale Factor Instability Noise in Mode-Split Open-Loop MEMS Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103803)|M. Vujadinović; T. Hiller; L. Blocher; T. Balslink; D. Radović; T. Northemann; A. Buhmann; B. Choubey|10.1109/INERTIAL56358.2023.10103803|MEMS inertial sensor;gyroscope;scale factor;bias instability;flicker noise;sensitivity;Allan deviation;MEMS inertial sensor;gyroscope;scale factor;bias instability;flicker noise;sensitivity;Allan deviation|
|[Modeling of Phase Noise in Mode-Split Open-Loop MEMS Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103979)|M. Vujadinović; T. Hiller; L. Blocher; D. Radovic; T. Balslink; T. Northemann; B. Choubey|10.1109/INERTIAL56358.2023.10103979|MEMS inertial sensor;gyroscope;phase noise;phase space;bias instability;frequency split;demodulation;MEMS inertial sensor;gyroscope;phase noise;phase space;bias instability;frequency split;demodulation|
|[A new high-g measurement system for severe perforation tests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104024)|M. Lavayssière; J. Willemin; A. Hottelet; N. Stephanopoli; C. Grein; C. Garaffa; E. Cabanillas; P. Ramahefa-Andry; S. Driussi; M. Gauroy; J. Boussac; S. Louwers|10.1109/INERTIAL56358.2023.10104024|deceleration-time measurements;instrumented projectile;impact;high-g shock;acceleration sensor;embedded data recorder;deceleration-time measurements;instrumented projectile;impact;high-g shock;acceleration sensor;embedded data recorder|
|[REDUCING THERMO-ELASTIC DAMPING OF MEMS RESONATORS USING A VIRTUAL SPRING](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103974)|C. Tang; Z. Wu; M. Heller; D. Nishinohara; T. Fujita; T. Ikehashi|10.1109/INERTIAL56358.2023.10103974|Thermo-elastic Damping (TED);resonator;quality factor;Thermo-elastic Damping (TED);resonator;quality factor|
|[Sensor Individual Non-Orthogonality Correction in Low-Cost MEMS Gyroscopes Using Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103806)|P. Tritschler; T. Hiller; T. Ohms; W. Mayer; A. Zimmermann|10.1109/INERTIAL56358.2023.10103806|Self-calibration;neural networks;meta-learning;MEMS inertial sensor;low-cost gyroscope;non-orthogonality;Self-calibration;neural networks;meta-learning;MEMS inertial sensor;low-cost gyroscope;non-orthogonality|
|[Laser Induced Chemical Etching of Quartz for MEMS sensors fabrication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103805)|M. Sirota; B. Lipavsky; D. Nuttman; N. Melech; O. HaLevy; S. Krylov|10.1109/INERTIAL56358.2023.10103805|Crystalline quartz MEMS;quartz anisotropic etch;laser assistant etch;force amplification;Crystalline quartz MEMS;quartz anisotropic etch;laser assistant etch;force amplification|
|[Development of Inertial Acceleration Measurement Device with Zero-Compliance Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103799)|T. Mizuno; Y. Ishino; M. Takasaki|10.1109/INERTIAL56358.2023.10103799|accelerometer;zero compliance;infinite stiffness;inertial mass;negative stiffness;accelerometer;zero compliance;infinite stiffness;inertial mass;negative stiffness|
|[71 kHz Frequency Modulated PiezoMEMS Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104005)|A. Ontronen; V. Kaajakari; K. Wjuga; A. Uno; S. Umezawa; Y. Aida|10.1109/INERTIAL56358.2023.10104005|MEMS gyroscope;frequency modulated;lissajous pattern;piezoelectric;gyroscope;MEMS gyroscope;frequency modulated;lissajous pattern;piezoelectric;gyroscope|
|[Optimization of Localization Error in Multi-Agent Systems through Cooperative Positioning: Autonomous Navigation in Partially Denied GNSS Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103968)|S. Shahkar; K. Khorasani|10.1109/INERTIAL56358.2023.10103968|Cooperative control;Multi-agent system formation;Autonomous localization;Sensor networks;Cooperative control;Multi-agent system formation;Autonomous localization;Sensor networks|
|[Detection and Identification of GNSS Spoofing Cyber-Attacks for Naval Marine Vessels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104008)|M. Taheri; M. Nematollahi; K. Khorasani|10.1109/INERTIAL56358.2023.10104008|GNSS Spoofing Cyber-attacks;Naval Marine Vessels;Detection and Identification of Cyber-attacks;GNSS Spoofing Cyber-attacks;Naval Marine Vessels;Detection and Identification of Cyber-attacks|
|[Prio-IMU: Prioritizable IMU Array for Enhancing Foot-mounted Inertial Navigation Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103991)|C. -S. Jao; D. Wang; A. M. Shkel|10.1109/INERTIAL56358.2023.10103991|IMU Array;ZUPT;Inertial Navigation;IMU Array;ZUPT;Inertial Navigation|
|[Towards a Better Understanding of Offset Changes Across Temperature in Mode-Split Open-Loop MEMS Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103945)|M. Vujadinović; T. Hiller; L. Blocher; T. Northemann; B. Choubey|10.1109/INERTIAL56358.2023.10103945|MEMS inertial sensor;gyroscope;temperature;zero-rate offset;quality factor;drive frequency;direct bias;MEMS inertial sensor;gyroscope;temperature;zero-rate offset;quality factor;drive frequency;direct bias|
|[Searching for the Origin of Zero-Rate Offset and Scale-Factor Drift in Nems-Based Nav-Grade Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104013)|A. Buffoli; P. Segala; M. Gadola; T. Verdot; P. Robert; G. Langfelder|10.1109/INERTIAL56358.2023.10104013|MEMS;NEMS;Gyroscope;Scale Factor Drift;Temperature;MEMS;NEMS;Gyroscope;Scale Factor Drift;Temperature|
|[Bandwidth vs ZRO Stability Trade-Off in Lissajous Frequency Modulated MEMS Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103804)|M. Bestetti; G. Mussi; G. Gattere; C. Padovani; A. G. Bonfanti; G. Langfelder|10.1109/INERTIAL56358.2023.10103804|gyroscope;frequency modulation;Lissajous;MEMS;stability;gyroscope;frequency modulation;Lissajous;MEMS;stability|
|[An autonomous north alignment method for motion simulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103977)|B. V. Exail; M. B. Exail; N. B. Exail|10.1109/INERTIAL56358.2023.10103977|North alignment;Northfinding;Motion simulator;Fiber Optics Gyroscope (FOG);North alignment;Northfinding;Motion simulator;Fiber Optics Gyroscope (FOG)|
|[MEMS Directional Underwater Acoustic Sensor Operating in Near Neutral Buoyancy Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104015)|J. Ivancic; J. Catterlin; G. Karunasiri; F. Alves|10.1109/INERTIAL56358.2023.10104015|MEMS acoustic sensors;directional acoustic sensor;accelerometer;neutral buoyancy;underwater;MEMS acoustic sensors;directional acoustic sensor;accelerometer;neutral buoyancy;underwater|
|[Efficient Online Compression for MEMS based BCG Wearable Sensors on ULP FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103939)|U. Kulau; A. Noshy; A. Ahmed|10.1109/INERTIAL56358.2023.10103939|Wearable;BCG;Data compression;MEMS;Ultra-low power;FPGA;Wearable;BCG;Data compression;MEMS;Ultra-low power;FPGA|
|[Manufacture of hemi-spherical resonators using printable fused silica glass](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103948)|Y. Atwa; H. Shakeel|10.1109/INERTIAL56358.2023.10103948|hemi-spherical resonator;fused silica;3D printing;printable glass;hemi-spherical resonator;fused silica;3D printing;printable glass|
|[Combined Rotation and Magnetic Field Sensor Based on Lissajous FM Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103987)|T. Tsukamoto; S. Tanaka|10.1109/INERTIAL56358.2023.10103987|;|
|[Virtually Rotated Multiple Mass Resonator Enabled by Electrostatic Frequency and Q-factor Tuning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103981)|J. Chen; T. Tsukamoto; G. Langfelder; S. Tanaka|10.1109/INERTIAL56358.2023.10103981|;|
|[Dynamic Quality Factor Equalization for Improved Bias Stability in Mode-Matched Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104012)|R. Q. Rudy; C. Hauser; R. R. Knight; J. S. Pulskamp|10.1109/INERTIAL56358.2023.10104012|Quality factor;mode-matching;gyroscope;resonators;piezoelectric;ferroelectric;Quality factor;mode-matching;gyroscope;resonators;piezoelectric;ferroelectric|
|[Efficient Quadrature Suppression for Improved Performance of a MEMS Vibratory Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103983)|R. Forke; A. Shaporin; S. Weidlich; D. Bülz; K. Hiller; H. Kuhn|10.1109/INERTIAL56358.2023.10103983|MEMS;gyroscope;angular rate;sensor;quadrature suppression;compensation;BDRIE;cavity-SOI;HAR;MEMS;gyroscope;angular rate;sensor;quadrature suppression;compensation;BDRIE;cavity-SOI;HAR|
|[A New Design of Mode-Matched (100) Silicon Ring Gyroscope with Chamfered Rectangle Springs Immune to Fabrication Error](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103986)|S. Okayama; A. Banerjee; J. Hirotani; T. Tsuchiya|10.1109/INERTIAL56358.2023.10103986|vibrating gyroscope;(100) silicon;mode match;fabrication error;vibrating gyroscope;(100) silicon;mode match;fabrication error|
|[Locomotive Syndrome Assessment in Older Adults Using a Single Inertial Measurement Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103944)|I. Hosseini; M. Ghahramani|10.1109/INERTIAL56358.2023.10103944|;|
|[A Navigation Grade, Software Defined Gyroscope and Extensions for Generic Vibratory Inertial Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104002)|D. Hayner; D. Challoner|10.1109/INERTIAL56358.2023.10104002|vibratory inertial sensors;digital phase lock loops;coherent demodulation;software defined gyroscope;software defined accelerometer;gyro servos;gyro force feedback;vibratory inertial sensors;digital phase lock loops;coherent demodulation;software defined gyroscope;software defined accelerometer;gyro servos;gyro force feedback|
|[Dynamical Encircling of a Lock-In Area with a Combination of Fast and Slow Dithering in a Ring Laser Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103982)|W. -S. Choi; J. -E. An; C. -J. Kim; K. -M. Shim|10.1109/INERTIAL56358.2023.10103982|Ring laser gyroscope (RLG);Frequency lock-in;Fast and slow dithering;Ring laser gyroscope (RLG);Frequency lock-in;Fast and slow dithering|
|[Quadrature Compensation and Demodulation Phase Reference Selection for FM Accelerometers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103973)|A. B. Sabater|10.1109/INERTIAL56358.2023.10103973|MEMS;FM Accelerometer;Phase;Quadrature;MEMS;FM Accelerometer;Phase;Quadrature|
|[Impact of Amplitude and Demodulation Errors on the Nonlinear Frequency Modulated Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104025)|A. B. Sabater|10.1109/INERTIAL56358.2023.10104025|MEMS;FM Gyroscope;Nonlinear;NFMG;Amplitude Error;Demodulation Error;Timing Error;PLL;MEMS;FM Gyroscope;Nonlinear;NFMG;Amplitude Error;Demodulation Error;Timing Error;PLL|
|[A Digital Twin to Model the Impact of Etch Profile on MEMS Gyroscope Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103938)|C. J. Welham; A. Parent; E. Valfer; T. Piirainen; M. Liukku; M. Partanen|10.1109/INERTIAL56358.2023.10103938|MEMS Gyroscope;Digital Twin;DRIE sidewall angle;Finite element analysis;Quadrature compensation;MEMS Gyroscope;Digital Twin;DRIE sidewall angle;Finite element analysis;Quadrature compensation|
|[Low frequency inertial sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103942)|A. Nelson; A. Hines; G. Valdes; J. Sanjuan; F. Guzman|10.1109/INERTIAL56358.2023.10103942|accelerometer;seismometer;gravimeter;inertial sensing;interferometry;resonator;accelerometer;seismometer;gravimeter;inertial sensing;interferometry;resonator|
|[A Low-Voltage Wideband AlN-on-Si Gyroscope With Sub 10-DPH Bias Instability Mode Matched Using Laser Trimming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104003)|Z. Liu; H. Wen; F. Ayazi|10.1109/INERTIAL56358.2023.10104003|MEMS;Gyroscope;Bulk-Acoustic Wave;Piezoelectric;Mode matching;MEMS;Gyroscope;Bulk-Acoustic Wave;Piezoelectric;Mode matching|
|[UMEMS: A Robust Technology Platform For Quality Automotive Inertial Sensor Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103951)|L. Liu; M. Lagouge; B. Steimle; A. Geisberger; J. McKillop; D. Monk|10.1109/INERTIAL56358.2023.10103951|MEMS;Inertial sensor;Accelerometer;Gyroscope;AlGe bonding;TSV;RDL;MEMS;Inertial sensor;Accelerometer;Gyroscope;AlGe bonding;TSV;RDL|
|[Out of Plane Double Differential Quadrature Compensation Electrodes with ThELMA-Double Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103994)|P. Fedeli; L. Falorni; G. J. Yallico Sanchez; M. Riani; G. Gattere|10.1109/INERTIAL56358.2023.10103994|MEMS;Gyroscope;ThELMA-Double;Quadrature Compensation;Scale Factor Stability;MEMS;Gyroscope;ThELMA-Double;Quadrature Compensation;Scale Factor Stability|
|[Verifying IMU Suitability for Recognition of Freshwater Mussel Behaviors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104026)|W. Jackson; A. Marchiori; S. J. Thomas; E. Capaldi; S. Reese|10.1109/INERTIAL56358.2023.10104026|freshwater mussel;gape;ecology;IMU;freshwater mussel;gape;ecology;IMU|
|[Study of IMU Mounting Position for ZUPT-Aided INS in the Case of Firefighter Crawling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104014)|A. R. Parrish; C. -S. Jao; D. Wang; A. M. Shkel|10.1109/INERTIAL56358.2023.10104014|ZUPT-aided INS;self-contained navigation;crawling;firefighter;first responder;ZUPT-aided INS;self-contained navigation;crawling;firefighter;first responder|
|[A Centrally-Anchored High-Q Tunable Piezoelectric MEMS Resonators FOR Wide Temperature Range RTC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103940)|Y. Long; Z. Liu; C. Wehner; A. Farrokh|10.1109/INERTIAL56358.2023.10103940|real time clock;AIN-on-silicon;electrostatic-tuning;temperature compensation;wide temperature range;real time clock;AIN-on-silicon;electrostatic-tuning;temperature compensation;wide temperature range|
|[Large Amplitude Linear Drive Quadruple Mass Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103988)|R. R. Knight; C. Scheri; J. S. Pulskamp; R. Q. Rudy; D. L. DeVoe|10.1109/INERTIAL56358.2023.10103988|MEMS;gyroscope;QMG;nonlinear;angle random walk;MEMS;gyroscope;QMG;nonlinear;angle random walk|
|[c-Axis parallel ZnO piezoelectric multilayer for BAW gyroscope applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103993)|A. Hanai; S. Kudo; K. Ekida; J. Jia; T. Yanagitani|10.1109/INERTIAL56358.2023.10103993|MEMS gyroscope;c-axis parallel ZnO;ion beam assisted deposition;MEMS gyroscope;c-axis parallel ZnO;ion beam assisted deposition|
|[Shear mode bulk acoustic wave type gyroscope based on c-axis tilted ScAlN piezoelectric films](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104023)|M. Matsumura; Y. Koike; R. Seki; T. Yanagitani|10.1109/INERTIAL56358.2023.10104023|BAW;piezoelectric gyroscope;shear wave;ScAlN;BAW;piezoelectric gyroscope;shear wave;ScAlN|
|[“Sugar-Cube”: Pedestrian Hardware Platform that Fits in the Sole of a Shoe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103995)|A. R. Parrish; C. -S. Jao; D. Wang; A. M. Shkel|10.1109/INERTIAL56358.2023.10103995|Indoor navigation;Pedestrian Navigation;Inertial Navigation Platform;Foot-mounted IMU;Zero Velocity Update;Altimeter;Indoor navigation;Pedestrian Navigation;Inertial Navigation Platform;Foot-mounted IMU;Zero Velocity Update;Altimeter|
|[Non-Destructive Characterization of High Aspect-Ratio Structures Using 3D X-Ray Microscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103937)|E. L. Bentley; S. Prabhu; S. Singh; J. Y. Cho; K. Najafi|10.1109/INERTIAL56358.2023.10103937|3D X-Ray Microscopy;non-destructive testing;failure analysis;shell resonators;birdbath;fused-silica;gyroscopes;3D X-Ray Microscopy;non-destructive testing;failure analysis;shell resonators;birdbath;fused-silica;gyroscopes|
|[A high-performance resonant MEMS accelerometer with a residual bias error of 30 μg and scale factor repeatability of 2 ppm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103976)|L. Gurung; T. Miani; G. Sobreviela-Falces; D. Young; C. Baker; A. Seshia|10.1109/INERTIAL56358.2023.10103976|MEMS;accelerometer;inertial sensor;resonant;sensor;bias;scale factor;high-performance;navigation;VBA;MEMS;accelerometer;inertial sensor;resonant;sensor;bias;scale factor;high-performance;navigation;VBA|
|[An Inverted Pendulum Model of Walking for Predicting Navigation Uncertainty of Pedestrian in Case of Foot-mounted Inertial Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104017)|C. -S. Jao; E. Sangenis; P. Simo; A. Voloshina; A. M. Shkel|10.1109/INERTIAL56358.2023.10104017|IMU;ZUPT;walking simulation;navigation;IMU;ZUPT;walking simulation;navigation|
|[Simulation of Anchor Loss in MEMS Resonators Using Perfectly Matched Layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103970)|D. Schiwietz; L. R. More; E. M. Weig; P. Degenfeld-Schonburg|10.1109/INERTIAL56358.2023.10103970|;|
|[Indirect Excitation of micro-HRG Using Segmented Piezoelectric ALD PHT Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103950)|D. Wang; N. A. Strnad; Y. Wang; A. R. Parrish; R. R. Benoit; R. R. Knight; A. M. Shkel|10.1109/INERTIAL56358.2023.10103950|Fused Quartz;Hemispherical Resonator Gyroscope;Dual-Shell Gyroscope;Metallization;Quality factor;Piezoelectric;ALD PHT;Fused Quartz;Hemispherical Resonator Gyroscope;Dual-Shell Gyroscope;Metallization;Quality factor;Piezoelectric;ALD PHT|
|[Gold Single-Axis Differential Capacitive MEMS Accelerometer With Proof-Mass Position Control Electrode Fabricated by Post-CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103943)|A. Onishi; K. Miyado; D. S. Tenneti; K. Machida; P. Chakraborty; M. Sone; Y. Miyake; H. Ito|10.1109/INERTIAL56358.2023.10103943|MEMS accelerometer;gold proof-mass;proof-mass position control;post-CMOS;MEMS accelerometer;gold proof-mass;proof-mass position control;post-CMOS|
#### **2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS)**
- DOI: 10.1109/ICAECCS56710.2023
- DATE: 6-7 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Ideal Behavior of Vernier and Flash TDCs Implemented in a Spartan-6 FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104626)|W. Chouial; M. Maamoun|10.1109/ICAECCS56710.2023.10104626|Time to digital converter (TDC);Vernier TDC;tapped delay line (TDL) TDC;uniformity of the TDC binning;ideal behavior of TDC;field programmable gate array (FPGA);Time to digital converter (TDC);Vernier TDC;tapped delay line (TDL) TDC;uniformity of the TDC binning;ideal behavior of TDC;field programmable gate array (FPGA)|
|[Improved Efficiency and Performance of the Energy Storage System for an EV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104767)|R. Moumni; I. Benlaloui; K. Laroussi|10.1109/ICAECCS56710.2023.10104767|Electric Vehicle (EV);Battery Electric Vehicle (BEV);State of Charge (SOC);Electric Vehicle (EV);Battery Electric Vehicle (BEV);State of Charge (SOC)|
|[Energy Harvesting Based on SLIPT in I2V-VLC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105093)|S. Refas; D. Acheli; S. Yahia; Y. Meraihi|10.1109/ICAECCS56710.2023.10105093|Energy harvesting;traffic light-to-vehicle;VLC;SLIPT;Energy harvesting;traffic light-to-vehicle;VLC;SLIPT|
|[Dynamic Modeling and Control of Continuum Robots Using an Optimized PID Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105092)|A. Ghoul; S. Djeffal; K. Kara; A. Aouaichia|10.1109/ICAECCS56710.2023.10105092|Continuum robot;Dynamic modeling;Proportional integral derivative controller;Adaptive particle swarm optimization.;Continuum robot;Dynamic modeling;Proportional integral derivative controller;Adaptive particle swarm optimization.|
|[A Digital Signature Based on PKI To Authentication and Secure Exchanging data used in water boreholes intelligent Decision Support System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105057)|K. Semar-Bitah; F. Z. Bouderbala; Y. Ghebghoub|10.1109/ICAECCS56710.2023.10105057|Digital signature;PKI;PKCS;Linux;Certificate;Private/Public Keys;cryptography;Digital signature;PKI;PKCS;Linux;Certificate;Private/Public Keys;cryptography|
|[Low-Cost ECG Monitoring System with Classification Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104707)|H. Saadi; M. Ferroukhi; Y. L. Elghandja; F. Lahmari|10.1109/ICAECCS56710.2023.10104707|ECG acquisition;ESP32;MIT-BIH database;Classification;CNN;IoT;Web platform;ECG acquisition;ESP32;MIT-BIH database;Classification;CNN;IoT;Web platform|
|[Efficiency comparison of silicon and silicon carbide MOSFETs in a PV system application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104789)|E. Bouchetob; B. Nadji; I. Mahdi|10.1109/ICAECCS56710.2023.10104789|PV system;SiC Mosfet;Si Mosfet;DC-DC converter;MPPT;Co-Simulation;PV system;SiC Mosfet;Si Mosfet;DC-DC converter;MPPT;Co-Simulation|
|[Fuzzy Fast Terminal SMC for Underactuated Mechanical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104963)|A. Bendenidina; K. Guesmi; A. Rebai|10.1109/ICAECCS56710.2023.10104963|Underactuated systems;Fast terminal SMC;Fuzzy tuning;Stability;Underactuated systems;Fast terminal SMC;Fuzzy tuning;Stability|
|[Two-Stage Converter Based on Droop Control and IDA-PBC in Islanded Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105008)|C. Zakaria; B. Smain; T. Abdelhalim|10.1109/ICAECCS56710.2023.10105008|boost converter;droop control;IDA-PBC control;islanded;PV array;boost converter;droop control;IDA-PBC control;islanded;PV array|
|[Local Directional Patterns for Plant Leaf Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104754)|A. Mezenner; H. Nemmour; Y. Chibani; A. Hafiane|10.1109/ICAECCS56710.2023.10104754|Convolutional Neural Networks. Local Directional Patterns;Plant Disease Detection;Support Vector Machines.;Convolutional Neural Networks. Local Directional Patterns;Plant Disease Detection;Support Vector Machines.|
|[a Compact Size FSS Unit Cell Design For UWB Filtering and Shielding Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105125)|S. E. I. Daira; M. Lashab; M. Belattar|10.1109/ICAECCS56710.2023.10105125|Frequency Selective Surface;Ultra-Wide Band;Shielding effectiveness;Stop-band filter;Frequency Selective Surface;Ultra-Wide Band;Shielding effectiveness;Stop-band filter|
|[Reconfigurable Wide Band Antenna with Gain Enhancement using Metamaterial Superstrate for Ku Band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104698)|K. Abdelhak; D. Mustapha|10.1109/ICAECCS56710.2023.10104698|metamaterial (MTM);Pin diodes;Ku band;reconfigurable frequency;superstrate;metamaterial (MTM);Pin diodes;Ku band;reconfigurable frequency;superstrate|
|[Efficient Probabilistic Counter-Based Broadcast for Low-Power & Lossy Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105089)|D. A. Fedila; M. O. Khaoua|10.1109/ICAECCS56710.2023.10105089|LLN;probabilistic broadcast;counter-based broadcast;IEEE 802.15.4;Cooja simulation;LLN;probabilistic broadcast;counter-based broadcast;IEEE 802.15.4;Cooja simulation|
|[FSO and MmWave Technologies for 5G Mobile Networks: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105065)|S. Derouiche; S. Kameche; H. E. Adardour|10.1109/ICAECCS56710.2023.10105065|5G;FSO;mmWave;small cell;RF-FSO;5G;FSO;mmWave;small cell;RF-FSO|
|[Characterization of an ultrasound reception chain based of a PVDF membrane hydrophone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105038)|W. Djerir; R. Halimi; A. Allag; T. Boutkedjirt|10.1109/ICAECCS56710.2023.10105038|PVDF membrane;ultrasound reception chain;transfer function;impulse response;transfer Matrix;PVDF membrane;ultrasound reception chain;transfer function;impulse response;transfer Matrix|
|[Gossip-Based Interest Forwarding in NDN over MANETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104735)|A. S. O. Khaoua; F. Bey; A. Boukra|10.1109/ICAECCS56710.2023.10104735|MANETs;NDN;IEEE 802.11;Performance Analysis.;MANETs;NDN;IEEE 802.11;Performance Analysis.|
|[Statistical examination of wind energy’s contribution to carbon dioxide emissions reduction* : *Study: Adrar, located in Southern Algeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105015)|M. Benmedjahed; A. Dahbi; A. Khelfaoui; A. Hadidi; S. Mouhadjer; O. Djaafri|10.1109/ICAECCS56710.2023.10105015|Wind;Weibull;Energy;Natural Gaz;Carbon;Wind;Weibull;Energy;Natural Gaz;Carbon|
|[Analysis of a Novel 4D Chaotic Oscillator for Communication Systems Up to 6 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104866)|B. Mohammed; K. Samir; O. Achour|10.1109/ICAECCS56710.2023.10104866|secure communications;Colpitts oscillator;BFG410W bipolar transistor;bifurcation diagram;secure communications;Colpitts oscillator;BFG410W bipolar transistor;bifurcation diagram|
|[Enhancement of the AR-MED Deconvolution Process using Ensemble Empirical Mode Decomposition in Bearing Fault Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104709)|Y. Damine; N. Bessous; A. C. Megherbi; S. S. S. S. Sbaa; A. Ünsal|10.1109/ICAECCS56710.2023.10104709|feature extraction;autoregressive filter;ensemble empirical mode decomposition;minimum entropy deconvolution;bearing fault diagnosis;feature extraction;autoregressive filter;ensemble empirical mode decomposition;minimum entropy deconvolution;bearing fault diagnosis|
|[Running Convolutional Neural Network on tiny devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105032)|S. Daoudi; M. E. -A. Bellebna; M. Elbahri; S. Mazari; N. Alaoui|10.1109/ICAECCS56710.2023.10105032|CNN;Embedded system devices;Real time processing;Pruning & Quantization;CNN;Embedded system devices;Real time processing;Pruning & Quantization|
|[Performance Evaluation of SDN-WISE in Mobile Wireless Sensors Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104806)|A. Tcherak; M. O. Khaoua; S. Loucif|10.1109/ICAECCS56710.2023.10104806|Wireless Sensors Networks (WSNs);Internet of Things (IoT);Software-Defined Networking (SDN);SDN-WISE;Mobility;Wireless Sensors Networks (WSNs);Internet of Things (IoT);Software-Defined Networking (SDN);SDN-WISE;Mobility|
|[FPGA Implementation using Novel Co-Design Approach of Real-Time Speech Chaos based Crypto-Watermarking Prototype](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104859)|M. S. Azzaz; R. Kaibou; A. Smahi|10.1109/ICAECCS56710.2023.10104859|Watermarking;FPGA;LSB;Vivado;Speech;Co-Design.;Watermarking;FPGA;LSB;Vivado;Speech;Co-Design.|
|[Direct vector control for doubly fed induction generator-based wind turbine using sliding mode controller tested using RT-LAB OP5600](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104680)|S. Khelifa; A. Semmah; S. Bechekir; I. Yaichi|10.1109/ICAECCS56710.2023.10104680|SMC;PI;WECS;vector control;real time;RT-OP5600;SMC;PI;WECS;vector control;real time;RT-OP5600|
|[Optimized FLPID using TLBO algorithm: Applied to Quadrotor (UAV) system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104893)|S. Chafea; K. Kamel; B. Mohamed|10.1109/ICAECCS56710.2023.10104893|PID controller;fuzzy logic;teaching learning based optimization algorithm;unmanned aerial vehicle;altitude and orientation stabilization.;PID controller;fuzzy logic;teaching learning based optimization algorithm;unmanned aerial vehicle;altitude and orientation stabilization.|
|[Single-Phase Electric Meter Exhibiting Wireless Connectivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105127)|A. O. Gliwan; M. E. Aifa; M. B. Abobaker|10.1109/ICAECCS56710.2023.10105127|Electric meter;Smart City;RF;Arduino.;Electric meter;Smart City;RF;Arduino.|
|[Decision Feedback Equalizer based Affine Projection and Normalized Least Mean Square Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105130)|F. Lounoughi; M. Djendi|10.1109/ICAECCS56710.2023.10105130|adaptive equalization;decision feedback equalizer;NLMS algorithm;AP algorithm;mean square error;adaptive equalization;decision feedback equalizer;NLMS algorithm;AP algorithm;mean square error|
|[Decision-Directed Channel Equalizer Scheme Based on the Recursive Non Quadratic Adaptive Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104525)|F. Lounoughi; M. Djendi|10.1109/ICAECCS56710.2023.10104525|DFE;Equalizer;Rayleigh Channel;adaptive algorithm;SNR;RNQ;MSE;DFE;Equalizer;Rayleigh Channel;adaptive algorithm;SNR;RNQ;MSE|
|[Study of atmospheric attenuation on image quality assessment in FSO transmission operating at 850, 1064 and 1550 nm wavelengths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105131)|A. Djir; F. Meskine; M. L. Tayebi|10.1109/ICAECCS56710.2023.10105131|Image transmission;Free Space Optics (FSO);Atmospheric attenuation;Operating wavelengths;PSNR;Q-factor;SNR;Image transmission;Free Space Optics (FSO);Atmospheric attenuation;Operating wavelengths;PSNR;Q-factor;SNR|
|[FPGA Implementation of Secure Video Network Communication using Chaotic Cryptosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104907)|B. Madani; M. S. Azzaz; S. Sadoudi; R. Kaibou|10.1109/ICAECCS56710.2023.10104907|Secure video communication;Chaos encryption FPGA;Co-design;TCP-IP;Secure video communication;Chaos encryption FPGA;Co-design;TCP-IP|
|[An optimized fuzzy computed torque control for the robot manipulator PUMA 560](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104647)|A. Aouaichia; K. Kara; A. Ghoul|10.1109/ICAECCS56710.2023.10104647|fuzzy controller;computed torque control;robot manipulator;intelligent control;fuzzy controller;computed torque control;robot manipulator;intelligent control|
|[Compact Super Wideband Two-element MIMO Antenna for 5G Millimeter-Wave Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105086)|Y. Benghanem; A. Mansoul; L. Mouffok|10.1109/ICAECCS56710.2023.10105086|Super-wideband (SWB);multiple input multiple output (MIMO);isolation;5G millimeter-wave (MMW);Super-wideband (SWB);multiple input multiple output (MIMO);isolation;5G millimeter-wave (MMW)|
|[Modeling and Design of Primary Control’s Inner Loops for Droop-Controlled Three-Phase Inverters within a Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105047)|C. A. Hammouda; R. Bradai; R. Boukenoui; A. Kherbachi; A. Bendib|10.1109/ICAECCS56710.2023.10105047|dq0-frame;Droop control;Inner control design;Microgrid;Modeling;Voltage-controlled three-phase inverters.;dq0-frame;Droop control;Inner control design;Microgrid;Modeling;Voltage-controlled three-phase inverters.|
|[Meteorological sensor network based on ESP8266](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105060)|S. B. Merrah; A. Adane|10.1109/ICAECCS56710.2023.10105060|Smart systems;Wireless Sensor Network;IoT;NodeMCU ESP8266;Raspberry Pi;Smart systems;Wireless Sensor Network;IoT;NodeMCU ESP8266;Raspberry Pi|
|[Fractional Order PID Tuned using Symbiotic Organism Search Algorithm for Field Oriented Control of The Permanent Magnet Synchronous Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104667)|T. Nouaoui; A. Dendouga; A. Bendaikha|10.1109/ICAECCS56710.2023.10104667|Symbiotic organism search;Fractional order PID;Fractional calculus;permanent magnet synchronous motor;metaheuristic algorithm;space vector pulse width modulation;PSO;Symbiotic organism search;Fractional order PID;Fractional calculus;permanent magnet synchronous motor;metaheuristic algorithm;space vector pulse width modulation;PSO|
|[N-policy Priority Queueing Model for Energy and Delay Minimization in Wireless Sensor Networks Using Markov Chains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104978)|B. Boutoumi; N. Gharbi|10.1109/ICAECCS56710.2023.10104978|WSN;Priority vacation queueing;Energy conservation;Latency;CTMC;WSN;Priority vacation queueing;Energy conservation;Latency;CTMC|
|[Blockchain-based Resource Registration in Constrained IoT Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104987)|K. Zaghouani; B. Djamaa; A. Yachir|10.1109/ICAECCS56710.2023.10104987|Internet of Things;Blockchain;Resource Directory;CoAP;Resource Registration;Internet of Things;Blockchain;Resource Directory;CoAP;Resource Registration|
|[Image Forest Fire Segmentation Using Dirichlet Process Mixture Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104611)|M. Khorchef; N. Ramou; Y. Boutiche; A. Guessoum; N. Chetih|10.1109/ICAECCS56710.2023.10104611|Nonparametric Bayesian models;Chinese restaurant process;Gibbs sampling methods;mixture models;Dirichlet process;image segmentation;Nonparametric Bayesian models;Chinese restaurant process;Gibbs sampling methods;mixture models;Dirichlet process;image segmentation|
|[Improvement of PMSM Performances Using Nonlinear Model Predictive Control and Space Vector Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104640)|A. Kasri; K. Ouari|10.1109/ICAECCS56710.2023.10104640|Direct torque control;Nonlinear model predictive control;Permanent magnet synchronous motor;Space vector modulation;Direct torque control;Nonlinear model predictive control;Permanent magnet synchronous motor;Space vector modulation|
|[Set-Membership Parial Update SMFTF Algorithm For Acoustic Echo Cancellation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104623)|M. A. Ramdane; A. Benallal; T. Ayoub|10.1109/ICAECCS56710.2023.10104623|adaptive filtering;partial update;set membership;steady state;computational complexity;adaptive filtering;partial update;set membership;steady state;computational complexity|
|[An intelligent monitoring system of greenhouse: A Belief Functions Theory-Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104825)|K. Chemoun; S. Oubabas; T. Sadoun; R. Aoudjit; M. Gilg|10.1109/ICAECCS56710.2023.10104825|Smart greenhouse;automatic and intelligent monitoring;decision making;theory of belief functions;Smart greenhouse;automatic and intelligent monitoring;decision making;theory of belief functions|
|[Toward Building Another Arabic Voice Command Dataset for Multiple Speech Processing Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105079)|M. Lichouri; K. Lounnas; A. Bakri|10.1109/ICAECCS56710.2023.10105079|Arabic Voice Command;speech recognition;speaker identification;gender recognition;accent recognition;spoken language understanding;Arabic Voice Command;speech recognition;speaker identification;gender recognition;accent recognition;spoken language understanding|
|[Design and Analysis of Welding Power Supply with Active Current Injection PFC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104603)|B. Babes; N. Hamouda; S. Kahla; A. Reddaf; O. Aissa; A. Beddar|10.1109/ICAECCS56710.2023.10104603|Arc welding power supply (AWPS);front-end three-phase PFC rectifier;power factor (PF);fractional-order PID regulator;Arc welding power supply (AWPS);front-end three-phase PFC rectifier;power factor (PF);fractional-order PID regulator|
|[Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104822)|A. C. Mazari; H. Kheddar|10.1109/ICAECCS56710.2023.10104822|Covid-19 detection;Radiography images;Chest Xray;Image classification;CNN;ResNet50;MobileNetV2;ImageNet;Covid-19 detection;Radiography images;Chest Xray;Image classification;CNN;ResNet50;MobileNetV2;ImageNet|
|[Transfer Learning with Image Data Augmentation for Parking Occupancy Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105107)|A. Khalfi; M. Guerroumi|10.1109/ICAECCS56710.2023.10105107|classification of images;CNN;data augmentation;deep learning;PKLot;Inception V3;classification of images;CNN;data augmentation;deep learning;PKLot;Inception V3|
|[Low-cost BLE based intravenous monitoring and control infusion system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104683)|S. Abdullah; K. Kanwal; A. Hafid; S. Difallah|10.1109/ICAECCS56710.2023.10104683|Biomedical;intravenous infusion;remote monitoring;IoT;embedded system;Biomedical;intravenous infusion;remote monitoring;IoT;embedded system|
|[Adoption and Application of Blockchain Technology in IoT: Survey and Open Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105010)|K. Zaghouani; A. Yachir; B. Djamaa; A. Boutouba|10.1109/ICAECCS56710.2023.10105010|IoT;Blockchain;Survey;Open issues.;IoT;Blockchain;Survey;Open issues.|
|[Iterative Method Based Optimization of Wireless Power Transfer for Biomedical Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104669)|B. Fatima; B. Aimad; N. Fares|10.1109/ICAECCS56710.2023.10104669|wireless power transfer;power transfer efficiency;Iterative method;biomedical device;wireless power transfer;power transfer efficiency;Iterative method;biomedical device|
|[ECG Data Visualization: Combining the power of Grafana and InfluxDB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104857)|A. Aggoune; Z. Benratem|10.1109/ICAECCS56710.2023.10104857|ECG signal;data visualization methods;interactive data visualization;InfluxDB;Grafana;ECG signal;data visualization methods;interactive data visualization;InfluxDB;Grafana|
|[Improvement of the 2.4 GHz SDR Radar- range using optimised Cantennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104835)|A. Adane; S. Himene; D. Cherfi|10.1109/ICAECCS56710.2023.10104835|SDR;SDR-radar;Doppler-radar;Cantenna;GnuRadio;Adalm-Pluto;SDR;SDR-radar;Doppler-radar;Cantenna;GnuRadio;Adalm-Pluto|
|[Investigation of an octagonal AMC reflector backed wideband antenna for gain stabilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104775)|S. Chenaoui; L. Mouffok; S. Hebib|10.1109/ICAECCS56710.2023.10104775|Bowtie antenna;wideband antenna;impedance bandwidth;octagonal AMC reflector;steady gain;Bowtie antenna;wideband antenna;impedance bandwidth;octagonal AMC reflector;steady gain|
|[Design of a thermal stabilizer based on USB data acquisition and control using LabVIEW and PIC microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104863)|H. Hamil; M. S. Azzaz; S. Sakhi; R. Kaibou; A. Hamil|10.1109/ICAECCS56710.2023.10104863|Temperature;Microcontroller;LabVIEW;USB;Acquisition;Control;Power BJT.;Temperature;Microcontroller;LabVIEW;USB;Acquisition;Control;Power BJT.|
|[Effect of FSS structures on Radiation Properties of a Monopole Antenna with Anisotropic Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104774)|F. Guidoum; M. L. Tounsi; M. C. E. Yagoub|10.1109/ICAECCS56710.2023.10104774|monopole antenna;FSS;anisotropy;monopole antenna;FSS;anisotropy|
|[Chaos based fingerprint image encryption in the wavelet domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104939)|M. L. Benseddik; K. Zebbiche; M. S. Azzaz; S. Sadoudi; K. Loukhaoukha|10.1109/ICAECCS56710.2023.10104939|Image encryption;Fingerprint Images;Chaos;Lorenz system;Wavelet Transform;Rubik’s cube;Image encryption;Fingerprint Images;Chaos;Lorenz system;Wavelet Transform;Rubik’s cube|
|[Finite Control Set-Model Predictive Control for Grid connected Shunt Active Power Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105128)|D. E. Zabia; O. Kraa; H. Afghoul; T. L. Belahcene; S. A. Krim; F. Abdelmalek|10.1109/ICAECCS56710.2023.10105128|Grid System;Model Predictive Control;Shunt Active Power Filter;Synchronous Reference frame;Grid System;Model Predictive Control;Shunt Active Power Filter;Synchronous Reference frame|
|[Blockchain for medical security data: a review and perspectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104749)|H. R. Rahal; S. Slatnia; O. Kazar; E. Barka|10.1109/ICAECCS56710.2023.10104749|Blockchain;Consensus Protocol;Smart Contract;Medical Data;Blockchain;Consensus Protocol;Smart Contract;Medical Data|
|[Smart weather station using development boards for environmental applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104678)|A. Adane; I. A. Haicheur; A. R. Lebza|10.1109/ICAECCS56710.2023.10104678|Weather station;smart system;Arduino;Raspberry Pi3;interface development;DHT11;Solar module;MQ135;Weather station;smart system;Arduino;Raspberry Pi3;interface development;DHT11;Solar module;MQ135|
|[ECG Signal Denoising Based on Wavelet Transform and Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105043)|A. Abdallah; B. Billel; A. Nail; S. Abdelkerim|10.1109/ICAECCS56710.2023.10105043|Electrocardiogram;Wavelet Transform;Genetic Algorithm;Power Line Inteference;Electrocardiogram;Wavelet Transform;Genetic Algorithm;Power Line Inteference|
|[A review on early forest fire detection using IoT-enabled WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104887)|A. Sairi; S. Labed; B. Miles; A. Kout|10.1109/ICAECCS56710.2023.10104887|wireless sensor networks;forest fire;Internet of Things;sensors node.;wireless sensor networks;forest fire;Internet of Things;sensors node.|
|[Stochastic optimization algorithms for parameter identification of three phase induction motors with experimental verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104526)|R. Houili; M. Y. Hammoudi; A. Betka; A. Titaouine|10.1109/ICAECCS56710.2023.10104526|Induction motors;Parameters identification;Metaheuristic techniques;optimization;Induction motors;Parameters identification;Metaheuristic techniques;optimization|
|[RF Performance Analysis of Conventional and Recessed Gate AlGaN/GaN MOSHEMT using β–Ga2O3 as Dielectric Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104839)|N. Amina; M. Zitouni; T. Zineeddine|10.1109/ICAECCS56710.2023.10104839|AlGaN/GaN MOSHEMT;β–Ga2O3 gate dielectric;recessed gate;Maximum oscillation frequency;SILVACO TCAD.;AlGaN/GaN MOSHEMT;β–Ga2O3 gate dielectric;recessed gate;Maximum oscillation frequency;SILVACO TCAD.|
|[Comparative Study Between Sensorless Vector Control of PMSM Drives based on MRAS, SMO and EKF Observers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104927)|B. Bendjedia; S. Chouireb|10.1109/ICAECCS56710.2023.10104927|sensorless control;MRAS;SMO;EKF;electric vehicle;observers;Permanent Magnet Synchronous Motor(PMSM);sensorless control;MRAS;SMO;EKF;electric vehicle;observers;Permanent Magnet Synchronous Motor(PMSM)|
|[Intelligent system for detecting faults in the industrial area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104642)|A. Benmohamed; A. Bouguerra|10.1109/ICAECCS56710.2023.10104642|Action recognition;Action prediction;Object detection;Deep learning;Long-short term memory;human Skeleton;Action recognition;Action prediction;Object detection;Deep learning;Long-short term memory;human Skeleton|
|[Dealing with extremly Unbalanced Data and Detecting Insider Threats with Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105103)|S. Besnaci; M. Hafidi; M. Lamia|10.1109/ICAECCS56710.2023.10105103|insider threat detection;internal threats;unbalanced data;deep learning;LSTM algorithm;SMOTE technique;insider threat detection;internal threats;unbalanced data;deep learning;LSTM algorithm;SMOTE technique|
|[Active Disturbance Rejection Control of Unconventional Quadrotor based on Grey Wolf Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104628)|K. Kadri; F. Boudjema; Y. Bouzid; M. Ghazali; H. Sahouli|10.1109/ICAECCS56710.2023.10104628|Active Disturbance Rejection Control;extended state observer;Grey Wolf optimization;unconventional quadrotor;Active Disturbance Rejection Control;extended state observer;Grey Wolf optimization;unconventional quadrotor|
|[Toward a New Multirotor Design With Spatial Configuration and Tilted Rotors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104607)|M. Ghazali; Y. Bouzid; M. Belhocine; S. H. Derrouaoui|10.1109/ICAECCS56710.2023.10104607|UAV;Multirotor;Spatial configuration;Newton Euler formalism;Fully-actuation;Allocation matrix;UAV;Multirotor;Spatial configuration;Newton Euler formalism;Fully-actuation;Allocation matrix|
|[Semantic Role Labeling of Arabic Emotional Text in Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104732)|F. Senator; H. Boutouta; A. Lakhfif; C. Mediani|10.1109/ICAECCS56710.2023.10104732|corpus;annotation tool;emotion;semantic role;twitter;arabic language;corpus;annotation tool;emotion;semantic role;twitter;arabic language|
|[DC/DC buck Converter Prototype for Educational Nanosatellite Power Sub-System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104993)|B. Houari; B. Aissa; L. Lakhdar; B. E. Yazid|10.1109/ICAECCS56710.2023.10104993|Electrical power system (EPS);Nanosatellite;DC-DC Buck converters.;Electrical power system (EPS);Nanosatellite;DC-DC Buck converters.|
|[Chaos-based Key Generator using Artificial Neural Networks Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105105)|A. Kadir; M. S. Azzaz; R. Kaibou|10.1109/ICAECCS56710.2023.10105105|ANN;Chaotic systems;Deep learning;cryptography;key generation;ANN;Chaotic systems;Deep learning;cryptography;key generation|
|[A comprehensive study on multimedia DeepFakes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104814)|A. Boutadjine; F. Harrag; K. Shaalan; S. Karboua|10.1109/ICAECCS56710.2023.10104814|DeepFake;artificial intelligence;deep learning;DeepFake detection.;DeepFake;artificial intelligence;deep learning;DeepFake detection.|
|[A New Rfid Anti-Collision Technique Based On Time-Hopping Sub Slots Early Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105020)|F. Abderrahmene; B. Mustapha; K. Abdenour|10.1109/ICAECCS56710.2023.10105020|Time-Hopping;Anti-collision;RFID;Estimation;ALOHA;Time-Hopping;Anti-collision;RFID;Estimation;ALOHA|
|[A Combined Emission/Economic Dispatch Problem Considering NPPs Power Ramp Constraint](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104726)|R. Benabid; S. Chennai; A. Messai; D. Kemikem|10.1109/ICAECCS56710.2023.10104726|economic dispatch;load following;power ramp limit;optimization;nuclear power plant;CO2 emission;economic dispatch;load following;power ramp limit;optimization;nuclear power plant;CO2 emission|
|[One-dimensional Gaussian Density Function Segmentation Based on Piecewise Linear Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104618)|N. Tahir; M. Boudraa; E. -S. Lamini; A. A. E. Ouchdi|10.1109/ICAECCS56710.2023.10104618|Gaussian density function;Polynomial Approximation;CAM;VLSI;Gaussian density function;Polynomial Approximation;CAM;VLSI|
|[Adaptive Fractional PI Controller for Speed Control of Asynchronous motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104802)|B. Nemouchi; S. E. Rezgui; H. Benalla; K. Nebti|10.1109/ICAECCS56710.2023.10104802|Asynchronous motor;advanced control;reference model adaptive control;fractional order PI;MIT rule;Asynchronous motor;advanced control;reference model adaptive control;fractional order PI;MIT rule|
|[AMR Navigation Based on Dynamic Control Sampling Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104954)|S. Bouraine; I. Hasani; A. Djoudi; A. Gourine; B. Kazed|10.1109/ICAECCS56710.2023.10104954|autonomous mobile robot;navigation;obstacle avoidance;embedded system;Robot Operationg System;autonomous mobile robot;navigation;obstacle avoidance;embedded system;Robot Operationg System|
|[An optimal coordination of directional overcurrent relays using a Gorilla troops optimizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105106)|M. Oussama; B. Mohamed; B. Hamza; K. Aissa; E. Ahmed; B. Rafik|10.1109/ICAECCS56710.2023.10105106|Gorilla troops algorithm;GTO;time dial setting (TDS);plug setting (PS);DOCRs;optimal coordination;Gorilla troops algorithm;GTO;time dial setting (TDS);plug setting (PS);DOCRs;optimal coordination|
|[Performance Analysis of Various Rectifier Topologies for 2.45 GHz RF Energy Harvesting Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105121)|D. Bouchair; F. Boukerroum|10.1109/ICAECCS56710.2023.10105121|Rectenna;Rectifier circuit;Schottky diode;Conversion efficiency;Output voltage;Rectenna;Rectifier circuit;Schottky diode;Conversion efficiency;Output voltage|
|[An Overview of the Rectenna System to Power Implantable Biomedical Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104980)|M. Benhabiles; F. Boukerroum|10.1109/ICAECCS56710.2023.10104980|Wireless power transfer;biomedical implantable devices;Rectenna system;Wireless power transfer;biomedical implantable devices;Rectenna system|
|[Simulation of a pentagon-shaped wideband miniature antenna for 5G mobile networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104952)|F. Z. Moussa; Y. Belhadef; S. Ferouani|10.1109/ICAECCS56710.2023.10104952|Patch antenna;pentagon;metamaterials;CSRR;5G;DGS;Patch antenna;pentagon;metamaterials;CSRR;5G;DGS|
|[Deep Learning with EfficientNetB1 for detecting brain tumors in MRI images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104761)|S. Benkrama; N. E. H. Hemdani|10.1109/ICAECCS56710.2023.10104761|MRI-based brain tumor;Convolutional neural network;EfficientNetBl;Spark system;MRI-based brain tumor;Convolutional neural network;EfficientNetBl;Spark system|
|[Design of Miniaturized Multiple Slots Bi-Band PIFA Antenna for 915 and 2450 MHz ISM Telemetry Using Multilayer Human Body Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104588)|A. Abbas; F. Bouttout; A. Djellid|10.1109/ICAECCS56710.2023.10104588|Implantable PIFA Antenna;Slots;ISM Frequency Bands;SAR;Multilayer Human Body Model;Implantable PIFA Antenna;Slots;ISM Frequency Bands;SAR;Multilayer Human Body Model|
|[Development and Testing of Low-Cost Multi-Electronic Load Prototype for Nanosatellites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104914)|L. Lakhdar; B. Aissa; B. Houari; B. E. Yazid|10.1109/ICAECCS56710.2023.10104914|Electrical power system (EPS);Nanosatellite mission;Batteries tests;Hardware in the loop (HIL);Electrical power system (EPS);Nanosatellite mission;Batteries tests;Hardware in the loop (HIL)|
|[Upgraded-ABC Algorithm for Antenna Selection in Energy Efficient Massive MIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105114)|F. Bouchibane; H. Tayakout; N. Ziane; F. Siahmed; S. Hebib|10.1109/ICAECCS56710.2023.10105114|Massive MIMO;Antenna Selection;Multi-User ABC algorithm;Energy Efficiency;Gbest-guided ABC;UABC;Massive MIMO;Antenna Selection;Multi-User ABC algorithm;Energy Efficiency;Gbest-guided ABC;UABC|
|[A New Enhanced Chaotic Key-Based Medical Image Cryptosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105059)|Y. Sadou; S. Dib; M. Grimes|10.1109/ICAECCS56710.2023.10105059|Chaos;Arnold Cat Map;medical image;Cryptosystem;Sine-Tent;Confusion;Chaos;Arnold Cat Map;medical image;Cryptosystem;Sine-Tent;Confusion|
|[Comparison between MPPTs for PV systems using P&O and Grey Wolf controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104731)|F. Abdelmalek; H. Afghoul; F. Krim; D. E. Zabia; T. L. Belahcene; S. A. Krim|10.1109/ICAECCS56710.2023.10104731|MPPT;PV generator;P & O;GWO;BOOST converter;MPPT;PV generator;P & O;GWO;BOOST converter|
|[Vision-based Indoor Lighting Assessment Approach for Daylight Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104917)|S. Abderraouf; A. Mustapha; I. Abdelhamid; M. Haithem|10.1109/ICAECCS56710.2023.10104917|Energy consumption;daylight harvesting;indoor light;computer vision;expert systems;ROC and AUC;Energy consumption;daylight harvesting;indoor light;computer vision;expert systems;ROC and AUC|
|[A Sensitivity Based Methodology for power loss and voltage deviation in radial distribution network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104769)|A. Chanane; M. Belazzoug|10.1109/ICAECCS56710.2023.10104769|loss sensitivity indices;active power;voltage profile;radial distribution network;loss sensitivity indices;active power;voltage profile;radial distribution network|
|[High selective single mode plasmonic filters based on MIM coupled with pill resonator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105064)|I. Abderrahmane; B. Hadjira; A. Mehadji; H. Bensalah; R. Bachir|10.1109/ICAECCS56710.2023.10105064|Plasmonics;HPF;sub-wavelength;FEM method;diffraction limit;SPPs;Plasmonics;HPF;sub-wavelength;FEM method;diffraction limit;SPPs|
|[Image Watermarking Technique Using Convolutional Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105035)|E. Rebahi; M. Hemis; B. Boudraa|10.1109/ICAECCS56710.2023.10105035|Digital watermarking;Convolutions neural networks;Auto-encoder;security;Data Hiding.;Digital watermarking;Convolutions neural networks;Auto-encoder;security;Data Hiding.|
#### **2023 IEEE Underwater Technology (UT)**
- DOI: 10.1109/UT49729.2023
- DATE: 6-9 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Anomaly Detection of Underwater Gliders Verified by Deployment Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103445)|R. Yang; M. Hou; C. Lembke; C. Edwards; F. Zhang|10.1109/UT49729.2023.10103445|anomaly detection;glider navigation;anomaly detection;glider navigation|
|[Estimation of Sediments in Underwater Wall Corners using a Mechanical Scanning Sonar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103394)|C. F. Gonçalves; N. A. Cruz; B. M. Ferreira|10.1109/UT49729.2023.10103394|Underwater sonar processing;feature detection;sediment detection using sonar;Underwater sonar processing;feature detection;sediment detection using sonar|
|[Imitating dolphins: Nature-inspired boundary modulation to reduce frictional drag](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103391)|S. Matt; H. Shi; X. Tan; A. Thombs; W. Hou|10.1109/UT49729.2023.10103391|flow control;boundary actuation;Digital Particle Image Velocimetry;numerical tank;nature-inspired engineering;flow control;boundary actuation;Digital Particle Image Velocimetry;numerical tank;nature-inspired engineering|
|[Robust Control of A Novel Small AUV With Titlting Tunnel Thrusters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103415)|M. Fernandes; S. R. Sahoo; M. Kothari|10.1109/UT49729.2023.10103415|Autonomous Underwater Vehicle (AUV);Sliding Mode Control (SMC);Tilting Tunnel Thruster;Quaternions;Autonomous Underwater Vehicle (AUV);Sliding Mode Control (SMC);Tilting Tunnel Thruster;Quaternions|
|[Arctic ice underwater noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103407)|N. P. Chotiros|10.1109/UT49729.2023.10103407|ice;ambient noise;crack;resonance;ice;ambient noise;crack;resonance|
|[Multi-band, calibrated backscatter from high frequency multibeam systems as an efficient tool for seabed monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103390)|A. Kruss; M. Rucinska; A. Grzadziel; M. Waz; P. Pocwiardowski|10.1109/UT49729.2023.10103390|multibeam echosounder;habitat mapping;bottom classification;calibrated backscatter;multibeam echosounder;habitat mapping;bottom classification;calibrated backscatter|
|[Practice of marine environmental impact assessment and monitoring using ISO standards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103398)|H. Yamamoto; T. Miwa; H. Koshikawa; M. Kawachi; K. Inomata; K. Tsutsumi; K. Iijima; M. Kyo; K. Yoshida; S. Kawagucci|10.1109/UT49729.2023.10103398|environment;assessment;monitoring;deep-sea;mining;ISO;environment;assessment;monitoring;deep-sea;mining;ISO|
|[Digital Adaptive Optical Imaging for Oceanic Turbulence Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103385)|B. Neuner; S. D. Lilledahl; B. Laxton; K. R. Drexler|10.1109/UT49729.2023.10103385|imaging;propagation;turbulence;interferometry;undersea;QR Codes;imaging;propagation;turbulence;interferometry;undersea;QR Codes|
|[An Experimental Study on the Stability of a Catamaran Boat Equipped with an Underwater Camera](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103444)|Q. Li; G. Xu; J. Zhou; S. Dong; Y. Mizukami; D. Kitazawa|10.1109/UT49729.2023.10103444|underwater camera;catamaran boat;stability performance;flexible rope connection;underwater camera;catamaran boat;stability performance;flexible rope connection|
|[A Twin LS-SVM-based maneuvering predictor for marine crafts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103436)|T. Pei; C. Yu; L. Lian|10.1109/UT49729.2023.10103436|marine crafts;system identification;maneuvering prediction;LS-SVM;marine crafts;system identification;maneuvering prediction;LS-SVM|
|[Ocean Bottom Detector: frontier of technology for understanding the mantle by geoneutrinos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103417)|H. Watanabe; N. Abe; E. Araki; T. Araki; K. Inoue; T. Kasaya; M. Kyo; W. F. McDonough; T. Sakai; N. Sakurai; K. Ueki; H. Yoshida|10.1109/UT49729.2023.10103417|Geoneutrinos;Radiogenic heat in the Earth;Geoneutrinos;Radiogenic heat in the Earth|
|[Investigation on GNSS-A precise point positioning based on adaptively robust filter considering the horizontal heterogeneity of sound speed structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103431)|S. Zhao; Y. Yokota; Z. Wang; S. Xue|10.1109/UT49729.2023.10103431|kinematic GNSS-Acoustic positioning;sound speed structure (SSS);horizontal heterogeneity;adaptively robust filter;kinematic GNSS-Acoustic positioning;sound speed structure (SSS);horizontal heterogeneity;adaptively robust filter|
|[Subseafloor tectonic phenomena along the Japan Trench and the Nankai Trough revealed from recent GNSS-A observation at Japan Coast Guard’s SGO-A sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103402)|Y. Nakamura; T. Ishikawa; S. -I. Watanabe; K. Nagae; Y. Yokota|10.1109/UT49729.2023.10103402|GNSS-A;seafloor geodesy;SGO-A;postseismic movements;slow slip events;GNSS-A;seafloor geodesy;SGO-A;postseismic movements;slow slip events|
|[Construction of the borehole observatory using the deep-sea Boring Machine System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103419)|T. Yokobiki; S. Nishida; S. Tsuji; T. Murashima; K. Takase; K. Takahashi; E. Araki|10.1109/UT49729.2023.10103419|borehole observatory;Deepsea Boring Machine System;Cabled observation system;borehole observatory;Deepsea Boring Machine System;Cabled observation system|
|[Self-Noise Suppression for AUV without Clean Data: a Noise2Noise Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103424)|W. Zhou; J. Li|10.1109/UT49729.2023.10103424|autonomous underwater vehicle;Noise2Noise;noise removal;self-noise;autonomous underwater vehicle;Noise2Noise;noise removal;self-noise|
|[Effective sampling in tomographic current mapping using two moving vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103438)|K. -Y. Chen; C. -F. Huang; S. -W. Huang; J. Guo|10.1109/UT49729.2023.10103438|Moving vehicle tomography;Radon transform;open-boundary modal analysis;current inversion;Moving vehicle tomography;Radon transform;open-boundary modal analysis;current inversion|
|[Research and development for practical use of underwater optical wireless communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103412)|T. Sawa|10.1109/UT49729.2023.10103412|uowc;PMT;optical fiber;image senor;tracking;uowc;PMT;optical fiber;image senor;tracking|
|[Similar Object Identification in Sonar Images by Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103420)|I. Shogo; S. Masahiko|10.1109/UT49729.2023.10103420|Sonar Image;Machine Learning;YOLO;Sonar Image;Machine Learning;YOLO|
|[Experiments for long-range high-rate underwater acoustic MIMO communication using adaptive passive time reversal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103409)|Y. Kida; M. Deguchi; Y. Watanabe; T. Shimura|10.1109/UT49729.2023.10103409|Time Reversal;MIMO;Underwater Acoustic Communication;Long distance;Time Reversal;MIMO;Underwater Acoustic Communication;Long distance|
|[Data-Driven Underwater Navigation workshop: AUV Close-Range Localization and Guidance Employing an Electro-Magnetic Beacon](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103448)|Y. Gutnik; N. Cohen; I. Klein; M. Groper|10.1109/UT49729.2023.10103448|Underwater navigation;electromagnetic sensing;underwater docking;Underwater navigation;electromagnetic sensing;underwater docking|
|[Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103453)|N. Cohen; Z. Yampolsky; I. Klein|10.1109/UT49729.2023.10103453|Autonomous underwater vehicle (AUV);Inertial navigation system (INS);Doppler velocity log (DVL);Deep Learning;Transformer;Autonomous underwater vehicle (AUV);Inertial navigation system (INS);Doppler velocity log (DVL);Deep Learning;Transformer|
|[On the Enhancement of an Ocean Glider Navigation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103410)|A. Weizman; M. Groper; I. Klein|10.1109/UT49729.2023.10103410|Ocean Gilder;Accelerometers;Deep Learning;Navigation;Ocean Gilder;Accelerometers;Deep Learning;Navigation|
|[ProNet: Adaptive Process Noise Estimation for INS/DVL Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103449)|B. Or; I. Klein|10.1109/UT49729.2023.10103449|Autonomous underwater vehicles;Doppler Velocity Log;Deep Neural Network;Inertial Measurement Unit;Inertial Navigation System;Extended Kalman Filter;Autonomous underwater vehicles;Doppler Velocity Log;Deep Neural Network;Inertial Measurement Unit;Inertial Navigation System;Extended Kalman Filter|
|[Introduction to Indonesian Cable-based Subsea Tsunameter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103368)|M. A. Purwoadi; Y. Anantasena; W. W. Pandoe; J. Widodo; A. E. Sakya|10.1109/UT49729.2023.10103368|tsunami;Indonesia;cable-based tsunameter;tsunami;Indonesia;cable-based tsunameter|
|[Precise bathymetry with an underwater vehicle for seafloor crustal movement observation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103433)|S. Nishida; Y. Machida; H. Matsumoto; E. Araki; S. Ishibashi|10.1109/UT49729.2023.10103433|bottom pressure;bathymetry;in-situ calibration;crustal deformation;DONET;bottom pressure;bathymetry;in-situ calibration;crustal deformation;DONET|
|[Simultaneous seafloor seismic observation by distributed acoustic sensing and accelerometer using off-Sanriku optical cable observation system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103378)|M. Shinohara; T. Yamada; T. Akuhara; K. Mochizuki; H. Takano; S. Sakai|10.1109/UT49729.2023.10103378|distributed acoustic sensing (DAS);seafloor cable;earthquake observation;distributed acoustic sensing (DAS);seafloor cable;earthquake observation|
|[Real-time tsunami damage prediction using DONET and the implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103434)|N. Takahashi; N. Chikasada; K. Imai|10.1109/UT49729.2023.10103434|tsunami inundation;real-time damage prediction;tsunami debris;DONET;tsunami inundation;real-time damage prediction;tsunami debris;DONET|
|[Consideration of meteotsunami real-time forecasting method using high quality atmospheric and ocean bottom pressure records](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103430)|N. Y. Chikasada|10.1109/UT49729.2023.10103430|meteotsunami;meteorological tsunami;tsunami forecasting;Tonga volcano eruption;pressure observation;meteotsunami;meteorological tsunami;tsunami forecasting;Tonga volcano eruption;pressure observation|
|[Development and Construction of Nankai Trough Seafloor Observation Network for Earthquakes and Tsunamis: N-net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103206)|S. Aoi; T. Takeda; T. Kunugi; M. Shinohara; T. Miyoshi; K. Uehira; M. Mochizuki; N. Takahashi|10.1109/UT49729.2023.10103206|N-net;Nankai Trough earthquake;earthquake and tsunami observation;seafloor observation network for earthquakes and tsunamis;early warning;N-net;Nankai Trough earthquake;earthquake and tsunami observation;seafloor observation network for earthquakes and tsunamis;early warning|
|[A Position Correction Model for AUV Navigation with Sequential Learning-Assisted State Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103372)|X. Zhang; Y. Qiu; A. Ohya; A. Yorozu; B. He|10.1109/UT49729.2023.10103372|Autonomous Underwater Vehicle;Navigation and Localization;Unscented Kalman Filter;Position Correction Model;Sequential Learning;Autonomous Underwater Vehicle;Navigation and Localization;Unscented Kalman Filter;Position Correction Model;Sequential Learning|
|[Mechanical Imaging Sonar-based AUV Wall Following in a Water Tank](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103400)|Y. -C. Chou; J. -C. Jiang; H. -H. Chen; C. -C. Wang|10.1109/UT49729.2023.10103400|autonomous underwater vehicle;underwater wall following;relative navigation;mechanical imaging sonar;Doppler velocity log;gyrocompass;autonomous underwater vehicle;underwater wall following;relative navigation;mechanical imaging sonar;Doppler velocity log;gyrocompass|
|[Group Formation of Autonomous Underwater Vehicles that Optimizes Energetic Efficiency in Cruising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103369)|G. Li; R. Godoy-Diana; L. Duan; B. Thiria|10.1109/UT49729.2023.10103369|Computational Fluid Dynamics;Autonomous Underwater Vehicle (AUV);energy-saving;underwater robot group;optimization;group formation;Computational Fluid Dynamics;Autonomous Underwater Vehicle (AUV);energy-saving;underwater robot group;optimization;group formation|
|[Development of a Basic Formation Control System for Heterogeneous Autonomous Marine Vehicles and its Sea Trials in Suruga Bay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103439)|A. Okamoto; K. Kim; M. Sasano; T. Sato; S. Inaba; S. Kondo; H. Matsumoto; T. Murashima; T. Shimura; T. Fujiwara; H. Osawa|10.1109/UT49729.2023.10103439|autonomous underwater vehicle;autonomous surface vehicle;basic formation control;autonomous underwater vehicle;autonomous surface vehicle;basic formation control|
|[Towards an Open-Source Benchmark for Underwater Object Detection and Pose Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103392)|I. B. Saksvik; H. Weydahl; H. Teigland; A. Alcocer; V. Hassani|10.1109/UT49729.2023.10103392|Benchmark dataset;fiducial markers;object detection;pose estimation;Benchmark dataset;fiducial markers;object detection;pose estimation|
|[SquidJam: A Video Annotation Ecosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103435)|M. Sangekar; A. Friedman; M. Hidaka; T. Hosono; D. Lindsay|10.1109/UT49729.2023.10103435|Annotations;Marine ecosystems;Scientific analyses;Annotations;Marine ecosystems;Scientific analyses|
|[Advanced subsea imaging technique of digital holography: in situ measurement of marine microscale plankton and particles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103440)|Z. Liu; S. Giering; T. Takahashi; T. Thevar; M. Takeuchi; N. Burns; B. Thornton; J. Watson; D. Linsday|10.1109/UT49729.2023.10103440|digital holography;submersible holographic cameras;marine microscale particles and plankton;hologram processing;digital holography;submersible holographic cameras;marine microscale particles and plankton;hologram processing|
|[RamaCam: autonomous in-situ monitoring system of marine particles by combining holography and Raman spectroscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103388)|T. Takahashi; Z. Liu; T. Thevar; N. Burns; M. Sangekar; D. Lindsay; J. Watson; B. Thornton|10.1109/UT49729.2023.10103388|Marine particles;Raman spectroscopy;Holography;In-situ optical sensing;Marine particles;Raman spectroscopy;Holography;In-situ optical sensing|
|[Improving the Quality of Underwater Wireless Optical Communications in Uncertain Ocean Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103370)|Y. Weng; T. Matsuda; T. Maki|10.1109/UT49729.2023.10103370|underwater wireless optical communication;reinforcement learning;autonomous underwater vehicle;underwater wireless optical communication;reinforcement learning;autonomous underwater vehicle|
|[Seabed Object’s Height Estimation Method Utilizing Tilt Angle Changes of Imaging Sonar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103406)|M. Sung; Y. -W. Song; S. -C. Yu|10.1109/UT49729.2023.10103406|Forward Scan Sonar;Sonar mapping;Underwater mapping;Underwater 3D reconstruction;Forward Scan Sonar;Sonar mapping;Underwater mapping;Underwater 3D reconstruction|
|[Towards sensor agnostic artificial intelligence for underwater imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103403)|M. Massot-Campos; T. Yamada; B. Thornton|10.1109/UT49729.2023.10103403|Underwater imaging;artificial intelligence;image formation;Underwater imaging;artificial intelligence;image formation|
|[Application of Ambient Pressure-Driven Pumping Technology towards Ultra Low-Power Underwater Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103205)|T. Fukuba; A. Fujiwara; K. Nishiguchi; M. Bergaud; S. Grall; S. Li; S. -H. Kim; N. Clément|10.1109/UT49729.2023.10103205|deep-sea;sensor;in situ measurement;pressure-driven pumping;nanoFET;deep-sea;sensor;in situ measurement;pressure-driven pumping;nanoFET|
|[Modelling our way out of a featureless correspondence problem for automatic calibration of laser stripe mapping systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103452)|D. Stanley; A. Bodenmann; M. Massot-Campos; B. Thornton|10.1109/UT49729.2023.10103452|calibration;laser stripe;seafloor mapping;featureless;extrinsics;structured light;bundle adjustment;correspondence problem;design;underwater robotics;calibration;laser stripe;seafloor mapping;featureless;extrinsics;structured light;bundle adjustment;correspondence problem;design;underwater robotics|
|[GPS-Based Hydrophone Drifter for Underwater Locator Beacon Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103411)|C. -C. Wang; W. -C. Yu; H. -H. Chen; P. -C. Chen|10.1109/UT49729.2023.10103411|component;formatting;style;styling;insert (key words);component;formatting;style;styling;insert (key words)|
|[Towards Reconstruction of 3D Geometry of Underwater Rubble Mounds via Structure from Motion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103396)|S. Takao; T. Kita; T. Hirabayashi|10.1109/UT49729.2023.10103396|Underwater Image;Structure from motion;3D reconstruction;Marine Construction;Underwater Image;Structure from motion;3D reconstruction;Marine Construction|
|[Prototyping of Automatic Navigation of Underwater Robot for Underwater Visual Inspection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103377)|T. Kita; T. Tanaka|10.1109/UT49729.2023.10103377|underwater robot;automatic navigation;inspection;underwater robot;automatic navigation;inspection|
|[FOTOAN: A Novel Deepwater Anchoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103371)|K. R. S; N. Saha; S. R|10.1109/UT49729.2023.10103371|FOTOAN;deep water;embedment depth;pull-out resistance;1g tests;FOTOAN;deep water;embedment depth;pull-out resistance;1g tests|
|[Experimental Study of kW-class Wireless Charging System for Autonomous Underwater Vehicle with Magnetic Resonance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103376)|R. Hasaba; S. Yamaguchi; T. Yagi; K. Eguchi; H. Satoh; Y. Koyanagi; T. Ura|10.1109/UT49729.2023.10103376|Coils;magnetic induction;charging system;sea measurements;wireless power transfer;Coils;magnetic induction;charging system;sea measurements;wireless power transfer|
|[Trial Experiment of Positioning of Underwater Backhoe Using Time-of-flight of Acoustic Signal Group and Database Matching for Realization of Unmanned and Remote Underwater Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103442)|T. Yoshihara; T. Ebihara; K. Mizutani|10.1109/UT49729.2023.10103442|underwater acoustic positioning;shallow water;multipath;underwater acoustic positioning;shallow water;multipath|
|[A Search Strategy and Vessel Detection in Maritime Environment Using Fixed-Wing UAVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103204)|M. Peti; A. Milas; N. Kraševac; M. Križmančić; I. Lončar; N. Mišković; S. Bogdan|10.1109/UT49729.2023.10103204|search strategy;fixed-wing UAV;detection;marine environment;MBZIRC;simulation;search strategy;fixed-wing UAV;detection;marine environment;MBZIRC;simulation|
|[High-resolution visual seafloor mapping and classification using long range capable AUV for ship-free benthic surveys](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103421)|A. Bodenmann; J. Cappelletto; M. Massot-Campos; D. Newborough; E. Chaney; R. Marlow; R. Templeton; A. B. Phillips; B. J. Bett; C. Wardell; B. Thornton|10.1109/UT49729.2023.10103421|3D seafloor mapping;AUV;over-the-horizon operation;image classification;3D seafloor mapping;AUV;over-the-horizon operation;image classification|
|[Development of edge computing underwater sound recorder to monitor deep sea soundscape](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103375)|M. Katagiri; S. Kawagucci; H. K. Watanabe; K. Tanaka; T. Akamatsu|10.1109/UT49729.2023.10103375|autonomous recorder;underwater acoustics;environmental impact assessment;noise pollution;ocean observation;autonomous recorder;underwater acoustics;environmental impact assessment;noise pollution;ocean observation|
|[Shallow Water Seagrass Survey at Studland Bay with the AUV Smarty200](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103389)|M. Massot-Campos; T. Yamada; B. Walker-Rouse; K. Collins; J. Leyland; H. Kassem; B. Thornton|10.1109/UT49729.2023.10103389|Autonomous Underwater Vehicle (AUV);Seafloor Imaging;Machine Learning;Seagrass;Autonomous Underwater Vehicle (AUV);Seafloor Imaging;Machine Learning;Seagrass|
|[Sea Pollution: Analysis and Monitoring using Unmanned Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103429)|T. L. Antunes; N. P. Santos; R. P. Moura; V. Lobo|10.1109/UT49729.2023.10103429|Environmental monitoring;Pollution prevention;Pollution control;Sea pollution;Unmanned vehicles;Environmental monitoring;Pollution prevention;Pollution control;Sea pollution;Unmanned vehicles|
|[Experimental demonstration of equalization with phase lock loops against Doppler shifts of multipath signals on underwater acoustic communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103373)|M. Deguchi; Y. Kida; T. Shimura|10.1109/UT49729.2023.10103373|underwater acoustic communication;single carrier modulation;Doppler shift;multipath signals;doubly selective channels;underwater acoustic communication;single carrier modulation;Doppler shift;multipath signals;doubly selective channels|
|[Simplification of full spectrum soundscape analysis using specific frequencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103408)|L. Chiu-Leung|10.1109/UT49729.2023.10103408|Soundscapes;Hong Kong International Airport;Bioacoustics;frequency analysis;Acoustic events;Soundscapes;Hong Kong International Airport;Bioacoustics;frequency analysis;Acoustic events|
|[Correlation of dynamic internal waves with foraging activity of marine mammals in the South China Sea](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103387)|L. Chiu; Y. J. Yang; K. -H. Fu|10.1109/UT49729.2023.10103387|internal waves;marine mammals;oraging activity;South China Sea;internal waves;marine mammals;oraging activity;South China Sea|
|[Utilization of Several types of Bio-mimic Pulse Train for Acoustic Localization in Coastal Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103441)|H. Ogasawara; E. Sato; S. Urakawa; T. Kuroyama; K. Mori|10.1109/UT49729.2023.10103441|bio-mimic signal;underwater acoustic localization;inter-click interval;detection error;bio-mimic signal;underwater acoustic localization;inter-click interval;detection error|
|[Evaluation of Optical-Flow-Based Feature Matching for Underwater Vehicle’s Displacement Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103395)|H. -H. Chen; C. -W. Tsai|10.1109/UT49729.2023.10103395|optical flow;feature matching;underwater vehicles;displacement estimation;feature richness;CLAHE;optical flow;feature matching;underwater vehicles;displacement estimation;feature richness;CLAHE|
|[Developments of standardization and quality control for AUV bathymetric data through sea trials of "AUV-NEXT"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103401)|M. Sumiyoshi; R. Nagasawa; K. Nagahashi; T. Hyakudome; T. Nakatani; T. Aso; Y. Yokota|10.1109/UT49729.2023.10103401|Autonomous Underwater Vehicle (AUV);Bathymetric survey data;Quality control;Bathymetric side scan sonar (Bathy-SSS);Multibeam echosounder (MBES);Autonomous Underwater Vehicle (AUV);Bathymetric survey data;Quality control;Bathymetric side scan sonar (Bathy-SSS);Multibeam echosounder (MBES)|
|[Geometric Registration of Benthic Imagery for Learning Appearance-Based Place Recognition over Multiple Sessions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103451)|M. K. Larsen; O. Pizarro; M. Ludvigsen|10.1109/UT49729.2023.10103451|benthic imaging;multi-session;registration;place recognition;contrastive learning;benthic imaging;multi-session;registration;place recognition;contrastive learning|
|[Economic Feasibility Analyses for Seafloor Massive Sulfide Mining in Okinawa Trough Area, Japan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103414)|T. Yamazaki; N. Nakatani; R. Arai|10.1109/UT49729.2023.10103414|economy;hydraulic lifting;mechanical lifting;ore separation on seafloor;seafloor massive sulfides;deep-sea mining;economy;hydraulic lifting;mechanical lifting;ore separation on seafloor;seafloor massive sulfides;deep-sea mining|
|[An Economic Feasibility Analysis for Combined Mining of Cobalt-rich Crusts and Phosphorous Ores in North-west Pacific](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103423)|T. Yamazaki; N. Nakatani; R. Arai|10.1109/UT49729.2023.10103423|cobalt-rich crust;combined mining;economy;mechanical lifting;phosphorous ore;deep-sea mining;cobalt-rich crust;combined mining;economy;mechanical lifting;phosphorous ore;deep-sea mining|
|[Underwater sound observation using the Edokko Mark I as a platform of acoustic environment assessment for marine mineral resource development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103425)|Y. Onishi; H. Naganuma; Y. Yamamoto; M. Nagao; N. Saito; A. Suzuki; K. Takami; M. Shimazu; T. Akamatsu|10.1109/UT49729.2023.10103425|Underwater sound;The Edokko Mark I;Acoustic environmental assessment;Soundscape;Marine mineral resources;Underwater sound;The Edokko Mark I;Acoustic environmental assessment;Soundscape;Marine mineral resources|
|[Enhancing the Coverage of Underwater Robot Based Mn-crust Survey Area by Using a Multibeam Sonar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103443)|U. Neettiyath; M. Sangekar; K. Nagano; T. Koike; B. Thornton; H. Sugimatsu; H. Hino; A. Suzuki|10.1109/UT49729.2023.10103443|;|
|[DHC sensorics of bubble methane in situ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103422)|V. Dyomin; I. Polovtsev; A. Olshukov; A. Davydova|10.1109/UT49729.2023.10103422|underwater digital holography of particles;methane;bubbles;marine particles;methane release;gas transfer;Arctic studies;underwater digital holography of particles;methane;bubbles;marine particles;methane release;gas transfer;Arctic studies|
|[Heterogeneous marine robotic system for environmental monitoring missions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103383)|F. Ferreira; A. Babić; M. Oreč; N. Mišković; C. Motta; R. Ferretti; A. Odetti; S. Aracri; G. Bruzzone; M. Caccia; F. Braga; G. Manfè; G. Lorenzetti; G. Scarpa; F. De Pascalis|10.1109/UT49729.2023.10103383|environmental protection;ocean monitoring;Autonomous Surface Vehicles;buoys;environmental protection;ocean monitoring;Autonomous Surface Vehicles;buoys|
|[A Network of Underwater Flow Sensors for Long-term Estimation of Fish Feeding Activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103416)|D. Solpico; Y. Nishida; K. Mishima; K. Ishii; T. Suetsugu; Y. Yatsunami|10.1109/UT49729.2023.10103416|flow speed;sensor network;fish cage;feeding;aquaculture;flow speed;sensor network;fish cage;feeding;aquaculture|
|[Numerical Analysis of the Motion of an Automated Fish Guiding System for Set Net Fishery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103203)|D. Furuichi; S. Dong; Q. Li; J. Zhou; Y. Mizukami; D. Kitazawa|10.1109/UT49729.2023.10103203|set net;fish guiding system;flexible hose;lumped mass;bending stiffness;set net;fish guiding system;flexible hose;lumped mass;bending stiffness|
|[Development of Moon Jellyfish Removal ROV System and Its Sea Trial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103427)|J. Ahn|10.1109/UT49729.2023.10103427|ROV;moon jellyfish removal work;sea trial;ROV;moon jellyfish removal work;sea trial|
|[Development of the Autonomous Core Sampling System for AUV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103418)|S. Inoue; S. Takashima; K. Nagano; K. Masuda; S. Taoka; B. Thornton; H. Sugimatsu; Y. Nishida; I. Koike; T. Ura|10.1109/UT49729.2023.10103418|AUV;Core Sampling;Sampling;AUV;Core Sampling;Sampling|
|[Development of a Hybrid Underwater Vehicle for Visual Inspection of Bridge Piers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103382)|H. Kondo; S. Kobayashi; T. Tashiro; N. Saigo; T. Hiraike; K. Kuroki|10.1109/UT49729.2023.10103382|AUV;ROV;vehicle design;visual inspection;photogrammetry;AUV;ROV;vehicle design;visual inspection;photogrammetry|
|[Utilization of cement-based materials for deep sea infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103374)|K. Takahashi; M. Kobayashi; Y. Kawabata; T. Kasaya; M. Iwanami; T. Yamanaka; S. Nomura; H. Makita|10.1109/UT49729.2023.10103374|cementitious materialst;durability;in-situ monitoring;in-situ casting;microorganisms;cementitious materialst;durability;in-situ monitoring;in-situ casting;microorganisms|
|[Essential resource management in dredging hydrographic surveying using USV and SV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103381)|P. Pocwiardowski|10.1109/UT49729.2023.10103381|dredging;survey;hydrography;bathymetry;USV;dredging;survey;hydrography;bathymetry;USV|
|[Efficient real-time dredging monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103426)|P. Pocwiardowski|10.1109/UT49729.2023.10103426|dredging monitoring;bathymetry;survey;dredging monitoring;bathymetry;survey|
|[Forecasting Underwater Positioning Base and Station for Coastal Construction and Inspection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103450)|M. Yoshie; T. Sato|10.1109/UT49729.2023.10103450|underwater construction;AUV;ROV;coastal engineering;positioning system;benchmark;monitoring;underwater construction;AUV;ROV;coastal engineering;positioning system;benchmark;monitoring|
|[Information-Preserved Blending Method for Forward-Looking Sonar Mosaicing in Non-Ideal System Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103367)|J. Su; X. Tu; F. Qu; Y. Wei|10.1109/UT49729.2023.10103367|Forward-looking sonar;image mosaic;image blending;underwater inspection;Forward-looking sonar;image mosaic;image blending;underwater inspection|
|[Semantic Segmentation of seafloor images in Philippines based on semi-supervised learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103432)|S. Wang; K. Mizuno; S. Tabeta; T. Kei|10.1109/UT49729.2023.10103432|semi-supervised learning;semantic segmentation;seafloor images;marine organism;deep learning;semi-supervised learning;semantic segmentation;seafloor images;marine organism;deep learning|
|[Neural Network based CPG Control Method of Undulating Fin in Underwater Biomimetic Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103202)|T. Kim; M. -j. Kim; S. -C. Yu|10.1109/UT49729.2023.10103202|underwater biomimetic robot;soft robotics;nonlinear control;CPG-control;neural network;underwater biomimetic robot;soft robotics;nonlinear control;CPG-control;neural network|
|[Marine Snow Simulation and Elimination in Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103393)|J. P. Coffelt; N. Nowald; P. Kampmann|10.1109/UT49729.2023.10103393|marine snow;underwater robotics;sim2real;deep learning;computer vision;marine snow;underwater robotics;sim2real;deep learning;computer vision|
|[Performance Comparison of Differential OFDM Employing Multiple Differential Detection for Underwater Acoustic Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103405)|T. Fujita; H. Kubo|10.1109/UT49729.2023.10103405|differential orthogonal frequency division multiplexing;trellis-coded modulation;doubly-selective channel;channel prediction;multiple differential detection;differential orthogonal frequency division multiplexing;trellis-coded modulation;doubly-selective channel;channel prediction;multiple differential detection|
|[Development and Sea Trail of the Floating Kuroshio Turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103379)|L. -J. Mu; S. -W. Huang; T. -E. Hou; E. Chen; Y. -T. Hung; F. -C. Chiu; J. Guo|10.1109/UT49729.2023.10103379|Renewable Energy;Floating Kuroshio Turbine;Power Generation;Hydrofoil floating body;Buoyancy Control System;Mooring Test;Renewable Energy;Floating Kuroshio Turbine;Power Generation;Hydrofoil floating body;Buoyancy Control System;Mooring Test|
|[Development of long-term observation system using cable-restricted underwater vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103413)|Y. Tanaka; Y. Nishida; J. Fukuda; K. Ishii|10.1109/UT49729.2023.10103413|Long-term observation;Cable-restricted underwater vehicle;Involute curve;Long-term observation;Cable-restricted underwater vehicle;Involute curve|
|[Hydrodynamic Parameter Estimation of A 20kW Floating Kuroshio Turbine Operating in Steady State](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103397)|T. -E. Hou; L. -J. Mu; S. -W. Huang; E. Chen; F. -C. Chiu; J. Guo|10.1109/UT49729.2023.10103397|renewable energy;ocean energy;ocean current turbine;Kalman filter;depth control;renewable energy;ocean energy;ocean current turbine;Kalman filter;depth control|
|[Vector Field-Based Guidance Method for Collision Avoidance of Unmanned Surface Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103386)|Q. -D. Chen; S. -W. Huang; M. -H. Ho; F. -Y. Chung; C. -H. Chu; C. -M. Liao; J. Guo|10.1109/UT49729.2023.10103386|ship simulator;ship berthing;autopilot;collision avoidance;unmanned surface vehicles;ship simulator;ship berthing;autopilot;collision avoidance;unmanned surface vehicles|
|[Hydrodynamic study of hybrid aerial underwater vehicle in the wind, wave, and current environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103380)|T. Wei; Z. Zhou; Y. Wang; Z. Zeng; L. Lian|10.1109/UT49729.2023.10103380|Hybrid aerial underwater vehicle;Computational fluid dynamics;Wave;Current;Hybrid aerial underwater vehicle;Computational fluid dynamics;Wave;Current|
|[Observability analysis of acoustic positioning for multi-agent underwater vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103447)|Y. Sekimori; U. Neettiyath; C. Kawamura; S. Chun; T. Maki|10.1109/UT49729.2023.10103447|autonomous underwater vehicle;multi-agent system;acoustic positioning;self-localization;observability;autonomous underwater vehicle;multi-agent system;acoustic positioning;self-localization;observability|
|[Video analysis of an ecological monitoring at 2022 spring using Deep-sea deployed camera (EDOKKO Mark 1 series) in the seabed of off Minamitori Island](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103399)|N. Yoshioka; T. Miwa; S. Kawagucci; H. Yamamoto|10.1109/UT49729.2023.10103399|underwater video system;video monitoring;EDOKKO Mark 1;underwater video system;video monitoring;EDOKKO Mark 1|
|[Time-Synchronized Projector–Camera System Design and Underwater Sharpening Image Acquisition Experiment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10103384)|J. Han; H. Yamamoto; Y. Nishida; S. Yasukawa|10.1109/UT49729.2023.10103384|underwater image acquisition;underwater sharpening image;projector–camera system;underwater image acquisition;underwater sharpening image;projector–camera system|
#### **2023 IEEE 20th International Conference on Software Architecture (ICSA)**
- DOI: 10.1109/ICSA56044.2023
- DATE: 13-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Selection Model of Privacy Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092534)|S. Y. Chia; X. Xu; M. Ding; D. Smith; H. -Y. Paik; L. Zhu|10.1109/ICSA56044.2023.00009|Privacy pattern;Architectural pattern;Design;Privacy pattern;Architectural pattern;Design|
|[Feature-based software architecture analysis to identify safety and security interactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092690)|Priyadarshini; S. Greiner; M. Massierer; O. -E. -K. Aktouf|10.1109/ICSA56044.2023.00010|model-based software architecture analysis;feature interaction;feature-based software engineering;functional safety feature;cybersecurity feature;unified modeling language (UML);autonomous vehicles;model-based software architecture analysis;feature interaction;feature-based software engineering;functional safety feature;cybersecurity feature;unified modeling language (UML);autonomous vehicles|
|[A Pattern-Oriented Reference Architecture for Governance-Driven Blockchain Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092670)|Y. Liu; Q. Lu; G. Yu; H. -Y. Paik; L. Zhu|10.1109/ICSA56044.2023.00011|Software engineering;reference architecture;blockchain governance;decision rights;incentive;accountability;pattern;Software engineering;reference architecture;blockchain governance;decision rights;incentive;accountability;pattern|
|[Performance Modeling and Analysis of Design Patterns for Microservice Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092637)|R. Pinciroli; A. Aleti; C. Trubiani|10.1109/ICSA56044.2023.00012|Software Architecture;Model-based Performance Analysis;Microservices;Design Patterns;Software Architecture;Model-based Performance Analysis;Microservices;Design Patterns|
|[From monolithic to microservice architecture: an automated approach based on graph clustering and combinatorial optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092594)|G. Filippone; N. Qaisar Mehmood; M. Autili; F. Rossi; M. Tivoli|10.1109/ICSA56044.2023.00013|microservices;architecture migration;system decomposition;graph clustering;combinatorial optimization;microservices;architecture migration;system decomposition;graph clustering;combinatorial optimization|
|[Quality Metrics in Software Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092705)|S. Silva; A. Tuyishime; T. Santilli; P. Pelliccione; L. Iovino|10.1109/ICSA56044.2023.00014|Quality Attributes;Quality Metrics;Software Architecture;Quality Attributes;Quality Metrics;Software Architecture|
|[Standardisation in Digital Twin Architectures in Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092613)|E. Ferko; A. Bucaioni; P. Pelliccione; M. Behnam|10.1109/ICSA56044.2023.00015|Software Architecture;Digital Twin;ISO 23247;Reference Architecture;Software Architecture;Digital Twin;ISO 23247;Reference Architecture|
|[Access Control Enforcement Architectures for Dynamic Manufacturing Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092717)|B. Leander; A. Čaušević; T. Lindström; H. Hansson|10.1109/ICSA56044.2023.00016|Cybersecurity;Access Control;Industrial Automation and Control Systems;Dynamic Manufacturing;Cybersecurity;Access Control;Industrial Automation and Control Systems;Dynamic Manufacturing|
|[Software Architecture for IoT-based Indoor Positioning Systems for Ambient Assisted Living](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092619)|L. F. Mendes; P. A. C. Aguilar; C. I. M. Bezerra|10.1109/ICSA56044.2023.00017|ambient assisted living;fog computing;indoor positioning system;internet of things;software architecture;ambient assisted living;fog computing;indoor positioning system;internet of things;software architecture|
|[A Pub/Sub-Based Mechanism for Inter-Component Exception Notification in Android Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092691)|F. Freitas; L. S. Rocha; P. Henrique M. Maia|10.1109/ICSA56044.2023.00018|technological;exception handling mechanism;inter-component communication;exception notification;android apps;technological;exception handling mechanism;inter-component communication;exception notification;android apps|
|[The beauty of software architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092713)|C. M. Aldenhoven; R. S. Engelschall|10.1109/ICSA56044.2023.00019|Software Architectures;Design;Health implications;Education;Software Architectures;Design;Health implications;Education|
|[Where and What do Software Architects blog? : An Exploratory Study on Architectural Knowledge in Blogs, and their Relevance to Design Steps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092642)|M. Soliman; K. Gericke; P. Avgeriou|10.1109/ICSA56044.2023.00020|Architecture knowledge;Architecture design decisions;blog articles;Architecture knowledge;Architecture design decisions;blog articles|
|[Detecting Inconsistencies in Software Architecture Documentation Using Traceability Link Recovery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092702)|J. Keim; S. Corallo; D. Fuchß; A. Koziolek|10.1109/ICSA56044.2023.00021|Inconsistency Detection;Traceability Link Recovery;Consistency;Documentation;Software architecture;Software engineering;Inconsistency Detection;Traceability Link Recovery;Consistency;Documentation;Software architecture;Software engineering|
|[Architecting Digital Twins Using a Domain-Driven Design-Based Approach*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092715)|A. Macías; E. Navarro; C. E. Cuesta; U. Zdun|10.1109/ICSA56044.2023.00022|Digital Twin;Domain-Driven Design;Hexagonal Architecture;Microservice;Bounded Context;Design Science;Digital Twin;Domain-Driven Design;Hexagonal Architecture;Microservice;Bounded Context;Design Science|
|[An Integrated Approach to Package and Class Code- to-Architecture Mapping Using InMap](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092679)|Z. T. Sinkala; S. Herold|10.1109/ICSA56044.2023.00023|automated source code mapping;software architecture consistency;software architecture conformance;software maintenance;automated source code mapping;software architecture consistency;software architecture conformance;software maintenance|
|[A Recommender System for Recovering Relevant JavaScript Packages from Web Repositories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092660)|H. C. Vazquez; J. A. Diaz-Pace; S. A. Vidal; C. Marcos|10.1109/ICSA56044.2023.00024|technology selection;component-based design;Javascript packages;software repositories;technology selection;component-based design;Javascript packages;software repositories|
#### **2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)**
- DOI: 10.1109/ECEI57668.2023
- DATE: 3-5 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Challenges of Digital Transition in Engineering Careers: Solution Based on Standardized Renewable Source-based Remote Laboratories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105326)|M. Martínez; F. Segura; J. M. Andújar; L. Del Zotto; M. Tabakovic; T. O'Mahony|10.1109/ECEI57668.2023.10105326|remote laboratory;renewable energy;training;European network;remote laboratory;renewable energy;training;European network|
|[Analysis of Ground Ponding Characteristics of Ancient Buildings Based on 3D Point Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105260)|P. Chen; Y. He; T. Zhong; L. Zhong; L. Chen; Z. Xie; Y. Lai|10.1109/ECEI57668.2023.10105260|Ancient buildings;groundwater;section analysis;fitting plane;Ancient buildings;groundwater;section analysis;fitting plane|
|[Application of Computer Virtual Reality Technology in College Sports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105332)|D. Zhou|10.1109/ECEI57668.2023.10105332|College Sports;Virtual Reality Technology;Teaching Research;Teaching Effect;College Sports;Virtual Reality Technology;Teaching Research;Teaching Effect|
|[Application of Virtual Machine Technology in Teaching Mode of Financial Budget in Vocational Colleges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105415)|K. Liu; F. Cui|10.1109/ECEI57668.2023.10105415|Virtual Machine Technology;Vocational Colleges;Financial Budget;Teaching Model;Virtual Machine Technology;Vocational Colleges;Financial Budget;Teaching Model|
|[Application Scenarios and Patterns of Virtual Digital VR Technology in Environmental Art Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105406)|Y. Liu|10.1109/ECEI57668.2023.10105406|Virtual Digitization;VR;Environmental Art Design;Application Scenario;Virtual Digitization;VR;Environmental Art Design;Application Scenario|
|[Ario Game: Learning English Game Development with Python on Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105395)|P. Netinant; S. Chuencheevajaroen; M. Rakhiran|10.1109/ECEI57668.2023.10105395|Gamification;Mario game;game development;Pygame;Python;Object orientation;Raspberry Pi;Gamification;Mario game;game development;Pygame;Python;Object orientation;Raspberry Pi|
|[Automatic Patch Generation System for Smart Contract](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105422)|S. Guo|10.1109/ECEI57668.2023.10105422|security scheme;blockchain;smart contract;SolSaviour;EVMPatch;Slither;Solidity;SafeMath;security scheme;blockchain;smart contract;SolSaviour;EVMPatch;Slither;Solidity;SafeMath|
|[Bearing Fault Diagnosis Based on an Advanced Method: ID-CNN-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105356)|C. -J. Chang; C. -C. Chen; B. -H. Chen|10.1109/ECEI57668.2023.10105356|1D-CNN;LSTM;Bearing Fault Diagnosis;Vibration Signal;Ball Bearing;1D-CNN;LSTM;Bearing Fault Diagnosis;Vibration Signal;Ball Bearing|
|[Class Attendance System using Unimodal Face Recognition System based on Internet of Educational Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105374)|P. Netinant; N. Akkharasup-Anan; M. Rakhiran|10.1109/ECEI57668.2023.10105374|Image Processing;Internet of Things;Raspberry pi;Python;OpenCV;Face Recognition;Google Sheets;Class attendance;Image Processing;Internet of Things;Raspberry pi;Python;OpenCV;Face Recognition;Google Sheets;Class attendance|
|[Cloud-based Architecture for Education Informatization Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105357)|H. Guan; L. Zheng; X. Yuan; S. Han; S. Li|10.1109/ECEI57668.2023.10105357|Cloud Architecture Technology;Education Informatization;J2ee Technology;Weighted Polling Algorithm;Cloud Architecture Technology;Education Informatization;J2ee Technology;Weighted Polling Algorithm|
|[Clustering Research on Learning Behavior of Online Moral Education Course Based on K-Means Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105343)|D. Wang|10.1109/ECEI57668.2023.10105343|K-Means;Algorithm;On-Line;Moral Education Curriculum;Learning Behavior;Clustering;K-Means;Algorithm;On-Line;Moral Education Curriculum;Learning Behavior;Clustering|
|[Intrusion Detection System Based on Probabilistic Suffix Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105322)|H. Yang; H. Fu; C. Wu|10.1109/ECEI57668.2023.10105322|network security;probabilistic suffix tree;system call sequence;intrusion detection;threat identification;network security;probabilistic suffix tree;system call sequence;intrusion detection;threat identification|
|[Combining Big Data and GIS Interface to Achieve Effectiveness of E-government](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105399)|I. -C. Lin|10.1109/ECEI57668.2023.10105399|component;formatting;style;styling;insert;component;formatting;style;styling;insert|
|[Computer and AI Compound Knowledge Points Mining in Line with Human Resources Demand of AI Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105365)|C. Liu; Y. Zhang; C. Xie|10.1109/ECEI57668.2023.10105365|AI Industry Field;AI Job;Human resources Demand;Compound Knowledge Points;Association Mining;AI Industry Field;AI Job;Human resources Demand;Compound Knowledge Points;Association Mining|
|[Design and Application of English Vocabulary Computer Aided Learning System Based on Associative Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105412)|J. Chen|10.1109/ECEI57668.2023.10105412|Associative memory;English vocabulary;computer;Assisted learning;system design;Associative memory;English vocabulary;computer;Assisted learning;system design|
|[Design and Application of Landscape Design Space Perception Teaching Based on VR Virtual Environment Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105359)|Q. Liu; Y. Li|10.1109/ECEI57668.2023.10105359|VR;Virtual Environment;Landscape Design;Spatial Perception;Teaching;VR;Virtual Environment;Landscape Design;Spatial Perception;Teaching|
|[Design and Application of Virtual Simulation Teaching Function of Chime Performance and Creation Based on VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105401)|L. Li|10.1109/ECEI57668.2023.10105401|VR;Chime Playing;Creation;Virtual Simulation Teaching;VR;Chime Playing;Creation;Virtual Simulation Teaching|
|[Design and Implementation of The Big-Data-Based English Digital Teaching Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105369)|R. Dang|10.1109/ECEI57668.2023.10105369|design;implementation;big-data-based;digital teaching platform;design;implementation;big-data-based;digital teaching platform|
|[Design and Strategy Research of Online Learning Process Evaluation System Based on Big Data Intelligent Analysis Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105307)|L. Sun|10.1109/ECEI57668.2023.10105307|Big Data;Intelligent Analysis;Online Learning;Process Evaluation;Big Data;Intelligent Analysis;Online Learning;Process Evaluation|
|[Design of French Teaching Effect Analysis Model Based on Random Forest Algorithm and Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105257)|X. Luan|10.1109/ECEI57668.2023.10105257|Random Forest;Algorithm;French;Teaching Effectiveness;Random Forest;Algorithm;French;Teaching Effectiveness|
|[Designing and Implementation of Web-Enhanced Inquiry Learning for Literacy in Science Platform for Post COVID-19 Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105362)|N. Srisawasdi; P. Chaipidech; P. Pondee; K. Chaipah; P. Panjaburee; W. Khaokhajorn; S. Premthaisong; K. Tuamsuk|10.1109/ECEI57668.2023.10105362|scientific inquiry;pedagogy;online learning;scientific competencies;digital technology;scientific inquiry;pedagogy;online learning;scientific competencies;digital technology|
|[Development and Application of Spoken French Corpus Based on AI Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105317)|X. Luan|10.1109/ECEI57668.2023.10105317|Artificial Intelligence;Speech Recognition;French;Oral Language;Corpus;Artificial Intelligence;Speech Recognition;French;Oral Language;Corpus|
|[Mechanisms for Enhancing Network Services by Live Migration in the Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105337)|M. -H. Wu; C. -Y. Chiu; J. -Z. Wu; J. -H. Huang; J. -X. Chen; H. -J. Wang|10.1109/ECEI57668.2023.10105337|Live migration;NFQUEUE;NAT router;Three-way handshake;Live migration;NFQUEUE;NAT router;Three-way handshake|
|[Development and Application of Teaching Model for Medical Humanities Education using Artificial Intelligence and Digital Humans Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105419)|S. Jin; X. Zhang; X. Li; M. Cheng; X. Cui; J. Liu|10.1109/ECEI57668.2023.10105419|medical humanities education;artificial intelligence;digital human;big data;teaching model;medical humanities education;artificial intelligence;digital human;big data;teaching model|
|[Development of Mobile Augmented Reality of Series Circuits for Science Learning in Primary School Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105254)|P. Kusonyang; P. Pondee; N. Srisawasdi; P. Chaipidech; S. Premthaisong; W. Khaokhajorn|10.1109/ECEI57668.2023.10105254|Mobile Augmented Reality;Series Circuits;Primary Students;Mobile Augmented Reality;Series Circuits;Primary Students|
|[Effects of Decision Tree-based Online Gaming Framework on Students' Digital Citizenship Behaviors and Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105413)|P. Panjaburee; G. -J. Hwang; U. Intarakamhang; N. Srisawasdi; S. Poompimol; P. Tapingkae|10.1109/ECEI57668.2023.10105413|Quality education;essential skill;technology-enhanced learning;learning analytic;Quality education;essential skill;technology-enhanced learning;learning analytic|
|[Enhancing Health Training Education Experience with Volumetric Video and Wearable Mixed Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105262)|X. Bi; T. Lv; Z. Cheng; X. Hong; N. Miao; R. Li; G. Wang; G. Ren|10.1109/ECEI57668.2023.10105262|Internet of Things;Volumetric Video;Health Training;Internet of Things;Volumetric Video;Health Training|
|[Evaluation of Learning Effect of Reverse Teaching Design Based on BP Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105354)|C. Wang|10.1109/ECEI57668.2023.10105354|BP neural network;reverse instructional design;learning effect evaluation;BP neural network;reverse instructional design;learning effect evaluation|
|[Extraction of STEM Knowledge Relationship in Physical Education Course Textbooks Based on KNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105373)|Z. Shan; F. Liang|10.1109/ECEI57668.2023.10105373|KNN;Physical Education Curriculum;Teaching Materials;STEM Knowledge Points;Relation Extraction;KNN;Physical Education Curriculum;Teaching Materials;STEM Knowledge Points;Relation Extraction|
|[Few-shot Learning for Bagel Defect Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105421)|H. -Y. Liao; C. -C. Chen; A. -B. Cheng|10.1109/ECEI57668.2023.10105421|few-shot learning;defect detection;few-shot learning;defect detection|
|[Fusion System Design of Multi-sensors and Visual Algorithm For Elderly Fall Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105380)|G. Cao; J. Yang; M. Chen|10.1109/ECEI57668.2023.10105380|Fall detection;Multi-sensor integrated detection;Visual fall detection;Fall detection;Multi-sensor integrated detection;Visual fall detection|
|[Gradient Boost Decision Tree-based Research on Kindergarten Children's Cognitive Law of Mathematical Knowledge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105259)|Y. Yang; X. Li|10.1109/ECEI57668.2023.10105259|Gradient boost decision tree (GBDT);machine learning;children's cognitive law;mathematical knowledge module;Gradient boost decision tree (GBDT);machine learning;children's cognitive law;mathematical knowledge module|
|[Improved Adaboost Algorithm Method-Based Research on Influence of Pupils' Learning Habits on English Vocabulary Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105320)|X. Wu; L. Jiang|10.1109/ECEI57668.2023.10105320|Adaboost;ensemble learning;learning habits;English vocabulary;children;Adaboost;ensemble learning;learning habits;English vocabulary;children|
|[Protection Mechanism in JavaScript Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105425)|M. -H. Wu; C. -Y. Chiu; J. -H. Huang; J. -X. Chen; H. -J. Wang; Y. Lin|10.1109/ECEI57668.2023.10105425|Malicious JavaScript;VirusTotal;Protection;Malicious JavaScript;VirusTotal;Protection|
|[Improved Meanshift Tracking Algorithm Based on Optical flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105361)|X. Yang; Q. Li; C. He|10.1109/ECEI57668.2023.10105361|Mean shift;Region Detection;Object Tracking;Optical flow;Mean shift;Region Detection;Object Tracking;Optical flow|
|[Integrating 360° Virtual Learning Environment to Support Out-Of-Class Inquiry Activity for Preservice Teachers: A Preliminary Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105325)|P. Chaipidech; N. Srisawasdi|10.1109/ECEI57668.2023.10105325|virtual reality environment;preservice teacher;science;inquiry learning;virtual reality environment;preservice teacher;science;inquiry learning|
|[Intelligent Evaluation Model of English Translation Content Quality Based on Improved Neural Network Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105339)|P. Yang|10.1109/ECEI57668.2023.10105339|Neural Network Algorithm;English Translation;Content Quality;Evaluate;Neural Network Algorithm;English Translation;Content Quality;Evaluate|
|[Investigation of Learning Attraction Towards Digital Board Game by Using Eye Tracking Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105383)|N. Nukprach; P. Chaipidech; N. Srisawasdi|10.1109/ECEI57668.2023.10105383|Digital Board Game;Eye Tracking;Students' Perception;Students' Attraction;Physics;Digital Board Game;Eye Tracking;Students' Perception;Students' Attraction;Physics|
|[IoT OS Teaching Material Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105261)|S. -Y. Yuan; F. -C. Lui; H. -C. Huang; T. -W. Huang; W. -S. Liu|10.1109/ECEI57668.2023.10105261|FPGA;IoT;OS Teaching;FPGA;IoT;OS Teaching|
|[Learning Effect Evaluation of Online Course Based on Linear Regression Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105417)|P. Chen; H. Han; M. Zhang|10.1109/ECEI57668.2023.10105417|online courses;learning effect;linear regression;correlation analysis;online courses;learning effect;linear regression;correlation analysis|
|[Machine Learning Detection of Ransomware by Lightweight Mini-filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105315)|C. -Y. Chiu; M. -H. Wu; J. -H. Huang; J. -X. Chen; H. -J. Wang|10.1109/ECEI57668.2023.10105315|Ransomware;minfilter;windows driver;Ransomware;minfilter;windows driver|
|[Micro Expression Recognition by Machine Learning Based Profit Function Analysis in Intelligent Marketing of Financial Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105404)|J. Zhang; Z. Dong; S. -J. Wang|10.1109/ECEI57668.2023.10105404|microexpression recognition (MER);machine learning;intelligent marketing;profit function;deep local-holistic network (DLHN);microexpression recognition (MER);machine learning;intelligent marketing;profit function;deep local-holistic network (DLHN)|
|[Multiple Linear Regression and Bagging-based Analysis and Modeling of Influence of Mother's Socio-economic Attributes on Anxiety of Online Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105352)|L. Liu; L. Jiang|10.1109/ECEI57668.2023.10105352|Bagging;ensemble learning;multiple linear regression;online education;anxiety;Bagging;ensemble learning;multiple linear regression;online education;anxiety|
|[Natural Language Processing Algorithms for Divergent Thinking Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105336)|H. Lee; W. Zhou; H. Bai; W. Meng; T. Zeng; K. Peng; S. Tong; T. Kumada|10.1109/ECEI57668.2023.10105336|Divergent Thinking Assessment;Natural Language Processing;Alternative Uses Task;Crowdsourcing;Divergent Thinking Assessment;Natural Language Processing;Alternative Uses Task;Crowdsourcing|
|[Transformation of Online Education Platform by Internet Technology under China's Double Reduction Policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105370)|Y. Zhang; G. Chen; J. Fang|10.1109/ECEI57668.2023.10105370|online education;Internet;transformation;Analytic Hierarchy Process;online education;Internet;transformation;Analytic Hierarchy Process|
|[Novel Automatic Feature Engineering for Carbon Emissions Prediction Base on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105367)|Z. -J. Lee; Y. Lin; Z. Yang; Z. -Y. Chen; W. -G. Fan; C. -H. Lee|10.1109/ECEI57668.2023.10105367|Automatic Feature Engineering;Carbon Emissions;Self-organizing Map;Feature Selection;Deep Learning;Automatic Feature Engineering;Carbon Emissions;Self-organizing Map;Feature Selection;Deep Learning|
|[Prediction of Pork Price Based on PCA-BP Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105313)|Z. Liu; F. Mei; C. Li; Q. Yang|10.1109/ECEI57668.2023.10105313|BP neural network;Principal component analysis;Data simulation;Price prediction;BP neural network;Principal component analysis;Data simulation;Price prediction|
|[Privacy Protection Scheme for Personal Health Record System Using Blockchain Based on Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105420)|C. -P. Lin; Z. -Y. Wu; C. -H. Liu|10.1109/ECEI57668.2023.10105420|Personal Health Records;Blockchain Technology;Homomorphic Encryption;Health Promotion;Personal Health Records;Blockchain Technology;Homomorphic Encryption;Health Promotion|
|[Public Comment Analysis Model of Network Media Based on Big Data Mining and Implementation Plans](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105382)|X. Zhang|10.1109/ECEI57668.2023.10105382|big data;network media;public comment analysis;analysis model;key technology;big data;network media;public comment analysis;analysis model;key technology|
|[Quantitative Expression of Elderly Multi-Modal Emotions with Spoken Dialogue Agent and Edge AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105327)|S. Chen; H. Ozono; M. Nakamura; K. Yasuda|10.1109/ECEI57668.2023.10105327|quantitative expression;multi-modal emotions;spoken dialogue agent;edge AI;assistive technology;quantitative expression;multi-modal emotions;spoken dialogue agent;edge AI;assistive technology|
|[Random Forest Algorithm-based Modelling and Neural Network Analysis Between Social Anxiety Disorder of Childhood and Parents' Socioeconomic Attributes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105416)|G. Li; L. Jiang|10.1109/ECEI57668.2023.10105416|machine learning;random forest;logistic regression;social anxiety disorder of childhood (SADC);socio-economic attributes;machine learning;random forest;logistic regression;social anxiety disorder of childhood (SADC);socio-economic attributes|
|[Reform of English Listening Course Teaching Mode in Vocational Colleges Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105346)|D. Liao|10.1109/ECEI57668.2023.10105346|artificial intelligence;English listening course;teaching mode;artificial intelligence;English listening course;teaching mode|
|[Remote System Design of Urban Underground Comprehensive Pipe Gallery Inspection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105255)|M. Chen; G. Cao; J. Yang|10.1109/ECEI57668.2023.10105255|SLAM;UNET;Unmanned Patrol Inspection;Image Recognition;Intelligent Cruise;SLAM;UNET;Unmanned Patrol Inspection;Image Recognition;Intelligent Cruise|
|[Research and Construction of Online Gambling Early Warning Platform Based on Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105256)|Y. Xu; M. Tu; B. Xiong; Z. Wu; G. Chen; Z. Tao|10.1109/ECEI57668.2023.10105256|big data;online gambling;data mining;crime early warning;big data;online gambling;data mining;crime early warning|
|[Research and Countermeasures on Changes in Employment Trends of College Students in Post-epidemic Era under Big Data Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105265)|N. Fang|10.1109/ECEI57668.2023.10105265|big data;post-epidemic era;changes in the employment trend of college students;countermeasures and suggestions;big data;post-epidemic era;changes in the employment trend of college students;countermeasures and suggestions|
|[Design of New Push-pull Isolation Transformer Drive Circuit Based on Single-chip Microcomputer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105408)|S. Deng; H. Shen; Z. Xiao; H. Zhu; J. Ye; H. Xiao|10.1109/ECEI57668.2023.10105408|isolated power supply;push-pull converter;dead time;complementary signals;Charge pump boost circuitry;isolated power supply;push-pull converter;dead time;complementary signals;Charge pump boost circuitry|
|[Research on Algorithm of Selecting Courses and Combining Classes in College Physical Education Network Teaching Platform Based on Linear Equilibrium](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105344)|T. Hu; B. Zhang|10.1109/ECEI57668.2023.10105344|Linear Equilibrium;Colleges And Universities;Sports;Network Teaching Platform;Course Selection;Algorithm;Linear Equilibrium;Colleges And Universities;Sports;Network Teaching Platform;Course Selection;Algorithm|
|[Research on Application of Education Big Data Integrated with Artificial Intelligence Technology in Teaching Field](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105308)|L. Sun|10.1109/ECEI57668.2023.10105308|Artificial Intelligence;Education;Big Data;Teaching;Artificial Intelligence;Education;Big Data;Teaching|
|[Research on Construction of Three Rivers and Six Banks Cultural and Creative Product System in Dongguan from Perspective of Cultural and Tourism integration: DFuzzy Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105360)|Y. Feng; W. Liu; S. -L. Hsueh|10.1109/ECEI57668.2023.10105360|cultural resources;cultural and creative products;Delphi method;fuzzy logic theory;cultural resources;cultural and creative products;Delphi method;fuzzy logic theory|
|[Research on English-Chinese Translation Quality Evaluation Agorithm Based on Cross-Language Pre-Training Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105371)|P. Yang|10.1109/ECEI57668.2023.10105371|Cross-Language;Pre-Training Mode;English-Chinese Translation;Quality Evaluation;Algorithm;Cross-Language;Pre-Training Mode;English-Chinese Translation;Quality Evaluation;Algorithm|
#### **2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)**
- DOI: 10.1109/ICSA-C57050.2023
- DATE: 13-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Architecting a big data-driven software architecture for smart street lighting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092664)|M. Ali; P. Scandurra; F. Moretti; L. Blaso|10.1109/ICSA-C57050.2023.00019|Big data-driven software architecture;smart street lighting;smart city platforms;PELL;city analytics;Big data-driven software architecture;smart street lighting;smart city platforms;PELL;city analytics|
|[TOSCA for Microservice Deployment in Distributed Control Systems: Experiences and Lessons Learned](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092611)|H. Koziolek; R. Hark; N. Eskandani; P. S. Nguyen; P. Rodriguez|10.1109/ICSA-C57050.2023.00020|software architecture;microservice;deployment;OASIS TOSCA;distributed control systems;case study;modeling;Azure;StarlingX;software architecture;microservice;deployment;OASIS TOSCA;distributed control systems;case study;modeling;Azure;StarlingX|
|[The Quality-Driven Refactoring Approach in BIM Italia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092590)|R. Capuano; F. Vaccaro|10.1109/ICSA-C57050.2023.00021|refactoring;microservices;quality;antipatterns;migration;refactoring;microservices;quality;antipatterns;migration|
|[Documenting Software Architecture Design in Compliance with the ISO 26262: a Practical Experience in Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092726)|D. Amalfitano; M. De Luca; A. Rita Fasolino|10.1109/ICSA-C57050.2023.00022|Documenting Software Architecture;Software Architecture Design;ISO 26262;Industrial Survey;Industrial Experience;Documenting Software Architecture;Software Architecture Design;ISO 26262;Industrial Survey;Industrial Experience|
|[A Reconfigurable Industry 4.0 Middleware Software Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092655)|S. C. Jepsen; B. Siewertsen; T. Worm|10.1109/ICSA-C57050.2023.00023|Reconfigurability;Industry 4.0 Middleware Software Architecture;Cyber-Physical System;Reconfigurability;Industry 4.0 Middleware Software Architecture;Cyber-Physical System|
|[Variability Features: Extending Sustainability Decision Maps via an Industrial Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092542)|M. Funke; P. Lago; R. Verdecchia|10.1109/ICSA-C57050.2023.00024|Software Architecture;Software Sustainability;Case Study;Variability Features;Lessons Learned;Software Architecture;Software Sustainability;Case Study;Variability Features;Lessons Learned|
|[Experiences on a Frameworkless Micro-Frontend Architecture in a Small Organization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092645)|J. Männistö; A. -P. Tuovinen; M. Raatikainen|10.1109/ICSA-C57050.2023.00025|Micro-frontend;Microservice;Software architecture;Software Architecture Assessment;Micro-frontend;Microservice;Software architecture;Software Architecture Assessment|
|[The Impact of Remote Work on Architectural Decisions in a Start-Up Company - An Industrial Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092587)|A. Rodrigues Paris; E. Guerra|10.1109/ICSA-C57050.2023.00026|architectural decisions process;remote work;agile;startup;architectural decisions process;remote work;agile;startup|
|[Robust Automated Fiber Tracking: A Hybrid Software Architecture for Processing and Visualizing Diffusion Magnetic Resonance Imaging Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092663)|S. v. Gompel; G. Schouten; W. Huijbers; M. Versluis|10.1109/ICSA-C57050.2023.00027|diffusion MRI;software architecture;fiber tracking;image processing;diffusion MRI;software architecture;fiber tracking;image processing|
|[Microservice Logical Coupling: A Preliminary Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092730)|D. A. d’Aragona; L. Pascarella; A. Janes; V. Lenarduzzi; D. Taibi|10.1109/ICSA-C57050.2023.00028|Microservices;Logical Coupling;Empirical Software Engineering;Microservices;Logical Coupling;Empirical Software Engineering|
|[Towards Better Trust in Human-Machine Teaming through Explainable Dependability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092719)|M. M. Bersani; M. Camilli; L. Lestingi; R. Mirandola; M. Rossi; P. Scandurra|10.1109/ICSA-C57050.2023.00029|Human-machine teaming;formal analysis;statistical model checking;explainable AI;Human-machine teaming;formal analysis;statistical model checking;explainable AI|
|[Towards a Reference Component Model of Edge-Cloud Continuum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092668)|D. Khalyeyev; T. Bureš; P. Hnětynka|10.1109/ICSA-C57050.2023.00030|Edge-cloud continuum;edge computing;fog computing;Internet of Things;component model;Edge-cloud continuum;edge computing;fog computing;Internet of Things;component model|
|[Ethics-Aware DecidArch Game: Designing a Game to Reflect on Ethical Considerations in Software Architecture Design Decision Making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092589)|R. Alidoosti; P. Lago; E. Poort; M. Razavian|10.1109/ICSA-C57050.2023.00031|software architecture;design decision making;ethics;game;software architecture;design decision making;ethics;game|
|[User Interface and Architecture Adaption Based on Emotions and Behaviors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092612)|M. T. Moghaddam; M. Alipour; M. B. Kjærgaard|10.1109/ICSA-C57050.2023.00032|Software Architecture;Emotions;Behaviors;Reinforcement Learning;User Interface;Emergency;Software Architecture;Emotions;Behaviors;Reinforcement Learning;User Interface;Emergency|
|[Evolvability of Machine Learning-based Systems: An Architectural Design Decision Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092638)|J. Leest; I. Gerostathopoulos; C. Raibulet|10.1109/ICSA-C57050.2023.00033|software architecture;machine learning;design decisions;quality attributes;evolvability;concept drift;software architecture;machine learning;design decisions;quality attributes;evolvability;concept drift|
|[Monolith Microservices Identification: Towards An Extensible Multiple Strategy Tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092693)|T. Lopes; A. R. Silva|10.1109/ICSA-C57050.2023.00034|Monolith Decomposition Strategies;Microservices;Experimentation Environment;Metrics;Monolith Decomposition Strategies;Microservices;Experimentation Environment;Metrics|
|[Identifying Anti-Patterns in Distributed Systems With Heterogeneous Dependencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092708)|H. Fang; Y. Cai; R. Kazman; J. Lefever|10.1109/ICSA-C57050.2023.00035|Software Architecture;Software Maintenance;Software Quality;Software Architecture;Software Maintenance;Software Quality|
|[Towards a Conceptual Characterization of Antifragile Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092694)|V. Grassi; R. Mirandola; D. Perez-Palacin|10.1109/ICSA-C57050.2023.00036|Dependability;Antifragility;Conceptual Model;Dependability;Antifragility;Conceptual Model|
|[Towards the Assisted Decomposition of Large-Active Files](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092677)|J. Lefever; Y. Cai; R. Kazman; H. Fang|10.1109/ICSA-C57050.2023.00037|refactoring;design improvement;software architecture;refactoring;design improvement;software architecture|
|[Development and Integration of Self-Adaptation Strategies for Robotics Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092718)|E. Alberts|10.1109/ICSA-C57050.2023.00038|robotics;runtime uncertainty;self-*;reinforcement learning;non-functional requirements;robotics;runtime uncertainty;self-*;reinforcement learning;non-functional requirements|
|[Analyzing the Use of Blockchains for Challenges in Inter-organizational Business Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092624)|M. Kjäer|10.1109/ICSA-C57050.2023.00039|inter-organizational BPM;collaborative processes;challenges;enactment;EDI;trusted third party;blockchain;inter-organizational BPM;collaborative processes;challenges;enactment;EDI;trusted third party;blockchain|
|[AI And Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092659)|R. Omar|10.1109/ICSA-C57050.2023.00040|Green AI;Energy Efficiency;Machine Learning;Artificial Intelligence;Deep Learning;Accuracy;DL frameworks;Data-Centric;Energy consumption;Green AI;Energy Efficiency;Machine Learning;Artificial Intelligence;Deep Learning;Accuracy;DL frameworks;Data-Centric;Energy consumption|
|[Sustainability-Aware Software Architecting for the Future Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092596)|I. Fatima|10.1109/ICSA-C57050.2023.00041|Software Architecture;Sustainability Dimen-sions;Cloud-Based Software Services;Green Software;Software for Sustainability;Sustainability Indicators;Software Architecture;Sustainability Dimen-sions;Cloud-Based Software Services;Green Software;Software for Sustainability;Sustainability Indicators|
|[Formalizing the Relationship between Security Policies and Objectives in Software Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092601)|Q. Rouland; B. Hamid; J. -P. Bodeveix; J. Jaskolka|10.1109/ICSA-C57050.2023.00042|engineering secure systems;software architecture;metamodeling;security properties;formal methods;reuse;engineering secure systems;software architecture;metamodeling;security properties;formal methods;reuse|
|[An Exploratory Study of Architectural Style and Effort Estimation for Multi-Tenant Microservices-Based Software as a Service (SaaS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092712)|E. L. Ouh; B. Kok Siew Gan|10.1109/ICSA-C57050.2023.00043|architecture;design;multi-tenant;web engineering;effort estimation;architecture;design;multi-tenant;web engineering;effort estimation|
|[An Approach for Evaluating the Potential Impact of Anti-Patterns on Microservices Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092669)|R. Matar; J. Jahić|10.1109/ICSA-C57050.2023.00044|microservices;design decisions;anti-patterns;performance testing;microservices;design decisions;anti-patterns;performance testing|
|[Role-playing software architecture styles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092646)|L. M. Castro|10.1109/ICSA-C57050.2023.00045|learning styles;role-playing;software architecture models;learning styles;role-playing;software architecture models|
|[Multi-objective Software Architecture Refactoring driven by Quality Attributes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092626)|D. D. Pompeo; M. Tucci|10.1109/ICSA-C57050.2023.00046|refactoring;multi-objective optimization;software architecture;performance;refactoring;multi-objective optimization;software architecture;performance|
|[Capturing notable architectural decisions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092651)|N. B. Harrison; G. Rudolph; J. Tang; L. Thackeray; D. Wagstaff|10.1109/ICSA-C57050.2023.00047|Architectural decisions;Architectural documentation;Quality Attributes;Architectural decisions;Architectural documentation;Quality Attributes|
|[Deep Reinforcement Learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092680)|H. Zhang; J. Li; Z. Qi; A. Aronsson; J. Bosch; H. H. Olsson|10.1109/ICSA-C57050.2023.00048|Reinforcement Learning;Machine Learning;Software Engineering;Emergency Communication Network;Multi-Agent;Decentralized Architecture;Reinforcement Learning;Machine Learning;Software Engineering;Emergency Communication Network;Multi-Agent;Decentralized Architecture|
|[Towards a Product Line Architecture for Digital Twins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092540)|J. Pfeiffer; D. Lehner; A. Wortmann; M. Wimmer|10.1109/ICSA-C57050.2023.00049|Digital twins;domain-specific languages;product lines;software integration;Digital twins;domain-specific languages;product lines;software integration|
|[A Review of Software Architecture Evaluation Methods for Sustainability Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092709)|I. Fatima; P. Lago|10.1109/ICSA-C57050.2023.00050|systematic literature review;software architecture;architecture evaluation;sustainability;systematic literature review;software architecture;architecture evaluation;sustainability|
|[Designing an Architecture Team: An Experience Report](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092641)|M. S. Costa; A. B. Kammer; C. França; R. A. Costa|10.1109/ICSA-C57050.2023.00051|Team Formation;Software Architecture;Architecture Team Formation;Software Architecture Team;Team Formation;Software Architecture;Architecture Team Formation;Software Architecture Team|
|[BlockArch’23 - Fourth International Workshop on Blockchain-Based Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092711)|M. Kassab; V. V. Graciano Neto; G. Destefanis|10.1109/ICSA-C57050.2023.00052|;|
|[A Blockchain Design Supporting Verifiable Reputation-based Selection of Committee Members and IPFS for Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092666)|S. C. Adams; Y. Zheng|10.1109/ICSA-C57050.2023.00053|blockchain;consensus;IPFS;blockchain;consensus;IPFS|
|[Blockchain Scalability and Security: Communications Among Fast-Changing Committees Made Simple](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092697)|A. Mariani; G. Mariani; D. Pennino; M. Pizzonia|10.1109/ICSA-C57050.2023.00054|blockchain;scalability;consensus;sharding;Kademlia;blockchain;scalability;consensus;sharding;Kademlia|
|[Use of Blockchain in Securing IoT systems with Resource Constrained Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092600)|R. K. Sharma; N. Goveas|10.1109/ICSA-C57050.2023.00055|IoT;Blockchain;Non-Fungible Tokens;Micro-controller;IoT;Blockchain;Non-Fungible Tokens;Micro-controller|
|[Towards a Scalable Dual-Sided Blockchain Architecture with Concurrency Protocols](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092650)|A. Nazir; M. Singh; G. Destefanis; M. Kassab; J. Memon; R. Neykova; R. Tonelli|10.1109/ICSA-C57050.2023.00056|concurrency;blockchain;fork-chain;concurrency;blockchain;fork-chain|
|[An Efficient and Decentralized Blockchain-based Commercial Alternative](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092704)|M. Zeggari; R. Lambiotte; A. Abadi; M. Kassab|10.1109/ICSA-C57050.2023.00057|blockchain;smart contract;distributed systems;online commerce;marketplace;game theory;trustless;blockchain;smart contract;distributed systems;online commerce;marketplace;game theory;trustless|
|[Securing a National Driver and Vehicle Registration System with Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092687)|L. Florez; D. Correal|10.1109/ICSA-C57050.2023.00058|Motor Vehicle Registration System;Blockchain;Decentralized Identity;Architecturally Significant Requirements (ASRs);Microservices;Motor Vehicle Registration System;Blockchain;Decentralized Identity;Architecturally Significant Requirements (ASRs);Microservices|
|[Joint Workshop on Model-Driven Engineering for Software Architecture (MDE4SA) and International Workshop on Automotive System/Software Architectures (WASA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092632)|A. Bucaioni; A. Di Salle; L. Iovino; S. Kugele; Y. Dajsuren|10.1109/ICSA-C57050.2023.00059|;|
|[Timing Predictability and Performance Standoff in Component-based Vehicle Software on Multi-core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092661)|S. Mubeen|10.1109/ICSA-C57050.2023.00060|Component based software engineering;model based development;automotive software;Timing analysis;Multi core;Component based software engineering;model based development;automotive software;Timing analysis;Multi core|
|[Quality Attributes Optimization of Software Architecture: Research Challenges and Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092647)|D. Di Pompeo; M. Tucci|10.1109/ICSA-C57050.2023.00061|refactoring;multi-objective optimization;software architecture;performance;refactoring;multi-objective optimization;software architecture;performance|
|[Model-based Confidentiality Analysis under Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092633)|S. Hahner; T. Bitschi; M. Walter; T. Bureš; P. Hnětynka; R. Heinrich|10.1109/ICSA-C57050.2023.00062|Model-driven Security;Software Architecture;Uncertainty;Confidentiality;Data Flow Analysis;Access Control;Model-driven Security;Software Architecture;Uncertainty;Confidentiality;Data Flow Analysis;Access Control|
|[A customizable approach to assess software quality through Multi-Criteria Decision Making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092593)|F. Basciani; D. Di Pompeo; J. Di Rocco; A. Pierantonio|10.1109/ICSA-C57050.2023.00063|Quality Model;Quality Assessment;Multi-Criteria Decision Making (MCDM);Quality Model;Quality Assessment;Multi-Criteria Decision Making (MCDM)|
|[Towards supporting malleable architecture models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092539)|R. Jongeling; F. Ciccozzi|10.1109/ICSA-C57050.2023.00064|;|
|[Automatic Derivation of Vulnerability Models for Software Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092544)|Y. R. Kirschner; M. Walter; F. Bossert; R. Heinrich; A. Koziolek|10.1109/ICSA-C57050.2023.00065|Component-based;Context-Aware QoS Model;Modeling and prediction;Software architecture;Security;Component-based;Context-Aware QoS Model;Modeling and prediction;Software architecture;Security|
|[Modeling Data Analytics Architecture for IoT Applications using DAT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092710)|M. Abughazala; H. Muccini|10.1109/ICSA-C57050.2023.00066|Data Architecture;Modeling Data Analytics Architecture;Big Data;IoT;Data Architecture;Modeling Data Analytics Architecture;Big Data;IoT|
|[Metamodel portioning for flexible and secure architectural views](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092643)|M. Latifaj; F. Ciccozzi; A. Cicchetti|10.1109/ICSA-C57050.2023.00067|model-driven engineering;metamodel portioning;access control;access permissions;architectural views;co-evolution;model-driven engineering;metamodel portioning;access control;access permissions;architectural views;co-evolution|
|[Distributed Integration of Electronic Control Units for Automotive OEMs: Challenges, Vision, and Research Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092630)|S. Tziampazis; O. Kopp; M. Weyrich|10.1109/ICSA-C57050.2023.00068|Electronic Control Unit;Automotive;Integration;Testing;Distributed;Electronic Control Unit;Automotive;Integration;Testing;Distributed|
|[Continuous Safety Assessment of Updated Supervised Learning Models in Shadow Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092729)|H. Guissouma; M. Zink; E. Sax|10.1109/ICSA-C57050.2023.00069|DevOps;safety monitoring;OTA updates;contract-based design;supervised learning;DevOps;safety monitoring;OTA updates;contract-based design;supervised learning|
|[Safety-Aware Deployment Synthesis and Trade-Off Analysis of Apollo Autonomous Driving Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092682)|T. Terzimehić; S. Barner; Y. G. Dantas; U. Schöpp; V. Nigam; P. Ke|10.1109/ICSA-C57050.2023.00070|Autonomous vehicles;model-driven development;design space exploration;architecture pattern;safety;Autonomous vehicles;model-driven development;design space exploration;architecture pattern;safety|
|[2nd International Workshop on the Foundations of Infrastructure Specification and Testing : FIST 2023](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092653)|L. Baresi; G. Quattrocchi; D. A. Tamburri|10.1109/ICSA-C57050.2023.00071|;|
|[Towards Reliable Infrastructure as Code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092598)|D. Sokolowski; G. Salvaneschi|10.1109/ICSA-C57050.2023.00072|Infrastructure as Code;Cloud Engineering;Fuzzing;Property-based Testing;Verification;Infrastructure as Code;Cloud Engineering;Fuzzing;Property-based Testing;Verification|
|[Quality Assurance for Infrastructure Orchestrators: Emerging Results from Ansible](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092684)|Y. Zhang; M. Rahman; F. Wu; A. Rahman|10.1109/ICSA-C57050.2023.00073|ansible;devops;infrastructure as code;ansible;devops;infrastructure as code|
|[Practitioner Perceptions of Ansible Test Smells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092644)|Y. Zhang; F. Wu; A. Rahman|10.1109/ICSA-C57050.2023.00074|ansible;devops;empirical study;infrastructure as code;testing;smells;ansible;devops;empirical study;infrastructure as code;testing;smells|
|[Game-theory strategies for open-source Infrastructure-as-Code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092616)|A. E. de la Fuente Ruiz; G. Novakova Nedeltcheva|10.1109/ICSA-C57050.2023.00075|Infrastructure-as-Code (IaC);Open-Source Systems;Game theory;Business strategy;Infrastructure-as-Code (IaC);Open-Source Systems;Game theory;Business strategy|
|[Architecting MLOps in the Cloud: From Theory to Practice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092592)|I. Kumara; F. Pecorelli; G. Catolino; R. Kazman; D. A. Tamburri; W. -J. Van Den Heuvel|10.1109/ICSA-C57050.2023.00076|Machine Learning Operations;MLOps;Architecture Design;Cloud;Machine Learning Operations;MLOps;Architecture Design;Cloud|
|[Blended Modelling for Software Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092602)|M. Latifaj; F. Ciccozzi; M. W. Anwar; K. Aslam; I. Malavolta|10.1109/ICSA-C57050.2023.00077|software architecture modeling;model driven engineering;collaborative architecting;software architecture modeling;model driven engineering;collaborative architecting|
|[Distributed Systems — What Every Software Architect Should Know](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092692)|I. Gorton|10.1109/ICSA-C57050.2023.00078|distributed systems;software architecture;distributed systems;software architecture|
#### **2023 IEEE World Engineering Education Conference (EDUNINE)**
- DOI: 10.1109/EDUNINE57531.2023
- DATE: 12-15 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Plenary: Reimaging Engineering - Toward the next generation of Engineering Education, merging technologies in a connected world](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102847)|C. R. Brito; M. M. Ciampi; O. Clua; M. Feldgen|10.1109/EDUNINE57531.2023.10102847|Engineering practice;Complex systems;Social responsibility;Science and Technology;Engineering practice;Complex systems;Social responsibility;Science and Technology|
|[Plenary - How to Insert the STEAM Approach in University Academic Portfolios? A Disruptive Bet for a Relationship Model: URSTEAM of the Universidad Del Rosario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102822)|R. A. Méndez-Romero; G. Castelblanco-Arias|10.1109/EDUNINE57531.2023.10102822|STEM-STEAM education;higher education;curricular innovation;pedagogical innovation;STEM-STEAM education;higher education;curricular innovation;pedagogical innovation|
|[Plenary: Evaluation of Technological Development Projects With Social Impact](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102837)|R. G. Lerena|10.1109/EDUNINE57531.2023.10102837|Technological development projects;Science and technology assessment;Social impact of technology;Social technological development projects;Technological development projects;Science and technology assessment;Social impact of technology;Social technological development projects|
|[Plenary: A Perspectives Approach - Integrating the Entrepreneurial Mindset into the Engineering Classroom](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102911)|L. Bosman; K. Shirey; S. Fernhaber|10.1109/EDUNINE57531.2023.10102911|entrepreneurship;undergraduate;engineering;entrepreneurship;undergraduate;engineering|
|[Plenary: What I Wish They Taught Me in College](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102898)|D. Budny; B. Bernal; N. Celmo; B. Newborn; F. Kremm; J. Nolan-Kremm|10.1109/EDUNINE57531.2023.10102898|engineering curriculum;engineering skills;engineering curriculum;engineering skills|
|[Panel: Focus on Women Engineering - Toward the Next Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102838)|C. Del Camen Díaz Hernani; M. T. Copello; N. P. Lizana; A. C. da Silva-Ovando; A. Luna; S. E. Llerena; C. Obregón; C. Bueno|10.1109/EDUNINE57531.2023.10102838|Visible role models;gender stereotypes;STEM careers;Visible role models;gender stereotypes;STEM careers|
|[Teaching web development courses using flipped classroom and Discord: a two-year experience in the Peruvian context during the COVID-19 pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102865)|S. Lucho; G. B. Gardini; J. M. E. Bueno|10.1109/EDUNINE57531.2023.10102865|flipped classroom;active learning;autonomous learning;java;discord;flipped classroom;active learning;autonomous learning;java;discord|
|[Introducing Data Literacy in the Classroom using Sound Exploration Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102886)|N. Akshay; V. Minces|10.1109/EDUNINE57531.2023.10102886|data science;education;multimodal learning;STEAM;online learning;data science;education;multimodal learning;STEAM;online learning|
|[The Effectiveness of a Mobile Educational Platform for Engaging Students in Out-of-class Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102869)|M. Fuad; M. Akbar; W. D. Croslen|10.1109/EDUNINE57531.2023.10102869|Mobile learning platform;gamification;scaffolding;peer influence;interactive problem-solving;Mobile learning platform;gamification;scaffolding;peer influence;interactive problem-solving|
|[Experiences with Micro-Credentials at UC3M: Academic and Technological Aspects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102848)|C. A. Hoyos; C. D. Kloos|10.1109/EDUNINE57531.2023.10102848|micro-credentials;lifelong learning;upskilling;reskilling;European Digital Credentials for learning (EDC);micro-credentials;lifelong learning;upskilling;reskilling;European Digital Credentials for learning (EDC)|
|[Qualitative Assessment of the Educational Use of an Electrical Engineering YouTube Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102890)|R. Lijo; E. Quevedo; J. J. Castro|10.1109/EDUNINE57531.2023.10102890|educational tool;engineering education;STEM Education;STEM;videos;YouTube;educational tool;engineering education;STEM Education;STEM;videos;YouTube|
#### **2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)**
- DOI: 10.1109/ICBDA57405.2023
- DATE: 3-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Select-Them-Repair-Them: Automatically Optimize Pseudocode-to-Code Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104712)|Q. Yu; N. Gu|10.1109/ICBDA57405.2023.10104712|code generation;pseudocode-to-code generation;machine learning;data mining;data science;code generation;pseudocode-to-code generation;machine learning;data mining;data science|
|[Distributed or Centralized: An Experimental Study on Spatial Database Systems for Processing Big Trajectory Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105005)|S. Xiong; X. Ouyang; W. Xiong|10.1109/ICBDA57405.2023.10105005|trajectory data management;spatial NoSQL;spatial database;trajectory data management;spatial NoSQL;spatial database|
|[Engineering Large Wearable Sensor Data towards Digital Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104923)|H. Zhang; G. Ruan; R. Giesting; L. Miller; N. Patel; J. Ji; Y. L. Yang; J. Yang|10.1109/ICBDA57405.2023.10104923|data platform;data visualization;data engineering;data pipeline;digital biomarker;data platform;data visualization;data engineering;data pipeline;digital biomarker|
|[Multi-task Federated Learning Medical Analysis Algorithm Integrated Into Adapter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104867)|Y. Zhao; T. Zhao; P. Xiang; Q. Li; Z. Chen|10.1109/ICBDA57405.2023.10104867|federated learning;adapter;multi-task;medical analysis;federated learning;adapter;multi-task;medical analysis|
|[Style Semantic Disentangle Network for Multi-Source Domain Generalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105061)|S. Guo; H. Luo; Y. Zhu; F. Zhao|10.1109/ICBDA57405.2023.10105061|Domain Generalization;Deep learning;Data Augmentation;Feature Stylization;Domain Generalization;Deep learning;Data Augmentation;Feature Stylization|
|[How Does Big Data in Tax Administration Limited Corporate Earnings Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105058)|X. Sun; R. Juanatas; J. Niguidula; S. Pan|10.1109/ICBDA57405.2023.10105058|big data tax administration;profit manipulation;Golden tax phase III;big data tax administration;profit manipulation;Golden tax phase III|
|[Big Data Oriented Graph Division and Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104843)|Z. Zhang; Y. Zhao; X. Fan; B. Zhang|10.1109/ICBDA57405.2023.10104843|graph division;big data computing;big data storage;graph division;big data computing;big data storage|
|[Generalized Weierstrass-Mandelbrot with Disturbance for Big Data Applications in Economic and Financial Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104671)|L. Zhang|10.1109/ICBDA57405.2023.10104671|disturbance;time series;nonlinear features;economic and financial systems;disturbance;time series;nonlinear features;economic and financial systems|
|[Research on Basic Clinical Treatment Pattern Mining Based on Electronic Medical Record Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104953)|Q. Lu; X. Zheng; J. Chen|10.1109/ICBDA57405.2023.10104953|electronic medical record;data mining;clinical treatmentpattern;symptomatic treatment;electronic medical record;data mining;clinical treatmentpattern;symptomatic treatment|
|[A Survey on Application of Blockchain Technology in Drug Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104779)|X. Xu; N. Tian; H. Gao; H. Lei; Z. Liu; Z. Liu|10.1109/ICBDA57405.2023.10104779|blockchain regulation;drug supply chain;governing blockchain by blockchain;counterfeit drug governance;blockchain regulation;drug supply chain;governing blockchain by blockchain;counterfeit drug governance|
|[A Trustable and Traceable Blockchain-based Secondhand Market with Committee Consensus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104957)|S. Huang; D. Hou; Z. Peng; Y. Dong; J. Zhang|10.1109/ICBDA57405.2023.10104957|Secondhand Market;Consortium Blockchain;Trust Issues;Committee-based Consensus Mechanism;Practical Byzantine Fault Tolerance;Secondhand Market;Consortium Blockchain;Trust Issues;Committee-based Consensus Mechanism;Practical Byzantine Fault Tolerance|
#### **2023 Future of Educational Innovation-Workshop Series Data in Action**
- DOI: 10.1109/IEEECONF56852.2023
- DATE: 16-18 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Comparing the Use of Virtual Models vs. Fieldwork in Developing Geomatics Skills in Undergraduate Engineering Education During the COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104787)|L. M. Yeomans-Galli; M. P. Vela-Coiffier; R. V. Gutiérrez-Hernández; R. Ballinas-González|10.1109/IEEECONF56852.2023.10104787|Geomatics skills;virtual models;terrestrial laser scanning;photogrammetry;COVID-19 pandemic;higher education;educational innovation;Geomatics skills;virtual models;terrestrial laser scanning;photogrammetry;COVID-19 pandemic;higher education;educational innovation|
|[Factors to improve online education: A study on the impact of COVID-19 on Delhi students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104773)|V. V. Pineda-Romero; C. E. Orozco-Mora; H. G. Ceballos|10.1109/IEEECONF56852.2023.10104773|COVID-19;Online Education;Learning Analytics;Educational Innovation;Higher Education;COVID-19;Online Education;Learning Analytics;Educational Innovation;Higher Education|
|[Experimental Survey’s results for IoT Projects with Tinkercad Circuits Prototypes for Virtual Classes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104663)|A. C. Bento; D. C. Gatti|10.1109/IEEECONF56852.2023.10104663|IoT;Arduino;Tinkercad;Circuits;Prototype;Education;IoT;Arduino;Tinkercad;Circuits;Prototype;Education|
|[Towards assessment and attainment of Engineering Graduate Attributes in Outcome Based Education (OBE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104817)|M. Mahrishi; P. K. Jain; S. Hosseini|10.1109/IEEECONF56852.2023.10104817|Assessment methods;Attainment;Course Outcomes;Graduate Attributes;Higher Education Institutions (HEIs);Outcome-based Education;Program Outcomes;Assessment methods;Attainment;Course Outcomes;Graduate Attributes;Higher Education Institutions (HEIs);Outcome-based Education;Program Outcomes|
|[Emerging Perspectives on Sustainability in Business Schools: A Systematic Literature Review of Pedagogical Tools in Teaching Sustainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104976)|A. Y. Ar; Y. D. Ward; E. H. García; A. Abbas|10.1109/IEEECONF56852.2023.10104976|management education;educational innovation;curriculum building;higher education;PRISMA;management education;educational innovation;curriculum building;higher education;PRISMA|
|[Students’ Satisfaction and Recommendation of Sostek Application based on Sustainable Development Learning in Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104881)|M. E. Núñez; M. X. Rodriguez-Paz; A. Abbas|10.1109/IEEECONF56852.2023.10104881|Educational innovation;e-learning;higher education;soft skills;sustainable development;Educational innovation;e-learning;higher education;soft skills;sustainable development|
|[Experimental Survey Results on Azure.DevOps Application for Management Student’s Projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104593)|A. C. Bento; D. A. C. Delgado; S. Camacho-Leon|10.1109/IEEECONF56852.2023.10104593|projects;Azure DevOps;class;computing;education;projects;Azure DevOps;class;computing;education|
|[Instructional Usability and Learner-User eXperience Assessment in a Virtual Reality Educational Milieu: A Deductive Tech-Instructionality Model from EdTech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104873)|N. Assaf; L. F. Morán-Mirabal|10.1109/IEEECONF56852.2023.10104873|Learner-Interface Interaction;interface design and assessment in STEM;Tech-enhanced Learning & Techmediated education;EdTech Onto-epistemology;Educational Innovation & Remote EdTech Research in Covid times;Higher Education;human factors & cognitive ergonomics;snowball heuristic inquiry technique;Learner-Interface Interaction;interface design and assessment in STEM;Tech-enhanced Learning & Techmediated education;EdTech Onto-epistemology;Educational Innovation & Remote EdTech Research in Covid times;Higher Education;human factors & cognitive ergonomics;snowball heuristic inquiry technique|
|[Expanding the Concept of Learning Space in Biomedical Engineering Education using Wearable Devices and Cloud-based Collaborative Programming Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104837)|L. Montesinos; A. Santos-Diaz; D. E. Salinas-Navarro; L. Cendejas-Zaragoza|10.1109/IEEECONF56852.2023.10104837|engineering education;learning spaces;technology-enabled learning;wearable devices;cloud computing technology;higher education;educational innovation;engineering education;learning spaces;technology-enabled learning;wearable devices;cloud computing technology;higher education;educational innovation|
|[Improving Learning Experiences of Business Students in the Classroom Through Emotions in Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104795)|G. M. R. García; N. R. Vazquez; C. A. P. Grimaldo|10.1109/IEEECONF56852.2023.10104795|Emotions;engagement;academic commitment;educational innovation;higher education;Emotions;engagement;academic commitment;educational innovation;higher education|
|[Students’ Understanding of the Mean Through Technology-Mediated Analysis of Real-Life Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104617)|E. Padilla; E. Campos|10.1109/IEEECONF56852.2023.10104617|higher education;educational innovation;student understanding;onto-semiotic approach;real-data statistics;statistical thinking;measures of central tendency;higher education;educational innovation;student understanding;onto-semiotic approach;real-data statistics;statistical thinking;measures of central tendency|
#### **2023 Somaiya International Conference on Technology and Information Management (SICTIM)**
- DOI: 10.1109/SICTIM56495.2023
- DATE: 24-25 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Biometric-based Unique Identification for Bovine Animals — Comparative Study of Various Machine and Deep Learning Computer Vision Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105004)|N. Patel; H. Jain; V. S. Lonkar; D. Singh|10.1109/SICTIM56495.2023.10105004|Livestock Identification;Muzzle Recognition;Cattle Recognition;Cattle Biometric;Muzzle Signature;SIFT;Computer Vision;LBP;Deep Learning;Image Classification;Key point Descriptor;Scale-Invariant Feature Transform;Local Binary Pattern;Livestock Identification;Muzzle Recognition;Cattle Recognition;Cattle Biometric;Muzzle Signature;SIFT;Computer Vision;LBP;Deep Learning;Image Classification;Key point Descriptor;Scale-Invariant Feature Transform;Local Binary Pattern|
|[Review of Groundwater Potential Storage and Recharge Zone Map Delineation Using Statistics based Hydrological and Machine Learning based Artificial Intelligent Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104829)|D. Singh; V. Sharma|10.1109/SICTIM56495.2023.10104829|Groundwater estimation;groundwater potential storage zone;recharge zone;hydrological model;machine learning;Groundwater estimation;groundwater potential storage zone;recharge zone;hydrological model;machine learning|
|[Significance of Cyber Security of IoT devices in the Healthcare Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104657)|A. Tuscano; S. Joshi|10.1109/SICTIM56495.2023.10104657|Internet of Things (IoT);Cyber-security;Healthcare;Risk Management;Cyber-attacks;Internet of Things (IoT);Cyber-security;Healthcare;Risk Management;Cyber-attacks|
|[Cryptocurrency with Blockchain Technology — A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104971)|L. J. Parab; P. P. Nitnaware; S. A. Patil|10.1109/SICTIM56495.2023.10104971|Blockchain;Cryptocurrency;Consensus Algorithm;Decentralize;Security;Blockchain;Cryptocurrency;Consensus Algorithm;Decentralize;Security|
|[Federated Learning to Preserve the Privacy of User Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104860)|H. Shah; R. Patel; P. Tawde|10.1109/SICTIM56495.2023.10104860|Federated Learning;Healthcare;Privacy;Distributed Machine Learning;Logistic Regression;Federated Learning;Healthcare;Privacy;Distributed Machine Learning;Logistic Regression|
|[Machine Learning Techniques for Heart Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104919)|K. Wankhede; B. Wukkadada; S. Rajesh; S. Nair|10.1109/SICTIM56495.2023.10104919|Machine learning;heart disease;predictive analytics;HDPS;AdaBoost;Machine learning;heart disease;predictive analytics;HDPS;AdaBoost|
|[Teachers’ Perception about the Use of QR Code in Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104743)|R. Bala; S. Harnal; M. Gupta|10.1109/SICTIM56495.2023.10104743|Classroom;Education;Learning;QR;Technology;Classroom;Education;Learning;QR;Technology|
|[Alzheimer Prediction using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104951)|B. Wukkadada; K. Wankhede; S. Rajesh; C. Ria; T. Chakraborty|10.1109/SICTIM56495.2023.10104951|support vector machine;KNN;Logistic regression blueprint;Decision-tree blueprint;Random-Forests;support vector machine;KNN;Logistic regression blueprint;Decision-tree blueprint;Random-Forests|
|[Design and Control of Omnidirectional Conveyor Model Using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104684)|R. Bangal; S. Nalawade; C. Dusane|10.1109/SICTIM56495.2023.10104684|Smart Conveyor;omnidirectional;image processing;holonomic motion;etc;Smart Conveyor;omnidirectional;image processing;holonomic motion;etc|
|[Leveraging IoT Technologies in Retail Industry to improve Customer Experience: Current Applications and Future Potential](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104882)|S. Saxena; T. Dhote|10.1109/SICTIM56495.2023.10104882|IoT;Retail;Customer Experience;Customer Satisfaction;Digitization;Smart Retail Stores;IoT;Retail;Customer Experience;Customer Satisfaction;Digitization;Smart Retail Stores|
|[Internet of Things (IOT) Driven Digital Transformation in BFSI Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104797)|D. Gupta; P. Kulkarni|10.1109/SICTIM56495.2023.10104797|Internet of Things;BFSI;Digital Transformation;Internet of Things;BFSI;Digital Transformation|
#### **2023 IEEE International Symposium on Power Line Communications and its Applications (ISPLC)**
- DOI: 10.1109/ISPLC57122.2023
- DATE: 21-23 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Characterization of the LV distribution grid for the deployment of a pilot BB-PLC network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104185)|J. González-Ramos; I. Angulo; A. Arrinda; I. Fernández; A. Gallarreta; D. de la Vega; A. Sendin; I. Berganza; R. Ayala; J. S. Gómez|10.1109/ISPLC57122.2023.10104185|Broadband Power Line Communications;channel characterization;Low Voltage grid;pilot network;Broadband Power Line Communications;channel characterization;Low Voltage grid;pilot network|
|[A Statistical Model for Indoor SISO PLC Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104183)|I. Povedano; F. Crespo; F. J. Cañete; J. A. Cortés; L. Díez|10.1109/ISPLC57122.2023.10104183|power line communications;channel model;top-down;statistical;log-normal;power line communications;channel model;top-down;statistical;log-normal|
|[Temperature and Frequency Behavior of Low Voltage Cables for Broadband Powerline Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104187)|P. Lutat; M. Kurth; A. Ulbig|10.1109/ISPLC57122.2023.10104187|Powerline Communication;Cable Attenuation;Laboratory Measurement;Channel Characterization;Powerline Communication;Cable Attenuation;Laboratory Measurement;Channel Characterization|
|[Calibration Method for an On-Line PLC Blocking Filter Characterization System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104186)|C. Szymczyk; N. Nieß; J. N. Breitenbach; G. Bumiller|10.1109/ISPLC57122.2023.10104186|;|
|[Comprehensive Approach to on Line EMI Filter Characterization at Full Load Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104179)|N. Nieß; J. N. Breitenbach; C. Szymczyk; G. Bumiller|10.1109/ISPLC57122.2023.10104179|Impedance Measurement;Impedance;Voltage Measurement;Current Measurement;Electromagnetic Compatibility;Impedance Measurement;Impedance;Voltage Measurement;Current Measurement;Electromagnetic Compatibility|
|[Performances of PRIME PLC-RF Hybrid Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104180)|A. Sanz; E. Manero; B. Melguizo; J. C. Ibar|10.1109/ISPLC57122.2023.10104180|PRIME;Power Line Communications;PLC;Radio Frequency Communication;RF;Communications Protocol;AMI;PRIME;Power Line Communications;PLC;Radio Frequency Communication;RF;Communications Protocol;AMI|
|[A Proof of Concept Implementation of LiFi over Power Line Networks based on ITU-T G.hn](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104189)|L. Bober; D. Schulz; V. Jungnickel; A. Mengi|10.1109/ISPLC57122.2023.10104189|LiFi;optical wireless communication;power line communication;proof of concept;ITU-T G.9960;LiFi;optical wireless communication;power line communication;proof of concept;ITU-T G.9960|
|[Evaluating Various Machine Learning Techniques in Selecting Different Modulations in G3-PLC Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104174)|K. Razazian; M. C. Bali|10.1109/ISPLC57122.2023.10104174|G3-PLC;Hybrid;link quality prediction;machine learning;logistic regression;support vector machine;random forest classifier;artificial neural networks;G3-PLC;Hybrid;link quality prediction;machine learning;logistic regression;support vector machine;random forest classifier;artificial neural networks|
|[Achievable Throughput in In-Band Full-Duplex Broadband Power Line Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104178)|V. Korzhun; A. M. Tonello|10.1109/ISPLC57122.2023.10104178|PLC;IBFD;MIMO;channel estimation;PLC;IBFD;MIMO;channel estimation|
|[On Defining and Retrieving an Invariant Channel Model in Wired Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104176)|A. M. Tonello; M. De Piante|10.1109/ISPLC57122.2023.10104176|;|
|[General methodology of the in-field diagnostic and localization of noise source in G3-PLC network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104173)|P. Musil; P. Mlýnek; M. Rusz; L. Benešl; J. Sláčik|10.1109/ISPLC57122.2023.10104173|Power Line Communications;Methodology;G3–PLC;Smart Grids;Field Measurement;Noise Localization;Power Line Communications;Methodology;G3–PLC;Smart Grids;Field Measurement;Noise Localization|
#### **2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)**
- DOI: 10.1109/ICIDCA56705.2023
- DATE: 14-16 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Text Detection based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099672)|A. Thilagavathy; K. H. Suresh; K. T. Chowdary; M. Tejash; V. L. Chakradhar|10.1109/ICIDCA56705.2023.10099672|Optical Character Recognition;handwritten text;Convolutional neural networks;Python;Optical Character Recognition;handwritten text;Convolutional neural networks;Python|
|[Machine Learning System For Indolence Perception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099959)|S. M. Udhaya Sankar; N. J. Kumar; D. Dhinakaran; K. S. S; A. R|10.1109/ICIDCA56705.2023.10099959|Eye;Drowsiness;Detection;Opencv;Alarm;Eye;Drowsiness;Detection;Opencv;Alarm|
|[Cyclone Intensity Estimation on INSAT 3D IR Imagery Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099964)|K. Vayadande; T. Adsare; T. Dharmik; N. Agrawal; A. Patil; S. Zod|10.1109/ICIDCA56705.2023.10099964|Cyclone;Convolutional Neural Networks (CNN);Deep Learning;Ensemble Learning;Intensity estimation;Cyclone;Convolutional Neural Networks (CNN);Deep Learning;Ensemble Learning;Intensity estimation|
|[ENSEMBLED CROPIFY – Crop & Fertilizer Recommender System with Leaf Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100117)|K. Devi Priya; A. S. Samyogitha; A. V. Krishna Reddy; B. Divya Sri|10.1109/ICIDCA56705.2023.10100117|Machine Learning;Image Processing;Neural Networks;Random Forest;Convolutional Neural Networks;Crop Recommender System;Fertilizer Suggestion;Leaf Disease Prediction System;Machine Learning;Image Processing;Neural Networks;Random Forest;Convolutional Neural Networks;Crop Recommender System;Fertilizer Suggestion;Leaf Disease Prediction System|
|[An Android App Scanner for Detecting the Freshness and Age of Poultry Eggs using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099760)|N. Aniyan; P. M. Jacob; P. P; R. Raj; G. Ghosh; A. Suresh|10.1109/ICIDCA56705.2023.10099760|Image processing;Egg Freshness detection;Non-destructive;Grading;Image processing;Egg Freshness detection;Non-destructive;Grading|
|[A Survey- Wheat Plant Diseases Recognition System using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099708)|T. Goyal; A. R. Patil; P. Nahar; P. D. Bhise|10.1109/ICIDCA56705.2023.10099708|Convolutional Neural Network;Deep Learning;Machine Learning;Random Forest;Region based Convolutional Neural Network;Convolutional Neural Network;Deep Learning;Machine Learning;Random Forest;Region based Convolutional Neural Network|
|[Obstacle Detection and Safe Navigation in Unexpected Situations of Intelligent Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100210)|C. Sheebajoice; R. S. Krishna Reddy; T. R. Varun Kumar; T. V|10.1109/ICIDCA56705.2023.10100210|obstacle;Intelligent vehicle;Navigation;WSN;Autonomous vehicle;obstacle;Intelligent vehicle;Navigation;WSN;Autonomous vehicle|
|[Energy-Efficient Cooperative Image Transmission in Multi-Hop Wireless Multimedia Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100081)|S. K. Manthati; S. S. S; K. Y. Nanubothula; P. K. Devulapalli|10.1109/ICIDCA56705.2023.10100081|2D-Discrete wavelet transforms(2D-DWT);Wireless multimedia sensor networks (WSN);Decode and Forward (DF);2D-Discrete wavelet transforms(2D-DWT);Wireless multimedia sensor networks (WSN);Decode and Forward (DF)|
|[Preventing Data Leakage and Traffic Optimization in Software-Defined Programmable Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099703)|S. Reddy Vanga; G. Babu; V. K. A. Kumar; S. Aurelia; B. Sreedhar; S. Majji|10.1109/ICIDCA56705.2023.10099703|Software Define Network;Deep packet inspection;Traffic classification;Data Leakage;Software Define Network;Deep packet inspection;Traffic classification;Data Leakage|
|[IMS enabled Centralized UAV control system with seamless connectivity over mobile network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099619)|N. Mahajan; S. Kaushal; H. Kumar|10.1109/ICIDCA56705.2023.10099619|UAV;IMS;Machine to Machine;Air Traffic Management;IoT;UAV;IMS;Machine to Machine;Air Traffic Management;IoT|
|[An Improved Key Management System - DES Ultimate v1.1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099980)|A. K. Sharma; S. Wadhawan; Dalip; G. Habib|10.1109/ICIDCA56705.2023.10099980|Algorithm;Cipher;Confidentiality;Confusion;Diffusion;Encryption;Exclusive-Or;Decryption;Self-Invertible Matrix;Integrity;Permutation;Round;Secret-Key;Security;Substitution Box (s-box);Algorithm;Cipher;Confidentiality;Confusion;Diffusion;Encryption;Exclusive-Or;Decryption;Self-Invertible Matrix;Integrity;Permutation;Round;Secret-Key;Security;Substitution Box (s-box)|
|[Role of Blockchain in Manufacturing and Logistics Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100268)|A. Siddiqui; A. Chirputkar; P. Ashok|10.1109/ICIDCA56705.2023.10100268|Blockchain;Asset Tracking;Smart Contracts;IoT;Industry 4.0;Supply Chain;Manufacturing;Cryptography;Security;Blockchain;Asset Tracking;Smart Contracts;IoT;Industry 4.0;Supply Chain;Manufacturing;Cryptography;Security|
|[An IoT Based Agricultural Management Approach Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099598)|M. Kasiselvanathan; G. Sekar; J. Prasad; S. Lakshminarayanan; C. Sharanya|10.1109/ICIDCA56705.2023.10099598|Machine Learning;Internet of Things;Agriculture;Irrigation;Crop Monitoring;Machine Learning;Internet of Things;Agriculture;Irrigation;Crop Monitoring|
|[Understanding the Behaviour of Android SMS Malware Attacks With Real Smartphones Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099595)|A. Kumar; I. Sharma; A. Sharma|10.1109/ICIDCA56705.2023.10099595|Network Security;Android Malware;Sniffer Attack;Exploratory Data Analysis;Cryptojacking;Ransomware;Network Security;Android Malware;Sniffer Attack;Exploratory Data Analysis;Cryptojacking;Ransomware|
|[Blockchain in Telecom: Challenges and The Way Forward](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099882)|C. RS; P. Kulkarni|10.1109/ICIDCA56705.2023.10099882|Blockchain;5G;Smart contracts;Telecom industry;Telecom service provider;Blockchain;5G;Smart contracts;Telecom industry;Telecom service provider|
|[Impact of Blockchain Technology on E-Commerce](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100222)|N. Verma; P. Kulkarni|10.1109/ICIDCA56705.2023.10100222|Blockchain technology;E-Commerce industry;Supply chain management;Blockchain technology;E-Commerce industry;Supply chain management|
|[NFT Marketplace using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100326)|S. Kazi; A. Kazi; L. D'souza; A. Loke|10.1109/ICIDCA56705.2023.10100326|Blockchain;Decentralized Applications (dApps);Smart Contracts;Wallets;Non-Fungible Token (NFT);Blockchain;Decentralized Applications (dApps);Smart Contracts;Wallets;Non-Fungible Token (NFT)|
|[Pyciuti: A Python Based Customizable and Flexible Cybersecurity Utility Tool for Penetration Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099938)|M. M; K. B. Babu; S. G|10.1109/ICIDCA56705.2023.10099938|Python;Security;Network;Malware;OSINT;Python;Security;Network;Malware;OSINT|
|[Employing Supervised Learning Techniques for DDoS Attack Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099834)|A. Kumar; I. Sharma|10.1109/ICIDCA56705.2023.10099834|DDoS attack;Machine Learning;Supervised Learning;Decision Tree;Logistic Regression;Random Forest;K-Nearest Neighbors;DDoS attack;Machine Learning;Supervised Learning;Decision Tree;Logistic Regression;Random Forest;K-Nearest Neighbors|
|[EICF – An Enhanced Intelligent Cloud-based Framework for Automated Product Quality Assurance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100073)|A. Arora; R. Gupta|10.1109/ICIDCA56705.2023.10100073|Manufacturing;Quality Assurance;Customer Satisfaction;Visual Inspection;Cloud Computing;Manufacturing;Quality Assurance;Customer Satisfaction;Visual Inspection;Cloud Computing|
|[Black Box Testing Design Intended for Vehicle Surveillance and Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099824)|N. J. Kumar; S. M. U. Sankar; B. M. Kumar; A. Praveenkumar; R. G. Ranjith|10.1109/ICIDCA56705.2023.10099824|Internet of Things;Sensors;black box;Theft;Global System for Mobile;Global Positioning System;Internet of Things;Sensors;black box;Theft;Global System for Mobile;Global Positioning System|
|[AI-Driven Deep Structured Learning for Cross-Site Scripting Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099960)|S. Abhishek; R. Ravindran; A. T; S. V|10.1109/ICIDCA56705.2023.10099960|Code Injection;Cross-site scripting;Text Convolutional Layers;Machine Learning;Deep Learning;Code Injection;Cross-site scripting;Text Convolutional Layers;Machine Learning;Deep Learning|
|[EHR: Patient Electronic Health Records using Blockchain Security Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099789)|M. Jain; D. Pandey; N. P. Singh|10.1109/ICIDCA56705.2023.10099789|Index Tenns-Blockchain;Decentralization;Consensus;Scalability;Smart Contract;Firebase;ReactJS;Solidity;Ethereum;Index Tenns-Blockchain;Decentralization;Consensus;Scalability;Smart Contract;Firebase;ReactJS;Solidity;Ethereum|
|[Machine Learning based Iris Recognition Modern Voting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099580)|A. D. K; D. V. S; R. G; M. P. D; P. Balasubrarnanie; S. Hamsanandhini|10.1109/ICIDCA56705.2023.10099580|Aadhar incorporation;Automated biometric identification;Digitalization;Feature extraction;Machine learning;Aadhar incorporation;Automated biometric identification;Digitalization;Feature extraction;Machine learning|
|[Improvement of Graph-Based Assets using Blockchain Quality Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099935)|R. T. Selvi; T. C. S. Lakshmi; D. Karunkuzhali; R. A. A. Rosaline|10.1109/ICIDCA56705.2023.10099935|Acyclic-Direct-Diagram;Token Architecture;Etherum;Smart Contracts;Supply Chain;Acyclic-Direct-Diagram;Token Architecture;Etherum;Smart Contracts;Supply Chain|
|[An Analytical Survey on Optimized Authentication Techniques in OneM2M](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099946)|P. Saini; N. Mahajan; S. Kaushal; H. Kumar|10.1109/ICIDCA56705.2023.10099946|ONEM2M Evolution;Introduction to OneM2M;ONEM2M Security;Vulnerabilities;Authentication Schemes;ONEM2M Evolution;Introduction to OneM2M;ONEM2M Security;Vulnerabilities;Authentication Schemes|
|[The Impact of Blockchain Technology to Protect Image and Video Integrity from Identity Theft using Deepfake Analyzer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099668)|J. A. Costales; S. Shiromani; M. Devaraj|10.1109/ICIDCA56705.2023.10099668|blockchain technology;Deepfake;Consensus Algorithm;Convolutional Neural Network Algorithm;SHA-256 Hashing Algorithm;blockchain technology;Deepfake;Consensus Algorithm;Convolutional Neural Network Algorithm;SHA-256 Hashing Algorithm|
|[Detailed Investigation on Blockchain Smart Contracts Application and its Usefulness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099575)|M. Arumugam; J. Panduro-Ramirez; J. Padilla-Caballero; P. Gildhiyal; M. Goel; D. K. Jang Bahadur Saini|10.1109/ICIDCA56705.2023.10099575|Blockchain;Digital;Smart;Contract;Technology;Application;Blockchain;Digital;Smart;Contract;Technology;Application|
|[A Review on Digital Twin Technology in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099646)|M. Shrivastava; R. Chugh; S. Gochhait; A. B. Jibril|10.1109/ICIDCA56705.2023.10099646|Digital twin;industry 4.0;Healthcare;Emerging technologies;Simulation;Machine learning;Digital twin;industry 4.0;Healthcare;Emerging technologies;Simulation;Machine learning|
|[Security In Smartphone: A Comparison of Viruses and Security Breaches in Phones and Computers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100128)|A. Deep; S. Gochhait|10.1109/ICIDCA56705.2023.10100128|Malware;malicious assaults;security risks;hacking;computer virus attacks;information security;smart-phones;Malware;malicious assaults;security risks;hacking;computer virus attacks;information security;smart-phones|
|[Information Security Issues and Challenges: Perspective of Industry 4.0 Paradigm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099832)|M. Pooja; M. Damle|10.1109/ICIDCA56705.2023.10099832|Index Tenns-Information and Communication Technology;Cyber-security;Supply chain attacks;Industrial Internet of Things;Malware attacks;Distributed Ledger Technology;Personal Identifiable Information;Index Tenns-Information and Communication Technology;Cyber-security;Supply chain attacks;Industrial Internet of Things;Malware attacks;Distributed Ledger Technology;Personal Identifiable Information|
|[Malware Intrusion in Smart Traffic System and Rectification using Djikstra Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100019)|K. P; M. Damle|10.1109/ICIDCA56705.2023.10100019|National Transportation Communications for Intelligent Transportation System (ITS) Protocol (NTCIP);Vanet MobiSim;service set identifier (SSID);Spoofing/Parody Attack;National Transportation Communications for Intelligent Transportation System (ITS) Protocol (NTCIP);Vanet MobiSim;service set identifier (SSID);Spoofing/Parody Attack|
|[Detecting Distributed DoS Attacks on SDN using Machine Learning (ML) Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099680)|A. K. Kurakula; K. Akhila; M. Bhavya; M. V. Sai|10.1109/ICIDCA56705.2023.10099680|Software-Defined Network (SDN);DDoS;Decision Tree;Software-Defined Network (SDN);DDoS;Decision Tree|
|[Image Security Enhancement on Cloud Storage using AES Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100267)|B. Prajanati; V. S. Gutte; K. Kolhe|10.1109/ICIDCA56705.2023.10100267|Cloud;Cryptography;Encryption;Decryption;AES Algorithm;Digital Signature;Cloud;Cryptography;Encryption;Decryption;AES Algorithm;Digital Signature|
|[Detection of Fake Online Reviews by using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099776)|C. Silpa; P. Prasanth; S. Sowmya; Y. Bhumika; C. H. S. Pavan; M. Naveed|10.1109/ICIDCA56705.2023.10099776|Sentimental Analysis;Text mining;Product review;Machine Learning;Sentimental Analysis;Text mining;Product review;Machine Learning|
|[A Shared Network Security System for Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100001)|T. Arumugam; P. Senthilraja; A. Gnanabaskaran|10.1109/ICIDCA56705.2023.10100001|Cloud Computing;Data Center;Security;Multi-Tenant;Packet Delay;Throughput;Unified Threat Management;Cloud Computing;Data Center;Security;Multi-Tenant;Packet Delay;Throughput;Unified Threat Management|
|[Decentralized E-health Patient Record Management System using Blockchain and IPFS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100060)|R. Bhalerao; P. Gite; P. Patil; R. Gupta; S. Singh|10.1109/ICIDCA56705.2023.10100060|Blockchain;IPFS (Inter Planetary File System);Cryptography;E-Health Record;Hospital;Blockchain;IPFS (Inter Planetary File System);Cryptography;E-Health Record;Hospital|
|[Strengthening Supply Chain Integrity with Blockchain-based Anti-Counterfeiting Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100264)|S. Prajapati; J. Gadhari; T. Sawant; J. Kini; S. Solanki|10.1109/ICIDCA56705.2023.10100264|Blockchuin;Smart Contracts;QR (Quick Response) code;Blockchuin;Smart Contracts;QR (Quick Response) code|
|[Supervised Learning Methods for Identifying Credit Card Fraud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100266)|I. Dawar; N. Kumar; G. Kaur; S. Chaturvedi; A. Bhardwaj; M. Rana|10.1109/ICIDCA56705.2023.10100266|Credit Card;Credit Card Fraud;Supervised Learning;Regression Model;Random Forest;Credit Card;Credit Card Fraud;Supervised Learning;Regression Model;Random Forest|
|[An IoT Integrated Sensor Technologies for the Enhancement of Hospital Waste Segregation and Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099836)|D. Selvakarthi; D. Sivabalaselvamani; M. A. Wafiq; G. Aruna; M. Gokulnath|10.1109/ICIDCA56705.2023.10099836|Hospital waste;Segregator;deep learning;Sensors;IoT and Overflow;Hospital waste;Segregator;deep learning;Sensors;IoT and Overflow|
|[Non-Invasive Method of Detecting Anemia using AI & IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099915)|A. A; V. K. K; A. Bedant; M. Reddy C; K. Abhisek|10.1109/ICIDCA56705.2023.10099915|Oximeter;Spirometer;SpO2 level;Healthcare;Oximeter;Spirometer;SpO2 level;Healthcare|
|[Automated Home Life using IoT and Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100258)|S. V. Annam; N. Neelima; N. Parasa; D. Chinamuttevi|10.1109/ICIDCA56705.2023.10100258|Home Automation;Speech Recognition;Blynk Software;Node Microcontroller Unit ESP8266 board;Arduino Uno;Servo Motor;Home Automation;Speech Recognition;Blynk Software;Node Microcontroller Unit ESP8266 board;Arduino Uno;Servo Motor|
#### **2023 International Conference on Communication System, Computing and IT Applications (CSCITA)**
- DOI: 10.1109/CSCITA55725.2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Shared Rainwater Harvesting System Using Google Map API And Edge Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104862)|B. Rajan; K. Nadar; T. Tripathi; K. Wagaskar|10.1109/CSCITA55725.2023.10104862|GIS;Rainwater harvesting sharing system;machine learning;tank depth estimation;segmentation of rooftop;best tank placement position;rainwater harvesting tank sharing;deep learning;computer vision;GIS;Rainwater harvesting sharing system;machine learning;tank depth estimation;segmentation of rooftop;best tank placement position;rainwater harvesting tank sharing;deep learning;computer vision|
|[AVA: A Photorealistic AI Bot for Human-like Interaction and Extended Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104672)|V. Narkhede; O. Surushe; S. Kulkarni; H. Solanki; T. Ekbote; D. Joshi|10.1109/CSCITA55725.2023.10104672|Artificial Intelligence;Efficiency;Interview;Workforce;Artificial Intelligence;Efficiency;Interview;Workforce|
|[Deep Learning Based Marathi Sentence Recognition using Devnagari Character Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104985)|R. Patil; B. Narkhede; S. Gaonkar; T. Dave|10.1109/CSCITA55725.2023.10104985|Devnagari sentence recognition;Character recognition;Marathi Barakhadi;KNN classification;Devnagari sentence recognition;Character recognition;Marathi Barakhadi;KNN classification|
|[Efficient Detection of Small and Complex Objects for Autonomous Driving Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104969)|A. Sharma; R. Gupta|10.1109/CSCITA55725.2023.10104969|Convolutional Neural Network (CNN);Depth-wise Separable Convolution;You Only Look Once (YOLO);Intersection over Union (IoU);Non-Max Suppression;Small Object Detection.;Convolutional Neural Network (CNN);Depth-wise Separable Convolution;You Only Look Once (YOLO);Intersection over Union (IoU);Non-Max Suppression;Small Object Detection.|
|[Implementation of Exploratory Data Analysis on Weather Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104864)|S. Adivarekar; S. Nanwani; N. Mandal; T. Sarode|10.1109/CSCITA55725.2023.10104864|EDA;Random Forest;Climate;Weather;Loss Function;EDA;Random Forest;Climate;Weather;Loss Function|
|[Deep Learning Model for Simulating Self Driving Car](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104750)|K. Bhujbal; D. M. Pawar|10.1109/CSCITA55725.2023.10104750|Self-Driving cars;CNN (Convolutional Neural Network);Autonomous Cars;Machine Learning;Image Augmentation;Deep Learning;Image pre-processing;Behavioural Cloning;Computer Vision.;Self-Driving cars;CNN (Convolutional Neural Network);Autonomous Cars;Machine Learning;Image Augmentation;Deep Learning;Image pre-processing;Behavioural Cloning;Computer Vision.|
|[Summarization of Video Clips using Subtitles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105116)|E. Anil; S. Sebastian; J. Johnson; J. Rane; K. P. Karunakaran|10.1109/CSCITA55725.2023.10105116|Educational videos;NLP;Textrank;LexRank;LSA;Educational videos;NLP;Textrank;LexRank;LSA|
|[Investigative Analysis of Hospital Module In MIMIC-IV Database for Neonatal Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105050)|M. Ranade|10.1109/CSCITA55725.2023.10105050|MIMIC-IV;Big data analysis;Electronic Health Records;AI in neonatal healthcare;MIMIC-IV;Big data analysis;Electronic Health Records;AI in neonatal healthcare|
|[Reliability Stripe Coagulation in Two Failure Tolerant Storage Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104897)|T. Schwarz; J. R. Santiago|10.1109/CSCITA55725.2023.10104897|Erasure Correcting Codes;Extension of Codes;Linear Codes;Storage Systems;Erasure Correcting Codes;Extension of Codes;Linear Codes;Storage Systems|
|[Efficient Video Anomaly Detection using Residual Variational Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104785)|A. Kumar; M. Khari|10.1109/CSCITA55725.2023.10104785|Deep Learning;Variational autoencoder;Anomaly;ConvLSTM;Deep Learning;Variational autoencoder;Anomaly;ConvLSTM|
|[Web-3 Music Player on Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105088)|A. Agrawal; A. M. Gujar; A. Chovatiya; H. G. Sheth; T. Singh|10.1109/CSCITA55725.2023.10105088|Peer-to-Peer(P2P);Decentralized;Blockchain;Solidity;Peer-to-Peer(P2P);Decentralized;Blockchain;Solidity|
|[Situational Portfolio Forecasting and Allocation with Deep-Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104979)|M. Joshi; A. Deshpande; D. Ambawade|10.1109/CSCITA55725.2023.10104979|Portfolio;Optimization;Deep-Learning;LSTM;ARIMA. Sharpe Ratio;Volatility;Forecasting;Portfolio;Optimization;Deep-Learning;LSTM;ARIMA. Sharpe Ratio;Volatility;Forecasting|
|[Recognition of Emotions Based on Facial Expressions Using Bidirectional Long-Short-Term Memory and Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105040)|S. B. Dhekale; D. K. Shedge|10.1109/CSCITA55725.2023.10105040|;|
|[Survey on Visual Speech Recognition using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104811)|R. Chand; P. Jain; A. Mathur; S. Raj; P. Kanikar|10.1109/CSCITA55725.2023.10104811|Lip Reading;CNN;RNN;Challenges;Deep Learning;Viseme;LRW;Visual Ambiguity;Keyframe;Tracking;Lip Reading;CNN;RNN;Challenges;Deep Learning;Viseme;LRW;Visual Ambiguity;Keyframe;Tracking|
|[P2P Negotiation Framework for trading Carbon Credits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104824)|A. Shigwan; A. Aguiar; D. D’Abreo; S. Jaswal|10.1109/CSCITA55725.2023.10104824|P2P;Negotiation;Automation;Carbon Credits;Fuzzy Logic;Fuzzy Controllers;P2P;Negotiation;Automation;Carbon Credits;Fuzzy Logic;Fuzzy Controllers|
|[NeuralBee - A Beehive Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104935)|Y. Mahajan; D. Mehta; J. Miranda; R. Pinto; V. Patil|10.1109/CSCITA55725.2023.10104935|Honey Bees;Varroa mite;object detection;Mel spectrogram;audio signal analysis;Mel-frequency cepstral coefficients (MFCCs);Honey Bees;Varroa mite;object detection;Mel spectrogram;audio signal analysis;Mel-frequency cepstral coefficients (MFCCs)|
|[Supply Chain Authentication for Vaccine Passport Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104947)|A. Dhamelia; G. Harpanhalli; A. Doshi; A. Kabsuri; N. Rai|10.1109/CSCITA55725.2023.10104947|Blockchain;Immunization;Vaccine;COVID-19;Blockchain;Immunization;Vaccine;COVID-19|
|[Stock Portfolio Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105068)|S. Shinde; A. Ware; S. Yadav; A. Paul; R. Yadav|10.1109/CSCITA55725.2023.10105068|;|
|[Audio Source Separation using Wave-U-Net with Spectral Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104853)|V. Patkar; T. Parmar; P. Narvekar; V. Pawar; J. Gomes|10.1109/CSCITA55725.2023.10104853|Audio-Separation;Wave-U-Net;Spectral-Loss-Function;CNN;Machine-Learning;Audio-Separation;Wave-U-Net;Spectral-Loss-Function;CNN;Machine-Learning|
|[GuitarGuru: A Realtime Guitar Chords Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104798)|V. Nagpurkar; N. Pattankar; T. Nayak; A. D’Souza; N. Henriques|10.1109/CSCITA55725.2023.10104798|CNN;Guitar Chord Recognition;Neural Networks;TensorFlow;Keras;OpenCV;Mediapipe.;CNN;Guitar Chord Recognition;Neural Networks;TensorFlow;Keras;OpenCV;Mediapipe.|
|[Algorithm Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104848)|A. Suvarna; J. Shah; S. Shettigar; V. Soni; A. Mathur|10.1109/CSCITA55725.2023.10104848|pedagogy;algorithm;soft computing;simulator;interactive;active engagement;fuzzy logic;pedagogy;algorithm;soft computing;simulator;interactive;active engagement;fuzzy logic|
|[Computer Vision for Industrial Safety and Productivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104764)|S. Shetye; S. Shetty; S. Shinde; C. Madhu; A. Mathur|10.1109/CSCITA55725.2023.10104764|computer vision;industrial safety;YOLOv3;computer vision;industrial safety;YOLOv3|
|[Complementary Product Recommendation using Siamese Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104621)|R. Rai; M. Patel; P. Varma; D. Parvaiz; S. Chapaneri; D. Jayaswal|10.1109/CSCITA55725.2023.10104621|Recommender systems;Siamese Neural Network;LSTM;E-commerce;Recommender systems;Siamese Neural Network;LSTM;E-commerce|
|[dExCount: A decentralized cross-chain discount web app for Token Sale](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104639)|K. Sonawane; Y. S. Nayal; D. Palekar; H. Shetty; V. Pinto|10.1109/CSCITA55725.2023.10104639|Blockchain;Decentralized applications;Discount;crypto-currencies;Blockchain;Decentralized applications;Discount;crypto-currencies|
|[Vehicle Damage Analysis Using Computer Vision: Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105039)|S. Doshi; A. Gupta; J. Gupta; N. Hariya; A. Pavate|10.1109/CSCITA55725.2023.10105039|Computer Vision;Convoutional Neural Network(CNN);Mask R-CNN;Computer Vision;Convoutional Neural Network(CNN);Mask R-CNN|
|[Logo Detection Using Machine Learning Algorithm : A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105056)|J. Sanghvi; J. Rathod; S. Nemade; H. Panchal; A. Pavate|10.1109/CSCITA55725.2023.10105056|Image processing & Recognition;Brand protection;Object detection & Localization;Computer vision;Image processing & Recognition;Brand protection;Object detection & Localization;Computer vision|
|[BeRedy (Period Tracker & PCOS Diagnosis)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104755)|A. Karia; A. Poojary; A. Tiwari; L. Sequeira; M. K. Sokhi|10.1109/CSCITA55725.2023.10104755|PCOS;Machine learning;Random Forest;Chatbot;PCOS;Machine learning;Random Forest;Chatbot|
|[CNN Based Image Descriptor for Polycystic Ovarian Morphology from Transvaginal Ultrasound](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104931)|P. H. Panicker; K. Shah; S. Karamchandani|10.1109/CSCITA55725.2023.10104931|PCOS;segmentation;CNN;blob detection;PCOS;segmentation;CNN;blob detection|
|[An Effective Technique for Single Image Haze Removal using MSMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104784)|V. Varshney; J. Panda; R. Gupta|10.1109/CSCITA55725.2023.10104784|Haze removal;contrast enhancement;fusion;color preservation;denoisung;morphological;Haze removal;contrast enhancement;fusion;color preservation;denoisung;morphological|
|[Character Segmentation of Devnagari Script in Printed Document Images using Projection Profiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104858)|P. Patil; K. Noronha|10.1109/CSCITA55725.2023.10104858|OCR;projection profile;segmentation accuracy;OCR;projection profile;segmentation accuracy|
|[Advanced Irrigation and Cultivation System Based on Machine Learning in IOT Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104638)|A. Chaugule; P. Gupta|10.1109/CSCITA55725.2023.10104638|Ensemble;Irrigation Management;IoT;Agriculture;Smart Farming;Sensor;Machine Learning;Advanced Agriculture;Precision Agriculture;Digital Agriculture;Ensemble;Irrigation Management;IoT;Agriculture;Smart Farming;Sensor;Machine Learning;Advanced Agriculture;Precision Agriculture;Digital Agriculture|
|[Design Of Corner Truncated Square Microstrip Antenna For Circular Polarized Response in Global Positioning System L5 Band Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104717)|A. A. Deshmukh; S. Nagaokar; S. Tawde; V. P. Chavali|10.1109/CSCITA55725.2023.10104717|Circular-polarization;Square microstrip antenna;Corner truncation;Rectangular-slot;Global positioning system;Circular-polarization;Square microstrip antenna;Corner truncation;Rectangular-slot;Global positioning system|
|[Energy Efficient Node Re-positioning Algorithm for Uniform node Distribution in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104821)|A. Jain|10.1109/CSCITA55725.2023.10104821|Wireless Sensor Networks;Uniform distribution;Energy Efficiency;Mobility;Degree Centrality;Coverage;Repositioning.;Wireless Sensor Networks;Uniform distribution;Energy Efficiency;Mobility;Degree Centrality;Coverage;Repositioning.|
|[Evaluation of illumination and received power for realistic LED layout for indoor VLC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105123)|M. Mukherjee; K. Noronha|10.1109/CSCITA55725.2023.10105123|Illuminance;Light emitting diodes;Luminous intensity;Received power;Visible light communication;Illuminance;Light emitting diodes;Luminous intensity;Received power;Visible light communication|
|[Malicious User Detection using Honeywords](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104807)|S. Thakur; S. Chaudhari; B. Joshi|10.1109/CSCITA55725.2023.10104807|honeywords;malicious;BLAST algorithm;QR Code;honeywords;malicious;BLAST algorithm;QR Code|
|[Design and Development of Smart Waste Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104960)|P. Jain; T. Chaudhary; S. Gajjar|10.1109/CSCITA55725.2023.10104960|Smart waste management;Internet of things;Cloud;IFTTT;ThingSpeak;MIT app inventor;GPS;Smart waste management;Internet of things;Cloud;IFTTT;ThingSpeak;MIT app inventor;GPS|
|[Smart Living Solution to Optimize Building Systems for Efficient Energy Usage and Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104852)|E. Masrani; D. Patel; M. Khatri; E. Martis; N. Gaur|10.1109/CSCITA55725.2023.10104852|smart living;IoT;automation;smart home;building management;smart city;smart living;IoT;automation;smart home;building management;smart city|
|[A Machine Learning Perspective in an Effective Monitoring of Thermal Performance of Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104801)|S. S. Nayyer; J. Hozefa; M. Rahul; C. Mandhar|10.1109/CSCITA55725.2023.10104801|Gaussian Process Regression (GPR);HST;LoL;Machine Learning (ML);Parameter Estimation;TOT;Gaussian Process Regression (GPR);HST;LoL;Machine Learning (ML);Parameter Estimation;TOT|
|[Securing VANET Communication Using Block Chain: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10105098)|N. Pathak; P. R. Patil|10.1109/CSCITA55725.2023.10105098|MDS;V2X;BSM;OMNET++;MDS;V2X;BSM;OMNET++|
|[IoE-Based Predictive Oxygen Inventory Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104693)|A. Dhamelia; G. Harpanhalli; A. Doshi; A. Kabsuri; M. Lopes; G. Singh|10.1109/CSCITA55725.2023.10104693|Internet of Everything;Hypoxia;Recurrent Neural Network (RNN);Inventory Management;COVID-19;Internet of Everything;Hypoxia;Recurrent Neural Network (RNN);Inventory Management;COVID-19|
|[A Framework for Development of a Virtual Campus Tour](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104840)|J. Dsouza; S. Ger; L. Wilson; N. Lobo; N. Rai|10.1109/CSCITA55725.2023.10104840|Virtual Reality (VR);3d Modelling;Unreal engine;objects;Blender 3D;Virtual Reality (VR);3d Modelling;Unreal engine;objects;Blender 3D|
|[Design and Development of Solar Powered Phase Shifted Full Bridge Converter for EV Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104932)|S. Gawande; S. Thale|10.1109/CSCITA55725.2023.10104932|Electric vehicle;DC charging;PSFB;Solar PV;Zero voltage switching;Electric vehicle;DC charging;PSFB;Solar PV;Zero voltage switching|
#### **2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)**
- DOI: 10.1109/iCoMET57998.2023
- DATE: 17-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Comparative Survey of Solutions to Russel's Paradox](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099262)|F. Rubab; S. Hafeez; M. H. Qazi; M. H. Syed; B. I. Azeemi; A. J. Kayani|10.1109/iCoMET57998.2023.10099262|Logic;Bertrand Russell;Paradox;Set Theory;Logic;Bertrand Russell;Paradox;Set Theory|
|[Introducing VAIFU: A Virtual Agent for Introducing and Familiarizing Users in VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099242)|M. H. Qazi; M. P. Qazi|10.1109/iCoMET57998.2023.10099242|Human-Computer Interaction;Virtual Reality;Embodied Conversational Agents;Natural Language Processing Applications;Human-Computer Interaction;Virtual Reality;Embodied Conversational Agents;Natural Language Processing Applications|
|[Performance Analysis and Quantification of BeiDou Navigation Satellite System (BDS-3)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099200)|H. Magsi; M. A. Shah; S. H. Hussain Shah; Faiza; A. Hussain; A. M. Soomar; F. A. Chachar|10.1109/iCoMET57998.2023.10099200|GNSS;BDS-3;accuracy;positioning;GNSS;BDS-3;accuracy;positioning|
|[Towards a Protein-Protein Interactions Framework using Graph Analytics on Apache Spark](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099075)|H. Umbrin; M. Aamir; J. Ferzund; H. M. A. Tahir; R. M. A. Latif|10.1109/iCoMET57998.2023.10099075|data science;protein-protein interaction (PPI);PageRank;graphx;data science;protein-protein interaction (PPI);PageRank;graphx|
|[Social Media Competitive Analysis: A Case Study of Laptop Brands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099132)|T. Hassan; A. Ahmed; M. Anwar; M. B. Ali Gilani; M. A. Qureshi|10.1109/iCoMET57998.2023.10099132|Text Mining;social media;Content analysis;Facebook. Competitive analysis;Case study;Sentiment Analysis;Big data;Text Mining;social media;Content analysis;Facebook. Competitive analysis;Case study;Sentiment Analysis;Big data|
|[Promising Features of Wind Energy: A Glance Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099282)|R. Asghar; M. J. Anwar; H. Wadood; H. Saleem; N. Rasul; Z. Ullah|10.1109/iCoMET57998.2023.10099282|consumer;GHG;fossil fuels;environment;consumer;GHG;fossil fuels;environment|
|[Image Caption Generation Related to Object Detection and Colour Recognition Using Transformer-Decoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099161)|Z. U. Kamangar; G. M. Shaikh; S. Hassan; N. Mughal; U. A. Kamangar|10.1109/iCoMET57998.2023.10099161|Image captioning;deep learning;computer vision;transformer;natural language processing;Image captioning;deep learning;computer vision;transformer;natural language processing|
|[Electrical Energy Audit and Analysis of Energy Conservation Opportunities at University Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099073)|S. A. Jiskani; S. A. Shaikh; Q. A. Memon; M. A. Bhutto; M. F. Shaikh; M. Kumar|10.1109/iCoMET57998.2023.10099073|Energy audit;Energy conservation;Daylighting;Energy audit;Energy conservation;Daylighting|
|[Vehicle response control during stopping a train with dynamics of the train and driver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099279)|M. R. Siddique; S. I. Ali Shah; A. Ahmed; M. Mehmood|10.1109/iCoMET57998.2023.10099279|control;trains;braking system;modern;closed loop;control;trains;braking system;modern;closed loop|
|[Visual-Inertial State-Estimation Using Ground Station for UAV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099145)|A. Khan; H. Ullah; U. Faisal; N. Mazhar; M. Y. Khan|10.1109/iCoMET57998.2023.10099145|Flight controller;monocular visual-inertial systems (VINS-mono);Unmanned Aerial Vehicles;state estimation;IMU;Flight controller;monocular visual-inertial systems (VINS-mono);Unmanned Aerial Vehicles;state estimation;IMU|
|[Broadband computer-generated holography (CGH)-based Bessel beam generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099083)|I. Javed; M. Asad; Y. M. Rind; A. J. Satti; M. Zubair; M. Q. Mehmood|10.1109/iCoMET57998.2023.10099083|Multifunctional metasurface;CGH;vortex beam;optical metasurface;Multifunctional metasurface;CGH;vortex beam;optical metasurface|
|[Disease estimation using robust AI methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099377)|A. R. Shah; I. Javed; U. A. Shams; M. A. Naverd; M. Q. Mehmood|10.1109/iCoMET57998.2023.10099377|RBCs;WBCs;Platelets;YOLO;Blood count;Machine learning;RBCs;WBCs;Platelets;YOLO;Blood count;Machine learning|
|[Future Prospective of HVDC System in Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099224)|M. Ahmad; M. Yousaf Ali Khan; E. Mustafa; N. H. Baloch; M. A. Khan|10.1109/iCoMET57998.2023.10099224|Green House Gases (GHG);China Pakistan Economic Corrdior (CPEC);High Voltage Direct Current (HVDC);Renewable Energy Resources;Green House Gases (GHG);China Pakistan Economic Corrdior (CPEC);High Voltage Direct Current (HVDC);Renewable Energy Resources|
|[A Survey on Machine Learning Approaches in Cryptocurrency: Challenges and Opportunities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099130)|H. Mujlid|10.1109/iCoMET57998.2023.10099130|Cryptocurrency;Price prediction;Machine learning;Overfitting;Interoperability;Cryptocurrency;Price prediction;Machine learning;Overfitting;Interoperability|
|[Performance Analysis of Outer Rotor Flux Reversal Machine at Different Rotor Poles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099146)|N. Ahmad; Tahsinullah; S. Ahmed; H. Shahbaz; S. Khan|10.1109/iCoMET57998.2023.10099146|Permanent magnet outer rotor flux reversal machine (PMORFRM);different rotor pole analysis;electric vehicles (EVs);hybrid electric vehicles (REVs);direct drive application;Permanent magnet outer rotor flux reversal machine (PMORFRM);different rotor pole analysis;electric vehicles (EVs);hybrid electric vehicles (REVs);direct drive application|
|[A Competitive Numerical Model for Virus Transmission in a Computer Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099088)|M. S. Ehsan; M. Rafiq; A. Raza|10.1109/iCoMET57998.2023.10099088|Computer virus;Continuous Dynamical System;Numerical Analysis;Convergence;Computer virus;Continuous Dynamical System;Numerical Analysis;Convergence|
|[Artificial neural network based control of wind powered small scale DC generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099318)|N. Ahmed; M. Nasir; M. Arslan Saleem; S. Murtaza; S. Abdullah; E. Kamal|10.1109/iCoMET57998.2023.10099318|Renewable energy resources;wind energy;artificial neural networks;robust and intelligent control;permanent magnet generator;Renewable energy resources;wind energy;artificial neural networks;robust and intelligent control;permanent magnet generator|
|[The Influential factors for Pollution Emissions in manufacturing business](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099283)|B. Sadiq; T. Zhongfu; T. Bashir; A. Naseem; D. Khan|10.1109/iCoMET57998.2023.10099283|Pollution Emissions;Recycling;Quality Management System;Environmental Engineering;Pollution Emissions;Recycling;Quality Management System;Environmental Engineering|
|[Implications of Energy Management Practices to Reduce Carbon Emissions in Pakistani Manufacturing Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099188)|T. Bashir; T. Zhongfu; B. Sadiq; A. Naseem; B. Jawed|10.1109/iCoMET57998.2023.10099188|Energy Management;Energy Policy;Carbon Emissions;Manufacturing Industries;Energy Management;Energy Policy;Carbon Emissions;Manufacturing Industries|
|[Deep Learning-Based Identification of Skin Cancer on Any Suspicious Lesion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099160)|M. Aaqib; M. Ghani; A. Khan|10.1109/iCoMET57998.2023.10099160|Melanoma detection;deep learning;convolutional neural networks;machine learning;the classification of skin lesions;Melanoma detection;deep learning;convolutional neural networks;machine learning;the classification of skin lesions|
|[Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099260)|G. Zaman Khan; I. Ali Shah; Farhatullah; M. A. Hassan; H. Junaid; F. Sardar|10.1109/iCoMET57998.2023.10099260|Malaria disease;transfer learning;classification;Malaria disease;transfer learning;classification|
|[A Framework for Daily Living Activity Recognition using Fusion of Smartphone Inertial Sensors Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099271)|S. Khan; S. M. A. Shah; S. H. Noorani; A. Arsalan; M. Ehatisham-ul-Haq; A. Raheel; W. Ahmed|10.1109/iCoMET57998.2023.10099271|Human Activity;Machine Learning;Smart-phone Sensor;Out-of-lab;Random Forest;Human Activity;Machine Learning;Smart-phone Sensor;Out-of-lab;Random Forest|
|[A Deep Learning-based Solution for Identification of Figurative Elements in Trademark Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099183)|A. Uzairi; A. Kurti; Z. Kastrati|10.1109/iCoMET57998.2023.10099183|Trademark image;trademark conceptual similarity;Figurative elements;deep learning;image classification;Trademark image;trademark conceptual similarity;Figurative elements;deep learning;image classification|
|[Impact of green human resource practices on work performance of Renewable Projects in Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099214)|F. Hussain Awan; A. Anwar; K. Jamil; R. F. Gul; S. Mustafa; S. M. Bajkani|10.1109/iCoMET57998.2023.10099214|Green human resource;renewable energy;Pakistan;energy sector;Green human resource;renewable energy;Pakistan;energy sector|
|[Polyimide Substrate based Compact Antenna for Terahertz Wireless Communication Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099226)|S. Ahmed; S. Bano; B. Iftikhar; S. I. Naqvi; Y. Amin|10.1109/iCoMET57998.2023.10099226|microstrip patch antenna;polyimide substrate;THz applications;wideband;microstrip patch antenna;polyimide substrate;THz applications;wideband|
|[Computationally Efficient Numerical Analysis of Rotavirus Epidemic Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099312)|M. Rafiq; M. Sarwar Ehsan; A. Nisar; S. Abbas|10.1109/iCoMET57998.2023.10099312|Rotavirus;Numerical Methods;Stability Analysis;Convergence;Comparison;Rotavirus;Numerical Methods;Stability Analysis;Convergence;Comparison|
|[Efficient and Unique Learning of The Complex Receiver Structure of Galileo E5 AltBOC Using an Educational Software in Matlab](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099156)|I. Ali; S. Khan; M. Ali; K. Mujeeb|10.1109/iCoMET57998.2023.10099156|AltBOc;Engineering Education;Galileo E5 signal;Signal acquisition;Signal tracking;AltBOc;Engineering Education;Galileo E5 signal;Signal acquisition;Signal tracking|
|[Comparative Analysis of finned absorber plate with and without black paint in Solar Air Heater](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099171)|R. Rai; A. R. Larik; K. Ahmed; S. Kumaramasy; A. A. Zaidi|10.1109/iCoMET57998.2023.10099171|Solar Air Heater;sustainable system;energy efficient absorber;black paint finned absorber;solar energy;Solar Thermal Energy;Solar Air Heater;sustainable system;energy efficient absorber;black paint finned absorber;solar energy;Solar Thermal Energy|
|[An Automated Human Action Recognition and Classification Framework Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099190)|S. Waheed; R. Amin; J. Iqbal; M. Hussain; M. A. Bashir|10.1109/iCoMET57998.2023.10099190|human action recognition;deep learning;automated human action classification;human action recognition;deep learning;automated human action classification|
|[Vehicle Detection using Artificial Intelligence based Algorithm in Intelligent Transportation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099381)|M. Waqar; M. Ishaq; M. H. Afzal; S. Iqbal|10.1109/iCoMET57998.2023.10099381|Vehicle Detection;You Look Only Once;YOLO;Neural Network;Intelligent Transportation System;MATLAB;Vehicle Detection;You Look Only Once;YOLO;Neural Network;Intelligent Transportation System;MATLAB|
|[Automated Report Generation: A GRU Based Method for Chest X-Rays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099311)|W. Akbar; M. Inam Ul Haq; A. Soomro; S. Muhammad Daudpota; A. Shariq Imran; M. Ullah|10.1109/iCoMET57998.2023.10099311|Chest X-rays;Medical reports;Convolutional Neural Networks;Gated Recurrent Units;Radiology reports generation;Text generation;Medical imaging;Chest X-rays;Medical reports;Convolutional Neural Networks;Gated Recurrent Units;Radiology reports generation;Text generation;Medical imaging|
|[Multi-layer Convolutional Approach for Lung Cancer Detection using CXR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099261)|S. Javed; S. M. Anwar; M. B. Umair|10.1109/iCoMET57998.2023.10099261|ImageNet;VGG16;DenseNet;Deep Learning;CNN;Adenocarcinoma;Large Cell Carcinoma;Squamous Cell Carcinoma;ImageNet;VGG16;DenseNet;Deep Learning;CNN;Adenocarcinoma;Large Cell Carcinoma;Squamous Cell Carcinoma|
|[Implications of Social Networking Sites for Consumer Motivations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099090)|K. Jamil; L. Dunnnan; A. Anwar; R. F. Gul; S. Mustafa; D. Z. Alvi|10.1109/iCoMET57998.2023.10099090|Social Networking Sites;Online Shopping;Consumer Motivation;Purchase Decision;Social Networking Sites;Online Shopping;Consumer Motivation;Purchase Decision|
|[Evaluating the Effectiveness of Flat Plate Solar Collector for Water Heating in Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099269)|M. Umer; A. Akbar; S. Dilshad; U. J. Kalair; N. Abas|10.1109/iCoMET57998.2023.10099269|TRNSYS;flat plate collector;solar water heating;renewable energy;TRNSYS;flat plate collector;solar water heating;renewable energy|
|[Design Optimization and Performance Analysis of Rectangular Structured Moving Magnet Linear Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099334)|H. Ahmad; M. Jawad; Z. Ahmad; A. Ahmad|10.1109/iCoMET57998.2023.10099334|Linear actuator;Moving magnet;Rectangular topology;Electromagnetic force;Linear actuator;Moving magnet;Rectangular topology;Electromagnetic force|
|[Comparison of ANN Global Horizontal Irradiation predictions with Satellite Global Horizontal Irradiation using Statistical evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099300)|F. Nawab; A. Ibrahim; S. Zeeshan Suheel; A. Ahmed Goje|10.1109/iCoMET57998.2023.10099300|Artificial Neural Networks;Solar Irradiation;Statistical evaluation;Prediction;Artificial Neural Networks;Solar Irradiation;Statistical evaluation;Prediction|
|[Towards Classification and Analysis of Ransomware Detection Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099204)|M. Ashraf; M. Asif; M. B. Ahmad; A. Ayaz; A. Nasir; U. Ahmad|10.1109/iCoMET57998.2023.10099204|Ransomware;Malware;Encryption;Detection;Classification;Locker;Crypto;Ransomware;Malware;Encryption;Detection;Classification;Locker;Crypto|
|[Design and Simulation for hydro turbine through Computational Fluid Dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099207)|M. A. Abbas; M. Hussain; S. Muhammad|10.1109/iCoMET57998.2023.10099207|Pelton turbine;Computational Fluid Dynamics);6-DOF;2D Dimensions;ANSYS CFX;SolidWork;Transient Flow;k-epsilon model;Pelton turbine;Computational Fluid Dynamics);6-DOF;2D Dimensions;ANSYS CFX;SolidWork;Transient Flow;k-epsilon model|
|[Potential of SIFT, SURF, KAZE, AKAZE, ORB, BRISK, AGAST, and 7 More Algorithms for Matching Extremely Variant Image Pairs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099250)|S. A. Khan Tareen; R. H. Raza|10.1109/iCoMET57998.2023.10099250|SIFT;SURF;KAZE;AKAZE;ORB;BRISK;AGAST;FAST;MSER;MSD;GFTT;Harris Corner Detector based GFTT;Harris Laplace Detector;Star Detector;CenSurE;SIFT Descriptor;Edge Detectors;Line Detectors;FLANN;K-D Trees;Multi-Probe LSH;Nearest Neighbors Distance Ratio;SIFT;SURF;KAZE;AKAZE;ORB;BRISK;AGAST;FAST;MSER;MSD;GFTT;Harris Corner Detector based GFTT;Harris Laplace Detector;Star Detector;CenSurE;SIFT Descriptor;Edge Detectors;Line Detectors;FLANN;K-D Trees;Multi-Probe LSH;Nearest Neighbors Distance Ratio|
|[Deep learning-based video anonymization for security and privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099232)|J. G. Fossum; B. N. Skinstad; S. Yamin; M. Ullah; F. A. Cheikh|10.1109/iCoMET57998.2023.10099232|Anonymization;Detectron;Facial detection;GDPR;Object detection;Privacy;Video censorship;Anonymization;Detectron;Facial detection;GDPR;Object detection;Privacy;Video censorship|
|[Urdu Speech Emotion Recognition using Speech Spectral Features and Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099289)|S. Taj; G. M. Shaikh; S. Hassan; Nimra|10.1109/iCoMET57998.2023.10099289|Speech Features;Urdu;Deep Learning;ID-Convolutional Neural Network (CNN);Speech Features;Urdu;Deep Learning;ID-Convolutional Neural Network (CNN)|
|[Label Smoothing Loss with Dual-Stream Network Using Separable Convolutional Layers for Retinopathy Grading and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099097)|M. A. Kaloi; A. Ali; I. A. Babar; K. Mujeeb|10.1109/iCoMET57998.2023.10099097|;|
|[Sustainable Performance of Energy sector Organizations through Green Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099337)|A. Anwar; L. Dunnan; K. Jamil; S. Mustafa; F. H. Awan; U. K. Ansari|10.1109/iCoMET57998.2023.10099337|Green supply chain management;Sustainable performance;Energy sector;Pakistan;Green supply chain management;Sustainable performance;Energy sector;Pakistan|
|[A New Method of Unintentional Islanding detection for Multiple Distributed energy resources based Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099374)|N. A. Larik; M. Ahmed; L. L. Zhang; J. A. Jamali; Q. H. Wu|10.1109/iCoMET57998.2023.10099374|Multiple energy resources;Islanding;Non-detection zone;PAssive detection methods;Multiple energy resources;Islanding;Non-detection zone;PAssive detection methods|
|[Numerical Investigation of the MHD Casson Nanofluid Flow over Permeable Stretching/Shrinking Surface with Radiation Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099251)|S. Kumar; A. Ali Shaikh; S. Feroz Shah; H. Bux Lanjwani|10.1109/iCoMET57998.2023.10099251|Casson;Nanofluid;Stretching/shrinking;Porosity;Shooting method;Casson;Nanofluid;Stretching/shrinking;Porosity;Shooting method|
|[Tracking error control of robotic manipulator using optimal integral sliding mode control in the presence of external disturbances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099072)|M. Waseem; I. Ali|10.1109/iCoMET57998.2023.10099072|Optimal Integral sliding mode control(OISMC);Linear Quadratic regulator(LQR);Robotic manipulator;Integral Sliding mode control (ISMC);Optimal Integral sliding mode control(OISMC);Linear Quadratic regulator(LQR);Robotic manipulator;Integral Sliding mode control (ISMC)|
|[Skip Connections' Importance in Biomedical Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099184)|A. Yaqoob; F. Rehman; H. Sharif; M. H. Mahmood; S. Sharif; A. Ahmad; C. N. Ali; A. Hussain; M. Khan|10.1109/iCoMET57998.2023.10099184|Semantic Segmentation;Deep learning;FCN;ResNet;Skip Connections encoder-decoder models;Semantic Segmentation;Deep learning;FCN;ResNet;Skip Connections encoder-decoder models|
|[Machine Learning in Banking Risk Management - A Brief Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099339)|H. Shakeel; H. Sharif; F. Rehman; B. Rasool; A. Mahmood; H. Maqsood; H. Kirn; C. N. Ali; M. Bilal|10.1109/iCoMET57998.2023.10099339|Avoid risks;Trickery;Credit worthiness;machine learning;Avoid risks;Trickery;Credit worthiness;machine learning|
|[A Brief Overview of Deep Learning Approaches for IoT Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099306)|M. Hassaan Khalid; H. Sharif; F. Rehman; M. Naeem Ullah; S. Shaukat; H. Maqsood; C. Nouman Ali; A. Hussain; I. Iftikhar|10.1109/iCoMET57998.2023.10099306|IoT;security;Deep neural networks;smart applications;IoT;security;Deep neural networks;smart applications|
|[From Cloud Down to Things: An Overview of Machine Learning in Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099119)|M. Humayoun; H. Sharif; F. Rehman; S. Shaukat; M. Ullah; H. Maqsood; C. N. Ali; R. Iftikhar; A. H. Chandio|10.1109/iCoMET57998.2023.10099119|IoT;Machine;style;styling;insert;IoT;Machine;style;styling;insert|
|[A Systematic Review of Intrusion Detection Systems in Internet of Things Using ML and DL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099142)|A. Hussain; H. Sharif; F. Rehman; H. Kirn; A. Sadiq; M. S. Khan; A. Riaz; C. N. Ali; A. H. Chandio|10.1109/iCoMET57998.2023.10099142|DL;IoT;Intrusion detection;ML;Security attacks;DL;IoT;Intrusion detection;ML;Security attacks|
|[Application of Artificial Neural Networks inSatellite Imaging – A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099324)|H. Sharif; F. Rehman; A. Rida; C. Nouman Ali; R. Zeeshan Zulfiqar; S. Akram; H. Kirn; A. Hussain; R. Iftikhar|10.1109/iCoMET57998.2023.10099324|satellite imaging;neural networks;deep learning;artificial neural networks;satellite imaging;neural networks;deep learning;artificial neural networks|
|[Artificial Intelligence based prediction system for General Medicine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099078)|G. Nadeem; Y. Rehman; A. Khaliq; H. Khalid; M. I. Anis|10.1109/iCoMET57998.2023.10099078|BSN;Deep Learning;E-Health Shield;K-Nearest Neighbors;Machine Learning;BSN;Deep Learning;E-Health Shield;K-Nearest Neighbors;Machine Learning|
|[LabView based Automated Motor Test Bench for Induction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099192)|M. Sohaib; H. Shaukat; T. Tauqeer; A. Shahid; U. Younis; R. Hafiz|10.1109/iCoMET57998.2023.10099192|LabView;predictive maintenance;motor test bench;LabView;predictive maintenance;motor test bench|
|[A Review Based On Comparative Analysis of Techniques Used in Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099182)|K. Memon; F. A. Umrani; A. Baqai; Z. S. Syed|10.1109/iCoMET57998.2023.10099182|Precision Agriculture;Wireless sensor networks;IOT;Machine Learning;Precision Agriculture;Wireless sensor networks;IOT;Machine Learning|
|[Design of TeleHaptic Simulators for Various Control Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099140)|F. Qureshi; R. Uddin|10.1109/iCoMET57998.2023.10099140|;|
|[Performance comparison of Webservers load balancing using HAProxy in SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099326)|A. Khaliq; M. A. Tahir; G. Nadeem; S. H. Adil; J. Jamshid; J. A. Memon|10.1109/iCoMET57998.2023.10099326|HAProxy;Httperf;High availability;Load balancing;Round robin;SDN;HAProxy;Httperf;High availability;Load balancing;Round robin;SDN|
|[Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099227)|M. Ikram; S. Ahmed; S. N. Khan Marwat|10.1109/iCoMET57998.2023.10099227|Decentralized Energy Trading;Local Energy Markets;Multi-agent Control;Dual Auction Consensus;Smart Grid;Decentralized Energy Trading;Local Energy Markets;Multi-agent Control;Dual Auction Consensus;Smart Grid|
|[CBAI: Cloud-Based Agile Infrastructure for Enhancing Distributed Agile Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099284)|M. Ali; S. M. Cheema; Z. Aslam; A. Naz; N. Ayub|10.1109/iCoMET57998.2023.10099284|agile development;cloud computing;cloud-based agile Infrastructure;agile development;cloud computing;cloud-based agile Infrastructure|
|[User Feedback Severity Level Identification and Classification through Deeper Analysis of Text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099177)|M. Umair; S. Aun Irtaza; S. Salim|10.1109/iCoMET57998.2023.10099177|Toxic Comments;MPL;Bag of words;Online Reviews;Binary Classifications;Toxic Comments;MPL;Bag of words;Online Reviews;Binary Classifications|
|[Ceramic Waveguide Filters with Improved Stop band Rejection and Transmission Zeros](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099210)|S. Khan; S. Afridi; L. Iram; T. Khan|10.1109/iCoMET57998.2023.10099210|Ceramic;Attenuation;Poles;TEM;Blind holes;Ceramic;Attenuation;Poles;TEM;Blind holes|
|[An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099357)|G. Zaman Khan; I. Ali Shah; Farhatullah; M. Ikram Ullah; I. Ullah; M. Ihtesham; H. Junaid; S. Yousafzai; F. Sardar|10.1109/iCoMET57998.2023.10099357|Lung cancer;CNN model;classification;Lung cancer;CNN model;classification|
|[Automatic Solar Based Water Heating System Through Convex Lenses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099173)|A. Samim; Narjis; A. R. Khatri|10.1109/iCoMET57998.2023.10099173|Solar energy;Convex lenses;automatic system;Solar energy;Convex lenses;automatic system|
|[Design and Development of Android Based Therapeutic Heel Sole](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099293)|S. A. Semab; F. Amin; A. Mazhar; M. Z. Ur Rehman; M. A. Waris; Z. Zia|10.1109/iCoMET57998.2023.10099293|Planter Fasciitis;Heel pad;Heel therapy;Smart Sole;Heel pain;Planter Fasciitis;Heel pad;Heel therapy;Smart Sole;Heel pain|
|[Future Prospective of Smart Meters, Net Metering and Electricity Market for Power Distribution companies in Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099361)|M. Ahmad; M. Yousaf Ali Khan; A. Nawaz; E. Mustafa; N. H. Baloch|10.1109/iCoMET57998.2023.10099361|National Transmission and Dispatch Company (NTDC);National Electric Power Regulatory Authority (NEPRA);Competitive Trading Bilateral Contract Market (CTBCM);Water and Power Develpoment Authority (WAPDA);Distribution Companies (DISCO's);Advance Metering Infrastructure (AMI);Generation Companies (GENCO's);Central Power Purchase Agency (CPPA)Bulk Power Consumer (BPC);National Transmission and Dispatch Company (NTDC);National Electric Power Regulatory Authority (NEPRA);Competitive Trading Bilateral Contract Market (CTBCM);Water and Power Develpoment Authority (WAPDA);Distribution Companies (DISCO's);Advance Metering Infrastructure (AMI);Generation Companies (GENCO's);Central Power Purchase Agency (CPPA)Bulk Power Consumer (BPC)|
|[A Highly Selective Dual Bandpass Filter using Couple Line Resonator for Modern Wireless Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099212)|B. Mushtaq; M. A. Rehman; A. Hussain; M. J. Abbass|10.1109/iCoMET57998.2023.10099212|Dual-band;Bandpass Filter;Short Circuit Stub;Couple Line;Step Impedance Resonator;Microwave Filters;Dual-band;Bandpass Filter;Short Circuit Stub;Couple Line;Step Impedance Resonator;Microwave Filters|
|[Energy Management System in Industrial Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099134)|S. Ali; R. A. Shah; F. H. Malik; H. S. Hashmi|10.1109/iCoMET57998.2023.10099134|Conditional Value at Risk;Energy Management System;Risk Aversion;Resilience;unexpected conditions;Conditional Value at Risk;Energy Management System;Risk Aversion;Resilience;unexpected conditions|
|[Centralized Accessibility of VM for Distributed Trusted Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099351)|U. Alias Kashif; Z. Ali Memon; S. Ahmed Ghanghro; W. Ahmed Channa; A. Soomro|10.1109/iCoMET57998.2023.10099351|Trusted Cloud computing;Distributed Trust Models;Virtual machine integrity;Trusted Cloud computing;Distributed Trust Models;Virtual machine integrity|
|[Real-Time Detection of Road-Based Objects using SSD MobileNet-v2 FPNlite with a new Benchmark Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099364)|S. Kumar; R. Kumar; Saad|10.1109/iCoMET57998.2023.10099364|;|
|[Performance Evaluation of Deep Learning Models for Leaf Disease Detection: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099223)|W. Akbar; A. Soomro; M. Ullah; M. Inam Ul Haq; S. Ullah Khan; T. Ali Shah|10.1109/iCoMET57998.2023.10099223|Convolutional Neural Network;Plant Disease;Deep Learning;Convolutional Neural Network;Plant Disease;Deep Learning|
|[Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099175)|S. Noor; S. U. Bazai; M. I. Ghafoor; S. Marjan; S. Akram; F. Ali|10.1109/iCoMET57998.2023.10099175|Generative Adversarial Networks;GAN Based Anomaly Detection Methods;Anomaly Detection;Generative Adversarial Network;Anomaly Detection Techniques;Generative Adversarial Networks;GAN Based Anomaly Detection Methods;Anomaly Detection;Generative Adversarial Network;Anomaly Detection Techniques|
|[Novel Method of Calculating the Coefficient of Asymmetry in the Negative Sequence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099113)|K. I. Khosiljonovich; A. Saleem; A. Iqbal; K. M. Mutalibjon qizi; E. Khojiakbar; M. Mateen|10.1109/iCoMET57998.2023.10099113|power quality;voltage asymmetry;negative sequence coefficient;power indicators;power quality;voltage asymmetry;negative sequence coefficient;power indicators|
|[Efficient & Sustainable Intrusion Detection System Using Machine Learning & Deep Learning for IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099152)|M. S. Amir; G. Bhatti; M. Anwer; Y. Iftikhar|10.1109/iCoMET57998.2023.10099152|IoT;IDS;Random Forest;Man in the Middle;Decision Tree;Bi-LSTM;IoT;IDS;Random Forest;Man in the Middle;Decision Tree;Bi-LSTM|
|[Degradation of Silicon Rubber-based Nuclear Power Plant I&C Cable under Accelerated Thermal Aging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099299)|U. Ibrahim; I. Ahmed; N. Ahmed; A. Rehman Abbasi; E. Mustafa; H. Ullah|10.1109/iCoMET57998.2023.10099299|nuclear power plants;low voltage cables;thermal aging;dissipation factor;hardness;electrical aging markers;nuclear power plants;low voltage cables;thermal aging;dissipation factor;hardness;electrical aging markers|
|[Towards 5G, 6G and 7G Sustainable and Potential Applications Using Blockchain: Comparative Analysis and Prospective Challenges*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099241)|U. Iftikhar; M. Anwer; R. Butt; G. Ahmed|10.1109/iCoMET57998.2023.10099241|Industry 4.0;Blockchain;IoT;5G;6G and 7G;Industry 4.0;Blockchain;IoT;5G;6G and 7G|
|[A Survey on Cancer Molecular Subtype Classification using Deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099055)|M. Wahid; G. Ahmed; S. Hussain; A. A. Ansari|10.1109/iCoMET57998.2023.10099055|Molecular Sub-typing;Deep Learning;Omics Data;K-means cluster;Molecular Sub-typing;Deep Learning;Omics Data;K-means cluster|
|[Numerical Analysis of Cs2 TiBr6 Perovskite Solar Cell Using ETL-CdS and HTL-CuI Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099154)|M. Z. Arif; M. Z. Alam; N. K. Chowdhury; A. Dutta; S. Baruri|10.1109/iCoMET57998.2023.10099154|Perovskite solar cell;SCAPS-1D;Metal work Function;Absorber layer;Perovskite solar cell;SCAPS-1D;Metal work Function;Absorber layer|
|[Towards a Digital Twin for Lifelong Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099333)|M. M. Yamin; A. S. Imran; B. Katt|10.1109/iCoMET57998.2023.10099333|Digital twin;life-long learning;personalized recommendations;recommender systems;chatbots;MOOCs;Digital twin;life-long learning;personalized recommendations;recommender systems;chatbots;MOOCs|
|[Design of a Buck Converter with an Analogue PI Controller for Wide Load Range Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099295)|M. Adeel; U. Fayyaz; U. Rafi; M. Umar Farooq|10.1109/iCoMET57998.2023.10099295|Buck Converter;PI controller;OP-Amps;Buck Converter;PI controller;OP-Amps|
|[Visualizing Research on Explainable Artificial Intelligence for Medical and Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099343)|S. Ali; A. S. Imran; Z. Kastrati; S. M. Daudpota|10.1109/iCoMET57998.2023.10099343|xai;explainable;interpretability;artificial intelligence;machine learning;deep learning;medical;healthcare;bibliometric analysis;xai;explainable;interpretability;artificial intelligence;machine learning;deep learning;medical;healthcare;bibliometric analysis|
#### **WSA & SCC 2023; 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding**
- DATE: 27-27 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Tree-Based Algorithm for Device Coordination in Over-the-Air Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104034)|M. A. Sedaghat; A. Bereyhi; R. R. Mueller; S. Asaad||;|
|[Overhead Reduction in UAV-Assisted Federated Learning with Fast-Varying Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104035)|S. Dai; S. Maghsudi; L. Thiele; S. Stanczak||;|
|[Model-Driven Deep Joint Source-Channel Coding over Time-Varying Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104036)|C. Karamanli; T. -Y. Tung; D. Guenduez||;|
|[Distributed Compression for Partially Cooperating Sensors and Gaussian Relevant Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104037)|S. Steiner; V. Kuehn||;|
|[Evaluation of the Fractional Approach for Iterative Algorithms in Compressed Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104038)|C. Sippel; R. F. H. Fischer||;|
|[Comparison of Damping Approaches for AMP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104039)|E. Sterk; C. Sippel; R. F. H. Fischer||;|
|[Vector Coded Caching Substantially Boosts MU-MIMO: Pathloss, CSI and Power-allocation Considerations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104040)|H. Zhao; P. Elia||;|
|[Deep-LaRGE: Higher-Order SVD and Deep Learning for Model Order Selection in MIMO OFDM Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104041)|B. Vilas Boas; W. Zirwas; M. Haardt||;|
|[Analysis of Intelligent Surface-Aided MIMO Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104042)|J. A. Russer; D. Semmler; M. Joham; J. A. Nossek; W. Utschick||;|
|[Rate Region of MIMO RIS-assisted Broadcast Channels with Rate Splitting and Improper Signaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104043)|M. Soleymani; I. Santamaria; E. Jorswieck||;|
|[Alternating Minimization for the Downlink of Wideband IRS-Aided mmWave MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104044)|D. Perez-Adan; M. Joham; O. Fresnedo; J. P. Gonzalez-Coma; W. Utschick; L. Castedo||;|
|[Distributed Coordinated Beamforming for RIS-Aided Dynamic TDD Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104045)|G. C. Nwalozie; M. Haardt||;|
|[Four-Dimensional Hurwitz Signal Constellations With Convenient Bit Mapping and Set Partitioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104046)|S. Stern; M. Basler; R. F. H. Fischer||;|
|[Geometrical Properties of Balls in Sum-Rank Metric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104527)|C. Ott; H. Liu; A. Wachter-Zeh||;|
|[Learning a Time-Frequency Predistortion for Optical Coherent Digital Sub-Carrier Multiplexing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104528)|T. Uhlemann; V. Aref; S. ten Brink||;|
|[Comparison of Electronic and Optoelectronic Signal Generation for Wireless THz Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104529)|J. Dittmer; P. Matalla; C. Fuellner; S. Wagner; A. Tessmann; C. Koos; S. Randel||;|
|[Statistical-CSI-Based Antenna Selection and Precoding in Uplink MIMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104530)|C. Ouyang; A. Bereyhi; S. Asaad; R. R. Mueller; H. Yang||;|
|[Robust Transmit Beamforming Using OSTBC in a Multicast Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104531)|J. Li; F. Wang; Y. Huang||;|
|[A Low Complexity Rate-Splitting Bilinear Precoder for Massive MIMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104532)|M. Bjelkanovic; D. Ben Amor; M. Joham; W. Utschick||;|
|[Lattice-Reduction-Aided Preequalization for Physical-Layer Security in Wireless THz-Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104533)|R. Schulz; R. F. H. Fischer||;|
|[Automorphism Ensemble Polar Code Decoders for 6G URLLC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104534)|C. Kestel; M. Geiselhart; L. Johannsen; S. ten Brink; N. Wehn||;|
|[Space-Efficient Quantized Polar Decoders Designed using the Information Bottleneck Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104535)|S. A. A. Shah; M. Stark; G. Bauch||;|
|[Semi-Deterministic Subspace Selection for Sparse Recursive Projection-Aggregation Decoding of Reed-Muller Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104536)|J. Voigt; H. Jaekel; L. Schmalen||;|
|[Polar Coding for Physical-Layer Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104537)|J. Pfeiffer; R. F. H. Fischer||;|
|[Analysis of dense array massive MIMO with practical constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104538)|N. V. Deshpande; S. R. Khosravirad; J. Du; H. Viswanathan; M. R. Castellanos; R. W. Heath||;|
|[MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104539)|S. Mashdour; R. C. de Lamare; A. Schmeink; J. P. S. H. Lima||;|
|[Classification of PC Baseband Signals from Wireless Egress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104540)|M. A. Leghari; S. M. Pralle; S. F. Peik; S. Luetje; W. Henkel||;|
|[Compressive Sensing based Angle-of-Arrival Estimation of a Single Light Source using a Liquid Crystal Display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104541)|A. Harlakin; M. Schurwanz; J. Mietzner; P. A. Hoeher||;|
|[Intelligent Reflecting Surface Enabled Wireless System with Antenna Selection at Source Under Transceiver Hardware Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104542)|C. Kumar; A. Kumar; S. Kashyap||;|
|[Intercarrier Interference at Terahertz Frequencies for IEEE Std 802.15.3d Multiband Transmissions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104543)|J. M. Eckhardt; C. Herold; T. Kuerner||;|
|[Performance Evaluation of Array Calibration for Angle-of-Arrival-Based 5G Positioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104544)|M. Henninger; S. Sengupta; S. Mandelli; S. ten Brink||;|
|[Antenna Array Calibration Via Gaussian Process Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104545)|S. S. Tambovskiy; G. Fodor; H. M. Tullberg||;|
|[MIMO Systems with Reconfigurable Antennas: Joint Channel Estimation and Mode Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104546)|F. Armandoust; E. Tohidi; M. Kasparick; L. Wang; A. H. Gokceoglu; S. Stanczak||;|
|[Deep Reinforcement Learning for mmWave Initial Beam Alignment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104547)|D. Tandler; S. Doerner; M. Gauger; S. ten Brink||;|
|[Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation Systems with Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104548)|D. Nicolas Bailon; V. Kuehn; S. Shavgulidze; J. Freudenberger||;|
|[Learning End-to-End Channel Coding with Diffusion Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104549)|M. Kim; R. Fritschek; R. F. Schaefer||;|
|[Spiking Neural Network Decision Feedback Equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104550)|E. -M. Bansbach; A. von Bank; L. Schmalen||;|
|[An Improved Data Collection Framework for Enabling ML-based QoS Prediction for Vehicular Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104551)|A. Krause; A. Palaios; A. Kumar; P. Schulz; G. Fettweis||;|
|[Joint Communication and Sensing Beamforming for Passive Object Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104552)|M. U. Baig; J. Vinogradova; G. Fodor; C. Mollen||;|
|[DFT-spread OFDM Frequency Domain Processing for Joint MIMO Radar and Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104553)|M. Schurwanz; J. Mietzner; P. A. Hoeher||;|
|[Ray Tracing Based Radio Channel Modelling Applied to RIS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104554)|J. Pyhtilae; J. Kokkoniemi; P. Sangi; N. Vaara; M. Juntti||;|
|[Empirical Evaluation of Distributional Shifts in FDD Systems Based on Ray-Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104555)|M. Baur; V. Rizzello; N. Alvarez Prado; W. Utschick||;|
|[ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104556)|F. Euchner; T. Schneider; M. Gauger; S. ten Brink||;|
|[RIS-Based Channel Modeling and Prototypical Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104557)|K. Weinberger; S. Tewes; M. Heinrichs; R. Kronberger; A. Sezgin||;|
|[LEO-PNT With Starlink: Development of a Burst Detection Algorithm Based on Signal Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104558)|W. Stock; C. A. Hofmann; A. Knopp||;|
|[Polarization and Correlation in MIMO Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104559)|A. Tukmanov; J. Weng; R. Husbands||;|
|[Suboptimal Position Control as Enabler for Low-Cost Distance Estimation in Dynamic Multipath Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104560)|M. Kokorsch; G. Dietl||;|
|[An ultra reliable low latency Cloud RAN implementation in GNU Radio for automated guided vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104561)|J. Demel; C. Bockelmann; A. Dekorsy||;|
|[A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104562)|S. Gracla; C. Bockelmann; A. Dekorsy||;|
|[Autoencoder-based Joint Communication and Sensing of Multiple Targets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104563)|C. Muth; L. Schmalen||;|
|[Ensemble Belief Propagation Decoding for Short Linear Block Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104564)|K. Kraft; M. Herrmann; O. Griebel; N. Wehn||;|
|[Optimizing Serially Concatenated Neural Codes with Classical Decoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104565)|J. Clausius; M. Geiselhart; S. ten Brink||;|
|[LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104566)|H. Nguyen-Kha; V. N. Ha; E. Lagunas; S. Chatzinotas; J. Grotz||;|
|[A Comparison between RSMA, SDMA, and OMA in Multibeam LEO Satellite Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104567)|A. Schroeder; M. Roeper; D. Wuebben; B. Matthiesen; P. Popovski; A. Dekorsy||;|
|[Beamforming performance of satellite swarm-based antenna arrays for 6G direct-to-cell connectivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104568)|D. Tuzi; T. Delamotte; A. Knopp||;|
|[Beam Splash Mitigation for NGSO Spectrum Coexistence between Feeder and User Downlink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104569)|E. Lagunas; A. Perez-Neira; J. Grotz; S. Chatzinotas; B. Ottersten||;|
|[Enabling Effective Multi-Link Data Distribution in NTN-based 6G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104570)|T. de Cola||;|
|[Beam-Based Resource Allocation in THz-NOMA Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104571)|Z. Ding; H. V. Poor||;|
|[Secure Communication in Multifunctional Active Intelligent Reflection Surface-assisted Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104572)|S. Hu; C. Liu; D. W. K. Ng; J. Yuan||;|
|[RISnet: a Dedicated Scalable Neural Network Architecture for Optimization of Reconfigurable Intelligent Surfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104573)|B. Peng; F. Siegismund-Poschmann; E. A. Jorswieck||;|
|[RIS-enhanced Resilience in Cell-Free MIMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104574)|K. Weinberger; R. -J. Reifert; A. Sezgin; E. Basar||;|
|[Federated Learning with Integrated Over-the-Air Computation and Sensing in IRS-assisted Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104575)|P. Zheng; Y. Zhu; M. Bouchaala; Y. Hu; S. Stanczak; A. Schmeink||;|
|[On the Degrees of Freedom of RIS-Aided Holographic MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104576)|J. C. Ruiz-Sicilia; X. Qian; M. Di Renzo; V. Sciancalepore; M. Debbah; X. Costa-Perez||;|
|[A Comparative Study of Subspace-based Superresolution Path Delay Estimation Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104577)|Z. Li; A. Nimr; P. Schulz; G. Fettweis||;|
|[Multi-Scatter-Point Target Estimation for Sensing-Assisted OTFS Automotive Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104578)|S. K. Dehkordi; J. C. Hauffen; P. Jung; R. Hernangomez; G. Caire; S. Stanczak||;|
|[Sensing-assisted Physical Layer Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104579)|N. Su; F. Liu; C. Masouros||;|
|[Adaptive Energy-Efficient Waveform Design For Joint Communication and Sensing using Multiobjective Multiarmed Bandits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104580)|A. R. Balef; S. Maghsudi; S. Stanczak||;|
|[6GEM Perspective on Joint Communication and Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104581)|G. vom Boegel; A. Sezgin; N. Pohl; M. Vossiek; M. Weimer; J. Wessel; M. Haferkamp; S. S. Sivadevuni; T. Koegel; S. Haeger; S. Boecker; J. Geiss||;|
|[Joint communication and target detection with multiple antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10104582)|H. Joudeh||;|
#### **2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)**
- DOI: 10.1109/ITIKD56332.2023
- DATE: 8-9 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Welcome Message from the Conference Chair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100107)|W. S. Awad|10.1109/ITIKD56332.2023.10100107|nan;Internet of Things;Technological innovation;Information technology;Computer crime;Knowledge discovery;Social networking (online);Social implications of technology|
|[Arabic Sign Language Recognition Using EfficientnetB1 and Transfer Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099710)|B. A. Dabwan; M. E. Jadhav; Y. A. Ali; F. A. Olayah|10.1109/ITIKD56332.2023.10099710|CNN;Sign language;EfficientnetB1;Image processing;Deep Learning;Transfer Learning;Training;Visualization;Technological innovation;Transfer learning;Gesture recognition;Assistive technologies;Convolutional neural networks|
|[A Knowledge Management Framework for Presales Operations in High-Technology Companies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100035)|M. Abdelaziz; T. Khalil; M. M. Awny|10.1109/ITIKD56332.2023.10100035|Knowledge Management;Presales;Framework;Process;Technological innovation;Roads;Bibliographies;Companies;Knowledge discovery;Knowledge management;Stakeholders|
|[The Era of Internet of Things: Towards better security using machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099608)|H. Abdulla; H. Al-Raweshidy; W. Awad|10.1109/ITIKD56332.2023.10099608|IoT;Machine Learning;Security;Anomaly Detection;Intrusion Detection System;Industries;Technological innovation;Smart cities;Ecosystems;Information security;Machine learning;Medical services|
|[Artificial Intelligence into Multimedia Deepfakes Creation and Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099744)|M. A. Khder; S. Shorman; D. T. Aldoseri; M. M. Saeed|10.1109/ITIKD56332.2023.10099744|Artificial intelligence;deepfakes;Photo deepfakes;Audio deepfakes;video deepfakes;Deepfakes;Technological innovation;Uncertainty;Social networking (online);Visual communication;Hate speech;Production|
|[Sterilization Robotic Approach in Hospitals: Case Study at King Hamad Hospital Bahrain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099561)|A. AL-Mofleh; L. Albalooshi; O. Najam; B. AlMannaei; M. Alseddiqi|10.1109/ITIKD56332.2023.10099561|service robots;healthcare;disease prevention and management;Environmental cleaning;Technological innovation;Schedules;Radiation effects;Hospitals;Service robots;Navigation;Cleaning|
|[The Role of Knowledge Sharing in Reinforcing Civic Engagement Practices in the context of Artificial Intelligence: The Case of the Kingdom of Bahrain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100329)|H. Al Ansari; F. Alnasser|10.1109/ITIKD56332.2023.10100329|knowledge Management;knowledge sharing;civic engagement;trust;value;Technological innovation;Data analysis;Social networking (online);Sociology;Government;Chatbots;Knowledge discovery|
|[Investigating the Value of Using Emotionally Intelligent Artificial Conversational Agents to Carry out Assessments in Higher Education: Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099518)|S. Alaswad; T. Kalganova; W. Awad|10.1109/ITIKD56332.2023.10099518|Artificial Intelligence;Conversational Agents;Human Computer Interaction;Wizard-of-Oz;Higher Education;Technological innovation;Source coding;Education;Anxiety disorders;Knowledge discovery;Reliability;Usability|
|[Energy-Efficiency in Cloud Datacenters: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099685)|M. Alghamdi; G. Alsaab; N. Alsunbol; L. Albraheem|10.1109/ITIKD56332.2023.10099685|component;formatting;style;styling;insert;Cloud computing;Data centers;Technological innovation;Power demand;Processor scheduling;Cooling;Government|
|[Using Single Time of Quantum Computer for Shor's Factoring Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099702)|Y. Alhammadi|10.1109/ITIKD56332.2023.10099702|Factoring algorithm;Shor's factoring algorithm;polynomial time;Technological innovation;Quantum computing;Knowledge discovery|
|[Fraud Classification In Financial Statements Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100257)|A. H. Ali Mohamed; S. Subramanian|10.1109/ITIKD56332.2023.10100257|Machine Learning;Classification;Random Forest;Credit Card Fraud;Accuracy;Financial Statements;Technological innovation;Machine learning algorithms;Forestry;Credit cards;Fraud;Classification algorithms;Safety|
|[Image Process Based Recommender System for Social Media Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100114)|L. A. Ali Alkhatib; S. Subramanian|10.1109/ITIKD56332.2023.10100114|Social media;Convolutional Neural Network (CNN);Recommender system;Image classification;Technological innovation;Social networking (online);Brand management;Text categorization;Neural networks;Software algorithms;Nonhomogeneous media|
|[Deep Learning Based An Optimized Predictive Academic Performance Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099652)|A. A. Almahdi; B. T. Sharef|10.1109/ITIKD56332.2023.10099652|Educational Early Warning System;Deep Learning;Predictive Analytics;Learning Analytics;Machine Learning;Support vector machines;Analytical models;Education;Artificial neural networks;Predictive models;Alarm systems;Feature extraction|
|[Visual Paragraph Generation: Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099830)|K. Almohsen|10.1109/ITIKD56332.2023.10099830|Image description;visual paragraph generation;image captioning;dense captioning;paragraph captioning;Measurement;Visualization;Technological innovation;Coherence;Big Data;Linguistics;Knowledge discovery|
|[Improving The Security of E-Exam Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100104)|F. Alnasser; A. Elrashidi|10.1109/ITIKD56332.2023.10100104|Data security;E-exams;Internet of Things;Cloud;Encryption;Cloud computing;Technological innovation;Costs;Computational modeling;Biological system modeling;Authentication;SQL injection|
|[Cloud-Based Big Data Analytics on IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100150)|G. Alrehaili; N. Galam; R. Alawad; L. Albraheem|10.1109/ITIKD56332.2023.10100150|Cloud Computing;IoT;Data Analytics;Industries;Technological innovation;Data analysis;Protocols;Big Data;Market research;Knowledge discovery|
|[A framework for Labor Market Analysis using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099838)|N. Alsayed; W. S. Awad|10.1109/ITIKD56332.2023.10099838|Labor Market Information;Labor Market Intelligence;Machine Learning;Analysis;NLP;Detect News;Industries;Analytical models;Technological innovation;Machine learning algorithms;Fluctuations;Merging;Machine learning|
|[Cost Saving Model For Lighting in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099893)|F. A. Mohamed Ali Alsebea; S. Subramanian; R. Binsaddig|10.1109/ITIKD56332.2023.10099893|IoT;Smart City;Machine Learning;Random Forest Regressor;Legged locomotion;Technological innovation;Costs;Smart cities;Prediction algorithms;Recording;Internet of Things|
|[The Technical Challenges in Orthotic Exoskeleton Robots with Future Directions: a Review Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099850)|R. Andersson; N. Björsell|10.1109/ITIKD56332.2023.10099850|Orthoses Exoskeletons;Wearable Robots;Technical Challenges;Design Aspects;Exoskeleton Future Directions;Industries;Technological innovation;Service robots;Exoskeletons;Fixtures;Insurance;Knowledge discovery|
|[Hybrid Resource Scheduling Algorithms in Heterogeneous Distributed Computing: a Comparative Study and Further Enhancements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100280)|M. Ba; A. Fall; B. S. Haggar|10.1109/ITIKD56332.2023.10100280|Cloud Computing;Scheduling Algorithm;Load Balancing;High-Performance Computing;Clustering;Energy-aware;CloudSim;Measurement;Technological innovation;Runtime;Scheduling algorithms;Heuristic algorithms;Clustering algorithms;Knowledge discovery|
|[Voice Command Based Remote Surgery Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100066)|M. Badow; M. Al-Qassimi; S. Safdar; A. Al-Felaij; R. Al-Murbati|10.1109/ITIKD56332.2023.10100066|Remote Surgery;Surgery Simulator;Monitored Surgery;Online Surgery;Simulated Surgery;Heating systems;Visualization;Technological innovation;Surgery;Speech recognition;Medical services;Knowledge discovery|
|[The Impact of the Data Warehouse on Decision Making Quality and Speed in Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099501)|A. Bahaaudeen|10.1109/ITIKD56332.2023.10099501|data warehouse;decision making process;system quality;information quality;decision quality;decision speed;higher education;Productivity;Technological innovation;Education;Decision making;Warehousing;Data warehouses;Knowledge discovery|
|[Generation and retrieval of procedural memory using natural intelligence for an articulated robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100232)|S. Chattoraj; T. Kalganova|10.1109/ITIKD56332.2023.10100232|natural intelligence;procedural memory;robotic automation;Technological innovation;Automation;Supply chains;Kinematics;Transforms;Manipulators;Trajectory|
|[Exploring The Role of Big Data Algorithm Recommendation in Smart Cities- Taking Book recommendation As an Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099656)|Y. Cheng; H. Chen; L. Xu; K. Chen; X. Wang; Z. Huang|10.1109/ITIKD56332.2023.10099656|big data;recommendation system;collaborative filtering;Weight measurement;Technological innovation;Solid modeling;Smart cities;Terminology;Collaborative filtering;Big Data|
|[Designing of Intrusion Detection System Using an Ensemble of Artificial Neural Network and Honey Badger Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100161)|R. Chinnasamy; M. Subramanian; N. Sengupta|10.1109/ITIKD56332.2023.10100161|cyber security;Intrusion detection system;honey badger optimization;artificial intelligence;Training;Technological innovation;Software algorithms;Network intrusion detection;Artificial neural networks;Knowledge discovery;Software|
|[The Impact of Cashless Payment in Application-Based Transportation on Gen Z User Behavior in Jakarta](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100198)|M. Christian; H. Yulita; L. R. Girsang; S. Wibowo; E. R. Indriyarti; S. Sunarno|10.1109/ITIKD56332.2023.10100198|cashless;payment;digital marketing;technology;innovation;Technological innovation;Costs;Shape;Transportation;Switches;Knowledge discovery;Behavioral sciences|
|[Resistant to Technology and Digital Banking Behavior Among Jakarta's Generation Z](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099594)|M. Christian; H. Yulita; Y. Yuniarto; S. Wibowo; E. R. Indriyarti; S. Sunarno|10.1109/ITIKD56332.2023.10099594|technology;resistant;digital;banking;PLS-SEM;Resistance;Technological innovation;Banking;Knowledge discovery;Mathematical models;Behavioral sciences;Security|
|[Machine Learning Schemes for Leak Detection in IoT-enabled Water Transmission System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100175)|F. Ebisi; I. P. Nikolakakos; J. V. Karunamurthi; A. N. Ahmed Binahmed Alnuaimi; E. Al Buraimi; S. Alblooshi|10.1109/ITIKD56332.2023.10100175|Anomaly detection;IoT enabled infrastructure;Machine learning;Smart water leak Test Rig;water supply network;Deep Learning;Technological innovation;Recurrent neural networks;Smart cities;Support vector machine classification;Static VAr compensators;Valves;Sensors|
|[Determining Linguistic Features of Hate Speech from 2016 Philippine Election-Related Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100008)|R. C. K. Enriquez; M. R. J. E. Estuar|10.1109/ITIKD56332.2023.10100008|Hate Speech Detection;Linguistic Features;Text Classification;Machine Learning;Natural Language Processing;Technological innovation;Social networking (online);Voting;Hate speech;Psychology;Linguistics;Feature extraction|
|[Hybrid Deep Learning for Channel Estimation and Power Assignment for MISO-NOMA System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099781)|M. Gaballa; M. Abbod; S. Alnasur|10.1109/ITIKD56332.2023.10099781|DL;LSTM;CNN;NOMA;KKT conditions;Deep learning;Training;Reactive power;NOMA;Technological innovation;Neural networks;Channel estimation|
|[Evaluating Gender Bias in Pre-trained Filipino FastText Embeddings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100022)|L. C. Gamboa; M. R. Justina Estuar|10.1109/ITIKD56332.2023.10100022|word embedding;gender bias;Filipino;FastText;Word Embedding Association Test;principal component analysis;Technological innovation;Semantics;Knowledge discovery;Principal component analysis|
|[Data Mining Hospital Treatment and Discharge Summary of Sickle Cell Disease Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099773)|M. Gollapalli; A. Alfaleh|10.1109/ITIKD56332.2023.10099773|Sickle cell disease;data mining;classification models;machine learning;knowledge extraction;Support vector machines;Hospitals;Red blood cells;Pain;Urban areas;Clustering algorithms;Knowledge discovery|
|[Semantic-based Job Recommendation Framework Using OWL Ontology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099674)|E. H. Alkhammash|10.1109/ITIKD56332.2023.10099674|OWL ontology;Job recommendation system;EasyRDF;Technological innovation;OWL;Semantics;Crawlers;Ontologies;Resource description framework;Servers|
|[Design, Development, and Implementation of Internet of Things Enabled Laboratory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099704)|Z. N. Haitaamar; B. Ramachandran; J. V. Karunamurthy|10.1109/ITIKD56332.2023.10099704|Internet-of-Things (IoT);Energy Efficiency;Optimization;Wireless Sensor Network (WSN);MQTT;Wireless sensor networks;Technological innovation;Ecosystems;Knowledge discovery;Energy efficiency;Software;Internet of Things|
|[Parallel Self Organizing Neural Network (PSONN) Prediction of Water Saturation in Carbonate Reservoirs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099865)|G. Hamada; A. Al Gathe; A. Al Khudafi|10.1109/ITIKD56332.2023.10099865|Water saturation;Artificial intelligence;PSONN;carbonate reservoir;Technological innovation;Statistical analysis;Oils;Neural networks;Reservoirs;Rocks;Prediction algorithms|
|[Fake News Detection: A Graph Mining Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099626)|H. H. Hasan Ahmed Abdulla; H. H. Abdulla|10.1109/ITIKD56332.2023.10099626|Fake News;Machine Learning;Graph Mining;Natural Language Processing;Social Media;Text Mining;Technological innovation;Social networking (online);Training data;Linguistics;Feature extraction;Knowledge discovery;Data mining|
|[Propose a Recommender System to Dynamically Align Higher Education Curriculums With 4IR Market Needs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099924)|Z. Hasan; S. S. Baskaran|10.1109/ITIKD56332.2023.10099924|Fourth Industrial Revolution;K-Means;Cosine Similarity;TF-IDF;Clustering;Gap Analysis;Industries;Technological innovation;Heuristic algorithms;Clustering algorithms;Knowledge discovery;Classification algorithms;Fourth Industrial Revolution|
|[A Systematic Literature Review of Blockchain for Higher Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100049)|F. Kabashi; H. Snopce; A. Aliu; A. Luma; L. Shkurti|10.1109/ITIKD56332.2023.10100049|systematic review;blockchain;education;application;Technological innovation;Systematics;Databases;Scalability;Bibliographies;Education;Knowledge discovery|
|[Predicting Acute Respiratory Failure Using Fuzzy Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099746)|F. Khalaf; S. S. Baskaran|10.1109/ITIKD56332.2023.10099746|Fuzzy Classifier;Artificial Neural Network (ANN);Acute Respiratory Failure;Prediction;Technological innovation;Neural networks;Medical services;Knowledge discovery;Respiratory system;Medical diagnostic imaging;Diseases|
|[Weapon detection system for surveillance and security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099733)|S. Khalid; A. Waqar; H. U. Ain Tahir; O. C. Edo; I. T. Tenebe|10.1109/ITIKD56332.2023.10099733|weapon detection;handguns;Yolov5;Deep learning;Measurement;Technological innovation;Weapons;Object detection;Video surveillance;Real-time systems|
|[Hybrid Education and Institutional Readiness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100289)|N. K. Malik; A. Al-Hattami; I. Elmahdi; A. Abusin|10.1109/ITIKD56332.2023.10100289|hybrid education;educational access;educational policies;institutional readiness;innovative teaching;educational technology;Technological innovation;Pediatrics;Pandemics;Education;Collaboration;Market research;Knowledge discovery|
|[Online Games Data Storing Methods, Vulnerabilities, and Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099671)|M. A. Khder; S. Shorman; A. F. Ali; M. O. Al-Mudaifa|10.1109/ITIKD56332.2023.10099671|online games;save state;Massively Multiplayer Online Games;Technological innovation;File systems;Databases;Computer bugs;Memory;Games;Companies|
|[Review Study of the Impact of Artificial Intelligence on Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099788)|M. A. Khder; S. Shorman; D. A. Showaiter; A. S. Zowayed; S. I. Zowayed|10.1109/ITIKD56332.2023.10099788|Artificial intelligence;machine learning;cyber security;cyber-attacks;deep learning;cyberspace;Technological innovation;Law;Organizations;Knowledge discovery;Malware;Personnel;Artificial intelligence|
|[Social Engineering via Personality Psychology - Bypassing Users Based on Their Personality Pattern To Raise Security Awareness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100048)|A. M. Yaser Al-Bustani; A. K. Almutairi; A. Alrashed; A. W. Muzaffar|10.1109/ITIKD56332.2023.10100048|Social Engineering;Cyber Attacks;Behavior Psychology;Phishing;URL Phishing;DiSC;Personality Model;Personality Patterns;RBYG Behavior Patterns;RBYG Personality Test;Personality Test;Uniform resource locators;Technological innovation;Social networking (online);Unsolicited e-mail;Phishing;Psychology;US Department of Homeland Security|
|[Automatic Content Searching Model During Blended Learning Class Sessions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099657)|M. M. Abdulkarim; M. Alsaeed; S. Safdar|10.1109/ITIKD56332.2023.10099657|Blended Learning;Searching Techniques;TF-IDF;Voice Based Searching;Learning experience;Technological innovation;Electronic learning;Education;Speech recognition;Search problems;Knowledge discovery;Libraries|
|[COVID-19 Contact-Tracing Networks Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099670)|J. Moosa; W. Awad; T. Kalganova|10.1109/ITIKD56332.2023.10099670|COVID-19;Contact Tracing;SARS-CoV-2;WHO;COVID-19;Economics;Technological innovation;Ethics;Bibliographies;Knowledge discovery;Diseases|
|[Accuracy and Privacy Evaluation of detected communities using Attributed-Based Label Propagation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100272)|J. Moosa; W. Awad; T. Kalganova|10.1109/ITIKD56332.2023.10100272|Community Detection;NMI;ARI;RI;VI;Weight measurement;Privacy;Technological innovation;Social networking (online);Network analyzers;Knowledge discovery;Time measurement|
|[Programming courses Teaching methods Before, During, and After COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099736)|J. Moosa; A. Bahaaudeen|10.1109/ITIKD56332.2023.10099736|Online teaching;COVID;LMS;teaching and assessment methods;COVID-19;Learning systems;Technological innovation;Distance learning;Pandemics;Education;Knowledge discovery|
|[A Deep Learning Model for Classification of EEG Signals for Neuromarketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100014)|S. M. Usman; S. M. Ali Shah; O. C. Edo; J. Emakhu|10.1109/ITIKD56332.2023.10100014|EEG;Deep Learning;SVM;DT;Neuromarketing;Deep learning;Technological innovation;Tracking;Neuromarketing;Sensitivity and specificity;Brain modeling;Electroencephalography|
|[A Study of Machine Learning Techniques based on Human Daily Living Activities via Inertial Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099820)|Z. Mustafa|10.1109/ITIKD56332.2023.10099820|human activity recognition (HAR);inertial measurement unit (IMU);random forest (RF);k-nearest neighbours (KNN);feature extraction;classification;hidden Markov models (HMM);Technological innovation;Measurement units;Inertial sensors;Computational modeling;Hidden Markov models;Data models;Reliability|
|[The Impact of Data Augmentation on Sentiment Analysis of Translated Textual Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099851)|T. Omran; B. Sharef; C. Grosan; Y. Li|10.1109/ITIKD56332.2023.10099851|Data augmentation;LSTM;translation-based;Modern standard Arabic;Bahraini dialects;Deep learning;Sentiment analysis;Technological innovation;Training data;Knowledge discovery;Boosting;Standards|
|[Negative Impact of Social Media Advertisements on Branding in Digital Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099505)|O. P. Oriakhi; A. Amin; S. Safdar|10.1109/ITIKD56332.2023.10099505|Social Media Advertisement;Brand Image;Purchase Intention;Negative comments/opinions;Controversial brand post;Fake news;Technological innovation;Analytical models;Social networking (online);Brand management;Knowledge discovery;Fake news;Advertising|
|[Improving Recommendation System by using a knowledge Graph Database for Maintenance of Rolling Stock](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099517)|Z. Ragala; A. Retbi; S. Bennani|10.1109/ITIKD56332.2023.10099517|Recommendation system;Rolling stock;Knowledge acquisition;Knowledge-based system;Content-based filtering;Collaborative filtering;KNN;Technological innovation;Tree graphs;Profitability;Knowledge graphs;Maintenance engineering;Prediction algorithms;Knowledge discovery|
|[IoT-based Test facility for a Water Transmission Line with Leak Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099751)|B. Ramachandran; H. Y. Youssef; J. V. Karunamurthi; F. Ebisi; A. N. Ahmed Alnuaimi; J. Najjar|10.1109/ITIKD56332.2023.10099751|internet of things;water leakage detection;sensors;test facility;monitoring and control;utility company;Technological innovation;Test facilities;Power transmission lines;Pipelines;Companies;Valves;Propagation losses|
|[Towards an approach for weaving Open Digital Rights Language into Role-Based Access Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100036)|A. S. Alshamsi; Z. Maamar; M. -A. Kuhail|10.1109/ITIKD56332.2023.10100036|Access control;ODRL;Policy;RBAC;Access control;Technological innovation;Organizations;Knowledge discovery;Weaving;Safety|
|[Blockchain-enabled Platform for a Meta Customer Loyalty Program](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099907)|A. A. Shaikh; N. Dahmani; S. Khan; R. Sharma|10.1109/ITIKD56332.2023.10099907|digital business;blockchain platform;transparency;accountability;fairness;ethics;Technological innovation;Strips;Ethics;Knowledge discovery;Mobile applications;Blockchains;Object recognition|
|[Baseline Estimation in Face Detection for AI Proctored Examinations through Convoluted Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100328)|A. Shata; Z. N. Haitaamar; A. Benkrid|10.1109/ITIKD56332.2023.10100328|AI;Online Examinations;MTCNN;Baseline Estimation;Face Detection;COVID-19;Technological innovation;Pandemics;Neural networks;Education;Estimation;Knowledge discovery|
|[Hardware doppler shift emulation and compensation for LoRa LEO satellite communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100192)|V. Subramanian; J. V. Karunamurthy; B. Ramachandran|10.1109/ITIKD56332.2023.10100192|Internet-of-Things (IoT);LoRa;LEO satellite;Doppler effect;Doppler emulation;Doppler compensation;Doppler shift;Wireless communication;Wireless sensor networks;Satellites;Protocols;Transmitters;Low earth orbit satellites|
|[Multi-Factor Authentication Modeling using Petri Nets: Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099567)|M. W. Alawadhi; W. Shaker Awad|10.1109/ITIKD56332.2023.10099567|Multi-Factor Authentication;Petri Nets;Modelling;Cyber Security;Technological innovation;Multi-factor authentication;Computer hacking;Petri nets;Data protection;Organizations;Benchmark testing|
|[DEWA R&D Data Lake: Big Data Platform for Advanced Energy Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099717)|H. Y. Youssef; M. Ashfaque; J. V. Karunamurthy|10.1109/ITIKD56332.2023.10099717|utility data;big data platform;internet of things;digital transformation;advanced data analytics;Technological innovation;Data analysis;Soft sensors;Digital transformation;Decision making;Data visualization;Big Data applications|
#### **2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)**
- DOI: 10.1109/ICICACS57338.2023
- DATE: 24-25 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Deep Learning based Breast Image Classification Study for Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100206)|C. Sarada; V. Dattatreya; K. V. Lakshmi|10.1109/ICICACS57338.2023.10100206|Mammogram;Histopathology;Magnetic Resonance Imaging (MRI);Ultrasound Modality;Classification;Diagnosis;Deep Learning (DL);Convolutional Neural Networks (CNN). Transfer Learning (TL);Artificial Neural Network (ANN);University of Michigan Health System (UM);Film-field digital mammography (FFDM);Radial basis function (RBF);Lung Image Database Consortium (LIDC);Deep Convolutional Neural Network (DCNN);Ultrasonic imaging;Magnetic resonance imaging;Transfer learning;Neural networks;Manuals;Breast cancer;Mammography|
|[Analysis of High Performance Low Power Full Adder Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100024)|P. A. Shraavya; C. R. Shetty; Nidhi; D. P. Suvarna; Roopashree; A. Bekal|10.1109/ICICACS57338.2023.10100024|Integrated circuits (IC);Full Adder;Very Large-Scale Integration (VLSI);Speed;Image processing;High performance computing;Digital signal processors;Signal processing algorithms;Computer architecture;Very large scale integration;Hybrid power systems|
|[Effective Reduction of Power Consumption with Thermo Electric Module (TEM) and Wireless Sensor Nodes using Embedded System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099582)|N. B. Abhishakth; N. D. Kamal; G. M. Arasan; P. Baraneedharan|10.1109/ICICACS57338.2023.10099582|Thermo Electric Modules (TEM);Thermo Electric Generator (TEG);Embedded system;Wireless sensor node;Power Consumption;Wireless communication;Resistance;Productivity;Wireless sensor networks;Embedded systems;Power demand;Real-time systems|
|[Development of Miniature Wearable Ultra-Wideband Antenna Using Qualified Analysis of Different Substrate Material for BCC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100012)|M. A. Chimanna; P. S. Dhumal; M. J. Sagede|10.1109/ICICACS57338.2023.10100012|wearable UWB antenna;gain;efficiency;radiation pattern;VSWR;Wireless communication;Surface impedance;Resonant frequency;Bandwidth;Reflector antennas;Ultra wideband antennas;Rubber|
|[Design and Development of Stacked Multi-Permittivity Dielectric Resonator Antenna (DRA) for Ultra-Wide Band (UWB) Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099748)|R. M L; M. G R|10.1109/ICICACS57338.2023.10099748|DRA;stacked DR;multi-frequency;springing effect and ultra-wideband (UWB);Resonant frequency;Dielectric losses;Bandwidth;Silicon;Dielectric resonator antennas;Ultra wideband antennas;Frequency measurement|
|[Area Optimised Efficient Multiplication Using Modified Round Square Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099920)|G. R. L. V. N. S. Raju; T. N. Nikitha; G. C. Ram; U. G. Chary; J. N. V. Vardhan; J. D. Kumar|10.1109/ICICACS57338.2023.10099920|Round square approximation;Multiplier;Approximate multiplier;Energy consumption;Power demand;Costs;Very large scale integration;Hardware;Energy efficiency;Compressors|
|[Comparative Analysis of Antenna Design for Avionics Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099653)|L. Dash; D. Sharma; S. K B; I. Jennifer; B. K S; C. G S|10.1109/ICICACS57338.2023.10099653|patch;wraparound;monopole antenna;gain;Measurement;Integrated circuits;Rockets;Wires;Resonant frequency;Bandwidth;Aerospace electronics|
|[Quantum Machine Learning in Healthcare: Developments and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100075)|S. Rani; P. Kumar Pareek; J. Kaur; M. Chauhan; P. Bhambri|10.1109/ICICACS57338.2023.10100075|healthcare;machine learning;quantum bits;quantum computing;quantum machine learning;Integrated circuits;Quantum computing;Machine learning algorithms;Quantum algorithm;Communication systems;Computational modeling;Quantum mechanics|
|[Combining K-Means and Gaussian Mixture Model for better accuracy in prediction of Ductal Carcinoma in Situ (DCIS)- Breast Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099971)|P. N. Singh; P. Mohan; R. Rajput|10.1109/ICICACS57338.2023.10099971|DCIS;Feature Extraction;Estimation Maximization;Gaussian Mixture Model;K-Means;Shape;Supervised learning;Clustering algorithms;Predictive models;Prediction algorithms;Breast cancer;Classification algorithms|
|[Deepfake Video Detection System Using Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099618)|S. R. B. R; P. Kumar Pareek; B. S; G. G|10.1109/ICICACS57338.2023.10099618|Convolutional Neural Network;ResNet50;LSTM;Deep Fake;GAN;Training;Deep learning;Deepfakes;Visualization;Neural networks;Media;Service-oriented architecture|
|[Secure Three Dimensional Path Planning in Dynamic Environment Using Internet of Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099984)|P. Agarwal; S. Sharma; R. Tyagi|10.1109/ICICACS57338.2023.10099984|IoV;VANETs;3D path optimization;dynamic environment vehicles;Solid modeling;Three-dimensional displays;Urban areas;Vehicular ad hoc networks;Path planning;Real-time systems;Safety|
|[IOT Based Surveillance Robot with Bomb Diffusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100025)|P. V. Phani Srikar; P. Sumanth Kumar; M. Divya; P. G. V. Vinay Kumar; P. Vyshnavi|10.1109/ICICACS57338.2023.10100025|surveillance;illegal smuggling;night vision IR camera;bomb diffusion;robotic arm;metal detector;Weapons;Surveillance;Robot vision systems;Detectors;Cameras;Universal Serial Bus;Safety|
|[Machine Learning Classifiers to Detecting Epileptic Seizures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100101)|M. Tushar Deshmukh; S. R. Suralkar|10.1109/ICICACS57338.2023.10100101|Epileptic Seizures;machine learning;electroencephalogram (EEG);electrocorticography (ECoG);epilepsy;Neurological diseases;Location awareness;Neurons;Epilepsy;Machine learning;Media;Electroencephalography|
|[A New Approach to Recognize a Patient with Diabetic Retinopathy using Pre-trained Deep Neural Network EfficientNetB0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099647)|S. H; V. Petli; V. K Jalihal; J. B K; S. Patil|10.1109/ICICACS57338.2023.10099647|Artificial Intelligence;Diabetic retinopathy (DR);Image Processing;Kaggle's;Neural organization;Technological innovation;Retinopathy;Transfer learning;Organizations;Solids;Retina;Diabetes|
|[A Novel Approach for Recognition of Face by Using Squeezenet Pre-Trained Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100097)|S. H; V. V. M; V. Petli; N. B. Patil|10.1109/ICICACS57338.2023.10100097|Con v_ base;convolutional neural network-complex features;trained network;transfer learning;Training;Deep learning;Integrated circuits;Face recognition;Neural networks;Transfer learning;Training data|
|[Autoencoder for Image Retrieval System using Deep Learning Technique with Tensorflow and Kears](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099675)|K. Wangi; A. Makandar|10.1109/ICICACS57338.2023.10099675|Convolutional autoencoder;Image Retrieval;Keras;Tensorflow;Approximate Nearest Neighbor;Deep learning;Training;Visualization;Shape;Image retrieval;Neural networks;Market research|
|[Area Efficient Reliable QCA Adder and Subtractor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099724)|A. T. Vanaraj; R. S. K; M. R; G. Lakshminarayanan; A. Venkitachalam|10.1109/ICICACS57338.2023.10099724|Adder;Area;QCA;Reliability;Subtractor;Integrated circuits;Fabrication;Fault tolerance;Fault tolerant systems;Quantum dots;Logic gates;Reliability engineering|
|[Deep Learning approaches for Automated Detection of Fake Indian Banknotes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100265)|H. Prakash; A. Yadav; U. P; C. Jha; G. K. Sah; A. Naik|10.1109/ICICACS57338.2023.10100265|Convolutional Neural Networks;Counterfeit notes detection;Deep Learning;Banknote security;Recurrent neural networks;Deep learning;Integrated circuits;Recurrent neural networks;Government;Feature extraction;Security;Convolutional neural networks|
|[Predictive analysis of Unemployment rate Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100121)|A. P. Nirmala; A. V; A. Prasad; A. M; D. Babu P|10.1109/ICICACS57338.2023.10100121|unemployment rate;Exploratory Data Analysis;LSTM;Linear Regression;Adam;MSE;dense;Training;Integrated circuits;Analytical models;Data analysis;Linear regression;Machine learning;Predictive models|
|[Glaucoma Detection using HOG and Feed-forward Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099506)|A. S; M. R. Bharamagoudra; B. K. P; R. R. Pujari; V. A. Hanamanal|10.1109/ICICACS57338.2023.10099506|Glaucoma;Retinopathy;HOG;feed-forward neural networks;Wavelet transforms;Integrated circuits;Histograms;Retinopathy;System performance;Dynamic range;Feature extraction|
|[Fake News Detection Using Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100208)|U. P; A. Naik; S. Gurav; A. Kumar; C. S R; M. B S|10.1109/ICICACS57338.2023.10100208|LSTM (Long Short-Term Memory);Hoax news;NLP (Natural language Processing);Machine Learning;Deep learning;Sentiment analysis;Social networking (online);Text categorization;Semantics;Syntactics;Feature extraction|
|[Early Diagnosis of Types of Glaucoma Using Multi Feature Analysis Based on DBN Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100195)|L. Sunkara; B. L. Vema; H. L. Prasanna Rajulapati; A. Mukkapati; V. Aruna|10.1109/ICICACS57338.2023.10100195|Deep Belief Network (DBN);Multi-Feature Analysis;Congenital Glaucoma;Secondary Glaucoma;Open Angle Glaucoma;Angle Closure Glaucoma;Optical losses;Integrated circuits;Visualization;Neural networks;High-resolution imaging;Retina;Feature extraction|
|[Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099529)|K. N. R; P. Ranjana|10.1109/ICICACS57338.2023.10099529|Autism Spectrum Disorder;fuzzy logic;recurrent neural network;Deep learning;Fuzzy logic;Autism;Pathology;Recurrent neural networks;Sensitivity;Medical treatment;Psychology|
|[Fine Tuned DBN Model for Food Ingredient Recognition: Introduction to Self-improved Tasmanian Devil Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099841)|S. A. Madival; S. S. Jawaligi|10.1109/ICICACS57338.2023.10099841|Food Ingredients;Deep features;Textual features;DBN;ITDO algorithm;Support vector machines;Measurement;Integrated circuits;Finite impulse response filters;Communication systems;Classification algorithms;Tuning|
|[Fashion Forecasting using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099844)|N. Thoiba Singh; L. Chhikara; P. Raj; S. Kumar|10.1109/ICICACS57338.2023.10099844|deep learning;convolutional neural network (CNN);feature extraction;recommender system;fashion forecasting;Solid modeling;Filtering;Social networking (online);Time series analysis;Market research;Forecasting;Integrated circuit modeling|
|[A Modern Podcast Player for Mobile Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100023)|N. T. Singh; P. Kaur; S. Kaur; D. Kaur|10.1109/ICICACS57338.2023.10100023|Application Programming Interface;software development kit;podcast;NoSQL;flutter;Performance evaluation;Computer languages;Social networking (online);Operating systems;User interfaces;Programming;Software|
|[ECG Compression Techniques of Spiht Decoder for Mobile Health Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099642)|V. B. K. L. Aruna; E. Chitra; M. Padmaja|10.1109/ICICACS57338.2023.10099642|ECG signal compression;SPIHT decoder;Wavelets;Noise removal;m-Health applications;Wireless communication;Image coding;Databases;Measurement uncertainty;Electrocardiography;Decoding;Noise measurement|
|[Different Machine Learning Approch's for Diagnosis of Alzheimer's Disease and Vascular Dementia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100185)|V. Pentela; B. R. Nilaya Vendra; D. T. Reddy Putluri; V. Kumar Bodapati; S. M. Nimmagadda|10.1109/ICICACS57338.2023.10100185|Alzheimer's Disease;Vascular Dementia;imaging and Machine Learning Approach's;Support vector machines;Radio frequency;Proteins;Integrated circuits;Machine learning algorithms;Sensitivity;Magnetic resonance imaging|
|[Prevalent Cyber Attacks and Defense](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099516)|V. R. B; R. S|10.1109/ICICACS57338.2023.10099516|Cyber-attacks;Ransomware;Phishing;Malware;Man in the middle attack;Integrated circuits;Earth;Phishing;Government;Education;Cyberspace;Cyber warfare|
|[Mechatronics Computer Numerical Control Tool to Manufacture Calligraphy Board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100068)|P. B. Shetty; B. Praveen; A. S. U; G. J. Naveen|10.1109/ICICACS57338.2023.10100068|CNC;Machine learning;Mechatronics;Calligraphy;Solid modeling;Mechatronics;Machine learning algorithms;Codes;Three-dimensional displays;Machine learning;Production|
|[LDWPSO based Bi-LSTM Model for Predicting the Missing Data in PHRs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099845)|P. K. Pareek; A. N. Prasad; G. G; N. C. P|10.1109/ICICACS57338.2023.10099845|Personal health records;Missing Data;Bi-Directional Long Short-Term Memory;Linearly decreasing weight particle swarm optimization;Korean National Health Nutrition Examination Survey;Support vector machines;Radio frequency;Noise reduction;Machine learning;Predictive models;Data models;Particle swarm optimization|
|[Adaptive Voting Mechanism with Artificial Butterfly Algorithm based Feature Selection for IDS in MANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099861)|P. B.D; A. Prasad N; Dhanraj; M. T N|10.1109/ICICACS57338.2023.10099861|Intrusion Detection Systems;Artificial Butterfly algorithm;Adaptive voting mechanism;Mobile ad hoc networks;Boosted Regression Trees;Adaptation models;Wireless sensor networks;Wireless networks;Intrusion detection;Feature extraction;Ad hoc networks;Classification algorithms|
|[Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100324)|P. K. Pareek; A. P. N; C. Srinivas; J. B. N|10.1109/ICICACS57338.2023.10100324|Rainfall Prediction;Multi-layer perceptron;Karnataka Subdivision;Data Mining;Honey Badger procedure;Multivariate Convolutional Long Short-Term Memory;Deep learning;Data analysis;Correlation;Time series analysis;Droughts;Predictive models;Data models|
|[Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099916)|P. K. Pareek; R. I M; J. B. N; L. H. M|10.1109/ICICACS57338.2023.10099916|Agriculture;Grape Leaves Disease Detection;Firefly with cyclic randomization;Convolutional Neural Network;K-means clustering;component;formatting;style;styling;insert;Computers;Image segmentation;Computational modeling;Pipelines;Crops;Real-time systems;Convolutional neural networks|
|[Prediction of Floods in Kerala using Hybrid Model of CNN and LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099867)|P. Kumar Pareek; C. Srinivas; N. S; M. D S|10.1109/ICICACS57338.2023.10099867|Flood Prediction;Long short-term memory;Land use and land cover;Vembanad Lake System;Training;Biological system modeling;Time series analysis;Training data;Predictive models;Lakes;Feature extraction|
|[Scheduling the Task of User in Cloud computing using Hybrid Procedure of PSO and Lion Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100224)|P. B. D.; U. M; S. N; M. B. R|10.1109/ICICACS57338.2023.10100224|Particle Swarm Optimization;Lion Algorithm;CloudSim toolkit;Workloads;Task Scheduling;Makespan;Cloud Computing;Computers;Cloud computing;Schedules;Processor scheduling;Computational modeling;Virtual environments;Virtual machining|
|[U-Net based Segmentation and Transfer Learning Based-Classification for Diabetic-Retinopathy Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099813)|B. D. Parameshachari; B. M. Nalini; H. M. LeenaShruthi; P. Diggi|10.1109/ICICACS57338.2023.10099813|Artificial intelligence;Diabetic Retinopathy;Transfer learning;Asia Pacific Tele-Ophthalmology Society;Data augmentation;Hand-crafted feature extraction;Deep learning;Training;Visualization;Retinopathy;Transfer learning;Visual impairment;Blood vessels|
|[An Enhanced Genetic Algorithm for Solving Trajectory Planning of Autonomous Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099994)|A. Kishore Kumar; A. Alemran; D. A. Karras; S. Kant Gupta; C. Kumar Dixit; B. Haralayya|10.1109/ICICACS57338.2023.10099994|Autonomous Robots;Upgraded Genetic Algorithm;Trajectory Planning;Distance Matrix;Limiting;Trajectory planning;Trajectory tracking;Simulation;Sociology;Sensors;Task analysis|
|[IoT Preserving Patient-Centric Models for Privacy Preserving Based Personal Health Records Sharing in Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100155)|P. Mohan|10.1109/ICICACS57338.2023.10100155|Rail Fence Data Encryption (RFDE);Cloud Service Providers (CSP);Personal Health Records (PHRs);Internet of Things (IoT);Privacy Patient-Centric Models;Rails;Access control;Cloud computing;Privacy;Data privacy;Ciphers;Encryption|
|[Detection of QRS Complexes in ECG Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100203)|R. N; M. Anto Bennet|10.1109/ICICACS57338.2023.10100203|Heart Rate Variability;QRS detection;Cardiac Diseases;Rhythm Disorders;Automatic Classification;Integrated circuits;Heart beat;Communication systems;Electrocardiography;Rhythm;Heart rate variability;Diseases|
|[Learning Future Terrorist Targets using Attention Based Hybrid CNN and BiLSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100298)|N. Nayak; M. Rayachoti; A. M. Gupta; G. P. Prerna; S. M. V; D. Annapurna|10.1109/ICICACS57338.2023.10100298|GTD;Framework;Deep Learning;BiLSTM;CNN;LSTM;Attention Mechanism;Deep learning;Communication systems;Terrorism;Time series analysis;Organizations;Predictive models;Feature extraction|
|[Automatic Identification of Epileptic Seizure Using Kelm Optimized By Grey Wolf Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099688)|D. Saranya; A. Bharathi|10.1109/ICICACS57338.2023.10099688|Support Vector Machine;Extreme Learning Machine;Kernel Extreme Learning Machine;Grey Wolf Algorithm;Training;Extreme learning machines;Epilepsy;Classification algorithms;Clinical diagnosis;Integrated circuit modeling;Particle swarm optimization|
|[A Wavelet Transform Algorithm based Detection and Classification of Sleep Apnea for Monitoring of Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099785)|B. G. Pillai; M. J. A; V. J. Babu; A. S. Kumar Reddy; A. Siddiqua|10.1109/ICICACS57338.2023.10099785|IRA;NREM;AC;EEG;WT;Wavelet transforms;Time-frequency analysis;Wavelet domain;Event detection;Coherence;Feature extraction;Sleep apnea|
|[The Application of Machine Learning Algorithms to the Classification of EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099996)|S. Gurumoorthy; S. Avikkal; R. M; K. Radha; R. N|10.1109/ICICACS57338.2023.10099996|attention deficit hyperactivity disorder (ADHD);Electroencephalography (EEG);cognitive states and ML models;Visualization;Machine learning algorithms;Metaheuristics;Brain modeling;Feature extraction;Electroencephalography;Task analysis|
|[Blockchain-based Wireless Sensor Network Security Through Authentication and Cluster Head Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099593)|D. Paulraj; L. R; T. Jayasudha; M. Ishwarya Niranjana; T. Daniya; F. Daniel Shadrach|10.1109/ICICACS57338.2023.10099593|AES;block chain;sensor;security;authentication;loT;Wireless sensor networks;Costs;Simulation;Scalability;Smart contracts;Authentication;Network security|
|[Automatic English Essay Scoring Algorithm Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099945)|L. Wu|10.1109/ICICACS57338.2023.10099945|machine learning;random forest algorithm;automatic scoring of English essays;Pearson correlation coefficient;Radio frequency;Integrated circuits;Machine learning algorithms;Education;Natural languages;Machine learning;Predictive models|
|[An Improved Analysis of Tourism Management in Enhanced Forest Environment using Block Chain based Internet of Things Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100319)|A. Juyal; S. Baijwan|10.1109/ICICACS57338.2023.10100319|tourism;human;annual;regular work;tourist pollution;negligence;Urban areas;Government;Wildlife;Sea measurements;Forestry;Regulation;Pollution measurement|
|[Deep Learning based Brain Tumor Detection with Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100253)|V. Sandya; V. Baligeri; B. Lal; V. Petli; P. K. S|10.1109/ICICACS57338.2023.10100253|brain tumor;deep learning;kirsch's edge detectors;canfes;co-active adaptive neuro-fuzzy expert system classifier;Training;Adaptation models;Three-dimensional displays;Adaptive systems;Image edge detection;Magnetic resonance imaging;Transforms|
|[The Smart Energy and Power Estimation of Electric Vehicle Battery Using Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100037)|S. Kumar Shah; M. Singh|10.1109/ICICACS57338.2023.10100037|energy;vehicle;electric vehicle;charging;lithium;batteries;deep learning;Lithium-ion batteries;Deep learning;Manifolds;Pollution;Urban areas;Estimation;Electric vehicles|
|[A Fast Alignment Algorithm for Posture Measurement of Autistic Children in Swimming using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099638)|H. Pan; Z. Lv; Z. Liu|10.1109/ICICACS57338.2023.10099638|computer vision;autistic children swimming;posture measurement;fast alignment algorithm;Training;Computer vision;Motor drives;Neural networks;Inertial navigation;Robustness;Velocity measurement|
|[Advantages of 5G Slicing Technology in the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099520)|B. Jin; F. Long; F. Xia; S. Chen; H. Xu; W. Zhan; W. Feng; R. Zhang|10.1109/ICICACS57338.2023.10099520|Application Advantages;Mobile Health Care;IoT Technology;5G Slicing Technology;Technological innovation;5G mobile communication;Smart cities;Network slicing;Telemedicine;Systems architecture;Surgery|
|[Stock Price Prediction using LSTM and TLBO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100074)|K. Kumar M; V. R C; I. C. K. P; G. Shivakanth; K. Rai B|10.1109/ICICACS57338.2023.10100074|long short-term memory (lstm);teaching and learning based optimization (tlbo);machine learning;stock prediction;processing time;Deep learning;Symbiosis;Image recognition;Education;Time series analysis;Predictive models;Prediction algorithms|
|[A Tumor Classification Algorithm Utilizing Extreme Gradient Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100241)|C. Sita Kameswari; R. V; K. S. R. Radhika; T. S. Sri|10.1109/ICICACS57338.2023.10100241|Posterior Probability;Scatter Diagram;Analysis of covariance;Gradient Descent;Feature Scalability;Supervised learning Classifier;Deep learning;Machine learning algorithms;Communication systems;Detectors;Boosting;Breast cancer;Classification algorithms|
|[Fuzzy Judgment-based Mental Health Assessment System for College Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099884)|L. Zhu; Y. Zhang|10.1109/ICICACS57338.2023.10099884|fuzzy judgment;university students' psychology;psychological health;assessment system;Integrated circuits;Communication systems;Education;Force;Mental health;Time factors;Testing|
|[Design System of Plant Decorative Ceramic Pattern Based on Multi-Objective Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100079)|F. Shuai|10.1109/ICICACS57338.2023.10100079|multi-objective genetic algorithm;plant decoration;ceramic pattern;design system;Transforms;Microcomputers;Software;Mathematical models;Ceramics;System analysis and design;Pattern matching|
|[Application of CORBA-based Distributed Database in Virtual Organization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099925)|H. Xu; X. Feng; Y. Liang|10.1109/ICICACS57338.2023.10099925|data distribution;virtual organization;work collaboration;object characteristics;Integrated circuits;Object oriented modeling;Web and internet services;Distributed databases;Organizations;Production;Internet|
|[An Efficient SFLA and CUCKOO Search Hybridization for Source Distribution in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100133)|V. Malsoru; A. Patil; S. M. Kusuma; G. Shivakanth; N. Samanvita|10.1109/ICICACS57338.2023.10100133|sfla;cs;cloud computing;habccs;Integrated circuits;Cloud computing;Computational modeling;Prototypes;Memory;Bandwidth;Safety|
|[Optimization Design of Traditional Building Space Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100201)|P. Liu|10.1109/ICICACS57338.2023.10100201|artificial intelligence;traditional architecture;space optimization;space design;Integrated circuits;Earth;Architecture;Buildings;Layout;Artificial intelligence;Optimization|
|[Cryptography based Network Security Analysis using Secure Hashed Identity Message Authentication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100004)|S. Pandey; S. Lahoti|10.1109/ICICACS57338.2023.10100004|proxy re-encryption;decryption Cryptography;Hashed Message Encryption;Network security;time consuming;Costs;Wireless networks;Network security;Time measurement;Encryption;Delays;Complexity theory|
|[Research on Key Technologies of Sensing and Communication for Distributed Photovoltaic Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099985)|Y. Ma; D. Li; X. Zhang; J. Chen|10.1109/ICICACS57338.2023.10099985|distributed photovoltaic;integrated communication;data sensing;carrier;wireless;Photovoltaic systems;Wireless communication;Wireless sensor networks;Distributed databases;Collaboration;Computer architecture;Power system stability|
|[Secure Data Communication Using Padding Key Encryption Cryptography Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099570)|A. Mittal; F. Sidney|10.1109/ICICACS57338.2023.10099570|Cryptography;Padding Key Encryption (PKE) algorithm;cipher-text;private key;secret key;encryption and decryption;Integrated circuits;Simulation;Data protection;Receivers;Encryption;Internet;Data communication|
|[Distributed Intelligent Scheduling Algorithm for Wireless Communication Network Link Resources Based on Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099949)|Y. Feng|10.1109/ICICACS57338.2023.10099949|data analysis;wireless communication;network link;intelligent scheduling;Performance evaluation;Data analysis;Scheduling algorithms;Wireless networks;Quality of service;Scheduling;Routing protocols|
|[A Novel Power Grid Security Situation Assessment Method for Intelligent Dispatching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099698)|H. Li; Q. Ren; X. Lu; S. Cao|10.1109/ICICACS57338.2023.10099698|scheduling theory;online monitoring method;power grid;security posture;Smoothing methods;Regulators;Simulation;Power grids;Dispatching;Safety;Security|
|[Grasshopper Optimized Tuning of Support Vector Regression for Day a Head Prognostic Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099778)|J. C; M. V; K. C; S. S; N. N|10.1109/ICICACS57338.2023.10099778|Support Vector Regression;Grasshopper Optimization Algorithm;load forecasting;Support vector machines;Integrated circuits;Power demand;Fluctuations;Prediction algorithms;Power systems;Forecasting|
|[IoT Based Vertical Hydroponic Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100317)|S. M; D. N. Kumar; H. S. Vamsi; B. Ranganayakulu|10.1109/ICICACS57338.2023.10100317|DHT11;LDR;Raspberry Pi3;Android App;PH Sensor;Moisture sensor;Support vector machines;Technological innovation;Machine learning algorithms;Urban areas;Hydroponics;Media;Soil|
|[Secured Data Transmission in Multiplexing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099954)|J. C. Gudda; V. Katkam; N. SL; K. A. Bhaskar; N. I. G|10.1109/ICICACS57338.2023.10099954|xilinx ise 14.2;mux;decoder;de-mux;together with mod-3 counter and dwdm and wdm;Optical fibers;Optical fiber cables;Interference;Optical fiber networks;Wavelength division multiplexing;Throughput;Decoding|
|[Automated Real Time Wakefulness State Observation of Driver based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099635)|M. Bindhu; V. M; V. R; A. S|10.1109/ICICACS57338.2023.10099635|Convolution Neural Network (CNN);Rectified Linear Unit (ReLu);Viola Jones Algorithm;Haar Features;SoftMax;fatigue detection;Convolution;Roads;Neural networks;Wheels;Machine learning;Streaming media;Feature extraction|
|[Design and Application of Big Data Technology Management for the Analysis System of High Speed Railway Operation Safety Rules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100171)|N. Yang; M. Chen|10.1109/ICICACS57338.2023.10100171|big data technology management;high speed rail operations;safety laws;analysis systems;Industries;Analytical models;Machine learning algorithms;Transportation;Safety management;Machine learning;Feature extraction|
|[Command Information System Network Modeling and Analysis Based on Bipartite Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100039)|J. Zheng; D. Liu|10.1109/ICICACS57338.2023.10100039|command information system network modeling;complex network;bipartite graph;affiliation network;Military communication;Command and control systems;Analytical models;Simulation;Complex networks;Bipartite graph;Communication networks|
|[Design and Analysis of Heart Attack Prediction System Using ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099819)|M. S. Manoj; K. Madhuri; K. Anusha; K. U. Sree|10.1109/ICICACS57338.2023.10099819|Heart disease;Chronic disease;Electrocardiogram;Myocardial infarction;Machine learning;Radio frequency;Cloud computing;Machine learning algorithms;Cardiac arrest;Predictive models;Prediction algorithms;Real-time systems|
|[An Improved Analysis of Renewable Energy based Storage System using Solar Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099936)|D. Singh; S. Bharti|10.1109/ICICACS57338.2023.10099936|renewable energy;storage system;solar;deep learning model;Earth;Deep learning;Renewable energy sources;Oils;Atmospheric modeling;Water heating;Production|
|[Application of Hydraulic Synchronous Jacking Span Based On 3D Mechanical Drawing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099743)|H. Mao; F. Duan; P. Dong; L. Zheng; J. Ye; J. Cai|10.1109/ICICACS57338.2023.10099743|3D mechanical drawing;yydraulic synchronous jacking;straddling frame;system design;Road transportation;Solid modeling;Three-dimensional displays;Shape;Hydraulic systems;Stability analysis;Software|
|[PSO Algorithm-Based Management Method and Measurement of Enterprise Employee Performance Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099919)|M. Li|10.1109/ICICACS57338.2023.10099919|PSO algorithm;enterprise measurement;employee performance;evaluation management;Performance evaluation;Productivity;Integrated circuits;Communication systems;Companies;Position measurement;Classification algorithms|
|[Service Level Trust Key Encryption based Cloud Security using Starvation End-Point Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099816)|S. Mishra; M. Chitkara|10.1109/ICICACS57338.2023.10099816|Cloud computing;cryptography;public key policy;Symmetric key;security;starvation encryption;Access control;Integrated circuits;Data privacy;Protocols;Cloud computing security;Public key cryptography;Data systems|
|[Intelligent English Recognition System Based on Computer Image Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099563)|O. Chen|10.1109/ICICACS57338.2023.10099563|computer image;intelligent English recognition;OCR algorithm;image processing;Integrated circuits;Image recognition;Layout;Image retrieval;Information processing;Feature extraction;Market research|
|[Prediction of Road Accidents in the Different States of India using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099519)|M. G; R. H. R|10.1109/ICICACS57338.2023.10099519|Accident;collision;decision tree regressor;machine learning model;prediction;random forest regressor;road;visualization;weather;Measurement;Analytical models;Road accidents;Roads;Predictive models;Root mean square;Integrated circuit modeling|
|[Comprehensive Evaluation Study on the Greenness Level of Power Equipment Supply Chain under the Dual Carbon Target](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099762)|K. Pan; T. Wang; X. Yu; X. Zhu; N. Kang; M. Gu|10.1109/ICICACS57338.2023.10099762|dual carbon targets;power equipment;green supply chain;comprehensive evaluation;Procurement;Integrated circuits;Databases;Supply chains;Green products;Government;Companies|
|[Improve Firefly Heuristic Optimization Scheme for Web based Information Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100016)|S. Pandey; S. Lahoti|10.1109/ICICACS57338.2023.10100016|Information retrieval;user interest rate;stop words;tokenization;pre-processing;appropriate information;Heuristic algorithms;Integrated circuit interconnections;Information retrieval;Tokenization;Internet;Optimization|
|[Simulation Analysis of Plane Six-Bar Push Mechanism Based on Matlab](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099716)|J. Lin|10.1109/ICICACS57338.2023.10099716|kinematic analysis;dynamic simulation;vector equation;six-bar mechanism;Integrated circuits;Analytical models;Visualization;Transmission line matrix methods;Tracking;Kinematics;Software|
|[Location based Energy Efficient Routing Protocol for Improving Network Lifetime in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099892)|C. Karthick; C. Kathirvel; C. Jeevakarunya; P. Deepa|10.1109/ICICACS57338.2023.10099892|Energy model;cluster routing;network topology;multipath discovery;localization and data transmission;Location awareness;Wireless sensor networks;Network topology;Simulation;Throughput;Energy efficiency;Delays|
|[Application Research of K-means Algorithm based on Big Data Background](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099551)|Y. Yang; Q. Chen; T. y. Huang; P. K. Pareek|10.1109/ICICACS57338.2023.10099551|Big data;Data mining;Clustering algorithm;K-means algorithm;Integrated circuits;Communication systems;Decision making;Clustering algorithms;Big Data;Data mining;Business|
|[An Improved Numerical Method for Calculating the Fundamental Construction of Digital Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099687)|A. Gehlot; A. Joshi|10.1109/ICICACS57338.2023.10099687|quantum;mechanics;theory;physical;atomic;superposition;entanglement;Computers;Integrated circuits;Quantum computing;Liquids;Quantum mechanics;Multitasking;Organisms|
|[An Innovation Development of Document Management and Security Model for Commercial Database Handling Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099732)|R. Singh; P. Gildhiyal|10.1109/ICICACS57338.2023.10099732|document management;security;database handling systems;sql servers;Integrated circuits;Technological innovation;Databases;Organizations;Portable document format;Planning;Servers|
|[Design of Financial Data Security Storage System for Public Institutions Based on Particle Swarm Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099846)|A. Chen|10.1109/ICICACS57338.2023.10099846|particle swarm optimization algorithm;public institutions;financial data;secure storage;Integrated circuits;Systematics;Data security;Memory;Information processing;Regulation;Resource management|
|[The Intelligent Detection and Treatment of Schizophrenia Disease Using the Fuzzy Logical Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099885)|G. Thakur; R. S. Jha|10.1109/ICICACS57338.2023.10099885|schizophrenia;mental;Psychosis;paranoia;convulsive;detection;Integrated circuits;Mental disorders;Psychology;Color;Medical services;Human factors;History|
|[Estimation of Transmission Bandwidth for VoIP Signals over IP Packet Transmission Network using Capacity Computing Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100177)|R. Singh; A. Joshi|10.1109/ICICACS57338.2023.10100177|telephone network;bandwidth;VoIP;transmission;voice;signal;UDP;IP;network;connection;Wireless communication;Coaxial cables;Estimation;Bandwidth;Streaming media;Telephone sets;Central Processing Unit|
|[Wireless Level Monitoring of Interfacing Two-Tank System through User Datagram Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099621)|R. Pilli; T. Rajkumar; K. N. Shreenath; K. R. P. Kumar; P. J. Josephson; B. Pant|10.1109/ICICACS57338.2023.10099621|Interacting two-tank system;Data Acquisition;UDP;Android;PID Controller;Wireless communication;Industries;Protocols;Fluids;Software packages;Data acquisition;Data transfer|
|[An Intelligent Estimation of Solar Photo-Voltaic Capacity Measurement Using Solar Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100247)|S. Kumar Shah; M. Singh|10.1109/ICICACS57338.2023.10100247|solar;battery;hydrogen;oxygen;water;machine learning;Deep learning;Temperature measurement;Costs;Estimation;Water heating;Solar energy;Prediction algorithms|
|[An Improved Industrial Analysis of Electrical Equipment using Deep Learning based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100327)|S. V. Akram; B. Rawat|10.1109/ICICACS57338.2023.10100327|modern;electrical;equipments;screws;wood;plastic;length;Resistors;Neon;Urban areas;Wires;Fasteners;Skin;Plastics|
|[Cost Model and Algorithm Analysis of Food Emergency Logistics Distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100127)|L. Haiyan|10.1109/ICICACS57338.2023.10100127|Logistics distribution;Food emergency;Cost model;Analytical models;Costs;Snow;Supply chains;Transportation;Time factors;Resource management|
|[Deep Learning Based Cancer Detection in Bone Marrow using Histopathological Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100116)|S. Rani B; G. B; S. G Shivaprasad Yadav; G. Shivakanth; M. B M|10.1109/ICICACS57338.2023.10100116|cnn algorithm;plane extraction;linear distinction stretching;world thresholding;gray level co-occurrence matrix;sickle cell anemia;White blood cells;PSNR;Microscopy;Clustering algorithms;Cells (biology);Ash;Bones|
|[Correlation Analysis and Application Research of Tourism Data Based on Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100282)|H. Zhang; H. Li; D. Li; H. Wang|10.1109/ICICACS57338.2023.10100282|big data;five-dimensional paradigm;web crawler;tourist resources;data analysis;Analytical models;Correlation;Tourism industry;Big Data;Market research;Stability analysis;Software|
|[Medical Image Segmentation Algorithm Based on Deep Learning and Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100142)|Q. Gao|10.1109/ICICACS57338.2023.10100142|deep learning;convolutional neural networks;medical image segmentation algorithms;Deep learning;Location awareness;Image segmentation;Analytical models;Neural networks;Transforms;Feature extraction|
|[An Improved Fuzzy Logic Method for Calculating Humidity in Workplaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099772)|A. Juyal; R. Rautela|10.1109/ICICACS57338.2023.10099772|improved fuzzy logic;humidity;nasal mucosa;dryness;Heating systems;Microorganisms;Instruments;Nose;Music;Moisture;Humidity|
|[A Deep Learning Method for Predicting Disease Impact in Indolent Schizophrenia Applied to Psychiatry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099695)|G. Thakur; R. Shyam Jha|10.1109/ICICACS57338.2023.10099695|schizophrenia;mental;Psychosis;paranoia;convulsive;detection;Deep learning;Integrated circuits;Pathology;Communication systems;Mental disorders;Medical services;Diseases|
|[Research on Xi'an Image Building Based on SPSS Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100026)|Y. Zhang; C. G. Lai|10.1109/ICICACS57338.2023.10100026|Xi'an release;WebChat;SPSS;city image;Principal Component Analysis;Integrated circuits;Data analysis;Correlation;Social networking (online);Image communication;Urban areas;Government|
|[An Improved Analysis and Treatment Regimen and Modern Antibacterial Agents using AI based Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100312)|N. Misra; T. Prashar|10.1109/ICICACS57338.2023.10100312|urinary infection;urinary system;children;diet;fruits;pain;syndrome;Fungi;Pregnancy;Integrated circuits;Pediatrics;Pain;Medical treatment;Antibacterial activity|
|[Construction of Economic Index System of High-Tech Industry based on Data Mining Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099803)|R. Huang; D. Li|10.1109/ICICACS57338.2023.10099803|Data mining;High tech industry;Economic indicators;System construction;Industries;Economics;Web pages;Production;Writing;Indexes;Data mining|
|[Improving The Hiding Capacity of Image Steganography with Stego-Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100146)|P. Grandhe; A. M. Reddy; K. Chillapalli; K. Koppera; M. Thambabathula; L. P. Reddy Surasani|10.1109/ICICACS57338.2023.10100146|Cryptography;Digital image processing;Blind Hide Algorithm;Encryption;steganography;stego-image;Bulk Analysis;Benchmark Analysis;Stego Analysis;Decryption;Integrated circuits;Steganography;Costs;Communication systems;Image processing;Government;Communication channels|
|[Design of Archives Management System Based on Data Mining Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099880)|X. Ru|10.1109/ICICACS57338.2023.10099880|data mining technology;archives management system;database;SQL query;SQL update;Integrated circuits;File systems;Software algorithms;Relational databases;Programming;Data collection;Software|
|[Optimization Simulation of Cultural Communication Algorithm Based on Artificial Intelligence Data Mining Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099589)|Z. Wang; S. Dadparvar|10.1109/ICICACS57338.2023.10099589|artificial intelligence;DM;cultural communication;algorithm optimization simulation;Analytical models;Media;Search problems;Reflection;Global communication;Cultural differences;Data mining|
|[Total Factor Productivity of Logistics Industry Based on DEA-Malmquist Index Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100126)|Y. Huang; P. Yang|10.1109/ICICACS57338.2023.10100126|logistics industry;TFP;DEA-Malmquist index model;Industries;Productivity;Pandemics;Market research;Data models;Software;Indexes|
|[Dimensional Reduction Method based on Big Data Techniques for Large Scale Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100261)|M. Kumbhkar; P. Shukla; Y. Singh; R. A. Sangia; D. Dhabliya|10.1109/ICICACS57338.2023.10100261|Dimensionality Reduction;Big Data Techniques;Large Scale Dataset;Hadoop and MapReduce;Costs;Scalability;Decision making;Big Data;Feature extraction;Encoding;Cognition|
|[Research on Target Extraction System of UAV Remote Sensing Image Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100183)|W. Xie|10.1109/ICICACS57338.2023.10100183|artificial intelligence;unmanned aerial vehicle;remote sensing image;target extraction;Deep learning;Training;Landslides;Target recognition;Roads;Object detection;Autonomous aerial vehicles|
|[Design and Implementation of System for Rider's Safety Against Extreme Climatic Conditions and Uneven Road](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100087)|P. Sivakumar; K. S. Kumar; A. S. Tejaswani; G. Mounika; M. R. Kumar|10.1109/ICICACS57338.2023.10100087|ARM-7;GPS;GSM;Voice Recognition;Canopy;Potholes;Temperature sensors;Temperature measurement;Rain;Roads;Government;Maintenance engineering;Sensor systems|
|[Computer Aided Design of Arts And Crafts Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100313)|C. Xie|10.1109/ICICACS57338.2023.10100313|Arts and crafts products;Computer aided design;Solid modeling;Art;Design automation;Systematics;Stars;Production;Rapid prototyping|
|[Quality Inspection Method of Building Material Concrete Based on Ultrasonic Tomography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100138)|H. Wang|10.1109/ICICACS57338.2023.10100138|ultrasonic tomography;imaging technology;building materials;concrete quality;Integrated circuits;Building materials;Quality control;Human factors;Tomography;Inspection;Feature extraction|
|[Design of TCM Research Demand System Based on Data Mining Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099881)|Y. Yang; Y. Huang; L. Yang; H. Liu|10.1109/ICICACS57338.2023.10099881|data mining;traditional Chinese medicine;scientific research demand;system design;Integrated circuits;Databases;Communication systems;Medical services;User experience;Software;Classification algorithms|
|[Resource Sharing Platform Based on Machine Learning and Data Fusion Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100105)|Y. Fu|10.1109/ICICACS57338.2023.10100105|machine learning;data fusion;educational resources;sharing platform;Education;Data integration;Integrated circuit interconnections;Machine learning;Learning (artificial intelligence);Big Data;Software|
|[An Improved System built on Artificial Intelligence for Sorting and Handling Plastic Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100302)|G. Krishna; A. Sharma|10.1109/ICICACS57338.2023.10100302|artificial intelligence;sorting and handling plastic materials;groundwater;microorganisms;Earth;Water;Sociology;Soil;Water pollution;Plastics;Solar radiation|
|[Analysis of the Characteristics of Film and Television Music based on Internet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099990)|N. Jing|10.1109/ICICACS57338.2023.10099990|Internet;Film and television music;Characteristic analysis;Integrated circuits;TV;Art;Education;Production;Media;Real-time systems|
|[Algorithm of Highway Toll Sorting Based on Matrix Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099637)|Z. Xie|10.1109/ICICACS57338.2023.10099637|Matrix operation;expressway;Charge sorting algorithm;Integrated circuits;Costs;Roads;Heuristic algorithms;Data transfer;Mathematical models;Dynamic programming|
|[An Improved Method for Deep Learning-based Analysis of Environmental Issues and Remote Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099699)|S. Gildhiyal; R. Prasad|10.1109/ICICACS57338.2023.10099699|ecosystem;natural;environment;pollution;world;lifestyle;instability;harmful;noise;heat;light;energy;Military communication;Satellites;Receivers;Air pollution;Water pollution;Trajectory;Nitrogen|
|[Research on the Application of Cross-Regional Sharing of Blockchain-Based Electronic Health Records](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100218)|R. Wang; J. Tu; C. Chu|10.1109/ICICACS57338.2023.10100218|electronic health records;blockchain;data sharing;encryption technology;Cloud computing;Hospitals;Permission;Non-repudiation;Data models;Blockchains;Performance analysis|
|[Application of L-M Algorithm in High-Rise Structure System Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099497)|K. Chen; W. Xiaoqi|10.1109/ICICACS57338.2023.10099497|High rise structure;L-M algorithm;System selection;Nonlinear equations;Shape;Buildings;Soil properties;Artificial neural networks;Robustness;Real-time systems|
|[Multi Path TCP Over Network Coding for Wireless Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099667)|K. P. Priya; M. S; M. Rai S; C. T. Kalaivani; M. Tejesh; M. Hasan M|10.1109/ICICACS57338.2023.10099667|Wireless sensor network;Expected Transmission Count;Routing;SDN controller;Wireless sensor networks;Wireless networks;Voting;Packet loss;Routing;Routing protocols;Sensors|
|[Construction of Clustered Online Teaching System based on Data Center Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100199)|X. Yufeng; X. Siqiang|10.1109/ICICACS57338.2023.10100199|Data center;Online teaching;Clustering;Integrated circuits;Data centers;Costs;Distance learning;Education;Media;Market research|
|[The Relationship Between Foreign Trade and Economic Growth Based on the Import and Export Trade Forecasting Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100109)|Q. Zhu|10.1109/ICICACS57338.2023.10100109|opening to the outside world;foreign trade;import and export trade forecasting algorithm;economic growth;Integrated circuits;Solid modeling;Analytical models;Economic indicators;Communication systems;Elasticity;Predictive models|
|[Analytical Scheme of the Color Test Carried Out on a Mental Disorder Patient-Time basis to Assess the Specific Meaning of Color](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099929)|R. Singh; A. Joshi|10.1109/ICICACS57338.2023.10099929|information;manifestations;disease;mental disorder;symptoms;schizophrenia;psychosis;Drugs;Training;Integrated circuits;Art;Hospitals;Communication systems;Mental disorders|
|[Optimization of Newton's Iterative Algorithm for Fractal Art Graphic Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100321)|J. Wei; Y. Tan|10.1109/ICICACS57338.2023.10100321|graphics;points;time series;Algorithm;Graphics;Integrated circuits;Visualization;Manuals;Iterative algorithms;Iterative methods;Optimization|
|[Deep Learning Technology in Film and Television Post-Production](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100148)|C. Zhang; K. M R|10.1109/ICICACS57338.2023.10100148|deep learning;film and television post;facial recognition;digital image;Deep learning;Production systems;TV;Green products;Production;Streaming media;Motion pictures|
|[Machine Learning Based Sentiment Analysis and Swarm Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100262)|R. K. Patra; B. Patil; T. S. Kumar; G. Shivakanth; M. B. M|10.1109/ICICACS57338.2023.10100262|natural language processing;accuracy;sensitivity;specificity;precision;recall (90.2 percent);f-measure;Sentiment analysis;Sensitivity;Social networking (online);Simulation;User-generated content;Stars;Machine learning|
|[Prediction of Electric Energy Substitution Potential under Multiple Scenarios Based on SSA-ELM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099765)|K. Ge; Y. Wang; B. Hu; T. Zeng; L. He; J. Zhao|10.1109/ICICACS57338.2023.10099765|amorous scene;electric energy substitution potential;extreme learning machine;sparrow search algorithm;Electric potential;Energy consumption;Communication systems;Oils;Coal;Predictive models;Prediction algorithms|
#### **2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)**
- DOI: 10.1109/ECCE57851.2023
- DATE: 23-25 Feb. 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Bandgap Analysis of InAs/InGaN Quantum Dot Intermediate Band Solar Cell (QDIBSC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101673)|A. Das; M. M. Rahman; M. A. Matin; N. Amin|10.1109/ECCE57851.2023.10101673|Efficiency;Bandgap;Material;Solar cell;Fill Factor;Photonic band gap;Photovoltaic cells;Quantum dots;Stability analysis;Junctions;Thermal stability|
|[Sexual Harassment Detection using Machine Learning and Deep Learning Techniques for Bangla Text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101522)|M. Islam; M. Rahman; M. T. Ahmed; A. Z. Muhammad Islam; D. Das; M. M. Hoque|10.1109/ECCE57851.2023.10101522|Cyberbullying;Sexual Harassment;Machine Learning;Deep Learning;Sentiment Analysis;Natural Language Processing;Text analysis;Deep learning;Support vector machines;Machine learning algorithms;Toxicology;Cyberbullying;Filtering algorithms;Classification algorithms|
|[Optimization strategies for Micro-grid energy management and scheduling systems by Sine cosine Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101663)|M. A. Islam; M. A. Zardar; M. Shafiullah; A. Nadia|10.1109/ECCE57851.2023.10101663|Community Micro-grid;Distributed generator;Renewable energy;Sine Cosine Algorithm;Energy storage system;Meta-heuristic;Degradation;Renewable energy sources;Costs;Processor scheduling;Metaheuristics;Microgrids;Wind power generation|
|[A Comprehensive Analysis of Load Shedding with DSM in a Power Grid with IEEE 14 Bus System Adoption of Chittagong Zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101510)|S. M. Salam; N. Mohammad|10.1109/ECCE57851.2023.10101510|Telecommunication Power;Optimal Power Flow;Power quality;Energy;Demand Side Management;Costs;Supply and demand;Demand side management;Load shedding;Production;Energy efficiency;Power grids|
|[Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101619)|S. Lyu; M. H. Chowdhury; R. C. C. Cheung|10.1109/ECCE57851.2023.10101619|EEG;Extreme learning machine;ELM;Hard-ware/software co-design;BCI;Training;Power demand;Sensitivity;Extreme learning machines;Sleep;Signal processing;Electroencephalography|
|[Measurement and Modeling of GaAs Based Nano-pHEMT: Small Signal to Large Signal Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101628)|M. S. Alam; M. A. Alim; A. A. Rezazadeh|10.1109/ECCE57851.2023.10101628|GaAs pHEMT;measurements;simulation;DC and RF characterization;small signal and large signal analysis;Radio frequency;Analytical models;Computational modeling;Gallium arsenide;Data models;Signal analysis|
|[Alzheimer's Disease Classification From 2D MRI Brain Scans Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101539)|R. A. Hridhee; B. Bhowmik; Q. D. Hossain|10.1109/ECCE57851.2023.10101539|AIzheimer's Disease (AD);Magnetic Resonance Imaging (MRI);Convolutional Neural Network (CNN);Support vector machines;Three-dimensional displays;Computational modeling;Magnetic resonance imaging;Medical services;Brain modeling;Convolutional neural networks|
|[Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101655)|N. Mahfuz; P. C. Shill|10.1109/ECCE57851.2023.10101655|object-oriented (OO);class;composed dagging technique;classification;Measurement;Software metrics;Object oriented modeling;Multilayer perceptrons;Predictive models;Software;Classification algorithms|
|[Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101588)|T. A. Belal; G. M. Shahariar; M. H. Kabir|10.1109/ECCE57851.2023.10101588|deep learning;toxic comment;Bangla BERT;classification;multi-label;lime;interpretation;Deep learning;Toxicology;Pipelines;Transfer learning;Predictive models;Transformers;Natural language processing|
|[Architecture and Design of a New Non-Quadrature Vector-Sum Microwave Phase Shifter at 10 GHz With Maximum Residual Phase Error of 1.80°](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101495)|M. Kebe; S. Abdullah; R. E. Amaya; M. C. E. Yagoub|10.1109/ECCE57851.2023.10101495|phase shifter;vector-sum phase shifter;digital phase shifter;analog phase shifter;vector synthesizer;phase path selector;vector subtractor;beamformers;phased-array;phased-array systems;Phased arrays;Power demand;Simulation;Phase shifters;Computer architecture;Insertion loss;Microwave communication|
|[Protein Structure Prediction in Structural Genomics without Alignment Using Support Vector Machine with Fuzzy Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100743)|S. Saha; P. C. Shill|10.1109/ECCE57851.2023.10100743|Fuzzy Logic;Support Vector Machine;Protein Secondary Structure;Protein Function;Structure Based Drug Designing;Proteins;Support vector machines;Fuzzy logic;Drugs;Training;Simulation;Benchmark testing|
|[Impact of Transmission Line Capacity Expansion on Electricity Operation Cost and Selling Price](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101541)|N. Mohammad; D. K. Chowdhury|10.1109/ECCE57851.2023.10101541|Power System;Operation Cost;Optimal Power Flow;Generator;Transmission Line;Reactive power;Power transmission lines;Costs;Simulation;Systems operation;System performance;Generators|
|[AI-based Consumers' Preference Prediction Using a Research-grade BCI and a Commercial-grade BCI for Neuromarketing: A Systematic Comparison](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101563)|F. Ishtiaque; F. R. Mashrur; M. T. I. Miya; K. M. Rahman; R. Vaidyanathan; S. F. Anwar; F. Sarker; K. A. Mamun|10.1109/ECCE57851.2023.10101563|Brain Computer Interface;Neuromarketing;EEG;Signal Processing;Machine Learning;Support vector machines;Systematics;Neuromarketing;Feature extraction;Prediction algorithms;Electroencephalography;Brain-computer interfaces|
|[Attention-based RNN architecture for detecting multi-step cyber-attack using PSO metaheuristic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101590)|P. B. Udas; K. S. Roy; M. E. Karim; S. M. Azmat Ullah|10.1109/ECCE57851.2023.10101590|Cyber Security;Deep Learning;Attention Layer;Feature Selection;Network Intrusion Detection;Particle Swarm Optimization;Measurement;Recurrent neural networks;Computational modeling;Metaheuristics;Network intrusion detection;Computer architecture;Stability analysis|
|[Design of an Efficient MPPT for Grid Connected Photovoltaic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101633)|S. M. Salam; N. Mohammad; S. S. Islam; F. Hossain; I. A. Chowdhury; M. I. Uddin|10.1109/ECCE57851.2023.10101633|Solar PV;Fuzzy logic;Optimization;Power;Photovoltaic systems;Renewable energy sources;Software packages;Simulation;Power quality;Switches;Pulse width modulation|
|[Output Voltage Stability of a DC-DC Buck Converter via an Improved Backstepping Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101543)|M. Z. Alam; T. K. Roy; S. K. Ghosh; N. Mohammad; L. C. Paul|10.1109/ECCE57851.2023.10101543|Buck converters;improve backstepping technique;Lyapunov theory;voltage stability;Asymptotic stability;Backstepping;Buck converters;Uncertainty;Perturbation methods;Process control;Mathematical models|
|[Absorber Layer Thickness Dependent Performance Evaluation of Perovskite Solar Cell for different Electron Transport Layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101620)|M. Hasan; S. M. Sultana; M. J. Ferdous; I. J. Khan; M. F. Nayan|10.1109/ECCE57851.2023.10101620|Perovskite solar cell;Absorber layer thickness;SCAPS-1D;Hole Transport Layer;Electron Transport Layer;Efficiency;Performance evaluation;II-VI semiconductor materials;Photovoltaic cells;Simulation;Zinc oxide;Electron optics;Perovskites|
|[Deep Transfer Learning for Early Parkinson's Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101591)|N. Afroz; B. Ahmed|10.1109/ECCE57851.2023.10101591|Parkinson's Disease (PD);Convolutional Neural Network (CNN);Transfer Learning (TL);Ensemble Method;Spirals;Parkinson's disease;Transfer learning;Pipelines;Loss measurement;Recording;Ensemble learning|
|[Hardware/Software Co-design of an ECG- PPG Preprocessor: A Qualitative & Quantitative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101536)|A. Chowdhury; D. Das; R. C. C. Cheung; M. H. Chowdhury|10.1109/ECCE57851.2023.10101536|Electrocardiogram (ECG);Photoplethysmo-gram (PPG);Field Programmable Gate Array (FPGA);Correlation coefficient;Power demand;Finite impulse response filters;Digital systems;Statistical analysis;Wearable computers;IIR filters|
|[Effects of Multifunctional Interlayers on the Performance of Perovskite Solar Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101601)|M. S. Mahtab Dipto; M. J. Islam; M. R. Kaysir; J. Atai|10.1109/ECCE57851.2023.10101601|Perovskite solar cell;Interlayer;Charge generation rate;Photon density Efficiency;Photovoltaic cells;Optical device fabrication;Charge carrier processes;Electron optics;Perovskites;Software;Optical materials|
|[94.5 GHz Dual-loop Optoelectronic Oscillator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101595)|G. K. M. Hasanuzzaman; S. Iezekiel; A. Kanno|10.1109/ECCE57851.2023.10101595|Optoelectronic oscillator;phase noise;dual-loop OEO;Optical fiber amplifiers;Phase noise;Vibrations;Frequency modulation;Instruments;Optical fiber couplers;Optical fiber communication|
|[A Bi-directional Temporal Sequence Approach for Condition Monitoring of Broken Rotor Bar in Three-Phase Induction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101518)|F. Rayhan; M. S. Shaurov; M. A. Nashrah Khan; S. Jahan; R. Zaman; M. Z. Hasan; T. Rahman; E. A. Bhuiyan|10.1109/ECCE57851.2023.10101518|Broken rotor bar (BRB);fault diagnosis;induction motor (IM);LSTM;Fault diagnosis;Condition monitoring;Deep learning;Induction motors;Rotors;Bidirectional control;Feature extraction|
|[Security of an Audio using Multiple Watermarking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101632)|A. Singha; M. A. Ullah|10.1109/ECCE57851.2023.10101632|digital watermarking;DWT;multiple watermarking;SVD;Watermarking;Transforms;Robustness;Discrete wavelet transforms;Singular value decomposition|
|[Securing E-Passport Management Using Private-Permissioned Blockchain and IPFS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101496)|N. Jahan; S. Reno; M. Ahmed|10.1109/ECCE57851.2023.10101496|Blockchain;E-Passport;Hyperledger;Chain-code;Biometrics;Radio-Frequency Identification (RFID);Distributed ledger;Government;Documentation;Fingerprint recognition;Blockchains;InterPlanetary File System;History|
|[SHD: Development of an Smart Headband for Deafblind People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101656)|M. R. Islam; M. A. Israk; Ferdib-Al-Islam; J. Majumder; S. Rahman|10.1109/ECCE57851.2023.10101656|STM32F401CC;LM358P IC;Headband;Audio Level;Deafblind;Vibrations;Resistors;Microwave integrated circuits;Navigation;Microcontrollers;Roads;Blindness|
|[An Effective Framework for Identifying Pneumonia in Healthcare Using a Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101548)|M. R. Hasan; S. M. A. Ullah; M. E. Karim|10.1109/ECCE57851.2023.10101548|Pneumonia;Deep Learning;CNN;Max Polling;Rectified Linear Unit (ReLU);Sigmoid Function;Training;Pediatrics;Pulmonary diseases;Transfer learning;Medical services;Predictive models;Convolutional neural networks|
|[The Analysis of Acoustic Signal Refraction Effect on Distance Measurement between Beacon Node and Underwater Wireless Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101530)|M. M. Rahman|10.1109/ECCE57851.2023.10101530|Acoustic Signal;Distance Error;Mathematical Model;Receiving Angles of Acoustic Signal;Wireless communication;Temperature sensors;Wireless sensor networks;Analytical models;Computational modeling;Acoustics;Distance measurement|
|[Investigation of the Impact of Sea Conditions on the Sea Surface Reflectivity in Maritime Radar Sea Clutter Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101598)|N. Das; M. S. Hossain|10.1109/ECCE57851.2023.10101598|Sea State;Polarization;Grazing Angle;Sea Surface Reflectivity;Sea Clutter;Reflectivity;Sea surface;Radar clutter;Sea state;Surface roughness;Rough surfaces;Clutter|
|[Improved Design and Comparison of a Low Power CNTFET based on D Flip-Flop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101615)|J. H. Ridoy; A. Y. Rahman; F. R. Saquib|10.1109/ECCE57851.2023.10101615|CNTFET;flip-flop;leakage current;leakage power consumption;CMOS;Performance evaluation;Power demand;Power measurement;Shape;Nanoscale devices;CNTFETs;Sequential circuits|
|[IoT-based Efficient Streetlight Controlling, Monitoring and Real-time Error Detection System for Smart Cities in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101600)|A. T. M. M. M. Chowdhury; J. Sultana; M. S. U. Sourav|10.1109/ECCE57851.2023.10101600|Error detection;energy efficiency;IoT;streetlight monitoring & controlling;Electric potential;Cloud computing;Costs;Smart cities;Fault detection;Lighting;Real-time systems|
|[Design and Performance Evaluation of an FPGA based EOG Signal Preprocessor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101512)|D. Das; A. Chowdhury; A. I. Sanka; M. H. Chowdhury|10.1109/ECCE57851.2023.10101512|Electrooculogram;FPGA;Xilinx System Gen-erator;FDATool;FIR Filter;IIR Filter;Performance evaluation;Electrooculography;Power demand;Statistical analysis;IIR filters;Hardware;Software|
|[Study of Different Candidates of Modulation Schemes for 5G Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101611)|T. Sultana; R. A. Akhi; J. H. Turag; S. Najeeb|10.1109/ECCE57851.2023.10101611|Modulation scheme;4G;5G;OFDM;F-OFDM;UFMC;Power Spectral Density (PSD);Resource Block (RB);Constellation diagram;PAPR;BER;Sub-band;Subcarrier;Industries;Constellation diagram;5G mobile communication;Communication systems;Phase modulation;Computational modeling;Modulation|
|[A Comparative Analysis for Stroke Risk Prediction Using Machine Learning Algorithms and Convolutional Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101567)|M. J. Ferdous; R. Shahriyar|10.1109/ECCE57851.2023.10101567|stroke;machine learning algorithms;stacking classifier of six ensemble methods;convolutional neural network;ROC curve;Training;Machine learning algorithms;Stacking;Stroke (medical condition);Prediction algorithms;Classification algorithms;Convolutional neural networks|
|[An Effective Deep CNN Model for Multiclass Brain Tumor Detection Using MRI Images and SHAP Explainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101503)|S. Ahmed; S. N. Nobel; O. Ullah|10.1109/ECCE57851.2023.10101503|Brain tumor detection;Deep convolutional neural network;data augmentation;deep learning;MRI;SHAP;XAI;Clinical diagnosis;Deep learning;Visualization;Additives;Magnetic resonance imaging;Predictive models;Brain modeling;Numerical models|
|[Human Skin Diseases Detection and Classification using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101636)|S. Ahmed; M. Abiduzzaman; M. H. Rajib; R. Rahaman; S. Hussain; H. B. Rashid; A. H. Wadud; T. M. Amir-Ul-Haque Bhuiya|10.1109/ECCE57851.2023.10101636|Deep learning;CNN;Normalization;Augmentation;Morphological Transformation;Deep learning;Systematics;Computational modeling;Image processing;Melanoma;Medical services;Predictive models|
|[Emission and Valve Point Loading Cost Using Superiority of Feasible Solutions-Moth Flame Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101593)|M. K. Alam; M. H. Bin Sulaiman; M. S. Sayem; S. Imtiaz; M. M. A. Ringku; R. Khan|10.1109/ECCE57851.2023.10101593|Superiority of Feasible Solutions-Moth Flame Optimization;Valve-point Effect;Optimal Power Flow;Energy Efficiency;power system optimization;Costs;Simulation;Loading;Fires;Valves;Steady-state;Power system reliability|
|[Segmented Nonnegative Matrix Factorization for Hyperspectral Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101584)|M. H. Bari; T. Ahmed; M. I. Afjal; A. M. Nitu; M. P. Uddin; M. A. Marjan|10.1109/ECCE57851.2023.10101584|Remote sensing;Hyperspectral images;Feature extraction;PCA;Segmented PCA;NMF;Segmented NMF;Support vector machines;Analytical models;Image segmentation;Image resolution;Computational modeling;Feature extraction;Reliability|
|[Bengali-English Neural Machine Translation Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101491)|N. Paul; I. Faruki; M. I. Pranto; M. T. Rouf Shawon; N. C. Mandal|10.1109/ECCE57851.2023.10101491|Machine Translation;Natural Language Processing;Bengali-to-English;Neural Machine Translation;Measurement;Deep learning;Vocabulary;Logic gates;Machine translation;Task analysis;Long short term memory|
|[Study on Accuracy Improvement of Mental Arithmetic Task Classification Using Different Classifiers with DWT Feature Extraction Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101596)|T. I. Touhid; M. Anam; M. R. Alam; M. Foysal; S. Shaiham|10.1109/ECCE57851.2023.10101596|Mental Arithmetic;fNIRS;BCI;DWT;GentleBoost;Random Forest;Brain;Transforms;Feature extraction;Brain-computer interfaces;Discrete wavelet transforms;Hemodynamics;Task analysis|
|[Seagull Optimization Algorithm for Solving Economic Load Dispatch Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101516)|M. Hanif; N. Mohammad; K. Biswas; B. Harun|10.1109/ECCE57851.2023.10101516|Objective function;statistical comparison;valve point effect;constraints;SOA;Economics;Statistical analysis;Metaheuristics;Generators;Performance analysis;Fuels;Particle swarm optimization|
|[Deep CNN-GRU Based Human Activity Recognition with Automatic Feature Extraction Using Smartphone and Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101550)|M. A. Khatun; M. A. Yousuf; M. A. Moni|10.1109/ECCE57851.2023.10101550|sensors;smartphone;human activity recognition;deep learning;CNN;FC;GRU;Deep learning;Computational modeling;Wearable computers;Logic gates;Feature extraction;Data models;Convolutional neural networks|
|[A Survey on Neural and Non-Neural Network Based Approaches to Classify Images and Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101667)|N. B. Noor; O. Das|10.1109/ECCE57851.2023.10101667|Convolutional Neural Network;Food Image Classification;Transfer Learning;Tensorflow;Keras;Electroen-cephalogram (EEG);Epileptic Seizure;Deep learning;Neural networks;Transfer learning;Pattern classification;Brain modeling;Feature extraction;Electroencephalography|
|[Sentiment Polarity Detection Using Machine Learning and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101494)|A. R. Mehul; S. M. Mahmood; T. Tabassum; P. Chakraborty|10.1109/ECCE57851.2023.10101494|Natural Language Processing (NLP);Support Vector Machine(SVM);Long Short Term Memory (LSTM);Machine Learning;Deep Neural Network;Support vector machines;Deep learning;Training;Computational modeling;Neural networks;Predictive models;Feature extraction|
|[A Comparative Analysis on Predicting Brain Tumor from MRI FLAIR Images Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101559)|M. S. K. Akash; M. A. Mamun|10.1109/ECCE57851.2023.10101559|Brain Tumors;Magnetic resonance imaging;Deep learning;FLAIR modality;Image segmentation;Convolutional neural network;Biomedical imaging;Deep learning;Image segmentation;Three-dimensional displays;Sensitivity;Computer architecture;Feature extraction;Biological neural networks|
|[Polarization-Insensitive Terahertz Tunable Broadband Metamaterial Absorber on U-shaped Graphene Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101545)|A. B. M. A. Hossain; A. Khaleque; N. M. Shahriar; M. S. Hosen; K. S. R. Shaha; M. Mizan|10.1109/ECCE57851.2023.10101545|absorber;broadband;graphene;metamaterial;tunable;terahertz;polarization-insensitive;Performance evaluation;Absorption;Simulation;Graphene;Metamaterials;Magnetic materials;Broadband communication|
|[Exploratory Perspective of PV Net-Energy-Metering for Residential Prosumers: A Case Study in Dhaka, Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101637)|M. S. A. A. F. Shiblee; M. Z. Rahman; H. Monir; M. A. Kabir|10.1109/ECCE57851.2023.10101637|Net Metering;photovoltaic system (PV);NPV;residential load;Meters;Buildings;Urban areas;Government;Fossil fuels;Data models;Performance analysis|
|[Model Predictive Control of an Active Front End Rectifier: Robustness Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101487)|R. Hossain; M. N. Hossain; A. Chowdhury; K. Hasan; M. R. T. Hossain|10.1109/ECCE57851.2023.10101487|AC-DC converter;Robust model;Model predictive control (MPC);Dynamic references;Reactive power;Total harmonic distortion;Voltage measurement;Switching frequency;Rectifiers;Modulation;Robustness|
|[A Novel Low-Cost Monitoring System for Sleep Apnea Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101614)|S. A. Sumona; W. B. N. Aurthy|10.1109/ECCE57851.2023.10101614|Sleep apnea;sleep monitoring;wearable;low cost and user friendly;Wires;Medical services;Electrocardiography;Sleep apnea;Sensor systems;Mobile applications;Biomedical monitoring|
|[Numerical Analysis of Coronary Stent Alloy Materials During Radial Expansion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101627)|S. Paul; T. I. Palash; A. D. Roy; A. K. Debnath|10.1109/ECCE57851.2023.10101627|FEM;Stent;Alloy;Palmaz-Schatz;Numerical Analysis;Deformation;Corrosion;Computational modeling;Simulation;Metals;Steel;Plastics|
|[Selective HybridNET: Spectral-Spatial Dimensionality Reduction for HSI Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101534)|M. R. Islam; M. T. Islam; M. Sohrawordi|10.1109/ECCE57851.2023.10101534|Incremental Principal Component Analysis (iPCA);3D-2D Convolutional Neural Net (CNN);Spectral-Spatial Feature Extraction (SSFE);Hyperspectral Image (HSI) Classification;Dimensionality reduction;Solid modeling;Analytical models;Three-dimensional displays;Computational modeling;Feature extraction;Data models|
|[IoT Based Smart Soil Fertilizer Monitoring And ML Based Crop Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100744)|M. D. Hossain; M. A. Kashem; S. Mustary|10.1109/ECCE57851.2023.10100744|Internet of Things(IoT);Machine Learning(ML);Wireless Sensor Networks (WSN);Potential of Hydrogen(pH);Crop Recommendation;Temperature measurement;Wireless sensor networks;Soil measurements;Crops;Soil;Sensor phenomena and characterization;Sensor systems|
|[A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101540)|M. A. Hussen Wadud; A. Rahman; M. J. Islam; T. M. Amir-Ul-Haque Bhuiyan; M. J. Hossain; R. Hossen|10.1109/ECCE57851.2023.10101540|Blockchain;Security;Patient Record;Cloud Com-puting;Data Monitoring;Access Control;Privacy;Cloud computing;Privacy;Computer architecture;Oral communication;Safety;Reliability;Internet of Things|
|[Majority Voting Ensemble Approach for Predicting Diabetes Mellitus in Female Patients from Unbalanced Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101629)|F. Muntasir; M. S. Anower; M. Nahiduzzaman|10.1109/ECCE57851.2023.10101629|Machine learning;SMOTE;Random Forest;XGBoost;Voting Ensemble Technique;Diabetes prediction;SHAP Analysis;Training;Machine learning algorithms;Predictive models;Prediction algorithms;Boosting;Diabetes;Classification algorithms|
|[Devising an IoT-Based Water Quality Monitoring and pH Controlling System for Textile ETP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101616)|A. D. Gupta; M. M. Islam; M. R. Islam; Z. Sadek; T. R. Toha; A. Mondol; S. M. M. Alam|10.1109/ECCE57851.2023.10101616|ETP(Effluent Treatment Plant);IoT;Monitoring;pH Controlling;Water quality parameters;Industries;Process control;Water quality;Control systems;Water pollution;Wastewater treatment;Monitoring|
|[S-shaped Metamaterial Absorber for K-band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101515)|S. Dey; M. S. Mia; A. A. Mamun; T. Alam; M. T. Islam; R. Azim|10.1109/ECCE57851.2023.10101515|Absorber;Metamaterial;K-band;S-shape;Satellites;K-band;Spaceborne radar;Superresolution;Metamaterials;Software;Radar applications|
|[Experimental Investigation of Breakdown Characteristics Towards Comparative Study of Transformer Oil Versus Cottonseed Oil for High Voltage Insulation Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101586)|U. Modak; M. A. G. Khan|10.1109/ECCE57851.2023.10101586|Transformer oil;Breakdown Voltage;Uniform field;non-uniform field;Cottonseed oil (CSO);Electrodes;Viscosity;Vegetable oils;Electric breakdown;Shape;Purification;High-voltage techniques|
|[Establishing an Internet of Things (IoT)-enabled solar-powered smart water treatment system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101624)|F. Alam; A. Gupta; A. Saha; S. M. Salimullah|10.1109/ECCE57851.2023.10101624|PLC;Drinking Water;Internet of Things (IoT);Solar Energy;Surface contamination;Sociology;Programmable logic devices;SCADA systems;Solar energy;Water pollution;Rivers|
|[Ferroelectric BiMnO3 in BSF layer and Zinc doped CdS in buffer layer: Boosting up the performance of CZTS solar cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101529)|M. A. Islam Sakib; M. T. Ahmed; J. P. Dhar|10.1109/ECCE57851.2023.10101529|CZTS;ferroelectric;BiMnO3;BSF;GPVDM software;Photon density;Buffer layers;Photovoltaic cells;Ferroelectric materials;Short-circuit currents;Optical buffering;Voltage;Numerical models|
|[Ensemble Based Machine Learning Model for Early Detection of Mother's Delivery Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101558)|M. Hasan; M. J. Zobair; S. Akter; M. Ashef; N. Akter; N. B. Sadia|10.1109/ECCE57851.2023.10101558|Mother's Mode of Delivery;machine learning;ensemble learning;Radio frequency;Support vector machines;Pediatrics;Machine learning algorithms;Pipelines;Stochastic processes;Static VAr compensators|
|[Comparative Study of Three-Phase Bridge Converter Voltage Control Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101643)|S. M. Hossain; M. Rasel; F. Amin; S. Islam|10.1109/ECCE57851.2023.10101643|bridge voltage source inverter;induction machine;pulse width modulation;total harmonic distortion;voltage control;Total harmonic distortion;Renewable energy sources;Switching frequency;Bridge circuits;Pulse width modulation;Power system harmonics;Harmonic analysis|
|[A Shuffling Building Block and Augmentation Parameter Tuning Techniques to Handle Small Medical Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101552)|T. H. Nehal; A. A. Khan; S. A. Shifa; L. Saiyara; U. Hossain; A. K. M. E. Islam|10.1109/ECCE57851.2023.10101552|medical imaging;computer vision;deep learning;Architecture;Microscopy;Computer architecture;Information filters;Reliability;Medical diagnosis;Medical diagnostic imaging|
|[Comparative Performance Analysis of Feature Selection for Mortality Prediction in ICU with Explainable Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101553)|N. Tasnim; S. A. Mamun|10.1109/ECCE57851.2023.10101553|Principal Component;Explainable Artificial Intelligence;Machine Learning;MIMIC-III;Feature Selection;Support vector machines;Analytical models;Machine learning algorithms;Decision making;Machine learning;Medical services;Predictive models|
|[Analysis and Modeling of an Oval Shaped Silica PCF for Supercontinuum Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101580)|M. S. Hossain; F. Hoque; M. R. Karim|10.1109/ECCE57851.2023.10101580|novel hexagonal PCF;oval air holes;lattice pitch;confinement loss;dispersion;air filling fraction;Silicon compounds;Optical losses;Supercontinuum generation;Lattices;Photonic crystal fibers;Filling;Optical pumping|
|[Ring Oscillator Based Voltage Controlled Oscillator Design for IoT Based Wireless Patient Monitoring Station in 50 nm CMOS Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101565)|M. S. K. Hemel; K. N. Minhad; K. J. A. Ooi; M. B. I. Reaz; M. S. Amin; M. A. S. Bhuiyan|10.1109/ECCE57851.2023.10101565|CMOS;IoT;RO-VCO;Transceiver;Oscillator;Patient monitoring system;Wireless communication;Ring oscillators;Radio frequency;Patient monitoring;Voltage-controlled oscillators;Medical services;Logic gates|
|[Speech Emotion Recognition from Audio Files Using Feedforward Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101492)|K. Alam; N. Nigar; H. Erler; A. Banerjee|10.1109/ECCE57851.2023.10101492|machine learning;deep learning;speech emotion recognition;Emotion recognition;Filtering;Speech recognition;Feedforward neural networks;Human voice|
|[Design and Optimization of a Passive Micromixer with Kite-Shaped Chambers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101610)|I. Z. Nishu; M. F. Samad|10.1109/ECCE57851.2023.10101610|passive micromixer;microchannel;mixing index;pressure drop;Taguchi method;Bridges;Fluids;Shape;Simulation;Numerical simulation;Software;Indexes|
|[Proposal of Single-Feed Microstrip Antenna for Quad-Polarization Agility With Short-Ended Microstrip- Line Perturbation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101594)|M. Hasan; H. W. Htun; E. Nishiyama; I. Toyoda|10.1109/ECCE57851.2023.10101594|polarization agile antenna;quad-polarization;single feed;CP/LP switch;Polarization;Wireless sensor networks;PIN photodiodes;Perturbation methods;Microstrip antennas;Switches;Bandwidth|
|[Impact of Vacancies in Monolayer $1\mathrm{T}-\text{TiTe}_{2}$ for Optoelectronic and Spintronic Applications: A First-Principles Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101533)|S. M. Ta-Seen Afrid; Asad-Uz-Zaman|10.1109/ECCE57851.2023.10101533|TMD;DFT;Vacancy;Optoelectronics;Spintronics;Magnetization;Bader Charge Analysis;Dynamic Stability;Dielectric Functions;Absorption Coefficient;Optical polarization;Absorption;Magnetoacoustic effects;Optical imaging;Orbits;Stability analysis;Optical modulation|
|[Load Frequency Analysis Using Optimal Virtual Inertia and SMES Considering A Random Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101617)|T. A. Chowdhury; A. Dhar; M. S. Alam; F. S. Al–Ismail; M. A. Ullah|10.1109/ECCE57851.2023.10101617|RESs;Micro-grid;Wind power;Load frequency control (LFC);Rate of change of frequency (ROCOF);Virtual inertia;SMES;Frequency stability;Power imbalance;Renewable energy sources;PI control;Power system stability;Stability analysis;Mathematical models;Inverters;Superconducting magnetic energy storage|
|[Intelligent training system for snowboard big air](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101605)|L. Jiang; B. Huo; Q. Sun; X. Chen; X. Gao|10.1109/ECCE57851.2023.10101605|artificial intelligence;snowboard big air;kinematic;kinetics;Training;Visualization;Three-dimensional displays;Tracking;Space technology;Force;Kinematics|
|[An Optimal Technique for Computation-intensive Task Allocation at Virtual Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101526)|A. U. Chowdhury; M. S. Hossen; M. I. A. Zahed|10.1109/ECCE57851.2023.10101526|Heuristic;optimization;task-allocation;virtual machine;Cloud computing;Costs;Heuristic algorithms;Computational modeling;Simulation;Virtual machining;Resource management|
|[Pothole Detection and Estimation of Repair Cost in Bangladeshi Street: AI-based Multiple Case Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101579)|M. S. Hossain; R. B. Angan; M. M. Hasan|10.1109/ECCE57851.2023.10101579|object tracking;object detection;YOLO;YOLOv4-tiny;TensorFlow;potholes detection;repair method;cost estimation;Analytical models;Convolution;Roads;Computational modeling;Neural networks;Estimation;Developing countries|
|[Brain Tumor Classification Using Watershed Segmentation with ANN Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101528)|F. S. Chowdhury; T. Noor; M. S. Islam; M. K. Alam|10.1109/ECCE57851.2023.10101528|ANN;statistical feature;watershed;brain tumor;Image segmentation;Magnetic resonance imaging;Artificial neural networks;Watersheds;Feature extraction;Classification algorithms;Clinical diagnosis|
|[A Dual-band MIMO Antenna for 5G sub-6 GHz/WiFi/WiMAX/WLAN/Bluetooth/C-band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101568)|N. Das; M. S. Mia; M. T. Islam; A. Mostafa; K. Dhar; R. Azim|10.1109/ECCE57851.2023.10101568|MIMO;Bluetooth;antenna;5G sub-6 GHz;WiMAX;WLAN;C-band;5G mobile communication;Dual band;Diversity methods;WiMAX;Antennas;MIMO communication;Gain|
|[Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101597)|M. S. Rana; N. K. Podder; H. K. Rana; M. I. Hasan; M. S. Azam; M. A. Rahim; S. M. Hasan Sazzad Iqbal; S. Saha|10.1109/ECCE57851.2023.10101597|Parkinson's disease;Alzheimer's Diseases;Amyotrophic lateral sclerosis;Huntington's Disease and Multiple Sclerosis disease;Proteins;Peripheral nervous system;Multiple sclerosis;Systematics;Parkinson's disease;Genomics;Phylogeny|
|[Dynamic Hand Gesture Recognition using Sequence of Human Joint Relative Angles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101606)|S. Ishrak; M. B. Munir; M. H. Kabir|10.1109/ECCE57851.2023.10101606|Gesture Recognition;Dynamic Gestures;Joint Relative Angle (JRA);Sign Language;Kinect;Skeletal Model;Sign Language Translation;Solid modeling;Three-dimensional displays;Machine learning algorithms;Computational modeling;Training data;Gesture recognition;Skeleton|
|[An Improved Framework for Reliable Cardiovascular Disease Prediction Using Hybrid Ensemble Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101564)|T. Mahmud; A. Barua; M. Begum; E. Chakma; S. Das; N. Sharmen|10.1109/ECCE57851.2023.10101564|Cardiovascular disease;Hybrid ensemble Approach;Hyperparameter Tuning;Support vector machines;Heart;Stacking;Stroke (medical condition);Rhythm;Reliability engineering;Ensemble learning|
|[Recognition of Bengali Handwritten Digits Using Spiking Neural Network Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101535)|S. Bhattacharjee; M. B. Uddin Sifat; J. B. Kibria; N. S. Pathan; N. Mohammad|10.1109/ECCE57851.2023.10101535|SNN;OCR;Bengali Digit Recognition;NumtaDB;Training;Support vector machines;Handwriting recognition;Computational modeling;Brain modeling;Sparse matrices;Security|
|[Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101647)|S. Dhar; M. S. Islam|10.1109/ECCE57851.2023.10101647|Induction motor;mamdani type fuzzy logic controller;starting current;acceleration torque;acceleration time;Fuzzy logic;Thyristors;Induction motors;Torque;Firing;Decision making;Artificial neural networks|
|[An Efficient Technique of Predicting Toxicity on Music Lyrics Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101658)|N. Bin Noor; I. Ahmed|10.1109/ECCE57851.2023.10101658|Spotify Dataset;Logistic Regression;SVM;Natural Language Processing (NLP);Toxic Songs;TF-IDF Vectorizer;Industries;Support vector machines;Toxicology;Machine learning algorithms;Computational modeling;Music;Organizations|
|[A Text Independent Speech Emotion Recognition Based on Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101666)|S. Sarker; K. Akter; N. Mamun|10.1109/ECCE57851.2023.10101666|Speech Emotion Recognition;Convolutional Neural Network;Mel-Frequency Cepstral Coefficient;Log-Mel Spectrogram;Emotion recognition;System performance;Frequency-domain analysis;Speech recognition;Convolutional neural networks;Reliability;Mel frequency cepstral coefficient|
|[KNNTree: A New Method to Ameliorate K-Nearest Neighbour Classification using Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101569)|N. Islam; M. Fatema-Tuj-Jahra; M. T. Hasan; D. M. Farid|10.1109/ECCE57851.2023.10101569|Classification;Decision Tree;K-Nearest Neighbour;Hybrid Model;Supervised Learning;Machine learning algorithms;Computational modeling;Supervised learning;Training data;Machine learning;Data science;Benchmark testing|
|[Indium Tin Oxide Coated Surface Plasmon Resonance Based Biosensor for Cancer Cell Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101514)|M. J. Hasan; R. H. Munshi; F. A. Shefat; S. Akter|10.1109/ECCE57851.2023.10101514|Cancer cells;Cancer Sensing and Detection;Mono-core Bowl shaped SPR;Birefringence;Sensitivity;Sensitivity;Optical polarization;Biomedical optical imaging;Biological system modeling;Indium tin oxide;Optical variables control;Finite element analysis|
|[Deep Learning Approach to Determine the Optical Characteristics of Photonic Crystal Fiber for Orbital Angular Momentum Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101646)|M. I. H. Upal; D. Dutta; R. Rafi; M. R. Karim; M. R. Alam; S. Ghosh|10.1109/ECCE57851.2023.10101646|Artificial Neural Network;Deep Learning;Orbital Angular Momentum;Photonic Crystal Fiber;Optical fibers;Deep learning;Photonic crystal fibers;Refractive index;Artificial neural networks;Optical fiber networks;Optical variables control|
|[Design and Performance Analysis of a Silicon Carbide Microneedle for Ibuprofen Drug Delivery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101639)|R. U. Jaman; M. F. Samad|10.1109/ECCE57851.2023.10101639|microneedle;transdermal drug delivery;silicon carbide;Von Mises Stress;ibuprofen;Drugs;Silicon carbide;Pain;Lumen;Solids;Drug delivery;Skin|
|[Stacking Ensemble Technique for Multiple Medical Datasets Classification: A Generalized Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101523)|N. Jannat; S. M. Mahedy Hasan; A. H. Efat; M. F. Taraque; M. Mitu; M. A. Mamun; M. F. Faruk|10.1109/ECCE57851.2023.10101523|Machine Learning;Medical Dataset;Cross-validation;Ensemble Learning;Hyperparameter Tuning;Support vector machines;Radio frequency;Heart;Pain;Magnetic resonance imaging;Stacking;Predictive models|
|[Combination of the Features of Pre-trained Xception and VGG16 Models to Identify Childhood Pneumonia from Chest X-Ray Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101489)|M. M. A. Prodhan; M. A. Yousuf|10.1109/ECCE57851.2023.10101489|Pre-trained Model;Xception;VGG16;LSTM;Binary classification;Pneumonia Detection;Training;Radiography;Pediatrics;Computational modeling;Pulmonary diseases;Transfer learning;Lung|
|[Machine Learning based Load and Temperature Behavior Clustering and Peak Shifting Implementation on Bangladeshi Grid Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100746)|S. S. Oyshee; S. R. Anik; M. J. Ul Kabir Chowdhury; M. A. Kabir|10.1109/ECCE57851.2023.10100746|Demand side management;peak shifting;z-normalization piecewise-aggregate approximation;k-Means clustering;time series clustering;machine learning;Pearson correlation coefficient;Correlation coefficient;Temperature distribution;Temperature dependence;Correlation;Time series analysis;Clustering algorithms;Approximation algorithms|
|[An Efficient Modulation Strategy for Modular Multilevel Cascaded Inverter Used in Solar PV Fed Induction Motor Drive Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101634)|S. Haq; M. K. Hosain; S. P. Biswas|10.1109/ECCE57851.2023.10101634|Pulse width modulation;induction motor;modular cascaded multilevel inverter;solar photovoltaic;Photovoltaic systems;Total harmonic distortion;Torque;Power quality;Switches;Stators;Pulse width modulation|
|[A T-F Masking based Monaural Speech Enhancement using U-Net Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101608)|K. Akter; N. Mamun; M. A. Hossain|10.1109/ECCE57851.2023.10101608|Spectral-masking;U-Net;Speech enhancement;Deep learning;Single-channel Speech Enhancement;Performance evaluation;Time-frequency analysis;Computational modeling;Computer architecture;Speech enhancement;Feature extraction;Noise measurement|
|[Detection of Colon Cancer Using Inception V3 and Ensembled CNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101654)|I. J. Swarna; E. K. Hashi|10.1109/ECCE57851.2023.10101654|colon cancer;Inception V3;ensemble;deep learning;image classification;Deep learning;Costs;Computational modeling;Transfer learning;Predictive models;Data models;Colon|
|[Ensemble Machine Learning Approach For Agricultural Crop Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101585)|A. Islam; I. Khair; S. Hossain; R. A. Ifty; M. N. Arefin; M. J. A. Patwary|10.1109/ECCE57851.2023.10101585|Crop Prediction;Regression;Ensemble Method;Weather;Agriculture;Machine learning algorithms;Profitability;Error analysis;Employment;Crops;Weather forecasting;Machine learning|
|[Automated Gastrointestinal Tract Image Segmentation Of Cancer Patient Using LeVit-UNet To Automate Radiotherapy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101574)|M. J. Alam; S. Zaman; P. C. Shill; S. Kar; M. A. Hakim|10.1109/ECCE57851.2023.10101574|Gastrointestinal tract cancer;GI tract image seg-mentation;Medical image segmentation;Deep Learning;LeVit-UNet;Transformer;Image segmentation;Stomach;Intestines;Computer architecture;Transformers;Gastrointestinal tract;Radiation therapy|
|[A Design of Orthogonal Switchable Feed Networks Integrated Microstrip Antenna for Six Polarization States](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101603)|M. A. Rahman; M. N. Rahman; M. A. Hossain|10.1109/ECCE57851.2023.10101603|hexad-polarization;phase shifter;polarization reconfigurable antenna;quadrature length shifter;switchable feed network;Polarization;Patch antennas;Phase shifters;Resonant frequency;Adaptive arrays;Microstrip antennas;Switches|
|[Person Re-Identification: A Lightweight Feature Extraction Architecture for Image Based Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101571)|M. Z. Hassan; S. M. M. Ahsan|10.1109/ECCE57851.2023.10101571|Person Re-Identification;CNN;Siamese Net-work;Deep Learning;Training;Computational modeling;Search methods;Lighting;Computer architecture;Predictive models;Feature extraction|
|[COMSOL-Based Modeling and Simulation of ISFET pH Sensor Using Si02 Sensing Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101521)|S. Hossain; M. T. Rahman|10.1109/ECCE57851.2023.10101521|COMSOL;Ion Sensitive Field Effect Transistor (ISFET);Gate voltage;Sensitivity;Electrolyte;pH sensor;Electric potential;Semiconductor device measurement;Sensitivity;Voltage measurement;Field effect transistors;Logic gates;Electrolytes|
|[Sustainability of Machine Learning Models: An Energy Consumption Centric Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101532)|M. S. Islam; S. N. Zisad; A. -L. Kor; M. H. Hasan|10.1109/ECCE57851.2023.10101532|Machine learning;Energy consumption;Classification models;Green AI;Carbon footprint;Microsoft Joulemeter;Measurement;Energy consumption;Machine learning algorithms;Biological system modeling;Computational modeling;Gaussian processes;Carbon dioxide|
|[Investigating Heuristic and Optimization Energy Management Algorithms to Minimize Residential Electricity Costs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101630)|S. Barua; N. Mohammad|10.1109/ECCE57851.2023.10101630|Solar PV;Energy Storage;Energy Management System;State-of-Charge;Optimization;Heuristic;Electricity Tariffs;Renewable energy sources;Costs;Tariffs;Optimization methods;Stochastic processes;Power system stability;Stability analysis|
|[The Use of Plasmonic Metal Nanoparticles to Enhance The Opto-electronic Performance of Thin-Film/Ultrathin Film CdTe Solar Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101669)|A. Al Suny; R. B. Sultan; S. Tohfa; A. J. Haque; M. H. Chowdhury|10.1109/ECCE57851.2023.10101669|CdTe;thin-film solar cell;solar cell;nanoparticle;plasmon resonance;plasmonics;surface plasmon;FDTD;finite-difference time-domain;renewable energy;Nanoparticles;Silver;II-VI semiconductor materials;Photovoltaic cells;Voltage;Plasmons;Silicon|
|[SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101527)|N. M. Fahad; S. Sakib; M. A. Khan Raiaan; M. S. Hossain Mukta|10.1109/ECCE57851.2023.10101527|Skin cancer;deep Learning;CNN;image pre-processing;data balance;shallow;Measurement;Computational modeling;Transfer learning;Computer architecture;Skin;Robustness;Lesions|
|[Simulation and Numerical Scheme of Molecular Transport Due to Dielectrophoresis and Electroporation in a Giant Unilamellar Vesicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101507)|S. Saha; M. S. Ishtiaque; M. I. Hossain; M. K. Alam|10.1109/ECCE57851.2023.10101507|Electroporation;electrophoresis;molecular transport;biomedical engineering;Analytical models;Biological system modeling;Simulation;Electroporation;Organisms;Numerical models;Planning|
|[juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset for Optical Character Recognition Using BiT-based M-ResNet-101x3 Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101581)|M. M. Hasan; M. A. Hossain; A. Y. Srizon; A. Sayeed|10.1109/ECCE57851.2023.10101581|Big Transfer;M-ResNet-101x3;Convolutional Neural Network;Handwritten compound character;Transfer Learning;Databases;Computational modeling;Optical character recognition;Computer architecture;Convolutional neural networks;Compounds|
|[Implementation of Liver Segmentation from Computed Tomography (CT) Images Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101544)|M. A. H. Ifty; M. S. S. Shajid|10.1109/ECCE57851.2023.10101544|Medical image processing;Liver segmentation;MONAI;Computed Tomography (CT);Deep learning;UNet;Deep learning;Image segmentation;Computed tomography;Computational modeling;Volume measurement;Transfer learning;Liver|
|[A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101612)|N. Taki; E. Showan; U. Chowdhury; F. Tasnim|10.1109/ECCE57851.2023.10101612|Fake information detection;NLP;TF-IDF;MLP;LSTM;Bi-LSTM;GRU;Deep learning;Machine learning algorithms;Social networking (online);Text categorization;Predictive models;Classification algorithms;Fake news|
|[A Blockchain-based Technique to Prevent Grade Tampering: A University Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101502)|M. A. Habib; M. M. Hossen Manik; S. Zaman|10.1109/ECCE57851.2023.10101502|Blockchain;Grade Tampering;Encryption;Decryption;Privacy;Cross-site scripting;Education;SQL injection;Blockchains;Servers;Indexes|
|[PUF-based Hardware Trojan: Design and Novel Attack on Encryption Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101599)|M. A. Bokor Siddik; S. H. Alam|10.1109/ECCE57851.2023.10101599|hardware trojan;hamming distance;FPGA;ring oscillator;physical unclonable function;hardware security;Layout;Inspection;Physical unclonable function;Hardware;Stability analysis;Encryption;Trojan horses|
|[Classification of Breast Tumor Using Radon Cumulative Distribution Transform Nearest Subspace Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101642)|S. Ahmed; M. Jahan; N. S. Pathan; Q. D. Hossain|10.1109/ECCE57851.2023.10101642|Pattern Recognition;R-CDT;Nearest Subspace;Image classification;Breast Tumor;Computer-aided diagnosis (CAD);ICIAR;Bioimaging;Deep learning;Breast tumors;Ultrasonic imaging;Histopathology;Radon;Neural networks;Graphics processing units|
|[Brain Tumor Detection and Classification by SVM Algorithm and Performance Analysis Through CNN Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101618)|S. A. Shimanto; M. K. Hosain; S. P. Biswas; M. S. Islam|10.1109/ECCE57851.2023.10101618|machine learning;malignant;support vector machine (SVM);convolutional neural network (CNN);anisotropic filtering (ADF);brain tumor;Support vector machines;Image segmentation;Machine learning algorithms;Sensitivity;Filtering;Feature extraction;Brain modeling|
|[FPGA Implementation of Educational RISC- V Processor Suitable for Embedded Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101508)|M. H. Banna Saif; N. U. Sadad; M. N. Islam Mondal|10.1109/ECCE57851.2023.10101508|FPGA;RISC-V;CPU Design;Assembler;Codes;Instruction sets;Computer architecture;Logic gates;Hardware;Task analysis;Field programmable gate arrays|
|[Malaria Parasite Detection Using CNN-Based Ensemble Technique on Blood Smear Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101524)|K. M. T. Ahmed; Z. Rahman; R. Shaikh; S. I. Hossain|10.1109/ECCE57851.2023.10101524|Blood smear images;convolutional neural net-works (CNN);computer-aided diagnosis (CADx);deep learning (DL);machine learning (ML);malaria parasite detection;pre-trained models;Red blood cells;Computational modeling;Microscopy;Microprocessors;Computer architecture;Benchmark testing;Personnel|
|[Evaluating the Efficacy of a Three-Phase AC-A C Boost Converter Configuration Using a Bidirectional Switch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101551)|A. U. Chowdhury; M. R. T. Hossain|10.1109/ECCE57851.2023.10101551|boost converter;single bidirectional switch;power quality;voltage mode feedback control;Legged locomotion;Costs;AC-AC converters;Power quality;Switching loss;Switches;Feedback control|
|[Comparative Study of the Open-loop Boost Converter and the Closed-loop PID Controlled Boost Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101638)|M. N. Alam; N. Bashar; S. Sarker; S. A. Lopa; T. Ahmed|10.1109/ECCE57851.2023.10101638|boost converter;renewable energy;open loop;close loop;PID controller;Inductance;Simulation;Capacitors;Voltage;Harmonic analysis;Capacitance;Distortion|
|[Drowsiness and Lethargy Detection Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101607)|M. A. D. Siddik; M. S. Rahman|10.1109/ECCE57851.2023.10101607|drowsiness detection;machine learning;eye aspect ratio;mouth aspect ratio;pupil circularity;nose length;chin length;3D face mesh;eye closing;rapid eye blinking;yawning;head posing down;head posing up;CNN;Three-dimensional displays;Mouth;Nose;Machine learning;Feature extraction;Classification algorithms;Convolutional neural networks|
|[A Statistical Investigation of Spatial Consistency and Human Blockage Consideration based mmWave Channel Modeling for 5G Back-Haul Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100745)|M. R. Imrose; M. T. Hassan; M. R. Hassen; M. M. Mowla|10.1109/ECCE57851.2023.10100745|5G;mmWave;Back-Haul;Channel Modeling;Power Delay Profile;Solid modeling;Fluctuations;5G mobile communication;Atmospheric modeling;Computational modeling;Channel estimation;Delays|
|[Converting Municipal Solid Waste into Electrical Energy: A Renewable Solution in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101566)|M. S. Hasan; M. S. Hossain; M. R. Hayder|10.1109/ECCE57851.2023.10101566|Municipal Solid Waste (MSW);waste to energy (WTE);incineration;renewable energy;Waste management;Waste materials;Renewable energy sources;Incineration;Roads;Sociology;Production|
|[Non-Invasive Blood Glucose Measurement Device: Performance analysis of Diffused Reflectance method and Diffused Transmittance method using Near Infrared Light](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101505)|T. R. Khan; A. Mostofa; M. Dey|10.1109/ECCE57851.2023.10101505|Near Infrared Light;LED;Regression Analysis;Diffused Reflectance;Diffused transmittance;Blood Glucose;Non-Invasive Glucometer;Clark Grid Error Analysis;Reflectivity;Costs;Error analysis;Reflection;Glucose;Diabetes;Biomedical monitoring|
|[A New Method for Learning Decision Tree Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101557)|Z. Saurav; M. M. Mitu; N. S. Ritu; M. A. Hasan; S. Arefin; D. M. Farid|10.1109/ECCE57851.2023.10101557|Decision Tree;Data Mining;Machine Learning;Supervised Learning;Training;Machine learning algorithms;Training data;Big Data;Data models;Classification algorithms;Decision trees|
|[Optimal Cost and Component Configuration Analysis of Micro-grid Using GWO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101554)|M. S. -U. Islam; M. A. B. Zafar; A. I. Ikram; T. A. Chowdhury; M. S. R. Sachha; S. Hossain|10.1109/ECCE57851.2023.10101554|Micro-grid;Grey wolf optimization;Waste to energy;Energy management;Sustainable energy;Generation;Renewable energy sources;Costs;Pollution;Optimization methods;Production;Batteries;Wind turbines|
|[Effective Secrecy Throughput Analysis Over Underwater Wireless Optical Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101499)|A. R. Chowdhury; M. K. Kundu; A. S. M. Badrudduza|10.1109/ECCE57851.2023.10101499|underwater;security;air bubbles;temperature gradient;eavesdropper;Wireless communication;Radio frequency;Temperature distribution;Monte Carlo methods;Optical mixing;Probability;Physical layer security|
|[Electric Vehicle Charging Station with Solar-Grid Interactive System for Maximum Power Exchange](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101531)|J. K. Monny; M. A. Al Noman; R. K. Das; M. A. Razzak|10.1109/ECCE57851.2023.10101531|EV;solar station;GTI;transformer less;PV;MPPT;high-efficiency;Photovoltaic systems;Switching frequency;Magnetic cores;System performance;Prototypes;Transformers;Electric vehicle charging|
|[Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101650)|M. Hussain; N. Sharmin; S. B. Shafiul|10.1109/ECCE57851.2023.10101650|Support Vector Regression;Random Forest;Soil Moisture;Meteorological forcing;Metropolitans;Bangladesh;Radio frequency;Support vector machines;Training;Biological system modeling;Soil moisture;Urban areas;Weather forecasting|
|[Detection and Recognition of Bangladeshi Vehicles' Nameplates Using YOLOV6 and BLPNET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101501)|C. Sinthia; M. H. Kabir|10.1109/ECCE57851.2023.10101501|Yolov6;BLPNET;Traffic jam;Detection;Recognition;License plates;Convolution;Computational modeling;Licenses;Character recognition;Automobiles;Artificial intelligence;License plate recognition|
|[Design of Mini-Grid for Solar Home Systems with NPV in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101668)|M. N. Sakib; M. A. Matin|10.1109/ECCE57851.2023.10101668|SHS;COE;grid;solar;RE;PV;Net Metering;Mini-Grid;NPV;Photovoltaic systems;Renewable energy sources;Electric potential;Costs;Government;Energy resolution;Market research|
#### **2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)**
- DOI: 10.1109/ICISCoIS56541.2023
- DATE: 9-11 Feb. 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Sell and Buy Homegrown Vegetables and Fruits Online Using E-Commerce UML Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100576)|C. Preethi; V. S. S. Saran; M. Meikannan; S. S. Hammed; K. Haripriya; B. A. Kumar|10.1109/ICISCoIS56541.2023.10100576|E-commerce;M-Commerce;E-app to sell and buy homegrown veggies;sustainable development);Roads;Unified modeling language;Soil;Software;Mobile handsets;Minerals;Internet|
|[Improved Faster RCNN-based Nighttime Pedestrian Detection Using RGB Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100389)|S. Devi; R. Dayana; P. Malarvezhi|10.1109/ICISCoIS56541.2023.10100389|Low light illumination;Pedestrian detection;Deep learning;Attention mechanism;Autonomous vehicle;Representation learning;Shape;Surveillance;Pipelines;Lighting;Benchmark testing;Transformers|
|[Laser micromachining of Monel-400 and the investigation on the effect of process parameters on top kerf quality characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100498)|M. R. P. Kumar; S. Karthick; C. Niranjan; R. Shanmathi; D. Arulkirubakaran|10.1109/ICISCoIS56541.2023.10100498|Laser micro machining;Top kerf characteristics;Laser power;Cutting speed;Taguchi method;Drag;Power lasers;Force;Machining;Micromachining;Gas lasers;Security|
|[Machine learning for diagnosis of polycystic ovarian syndrome (PCOS/PCOD)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100490)|V. Srinithi; R. Rekha|10.1109/ICISCoIS56541.2023.10100490|Machine learning;PCOS;algorithms;ovaries;follicles;detection;ultrasonic images;CNN;Hair;Pediatrics;Machine learning algorithms;Ultrasonic imaging;Medical treatment;Manuals;Prediction algorithms|
|[A Review towards Fault-tolerable Load Balancing in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100345)|V. Siruvoru; S. Aparna; N. VijayKumar; V. Siruvoru|10.1109/ICISCoIS56541.2023.10100345|Load-balancing;Fault- Tolerance;Static load-balancing algorithm;Dynamic load-balancing algorithm;Virtual Machines;Cloud-Computing;Resistance;Cloud computing;Fault tolerance;Processor scheduling;Fault tolerant systems;Load management;Security|
|[A Review of Convolutional Neural Networks, its Variants and Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100412)|S. P; R. R|10.1109/ICISCoIS56541.2023.10100412|Deep learning;CNN;variants;region-based models;attention-based mechanisms;applications;Deep learning;Computer vision;Image recognition;Object detection;Network architecture;Feature extraction;Convolutional neural networks|
|[Parkinson Disease Identification Using Hand Written Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100446)|Priya; H. T; J. P; R. S|10.1109/ICISCoIS56541.2023.10100446|CNN;Fully Connected Neural Network;Voice and Image Dataset;Support vector machines;Parkinson's disease;Predictive models;Prediction algorithms;Data models;Hybrid power systems;Convolutional neural networks|
|[A Novel Hybrid Deep Learning Model for Botnet Attacks Detection in a Secure IoMT Environment *](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100396)|A. K. Kumar; K. Vadivukkarasi; R. Dayana|10.1109/ICISCoIS56541.2023.10100396|Botnet attack;Internet of Medical Things;Security;Health care;Hybrid;Deep learning;Measurement;Industries;Botnet;Internet of Medical Things;Malware;Internet of Things|
|[Comment Generator for Java using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100539)|S. Saranya; S. Devi; R. M. Suruthi; N. M; S. B|10.1109/ICISCoIS56541.2023.10100539|Deep learning;comment generator;Neural Network;Java;project management;Deep learning;Java;Codes;Generators;Security;Intelligent systems|
|[Machine Learning Framework for Analyzing Disaster-Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100450)|R. Manimegalai; S. Kavisri; M. Vasundhra; R. K. Grace|10.1109/ICISCoIS56541.2023.10100450|social media;tweet processing;text classification;disaster response;machine learning;Training;Support vector machines;Machine learning algorithms;Social networking (online);Blogs;Boosting;Teamwork|
|[Autonomous Automobile Security To Defend Against De-Authentification And Key Reinstallation Attacks In Wi-Fi Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100593)|S. R; A. P|10.1109/ICISCoIS56541.2023.10100593|Distributed Denial of Service;De-authentication;Dis-association;MAC Address;4-Way Handshake;Key Re-installatio n;Protocols;Wireless networks;Transportation industry;Radar;Cameras;Software standards;Autonomous automobiles|
|[Design of Landmark Identification and Path Detection Model on the basis of Air Quality Index](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100608)|S. Singh; M. Kaur; P. Tanwar|10.1109/ICISCoIS56541.2023.10100608|Air-Quality Index (AQI);Geo-Coding;Application Programming Interface (API);Shortest Route;Air Pollution;Pollution;Urban areas;Organizations;Programming;Libraries;Software;Software reliability|
|[Detection and Recognition of Face Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100435)|S. M; E. R. G; N. M; J. K; N. V; P. R|10.1109/ICISCoIS56541.2023.10100435|Facial Detection;Facial Recognition;Deep Learning;Haarcascade classifier;Face recognition;Neural networks;Object detection;Feature extraction;Libraries;Face detection;Object recognition|
|[Machine Learning Based Personality Classification for Carpooling Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100353)|M. Anas; G. C; K. G|10.1109/ICISCoIS56541.2023.10100353|Carpooling;XG-Boost;Tweet analysis;Personality classification;Support vector machines;Machine learning algorithms;Roads;Stochastic processes;Tokenization;Classification algorithms;Automobiles|
|[Wavelet based Convolutional Autoencoder for Medical Image Compression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100599)|M. Venugopal; K. Palanisamy|10.1109/ICISCoIS56541.2023.10100599|Image compression;CNN;Autoencoders;Medical Image compression;Deep learning;Learned image compression;Wavelet;Measurement;Image coding;Transform coding;Decoding;High frequency;Security;Task analysis|
|[Conjoint Analysis based Predictive Analytics to study the employee attitude towards virtual and digital work practices in IoT work environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100481)|G. Nagpal; A. Nagpal; N. V. K. Jasti|10.1109/ICISCoIS56541.2023.10100481|virtual work;flexible work;working hours;work-life balance;work practice profiles;conjoint analysis;Productivity;Schedules;Pandemics;Roads;Urban areas;Companies;Manufacturing|
|[Analysis of Trust aware Web Services using Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100515)|N. H. Priya; N. G. Rani; S. Shymalagowri; R. Sivasaran|10.1109/ICISCoIS56541.2023.10100515|nan;Privacy;Web services;Federated learning;Linear regression;Quality of service;Throughput;Prediction algorithms|
|[A Mobile App for Age and Gender Identification Using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100432)|J. N. V. R. S. Kumar; B. M. Babu; M. Vignesh; P. Manoi; T. M. N. Vamsi; G. Kamal|10.1109/ICISCoIS56541.2023.10100432|Age Estimation;Convolutional Neural Network;Gender Acknowledgment;Face detection;classification;Deep learning;Layout;Lighting;Predictive models;Multitasking;Mobile applications;Security|
|[Optimization of Wire Arc Additive Manufacturing Parameters Using Taguchi Grey Relation Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100337)|J. P. Kumar; R. J. Raman; N. J. Festus; M. Kanmani; S. P. Nath|10.1109/ICISCoIS56541.2023.10100337|Wire arc additive manufacturing;Optimized parameters: welding current;contact tip-to-work distance;Nozzle travel speed c;Taguchi L9 orthogonal array;grey relational analysis;macro-structure analysis;Shape;Welding;Wires;Optical wavelength conversion;Production;Three-dimensional printing;Steel|
|[Parametrization of Optical Flow Kymograms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100493)|B. Panchami; S. P. Kumar; A. J. Rajkumar; V. Rishika; S. H. Ramesh|10.1109/ICISCoIS56541.2023.10100493|Laryngeal high-speed video endoscopy;Optical flow kymogram;Glottal optical flow waveform;Facilitative playbacks;Vibrations;Visualization;Pathology;Image processing;Propagation;Motion estimation;Dynamics|
|[Development of Novel Flying Capacitor Multilevel Inverter with reduced components & Harmonic mitigation by Soft Computing Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100427)|P. Vivek; N. B. Muthuselvan; S. Sivaranjani|10.1109/ICISCoIS56541.2023.10100427|Pulse width modulation (PWM);Multilevel Inverter (MLI);Particle Swarm Optimization (PSO);Selective Harmonic Elimination (SHE);Flying Capacitor Multilevel Inverter (FCMLI);Switched Capacitance Multilevel Inverter (SCMLI) & Total Harmonic Distortion (THD);Total harmonic distortion;Capacitors;Voltage;Switches;Pulse width modulation;Multilevel inverters;Harmonic analysis|
|[Enhancement of Energy Efficiency and Network Lifetime Using Modified Cluster Based Routing in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100580)|P. Satyanarayana; T. Sushma; M. Arun; V. S. Raiu Talari; S. Gopalakrishnan; V. G. Krishnan|10.1109/ICISCoIS56541.2023.10100580|Clustering;Routing;Wireless sensor networks;Energy efficiency;Network lifetime;Wireless sensor networks;Schedules;Scalability;Throughput;Routing;Security;Task analysis|
|[Simulation and Synthesis of Current Mode Logic and Reversible Logic Based Arithmetic Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100442)|J. Ramesh; K. Madhumitha; B. Soundaryalaxmi; D. A. Rao; R. Kanisha|10.1109/ICISCoIS56541.2023.10100442|MOS Current Mode Logic;Reversible Logic;Static CMOS Logic;Power;Semiconductor device modeling;Power demand;Logic gates;CMOS technology;Power dissipation;Security;Integrated circuit modeling|
|[Sentiment Analysis Using VADER and Logistic Regression Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100565)|P. Dhanalakshmi; G. A. Kumar; B. S. Satwik; K. Sreeranga; A. T. Sai; G. Jashwanth|10.1109/ICISCoIS56541.2023.10100565|Sentiment;VADER;Lexicon;Logistic regression;Sentiment analysis;Public relations;Social networking (online);Mood;Blogs;Training data;Companies|
|[A Convolutional Recurrent Neural Network-Based Model For Handwritten Text Recognition To Predict Dysgraphia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100514)|N. N. Doshi; M. U. Maniyar; K. K. Shah; N. D. Sarda; M. Narvekar; D. Mukhopadhyay|10.1109/ICISCoIS56541.2023.10100514|Dysgraphia;Dyslexia;handwriting;Optical Character Recognition (OCR);risk;prediction;Convolutional Neural Network (CNN);Recurrent Neural Network (RNN);Recurrent neural networks;Machine learning algorithms;Text recognition;Sociology;Manuals;Predictive models;Prediction algorithms|
|[PharmaSafe - Blockchain-Based Counterfeit Detection in the Pharmaceutical Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100382)|D. Sivaganesan; A. S. Kareshmmaa; A. K. S. Shankar; A. P. B|10.1109/ICISCoIS56541.2023.10100382|Blockchain;Ethereum;Ganache;Smart contracts;DApp;Web3.js;Supply chain;Drug forging;Drugs;Costs;Distributed ledger;Supply chains;Smart contracts;Decentralized applications;Blockchains|
|[Preliminary Diagnosis of COVID-19 using Speech Processing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100482)|N. Yerramsetty; K. Deekshitha; M. Mantrala; Latha|10.1109/ICISCoIS56541.2023.10100482|COVID-19;speech samples;MFCC;Random Forest Classifier;COVID-19;Pulmonary diseases;Recording;Naive Bayes methods;Linear discriminant analysis;Security;Speech processing|
|[Communication Assistance Based on Augmentative and Alternative Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100399)|M. Kumar; Y. Vats; P. Tanwar|10.1109/ICISCoIS56541.2023.10100399|Augmentative and Alternative Communication (AAC);Disorder;PECS;Communication Assistance;Java;Computer languages;XML;Grasping;Security;Intelligent systems|
|[Wisconsin Breast Cancer Detection Using L1 Logistic Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100387)|R. Rekha; K. L. Vinoci|10.1109/ICISCoIS56541.2023.10100387|Logistic Regression;Lasso Regularization;Wisconsin Breast Cancer Dataset;Deep learning;Feature extraction;Breast cancer;Security;Intelligent systems;Logistics|
|[Bus Management System Through MEAN Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100416)|K. K. Baseer; M. J. Pasha; M. Jayasunitha; K. Swetha; B. M. Joseph|10.1109/ICISCoIS56541.2023.10100416|Mongodb;Nodejs;JavaScript;Gps;Visualization;Databases;Urban areas;Organizations;Search problems;Web servers;Complexity theory|
|[Long Short-Term Memory Networks for Email Spam Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100445)|V. S. Vinitha; D. K. Renuka; L. A. Kumar|10.1109/ICISCoIS56541.2023.10100445|Neural Network;Recurrent Neural Network;Vanishing Gradient;Exploding Gradient;Spam Emails;Long Short-Term Memory Networks;Deep learning;Recurrent neural networks;Machine learning algorithms;Computer viruses;Unsolicited e-mail;Memory management;Information filters|
|[Analyzing WhatsApp Chat Using Python Libraries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100443)|Ranjan; B. Gupta; V. Kapoor; D. Bansal|10.1109/ICISCoIS56541.2023.10100443|Chat analysis;Insight;WhatsApp;Python;Freeware;Data analysis;Data visualization;Oral communication;Message services;Libraries;Security|
|[Design of a UWB Antenna Utilising Fractal Geometry and EBG Structures for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100529)|M. Jayaram; K. N. Devnesh; B. M. Anjaneyulu|10.1109/ICISCoIS56541.2023.10100529|UWB antenna;EBG structures;fractal geome-try;efficiency;wireless;gain;Wireless communication;Microwave antennas;Wireless LAN;Satellite antennas;Satellites;Ultra wideband antennas;Fractals|
|[Luna: An interactive Unified Device Management Gateway for Smarter IoT Ecosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100343)|S. Priya; A. SP; H. R; H. S; L. C; S. R|10.1109/ICISCoIS56541.2023.10100343|nan;Ecosystems;Transfer learning;Logic gates;Chatbots;User experience;Real-time systems;Internet of Things|
|[Optimization Techniques for Deep Learning Based House Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100566)|J. Vijava; P. S. Kumar; M. Sorgile; M. S. SV vasanth|10.1109/ICISCoIS56541.2023.10100566|House price prediction;Deep Learning;Optimization techniques;Ant Colony Optimization;PSO;Deep learning;Systematics;Layout;Whales;Predictive models;Prediction algorithms;Genetics|
|[Design and Fabrication of Dual band Slotted Microstrip Patch Antenna - 3.5 Ghz and 2.4 Ghz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100371)|D. M; R. P; S. P. S; A. H; M. R. A; S. S|10.1109/ICISCoIS56541.2023.10100371|microstrip patch antenna;2.4Ghz;3.5Ghz;Antenna Fabrication;Fabrication;Patch antennas;Dual band;Resonant frequency;Microstrip antennas;Dielectric resonator antennas;Microstrip|
|[Twitter Sentiment Analysis Using Clustering based Cuckoo Search Algorithm and Ensemble Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100606)|G. Sudhamathy; N. Valliammal|10.1109/ICISCoIS56541.2023.10100606|Clustering based cuckoo search algorithm;Multi-support vector machine;Naïve Bayes;Sentiment analysis;Twitter data;Training;Sentiment analysis;Social networking (online);Computational modeling;Blogs;Clustering algorithms;Feature extraction|
|[Sentiment Analysis on Movie Reviews: A Comparative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100367)|S. B; S. S; G. D. T; V. R. N|10.1109/ICISCoIS56541.2023.10100367|Sentiment Analysis;Movie reviews;Polarity of Reviews;Ensemble models;Feature extraction;Sentiment analysis;Analytical models;Social networking (online);Stacking;Support vector machine classification;Motion pictures;Feature extraction|
|[Measuring the Effects of Employee Face-to-Face interactions on their Productivity Through IR Badges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100583)|A. A. Marri; B. B. Jarn; W. Mansoor; S. Atalla; S. Miniaoui|10.1109/ICISCoIS56541.2023.10100583|Employee productivity;IR;Sociometric Badges;performance;copresence;Knowledge Transfer;Productivity;Machine learning algorithms;Organizations;Internet of Things;Security;Intelligent systems|
|[Intelligent Intravenous Syringe Pump](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100431)|D. Gopinath; R. D. Nayagam; G. Sivakumar; S. Karthik; P. D. Chakravarthi|10.1109/ICISCoIS56541.2023.10100431|Syringe;DC Servo motor;ATMEGA 382P microcontroller;Timer module;Pediatrics;Microcontrollers;Point of care;Programming;Needles;Glucose;Security|
|[Evolution of Blockchain and Smart Contracts: A State of the Art Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100456)|C. K.; R. Kesavamoorthy|10.1109/ICISCoIS56541.2023.10100456|Blockchain;smart contracts;decentralised network;transactions;peer-to-peer network;Shape;Smart contracts;Organizations;Bitcoin;Blockchains;Trajectory;Security|
|[A Survey on Security and Network management of SDWSN with ML Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100545)|J. l. A; J. P. P M|10.1109/ICISCoIS56541.2023.10100545|Wireless Sensor Network (WSN);Software Defined Network (SDN);Software Defined Wireless Sensor Networks (SDWSN);Machine Learning (ML);Internet of Things (IoT);Wireless sensor networks;Art;Software;Security;Task analysis;Software defined networking;Intelligent systems|
|[Certain Investigation on Eeg Signal Processing Using Auto Regression Feature for Various Colour Stimuli](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100447)|S. K; P. M.|10.1109/ICISCoIS56541.2023.10100447|Auto Regressive (AR) feature;Feed Forward Neural Network (FFNN);Electroencephalogram (EEG);Band-pass filters;Computational modeling;Color;Signal processing;Feature extraction;Brain modeling;Electroencephalography|
|[Towards Robust Speech Recognition Model Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100390)|L. A. Kumar; D. K. Renuka; M. C. S. Priya|10.1109/ICISCoIS56541.2023.10100390|Visual speech recognition;Lipreading;Word Error Rate;Audio Visual Speech Recognition;Language Model;Deep learning;Visualization;Error analysis;Lips;Decoding;Noise measurement;Security|
|[Electronic Health Record Systems for Enhanced Medical Care: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100356)|S. Thakur; B. Gupta; U. Mathur; D. Bansal|10.1109/ICISCoIS56541.2023.10100356|Health information management;Electronic Health Records (EHR);Wearables in EHR;EHR security;Training;Technological innovation;Hospitals;Wearable computers;Authentication;Blockchains;Security|
|[An earlier serial lactate determination analysis of cardiac arrest patients using a medical machine learning model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100454)|M. A. Mohammed; M. A. Mohammed; V. A. Mohammed; J. Logeshwaran; N. Jiwani|10.1109/ICISCoIS56541.2023.10100454|Cardiac arrest;blood;organs;tissues;oxygen;nutrients;earlier;serial lactate;accuracy;precision;recall;f1-score;Heart;Proteins;Sociology;Pumps;Cardiac arrest;Security;Statistics|
|[PYNQ-Z2 Based Hardware Implementation of Delay-Aware SFC Placement in 5G Networks Using PSO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100344)|P. Visalakshi; S. Nivetha|10.1109/ICISCoIS56541.2023.10100344|Network Function Virtualization;Resource orchestration;particle swarm optimization;Service function chain (SFC);Virtual network function (VNF);PYNQ-Z2;Location awareness;Service function chaining;Sociology;Throughput;Hardware;Delays;Network function virtualization|
|[Physical Violence Detection in Videos Using Keyframing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100407)|B. J; D. R. A; V. K. B; C. G|10.1109/ICISCoIS56541.2023.10100407|Violence detection;Keyframe extraction;Spatial-temporal CNN;Video on demand;Computational modeling;Surveillance;Sampling methods;Motion pictures;Encoding;Security|
|[A Smart Air Pollutants Monitoring System Using IOT Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100535)|M. Pyingkodi; N. R. W. Blessing; K. Thenmozhi; K. Kanimozhi; L. Pavithira; P. K. S. Balakrishnan|10.1109/ICISCoIS56541.2023.10100535|IoT sensors;Air pollutants;Gas sensor;MQ135;MQ2;MQ7;Cloud computing;Microprocessors;Air pollution;Hardware;Sensors;Servers;Internet of Things|
|[Toddler Activity Analysis Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100553)|I. Kala; B. P; D. R; D. S|10.1109/ICISCoIS56541.2023.10100553|Cnn;Cloud;Dataset;Lstm;Model;Training;Pediatrics;Surveillance;Machine learning;Activity recognition;Predictive models;Cameras|
|[A Critical Survey on Security Issues in Cognitive Radio Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100508)|K. Sudha; K. A. Kumari; D. Varunika|10.1109/ICISCoIS56541.2023.10100508|cognitive radio networks;cognitive radios;cognitive re-configurability;cognitive capability;Technological innovation;Social networking (online);Wireless networks;Radio interferometry;Cognitive radio;Security;Communication system security|
|[Healthcare Data Security using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100572)|P. V. A; R. Dayana; K. Vadivukkarasi|10.1109/ICISCoIS56541.2023.10100572|Blockchain Technology;Healthcare;Privacy;Security;Privacy;Data security;Supply chains;Smart contracts;Memory;Medical services;Organizations|
|[Performance Analysis of DRL Algorithms in V2V Resource Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100573)|K. M; A. K. V; S. Chandrakumar; H. C|10.1109/ICISCoIS56541.2023.10100573|Deep Reinforcement Learning;Sub-band;power levels;Resource Allocation;V2V Communication;Deep learning;Error analysis;Vehicular ad hoc networks;Reinforcement learning;Interference;Safety;Performance analysis|
|[COVID-19 Detection from Pulmonary CT Images using Neural Networks based on Dropout-Driven Hidden Layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100361)|S. V. Ligi; R. Kumar; S. Dhanalakshmi|10.1109/ICISCoIS56541.2023.10100361|COVID-19;Artificial Intelligence;Convolutional Neural Network;Dropout;Hidden layers;COVID-19;Sensitivity;Computed tomography;Computational modeling;Neural networks;Lung;Computer architecture|
|[Digital Signal Processing on 3-Axis Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100369)|S. Krishnakumar; V. K. R; M. V. K; S. N|10.1109/ICISCoIS56541.2023.10100369|Cortex M4F;Fast Fourier Transform;signal processing;band stop filter;critical frequency;Accelerometers;Vibrations;Microcontrollers;Fast Fourier transforms;Signal processing algorithms;Market research;Information filters|
|[Machine Vision based Intelligent Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100502)|D. Sivarai; P. D. Rathika; K. R. Vaishnavee; K. G. Easwar; P. Saranyazowri; R. Hariprakash|10.1109/ICISCoIS56541.2023.10100502|YOLOv5;motion and human detection;surveillance system;telegram bot;Visualization;Surveillance;Machine vision;Streaming media;Chatbots;Cameras;Motion detection|
|[Digit Recognition of MNIST Handwritten Using Convolutional Neural Networks (CNN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100602)|S. M; E. R. G; N. M; A. M; K. K; P. V. Siddhartha|10.1109/ICISCoIS56541.2023.10100602|Pattern recognition;handwritten recognition;digit recognition;machine learning;Handwriting recognition;Machine learning algorithms;Personal digital devices;Organizations;Machine learning;Convolutional neural networks;Security|
|[Offload Decision Making for Web Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100419)|S. S. G; B. K. G; T. V. R; H. B; A. M; B. A|10.1109/ICISCoIS56541.2023.10100419|Compute unit offloading;edge computing;machine learning;regression;clustering algorithms;Performance evaluation;Productivity;Computational modeling;Decision making;Predictive models;Mobile handsets;User experience|
|[Dynamic Personalized Ads Recommendation System using Contextual Bandits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100575)|S. G. S; A. Srikanth; G. G; G. M; G. D. T; L. C. R; L. H|10.1109/ICISCoIS56541.2023.10100575|reinforcement learning;recommendation;advertisements;contextual bandits;Supervised learning;Reinforcement learning;Security;Intelligent systems;Recommender systems|
|[Classification of Brain Disease & MRI-Based Age Estimation Using Deep Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100478)|S. Kamakshi; P. Penchalaiah; P. Bhasha|10.1109/ICISCoIS56541.2023.10100478|MRI;Age estimation;CNN;Deep Learning;SVM;Machine Learning;Support vector machines;Deep learning;Machine learning algorithms;Magnetic resonance imaging;Estimation;Prediction algorithms;Feature extraction|
|[Future Database Technologies for Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100525)|G. Rani; T. Sharma; A. Sharma|10.1109/ICISCoIS56541.2023.10100525|Big Data;Big data Analytics;NoSQL;Hadoop;Costs;Social networking (online);Decision making;Data visualization;Footwear;Big Data;Real-time systems|
|[Review of S3 Data Summarization and Visualization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100403)|K. More; P. Jawale; F. Francis; A. Narote|10.1109/ICISCoIS56541.2023.10100403|AutoML;LDA;Cloud;Big Data;Natural Language;Transformer;Amazon Web Service;Web services;Social networking (online);Data visualization;Organizations;Documentation;Big Data;Natural language processing|
|[Secured machine learning using Approximate homomorphic scheme for healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100547)|S. S. Gowri; S. Sadasivam; N. H. Priya; D. P. T A|10.1109/ICISCoIS56541.2023.10100547|Machine Learning;Homomorphic Encryption;Privacy Preservation;Training;Productivity;Data privacy;Training data;Machine learning;Organizations;Prediction algorithms|
|[A Study on Machine Learning Techniques for Precision Medicine Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100600)|P. Sugavaneshwari; K. G. Saranya|10.1109/ICISCoIS56541.2023.10100600|Machine Learning;Recommendation system;Precision medicine;Random Forest;LASSO;SVM;Boruta;Deep Learning;PSN;Naïve Base;LDA;Precision medicine;Genomics;Machine learning;Predictive models;Feature extraction;Security;Intelligent systems|
|[A Hybrid Homomorphic Model with RSA Algorithm and Modified Enhanced Homomorphic Encryption Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100372)|T. P. Kamatchi; K. A. Kumari|10.1109/ICISCoIS56541.2023.10100372|Homomorphic scheme;RSA;Cloud Security;Modified Enhanced Homomorphic encryption;Smart cities;Computer hacking;Computational modeling;Cloud computing security;Urban areas;Noise reduction;Data models;Servers|
|[The Intelligent Heart Rate Monitoring Model for Survivability Prediction of Cardiac Arrest Patients Using Deep Cardiac Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100413)|A. Ahmad; H. K. Hussain; H. Tanveer; T. Kiruthiga; K. Gupta|10.1109/ICISCoIS56541.2023.10100413|Cardiac monitoring;heart;healthcare;early diagnosis;disease;treatment;recovery;survivability;prediction;Pain;Heart beat;Surgery;Shoulder;Cardiac arrest;Predictive models;Valves|
|[Development of an Automated Hand Gesture Software to Control Volume for Computer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100363)|S. M; E. R. G; N. M; R. S; B. T. Naik; P. V. Reddy|10.1109/ICISCoIS56541.2023.10100363|volume control system;OpenCV;MediaPipe;Portable computers;Thumb;Gesture recognition;Control systems;Software;Real-time systems;Security|
|[An IoT Based Smart Water Contamination Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100559)|M. N. Vamsi Thalatam; P. Lanka; J. N. V. R. S. Kumar|10.1109/ICISCoIS56541.2023.10100559|Arduino Uno;ESP 8266;Analog;pH sensor;turbidity sensor;temperature sensor;Thing Speak cloud;Temperature measurement;Temperature sensors;Cloud computing;Water quality;Water pollution;Real-time systems;Pollution measurement|
|[Infrared and Visible Image Fusion using Enhanced Thermal Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100444)|C. G; B. J; V. K. B; S. K. P|10.1109/ICISCoIS56541.2023.10100444|Image Fusion;Encoder;Decoder;Deep Learning;Visible Image;Thermal Image;Computer vision;Lighting;Decoding;Security;Spatial resolution;Intelligent systems;Image fusion|
|[Privacy Preservation Using Federated Learning for Credit Card Transactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100577)|H. P. N; P. D. Rathika; P. A|10.1109/ICISCoIS56541.2023.10100577|nan;Privacy;Federated learning;Computer hacking;Distributed databases;Credit cards;Data models;Real-time systems|
|[A Multimodal Approach for Crime Pattern Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100528)|G. Sindhu; K. Umamaheswari|10.1109/ICISCoIS56541.2023.10100528|Criminals;Crime Rate;Real world crime;Classifying the crimes;Economics;Law enforcement;Statistical analysis;Force;Safety;Data mining;Security|
|[Radar Based Target Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100358)|G. Santhanamari; M. J. P. S; D. B; K. S; A. R|10.1109/ICISCoIS56541.2023.10100358|c radar;human and animal target detection;spectrogram;range of red cells;Patient monitoring;Wildlife;Radar detection;Radar;Object detection;Millimeter wave radar;Robustness|
|[Amazon Employee Access System using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100530)|G. E. Rani; M. Sakthimohan; M. Navaneethakrishnan; S. Mahendran; S. Dhivya; M. Jayaprakash|10.1109/ICISCoIS56541.2023.10100530|Catboost;Decision tree;Random Forest algorithms;Machine learning algorithms;Prediction algorithms;Boosting;Encoding;Data models;Classification algorithms;Decision trees|
|[Detection of ARP Spoofing Attacks in Software Defined Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100567)|S. N; A. K. V; S. Krishnakumar|10.1109/ICISCoIS56541.2023.10100567|ARP Spoofing;Dsniff;Latency;Mininet;RYU-SDN Controller;Software Defined Network;Protocols;Software algorithms;Memory management;Packet loss;Security;IP networks;Software defined networking|
|[Subjective Time Estimation to Measure the Cognitive Load of Interactive Mobile User Interfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100483)|K. Anamalamudi; B. R. Surampudi; P. Peddapalyam; R. A. Modin|10.1109/ICISCoIS56541.2023.10100483|Human-Computer Interaction;Duration Judgement;Usability;User Experience;Design Heuristics;Cognitive Load;Prospective Time Estimation;Human computer interaction;Estimation;Cognitive load;Time measurement;User experience;Mobile applications;Security|
|[Vertical Farming Algorithm using Hydroponics for Smart Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100527)|B. Anuradha; R. Pradeep; E. Ahino; A. Dhanabal; R. J. Gokul; S. Lingeshwaran|10.1109/ICISCoIS56541.2023.10100527|Vertical Farming;Hydroponics;Smart Agriculture;Internet of Things;Smart agriculture;Soft sensors;Urban areas;Sociology;Hydroponics;Crops;Switches|
|[Emotion Recognition using Autoencoders: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100563)|M. Mohana; P. Subashini|10.1109/ICISCoIS56541.2023.10100563|Facial Emotion Recognition;Autoencoder;Dimensionality Reduction;ANN;CNN;Deep learning;Training;Emotion recognition;Face recognition;Neural networks;Feature extraction;Complexity theory|
|[Optimization of Heterogeneous Task Scheduling in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100614)|S. S. Gawali; M. B. Gawali|10.1109/ICISCoIS56541.2023.10100614|Task scheduling;Resource allocation;Cloud Computing;Cloud computing;Schedules;Processor scheduling;Random access memory;Bandwidth;Virtual machining;Central Processing Unit|
|[A Comparative Analysis of Resource Allocation in VANET using DSRC and C-V2X](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100441)|K. M; A. K V|10.1109/ICISCoIS56541.2023.10100441|C-V2X;DSRC;VANET;Resource allocation;5G mobile communication;Vehicular ad hoc networks;Resource management;Security;Intelligent systems;Dedicated short range communication;Vehicle-to-everything|
|[A Hybrid feature extraction method with machine learning for detecting the presence of network attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100339)|V. Santhi; J. Priyadharshini; M. Swetha; K. Dhanavandhana|10.1109/ICISCoIS56541.2023.10100339|Machine learning;feature selection;accuracy;intrusion detection;network attacks;binary classification;Training;Error analysis;Machine learning;Telecommunication traffic;Predictive models;Network security;Feature extraction|
|[A comparitive analysis on various machine learning techniques for breast cancer detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100438)|P. Dedeepya; A. J. D. Krupa; S. Dhanalakshmi|10.1109/ICISCoIS56541.2023.10100438|Breast cancer;Support vector machine;Decision tree;Random forest;Wisconsin Breast Cancer Dataset;Support vector machines;Training;Measurement;Machine learning algorithms;Support vector machine classification;Organizations;Breast cancer|
|[Spoken Language Translation using Conformer model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100421)|L. A. Kumar; D. K. Renuka; V. H. Priya; S. Sudarshan|10.1109/ICISCoIS56541.2023.10100421|Spoken language translation;Automatic speech recognition;Machine translation;Deep learning;Vocabulary;Neural networks;Switches;Transformers;Grammar;Security|
|[Smart IOT Based Pothole Detection and Filling System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100458)|P. Gurwani; R. Mandal; S. Chaudhari; M. Jadhav; S. Sonawane|10.1109/ICISCoIS56541.2023.10100458|Potholes;Internet-of-Things;ultrasonic sensor;Raspberry Pi;Plastic trash;Databases;Ultrasonic variables measurement;Roads;Urban areas;Maintenance engineering;Filling;Acoustics|
|[Improving Smart Home Safety with Face Recognition using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100592)|A. S. Kumar; R. Rekha|10.1109/ICISCoIS56541.2023.10100592|Machine learning;Data Management;Data processing;Algorithm;Automatic Assistance;Databases;Face recognition;Smart homes;Machine learning;Safety;Security;Internet of Things|
|[Detection and Prevention of Cyber-Attacks in Cyber-Physical Systems based on Nature Inspired Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100513)|S. Simonthomas; R. Subramanian|10.1109/ICISCoIS56541.2023.10100513|Cyber-Attacks;Cyber-Physical Systems;Genetic Algorithm;Deep Feedforward Neural Network;Simulation;Cyber-physical systems;Benchmark testing;Prediction algorithms;Smart grids;Feedforward neural networks;Security|
|[Design of fuzzy controller for MPPT of solar photovoltaic system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100594)|S. Arunagirinathan; C. Subramanian|10.1109/ICISCoIS56541.2023.10100594|Solar photovoltaic system (SPV);MPPT;P&O;ANN;Fuzzy logic controller (FLC);Maximum power point trackers;Photovoltaic systems;Fuzzy logic;Artificial neural networks;Solar system;Security;Intelligent systems|
|[Evolution of Consensus Algorithms in Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100388)|H. P. K; G. K. S|10.1109/ICISCoIS56541.2023.10100388|Blockchain;Distributed ledger;Decentralization;Consensus algorithms;Agreement;Industries;Privacy;Distributed ledger;Scalability;Buildings;Consensus algorithm;Blockchains|
|[Secured Data Sharing of Medical Images for Disease diagnosis using Deep Learning Models and Federated Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100542)|A. R; K. Renuka D; O. S; S. R|10.1109/ICISCoIS56541.2023.10100542|Privacy and Security in Healthcare;Federated Learning;CNN;Deep learning;Federated learning;Pulmonary diseases;Atmospheric modeling;Data models;Encryption;Classification algorithms|
|[Research on Homomorphic Encryption for Arithmetic of Approximate Numbers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100464)|D. D; A. K. K.; R. M|10.1109/ICISCoIS56541.2023.10100464|Homomorphic Encryption for Arithmetic of Approximate Numbers [HEAAN];Homomorphic Encryption [HE];Data privacy;Real-time systems;Homomorphic encryption;Intelligent systems;Arithmetic|
|[Hybrid Convolutional Neural Network - Long Short-Term Memory Model for Automated Detection of Sleep Stages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100489)|G. Sudhamathy; N. Valliammal; P. Subashini; T. T. Dhivyanrabha; R. G. Sneha|10.1109/ICISCoIS56541.2023.10100489|CNN;Deep learning;Hybrid model;LSTM;Polysomnography;PSG;Sleep disorder;Sleep stage classification;Deep learning;Analytical models;Medical services;Sleep apnea;Classification algorithms;Convolutional neural networks;Security|
|[Gemini: Graph-based Versatile News Search Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100586)|S. Babu; S. Samudrala; V. Pulabaigari; Y. C. A. Padmanabha Reddy; C. M. Babu|10.1109/ICISCoIS56541.2023.10100586|Deep Learning;Content Deduplication;Search Engine;News Search;Productivity;Databases;Soft sensors;Semantics;Search engines;Natural language processing;Data models|
|[AI -Based mock interview evaluator: An emotion and confidence classifier model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100589)|R. Mandal; P. Lohar; D. Patil; A. Patil; S. Wagh|10.1109/ICISCoIS56541.2023.10100589|deep learning;CNN;categorical emotions;NLP;confidence evaluation;web scraping;Knowledge engineering;Knowledge based systems;Anxiety disorders;Semantics;Speech recognition;Natural language processing;Libraries|
|[Automatic Tonic Pitch Estimation in South Indian Classical Music using Frequency- ratio Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100503)|M. A. Aiswarya; M. S. Sinith; R. Rajan|10.1109/ICISCoIS56541.2023.10100503|Tonic pitch;Swara;South Indian Classical Music;TMS320C6713 digital signal processor;Histograms;Instruments;Digital signal processors;Music;Frequency estimation;Real-time systems;Mathematical models|
|[Facemask detection using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100472)|V. Jyothsna; B. N. Madhuri; K. S. Lakshmi; K. Himaja; B. Naveen; K. D. Royal|10.1109/ICISCoIS56541.2023.10100472|COVID-19;HAAR-CASCADE technique;faces with mask;faces;without a mask;COVID-19;Deep learning;Government;Cameras;Security;Task analysis;Intelligent systems|
|[A Comprehensive Review on Word Embedding Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100347)|A. Neelima; S. Mehrotra|10.1109/ICISCoIS56541.2023.10100347|Bag of words;Conventional word embedding;Static word embedding;Sentiment analysis;Text categorization;Bit error rate;Artificial neural networks;Predictive models;Linguistics;Transformers|
|[Design and Analysis of Tilted Grating Structure for Simultaneous Temperature and Humidity Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100409)|B. Raju; R. Kumar; S. Dhanalakshmi|10.1109/ICISCoIS56541.2023.10100409|Fiber Bragg Grating;Tilted FBG;Simultaneous measurement;COMSOL;Temperature measurement;Temperature sensors;Thermal expansion;Claddings;Temperature;Humidity measurement;Fiber gratings|
|[Image Based Plant Disease Classification with Cause and Severity Analysis and Remedial Action](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100541)|G. N. Rani; D. D. R; M. S. P; S. B. S; S. M. V. C; S. B|10.1109/ICISCoIS56541.2023.10100541|Machine Learning;Dataset Classification;Disease Prediction;CNN;SVM;Plant diseases;Machine learning algorithms;Image color analysis;Shape;Crops;Feature extraction;Prediction algorithms|
|[Efficient Hardware Implementation of PRESENT Lightweight Cipher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100604)|R. Parthasarathy; P. Saravanan|10.1109/ICISCoIS56541.2023.10100604|Lightweight cryptography;PRESENT cipher;FPGA/ASIC implementation;Ciphers;Scheduling algorithms;Side-channel attacks;Germanium;Hardware;Safety;Iterative methods|
|[Random forest based feature ranking to evaluate the effect of motion artifact on different clinical features of PPG Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100487)|P. Ghosal; S. Himavathi; E. Srinivasan|10.1109/ICISCoIS56541.2023.10100487|Random Forest Algorithm;Biomedical signal processing;Peak detection;Motion artefact;Photoplethysmogram;Accelerometers;Statistical analysis;Fingers;Signal processing algorithms;Feature extraction;Security;Detection algorithms|
|[Improving security using Swarm intelligence based optimal pixel selection in Image steganography-A Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100500)|G. Mahalakshmi; S. Sarathambekai; T. Vairam|10.1109/ICISCoIS56541.2023.10100500|image steganography;PSNR;MSE;swarm intelligence;spatial domain;high capacity;Steganography;Magnetic resonance imaging;Computed tomography;Medical services;Media;Safety;Recording|
|[Predictive Codec of Medical Image Compression Using Subb and Thresholding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100453)|P. Viswanathan; K. Palanisamy|10.1109/ICISCoIS56541.2023.10100453|Wavelet-based;Subband;Thresholding;Medical Image Compression;Predictive Coding;Image coding;Thresholding (Imaging);Telemedicine;Magnetic resonance imaging;Transforms;Predictive models;Discrete wavelet transforms|
|[Analysis of Key Policy-Attribute Based Encryption Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100504)|J. Madhushree; K. A. Kumari; T. P. Kamatchi|10.1109/ICISCoIS56541.2023.10100504|KP-ABE;CP-ABE;EHR;EMR;Ciphers;Data privacy;Cloud computing;Transmitters;Text recognition;Medical services;Sensor phenomena and characterization|
|[ARWeather: Weather Forecasting and Visualization using Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100520)|R. Manimegalai; S. Arawind; G. V. S. R. Jegan; B. Gomathi|10.1109/ICISCoIS56541.2023.10100520|Augmented Reality;ARWeather;Weather Forecast;Mobile Application;Weather Visualisation;Visualization;Rain;Snow;Atmosphere;Security;Reliability;Wind forecasting|
|[Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100499)|B. Sangeetha; A. A. Joshuaa; A. Roshini; P. Sujaybharath; K. Sutharsan|10.1109/ICISCoIS56541.2023.10100499|Reverse Transcription-Polymerase Chain Reaction [RT-PCR];COVID-19 Detection;you only look once [yolo];Localization of COVID-19 using Lung CT images;Medical Image Processing;COVID-19;Deep learning;Computer vision;Computer viruses;Computational modeling;Pulmonary diseases;Lung|
|[Detection of Cyber Attacks by using Gray Wolf Optimizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100556)|D. Srivastava; S. Hooda; R. Gill; C. Singla|10.1109/ICISCoIS56541.2023.10100556|Grey wolf optimizer;KNN;SVM;GRNN;KDD CUP1999;Intrusion Detection System;Support vector machines;Leadership;Optimization methods;Intrusion detection;Sensitivity and specificity;Feature extraction;Classification algorithms|
|[An Analysis on Augmentative and Assistive Technology for the Speech Disorder People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100439)|N. Ramkumar; D. K. Renuka|10.1109/ICISCoIS56541.2023.10100439|Brain Computing Interface;Cognition and Wearable technology;Performance evaluation;Technological innovation;Metaverse;Wearable computers;Pipelines;Morphology;Aging|
|[Iris tumor recognition based on hybrid classical and quantum neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100451)|K. T; S. S; S. Manikumar|10.1109/ICISCoIS56541.2023.10100451|Iris Tumor data-set;classification;classical-quantum learning;parameterized quantum circuit;Training;Computational modeling;Neural networks;Feature extraction;Convolutional neural networks;Integrated circuit modeling;Quantum circuit|
|[Online Education Pedagogy Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100536)|S. Sakthivel; K. M. Prabhakaran|10.1109/ICISCoIS56541.2023.10100536|Head Pose Estimation;Attention state;Open CV;K-means clustering;Facial Feature Detection;Tracking;Education;Text categorization;Switches;Software;Security;Biomedical monitoring|
|[A Review on Near Infrared Spectroscopic Technique for the Prognosis of Leukemia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100374)|P. Divyabharathi; N. Devarasu|10.1109/ICISCoIS56541.2023.10100374|Spectroscopy;Blood cells;Leukemia screening;Biomarker;Spectroscopy;Art;Cells (biology);Security;Prognostics and health management;Intelligent systems;Medical diagnostic imaging|
|[Multimodal Biometric System Using Hybrid Convolutional Neural Network (HCNN) Based on Face and Finger Vein Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100402)|A. G; T. T; S. S|10.1109/ICISCoIS56541.2023.10100402|Multimodal Biometric recognition;Information security;Hybrid convolutional neural network;Face and finger vein;Support vector machines;Fuses;Veins;Face recognition;Fingers;Neural networks;Information security|
|[Data Anomaly Detection in Wireless Sensor Networks Using $\beta$ -Variational Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100379)|A. J. S; H. S; N. K; S. K. B. G; G. R; J. B. S|10.1109/ICISCoIS56541.2023.10100379|Anomaly Detection;Data imbalance;VAE;Training;Deep learning;Wireless sensor networks;Gaussian distribution;Data models;Security;Intelligent systems|
|[Classification of Oral Squamous Carcinoma Histopathological images using Alex Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100510)|T. Saraswathi; V. M. Bhaskaran|10.1109/ICISCoIS56541.2023.10100510|oral squamous cell carcinoma(OSCC);histopathology image;batch normalization;drop out;Tongue;Histopathology;Biopsy;Mouth;Classification algorithms;Convolutional neural networks;Security|
|[Multi-label Bird Species Classification Using Ensemble of Pre-trained Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100519)|A. Noumida; R. Mukund; N. M. Nair; R. Rajan|10.1109/ICISCoIS56541.2023.10100519|multi-label;sequential;augmentation;pre-trained CNN;ensemble learning;Databases;Biological system modeling;Atmospheric modeling;Transfer learning;Predictive models;Birds;Audio recording|
|[Performance Comparison of LSTM and XGBOOST for Ether Price Prediction from Spam Filtered Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100425)|K. Sathiyapriya; S. Vankadara; K. S. Babu; M. Muralidharan|10.1109/ICISCoIS56541.2023.10100425|Twitter data;sentiment analysis;LSTM;XGBoost;price prediction;C5.0;spam filtration;Social networking (online);Filtration;Biological system modeling;Blogs;Weather forecasting;Pricing;Predictive models|
|[Hand Gesture recognition using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100557)|S. Sangeetha; B. Srivathsan; D. C; R. Gunaseelan; S. G; S. K|10.1109/ICISCoIS56541.2023.10100557|Hand gestures;Deaf;Dumb;Hand sign;Deep learning;Convolution neural network;YOLO;Image recognition;Feature extraction;Gesture recognition;Training;Measurement;Deep learning;Transfer learning;Gesture recognition;Transforms;Assistive technologies|
|[Single-Image Haze Reduction Using a Straightforward Additive Model with a Haze Smoothness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100411)|K. Sundari; K. Jebastin; K. Dhinakaran|10.1109/ICISCoIS56541.2023.10100411|Prior haze smoothness;addition model;single image haze removal;Visualization;Computer vision;Additives;Computational modeling;Production;Security;Task analysis|
|[Secure Practices to Prevent Cyber Attacks in E-Commerce Sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100357)|H. K; P. R|10.1109/ICISCoIS56541.2023.10100357|Spam;Ransomware;Malware;Phishing;cybercriminals;entrepreneurs;OWASP top10;COVID-19;Uncertainty;Pandemics;Computer hacking;Buildings;User interfaces;Malware|
|[Automatic Recognition of Continuous Malayalam Speech using Pretrained Multilingual Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100598)|K. Manohar; G. G. Menon; A. Abraham; R. Rajan; A. R. Jayan|10.1109/ICISCoIS56541.2023.10100598|Self-supervised Learning;Speech Recognition;Pretrained Models;Low-resource language;Malayalam;Training;Adaptation models;Error analysis;Self-supervised learning;Transformers;Reproducibility of results;Recording|
|[Vision Based Collision Detection And Avoidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100476)|R. Rekha; T. Hariprasath; P. Raj; R. Raveena; S. Samyuktha|10.1109/ICISCoIS56541.2023.10100476|CNN;LSTM;simulation;Productivity;Scalability;Neural networks;Machine learning;Trajectory;Security;Collision avoidance|
|[Efficacy of ELECTRA-based Language Model in Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100342)|M. J. B; A. A. S. J; A. R. S. M; R. Rajan|10.1109/ICISCoIS56541.2023.10100342|Sentiment Analysis;Deep Learning;Transformers;BERT;XLNet;RoBERTa;ELECTRA;Sentiment analysis;Analytical models;Text recognition;Social networking (online);Computational modeling;Bit error rate;Speech recognition|
|[Hybrid Deep Learning Classification Model for Attention-Deficit-Hyperactivity Disorder using functional Magnetic Resonance Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100467)|U. R. K; A. P. P|10.1109/ICISCoIS56541.2023.10100467|Attention deficit hyperactivity disorder;Bidirectional long short-term memory;Convolution neural network;Functional magnetic resonance imaging;Deep learning;Solid modeling;Pediatrics;Three-dimensional displays;Functional magnetic resonance imaging;Brain modeling;Feature extraction|
|[Several Energy-Efficient Routing Protocols, Design-based Routing Problems and Challenges in IoT-Based WSN: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100555)|M. Pahuja; D. Kumar|10.1109/ICISCoIS56541.2023.10100555|WSN (Wireless Sensor Network);IoT (Internet-of-Things);EERPs (Energy-Efficient Routing Protocols);Wireless communication;Wireless sensor networks;Weather forecasting;Medical services;Energy efficiency;Routing protocols;Agriculture|
|[Smart traffic management system through optimized Network Architecture for the smart city paradigm shift](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100338)|S. R; D. Sasikumar; G. Sriram; K. Nelson; P. Harish|10.1109/ICISCoIS56541.2023.10100338|Smart city;transportation;traffic control;communications;time delay;reinforcement algorithm;DQN;Smart cities;Traffic control;Network architecture;Delays;Smart transportation;Automobiles;Security|
#### **2023 2nd International Conference for Innovation in Technology (INOCON)**
- DOI: 10.1109/INOCON57975.2023
- DATE: 3-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Visualising Chemistry Experiments Using NLP and Computer Graphics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101068)|M. Suresh; V. A. N. Nikhilesh; N. R. S. Rao; K. P. Kumar; G. Dayalan|10.1109/INOCON57975.2023.10101068|NLP;Computer Graphics;Web tutorial;Chemistry experiments;Virtual learning;Text to image;Visualization;Technological innovation;Image segmentation;Chemistry;Navigation;Prototypes;Tutorials|
|[Data Protection Regulations Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101061)|B. S. Shishodia; M. J. Nene|10.1109/INOCON57975.2023.10101061|General Data Protection Regulation;Blockchain;Transparency;Technology Neutrality;Personal Data;Data Controller;Data Processor;Technological innovation;Program processors;Regulators;Roads;Insurance;Regulation;Blockchains|
|[GSM Based Home Automation Smart Controlled Power Outlet using Android Phone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101094)|D. C.Malunao; G. J. O. Fernando; R. R. Tejada|10.1109/INOCON57975.2023.10101094|Arduino;Bluetooth;GSM;Home Automation;Smartphone;GSM;Natural resources;Technological innovation;Home automation;Power demand;Authentication;Smart homes|
|[A New Priority Support Scheme for Asymmetric Multi-hop Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101074)|K. -S. Chan; P. Murdiyat; K. -S. Chung|10.1109/INOCON57975.2023.10101074|priority support;multi-hop wireless sensor network;shared MAC;multi-channel;asymmetric traffic;multi-hop transmission;Wireless communication;Wireless sensor networks;Technological innovation;Simulation;Spread spectrum communication;Collision avoidance|
|[Deep Learning Approach in Cotton Plant Disease Detection: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101359)|S. Bera; P. Kumar|10.1109/INOCON57975.2023.10101359|CNN;plant’s Disease Detection;Cotton Plant;Anthracnose;Deep learning;Plant diseases;Visualization;Technological innovation;Plants (biology);Neural networks;Production|
|[Detection of Missing Persons Using Mobile App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101097)|M. D. Inavolu; D. Venna; G. V. Kallepalli; S. S. Surapaneni|10.1109/INOCON57975.2023.10101097|Android Studio;Firebase;Mobile Application;Police;Public.;Bridges;Technological innovation;Law enforcement;Databases;Rail transportation;Mobile applications|
|[Financial and Management Accounting: The Analysis of Financial Models for comparison](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101306)|P. Singh; A. Gupta; P. Dwivedi|10.1109/INOCON57975.2023.10101306|Financial Models;LSTM;MA;comparison;Data Analytics;financial data models comparison;stock price analysis models;Management accounting;Economics;Analytical models;Technological innovation;Data analysis;Biological system modeling;Predictive models|
|[A Comparative Analysis of GAN and VAE based Synthetic Data Generators for High Dimensional, Imbalanced Tabular data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101315)|A. Kiran; S. S. Kumar|10.1109/INOCON57975.2023.10101315|CTGAN;TVAE;Synthetic Data Vault;ADASYN;Deep learning;Industries;Technological innovation;Data privacy;Recurrent neural networks;Generative adversarial networks;Generators|
|[CX-R Classification Using DCNN Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101085)|R. Ananya; P. M. Prathibhavani; K. R. Venugopal|10.1109/INOCON57975.2023.10101085|Covid-19;DCNN;Chest X-Ray;COVID-19;Training;Measurement;Technological innovation;Health and safety;Convolutional neural networks;Reliability|
|[Intelligent Waste Segregation Technique Using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101021)|D. Nagajyothi; S. A. Ali; V. Jyothi; P. Chinthapalli|10.1109/INOCON57975.2023.10101021|CNN;VGG-16;Convolutional Neural Network;Deep learning;Wastesegregation.;Training;Productivity;Waste materials;Image recognition;Computational modeling;Predictive models;Solids|
|[Image Enhancement for Pedestrian Detection at Night Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101014)|D. Nagajyothi; P. S. Charan; M. Zeeshan; V. Jyothi|10.1109/INOCON57975.2023.10101014|CNN;Low light image dateset;Enhancement;SVM HOG;Gamma Correction.;Training;Support vector machines;Technological innovation;Neural networks;Training data;Predictive models;Feature extraction|
|[Image Registration using Shi-Tomasi and SIFT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101131)|S. S. Patankar; S. G. Kadam; A. Jadhav; M. Gore|10.1109/INOCON57975.2023.10101131|Scale-Invariant Feature Transform (SIFT);Euclidean Distance;Pearson Correlation Coefficient;Image Registration;Image registration;Technological innovation;Satellites;Military computing;Estimation;Transforms;Object detection|
|[Classification of Dynamic Comparator based Upon Different Methodologies.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101336)|S. S. Ail; N. V. Yajnesh; S. Shetty|10.1109/INOCON57975.2023.10101336|Comparator;pre-amplifier;latch;speed;low power;Analog to Digital Convertor.;Technological innovation;Latches;Voltage;Tail;Converters;Delays;Transistors|
|[Smart Authentication using Human Ear](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100984)|Y. T; P. R; S. S; N. S. Kumar; N. Priyadarshi; S. S; N. Kumar|10.1109/INOCON57975.2023.10100984|Authentication;Biometric;Ear recognition;Pattern recognition;Raspberry pi;Advanced Risc Machine;High Definition Multimedia Interface;Universal Serial Bus;Training;Technological innovation;Reduced instruction set computing;Biometrics (access control);Shape;Face recognition;Authentication|
|[Application Analysis of Computer Technology in Cross-Border E-Commerce Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101374)|Z. Dong|10.1109/INOCON57975.2023.10101374|Computer technology;Cross border E-commerce;International trade;Technological innovation;Companies;Regulation;Safety;Quality assessment;Electronic commerce|
|[Intelligent Campus Student Information Management System Based on Cloud Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101285)|Y. Zhou; L. Hou|10.1109/INOCON57975.2023.10101285|Cloud platform;Smart campus;Student management system;Technological innovation;Program processors;Databases;Data security;Education;Information management;Recording|
|[AGC of 2-Area Restructured Power System using Polar Fuzzy Controller and Energy Storage Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101346)|R. N. Mishra; N. Kumar; M. Zuhaib|10.1109/INOCON57975.2023.10101346|AC tie line;automatic generation control (AGC);dynamic stability;polar fuzzy logic controller (PFC) proportional integral derivative (PID). redox flow batteries (RFB).;Fuzzy logic;Technological innovation;Software packages;Perturbation methods;Power system dynamics;Knowledge based systems;Power system stability|
|[A RealTime Image Recognition Method of Power AI Based on Quadtree Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101145)|X. Pengcheng; H. Zhenlin; Z. Liuqi; W. Ning; Z. Hanghang; Z. Yuheng|10.1109/INOCON57975.2023.10101145|Quadtree algorithm;Electricity;artificial intelligence;image recognition;Image quality;Image segmentation;Technological innovation;Image recognition;Target recognition;Traffic control;Real-time systems|
|[Application of Beidou technology in infrastructure acceptance of overhead transmission lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101101)|L. Nan; Z. Wulue; Y. Wenjun; Z. Xin; C. Daxuan; L. Kai|10.1109/INOCON57975.2023.10101101|Beidou technology;Line layout;Overhead transmission line;Underground power cables;Technological innovation;Satellites;Power grids;Planning;Security;Reliability|
|[Firmware-based approach for efficient Power Management in Mobile Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101057)|B. R. Harsha; P. George; P. Tom; V. P. Hiremath; K. P. Bhat D; D. Deshatty|10.1109/INOCON57975.2023.10101057|ACPI;DPTF;DPPM;USB-C;USB-PD;BMS;ASL;Battery State Of Charge;Adaptor ratings;MCU;Performance evaluation;Technological innovation;Reactive power;Power demand;Power system management;System performance;Voltage|
|[Application of Classification Management Method of Vehicle Electromagnetic Compatibility based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101159)|Y. Wang|10.1109/INOCON57975.2023.10101159|Electromagnetic compatibility;artificial intelligence;Hierarchical management;Technological innovation;Immunity testing;Time measurement;Reliability;Artificial intelligence;Transient analysis;Surges|
|[Application of Particle Swarm Clustering Algorithm in Power Engineering Cost Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101058)|Z. Dong; N. Li; Y. Wang; Y. Ling; H. Zhang; X. Zhao|10.1109/INOCON57975.2023.10101058|Particle swarm optimization algorithm;Clustering algorithm;Power engineering cost;Power engineering;Technological innovation;Renewable energy sources;Costs;Wind speed;Clustering algorithms;Estimation|
|[Construction of Computer Frontend Resource Sharing Platform based on Web](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101369)|J. He|10.1109/INOCON57975.2023.10101369|Web;Computer front end;Resource sharing platform;Technological innovation;Weapons;Education;Systems architecture;Software;Internet;Resource management|
|[Design of Medical Image Registration System Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101288)|T. Feng; R. Feng|10.1109/INOCON57975.2023.10101288|Medical image registration;Deep learning;Convolutional neural network;Deep learning;Image registration;Adaptation models;Technological innovation;Supervised learning;Lung;Task analysis|
|[Persistent Monitoring of Railway Bridge Piers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101355)|M. G. Sankar; D. Rajeswararao; M. S. Kennanya; M. K. Sri; D. N. Rao|10.1109/INOCON57975.2023.10101355|Gyro MPU6050 triple axis accelerometer;ESP8266;Firebase real time database;Tilt in bridge piers;Bridges;Technological innovation;Structural panels;Soil;Real-time systems;Sensors;Safety|
|[Trace Image Detection Technology Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101242)|Z. Ji; Y. Wang; Y. Wang|10.1109/INOCON57975.2023.10101242|computer;Deep learning;Trace inspection;Productivity;Deep learning;Technological innovation;Image coding;Costs;Digital images;Inspection|
|[Question Answering Retrieval Method for Knowledge-Based with Dynamic Programming Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101356)|L. Shouyu; J. Wei; Y. Yuanwei; Z. Kun; H. Yubin; L. Yingchen|10.1109/INOCON57975.2023.10101356|Dynamic programming algorithm;Knowledge atlas;Question and answer retrieval;Technological innovation;Heuristic algorithms;Knowledge based systems;Decision making;Formal languages;Information retrieval;Question answering (information retrieval)|
|[Hotel Information System Management Audit Using COBIT 2019 - APO12](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101245)|J. N. Utamajaya; S. H. Supangat; F. L. Gaol; B. Ranti|10.1109/INOCON57975.2023.10101245|APO12;COBIT 5;Information technology;Hotel Management;Risk Management;Technological innovation;Companies;Risk management;Information technology;Interviews;Information systems|
|[EEG-based Emotion Classification - A Theoretical Perusal of Deep Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101002)|K. Sana Parveen; J. T. Panachakel; H. Ranjana; S. Sidharth; A. A. Samuel|10.1109/INOCON57975.2023.10101002|Emotion Recognition;EEG;Emotion Classification;LSTM;CNN;Deep Learning;Deep learning;Learning systems;Emotion recognition;Technological innovation;Neural activity;Feature extraction;Brain modeling|
|[Blockchain Based Inter-Organizational Secure File Sharing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101350)|R. Mathwale; R. Ramisetty|10.1109/INOCON57975.2023.10101350|blockchain;IPFS;file-sharing;Resistance;Technological innovation;Distributed ledger;Smart contracts;Organizations;Metadata;Fabrics|
|[Analysis of Discrete Wavelet Transforms for Different Crops using Sentinel-1 SAR data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101008)|S. Shaik; K. S. Kishor; B. M. Dodamani|10.1109/INOCON57975.2023.10101008|Crop classification;DWT analysis;temporal crop growth patterns;Sentinel-IA SAR data;Histograms;Crops;Transforms;Writing;Wavelet analysis;Radar polarimetry;Discrete wavelet transforms|
|[On Improved Performance of Underwater VLC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101167)|A. K. Das; A. Pramanik; A. R. Chowdhury; L. Ramakrishnan|10.1109/INOCON57975.2023.10101167|underwater wireless communication (UWC);underwater visible light communication (UVLC);IoUT;light scattering;underwater channel model.;Band-pass filters;Wireless communication;Underwater communication;Technological innovation;Monte Carlo methods;System performance;Simulation|
|[Medium-Term Load Forecasting Using ANN and RNN in Microgrid Integrating Renewable Energy Source](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101126)|F. Dewangan; M. Biswal|10.1109/INOCON57975.2023.10101126|load forecasting;machine learning;ANN;RNN;Technological innovation;Renewable energy sources;Recurrent neural networks;Load forecasting;Atmospheric modeling;Artificial neural networks;Microgrids|
|[Deep Learning based Approach for Prediction of Diabetes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101241)|S. Priyanka; C. Kavitha; M. P. Kumar|10.1109/INOCON57975.2023.10101241|Diabetes;Deep Learning;PIMA Database;MESSIDOR dataset;training;testing;accuracy;Deep learning;Training;Technological innovation;Feature extraction;Prediction algorithms;Blood pressure;Diabetes|
|[A Comprehensive Performance Evaluation of Sequence Component based Directional Relaying Philosophies for Inverter Integrated Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101136)|S. Dash; M. K. Jena; P. D. Achlerkar|10.1109/INOCON57975.2023.10101136|Directional relaying;inverter based resources;line protection;Fault diagnosis;Performance evaluation;Codes;Voltage measurement;Philosophical considerations;Current measurement;Transformers|
|[Hybrid Energy Source Based BLDC Motor Drive for Electric Vehicle Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101225)|M. Kumar; K. P. Panda; J. Moharana; R. Thakur; G. Panda|10.1109/INOCON57975.2023.10101225|BLDC Motor Speed Control;Electric Vehicle;Renewable Energy Source;Solar Photovoltaic (PV);PID Controller;Fuzzy Logic Controller;Hybrid Fuzzy-PI Controller.;Technological innovation;Renewable energy sources;Brushless DC motors;Velocity control;Solar energy;Fossil fuels;Real-time systems|
|[Impact of Machine Learning on Regional Languages Processing: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101023)|G. Spandan; S. H. Brahmananda|10.1109/INOCON57975.2023.10101023|Machine Translation;Natural Language Processing;Technological innovation;Image processing;Machine learning;Machine translation;Speech processing|
|[Estimation of Periodontal Bone Loss Using SVM and Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101155)|S. Sameer; J. Karthik; M. S. Teja; P. A. Vardhan|10.1109/INOCON57975.2023.10101155|Periodontitis;SVM;Random Forest;Machine Learning;Dental Restoration;Support vector machines;Technological innovation;Estimation;Teeth;Bones;Feature extraction;Dentistry|
|[A Study To Detect Emotions From Twitter Text Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101258)|Anusha; S. A. Shenoy; S. V. Harish|10.1109/INOCON57975.2023.10101258|Depression analysis;Machine Learning;Sentiment Analysis;Twitter;Tweets;Naïve Bayes;Logistic Regression;Technological innovation;Machine learning algorithms;Social networking (online);Blogs;Machine learning;Depression;Information exchange|
|[High-Speed Data Transfer using AXI Protocol for Image Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101371)|R. Raj; S. K. Miskin; N. Bhatia|10.1109/INOCON57975.2023.10101371|Convolutional neural networks;Advanced extensible Interface;Rectified linear activation unit;Hardware accelerators;Protocols;Machine learning algorithms;Computational modeling;Software algorithms;Neural networks;Graphics processing units;Parallel processing|
|[Lithium Ion Battery Modeling and State of Charge Estimation using Kalman Filter based Observer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101333)|K. D. Rao; K. Y. S. Srinivas; L. Sucharita; T. Vineela; A. Daveed|10.1109/INOCON57975.2023.10101333|MATLAB Simulink;state of charge;Kalman filter;battery management system;Lithium-ion batteries;Estimation;Battery management systems;Electric vehicles;Mathematical models;State of charge;Kalman filters|
|[Multiclass Artificial Neural Network based Facial Expression Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101209)|N. Begum; A. S. Mustafa|10.1109/INOCON57975.2023.10101209|Facial Recognition;Facial Detection;Multiclass Artificial Neural Network (MCANN);Facial Feature Mapping;Deep Learning;Computer Vision;Deep learning;Emotion recognition;Technological innovation;Runtime;Face recognition;Programming;Security|
|[Design, Simulation, Optimization and performance of a MEMS Based Piezoelectric Energy Harvester](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101260)|S. Parvathi; S. S. Raju; M. Srikanth; Y. Geetha Kusuma|10.1109/INOCON57975.2023.10101260|cantilever beam;piezoelectric;stress;deflection;non-traditional geometry;Vibrations;Geometry;Shape;Simulation;Voltage;Transforms;Sensors|
|[Customer Segmentation Analysis Using LRFM Based Product and Brand Dimensions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100974)|M. Husnah; R. A. Vinarti|10.1109/INOCON57975.2023.10100974|Customer Segmentation;Fuzzy C-Means;LRFM;Product;Brand;Technological innovation;Clustering algorithms;Companies;Behavioral sciences;Recording;High frequency;Information technology|
|[Ant Colony Algorithm for Distributed Constrained Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101321)|F. Zhang|10.1109/INOCON57975.2023.10101321|Distributed;Ant colony artificial intelligence;Privacy;Technological innovation;Costs;Navigation;Message passing;Robot control;Estimation|
|[Aircraft Engine Multi-Condition Detection Method Based on Single Classification Limit Learning Machine Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101108)|W. Hong|10.1109/INOCON57975.2023.10101108|airplane engine;Single classification limit learning;Working condition detection;Employee welfare;Degradation;Atmospheric modeling;Buildings;Clustering algorithms;Data models;Classification algorithms|
|[Algorithm Analysis and Application of Digital Signal Processing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101009)|W. Yuanmin; Z. Li|10.1109/INOCON57975.2023.10101009|Digital signal processing;algorithm analysis;Technological innovation;Digital signal processors;Signal processing algorithms;Digital signal processing;Control systems;Real-time systems;Filtering theory|
|[BP Neural Network Algorithm for Computer Network Security Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101368)|L. Xia; Y. Zheng; J. Gu|10.1109/INOCON57975.2023.10101368|BP neural network;Network security;evaluate;Backpropagation;Heuristic algorithms;Neural networks;Analytic hierarchy process;Network security;Computer networks;Behavioral sciences|
|[Urban Land Subsidence Using Remote Sensing Satellite Radar Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101166)|W. Wu|10.1109/INOCON57975.2023.10101166|Remote sensing satellite radar;Urban surface subsidence;Interferometry;Radar remote sensing;Satellites;Spaceborne radar;Time series analysis;Interference;Radar imaging;Radar interferometry|
|[Predicting Stock Price Movement for The Inefficient Market: Case of Hanoi Stock Exchange (HNX)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101296)|B. T. Khoa; T. T. Huynh; N. T. D. Huong|10.1109/INOCON57975.2023.10101296|SVM;Accuracy;HNX-index;Technical Analysis;Predict;Support vector machines;Technological innovation;Fluctuations;Predictive models;Prediction algorithms;Indexes;Stock markets|
|[Design of Computer Data Structure in Large Retail Business Management Information System Based on RFID](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101038)|M. Hao|10.1109/INOCON57975.2023.10101038|Computer data structure;Large scale retail business;mis;Technological innovation;Costs;Process control;Management information systems;Data structures;Information technology;Radiofrequency identification|
|[Body Posture Correction and Hand Gesture Detection Using Federated Learning and Mediapipe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101124)|R. Singhal; H. Modi; S. Srihari; A. Gandhi; C. O. Prakash; S. Eswaran|10.1109/INOCON57975.2023.10101124|Federated Learning;MediaPipe;Gesture Detection;Body Posture Correction;Sitting Posture Correction;Technological innovation;Privacy;Federated learning;Medical services;Streaming media;Feature extraction;Real-time systems|
|[Solving Food Issues in Government Schools and Tracking the School Location Through QGIS Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101199)|I. Harshitha; G. Anuradha; C. Swaroop; B. Jessica|10.1109/INOCON57975.2023.10101199|Firebase;QGIS Mapping;Flutter;Technological innovation;Government;Fingers;Task analysis;Pupils|
|[A Novel Node Similarity Measure for Efficient Email Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101323)|K. B. Aruna; M. M. Kodabagi|10.1109/INOCON57975.2023.10101323|Email classification;Node similarity;Text classification;Real-Time dataset;Multi-class classification;Training;Technological innovation;Social networking (online);Buildings;Support vector machine classification;Real-time systems;Electronic mail|
|[Application of Hybrid Algorithm in Robot Intelligent Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101194)|Y. Qin; W. Ouyang|10.1109/INOCON57975.2023.10101194|robot;Hybrid algorithm;intelligent control;Uncertain systems;Technological innovation;Energy consumption;Adaptation models;Robot control;Decision making;Task analysis|
|[Basic Network Construction and Network Security Design Analysis of Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101212)|J. Gu; L. Xia; H. Wang|10.1109/INOCON57975.2023.10101212|Cloud computing;Network construction;Network security design;Cloud computing;Technological innovation;Network security;Stability analysis;Planning;Servers;Time factors|
|[Construction and Design of Production Planning and Scheduling Model of Remanufacturing System Based on AHP Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101299)|Y. Xu; L. Zhu; H. Shan|10.1109/INOCON57975.2023.10101299|AHP algorithm;Remanufacturing system;Production plan;scheduling model;Technological innovation;Job shop scheduling;Pollution;Production planning;Production;Analytic hierarchy process;Prediction algorithms|
|[Automated Script Evaluation using Machine Learning and Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101281)|S. M. Chavan; M. S. Prerana; R. Bathula; S. Saikumar; G. Dayalan|10.1109/INOCON57975.2023.10101281|GCP OCR (Google Cloud Platform - Optical character recognition);Bidirectional Encoder Representations from Transformers (BERT);Gradient-Boosted Decision Trees (GBDT);Natural Language Toolkit (NLTK);Support Vector Machine (SVM);Convolutional Neural Network (CNN);Generative Pre-trained Transformer 3rd generation (GPT-3);Long short-term memory (LSTM);Natural Language Processing (NLP);Support vector machines;Technological innovation;Text recognition;Optical character recognition;Optical fiber networks;Transformers;Natural language processing|
|[An Efficient VLSI Architecture for Analysis of Area, Delay and Power Consumption-A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101290)|S. Ab; Bhoomika; N. Mayya; P. Sanadhani; P. Cs|10.1109/INOCON57975.2023.10101290|ALU;Ripple Carry Adder;Carry Look-Ahead Adder;Carry Skip Adder and Carry Save Adder;Computers;Analytical models;Technological innovation;Program processors;Power demand;Very large scale integration;Delays|
|[Alzheimer’s Disease Classification On sMRI Images Using Convolutional Neural Networks And Transfer Learning Based Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101314)|P. A. Sai; C. Anupama; R. V. Kiran; P. B. Reddy; N. N. Goutham|10.1109/INOCON57975.2023.10101314|Alzheimer’s disease;Convolutional Neural Networks (CNN);sMRI;Transfer Learning;Alexnet;Deep-learning;ADNI;Training;Neuroimaging;Deep learning;Technological innovation;Transfer learning;Feature extraction;Convolutional neural networks|
|[Implementation of Electric Vehicles Charging Station and Battery Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101213)|R. Sushmitha; K. Asha; J. Tejaswini; V. Hemanthakumari; K. Harshitha; N. K. Suryanarayana|10.1109/INOCON57975.2023.10101213|charging strategies;Electric vehicles;Battery Management system;Grid capacity;Efficiency;Technological innovation;Pollution;Planets;Battery management systems;Charging stations;Electric vehicles;Batteries|
|[A Modified UNet based Framework towards Early Detection of Autism using EEG Waves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101224)|N. Nagashree; M. Chitralekha; S. M. Harsha; S. M. Sai Usha Sree; M. Chinmayee; S. H. Basavaraj|10.1109/INOCON57975.2023.10101224|EEG;ASD;UNet;MRI;Spectrogram;Neurological diseases;Deep learning;Autism;Technological innovation;Magnetic resonance imaging;Computed tomography;Electroencephalography|
|[Application of Web Data Mining Technology in Computer Information Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101265)|L. Dong|10.1109/INOCON57975.2023.10101265|Web data mining technology;computer;information management;Technological innovation;Databases;Clustering algorithms;Web mining;Filtering algorithms;Information filters;Information management|
|[Construction of Computer Front-End Resource Sharing Platform Based on Web](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101273)|J. He|10.1109/INOCON57975.2023.10101273|Web;Computer front end;Resource sharing platform;Technological innovation;Computational modeling;Education;Systems architecture;Computer architecture;Software;Data models|
|[Transmission Line Fault Diagnosis and Location System in Distribution Network Based on PSO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101049)|J. Qin; H. Chu; D. Yu|10.1109/INOCON57975.2023.10101049|Distribution network;Transmission line;Fault diagnosis;positioning system;Technological innovation;Power transmission lines;Distribution networks;Voltage;Fault location;Mathematical models;Active distribution networks|
|[Video Image Processing System Based on Directshow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101332)|Z. He|10.1109/INOCON57975.2023.10101332|Video image;DirectShow;processing system;Technological innovation;Costs;Image processing;Scalability;Multimedia systems;User interfaces;Streaming media|
|[Design of DC Motor Speed Control System based on PID Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101233)|T. Li|10.1109/INOCON57975.2023.10101233|DC motor;PID algorithm;Speed control system;Fuzzy control;Technological innovation;PI control;Velocity control;Transfer functions;Regulation;Data models|
|[Multi-View Image Reconstruction Algorithm Based on Virtual Reality Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101192)|X. Liao; L. Wu|10.1109/INOCON57975.2023.10101192|Virtual reality technology;Multi-point image reconstruction algorithm;Image generation;Training;Convolutional codes;Convolution;Superresolution;Virtual reality;Feature extraction;Encoding|
|[Frequency Conversion Speed Control System with Full Rotary Rim Propeller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101338)|H. Luo; C. Xu|10.1109/INOCON57975.2023.10101338|speed control;control methods;Full Rotary Rim Propeller;Space vector pulse width modulation;Analytical models;Propellers;Velocity control;Rotors;Modulation;Frequency conversion|
|[UAV Information Detection and Processing System Based on DTN algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101076)|L. Wu; X. Liao|10.1109/INOCON57975.2023.10101076|DTN algorithm;Unmanned aerial vehicle (uav);Information detection and processing system;Analytical models;Technological innovation;Satellites;Hidden Markov models;Predictive models;Probability;Autonomous aerial vehicles|
|[Heavy Metal Analysis Platform for Atmospheric Fine Particulate Matter Based on AHP Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101006)|F. Ying; H. Zou; L. Fan; J. Liu; F. Li|10.1109/INOCON57975.2023.10101006|AHP algorithm;Atmospheric fine particulate matter;Heavy metal analysis platform;Analytical models;Monte Carlo methods;Atmospheric modeling;Soil;Feature extraction;Wavelet analysis;Nickel|
|[Wheat Yield Prediction using Temporal Fusion Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101144)|T. Junankar; J. K. Sondhi; A. M. Nair|10.1109/INOCON57975.2023.10101144|Temporal Fusion Transformers;Machine Learning;Deep Learning;Crop Yield Prediction;Time Series Forecasting;Deep learning;Technological innovation;Machine learning algorithms;Time series analysis;Crops;Predictive models;Transformers|
|[Error Modeling Analysis of Parallel Robot Based on Improved Fish Swarm Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101208)|L. Chen|10.1109/INOCON57975.2023.10101208|Error modeling and analysis;Parallel robot;Fish swarm algorithm;Improved version;Parallel robots;Analytical models;Technological innovation;5-DOF;Simulation;Prototypes;Transforms|
|[Records of Patient Health Data and Medical Information Monitoring Using IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101301)|K. Sarada; K. Saikumar; B. Dudi; M. A. Alkhafaji; K. S. Mohsen|10.1109/INOCON57975.2023.10101301|Application-Score;data sharing;IoT;medical records;secure data;Sensitivity;Ultrasonic imaging;Magnetic resonance imaging;Sensors;Servers;Medical diagnosis;Software tools|
|[Compiling with MultiCores](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101054)|Farhanaaz; V. Sanju|10.1109/INOCON57975.2023.10101054|Compiler;Multicore;Compiler Architecture;Multicore Architecture;Shared Memory;Distributed Memory;Parallelism;Technological innovation;Program processors;Codes;Multicore processing;Parallel programming;Switches;Parallel processing|
|[Improved BEB Algorithm for Computer Network Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101237)|H. Wang; J. Gu; L. Xia|10.1109/INOCON57975.2023.10101237|Improved beb algorithm;Network optimization;computer;Wireless communication;Wireless sensor networks;Technological innovation;Simulation;Optimization methods;Quality of service;Spread spectrum communication|
|[Dynamic Program Slicing of Agent-oriented Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101284)|A. Mishra; M. R. Kabat; D. P. Mohapatra|10.1109/INOCON57975.2023.10101284|Agents;Behaviors;Message Passing;Agent Dependence Graph;Java Agent Development Framework (JADE);Multi-Agent Systems (MAS);Technological innovation;Heuristic algorithms;Computational modeling;Software algorithms;Programming;Maintenance engineering;Software systems|
|[Synthetic Data Generation Using Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101072)|P. Thogarchety; K. Das|10.1109/INOCON57975.2023.10101072|Genetic Algorithm;Synthetic Data Generation;Heuristic Search;Training;Technological innovation;Machine learning algorithms;Finance;Machine learning;Genetics;Data models|
|[Modern Face Recognition Attendance System Using Open CV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101232)|K. Ugale; K. Lohare; R. Jaid; A. Ttuteja|10.1109/INOCON57975.2023.10101232|Wi-Fi;DCT;Open CV;PCA;Value Regression Support CV;FR;DCN;Technological innovation;Face recognition;Media;Cameras;Retina;Real-time systems;Face detection|
|[Vaccine Supply Forecasting and Optimization using Deterministic and Probabilistic Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101255)|S. C. Banerjee; S. Banerjee; R. K. Jain|10.1109/INOCON57975.2023.10101255|Vaccine dosage forecasting;supply optimization;Time-series analysis;ARIMA;Holt-Winter’s Method;Technological innovation;Schedules;Pandemics;Time series analysis;Sociology;Probabilistic logic;Market research|
|[Water Surface Solid Waste Cleaning Robot For Ponds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101026)|S. N. Chaudhari; A. S. Botre; T. S. Wable; M. Gawande|10.1109/INOCON57975.2023.10101026|Garbage Extractor;Water surface cleaner;Solid Waste Extractor;Surface cleaning;Waste materials;Technological innovation;Prototypes;Belts;Surface tension;Rubber|
|[Intelligent System for Vehicles License Plate Recognition Using a Hybrid Model of GAN, CNN and ELM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101051)|B. Nirmala; S. Nithya; R. Vidhiya; K. K. Sunalini; B. H. Kumar; B. Varadharajan|10.1109/INOCON57975.2023.10101051|Support Vector Machine (SVM);Convolutional Neural Network (CNN);License Plate Recognition (LPR);Extreme Learning Machine (ELM);Generative Adverserial Networks (GAN);Training;Support vector machines;Technological innovation;Neural networks;Lighting;Licenses;Generative adversarial networks|
|[Field Programmable Gate Array(FPGA): An Innovation In Hardware Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101210)|Farhanaaz; V. Sanju|10.1109/INOCON57975.2023.10101210|FPGA;Embedded microprocessor;Microcontroller;Programming languages;Hardware Description Language;Technological innovation;Limiting;Codes;Microcontrollers;Computer architecture;Parallel processing;Hardware|
|[A Survey of Various Sentiment Analysis Techniques of Whatsapp](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101105)|R. Kaushal; R. Chadha|10.1109/INOCON57975.2023.10101105|Sentiment Analysis;Machine Learning;Whatsapp Data;Lexical Analysis;Sentiment analysis;Freeware;Technological innovation;Ethics;Social networking (online);Sociology;Psychology|
|[An evaluation on E-learning with Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101254)|S. K. Singh; A. Sharma|10.1109/INOCON57975.2023.10101254|Cloud Architecture;Cloud Computing;E-learning;Distance Education;Information Technology;virtualization;Cloud computing;Technological innovation;Electronic learning;Software as a service;Platform as a service;Education;Transforms|
|[Content Detection of Web Pages using HTML2Canvas and YOLOV3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101172)|K. Gorro; L. Feliscuzo; C. L. S. Romana|10.1109/INOCON57975.2023.10101172|You only look once (Yolo);Deep Learning;Training;Technological innovation;Layout;Web pages;Transform coding;Object detection;Games|
|[Recommendation System for E-Commerce Apparel Stores based on Text-Semantics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101358)|B. B. Dash; U. C. De; T. M. Behera; T. Samant; S. Banerjee|10.1109/INOCON57975.2023.10101358|Recommendation System;Average W2V;IDF Weighted W2V;Text-Semantics;Information Retrieval;Technological innovation;Social networking (online);Real-time systems;Electronic mail;Electronic commerce;History;Floods|
|[Sentiment Analysis for Real-Time Micro Blogs using Twitter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101366)|R. Banu; G. F. A. Ahammed; G. Divya; V. D. Reddy; N. Bhaskar; M. Kanthi|10.1109/INOCON57975.2023.10101366|SVM;Random Forest;Naïve Bayes;ML;SA;Support vector machines;Sentiment analysis;Technological innovation;Social networking (online);Mood;Blogs;Forestry|
|[“Cardioxy” – A Novel and Portable Instrumentation Amplifier based and IoT enabled ECG device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101028)|S. Raptan; A. Bhattacharyya; N. Das; I. Pandey; S. Bhattacharjee|10.1109/INOCON57975.2023.10101028|Instrumentation amplifier;LM741;ECG device;IoT;ThingSpeak;Operational amplifiers;Heart;Visualization;Technological innovation;Costs;Pandemics;Instruments|
|[Digital Image Enhancement using Conventional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100995)|P. D. S. Prasad; R. Tiwari; M. L. Saini; Savita|10.1109/INOCON57975.2023.10100995|Deep learning;Convolutional neural network;Image Super Resolution;High Resolution;Low Resolution;SRCNN;Headphones;Technological innovation;Digital images;Superresolution;Neural networks;Virtual reality;Cameras|
|[Bone Cancer Identification and Separation Using K-Means and KNN Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101031)|S. V. Shashikala; H. R. Uma; A. N. Sunad Kumara; N. L. Taranath; L. Singh; D. Sisodia|10.1109/INOCON57975.2023.10101031|KNN;K-Means;CT Scan;DNA;Support vector machines;Image segmentation;Technological innovation;Computed tomography;Bones;Cancer detection;Data mining|
|[A Closed Space Parameter Monitoring and Visualization System using Power Business Intelligence (Power BI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101302)|S. C. Jain; M. A. Gaydhane; O. M. Koli; R. P. Saraf; G. C. Mhaske; P. V. Pol|10.1109/INOCON57975.2023.10101302|Parameter Measuring;Temperature;Humidity;Pressure;Soil Moisture;Air Quality;Google Sheets;PowerBI;Dashboard;Temperature measurement;Temperature sensors;Soil measurements;Soil moisture;Real-time systems;Internet;Sensors|
|[Sign Language Conversion using Hand Gesture Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101099)|Y. Dhamecha; R. Pawar; A. Waghmare; S. Ghosh|10.1109/INOCON57975.2023.10101099|component;formatting;style;styling;insert;Computers;Technological innovation;Codes;Gesture recognition;Auditory system;User interfaces;Assistive technologies|
|[An IOTML Based Food Freshness Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101203)|Rashmi; V. A. Navyashree; S. Sai Geetha; B. K. Pavan; C. Deepti|10.1109/INOCON57975.2023.10101203|Food donation;food waste reduction;food spoilage detection;food freshness detection;MQ2;MQ135;Technological innovation;Sensors;Carbon|
|[Svelte.js: The Most Loved Framework Today](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101104)|S. Bhardwaz; R. Godha|10.1109/INOCON57975.2023.10101104|svelte.js;web development;front end;APIs;reactivity;Technological innovation;Codes;Buildings;Syntactics;Writing;Libraries;Complexity theory|
|[Bearing Error Diagnosis Using Deep Learning and Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101200)|R. Martin; R. Achary; C. J. Shelke|10.1109/INOCON57975.2023.10101200|Deep learning;Imps;Cwru;Predictive analytics;Vibrations;Deep learning;Time-frequency analysis;Technological innovation;Tensors;Databases;Fault detection|
|[Object Detection System using Arduino for Military Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101352)|J. C. N; A. K; S. A. C; T. N. M; V. R. S; S. N. K|10.1109/INOCON57975.2023.10101352|RADAR;Steeper motor;ultrasonic sensor;Meters;Technological innovation;Radar detection;Radar;Robot sensing systems;Acoustics;Time factors|
|[Analytical Approach for Soil and Land Classification Using Image Processing with Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101169)|Y. Aparna; G. Somasekhar; N. Bhaskar; K. S. Raju; G. Divya; K. R. Madhavi|10.1109/INOCON57975.2023.10101169|Agriculture;Soil;Land;Image processing;Deep Learning (DL);Deep learning;Productivity;Technological innovation;Soil measurements;Crops;Tail;Soil|
|[Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101073)|A. Singh; B. Shukla; J. Jos|10.1109/INOCON57975.2023.10101073|Consumer Price Index;RNN;LSTM;Stacked LSTM;GRU;ARIMA;SARIMA;Holt-Winters;Technological innovation;Statistical analysis;Economic indicators;Neural networks;Time series analysis;Predictive models;Macroeconomics|
|[Detection of Credit Card Fraudulent Transactions Utilizing Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101137)|A. N. Ahmed; R. Saini|10.1109/INOCON57975.2023.10101137|Fraud Detection;AI;Credit Card;Data Imbalance;Machine learning Algorithms;Support vector machines;Deep learning;Technological innovation;Machine learning algorithms;Credit cards;Fraud;Classification algorithms|
|[AI Applications in Smart City Employing Technology Adoption Model: Hofstede’s Cultural Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101114)|S. A. A. Bokhari; S. Myeong|10.1109/INOCON57975.2023.10101114|Artificial Intelligence Applications;Technology Acceptance Model (TAM);Cultural Dimensions;Perceived Ease of Use;Perceived Usefulness;Technological innovation;Smart cities;Chatbots;Data models;Behavioral sciences;Regression analysis;Cultural differences|
|[Design of Agro-photovoltaic System for Optimized Energy Generation and Crop Yield using Fuzzy Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101340)|S. G. Gulhane; A. R. Phadke|10.1109/INOCON57975.2023.10101340|Agro-Photovoltaic;Renewable;Vertical BiFacial;Fuzzy logic;Photovoltaic systems;Fuzzy logic;Resistance;Technological innovation;Sociology;Crops;Production|
|[Low Power DET Flip-Flops Using C-Element](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101275)|A. M. Abhishek; M. B. Veena; S. Archana|10.1109/INOCON57975.2023.10101275|DET;VLSI;dual edge triggered FF;MUX;Technological innovation;Latches;Power demand;System performance;Voltage;Very large scale integration;Throughput|
|[Artificial Intelligence Based Hybrid Models for Prediction of Stock Prices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101297)|H. Singh; M. Malhotra|10.1109/INOCON57975.2023.10101297|Deep Learning;Stock prediction;Stacked LSTM;Convolutional network;Hybrid models;Stock Market;Deep learning;Analytical models;Technological innovation;Bidirectional control;Predictive models;Convolutional neural networks;Forecasting|
|[Low-voltage and Low-power Systems using Common Gate Comparator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101158)|K. S. Praveen; S. P. Mamatha; M. B. Veena|10.1109/INOCON57975.2023.10101158|Multi-threshold CMOS (MTCMOS);Adder;Full Adder;Power;Area;Parallel Adder;Low voltage;Technological innovation;Power demand;Power system management;Microprocessors;Switches;Logic gates|
|[Educative Reality - Augmented Reality Application for Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101270)|N. Kshirsagar; G. Pandey; A. Prakash; I. S. Chauhan; S. Kumar|10.1109/INOCON57975.2023.10101270|AR;Education;Learning;Pedagogy;AR Foundation;Unity3D;Blender;Gaming;Visualization;Solid modeling;Technological innovation;Three-dimensional displays;Education;Streaming media;Servers|
|[An IoT-based Weather Monitoring System for Upcountry Farming in Sri-Lanka](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101282)|A. M. Manoj; T. D. B. Weerasinghe|10.1109/INOCON57975.2023.10101282|IoT for Agriculture;Sri Lankan Agriculture;Weather Monitoring;Temperature sensors;Smart agriculture;Technological innovation;Sociology;Agriculture;Sensor systems;Temperature control|
|[Computer Vision based Activity Recognition: Studying and Chit chatting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101091)|S. Shilaskar; R. Ekambaram; R. Rajandekar; R. Sisodiya|10.1109/INOCON57975.2023.10101091|Activities;Chitchat;Computer Vision;Image recognition;Machine Learning;Studying;Technological innovation;Emotion recognition;Computer vision;Machine learning algorithms;Image recognition;Face recognition;Surveillance|
|[A Study of Machine Learning Algorithms for Predicting Heart Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100980)|D. Mondal; R. Saini|10.1109/INOCON57975.2023.10100980|SVM;Random Forest;confusion matrix;Logistic Regression;Adaboost;XG-boost;python programming;Naive Bayes;Decision Tree;correlation matrix;Heart;Training;Deep learning;Support vector machines;Technological innovation;Computational modeling;Machine learning|
|[Heart Disease Prediction: Optimization of Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101268)|D. Mondal; R. Saini|10.1109/INOCON57975.2023.10101268|Heart disease;Machine learning;Features;Algorithms;Support vector machines;Heart;Technological innovation;Machine learning algorithms;Big Data;Prediction algorithms;Decision trees|
|[Powering up the IT Equipment and Cooling Units of Cloud Data Centers using Photovoltaic-fed Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101170)|S. B. Prusty; L. R. Behera; P. S. Roy; S. Padhee|10.1109/INOCON57975.2023.10101170|Cloud computing;distributed energy sources;air conditioning system;data center;solar microgrid;economic analysis;Economics;Data centers;Cloud computing;Power demand;Atmospheric modeling;Microgrids;Computer architecture|
|[Simulation of working of Chemical Reactor in LABVIEW platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101180)|L. S. Thota; H. Thota; S. B. Changalasetty; A. S. Badawy; W. Ghribi; M. Rahmathullah; S. A. Basha; H. Bangali|10.1109/INOCON57975.2023.10101180|Chemical reactor;Simulation;Modelling;LabVIEW;Virtual Instruments VI;front panel;block diagram;Industries;Technological innovation;Instruments;Crystallization;Chemical reactions;Stability analysis;Safety|
|[Design and Implementation of a Method for Diagnosing Various Stages of Alzheimer’s Illness Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101263)|P. Rachana.; B. Rajalakshmi; P. Guna Keerthi.; S. Kavya.; R. Likitha.|10.1109/INOCON57975.2023.10101263|Alzheimer’swDisease;MRI;ADNI;Deep Learning;Diagnosis;Image segmentation;Technological innovation;Solid modeling;Three-dimensional displays;Magnetic resonance imaging;Multitasking;Convolutional neural networks|
|[Comparative Analysis of Maximum Power Point Tracking Techniques for Photovoltaic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100994)|A. Choorakuzhiyil; K. Parate; J. Joshi|10.1109/INOCON57975.2023.10100994|Photovoltaic Module;Maximum Power Point Tracking;Perturb and Observe Algorithm;Incremental Conductance Algorithm;Maximum power point trackers;Renewable energy sources;Technological innovation;Computational modeling;Simulation;Solar energy;Systems modeling|
|[Using Sysbench, Analyze the Performance of Various Guest Virtual Machines on A Virtual Box Hypervisor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101143)|A. Pokharana; R. Gupta|10.1109/INOCON57975.2023.10101143|Virtual Machine;Guest OS;Analysis;Sysbench;Benchmarking;Results;Virtual Box;Hypervisor;Computers;Technological innovation;Virtual machine monitors;Linux;Benchmark testing;Virtual machining;Real-time systems|
|[Imposter detection with canvas and WebGL using Machine learning.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101070)|M. S. Prathima; S. P. Milena; P. Rm|10.1109/INOCON57975.2023.10101070|fingerprinting;Authentication;Canvas;WebGL;Machine Learning;Technological innovation;Machine learning algorithms;Authentication;Organizations;Machine learning;Fingerprint recognition;Software|
|[Performance Investigation of Three-phase Inverters with Conventional and Novel Topologies of MLI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101370)|S. Choubey; H. P. Vemuganti|10.1109/INOCON57975.2023.10101370|Power loss;Cascaded H-bridge (CHB);multilevel inverter (MLI);Reduced switch count (RSC);T-type;Multilevel dc-link MLDCL;Reduced carrier PWM;Performance evaluation;Technological innovation;Switching frequency;Switching loss;Switches;Pulse width modulation;Reliability engineering|
|[Identification of Aerial Image Land Use using Fused Thepade SBTC and Adaptive Thresholding with MachineLearning Ensemble](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101128)|S. D. Thepade; S. S. Adrakatti|10.1109/INOCON57975.2023.10101128|Thepade SBTC;Adaptive Thresholding;Land Use;Feature Fusion;Machine Learning;Ensemble;Performance evaluation;Learning systems;Technological innovation;Machine learning algorithms;Correlation;Machine learning;Forestry|
|[A Comprehensive Study on Blockchain: Transforming the World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101303)|S. N. Thakur; S. Maurya; B. Rawat|10.1109/INOCON57975.2023.10101303|Blockchain;Shared-ledger;Bitcoin;Decentralized;Technological innovation;Distributed ledger;Government;Bitcoin;Medical services;Regulation;Blockchains|
|[E-Commerce: Reach Customers and Drive Sales with Data Science and Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101132)|D. Sharma; S. Maurya; R. Punhan; M. K. Ojha; P. Ojha|10.1109/INOCON57975.2023.10101132|E-commerce;Data Science;Big Data Analytics;Customer behavior;Personalization;Industries;Customer satisfaction;Companies;Pricing;Big Data;Data science;Behavioral sciences|
|[An IoT Solutions for Ungulates Attacks in Farmland](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100983)|R. Raju; T. M. Thasleema|10.1109/INOCON57975.2023.10100983|Machine Learning (ML);Crops;Deep Learning (DL);Precision Agriculture (PA);Internet of Things (IoT);Temperature sensors;Smart agriculture;Irrigation;Insects;Crops;Soil;Sensor systems|
|[Deployment of Blockchain in Cloud Computing- A Comprehensive Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101317)|S. Khare; R. Sahay; R. Kumar|10.1109/INOCON57975.2023.10101317|Cloud computing;Block chain;Bit coin;security;Data protection;Authentication;Cloud infrastructure security;Computers;Cloud computing;Technological innovation;Costs;Companies;Blockchains;Topology|
|[GAN, CNN and ELM Based Breast Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101250)|V. Kate; R. Ushasree; R. M. Tharsanee; M. T. Kukreja; S. Saraf; B. Varadharajan|10.1109/INOCON57975.2023.10101250|Convolutional Neural Network (CNN);Extreme Learning Machine (ELM);Generative Adverserial Networks (GAN);Technological innovation;Uncertainty;Costs;Extreme learning machines;Image processing;Generative adversarial networks;Breast cancer|
|[Modeling and Simulation of the Power Train for Fuel Cell Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101179)|P. Mounica; N. Karuppiah; U. Pranavi|10.1109/INOCON57975.2023.10101179|fuel cell;hybrid car;sustainable mobility;hydrogen economy;power train architecture;performance;Power system measurements;Technological innovation;Density measurement;Transportation industry;System performance;Traction motors;Automobiles|
|[Big Data Security management through Task Role Based Access Control Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101117)|S. Pandey; S. Maurya|10.1109/INOCON57975.2023.10101117|Big data;Access control;Task;Role;Apache Hadoop;Authorization;Technological innovation;Social networking (online);Security management;Ecosystems;Distributed databases;Big Data|
|[Data Hiding Scheme using Difference Expansion and Modulus Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100991)|M. S. Hossen; T. Ahmad; N. J. D. L. Croix|10.1109/INOCON57975.2023.10100991|Data hiding;image Steganography;difference expansion;data security;infrastructure;Measurement;Image quality;Steganography;Technological innovation;PSNR;Digital images;Media|
|[Prediction of Smart Building and Smart City Resources using AI-techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101326)|J. S. Saini; S. Arora; S. Kamboj|10.1109/INOCON57975.2023.10101326|Smart Building;Smart City;Big Data;Artificial Intelligence;Machine Learning;Deep Learning;Industries;Technological innovation;Smart buildings;Smart cities;Predictive models;Communications technology;Artificial intelligence|
|[A Machine Learning Model for Content-Based Image Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101215)|Kunal; B. Singh; E. K. Kaur; C. Choudhary|10.1109/INOCON57975.2023.10101215|CBIR;Image Processing;Model;classifier;Robust;Computer science;Technological innovation;Databases;Image processing;Computational modeling;Image retrieval;Memory|
|[Should AI Technologies Replace the Human Jobs?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101202)|A. Trivedi; E. K. Kaur; C. Choudhary; Kunal; P. Barnwal|10.1109/INOCON57975.2023.10101202|Artificial Intelligence;techniques;chatbot;chat gbt;prediction;classification;Productivity;Industries;Technological innovation;Image recognition;Human intelligence;Natural languages;Chatbots|
|[Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101327)|V. Khanna; J. Joshua; R. Pramila|10.1109/INOCON57975.2023.10101327|Deep Learning;Pre-training;Time Series Forecasting;Machine Learning;Technological innovation;Correlation;Computational modeling;Time series analysis;Complexity theory;Computational efficiency;Forecasting|
|[Mental Health Monitor using Facial Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101000)|D. Naveen; P. Rachana; S. Swetha; S. Sarvashni|10.1109/INOCON57975.2023.10101000|Machine learning;OpenCV;Sentimental analysis;Technological innovation;Emotion recognition;Mood;Face recognition;Mental health;Predictive models;Market research|
|[Multilayer Convolutional Neural Network Based Approach to Detect Apple Foliar Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101125)|B. Biswas; R. K. Yadav|10.1109/INOCON57975.2023.10101125|Apple Foliar Disease;Artificial Intelligence (AI);Convolutional Neural Network (CNN);Deep Learning (DL);Machine Learning (ML);Plant Disease Detection;Plant diseases;Technological innovation;Machine learning algorithms;Crops;Nonhomogeneous media;Data models;Convolutional neural networks|
|[An Innovative Way of Building Website Using HCI Principles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100973)|O. Nankar; S. Patil; S. Gonge; A. Jain; R. Joshi; K. Kotecha|10.1109/INOCON57975.2023.10100973|Human Computer Interaction;Usability;Websites;User Experiences;Principles;Analysis;Human computer interaction;Technological innovation;Buildings;Search engines;User experience;Usability;Business|
|[Development of Breathe Analysis Technique Using Labview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101062)|T. P. Kausalya Nandan; M. Anusha; M. C. Chinnaiah; P. S. Raju|10.1109/INOCON57975.2023.10101062|LabVIEW;Microsoft Speech;Optical Character Recognition (OCR);Text to Speech Conversion (TTS);Breathe analysis;Pranayama;Instructions;Headphones;Technological innovation;Heart beat;Optical character recognition;Speech recognition;Electrocardiography;Speech enhancement|
|[Design And Analysis Of Physical Unclonable Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101289)|U. K. Anchana; S. Singh; M. Mogireddy; E. Kadavergu|10.1109/INOCON57975.2023.10101289|Physical Unclonable Function (PUF);Integrated Circuit (IC);Complementary Metal-Oxide Semiconductor (CMOS);Linear Feedback Shift Register (LFSR);Integrated circuits;Ring oscillators;Temperature dependence;Technological innovation;Authentication;Voltage;Physical unclonable function|
|[Evaluation of Potential Flying Cars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101276)|C. Choudhary; Aastha; G. K. Saini; Kunal; M. Saxena|10.1109/INOCON57975.2023.10101276|Flying car;(VTOL);Model;Data;Technological innovation;Roads;Sociology;Safety;Automobiles;Statistics;Traffic congestion|
|[Cybersecurity and Prevention in the Quantum Era](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101186)|A. Dwivedi; G. K. Saini; U. I. Musa; Kunal|10.1109/INOCON57975.2023.10101186|Cybersecurity;Quantum Computers;Public Key Encryption;Shor’s algorithm;Refactoring;Quantum Cryptography;QKD;Grover’s Algorithm;Technological innovation;Quantum computing;Current measurement;Software algorithms;Organizations;Software;Encryption|
|[An Optimized Predictive Model Based on Deep Neural Network for Detection of Skin Cancer and Oral Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101118)|G. Sharma; R. Chadha|10.1109/INOCON57975.2023.10101118|Cancer detection;Deep Learning;Machine Learning;Feature Extraction;Exploratory Data analysis.;Deep learning;Technological innovation;Neural networks;Medical services;Predictive models;Prediction algorithms;Feature extraction|
|[Advanced Healthcare Chat Bot using Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101239)|S. Santhosham; C. P. Sah|10.1109/INOCON57975.2023.10101239|Chatbot;HealthCare;Natural Language Processing;Technological innovation;Costs;Medical services;Chatbots;Object recognition;Artificial intelligence;Medical diagnostic imaging|
|[Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101206)|M. S. A. Basha; C. Prabhavathi; V. Khangembam; M. M. Sucharitha; P. M. Oveis|10.1109/INOCON57975.2023.10101206|Machine Learning;Classifier;Regressors;Classification report;Accuracy Scores.;Technological innovation;Measurement uncertainty;Support vector machine classification;Static VAr compensators;Predictive models;Boosting;Feature extraction|
|[Dual Vt 7T SRAM Based In-Memory Compute Adder for Convolution Neural Network Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101365)|B. Joseph; R. K. Kavitha|10.1109/INOCON57975.2023.10101365|SRAM;In-Memory Computation (IMC);neural networks;Artificial Intelligence (AI);Machine Learning (ML);Technological innovation;Microprocessors;Neural networks;Memory management;Random access memory;Delays;Sensors|
|[Fraudulence Detection of Medical Images using Pixel Level Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101217)|T. A. Goud; G. A. E. Satish Kumar; V. M. Shalini; B. A. Goud|10.1109/INOCON57975.2023.10101217|Hybrid Median Filter;Support Vector Machine;Extreme Learning Machine;BSR;Support vector machines;Technological innovation;Privacy;Noise reduction;Medical services;Mammography;Classification algorithms|
|[Skin Cancer Detection Using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101344)|D. Hemalatha; K. N. Latha; P. M. Latha|10.1109/INOCON57975.2023.10101344|Skin Lesion;Deep Learning;Melanoma;Convolutional Neural Network;Dermatology.;Image segmentation;Melanoma;Predictive models;Feature extraction;Skin;Real-time systems;Lesions|
|[Control Strategy for Renewable Energy System Using Transformerless HERIC Bridge Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101335)|V. Saravanakrishnan.; N. Dhanush.; S. Israk Hussian.; G. Thamizhselvan.; A. Janagiraman|10.1109/INOCON57975.2023.10101335|Transformer-less Inverter (HERIC);Zeta Converter;Maximum Power Point Tracking;Power Decoupling;Single Phase System;PV Panel;Memetics;Total harmonic distortion;Technological innovation;Wheels;Switches;Transformers;Harmonic analysis|
|[A Review Paper on Hybrid Cryptographic Algorithms in Cloud Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101044)|H. Sharma; R. Kumar; M. Gupta|10.1109/INOCON57975.2023.10101044|cloud network;cryptography;cryptographic algorithm;symmetric;asymmetric;Cloud computing;Technological innovation;Companies;Encryption;Robots|
|[Sentiment Analysis of Twitter Data: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100998)|S. Singh; P. Kumar|10.1109/INOCON57975.2023.10100998|Twitter;Sentiment Analysis;Machine Learning;Natural Language Processing;Preprocessing;Feature extraction;Classifiers;Sentiment analysis;Analytical models;Technological innovation;Social networking (online);Voting;Scalability;Blogs|
|[Ensemble deep learning fusion for detection of colorization based image forgeries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101337)|S. S; D. R. G. K|10.1109/INOCON57975.2023.10101337|Fake colorization;multichannel;ensemble;modified Dense net;Deep learning;Image forensics;Technological innovation;Histograms;Image color analysis;Splicing;Digital images|
|[Classification of Rolling Bearing Fault Based on Long Short Term Memory Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101096)|S. Kumar; D. Ganga|10.1109/INOCON57975.2023.10101096|Fault diagnosis and fault classification;Deep learning and Long-short term memory;Fault diagnosis;Deep learning;Training;Technological innovation;Neural networks;Rolling bearings;Receivers|
|[An Automated Static CMOS Schematic Generation Using MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101160)|K. G; S. K. K; S. S. Jacob; R. G|10.1109/INOCON57975.2023.10101160|CMOS;automation;schematic;semiconductor chips;area efficiency;Integrated circuits;Technological innovation;Visualization;Power demand;Voltage;Software;Transistors|
|[Automating the Machine Learning Process using PyCaret and Streamlit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101357)|N. Sarangpure; V. Dhamde; A. Roge; J. Doye; S. Patle; S. Tamboli|10.1109/INOCON57975.2023.10101357|AutoML;Streamlit;PyCaret;Iris dataset;Productivity;Industries;Technological innovation;Analytical models;Pipelines;User interfaces;Predictive models|
|[Dark Web Crawling for Cybersecurity: Insights into Vulnerabilities and Ransomware Discussions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101162)|A. Dalvi; P. Kulkarni; A. Kore; S. G. Bhirud|10.1109/INOCON57975.2023.10101162|Log4j Vulnerabilities;REvil Ransomware;WannaCry Ransomware;Dark web;Technological innovation;Dark Web;Ransomware;Engines;Cyberattack|
|[Transforming College Counseling: An In-House Solution for Student Mental Wellness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101364)|A. Dalvi; P. Kapur; R. Ashtekar; A. Gorugantu; I. Siddavatam|10.1109/INOCON57975.2023.10101364|Academic;Student Counseling;Mental Health;We application;Mobile application;Employee welfare;Bridges;Technological innovation;Medical treatment;Mental health;Depression|
|[Disease Classification in Bell Pepper Plants Based on Deep Learning Network Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101269)|M. P. Mathew; S. Elayidom; V. Jagathyraj|10.1109/INOCON57975.2023.10101269|Deep learning;plant diseases classification;Vgg16;Vgg19;AlexNet;CNN;Artificial Intelligence;Deep learning;Technological innovation;Transfer learning;Crops;Production;Feature extraction;Agriculture|
|[Discrete Teaching Learning-Based Optimization for Multi-Hole Drilling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101171)|V. P. Rathod; S. P. Kadam; O. P. Yadav; A. P. S. Rathore|10.1109/INOCON57975.2023.10101171|Travelling Salesmen Problem;Simulated Annealing;Multi-hole drill path sequencing;Discrete Teaching-Learning-Based Optimization.;Drilling;Manufacturing industries;Sequential analysis;Technological innovation;NP-hard problem;Education;Evolutionary computation|
|[An Optimized Ant Gardient Convolutional Neural Network for Disease Detection in Apple Leaves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101362)|A. Kaur; R. Chadha|10.1109/INOCON57975.2023.10101362|Apple Leave Disease;OAG-CNN (Optimized Ant Gradient-Convolutional Neural Network);Feature Extraction;SGD (Stochastic Gradient Descent);ACO (Ant Colony Optimization) method);Technological innovation;Image segmentation;Databases;Stochastic processes;Production;Real-time systems;Complexity theory|
|[Advancing Crypto Ransomware with Multi Level Extortion: A Peril to Critical Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100971)|C. S. Anand; R. Shanker|10.1109/INOCON57975.2023.10100971|ransomware;crypto-ransomware;ransomware-as-a-service;cyber security;crypto warfare;cyberattack;malware.;Technological innovation;Hospitals;Weapons;Government;Cyber warfare;Market research;Virtual machining|
|[Design of 8-bit ALU Using OAI Logic at Subthreshold Voltage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100982)|S. K. Velagaleti; D. Lahari|10.1109/INOCON57975.2023.10100982|FullAdder (FA);Carry Extender (CE);Arithmetic Extender (AE);Logic Extender (LE);Technological innovation;Power demand;Simulation;CMOS technology;Mathematical models;Power dissipation;Task analysis|
|[An Integrated Security System for Bank Lockers Using Gated D-Latch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101343)|S. Velagaleti|10.1109/INOCON57975.2023.10101343|Bank Locker System;Power Consumption;Supply Voltage;Sequential circuit;State diagram;Siren;Temperature measurement;Semiconductor device modeling;Technological innovation;Power demand;Logic gates;CMOS technology;Sequential circuits|
|[A Low Power APB with an Area Efficient Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100979)|S. Velagaleti|10.1109/INOCON57975.2023.10100979|AMBA;APB Bridge;LFSR;AXI;UART;Bridges;Technological innovation;Power demand;Microcontrollers;Data security|
|[Decision Assist System for Patients using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101188)|K. Roy; S. Sagnika; S. Bilgaiyan|10.1109/INOCON57975.2023.10101188|Machine Learning;Data Analysis;Bagging;Boosting;Neural Network;Analytical models;Costs;Neural networks;Medical treatment;Predictive models;Boosting;Data models|
|[Gain and Bandwidth Enhancement of Sierpinski Carpet Based Fractal Patch Antenna Using Stacked Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101022)|B. Kattimani; R. R.Patil|10.1109/INOCON57975.2023.10101022|Fractal Microstrip patch Antenna (FMPA);Bandwidth;Gain;Return Loss;Technological innovation;Patch antennas;Stacking;Resonant frequency;Microstrip antennas;Microstrip components;Fractals|
|[A Design of Face Recognition Model with Spatial Feature Extraction using Optimized Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101149)|S. Santhosh; S. V. Rajashekararadhya|10.1109/INOCON57975.2023.10101149|Face Recognition;Spatial Feature Extraction;Optimized Support Vector Machine;Rat Swarm Optimizer;Support vector machines;Computer vision;Technological innovation;Filtering;Face recognition;Lighting;Rats|
|[Interactive Communication Robot Handling Crowd Management and Content Delivery in Museums Employing Crowd Counting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101373)|S. Ghosh; A. Roy; N. Dasgupta; M. Bagchi; A. Chatterjee; N. K. Das|10.1109/INOCON57975.2023.10101373|crowd count;museum;virtual assistant;Museum IoT;crowd handling;content delivery.;Multiplexing;Manifolds;Location awareness;Technological innovation;Surveillance;Knowledge based systems;Humanoid robots|
|[ThresholdedReLU Orthogonal Layer Weight Regularized Densely Connected Convolutional Networks CNN for Strawberry Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101003)|M. S. Devi; A. Kumar; S. Ravikumar; R. Yadav; D. Singh|10.1109/INOCON57975.2023.10101003|Convolution;CNN;deep learning;pooling;accuracy;Convolutional codes;Training;Adaptation models;Analytical models;Training data;Data models;Power capacitors|
|[Depression Prediction from Combined Reddit and Twitter Data using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101174)|L. D’Cruz; V. Dubey; P. Thakur|10.1109/INOCON57975.2023.10101174|Social network sites;depression prediction;NLP;Twitter;Reddit;mental health.;Measurement;Technological innovation;Text analysis;Social networking (online);Blogs;Mental health;Predictive models|
|[Detection of High Impedance Fault in Low Voltage Distribution System using Discrete Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101138)|D. Kumar; A. Kumar; R. Singh; M. Sarwar|10.1109/INOCON57975.2023.10101138|distribution system;high impedance fault;discrete wavelet transform;HIF modelling;multi resolution analysis;fault detection;Low voltage;Technological innovation;Power system harmonics;Harmonic analysis;Transformers;Mathematical models;Discrete wavelet transforms|
|[ROS Based Wireless Teleoperation System for Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100975)|A. Gomes; M. Nagavekar; J. M. Dsouza|10.1109/INOCON57975.2023.10100975|ROS;Controller;Wireless communication;Teleoperation;Wireless communication;Technological innovation;Service robots;Navigation;Operating systems;Transceivers;Real-time systems|
|[Space Agriculture and Mechatronic Technologies: Micro-Review and Multi-Collaborative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101312)|R. Barreto; J. Cornejo; R. Palomares; J. A. Cornejo; J. C. Suárez-Quispe; M. Vargas; C. Valenzuela; J. C. Chavez; J. Valdivia|10.1109/INOCON57975.2023.10101312|agriculture;mechatronics;microgravity;space crops;plants.;Earth;Technological innovation;Renewable energy sources;Space technology;Plants (biology);Aerospace electronics;Agriculture|
|[CFD Simulation of Tier 4 Data Center for Cooling and Backup Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101234)|R. Balakrishnan; M. Munirajulu|10.1109/INOCON57975.2023.10101234|Computational Fluid Dynamics (CFD);cooling;backup power;data center;performance-based design;Data centers;Technological innovation;Cooling;Computational fluid dynamics;Computational modeling;Predictive models;Ventilation|
|[Deep Convolutional Neural Network-based Automatic Detection of Brain Tumour](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101238)|I. Paul; A. Sahu; P. K. Das; S. Meher|10.1109/INOCON57975.2023.10101238|Brain Tumor;Deep Learning;Machine Learning;Data Augmentation;Convolutional Neural Network;MRI;Training;Representation learning;Technological innovation;Costs;Computational modeling;Brain modeling;Data models|
|[Recogniition and Speech Conversion of Devnagri Script using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101034)|S. Agrawal; N. Agrawal|10.1109/INOCON57975.2023.10101034|Optical character Recognition;Devanagari Script;CNN;Text to Speech;Training;Convolution;Computational modeling;Neural networks;Speech recognition;Ink;Writing|
|[Design and Development of Dual Loop Motion Control System for BLDC motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101075)|A. Sarkar; A. Khetan; L. John; H. Sabunwala; E. Gupta; A. Jain; A. Kumar; N. Gupta|10.1109/INOCON57975.2023.10101075|BLDC motor;Control systems;STM Nucleo;Step response;MATLAB Simulink;Technological innovation;PI control;Software packages;Rotors;Oscilloscopes;Open systems;Pulse width modulation|
|[A Compact Circularly Polarized Slot Antenna With Wide Axial Ratio Bandwidth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101189)|S. Mitra; U. Nayak; P. Chongder|10.1109/INOCON57975.2023.10101189|Circular Polarization;Axial Ratio Bandwidth (ARBW);Slot Antenna;Microstrip Feed Line;Dual Port;Antenna measurements;Wireless LAN;Polarization;Technological innovation;Slot antennas;Simulation;Perturbation methods|
|[Cardiac disease detection and analysis – a Machine Learning based approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101078)|S. A. Farooq; A. S. Bhuyan; R. Sharma; H. Rao; M. Qamar; S. Das|10.1109/INOCON57975.2023.10101078|Machine Learning;Decision Tree Classifier;Cardiac Diseases;Simple Classifier.;Heart;Technological innovation;Machine learning algorithms;Cardiac disease;Organizations;Feature extraction;Classification algorithms|
|[Smart Systems as Futuristic Approach Towards Agriculture Development:A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101109)|K. Kalbande; W. Patil|10.1109/INOCON57975.2023.10101109|Internet of Things (IoT);Agricultural System;Machine Learning;Artificial Intelligence;Smart System;Robotic System;Wireless communication;Technological innovation;Plant diseases;Service robots;Crops;Spraying;Machine learning|
|[A Novel Way to Detect the Islanding Condition Using PSO and Control the Voltage Current of DG Using A PI Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101107)|G. C. Babu; C. Srinivas; E. Gopi; J. E. S. Bhaskar; G. P. S. S. Nagaraj; K. Manohar|10.1109/INOCON57975.2023.10101107|Distributed generation (DG);grid-connected;intentional islanding operation scenario;load shedding;Particle Swarm Optimization (PSO);and synchronization;Technological innovation;Renewable energy sources;Islanding;PI control;Load shedding;Synchronization;Voltage control|
|[Facial Emotional Recognition Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101347)|D. Raj; M. A. Wassay|10.1109/INOCON57975.2023.10101347|Facial Emotion Recognition;Convolutional Neural Network;Accuracy;Feature Extraction;Classification;Emotion recognition;Pediatrics;Autism;Technological innovation;Face recognition;Psychology;Medical services|
|[Deep-learning Residual Network Based Image Analysis for An Efficient Two-Stage Recognition of Neurological Disorders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101037)|N. D. S. Battula; H. R. Kambhampaty; Y. Vijayalata; R. N. Ashlin Deepa|10.1109/INOCON57975.2023.10101037|Classification;Deep Convolutional Neural networks;ResNet Brain MRI;Alzheimer’s disease;Parkinson’s Disease;Autism spectrum Disorder;Neurological diseases;Training;Technological innovation;Image recognition;Magnetic resonance imaging;Classification algorithms;Task analysis|
|[Implementation of Braun and Baugh-Wooley Multipliers Using QCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101300)|P. Kishore; R. Sirimalla; K. S. Sushma; R. S. Reddy|10.1109/INOCON57975.2023.10101300|QCA;Braun multiplier;Baugh-Wooley multiplier;high-speed;Cellular Automata;Power dissipation;Program processors;Power demand;Array signal processing;Quantum dots;Digital signal processors;Computer architecture;Logic gates|
|[A Novel Offloading and Resource Allocation Scheme for Time-critical Tasks in Heterogeneous Internet of Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101035)|J. Liu; Y. Wang; W. Zhang; K. Tian|10.1109/INOCON57975.2023.10101035|Mobile Edge Computing (MEC);Offloading;Resource Allocation;Agglomerative Clustering;Technological innovation;Multi-access edge computing;Simulation;Wireless networks;Clustering algorithms;Quality of service;Resource management|
|[An Experimental Analysis on Mitigating the Effects of Malicious Nodes in a Federated Learning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101043)|B. Chempavathy; S. K. Shibi; B. M. Sundaram; S. Kotturi; S. B. Reddy|10.1109/INOCON57975.2023.10101043|Deep learning;Federated computing;IoT;Cyber security;Performance evaluation;Deep learning;Technological innovation;Federated learning;Computational modeling;Internet of Things;Security|
|[Improving the Quality and Readability of Ancient Brahmi Stone Inscriptions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101244)|P. Rajnish; K. Prajwal Kamath; B. Kumar; M. Nishanth; P. Preethi|10.1109/INOCON57975.2023.10101244|Optical Character Recognition;Deep Learning;Denoising;Segmentation;Brahmi Inscriptions;Inception ResNet;Graph Algorithm;Technological innovation;Image segmentation;Handwriting recognition;Image recognition;Pipelines;Asia;Optical imaging|
|[Modelling of Charging and Discharging of Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101093)|S. Misra; B. Jha; V. M. Mishra|10.1109/INOCON57975.2023.10101093|Electric vehicle (EV);vehicle to grid (V2G);grid to vehicle (G2V);vehicle to vehicle (V2V);vehicle to home (V2H).;Vehicle-to-grid;Technological innovation;Software packages;Simulation;Power quality;Power system stability;Electric vehicles|
|[Comparing Gradient Descent and Genetic Algorithm for Optimization In Regression Analysis For Handling Nonlinearity In Liver Cirrhosis: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101184)|H. S. Yadav; R. K. Singhal|10.1109/INOCON57975.2023.10101184|Machine Learning;Loss Function;optimization;Deep Learning;Gradient Descent.;Deep learning;Technological innovation;Machine learning algorithms;Linear regression;Neural networks;Fitting;Predictive models|
|[Development of Cost-Effective Precision Spraying Techniques Using Sensor Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101110)|S. S. Patil; Y. M. Patil; S. B. Patil|10.1109/INOCON57975.2023.10101110|Variable rate application;Precision agriculture;Ultrasonic Sensor;Canopy measurement;Technological innovation;Ultrasonic imaging;Liquids;Ultrasonic variables measurement;Shape;Volume measurement;Spraying|
|[Classification and Prediction of Liver Disease Diagnosis Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101221)|H. S. Yadav; R. K. Singhal|10.1109/INOCON57975.2023.10101221|Chronic liver disease;confusion matrix;machine learning;Logistic regression;hyper Parameters tunning;Random Forest classifiers;Technological innovation;Machine learning algorithms;Liver diseases;Computational modeling;Medical services;Machine learning;Predictive models|
|[A Behavior Modelling and Analysis of Lithium Ion Battery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101040)|A. D. J. Raju; S. S. Manohar; A. A. S. Beula|10.1109/INOCON57975.2023.10101040|Battery Management System (BMS);Machine Learning;State of Charge (SoC);State of Health (SoH).;Lithium-ion batteries;Temperature sensors;Temperature measurement;Analytical models;Temperature;Machine learning;Mathematical models|
|[High Impedance Fault Detection in Distribution Networks using Unsupervised Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101324)|M. Bhatnagar; A. Yadav; A. Swetapadma|10.1109/INOCON57975.2023.10101324|Unsupervised learning;Autoencoders;High Impedance Fault (HIF) detection;Quadratic Discriminant Analysis (QDA);Fault diagnosis;Technological innovation;Fault detection;Distribution networks;Voltage;Feature extraction;Impedance|
|[Linear Active Disturbance Rejection Control of Buck-boost PFC based LED Driver Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101168)|S. Dahiya; M. M. Garg; K. S. Mani|10.1109/INOCON57975.2023.10101168|Active Power Factor Correction;Buck-Boost PFC;Controller Design;LED Driver;Linear Active Disturbance Rejection Control;Power Factor Correction;Robust control;Reactive power;Transient response;Voltage fluctuations;Observers;Power factor correction;Light emitting diodes|
|[Underwater and Coastal Seaweeds Detection for Fluorescence Seaweed Photos and Videos using YOLOV3 and YOLOV5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101342)|E. Ranolo; K. Gorro; A. Ilano; H. Pineda; C. Sintos; A. J. Gorro|10.1109/INOCON57975.2023.10101342|Yolo;Deep Learning;Computer Vision;Augmentation;Artificial Intelligence;Training;Image quality;Technological innovation;Oceans;Ecosystems;Sea measurements;Object detection|
|[Developing of NPA predictive Model for Pawning Advances in Sri Lankan Banking Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101183)|N. Bamunuarachchi; C. D. Silva|10.1109/INOCON57975.2023.10101183|Banking Sector;Non - Performing Loans;Profitability;Machine Learning;Inflation;Support vector machines;Gold;Biological system modeling;Banking;Predictive models;Feature extraction;Data models|
|[Applications of Blockchain Technology in Project Management — A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101235)|R. Perera; R. Wickramarachchi; C. Rajapakse|10.1109/INOCON57975.2023.10101235|blockchain;project management;applications of blockchain technology;decentralized applications;Technological innovation;Systematics;Supply chains;Ecosystems;Project management;Insurance;Finance|
|[Development of Brake System Plausibility Device of an FSAE Race Car](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100997)|D. C. Meena; A. Motla; A. Majumdar; A. K. Ghosh|10.1109/INOCON57975.2023.10100997|FSAE;Safety Systems;Electric Vehicles;Brake System Plausibility Device (BSPD);Technological innovation;Energy resources;Life estimation;Mechanical variables measurement;Robustness;Safety;Brakes|
|[Analysis of Government Flagship Programs using Public Feedbacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101351)|G. Chauhan; S. Puri; R. Nahta|10.1109/INOCON57975.2023.10101351|aspect approach;lexicon method;machine learning;sentiment analysis;natural language processing;Government policies;Learning systems;Technological innovation;Sentiment analysis;Education;Medical services;Games|
|[Interpreting Machine and Deep Learning Models for PDF Malware Detection using XAI and SHAP Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101116)|T. Rahman; N. Ahmed; S. Monjur; F. M. Haque; M. I. Hossain|10.1109/INOCON57975.2023.10101116|malware;PDF;cyber-security;chi-square;K-fold;rmse;SGD;machine-learning;detection;algorithm;XG-Boost Classifier;Single Layer Perceptron;ANN;Deep learning;Analytical models;Technological innovation;Machine learning algorithms;Tensors;Stochastic processes;Artificial neural networks|
|[CO2 Emission Rating by Vehicles Using Data Science](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101272)|A. Julian; S. Karthick; T. Chandrakishore; A. Chandramohan|10.1109/INOCON57975.2023.10101272|CO2;accuracy;global warming;emission;Industries;Technological innovation;Machine learning algorithms;Roads;Transportation;Carbon dioxide;Machine learning|
|[A New Concatenated Method for Deep Curve Estimation Using Low Weight CNN for Low Light Image Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101064)|R. Kalyan; R. K. Jototh; R. Tiruveedhula|10.1109/INOCON57975.2023.10101064|LE Curves;non-reference Loss Function;DCE NET;Training;Technological innovation;Estimation;Dynamic range;Generative adversarial networks;Task analysis;Image enhancement|
|[Analysis of EMI Mitigation Techniques in High Speed Printed Circuit Board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101216)|M. Anandan; L. P. Sajitha; S. Geerthik|10.1109/INOCON57975.2023.10101216|Electro Magnetic Compatibility;EMI-Electro Magnetic Inteference;IDC-Inter Digitated Capacitor;HIDDS-Hybrid Inter Digital Defected ground Structure;Band-pass filters;Magnetic separation;Wireless networks;Electromagnetic interference;Insertion loss;Microwave communication;Microwave theory and techniques|
|[Object Detection System in Adverse Weather Conditions Using Ann](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101071)|J. A. Joshua; L. Narasiman; V. Yogeshwaran; M. V. Anand|10.1109/INOCON57975.2023.10101071|Accident prevention;Thermal infrared camera;Object detection;Low visibility;Adverse weather conditions;Artificial neural network (ANN);Image processing;Computer vision;Technological innovation;Rain;Snow;Roads;Supply chains;Urban areas;Transportation|
|[Self-Operated and Efficient Recruitment Procedure Using Natural Language Toolkit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101113)|A. Julian; S. Raja Sahaya Subeka|10.1109/INOCON57975.2023.10101113|Natural language processing (NLTK);GitHub;Object Character Recognition (OCR);POS tagging;Redis;Technological innovation;Text recognition;Databases;Resumes;Optical character recognition;Companies;Cache storage|
|[Network Optimization of Large-Scale CNNs for Applications in Autonomous Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101330)|H. R. Kambhampaty; N. D. S. Battula; S. Rentala; S. C. Challa; A. K|10.1109/INOCON57975.2023.10101330|Network Optimization;CNN;Point-Cloud;3D Object Detection;Autonomous Systems;Training;Quantization (signal);Three-dimensional displays;Autonomous systems;Tracking;Image edge detection;Object detection|
|[Inherent Constant Current Constant Voltage (CCCV) Charging of Electric Vehicles By Using LCL Resonant Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101164)|K. K. Gautam; A. Chatterjee; D. Prasad|10.1109/INOCON57975.2023.10101164|Constant current (CC) charging;constant voltage (CV) charging;LCL resonant converter;high efficiency;zero-voltage-switching (ZVS);zero-current-switching (ZCS);Technological innovation;Switching frequency;Rectifiers;RLC circuits;Prototypes;Resonant converters;Zero voltage switching|
|[Machine Learning based Land Use Identification of Aerial Images with Fusion of Thepade SBTC and Triangle Thresholding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101262)|S. D. Thepade; A. P. Bhalerao|10.1109/INOCON57975.2023.10101262|Feature Extraction;Image processing;TSBTC;Triangle thresholding;Technological innovation;Satellites;Machine learning algorithms;Urban planning;Predictive models;Feature extraction;Classification algorithms|
|[Balancing Techniques for Improving Automated Detection of Hate Speech and Offensive Language on Social Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101157)|B. A. C. Reddy; G. K. Chandra; D. S. Sisodia; A. Anuragi|10.1109/INOCON57975.2023.10101157|Sentimental analysis;Hate speech;offensive language;TF-ID features;logistic regression;Sampling techniques;Support vector machines;Technological innovation;Social networking (online);Hate speech;Blogs;Text categorization;Feature extraction|
|[A Review of Deep Learning Approaches for Human Gait Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101267)|A. Pundir; M. Sharma|10.1109/INOCON57975.2023.10101267|Gait Recognition;Deep Learning(DL);Convolutional Neural Network (CNN);Recurrent Neural Network (RNN);Autoencoders;Deep Belief Networks;Generative Adversarial Networks(GAN);Measurement;Deep learning;Technological innovation;Biological system modeling;Neural networks;Security;Gait recognition|
|[Involvement of Functional Programming in Language Processing and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101253)|D. C. Dobhal; B. Kumar; P. Das|10.1109/INOCON57975.2023.10101253|Functional programming;Machine Learning;Language process;Mathematical program;Data collection;Language support;Technological innovation;Machine learning;Learning (artificial intelligence);Data collection;Functional programming;Business|
|[The Effect of Virtual Reality in the Modern System of Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101080)|D. C. Dobhal; H. S. Negi; P. Das|10.1109/INOCON57975.2023.10101080|Virtual reality;effect of virtual reality;modern education system;application of virtual reality;etc;Training;Technological innovation;Tracking;Medical services;Immersive experience;Games;Sensor phenomena and characterization|
|[Using AI and Blockchain in Case of Algorithm Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101367)|N. Tripathi; A. K. Mishra|10.1109/INOCON57975.2023.10101367|Blockchain;AI-driven protection;Algorithm regulation;Big Data;Machine Learning;Privacy;Technological innovation;Modulation;Regulation;Blockchains;Artificial intelligence|
|[The Significance of using Data Extraction Methods for an Effective Big Data Mining Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101236)|M. Sharma; R. Gupta|10.1109/INOCON57975.2023.10101236|Big data mining;Data extraction methods;Data Mining process;Data warehouse;Data lake;Data analysis;Decision making;Artificial intelligence;Business intelligence;data-driven decision-making;Technological innovation;Data analysis;Decision making;Big Data;Data mining;Fuels;Business|
|[Improved Wireless Sensor Network Security Through Node Leach Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100985)|G. Mehta; Bhuvneshwari; A. K. Singh|10.1109/INOCON57975.2023.10100985|Active Attacks;Sinkhole;Leach;WSN;IDS;NS2;DOS;Wireless sensor networks;Technological innovation;Base stations;Power demand;Protocols;Packet loss;Organizations|
|[Effect of Mesh Generation and its Usage for Computational Domains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100987)|K. C. Purohit; S. Gupta; M. Manwal|10.1109/INOCON57975.2023.10100987|Mesh Generation;Triangles;Quadrilaterals;Dimensional structures;Geometric structure;CFD;Technological innovation;Mesh generation|
|[Implementation and Comparison of BUG Algorithms on ROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101325)|S. Gupta; C. S. Asha; J. M. D’Souza|10.1109/INOCON57975.2023.10101325|Bug 0;Bug 1;Bug 2;Robot Operating System;Technological innovation;Navigation;Operating systems;Simulation;Computer bugs;Robot sensing systems;Sensors|
|[Deep Overview of Virtualization Technologies Environment and Cloud Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101349)|M. A. Khan; A. Sharma|10.1109/INOCON57975.2023.10101349|Virtualization;Cloud Computing Models;Cloud Security;Cloud computing;Technological innovation;Information security;Computer architecture;Virtual machining;Hazards;Hardware|
|[Analysis of the Contribution of Computing for The Development of IT Service Centre](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101130)|S. K. Shukla; V. Vimal|10.1109/INOCON57975.2023.10101130|Computing process;IT service centre;Artificial intelligence;Output Unit;Storage;Technological innovation;Telephone sets;Software;Problem-solving;Business|
|[Modelling & Analysis of Various control MPPT techniques of Renewable Energy Sources in Micro-Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101112)|Y. Vijaykumar; V. Vemulapati; B. S. Murali; P. Naveen; K. Satyanarayana; G. Venumadhav|10.1109/INOCON57975.2023.10101112|Wind Power Energy;Solar Power Energy;(Maximum Power Point Tracking) MPPT;Micro Grid;Fuzzy logic;Natural resources;Renewable energy sources;Tracking loops;Technological innovation;Wind energy;Software packages|
|[Analysis of Smart Healthcare Application in 5G Using Manet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100976)|S. A. Sahaaya Arul Mary; A. Rajput; S. J. N. Kumar; M. Kandasamy; R. Lakhan|10.1109/INOCON57975.2023.10100976|MANET;Health care;data speed;5G networks;OFDM;Technological innovation;5G mobile communication;Hospitals;OFDM;Smart healthcare;Medical services;User experience|
|[Comparative Analysis of Different Shading Patterns in Case of Total Cross Tied Connection for Solar PV Panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101360)|R. Verma; S. Gupta; A. Yadav|10.1109/INOCON57975.2023.10101360|Partial shading;Photovoltaic array;Reconfiguration;Global maximum power Point (GMPP);Technological innovation;Renewable energy sources;Temperature;Fill factor (solar cell);Snow;Poles and towers;Voltage|
|[Image Recognition and Enhancement using Multi Scale Retinex and Histogram Equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101017)|K. R. Tejas; B. Rajalakhmi; P. Geethika; C. Indrani; R. S. Kolalapudi|10.1109/INOCON57975.2023.10101017|Deep -Learning;MSR;MASRCR;CNN;PET;HE;Histograms;Technological innovation;Rain;Image recognition;Image color analysis;Surveillance;Mobile applications|
|[Design and Implementation of Self-Charging BMS for Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101271)|M. K. Yarra; T. V. Krishna; G. Siva Gangadhar; L. Sandeep; G. Naga Venkata Balakrishna; C. S. Prakash|10.1109/INOCON57975.2023.10101271|Electrical Vehicles;Armature Winding Technology;Battery management system (BMS);Bi-Directional DC/DC converters;Solar PV System;Technological innovation;Windings;Rotors;Battery management systems;Voltage;Electric vehicles;Batteries|
|[Stock Prices Prediction Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101226)|A. Gupta; Akansha; K. Joshi; M. Patel; M. V. Pratap|10.1109/INOCON57975.2023.10101226|Stock Market Prediction;Machine Learning;Long Short-Term Memory(LSTM);Convolution Neural Networks(CNN);Recurrent Neural Network(RNN);Technological innovation;Machine learning;Companies;Prediction algorithms;Nonhomogeneous media;Market research;Convolutional neural networks|
|[Prediction of Cardiovascular Diseases Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101292)|J. F. Aradan; A. Pawar|10.1109/INOCON57975.2023.10101292|Machine Learning Algorithms;Heart Diseases;SVM;Decision Tree;Random Forest;Neural Networks;Fischer Score;Recursive Feature Elimination;Cross Validation;Heart;Support vector machines;Machine learning algorithms;Neural networks;Medical services;Predictive models;Feature extraction|
|[An Efficient framework for Metadata Extraction over Scholarly Documents using Ensemble CNN and BiLSTM Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101029)|P. Raghavendra Nayaka; R. Ranjan|10.1109/INOCON57975.2023.10101029|Scholarly data;Deep Neural Networks;Machine Learning;Algorithm Extraction;Metadata Extraction.;Deep learning;Machine learning algorithms;Software algorithms;Semantics;Neural networks;Metadata;Search engines|
|[An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101354)|P. Metipatil; P. Bhuvaneshwari; S. M. Basha; S. S. Patil|10.1109/INOCON57975.2023.10101354|Ensemble Machine Learning;Support Vector Machine;Naïve Bayes;Sequence Based Proteins;Cancer Gene Data;Support vector machines;Proteins;Technological innovation;DNA;Training data;Support vector machine classification;Machine learning|
|[Optimal Utilization of Water for Smart Farming Using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101227)|M. R. Barusu; P. N. Pavithra; P. S. R. Chandrika|10.1109/INOCON57975.2023.10101227|Internet of Things (IoT);Smart farming;ESP32;thingspeak;Smart agriculture;Cloud computing;Soil moisture;Moisture;Crops;Sensor systems;Real-time systems|
|[Early Detection of Alzheimer’s Disease Using SVM, Random Forest & FNN Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101298)|B. S. S. Varma; G. Kalyani; K. Asish; M. I. Bai|10.1109/INOCON57975.2023.10101298|Alzheimer’s disease (AD);machine learning model;dementia;medical data analysis;Support vector machines;Analytical models;Data analysis;Sociology;Predictive models;Data models;Alzheimer's disease|
|[Metal-Semiconductor – Metal structure on Graphene Doped ZnO Thin Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101294)|H. S. Kalyanapu; N. G. Vemuri; V. P. S. H. Rayapati; A. B. Yadav; G. P. Yella|10.1109/INOCON57975.2023.10101294|Nano Materials;ZnO/Graphene composite;1-vinyl 2-pyrrolidone;Sol-Gel method;polymers;Technological innovation;II-VI semiconductor materials;Graphene;Production;Zinc oxide;Nanomaterials;Thin film transistors|
|[Cumulative Summary List Driven Lightweight Frequent Closed High Utility Itemset Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101053)|S. Siva; S. Chaudhari|10.1109/INOCON57975.2023.10101053|Frequent Closed High-Utility Itemset Mining;Business Intelligence;Cumulative Summary List-Structure;Cumulative Utility List Structure;Lightweight HUIM;Technological innovation;Itemsets;Time measurement;Data mining;Business intelligence;Time factors;Synchronization|
|[Red Rot Disease Prediction in Sugarcane Using the Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101147)|V. Tanwar; S. Lamba; B. Sharma; A. Sharma|10.1109/INOCON57975.2023.10101147|Convolutional Neural Network and (CNN) hybrid;Red Rot;Sugarcane Diseases;Crop Disease;Deep learning;Technological innovation;Crops;Predictive models;Feature extraction;Cameras;Data models|
|[Prediction of Early Heart Attack Possibility Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100993)|K. Tn; S. C. P; M. S; A. Kodipalli; T. Rao; S. Kamal|10.1109/INOCON57975.2023.10100993|Coronary heart disease (CHD);feature selection;Diagnosis;Heart;Industries;Technological innovation;Cardiac disease;Predictive models;Data mining;Random forests|
|[Robust Multi-Bio-Metric Authentication Framework in Face and Iris recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100996)|S. M. R. Bagwan; G. Gupta; S. B.Thigale|10.1109/INOCON57975.2023.10100996|Multi-bio-metric;authentication;head mounted display;fuzzy logic classifier;computer vision;image process;Technological innovation;Biometrics (access control);Face recognition;Authentication;Color;Passwords;Safety|
|[Face, Iris, and Fingerprint based Robust Biometric Authentication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101122)|S. M. R. Bagwan; S. Kumar; S. B.Thigale|10.1109/INOCON57975.2023.10101122|Multi-bio-metric;authentication;head mounted display;fuzzy logic classifier;computer vision;image process;Support vector machines;Training;Technological innovation;Databases;Face recognition;Authentication;Fingerprint recognition|
|[Flower Classification Utilisizing Tensor Processing Unit Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101313)|K. S. Gill; A. Sharma; V. Anand; R. Gupta|10.1109/INOCON57975.2023.10101313|Convolutional Neural Network;Flower Classification;Adam Optimizer;Visualization;ResNet50 Model;Deep learning;Visualization;Tensors;Computational modeling;Transfer learning;Feature extraction;Object recognition|
|[Xception Model for Pneumothorax Classification using Chest X-ray Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101280)|R. Singh; A. Sharma; N. Sharma; R. Gupta|10.1109/INOCON57975.2023.10101280|pneumothorax;Deep learning;X-ray Images;Image Classification;collapsed lung;Xception model;Transfer learning;Training;Deep learning;Technological innovation;Image recognition;Predictive models;Prognostics and health management;Medical diagnostic imaging|
|[Brain Tumor Classification using VGG 16, ResNet50, and Inception V3 Transfer Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101252)|R. Pillai; A. Sharma; N. Sharma; R. Gupta|10.1109/INOCON57975.2023.10101252|Brain tumor;deep learning;transfer learning models;VGG16;InceptionV3;ResNet50;Deep learning;Technological innovation;Magnetic resonance imaging;Transfer learning;Brain modeling;Time-domain analysis;Time-varying systems|
|[Analysis and Evaluation of Medical Care Data using Analytic Fuzzy Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101089)|P. Batra; D. S. Sethi; K. Kandoi|10.1109/INOCON57975.2023.10101089|Medical Care Data;Analytical Fuzzy Process;and Internet of Medical Things;Technological innovation;Terminology;Wearable computers;Medical services;Analytic hierarchy process;Telecommunication traffic;Quality of service|
|[Three Level Password Authentication System using Cryptography Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101077)|I. Varshney; S. Sagar|10.1109/INOCON57975.2023.10101077|Three Level Password;Authentication System and Cryptography Approaches;Technological innovation;Online banking;Navigation;Face recognition;Authentication;Passwords;Companies|
|[Convolutional Neural Networks for Students Academic Performance Analaysis and Alerting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101291)|A. Khalid; A. Kumar|10.1109/INOCON57975.2023.10101291|ODL;LMS;Deep Learning;Deep learning;Technological innovation;Analytical models;Linear regression;Predictive models;Data models;Data mining|
|[Clustering Technique for Crime Rate Prediction and Warning to Users in Big Data Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101156)|K. Saxena; P. Shukla|10.1109/INOCON57975.2023.10101156|Crime;Numerous safety problem;Data mining;KNN (K-Nearest Neighbor) and Safe route;Training;Support vector machines;Technological innovation;Systematics;Face recognition;Prediction algorithms;Safety|
|[Secure Data Transmission using Cryptography for Internet of Things and Sensor Networks Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101069)|P. Chandani; M. Sharma|10.1109/INOCON57975.2023.10101069|Security;Privacy;Wireless Sensor Networks;Security requirements;and Security attacks;Wireless sensor networks;Technological innovation;Privacy;Authentication;Elliptic curve cryptography;Sensors;Error correction codes|
|[Customer Behavior Prediction using Deep Learning Techniques for Online Purchasing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101102)|Nisha; A. S. Singh|10.1109/INOCON57975.2023.10101102|Customer Behavior Prediction;Deep Learning Techniques;and Online Purchasing;Deep learning;Support vector machines;Technological innovation;Supervised learning;Sociology;Predictive models;Electronic commerce|
|[Multiple Agents based Disaster Prediction for Public Environments using Data Mining Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101148)|U. K. Malviya; S. P. S. Chauhan|10.1109/INOCON57975.2023.10101148|Geograpic data mining;Disasters;Detectors and Location estimators;Landslides;Earthquakes;Disaster management;Feature extraction;Real-time systems;Spatiotemporal phenomena;Data mining|
|[Leach Protocol with AODV based Data Transmission in Wireless Sensor Networks and IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101152)|B. Wadhwa; S. S. Yadav|10.1109/INOCON57975.2023.10101152|LEACH;IoT;Battery Capacity;Energy Efficient;and Throughput;Wireless sensor networks;Energy consumption;Technological innovation;Protocols;Limiting;Throughput;Energy efficiency|
|[RPL Routing Protocol for Data Transmisison in Internet of Things Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101305)|S. K. Nayak; S. Ghosh|10.1109/INOCON57975.2023.10101305|RPL Routing Protocol;Data Transmission;and Internet of Things Applications;Industries;Actuators;Technological innovation;Routing;Robustness;Routing protocols;Internet of Things|
|[Disaster Detection and Alert Generation for Smart City Scenario using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101261)|R. K. Singh; O. Prakash|10.1109/INOCON57975.2023.10101261|Detection and Alert Generation;Smart City;and IoT;Climate change;Smart cities;Internet of Things;Disasters;Monitoring;Deep learning|
|[Low Latency Consistency based Protocol for Fog Computing Systems using CoAP with Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101176)|S. Jha; D. Tripathy|10.1109/INOCON57975.2023.10101176|CoAP;Fog and Cloud computing;Cloud computing;Analytical models;Computational modeling;Medical services;Reinforcement learning;Inference algorithms;Delays|
|[Privacy and Security Control Approach for DDoS Attacks in Cyber Physical Systems using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101334)|A. P. Yadav; N. Mishra|10.1109/INOCON57975.2023.10101334|Privacy and Security;Control Approach;DDoS Attacks;Cyber Physical Systems;Deep Learning;Technological innovation;Security management;Software algorithms;Telecommunication traffic;Denial-of-service attack;Software;Regulation|
|[Cyber Attacks Detection and Prevention using Cryptography Algorithms for Industrial Automation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101087)|N. Chauhan; K. S. Kumar|10.1109/INOCON57975.2023.10101087|Cyber Attacks Detection;Prevention;Cryptography Algorithms;and Industrial Automation;Training;Automation;Industrial control;Frequency-domain analysis;Neural networks;Feature extraction;Robustness|
|[Renewable Energy Systems Energy Modeling using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101286)|S. P. Sharma; D. K. Yadav|10.1109/INOCON57975.2023.10101286|Convolutional neural network;Renewable energy;solar energy;and Deep learning;Deep learning;Renewable energy sources;Technological innovation;Predictive models;Complexity theory;Spatiotemporal phenomena;Smart grids|
|[Renewable Energy Systems Using Game Theory For Power Forecasting Energy Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101372)|P. Kumar; T. G. Kumar|10.1109/INOCON57975.2023.10101372|Energy Management;Stackelberg Game;Microgrid Energy Management;Wind Power Forecasting;Renewable energy sources;Technological innovation;Wind energy;Power supplies;Wind power generation;Predictive models;Numerical simulation|
|[Renewable Energy Storage Systems with Grid Connected Solar Using Multi-Objective Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101240)|P. Padamanabhan; R. Sharma|10.1109/INOCON57975.2023.10101240|Grid Connected Wind Energy;Photovoltaic Systems;Fuzzy Approach and Grid RES Assessment;Renewable energy sources;Technological innovation;Costs;Simulation;Mathematical models;Batteries;Optimization|
|[Intelligent Control and Multi-Level Inverter Design for Power Management using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101005)|A. Singh; K. S. Kaswan|10.1109/INOCON57975.2023.10101005|Multi-level inverter design;controller;renewable energy systems.;Training;Total harmonic distortion;Technological innovation;Switching frequency;Power system management;Rectifiers;Multilevel inverters|
|[Renewable Energy Systems with Improved Power Quality in Smart Grid using Fuzzy Logic Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101322)|R. K. C; R. K. Sharma|10.1109/INOCON57975.2023.10101322|Power Quality Improvement;Solar and Wind;Fuzzy Logic;PV-Wind Hybrid Renewable Energy Systems;Fuzzy logic;Renewable energy sources;Power filters;Technological innovation;Power quality;Wind power generation;Active filters|
|[An Innovation Development of Cost Wise Task Scheduling Model for Complex Transmission in Ultra Dense Cloud Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101059)|M. Arvindhan; H. Singh|10.1109/INOCON57975.2023.10101059|cost-wise;task scheduling;balancing;cost;maximize;ratio;priories tasks;Computers;Cloud computing;Technological innovation;Costs;Processor scheduling;Computational modeling;Neural networks|
|[Artificial Intelligence based Cyber Security Threats Identification in Financial Institutions Using Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100967)|D. Kumar; K. P. Kumar|10.1109/INOCON57975.2023.10100967|digital;cyber threats;financial institution;artificial intelligence;machine learning;Technological innovation;Machine learning algorithms;Phishing;Machine learning;Passwords;Prediction algorithms;Natural language processing|
|[The Smart Workflow Analysis Framework For Channel Allocation In Ultra Dense Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101079)|J. A. Blessy; D. K. Kushwaha|10.1109/INOCON57975.2023.10101079|cloud computing;local server;ubiquitous;convenient;on-demand;network access;Cloud computing;Technological innovation;Organizations;Reinforcement learning;Computer architecture;Channel allocation;Real-time systems|
|[The Performance Analysis of Network Security Management Model in High Speed Computer Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101329)|D. Damodharan; R. S. Kurmi|10.1109/INOCON57975.2023.10101329|technology;internet;communication;information;security;management;misuse;Analytical models;Firewalls (computing);Computational modeling;Authentication;Network security;Computer networks;Software|
#### **2023 International Conference on Emerging Smart Computing and Informatics (ESCI)**
- DOI: 10.1109/ESCI56872.2023
- DATE: 1-3 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Horizontal Max Pooling a Novel Approach for Noise Reduction in Max Pooling for Better Feature Detect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099648)|Y. More; K. Dumbre; B. Shiragapur|10.1109/ESCI56872.2023.10099648|Horizontal Max Pooling;HMax Pooling;Max Pooling;Pooling layer;Pooling operations;Convolutional Neural Network;Deep Neural Network;CNN;DNN;Deep CNN;Feature Extraction;Feature Extraction Algorithm;Feature Extraction Method;Image Feature Extraction;Image Features;Image Character;Deep learning;Convolution;Feature detection;Computational modeling;Neural networks;Noise reduction;Predictive models|
|[Improve Communication Skills using AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099941)|S. V. Jadhav; S. R. Shinde; D. K. Dalal; T. M. Deshpande; A. S. Dhakne; Y. M. Gaherwar|10.1109/ESCI56872.2023.10099941|Deep Learning;Artificial Neural Networks;Computer Vision;Natural Language Processing;Web Application;Industries;Deep learning;Modulation;Predictive models;Public speaking;Natural language processing;Artificial intelligence|
|[State of the Art Challenges and Technique for 5G and 6G using Software Defined network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099588)|S. Bendale; B. Gupta|10.1109/ESCI56872.2023.10099588|Software Defined network;Security;5G;6G;6G mobile communication;5G mobile communication;Computational modeling;Software;Security;History;Software defined networking|
|[Insight on Human Activity Recognition Using the Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099759)|S. S. Kulkarni; S. Jadhav|10.1109/ESCI56872.2023.10099759|CNN deep learning;Transfer Learning;RoI;Activity Recognition;Deep learning;Computational modeling;Transfer learning;Video sequences;Neural networks;Streaming media;Feature extraction|
|[Explicable AI for surveillance and interpretation of Coronavirus using X-ray imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099633)|T. Chauhan; S. Sonawane|10.1109/ESCI56872.2023.10099633|Explainable AI;Artificial intelligence;Black box systems;Transparent systems;Medical diagnosis;Chest X-ray images;COVID-19;Surveillance;Taxonomy;Pipelines;Closed box;Medical services;Reliability|
|[Cloud based Single Shot Detector Model for Speed Breaker Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099534)|S. Pawar; S. Nahar; M. D. Shaikh; V. Meher; S. Narwane|10.1109/ESCI56872.2023.10099534|Speed Breaker;Computer Vision;Deep Learning;Alert Application;Cloud Computing;Deep learning;Performance evaluation;Image segmentation;Roads;Lighting;Detectors;Cameras|
|[Predicting Risky Environment for Child Inside House using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100221)|N. Ahmad; S. Arya; D. Singh|10.1109/ESCI56872.2023.10100221|Deep Learning;Child Safety;Safe Playing;Computer Vision;Shallow CNN;Deep learning;Performance evaluation;Training;Computer vision;Pose estimation;Predictive models;Safety|
|[An Enhanced Intelligent Algorithm on Fault Location System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099804)|M. P. Shelke; A. Shaikh; S. Rai; M. S. Mujawar; D. Mulani|10.1109/ESCI56872.2023.10099804|Smart grids;fault location;support vector machine;genetic algorithm;Support vector machines;Power supplies;SCADA systems;Distribution networks;Fault location;Smart grids;Safety|
|[Student Engagement Prediction in MOOCs Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100007)|N. Ahmad; Z. Khan; D. Singh|10.1109/ESCI56872.2023.10100007|Deep Learning;Student Engagement;Engagement Prediction;MOOCs;Transfer Learning;Performance evaluation;Deep learning;Adaptation models;Cameras;Physiology;Sensors;Task analysis|
|[Performance analysis of VLC based Intelligent Transportation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099691)|M. Kumari|10.1109/ESCI56872.2023.10099691|visible light communication (VLC);orthogonal frequency division multiplexing (OFDM);light emitting diode (LED);red-green-blue (RGB);Avalanche photodiodes;Light emitting diodes;OFDM modulation;Pins;Performance analysis;Frequency division multiplexing;Informatics|
|[A Review on Traditional and Deep Learning based Object Detection Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099639)|B. R. Solunke; S. R. Gengaje|10.1109/ESCI56872.2023.10099639|Object detection;classification;Machine learning;Deep learning;CNN;Datasets;Training;Testing;Evaluation;Deep learning;Training;Technological innovation;Military computing;Object detection;Video surveillance;Security|
|[IOT Based Smart LandSlide Detection System (S-LDS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099562)|A. Amune; S. Patil; D. Ushir; A. Nangare|10.1109/ESCI56872.2023.10099562|Landslides;Frequency;IOT;NodeMCU;LEWS;Multiplexing;Landslides;Wireless sensor networks;Soil moisture;Voltage;Terrain factors;Internet of Things|
|[Real Time Face Recognition Based Attendance System using Multi Task Cascaded Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099879)|V. Chaudhari; S. Jain; R. Chaudhari; T. Chavan; P. Shahane|10.1109/ESCI56872.2023.10099879|Face detection;Face recognition;MTCNN;FaceNet;SVM;Support vector machines;Image quality;Image recognition;Target tracking;Face recognition;Heuristic algorithms;Systems operation|
|[Money Laundering Fraudulent Prediction Using Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099770)|A. Ahluwalia; I. Goyal; P. Bafna|10.1109/ESCI56872.2023.10099770|Money laundering;classifier;precision;data mining;Measurement;Subspace constraints;Prediction algorithms;Classification algorithms;Fraud;Macroeconomics;Informatics|
|[A Solution to finding Fruit Freshness Index using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100290)|S. A. Raja; T. R. V. Srivatsan; G. Maragatham|10.1109/ESCI56872.2023.10100290|Computer vision;histogram;k-means;dominant color;freshness index;Computer vision;Shape;Image color analysis;Manuals;Image capture;Feature extraction;Turning|
|[Analysis of Air Pollutants and its Inferences in Tamil Nadu](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100295)|S. Sasikala; S. R; R. D. D; C. D|10.1109/ESCI56872.2023.10100295|Air Pollution;WHO;Pollutants;Health;Big data;analytics;Machine Learning Technologies;Satellites;Sociology;Sea measurements;Machine learning;Big Data;Air pollution;Spatiotemporal phenomena|
|[Design of Hybrid Bioinspired Model For Multilane Departure Warning & Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100095)|R. A. Wakode; S. W. Mohod|10.1109/ESCI56872.2023.10100095|Lane Departure Warning System;Bioinspired;Ensemble;Biological system modeling;Roads;Computational modeling;Control systems;Real-time systems;Delays;Time factors|
|[Semantic Segmentation Based Leaf Disease Severity Estimation Using Deep Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099491)|R. A. Jamadar; A. Sharma|10.1109/ESCI56872.2023.10099491|Deep Learning;Leaf disease severity;Semantic segmentation;SegNet;Deep learning;Semantic segmentation;Memory management;Estimation;Manuals;Object recognition;Labeling|
|[Application of Cloud Computing in an Education Sector through Education and Learning as a Service and its Cost Benefit Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099586)|K. S. Sagale; M. D. Kokate; R. K. Agrawal|10.1109/ESCI56872.2023.10099586|Cloud Computing;Cloud Service Provider;ELaaS;Cost Benefit Analysis;CAGR;Computers;Cloud computing;Technological innovation;Scalability;Data security;Education;Decision making|
|[IoT Based Bio Shed for Agricultural Purpose](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099550)|V. Gaikwad; H. Mirgal; S. Kale; S. Jadhao; H. Khare; J. Singh|10.1109/ESCI56872.2023.10099550|Smart Bio-Shed;LM35;Rain Sensor;Arduino UNO;Temperature sensors;Temperature dependence;Rain;Temperature;Ultraviolet sources;Prototypes;Ventilation|
|[Threat Detection and Rescue using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100234)|H. R.; A. M.; A. Hameed|10.1109/ESCI56872.2023.10100234|LSTM;Anaconda Navigator;Jupyter Notebook;OpenCv’ TensorFlow;Navigation;Urban areas;Gesture recognition;Machine learning;Traffic control;Cameras;Software|
|[Web Based Book Recommendation System Using Collaborative Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099750)|K. Mankar; S. Pawar; H. Agarwal; T. Sangale; S. Kulkarni|10.1109/ESCI56872.2023.10099750|recommendation system;django;collaborative filtering;KNN;Training;Databases;Collaborative filtering;Machine learning;Computer architecture;Chatbots;Internet|
|[Improved Audio Filtration using IIR Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099677)|V. Gour; M. Gurnule; M. Kaif; A. Bherje; N. V. Bansode|10.1109/ESCI56872.2023.10099677|AWGN;FDA Tool;DSP hearing aid;frequency shaper;amplitude shaper;Filtration;Filtering;Auditory system;IIR filters;Digital signal processing;Hearing aids;Speech processing|
|[Smart Task Board: Complete web accessible solution to modern task management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100154)|S. M. Joshi; P. P. Joshi|10.1109/ESCI56872.2023.10100154|Computer vision;Machine learning;Regression;Web accessibility;Task management;Screen readers;Eye tracking;Visualization;Throughput;Web sites;Task analysis;Informatics;Monitoring;Guidelines|
|[Self- Regulated Robotic Illuminant Using NodeMcu(ESP8266)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099620)|V. Gaikwad; M. Doiphode; M. Gatke; G. Moona; C. Gavit; P. Ghuge|10.1109/ESCI56872.2023.10099620|IoT;Streetlight;NodeMcu;Smart city;Automation;Energy conservation;Energy consumption;Power demand;Microcontrollers;Lighting;Robot sensing systems;Energy efficiency;Servers|
|[Multi-Classification of Non-Proliferative Diabetic Retinopathy Through Integrated Machine Learning Approach in Fundus Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100091)|S. R; S. S; T. Raajaseharan|10.1109/ESCI56872.2023.10100091|machine learning;multi-level;multi-classification;non-proliferative-diabetic-retinopathy;integrated;Support vector machines;Visualization;Machine learning algorithms;Retinopathy;Feature extraction;Retina;Prediction algorithms|
|[A Comprehensive Review of Machine Learning for Financial Market Prediction Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099791)|R. M. Dhokane; O. P. Sharma|10.1109/ESCI56872.2023.10099791|Financial Market Prediction;Machine Learning (ML);Deep Learning (DL);Artificial Neural Networks (ANN);Support Vector Machines (SVM);Genetic Algorithm (GA);Support vector machines;Machine learning algorithms;Artificial neural networks;Prediction algorithms;Classification algorithms;Stock markets;Forecasting|
|[Smart Home Surveillance System Using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100088)|S. Sayyad; A. Momin; M. Shaikh; R. Mirajkar; P. Shelke|10.1109/ESCI56872.2023.10100088|Face Recognition;Cascade Classifier;Image Processing;Human Face;LBPH;Haar cascade;Privacy;Sleep;Law enforcement;Face recognition;Surveillance;Smart homes;Watches|
|[Bi-LSTM based Interdependent Prediction of Physiological Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099548)|P. Ghadekar; A. Bongulwar; A. Jadhav; R. Ahire; A. Dumbre; S. Ali|10.1109/ESCI56872.2023.10099548|ECG;PPG;Alignment;BiLSTM;Training;Filtering;Computational modeling;MIMICs;Electrocardiography;Predictive models;Data models|
|[An Optimal Approach to Vehicular CO2 Emissions Prediction using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099940)|S. Sahay; P. Pawar|10.1109/ESCI56872.2023.10099940|Vehicle Telematics;On-Board Diagnostics;OBD-II;LSTM;RNN;Deep Learning;CO2 Prediction;Climate change;Humanities;Computational modeling;Sensor phenomena and characterization;Data models;Sensor systems;Hazards|
|[Hybrid Gradient Boost based Heart Failure Prediction System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099903)|G. Athalye; A. Sarde; M. Badgujar; V. Gaikwad; S. Sondkar|10.1109/ESCI56872.2023.10099903|Machine learning;Feature detection;Cross-validation;Performance metrics;Heart Failure system;Hybrid Gradient boost;Heart;Obesity;Neural networks;Prediction algorithms;Lipidomics;Feature extraction;Reproducibility of results|
#### **2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)**
- DOI: 10.1109/GridEdge54130.2023
- DATE: 10-13 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[The Intelligent Grid: Integrating Data from All Sources for Use by All Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102736)|A. C. West|10.1109/GridEdge54130.2023.10102736|Cybersecurity;Data integration;IEC 61850;Remote access;nan|
|[Applying Edge Computing to Distribution Automation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102738)|R. Rodrigues; M. Razeghi-Jahromi; A. Melese; J. Stoupis|10.1109/GridEdge54130.2023.10102738|edge computing;data fusion;artificial intelligence;distribution automation;distributed intelligence;nan|
|[Improving Utility Cables Diagnostics and Prognostics using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102722)|S. Shekhar; S. Shekhar|10.1109/GridEdge54130.2023.10102722|Asset Management;Dielectric Cables;Deep Learning;Machine Learning;Partial Discharge;MATLAB;nan|
|[Tracking Periodic Voltage Sags via Synchrophasor Data in a Geographically Bounded Service Territory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102706)|X. Xu; C. Mishra; C. Wang; K. D. Jones; J. B. Starling; R. M. Gardner; L. Vanfretti|10.1109/GridEdge54130.2023.10102706|periodic voltage sags;power spectral density;spectral estimation analysis;spectrogram;synchrophasor data;nan|
|[Generator Aggregation and Power Grid Stability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102750)|J. M. Moloney; S. J. Williamson; C. L. Hall|10.1109/GridEdge54130.2023.10102750|power grid;stability;aggregation;nan|
|[Development of a NARX State-of-Charge Predictor based on Active Power Demand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102751)|A. Crain; E. Rebello; A. Sherwood; D. Jang|10.1109/GridEdge54130.2023.10102751|energy storage system;machine learning;modelling;NARX;neural network;state-of-charge prediction;nan|
|[Smart Electric Energy District (SEED): Analysis of a Senior Housing Campus in Pittsburgh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102723)|E. Cook; J. Valentine; K. Kelly-Pitou; S. Nguyen; W. Thai|10.1109/GridEdge54130.2023.10102723|Electrification;distributed energy resources;power grid;solar energy;nan|
|[Quantifying Transformer and Cable Degradation in Highly Renewable Electric Distribution Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102733)|W. Wang; R. Flores; G. Razeghi; J. Brouwer|10.1109/GridEdge54130.2023.10102733|DER;distribution transformer;electrification;infrastructure degradation;power cable;nan|
|[Power Flow Control Via Effective Dispatch of Modular FACTS Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102713)|S. Babaeinejadsarookolaee; T. Nudell; D. Schweer; M. Subramanian|10.1109/GridEdge54130.2023.10102713|optimal power flow control;power flow sensitivity;real time operation;FACTS device;nan|
|[Incremental Subgradient Method for EVs Smart Charging Flexibility in Wholesale Energy Markets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102709)|S. M. de Oca; P. Monzon; P. Belzarena|10.1109/GridEdge54130.2023.10102709|Demand Response;Electricity market;Smart Charge;Renewable Sources;nan|
|[EV Hosting Capacity and Voltage Unbalance: An Australian Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102721)|Y. Hou; J. Zhu; M. Z. Liu; W. J. Nacmanson; L. F. Ochoa|10.1109/GridEdge54130.2023.10102721|distribution networks;electric vehicle hosting capacity;Monte Carlo assessment;voltage unbalance factor;sensitivity diagram;nan|
|[Development, Demonstration, and Validation of Power Hardware-in-the-loop (PHIL) Testbed for DER Dynamics Integration in Southern California Edison (SCE)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102716)|M. Arifujjaman; R. Salas; A. Johnson; J. Araiza; F. Elyasichamazkoti; A. Momeni; S. Chuangpishit; F. Katiraei|10.1109/GridEdge54130.2023.10102716|Distributed energy resources (DERs);power hardware in the loop (PHIL);Real Time Digital Simulator (RTDS);photovoltaic (PV);inverter;protection system;nan|
|[Detection of Floating Neutral Condition in a Form 2S Electric Meter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102718)|I. V. Figueirido; L. P. Piyasinghe; L. R. Kremer|10.1109/GridEdge54130.2023.10102718|Advanced Metering Infrastructure;Data Acquisition System;edge computing;floating neutral;form 2S meter;nan|
|[Cost-benefit Analysis of Grid-Supportive Loads for Fast Frequency Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102707)|S. Subedi; M. Blonsky; Y. Son; B. Mather|10.1109/GridEdge54130.2023.10102707|Cost-benefit;fast frequency response;low-inertia;grid supportive loads;stability;nan|
|[Parallel Line Resonance Between Interagency Transmission Lines and the Effect on a De-Energized Line with Fixed Shunt Reactance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102730)|S. Ashmore; M. Majidi; M. Etezadi-Amoli|10.1109/GridEdge54130.2023.10102730|event analysis;shunt reactor;mutual coupling;zero sequence network;parallel line resonance;induced voltage;nan|
|[A Resilience-Driven Battery Energy Storage System Sizing Strategy for Grid Edge Radial Supplies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102731)|A. B. Nassif|10.1109/GridEdge54130.2023.10102731|Battery Energy Storage Systems;distribution planning;power system reliability;outage restoration;nan|
|[Modeling and Measurement of Load Rejection Overvoltage of Inverter-Based Resources Interconnected to Distribution Feeders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102705)|A. B. Nassif; K. A. Wheeler|10.1109/GridEdge54130.2023.10102705|Anti-Islanding Protection;Battery Energy Storage Systems;Distribution Systems;Load Rejection Overvoltage;nan|
|[Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102720)|L. Laurent; J. -S. Brouillon; G. Ferrari-Trecate|10.1109/GridEdge54130.2023.10102720|nan;nan|
|[Integration of a Smart Outlet-Based Plug Load Management System with a Building Automation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102749)|K. Chia; A. LeBar; V. Agarwal; M. Lee; J. Ikedo; J. Wolf; K. Trenbath; J. Kleissl|10.1109/GridEdge54130.2023.10102749|building automation system;commercial building;demand response;grid-interactive efficient buildings;plug load control;nan|
|[Effectively Managing Today’s Transformer Challenges for Increased Asset Reliability & Sustainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102732)|T. Hopkins|10.1109/GridEdge54130.2023.10102732|Hydrogen;transformer;reliability;sustainability;DGA;nan|
|[Emerging Coordinated Cyber-Physical-Systems Attacks and Adaptive Restoration Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102741)|M. Ali; X. Gao; A. Rahman; M. M. Hossain; W. Sun|10.1109/GridEdge54130.2023.10102741|Cyber-Physical-System;Multistage and Multiwave cyber-physical attacks;Smart grids;nan|
|[Volt-VAr Optimization of PV Smart Inverters in Unbalanced Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102744)|Z. Soltani; S. Ma; M. Ghaljehei; M. Khorsand|10.1109/GridEdge54130.2023.10102744|AC optimal power flow;distributed energy resources (DERs);PV smart inverters;Volt-VAr controller;nan|
|[Time Series Classification for Detecting Fault Location in a DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102748)|S. T. Ojetola; M. J. Reno|10.1109/GridEdge54130.2023.10102748|DC Microgrids;fault detection;machine learning;support vector machines;neural network;decision tree;nan|
|[Effective Microgrid Optimal Dispatch Settings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102729)|M. Farajollahi; A. Mojallal; M. R. Dadash Zadeh|10.1109/GridEdge54130.2023.10102729|microgrid;optimal dispatch;penalty factor;nan|
|[Real-Time T&D Co-Simulation for Testing Grid Impact of High DER Participation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102714)|V. Paduani; R. Kadavil; H. Hooshyar; A. Haddadi; A. Jakaria; A. Huque|10.1109/GridEdge54130.2023.10102714|Co-simulation;DER;Grid services;real-time simulation;transient stability;nan|
|[A scalable method for probabilistic short-term forecasting of individual households consumption in low voltage grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102724)|L. Botman; J. Lago; T. Becker; O. M. Agudelo; K. Vanthournout; B. De Moor|10.1109/GridEdge54130.2023.10102724|Short-Term Probabilistic Load Forecasting;Smart Meter;Household Consumption;Low Voltage Grid;nan|
|[Adaptive Chance Constrained MPC under Load and PV Forecast Uncertainties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102711)|A. Ghosh; C. Cortes-Aguirre; Y. -A. Chen; A. Khurram; J. Kleissl|10.1109/GridEdge54130.2023.10102711|MPC;chance constraints;forecast uncertainty;nan|
|[Estimating the Output of Behind the Meter Solar Farms by Breaking Irradiance Data into its Diffuse and Direct Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102742)|C. Ozatalar; R. Ahmad; P. Pambuh; H. Shah|10.1109/GridEdge54130.2023.10102742|Extraterrestrial Solar Radiation;Diffuse Radiation;Direct (beam) Radiation;Liu-Jordan model;Behind-the-Meter-Solar;nan|
|[Grid-Edge Dynamic Volt-VAr Control Solution to Mitigate System Impacts Caused by Vast EV Charging Infrastructure Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102743)|R. Karandeh; H. Chun; D. Tholomier|10.1109/GridEdge54130.2023.10102743|charging infrastructure;dynamic VAr Control;electric vehicle;grid-edge;non-wires alternative;voltage support;secondary circuit;nan|
|[A Novel Resilience-Oriented Cellular Grid Formation Approach for Distribution Systems with Behind-the-Meter Distributed Energy Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102719)|U. Kumar; F. Ding|10.1109/GridEdge54130.2023.10102719|distributed energy resource;self-organizing maps;grid resilience;nan|
|[Safety, Reliability and Efficiency – beyond the grid edge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102704)|H. Borland; M. McCormack|10.1109/GridEdge54130.2023.10102704|Continuity;efficiency;fault location;safety;reliability;wildfires;nan|
#### **2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)**
- DOI: 10.1109/PERCOM56429.2023
- DATE: 13-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Panel: Sustainability in Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099249)|G. Singh; G. D. Abowd; A. A. Chien; A. Jones; B. Islam; R. Dahiya; J. Hester|10.1109/PERCOM56429.2023.10099249|nan;Computer science;Sustainable development;Robot sensing systems;Computer architecture;Wearable computers;Ubiquitous computing;US Government|
|[HeadMon: Head Dynamics Enabled Riding Maneuver Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099215)|Z. Han; L. Xu; X. Dong; Y. Nishiyama; K. Sezaki|10.1109/PERCOM56429.2023.10099215|Human Activity Prediction;Head Movement;Mobile Computing;Pervasive computing;Measurement units;Head;Urban areas;Deep architecture;Prototypes;Inertial navigation|
|[CHAR: Composite Head-body Activities Recognition with A Single Earable Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099218)|P. Zhu; Y. Zou; W. Li; K. Wu|10.1109/PERCOM56429.2023.10099218|Composite activity recognition;Earable device;Multi-task learning;Pervasive computing;Performance evaluation;Legged locomotion;Headphones;Prototypes;Activity recognition;Multitasking|
|[DroneVLC: Exploiting Drones and VLC to Gather Data from Batteryless Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099247)|L. De Groot; T. Xu; M. Z. Zamalloa|10.1109/PERCOM56429.2023.10099247|Visible Light Communication;Backscatter;Bat-teryless;Drones;UAV;Dynamic Channel;Radio frequency;Frequency modulation;Photovoltaic cells;Lighting;Prototypes;Reliability;Standards|
|[PreActo: Efficient Cross-Camera Object Tracking System in Video Analytics Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099298)|T. -T. Nguyen; S. Y. Jang; B. Kostadinov; D. Lee|10.1109/PERCOM56429.2023.10099298|Edge Computing;Machine Learning;Object Tracking;Trajectory learning;Target tracking;Visual analytics;Collaboration;Streaming media;Cameras;Real-time systems;Trajectory|
|[Device-Free Multi-Person Indoor Localization Using the Change of ToF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099384)|A. Nomura; M. Sugasaki; K. Tsubouchi; N. Nishio; M. Shimosaka|10.1109/PERCOM56429.2023.10099384|Wi-Fi;UWB;Round Trip Time;Time of Flight;device-free indoor localization;indoor positioning;density estimation;Location awareness;Estimation;Training data;Receivers;Distance measurement;Data models;Transceivers|
|[hEARt: Motion-resilient Heart Rate Monitoring with In-ear Microphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099317)|K. -J. Butkow; T. Dang; A. Ferlini; D. Ma; C. Mascolo|10.1109/PERCOM56429.2023.10099317|earable;heart rate;motion artefact;in-ear audio;Heart rate;Legged locomotion;Deep learning;Irrigation;Wearable computers;Estimation;Ear|
|[Joint Estimation of the Distance and Relative Velocity of Obstacles via Smartphone Active Sound Sensing for Pedestrian Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099353)|T. Dissanayake; T. Maekawa; T. Hara|10.1109/PERCOM56429.2023.10099353|Active sound sensing;pedestrian safety;obstacles;Pervasive computing;Neural networks;Estimation;Computer architecture;Predictive models;Sensors;Safety|
|[Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099368)|M. Cominelli; F. Gringoli; F. Restuccia|10.1109/PERCOM56429.2023.10099368|Device-free sensing;Channel State Information;Wi-Fi 6;nan|
|[Highly Responsive Batteryless System for Indoor Light Energy Harvesting Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099205)|D. Kim; J. Ahn; H. Lee; H. Cha|10.1109/PERCOM56429.2023.10099205|Batteryless systems;energy-harvesting;nan|
|[GFL: Federated Learning on Non-IID Data via Privacy-Preserving Synthetic Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099110)|Y. Cheng; L. Zhang; A. Li|10.1109/PERCOM56429.2023.10099110|Federated Learning;Non-IID;Membership In-ference Attack;nan|
|[EMGSense: A Low-Effort Self-Supervised Domain Adaptation Framework for EMG Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099164)|D. Duan; H. Yang; G. Lan; T. Li; X. Jia; W. Xu|10.1109/PERCOM56429.2023.10099164|EMG sensing;biological heterogeneity;domain adaptation;self-supervised learning;Degradation;Training;Pervasive computing;Deep learning;Supervised learning;Neural networks;Gesture recognition|
|[SEEK: Detecting GPS Spoofing via a Sequential Dashcam-Based Vehicle Localization Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099105)|P. Jiang; H. Wu; Y. Zhao; D. Zhao; C. Xin|10.1109/PERCOM56429.2023.10099105|GPs spoofing;deep learning;vehicle localization’ image matching;Location awareness;Pervasive computing;Performance evaluation;Computer vision;Image matching;Lighting;Transportation|
|[Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099061)|M. Ohno; R. Ukyo; T. Amano; H. Rizk; H. Yamaguchi|10.1109/PERCOM56429.2023.10099061|Point cloud-based recognition;LiDAR;Privacy-preserving;Re-ID;Pedestrian tracking;Point cloud compression;Pervasive computing;Adaptation models;Privacy;Laser radar;Three-dimensional displays;Feature extraction|
|[mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099264)|A. Sen; A. Mandal; P. Karmakar; A. Das; S. Chakraborty|10.1109/PERCOM56429.2023.10099264|Dangerous Driving Behaviors;mmWave sensing;Pervasive computing;Visualization;Radar measurements;Radar detection;Radar;Sensors;Safety|
|[Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099197)|H. Haresamudram; I. Essa; T. Plötz|10.1109/PERCOM56429.2023.10099197|self-supervised learning;human activity recog-nition;contrastive learning;Representation learning;Convolutional codes;Annotations;Wearable computers;Supervised learning;Predictive coding;Data collection|
|[ALAE-TAE-CutMix+: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099138)|N. Ahmad; H. -f. Leung|10.1109/PERCOM56429.2023.10099138|ubiquitous computing;activity recognition;deep learning;attention;wearable sensors;data augmentation;Statistical analysis;Neural networks;Sensor phenomena and characterization;Benchmark testing;Ubiquitous computing;Data models;Sensors|
|[MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099301)|Z. Wu; Q. Wan; M. Zhao; Y. Ke; Y. Fang; Z. Liang; F. Xie; J. Cheng|10.1109/PERCOM56429.2023.10099301|Pressure image;body weight estimation;contrastive learning;Pervasive computing;Drugs;Privacy;Estimation;Lighting;Feature extraction;Physiology|
|[An Intent-based Framework for Vehicular Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099081)|T. He; A. N. Toosi; N. Akbari; M. T. Islam; M. A. Cheema|10.1109/PERCOM56429.2023.10099081|Vehicular Edge Computing;Intent-based Net-working;Software-Defined Networking;Resource Management;Virtual Network Embedding;Pervasive computing;Prototypes;Quality of service;Resource management;Vehicle dynamics;Task analysis;Software defined networking|
|[Learning the world from its words: Anchor-agnostic Transformers for Fingerprint-based Indoor Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099376)|S. M. Nguyen; D. V. Le; P. J. M. Havinga|10.1109/PERCOM56429.2023.10099376|Transformer;Self-Attention;CNNs;Indoor Localization;Indoor positioning;Deep Learning;Location awareness;Representation learning;Pervasive computing;Sensitivity;Fingerprint recognition;Transformers;Multitasking|
|[Ortho-CodeA: Orthogonal Codes Assisted Backscatter Multiple Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099216)|W. Wu; A. Hawbani; W. Gong|10.1109/PERCOM56429.2023.10099216|Internet of Things;backscatter multiple access;concurrent transmissions;spread spectrum;orthogonal codes;nan|
|[Characterisation of Wearable Electric-Field Communication Link for BAN Multimedia Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099133)|R. Cobden; M. Zeeshan; D. Roggen; R. J. Prance; A. Pouryazdan|10.1109/PERCOM56429.2023.10099133|Electric-Field;Capacitive-Communication;Body Area Network;Electrodes;Wireless communication;Wireless sensor networks;Wearable computers;Bit error rate;Radio transmitters;Receivers|
|[Zone-based Federated Learning for Mobile Sensing Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099308)|X. Jiang; T. On; N. Phan; H. Mohammadi; V. D. Mayyuri; A. Chen; R. Jin; C. Borcea|10.1109/PERCOM56429.2023.10099308|federated learning;smart phones;mobile sensing;edge computing;Training;Heart rate;Adaptation models;Data privacy;Federated learning;Systems architecture;Data models|
|[BrainNet: Improving Brainwave-based Biometric Recognition with Siamese Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099367)|M. Fallahi; T. Strufe; P. Arias-Cabarcos|10.1109/PERCOM56429.2023.10099367|brain biometrics;user authentication;computer security;electroencephalogram (EEG);Brain;Biometrics (access control);Filtering;Error analysis;Wearable computers;Current measurement;Passwords|
|[BumbleBee: Enabling the Vision of Pervasive ZigBee Backscatter Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099287)|Z. Xu; W. Gong|10.1109/PERCOM56429.2023.10099287|System;IoT;Backscatter;ZigBee;BLE;Radio frequency;Pervasive computing;Meters;Radio transmitters;Zigbee;Prototypes;Receivers|
|[DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099058)|D. Das; S. Chakraborty; B. Mitra|10.1109/PERCOM56429.2023.10099058|Driving behavior;spatial events;context analysis;Pervasive computing;Fluctuations;Behavioral sciences;Spatiotemporal phenomena;Safety;Task analysis;Intelligent systems|
|[Serverledge: Decentralized Function-as-a-Service for the Edge-Cloud Continuum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099372)|G. R. Russo; T. Mannucci; V. Cardellini; F. L. Presti|10.1109/PERCOM56429.2023.10099372|serverless;edge computing;offloading;Pervasive computing;Scalability;Decentralized control;Computer architecture|
|[Scheduled Spatial Sensing against Adversarial WiFi Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099079)|S. M. Hernandez; E. Bulut|10.1109/PERCOM56429.2023.10099079|WiFi sensing;security and privacy;human activity detection;Performance evaluation;Schedules;Transmitting antennas;Receiving antennas;Predictive models;Data models;Reflection|
|[Keynote: AI for Scientific Discovery and a Sustainable Future](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099237)|C. P. Gomes|10.1109/PERCOM56429.2023.10099237|nan;Artificial intelligence;Sustainable development;Cognition;Computer science;Pervasive computing;Optimization;Deep learning|
|[Keynote: Rising to the Challenge of Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099203)|R. Rajkumar|10.1109/PERCOM56429.2023.10099203|nan;Autonomous vehicles;Real-time systems;Pervasive computing;Connected vehicles;Wireless sensor networks;Transportation;Software|
|[Panel: Sustainability in Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099178)|G. Singh; G. Abowd; A. Chien; A. Jones; B. Islam; R. Dahiya; J. Hester|10.1109/PERCOM56429.2023.10099178|nan;Computer science;Sustainable development;Robot sensing systems;Computer architecture;Wearable computers;Ubiquitous computing;Sensors|
#### **2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)**
- DOI: 10.1109/CCWC57344.2023
- DATE: 8-11 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Coding Translation to Increase the Efficiency of Programmatic Data Analyses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099092)|D. Shilane|10.1109/CCWC57344.2023.10099092|programmatic data analysis;natural language processing;metaprogramming;interpreted programming languages;run-time efficiency;R language;Data analysis;Conferences;Software algorithms;Syntactics;Encoding;Software|
|[MGM for Aggregated General Distribution Queuing System in discrete time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099304)|T. Hoshiyama|10.1109/CCWC57344.2023.10099304|GI/G/s;Matrix-Geometric Method;Aggregation method;Performance evaluation;Gamma distribution;Adaptation models;Analytical models;Operations research;Production;Markov processes|
|[Radar Target Identification Using Multiclass Sparse Centroids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099137)|I. Jouny|10.1109/CCWC57344.2023.10099137|Radar target identification;sparse centroid classifier;Training;Target recognition;Conferences;Fitting;Training data;Radar;Feature extraction|
|[Parallel Computing of the FFT and of Spectral Filtering with Few to No Complex-Valued Operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099123)|D. H. Mugler|10.1109/CCWC57344.2023.10099123|parallel computing;FFT;parallel filtering;DCTIV;speedup;OFDM;Program processors;Filtering;Fast Fourier transforms;Frequency-domain analysis;OFDM;Conferences;Transforms|
|[Is the residential sector ready for prescriptive maintenance? A short analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099060)|G. Tzitziou; A. Dimara; A. Papaioannou; S. Krinidis; C. -N. Anagnostopoulos; D. Ioannidis; D. Tzovaras|10.1109/CCWC57344.2023.10099060|Maintenance;Prescriptive maintenance;residen-tial sector;prescriptions;maintenance good practices;Home appliances;Energy consumption;Costs;Conferences;Energy measurement;Maintenance engineering;Energy efficiency|
|[Distributed D2D Resource Allocation for Reducing Interference in Ultra-Dense Networks With Generic Imperfection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099049)|R. M. Radaydeh|10.1109/CCWC57344.2023.10099049|D2D networks;universal reuse ratio;co-channel interference minimization;deficient resource allocation;outage performance;array processing;interference cancelation;Performance evaluation;Transmitters;Aggregates;Receivers;Minimization;Device-to-device communication;Resource management|
|[Performance analysis of self-learning forwarding algorithms for Vehicle-to-Vehicle networks on Named Data Networking (NDN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099191)|S. R. Melati; L. V. Yovita; R. Mayasari|10.1109/CCWC57344.2023.10099191|VANET;NDN;Forwarding Strategy;Self-learning Forwarding;Wireless communication;Roads;Road vehicles;Vehicular ad hoc networks;Throughput;Routing;Routing protocols|
|[Electric Vehicle (EV) Preventive Diagnostic System: Solution for Thermal Management of Battery packs using AIOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099185)|L. Kumar; D. Choudhury; A. R. Paduri; S. Kumar; D. Sahoo; J. Murthy; N. Darapaneni|10.1109/CCWC57344.2023.10099185|AIOT;Li-ion batteries;Artificial Neural Network (ANN);Big data analytics;vehicle preventive maintenance;Temperature sensors;Temperature measurement;Temperature dependence;Temperature;Thermal management;Electric vehicles;Batteries|
|[Career Support Platform for Older Adults Powered by AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099112)|A. Jain; S. Durairaj; A. R. Paduri; P. Krishnan; P. Chalaiah; J. Chanda; N. Darapaneni|10.1109/CCWC57344.2023.10099112|Artificial Intelligence;Career guidance;older adults;Random Forest;NLP;Recommendation Engine;Engineering profession;Sociology;Focusing;Medical services;Forestry;Statistics;Older adults|
|[Watson-Crick grammar and its label languages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099063)|P. Paul; S. Ghosh; A. Mandal|10.1109/CCWC57344.2023.10099063|Label language;Szilard languages;Watson-Crick grammar;Turing recognizable language;Biotechnology;Computational modeling;Conferences;Biological system modeling;DNA;Parallel processing;Grammar|
|[Investigating the Performance of Steepest Descent, Newton-Raphson, and Fletcher-Reeves Approaches in Unconstrained Minimization Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099341)|M. S. Hossain; M. A. Simaan|10.1109/CCWC57344.2023.10099341|unconstrained minimization;Rosenbrock function;Steepest descent algorithm;Newton-Raphson algorithm;Fletcher- Reeves algorithm;Measurement;Conferences;Benchmark testing;Minimization;Trajectory;Performance analysis;Newton method|
|[A Smart Solution to Identify an Efficient Delivery Route Involving Several Destinations and Drivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099365)|S. Das|10.1109/CCWC57344.2023.10099365|Computational Intelligence;Systems and Software Engineering;Algorithms and Theory;Smart Embedded Com-puting;Real-Time Systems;High-Performance Computing;Runtime;Conferences;Companies;Software;Distance measurement;Internet;Resource management|
|[Face Recognition Based on Point Cloud Data Captured by Low-cost mmWave Radar Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099235)|Y. Zhong; C. Yuan; Y. Zou; H. Yao|10.1109/CCWC57344.2023.10099235|mmWave radar;face recognition;deep learning;PointNet;Point cloud compression;Performance evaluation;Face recognition;Radar;Sensor phenomena and characterization;Radar imaging;Radar antennas|
|[Autonomous Vehicle Driving Path Control with Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099122)|T. Tiong; I. Saad; K. T. K. Teo; H. b. Lago|10.1109/CCWC57344.2023.10099122|autonomous vehicle;deep reinforcement learning;deep deterministic policy gradient;mini-batch size;actor learning rate;Training;Deep learning;Space vehicles;Roads;Conferences;Reinforcement learning;Lead|
|[Process Proportional-Integral PI Control with Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099286)|T. Tiong; I. Saad; K. T. K. Teo; H. Bin Lago|10.1109/CCWC57344.2023.10099286|deep reinforcement learning;actor-critic;twin-delayed DDPG;process control;proportional-integral control;Industries;Deep learning;Training;Adaptation models;Computational modeling;Process control;Reinforcement learning|
|[Reinforcement Learning for a Raspberry Pi Smart Car for Lane Detection Using OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099118)|M. Nguyen; T. K. Mohd|10.1109/CCWC57344.2023.10099118|Raspberry-pi;vision;Smart Car;Image processing;Lane Detection;Performance evaluation;Deep learning;Lane detection;Prototypes;Reinforcement learning;Real-time systems;Nanoscale devices|
|[Investigating The Usability Issues In Mobile Applications Reviews Using A Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099350)|S. Alagmdi; A. Albanyan; S. Ludi|10.1109/CCWC57344.2023.10099350|Usability;Deep Learning;Human Computer Interaction;Deep learning;Analytical models;Conferences;Computational modeling;Companies;Cognition;Mobile applications|
|[Hybrid Quantum-Classical Machine Learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging Sensor Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099338)|S. Fadli; B. S. Rawal|10.1109/CCWC57344.2023.10099338|hybrid quantum-classical machine learning architecture;quantum neural networks;integrated quantum 11 earth;storm detection;lightning-atmosphere interaction;Training;Quantum computing;Neural networks;Lightning;Imaging;Computer architecture;Quantum state|
|[Optimizing Edge-Cloud Synergy for Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099258)|R. Singh; N. Kumar|10.1109/CCWC57344.2023.10099258|Application-level orchestration;Computational complexity;WAN;WLAN;Edge-Cloud synergy;Wide area networks;Wireless LAN;5G mobile communication;Conferences;Big Data;Software;Delays|
|[The Link Between a User's Religion and Mis/Disinformation Vulnerability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099194)|K. M. Caramancion|10.1109/CCWC57344.2023.10099194|Fake News;Misinformation;Disinformation;Information Warfare;Cyber Deception;Conferences;Digital forensics;Fake news;Information integrity|
|[Blockchain Development in Colab: An Ethereum-Based Bicycle Registry System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099050)|W. Downing; D. Harvey; D. Wagura; Y. Shi|10.1109/CCWC57344.2023.10099050|Blockchain;Colaboratory;Smart Contract;Ethereum;Storms;Social networking (online);Conferences;Smart contracts;Public key;Bicycles;Hardware|
|[Multi-Source Pandemic Data Visualization and Synchronization for Information Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099327)|Q. Zhang; J. Brokaw|10.1109/CCWC57344.2023.10099327|Synchronization;Data-Driven Documents (D3);dynamic visualization;data analysis;information extraction;feature enhancement;COVID-19;Text analysis;Pandemics;Sociology;Software algorithms;Data visualization;Web pages|
|[Analyzing Multimodal Datasets for Detecting Online COVID Misinformation: A Preliminary Survey Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099147)|A. Chattopadhyay; N. Beyene; S. Rana|10.1109/CCWC57344.2023.10099147|COVID;misinformation;detection;multimodal;datasets;online web content;textual;visual;cues;image infographic;COVID-19;Visualization;Social networking (online);Pandemics;Conferences;Taxonomy;Organizations|
|[EEG and fNIRS Analysis to Determine Acute Stress Resulting From Reaction Time Tests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099193)|J. De La Cruz; J. Law; N. -K. Oteng-Quarshie; K. George|10.1109/CCWC57344.2023.10099193|Electroencephalogram (EEG);Functional Near-Infrared Spectroscopy (fNIRS);Heart Rate Variability (HRV) Brain-Computer Interface (BCI);K-Nearest Neighbor (KNN);Brain modeling;Electroencephalography;Mathematical models;Data models;Classification algorithms;Reliability;Functional near-infrared spectroscopy|
|[Using Reinforcement Learning to Train Generative Adversarial Networks for Image Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099199)|A. Nguyen; R. Jin|10.1109/CCWC57344.2023.10099199|Reinforcement Learning;Generative Adversarial Networks;Image Generation;Deep learning;Image synthesis;Conferences;Computational modeling;Neural networks;Reinforcement learning;Generative adversarial networks|
|[An Enhanced Segmentation and Deep Learning Architecture for Early Diabetic Retinopathy Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099069)|R. R. Maaliw; Z. P. Mabunga; M. R. D. De Veluz; A. S. Alon; A. C. Lagman; M. B. Garcia; L. L. Lacatan; R. M. Dellosa|10.1109/CCWC57344.2023.10099069|attention-aware DCNN;atrous spatial pyramid pooling;blindness;fundus images;lesion detection;ophthalmology;Deep learning;Image segmentation;Retinopathy;Pipelines;Feature extraction;Diabetes;Lesions|
|[Explainable Neural Network Recognition of Handwritten Characters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099288)|P. Whitten; F. Wolff; C. Papachristou|10.1109/CCWC57344.2023.10099288|Explainable Artificial Intelligence;XAI;Neural Network;Machine Learning;Training Set Pruning;Training;Measurement;Handwriting recognition;Visualization;Training data;Computer architecture;Artificial neural networks|
|[A systematic review on artificial intelligence models applied to prediction in finance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099222)|O. Hijazi; K. Tikito; K. Ouazzani-Touhami|10.1109/CCWC57344.2023.10099222|Financial market;finance;prediction;artificial intelligence;forecasting;deep learning;Systematics;Instruments;Computational modeling;Conferences;Finance;Learning (artificial intelligence);Predictive models|
|[Deep Learning Applications in Brain Computer Interface Based Lie Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099109)|M. A. Khalil; J. Can; K. George|10.1109/CCWC57344.2023.10099109|Electroencephalography (EEG);Functional near-infrared spectroscopy (fNIRS);Heart Rate Variability (HRV);Blood Oxygen Saturation;Deep Learning;Brain Computer Interface (BCI);Deep learning;Training;Oxygen;Computational modeling;Brain modeling;Electroencephalography;Brain-computer interfaces|
|[Development of A Smart Non-Invasive Glucose Monitoring System With SpO2 and BPM for Diabetic Patient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099236)|K. A. M. Nabil; M. A. Islam; A. A. Noman; M. M. Khan|10.1109/CCWC57344.2023.10099236|NIR LED;ESP32;Glucometer;SpO2;Healthcare;COVID-19;Heart beat;Pulse measurements;Wavelength measurement;Light emitting diodes;Liquid crystal displays;Glucose|
|[Smart Supply Chain Management with Attribute-Based Encryption Access Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099268)|H. Shittu; M. Nabil|10.1109/CCWC57344.2023.10099268|Smart Supply Chain;Blockchain;Smart Contract;Raspberry Pi;RFID;Brownie;Solidity;Python;Vyper;Access control;Supply chain management;Costs;Supply chains;Smart contracts;Sensor phenomena and characterization;Decentralized applications|
|[Latency and Reliability Aware SDN Controller: A Role Delegation Function as a Service](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099225)|D. Wobuyaga; S. T. Arzo; H. Kumar; F. Granelli; M. Devetsikiotis|10.1109/CCWC57344.2023.10099225|Software Defined Networks;Control Plane;OpenFlow;Role Delegation;Performance evaluation;Conferences;Emulation;Microservice architectures;Computer architecture;Ultra reliable low latency communication;Network function virtualization|
|[Higher Order Sliding Mode Control for Speed Control of BLDC Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099280)|Z. Alqarni|10.1109/CCWC57344.2023.10099280|Brushless DC Motor;Higher Order Sliding Mode Control;Sliding Surface;Speed Control;Robust control;Torque;Brushless DC motors;Service robots;Simulation;Velocity control;Reliability theory|
|[FPGA-Based Digital FIR Filters With Small Coefficients and Large Data Input](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099131)|N. Chabini; R. Beguenane|10.1109/CCWC57344.2023.10099131|FIR;FPGA;Large Size;DSP;Multiplier;VHDL;Finite impulse response filters;Conferences;Logic gates;Encoding;Arrays;Field programmable gate arrays|
|[Incorporating an Integrated Software System for Stroke Prediction using Machine Learning Algorithms and Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099245)|M. J. Uddin Chowdhury; A. Hussan; D. A. Islam Hridoy; A. S. Sikder|10.1109/CCWC57344.2023.10099245|Stroke;Logistic Regression;Naï ve Bayes;Support Vector Machine;Decision Tree;Random Forest;K-Nearest Neighbors;Artificial Neural Network;Support vector machines;Visualization;Machine learning algorithms;Software algorithms;Artificial neural networks;Stroke (medical condition);Prediction algorithms|
|[Generatively Augmented Neural Network Watchdog for Image Classification Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099329)|J. M. Bui; G. A. Amigo; R. J. Marks|10.1109/CCWC57344.2023.10099329|Neural Network Applications;Neural Network Architectures;Classifier;Image Classifier;Image coding;Filtering;Conferences;Neural networks;Training data;Dogs;Reliability|
|[Sensor Tracking System using Radar and Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099126)|C. M. Melgoza; K. George; J. Miho|10.1109/CCWC57344.2023.10099126|Radar;Transceiver;HB100;Jetson TX2;Parallel Programming;Computer Vision;YOLOv3;Object Detection;System Latency;Average Classification Accuracy;Computer vision;Surveillance;Radar detection;Object detection;Radar tracking;Hardware;Software|
|[Image Segmentation and Anomaly Detection using Doppler Data from Coffee-Can Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099309)|C. M. Melgoza; K. George; J. Miho|10.1109/CCWC57344.2023.10099309|Coffee-Can Radar;Doppler;Signal Processing;K-Means Clustering;Image Segmentation;Anomaly Detection;RX Detector;RX Score;Confidence Coefficient Signal-to-Noise Ratio;MATLAB;Radio frequency;Image segmentation;Computer vision;Radar detection;Radar;Detectors;Radar imaging|
|[Inductive Link Prediction Banking Fraud Detection System Using Homogeneous Graph-Based Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099180)|H. A. Bukhori; R. Munir|10.1109/CCWC57344.2023.10099180|banking fraud detection;graph-based fraud detection;classification;tree-based fraud detection;inference time;Computational modeling;Conferences;Machine learning;Banking;Predictive models;Fraud|
|[Camera with Artificial Intelligence of Things (AIoT) Technology for Wildlife Camera Trap System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099252)|H. -C. Huang; C. -H. Lin; J. Liu|10.1109/CCWC57344.2023.10099252|Edge AI;IoT;AIoT;Machine Learning;Image recognition;Wildlife;Cameras;Software;Real-time systems;Recording;Internet of Things|
|[Road Curb Detection Based on a Deep Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099233)|M. Zou; Y. Kageyama|10.1109/CCWC57344.2023.10099233|Road curb;autonomous driving;convolutional neural network;driving assistance;deep learning;Deep learning;Location awareness;Roads;Detectors;Predictive models;Feature extraction;Distance measurement|
|[Design of an Automated, Digital Water Tank Heating System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099095)|M. Njovana|10.1109/CCWC57344.2023.10099095|Automated;digital;CPLD;water heating;Temperature sensors;Temperature measurement;Water heating;Switches;Control systems;Valves;Timing|
|[Metal-Organic Frameworks and Porphyrins for Water Filtration Using Density Functional Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099165)|R. Kyung; I. H. Choi|10.1109/CCWC57344.2023.10099165|Water Filtration;Reactivity;Polarity;Nanoparticles;Metal-organic frameworks(MOFs);Porphyrins;Density Functional Theory (DFT);Optimization energies;LUMO/HOMO;Nanoparticles;Water;Analytical models;Thermodynamics;Filtration;Adsorption;Computational modeling|
|[Implementing Privacy on Public Digital Displays Using Smart Glasses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099103)|A. Cabrera; A. Rider; J. Xiong; H. El-Razouk|10.1109/CCWC57344.2023.10099103|Augmented Reality;Cryptography;Privacy;Smart Glasses;Privacy;Visualization;Online banking;Prototypes;Public key;Glass;Systems engineering and theory|
|[Study on Modified Public Key Cryptosystem Based on ElGamal and Cramer-Shoup Cryptosystems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099297)|S. -R. Kim; R. Kyung|10.1109/CCWC57344.2023.10099297|Public key cryptosystems;ElGamal cryptosystem;Cramer-Shoup cryptosystem;Diffie-Hellman(DDH) assumption;modulo multiplication;security;Hash functions;Correlation;Adaptive systems;Numerical analysis;Conferences;Public key cryptography;Encryption|
|[Security Analysis of Ransomware: A Deep Dive into WannaCry and Locky](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099114)|B. Fiore; K. Ha; L. Huynh; J. Falcon; R. Vendiola; Y. Li|10.1109/CCWC57344.2023.10099114|ransomware;malware;malware analysis;static analysis;dynamic analysis;reverse engineering;crypto ransomware;locker ransomware;WannaCry;Locky;malware detection;malware prevention;malware removal;obfuscation;Training;Reverse engineering;Telecommunication traffic;Static analysis;Behavioral sciences;Encryption;Ransomware|
|[Information Security using GNU Privacy Guard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099196)|D. Syed; A. H. Al-Ghushami; A. Zainab; S. M. Abdulhamid; M. S. D. A. Al-Kuwari|10.1109/CCWC57344.2023.10099196|Asymmetric key algorithm;GNU Privacy Guard;OpenPGP;Symmetric key algorithm;Privacy;Graphics processing units;Public key;Information security;Passwords;Electronic mail;Encryption|
|[Out-of-Band Analysis of GFDM Systems Based on Different Self-Interference Types](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099217)|M. R. G. Aghdam; R. Abdolee; B. M. Tazehkand; A. A. Jirdehi|10.1109/CCWC57344.2023.10099217|5G;GFDM;Interference;OOB;Pulse Shape;Sufficient conditions;Analytical models;Interference cancellation;Shape;Conferences;Prototypes;Modulation|
|[Adaptive Token Circulation to Avoid Malicious UEs Hoarding Tokens and Assure D2D Relay Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099176)|Y. -C. Wang; K. Yu|10.1109/CCWC57344.2023.10099176|circulation;device-to-device (D2D);hoarder;incentive;relay service;token;Online banking;Simulation;Conferences;Computational modeling;Finance;Packet loss;Throughput|
|[Zero-Effort Two-Factor Authentication Using Wi-Fi Radio Wave Transmission and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099124)|A. A. S. AlQahtani; T. Alshayeb|10.1109/CCWC57344.2023.10099124|Two-factor authentication;Wi-Fi radio waves;Machine learning;user authentication;zero-effort 2FA;ML;Wireless communication;Wireless sensor networks;Authentication;Prototypes;Medical services;Machine learning;Software|
|[Optimizing Smart Home Performance and User Convenience with RSSI-based Proximity Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099153)|A. A. S. AlQahtani; H. Alamleh|10.1109/CCWC57344.2023.10099153|Smart home;Smarthome;Smart home Management Center;IoT;RSSI;BLE;Performance evaluation;Smart cities;Sociology;Smart homes;Machine learning;Turning;Hardware|
|[Classification of Road Objects Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099093)|M. Patel; H. Elgazzar|10.1109/CCWC57344.2023.10099093|Road Object;Convolutional Neural Network;Traffic Sign;Classification Task;Roads;Computational modeling;Neural networks;Merging;Predictive models;Convolutional neural networks;Object recognition|
|[An IoT Intrusion Detection System Based on TON IoT Network Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099144)|G. Guo; X. Pan; H. Liu; F. Li; L. Pei; K. Hu|10.1109/CCWC57344.2023.10099144|Machine learning;Intrusion Detection Systems;Feature selection;TON_IoT;Performance evaluation;Learning systems;Correlation coefficient;Conferences;Computational modeling;Intrusion detection;Machine learning|
|[Airbnb Rental Price Prediction Using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099266)|A. Lektorov; E. Abdelfattah; S. Joshi|10.1109/CCWC57344.2023.10099266|Airbnb;Machine Learning;Regression;Decision Trees;K-Nearest Neighbors;Extra Trees;Support Vector Machines;Random Forests;XGBoost;Support vector machines;Measurement;Industries;Atmospheric modeling;Computational modeling;Urban areas;Forestry|
|[Machine Learning in Embedded Systems: Limitations, Solutions and Future Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099348)|E. Batzolis; E. Vrochidou; G. A. Papakostas|10.1109/CCWC57344.2023.10099348|machine learning;deep learning;artificial intelligence;embedded systems;Internet of Things (IoT);Embedded systems;Machine learning algorithms;Computational modeling;Machine learning;Prediction algorithms;Real-time systems;Robustness|
|[Prompting Large Language Models With the Socratic Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099179)|E. Y. Chang|10.1109/CCWC57344.2023.10099179|large language model;natural language processing;prompting;the Socratic method;Systematics;Computational modeling;Conferences;Writing;Chatbots;Cognition;Task analysis|
|[Machine Learning Enabled Intrusion Detection for Edge Devices in the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099276)|M. Alsharif; D. B. Rawat|10.1109/CCWC57344.2023.10099276|Intrusion Detection in IoT;IoT Datasets;Machine Learning Models;Cloud computing;Computational modeling;Intrusion detection;Optimized production technology;Delays;Internet of Things;Security|
|[Recent Advances in Cybersecurity and Fraud Detection in Financial Services: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099355)|A. Bajracharya; B. Harvey; D. B. Rawat|10.1109/CCWC57344.2023.10099355|Cyberattacks;cybercriminals;financial service industry;financial fraud detection;cybersecurity measures;Training;Regulators;Pandemics;Legislation;Organizations;Fraud;Risk management|
|[Recent Advances in Artificial Intelligence Enabled Tutoring Systems: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099098)|I. Yesir; D. B. Rawat|10.1109/CCWC57344.2023.10099098|Tutoring Systems;Artificial Intelligence;Chat bots;Learning Systems;Tutor Agent;Pandemics;Conferences;Education;Transportation;Entertainment industry;Tutorials;Companies|
|[Classification Challenges and Analysis of Traffic Patterns for Highly Congested Areas in Central Texas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099316)|D. Alonso; E. Alonso; D. Valles|10.1109/CCWC57344.2023.10099316|SVM;logistical regression;decision tree;traffic;unsupervised learning;majority voting;Training;Costs;Roads;Surveillance;Urban areas;Machine learning;Traffic control|
|[Applying Deep Reinforcement Learning for Detection of Internet-of- Things Cyber Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099349)|C. Rookard; A. Khojandi|10.1109/CCWC57344.2023.10099349|Reinforcement Learning;Intrusion Detection;Internet-of-Things;Q-Learning;Deep-Q-Network;Performance evaluation;Deep learning;Machine learning algorithms;Embedded systems;Network intrusion detection;Reinforcement learning;Internet of Things|
|[IoT Security: AI Blockchaining Solutions and Practices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099111)|H. Rajan; J. Burns; C. Jaiswal|10.1109/CCWC57344.2023.10099111|IoT;security;Blockchain;Artificial Intelligence;Machine Learning;privacy;decentralization;edge devices;Data privacy;Privacy;Data integrity;Computer architecture;Data processing;Hardware;Blockchains|
|[Short-Term AQI Forecasts using Machine/Deep Learning Models for San Francisco, CA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099064)|B. S. Chandar; P. Rajagopalan; P. Ranganathan|10.1109/CCWC57344.2023.10099064|AQI;Pollutants;XGBoost;Degradation;Atmospheric modeling;Railway accidents;Urban areas;Fires;Predictive models;Air quality|
|[Pragmatic Domestic Electrical Load Disaggregation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099375)|S. S. Chawathe|10.1109/CCWC57344.2023.10099375|Non-Intrusive Load Monitoring (NILM);Elec-trical Load Disaggregation;Smart Meters;Data Integration. Data Visualization;Energy consumption;Data privacy;Aggregates;Data visualization;Prototypes;Companies;Smart meters|
|[Canny Edge Detection on GPU using CUDA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099273)|M. Horvath; M. Bowers; S. Alawneh|10.1109/CCWC57344.2023.10099273|CUDA;Kernel;Compute;Edge Detection;Parallelism;Shared Memory;Tiling;Real-Time;Image resolution;Image edge detection;Instruction sets;Conferences;Graphics processing units;Debugging;Streaming media|
|[Consumer review Analysis using NLP and Data Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099278)|M. Nasimuzzaman; A. N. Merag; S. Afroj; M. M. Alam; M. H. K. Mehedi; A. A. Rasel|10.1109/CCWC57344.2023.10099278|Consumer review;Data mining;NLP;TF-IDF;SVM;Naive Bayes;logistic regression;multinomial naive bayes;Matplotlib;Seaborn;Itertools;Google Translate;Support vector machines;Sentiment analysis;Analytical models;Social networking (online);Sociology;Organizations;Data models|
|[Evaluating Question generation models using QA systems and Semantic Textual Similarity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099244)|S. Shaheer; I. Hossain; S. N. Sarna; M. H. Kabir Mehedi; A. A. Rasel|10.1109/CCWC57344.2023.10099244|Question Generation;Semantic Textual Similarity;Question Answering;BLEU;Measurement;Computational modeling;Conferences;Semantics;Computer architecture;Question answering (information retrieval);Context modeling|
|[Transformation of Visual Information into Bangla Textual Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099345)|N. Nawer; M. S. I. Khan; M. M. Alam; M. H. K. Mehedi; A. A. Rasel|10.1109/CCWC57344.2023.10099345|Bangla;Local Attention;Multi-Head Attention;VGG16;CuDNNLSTM;BLEU score;Visualization;Adaptation models;Quality assurance;Image resolution;Predictive models;Feature extraction;Transformers|
|[An Efficient Parallel Divide-and-Conquer Algorithm for Generalized Matrix Multiplication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099141)|J. Eagan; M. Herdman; C. Vaughn; N. Bean; S. Kern; M. Pirouz|10.1109/CCWC57344.2023.10099141|matrix multiplication;divide and conquer;CUDA;Performance evaluation;Symmetric matrices;Runtime;Scalability;Memory management;Graphics processing units;Signal processing algorithms|
|[Assessing the Impact of Matched Fragments' Relative Locations on Application Artifact Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099101)|O. Adegbehingbe; J. H. Jones|10.1109/CCWC57344.2023.10099101|Digital Forensics;Sub-file Blocks;Block Classification;Software Inference;Data Recovery;Matched filters;File systems;Conferences;Digital forensics;Behavioral sciences|
|[Informational Query Detection on Social Media Posts in Bengali Language Using Machine Learning And Transfer Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099305)|M. A. Rahman; S. I. Chowdhury; S. Rafan; N. Jannat; T. Aziz|10.1109/CCWC57344.2023.10099305|Ensemble;Transfer learning;SVM;AdaBoost;BERT;Analytical models;Social networking (online);Conferences;Transfer learning;Organizations;Natural language processing|
|[Classification of Lung Chest X-Ray Images Using Deep Learning with Efficient Optimizers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099228)|A. Asaithambi; V. Thamilarasi|10.1109/CCWC57344.2023.10099228|data augmentation;learning rate;VGG-16;VGG-19;Xception Net;ResNet-50;nodule and non-nodule classification;lung CXR image;Deep learning;Conferences;Lung;Computer architecture;X-ray imaging;Biomedical imaging;Tumors|
|[Predicting EURO Games Using an Ensemble Technique Involving Genetic Algorithms and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099366)|A. S. Randrianasolo|10.1109/CCWC57344.2023.10099366|Soccer predictions;Genetic Algorithms;Machine Learning;Ensemble Technique;Machine learning algorithms;Conferences;Games;Machine learning;Prediction algorithms;Genetic algorithms;Sports|
|[Deep learning for predictive alerting and cyber-attack mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099209)|A. Imeri; O. Rysavy|10.1109/CCWC57344.2023.10099209|Cyber threat intelligence;Situational awareness system;Deep residual network;Fuzzy C-means clustering;Training;Machine learning algorithms;System performance;Training data;Predictive models;Data models;Cyber threat intelligence|
|[A Review On Breaking the Limits of Time Series Forecasting Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099071)|A. Raneez; T. Wirasingha|10.1109/CCWC57344.2023.10099071|Time Series (TS) Forecasting;Neural Ordinary Differential Equations (NODEs);Liquid Time-constant Networks (LTCs);Ordinary Differential Equations (ODEs);Stochastic Differential Equations (SDEs);Deep learning;Liquids;Conferences;Time series analysis;Reinforcement learning;Ordinary differential equations;Natural language processing|
|[A Comparison Study on AI Language Detector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099219)|A. Singh|10.1109/CCWC57344.2023.10099219|artificial intelligence;computational intelligence;natural language understanding;language models;ethical concerns;Ethics;Codes;Law;Computational modeling;Plagiarism;Conferences;Detectors|
|[Social Engineering Incidents and Preventions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099202)|A. Kamruzzaman; K. Thakur; S. Ismat; M. L. Ali; K. Huang; H. N. Thakur|10.1109/CCWC57344.2023.10099202|social engineering;phishing;baiting;tailgating;keylogger;pretexting;Computer hacking;Computer viruses;Pandemics;Phishing;Psychology;Companies;Universal Serial Bus|
|[Network Packet Sniffing and Defense](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099148)|M. L. Ali; S. Ismat; K. Thakur; A. Kamruzzaman; Z. Lue; H. N. Thakur|10.1109/CCWC57344.2023.10099148|broadcast network transmission;promiscuous mode;hub-based LAN;switch-based LAN;Flooding attack;ARP spoofing;Computer hacking;Conferences;Information security;Credit cards;Encryption;Electronic commerce;Network intrusion|
|[The Metaverse and Conversational AI as a Threat Vector for Targeted Influence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099167)|L. Rosenberg|10.1109/CCWC57344.2023.10099167|Virtual Reality;Augmented Reality;Mixed Reality;Conversational AI;Virtual Spokespeople;Epistemic Agency;AI Manipulation Problem;Metaverse Regulation;LLMs;Democracy;Human computer interaction;Regulators;Computational modeling;Surveillance;Oral communication;Media;Real-time systems|
|[Data Analytics of Network Intrusion Based on Deep Neural Networks with Weights Initialized by Stacked Autoencoders and Deep Belief Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099108)|L. Wang; R. L. Mosher; P. Duett; T. C. Falls|10.1109/CCWC57344.2023.10099108|data analytics;network intrusion;cybersecurity;deep neural network;stacked autoencoder;deep belief network;dropout;Deep learning;Performance evaluation;Data analysis;Databases;Conferences;Neural networks;Network intrusion|
|[Analysis of Well-Known DNS over HTTPS Resolvers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099347)|K. Jerabek; O. Rysavy; I. Burgetova|10.1109/CCWC57344.2023.10099347|DNS over HTTPS;DoH;DNS security;Security;Privacy;Measurement;Privacy;Protocols;Conferences;Internet;Encryption;Blocklists;Security|
|[Template Attack Against AES in Counter Mode With Unknown Initial Counter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099238)|M. Tienteu; E. Smith; E. M. Santillan; K. Kornegay; P. Harvey; O. Toutsop; T. Yimer; V. Morris; K. Wandji|10.1109/CCWC57344.2023.10099238|AES;Counter mode;CTR;EM SCA;POI;Sidechannel Analysis;Template Attack;Conferences;Software algorithms;Software;Forgery;Encryption;Complexity theory;Standards|
|[MediSearch: Advanced Medical Web Search Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099048)|D. Trivedi; V. Gopalakrishnan; D. Dholariya|10.1109/CCWC57344.2023.10099048|web search engine;information retrieval;machine learning;disease classification;Privacy;Random access memory;Radiology;Bones;Skin;Task analysis;Web search|
|[Mobile App for the information management of pre-existing diseases towards preventing COVID-19 severity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099378)|W. Velasquez; R. J. Coronel; A. A. Loayza; V. S. Padilla; M. Filian-Gomez|10.1109/CCWC57344.2023.10099378|Cloud services;COVID-19;database;intensive care unit;WBAN;COVID-19;Cloud computing;Software algorithms;Machine learning;Real-time systems;Mobile applications;Batteries|
|[Performance Analysis of TensorFlow2 Object Detection API Models for Engineering Site Surveillance Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099270)|D. Grimes; D. Valles|10.1109/CCWC57344.2023.10099270|detection;TensorFlow2;security;analysis;surveillance;Training;Analytical models;Computational modeling;Surveillance;Object detection;Motion capture;Performance analysis|
|[Forecast Analysis of Visibility for Airport Operations with Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099100)|S. Saha; D. Valles|10.1109/CCWC57344.2023.10099100|forecast;LSTM;GRU;visibility;airport;flight-plan;Deep learning;Training;Atmospheric modeling;Computational modeling;Weather forecasting;Predictive models;Airports|
|[A Gradient Boosting Classifier to Predict Electric Power Consumption by Nuclear Power Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099208)|M. R. Khan; F. Fuad; M. A. Ibrahim|10.1109/CCWC57344.2023.10099208|Nuclear Power Plant (NPP);Electric Power Con-sumption (Megawatt);Machine Learning;Stochastic Gradient Boosting;Extreme Gradient Boosting;Time-Series Dataset;Power demand;Conferences;Computational modeling;Time series analysis;Stochastic processes;Predictive models;Boosting|
|[Application of LSTM Auto Encoder in Hardware Trojan Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099121)|A. Sumarsono; Z. Masters|10.1109/CCWC57344.2023.10099121|Hardware Trojan;Anomaly Detection;LSTM;Autoencoder;Machine Learning;Integrated Circuits;Integrated circuits;Neural networks;Ethernet;Throughput;Hardware;Trojan horses;Reliability|
|[Red Team Ethical Physical Penetration Testing Simulations using Open Source Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099053)|C. DeCusatis; C. Danyluk; D. MacCarthy; J. Shapiro; N. Regan|10.1109/CCWC57344.2023.10099053|Cybersecurity;physical;penetration;test;gamification;Octalysis;Training;Ethics;Conferences;Sociology;Hardware;Elevators;Statistics|
|[Spectral Clustering of Virus Spread through Computer Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099335)|A. Barnett; J. Guo|10.1109/CCWC57344.2023.10099335|Spectral Clustering;Graph Laplacian;computer network;Visualization;Computer viruses;Hospitals;Veins;Virtual environments;Organizations;Radiology|
|[Eyes on the Road: A Survey on Cyber Attacks and Defense Solutions for Vehicular Ad-Hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099187)|A. Hankins; T. Das; S. Sengupta; D. Feil-Seifer|10.1109/CCWC57344.2023.10099187|Vehicular Ad-Hoc Networks;Cyber Attacks;Defense Solutions;Machine Learning;Blockchain;Trust;Public Key Infrastructures;Roads;Conferences;Vehicular ad hoc networks;Safety;Internet;Security;Cyberattack|
|[Cybersecurity in Malware Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099047)|H. Lupsan; R. Ahmed; Y. Shi|10.1109/CCWC57344.2023.10099047|Malware Detection;Cybersecurity;Email Phishing;Ransomware;Trojan Malware;Injection Attack;Conferences;Games;Malware;Computer security|
|[Activity-Attack Graphs for Intelligence-Informed Threat COA Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099277)|C. Mckee; K. Edie; A. Duby|10.1109/CCWC57344.2023.10099277|Cyber threat intelligence;attack graphs;threat analysis;Itemsets;Conferences;Emulation;Automatic generation control;Data collection;Extensibility;Usability|
|[Review of Ransomware Attacks and a Data Recovery Framework using Autopsy Digital Forensics Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099169)|S. C. Nayak; V. Tiwari; B. K. Samanthula|10.1109/CCWC57344.2023.10099169|Ransomware;cryptovirology;encryption;cyber forensics;data recovery;Conferences;Autopsy;Digital forensics;Virtual machining;Behavioral sciences;Cryptocurrency;Ransomware|
|[File Allocation Chronology and its Impact on Digital Forensics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099265)|A. Bahjat; J. Jones|10.1109/CCWC57344.2023.10099265|forensic recovery;digital forensics;digital evidence;file slack;file fragment;event reconstruction;Sequential analysis;Correlation;File systems;Digital forensics;Focusing;Drives;Fats|
|[Automation of Buildings using Sign Interpretation Language to include People with Hearing Disabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099330)|W. Velasquez; M. Ruiz-Pena; G. Merizalde; D. Torres; M. S. Alvarez-Alvarado|10.1109/CCWC57344.2023.10099330|Kafka;Machine Learning;Sign Language;Spark Streaming;Training;Uniform resource locators;Automation;Webcams;Distributed databases;Auditory system;Computer architecture|
|[Ensemble of Gated Recurrent Unit and Convolutional Neural Network for Sarcasm Detection in Bangla](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099157)|N. Farhan; I. T. Awishi; M. H. K. Mehedi; M. M. Alam; A. A. Rasel|10.1109/CCWC57344.2023.10099157|Natural Language Processing;Artificial Intelligence;GRU;CNN;Sarcasm in Bangla;Deep learning;Conferences;Computational modeling;Natural languages;Bit error rate;Computer architecture;Logic gates|
|[A new Potential-Based Reward Shaping for Reinforcement Learning Agent](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099211)|B. Badnava; M. Esmaeili; N. Mozayani; P. Zarkesh-Ha|10.1109/CCWC57344.2023.10099211|Potential-based Reward Shaping;Reinforcement Learning;Reward Shaping;Knowledge Extraction;Conferences;Transfer learning;Reinforcement learning;Games;Multitasking;Task analysis|
|[A Transfer Learning Approach For Efficient Classification of Waste Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099127)|M. H. K. Mehedi; I. Arafin; M. Hasan; F. Rahman; R. Tasin; A. A. Rasel|10.1109/CCWC57344.2023.10099127|VGG16;CNN;MobileNetV2;Transfer Learning;waste classification;deep learning;Waste materials;Pollution;Computational modeling;Conferences;Transfer learning;Neural networks;Predictive models|
|[Optimize Path Planning for Drone-based Wireless Power Transfer System by Categorized Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099314)|Y. Xing; A. Verma|10.1109/CCWC57344.2023.10099314|UAV-enabled System;Wireless Power Transfer;Deep Reinforcement Learning;Path Planning;Deep learning;Performance evaluation;Reinforcement learning;Wireless power transfer;Path planning;Planning;Internet of Things|
|[A Comparative Study of Machine Learning Algorithms for Detecting Breast Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099106)|R. H. Khan; J. Miah; M. M. Rahman; M. Tayaba|10.1109/CCWC57344.2023.10099106|Breast cancer;Machine learning;Artificial Intelligence;XGBoost;Industries;Machine learning algorithms;Sensitivity;Medical services;Prediction algorithms;Breast cancer;Hazards|
|[System-information rationing of digital twins accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099319)|M. Korablyov; S. Lutskyy; I. Ivanisenko; O. Fomichov|10.1109/CCWC57344.2023.10099319|system-information rationing;system information;logarithmic unit measure;Planck threshold of sensitivity;coefficient of system-information rationing;Production systems;Sensitivity;Measurement units;Conferences;Production;Regulation;Data models|
|[PAM Constellations Minimizing Symbol-error Probability with Constrained Peak-to-average Power Ratios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099080)|B. Wiens; D. C. Lee|10.1109/CCWC57344.2023.10099080|Signal constellation;symbol-error probability;peak-to-average power ratio;non-uniform constellation;Radio frequency;Shape;Communication systems;Merging;Symbols;Power amplifiers;Peak to average power ratio|
|[Bounds on Pairwise Error Probability for LDPC coded CF-mMIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099052)|C. Fadnis; S. Chouhan|10.1109/CCWC57344.2023.10099052|Cell-free massive MIMO;Low-Density Parity-Check (LDPC) code;Wishart's distribution;pairwise error probability (PEP) bounds;Wireless communication;Pairwise error probability;Monte Carlo methods;Upper bound;Quadrature amplitude modulation;Interference;Throughput|
|[Effect of Node Position and Human Activity on BER Performance and Data Throughput in Off-Body Medical Body Area Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099116)|N. El-Maradny; H. Shaban; N. Elmadany|10.1109/CCWC57344.2023.10099116|MBAN (Medical Body Area Network);Off-body MBAN;Data Throughput;Bit Error Rate;Legged locomotion;Wrist;Transmitters;Conferences;Bit error rate;Throughput;Body area networks|
|[Analyzing Affect During Virtual Meetings to Improve Quality of Interaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099285)|E. Ashoori; S. Dávila-Montero; A. J. Mason|10.1109/CCWC57344.2023.10099285|affect recognition;neural networks;virtual meetings;improved interactions;technologies for psychological assessments;Measurement;Portable computers;Conferences;Neural networks;Psychology;Feature extraction;Social intelligence|
|[PAPR Reduction Techniques for MC-CDMA System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099102)|M. Ali; R. K. Rao; V. Parsa|10.1109/CCWC57344.2023.10099102|Orthogonal Frequency Division Multiplexing (OFDM);Multi-carrier Code Division Multiple Access (MC-CDMA);PAPR;Complex PN-sequence;Clipping Technique;PTS Technique;Multicarrier code division multiple access;Gold;Correlation;Partial transmit sequences;Conferences;Symbols;Peak to average power ratio|
|[System Monitoring and Data logging using PLX-DAQ for Solar-Powered Oil Well Pumping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099099)|O. Chidolue; T. Iqbal|10.1109/CCWC57344.2023.10099099|Photovoltaic;Oil Well;System Monitoring PLX DAQ;Arduino;Solar Energy;Photovoltaic systems;Microcontrollers;Oils;Switches;Real-time systems;Solar system;Sensors|
|[Data Science Analysis of Malicious Advertisements and Threat Detection Automation for Cybersecurity Progress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099325)|S. Nguyen; D. Bein|10.1109/CCWC57344.2023.10099325|machine learning;data science;cybersecurity;Google Ad;malicious URL;shortening service;Uniform resource locators;Companies;Search engines;Feature extraction;Market research;Internet;Data mining|
|[Electroencephalogram-based Emotion Recognition with Hybrid Graph Convolutional Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099220)|R. A. Nahin; M. T. Islam; A. Kabir; S. Afrin; I. A. Chowdhury; R. Rahman; M. G. R. Alam|10.1109/CCWC57344.2023.10099220|Electroencephalogram (EEG);Signal Processing;Emotion Recognition;Deep learning;Hybrid model;Graph Convolutional Network (GCN);Convolutional Neural Network;Emotion recognition;Analytical models;Convolution;Computational modeling;Brain modeling;Feature extraction;Electroencephalography|
|[Unsupervised Network Intrusion Detection Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099151)|S. Alam; Y. Alam; S. Cui; C. M. Akujuobi|10.1109/CCWC57344.2023.10099151|IDS;DDoS;CNN;Unsupervised Learning;Training;Network intrusion detection;Computer architecture;Telecommunication traffic;Machine learning;Convolutional neural networks;Task analysis|
|[Speech emotion recognition for psychotherapy: an analysis of traditional machine learning and deep learning techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099344)|N. Shah; K. Sood; J. Arora|10.1109/CCWC57344.2023.10099344|speech;emotion recognition;Machine Learning;MFCCs;deep learning;Boosting;CNN;LSTM;Deep learning;Emotion recognition;Cepstral analysis;Conferences;Speech recognition;Boosting;Task analysis|
|[Detecting Hate Speech on Social Media with Respect to Adolescent Vulnerability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099373)|A. Chiu; K. Sood; A. Rincon; D. Doran|10.1109/CCWC57344.2023.10099373|Machine Learning;Hate speech recognition;Classification;SVM;K-Nearest Neighbor;Naive Bayes;Ensemble Method;Support vector machines;Ethics;Social networking (online);Conferences;Hate speech;Blogs;Speech recognition|
|[Wildfire Time Estimation using a Machine Learning Algorithms Interaction Mechanism for Endemic Tree Species Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099174)|W. Velasquez; G. Sanchez-Guzman; F. Jijon-Veliz; M. S. Alvarez-Alvarado|10.1109/CCWC57344.2023.10099174|Prediction;Recognition;Tree;Wildfire;Training;Forestry;Vegetation;Computer architecture;Predictive models;Reconnaissance;Prediction algorithms|
|[A Machine Learning Model for Early Prediction of Parkinson's Disease from Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099230)|L. Igene; A. Alim; M. H. Imtiaz; S. Schuckers|10.1109/CCWC57344.2023.10099230|Accelerometer;ANOVA;Machine Learning;Parkinson's disease;PCA;SVM;Neurological diseases;Program processors;Parkinson's disease;Computational modeling;Conferences;Thigh;Machine learning|
|[Soft Voting Strategy for Multi-Modal Emotion Recognition Using Deep-learning- Facial Images and EEG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099070)|U. Chinta; J. Kalita; A. Atyabi|10.1109/CCWC57344.2023.10099070|EEG;feature extraction;emotion analysis;multi-modal integration;Gated Recurrent Unit;Emotion recognition;Visualization;Neural networks;Psychology;Medical services;Gaze tracking;Brain modeling|
|[A Framework Pipeline to Address Imbalanced Class Distribution Problem in Real-world Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099163)|U. Chinta; A. Atyabi|10.1109/CCWC57344.2023.10099163|facial expression;Autism Spectrum Disorder;convolutional neural network;Imbalance classification;Support vector machines;Radio frequency;Neuroimaging;Autism;Pipelines;Computer architecture;Feature extraction|
|[Non-Intrusive Load Monitoring Method for Appliance Identification Using Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099248)|A. S. Nuran; M. A. Murti; F. Y. Suratman|10.1109/CCWC57344.2023.10099248|non-intrusive load monitoring;energy disaggregation;machine learning;energy consumption;appliance;Support vector machines;Home appliances;Load monitoring;Fans;TV;Power demand;Telephone sets|
|[The Impact of Dynamic Adjustment of Swarm Behavior on Particle Swarm Optimization Performance using Benchmark Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099117)|M. Nadhir Ab Wahab; S. Nefti-Meziani; A. Atyabi|10.1109/CCWC57344.2023.10099117|Particle Swarm Optimization;Dynamic Be-haviour;Dynamic Parameter tuning;Dynamic Acceleration Co-efficients;Technological innovation;Metaheuristics;Optimization methods;Benchmark testing;Search problems;Particle measurements;Behavioral sciences|
|[Automatic Detection of Microplastics in the Aqueous Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099253)|M. A. B. Sarker; U. Butt; M. H. Imtiaz; A. B. Baki|10.1109/CCWC57344.2023.10099253|Deep learning;DeepSORT;Microplastics;YOLOv5;Training;Measurement;Shape;Image color analysis;Pipelines;Green products;Prototypes|
|[Design and Implementation Improvements for RFID Based Tactile Communication Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099346)|B. Rivera; A. Liu; K. Luong; K. George|10.1109/CCWC57344.2023.10099346|Radio-Frequency Identification;Near Field Communication;Design Improvement;Radio frequency;Presses;Embedded systems;Prototypes;Product design;Delays;Time factors|
|[Introducing the Scientific Image Analysis Application: A Free and User-Friendly Program for Extracting Bioinformatics From Digital Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099135)|I. Bentley; J. Ralston; S. D. Garman; O. Hershberger; C. M. Probst; C. Washer|10.1109/CCWC57344.2023.10099135|bioinformatics;color correction;graphical out-put;image analysis;morphometrics;museum specimens;Image analysis;Costs;Image color analysis;Digital images;Conferences;Length measurement;Museums|
|[A Survey on Data Integrity Attacks and DDoS Attacks in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099240)|Y. Tian; A. F. R. Nogales|10.1109/CCWC57344.2023.10099240|Cloud computing;cloud services;service availability;data integrity;cloud security attacks;mitigation approaches;Industries;Cloud computing;Data integrity;Computational modeling;Scalability;Denial-of-service attack;Forgery|
|[Using Cuckoo Filters to Improve Performance in Object Store-based Very Large Databases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099074)|R. K. N. Sai Krishna; C. Tekur; R. Bhashyam; V. Nannaka; R. Mukkamala|10.1109/CCWC57344.2023.10099074|Cloud databases;Cuckoo filter;Multi-version concurrency control;Object storage;Two-phase locking protocol;Protocols;Databases;Conferences;Metadata;Throughput;Information filters;Probabilistic logic|
|[Development of an Movil IoT System Using ESP8266 for the Detection of Pollutants in the Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099076)|J. A. Villanueva; X. Huaman Rojas; C. C. Rojo|10.1109/CCWC57344.2023.10099076|IoT;air pollution;smart sensors;ESP8266;Tuberculosis;Urban areas;Web pages;Real-time systems;Pollution measurement;Visual databases;Servers|
|[Cloud Service Misconfigurations: Emerging Threats, Enterprise Data Breaches and Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099296)|J. Guffey; Y. Li|10.1109/CCWC57344.2023.10099296|cloud misconfigurations;cloud security;IAM misconfiguration;S3 bucket misconfiguration;Twilio;Imperva breach;Capital One breach;DevSecOps;cloud scanning tools;Cloud computing;Costs;Scalability;Conferences;Organizations;Data breach;Maintenance engineering|
|[Multi-Agent-Based Simulation of Intelligent Network System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099195)|P. M. Tshakwanda; S. T. Arzo; M. Devetsikiotis|10.1109/CCWC57344.2023.10099195|nan;Deep learning;Intelligent networks;Machine learning algorithms;Design methodology;Conferences;Computational modeling;Microservice architectures|
|[Classification of Potato Disease with Digital Image Processing Technique: A Hybrid Deep Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099162)|F. T. J. Faria; M. Bin Moin; A. Al Wase; M. R. Sani; K. M. Hasib; M. S. Alam|10.1109/CCWC57344.2023.10099162|Deep learning;Image processing;MobileNet V2;LSTM;GRU;BiLSTM;Disease classification;Fungi;Deep learning;Image processing;Digital images;Conferences;Stochastic processes;Training data|
|[Smartphone Context Event Sequence Prediction with POERMH and TKE-Rules Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099166)|P. Goyal; M. K. Khan; C. Steil; S. M. Martel; R. Bryce|10.1109/CCWC57344.2023.10099166|Context-aware applications;sequence prediction;Android applications;smartphone application testing;sequential rule mining;context events;machine learning;se-quence prediction;software testing;Conferences;Predictive models;Prediction algorithms;Mobile applications;Behavioral sciences;Batteries;Reliability|
|[Detecting Network Transmission Anomalies using Autoencoders-SVM Neural Network on Multi-class NSL-KDD Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099056)|S. S. Khan; A. B. Mailewa|10.1109/CCWC57344.2023.10099056|NSL-KDD;Network Security;Intrusion Detection System;Deep Autoencoders;Anomaly Detection;Support Vector Machine (SVM);t-SNE;Deep Learning;Support vector machines;Training;Computational modeling;Neural networks;Predictive models;Feature extraction;Security|
|[Wind Speed Forecasting for Designing Sustainable Wastewater Treatment Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099313)|S. Somvanshi; E. Z. Fainman; K. Ikehata; D. V. Molina; T. Jin|10.1109/CCWC57344.2023.10099313|wind speed forecasting;sustainable wastewater treatment plants;regression models;recurrent neural network;long short-term memory;Costs;Wind energy;Wind speed;Companies;Predictive models;Wind turbines;Numerical models|
|[Large-scale Investigations of AAC Usage Patterns: Trends, Autism, and Stacked Autoencoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099096)|A. Atyabi; L. Boccanfuso; J. C. Snider; M. Kim; E. Barney; Y. A. Ahn; B. Li; K. J. Dommer; F. Shic|10.1109/CCWC57344.2023.10099096|nan;Autism;Sociology;Market research;Data models;Stability analysis;Numerical models;Statistics|
|[Sentiment Analysis of Social Media Comments in Mauritius](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099291)|N. G. Sahib; M. A. J. Marianne; B. Gobin-Rahimbux|10.1109/CCWC57344.2023.10099291|Natural Language Processing;Sentiment Analysis;Kreol Morisien;Support vector machines;Sentiment analysis;Social networking (online);Conferences|
|[Machine Learning Based Cost prediction for Acquiring New Customers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099189)|G. L. V. Prasad; A. D. S. Nanda; N. Darapaneni; A. Bunga; S. K. Tadepalli; A. R. Paduri; S. Kishore; Y. Saini|10.1109/CCWC57344.2023.10099189|Customer Acquisition Cost (CAC);Exploratory Data Analysis (EDA);Regression models;Decision TreeRegressor;RandomForestRegrssor;Pipeline;Grid Search CV;Measurement;Costs;Computational modeling;Pipelines;Data science;Predictive models;Data models|
|[An Overview of Bengali Speech Recognition: Methods, Challenges, and Future Direction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099382)|N. Tasnia; M. Islam; M. S. Rony; N. Tanzim; K. M. Hasib; M. S. Alam|10.1109/CCWC57344.2023.10099382|Speech recognition;Automatic Speech Recognition;Bengali language;Language model;Bengali ASR;Human computer interaction;Recurrent neural networks;Conferences;Speech recognition;Writing;Transformers;Convolutional neural networks|
|[A Multi-modal Deep Learning Approach for Predicting Dhaka Stock Exchange](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099255)|M. N. R. Khan; O. Al Tanim; M. S. Salsabil; S. M. R. Reza; K. M. Hasib; M. S. Alam|10.1109/CCWC57344.2023.10099255|Dhaka Stock Exchange (DSE);LSTM (Long Short-Term Memory);Transformer;Gated recurrent unit (GRUs);Time Series Data;Moving Average;Prediction;Deep learning;Analytical models;Computational modeling;Predictive models;Transformers;Data models;Behavioral sciences|
|[IoBT Intrusion Detection System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099340)|B. Alkanjr; T. Alshammari|10.1109/CCWC57344.2023.10099340|IoBT;Intrusion Detection system;IDS;security;Machine Learning;Radio frequency;Intrusion detection;Sensor systems;Real-time systems;Sensors;Personnel;Internet of Things|
|[Active Learning Framework For Long-term Network Traffic Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099065)|J. Pešek; D. Soukup; T. Čejka|10.1109/CCWC57344.2023.10099065|Active Learning;Dataset Quality;Network traffic analysis;Adaptation models;Computational modeling;Telecommunication traffic;Machine learning;Network security;Data models;Software|
|[Explainable Autonomic Cybersecurity For Industrial Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099307)|V. Manoj; S. Wenda; N. Sihan; C. Rouff; L. Watkins; A. Rubin|10.1109/CCWC57344.2023.10099307|Intrusion Detection;Explainable;Machine Learning;Industrial Control Network;Integrated circuits;Training;Industrial control;Computational modeling;Intrusion detection;Feature extraction;Decision trees|
|[Design of Energy Efficient Ring Oscillator and Full Adder Circuit using Compact Model of MoS2 Channel TFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099371)|N. O. Adesina; M. A. U. Khan; J. Xu|10.1109/CCWC57344.2023.10099371|TFET;verilog-A;cadence;inverter;full adder;ring oscillator;Ring oscillators;TFETs;Very large scale integration;Inverters;Delays;Sulfur;Transistors|
|[Experimental Analyses of a Noise-Based True Random Number Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099239)|N. O. Adesina; B. Wang; W. Morell; M. A. Ullah Khan|10.1109/CCWC57344.2023.10099239|Electrical noise;entropy;NIST SP800-22 tests;post processing;TRNG;Resistors;Operational amplifiers;Power demand;Electric breakdown;NIST;White noise;Generators|
|[Adaptive DFE Utilizing Fixed Step Error Signal for Multi-Gb/s Serial Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099051)|A. R. Altaee|10.1109/CCWC57344.2023.10099051|VLSI;Wire-line communication;Error detection;Timing Jitter;Adaptive DFE;CMOS Equalizer;Serial links;Semiconductor device modeling;Performance evaluation;Adaptation models;Conferences;Symbols;Computer architecture;CMOS technology|
|[Sign-Sign LMS-Based Adaptive DFE for 4PAM Multi-Gbps Serial Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099332)|A. R. Altaee|10.1109/CCWC57344.2023.10099332|VLSI;Wire-line communication;Error detection;Timing Jitter;Adaptive DFE;CMOS Equalizer;Serial links;Semiconductor device modeling;Analytical models;Adaptation models;Simulation;Symbols;Voltage;Jitter|
|[Process Variation's Effect on Various Threshold Voltage Assignments in 6T SRAM Designs Using 12nm FinFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099370)|U. R. Irin; S. Barua; M. M. Azmir; T. Hassan; D. Mohammed|10.1109/CCWC57344.2023.10099370|Low power;Process variation;FinFET;6T SRAM cell;Threshold-voltage;Simulations;read-write delay;read-write energy;Leakage power;Performance evaluation;Semiconductor device modeling;Latches;Power demand;Layout;Random access memory;CMOS technology|
|[A Look into the Vulnerabilities of Automatic Dependent Surveillance-Broadcast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099369)|C. Clay; M. Khan; B. Bajracharya|10.1109/CCWC57344.2023.10099369|ADS-B;Aviation;Cybersecurity;ATC;IoT;IoV;Protocols;Atmospheric modeling;Packet loss;Data integration;Radar;Time measurement;Safety|
|[Grouping Patients for Ridesharing in Non-emergency Medical Care Services*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099125)|F. -S. Hsieh; J. -Z. Huang|10.1109/CCWC57344.2023.10099125|k-means;non-emergency medical services;health care;transportation;ridesharing;Hospitals;Conferences;Clustering methods;Clustering algorithms;Automobiles;Vehicles|
|[OC-SMOTE-NN: A Deep Learning-based Approach for Imbalanced Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099267)|N. Karunasingha; B. G. Jayasekara; A. Hevapathige|10.1109/CCWC57344.2023.10099267|class imbalance;deep learning;data mining;SMOTE;orthogonality;Adaptation models;Computational modeling;Conferences;Manuals|
|[A Comparative Study of Machine Learning classifiers to analyze the precision of Myocardial Infarction prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099059)|R. H. Khan; J. Miah; S. A. Abed Nipun; M. Islam|10.1109/CCWC57344.2023.10099059|Machine Learning;Myocardial Infarction;Heart Disease;LightGBM;Heart;Support vector machines;Machine learning algorithms;Hospitals;Conferences;Machine learning;Myocardium|
|[Data Complexity for Identifying Suitable Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099201)|P. Anand Ramalingam; N. Fathima; P. Supriya; P. Shetty; M. Sanyal; P. Yeshaswini; M. Rao; N. Darapaneni; A. R. Paduri|10.1109/CCWC57344.2023.10099201|linear separability;purity;computation time;optimal algorithm;classification;Measurement;Machine learning algorithms;Conferences;Computational modeling;Buildings;Data models;Classification algorithms|
|[A Survey of Advanced Methods for Efficient Text Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099322)|D. Antony; S. Abhishek; S. Singh; S. Kodagali; N. Darapaneni; M. Rao; A. R. Paduri; S. BG|10.1109/CCWC57344.2023.10099322|Natural Language Processing;Text Extraction;Abstractive Summartization;Extractive Summartization;Natural Language Generation;Conferences;Semantics;Natural languages;Oral communication;Reliability;Data mining;Open source software|
|[A Study of Text Summarization in the Medical Domain using BERT and its Variants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099139)|T. S. Shaik; J. Bharath; N. Darapaneni; S. Patra; S. A. Vishal; S. Manthripragada; A. K. Kagita; M. Rao; A. R. Paduri|10.1109/CCWC57344.2023.10099139|Text Summarization;PubMed;BERT;BioBert;Rogue;BLURB;Vocabulary;Databases;Computational modeling;Conferences;Biological system modeling;Bit error rate;Traffic control|
|[A Semi-Supervised Multi-Spike Learning Algorithm for Deep Spiking Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099067)|X. Zhao; X. Lin; Z. Zhang|10.1109/CCWC57344.2023.10099067|deep spiking neural network;semi-supervised learning;STDP;broadcast alignment;pattern classification;Training;Digital images;Conferences;Supervised learning;Neural networks;Semisupervised learning;Benchmark testing|
|[Heart Disease Detection Using ML](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099294)|R. C. Das; M. C. Das; M. A. Hossain; M. A. Rahman; M. H. Hossen; R. Hasan|10.1109/CCWC57344.2023.10099294|Hearth disease;Machine Learning Technique;heart disease prediction;classification algorithms;regression model formatting;Heart;Machine learning algorithms;Sensitivity;Sociology;Predictive models;Stroke (medical condition);History|
|[Capacity Bounds Analysis of 5G networks in different propagation environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099143)|A. I. Zreikat|10.1109/CCWC57344.2023.10099143|Capacity Bounds;5G Networks;Propagation environments;COST-231 Hata model;Performance Evaluation;5G mobile communication;Diffraction;Wireless networks;Receivers;Reflection;Planning;Numerical models|
|[Smart Cup for a Smart Pill Dispenser for Verification of Pill Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099363)|S. R. Minera; A. Nuerbiya; A. Espinoza; K. George; A. Panangadan|10.1109/CCWC57344.2023.10099363|Accelerometer;Gyroscope;IR proximity Sensor;Wireless Charging;Portable;Assistive Technology;Arduino RP2040 Connect;LSM6DSOX IMU;Accelerometers;Conferences;Gyroscopes|
|[Proposal of Client-Server Based Vertical Handover Scheme Using Virtual Routers for Edge Computing in Local 5G Networks and WLANs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099150)|K. Ito; N. Izuka|10.1109/CCWC57344.2023.10099150|edge computing;local 5G;WLAN;vertical handover;dynamic routing;virtual router;Wireless LAN;5G mobile communication;Diffraction;Operating systems;Wireless networks;Handover;Routing protocols|
|[User Optimum Locations in Cellular Networks for Tradeoff of Throughput and User Satisfaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099320)|J. Zhang; X. Li|10.1109/CCWC57344.2023.10099320|Location;throughput;priority;satisfaction;in-centive;Cellular networks;Wireless communication;Base stations;Simulation;Conferences;Throughput;Optimization|
|[A Blockchain Application on Bootstrapping Mobile Nodes within VANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099315)|E. W. Bowlin; M. S. Khan; B. Bajracharya|10.1109/CCWC57344.2023.10099315|Blockchain;Bootstrapping;Mobile-nodes;VANET;Conferences;Vehicular ad hoc networks;Machine learning;Blockchains;Mobile nodes;Security;Vehicle dynamics|
|[Benchmark Signals with Inter-harmonics for the Development of Harmonic Estimation Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099243)|M. A. Aziz Jahan; S. Tiwari; F. B. Costa|10.1109/CCWC57344.2023.10099243|Harmonic estimation;Inter-Harmonics estimation;Power quality;Classical Discrete Fourier Transform;Design methodology;Discrete Fourier transforms;Estimation;Benchmark testing;Harmonic analysis;Real-time systems;Harmonic distortion|
|[60 GHz Wi-Fi as a Tractor-Trailer Wireless Harness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099274)|A. Elhadeedy; J. Daily|10.1109/CCWC57344.2023.10099274|Autonomous Vehicles;Named Data Networking;Trucks;Trailers;Wireless Harness;Wi-Fi;Wireless communication;Connectors;Wireless sensor networks;Wires;Agricultural machinery;Sensor phenomena and characterization;Software|
|[Post Prioritization Techniques to Improve Code Coverage for SARSA Generated Test Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099120)|M. K. Khan; R. Michaels; D. Williams; B. Dinal; B. Gurkas; A. Luloh; R. Bryce|10.1109/CCWC57344.2023.10099120|Android Testing;Context Aware Environments;Mobile Application Testing;Reinforcement Learning;Test Suite Generation;Test Suite Prioritization;Codes;Conferences;Reinforcement learning;Test pattern generators|
|[Secure Access Control for Healthcare Information Systems: A Body Area Network Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099172)|R. Abdunabi; R. Basnet; M. A. Amin|10.1109/CCWC57344.2023.10099172|Access Control;Privacy and Security;Blockchain;Smart Contract;HCIS;STABAC;BAN;Access control;Smart contracts;Systems architecture;Medical services;Body area networks;Privacy breach;Blockchains|
|[Counter-surveillance Technique by Diversifying Transmission Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099077)|H. Alamleh|10.1109/CCWC57344.2023.10099077|Counter-surveillance;data communications;data concealment;data transmission;network security;Scapy;transmission control protocol;Wireless communication;Surveillance;Sockets;Network security;Information and communication technology;Data communication;Servers|
|[CNN Based Study of Improvised Food Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099066)|T. Raman; S. Kumar; A. R. Paduri; G. Mahto; S. Jain; B. K; N. Darapaneni|10.1109/CCWC57344.2023.10099066|Food classification;Deep Learning;CNN;Transfer learning;Background removal;Data augmentation;Food-101;Conferences;Transfer learning;Buildings;Feature extraction;Convolutional neural networks;Iterative methods;Task analysis|
|[Medical Radiology Image processing for Pneumonia Detection Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099292)|V. Singh; A. Manay; R. P. Singh; S. Tomer; C. A; N. Darapaneni; V. Manoj; A. Rastogi; A. R. Paduri|10.1109/CCWC57344.2023.10099292|Deep Learning;Pneumonia Detection;Convolution Neural Networks (CNNs);Transfer Learning;Chest X-Rays;Medical Imaging;Training;Image segmentation;Pulmonary diseases;Computational modeling;Lung;Predictive models;Feature extraction|
|[Deep Semi-Supervised Learning With Contrastive Learning in Large Vocabulary Automatic Chord Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099263)|C. Li; Y. Li; H. Song; L. Tian|10.1109/CCWC57344.2023.10099263|Automatic chord estimation;contrastive learning;semi-supervised learning;teacher-student network;Vocabulary;Conferences;Computational modeling;Neural networks;Semisupervised learning;Data models|
|[A Review of Current Techniques for Robotic Arm Manipulation and Mobile Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099115)|T. Sieusankar; B. Chandrasekaran|10.1109/CCWC57344.2023.10099115|degrees-of-freedom;end-effector;kinematics;manipulator;pick-and-place;robotic arm;trajectory planning;Service robots;Navigation;Bibliographies;Conferences;Kinematics;Manipulators;Planning|
|[Symbolic and Flat Affect Humanoid Head Design for Modular Humanoid Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099206)|P. Chaurasia; R. A. Viji; P. M; A. P. Chavan; A. H. M. Reddy; P. R|10.1109/CCWC57344.2023.10099206|—Humanoid Head;Symbolic Humanoid Head;Flat Affect Head;Humanoid head design;Humanoid robot;Eye mechanism;Industries;Torso;Visualization;Face recognition;Social robots;Humanoid robots;Computer architecture|
|[Cable-Driven Parallel Robot for Warehouse Monitoring Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099136)|A. Zargari; Z. A. Castrejon; D. Kim; P. Y. Oh|10.1109/CCWC57344.2023.10099136|cable driven robot;warehouse inventory;skycam;Parallel robots;Computer vision;Manufacturing processes;Conferences;Robot vision systems;Inventory management;Land vehicles|
|[Automation of Agave Americana L fiber for the production of reinforced earthenware blocks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099087)|A. J. Valenzuela-Inga; J. G. Benito-Zuñiga; J. A. Huamán-Chavez; H. K. Hinostroza-Maravi; S. I. Del Carpio-Ramirez; G. Perez-Campomanes|10.1109/CCWC57344.2023.10099087|Automation;earth blocks;mechanical properties;Agave fiber;Agave set;Earth;Productivity;Automation;Transportation;Soil;Optical fiber communication;Mechanical factors|
|[Mechatronic design for load-bearing masonry construction based on BIM methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099229)|L. W. Torpoco-Lopez; T. J. Cochachi-Rubio; R. M. Olivera-Pérez; S. I. Del Carpio-Ramirez; J. R. Ortiz-Zacarias; G. Perez-Campomanes|10.1109/CCWC57344.2023.10099229|Simulation;Computational modeling;Masonry;Robot localization;Building Information Modeling;Automation;Solid modeling;Three-dimensional displays;Robot kinematics;Computational modeling;Mathematical models;Software;Regulation|
|[A Blockchain-based IoT Security Solution Using Multichain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099128)|S. Ismail; H. Reza; H. K. Zadeh; F. Vasefi|10.1109/CCWC57344.2023.10099128|IoT;Blockchain;Multichain;Security;Scalability;Conferences;Supply chains;Buildings;Blockchains;Planning;Security|
|[Integration of an IoT System for Monitoring the Thermal Comfort of a Barn with a Trombe Wall in the High Andean Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099104)|J. A. Villanueva; X. H. Rojas; C. C. Rojo|10.1109/CCWC57344.2023.10099104|IoT;automatic system;smart sensors;ESP8266;Temperature sensors;Temperature measurement;Temperature distribution;Animals;Databases;Economics;Agriculture;Climate change;Internet of Things;South America|
|[FLODAREM: Intelligent Flood Detection and Dam Reservoir Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099091)|P. K. Sharma; S. Basu; K. Bairagi; A. Ahmed|10.1109/CCWC57344.2023.10099091|Flood detection;Sensors;Monitoring;Dam reservoir;Solar power;Microcontroller;Temperature sensors;Dams;Prototypes;Humidity;Maintenance engineering;Reservoirs;Sensor systems|
|[A Quantum Key Distribution Network Routing Performance Based on Software-Defined Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099323)|V. Monita; R. Munadi; I. D. Irawati|10.1109/CCWC57344.2023.10099323|QKD network;SDN;OSPF;RIP;Packet loss;Network security;Routing;Throughput;Routing protocols;Quantum key distribution;Delays|
|[Infrastructure-as-Code in Open-Networking: Git, Ansible, and Cumulus-Linux Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099084)|G. Salazar-Chacón; D. M. Parra|10.1109/CCWC57344.2023.10099084|Open-Networking;Network Automation Server;Cumulus-Linux;Ansible;VXLAN;Git;Automation;Linux;Emulation;Pipelines;Process control;Tunneling;Servers|
|[Avionics Design of a Sub-Orbital Launch and Recovery System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099168)|R. E. Yanez; B. Williams; Y. Choi|10.1109/CCWC57344.2023.10099168|Aerospace;Microgravity;Recovery Systems;High Altitude;Test Platform;Industries;Manufacturing processes;Power distribution;Aerospace electronics;Orbits;Manufacturing;Telecommunications|
|[A Discriminative DeepLab Model (DDLM) for Surface Anomaly Detection and Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099181)|N. K. Gyimah; K. D. Gupta; M. Nabil; X. Yan; A. Girma; A. Homaifar; D. Opoku|10.1109/CCWC57344.2023.10099181|Anomaly Detection;Anomaly Localization;Re-constructive sub-network;Discriminative sub-network;Location awareness;Visualization;Conferences;Computational modeling;Manuals;Anomaly detection;Image reconstruction|
|[Analysis of Automated Skin Disease Classification Exploiting Different Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099354)|T. R. Anik; P. Talukder; I. Faruki; I. S. Ibn Rahman; E. Hossain|10.1109/CCWC57344.2023.10099354|Machine Learning;Deep Learning;Computer Vision;Pattern Recognition;Skin Diseases;Image Processing;Deep learning;Support vector machines;Computational modeling;Neural networks;Melanoma;Feature extraction;Skin|
|[A Low Parametric CNN Based Solution to Efficiently Detect Brain Tumor Cells from Ultrasound Scans](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099302)|M. A. Islam; S. A. Noshin; M. R. Islam; M. F. Razy; S. Antara; M. T. Reza; M. Z. Parvez|10.1109/CCWC57344.2023.10099302|Brain Tumor;CNN;Binary Classification;Deep Learning;Binary Cross Entropy;Dataset;Br35H;MRI;CAD;Training;Solid modeling;Computational modeling;Magnetic resonance imaging;Brain modeling;Data models;Reliability|
|[Identification of Motifs in Aptamers Using MEME Analysis to aid design of Aptasensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099358)|A. Takayama; A. Medina; S. Pecic; A. Mohapatra|10.1109/CCWC57344.2023.10099358|Aptamers;MEME analysis;motifs;biosensors;steroids;Measurement;Costs;Target recognition;Databases;Conferences;Antibodies;Biosensors|
|[Predicting motifs and secondary structure of steroid aptamers using APTANI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099310)|A. Medina; A. Takayama; A. Mohapatra; S. Pecic|10.1109/CCWC57344.2023.10099310|Aptamers;SELEX;motifs;secondary structure;APTANI;Visualization;Systematics;RNA;Hydrogen;Throughput;Libraries;Iterative methods|
|[Fixed Wing UAV-based Non-Terrestrial Networks for 5G millimeter wave Connected Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099281)|M. S. Alamgir; B. Kelley|10.1109/CCWC57344.2023.10099281|Unmanned aerial vehicles (UAV);5G millimeter wave Connected Vehicles;Non-terrestrial Networks;High altitude platform stations (HAPS);Beamforming;Beam Selection;multi-armed bandit;Rotary wing UAV;Fixed wing UAV;Meters;Analytical models;Connected vehicles;5G mobile communication;Simulation;Machine learning;Autonomous aerial vehicles|
|[Fuzzy Classification of Color Carrots (Dacus Carota) using Raspberry Pi towards Farming 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099149)|M. J. Villaseñor-Aguilar; J. A. Padilla-Medina; J. Prado-Olivarez; S. Martinez-Diaz; I. -I. Méndez-Gurrola; A. I. Barranco-Gutiérrez|10.1109/CCWC57344.2023.10099149|Agriculture;Carrots;Raspberry Pi 3;Fuzzy Logic;Pixel;Training;Wireless communication;Energy consumption;Wireless sensor networks;Image color analysis;Agriculture;Sensors|
|[DS-HPE: Deep Set for Head Pose Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099159)|V. Menan; A. Gawesha; P. Samarasinghe; D. Kasthurirathna|10.1109/CCWC57344.2023.10099159|Head Pose Estimation;Deep Sets;Landmark-based method;Human computer interaction;Computer vision;Conferences;Pose estimation;Graphics processing units;Computer architecture;Benchmark testing|
|[The Precise 3D Reconstruction of Human Faces Based on 2D Photograph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099272)|X. Suo|10.1109/CCWC57344.2023.10099272|photogrammetry;3D reconstruction;Camera;Multiple Views;2D images;depth perception;Interpolation;Computer vision;Three-dimensional displays;Face recognition;Conferences;Computer architecture;Cameras|
|[Probability Density Functions of the Subspace-Based Direction of Arrival Estimators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099094)|R. Al Kinani; K. Adhikari|10.1109/CCWC57344.2023.10099094|Direction of arrival;coprime array;ESPRIT;MNM;MUSIC;nested array;probability density function;Direction-of-arrival estimation;Conferences;Probability density function;Gaussian distribution;Density functional theory;Multiple signal classification;Signal to noise ratio|
|[A Robust Sparse Fractal Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099275)|K. Goel; M. Aggarwal; S. Kar|10.1109/CCWC57344.2023.10099275|Sparse array;fractal array;DOA(direction of arrival estimation);DOF(degree of freedom);MUSIC(Multiple Signal Classification);ESPRIT(Estimation of Signal Parameters via Rotational Invariance Technique algorithms);ULA(uniform linear array);Geometry;Direction-of-arrival estimation;Conferences;Estimation;Mean square error methods;Fractals;Robustness|
|[Continuous Time Digital Signal Processing and Signal Reconstruction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099213)|P. Jungwirth; W. M. Crowe|10.1109/CCWC57344.2023.10099213|digital signal processing;DSP;continuous time;CT-DSP;signal reconstruction;Nonlinear distortion;Signal processing algorithms;Linearity;Digital signal processing;Reconstruction algorithms;Signal reconstruction;Complexity theory|
|[Age of Information in Multichannel Slotted ALOHA: Should Collided Users Send First?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099107)|Y. Lai; T. -T. Chan; J. Liang; H. Pan|10.1109/CCWC57344.2023.10099107|Age of information;information freshness;multichannel slotted Aloha;random access;Protocols;Conferences;Computational modeling;Information age;Time measurement|
|[Full-Duplex mmWave Massive MIMO Systems with A Joint Self-Interference Cancellation Design Based on Zero-Space Projection and Angular Excluding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099379)|X. Xia; J. Zhao; F. Xu|10.1109/CCWC57344.2023.10099379|full-duplex;mm Wave;hybrid beamforming;self-interference cancellation;Radio frequency;Interference cancellation;Program processors;Array signal processing;Massive MIMO;Full-duplex system;Arrays|
|[A Survey of Data Dissemination Schemes in Secure Inter-Vehicle Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099380)|S. Bayan; U. Mohammad|10.1109/CCWC57344.2023.10099380|Data Dissemination;Delay-based dissemination;Probability-Based dissemination;Cluster-Based dissemination;Geocast-Based Dissemination;Security;V ANET;Roads;Computational modeling;Distributed databases;Routing;Communications technology;Data models;Safety|
|[Ultra-Wideband Edge Trimmed Bowtie Antenna for X-band Radar Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099062)|P. Priyalatha; P. Choudhary; R. Kumari|10.1109/CCWC57344.2023.10099062|Ultra-wideband;X-band;radar;radial stub;tapered feedline;edge trimmed patch;bowtie;defense communication;Wireless communication;Conferences;Bandwidth;Radar;Radar antennas;Antenna feeds;Ultra wideband antennas|
|[New Reward-Clipping Mechanism in Deep -Learning Enabled Internet of Things in 6G to Improve Intelligent Transmission Scheduling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099362)|M. Alhartomi|10.1109/CCWC57344.2023.10099362|AI;IoT;URLLC;packet error rate;6G;deep-RL;nan|
|[Compact Low-pass Filtering-response Wilkinson Power Divider with Wide Harmonic Suppression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099359)|A. Mandal; T. Moyra; P. Paul|10.1109/CCWC57344.2023.10099359|Wilkinson;Low-pass;T-shaped stub;Filtering power divider;Wide attenuation band;Defected ground structures;Equal power division;Power dividers;Microstrip filters;Microstrip resonators;Resonator filters;Low-pass filters;Insertion loss;Attenuation|
|[Performance of Battery-free BackCom in Uplink NOMA Systems with Joint Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099336)|S. Angelucci; R. Valentini; P. Di Marco; F. Santucci|10.1109/CCWC57344.2023.10099336|NOMA;Backscattering;SIC;Joint Detection;Performance evaluation;NOMA;Interference cancellation;Aggregates;Bit error rate;Detectors;Reflection coefficient|
|[Econometrics and Manufacturing Industries Retail Volumes Forecast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099352)|K. Salunkhe; S. Gonge; R. Joshi; K. Kotecha; V. Basalalli; P. Shah|10.1109/CCWC57344.2023.10099352|Manufacturing Industries;Economic Factors;Prediction;Manufacturing industries;Leadership;Conferences;Collaboration;Production;Market research;Manufacturing|
|[Big Data Approach For IoT Botnet Traffic Detection Using Apache Spark Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099385)|O. Arokodare; H. Wimmer; J. Du|10.1109/CCWC57344.2023.10099385|Apache Spark;big data;intrusion detection system;machine learning;Support vector machines;Machine learning algorithms;Conferences;Botnet;Intrusion detection;Cluster computing;Big Data|
|[Predicting Chronic Kidney Disease Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099221)|A. Farjana; F. T. Liza; P. P. Pandit; M. C. Das; M. Hasan; F. Tabassum; M. H. Hossen|10.1109/CCWC57344.2023.10099221|Kidney disease;Machine Learning Technique;Kidney disease prediction;classification algorithms;LighGBM;Support vector machines;Schedules;Machine learning algorithms;Sociology;Static VAr compensators;Predictive models;Chronic kidney disease|
|[Interpretable Bangla Sarcasm Detection using BERT and Explainable AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099331)|R. Anan; T. S. Apon; Z. T. Hossain; E. A. Modhu; S. Mondal; M. G. R. Alam|10.1109/CCWC57344.2023.10099331|Machine Learning;Natural Language Processing;Sarcasm Detection;BERT;Sentiment analysis;Machine learning algorithms;Video on demand;Social networking (online);System performance;Memory management;Machine learning|
|[Short- Term Electric Vehicle Demand Forecasts and Vehicle-to-Grid (V2G) Idle- Time Estimation Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099356)|P. Rajagopalan; J. Thornby; P. Ranganathan|10.1109/CCWC57344.2023.10099356|Electric Vehicles;Charging station;Machine Learning;Vehicle- to-Grid;Vehicle-to-grid;Load forecasting;Static VAr compensators;Estimation;Support vector machine classification;Artificial neural networks;Charging stations|
|[Towards Inclusive Privacy Consenting for GDPR Compliance in Visual Surveillance: A Survey Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099256)|A. Chattopadhayay; I. Rijal|10.1109/CCWC57344.2023.10099256|inclusive;privacy;consenting;GDPR;compliance;visual;surveillance;systems;public;marginalized;under-represented;under-served;under-privileged;Visualization;Privacy;Technological innovation;Data privacy;TV;Surveillance;Sociology|
|[Machine Learning Based Pedestrian Detection and Tracking for Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099089)|T. Ward; S. Rashad; H. Elgazzar|10.1109/CCWC57344.2023.10099089|Autonomous vehicles;Computer vision;Machine learning;Object detection;Object recognition;Roads;Object detection;Machine learning;Predictive models;Mathematical models;Trajectory;Safety|
|[Proactive Actuator Update Management for Reliability Enhancement in Cyber-Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099068)|C. M. Krishna|10.1109/CCWC57344.2023.10099068|Feedback delay;cyber-physical systems;task scheduling;real-time systems;Actuators;Conferences;Cyber-physical systems;Reliability;Thermal stresses;Stress|
|[Design & Verification of AMBA AHB-Lite Memory Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099257)|A. Kommineni; M. K. Gundu; Y. Kim; S. Jadhav|10.1109/CCWC57344.2023.10099257|Verilog;SoC;Memory Controller;AMBA 3;AHB-Lite;System Verilog;verification;Employee welfare;Protocols;Program processors;Conferences;Generators;System-on-chip;Hardware design languages|
|[Design of an Automation System for the Production of Soil-Cement Blocks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099085)|D. F. Matamoros-Paitán; E. J. Dolorier-Flores; F. Alanya-Almonacid; A. J. Valenzuela-Inga; J. G. Benito-Zuñiga; J. A. Huamán-Chavez; L. N. Mantari-Ramos; G. Perez-Campomanes|10.1109/CCWC57344.2023.10099085|Compressive strength;soil blocks;cost analysis;automation;Resistance;Gases;Costs;Automation;Process control;Vitrification;Software|
|[Exploring Technical Capabilities of Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099360)|T. K. Mohd; E. M. Tesfa|10.1109/CCWC57344.2023.10099360|Unmanned aerial vehicles;tello;drones;UAVs;quadcopters;DJIMavic2;Sky Viper Journey;DJI-Mavic3;QuadAirDrone;Computer hacking;Law enforcement;Conferences;Autonomous aerial vehicles;Safety;Critical infrastructure;Security|
|[Mechatronic design and monitoring of a tuned mass damper in structural vibrations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099321)|J. L. Nizama-Mallqui; E. R. Mayta-Mercado; I. E. Sarmiento-Ñaupa; A. J. Valenzuela-Inga; J. G. Benito-Zuñiga; L. N. Mantari-Ramos; G. Perez-Campomanes|10.1109/CCWC57344.2023.10099321|Shock absorber;Structural Vibration;Arduino;Gyroscope;Vibrations;Visualization;Displacement control;Buildings;Earthquakes;Shock absorbers;Software|
#### **2023 IEEE International Conference on Mechatronics (ICM)**
- DOI: 10.1109/ICM54990.2023
- DATE: 15-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Intelligent Static Calibration of Industrial Robots using Artificial Bee Colony Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101918)|M. A. Khanesar; M. Yan; P. Kendal; M. Isa; S. Piano; D. Branson|10.1109/ICM54990.2023.10101918|Industrial robot calibration;laser tracker system;intelligent optimization;artificial bee colony;Three-dimensional displays;Mechatronics;Service robots;Measurement by laser beam;Kinematics;Position measurement;Open loop systems|
|[Energy Localization in Spring-Motor Coupling System by Switching Mass Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101963)|K. Miyahara; S. Katsura|10.1109/ICM54990.2023.10101963|energy control;energy localization;mass control;spring-motor coupling system;Couplings;Location awareness;Mechatronics;Switches;Control systems;Regulation;Mechanical energy|
|[Assistance Torque Control Based on Musculoskeletal Hexagon Output Distribution for Upper Limb Exoskeleton](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101922)|H. Morishita; T. Murakami|10.1109/ICM54990.2023.10101922|assistive exoskeleton;bi-articular muscle;musculoskeletal model;physical human robot interaction;Torque;Calculators;Exoskeletons;Torque control;Muscles;Particle measurements;Safety|
|[Control Architectures for Metamaterials in Vibration Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101931)|V. F. Buskes; M. B. Kaczmarek; J. C. Veenstra; C. Coulais; S. H. HosseinNia|10.1109/ICM54990.2023.10101931|Mechatronic Systems;Vibration Control;Metamaterials;Active metamaterials;Decentralised control;Damping;Actuators;Mechatronics;Regulators;Force;Computer architecture;Metamaterials|
|[Discrete-time adaptive pole placement control of a multi-inertia system with high-frequency resonance and time-delay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101896)|T. Seki; Y. Masui; N. Ushiro; N. Uchiyama|10.1109/ICM54990.2023.10101896|adaptive control;pole placement;multi-inertia system;PI control;PI control;Torque;Fluctuations;Mechatronics;Simulation;Design methodology;Angular velocity|
|[Adaptive Velocity Estimation for Lagrangian Systems using Modulating Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101935)|M. Noack; J. Reger; J. Jouffroy|10.1109/ICM54990.2023.10101935|modulating functions;robotic equation;Lagrange formalism;simultaneous parameter and state estimation;Adaptation models;Tensors;Kinematics;Transforms;Robot sensing systems;Mathematical models;Data models|
|[Mechatronic Design for Multi Robots-Insect Swarms Interactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102026)|F. Rekabi-Bana; M. Stefanec; J. Ulrich; E. E. Keyvan; T. Rouček; G. Broughton; B. Y. Gündeǧer; Ö. Sahin; A. E. Turgut; E. Şahin; T. Krajník; T. Schmickl; F. Arvin|10.1109/ICM54990.2023.10102026|nan;Visualization;Mechatronics;Tracking;Databases;Machine vision;Insects;Data models|
|[Fitting-based Cutting Force Estimation for Machine Tool with Encoder Resolution Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101950)|T. Hayashi; H. Fujimoto; Y. Isaoka; Y. Terada|10.1109/ICM54990.2023.10101950|Machine tool;cutting force estimation;twoinertia system;disturbance observer;Industries;Quantization (signal);Mechatronics;Force;Estimation;Observers;Machine tools|
|[A New Design of Redundant 7-DOF Parallel Robot with Large Workspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101947)|S. Sakurai; S. Katsura|10.1109/ICM54990.2023.10101947|kinematics;parallel robot;redundant robot;motion control;Robust control;Parallel robots;Mechatronics;Redundancy;Position control;Prototypes;Kinematics|
|[Minimum curvature path planning for a dual stage positioning system*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101873)|M. Monte; R. Oboe; E. Siego; D. Pilastro; S. Bizzotto|10.1109/ICM54990.2023.10101873|Dual-stage positioning system;Path Planning;Splines;Quadratic programming;Vibrations;Actuators;Mechatronics;Programming;End effectors;Trajectory;Quadratic programming|
|[State-oriented evaluation of observability and sensor placement for mechanical estimation applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102033)|J. Staiger; L. Mazzanti; F. Naets|10.1109/ICM54990.2023.10102033|Observability;Gramians;sensor selection;estimation;Measurement;Sensor placement;Mechatronics;Estimation;Observability;Ellipsoids|
|[Preliminary Study of Object Recognition by Converting Physical Responses to Images in Two Dimensions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101938)|K. Yane; T. Nozaki|10.1109/ICM54990.2023.10101938|object recognition;convolutional neural network;hybrid control;motion generation;Robot motion;Recurrent neural networks;Simulation;Robot vision systems;Force;Estimation;Machine learning|
|[Modified A* Algorithm for Optimal Motion Trajectory Generation of Rotary Cranes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102084)|A. Farrage; H. Takahashi; K. Terauchi; S. Sasai; H. Sakurai; M. Okubo; N. Uchiyama|10.1109/ICM54990.2023.10102084|Rotary crane;Trajectory generation;A* algorithm;Time-optimization;Cranes;Mechatronics;Simulation;Heuristic algorithms;Dynamics;Kinematics;Trajectory|
|[Grasping complex shapes with the integration of high-speed vision and machine learning in a dynamic situation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101926)|H. Kawahara; T. Senoo; I. Ishii|10.1109/ICM54990.2023.10101926|nan;Target tracking;Mechatronics;Limiting;Shape;Machine vision;Computational modeling;Estimation|
|[High-performance Aluminum Scandium Nitride MEMS energy harvester with wafer-level integrated micromagnets for contactless rotational motion harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101917)|T. Dankwort; M. Ahmed; S. Grünzig; A. Khare; B. Gojdka|10.1109/ICM54990.2023.10101917|MEMS energy harvesting;magnetic plucking;AlScN;machine monitoring;IoT;IIoT;Micromechanical devices;Degradation;Mechatronics;Piezoelectric transducers;Scandium;Broadband communication;Monitoring|
|[A Voice-Controlled Motion Reproduction Using Large Language Models for Polishing Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101966)|Y. Tanaka; S. Katsura|10.1109/ICM54990.2023.10101966|motion control;motion reproduction system;natural language processing;large language models;polishing robot;transfer of skills;human-robot interaction;robot teaching;Service robots;Source coding;Robot control;Process control;Speech recognition;Production facilities;Task analysis|
|[Estimating friction coefficient using generative modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101932)|M. Otoofi; W. J. B. Midgley; L. Laine; H. Leon; L. Justham; J. Fleming|10.1109/ICM54990.2023.10101932|tyre-road friction;semantic segmentation;regression;deep neural networks;Visualization;Friction;Semantic segmentation;Force;Estimation;Feature extraction;Real-time systems|
|[Development of Magnetic Geared Screw Two-Degree-of-Freedom Motor with Halbach Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102004)|Y. Hatta; K. Ito|10.1109/ICM54990.2023.10102004|Magnetic geared screw;Magnetic screw;Magnetic gear;Geared motor;2-DOF motor;Torque;Mechatronics;Force;Magnetic gears;Fasteners;Magnetic analysis|
|[Dual MPC for Adaptive Cruise Control with Unknown Road Profile](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102091)|Z. Li; J. Jiang; W. -H. Chen|10.1109/ICM54990.2023.10102091|Adaptive Cruise Control;Dual Model Predictive Control;Unknown Road Profile;nan|
|[Data-Driven Iterative optimization of TDOF Controller with Rational Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102075)|T. Kong; H. Jung; S. Oh|10.1109/ICM54990.2023.10102075|iterative optimization;data-driven method;feedforward controller;disturbance observer;Mechatronics;Simulation;Resonant frequency;Iterative algorithms;Disturbance observers;Feedback control;Feedforward systems|
|[Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101984)|Y. Li; E. -J. v. Kampen|10.1109/ICM54990.2023.10101984|adaptive control;optimal control;UAV;value iteration;Adaptation models;Mechatronics;Costs;Simulation;Optimal control;Autonomous aerial vehicles;Approximation algorithms|
|[Soft Robotic Tongue that Mimicking English Pronunciation Movements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102027)|E. Krisdityawan; S. Yokota; A. Matsumoto; D. Chugo; S. Muramatsu; H. Hashimoto|10.1109/ICM54990.2023.10102027|pneumatic network tongue;soft robotics;FEM simulation;English pronunciation;Actuators;Solid modeling;Tongue;Three-dimensional displays;Mechatronics;Deformation;Soft robotics|
|[Integrated Hydromechatronic Actuators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101880)|A. Plummer|10.1109/ICM54990.2023.10101880|additive manufacture;digital hydraulics;digital pumps;switched inertance;servovalves;servohydraulics;Robust control;Additives;Pumps;Switches;Seals;Valves;Energy efficiency|
|[A Novel Disturbance Device for Aerial Manipulation Experiments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101979)|B. J. Marshall; J. Knowles; Y. Yan; C. Liu|10.1109/ICM54990.2023.10101979|Aerial manipulator;environmental interaction;observer design;Mechatronics;Sockets;Friction;Force;Dynamics;Observers;Task analysis|
|[A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102013)|S. Yu; X. Pan; A. Georgiou; B. Chen; I. M. Jaimoukha; S. A. Evangelou|10.1109/ICM54990.2023.10102013|nan;Biological system modeling;Computational modeling;Electric vehicles;Energy efficiency;Robustness;Numerical models;Safety|
|[Generation of Self-oscillation in a Flexible Rope using Boundary Two-Relay Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102093)|L. T. Aguilar; Y. Orlov|10.1109/ICM54990.2023.10102093|self-oscillation;flexible rope;distributed parameter system.;Asymptotic stability;Mechatronics;Tracking;Partial differential equations;Simulation;Ordinary differential equations;Stability analysis|
|[Mechatronic Swarm and its Virtual Commissioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101912)|T. Lyu; A. Lashchev; S. Patil; U. D. Atmojo; V. Vyatkin|10.1109/ICM54990.2023.10101912|Intelligent mechatronics;swarm intelligence;plug and produce;IEC 61499;virtual commissioning.;Mechatronics;Programming;IEC Standards;Standards|
|[Robotic Control for Vibration Reduction of Swinging Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101973)|R. v. d. Kruk; A. J. v. Noorden; T. Oomen; R. v. d. Molengraft; H. Bruyninckx|10.1109/ICM54990.2023.10101973|trajectory generation;motion control;suspended products;food packaging;input shaping;suction cup gripper;Vibrations;Liquids;Dairy products;Robot kinematics;Bandwidth;Aerospace electronics;Trajectory|
|[Accuracy Assessment of Hand-Eye Calibration Techniques in Uncertain Environments for Vision Guided Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102016)|I. Enebuse; B. K. K. Ibrahim; I. M. Foo; R. S. Matharu; H. Ahmed|10.1109/ICM54990.2023.10102016|Hand-eye calibration;Passive calibration;Robot-hand transform;Vision guided robot;Computer vision;Active calibration.;Uncertainty;Mechatronics;Decision making;Calibration;Robots|
|[Stable Electrocardiogram Measurement Using Capacitive-Coupled Electrodes*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102001)|R. Fujita; T. Ohhira; H. Hashimoto|10.1109/ICM54990.2023.10102001|capacitance-coupled electrodes;Analog circuit;Electrocardiogram;R-R wave Interval;Overlapping clothing;Electrodes;Clothing;Electrocardiography;Signal processing;Sensor phenomena and characterization;Capacitance;Skin|
|[A multimodal teleoperation interface for human-robot collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102060)|W. Si; T. Zhong; N. Wang; C. Yang|10.1109/ICM54990.2023.10102060|Immersive teleoperation;Human-in-the-loop;Human-robot interface;Visualization;Solid modeling;Multimodal sensors;NASA;Collaboration;Virtual environments;Teleportation|
|[Direct Yaw Moment Control for Electric Vehicles with Variable-Rate-Slip-Ratio-Limiter Based Driving Force Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102086)|T. Ueno; B. -M. Nguyen; H. Fujimoto|10.1109/ICM54990.2023.10102086|Direct yaw moment control;Driving force control;Electric vehicles;Slip ratio control;Variable-rate-slip-ratiolimiter;Yaw-rate control;Mechatronics;Friction;Force;Wheels;Electric vehicles;Real-time systems;Force control|
|[Machine Learning-Based Performance Improvement of Bilateral Teleoperation with Hydraulic Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101985)|Y. Saito; H. Asai; T. Kitamura; K. Ohnishi|10.1109/ICM54990.2023.10101985|bilateral control;hydraulic actuator;teleoperation;machine learning;long short-term memory;force sensorless control;Recurrent neural networks;Mechatronics;Force;Time series analysis;Hydraulic actuators;Estimation;Machine learning|
|[Local Path Planning with Turnabouts for Mobile Robot by Deep Deterministic Policy Gradient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101921)|T. Nakamura; M. Kobayashi; N. Motoi|10.1109/ICM54990.2023.10101921|Motion control;reinforcement learning;mobile robot;robotics;path planning;Mechatronics;Roads;Reinforcement learning;Turning;Path planning;Production facilities;Planning|
|[Stiffness estimation of a lumped mass-spring system using sliding DFT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102017)|F. Vanbecelaere; M. Monte; K. Stockman|10.1109/ICM54990.2023.10102017|Parameter estimation;Sliding Discrete Fourier Transform (SDFT);Modal analysis;model order reduction;Adaptation models;Computational modeling;Discrete Fourier transforms;Estimation;Frequency estimation;Time measurement;Reduced order systems|
|[Evaluation Framework for the Comparison of Modular Drivetrain Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101900)|D. v. Os; K. Laurijssen; H. Vansompel; P. Sergeant; N. Divens; K. Stockman|10.1109/ICM54990.2023.10101900|Modularity;Simulation;Mechatronic systems;Evaluation framework;Shafts;Energy consumption;Visualization;Costs;Torque;Mechatronics;Tracking|
|[An Overview on Hybrid Integrator-Gain Systems with applications to Wafer Scanners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102062)|M. Heertjes; S. v. D. Eijnden; B. Sharif|10.1109/ICM54990.2023.10102062|hybrid integrator-gain systems;motion systems;nonlinear PID-control;reset control;Semiconductor device modeling;Uncertainty;PI control;Mechatronics;Control design;Switches;Stability analysis|
|[Contact Force Distribution Using Centroidal Momentum Feedback for Quadruped Locomotion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101987)|E. C. Ozcinar; O. Bebek; B. Ugurlu|10.1109/ICM54990.2023.10101987|quadruped walking;locomotion control;contact force distribution;Legged locomotion;Torso;Fluctuations;Computational modeling;Dynamics;Force;Angular velocity|
|[Rack force estimation from standstill to high speeds by hybrid model design and blending](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102078)|F. Snobar; A. Michalka; M. Horn; C. Strohmeyer; K. Graichen|10.1109/ICM54990.2023.10102078|nan;Mechatronics;Computational modeling;Force;Dynamics;Estimation;Production;Tires|
|[Drowsy Driver Detection System For Poor Light Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101972)|A. Ibnouf; A. Fadlallah; M. Ali; A. Zidouri|10.1109/ICM54990.2023.10101972|Drowsy Driver;Image Enhancement;Face Recognition;Eye Detection;Deep learning;Face recognition;Transportation industry;Lighting;Safety;Convolutional neural networks;Standards|
|[Variable Stiffness Improves Safety and Performance in Soft Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102076)|M. Aydin; E. Sariyildiz; C. D. Tawk; R. Mutlu; G. Alici|10.1109/ICM54990.2023.10102076|soft gripper;monolithic;compliant actuator;3D printing;variable stiffness;pneumatic control;Bellows;Service robots;Trajectory tracking;Scalability;Fingers;Grasping;Soft robotics|
|[Handheld Device Design for Robotic Teleoperation based on Multi-Sensor Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102054)|L. Xie; D. Huang; Z. Lu; N. Wang; C. Yang|10.1109/ICM54990.2023.10102054|Teleoperation;Handheld device;Visual-Inertial fusion;Error State Kalman Filter;Micromechanical devices;Visualization;Tracking;Position measurement;Robot sensing systems;Fiducial markers;Sensors|
|[Developing power-assisted two-wheeled luggage-carrying robot for stair-lifting using admittance control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101928)|K. Nagaya; T. Ohhira; H. Hashimoto|10.1109/ICM54990.2023.10101928|Two-Wheeled Inverted Pendulum Robot;Human Interaction;Power-Assist;Human Load Reduction;Mechatronics;Force;Stairs;Behavioral sciences;Robots;Admittance control|
|[Frequency-domain Analysis for Infinite Resets Systems*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101919)|X. Zhang; S. H. HosseinNia|10.1109/ICM54990.2023.10101919|reset control system;infinite-reset systems;frequency-domain analysis;sensitivity functions;Analytical models;Sensitivity;Mechatronics;Frequency-domain analysis;Predictive models;Control systems;Time factors|
|[A Stability Analysis for the Reaction Torque Observer-based Sensorless Force Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101940)|E. Sariyildiz|10.1109/ICM54990.2023.10101940|discrete-time control;disturbance observer;reaction force observer;robust force control;robust stability and performance.;Torque;Mechatronics;Microcontrollers;Force;Stability analysis;Disturbance observers;Design tools|
|[Energy-Efficient Control of Bearingless Linear Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102047)|R. Hosseinzadeh; F. Martin; M. Hinkkanen|10.1109/ICM54990.2023.10102047|Artificial neural networks;bearingless;energy efficiency;linear actuator;magnetic levitation;table lookup;Three-dimensional displays;Simulation;Production;Artificial neural networks;Prediction algorithms;Minimization;Permanent magnet motors|
|[A Coil Temperature Estimation for Disk Rotor Type Brushless DC Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101907)|M. Hattori; S. Murakami; T. Ohhira; H. Hashimoto|10.1109/ICM54990.2023.10101907|Electrical motor;Temperature estimation;Torque;Temperature measurement;Torque;Thermometers;Mechatronics;Brushless DC motors;Current measurement;Estimation|
|[Automotive Digital Twins: A Traversal Algorithm for Virtual Testing of Software over-the-air Updates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102057)|T. Fuchs; M. Zinser; K. Renatus; B. Bäker|10.1109/ICM54990.2023.10102057|Digital Twin;Over-the-Air;Software Updates;Traversal Algorithnb Virtual Testing;Mechatronics;Computer aided software engineering;Soft sensors;Software algorithms;Market research;Data models;Hardware|
|[Q-learning-based feedback linearization method for unknown dynamics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101884)|Y. Sun; X. Chen; W. He; L. Wang; E. F. Fukushima; J. She|10.1109/ICM54990.2023.10101884|Unknown nonlinear system;input-output feedback linearization;equivalent input interference;Q-learning;Linear systems;Q-learning;Mechatronics;System dynamics;Simulation;Neural networks;Position control|
|[Energy-efficient automated driving: effect of a naturalistic eco-ACC on a following vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102074)|J. Fleming; W. J. B. Midgley|10.1109/ICM54990.2023.10102074|Optimal control;eco-driving;automated driving;adaptive cruise control;ADAS;Energy loss;Uncertainty;Mechatronics;Microscopy;Optimal control;Mechanical power transmission;Energy efficiency|
|[H∞ Control of the Furuta Pendulum with Backlash and Analysis of the Effect of Bounded-Disturbance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101944)|M. Suwa; K. Hirata; Y. Nakamura|10.1109/ICM54990.2023.10101944|backlash nonlinearity;reachable set;Furuta pendulum;H∞ control;Mechatronics;Aerospace electronics;Numerical simulation;Time factors;Closed loop systems;Reachability analysis|
|[Super-Twisting Algorithm-based Sliding Mode Observer for Open-Circuit Fault Diagnosis in PWM Voltage Source Inverter in an In-Wheel Motor Drive System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102000)|M. Hashemi; M. Stolz; D. Watzenig|10.1109/ICM54990.2023.10102000|in-wheel motors;voltage source inverter;open switch fault diagnosis;super-twisting algorithm;sliding mode observer;Motor drives;Voltage source inverters;Signal processing algorithms;Observers;Pulse width modulation;Permanent magnet motors;Synchronous motors|
|[Development of cart providing constant steerability regardless of loading weight or position : 2nd Report: Proposal of the operational interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101891)|S. Aoki; S. Yokota; A. Matsumoto; D. Chugo; S. Muramatsu; H. Hashimoto|10.1109/ICM54990.2023.10101891|mechatronics;sensing;active steering caster;shopping cart;passive robotics;Force measurement;Torque;Mechatronics;Force;Loading;Strain measurement;Torque measurement|
|[State-parameter estimation for a helical gear transmission with pitting defects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101989)|T. Van der Veken; J. M. I. Jordan; B. Blockmans; M. Kirchner; F. Naets|10.1109/ICM54990.2023.10101989|diagnostics;gear;pitting;estimation;Shafts;Interpolation;Mechatronics;Gears;Estimation;Predictive models;Maintenance engineering|
|[Improved Intersample Behaviour of Non-Minimum Phase Systems using State-Tracking Iterative Learning Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102029)|L. Oei; K. Tsurumoto; W. Ohnishi|10.1109/ICM54990.2023.10102029|iterative learning control;multirate inversion;intersample behaviour;non-minimum phase;Mechatronics;Tracking;Task analysis;Oscillators;MIMO communication;Iterative learning control|
|[Posture Stabilization Control Compensating Variation of Body Center of Gravity in Underactuated System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101996)|H. Yajima; K. Ishizaki; Y. Miyata; M. Nawa; N. Kato; T. Murakami|10.1109/ICM54990.2023.10101996|Two-wheeled robot;Posture Stabilization Control;Repulsive compliance control(RCC);Target tracking;Mechatronics;Sociology;Aging;Steady-state;Statistics;Robots|
|[Model-free Road Friction Estimation using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102087)|J. B. William Midgley; J. Fleming; M. Otoofi|10.1109/ICM54990.2023.10102087|neural network;machine learning;magic formula;ABS;braking;friction estimation;nan|
|[Cooperative optimization-Based Efficient Autonomous Parameter Design for Cascade Feedback Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101957)|E. Kuroda; H. Noda; Y. Maeda; M. Iwasaki|10.1109/ICM54990.2023.10101957|autonomous design;cascade feedback control system;cooperative optimization;fast steering mirror;sensitivity characteristic;Sensitivity;Robust stability;Design methodology;Perturbation methods;Optimization methods;Control systems;Feedback control|
|[Levenberg-Marquardt method based Precise Angle Estimation for Eccentric Magnetic Absolute Encoders*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102073)|A. Takeyama; S. Komatsuzaki; T. Ohhira; H. Hashimoto|10.1109/ICM54990.2023.10102073|Magnetic Encoder;Sensor;Neural Network;PLL;Adaptive Filter;Adaptation models;Mechatronics;Magnetic separation;Neurons;Estimation;Harmonic analysis;Steady-state|
|[Simultaneous Ultrasonic Power Transfer and Depth Feedback for Active Medical Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101914)|T. Li; H. Fu; S. Theodossiades; S. Korossis|10.1109/ICM54990.2023.10101914|wireless power transfer;ultrasound;piezoelectricity;medical implants;position feedback;Wireless communication;Transmitters;Piezoelectric transducers;Wires;Receivers;Implants;Wireless power transfer|
|[Sliding Mode-Based Design of Unified Force and Position Control for Series Elastic Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101901)|M. Yokoyama; T. Shimono; T. Uzunović; Asif Šabanović|10.1109/ICM54990.2023.10101901|position control;force control;sliding mode control;two-mass system;series elastic actuator;Manifolds;Actuators;Transient response;Simulation;Force;Position control;Force control|
|[Sensor Fusion Helps to Improve Strip Speed Measurement in Cold Rolling Mills](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102056)|P. Ettler|10.1109/ICM54990.2023.10102056|sensor fusion;polynomial regression;inversevariance weighting;rolling mills;skin-pass mill;cluster mill;Strips;Mechatronics;Filtering;Delay effects;Metals;Sensor fusion;Velocity measurement|
|[Artificial Intelligence Enabled Digital Twin For Predictive Maintenance in Industrial Automation System: A Novel Framework and Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101971)|M. Siddiqui; G. Kahandawa; H. S. Hewawasam|10.1109/ICM54990.2023.10101971|Industry 4.0;Advance Manufacturing;Predictive Maintenance. Digital Twin;Artificial Intelligence;Industrial Automation System;Automation;Manufacturing processes;Mechatronics;Prediction algorithms;Real-time systems;Digital twins;Manufacturing|
|[Active Reduction of Gear Mesh Vibrations by Drive Torque Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101958)|D. Reitmeier; A. Mertens|10.1109/ICM54990.2023.10101958|Acoustic noise;Adaptive control;Vibration control;Electric vehicles;Vibrations;Shafts;Torque;Mechatronics;Gears;Torque control;Power transmission|
|[Robust two-degrees-of-freedom control of hydraulic drive with remote wireless operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101993)|R. Checchin; M. Ruderman; R. Oboe|10.1109/ICM54990.2023.10101993|PID controller;robust control design;hydraulic system;communication delay;remote control;robust stability;Wireless communication;Uncertain systems;Uncertainty;Robust stability;Hydraulic drives;Delay effects;Stability criteria|
|[Computationally-efficient Motion Cueing Algorithm via Model Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101964)|A. Chadha; V. Jain; A. M. R. Lazcano; B. Shyrokau|10.1109/ICM54990.2023.10101964|Motion cueing algorithm;driving simulator;model predictive control;Industries;Mechatronics;Tracking;Virtual environments;Prediction algorithms;Real-time systems;Human in the loop|
|[A basic study on admittance control using torsional torque control for a two-inertia system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101936)|T. Hayashi; S. Yamada; H. Fujimoto|10.1109/ICM54990.2023.10101936|Admittance control;Vibration suppression;Torsional control;Two-inertia system;Human-robot interaction;Vibrations;Mechatronics;Torque control;Resonant frequency;Time factors;Admittance control;Robots|
|[Improved Nonlinear Estimation in Thermal Networks Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10102071)|M. Schumann; S. Ebersberger; K. Graichen|10.1109/ICM54990.2023.10102071|nonlinear estimation;unscented Kalman filter;artificial neural network;Gaussian process regression;system identification;thermal network;Thermal resistance;Machine learning;Artificial neural networks;Sensors;Kalman filters;Electrical resistance measurement;Transistors|
#### **2023 15th International Conference on Developments in eSystems Engineering (DeSE)**
- DOI: 10.1109/DeSE58274.2023
- DATE: 9-12 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[iCan: Psychological Look in Future Using Augmented Reality Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099502)|H. Rastegari; H. Kolivand; P. Hawkins|10.1109/DeSE58274.2023.10099502|Augmented Reality;Psychology;future life;what I can do;Solid modeling;Three-dimensional displays;Systematics;Operating systems;Psychology;Mobile communication;User experience|
|[Prediction of Component Level Degradation in a Hydraulic Rig using Machine Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100050)|S. Rajasekar|10.1109/DeSE58274.2023.10100050|Predictive Maintenance;Machine Learning;Hydraulic Rig;Supervised;Unsupervised;Degradation;Supervised learning;Hydraulic systems;Feature extraction;Sensor systems;Robustness;Sensors|
|[AI-Based Portable Gesture Recognition System for Hearing Impaired People Using Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099999)|N. E. Al-Qaisy; B. R. Al-Kaseem; Y. Al-Dunainawi|10.1109/DeSE58274.2023.10099999|Artificial Intelligence;Neural Network;Sign Language Recognition;Smart Glove;Wearable Sensors;Training;Gesture recognition;Artificial neural networks;Assistive technologies;Sensor systems;Real-time systems;Usability|
|[Estimation the Design Paramters of Surface Course Asphalt Concrete by Cyclic and Static Loading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100288)|H. R. Salih; T. H. Fadil; A. S. Mahmoud|10.1109/DeSE58274.2023.10100288|Asphalt mixtures;Creep rate;Permanent deformation;cyclic and static loading tests;Asphalt;Deformation;Aggregates;Surface resistance;Creep;Loading;Estimation|
|[Predicting Strength Criteria of Hardened Concrete Containing Waste Glass Powder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099944)|S. S. Mahmoud; S. M. Hama; A. S. Mahmoud|10.1109/DeSE58274.2023.10099944|Mechanical Properties;Ultrasonic;splitting;Waste glass;Flexural strength;Silicon compounds;Powders;Glass;Production;Acoustics;Raw materials;Mechanical factors|
|[“Industrie 4.0” and Smart Manufacturing: A State of the Art Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100115)|A. T. Hassan; L. A. H. Al-Kindi; A. B. Abdulghafour|10.1109/DeSE58274.2023.10100115|Industry 4.0;Smart Manufacturing Model;Internet of Thing;Cyber Physical System;Big Data;Service robots;Decision making;Supply chains;Big Data;Real-time systems;Production facilities;Information and communication technology|
|[Deep learning model for binary classification of COVID-19 based on Chest X-Ray](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099555)|R. S. Saeed; B. K. Oleiwi|10.1109/DeSE58274.2023.10099555|Chest X-Ray;Convolutional neural network;COVID-19;Deep learning;Disease detection;Medical application;Medical images;COVID-19;Training;Deep learning;Ultrasonic imaging;Pandemics;Lung;X-ray imaging|
|[Deep Audio Embeddings and Attention Based Music Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100058)|S. Gupta|10.1109/DeSE58274.2023.10100058|music emotion recognition;music information re-trieval;music recommendation;playlist generation;audio embed-dings;attention networks;Training;Deep learning;Emotion recognition;Neural networks;Buildings;Music;Medical treatment|
|[The Probability of Connectivity in the 2 Way / 2 Lane Platoon-Based V2I Communication Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099509)|A. W. K. Al-Nasir; F. S. Mubarek|10.1109/DeSE58274.2023.10099509|VANETs;platoon;connectivity probability;V2I network;two-way road;two-lane road;Shape;Roads;Vehicle-to-infrastructure;Government;Routing;Mathematical models;Routing protocols|
|[Optimal Stability of Brushless DC Motor System Based on Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099574)|Y. A. Mashhadany; M. A. Lilo; S. Algburi|10.1109/DeSE58274.2023.10099574|Optimal Stability;Optimal Control;DC-link Variable Control;Matlab - Simulation;PID Controller;Brushless DC motors;Power system stability;Multilevel inverters;Transformers;Mathematical models;Transient analysis;Voltage control|
|[Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099655)|A. S. Alatrany; A. Hussain; S. S. J. Alatrany; J. Mustafina; D. Al-Jumeily|10.1109/DeSE58274.2023.10099655|Alzheimer's disease;ADNI;LOAD;Machine Learning;Early Prediction;Neuroimaging;Machine learning algorithms;Machine learning;Benchmark testing;Aging;Genetics;Linear discriminant analysis|
|[COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099799)|A. Yadav|10.1109/DeSE58274.2023.10099799|COVID-LiteNet;COVID-19 Detection;convolutional neural network;Deep Learning;Machine Learning;Chest X-Ray;Computer Vision;COVID-19;Industries;Pandemics;Instruments;Medical services;Convolutional neural networks;Task analysis|
|[Advanced Optimization Techniques & Its Application in AI-Powered Breast Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099678)|S. Das; S. Mukherjee|10.1109/DeSE58274.2023.10099678|Optimization;Weighted Blending;Ensemble;Breast cancer classification;Random Forest;XGBoost;Visualization;Sensitivity;Lung cancer;Machine learning;Manuals;Forestry;Needles|
|[Double Dual Convolutional Neural Network (D2CNN) for Copy-Move Forgery Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100318)|M. H. Farhan; K. Shaker; S. Al-Janabi|10.1109/DeSE58274.2023.10100318|Image forensics;copy-move forgery;deep learning;convolutional neural network;Measurement;Analytical models;Social networking (online);Digital images;Neural networks;Feature extraction;Forgery|
|[An Efficient Approach for Resilience and Reliability Against Cascading Failure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100283)|D. M. Vistro; A. U. Rehman; Z. Hameed|10.1109/DeSE58274.2023.10100283|Cascading Failure;Resilience;Reliability;Cascading Failure Resilience System (CSFR);Cloud computing;Costs;Simulation;Power system protection;Reliability engineering;Reliability;Resource management|
|[A Hybrid Digital and Optical Double Color Image Encryption Scheme Using a Nine-Dimensional Chaotic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100051)|R. A. Muttashar; R. S. Fyath|10.1109/DeSE58274.2023.10100051|Hybrid digital/optical encryption;Nine-dimensional (9D) chaotic;Double color image encryption;Fourier transforms;Image color analysis;Simulation;Color;Optical imaging;Entropy;Robustness|
|[Preliminary study: Professional Collaboration Application with Project Management Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099939)|T. K. Jun; S. Ramiah|10.1109/DeSE58274.2023.10099939|Project Management;Online Collaboration;Productivity;Productivity;Costs;Software architecture;Bibliographies;Collaboration;Project management;Computer architecture|
|[CNN Aided Surface Inspection for SMT Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099694)|M. C. Loo; R. Logeswaran; Z. A. A. Salam|10.1109/DeSE58274.2023.10099694|Automated optical inspection;convolution neural network;transfer learning;surface inspection;Training;Machine learning algorithms;Convolution;Image edge detection;Inspection;Classification algorithms;Convolutional neural networks|
|[Architectural Design and Recommendations for a Smart Wearable Device for Women's Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099522)|Manahil; R. Abdulla; M. E. Rana|10.1109/DeSE58274.2023.10099522|Women Safety;Wearable Devices;Smart Devices;Location-tracking;Fall-detection;Training;Visualization;Wearable computers;Sensor systems;Data mining;Fall detection;Intelligent sensors|
|[Early Prediction of COVID-19 Infection with IoT and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099492)|C. M. Pan; K. Shanmugam; M. E. Rana; M. Jayabalan|10.1109/DeSE58274.2023.10099492|Ubiquitous Computing;Wearable Sensors;Machine Learning;COVID-19 Infection Prediction;COVID-19;Computer viruses;Hospitals;Machine learning;Ubiquitous computing;Prediction algorithms;Feeds|
|[Improved Traditional Fitness Model by Applying Big Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100118)|M. E. Rana; L. Yanyu; V. A. Hameed; K. B. Nowshath|10.1109/DeSE58274.2023.10100118|Weight Loss;Fitness Model;Machine Learning;Regression;R Square;Mean Absolute Error (MAE);Industries;Resistance;Analytical models;Buildings;Muscles;Predictive models;Big Data|
|[Technology-Driven Implementation of Smart Entrances in Public Places During the COVID-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100044)|M. C. Yuen; M. E. Rana; K. Shanmugam; R. Abdulla|10.1109/DeSE58274.2023.10100044|Thermal Cameras;Fingerprint Recognition;Face Recognition;Iris Recognition;Object Detection;Cloud Computing;COVID-19;Temperature measurement;Cloud computing;Pandemics;Face recognition;Authentication;Object detection|
|[Automated Face Mask Detection using Artificial Intelligence and Video Surveillance Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099878)|G. Y. Shien; K. Shanmugam; M. E. Rana|10.1109/DeSE58274.2023.10099878|Ubiquitous Computing;Face Mask Detection;CCTV;Video Surveillance Management System;Convolutional Neural Network (CNN);COVID-19;Limiting;Automation;Face recognition;Computational modeling;Neural networks;Employment|
|[Implement of Intelligent Controller for 6DOF Robot Based on a Virtual Reality Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099597)|Y. A. Mashhadany; A. A. A. Alrawi; Z. T. Ibraheem; S. Algburi|10.1109/DeSE58274.2023.10099597|Intelligent Controller;6-DOF Robot;2DOF-PID Controller;Virtual Reality model;VRML Builder;D-H parameters;Solid modeling;Visualization;Process control;Virtual reality;Manipulators;Control systems;Mathematical models|
|[AD-Hoc Routing Protocols in WSN-WiFi based IoT in Smart Home](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099981)|S. W. Nourildean; M. D. Hassib; Y. A. Mohammed|10.1109/DeSE58274.2023.10099981|WSN;IoT;ZigBee;AODV;smart home;Wireless sensor networks;Zigbee;Smart homes;Throughput;Routing protocols;Peer-to-peer computing;Sensors|
|[Intelligent Detection System for a Distributed Denial-of - Service (DDoS) Attack Based on Time Series](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100180)|M. S. I. Alsumaidaie; K. M. A. Alheeti; A. K. Al-Aloosy|10.1109/DeSE58274.2023.10100180|Machine learning;DDoS detection;Cyber Security;Time series analysis;Machine learning algorithms;Computer hacking;Time series analysis;Surge protection;Companies;Programming;Denial-of-service attack|
|[Aspect-Based Sentiment Analysis on Movie Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099815)|B. W. S. Kit; M. H. Joseph|10.1109/DeSE58274.2023.10099815|Sentiment Analysis;Aspect-Based;Text Analysis;Machine Learning;Polarity;Movie;Reviews;Industries;Sentiment analysis;Machine learning;Companies;Predictive models;Motion pictures;Decision trees|
|[The Acceptance and Readiness of Micro-credentials and its Barriers in the Tech-related Job Market in Malaysia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099634)|K. Yueh; I. F. B. Kamsin; J. C. C. Fuh|10.1109/DeSE58274.2023.10099634|Micro-credentials;online learning;e-learning;tech industry;Industries;COVID-19;Pandemics;Ecosystems;Fourth Industrial Revolution;Personnel;Business|
|[Hybrid Zero-knowledge Access Control System in e-Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099775)|E. T. W. Chin; I. F. B. Kamsin; S. B. Amin; N. K. B. Zainal|10.1109/DeSE58274.2023.10099775|Access Control;e-Health;privacy;Access control;Authorization;Industries;Privacy;Scalability;Medical services;Search engines|
|[A Complete Log Files Security Solution Using Anomaly Detection and Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100200)|T. K. Chan; I. F. B. Kamsin; S. Amin; N. K. Zainal|10.1109/DeSE58274.2023.10100200|cybersecurity;blockchain technology;anomaly detection;log analysis;Scalability;Sociology;Intrusion detection;Sampling methods;Feature extraction;Blockchains;Fourth Industrial Revolution|
|[Design and Implementation of Algorithmic Stock Trading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100293)|P. Blackmun; S. Al-Sudani; D. Al-Jumeily|10.1109/DeSE58274.2023.10100293|Stock Trading;Algorithmic Methods;Financial Markets;Psychology;High frequency;Low latency communication;Investment;Testing|
|[Improving Prediction for taxi demand by using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099731)|M. M. Ibrahim; F. S. Mubarek|10.1109/DeSE58274.2023.10099731|Vehicular Social network VSN;passengers prediction;LSTM;ANN;Linear regression;Taxi request;Random Forest;Training;Error analysis;Social networking (online);Linear regression;Search problems;Task analysis;Public transportation|
|[Sentiment Classification of Drug Reviews Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099735)|M. A. -A. A. Hameed; K. Shaker; H. A. Khalaf|10.1109/DeSE58274.2023.10099735|Sentiment Classification;Drug Reviews;TF-IDF;Machine Learning;Drugs;Measurement;Sentiment analysis;Support vector machine classification;Feature extraction;Naive Bayes methods;Public healthcare|
|[A System Implementation: Point-of-Sales (POS) System Integrated with Business Intelligence (BI) Capability Focused on SME in Indonesia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099802)|F. M. Al Kautsaf; M. N. bin Mohd Nizam; K. S. Harun|10.1109/DeSE58274.2023.10099802|Business Intelligence;Point of Sales System;Business Decision Making;Small and Medium Enterprise (SME);Microsoft Power BI;Business Insights;Visualisation;Catalysts;Decision making;Libraries;Mobile applications;Business intelligence;Visual databases;Organizational aspects|
|[Camel Detection and Monitoring Using Image Processing and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100123)|M. Madi; Y. Basha; Y. Albadersawi; F. Alenezi; S. A. Mahmoud; D. h. Abd; D. Al-Jumeily; W. Khan; A. J. Hussien|10.1109/DeSE58274.2023.10100123|classification;Animal-vehicle collision;image processing;camel behavior;Degradation;Computer vision;Roads;Image processing;Wildlife;Machine learning;Behavioral sciences|
|[Design and Implementation a Low-Cost Smart House Automation System using Bluetooth and Sensor Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099823)|W. A. Majeed; S. A. Aliesawi|10.1109/DeSE58274.2023.10099823|Arduino;SHS;Bluetooth;Wireless Technology;Temperature sensors;Wireless sensor networks;Bluetooth;Personal digital devices;Speech recognition;Sensor systems;Safety|
|[Intracranial hemorrhage detection and classification from CT images based on multiple features and machine learning approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099988)|M. A. A. Majeed; O. M. Al Okashi; A. T. Alrawi|10.1109/DeSE58274.2023.10099988|Hemorrhage;CT Scan;Brain;Machine learning;Radio frequency;Training;Support vector machines;Computed tomography;Feature extraction;Brain modeling;Hemorrhaging|
|[Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100062)|R. D. Jalal; S. A. Aliesawi|10.1109/DeSE58274.2023.10100062|UWSNS;cluster-based routing;TEEN;particles swarm optimization algorithm;BAT;Wireless sensor networks;Protocols;Power demand;Target recognition;Clustering algorithms;Routing protocols;Energy efficiency|
|[COVID QA Network: A Specific Case of Biomedical Question Answering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099510)|A. Kumar; R. Bhargava; M. Jayabalan|10.1109/DeSE58274.2023.10099510|Question answering;question classification;transfer learning and domain adaptation;fine-tuning;BERT;COVID-19;Biological system modeling;Transfer learning;Bit error rate;Training data;Predictive models;Transformers|
|[Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100226)|K. M. A. Alheeti; A. Alzahrani; O. H. Jasim; D. Al-Dosary; H. M. Ahmed; M. S. Al-Ani|10.1109/DeSE58274.2023.10100226|Cyber-attacks;intrusion detection system;KNN;MSCAD;accuracy;Measurement;Training;Intrusion detection;Machine learning;Malware;Complexity theory;Reliability|
|[Performance Comparison of an implemented Wired and Wireless Micro Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099600)|A. O. Abdulridha; I. B. Al-Mashhadani; S. M. Hashim; K. A. Reshak|10.1109/DeSE58274.2023.10099600|Smart grid;Wire;Wireless;Smart Meter;Smart Socket;GSM;ZigBee;Bluetooth;PLC;SDG9;Wireless communication;Industries;GSM;Power cables;Zigbee;Voltage;Smart meters|
|[An Intelligent Routing Approach for Multimedia Traffic Transmission Over SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100250)|M. A. Jameel; T. Kanakis; S. Turner; A. Al-Sherbaz; W. S. Bhaya; M. Al-khafajiy|10.1109/DeSE58274.2023.10100250|multimedia traffic;QoE;QoS;SDN;reinforcement learning;Network topology;Telecommunication traffic;Bandwidth;Streaming media;Routing;Throughput;Delays|
|[Using Simulation for Investigating Emergency Traffic Situations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099681)|I. Makarova; G. Yakupova; P. Buyvol; E. Mukhametdinov; A. Abashev; J. Mustafina|10.1109/DeSE58274.2023.10099681|intelligent transport systems;traffic accident;traffic safety;emergencies;simulation model;Databases;Roads;Computational modeling;Urban areas;Stochastic processes;Time measurement|
|[BEsafe - Validating URLs and Domains with the aid of Indicator of Compromise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099965)|C. D. Khai; J. Juremi|10.1109/DeSE58274.2023.10099965|URL;Domains;Indicator of Compromise;Uniform resource locators;Privacy;Pandemics;Navigation;Phishing;Turning;Browsers|
|[Towards Building a System for Predicting Diabetes and related conditions using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099719)|U. Selv; S. Al-Sudani|10.1109/DeSE58274.2023.10099719|Diabetes prediction;Blood glucose level prediction;Long Short-Term Memory (LSTM);Recurrent Neural Network (RNN);Backpropagation Neural Network (BPNN);K-Nearest Neighbour (KNN);Recurrent neural networks;Sensitivity;Machine learning;Medical services;Predictive models;Diabetes;Glucose|
|[Dezvent - Digitalizing Attendance System with 2FA and Face Recognition Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100308)|Z. L. M. Yi; J. Juremi|10.1109/DeSE58274.2023.10100308|Web Attendance;2FA;Image Recognition;Face recognition;Authentication;Manuals;Security;Cyberattack;Testing|
|[Enhancing Intrusion Prevention in Snort System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099757)|S. Abdulrezzak; F. A. Sabir|10.1109/DeSE58274.2023.10099757|DoS;intrusion detection system;intrusion prevention system;Snort;Data centers;Force;Intrusion detection;Libraries;Internet;Safety;Security|
|[Inverse Kinematics Optimization for Humanoid Robotic Legs Based on Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100167)|H. S. Radeaf; M. Z. Al-Faiz|10.1109/DeSE58274.2023.10100167|inverse kinematics;D-H parameters;optimization;humanoid robotic legs;Legged locomotion;Atmospheric measurements;Humanoid robots;Kinematics;Particle measurements;Mathematical models;End effectors|
|[Identification of authentic and counterfeit Viagra tablets using near-infrared spectroscopic methods and machine learning algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100015)|S. Rowlands; D. A. -J. Obe; S. Assi|10.1109/DeSE58274.2023.10100015|Viagra;Authentic;Counterfeit;Near-infrared spectroscopy;Machine learning algorithms;Principal Component Analysis;Machine learning algorithms;Wavelength measurement;Raman scattering;Clustering algorithms;Morphology;Manufacturing;Public healthcare|
|[A comparative Time Series analysis of the different categories of items based on holidays and other events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099814)|S. Ghareeb; M. Mahyoub; J. Mustafina|10.1109/DeSE58274.2023.10099814|Machine Learning;Retail;Sales forecasting;supply chain;Time series;Machine learning algorithms;Social networking (online);Time series analysis;Urban areas;Predictive models;Boosting;Feature extraction|
|[Analysis of Feature Selection and Phishing Website Classification Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099697)|S. Ghareeb; M. Mahyoub; J. Mustafina|10.1109/DeSE58274.2023.10099697|Phishing website;cyber-attacks;Machine Learning;Random Forest;Logistic Regression;Ensemble Model;Deep Learning;Uniform resource locators;Radio frequency;Phishing;Flight recording;Sociology;Forestry;Feature extraction|
|[Sign Language Recognition using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100055)|M. Mahyoub; F. Natalia; S. Sudirman; J. Mustafina|10.1109/DeSE58274.2023.10100055|Sign Language;Sign Language Recognition;Deep Learning;Convolutional Neural Networks;Deep learning;Solid modeling;Three-dimensional displays;Gesture recognition;Assistive technologies;Transformers;Data models|
|[Low-Distortion MMSE Estimator for Speech Enhancement Based on Hahn Moments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100112)|A. S. Al-Zubaidi; B. M. Mahmmod; S. H. Abdulhussain; M. A. Naser; A. Hussain|10.1109/DeSE58274.2023.10100112|Hahn polynomials;Hahn moments;Speech Enhancement Algorithm;MMSE;Wiener filters;Laplace equations;Simulation;Nonlinear distortion;Signal processing algorithms;Sea measurements;Speech enhancement|
|[Automated Plant Disease Diagnosis in Apple Trees Based on Supervised Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099689)|P. Aich; A. A. Ataby; M. Mahyoub; J. Mustafina; Y. Upadhyay|10.1109/DeSE58274.2023.10099689|Machine Learning;Apple Trees;Disease Detection;Classification;Identification;Productivity;Plant diseases;Image color analysis;Biological system modeling;Crops;Machine learning;Robustness|
|[AIRBNB Price Prediction Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099909)|M. Mahyoub; A. A. Ataby; Y. Upadhyay; J. Mustafina|10.1109/DeSE58274.2023.10099909|Airbnb;XGBoost;Linear Regression;ANN;KNN;Analytical models;Atmospheric modeling;Biological system modeling;Pricing;Sharing economy;Predictive models;Tag clouds|
|[Abstract Pattern Image Generation using Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099871)|M. Mahyoub; S. H. Abdulhussain; F. Natalia; S. Sudirman; B. M. Mahmmod|10.1109/DeSE58274.2023.10099871|Abstract Pattern;Image Synthesis;Generative Adversarial Networks;Deep Convolutional GAN;Wasserstein GAN;Training;Industries;Deep learning;Image synthesis;Generative adversarial networks;Hyperparameter optimization;Textiles|
|[Semantic Segmentation and Depth Estimation of Urban Road Scene Images Using Multi-Task Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099504)|M. Mahyoub; F. Natalia; S. Sudirman; A. H. J. Al-Jumaily; P. Liatsis|10.1109/DeSE58274.2023.10099504|Urban Road Scene Analysis;Deep Learning;Multi-Task Networks;Semantic Segmentation;Depth Estimation;Training;Semantic segmentation;Roads;Estimation;Multitasking;Decoding;Task analysis|
|[Palm-sized Near-Infrared Spectroscopy and Machine Learning Analytics for the Detection of Endogenous Constituents and Drugs in Human Fingernails](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100270)|M. Wilson; D. A. -J. Obe; I. Abbas; I. Khan; J. Birkett; L. Tang; S. Assi|10.1109/DeSE58274.2023.10100270|Fingernails;Drug detection;Cocaine;Near-infrared spectroscopy;Machine learning analytics;Self-organising Maps;Drugs;Water;Proteins;Spectroscopy;Nails;Morphology;Machine learning|
|[Exploring the authentication of COVID-19 vaccines using Surface-enhanced handheld Raman spectroscopy (SERS) equipped with orbital Raster scattering and machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100028)|M. Watson; D. Al-Jumeily; J. Birkett; I. Khan; S. Assi|10.1109/DeSE58274.2023.10100028|Vaccine;Surface Enhanced Raman Spectroscopy;Excipients;Colloid;Correlation in Wavelength Space;Principal Component Analysis;COVID-19;Sensitivity;Pandemics;Raman scattering;Stability criteria;Authentication;Machine learning|
|[Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099479)|M. Mahyoub; T. Coombs; M. Jayabalan; J. Mustafina; A. Hussain|10.1109/DeSE58274.2023.10099479|Covid-19;X-RAY image;Lung;Infection;Engineering;COVID-19;Training;Computational modeling;Pulmonary diseases;Transfer learning;Lung;Robustness|
|[Data Augmentation Using Generative Adversarial Networks to Reduce Data Imbalance with Application in Car Damage Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100274)|M. Mahyoub; F. Natalia; S. Sudirman; P. Liatsis; A. H. Jasim Al-Jumaily|10.1109/DeSE58274.2023.10100274|Image Classification;Deep Learning;Generative Adversarial Networks;Data Augmentation;Car Insurance Claim;Training;Deep learning;Insurance;Manuals;Inspection;Generative adversarial networks;Data models|
|[Deep Learning-Based Skin Cancer Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100194)|S. M. N; A. Hussain; D. Al-Jumeily; B. M. Mahmmod; S. H. Abdulhussain|10.1109/DeSE58274.2023.10100194|component;formatting;style;styling;insert;Pandemics;Transfer learning;Predictive models;Market research;Skin;Sun;Skin cancer|
|[Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100119)|M. Mahyoub; F. Natalia; S. Sudirman; A. H. Jasim Al-Jumaily; P. Liatsis|10.1109/DeSE58274.2023.10100119|Brain Tumor;Image Segmentation;Magnetic Resonance Imaging;Residual Networks;Deep Learning;Training;Image segmentation;Magnetic resonance imaging;Transfer learning;Sociology;Brain modeling;Hyperparameter optimization|
|[Improvement of the Personnel Delivery System in the Mining Complex using Simulation Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099877)|I. Makarova; G. Mavlyautdinova; V. Mavrin; P. Buyvol; A. S. Alatrany; J. Mustafina|10.1109/DeSE58274.2023.10099877|Arctic zone;transport;transport infrastructure;shift method;environmental friendliness;compressed natural gas;liquefied hydrocarbon gas;liquefied natural gas;Analytical models;Schedules;Systematics;Winches;Energy efficiency;Safety;Arctic|
|[Gesture Recognition Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099640)|V. Sharma; H. Kolivand; S. Asadianfam; D. Al-Jumeily; M. Jayabalan|10.1109/DeSE58274.2023.10099640|Gesture recognition;Vision based gesture recognition;Graph based gesture recognition;computer vision;Computer science;Computer vision;Gesture recognition;Computational efficiency;Classification algorithms|
|[Point-based Gesture Recognition Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099660)|V. Sharma; H. Kolivand; S. Asadianfam; D. Al-Jumeily; M. Jayabalan|10.1109/DeSE58274.2023.10099660|Point based gesture recognition;Point Clouds nearest neighbors and sampling;Gesture recognition;Point cloud compression;Gesture recognition|
|[Identifying a Student as a Subject of Educational Activity: Methodology and Web-based Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100140)|N. Chernova; R. Akhunzyanova; A. Alatrani; J. Mustafina|10.1109/DeSE58274.2023.10100140|computer program;software development;mediation means control;educational setting;participation;AI-enhanced education;Knowledge engineering;Sensitivity;Attitude control;Education;Process control;Collaboration;Linguistics|
|[Transformer Based Approach for Depression Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099629)|A. A. Khaparde; R. Das; R. Bhargava|10.1109/DeSE58274.2023.10099629|Mental Health;Depression Detection;Deep Learning;BERT;Training;Deep learning;Social networking (online);Bit error rate;Transfer learning;Mental health;Depression|
|[A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099796)|M. Mahyoub; S. Ghareeb; J. Mustafina|10.1109/DeSE58274.2023.10099796|Home Loan default;Machine Learning;LIME;SHAP;Random Forest;Gradient Boosting;Analytical models;Metaverse;Forestry;Predictive models;Boosting;Feature extraction;Prediction algorithms|
|[A Comparative Study on Transformer-based News Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099798)|A. Choudhary; M. Alugubelly; R. Bhargava|10.1109/DeSE58274.2023.10099798|Text Summarization;Natural Language Processing;Transformers;GPT2;BERT;XLNet;T5;BART;Deep learning;Technological innovation;Analytical models;Bit error rate;Transformers;Natural language processing|
|[Predicting the Effectiveness of ‘Stop and Search’ Police Interventions Using Advanced Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100242)|B. Marimbire; A. Al-Nahari; W. K. Ahmadzai; D. Al-Jumeily; W. Khan|10.1109/DeSE58274.2023.10100242|Stop and search interventions;Crime deter;Predictive policing;Crime prediction;Police interventions;Crime suspects;Smoothing methods;Law enforcement;Weapons;Predictive models;Prediction algorithms;Search problems;Classification algorithms|
|[Investigation on the Integrated Cloud and BlockChain (ICBC)Technologies to Secure Healthcare Data Management Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100065)|A. Mudheher Badr; L. Chaari Fourati; S. Ayed|10.1109/DeSE58274.2023.10100065|Healthcare;Medical Internet of Things;Secu-rity;Cloud Computing;Blockchain;Cloud computing;Technological innovation;Privacy;Taxonomy;Medical services;Computer architecture;Blockchains|
|[Agriculture 4.0 from IoT, Artificial Intelligence, Drone, & Blockchain Perspectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099927)|A. N. JASIM; L. C. FOURATI|10.1109/DeSE58274.2023.10099927|Drone;UAVs;Smart agriculture;Internet of Things (IoT);Blockchain technology;Security System;Artificial intelligence;Smart agriculture;Blockchains;Internet of Things;Security;Resource management;Artificial intelligence;Smart devices|
|[Real- Time Healthcare Monitoring and Treatment System Based Microcontroller with IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099758)|M. K. Awsaj; Y. A. Mashhadany; L. C. Fourati|10.1109/DeSE58274.2023.10099758|health care system;sensors;vital signs;ESP32;Raspberry Pi 4;medical alarm;technical alarm;Temperature measurement;Temperature sensors;Pandemics;Humidity measurement;Medical services;Humidity;Pressure measurement|
|[Proposed Deep Learning System for Arabic Text Detection and Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100235)|G. J. Salman; M. S. M. Altaei|10.1109/DeSE58274.2023.10100235|OCR;Arabic Text Detection;Arabic Text Recognition;CNN;Deep learning;Training;Text recognition;Convolution;Neural networks;Buildings;Feature extraction|
|[A Hybrid Modified ABC-PSO Algorithm for Optimal Robotic Path Planner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100021)|N. I. Khalil; H. N. Abdullah; L. A. Hassnawi|10.1109/DeSE58274.2023.10100021|Path planning;Artificial Bee Colony;Particle Swarm Optimization;Measurement;Power demand;Navigation;Artificial bee colony algorithm;Path planning;Planning;Mobile robots|
|[A Tri-Classes Method for Studying the Impact of Nodes and Sinks Number on Received Packets Ratio of MANETs Routing Protocols](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100163)|H. A. Ahmed; H. A. A. Al-Asadi|10.1109/DeSE58274.2023.10100163|MANETs;WSNs;routing protocols;AODV;OLSR;DSDV;DSR;Received Packets Ratio (RPR);Network topology;Simulation;Vehicular ad hoc networks;Routing;Throughput;Ad hoc networks;Routing protocols|
|[Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099854)|S. A. Jerjees; H. J. Mohammed; H. S. Radeaf; B. M. Mahmmod; S. H. Abdulhussain|10.1109/DeSE58274.2023.10099854|Speech Enhancement;Deep learning;Charlier polynomials;Charlier moments;Deep learning;Discrete transforms;Machine learning algorithms;Databases;Neural networks;Nonlinear distortion;Speech enhancement|
|[Vision-Based Fatigue Detection In Drivers Using Multi-Facial Feature Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099741)|S. Das; R. Bhargava|10.1109/DeSE58274.2023.10099741|Face Detection;Fatigue Classification;MTCNN;Dlib;EAR;MAR;LSTM;Training;Road accidents;Mouth;Feature extraction;Fatigue;Brain modeling;Multitasking|
|[A Transfer Learning Based Intrusion Detection System for Internet of Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099623)|A. Haddaji; S. Ayed; L. C. Fourati|10.1109/DeSE58274.2023.10099623|IoV security;Transfer Learning;CAN Bus attacks;Intrusion Detection;Deep learning;Training;Protocols;Transfer learning;Intrusion detection;Data models;Behavioral sciences|
|[BlockChain-based Cooperative UAVs for Secure Data Acquisition and Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100072)|B. Najeh; A. I. Hentati; M. Fourati; L. C. Fourati; A. Alanezi|10.1109/DeSE58274.2023.10100072|Unmanned Aerial Vehicles;BlockChain;Data Acquisition;Data storage;Performance evaluation;Bridges;Data acquisition;Autonomous aerial vehicles;Blockchains;Internet of Things;Time factors|
|[K-Nearest Neighbor Algorithm for Efficient Heart Disease Classification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099808)|A. S. Abdalkafor; K. M. A. Alheeti|10.1109/DeSE58274.2023.10099808|Heart Disease;Classification;Outliers;Correlation Coefficient;Cosine similarity;Heart;Support vector machines;Databases;Medical services;Feature extraction;Prediction algorithms;Classification algorithms|
|[Multi-UAVs-based SDN, IoT, and Cloud Architecture for Hostile Areas Supervision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099609)|A. I. Hentati; L. C. Fourati; L. Krichen; A. Alanezi|10.1109/DeSE58274.2023.10099609|Unmanned Aerial Vehicles;SDN;IoT;Cloud;Area supervision;Cellular networks;Cloud computing;Wireless sensor networks;Collaboration;Computer architecture;Space-air-ground integrated networks;Delays|
|[Optimal Sensor Placement Strategy for Structural Health Monitoring with Application of the Aqueduct El Hnaya of Carthage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100124)|W. DOGHRI; A. SADDOUD; L. CHAARI|10.1109/DeSE58274.2023.10100124|Sensors Placement;Structural Health Monitoring;Wireless Sensors Networks;Finite Element Modeling;Sensor placement;Wireless sensor networks;Maintenance engineering;Mathematical models;Finite element analysis;Safety;Cultural differences|
|[Study on Communicative Robots Assisting Elderly Persons](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099833)|S. Taleb; L. C. FOURATI; M. FOURATI|10.1109/DeSE58274.2023.10099833|Assistive Robot;Elderly Person;Internet of Things (IoT);Support services;AI;Knowledge engineering;Communication systems;Humanoid robots;Focusing;Assistive robots;Older adults;Robots|
|[Recommendations for Developing an Affordable IoT-Based Flood Monitoring and Early Warning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099530)|K. S. Pillay; K. Shanmugam; M. E. Rana|10.1109/DeSE58274.2023.10099530|Flood Warning;Flood Monitoring;Flood Impact and Disaster;Flood Causes;Internet of Things (IoT);Wiring;Wires;Government;Prototypes;Weather forecasting;Electronic components;Sensors|
|[The Role and Potential Applications of Cloud Computing in the Banking Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099662)|M. E. Rana; L. Z. Ji|10.1109/DeSE58274.2023.10099662|Cloud Computing;Banking Industry;Cloud Models;Cloud Applications for Banking;Industries;Cloud computing;Fault tolerance;Data centers;Computational modeling;Fault tolerant systems;Banking|
|[An Exploratory Study on the Impact of Hosting Blockchain Applications in Cloud Infrastructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100137)|T. H. Chee; M. E. Rana|10.1109/DeSE58274.2023.10100137|Cloud Computing;Blockchain;Cloud Service Providers (CSP);Cloud Services for Blockchain;Cloud computing;Privacy;Costs;Buildings;Blockchains;Security;Servers|
|[The Impact of the COVID-19 Pandemic on Retrenchment, Vaccinations, and Global Happiness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100157)|N. W. S. Jackson; J. Sasikumar; W. Y. Hung; O. R. Khan; V. N. Z. Hui; S. Al-Sudani; H. Guo; Z. Zhang; Z. Wang|10.1109/DeSE58274.2023.10100157|COVID-19;Retrenchment rate;Vaccine hesitancy;World happiness;Vaccination rate;COVID-19;Correlation;Pandemics;Economic indicators;Soft sensors;Biological system modeling;Vaccines|
|[Modify Multiple Object Detection and Tracking to Improve the Execution Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10099782)|R. N. Razak; H. N. Abdullah|10.1109/DeSE58274.2023.10099782|Detection;tracking;background subtraction;Kalman filter;Multiple Object;Tracking;Detectors;Filtering algorithms;Traffic control;Prediction algorithms;Cameras;Real-time systems|
|[BrandTrend: Understanding the Trending Games and Gaming Influencers for Better Gaming Peripheral Promotion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100248)|T. W. Z. Ashley; L. J. Han; Derrick; K. Tan; R. K. T. Avin; A. Kaur; S. Al-Sudani; Z. Wang|10.1109/DeSE58274.2023.10100248|Brand;Influcence;Games;Promotion;Youtube;Twitch;Sentiment analysis;Video on demand;Data analysis;Social networking (online);Collaboration;Games;Companies|
#### **2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)**
- DOI: 10.1109/EuroSimE56861.2023
- DATE: 16-19 April 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Studying Asymmetric Warpage Behavior of Panel-Level Packages Using Process Modeling Techniques and Viscoelasticity Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100838)|Z. Shu; K. N. Chiang|10.1109/EuroSimE56861.2023.10100838|nan;nan|
|[Using Grid Search Methods and Parallel Computing to Reduce AI Training Time for Reliability Lifetime Prediction of Wafer-Level Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100751)|C. Y. Chang; C. H. Lee; K. N. Chiang|10.1109/EuroSimE56861.2023.10100751|Wafer Level Packaging;Finite Element Analysis;Thermal Cycle Load;Reliability Estimation;Machine Learning;Parallel Computing;Grid Search;Semiconductor device modeling;Analytical models;Computational modeling;Search methods;Semiconductor device reliability;Graphics processing units;Machine learning|
|[Improved nanoindentation methods for polymer based multilayer film cross-sections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100815)|P. Christoefl; J. E. Jakes; J. Geier; G. Pinter; G. Oreski; D. Stone; C. Teichert|10.1109/EuroSimE56861.2023.10100815|nan;Uncertainty;Films;Predictive models;Packaging;Nonhomogeneous media;Mechanical factors;Polymers|
|[Power Semiconductor Die Passivation Layer Stress Mechanism Investigation and optimization by Numerical Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100807)|Z. Zhou; H. Fan; A. Brown|10.1109/EuroSimE56861.2023.10100807|nan;Tensile stress;Simulation;Thermomechanical processes;Electromagnetic compatibility;Topology;Reliability;Stress|
|[The Effect of Geometric and Material Uncertainty on Debonding Warpage in Fan-Out Panel Level Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100786)|H. L. Chen; K. N. Chiang|10.1109/EuroSimE56861.2023.10100786|nan;Thermal expansion;Uncertainty;Process modeling;Simulation;Packaging;Predictive models;Electromagnetic compatibility|
|[FEM simulation of influence of different polymeric module materials and layouts on thermomechanical deformations in strings of shingled solar cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100805)|M. Lang; G. Oreski; E. Helfer; P. Fuchs; A. Halm; M. Klenk|10.1109/EuroSimE56861.2023.10100805|nan;Thermal expansion;Anisotropic magnetoresistance;Photovoltaic cells;Microprocessors;Thermomechanical processes;Computer architecture;Numerical models|
|[Finite Element-Based Monitoring of Solder Degradation in Discrete SiC MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100794)|B. Kilian; J. Gleichauf; Y. Maniar; O. Wittler; M. Schneider-Ramelow|10.1109/EuroSimE56861.2023.10100794|nan;Degradation;MOSFET;Silicon carbide;Thermal resistance;Thermomechanical processes;Power electronics;Finite element analysis|
|[Finite Element Model for Prediction of Back-End-of-Line Process Induced Wafer Bow for Patterned Wafer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100826)|P. K. Singh; K. V. Machani; D. Breuer; M. Hecker; K. Meier; F. Kuechenmeister; M. Wieland; K. Bock|10.1109/EuroSimE56861.2023.10100826|Wafer bow;wafer warpage;finite element modelling;back-end-of-line;Semiconductor device modeling;Fabrication;Micromechanical devices;Electronics industry;Metals;Predictive models;Finite element analysis|
|[Impact of Viscoelastic Properties on Package Warpage Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100768)|D. Spini; M. Rovitto|10.1109/EuroSimE56861.2023.10100768|nan;Analytical models;Temperature;Deformation;Simulation;Electromagnetic compatibility;Mathematical models;Numerical models|
|[Investigating the Occurrence of Bifurcation in Large Metalized Wafers using ANSYS Layered Shell Elements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100793)|V. Vinciguerra; M. Boutaleb; G. L. Malgioglio; A. Landi; F. Roqueta; M. Renna|10.1109/EuroSimE56861.2023.10100793|Bifurcation;Wafer;Die;Warpage;Curvature;Finite Element Analysis (FEA);ANSYS Layered Shell Elements;nan|
|[Influence of the quality of material models on warpage and lifetime prediction by finite element simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100777)|J. Zündel; M. Weninger; T. Krivec; M. Frewein; S. Waschnig|10.1109/EuroSimE56861.2023.10100777|nan;Temperature measurement;Biological system modeling;Materials reliability;Predictive models;Dielectric measurement;Dielectrics;Behavioral sciences|
|[Temperature Field Simulation and optimization for Horizontal 6-inch 4H-SiC Epitaxial CVD Reactor by Induction Heating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100813)|Z. Tang; J. Tian; C. Mao; N. Zhang; J. Huang; J. Fan; G. Zhang|10.1109/EuroSimE56861.2023.10100813|nan;Solid modeling;Temperature distribution;Analytical models;Silicon carbide;Graphite;Finite element analysis;Epitaxial growth|
|[Packaging Induced Stresses in Embedded and Molded GaN Power Electronics Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100830)|S. Akbari; J. Holmberg; D. Andersson; M. Mishra; K. Brinkfeldt|10.1109/EuroSimE56861.2023.10100830|nan;nan|
|[Design of Power Modules Using Containers Filled With Phase Change Materials as Device Top Interconnection for Power Peak Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100726)|R. Khazaka; Y. Avenas; R. Hanna; S. Azzopardi|10.1109/EuroSimE56861.2023.10100726|nan;Phase change materials;Cooling;Wires;Multichip modules;Containers;Sugar industry;Copper|
|[Hydrolysis Mechanism Analysis of (Ca, Sr)AlSiN₃:Eu²⁺ Red Phosphor Aged Under Pressure Cooker Test and 85°C&85%RH Test: Kinetics Modeling and First-principles Calculation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100750)|M. Wen; B. Guo; S. Chen; X. Hu; X. Fan; G. Zhang; J. Fan|10.1109/EuroSimE56861.2023.10100750|nan;Degradation;Resistance;Simulation;Phosphors;Humidity;Aging;Rendering (computer graphics)|
|[Analytical and experimental studies on the damage evolution of SAC solder alloys](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100753)|S. Glane; A. Morozov; W. H. Müller; T. Hauck; G. R. Mazumder; M. A. Haq; J. Suhling|10.1109/EuroSimE56861.2023.10100753|nan;Temperature distribution;Creep;Data models;Mathematical models;Numerical models;Behavioral sciences;Stress|
|[AI surrogate models for error analysis in optical systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100829)|P. Meszmer; N. Mundada; M. Tavakolibasti; B. Wunderle|10.1109/EuroSimE56861.2023.10100829|nan;Laser radar;Error analysis;Optical computing;Predictive models;Optical fiber networks;Optical receivers;Data models|
|[Metal-based Direct Multi-jet Impingement Cooling Solution for Autonomous Driving High-Performance Vehicle Computer (HPVC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100758)|R. Moloudi; T. Grün; W. Verleysen; B. Vandevelde; S. G. C. Cleuren; D. May; B. Wunderle|10.1109/EuroSimE56861.2023.10100758|nan;Thermal management of electronics;Micromechanical devices;Industries;Liquid cooling;Metals;Three-dimensional printing;Thermal management|
|[Transient Thermal 2D FEM Analysis of SiC Mosfet in Short-Circuit Operation Including Solidus-Liquidus Phase Transition of the Aluminum Source Electrode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100775)|E. Sarraute; T. Cazimajou; F. Richardeau|10.1109/EuroSimE56861.2023.10100775|nan;Semiconductor device modeling;Electrodes;Heating systems;MOSFET;Silicon carbide;Thermal conductivity;Power transistors|
|[Trigger specific failure in LED system by power and duty cycle patterns lifetime testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100790)|J. Magnien; L. Mitterhuber; K. Fladischer; J. Rosc; E. Kraker|10.1109/EuroSimE56861.2023.10100790|nan;Heating systems;Performance evaluation;Solid modeling;Semiconductor devices;Light emitting diodes;Fatigue;Boundary conditions|
|[Simulation of Temperature Driven Microflows Using a Lattice Boltzmann Method in Slip and Moderate Transition Regimes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100812)|A. Selmi; S. Bhapkar; C. Nagel; A. Kummerländer; M. J. Krause|10.1109/EuroSimE56861.2023.10100812|nan;Micromechanical devices;Temperature distribution;Fluids;Monte Carlo methods;Computational modeling;Boundary conditions;Mathematical models|
|[Deformation analysis of QFN packages for validation of thermo-mechanical finite element simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100821)|C. Nawghane; T. Moncond’Huy; B. Vandevelde; P. Vernhes; R. Cruz|10.1109/EuroSimE56861.2023.10100821|nan;nan|
|[Microstructure Analysis Based on 3D reconstruction Model and Transient Thermal Impedance Measurement of Resin-reinforced Sintered Ag layer for High power RF device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100799)|X. Hu; H. A. Martina; R. H. Poelma; J. L. Huang; H. Rijckevorsel; H. Scholten; E. C. P. Smits; W. D. Van Driel; G. Q. Zhang|10.1109/EuroSimE56861.2023.10100799|Hybrid Ag Sintering;Pressureless Sintering;3D Reconstruction;Microstructure Analysis;Tortuosity;Transient Thermal Impedance;LDMOS Body Diode Measurement;Temperature measurement;Semiconductor device measurement;Solid modeling;Three-dimensional displays;Thermal resistance;Sintering;Electronic packaging thermal management|
|[Non-intrusive electro-thermo-mechanical reduced model for diagnosis and prognostic on IGBT power modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100766)|L. Schuler; L. Chamoin; Z. Khatir; M. Bouarroudj; M. Ouhab|10.1109/EuroSimE56861.2023.10100766|nan;Insulated gate bipolar transistors;Micromechanical devices;Computational modeling;Wires;Multichip modules;Software;Behavioral sciences|
|[An Advanced Finite Element Model of the Cu Pillar Solder Reflow Assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100808)|C. Zhibo; P. Baran; O. Aslihan; H. Bruno; C. Corrado; K. Mehmet|10.1109/EuroSimE56861.2023.10100808|nan;Semiconductor device modeling;Visualization;Thermomechanical processes;Throughput;Finite element analysis;Wafer scale integration;Flip-chip devices|
|[Equation Informed Neural Networks with Bayesian Inference Improvement for the Coefficient Extraction of the Empirical Formulas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100752)|C. Yuan; J. Y. Wang; C. E. Lee; K. -N. Chiang|10.1109/EuroSimE56861.2023.10100752|Equation-informed neural networks;Coefficient extraction;Empirical functions;Bayesian inference;Monte Carlo Markov Chain.;Training;Micromechanical devices;Monte Carlo methods;Neural networks;Markov processes;Mathematical models;Robustness|
|[Thermomechanical and Electrical Material Characterization for a DLP Printing Process Simulation of Electrically Conductive Parts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100754)|A. Thalhamer; E. Rossegger; S. Hasil; K. Hrbinič; V. Feigl; M. Pfost; P. Fuchs|10.1109/EuroSimE56861.2023.10100754|nan;Temperature measurement;Heating systems;Solid modeling;Thermomechanical processes;Conductivity;Three-dimensional printing;Thermal conductivity|
|[Lifetime modeling of copper metallization for SiC power electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100835)|D. Losbichler; M. Klingler; S. Orso; B. Wunderle|10.1109/EuroSimE56861.2023.10100835|nan;Semiconductor device modeling;Annealing;Metallization;Silicon carbide;Aluminum;Fatigue;Power electronics|
|[A simple modeling of ferroelectric actuator based on phenomenological model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100791)|B. H. Nguyen; M. Zunic; G. B. Torri; V. Rochus|10.1109/EuroSimE56861.2023.10100791|Phenomenological;Ferroelectric;PZT;Micro-actuator;Micromechanical devices;Actuators;Thermal variables measurement;Mechanical variables measurement;Robustness;Numerical models;Finite element analysis|
|[Study on Sintering Mechanism and Mechanical Properties of Nano-Cu based on Molecular Dynamics Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100810)|C. Qian; D. Hu; X. Liu; X. Fan; G. Zhang; J. Fan|10.1109/EuroSimE56861.2023.10100810|Nano Cu sintering;molecular dynamics simulation;Nanoflake;Shearing simulation;Deformation;Sintering;Mechanical factors;Neck;Behavioral sciences;Nanostructures;Noise measurement|
|[Prediction of thermo-mechanical properties of PCB conductive layers using convolutional neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100776)|M. Shevchuk; C. Schipfer; M. Haselmann; Q. Tao; P. Fuchs|10.1109/EuroSimE56861.2023.10100776|nan;Training;Computational modeling;Thermomechanical processes;Printed circuits;Predictive models;Thermal conductivity;Convolutional neural networks|
|[A 3-axis capacitive nonlinear MEMS energy harvester simulation: unidirectivity no more?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100770)|B. Vysotskyi; V. Rochus|10.1109/EuroSimE56861.2023.10100770|nan;Micromechanical devices;Vibrations;Power system measurements;Sensitivity;Density measurement;Autonomous systems;Bandwidth|
|[Modelling Creep Behaviour in Sintered Silver using User-Programmable Features in ANSYS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100819)|F. Forndran; J. Heilmann; M. Metzler; M. Leicht; B. Wunderle|10.1109/EuroSimE56861.2023.10100819|nan;Micromechanical devices;Silver;Creep;Multichip modules;Materials reliability;Fatigue;Finite element analysis|
|[Characterization and simulation of delamination on package-level considering sub-critical interfacial fracture-parameters under cyclic loading.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100778)|R. Kniely; J. Heilmann; F. Huber; B. Wunderle|10.1109/EuroSimE56861.2023.10100778|nan;Loading;Bending;Fatigue;Silicon;Behavioral sciences;Microelectronics;Reliability|
|[Frame detachment simulation of PV modules under mechanical load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100801)|D. C. Joseph; A. S. López; P. Romer; A. J. Beinert|10.1109/EuroSimE56861.2023.10100801|nan;Analytical models;Computational modeling;Mathematical models;Finite element analysis;Structural engineering;Laminates;Thermal loading|
|[Predictive thermo-mechanical models for the quantification of electro-mechanical interactions during production and field-life](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100836)|J. J. M. Zaal; C. He; J. A. M. Claes; J. v. Herk; A. O. Adojutelegan; X. Cheng|10.1109/EuroSimE56861.2023.10100836|nan;Semiconductor device modeling;Temperature measurement;Resistors;Temperature distribution;Thermomechanical processes;Analog circuits;Thermal loading|
|[Evaluation of thermomechanical behavior of electronic devices through the use of a reduced order modelling approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100800)|M. Weninger; J. Zündel; T. Krivec; M. Frewein; S. Waschnig; P. Fuchs; C. Obst|10.1109/EuroSimE56861.2023.10100800|nan;Micromechanical devices;Temperature;Computational modeling;Simulation;Thermomechanical processes;Printed circuits;Predictive models|
|[Leadframe-Epoxy Moulding Compound Adhesion: a Micromechanics-driven Investigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100792)|A. D. Porta; S. Mariani; M. Rovitto; L. Andena; S. Zalaffi|10.1109/EuroSimE56861.2023.10100792|nan;Adhesives;Surface morphology;Morphology;Lead;Electromagnetic compatibility;Surface roughness;Rough surfaces|
|[Reduction of empiricism in the solder joint reliability assessment of QFN packages by using thermo-mechanical simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100761)|M. v. Soestbergen; R. Roucou; M. Rebosolan; J. J. M Zaal|10.1109/EuroSimE56861.2023.10100761|nan;Measurement;Correlation;Computational modeling;Thermomechanical processes;Failure analysis;Fatigue;Intermetallic|
|[Impact of mechanical material modeling on the solder joint fatigue analysis of a leadless package mounted at different positions inside a generic aluminum ECU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100779)|M. Niessner; A. Gyarmati; H. Guettler|10.1109/EuroSimE56861.2023.10100779|nan;Analytical models;Temperature;Thermomechanical processes;Predictive models;Fatigue;Behavioral sciences;Microelectronics|
|[Frequency Analysis of Dual-Phase-Lag Heat Conduction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100822)|A. Sobczak; G. Jabłoñski; M. Janicki|10.1109/EuroSimE56861.2023.10100822|nan;Solid modeling;Analytical models;Temperature;Thermal resistance;Taylor series;Solids;Mathematical models|
|[A Simulation-Based Design Approach for Optimized Performance of Cu-Mo-Cu Clips in High-Power Semiconductor Modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100818)|M. Packwood; X. Li; M. Morshed; H. Neal; Y. Wang|10.1109/EuroSimE56861.2023.10100818|nan;Procurement;Micromechanical devices;Thermal expansion;Composite materials;Simulation;Switches;Microelectronics|
|[Determination of Lemaitre Damage Parameters for Al H11 Wire Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100825)|S. Kuttler; B. E. Abali; O. Wittler|10.1109/EuroSimE56861.2023.10100825|nan;Micromechanical devices;Sensitivity;Wires;Numerical simulation;Blanking;Numerical models;Microelectronics|
|[A Probability Soft-Error Model for a 28-nm SRAM-based FPGA under Neutron Radiation Exposure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100757)|G. B. Thieu; J. Schmechel; K. Weide-Zaage; K. Schmidt; D. Hagenah; G. Payá-Vayá|10.1109/EuroSimE56861.2023.10100757|nan;Radiation effects;Fault tolerance;Analytical models;Fault tolerant systems;Random access memory;Neutrons;Hardware|
|[Improving the Vibration Reliability of SAC Flip-Chip Interconnects Using Underfill](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100809)|R. Höhne; K. Meier; M. Reim; M. Lehmann; K. -H. Bock|10.1109/EuroSimE56861.2023.10100809|Harsh environment reliability;harmonic vibration;Flip-Chip;failure analysis;reliability analysis;lead-free solder alloy;nan|
|[FEM Modelling of Ag-Sinter Joints with Respect of Porosity and Sinter Pressure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100831)|M. Roellig; R. Schwerz; J. Meyer; K. Meier|10.1109/EuroSimE56861.2023.10100831|nan;nan|
|[Modification of Prony Series Coefficients to Account for Thermo-Oxidative Ageing Effects within Numerical Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100845)|M. v. Dijk; O. Wittler; S. Wagner; M. Schneider-Ramelow|10.1109/EuroSimE56861.2023.10100845|nan;nan|
|[Application of AI-enabled Simulation in Power Package Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100811)|H. Fan; P. Yao; H. Chen|10.1109/EuroSimE56861.2023.10100811|nan;Performance evaluation;Materials reliability;Machine learning;Reliability engineering;Electromagnetic compatibility;Compounds;Soldering|
|[Characterization of Polysilicon Strength through On-Chip Testing at MEMS Stoppers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100798)|T. V. F. D. Valle; A. Ghisi; B. D. Masi; S. Mariani; F. Rizzini; G. Gattere; C. Valzasina|10.1109/EuroSimE56861.2023.10100798|nan;Geometry;Uncertainty;Tensile stress;Films;Microfabrication;Force;System-on-chip|
|[Models of Bifurcation and Gravity Induced Deflection in Wide Band Gap 4H-SiC Semiconductor Wafers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100763)|V. Vinciguerra; G. L. Malgioglio; A. Landi; M. Renna|10.1109/EuroSimE56861.2023.10100763|Bifurcation;Wafer;Warpage;Curvature;Finite Element Analysis (FEA);Simulations;Gravity Induced Deflection;Semiconductor device modeling;Silicon carbide;Photonic band gap;Bifurcation;Semiconductor device manufacture;Finite element analysis;Thermal analysis|
|[Strain Measurements and Thermo-Mechanical Simulation of SnAgCu vs. low melting point alloy (LMPA-Q) solder joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100841)|B. Vandevelde; R. Labie; R. Lauwaert; R. Dudek; P. Gromala; M. Eichorst|10.1109/EuroSimE56861.2023.10100841|nan;nan|
|[Neuron-Electrode Interface with Hodgkin-Huxley Model in ANSYS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100828)|U. Fitzer; D. Hohlfeld; T. Bechtold|10.1109/EuroSimE56861.2023.10100828|nan;Micromechanical devices;Medical treatment;Implants;Software;Numerical models;Finite element analysis;Behavioral sciences|
|[Failure Analysis of Sintered Layers in Power Modules Using Laser Lock-in Thermography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100762)|S. Panahandeh; D. May; C. Grosse-Kockert; B. Wunderle; M. A. Ras|10.1109/EuroSimE56861.2023.10100762|nan;Electronic components;Laser sintering;Failure analysis;Production;Inspection;Conductivity;Thermal conductivity|
|[TC Reliability Enhancement Technology for SSD with Low Temperature Solder Paste Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100767)|J. Kim; Y. Jung; E. Oh; Y. Cinar; J. Jeong; S. Lee; J. Park|10.1109/EuroSimE56861.2023.10100767|nan;Micromechanical devices;Mass production;Temperature;Random access memory;Materials reliability;Reliability engineering;Microelectronics|
|[FEA-based Layout Optimization of E1.S Solid-State Drive to Improve Thermal Cycling Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100844)|E. Oh; J. Kim; Y. Cinar; W. Kim; B. Lee; M. Jang; N. Song; S. Lee; J. Park|10.1109/EuroSimE56861.2023.10100844|nan;Temperature;Solid state drives;Layout;Fasteners;Fatigue;Reliability;Plastics|
|[Model order reduction for nonlinear modal analysis of MEMS devices: theory and recent advancements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100804)|A. Opreni; P. Degenfeld-Schonburg|10.1109/EuroSimE56861.2023.10100804|nan;Micromechanical devices;Vibrations;Computational modeling;Stationary state;Mathematical models;Data models;Numerical models|
|[Ab initio derived force field potential for the accurate simulation of thermal transport in AlN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100760)|S. Fernbach; E. Kraker; N. Bedoya-Martínez|10.1109/EuroSimE56861.2023.10100760|nan;Micromechanical devices;Force;Fitting;Lattices;Phonons;Thermal force;Thermal conductivity|
|[Characterization and simulation of four bending test to estimate resin-copper adhesion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100749)|A. Sitta; G. Mauromicale; M. A. Torrisi; G. Sequenzia; G. D’Arrigo; M. Calabretta|10.1109/EuroSimE56861.2023.10100749|nan;Solid modeling;Adhesives;Shape;Bending;Predictive models;Solids;Numerical models|
|[Fully-Coupled Transient Modeling of Highly Miniaturized Electrostatic Pull-In Driven Micropumps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100814)|W. Hölzl; M. Seidl; G. Schrag|10.1109/EuroSimE56861.2023.10100814|nan;Micromechanical devices;Adaptation models;Fluids;Computational modeling;Micropumps;Behavioral sciences;Finite element analysis|
|[Simulation Methods for LED Multi-Domain Models Parameter Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100832)|R. Al-Zubaidi|10.1109/EuroSimE56861.2023.10100832|nan;Computational modeling;Force;Measurement uncertainty;Light emitting diodes;Mathematical models;Adaptive optics;Numerical models|
|[Effect of Undercut due to Isotropic Etch while Releasing on the Performance of TPoS Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100769)|J. Bijay; K. N. Bhadri Narayanan; A. Sarkar; A. DasGupta; D. R. Nair|10.1109/EuroSimE56861.2023.10100769|nan;Semiconductor device modeling;Q-factor;Performance evaluation;Geometry;Q measurement;Resonant frequency;Predictive models|
|[Towards System-level Simulation of an Electromagnetic Energy Harvester Model via Equivalent Circuit Extraction from ANSYS Maxwell 3D](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100816)|C. Yuan; D. Hohlfeld; T. Bechtold|10.1109/EuroSimE56861.2023.10100816|nan;Wireless communication;Micromechanical devices;Solid modeling;Three-dimensional displays;Power supplies;Software;Finite element analysis|
|[Supportless 5-Axis 3D-Printing and Conformal Slicing: A Simulation-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100840)|L. S. G; M. Pawel; L. Marius; L. M. Faller|10.1109/EuroSimE56861.2023.10100840|additive manufacturing;conformal slicing;kinematics;5-axismaker;structural strength;Three-dimensional displays;Software algorithms;Force;Kinematics;Three-dimensional printing;Software;Mathematical models|
|[Warpage of Fan-Out Panel Level Packaging – Experimental and Numerical Study of Geometry and Process Influence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100849)|A. Stegmaier; O. Hölck; M. v. Dijk; H. Walter; O. Wittler; M. Schneider-Ramelow|10.1109/EuroSimE56861.2023.10100849|nan;Fabrication;Production;Packaging;Predictive models;Market research;Electromagnetic compatibility;Data models|
|[Thermo-Mechanical Super-Element of a Packaged-Chip Model for Re-Integrating Reduced State-Space Models into Finite Element Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100846)|C. B. Umunnakwe; I. Zawra; E. B. Rudnyi; M. Niessner; T. Bechtold|10.1109/EuroSimE56861.2023.10100846|nan;nan|
|[Failure Prediction and Analysis of an IGBT Module for Industrial Applications Subjected to Passive and Power Cycling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100806)|R. Dudek; A. Otto; R. Döring; A. Mathew; X. Liu; S. Rzepka|10.1109/EuroSimE56861.2023.10100806|nan;Insulated gate bipolar transistors;Wires;Thermomechanical processes;Predictive models;Reliability;Bonding;Substrates|
|[A Continuously Updated Package-Degradation Model reflecting Thermomechanical Changes at Different Thermo-Oxidative Stages of Moulding Compound](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100833)|A. Inamdar; M. v. Soestbergen; A. Mavinkurve; W. v. Driel; G. Zhang|10.1109/EuroSimE56861.2023.10100833|nan;Vibrations;Temperature;Thermomechanical processes;Aging;Electronic packaging thermal management;Electromagnetic compatibility;Microelectronics|
|[Numerical Simulation of Crosstalk Effects in PMUT Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100789)|O. M. O. Abdalla; G. Massimino; C. D’Argenzio; M. Colosio; M. Soldo; F. Quaglia; A. Corigliano|10.1109/EuroSimE56861.2023.10100789|nan;Couplings;Vibrations;Transducers;Atmospheric modeling;Crosstalk;Acoustic measurements;Acoustic arrays|
|[Humidity Sensing for free—advanced thermoacoustic signal models in miniaturized photoacoustic gas sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100759)|S. Essing; M. Trautmann; D. Tumpold; G. Schrag|10.1109/EuroSimE56861.2023.10100759|nan;Temperature sensors;Atmospheric measurements;Humidity measurement;Humidity;Thermal conductivity;Sensor systems;Sensors|
|[Efficient Simulation of the Effect of Solder Voids and Tilting on the Cooling of Power Semiconductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100817)|N. Jahn; M. Pfost|10.1109/EuroSimE56861.2023.10100817|nan;Semiconductor device modeling;Training;Micromechanical devices;Cooling;Computational modeling;Simulation;Training data|
|[Analyzing the Impact of Die Positions inside the Power Module on the Reliability of Solder Layers for Different Power Cycling Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100764)|B. P. Singh; S. Li; K. R. Choudhury; S. Norrga; H. -P. Nee|10.1109/EuroSimE56861.2023.10100764|Power module;finite element method;power cycling;solder;viscoplasticity;lifetime estimation;Heating systems;Temperature distribution;Temperature dependence;Analytical models;Multichip modules;Finite element analysis;Thermal analysis|
|[Manufacturing of an In-Package Relative Humidity Sensor for Epoxy Molding Compound Packages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100771)|R. Sattari; H. v. Zeijl; G. Zhang|10.1109/EuroSimE56861.2023.10100771|In-package relative humidity sensor;epoxy molding compound;shielded interdigital electrodes;encapsulation layer;electrical field lines;through polymer vias;Fabrication;Encapsulation;Electrodes;Sensitivity;Humidity;Electromagnetic compatibility;Capacitance|
|[Size Scaling of Brittle Strength using Multi-Mode Weibull Distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100803)|S. Ananiev; G. M. Reuther; N. D. Vecchio; P. Altieri-Weimar|10.1109/EuroSimE56861.2023.10100803|nan;Micromechanical devices;Bending;Microelectronics;Weibull distribution;Stress;Testing|
|[Micro-cantilever Bending Test of Sintered Cu nanoparticles for Power Electronic Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100756)|L. Du; D. Hu; R. Poelm; W. V. Driel; K. Zhang|10.1109/EuroSimE56861.2023.10100756|nan;Nanoparticles;Thermal resistance;Bending;Silicon;Power electronics;Time measurement;Thermal analysis|
|[A Reliability Assessment Approach for A LIF Neurons Based Spiking Neural Network Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100785)|J. Li; B. Sun; X. Xie|10.1109/EuroSimE56861.2023.10100785|nan;Performance evaluation;Fault tolerance;Neurons;Fault tolerant systems;Hardware;Robustness;Circuit faults|
|[Multi-scale electro-thermo-mechanical simulation of a SiC MOSFET transitor during short-circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100834)|F. Loche-Moinet; L. Theolier; E. Woirgard|10.1109/EuroSimE56861.2023.10100834|nan;Heating systems;Semiconductor device modeling;Micromechanical devices;MOSFET;Silicon carbide;Thermomechanical processes;Microelectronics|
|[Application of Machine Learning to Recognize Wire Bond Lift-Off in Power Electronics Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100782)|H. Huai; N. Chidanandappa; J. Wilde|10.1109/EuroSimE56861.2023.10100782|nan;Support vector machines;Solid modeling;Machine learning algorithms;Magnetostatics;Simulation;Wires;Machine learning|
|[Modeling of in-plane distortions and overlay errors encountered during 3-D NAND flash device fabrication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100780)|O. O. Okudur; M. Gonzalez; G. Van Den Bosch; M. Rosmeulen|10.1109/EuroSimE56861.2023.10100780|nan;Fabrication;Micromechanical devices;Solid modeling;Nonlinear distortion;Microelectronics;Integrated circuit modeling;Flash memories|
|[Optimal design of piezoelectric MEMS for vibration monitoring system with nanoionics zero-energy memory elements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100827)|A. Shaporin; C. Stöckel; M. Melzer; F. Schaller; R. Forke; S. Zimmermann; H. Kuhn|10.1109/EuroSimE56861.2023.10100827|nan;Vibrations;Micromechanical devices;Wireless communication;Wireless sensor networks;Transducers;Vibration measurement;Sensor systems|
|[PCBA reliability simulation in the cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100837)|H. Ziegelwanger|10.1109/EuroSimE56861.2023.10100837|nan;Micromechanical devices;Shape;Printed circuits;Layout;Software;Software reliability;Microelectronics|
|[Stress Recovery in the Reduced Space for Parametric Reduced Models in Microelectronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100787)|I. Zawra; C. B. Umunnakwe; M. v. Soestbergen; E. B. Rudnyi; T. Bechtold|10.1109/EuroSimE56861.2023.10100787|nan;Micromechanical devices;Linear systems;Temperature dependence;Real-time systems;Microelectronics;Digital twins;Parametric statistics|
|[Matrix Interpolation-Based Parametric Model Order Reduction of Electromagnetic Systems with Translational Movement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100820)|A. Schütz; T. Bechtold|10.1109/EuroSimE56861.2023.10100820|nan;Couplings;Micromechanical devices;Electromagnetic devices;Computational modeling;Numerical models;Parametric statistics;Microelectronics|
|[Fatigue behavior of Au, Cu and PCC fine wire bond connections for power LED applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100839)|B. Czerny; S. Schuh|10.1109/EuroSimE56861.2023.10100839|nan;Gold;Wires;Materials reliability;Fatigue;Light emitting diodes;Reliability engineering;Microelectronics|
|[Probabilistic and Physics-Informed Machine Learning for Predictive Maintenance with Time Series Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100781)|P. -A. Vu; E. Aldea; M. Bouarroudj; S. L. Hégarat-Mascle|10.1109/EuroSimE56861.2023.10100781|nan;Measurement;Training;Uncertainty;Time series analysis;Neural networks;Predictive models;Probabilistic logic|
|[Influence of the Bond Foot Angle on Active Power Cycling Lifetime of Wire Bonds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100802)|M. Sippel; Y. F. Tan; R. Schmidt; P. Botazzoli; M. Sprenger; J. Franke|10.1109/EuroSimE56861.2023.10100802|nan;Geometry;Thermal expansion;Simulation;Wires;Layout;Multichip modules;Reliability engineering|
|[MEMS Cantilever on High-Cycle Fatigue Testing of thin Metal Films](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100848)|N. Jöhrmann; C. Stöckel; B. Wunderle|10.1109/EuroSimE56861.2023.10100848|nan;Micromechanical devices;Temperature measurement;Scanning electron microscopy;Aluminum;Life estimation;Fatigue;Surface roughness|
|[Warpage of transfer-molded automotive power modules – experimental characterization, numerical simulation and optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100795)|M. Sprenger; M. Krämer; E. Tolyschew; M. Steinau; D. Renner; B. Ottinger; J. Franke|10.1109/EuroSimE56861.2023.10100795|nan;Micromechanical devices;Rapid thermal processing;Manufacturing processes;Multichip modules;Predictive models;Numerical simulation;Numerical models|
|[Undoped and Doped Solder Performance under High Strain Rates and Wide Operating Temperatures after Prolonged Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100788)|P. Lall; V. Mehta; V. Yadav; M. Saha; J. Suhling|10.1109/EuroSimE56861.2023.10100788|nan;Temperature measurement;Vibrations;Temperature distribution;Electric shock;Computational modeling;Predictive models;Strain measurement|
|[AI and Feature-Vector Based Damage Monitoring and Remaining Useful-Life Assessment for Electronics Assemblies in Mechanical Shock and Vibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100747)|P. Lall; T. Thomas|10.1109/EuroSimE56861.2023.10100747|nan;Vibrations;Micromechanical devices;Electric shock;Mission critical systems;Predictive models;Time measurement;Microelectronics|
|[Degradation of silicone-based sealing materials used in microelectronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100796)|M. Y. Mehr; P. Hajipour; H. v. Zeijl; W. D. van El; T. Cooremans; F. D. Buyl; G. Q. Zhang|10.1109/EuroSimE56861.2023.10100796|nan;nan|
|[UV LEDs: Performance and Reliability for Commercial InGaN and AlGaN LED Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100797)|J. L. Davis; K. Rountree; R. Pope; K. Riter; C. Clayton; A. Dart; M. McCombs; A. Wallace|10.1109/EuroSimE56861.2023.10100797|nan;Degradation;Temperature measurement;Voltage measurement;Current measurement;Aging;Light emitting diodes;Wide band gap semiconductors|
|[Effect of Thermomigration on Electromigration in SWEAT Structures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100774)|Z. Cui; X. Fan; G. Zhang|10.1109/EuroSimE56861.2023.10100774|nan;Electromigration;Micromechanical devices;Temperature distribution;Simulation;Conductors;Thermal force;Mathematical models|
|[FE-Analysis of Deformation State during a Four-Point Bending Experiment on Soldered MLCCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100847)|S. G. Anthati; V. Serea; E. Wiss; S. Wiese|10.1109/EuroSimE56861.2023.10100847|nan;Deformation;Simulation;Force;Capacitors;Bending;Thermal force;Numerical models|
|[Reactive Die Bonding on LTCC Substrates – Analysis by CFD Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100772)|E. Wiss; A. Yuile; A. Schulz; J. Müller; S. Wiese|10.1109/EuroSimE56861.2023.10100772|nan;Temperature measurement;Heating systems;Micromechanical devices;Adaptation models;Atmospheric modeling;Computational fluid dynamics;Computational modeling|
|[Validation of the thermal path with one phase liquid cooling for HPC in harsh environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100823)|T. Grün; D. May; H. Straub; G. P. Jakub; W. Verleysen; B. Wunderle|10.1109/EuroSimE56861.2023.10100823|Automotive HPC;liquid cooling;enhanced pin-fin;thermal characterization;Micromechanical devices;Manifolds;Liquid cooling;Thermal resistance;Cold plates;Three-dimensional printing;Microelectronics|
|[Effect of Elastic-Plastic Anisotropy of Solder Grains on Variability of Cyclic Mechanical Bending Durability in SAC305 CSP Assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100850)|A. Bharamgonda; A. Dasgupta; A. Deshpande; T. Hauck; Y. Chen|10.1109/EuroSimE56861.2023.10100850|SAC305;oligocrystalline joint;grain-scale modelling;global-local modelling;cyclic mechanical bending.;nan|
|[Thermal management of vertical GaN transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100765)|L. Mitterhuber; V. Leitgeb; M. Krainz; R. Strauss; T. Kaden; E. B. Treidel; F. Brunner; C. Huber; E. Kraker|10.1109/EuroSimE56861.2023.10100765|nan;Performance evaluation;Thermal resistance;Conductivity;Thermal conductivity;Thermal management;Silicon;Thermal analysis|
|[Metamaterials and MEMS (MetaMEMS): a promising trend in Microsystems technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100824)|R. Ardito; C. Comi; V. Zega; A. Corigliano|10.1109/EuroSimE56861.2023.10100824|nan;Micromechanical devices;Vibrations;Geometry;Propagation;Prototypes;Aerospace electronics;Market research|
|[Simulation of Cu bulge-out by cyclic Cu surface diffusion FEM in Cu/SiCN hybrid bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100784)|Y. W. Tsau; J. D. Messemaeker; M. Gonzalez; M. Seefeldt; E. Beyne; I. D. Wolf|10.1109/EuroSimE56861.2023.10100784|nan;Semiconductor device modeling;Feature extraction;Surface fitting;Surface roughness;Surface topography;Finite element analysis;Rough surfaces|
|[Over-current Capability of SiC Devices for Short Power and Heat Pulses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100755)|S. Bhadoria; H. -P. Nee|10.1109/EuroSimE56861.2023.10100755|High-temperature;metals;over-currents;phase change materials;power modules;semiconductor devices;silicon carbide;Heating systems;Phase change materials;Silver;Silicon carbide;Graphite;Diamonds;Thermal conductivity|
|[Two-Phase Flow Simulation of Capillary Underfilling as a Design Tool for Heterogenous Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100773)|L. C. Stencel; J. Strogies; R. Knofe; B. Müller; C. Borwieck; M. Heimann|10.1109/EuroSimE56861.2023.10100773|Capillary underfill;power packaging;CFD simulation;FVM;VOF;vouformation;design tool;nan|
|[Thermal Stresses in a Bi-Layer Assembly in Electronics Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100842)|M. T. Vellukunnel; M. Khanal; X. Fan|10.1109/EuroSimE56861.2023.10100842|nan;nan|
|[Analytical Solution for Moisture Diffusion with Initial Non-Uniform Moisture Concentration used in Bake Time Study in Electronics Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100783)|M. Khanal; J. Zhou; X. Fan|10.1109/EuroSimE56861.2023.10100783|nan;Micromechanical devices;Sensitivity;Absorption;Parametric study;Ovens;Moisture;Packaging|
|[Anand model calibration for SAC305 solder joints based on the evolution of the shear stress and strain hysteresis loops for different thermal cycling conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100843)|J. -B. Libot; Z. Bussière; L. Mahfouz; J. Alexis; O. Dalverny|10.1109/EuroSimE56861.2023.10100843|nan;Instruments;Creep;Thermomechanical processes;Lead;Strain measurement;Data models;Soldering|
|[Comparison of finite element approaches for Si wafer buckling calculation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100748)|C. Sautot; J. -C. Craveur; M. Boutaleb; F. Roqueta|10.1109/EuroSimE56861.2023.10100748|Buckling;bifurcation;finite element analysis;wafer;perturbation force;geometric imperfection.;Semiconductor device modeling;Temperature;Shape;Perturbation methods;Force;Bifurcation;Thermal force|
#### **2023 IEEE Workshop on Microelectronics and Electron Devices (WMED)**
- DOI: 10.1109/WMED58543.2023
- DATE: 31-31 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Single-Ended Signaling for Energy-Efficient Short-Reach Communication with High-Bandwidth Density: Invited Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097442)|J. M. Wilson|10.1109/WMED58543.2023.10097442|Ground Referenced Signaling;High-Speed Serial Link;Single-Ended Signaling;Multi-Chip Modules;AI;HPC;Transmitters;Power supplies;Prototypes;Production;Receivers;Jitter;CMOS technology|
|[Atomic Layer Processing of MoS2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097444)|W. Jen; J. D. Hues; J. Soares; S. Letourneau; M. Lawson; D. Choudhury; A. U. Mane; Y. Lu; Y. Wu; S. M. Hues; L. Li; J. W. Elam; E. Graugnard|10.1109/WMED58543.2023.10097444|nan;Electric potential;Photonic band gap;Conferences;Atomic layer deposition;Electron mobility;Microelectronics;Sulfur|
|[Metal-to-Metal Flip-Chip Bonding for High-Temperature 3D SiC IC Integration and Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097447)|F. Li; J. Shi|10.1109/WMED58543.2023.10097447|flip-chip bonding;gold-to-gold;metal-to-metal;reliability;high temperature;integrated circuit (IC);3D IC;3D packaging;Integrated circuits;Gold;Silicon carbide;Aging;Packaging;Conductors;Thermal conductivity|
|[An All-Optical 2-Bit Adder Composed of Fabry-Perot Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097433)|J. Ellaine Houle; D. Sullivan; A. Zadehgol; M. G. Kuzyk|10.1109/WMED58543.2023.10097433|FDTD;optical logic;Solid modeling;Analytical models;Three-dimensional displays;Aerospace electronics;Fabry-Perot;Behavioral sciences;Optical bistability|
|[Power management architectures for high performance NAND systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097445)|L. Nubile; B. Iorio; M. Yu; J. Binfet; M. Piccardi; R. Cardinali; W. Di Francesco; A. Mohammadzadeh|10.1109/WMED58543.2023.10097445|Flash memories;Current control;Power system control;Low-power electronics;Microcontrollers;Distributed management;Performance evaluation;Portable computers;Solid state drives;Power system management;Control systems;Microelectronics;Flash memories|
|[R(t)-Based Spike-Timing-Dependent Plasticity in Memristive Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097441)|F. Afrin; K. D. Cantley|10.1109/WMED58543.2023.10097441|Spike-Timing-Dependent Plasticity;R(t) element;memristor;Spiking Neural Network;spike triplet learning;Shape;Neuromorphics;Biological system modeling;Scalability;Parallel processing;Behavioral sciences;Microelectronics|
|[Quantum Time-Domain Simulation in 3-D Electron Nano-devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097446)|K. Antonov; D. Sullivan|10.1109/WMED58543.2023.10097446|FDTD;MOSFET;transmission;PML;Performance evaluation;Solid modeling;Conferences;Nanoscale devices;Microelectronics;Transistors;Time-domain analysis|
#### **2023 IEEE Applied Sensing Conference (APSCON)**
- DOI: 10.1109/APSCON56343.2023
- DATE: 23-25 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Efficient but Effective Perceptual Quality Model of Screen Content Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101066)|T. Tang; C. You; R. Zhang|10.1109/APSCON56343.2023.10101066|Image quality assessment;visual perception;human visual system;Image quality;Deep learning;Computational modeling;Image edge detection;Visual systems;Feature extraction;Animation|
|[Impedimetric study of poly-butyl thiophenebased sensor for detection of VOCs and mixtures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101185)|P. Kaur; S. Bagchi; A. P. Bhondekar|10.1109/APSCON56343.2023.10101185|Classification;Conducting Polymers;Impedance Spectroscopy;Machine Learning;Mixtures;Support Vector Machine;Volatile Organic Compounds.;Volatile organic compounds;Resistance;Spectroscopy;Support vector machine classification;Oxidation;Frequency response;Sensors|
|[Multi-level Fusion of Multi-spectral Images to Detect the Artificially Ripened Banana](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101030)|N. Vetrekar; R. Ramachandra; R. S. Gad|10.1109/APSCON56343.2023.10101030|Multi-spectral imaging;Artificial ripening;Feature extraction;Score fusion;Classification;Support vector machines;Multispectral imaging;Databases;Sensors|
|[A Study on Pulse Wave Signals based on Fibre Bragg Grating Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101328)|M. Mishra; P. K. Sahu|10.1109/APSCON56343.2023.10101328|Pulse Wave Signals (PWS);Arterial Pulse Wave (APW);Fiber Bragg Gratings (FBG);Fiber Bragg Grating Array (FBGA);Optical Sensors;Coherence;Medical Applications and Diagnosis.;Heart rate;Coherence;Fiber gratings;Physiology;Blood pressure;Sensors;Arteries|
|[Degradable nanofibers-based capacitive pressure sensor for underwater monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101231)|X. Karagiorgis; A. Beniwal; P. Skabara; R. Dahiya|10.1109/APSCON56343.2023.10101231|Degradable nanofibers;electrospinning;pressure sensor;underwater monitoring;flexible electronics;Pressure sensors;Encapsulation;Electrodes;Sensitivity;Wearable computers;Aluminum;Polyimides|
|[B2EH: Batteryless BLE Sensor Network Using RF Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101211)|T. -H. Dang; N. -H. Dang; V. -T. Tran; W. -Y. Chung|10.1109/APSCON56343.2023.10101211|Radio-frequency energy harvesting;Wireless power transfer;BLE;Radio frequency;Wireless communication;Wireless sensor networks;Power system management;Wireless power transfer;Sensor systems;Mobile nodes|
|[Development of landslide forecasting system using deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101223)|A. Joshi; D. P. Kanungo; R. K. Panigrahi|10.1109/APSCON56343.2023.10101223|WSN-AI;Landslide fore-warning;Landslide prediction;Deep learning;Sensor network;Wireless sensor networks;Weather forecasting;Predictive models;Prediction algorithms;Terrain factors;Real-time systems;Hazards|
|[Intra Plant Body Signal Transmission Using Capacitive and Galvanic Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100978)|G. Kumari; N. P. Pathak|10.1109/APSCON56343.2023.10100978|Intra Body Communication;Capacitive Coupling;Galvanic Coupling;Plant Body Communication;Couplings;Electrodes;Transmitters;Voltage;Receivers;Attenuation;Physiology|
|[Flying Path Optimization of Rechargeable UAV for Data Collection in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101011)|Y. Zhu; S. Wang|10.1109/APSCON56343.2023.10101011|Data collection;path planning;unmanned aerial vehicle;wireless sensor network.;Wireless sensor networks;Data collection;Traveling salesman problems;Autonomous aerial vehicles;Path planning;Batteries;Sensors|
|[An Array of Bandpass Detectors for Measuring Beam Spectral Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101178)|M. Jahangiri; P. Sberna; A. Sammak; S. Nihtianov|10.1109/APSCON56343.2023.10101178|radiation beam spectral components;detector array;integrated optical windows;Optical filters;Sensitivity;Stimulated emission;Wavelength measurement;Ultraviolet sources;Detectors;Vacuum technology|
|[Printed and Flexible Capacitive Pressure Sensors for Soft Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101193)|M. T. Vijjapu; S. Khan; S. H. Abdullah; M. Jose; J. Zikulnig; L. Rauter; L. -M. Faller; J. Kosel|10.1109/APSCON56343.2023.10101193|Pressure sensors;Force sensors;PDMS composite;capacitive sensors;tactile sensors;robotics;NiZnFeO nanoparticles.;Pressure sensors;Electrodes;Nanoparticles;Fabrication;Sensitivity;Robot sensing systems;Capacitive sensors|
|[Discrimination of VOCs using Chemiresistive Sensor Array - Towards electronic nose applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101331)|N. Vadera; S. Dhanekar|10.1109/APSCON56343.2023.10101331|Electronic nose;Metal oxide sensors;Gas sensors;Volatile organic compounds;Sensor Array;Principal Component Analysis.;Volatile organic compounds;Sensitivity;Parallel processing;Electronic noses;Sensors;Indoor environment;Monitoring|
|[Remote sensing using drone and machine learning for computation of rooftop solar energy potential](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101182)|P. S. Prakash; P. R. Vyas|10.1109/APSCON56343.2023.10101182|Deep learning;Unmanned Aerial Vehicle;Solar energy;Renewable energy sources;Computational modeling;Buildings;Solar energy;Data models;Spatial databases;Sensors|
|[DL-RAP: Deep-Learning based Real-time Accident Diffusion Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101065)|B. Zhang; X. Zhao; H. Chen|10.1109/APSCON56343.2023.10101065|Habitat and Environmental Monitoring;Computational Fluid Dynamics;Diffusion Prediction Model;Transformer Neural Network;Neural networks;Time series analysis;Predictive models;Reliability theory;Transformers;Software;Real-time systems|
|[An Improved Thompson Sampling Method for Dynamic Spectrum Access in Non-Stationary Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101010)|S. Ye; S. Wang|10.1109/APSCON56343.2023.10101010|Dynamic spectrum access;non-stationarity;sensor networks;Thompson sampling.;Wireless sensor networks;Heuristic algorithms;Dynamic spectrum access;Time-varying channels;Probability;Sampling methods;Sensors|
|[Soil pH Sensing with an All-Solid-Electrode Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101246)|A. V. Akshaya; N. Nair; M. J. Bosco; G. K. Ananthasuresh; J. Joseph|10.1109/APSCON56343.2023.10101246|nan;Electrodes;Fabrication;Soil measurements;Soil;Maintenance engineering;Sensors;Reliability|
|[Design and Characterization of a Frequency Modulated Continuous Wave Transceiver-based Ultrasound Imaging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101348)|D. D. Lawania; B. Mukherjee; A. Jain|10.1109/APSCON56343.2023.10101348|ultrasound imaging;FMCW;time delay spectrometry;Performance evaluation;Spectroscopy;Ultrasonic imaging;Frequency modulation;Power demand;Ultrasonic variables measurement;Pulse measurements|
|[A novel and compact photoacoustic sensing system to estimate thermophysical properties of the lubricant oil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101150)|A. Gorey; A. Sinharay; C. Bhaumik; T. Chakravarty; A. Pal|10.1109/APSCON56343.2023.10101150|thermophysical properties;photoacoustic sensing system;lubricant oil;Temperature measurement;Viscosity;Temperature sensors;Oils;Lubricants;Inspection;Feature extraction|
|[Reliability-Aware Energy-Efficient Joint Resource Allocation for Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101304)|H. Peng; J. Zhang; Z. Li; T. Tang|10.1109/APSCON56343.2023.10101304|edge computing;reliability;alternating iteration;energy consumption;Performance evaluation;Energy consumption;Processor scheduling;Computational modeling;Simulation;Sensors;Reliability|
|[Detecting moments of distraction during meditation practice based on changes in the EEG signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101045)|P. Pandey; J. Rodriguez-Larios; K. P. Miyapuram; D. Lomas|10.1109/APSCON56343.2023.10101045|nan;Protocols;Machine learning algorithms;Machine learning;Feature extraction;Brain modeling;Electroencephalography;Sensors|
|[Portable H2S gas detection and alert system prototype using highly sensitive nanocrystalline SnO2 thin films : Sensor for environmental monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101012)|S. Kanth; A. K. Chikara; S. Choudhury; C. A. Betty|10.1109/APSCON56343.2023.10101012|electronic alarm; chemisresistive sensor; nanostructured SnO2;gas sensor;electronic alarm;chemisresistive sensor;nanostructured SnO2, gas sensor;Gases;Chemical sensors;Three-dimensional displays;Transmitters;Nanoscale devices;Sensor systems;Hazards|
|[UAV-assisted 3D Trajectory Planning and Data Collection in Wireless Powered IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101001)|Z. Li; H. Li; T. Tang|10.1109/APSCON56343.2023.10101001|UAV;age of information;energy harvesting;trajectory planning;Wireless communication;Wireless sensor networks;Three-dimensional displays;Trajectory planning;Simulation;Data collection;Information age|
|[Floodet: A Lightweight Edge AI Model for Intelligent Flood Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101310)|W. Ou; L. Zeng; X. Chen|10.1109/APSCON56343.2023.10101310|F1ood detection;edge intelligence;lightweight model;Image edge detection;Computational modeling;Semantic segmentation;Roads;Urban areas;Real-time systems;Sensors|
|[DC-ST: A Short-term traffic flow prediction Approach based on Distance Correlation and Spatial-Temporal Dependence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101007)|C. Liu; L. Lin; Y. Zhang; Y. Chen; H. Wang; L. Chen|10.1109/APSCON56343.2023.10101007|Intelligent Transportation System;Short-term Traffic Flow;Prediction Model;Temporal-spatial dependency;Correlation;Uncertainty;Filtering;Redundancy;Transportation;Predictive models;Traffic control|
|[Directed Transimission of Ca2+ Signals in Three-dimention Biological Cell Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101103)|P. He; M. Su; Y. Cui; Y. Chen; M. Hua; K. Wang|10.1109/APSCON56343.2023.10101103|mobile molecular communication;calcium signal;gap junction;chemotaxis;Analytical models;Solid modeling;Biological system modeling;Calcium;Simulation;Molecular communication;Cells (biology)|
|[An Effective Evolutionary Neural Architecture Search for Bike-Sharing System Demand Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101084)|B. -H. Chen; Y. -Y. Cai; C. -Y. Huang; C. -W. Tsai|10.1109/APSCON56343.2023.10101084|Neural architecture search;Bike sharing system;and metaheuristic algorithm;Training;Codes;Computational modeling;Neural networks;Computer architecture;Simulated annealing;Prediction algorithms|
|[A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101228)|L. Peng; D. Luo; Y. Xiao; F. Li|10.1109/APSCON56343.2023.10101228|photonics-aided system;OFDM;phase noise;radar and communication integration;Phase noise;Laser radar;Image resolution;Communication systems;OFDM;Bandwidth;Radar imaging|
|[Acoustic based Machine Anomaly Detection using Beamforming and Sequential Transform Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100992)|S. Sahu; K. Kumar; A. Majumdar; A. A. Kumar; M. G. Chandra|10.1109/APSCON56343.2023.10100992|Source Separation;Beamforming;Multichannel;Sequential Transform Learning;Anomaly Detection;Source separation;Array signal processing;Neural networks;Transforms;Maintenance engineering;Acoustics;State-space methods|
|[MoS2 based nanomechanical bolometer for combined radiation sensing and the estimation of material properties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101248)|S. Saxena; T. B. Ashagre; D. Rakshit; S. Das; V. R. Rao|10.1109/APSCON56343.2023.10101248|Bolometer;photodetector;MoS2;PDMS;sensors;Performance evaluation;Absorption;Photonic band gap;Thermal sensors;Bolometers;Photodetectors;Mathematical models|
|[Monte Carlo method based model for augmenting data towards lubricant oil state analysis in heavy machine industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101307)|S. Chatterjee; A. Gorey; S. Gain; A. Sinharay; C. Bhaumik; T. Chakravarty; A. Pal|10.1109/APSCON56343.2023.10101307|Model;Monte Carlo;k-wave toolbox;lubricant oil.;Industries;Analytical models;Technological innovation;Computational modeling;Lubricants;Machine learning;Lubricating oils|
|[Embedded Machine Learning on Accelerometer Data for Exercise Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101165)|R. -u. -. Nissa; N. C. Karmakar; M. S. Baghini|10.1109/APSCON56343.2023.10101165|embedded machine learning;inertial sensor;mems accelerometer;physical therapy;Accelerometers;Performance evaluation;Microcontrollers;Wearable computers;Medical treatment;Machine learning;Brain modeling|
|[Aeroelastic Analysis in a Hybrid Composite by Embedding Shape Memory Alloy*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101339)|K. S. Tandel; R. Bhanumurthy; H. Meghashyam|10.1109/APSCON56343.2023.10101339|Aeroelasticity;Flutter;Shape Memory Alloy(SMA);Mode shapes;Damping;Nonlinear transient dynamic.;Time-frequency analysis;Shape memory alloys;Aerodynamics;Sensors;Intelligent structures;Time-domain analysis;Transient analysis|
|[Analysis of Interpolation Techniques for a Flexible Sensor Mat for Plantar Pressure Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101098)|A. Fatema; I. Kuriakose; R. Gupta; A. M. Hussain|10.1109/APSCON56343.2023.10101098|F1exible pressure sensor;Interpolation;Piezoresistive;Plantar pressure;Velostat.;Pressure sensors;Fabrication;Weight measurement;Interpolation;Visualization;Pressure measurement;Reliability|
|[A dual-slope RDC using T-Network for Low Resistance Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101142)|G. Singh; S. Sohail; T. Islam|10.1109/APSCON56343.2023.10101142|Low resistance;T-network;dual-slope integrator;resistance to digital converter (RDC);lead resistance compensation;two-wire.;Resistance;Voltage measurement;Sensitivity;Current measurement;Simulation;Capacitors;Measurement techniques|
|[Labour Monitoring in Pregnant Women using Electrocardiography and Electromyography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101197)|A. Tiwari; S. Chauhan; S. Bharatala; T. Ajay; N. Paleru; A. M. Hussain|10.1109/APSCON56343.2023.10101197|Electrocadiography;Electromyography;Labour monitoring;Maternal heart rate;Uterine contractions;Heart rate;Pregnancy;Pediatrics;Wearable computers;Medical services;Electrocardiography;Electromyography|
|[Fabrication and Characterization of a Flexible Transparent Nozzle/Diffuser Micropump](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101187)|S. Malkurthi; D. Niteesh; S. Bhattacharjee; A. M. Hussain|10.1109/APSCON56343.2023.10101187|Microfluidics;Micropump;Nozzle/diffuser;PDMS;Transparent;Fabrication;Volume measurement;Bending;Micropumps;Lab-on-a-chip;Silicon;Sensors|
|[Chemosensor for Colorimetric Detection of Fluoroquinolone Antibiotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101220)|T. Mathai; T. Pal; S. Mukherji|10.1109/APSCON56343.2023.10101220|sensors;fluoroquinolone antibiotics;antimicrobial resistance;chemosensor;counterfeit medicines;Microorganisms;Antibiotics;Developing countries;Sensors;Testing;Immune system|
|[Detection of Microcystin-LR in Water using Polyaniline Coated U-bent Fiber Optic Biosensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101129)|A. K. Mandal; T. Pal; S. Mukherji; S. Mukherji|10.1109/APSCON56343.2023.10101129|microcystin;biosensor;polyaniline;fiber optic;Optical fibers;Optical fiber sensors;Biomedical optical imaging;Optical buffering;Organizations;Antibodies;Biosensors|
|[Gold Nanoparticles Coated Aptamer-based Fiber Optic LSPR Biosensor for Arsenic Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101266)|A. Shukla; T. Pal; S. Mukherjia|10.1109/APSCON56343.2023.10101266|Aptamer-based sensor;LSPR;fiber-optic;arsenite;biosensor.;Optical fibers;Nanoparticles;Gold;Optical fiber sensors;Arsenic;Organizations;Sensors|
|[Flexible Writing Pad based on a Piezoresistive Thin Film Sensor Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101025)|M. D. Gupta; L. Lakshmanan; A. Fatema; A. M. Hussain|10.1109/APSCON56343.2023.10101025|Flexible PCB;Flexible writing pad;Piezoresistive material;Pressure sensor;Velostat;Transmission line matrix methods;Voltage;Writing;Bending;Predictive models;Probabilistic logic;Thin film sensors|
|[Non Line of Sight (NLoS) Path Loss Evaluation of Wi-SUN in an Urban Landscape](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101363)|A. Gupta; M. Ruthwik; A. Saxena; A. M. Hussain|10.1109/APSCON56343.2023.10101363|Path Loss;RSSI;Smart city;Urban deployment;Wi-SUN;Wireless communication;Wireless sensor networks;Protocols;Smart cities;Termination of employment;Topology;Sensors|
|[Effect of rapid thermal annealing on material, electrical and sensing characteristics of Ag-doped CaTiO3-CuO thin film carbon dioxide gas sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101278)|R. S. B; S. H. R; N. Bhat|10.1109/APSCON56343.2023.10101278|gas sensor;semiconductor metal oxide;CO₂ gas;Grain size;Temperature sensors;Temperature measurement;Resistance;Annealing;Temperature;Films|
|[Efficient Prediction of Segment Kinematics and Dynamics from Motion Capture Data Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100990)|D. N. Chowdhury; A. Ahmed; M. Sreenivasa|10.1109/APSCON56343.2023.10100990|artificial neural networks;deep learning;dynamics;feature selection;inertial motion capture;kinematics;Training;Deep learning;Motion segmentation;Dynamics;Artificial neural networks;Medical services;Motion capture|
|[Evaluation of low-cost particulate matter sensor in indoor and outdoor micro-environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100969)|J. Aswin Giri; S. Shiva Nagendra|10.1109/APSCON56343.2023.10100969|Air quality monitoring;Sensors;Evaluation;Atmospheric measurements;Instruments;Urban areas;Size measurement;Particle measurements;Market research;Air quality|
|[Selective sensor platform for the measurement of 0.5 ppm of CH4 for Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101088)|R. G. Anjitha; P. K. Basu|10.1109/APSCON56343.2023.10101088|Precision Agriculture;Comsol Multiphysics;Device fabrication;Temperature sensors;Semiconductor device measurement;Gases;X-ray scattering;Temperature;Sensitivity;Transmitters|
|[Radar and Camera Fusion for Multi-Task Sensing in Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101274)|K. Shi; S. He; J. Chen|10.1109/APSCON56343.2023.10101274|multi-modal fusion;mmWave radar;object detection;autonomous driving;Three-dimensional displays;Radar detection;Radar;Object detection;Cameras;Multitasking;Transformers|
|[IoT based Data Sensing System for AutoGrow, an Autonomous greenhouse System for Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101100)|P. Patil; R. Kestur; M. Rao; A. C†|10.1109/APSCON56343.2023.10101100|Data Sensing;IoT;Controlled Environment Agriculture(CEA);Precision Farming;AI.;Temperature sensors;Irrigation;Climate change;Instruments;Green products;Prototypes;Air pollution|
|[Design and Development of a Wireless Condition Monitoring System Biased Using Through Wall Power Transfer Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101041)|A. Pal; R. Anandanatarajana; S. Kaluvan; T. B. Scott; U. Mangalanathan; G. Herrmann|10.1109/APSCON56343.2023.10101041|through wall power transfer;piezoelectric energy harvesting;IoT;FEM analysis;resonant frequency;Temperature measurement;Temperature sensors;Condition monitoring;Wireless communication;Wireless sensor networks;Power measurement;Resonant frequency|
|[Brain Activity Recognition using Deep Electroencephalography Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100986)|R. Johri; P. Pandey; K. P. Miyapuram; D. Lomas|10.1109/APSCON56343.2023.10100986|Human-Centered Computing;EEG Sensor;Machine Learning;Brain Activity;Convolution;Wearable computers;Music;Spatial filters;Motion pictures;Feature extraction;Brain modeling|
|[Advantage of droplet encapsulation scheme in microflow cytometer based detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100972)|K. Mirkale; A. K. Sen|10.1109/APSCON56343.2023.10100972|microflow cytometer;CTC;fluorescence;optical signal;focusing;Encapsulation;Optical losses;Geometry;Optical device fabrication;Force;Focusing;Fluorescence|
|[Design and Optimization of a Wearable Sonomyography Sensor for Dynamic Muscle Activity Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101042)|A. T. Kamatham; B. Mukherjee|10.1109/APSCON56343.2023.10101042|Sonomyography;ultrasound imaging;muscle activity sensing;Image sensors;Ultrasonic imaging;Transducers;Tungsten;Bandwidth;Muscles;Biomedical monitoring|
|[Design and Development of Low-cost Environmental Sensors for Urban Noise Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101341)|L. Pradeep; S. M. Shiva Nagendra|10.1109/APSCON56343.2023.10101341|Low-cost sensors;Noise;Noise sensor;Internet of things;nan|
|[Printable Fused Silica Based Microchamber Integrated With Graphene Chemi-Resistive Sensors For Direct On-Chip Soil Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101020)|S. A. Jose; Y. Atwa; N. Mitchell; H. Shakeel|10.1109/APSCON56343.2023.10101020|graphene chemi-resistor;direct injection;vapor sensing;printable gas chamber;soil testing;Silicon compounds;Volatile organic compounds;Ultraviolet sources;Graphene;Glass;Soil;System-on-chip|
|[Design of round corner rectangular planar sensor with DGS for measurement of permittivities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101318)|S. Kaur; S. Singh; M. M. Sinha|10.1109/APSCON56343.2023.10101318|DGS;MPAS;permittivity;sensitivity;PRFS;Wireless communication;Sensitivity;Soil measurements;Simulation;Dielectric materials;Soil moisture;Resonant frequency|
|[PEDOT:PSS based Disposable Humidity Sensor for Skin Moisture Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101060)|A. Beniwal; R. Dahiya|10.1109/APSCON56343.2023.10101060|Disposable humidity sensor;eco-friendly;PEDOT:PSS;printed electronics;skin moisture monitoring;Temperature sensors;Temperature distribution;Moisture;Humidity;Switches;Medical services;Skin|
|[Sensitivity Analysis of a Flexible Piezoresistive Sensor for Efficient Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101146)|L. Singh; M. Bhattacharjee|10.1109/APSCON56343.2023.10101146|flexible pressure sensors;sensor packaging issues;piezoresistive sensor.;Pressure sensors;Fabrication;Sensitivity analysis;Packaging;Pressure measurement;Piezoresistance|
|[Sensing System Assisted Novel PID Controller for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101279)|D. Baidya; S. Dhopte; M. Bhattacharjee|10.1109/APSCON56343.2023.10101279|Electric vehicles;Dandelion Optimization;PID control;DC-DC converter;gearbox;PI control;Simulation;Education;Transportation;Electric vehicles;Robustness;Sensors|
|[Yes/No type swab based colorimetric paper biosensor for detection of chlorpyrifos on agricultural produce : A nondestructive sensing approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101345)|T. Pal; S. Mukherji|10.1109/APSCON56343.2023.10101345|swab based biosensor;colorimetric biosensor;Yes/No biosensor;chlorpyrifos;pesticides;maximum residual limit;Chemistry;Biomedical optical imaging;Toxic chemicals;Color;Biosensors;Optical sensors|
|[Design and Development of a Novel Bluetooth Based Vehicle Scanner : Perspective based on new MCUs with integrated Wi-Fi and Bluetooth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101287)|A. R. Kulkarni; N. Kumar; R. Rao|10.1109/APSCON56343.2023.10101287|Bluetooth;Wi-Fi;IoT;Traffic sensors;Smart city;Fabrication;Bluetooth;Portable computers;Smart cities;Hardware;Real-time systems;Automobiles|
|[Deep Reinforcement Learning Empowered Particle Swarm Optimization for Aerial Base Station Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101173)|J. Song; B. Zhang; J. Lia|10.1109/APSCON56343.2023.10101173|Unmanned Aerial Vehicles (UAVs);particles swarms optimization (PSO);deep deterministic policy gradient(DDPG);Deep learning;Base stations;NP-hard problem;Heuristic algorithms;Simulation;Reinforcement learning;Quality of service|
|[Stronger correlation of music features with brain signals predicts increased levels of enjoyment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101229)|P. Pandey; P. S. Bedmutha; K. P. Miyapuram; D. Lomas|10.1109/APSCON56343.2023.10101229|EEG;Aesthetic;Music;CCA;Measurement;Correlation;Feature extraction;Acoustics;User experience;Real-time systems;Multiple signal classification|
|[A reusable and reagent-free solid-state sensor for chloride detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101018)|V. Patel; V. Ramesh; P. Maske; A. Ghosh; R. Srivastava|10.1109/APSCON56343.2023.10101018|solid-state sensor;reagent free;chloride sensor;silver-silver chloride electrode;sensor reuse;Chemical sensors;Silver;Fluids;Wires;Interference;Sensors;Calibration|
|[Distributed Fiber-Optic Calorimetric Dosimeter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100988)|A. V. Tregubov; V. V. Prikhodko; A. S. Alekseyev; S. G. Novikov; G. V. Tertyshnikova; A. V. Zhukov|10.1109/APSCON56343.2023.10100988|calorimetric dosimeter;optical fiber sensor;remote sensing;stimulated Brillouin scattering;Temperature sensors;Temperature measurement;Optical fibers;Optical fiber sensors;Temperature distribution;Temperature dependence;Sensitivity|
|[DigiFresh - Quality Assurance of High Value Foods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101361)|J. Dutta; P. Deshpande; S. Selvan; B. Rai|10.1109/APSCON56343.2023.10101361|Food Quality;multimodal sensing;IoT;Quality Assessment;Shelf life;Economics;Quality assurance;Dairy products;Supply chains;Prototypes;Predictive models;Fish|
|[Analysis of impedance sensor probes for electric field treatment during post-harvest processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101095)|T. Mohan; K. J. Suja; K. Sunitha|10.1109/APSCON56343.2023.10101095|Pulsed Electric Field;Post-harvest processing;Impedance Sensor;IDE;Finite Element Method;Electrodes;Impedance measurement;Interference;Agricultural products;Software;Sensors;Capacitance measurement|
|[Spectroscopic Studies and Numerical Modelling on Nanoparticle based Toxic Heavy Metal Sensor for the Development of a Low-Cost Prototype in Field Use](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101024)|N. Pan; R. Ghosh; D. Mukherjee; N. Bhattacharyya; L. Roy; A. Banerjee; S. Singh; R. T. Goswami; M. Mitra; A. Chattopadhyay; S. K. Pal|10.1109/APSCON56343.2023.10101024|Plasmonic nanoparticle;Lead;environmental pollution;self-aggregation;numerical modelling;Nanoparticles;Gold;Pollution;Toxicology;Biological system modeling;Prototypes;Lead|
|[Analysis of Multi-Indices from Hyperspectral strip along Climatic Gradient as Surrogates of Climate Change](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101295)|M. Shoshany; J. Chang; Y. Oh|10.1109/APSCON56343.2023.10101295|Hyperspectral imagery;Spectral Indices;climatic gradient;aridity;desert.;Training;Strips;Sensitivity;Ecosystems;Vegetation mapping;Market research;Global warming|
|[Fitness Activity Classification Using mmWave Radar Point-Cloud And Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101127)|G. Tiwari; S. Gupta|10.1109/APSCON56343.2023.10101127|Human activity recognition;HAR;radar;mmwave;machine learning;privacy;contactless;fitness tracker;point-cloud;IWR1642;Point cloud compression;Machine learning algorithms;Training data;Machine learning;Radar tracking;Data models;Doppler radar|
|[Wearable sensing module for Table Tennis stroke detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101257)|S. Ghosh; Y. Gholap; S. Tallur|10.1109/APSCON56343.2023.10101257|Table tennis;sports analytics;IMU;body sensor;machine learning;neural network;tinyML;TensorFlow;Quantization (signal);Neural networks;Predictive models;Stroke (medical condition);Sensor phenomena and characterization;Feature extraction;Real-time systems|
|[Design of a sensor for real-time measurement of high molarity(12M-16M) sodium hydroxide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101222)|V. M. M. Shri; M. Sivaramakrishna; S. Chitrakkumar|10.1109/APSCON56343.2023.10101222|sodium hydroxide;molarity;refractive index;pulsating sensor;fast breeder reactors;scattering;Sodium;Hydrogen;Real-time systems;Sensors;Inductors|
|[Tracking Moored Vessel Movement in Multiple DOF using Active Sensing Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101293)|R. Kerstens; W. Jansen; G. d. Borrekens; S. Ides; J. Steckel|10.1109/APSCON56343.2023.10101293|nan;Laser radar;Tracking;Sonar measurements;Sonar;Seaports;Radar tracking;Sensors|
|[Modelling of a Highly Sensitive Polymer Composite Tactile Pressure Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101230)|S. H. Abdullah; L. -M. Faller; M. T. Vijjapu; J. Kosel; S. Khan|10.1109/APSCON56343.2023.10101230|Pressure sensors;Capacitive sensors;NiZnFeO nanoparticles;PDMS composite;COMSOL;FEM simulation;Nanoparticles;Pressure sensors;Electrodes;Sensitivity;Tactile sensors;Soft robotics;Large scale integration|
|[An Analog Linearizing Circuit for TMR Angle Sensor with Flexible Measurement Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101196)|K. B. Nandapurkar|10.1109/APSCON56343.2023.10101196|Angle Sensor;Analog Output;Linearizing Circuit;Phase-Imbalance;Tunneling Magneto-Resistance;Capacitance;Sensors;Tunneling magnetoresistance;Integrated circuit modeling;Magnetic circuits;Magnetic tunneling|
|[Fully automated, real-time monitoring of ambient water vapour using a compact 1392 nm tunable diode laser-based system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101316)|D. Paul; S. De; K. T. V. Grattan; A. L. Chakraborty|10.1109/APSCON56343.2023.10101316|Water vapour measurement;wavelength modulation spectroscopy;remote pollution monitoring;Weight measurement;Semiconductor device measurement;Spectroscopy;Absorption;Modulation;Distributed feedback devices;Size measurement|
|[Optimizing of Bathing Water Heater using Microwave and Microcontroller Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101161)|P. Karunakaran; M. S. Osman; B. H. Hossain; K. John; P. Karunakaran; S. Karunakaran; A. Karunakaran|10.1109/APSCON56343.2023.10101161|Bathing;water heater;microwaves;magnetron;Electromagnetic heating;Microwave technology;Water heating;Microwave ovens;Solids;Water pumps;Copper|
|[Handling Gas Entrainment Issues in Coriolis Flow Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101264)|S. Mahalingam; M. Arsalan|10.1109/APSCON56343.2023.10101264|Coriolis flow sensor;gas entrainment;multiphase flow;data analytics;machine learning;Vibrations;Uncertainty;Density measurement;Neural networks;Measurement uncertainty;Predictive models;Vibration measurement|
|[Spatial Field Fusion Network (SFFNet) for Panoramic Dental X-ray Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101175)|A. Mantravadi; K. Raj; R. Pawar; S. C. T. R; N. K. S|10.1109/APSCON56343.2023.10101175|nan;Deep learning;Training;Performance evaluation;Image segmentation;Solid modeling;Computational modeling;X-rays|
|[Lossy Mode Resonance based Optical Fiber Sensor using Polyvinylpyrrolidone/Chitosan composite for identification of Cadmium ions in water](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101207)|A. Prasanth; M. Velumani; S. Narasimman; Z. C. Alex|10.1109/APSCON56343.2023.10101207|lossy mode resonance;cadmium;optical fiber;tin oxide;pvp/chitosan;Optical fiber losses;Cadmium;Optical fiber sensors;Sensitivity;Tin;Soil;Ions|
|[ACR2UNet: Semantic Segmentation of Remotely Sensed Images using Residual-Recurrent UNet and Asymmetric Convolutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101256)|A. Putty; A. B|10.1109/APSCON56343.2023.10101256|Remotely Sensed Images;Semantic Segmentation;Asymmetric Convolution;Residual connections;Recurrent network;UNet;Land-use and land-cover classes;Semantic segmentation;Semantics;Feature extraction;Sensors;Numerical models;Environmental monitoring;Data mining|
|[Weighted Frequency Subband Compounding in Ultrasonic Imaging Sensor Consisting of a Single Transducer and a Random Coding Mask](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101047)|M. Syaryadhi; N. Tagawa; M. Yang|10.1109/APSCON56343.2023.10101047|single sensor;random coding mask;frequency subband compounding;ultrasonic imaging sensors;microscopy;Image resolution;Ultrasonic imaging;Transducers;Imaging;Encoding;Mathematical models;Software|
|[Fabrication and Characterization of Liquid Phase Exfoliated MoS2 Nanosheet for Gas Sensing Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101319)|R. Gond; A. Rawat; M. Baghoria; B. Prakash; B. Rawat|10.1109/APSCON56343.2023.10101319|Gas sensor;MoS2-CTAB;MoS2-SDS;Response;Temperature sensors;Temperature measurement;Printing;Chemical sensors;Liquids;Sensitivity;Sensor phenomena and characterization|
|[Mental Stress Detection using EEG and Recurrent Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100977)|A. Patel; D. Nariani; A. Rai|10.1109/APSCON56343.2023.10100977|Mental Stress;Emotion classification;EEG;Deep learning;Bi LSTM;BiGRU;Radio frequency;Emotion recognition;Human factors;Brain modeling;Feature extraction;Prediction algorithms;Electroencephalography|
|[Design and Experimental Analysis of Triboelectric Energy Harvester with In-house Set-up](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101013)|S. Khan; B. Mukherjee|10.1109/APSCON56343.2023.10101013|Triboelectric Energy Harvester;Design;Experimentation;In-house set-up.;Vibrations;Simulation;Power system management;Voltage;Sensors;Energy harvesting;Internet of Things|
|[Sensor based Image Reconstruction in Electrical Impedance Tomography using Open-Source Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101198)|P. Chimurkar; P. Trimukhe; D. Berwal; M. S. Baghini|10.1109/APSCON56343.2023.10101198|EIT;electrical impedance tomography;phantom;electrode sensor;reconstruction of image;Electrodes;Electrical impedance tomography;Shape;Impurities;Phantoms;Voltage;Steel|
|[Open Source AI-Enhanced 3D-Printed Insulin Pump](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101219)|F. Fawzi; M. Sedky; Y. Abohammar; H. Sharara; M. Serry|10.1109/APSCON56343.2023.10101219|Artificial Intelligence;insulin pump;bolus injection;Internet-of-Things;micropump;3D printing;drug delivery;wireless;reconfigurable;refillable;Three-dimensional displays;Power demand;Insulin pumps;Machine learning;Predictive models;Sensors;Glucose|
|[Metadata Enhanced Security Watermarks for Sensor Data Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101177)|A. R. Kondapuram; A. Treytl; H. Ruotsalainen; T. Sauter|10.1109/APSCON56343.2023.10101177|Sensor attack detection;LSB watermarking;LoRaWAN;Metadata;IoT;Home Automation Systems.;Home automation;Data protection;Watermarking;Metadata;Logic gates;Hardware;Sensors|
|[Design of round corner rectangular planar sensor with circular slot for estimation of permittivity and conductivity of material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101353)|S. Kaur; S. Singh; M. M. Sinha|10.1109/APSCON56343.2023.10101353|Conductivity;permittivity;reflection coefficient;round corner;nan|
|[Experimental Investigation of Leaf Wetness Sensing Properties of MoS2 Nanoflowers Based Flexible Leaf Wetness Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101153)|P. Khaparde; K. S. Patle; Y. Agrawal; A. Roy; V. S. Palaparthy|10.1109/APSCON56343.2023.10101153|Leaf wetness sensor;plant disease;Molybdenum disulfide (MoS2) sensors;Plant diseases;Morphology;Sensor phenomena and characterization;Oxidation;Sensor systems;Frequency measurement;Sulfur|
|[Real-Time Missing Data Estimation in Water Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101123)|J. Bhardwaj; C. Harman; H. S. G. Pussewalage; L. R. Cenkeramaddi|10.1109/APSCON56343.2023.10101123|Water Distribution Networks;Recurrent Neural Networks;Real-time control;and Data Driven Kalman Filter;Recurrent neural networks;System dynamics;Approximation error;Mathematical models;Data models;Real-time systems;Numerical models|
|[Sensing the Environment with 5G Scattered Signals (5G-CommSense): A Feasibility Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101090)|S. Jana; A. K. Mishra; M. Z. A. Khan|10.1109/APSCON56343.2023.10101090|ASIN;CommSense;5G New Radio;Tapped Delay Line;Cluster Delay Line;nan|
|[Development and Validation of Offset Current Compensation Technique for Optical Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101309)|S. Kumar; S. S. Khan; R. Patil; M. Ahmad; L. Somappa; S. Malik|10.1109/APSCON56343.2023.10101309|Trans-impedance amplifier;optical sensors;in-frared LED;photo-diode;photoplethysmopgrahy(PPG);Feedback loop;Shape;Prototypes;Medical services;Photoplethysmography;Sensor systems;Recording|
|[A Resistance Change Detection Circuitry for Thread Based Resistive Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101135)|N. Samaddar; M. I. Wani; V. Maharshi; S. Sonkusale; S. Malik|10.1109/APSCON56343.2023.10101135|Flexible Sensors;Thread Sensors;Resistive sensors;Resistance Change;Signal Conditioning Circuits;Resistance;Pressure sensors;Feedback loop;Sensitivity;Measurement uncertainty;Medical services;Sensor phenomena and characterization|
|[Enhanced TCR results of TiOx thin films for uncooled infrared microbolometers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101033)|Y. A. K. Reddy; P. V. Karthik Yadav; T. R. Barathy; B. Ajitha; I. Yadav; S. Gupta|10.1109/APSCON56343.2023.10101033|TiOx;Thin film;Resistivity;Microbolometer;Resistance;Fabrication;Electrodes;Temperature;Films;Conductivity;Ions|
|[Development of Time-Multiplexed Magnetic-Induction Based Ranging Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101039)|M. Chavda; A. Chandola; S. Malik; C. O’Sullivan; A. S. Holmes|10.1109/APSCON56343.2023.10101039|Magnetic induction;ranging;time division multiplexing;indoor positioning;Transmitters;Prototypes;Time division multiplexing;Robot sensing systems;Distance measurement;Complexity theory;Sensors|
|[Comparative Study and Experimental Validation of Phase-Sensitive-Detection Techniques for Sensor Lock-in Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101083)|R. Patil; M. I. Wani; T. Islam; M. S. Baghini; L. Somappa; S. Malik|10.1109/APSCON56343.2023.10101083|lock-in amplifier (LIA);multiplier;switches;and phase sensitive detection (PSD).;Low voltage;Power demand;Sensitivity;Voltage;Harmonic analysis;Sensor systems;Amplifiers|
|[Multi Task Learning for Plant Leaf Segmentation and Counting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101308)|B. Chaudhury; V. Joshi; P. Mitra; A. S. Sahadevan|10.1109/APSCON56343.2023.10101308|plant phenotyping;multi-task learning;leaf-counting;deep learning;Deep learning;Image segmentation;Shape;Computational modeling;Computer architecture;Multitasking;Feature extraction|
|[A ΔC Detection Circuit for Capacitive Sensing Based Prosthetic Control Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101055)|B. Nakum; M. I. Wani; K. M. Ehsan; S. Malik|10.1109/APSCON56343.2023.10101055|capacitive sensor;auto-zeroing;phase-sensitive detection (PSD);prosthetic limb;Prototypes;Manuals;Capacitance;Capacitive sensors;Sensors;Impedance;Spatial resolution|
|[CMOS-MEMS Nano Force Sensor with Sub-μm U-Channel Suspended Gate SOIFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101320)|P. Martha; N. Kadayinti; V. Seena|10.1109/APSCON56343.2023.10101320|Nano-Force Sensor;SGFET;U-Channel;Micromechanical devices;Wafer bonding;Sensitivity;Atmospheric modeling;Air gaps;Resonant frequency;Voltage|
|[A Microwave Sensor for Grain Moisture-Content Measurement Designed with Surface Wave Transmission Line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101067)|S. V. Yadav; A. Chittora|10.1109/APSCON56343.2023.10101067|Agriculture;Moisture content;Microwave sensor;Surface wave transmission line;Microwave measurement;Surface waves;Moisture measurement;Moisture;Microwave sensors;Containers;Transmission line measurements|
|[A Novel Dual Torsional MEMS Suspended Gate FET (DTM-SGFET) Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100970)|R. Raeann Jesma; N. M. Sreevatsava; V. Seena|10.1109/APSCON56343.2023.10100970|CMOS-MEMS;Torsional Accelerometer;SGFET;DTM-SGFET;Accelerometers;Micromechanical devices;Sensitivity;Field effect transistors;Air gaps;Logic gates;Dynamic range|
|[Acetone and Benzene Detection using MEMS Electro-thermal Actuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101134)|V. K. Chalka; M. Chauhan; S. Dhanekar; K. Rangra|10.1109/APSCON56343.2023.10101134|MEMS;thermal actuation;acetone and benzene sensor;breath sensor;Micromechanical devices;Volatile organic compounds;Temperature sensors;Sensitivity;Thermal resistance;Lung cancer;Software|
|[Flexible Vibrational Energy Harvester with Groove Design Using BTO/PVDF-TrFE Film for Higher Output](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101032)|S. S. Chauhan; N. T. Beigh; D. Mukherjee; D. Mallick|10.1109/APSCON56343.2023.10101032|F1exible;Piezoelectric;PVDF;Energy Harvester;Vibration;nan|
|[Silicon Nitride Microcantilever-Based Temperature Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101115)|H. K. Verma; D. Khan; M. Kandpal; S. N. Behra; J. Singh; A. Naik|10.1109/APSCON56343.2023.10101115|micro-cantilever;silicon nitride cantilever;strain;temperature sensing;frequency stability;Allan deviation;Temperature measurement;Temperature sensors;Temperature distribution;Sensitivity;Electrostatic measurements;Resonant frequency;Silicon nitride|
|[UAV sensing-based semantic image segmentation of litchi tree crown using deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101133)|D. Chakraborty; B. Deka|10.1109/APSCON56343.2023.10101133|Deep Learning;Semantic Segmentation;Unnamed Aerial Vehicle (UAV);Photogrammetry;Orthomosaic;Deep learning;Semantic segmentation;Semantics;Crops;Bending;Autonomous aerial vehicles;Data models|
|[Software Reconfigurable Frequency Readouts with Coarse Voltage Quantizers for Sensor Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100989)|L. Somappa; S. Malik; M. S. Baghini|10.1109/APSCON56343.2023.10100989|Frequency readout;Sensor interface;reconfigurability;oscillator;sensor nodes;IoT;Software algorithms;Logic gates;Size measurement;Software;Hardware;Frequency measurement;Sensors|
|[Design and Characterization Methodology of Capacitive Sensors for Soil Moisture Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101081)|S. V. Devaraj; K. Shaikh; V. Gaur; L. Somappa; M. S. Baghini|10.1109/APSCON56343.2023.10101081|Capacitive;Soil Moisture Sensor;Low-Cost;Coplanar.;Sensitivity;Soil measurements;Design methodology;Soil moisture;Printed circuits;Capacitive sensors;Sensors|
|[Impact of Annealing on Soil Moisture Sensing Properties of Graphene Oxide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10100999)|K. S. Patle; P. Khaparde; S. Jain; S. Shah; Y. Sheth; Y. Agrawal; V. S. Palaparthy|10.1109/APSCON56343.2023.10100999|Soil moisture sensor;Graphene oxide;sensing mechanism;Temperature sensors;Temperature measurement;Silicon compounds;Annealing;Temperature;Soil moisture;Graphene|
|[System Architecture and Software Design of a Handheld High Throughput Phenotyping Device: AgRECA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101056)|K. E. Cholachgudda; R. C. Biradar; K. Y. O. Akansie; A. A. Sannabhadti; G. D. Devanagavi|10.1109/APSCON56343.2023.10101056|High throughput phenotyping;phenotyping platforms;imaging sensors;handheld device;software design;Image sensors;Software design;Data acquisition;Systems architecture;Thermal sensors;Throughput;Hardware|
|[A two-axis force sensor using a decoupling compliant mechanism for calibrating magnetic robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101247)|S. Bhat; G. K. Ananthasuresh|10.1109/APSCON56343.2023.10101247|Compliant mechanism;millinewton;3D printing;FDM;PLA;Force measurement;Manufacturing processes;Three-dimensional displays;Magnetic resonance imaging;Image processing;Force;Magnetic forces|
|[Fabrication of Silk nanofibers with in-situ Crystallization for Large-area Tactile Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101140)|C. Parameswaran; D. Shakthivel; R. Dahiya|10.1109/APSCON56343.2023.10101140|crystallinity;electrospinning;silk fibroin;tactile sensor;yeast autolysis;Mechanical sensors;Fabrication;Pressure sensors;Performance evaluation;Sensitivity;X-ray scattering;Tactile sensors|
|[Design and Simulation of Tactical Grade Capacitive Based MEMS Vibratory Ring Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101036)|V. Venkataram; P. Chabbi; V. K. Rao|10.1109/APSCON56343.2023.10101036|Vibrating Ring Gyroscope (VRG);High scale factor;capacitive detection;high linearity;tactical grade;Micromechanical devices;Structural rings;Linearity;Robot sensing systems;Autonomous aerial vehicles;Stability analysis;Gyroscopes|
|[Visual sensor network based early onset disease detection for strawberry plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101121)|D. Usman; A. Hossein; A. Vahid; N. Chris; I. Andrey|10.1109/APSCON56343.2023.10101121|Internet of Things;Deep Learning;Visual Sensor Network;Strawberry Diseases;Convolutional Neural Network;Visualization;Plant diseases;Plants (biology);Neural networks;Ecosystems;Prototypes;Sensors|
|[Characterizing the Dynamics of Surface Electromyography Signals in Muscle Fatigue through Visibility Motif Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101311)|N. Makaram; R. Swaminathan|10.1109/APSCON56343.2023.10101311|Muscle Fatigue;Surface Electromyography;Visibility graph motif;Transition Network;Training;Muscles;Fatigue;Electromyography;Real-time systems;Entropy;Sensors|
|[RFID-enhanced Connected Lane Markings: Design Constraints and Requirements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101139)|D. Suo; R. Bhattacharyya; J. Melià-Seguí; S. Sarma|10.1109/APSCON56343.2023.10101139|RFID;connected vehicles;lane markings;intelligent transportation systems;Radio frequency;Snow;Roads;Propagation;Transportation;Sensors;Safety|
|[Environmental Testing Methodology for Real-Time Soil Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101082)|T. R. Naik; K. M. Shaikh; S. V. Devaraj; S. N. Doddapujar; R. B. Dheeraj; R. Patkar; M. S. Baghini|10.1109/APSCON56343.2023.10101082|agriculture;soil health monitoring;soil moisture;soil temperature;sensor;climatic testing;environmental testing;precision agriculture;sustainable agriculture;Temperature sensors;Temperature measurement;Temperature distribution;Humidity;Soil;Sensor systems;Agriculture|
|[Development of MoSe2/Ti3 C2 Tx (MXene) Nanohybrid based Flexible Electromechanical Sensor for Artificial E-Skin Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101259)|V. Adepu; C. R. Kolli; P. Sahatiya|10.1109/APSCON56343.2023.10101259|MoSe2/Ti3C2Tx Nanohybrid;Flexible Electronics;Electromechanical Sensor;Artificial E-Skin;Sensitivity;Cellulose;Strain measurement;Skin;Real-time systems;Substrates;Electromechanical sensors|
|[Wi-Fi Sensing based Real-Time Activity Detection in Smart Home Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101249)|A. K. Sahoo; V. Kompally; S. K. Udgata|10.1109/APSCON56343.2023.10101249|WiFi sensing;channel state information;edge computing;activity recognition;Wireless communication;Wireless sensor networks;Machine learning algorithms;Smart homes;Computer architecture;Real-time systems;Sensors|
|[Non-Linearity Switching in PMUTs for Enhanced Sensitivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101190)|S. H. Paladugu; P. Singh; A. Rangarajan; R. Pratap|10.1109/APSCON56343.2023.10101190|Non-linearity;PMUTs;Sensitivity;Sensing;Softening;Hardening;Linear zone;Micromechanical devices;Q-factor;Viscosity;Sensitivity;Liquids;Switches;Mathematical models|
|[Reusable porous alumina-based adsorber for removal of copper ions from top sediments layers of water bodies and effluents discards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101119)|V. Maharshi; P. Vinayak; M. Singh; A. Agarwal; B. Mitra|10.1109/APSCON56343.2023.10101119|Porous alumina membrane;anodization;chronoamperometry;adsorption;heavy metal ions;Electric potential;Toxicology;Surface waves;Ions;Sensors;Copper;Sediments|
|[Wet Porous Electrode Glow Discharge Optical Emission Spectroscopy Chip For Low Cost Rapid Analysis Of Aqueous Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101201)|M. Kumar; H. S. Devi; M. Singh; B. Mitra|10.1109/APSCON56343.2023.10101201|Microplasma glow discharge;atomic emission spectroscopy;wet chemical sensor;Integrated optics;Spectroscopy;Liquids;Stimulated emission;Optical device fabrication;Lead;Optical sensors|
|[Transparent Flexible Capacitive Pressure Sensor Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101243)|N. M. Naira; D. Shakhtivel; R. Dahiya|10.1109/APSCON56343.2023.10101243|Transparent electronics;flexible electronics;ZnO nanowires;pressure sensor;capacitive sensor.;Pressure sensors;Performance evaluation;Sensitivity;II-VI semiconductor materials;Shape;Zinc oxide;Robot sensing systems|
|[Micromolding-in-Capillary based Fabrication of Liquid Metal Patterned Structures for Soft Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101205)|R. Chirila; A. S. Dahiya; R. Dahiya|10.1109/APSCON56343.2023.10101205|liquid metals;3D printing;stretchable;molding;electronics;Resistance;Temperature sensors;Temperature measurement;Temperature distribution;Liquids;Shape;Metals|
|[Electrochemical Detection of Fe2+ ions in Water using 2-Dimensional g-C3N4 modified Glassy Carbon Electrode-based Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101086)|D. K. Neethipathi; A. Beniwal; P. Ganguly; A. Bass; M. Scott; R. Dahiya|10.1109/APSCON56343.2023.10101086|Electrochemical sensor;g-C3N4;glassy carbon electrode;Fe2+ ions detection;Electrodes;Scanning electron microscopy;X-ray scattering;X-ray diffraction;Ions;Electrolytes;Iron|
|[A Wearable Device for Detecting and Analyzing Gait Changes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101106)|A. J. Muley; K. Sasidhar; R. Dhokai|10.1109/APSCON56343.2023.10101106|Wearable device;accelerometer;gait analysis;Legged locomotion;Performance evaluation;Tracking;Wearable computers;Packaging;Market research;Sensors|
|[Understanding Conversational Usage Patterns between English and Hindi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101218)|N. Tarware; M. Mungra; H. Parmar; Y. Chaudhari; K. Sasidhar|10.1109/APSCON56343.2023.10101218|Conversational agents;Alexa;regional accents;nan|
|[A Battery-less NFC Sensor Transponder for Cattle Health Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101019)|D. R. Gawade; B. V. B. Roy Simorangkir; S. Kumar; M. Pigeon; M. Belcastro; N. Rather; J. L. Buckley; B. O’Flynn|10.1109/APSCON56343.2023.10101019|battery-less NFC sensor;cattle health monitoring;flexible;RFID and screen-printed contact lens;Temperature measurement;Wireless communication;Temperature sensors;Wireless sensor networks;Prototypes;Cows;Sensors|
|[Extracting Leaf Wetness Duration using Baseline Correction through Group-Sparse Total Variation Method for LW Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101048)|R. Saini; K. S. Patle; S. Shah; A. Kumar; V. S. Palaparthy|10.1109/APSCON56343.2023.10101048|Group-Sparsity Total Vanation (GS-TV);Leaf wetness duration (LWD);Leaf wetness sensor (LWS);Plant disease detection;Wavelet transforms;Plant diseases;TV;Crops;Manuals;Filtering algorithms;Information filters|
|[Au Deposited Carbon-thread Electrode for Lead ions Detection in Water Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101283)|S. A. Lahari; K. Amreen; S. K. Dubey; P. Rn; S. Goel|10.1109/APSCON56343.2023.10101283|lead;water quality;carbon thread;Differential pulse voltammetry;Au nanoparticles;Nanoparticles;Gold;Surface contamination;Surface morphology;Lead;Ions;Water pollution|
|[Modified Carbon-thread based Miniaturized Electrochemical Platform for Real Time Serotonin Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101111)|S. Kumar; K. Amreen; S. K. Dubey; S. Goel|10.1109/APSCON56343.2023.10101111|Serotonin;Microfluidics;carbon thread;Electrochemical Sensing.;Performance evaluation;Fabrication;Microelectrodes;Neurotransmitters;Ink;Stability analysis;Reproducibility of results|
|[SrTiO3-TiO2 heterostructured nanotubes array for selective acetone sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101092)|R. Bhardwaj; A. Hazra|10.1109/APSCON56343.2023.10101092|SrTiO3-TiO2 nanotubes array;VOC sensing;high response;selective detection;stability;Temperature sensors;Temperature distribution;X-ray scattering;Surface morphology;Stability analysis;Sensors;Titanium dioxide|
|[Fullerene- MoSe2 nanocomposite-based sensor for selective detection of formaldehyde](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101195)|R. Bhardwaj; A. Hazra|10.1109/APSCON56343.2023.10101195|MoSe2 nanoflowers;fullerene clusters;nanocomposite;formaldehyde selective;room temperature;Temperature sensors;Temperature measurement;Volatile organic compounds;Nanoparticles;Temperature;Surface morphology;Fullerenes|
|[A monitored miniature dialysis apparatus with silicon nanoporous membrane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101015)|A. Kumar; F. V. T; S. Kannan; S. Sengupta; E. Bhattacharya|10.1109/APSCON56343.2023.10101015|Silicon nanoporous membrane; temperature;pressure and bubble sensors;Inline monitoring;nan|
|[Molecular analysis of Sweat for Evidence based Ayurvedic Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101052)|P. Soni; S. Singh; U. Singh; A. Agarwal|10.1109/APSCON56343.2023.10101052|Non-Invasive detection;Sweat;biomarkers;SERS;Nanoparticles;Ayurveda;Correlation;Precision medicine;Molecular biophysics;Fingerprint recognition;Sensors;Reliability;Blood|
|[Salivary Analysis for Evidence based Ayurvedic Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101016)|P. Nandi; S. Singh; A. Agarwal|10.1109/APSCON56343.2023.10101016|SERS;Ayurveda;Modern Medical science;COVID-19;Proteins;Silver;Raman scattering;Sensors;Reliability;Nanopores;Medical diagnostic imaging|
|[A two-dimensional (2D) WSe2 -based binary composite for ultrasensitive trace level room temperature NH3 sensing for non-invasive diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101063)|D. Sharma; A. Pal; N. Bhat|10.1109/APSCON56343.2023.10101063|Ammonia;Chemiresistive;WSe2;Fe3O4 Nanoparticles;Ammonia;Chemiresistive;WSe2,Fe3O4 Nanoparticles;Temperature sensors;Performance evaluation;Nanoparticles;Temperature distribution;Pollution;Two dimensional displays;Robustness|
|[Trace Level Molecular Detection in Organic Honey Relevant for Therapeutic Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101027)|S. Singh; S. K. Keshi; A. Agarwal|10.1109/APSCON56343.2023.10101027|Honey;SERS;Nanosensor;Therapeutics;Traditional medicine;Enzymes;Antibiotics;Raman scattering;Amino acids;Quality assessment;Sensors;Sugar|
|[Conducting Yarn based Capacitive Humidity Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101251)|A. Kumari; A. Agarwal; A. Sengupta; Y. Garg|10.1109/APSCON56343.2023.10101251|conducting yarns;e-textiles;wearable electronics;humidity sensor;smart fabrics;Wearable computers;Tungsten;Humidity;Sensor phenomena and characterization;Capacitive sensors;Fourth Industrial Revolution;Yarn|
|[Rapid Detection of Inflammatory Biomarkers using Surface Enhanced Raman Spectroscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101191)|B. Akilandeshwari; S. Singh; A. Agarwal; S. Jha|10.1109/APSCON56343.2023.10101191|Inflammation;cytokines;biomarkers;SERS;Raman signal;Nanoparticles;Gold;Pathology;Raman scattering;Biomarkers;Physiology;Sensors|
|[A woven wristband for spatiotemporal body temperature sensing for healthcare applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10101204)|K. Golwala; S. Sarma; A. Agarwal; Y. Garg|10.1109/APSCON56343.2023.10101204|Textile based sensors;Temperature sensors;Temperature Coefficient of Resistance (TCR);optimal length;optimal number of yarns;conducting yarns;Temperature sensors;Temperature measurement;Temperature distribution;Weaving;Fabrics;Sensors;Yarn|
#### **2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)**
- DOI: 10.1109/REEPE57272.2023
- DATE: 16-18 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Adaptive Protection Scheme for Overcurrent and Directional Overcurrent Relays In Distribution Networks with Distributed Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086756)|M. K. Elmahgoub; M. Ezzat; A. Magdy|10.1109/REEPE57272.2023.10086756|Adaptive protection;overcurrent relays;directional relays;distributed generation;relay coordination;smart grid;Renewable energy sources;Adaptation models;Adaptive systems;Sensitivity;Distribution networks;Stability analysis;Smart grids|
|[Experimental Study on Torrefaction of Food Waste on The High-temperature Furnace HT 08.17](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086886)|N. D. Khuong; S. T. Aleksandrovna|10.1109/REEPE57272.2023.10086886|food waste;municipal solid waste;torrefaction;energy yield;mass yield;elemental analysis;proximate analysis;Food waste;Power engineering;Temperature;Furnaces;Moisture;Solids;Fossil fuels|
|[Development of a distributed digital microgrid generation system and features of its application in the retail electricity market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086891)|M. S. Ivanitckii; M. M. Sultanov; E. V. Kuryanova|10.1109/REEPE57272.2023.10086891|microgeneration plants;addition of hydrogen;efficiency;power generation;electrical load;Analytical models;Power measurement;Law;Microgrids;Production;Reliability theory;Control systems|
|[Protection of power facilities personnel from electric fields of industrial frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086848)|I. V. Korolev; A. A. Zakrevsky; N. V. Vasileva|10.1109/REEPE57272.2023.10086848|electrosafety;electrical field of power frequency;individual protection equipment;shielding;Electrodes;Technological innovation;Head;Grounding;Current measurement;Time measurement;Safety|
|[Development of a comprehensive cybersecurity solution for an automated process control system of a digital substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086782)|V. Shikhin; O. Trutneva|10.1109/REEPE57272.2023.10086782|Cybersecurity;automated process control system;digital substation;cyber threats;Substations;Information resources;Power measurement;Process control;Digital signal processing;Reliability engineering;Software|
|[Influence of nonlinear load on the measurement of harmonic impedance of the power supply system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086795)|A. N. Skamyin; I. V. Dobush; I. A. Gurevich|10.1109/REEPE57272.2023.10086795|energy system;impedance evaluation;harmonics;nonlinear load;semiconductor converters;rectifiers;power converters;Couplings;Semiconductor device measurement;Thyristors;Impedance measurement;Power measurement;Power supplies;Energy measurement|
|[Influence of the placement of buildings outside the territory of the thermal power station sanitary protection zone on noise reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086736)|V. B. Tupov; A. B. Mukhametov|10.1109/REEPE57272.2023.10086736|noise reduction;barrier;thermal power plant;Fans;Power engineering;Cooling;Buildings;Noise reduction;Poles and towers;Thermal noise|
|[A Comparison Between the use of High and Low Pass Filters in VSC-MTDC Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086783)|I. Alwazah; A. S. Al-Akayshee; R. V. Bulatov; R. R. Nasyrov|10.1109/REEPE57272.2023.10086783|AC and DC filters;VSC- MTDC;Three-level PWM;HVDC Syria;DESERTEC;Shape;Simulation;Low-pass filters;Pulse width modulation;Control systems;Power systems;Voltage control|
|[Hybrid Solar Thermal Power Plant Potential in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086738)|A. A. Alam; D. A. Anatolevich|10.1109/REEPE57272.2023.10086738|solar energy;renewable energy;concentrated solar power (CSP);hybrid plants;combined cycle power plant (CCPP);integrated solar combine cycle (ISCC);Industries;Renewable energy sources;Power engineering;Sociology;Solar energy;Generators;Software|
|[Development of model for optimizing thermal power plant operation regimes while taking into account the criteria of efficiency and reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086857)|M. Sultanov; A. Konstantinov|10.1109/REEPE57272.2023.10086857|thermal power plants;operation regimes;optimization;reliability;economy;Power engineering;Thermal factors;Thermal engineering;Mathematical models;Reliability;Task analysis;Transient analysis|
|[Comparison of the dynamic and static measurements of hydrophobicity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086764)|K. D. Poluektova; I. A. Gulenko; S. A. Vasilkov|10.1109/REEPE57272.2023.10086764|contact angle;corona discharge;hydrophobicity;receding angle;silicone rubber;water immersion;Partial discharges;Rain;Power measurement;Surface discharges;Contacts;Moisture;High-voltage techniques|
|[Economic assessment of the prospects for the use of CAES to maintain the modes of the power system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086787)|D. V. Semin; A. V. Fedyukhin; S. A. Dronov; D. P. Medov|10.1109/REEPE57272.2023.10086787|energy storages;renewable energy sources;compressed air energy storage;thermal power plant;peak power plant;Renewable energy sources;Power engineering;Costs;Cogeneration;Biological system modeling;Production;Wind farms|
|[CES and TCSC based Supplementary Controller for Stability of Renewable Energy Integrated Nonlinear Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086804)|C. N. Sai Kalyan; C. Rami Reddy; B. S. Goud; M. Bajaj; M. A. Tolba; S. Kamel|10.1109/REEPE57272.2023.10086804|DSOT algorithm;TIDF controller;TANLPS model;GTPP integration;CES-TCSC mechanism;Performance evaluation;Thyristors;Regulators;Sensitivity analysis;Simulation;Power system stability;Stability analysis|
|[Revealing the Significance of Time Delays on the Performance of Diverse Source Power Systems under Fruit Fly Optimization Tuned 3DOFTID Regulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086832)|C. N. Sai Kalyan; B. Srikanth Goud; C. R. Reddy; M. Bajaj; V. N. Tulsky; S. Kamel|10.1109/REEPE57272.2023.10086832|fruit fly optimization;load frequency control;3DOFTID;time delays;10%SLD;Robust control;Time-frequency analysis;Regulators;Delay effects;System performance;Simulation;Superluminescent diodes|
|[Fruit Fly Optimization Technique Based Regulator for LFC of Conventional Power System with the Integration of Plugin Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086898)|C. N. Sai Kalyan; B. Srikanth Goud; C. R. Reddy; M. Bajaj; V. N. Tulsky; S. Kamel|10.1109/REEPE57272.2023.10086898|TIDN controller;TACPS model;10%SLD;fruit fly optimization;PEVs integration;Regulators;System dynamics;Heuristic algorithms;Power system dynamics;Power system stability;Electric vehicles;Superluminescent diodes|
|[Optimal Configuration of Stand-Alone Hybrid Energy System in New Tiba City, Luxor, Egypt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086845)|Z. A. El-Aziem; S. Kamel; H. A. El-Sattar; L. Nasrat; M. A. Tolba|10.1109/REEPE57272.2023.10086845|Small-grid;optimal;Hybrid;energy system;Pelican Optimization Algorithm;Photovoltaic systems;Costs;Power supplies;Urban areas;Production;Power system stability;Generators|
|[Investigation of fault location on extra-high voltage lines with synchrophasors based on real transient waveforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086766)|A. A. Yablokov; I. E. Ivanov; F. A. Kulikov; A. V. Panaschatenko; Y. A. Umnov; A. R. Tychkin|10.1109/REEPE57272.2023.10086766|digital fault recorder;fault location;phasor measurement unit;transient waveform;synchrophasor;Power engineering;Power transmission lines;Power measurement;Fault location;Transmission line measurements;Phasor measurement units;Software|
|[Nanostructured coatings of TiCxNy as a sublayer of electrodes for devices with a proton exchange membrane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086752)|M. V. Sinyakov; D. D. Spasov; R. M. Mensharapov; A. A. Zasypkina; Y. S. Pak; N. A. Ivanova|10.1109/REEPE57272.2023.10086752|corrosion;current collector;titanium coatings;magnetron sputtering;electrochemical devices;Protons;Electrochemical devices;Catalysts;X-ray diffraction;Nanoscale devices;Coatings;Titanium compounds|
|[A Novel Model of Integrated Combined Cycle Power Plants with ORC, ABRC and Hydrogen](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086809)|B. B. Dakkah; I. A. Sultanguzin; Y. V. Yavorovsky; N. A. Belekhova|10.1109/REEPE57272.2023.10086809|gas turbine;combined cycle power plant;Brayton cycle;Rankine cycle;ORC;absorption refrigeration cycle;thermodynamic model;greenhouse gases;recuperator;Methane;Thermodynamics;Absorption;Hydrogen;Production;Mathematical models;Waste heat|
|[Effect of hydrogen pressure on the electrochemical hydrogen pump performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086860)|B. V. Ivanov; N. A. Ivanova; R. M. Mensharapov; D. D. Spasov; V. V. Shkandybina; M. V. Sinyakov|10.1109/REEPE57272.2023.10086860|electrochemical hydrogen pump;hydrogen;compression;purification;IV curve;pressure influence;fusion fuel cycle;Temperature measurement;Temperature distribution;Temperature dependence;Purification;Hydrogen;Aerospace electronics;Kinetic theory|
|[The Research on Heat Loss Reduction of Prefabricated Building in Ulan-Bator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086754)|T. Tsetsgee; V. Y. Chaikin; I. A. Sultanguzin; I. D. Kalyakin; Y. V. Yavorovsky; N. Batbayar|10.1109/REEPE57272.2023.10086754|building insulation;thermal resistance;thermal load;program designPH and PHPP;Prefabricated construction;Insulation;Thermal resistance;Buildings;Production;Windows;Solar panels|
|[Estimation of the Effect of Non-Synchronization of Voltage and Current Samples on the Active Power Measurement Error](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086844)|A. N. Serov; K. A. Ivanenko; S. A. Podobuev; L. S. Serdyukova; A. A. Shatokhin|10.1109/REEPE57272.2023.10086844|aperture jitter;aperture time;measurement error;active power;synchronous sampling;simulation modeling;Time-frequency analysis;Analytical models;Voltage measurement;Power measurement;Transducers;Current measurement;Apertures|
|[Comparative analysis of evaluation approaches for the climatic factors influence on power grid facilities reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086808)|O. A. Loktionov; M. A. Zabelin; E. A. Belova|10.1109/REEPE57272.2023.10086808|power grid facilities;overhead lines;transmission lines;reliability;climate changes;wind loads;extreme weather conditions;Wind;Power engineering;Power transmission lines;Reliability engineering;Power grids;Data models;Power system reliability|
|[Numerical Simulation of the Gas Cooling Turbogenerator System with the Gap-Pickup Rotor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086762)|D. Verkhovtsev; S. Gulay; A. Permut; M. Maiantcev; A. Liamin|10.1109/REEPE57272.2023.10086762|Electrical machine;turbogenerator cooling system;rotor ducts with a diagonal flow design;gas separation barrier;Solid modeling;Ducts;Turbogenerators;Rotors;Stator windings;Fluid flow;Numerical simulation|
|[Mathematical modeling of methane-hydrogen fuel combustion processes in Aspen Plus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086881)|S. A. Dronov; A. V. Fedyukhin; D. V. Semin; A. S. Malenkov; A. G. Gusenko|10.1109/REEPE57272.2023.10086881|Hydrogen;hydrogen boiler;methane-hydrogen fuel;hydrogen energy;mathematical modeling;Aspen Plus;Industries;Power engineering;Simulation;Hydrogen;Exhaust gases;Boilers;Mathematical models|
|[Numerical Simulation of Non-coalescence Regime for Two Conducting Droplets at High Voltage Electrostatic Separation of Water-in-oil Emulsion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086869)|V. A. Chirkov; D. D. Saifullin; G. O. Utiugov; A. V. Samusenko|10.1109/REEPE57272.2023.10086869|arbitrary Lagrangian-Eulerian method;electrocoalescence;electrospraying;numerical simulation;two-phase liquid;water-in-oil emulsion;Liquids;Oils;Computational modeling;High-voltage techniques;Numerical simulation;Threshold voltage;Data models|
|[Effective Numerical Solution to the Problem of Temperature Oscillations in an Isolated Heat Accumulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086779)|M. Purdin; R. Magomedova|10.1109/REEPE57272.2023.10086779|heat accumulator;heat waves;steady oscillations;Thermal expansion;Power engineering;Temperature;Perturbation methods;Numerical simulation;Mathematical models;Fourier series|
|[General Analytical Solution to the Problem of Propagation of Thermal Oscillations in Semi-bounded Solids of Various Shapes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086834)|M. Purdin; R. Magomedova|10.1109/REEPE57272.2023.10086834|heat accumulation;heat waves;semi-bounded body;steady oscillations;Comets;Heat treatment;Temperature;Shape;Solids;Boundary conditions;Distance measurement|
|[Thermodynamic characteristics of the processing of gaseous hydrocarbons in the SOFC reforming system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086715)|A. A. Filimonova; E. S. Mayorov|10.1109/REEPE57272.2023.10086715|reforming;solid oxide fuel cell (SOFC);hydrogen energy;Methane;Temperature distribution;Power engineering;Hydrogen;Fuel cells;Hydrocarbons;Solids|
|[Production, storage and use of hydrogen for seasonal accumulation in energy efficient house](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086819)|N. A. Belekhova; I. A. Sultanguzin; Y. V. Yavorovsky; B. B. Dakkah; V. Y. Chaikin|10.1109/REEPE57272.2023.10086819|passive house;energy efficient house;renewable energy;solar energy;hydrogen;zero carbon footprint;Resistance;Renewable energy sources;Power engineering;Hydrogen;Solar energy;Production;Energy efficiency|
|[Impact of water coolant flow on thermal characteristics of hybrid cooling tower](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086871)|V. V. Kharkov; I. N. Madyshev; A. O. Mayasova; V. E. Zinurov|10.1109/REEPE57272.2023.10086871|heat transfer coefficient;tube radiator;thermal resistance;biofouling;Water;Irrigation;Thermal resistance;Poles and towers;Water heating;Coolants;Electron tubes|
|[A study of an approach to the construction of high-power with high-voltage supplies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086935)|D. A. Seregin; I. P. Voronin; M. S. Pavlova; D. V. Mostovoy; V. D. Gromov; A. M. Stoynova|10.1109/REEPE57272.2023.10086935|high-power static converter;high-power supply;high-voltage power supply;multi-cell converter;scalable converter;Power supplies;Simulation;Rectifiers;High-voltage techniques;Mathematical models;Inverters;Numerical models|
|[A Review of Robotic Gloves Applied for Remote Control in Various Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086822)|G. R. Saypulaev; I. V. Merkuryev; M. R. Saypulaev; V. K. Shestakov; N. V. Glazkov; D. R. Andreev|10.1109/REEPE57272.2023.10086822|robotic glove;review;smart glove;estimation;sensor;Patents;Thermal sensors;Filtering algorithms;Robot sensing systems;Market research;Artificial intelligence;Robots|
|[Parallel Connection of Voltage Inverters Stability Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086818)|D. A. Seregin; V. D. Gromov; A. V. Badalyan; M. I. Fedorova; M. S. Pavlova; A. M. Stoynova|10.1109/REEPE57272.2023.10086818|control;stability;active power;reactive power;frequency;hodograph;voltage inverters;analysis;Reactive power;Power engineering;Software packages;Stability criteria;Control systems;Inverters;Synchronization|
|[Stationary Component of Heat Losses from Ground Heat Accumulators and Heat Exchangers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086934)|M. Purdin; R. Magomedova|10.1109/REEPE57272.2023.10086934|heat accumulator;heat losses in the ground;ground overheating;Heating systems;Temperature dependence;Power engineering;Heat engines;Soil;Numerical simulation;Mathematical models|
|[The concept of a software and technological platform for digital twins based on energy dynamics methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086710)|I. E. Starostin; A. A. Drujinin|10.1109/REEPE57272.2023.10086710|digital twins;energy dynamics;microservice architecture;Thermodynamics;Power engineering;Scalability;Computer architecture;Aerospace electronics;Software;Digital twins|
|[Classification of vibration diagnostic systems Brush-collector assembly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086895)|O. Filina; A. Khusnutdinov; K. Vakhitov; A. Abdyllina; O. Salnikova|10.1109/REEPE57272.2023.10086895|brush contact;approximation;electric brush;vibration;brush holder;Vibrations;Power engineering;Brushes;Switchgear;Predictive models;Mathematical models;Rail transportation|
|[Probabilistic-statistical analysis of the maximum electrical load of apartment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086879)|V. A. Khomichev; G. V. Shvedov|10.1109/REEPE57272.2023.10086879|Electricity consumption;load flow analysis;distribution law of a random variable;electrical load profile;individual apartment;Pearson's chi-squared test;Power engineering;Power demand;Buildings;Electric variables measurement;Gaussian distribution;Probabilistic logic;Market research|
|[Investigation of bacterial contamination of a mixed-bed filter of a TPP water treatment plant by IR spectroscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086798)|A. Y. Vlasova; O. E. Babikov|10.1109/REEPE57272.2023.10086798|thermal power plants;water treatment plants;water purification;mixed-bed filter;infrared spectroscopy;bacterial contamination;Spectroscopy;Microorganisms;Thermal engineering;Ions;Water pollution;Resins;Contamination|
|[Application of Dissipative Variable Cross-Section Noise Silencers at Industrial Facilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086876)|A. A. Taratorin; A. B. Mukhametov|10.1109/REEPE57272.2023.10086876|energy efficiency;dissipative silencers;fans;acoustic efficiency;aerodynamic drag;Power measurement;Ventilation;Industrial facilities;Boilers;Aerodynamics;Energy efficiency;Acoustics|
|[Software and algorithmic support for online assessment of transformer substation technical condition 35/6(10) kV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086828)|A. R. Galyautdinova; I. V. Ivshin; O. V. Vladimirov; M. F. Nizamiev; A. H. Safiullin|10.1109/REEPE57272.2023.10086828|software;transformer substation;online assessment;technical condition;control;diagnostics;Power engineering;Substations;Software algorithms;Transformers;Prediction algorithms;Software;Software reliability|
|[Decarbonization methods of electric energy production in a solid oxide fuel cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086926)|F. A. A; V. A. Y; K. R. F; B. O. E|10.1109/REEPE57272.2023.10086926|solid oxide fuel cell (SOFC);carbon dioxide;carbon dioxide capture and utilization;thermal power plants;absorption;Presses;Waste materials;Dairy products;Absorption;Sodium;Fuel cells;Carbon dioxide|
|[Annual cycle of a seasonal heat accumulator of thermal energy of an energy-efficient house](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086839)|V. Y. Chaikin; T. Tsetsgee; B. B. Dakkah; I. A. Sultanguzin; Y. V. Yavorovsky; A. N. Nechaev|10.1109/REEPE57272.2023.10086839|renewable energy resources;passive house;heat accumulator;energy efficiency;underground heat accumulator;heat pump;Insulation;Renewable energy sources;Power engineering;Heat pumps;Simulation;Batteries;Solar heating|
|[The Hybrid Method for Parameters Estimation of a Single-loop Equivalent Circuit of Induction Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086767)|V. A. Pavlukov; S. N. Tkachenko|10.1109/REEPE57272.2023.10086767|induction motor;T-shaped equivalent circuit;parameters estimation;nonlinear algebraic equations system;non-iterative method profile;Resistance;Induction motors;Torque;Rotors;Stators;Mathematical models;Thermal analysis|
|[The Effect of New Specific Electrical Loads on Residential Complexes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086815)|Y. I. Soluyanov; A. I. Fedotov; A. R. Akhmetshin; V. I. Soluyanov|10.1109/REEPE57272.2023.10086815|smart grids;load profile;load management;energy efficiency;Costs;Substations;Power supplies;Buildings;Urban areas;Sociology;Power transformers|
|[Development and research of the current measurement circuit protection of a resonant test device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086882)|R. T. Khazieva; A. V. Mukhametshin|10.1109/REEPE57272.2023.10086882|current measurement;Schottky diodes;breakdown (discharge) process;resonant test device;high voltage technology;Schottky diodes;Insulation;Voltage measurement;Electric breakdown;Current measurement;Prototypes;RLC circuits|
|[Digital infrastructure development for semiconductor devices controlling for electric power complex](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086732)|M. I. Fedorova; I. I. Zhuravlev; D. P. Khmelyuk; A. E. Bannov; I. B. Yakovlev|10.1109/REEPE57272.2023.10086732|Smart Grid;computer modeling;SCADA;power electronics;digital control systems;Digital control;Semiconductor device modeling;Computational modeling;Power electronics;Mathematical models;Power systems;Synchronization|
|[The SCADA System Digital Infrastructure Implementation in Distribution Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086846)|M. I. Fyodorova; I. I. Zhuravlev; D. S. Kuzenev; A. E. Bannov; D. P. Khmelyuk|10.1109/REEPE57272.2023.10086846|Distribution networks;reactive power compensator;Smart Grid;APCS;reactive power;computer simulation;SCADA;power electronics;digital control systems;Reactive power;Regulators;Software algorithms;SCADA systems;Process control;Distribution networks;Power electronics|
|[Determination of the Damage Location on Multiphase and Multi-Wire Power Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086853)|S. G. Tiguntsev; S. B. Usmonov; K. V. Shafarevich|10.1109/REEPE57272.2023.10086853|multiphase power transmission lines;the location of damage;of telegraphic equations;Resistance;Voltage measurement;Power transmission lines;Current measurement;Wires;Differential equations;Transmission line measurements|
|[Air cooling simulation in an apartment building premises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086781)|A. A. Nikaeva; A. A. Vorobyov; M. V. Gorelov|10.1109/REEPE57272.2023.10086781|math modelling;COMSOL Multiphysics;thermal storage;heating;heat losses;residential building;Heating systems;Schedules;Power engineering;Temperature;Cooling;Atmospheric modeling;Buildings|
|[Analysis and optimization of the operation of deaeration devices on heat sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086777)|O. V. Pazushkina; M. V. Zolin|10.1109/REEPE57272.2023.10086777|thermal power station;thermal deaeration of water;vacuum deaerator;gas exhaust devices;water-jet and steam-jet ejectors;Temperature distribution;Thermal engineering;Water heating;Switches;Vacuum technology;Boilers;Thermal analysis|
|[Justification of the scope of competitive application of liquefied natural gas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086830)|O. N. Medvedeva; S. D. Perevalov|10.1109/REEPE57272.2023.10086830|liquefied natural gas;logistic model;LNG plant;optimal gas delivery radius;Power engineering;Costs;Biological system modeling;Sociology;Pipelines;Transportation;Production|
|[Producing hydrogen by electrolysis to ensure passage of the minimum electrical load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086724)|E. T. Ilyin; D. I. Levenok; D. A. Lymarev|10.1109/REEPE57272.2023.10086724|Hydrogen;Thermal load;Adjustment range;Combined generation;Load curve;Cogeneration;Hydrogen;Fuel cells;Water heating;Production;Boilers;Electrochemical processes|
|[Simulation of the Condensed Phase Particles Electrization in Combustion Products of the Model Hybrid Rocket Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086842)|A. V. Rudinskii; A. V. Kravchenko; V. E. Evstigneeva|10.1109/REEPE57272.2023.10086842|high-enthalpy two-phase flow;C-phase;electric charge;model hybrid rocket engine;diagnostics;Temperature sensors;Temperature measurement;Rockets;Solid modeling;Solids;Mathematical models;Combustion|
|[Methods and Principles of Carbon Footprint Calculation. Relevance of Application of These Approaches in Russia and the World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086925)|T. M. Vitalievich; C. A. Kirillovich; K. A. Aleksandrovna; I. M. Vitalievich; R. A. Pavlovna|10.1109/REEPE57272.2023.10086925|Climate Change;Climate Change Adaptation Strategy;Carbon Dioxide Emissions;Sustainable Development Education;Low-carbon economy;carbon footprint;Power engineering;Government;Anthropomorphism;Production;Market research;Reflection;Sustainable development;Climate change|
|[Configuring an Off-Grid Hybrid Renewable Energy System in the Arctic Zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086709)|A. Klokov; E. Loktionov|10.1109/REEPE57272.2023.10086709|photovoltaic;solar energy;wind energy;battery energy storage;hybrid power system;stand-alone power system;Renewable energy sources;Costs;Power supplies;System performance;Energy resolution;Reliability engineering;Real-time systems|
|[Impact of Dopants Incomplete Ionization on Resistivity Profiles in Triple-Doped Multicrystalline Silicon](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086909)|R. I.|10.1109/REEPE57272.2023.10086909|incomplete ionization;multicrystalline silicon;compensation level;boron;gallium;phosphorus;Power engineering;Boron;Gallium;Impurities;Ionization;Conductivity;Phosphorus|
|[Energy Theft Detection in Smart Grids via Explainable Attention Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086919)|D. O. Ishkov; V. I. Terekhov; K. S. Myshenkov|10.1109/REEPE57272.2023.10086919|Power Consumption;Smart Grids;Electricity Theft;Machine Learning;Deep Learning.;Training;Radio frequency;Time-frequency analysis;Time series analysis;Transfer learning;Production;Smart meters|
|[A Coordination of the Capacities of the Hybrid Renewable Energy System and the Seasonal Variable Load Following the Intermittent Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086734)|A. Tutunin; E. Loktionov|10.1109/REEPE57272.2023.10086734|solar;wind;photovoltaics;permafrost thermal stabilization;adaptive load;hybrid system;weather data;Renewable energy sources;Power demand;Costs;Heat pumps;Power system stability;Inverters;Hybrid power systems|
|[Modeling of the work of the UFLS using the new component of the UFLS in the PSCAD/EMTDC software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086833)|B. M. Giyoev; B. F. Ibragimov; A. M. Rasulov; M. M. Mahmadsaidzoda; B. A. Gayurov|10.1109/REEPE57272.2023.10086833|computing system;modeling;analysis;verification;component;under frequency load shedding;PSCAD;Power engineering;Computational modeling;Process control;Load shedding;Market research;Software;Power systems|
|[Optimal Overcurrent Relay Coordination For Small Scale Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086772)|M. G. A. ElSamahy; E. Beshr; K. E. Bahrawy|10.1109/REEPE57272.2023.10086772|micro-grid;Relay Coordination;OCR;optimization techniques;GA;PSO;IEEE 4-bus;Micro grid System;Software packages;Software algorithms;Microgrids;Linear programming;Timing;Safety;Security|
|[Design and Analysis of Control Strategies for a Stand-Alone VSI with LC Output Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086735)|A. Gamal; M. A. Tolba; M. Elhasheem|10.1109/REEPE57272.2023.10086735|Two-level voltage source inverter (2L-VSI);proportional integral derivative (PID);proportional resonance (PR);voltage total harmonic distortion (THDv);proportional Integral (PI);Transient response;Total harmonic distortion;Voltage source inverters;Power quality;Loading;Transfer functions;Delays|
|[Permanent Magnet Wind Generator with Double Excitation for Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086861)|L. L. Amuhaya; N. M. Muchuka; U. B. Akuru; M. J. Kamper; E. S. Obe|10.1109/REEPE57272.2023.10086861|buried PM;double excitation;smart grids;variable flux;wind energy conversion;Coils;Torque;Rotors;Generators;Permanent magnets;Synchronous generators;Topology|
|[Reliability analysis of power distribution network in urban area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086755)|D. Gabdushev; A. Vanin|10.1109/REEPE57272.2023.10086755|power distribution network;fault log processing;equipment reliability;power supply reliability;Power cables;Urban areas;Aluminum;Power cable insulation;Distribution networks;Oil insulation;Aging|
|[Smart Charging of Electric Vehicles in Charging Stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086885)|D. A. Hussien; W. A. Omran; R. M. Sharkawy|10.1109/REEPE57272.2023.10086885|Smart charging;G2V and V2G;Maximize revenue;Electric Vehicles;PSO;Economics;Power engineering;Charging stations;Electric vehicles;Particle swarm optimization;Optimization;Smart charging|
|[Design of a Compact, Dual-band, and Single-Fed Patch Antenna for 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086901)|R. Hassan; S. K. Eldayasti; M. A. Aboul-Dahab|10.1109/REEPE57272.2023.10086901|Dual-band;single-fed;DGS;compact size;5G Applications.;Antenna measurements;Slot antennas;5G mobile communication;Patch antennas;Dual band;Resonant frequency;Microstrip antennas|
|[Islanded microgrid with robust power sharing and voltage stabilization control structure using model predictive control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086727)|Z. Khaled; M. Alhasheem; H. M. Elhelw|10.1109/REEPE57272.2023.10086727|VSC;primary control;power sharing;smart grid;Model predictive control;droop control;secondary control layer;Reactive power;Simulation;Predictive models;Robustness;Voltage control;Wind forecasting;Transient analysis|
|[Microgrid Applications: Effects of Communication Systems on Secondary Control Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086711)|K. Elewa; M. Alhasheem; H. M. Elhelw|10.1109/REEPE57272.2023.10086711|VSC;communication protocols;smart grid;Model predictive control;droop control;secondary control layer;Transient response;Protocols;Power quality;Microgrids;Control systems;Delays;Time factors|
|[Lightning direction finding system as a tool to improve reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086801)|A. G. Gruzdov; A. A. Voloshin; E. A. Voloshin; E. E. Pashkovskaya|10.1109/REEPE57272.2023.10086801|reliability of power supply;thunderstorm phenomena;lightning direction finding system;Technical requirements;Power engineering;Systems operation;Lightning;Sensor phenomena and characterization;Reliability engineering;Discharges (electric)|
|[Description of nonlinear deformation diagrams of electrotechnical materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086760)|T. B. Duishenaliev; T. N. Dogadina; D. S. Dikarev|10.1109/REEPE57272.2023.10086760|metals;alloys;material constants;load-deformation diagram equation;radial deformation;axial stress;hoop stress;Power engineering;Tensors;Systematics;Deformation;Creep;Metals;Boundary conditions|
|[Analysis of energy transformer efficiency in heat supply system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086850)|R. Valiev; R. Ponomarev; A. Mukatdarov|10.1109/REEPE57272.2023.10086850|Energy transformers;steam-compression heat pumps;heat supply system;thermodynamic analysis;efficiency;Temperature dependence;Thermodynamics;Schedules;Costs;Heat pumps;Water heating;Transformers|
|[Wind Turbines Analysis and Analytics of Power Generation by using Long Short Term Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086892)|S. A. Nagy; Z. O. Yousef; M. A. Tolba|10.1109/REEPE57272.2023.10086892|Wind Turbines;Time series;Power prediction;Deep learning;Forecasting;Productivity;Analytical models;Machine learning algorithms;Time series analysis;Predictive models;Data models;Wind turbines|
|[Two-Stage Single-Phase EV On-Board Charger Based on Interleaved Bridgeless AC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086793)|M. Sameh Abdelmomen; I. Abdelsalam; M. S. Hamad|10.1109/REEPE57272.2023.10086793|Interleaved converter;EV battery charger;Bridgeless converter;Current control;Switching frequency;Simulation;Resonant frequency;DC-DC power converters;Switches;Zero voltage switching|
|[LQR Stability Control of Scissor Pair Gyro-Stabilized Two Wheel Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086769)|A. Amin; M. A. Fouz; A. Elsawaf|10.1109/REEPE57272.2023.10086769|Control Moment Gyro;Gyro-Stabilizer;Inverted Pendulum;Equilibrium Tilt Angle;Two Wheel Robot;LQR;Power engineering;Torque;Regulators;Wheels;Stability analysis;Mathematical models;Software|
|[Effect of Impact Damages on the Strength of Wind Turbine Blade](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086749)|V. I. Mitryaykin; T. A. Zaitseva; O. N. Bezzametov; M. E. Kuznetsov|10.1109/REEPE57272.2023.10086749|carbon mechanical disorders;stress-strain state;impact load;integral constructions;composite material;Resistance;Blades;Weaving;Behavioral sciences;Wind turbines;Polymers;Carbon|
|[Phasor Measurement Unit Optimal Allocation Utilizing Discrete Water Cycle Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086708)|N. M. Tawfik; N. H. El-Amary; L. Nasrat|10.1109/REEPE57272.2023.10086708|Phasor Measurement Unit (PMU);Optimal Phasor Measurement Placement (OPP);Discrete Water Cycle Optimization (DWCO);Tree Search Method (TSM);Voltage measurement;Search methods;Phasor measurement units;Topology;Resource management;Communication networks;Wide area measurements|
|[Analysis of The Three-Phase AC Short-Circuit And The DC Short-Circuit in a VSC-MTDC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086911)|I. Alwazah; M. V. Burmeyster; M. A. Tolba; R. R. Nasyrov|10.1109/REEPE57272.2023.10086911|AC and DC fault;VSC- MTDC;HVDC Syria;DESERTEC;Resistance;Employee welfare;Software packages;System performance;Capacitors;High-voltage techniques;Voltage|
|[Methodology for determining the optimal configuration of a wind farms complex and its approval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086812)|G. V. Deryugina; E. V. Ignatiev; N. V. Sychev|10.1109/REEPE57272.2023.10086812|wind farms complex;asynchronous supply of the wind resource;wind gradient model;optimization;configuration;energy system;Wind;Power engineering;Wind energy;Optimization methods;Wind farms;Wind power generation;Market research|
|[The Influence of Working Fluid on Stirling Engine Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086916)|S. A. Salih; B. A. Aljashaami; M. A. Qasim; A. H. Mola; S. E. Shcheklein; A. M. Dubinin|10.1109/REEPE57272.2023.10086916|Keywords— Stirling engine;numerical analysis;working fluid;thermodynamic analysis.;Viscosity;Thermodynamics;Power engineering;Fluids;Hydrogen;Conductivity;Thermal conductivity|
|[Solving the problem of emergency protection to install a nuclear reactor to subcritical mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086851)|N. N. Oshkanov; M. A. Metelnikova; G. A. Vaulin|10.1109/REEPE57272.2023.10086851|chain reaction;emergency protection;kinetics;multiplication factor;nuclear reactor;reactivity;Fission reactors;Power engineering;Limiting;Tail;Neutrons;Kinetic theory;Behavioral sciences|
|[Electrochemical determination of silver nanoparticles on flexible laser reduced graphene oxide sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086797)|S. A. Aljasar; E. S. Marchenko|10.1109/REEPE57272.2023.10086797|voltammetry sensor;laser reduced graphene oxide;silver nanoparticles;flexible senor;electrochemical sensor;Nanoparticles;Electrodes;Silver;Scanning electron microscopy;Graphene;Raman scattering;Surface morphology|
|[Simulation of the two-phase flow through the hole of the submerged perforated sheet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086741)|A. S. Nikulin; S. A. Tokarev; V. I. Melikhov|10.1109/REEPE57272.2023.10086741|hydraulic resistance;submerged perforated sheet;two-phase flow;steam generator;mathematical modeling;Resistance;Solid modeling;Fluids;Codes;Tracking;Shape;Hydraulic systems|
|[Development of a Method for Calculating ASW Frequencies in VVER-440 Pressurizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086765)|K. N. Proskuryakov; R. M. Ismail; A. V. Anikeev|10.1109/REEPE57272.2023.10086765|DAMP;Helmholtz resonator;pressurizer;auto-spectral power density;electroacoustic analogues;ASW;VVER-440;Pipelines;Software algorithms;Resonant frequency;Acoustic measurements;Software;Frequency measurement;Acoustic field|
|[Digital Technology for Constructing the Acoustic Field of Reactor Plants of the VVER Type](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086792)|K. N. Proskuryakov; A. V. Anikeev; R. M. Ismail|10.1109/REEPE57272.2023.10086792|DAMNR;VVER;CFD;ASW;Helmholtz resonator;vibration;Power engineering;Thermal engineering;Predictive models;Production facilities;Acoustic field;Digital twins;Thermal analysis|
|[Compact modeling and digital twins of capacitive fractal microsystems: characteristics variations caused by heavy charged particles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086770)|V. V. Terekhov; A. A. Glushko; V. V. Makarchuk; E. V. Rezchikova; V. A. Shakhnov; L. A. Zinchenko|10.1109/REEPE57272.2023.10086770|MEMS;microsystems;heavy charged particles;ions;digital twin;model;computer simulation;Micromechanical devices;Space vehicles;Power engineering;Neural networks;Estimation;Machine learning;Predictive models|
|[Model of the conductivity of a composite material on a dielectric matrix and a conductive filler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086887)|A. Matasov; A. Evsiukov; R. Shcherbakov|10.1109/REEPE57272.2023.10086887|Composite materials;dielectric materials;tunneling;electrical conductivity;Resistance;Electric potential;Temperature dependence;Power engineering;Composite materials;Shape;Conductivity|
|[Equivalent Number of Bits of Major ADC Types Considering the Evaluation of Their Dynamic Error](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086805)|L. K. Samoilov; N. N. Prokopenko; V. E. Chumakov; A. V. Bugakova|10.1109/REEPE57272.2023.10086805|sequential count ADC;push-pull integration ADC;successive approximation ADC;dynamic error;equivalent number of bits;information delay;signal spectral density;Time-frequency analysis;Power engineering;Cutoff frequency;Process control;Control systems;Frequency conversion;Delays|
|[Occurrence of fatigue cracks on the surface of aluminum wire](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086924)|K. V.G.; N. D.Sh.; L. A.I.; B. A.V.|10.1109/REEPE57272.2023.10086924|Overhead lines;aluminum wire;accumulation of dislocations;vacancies;crack;Power engineering;Power transmission lines;Wires;Aluminum;Fatigue;Reliability engineering;Power systems|
|[Thermal stresses using Finite Elements Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086771)|M. A. Raad; A. Y. Popov|10.1109/REEPE57272.2023.10086771|computer-aided design (CAD);thermal stress;solids;finite element method;3d-meshing;meshing algorithms;Delaunay algorithm;Heating systems;Solid modeling;Shape;Computational modeling;Solids;Approximation algorithms;Finite element analysis|
|[Metallized film capacitors operation under the ramp voltage test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086810)|D. Y. Glivenko; I. O. Ivanov; A. A. Hojamov; A. V. Pechnikov|10.1109/REEPE57272.2023.10086810|metallized film capacitors;self-healing effect;rump voltage test;«soft» treatment;parametric and catastrophic failures;Resistance;Insulation;Metallization;Electric breakdown;Capacitors;Dielectric losses;Data processing|
|[High-Speed Operational Amplifiers Based on the Advanced Derivatives of the Class AB Intermediate Stage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086923)|N. Prokopenko; V. Chumakov; D. Kleimenkin|10.1109/REEPE57272.2023.10086923|operational amplifier;maximum slew rate;input stage;intermediate stage;folded cascode;JFETs;GaAs transistors;internal channel CMOS transistors;Operational amplifiers;Power engineering;Computer simulation;Current measurement;Gallium arsenide;Computer architecture;CMOS technology|
|[Reducing odd-order nonlinear distortions in a low noise amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086746)|A. M. Ibragimov; M. N. Mikhailov; E. P. Smirnov|10.1109/REEPE57272.2023.10086746|radar;nonlinear equalization;intermodulation distortion;low noise amplifier;receiver nonlinearity;Low-noise amplifiers;Power engineering;Equalizers;Search methods;Nonlinear distortion;Receivers;Dynamic range|
|[Equivalent Number of Bits of the ADC in the Implementation of Its Input Driver Based on High-Speed Operational Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086712)|L. K. Samoilov; N. Prokopenko; V. Chumakov|10.1109/REEPE57272.2023.10086712|physical quantity sensor;analog-to-digital converter;high-speed operational amplifier;sensing element;signal delay in the ADC driver;Operational amplifiers;Power engineering;Bit rate;Mathematical analysis;Control systems;Delays;Finite element analysis|
|[Investigation of the effect of multiple oxidation and ion sputtering on the formation of inhomogeneous oxide layers on the surface of an ultrathin metal film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086730)|A. V. Lubenchenko; D. A. Ivanov; D. S. Lukiantsev; M. B. Smirnov; O. N. Pavlov|10.1109/REEPE57272.2023.10086730|metal oxide films;controlled film formation;layered chemical and phase analysis;X-ray photoelectron spectroscopy;Spectroscopy;Films;Atmospheric modeling;Ions;Nonhomogeneous media;Oxidation;Sputtering|
|[Synchronous execution of program instructions in a distributed data transmission network based on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086927)|A. A. Deev; A. A. Kalshchikov|10.1109/REEPE57272.2023.10086927|time synchronization;subnanosecond synchronization;program instructions execution;distributed data transmission network.;Performance evaluation;Optical fibers;Power engineering;Service robots;Real-time systems;System-on-chip;Synchronization|
|[Modeling the Algorithm for Determining the Coordinates of Radio Radiation Sources Using the Maximum Likelihood Method in TDoA Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086761)|A. A. Chugunov; R. S. Kulikov; N. I. Petukhov; A. P. Malyshev; S. A. Serov; O. V. Glukhov|10.1109/REEPE57272.2023.10086761|maximum likelihood method;trajectory filtration;passive location;multiposition radar;radar systems;non-linear approximation;Maximum likelihood estimation;Power engineering;Radar;Approximation algorithms;Trajectory|
|[Computer simulation of the biomorphic neuroprocessor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086889)|A. Ebrahim; A. Busygin; S. Udovichenko|10.1109/REEPE57272.2023.10086889|MDC-SPICE;simulation;biomorphic neuroprocessor;networks;Neurons;Sociology;SPICE;Hardware;Software;Delays;Statistics|
|[Optimization of navigation algorithms parameters in fingerprint method by received Wi-Fi signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086865)|A. P. Malyshev; S. A. Chuykin; N. I. Petukhov; P. Y. Anuchin; T. A. Brovko; A. D. Evseev|10.1109/REEPE57272.2023.10086865|Radio navigation;Wi-Fi;indoor positioning;fingerprint;Meters;Weight measurement;Coordinate measuring machines;Measurement uncertainty;Signal processing algorithms;Radio navigation;Estimation|
|[Analysis of EKF, SR-UKF and Particle filter for ToF/AoA Local Navigation System and IMU Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086716)|N. I. Petukhov; K. V. Kochka; A. D. Evseev; T. A. Brovko; S. V. Orobchenko; I. V. Korogodin|10.1109/REEPE57272.2023.10086716|ToF;AoA;local navigation system;PDR;EKF;UKF;particle filter;integrated system;Global navigation satellite system;Power measurement;Atmospheric measurements;Measurement uncertainty;Radio navigation;Particle measurements;Particle filters|
|[Research of a multifunctional integrated electromagnetic component as an LC filter in DC/DC converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086824)|R. R. Sattarov; R. T. Khazieva; M. D. Ivanov|10.1109/REEPE57272.2023.10086824|DC/DC converters;LC filter;ripple current;voltage undershoot;multifunctional integrated electromagnetic component;Inductance;Transient response;Power engineering;Costs;Power supplies;Capacitance;Steady-state|
|[Quality assessment of a satellite communication system using a weather-dependent parametric model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086722)|M. E. Korchagin; G. M. Seregin|10.1109/REEPE57272.2023.10086722|Internet access;Communications Technologies;BER;SER;signal propagation;Precipitation;Simulation;Probability;Attenuation;Data models;Telecommunications;Quality assessment|
|[Aspects of effective planning of electroporation procedure using finite element analysis methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086849)|A. Briko; V. Kapravchuk; E. Sorokina; M. Baryshnikova; V. Kosyrev; V. Kosorukov|10.1109/REEPE57272.2023.10086849|irreversible electroporation;tumor ablation;planning system;modeling;nanoknife;domestic equipment;Electrodes;Visualization;Three-dimensional displays;Shape;Electroporation;Planning;Finite element analysis|
|[On-Board Intelligent Decision Support System Development for Aviation Complexes Using Machine-Oriented Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086920)|A. Vorobev; I. Elesin; A. Shevadronov|10.1109/REEPE57272.2023.10086920|aircraft expert systems;decision support systems;knowledge based systems;computer aided analysis;computer aided software engineering;Decision support systems;Visualization;Solid modeling;Power engineering;Instruments;Pipelines;Aerospace electronics|
|[Burning Gas Sensor Module Adapter Development and Test Operation Results Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086897)|A. A. Ivantsov; E. A. Mironova; M. Y. Ryakhina|10.1109/REEPE57272.2023.10086897|optic sensor;electronic module;gas analyzer;burning gas;Temperature measurement;Temperature sensors;Temperature distribution;Sensitivity;Prototypes;Transfer functions;Optical receivers|
|[MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086785)|D. A. Vidmanov; A. N. Alfimtsev|10.1109/REEPE57272.2023.10086785|user interface;interface synthesis;adaptive;intelligent;mobile interface;agent;multi-agent;reinforcement learning;deep learning;innovation system;Adaptation models;Processor scheduling;Computational modeling;Ecosystems;Reinforcement learning;User interfaces;Markov processes|
|[Procedure for Locating Trees and Estimating Diameters Using LiDAR Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086843)|I. A. Grishin; V. I. Terekhov|10.1109/REEPE57272.2023.10086843|forest management;forest ecosystem;forest auditing;segmentation;Power engineering;Laser radar;Forestry;Approximation algorithms;Data processing;Filling;Partitioning algorithms|
|[The Robot-Guide for Indoor Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086707)|V. A. D.; K. D. S.; K. A. N.; D. S. S.; K. A. I.|10.1109/REEPE57272.2023.10086707|robot;Raspberry Pi;lidar;speech recognition;ROS;web;navigation;Computer vision;Software algorithms;Signal processing algorithms;Speech recognition;Robot sensing systems;Software;Libraries|
|[Optical Properties of MoS2 Films Fabricated on Ceramic Substrates by Pulse Laser Deposition (PLD) Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086821)|A. A. Pospelov; A. E. Shupenev; K. V. Zayats; S. B. Bychkov; D. A. Yu; A. G. Grigoriants|10.1109/REEPE57272.2023.10086821|pulsed laser deposition;optical coating;molybdenum disulfide;aluminum nitride;laser-induced damage threshold;laser treatment;thin films;Reflectivity;Resistance;Power system measurements;Density measurement;Optical device fabrication;Coatings;Optical reflection|
|[Malicious Node Detection in Wireless Sensor Networks: Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086790)|L. Darwish; M. Nassr; F. Ghosna; H. M. Fardoun; D. K. Voronkova; M. Anbar|10.1109/REEPE57272.2023.10086790|wireless sensor network WSN;malicious node;throughput;power consumption;delay;Wireless sensor networks;Power engineering;Power demand;Network security;Throughput;Delays;Reliability|
|[Synthesis of the UWB signal processing algorithm on an artificial neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086841)|S. A. Serov; A. A. Chugunov; R. S. Kulikov; A. P. Malyshev; O. V. Glukhov; T. A. Brovko|10.1109/REEPE57272.2023.10086841|UWB;neural network;map of the depths;ultra-wideband signals;Power engineering;Radio navigation;Signal processing algorithms;Artificial neural networks;Information processing;Reflection;Computational efficiency|
|[Loosely Coupled UWB/Stereo Camera Integration for Mobile Robots Indoor Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086807)|O. V. Glukhov; I. A. Akinfiev; A. D. Razorvin; A. A. Chugunov; D. A. Gutarev; S. A. Serov|10.1109/REEPE57272.2023.10086807|mobile robots;indoor positioning;EKF;UWB;stereo camera;Time-frequency analysis;Power measurement;Current measurement;Robot vision systems;Radio navigation;Gain measurement;Position measurement|
|[Design of compact ultra-wideband spiral antenna for ground-penetrating radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086757)|A. B. Borzov; K. P. Likhoedenko; V. B. Suchkov; M. V. Artyushkin; G. M. Seregin|10.1109/REEPE57272.2023.10086757|ultra-wideband;air-drone;ground penetrating radar;microstrip spiral antenna;optimization algorithm;Spirals;Ground penetrating radar;Snow;Microstrip antennas;Mobile antennas;Radar antennas;Ultra wideband antennas|
|[Simulation of energy absorption in a device for microwave heating of liquid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086794)|N. N. Kisel; A. A. Vaganova; A. I. Panychev|10.1109/REEPE57272.2023.10086794|waveguide;microwave heating;simulation;specific absorption rate;SAR;electric field;Performance evaluation;Electromagnetic heating;Liquids;Absorption;Power distribution;Electromagnetic waveguides;Microwave devices|
|[Investigation of the influence of the CSRR resonator parameters on the characteristics of a Ku-band microstrip SIW antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086743)|N. N. Kisel; A. I. Panychev; A. A. Vaganova|10.1109/REEPE57272.2023.10086743|microstrip antennas;substrate integrated waveguides;SIW;complementary split ring resonators;CSRR;simulation;Microstrip resonators;Codes;Computational modeling;Resonant frequency;Microstrip antennas;Electromagnetic waveguides;Computational electromagnetics|
|[Design and Optimization of Helmholtz Coils For Magnetic Fields](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086864)|M. Saqib; Y. Xu; S. A. Aljasar; A. L. Hui|10.1109/REEPE57272.2023.10086864|Helmholtz coils;axial magnetic field;transverse magnetic field;CuCr electrode;sensor;simulation;Coils;Electrodes;Magnetic field measurement;Voltage measurement;Current measurement;Biomedical measurement;Hall effect|
|[Neuro-fuzzy approach to identification of electromagnetic fields of electrostatic discharge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086745)|A. B. Bakasova; A. Uulu Askat|10.1109/REEPE57272.2023.10086745|neuro-fuzzy inference;electrostatic discharge;electromagnetic environment;identification of electromagnetic fields;Training;Visualization;Neural networks;Electrostatic discharges;Finite element analysis;Takagi-Sugeno model;Spatiotemporal phenomena|
|[Reducing Delay for Delay-Sensitive Applications in Smart Home Networks Using Openflow Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086840)|M. Alkubeily; S. A. Sakulin; B. Hasan; H. M. Fardoun|10.1109/REEPE57272.2023.10086840|IoT Network;smart home network;Quality of Service;Power engineering;Costs;Bandwidth;Smart homes;Quality of service;Routing;Throughput|
|[Design an Adaptive Trajectory to Support UAV Assisted VANET Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086859)|M. Alkubeily; S. A. Sakulin; B. Hasan|10.1109/REEPE57272.2023.10086859|Ad Hoc connectivity;VANET;UAV_assisted VANET;Sensitivity;Simulation;Vehicular ad hoc networks;Autonomous aerial vehicles;Throughput;Land vehicles;Delays|
|[Application of Island Thin Films for Microelectronics Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086877)|S. V. Kiryanov; N. O. Yurkin; A. D. Kouptsov; S. V. Sidorova|10.1109/REEPE57272.2023.10086877|island film;aluminum oxide;vacuum;thermal evaporation;magnetron sputtering;tunneling;emission;potential barrier;Resistance;Power engineering;Films;Memristors;Switches;Conductivity;Nickel|
|[Detecting and Mitigating Wormhole Attack Effect in MANETs Based on Hop Count Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086929)|M. Knaj; M. Anbar; F. Ghosna; M. Nassr; D. K. Voronkova|10.1109/REEPE57272.2023.10086929|MANETs;Wormhole Attack;AODV;Hop Count;NS-2.35;Performance evaluation;Power engineering;Throughput;Ad hoc networks;Delays;Security;Mobile computing|
|[Calculation, modeling and programming of a three-link manipulator robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086733)|S. Vtyurina; D. Shilin|10.1109/REEPE57272.2023.10086733|manipulator robot;direct kinematics problem;inverse kinematics problem;attainability domain;Training;Power engineering;Automation;Service robots;Kinematics;Production;Programming|
|[Autoregressive Models for Solving the Problem of Forecasting Active Energy Complexes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086906)|M. A. Durova; A. N. Zein; S. V. Borisova; A. A. Mishin; D. Kan; S. K. Osipov|10.1109/REEPE57272.2023.10086906|machine learning;autoregression;time series forecasting;ARIMA;SARIMA;Analytical models;Energy consumption;Power engineering;Time series analysis;Predictive models;Strategic planning;Forecasting|
|[Investigation of the Correlation Method Accuracy for Measuring Time Intervals in Ultrasonic Flowmeters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086806)|S. I. Gerasimov; V. D. Glushnev|10.1109/REEPE57272.2023.10086806|time delay estimation;transit-time;truncation;interpolation;time-of-flight;TDE;Correlation;Transducers;Ultrasonic imaging;Ultrasonic variables measurement;Harmonic analysis;Flowmeters;Time measurement|
|[Designing the system for detecting unsuitable marble stones for an industrial process using convolutional neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086731)|S. Ivan; D. Shilin|10.1109/REEPE57272.2023.10086731|convolutional neural network;object detection;data classification;marble processing;Measurement;Power engineering;Computer vision;Data analysis;Industrial control;Neural networks;Belts|
|[Development of the digital twin for a multi-purpose processing plant training simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086858)|I. Zabarin; D. Shilin|10.1109/REEPE57272.2023.10086858|digital twin;automated process control system;process simulation;digital simulator;Training;Solid modeling;Power engineering;Three-dimensional displays;Process control;Virtual reality;Search problems|
|[Active Content Publication System for STEM Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086719)|G. G. Ambarjian; A. A. Lipskya; V. F. Ochrov; A. I. Tikhonov|10.1109/REEPE57272.2023.10086719|STEM;STEAM;active content;interactive web applications;educational content publishing;Python;Django;Plotly Dash;Power engineering;Publishing;Databases;Source coding;Laboratories;Education;Web pages|
|[Evaluation of Multiple Bioradar-based Fall Detection System’s Performance on a Test Sample including Recordings with Pets, moving Appliances and Household Activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086773)|D. Bezdetnyy; V. Slizov; L. Anishchenko|10.1109/REEPE57272.2023.10086773|bioradiolocation;fall detection;interference;pets;deep learning;wavelet analysis;Training;Home appliances;Power engineering;Sociology;Interference;Predictive models;Recording|
|[Optimisation of the CFD-model calculation method for natural convection in large volume](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086863)|L. O. Iakovlev; I. O. Morozov|10.1109/REEPE57272.2023.10086863|natural convection in big volume;optimising CFD-model;reducing computational effort;cold bridges;LES-model;Solid modeling;Analytical models;Power engineering;Software packages;Computational modeling;Finite element analysis;Servers|
|[Application of machine vision technology for video recordings of the combustion process of energetic materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086813)|A. Kaida; F. Gubarev; A. Mostovshchikov|10.1109/REEPE57272.2023.10086813|high-speed imaging;thresholding;binarization;Otsu’s method;combustion analysis;Thresholding (Imaging);Machine vision;Aluminum;Pipelines;Lasers;Video sequences;Lighting|
|[High-Precision Computing based on the CUDA Architecture in Residual Number Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086778)|V. A. Chumychkin; N. A. Galanina|10.1109/REEPE57272.2023.10086778|Residue number system;RNS;parallel algorithms;graphics processing unit;GPU;CUDA;Power engineering;Neural networks;Graphics processing units;Computer architecture;Dynamic range;Software;Libraries|
|[Data processing model for equipment monitoring and diagnostics systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086875)|M. M. Sultanov; I. A. Boldyrev; P. D. Menshikov|10.1109/REEPE57272.2023.10086875|technical condition;condensers;monitoring system;Power measurement;Software packages;Measurement uncertainty;Maintenance engineering;Time measurement;Software measurement;Remuneration|
|[Estimation of the optimal record interval of the technological parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086827)|M. M. Sultanov; I. A. Boldyrev; K. V. Evseev; N. S. Khlyustov|10.1109/REEPE57272.2023.10086827|machine learning;data analysis;signal processing;control system;thermal power plant;software and hardware complex;Power engineering;Machine learning algorithms;Signal restoration;Estimation;Machine learning;Writing;Control systems|
|[Hardware optimized digital resamplers based on half-band filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086717)|B. Nikita; A. Degtyarev; S. Bakhurin|10.1109/REEPE57272.2023.10086717|Half-band filters;digital resamplers;non-linear processing;hardware optimization;Power engineering;Quantization (signal);Finite impulse response filters;Low-pass filters;Signal processing algorithms;Bandwidth;Filtering algorithms|
|[Optimization of the Algorithm for the Placement of Reference Points in the Positional Local Navigation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086912)|R. S. Kulikov; S. V. Orobchenko; M. M. Zainutdinov; S. S. Erochkina; A. O. Zhirnova; A. V. Pavlovsky|10.1109/REEPE57272.2023.10086912|genetic algorithm;positional local navigation system;GDOP;meta-genetic algorithm;Geometry;Economics;Power engineering;Radio navigation;Minimization;Task analysis;Optimization|
|[Improving the information veracity of the complex of multiparametric control of the relaxometer based on a neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086740)|G. A. Ovseenko; R. S. Kashaev; O. V. Kozelkov; T. K. Filimonova; T. S. Evdokimova; A. M. Mardanova|10.1109/REEPE57272.2023.10086740|neural network;relaxometry;proton;control;parameters;Protons;Training;Software algorithms;Sociology;Magnetic resonance;Artificial neural networks;Software|
|[The possibility of artificial neural network application in prototyping in instrument making industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086823)|G. A. Ovseenko; R. S. Kashaev; I. A. Koshkina; V. V. Kosulin; G. A. Khamatgaleeva; R. A. Ulengov|10.1109/REEPE57272.2023.10086823|neural network;honeycomb panels;defect classification system;Training;Industries;Power engineering;Instruments;Nondestructive testing;Artificial neural networks;Machining|
|[Convolutional Neural Network to Detect Support and Suspend Insulators on Infrared Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086902)|A. D. Zaripova|10.1109/REEPE57272.2023.10086902|infrared images;convolutional neural network;image processing;high-voltage insulators;Training;Measurement;Isolators;Power engineering;Substations;Neural networks;Insulators|
|[Pressure influence on morphological and functional changes in forearm tissues research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086803)|E. Romanova; E. Leontyeva; V. Kapravchuk; T. Goidina; A. Briko; A. Kobelev|10.1109/REEPE57272.2023.10086803|sonomiography;bioelectric impedance;forearm muscles;muscle contraction;transducer clamping;Electrodes;Ultrasonic imaging;Transducers;Force;Propioception;Pressing;Muscles|
|[Determination of Deformations of Transport Infrastructure Objects Using Optical Information Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086799)|D. A. Loktev; A. A. Loktev; K. A. Izotov|10.1109/REEPE57272.2023.10086799|fiber-optic technologies;track facilities;monitoring system;recognition of defects;transportation system.;Optical fibers;Temperature measurement;Temperature sensors;Rails;Optical fiber sensors;Deformation;Shape|
|[Determining the Dynamic Characteristics of Moving Vehicles Using Image Blur Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086893)|D. A. Loktev; A. A. Loktev; V. V. Korolev; A. A. Linenko|10.1109/REEPE57272.2023.10086893|object detection;mass determination;image analysis;object blur;dynamic characteristics;transportation system.;Roads;Wheels;Kinematics;Streaming media;Mathematical models;Surface roughness;Trajectory|
|[Determining The DOA of Jamming Signals Using Root-Music and MVDR Algorithms for Planar Elliptical Digital Antenna Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086894)|A. Propastin; V. Prokhorenko|10.1109/REEPE57272.2023.10086894|DOA;DAA;Root-MUSIC;MVDR;planar DAA;elliptical DAA;compensating radiation pattern;adaptive beamforming;array processing;Power engineering;Direction-of-arrival estimation;Azimuth;Transmitting antennas;Receiving antennas;Interference;Directive antennas|
|[Parameter Optimization of Broadband Interference-Suppressing Filters of Hi-Performance Power Supplies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086788)|I. V. Chukhraev; V. E. Drach|10.1109/REEPE57272.2023.10086788|interference-suppressing;filter;QUCS;components;high-frequency interference;frequency response;Inductance;Power filters;Power supplies;Simulation;Capacitors;Attenuation;Transformers|
|[Accuracy of Feature Extraction Approaches in the Task of Recognition and Classification of Isolated Words in Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086720)|A. Messerle; Y. Gorshkov|10.1109/REEPE57272.2023.10086720|mel frequency cepstral coefficients;feature extraction;speech recognition;keyword search;hidden Markov models;speech processing;Wavelet transforms;Training;Power engineering;Speech recognition;Markov processes;Feature extraction;Classification algorithms|
|[Decision Making for Advanced Driver Assistance Systems for Public Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086753)|Y. Wassouf; E. M. Korekov; V. V. Serebrenny|10.1109/REEPE57272.2023.10086753|Advanced Driver Assistance System (ADAS);Public transport;RADAR systems;Decision making;road safety;Power engineering;Software packages;Roads;Decision making;Safety;Time factors;Advanced driver assistance systems|
|[Research of Different Neural Network Architectures for Audio and Video Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086862)|A. Kanev; M. Nazarov; D. Uskov; V. Terentyev|10.1109/REEPE57272.2023.10086862|Deep learning;neural network;denoising;audio noises;gaussian noise;Temperature sensors;Visualization;Power engineering;Gaussian noise;Neural networks;Noise reduction;Filtering algorithms|
|[Software Development Methodologies: Analysis and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086852)|D. A. Gurianov; K. S. Myshenkov; V. I. Terekhov|10.1109/REEPE57272.2023.10086852|life cycle;software development methodologies;agile;classification;SDGs goal 9;innovation management;resource-use efficiency;industrial performance;information;and communications technology for development;value chain management;Training;Power engineering;Costs;NASA;Machine learning;Predictive models;Software|
|[Relational Database Performance Comparation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086872)|D. S. Gudilin; A. E. Zvonarev; B. S. Goryachkin; D. A. Lychagin|10.1109/REEPE57272.2023.10086872|databases;PostgreSQL;MySQL;MS SQL;performance;DBMS;Power engineering;Relational databases;Optimization|
|[Development of an Algorithm for Optimal Encoding of WAV Files Using Genetic Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086837)|M. V. Belodedov; R. V. Fonkants; R. R. Safin|10.1109/REEPE57272.2023.10086837|resource use efficiency;innovation;audio encoding;WAV file;genetic algorithm;data compression;Power engineering;Encoding;Huffman coding;Genetic algorithms;Testing|
|[Automation of the Behavioral Safety Audit of a Transport Operator Based on the Recognition of His Emotional State in Real Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086820)|D. A. Loktev; L. A. Illarionova; A. A. Loktev|10.1109/REEPE57272.2023.10086820|behavioral security audit;emotion recognition;image analysis;convolutional neural networks;transport operator;Training;Face recognition;Mouth;Streaming media;Muscles;Real-time systems;Skin|
|[The Choice of the Cost Parameter in the PELT Method for Determining the Change Points for Time Series Parameters in the Hidden Channel Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086883)|A. N. Smirnov|10.1109/REEPE57272.2023.10086883|PELT method;cost function parameter selection;hidden data channel;time series change point search;Measurement;Power engineering;Costs;Time series analysis;Cost function;Security;Data communication|
|[Equivalence of logical operations and other operations in Python programming language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086928)|V. S. Popov|10.1109/REEPE57272.2023.10086928|Boolean function;operator;programming operation;logical equivalences;truth table;Power engineering;Casting;Boolean functions;Codes;Logic gates;Python;Arithmetic|
|[Transcription of Arabic and Turkish Music Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086868)|I. S. Wahbi; H. T. Bahbouh; K. M. Yahia|10.1109/REEPE57272.2023.10086868|Music transcription;f0 estimation;Onset detection;Eastern music;Deep learning;CNN;Training;Measurement;Power engineering;System performance;Instruments;Neural networks;Music|
|[Estimating Chest Geometry Assumptions in Modeling Precordial Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086826)|A. Kozlova; S. Mezentseva; A. Tihomirov|10.1109/REEPE57272.2023.10086826|electrical impedance;impedance cardiography;3D model chest;physical modeling;Heart;Geometry;Electrodes;Shape;Current measurement;Shape measurement;Mathematical models|
|[Evaluation of the Informative Value of ECG Fiducial Points in Identification Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086918)|S. V. Mezentseva; A. N. Tichomirov|10.1109/REEPE57272.2023.10086918|identification;Electrocardiogram;ECG;biometrics;amplitude features;temporal features;Heart;Correlation coefficient;Power engineering;Codes;Biometrics (access control);Passwords;Electrocardiography|
|[Extract-Load-Transform (ELT) Process Runtime Analysis and Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086728)|A. E. Zvonarev; D. S. Gudilin; D. A. Lychagin; B. S. Goryachkin|10.1109/REEPE57272.2023.10086728|optimization;ELT;Python;PostgreSQL;flow;procedure;DBMS;Power engineering;Runtime;Data conversion;Process control;Market research;Artificial intelligence;Optimization|
|[Research of Respiratory Waves in Space Flight and Liquid Respiration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086870)|A. E. Larionov; P. V. Luzhnov|10.1109/REEPE57272.2023.10086870|cardiosignal;isolation of the respiratory wave;cardiorespiratory system;microgravity;liquid breathing;Power engineering;Liquids;Signal processing algorithms;Signal processing;Resource management;Task analysis;Respiratory system|
|[An Approach to eye-brain-computer interface development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086838)|L. R. Kondaurov; E. A. Baykova; A. N. Dmitriev; E. S. Boeva; E. A. Romanova; N. Haddad|10.1109/REEPE57272.2023.10086838|brain-computer interface;evoked potential P300;eye tracker;habituation;synchronization;Power engineering;Tracking;Shape;Prototypes;Synchronous motors;Electroencephalography;Brain-computer interfaces|
|[Machine Learning approach for predicting suitable wavelengths in OFDM-FSO system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086914)|R. Younes; M. Nassr; M. Anbar; F. Ghosna; H. A. A. Alasadi; D. K. Voronkova|10.1109/REEPE57272.2023.10086914|FSO;Machine Learning Algorithms (MLAs);Random Forest (RF);Linear Regression (LR);wavelength;Radio frequency;Power engineering;Machine learning algorithms;Rain;OFDM;System performance;Prediction algorithms|
|[Estimation of rotation measurement error of objects using computer simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086903)|E. V. Shmatko; N. Y. Sivov; A. Y. Poroykov|10.1109/REEPE57272.2023.10086903|optical instrumentation;object position estimation;object rotation;fiducial markers;ArUco;measurement error;computer simulation;Unity3D;Measurement errors;Instruments;Computational modeling;Computer simulation;Measurement uncertainty;Optical variables measurement;Position measurement|
|[Diamond-Like Silicon-Carbon Films Preparation at Different Frequencies of Plasma-Chemical Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086836)|T. Chukanova; A. Barinov; V. Yemets; D. Zezin; A. Popov|10.1109/REEPE57272.2023.10086836|silicon-carbon films;preparation;morphology;conductivity;hardness;elastic modulus;Plasma properties;Films;Surface morphology;Mechanical factors;Electric fields;Inductors;Frequency control|
|[Mathematical model of deformations of the antenna frame for space communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086758)|A. D. Ivanitskii; M. N. Kirsanov|10.1109/REEPE57272.2023.10086758|spatial truss;dome;Mohr integral;number of panels;induction;Maple;deflection;Power engineering;Deformation;Space communications;Belts;Mathematical models;Rigidity;Reliability|
|[Assessment of Students’ Performance According to the Characteristics of the Respiratory Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086750)|S. A. Andreeva; V. S. Malyshev; E. V. Fedorova; A. M. Borovkova|10.1109/REEPE57272.2023.10086750|mental performance;students;respiratory cycle characteristics;psychosocial risks;Power engineering;Anxiety disorders;Human factors;Rhythm;Acoustic measurements;Acoustics|
|[On the feasibility of evaluating the performance of strain gauge force sensors using open FEM software Code_Aster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086932)|S. I. Gavrilenkov|10.1109/REEPE57272.2023.10086932|strain gauge load cell;Finite Element Method;open FEM codes;error;simulation;nonlinearity;Power engineering;Simulation;Strain measurement;Reliability engineering;Software;Finite element analysis;Software reliability|
|[The need for using various interpretation methods for power transformer DGA results](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086721)|V. D. Bitney; V. Y. Ulyanov; M. A. Moiseev|10.1109/REEPE57272.2023.10086721|Gas chromatographic analysis;transformer;transformer oil;monitoring;dissolved gas analysis (DGA);faults;Training;Gases;Power engineering;Oils;Companies;Oil insulation;Dissolved gas analysis|
|[Effect of the Reactive Power Consumption Mode on the Technical Condition of the T3FP-110-2MU3 Turbogenerator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086802)|V. D. Bitney; N. N. Smotrov; A. A. Timofeev; A. V. Okhlopkov|10.1109/REEPE57272.2023.10086802|electric power plant;operating reliability;reactive power control;operating modes;turbogenerator;operation;stator winding;Reactive power;Stator cores;Insulation;Windings;Turbogenerators;Stator windings;Maintenance engineering|
|[Impact of the "well-being" system indicators on the safety culture level and health of power industry employees](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086742)|O. A. Loktionov; N. V. Vasileva; O. E. Kondrateva|10.1109/REEPE57272.2023.10086742|well-being;power industry;occupational safety;safety culture level;labor health;ESG;SDG;Power engineering;Occupational safety;Process control;Power industry;Safety;Unemployment;Injuries|
|[Determination of Indicators for Calculating Environmental Risk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086775)|A. M. Borovkova; D. D. Volkova; E. V. Fedorova; N. V. Zvonkova|10.1109/REEPE57272.2023.10086775|environmental risk;negative impact;environmental management;evaluation indicators;environmental safety;Natural resources;Industries;Power engineering;Leadership;Safety;Risk management;Object recognition|
|[Calculation of individual elements of enclosing structures of a continuous steelmaking unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086855)|K. V. Strogonov; A. A. Borisov; V. A. Murashov; D. D. Lvov|10.1109/REEPE57272.2023.10086855|energy efficiency;steel;iron reduction;hydrogen;melt;refractory materials;garnissage;Power engineering;Refining;Production;Iron;Energy efficiency;Steel;Inductors|
|[Mathematical Model of Electric Arc in Low-voltage DC Contactors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086791)|A. Y. Verstunin; N. A. Vedeshenkov|10.1109/REEPE57272.2023.10086791|electric arc;mathematical model;electric arc with variable geometric parameters;arc extinguishing chamber;DC contactors;AC contactors;Low voltage;Power engineering;Software packages;Atmospheric modeling;Contactors;Mathematical models;Stability analysis|
|[A New Approach for Evaluating the Topological Accuracy of Circuit Boards Made by 3D Printing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086768)|O. N. Smirnova; J. S. Bobrova; K. M. Moiseev|10.1109/REEPE57272.2023.10086768|Printed Circuit Board;3D-printing;rapid prototyping;topological accuracy;alignment error;error decomposition;Power engineering;Shape;Printed circuits;Production;Three-dimensional printing;Distortion;Mathematical models|
|[Problems and Measures Ensuring Further Utilization of the Russian Segment of the International Space Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086899)|N. O. Romanov; D. A. Skvortsova|10.1109/REEPE57272.2023.10086899|power supply;reliability of electronic components;ISS;long-lasting in space conditions;Resistance;Power engineering;Power supplies;Electronic components;Production;Maintenance engineering;International Space Station|
|[Research of the Characteristics of the Parabolic Reflector during Its Natural Oscillations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086835)|P. K. Alekseevich; S. G. Mikhailovich|10.1109/REEPE57272.2023.10086835|technologies;communication;network;satellite;modal;Reflectivity;Vibrations;Titanium alloys;Power engineering;Surface waves;Distortion;Reflector antennas|
|[Comparative Analysis of Deuterium Interaction with a Target Made of Steel and Doped Tungsten](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086739)|N. Batrak; N. Kopaleishvili|10.1109/REEPE57272.2023.10086739|deuterium;steel;tungsten;near-wall plasma;first wall;RUSFER-EK-181;low-activated structural materials;Deuterium;Power engineering;Computational modeling;Memory management;Tungsten;Ions;Mathematical models|
|[Analysis of Solar Refrigeration Systems and Development of a Combined Absorption Cooling Installation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086913)|I. R. Juraev; S. S. Dorzhiev|10.1109/REEPE57272.2023.10086913|renewable energy;solar cooling;absorption refrigeration system;adsorption cooling;thermal energy storage;Solid modeling;Renewable energy sources;Power engineering;Costs;Three-dimensional displays;Absorption;Adsorption|
|[Characteristics of Polymetallic Structures Obtained by Direct Metal Deposition of Stainless Steel, Chromium Bronze and Lead for Antifriction Parts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086737)|I. A. Lomakin; A. A. Kholopov; D. O. Bogatyrev; I. N. Shiganov|10.1109/REEPE57272.2023.10086737|direct metal deposition;laser cladding;copper alloys;stainless steels;lead;bimetallic structures;immiscible components;Resistance;Power engineering;Powders;Composite materials;Lasers;Metals;Production|
|[First-Order Logical Equations with Parameter and their Exhaustive Search Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086751)|V. S. Popov|10.1109/REEPE57272.2023.10086751|logical equations;logical equations with parameter;first-order logic;predicate logic;exhaustive search;USE;EGE;Computer science;Power engineering;Computer languages;Algebra;Search problems;Task analysis;Optimization|
|[Features of Approaches to Information Educational Components Design for Energy Universities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086744)|N. N. Tsybov; Z. T. Galbaev; M. V. Burmeyster|10.1109/REEPE57272.2023.10086744|educational information systems;didactic efficiency;psychodiagnostics;personality traits;Training;Power engineering;Computer aided instruction;Philosophical considerations;Electronic learning;Psychology;Control theory|
|[Application of numerical simulation methods to improve the efficiency of cooling systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086888)|A. Ermakov; R. Salakhov; R. Khismatullin|10.1109/REEPE57272.2023.10086888|cooling system;pump;PID controller;internal combustion engine;WHTC;simulation;Simulation;Coolants;Numerical simulation;Belts;Mathematical models;Stability analysis;Transient analysis|
|[Social Dilemma in a Heterogeneous Traffic Flow Consisting of Personal and Public Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086880)|N. V. Bykov; M. A. Kostrov|10.1109/REEPE57272.2023.10086880|social dilemmas;heterogeneous traffic;cellular automata;buses traffic;defectors;cooperators;Power engineering;Computational modeling;Roads;Computer simulation;Automata;Topology;Automobiles|
#### **2023 6th International Conference on Energy Conservation and Efficiency (ICECE)**
- DOI: 10.1109/ICECE58062.2023
- DATE: 15-16 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Air Quality of Karachi and Lahore: How does the energy industry of Pakistan affect it?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092488)|M. Tariq|10.1109/ICECE58062.2023.10092488|AQI;Energy Usage;Proportion Test;Industries;Kilns;Urban areas;Sociology;Energy conservation;Organizations;Air pollution|
|[Smart Energy Conservation in Data Centers Using Machine Learning Based Software-Defined Networking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092487)|H. Aslam; S. Munawar|10.1109/ICECE58062.2023.10092487|Power Efficiency;Data Centers;Deep Learning;Cloud DC;Energy Conservation;Energy Efficiency;Artificial Intelligence (AI);software-defined networking;Energy consumption;Data centers;Renewable energy sources;Network topology;Power control;Traffic control;Topology|
|[Optimal Dispatch of Distributed Generators in Multiple Microgrids Using Hybrid PSO-GWO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092500)|U. Hassan; A. Ahmad|10.1109/ICECE58062.2023.10092500|microgrids;optimal dispatch;particle swarm optimization;grey wolf optimization;dispatchable and non-dispatchable DGs;Photovoltaic systems;Renewable energy sources;Costs;Microgrids;Hybrid power systems;Wind turbines;Reliability|
|[Health Monitoring and Power Quality Analysis of Induction Motors at Tube Well Sites of WASA Lahore](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092506)|U. Liaqat; M. Sohaib; H. Shaukat; U. Younis; R. Hafiz; T. Tauqeer|10.1109/ICECE58062.2023.10092506|Induction Motor;Energy Conservation;Health Monitoring;Power Quality Analysis;Tube Wells;Induction motors;Costs;Power quality;Urban areas;Energy conservation;Voltage;Maintenance engineering|
|[Determination of Thermal Properties of Eutectic Phase change materials (EPCM) using the T-history method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092494)|S. Khan; M. A. Kamran; M. Junaid; R. M. Gul|10.1109/ICECE58062.2023.10092494|Phase change materials;T-history method;Sensible and latent heat;Glauber salt;charging and discharging;Salt hydrates;Heating systems;Phase change materials;Temperature distribution;Costs;Energy conservation;Crystallization;Conductivity|
|[Multi-agent and Reinforcement Learning Schemes for Demand Response Estimation in Distributed Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092514)|M. Ikram; S. Ahmed; S. N. K. Marwat; M. Nasir|10.1109/ICECE58062.2023.10092514|Demand response;Multi-agent control;Deep Q-network;Reinforcement learning;Deep deterministic policy gradient;Networked microgrids;Distributed Smart grid;Costs;Estimation;Microgrids;Reinforcement learning;Mathematical models;Power system reliability;Smart grids|
|[Analysis and Design of the Subnominal Operation of Voltage-Source Parallel Resonant Class E Frequency Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092515)|T. Li; M. Li; Z. Dai; J. Pang; W. Shi; H. Cao|10.1109/ICECE58062.2023.10092515|frequency multiplier;voltage-source parallel resonant class E PA;soft-switching conditions;subnominal;Analytical models;Energy conservation;Resonant frequency;Voltage;Zero voltage switching;SPICE;Circuit synthesis|
|[Structural Optimization of Interuniversity Campus with System Analysis of the Territorial Dispersion of Residential and Educational Facilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092485)|E. Prokshits; P. Moskalev; O. Sotnikova; I. Zolotukhina; D. Vasenin; N. Savvin|10.1109/ICECE58062.2023.10092485|interuniversity campus;spatial structure;system analysis;cluster analysis;energy-efficient technologies;Economics;Technological innovation;Urban areas;Sociology;Energy efficiency;Trajectory;Statistics|
|[A Case Study on Robust Power/Energy Balancing Driven Cost Optimization for Sizing Energy Storage, Power Generators and Consumers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092517)|S. Javaid; M. Kaneko; Y. Tan|10.1109/ICECE58062.2023.10092517|Energy storage systems;renewable energy sources;power fluctuations;cost optimization;power/energy balancing;Photovoltaic systems;Renewable energy sources;Costs;Wind power generation;Controllability;Generators;Global warming|
|[Deep Learning-based Predictive Modeling of Building Energy Usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092502)|F. Hamayat; Z. Akram; S. Zubair|10.1109/ICECE58062.2023.10092502|CNN;Bi-LSTM;Black-Box Model;Energy Demand Modelling;Power Consumption (PC) Prediction;Energy consumption;Power demand;Buildings;Time series analysis;Closed box;Predictive models;Prediction algorithms|
|[Design of Weather Monitoring System for Intermittent Power Generation using Blynk Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092509)|Syafii; T. K. Agung; Aulia|10.1109/ICECE58062.2023.10092509|blynk platform;esp8266;intermittent;internet of thing;weather station;Temperature sensors;Fluid flow measurement;Wind energy;Wind speed;Humidity;Wind power generation;Liquid crystal displays|
|[Improved Power Quality of Distribution Grid by integrating DVR with Super Twisting Sliding Mode Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092513)|I. Ahmad; A. U. Khattak; M. F. Ullah|10.1109/ICECE58062.2023.10092513|Power quality;voltage sag;voltage swell;Dynamic voltage restorer;super twisting sliding mode controller and ultra-capacitor;Total harmonic distortion;Voltage fluctuations;Power quality;Power system dynamics;Power distribution;Transformers;Time factors|
|[Non-intrusive Status Prediction of Residential Appliances using Supervised Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092511)|M. Z. Abbas; I. A. Sajjad; R. Liaqat; T. Khursheed; M. Awais; M. S. Javed|10.1109/ICECE58062.2023.10092511|NILM;load disaggregation;energy conservation;supervised learning;Support vector machines;Training;Home appliances;Machine learning algorithms;Aggregates;Energy conservation;Supervised learning|
|[Eco Gym: Electricity Generation from Manual Treadmill](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092495)|Y. Aftab; M. M. Ghauri; H. Rashad; A. Ahmad; A. Waleed|10.1109/ICECE58062.2023.10092495|Energy;Electricity;Treadmill;Dynamo generator;Renewable;Electric potential;Obesity;Energy conservation;Production;Manuals;Generators;Flywheels|
|[Assessment of Passive Design-Based Energy Efficiency Improvement Opportunities Through Energy Audit of Residential Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092497)|F. Arif; F. Saeed; N. Azhar|10.1109/ICECE58062.2023.10092497|energy audit;passive design;energy efficiency;Insulation;Energy consumption;Wind;Energy conservation;Energy measurement;Ventilation;Energy efficiency|
|[Green Retrofitting of Educational Building for Enhancing the Building's Performance (Retrofitting of IBM building UET Lahore)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092496)|H. Riaz; N. Amina; I. Shahbaz; R. Asif; H. Mohsin; A. Irfan|10.1109/ICECE58062.2023.10092496|Green retrofitting;Building envelope;Building insulation;Building performance;Building system;Insulation;Energy consumption;Energy loss;HVAC;Green buildings;Buildings;Lighting|
|[Sustainable Strategy to Develop Organic Bio Refinery Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092498)|T. Qureshi; M. Farooq; M. H. Siddiqi; M. Adnan; M. Farhan; Q. Ali|10.1109/ICECE58062.2023.10092498|Biomass;Clean energy;Waste to energy;XRD;GCV;SEM;FTIR;TGA;Degradation;Scanning electron microscopy;Climate change;Temperature;X-ray scattering;Biological system modeling;Ignition|
|[Qualitative risk evaluation of occupational health and safety measures of under-construction Indus Highway N-55: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092486)|A. Khan; M. Shakeel; M. Khan; N. Sohail; M. Shahab; M. Wasil|10.1109/ICECE58062.2023.10092486|N-55 Highway;OHMS;OH&S;safety;hazards;Vibrations;Musculoskeletal system;Phase measurement;Roads;Occupational health;Skin;Planning|
|[Multiobjective Optimal Power Flow (MO-OPF) using Hybrid Harris Hawk-Particle Swarm Optimization Algorithm Hy(HHO-PSO)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092489)|R. Ashfaq; I. A. Sajjad|10.1109/ICECE58062.2023.10092489|OPF;AI;Harris Hawk Optimization (HHO);Particle swarm optimization (PSO);Power system;Multi-Objective Optimization;Fuel cost;Emission;Environment;Energy conservation;Hybrid power systems;Generators;Power system reliability;Reliability;Fuels;Particle swarm optimization|
|[NUMERICAL ANALYSIS OF PYROLYSIS OF PLASTIC USING OXY-HYDROGEN GAS PRODUCED BY ELECTROLYSIS OF SOLAR ENERGY](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092504)|M. Hamza; A. Ali; S. Abbas; F. BiBi; K. Afzal; Z. Abbas; H. Ashraf|10.1109/ICECE58062.2023.10092504|Oxy-Hydrogen;Pyrolysis;COCO simulator;Solidworks;Plastic;Analytical models;Liquids;Spirals;Costs;Computational fluid dynamics;Computational modeling;Software|
|[Design, Fabrication and Performance Enhancement of Photovoltaic Thermal Collector by using different Heat Transfer Fluids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092501)|F. Umer; S. A. Rehman; A. Haseeb; A. Waqas; H. Nazir|10.1109/ICECE58062.2023.10092501|Photovoltaic Thermal Collector;PVT Efficiency;Performance Analysis;Solar Energy Conversion Efficiency;Photovoltaic systems;Temperature measurement;Fluids;Pumps;Water heating;Coolants;Thermal analysis|
|[Investigating the macroscopic spray characteristics of Mustard biodiesel in a control volume chamber (CVC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092491)|M. u. Haq; A. T. Jafry; H. Ullah|10.1109/ICECE58062.2023.10092491|Biodiesel;Biofuel;Penetration Length;Sauter mean diameter;Spray projected area;Viscosity;Heating systems;Oils;Fossil fuels;Behavioral sciences;Fuels;Biofuels|
|[Electric Vehicle Readiness in Sabah: Overview of Market Forecast and Adoption Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092507)|M. Songkin; M. Y. Hj Jaafar|10.1109/ICECE58062.2023.10092507|Electric Vehicle;Electric Vehicle Infrastructure;transportation electrification;EV;Econometric;Pollution;Government;Carbon dioxide;Predictive models;Electric vehicles;Regulation;Pollution measurement|
|[A Controller Hardware in Loop Framework for Microgrid Control Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092516)|M. A. Aslam; S. A. R. Kashif; M. Adeel; M. U. Shahid; M. Iqbal; M. A. Riaz|10.1109/ICECE58062.2023.10092516|Smart Grid;Microgrid;Microgrid Control Validation;Hardware in Loop (HIL);PI control;Costs;Energy conservation;Microgrids;Hardware;Real-time systems|
|[Erosion of pipe bends for multiphase flow: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092492)|M. Abdullah; M. R. Khan; U. K. Uz Zaman; B. Anjum; M. A. Khan; A. R. Mazhar|10.1109/ICECE58062.2023.10092492|Silt Erosion;pipeline erosion;Erosion model;CFD Analysis of elbow erosion;Industries;Geometry;Viscosity;Pipelines;Particle production;Production;Hydrocarbons|
|[Dielectric-Metal Nanostructures for Enhanced Scattering and Absorption in Solar Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092493)|S. Latif; M. Alam; Y. Massoud|10.1109/ICECE58062.2023.10092493|Plasmonics Nanoshell;Absorption Enhancement;Metamaterial;Mie Solution;Scattering;Nanoparticles;Optical polarization;Absorption;Photovoltaic cells;Scattering;Linear circuits;Metasurfaces|
#### **2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)**
- DOI: 10.1109/IEMECON56962.2023
- DATE: 10-11 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Automatic flood detection by leveraging deep convolutional neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092315)|D. P. Wilfried; N. Rai; K. Singla|10.1109/IEMECON56962.2023.10092315|flood mapping;Sentinel-1;Random Forest Classification;U-Net;deep learning;Satellites;Microwave communication;Microwave theory and techniques;Robustness;Radar polarimetry;Floods;Internet of Things|
|[Spatially Varying Regularization for Microwave Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092373)|D. Magdum; S. Kadam; A. Abhyankar; S. Ghaisas; M. Erramshetty; A. Magdum|10.1109/IEMECON56962.2023.10092373|inverse solution;microwave imaging;regularization parameter;spatially varying regularization;Tikhonov regularization;Object detection;Microwave communication;Speckle;Microwave theory and techniques;Internet of Things;Image reconstruction;Standards|
|[The Generalized Average Entropy with Applications to some Satellite Image Thresholding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092323)|O. A. Kittaneh|10.1109/IEMECON56962.2023.10092323|Average Entropy;Entropy;Image Thresholding;Truncated Distributions;Satellites;Microwave communication;Solids;Microwave theory and techniques;Entropy;Internet of Things;Microwave imaging|
|[Comparative Analysis of various Deep learning techniques for Plant leaf disease detection: A practical approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092330)|K. Chauhan; G. U. Devi|10.1109/IEMECON56962.2023.10092330|ResNet152V2;YOLO3;YOLO5;Validation;Image Processing;Crops;Training;Deep learning;Plant diseases;Transfer learning;Predictive models;Soil;Microwave theory and techniques|
|[Evaluation of BLER and throughput during the coexistence of both 4G LTE and 5G NR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092362)|J. K. Ray; R. Sultana; R. Bera; Q. M. Alfred; S. Sil|10.1109/IEMECON56962.2023.10092362|DRiVE;DSS;RAT;Throughput;Throughput Fraction;4G LTE;5G NR;ESS;Technological innovation;5G mobile communication;Interference;Microwave communication;Throughput;Microwave oscillators;Cognition|
|[L-Slotted Rectangular Patch Antenna with DGS for mm Wave Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092365)|A. Chowdhury; P. Ranjan|10.1109/IEMECON56962.2023.10092365|Slot;mm Wave;DGS;Surface Current Distribution;Substrate;Microwave antennas;Slot antennas;5G mobile communication;Surface waves;Resonant frequency;Bandwidth;Software|
|[Parking Space Monitoring – A LoRa Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092339)|B. C. Mallikarjun; G. Anantha Krishna; B. V. Mahantesha; K. T. Monish; E. Kishore|10.1109/IEMECON56962.2023.10092339|Vacant Parking Space Detection;Image Processing;LoRa;OpenCV;Node to Node;Wireless communication;Wireless sensor networks;Smart cities;Image edge detection;Streaming media;Cameras;Monitoring|
|[Segmenting Personal Protective Equipment Using Mask R-CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092308)|S. Mathur; T. Jain|10.1109/IEMECON56962.2023.10092308|Instance Segmentation;Deep Learning;Safety Equipment;Computer Vision;Mask R-CNN;Deep learning;Personal protective equipment;Image segmentation;Computer vision;Employment;Microwave devices;Software|
|[Prediction of Diabetes using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092335)|K. Sidana|10.1109/IEMECON56962.2023.10092335|Accuracy;Diabetes Disease;Healthcare;Machine learning;nan|
|[Credit Risk Prediction using Extra Trees Ensemble Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092325)|T. Saha; S. K. Biswas; S. Sanyal; S. K. Parui; B. Purkayastha|10.1109/IEMECON56962.2023.10092325|SMOTE;ENN;Oversampling;Undersampling;Ensemble Methods;Support vector machines;Predictive models;Microwave communication;Microwave theory and techniques;Feature extraction;Internet of Things;Expert systems|
|[Optimal Status Updates in Cognitive Radio-Enabled IoT Networks: An Age of Information Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092292)|K. N. Vaishnavi; V. Baluwale; R. Kishore; Y. K. Moorthy; S. Gurugopinath|10.1109/IEMECON56962.2023.10092292|Age of information;cognitive radios;internet of-things;status updates;time slot structure.;Radio transmitters;Information age;Minimization;Microwave theory and techniques;Time measurement;Sensors;Internet of Things|
|[IoT based all in one Security System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092364)|I. Varshney; A. Chowdhury; A. Ahmad; H. Banerjee|10.1109/IEMECON56962.2023.10092364|Internet of things(IoT);security;sensors;GSM module;Temperature sensors;GSM;Home automation;Lightning;Microwave communication;Modems;Motion detection|
|[Supervised Machine Learning Approaches for Brain Stroke Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092374)|D. v. Desai; T. Jain; P. Tiwari|10.1109/IEMECON56962.2023.10092374|Machine Learning;Classification;KNN;Logistic Regression Model;Random Forest Model;Decision Tree Model;SVM Model;Naive Bayes Model;Support vector machines;Forestry;Predictive models;Stroke (medical condition);Microwave communication;Brain cells;Brain modeling|
|[Energy-efficient Security Technique Implementation for Selective Forwarding Attack in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092329)|H. Banerjee; S. Yadav|10.1109/IEMECON56962.2023.10092329|Security Protocols;WSN;Sensor Node;WSN attacks;Selective Forwarding Attack;Threats Introduction (Heading 1);Wireless sensor networks;Solid modeling;Analytical models;Protocols;Surveillance;Open systems;Routing|
|[Forestry 4.0 – An Overview of Authentication in the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092313)|J. Chen; A. R. Bektas; J. Roßmann|10.1109/IEMECON56962.2023.10092313|Internet of Things;Forestry 4.0;Authentication;Security;Smart cities;Phishing;Scalability;Authentication;Forestry;Smart homes;Routing|
|[Dermatological Disease Detection Employing Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092304)|S. Mathur; T. Jain|10.1109/IEMECON56962.2023.10092304|Image Classification;Deep Learning;Skin Diseases;Computer Vision;Deep learning;Computer vision;Computational modeling;Transfer learning;Software algorithms;Developing countries;Skin|
|[Solution to Web Scraping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092327)|C. Biswas; R. Mallick; S. Paul; D. Mukherjee|10.1109/IEMECON56962.2023.10092327|Scraping;Web scraping;Anti-web scraping.;Computer hacking;Instruments;Microwave communication;Microwave theory and techniques;Internet of Things;CAPTCHAs|
|[Secure Beacon Node Selection for Localization in Underwater Acoustic Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092361)|S. Saha; A. Khan; R. Arya|10.1109/IEMECON56962.2023.10092361|localization coverage;secure localization;malicious nodes;underwater acoustic sensor networks;Location awareness;Surveillance;Microwave communication;Microwave theory and techniques;Safety;Security;Reliability|
|[An Efficient Violence Detection System from Video Clips using ConvLSTM and Keyframe Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092302)|S. K. Parui; S. K. Biswas; S. Das; M. Chakraborty; B. Purkayastha|10.1109/IEMECON56962.2023.10092302|Violence Detection;Keyframe Extraction;Hockey Videos;CNN;LSTM;ConvLSTM;Urban areas;Neural networks;Manuals;Feature extraction;Video surveillance;Data models;Spatiotemporal phenomena|
|[An Energy Efficient Routing Protocol based on Melioration in Distributed Energy Efficient Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092306)|R. P. R. Sadi; C. Dadhirao|10.1109/IEMECON56962.2023.10092306|Wireless Sensor Network;Routing Protocol;Network Lifetime;Energy consumption;Wireless communication;Energy consumption;Wireless sensor networks;Simulation;Routing;Throughput;Energy efficiency|
|[Designing with Simulation Results for Magnetically-/Electrically-tunable Dual-band and Triple-band Microwave Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092298)|S. K. Samanta; S. Ghorai; R. Saha; R. Pradhan; D. Syam|10.1109/IEMECON56962.2023.10092298|Metamaterials;Microstrip Line;Microwave Filter;Split-Ring Resonator;Microstrip filters;Microstrip resonators;Magnetic separation;Resonator filters;Dual band;Microwave communication;Microwave theory and techniques|
|[Adaptive Conditional Sharing of Network Slices and Monetization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092301)|G. Bhatnagar; S. Gupta; M. Verma|10.1109/IEMECON56962.2023.10092301|Network Slice;Network Slice Instance (NSI);Service Profile;allocateNsi;NSMS_Provider;NSMS_Consumer;Adaptive systems;5G mobile communication;Tariffs;Traffic control;Microwave communication;Telecommunications;Noise measurement|
|[3D Modelling and Rendering Using Autodesk 3ds Max](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092375)|P. Dangi; A. Jain; D. Samanta; S. Dutta; A. Bhattacharya|10.1109/IEMECON56962.2023.10092375|Blended Education System;Outcome Based Learning;Effective Education System;Sector Skill Development.;Solid modeling;Three-dimensional displays;Shape;Education;Lighting;Lattices;Microwave communication|
|[Performance investigation for analysis of Predicting and understanding human depression behavior from social network analysis using SVM model: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092317)|P. Mishra; G. U. Devi|10.1109/IEMECON56962.2023.10092317|data mining;Machine learning;depression;Support vector machines;Sentiment analysis;Social networking (online);Sociology;Predictive models;Depression;Microwave theory and techniques|
|[Smart Healthcare Monitoring System Using IoT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092300)|A. S. Kishore; G. R. Chinni; G. JayaLakshmi; K. S. K. Reddy|10.1109/IEMECON56962.2023.10092300|Arduino UNO;Arduino software;SVM;temperature;heart rate;spo2 sensors;Temperature sensors;Support vector machines;Patient monitoring;Medical services;Microwave communication;Sensor systems;Software|
|[Design and Fabrication of an IoT based Air Purifier using HEPA Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092366)|A. Choudhary; L. Saini; A. Ahmad; H. Banerjee; F. Gazi|10.1109/IEMECON56962.2023.10092366|Internet of Things (IoT);HEPA Filter;Indoor Air Quality;Air Pollution.;Air cleaners;Microwave measurement;Air pollution;Information filters;Sensors;Timing;Internet of Things|
|[Secure Medical Data Abstraction Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092311)|S. S. Shaik; C. Rupa; V. Yadlapalli|10.1109/IEMECON56962.2023.10092311|Image Steganography;Convolutional Neural Network(CNN) model;Deep Learning;MRI images;Security;DICOM images;Steganography;Data privacy;Medical conditions;Magnetic resonance imaging;Microwave communication;Microwave theory and techniques;Convolutional neural networks|
|[Duo Satellite Based Surface Temperature Comparative Study of Jaipur City Using Soft Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092290)|S. Chauhan; A. S. Jethoo; U. K. Das|10.1109/IEMECON56962.2023.10092290|LST;GIS;Remote Sensing;MODIS;BPNN;Training;Temperature sensors;Temperature distribution;Computational modeling;Urban areas;Land surface;Predictive models|
|[Grape leaf disease prediction using various machine learning techniques: - A technical review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092353)|R. Patil; A. More|10.1109/IEMECON56962.2023.10092353|Grape leaf disease;SVM;Convolution brain network (CNN) k mean clustering;fuzzy logic and DL;Support vector machines;Fuzzy logic;Image color analysis;Pipelines;Neural networks;Feature extraction;Microwave theory and techniques|
|[FinFET-Premised 1-Bit Comparator Design Using CPTL and DCVSPG Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092338)|R. Rajora; K. Sharma; A. Sharma; L. Gupta|10.1109/IEMECON56962.2023.10092338|Complementary Pass Transistor Logic;Comparator;DCVSPG (Differential cascode voltage switch with pass gate);FinFET;Power Dissipation;Performance evaluation;Voltage;Logic gates;FinFETs;Delays;Power dissipation;Microwave transistors|
|[Indian Sign Language Recognition System for Emergency Words by Using Shape and Deep Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092312)|S. Das; S. K. Biswas; B. Purkayastha|10.1109/IEMECON56962.2023.10092312|Sign Language (SL);Handcrafted feature;Zernike Moments (ZM);Hu Moments (HM);Shape;Neural networks;Gesture recognition;Assistive technologies;Microwave communication;Feature extraction;Microwave theory and techniques|
|[Low Voltage Squarer/Divider Circuit based on FGMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092333)|A. Chhabra; B. Aggarwal; R. Senani|10.1109/IEMECON56962.2023.10092333|Low voltage;Squarer/Divider;Full Wave Rectifier;FGMOS;MOS translinear loop;Analog signal processing;Temperature sensors;Low voltage;Temperature distribution;Sensitivity;Power demand;Simulation;Bandwidth|
|[Precision Agriculture-Machine Learning Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092314)|B. C. Mallikarjun; V. Y. Gowd; S. Gagan; K. P. Vishwas; L. Gagan Kumar|10.1109/IEMECON56962.2023.10092314|Machine Learning;Internet of things;NodeMCU-ESP12E;Temperature;Humidity;Soil Moisture;Temperature sensors;Irrigation;Soil measurements;Moisture measurement;Soil moisture;Valves;Temperature control|
|[Low Complexity Signal Detection Technique for SFBC-OFDM Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092336)|J. P. Patra; B. K. Muni; B. B. Pradhan; S. T. Prasad|10.1109/IEMECON56962.2023.10092336|SFBC;OFDM;ASI;Signal Detection;MDF;Maximum likelihood detection;Interference cancellation;OFDM;Simulation;Symbols;Receivers;Microwave communication|
#### **2023 20th Learning and Technology Conference (L&T)**
- DOI: 10.1109/LT58159.2023
- DATE: 26-26 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Embracing the Metaverse: The Future of Islamic Teaching and Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092367)|B. Popova|10.1109/LT58159.2023.10092367|Metaverse;Lifelogging;Immersive Virtual Reality;Mirror Worlds;Islam;Ethics;Metaverse;Open Access;Education;Psychology;Legislation;Virtual reality|
|[Mapping the Scientific Landscape of Metaverse Using VOSviewer and Bibliometrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092363)|T. Brahimi; H. Haneya|10.1109/LT58159.2023.10092363|Metaverse;virtual reality;augmented reality;Bibliometric;Vosviewer;Privacy;Metaverse;Bibliometrics;Social sciences;Education;Market research;Blockchains|
|[The Redefinition of mHealth Applications in the Metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092356)|P. Elkafrawy; H. Abbas; J. AlFarra; L. Alam; M. Junaid|10.1109/LT58159.2023.10092356|metaverse;mHealth;mobile health;applications;medical metaverse;Internet of Medical Things;Training;Industries;Metaverse;Databases;Extended reality;Mixed reality;Medical services|
|[Non-Invasive BCI by using EMD and Machine Learning: A Metaverse Interaction Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092357)|M. Ali; N. Alsaedi; S. M. Qaisar|10.1109/LT58159.2023.10092357|Brain Computer Interface;Machine learning algorithms;Feature extraction;Classification;Support vector machines;Image segmentation;Machine learning algorithms;Metaverse;Virtual reality;Feature extraction;Electroencephalography|
|[Activity Provenance for Rights and Usage Control in Collaborative MR using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092326)|M. S. Siddiqui|10.1109/LT58159.2023.10092326|collaborative MR;virtual reality;blockchain;usage and rights control;Measurement;Distributed ledger;Collaboration;Mixed reality;Virtual reality;Permission;Throughput|
|[Using Augmented Reality to improve Nutritional Educational for Type 1 Diabetic Children and Adolescents: Quantitative study of Patient Knowledge Retention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092297)|N. Alshebil; K. Alkadi; N. Yenugadhati; M. A. Dubayee|10.1109/LT58159.2023.10092297|Education;Augmented Reality;Nutrition Knowledge;Type 1 Diabetes;Pediatrics;Education;Medical services;Aging;Diabetes;Augmented reality;Videos|
|[Understanding Blockchain technology in Islamic social finance and its opportunities in metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092287)|S. Kunhibava; A. Muneeza; Z. Mustapha; M. Khalid|10.1109/LT58159.2023.10092287|Blockchain;Islamic Social Finance;Blossom Finance;Metaverse;Technological innovation;Metaverse;Instruments;Education;Finance;Companies;Blockchains|
|[Predicting COVID-19 Mortalities for Patients with Special Health Conditions Using an Agent-Based Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092342)|E. Mazurkiewicz; S. A. Seesi; A. Abdel Raouf|10.1109/LT58159.2023.10092342|Agent-Based Model;COVID-19;comorbidity;COVID-19;Measurement;Limiting;Metaverse;Pandemics;Infectious diseases;Predictive models|
|[A Decentralised Instant Messaging Application with End-to-End Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092319)|D. Mashru; G. M. Mangipudi; H. Swamy; S. Halangali; S. E|10.1109/LT58159.2023.10092319|Instant Messenger;End-to-End Encryption;Decentralisation;Fault Tolerance;Signal Protocol;Privacy;Fault tolerance;Protocols;Fault detection;Fault tolerant systems;Instant messaging;Feature extraction|
|[AI-Based Use-Pattern Generative Hybrid Spaces for Indoor and Outdoor Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092345)|S. Allam|10.1109/LT58159.2023.10092345|Keywords—Artificial Intelligence & Machine learning;Generative Space;Generative Adversarial Network;Internet of things (IOT);Green Nodes;nan|
|[Blockchain in Healthcare for Achieving Patients’ Privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092352)|E. Elgamal; W. Medhat; M. A. Elfatah; N. Abdelbaki|10.1109/LT58159.2023.10092352|blockchain;Ethereum;healthcare;big data;smart contract;EMRs’ access control;Heating systems;Privacy;Data privacy;Metaverse;Medical services;Organizations;Throughput|
|[An Approach for Detecting Missed Tissue Proteins in Autoimmune Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092296)|R. Elnemr; M. Rafea; P. Elkafrawy|10.1109/LT58159.2023.10092296|disease diagnosis;autoimmune diseases;missed tissue protein;Erythrocytes Dynamic Antigens Store (EDAS);Proteins;Antigens;Tissue damage;Laboratories;Mathematical models;Data models;Prognostics and health management|
|[Blockchain Application on Big Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092368)|E. Elgamal; W. Medhat; M. A. Elfatah; N. Abdelbaki|10.1109/LT58159.2023.10092368|Blockchain;Big Data;Healthcare;IOT;Agriculture;Electronic Voting;Social Network;Industries;Access control;Privacy;Systematics;Distributed ledger;Systems architecture;Medical services|
|[Detection of Hydrogen Leakage Using Different Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092303)|M. F. El-Amin|10.1109/LT58159.2023.10092303|hydrogen leakage;turbulent jet;machine learning;artificial neural networks;random forest;random tree;gradient boosting regression;decision tree;hyperparameters tuning;Radio frequency;Nanoparticles;Correlation;Atmospheric modeling;Hydrogen;Artificial neural networks;Predictive models|
|[Machine Learning Prediction for Nanoparticles Behavior in Hydrocarbon Reservoirs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092310)|M. F. El-Amin; B. Alwated|10.1109/LT58159.2023.10092310|Nanoparticles;EOR;machine learning;artificial neural networks;random forest;gradient boosting regression;decision tree;Nanoparticles;Measurement;Data preprocessing;Forestry;Predictive models;Boosting;Numerical models|
|[Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092348)|R. H. M. Aly; K. Hussien Rahouma; A. I. Hussein|10.1109/LT58159.2023.10092348|Hybrid Robot;Metaheuristic techniques;Crow Search Optimization (CSO);Emperor Penguin Optimization (EPO);Satin Bowerbird optimization (SBO);and PID controller;PI control;Simulation;Search methods;Robot control;Metaheuristics;Optimization methods;Kalman filters|
|[Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092334)|C. R. Kumar. J; M. A. Majid|10.1109/LT58159.2023.10092334|Metaverse;WSN;Energy efficiency;High performance;IoT;Carry resist adder;Carry propagation adder;Booth algorithm;Partial product;Approximate adder;Approximate multiplier;Wireless communication;Wireless sensor networks;Metaverse;Finite impulse response filters;Resists;Energy efficiency;Delays|
|[Adaptive Optics Rotational Design and Electro-Magnetic Actuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092369)|A. Alzaydi|10.1109/LT58159.2023.10092369|Adaptive-Mirror;Adaptive Optics;Electromagnetic Actuation;Image Focusing;Optical filters;Costs;Sensitivity;Magnetic separation;Impurities;Prototypes;Virtual reality|
|[Electric Vehicle Performance Evaluation Using UDDS, NYCC and WLTP Drive Cycles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092321)|L. Almatrafi; S. Badaam; S. M. Qaisar|10.1109/LT58159.2023.10092321|Electric vehicles (EV);MATLAB;SIMULINK;Simscape;Drive cycles;SoC;Urban Dynamometer Driving Schedule (UDDS);New York City Cycle (NYCC);Worldwide Harmonized Light Vehicles Test Procedure (WLTP);Schedules;Power demand;Software packages;Urban areas;Electric vehicles;Mathematical models;Dynamometers|
|[Self-Balancing System and Control Design for Two-Wheeled Single-Track Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092328)|A. Alzaydi|10.1109/LT58159.2023.10092328|Two-Wheeled;Single-Track;Vehicles;Self-Balancing;Control Design;Feedback loop;Fans;PI control;Transmitters;Control design;Motorcycles;Receivers|
|[Solid-State 3D Models of Lumbar Vertebral Segments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092316)|V. Mavrych; O. Bolgova; I. Shypilova; A. Eryomin|10.1109/LT58159.2023.10092316|lumbar segments;finite element model (FEM);stress analysis;Solid modeling;Analytical models;Three-dimensional displays;Biological system modeling;Computational modeling;Spine;Finite element analysis|
|[A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092331)|C. R. Kumar. J; M. Arunsi. B; M. A. Majid|10.1109/LT58159.2023.10092331|Artificial Intelligence;Machine Learning;IoT;Covid-19;Neural network;LWL;Decision tree;AdaBoost.M1;Support vector machine;Hoeffding tree;Naïve Bayes classifier;Bayesian network classifier;Logistic regression;Random forest;K-means Clustering;Linear regression;COVID-19;Machine learning algorithms;Pandemics;Biological system modeling;Clustering algorithms;Artificial neural networks;Prediction algorithms|
|[Design of a DC/DC Converter with a PID Controller and Backpropagation Neural Network for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092291)|A. I. Hussein; B. Shigdar; L. Almatrafi; B. Alaidroos; F. Alsharif; R. H. M. Aly|10.1109/LT58159.2023.10092291|Electric vehicles (EVs);Backpropagation Neural Network (BPNN);Dc/Dc converter;and PID controller;Backpropagation;Simulation;Neural networks;Modulation;Power transmission;Voltage;Electric vehicles|
|[Efficient Tradeoff between Throughput and Energy Efficiency of Massive-MIMO Technique for Satellite Communication applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092283)|A. I. Hussein; I. Salah; K. H. Rahouma; M. Mourad Mabrook|10.1109/LT58159.2023.10092283|Throughput;Massive-MIMO;Energy efficiency;Next generation;Satellite communications;Spectral efficiency;Precoding;Wireless networks;Simulation;Satellite broadcasting;Massive MIMO;Throughput|
|[Malscanner – File Behavior Analysis using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092346)|B. Abdulrahman; A. Qanadeely; A. Al-Hassan; O. Al-Ghamdi; N. Al-Sukaibi; N. A. Saqib|10.1109/LT58159.2023.10092346|Malware Analysis;Machine Learning;Static Analysis;Dynamic Analysis;Sandboxing;Training;Systematics;Machine learning;Feature extraction;Malware;Behavioral sciences;Blockchains|
|[Tailoring Arduino for Interactive Digital Fabrication: Mechanism, Algorithms, Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092318)|S. Allam; S. Alacame|10.1109/LT58159.2023.10092318|Digital fabrication;Arduino application;Computational Pedagogy;IOT;Interactive design;Fabrication;Visualization;Heuristic algorithms;Education;Focusing;Kinetic theory;Sensors|
|[The Future Metavertainment Application development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092341)|L. Ghryani; A. M. Sidiya; R. Almahdi; H. Alzaher|10.1109/LT58159.2023.10092341|Metaverse;Entertainment;Future;applications;Art;Metaverse;Shape;Education;Entertainment industry;Media;Market research|
|[Automatic Detection of Some Tajweed Rules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092350)|D. Omran; S. Fawzi; A. Kandil|10.1109/LT58159.2023.10092350|Quranic recitation rules;Qalqalah rule;the Mel Frequency Cepstral Coefficients;Convolutional Neural Networks (CNN);Training;Neural networks;Feature extraction;Pattern recognition;Convolutional neural networks;Mel frequency cepstral coefficient|
|[A Survey on BERT and Its Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092289)|S. Aftan; H. Shah|10.1109/LT58159.2023.10092289|BERT;Machine Learning;Natural Language Processing model;bidirectional encoder;Deep learning;Computer science;Text mining;Text analysis;Bit error rate;Predictive models;Transformers|
|[Arabic English Speech Emotion Recognition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092295)|M. E. Seknedy; S. Fawzi|10.1109/LT58159.2023.10092295|Bilingual speech emotion recognition;machine learning;MFCC;prosodic features;Support vector machines;Training;Emotion recognition;Metaverse;Computational modeling;Speech recognition;Ensemble learning|
|[A proposed Array of Quadrifilar Helix Antenna for CubeSat applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092351)|N. A. -D. M. Salem; A. I. Hussein; M. M. Mabrook|10.1109/LT58159.2023.10092351|Quadrifilar Helical antenna;CubeSat antenna;four-element quadrifilar helical antenna;Moment Method;nan|
|[The usefulness of Teaching Technology Toolkit (3T) in selecting appropriate learning tools for an engaging online learning solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092358)|F. M. A. Backer Maricar; M. Fadzil Zaiiial Anuar; M. O. Bakhi; H. Z. Baloch|10.1109/LT58159.2023.10092358|Usefulness;teaching technology;delivery tool;learning technology;decision making tool;Decision making;Educational technology;Hybrid learning|
|[Data Mining and Visualization of Space Technology Research Trends in the Arab World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092340)|T. Brahimi|10.1109/LT58159.2023.10092340|Data mining;Space exploration;Arab world;Bibliometric;VOSviewer;nan|
|[Covid -19 Social Distance Analysis Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092349)|S. Alsulami; D. Alghamdi; S. BinMahfooz; K. Moria|10.1109/LT58159.2023.10092349|nan;COVID-19;Tracking;Computer viruses;Government;Human factors;Machine learning;Cameras|
#### **2023 Systems of Signals Generating and Processing in the Field of on Board Communications**
- DOI: 10.1109/IEEECONF56737.2023
- DATE: 14-16 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Implementation of the Format of Video Lectures for Disciplines of the Department of General Communication Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092106)|A. S. Adzhemov; A. Y. Kudryashova; N. E. Poborchaya|10.1109/IEEECONF56737.2023.10092106|video lectures;multimedia presentation;innovative learning technologies;teleprompter;chroma key;Technological innovation;Schedules;Education;Organizations;Streaming media;Recording|
|[Experimental Investigation and Comparison of Modulation Types for High Capacity Broadband Transmission System to Support 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092177)|A. M. Almufti; O. G. Morozov; R. S. Misbakhov; Y. K. Garovov; A. M. Niyazgulyeva|10.1109/IEEECONF56737.2023.10092177|high capacity 5G transmission;terabit/s over single mode optic fiber;BER and EVM for DP-PSK and DP-QAM;high order modulation for 5G networks;Optical fibers;5G mobile communication;Optical design;Bit error rate;WDM networks;Optical receivers;Adaptive optics|
|[Artificial Intelligence for Cyber Security Goals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092079)|D. F. Amirov|10.1109/IEEECONF56737.2023.10092079|artificial intelligence;machine learning;cyber security;Graphics;Machine learning;Artificial neural networks;Data models;Real-time systems;Security;Computer crime|
|[Some Features of AnyLogic Integration into the University’s Education System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092076)|V. M. Antonova; E. E. Malikova; V. E. Bogomolov; A. Y. Malikov|10.1109/IEEECONF56737.2023.10092076|simulation;Anylogic software;Network Functions Virtualization (NFV);Internet of Things (IoT);Future Generation Networks (FGN);handover;data center;Software Defined Networks (SDN);Training;Visualization;Flowcharts;Software;Telecommunications;Complexity theory;Communication networks|
|[Multi-Band Bandpass Microstrip Filters Based on Two Codirectional Hairpin Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091997)|G. M. Aristarkhov; I. N. Kirillov; O. V. Arinin; A. V. Markovskiy; A. D. Doronina|10.1109/IEEECONF56737.2023.10091997|Multiband filters;microstrip hairpin resonators;poles and zeros of working attenuation;topology;Band-pass filters;Microstrip filters;Time-frequency analysis;Microstrip resonators;Resonator filters;Resonant frequency;Attenuation|
|[Empirical Model for Studying Preferences in the Field of Intelligent Transport System of Europe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092018)|Y. S. Artamonova; G. V. Brega; E. N. Vasilieva; A. A. Antipov|10.1109/IEEECONF56737.2023.10092018|intellectual transport system of Europe;road safety;transport communications;digitalization of society;Law;Urban areas;Europe;Transportation;Road safety;Regulation;Safety|
|[Some Aspects of the Synthesis of Automated Singularly Perturbed Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092016)|V. S. Artemyev; M. N. Makhiboroda; S. L. Yablochnikov; I. O. Yablochnikova; F. K. Khutugova|10.1109/IEEECONF56737.2023.10092016|intelligent systems;control of moving objects;singularly perturbed systems;mathematical modeling;sequential approximation method;Analytical models;Process control;Optimal control;Differential equations;Predictive models;Mathematical models;Approximation methods|
|[The Energy Spectrum of the Noise Modulation Function under the Influence of Slow Multiplicative Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092183)|V. M. Artyushenko; V. I. Volovach|10.1109/IEEECONF56737.2023.10092183|multiplicative (modulating) noise;noise modulation function;energy spectrum;amplitude distortion;phase distortion;narrowband normal random process;non-stationary pulse-fluctuation process;slow multiplicative noise;Frequency modulation;Phase modulation;Random processes;Phase distortion;Manganese;Clocks|
|[Development of a Hardware and Software Complex for Optimizing Logistics Activities in the Field of Consumer Waste Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092123)|I. A. Asmanov; V. V. Zavyazkina; D. G. Moroz; A. I. Zhukov|10.1109/IEEECONF56737.2023.10092123|intelligent transport systems;filling control device;solid municipal waste;methods for constructing optimal routes;genetically algorithm;Waste management;Process control;Containers;Routing;Software;Hardware;Sensors|
|[Classification of NOMA Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092155)|M. G. Bakulin; B. R. T. B. C.; V. B. Kreyndelin; D. Y. Pankratov; A. E. Smirnov|10.1109/IEEECONF56737.2023.10092155|Non-orthogonal multiple access;NOMA classification;signal processing algorithms;multiuser detection;5G;6G;wireless communication systems;6G mobile communication;NOMA;Analytical models;Baseband;Symbols;Downlink;Mathematical models|
|[Agricultural Robot Software with Machine Vision and Cognitive Artificial Intelligence Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092087)|S. B. Benevolensky; A. A. Sirotkin; A. A. Kiryanov; V. Yu. Sirotkin; V. S. Ershov|10.1109/IEEECONF56737.2023.10092087|machine vision;artificial intelligence;cognitive module;software;intelligent transport systems;Plants (biology);Software algorithms;Crops;Vegetation mapping;Prototypes;Streaming media;Software|
|[Development of the Spatial Model of an Urban Passenger Transport Route for the Safe Movement of Passenger Vehicles with a Partially Autonomous (Robotic) Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092181)|V. N. Bogumil; I. V. Konin; V. M. Vlasov; H. M. Eldiba; M. J. Duque Sarango|10.1109/IEEECONF56737.2023.10092181|urban passenger transport;spatial model of an urban passenger transport route;Bezier curves;intelligent transport system;safety;Control systems;Software systems;Mathematical models;Routing protocols;Real-time systems;Safety;Trajectory|
|[Developing an AI-System for Analyzing Games of Team Sports Using VR/AR Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092099)|D. V. Braslavskiy; E. A. Skorodumova|10.1109/IEEECONF56737.2023.10092099|machine-learning;object-detection;object-tracking;person-re-identification;3d-human-pose-estimation;image-inpainting;sports;Training;Solid modeling;Games;Streaming media;Cameras;Real-time systems;Task analysis|
|[Software Model of a 5G Data Transmission System with Increased Spectral Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092088)|V. V. Butenko; M. V. Ivankovich; E. M. Lobov; A. A. Kuchumov; E. O. Lobova|10.1109/IEEECONF56737.2023.10092088|FBMC-OQAM;5G;throughput;spectral efficiency;signal generation;Spectral efficiency;5G mobile communication;Symbols;Throughput;Data models;Decoding;Data communication|
|[Spectrum Occupancy Prediction Algorithm Using Artificial Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092167)|D. S. Chirov; E. O. Kandaurova|10.1109/IEEECONF56737.2023.10092167|cognitive radio system;dynamic spectrum access;artificial neural networks;LSTM;spectrum occupancy prediction;Training;Time-frequency analysis;Recurrent neural networks;Dynamic spectrum access;Predictive models;Prediction algorithms;Cognitive radio|
|[Prediction of Flows in an Automotive Catalytic Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092061)|N. G. Churbanova; M. A. Trapeznikova; A. G. Churbanov; V. V. Emets|10.1109/IEEECONF56737.2023.10092061|converter;porous medium;generalized Navier-Stokes equations;FEniCS;flow prediction;Geometry;Resistance;Software algorithms;Prediction algorithms;Mathematical models;Software;Isothermal processes|
|[The Mathematical Model of the Internet of Things Traffic Servicing in Case of its Impulse Nature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092064)|T. Dawood; M. S. Stepanov; C. Naoussi; B. Joao; K. Yahia|10.1109/IEEECONF56737.2023.10092064|Internet of Things;automation;LTE mobile networks;mathematical modeling;Costs;Automation;Production;Mathematical models;Planning;Numerical models;Internet of Things|
|[Cloud Data Formats Transformation Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092056)|V. A. Dokuchaev; D. Petukhov|10.1109/IEEECONF56737.2023.10092056|NoSQL;cloud;infrastructure;OpenStack;data formats;Big Data;Automation;Software algorithms;XML;Memory;Transforms;Writing;Maintenance engineering|
|[Reverse Intermodulation Distortion in Current Mode and Bridge Class D RF Power Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092117)|A. V. Dolgopyatova; O. V. Varlamov|10.1109/IEEECONF56737.2023.10092117|reverse intermodulation distortions;current mode class D (CMCD);bridge mode class D (BMCD);RF Power Amplifier;Internet of Things;5G;MIMO;Resistance;Radio frequency;Intermodulation distortion;Frequency modulation;Power amplifiers;Bridge circuits;Switches|
|[Methodology for Organizing Scientific Work in Telecommunications University](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092104)|S. Dymkova|10.1109/IEEECONF56737.2023.10092104|scientific work;information systems;integral indicator;research process;Bibliometrics;Organizations;Market research;Telecommunications;Task analysis;Engines;Qualifications|
|[Efficiency Evaluation of Noise Based Camera Measurements Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092086)|D. Egorov; A. Egorova; A. Potashnikov|10.1109/IEEECONF56737.2023.10092086|maging;camera;image quality;spatial resolution;methods for measuring spatial resolution;spatial frequency response;Photography;Parameter estimation;Market research;Hardware;Frequency response;Digital cameras;Frequency measurement|
|[Methodology of Researching Perception Identity of Regions of Users' Interests While Viewing Streaming Video Containing Various Content and Compression Artefacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092038)|A. Egorova; R. Baryshev; A. Mozhaeva|10.1109/IEEECONF56737.2023.10092038|eye tracking;regions of interest;media content;compression artefacts;visual attention patterns;perceptual identity;video sequences;eye tracker;subjective quality evaluation;TV;Video sequences;Gaze tracking;Streaming media;Media;Telecommunications;Radio broadcasting|
|[Analytical Queueing Model of Functioning of the On-board Navigation Complex Database](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092037)|A. G. Erokhin; M. F. Vanina; E. A. Frolova|10.1109/IEEECONF56737.2023.10092037|queueing theory;service quality indicators;database;model;allocating;on-board information system;analytical model;simulation model;Measurement;Analytical models;Databases;Navigation;Computational modeling;Safety;Servers|
|[Results of the Assessment of the Adequacy of the Mathematical Model of the Hydrodynamic Transmission of a Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092090)|S. A. Eruslankin; G. S. Mazlumyan; A. N. Sova|10.1109/IEEECONF56737.2023.10092090|drive system;torque converter;hydrodynamic drive;design;optimization;model;adequacy;blade system;Analytical models;Loading;Probability;Linear programming;Hydrodynamics;Transformers;Mathematical models|
|[Analysis of Software Tools Allowing the Development of Cross-Platform Applications for Mobile Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092148)|T. Fatkhulin; R. Alshawi; A. Kulikova; A. Mokin; A. Timofeyeva|10.1109/IEEECONF56737.2023.10092148|development;mobile applications;cross-platform;tool;performance;framework;Operating systems;Mobile handsets;Mobile applications;C# languages;Security;Software tools;Task analysis|
|[Software-Defined Radio Network Positioning Technology Design. Receiver Processing Procedures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092150)|G. Fokin; D. Volgushev|10.1109/IEEECONF56737.2023.10092150|positioning;SDR;LTE;procedures;CRS;Global navigation satellite system;Current measurement;Urban areas;Receivers;Position measurement;Software;Time measurement|
|[Application of the Method of Majority Coding to Determine the Optimal Route for Data Transmission in the Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092067)|D. V. Gadasin; A. V. Shvedov; Y. D. Egorova; I. R. Shaidullina|10.1109/IEEECONF56737.2023.10092067|Network;metric;route;routing;routing protocols;majority coding;quality of service;QoS;Routing Information Base;RIB;Forwarding Information Base;FIB;Codes;Symbols;Quality of service;Routing;Mathematical models;Routing protocols;Encoding|
|[Virtual Advanced Driver-Assistance Systems Tests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092046)|V. V. Gaevskiy; N. V. Popov; S. R. Kristalniy; M. A. Toporkov; A. N. Andreev|10.1109/IEEECONF56737.2023.10092046|ADAS;MIL;HIL;SIL;PreScan;AEBS;LSS;AV;scenario;radar sensor;virtual testing;occlusion;Software packages;Roads;Software algorithms;Systems architecture;Radar;Sensor systems;Sensors|
|[Space-Frequency Beamforming Algorithms Comparison with a Circular Antenna Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092000)|E. I. Glushankov; V. I. Tsarik|10.1109/IEEECONF56737.2023.10092000|interference-protected satellite navigation;space-frequency processing;digital beamformer;circular antenna array;sample correlation matrix;Satellite antennas;Satellites;Correlation;Array signal processing;Computational modeling;Parallel processing;Satellite navigation systems|
|[On the Task of Classifying Sound Patterns in Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092004)|M. Gorodnichev; G. Mkrtchian; Y. Furletov|10.1109/IEEECONF56737.2023.10092004|deep learning;sound patterns;audio;spectogram;convolutional neural networks;Training;Time-frequency analysis;Recurrent neural networks;Transportation;Computer architecture;Multilayer perceptrons;Classification algorithms|
|[The Special Aspects of Chinese Teaching Methodology for Radio Engineering Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092007)|R. T. Govorova; E. G. Kukharenko|10.1109/IEEECONF56737.2023.10092007|Radio-engineering;terminology;scientific-technical terminology of the Chinese language;teaching terminological vocabulary and methodology for radio-engineering students;Vocabulary;Terminology;Education;Semantics;Grammar;Compounds;Engineering students|
|[A Simulation Study on the Detection of a Cyclostationary Signal buried in a Stationary Noise for Unknown Power Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092160)|O. Guschina|10.1109/IEEECONF56737.2023.10092160|cyclostationary process;spectral correlation function;signal detection;ROC curves;detection performance;Correlation;Power measurement;Detectors;Receivers;Probability;Numerical simulation;Noise measurement|
|[Research of Resources Fiber-Optic Communication Lines Based on Wavelong Multiplexing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092043)|B. G. Ibrahimov; Z. N. Huseynov; Z. A. Ismaylov; I. M. Mammadov; T. A. İsmaylov|10.1109/IEEECONF56737.2023.10092043|Fiber-optic communication lines;network resource;bit rate;throughput;OSNR;DWDM systems;wavelength;channel resource;spectral efficiency;Optical fibers;Spectral efficiency;Modulation;Channel estimation;Optical fiber networks;Probability;Wavelength division multiplexing|
|[Ensuring the Safety of an Autonomous Object, Taking Into Account the Movement of Other Road Users](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092083)|Z. V. Ilyichenkova; S. M. Ivanova; A. I. Volkov; A. Y. Ermakova|10.1109/IEEECONF56737.2023.10092083|safety;optimal trajectory;adaptation;prediction;neural network;Computer languages;Roads;Neural networks;Buildings;Real-time systems;Safety;Trajectory|
|[M-Sequence Based Partial Transmit Sequence PAPR Reduction Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092029)|I. I. Ishmiev; S. S. Loginov|10.1109/IEEECONF56737.2023.10092029|PTS;PAPR;OFDM;CCDF;M-sequence;Wireless communication;Phase modulation;Partial transmit sequences;Computer simulation;Peak to average power ratio;Digital communication;Gain|
|[Method for Diagnosing the Ultimate Bandwidth of Variable Transionospheric Radio Communication Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092003)|D. V. Ivanov; N. V. Ryabova; V. A. Ivanov; A. A. Kislitsin|10.1109/IEEECONF56737.2023.10092003|transionospheric radio channel;coherence bandwidths;total electron content;group-delay dispersion;GNSS;Time-frequency analysis;Global navigation satellite system;Limiting;Machine learning algorithms;Channel estimation;Coherence;Throughput|
|[Digital Signal Generators Based on the Lorentz System Implemented Using Fixed-Point Numbers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092093)|K. M. Kafarov; S. S. Loginov; E. A. Bobina|10.1109/IEEECONF56737.2023.10092093|dynamic chaos;dynamic systems;statistical properties;auto-correlation function;cross-correlation function;Chaotic communication;Signal generators;Generators|
|[Signal-Code Constructions for Wideband Signals Based on M-Cpfsk and Non-Binary Error-Correcting Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091988)|N. A. Kandaurov; E. O. Lobova; V. O. Varlamov; K. E. Telengator|10.1109/IEEECONF56737.2023.10091988|signal-code constructs;wideband;M-Cpfsk;LDPC;turbo-like;Radio links;Autonomous aerial vehicles;Error correction codes;Decoding;Broadband communication;Wideband;Signal to noise ratio|
|[The Time of Entering into Synchronism During Synchronization According to the Cyclic Prefix OFDM Symbols Formed by the Sum of Information and Multiphase Sequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092188)|T. P. Kiseleva|10.1109/IEEECONF56737.2023.10092188|LTE OFDMA technology;multiphase CAZAC (Constant amplitude zero autocorrelation waveform) sequences;average time of entry into synchronism;Rayleigh communication channel;ETU delay profiles;QPSK modulation;64QAM modulation;autocorrelation function cyclic prefix (ACF CP);Frequency modulation;OFDM;Symbols;Filling;Mathematical models;Delays;Synchronization|
|[Assessment of Information Capacity of Compressed Video Sequences for Information Security Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092068)|A. Komina; K. Nezhivleva; S. Votyakov; N. Stepanov; I. Vlasuyk|10.1109/IEEECONF56737.2023.10092068|steganography;video databases;subjective assessments;perception threshold;compressed video sequences;information security;Steganography;TV;Limiting;Video sequences;Information security;Streaming media;Visual systems|
|[Determination of Optimal Maximum Usable Frequency Prediction Periods for Ionospheric Radio Channels Using the XGBoost Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092186)|N. A. Konkin|10.1109/IEEECONF56737.2023.10092186|HF communication;sounding;ionosphere;maximum usable frequency;machine learning;time series;XGBoost;hyperparameter;hyperopt;Training;Machine learning algorithms;Databases;Heuristic algorithms;Communication channels;Machine learning;Predictive models|
|[Fiber – Optical Lines Reliability: The Economic Aspect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092059)|V. N. Korshunov; I. A. Ovchinnikova; N. I. Likhachev; N. A. Shishova; P. A. Semenov|10.1109/IEEECONF56737.2023.10092059|fiber-optical cable lines;reliability;durability;throughput;economic indicators;Optical fiber losses;Optical losses;Power cables;Optical fiber cables;Optical attenuators;Propagation losses;Optical fiber communication|
|[Implementation of Electronic Educational and Methodological Complexes for Students of IT Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092173)|A. Y. Kudryashova; A. S. Adzhemov; N. V. Toutova|10.1109/IEEECONF56737.2023.10092173|electronic educational environment;IT students;electronic educational and methodical complexes;interactive tests;electronic textbooks;Portable computers;Electronic learning;Annotations;Education;Streaming media;Information technology;Video recording|
|[Implementation of the Data Fabric Architecture as a Sustainable Development of Industrial Platform Technologies in Road Transport Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092048)|N. G. Kuftinova; A. V. Ostroukh; O. I. Maksimychev; C. B. Pronin; A. K. Yadav|10.1109/IEEECONF56737.2023.10092048|Data Fabric (DF);Road Transport Systems;Data Model;Metadata;Decomposition Model;Distributed Data Protocol;Logical data architecture;Architectural quantum object;Analytical models;Roads;Standards organizations;Distributed databases;Computer architecture;Organizations;Throughput|
|[Improving the Efficiency of Information Systems Management Through the Introduction of the Authority Criticality Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091987)|E. G. Kukharenko; A. S. Novikova; V. V. Voskobovich; A. M. Kukharenko|10.1109/IEEECONF56737.2023.10091987|The authority criticality matrix;Information process management;Complex information systems;automation;business;organization of information processes;Access control;Automation;Design methodology;Companies;Risk management;Indexes;Computer security|
|[Design and Research of an Antenna Array for a Satellite Communication System in the 435 MHz Band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092058)|O. Kuvshinov; A. Ruzhitskii; D. Stafeev; E. Baev; I. Malygin|10.1109/IEEECONF56737.2023.10092058|satellite tracking;Yagi-Uda antenna;antenna array;nanosats;HFSS;Space vehicles;Satellite antennas;Yagi-Uda antennas;Array signal processing;Transmitting antennas;Receiving antennas;Telemetry|
|[Fiber-Optical System for Measuring the Temperature of the KAI-1 Cubesat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092069)|A. A. Kuznetsov; I. I. Vasilèv; S. R. Valiullin; K. A. Lipatnikov|10.1109/IEEECONF56737.2023.10092069|cubesat;fiber Bragg gratings;fiber optic temperature sensors;photonics;nano satellite;Meters;Temperature measurement;Space vehicles;Optical fiber sensors;Temperature distribution;Thermocouples;Thermal loading|
|[Research of the Electrodynamic Parameters of UHF RFID Tags](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092062)|I. R. Lavrukhin; A. A. Yelizarov; S. V. Bashkevich; I. V. Nazarov|10.1109/IEEECONF56737.2023.10092062|electrodynamic parameters;UHF;RFID;tag;bending;S11;VSWR;radiation patterns;Electrodynamics;Deformation;Shape;Bending;Numerical simulation;Topology;Object recognition|
|[Development of a Mathematical Model for Analysis of the Information Structure of a Software-Defined Optical Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092172)|Y. Leokhin; T. Fatkhulin|10.1109/IEEECONF56737.2023.10092172|software-defined optical networks;structure;characteristics;mathematical model;method;service;data flows;parameters;network node;Analytical models;Optical fiber networks;Mathematical models;Data models|
|[Evaluation of Influence of the Radio Receiving Devices Heterodynes Functioning Quality on the Quadrature Amplitude Modulation Digital Signals Reception Noise Immunity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091999)|G. Leon; D. S. Koptev|10.1109/IEEECONF56737.2023.10091999|oscillation synthesizers of heterodynes;potential noise immunity;quadrature amplitude modulation;integral relative frequency instability;spectral power density of phase noise;Phase noise;Frequency synthesizers;Power system measurements;Frequency modulation;Power transmission lines;Density measurement;Quadrature amplitude modulation|
|[Resolution Time Theory Broadband Communications in Problem of Data Dependent Jitter in Frequency Selective Channels with PAM-n-Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092097)|I. M. Lerner; A. N. Khairullin; R. R. Fayzullin; D. V. Shushpanov; V. I. Il’in|10.1109/IEEECONF56737.2023.10092097|ISI;resolution time;PAM-signals;FTN;LAN;Time-frequency analysis;Shape;Memory management;Channel estimation;Estimation;Jitter;Data transfer|
|[Restoration of Complex Signals Distorted by Aliasing as a Result of Bandpass Sub-Nyquist Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092081)|V. Lesnikov; T. Naumovich; A. Chastikov; A. Metelyov|10.1109/IEEECONF56737.2023.10092081|bandpass sampling;sub-Nyquist sampling;aliasing;alias;unaliasing;signal restoration;Digital signal processing;Distortion|
|[Synthesis of the Optimal Dispersion Slope and Phase Joint Filtering Algorithm for the Broadband Signal in the Ionospheric Radio Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092032)|E. M. Lobov; E. O. Lobova; V. O. Varlamov|10.1109/IEEECONF56737.2023.10092032|broadband signal;HF channel;frequency dispersion;joint estimation;optimal filtering;Phase modulation;Filtration;Estimation;Bandwidth;Filtering algorithms;Radio links;Broadband communication|
|[Self-Directed Learning Optimisation Toolkits for Electronics and Electrical Engineering Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092122)|E. V. Lopatina; S. N. Maltseva; A. Y. Pavlova|10.1109/IEEECONF56737.2023.10092122|self-directed learning;blended learning;online education tools;ESP for engineers;professional competency;EdTech;Electrical engineering;Electric potential;Pandemics;Education;Writing;Media;Aerospace electronics|
|[Modeling of Partially Filled Waveguide Structures with Magnetic Walls on a Mushroom-Shaped Metamaterial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092080)|M. A. Mashkova; V. N. Karavashkina; A. A. Yelizarov; A. A. Skuridin; E. A. Zakirova|10.1109/IEEECONF56737.2023.10092080|mushroom metamaterial;rectangular waveguide;reflection coefficient;gain;VSWR;bandstop filter;Waveguide transitions;Waveguide components;Magnetic separation;Rectangular waveguides;Metamaterials;Magnetic materials;Dielectrics|
|[Software Demo Versions of Modern Measuring Equipment Using for Telecommunication Disciplines Studying](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092130)|A. E. Mikenin; G. A. Prokurat; A. V. Pestryakov|10.1109/IEEECONF56737.2023.10092130|Spectrum analyzer;software;computer analysis;signals investigation;laboratory practice;Training;Conferences;Instruments;Software algorithms;Software;Communications technology;Telecommunications|
|[Intelligent Transport Systems Software as a Source of Transport Security Threats](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092129)|I. F. Mikhalevich|10.1109/IEEECONF56737.2023.10092129|continuous delivery;continuous integration;cyberattack;cybersecurity incident;cyberspace;open sourse;public repository;software supply chain;transportation security incident;vulnerability analysis;Industries;Supply chains;Market research;Software;Real-time systems;System software;Software reliability|
|[Development of Models of Secretly Influence on the Wireless Networks Infrastructure Using Signal-Like Interference and Evaluation of Their Resistance to Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091998)|V. Y. Mikhaylov; R. B. Mazepa; A. A. Abramov; N. A. Yakush|10.1109/IEEECONF56737.2023.10091998|IEEE 802.11;USRP;LabVIEW;signal-like interference;EAPOL;DoS-impact;IDS/IPS-systems;Wireless networks;Interference;Broadcasting;Software;Hardware;Stability analysis;Recording|
|[Intensification of Intelligent Automated Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091978)|N. V. Mokrova; S. L. Yablochnikov; A. B. Semenov; I. K. Kuchieva|10.1109/IEEECONF56737.2023.10091978|generalized principles;intensification transport systems;hierarchiti;selectivity;openness;mutual consistency;control factors;physical impacts;Economics;Production systems;Systematics;Process control;Optimal control;Production;Synchronization|
|[Methodology for Determining the Minimum Capacity of Traction Batteries of Electric Buses Based on the Operational Characteristics of Routes Using Artificial Intelligence Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092121)|D. G. Moroz; A. I. Zhukov; V. M. Makurina; P. V. Akopov; M. A. Kudryshov|10.1109/IEEECONF56737.2023.10092121|electric bus with ultrafast charging;traction batteries;decision tree;Power demand;Transportation;Production;Organizations;Charging stations;Turning;Batteries|
|[Two-frequency DSB-SC Modulation for Relative Frequency Response Measurement of Mach-Zehnder Amplitude Modulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092131)|O. G. Morozov; A. Zh. Sakhabutdinov; V. S. Sokolov; R. Sh. Misbakhov; Y. Garovov|10.1109/IEEECONF56737.2023.10092131|microwave photonics;amplitude electro-optical Mach-Zehnder modulator;relative frequency response;ultra-narrow-band discrete frequencies package;Il’in-Morozov method;doubleband two-frequency with suppressed-carrier modulation;Microwave measurement;Frequency modulation;Power measurement;Wavelength measurement;Masers;Microwave theory and techniques;Frequency response|
|[Algorithm for Predicting Pedestrian Behavior on Public Roads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091967)|M. Moseva; K. Polyantseva; M. Gorodnichev; A. Pavlikov|10.1109/IEEECONF56737.2023.10091967|pedestrian behavior;deep learning;alpha pose;open pose;detectron;Torso;Service robots;Pose estimation;Cameras;Libraries;Behavioral sciences;Task analysis|
|[Analysis of Electrocardiosignals by Z Lead on Presence of Low-Amplitude High-Frequency Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092101)|O. A. Mukhametzyanov; T. F. Shcherbakova; D. V. Libina; S. S. Sedov|10.1109/IEEECONF56737.2023.10092101|digital signal processing;telemedicine systems;ventricular late potentials;signal averaging;Z lead;nan|
|[Ultra-Reliable Communications: Basic Concepts, Challenges and Open Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092044)|V. Netes|10.1109/IEEECONF56737.2023.10092044|Ultra-reliable communications;5G and 6G networks;dependability;reliability;availability;failure criterion;redundancy;common cause failures;cost;6G mobile communication;5G mobile communication;Atmospheric measurements;Mission critical systems;Packet loss;Transportation;Particle measurements|
|[Investigation of the Possibility of Using Optical Cables in Remote Control Systems Operating in Underwater Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092063)|I. A. Ovchinnikova; E. B. Vasiliev; P. A. Semenov; A. S. Khahichev; A. G. Koriakin|10.1109/IEEECONF56737.2023.10092063|optical cable;optical fiber;optical microcable;reflectometry;Mandelstam-Brillouin scattering;salinity;salt solution;IR spectrometry;polyacrylate;deformation;Optical fibers;Underwater cables;Thermomechanical processes;Optical attenuators;Optical fiber cables;Scattering;Communication cables|
|[Estimation of the Power of Algebraic Geometric Codes Designed to Construct a Post-Quantum Algorithm for Ensuring Information Security of On-board Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092118)|K. N. Pankov; M. M. Glukhov|10.1109/IEEECONF56737.2023.10092118|Internet of Things;IoT;Information Security;On-Board System;Post Quantum cryptography;NIST PQC;Error-Correcting Codes;Algebraic-Geometry Codes;Gilbert–Varshamov bound;nan|
|[Determining the Amount of Information in One Information Bit of Text Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091972)|K. A. Panteleeva; A. V. Shvedov; D. D. Gadasin; D. V. Gadasin|10.1109/IEEECONF56737.2023.10091972|NLP;natural language processing;Shannon-Fano coding;information entropy;machine learning;text preprocessing;tokenization;word processing;information value;Text recognition;Software algorithms;Symbols;Software;Natural language processing;Hardware;Data communication|
|[Ensuring the Reliability of a Highly Loaded Vehicle Monitoring and Traffic Control Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092031)|K. Polyantseva; M. Gorodnichev; M. Moseva|10.1109/IEEECONF56737.2023.10092031|high loaded system;traffic control;traffic monitoring;reliability;safety;Fault tolerance;Fault tolerant systems;Distributed databases;Big Data;Traffic control;Product development;Task analysis|
|[Minimizing Distortion when Companding an Audio Signal in Transmission Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092098)|O. B. Popov; T. V. Chernysheva; K. V. Orlov; P. S. Sapronov|10.1109/IEEECONF56737.2023.10092098|Segment;gain;window function;dynamic range;signal-to-noise ratio;distortion;bit depth;Shape;Shape measurement;Modulation;Speech recognition;Dynamic range;Broadcasting;Distortion|
|[Development of a Metrological System for Measuring the Characteristics of Single Photon Detectors Based on an Educational Platform EMQOS 1.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092001)|J. Rabenandrasana; A. V. Bachus; T. V. Kazieva; N. S. Trofimov; M. V. Boltanskii|10.1109/IEEECONF56737.2023.10092001|Quantum cryptography;quantum communication;quantum key distribution;single photon detector;single-photon Fock state;single photon source;optical fiber;Wavelength measurement;Detectors;Particle measurements;Time measurement;Photodetectors;Optical fiber communication;Quantum key distribution|
|[Information Theory Pattern Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092020)|S. Rozhkov; V. Voronov|10.1109/IEEECONF56737.2023.10092020|information system;pattern recognition algorithm;information space;scene image;Fuzzy sets;Image recognition;Geometric modeling;Reliability theory;Pattern recognition;Behavioral sciences;Information theory|
#### **2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH)**
- DOI: 10.1109/INFOTEH57020.2023
- DATE: 15-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Optimization and proper selection arrangement of solar panels on the rooftop PV plant 400 kW AC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094083)|M. S. Đurović; Ž. V. Despotović|10.1109/INFOTEH57020.2023.10094083|component;optimization;PV solar;solar panels;inverter;string;simulation;Simulation;Production planning;Solar energy;Production;Ventilation;Energy efficiency;Numerical models|
|[Experimental Setup for Testing Low Energy Harvesting Devices Based on Lead-Free NBT-BT-PVDF Composite Flexible Films](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094150)|Ž. V. Despotović; M. Vijatović Petrović; J. Bobić; A. Džunuzović; E. Mercadelli|10.1109/INFOTEH57020.2023.10094150|energy harvesting; NBT-BT;PVDF;electronics;measuring;excitation force;pulse voltage;Vibrations;Voltage measurement;Force;Voltage;Lead;Energy harvesting;Polymers|
|[Contrast Enhancing by Applying Histogram Analysis in Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094055)|T. Petrova; Z. Petrov|10.1109/INFOTEH57020.2023.10094055|analysis of histogram;quality of images;brightness;contrast;Histograms;Image color analysis;Digital images;Brightness;Color;Distortion|
|[Assessment of Gross Errors and Uniformity of Readings of Sensors for Relative Humidity Measurement in Indoor Premises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094132)|S. Zaharieva; I. Stoev; A. Borodzhieva; T. Petrova|10.1109/INFOTEH57020.2023.10094132|relative humidity;gross errors evaluation;uniformity of readings;microclimate;IoT;Temperature measurement;Temperature sensors;Meters;Humidity measurement;Measurement uncertainty;Humidity;Sensor systems|
|[HTTP web server implementation in air parameters monitoring system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094075)|T. Begović; N. Kukrić; S. Lubura|10.1109/INFOTEH57020.2023.10094075|embedded system;http web server;parameters monitoring;LwIP stack;Embedded systems;Web servers;Software;Hardware;Monitoring|
|[An Intelligent Robot Sorting System By Deep Learning On RGB-D Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094093)|J. Zheng; L. Chen; Y. Li; Y. A. Khan; H. Lyu; X. Wu|10.1109/INFOTEH57020.2023.10094093|component;intelligent manufacturing;flexible manufacturing;intelligent robot sorting system;microservice;machine vision;YOLOv5s;Deep learning;Visualization;Solid modeling;Three-dimensional displays;Service robots;Microservice architectures;Software|
|[DB-MVSNet: Unsupervised multi-view 3D reconstruction algorithm with two branches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094116)|J. Zheng; S. Li; Y. A. Khan; Y. Li; H. Lyu; H. Wang|10.1109/INFOTEH57020.2023.10094116|MVS;unsupervised learning;3D reconstruction;Solid modeling;Surface reconstruction;Three-dimensional displays;Clustering algorithms;Lighting;Estimation;Reflection|
|[Impact of Vulnerability Assesment and Penetration Testing (VAPT) on Operating System Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094095)|J. Softić; Z. Vejzović|10.1109/INFOTEH57020.2023.10094095|Vulnerability Assessment;Pentest;Penetration testing;cybersecurity;VAPT;Phishing;Operating systems;Organizations;Passwords;Malware;Security;Monitoring|
|[Transforming Dublin Core (X)HTML Descriptions to RDF Model Using RDF Mapping Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094128)|T. Georgieva-Trifonova|10.1109/INFOTEH57020.2023.10094128|metadata;web page;Dublin Core;RDF Mapping Language;Content management;Semantics;Web pages;Transforms;Metadata;Resource description framework;Task analysis|
|[Estimating knowledge level for job applicants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094123)|F. Ćatibušić; Z. Vejzović|10.1109/INFOTEH57020.2023.10094123|algorithm;computer science;human resources;Social networking (online);Focusing;Production;Machine learning;Solids;Prediction algorithms;Market research|
|[Software Simulation for the Airspace Reconfiguration Project in Europe and its Impact on Bosnia and Herzegovina Airspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094177)|D. Džubur; S. Čaušević; S. Muhić; S. Kahriman|10.1109/INFOTEH57020.2023.10094177|Free Route Airspace;Airspace re-configuration project;SECSI FRA;FRA IT;Bosnia and Herzegovina airspace;Costs;Atmospheric modeling;Europe;Routing;Software;Fuels|
|[Dueling Double Deep Q-Network for indoor exploration in factory environments with an unmanned aircraft system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094171)|A. Seel; F. Kreutzjans; B. Küster; M. Stonis; L. Overmeyer|10.1109/INFOTEH57020.2023.10094171|Unmanned Aircraft System (UAS);Deep Reinforcement Learning (DRL);Deep Q-Network (DQN);factory planning;autonomous exploration;autonomous navigation;GNSS-denied environment;Training;Virtual environments;Reinforcement learning;Production facilities;Aircraft navigation;Planning;Manufacturing|
|[Guest file system behavior for type-2 hypervisor-based virtualization in VMware Workstation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094170)|B. Đorđević; N. Kraljević; N. Davidović|10.1109/INFOTEH57020.2023.10094170|Virtualization;Filebench;Hypervisors;VMware;Workstation Player;Virtual Machine;Virtual machine monitors;File systems;Operating systems;Virtual machining;Time measurement;Workstations;Behavioral sciences|
|[Speech quality assessment in visible light communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094155)|M. Mlađen; J. Banović; J. Galić; G. Gardašević; M. Petković|10.1109/INFOTEH57020.2023.10094155|VLC;PESQ;ViSQOL;Hamming encoding;RS encoding;Reed-Solomon codes;Speech coding;Communication channels;Bandwidth;Digital communication;Quality assessment;Reliability|
|[Performance Comparison of hardware RAID5 and RAID5+0 configurations under Linux environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094176)|B. Djordjević; V. Timčenko; N. Kraljević; A. Popić; N. Davidović|10.1109/INFOTEH57020.2023.10094176|RAID;RAID configuration;performance;Linux Ubuntu;Linux;Redundancy;Writing;Drives;Hardware;Software;Performance analysis|
|[Comparison of VMware Workstation, VirtualBox and MS Hyper-V hypervisor performance with MS Windows OS based guests](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094080)|B. Djordjević; V. Timčenko; N. Kraljević; I. Jovičić; N. Davidović|10.1109/INFOTEH57020.2023.10094080|Windows 10 Pro;VMware Workstation player;VirtualBox;MS Hyper-V;virtual machines;Virtual machine monitors;Operating systems;Computer industry;Hardware;Virtual machining;Workstations;Delays|
|[A Novel Machine Learning Based Traffic Congestion Recognition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094179)|N. Bereczki; V. Simon|10.1109/INFOTEH57020.2023.10094179|C-ITS;Machine learning;Congestion detection;AI;Road accidents;Roads;Transportation;Pressing;Machine learning;Sensors;Reliability|
|[Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094135)|D. Dimitrić; V. Risojević; M. Simić|10.1109/INFOTEH57020.2023.10094135|in-distribution (ID);out-of-distribution (OOD);OOD detection;remote sensing image classification;deep nearest neighbors;maximum softmax probability;Training;Image sensors;Deep learning;Training data;Detectors;Benchmark testing;Sensors|
|[Smart waste management solution model for practical implementation: Integrated system approach focused on business processes and end-user experience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094100)|S. Muhić; S. Čaušević; D. Džubur; S. Kahriman; N. Goran|10.1109/INFOTEH57020.2023.10094100|component;smart waste management;integrated system approach;IoT;AI/ML;Internet of Things;machine learning;artificial intelligence;nan|
|[Towards green data centers: Energy efficiency and performance evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094184)|N. Salihović; B. Memić; A. Čolaković; E. Avdagić-Golub; A. H. Džubur|10.1109/INFOTEH57020.2023.10094184|Green IoT;Green data centers;GreenCloud simulator;Energy efficiency;Performance evaluation;Data centers;Energy consumption;Green products;Energy efficiency;Topology;Internet of Things|
|[IoT-Based Device for Indoor Premises Relative Humidity Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094088)|S. Zaharieva; I. Stoev; A. Borodzhieva|10.1109/INFOTEH57020.2023.10094088|relative humidity;electronic device;IoT;comfortable conditions criteria;dehumidifier;Buildings;Moisture;Humidity;Humidity control;Mobile applications;Remote control|
|[A Quick Heuristic Algorithm for Enforcing the Liveness of S3PR Petri Nets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094203)|A. Karatkevich; I. Grobelna|10.1109/INFOTEH57020.2023.10094203|control systems;flexible manufacturing systems;liveness;Petri nets;NP-hard problem;Heuristic algorithms;Petri nets;Phantoms;System recovery;Control systems;Approximation algorithms|
|[A 1.5 GHz-1.8 GHz Voltage Controlled Oscillator for Passive LC Sensors Readout](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094084)|M. Simić|10.1109/INFOTEH57020.2023.10094084|Voltage-controlled oscillators;passive LC circuits;resonant frequency estimation;Couplings;Voltage measurement;Voltage-controlled oscillators;Resonant frequency;Systems architecture;Frequency estimation;Sensors|
|[Long Range Multisensor Imaging Systems : Procedure for selecting MSIS components based on simplified performance model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094188)|S. Vujić; B. Livada; D. Perić; M. Radisavljević; S. Vujić; B. Livada; D. Perić; M. Radisavljević; V. Milićević|10.1109/INFOTEH57020.2023.10094188|model;requirements;system design;multi-sensor imaging system;long-range surveillance;target acquisition;electro-optics;inrared;Analytical models;Image recognition;Imaging;Computer architecture;Software;Software measurement;Task analysis|
|[Predicting Song Success: Understanding Track Features and Predicting Popularity Using Spotify Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094172)|L. Vardo; J. Jerkić; E. Žunić|10.1109/INFOTEH57020.2023.10094172|Prediction;Music;Spotify;Artificial Intelligence;Machine Learning;Training;Analytical models;Support vector machine classification;Static VAr compensators;Predictive models;Prediction algorithms;Classification algorithms|
|[Dispersion Management of Light Pulse Transparency in Quantum Metamaterials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094191)|Z. Ivić; D. Chevizovich; Ž. Pržulj; I. Ivković|10.1109/INFOTEH57020.2023.10094191|photonic band gap materials;soliton;transparency windows;Photonic band gap;Slow light;Qubit;Solitons;Production;Metamaterials;Dispersion|
|[Slow Light in Quantum Metamaterials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094056)|Z. Ivić; V. Nikolić; D. Chevizovich; Ž. Pržulj|10.1109/INFOTEH57020.2023.10094056|superconducting quantum metamaterial;qubit;qubit-photon coupling;Couplings;Slow light;Optical solitons;Metamaterials|
|[Primer Sensitivity Test Box](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094119)|V. Drincevic; V. Milicevic; S. Nijemcevic|10.1109/INFOTEH57020.2023.10094119|primer;sensitivity;software;PSTB;MAFC;MNFC;Military standards;Sensitivity;Weapons;Data acquisition;Explosives;Reliability;Test equipment|
|[Maintaining a stable point-to-point remote control link based on transceivers nRF24L01+ PA/LNA in presence of interferences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094147)|E. Saletović; E. Babović; Đ. Hadžić|10.1109/INFOTEH57020.2023.10094147|point-to-point;remote;link;nRF24L01+ PA/LNA;interferences;channel;algorithm;microcontroller;Wireless communication;Microcontrollers;Transmitters;Adaptive algorithms;Transceivers;Hardware;Regulation|
|[Fuze vAF-M17 Field Test Software for vAFRSW](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094157)|M. V. Živković; S. Nijemčević|10.1109/INFOTEH57020.2023.10094157|fuze;field;software;Vibrations;Temperature sensors;Temperature;Weapons;Switches;Software;Production facilities|
|[PEM Fuel Cell Modeling by Application of the Rough Sets Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094050)|A. Stjepanovic; M. Stojcic; M. Kostadinovic; G. Jausevac; G. Jotanovic; G. Kuzmic|10.1109/INFOTEH57020.2023.10094050|PEM fuel cells;Nafion membrane;Artificial Neural Network;Model of PEM fuel cells;Rough Sets Theory;FCEV;Protons;Electrodes;Fuel cells;Rough sets;Electric vehicles;Fuels;Standards|
|[Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094154)|E. Krupalija; E. Cogo; Š. Bećirović; I. Prazina; D. Pozderac; I. Bešić|10.1109/INFOTEH57020.2023.10094154|test case minimization;test case prioritization;causeeffect graphs;black-box testing;software quality;Measurement;Fault detection;Software algorithms;Closed box;Software quality;Minimization;Real-time systems|
|[Accelerating Sorting on GPUs: A Scalable CUDA Quicksort Revision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094180)|M. Mujić; I. Ćatić; S. Behić; A. Hadžibajramović; N. Nosović; T. Hrnjić|10.1109/INFOTEH57020.2023.10094180|CUDA-Quicksort;CUDA;quicksort;paralelization;sorting;parallel algorithm;Java;Multicore processing;Scalability;Graphics processing units;Distributed databases;Performance gain;Virtual machining|
|[Design of micro scale wind turbine blade for low wind speed applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094182)|W. P. J. M. Warnakula; A. C. Vidanapathirana; R. M. T. C. B. Ekanayake; S. S. D. D. B. Senanayake|10.1109/INFOTEH57020.2023.10094182|Five Bladed;HAWT;Renewable Energy;wind energy;Economics;Renewable energy sources;Blades;Wind speed;Wind power generation;Software;Fossil fuels|
|[Real-Time Video Fusion Implemented In GStreamer Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094131)|A. Simic; B. Vukasovic; I. Popadic; M. Peric; D. Peric; A. Simic; M. Peric; D. Peric|10.1109/INFOTEH57020.2023.10094131|image fusion;gstreamer;homography;laplacian pyramids;long range surveillance;Image quality;Image registration;Laplace equations;Surveillance;Streaming media;Cameras;Real-time systems|
|[On similarity of PPI subnetworks induced by important proteins: A case study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094129)|M. Jaguzovic; N. Vilendečić; M. Grbić; D. Matić|10.1109/INFOTEH57020.2023.10094129|protein complexes;PPI networks;graph centrality;graph similarity;Proteins;Phase measurement;Biology;Labeling;Gene expression;Kernel;Standards|
|[Ultrasonic Cane Control System for Blind People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094102)|A. Manukova; D. Dimitrova|10.1109/INFOTEH57020.2023.10094102|ultrasonic;cane;electronic control system;blind people;Ultrasonic imaging;Visual impairment;Laboratories;Blindness;Control systems;Acoustics;Reliability|
|[Simulation of the traffic light optimization algorithm based on the current traffic frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094169)|L. Tarjan; I. Šenk; M. Stanojević; M. Babić; D. Jovanović; D. Rikić; T. Srđan|10.1109/INFOTEH57020.2023.10094169|semafori;traffic jams;optimization;PLC;Codesys (key words);Time-frequency analysis;Visualization;Urban areas;Time measurement;Frequency measurement;Automobiles;Optimization|
|[Image Classification with Transfer Learning Using a Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094197)|M. Bajramović; E. Žunić|10.1109/INFOTEH57020.2023.10094197|image classification;Densely Connected CNN;transfer learning;fine-tuning;Training;Adaptation models;Analytical models;Transfer learning;Graphics processing units;Training data;Data models|
|[A faster path to sustainability: the use of Digital Twins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094074)|M. Maksimović|10.1109/INFOTEH57020.2023.10094074|Digital Twin;sustainability;SDGs;Industries;Smart cities;Digital twins;Sensors;Performance analysis;Internet of Things;Sustainable development|
|[Decision-making AI for customer worthiness and viability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094207)|E. Marevac; S. Patković; E. Žunić|10.1109/INFOTEH57020.2023.10094207|customer worthiness;customer viability;artificial intelligence;predictive analytics;vintage analysis;exploratory data analysis (EDA);data visualisation;Training;Industries;Data analysis;Customer services;Soft sensors;Decision making;Companies|
|[Testing the intensity of erosive wear of selected steels depending on the angle of incidence of the abrasive stream](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094092)|W. Tarasiuk; T. Węgrzyn; B. S. Lasota; D. Dziki; U. Gawlik-Dziki; M. Liszewski|10.1109/INFOTEH57020.2023.10094092|erosive wear;steels;intensity of wear;ductile and brittle materials;Resistance;Heat treatment;Abrasives;Steel;Testing|
|[MAG Welding of austenite steel for the structure of antenna mounts and supports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094198)|T. Węgrzyn; B. S. Lasota; A. P. Silva; W. Tarasiuk; R. A. Jarząbska; A. Döring|10.1109/INFOTEH57020.2023.10094198|Component;civil engineering;antenna;MAG welding;316L steel;Ferrites;Resistance;Welding;Microscopy;Austenite;Steel;Argon|
|[Mechanics of polytetrafluoroethylene-based composites densification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094136)|B. Krupicz; W. Tarasiuk; V. Barsukov; V. Barsukov; A. Kosarac|10.1109/INFOTEH57020.2023.10094136|polytetrafluoroethylene;compaction;interparticle friction;external friction;compacting pressure;Analytical models;Friction;Predictive models;Compaction;Pressure measurement;Stress|
|[Edible electronics components for biomedical applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094196)|M. Radovanović; K. Krnić; M. Simić; G. Stojanović|10.1109/INFOTEH57020.2023.10094196|edible components;electronics;interdigital capacitor;Performance evaluation;Gold;Dairy products;Capacitors;Aluminum;Conductors;Capacitance|
|[Trends and Applications in Patent Databases for the Blockchain Technology on Use Case : Products Traceability in Food Supply Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094073)|D. Kukolj; J. Mastilović; Ž. Kevrešan; R. Kovač; G. Ostojić; S. Stankovski; S. Nemet|10.1109/INFOTEH57020.2023.10094073|patent analytics;technology analysis;blockchain;food supply chain;food traceability;Patents;Databases;Supply chains;Production;Market research;Agriculture;Blockchains|
|[The Use of Virtual Microscopy in the Teaching of Histology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094076)|M. Ljubojević; M. Savić; V. Ljubojević; B. Milinković; D. Mijić|10.1109/INFOTEH57020.2023.10094076|whole slide imaging;virtual microscopy;histology;Visualization;Histopathology;Pandemics;Microscopy;Education;Manuals;Regulation|
|[Stockwell Transform and its Modifications in Signal Processing Courses: Comparison and Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094190)|S. Maksimović; S. Gajić|10.1109/INFOTEH57020.2023.10094190|Stockwell transformation;signal and image processing;transformations in teaching;Time-frequency analysis;Image resolution;Image coding;Transforms;Discrete wavelet transforms;Discrete cosine transforms;Standards|
|[Power Quality Investigation of Residential Low-Wattage LED Lamps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094066)|H. A. Abdulmajeed Salbi; P. Kiss|10.1109/INFOTEH57020.2023.10094066|LED lamps;power quality;harmonic distortion;Reactive power;LED lamps;Shape;Power quality;Distortion;Topology;Software measurement|
|[Automated Authorship Attribution using CNG Distance on Blog Posts in the Serbian Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094205)|V. Kešelj|10.1109/INFOTEH57020.2023.10094205|Natural Language Processing (NLP);Text Classification;Automated Authorship Attribution;CNG distance;Training;Blogs;Focusing;Europe;Benchmark testing;Natural language processing;Object recognition|
|[On Significance of the Kalman Filter in an Automotive Visual Perception System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094121)|B. Kisačanin; D. Zhang; E. Viscito; Z. Nikolić|10.1109/INFOTEH57020.2023.10094121|Kalman Filter;Advanced Driver Assistance Systems;Autonomous Driving;Automotive visual perception;Reliability engineering;Particle filters;Kalman filters;Task analysis;Visual perception;Automotive engineering|
|[Common programmers mistakes in education and practicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094051)|D. Damyanov; Z. Varbanov|10.1109/INFOTEH57020.2023.10094051|Training;training errors;programming problems;process improvement;Training;Codes;Buildings;Writing;Software;Encoding;Personnel|
|[Two axis gimbal system design analysis : Simplified model supporting system components selection for use in MSIS systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094200)|M. Radisavljević; S. Vujić; M. Perić; N. Košanin; M. Stanković; Đ. Vulović; B. Livada; M. Radisavljević; S. Vujić; M. Perić; B. Livada|10.1109/INFOTEH57020.2023.10094200|Two axis gimbal;model;design;component selection;gyro stabilization;multisensor imaging system;Performance evaluation;Analytical models;Azimuth;Imaging;Performance analysis;Task analysis|
|[Integrated information solution as a platform for data exchange in modernized ADRIREP system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094161)|M. Šorović; T. Maričević; D. Brčić; Z. Đurović; V. Frančić; N. Kapidani; Ž. Lukšić|10.1109/INFOTEH57020.2023.10094161|EUREKA Project;ADRIREP system;Adriatic-Ionian Region;VTMIS;ship reporting;navigational safety;cooperation;Navigation;Redundancy;Data collection;Regulation;Safety;Artificial intelligence;Monitoring|
|[In pursuit of appropriate private blockchain platform for higher education institutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094098)|D. Čeke; N. Buzađija|10.1109/INFOTEH57020.2023.10094098|blockchain;digital certificates;higher education;security;Data privacy;Distributed ledger;Education;Fabrics;Blockchains;Security;Task analysis|
|[Intro basics of modeling user rights management for the university diploma issuing process with the support of the Hyperledger Fabric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094112)|D. Čeke; N. Buzađija|10.1109/INFOTEH57020.2023.10094112|blockchain;Hyperledger Fabric;higher education;nan|
|[Information Systems Integration in E-Government: A Bibliometric Analysis Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094140)|A. Milićević; T. Vučković; D. Krstić; A. Ivić|10.1109/INFOTEH57020.2023.10094140|information systems integration;e-government;public sectors;bibliometrix;Productivity;Databases;Bibliometrics;Knowledge based systems;Market research;Data processing;Information management|
|[Automatic Testing of the Correctness of a Website Layout using the Layout Graph: An Experiment Report](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094139)|E. Bašić; S. Hadžijusufović; I. Prazina; D. Pozderac; V. Okanović|10.1109/INFOTEH57020.2023.10094139|website;responsive web design;layout graph;responsive design failures;Quality assurance;Portable computers;Automatic testing;Layout;Loading;Web pages;Manuals|
|[Phase-Domain Synchronous Machine Model with Main Flux Saturation for EMTP-type Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094208)|E. Mostajeran; J. Jatskevich|10.1109/INFOTEH57020.2023.10094208|Electromagnetic transient program;main flux saturation;phase-domain model;single-saturation factor;synchronous machine modeling;Magnetic flux;Computational modeling;Magnetic separation;Numerical models;Synchronous machines;PSCAD;Transient analysis|
|[C64 Emulation on Modern Operating System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094134)|M. Švenda; B. Cafuta; I. Dodig; D. Cafuta|10.1109/INFOTEH57020.2023.10094134|component;Commodore 64;emulation;testing;Performance evaluation;Portable computers;Embedded systems;Emulation;Loading;Focusing;Games|
|[Towards Digital Agriculture in Bosnia and Herzegovina: Digital Solutions for Small Farmers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094183)|D. Mijić; M. Sikimić; G. Vico; M. Ljubojević|10.1109/INFOTEH57020.2023.10094183|digitalization;agriculture;small farms;IoT;ICT;Smart agriculture;Productivity;Costs;Digital transformation;Developing countries;Sustainable development|
|[Potential of Mass Customization using Product Configurators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094151)|A. Gutai; S. Havzi; D. Nikolic; D. Dakic|10.1109/INFOTEH57020.2023.10094151|mass customization;product configurator;design automation;Product customization;Solid modeling;Mass production;Mass customization;Costs;Three-dimensional displays;Production|
|[Optimization on ITB Metaverse Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094107)|A. A. Putra Gary; E. Husni|10.1109/INFOTEH57020.2023.10094107|Metaverse;Digital Twin;LOD Optimization;Asset Management;Unreal Engine;Metaverse;Buildings;Collaboration;Immersive experience;Logic gates;Asset management;Digital twins|
|[An improved experimental power distribution system simulator for the analysis of power quality parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094061)|A. Simović; S. Jokić; A. Lemez; Z. Stojković|10.1109/INFOTEH57020.2023.10094061|experimental simulator;load balancing;power quality;regulating transformer;voltage imbalance;Low voltage;Power quality;Wires;Distribution networks;Switches;Transformers;Regulation|
|[5G for Mission Critical Communications: RESPOND-A Project Experiences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094163)|Z. Paladin; E. Kočan; Ž. Lukšić; N. Kapidani; M. -A. Kourtis; M. C. Batistatos|10.1109/INFOTEH57020.2023.10094163|5G;mission-critical communications;First responders;pilots;5G mobile communication;Surveillance;Mission critical systems;Earthquakes;Forestry;Virtual reality;Seaports|
|[Blockchain Technology for Weather Data Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094082)|M. Grebovic; T. Popovic; R. S. Grebovic|10.1109/INFOTEH57020.2023.10094082|weather data;distributed network;blockchain;hash function;smart contract;Electric breakdown;Publishing;Smart contracts;Memory;Weather forecasting;Manuals;Data systems|
|[Tuning the Maximum Power Extraction Loop in the Improved Droop Controller of Virtual Synchronous Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094108)|M. Wang; E. Mostajeran; J. Jatskevich|10.1109/INFOTEH57020.2023.10094108|Droop controller;parameter tuning;small-signal analysis;synchronverter;virtual synchronous generator;Renewable energy sources;System dynamics;Software packages;Power system dynamics;Low-pass filters;Synchronous generators;Power grids|
|[Average-Value Modeling of Hybrid LCC-VSC HVDC Systems with Direct Interfacing in PSCAD/EMTDC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094118)|P. S. Hosseinian; S. Ebrahimi; J. Jatskevich|10.1109/INFOTEH57020.2023.10094118|Average-value mode;conductance matrix;hybrid;HVDC;LCC;nodal analysis;simulation;VSC;nan|
|[A conceptual model of agile meetings' problems and their relationships with organizational issues in IT industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094204)|M. Gaborov; Z. Stojanov; M. Kavalić; I. Vecštejn; S. Popov|10.1109/INFOTEH57020.2023.10094204|meeting problems;organizational issues;agile;IT industry;conceptual model;Industries;Analytical models;Standards organizations;Project management;Companies;Software tools;Guidelines|
|[Power System Decarbonization and Electrical Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094047)|G. Dobric|10.1109/INFOTEH57020.2023.10094047|decarbonisation;electrical vehicles;smart grid;Systems operation;Low-carbon economy;Production;Electric vehicles;Control systems;Power systems;Sustainable development|
|[UML-based Forward Database Engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094162)|Z. Spasic; A. Vukotic; D. Brdjanin; D. Banjac; G. Banjac|10.1109/INFOTEH57020.2023.10094162|AMADEOS;business process model;conceptual database model;database design;forward engineering;relational database model;UML class diagram;Codes;Databases;Unified modeling language;Standards;Business|
#### **2023 IEEE 3rd International Conference in Power Engineering Applications (ICPEA)**
- DOI: 10.1109/ICPEA56918.2023
- DATE: 6-7 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Towards a Sustainable Mosque Building-Assessment Study Based on Dubai Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093174)|H. Taleb; I. M. AlHamad; S. Alaryani; R. Khader|10.1109/ICPEA56918.2023.10093174|Energy Modeling;Intermittent Occupancy Buildings;Sustainable Strategies;Energy Saving;Power Consumption;Insulation;Energy consumption;Power demand;Buildings;Water heating;Lighting;Software|
|[Determination of Excitation Current Range to Reduce Self-Heating using LTSpice for Resistance Temperature Detector in Aircraft System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093215)|P. S. M. Saad; A. Ahmad; H. Hashim|10.1109/ICPEA56918.2023.10093215|Resistance Temperature Detector (RTD);Pt 100;Self-heating;Excitation current;LTspice Simulator;Temperature sensors;Resistance;Temperature measurement;Temperature distribution;Power engineering;Detectors;Sensors|
|[An Efficient Method for Available Transfer Capability Calculation Considering Cyber-Attacks in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093168)|M. Eidiani; H. Zeynal; Z. Zakaria|10.1109/ICPEA56918.2023.10093168|Cyberattack;State Estimation;DIgSILENT Power Factory;Available Transfer Capability;SCADA;Power engineering;Computer hacking;Simulation;Power system dynamics;Production facilities;Power markets;Software reliability|
|[Techno-Economical Analysis of Hybrid PV System for School Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093195)|M. R. Akbar; Aripriharta; A. Mizar; M. Muladi; H. -Y. Ching; M. A. Amin|10.1109/ICPEA56918.2023.10093195|Solar Power Plant;Economic Analysis;Payback Period;Natural resources;Renewable energy sources;Power engineering;Costs;Profitability;Solar energy;Inverters|
|[A Comprehensive Study on The Renewable Energy Integration Using DIgSILENT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093153)|M. Eidiani; H. Zeynal; Z. Zakaria|10.1109/ICPEA56918.2023.10093153|Harmonic;Energy storage systems;Contingency analysis;Island mode;Renewable energy;Reliability;Renewable energy sources;Power engineering;System integration;Distortion;Power grids;Software;Regulation|
|[Simulation Analysis for 33 kV Porcelain Insulator String Based on Room Temperature Vulcanize Coating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093219)|M. Othman; S. H. Kamal Hamadi; M. Isa|10.1109/ICPEA56918.2023.10093219|coating;contamination;insulator;Room Temperature Vulcanize (RTV);Porcelain;Power transmission lines;Design automation;Voltage;Insulators;Water pollution;Finite element analysis|
|[Investigation of Acetylene Gas and Internal Hotspot in the Low Voltage Bushing of Power Transformer #1 Telukjambe Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093192)|L. Yahya; A. Bagaskara; D. Prima Rahmad|10.1109/ICPEA56918.2023.10093192|transformer;dissolved gas;hot spot;SFRA;winding resistance;Resistance;Substations;Purification;Oils;Windings;Oil insulation;Maintenance engineering|
|[Islanding Scheme Design for Generators and Assessment of Impact of RGMO on Frequency Stability of Island](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093201)|S. Ghosh; P. Mukherjee; A. P. Singh; P. Jyoti Borah; S. Kumar Sahay; S. Mondal; S. Konar|10.1109/ICPEA56918.2023.10093201|RGMO;Islanding scheme;Automatic Under frequency load shedding scheme(AUFLS);Indian Electricity Grid Code (IEGC);Automatic generation control(AGC);Load generation Balance report(LGBR);Maximum Continuous Rating (MCR);Time-frequency analysis;Islanding;Codes;Urban areas;Load shedding;Voltage;Power system stability|
|[Thermal Characterization of Power Gallium Nitride Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093146)|J. Kim|10.1109/ICPEA56918.2023.10093146|thermal transient measurement;structure function;thermal model calibration;HEEDS;Power measurement;Thermal engineering;Thermal management;Software;Calibration;Thermal analysis;Transistors|
|[Scaling-Free Transformations for Stability-Guaranteed Variable Notch-Frequency Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093218)|T. -B. Deng|10.1109/ICPEA56918.2023.10093218|Frequency-component removal;notch frequency (NF);notch-frequency (NF) filter;stability;Power engineering;Stability criteria;Interference;Noise measurement;Notch filters|
|[Development of Prototype Hybrid Photovoltaic-Thermoelectric (PV-TEG) System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093157)|W. C. Khang; S. Amely Jumaat; M. A. Johar; U. Saleh Abu Bakar|10.1109/ICPEA56918.2023.10093157|Solar energy;photovoltaic;thermoelectric generator;photovoltaic-thermoelectric hybrid power generation system;Photovoltaic systems;Power engineering;Prototypes;Voltage;Hybrid power systems;Generators;Waste heat|
|[Development of Automated Nutrient Composition Control for Fertigation System Using IoT Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093152)|M. A. Danial Bin Azman; S. Amely Jumaat; M. A. Johar; U. Saleh Abu Bakar|10.1109/ICPEA56918.2023.10093152|Internet of Things;automated fertigation system;solar power;Meters;Crops;Control systems;Water pumps;Water pollution;Internet of Things;Solar panels|
|[A Robust State Estimation Method for Unsymmetrical Three-Phase Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093166)|H. A. Adamu; X. -P. Zhang|10.1109/ICPEA56918.2023.10093166|States;WLS;Welsch distribution function;error measurement;influence function;weight function;3-phase bus network;Power engineering;Measurement errors;Power measurement;Measurement uncertainty;Mathematical models;Power systems;Performance analysis|
|[Optimal Charging Scheduling of Electric Vehicle Considering Minimum Power Loss using Firefly Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093224)|H. Mohamad; N. M. Razali; K. I. K. Shah; N. A. Salim; K. Naidu|10.1109/ICPEA56918.2023.10093224|electric vehicle;charging scheduling;distribution network;firefly algorithm;minimum power loss;Simulation;Tariffs;Voltage;Programming;Power system stability;Scheduling;Electric vehicle charging|
|[UV-Vis Spectra and Moisture Content of Retrofilled Aged Mineral Oil with Synthetic Ester](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093173)|N. A. Othman; H. Zainuddin; M. S. Yahaya; N. Azis; Z. Ibrahim; M. A. C. Musni|10.1109/ICPEA56918.2023.10093173|Retrofilling;power transformer;synthetic ester;mineral oil;moisture content;UV-Vis spectra;Degradation;Spectroscopy;Oils;Moisture;Aging;Oil insulation;Transformers|
|[Short-circuit Fault Detection in Power Transformer Using Frequency Response Analysis bipolar signature of Inductive Inter-Winding Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093162)|A. A. Alawady; S. Mgammal Al-Ameri; Z. Abdul-Malek; M. F. Mohd Yousof; A. Ahmed Salem|10.1109/ICPEA56918.2023.10093162|Power transformer;winding short circuit;frequency response analysis (FRA);Inductive inter-winding;bipolar signature;Power transmission lines;Fault detection;Windings;RLC circuits;Transmission line measurements;Electrical fault detection;Frequency response|
|[A Novel Active Cooling Technique for Photovoltaic in Harsh Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093189)|M. S. Raja; A. J. Abid; Z. S. Al-Sagar|10.1109/ICPEA56918.2023.10093189|Solar Panels;Cooling of Solar Panels;Photovoltaic cell;Temperature;Photovoltaic systems;Productivity;Temperature distribution;Power engineering;Irrigation;Cooling;Water heating|
|[A Systematic Review for Enhancing Solar Photovoltaic System Efficiency by Reducing the Panel Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093223)|M. S. Raja; A. J. Abid; Z. S. Al-Sagar|10.1109/ICPEA56918.2023.10093223|Active Cooling System;PV Panel;Solar system in Haresh Area;Irrigation System;Hot Spot;Temperature;Productivity;Renewable energy sources;Temperature dependence;Power engineering;Costs;Systematics;Cooling|
|[Four Bioinspired Optimization Techniques in PV MPPT under Uniform and Non-Uniform Shading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093225)|H. S. Ahmed; A. J. Abid; A. A. Obed|10.1109/ICPEA56918.2023.10093225|MPPT;Bio-inspired;CS;PSO;GWO;HHO;Partial shading;Maximum power point trackers;Photovoltaic systems;Radiation effects;Renewable energy sources;Power engineering;Biological system modeling;Metaheuristics|
|[Green Hydrogen Scale Prediction Based on System Dynamics Model for Carbon Neutrality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093163)|H. Yang; C. Zhao; Z. Li; T. Lu; J. Zhang|10.1109/ICPEA56918.2023.10093163|Carbon neutralization;hydrogen production;subsidy policies;system dynamics model;Industries;Power engineering;System dynamics;Hydrogen;Finance;Production;Carbon neutral|
|[Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093214)|T. Z. Wei Chang; S. Chiang Tan; K. S. Sim; C. Peng Lim; P. Y. Goh|10.1109/ICPEA56918.2023.10093214|Imbalanced data classification;power systems;condition monitoring;SMOTE;Fuzzy ARTMAP;Condition monitoring;Fault diagnosis;Power engineering;Machine learning algorithms;Fault detection;Data models;Sensor systems|
|[Distribution Network State Monitoring using Compressive Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093180)|T. Yuanchun; L. Cui; Z. Zhaozheng|10.1109/ICPEA56918.2023.10093180|power distribution network;compressive sensing;signal reconstruction;Renewable energy sources;Simulation;Signal sampling;Distribution networks;Real-time systems;Sensors;Matrix decomposition|
|[A Multi-View Clustering based Dynamic Partitioning Method for Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093151)|L. Cui; X. Bingsen; L. Zhenglong|10.1109/ICPEA56918.2023.10093151|distribution network;multi-view clustering;kmeans;area partitioning;Schedules;Power engineering;Power demand;Heuristic algorithms;Simulation;Clustering algorithms;Distribution networks|
|[Implementation of Certainty Level (CL) to Condition Assessment Factor (CAF) Index on High-Voltage Circuit-Breaker (HVCB) on Sulmapana Region](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093159)|E. Yulianto; R. A. Prasojo|10.1109/ICPEA56918.2023.10093159|assessment;circuit-breaker;certainty-level;Resistance;Insulation;Electric breakdown;Circuit breakers;Aggregates;High-voltage techniques;Maintenance engineering|
|[Line-Interactive Transformerless Bidirectional Buck-Boost Uninterruptable Power Supply System With Battery Control Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093170)|N. A. Rahman; N. Sukimin|10.1109/ICPEA56918.2023.10093170|Uninterruptible Power Supply (UPS);Buck-Boost Converter;Battery Control Algorithm;Total harmonic distortion;Power engineering;PI control;Power supplies;Simulation;Transformers;Batteries|
|[Prioritizing Load Break Switch Placement: Application of Fuzzy AHP-TOPSIS Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093205)|N. S. Sitohang; M. Isa Irawan; H. S. Saragi; A. Saragih; R. Simanjuntak|10.1109/ICPEA56918.2023.10093205|FAHP;TOPSIS;reliability;LBS;prioritizing;Power engineering;Analytical models;Sensitivity analysis;Bibliographies;Key performance indicator;Switches;Companies|
|[Risk Management of Stochastic Power Generation Outages by Load Curtailment Program](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093158)|K. Alqunun|10.1109/ICPEA56918.2023.10093158|Load Curtailment;Economic Dispatch;Unit Commitment;Mixed Integer Programming (MIP);GAMS software;Schedules;Costs;Energy resources;Stochastic processes;Software;Power systems;Mixed integer linear programming|
|[Comparative Study of Power System Security Assessment using Deterministic and Probabilistic Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093198)|N. Aminudin; I. Musirin; R. H. Salimin; M. A. T. Mat Yusoh; S. R. A. Rahim; Y. Yusof|10.1109/ICPEA56918.2023.10093198|Risk based Security Assessment;L-index;Severity;Line outages;Probability;Deterministic Assessment;Power transmission lines;Systems operation;Contingency management;Voltage;Power system stability;Probabilistic logic;Stability analysis|
|[ANFIS-Based New Approach for an Optimal Lithium-Ion Battery Charging Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093226)|S. S. Hussein; A. J. Abid; A. A. Obed|10.1109/ICPEA56918.2023.10093226|ANFIS;CC-CV;Energy Storage System;Renewable Energy System;Lithium-Ion Battery;Lithium-ion batteries;Training;Lead acid batteries;Power engineering;Adaptive systems;Simulation;Control systems|
|[Influence of Deep Trap Density and Injection Barrier Height towards Accumulation of Space Charge within Dielectrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093193)|I. D. Salim; N. H. Nik Ali; A. M. Ariffin; A. Doolgindachbaporn; T. M. Kuan; M. Abdul Talib Mat Yusoh|10.1109/ICPEA56918.2023.10093193|HVDC cable;space charge;ageing;low-density polyethylene;cross-linked polyethylene;BCT model;Insulation;Polyethylene;Power cables;Electric breakdown;HVDC transmission;Accelerated aging;High-voltage techniques|
|[Artificial Neural Network Prediction to Identify Solar Energy Potential In Eastern Indonesia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093184)|D. Aryani; S. Pranoto; F. Fajar; A. N. Intang; F. Zulmi Rhamadhan|10.1109/ICPEA56918.2023.10093184|artificial neural network;prediction;solar energy;Training;Radiation effects;Power engineering;NASA;Estimation;Artificial neural networks;Solar energy|
|[Generation Expansion Planning Considering Photovoltaic (PV) and Wind turbine Systems Using Optimization of Evolutionary Programming (EP) Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093202)|A. B. Mashudi; M. M. Othman; M. Ahmadipour; K. Hasan|10.1109/ICPEA56918.2023.10093202|Forced Outage Rate (FOR);Evolutionary Programming (EP);generating unit (GU);optimization;Markov model;Monte-Carlo;reliability;renewable energy;Loss of Load Expectation (LOLE);Costs;Sociology;Programming;Generators;Wind turbines;Planning;Reliability|
|[Optimal Allocation of Photovoltaic (PV) System Incorporating Energy Storage System (ESS) using Evolutionary Programming (EP) for Power System Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093165)|S. B. S. Z. Abidin; M. M. Othman; M. Ahmadipour; K. Hasan|10.1109/ICPEA56918.2023.10093165|Photovoltaic (PV);Energy Storage System (ESS);Force Outage Rate (FOR);Loss of Load Expectation (LOLE);Expected Unserved Energy (EUE);Evolutionary Programming (EP);Photovoltaic systems;Uncertainty;Stability criteria;Programming;Markov processes;Power system reliability;Reliability|
|[Optimal Allocation of Photovoltaic (PV) System Considering Weather Conditions using Evolutionary Programming (EP) for Enhanced Power System Resiliency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093211)|N. T. E. M. Jailaini; M. M. Othman; M. Ahmadipour; K. Hasan|10.1109/ICPEA56918.2023.10093211|Photovoltaic (PV);Forced Outage Rate (FOR);Evolutionary Programming (EP);generating unit (GU);optimization;Markov model;reliability;renewable energy;weather condition;Loss of Load Expectation (LOLE);Expected Unserved Energy (EUE);Photovoltaic systems;Markov processes;Programming;Mathematical models;Power system reliability;Resource management;Statistics|
|[Energy Efficiency using Dynamic Voltage Restorer (DVR) Integrated Photovoltaic and Energy Storage Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093179)|M. S. M. Tamam; M. M. Othman; K. Hasan; M. Ahmadipour|10.1109/ICPEA56918.2023.10093179|Solar Photovoltaic (PV);Dynamic Voltage Restorer (DVR);Supercapacitor(SCAP);power quality disturbance;Photovoltaic systems;Shunts (electrical);Voltage fluctuations;Simulation;Power quality;Power system dynamics;Supercapacitors|
|[Dynamic Voltage Restorer (DVR) using Supercapacitor for Power Quality Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093217)|J. A. Tigong; M. Murtadha Othman; K. Hasan; M. Ahmadipour|10.1109/ICPEA56918.2023.10093217|Dynamic Voltage Restorer (DVR);Harmonic;Hysteresis Voltage Control;Supercapacitor (SCAP);Voltage Swell/Sag;Reactive power;Voltage fluctuations;Simulation;Power quality;Capacitors;DC-DC power converters;Supercapacitors|
|[Structure and DC Breakdown Properties of Polypropylene/Elastomer Blends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093160)|S. N. H. Kamarudin; K. Y. Lau; N. A. Ahmad; N. A. Azrin; C. W. Tan; K. Y. Ching|10.1109/ICPEA56918.2023.10093160|polypropylene;elastomer;blend;DC breakdown strength;Spectroscopy;Temperature;Polyethylene;Electric breakdown;Elastomers;High-voltage techniques;Electric variables|
|[Effect of Elastomer Content on AC Breakdown Performance of PP Blends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093188)|S. N. H. Kamarudin; K. Y. Lau; N. A. Ahmad; N. A. Azrin; A. B. A. Ghani; N. A. N. Arifin|10.1109/ICPEA56918.2023.10093188|polypropylene;elastomer;blend;AC breakdown analysis;Insulation;Electric potential;Polyethylene;Electric breakdown;Morphology;Elastomers;High-voltage techniques|
|[Aquaponic Analysis Using the PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093178)|A. M. Tyassilva; A. Aripriharta; R. A. Ihsan; R. Alfadel Saputra; S. Omar; H. -Y. Ching|10.1109/ICPEA56918.2023.10093178|Aquaponics;Inverters;Solar Panels;Technological innovation;Renewable energy sources;Tracking;Solar energy;Inverters;Water pumps;Batteries|
|[Educational Building’s Energy Consumption Independent Variables Analysis using Linear Regression Model: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093222)|R. F. Mustapa; A. Hamizah Mohd Nordin; M. A. Hairuddin; W. Suhaifiza W Ibrahim; S. A. Mohd Saleh; N. Y. Dahlan; I. M. Yassin|10.1109/ICPEA56918.2023.10093222|energy consumption;independent variables;regression model;educational building;and baseline;Energy consumption;Analytical models;Power engineering;Correlation;Buildings;Linear regression;Laboratories|
|[Accelerant Facilitation for an Adaptive Weighting-Based Multi-Index Assessment of Cyber Physical Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093212)|S. Chan; P. Nopphawan|10.1109/ICPEA56918.2023.10093212|Smart Grid;Adaptive Weighting Methodology;Artificial Intelligence;Power System Operations and Planning;Decision Engineering;Data Analytics in Power Engineering;Power engineering;Fuzzy sets;Adaptive systems;Veins;Benchmark testing;Power system stability;Entropy|
|[Perovskite PV MPPT Design for BIPV Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093206)|S. I. Olatunji; A. Marzki; A. Ng; I. A. Ukeagbu|10.1109/ICPEA56918.2023.10093206|Perovskite Solar Cell (PSC);Maximum Power Point Tracking (MPPT);DC-DC Boost Converter;Building Integrated Photovoltaic (BIPV);Cadence Virtuoso Software;Maximum power point trackers;Renewable energy sources;Photovoltaic cells;Short-circuit currents;Software algorithms;Voltage;Solar energy|
|[Resource allocation for NOMA/IRS network with energy harvesting in presence of Hardware Impairment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093209)|H. N. Thi; T. X. Kieu; L. H. Truong; A. Le Thi|10.1109/ICPEA56918.2023.10093209|Non-orthogonal multiple access;intelligent reflecting surface;hardware impairment;energy harvesting;power-splitting architecture;energy efficiency;Measurement;NOMA;Transmitters;System performance;Relay networks;Hardware;Resource management|
|[Structure of Polypropylene, Ethylene-Propylene- Diene-Monomer and Magnesium Oxide for the Formulation of PP Blend Nanocomposites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093200)|N. A. Johari; K. Y. Lau; Z. Abdul-Malek; M. R. M. Esa; C. W. Tan; R. Ayop|10.1109/ICPEA56918.2023.10093200|polypropylene;ethylene-propylene-diene monomer;magnesium oxide;structure;thermal;Heating systems;Temperature;Nanocomposites;Magnesium oxide;Raw materials;Dielectrics;Behavioral sciences|
|[Direct Current Breakdown Properties of Polypropylene Nanocomposites Containing Magnesium Oxide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093176)|N. A. Johari; K. Y. Lau; Z. Abdul-Malek; M. R. M. Esa; C. W. Tan; R. Ayop|10.1109/ICPEA56918.2023.10093176|breakdown strength;elastomer;ethylene-propylene-diene monomer;magnesium oxide;polypropylene;Nanoparticles;Power engineering;Nanocomposites;Loading;Magnesium oxide;Rigidity;Dielectric breakdown|
|[Classification of Faults on the Shipboard Distribution Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093171)|M. A. Talib Mat Yusoh; A. Farid Abidin; N. B. Mohamad Bakar Mohd Basri; N. Zulaily Mohamad; N. H. Nik Ali; C. Chin Aun|10.1109/ICPEA56918.2023.10093171|Power Quality;Voltage Sag;Voltage Swell;Voltage Swell + Transient;Ensemble Bagged tree;KNN;SVM;Support vector machines;Voltage fluctuations;Navigation;Power quality;Feature extraction;Power systems;Safety|
|[Power Transformer Insulation System Health Index with Missing Data Prediction using Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093216)|G. Chintia; R. A. Prasojo; Suwarno|10.1109/ICPEA56918.2023.10093216|Health Index;Power Transformer;Condition Monitoring and Diagnostics;Random Forest;Missing Value;Power engineering;Forestry;Indexes;Monitoring;Power transformer insulation|
|[Optimizing Power Plant Development for Fakfak System using Generation Expansion Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093194)|A. M. Reza; A. Nur Arif Wicaksono; A. B. Heksaprilla; W. Setyadi; M. Firmansyah; M. Asyari|10.1109/ICPEA56918.2023.10093194|RUPTL;Generation Expansion Planning;Renewable Energy;Fakfak;Photovoltaic systems;Power engineering;Costs;Power supplies;Carbon dioxide;Planning;Power generation|
|[Combustion Consumables Cost Analysis in 110 MW gross CFB type CFPP Biomass Co-firing Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093210)|A. M. Reza; F. Chariri; A. O. Yurwendra; A. A. Prakoso; M. Rifaldi|10.1109/ICPEA56918.2023.10093210|Renewable energy;CFPP;CFB;Biomass co-firing;PKS;SO2;Limestone;Power engineering;Coal;Switches;Boilers;Combustion;Biomass;Cost benefit analysis|
|[Health Index prediction using Artificial Neural Network (ANN) on Historical Data of Power Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093199)|G. A. Sudrajad; S. Suwarno; R. A. Prasojo|10.1109/ICPEA56918.2023.10093199|Power transformer;Health Index;Expected life transformer;and Artificial Neural Network;Costs;Artificial neural networks;Prediction methods;Oil insulation;Maintenance engineering;Prediction algorithms;Power system reliability|
|[Three-Phase Power Transformer Fault Diagnosis Based on Support Vector Machines and Bees Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093147)|O. Abdusalam; F. Anayi; M. Packianather|10.1109/ICPEA56918.2023.10093147|Inrush Currents;Internal Fault;External Fault and Support Vector Machine (SVM);Support vector machines;Fault diagnosis;Fault detection;Transforms;Feature extraction;Data models;Classification algorithms|
|[Power Quality Control Strategy of MMC Rectifier as Solid State Transformer in MVAC Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093220)|K. G. H. Mangunkusumo; M. Ridwan; Sriyono; F. D. Wijaya; R. Irnawan; Y. F. Sidik; P. P. Oktarina|10.1109/ICPEA56918.2023.10093220|Solid State Transformer;Modular Multilevel Converter;Rectifier;Power Quality;Sag;Swell;Current control;Reactive power;Network topology;Simulation;Power quality;Rectifiers;Distribution networks|
|[Impact of Incandescent Light and LED on Electricity Fee and Carbon Emission Cost at an Airport in Malaysia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093154)|W. M. R. Jamaludin; W. M. Wan; N. H. Nik Ali; N. A. M. Isa|10.1109/ICPEA56918.2023.10093154|Airport;Lighting;Incandescent Light;LED;Electricity Fee;Carbon Emission Cost;Filament lamps;Costs;Atmospheric modeling;Energy conservation;Lighting;Carbon dioxide;Light emitting diodes|
|[A New MBC-TSI Topology for Microinverter PV Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093196)|A. Razi; M. N. Hidayat; A. Jidin; S. A. A. Shukor; S. Z. M. Noor|10.1109/ICPEA56918.2023.10093196|Photovoltaic System;Microinverter;OGPV;SELCO;Two-stage PV;Photovoltaic systems;Network topology;Software packages;Simulation;Switches;Predictive models;Mathematical models|
|[Comparison of Enhanced Isolation Forest and Enhanced Local Outlier Factor in Anomalous Power Consumption Labelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093186)|R. ELHadad; Y. -F. Tan; W. -N. Tan|10.1109/ICPEA56918.2023.10093186|Anomalous Power Consumption;Isolation Forest;Local Outlier Factor;Smart Meter;Automated Data Labelling;Meters;Power engineering;Power demand;Machine learning algorithms;Forestry;Prediction algorithms;Smart meters|
|[Random Forest (RF) with Daubechies Wavelet and Multiple Time Lags (MTL) for Solar Irradiance Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093181)|R. N. Redan; M. Murtadha Othman; K. Hasan; M. Ahmadipour|10.1109/ICPEA56918.2023.10093181|Photovoltaic;Solar Irradiance Forecasting;Random Forest;Multiple Time Lags;Daubechies Wavelets;Radio frequency;Training;Sensitivity analysis;Weather forecasting;Feature extraction;Stability analysis;Forecasting|
|[Communication Network Selection for Distribution Network Based on Analytic Hierarchy Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093204)|W. Ge; Q. Wei; W. Hong; Y. Wu; C. Li|10.1109/ICPEA56918.2023.10093204|Analytic Hierarchy Process;distribution network;network selection;Wireless communication;5G mobile communication;Distribution networks;Optical fiber communication;Smart grids;Communication networks;Security|
|[Optimized Allocation of Lightning Protection System Using PSOGSA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093208)|J. -W. Tang; C. -L. Wooi; W. -S. Tan; H. N. Afrouzi; S. N. bin Md Arshad Hashim; M. Othman|10.1109/ICPEA56918.2023.10093208|Lightning protection system;SAIFI;Particle Swarm Optimization (PSO);Gravitational Search Algorithm (GSA);Optimal location;Power engineering;Lightning protection;Distribution networks;Power system reliability;Reliability;Resource management;Particle swarm optimization|
|[A Review of Optimization Approaches for Optimal Sizing and Placement of Battery Energy Storage System (BESS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093172)|S. S. Mat Isa; M. Nizam Ibrahim; A. Mohamad; N. Y. Dahlan; S. Nordin|10.1109/ICPEA56918.2023.10093172|optimization;location;sizing;battery energy storage system;Climate change;Energy storage;Batteries;Optimization;Global warming;Carbon emissions;Resource management;Renewable energy sources|
|[Determination of Optimal Distributed Generation Penetration Level in Distribution Networks based on Normalized Impact Factor Score](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093149)|M. S. Turiman; M. Khairun Nizam Mohd Sarmin; N. Saadun; M. F. Zamri; H. Ali; Q. Mohammad|10.1109/ICPEA56918.2023.10093149|Distributed generation (DG);renewable energy (RE);solar PV generation;optimal DG penetration level;normalized impact factor;Renewable energy sources;Analytical models;Green products;Government;Distribution networks;Voltage;Transformers|
|[Review of Artificial Neural Network Approaches for Predicting Building Energy Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093183)|S. S. Md Ramli; M. Nizam Ibrahim; A. Mohamad; K. Daud; A. M. Saidina Omar; N. Darina Ahmad|10.1109/ICPEA56918.2023.10093183|Forecasting;Energy Consumption;Building;Artificial Neural Networks;Data Driven;Energy consumption;Power engineering;Soft sensors;Buildings;Artificial neural networks;Organizations;Predictive models|
|[Hotspot Detection of Solar Photovoltaic System: A Perspective from Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093148)|N. H. Binti Ishak; I. Sazanita Binti Isa; M. K. Bin Osman; K. Daud; M. S. Bin Jadin|10.1109/ICPEA56918.2023.10093148|hotspot;solar PV system;image processing;Photovoltaic systems;Renewable energy sources;Image processing;Fault detection;Solar energy;Cameras;Prediction algorithms|
|[System Transient Stability Due to Various Contingency Using Power World Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093191)|N. A. Salim; H. Mohamed; M. E. S. Bin Ensnat; Z. M. Yasin|10.1109/ICPEA56918.2023.10093191|Transient Stability;IEEE 9-bus system;Power World Simulator;Various Contingency;Critical Clearing Time;Time-frequency analysis;Contingency management;Rotors;Voltage;Switches;Power system stability;Stability analysis|
|[Centralized Protection and Control (CPC): Next Level Of Electrical Protection System Towards Digital Substation (Oil and Gas)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093221)|I. S. Bin A Halim; M. K. Bin M Hatta; I. T. H. H. Bin W Hussein; I. T. F. B. Shafe’i; T. N. A. B. Azmi; F. B. Othman|10.1109/ICPEA56918.2023.10093221|protection system;centralize;remote monitoring and operation;digitalization;Power engineering;Substations;Costs;Oils;Maintenance engineering;Natural gas industry;Hardware|
|[Condition Monitoring Studies on a New Ellipse Shape Profile of Modified Savonius Wind Turbine with Quarter Cylindrical Rotor House](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093156)|M. Radzi; A. Qoiyum; S. Sarip; M. N. Muhtazaruddin|10.1109/ICPEA56918.2023.10093156|Savonius wind turbine;rotor house;ellipse shape profile;rotating deflector;condition monitoring;rotational speed;bearing fault;Torque;Shape;Wind speed;Wind tunnels;Velocity control;Rotors;Wind turbines|
|[Mixed-Integer Linear Programming (MILP) Approach for Solving Derating Problems in Optimization of Thermal Power Plants Operation Considering Primary Energy Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093175)|N. Fauziyah; N. Hariyanto|10.1109/ICPEA56918.2023.10093175|derating;unit commitment;economic dispatch;coal transshipment;fuel blending;inventory problems;Pyomo;Costs;Uncertainty;Coal;Maintenance engineering;Mixed integer linear programming;Thermal analysis;Sulfur|
|[Optimal Placement of Distributed Generation and Capacitor in Distribution System for District Hospital in Malaysia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093197)|M. S. S. Anuar; M. N. Muhtazaruddin; M. A. A. Rahman; M. E. Amran|10.1109/ICPEA56918.2023.10093197|distributed generation (DG);capacitor;photovoltaic distributed generation (PV-DG);Artificial Bee Colony (ABC);Government policies;Renewable energy sources;Power engineering;Costs;Hospitals;Capacitors;Linear programming|
|[The Impact of Cavity Size on Electric Field Distribution and PD Inception Voltage in Epoxy-resin Insulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093169)|B. D. Aliyu; M. N. K. H. Rohani; U. Musa; Y. Jibril; S. H. Sulaiman; A. A. Mas’ud; F. Muhammad-Sukki|10.1109/ICPEA56918.2023.10093169|electric field;COMSOL multi-physics;finite element analysis;inception voltage;epoxy-resin insulation;Partial discharges;Insulation;Solid modeling;Three-dimensional displays;Simulation;High-voltage techniques;Solids|
|[Estimating Biomass Sources for a 10 MW Dendro Power Plant Using Leucaena Leucocephala Fuel Wood](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093187)|M. I. bin Muhamad; M. A. M. Radzi; M. L. Othman; H. Hizam; C. Gomes; M. A. Alias|10.1109/ICPEA56918.2023.10093187|Biomass resources;Dendro power plant;Energy generation;Fuel woods;Leucaena Leucocephala;Economics;Electric potential;Power engineering;Crops;Moisture;Companies;Biomass|
|[MPPT Design Using PSO Technique for Photovoltaic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093161)|M. N. S. Khairi; N. A. B. Bakhari; A. A. A. Samat; N. Kamarudin; M. H. Md Hussin; A. I. Tajudin|10.1109/ICPEA56918.2023.10093161|MPPT;PV System;PSO Technique;Buck-converter;Photovoltaic systems;Power engineering;Buck converters;Software;Mathematical models;Temperature control;Voltage control|
|[Correlation of the NOx Emission and Required Area for Designing SCR Facility in Gas Engine Power Plant Based on the Emission Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10093177)|A. M. Reza; M. Arif Susetyo; A. B. Heksaprilla; W. Setyadi|10.1109/ICPEA56918.2023.10093177|NOx;NOx regulation;GEPP;SCR;ammonia;Ammonia;Power engineering;Estimation;Regulation;Nitrous oxide;Fuels;Sulfur|
#### **2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)**
- DOI: 10.1109/IITCEE57236.2023
- DATE: 27-28 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[The Significance of Edge AI towards Real-time and Intelligent Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090926)|C. Surianarayanan; P. Raj; S. K. Niranjan|10.1109/IITCEE57236.2023.10090926|Edge computing;edge analytics;edge clouds;edge AI;edge-native;serverless edge;blockchain for edge devices and data;5G for Edge AI;On-device data processing;edge intelligence;machine vision;Training;Web pages;Real-time systems;Servers;Artificial intelligence;Task analysis;Surges|
|[Potential usage of AI in Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091093)|P. Ojha; S. K. Niranjan|10.1109/IITCEE57236.2023.10091093|Artificial Intelligence;Block chain Technology;Electric potential;Codes;Distributed ledger;Smart contracts;Decision making;Blockchains;Cryptocurrency|
|[Classification of Blood Cell Data using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090986)|S. Yepuri; A. Marandi|10.1109/IITCEE57236.2023.10090986|Machine Learning;Leukemia;Blood Cell Data;Filtering;Network models;Deep learning;Machine learning algorithms;Neural networks;Feature extraction;Data models;Convolutional neural networks;Medical diagnosis|
|[Quality Improvement of Image Datasets using Hashing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091044)|A. Joshi; A. V. Shet; A. S. Thambi; S. R|10.1109/IITCEE57236.2023.10091044|Image processing;Quality improvement;d-Hash;p-Hash;a-Hash;Training;Hash functions;Image processing;Computational modeling;Digital images;Manuals;Nonhomogeneous media|
|[Design and Analysis of a Low Power UWB Transmitter Using Second Derivative Gaussian Pulse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091010)|M. A. Islam; M. Ariful; A. S. Jasir; A. A. Pratyay|10.1109/IITCEE57236.2023.10091010|BPSK modulator;Gaussian 2nd order pulse generator;UWB transmitter;Voltage Controlled Ring Oscillator;Ring oscillators;Frequency modulation;Power demand;Transmitters;Radar tracking;Binary phase shift keying;Transistors|
|[Smart Mining Helmet with Body Vitals and Location Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090871)|S. Kurundkar; K. M. Bhure; A. D. Kamath; A. J. Tyagi; A. Barde; A. A. Pimpalgaonkar|10.1109/IITCEE57236.2023.10090871|Internet of things;Mining helmet;Body temperature and heartbeat sensor;Ambience temperature and humidity sensor;impact sensors and location tracking;ThingSpeak;Temperature sensors;Head;Heart beat;Economic indicators;Humidity;Companies;Safety|
|[Analysis of Flat and Hierarchical Routing Structure Supported Protocol in Mobile Ad-hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091055)|M. S. Nidhya; R. Arumugam; A. Kannagi; T. Revathi; M. P. Karthikeyan|10.1109/IITCEE57236.2023.10091055|Hierarchical;Flat;Ad-hoc;bandwidth;overhead;proactive;reactive;Protocols;Network topology;Scalability;Memory management;Bandwidth;Network analyzers;Maintenance engineering|
|[Altered Neural Net for Breast Histological Image Categorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091027)|R. Uppada; N. Radha|10.1109/IITCEE57236.2023.10091027|Image Classification;Computer-Aided Analysis;Support-Vector Machine;Neural Networks;Support vector machines;Sensitivity;Level set;Neural networks;Biopsy;Manuals;Breast tissue|
|[Classification of Breast Cancer Mammographic Images Using A Light-Weighted Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091078)|P. Kaur; A. Kaur|10.1109/IITCEE57236.2023.10091078|deep-learning;classification;convolutional neural network;Deep learning;Solid modeling;Design automation;Neural networks;Mammography;Breast cancer;Convolutional neural networks|
|[Deep Learning Based Brain Tumor Detection and Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091009)|S. R; S. S S; V. Kusanur; N. B V; P. K. Shetty|10.1109/IITCEE57236.2023.10091009|CNN;MRI;BF;Image segmentation;Thresholding (Imaging);Hospitals;Convolution;Magnetic resonance imaging;Gray-scale;Brain modeling|
|[Alzheimer's Disease Classification Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090947)|C. CH; R. R. A; M. K. R. Jansi|10.1109/IITCEE57236.2023.10090947|AD DETECTION;DEEP LEARNING KERAS MODEL;CNN MODEL;Deep learning;Neuroimaging;Support vector machines;Systematics;Computational modeling;Medical services;Feature extraction|
|[Diabetic Retinopathy Screening Using CNN (ResNet 18)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090968)|A. Kodumuru; K. Kesireddy; K. R. Jansi|10.1109/IITCEE57236.2023.10090968|DR screening;manual screening;deep learning Machine learning;cnn;time-consuming;fundus images;RESNET;Photography;Adaptation models;Analytical models;Retinopathy;Network topology;Diabetes;Topology|
|[A Review of Computer Assistance in Dermatology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090919)|S. Reshma; R. S. R|10.1109/IITCEE57236.2023.10090919|Lesion classification;Malignancy;Seementation;Deep learning;Computational modeling;Biopsy;Skin;Lesions;Convolutional neural networks;Time complexity|
|[Anomaly Detection for Video Surveillance using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091090)|G. Yadav; A. L. Shrivastava|10.1109/IITCEE57236.2023.10091090|Video Surveillance;Anomaly Detection;CNN;Recurrent Neural Network;Deep Learning;Portable computers;Video tracking;Neural networks;Streaming media;Video surveillance;Real-time systems;Behavioral sciences|
|[Parkinson's Disease Detection Using Acoustic features from Speech recordings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090464)|S. Dheer; M. Poddar; A. Pandey; S. Kalaivani|10.1109/IITCEE57236.2023.10090464|Parkinson's disease recognition;Voice;Acoustic features Statistical pooling KNN;NN;LDA;Decision tree classifier;GB;UPDRS;Neurological diseases;Parkinson's disease;Machine learning;Artificial neural networks;Speech recognition;Speech enhancement;Acoustics|
|[Music Note Recognition Based Physical Therapy in Parkinson's Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090886)|A. Varughese; K. S|10.1109/IITCEE57236.2023.10090886|nan;Audio systems;Signal processing;Matlab;Music;Parkinson's disease|
|[Nonlinear Prediction of Speech Signal using Short Term Forecasting Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091026)|C. N; M. M. D|10.1109/IITCEE57236.2023.10091026|Nonlinear modeling;time series;speech prediction;forecasting;chaos theory;Chaos;Computational modeling;Time series analysis;Stochastic processes;Predictive models;Prediction algorithms;Delays|
|[Impact of Channel Length Scaling on the Performance of Recessed GaAs Channel DGJLFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090908)|A. A. Pratyay; T. Nahar; M. S. Hasan; S. Sultana; M. R. Islam|10.1109/IITCEE57236.2023.10090908|GaAs;Recessed;Electric field profile;Double gate;Short channel effects;Junction-less;Performance evaluation;Gallium arsenide;Field effect transistors;Logic gates;Electric variables;Threshold voltage;Silicon|
|[Detection of Human Brain Tumors Using an UWB Patch Antenna at 28GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090949)|S. P. N; B. S; K. K. N; P. A. Vijaya|10.1109/IITCEE57236.2023.10090949|Inset fed antenna;UWB;detection of tumor in the Brain;Measurement;Patch antennas;Bandwidth;Voltage;Ultra wideband antennas;Electric fields;Tumors|
|[Realization Criteria of Terminal Radiation Disposition on Circular Aperture Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090880)|S. H. P; A. Nallathambi; S. Nallathambi|10.1109/IITCEE57236.2023.10090880|Terminal Radiation Disposition;TRD;Scilab integration;Circular Aperture;XML;SciLab;Databases;Computational modeling;Aperture antennas;Software;Mathematical models;Data models;Antenna radiation patterns|
|[Feedback Linearization Based Robust Integral Optimal Sliding Mode Control of Electromagnetic Levitation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091006)|A. Pandey; D. M. Adhyaru|10.1109/IITCEE57236.2023.10091006|Robust control;sliding mode control;integral optimal sliding mode control;feedback linearization;modelling;electromagnetic levitation system;Stability criteria;Riccati equations;Optimal control;Levitation;Mathematical models;Robustness;Feedback linearization|
|[Vehicle Braking System using MSP430 Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090975)|J. Sodhani; H. P. Agrawal; D. Jain|10.1109/IITCEE57236.2023.10090975|MSP 430 microcontroller;IR sensor;LCD Display;DC supply;Road accidents;Microcontrollers;Roads;Sociology;Timing;Safety;Brakes|
|[Tuning of Fuzzy Controller by Variable Clustered Fuzzy Rules and Its Application to Overhead Crane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090889)|I. Naskar; A. K. Pal; N. K. Jana|10.1109/IITCEE57236.2023.10090889|fuzzy C-Means clustering algorithm and similarity analysis;fuzzy controller tuning;fuzzy Rule extraction;Self-tuning fuzzy proportional plus derivative controller;Cranes;Computational modeling;Clustering methods;Process control;Feature extraction;Stability analysis;Computational efficiency|
|[Identification of Key Genes Involved in Polycystic Ovary Syndrome in Obese Patients: A Bioinformatics Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091003)|M. Kaur; N. Dahiya; A. Sharma; V. Singh; R. Gupta|10.1109/IITCEE57236.2023.10091003|PCOS;obesity;microarray;differential gene expression;Obesity;Sequential analysis;RNA;Throughput;Genetics;Gene expression;Bioinformatics|
|[Cassava Disease Classification with Knowledge Distillation for use in Constrained Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090898)|S. Shivganesh; A. Ambalavanan; A. C; B. R. Selvamani|10.1109/IITCEE57236.2023.10090898|Cassava;Knowledge Distillation;Constrained Devices;CNN;Performance evaluation;Deep learning;Knowledge engineering;Computational modeling;Transfer learning;Predictive models;Resource management|
|[Metal Surface Defect Detection using Object Detection Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090466)|H. Shah; V. Patil|10.1109/IITCEE57236.2023.10090466|Surface defect detection;visual inspection;object detection;mAP;Manufacturing industries;Visualization;Oils;Merging;Metals;Object detection;Manuals|
|[A Pre-trained YOLO-v5 model and an Image Subtraction Approach for Printed Circuit Board Defect Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090861)|A. K. Lailesh; J. A. Richi; N. Preethi|10.1109/IITCEE57236.2023.10090861|printed circuit board;defect detection;YOLO-v5;image subtraction;structural similarity index;computer vision;electronic manufacturing;automatic optical inspection;Industries;Training;Visualization;Uncertainty;Printed circuits;Training data;Manuals|
|[Controlling of Electrical Vehicle Charging Conditions using PV based Multi-Mode Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090924)|S. Vendoti; A. C. Shekhar; S. V. V. G. Bapuji; G. L. Reddy|10.1109/IITCEE57236.2023.10090924|Electric Vehicle;charging station;vehicle to home;energy management system;Voltage fluctuations;Simulation;Production;Charging stations;Pulse width modulation;Generators;Electric vehicle charging|
|[Model Based Design Accelerometer Control system in EV-ECU For HIL Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091049)|D. Kalyan; K. Sudhakar; P. P. Venkata Sai; J. S. V. Gopala Krishna|10.1109/IITCEE57236.2023.10091049|Electric vehicle;Hil Simulation;Accelerometer behavior;test platform;Modular design;Accelerometers;Regulators;Software packages;Hardware-in-the-loop simulation;Sensor phenomena and characterization;Mathematical models;Hardware|
|[Dynamic Programming-Based Energy Management Strategy Optimization for PV System Hybrid Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090989)|V. Suresh; M. S. Bhaskar; K. D. Kumar; K. B. Chandra|10.1109/IITCEE57236.2023.10090989|Vehicle electric hybrid VEH;fuel consumption;hybrid vehicle model;rules-based energy management;optimization algorithm;Grey wolf optimizer;Metaheuristics;Sociology;Regulation;Hybrid electric vehicles;State of charge;Automobiles;Reliability|
|[Comparison of Schmitt Trigger Oscillators using logic gates in 45nm CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091074)|V. Bindushree; G. Pavithra; S. Shruthi; V. K. S. Patel|10.1109/IITCEE57236.2023.10091074|Oscillator;CMOS;Schmitt trigger;Communication systems;Logic gates;CMOS technology;Libraries;Delays;Power dissipation;Oscillators|
|[VLSI Synthesis of Multiply and Accumulate Structures Using Distributed Arithmetic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091002)|M. Bharathi; Y. J. M. Shirur|10.1109/IITCEE57236.2023.10091002|Distributed Arithmetic (DA);1BAAT (One Bit at a Time);2BAAT (Two Bit at a Time);LUT (Look-Up Table;General Purpose Processor (GPP);Performance evaluation;Program processors;Buildings;Digital signal processors;Computer architecture;Very large scale integration;Real-time systems|
|[A Robust Model for the Identification of Bolt Loosening in Steel Structures Based on Modified Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090969)|S. Shankari; S. Rohini|10.1109/IITCEE57236.2023.10090969|Nut Loosening;nuts and bolts;Convolutional Neural Network;LSTM;SHM;Steel Structure;Bridges;Deep learning;Statistical analysis;Computer architecture;Fasteners;Throughput;Steel|
|[Rock / Mine Classification Using Supervised Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091031)|S. K; K. R; P. S. Kumar; R. V; G. R. J. Lakshmi|10.1109/IITCEE57236.2023.10091031|Light gradient boosting algorithm;Rock/mine classification;Machine Learning algorithms (MLA);Accuracy;Precision;Execution time;Training;Machine learning algorithms;Sonar measurements;Sonar;Tunneling;Rocks;Boosting|
|[Crowdfunding Fraud Prevention using Smart Contracts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091077)|A. Kumar; J. Lamba; B. S. Rawal; M. Sathiyanarayanan; N. Alvarez|10.1109/IITCEE57236.2023.10091077|Blockchain;Explorer;Addresses;NFTs;Wallets;System performance;Smart contracts;Optimized production technology;Companies;Blockchains;Fraud;Stakeholders|
|[Identifying Cyber Extremism Sentiments using ROBERTA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090910)|S. Patel; K. Raja; J. S. Duela; T. M. Chen; M. Sathiyanarayanan|10.1109/IITCEE57236.2023.10090910|extremism;natural language processing;ROBERTA;Training;Ethics;Image recognition;Law enforcement;Natural languages;Machine learning;Organizations|
|[The Intercorrelation between Zero trust, Dark web, and its Implications in the field of Digital Forensics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091054)|G. Gopan; A. Subramanian; B. S. K B; K. Duraipandian; M. Sathiyanarayananan|10.1109/IITCEE57236.2023.10091054|Zero Trust;Dark Web;Digital Forensics;Data Breach;Dark Web;Digital forensics;Organizations;Data breach;Market research;Zero Trust|
|[Predicting Abnormal User Behaviour Patterns in Social Media Platforms based on Process Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091025)|S. G; D. Chandrasekaran; M. D. Sre; M. Sathiyanarayanan|10.1109/IITCEE57236.2023.10091025|Cyber Bulling;Cybercrime;social media;Process Mining;Data Mining;Visualization;Publishing;Soft sensors;Cyberbullying;Data science;Prediction algorithms;Behavioral sciences|
|[Cerebellum-Inspired Artificial Neural Networks Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090893)|A. R. Nurutdinov; R. K. Latypov|10.1109/IITCEE57236.2023.10090893|cerebellum model;artificial neural network;analog computing;Cerebellum;Neuromorphics;Computational modeling;Computer architecture;Differential equations;Machine learning;Brain modeling|
|[The application and enlightenment of augmented reality technology in social disorders of autistic patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091063)|W. Zhu; W. Zhao; H. Xu|10.1109/IITCEE57236.2023.10091063|Autism spectrum disorder;ASD;Augmented reality;AR;Social communication barriers;Autism;Behavioral sciences;Augmented reality|
|[Fraud Detection in Banking Transactions Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091067)|R. Achary; C. J. Shelke|10.1109/IITCEE57236.2023.10091067|Credit card fraud;fraudulent transactions;KNN classifier;Random Forest;XGBoost;Blockchain;Artificial intelligence;Machine learning algorithms;Correlation;Demography;Computational modeling;Finance;Banking;Forestry|
|[Dual Power Supply with Real Time GPS Tracking for Vehicular Application Using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091039)|P. R. Kshirsgar; H. Manoharan; E. A. B; B. A. V; K. K; V. P|10.1109/IITCEE57236.2023.10091039|Electric Vehicle;GPS;Self charge;Arduino Nano;Arduino Nano 33 BLE sense Board;ESP8266;Prototypes;Wheels;Electric vehicles;Real-time systems;Sensors;Batteries;Solar panels|
|[Elliptic Curve Scalar Multiplication over Prime Field for both Affine and Jacobian Coordinates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090927)|R. Raiashree; S. A. Durai; M. S. Murali; P. Manideep; M. R. Kannan|10.1109/IITCEE57236.2023.10090927|Group Operation;EC Point Addition;EC Point Doubling;Affine Coordinate;Jacobian Coordinate and Finite Prime field;Jacobian matrices;Elliptic curves;Elliptic curve cryptography;Software;Hardware;Fourth Industrial Revolution;Error correction codes|
|[Hybrid Feature Hashing and Recurrent Neural Network based LSTM Architecture for Real Time Malware Analysis and Detection Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090960)|V. Peroumal; O. M; A. Tiwari; S. V; B. Mathew|10.1109/IITCEE57236.2023.10090960|Accuracy;False Positive;False Negative;LSTM;Malware Detection;Neural Network;Python Framework;Recurrent;Training;Recurrent neural networks;Computational modeling;Feature extraction;Boosting;Malware;Real-time systems|
|[Correction and Estimation of Workout Postures with Pose Estimation using AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090463)|T. Hande; B. Kakirwar; A. V. Bharadwaja; P. Kshirsagar; A. Gupta; P. Vijayakumar|10.1109/IITCEE57236.2023.10090463|Computer Vision;Deep Convolutional Networks;Posture Analysis;Workout Recognition;Training;Pose estimation;Cameras;Real-time systems;Labeling;Artificial intelligence;Injuries|
|[Design of Novel Coupled FED MIMO Antennas for Future Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090888)|K. Thilagam|10.1109/IITCEE57236.2023.10090888|Coupled FED;MIMO;smartphones;5G antennas;Wireless communication;Wireless LAN;5G mobile communication;Patch antennas;Bandwidth;Broadband antennas;Wrapping|
|[Design and development of information management system using QR code based technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091058)|S. Prasad; M. P; R. Adhyapak; A. N. Pai; V. Gadad; S. Rajendran|10.1109/IITCEE57236.2023.10091058|QR Code;Website;Education;Campus tour;Library;Uniform resource locators;Industries;QR codes;Information management;Security;Task analysis;Automotive engineering|
|[Traffic Congestion Prediction Based on Spatio-Temporal Graph Structure Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090902)|C. Bannur; C. Bhat; G. Goutham; H. R. Mamatha|10.1109/IITCEE57236.2023.10090902|Traffic Prediction;Spatiotemporal data;GTFS data;Public Transit Systems;Graph Neural Network;Neo4j;Knowledge engineering;Roads;Computational modeling;Urban areas;Predictive models;Graph neural networks;Spatiotemporal phenomena|
|[Implementation of Realtime design of crowd Enumeration via tracking using AI system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090914)|M. R. Kounte; J. Rishitha; S. S. Setty; S. S|10.1109/IITCEE57236.2023.10090914|Crowd Enumeration;Deep Learning (DL);Intelligent Machine (IM);Convolution Neural Network (CNN)and Machine learning (ML);Deep learning;Schedules;Profitability;Computational modeling;Neural networks;Prototypes;Object detection|
|[Wheelchair and PC Volume Control Using Hand Gesture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090900)|M. Varalatchoumy; S. Srividhya; M. Praveen; M. Ananda|10.1109/IITCEE57236.2023.10090900|WheelChair;Volume;Hand Gesture;Wireless communication;Performance evaluation;Webcams;Wheelchairs;Process control;Propulsion;Real-time systems|
|[An Analysis of Cotton Crop for Detection of Pests and Diseases Using ML and DL Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090894)|K. S. Susheel; R. Rajkumar|10.1109/IITCEE57236.2023.10090894|Machine Learning;Deep Learning;Image Processing;Cotton Pests and Diseases;Plant Fiber;Segmentation Technique;Support vector machines;Analytical models;Soft sensors;Crops;Production;Optical fiber networks;Classification algorithms|
|[Improvised Round Robin scheduling Algorithm with the Calculated Time Quantum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090906)|M. B. M; A. P. Kumar; S. P. Rajur|10.1109/IITCEE57236.2023.10090906|Time quantum;Variance;Round Robin;Mean;Quantum computing;Round robin;Standards|
|[Performance Analysis of Dielectric Modulated Dual Material Double Gate Hetero Stack (DM-DMDG-HS) TFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090967)|J. Kumar; R. Chaudhary; R. Saha|10.1109/IITCEE57236.2023.10090967|Biosensors;Cavity;HS DMDG TFET;Sensitivity;TFET;Proteins;Sensitivity;TFETs;Sensitivity analysis;Fill factor (solar cell);Molecular biophysics;Logic gates|
|[Power Quality Enhancement Using Active Shunt Harmonic Filter Based on PV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090903)|S. Gupta; A. Gill; M. Singh|10.1109/IITCEE57236.2023.10090903|SAHF;P&O algorithm;Harmonics;THD;PV system;MPPT technique;Power quality;power enhancement;VSI;Hysteresis controller;Reactive power;Renewable energy sources;Passive filters;Power quality;Harmonic filters;Harmonic analysis;Active filters|
|[MATLAB Simulation of Nine Level Modular Multilevel Inverter for HVDC Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091005)|K. Jayaram|10.1109/IITCEE57236.2023.10091005|Multilevel Inverter;Modular multilevel converter (MMC);MMC topologies;HVDC Applications;Multilevel converters;Software packages;HVDC transmission;Bridge circuits;Switches;Multilevel inverters;Topology|
|[Loadability Enhancement of Power System using Sine Cosine Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091062)|M. K. Kar; P. K. Tripathy|10.1109/IITCEE57236.2023.10091062|Loadability;FACTS Controllers;Optimal Power Flow;Power Los Minimization;SCA;Power transmission lines;Simulation;Loading;Metaheuristics;Flexible AC transmission systems;Power systems;Resource management|
|[An Intelligent Control for M-STATCOM Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091076)|R. Sharma; V. H. Makwana|10.1109/IITCEE57236.2023.10091076|STATCOM;Power Quality;Voltage sag;Modular Multilevel Inverter;Distributed generation;THD;Reactive power;Uncertainty;Software packages;System performance;Power quality;Power system dynamics;Stars|
|[An Integrated Approach based on Supervised and Unsupervised ML algorithms for Efficient Design of Demand Response Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090913)|S. Srivastava; A. Kanungo; T. Jain|10.1109/IITCEE57236.2023.10090913|Clustering;decision tree classifier;demand response;demand features;temporal features;Machine learning algorithms;Demand side management;Clustering algorithms;Machine learning;Demand response;Smart meters;Labeling|
|[The Dashboard Infrastructure of Electric Vehicles with IoT and Wireless Power Transfer System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091082)|C. S. Tiruvuri; K. Dusarlapudi; K. Sudhakar; J. S. V. G. Krishna; A. P. Ranjani; P. P. V. Sai|10.1109/IITCEE57236.2023.10091082|WPT;IoT Sensors;Thingspeak;Wireless communication;Wireless sensor networks;Transmitters;Wireless power transfer;Light emitting diodes;Power systems;Batteries|
|[eChoupal: The Ticker Tape of Agricultural Markets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090873)|S. Garg|10.1109/IITCEE57236.2023.10090873|ICT Intervention;E-Choupal;Agricultural Tech;Shape;Supply chains;Market research;Information and communication technology;Consumer electronics|
|[TagAlong: An Assistive Device for Alzheimer Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090899)|M. A. Lobo; K. Sowmya; H. Mimani; A. S. Savanth|10.1109/IITCEE57236.2023.10090899|IoT;Alzheimer;GPS Tracking;Health Parameters;Reminder Alerts;Android Application;Software;Behavioral sciences;Sensors;Internet of Things;Assistive devices;Software measurement;Servers|
|[Role and Interval Changing Algorithm (RICA) for Energy Consumption Node in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091040)|N. M. S; B. Veronica; H. Shaheen; S. Azahad; L. Jayanthi|10.1109/IITCEE57236.2023.10091040|Sensor node;energy;RICA;interval rate;leaf node;Energy consumption;Wireless sensor networks;Clustering algorithms;Computer architecture;Energy states;Energy efficiency;Sensors|
|[Analysis of BER Performance over AWGN and Rayleigh Channels using FSK and PSK Modulation Schemes in LoRa Based IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091096)|R. Swathika; S. M. D. Kumar|10.1109/IITCEE57236.2023.10091096|BER;IoT AWGN;Rayleigh;CSS;Spreading Factor;MATLAB;FSK;PSK;Modulation;Phase shift keying;Analytical models;Codes;Chirp;Bit error rate;Frequency shift keying;Rayleigh channels|
|[A Smart Hydroponic Farming System Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090860)|L. S. Kondaka; R. Iyer; S. Jaiswal; A. Ali|10.1109/IITCEE57236.2023.10090860|Deep Water Culture (DWC);Nutrients Film Technique (NFT);Hybrid System;Internet of Things (IOT) flask server;hydroponic;random forest regression model;web-app;Temperature sensors;Temperature measurement;Computational modeling;Hydroponics;Crops;Machine learning;Control systems|
|[Energy-Efficient Resource Allocation and Routing Protocols for IoT-based WSN: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091045)|A. Kumar; K. K. Agrawal|10.1109/IITCEE57236.2023.10091045|IoT (Internet-of-things);Routing Protocols;Resource allocation;route discovery;Wireless sensor networks;Smart cities;Optimization methods;WiMAX;Routing protocols;Safety;Resource management|
|[Evaluation of Solar Energy Generation and Radiation Prediction Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091088)|J. Gupta; G. Shrivastava; S. S. Raghuwanshi|10.1109/IITCEE57236.2023.10091088|machine learning;photovoltaic generation;forecasting;neural networks;regression;Temperature dependence;Renewable energy sources;Machine learning algorithms;Art;Solar energy;Machine learning;Production|
|[Wind Turbine System based on Fuzzy Logic based MPPT Controller and Boost type Vienna Rectifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091023)|D. Reddy; V. Kulkarni; S. Ramaswamy|10.1109/IITCEE57236.2023.10091023|MPPT;Vienna Rectifier;Wind Turbine System;Fuzzy Logic Controller and SVPWM;Maximum power point trackers;Fuzzy logic;Wind speed;Velocity control;Rectifiers;Wind power generation;Harmonic analysis|
|[Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090956)|T. S. Y; L. C. M; N. G; R. S R|10.1109/IITCEE57236.2023.10090956|Covid-19;monkey pox;Machine Learning (ML);Supervised Learning (SL);Unsupervised Learning (USL);COVID-19;Support vector machines;Machine learning algorithms;Computer viruses;Supervised learning;Prediction algorithms;Classification algorithms|
|[Comprehensive Study on RS_FMRI and EEG Using Deep Learning Approach for Brain Stroke](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090864)|S. Mounika; R. S. R|10.1109/IITCEE57236.2023.10090864|f-MRI;EEG;rs-fMRI;Low-High Order Functional Connectivity;Brain Stroke;Machine Learning;Deep Learning;Deep learning;Neuroscience;Cerebral cortex;Cerebrum;Functional magnetic resonance imaging;Feature extraction;Electroencephalography|
|[Real time Pothole Detection System – An Application facilitating public safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091000)|L. Panda; K. B. Sri; R. S R|10.1109/IITCEE57236.2023.10091000|pothole;deep-learning;Inception V3;real time;Support vector machines;Deep learning;Technological innovation;Roads;Artificial neural networks;Production;Real-time systems|
|[Traffic Sign Classification using Le-Net Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090988)|A. Nalla; J. Gottiukkala; R. S R|10.1109/IITCEE57236.2023.10090988|LeNet;Traffic sign;CNN;Costs;Computer architecture;Maintenance engineering;Cameras;Hardware;Autonomous automobiles;Safety|
|[Ultrasonic Sensor Based Door Security Camera with Wireless Data Transfer in Telegram Bot Using WIFI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090954)|N. Raghu; I. Miah; A. B. R. Tonmoy|10.1109/IITCEE57236.2023.10090954|Internet of Things;Ultrasonic sensor;MQTT cloud;Telegram BoT;Wireless communication;Wireless sensor networks;Cameras;Chatbots;Data transfer;Acoustics;Sensors|
|[Informetric Analysis of Researches on Application of Artificial Intelligence in Legal Practice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091079)|S. Chen; J. Wang; Q. Zhang|10.1109/IITCEE57236.2023.10091079|AI;Legal Practice;Informetric Analysis;Legal Service;Deep learning;Epidemics;Electric potential;Law;Electric variables measurement;Documentation;Position measurement|
|[Intelligent Tourism Platform Model Construction Based on Artificial Intelligence: Taking Wudang Mountain as an example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091092)|M. Du; W. Zhao|10.1109/IITCEE57236.2023.10091092|artificial intelligence;intelligent travel;voice interaction;virtual reality;Economics;Solid modeling;Pandemics;Tourism industry;Virtual environments;Transportation;Natural language processing|
|[Stock Movement Prediction via Temporal Convolutional Network and Interactive Attention Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091033)|S. Guo; H. Ai; S. Li|10.1109/IITCEE57236.2023.10091033|Stock movement prediction;Temporal Convolution Network;Interactive Attention Network;Knowledge engineering;Convolution;Social networking (online);Knowledge graphs;Share prices;Predictive models;Feature extraction|
|[Intelligent diagnosis system of cardiovascular disease based on case-based reasoning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091094)|C. Zhang; Q. Zhu; H. Li|10.1109/IITCEE57236.2023.10091094|case-based reasoning(CBR);cardiovascular disease;case retrieval;weight assignment;introspective learning;Heuristic algorithms;Decision making;Optimization methods;Receivers;Medical services;Dynamic scheduling;Cognition|
|[Research on the Impact of High-Speed Railway on the Regional Industrial Structure of Beijing-Tianjin-Hebei Region Based on GWR Model Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090878)|S. Yao; S. Yao; Y. Liu; Z. Zhang; Y. Wang|10.1109/IITCEE57236.2023.10090878|high-speed railway;GWR;Beijing-Tianjin-Hebei region;economic distribution;Analytical models;Graphical models;Economic indicators;Computational modeling;Urban areas;Market research;Rail transportation|
|[An Iot-Based Covid Patient Health Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090868)|R. Pandurangan; R. Prathipa; G. Sundari|10.1109/IITCEE57236.2023.10090868|Arduino;JAVA;IoT;data acquisition unit;mobile application etc;Temperature sensors;Temperature measurement;Temperature distribution;Technological innovation;Patient monitoring;Java;Microcontrollers|
|[Animated and Oil Painting Image Generation from Real World Images using Super-Pixel based SLIC Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090962)|V. SV; S. S; S. G; M. V|10.1109/IITCEE57236.2023.10090962|Cartoon Image;Oil Painting;SLIC;Super-Pixel;Machine Learning;Image quality;Privacy;Machine learning algorithms;Social networking (online);Image synthesis;Clustering algorithms;Media|
|[A Survey on Copy Move Forgery Detection (CMFD) Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090884)|A. R; V. K. S. B; S. B. M|10.1109/IITCEE57236.2023.10090884|Copy Move Forgery Detection;Speeded Up Robust Feature;Discrete Cosine Transform;Discrete Wavelet Transform;Wavelet transforms;Image segmentation;Digital images;Forensics;Copyright protection;Feature extraction;Forgery|
|[Image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090879)|S. K. R; B. Rajalakhmi; P. Geethika; I. C; R. S. Kolalapudi|10.1109/IITCEE57236.2023.10090879|Deep-Learning;MSR;MASRCR;CNN;PET;HE;Histograms;Rain;Image recognition;Military computing;Image color analysis;Surveillance;Mobile applications|
|[Classification of Brahmi script characters using HOG features and multiclass error-correcting output codes (ECOC) model containing SVM binary learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091084)|A. S. Nagane; C. H. Patil; S. M. Mali|10.1109/IITCEE57236.2023.10091084|Brahmi Script;OCR;feature extraction;classification;Support vector machines;Codes;Computational modeling;Optical character recognition;Feature extraction;Character recognition|
|[Power Spectral Density Analysis of Decomposed EMG Signals for Dominant and Non-dominant Hands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091030)|K. Mahajan; R. Shriram; N. Daimiwal; S. Gandhi|10.1109/IITCEE57236.2023.10091030|Biceps brachii;Dominating hand;EMG;Non-dominating hand;PSD;Power measurement;Density measurement;Force;Central nervous system;Muscles;Electromyography|
|[Determination of Feet Elevation for the Development of a Robotic Squat-Assist Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090952)|H. K. Ramachandran; A. Thirupathi|10.1109/IITCEE57236.2023.10090952|assistive technology;squatting;constipation;squat-assist;elder care;rehabilitation;Torso;Legged locomotion;Knee;Sociology;Bending;Statistics;Robots|
|[Planar Thin Film Resistor Based Wilkinson Power Divider Realization & Its Characterization at S-band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090985)|R. Behera; S. S. Lonkadi; A. Andhiwal; K. Singh|10.1109/IITCEE57236.2023.10090985|Broad Bandwidth;Microstrip;Power Divider;Thin film Resistor;WPD;Resistors;Power dividers;Thermal resistance;Chromium;Transformers;Stability analysis;Topology|
|[A Planar Concentric Circular Ultra-wideband 5/6 GHz Notch Antenna for Wireless Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090998)|S. K. Dash; J. S. Varma; B. K. Behera; B. Maity; S. Mohapatra; J. R. Panda|10.1109/IITCEE57236.2023.10090998|Slotted U-shape;UWB antennas;Concentric-circular;Wireless communication;Wireless LAN;Slot antennas;Shape;Microstrip antennas;Power system stability;Ultra wideband antennas|
|[FPGA Implementation Of Reconfigurable Address Generator For Various Standard Interleaver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090916)|V. K. Perumal; K. Mahalingam; D. P. Raja|10.1109/IITCEE57236.2023.10090916|nan;Wireless LAN;Phase shift keying;Protocols;Computer architecture;WiMAX;Generators;Hardware|
|[COVID-19 SOP Compliance And Monitoring Electronic System For Business And Public Places Using Arduino Uno](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090961)|S. Mahendrakumar; N. S. Kumar; G. Chandrasekaran; K. Vanchinathan; K. Vanitha; N. Priyadarshi; M. S. Bhaskar; N. Kumar|10.1109/IITCEE57236.2023.10090961|COVID-19;Mask detection;Sanitizer;Temperature;Door open;MATLAB;COVID-19;Temperature sensors;Temperature measurement;Embedded systems;Computer viruses;Webcams;Safety|
|[Comparison of Machine Learning Algorithms for Prediction of Stock Prices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090974)|M. Bansal; D. Sharma|10.1109/IITCEE57236.2023.10090974|machine learning;supervised learning;machine learning algorithms;regression;classification;Machine learning algorithms;Machine learning;Predictive models;Measurement;Data models;Prediction algorithms;Computational modeling|
|[Enhancing the Prediction of Diabetics using Bagging Ensambler Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090921)|M. A. Kumar; G. Manivasagam; K. Kathirvel; V. Kavitha; A. Gupta|10.1109/IITCEE57236.2023.10090921|Machine Learning;Bagging;Classifications;Ensemble Methods;Support vector machine classification;Forestry;Cardiac arrest;Stroke (medical condition);Diabetes;Ensemble learning;Bagging|
|[Empirical Analysis of QoS & Security Aware IoV Routing Models from a Statistical Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090862)|P. Ulhe; S. Asole|10.1109/IITCEE57236.2023.10090862|IoV;Routing;Machine;Learning;Delay;Throughput;Security;PDR;Paths;Measurement;Analytical models;Costs;Biological system modeling;Computational modeling;Scalability;Quality of service|
|[Leak Localization Using Graph Based Reinforcement Leaning for Subterranean Electrical Cables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090895)|I. Kar; L. Chakraborty; R. Das|10.1109/IITCEE57236.2023.10090895|Leak detection;reinforcement learning;Extreme anomaly detection;Location awareness;Measurement;Deep learning;Q-learning;Fluids;Maintenance engineering;Dielectrics|
|[Classification of potato diseases using deep learning apprach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090983)|B. Samatha; D. K. Rao; T. Syamsundararao; G. Mani; N. Karyemsetty; M. V. B. T. Santhi|10.1109/IITCEE57236.2023.10090983|Deep Learning;image processing;Potato's;segmentation;Deep learning;Support vector machines;Image segmentation;Neural networks;Inspection;Feature extraction;Skin|
|[Bit Error Rate Prediction Analysis on modified dyadic wavelet transform based Channel Estimation in comparison with Traditional wavelets for MIMO OFDM system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090964)|M. Sreelekha; A. Raja; T. J. Nagalakshmi|10.1109/IITCEE57236.2023.10090964|Novelmodified dyadic wavelet transform;Traditional wavelet;Orthogonal Frequency Division Multiplexing;Bit Error Rate Prediction;Multiple Input Multiple Output;Wireless Communication;Wavelet transforms;Statistical analysis;OFDM;Bit error rate;Channel estimation;Predictive models;Wavelet analysis|
|[Performance Analysis of a Sensorless DC Motor Using Neuro Fuzzy Logic Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090872)|G. T. S. Rajan; V. Sivachidambaranathan; J. B. P. Glady; R. Babu; G. Subramanian; S. D. S. Jebaseelan; A. S. M. Antony|10.1109/IITCEE57236.2023.10090872|Brushless DC Motor;Neuro Fuzzy Logic Control;Hall Sensor;Back EMF method;PID Controller;Fuzzy logic;Motor drives;Brushless DC motors;Velocity control;Performance analysis|
|[Hybrid Grid Charging Station for Electric Vehicle Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091021)|G. T. S. Rajan; V. Sivachidambaranathan; A. R. Babu; A. S. M. Antony; J. B. P. Glady; S. D. Sundarsingh Jebaseelan|10.1109/IITCEE57236.2023.10091021|Electric Vehicle;Grid;PV;IoT;Focusing;Transportation;Tariffs;Charging stations;Reliability;Energy storage|
|[A Review on Various Supply Sources for DC Micro Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091064)|C. S. Kala; G. Mahesh; B. T. Rao; G. Swapna|10.1109/IITCEE57236.2023.10091064|Photovoltaic (PV) systems;Storage Systems;Transducers;DC Micro Grids;Photovoltaic systems;Renewable energy sources;Transducers;Power demand;Costs;Green products;Solar energy|
|[A Novel Asymmetric 21-Level Inverter with PV System fed to Motor Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090875)|D. R. Kishore; K. M. Syam; T. R. B. Rao; P. S. Kumar|10.1109/IITCEE57236.2023.10090875|PV System;Sepic Converter;MPPT;ANFIS;MLI;THD;Total harmonic distortion;Renewable energy sources;Fluctuations;Software packages;System performance;Maintenance engineering;DC motors|
|[Grid-Connected Photovoltaic Inverter with a High-Gain Reboost Luo Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090892)|S. Kollati; T. S. Krishna; P. C. Kumar; D. D. Bhargavi|10.1109/IITCEE57236.2023.10090892|PV System;DC-DC Converter;reboost luo converter;low DC voltage;Photovoltaic systems;PI control;Voltage source inverters;Switching frequency;Switching loss;Steady-state;Solar system|
|[Active and reactive power regulation utilising an ANN controller for a PV Fed Trans Quasi-Z Source Network with a Multilevel Grid-Tied System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090990)|K. Saritha; K. D. Kumar; K. Anil; S. Ansari|10.1109/IITCEE57236.2023.10090990|PV System;Z Source;Shoot through;Non Shoot through;Seven Level MLI;Photovoltaic systems;Reactive power;Network topology;Inverters;Software;Regulation;Topology|
|[Fall Detection Among Elderly Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090887)|B. M. Sundaram; B. Rajalakshmi; R. K. Mandal; S. Nair; S. S. Choudhary|10.1109/IITCEE57236.2023.10090887|Fall Detection/Prediction;Vision Surveillance;Sensors;Performance evaluation;Deep learning;Visualization;Time-frequency analysis;Surveillance;Sensor systems;Older adults|
|[Sensor Data Transforming into Real-Time Healthcare Evaluation: A Review of Internet of Things Healthcare Monitoring Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091059)|A. Sundas; S. Badotra; G. Singh|10.1109/IITCEE57236.2023.10091059|Internet of Things;Self-care;Telecare;Health monitoring systems;healthcare;machine learning;data mining;Costs;Hospitals;Surveillance;Computer architecture;Machine learning;Internet of Things;Data mining|
|[COVID-19 prediction with Chest X-Ray images using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090951)|P. Maddula; P. Srikanth; P. K. Sree; P. B. V. R. Rao; P. T. S. Murty|10.1109/IITCEE57236.2023.10090951|Convolutional Neural Networks (CNN);COVID-19;VGG 19;chest X-Ray;COVID-19;Pathology;Sensitivity;Pandemics;X-rays;Convolutional neural networks;Public healthcare|
|[Centralized Virtual Mapping Algorithm in Virtual Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091097)|G. Florance; R. J. Anandhi|10.1109/IITCEE57236.2023.10091097|Internet service provider;Virtual Node;Network Virtualization;Virtual Network;Virtual Link;VN mapping;Performance evaluation;Protocols;Web and internet services;Collaboration;Routing;Load management;Virtualization|
|[Grey Wolf Optimization Aided Mppt Algorithm with Sepic Converter for Grid Integrated Pv System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090912)|R. K. Dondapati; P. Y. Naidu; L. Sai; Z. Himahitho|10.1109/IITCEE57236.2023.10090912|Electric Vehicles (EVs);Renewable energy sources (RES);Single ended primary inductance converter (SEPIC);Grey Wolf Optimization (GWO);Voltage source inverter (VSI);Total harmonic distortion (THD);Total harmonic distortion;Pollution;Voltage fluctuations;Voltage source inverters;Electric vehicles;Synchronization;Energy management|
|[Implementation of four-phase interleaved DC–DC boost converter for electric vehicle power system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090980)|S. Vendoti; K. Ravi Shankar; P. Chinni Gopal; K. R. S. Akhil Kumar|10.1109/IITCEE57236.2023.10090980|FP-IBC converter;Boost Converter;Electric Vehicle;3-Phase Inverter;PWM Technique and THD;Low voltage;Switching frequency;DC-DC power converters;Switches;High-voltage techniques;Control systems;Load management|
|[Application of Blockchain in Digital Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091070)|S. Baskar; P. V. Gopirajan|10.1109/IITCEE57236.2023.10091070|Blockchain;digital;EHR;healthcare;Industries;Wireless networks;Medical services;Companies;Blockchains;Electronic healthcare;Monitoring|
|[Smart Trash Bin-An Effective Solution for Rural and Under Developed Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090865)|N. V. M. Kumar; G. Nagappan; I. S. Deepak; J. Charishma; K. B. Sruthi|10.1109/IITCEE57236.2023.10090865|IoT;Internet of Things;Sensors;smart circuitry;Urban areas;Sociology;Containers;Writing;Sensor systems;Internet of Things;Statistics|
|[Spoken Dialog Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090867)|M. V. Anand; R. Aarthi; R. Abinava|10.1109/IITCEE57236.2023.10090867|Human Computer Interaction (HCI);Spoken Dialog System (SDS);Interactive voice response;Ticket booking;Dialog management;Human computer interaction;Computational modeling;Natural languages;Weather forecasting;Speech recognition;Agriculture;Task analysis|
|[To Prevent Copy Right Infringement Piracy Plagiarism of NCERT Text Books](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090901)|N. V. Ravindhar; G. Nagappan; G. Lokesh; P. Punith; P. Prabhu|10.1109/IITCEE57236.2023.10090901|Plagiarism;Detector;Copyrights;Infringement NCERT Book;Homepage;Teampage;Costs;Publishing;Plagiarism;Materials reliability;Detectors;Writing;Guidelines|
|[Short Term Load Forecasting Using ANN and WNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091081)|M. Ulagammai|10.1109/IITCEE57236.2023.10091081|Artificial neural network;Multi resolution analysis;short term load forecasting;wavelet neural network;TNEB;Load forecasting;Artificial neural networks;Predictive models;Wavelet analysis;Prediction algorithms;Power system reliability;Reliability|
|[Quality of Service Factor based Unfailing Route Formation in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090966)|S. Yuvarani; A. Gayathri; K. J. Velmurugan; V. Meenakshi; S. Sadhana; C. Srinivasan|10.1109/IITCEE57236.2023.10090966|Quality of Service;Wireless Sensor Network;Unfailing Route Formation Opponent node detection;Reinforcement Learning;Wireless communication;Wireless sensor networks;Computational modeling;Packet loss;Quality of service;Reinforcement learning;Delays|
|[Semantic Analysis of Auto-generated Sentences using Quantum Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091087)|L. K. Kumar Pallapothu; V. M. Sunanda Vulavalapudi; P. C. Evuru; P. Medisetty; K. B. Prakash; G. Swain|10.1109/IITCEE57236.2023.10091087|Quantum Computing;Natural Language Processing;Quantum Hardware;Quantum Simulator;Training;Program processors;Semantics;Force;Linguistics;Natural language processing;Hardware|
|[Impact of Dielectric Substrate on the performance of Microstrip Patch Antenna at millimeter wave frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091083)|P. K. Desai; S. Bindu|10.1109/IITCEE57236.2023.10091083|Dielectric Substrate;Microstrip Patch Antenna;millimeter wave frequency;Patch antennas;Simulation;Impedance matching;Dielectric substrates;Millimeter wave technology;Microstrip antennas;Reflection coefficient|
|[Wearable Antenna For Remote Health Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091015)|R. P; Sumathi; S. S. Kaushik; K. B; C. S P|10.1109/IITCEE57236.2023.10091015|Micro strip Patch Antenna;NWCF;e-Textile Antenna;Denim cotton;FR4(lossy);ISM;WBAN (wireless body area network);Wireless communication;Wireless sensor networks;Dielectric losses;Solids;Fabrics;Copper;Older adults|
|[Hydrocapsule: Water generation using peltier and electricity generation by electrolysis of water](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091052)|K. M; N. M. Harapanhalli; I. Jigajinni; K. K. N|10.1109/IITCEE57236.2023.10091052|Drinking water;Electricity;Hydrocapsule;pH value;Natural coagulants;Natural resources;Economics;Earth;Purification;Ecosystems;Wind power generation;Power system reliability|
|[Implementation and Analysis of Various Kalman Filtering Techniques for Target Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090994)|R. P. Rao; R. Ram; B. R. Reddy|10.1109/IITCEE57236.2023.10090994|Radio Detection and Ranging;Extended Kalman Filter;Unscented Kalman Filter;Cubature Kalman Filter;Target tracking;Filtering;Radar;Radar tracking;Mathematical models;Real-time systems;Particle filters|
|[Arduino Uno Based Swarm Intelligence Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090948)|R. N; S. S; A. K. S|10.1109/IITCEE57236.2023.10090948|Swarm Intelligence;Swarm Robotics;Arduino;Industries;Service robots;Navigation;Robot kinematics;Energy resolution;Swarm robotics;Chatbots|
|[Spectral Unmixing for End Member Extraction and Abundance Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090877)|A. Khan; A. D. Vibhute; C. H. Patil; S. Mali|10.1109/IITCEE57236.2023.10090877|Hyperspectral unmixing;Bilinear mixing model;Endmember extraction;nonlinear unmixing;Generalized Bilinear abundance estimation;Reflectivity;Graphical models;Estimation;Lighting;Feature extraction;Libraries;Data models|
|[Blockchain Solution to Electronic Healthcare Records](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090970)|P. Vidap; A. Bhargav; R. Paswan; A. Jewalikar|10.1109/IITCEE57236.2023.10090970|Electronic Healthcare Records;Permissioned Blockchain;Smart Contracts;Chaincode;Ordering Service;Hyperledger Fabric;Transaction Log;Certification Authority;Public Key Interface;World State;Privacy;Smart contracts;Information security;Fabrics;Electronic healthcare;Blockchains;Reliability|
|[Link Prediction in Social Networks using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091004)|G. K; R. Rajasekaran; V. S. R. P. V. Krishna Varun; D. Vuyyuru; B. Thirumaleshwari Devi|10.1109/IITCEE57236.2023.10091004|SVM;Link Prediction;SIN;ML;Social networking (online);Blogs;Directed graphs;Machine learning;Streaming media|
|[IoT based Smart Water Meter for Water Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091019)|A. B. R; A. S. Kadam; A. Kulkarni; P. R. Sankpal|10.1109/IITCEE57236.2023.10091019|smart city;volumetric pricing;IoT;smart metering;water consumption;Meters;Natural resources;Volume measurement;Urban areas;Pricing;Water conservation;Logic gates|
|[Sentiment and Statistical Analysis on Custom Twitter Dataset for 2022 Russo-Ukrainian Conflict](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090995)|H. Thakkar; A. Patil; O. Saudagar; A. Yenkikar|10.1109/IITCEE57236.2023.10090995|Custom Twitter Dataset;Russo-Ukrainian Conflict;Natural Language Processing based Pipeline;Sentiment and Statistical Analysis;Deep learning;Analytical models;Sentiment analysis;Social networking (online);Statistical analysis;Computational modeling;Blogs|
|[Robotic Process Automation (RPA): A software bot for healthcare sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090996)|B. A J; A. N; I. S|10.1109/IITCEE57236.2023.10090996|RPA software bot;RPA tools;EMR;ACME;Intelligent automation;Digital systems;MIMICs;Insurance;Medical services;Media;Chatbots|
|[Power train design and build up of EV born car using power flow analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090958)|V. K; R. V. Parimala; C. PN|10.1109/IITCEE57236.2023.10090958|Power flow analysis;Full Electric Vehicle;Motor and battery sizing;Aerodynamic drag force;Analytical models;Force;Life estimation;Permanent magnet motors;Synchronous motors;Electric vehicles;Mathematical models|
|[A survey on cloud security threats using Deep learning algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090981)|V. S Badiger; D. K. Shyam|10.1109/IITCEE57236.2023.10090981|Cloud computing;Deep learning;threats;unauthorized access;DOS;Phishing;Malware;Deep learning;Computers;Cloud computing security;Area measurement;Software;Security;Computer crime|
|[Machine Learning in Soil Borne Diseases, Soil Data Analysis & Crop Yielding: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091016)|C. Kiruthiga; K. Dharmarajan|10.1109/IITCEE57236.2023.10091016|Agriculture;Horticulture;Data Mining;Machine learning Algorithm;Support vector machines;Machine learning algorithms;Data analysis;Crops;Soil;Prediction algorithms;Market research|
|[An Extensive Review on Different Strategies of Multimedia Data Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091056)|P. Kumar; J. Kaur; R. Sandhu; M. Wamique; A. Yadav|10.1109/IITCEE57236.2023.10091056|Multimedia Mining;Static data;Dynamic Data;Clustering;Social networking (online);Soft sensors;Multimedia computing;Computer architecture;Media;Streaming media;Filtering algorithms|
|[Comparative Study of Scheduling Algorithms for Multiprocessor Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091017)|M. Sreenath; P. A. Vijaya|10.1109/IITCEE57236.2023.10091017|Scheduling Algorithms;Multiprocessor;Real time;Performance;Power demand;Job shop scheduling;Embedded systems;Scheduling algorithms;Data processing;Real-time systems;Functional analysis|
|[Hybrid Spectrum-handoff in Cognitive Radio Ad Hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090925)|M. Uprit; R. N. Raj|10.1109/IITCEE57236.2023.10090925|Cognitive radio network;Dynamic spectrum access;Spectrum Handoff;Channel assignment;Primary user;Degradation;Dynamic spectrum access;Quality of service;Ad hoc networks;Sensors;Cognitive radio;Resource management|
|[An Analysis of the Effectiveness of the Naive Bayes Algorithm and the Support Vector Machine for Detecting Fake News on Social Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090978)|P. S. Vadlamudi; M. Gunasekaran; T. J. Nagalakshmi|10.1109/IITCEE57236.2023.10090978|Fake News;Support Vector Machine;Naive Bayes;Social Media;Machine Learning;Novel Hybrid Approach;Social networking (online);Statistical analysis;Support vector machine classification;Machine learning;Naive Bayes methods;Fake news;Kernel|
|[Credit Card Fraud Detection using ML: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091035)|S. Bonkoungou; N. R. Roy; N. H. A. -E. Ako; U. Batra|10.1109/IITCEE57236.2023.10091035|credit card fraud;online transaction;fraudulent transaction;Support vector machines;Computational modeling;Neural networks;Hidden Markov models;Credit cards;Fraud;Classification algorithms|
|[Cloud Oriented Multiple Owner Keyword Search for Shared Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090866)|N. A. V; D. M. R; S. Rallapalli; S. Dhareshwar; P. K. Pareek; M. Y. K|10.1109/IITCEE57236.2023.10090866|Multi-Proprietor;Open-Key;Encryption;Decryption;Cipher Text;Manifolds;Keyword search;Encryption;Servers|
|[Detection Of Snowball and Jersey Colour And Jersey Number Using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091073)|D. C. Shubhangi; B. Gadgay; M. Begum; M. Waheed|10.1109/IITCEE57236.2023.10091073|Snowball;CNN(Convolution Neural Network);Players;Rectified Linear Unit (ReLU);Softmax layer;Fluids;Image color analysis;Snow;Neural networks;Games;Ice;Reliability|
|[A Knowledge based Approach for User Profiling from the Multilingual Texts in the Social Media Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090911)|S. Nag; S. Dalui; S. Ghosh; Alok; R. Pal; A. Pal|10.1109/IITCEE57236.2023.10090911|User Profiling;Multilingual Text;Kaggle Dataset;WordNet;Sense Hirarchy;Sentiment analysis;Social networking (online);Knowledge based systems;Training data;Electronic commerce;Task analysis|
|[Blockchain and IoT Based Smart Agriculture and Food Supply Chain System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090897)|D. Kumar; R. K. Dwivedi|10.1109/IITCEE57236.2023.10090897|Agriculture;Smart Model;Blockchain;Smart Contracts;IoT;Smart agriculture;Computational modeling;Supply chains;Data collection;Data models;Blockchains;Safety|
|[Blockchain Enabled Autonomous Vehicle Based Vehicular IoT System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090950)|Hemani; D. S. Singh; R. K. Dwivedi|10.1109/IITCEE57236.2023.10090950|Blockchain;Autonomous Vehicle (AV);Decentralized;Distributed;Ledger;Industries;Electric potential;Transportation;Blockchains;Autonomous vehicles|
|[Optimal Placement of Renewable Energy Sources in Low Power Distribution System For Techno-Economic Performance Benefits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091007)|S. Patnaik; P. Parida; A. Naik; P. Pradhan; S. Satpathy; D. Kisan; M. R. Nayak|10.1109/IITCEE57236.2023.10091007|Wind turbine generators (WTG);Photovoltaic (PV) system;Battery Energy Storage system (BESS);Radial distribution system (RDS);Cat and Mouse Based Optimizer (CMBO) multi-objective algorithm;Economics;Renewable energy sources;Energy loss;Tariffs;Voltage;Mice;Generators|
|[Modelling and Simulation of Solar Water Pump Using Arduino Uno in Proteus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091075)|K. Bharti; S. K. Singh; S. K. Jha; R. Gupta|10.1109/IITCEE57236.2023.10091075|Solar Panel;Battery;Charger Controller;Inverter;Motor;Pump;Arduino-UNO;GSM Module;Maximum power point trackers;Irrigation;Induction motors;Costs;Biological system modeling;Switches;DC motors|
|[Design and Implementation of Arithmetic based FIR Filters for DSP Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090953)|G. S; E. S; C. Bhavya; Y. J. M. Shirur|10.1109/IITCEE57236.2023.10090953|Distributed Arithmetic (DA);Look Up Table (LUT);Multiply and Accumulate (MAC) unit;Finite Impulse Response (FIR) filter;Program processors;Finite impulse response filters;Wearable computers;Digital signal processing;Computer architecture;Watches;Computational efficiency|
|[Comparative Analysis of LEAP and PTL techniques in 18nm FinFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090955)|G. Rana; K. Sharma; A. Sharma; A. Sharma|10.1109/IITCEE57236.2023.10090955|FinFET;Logic Circuits Complementary Pass Transistor Logic;Pass Transistor Logics;lean integration with pass transistors;Transient response;Logic circuits;FinFETs;Delays;Power dissipation;Transistors;Low-power electronics|
|[A Novel and Efficient Multi-Band Wireless Communication System for Healthcare Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090991)|R. Renugadevi; T. Sethukarasi|10.1109/IITCEE57236.2023.10090991|MM wave;WPT;Internet of Things (IoT);Antenna;Wireless Technology;Wireless communication;Technological innovation;Transmitters;Millimeter wave technology;Medical services;Tablet computers;Low-power electronics|
|[A Real TimeVehicle Detection Based on Modified Convolutional Neural Networks in Different Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090965)|S. Johnson; B. P. Laxmi|10.1109/IITCEE57236.2023.10090965|Vehicle detection;Traffic Image;YoLo;AI;Deep Learning;SPPN;Classification;Training;Roads;Vehicle detection;Surveillance;Urban areas;Traffic control;Convolutional neural networks|
|[Inferring User Image-Search Goals Using SVM and Re-ranking process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091036)|K. S. Rekha; P. P. V; A. Gayathri; S. K. Mohapatra|10.1109/IITCEE57236.2023.10091036|Image mining;SVM classifier;Re-ranking process;Image Datasets;Object recognition;Support vector machines;Visualization;Portable computers;Image recognition;Image databases;Search engines;Search problems|
|[Energy Proficient and Dependable Cluster Routing in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091001)|S. Hemavathi; J. G. J; G. Geetha; K. K. K; R. Kohila|10.1109/IITCEE57236.2023.10091001|Dependable Cluster Routing;Transmission Faith;Average Energy Faith;Wireless Sensor Network;Bandwidth;Wireless sensor networks;Simulation;Packet loss;Bandwidth;Routing;Energy efficiency;Delays|
|[Relay Awake Feature-based Efficient Route Formation in Mobile Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091091)|G. Kavitha; P. Ramanathan; R. Thamizhamuthu; M. R. Joel; J. Lenin|10.1109/IITCEE57236.2023.10091091|Relay Awake Feature;Mobile Wireless Sensor Networks;communication key function;remaining energy;link weight;network lifetime;Weight measurement;Wireless communication;Wireless sensor networks;Simulation;Organizations;Throughput;Loss measurement|
|[Intelligent Power Control Models for the IOT Wearable Devices in BAN Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090918)|A. P; M. Meenakumari; S. L; R. N; S. Jayaprakash; S. Murugan|10.1109/IITCEE57236.2023.10090918|wireless body area network;Internet of things;Energy harvesting;Maximum power point tracking;Wi-Fi;Wireless communication;Maximum power point trackers;Temperature sensors;Heart rate;Wireless sensor networks;Body area networks;Software|
|[A Novel Characterization of the Fake News in Twitter Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090869)|B. Sreedevi; L. P. Narendruni; B. Chitradevi; M. Santhanalakshmi; G. Sumathi|10.1109/IITCEE57236.2023.10090869|Long Term Short Memory;Fake news;Auto Regressive Integrated Moving Average;Twitter;Deep learning;Prediction networks;Sensitivity;Social networking (online);Blogs;Predictive models;Mathematical models;Data models;Security|
|[Hidden Markov Model with Machine Learning-Based Black hole Attack Identification in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090993)|K. Balasubadra; X. S. A. Shiny; P. P V; P. Solainayagi; S. P. Maniraj|10.1109/IITCEE57236.2023.10090993|black hole attack identification;Hidden Markov Model;Wireless Sensor Network;supervised learning;unsupervised learning;Training;Wireless sensor networks;Simulation;Computational modeling;Hidden Markov models;Packet loss;Machine learning|
|[Privacy-Preserving Data Mining Process in Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091069)|T. C. S. Lakshmi; R. A. A. Rosaline; R. T. Selvi; D. Karunkuzhali; S. Lavanya|10.1109/IITCEE57236.2023.10091069|Data mining;Privacy-Preserving;EM algorithm;Reconstructed Distribution function;Trade-offs;Information loss;Privacy;Maximum likelihood estimation;Data privacy;Legislation;Tutorials;Data breach;Reliability theory|
|[Biomedical Image Classification Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091043)|D. Sharma; I. Mishra; R. Parepalli; J. S|10.1109/IITCEE57236.2023.10091043|Convolutional Neural Network (CNN);Modelling;Keras API;Google Colab;Kaggle;Covid-19 detector;Deep learning;Analytical models;Neural networks;Data visualization;Data models;Internet;Convolutional neural networks|
|[Design and Implementation of 32-Bit MIPS RISC Processor with Flexible 5-Stage Pipelining and Dynamic Thermal Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091038)|A. N J; I. Mishra; T. C. Ghorpade; L. M; N. H; M. K. S|10.1109/IITCEE57236.2023.10091038|RISC;CISC;MIPS;ALU;ISA;Reduced instruction set computing;Power demand;Microprocessors;Computational modeling;Process control;Computer architecture;Thermal management|
|[Design and Development of PV Based Hybrid Multilevel Inverter with Ziegler-Nichols Tunning Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090971)|S. Devi; S. K. Sahoo|10.1109/IITCEE57236.2023.10090971|PV System;Boost converter method;Total harmonic distortion;Hybrid Multi level Inverter;PI control;Switching loss;Voltage;High-voltage techniques;Multilevel inverters;Harmonic analysis;Mathematical models|
|[Analysis of the Impacts of Consuming Red and Processed Meat on Colorectal Cancer and the role of Machine Learning in Clinical Diagnostics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091060)|C. Navaneethan; S. T. Prasath|10.1109/IITCEE57236.2023.10091060|Colorectal Cancer (CRC);Cancer Risk;Machine Learning;Red meat;Processed Meat;Industries;Dairy products;Systematics;Machine learning;Hazards;Medical diagnostic imaging;Cancer|
|[Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091034)|K. Agarwal; S. Deepa; R. V. SivaBalan; C. Balakrishnan|10.1109/IITCEE57236.2023.10091034|Twitter;Natural Language Processing;Sentiment Analysis;Machine Learning;Logistic Regression;Random Forest;classifier;Support Vector Machines;Decision Tree;Gaussian Naive Bayes;XGBoost;Sentiment analysis;Technological innovation;Machine learning algorithms;Social networking (online);Education;Blogs;Support vector machine classification|
|[Cloud-based Application for managing placement related information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091095)|S. Aravind; M. Ashik; M. L. Chayadevi; A. J. Kennedy; C. R. Bhatkal; A. P. Patil|10.1109/IITCEE57236.2023.10091095|cloud computing;Kubernetes;sentiment analysis;Vader-sentiment analysis;Data visualization;Companies;Interviews|
|[Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090882)|T. Kumari; R. Kumar; R. K. Dwivedi|10.1109/IITCEE57236.2023.10090882|Blockchain;Consensus;Electronic Healthcare System (EHS);Hybrid Consensus Mechanism (HCM);Reward;Reputation;Punishment;Computational modeling;Memory management;Insurance;Medical services;Consensus protocol|
|[Impact of Artificial Intelligence (AI) in Talent Acquisition Process: A study with reference to IT Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090973)|P. V. Yadav; U. S. Kollimath; T. V. Chavan; D. T. Pisal; S. A. Giramkar; S. M. Swamy|10.1109/IITCEE57236.2023.10090973|Artificial intelligence;Talent Acquisition process;Human Resource Professionals;Industries;Job shop scheduling;Urban areas;Standards organizations;Resumes;Pipelines;Organizations|
|[Measuring perception towards AI-based chatbots in Insurance Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091024)|D. Mangla; R. Aggarwal; M. Maurya|10.1109/IITCEE57236.2023.10091024|Artificial Intelligence;chatbots;perception;insurance sector;Industries;Education;Insurance;Banking;Chatbots;Particle measurements;Electronic commerce|
|[Heart Disease Prediction Analysis Using Hybrid Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090896)|M. Gagoriya; M. K. Khandelwal|10.1109/IITCEE57236.2023.10090896|Machine Learning;Prediction Analysis;Heart Disease;Heart;Machine learning algorithms;Machine learning;Prediction methods;Prediction algorithms;Data models;Data mining|
|[Breast Cancer classification using Neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091020)|V. Asha; B. Saju; S. Mathew; A. M. V; Y. Swapna; S. P. Sreeja|10.1109/IITCEE57236.2023.10091020|Artificial Neural Network;Breast Cancer Detection;Convolutional Neural Network;Deep Neural Network;Malignant Cancer;Benign Cancer;Deep learning;Computational modeling;Medical services;Feature extraction;Breast cancer;Convolutional neural networks;Medical diagnostic imaging|
|[Sign Language Recognition System using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091051)|A. Deshpande; A. Shriwas; V. Deshmukh; S. Kale|10.1109/IITCEE57236.2023.10091051|American Sign Language;Human Computer Interface;Human computer interaction;Deep learning;Computer interfaces;Systematics;Symbols;Lighting;Gesture recognition|
|[Automated Awning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090905)|H. U; H. P. N; K. Bhatta; L. Bhaskar|10.1109/IITCEE57236.2023.10090905|Arduino UNO;FC37 rain water-sensor;Microservo SG90;Rain;Costs;Microcontrollers;Crops;Lighting;Monsoons;Detectors|
|[Efficient Compression Technique For Large Size Binary Sparse Matrix Using Modified Run Length Encoding For Memory Constraint Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091013)|N. G S; P. Pandey; M. Hota; M. Goel|10.1109/IITCEE57236.2023.10091013|Binary Sparse Matrix;Run Length Encoding;Sparsity;Embedded Systems;Embedded systems;Upper bound;Memory management;Encoding;Mathematical models;System-on-chip;Sparse matrices|
|[Design and Implementation of Synthesizable Two-Level Cryptosystem for High-Security enabled Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091066)|Y. J. M. Shirur; N. I. K S; S. K S; U. V N|10.1109/IITCEE57236.2023.10091066|Elliptic Curve Cryptography (ECC);Encryption scheme;Left to right Point / Scalar Multiplication;Elliptic Curve Diffie Hellman (ECDH);Ciphers;Memory management;Elliptic curve cryptography;Encryption;Error correction codes;Task analysis|
|[Design of 64-bit Floating-Point Arithmetic and Logical Complex Operation for High-Speed Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091011)|D. Bhuvana Suganthi; M. Shivaramaiah; A. Punitha; M. K Vidhyalakshmi; S. Thaiyalnayaki|10.1109/IITCEE57236.2023.10091011|Floating Point;Xilinx;Complex Numbers;Arithmetic Operations;Memory management;Signal processing algorithms;Signal processing;Hardware;Delays;Hardware design languages;Floating-point arithmetic|
|[Biomechanically Sourced Hybrid Energy Harvester - Towards a Sustainable Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090963)|L. S. G; A. H. P R; S. S. E; A. R; U. MV; H. J|10.1109/IITCEE57236.2023.10090963|Energy;electricity generation;green energy harvesters;piezoelectric;triboelectric;electromagnetic generator;solar;hybrid floor-tile;Biomechanics;Earth;Pollution;Magnetic sensors;Green products;Generators;Energy harvesting|
|[Analysing Cyber Security Vulnerabilities using Click Jacking and HostHeader Injection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090465)|B. P. Kumar; K. S. Vaishnavi; K. G. Phani Sri Nitya; P. P. Durga; K. B. Prakash; G. P. S. Varma|10.1109/IITCEE57236.2023.10090465|Vulnerabilities;IP address;Ports;Host Header;Reverse IP;Codes;Art;IP networks;Security;Computer crime;Business|
|[India's Transition towards Renewable Energy Generation and Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091085)|S. Sen; S. S. Ranagarajan; R. Kumar; C. K. Shiva; D. Yadeo; N. Shruthi|10.1109/IITCEE57236.2023.10091085|Cost-benefit analysis;Energy management;Human development;Power system grid;Renewable energy sources;Economics;Photovoltaic systems;Renewable energy sources;Power engineering;Green products;Microgrids;Market research|
|[Overview of Substation Protection and Control Perspective to Active Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091072)|S. Sen; S. S. Rangarajan; C. K. Shiva; S. N; A. Mohammed; P. Jadhav; A. Nalla|10.1109/IITCEE57236.2023.10091072|Control system;Energy management;Power system protection;Renewable energy sources;Substation;Renewable energy sources;Substations;Uncertainty;Power supplies;Substation protection;Distribution networks;Market research|
|[High Efficiency Bridgeless Single-Power-Conversion Battery Charger for Light Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091057)|M. S. Kumar; S. S. Rangarajan; C. K. Shiva; S. Nookala; K. Sriram; N. S. Deepthi; D. Vipul; G. Nishitha|10.1109/IITCEE57236.2023.10091057|Bridge diode;series resonance circuit;power factor correction;Battery chargers;Reactive power;Electric potential;Employment;RLC circuits;Prototypes;Bridge circuits|
|[Deep Learning-Based Drowsiness Detection System Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090863)|N. Himaswi; K. Sathvika; S. Pathima; S. K. K|10.1109/IITCEE57236.2023.10090863|Drowsiness Detection;Face Recognition;Image processing;EAR;Haar Cascade;Raspberry Pi board;Support vector machines;Road accidents;Sleep;Lips;Face recognition;Wheels;Streaming media|
|[Implementation Of Grover's and Shor's Algorithms In Quantum Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091029)|D. K. Kumar; E. H. Venkata Krishna; R. Ushasri; V. Jahnavi; K. B. Prakash; S. Imambi|10.1109/IITCEE57236.2023.10091029|Quantum Machine Learning;Quantum Computing;Qubits;Grover's Algorithm;Shor's Algorithm;Kernel;Quantum Entanglement;Machine learning algorithms;Quantum algorithm;Quantum entanglement;Qubit;Machine learning;Predictive models;Parallel processing|
|[Histopathological Cancer Detection with Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091041)|N. Sail; S. Nadkarni|10.1109/IITCEE57236.2023.10091041|Histopathology;Deep Learning;Convolutional Neural Network (CNN);Machine Learning;Metastatic Cancer;Support vector machines;Deep learning;Digital images;Subspace constraints;Self-supervised learning;Mathematical models;Metastasis|
|[Cogeneration Plants Design Optimization by Integrating Them with Renewable Energy Towards Sustainable Energy and Environmental Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090890)|A. V. R. N. B. M. Rao; A. K. Singh|10.1109/IITCEE57236.2023.10090890|Renewable energy integration;Cogeneration;back pressure turbine cogeneration systems;energy storage devices;Industries;Renewable energy sources;Pollution;Cogeneration;Fossil fuels;Security;Steel|
|[Intelligent Islanding Classification with MLPNN for Hybrid Distributed Energy Generations in Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091089)|I. A. Saifi; A. Haque; M. Amir; V. S. Bharath Kurukuru|10.1109/IITCEE57236.2023.10091089|Islanding classification;µG;low voltage ride through;distributed energy resources (DERs);Machine learning applications;Training;Islanding;Tropical cyclones;Neural networks;Microgrids;Voltage;Power system stability|
|[Predicting Breast Cancer Using Classical Machine Learning and Deep Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090883)|V. L. K. Vasista; K. Sona; J. Pedarla; B. Sahithi; T. K. R. K. Rao; K. B. Prakash|10.1109/IITCEE57236.2023.10090883|breast cancer;deep learning;random forest;ANN;RNN;Deep learning;Machine learning algorithms;Computational modeling;Europe;Forestry;Prediction algorithms;Breast cancer|
|[Comparative Analysis of different Heart Disease Prediction Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091028)|K. Rethik; A. Singh; D. Singh; M. Rakhra|10.1109/IITCEE57236.2023.10091028|Heart Disease;Machine Learning;Cardiovascular Disease;Prediction;Heart;Solid modeling;Analytical models;Computational modeling;Predictive models;Stroke (medical condition);Stress|
|[Blockchain-based EHR System for Indian Healthcare Industry using Aadhar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091065)|S. Singh; M. Rakhra; A. Malik; D. Singh|10.1109/IITCEE57236.2023.10091065|Blockchain;UPI (Unique Patient Identifier);EHR (Electronic Health Record);Aadhar card;Industries;Pandemics;Standardization;Medical services;Developing countries;Blockchains;Delays|
|[Enhancing the Lifetime of Wireless Sensor Networks by using the LEACH-Based Clustering Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090982)|N. T. Singh; A. Chaudhary; A. L. Yadav; A. Pandey|10.1109/IITCEE57236.2023.10090982|Wireless Sensor Network;Lifetime;Clustering;LEACH;MESH;Wireless communication;Wireless sensor networks;Energy consumption;Power demand;Simulation;Scalability;Clustering methods|
|[ANND: Identification and Prediction of Tooth Decay based on Artificial Neural Network and DenseNet Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091047)|A. G. Soundari; R. Dhanalakshmi; V. C B; B. Rajasekar; S. M. Basha|10.1109/IITCEE57236.2023.10091047|dental caries;multi-modality;hybrid neural network;convolutional neural network;Microorganisms;Soft sensors;Sociology;Teeth;Medical services;Predictive models;Dentistry|
|[Exploring the Role of Mining Wireless Framework in Identifying Human Privacy Vulnerabilities in Internet of Things Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090992)|R. Dhanalakshmi; V. C B; B. Rajasekar; S. M. Basha; A. G. Soundari|10.1109/IITCEE57236.2023.10090992|Security and privacy in the Internet of Things;leaking user data;people-tracking;Wireless communication;Wireless sensor networks;Data privacy;Smart homes;Software;Communication system security;Internet of Things|
|[Identification of Fatigue Drivers Based on Multiple Convolutional Neural Networks in Accelerometry Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090870)|V. C. B; B. Rajasekar; S. M. Basha; A. G. Soundari; R. Dhanalakshmi|10.1109/IITCEE57236.2023.10090870|AI;CNN;drowsiness;tiredness;long drive;sleeping;Deep learning;Accelerometers;Thigh;Fatigue;Convolutional neural networks|
|[An Efficient Beam Splitter Scheme for Optimization Based on Modified Optical Wireless Power Transmission Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090984)|V. Ramkumar; P. A; N. Vanathi; S. Karpagam; K. P. Chandran; R. T. Prabu|10.1109/IITCEE57236.2023.10090984|Long-range power transfer;high-power output;light-emitting diode (LED);Fresnel lens;gallium arsenide (GaAs) solar cell;Meters;Photovoltaic cells;Production;Light emitting diodes;Software;Wireless power transmission;Safety|
|[An Integrated Optimal Resource Management Scheduling for Dual Target in Remote MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090885)|L. H. M; R. Renugadevi; T. Sethukarasi|10.1109/IITCEE57236.2023.10090885|schedule optimization;resource allocation;networked radar;multi-target imaging;Schedules;Processor scheduling;MIMO radar;Imaging;Radar;Optimal scheduling;Radar imaging|
|[Performance Analysis of a Wireless IoT Networks with Robust Resource Distribution Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090904)|S. Rohini; S. Shankari|10.1109/IITCEE57236.2023.10090904|Wireless power transmission;Internet of Things;reduced data packet sizes;longer range communications;Wireless communication;Codes;Error analysis;Packet loss;Throughput;Hybrid power systems;Performance analysis|
|[Automatic Helmet And License Plate Recognization using YOLOv5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090976)|M. L. Manogna; K. Dakshinya; B. S. Ratnakar; D. R. Rao|10.1109/IITCEE57236.2023.10090976|Optical Character Recognition;Convolution Neural Network;OpenCV;Video Surveillance System;YOLO;Head;Surveillance;Optical device fabrication;Optimized production technology;Optical fiber networks;Optical imaging;Safety|
|[Countering Dis-information by Multimodal Entailment via Joint Embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090876)|A. Bobde; S. Tawari; V. Narnaware; A. Suryawanshi; P. Kshirsagar|10.1109/IITCEE57236.2023.10090876|Multimodal Deep Learning;Transformer;Fact Checking;Factify;Co-Attention;Social networking (online);Task analysis;Fake news|
|[Analysis of a Multichannel Learning Mechanism for Speech Detection in Social Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091086)|B. Samatha; N. Karyemsetty; D. S. Kumar; D. K. Rao; G. Mani; T. Syamsundararao|10.1109/IITCEE57236.2023.10091086|nan;Deep learning;Representation learning;Voice activity detection;Learning systems;Machine learning algorithms;Social networking (online);Hate speech|
|[Heart Disease Prediction using Clustered Genetic Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091050)|J. Vijaya|10.1109/IITCEE57236.2023.10091050|Heart disease prediction;Machine learning;Segmentation;Optimization;Genetic Algorithm;Heart;Clustering algorithms;Whales;Predictive models;Prediction algorithms;Genetics;Classification algorithms|
|[Block Chain Based File Tracking and Management System for Pension Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091068)|S. Geetha; Teena; C. ML; K. Jayaram|10.1109/IITCEE57236.2023.10091068|Pension chain;block chain;decentralization;digitization;e-Administration;security;superannuation;straightforwardness;Technological innovation;Profitability;Biological system modeling;Government;Standards organizations;Documentation;Medical services|
|[Classifying Chest X-rays for COVID-19 using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090915)|S. P. Sreeja; V. Asha; B. Saju; P. K. C; P. Manasa; V. C. R|10.1109/IITCEE57236.2023.10090915|Deep Learning;CXR images;Classification CNN and COVID-19;COVID-19;Training;Deep learning;Solid modeling;Pulmonary diseases;Lung;Data models|
|[An Approach to Disambiguate the Innate Sense of a Message with the Help of Emoji](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090922)|S. Bhattacharyya; S. Maity; S. Maiti; A. R. Pal; A. Pal|10.1109/IITCEE57236.2023.10090922|User profiling;Emoji;Sentiment analysis;LSTM;RNN;Training;Sentiment analysis;Emotion recognition;Visualization;Social networking (online);Text recognition;Mood|
|[Performance Estimation of Real Estate Business Price Using the Regression Analysis of Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091037)|S. Geetha; P. Diana; P. S|10.1109/IITCEE57236.2023.10091037|Real Estate Prediction;Random Forest Regression;Linear Regression;Decision Tree;Costs;Urban areas;Forestry;Artificial neural networks;Organizations;Machine learning;Ontologies|
|[Reduced ripple in the torque output of the SRM drive by the use of the SVM-based converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090972)|C. SivaGanesh; A. V. V. S. P. Kumar; G. R. S. Prudhvi; Y. V. Krishna|10.1109/IITCEE57236.2023.10090972|switched reluctance motor;torque sharing function;torque ripple;SVM based converter;Insulated gate bipolar transistors;Torque;Windings;Torque control;Switched reluctance motors;Aerospace electronics;Turning|
|[Automatic Generation Control and Its Comparative Analysis for Interconnected Thermal and Hydrothermal Power System by ERWCA Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090881)|G. Sahoo; R. K. Sahu; S. Panda|10.1109/IITCEE57236.2023.10090881|IHTPS;ITPS;ERWCA;2DOFPIDF;AGC;Analytical models;Computational modeling;Automatic generation control;Power system stability;Superluminescent diodes;Robustness;Thermal analysis|
|[Energy Flow Optimization in Electric Vehicle Charging Station with Integration of Lens Wind Turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090907)|A. Sachithanandam; J. R|10.1109/IITCEE57236.2023.10090907|Lens Wind Turbine;Electric Vehicle Charging Station;Power Flow;Controller;Semiconductor device modeling;Switches;Pulse width modulation;Control systems;Routing;Electric vehicle charging;Mathematical models|
|[Design of 8-bit Register Using OAI Logic at Subthreshold](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091008)|D. Lahari; S. Velagaleti; V. Nikitha|10.1109/IITCEE57236.2023.10091008|MUX D Flipflop;CMOS;OAI;Register;Power demand;Simulation;CMOS technology;Mathematical models;Registers;Power dissipation|
|[Audio Watermark Insertion and Enrichment of Speech via Kalman Filter and LSB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090891)|A. Sulakhe; S. Mukherjee; B. Vishwakarma; K. S|10.1109/IITCEE57236.2023.10090891|Least Significant bit;Kalman filter;signal processing Introduction;Maximum likelihood detection;Watermarking;Nonlinear filters;Speech enhancement;Media;Filtering algorithms;Kalman filters|
|[Performance Analysis of Efficiently trusted AODV serving Security in MANET under Blackhole Attack Using Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091046)|I. Nausheen; A. Upadhyay|10.1109/IITCEE57236.2023.10091046|Mobile Ad-hoc Network (MANET);Security attacks;Routing protocols;AODV;GA;Wireless communication;Wireless sensor networks;Throughput;Routing protocols;Performance analysis;Topology;Security|
|[Designing Rectangular Patch Antennafor broad band 5G Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090909)|C. T. Selvi; P. Ramachandran; M. Saravanan; K. Senthilvel; M. S. Millet|10.1109/IITCEE57236.2023.10090909|Rectangular patch antenna;bandwidth;duroid substrate;return loss;VSWR;WLAN;5G mobile communication;Patch antennas;Resonant frequency;Microstrip antennas;Internet of Things;Microstrip;High frequency|
|[Internet of Things and Edge Computing for Real Time Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091014)|C. S. Thondebhavi Shanthakumar; N. Harish; Eshanya; A. Giridharan|10.1109/IITCEE57236.2023.10091014|Edge computing;Cloud Computing;Cisco Packet Tracer;Internet of Things;Cloud computing;Network topology;5G mobile communication;Computational modeling;Wireless networks;Real-time systems;Topology|
|[Malware Detection across Clusters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090979)|M. C V; A. N. Nandakumar|10.1109/IITCEE57236.2023.10090979|Deep neural networks;Distributed computing;Trust based malware detection (TMD);Deep learning;Knowledge engineering;Java;Simulation;Neural networks;Software algorithms;Clustering algorithms|
|[Virtual User Interface for Differently Abled People Using Vision Transformer Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091018)|S. H. Sri; Y. Sai; P. Anil; A. R. Pratap|10.1109/IITCEE57236.2023.10091018|Video stream;OpenCV;Image processing;pyautogui;mouse events;VisionTransformer;dlib;Computers;Webcams;Image processing;Computational modeling;Streaming media;User interfaces;Transformers|
|[Assessing the Suitability of Meta-analysis for Biomarker Identification in Coronary Artery Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090920)|R. K. Pradhan; S. Subham|10.1109/IITCEE57236.2023.10090920|Meta-analysis;Differentially Expressed Genes;Gene Enrichment;Bioinformatics;Coronary Artery Disease;Data analysis;Statistical analysis;Data integrity;Systems biology;Genomics;Gene expression;Bioinformatics|
|[Deep Learning Network for Vision-Based UAV Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091022)|T. Prabhakar; M. Almutairi; H. Almutairi; S. Vasamsetti; M. V. R. Rao; A. Kunda|10.1109/IITCEE57236.2023.10091022|Unmaned aerial vehicle (UAV);Deep Learning;Recognition;Aviation;Deep learning;Visualization;Image processing;Inspection;Radar imaging;Autonomous aerial vehicles;Radar tracking|
|[Deep Learning Based Application in Detecting Wrinkle and Predicting Age](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090987)|P. M O; V. Y; A. Raj|10.1109/IITCEE57236.2023.10090987|face recognition;feature extraction method;wrinkles detection;age prediction;Support vector machines;Hair;Image color analysis;Face recognition;Employment;Mental health;Detectors|
|[Artificial Intelligence and IoT based detection of pesticide in organic fruits and vegetables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091032)|S. Sujitha; S. S. K; M. K. H V; N. R. N; S. N. R; M. K. P|10.1109/IITCEE57236.2023.10091032|Artificial Intelligence;IoT;detection of pesticide;organic fruits and vegetables;sensors;Temperature sensors;Chemical sensors;Microcontrollers;Sociology;Vegetation mapping;Production;Software|
|[Comparative Performance Analysis of Quantum Algorithm with Machine learning Algorithms on Diabetes Mellitus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090957)|B. Prakash; B. Naveen; M. Akhiluzzama; P. Rajarajeswari|10.1109/IITCEE57236.2023.10090957|Kernel Principle Component Analysis (KPCA);Diabetes Mellitus;Support Vector Machine (SVM);Quantum Computing;Quantum Machine Learning Algorithms (QMLA);Support vector machines;Machine learning algorithms;Quantum algorithm;Computational modeling;Machine learning;Predictive models;Data models|
|[A Blockchain-based Model for Maternal Health Information Exchange and Prediction of Health Risks using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090997)|K. P. Kalita; S. K. Chettri; R. K. Deka|10.1109/IITCEE57236.2023.10090997|Antenatal care;blockchain;machine learning;maternal mortality;e-healthcare;Pregnancy;Pediatrics;Machine learning algorithms;Data security;Medical services;Machine learning;Predictive models|
|[Analysis of DDoS Attacks in Software Defined Networking using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090959)|A. Sharma; H. S. Chauhan; H. Kaur; H. Babbar|10.1109/IITCEE57236.2023.10090959|Software Defined Networking;DDoS attacks;Machine Learning;BoT-IoT;Performance Metrics;Measurement;Protocols;Machine learning;Fingerprint recognition;Denial-of-service attack;Software defined networking;Computer crime|
|[Case Studies and Use case Scenarios of CPS-IoT: Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090977)|D. Nagpal; G. Garg; H. Babbar|10.1109/IITCEE57236.2023.10090977|The Cyber-Physical system;Internet of things;cloud base technology;security;privacy;Cloud computing;Computer architecture;Big Data;Malware;Fourth Industrial Revolution;Security;Smart devices|
|[Evaluation and Analysis: Internet of Things using Machine Learning Algorithms for Detection of DDoS Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090917)|A. Sharma; H. Babbar|10.1109/IITCEE57236.2023.10090917|Internet of Things;Security;DDoS Attacks;Machine Learning;Datasets;Radio frequency;Machine learning algorithms;Denial-of-service attack;Prediction algorithms;Internet of Things;Security;Decision trees|
|[K-Means Clustering for Prophesy of Freshmen's Attainment with Euclidean Execution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090874)|A. Kaur; H. Vaid; L. Mukhija|10.1109/IITCEE57236.2023.10090874|K-Mean Clustering;Calculation;Euclidean;Execution Outcomes;Education;Estimation;Euclidean distance;Benchmark testing;Numerical models;Standards|
|[The Role of Machine Learning Analysis and Metrics in Retailing Industry by using Progressive Analysis Pattern Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091071)|K. Suresh; A. C. Donald; R. Subramani; N. R. Robert|10.1109/IITCEE57236.2023.10091071|Machine learning;Pattern Mapping;Utility mining Approach;Progressive analysis;Customer Prediction methodology;Measurement;Industries;Machine learning algorithms;Itemsets;Databases;Machine learning;Planning|
|[Sensor Node Communication based Selfish Node Detection in Mobile Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091048)|S. J. J. Thangaraj; N. Ramshankar; E. Srividhya; S. Jayanthi; R. Kumudham; C. Srinivasan|10.1109/IITCEE57236.2023.10091048|Selfish Node Detection;Mobile Wireless Sensor Networks;Energy Efficiency;Node Categorization;Ratio of Node Communication;Wireless communication;Wireless sensor networks;Routing;Energy states;Data models;Real-time systems;Topology|
|[Application of Machine Learning for Malicious Node Detection in IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091042)|J. Prasad; M. Kasiselvanathan; S. Lakshminarayanan; G. Sekar; A. H|10.1109/IITCEE57236.2023.10091042|Machine learning;malicious node;accuracy;IoT;ANN;Radio frequency;Support vector machines;Machine learning algorithms;Area measurement;Artificial neural networks;Prediction algorithms;Real-time systems|
|[Machine Learning and Advanced Technology Based Fire Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090923)|C. Malarvizhi; P. Dass; P. Karthikeyani; V. Sudha; S. Iniyan|10.1109/IITCEE57236.2023.10090923|CNN;Security;Image classification;Safety;Fire detection;Performance evaluation;Machine learning algorithms;Convolution;Neural networks;Layout;Memory management;Cameras|
|[Towards Smart Data Mining Support Based on Annotation Beneficial Grouping Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091053)|S. Kavitha; K. Pushpavathi; S. Mahalakshmi|10.1109/IITCEE57236.2023.10091053|Data Mining;Classification;Data Model;Decision Tree;KDDCUP;Annotations;Computational modeling;Prototypes;Computer architecture;Reliability theory;Ontologies;Data models|
|[QoS Based Efficient Link and Consistent Routing in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091080)|S. Sivakumar; R. Anusuya; V. Nagaraju; L. P. Narendruni; R. Thamizhamuthu|10.1109/IITCEE57236.2023.10091080|Wireless Sensor Networks;link QoS factor;Received Signal Strength Indication;Consistency Achievement;Forecasted Consistency;Communication Rate;Left-over Energy;simulation analysis;Wireless communication;Wireless sensor networks;Network topology;Energy measurement;Quality of service;Predictive models;Routing|
|[Interior Angle Computation for detecting Adversary Sensor in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091012)|R. Saranya; K. Anitha; A. A. Khan; M. Sivakumar; R. Arthi|10.1109/IITCEE57236.2023.10091012|Detecting Adversary Sensor;Interior Angle Computation;Wireless Sensor Networks;Simulation Analysis;Location Verification;Wireless sensor networks;Simulation;Receivers;Binary trees;Routing;Security|
|[Smart Technique to Prevent Flood Disaster due to High Rainfall in Coastal Areas using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090999)|A. Balaji; V. P. Srinivasan; J. Rangarajan; V. Nagaraju; B. Bharathi; S. Murugan|10.1109/IITCEE57236.2023.10090999|Disaster management;Flood Monitoring;IoT;Coastal areas;Riverbank;Temperature sensors;Temperature measurement;Temperature;Soil;Sensor systems;Rivers;Sensors|
#### **2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)**
- DOI: 10.1109/EEBDA56825.2023
- DATE: 24-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Modern Power System Stabilizer Tuning Approach on Site Using Multi-objective Lighting Flash Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090787)|Q. Chang; Y. Lei; X. He; C. Li; B. Liu|10.1109/EEBDA56825.2023.10090787|PSS tuning;MOLFA;phase compensation;field test;Damping;Lightning;Lighting;Power system stability;Mathematical models;Power grids;Tuning|
|[Design of series voltage compensation device based on rectifier-inverter unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090736)|W. Shiqing; C. Gang; L. Yitao|10.1109/EEBDA56825.2023.10090736|Distribution network;low voltage;rectifier-inverter;series voltage compensation;Low voltage;Voltage fluctuations;Power supplies;Simulation;Rectifiers;Power system stability;Stability analysis|
|[Application of data mining in logistics industry in the era of big data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090765)|L. Jinyang|10.1109/EEBDA56825.2023.10090765|Big data;data mining;logistics;Industries;Electrical engineering;Big Data;Data mining;Floods;Logistics|
|[Network Information Resource Allocation under Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090770)|X. Mou; H. Chen|10.1109/EEBDA56825.2023.10090770|game theory;Nash equilibrium solution;big data;network information;resource allocation;Electrical engineering;Information resources;Optimization methods;Games;Big Data;Nash equilibrium;Data models|
|[Research and Design of an E-Commerce Platform for Ceramic Crafts Based on UML Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090540)|Y. Yang|10.1109/EEBDA56825.2023.10090540|UML model;E-commerce platform design;Ceramic crafts;Data structure design;Electrical engineering;Analytical models;Codes;Unified modeling language;Big Data;Data structures;Data models|
|[Research on the optimization of same-city fresh food delivery path based on improved Hopfield neural network algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090685)|D. Zhou|10.1109/EEBDA56825.2023.10090685|fresh food delivery;Hopfield neural network algorithm;smart logistics;Industries;Electrical engineering;Urban areas;Neural networks;Hopfield neural networks;Big Data;Data models|
|[Structure Design of Self-service Data Governance about Sea-based Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090780)|H. Jing; X. Xianchun; W. Luyao|10.1109/EEBDA56825.2023.10090780|data governance;self-service;metadata;Electrical engineering;Permission;Big Data;Data governance;Security;Self-service|
|[Intelligent Garbage Sorting Assistant Based on Mobile Internet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090485)|Z. Wenyan; S. Qin|10.1109/EEBDA56825.2023.10090485|Intelligent garbage classification;Mobile Internet;Deep learning;Android;MySQL;Image recognition;Operating systems;Urban areas;XML;Broadcasting;Robustness;Classification algorithms|
|[Automatic Diagnosis of COVID-19 Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090604)|Z. Li; S. Yu|10.1109/EEBDA56825.2023.10090604|COVID-19 diagnosis;machine learning;SVM;Random Forest;COVID-19;Training;Support vector machines;Machine learning algorithms;Feature extraction;Data models;Vaccines|
|[Prediction from Breast Cancer Images by Logistic Regression and AlexNet Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090519)|T. Wen|10.1109/EEBDA56825.2023.10090519|breast cancer;AlexNet;logistic regression;CNN;Training;Electrical engineering;Shape;Predictive models;Prediction algorithms;Breast cancer;Data models|
|[Research on Cross-Border E-Commerce Customer Service System under the Integration of Computer Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090598)|S. Gu|10.1109/EEBDA56825.2023.10090598|Computer;big data;cross-border e-commerce;customer service system;Customer services;Information services;Production;Big Data;Stability analysis;Real-time systems;Behavioral sciences|
|[Electric vehicle load optimization model considering peak and valley electricity price time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090819)|Z. Zhang; W. Li; X. Li|10.1109/EEBDA56825.2023.10090819|electric vehicle;peak-valley electricity price;genetic algorithm;user responsiveness;Electrical engineering;Power demand;Distribution networks;Linear programming;Mathematical models;Power grids;Electric vehicle charging|
|[Data Recovery of Power Plant Platform Based on Generative Adversarial Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090824)|J. Jia; T. Xu; M. Li; X. Li; W. Pan|10.1109/EEBDA56825.2023.10090824|GAN;Power grid platform;Data repair;Prompt mechanism;Electrical engineering;Data acquisition;Maintenance engineering;Big Data;Generative adversarial networks;Excavation;Power grids|
|[Ionospheric Delay Inversion in the Working Area of Spacecraft TT&C Ship Based on BDS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090573)|F. Zhang; Y. Ren; S. Cheng; Y. Zou|10.1109/EEBDA56825.2023.10090573|BDS;ionospheric delay;inversion;MATLAB;Space vehicles;Satellite broadcasting;Area measurement;Sea measurements;Interference;Position measurement;Real-time systems|
|[Operation optimization of power and natural gas integrated energy system based on multi-objective optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090550)|M. Weizhe; H. Fuquan; L. Zizhao; W. Yixuan; Q. Hui; H. Xiaofeng|10.1109/EEBDA56825.2023.10090550|multi-objective optimization;power and natural gas optimization;integrated energy;multi-objective particle swarm;Couplings;Electrical engineering;Analytical models;Renewable energy sources;Costs;Wind power generation;Power grids|
|[Design of Monitoring System for Wind-Solar Hybrid Power Supply System in Laboratory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090847)|Y. Xiao; G. Zang; L. Zhang|10.1109/EEBDA56825.2023.10090847|wind-solar hybrid power supply system;monitoring system;complementary operation management;data collection;Photovoltaic systems;Power supplies;Digital signal processors;Signal processing algorithms;Wind power generation;Hybrid power systems;Software|
|[Nature-inspired Path Planning for Robot Swarms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090591)|Z. Quan|10.1109/EEBDA56825.2023.10090591|path planning;ant colony system;artificial bee colony;firefly algorithm;Electrical engineering;Systematics;Robot kinematics;Big Data;Path planning;Planning;Optimization|
|[Solving the Traveling Saleman Problem Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090654)|J. Liang|10.1109/EEBDA56825.2023.10090654|traveling salesman problem;reinforcement learning;combinatorial optimization;Learning systems;Electrical engineering;Q-learning;Costs;Traveling salesman problems;Big Data;Data models|
|[Research on Logistics Distribution Route Optimization Based on Ant Colony Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090583)|S. Guo|10.1109/EEBDA56825.2023.10090583|ant colony algorithm;logistics;delivery route;Industries;Electrical engineering;Job shop scheduling;Costs;Path planning;Mathematical models;Resource management|
|[Research on Digital Image Encryption Based on Chaotic Syste](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090528)|G. Niu|10.1109/EEBDA56825.2023.10090528|Digital image encryption;Logistic map;Key;Pixel replacement;Electrical engineering;Chaos;Privacy;Image recognition;Digital images;Big Data;Information age|
|[Research of Boosting Algorithm Machine Learning in Logistics Enterprise Financial Risk Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090779)|Y. Zhang|10.1109/EEBDA56825.2023.10090779|machine learning;risk early warning;algorithm;logistics enterprises;Electrical engineering;Machine learning algorithms;Predictive models;Financial management;Big Data;Prediction algorithms;Boosting|
|[OS-Net: A novel oriented ship detector based on RetinaNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090805)|C. Jiao|10.1109/EEBDA56825.2023.10090805|OS-Net;Rotate box detection;RetinanNet;ConvNext;Cutout;Electrical engineering;Target recognition;Maintenance engineering;Inspection;Feature extraction;Data models;Security|
|[A regulated identity management system based on blockchain platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090716)|X. Qian; J. Li; Y. Liu|10.1109/EEBDA56825.2023.10090716|Block chain;Identity Markers;Controlled Anonymous Authentication;Electrical engineering;Privacy;Systematics;Smart contracts;Authentication;Big Data;Blockchains|
|[Research on Accurate Portrait Construction of Online Platform Learners Based on Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090566)|H. Liu|10.1109/EEBDA56825.2023.10090566|Data analysis;xAPI;Online platform learners;Precise portrait construction;Visualization;Data analysis;Databases;Statistical analysis;Soft sensors;Data preprocessing;Big Data|
|[Method of Clothing Image Retrieval Based on Convolution Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090806)|Z. Wenyan; S. Qin|10.1109/EEBDA56825.2023.10090806|image retrieval;feature fusion;convolutional neural network;similarity measure;Analytical models;Histograms;Image color analysis;Fuses;Image retrieval;Clothing;Neural networks|
|[Knowledge Encapsulation and Application Based on Domain Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090622)|X. Chen; J. Wang; H. Zhang; Q. Hu|10.1109/EEBDA56825.2023.10090622|liquid oxygen and liquid hydrogen;domain knowledge graph;knowledge encapsulation;label cluster;information recommendation;Encapsulation;Training;Liquids;Scalability;Knowledge acquisition;Hydrogen;Clustering algorithms|
|[Research on the Application of Computer Big Data Technology in Information Security Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090574)|Y. Zhang|10.1109/EEBDA56825.2023.10090574|computer;big data;information security management;encryption;security system;Databases;Security management;Information security;Web pages;Safety management;Passwords;Big Data|
|[Attribute prediction of Pokémon images based on convolutional neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090499)|J. J. Chu; Z. Shan; X. Sun|10.1109/EEBDA56825.2023.10090499|Pokémon;Attribute prediction;convolutional neural network;image enhancement;Electrical engineering;Computer vision;Image recognition;Convolution;Neural networks;Games;Predictive models|
|[Control strategy of three-phase PWM rectifier based on improved double closed loop sliding mode control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090601)|X. Zhang; C. Liu; Y. Zhang; Y. Qu|10.1109/EEBDA56825.2023.10090601|PWM rectifier;Variable structure control;Chattering suppression;Dynamic response characteristics;Anti interference;Reactive power;PI control;Rectifiers;Pulse width modulation;Power system harmonics;Harmonic analysis;Indexes|
|[Research on Software Reliability Evaluation Based on Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090760)|X. Li|10.1109/EEBDA56825.2023.10090760|software reliability;reliability evaluation;software architecture;reliability testing;Software testing;Software architecture;Software algorithms;Computer architecture;Software quality;Software systems;Software|
|[Identification of Grape Leaf Diseases and Insect Pests Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090689)|Z. Yao|10.1109/EEBDA56825.2023.10090689|grape;leaf diseases;artificial intelligence;Training;Image recognition;Insects;Pipelines;Neural networks;Transfer learning;Feature extraction|
|[Multivariate Network Intrusion Detection Methods Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090554)|R. Liu|10.1109/EEBDA56825.2023.10090554|Network Security;Machine learning;Neural networks;Intrusion detection;Training;Support vector machines;Machine learning algorithms;Machine learning;Feature extraction;World Wide Web;Pattern recognition|
|[Research on People Counting Methods of Subway Cars Based on Improved Random Hough Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090833)|Y. Ling; T. Hongjin|10.1109/EEBDA56825.2023.10090833|Background difference;canny operator;random Hough transform;people counting;Electrical engineering;Head;Image edge detection;Heuristic algorithms;Transforms;Streaming media;Feature extraction|
|[Optical Remote Sensing Ship Object Detector Based on Improved GWD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090822)|Y. Cui|10.1109/EEBDA56825.2023.10090822|Ship Detection;Deep Learning;Rotated Detector;Data Augmentation;Deformable Convolution;GWD (Gaussian Wasserstain distance);Object detection;Detectors;Optical detectors;Feature extraction;Optical imaging;Robustness;Mathematical models|
|[Application of cloud computing technology and standard dictionary in elevator Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090705)|H. Yang; Y. Fan; L. Dai|10.1109/EEBDA56825.2023.10090705|cloud computing technology;elevator;Internet of Things;Computers;Cloud computing;Dictionaries;Big Data;Elevators;Internet of Things;Servers|
|[Grid Load Forecasting Based on Dual Attention BiGRU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090646)|W. Xiang; W. Zhanxia; G. Junxiong; Z. Zhanhao; H. Lincheng; W. Shunjiang; Z. Xiuyu|10.1109/EEBDA56825.2023.10090646|Attention;SEQ2SEQ;BIGRU;Electrical engineering;Correlation;Load forecasting;Neural networks;Predictive models;Prediction algorithms;Data models|
|[Research of the Routing Strategy under the Constraint of Privileged Load in Complex Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090775)|X. Wang|10.1109/EEBDA56825.2023.10090775|complex network;privileged load;routing strategy;congestion;Transportation;Complex networks;Telecommunication traffic;Routing;Reliability engineering;Real-time systems;Mathematical models|
|[Research on road image enhancement based on histogram equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090505)|D. Zhang|10.1109/EEBDA56825.2023.10090505|image enhancement;histogram equalization;road images;Electrical engineering;Economics;Histograms;Roads;Lighting;Gray-scale;Video surveillance|
|[Research on Optimal Dispatching Method of Integrated Energy Network in Agricultural Industrial Park Considering Demand Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090699)|H. He; X. Chen; Q. Wang; Y. Zhao; S. Wang|10.1109/EEBDA56825.2023.10090699|demand response;agricultural park;optimize scheduling;source lotus interactive;particle swarm algorithm;Electrical engineering;Analytical models;Renewable energy sources;Optimal scheduling;Linear programming;Demand response;Dispatching|
|[Field Distribution in Shielding Case from Space Electromagnetic Interference during Switching Operation in Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090560)|B. Zhang; X. Meng; Y. Huang; J. Qu; L. Guo; S. Fang|10.1109/EEBDA56825.2023.10090560|substation;electromagnetic shield;observation point;shielding effectiveness;Analytical models;Substations;Electromagnetic interference;Switches;Software;Software measurement;Electromagnetics|
|[Research and Improvement of Greedy Projection Triangulation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090681)|Z. Yao; Y. Gao; H. Guo; Y. Song; X. Zhou; W. Zhou|10.1109/EEBDA56825.2023.10090681|Point cloud registration;3D reconstruction of point cloud;RANSAC algorithm;ICP algorithm;Greedy projective triangulation;Point cloud compression;Surface reconstruction;Solid modeling;Maximum likelihood estimation;Three-dimensional displays;Reverse engineering;Virtual reality|
|[Recent progress of infrared stealth technology for ships](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090647)|N. Li; K. Li|10.1109/EEBDA56825.2023.10090647|Ships;Stealth;Infrared;Progress;Application;Electrical engineering;Big Data;Marine vehicles|
|[Design of AD Conversion Controlled by Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090791)|H. Yu|10.1109/EEBDA56825.2023.10090791|51 microcontroller;AD converter;ADS7825;Electrical engineering;Interpolation;Microcontrollers;Filtering;Process control;Big Data|
|[Displacement Calculation and Simulation Study under Body Frame System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090504)|J. Qiu; C. Liu; Q. Zhang; D. Zheng|10.1109/EEBDA56825.2023.10090504|Inertial strapdown navigation system;carrier coordinate system;Coordinate transformation;Electrical engineering;Coordinate measuring machines;Navigation;Error analysis;Measurement uncertainty;Data acquisition;Displacement measurement|
|[Preparation study of load spectrum of traction system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090605)|B. Liu; C. Liu; D. Zhuang; J. Qiu|10.1109/EEBDA56825.2023.10090605|Shearer traction system;Load spectrum;Rainflow counting;Probability distribution;Employee welfare;Electrical engineering;Loading;Inertial navigation;Reliability theory;Gaussian distribution;Big Data|
|[A DDoS attack detection algorithm based on improved grid search to optimize SVM in SDN environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090555)|M. Huang; B. Zhao|10.1109/EEBDA56825.2023.10090555|SVM;grid search algorithm;Particle Swarm Optimization;DDoS;Support vector machines;Electrical engineering;Intrusion detection;Denial-of-service attack;Feature extraction;Detection algorithms;Software defined networking|
|[An Improved Pointer Chinese Text Summarization Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090588)|D. Ma; C. Yang; Q. Chen|10.1109/EEBDA56825.2023.10090588|abstractive summarization;pointer generation network;Convolutional Gated Unit(CGU);attention mechanism;coverage mechanism;Training;Electrical engineering;Recurrent neural networks;Dictionaries;Semantics;Logic gates;Information filters|
|[A Text Classification Model Based on Gaussian Multi-Head Self Attention Mechanism for Chinese Medical Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090693)|M. Chen; C. Yao; X. Li; L. Shen|10.1109/EEBDA56825.2023.10090693|Text Classification;Transformer;CNN;Electrical engineering;Couplings;Fuses;Text categorization;Transformers;Data models;Classification algorithms|
|[A review of the virtual power plant with the vehicle to grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090562)|S. Nie|10.1109/EEBDA56825.2023.10090562|virtual power plant;vehicle to grid;electric vehicles;Vehicle-to-grid;Supply and demand;Virtual power plants;Dynamic scheduling;Power grids;Discharges (electric);Batteries|
|[Research on Technology and Standards of Underwater Sensing and Communication for Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090815)|H. Yang; X. Guo; X. Zhang; G. Lei|10.1109/EEBDA56825.2023.10090815|underwater sensing;underwater communication IoT;acoustic sensor network;standardization;Electrical engineering;Big Data;Communications technology;Sensors;Internet of Things;Standards|
|[A Non-Intrusive Load Monitoring Model Based on Graph Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090820)|X. Jiao; G. Chen; J. Liu|10.1109/EEBDA56825.2023.10090820|non-invasive load monitoring;multi-modal device;Graph Neural Networks;mean absolute error;Performance evaluation;Load monitoring;Correlation;Feature extraction;Data models;Graph neural networks;Data mining|
|[Modeling Method of Dynamic Knowledge Graph for Distributed Manufacturing Resources in Cloud Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090541)|S. Zhou; D. Tang; Y. Zhang; S. Zhang|10.1109/EEBDA56825.2023.10090541|cloud manufacturing;data acquisition and transmission;knowledge graph;resource modeling;Visualization;Heuristic algorithms;Data acquisition;Time series analysis;Distributed databases;Knowledge graphs;Dynamic scheduling|
|[Working principle and application analysis of UART](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090571)|L. Cao; J. Chen; J. Li|10.1109/EEBDA56825.2023.10090571|UART;Communication Protocol;Serial communication;Baud rate generator;Military communication;Costs;Power demand;Transmitters;Receivers;Production;Stability analysis|
|[Analysis and Comparison of Asynchronous FIFO and Synchronous FIFO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090586)|E. Xie; J. Zhou|10.1109/EEBDA56825.2023.10090586|Comparison;Asynchronous FIFO;Synchronous FIFO;Integrated circuits;Electrical engineering;Big Data;Hardware;Registers;Clocks|
|[Design and Implementation of HDB3 Clock Recovery Module with Large Frequency Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090854)|H. Li; S. Lu; Z. Zhao|10.1109/EEBDA56825.2023.10090854|Clock Recovery Module;Large Frequency Range;Real-Time Control;Meters;Frequency measurement;System-on-chip;Synchronization;Frequency synchronization;Integrated circuit modeling;Task analysis|
|[Analysis and Comparison of UART, SPI and I2C](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090677)|J. Chen; S. Huang|10.1109/EEBDA56825.2023.10090677|UART;SPI;I2C;Resistance;Protocols;Power demand;Transmitters;Wires;Full-duplex system;Receivers|
|[The Principle and Applications of Asynchronous FIFO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090696)|Z. Hao; L. Liu; B. Tian|10.1109/EEBDA56825.2023.10090696|Asynchronous FIFO;Metastable state;Gray Code;Integrated circuits;Electrical engineering;Codes;Big Data;Stability analysis;Reflective binary codes;Synchronization|
|[Optimization of UART Communication Protocol Based on Frequency Multiplier Sampling Technology and Asynchronous FIFO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090630)|W. Wang|10.1109/EEBDA56825.2023.10090630|UART;Communications Protocol;Multiplier Sampling;Asynchronous FIFO;Protocols;Transmitters;Receivers;Control systems;Mathematical models;Delays;Data communication|
|[Gaussian Wasserstein distance based ship target detection algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090840)|S. Wang|10.1109/EEBDA56825.2023.10090840|remote sensing target detection;GWD model;ConvNeXt backbone network;cutout data enhancement;Training;Scalability;Object detection;Feature extraction;Data models;Robustness;Data mining|
|[Prediction of Electric Vehicle Charging Demand in Rural Areas Based on Driving Track Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090607)|J. Xu; H. Wang; W. Zhang; T. Wang; C. Wu|10.1109/EEBDA56825.2023.10090607|Beidou system;charge schedule strategy;electric vehicle positioning;travel track prediction;economical scheduling;Economics;Schedules;Charging stations;Big Data;Prediction algorithms;Electric vehicle charging;Scheduling|
|[Electromagnetic Interference Protection Method of Beidou Positioning Terminal Near High Voltage Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090726)|P. Wu; L. Ge; J. Guan; G. Chen|10.1109/EEBDA56825.2023.10090726|Beidou positioning terminal;electromagnetic interference;shielding characteristic;electromagnetic compatibility;Couplings;Analytical models;Power transmission lines;Electromagnetic interference;Space radiation;Directive antennas;Transmission line measurements|
|[Spatial Electromagnetic Interference Calculation of Arc Discharge in High Voltage Transmission System Based on FDTD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090615)|H. Li; P. Wu; T. Zheng; W. Luan; X. Wu|10.1109/EEBDA56825.2023.10090615|Transient electromagnetic field;FDTD;arc discharge;Power transmission lines;Dipole antennas;Electromagnetic interference;Arc discharges;High-voltage techniques;Magnetic fields;Transient analysis|
|[Data Analysis of Abnormal Harmonic of Offshore Wind Farm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090525)|Z. Yang; Y. Wen; S. Ye; Q. Guo; L. Zhao|10.1109/EEBDA56825.2023.10090525|offshore wind farm;harmonics;monitoring data;operation states;association analysis;Histograms;Reactive power;Correlation;Wind speed;Wind farms;Power system harmonics;Wind power generation|
|[Research on Security and Electronic Countermeasure of Wireless Quantum Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090782)|W. Haoming; Z. Weikang; L. Nan|10.1109/EEBDA56825.2023.10090782|wireless quantum Communication;electronic countermeasure;effectiveness;reliability;Wireless communication;Measurement by laser beam;Interference;Reconnaissance;Radar countermeasures;Communication system security;Reliability|
|[GAN-based image deblurring: A comparison](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090713)|Q. Feng; L. Jiang; Y. Ji; R. Zhang|10.1109/EEBDA56825.2023.10090713|Image deblurring;GAN;DeblurGAN;Machine learning algorithms;Image synthesis;Image edge detection;Heuristic algorithms;Generative adversarial networks;Inference algorithms;Image restoration|
|[Research on Resource Search Technology and Student Diagnostic Evaluation in Distance Education System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090778)|S. Hui|10.1109/EEBDA56825.2023.10090778|P2P mode;knowledge hierarchy structure;distance education;search technology;double ring structure;Structural rings;Knowledge engineering;Electrical engineering;Fault tolerance;Scalability;Heuristic algorithms;Education|
|[Research on Label Recognition Method of Power Grid Item Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090763)|B. Guangyu; Z. Xin; Z. Yongqiao; Z. Pengyuan; S. Pengfei; Q. Zheng|10.1109/EEBDA56825.2023.10090763|Deep learning;Power grid item;Label identification;Word2vec + the SVM model;BiLSTM-CRF sequence labeling model;Support vector machines;Knowledge engineering;Adaptation models;Text recognition;Heuristic algorithms;Semantics;Power system dynamics|
|[Construction and Accurate Retrieval Method of Knowledge Graph of Automobile Engine Fault](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090855)|J. Tang; C. Xu; W. Zhang|10.1109/EEBDA56825.2023.10090855|engine fault;knowledge graph;entity link;Subgraph matching;semantic search;Knowledge engineering;Visualization;Semantic search;Decision making;Knowledge graphs;Maintenance engineering;Feature extraction|
|[Research on bank customer churn model based on attention network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090614)|Y. Wang; S. Zheng; G. Liu; J. Li|10.1109/EEBDA56825.2023.10090614|customer churn prediction;attention weight;LSTM;GRU;Electrical engineering;Neural networks;Finance;Banking;Predictive models;Prediction algorithms;Data models|
|[High-precision GPS Signal Tracking Method Based on TDOA/FDOA Phase Fringes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090661)|T. Jiang; W. Cui; W. Liu|10.1109/EEBDA56825.2023.10090661|Global Positing System (GPS);Code tracking;Time Difference Of Arrival (TDOA);Carrier tracking;Frequency Difference Of Arrival (FDOA);Phase stripe;Hough transform;Tracking loops;Time-frequency analysis;Codes;Tracking;Heuristic algorithms;Receivers;Mean square error methods|
|[Research on Gender Difference of Car Following Behavior Based on Natural Driving Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090490)|Z. Xing; H. Fu; H. Zhang; Z. Ji|10.1109/EEBDA56825.2023.10090490|gender difference;natural driving data;car following behavior;mathematical statistics;model adaptability;Electrical engineering;Analytical models;Correlation;Statistical analysis;Roads;Data acquisition;Radar|
|[Research on the design of maximum output power of wave energy device under the condition of pendulum motion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090743)|Z. Jia; W. Han; Z. Sun|10.1109/EEBDA56825.2023.10090743|Eccentric oscillator model;Ordinary differential equation;Heuristic algorithm;Damping;Renewable energy sources;Pollution;Heuristic algorithms;Ordinary differential equations;Linear programming;Mathematical models|
|[Multimodal emotion recognition based on feature fusion and residual connection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090537)|X. Du; J. Yang; X. Xie|10.1109/EEBDA56825.2023.10090537|Bi-LSTM;Feature fusion;Multimodal emotion recognition;Multi-head attention;Human computer interaction;Electrical engineering;Emotion recognition;Education;Medical services;Big Data;Feature extraction|
|[Research on third-order damping technology of ESGN vertical channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090745)|J. Dai; C. Liu; Q. Zhang; D. Zheng|10.1109/EEBDA56825.2023.10090745|electrostatic suspended gyroscope navigator (ESGN);vertical channel;damping technology;Damping;Meters;Electrical engineering;Analytical models;Navigation;Big Data;Gyroscopes|
|[To prevent the LAN IP address from being preempted and conflict resolution countermeasures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090484)|L. Zhang; W. Zhu; J. Xu|10.1109/EEBDA56825.2023.10090484|LAN;IP address;ARP deception;Enterprise;IP address conflict;countermeasures;Productivity;Electrical engineering;Wireless LAN;Protocols;Computer hacking;Data security;Big Data|
|[Network Monitoring and Security Protection Design of Wind Farm Centralized Control Center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090491)|C. Yang; W. Zhang|10.1109/EEBDA56825.2023.10090491|Wind Farm Centralized Control Center;Network Monitoring;Security Protection;Concurrent Performance;Statistical analysis;Transforms;Wind power generation;Wind farms;Wind turbines;Safety;Reliability|
|[Pure Azimuth Passive Positioning in Attempted UAV Formation Flight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090746)|X. Li; M. Wang|10.1109/EEBDA56825.2023.10090746|Pure azimuthal passive localization;triangulation localization method;two-station cross-localization method;greedy algorithm;Greedy algorithms;Location awareness;Azimuth;Heuristic algorithms;Estimation;Mathematical models;Planning|
|[Intelligent energy-saving parking system design based on deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090619)|Y. Chen; Y. Mao; C. Qi; G. Zhao; Y. Zhao|10.1109/EEBDA56825.2023.10090619|Intelligent energy-saving parking;electricity efficiency;management;system;Deep learning;Costs;Power supplies;Urban areas;Lighting;Production;Sensor systems|
|[A Parameter-Optimized CNN Using WOA and Its Application in Fault Diagnosis of Bearing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090663)|Y. Cui; S. Zhang; Z. Zhang|10.1109/EEBDA56825.2023.10090663|bearing fault diagnosis;Convolution neural network;Group optimization algorithm;Fault diagnosis;Training;Wavelet transforms;Adaptation models;Time-frequency analysis;Classification algorithms;Convolutional neural networks|
|[PISA architecture chip resource scheduling algorithm design and implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090793)|Z. Tao|10.1109/EEBDA56825.2023.10090793|Resource scheduling scenarios;mixed integer programming models;decision variables;lingo solvers;particle swarm optimization;Integer programming;Limiting;Scheduling algorithms;Heuristic algorithms;Pipelines;Process control;Scheduling|
|[Short-term Power Load Forecasting Based on Particle Swarm Optimization Long Short-term Memory Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090722)|Z. Zhang; W. Xu; Q. Gong|10.1109/EEBDA56825.2023.10090722|particle swarm optimization;power load forecasting;prediction accuracy;long short-term memory neural network;Load forecasting;Predictive models;Prediction algorithms;Transformers;Explosions;Stability analysis;Particle swarm optimization|
|[Multi-objective optimal configuration of two-stage reactive power compensation in power grids with power loss index](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090769)|G. Ting; X. Liangde; C. Zhonghao; Y. Fan|10.1109/EEBDA56825.2023.10090769|Reactive power compensation;optimal allocation;power loss index;penalty function;remora optimization algorithm;Reactive power;Costs;Simulation;Voltage;Search problems;Power grids;Whale optimization algorithms|
|[Research on Digital Twin Optimization Algorithm of Logistics Distribution Based on Computer Virtual Reality Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090748)|L. Liu; Y. Fan|10.1109/EEBDA56825.2023.10090748|computer;virtual reality;logistics distribution;digital twinning;optimization algorithm;vehicle scheduling;optimal path;Solid modeling;Visualization;Time-frequency analysis;Vehicle routing;Virtual reality;Software systems;Digital twins|
|[Research on 3D Visualization Logistics Intelligent Scheduling System Based on Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090776)|Y. Fan; W. Yao|10.1109/EEBDA56825.2023.10090776|digital twinning;visualization;3d;logistics intelligent scheduling system;scheduling visualization;Electrical engineering;Solid modeling;Three-dimensional displays;Scalability;Dispatching;Scheduling;Digital twins|
|[Research on Digital Twinning Technology in the Construction of Computer Digital Logistics Intelligent Twinning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090660)|W. Yao; L. Liu|10.1109/EEBDA56825.2023.10090660|digital twinning;computer;digital logistics;logistics twin system;intelligent logistics decision;Couplings;Heuristic algorithms;Computational modeling;Decision making;Interference;Real-time systems;Feedback control|
|[Analysis of energy storage operation and configuration models for high ratio wind power systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090517)|X. Li|10.1109/EEBDA56825.2023.10090517|Nonlinear programming model;system power balance;Particle Swarm optimization;Wind energy generation;Costs;Carbon dioxide;Wind power generation;Real-time systems;Thermal analysis;Wind turbines|
|[Blockchain Security Risk Monitoring of Power Supply Chain Based on Fuzzy Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090552)|Z. He|10.1109/EEBDA56825.2023.10090552|Fuzzy Neural Network;Power Supply Chain;Block chain;Security Risk Monitoring;Supply chains;Smart contracts;Fuzzy neural networks;Power grids;Mathematical models;Blockchains;Security|
|[Long-term Power Generation Forecasting Method Based on BAS-BP Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090568)|M. Cai; F. Xing; P. Cui|10.1109/EEBDA56825.2023.10090568|electricity generation forecasting;BAS-BP neural networks;beetle antennae search algorithm;model optimization;Supply and demand;Neural networks;Time series analysis;Predictive models;Prediction algorithms;Market research;Data models|
|[Improved Topologies Towards Existing Types of Single Phase Rectifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090627)|S. Gou|10.1109/EEBDA56825.2023.10090627|Single Phase Buck-boost rectifier;Single Phase Zeta rectifier;Performance evaluation;Reactive power;AC-DC power converters;Total harmonic distortion;Rectifiers;Power transmission;Production|
|[Design model of maximum output power of wave energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090575)|B. Liu; R. Zhao; W. Wang|10.1109/EEBDA56825.2023.10090575|wave energy;Force analysis;Average maximum output power;and hydrodynamic coefficients and models;Analytical models;Renewable energy sources;Green products;Force;Hydrodynamics;Reliability engineering;Data models|
|[Pipe scale cleaning device based on YOLO algorithm and Carmen vortex street](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090514)|R. Cheng; T. Hou|10.1109/EEBDA56825.2023.10090514|Carmen vortex;bionic;pipeline;water scale;yolo;algorithm;Training;Electrical engineering;Computer vision;Impellers;Big Data;Cameras;Cleaning|
|[Real-time Video Fire Detection via Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090723)|J. Huang; X. Ma; Y. Wang; X. Li|10.1109/EEBDA56825.2023.10090723|Flame detection;Transfer learning;Embedded usage;CNN;Image color analysis;Fires;Streaming media;Real-time systems;Classification algorithms;Sensors;Safety|
|[Research on Effect of High Temperature on SiC MOSFET Electrical Characteristic Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090486)|H. Guo; Z. Wang; S. Guo; L. Shuai; Y. Wei|10.1109/EEBDA56825.2023.10090486|power electronic;rail transit;SiC MOSFET;high temperature;Rails;Resistance;MOSFET;Temperature distribution;Silicon carbide;SPICE;Threshold voltage|
|[Research and Development of Hybrid Impact Tester Based on Hydraulic Servo System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090711)|N. Cheng; X. Gao; Y. Liu; L. Wang|10.1109/EEBDA56825.2023.10090711|impact tester;hydraulic servo system;drop hammer tester;extened state observer;Vibrations;Electrical engineering;Simulation;Observers;Mathematical models;Electrohydraulics;Servomotors|
|[Research on Control Strategy of Offshore Wind Power Transmission System via MMC-HVDC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090742)|X. Ren; H. Huang; Y. Wang; Y. Cui; P. Li|10.1109/EEBDA56825.2023.10090742|Offshore wind farms;MMC;Frequency control;Power operation interval optimization;Time-frequency analysis;Heuristic algorithms;Wind speed;Wind farms;Wind power generation;Power system harmonics;Harmonic analysis|
|[Bearing Health Detection Method Based on Wavelet Time-Frequency Spectrum and Parametric Improved CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090603)|Y. Liu|10.1109/EEBDA56825.2023.10090603|Alexnet;wavelet time-frequency spectrum;deep learning theory;fault diagnosis;Wavelet transforms;Training;Fault diagnosis;Time-frequency analysis;Data centers;Neural networks;White noise|
|[Intelligent Agricultural Data Collection and Analysis System Based on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090487)|X. Wu; G. Li|10.1109/EEBDA56825.2023.10090487|Smart Agriculture;Data Collection;Internet of Things Technology;Analysis System;Smart agriculture;File systems;Memory;Production;Big Data;Internet of Things;Data communication|
|[A Multi-stage dynamic evaluation method of distribution network with renewable energy based on hierarchical incentive control line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090781)|Z. Cai; K. Yang; Z. Huang; J. Cao; Z. Ou|10.1109/EEBDA56825.2023.10090781|Distribution network with high permeability renewable energy;Distribution network planning evaluation index system;Static evaluation method;Multi-stage dynamic evaluation;Renewable energy sources;Power supplies;Heuristic algorithms;Power system dynamics;Distribution networks;Maintenance engineering;Planning|
|[Recognition of Italian Gesture Language Based on Augmented YoloV5 Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090818)|N. Zhang; Y. Huang; Z. Sun|10.1109/EEBDA56825.2023.10090818|YOLO;Keep Augment;Italian static gesture;Hand gesture recognition;Training;Electrical engineering;Analytical models;Runtime;Gesture recognition;Production;Streaming media|
|[Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in PCB Defect Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090731)|M. Ye; H. Wang; H. Xiao|10.1109/EEBDA56825.2023.10090731|Industrial Defect Detection;Slimneck;Attention mechanism;YOLOv5;Electrical engineering;Computational modeling;Object detection;Network architecture;Feature extraction;Data models;Neck|
|[Research on Ore Processing Quality Prediction Based on XGBoost ESN Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090546)|J. Zhang; W. Xu; X. Zhang|10.1109/EEBDA56825.2023.10090546|XGBoost;ESN;factor analysis;ore quality prediction;Matlab;Analytical models;Temperature distribution;Time series analysis;Predictive models;Prediction algorithms;Data models;Software|
|[Risk Prediction of Diabetes Based on Spark and Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090801)|Z. Dai; Z. Hu; T. Shen; Y. Zhang|10.1109/EEBDA56825.2023.10090801|Diabetes;Random forest;Disease prediction;Machine learning;spark;Training;Machine learning algorithms;Computational modeling;Predictive models;Prediction algorithms;Data models;Diabetes|
|[Pavement crack identification and detection based on multi-task learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090657)|Z. Chen; X. Huang; S. Liu|10.1109/EEBDA56825.2023.10090657|Pavement crack detection;Multi-task learning;Deep learning;Image segmentation;Training;Adaptation models;Roads;Semantic segmentation;Multitasking;Data models;Robustness|
|[Research on Indoor Target Positioning System Based on Image Feature Extraction and Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090701)|P. Lu; J. Zhang; X. Lin; J. Qi; Y. Chen; K. Yang|10.1109/EEBDA56825.2023.10090701|ORB algorithm;feature extraction;image recognition;Indoor positioning;Knowledge engineering;Electrical engineering;Image recognition;Target recognition;Object detection;Feature extraction;Hardware|
|[Modulation identification method based on time-frequency analysis and support vector machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090561)|X. Huang; X. Li|10.1109/EEBDA56825.2023.10090561|modulation recognition;low signal-to-noise ratio;support vector machine;time-frequency analysis;wavelet energy entropy;Support vector machines;Time-frequency analysis;Modulation;Frequency shift keying;Feature extraction;Entropy;Pattern recognition|
|[Query performance optimization method for blockchain carbon footprint data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090788)|T. Xu; T. Chen; Y. Zhang; X. Zuo; M. Liu; L. Zhang; Y. Feng; Y. Wang; C. Yu; Y. Zhao|10.1109/EEBDA56825.2023.10090788|blockchain;query;database;index;optimization;Electrical engineering;Query processing;Memory;Big Data;Blockchains;Carbon footprint;Optimization|
|[A Review of Research on Testability Technology for Complex Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090608)|H. Zhu; M. Qiao; Y. Shao; P. Sun|10.1109/EEBDA56825.2023.10090608|testability;fault diagnosis;reliability;Performance evaluation;Fault diagnosis;Process control;Maintenance engineering;Prediction algorithms;Real-time systems;Safety|
|[Identifying interphase short circuits in the tator of a multi-phase generator using coils](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090579)|H. Zhu; M. Qiao; Y. Shao; P. Sun|10.1109/EEBDA56825.2023.10090579|interphase short circuits;Harmonic characteristics;Fault identification;Fault diagnosis;Short-circuit currents;Air gaps;Windings;Stator windings;Rotors;Harmonic analysis|
|[Research on fuzzy comprehensive forecasting of large user power unproductive load based on demand response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090559)|S. Gu; Z. Zheng; Y. Li; J. Song; Y. Liu; W. Zhang|10.1109/EEBDA56825.2023.10090559|demand response;Power load;Fuzzy prediction;Distributed nodes;Overclocking power supply;Analytical models;Load forecasting;Computational modeling;Predictive models;Prediction algorithms;Demand response;Data models|
|[Method for Initial Point Positioning of V-bevel Welding Seam Based on Binocular Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090544)|S. Sun; J. He; T. Wang; K. Wang; Z. Lei|10.1109/EEBDA56825.2023.10090544|binocular vision;weld initial point;target detection;hand-eye calibration;Welding;Robot kinematics;Image edge detection;Robot vision systems;Neural networks;Vision sensors;Cameras|
|[High-speed Real-time Infrared Image Target Detection Method Based on Intelligent Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090686)|Y. Gang; L. Pengyuan; H. Jian; Z. Bing; J. Yunfei; Y. Xiong|10.1109/EEBDA56825.2023.10090686|Intelligent platform;YOLOv5;Target detection;Infrared image;Infrared detectors;Electrical engineering;Object detection;Big Data;Real-time systems|
|[Research on Network Information Security Model Based on Computer Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090710)|J. Xiao; Y. Li; T. Yan; L. Li|10.1109/EEBDA56825.2023.10090710|computer;artificial intelligence;network information security;rough set algorithm;situational awareness;Support vector machines;Computational modeling;Simulation;Rough sets;Intrusion detection;Reliability theory;Network security|
|[UAV positioning based on pure azimuth passive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090790)|Y. Wu; W. Wang; G. Zhang; K. Yang; Y. Chen|10.1109/EEBDA56825.2023.10090790|pure orientation passive;localization model;greedy algorithm;recursive algorithm;Location awareness;Greedy algorithms;Space vehicles;Costs;Azimuth;Weapons;Satellite broadcasting|
|[Study on Passive Pure Azimuth Positioning Model and Position Adjustment Model of UAV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090738)|J. Li; Z. Li; R. Wang|10.1109/EEBDA56825.2023.10090738|Bayesian estimation;Passive and pure azimuth positioning;Dynamic programming;Analog annealing algorithm;Location awareness;Azimuth;Heuristic algorithms;Working environment noise;Simulated annealing;Interference;Probabilistic logic|
|[Pure azimuthal passive positioning model for UAVs flying in circular formation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090707)|S. Li; Y. Chen; T. Jiang|10.1109/EEBDA56825.2023.10090707|Formation flight;effective positioning model;trajectory equation;position adjustment model;Target tracking;Azimuth;Surveillance;Electromagnetic scattering;Simulated annealing;Interference;Mathematical models|
|[RSSI-KNN: A RSSI Indoor Localization Approach with KNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090664)|X. Zheng; R. Cheng; Y. Wang|10.1109/EEBDA56825.2023.10090664|KNN;RSSI;Trilateration;Fingerprinting;Location awareness;Wireless communication;Transmitters;RF signals;Zigbee;Mean square error methods;Prediction algorithms|
|[Pure azimuth passive localization in attempted formation flight of UAVs under different constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090729)|L. Jin; Y. Xu; Q. Mei|10.1109/EEBDA56825.2023.10090729|Kalman filtering algorithm;Single objective optimization;Hierarchical analysis;Analytical models;Adaptation models;Shape;Heuristic algorithms;Filtering algorithms;Data models;Indexes|
|[Optimization of additional heat production performance of air conditioning compressor based on hybrid particle swarm optimization algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090644)|Y. Wu; F. Zhang; Y. Su|10.1109/EEBDA56825.2023.10090644|Energy-efficient optimization;superposition principle;Crank-Nicolson;neural network;hybrid particle swarm algorithm;Analytical models;Software algorithms;Neural networks;Mathematical models;Data models;Software;Particle swarm optimization|
|[Motion and Power Study of Wave Energy Devices Based on Runge-Kutta Method and Variable Step Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090672)|G. Shi; Y. Feng; J. He; Y. Li|10.1109/EEBDA56825.2023.10090672|movement model;Runge-Kutta method;variable step size search algorithm;Damping;Performance evaluation;Analytical models;Renewable energy sources;Ocean waves;Mathematical models;Stability analysis|
|[Ultra-compact dual-wavelength-dual-mode (de)multiplexer utilizing topology optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090624)|Z. Zhao; Y. Jiang; Y. Deng; P. Cao|10.1109/EEBDA56825.2023.10090624|Micro-nano Photonics;Inverse Design;Mode-division Multiplexing (MDM);Wavelength-division Multiplexing (WDM);Topology Optimization;Optical losses;Silicon-on-insulator;Crosstalk;Insertion loss;Wavelength division multiplexing;Optical fiber communication;Topology|
|[Blind Lane Detection Algorithm Based on Improved YOLOv5s](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090556)|X. Gong; B. Pan; Z. Yang; W. Liu; H. Zheng|10.1109/EEBDA56825.2023.10090556|Blind Lane Scene Detection;YOLOv5s;Lightweight;BiFPN;Ghost Module;Coordinate Attention;Performance evaluation;Electrical engineering;Costs;Lane detection;Computational modeling;Blindness;Big Data|
|[Optimal parking research based on path and power optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090572)|B. Li|10.1109/EEBDA56825.2023.10090572|Auto parking;Path optimization;Time optimization;Dynamic programming;Difference-in-difference method;Space vehicles;Adaptation models;Technological innovation;Urban areas;Planning;Safety;Automobiles|
|[Wind Speed Prediction with LSTM and BPNN Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090772)|W. Zhang; F. Wang; Y. Yang|10.1109/EEBDA56825.2023.10090772|Wind speed prediction;Back Propagation Neural Network (BPNN);Long Short-Term Memory (LSTM);Training;Renewable energy sources;Temperature;Wind speed;Neural networks;Wind power generation;Predictive models|
|[AureNet: A Real-Time Arbitrary-oriented and Ship-based Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090508)|M. Liu; Y. Chen; D. Ding|10.1109/EEBDA56825.2023.10090508|Focal Loss;AureNet Retinanet;Yolox-HSV;Web and internet services;Transportation;Object detection;Feature extraction;Real-time systems;Robustness;Sensors|
|[Rev-RetinaNet: PCB defect detection algorithm based on improved RetinaNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090524)|J. Tang; Y. Zhao; D. Bai; Q. Liu|10.1109/EEBDA56825.2023.10090524|Rev-RetinaNet;ConvNext;RetinaNet;YOLOXHSVRandomAug;Deep learning;Costs;Perturbation methods;Printed circuits;Imaging;Lighting;Production|
|[Design and Implementation of Travel Website Based on Java Web](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090730)|Y. Guo|10.1109/EEBDA56825.2023.10090730|JavaWeb;Travel websites;B/S structure;MySQL;Electrical engineering;Java;Databases;Tourism industry;Maintenance engineering;Containers;Information filters|
|[Distributed Data Multi-Level Storage Encryption Method Based on Full-Flow Big Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090798)|Y. Ma; Y. Zhao; Z. Zhang; J. Wang|10.1109/EEBDA56825.2023.10090798|big data analysis;distributed data;multi-level storage;encryption algorithm;Data security;Storage management;Software algorithms;Distributed databases;Collaboration;Big Data;Network coding|
|[Research on location planning of 5G base station based on DBSCAN clustering algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090761)|L. Jiang; B. Huang; L. Chen; Z. Li|10.1109/EEBDA56825.2023.10090761|DBSCAN clustering algorithm;traversal algorithm;time complexity;location planning;Base stations;Costs;5G mobile communication;Software algorithms;Clustering algorithms;Software;Data models|
|[AC current online calibration device based on digital image recognition and fiber optic sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090834)|X. Chen; J. Zhang; Q. Zhang|10.1109/EEBDA56825.2023.10090834|Digital image recognition;fiber optic current sensor;AC current;online calibration;Optical fibers;Optical fiber sensors;Image recognition;Digital images;Current measurement;Measurement uncertainty;Sensor systems|
|[Pure orientation passive positioning of UAVs in different formations and initial positions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090703)|M. Jiang; J. Liu; Z. Li|10.1109/EEBDA56825.2023.10090703|Optimization algorithm;dynamic programming algorithm;UAV positioning;Greedy algorithms;Electrical engineering;Transmitters;Heuristic algorithms;Interference;Position measurement;Programming|
|[A study of base station establishment site selection based on cluster analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090850)|R. Luo|10.1109/EEBDA56825.2023.10090850|K-mean clustering;linear programming model;simulation;region cutting;python;Base stations;Solid modeling;Analytical models;Clustering algorithms;Programming;Mobile communication;Data models|
|[Application of Pure Azimuth Passive Positioning in UAV Formation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090616)|H. Yang; Q. Wang; W. Sun; M. Liu|10.1109/EEBDA56825.2023.10090616|Greedy algorithm;Partitioning algorithm;Outer circle intersection;Penalty factor;Electrical engineering;Limiting;Transmitters;Electromagnetic scattering;Interference;Big Data;Robustness|
|[Improved K-Nearest Neighbor Missing Data Classification Based on Interval Value Imputation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090609)|Z. Zhang; C. Tang|10.1109/EEBDA56825.2023.10090609|KNN;incomplete dataset;interval value imputation;Training;Electrical engineering;Euclidean distance;Big Data;Classification algorithms|
|[Analysis of control strategy of three-phase bridge fully controlled rectifier circuit based on PID principle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090617)|X. Zhang; Z. Liu; X. Wang; Z. Ji|10.1109/EEBDA56825.2023.10090617|IGBT;rectifier circuit;PID control;trigger angle;Analytical models;PI control;Rectifiers;Bridge circuits;Regulation;Power system reliability;PD control|
|[Study on the maximum output power of wave energy under vertical oscillation and longitudinal rocking motion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090633)|Q. Li|10.1109/EEBDA56825.2023.10090633|Multivariate second-order nonlinear differential equations;objective programming models;adaptive particle swarm algorithms;Analytical models;Renewable energy sources;Adaptation models;Differential equations;Kinematics;Programming;Mathematical models|
|[UAV passive positioning model based on multi-objective iterative optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090668)|T. Yang; W. Yu; L. Zhang|10.1109/EEBDA56825.2023.10090668|Pure azimuthal passive positioning;three-point positioning;multi-objective iterative mode;Analytical models;Satellites;Surgery;Satellite navigation systems;Path planning;Iterative algorithms;Iterative methods|
|[Formation Adjustment of Bearings-only Passive Location UAV Based on Triangulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090640)|R. Jiao|10.1109/EEBDA56825.2023.10090640|Triangulation;Least Squares;Passive Location;Queue Adjustment;Electrical engineering;Databases;Interference;Big Data;Autonomous aerial vehicles;Electromagnetics|
|[A multi-objective traffic flow detection system based on an improved yolov4 algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090516)|X. Chen; Y. Zhai|10.1109/EEBDA56825.2023.10090516|Intelligent traffic;YOLOv4 algorithm;Deepsort;Deep learning;Multi-target tracking;Traffic statistics;Deep learning;Electrical engineering;Electric potential;Target tracking;Limiting;5G mobile communication;Transportation|
|[Research on emergency supplies distribution in 5G network environment based on K-class mean value algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090846)|Z. Sun; Z. Jia; W. Han|10.1109/EEBDA56825.2023.10090846|TSP-D;kmeans clustering;two-layer mixed integer programming model;Integer programming;Analytical models;5G mobile communication;Heuristic algorithms;Roads;Clustering algorithms;Transforms|
|[Bearing health monitoring based on SSA optimization of AlexNet hyperparameter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090628)|Y. Xie; Q. Wang; H. Yu|10.1109/EEBDA56825.2023.10090628|Bearing fault diagnosis;Sparrow Optimization Algorithm;convolutional neural network;Training;Time-frequency analysis;Oceans;Sociology;Neural networks;Convolutional neural networks;Statistics|
|[Laser slam-based autonomous navigation for fire patrol robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090785)|C. Liu; W. He; X. Cai; Z. Xie; X. Zhang|10.1109/EEBDA56825.2023.10090785|Ship;Fire Patrol Robot;McNamee Wheel;Robot-Link V4.0 WIFI Digital Transmission Module;Technological innovation;Robot vision systems;Wheels;Cameras;Hardware;Sensors;Mobile robots|
|[Base station planning problem based on genetic algorithm and K-Means clustering algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090812)|Z. Wang|10.1109/EEBDA56825.2023.10090812|Multi-objective optimization model;Genetic algorithm;K-Means clustering algorithm;Optimization model;Base stations;Costs;Clustering algorithms;Mathematical models;Data models;Software;Planning|
|[Research on the maximum output power of wave energy based on micro-amplitude wave theory and potential flow theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090623)|P. Chen|10.1109/EEBDA56825.2023.10090623|Micro-amplitude wave theory;potential flow theory;finite element analysis;Simulink simulation;Damping;Analytical models;Solid modeling;Laplace equations;Force;Mathematical models;Data models|
|[Temperature Field Simulation of Partial Discharge in Oil-Paper Capacitor Bushing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090841)|P. Peng; Y. Liu; X. Wan; L. Ou|10.1109/EEBDA56825.2023.10090841|capacitor bushing;thermal field;partial discharge;finite element method;Partial discharges;Temperature measurement;Heating systems;Temperature distribution;Three-dimensional displays;Capacitors;Insulators|
|[Short-term power load forecasting based on PSO-GRU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090721)|X. Jiang; Y. Zhang; Y. Liu|10.1109/EEBDA56825.2023.10090721|electricity load forecasting;gated recurrent unit;particle swarm optimization;forecasting accuracy;Analytical models;Load forecasting;Simulation;Predictive models;Prediction algorithms;Data models;Stability analysis|
|[Sound Field Simulation and Excessive Noise Analysis of Station Boundary for Two Typical 220kV Substations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090659)|T. Qi; C. Wei; C. Hao; H. Sheng; W. Cai; J. Xiao; W. Peng|10.1109/EEBDA56825.2023.10090659|Full idoor substation;half indoor substation;noise emmission;Fans;Insulation;Analytical models;Substations;Layout;Production facilities;Software|
|[Application of mutual information and improved support vector machine in power load forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090531)|K. Zhu|10.1109/EEBDA56825.2023.10090531|Power load-forecast;Mutual information;predictive modeling;Support vector machines;Correlation;Atmospheric modeling;Predictive models;Prediction algorithms;Data models;Forecasting|
|[A 5G-Based Big Data Security Access Processing Method and Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090774)|A. Abduklimu; P. Yan; G. Wang; H. Liu; J. Xie|10.1109/EEBDA56825.2023.10090774|5G Era;Communication Requirements;Data Encryption;Data Security Transmission;Industries;5G mobile communication;Software algorithms;Authentication;Big Data;Throughput;Power grids|
|[Research on Location Decision Optimization of Communication Station Based on Simulated Annealing Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090704)|Z. Su; Y. Zhang; Z. Zu; Z. Sun|10.1109/EEBDA56825.2023.10090704|biobjective programming model;Monte Carlo simulation;Simulated annealing particle swarm optimization;Electrical engineering;Base stations;Costs;Biological system modeling;Simulated annealing;Programming;Linear programming|
|[Mobile communication network site planning and regional clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090816)|H. Lin; J. Lin; J. Lin; K. You|10.1109/EEBDA56825.2023.10090816|site planning;particle swarm optimization;analytic hierarchy process;k-means;Base stations;Analytical models;Costs;Buildings;Transportation;Analytic hierarchy process;Mobile communication|
|[UAV Point-point Analysis Based on Analytic Geometric and Iterative Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090795)|P. Jiang; S. He; S. Qian|10.1109/EEBDA56825.2023.10090795|Pure azimuth passive localization;iterative algorithm;analytic geometry;target planning;Analytical models;Azimuth;Atmospheric modeling;Computational modeling;Media;Autonomous aerial vehicles;Data models|
|[Web Random Token Generation Technology Based on Asymmetric Encryption Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090549)|Y. Ling; X. Li; D. Bin; C. Yang; J. Lu|10.1109/EEBDA56825.2023.10090549|Asymmetric Encryption Technology;WEB Technology;Random Token Generation;Technical Research;Industries;Electrical engineering;Data security;Big Data;Hardware;Encryption;Internet|
|[A Hybrid Structure Speech Coding Scheme Based on MELPe and LPCNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090529)|Y. Ding; X. Yu|10.1109/EEBDA56825.2023.10090529|speech coding;speech synthesis;MELPe;LPCNet;Deep learning;Training;Electrical engineering;Speech coding;Jitter;Feature extraction;Hybrid power systems|
|[Research on Intelligent Home System Based on Computer Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090733)|S. He|10.1109/EEBDA56825.2023.10090733|computer;smart home;control system;the sensor;remote monitoring;the remote control;Technical requirements;Smart homes;Speech recognition;Syntactics;Wavelet packets;System software;Internet of Things|
|[The Research of Development of New Energy Industrial Internet Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090802)|X. Xie; H. Xie; X. Fan; C. Luo; T. Xu; H. Li; L. Ge|10.1109/EEBDA56825.2023.10090802|New Energy;Industrial mechanism model;Microservices;Industrial application;Industries;Supply chains;Production;Maintenance engineering;Big Data;Internet;Manufacturing|
|[A multi-index comprehensive evaluation method for key technology development of AC-DC distribution network based on neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090848)|J. Wang; T. Zhang; M. Cheng; L. Zhou; Y. Zhang; R. Wang|10.1109/EEBDA56825.2023.10090848|Multi-index comprehensive evaluation;FAHP;AC-DC distribution network;Technology maturity;BPNN;Economics;Electrical engineering;Neural networks;Distribution networks;Carbon neutral;Power systems;Planning|
|[Simulation Analysis of Time Domain Broadening of Laser Propagation in Seawater](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090670)|F. Wang|10.1109/EEBDA56825.2023.10090670|Time domain broadening;Optical communication system;Laser pulse;Electrical engineering;Analytical models;Solid modeling;Impurities;Scattering;Solids;Optical fiber communication|
|[Numerical Simulation of New Energy Vehicle Battery Pack under Different Working Conditions Based on PCM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090673)|M. Zhang; S. Wang; J. Wang; Y. Cao; Y. Bai; H. Wei|10.1109/EEBDA56825.2023.10090673|power battery;phase change material;limit temperature;numerical simulation;Phase change materials;Temperature distribution;Insulation;Cooling;Thermal engineering;Heat engines;Thermal management|
|[Research on Data Synchronization Consistency Comparison System Based on Computer Heterogeneous Database](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090483)|X. Ding; Z. Jia; C. Liu; S. Zheng; Q. Shang; J. Li|10.1109/EEBDA56825.2023.10090483|computer;heterogeneous data;database;data synchronization consistency;Analytical models;Correlation;Computational modeling;Semantics;XML;Routing;Linear programming|
|[Research on Heterogeneous Data Consistency Comparison System Based on Computer Mobile Database](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090590)|Y. Jiang; Y. Ai; X. Zhang; L. Xia; W. Liu; J. Li|10.1109/EEBDA56825.2023.10090590|computer;mobile database;heterogeneous data;data consistency;Electrical engineering;Solid modeling;Three-dimensional displays;Databases;Heuristic algorithms;Computational modeling;Computer architecture|
|[Research on the design of a target control platform for engineering projects under big data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090783)|J. Song|10.1109/EEBDA56825.2023.10090783|big data;engineering project;target control;platform design;Training;Costs;Process control;Quality control;Big Data;Big Data applications;Real-time systems|
|[A Bearing Fault Diagnosis Method Based on Branch Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090533)|J. Hu; H. Yan|10.1109/EEBDA56825.2023.10090533|intelligent fault diagnosis;deep learning;class imbalance;Convolution Neural Network;Training;Fault diagnosis;Neural networks;Rolling bearings;Data models;Stability analysis;Safety|
|[Application of Artificial Intelligence, Big Data and Cloud Computing in Optimizing Elevator Safety Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090755)|Y. Hu|10.1109/EEBDA56825.2023.10090755|artificial intelligence;big data;cloud computing;elevator safety;Industries;Cloud computing;Safety management;Big Data;Prediction algorithms;Reliability engineering;Elevators|
|[Research of integrating prior knowledge into abnormal behavior recognition model of EV charging station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090512)|W. Zhang; Y. Ji; S. Wang; L. Gu; G. Cao|10.1109/EEBDA56825.2023.10090512|image recognition;electric charging station;prior knowledge;class activation;neural network;Knowledge engineering;Training;Target recognition;Heuristic algorithms;Neural networks;Feature extraction;Electric vehicle charging|
|[The Xiangshan IOUT Digital Branch Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090522)|Z. Duan; J. Hu; A. Yi; Y. Zhang; Y. Chen|10.1109/EEBDA56825.2023.10090522|Internet of Underwater Things;Fault Isolation;Branch Unit;Current control;Underwater cables;Electrical engineering;Protocols;Wires;Sea floor;Reliability engineering|
|[Method of Continuous Electrolytic Loop-closing for 30° Phase-angle Difference Line Based on Three-Level Protection of Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090678)|L. Yuan; Z. Liu; D. Li|10.1109/EEBDA56825.2023.10090678|Distribution Network Automation;Three-Level Protection;Trend Change;Continuous Electrolytic Loop and closing;30 °Phase-angle Difference;Automation;Power supplies;Contacts;Distribution networks;Switches;Power system reliability;Circuit faults|
|[Application Research on the Measurement of Unintended Car Movement Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090773)|L. Kong; B. Yu; C. Tian; Y. Wu|10.1109/EEBDA56825.2023.10090773|Unintended Car Movement;breaking subsystem;measurement;Electrical engineering;Electric variables measurement;Maintenance engineering;Big Data;Elevators;Manufacturing;Automobiles|
|[Knowledge Graph Relationship Prediction Model for Intelligent Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090480)|L. Pengyuan; Y. Gang; Y. Xiong; H. Jian; Z. Bing; G. Libing|10.1109/EEBDA56825.2023.10090480|intelligent search;knowledge graph;neural network;relation prediction model;Computational modeling;Neural networks;Semantics;Knowledge graphs;Predictive models;Search problems;Data models|
|[Research on Deduplication Technology Based on B+ Tree Index and Hash Index](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090509)|H. Guan|10.1109/EEBDA56825.2023.10090509|deduplication technology;B+ tree Index;Hash Index;Hash functions;Protocols;Statistical analysis;Metadata;Fingerprint recognition;Search problems;Indexes|
|[Research on Grid Connection of Virtual Synchronous Generator and Diesel Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090631)|W. Zhan; L. Hu; G. Yang; X. Yao; Z. Zhu; M. Yang|10.1109/EEBDA56825.2023.10090631|Grid connected inverter;Diesel generator;Virtual synchronous generator;Microgrid;Fluctuations;Voltage fluctuations;Microgrids;Switches;Inverters;Synchronous generators;Generators|
|[Research on Carbon Reduction System of 110kV Substation Based on Computer Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090638)|Z. Wang; Q. Xu; J. Yang|10.1109/EEBDA56825.2023.10090638|energy saving and economic operation;power system planning;operation simulation;low carbon;Economics;Substations;Computational modeling;Systems operation;Carbon dioxide;Big Data;Software systems|
|[Electric heating optimization strategy and economic comparison based on heat load demand regulation capability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090688)|P. Wang; Q. Wang; C. Li; S. Liu; T. Wang|10.1109/EEBDA56825.2023.10090688|Heat load demand;Electric heating optimization control;Electricity price curve;Thermal characteristics of buildings;Energy consumption;Analytical models;Temperature;Buildings;Heat engines;Thermal analysis;Temperature control|
|[CRM-SBKG: Effective Citation Recommendation by Siamese BERT and Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090832)|M. Yuan; J. Wan; D. Wang|10.1109/EEBDA56825.2023.10090832|knowledge graph;citation recommendation;BERT model;graph convolutional network;Siamese network;Knowledge engineering;Electrical engineering;Publishing;Logic programming;Bit error rate;Knowledge graphs;Mixture models|
|[Design of a Docking & Charging Device for Unmanned Surface Vehicle (USV)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090671)|J. Hu; B. He; Z. Xing; Z. Duan; C. Yang|10.1109/EEBDA56825.2023.10090671|Unmanned Surface Vehicle(USV);Contactless Power Transfer(CLPT);Resonance compensation;Surface docking;Battery chargers;Autonomous underwater vehicles;RLC circuits;Prototypes;Couplers;Water quality;Surface charging|
|[State Estimation of Distribution Network Equipment Based on Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090507)|C. Liu; W. Li; Y. Zhang; Z. Zhou; Y. Liu; N. Li|10.1109/EEBDA56825.2023.10090507|Distribution network;State assessment;Genetic algorithm;Power supplies;Urban areas;Distribution networks;Maintenance engineering;Probability;Safety;Risk management|
|[Infrared Target Detection Based on the Fusion of Attention Mechanism and YOLOv5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090650)|Z. Wei; J. Zhao; X. Chen; A. Wang; F. Li; Y. Gu|10.1109/EEBDA56825.2023.10090650|Infrared image;Target Detection;Attention Mechanism;YOLOv5;Electrical engineering;Deep learning;Focusing;Data visualization;Object detection;Forestry;Big Data|
|[An Algorithm for GNSS Timing Interference Analysis Based on Multi-Feature Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090585)|H. Guo; H. Yang; S. Wang; X. Wang|10.1109/EEBDA56825.2023.10090585|GNSS timing;timing interference analysis;quality degradation model;multi-feature integration;Degradation;Electrical engineering;Global navigation satellite system;Analytical models;Interference;Feature extraction;Real-time systems|
|[A fault diagnosis method for ship shaft system based on empirical wavelet transform and particle filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090500)|Y. He; C. Ni|10.1109/EEBDA56825.2023.10090500|empirical wavelet transform;particle filter;ship shaft system;fault diagnosis;Wavelet transforms;Vibrations;Shafts;Fault diagnosis;Filtering algorithms;Feature extraction;Information filters|
|[LoRa-based smart greenhouse control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090493)|C. Zhang; J. Yang|10.1109/EEBDA56825.2023.10090493|IoT technology;LoRa;smart greenhouse;dynamic monitoring;data gateway;Wireless communication;Irrigation;Costs;Crops;Web pages;Greenhouses;Production|
|[Research on GPS Spoofing Signal Identification Technology Based on Inertial Information Assistance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090570)|Y. Shuai; Z. Chao|10.1109/EEBDA56825.2023.10090570|GPS receiver;Pseudorange;Positioning result;Spoofing strategy;INS;Electrical engineering;Simulation;Receivers;Inertial navigation;Big Data;Software;Object recognition|
|[Research on the Evolution Scale of Network Topology Based on FlexE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090496)|Z. Wang; J. Pan; H. Ding; K. Gao; Y. Wang; F. Dong; S. Zhu; W. Wang|10.1109/EEBDA56825.2023.10090496|FlexE;Network Slice;Network topology;Electrical engineering;Power demand;Network topology;Big Data;Data models;Topology|
|[Image Classification for Pneumonia and the Normal Based on Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090831)|Z. Rui; C. Ying; B. Xintong; T. T. Toe|10.1109/EEBDA56825.2023.10090831|Convolutional Neural Network;Image classification;Pneumonia;deep learning;Measurement;Electrical engineering;Analytical models;Pulmonary diseases;Entropy;Data models;Classification algorithms|
|[A novel method for micro-nano channel alignment with automatic features based on reconstructed Hough transform theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090523)|M. Ge; G. Sun; G. Zhang; L. Gu; M. Zhang; J. Wang|10.1109/EEBDA56825.2023.10090523|Micro-nano channel;Hough transform theory;Automatic alignment;Alignment accuracy;Threshold;Electrical engineering;Three-dimensional displays;Microscopy;Transforms;Production;Manuals;Fluorescence|
|[An Ansible-based Distributed Application Architecture Rapid Deployment Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090753)|A. Ning|10.1109/EEBDA56825.2023.10090753|Kubernetes;Container;High Availability;Cloud Computing;Electrical engineering;Costs;Computer architecture;Standardization;Manuals;Maintenance engineering;Containers|
|[Research on noise control technology of 110kV indoor substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090720)|X. Liu; S. Hu; C. Yang; T. Huang; W. Chen; Q. Tang; H. Cao; L. Lu|10.1109/EEBDA56825.2023.10090720|110 kV substaion;noise;control technology;Electrical engineering;Fans;Substations;Capacitors;Transformers;Acoustics;Software|
|[Research on Ventilation and Noise Reduction Technology of High-power Fast Charging Pile](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090829)|S. Hu; J. Zhou; W. Chen; X. Liu; J. Yang; Q. Tang; H. Cao; L. Lu|10.1109/EEBDA56825.2023.10090829|Fast charging station;Ventilation;noiseReduction;Heating systems;Performance evaluation;Fans;Power measurement;Silicon carbide;Noise reduction;Solids|
|[Research on Man-machine System Safety Auxiliary System Based on Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090764)|F. Qiang|10.1109/EEBDA56825.2023.10090764|big data;man-machine system;drive safely;auxiliary system;Software packages;Wheels;Predictive models;Control systems;Mathematical models;Data models;Safety|
|[Optimal Meter Configuration Based on Harmonic State Estimation Model and Binary Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090766)|W. Wei; H. Yi; H. Zhang; Q. Wang; Z. Yang; F. Zhuo|10.1109/EEBDA56825.2023.10090766|harmonic state estimation;optimal configuration;binary particle swarm optimization;Meters;Distribution networks;Harmonic analysis;Topology;State estimation;Particle swarm optimization;Observability|
|[Lightweight Access Control Scheme for Attribute Change Based on Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090813)|H. Huang; X. Ran; X. Zhao|10.1109/EEBDA56825.2023.10090813|Attribute based encryption;Access control;Blockchain;Attribute change;Access control;Costs;Public key;Resists;Blockchains;Outsourcing;Internet of Things|
|[Research on Self-calibration Technology for Online Monitoring of Substation Boundary Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090754)|L. Ling; D. Xinyu; Z. Zhou; C. Hao; H. Sheng; C. Wei; Z. Weihua|10.1109/EEBDA56825.2023.10090754|Substation boundary noise;Self-calibration;Electrostatic actuator;Electrical engineering;Substations;Voltage;Big Data;Frequency response;Calibration;Monitoring|
|[A multi-modal decision fusion neural network for emotion recognition using facial information and EEG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090625)|Y. Guo; W. Huang|10.1109/EEBDA56825.2023.10090625|MDFNN;EEG;Multi-modal;GRU;enumeration;Adaboost;Emotion recognition;Visualization;Power demand;Face recognition;Neural networks;Logic gates;Prediction algorithms|
|[Game Image Detection and Application Based on Improved YOLOv5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090557)|Y. Cui; M. Si; Q. Li|10.1109/EEBDA56825.2023.10090557|Deep learning;YOLOv5;aimbot;TensorRT;Deep learning;Training;Video games;Technological innovation;Image recognition;Computational modeling;Games|
|[Research on Electric Vehicle Driverless Test System Based on Computer Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090567)|Y. Yuan|10.1109/EEBDA56825.2023.10090567|computer;big data;driverless electric vehicles;test system platform;Velocity control;Rectifiers;Big Data;Electric vehicles;Software systems;Stability analysis;Robustness|
|[Intrusion detection of intelligent substation video surveillance based on average background interframe difference method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090553)|S. Qiao; Q. Zheng; W. Li; S. Yang; H. Zhang|10.1109/EEBDA56825.2023.10090553|intelligent substation;video surveillance;intrusion detection;movement target;the average background;Couplings;Electrical engineering;Analytical models;Substations;Intrusion detection;Big Data;Video surveillance|
|[Study on motion characteristics and power of wave energy device based on Runge-Kutta algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090768)|Z. Wang; Z. Fan; W. Wang|10.1109/EEBDA56825.2023.10090768|wave energy converter;PTO;wave power Nonlinear equations of motion;Runge-Kutta;Damping;Sea surface;Renewable energy sources;Surface waves;Surface resistance;Mathematical models;Springs|
|[Research on Integrated Power Electronic Monitoring System of Digital Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090687)|Y. Ren|10.1109/EEBDA56825.2023.10090687|digital change station;power grid monitoring;failure prediction;support vector machine (SVM) model;variational mode decomposition;Support vector machines;Substations;Unified modeling language;Time series analysis;Predictive models;Power grids;Real-time systems|
|[Application Research of Microcomputer Relay Protection in Power System Analysis System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090495)|Y. Ren; J. Zhu|10.1109/EEBDA56825.2023.10090495|microcomputer relay protection;automatic test system;test cases;regression test;distributed system;Optical filters;Low-pass filters;Protective relaying;Voltage;Interference;Microcomputers;Transformers|
|[A Study on Improved Face Recognition Method Based on Multi-Detection Network and Facial Abnormal Part Beautification Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090502)|H. Cheng; B. Cao; Y. Deng; W. Zhang|10.1109/EEBDA56825.2023.10090502|Fusion network;face recognition;facial abnormal beautification;information encryption;privacy protection;Location awareness;Electrical engineering;Data privacy;Privacy;Image recognition;Target recognition;Face recognition|
|[A Chinese NER Method Based on Chinese Characters' Multiple Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090838)|D. Li; H. Zhang; J. Wang; S. Li|10.1109/EEBDA56825.2023.10090838|Chinese Named Entity Recognition;Word vector fusion;BERT;CRF;Electrical engineering;Computational modeling;Blogs;Bit error rate;Big Data;Robustness;Encoding|
|[UAV Formation Positioning Model Based on Plane Geometry Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090747)|Z. He; Z. Lyu; X. Dong|10.1109/EEBDA56825.2023.10090747|Plane Geometry Analysis;Cosine theorem;Data comparison;UAV formation;UAV positioning;Geometry;Electrical engineering;Analytical models;Computational modeling;Big Data;Data models;Real-time systems|
|[Data mining algorithm based on particle swarm optimized K-means](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090842)|Z. Cuicui; S. Jiali; S. Qi; W. Peng; L. Runxuan; L. Yiwang; L. Huan|10.1109/EEBDA56825.2023.10090842|Particle swarm;K-means;data mining;clustering;Electrical engineering;Clustering algorithms;Big Data;Reliability;Indexes;Data mining;Particle swarm optimization|
|[Research on HPLC Technology System of Client-side Power Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090737)|D. Zhong’an; G. Chen; W. Ruoxue; L. Yijing|10.1109/EEBDA56825.2023.10090737|Client side;power internet of things;HPLC technology system;channel monitoring and fault diagnosis;Fault diagnosis;Wireless communication;Wireless sensor networks;Urban areas;Solids;Sensors;Smart grids|
|[Commercial load data anomaly detection based on LOWESS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090648)|S. Wang; M. Li; Y. Liu; Y. Chen; J. Liu; S. Li; L. Yue; S. Li|10.1109/EEBDA56825.2023.10090648|load data;anomaly detection;LOWESS;STL decomposition;Electrical engineering;Smoothing methods;Data integrity;Buildings;Big Data;Market research;Cleaning|
|[Application of Automatic Degassing Unit in Dissolved Gas Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090844)|Q. Qi; G. Qi; T. Xu; R. Zhang|10.1109/EEBDA56825.2023.10090844|automatic degassing unit;transformer oil;dissolved gas analysis;Gas chromatography;Sensitivity;Oils;Manuals;Oil insulation;Dissolved gas analysis;Transformers|
|[Short-term load forecasting based on PCA-ILSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090767)|L. Yi; W. Yong; Y. Yanfeng; C. Jinjie; F. Rao|10.1109/EEBDA56825.2023.10090767|short-term load forecasting;Principal component analysis;Sparrow search optimization;Long and short term memory network;Load forecasting;Simulation;Neurons;Redundancy;Predictive models;Prediction algorithms;Data mining|
|[Short-term load forecasting based on PSO-ELM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090675)|H. Bin; Z. Xiang; L. Zhifang; S. Yixiang; C. Wei|10.1109/EEBDA56825.2023.10090675|short-term;PSO;ELM;Sensitivity;Load forecasting;Extreme learning machines;Simulation;Neurons;Predictive models;Prediction algorithms|
|[A disturbance localization method for power system based on group sparse representation of compressed sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090771)|Z. Wang; M. Jiao; D. Wang; M. Liu; M. Jiang; H. Wang; S. Li|10.1109/EEBDA56825.2023.10090771|disturbance location;compressed sensing;group sparse representation;entropy power method;GOMP algorithm;Location awareness;Dictionaries;Simulation;Reconstruction algorithms;Entropy;Robustness;Power systems|
|[Improved clustering analysis algorithm for density grid data streams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090636)|F. Bin; Z. Shi; H. Jiahao; L. Haozhi|10.1109/EEBDA56825.2023.10090636|density grid;clustering;boundary;Grain size;Electrical engineering;Clustering methods;Heuristic algorithms;Clustering algorithms;Big Data;Real-time systems|
|[Research on Energy Saving System of Energy Controller Based on Ubiquitous Access of Electric Internet of Things Terminal to Low-Voltage Boutique Platform Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090515)|F. Zhou; H. Ruan; J. Zheng; Q. He|10.1109/EEBDA56825.2023.10090515|Electric Internet of Things;Low Pressure Platform Area;Energy Controller;Energy Saving System;Meters;Low voltage;Protocols;Control systems;Device-to-device communication;Sensors;Safety|
|[Research on Load Forecasting and Early Warning System of Distribution Network Equipment Based on Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090582)|K. Yang; K. Lin; R. Chen|10.1109/EEBDA56825.2023.10090582|Artificial Intelligence;Distribution Network;Electric Power Equipment;Load Warning;Systems operation;Data visualization;Distribution networks;Alarm systems;Transformers;Prediction algorithms;Software|
|[The routing requirement analysis and scheme design of air communication AD hoc network in demonstration and training ground](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090839)|Z. Fengxiao; X. Zhao|10.1109/EEBDA56825.2023.10090839|AD hoc networking;wireless;routing;Performance and training ground;Training;Atmospheric modeling;Optical fiber networks;Routing;Market research;Routing protocols;Data models|
|[A Method of Radar HRRP Aircraft Type Identification and Rejection Based on LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090595)|L. Yonggang; G. Libing; W. Yi; Y. Haimin; H. Shangcheng; H. Jian|10.1109/EEBDA56825.2023.10090595|LSTM;HRRP;Aircraft type identification;Aircraft type rejection;Target recognition;Shape;Atmospheric modeling;Airborne radar;Radar detection;Object detection;Autonomous aerial vehicles|
|[Wavelength division multiplexers and some experimental analysis in the field of optical communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090758)|Z. Gong|10.1109/EEBDA56825.2023.10090758|Wavelength Division Multiplexing;Optical Fiber Communication;Thin Film Filter;Differential Scanning Laser Doppler Velocimetry;Digital Holography;Stimulated emission;Optical fiber networks;Holography;Wavelength division multiplexing;Holographic optical components;Adaptive optics;Optical films|
|[The application of embedded microcontroller in motor control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090799)|L. Xu; P. Chen|10.1109/EEBDA56825.2023.10090799|motor control system;dsPIC20F embedded microcontroller;application analysis;Meters;Electrical engineering;Motor drives;Microcontrollers;Production;Big Data;Control systems|
|[Antarctic Temperature Prediction Based on the Long Short Term Memory Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090643)|S. Duan; Y. Tang; Z. Ye; M. Zhang|10.1109/EEBDA56825.2023.10090643|Antarctic temperatures;Dislocation processing;LSTM;Global warming;Temperature distribution;Analytical models;Linear regression;Predictive models;Market research;Feature extraction;Antarctica|
|[Missing Data Imputation in Bridge Monitoring System Based on the Prediction Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090750)|J. Xu|10.1109/EEBDA56825.2023.10090750|BP neural network;Support vector machine;Time series prediction;Gaussian Regression;Missing data prediction;Support vector machines;Bridges;Time series analysis;Neural networks;Predictive models;Prediction algorithms;Data models|
|[Design and research on resource management of scale edge data center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090714)|H. Zou; H. Liu; M. Tang; L. Zhang|10.1109/EEBDA56825.2023.10090714|Edge computing;Data center;Kubernetes;Resource management;Electrical engineering;Data centers;Energy consumption;Processor scheduling;Computer architecture;Resource management;Servers|
|[License Plate Character Recognition with Lightweight Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090808)|B. Lin; F. He; Z. Xu; L. Lin; Y. Guo|10.1109/EEBDA56825.2023.10090808|license plate recognition;CNN;data enhancement;Electrical engineering;Transportation;Graphics processing units;Feature extraction;Robustness;Real-time systems;Data models|
|[5G Access Protocol for EMU Terminals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090676)|C. Che; J. Cui; Z. Wang; G. Shi; Z. Liu|10.1109/EEBDA56825.2023.10090676|Train Communication Network;Tunnel Protocol;Intelligent EMU;Communication Latency;Encapsulation;Electrical engineering;Multicast algorithms;5G mobile communication;Unicast;Wireless networks;Scalability|
|[Implementation of FPGA-based hardware acceleration system for U-Net networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090536)|P. Chen; L. Xu|10.1109/EEBDA56825.2023.10090536|U-Net;hardware acceleration system;FPGA;cyclic flow and unfolding;Electrical engineering;Constraint optimization;Image recognition;Neural networks;Parallel processing;Big Data;Hardware acceleration|
|[Research and Implementation of Information Security System Based on Chaos Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090610)|Y. Wu; Y. Qin; G. Han|10.1109/EEBDA56825.2023.10090610|Chaotic algorithm;Information security;system design;Electrical engineering;Chaotic communication;Information security;Information processing;Complexity theory;Encryption;Synchronization|
|[Design of an Access Control System for Unmanned Bathroom Based on Image Processing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090513)|J. Sun; J. Wang; T. Liu; B. Cheng; Y. Li|10.1109/EEBDA56825.2023.10090513|Image processing;Server;Access control system;Access control;Databases;Image processing;Signal processing algorithms;Switches;Signal processing;Reliability engineering|
|[Research on application of 3D modeling in oil and gas field development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090558)|J. Bi; T. Ren; B. Wang; B. Xia; H. Xu|10.1109/EEBDA56825.2023.10090558|3D modeling;reservoir research;geostatistics;variogram;Solid modeling;Analytical models;Three-dimensional displays;Graphical models;Geology;Oils;Reservoirs|
|[Analysis of Operation Failure of Transformer Oil Chromatography Online Monitoring Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090506)|R. Zhang; Q. Qi; G. Qi; Q. Liu|10.1109/EEBDA56825.2023.10090506|transformer oil;online monitoring;faults analysis;gas chromatography;photoacoustic spectroscopy;Spectroscopy;Substations;Oils;Oil insulation;Maintenance engineering;Transformers;Personnel|
|[Improvement of IIR Digital Band-Pass Filter by the Digital Frequency Band Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090548)|J. Sun; J. Wang; Y. Wang; J. Li; B. Cheng|10.1109/EEBDA56825.2023.10090548|Method of digital frequency band transformation;Digital band-pass filter;Digital filter;Band-pass filters;Electrical engineering;Low-pass filters;Prototypes;IIR filters;Filtering algorithms;Big Data|
|[Energy consumption prediction of public buildings based on PCA-RF-AdaBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090762)|W. Li|10.1109/EEBDA56825.2023.10090762|Building energy consumption;energy consumption prediction;principal component analysis;RF AdaBoost;Energy consumption;Analytical models;Adaptation models;Correlation;Buildings;Redundancy;Predictive models|
|[Active noise control of refrigerator based on improved notch algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090830)|C. Gui; H. Zhao; X. Sun; D. Ren|10.1109/EEBDA56825.2023.10090830|Refrigerator noise;improved delay notch algorithm;Active noise control;incoherent signal interference;Vibrations;Working environment noise;Noise reduction;Interference;Harmonic analysis;Control systems;Numerical simulation|
|[General particle swarm optimization algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090725)|W. Chuanjun; W. Ling; R. Xuejing|10.1109/EEBDA56825.2023.10090725|Particle swarm optimization algorithm;particle swarm optimization;population optimal;individual previous dynasties;algorithm complexity;Electrical engineering;Simulation;Iterative algorithms;Data models;Complexity theory;Particle swarm optimization;Standards|
|[Research on Sports Live Broadcast System under Computer Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090702)|W. Zhang|10.1109/EEBDA56825.2023.10090702|computer;big data;sports live broadcasting system;detection of SURF feature points;Target tracking;TV;Splicing;Simulation;Streaming media;Big Data;Feature extraction|
|[Transfer learning for bearing fault diagnosis based on improved residual network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090821)|Z. Liu; S. Su; J. Cao; P. Yu|10.1109/EEBDA56825.2023.10090821|bearing;fault diagnosis;transfer learning;transfer model;residual network;Training;Fault diagnosis;Employee welfare;Electrical engineering;Deep learning;Machine learning algorithms;Transfer learning|
|[Research on key technologies of high precision post processing kinematics based on multi system and multi frequency GNSS data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090489)|C. Li; X. Wang; Y. Wang|10.1109/EEBDA56825.2023.10090489|GNSS;PPK;partial ambiguity;Global navigation satellite system;Time-frequency analysis;Satellites;Roads;Kinematics;Intellectual property;User experience|
|[Research on Multifunctional Inspection System of Power Industry Equipment Based on Infrared Ultrasonic Integration Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090817)|P. Wu; M. Yu; W. Cao|10.1109/EEBDA56825.2023.10090817|infrared ultrasonic wave;multifunctional;power equipment inspection system;equipment inspection;equipment health index;Substations;Service robots;Simulation;Voltage;Inspection;Acoustics;Power industry|
|[Study of binocular parallax estimation algorithms with different focal lengths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090658)|M. Zheng; Z. Wang; H. Li; J. Lu|10.1109/EEBDA56825.2023.10090658|different focal lengths;stereo matching;parallax estimation;Electrical engineering;Estimation;Big Data;Cameras;Stereo vision;Standards;Monitoring|
|[Research of Irrigation Optimal Control for Green Island Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090593)|K. Hu; X. Guo|10.1109/EEBDA56825.2023.10090593|Irrigation;Green Island;Optimal Control;Control System;HMI;Employee welfare;Irrigation;Force;Optimal control;Human factors;Switches;Real-time systems|
|[Image analysis of crystallizer flux crystal sequence based on OCR model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090715)|Y. Hou; M. Zhang; X. Feng|10.1109/EEBDA56825.2023.10090715|fused crystal;color space;single regression;OCR;Heating systems;Casting;Temperature;Image color analysis;Smelting;Optical character recognition;Crystallization|
|[Research on the application of service choreography in the intelligent customer service system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090735)|Z. Shuo; Y. Rui; X. Yin; Z. Yan; Z. Wei; A. Yeteng; H. Wei; C. Long; K. Na; X. Liyang|10.1109/EEBDA56825.2023.10090735|Intelligent customer service;business process;service choreography;Zeebe;Electrical engineering;Costs;Customer services;Scalability;Manuals;Big Data;Scheduling|
|[Research on intention management and intention switching technology of task-based dialogue based on hierarchical attention network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090784)|A. Yeteng; Z. Shuo; Y. Rui; S. Pengfei; Z. Yumeng; Z. Hu; X. Liyang; K. Na; X. Yin; Z. Wenhua|10.1109/EEBDA56825.2023.10090784|Dialogue system;Intent identification;Intention switching;Hierarchical network;Attention mechanism;Fuses;Semantics;Redundancy;Oral communication;Switches;Maintenance engineering;Power systems|
|[Research on power customer service composite intention recognition based on dependency syntax and graph attention network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090599)|S. Lintan; Z. Wei; W. Chenfei; Z. Shuo; Z. Huimin; L. Ziqian; Z. Yumeng; Z. Yan; L. Min; X. Liyang|10.1109/EEBDA56825.2023.10090599|Intention recognition;Dependency parsing;Attention network;Power customer service;Electrical engineering;Analytical models;Text recognition;Customer services;Neural networks;Syntactics;Natural language processing|
|[Building a Robot Path Optimization in Prefabricated Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090581)|T. Li|10.1109/EEBDA56825.2023.10090581|prefabricated building;construction robot;Genetic – Ant colony algorithm;experimental measurement;Prefabricated construction;Software algorithms;Genetics;Path planning;Software;Particle swarm optimization;Sustainable development|
|[Research on the Application of Computer Virtual Reality Technology in Korean Language Teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090698)|W. Yao|10.1109/EEBDA56825.2023.10090698|Korean teaching;online teaching system;virtual simulation technology;learning efficiency;Computers;Industries;Cloud computing;Solid modeling;Education;Multimedia computing;Machine learning|
|[Research on Automotive TSN network Scheduling Analysis and Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090662)|Z. Libin; Z. Hongchen; X. Wenchen; Z. Zhuxin|10.1109/EEBDA56825.2023.10090662|TSN;Time-sensitive networking;Traffic scheduling algorithm;network simulation;Intelligent connected cars;Electrical engineering;Analytical models;Connected vehicles;Scheduling algorithms;Network topology;Simulation;Big Data|
|[Estimating Hurricane Intensity from Satellite Imagery Using Deep CNNs Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090706)|R. Xu; Z. Wu; J. Wang; H. Li|10.1109/EEBDA56825.2023.10090706|extreme climate;tropical cyclones;hurricane;feature extraction;deep learning;Deep learning;Satellites;Tropical cyclones;Prediction algorithms;Hurricanes;Robustness;Safety|
|[Research on Application of Computer Virtual Reality Technology in 3D Modeling System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090612)|Z. Hui|10.1109/EEBDA56825.2023.10090612|3D real scene model;computer simulation;building simulation;exterior wall damage;Solid modeling;Visualization;Three-dimensional displays;Computational modeling;Simulation;Urban areas;Redundancy|
|[Short-term PV Power Generation Prediction Based on QFWA-SVM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090655)|W. Guilian; L. Tingting; T. Lu; L. Jinlin|10.1109/EEBDA56825.2023.10090655|Photovoltaic power generation;Quantum fireworks algorithm;Support vector machine;Evaluation index;Photovoltaic systems;Support vector machines;Electrical engineering;Predictive models;Prediction algorithms;Data models;Indexes|
|[Analysis of Deicing Jump Characteristics of Double Circuit Overhead Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090845)|L. Yong; D. Shaoping; X. Zhengtao; H. Xianxu|10.1109/EEBDA56825.2023.10090845|double circuit line;deicing jump;static;dynamic;Electrical engineering;Power transmission lines;Poles and towers;Ice thickness;Electrical safety;Flashover;Conductors|
|[Research on application scenarios of operation and maintenance technology based on business perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090580)|L. Sun; J. Mu; H. Zhang; J. Lv; W. Yang; X. Chen; J. Zhu|10.1109/EEBDA56825.2023.10090580|Power Internet;Application;Business perspective;Operation and maintenance technology;scene;Analytical models;Technological innovation;Smart cities;Big Data;Internet;Power systems;Indexes|
|[Research on scheduling strategy of virtual power plant based on multi-objective optimization of load-generating side](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090584)|W. Xin; W. Xudong; X. Lei; L. Chang; L. Bo|10.1109/EEBDA56825.2023.10090584|Multiple objectives;Virtual power plant;Load;Optimized scheduling;Electrical engineering;Renewable energy sources;Energy consumption;Costs;Optimal scheduling;Virtual power plants;Scheduling|
|[Design of intelligent agricultural environmental big data collection system based on ZigBee and NB-IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090649)|L. Niu|10.1109/EEBDA56825.2023.10090649|ZigBee;NB-IoT;big data;intelligent agriculture;environmental monitoring;system design;Temperature sensors;Temperature measurement;Cloud computing;Temperature;Zigbee;Production;Big Data|
|[Design and realization of measuring total harmonic distortion of periodic signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090718)|J. Xie; C. Zhang|10.1109/EEBDA56825.2023.10090718|embedded system design;automatic gain control;fast discrete Fourier transform;Total harmonic distortion;Embedded systems;Software algorithms;Measurement uncertainty;Electric variables measurement;Gain measurement;Software|
|[Research on Corner Reflector Array Fitting Method for Ship Scattering Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090547)|T. Jiang; J. Luo; Z. Yu|10.1109/EEBDA56825.2023.10090547|corner reflector;scattering characteristic;synthesis of scattering center;array optimization algorithm;Radar cross-sections;Fitting;Electromagnetic scattering;Radar detection;Reflector antennas;Radar countermeasures;Arrays|
|[Design of X-band all Metal Transmit Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090587)|T. Jiang; T. Sun; M. Cai; Z. Yu|10.1109/EEBDA56825.2023.10090587|all metal;Array;antenna efficiency;Relative bandwidth;Electrical engineering;Shape;Simulation;Transmitting antennas;Metals;Bandwidth;Big Data|
|[Research on Simulation Model of Ship Wakes on Rough Sea Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090596)|M. Cai; Z. Yu; N. Bi|10.1109/EEBDA56825.2023.10090596|Rough sea surface;electromagnetic scattering;Kelvin wake;internal wave wake;Sea surface;Analytical models;Electric potential;Surface waves;Kelvin;Electromagnetic scattering;Approximation algorithms|
|[Research on a simplified algorithm for large-scale microstrip antenna array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090684)|Y. Ziying; C. Mingjuan; L. Jiajun; C. Bin; Q. Fan; W. Xuhong|10.1109/EEBDA56825.2023.10090684|antenna array distribution;equivalent source algorithm;microstrip antenna;trigonometric period;simplified algorithm;Electrical engineering;Microstrip antenna arrays;Simulation;Microstrip antennas;Big Data;Microstrip;Arrays|
|[Multi-label Image Transient Background Information Recognition Based on Graph Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090521)|Y. Kong; W. Wang|10.1109/EEBDA56825.2023.10090521|Multi-label transient background recognition;Semantic attention module;Graph convolutional neural network;Electrical engineering;Computer vision;Image recognition;Convolution;Semantics;Brain modeling;Data models|
|[A Dual Coupling LCC-LCC Topology Based WPT System for Wireless Slip Ring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090535)|Y. Yang; X. Liu; H. Zhang; Y. Shi|10.1109/EEBDA56825.2023.10090535|Wireless slip ring;Current rising;Dual-coupling LCC-LCC topology;Couplings;Wireless communication;Structural rings;Heating systems;Electrical engineering;Simulation;Big Data|
|[Design on Weak Supervision and Self-learning Spatiotemporal Data Annotation System for Large-scale Complex Scenes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090825)|F. Yang; Z. Wu; L. Xu|10.1109/EEBDA56825.2023.10090825|Weak Supervision Annotation;Text Annotation;Image Annotation;Manual Annotation;Self-Learning Automatic Annotation;Electrical engineering;Annotations;Education;Manuals;Tagging;Big Data;Spatial databases|
|[Meteorological Data Transmission Management System Based on Multi-source Satellite Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090642)|Z. Li; N. Ye; Y. He|10.1109/EEBDA56825.2023.10090642|Association Rules;Multi-Source Satellite Data;Meteorological Data;Overall System;System Design;Industries;Satellites;Weather forecasting;Software;Robustness;Real-time systems;Data communication|
|[Design and Implementation of Epidemic Big Data Visualization Platform Based on Vue + ECharts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090728)|J. Xie; Z. Yang; Z. Jun; Z. Xi; Y. Tang|10.1109/EEBDA56825.2023.10090728|Data Visualization;Vue Framework;ECharts chart;ElementUI component;Data Management;COVID-19;Electrical engineering;Epidemics;Visualization;Pandemics;Data visualization;Big Data|
|[Mask Wear Detection Algorithm Based on YOLOv5m](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090551)|X. Zhenjiu; C. Sihang; S. Ting|10.1109/EEBDA56825.2023.10090551|mask;mask wear detection;attention mechanism;double scale feature fusion;YOLOv5m;Solid modeling;Epidemics;Target recognition;Object detection;Feature extraction;Solids;Prediction algorithms|
|[Research on A Route Planning Method for Military Transport Aircraft Based on GWO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090543)|J. Lu; X. Gan; Y. Wei; S. Li|10.1109/EEBDA56825.2023.10090543|Aviation network;Military transport aircraft;GWO algorithm;Route planning;Knowledge engineering;Atmospheric modeling;Transportation;Military aircraft;Graph theory;Data models;Planning|
|[An oil painting image retrieval strategy based on feedback mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090645)|S. Wang; S. Gong|10.1109/EEBDA56825.2023.10090645|oil painting image;retrieval strategy;feedback mechanism;Databases;Image color analysis;Oils;Image retrieval;Semantics;Layout;Feature extraction|
|[Research on oil painting recommendation algorithm based on collaborative filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090814)|S. Wang; S. Gong|10.1109/EEBDA56825.2023.10090814|online network courses;security knowledge;Intrusion detection model;Oils;Collaborative filtering;Search methods;Web and internet services;Search engines;Predictive models;Prediction algorithms|
|[An Improved YOLOv5 Algorithm of Target Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090620)|L. Hu|10.1109/EEBDA56825.2023.10090620|Apple Picking Robot;Target image;Recognition;YOLOv5;Improved Algorithm;Electrical engineering;Target recognition;Robot kinematics;Process control;Object detection;Big Data;Information retrieval|
|[Construction of domain knowledge graph based on open source intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090635)|Y. Hu; L. He; X. Tang; G. Luo; S. He; Q. Fang|10.1109/EEBDA56825.2023.10090635|Open source intelligence;knowledge graph;domain knowledge;Electrical engineering;Deep learning;Knowledge acquisition;Semantics;Knowledge based systems;Knowledge graphs;Manuals|
|[Research on Image Analysis and Processing Technology Based on Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090749)|Y. Hu; Q. Xi; L. Xiao; Q. Fang; Y. Hu|10.1109/EEBDA56825.2023.10090749|Spark;Image Processing;Big Data;Data Set;Cluster;Image quality;Superresolution;Clustering algorithms;Big Data;Hardware;Sparks;Image reconstruction|
|[Decentralized observer based output tracking for linear time-invariant interconnected systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090777)|Z. Mao; Y. Tseng|10.1109/EEBDA56825.2023.10090777|Decentralized control;observer-based;output tracking;Linear systems;Electrical engineering;Uncertainty;Computer simulation;Computational modeling;Estimation;Optimal control|
|[Test Report of Power System Stabilizer (PSS) of the 2nd unit in a Certain Thermal Power Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090803)|T. Wang; J. Huang; L. Yu; Q. Wei; D. Wang; P. Zhang|10.1109/EEBDA56825.2023.10090803|power system stabilizer (PSS);excitation system;parameters testing;Damping;Reactive power;Regulators;Interference;Power system stability;Generators;Regulation|
|[Image Recognition Technology and Digital Twin in the Power Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090641)|S. Wang; Y. Zhao|10.1109/EEBDA56825.2023.10090641|artificial intelligence;image recognition;power system;digital twin;Electrical engineering;Image recognition;Big Data;Power industry;Digital twins;Power systems|
|[Short-term Prediction of Multidimensional Data in Power System Based on CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090611)|X. Gong; W. Zhang; L. Jiang; Y. Shi; Y. Liu; Y. Zhang; Q. Miao|10.1109/EEBDA56825.2023.10090611|time series prediction;CNN;power system;multidimensional data prediction;Convolution;Time series analysis;Predictive models;Feature extraction;Data models;Power systems;Smart grids|
|[Design of a Vessel Dynamic Location System Observer Using the Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090563)|J. Mei; T. Zhong; C. Deng|10.1109/EEBDA56825.2023.10090563|DP system;Neural network;Observer;Uniformly finally bounded;Uncertainty;Nonlinear equations;Neural networks;Nonlinear distortion;Dynamics;Estimation;Observers|
|[Analysis of ATC-type Stair Structure by Axonometric Projection Drawing Demo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090600)|F. Yuchu; L. Huan; Y. Qian|10.1109/EEBDA56825.2023.10090600|Axonometric projection;ATC-type stair;drawing;demonstration;Electrical engineering;Solid modeling;Cognitive processes;Engineering drawings;Education;Layout;Stairs|
|[Research on keywords extraction technology in intelligent evaluation scheme for airport emergency plan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090545)|S. Hu; B. Wang; Y. Chen|10.1109/EEBDA56825.2023.10090545|TF-IDF;Emergency plan evaluation;Evaluation indicators;Keywords extraction;Machine learning;Electrical engineering;Atmospheric modeling;Standardization;Machine learning;Big Data;Airports;Security|
|[Fractional Fourier Transform Based Channel Estimation in Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090634)|J. Peng; Y. Wang|10.1109/EEBDA56825.2023.10090634|Massive MIMO;channel estimation;fractional fourier transform;Multiplexing;Electrical engineering;Fourier transforms;Spectral efficiency;Simulation;Channel estimation;Estimation|
|[Thermal analysis and design of solar heat pump drying wheat system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090734)|K. Wang; T. Jiang; J. Shi|10.1109/EEBDA56825.2023.10090734|Solar energy;Heat pump;Combined drying;design;hermal calculation;Thermodynamics;Heat pumps;Solar energy;Stability analysis;Thermal analysis;Safety;Thermal pollution|
|[Blockchain-Based Supply Chain Financial Ciphertext Retrieval System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090690)|Z. Sun; B. Liu|10.1109/EEBDA56825.2023.10090690|Blockchain;Supply Chain Finance;Searchable Encryption;Multi-Keywords;Smart Contracts;Privacy;Supply chains;Finance;Resists;Blockchains;Encryption;Indexes|
|[Application of data storage management system in blockchain-based technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090564)|X. Wang; J. Jia; Y. Cao; J. Du; A. Hu; Y. Liu; Z. Wang|10.1109/EEBDA56825.2023.10090564|Blockchain technology;data;storage;management;Electrical engineering;Delay effects;Memory;Information sharing;Ethernet;Maintenance engineering;Blockchains|
|[ECLSTM: An Efficient Channel Attention-based Spatio-temporal Fusion Method for Fault Detection of Instruments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090740)|S. Dou; Z. Fan; Y. Yang; J. Qin; L. Li; Y. Han|10.1109/EEBDA56825.2023.10090740|CNN;Efficient channel attention;Fault detection;Instruments;LSTM;Electrical engineering;Instruments;Fault detection;Neural networks;Memory management;Predictive models;Big Data|
|[Fault diagnosis method of fan bearing at non- extended end of motor based on resonant sparse decomposition and variational modal decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090501)|J. Huang; W. Lin|10.1109/EEBDA56825.2023.10090501|fault detection;feature extraction;resonant sparse decomposition;variational modal decomposition;Fault diagnosis;Vibrations;Q-factor;Fans;Electric shock;Resonant frequency;Data mining|
|[Research on Security Protection of Space-Earth Integrated Network Wireless Link Based on Consortium Blockchain Technology and Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090811)|Z. Yang; L. Shaofeng; T. Chenyang|10.1109/EEBDA56825.2023.10090811|Space-earth Integrated Network;Consortium Blockchain;Wireless Link Security;Wireless communication;Electrical engineering;Network topology;Security management;Network security;Heterogeneous networks;Blockchains|
|[Research on the fatigue detection method of operators in digital main control rooms of nuclear power plants based on multi-feature fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090719)|G. Liu; J. Liu; L. Dai|10.1109/EEBDA56825.2023.10090719|Operators in the digital main control rooms of nuclear power plants;Fatigue detection;Multi-feature Fusion;Machine Learning;Support vector machines;Fatigue;Feature extraction;Cognitive load;Magnetic heads;Real-time systems;Classification algorithms|
|[Temporal Convolutional Knowledge Tracking Model with Embedded Graph Association Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090542)|Z. Zhong; Z. Liu; W. Gu|10.1109/EEBDA56825.2023.10090542|knowledge tracing;temporal convolutional network;sequence modeling;graph attention network;deep learning;Knowledge engineering;Deep learning;Measurement;Convolution;Neural networks;Predictive models;Feature extraction|
|[>Research on Risk Analysis of an Airport Based on QAR Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090757)|X. Wang; M. Liu; X. Zhao|10.1109/EEBDA56825.2023.10090757|airport risks;QAR data;flight quality;wind shear;core risks;Electrical engineering;Safety management;Big Data;Airports;Indexes;Risk management;Data mining|
|[A Chinese Implicit Sentiment Analysis Model Based on Relational Weighted Graph Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090518)|Z. Dai; Q. Zhang; Z. Wang; S. Di|10.1109/EEBDA56825.2023.10090518|Chinese implicit sentiment analysis;heterogeneous text graph;weighted graph convolution network;syntactic information;Knowledge engineering;Electrical engineering;Sentiment analysis;Analytical models;Dictionaries;Convolution;Knowledge graphs|
|[Research on HDR Display Halo Evaluation Algorithm Based on Image Local Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090837)|L. Yu; C. Haitao; Y. Yun|10.1109/EEBDA56825.2023.10090837|Mini-LED;HDR;Halo;multi-block;Local dimming;Integrated circuits;Image quality;Analytical models;Visualization;Power demand;Machine vision;Numerical simulation|
|[An Optimized CNN-SVM Algorithm for UAV Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090683)|W. Xiaowei|10.1109/EEBDA56825.2023.10090683|CNN convolutional neural network;support vector machine;drone;UAV;anomaly detection;Support vector machines;Predictive models;Feature extraction;Autonomous aerial vehicles;Data models;Convolutional neural networks;Task analysis|
|[Elastic-Net Regularized Online Portfolio Selection with Transaction Costs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090727)|X. Yao; N. Zhang|10.1109/EEBDA56825.2023.10090727|online portfolio selection;transaction costs;elastic-net regularization;linearized augmented Lagrangian method;Costs;Closed-form solutions;Computational modeling;Machine learning;Minimization;Mathematical models;Numerical models|
|[Model Research of Energy Storage Resources Participating in the Electricity Spot Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090667)|Y. Wu; W. Zou; X. Zhu; S. Liu|10.1109/EEBDA56825.2023.10090667|energy storage resources;electricity spot market;clearing;spot energy market;Electrical engineering;Switches;Electricity supply industry;Control systems;Mathematical models;Regulation;Data models|
|[Research on voiceprint Recognition system based on ECAPA-TDNN-GRU architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090565)|C. Haitao; L. Yu; Y. Yun|10.1109/EEBDA56825.2023.10090565|ECAPA-TDNN-GRU;voiceprint Recognition;Audio pre-processing;Neural networks;Speech recognition;Logic gates;Feature extraction;Propagation losses;Nonhomogeneous media;Market research|
|[Research on azimuth-only passive location technology of UAV carrying formation based on Newton iterative algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090532)|X. Wang; A. Quan; C. Hu; Y. Bi|10.1109/EEBDA56825.2023.10090532|passive positioning;Newton iterative algorithm;triangulation positioning method;nonlinear programming model;Electrical engineering;Analytical models;Interference;Programming;Big Data;Data models;Iterative algorithms|
|[A simulation study of passengers with disabilities of security screening in airport based on AnyLogic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090717)|W. Ye; N. He; J. Wang|10.1109/EEBDA56825.2023.10090717|Simulation;Passengers with disabilities;Security screening;Airport;Training;Analytical models;Object oriented modeling;Atmospheric modeling;Inspection;Airports;Data models|
|[Distributed log collection for business processes based on ELK architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090786)|Y. Zong|10.1109/EEBDA56825.2023.10090786|distributed environment;low-code;log collection system;business process;Electrical engineering;Codes;Microservice architectures;Writing;Maintenance engineering;Load management;Real-time systems|
|[A Multimodal Features Fusion Classification Method Based on Stockwell Transform and Deep Learning for Power Quality Disturbances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090794)|P. Zhang; H. Chen|10.1109/EEBDA56825.2023.10090794|Power quality disturbances (PQDs);deep learning (DL);multimodal;Stockwell transform (ST);Deep learning;Renewable energy sources;Simulation;Power quality;Transforms;Benchmark testing;Wind power generation|
|[Research on Data Communication Delay Prediction Method for Flight Simulators Based on ARIMA-LSTM Hybrid Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090639)|G. Yu; H. Wang; Y. Peng; M. Tan; J. Song; J. Zhou|10.1109/EEBDA56825.2023.10090639|ARIMA-LSTM;time delay jitter;prediction;Transmitters;Delay effects;Simulation;Urban areas;Predictive models;Jitter;Streaming media|
|[Modeling and Simulation Design of New Energy Digital Trackless Train System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090852)|Z. Wei; S. Huang; H. Lan; W. Li; L. Shan|10.1109/EEBDA56825.2023.10090852|modeling and simulation;new energy;intelligent model;green symbiosis;Rails;Symbiosis;Technological innovation;Computational modeling;Transportation;Traffic control;Systems modeling|
|[A Survey of Database Knob Tuning with Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090597)|J. Wang; L. Liu; C. Li|10.1109/EEBDA56825.2023.10090597|DBMS;knob tuning;Bayesian Optimization;Reinforcement Learning;Deep Learning;Electrical engineering;Deep learning;Machine learning algorithms;Reinforcement learning;Big Data;Database systems;Bayes methods|
|[Research on Key Technologies of Chinese Spelling Check Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090481)|Y. Fan; Y. Bowen|10.1109/EEBDA56825.2023.10090481|Chinese spelling check;Chinese text error diagnosis;machine learning;Performance evaluation;Technological innovation;Information processing;Search engines;Filtering algorithms;Feature extraction;Information filters|
|[Survey of line selection algorithms for low current single-phase-to-ground fault](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090752)|W. Yang; X. Zhu; Y. Guo; P. Li; P. Gan|10.1109/EEBDA56825.2023.10090752|distribution network;small current grounding system;fault line-selection;Electrical engineering;Systematics;Power supplies;Grounding;Distribution networks;Big Data;Reliability engineering|
|[Cloud API Oriented Analysis on Rolling Upgrade Dependability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090527)|Z. Yu; M. Fu|10.1109/EEBDA56825.2023.10090527|Rolling Upgrade;Cloud API;Empirical Study;Electrical engineering;Cloud computing;Web services;Big Data|
|[YOLOv5-based Object Detection for Food Freezer Warehouses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090653)|Y. Kong; L. Liu; Z. Liang; Y. Huang; F. Xiong; M. Qin|10.1109/EEBDA56825.2023.10090653|deep learning;object detection;YOLOv5;warehouse security;Electrical engineering;Solid modeling;Safety management;Training data;Object detection;Data models;Security|
|[Enterprise User Data Governance Based on Crawler and Text Similarity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090592)|J. Jiang; F. Xiang; X. Yin; Z. Zhang; X. Cheng; P. Kuang|10.1109/EEBDA56825.2023.10090592|social unified credit code;Data governance;Web crawler;Text similarity;Electrical engineering;Codes;Crawlers;Big Data;Libraries;Data governance;Power systems|
|[Combining Forecasting and Multi-Agent Reinforcement Learning Techniques on Power Grid Scheduling Task](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090669)|C. Yang; J. Zhang; F. Lin; L. Wang; W. Jiang; H. Zhang|10.1109/EEBDA56825.2023.10090669|forecasting;reinforcement learning;power grid scheduling;Greenhouse effect;Reinforcement learning;Solar energy;Microgrids;Scheduling;Global warming;Solar power generation|
|[Research on Air Conditioning Energy Consumption Simulation and Optimization Strategy Based on TRNSYS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090827)|X. Zhang|10.1109/EEBDA56825.2023.10090827|Air-conditioning system;energy consumption;simulation platform;operation optimization;Air conditioning;Energy consumption;Temperature distribution;Atmospheric modeling;Buildings;Water conservation;Mathematical models|
|[Research on Anti-Obscure Target Tracking Method Based on Feature Adaptive Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090759)|C. Liqun; D. Shiqi|10.1109/EEBDA56825.2023.10090759|Feature fusion;Adaptive scale;Re-detection;Support vector machines;Target tracking;Sensitivity;Fuses;Filtering;Lighting;Estimation|
|[Production model based on the combined prediction of time series and XGBoost neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090741)|B. Duan; J. Zhang; Z. Zeng|10.1109/EEBDA56825.2023.10090741|multi-variety small batch material;entropy weight Topsis;time series;XGBoost neural network;combined prediction model;Time series analysis;Neural networks;Globalization;Production materials;Mean square error methods;Predictive models;Prediction algorithms|
|[Research on Nondestructive Testing Technology of Grounding Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090652)|J. Shou; P. Fang; M. Xu; J. Chen; S. Zhu|10.1109/EEBDA56825.2023.10090652|grounding network;detection;monitoring;Wireless communication;Voltage measurement;Grounding;Surface waves;Corrosion;Nondestructive testing;Stationary state|
|[Research on Simulation Model of Explosion Impulse Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090665)|X. Liu|10.1109/EEBDA56825.2023.10090665|explosion impulse noise;explosive ammunition;finite element;self-programming;simulation;Electrical engineering;Analytical models;Shock waves;Fluids;Simulation;Explosions;Data models|
|[Short-term power load forecasting based on similar day selection and improved LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090732)|Z. Zhou; H. Chen; W. Wang|10.1109/EEBDA56825.2023.10090732|short-term power load;forecasting;similar day selection;LSTM;Training;Industries;Load forecasting;Time series analysis;Predictive models;Mathematical models;Data models|
|[Visual Local Path Planning Based on Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090576)|X. Shen|10.1109/EEBDA56825.2023.10090576|visual navigation;deep reinforcement learning;visual SLAM;local path planning;Deep learning;Legged locomotion;Visualization;Simultaneous localization and mapping;Navigation;Reinforcement learning;Turning|
|[Pursuit-escape Strategy for Autonomous Agents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090700)|H. Cai|10.1109/EEBDA56825.2023.10090700|Autonomous robots;Pursuit-escape strategies;Q-learning algorithm models;Reinforce learning;Environment interference;Training;Q-learning;Computational modeling;Web and internet services;Training data;Games;Interference|
|[Comparative study on model based test of automotive automatic control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090694)|Z. Gao|10.1109/EEBDA56825.2023.10090694|EUC;Maximum coverage;CANoe;GraphWalker Tools;Industries;Electrical engineering;Prototypes;Big Data;Software;Automotive engineering;Testing|
|[Matching Analysis of Four-Wheel Drive Pure Electric Power System Based on Adhesion Coefficient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090656)|J. Li; T. Yang; Z. Wang; S. Wei; P. Yu; G. Ren|10.1109/EEBDA56825.2023.10090656|Four-wheeldrive;Pure electric;Adhesion coefficient;Matching;Torque;Adhesives;Roads;Power distribution;Power system stability;Drives;Stability analysis|
|[A Review of Point Cloud Registration Methods Based on Laser SLAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090835)|B. Wang; J. Han; J. Huang|10.1109/EEBDA56825.2023.10090835|point cloud registration;slam;iteration nearest point;normal distribution;Point cloud compression;Simultaneous localization and mapping;Laser radar;Iterative closest point algorithm;Lasers;Process control;Transforms|
|[Research on intelligent storage monitoring of power grid based on YOLOv5 converged attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090797)|T. Xiaodong; G. Zhan; W. Liujun; W. Yong; C. Lianxin|10.1109/EEBDA56825.2023.10090797|YOLOv5;SIoU loss function;channel attention;Electrical engineering;Warehousing;Object detection;Real-time systems;Power grids;Safety;Task analysis|
|[Detection of bolts and nuts of automobile sheet metal parts based on YOLOV7](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090809)|Y. Zhang; J. Miao; C. Liu|10.1109/EEBDA56825.2023.10090809|Automotive sheet metal parts;bolt;nut;YOLOV7;Industries;Training;Shape;Computational modeling;Metals;Object detection;Manuals|
|[Research on Digital Media Tourism Publicity System under Computer Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090510)|L. Yong; X. Dan|10.1109/EEBDA56825.2023.10090510|computer;big data;digital media;tourism publicity system;Technical requirements;Electrical engineering;Visualization;Computational modeling;Computer architecture;Media;Big Data|
|[Optimization of Vehicle Blind Zone Monitoring (BSD) Evaluation Scheme Based on Image Processing and Radar Ranging Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090708)|Z. Boxu; Z. Cheng; Z. Libin; L. Meixia|10.1109/EEBDA56825.2023.10090708|blind spot detection;intelligent car;driving automation;Industries;System performance;Roads;Standardization;Radar;Radar imaging;Solids|
|[Research on Advanced Adaptive Cruise Control (ACC) Testing Algorithm and Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090539)|L. Weijian; X. Minghui; Y. Xiaoyong; L. Jianqiang|10.1109/EEBDA56825.2023.10090539|adaptive cruise control;ACC;intelligent driving;ADAS;Employee welfare;Electrical engineering;System verification;Roads;Companies;Automobiles;Tuning|
|[Research of Automotive Ethernet Media Dependent Interface (MDI) Designing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090613)|X. Wenchen; Z. Libin; Z. Zhipeng; J. Huating|10.1109/EEBDA56825.2023.10090613|automotive Ethernet;Media-dependent interfaces;Return loss;Electrical engineering;Ethernet;Organizations;Media;Turning;Physical layer;Research and development|
|[Research on Security Sandbox System Based on Computer Big Data Hyperledger Fabric Blockchain Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090679)|W. Tong; F. Qiu|10.1109/EEBDA56825.2023.10090679|computer;big data;hyperledger fabric;blockchain;secure sandbox system;Electrical engineering;Privacy;Data privacy;Distributed ledger;Containers;Big Data;Fabrics|
|[A New Porosity Prediction Method Based on Deep Learning of TabNet Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090680)|Z. Liu|10.1109/EEBDA56825.2023.10090680|porosity prediction;TabNet;DeepLearning;reservoir parameter prediction;Deep learning;Electrical engineering;Time series analysis;Neural networks;Fluid flow;Predictive models;Reservoirs|
|[Analysis and Research on Safety Protection Risks of Intelligent Power Terminals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090618)|H. Chen; B. Xu; F. Zhai; B. Gao|10.1109/EEBDA56825.2023.10090618|intelligent power terminal;safety risk;safety protection;energy controller;Electrical engineering;Power measurement;Operating systems;Energy measurement;Big Data;Safety;Security|
|[AC fault ride through control strategy for hybrid HVDC system considering renewable energy consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090497)|F. Feng; Y. Lu; C. Yang; H. Xiao; C. Meng; J. Wang|10.1109/EEBDA56825.2023.10090497|HVDC transmission system;passive network;new energy consumption;induction motor;Passive networks;Energy consumption;HVDC transmission;Load shedding;Hybrid power systems;Regulation;Real-time systems|
|[LGA-based short-term power load time series forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090796)|X. Li; J. Wang; R. Tian|10.1109/EEBDA56825.2023.10090796|Short-term power time series forecast;LGA;GRU;LSTM;Load forecasting;Time series analysis;Sociology;Predictive models;Robustness;Data models;Power system reliability|
|[Edge Cloud Manufacturing Service Platform and the Resource Allocation Optimization Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090674)|S. Ruan; Z. Zhang; D. Tang|10.1109/EEBDA56825.2023.10090674|edge cloud manufacturing service platform;service;modeling;Cloud computing;Software algorithms;Maintenance engineering;Software;Real-time systems;Virtual machining;Manufacturing|
|[Research on Chip Level Inspection and Maintenance System of Electronic Products under Computer Digitization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090511)|H. Sun|10.1109/EEBDA56825.2023.10090511|generalized hough variation;chip detection;object recognition;computer;go digital;Software design;Simulation;Software algorithms;Transforms;Parallel processing;Hardware;Software|
|[Simulation Research of AEB System Based on Virtual Scene](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090853)|Z. Zhuxin; T. Yongjun; L. Meixia|10.1109/EEBDA56825.2023.10090853|AEB system;virtual scene;co-simulation;ADAS system;Space vehicles;Analytical models;Statistical analysis;Soft sensors;Software algorithms;Turning;Mathematical models|
|[Design of GIS Based Mobile Data Acquisition System for Agricultural Green Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090538)|Y. Li; C. Zhang; L. Tian|10.1109/EEBDA56825.2023.10090538|Data mobility;Intelligent acquisition system;System design;GIS technology;Green development;Green products;Data acquisition;Production;Big Data;Market research;Real-time systems;Agriculture|
|[Research on Sports Video Image Teaching System Based on Computer 3D Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090695)|W. Zhang|10.1109/EEBDA56825.2023.10090695|computer;three-dimensional technology;sports video;physical education;basketball teaching system;Training;Solid modeling;Three-dimensional displays;TV;Watches;Animation;Turning|
|[Research on EEG signal emotion recognition based on improved GAPSO-SVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090851)|J. Peng; R. Liu|10.1109/EEBDA56825.2023.10090851|EEG signals;emotion;PSO;GA;SVM;Support vector machines;Electrical engineering;Emotion recognition;Big Data;Brain modeling;Electroencephalography;Classification algorithms|
|[Navigation and obstacle avoidance technology for warehouse tracking AGVs based on multi-sensor information fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090828)|N. Zhang|10.1109/EEBDA56825.2023.10090828|AGV;embedded;grey scale sensor;Bluetooth;laser sensor;multi-sensor combination Introduction;Electrical engineering;Remotely guided vehicles;Navigation;Control systems;Real-time systems;Trajectory;Collision avoidance|
|[Analysis of simultaneous localization and mapping technology for mobile robot based on binocular vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090692)|Z. Xue|10.1109/EEBDA56825.2023.10090692|Stereo-vision;G2O;triangulate;SLAM;Electrical engineering;Visualization;Simultaneous localization and mapping;Robot vision systems;Pose estimation;Computer architecture;Feature extraction|
|[Research on optimal route planning and delivery strategy of multiple robots using HCA algorithm in a restaurant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090578)|Z. Wang|10.1109/EEBDA56825.2023.10090578|Path planning;Mobile robot;Collision avoidance;Catering industry;Industries;Costs;Service robots;Roads;Collaboration;Morphology;Planning|
|[Research on real-time path planning and obstacle avoiding for mobile robot swarms based on an advanced artificial potential field method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090534)|B. Bao|10.1109/EEBDA56825.2023.10090534|artificial potential method;swarm robot;path planning;obstacle avoidance;automatic tracking;Technical requirements;Navigation;Dynamics;Path planning;Real-time systems;Space exploration;Mobile robots|
|[Research on Multi-AGVs Cooperative Transportation Strategy in Warehouse Logistics Environment Based on HCA Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090651)|Y. Li|10.1109/EEBDA56825.2023.10090651|AGV;HCA;Cooperative Transportation Strategy Cooperative Transportation Strategy;Warehouse Logistics Environment;Remotely guided vehicles;Navigation;Service robots;Simulation;Roads;Warehousing;Transportation|
|[Research on trajectory tracking control algorithm in 4WS mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090629)|X. Wang; S. Wang|10.1109/EEBDA56825.2023.10090629|four steering wheels;trajectory tracking;model predictive control;control time domain;sample time;Training;Target tracking;Fluctuations;Trajectory tracking;Wheels;Predictive models;Prediction algorithms|
|[Design of Track Tracking Controller for Tracked Car Based on Pure Tracking Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090744)|Z. Yifu|10.1109/EEBDA56825.2023.10090744|pure tracking algorithm;crawler car;trajectory tracking;Simulink simulation;Trajectory tracking;Software packages;Heuristic algorithms;Roads;Dynamics;Process control;Kinematics|
|[Design and fabrication of the power generation floor based on the mechanical conversion method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090792)|Y. Zheng|10.1109/EEBDA56825.2023.10090792|Mechanical energy;pressure power generation;power generation;Performance evaluation;Legged locomotion;Fabrication;Power supplies;Oils;Green products;Mechanical energy|
|[Research of high voltage permanent magnet synchronous frequency converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090739)|W. Lei|10.1109/EEBDA56825.2023.10090739|Scraper drive;high Voltage permanent magnet synchronous;Frequency conversion integrated machine;Drive system;Belt conveyor drive;Torque;Transportation;Voltage;Frequency conversion;Synchronous motors;Permanent magnets;Coal mining|
|[Computer Simulation Analysis Applied in Intelligent Lightweight Sorting Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090606)|T. Yang; X. Li|10.1109/EEBDA56825.2023.10090606|manipulator;glass fiber composite materials;sorting;Resistance;Electrical engineering;Energy consumption;Composite materials;Computer simulation;Glass;Big Data|
|[Analysis of energy storage operation and configuration of high-proportion wind power system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090626)|Y. Du|10.1109/EEBDA56825.2023.10090626|power balancing;nonlinear programming;traversal algorithms;wind power abandonment;Wind;Costs;Wind energy;Wind power generation;Programming;Linear programming;Planning|
|[Study on Wind Power Prediction Based on EEMD-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090691)|R. Cai; W. Liu; Z. Zhu|10.1109/EEBDA56825.2023.10090691|wind power;ensemble empirical mode decomposition;long short-term memory;new energy;prediction model;Simulation;Neural networks;Solar energy;Predictive models;Wind power generation;Prediction algorithms;Hyperparameter optimization|
|[Research on proportional parameter of random propagation algorithm with noisy labels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090751)|Z. Guo|10.1109/EEBDA56825.2023.10090751|Label propagation;hyperspectral image classification;noisy label;proportional parameter;superpixel segmentation;Training;Electrical engineering;Image segmentation;Focusing;Classification algorithms;Noise measurement;Monitoring|
|[Research on vehicle detection algorithm based on improved YOLOV5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090709)|S. Deng; Z. He; M. Niu|10.1109/EEBDA56825.2023.10090709|vehicle detection;Yolov5;MobileNetV3;attentional mechanism;Training;Electrical engineering;Solid modeling;Costs;Vehicle detection;Object detection;Big Data|
|[Research on Short-term Voltage Stability with Dynamic Load and Short-circuit Location](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090530)|Z. Dong; X. Lu; J. Wang; Z. Qin; T. Xing|10.1109/EEBDA56825.2023.10090530|short-term voltage stabilization;dynamic load;WECC Aggregation model;test system;Analytical models;Induction motors;System dynamics;Power system dynamics;Voltage;Power system stability;Stability analysis|
|[A high available and strong generalization capability data-driven-based wireless fingerprint positioning model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090621)|X. Wu; M. Hu; G. Dong|10.1109/EEBDA56825.2023.10090621|indoor positioning;Wi-Fi fingerprint;machine learning;binary classification;Location awareness;Training;Support vector machines;Machine learning algorithms;Databases;Atmospheric modeling;Fingerprint recognition|
|[Aircraft Trajectory Prediction Based on Residual Recurrent Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090482)|Z. Fan; J. Lu; Z. Qin|10.1109/EEBDA56825.2023.10090482|Unpowered aircraft;Trajectory prediction;Residual recurrent neural network;Recurrent neural networks;Trajectory planning;Atmospheric modeling;Predictive models;Prediction algorithms;Mathematical models;Libraries|
|[Intelligent control system of waste gas treatment based on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090682)|Q. Xu; Z. Liu; D. Chen|10.1109/EEBDA56825.2023.10090682|Internet of Things Technology;waste gas treatment system;intelligent control system;Wireless communication;Electrical engineering;Purification;Process control;Control systems;Real-time systems;Raw materials|
|[Design of restaurant intelligent seat-seeking system based on ESP32](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090804)|T. Ying; D. Zhu; Y. Zou; Y. Huang; P. Zhao|10.1109/EEBDA56825.2023.10090804|Edge technology;Internet of Things technology;ESP32;Intelligent seat seeking;Cloud computing;Social networking (online);Data visualization;Message services;Real-time systems;Libraries;Internet of Things|
|[Design and Realization of SCM Network Based on a New Symmetry Array with Five Feed Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090843)|Y. Liu; J. He|10.1109/EEBDA56825.2023.10090843|SCM network;Summation and difference signals;Across coupling;Electrical engineering;Couplings;Radiation effects;Target tracking;Phase modulation;Design methodology;Adaptive arrays|
|[Analysis on the Deterioration of the Withstand Voltage Level of HVDC Thyristors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090492)|H. Cui|10.1109/EEBDA56825.2023.10090492|DC transmission;valve tower thyristor;blocking characteristics;Silicon compounds;Thyristors;HVDC transmission;Process control;Materials reliability;Valves;Reliability engineering|
|[Aggregation test of public power load under peak power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090594)|F. Men|10.1109/EEBDA56825.2023.10090594|Public power consumption;load aggregation;double-layer game;power peak;interactive operation;Home appliances;Load forecasting;Clustering methods;Games;Smart homes;Temperature control;Interoperability|
|[Algorithmic Study of Gibbs Sampling Selection Based on Information Criterion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090800)|S. Shang; Y. Liu; R. Wang; R. Cao|10.1109/EEBDA56825.2023.10090800|Feature selection;random search;Gibbs sampling;information criterion;algorithm research;Training;Process control;Probability;Big Data;Feature extraction;Search problems;Data models|
|[Discussion on the setting of double-density magnetic recording belt speed for high-orbit satellites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090637)|L. Yang; H. Jing|10.1109/EEBDA56825.2023.10090637|magnetic recording;tape speed;recording wavelength;gap loss;Electrical engineering;Satellites;Big Data;Belts;Distortion;Magnetic heads;Frequency response|
|[Research on a shipborne ionospheric scintillation detection method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090498)|J. Zhou; X. Wu; G. Wu; Y. Su|10.1109/EEBDA56825.2023.10090498|ionospheric scintillation;radio tracking;Kriging-Kalman method;shipborne;Electrical engineering;Interpolation;Big Data;Kalman filters|
|[Analysis and examination of instrumentation tape recorder’s flutter and time base error](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090494)|L. Yang; H. Jing|10.1109/EEBDA56825.2023.10090494|measuring tape drive;jitter;time base error;spindle motor;Measurement uncertainty;Process control;Jitter;Drives;Reliability engineering;Time measurement;Real-time systems|
|[Research on a network covert channel based on blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090526)|J. Du; L. Li; X. Xiong; T. Niu|10.1109/EEBDA56825.2023.10090526|covert channel;blockchain;concealment;efficiency;Electrical engineering;Costs;Databases;Operating systems;Smart contracts;Buildings;Bitcoin|
|[Gesture Recognition Technology Based on Depth Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090836)|H. Wei|10.1109/EEBDA56825.2023.10090836|gesture recognition;convolution;neural network;intelligence;Training;Solid modeling;Neurons;Surgery;Gesture recognition;Virtual reality;Robustness|
|[A Real-time Logistics Scheduling Method of Digital Twin Workshop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090589)|Y. He; Z. Zhang; D. Tang|10.1109/EEBDA56825.2023.10090589|digital twin;discrete workshop;reinforcement learning;real-time scheduling;Electrical engineering;Job shop scheduling;Conferences;Reinforcement learning;Real-time systems;Iterative algorithms;Data models|
|[Research on EMC simplified analysis method of coupling communication cable between EMUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090789)|Y. Liu; Y. Sun|10.1109/EEBDA56825.2023.10090789|EMU;communication cable;EMC;harness equivalent;Couplings;Sensitivity;Power cables;Simulation;Electromagnetic interference;Wires;Voltage|
|[Research on Simple Power Consumption Based on AES Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090666)|C. Zhang; Y. Jia; L. Zhu; Z. Zhang|10.1109/EEBDA56825.2023.10090666|AES;Side Channel Attack;Intrusive attack;power waste;Electrical engineering;Energy consumption;Power demand;Big Data;Stability analysis;Encryption|
|[Research on a comprehensive evaluation method based on multi-indicator solitary network frequency control strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090569)|L. Dexin; C. Jingyi; W. Wei; G. Song|10.1109/EEBDA56825.2023.10090569|Orphan network;frequency control;evaluation index;entropy method;Electrical engineering;Costs;Big Data;Entropy;Indexes;Frequency control;Network systems|
|[Research on Fruit Recognition Method Based on Improved YOLOv4 Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090849)|C. Yi; W. Wu; L. Yang; R. Jia|10.1109/EEBDA56825.2023.10090849|Image processing;Fruit recognition;YOLOv4;Attention mechanism;Average precision;Electrical engineering;Image recognition;Target recognition;Pipelines;Object detection;Big Data;Feature extraction|
|[Application Research of Computer Virtual Reality Technology in Digital Media System Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090807)|Z. Wang; C. Zhu|10.1109/EEBDA56825.2023.10090807|virtual reality;digital media;virtual resources;system design;Structured Query Language;Protocols;Software algorithms;Virtual reality;Computer architecture;Media;Hardware|
|[Firewall Technology Strategy Analysis and Application Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090632)|R. Liu|10.1109/EEBDA56825.2023.10090632|Stateful firewall;Securtiy zone;VPN;NAT;ENSP;Electrical engineering;Analytical models;Protocols;Firewalls (computing);Filtering;Network security;Big Data|
|[Research on Efficient Parallelization of Spectral Clustering Algorithm Based on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090712)|C. Wei|10.1109/EEBDA56825.2023.10090712|big date;spectral clustering;distributed parallelization;Industries;Heuristic algorithms;Linear regression;Clustering algorithms;Parallel processing;Big Data;Partitioning algorithms|
|[Speech Emotion Feature Extraction Method Based on Improved MFCC and IMFCC Fusion Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090810)|Z. Wang; J. Yan; Y. Wang; X. Wang|10.1109/EEBDA56825.2023.10090810|Speech emotion recognition;variational modal decomposition;feature extraction;Meier frequency cepstral coefficients;principal component analysis;Emotion recognition;Time-frequency analysis;Databases;Speech recognition;Feature extraction;Data mining;Mel frequency cepstral coefficient|
|[Network Log Big Data Analysis Processing Based on Hadoop Cluster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090697)|A. Ning|10.1109/EEBDA56825.2023.10090697|Hadoop;HDFS;MapReduce;Hive;Pig;Big data analysis;Electrical engineering;Splicing;Refining;Ecosystems;Intrusion detection;Big Data;IP networks|
|[Application Research of Computer Network Technology in Electronic Wireless Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090488)|W. Lv|10.1109/EEBDA56825.2023.10090488|computer;electronic wireless transmission;system design;remote communication;network protocol;Wireless sensor networks;Protocols;Wireless networks;Software algorithms;Reliability engineering;Software;Internet of Things|
|[Research on Network Application Automation System Based on Computer Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090826)|W. Lv|10.1109/EEBDA56825.2023.10090826|computer;artificial intelligence;network automation;fading channel;Wireless sensor networks;Automation;Substation automation;Wireless networks;Simulation;Systems architecture;Optical fiber networks|
|[Research on glass relics based on machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090756)|J. Zhao; Z. Zheng; C. Fang; Y. Huang; B. Zhang|10.1109/EEBDA56825.2023.10090756|Decision tree algorithm;multilayer perceptron;five fold cross validation;Sensitivity;Neural networks;Glass;Lead;Predictive models;Prediction algorithms;Data models|
|[The Comparison and Analysis of Autonomous Food Delivery Robot Based on Artificial Potential Field and Breadth-First Search Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090724)|R. Wang|10.1109/EEBDA56825.2023.10090724|Artificial Potential Fields;Breadth-First Search;path planning;obstacle avoidance;Electrical engineering;Electric potential;Search methods;Big Data;Path planning;Planning;Collision avoidance|
|[Research on Multi-robot Material Picking and Autonomous Path Planning System in Industrial Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090823)|Z. Jiang|10.1109/EEBDA56825.2023.10090823|industrial 4.0;multi-robot path planning;HCA algorithm;PID control;Industries;Electrical engineering;Service robots;Navigation;Grasping;Manipulators;Path planning|
|[Analysis of Cultural Group Communication Behavior Based on Deep Belief Network Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090520)|W. Mi|10.1109/EEBDA56825.2023.10090520|deep belief network algorithm;group culture group communication;cultural group communication model;abnormal data capture;Training;Analytical models;Simulation;Manuals;Feature extraction;Data models;Behavioral sciences|
|[Research and Application of Archive Data Management System Based on Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10090602)|L. Yang; S. Fang; L. Sheng; L. Dandan; S. Hangxuan; W. Yingying; L. Nan; C. Xin|10.1109/EEBDA56825.2023.10090602|blockchain;archival datah;HyperLedger;hash;Electrical engineering;Distributed ledger;Memory;Big Data;Blockchains;Data mining|
#### **2023 57th Annual Conference on Information Sciences and Systems (CISS)**
- DOI: 10.1109/CISS56502.2023
- DATE: 22-24 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[FPGA Based Emulation Of B92 QKD Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089628)|U. Mujahid; M. Khalid; M. Najam-ul-Islam|10.1109/CISS56502.2023.10089628|Quantum computing;FPGA emulation;B92;Quantum key distribution;Quantum system;Quantum algorithm;Protocols;Costs;Emulation;Qubit;Performance analysis|
|[An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089765)|H. Friedman; A. R. Maina-Kilaas; J. Schalkwyk; H. Ahmed; J. Haddock|10.1109/CISS56502.2023.10089765|nonnegative matrix factorization;topic modeling;point cloud distance;data set distance;Point cloud compression;Additive noise;Plagiarism;Transfer learning;Noise reduction;Noise measurement;Task analysis|
|[Search for Extraterrestrial Intelligence as One-Shot Hypothesis Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089645)|I. George; X. Chen; L. R. Varshney|10.1109/CISS56502.2023.10089645|Quantum Information Theory;Search for Extraterrestrial Intelligence;Asymmetric Hypothesis Testing;One-Shot Information Theory;Quantum computing;Quantum mechanics;Focusing;Benchmark testing;Computational efficiency;Standards;Information theory|
|[Interference Mitigation in Blind Source Separation by Hidden State Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089636)|A. Ghosh; A. M. Haimovich; J. A. Dabin|10.1109/CISS56502.2023.10089636|Blind Source Separation;Direction of Arrival;Hidden Markov Model;Jammer;Sparse Representation;Radio frequency;Direction-of-arrival estimation;Filtering;Hidden Markov models;Interference;Markov processes;Blind source separation|
|[A computational method for improving the data acquisition process in the Laser Metal Deposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089700)|M. Mu'az Imran; G. Jung; Y. Kim; P. E. Abas; L. C. De Silva; Y. B. Kim|10.1109/CISS56502.2023.10089700|Outliers Detection;Data Stream;Additive Manu-facturing;Knowledge Representation and Acquisition;Spatters;Laser theory;Data analysis;Data acquisition;Metals;Estimation;Laser stability;Stability analysis|
|[Zeroing the Output of Nonlinear Systems Without Relative Degree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089661)|W. S. Gray; K. Ebrahimi-Fard; A. Schmeding|10.1109/CISS56502.2023.10089661|nonlinear control systems;zero dynamics;Chen-Fliess series;Algebra;Heuristic algorithms;Control systems;Nonlinear systems|
|[Parallel and Serial Two-Sensor Distributed Detection Considering Binary Symmetric Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089660)|X. Sun; L. Cao; R. Viswanathan|10.1109/CISS56502.2023.10089660|Wireless sensor network;Distributed detection;Binary symmetric channel;Error-resilience;Genetic algorithm;Wireless communication;Wireless sensor networks;Codes;Network topology;Sensor fusion;Sensor systems;Sensors|
|[A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089677)|A. Emadi; T. Lipniacki; A. Levchenko; A. Abdi|10.1109/CISS56502.2023.10089677|cell decision making;biochemical signals;detection theory;Neyman-Pearson detector;A20 deficiency;cancer;Pathology;Communication system signaling;Biological system modeling;Computational modeling;Decision making;Predictive models;Data models|
|[Block turbo decoding with ORBGRAND](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089737)|K. Galligan; M. Médard; K. R. Duffy|10.1109/CISS56502.2023.10089737|GRAND;Block Turbo Decoding;Product Codes;List Decoding;Additive noise;Maximum likelihood estimation;Energy consumption;Product codes;Redundancy;Complexity theory;Reliability|
|[Particle Thompson Sampling with Static Particles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089653)|Z. Zhou; B. Hajek|10.1109/CISS56502.2023.10089653|stochastic bandit;Thompson sampling;particles;Atmospheric measurements;Particle measurements|
|[Distributed Policy Gradient with Heterogeneous Computations for Federated Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089771)|Y. Zhu; X. Gong|10.1109/CISS56502.2023.10089771|nan;Federated learning;Heuristic algorithms;Decision making;Reinforcement learning;Benchmark testing;Trajectory;Complexity theory|
|[Physical layer insecurity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089749)|M. Médard; K. R. Duffy|10.1109/CISS56502.2023.10089749|nan;Measurement;Simulation;Physical layer security;Linear codes;Prediction algorithms;Error correction codes;Reliability|
|[Policy Gradients for Probabilistic Constrained Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089763)|W. Chen; D. Subramanian; S. Paternain|10.1109/CISS56502.2023.10089763|reinforcement learning;probabilistic constraint;safe policy;policy gradient;Navigation;Optimization methods;Reinforcement learning;Probabilistic logic;Linear programming;Approximation algorithms;Safety|
|[Multi-armed Bandit Learning on a Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089744)|T. Zhang; K. Johansson; N. Li|10.1109/CISS56502.2023.10089744|nan;Uncertainty;Decision making;Benchmark testing;Manipulators;Planning|
|[Extended Abstract: Learning in Low-rank MDPs with Density Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089731)|A. Huang; J. Chen; N. Jiang|10.1109/CISS56502.2023.10089731|reinforcement learning;low-rank MDPs;density features;Additives;Estimation;Reinforcement learning;Function approximation;Standards|
|[Optimal Decision Making with Rational Inattention Using Noisy Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089696)|Y. Zhao; A. Abdi; M. Dean; A. Abdi|10.1109/CISS56502.2023.10089696|Decision making;rational inattention;noisy signals;Costs;Decision making;Psychology;Finance;Data collection;Distortion;Noise measurement|
|[Physical geometry of channel degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089672)|S. Huntsman|10.1109/CISS56502.2023.10089672|nan;Geometry;Degradation;Behavioral sciences;Physics|
|[Federated Learning With Server Learning for Non-IID Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089643)|V. S. Mai; R. J. La; T. Zhang; Y. Huang; A. Battou|10.1109/CISS56502.2023.10089643|Federated learning;Distributed optimization;Degradation;Computer aided instruction;Federated learning;Distance learning;Distributed databases;Performance gain;Servers|
|[Evaluating FFT performance of the C and Rust Languages on Raspberry Pi platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089631)|M. P. Rooney; S. J. Matthews|10.1109/CISS56502.2023.10089631|Edge computing;Rust;C;Fast Fourier Transform;Raspberry Pi;Performance evaluation;Energy consumption;Fast Fourier transforms;Memory management;Energy measurement;Digital signal processing;Real-time systems|
|[Impedance Estimation at Multi-Antenna Receivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089650)|S. Wu; B. L. Hughes|10.1109/CISS56502.2023.10089650|Antenna Impedance Estimation;Maximum-Likelihood Estimator;Training;Measurement;Maximum likelihood estimation;Impedance matching;Channel estimation;Receiving antennas;Rayleigh channels|
|[An Overview of Quantum-Safe Approaches: Quantum Key Distribution and Post-Quantum Cryptography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089619)|G. Xu; J. Mao; E. Sakk; S. P. Wang|10.1109/CISS56502.2023.10089619|Quantum-safe Application;Quantum Key Distribution;Post-Quantum Cryptography;Quantum computing;Protocols;Systematics;Computational modeling;Network security;NIST;Quantum key distribution|
|[A Deep Transfer Learning based approach for forecasting spatio-temporal features to maximize yield in cotton crops](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089748)|K. C. Gadepally; S. B. Dhal; M. Bhandari; J. Landivar; S. Kalafatis; K. Nowka|10.1109/CISS56502.2023.10089748|deep transfer learning;canopy cover;canopy height;excess green index;Measurement;Biological system modeling;Transfer learning;Crops;Predictive models;Data models;Indexes|
|[Secret Key Generation from MIMO Channel With or Without Reciprocity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089738)|Y. Hua; A. Maksud|10.1109/CISS56502.2023.10089738|Secret key generation;secret key capacity;degree of freedom;channel probing;pre-processing;Computational modeling;Coherence;Antennas;MIMO communication|
|[Generative Versus Discriminative Data-Driven Graph Filtering of Random Graph Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089638)|L. Dabush; N. Shlezinger; T. Routtenberg|10.1109/CISS56502.2023.10089638|Graph signal processing;linear estimation;graph filters;generative learning;discriminative learning;Maximum likelihood detection;Training data;Nonlinear filters;Signal processing;Information filters;Data models;Mathematical models|
|[Toward Designing an Attentive Deep Trajectory Predictor Based on Bluetooth Low Energy Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089682)|W. Lu; X. Ma; X. Zhang; Z. Yang; Q. Wang; C. Liu; T. Yang|10.1109/CISS56502.2023.10089682|Bluetooth Low Energy;Attention;Phase Sensitive Channels;Embedding Ranking Loss;Location awareness;Deep learning;Sensitivity;Layout;Filter banks;Feature extraction;Robustness|
|[Searching for the Most Probable Combination of Class Labels Using Etcetera Abduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089729)|A. S. Gordon; A. Feng|10.1109/CISS56502.2023.10089729|I.2.6.g Machine learning;I.2.3.d Inference engines;I.2.4 Knowledge Representation Formalisms and Methods;Computer vision;Computational modeling;Knowledge representation;Benchmark testing;Probability distribution;Labeling;Task analysis|
|[Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089720)|S. Bhatnagar; P. L.A.|10.1109/CISS56502.2023.10089720|Stochastic Optimization;Generalized Simultaneous Perturbation Stochastic Approximation (G-SPSA);Algorithms with Low Estimator Bias;Atmospheric measurements;Perturbation methods;Particle measurements;Noise measurement;Convergence|
|[Slime Mold Algorithm-Based Performance Improvement of PD-Type Indirect Iterative Learning Fuzzy Control of Tower Crane Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089708)|R. -E. Precup; R. -C. Roman; E. -L. Hedrea; E. M. Petriu; C. -A. Bojan-Dragos; A. -. I. Szedlak-Stinean|10.1109/CISS56502.2023.10089708|indirect Iterative Learning Control;Proportional-Derivative learning rule;Proportional-Integral-fuzzy control;Slime Mold Algorithm;tower crane systems;Fuzzy control;Cranes;PI control;Poles and towers;Metaheuristics;Position control;Linear programming|
|[Feasibility of Regression Modeling and Biomarker Analysis for Epileptic Seizure Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089755)|D. L. Tanner; M. Privitera; M. Rao|10.1109/CISS56502.2023.10089755|Epilepsy;Seizure prediction;Logistic regression;Biomarkers;Analytical models;Uncertainty;Biological system modeling;Precision medicine;Epilepsy;Medical services;Biomarkers|
|[Federated Learning via Indirect Server-Client Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089783)|J. Bian; C. Shen; J. Xu|10.1109/CISS56502.2023.10089783|nan;Schedules;Machine learning algorithms;Federated learning;Distance learning;Optimization methods;Sensors;Servers|
|[Distributed Detection with Unreliable Reporting Channels: Quantize or Not Quantize?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089620)|L. Cao; R. Viswanathan|10.1109/CISS56502.2023.10089620|Distributed detection;centralized or decentralized;quantize or not quantize;Modulation;Bandwidth;Sensor fusion;Distortion;Sensors;Optimization|
|[Tools and Visualizations for Exploring Classification Landscapes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089673)|W. Powers; L. Shi; L. S. Liebovitch|10.1109/CISS56502.2023.10089673|machine learning;image classification;data visualization;deep learning networks;loss landscapes;Deep learning;Training;Iris;Neural networks;Data visualization;Predictive models;Data models|
|[Application of Machine Learning to Quantum Cascade Laser Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089756)|A. C. Hernandez; C. F. Gmachl|10.1109/CISS56502.2023.10089756|quantum cascade laser;design;machine learning;regression;figure of merit;Training;Machine learning algorithms;Neural networks;Training data;Machine learning;Data collection;Prediction algorithms|
|[Communication-Efficient Federated Learning with Channel-Aware Sparsification over Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089723)|R. Jin; P. Dai; K. Xiong|10.1109/CISS56502.2023.10089723|nan;Training;Time division multiple access;Quantization (signal);Federated learning;Wireless networks;Computational modeling;Training data|
|[Outlier Detection for Generative Models with Performance Guarantees](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089758)|J. Gao; J. Yi; W. Xu|10.1109/CISS56502.2023.10089758|Generative model;outlier detection;recovery guarantees;neural network;nonlinear activation function;Neural networks;Pollution measurement;Anomaly detection;Optimization|
|[Towards an Improved Hyperdimensional Classifier for Event-Based Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089705)|N. Anwar; C. Parameshwara; C. Fermüller; Y. Aloimonos|10.1109/CISS56502.2023.10089705|nan;Visualization;Neuromorphics;Fitting;Pose estimation;Vision sensors;Encoding;Surface fitting|
|[Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089766)|C. Quan; B. Geng; Y. S. Han; P. K. Varshney|10.1109/CISS56502.2023.10089766|component;formatting;style;styling;insert;Simulation;Decision making;Collaboration;Sensor systems;Sensors;Man-machine systems|
|[Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089635)|R. Jin; X. He; H. Dai|10.1109/CISS56502.2023.10089635|nan;Training;Privacy;Differential privacy;Machine learning algorithms;Stochastic processes;Training data;Machine learning|
|[Prediction and functional characterization of transcriptional activation domains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089768)|S. Mahatma; L. Van den Broeck; N. Morffy; M. V. Staller; L. C. Strader; R. Sozzani|10.1109/CISS56502.2023.10089768|Multilayer neural network;transcriptional activation domains;feature importance;unsupervised clustering;Limiting;Neural networks;Pipelines;Machine learning;Nonhomogeneous media;Amino acids;Gene expression|
|[An ALOHA multi-user game with tradeoff between throughput and transmission proportional fairness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089707)|A. Garnaev; W. Trappe|10.1109/CISS56502.2023.10089707|Equilibrium;ALOHA;Proportional Fairness;Measurement;Base stations;Protocols;Costs;Switches;Games;Throughput|
|[Error Detection Strategies for CRC-Concatenated Polar Codes under Successive Cancellation List Decoding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089769)|A. Sauter; B. Matuz; G. Liva|10.1109/CISS56502.2023.10089769|nan;Monte Carlo methods;Error analysis;Design methodology;Encoding;Decoding;Polar codes;Cyclic redundancy check codes|
|[Perspicuity of Evacuation Behavior in Communities during Hurricanes using Large-scale Mobility Patterns and Communal Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089754)|H. Anand; M. Shafiee-Jood; N. Alemazkoor|10.1109/CISS56502.2023.10089754|community behavior;evacuation orders;mobility;big data;machine learning;hurricane Dorian;Costs;Sociology;Emergency services;Hurricanes;Behavioral sciences;Stakeholders;Indexes|
|[Data-Driven Analysis and Optimization for Urban Energy Systems Equitable Resilience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089717)|G. Ebbrecht; J. Chen|10.1109/CISS56502.2023.10089717|nan;COVID-19;Pandemics;Urban areas;Decision making;Machine learning;Predictive models;Electric vehicle charging|
|[Differentially Private Community Detection over Stochastic Block Models with Graph Sketching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089679)|M. Seif; A. J. Goldsmith; H. V. Poor|10.1109/CISS56502.2023.10089679|Differential Privacy;Graphs;Community Detection;Graph Sketching;Subsampling;Privacy;Maximum likelihood estimation;Sufficient conditions;Maximum likelihood detection;Differential privacy;Image edge detection;Stochastic processes|
|[On the determination of GRAND noise sequences by employing integer compositions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089775)|S. Jones; A. B. Cooper|10.1109/CISS56502.2023.10089775|GRAND;decoding;composition;noise sequence;two-state Markov channel;Additive noise;Binary codes;Markov processes;Mathematical models;Generators;Decoding;Channel models|
|[Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089777)|M. Mortaheb; S. Ulukus|10.1109/CISS56502.2023.10089777|nan;Training;Degradation;Correlation;Federated learning;Heuristic algorithms;Multitasking;Task analysis|
|[Towards Equalization of Mixed Multi-user OFDM Signals Over a Doubly-Dispersive Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089639)|K. Toland; P. Taiwo; A. Cole-Rhodes|10.1109/CISS56502.2023.10089639|delay-Doppler;OFDM;MIMO communication;doubly-dispersive channel;Phase shift keying;Frequency modulation;Equalizers;OFDM;Soft sensors;Symbols;Receivers|
|[Improving Particle Thompson Sampling through Regenerative Particles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089647)|Z. Zhou; B. Hajek|10.1109/CISS56502.2023.10089647|stochastic bandit;Thompson sampling;particles;Particle filters;Stochastic processes;Particle measurements;Covariance matrices;Atmospheric measurements;Tuning;Surface treatment|
|[Decoding EEG Signals with Visibility Graphs to Predict Varying Levels of Mental Workload](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089662)|A. Teymourlouei; R. J. Gentili; J. Reggia|10.1109/CISS56502.2023.10089662|Mental workload (MWL);electroencephalography (EEG);machine learning;classification;visibility graph (VG);horizontal visibility graph (HVG);Machine learning algorithms;Frequency-domain analysis;Neural networks;Time series analysis;Filtering algorithms;Prediction algorithms;Feature extraction|
|[ACRE: Actor Critic Reinforcement Learning for Failure-Aware Edge Computing Migrations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089694)|M. Siew; S. Sharma; C. Joe-Wong|10.1109/CISS56502.2023.10089694|Edge Computing;Service migration;Resilient Resource Allocation;Training;Costs;Monte Carlo methods;Reinforcement learning;Quality of service;Real-time systems;Delays|
|[Game and Prospect Theoretic Hardware Trojan Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089630)|S. Nan; L. Njilla; S. Brahma; C. A. Kamhoua|10.1109/CISS56502.2023.10089630|nan;Analytical models;Costs;Simulation;Games;Nash equilibrium;Hardware;Behavioral sciences|
|[Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089741)|X. Zhang; Q. Zhu; H. V. Poor|10.1109/CISS56502.2023.10089741|Sixth generation (6G) wireless networks;massive ultra-reliable and low-latency communications (mURLLC);massive multiple-input and multiple-output (massive-MIMO);rate splitting;6G mobile communication;Fading channels;Closed-form solutions;Channel capacity;Wireless networks;Interference;Quality of service|
|[Stochastic Mean-Shift for Speaker Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089776)|I. Lapidot|10.1109/CISS56502.2023.10089776|Speaker clustering;mean-shift;stochastic mean-shift;stochastic training;PLDA;Clustering algorithms;Probabilistic logic;Linear discriminant analysis;Task analysis;Standards|
|[When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089678)|D. Deniz; E. Ros; C. Fermller; F. Barranco|10.1109/CISS56502.2023.10089678|Event processing;manipulation action recognition;Visualization;Neuromorphics;Neural networks;Human-robot interaction;Sensor phenomena and characterization;Feature extraction;Real-time systems|
|[The ε-Effective Capacity for Statistical Delay and Error-Rate Bounded QoS Provisioning Over 6G CF M-MIMO Wireless Networks Using HARQ-IR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089782)|X. Zhang; J. Wang; H. V. Poor|10.1109/CISS56502.2023.10089782|Statistical delay and error-rate bounded QoS;cell-free m-MIMO;FBC;HARQ-IR;$\epsilon$-effective capacity;outage probability;6G mobile wireless networks;6G mobile communication;Analytical models;Protocols;Wireless networks;Systems architecture;Quality of service;Transforms|
|[Maximum Zero-Outage Secrecy Capacity of Fading Wiretap Channels with Finite Alphabets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089618)|X. Xu; K. -L. Bessert; P. -H. Lin; E. A. Jorswieck|10.1109/CISS56502.2023.10089618|nan;Fading channels;Systematics;Transmitters;System performance;Receivers;Probability;Linear programming|
|[Near-optimal Sampling to Optimize Communication Over Discrete Memoryless Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089651)|M. A. Tope; J. M. Morris|10.1109/CISS56502.2023.10089651|nan;Monte Carlo methods;Channel capacity;Aggregates;Approximation algorithms;Picture archiving and communication systems;Memoryless systems;Complexity theory|
|[Entropy-based scheduling performance in real-time multiprocessor systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089704)|C. A. Rincon; D. Rivas; A. M. K. Cheng|10.1109/CISS56502.2023.10089704|Entropy-based scheduling;real-time systems;multiprocessors;performance analysis;Scheduling algorithms;Real-time systems;Scheduling;Entropy;Performance analysis;Task analysis;Multiprocessing systems|
|[Teaching Reinforcement Learning Agents via Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089695)|K. Yang; C. Shi; C. Shen|10.1109/CISS56502.2023.10089695|nan;Costs;Education;Reinforcement learning;Behavioral sciences;Task analysis;Convergence|
|[Multi-Output Career Prediction: Dataset, Method, and Benchmark Suite](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089642)|S. Singh; A. Gupta; S. S. Baraheem; T. V. Nguyen|10.1109/CISS56502.2023.10089642|career prediction;dataset;multi-class multi-output classification;benchmark suite;Industries;Engineering profession;Social networking (online);Soft sensors;Companies;Benchmark testing;Market research|
|[Rebalancing Techniques for Asynchronously Distributed EEG Data to Improve Automatic Seizure Type Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089669)|N. McCallan; S. Davidson; K. Y. Ng; P. Biglarbeigi; D. Finlay; B. L. Lan; J. McLaughlin|10.1109/CISS56502.2023.10089669|Electroencephalography;Epileptic seizure;Classification;Machine learning;ADASYN;SMOTE;Visualization;Hospitals;Distributed databases;Epilepsy;Nervous system;Brain modeling;Electroencephalography|
|[Real-time Fitness Activity Recognition and Correction using Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089773)|M. M. Varghese; S. Ramesh; S. Kadham; V. M. Dhruthi; P. Kanwal|10.1109/CISS56502.2023.10089773|Exercise Recognition;key points;real-time;LSTM-RNN;GAN;Conv-LSTM;MediaPipe;Measurement;Pandemics;Computational modeling;Neural networks;Computer architecture;Production;Generative adversarial networks|
|[Piecewise Linear and Stochastic Models for the Analysis of Cyber Resilience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089725)|M. J. Weisman; A. Kott; J. Vandekerckhove|10.1109/CISS56502.2023.10089725|nan;Degradation;Analytical models;Stochastic processes;Differential equations;Resists;Mathematical models;Malware|
|[Artificial Intelligence-Assisted Laparoscopic Cholecystectomy in a Preclinical Swine Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089690)|K. M. Ali; M. Saruwatari; K. Jawed; Y. Kim; S. Park; S. Cha; B. Ning; R. J. Cha|10.1109/CISS56502.2023.10089690|artificial intelligence;laparoscopic surgery;computer-aided detection;bile duct injury;Laparoscopes;In vivo;Minimally invasive surgery;Ducts;Fluorescence;Artificial intelligence;Injuries|
|[Learning of Doppler Tolerant Radar Detectors for Noise Waveforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089751)|K. P. Wensell; J. Zhou; A. M. Haimovich; E. A. Young; L. T. Vo|10.1109/CISS56502.2023.10089751|Radar detection;noise waveforms;Doppler tolerance;neural networks;Training;Doppler shift;Neural networks;MIMICs;Radar detection;Training data;Detectors|
|[Coarse-Grained High-speed Reconfigurable Array-based Approximate Accelerator for Deep Learning Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089735)|K. Mercado; S. Bavikadi; S. M. PD|10.1109/CISS56502.2023.10089735|nan;Deep learning;Neural networks;Speech recognition;Resource management;Convolutional neural networks;Computational complexity;Field programmable gate arrays|
|[Output Reachability of Chen-Fliess series: A Newton-Raphson Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089740)|I. P. Avellaneda; L. A. Duffaut Espinosa|10.1109/CISS56502.2023.10089740|Nonlinear systems;Chen-Fliess series;Reacha-bility;Optimization;Newton-Raphson;Adaptation models;Systematics;Computational modeling;NASA;Optimization methods;Approximation algorithms;Control systems|
|[Development of multifunctional hyperspectral/near-infrared imaging camera system for intraoperative tissue characterization and assessment in vivo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089670)|C. Lee; A. Naik; S. Naik; M. Saruwatari; K. Jawed; K. M. Ali; B. Ning; R. J. Cha|10.1109/CISS56502.2023.10089670|nan;In vivo;Visualization;Image color analysis;Surgery;Rodents;Rats;Cameras|
|[Multi-agent Deep Reinforcement Learning for Multi-Cell Interference Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089622)|M. Dahal; M. Vaezi|10.1109/CISS56502.2023.10089622|nan;Deep learning;Power measurement;Array signal processing;Spectral efficiency;Power control;Millimeter wave technology;Interference|
|[Distributed Detection in EH-Powered Mobile WSNs: Adaptive Transmission over Temporally Correlated MIMO Channels with Limited Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089747)|G. Ardeshiri; A. Vosoughi|10.1109/CISS56502.2023.10089747|nan;Fading channels;Measurement;Wireless sensor networks;Quantization (signal);Transmitting antennas;Symbols;Mobile antennas|
|[Stopping Criteria for Compressive Sensing OFDM Channel Estimation using OMP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089664)|J. Franklin; A. B. Cooper III|10.1109/CISS56502.2023.10089664|Compressed Sensing;Sparse Recovery;Channel Estimation;OFDM;OFDM;Channel estimation;Matching pursuit algorithms;Sensors;Frequency division multiplexing;Compressed sensing;Multipath channels|
|[Greedy Centroid Initialization for Federated K-means](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089666)|K. Yang; M. M. Amiri; S. R. Kulkarni|10.1109/CISS56502.2023.10089666|K-means;Clustering;Federated Learning;Machine Learning;Greedy algorithms;Clustering algorithms;Partitioning algorithms;Servers|
|[Stacking multiple optimal transport policies to map functional connectomes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089693)|J. Dadashkarimi; M. Rosenblatt; A. Karbasi; D. Scheinost|10.1109/CISS56502.2023.10089693|optimal transport;connectome;neuroimaging;Training;Neuroimaging;Stacking;Training data;Transforms;Functional magnetic resonance imaging;Topology|
|[An Accelerated Asynchronous Distributed Method for Convex Constrained Optimization Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089633)|N. Abolfazli; A. Jalilzadeh; E. Y. Hamedani|10.1109/CISS56502.2023.10089633|Multi-agent distributed optimization;asyn-chronous algorithm;constrained optimization;convergence rate;Optimization;Convergence|
|[A Machine Learning Approach to Predict the Optical Properties of a Nanocube via Gaussian Process Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089714)|E. G. Ozaktas; A. Naef; G. Tagliabue|10.1109/CISS56502.2023.10089714|machine learning;plasmonics;Gaussian Process Regression;nanocubes;nanoparticles;optical cross section;Training;Measurement;Visualization;Scattering;Gaussian processes;Machine learning;Predictive models|
|[AI/ML Systems Engineering Workbench Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089781)|K. Nyarko; P. Taiwo; C. Duru; E. Masa-Ibi|10.1109/CISS56502.2023.10089781|Machine Learning;Artificial Intelligence;System Workbench;RESTful API;Cloud Computing;AutoML;Training;Computational modeling;Transfer learning;Benchmark testing;Natural language processing;Software;Metasearch|
|[Net Load Forecast based on Behind-the-Meter Disaggregation of Smart Meter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089621)|M. Jia; K. Pu; Y. Zhao|10.1109/CISS56502.2023.10089621|nan;Training;Load forecasting;Neural networks;Weather forecasting;Power distribution;Solar energy;Prediction algorithms|
|[SPSA-Based Switch Updating Algorithm for Constrained Stochastic Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089743)|Z. Jia; Z. Wei|10.1109/CISS56502.2023.10089743|Constrained Optimization;Stochastic Approximation;SPSA;Stochastic processes;Switches;Approximation algorithms;Loss measurement;Reliability;Quadratic programming;Time complexity|
|[Model Segmentation for Storage Efficient Private Federated Learning with Top $r$ Sparsification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089698)|S. Vithana; S. Ulukus|10.1109/CISS56502.2023.10089698|nan;Data privacy;Costs;Federated learning;Data models|
|[Linearization of Non-Uniform Quantizers via Adaptive Non-Subtractive Dithering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089625)|M. Kasher; P. Spasojevic; M. Tinston|10.1109/CISS56502.2023.10089625|dithering;quantization;analog-to-digital conversion;linearization;non-linear;lloyd-max;Quantization (signal);Correlation;Measurement uncertainty;Transfer functions|
|[Performance Analysis of LOS THz Systems Under Misalignment and Deterministic Fading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089646)|R. Abdalla; A. B. Cooper|10.1109/CISS56502.2023.10089646|Terahertz communications;Line-of-sight;Misalignment;Average error probability;Deterministic fading;Path loss;Molecular absorption;Performance assessment;Fading channels;Wireless communication;Monte Carlo methods;Error probability;Absorption;Transmitting antennas;Symbols|
|[Boolean Factor Graph Modeling and Analysis of Gene Graphs: Budding Yeast Cell-Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089697)|S. Kotiang; A. Eslami|10.1109/CISS56502.2023.10089697|Boolean networks;factor graph;network perturbation;systems biology;Analytical models;Biological system modeling;Computational modeling;Perturbation methods;Biological systems;Predictive models;Prediction algorithms|
|[Quantifying Phase- Amplitude Modulation in Neural Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089691)|V. Subritzky-Katz; A. L. Sampson; E. Emeric; W. Lipski; S. Moreira-González; J. González-Martínez; S. Sarma; V. Stuphorn; E. Niebur|10.1109/CISS56502.2023.10089691|phase-amplitude modulation;cross-frequency coupling;sEEG;EEG;ECoG;Couplings;Frequency modulation;Phase measurement;Phase modulation;Shape;Amplitude modulation;Recording|
|[Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089641)|X. Zhang; Q. Zhu; H. V. Poor|10.1109/CISS56502.2023.10089641|6G wireless networks;SDN;scaling-law;heterogeneous statistical QoS provisioning;transport capacity;6G mobile communication;Wireless networks;Microprocessors;Computer architecture;Quality of service;MISO communication;Behavioral sciences|
|[Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089734)|H. Rajoli; F. Lotfi; A. Atyabi; F. Afghah|10.1109/CISS56502.2023.10089734|Deep metric learning;Triplet loss;Facial expression recognition;Negative-samples selection;Measurement;Deep learning;Face recognition;Simulation;Neural networks|
|[Prospect Theoretic Contract Design in a Stackelberg Game via Bayesian Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089699)|E. Jamalinia; P. Venkitasubramaniam|10.1109/CISS56502.2023.10089699|Bayesian inference;prospect theory;Stackelberg game;Ethics;Simulation;Decision making;Games;Numerical simulation;Bayes methods;Noise measurement|
|[Performance Analysis of Model-Free Control and PID Control on a Class of Nonlinear MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089683)|J. C. Peng; J. C. Spall|10.1109/CISS56502.2023.10089683|Model-free control;Neural Networks;SPSA;PID;Stochastic Search and Optimization;Sufficient conditions;PI control;Simulation;Perturbation methods;Neural networks;Stochastic processes;Performance analysis|
|[Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089721)|C. M. King; S. T. Do; J. Chen|10.1109/CISS56502.2023.10089721|nan;System performance;Computational modeling;Control systems;Approximation algorithms;Data models;Convex functions;Computational efficiency|
|[Tunable complexity benchmarks for evaluating physics-informed neural networks on coupled ordinary differential equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089728)|A. New; B. Eng; A. C. Timm; A. S. Gearhart|10.1109/CISS56502.2023.10089728|nan;Training;Laplace equations;Partial differential equations;Neural networks;Benchmark testing;Ordinary differential equations;Network architecture|
|[A Comprehensive Study of Gradient Inversion Attacks in Federated Learning and Baseline Defense Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089719)|P. R. Ovi; A. Gangopadhyay|10.1109/CISS56502.2023.10089719|Model inversion attacks;Gradient leakage attacks;Mixed quantization;Federated learning;Training;Data privacy;Privacy;Protocols;Machine learning algorithms;Federated learning;Training data|
|[Langevin Monte Carlo with SPSA-approximated Gradients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089715)|S. Sun; J. C. Spall|10.1109/CISS56502.2023.10089715|Langevin Monte Carlo;Stochastic Approximation;SPSA;Gradient-free Optimization;Sampling;Monte Carlo methods;Approximation algorithms;Sampling methods;Inference algorithms;Bayes methods;Proposals;Convergence|
|[Strategic multi-task coordination over regular networks of robots with limited computation and communication capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089772)|Y. Wei; M. M. Vasconcelos|10.1109/CISS56502.2023.10089772|nan;Robot kinematics;Nash equilibrium;Multitasking;Task analysis;Multi-agent systems|
|[Body-mounted MR-HIFU Robotic System: Mechanical Design and Accuracy Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089665)|A. Nankani; P. Yarmolenko; K. Sharma; K. Cleary; R. Monfaredi|10.1109/CISS56502.2023.10089665|HIFU;MRI;body-mounted;robot;musculoskeletal;Musculoskeletal system;Ultrasonic imaging;Transducers;Magnetic resonance imaging;Prototypes;Optical variables measurement;Lesions|
|[Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089692)|Y. E. Sagduyu; T. Erpek; S. Ulukus; A. Yener|10.1109/CISS56502.2023.10089692|Semantic communications;deep learning;ad-versarial machine learning;backdoor attacks;Trojan attacks;Training;Transmitters;Design methodology;Semantics;Training data;Receivers;Trojan horses|
|[Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089655)|W. A. Suttle; A. Koppel; J. Liu|10.1109/CISS56502.2023.10089655|reinforcement learning;exploration vs. exploitation;sparse rewards;Gradient methods;Reinforcement learning;Benchmark testing|
|[A Novel Feature Selection Technique for Intrusion Detection System Using RF-RFE and Bio-inspired Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089745)|Z. A. Tonni; R. Mazumder|10.1109/CISS56502.2023.10089745|Intrusion;Feature Selection;Bio-inspired Optimization;Biological system modeling;Intrusion detection;Feature extraction;Software;Hardware;Complexity theory;Security|
|[Forward and Backward Private Dynamic Searchable Encryption with Better Space Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089760)|Y. Liu; Y. Watanabe; J. Shikata|10.1109/CISS56502.2023.10089760|dynamic searchable encryption;forward privacy;backward privacy;Privacy;Data privacy;Databases;Encryption;Sun|
|[Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089648)|Y. Jiang; W. Yu; D. Song; W. Cheng; H. Chen|10.1109/CISS56502.2023.10089648|imitation learning;prototype;interpretable machine learning;dynamic treatment regimes;Learning systems;Visualization;MIMICs;Decision making;Prototypes;Medical services;Cognition|
|[C-DIEGO: An Algorithm with Near-Optimal Sample Complexity for Distributed, Streaming PCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089668)|M. Zulqarnain; A. Gang; W. U. Bajwa|10.1109/CISS56502.2023.10089668|Dimension reduction;distributed learning;Oja's method;principal component analysis;streaming data;Representation learning;Dimensionality reduction;Machine learning algorithms;Sociology;Distributed databases;Training data;Covariance matrices|
|[Offline Reinforcement Learning for Price-Based Demand Response Program Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089681)|C. Xu; B. Liu; Y. Zhao|10.1109/CISS56502.2023.10089681|nan;Learning systems;Heuristic algorithms;Decision making;Pricing;Reinforcement learning;Markov processes;Demand response|
|[Energy-efficient Edge Approximation for Connected Vehicular Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089724)|D. Katare; A. Y. Ding|10.1109/CISS56502.2023.10089724|3D maps;Approximation;Data Compression;Energy Efficiency;Edge AI;HD map;Model compression;Energy consumption;Three-dimensional displays;Pipelines;Ecosystems;Focusing;Data processing;Energy efficiency|
|[Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089767)|F. Wang; X. Zhang|10.1109/CISS56502.2023.10089767|UAV;active IRS;multi-objective optimization;hierarchical DRL;DDPG;Energy consumption;Communication systems;Wireless networks;Reinforcement learning;Autonomous aerial vehicles;Minimization;Thermal noise|
|[Multi-Objective Design Optimization for Image Classification Using Elastic Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089753)|L. Pan; Y. Zhang; Y. Zhou; H. Huttunen; S. S. Bhattacharyya|10.1109/CISS56502.2023.10089753|Adaptive Neural Network;Multi-Objective Optimization;Image Classification;Convolutional Neural Network;Measurement;Runtime;Neural networks;Switches;Real-time systems;Hardware;Convolutional neural networks|
|[Advancing Temporal Multimodal Learning with Physics Informed Regularization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089632)|N. Deshpande; H. Park; V. Pandey; G. Yoon|10.1109/CISS56502.2023.10089632|Optimal Learning;Regularization;Spurious Correlation;Multimodal Probability Distribution;Spatiotemporal Correlation;Uncertainty;Mixture models;Machine learning;Predictive models;Probability density function;Probability distribution;Spatiotemporal phenomena|
|[Architecture Analysis for Symmetric Simplicial Deep Neural Networks on Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089667)|N. Rodríguez; M. Villemur; P. Julián|10.1109/CISS56502.2023.10089667|Neuromorphic Computing;Neural Network Accelerators;Digital Architectures;Neuromorphics;Shape;Computational modeling;Neural networks;Writing;Data transfer;Hardware|
|[Inducing Dynamic Group Sparsity on Vagus Nerve Recordings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089732)|K. Aboumerhi; R. Etienne-Cummings|10.1109/CISS56502.2023.10089732|compressive sampling;neural encoding;nerve recordings;sparse representation;medical devices;Neuromorphics;Neurons;Matching pursuit algorithms;Implants;Bandwidth;Hardware;Recording|
|[Retinomorphic Channel Design and Considerations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089617)|J. P. Sengupta; A. G. Andreou|10.1109/CISS56502.2023.10089617|neuromorphic vision;system design;information theory;retinomorphic engineering;Measurement;Visualization;Costs;Computer architecture;Jitter;Vision sensors;Retina|
#### **2023 35th International Conference on Microelectronic Test Structure (ICMTS)**
- DOI: 10.1109/ICMTS55420.2023
- DATE: 27-30 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Discrete current limiting circuit for emerging memory programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094099)|L. Laborie; P. Trotti; K. Veyret; C. Cagli|10.1109/ICMTS55420.2023.10094099|RRAM;discrete current limiting circuit;DCL;memory programming;current overshoot;parasitic capacitance;test structure;Resistors;Limiting;Microprocessors;Resistive RAM;Modulation;Computer architecture;Programming|
|[Test Methodology Development for Investigating CeRAM at Elevated Temperatures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094065)|A. A. Gruszecki; R. Prasad; S. V. Suryavanshi; G. Yeric; C. D. Young|10.1109/ICMTS55420.2023.10094065|nan;Resistance;Performance evaluation;Temperature dependence;Temperature;Random access memory;Microelectronics;Leakage currents|
|[Real-time electrical measurements during laser attack on STT-MRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094166)|N. Yazigy; J. Postel-Pellerin; V. D. Marca; R. C. Sousa; A. -L. Ribotta; G. D. Pendina; P. Canet|10.1109/ICMTS55420.2023.10094166|STT-MRAM;laser attack;electrical characterization;non-volatile memories;security;reliability.;Radiation effects;Semiconductor lasers;Optical switches;Power lasers;Measurement by laser beam;Optical variables measurement;Real-time systems|
|[Automated RRAM measurements using a semi-automated probe station and ArC ONE interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094156)|A. G. Panca; A. Serb; S. Stathopoulos; S. K. Garlapati; T. Prodromakis|10.1109/ICMTS55420.2023.10094156|automation;memristor;RRAM;characterization;Performance evaluation;Benchmark testing;Rapid prototyping;Microelectronics;Probes|
|[Analysis of Critical Schottky Distance Effect and Distributed Set Voltage in HfO2-based 1T-1R Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094175)|S. -K. Lin; T. -C. Chang; W. -C. Huang; Y. -F. Tan; C. -H. Lien|10.1109/ICMTS55420.2023.10094175|Resistive Random Access Memory (RRAM);onetransistor- one-resistor (1T-1R);HfO2;Schottky Distance;Resistance;Correlation;Schottky barriers;Fitting;Random access memory;Switches;Voltage|
|[Static and LFN/RTN Local and Global Variability Analysis Using an Addressable Array Test Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094087)|O. Gauthier; S. Haendler; R. Beucher; P. Scheer; Q. Rafhay; C. Theodorou|10.1109/ICMTS55420.2023.10094087|matching;low frequency noise;Random Telegraph Noise;variability;addressable test structure;statistical analysis;Performance evaluation;Time-frequency analysis;Statistical analysis;Switches;Logic gates;Microelectronics;Low-frequency noise|
|[An Extended Method to Analyze Boron Diffusion Defects in 16 nm Node High-Voltage FinFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094125)|T. -T. Kuo; Y. -C. Chen; T. -C. Chang; F. -M. Ciou; C. -H. Yeh; P. -H. Chen; S. M. Sze|10.1109/ICMTS55420.2023.10094125|Boron Diffusion;defect detection;power spectrum density (PSD);weighted time lag plot (W-TLP);high-voltage FinFET (HV FinFET).;Degradation;Boron;Fluorine;High-voltage techniques;Voltage;FinFETs;Software|
|[VSS-Bias-Based Measurement of Random Telegraph Noise in Hybrid SRAM PUF after Hot Carrier Injection Burn-in](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094138)|K. Liu; Y. Tang; S. Xu; H. Shinohara|10.1109/ICMTS55420.2023.10094138|hot carrier injection (HCI);physically unclonable function (PUF);random telegraph noise (RTN);static random access memory (SRAM);test structure;Human computer interaction;Semiconductor device measurement;Voltage measurement;Random access memory;Hot carrier injection;Microelectronics;Noise measurement|
|[Distributed field plate effects in split-gate trench MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094167)|R. Tambone; A. Ferrara; F. Magrini; A. Hoffmann; A. Wood; G. Noebauer; E. Gondro; R. J. E. Hueting|10.1109/ICMTS55420.2023.10094167|Field plate;Distributed effects;TLP;Test structure;MOSFET;Split-gate;TCAD;Semiconductor device modeling;MOSFET;Solid modeling;Transmission lines;Simulation;Split gate flash memory cells;Predictive models|
|[Measuring of parasitic resistance of stacked chip of Si power device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094137)|T. Ohguro; H. Kojima; T. Hara; T. Nishiwaki; K. Kobayashi|10.1109/ICMTS55420.2023.10094137|resistance;Ron;MOSFET;LVMOS;stacked chip;Resistance;Semiconductor device measurement;Power measurement;Silicon;Microelectronics;Electrical resistance measurement|
|[New Extraction Method for Intrinsic Qrr of Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094069)|T. Hara; S. Nakajima; T. Ohguro; K. Miyashita|10.1109/ICMTS55420.2023.10094069|Power MOSFET;Qrr;Qoss;Reverse Recovery;Diode;Parasitic Inductance;Inductance;MOSFET;Analytical models;Power measurement;Discharges (electric);Time measurement;Microelectronics|
|[On-Resistance Measurements of Low Voltage MOSFET at wafer level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094127)|K. Oasa; T. Nishiwaki; T. Ohguro; Y. Saito; Y. Kawaguchi|10.1109/ICMTS55420.2023.10094127|test element group;TEG;on-resistance;Ron;MOSFET;LVMOS;wafer level;Electrodes;MOSFET;Low voltage;Surface resistance;Kelvin;Life estimation;Size measurement|
|[Comparative study on characteristics of GaN-based MIS-HEMTs with Al2O3 and Si3N4 gate insulators under Hot Carrier Degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094072)|P. -Y. Wu; X. -Y. Tsai; T. -C. Chang; T. -M. Tsai; S. M. Sze|10.1109/ICMTS55420.2023.10094072|power devices;metal-insulator-semiconductorhigh electron mobility transistors (MIS-HEMTs);impact ionization;Al2O3/Si3N4 insulator MIS-HEMTs.;Degradation;Analytical models;HEMTs;Logic gates;Insulators;Reliability engineering;Ions|
|[The Pressing Probe Needle Technique for Characterizing Mechanical Stress Sensitivity of Semiconductor Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094063)|H. Tuinhout; O. Dieball|10.1109/ICMTS55420.2023.10094063|semiconductor device characterization;piezoelectric effects;test method;parametric test system;motorized probe positioner;out-of-plane mechanical stress;Sensitivity;Semiconductor devices;Layout;Tungsten;Pressing;Analog circuits;Needles|
|[A multi-contact six-terminal cross-bridge Kelvin resistor (CBKR) structure for evaluation of interface uniformity of the Ti-Al alloy/p-type 4H-SiC contact](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094097)|Y. -L. Chen; S. -H. Lai; J. -H. Lin; B. -Y. Tsui|10.1109/ICMTS55420.2023.10094097|ohmic contact;silicon carbide;specific contact resistance;cross-bridge Kelvin resistor;Resistors;Integrated circuits;Transmission electron microscopy;Silicon carbide;Kelvin;Microscopy;Conductivity|
|[Test Structure for Evaluation of Pad Size for Wafer Probing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094145)|B. Smith; D. Hall; G. Tranquillo|10.1109/ICMTS55420.2023.10094145|wafer probe;test structure;probe pad size;Resistance;Contacts;Wires;Needles;Size measurement;Pins;Microelectronics|
|[Test Bench for Biopotential Instrumentation Amplifier using Single-Ended to Differential Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094130)|S. Thanapitak; P. Sedtheetorn; P. Chanyagorn; T. Chulajata; S. Bhatranand; P. Phattanasri|10.1109/ICMTS55420.2023.10094130|Instrumentation Amplifier;Biopotential Signal;Electrode Offset;Test Bench;Verification;ECG;Electrodes;Impedance measurement;Power supplies;Instruments;Distortion;Differential amplifiers;Microelectronics|
|[Measurement of Temperature Effect on Comparator Offset Voltage Variation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094194)|Y. Iwata; T. Kitamura; M. Islam|10.1109/ICMTS55420.2023.10094194|Comparator;Offset Voltage;Variation;Mismatch;Temperature Drift;ADC;Distribution Tuning;Temperature measurement;Temperature distribution;Voltage measurement;Automation;Estimation;Size measurement;Distance measurement|
|[Variability of MOSFET Series Resistance Extracted from Individual Devices: Is Direct Variability Measurement Possible?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094106)|K. Takeuchi; T. Mizutani; T. Saraya; M. Kobayashi; T. Hiramoto|10.1109/ICMTS55420.2023.10094106|MOSFET;series resistance;variability;Resistance;Geometry;MOSFET;Correlation;Interference;Microelectronics;Electrical resistance measurement|
|[Variability Evaluation of MOS-gated PNPN Diode for Hardware Spiking Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094054)|T. Takada; T. Mori; J. Ida|10.1109/ICMTS55420.2023.10094054|feedback;inter spike interval;neuromorphic;silicon on insulator;spiking neural network;thyristor;Neurons;Metals;Voltage;Logic gates;Hardware;Semiconductor diodes;Microelectronics|
|[Effect of Quadruple Size Transistor on SRAM Physically Unclonable Function Stabilized by Hot Carrier Injection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094187)|S. Xu; K. Liu; Y. Tang; R. Zhang; H. Shinohara|10.1109/ICMTS55420.2023.10094187|Static random-access memory (SRAM);physically unclonable function (PUF);hot carrier injection (HCI);mismatch distribution;stability reinforcement;Human computer interaction;Semiconductor device modeling;Semiconductor device measurement;Stability criteria;Random access memory;Hot carrier injection;Physical unclonable function|
|[Test Circuit Design for Accurately Characterizing Cells’ Output Currents in a Read-Decoupled 8T SRAM Array for Computing-in-Memory Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094078)|H. -C. Hong; L. -Y. Lin; B. -C. Chen|10.1109/ICMTS55420.2023.10094078|CIM;RD8T;SRAM;current;characterization;Fabrication;Graphical models;Nonvolatile memory;SRAM cells;Phase change random access memory;Energy efficiency;Circuit synthesis|
|[Design and Analysis of Discrete FET Monitors in 7nm FinFET Product for Robust Technology Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094206)|V. Vidya; N. Zamdmer; T. Mechler; K. Onishi; D. Chidambarrao; B. W. Jeong; Y. G. Ko; C. H. Lee; J. Sim; M. Angyal; E. Crabbe|10.1109/ICMTS55420.2023.10094206|Bulk FinFET;Fabless;IBM Product;Semiconductor device modeling;Correlation;Layout;Logic gates;Solids;Foundries;Microelectronics|
|[An Electrical Inline-Testable Structure to Monitor Gate-Source/Drain Short Defect Caused by Imperfect Fin-Cut Patterning in FinFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094149)|H. Zhu; K. Onishi; S. Wu; A. Yang; B. -W. Jeong; S. -J. Lim; N. Jing; C. -H. Lee; D. Conrady; D. Chidambarrao|10.1109/ICMTS55420.2023.10094149|MOSFET;FinFET;Defect;Inline Test;Electrical Test;Layout Design;Integrated circuits;Systematics;Layout;Failure analysis;Logic gates;FinFETs;Silicon|
|[Wafer Level Reliability Monitoring of NBTI Using Polysilicon Heater Structures for Production Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094064)|Y. -H. Cheng|10.1109/ICMTS55420.2023.10094064|Negative bias temperature instability;NBTI;poly heater;threshold voltage shift;fast wafer level reliability;Temperature measurement;Negative bias temperature instability;Heating systems;Semiconductor device reliability;Production;Velocity measurement;Voltage control|
|[Application of Greek cross structures for process development of electrochemical sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094096)|M. Zhang; S. Zhang; C. Dunare; J. R. K. Marland; J. G. Terry; S. Smith|10.1109/ICMTS55420.2023.10094096|microelectronic test structures;electrochemical sensors;Greek cross;Van der Pauw structures.;Bridges;Resistors;Resistance;Fabrication;Semiconductor device measurement;Silver;Current measurement|
|[Test Structures for Studying Coplanar Reverse- Electrowetting for Vibration Sensing and Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094057)|A. Moyo; M. W. Shahzad; J. G. Terry; S. Smith; Y. Mita; Y. Li|10.1109/ICMTS55420.2023.10094057|Reverse-Electrowetting on Dielectric;Electric Double layer;Energy Harvesting;Vibrations;Electrodes;System integration;Packaging;Sensors;Microelectronics;Dielectrics|
|[Damage Assessment Structure of Thermal-Annealing Post-Processing on CMOS LSIs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094159)|Y. Okamoto; N. Makimoto; K. Misumi; T. Kobayashi; Y. Mita; M. Ichiki|10.1109/ICMTS55420.2023.10094159|CMOS post-process;thermal budget;PZT;damage assessment;Ring oscillators;Wiring;Degradation;MOSFET;Annealing;Temperature;Monolithic integrated circuits|
|[Improving Performance of FBARs by Advanced Low-Temperature High-Pressure Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094148)|Y. -F. Tu; T. -C. Chang; K. -J. Zhou; W. -C. Hung; T. -T. Kuo; C. -H. Lien|10.1109/ICMTS55420.2023.10094148|supercritical fluid (SCF);thin film bulk acoustic resonator (FBAR);piezoelectric material;Q-factor;Solvents;Fluids;Piezoelectric materials;Surface tension;Etching;Reflection coefficient|
|[Solderable Multisided Metal Patterns Enables 3D Integrable Direct Laser Written Polymer MEMS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094101)|L. Ivy; A. Lal|10.1109/ICMTS55420.2023.10094101|Microfabrication;manufacturing processes;threedimensional printing;microelectromechanical systems (MEMS);two-photon polymerization (TPP);metal-insulator structures;metallization.;Resistors;Printing;Three-dimensional displays;Lasers;Metals;Pressing;Writing|
|[Accurate Gate Charge Modeling of HV LDMOS Transistors for Power Circuit Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094091)|X. Jie; R. v. Langevelde; K. Xia; L. Chao; C. C. McAndrew; Q. Zhang; M. Bacchi; W. Li|10.1109/ICMTS55420.2023.10094091|SPICE modeling;HV LDMOS;gate charge;bias dependent capacitance;Analytical models;Computational modeling;Logic gates;Capacitance;SPICE;Microelectronics;Transistors|
|[Introducing Transfer Learning Framework on Device Modeling by Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094067)|K. Niiyama; H. Awano; T. Sato|10.1109/ICMTS55420.2023.10094067|nan;Performance evaluation;Extrapolation;Transfer learning;Microelectronics;Transistors|
|[Technology-Dependent Modeling of MOSFET Parasitic Capacitances for Circuit Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094071)|D. Navarro; C. Tanaka; K. Adachi; T. Naito; K. Tada; A. Hokazono|10.1109/ICMTS55420.2023.10094071|overlap capacitance;RF-CMOS;MOSFET modeling;MOSFET;Circuit simulation;Simulation;Modulation;Logic gates;Capacitance;Microelectronics|
|[Bridging Large-Signal and Small-Signal Responses of Hafnium-Based Ferroelectric Tunnel Junctions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094178)|M. Massarotto; M. Segatto; F. Driussi; A. Affanni; S. Lancaster; S. Slesazeck; T. Mikolajick; D. Esseni|10.1109/ICMTS55420.2023.10094178|Ferroelectric;Hafnium Zirconium Oxide (HZO);Ferroelectric Tunnel Junction (FTJ);Experimental Characterization;Small Signal Analysis;Zirconium;Neuromorphic engineering;Switches;Iron;Time measurement;Microelectronics;Junctions|
|[Demonstration of frequency doubler application using ZnO-DNTT anti-ambipolar switch device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094079)|Y. Lee; H. J. Hwang; B. H. Lee|10.1109/ICMTS55420.2023.10094079|Anti-ambipolar switch;ZnO;DNTT;heterojunction;low temperature;frequency doubler;Performance evaluation;Temperature;Switches;Heterojunctions;Signal processing;Electric variables;Frequency conversion|
|[Identifying nano-Schottky diode currents in silicon diodes with 2D interfacial layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10094164)|T. Knežević; L. K. Nanver|10.1109/ICMTS55420.2023.10094164|diode characterization;pure boron;nanoSchottky;2D interfacial layers;bipolar devices;Temperature measurement;Schottky diodes;Voltage measurement;Current measurement;Schottky barriers;Size measurement;Silicon|
#### **2023 36th International Conference on VLSI Design and 2023 22nd International Conference on Embedded Systems (VLSID)**
- DOI: 10.1109/VLSID57277.2023
- DATE: 8-12 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Hardware Architecture and FPGA Implementation of Low Latency Turbo Encoder for Deep-Space Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089918)|M. Pathak; R. Shrestha|10.1109/VLSID57277.2023.00016|Turbo encoding;field programmable gate array (FPGA);puncturing;digital VLSI architecture;latency;serialization;Space missions;Very large scale integration;Logic gates;Hardware;Real-time systems;Low latency communication;Field programmable gate arrays|
|[Lightweight Approximate Multiplier with Improved Accuracy in FPGA for Error Resilient Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089951)|A. P. Padhy; B. P. Das|10.1109/VLSID57277.2023.00017|approximate computing;high performance;image sharpening;multiplier;reduced area;Embedded systems;Input variables;Multimedia computing;Machine learning;Very large scale integration;Data processing;Distance measurement|
|[Delay-Aware Control for Autonomous Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089979)|S. Ghosh|10.1109/VLSID57277.2023.00018|Perception-based Systems;Autonomous Systems;Controller Synthesis;Variable Delay;Control Performance;Embedded systems;Autonomous systems;Image processing;Switches;Very large scale integration;Control systems;Software|
|[Hardware implementation of Ring-LWE lattice cryptography with BCH and Gray coding based error correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089970)|S. Mondal; S. Patkar; T. K. Pal|10.1109/VLSID57277.2023.00019|Cryptography;PQC;RLWE;DFR;NewHope;BCH;Gray Code;BER;Bit error rate;Pipelines;Lattices;Graphics processing units;Bandwidth;Hardware;Encoding|
|[DRRA-based Reconfigurable Architecture for Mixed-Radix FFT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089926)|R. Kallapu; D. Stathis; S. Boppu; A. Hemani|10.1109/VLSID57277.2023.00020|Fast Fourier Transform;FFT butterfly radix 2 & 4;Coarse Grain Reconfigurable Architectures;Field Programmable Gate Array;Memory management;Signal processing algorithms;Computer architecture;Transforms;Switches;Very large scale integration;Hardware|
|[Live & Seamless Firmware Upgrade in Real Time Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089983)|S. Rao; B. Chidambaram; P. V.; K. Rajakumar; P. Prabhakara; P. Ravichandran; S. Ghotgalkar; A. Vanjari; M. Mody|10.1109/VLSID57277.2023.00021|High Availability;Firmware Upgrade;C2000;Server power;DC-DC buck converter;Costs;Buck converters;Systematics;Switches;Very large scale integration;Real-time systems;Hardware|
|[WIB-SAR: Write Intensity Based Selective Address Remapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089984)|A. N. S; D. Bhuinya; H. K. Kapoor|10.1109/VLSID57277.2023.00022|Address remapping;Write-variation;Phase Change Memory;Lifetime;Phase change materials;Nonvolatile memory;Multicore processing;Resistive RAM;Energy resolution;Random access memory;Very large scale integration|
|[Design of a Multi-Core Compatible Linux Bootable 64-bit Out-of-Order RISC-V Processor Core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089948)|S. S; S. S. Garag; A. Phegade; D. Gusain; K. Varghese|10.1109/VLSID57277.2023.00023|RISC-V;Out-of-Order Execution;Multi-Core Processor;Linux;System on Chip (SoC);Cache Coherence;MESI Protocol;Snooping;Out of order;Multicore processing;Linux;Random access memory;Booting;Very large scale integration;Hardware|
|[Voltage Boosted Schmitt Trigger Sense Amplifier (VBSTSA) With Improved Offset And Reaction Time For High Speed SRAMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089934)|G. Saraswat; A. Parashar|10.1109/VLSID57277.2023.00024|Sense Amplifier;SRAM;Negative boost circuit;Reaction Time;SA Offset;Embedded systems;Simulation;Random access memory;Voltage;Very large scale integration;CMOS technology;Sensors|
|[A Low Noise Bandgap Reference with 0.89 V Vref, 0.88 μVrms noise and 80 dB of PSRR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089968)|S. S; H. Shrimali|10.1109/VLSID57277.2023.00025|Low noise;voltage reference;decoupling capacitor;chopper stabilization amplifier;PVT corners;spot noise;root mean square (RMS) noise;power supply rejection ratio (PSRR);Temperature sensors;Sensitivity;Photonic band gap;Power supplies;Simulation;Voltage;Very large scale integration|
|[Radiation Hardened CMOS Programmable Bias Generator for Space Applications at 180nm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089920)|A. Yadav; A. Bulusu; S. Singh; S. Dasgupta|10.1109/VLSID57277.2023.00026|CMOS;PBG;TID;R-2R DAC;Folded Cascode Opamp;Fabrication;Temperature distribution;Power demand;Radiation hardening (electronics);Layout;Voltage;Very large scale integration|
|[A low-power resistive tail dynamic comparator with self-shut mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089953)|S. K. Dey; M. Sarkar; S. Chatterjee|10.1109/VLSID57277.2023.00027|dynamic comparator;Strong-Arm Latch;self-shut mechanism;resistive tail;energy efficiency;SAR ADC;Semiconductor device modeling;Latches;Embedded systems;Tail;Very large scale integration;CMOS process;Energy efficiency|
|[A Sense Amplifier Based Bulk Built-In Current Sensor for Detecting Laser-Induced Currents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089947)|D. Batabyal; S. K. Singh; R. K. Mishra; A. Grover|10.1109/VLSID57277.2023.00028|BBICS;Safety;Security;Laser Induced Current;Sense Amplifier;Single event transients;Sensitivity;Semiconductor lasers;Single event upsets;Side-channel attacks;Very large scale integration;Circuit faults|
|[Unifying Intrinsically-Operated Physically Unclonable Function and Random Number Generation in Analog Circuits: A Case Study on Successive Approximation ADC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089925)|A. Shylendra; S. Bhunia; A. R. Trivedi|10.1109/VLSID57277.2023.00029|nan;Correlation;Protocols;Voltage;Analog circuits;Very large scale integration;Physical unclonable function;Registers|
|[Programmable Delay Line With Inherent Duty Cycle Correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089928)|S. C. Nimmagadda; H. B. Dubey|10.1109/VLSID57277.2023.00030|Tx (Transmitter);Rx (Receiver);delay tap size;duty cycle correction (DCC);DC (duty cycle);HBM2E IO (High Bandwidth Memory Second Generation Extended IO);jitter;Duty cycle distortion (DCD);Most significant bit (MSB);DDR (double data rate);Read time margin;Odd and Even Read Eye;Automatic Test Equipment(ATE);Industries;Embedded systems;Random access memory;Linearity;Delay lines;Very large scale integration;Distortion|
|[A 2.25 GHz PLL with 0.05-2 MHz Inloop Phase Modulation and -70 dBc Reference Spur for Telemetry Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089977)|S. Jakkoju; D. J. Bandarupalli; A. Srikanth; S. Thomas; S. Saxena|10.1109/VLSID57277.2023.00031|fractional-N;jitter;phase modulator;phase noise;PLL;spur;Matched filters;Frequency modulation;Quantization (signal);Phase modulation;Prototypes;Very large scale integration;Jitter|
|[Ultra-Low Power Non-Uniform SAR ADC based ECG detector for Early Detection of Cardiovascular Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089952)|A. Ramkumar; A. Verma; B. P. Das|10.1109/VLSID57277.2023.00032|SAR-ADC;non-Uniform quantizer;ECG;cardiovascular diseases;ultra-low power;Voltage;Medical services;Electrocardiography;Very large scale integration;Real-time systems;Registers;Cardiovascular diseases|
|[Design Challenges and Techniques for 5nm FinFET CMOS Analog/Mixed-Signal Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089917)|S. Goyal; S. K. Wadhwa; D. Tripathi; G. Agrawal; K. Thakur; D. K. Jain; A. L. S. Loke; A. Kumar; M. K. Upadhyay; Bhawna; S. K. Dey|10.1109/VLSID57277.2023.00033|5nm finFET technology;leakage currents;gate-induced drain leakage;ring oscillators;Ring oscillators;Power demand;Layout;Very large scale integration;Logic gates;CMOS technology;FinFETs|
|[Design of Radiation Hardened 12T SRAM with Enhanced Reliability and Read/Write Latency for Space Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089931)|M. S. A. S; K. S; B. S. Reniwal; S. K. Vishvakarma|10.1109/VLSID57277.2023.00034|Soft Errors;Single Event Upset (SEU);Static Noise Margin (SNM);Read Delay;Write Delay;Critical Charge;SRAM;Wireless sensor networks;Radiation hardening (electronics);Microprocessors;Random access memory;Single event upsets;Computer architecture;Voltage|
|[Design and Analysis of Multibit Multiply and Accumulate (MAC) unit: An Analog In-Memory Computing Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089927)|S. Ananthanarayanan; B. S. Reniwal; A. Upadhyay|10.1109/VLSID57277.2023.00035|in-memory computing;current mirror;6T SRAM;multibit multiplication;Embedded systems;Current mirrors;Linearity;Random access memory;Computer architecture;Very large scale integration;In-memory computing|
|[A 2.5 GHz, 1-Kb SRAM with Auxiliary Circuit Assisted Sense Amplifier in 65-nm CMOS Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089965)|R. D. Kadhao; S. R. K.; N. K. Y. B.; V. M. H.; D. Dwivedi|10.1109/VLSID57277.2023.00036|Static Random Access Memory (SRAM);Static Voltage Noise Margin (SVNM);read and write delay;sense amplifier;Power demand;Latches;Embedded systems;Voltage;Very large scale integration;SRAM cells;Stability analysis|
|[A Common Mode Insensitive Process Tolerant Sense Amplifier Design for In Memory Compute Applications in 65nm LSTP Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089940)|B. Iqbal; A. Grover; H. Rawat|10.1109/VLSID57277.2023.00037|In memory compute;SRAM;Process Tolerant;Deep Neural Network(DNN);Sense Amplifier;Comparator;Power demand;Embedded systems;Random access memory;Voltage;Very large scale integration;Throughput;Data transfer|
|[Supply Noise and Peak Current Reduction in High-Speed Output Drivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089960)|D. Nedalgi; L. M. N; S. V. Siddamal|10.1109/VLSID57277.2023.00038|staggered switching;simultaneous switching noise;device reliability;peak current;supply noise;ground bounce;Performance evaluation;Inductance;Embedded systems;Simulation;Very large scale integration;FinFETs;Reliability engineering|
|[An Energy-Efficient and Robust 10T SRAM Based in-Memory Computing Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089955)|N. Srivastava; A. K. Rajput; M. Pattanaik; G. Kaushal|10.1109/VLSID57277.2023.00039|SRAM;Von-Neumann architecture (VNA);In-Memory Computing (IMC);energy-efficiency;Wireless sensor networks;Microprocessors;Random access memory;Computer architecture;Voltage;Very large scale integration;In-memory computing|
|[Memristor-based High Speed and Area Efficient Comparators in IMPLY Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089913)|N. Kaushik; B. Srinivasu|10.1109/VLSID57277.2023.00040|comparator;memristor;imply and emerging technologies;Embedded systems;Computational modeling;Memristors;Computer architecture;Very large scale integration;Computational efficiency|
|[Design of Hardware Efficient Approximate DCT Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089958)|V. S. B; V. Solanki; A. D. Darji|10.1109/VLSID57277.2023.00041|Integer DCT;real-valued DCT;Scalable DCT;HEVC;FPGA;Very large scale integration;Hardware;Complexity theory;Table lookup;Discrete cosine transforms;Hardware design languages;Field programmable gate arrays|
|[Design of Energy Efficient and Low Delay Posit Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089974)|L. B. R. K; H. R. S; K. Puli; S. R. R. Annapalli; V. Pudi|10.1109/VLSID57277.2023.00042|Posit Number System;IEEE Floating Point unit;Posit Multiplier;Multiplexing;Graphics;Embedded systems;Detectors;Very large scale integration;Dynamic range;Hardware|
|[Surmounting Challenges in the Design of Low Power Real Time Clock IP for Advanced FinFET Technology Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089956)|K. Sukumar; S. Vodnala; R. Ayyagari; A. Jain; T. Ganesan; R. R|10.1109/VLSID57277.2023.00043|Coin cell battery;G3 state;Ibat(active);Low-power standard cell library;Real Time Clock;Microprocessors;Computer architecture;Very large scale integration;FinFETs;Libraries;Real-time systems;Topology|
|[Dynamic Keeper for 1R1W 8T-SRAM to Enable Read Operation at 150c till 0.5v in 5nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089914)|V. Kumar; V. Sahu; A. Khanda; S. Kumar|10.1109/VLSID57277.2023.00044|8T-Bitcell;1R1W SRAM;Read Disturb Failure;Subthreshold Leakage;Read0/Read1 operation;Temperature aware circuit;bitline sensing;Temperature distribution;Temperature dependence;Simulation;Modulation;Voltage;Very large scale integration;FinFETs|
|[Translation of Array Expressions for in-Memory Computation on Memristive Crossbar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089976)|S. Pyne|10.1109/VLSID57277.2023.00045|Memristor;crossbar array;in-memory computation;array operation;translation;Degradation;Costs;Power demand;Processor scheduling;Memristors;Computer architecture;Programmable logic arrays|
|[ASPIRE: An Intermediate Representation for Abstract Security Policies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089915)|P. Bhamidipati; R. Vemuri|10.1109/VLSID57277.2023.00046|Vulnerabilities;security analysis;security policy;System-on-Chip;System Verilog Assertions;Embedded systems;Intellectual property;Very large scale integration;System-on-chip;Security;Hardware design languages|
|[MLTDRC: Machine Learning Driven Faster Timing Design Rule Check Convergence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089939)|S. Kundu; C. S. Padharia; R. S. Kerla|10.1109/VLSID57277.2023.00047|Timing DRC miscorrelation;faster convergence;Machine Learning;Random Forest Regression;Explainable AI;Runtime;Correlation;Machine learning;Forestry;Very large scale integration;Timing;Pins|
|[Machine Learning-based model for Single Event Upset Current Prediction in 14nm FinFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089982)|V. V; S. Mittal; V. Kumar|10.1109/VLSID57277.2023.00048|VLSI Engineering;Liner Energy Transfer (LET);Random Forest;Single Event Transient;TCAD Tools;Machine Learning;Radiation effects;Machine learning algorithms;Single event upsets;Predictive models;Very large scale integration;Prediction algorithms;FinFETs|
|[Transport-Free Placement of Mixers for Realizing Bioprotocol on Programmable Microfluidic Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089903)|M. Hirai; D. Kundu; S. Yamashita; S. Roy; H. Tomiyama|10.1109/VLSID57277.2023.00049|Programmable Microfluidic Device;Biochips;Mixing;No Transport Mixing;2x3 mixer;Schedules;Fluids;Embedded systems;Simulation;Transportation;Vegetation;Transforms|
|[Efficient 3D Modeling Methodology for High-Speed Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089967)|S. Thirubalan; S. K. Kopparti; D. T. H. Peng|10.1109/VLSID57277.2023.00050|Cascaded S-parameters;3D model;physical based model;printed circuit board (PCB);high-speed signal integrity (SI);vias;RF connectors;Differential channel;Scattering parameters;Connectors;Solid modeling;Analytical models;Three-dimensional displays;Resonant frequency;Loss measurement;Finite element analysis|
|[ISP: An Improved Slicing Pair Code for Skewed Slicing Floorplan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089949)|B. Nayak; B. N. B. Ray|10.1109/VLSID57277.2023.00051|Floorplan;VLSI;Skewed Slicing Tree;BFS Code;SP Code;ISP Code;Meters;Codes;Embedded systems;Art;Layout;Very large scale integration;Encoding|
|[Maximum Power Point Tracking using Buck-Boost converter for EH-PMIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089971)|S. Kotabagi; R. Nayak; S. Dalabanjan; V. P. N; P. L. Patil; S. Hemadri|10.1109/VLSID57277.2023.00052|Renewable energy;Maximum power;Power management integrated circuit;Maximum power point trackers;Renewable energy sources;Embedded systems;Power system management;Photovoltaic cells;Layout;Very large scale integration|
|[GRILAPE: Graph Representation Inductive Learning-based Average Power Estimation for Frontend ASIC RTL Designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089972)|R. M. B; P. Das; S. P. K. R; A. Acharyya|10.1109/VLSID57277.2023.00053|Power estimation;Graph neural networks;Machine learning in EDA;Computational modeling;Estimation;Logic gates;Very large scale integration;Predictive models;Throughput;Libraries|
|[Efficient MBIST Area and Test Time Estimator Using Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089923)|D. Jamal; R. Veetil|10.1109/VLSID57277.2023.00054|Memory Built In Self-Test;Machine Learning;Design for Test;Linear Regression;Polynomial Regression;Supervised Learning;Maximum likelihood estimation;Costs;Discrete Fourier transforms;Predictive models;Built-in self-test;Very large scale integration;Silicon|
|[A Novel AI Based Approach for Performance validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089938)|K. Naz; R. Jindal; S. Boothkuri|10.1109/VLSID57277.2023.00055|Performance validation;Artificial Intelligence;Coho (Core performance model);IPC (Instructions Per Cycle);Outlier (mis-correlated trace);Program processors;Computer bugs;Pipelines;Time to market;Manuals;Very large scale integration;Silicon|
|[Signal Agnostic Scalable Scan Wrapper Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089932)|H. H; R. T. Veetil; A. Gadi|10.1109/VLSID57277.2023.00056|IEEE P1500;core wrapping;SOC interconnect testing;Interconnect scan wrapper cells;TAM;Test access mechanism;test coverage;Measurement;Microprocessors;Computer bugs;Computer architecture;Switches;Very large scale integration;Logic gates|
|[Mutation Analysis and Model Checking Guided Test Generation for SoC Run-Time Monitors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089900)|S. Srinivasan; R. Vemuri|10.1109/VLSID57277.2023.00057|Model Checking;Security Vulnerability;Mutation Analysis;Run-Time Monitors;Test Program Generation;System-on-Chip;FPGA emulation;Code Coverage;Assertion Based Verification;Fabrication;Codes;Model checking;Very large scale integration;Silicon;Security;Test pattern generators|
|[Extending Action Recognition in the Compressed Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089924)|S. Abrams; V. Narayanan|10.1109/VLSID57277.2023.00058|nan;Preforms;Visual analytics;Redundancy;Transform coding;Computer architecture;Very large scale integration;Streaming media|
|[Design and Analysis of Posit Quire Processing Engine for Neural Network Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089906)|P. J. Edavoor; A. Raveendran; D. Selvakumar; V. Desalphine; D. S. G; G. Raut|10.1109/VLSID57277.2023.00059|Posit MAC Engine;Posit Quire Accumulators;Posit for AI/ML;Neural Network Processing Engine;Inference Accuracy;Deep learning;Analytical models;Computational modeling;Neural networks;Very large scale integration;Hardware;Resource management|
|[Fast and Robust Sense Amplifier for Digital In Memory Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089946)|K. Prasad; A. Srivastava; N. Baruah; J. Mekie|10.1109/VLSID57277.2023.00060|SRAM;In Memory Computing;Sense Amplifier;Embedded systems;Random access memory;Artificial neural networks;Very large scale integration;In-memory computing;Energy efficiency;Topology|
|[MOSCON: Modified Outer Product based Sparse Matrix-Matrix Multiplication Accelerator with Configurable Tiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089919)|N. G; N. S; K. S|10.1109/VLSID57277.2023.00061|Deep learning;Sparse matrix multiplication;Execution time;FPGA accelerator;Deep learning;Performance evaluation;Embedded systems;Very large scale integration;Performance gain;Logic gates;Hardware|
|[A Portable Ultra-low-cost Multi-Gas Sensing System-on-Module for Wireless Air Quality Monitoring Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089954)|A. Sharma; S. Divekar; R. Zele|10.1109/VLSID57277.2023.00062|Air pollution;air quality monitoring;portable;multi-gas sensing;ultra-low-cost;wireless sensor network;Wireless communication;Wireless sensor networks;Costs;Power demand;Very large scale integration;Air pollution;Transceivers|
|[FPGA based Smart and Sustainable Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089941)|S. Peraka; S. I. Ali; D. V. Mogili; A. K. Palivela; S. Reddy; J. Bavisetti; D. R. Y|10.1109/VLSID57277.2023.00063|DE10-Nano Cyclone V;FPGA;Azure Cloud;Sensors;Crop Recommendation;Smart Agriculture;Sustainable Agriculture;Soil Testing;Smart Irrigation;Weed Detection;Disease Detection;Voice commands;Web Application;Machine Learning Algorithms;Convolution Neural Networks;Yolo V5;XG Boost;Cloud computing;Irrigation;Crops;Prototypes;Soil;Real-time systems;Cyclones|
|[SV Based Fast & Accurate Verification Methodology for CTLE Adaptation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089937)|A. Jadhav; V. Bhide; T. N. V. Raghuram; T. Nandy|10.1109/VLSID57277.2023.00064|CTLE;Adaptation;SV;Equalization;LMS;PVT;RTL;Adaptation models;Frequency-domain analysis;Digital simulation;Very large scale integration;Decision feedback equalizers;Silicon;Mathematical models|
|[A 16Gbps 3rd Order CTLE Design for Serial Links with High Channel Loss in 16nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089943)|P. K. Thota; S. K. Rapina; B. R. Nistala|10.1109/VLSID57277.2023.00065|Equalizer;High speed link;Inter Symbol Interference (ISI);Lossy channel;Broadband receivers;Equalizers;Symbols;Receivers;Interference;Very large scale integration;FinFETs;Transceivers|
|[Enhanced Performance Parameters of Magnetic Tunnel Junction with Composite Dielectric Barrier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089936)|R. Sinha; J. Kaur|10.1109/VLSID57277.2023.00066|Magnetic Tunnel Junction;NEGF;TMR;2D materials;Spintronics;Performance evaluation;Analytical models;Computational modeling;Very large scale integration;Mathematical models;Dielectrics;Effective mass|
|[FEM modeling of gate resistance for 5 nm SGC/DGC Stacked Nanosheet Transistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089981)|V. Kumar; J. Patel; A. Datta; S. Dasgupta|10.1109/VLSID57277.2023.00067|Stacked Nanosheet;Gate Resistance;Single Gate Contact;Double Gate Contact;Resistance;Radio frequency;Analytical models;Three-dimensional displays;Logic gates;Very large scale integration;Capacitance|
|[Automatic Implementation and Evaluation of Error-Correcting Codes for Quantum Computing: An Open-Source Framework for Quantum Error Correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089950)|T. Grurl; C. Pichler; J. Fuß; R. Wille|10.1109/VLSID57277.2023.00068|nan;Computational modeling;Quantum mechanics;Very large scale integration;Reliability theory;Hardware;Error correction codes;Error correction|
|[Implementation of Probabilistic Bits (Pbits) using Low Barrier Magnets: Investigation and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089901)|A. Haroon; R. K. Ghosh; S. Saurabh|10.1109/VLSID57277.2023.00069|Probabilistic Spin Logic;Invertible Logic;Spin electronics;low barrier magnet;binary stochastic neuron;modified 1T-1MTJ embedded MRAM;probabilistic computing;Torque;Embedded systems;Very large scale integration;Traveling salesman problems;Probabilistic logic;Energy efficiency;Magnetic analysis|
|[Post Silicon Validation for I2C (SMBUS) Peripheral](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089904)|S. Shilaskar; A. Behare; K. Sonawane; S. Bhatlawande|10.1109/VLSID57277.2023.00070|Post Silicon Validation;SMBUS;AMBA APB;Corelis debugger;Lauterbach Trace 32;Codes;Protocols;Embedded systems;Buildings;Very large scale integration;Writing;Silicon|
|[Efficient FPGA Implementations of Lifting based DWT using Partial Reconfiguration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089930)|M. A. B. M; P. Bharadwaja|10.1109/VLSID57277.2023.00071|Convolution based DWT;Digital Signal Processing;Lifting based DWT;Partial Reconfiguration;PSNR;Three-dimensional displays;Image color analysis;Computer architecture;Transforms;Gray-scale;Very large scale integration|
|[Accelerating Defect Simulation in Analog and Mixed-Signal Circuits by Parallel Defect Injection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089933)|S. Sanyal; M. Bhattacharya; P. Dasgupta; A. Patra|10.1109/VLSID57277.2023.00072|nan;Voltage measurement;Embedded systems;Current measurement;Discrete Fourier transforms;Bridge circuits;Life estimation;Very large scale integration|
|[Mutual Information based Efficient Spike Encoding on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089921)|O. Gundoji; D. Banerjee; S. Dey; A. Pal|10.1109/VLSID57277.2023.00073|Spiking Neural Network;Neuromorphic Computing;Spike Encoding;FPGA;Rate Encoder;Mutual Information;Information Theory;Performance evaluation;Neuromorphic engineering;Time series analysis;Computer architecture;Encoding;Hardware;Software|
|[SANNA: Secure Acceleration of Neural Network Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089929)|A. Poptani; A. Mittal; R. Saiya; R. Kalayappan; S. Chandran|10.1109/VLSID57277.2023.00074|Hardware Trojans;Design-for-Trust;Neural Networks;Assisted Parallelization;Performance evaluation;Embedded systems;Source coding;Discrete Fourier transforms;Artificial neural networks;Very large scale integration;Hardware|
|[The Acceleration of OPUS Codec Using Processor - FPGA Co-processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089963)|S. Bezawada; B. P. Reddy|10.1109/VLSID57277.2023.00075|co-processing;hardware acceleration;Codecs;Codes;Computer architecture;Very large scale integration;Fabrics;Hardware;Software|
|[A 105-525MHz Integer-N Phase-Locked Loop in Indigenous SCL 180nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089909)|S. Nigam; M. Murali; H. S. Gupta; S. Saxena|10.1109/VLSID57277.2023.00076|Integer-N PLL;supply regulated wide range VCO;charge-pump with large dynamic range;Temperature measurement;Phase noise;Temperature distribution;Power demand;Jitter;Very large scale integration;Frequency measurement|
|[Evaluating the Impact of Transition Delay Faults in GPUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089942)|J. E. R. Condia; M. S. Reorda|10.1109/VLSID57277.2023.00077|Graphics Processing Units (GPUs);Functional units;Instruction-level fault impact;Embedded systems;Correlation;Codes;Graphics processing units;Very large scale integration;Logic gates;Software|
|[An Energy-Efficient Multi-bit Current-based Analog Compute-In-Memory Architecture and design Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089945)|D. Kushwaha; A. Joshi; N. Gupta; A. Sharma; S. Miryala; R. V. Joshi; S. Dasgupta; A. Bulusu|10.1109/VLSID57277.2023.00078|Accuracy;compute in-memory (CIM);multiplication and accumulation (MAC);signal-to-noise ratio (SNR);static random-access memory (SRAM);Semiconductor device modeling;Computational modeling;Design methodology;Computer architecture;Voltage;Predictive models;SPICE|
|[Reliability Enhancement of Hardware Trojan Detection using Histogram Augmentation Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089912)|V. Sankar; B. S; N. D. M; J. M|10.1109/VLSID57277.2023.00079|Histogram-based data augmentation;Hardware Trojan detection;Imbalanced Dataset;XGBoost;Training;Supply chains;Time to market;Machine learning;Very large scale integration;Feature extraction;Hardware|
|[DARK-Adders: Digital Hardware Trojan Attack on Block-based Approximate Adders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089794)|V. Mishra; N. Hassan; A. Mehta; U. Chatterjee|10.1109/VLSID57277.2023.00080|Approximate Computing;Hardware Security;Trojan attacks;Computer architecture;Machine learning;Very large scale integration;Probabilistic logic;Hardware;Trojan horses;Security|
|[True Random Number Generator based on Voltage-Gated Spintronic structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089905)|A. P. B; T. S. Warrier|10.1109/VLSID57277.2023.00081|True Random Number Generation;Magnetic tunnel junction (MTJ);Spin Orbit Torque (SOT);Voltage-gated SOT (VGSOT);Thermal stability;Torque;Stability criteria;NIST;Orbits;Generators;Circuit stability;Junctions|
|[A Novel Approach for Assisting Blind People Using a Smart Wearable Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089916)|S. Peraka; S. I. Ali; R. Sudheer; P. P. Kumar; G. Kondala; D. Samal|10.1109/VLSID57277.2023.00082|Viola Jones algorithm;Haar cascades;OpenCV;Py-YOLOV5 algorithm;ONNX model;Deep neural networks;Eigen faces;Principal Component Analysis;Azure Maps;Octa-polar segmentation;Dimensional ratio similarity;Py-Tesseract;Paddle OCR;Performance evaluation;Face recognition;Wearable computers;Visual impairment;Optical character recognition;Prototypes;Blindness|
|[Word-Level Structure Identification In FPGA Designs Using Cell Proximity Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089907)|A. Nathamuni-Venkatesan; R. -V. Narayanan; K. Pula; S. Muthukumaran; R. Vemuri|10.1109/VLSID57277.2023.00083|FPGA;reverse engineering;word level;grouping;Program processors;Embedded systems;Reverse engineering;Clustering algorithms;Very large scale integration;Benchmark testing;Table lookup|
|[Analysis and Design of Low Phase Noise 20 GHz VCO for Frequency Modulated Continuous Wave Chirp Synthesizers in mmWave Radars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089944)|H. Kambham; S. S. Chatterjee; A. S. Edakkadan; A. Srivastava|10.1109/VLSID57277.2023.00084|Low Phase Noise VCO;Tuning Range;FMCW radar;Phase noise;Semiconductor device modeling;Frequency synthesizers;Chirp;Voltage-controlled oscillators;Synthesizers;Radar|
|[InsectEye: An Intelligent Trap for Insect Biodiversity Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089975)|E. Homan; C. Mathis; C. Lee; H. Patch; C. Grozinger; V. Narayanan|10.1109/VLSID57277.2023.00085|Artificial Intelligence;Motion Detection;Entomology;Biodiversity;Tracking;Insects;Visual analytics;Soft sensors;Sociology;Very large scale integration;Hardware|
|[An mmWave Frequency Range Multi-Modulus Programmable Divider for FMCW Radar Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089899)|S. Mantha; A. S. Edakkadan; A. Sahni; A. Srivastava|10.1109/VLSID57277.2023.00086|FMCW;Frequency Synthesizer;PLL;Programmable Divider;Phase noise;Frequency synthesizers;Frequency modulation;Voltage-controlled oscillators;Very large scale integration;Frequency conversion;Transceivers|
#### **2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)**
- DOI: 10.1109/GlobConHT56829.2023
- DATE: 11-12 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Optimal Energy Trade in Retailer, Charging Station, and Electric Vehicles using a Stackelberg Game](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087684)|M. Adil; M. A. P. Mahmud; A. Z. Kouzani; S. Y. Khoo|10.1109/GlobConHT56829.2023.10087684|Energy Trading;multi-level Optimization;Re-tailer;Charging Stations;Social Welfare;Price Penalty;Stackelberg Game;Renewable energy sources;Uncertainty;Games;Pricing;Charging stations;Linear programming;Electric vehicles|
|[Operation of Unified Power Quality Conditioner with Photovoltaic Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087529)|A. Jain; K. P. Panda; S. Bhullar|10.1109/GlobConHT56829.2023.10087529|Harmonics;Photovoltaic;Power Quality;Unified Power Quality Conditioner (UPQC);Voltage Sag;Voltage Swell;Photovoltaic systems;Workability;Robust control;Shunts (electrical);Reactive power;Renewable energy sources;Software packages|
|[Transient Performance Enhancement of VSG-based Multiple Microgrid Clusters by Coordinating Active Power Regulator and Fault Current Limiter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087808)|L. Chen; J. Tang; R. Hu; Z. Zhao; Y. Jiang; S. Zheng|10.1109/GlobConHT56829.2023.10087808|Multiple microgrid clusters;active power regulator;fault current limiter;virtual synchronous generator;transient stability improvement;Renewable energy sources;Regulators;Fluctuations;Microgrids;Stability analysis;Mathematical models;Synchronous generators|
|[Device Simulation of PTB7:PC70BM based Plastic Solar Cells using OghmaNano Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087535)|K. Krishnakumar; A. Grover; P. Kumar|10.1109/GlobConHT56829.2023.10087535|PTB7;PC70BM;Organic Solar cells;Power Conversion Efficiency;Performance evaluation;Renewable energy sources;Photovoltaic cells;Short-circuit currents;Voltage;Zinc oxide;Software|
|[Technical Feasibility Analysis of Intentional Islanding for a Real Industrial Plant with Distributed Cogeneration: A Case Study on Dynamic Aspects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087640)|W. L. A. Costa; A. P. Leão; J. P. A. Vieira; M. A. S. Borges|10.1109/GlobConHT56829.2023.10087640|Cogeneration;Intentional Islanding;Isolated Operation;Reconnection;Synchronous Generator;Renewable energy sources;Islanding;Cogeneration;Simulation;Hydrogen;Load shedding;Industrial plants|
|[Review on Optimum Sizing of Stand-Alone Hybrid Renewable Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087627)|H. Shingrakhia; S. Joshi; H. Shah|10.1109/GlobConHT56829.2023.10087627|hybrid renewable energy source;localized cost of energy;net present cost;Levelized cost of energy;annual cost;cost of energy;sizing;Renewable energy sources;Costs;Wind energy;Power supplies;System performance;Software algorithms;Stability analysis|
|[Energy Demand Forecasting Using Machine Learning Perspective Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087679)|A. P. Piyal; S. Ahmed; K. F. Rahman; A. S. M. Mohsin|10.1109/GlobConHT56829.2023.10087679|Deep learning;LSTM;SARIMAX;Fbprophet;Max power Generation;load forecasting;Renewable energy sources;Machine learning algorithms;Load forecasting;Power supplies;Sociology;Hydrogen;Machine learning|
|[Energy efficiency Evaluation Study on the Air Source Heat Pump Drying System Based on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087498)|C. Lu; M. Ge; L. Song; J. Wu; G. Pan; H. Wang|10.1109/GlobConHT56829.2023.10087498|drying system;air source heat pump;remote monitoring;energy efficiency assessment;Automation;Heat pumps;Process control;Energy measurement;Energy efficiency;Safety;Indexes|
|[Distribution System Reconfiguration for Voltage Profile Improvement using Enhanced Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087590)|W. Mariño; J. Muñoz; M. Jaramillo; C. Barrera-Singaña; W. Pavón|10.1109/GlobConHT56829.2023.10087590|Power distribution networks;objective function;voltage fluctuations;particle swarm optimization;power quality;Renewable energy sources;Hydrogen;Distribution networks;Voltage;Power system harmonics;Linear programming;Reliability|
|[Salient Features of Transmission-Distribution Coordination for Secure System Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087409)|R. Vijay; P. Mathuria|10.1109/GlobConHT56829.2023.10087409|Ancillary services;benchmark schemes;distributed energy resources;flexibility;TSO-DSO coordination;Procurement;Renewable energy sources;Systems operation;Estimation;Benchmark testing;Distributed power generation;Power systems|
|[The Impacts of Maintenance Weather and Aging on Solar Power Generation Forecasting and Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087613)|S. Vyas; S. Bhuwania; S. Mishra; H. Patel; B. Tripathi|10.1109/GlobConHT56829.2023.10087613|Forecasting;Prediction;Vector auto regression;maintenance;weather;Photovoltaic systems;Renewable energy sources;Reactive power;Weather forecasting;Solar energy;Production;Maintenance engineering|
|[Implementation of Composite Storage System in a PV-Integrated DC Microgrid for Active Power Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087417)|A. Bharatee; P. K. Ray; A. Ghosh; G. Panda|10.1109/GlobConHT56829.2023.10087417|DC microgrid;PV;composite storage system;virtual droop technique;active power sharing;Renewable energy sources;Simulation;Power system management;Microgrids;Supercapacitors;Power system stability;Real-time systems|
|[Open-Circuit Faults Diagnosis in Inverter Switches using Current Control Prediction Method for PMSM Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087830)|K. V. Tejan; B. S. Rajpurohit|10.1109/GlobConHT56829.2023.10087830|Current Control Prediction (CCP);Open Circuit Faults (OCFs);Faults recognition;Permanent Magnet Synchronous Motor (PMSM);Current control;Fault diagnosis;Renewable energy sources;Prediction methods;Cost function;Synchronous motors;Inverters|
|[Comparative Analysis of different Machine learning Models for Load Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087406)|R. Bareth; M. Kochar; A. Yadav|10.1109/GlobConHT56829.2023.10087406|Short-term Load Forecasting;Machine learning models;Linear Regression;Decision Trees;Gaussian Process Regression;Bagged Tree;SVM;Support vector machines;Training;Analytical models;Load forecasting;Linear regression;Machine learning;Predictive models|
|[Multi-Objective Analysis for Optimal location and location of Distributed Generation Focused on Improving Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087886)|K. Benítez; M. Jaramillo; J. Muñoz; C. Barrera-Singaña; W. Pavón|10.1109/GlobConHT56829.2023.10087886|Distributed Power Generation;Renewable En-ergy Sources;Ant Colony Optimization;Power Quality;Renewable energy sources;Greenhouse effect;Power quality;Metaheuristics;Hydrogen;Voltage;Minimization|
|[THD Minimization in Electrical Distribution Networks Through Vector Space Control Implementation In Power Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087359)|F. Vaca; M. Jaramillo; J. Muñoz; C. Barrera-Singaña; W. Pavón|10.1109/GlobConHT56829.2023.10087359|Total harmonic distortion;Power distribution networks;DC-AC power converters;inverters;Space vector pulse width modulation;Space vector pulse width modulation;Total harmonic distortion;Renewable energy sources;Distribution networks;Power system stability;Aerospace electronics;Inverters|
|[Optimal Location of Battery Storage Systems to Minimize Losses in the Distribution System Considering Power Quality Indicators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087511)|S. Guerrero; C. Barrera-Singaña; A. Valenzuela; S. Y. Rojas; J. Muñoz; M. Jaramillo|10.1109/GlobConHT56829.2023.10087511|Voltage;BESS;Losses;Distribution System with battery storage;Renewable energy sources;Power quality;Voltage;Distribution networks;Batteries;Power system reliability;Resource management|
|[Operational Experience with a Vanadium Redox Flow Battery in a Off-Grid Renewable System at an Extreme Cold Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087645)|R. Bhattacharyya; P. C. Ghosh|10.1109/GlobConHT56829.2023.10087645|Energy storage;Vanadium redox flow battery;Off-grid system;Carbon footprint;Photovoltaic systems;Renewable energy sources;Hydrogen;Green products;Vanadium;Redox;Batteries|
|[Application of Pinch Analysis for optimal Sizing Of Thermal Management System in Fuel Cell Powered Heavy-Duty Trucks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087819)|N. Philip; P. C. Ghosh|10.1109/GlobConHT56829.2023.10087819|Pinch Analysis;Thermal Management;Phase Change Material;Cooling System;Radiator Area;Phase change materials;Resistance;Renewable energy sources;Power demand;Hydrogen;Fuel cells;Transportation|
|[Determination of The Maximum Power Point in Solar Photovoltaic Pumping Systems using P&O Perturbation Observation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087762)|B. Valenzuela; J. Muñoz; M. Jaramillo; C. Barrera-Singaña; W. Pavón|10.1109/GlobConHT56829.2023.10087762|Irrigation;photovoltaic systems;renewable energy sources;solar energy;sustainable development;water pumps;Photovoltaic systems;Maximum power point trackers;Irrigation;Renewable energy sources;Costs;System performance;Sociology|
|[A Charging Coordination Strategy for Seamless Integration of Plug-In Electric Vehicles into a Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087580)|J. P. Sahoo; S. Sivasubramani|10.1109/GlobConHT56829.2023.10087580|Grid-to-vehicle;Plug-in-electric Vehicles;Renewable Energy Sources;Solar and Wind powered charging stations;Vehicle-to-grid;Plug-in electric vehicles;Vehicle-to-grid;Renewable energy sources;Costs;Wind energy;Transportation industry;Simulation|
|[Protection Scheme for Low Voltage DC Microgrid Based on Voltage Rise Across Terminal Inductor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087891)|P. Chauhan; C. P. Gupta; M. Tripathy|10.1109/GlobConHT56829.2023.10087891|Relaying algorithm;microgrid protection;protection schemes;fault classification;Low voltage;Fault detection;Current measurement;Simulation;Microgrids;Robustness;Time measurement|
|[Modelling and Small Signal Stability analysis of DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087703)|Y. Tripathy; B. Tyagi|10.1109/GlobConHT56829.2023.10087703|DC microgrid;Eigenvalues and Eigenvectors;Microgrid;Pole-placement;Stability Analysis;State-feedback;Analytical models;State feedback;Microgrids;Aerospace electronics;Mathematical models;Stability analysis;Eigenvalues and eigenfunctions|
|[Performance Aspects of the Electrical Conversion Stages of a small multibladed wind energy system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087786)|C. Caruana; C. Grima; M. Mifsud|10.1109/GlobConHT56829.2023.10087786|Small-scale wind;multibladed rotor;permanent magnet generator;control;field tests;temperature effect;Temperature;Torque;Wind speed;Systems operation;Velocity control;Prototypes;Generators|
|[State-space Modelling and Stability Analysis of ANN controller for Grid-connected VSC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087412)|P. R. Bana; M. Amin|10.1109/GlobConHT56829.2023.10087412|Artificial neural network;Eigenvalues;PI-controller;Supervised learning;State-space modelling;Voltage source converter;Analytical models;Sensitivity analysis;Simulation;Closed box;Artificial neural networks;Power system stability;Control systems|
|[Comparative Analysis of Algorithms for the Optimum Placement of PMUs in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087710)|P. Riyas; S. A. Lakshmanan|10.1109/GlobConHT56829.2023.10087710|phasor measurement units;optimization;power systems;distribution networks;meta-heuristic methods;PMU;algorithm;synchro phasor technology;monitoring;controllability;observability;state estimation;Performance evaluation;Renewable energy sources;Voltage measurement;Costs;Phasor measurement units;Time measurement;Topology|
|[Active Power Control of Hybrid-HVDC Line Connected to Offshore DFIG-based Wind Farm During Mainland Grid Voltage Fluctuations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087847)|R. R. Patra; R. M. A. Asha; P. B. Krishna|10.1109/GlobConHT56829.2023.10087847|Doubly Fed Induction Generator (DFIG);High Voltage Direct Current (HVDC);Hybrid- HVDC Line;Line commutated Converter (LCC);Mainland grid;Offshore DFIG Wind Farm (ODWF);Voltage Source Inverter (VSI);Voltage fluctuations;Voltage source inverters;HVDC transmission;Power control;Doubly fed induction generators;Rectifiers;Wind farms|
|[Real-Time Simulation, Modelling, and Control of Low Inertial Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087447)|I. Alotaibi; M. Abido|10.1109/GlobConHT56829.2023.10087447|Low Inertial Microgrids;Real-Time Simulations;Virtual Inertia Control;Real-Time Digital Simulator (RTDS);Renewable energy sources;Adaptation models;Power system dynamics;Layout;Microgrids;Power system stability;Real-time systems|
|[A Comprehensive and Preferential Analysis of Demand Response Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087476)|G. Kansal; R. Tiwari|10.1109/GlobConHT56829.2023.10087476|DR;Customer benefit function;TOPSIS;AHP;Renewable energy sources;Regulators;ISO;Hydrogen;Analytic hierarchy process;Elasticity;Demand response|
|[MIMO controller design for two-input DC-DC converter without de-couplers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087549)|A. Trivedi; M. Veerachary|10.1109/GlobConHT56829.2023.10087549|Multi-input multi-output converter (MIMO);linear matrix inequality (LMI);bounded real-lemma (BRL);de-centralized controller;Renewable energy sources;Riccati equations;Hydrogen;Stability analysis;Performance analysis;Linear matrix inequalities;Matrix converters|
|[Detection Study of Static Eccentricity and Demagnetization Faults in IPMSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087871)|P. Verma; H. Misra; B. Singh Rajpurohit|10.1109/GlobConHT56829.2023.10087871|Eccentricity;Interior Permanent Magnet Synchronous Machine (IPMSM);Demagnetization;NdFeB;Permanent magnet machines;Fault diagnosis;Vibrations;Magnetic flux;Magnetoacoustic effects;Rotors;Synchronous motors|
|[Stability Robustness Assessment of Bypass Capacitor Based Buck Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087448)|M. Veerachary; Anu|10.1109/GlobConHT56829.2023.10087448|Buck converter;Fourth-order buck converter;Small-signal stability;Sensitivity Index;Asymptotic stability;Buck converters;Sensitivity;Uncertainty;Computational modeling;Simulation;Stability criteria|
#### **2023 IEEE 15th International Symposium on Autonomous Decentralized System (ISADS)**
- DOI: 10.1109/ISADS56919.2023
- DATE: 15-17 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Emotion monitoring sensor network using a drive recorder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092139)|J. Luo; H. Yoshimoto; Y. Okaniwa; Y. Hiramatsu; A. Ito; M. Hasegawa|10.1109/ISADS56919.2023.10092139|driving support system;emotion estimation;facial expression analysis;EEG;sensor network;nan|
|[Portable Device for Monitoring the Respiratory Rate in Home Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092089)|S. A. A. Flores; C. C. F. D. Castillo; D. S. S. Carreón; S. Z. Barreiro; J. Brieva; H. Ponce; E. Moya-Albor|10.1109/ISADS56919.2023.10092089|Respiratory rate;capnography;impedance plethysmography;photoplethysmography;breath sensing devices;signal processing;portable monitor;nan|
|[Scheduling Linear Workflows with Dynamically Adjustable Exit Tasks on Distributed Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092151)|G. L. Stavrinides; H. D. Karatza|10.1109/ISADS56919.2023.10092151|scheduling;distributed resources;partial computations;dynamically adjustable tasks;performance evaluation;linear workflows;nan|
|[Application prototype for the registration, checking, and monitoring of perishable foods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092185)|A. Cutipa; E. Williams; C. Arias; Gianfranco; Q. Tuesta; J. Enrique|10.1109/ISADS56919.2023.10092185|IoT;supply chain;tracking;perishable food;and cloud;A WS;nan|
|[Towards the Distributed Wound Treatment Optimization Method for Training CNN Models: Analysis on the MNIST Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092078)|H. Ponce; E. Moya-Albor; J. Brieva|10.1109/ISADS56919.2023.10092078|Convolution Neural Network;Wound Treatment optimization;Distributed Metaheuristic optimization;MNIST;Image Classification;Distributed Systems;Training;Analytical models;Federated learning;Metaheuristics;Wounds;Convolutional neural networks;Proposals|
|[A Computer Vision Approach to Terminus Movement Analysis of Viedma Glacier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092045)|E. Moya-Albor; A. Schwartzman; J. Brieva; M. Pardo; H. Ponce; R. Chávez-Domínguez|10.1109/ISADS56919.2023.10092045|Viedma Glacier;Glacier Terminus Movement;Segmentation;Computer Vision;Optical flow;Hermite transform;Global Warming;Climate Change;Environment;Green;nan|
|[Innovation by Connecting People, Skill, and Value: A Community Platform for Collaborative Job Hunting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092110)|N. Saito; P. Zhang; H. Hayashi; S. Sugano; K. Mori|10.1109/ISADS56919.2023.10092110|nan;nan|
|[A Unified Deep Learning Diagnostic Architecture for Big Data Healthcare Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092137)|S. Shafqat; Z. Anwar; Q. Javaid; H. F. Ahmad|10.1109/ISADS56919.2023.10092137|deep learning;diagnostic architecture;healthcare analytics;HPC;big data;nan|
|[A pipeline to collaborative AI models creation between Brazilian governmental institutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092030)|G. Souza; M. Figueredo; D. Sabino; N. Cacho|10.1109/ISADS56919.2023.10092030|Federated Learning;Distributed Learning;Privacy;Security;nan|
|[Fleet in the Loop: An Open Source approach for design and test of resilient vehicle architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092108)|D. Grimm; M. Schindewolf; E. Sax|10.1109/ISADS56919.2023.10092108|vehicles;simulation;virtualization;testing;resilience;vehicle architecture;CARLA;nan|
|[An Approach to Workload Generation for Cloud Benchmarking: a View from Alibaba Trace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092039)|J. Zhu; B. Lu; X. Yu; J. Xu; T. Wo|10.1109/ISADS56919.2023.10092039|benchmark;cloud computing;workload submission;nan|
|[Development of an Electric Powered Assisted Cycle with a Heart Rate Sensor Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092070)|R. G. Cedillo; D. M. López; E. M. Quintana; A. P. Guerra; J. P. S. H. Moreno; J. M. V. Hernández; E. Moya-Albor; H. Ponce; J. Brieva|10.1109/ISADS56919.2023.10092070|Electric-powered assisted cycle (EPACs);Heart Rate;Control System;Mechatronics;Physiological Control;Health;nan|
|[Chinese named entity recognition based on Heterogeneous Graph and Dynamic Attention Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092180)|Y. Wang; L. Lu; W. Yang; Y. Chen|10.1109/ISADS56919.2023.10092180|Chinese NER;Heterogeneous graph;Non-zero attention problem;Lexicon information;nan|
|[Access Control in Dynamic IoT Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092159)|I. -L. Yen; A. Tiwari; F. Bastani|10.1109/ISADS56919.2023.10092159|nan;nan|
|[DAOs & ADSs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091973)|S. A. Wright|10.1109/ISADS56919.2023.10091973|Decentralized Autonomous Organization;DAO;Autonomous Decentralized System;ADS;Industries;Data privacy;Decentralized autonomous organization;Transportation;Market research;Software;Data governance|
|[Policy-Based Task Allocation at Runtime for a Self-Adaptive Edge Computing Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092022)|V. P. Betancourt; M. Kirschner; M. Kreutzer; J. Becker|10.1109/ISADS56919.2023.10092022|Edge Computing;Task Allocation;Self-Adaptive Systems;Distributed Systems;Industrial Internet of Things;nan|
|[Resilient Streamlines Optimization for Baby Carriages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092040)|X. Zeng; Y. Guo; P. Ying; S. Ai|10.1109/ISADS56919.2023.10092040|resilience;baby carriage;streamline;Yolov5;AHP;nan|
|[Implementation of Smart Contract on Autonomous Decentralized Voting Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092152)|H. Takahashi; U. Lakhani|10.1109/ISADS56919.2023.10092152|NFT;marketplace;smart contract;voting blockchain;layer one blockchain;autonomous decentralized blockchain;nan|
|[NEOP: A Framework for Distributed Mobile Apps on Heterogeneous Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092023)|Y. Zhao; S. Jiang; W. Zhong; L. Wang; X. -F. Li|10.1109/ISADS56919.2023.10092023|Android ecosystem;app development;nan|
|[Design of a Soft Gripper Hand for a Quadruped Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091976)|J. González; J. Poza; J. Robles; H. Ponce; J. Brieva; E. Moya-Albor|10.1109/ISADS56919.2023.10091976|Gripper;soft robotics;bio-robotics;quadruped robots;mechatronics.;Robust control;Shape;Prototypes;Process control;Soft robotics;Production facilities;Quadrupedal robots|
|[Assessment of Situation Awareness and Automation in Performance-Based Navigation Procedures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091986)|C. Morales; S. Moral|10.1109/ISADS56919.2023.10091986|SA;PBN;SWIM;Logistic Regression;Automation;Navigation;Trajectory;Safety;Information management;Bayes methods;Autopilot|
|[Research on SWIM Cooperative Emergency Response and Resilient Disaster Recovery Based on Survivability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092065)|J. Lei; M. Sun; Z. Wu|10.1109/ISADS56919.2023.10092065|SWIM;security;emergency response;resilient disaster recovery;mutual assistance mechanism;nan|
|[Preliminary Feasibility Study of Quantum Key Distribution for Future Air Traffic Management Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092072)|N. Kanada; X. Lu|10.1109/ISADS56919.2023.10092072|Quantum Key Distribution;Aeronautical Communication;Aeronautical Navigation;Aeronautical Surveillance;System Wide Information Management;Surveillance;Traffic control;Mobile communication;Aircraft navigation;Safety;Quantum key distribution;Information management|
|[4D Trajectory Negotiation to Achieve Situational and Operational Awareness for Air Traffic Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091979)|X. Lu; K. Morioka; N. Kanada; T. Koga|10.1109/ISADS56919.2023.10091979|System Wide Information Management (SWIM);Trajectory Based Operations (TBO);4-Dimensional Trajectory (4DT);Air Traffic Management (ATM);Electronic Flight Bag (EFB);Collaboration;Systems architecture;Trajectory;Information management;Air traffic control;IP networks;Aircraft manufacture|
|[Collision Avoidance Method for Multirotor Small Unmanned Aircraft Systems in Multilateration Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092170)|G. Sato; H. Yokoi; D. Toratani; T. Koga|10.1109/ISADS56919.2023.10092170|Markov Decision Process;Collision Avoidance;Small Unmanned Aircraft System;Multilateration;nan|
|[Mining of Potential Relationships based on the Knowledge Graph of Industrial Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092024)|X. Zhang; Y. Lai|10.1109/ISADS56919.2023.10092024|knowledge reasoning;knowledge graph;graph neural network;nan|
|[Teaching Quantum Machine Learning in Computer Science](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092171)|G. De Luca; Y. Chen|10.1109/ISADS56919.2023.10092171|quantum machine learning;computer science education;quantum computing;machine learning;nan|
|[Intrusion Detection System for Industrial Control Systems Based on Imbalanced Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092008)|X. Dong; Y. Lai|10.1109/ISADS56919.2023.10092008|industrial control system;imbalanced data;oversampling algorithm;nan|
|[DDM: Study Of Deer Detection And Movement Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091943)|M. J. Siddique; K. R. Ahmed|10.1109/ISADS56919.2023.10091943|Deer Detection and Movement (DDM);Deer Vehicle Collisions (DVC);Detection Model (DM);Movement Model (MM);DeepLabCut (DLC) and YOLO version 5(YOLOv5);Deep learning;Road transportation;Computer vision;Computational modeling;Wildlife;Road safety;Inference algorithms|
|[Why Decentralize Deep Learning?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091996)|S. A. Wright|10.1109/ISADS56919.2023.10091996|Decentralization;Deep Learning;Deep learning;Privacy;Metaverse;Medical services;Big Data;Blockchains;Safety|
|[Real Time Deep Learning Algorithm for Counting Weed’s Growth Stages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092053)|A. M. Almalky; K. R. Ahmed|10.1109/ISADS56919.2023.10092053|Deep learning;Weeds detection;Weeds growth stages counting;YOLOv5;Deep learning;Crops;Production;Real-time systems;Agriculture;Classification algorithms|
|[Semi-supervised Learning in Distributed Split Learning Architecture and IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092050)|M. Barhoush; A. Ayad; A. Schmeink|10.1109/ISADS56919.2023.10092050|Algorithm Comparison;Big Data;Distributed Learning;Semi-supervised learning;Split Learning;Training;Data privacy;Computer aided instruction;Distance learning;Scalability;System performance;Supervised learning|
#### **2023 International Conference on Robotics and Automation in Industry (ICRAI)**
- DOI: 10.1109/ICRAI57502.2023
- DATE: 3-5 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089584)|M. Zafar; S. Shafique; F. Riaz; S. Abid; U. Raza; W. Holderbaum|10.1109/ICRAI57502.2023.10089584|Autonomous Trolley;Human labor assistance;Robotics;Automation;Target tracking;Service robots;Transportation;Object detection;Robot sensing systems;Real-time systems|
|[Robot Autonomous Exploration and Map Building Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089570)|X. Tian; B. Li; Y. Du; M. Wang|10.1109/ICRAI57502.2023.10089570|Rapidly-Exploring Random Tree (RRT);Boundary Exploration;Mapping;Industries;Simultaneous localization and mapping;Automation;Filtering;Buildings;Indoor environment;Mobile robots|
|[Collaborative Path Planning Algorithm for Multiple AGVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089588)|Y. Chen; M. Yuan; M. Cong; D. Liu|10.1109/ICRAI57502.2023.10089588|genetic algorithm;AGV;path planning;information entropy;Collaboration;Production;Path planning;Information entropy;Task analysis;Optimization;Robots|
|[Comparison Between ANSYS Fluent and Solidworks Internal Flow Simulation for Analysis of A Fuzzy Logic Controller-Based Heating/Cooling System in A Mobile Robot Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089571)|M. Afaq; R. Ahmad|10.1109/ICRAI57502.2023.10089571|Computational Fluid Dynamics;Mobile Robot Design;Fuzzy Logic Controller;Heating/Cooling Flow Simulation;ANSYS Fluent;Solidworks;Fans;Solid modeling;Fluids;Computational fluid dynamics;Computational modeling;Software;Finite element analysis|
|[Design and Control of a Self-Driving Vehicle using RP Lidar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089600)|A. Ikhtiar; D. Qurban; S. Samo|10.1109/ICRAI57502.2023.10089600|Autonomous Vehicle;self-driving vehicles;2D Mapping;LiDAR;obstacle detection;obstacle avoidance;Road accidents;Laser radar;Heuristic algorithms;Wheelchairs;Dynamics;Prototypes;Sensor systems|
|[Design, Modeling, and Analysis of Robotic Manipulator for Welding of Nuclear Spent Fuel Cask Lid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089575)|M. W. Faheem; S. M. Haris; A. R. Abbasi; H. Tariq|10.1109/ICRAI57502.2023.10089575|automation;hazardous;nuclear;robotic manipulator;orbital welding;Industries;Solid modeling;Automation;Welding;Weaving;Software;Orbits|
|[Tele-Operated Robotic Manipulator based Contactless Heartbeat Measurement Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089533)|A. H. Ali; S. M. H. Kazmi; R. Uddin|10.1109/ICRAI57502.2023.10089533|Robotic Manipulator;Human-Robot Interaction;Haptics;Heartbeat measurement;Force Feedback;Performance evaluation;Force measurement;Heart beat;Force;Stethoscope;Medical services;Manipulators|
|[Vision-Based Hybrid Detection For Pick And Place Application In Robotic Manipulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089602)|M. U. Anjum; U. S. Khan; W. S. Qureshi; A. Hamza; W. A. Khan|10.1109/ICRAI57502.2023.10089602|manipulator robot;UR5;universal robot;pick and place;computer vision;YOLO;RoboDK;cobots;Industries;Wrist;Uncertainty;Service robots;Statistical analysis;Robot kinematics;Robot vision systems|
|[A Novel Approach for Multi-Criteria Workspace Optimization of Parallel Kinematic Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089541)|S. M. M. H. Rasheed; A. Shujah; S. Ayub; A. Baqai; K. F. Ahmad|10.1109/ICRAI57502.2023.10089541|Parallel Kinematic Machines;Parallel Robots;Workspace Optimization;Singularities;Multi-Objective;Pareto Front;Jacobian matrices;Industries;Parallel robots;Service robots;Kinematics;End effectors;Complexity theory|
|[McKibben Pneumatic Artificial Muscle Robot Actuators - A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089581)|M. A. Khan; S. Shaik; M. H. Tariq; T. Kamal|10.1109/ICRAI57502.2023.10089581|Actuators;McKibben Pneumatic Artificial Muscles;Mathematical Modeling;Analysis;Control Strategies;Artificial muscles;Pneumatic actuators;Industries;Analytical models;Automation;Service robots;Biological system modeling|
|[Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089577)|F. Ul Hassan Asif Mattoo; U. S. Khan; T. Nawaz; N. Rashid|10.1109/ICRAI57502.2023.10089577|Action recognition;Sequential skeleton information;Deep neural network;Motion features;Geometric features;Feature fusion;Industries;Computational modeling;Pose estimation;Predictive models;Streaming media;Skeleton;Real-time systems|
|[Exploration of Unknown Environment using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089589)|A. Ali; S. Gul; T. Mahmood; A. Ullah|10.1109/ICRAI57502.2023.10089589|Double Deep Q-Network;Exploration;Mobile Robots;Navigation;Reinforcement Learning;Deep learning;Location awareness;Industries;Uncertainty;Service robots;Computational modeling;Reinforcement learning|
|[Detection of Grape Clusters in Images Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089582)|M. O. Shahzad; A. B. Aqeel; W. S. Qureshi|10.1109/ICRAI57502.2023.10089582|Object Recognition;Grapes;YOLO;Convolutional Neural Network;Deep Learning;Object Detection;Training;Shape;Pipelines;Neural networks;Lightning;Data models;Real-time systems|
|[Railway Track Joints and Fasteners Fault Detection using Principal Component Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089579)|M. Owais; I. Hussain; G. Shahzad; B. M. Khan|10.1109/ICRAI57502.2023.10089579|Principal Component Analysis;Eigenvectors;Euclidean Distance Classifier;Railway Track;Rail Fasteners;Fishplate;Training;Machine learning algorithms;Fault detection;Euclidean distance;Fasteners;Feature extraction;Rail transportation|
|[Video Stabilization using RAFT-based Optical Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089609)|R. Ashar; B. Sadiq; H. Mohiuddin; S. Ashraf; M. Imran; A. Ullah|10.1109/ICRAI57502.2023.10089609|Video Stabilization;RAFT;Optical Flow;Deep Learning-based;Industries;Deep learning;Visualization;Pipelines;Neural networks;Estimation;Transforms|
|[Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089539)|M. H. Asad; M. M. Asim; M. N. M. Awan; M. H. Yousaf|10.1109/ICRAI57502.2023.10089539|Natural disaster damage assessment;CNNs;Deep learning;semantic segmentation;Transformer;UAV;Industries;Visualization;Semantic segmentation;Public infrastructure;Random access memory;Transformers;Autonomous aerial vehicles|
|[Overall Survial Prediction from Brain MRI in Glioblastoma](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089587)|S. Yousaf; N. Ibrar; M. Majid; S. Anwar|10.1109/ICRAI57502.2023.10089587|CNN;SVM;ML;DL;BRATS;Support vector machines;Deep learning;Image segmentation;Computational modeling;Predictive models;Brain modeling;Feature extraction|
|[A Review of Activity Detection Methods Used in Videos Streaming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089567)|S. Khursheed; S. Khalid; F. Riaz; T. Shehryar|10.1109/ICRAI57502.2023.10089567|Human Activity Recognition;Untrimmed videos;Neural Networks;2D CNN;3D CNN;I3D;Location awareness;Three-dimensional displays;Computational modeling;Training data;Computer architecture;Streaming media;Convolutional neural networks|
|[Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089586)|A. H. Khan; Z. Zafar; M. Shahzad; K. Berns; M. M. Fraz|10.1109/ICRAI57502.2023.10089586|Remote sensing;Crop type classification;Sentinel-2;Satellite imagery;Time series data;Semantic segmentation;Visualization;Satellites;Semantic segmentation;Time series analysis;Crops;Optical imaging;Agriculture|
|[Design and Development of IoT Based Weather and Air Quality Monitoring Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089568)|M. Haris; B. Ubaid; U. Bibi; A. Said; S. N. us Saqib; S. Khan; M. Tufail|10.1109/ICRAI57502.2023.10089568|Air Quality Index(AQI);Internet of Things (IoT);ThingSpeak;Weather station;Temperature sensors;Cloud computing;Wind speed;Transportation;Air quality;Sensors;Internet of Things|
|[Dense Vehicular Ad hoc Network UAV Assisted Cooperative Routing Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089603)|S. Ayub; A. Shujah; S. M. M. H. Rasheed; T. Zafar; A. Bilal|10.1109/ICRAI57502.2023.10089603|Drone assisted vehicular networks;Intelligent transportation system;Route discovery method;UAV assisted cooperative routing scheme;Remote radio heads;End to end delay;Performance evaluation;Degradation;Vehicular ad hoc networks;Transportation;Routing;Throughput;Delays|
|[An Improved Cooperative Spectrum Sensing Scheme for Emulation Attacker Detection in Cognitive Radio Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089590)|M. N. Awan; S. U. Haq; S. Anwar|10.1109/ICRAI57502.2023.10089590|cognitive radio (CR);wireless sensor network (WSN);primary user emulation attacker (PUEA);cooperative spectrum sensing (CSS);Wireless sensor networks;Costs;Wireless networks;Simulation;Emulation;Robot sensing systems;Throughput|
|[Intelligent Transportation Systems in Smart City: A Systematic Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089543)|M. A. Hassan; R. Javed; Farhatullah; F. Granelli; X. Gen; M. Rizwan; S. H. Ali; H. Junaid; S. Ullah|10.1109/ICRAI57502.2023.10089543|Big Data;Big Data Analytics;Smart Cities;Intelligent Transportation Systems;Artificial Intelligence;SDN;Blockchain;VANETs;Industries;Wireless sensor networks;Smart cities;Big Data;Smart transportation;Safety;Information and communication technology|
|[Application of Remote Wireless Control Technology Based on LoRa System in Intelligent Agricultural Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089601)|J. Ru; Z. Dong; Z. Guangyuan; G. Zhiguang; W. Xuelin; S. Anwar|10.1109/ICRAI57502.2023.10089601|LoRa;Wireless remote control;Intelligent agricultural machinery;Wireless communication;Microcontrollers;Decision making;Agricultural machinery;Market research;Safety;Reliability|
|[Design and Analysis of Throwable Unmanned Ground Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089612)|H. Sohail; A. Hamza; N. Rashid; M. S. Ali; T. Ghani|10.1109/ICRAI57502.2023.10089612|Throwable UGV;ANSYS;PCTPE;Robot;Reconnaissance;Meters;Visualization;Three-dimensional displays;Surveillance;Wheels;Tail;Land vehicles|
|[Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089591)|F. Nasir; M. Haris; B. Khan; M. Tufail; M. T. Khan; Z. Dong|10.1109/ICRAI57502.2023.10089591|Agricultural robotics;Deep learning;Precision agriculture;Pressure control;Robot Navigation;Row detection;Service robots;Plants (biology);Spraying;Crops;Wheels;Robot sensing systems;Visual servoing|
|[Formation Control and Sub-Swarm Generation of Multirotor UAVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089546)|M. M. Shahzad; M. H. Asad; M. Haris; H. Munawar; M. H. Yousaf|10.1109/ICRAI57502.2023.10089546|sub-swarm generation;team connectivity;navigation;swarm robotics;Pattern formation;Service robots;Navigation;Surveillance;Insects;Swarm robotics;Formation control|
|[Autonomous Navigation and Mapping of Water Channels in a Simulated Environment Using Micro-Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089599)|S. I. Ullah; A. Muhammad|10.1109/ICRAI57502.2023.10089599|UAV;navigation;Mapping;Path Planning;ROS;Unreal engine;Airsim;RRT*;Meters;Irrigation;Automation;Navigation;Atmospheric modeling;Inspection;Path planning|
|[Dynamic Flexibility for Resource Sharing Initialization with Fame and Confidence of Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089611)|N. Fazal; Z. Ahmad; T. Khan|10.1109/ICRAI57502.2023.10089611|resource shortage;resource sharing;dynamic flexibility;Industries;Automation;System performance;Dynamic scheduling;Delays;Resource management;Reliability|
|[Autonomous Navigation and Mapping of Snake Robot for Urban Search and Rescue](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089544)|S. I. Ullah; T. Mahmood; Anayatullah|10.1109/ICRAI57502.2023.10089544|Snake robot;navigation;Mapping;Path Planning;ROS;CoppeliaSim;USAR;RRT*;Three-dimensional displays;Simultaneous localization and mapping;Navigation;Service robots;Linux;Simulation;Snake robots|
|[Variant Process Planning for a Job Shop](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089535)|M. Ali; H. Ali; H. M. Rizwan; M. H. Umer; H. Abdullah; H. Ullah; S. u. Rehman; W. A. Khan|10.1109/ICRAI57502.2023.10089535|Process Plan;Variant Process planning;Generative Process Planning;Computer Aided Process Planning;Pistons;Industries;Group technology;Databases;Process planning;Manuals;Software|
|[Machine Vision Based Predictive Maintenance for Machine Health Monitoring: A Comparative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089572)|I. Ul Haq; S. Anwar; T. Khan|10.1109/ICRAI57502.2023.10089572|Intelligent machining monitoring;Predictive Maintenance;Machine learning;Deep Learning and Fault diagnosis and Prognosis;Deep learning;Recurrent neural networks;Costs;Machining;Data models;Monitoring;System analysis and design|
|[Design and Fabrication of Inexpensive Portable Polar 3D Printer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089592)|S. B. H. Bukhari; T. Tanveer; A. Abid; S. Anwar|10.1109/ICRAI57502.2023.10089592|3D Printing;Additive Manufacturing;Fused Deposition Modelling;Polar Coordinates;Stereolithography;Industries;Heating systems;Temperature sensors;Temperature measurement;Solid modeling;Three-dimensional displays;Costs|
|[Design and Validation of an Automation Strategy for the Strip Test Process in the Semiconductor Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089538)|M. A. A. Rahman; C. Lin; P. G. Maropoulus; W. S. Teoh; A. A. A. Rahman; E. Mohamad|10.1109/ICRAI57502.2023.10089538|automation;strip test;manufacturing;quality;productivity;Productivity;Performance evaluation;Strips;Automation;Semiconductor devices;Batch production systems;Semiconductor device manufacture|
|[A Feature Extraction Algorithm for Hybrid Manufacturing and Its Application in Robot-Based Additive and Subtractive Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089593)|O. Cooke; H. Al-Musaibeli; R. Ahmad|10.1109/ICRAI57502.2023.10089593|feature extraction;remanufacturing;hybrid manufacturing;3D printing;Solid modeling;Visualization;Manufacturing processes;Additives;Three-dimensional displays;Automation;Feature extraction|
|[Role of Machine Learning in Power Analysis Based Side Channel Attacks on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089540)|A. Hasnain; Y. Asfia; S. G. Khawaja|10.1109/ICRAI57502.2023.10089540|Power Analysis;Profiling;Side Channel Attack (SCA);Machine Learning;Deep Learning;FPGA;Power Profiling Sources;CNN Model;Electric potential;Voltage measurement;Power measurement;Voltage fluctuations;Fluctuations;Oscilloscopes;Side-channel attacks|
|[SWYNT: Swin Y-Net Transformers for Deepfake Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089585)|F. Khalid; M. H. Akbar; S. Gul|10.1109/ICRAI57502.2023.10089585|Celeb-DF;Deepfake;Deepfake Detection;FaceForensics++;Swin Transformer;Swim Y-Net;U-Net;Performance evaluation;Industries;Deepfakes;Social networking (online);Transformers;Decoding;Reliability|
|[Generative Adversarial Network based Chest Disease Detection and Binary Mask Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089542)|M. A. Junaid; S. Anwar; G. Sikander; M. T. Khan|10.1109/ICRAI57502.2023.10089542|explainable artificial intelligence;generative adversarial networks;weakly supervised learning;binary masks generation;tuberculosis;Radiography;Tuberculosis;Infectious diseases;Data visualization;Generative adversarial networks;Lesions;Artificial intelligence|
|[Detection of Illegal Kiln Activity During SMOG Period](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089596)|U. Nazir; M. A. Ather; M. Taj|10.1109/ICRAI57502.2023.10089596|Google Earth Engine;Spectral properties;Brick Kiln;Sustainable Development Goals;Industries;Kilns;Gases;Organizations;National Institutes of Health;Pollution measurement;Internet|
|[Genetic Drift and its Effects on the Performance of Genetic Algorithm(GA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089573)|S. Ullah; M. Masood|10.1109/ICRAI57502.2023.10089573|Evolutionary Algorithm;Genetic Algorithm;Max One;Genetic Drift;Industries;Sociology;Metaheuristics;MIMICs;Force;Genomics;Search problems|
|[Clothing gripping and sorting based on a dual-arm collaborative robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089595)|Y. Du; Z. Z. Yuan; X. Tian; Z. Yang|10.1109/ICRAI57502.2023.10089595|dual-arm collaborative robot;forward and reverse kinematics;path planning;contour matching;clothes folding;Service robots;Clothing;Collaboration;Grasping;Manipulators;Path planning;Reliability|
|[Configuration Tool for Generating Multi-Type and Multi-Robot Work Cell Layout](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089607)|M. A. A. Rahman; K. N. Shikder; P. G. Maropoulos; E. Mohamad; A. A. A. Rahman; M. R. Salleh|10.1109/ICRAI57502.2023.10089607|industrial robots;work cell;layout optimization;reconfigurable manufacturing system;automation;Visual BASIC;Service robots;Shape;Welding;Layout;Manufacturing;Safety|
|[Housekeeping Using Multi-Agent Service Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089576)|M. F. Malik; H. Shahid; M. H. Saleem; H. Nazir; A. Nouman|10.1109/ICRAI57502.2023.10089576|service robotics;housekeeping;autonomous navi-gation;manipulation;action languages;robotic simulations;plan execution;monitoring;Industries;Automation;Service robots;Computational modeling;Benchmark testing;Planning;Task analysis|
|[Applications of Hybrid Conditional Planning in Service Robotics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089569)|A. Nouman; M. K. Saleem|10.1109/ICRAI57502.2023.10089569|service robotics;task planning;hybrid planning;motion planning;conditional planning;planning under uncertainty;task motion planning;Uncertainty;Service robots;Navigation;Knowledge based systems;Robot sensing systems;Hybrid power systems;Planning|
|[Odometry and Inertial Sensor-based Localization of a Snake Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089598)|R. Shahid; A. Baloch; H. Tahir; A. Ullah|10.1109/ICRAI57502.2023.10089598|localization;probabilistic techniques;sensor fusion;IMU;odometry;EKF;UKF;Location awareness;Industries;Measurement units;Snake robots;Inertial navigation;Disaster management;Robot sensing systems|
|[BactPNet: A Novel Automated Detection Approach for Bacterial Pneumonia Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089605)|S. M. I. Shah; M. A. Minhas; F. Hassan|10.1109/ICRAI57502.2023.10089605|Deep Learning;bacterial pneumonia;medical imaging;healthcare systems;Industries;Deep learning;Pediatrics;Microorganisms;Hospitals;Pulmonary diseases;Europe|
|[HRV Dynamical Analysis of Cardiac Autonomic Activity of Healthy Subjects with Age](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089610)|S. M. Z. S. Bukhari; I. Akhtar|10.1109/ICRAI57502.2023.10089610|heart rate variability;autonomic nervous system;Kruskal Wallis test;Nonlinear analysis;sympathovagal balance;Pediatrics;Correlation;Fluctuations;Frequency-domain analysis;Electrocardiography;Physiology;Entropy|
|[Deep Learning Based Phonocardiogram Signals Analysis for Cardiovascular Abnormalities Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089537)|S. S. Hussain; M. Ashfaq; M. S. Khan; S. Anwar|10.1109/ICRAI57502.2023.10089537|Artificial Intelligence;phonocardiogram;convolutional neural network;classification;PhysioNet;Heart;Deep learning;Training;Signal processing algorithms;Surgery;Computer architecture;Classification algorithms|
|[An Automated System for the Classification of Bronchiolitis and Bronchiectasis Diseases using Lung Sound Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089608)|S. A. F. Jaffery; S. Aziz; M. U. Khan; S. Z. Hussain Naqvi; M. Faraz; A. Usman|10.1109/ICRAI57502.2023.10089608|Bronchiolitis;Bronchiectasis;lung sounds;Discrete Wavelet Transform;MFCC;K-Nearest Neighbors;Feature extraction;Industries;Databases;Pulmonary diseases;Lung;Wavelet analysis;Recording;Discrete wavelet transforms|
|[A Novel Deep Learning Based Framework for Cardiac Arrest Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089604)|N. Fatima; A. Irtaza; R. Ali|10.1109/ICRAI57502.2023.10089604|Deep Learning;Machine Learning;Cardiac Arrest Prediction;Heart Disease Prediction;Support vector machines;Industries;Deep learning;Cepstrum;Cardiac arrest;Artificial neural networks;Electrocardiography|
|[Design and Adaptive Compliance Control of a Wearable Walk Assist Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089580)|S. H. Shah; M. S. Alam; M. Arsalan; I. ul Haq; S. G. Khan; J. Iqbal|10.1109/ICRAI57502.2023.10089580|adaptive control;wearable devices;lower limb exoskeleton;Legged locomotion;Knee;Performance evaluation;Adaptation models;Simulation;Exoskeletons;Assistive devices|
|[Control Simulation of Proportional-Integral and Sliding Mode Control for Precision Seed Planters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089606)|Y. Nawaz; M. U. Qadir; M. A. Khan; I. U. Haq; K. Shah; T. Khan|10.1109/ICRAI57502.2023.10089606|Seed metering systems (SMS);Proportional Integral (PI);Sliding mode control (SMC);Electric control systems (ECS);Electric-driven seed meters (EDSM);Signal tracking Seed metering control systems (SMCS);Electric-driven control system (EDCS);Meters;Industries;PI control;Automation;System performance;Production;Robustness|
|[Evaluation and Comparison of TRMS Performance for Classical and Optimal Control Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089583)|U. Ahmad; W. Ahmed|10.1109/ICRAI57502.2023.10089583|TRMS;Mathematical Modelling;PID;LQI;Kalman Filter;LQG;Jacobian matrices;Industries;Simulation;Rotors;Estimation;Optimal control;Transmission line measurements|
|[Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089578)|A. Khan; F. Nasir; M. Tufail; M. Haris; M. T. Khan; Z. Dong|10.1109/ICRAI57502.2023.10089578|Agriculture automation;Agricultural sprayer;Model predictive control;Precision agriculture;Pressure control;Transient response;Toxicology;Software packages;Spraying;Agriculture;Regulation;Real-time systems|
|[Sectional Trajectory Planning of Transmission Mechanism based on S-curve and Spline Curve](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089597)|C. Ming; Y. YuXuan; L. Dong; D. Yu; L. Bin|10.1109/ICRAI57502.2023.10089597|trajectory planning;genetic algorithm;spline;Vibrations;Adaptation models;Trajectory planning;Boats;Transportation;Graphite;Propagation losses|
|[Effect of Tine Shaped Furrow Opener on Dry Soil Using Discrete Element Modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089532)|A. Mohiz; F. E. Nasir; K. Shah|10.1109/ICRAI57502.2023.10089532|tillage;DEM simulation;furrow opener;tine shape;agriculture;Meters;Industries;Automation;Force;Crops;Soil;Software|
|[Determination of the Strain Energy and Stress Intensity Factors with the Crack's Inclination under biaxial loading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089534)|S. Khan; M. Junaid; F. N. Khan; T. Shehbaz|10.1109/ICRAI57502.2023.10089534|Fracture Mechanics;Biaxial Loading;Loading Factor;Stress Intensity Factor;J-Integral Method;Strain Energy Release Rate;Industries;Loading;Market research;Mathematical models;Distance measurement;Finite element analysis;Numerical models|
|[A Time Series Regression-based Model for Predicting the Spread of Dengue Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089545)|M. D. Waseem; A. Nawaz; U. Rasheed; A. Raza; M. O. Albarka|10.1109/ICRAI57502.2023.10089545|Machine Learning;Prediction;Dengue;Time Series Analysis;Industries;Machine learning algorithms;Time series analysis;Machine learning;Medical services;Predictive models;Prediction algorithms|
|[Improving Popular Textbook Recursive Algorithms with Tail Recursion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089594)|S. Ullah|10.1109/ICRAI57502.2023.10089594|Iteration;recursion;branched recursion;tail recursion;binary recursion;factorial;Fibonacci;binomial coefficient;algorithms;computer science;education;Industries;Codes;Heuristic algorithms;Software algorithms;Tail;Programming;Software|
|[Smart Railway Level Crossing System for Avoiding Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089536)|G. F. Mirza; Y. S. Junejo; F. K. Shaikh; B. S. Chowdhry; A. A. Shah|10.1109/ICRAI57502.2023.10089536|Railway level crossing;accident avoidance;Internet of Things;Global Positioning System;Hypertext Transfer Protocol;Databases;Roads;Logic gates;Cameras;Rail transportation;Sensor systems;Reliability|
|[Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089574)|C. D. Nguyen; F. E. Nasir; S. Khan; S. A. Khan; J. -M. Kim|10.1109/ICRAI57502.2023.10089574|bearings;vibration;envelope;signal;diagnosis;Vibrations;Fault diagnosis;Shafts;Time-frequency analysis;Filtering;Pumps;Machine learning|
#### **2023 10th Iranian Conference on Renewable Energy & Distributed Generation (ICREDG)**
- DOI: 10.1109/ICREDG58341.2023
- DATE: 15-16 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Investigating the Optimal Location Determination Index of Charging Stations from the Perspectives of Distribution Companies, Owners of Charging Stations and Owners of Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092112)|S. M. Rezvani; A. A. Shojaei; M. Nobahari|10.1109/ICREDG58341.2023.10092112|charge station;electric vehicle;optimal locating;electric distribution;Pollution;Urban areas;Transportation;Companies;Distribution networks;Charging stations;Fossil fuels|
|[A Novel Game Theory-based Participation Model of a Microgrid in a Competitive Electricity Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091971)|S. M. M. Tafreshi; P. Farhadi|10.1109/ICREDG58341.2023.10091971|microgrid;electricity market;game theory;electric vehicles;marginal clearing pricing;Renewable energy sources;Microgrids;Pricing;Electricity supply industry;Electric vehicles;Distributed power generation;Wind turbines|
|[A Fast Fault Detection Method for Protection of HVDC Transmission using Voltage of The PMFCL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092141)|A. Hosseinzadeh; H. Heydari|10.1109/ICREDG58341.2023.10092141|fault detection;PMFCL;SR;HVDC transmission;short-circuit current;Inductance;Renewable energy sources;Smoothing methods;HVDC transmission;Fault detection;Short-circuit currents;Voltage|
|[Optimal Low-Power SI-MIMO Converter for Renewable Energy Base Power Supply of Wireless Gas Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091970)|H. Karami; G. B. Gharehpetian; A. Baranov; I. Ivanov; D. Spirjakin|10.1109/ICREDG58341.2023.10091970|Wireless gas monitoring system;SI-MIMO;Renewable energy harvesting;Time-multiplexing control;Pulse width modulation;Wireless communication;Wireless sensor networks;Renewable energy sources;Power supplies;Switching frequency;Switches;Pulse width modulation|
|[Economic Evaluation of Supplying a Hotel by DGs in the condition of Khuzestan Province Using Sensitivity Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091995)|H. Gharibvand; G. B. Gharehpetian|10.1109/ICREDG58341.2023.10091995|PV;HOMER;Sensitivity analysis;Temperature effect;Hotel consumption;Temperature sensors;Photovoltaic systems;Renewable energy sources;Costs;Sensitivity analysis;Carbon dioxide;Software|
|[Implementation of the Internet of Things Technology in the Smart Power Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092002)|A. Meydani; A. Meidani; S. Shahablavasani|10.1109/ICREDG58341.2023.10092002|Internet of Things;Smart Grids;M2M Communication;Communication Technologies;Internet of Things;Climate change;Smart grids;Machine-to-machine communication;Communications technology|
|[Multi-Objective Optimization of Microgrid in the Presence of Distributed Energy Resources and Demand Response Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092013)|A. Mehrabani; S. M. Shobeiry; M. A. Rahimi; A. P. Neghab|10.1109/ICREDG58341.2023.10092013|demand response;microgrid;sizing;power model;optimization;Renewable energy sources;Monte Carlo methods;Wind speed;Microgrids;Demand response;Generators;Distributed power generation|
|[Optimal placement of D-STATCOM and PV solar in distribution system using probabilistic load models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10091990)|F. Fardinfar; M. J. K. Pour|10.1109/ICREDG58341.2023.10091990|D-STATCOM;Distributed Generation;Distribution System;Particle Swarm Optimization;Probabilistic Load;Monte Carlo;Monte Carlo methods;Metaheuristics;Voltage;Probabilistic logic;Automatic voltage control;Hybrid power systems;Generators|
|[Multi-Objective Optimization for Simultaneous Optimal Sizing & Placement of DGs and D-STATCOM in Distribution Networks Using Artificial Rabbits Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092092)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; R. Mohajery|10.1109/ICREDG58341.2023.10092092|Electricity Distribution Networks;Distributed Generation Sources;Artificial Rabbits Optimization Algorithm;D-STATCOM;Rabbits;Costs;Distribution networks;Voltage;Automatic voltage control;Power grids;Distributed power generation|
|[Fault detection and reconfiguration of the 12-pulse thyristor rectifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092138)|A. Z. Nejad; A. Dastfan|10.1109/ICREDG58341.2023.10092138|Fault detection;Reconfiguration;12-pulse rectifier;Clark transform;Thyristors;Power supplies;Fault detection;Rectifiers;Transforms;Electrical fault detection;Safety|
|[Effect of light spectrum on the performance of SMFC with microalgae improved cathode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092142)|F. Baratzade; R. Gheshlaghi; M. A. Mahdavi|10.1109/ICREDG58341.2023.10092142|Biocathode;Light wavelength;Microalgae;Sediment microbial fuel cell;Renewable energy sources;Production systems;Power system measurements;Microorganisms;Organic materials;Fuel cells;Color|
|[Estimation of Solar Cell Circuit Model Parameters Using an Algorithm Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092027)|A. Samani; M. Khodsuz|10.1109/ICREDG58341.2023.10092027|solar cell system;two diode circuit model;particle swarm algorithm;Archimedes optimization algorithm;mean square error;Photovoltaic cells;Optimization methods;Estimation;Mean square error methods;Distributed power generation;Integrated circuit modeling;Particle swarm optimization|
|[Forecasting the PV Panel Power Based on Image Processing and Historical Outputs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092128)|P. Ramezanzadeh; H. Delkhosh; M. P. Moghaddam|10.1109/ICREDG58341.2023.10092128|Solar photovoltaic;Forecasting;Deep learning;Image processing;Historical data;Photovoltaic systems;Uncertainty;Sensitivity;Image processing;Clouds;Weather forecasting;Stochastic processes|
|[MPPT Control of a PMSG Connected to the Wind Turbine based on Deep Q-Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092119)|M. Arianborna; J. Faiz; A. Erfani-Nik|10.1109/ICREDG58341.2023.10092119|Wind Energy Conversion Systems;Maximum Power Point Tracking;Deep Reinforcement Learning;Maximum power point trackers;Deep learning;Q-learning;Uncertainty;Wind speed;Perturbation methods;Synchronous generators|
|[Optimal Design and Simulation of a Rooftop Photovolataic System in Faculty of Engineering Guilan University](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10092071)|Z. Jalali; S. -M. Moghaddas-Tafreshi|10.1109/ICREDG58341.2023.10092071|Design;Photovoltaic;PVSYST software;Rooftop PV system;Photovoltaic systems;Renewable energy sources;Greenhouse effect;Buildings;Software;Inverters;Power systems|
#### **2023 7th International Conference on Robotics, Control and Automation (ICRCA)**
- DOI: 10.1109/ICRCA57894.2023
- DATE: 5-7 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Research on Matching of Motor Control System Based on Electric Drive of Engineering Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087777)|Z. Feng|10.1109/ICRCA57894.2023.10087777|Engineering vehicle;Motor drive;Energy-saving control;Bus topology;Current control;Motor drives;Rotors;Stators;Inverters;Stability analysis;Topology|
|[Research on BIM and Numerical Control Technology Based on Mechatronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087655)|D. Qiao|10.1109/ICRCA57894.2023.10087655|Electromechanical;BIM;Numerical Control technology;Mechatronics;Industry;Electromechanical Installation;Electrical engineering;Solid modeling;Mechatronics;Design automation;Pipelines;Manufacturing;Indexes|
|[Optimization and Development of Marine Engineering Technology Based on Intelligent Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087708)|J. Zhang|10.1109/ICRCA57894.2023.10087708|Intelligent Control;Ship Turbine;Turbine Unit Weight;Improved Particle Swarm Algorithm;Water;Hydraulic turbines;Sensitivity analysis;Reliability engineering;Safety;Particle swarm optimization;Marine vehicles|
|[Research on Motor Control Technology of New Energy Vehicle Based on MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087680)|K. Ma; L. Ma; C. Kong|10.1109/ICRCA57894.2023.10087680|MATLAB;New Energy Vehicles;Motor Control;Technology;Industries;Motor drives;Oils;AC motors;Power system stability;Stability analysis;Safety|
|[Design and Implementation of an Embedded System for Water Quality Monitoring (WQM) Based on Internet of Things (IOT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087727)|M. Ali; G. Ling; H. Elmouazen|10.1109/ICRCA57894.2023.10087727|IOT;NodeMCU;PH sensor;WQM;Temperature sensors;Temperature measurement;Water quality;Robot sensing systems;Real-time systems;Hardware;Water pollution|
|[Research on Methods of Expressway Vehicle Detection under Abnormal Weather Conditions Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087528)|R. Cao; X. Ma; X. Chen; X. Ma; L. Hua; C. Zhao; T. Ma; X. Wang|10.1109/ICRCA57894.2023.10087528|Intelligent Transportation;Deep Learning;Vehicle Detection;Abnormal Weather Conditions;Deep learning;Training;Rain;Automation;Vehicle detection;Snow;Labeling|
|[Research Progress on Detection Technology of Small Magnetic Targets on Moving Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087597)|M. Chang; L. Xu; X. Xiong; J. Zhang; X. Li|10.1109/ICRCA57894.2023.10087597|Motion platform;multi-rotor UAV;magnetic field detection;unexploded ordnance;magnetic detector;Automation;Weapons;Detectors;Magnetic fields;Robots|
|[Virtual Potential Field-Based Motion Planning for Human-Robot Collaboration via Kinesthetically Guided Teleoperation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087678)|Y. Shi; T. Wang; J. Yu; S. Xiao; L. Xiong; L. Yang|10.1109/ICRCA57894.2023.10087678|human-robot collaboration;virtual potential field;force feedback;teleoperation;Medical robotics;Automation;Dynamics;Collaboration;Surgery;Planning;Safety|
|[Globally Perceived Obstacle Avoidance for Robots Based on Virtual Twin and Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087870)|R. Jiang; F. Ying; G. Zhang; Y. Xing; H. Liu|10.1109/ICRCA57894.2023.10087870|deep reinforcement learning;robot;obstacle avoid-ance;trajectory generation;Deep learning;Training;Torque;Reinforcement learning;Production facilities;Trajectory;Safety|
|[The Mobility and High Load-to-weight Ratio Oriented Design Criteria of Hybrid Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087507)|B. Yuan; H. Wang; X. Pan|10.1109/ICRCA57894.2023.10087507|Hybrid mechanism;Load-to-weight;Design criteria;Unit power;Power system measurements;Anisotropic magnetoresistance;Density measurement;Power transmission;Kinematics;Fasteners;Hybrid power systems|
|[Inference about Transient States of Innovative RCM Structure for Soft Tissue Surgery Using FEM Taking into Account Inputs from in Vitro Experiment on Cardiovascular Tissue](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087555)|G. Ilewicz|10.1109/ICRCA57894.2023.10087555|surgical system;cardiac surgery;in vitro;cardiovascular tissue;RCM;MIRS;FEM;CMOS sensor;Savitzky-Golay;Durbin-Watson;transient states;Semiconductor device modeling;Medical robotics;Surgery;Safety;Finite element analysis;Mechanical systems;Transient analysis|
|[Efficient Policy Learning for General Robotic Tasks with Adaptive Dual-memory Hindsight Experience Replay Based on Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087824)|M. Dong; F. Ying; X. Li; H. Liu|10.1109/ICRCA57894.2023.10087824|deep reinforcement learning;robot;hindsight experience replay (HER);policy learning;Training;Deep learning;Automation;Redundancy;Decision making;Reinforcement learning;Manipulators|
|[l1 PID Controller Synthesis via Delta Operator Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087867)|R. Li; K. Cao; J. Xie; H. Du; M. Zeng|10.1109/ICRCA57894.2023.10087867|PID control;l1 performance;delta operator approach;cone complementary linearization algorithm;Linear systems;Discrete-time systems;Systematics;PI control;Automation;PD control;Robots|
|[Research on Force Transmission Control of Bowden Cable-Fabrics Based on FEM and Fuzzy ADRC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087591)|D. Wang; X. Li; G. Ma; Z. Xu|10.1109/ICRCA57894.2023.10087591|Bowden cable-Fabrics;ADRC;Flexible exoskeleton;Finite element;Compensation;Analytical models;PI control;Friction;Simulation;Force;Propagation losses;Fabrics|
|[Deep Reinforcement Learning in Maximum Entropy Framework with Automatic Adjustment of Mixed Temperature Parameters for Path Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087467)|Y. Chen; F. Ying; X. Li; H. Liu|10.1109/ICRCA57894.2023.10087467|deep reinforcement learning;path planning;obstacle avoidance;maximum entropy;Deep learning;Reinforcement learning;Manuals;Entropy;Path planning;Temperature control;Task analysis|
|[Design of an Automated Dosing and Mixing System for the Production of Biodegradable Biomass Based on Sweet Potato Starch and Sugar Cane Fiber](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087874)|B. R. Izarra-Cano; L. G. Castro-Osores; C. A. Blas-Yaringaño; D. Huamanchahua|10.1109/ICRCA57894.2023.10087874|PLC;TIA Portal;HMI;biodegradable packaging;mixing;dosing;Optical fiber sensors;Soil;Valves;Software;Sensor systems;Sugar industry;Biomass|
|[Research on Intelligent Automatic Operating System of Marine Concrete Coating Aided by Computer Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087764)|Z. Guo|10.1109/ICRCA57894.2023.10087764|Computer Technology;Marine Concrete;Concrete Coating;Intelligent;Automatic Operation;Analog Simulation;Resistance;Operating systems;Corrosion;Heuristic algorithms;Simulation;Stability analysis;Coatings|
|[Design of A Portable Elevator Balance Coefficient Test Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087629)|J. Chen|10.1109/ICRCA57894.2023.10087629|elevator;portable;load capacity;balance coefficient;device;Shafts;Torque;Measurement uncertainty;Data acquisition;Robot sensing systems;Elevators;Time measurement|
#### **2023 International Conference on Smart Computing and Application (ICSCA)**
- DOI: 10.1109/ICSCA57840.2023
- DATE: 5-6 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Enhancing Data Security in the Cloud Platform by Encrypting the Used Key Dynamically](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087490)|L. K. A. Alsumair; K. A. Aldhlan|10.1109/ICSCA57840.2023.10087490|Security;Smart city;Cloud;Encryption algorithm;Dynamic key;AES;Blowfish;Sensitivity;Smart cities;Heuristic algorithms;Buildings;Encryption;Standards;Faces|
|[A Sequential-based Deep Learning Model for Dry Beans Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087861)|R. Sujatha; J. M. Chatterjee; A. Rohith; R. A. Ramadan|10.1109/ICSCA57840.2023.10087861|Dry beans (DBs);Keras;Multilayer Perceptron (MLP);Machine Learning (ML);Deep learning (DL);classification;Deep learning;Proteins;Industries;Machine learning algorithms;Shape;Supervised learning;Prediction algorithms|
|[Automatic Enlarged Lymph Node Detection by Volume Estimation from 3D Abdominal CT Images Based on Speed Up Robust Features and Maximum Intensity Projection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087456)|A. R. Al-Shamasneh|10.1109/ICSCA57840.2023.10087456|Enlarged lymph node;Speed up robust feature (SURF);Maximum intensity projection (MIP);abdominal area;detection;CT;Feature Extraction;Three-dimensional displays;Shape;Computed tomography;Volume measurement;Surgery;Observers;Feature extraction|
|[RTLB_Sched: Real Time Load Balancing Scheduler for CPU-GPU Heterogeneous Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087604)|T. A. Rahmani; G. Belalem; S. A. Mahmoudi|10.1109/ICSCA57840.2023.10087604|Heterogeneous Systems;Machine Learning;Scheduling;Load Balancing;Pycaret;Portable computers;Computational modeling;Source coding;Graphics processing units;Machine learning;Predictive models;Load management|
|[A Metamodeling Approach for Structuring and Organizing Cloud Forensics Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087425)|R. Al-Mugerrn; A. Al-Dhaqm; S. H. Othman|10.1109/ICSCA57840.2023.10087425|Semantic Metamodeling;Cloud Computing;Forensics Domain;Cloud Forensics;Cloud computing;Forensics;Computational modeling;Semantics;Taxonomy;Metamodeling;Task analysis|
|[Multi-culture Sign Language Detection and Recognition Using Fine-tuned Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087884)|M. F. Nurnoby; E. -S. M. El-Alfy|10.1109/ICSCA57840.2023.10087884|Multi-culture Sign Language;Sign Language Identification;Gesture Recognition;Convolutional Neural Networks;Deep Learning;Human computer interaction;Deep learning;Computer vision;Visual communication;Computational modeling;Gesture recognition;Computer architecture|
|[A Novel Sedentary Workforce Scheduling Optimization Algorithm using 2nd Order Polynomial Kernel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087492)|N. Patel; S. Trivedi; N. Faruqui|10.1109/ICSCA57840.2023.10087492|Scheduling;Machine Learning;SVM;Polynomial Kernel;optimization;Workforce;AI in HRM;Support vector machines;Productivity;Performance evaluation;Schedules;Veins;Employment;Optimal scheduling|
|[Complexity of IoT world - Review of challenges and opportunities in application development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087783)|N. S. Ebrahim|10.1109/ICSCA57840.2023.10087783|IoT (opportunities;prospects;challenges;protocols);nan|
|[Improvement of LEACH Protocol: W-LEACH Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087572)|S. A. Chafi; H. Abbad; M. K. Benhaoua; A. El Hassan Benyamina|10.1109/ICSCA57840.2023.10087572|Wireless Sensor Networks;Sensor;Power Consumption;LEACH;W-LEACH;Hierarchical Routing Protocol;Data Aggregation;Cluster Approach;Cluster;Cluster-Head;Base Station;Wireless communication;Wireless sensor networks;Protocols;Embedded systems;Routing;Routing protocols;Matlab|
|[Proposed Framework for Managing Customer Queries in Banking sector using Robotic Process Automation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087779)|S. Yadav; V. Bhardwaj; A. U. Rehman; N. Alsharabi|10.1109/ICSCA57840.2023.10087779|RPA;Robotic Process Automation;Banking sector;Customer Query Management;Customer Query Handling;Intelligent automation;Industries;Customer satisfaction;MIMICs;Banking;Organizations;Medical services|
|[A Review of Agile Methods for Requirement Change Management in Web Engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087734)|A. S. Alsharari; W. M. N. Wan Zainon; S. Letchmunan; B. A. Mohammed; M. S. Alsharari|10.1109/ICSCA57840.2023.10087734|Engineering;Agile Methodologies;Requirement Change Management;Software Engineering;nan|
|[Diabetes Prediction Using Machine Learning Classification Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087827)|P. Rani; R. Lamba; R. K. Sachdeva; P. Bathla; A. N. Aledaily|10.1109/ICSCA57840.2023.10087827|Diabetes;Support Vector Machine;Random Forest;Extreme Gradient Boosting;Decision Tree;Radio frequency;Heart;Support vector machine classification;Forestry;Diabetes;Classification algorithms;Glucose|
|[Deep learning based DeepFake video detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087584)|S. Guefrachi; M. Ben Jabra; N. A. Alsharabi; M. T. Ben Othman; Y. O. Alharabi; A. Alkholidi; H. Hammam|10.1109/ICSCA57840.2023.10087584|Deep-fake detection;Video authenticity;Fine-tuning;VGG16;Deep learning;Deepfakes;Computer vision;Social networking (online);Computational modeling;Neural networks;Transfer learning|
|[Face Recognition Accuracy Improving Using Gray Level Co-occurrence Matrix Selection Feature Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087414)|Vera; A. Kusnadi; I. Z. Pane; M. V. Overbeek; S. G. Prasetya|10.1109/ICSCA57840.2023.10087414|backpropagation;face recognition;feature extraction;GLCM;texture;Training;Backpropagation;Face recognition;Information security;Backpropagation algorithms;Feature extraction;Data mining|
|[Significance of Internet-of-Things Edge and Fog Computing in Education Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087582)|S. T. Siddiqui; M. R. Khan; Z. Khan; N. Rana; H. Khan; M. I. Alam|10.1109/ICSCA57840.2023.10087582|Edge computing;IoT Edge Computing;Educational system;IoT Devices;Cloud;Productivity;Cloud computing;Privacy;Systematics;Education;Systems architecture;Real-time systems|
|[An Enhanced Haar Cascade Face Detection Schema for Gender Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087742)|P. Sridhar; P. Chithaluru; S. Kumar; O. Cheikhrouhou; H. Hamam|10.1109/ICSCA57840.2023.10087742|Gender Recognition;Image Processing;Feature Extraction;Image Enhancement;Ethics;Face recognition;Neural networks;Nose;Mouth;Feature extraction;Behavioral sciences|
|[Modern, Secure Application Programming Interface Implementation using RFC 6238 and RFC 7617](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087735)|N. D. Wirasbawa; Y. Khaeruzzaman; A. Waworuntu|10.1109/ICSCA57840.2023.10087735|Application Programming Interface;Attendance System;RFC 6238;RFC 7617;Two-Factor Authentication;Measurement;Technology acceptance model;Authentication;Passwords;Programming;Internet;Security|
|[Generating Muslim Name using Character-Level Language Model in Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087673)|S. A. Ali Shah; W. M. S. Yafooz; A. -H. M. Emara; H. Sajid|10.1109/ICSCA57840.2023.10087673|Recurrent Neural Network;Neural Network;Deep Learning;Machine Learning;Artificial Intelligence;Muslim Person Name;Training;Deep learning;Vocabulary;Recurrent neural networks;Machine learning algorithms;Computational modeling;Symbols|
|[Forest Fire Detection by using MESA2DA Clustering Protocol based on Artificial Intelligence Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087479)|B. Gupta; S. Rana; S. K. Goyal; R. K. Gujral; A. N. Aledaily|10.1109/ICSCA57840.2023.10087479|Artificial Intelligence;Fuzzy logic;Clustering;Wireless Sensor Network;Fuzzy logic;Wireless communication;Wireless sensor networks;Protocols;Simulation;Weather forecasting;Forestry|
|[Internet of Things (IoT) based Model for Water Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087587)|M. A. Haque; S. Ahmad; A. E. M. Eljialy; M. Y. Uddin; D. Sonal|10.1109/ICSCA57840.2023.10087587|Internet of Things;Water distribution system;pH sensor;Flow sensor;Turbidity sensor;Temperature sensor;Arduino model;Analytical models;Computational modeling;System performance;Water quality;Water pollution;Real-time systems;Data models|
|[A Safeguard Agent for Intelligent Health-care Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087746)|A. Alzahrani|10.1109/ICSCA57840.2023.10087746|intelligent health-care environments;smart health care;Internet of Things;intelligent health-care security;intelligent security agent;Computational modeling;Collaboration;Reliability;Security;Internet of Things;Intelligent agents;Random forests|
|[Machine Learning Model for Predicting the Cuisine Category from a Dish Ingredients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087436)|F. Alshanketi|10.1109/ICSCA57840.2023.10087436|cuisine prediction;recipe ingredients;machine learning;Support vector machines;Machine learning;Predictive models;Feature extraction;Data models;Numerical models;Recording|
|[Synchronized Speech and Video Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087508)|A. S. Barve; P. Madhani; Y. Ghule; P. Potdukhe; K. Pawar; N. Bhandare|10.1109/ICSCA57840.2023.10087508|Speech Synthesis;LSTM;Multi Layer Perceptron;Voice Cloning;Attention based Encoder-Decoder;Training;Sentiment analysis;Lips;Pipelines;Dynamics;Synchronization;Speech processing|
|[A Referenced Framework on New Challenges and Cutting-Edge Research Trends for Big-Data Processing Using Machine Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087686)|M. Kirola; M. Memoria; M. Shuaib; K. Joshi; S. Alam; F. Alshanketi|10.1109/ICSCA57840.2023.10087686|Machine Learning;Data Mining;Big-Data Analytics;Big-Data Processing;Data analysis;Decision making;Distributed databases;Machine learning;Transforms;Big Data;Parallel processing|
|[Evaluation of AI-based Meta-scheduling Approaches for Adaptive Time-triggered System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087446)|D. Onwuchekwa; M. Dasandhi; S. Alshaer; R. Obermaisser|10.1109/ICSCA57840.2023.10087446|Adaptation;Artificial Intelligence;Artificial Neural Networks;Meta scheduling;Random Forest;Time-Triggered Systems;Schedules;Artificial neural networks;Computer architecture;Forestry;Cyber-physical systems;Explosions;Energy efficiency|
|[Blockchain-Based Online E-voting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087767)|Y. A. F. Ali; O. T. M. Ahmed; M. A. M. Diab; M. A. E. Sayed; M. K. Abd Elaziz; B. W. Aboshosha|10.1109/ICSCA57840.2023.10087767|Electronic Voting;Blockchain Technology;Machine Learning; Trust;Ethereum;Virtual Machine;Privacy;Automation;Face recognition;Smart contracts;Authentication;Machine learning;Blockchains|
|[Epileptic Seizure Detection Using the EEG Signal Empirical Mode Decomposition and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087614)|J. Nassir; M. Alasabi; S. M. Qaisar; M. Khan|10.1109/ICSCA57840.2023.10087614|Electroencephalogram;Empirical Mode Decomposition;Epileptic Seizure;Classification;Machine Learning;Support vector machines;Empirical mode decomposition;Epilepsy;Medical treatment;Pattern classification;Nonlinear filters;Sensitivity and specificity|
|[Implementation of Recency Techniques Monetary and K-Means for the Consumer Segmentation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087563)|P. Aubrey; A. A. Permana; J. C. Young|10.1109/ICSCA57840.2023.10087563|Recency;Frequency;Monetary;Silhouette;K-Means Clustering;Normalization;Standardization;Time-frequency analysis;Limiting;Clustering algorithms;Production;Machine learning;Data models;Elbow|
|[Evaluating machine learning algorithms for predicting house prices in Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087486)|T. Alshammari|10.1109/ICSCA57840.2023.10087486|House price prediction;Decision Tree Regression;Random Forest Regression;Linear Regression;Radio frequency;Economics;Measurement;Machine learning algorithms;Linear regression;Prediction algorithms;Random forests|
|[Comparative Study of Prognosis of Malware with PE Headers Based Machine Leaning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087532)|M. H. U. Sharif; M. AliMohammed; S. Hassan; M. H. Sharif|10.1109/ICSCA57840.2023.10087532|Chi-squared test;Malware Classification;Feature Extraction;Machine Learning;Malware Detection;Machine learning algorithms;Software algorithms;Machine learning;Feature extraction;Classification algorithms;Ransomware;Prognostics and health management|
|[Heart Disease Identification Based on Butterfly Optimization and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087885)|M. Asrar; J. Bawazir; S. I. Khan; S. M. Qaisar|10.1109/ICSCA57840.2023.10087885|PCG;Heart diseases;Variational Mode Decomposition;Butterfly Optimization;Algorithms;Classification;Machine learning;Heart;Support vector machines;Machine learning algorithms;Computational modeling;Artificial neural networks;Mathematical models;Recording|
|[AraDS: Arabic Datasets for Text Mining Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087675)|W. M. S. Yafooz; A. Alsaeedi; A. -H. M. Emara|10.1109/ICSCA57840.2023.10087675|Arabic dataset;text mining;sentiment analysis;classification;machine learning;Text mining;Sentiment analysis;Analytical models;Video on demand;Social networking (online);Video description;Digital transformation|
|[Efficient Traffic Management For Resolving Traffic Congestion On Motorways](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087443)|M. Arslan; R. A. Bakar; A. Ahad|10.1109/ICSCA57840.2023.10087443|Vehicle Platooning;vehicle to vehicle;style;Traffic formulation;Roads;Vehicular ad hoc networks;Switches;Automobiles;Distributed algorithms;Traffic congestion;Accidents|
|[Using Personal Key Indicators and Machine Learning-based Classifiers for the Prediction of Heart Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087430)|R. Jahed; O. Aseer; A. Al-Mousa|10.1109/ICSCA57840.2023.10087430|Machine learning;Heart disease;Coronary heart disease;myocardial infarction;Stochastic gradient descent;Decision tree classifier;Random forest classifier;Heart;Machine learning algorithms;Stochastic processes;Forestry;Stroke (medical condition);Predictive models;Data models|
|[Implementing an Ambient Air Quality Monitoring System in Spaces with Inadequate Ventilation using the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087523)|M. M. Khayyat; O. A. Aboulola|10.1109/ICSCA57840.2023.10087523|Ambient;Air Quality Index;Air pollution;Air quality;Environmental monitoring;Internet of Things;Roads;Air quality;Ventilation;Pollution measurement;Internet of Things;Indexes;Sustainable development|
|[Development of an Autograding System for Weld Bead Surface Quality using Feature Extraction and Mahalanobis-Taguchi System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087789)|N. Harudin; M. A. H. Norizhar; Z. M. Marlan; F. E. B. Selamat|10.1109/ICSCA57840.2023.10087789|Feature extraction;Image Processing technique;Mahalanobis-Taguchi System;Mahalanobis Distance;Autograding;Weld Bead Surface Quality;Manufacturing processes;Welding;System performance;Fixtures;Education;Transforms;Feature extraction|
|[Copper and Optical Networks Readiness Index Evaluation within Organizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087892)|A. M. Al BinAIi|10.1109/ICSCA57840.2023.10087892|Network Readiness Index (NRI);Copper Network Readiness Index (CNRI);Optical Network Readiness Index (ONRI);FTTx;CTTx;Network management;Network Performance;Quality of Service;Urban areas;Organizations;Optical fiber networks;Indexes;Copper;Communication networks|
|[Prediction of Diabetes Using Hybridization based Machine learning algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087491)|R. L. Naidu Boddeda; R. Prasad; S. S. Amiripalli; M. S. Jitendra|10.1109/ICSCA57840.2023.10087491|Machine learning;Diabetes;SVM;K-Nearest Neighbour;Decision Tree;Random forest;Logistic regression;PIMA data set;Support vector machines;Machine learning algorithms;Soft sensors;Standardization;Prediction algorithms;Diabetes;Classification algorithms|
|[Preemptive Diagnosis of Hepatitis C Using Machine Learning Techniques: A Retrospective Study in Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087834)|S. O. Olatunji; M. Farooqui; M. Alsaeed; R. Alqahtani; F. Alqassab; M. Alghamdi; F. Alshammari; A. Alansari; M. A. A. Khan; M. S. Ahmed; J. Alhiyafi|10.1109/ICSCA57840.2023.10087834|Hepatitis C;Preemptive Diagnosis;Machine Learning;Genetic Algorithm;Machine learning algorithms;Liver diseases;Hospitals;RNA;Machine learning;Organ transplantation;Classification algorithms|
|[Towards Diabetes Mellitus Prediction Based on Machine- Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087782)|H. El Bouhissi; R. E. Al-Qutaish; A. Ziane; K. Amroun; N. Yaya; M. Lachi|10.1109/ICSCA57840.2023.10087782|Machine-Learning;DNN;RF;SVM;Prediction;Diabetes;Pregnancy;Support vector machines;Radio frequency;Pathology;Prediction algorithms;Diabetes;Pancreas;Predictive analytics|
|[Energy-efficient routing to prevent void holes in heterogeneous 5G wireless sensor network using game theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087702)|A. Mateen; A. Ahad; S. Zia; I. Shayea; S. Ali|10.1109/ICSCA57840.2023.10087702|Void hole alleviation;Wireless sensor network;Energy efficiency;Network stability;Heterogeneous 5G network;Wireless sensor networks;5G mobile communication;Scalability;Packet loss;Routing;Stability analysis;Routing protocols|
|[DPIDNS:A Deep Packet Inspection Based IPS for Security Of P4 Network Data Plane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087377)|A. Ahad; R. A. Bakar; M. Arslan; M. H. Ali|10.1109/ICSCA57840.2023.10087377|switches with p4 programming;fast network speed;deep packet Inspection (DPI);Domain name system filtering;Protocols;Filtering;Packet loss;Inspection;Performance gain;Network security;Throughput|
|[Variational Quantum Algorithms for Solving Vehicle Routing Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087522)|M. Alsaiyari; M. Felemban|10.1109/ICSCA57840.2023.10087522|Quantum computing;VRP;VQE;QAOA;Quantum computing;Quantum algorithm;Vehicle routing;Software algorithms;Transportation;Approximation algorithms;Hardware|
|[A Unified Framework for Automating Software Security Analysis in DevSecOps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087568)|M. A. Aljohani; S. S. Alqahtani|10.1109/ICSCA57840.2023.10087568|DevSecOps;Security;Automation;DevOps;DAST;SAST;Pipelines;Computer architecture;Software;Application security;Security;Cultural differences;Testing|
|[Digital Transformation Model to Improve Educational Processes in Higher Education Applying Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087660)|A. F. Alshammari|10.1109/ICSCA57840.2023.10087660|Digital transformation;Big Data;Digital Disruption;Blended Learning;Virtual Classrooms;Moodle;Concurrent Triangulation Design;Engineering profession;Digital transformation;Computational modeling;Education;Decision making;Entrepreneurship;Big Data|
|[Adaptive Batch Normalization for Training Data with Heterogeneous Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087711)|W. Alsobhi; T. Alafif; W. Zong; A. E. Abdel-Hakim|10.1109/ICSCA57840.2023.10087711|Batch Normalization;Convolutional Neural Networks;Adaptive Batch Normalization;Heterogeneous Training Data;Training;Deep learning;Adaptive systems;Training data|
|[Offloading Techniques in Mobile Edge Computing (MEC) for Future Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087588)|A. F. Şahin; I. Shayea; İ. Yazici; A. E. Saleh; S. A. Saad|10.1109/ICSCA57840.2023.10087588|Mobile edge computing;multi-access edge computing;computational offloading;task offloading;machine learning;5G;IoT;IoV;Deep learning;Multi-access edge computing;Wireless networks;Collaboration;Network architecture;Heterogeneous networks;Mobile handsets|
|[Identifying the Gender of Human Cyber Attackers Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087688)|T. S. Almurayziq|10.1109/ICSCA57840.2023.10087688|machine learning;cyber-attack;human detection;artificial intelligence classification;Support vector machines;Economics;Social sciences;Machine learning;Artificial neural networks;Reliability;Cyberattack|
|[Broad Analysis of Deep Learning Techniques for Diabetic Retinopathy Screening](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087482)|S. Tiwari; A. Shukla; A. Jain; A. Alferaidi|10.1109/ICSCA57840.2023.10087482|Retinopathy Imaging;Optical coherence tomography (OCT) imaging;Deep Learning;Diabetic Retinopathy Diagnosis;Deep learning;Training;Retinopathy;Machine vision;Blindness;Tomography;Predictive models|
|[Improving placement of CSA-nets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087641)|M. Alahmadi; M. Koutny|10.1109/ICSCA57840.2023.10087641|Petri nets;visualisation;analyse;arc crossing;sorting;net placement;Visualization;Petri nets;Metaheuristics;Filtering algorithms;Information filters;Task analysis;Sorting|
|[An Exploratory Analysis of Effect of Adversarial Machine Learning Attack on IoT-enabled Industrial Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087713)|S. Trivedi; T. Anh Tran; N. Faruqui; M. M. Hassan|10.1109/ICSCA57840.2023.10087713|Gradient Boosting;Iterative Dichotomiser 3;Ad-versarial Machine Learning;Intrusion Detection System;Internet of Things;Industrial Control System;Adversarial Samples;Integrated circuits;Terminology;Industrial control;Intrusion detection;Production;Adversarial machine learning;Internet of Things|
|[The 5G Wireless Technology and a Significant Economic Growth and Sustainable Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087596)|A. Alkholidi; N. A. Alsharabi; H. Hamam; T. S. Alshammari|10.1109/ICSCA57840.2023.10087596|Techno-Economics Development;5G;6G;Wireless;business;Economics;Location awareness;5G mobile communication;Social networking (online);Wireless networks;Roads;Entertainment industry|
|[Improved Shepherding Model for Large-scale Swarm Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087385)|S. Elsayed; M. Hassanin|10.1109/ICSCA57840.2023.10087385|─Swarm control;Shepherding;Large-scale;Industries;Machine learning algorithms;Clustering algorithms|
|[Phishing Website Detection using XGBoost and Catboost Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087829)|K. Sadaf|10.1109/ICSCA57840.2023.10087829|Phishing websites;XGBoost;Catboost;Phishing detection;Cybersecurity;Uniform resource locators;Phishing;Tuning|
|[A Perceptual Data Cleansing Model (SDCM) for Reducing the Dirty Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087605)|M. A. Al-Madi; A. Abdel-Wahab; M. AlShanty; S. Bawazeer; M. AlZahrani|10.1109/ICSCA57840.2023.10087605|data cleaning;dirty data;supervised cleaning rules;datasets;outsourced errors;datarepository;Training;Computational modeling;Supervised learning;Clustering algorithms;Data warehouses;Cleaning;Data models|
|[Networks Intrusion Detection Using Optimized Hybrid Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087611)|P. Selvarajan; R. Salman; S. Ahamed; P. Jayasuriya|10.1109/ICSCA57840.2023.10087611|Convolutional Neural Network (CNN);Intrusion Detection System (IDS);Recurrent Neural Network (RNN);Machine Learning;optimization;Analytical models;Recurrent neural networks;Sensitivity;Computational modeling;Network intrusion detection;Machine learning;Remote working|
|[Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087570)|F. M. Al-Tbenawey; S. Alotaibi; M. Salem; S. Mir; Z. Mir|10.1109/ICSCA57840.2023.10087570|Kidney stones;detection;conventional neural networks (CNNs);K-Nearest Neighbor (KNN);MATLAB;Support vector machines;Image segmentation;Ultrasonic imaging;Ultrasonic variables measurement;Particle separators;Neural networks;X-rays|
|[An Innovative Deep Neural Network for Stress Classification in Workplace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087794)|N. Patel; S. Trivedi; N. Faruqui|10.1109/ICSCA57840.2023.10087794|HRM;Deep Learning;Stress Classification;Quantitative;Neural Network;Deep learning;Productivity;Performance evaluation;Human intelligence;Computer network reliability;Employment;Neural networks|
|[Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087682)|H. M. Alyahya; M. M. Ben Ismail; A. Al-Salman|10.1109/ICSCA57840.2023.10087682|Deep Learning;ResNet-18;Arabic Recognition;CNN;Machine Learning;COVID-19;Training;Handwriting recognition;Adaptation models;Pandemics;System performance;Computer architecture|
|[Project of CPEC on Pakistani TV Channels: Media Preferences and Project Understanding Influence awareness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087451)|M. W. Rana; S. Zhang; A. Yousaf|10.1109/ICSCA57840.2023.10087451|CPEC;Media projection;Pakistani TV Channels;Social Responsibility of Media;Economics;TV;Social networking (online);Roads;Government;Transportation;Games|
#### **2023 Second International Conference on Electronics and Renewable Systems (ICEARS)**
- DOI: 10.1109/ICEARS56392.2023
- DATE: 2-4 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Fuzzy Logic-based MPPT Control for Bifacial Photovoltaic Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085389)|N. Siddiqui; A. Verma; D. Shrivastava|10.1109/ICEARS56392.2023.10085389|Irradiance modeling;PV modeling;Bifacial cell modeling;MPPT;Fuzzy logic system;DC-DC converter;Maximum power point trackers;Photovoltaic systems;Fuzzy logic;Renewable energy sources;Power system measurements;Photovoltaic cells;Topology|
|[Portable Automatic System for Data Acquisition of Noise Pollution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085496)|C. Ciufudean; C. Buzduga|10.1109/ICEARS56392.2023.10085496|Noise;Pollution;Real time measurements;Arduino;Programming language;Data acquisition;Performance evaluation;Renewable energy sources;Pollution;Data acquisition;Urban areas;Web pages;Real-time systems|
|[A Novel Method for Fire Detection and Alarming Notification using IoT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084949)|S. Banka; B. Kanchanapalli; B. Annam; A. Akula; S. Thirukkovalluru; S. Jarpula|10.1109/ICEARS56392.2023.10084949|Flame sensor;NodeMCU;ThingSpeak;Internet of Things (IoT);MIT;Temperature sensors;Wireless communication;Temperature measurement;Visualization;Wireless sensor networks;Surveillance;Fires|
|[IoT based Smart and Automated Solar Panel Cleaning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085427)|V. S. G; R. R. K; M. S. N; K. S; K. K; H. K|10.1109/ICEARS56392.2023.10085427|Solar panel;Dust;Speck;Internet of Things;Android;Microcontroller;Cleaning;software;Renewable energy sources;Costs;Microcontrollers;Birds;Control systems;Cleaning;Batteries|
|[A Novel Approach on IoT Based Waste Water Management System in Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085533)|K. K; H. S; K. M; K. T|10.1109/ICEARS56392.2023.10085533|Sensors;temperature;wastewater;turbidity;cloud;Industries;Temperature sensors;Industrial waste;Water pollution;Real-time systems;Rivers;Wastewater|
|[A Deep Learning and IoT based Food Quality Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085194)|P. G. Krishna G; S. I; M. K. B; P. Sai Monika Y; S. K; S. B|10.1109/ICEARS56392.2023.10085194|Support Vector Machine (SVM);DNN;Convolution Neural Networks (CNN);Neural Networks;Sensors;Food Quality Detection;IoT;Support vector machines;Deep learning;Wireless communication;Wireless sensor networks;Renewable energy sources;Neural networks;Sensor systems|
|[Geo-Fencing and Overspeed Alert SMS System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085114)|H. Perusomula; V. Marriwada; S. K. Vallepu; K. Sesham; R. C. Gudapati; J. Garikipati|10.1109/ICEARS56392.2023.10085114|Geofencing;Speed detection;Emergency Gesture detection;GSM;Renewable energy sources;Satellite broadcasting;Radio navigation;Software;Safety;Task analysis|
|[Smart Controller for Air Conditioning in Car using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085112)|P. G; M. P; R. P; S. T; T. R. V. B; P. P|10.1109/ICEARS56392.2023.10085112|IoT;Smart Controller;Mobile Application;Researchable Battery;Temperature Sensor;Temperature sensors;Temperature measurement;Mood;Control systems;Mobile applications;Temperature control;Automobiles|
|[An Innovative Embedded System based Solution for Waste Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085069)|C. R; G. Guntanala; S. Bille; Y. Moguluri; J. Singamshetty|10.1109/ICEARS56392.2023.10085069|Swachh Bharath Mission;Arduino;Smart Dustbin;Waste management;Renewable energy sources;Pollution;Embedded systems;Maintenance engineering;Acoustics;Sensor systems|
|[An IoT based Low Cost E-Parking System in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085395)|P. G; M. T; G. C; R. B. C; G. V; S. M|10.1109/ICEARS56392.2023.10085395|IoT;Wearable Sensors;Biomechanics;Sports Applications;Space vehicles;Performance evaluation;Integrated optics;Chaos;Renewable energy sources;Costs;Smart cities|
|[IoT based Energy Efficient Smart Metering System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085037)|J. P; G. S. S; B. V; L. A; P. Nandankar; N. S. Kumar|10.1109/ICEARS56392.2023.10085037|IoT;Energy Efficient;Node MCU;PIC Microcontroller;Smart Metering;Meters;Microcontrollers;Tariffs;Systems architecture;Liquid crystal displays;Meter reading;Internet of Things|
|[Sports Applications of Biomechanics Wearable Sensors using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085068)|S. J S; N. M; T. S; P. G; A. P. M M; S. R|10.1109/ICEARS56392.2023.10085068|IoT;Wearable Sensors;Biomechanics;Sports Applications;Training;Biomechanics;Performance evaluation;Tracking;Biosensors;Biomedical monitoring;Wearable sensors|
|[Portable Automatic System for Locating Victims of Plane Crashes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085070)|C. Ciufudean; C. Buzduga|10.1109/ICEARS56392.2023.10085070|Plane crashes;Locating victims;Microcontroller;GPS;Data acquisition;Airplanes;Renewable energy sources;Satellites;Terminology;Roads;Sensor systems;Real-time systems|
|[Use of Internet of Things in the Tourism and Hospitality Realm: A Descriptive Bibliometric Study using Bibliometrix R - Tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085341)|A. Kumar Singh; P. Kumar Tyagi; A. Irshad; A. K. Singh; P. Tyagi; B. Benedict|10.1109/ICEARS56392.2023.10085341|Internet of things;Technology;Hospitality;Tourism;Hotel;Industries;Databases;Urban areas;Bibliometrics;Computer architecture;Big Data;Internet of Things|
|[An IoT Enabled Smart Flask](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085403)|R. Chauhan; S. Kaintura; N. Dhyani; R. Gowri; C. Bhatt|10.1109/ICEARS56392.2023.10085403|Keywords— Smart flask;hydration;Internet of Things (IoT);Water;health;Technological innovation;Renewable energy sources;Microorganisms;Liquids;Power supplies;Water heating;Mobile applications|
|[IoT-based Battery Health Monitoring System for Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085388)|V. Rukkumani; T. Anitha; P. A. Evangilin; P. Booja Aniruti; P. Deepthiga|10.1109/ICEARS56392.2023.10085388|ATMEGA328P microcontroller;Battery;Relay;Liquid Crystal Display (LCD);Internet of Things (IoT) Module.;Lithium-ion batteries;Lead acid batteries;Renewable energy sources;User interfaces;Lead;Electric vehicles;Lead compounds|
|[Agri-IoT: A Farm Monitoring and Automation System using Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085235)|V. V. Reddy S; B. Jaison; A. Balaji; D. Indumathy; S. Vanaja; J. J. Jeya Sheela|10.1109/ICEARS56392.2023.10085235|Agri-IoT;Robot;GSM;LED;Temperature;Humidity;Wi-Fi;Temperature sensors;Cloud computing;Bluetooth;Soil moisture;Humidity;Robot sensing systems;Solar panels|
|[Automated Aquaponics Farming using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085340)|M. S. Chandana; C. Sridevi; R. A. Chowdary; D. Likhitha|10.1109/ICEARS56392.2023.10085340|PH sensor;Digital Humidity and Temperature 11 sensor;Ultrasonic sensor;Servo motor;Node Micro Controller Unit;Temperature sensors;Temperature measurement;Prototypes;Humidity;Fish;Acoustics;Temperature control|
|[IoT and Face Recognition based Automated Door Lock System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085609)|G. Surla; S. Manepalli; N. A. Shaik; N. Saritha Gurram|10.1109/ICEARS56392.2023.10085609|Internet of Things (IoT);face recognition;voice message;One Time Password;Haar classifier;Raspberry Pi;Renewable energy sources;Databases;Face recognition;Real-time systems;Face detection;Security;Internet of Things|
|[Role of Neural Network, Fuzzy, and IoT in Integrating Artificial Intelligence as a Cyber Security System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084988)|P. Das; M. Illa; R. Pokhariyal; A. Latoria; Hemlata; D. J. B. Saini|10.1109/ICEARS56392.2023.10084988|Artificial intelligence;IoT;neural network;fuzzy;cybersecurity;Training;Renewable energy sources;Artificial neural networks;Nonhomogeneous media;Solids;Internet of Things;Artificial intelligence|
|[Hardware Implementation of Wireless Network for Tracking Mine Slopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085450)|G. Visalaxi; A. Muthukumaravel|10.1109/ICEARS56392.2023.10085450|Cloud Computing;Slope monitoring;Cloud interface;Realtime monitoring;Cloud computing;Renewable energy sources;Software design;Wireless networks;Surveillance;Industrial facilities;Terrain factors|
|[Traffic Aware Routing with Round Robin Technique for Equating the Load in Software Defined WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085019)|R. Senkamalavalli; M. Nalini; G. Kalaimani; P. Iyyanar|10.1109/ICEARS56392.2023.10085019|Traffic aware routing;Software defined networking;Congestion Control;Wireless sensor networks;Traffic-aware routing with round robin technique;Wireless sensor networks;Renewable energy sources;Packet loss;Telecommunication traffic;Channel allocation;Routing;Throughput|
|[An Improved Soft Computing based Congestion Control in Routing the Data in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085213)|K. Manojkumar; S. Devi|10.1109/ICEARS56392.2023.10085213|Communication network;congestion control;Wireless Sensor Network (WSN);Quality of Service (QoS);fuzzy logic system;artificial neural network;game theory based and particle swarm optimization techniques;Fuzzy logic;Wireless sensor networks;Renewable energy sources;Wireless networks;Packet loss;Quality of service;Organizations|
|[A Novel Technique for Evaluating Optimal Power Flow and SVC Performance using the ABC Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085237)|N. Kalpana|10.1109/ICEARS56392.2023.10085237|continuous Power Flow;Optimal power flow;artificial bee colony;optimization;static VAR compensator (SVC);active power losses;voltage stability;Renewable energy sources;Costs;Runtime;Sensitivity analysis;Static VAr compensators;Power system stability;Artificial bee colony algorithm|
|[A Novel Optimized Routing Protocol for Ad-hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085274)|A. Baliyan; S. Lamba; V. Tanwar|10.1109/ICEARS56392.2023.10085274|MANET;SDSR.DSR;RREQ;SRREQ;SRREP;Renewable energy sources;Costs;Packet loss;Disaster management;Throughput;Routing protocols;Delays|
|[Performance Evaluation of Optical Wireless Communication System in Fog Weather Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084953)|K. Venusamy; S. Kannadhasan; E. Prakash; P. K|10.1109/ICEARS56392.2023.10084953|Free Space Optics [FSO];Attenuation;Visibility;Signal to noise Ratio [SNR];Bit Error Rate [BER];Channel capacity;PIN photodiodes;Wireless networks;Bit error rate;Mathematical models;Silicon;Optical fiber communication;Optical transmitters|
|[Improving Energy Efficiency using Colliding Bodies Optimization based Routing Protocol for Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085674)|P. Prabakaran; R. Shalini; N. Hariharan; S. Marieswari; K. R. Kumar; M. G. L. Apoorva|10.1109/ICEARS56392.2023.10085674|Energy efficiency;Wireless sensor networks;Colliding bodies optimization;Fitness function;Wireless sensor networks;Renewable energy sources;Voting;Routing;Minimization;Routing protocols;Energy efficiency|
|[Self-Adaptive Multimedia Networked System for Effective Real-Time Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085042)|A. M. Dogra; M. Singh|10.1109/ICEARS56392.2023.10085042|Sensor Networks;Video Surveillance;Cameras;Intelligent System;Surveillance System;Renewable energy sources;Multimedia systems;Cameras;Video surveillance;Search problems;Real-time systems;Hardware|
|[Energy Efficiency in Wireless Sensor Network using Red Fox Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085283)|A. Balachandar; S. Usharani; P. M. Bala; P. V. Akshaya|10.1109/ICEARS56392.2023.10085283|Part;Layout;Wireless Sensor Network;Red Fox Optimization Algorithm;Wireless communication;Wireless sensor networks;Energy consumption;Detectors;Routing;Robot sensing systems;Routing protocols|
|[Song Recommendation based on Voice Tone Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085001)|R. R. Jaison C; M. Rajeswari|10.1109/ICEARS56392.2023.10085001|Song recommendation;Voice tone analysis;Deep learning model;Artificial Neural Network;Renewable energy sources;Electric potential;Machine learning algorithms;Mood;Music;Feature extraction;History|
|[Blockchain for Securing Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085685)|S. Rajendar; U. Thangavel; S. Devendran; V. Selvi; S. S. Muthumanickam|10.1109/ICEARS56392.2023.10085685|Autonomous vehicles;Blockchain;Electronic control unit;Transactions;Merkle tree root value;Industries;Renewable energy sources;Roads;Government;Communication channels;Blockchains;Safety|
|[Big Data Security through Privacy – Preserving Data Mining (PPDM): A Decentralization Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085646)|R. Josphineleela; S. Kaliapp; L. Natrayan; A. Garg|10.1109/ICEARS56392.2023.10085646|Data mining;Sustainable development;Big data;Privacy-Preserving Data Mining;PPDM;Gateway Security;Decentralized data distribution;Energy;Data privacy;Renewable energy sources;Databases;Data security;Information processing;Big Data;User interfaces|
|[Design and Development of an E-Healthcare Records Management System using Blockchain Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085635)|D. E; I. N. S; I. C. R; K. K. A|10.1109/ICEARS56392.2023.10085635|Cloud Computing;Privacy;Information Security;Blockchain;Cloud computing;Privacy;Bibliographies;Scalability;Standards organizations;Smart contracts;Medical services|
|[A Secure Resale Management System using Cloud Services and ReactJS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085477)|K. K. Baseer; M. Jahir Pasha; B. V. Srinivasulu; S. A. Moon|10.1109/ICEARS56392.2023.10085477|React.js;React-Redux;Ant-D;Cognito;Postman;Dynamo DB;Lambda;Serverless;Measurement;Renewable energy sources;Costs;Web services;Filtering;Databases;Logic gates|
|[An Efficient Machine Learning Approaches for Crop Recommendation based on Soil Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085361)|S. K; P. M; N. B; S. S|10.1109/ICEARS56392.2023.10085361|K-Nearest Neighbor;Crop;Gateway;Sensor;Crop prediction;Model selection;Technological innovation;Renewable energy sources;Machine learning algorithms;Crops;Soil;Predictive models;Prediction algorithms|
|[Image Security is Improved by Super Encryption using RSA and Chaos Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085142)|D. Venkata Rao; S. Pavan Kumar Reddy; C. Anusha; D. Bhoomika; R. Venkateswarlu|10.1109/ICEARS56392.2023.10085142|Super encryption;RSA and Chaos-based algorithms;Communication technologies;Picture encryption;Security;and Decryption;Chaos;Renewable energy sources;Force;Real-time systems;Encryption;Internet;Logistics|
|[AI & IoT based Control and Traceable Aquaculture with Secured Data using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085006)|V. P. Premkkumar; C. Gayathri; P. Priyadharshini; G. Praveenkumar|10.1109/ICEARS56392.2023.10085006|Artificial intelligence;Internet of Things;Aquaculture;Water quality;Temperature sensors;Temperature measurement;Water quality;Fish;Real-time systems;Internet of Things;Artificial intelligence|
|[Cyber Security Control Systems for Operational Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085345)|S. Sriram; G. Rajeshkumar; S. Sadesh; E. Saranya; K. Saranya; K. Venu|10.1109/ICEARS56392.2023.10085345|Operational Technology;Style of cyber security;Information Technology;Network security;Technological innovation;Renewable energy sources;Organizations;Oral communication;Control systems;Safety;Security|
|[Security System to Analyze, Recognize and Alert in Real Time using AI-Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085421)|N. Infantia H; S. G; K. M; P. A; S. Gomathi; J. Sivapriya|10.1109/ICEARS56392.2023.10085421|Caffe;Torch;OpenCV;DNN;and CNN;Backward Subtractor;Tensor Flow;CCTV;Deep learning;Training;Renewable energy sources;Face recognition;Ear;Feature extraction;Cameras|
|[An Integrated Security for Smart Farming and Monitoring System based on LiDAR Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085666)|K. M; U. S; M. C; S. Ravi; S. S; T. K. R|10.1109/ICEARS56392.2023.10085666|Security system;Light Detection and Ranging;Surveillance;Laser radar;Animals;Crops;Forestry;Microwave sensors;Electric fences;Sensor systems|
|[Controlled Drugs Administration using Block Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085179)|M. Lokhande; A. Sawalkar; G. Shelke; K. Shewale; A. Shaikh|10.1109/ICEARS56392.2023.10085179|Controlled drugs;Blockchain;Ethereum;Machine Learning;Advanced Data Analytics;Immutability;Transparency;Consensus;Smart Contract;Drugs;Ethics;Electric potential;Renewable energy sources;Machine learning;Control systems;Regulation|
|[A Combination of RSA Algorithm with Image Steganography to Ensure Enhanced Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085371)|B. Karthikeyan; B. Bharathkumar; G. Manikandan; R. Seethalakshmi|10.1109/ICEARS56392.2023.10085371|Steganography;cryptography;Rivest Shamir Adleman(RSA);cryptographic message syntax;image steganography;Steganography;Renewable energy sources;Syntactics;Encryption|
|[Concealment of Data using RSA Cryptography and Steganography Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085355)|C. L. Krishna; P. Anudeep; C. S. N. V. Sai Vijaya Lakshmi; M. V. P. Chandra Sekhara Rao; S. V. Chereddy; B. Rama Krishna|10.1109/ICEARS56392.2023.10085355|Cryptography;Steganography;RSA;LSB;Data Compression;DWT;Huffman coding;MSE;PSNR;SSIM;Measurement;Steganography;Renewable energy sources;Image coding;Information security;Transforms;Wavelet analysis|
|[Design and Implementation of Electronic Health Records using Ethereum Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085012)|K. K. Baseer; B. Jaya Naga Varma; B. Harish; E. Sravani; K. Y. Kumar; K. Varshitha|10.1109/ICEARS56392.2023.10085012|Blockchain;Ethereum;Smart Contracts;IPFS;EHR;Data privacy;Renewable energy sources;Costs;Smart contracts;Information sharing;Medical services;Blockchains|
|[Review on Cloud Security Attacks and Preventions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085396)|D. Sai Rohith; U. Venkata Sai; K. Mohan Venkat Sai Raju; S. Sarfaraz Ahmed; K. B. V. Brahma Rao; P. Yellamma|10.1109/ICEARS56392.2023.10085396|Cloud Computing;Data;Security;services;attacks;and threats;Cloud computing;Renewable energy sources;Costs;Cloud computing security;Operating systems;Companies;Tag clouds|
|[CPW Fed Ultrawideband Antenna for Microwave Imaging Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085165)|S. M; S. Nelaturi|10.1109/ICEARS56392.2023.10085165|Ultrawideband;coplanar waveguide feed;Slotted Patch;Microwave antennas;Antenna measurements;Microwave integrated circuits;Microwave communication;Transmission line measurements;Ultra wideband antennas;Coplanar waveguides|
|[Securing Banking Credentials from SQL Injection Attacks using AES Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085050)|N. C. Brintha; G. L. V. Sai Jaswanth; M. Anusha; J. Adi Narayana; D. Venkat|10.1109/ICEARS56392.2023.10085050|Securing banking Credentials;Encryption;Advanced Encryption Standard (AES) algorithm;Renewable energy sources;Codes;Online banking;Databases;SQL injection;Mobile handsets;Encryption|
|[Advanced Machine Learning Approach for Suspicious Coded Message Detection using Enigma Cipher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085339)|S. Hussain; P. Mohideen S|10.1109/ICEARS56392.2023.10085339|Cryptography;cryptanalysis;encryption;decryption;Machine Learning;Statistical natural language processing (SNLP);Social networking with instant messengers;Association rule mining (ARM);Suspicious message detection system;SMDs;Semantic Web;Ciphers;Renewable energy sources;Terrorism;Weapons;Government;Machine learning|
|[Analyzing Different Digital Image Authentication Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085193)|S. Kaur; A. Singh; A. Jindal|10.1109/ICEARS56392.2023.10085193|Watermarking;assaults;and tamper detection for images;Renewable energy sources;Electric potential;Digital images;Authentication;Watermarking|
|[Contemplate and Investigate a Network based Intrusion Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085479)|G. Kaur; K. Sharma|10.1109/ICEARS56392.2023.10085479|Network Security attacks;firewalls;IDS;NIDS;honeypot;SNORT;G-SNORT;Renewable energy sources;Firewalls (computing);Phishing;Intrusion detection;Network security;IP networks;Standards|
|[Smart Cities Hybridized to Prevent Phishing URL Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085315)|G. Swathi; M. Shwetha; P. Potluri; K. Murthy Raju; Y. Kumar; K. Rajchandar|10.1109/ICEARS56392.2023.10085315|Particle Swarm Optimization;Attacker;URL;Genetic algorithm;Feature selection;Training;Uniform resource locators;Renewable energy sources;Smart cities;Phishing;Feature extraction;Fraud|
|[Voice-based Virtual Assistant with Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085043)|C. Simon; M. Rajeswari|10.1109/ICEARS56392.2023.10085043|Automatic Speech Recognition;Artificial Intelligence;Natural Language Processing;Virtual Assistant;Performance evaluation;Renewable energy sources;Virtual assistants;Information security;Speech recognition;Passwords;Natural language processing|
|[A Hierarchical Taxonomy of Load Balancing in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084974)|SaiKrishna; c. Venkataramana; T. Sandeep; V. Varma; R. Suryakanth; M. Nageshwar Rao|10.1109/ICEARS56392.2023.10084974|Cloud computing;classification;load unbalancing;under loading;Cloud computing;Renewable energy sources;Systematics;Loading;Taxonomy;Load management;Computer performance|
|[A Systematic Literature Review on Security in Cloud Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085641)|K. S. S. Latha; H. V. Githiki; M. L. S. Morla; A. S. Vanama; Y. Tripathi|10.1109/ICEARS56392.2023.10085641|Cloud Computing;Security;Security Threats;Security Risk Evaluation;Vulnerability;Renewable energy sources;Systematics;Protocols;Cloud computing security;Computational modeling;Authentication;Software|
|[A Systematic Approach towards Security Concerns in Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085437)|M. V. Reddy; P. S. Charan; D. Devisaran; R. Shankar; P. M. Ashok Kumar|10.1109/ICEARS56392.2023.10085437|Cloud Computing;Security issue;Security Techniques;Cloud Security Tools;Blockchain in Cloud;Renewable energy sources;Costs;Systematics;Cloud computing security;Process control;Organizations;Virtual machining|
|[Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085047)|V. S. Narayana Tinnaluri; M. Vyankatesh Ghamande; S. Singh; R. Kuchipudi; M. Dattatraya Bhosale; R. Dharani|10.1109/ICEARS56392.2023.10085047|UAV;Mobile Edge Computing;offloading;Edge computing;delay optimal;Cloud computing;Computational modeling;Autonomous aerial vehicles;Software;Numerical models;Delays;Servers|
|[Gravitational Search Algorithm based System for Piezoelectric Energy Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085003)|R. Balamurugan; S. Duraimurugan; A. S. T. Arasu; P. Gokul|10.1109/ICEARS56392.2023.10085003|Piezo Electric Crystals;GS A;MPPT;Wireless power Transfer;Wireless communication;Legged locomotion;Vibrations;Wireless sensor networks;Portable computers;Wireless power transfer;Transforms|
|[Smart Vehicle Management based on Vehicular Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085171)|G. Manikanta Reddy; M. Vinod Reddy; V. Dinesh; C. Karthikeyan; P. M. Ashok Kumar|10.1109/ICEARS56392.2023.10085171|Vehicular cloud model (V C Model);Vehicular ad hoc networks;(Privacy Communication);v2x Communications;Cloud computing;Renewable energy sources;Road accidents;Roads;Motion detection;Ad hoc networks;Global Positioning System|
|[Distance based Similarity Search with Distinct Encrypted Image Storage on Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085021)|V. Mani; K. Akash; S. Mohamed Sulaiman|10.1109/ICEARS56392.2023.10085021|Encryption;Decryption;Duplication Checking;Image Search;Distance Measurement;Image Retrieval;Cloud computing;Renewable energy sources;Shape;Soft sensors;Keyword search;Feature extraction;Information retrieval|
|[Effective Management of IoT Devices that can Withstand Attacks on Cloud Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085408)|A. M; T. A|10.1109/ICEARS56392.2023.10085408|Internet of Things (IoT);cloud computing;control the data access;encryption is based on attribute;Access control;Cloud computing;Renewable energy sources;System performance;Neural networks;Authentication;Internet of Things|
|[Detecting and Preventing Unauthorized User Access to Cloud Services by CASBs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085406)|A. R. Reddy; K. L. Flower; J. Anitha; A. K. Kandru|10.1109/ICEARS56392.2023.10085406|security patterns;cloud computing;cloud brokers;SaaS;Amazon Web Services;Renewable energy sources;Web services;Vents;Companies;Switches;Malware;Workstations|
|[Fog Environment for Smart Cities with Multi-level Resource Sharing Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085430)|G. S; K. S; K. S; L. B|10.1109/ICEARS56392.2023.10085430|IoT;resource management;fog simulators;and fog computing are all terms used to describe the Internet of Things;Performance evaluation;Computational modeling;Quality of service;Throughput;Energy states;Mobile handsets;Internet of Things|
|[Artificial Intelligence Techniques for the wireless wearable Smart Healthcare Prediction System Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085051)|P. A.; G. V. Reddy; G. Ramachandran|10.1109/ICEARS56392.2023.10085051|Internet of things;Machine learning;Healthcare;Wireless;Wireless communication;Wireless sensor networks;Medical services;Transforms;Artificial intelligence;Biomedical monitoring;Intelligent sensors|
|[Financial Crisis Prediction using Feature Subset Selection with Quantum Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085208)|T. Balachander; N. Akhlaq; R. Bansal; S. A. Vasani; K. Singh; B. R. Mannar|10.1109/ICEARS56392.2023.10085208|Financial crisis prediction;Feature selection;Deep learning;Machine learning;Interactive search algorithm;Deep learning;Renewable energy sources;Computational modeling;Neural networks;Organizations;Predictive models;Prediction algorithms|
|[Early Diagnosis of Folium Disease in Solanum Lycopersicum using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085035)|V. S. Vutukuri; S. Regulagadda; L. C. Jasti; A. Cheekurthi|10.1109/ICEARS56392.2023.10085035|Agriculture;Convolutional Neural Network (CNN);EfficientNet;Pesticide;Renewable energy sources;Plants (biology);Neural networks;Crops;Agriculture;Classification algorithms;Convolutional neural networks|
|[Intelligent Video Analytics & Facial Emotion Recognition using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084928)|S. K. Nallapu; V. B. Boddukuri; D. V. V. A. L. S. Ganesh; K. Rithvik; V. Ganesan; M. M. Vutukuru|10.1109/ICEARS56392.2023.10084928|Expression Detection;Artificial Intelligence;Video Analytics;Video Statistics;Video Insights;Analytical models;Emotion recognition;Solid modeling;Renewable energy sources;Machine learning algorithms;Three-dimensional displays;Face recognition|
|[Conventional Protection of Power Transformers at Distribution Grid Side using Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085631)|S. B B; H. Bhat; S. Poornima; R. Bharanidharan; M. Sridharan; A. Banik|10.1109/ICEARS56392.2023.10085631|Smart grid;Transmission line faults;Artificial Neural Network;Classification;Renewable energy sources;Power transmission lines;Software packages;Neurons;Artificial neural networks;Smart grids;Topology|
|[Design of Neurofuzzy Models for Biological Oxygen Demand Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085535)|S. K. Sunori; A. Mittal; P. Juneja|10.1109/ICEARS56392.2023.10085535|Biological oxygen demand;Water pollution;Temperature;pH;Fuzzy;ANFIS;Grid partition;Subtractive clustering;Fuzzy logic;Renewable energy sources;Oxygen;Biological system modeling;Urban areas;Predictive models;Lakes|
|[Modified Grey Wolf Optimizer with Sparse Autoencoder for Financial Crisis Prediction in Small Marginal Firms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085618)|R. Bhattacharya; Kafila; S. H. Krishna; B. Haralayya; P. Nagpal; Chitsimran|10.1109/ICEARS56392.2023.10085618|Small marginal firms;Financial crisis;Predictive models;Machine learning;Grey wolf optimizer;Training;Renewable energy sources;Data preprocessing;Transforms;Machine learning;Predictive models;Prediction algorithms|
|[Artificial Intelligence Support System Design for Smart Offices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085205)|C. J. Swaroop; K. Sri Siva Koteswara Rao; K. S. Harshitha; M. Lakshmi Lahari; V. C. Jadala; R. Govindan|10.1109/ICEARS56392.2023.10085205|Cloud Computing;Machine Learning;Internet of Things;Mobile Android Applications;Smart Offices;Resistance;Cloud computing;Office automation;Technological innovation;Renewable energy sources;Transducers;Smart buildings|
|[Cardio Vascular Disease Prediction based on ANN Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085084)|A. K; A. M; D. K. K; G. P; G. N|10.1109/ICEARS56392.2023.10085084|Data mining;deep learning;cardiovascular disease prediction;machine learning;multi-layer perceptron;Deep learning;System performance;Artificial neural networks;Prediction algorithms;Data models;Classification algorithms;Data mining|
|[Prediction of Insufficient Accuracy for Human Activity Recognition with Limited Range of Age using K-Nearest Neighbor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085062)|S. Charan; S. M.S; S. R|10.1109/ICEARS56392.2023.10085062|Machine Learning;Novel K-Nearest Neighbour;Naive Bayes;E-Health;Electronic Gadgets;Age factor;Support vector machines;Renewable energy sources;Machine learning algorithms;Machine learning;Aging;Prediction algorithms;Naive Bayes methods|
|[SDNS: Artificial Neural Network Scheme to Solve the Nonlinear Skin Disease Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085024)|V. Ebenezer; S. R; S. S. Hiruthic; S. S. Sergius; R. Shivnesh|10.1109/ICEARS56392.2023.10085024|Artificial Neural Network;Levenberg-Marquardt Backpropagation;Deep Learning;Training;Renewable energy sources;Artificial neural networks;Learning (artificial intelligence);Complex networks;Skin;Data models|
|[Hybrid Convolutional Neural Network and Extreme Learning Machine for Kidney Stone Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085243)|P. R. G; P. Sapra; S. S. C. Mary; A. Chauhan; S. A. Parte; N. Nishant|10.1109/ICEARS56392.2023.10085243|Convolutional Neural Network (CNN);Extreme Learning Machine (ELM);Training;Support vector machines;Image segmentation;Histograms;Ultrasonic imaging;Speckle;Feature extraction|
|[Detecting Human Behavior from a Silhouette Using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085686)|N. S. Rao; G. Shanmugapriya; S. Vinod; R. S; S. P. Mallick; J. Gracewell J|10.1109/ICEARS56392.2023.10085686|Human Action Recognition (HAR);Convolutional Neural Network (CNN);Renewable energy sources;Films;Neural networks;Lighting;Video surveillance;Feature extraction;Convolutional neural networks|
|[Advanced Grape Leaf Disease Detection using Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085036)|K. V; R. D. K; P. G; J. M; B. K; R. B|10.1109/ICEARS56392.2023.10085036|Convolutional Neural Networks (CNN);leaf disease prediction;LeNet-5;Accuracy and Loss;Training;Plant diseases;Renewable energy sources;Pipelines;Neural networks;Medical services;Network architecture|
|[Detection of Retinal Degeneration via High-Resolution Fundus Images using Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085273)|M. V. Subbarao; J. T. S. Sindhu; N. N. S. Harshitha; K. P. Vasavi; A. S. Krishna; G. C. Ram|10.1109/ICEARS56392.2023.10085273|Retinal disorders;Ophthalmic diagnosis;deep learning;CNN;AlexNet;Deep learning;Training;Renewable energy sources;Retinopathy;Neural networks;Imaging;Retina|
|[Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085689)|L. Samineni; A. Peddi; A. Kasukurthi; M. V. P. C. S. Rao; G. Niharika; S. V. Chereddy|10.1109/ICEARS56392.2023.10085689|Sentiment Analysis;E-commerce;COVID-19;Amazon Reviews;Naïve Bayes (NB);Logistic Regression (LR);Decision Tree (DT-J48);Support Vector Machine (SVM);Ensemble model;Fake Review;Yelp;Data Resampling;Feature pruning;Parameter optimization;COVID-19;Support vector machines;Pandemics;Supervised learning;Writing;Feature extraction;Electronic commerce|
|[Medium-Voltage Drives (MVD) - Pulse Width Modulation (PWM) Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084995)|A. Alahmad; F. Kaçar; Ö. F. Farsakoğlu; C. P. Uzunoğlu|10.1109/ICEARS56392.2023.10084995|Medium Voltage Driver;topology;Control;PWM;Heating systems;Frequency modulation;Phase modulation;Switching loss;Switches;Medium voltage;Pulse width modulation|
|[AI Enabled Smart Campus for Health Safety and Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085423)|R. L. R. Singh; B. V; E. J; S. K|10.1109/ICEARS56392.2023.10085423|Face Mask Detection;Contactless Temperature;Electro Selective Pattern-32 Camera;Raspberry Pi;Temperature Scanning;Temperature sensors;Temperature measurement;COVID-19;Waste materials;Detectors;Streaming media;Real-time systems|
|[Handwritten Character Recognition Based on Adabelief Optimized Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085044)|S. K. Sahani; S. R. Kk; D. N|10.1109/ICEARS56392.2023.10085044|Handwritten Character Recognition;Convolutional Neural Network;Adam Optimizer;Adabelief Optimizer;Extended Modified National Institute of Standards and Technology dataset;Training;Handwriting recognition;Analytical models;Renewable energy sources;Text recognition;Feature extraction;Character recognition|
|[Enhancing Infants Early Communication through Sign Language Recognition System using Raspberry Pi and Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085246)|P. D. Cerna; C. S. Ututalum; R. S. Evangelista; A. T. Darkis; M. S. Asiri; J. A. Muallam-Darkis|10.1109/ICEARS56392.2023.10085246|Sign Language;Raspberry PI;Infants;Convolutional Neural Network;Enhance Communication;Deep learning;Pediatrics;Renewable energy sources;Machine learning algorithms;Lighting;Gesture recognition;Assistive technologies|
|[AI based Two Way Sign Language System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085670)|R. T; D. J; K. A; H. P. K. M; J. F. R; N. S|10.1109/ICEARS56392.2023.10085670|Sign language;Open Computer Vision;Convolutional Neural Network;google Text To Speech Synchronisation;Natural language processing;Renewable energy sources;Sociology;Gesture recognition;Auditory system;Assistive technologies;Streaming media;Real-time systems|
|[Cataract Disease Classification using Convolutional Neural Network Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085502)|S. Gayathri; S. Saran; P. S. Kumar; I. P. Singh|10.1109/ICEARS56392.2023.10085502|Cataract Disease;Convolutional Neural Network;Le-Net;Inception V3;Alex Net;Res-Net;Cataracts;Training;Renewable energy sources;Transfer learning;Surgery;Feature extraction;Convolutional neural networks|
|[Effect of VR Technological Development in the Age of AI on Business Human Resource Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085258)|M. V. Unni; R. S; R. Kar; R. Bh; V. V; J. M. Johnson|10.1109/ICEARS56392.2023.10085258|VR technology;Artificial Intelligence;Human Resource Management;Decision Making;Demand Analysis;Training;Solid modeling;Virtual reality;Resource management;Time factors;Human resource management;Artificial intelligence|
|[Early Identification of cervical cancer using K-Nearest Neighbor (KNN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085541)|N. Meenakshisundaram; G. Ramkumar|10.1109/ICEARS56392.2023.10085541|Key-Nearest Neighbor;Naive Bayes;Machine Learning;Preprocessing Data;Cervical Cancer;Charge coupled devices;Solid modeling;Renewable energy sources;Costs;Reliability;Medical diagnosis;Cervical cancer|
|[Melanocytic Pigmented Skin Lesion Detection and Classification using Hybrid Deep Features based on Fully Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085451)|A. Naresh; B. A. Reddy; G. P. Reddy; K. R. Kumari; M. S. Vaishnavi|10.1109/ICEARS56392.2023.10085451|Malignant Melanoma;Feature Extraction;Classification;FCNs;Random Forest;VGG16;ResNet50;Training;Renewable energy sources;Melanoma;Forestry;Feature extraction;Skin;Convolutional neural networks|
|[Copy Move Counterfeiting Identification based on CNN using Scalar Invariant Feature Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085602)|S. C; B. M; G. J; K. R|10.1109/ICEARS56392.2023.10085602|Deep learning;Forgery detection;Image features;Multimedia;Forensic analysis;Renewable energy sources;Image segmentation;Image forensics;Costs;Insurance;Feature extraction;Forgery|
|[Cosmetic Suggestion based on Skin Condition using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085539)|A. Kavitha; R. R; R. T; A. S; B. S. M; R. M|10.1109/ICEARS56392.2023.10085539|Artificial Intelligence;Convolutional algorithm;Cosmetic;Skin condition;Industries;Renewable energy sources;Machine learning algorithms;Customer services;Machine learning;Prediction algorithms;Skin|
|[Off-Policy Reinforcement based on a Safe Model Eco-Driving Education for Fully-Automated, Connected Hybrid Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085149)|S. S. Pande; N. B; K. K. Kumar; S. Sathish; L. Mounika; J. P. Patra|10.1109/ICEARS56392.2023.10085149|Eco-Driving;Electric Vehicle;Deep Reinforcement Learning (DRL);Off Policy-Learning;Fuel Consumption;Training;Biological system modeling;Roads;Velocity control;Reinforcement learning;Approximation algorithms;Behavioral sciences|
|[Artificial Neural Network and Process Optimization of Electrical Discharge Machining of Al 6463](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085204)|A. Pugazhenthi; R. Thiyagarajan; P. K. Srividhya; R. Udhayasankar; S. R|10.1109/ICEARS56392.2023.10085204|Hybrid metal matrix composite;ANOVA;stir casting;wear analysis;Renewable energy sources;Silicon carbide;Statistical analysis;Wires;Artificial neural networks;Predictive models;Mathematical models|
|[Artificial Neural Network and Optimization and Neural Network Approach for Al 7072 Alloy Reinforced with Nanoparticles of Tungsten Carbide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084946)|M. I. A; A. Pugazhenthi; R. Thiyagarajan; R. Udhayasankar|10.1109/ICEARS56392.2023.10084946|Hybrid metal matrix composite;ANOVA;stir casting;wear analysis;Nanoparticles;Resistance;Tungsten;Metals;Artificial neural networks;Predictive models;Stability analysis|
|[Enhancing Brain Tumor Detection Classification by Computational Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085176)|D. S. Islam; R. Udhayakumar; S. Kar; P. N; U. S. Aswal; D. K. J. B. Saini|10.1109/ICEARS56392.2023.10085176|Brain Tumor;Detection;Classification;Computational Intelligence;and Deep learning.;Training;Deep learning;Brain modeling;Task analysis;Statistics;Optimization;Computational intelligence|
|[The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085368)|C. Nayak; C. L. Rao; T. Alam; S. Singh; S. Islam; U. H. MaginmanI|10.1109/ICEARS56392.2023.10085368|Artificial intelligence;cyber security;Banking industry;Industries;Renewable energy sources;Image processing;Sociology;Software;Intelligent agents;Artificial intelligence|
|[An Analysis of the Effects and Interaction of Hyper Parameters in Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085483)|G. K. Arora; R. Mutha; M. S. Sangari; U. Aswal; A. Bhattacherjee; A. Agarwal|10.1109/ICEARS56392.2023.10085483|Neural;HyperTendril;AutoML;algorithm;networks;Deep learning;Renewable energy sources;Visual analytics;Search methods;Neural networks;Data models;Convolutional neural networks|
|[Machine Learning Algorithms for Predicting House Prices in a Smart city using its Real-Time Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085090)|M. R. Joel; M. Navaneethakrishnan; K. P. Sriram; S. P. Manonmani|10.1109/ICEARS56392.2023.10085090|Linear Regression;Machine Learning;Random Forest;Real-time Data;Lasso;Training;Industries;Renewable energy sources;Machine learning algorithms;Smart cities;Biological system modeling;Linear regression|
|[Pose Estimation Approach for Gait Analysis using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085311)|S. Jemimah Peace C; V. Ebenezer; B. Edwin; A. R; S. D; R. Thanka|10.1109/ICEARS56392.2023.10085311|Gait analysis;Machine learning;Pose estimation;physiotherapy;Legged locomotion;Renewable energy sources;Computer vision;Machine learning algorithms;Statistical analysis;Pose estimation;Machine learning|
|[Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085579)|P. Sudha; J. Jegathesh Amalraj; M. Sivakumar|10.1109/ICEARS56392.2023.10085579|Blockchain;FP;Return rate prediction;Machine learning;Deep learning;Deep learning;Renewable energy sources;Globalization;Bitcoin;Predictive models;Logic gates;Prediction algorithms|
|[Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085290)|R. Mahendran; M. A. Ala Walid; G. Vijaya Pratap; A. Pradeep; M. Soumya; P. Udhayaraja|10.1109/ICEARS56392.2023.10085290|Blood flow velocity;Deep learning;Harmony search algorithm;Decision making;Renewable energy sources;Computer vision;Ultrasonic imaging;Red blood cells;Computational modeling;Magnetic resonance imaging;Computed tomography|
|[IoT based Illness Prediction System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085553)|B. N. Lakshmi; M. Robinson Joel|10.1109/ICEARS56392.2023.10085553|Wearable technology;Machine learning;K-Nearest Neighbor;Support Vector Machine;Machine learning algorithms;Support vector machine classification;Machine learning;User interfaces;Robustness;Diabetes;Glucose|
|[Investigation of Fault Modes on PV based Cascaded H Bridge Multilevel Inverter for Submersible Induction Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085078)|C. Meyy; C. S. Ravichandran|10.1109/ICEARS56392.2023.10085078|PhotoVoltaic system;Maximum Power Point Technique;Sinusoidal Pulse Width Modulation;Hybrid bridge multilevel inverter;Submersible Asynchronous motor;Insulation;Renewable energy sources;Torque;Software packages;Simulation;Bridge circuits;Pulse width modulation|
|[Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084967)|J. Abraham; G. J. Cherian; N. Jayapandian|10.1109/ICEARS56392.2023.10084967|Artificial Intelligence;Cognitive Intelligence;Machine Learning;Humanizing;Social Interactions;;Ethics;Renewable energy sources;Systematics;Law;Machine learning;Regulation;Behavioral sciences|
|[A Comparative Study of the Stock Market using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085606)|K. Deevenapalli; S. P. Jampani; M. Venkata Bhagavan Shiva Sai Y; S. Sudulakunta; A. Chokka; S. Bulla|10.1109/ICEARS56392.2023.10085606|Stock market;Random Forest;Analysis;Prediction.;Machine learning algorithms;Biological system modeling;Support vector machine classification;Prediction algorithms;Market research;Reliability;Stock markets|
|[Cardiopulmonary Arrest Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085324)|A. Dumala; N. Annasani; P. Gubba; R. Guntupalli; V. Doppalapudi|10.1109/ICEARS56392.2023.10085324|Heart disease;Machine Learning;Predictive analysis;multilayer perceptron;Decision Tree;Heart;Machine learning algorithms;Machine learning;Medical services;Cardiac arrest;Multilayer perceptrons;Rhythm|
|[Students Community Portal using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085516)|P. V; L. S. K; P. Vyshnavi A; M. Ch; S. B. G|10.1109/ICEARS56392.2023.10085516|Doubt solving;Online student instructor interaction;Query resolution;Student community;Machine learning;Natural language processing;Support vector machines;Technological innovation;Renewable energy sources;Education;Neural networks;Machine learning;Public speaking|
|[Human Motion Capturing from a Video using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085484)|V. Dhanalakshmi; M. Ragul|10.1109/ICEARS56392.2023.10085484|Regions with Convolutional Neural Networks;RANSAC;Deep Learning;Geometry;Point cloud compression;Renewable energy sources;Image recognition;Annotations;Clothing;Manuals|
|[A Novel Real-time Automated Face Classification and Detection system using Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085500)|A. J. Naga Bhavani; G. Bhanu Sree; A. Yasaswini; B. Brahmam; P. K; A. D. Kumar|10.1109/ICEARS56392.2023.10085500|Machine Learning;Face Detection;Convolution Neural Network;Ensemble Transfer Learning;Decision Trees;Support vector machines;Renewable energy sources;Machine learning algorithms;Pandemics;Transfer learning;Real-time systems;Classification algorithms|
|[Crop And Fertilizer Recommendation to Improve Crop Yield using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085054)|R. Thendral; M. Vinothini|10.1109/ICEARS56392.2023.10085054|Convolutional Neural Networks;Diagnosing Plant Diseases;Long Short Term Memory;Using Deep Learning;Recurrent Neural Networks,;Deep learning;Renewable energy sources;Soil properties;Neural networks;Crops;Production;Data models|
|[Hybrid Noise Reduction And Enhancement of Audio Quality using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085247)|L. M; M. K|10.1109/ICEARS56392.2023.10085247|Acoustic Noise Cancellation (ANC);Subband Adaptive Filtering (SAF);Normalised Least Mean Fourth (NLMF);Least Mean Fourth (LMF);and Acoustic Echo Cancellation (AEC);Renewable energy sources;Noise reduction;Adaptive filters;Mean square error methods;Filtering algorithms;Speech enhancement;Noise cancellation|
|[Image Denoising for Smart Laser Osteotomy Using Deep Learning-based Fast Optical Coherence Tomography (OCT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085568)|S. N. Ghate; D. Kalpanadevi; K. Amudha; P. Velayutham; B. S; M. A. Shah|10.1109/ICEARS56392.2023.10085568|Denoising;Optical Coherence Tomography;Laser Osteotom;Frame-Averaging;Deep Learning;Deep learning;Time-frequency analysis;Optical coherence tomography;Noise reduction;Bones;Real-time systems;Time measurement|
|[An Ingenious Deep Learning Approach for Home Automation using Tensorflow Computational Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084944)|P. Ilampiray; A. Thilagavathy; C. S. H. U. Sahith; P. G. S. Varma; B. C. V; M. Dhanush|10.1109/ICEARS56392.2023.10084944|Internet of Things;Artificial Intelligent;On-device machine learning;Tensor flow;Deep Learning;Home Automation;Deep learning;Performance evaluation;Renewable energy sources;Home automation;Automation;TV;Machine learning algorithms|
|[Development of an Agro-Photovoltaic Transparent Solar Panel and DOCR for Agriculture and Grid System Usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084966)|M. E. Deowan; A. Kabir Nuhel; M. J. Naim; M. Mohibullah Sazid; I. Haider; F. Alam; P. Roy|10.1109/ICEARS56392.2023.10084966|PV;Solar;Power plant;agriculture;Grid etc;Renewable energy sources;Humanities;Sociology;Transformers;Agriculture;Inverters;Hardware|
|[Study and Outline of Vertical Take-Off and Landing (VTOL) System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085366)|K. Venusamy; S. N. R; D. Sam; B. W. John|10.1109/ICEARS56392.2023.10085366|Unmanned Aerial Vehicle (UAV);Vertical Take-Off and Landing (VTOL);Aircraft design;Force and Torque Equations;Types of VTOL;Industries;Renewable energy sources;Torque;Propellers;Roads;Autonomous aerial vehicles;Safety|
|[An Enhanced Machine Learning Technique for Text Detection using Keras Sequential model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085174)|R. Deepa; S. Gayathri; P. Chitra; J. J. Jasmine; R. R. Devi; A. Thilagavathy|10.1109/ICEARS56392.2023.10085174|Optical Character Recognition (OCR);handwritten text;Convolutional Neural Networks (CNN);Python;Training;Optical losses;Renewable energy sources;Text recognition;Optical character recognition;Neural networks;Machine learning|
|[An Improved Machine Learning and Deep Learning based Breast Cancer Detection using Thermographic Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085612)|D. Rajasekhar; M. Rafi D; S. Chandre; V. Kate; J. Prasad; A. Gopatoti|10.1109/ICEARS56392.2023.10085612|Breast Cancer;Clinical Study;Deep Learning;Benign;Malignant;Thermographic Images;Deep learning;Training;Renewable energy sources;Ultrasonic imaging;Computational modeling;Neural networks;Predictive models|
|[Deep Learning Model for Automated Kidney Stone Detection using VGG16](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085509)|V. N; B. S; D. p; D. L; H. P|10.1109/ICEARS56392.2023.10085509|Kidney Stone;Features extraction;Deep learning;Medical images;Convolutional neural network;Stomach;Solid modeling;Renewable energy sources;Pediatrics;Ultrasonic imaging;Pain;Computed tomography|
|[Topological Dependencies in Deep Learning for Mobile Edge: Distributed and Collaborative High-Speed Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084935)|Y. M. Abd Algani; A. S. Kumar; M. A. Ala Walid; B. S; P. Velayutham; A. Sasi Kumar|10.1109/ICEARS56392.2023.10084935|Deep neural network;high-speed inference;Edge computing;Cloud;machine intelligence;Deep learning;Training;Cloud computing;Fault tolerance;Image edge detection;Computational modeling;Fault tolerant systems|
|[Machine Learning based Candidate Recommendation System using Bayesian Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085615)|G. G. Manuval; T. T. George; B. P. Aby; M. Mathew; A. S. Chandran; N. Jayapandian|10.1109/ICEARS56392.2023.10085615|Bayesian Technique;Artificial Intelligence;Job Seeker;Decision Tree;Machine Learning;Service Learning,;Renewable energy sources;Machine learning algorithms;Collaborative filtering;Machine learning;Media;Prediction algorithms;Motion pictures|
|[Cotton Leaf Disease Detection using Convolutional Neural Networks (CNN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085551)|S. Sharmila; R. Bhargavi; R. Y. Anusha; K. Anusha; B. Divya|10.1109/ICEARS56392.2023.10085551|Dead leaves;Artificial Intelligence;Color Analysis in images;Feature Extraction;Deep Learning;Training;Deep learning;Renewable energy sources;Plant diseases;Plants (biology);Feature extraction;Libraries|
|[Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085425)|B. Krishnakumar; K. Kousalya; K. V. Indhu Prakash; S. Jhansi Ida; B. Ravichandra; R. G|10.1109/ICEARS56392.2023.10085425|Deep Learning;VGG16 Model;VGG19 Model;MobileNet V2 Model;InceptionV3 Model;Resnet50 V2 Model;Tensor Flow;Training;Analytical models;Renewable energy sources;Image color analysis;Computational modeling;Crops;Predictive models|
|[Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084996)|J. Jayachitra; N. Umarkathaf|10.1109/ICEARS56392.2023.10084996|Stress Detection;Electrocardiogram (ECG);Brain-Computer Interface (BCI);Electroencephalogram (EEG);Phonocardiogram (PCG);Deep learning;Renewable energy sources;Cells (biology);Brain modeling;Medical diagnosis;Blood;Stress|
|[Forecasting of Diabetes Disease using a Method based on Data Mining and Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085268)|S. Ramya; D. Kalaivani|10.1109/ICEARS56392.2023.10085268|Diabetes;Machine Learning;Conditional Decision Tree;Classification;Support vector machines;Renewable energy sources;Machine learning algorithms;Machine learning;Stroke (medical condition);Diabetes;Decision trees|
|[Alzheimer’s Disease Diagnosis using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085017)|S. P. A; S. S; A. S. Rajasekaran; S. N. P; S. R; N. R|10.1109/ICEARS56392.2023.10085017|Alzheimer’s disease;Deep Learning;Convolutional Neural Networks (CNN);LeNet-5;Accuracy and Loss;Deep learning;Neuroimaging;Analytical models;Renewable energy sources;Predictive models;Brain modeling;Data models|
|[Transformerless Inverter Topologies with Condensed Leakage Current Structure for Grid-tied Photovoltaic System: Design and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085563)|B. K; A. A; S. K. G; B. D; M. S|10.1109/ICEARS56392.2023.10085563|design;analysis;transformer-less;inverter;Photovoltaic systems;Renewable energy sources;Network topology;Transformers;Inverters;Mathematical models;Topology|
|[MDLNN Approach for Alcohol Detection using IRIS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085257)|A. V. Reddy; P. Sai Kumar; P. A. Khan; V. Subba Reddy Karumudi; P. G; S. Imambi|10.1109/ICEARS56392.2023.10085257|Modified Deep Learning Neural Network (MDLNN);Bacterial Foraging Optimization (BFO);Hybridization of Gaussian bandpass filter and Discrete Wavelet Transform (HGBFDWT);Adaptive Histogram Equalization (AHE);Renewable energy sources;Histograms;Transforms;Physiology;Behavioral sciences;Recording;Reliability|
|[Identification and Segmentation of Tumour in Brain MRI using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085383)|M. N; M. P; A. T; P. R. M; B. N. J; K. R|10.1109/ICEARS56392.2023.10085383|Tumour;Glioma;Magnetic resonance imaging (MRI);Image Segmentation.;Deep learning;Image segmentation;Technological innovation;Renewable energy sources;Magnetic resonance imaging;Spatial diversity;Task analysis|
|[Effective Machine Learning Techniques to Detect Fatty Liver Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085622)|N. V. Naik; D. Nasreen; S. C. Reddy; P. Lasya Sri|10.1109/ICEARS56392.2023.10085622|Machine Learning;Random Forest;eXtreme Gradient Boosting;Naïve Bayes;Artificial Neural Networks.;Radio frequency;Renewable energy sources;Ultrasonic imaging;Liver diseases;Pulmonary diseases;Neural networks;Software|
|[An Analysis of Abnormal Event Detection and Person Identification from Surveillance Cameras using Motion Vectors with Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085466)|P. Pandiaraja; S. R; P. M; L. R|10.1109/ICEARS56392.2023.10085466|Motion detection;Video surveillance system;Face detection;Deep learning;Alert system.;Training;Renewable energy sources;Supervised learning;Semisupervised learning;Cameras;Video surveillance;Safety|
|[Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085032)|R. Mohan; S. Chalasani; S. Suma Christal Mary; A. Chauhan; S. A. Parte; S. Anusuya|10.1109/ICEARS56392.2023.10085032|Convolutional Neural Network (CNN);Regularized Extreme Learning Machine (RELM);Extreme Learning Machine (ELM).;Training;Renewable energy sources;Extreme learning machines;Image processing;Wheels;Cameras;Software|
|[Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085489)|S. M; M. A. Ala Walid; D. Sarada Prasanna Mallick; R. Rastogi; A. Chauhan; A. Vidya|10.1109/ICEARS56392.2023.10085489|Convolutional Neural Network (CNN);Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (KELM).;Training;Renewable energy sources;Computer vision;Extreme learning machines;Melanoma;Skin;Convolutional neural networks|
|[Video based Facial Emotion Recognition System using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085245)|D. M; V. Pushpalatha; Y. P; J. S; D. A|10.1109/ICEARS56392.2023.10085245|Python;OpenCV;MobileNetV2;Transfer Learning;Deep learning;Computational modeling;Face recognition;Transfer learning;Feature extraction;Low latency communication;Recommender systems|
|[An Exploration of Machine Learning in Movie Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085299)|A. Manjusha; P. A. Kumar; S. Sreevani; C. Tejaswini; K. Padmanaban; A. Dinesh Kumar|10.1109/ICEARS56392.2023.10085299|Support Vector Machine (SVM);Naive Bayes and K-Nearest Neighbors Algorithm.;Support vector machines;Sentiment analysis;Machine learning algorithms;Scalability;Text categorization;Machine learning;Motion pictures|
|[Survey on Gestures Translation System for Hearing Impaired People in Emergency Situation using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085381)|K. K; A. J. M; K. M; V. B|10.1109/ICEARS56392.2023.10085381|Sign Language;Deep learning;Impairment people;Machine learning;Neural networks;Deep learning;Training;Renewable energy sources;Machine learning algorithms;Webcams;Gesture recognition;Auditory system|
|[A Survey of Price Prediction using Deep Learning Classifier for Multiple Stock Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085095)|K. K; R. V; S. K. K P; S. K. P S|10.1109/ICEARS56392.2023.10085095|Stock market data;Machine learning;Deep learning;Prediction;Closing values;Support vector machines;Machine learning algorithms;Social networking (online);Soft sensors;Neural networks;Prediction algorithms;Data models|
|[Controlled Islanding Solution for Blackout Prevention in Transmission Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085170)|A. Kayal; A. Alahmad|10.1109/ICEARS56392.2023.10085170|blackouts;cascading outages;controlled islanding;MATLAB Simulink;Renewable energy sources;Power measurement;Software packages;Power system protection;Load shedding;Power system stability;Reliability|
|[Doctormate- An Early Disease Prediction Approach using Multiple Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085617)|S. Kiruthiga|10.1109/ICEARS56392.2023.10085617|Decision Tree Algorithm;Random Forest Algorithm;Naive Bayes Algorithm;K Nearest Neighbour algorithm;Tkinter Interface;Industries;Schedules;Renewable energy sources;Machine learning algorithms;Hospitals;Prediction algorithms;Data models|
|[Sleeping Abnormalities Detection using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085009)|M. M; A. V; I. M; K. N|10.1109/ICEARS56392.2023.10085009|Artificial Intelligence;Insomnia;Machine learning;Deep learning;Sleeping disorder;Convolutional Neural Network;Deep learning;Renewable energy sources;Machine learning algorithms;Sleep;Predictive models;Brain modeling;Electroencephalography|
|[Salt Segment Identification in Seismic Images of Earth Surface using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085475)|L. D. N; R. Reddy Bochu; N. K. Buddha|10.1109/ICEARS56392.2023.10085475|Semantic Segmentation;Seismic images;UNet;ResNet;VGG16;Inceptionv3;Earth;Training;Measurement;Image segmentation;Renewable energy sources;Oils;Semantics|
|[Sign Language Prediction using Machine Learning Techniques: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084924)|D. Aggarwal; S. Ahirwar; S. Srivastava; S. Verma; Y. Goel|10.1109/ICEARS56392.2023.10084924|American Sign Language (ASL);MediaPipe;Harris Corner Algorithm;Convolutional Neural Network;Renewable energy sources;Machine learning algorithms;Neural networks;Gesture recognition;Oral communication;Machine learning;Assistive technologies|
|[Property Price Prediction, Litigation Detection and Prevention using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085347)|G. D. Devenagavi; B. M. Aleem; C. Saiswaroopa; J. Karthik; N. Chinnappannagari|10.1109/ICEARS56392.2023.10085347|Property price;Survey number;Barcode;Property litigation;Renewable energy sources;Computational modeling;Government;Machine learning;Color;Banking;Predictive models|
|[Performance Analysis of Deep Learning based Segmentation of Retinal Lesions in Fundus Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085616)|N. K. Baskaran; T. R. Mahesh|10.1109/ICEARS56392.2023.10085616|Deep Learning;Lesion segmentation;Diabetic Retinopathy;Exudate Detection;Fundus Images;Deep learning;Image segmentation;Renewable energy sources;Sensitivity;Retinopathy;Sensitivity and specificity;Retina|
|[Prediction of Brain Stroke in Human Beings using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085128)|T. N. Deepthi; S. Sharmila; M. Swarna; M. Gouthami; C. L. Akshaya|10.1109/ICEARS56392.2023.10085128|Machine Learning;KNN;Random Forest;stroke prediction;Heart;Radio frequency;Renewable energy sources;Machine learning algorithms;Stroke (medical condition);Prediction algorithms;Glucose|
|[A Survey on Prediction of Risk Related to Theft Activities in Municipal Areas using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085123)|J. T. K; G. J; P. S|10.1109/ICEARS56392.2023.10085123|Machine Learning;Deep Learning;Theft crime prediction;Demographic information;Temporal patterns;Linear Regression;Long Short Term Memory;Random Forest;XGBoost;Convolutional Neural Network;Deep learning;Training;Predictive models;Prediction algorithms;Feature extraction;Data models;Numerical models|
|[Medical Image Classification using Deep Learning Techniques: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084948)|M. Ayyannan; M. A; S. D; N. T; T. M; N. S. Kumar|10.1109/ICEARS56392.2023.10084948|Deep learning;Medical image processing;CT;MRI;Ultrasound;Deep learning;Renewable energy sources;Ultrasonic imaging;Magnetic resonance imaging;Computed tomography;Medical services;Biomedical image processing|
|[System for Predicting the Diabetes using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085329)|S. Alagarsamy; M. P; K. V. S. Kumar; M. M. Reddy; M. M. Naidu; M. V. Kumar|10.1109/ICEARS56392.2023.10085329|Diabetes Prediction;Machine Learning;Health Care Management;Predictive Analytics;Clinical Decision Support;Renewable energy sources;Machine learning algorithms;Machine learning;Medical services;Prediction algorithms;Real-time systems;Hazards|
|[Power Quality Enhancement of the Distribution Network by Multilevel STATCOM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085590)|B. Arthi; R. Thamizharasan; M. P. Mohandass; R. L. R. Singh|10.1109/ICEARS56392.2023.10085590|statcom;multilevel inverter;power quality;harmonics;alternate current;direct current;pulse-width modulation;active filters;Adaptation models;Reactive power;Adaptive systems;Power quality;Active filters;Automatic voltage control;Stability analysis|
|[Deep Learning based Image Enhancing Environment with Noise Suppression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084977)|Sivanantham; B. S. K. Reddy; J. SaiGnaneswar; K. Balaji; K. S. Vivek Reddy; K. L. Reddy|10.1109/ICEARS56392.2023.10084977|image Restoration;Convolution neural network;GAN;RGB;Degradation;Deep learning;Visualization;Renewable energy sources;Supervised learning;Noise reduction;Neural networks|
|[A Comparison of COVID-19 Detection using Deep Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085367)|S. P. Kumar; S. Murugan; B. Rubini|10.1109/ICEARS56392.2023.10085367|Deep Learning (DL);Convolutional Neural Network (CNN);Tuning;X-ray chest images;COVID-19;Deep learning;Training;Renewable energy sources;Neural networks;Predictive models;Convolutional neural networks|
|[YouTube and Movie Recommendation System Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084999)|S. S. K; M. Srivastava; Mohana|10.1109/ICEARS56392.2023.10084999|Youtube;Movie;KNN Algorithm;Artificial Intelligence;Machine Learning;Recommendation systems;Renewable energy sources;Video on demand;Systematics;Machine learning algorithms;Scalability;Machine learning;Organizations|
|[Prediction of Parkinson's Disease using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085443)|N. Alapati; N. Anusha; P. Joharika; N. J. Jerusha; P. Tanuja|10.1109/ICEARS56392.2023.10085443|Improved Accuracy;Random Forest Algorithm Parkinson's Disease Prediction;Radio frequency;Training;Support vector machines;Renewable energy sources;Machine learning algorithms;Parkinson's disease;Predictive models|
|[Prediction of Ground Water Level in Prosopis Juliflora Ecosystem using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085136)|S. Choudhary; M. Pundir|10.1109/ICEARS56392.2023.10085136|Prosopis Juliflora;Machine Learning;Ground Water Change;Rainfall Patterns;Land use land cover (LULC);ArcGIS;Linear Regression;Meters;Climate change;Rain;Machine learning algorithms;Linear regression;Vegetation;Machine learning|
|[A Review on Disease Prediction Approach using Data Analytics and Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085130)|A. E; A. Antonidoss|10.1109/ICEARS56392.2023.10085130|Dataset;Symptoms;Disease;Treatment;Machine Learning;Industries;Renewable energy sources;Machine learning algorithms;Pattern classification;Medical services;Switches;Predictive models|
|[Sentiment Analysis using Deep Learning: A Domain Independent Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085676)|M. Qamar; H. Rao; S. A. Farooq; A. Swagat Bhuyan|10.1109/ICEARS56392.2023.10085676|CNN;GRU;LSTM;Word Embedding;Tokenization;Lemmatization;Deep learning;Support vector machines;Sentiment analysis;Analytical models;Machine learning algorithms;Atmospheric modeling;Voting|
|[Deep Learning Approach to Automated Tomato Plant Leaf Disease Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085564)|I. Karthika; M. Megha; M. Roshni|10.1109/ICEARS56392.2023.10085564|Disease detection;Deep Learning Technique;Image Segmentation;Plant leaves;Deep learning;Training;Renewable energy sources;Biological system modeling;Crops;Agriculture;Classification algorithms|
|[Classification of Ultrasound PCOS Image using Deep Learning based Hybrid Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085400)|P. Chitra; K. Srilatha; M. Sumathi; F. V. Jayasudha; T. Bernatin; M. Jagadeesh|10.1109/ICEARS56392.2023.10085400|Polycystic Ovarian Syndrome;Deep learning;Transfer Learning;Hybrid Model;Ultrasound images;Pregnancy;Deep learning;Renewable energy sources;Ultrasonic imaging;Transfer learning;Imaging;Manuals|
|[An Enhanced Brain Tumor Detection Scheme using a Hybrid Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085267)|S. Dharshini; S. Geetha; S. Arya; N. Mekala; R. Reshma; S. P. Sasirekha|10.1109/ICEARS56392.2023.10085267|Brain Tumor;Machine Learning;BAT Algorithm;CNN;Early Detection;Clinical;MRI Images;Renewable energy sources;Machine learning algorithms;Shape;Magnetic resonance imaging;Computed tomography;Convolutional neural networks;Biological neural networks|
|[FPGA –based Optimized Design of Montgomery Modular Multiplier using Karatsuba Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085256)|A. T; S. S; R. A; S. K M|10.1109/ICEARS56392.2023.10085256|Montgomery Modular Multiplication (MMM);Karatsuba Multiplication;Cryptographic Hardware;Renewable energy sources;Elliptic curves;Delay effects;Computer architecture;Aerospace electronics;Hardware;Delays|
|[An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085157)|Y. R. M. R; K. A; R. D; R. Reshma; D. R. Santhosh; N. Mekala|10.1109/ICEARS56392.2023.10085157|Machine Learning;Credit Card;Fraudulent Transaction;Fraud Detection;Confidentiality;Support vector machines;Renewable energy sources;Machine learning algorithms;Sensitivity and specificity;Credit cards;Real-time systems;Fraud|
|[Opportunities of Machine Learning Techniques in the Catering Industry- A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085004)|G. Arvindaraj; B. Manikandan; R. Rokesh; M. Abinaya|10.1109/ICEARS56392.2023.10085004|Mobile Application;Big Data;Ingredient Planner;Multi-functional Model;Requirement Analysis;Artificial Intelligence;Machine Learning;Industries;Analytical models;Automation;Supply chain management;Computational modeling;Supply chains;Machine learning|
|[Deep Learning Approach for Pothole Detection - A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085203)|B. K. S. B; G. S; M. Kishore; S. R; A. D. J|10.1109/ICEARS56392.2023.10085203|Keywords— Pothole detection;traffic flow;Deep Learning,;Deep learning;Renewable energy sources;Costs;Systematics;Maintenance engineering;Road safety;Delays|
|[Implementing Deep Learning Techniques into Healthcare System Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085499)|H. K. Kochhar; M. Kilaru; N. Aljohani; H. Rai Goyal; D. Singh; D. J. Bahadur Saini|10.1109/ICEARS56392.2023.10085499|Keywords: Deep learning;techniques;Technology;Healthcare;AutoML;Technological innovation;Renewable energy sources;Medical services;Transforms;System improvement;Writing;Software|
|[Machine Learning and Speech Analysis Framework for Protecting Children against Harmful Online Content](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085565)|A. Dhoka; S. Pachauri; C. Nigam; S. Chouhan|10.1109/ICEARS56392.2023.10085565|Machine Learning (ML);Speech Recognition;Adult Content;Child Online Abuse;MFCC;ML Algorithm;Training;Solid modeling;Renewable energy sources;Machine learning algorithms;Speech analysis;Filtering;Training data|
|[Sentiments Analysis using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085432)|V. Joshi; S. Patel; R. Agarwal; H. Arora|10.1109/ICEARS56392.2023.10085432|Sentimental Analysis;Machine Learning;Naïve Byes Classifier;Accuracy;Twiter;Sentiment analysis;Renewable energy sources;Machine learning algorithms;Social networking (online);Databases;Blogs;Data visualization|
|[Intelligent Optimization Algorithm based Data Storage and Management: An Empirical Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085038)|S. Ayoub; R. K. R. Savaliya; P. G; N. Girdharwal; M. Verma; M. Tiwari|10.1109/ICEARS56392.2023.10085038|Data storage;Data management;Share creation;Image encryption;Key generation;Visualization;Renewable energy sources;Memory;Data compression;Receivers;Cryptography;Homomorphic encryption|
|[Performance Analysis of Olympic Games using Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084943)|V. Asha; S. P. Sreeja; B. Saju; N. C S; P. G. N; A. Prasad|10.1109/ICEARS56392.2023.10084943|Data analytics;Olympic games;Medal count;Athletes;Visualizing;Training;Renewable energy sources;Data analysis;Soft sensors;Decision making;Games;Predictive models|
|[Empowering Tourists with Context-Aware Recommendations using GAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085604)|E. E. Stephy; M. Rajeswari|10.1109/ICEARS56392.2023.10085604|Context-aware recommendation;Generative Adversarial Networks (GAN);tourism;Renewable energy sources;Supply and demand;Generative adversarial networks;Data models;History;Recommender systems;Web search|
|[Smart Attendance System using RFID and Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085186)|F. A. Jeffrey Vaz; G. AnusuyaDevi; A. M. Indhu Priya; R. Yamini Rajam; K. K. K|10.1109/ICEARS56392.2023.10085186|Google Database API (Application Programming Interface)- Firebase;Radio Frequency Identification;Real Time Clock;Automation;Raspberry Pi Pico W;Network Time Protocol;Visualization;Time-frequency analysis;Schedules;Renewable energy sources;Databases;Manuals;Remuneration|
|[Intelligent UV-Based Insect Hunter for Countryside Farming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085671)|E. C. Kumar; B. Vigneshwaran; S. J. Santhosh; A. J. Devaprakash|10.1109/ICEARS56392.2023.10085671|Agriculture;Solar power insect tarp;pesticides;Photovoltaic systems;Renewable energy sources;Insects;Crops;Prototypes;Light emitting diodes;Agriculture|
|[Machine Learning Algorithms based Detection and Analysis of Stress - A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084933)|R. R. A; S. Jyothirmy; G. Geethika; S. B. Sai; B. Saiteja; V. H. Prasad Reddy|10.1109/ICEARS56392.2023.10084933|Stress monitoring;Trier Social Stress Test;EEG Signals;Biomedical stress analysis;SVM;Support vector machines;Renewable energy sources;Machine learning algorithms;Bibliographies;Psychology;Human factors;Organizations|
|[A Review on Analysis of Cardiac Arrhythmia from Heart Beat Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085295)|R. R. A; P. Yesudasu; N. N. S. P. Revathi; P. R. L. Durga Prasad; K. Pujitha; V. R. Prabha K|10.1109/ICEARS56392.2023.10085295|Cardiac Arrhythmias;Ventricular Ectopic Beats;Supra-Ventricular Ectopic Beats;Atrial-Fibrillation;CNN;LSTM;Support vector machines;Pregnancy;Heart;Training;Heart beat;Arrhythmia;Electrocardiography|
|[Review on Blood Vessel Segmentation of Retina from Fundus Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085099)|D. Swetha; B. L. Srilakshmi; P. S. Swami; K. S. S. Kumar; M. B. Priya|10.1109/ICEARS56392.2023.10085099|Fundus Image;Retinal Blood Vessels;Vessel Segmentation;Supervised and Unsupervised Techniques;Machine Learning;Neural Network;Measurement;Image segmentation;Visualization;Retinopathy;Blood vessels;Retina;Feature extraction|
|[Traffic Rule Violation Recognition for Two Wheeler using YOLO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085537)|B. C. R; S. Joy; U. A. Reddy; R. C. Lal; V. R; A. D. M|10.1109/ICEARS56392.2023.10085537|YOLO (You Only Look Once) algorithm;Traffic Rule Violation Recognition;Helmet Detection;Renewable energy sources;Head;Surveillance;Software algorithms;Transportation;Regulation;Software|
|[Analysis of Driver Drowsiness Detection Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084986)|V. Kalisetti; V. S. C. Vasarla; S. B. Kolli; R. Varaparla; V. Enireddy; M. Mohammed|10.1109/ICEARS56392.2023.10084986|Drowsiness;Drowsiness Detection Methods;Biological-Base d measures;Vehicle-Based measures;Physiological Signals Analysis;Computer vision;Visualization;Renewable energy sources;Sensitivity;Computational modeling;Real-time systems;Signal analysis|
|[Multi-Categories Vehicle Detection For Urban Traffic Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085376)|V. Desai; S. Degadwala; D. Vyas|10.1109/ICEARS56392.2023.10085376|Multi-Categories Vehicle;Traffic Management;Transfer Learning;You Only Look Once (YOLO);Convolutional Neural Network;Renewable energy sources;Vehicle detection;Neural networks;Traffic control;Real-time systems;Sensors;Automobiles|
|[Performance of Recurrent Neural Networks in Liver Disease Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085202)|K. Nanthini; D. Sivabalaselvamani; D. Selvakarthi; D. Pavethran; N. Srinaath; K. S. Vignesh|10.1109/ICEARS56392.2023.10085202|Recurrent Neural Networks (RNN);Disease Detection;healthcare;data visualization;Deep learning;Renewable energy sources;Recurrent neural networks;Liver diseases;Classification algorithms;Cancer|
|[Proposed Approach for Dynamic Automobile Traffic Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085231)|D. Abin; A. Yadav; P. Bhagat; H. Mankar; S. Raut|10.1109/ICEARS56392.2023.10085231|Dynamic Traffic management;Graph theorem;Polynomial fitting;Wireless communication;Wireless sensor networks;Heuristic algorithms;Roads;Surveillance;Image processing;Urban areas|
|[Recommendation System for Job Opportunities based on Candidate Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085342)|E. K; S. Sivaranjani; A. M; S. B. M; S. M|10.1109/ICEARS56392.2023.10085342|Employment Matching;Employee Referrals;and HR Software;Renewable energy sources;Jobs listings;Employment;Software;Recommender systems;Consumer electronics;Business|
|[Hand Gesture based driver-vehicle interface Framework for automotive In-Vehicle Infotainment system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085653)|V. K. Kaliappan; P. K. Sureshkumar; N. P. Velliangiri; P. K. Loganathan; M. B. Selvaraj|10.1109/ICEARS56392.2023.10085653|Hand Gesture Recognition;Convolution Neural Network;Single Shot MultiBox Detector;Driver Safety;Deep learning;Visualization;Wheels;Detectors;Feature extraction;Cameras;Safety|
|[Solar PV based High Gain Converter for Microgrid Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085187)|M. Shunmathi; S. J. Fusic; V. Jayshree; K. J. V. Aishwarya; G. Sharanya|10.1109/ICEARS56392.2023.10085187|Maximum power point tracking;Discontinuous conduction mode;Photo voltaic system;Continuous conduction mode;Performance evaluation;Maximum power point trackers;Renewable energy sources;Capacitors;Voltage;Switches;High-voltage techniques|
|[A Review: Text Extraction from Stone Inscriptions and Translating to Modern Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085645)|M. P. Kurapati; H. C. Vallabhaneni; L. R. Burramukku; H. Reddy Soda; M. M. Vutukuru; S. Thatavarthi|10.1109/ICEARS56392.2023.10085645|Character Recognition;Stone inscriptions;AMSER;Renewable energy sources;Text recognition;Optical character recognition;Character recognition|
|[An Effective Technique using MobileNet-V2 for Face Mask Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085411)|T. Toshiba; A. Singh; M. Singh|10.1109/ICEARS56392.2023.10085411|Machine Learning;Deep Learning;Face mask;TensorFlow;COVID-19;F1-score;Scikit-Learn;COVID-19;Deep learning;Renewable energy sources;Pandemics;System performance;Urban areas;Cameras|
|[Design and Methodology of LOD and LOPD using Evolutionary Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085297)|C. Mythili; M. Yazhini Nivethitha|10.1109/ICEARS56392.2023.10085297|VLSI;LOPD circuit;LOD;cell area;power consumption;Renewable energy sources;Power demand;Design methodology;Buildings;Evolutionary computation;Very large scale integration;Logic gates|
|[Real-Time Face Mask Detection using Computer Vision and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085276)|C. N. Kumar; E. Nithin Computer; C. S. Krishna; C. Bindhu Madhavi|10.1109/ICEARS56392.2023.10085276|Covid 2 (SARS-CoV-2);SPP-NET;SSD;CNN;Faster R-CNN;Multi-Stage Object Detection;Computer vision;Renewable energy sources;Pandemics;Object detection;Streaming media;Real-time systems;Regulation|
|[An Enhanced Light GBM Model with Data Analytical Approach for Crop Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085596)|N. Bhatt; S. Varma|10.1109/ICEARS56392.2023.10085596|Classifier;Crop Recommendation;Agriculture Field;Soil Parameters;LightGBM;Logistic Regression.;Renewable energy sources;Computational modeling;Sociology;Crops;Soil;Agriculture;Stability analysis|
|[A Qualitative and Comprehensive Analysis of Software Testability Metrics and their Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085333)|S. Purohit; S. Singh; M. Agarwal; N. Verma|10.1109/ICEARS56392.2023.10085333|Software Testability Metric;Controllability;Observability;Source code metrics;Measurement;Renewable energy sources;Codes;Object oriented modeling;Production;Machine learning;Market research|
|[Analyzing the Bank Scam's Financial Fraud and its Technological Repercussions using Data Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085354)|S. H.K|10.1109/ICEARS56392.2023.10085354|Data Mining;Financial;Fraud;Detection;Statement;Renewable energy sources;Finance;Companies;Writing;Data models;Fraud;Data mining|
|[Music Recommendation System using Hybrid Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085059)|N. V. Durga Malleswari; K. Gayatri; K. Y. Sai Kumar; N. Likhita; P. K; D. Bhattacharyya|10.1109/ICEARS56392.2023.10085059|Personalized music recommendation;Collaborative filtering;Root Mean Squared Error (RMSE);Mean Absolute Error (MAE);Human computer interaction;Renewable energy sources;Music;Multiple signal classification;History;Web sites;Reliability|
|[Design and Evaluation of a Brain Signal-based Monitoring System for Differently-Abled People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085207)|I. R. P; S. Hari B; M. G|10.1109/ICEARS56392.2023.10085207|Differently-abled People;BCI (Brain- Computer Interface);EEG Signals (Electroencephalography);Feature Extraction;Machine Learning Algorithms;Hyperparameter Tuning;Support vector machines;Training;Uncertainty;Machine learning;Brain modeling;Electroencephalography;Real-time systems|
|[A Novel Approach for PE Malware Detection using Random Forest Algorithm and Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085463)|D. Soni; D. Shah; S. Ramolia; H. Joshi; S. Mehta|10.1109/ICEARS56392.2023.10085463|Portable Executable;Malware;Random Forest;Common Object File Format;Machine Learning;Classification;Radio frequency;Renewable energy sources;Machine learning algorithms;Codes;Computer hacking;Forestry;Malware|
|[Performance of OWC Device in Random Ocean Waves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085397)|K. Trivedi; A. R. Ray; S. Koley|10.1109/ICEARS56392.2023.10085397|Oscillating Water Column Device;Computational Fluid Dynamics;piston-type Wavemaker;Spectrum;ANSYS Fluent;Pistons;Sea surface;Renewable energy sources;Surface waves;Wave power;Sea floor;Orifices|
|[Survey on Customized Diet Assisted System based on Food Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085456)|K. Makanyadevi; P. S; S. R; S. S|10.1109/ICEARS56392.2023.10085456|Food recognition;Dietary consumption;Machine learning;Deep learning;Nutrition values;Renewable energy sources;Obesity;Image recognition;Software architecture;Shape;Medical services;Size measurement|
|[Design of Drowsiness and Yawning Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085310)|V. Dehankar; P. Jumle; S. Tadse|10.1109/ICEARS56392.2023.10085310|Driver drowsiness detection;Open CV;Landmark;alarm;Eye detection;yawning detection;Visualization;Renewable energy sources;Sleep;Vehicle safety;Sensor systems;Sensors;Automobiles|
|[Virtual Fit using Computer Vision and Trimesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084945)|V. Kishore Kumar Rejeti; S. Abdul; P. H. Prasanth; S. Ashok; S. M. Sameer|10.1109/ICEARS56392.2023.10084945|Databases;Computer Vision;Cascade Trainer;Haar Cascade;Trimesh;Mediapipe;Region;Products;Brands;Huge Capital;Models;Computer vision;Renewable energy sources;Databases;Multimedia Web sites;Object detection;Footwear;User experience|
|[Teledermatology-Teleremedy Technology to Diagnose Early Melanoma](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085079)|D. Wishma; C. K. Viknesh; R. Seetharaman; P. Ramaraj|10.1109/ICEARS56392.2023.10085079|Mobile teledermatology;Skin cancer;Melanoma;Screening;Andriod app;E-health;Training;Renewable energy sources;Privacy;Telemedicine;Web pages;Melanoma;Medical services|
|[A Peculiar Reading System for Blind People using OCR Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085372)|M. Raja; J. Deny; N. P; M. V|10.1109/ICEARS56392.2023.10085372|OCR;Raspberry pi;Raspberry Camera;Jump wires;Headset;Renewable energy sources;Databases;Text recognition;Optical character recognition;Blindness;Cameras;Libraries|
|[Parkinson’s Disease Identification using Vocal Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085210)|M. K. Dharani; R. Thamilselvan; R. Rajadevi; T. S; P. D; S. B|10.1109/ICEARS56392.2023.10085210|Parkinson’s Disease;Extream Gradient Boosting;Light Gradient boosting Machine;Support Vector Machine;Support vector machines;Deep learning;Analytical models;Renewable energy sources;Neurons;Feature extraction;Boosting|
|[Intelligent Email Automation Analysis Driving through Natural Language Processing (NLP)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085351)|C. Sathish; A. Mahesh; N. S. Karpagam; R. Vasugi; J. Indumathi; T. Kanchana|10.1109/ICEARS56392.2023.10085351|Email;Natural Language Processing (NLP);Spam;Analysis;Sentiment;and Message;Sentiment analysis;Renewable energy sources;Text analysis;Unsolicited e-mail;Phishing;Malware;Electronic mail|
|[Breast Cancer Classification using SVM and KNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085546)|J. S. S. Adapala; K. V. S. Gontla; V. Koka; S. L. Modugula; R. Mothukuri; S. Bulla|10.1109/ICEARS56392.2023.10085546|Breast Cancer;Classification;accuracy;Support Vector Machine;K-Nearest Neighbor;Heat map;Scatterplot;Support vector machines;Training;Performance evaluation;Renewable energy sources;Support vector machine classification;Medical services;Machine learning|
|[Surveillance Rover to Maintain Social Distancing in Crowded Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085672)|D. Y; D. D; S. B; Gayathri; Hanshikanisha|10.1109/ICEARS56392.2023.10085672|Social distancing;Robot;Thermal sensor;COVID-19;Obstacle detection;COVID-19;Temperature measurement;Temperature sensors;Pandemics;Wheels;Human factors;Robot sensing systems|
|[Diagnosis of Vitamin Deficiency in Human Beings using DNN Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085334)|E. K; S. K|10.1109/ICEARS56392.2023.10085334|Skin cancer;Multi-resolution analysis;Support vector machine;Medical image classification;Bendlet Transform;Maximum likelihood detection;Sensitivity;Filtering;Support vector machine classification;Transforms;Feature extraction;Skin|
|[Design and Fabrication of Multi-functioned Seed Sowing Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085567)|S. K. K. R; G. T; B. S. Jebaraj; V. S; V. M; K. C|10.1109/ICEARS56392.2023.10085567|Seed-Sowing;Plowing;Seed feeder;Wheels;Mechanical Assembly;Costs;Automation;Sociology;Wheels;Soil;Agriculture;Software|
|[Identification and Classification of Breast Cancer using Multilayer Perceptron Techniques for Histopathological Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085420)|S. G; G. Ramkumar|10.1109/ICEARS56392.2023.10085420|Histopathological images;machine learning;Multilayer Perceptron;and breast cancer;Renewable energy sources;Databases;Surgery;Machine learning;Multilayer perceptrons;Cancer detection;Breast cancer|
|[Football Prediction System using Gaussian Naïve Bayes Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085510)|A. V. P; R. D; S. N. S. S|10.1109/ICEARS56392.2023.10085510|football;Gaussian naïve bayes;machine learning;Big data;goals;outcomes;Renewable energy sources;Games;Machine learning;Prediction algorithms;Classification algorithms;Bayes methods;Decision trees|
|[Improving Heart Disease Prediction of Classifiers with Data Transformation using PCA and Relief Feature Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085401)|G. Varshini; A. Ramya; C. L. Sravya; V. Kumar; B. K. Shukla|10.1109/ICEARS56392.2023.10085401|Supervised Machine Learning;Heart Disease;Cardiovascular Diseases;classifiers;Relief feature selection;Principal Component Analysis;Heart;Support vector machines;Renewable energy sources;Machine learning algorithms;Medical services;Feature extraction;Random forests|
|[Prediction of Cervical Cancer using Multilayer Perceptron Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085636)|S. Sujanthi; A. S; H. K; S. S|10.1109/ICEARS56392.2023.10085636|Cervical cancer;Malignant tumour;Machine learning;Deep learning;Abnormal tissue;Deep learning;Renewable energy sources;Machine learning algorithms;Data analysis;Error analysis;Multilayer perceptrons;Prediction algorithms|
|[Clustering Multiple Views of Data through Many-objective Evolutionary Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085188)|K. P. Ponnaganti; M. V.P. Chandra Sekhara Rao; L. Pinninti; A. Peddi; S. V. Chereddy; P. Lakshmikanth|10.1109/ICEARS56392.2023.10085188|Multi-view;Clustering;Spectral clustering;Feature selection;Evolutionary algorithms;Renewable energy sources;Machine learning algorithms;Soft sensors;Clustering algorithms;Medical services;Machine learning;Evolutionary computation|
|[Prediction of Diabetic Patients with High Risk of Readmission using Smart Decision Support Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085491)|N. S. Kumar; N. Sathyanarayana|10.1109/ICEARS56392.2023.10085491|Classification;Diabetes Readmission;Feature Selection;Healthcare Analytics;Machine Learning;Drugs;Deep learning;Renewable energy sources;Hospitals;Predictive models;Feature extraction;Diabetes|
|[Deep Analysis on Patient Health Records using Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085440)|S. K; R. S. M. B; S. S; V. A. G; A. A|10.1109/ICEARS56392.2023.10085440|Digital Age;Augmented Reality;Health Records;Diagnosis;Treatment;Temperature sensors;Heart rate;Technological innovation;Renewable energy sources;Human anatomy;Surgery;Medical services|
|[Real-time Pothole Detection using YOLOv5 Algorithm: A Feasible Approach for Intelligent Transportation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085336)|B. K. S B; G. S; M. Kishore; S. R; A. D. J|10.1109/ICEARS56392.2023.10085336|Artificial intelligence (AI);Pothole detection;traffic flow;Deep Learning;YOLO V5;Machine Learning;Costs;Image edge detection;Computational modeling;Maintenance engineering;Real-time systems;Road safety;Automobiles|
|[A Comparative Study of Anti Theft Techniques and Android Applications based on Kotlin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085398)|D. Aggarwal; K. Verma; N. Sharma; K. Keshav|10.1109/ICEARS56392.2023.10085398|Anti-theft;Android;App;Smartphone;Security;Renewable energy sources;Law enforcement;Switches;Batteries;Smart phones;Consumer electronics|
|[Modeling of Dual Active Bridge with Extended Phase Shift and Dual Phase Shift Modulation Technique using Reduced Order Model Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085254)|V. K. Kaiwart; A. Jamatia; A. Chakrabarti; B. Das; P. R. Kasari; N. Laskar|10.1109/ICEARS56392.2023.10085254|Dual Active Bridge (DAB);Extended Phase Shift (EPS);Dual Phase Shift (DPS);Reduced Order Model (ROM);Bridges;Renewable energy sources;Phase modulation;Transfer functions;Microgrids;Mathematical models;Reduced order systems|
|[An Improved Automotive Battery Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085271)|M. P; K. R; S. T; S. S; G. U; G. R|10.1109/ICEARS56392.2023.10085271|Extended Kalman’s Filter;State of Charge;discharging rate;life span;lead acid battery;Lead acid batteries;Renewable energy sources;Software packages;Estimation;Lead;Real-time systems;Lead compounds|
|[Solar based Wireless Charging using Inductive Resistance for E-Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085501)|S. R. Senthil S; A. M; S. M; U. A. A|10.1109/ICEARS56392.2023.10085501|Electric car;Solar power;Inductive power transfer (IPT);Internal Combustion Engine (ICE);Coils;Resistance;Renewable energy sources;Inductive charging;Rectifiers;Wireless power transfer;Receivers|
|[Vehicle Safety and Support System using Android Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085550)|T. Prabhu; T. S. Ganesh; H. S. Karan; O. M. Yaswanth|10.1109/ICEARS56392.2023.10085550|Accident Detection;GPS;Eye-blink Sensor;Android Application;Accelerometer;Renewable energy sources;Electric potential;Vehicle safety;Vehicular ad hoc networks;Sensor systems;Real-time systems;Servers|
|[Various charging Methods in Electric Vehicle Application: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084952)|A. R. T. A; M. D; K. N R; M. J. K. A|10.1109/ICEARS56392.2023.10084952|Wireless Charging Technique;Electric Vehicle;Inductively Coupled Power Transfer;Capacitive Charging;Battery- swapping;V2G;V2V;Vehicle-to-grid;Renewable energy sources;Anxiety disorders;Inductive charging;Transportation;Vehicular ad hoc networks;Voltage|
|[A Python-based Grade Converter Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084961)|K. N. Naik; A. R. Patil; K. N. Patil; V. R. Sankhe; S. S. More; V. Brian Lobo|10.1109/ICEARS56392.2023.10084961|Cumulative grade point average;Tkinter;Python;Grade;Renewable energy sources;Error analysis;Operating systems;Graphical user interfaces|
|[Stability Analysis of Integrated Power System with Renewable Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085015)|S. Karthick; M. Ramesh Babu; M. Shyam; B. Jeyapoornima; S. R; S. Gomathi|10.1109/ICEARS56392.2023.10085015|Total Harmonic Distortion(THD);PI Controller;MP Controller;Response;Renewable energy sources;Total harmonic distortion;Reactive power;PI control;Power system stability;Power system harmonics;Automatic voltage control|
|[Characteristics Study of Capacitive Sensor to Identify Native Breed Cow Milk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085056)|H. J; B. V. T; B. S; D. S; A. K. A; M. Pradeep A|10.1109/ICEARS56392.2023.10085056|Capacitance;Milk;Native breed;Magnitude;Parallel plate capacitor;Cylindrical capacitor;Renewable energy sources;Dairy products;Capacitors;Cows;Capacitance;Phosphorus;Capacitive sensors|
|[Impact of Shadow or Dust on Solar Photovoltaic Power Generation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085410)|C. L. K R; V. A. R; K. Rajalakshmi; S. Simon; G. Stepha; G. T|10.1109/ICEARS56392.2023.10085410|Shadow Effect;Solar Photovoltaic Power Generation System;Polycrystalline solar panel;Dust Effect;Efficiency loss;Irradiance effect;Renewable energy sources;Temperature;Software;Mathematical models;Power systems;Solar panels;Sun|
|[Active Cell Balancing During Charging and Discharging of Lithium-Ion Batteries in MATLAB/Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085110)|D. Shylla; R. Swarnkar; H. R; S. H. Md Ali|10.1109/ICEARS56392.2023.10085110|Active Cell Balancing;Battery Management System;Buck-Boost Converter;Lithium-ion Battery;Electric Vehicle;Lithium-ion batteries;Resistors;Renewable energy sources;Runtime;Switches;Pulse width modulation;Mathematical models|
|[Measurement of Ocean Salinity using a Capacitance based Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084971)|A. C. R; B. V T; A. K P; P. P L; S. R; P. K. M|10.1109/ICEARS56392.2023.10084971|Salinity;Capacitance and LCR Meter;Electrodes;Temperature sensors;Temperature measurement;Meters;Temperature distribution;Salinity (geophysical);Sea measurements|
|[Implementation of Digital Modulation Techniques in High-Speed FPGA Board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085543)|S. Srinivasan; M. Kavitha; G. V. Rani; L. Manoharan; E. Terence; A. V. Siva|10.1109/ICEARS56392.2023.10085543|Digital Modulation;High Speed Field Programmable Gate Array (FPGA);Digital Oscilloscope;Binary Phase-Shift Keying (BPSK);Vivado tool;Verilog Hardware Descriptive Language (VHDL);Wireless communication;Phased arrays;VHDL;Spectral efficiency;Digital modulation;Frequency shift keying;Logic gates|
|[Thunderbolt Single Port Peripheral Reference Design using ALTIUM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085224)|A. S; K. P; M. P; H. S|10.1109/ICEARS56392.2023.10085224|Thunderbolt power supply;PCB;Altium;Printing;Renewable energy sources;Embedded systems;Power supplies;Printed circuits;Software;Hardware design languages|
|[A Study on Various Types of Lamps used in Domestic Sector and their Impact on Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085278)|S. Manoharan; B. Mahalakshmi; N. Preetha; A. K|10.1109/ICEARS56392.2023.10085278|Energy Efficient Lamps;Incandescent Lamps;Fluorescent Lamps;High-intensity Discharge Lamps LED Lamps;Bureau of Energy Efficiency;Energy Conservation;Productivity;Energy consumption;Renewable energy sources;Power demand;Lighting;Switches;Light emitting diodes|
|[A Comprehensive Review of Haptic Gloves: Advances, Challenges, and Future Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085607)|S. M; K. Venusamy; S. S; S. S; N. K. O|10.1109/ICEARS56392.2023.10085607|Haptics and Haptic Interfaces;Force feedback;Tactile feedback;Vibro-tactile feedback;Teleoperation;Optical filters;Force feedback;Surgery;Robot sensing systems;Real-time systems;Haptic interfaces;Space exploration|
|[Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085122)|S. Sujitha; M. N T; V. R; V. G R|10.1109/ICEARS56392.2023.10085122|Mechanized picking;Spraying;Dispensing seeds;and Ploughing;Costs;Bluetooth;Navigation;Microcontrollers;Humidity;Robot sensing systems;Seeds (agriculture)|
|[Design of MEMS based Micro-Pumps Transdermal Insulin Administration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085422)|S. Ramya; S. P. Kumar; S. P. Vinodhini; D. Lingaraja; G. D. Ram|10.1109/ICEARS56392.2023.10085422|Micro-needle;Micropump;Insulin;Piezoelectric;Electro osmotic micro-pump;Micromechanical devices;Solid modeling;Renewable energy sources;Fluids;Micropumps;Needles;Skin|
|[Design of Hybrid Systems with Transzsi-DVR for Mitigation of Power Quality Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085103)|S. Karishma; P. Sujatha|10.1109/ICEARS56392.2023.10085103|Trans-Impedance Source Inverter (TransZSI);Unit vector template –Maximum constant boost controller (UVT-MCBC);Dynamic Voltage Restorer (DVR);Maximum power point tracking (MPPT);Point of Common Coupling (PCC);and Permanent Magnet Synchronous Generator (PMSG);Voltage source inverters;Simulation;Power quality;Power system harmonics;Harmonic analysis;Synchronous generators;Stability analysis|
|[Development of Automatic Oil Filling System in Wood Pressed Oil Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085664)|R. P; M. T; D. S. A; K. Verma V; M. R|10.1109/ICEARS56392.2023.10085664|Controller;InfraRed Sensor;DC motor;Pump;Liquids;Automation;Oils;Pumps;Production;Containers;Control systems|
|[Switched Capacitor Integrated Electric Vehicle for Energy Recovery During Regenerative Mode using Dual Sliding Mode-Proportional Integral Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085659)|R. Sugamanchipalli; N. K; K. R; R. M|10.1109/ICEARS56392.2023.10085659|PMSM (Permanent Magnet Synchronous Motor);Regenerative braking;Electric vehicle;DSM-PI (Dual Sliding Mode-Proportional Integral);MATLAB (Matrix Laboratory);Renewable energy sources;PI control;Software packages;Capacitors;Switches;Electric vehicles;Stability analysis|
|[Automated Ink Level Sensing using Electromechanical System for Tex-Cone Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085459)|M. T; R. P; G. D; A. E; M. R|10.1109/ICEARS56392.2023.10085459|Tex Paper cones;Spinning mills;Top and base nose printing;Ink filling;Printing;Nose;Ink;Filling;Sensors;Manufacturing;Spinning|
|[Automatic Fish Feeder System using ATMEGA328p Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085291)|M. Manjusha; P. Sriram; N. Prabakaran|10.1109/ICEARS56392.2023.10085291|ESP8266;ATMega328p;L293D Motor driver;Infrared sensor;DS18B20 (direct-to-digital temperature) sensor;Mini water pump;Liquid crystal display;Ultrasonic sensor;Buzzer;Temperature sensors;Schedules;Renewable energy sources;Microcontrollers;Prototypes;Manuals;Fish|
|[Indoor Environment and Health Protocol Monitoring and Control System Integrated into a Smart Robot to Promote Safety on University Campuses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085327)|A. M. Nagayo; S. V. T. Sangeetha; M. Z. Al Ajmi; A. Y. M. Al Bulushi; M. S. A. Al Hinaai; L. Y. T. Al Hamadani|10.1109/ICEARS56392.2023.10085327|Robotics;Internet of Things;Transfer Learning;Machine Learning;Environment;Health Safety;Temperature sensors;Actuators;Protocols;Sensors;Indoor environment;Health and safety;Task analysis|
|[Energy Management System based on Interleaved Landsman Converter using Hybrid Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085115)|R. J; D. B. E; R. J C; T. P S|10.1109/ICEARS56392.2023.10085115|Solar Panel;Wind Mill;Rechargeable battery;Interleaved Landsman Converter;Rectifier;MATLAB simulation;Renewable energy sources;Wind;Wind energy;Power system stability;Hybrid power systems;Batteries;Power system reliability|
|[Embedded System based Independent Scientist Satellite Payload](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085081)|D. M; G. C; P. P; V. V|10.1109/ICEARS56392.2023.10085081|Weather balloon;Embedded system;Near space;Sensors;Temperature;Pressure and Humidity;Temperature sensors;Temperature measurement;Embedded systems;Atmospheric measurements;Satellite broadcasting;Humidity;Sensor systems|
|[Dual Control of Single-Phase Grid-Connected PV System for Harmonics Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085052)|B. L. Badela; S. Vudumudi; N. R. Kolipaka|10.1109/ICEARS56392.2023.10085052|PV Array;MPPT;Voltage Controller;Current Controller;Maximum power point trackers;Wind energy generation;Simulation;Power quality;Harmonic analysis;Inverters;Mathematical models|
|[A Multifunctional EV Charger using PV Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085326)|G. Seshavarthini; S. Suresh; S. Rathinamala; K. Pradeep|10.1109/ICEARS56392.2023.10085326|Microgrid;power system;Electric Vehicle (EV);Wind energy generation;Renewable energy sources;Wind energy;Microgrids;Wind power generation;Hybrid power systems;Generators|
|[Solar Based Water Jar Carrying System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085026)|P. B. Kokate; S. B. Malshikare; S. S. Panwal; C. S. Walke; K. V. Thakur|10.1109/ICEARS56392.2023.10085026|Arduino Uno;LN298N Motor Driver;Solar Panels;Ultrasonic Sensors;Rechargeable Batteries;Pulse Width Modulation (PWM);Renewable energy sources;Costs;Pollution;Pulleys;Prototypes;Solar energy;Maintenance engineering|
|[Thermal Analysis of 2.2kW Transformer of On-Board Charger for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085292)|B. K. Kushwaha; R. R.; S. Jadhav; S. T. S.|10.1109/ICEARS56392.2023.10085292|High Frequency Transformer;Core loss;Finite Element Analysis;Performance evaluation;Ferrites;Renewable energy sources;Transformer cores;Predictive models;Transformers;Electric vehicles|
|[Electrogastrogram Analysis of Unclear Stomach Pain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085520)|G. M; K. U. R; A. Prasath T; A. M|10.1109/ICEARS56392.2023.10085520|Electrogastrography(EGG);Gastric Dysrhythmias;Stomach pain;Fast Fourier Transform(FFT);Water Load Test(WLT);Stomach;Visualization;Renewable energy sources;Pathology;Pain;Sociology;Muscles|
|[A Study of the Effect of Optical Filters on the Performance of Solar Panel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085608)|C. L. K. R; G. Thangavel; K. C. V; A. V; J. D. R. K|10.1109/ICEARS56392.2023.10085608|Optical Filter;Efficiency of Solar panel;Water Coolant;Air Coolant;Photovoltaic (PV) module;Optical filters;Integrated optics;Radiation effects;Urban areas;Color;Voltage;Coolants|
|[Support Vector based classification for Adaptive Channel Equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085218)|D. C. Diana; R. Hema; G. N. Kumar; R. Rohith Kumar|10.1109/ICEARS56392.2023.10085218|Support vector machine;Channel Equalization;Adaptation models;Renewable energy sources;Adaptive systems;Support vector machine classification;Radial basis function networks;Adaptive equalizers;Mathematical models|
|[An Improved Miller Compensated Two Stage CMOS Operational Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085073)|Y. Thulasi; K. L. Krishna; D. Srinivasulu Reddy; V. Hemanthi; T. B. Reddy; T. Lalithapriya|10.1109/ICEARS56392.2023.10085073|amplifier;compensating circuit;phase margin;stability;settling time and CMOS design;Operational amplifiers;Semiconductor device modeling;Resistors;Current mirrors;Capacitors;Bandwidth;CMOS technology|
|[Design and Analysis of High-Speed All-Optical Logic Gate with Various Pulse Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085134)|M. Michael; E. C. Britto; Saranya; Vignesh|10.1109/ICEARS56392.2023.10085134|All-optical logic gates;Reflective semiconductor optical amplifiers;all-optical devices;Semiconductor optical amplifiers;Q-factor;Pulse generation;Renewable energy sources;Codes;Stimulated emission;Logic gates|
|[Spotted Hyena Optimized PI-PD Controller for Frequency control of Standalone μ-Grid Incorporating Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085648)|V. S. R. P. K. Neeli; M. V. Raghavendra; S. C. Basha; K. K. Chowdary; N. Sameera|10.1109/ICEARS56392.2023.10085648|PI-PD cascade controller;renewable and green energy sources;standalone microgrid;Spotted hyena optimizer;Renewable energy sources;Radiation effects;Loading;Merging;Microgrids;Wind power generation;Electric vehicles|
|[Study of Interfacing PLC With HMI for Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084927)|S. Sujitha; V. K. S; D. V S; J. Likitha; P. R M; J. R|10.1109/ICEARS56392.2023.10084927|HMI Interface;PLC;Automation;Industrial Application;Wiring;Renewable energy sources;Automation;Service robots;Process control;Control systems;Relays|
|[Design of Efficient Wireless Charging Pad Deployment and Maximizing the Power Transfer Technique for an Autonomous Electric Vehicle Charging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085244)|S. B; K. C; B. S; D. B. K. J; H. P Y|10.1109/ICEARS56392.2023.10085244|Electric Vehicles (EV);Wireless Power Transfer (WPT);Battery Technology (BT);Wireless communication;Technological innovation;Costs;Wires;Transportation;Receivers;Wireless power transfer|
|[An EEG-based Brain-Computer Interface for Stress Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085458)|S. N; J. J|10.1109/ICEARS56392.2023.10085458|Recognition of Emotion;Long Short-Term Memory Networks;Emotion Images;Feature Collection;Feature Extraction;Recurrent Neural Networks;Classification;Deep learning;Anxiety disorders;Human factors;Feature extraction;Brain modeling;Electroencephalography;Brain-computer interfaces|
|[An Enhanced and Interactive Training Model for Underground Coal Mines Using Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084970)|A. Jain; M. Jain; M. Patel; N. S. Rathore|10.1109/ICEARS56392.2023.10084970|Virtual Reality;Coal Mining;Mining Rescue Training;Blender;Virtual Training;Training;Headphones;Solid modeling;Renewable energy sources;Three-dimensional displays;Virtual reality;Real-time systems|
|[Detection of Intoxication in Automobile Drivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085153)|A. Rahul Harikumar; T. Grover; M. Kanchana|10.1109/ICEARS56392.2023.10085153|Intoxication;Sober;detection;CNN;Classification;Alcohol;Renewable energy sources;Face recognition;Automobiles;Task analysis;Vehicles|
|[An Improved Internet of Things (IoT) based Smart Prepaid Energy Meter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085592)|C. Selvarathi; S. M. Monishsurya; P. Naveen Kumar; K. Deepakraj|10.1109/ICEARS56392.2023.10085592|Internet of Things;Prepaid Electricity Meter;Node MCU(Micro Controller Unit);Smart Energy Meter;Meters;Wireless communication;Technological innovation;Zigbee;Passwords;Telephone sets;Real-time systems|
|[Design and Fabrication of Solar Powered Air Quality Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085436)|R. Lal Raja Singh; K. Arulselvan; A. Indhumathi; S. Iswarya; G. Namitha|10.1109/ICEARS56392.2023.10085436|air pollution;Internet of things;sensor;monitoring;temperature;Renewable energy sources;Costs;Urban areas;Air pollution;Sensor systems;Real-time systems;Sensors|
|[Optimization of Power System Transients in a Multi-Machine System using Unified Power Flow Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085357)|V. Patil; A. S. Jadhav; C. Nandakumara; R. V. Pawar; V. Koluragi; S. Gadad|10.1109/ICEARS56392.2023.10085357|FACTS;SSSC;STATCOM;UPFC;Pi Controller;Renewable energy sources;PI control;Flexible AC transmission systems;Rotors;Power system stability;Stability analysis;Voltage control|
|[Reverse Power Protection of Alternators in an IEEE-5 Bus System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085055)|C. Shanthini; P. Bhargavi; K. Deepa; P. V. Manitha|10.1109/ICEARS56392.2023.10085055|Reverse power;Institute of Electrical and Electronics Engineers (IEEE) 5 bus;Delay;Hold;Synchronous Generator;Renewable energy sources;Circuit breakers;Sociology;Generators;Alternators;Safety;Relays|
|[PV System-based Interleaved Converter for Grid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085578)|K. Rajesh; J. S. Priya; K. Gunasekaran; P. Duraipandy|10.1109/ICEARS56392.2023.10085578|Photovoltaic system;Maximum energy;Direct current to Direct Current converter;grid system;Perturb & observe algorithm;Maximum power point trackers;Photovoltaic systems;Renewable energy sources;Total harmonic distortion;Voltage source inverters;Magnetic separation;Voltage|
|[Modeling and Control of Solar PV Integrated Boost Converter fed Battery Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085482)|K. S. Mani; M. M. Garg; S. Dahiya|10.1109/ICEARS56392.2023.10085482|Boost Converter;Mathematical Modelling;Phase Margin;Proportional-Integral (PI) Controller;Solar Photovoltaics (PV) Modelling;Photovoltaic systems;PI control;Temperature;Atmospheric modeling;Frequency-domain analysis;Mathematical models;Temperature control|
|[A Fiber-Wireless Monitoring System with a QoE Instrument for Smart Grid Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085662)|H. Tabassum; N. L; S. P. Atti; A. Pasha; N. Shwetha; M. B. Neelagar|10.1109/ICEARS56392.2023.10085662|QoE;Fiber-Wireless Monitoring Systems (Fi-WMSs);Smart Grid;Teleprotection;Synchrophasors;Smart Sensor Channels (SSCs);Cross-layer;Optical fibers;IEEE 802.15 Standard;Optical fiber sensors;Cross layer design;Costs;Smart grids;Delays|
|[Voltage and Current Regulation of a Fuel Cell-Supercapacitor Based Hybrid Renewable Energy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085480)|K. Buts; L. Dewan; M. P. R. Prasad|10.1109/ICEARS56392.2023.10085480|Fuel Cell;Supercapacitor;Coordinated Control;Cascaded Control;Hybrid Control;Power Management;Current control;Renewable energy sources;Fuel cells;Supercapacitors;Hybrid power systems;Fuels;Voltage control|
|[Artificial Neural Network and Taguchi Analysis of Multi-Objective Optimisation of Wear Behaviour of Zro2 based Aluminium Nanocomposite](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084968)|S. P. Manikandan; K. P. Vetrivel; P. Thakre; K. Swarnalatha; N. P; G. Chandrasekar|10.1109/ICEARS56392.2023.10084968|Hybrid metal matrix composite;ANOVA;stir casting;wear analysis;Resistance;Renewable energy sources;Zirconium;Artificial neural networks;Predictive models;Parallel processing;Mathematical models|
|[Novel Control of Standalone Hybrid Wind-PV-Battery System for Improving Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084981)|K. Deepak; Y. Hazarathaiah; U. Chaithanya; N. A. Charan; C. M. Awaise; P. Sinjith|10.1109/ICEARS56392.2023.10084981|Photovoltaic;Wind;Renewable Energy Sources;Power Quality;Battery Storage;Standalone System;Photovoltaic systems;Renewable energy sources;Power quality;Microgrids;Hybrid power systems;Inverters;Batteries|
|[Performance Comparisons of Multilevel Inverter Topologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085387)|K. Deepak; U. Chaithanya; M. Lingmaiah; S. Abdul Siraj; K. Narahari Kalyan Kumar; N. Aravind|10.1109/ICEARS56392.2023.10085387|Multilevel inverter;SPWM;Neutral point clamped;H-Bridge;Renewable energy sources;Software packages;Shape;Voltage;Pulse width modulation;Multilevel inverters;Harmonic analysis|
|[Forest Fire Probability Prediction based on Humidity and Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085661)|R. Mekala; S. Srinath; S. Gokul; E. Balavigneshwar; R. Muralidharan|10.1109/ICEARS56392.2023.10085661|Convolutional Neural Network (CNN);Unmanned Aerial Vehicles (UAV);Logistic regression;Internet of Things (IOT) sensors;Aerial videos;Flask;Smoke sensors;Temperature sensors;Temperature distribution;Soft sensors;Weather forecasting;Forestry;Humidity;Predictive models|
|[MCSVM and MCRVM based Contingency Classification Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085481)|S. Prasher; L. Nelson; A. S. Sindhu; S. Sumathi; M. Jagdish|10.1109/ICEARS56392.2023.10085481|Principal component analysis;Contingency analysis;Support vector machine;Support vector machines;Contingency management;Voltage;Approximation algorithms;Mathematical models;Hardware;Data models|
|[Wavelet Entropy based Band Selection for Hyperspectral Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085053)|M. Jampana; P. Deekshita; S. S. Parvathaneni; B. L. N. P. Kumar|10.1109/ICEARS56392.2023.10085053|Band selection;Hyperspectral imaging;Wavelet entropy;Entropy;Support vector machine;Support vector machines;Dimensionality reduction;Renewable energy sources;Data analysis;Wavelet analysis;Information filters;Entropy|
|[Automatic Pothole and Humps on Roads Detection and Notification Alert](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085086)|K. Bhavana; S. Munappa; K. D. Bhavani; P. Deshmanth; A. Swathi; S. R. Vanga|10.1109/ICEARS56392.2023.10085086|Global Positioning System (GPS);Global System for Mobile communication (GSM);Pothole;Ultrasonic sensor;Renewable energy sources;Machine learning algorithms;Ultrasonic variables measurement;Roads;Snow;Acoustics;Time measurement|
|[Evaluation of Different Methods used to Clean Solar Panel Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085478)|A. Badhoutiya|10.1109/ICEARS56392.2023.10085478|Electrostatic cleaning;foam wiper;guide rails;jet spray;micro-particles;servo motors;Surface cleaning;Renewable energy sources;Radiation effects;Liquids;Spraying;Glass;Solar panels|
|[Novel Quad–Band Decagon Shaped Patch Antenna for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085160)|N. A. B; A. R; B. V; D. R|10.1109/ICEARS56392.2023.10085160|Square slots antenna;quadband;Voltage Standing Wave Radio;Decagon shaped patch;better return loss values;Wireless communication;Renewable energy sources;Slot antennas;Shape;Patch antennas;Dielectric materials;Dielectric resonator antennas|
|[Co-Existence of LTE Communication with WLAN in Unlicensed Bands: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085163)|H. K. Neelakantam; B. P. Makala; P. Kommoju; D. S. R. Ogirala; P. N. Suneel; M. K. D|10.1109/ICEARS56392.2023.10085163|Unlicensed bands;Wireless local-area network (WLAN);Throughput;Co-existence;Long-term evolution (LTE);Wireless LAN;Renewable energy sources;Heuristic algorithms;Interference;Throughput;Dynamic scheduling;Downlink|
|[Efficient Image Transmission in Underwater Communication using OFDM Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085091)|P. Balasubramani; S. Suresh; S. Kirubashankar; S. Kowsika; S. Guhan|10.1109/ICEARS56392.2023.10085091|discrete wavelet transform orthogonal frequency-division multiplexing channel;frames;power consumption;Wireless communication;Underwater communication;Transmitters;OFDM;Measurement uncertainty;Sea measurements;Transforms|
|[Improving Bandwidth & Gain using Trapezoidal Microstrip Feed with X-Shape DRA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085517)|B. Pujitha; S. Nausheen; H. Perusomula; S. Alekhya; C. Swapna; A. N. Kumar|10.1109/ICEARS56392.2023.10085517|Dielectric Resonator Antennas (DRA);Microchip Patches;Monopoles;X-shape;Weather Radar Systems;Microwave antennas;Dipole antennas;Radio navigation;Microstrip antennas;Bandwidth;Radar antennas;Dielectric resonator antennas|
|[A Compact Single-fed Multiband Antenna for Telematics Control Unit Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085102)|R. R; B. K K; S. K. M; S. R; S. J; S. T S|10.1109/ICEARS56392.2023.10085102|4G antenna;Telematics;Telematics control unit;multiband;Meander;Performance evaluation;Antenna measurements;Renewable energy sources;Shape;Bandwidth;Telematics;Downlink|
|[Microarray based Geonomic Biomarker Optimization for Cancer Prognosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084989)|J. T. K; K. S; N. H; R. K. P|10.1109/ICEARS56392.2023.10084989|Convolutional Neural Network Algorithm;Disease Prediction;Median Estimation;Particle Swarm Optimization Algorithm;Severity Analysis;Single-cell Ribonucleic acid sequencing;Sequential analysis;RNA;DNA;Prediction algorithms;Skin;Classification algorithms;Gene expression|
|[Design and Implementation of Circular Polarized Beam Steering CDRA for Satellite Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085178)|A. Kumar; R. S. Yaduvanshi|10.1109/ICEARS56392.2023.10085178|Cylindrical Dielectric Resonator Antenna (CDRA);Gain;LHCP and RHCP and Beam steering;Phased arrays;Polarization;Renewable energy sources;Beam steering;Satellites;Bandwidth;Dielectric resonator antennas|
|[An Analysis of Security Challenges in Internet of Things (IoT) based Smart Homes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085106)|M. R. Joel; G. Manikandan; G. Bhuvaneswari|10.1109/ICEARS56392.2023.10085106|Internet of Things (IoT);sensors;actuators;smart home;security;Radio frequency;Computers;Smart cities;Smart homes;Software;Sensors;Internet of Things|
|[Design and Development of a Smart Sprinkler Device for IoT-Integrated Plants Irrigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084960)|R. Josphineleela; K. V. Siva Reddy; M. V. S. S. Reddy; R. S. Rawat|10.1109/ICEARS56392.2023.10084960|Gardening;Energy;sprinklers;Sustainable development;Internet of things;the detection system;Temperature sensors;Irrigation;Temperature distribution;Moisture;Water heating;Spraying;Humidity|
|[IoT based Social Device Network with Cloud Computing Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085574)|T. Suman; S. Kaliappan; L. Natrayan; D. C. Dobhal|10.1109/ICEARS56392.2023.10085574|Energy;Cloud computing;Fog computing;IoT devices;social networking;Cloud data;Sustainable development;Cloud computing;Microwave integrated circuits;Social networking (online);Computer architecture;Switches;Cameras;Safety|
|[Low-Power High-Speed CNTFET-based 1-bit Comparator Design using CCT and STT Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084990)|R. Rajora; K. Sharma; L. Gupta; A. Sachdeva; A. Sharma|10.1109/ICEARS56392.2023.10084990|CNTFET comparators;Energy delay product;Power delay product;Sleep transistor technique;Chiral vector;Renewable energy sources;Logic circuits;Medical services;Very large scale integration;CNTFETs;Delays;Power dissipation|
|[Development of Programmed Autonomous Electric Heavy Vehicle: An Application of IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085492)|P. N. Reddy; P. Umaeswari; L. Natrayan; A. Choudhary|10.1109/ICEARS56392.2023.10085492|Energy;IOT;Autonomous Vehicle;Electric vehicle;Sustainable development;Vibrations;Laser radar;Automation;Torque;Cameras;Acoustics;Agriculture|
|[Study on Conveyor Belt System enabled with IoT in Postal and Courier Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085155)|K. Venusamy; A. H. M; K. M; M. S; C. P|10.1109/ICEARS56392.2023.10085155|Conveyor;Internet of Things;Postal service;Sorting and Segregating;Renewable energy sources;Costs;Operating systems;Programming;Belts;Software;Monitoring|
|[Resource Allocation in IoT-based Edge Computing using Deep Learning with Optimization Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085263)|Y. M. A. Algani|10.1109/ICEARS56392.2023.10085263|Edge computing;Internet of Things (IoT);resource allocation;deep learning;computational task;Deep learning;Cloud computing;Costs;Computational modeling;Computer architecture;Internet of Things;Resource management|
|[IoT based Automatic Electricity Cut off using LPG Gas Leakage Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085217)|R. B; M. D; G. K; S. N; S. R; K. T|10.1109/ICEARS56392.2023.10085217|GSM-Global System for Mobile Communication;LCD-Liquid Crystal Display;SMS-Short Message Service;IoT-Internet of Things;GSM;Fans;Renewable energy sources;Regulators;Prototypes;Switches;Modems|
|[IoT-based Real-Time System for Tracking and Monitoring the Health of Soldier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085150)|P. Sakthi; T. Vishnuram; N. Satheeshkumar; S. B. Sathishkumar|10.1109/ICEARS56392.2023.10085150|Global Positioning System;LM35 (Linear Monolithic35) sensor;Heartbeat sensor;battery;IoT (Internet of Things);Tilt Sensor;Temperature sensors;Temperature measurement;Temperature distribution;Real-time systems;Sensor systems;Safety;Internet of Things|
|[Cloud based Weather Station using IoT Technology to Track the Air Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085362)|S. Jayanthi; E. Srividhya; N. Ramshanka; S. T. R; P. B. Edwin Prabhakar|10.1109/ICEARS56392.2023.10085362|Weather station;Internet of Things;Air quality monitoring;Cloud server;ThingSpeak;Temperature measurement;Temperature sensors;Embedded systems;Moisture;Pollution measurement;Internet of Things;Servers|
|[Automated Cleaning Machine using Arduino UNO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085199)|B. N. Venkata; S. P. Boddapati; S. Vanka; V. R. Kumar Karimikonda; K. Mandava; M. Gunji|10.1109/ICEARS56392.2023.10085199|Home automation;Arduino UNO;Ultrasonic sensors;Infrared Sensors;Cleaning;and Mopping brushes;Technological innovation;Renewable energy sources;Brushes;Infrared sensors;Cleaning;Acoustics;Water pumps|
|[Biometric Aided Intelligent Security System Built using Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085572)|R. Josphineleela; D. Lekha; L. Natrayan; K. C. Purohit|10.1109/ICEARS56392.2023.10085572|Energy;IoT;Intelligent security;Biometric;Sustainable development;Renewable energy sources;MOSFET;Face recognition;Prototypes;Cameras;Safety;Security|
|[Automatic Industrial Fault Detection and IoT based Remote Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085353)|S. V. D; J. S; D. K; K. C; K. D|10.1109/ICEARS56392.2023.10085353|IoT;Node MCU;Sensors;WIFI Module;Temperature sensors;Industries;Temperature measurement;Automation;Fault detection;Sensor systems;Sensors|
|[IoT Detection based Energy Meter Integrated with Smart Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085200)|V. K. K; A. P; C. A; G. K. K; K. Deepthi|10.1109/ICEARS56392.2023.10085200|Energy Meter;Units;Arduino;IoT;Theft Detection;Meters;Cloud computing;Renewable energy sources;Programming;Hardware;Internet of Things;Smart devices|
#### **2023 4th International Conference on Advancements in Computational Sciences (ICACS)**
- DOI: 10.1109/ICACS55311.2023
- DATE: 20-22 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Open research knowledge graph for structuring scholarly contributions using transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089637)|M. Ali; A. Malik; M. Bashir|10.1109/ICACS55311.2023.10089637|knowledge graph;BERT;LSTM;Deep learning;Knowledge graphs;Transformers;Natural language processing;History;Task analysis|
|[A Novel Framework for Human Action Recognition Based on Features Fusion and Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089752)|T. F. N. Bukht; H. Rahman; A. Jalal|10.1109/ICACS55311.2023.10089752|Human action recognition;Action prediction;Decision Tree;SIFT;ORB;Computer vision;Statistical analysis;Shape;Image color analysis;Merging;Process control;Feature extraction|
|[Melanoma Lesion Segmentation and Classification Using SegNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089675)|H. Kibriya; I. Abdullah; F. Kousar|10.1109/ICACS55311.2023.10089675|Melanoma;SegNet;Deep Learning;Detection;Segmentation;Cancer;Hair;Measurement;Image segmentation;Melanoma;Manuals;Blood vessels;Optical imaging|
|[Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089674)|I. Akhter; A. Jalal|10.1109/ICACS55311.2023.10089674|Action detection;action classification;behavior prediction;crowded scenes;data optimization;random forest;T-distributed stochastic neighbor embedding;Visualization;Forestry;Feature extraction;Real-time systems;Data mining|
|[Automate Appliances via Gestures Recognition for Elderly Living Assistance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089778)|M. Muneeb; H. Rustam; A. Jalal|10.1109/ICACS55311.2023.10089778|Genetic algorithm;hand gestures recognition;inertial sensors;t-distributed stochastic neighbor embedding;Home appliances;Computer vision;Technological innovation;Wearable computers;System performance;Gesture recognition;Medical services|
|[Predicting Early Withdrawal of University Students: A Comparative Study between KNN and Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089706)|A. Tariq; A. Amin; Y. Masood; M. Muzaffar; J. Iqbal|10.1109/ICACS55311.2023.10089706|Student Drop Prediction;Dropping out of university;KNN performance;Decision Tree Comparison;Extra-TreesClassifier;Machine learning algorithms;Employment;Education;Predictive models;Prediction algorithms;Market research;Stakeholders|
|[Design and Analysis of an Improved Deep Ensemble Learning Model for Melanoma Skin Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089716)|M. H. Javid; W. Jadoon; H. Ali; M. D. Ali|10.1109/ICACS55311.2023.10089716|Convolution Neural Networks;Ensemble Learning;Deep Learning;Melanoma Skin Cancer;Data Augmentation;Imbalance Dataset;Stacking;Training;Convolution;Computational modeling;Stacking;Melanoma;Skin;Ensemble learning|
|[Detection of Tomato Leaf Disease Using Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089689)|A. Khalid; S. Akbar; S. A. Hassan; S. Firdous; S. Gull|10.1109/ICACS55311.2023.10089689|Convolutional neural networks;tomato leaf diseases;image processing;disease detection;Deep learning;Plant diseases;Plants (biology);Throughput;Agriculture;Real-time systems;Convolutional neural networks|
|[Natural Language Processing (NLP) based Extraction of Tacit Knowledge from Written Communication during Software Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089779)|M. Noor; Z. A. Rana|10.1109/ICACS55311.2023.10089779|tacit knowledge;unstructured knowledge;knowledge management;global software engineering;software lifecycle;project management;knowledge sharing;Knowledge engineering;Discussion forums;Organizations;Media;Software;Natural language processing;Knowledge management|
|[Multipath Mitigation for Single Frequency Stand-Alone Receivers Using Wavelet Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089676)|A. Zulfiqar; S. Z. Farooq|10.1109/ICACS55311.2023.10089676|Global Navigation Satellite Systems (GNSS);Single Frequency (SF) Receiver;Positioning Accuracy;Multipath Mitigation;Wavelet Denoising;Global navigation satellite system;Codes;Filtering;Noise reduction;Receivers;Signal processing;Position measurement|
|[Deep Activity Recognition based on Patterns Discovery for Healthcare Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089764)|M. Javeed; A. Jalal|10.1109/ICACS55311.2023.10089764|Activity recognition;convolutional neural network;deep learning;healthcare monitoring;motion pattern;Performance evaluation;Deep learning;Filtration;Medical services;Computer architecture;Smart homes;Activity recognition|
|[Cyber Security Intrusion Detection Using Deep Learning Approaches, Datasets, Bot-IOT Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089688)|I. Manan; F. Rehman; H. Sharif; C. N. Ali; R. R. Ali; A. Liaqat|10.1109/ICACS55311.2023.10089688|Cyber Security;Intrusion Detection;Deep Learning;Bot-IoT dataset;Deep learning;Analytical models;Noise reduction;Neural networks;Key performance indicator;Intrusion detection;Feeds|
|[Artificial Neural Network for Human Object Interaction System Over Aerial Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089722)|M. Pervaiz; A. Jalal|10.1109/ICACS55311.2023.10089722|Human object recognition;artificial neural network;visual classification;aerial images;games action dataset;Visualization;Image recognition;Video on demand;Artificial neural networks;Object detection;Games;Activity recognition|
|[Track Coalescence Avoidance in Multi-target Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089762)|S. A. Memon; W. -G. Kim; T. Yazdan|10.1109/ICACS55311.2023.10089762|detection;false-track discrimination;multi-targets;track coalescence avoidance;Smoothing;Couplings;Maximum likelihood detection;Target tracking;Smoothing methods;Simulation;Measurement uncertainty;Nonlinear filters|
|[Mobility and Content Retrieval in Vehicular Named Data Network: Challenges and Countermeasures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089761)|M. Abdullah; A. Kiran; U. Azam|10.1109/ICACS55311.2023.10089761|Internet of Vehicles (IoV);Intelligent Transportation System (ITS);broadcast storm;Named Data Network (NDN);Measurement;Storms;Information sharing;Transportation;TCPIP;Traffic control;Road safety|
|[3D Shape Estimation from RGB Data Using 2.5D Features and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089663)|H. Ashfaq; A. Jalal|10.1109/ICACS55311.2023.10089663|Machine learning;Data mining;neural network;Big data analytics;3D bounding box;Deep learning;Solid modeling;Three-dimensional displays;Machine learning algorithms;Electronic learning;Computational modeling;Virtual reality|
|[An Optimized Algorithm for Human Portrait Image Segmentation Using U-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089739)|A. Jamil; M. Ashraf; A. Farooq; M. B. Khan; U. Ahmed; M. Umair|10.1109/ICACS55311.2023.10089739|Image Segmentation;U-net;Human Portrait Segmentation;Trimap;Image Matting;Training;Image segmentation;Image analysis;Shape;Image color analysis;Computer architecture;Indexes|
|[Object Detection and Segmentation for Scene Understanding via Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089658)|B. R. Chughtai; A. Jalal|10.1109/ICACS55311.2023.10089658|Image segmentation;Object detection;and recognition;Shi-Tomasi corner detection;SIFT;Random forest;Tensor flow;Image segmentation;Visualization;Computer vision;Tensors;Image color analysis;Object detection;Transforms|
|[Highway Traffic Surveillance Over UAV Dataset via Blob Detection and Histogram of Gradient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089709)|A. M. Qureshi; A. H. Butt; A. Jalal|10.1109/ICACS55311.2023.10089709|object detection;pattern recognition;aerial images;template matching;Deep learning;Shape;Computational modeling;Vehicle detection;Surveillance;Roads;Lighting|
|[A Machine Learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089759)|M. Mudassar; M. Afzal; T. Muhammad|10.1109/ICACS55311.2023.10089759|Heart Failure;Imbalance Learning;Classification;Ensemble vs. non-Ensemble Classifiers;Heart;Training;Computational modeling;Machine learning;Predictive models;Cardiovascular diseases;Reliability|
|[Detection of Lungs Cancer Through Computed Tomographic Images Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089652)|M. Abid; S. Akbar; S. Abid; S. A. Hassan; S. Gull|10.1109/ICACS55311.2023.10089652|computed tomography;deep learning;lung cancer;convolutional neural network;chest CT images;Deep learning;Image segmentation;Sensitivity;Computed tomography;Lung cancer;Lung;Medical services|
|[Automated White Matter Segmentation in MR Images Using Residual UNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089627)|Y. Ali; M. A. Iftikhar; Q. Abbas; T. Wahab|10.1109/ICACS55311.2023.10089627|Segmentation;U-Net;Residual Block;Training;Image segmentation;Wearable Health Monitoring Systems;Visualization;Magnetic resonance imaging;White matter;Task analysis|
|[Deep Convolutional Neural Network-based Framework for Apple Leaves Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089774)|S. Firdous; S. Akbar; S. A. Hassan; A. Khalid; S. Gull|10.1109/ICACS55311.2023.10089774|Apple leaves disease detection;convolutional neural networks;image processing;canny feature extraction;model scaling;Deep learning;Plant diseases;Analytical models;Computational modeling;Plants (biology);Training data;Feature extraction|
|[Multi-Pedestrians Anomaly Detection via Conditional Random Field and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089730)|F. Abdullah; A. Jalal|10.1109/ICACS55311.2023.10089730|Anomaly Detection;Conditional random field;Conditional probability;Context understanding;Deep Learning;Jaccard similarity;Objects segmentation;Temporal association;Deep learning;Analytical models;Terrorism;Wheels;Watersheds;Transforms;Video surveillance|
|[Software Project Management - Gap between Theory and Practice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089727)|N. Ahmad; A. A. Malik|10.1109/ICACS55311.2023.10089727|academia-industry gap;empirical study;IT industry;software development;software project management;survey;Industries;Training;Schedules;Creep;Project management;Focusing;Companies|
|[Experimenting Ensemble Machine Learning for DDoS Classification: Timely Detection of DDoS Using Large Scale Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089656)|H. Amaad; H. Mughal|10.1109/ICACS55311.2023.10089656|Distributed Denial of Service (DDoS);Ensemble Machine Learning;Random Forest;DDoS Detection;Boosting Trees;CIC-DDoS2019;Radio frequency;Computer hacking;Computational modeling;Forestry;Denial-of-service attack;Boosting;Computer crime|
|[Vehicle Detection and Tracking Using Kalman Filter Over Aerial Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089701)|A. M. Qureshi; A. Jalal|10.1109/ICACS55311.2023.10089701|object detection;segmentation and tracking;aerial images;trajectories;Tracking;Vehicle detection;Computational modeling;Lighting;Cameras;Trajectory;Planning|
|[Anti-social Behavior Detection using Multi-lingual Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089659)|H. Z. Ali; A. Rashid|10.1109/ICACS55311.2023.10089659|Natural Language Processing;Deep Learning;Sentiment Analysis;Anti-social Behavior;Training;Analytical models;Sentiment analysis;Dictionaries;Social networking (online);Computational modeling;Natural languages|
|[Anti-Ant Framework for Android Malware Detection and Prevention Using Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089629)|M. Awais; M. A. Tariq; J. Iqbal; Y. Masood|10.1109/ICACS55311.2023.10089629|Android Malware;detection;Prevention;Machine Learning;Support vector machines;Training;Analytical models;Supervised learning;Feature extraction;Malware;Behavioral sciences|
|[Common Problems in Software Requirement Engineering Process: An Overview of Pakistani Software Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089703)|S. Khalid; U. Rasheed; M. Muneer; W. H. Butt; R. Mehmood; U. Qamar|10.1109/ICACS55311.2023.10089703|requirement engineering phases;requirement engineering process;software industry;software practices;Industries;Organizations;Market research;Software;Requirements engineering;Faces|
#### **2023 14th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)**
- DOI: 10.1109/PEDSTC57673.2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Multiphase Voltage Multiplier Circuit with Interleaved Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087147)|A. Babanezhad; R. Beiranvand|10.1109/PEDSTC57673.2023.10087147|diode-capacitor circuit;high voltage gain;interleaved;multiphase;voltage multiplier;Low voltage;Simulation;Capacitors;Voltage;Switches;Pulse width modulation;Frequency conversion|
|[High Step Down/Step Up Full Soft Switching Bidirectional DC-DC Converter Without Auxiliary Switches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087090)|M. Kholousi; M. Elhami; S. Tohidi|10.1109/PEDSTC57673.2023.10087090|bidirectional dc-dc converter;soft switching;nonauxiliary switch;high voltage gain;Simulation;Soft switching;Voltage;DC-DC power converters;Switches;Zero voltage switching;Software|
|[A New Approach To Control Transmission Power From Renewable Energy Resources To The Grid Side Via Hybrid DC/DC/AC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087144)|R. A. Asl; A. Alizadeh Asl|10.1109/PEDSTC57673.2023.10087144|Active and reactive powers control;Flexible AC transmission system (FACTS);Hybrid PV/FC/Battery system;Application of renewable energy resources;Maximum power point trackers;Renewable energy sources;Reactive power;Uncertainty;Microgrids;DC-DC power converters;Inverters|
|[Sensorless Control of a Surface-mounted PMSM by an Improved Flux Observer with RLS Parameter Identification Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087128)|A. Y. Darani; B. Mirzaeian Dehkordi|10.1109/PEDSTC57673.2023.10087128|Sensorless Control;PMSM;discrete-time Flux Observer;RLS;Parameter Identification;Radio frequency;Couplings;Estimation error;Parameter estimation;Simulation;Surface resistance;Rotors|
|[A High-Voltage Gain DC/DC Resonant SC Converter for High-Power and Wide Input Voltage and Load Variation Ranges Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087089)|S. A. Zarandi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087089|High step-up dc/dc converter;switched-capacitor converter (SCC);resonant converter;zero-current switching (ZCS);zero-voltage switching (ZVS);Switching frequency;Resonant frequency;Switching loss;Switches;High-voltage techniques;Zero voltage switching;Control systems|
|[Balancing of Capacitor Voltages with a Reduced Number of Voltage and Current Sensors in Alternate Arm Multilevel Converter (AAMC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087151)|M. B. Hashkavayi; S. M. Barakati; M. R. Haredasht; V. Barahouei; S. H. Torabi|10.1109/PEDSTC57673.2023.10087151|alternate arm multilevel converter (AAMC);high-power applications;balancing of the capacitor voltages;voltage and current sensors;Multilevel converters;Software packages;Simulation;Capacitors;Switches;Capacitive sensors;Sensors|
|[A New Non-Isolated High Step-Up DC-DC Converter Suitable for Renewable Energy Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087167)|A. A. Asl; R. Alizadeh Asl; S. H. Hosseini|10.1109/PEDSTC57673.2023.10087167|high step-up DC-DC converter;voltage lift (VL);switch inductor(SL);single-input single-output converter (SISO);Renewable energy sources;Voltage;Switches;DC-DC power converters;SISO communication;Software;Power electronics|
|[Model Predictive Control of a Single Stage Power Factor Correction for Inductive Power Transfer Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087142)|S. Navaiyan-Kalat; S. Vaez-Zadeh; A. Babaki; M. Pakzaban|10.1109/PEDSTC57673.2023.10087142|Electric vehicles;high-frequency inverter;inductive power transfer;model predictive control;power factor correction;reactive power;voltage regulation;Reactive power;Simulation;Resonant frequency;Power factor correction;Control systems;Inverters;Topology|
|[Optimization and Improvement of Reduced Ferrite DWPT Magnetic Link Structure with New Passive Reflection Windings Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087065)|M. Pakzaban; S. Vaez-Zadeh; S. Navaiyan-Kalat|10.1109/PEDSTC57673.2023.10087065|dynamic wireless power transfer;DWPT;electric vehicles;ferrite cores;magnetic link optimization;reflection windings;Ferrites;Couplings;Coils;Magnetic flux;Magnetic cores;Windings;Reflection|
|[Fault-Tolerant Operation Approach for Nested Neutral Point Clamp (NNPC) Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087114)|V. Barahouei; S. Masoud Barakati; M. Rahmani Haredasht; M. Bagheri Hashkavayi; M. Zoraghi Jedi|10.1109/PEDSTC57673.2023.10087114|nested neutral point clamp (NNPC) converter;reliability;fault-tolerant;redundant phase;Fault tolerance;System performance;Simulation;Fault tolerant systems;Switching loss;Switches;Stability analysis|
|[Small-Signal Modeling of a Resonant SCC for PWM, Phase-Shifted and Frequency Modulation Control Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087110)|S. AfsharZarandi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087110|Extended describing function (EDF);frequency modulation;large-signal modeling;phase-shifted control;pulse width modulation (PWM);resonant switched-capacitor converter (SCC);small-signal modeling;Analytical models;Frequency modulation;Simulation;Soft switching;Switches;Resonant converters;Pulse width modulation|
|[Current-Based Fault Detection of Photovoltaic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087002)|N. Pourshahbaz; A. Eskandari; J. Milimonfared; M. Aghaei|10.1109/PEDSTC57673.2023.10087002|photovoltaics (PV);fault detection;line-to-line faults;open-circuit faults;Current-Base;Photovoltaic systems;Fault detection;Feature extraction;Real-time systems;Power electronics;Behavioral sciences;Safety|
|[An Adaptive Technique of MPPT for PV Array Under Partial and Dynamic Shading Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087181)|A. Kouchaki; A. Eskandari; J. Milimonfared; M. Aghaei|10.1109/PEDSTC57673.2023.10087181|adaptive algorithm;maximum power point tracking;photovoltaic (PV) system;partial shading conditions;Maximum power point trackers;Temperature sensors;PI control;Adaptive systems;Software packages;Heuristic algorithms;DC-DC power converters|
|[An Extendable Voltage Multiplier Based Multi-Input DC/DC Converter with Soft-Switching Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087179)|K. C. Omran; R. Beiranvand|10.1109/PEDSTC57673.2023.10087179|Dc–dc converter;high voltage ratio;non-isolated;renewable energy;soft-switching;voltage multiplier circuit;Renewable energy sources;Costs;Capacitors;Voltage;DC-DC power converters;Zero voltage switching;Topology|
|[Photovoltaic Grid-Tied VSI Control Considering Parameter Uncertainty Based on DOB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087081)|S. M. Ghahfarokhi; M. G. Esfahani; M. Hajian|10.1109/PEDSTC57673.2023.10087081|disturbance observer;robust control;photovoltaic grid-tied system;Photovoltaic systems;Uncertain systems;Simulation;Drives;Disturbance observers;Robustness;Power electronics|
|[Improved Commutation in Sensorless Control of Brushless DC Motors by Index Pulse Comparison](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087005)|A. Ramezankhani; D. Arab Khaburi; M. Bagheri Sadr; F. Bodaghi; M. Reza Arabshahi; J. Rodriguez|10.1109/PEDSTC57673.2023.10087005|Brushless direct current motor;Index Pulse Comparison;Sensorless;Back electromotive force (EMF);Zero-crossing point;Index Pulse;Commutation Pulse (CP);Torque;Brushless DC motors;Commutation;Simulation;Sensorless control;Production;Mathematical models|
|[A Bidirectional Transformerless Direct Ac-Ac Converter for Improved Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087095)|S. Mohsen Mortazavi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087095|Direct ac-ac converter;dynamic voltage restorer (DVR);power quality;series compensator;switched capacitor;AC-AC converters;Simulation;Power quality;Mathematical analysis;Capacitors;Transformers;Power electronics|
|[Optimum Efficiency and Maximum Possible Power Transfer Analysis of Double-Vehicle Wireless Charging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087182)|S. Hossein Mousavi; S. Vaez-Zadeh; A. Babaki|10.1109/PEDSTC57673.2023.10087182|Double receiver;Dynamic charging;Electric vehicles;Efficiency Analysis;Wireless power transfer;Buck converters;Optimization methods;Resonant frequency;Receivers;Frequency conversion;Electric vehicles;Mathematical models|
|[Investigation of Broadband Excitation Signals for Online Battery Impedance Spectroscopy and Model Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087135)|H. Haji Abbasali; S. Jafarabadi Ashtiani|10.1109/PEDSTC57673.2023.10087135|Lithium-ion batteries;electrochemical impedance spectroscopy (EIS);broadband excitation signals;battery modelling;Impedance measurement;Power system harmonics;Harmonic analysis;Battery charge measurement;Time measurement;Batteries;Broadband communication|
|[A Novel Continuous Input Current High Step up DC-DC Converter for Low Level DC Voltage Resource Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087004)|F. Barmoudeh; A. Salemnia|10.1109/PEDSTC57673.2023.10087004|High step up DC-DC converter;Photovoltaic;Renewable energy resources (RERs);Coupled inductor;Photovoltaic systems;Maximum power point trackers;Inductance;Voltage;Switches;High-voltage techniques;DC-DC power converters|
|[Analysis of a Double Stator Flux Reversal Permanent Magnet Machine With Halbach Array Magnets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087099)|B. Aslani; S. A. Gholamian; S. Ehsan Abdollahi|10.1109/PEDSTC57673.2023.10087099|Flux reversal permanent magnet (FRPM) machine;double stator;Halbach array magnet;Torque performance;Permanent magnet machines;Magnetic flux;Torque;Rotors;Stators;Permanent magnets;Magnetic analysis|
|[Study of An Improved Yoke Permanent Magnet Motor With Booster Magnets Between Stator Modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087170)|M. Amirkhani; M. Mirsalim|10.1109/PEDSTC57673.2023.10087170|stator-PM;demagnetization;low torque ripple;high torque density;biased-flux;Magnetic flux;Stator cores;Reactive power;Torque;Magnetic cores;Stator windings;Permanent magnet motors|
|[Robust Synchronizing Method for Unbalanced Weak Grid Connected Converters with High Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087107)|P. Jamallo; S. Vaez-Zadeh; A. Jabbarnejad|10.1109/PEDSTC57673.2023.10087107|power converters;renewable energy sources;unbalanced weak grid;Couplings;Renewable energy sources;Reactive power;Power transmission lines;Simulation;System performance;Estimation|
|[A Bidirectional Transformerless Direct Ac-Ac Converter for Improved Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087098)|S. M. Mortazavi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087098|Direct ac-ac converter;dynamic voltage restorer (DVR);power quality;series compensator;switched capacitor;AC-AC converters;Simulation;Power quality;Mathematical analysis;Capacitors;Transformers;Power electronics|
|[Fault Diagnosis of a Stand-Alone Wind System Using Supervised Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087119)|H. Mohammadkazemi; R. Peykarporsan; E. Afjei|10.1109/PEDSTC57673.2023.10087119|machine learning;fault detection;wind system;feature engineering;protection;Support vector machines;Fault detection;Doubly fed induction generators;Rotors;Stators;Transformers;Feature extraction|
|[On the Effect of Parasitic Capacitances on the Voltage Sharing of Series-Connected Diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087154)|A. Ghaderi; A. Azam Rajabian; S. Mohsenzade|10.1109/PEDSTC57673.2023.10087154|Diodes;High-voltage;Pulsed Power;Parasitic Elements;Power supplies;Capacitors;Power system protection;Voltage;SPICE;Software;Power electronics|
|[Torque Ripple Reduction and Radial Force Mitigation in the Switched Reluctance Motor Using a Novel Rotor Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087000)|A. Sohrabzadeh; H. Torkaman; E. Afjei|10.1109/PEDSTC57673.2023.10087000|switched reluctance motor;torque ripple;radial force;finite element method;Radio frequency;Vibrations;Three-dimensional displays;Force;Rotors;Switches;Switched reluctance motors|
|[Multi-Objective Optimization of an Axial-Flux Magnetic Gear Using Taguchi Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087138)|M. Abolghasemi; A. Ghaheri; S. M. Saghin; S. E. Afjei|10.1109/PEDSTC57673.2023.10087138|axial-flux;design of experiments;finite element;magnetic gear;Taguchi method;Electromagnetic devices;Torque;Magnetic gears;Particle measurements;Industrial facilities;Power electronics;Torque measurement|
|[Performance Investigation and Improvement of a Double-Layer Outer Rotor Consequent-Pole Permanent Magnet Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087177)|M. Naeimi; K. Abbaszadeh; J. Gyselinck|10.1109/PEDSTC57673.2023.10087177|permanent magnet;consequent-pole motor;double-layer rotor;torque ripple;average torque;Torque;Magnetic flux leakage;Atmospheric modeling;Air gaps;Rotors;Permanent magnet motors;Harmonic analysis|
|[Maximum Power Point Tracking-Based Control Strategy for PMSG Wind Energy Conversion System Using a Combined Fuzzy-Model Predictive Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087073)|E. Akbari; M. S. Shadlu|10.1109/PEDSTC57673.2023.10087073|Maximum power point tracking;wind turbine;permanent magnet synchronous generator;fuzzy logic controller;model predictive control;Maximum power point trackers;Renewable energy sources;Fluctuations;Wind speed;Simulation;Mathematical models;Wind turbines|
|[Evaluation of Turning on/off Delay Time Interval as a Precursor for IGBTs Gate Oxide Degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087106)|M. Jazayeri; S. Mohsenzade|10.1109/PEDSTC57673.2023.10087106|IGBT;Reliability;Condition Monitoring;Gate-Oxide Degradation;Degradation;Insulated gate bipolar transistors;Simulation;Logic gates;Turning;Reliability engineering;Threshold voltage|
|[A Bidirectional CLLC Resonant Converter for EV Battery Charger Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087140)|R. Takarli; M. Adib; A. Vahedi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087140|Bidirectional converter;bidirectional CLLC;EV battery charger;G2V;high-input voltage;low-stress voltage inverter;resonant converter;V2G;Battery chargers;Magnetomechanical effects;Soft switching;Switching loss;Resonant converters;Zero voltage switching;Topology|
|[of Different Rotor Positions on the Performance of a Coreless Axial-Flux Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087130)|O. Aghapour; M. N. Azari; S. Mehdi Mirimani|10.1109/PEDSTC57673.2023.10087130|Axial-flux permanent magnet machines;Finite Element Method;Coreless machine;Different rotor positions;Total harmonic distortion;Solid modeling;Torque;Three-dimensional displays;Magnetic cores;Rotors;Generators|
|[L-Type Modular Outer-Rotor Consequent-Pole Motor With Low Torque Ripple and High Permanent Magnet Utilization Ratio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087052)|M. Naeimi; K. Abbaszadeh; J. Gyselinck|10.1109/PEDSTC57673.2023.10087052|modular rotor;consequent-pole motor;outer-rotor;torque ripple;permanent magnet;Torque;Atmospheric modeling;Air gaps;Rotors;Voltage;Permanent magnet motors;Permanent magnets|
|[A Wide Input Range Two-Channel Interleaving Boost PFC Rectifier with High DC Bus Voltage and Small Inductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087088)|A. M. Kamalkhani; R. Asgarniya; E. Afjei|10.1109/PEDSTC57673.2023.10087088|PFC rectifier;dual-boost;interleaving;wide input voltage range;high DC bus voltage;multilevel;Inductance;Simulation;Noise reduction;Rectifiers;Voltage;High-voltage techniques;Power electronics|
|[MRAS Sensorless Control of the SynRM Based on a Simplified Finite Position Set Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087132)|S. R. Eftekhari; A. Mosallanejad; H. Pairo|10.1109/PEDSTC57673.2023.10087132|sensorless control;MRAS mrthod;iteration algorithm;synchronous reluctance motor;Adaptation models;PI control;Microcontrollers;Heuristic algorithms;Software algorithms;Rotors;Prediction algorithms|
|[A Single Switch Common-ground Ultra-high Gain Non-isolated DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087183)|S. Mahdizadeh; M. T. Monfared; S. E. Fazeli; M. T. Bina|10.1109/PEDSTC57673.2023.10087183|DC-DC converter;Continuous input current;high gain converter;Simulation;Voltage;Switches;High-voltage techniques;Feature extraction;Power electronics;Behavioral sciences|
|[A High Step-Up Converter with Continuous Input Current and Auxiliary Circuit to Realize Soft-Switching Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087131)|M. M. Jouzdani; M. Shaneh; T. Nouri; H. Saeidi|10.1109/PEDSTC57673.2023.10087131|Soft-switching;ZVS;ZCS;Coupled inductor;Breakdown voltage;Simulation;Switches;High-voltage techniques;Zero voltage switching;Stability analysis;Power electronics|
|[A Step-up DC-DC Converter With High Voltage Gain and Eliminated Right Half Plane Zero](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087149)|S. M. Salehi; A. Yazdian|10.1109/PEDSTC57673.2023.10087149|DC-DC converter;step-up;minimum-phase;no right half plane zero;coupled inductor;Simulation;Transfer functions;High-voltage techniques;DC-DC power converters;Voltage;Switches;Stability analysis|
|[Low-Voltage Stress CI-based Quadratic Boost Converter for Distributed Energy Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087178)|M. Dezhbord; M. Babalou; S. Mohamadian; C. Cecati|10.1109/PEDSTC57673.2023.10087178|power electronics;renewable energy sources (RESs);DC-DC converter;quadratic boost converter.;Renewable energy sources;Inductance;Simulation;Voltage;Switches;Software;Topology|
|[Model Predictive Control of Alternate Arm Converter with a Short-Overlap Period Under Steady-State and Transient Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087124)|E. Akbari; M. S. Shadlu|10.1109/PEDSTC57673.2023.10087124|Alternate arm converter;Short-overlap operation mode;Model predictive control;Energy balancing;Steady-state and transient conditions.;Total harmonic distortion;Simulation;Switching frequency;Rectifiers;Predictive models;Mathematical models;Steady-state|
|[Power Loss Reduction Using SOS Algorithm in SEPIC LED Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087091)|S. R. Movahhed Ghodsinya; E. Azimirad; A. Mahmoodi; M. Mehrabi|10.1109/PEDSTC57673.2023.10087091|Power losses;SEPIC Converter;Symbiotic Organisms Search Algorithm.;Symbiosis;LED lamps;Power demand;Simulation;Metaheuristics;Optimization methods;Electronic components|
|[Controllable DC Fault Current Limiter with Loss Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087155)|A. Jafari; S. Mohsenzade; A. A. Razi-Kazemi|10.1109/PEDSTC57673.2023.10087155|Fault Current Limiter (FCL);Controllable;DC circuit breaker (DCCB);Saturation Magnetization;Magnetic cores;Circuit breakers;Windings;Controllability;Finite element analysis;Topology;Voltage control|
|[Analysis, Modeling, and Simulation of a PM Linear Actuator with Surface Motion Ability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087113)|S. Hasanzadeh; H. Besharatifard|10.1109/PEDSTC57673.2023.10087113|magnetic actuator;electromagnetic force;magnetic material;steal structure;propulsion force;Pistons;Actuators;Analytical models;Force;Mathematical models;Iron;Electron tubes|
|[Improved Half-Bridge Switched Boost Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087059)|A. Bahador; E. Babaei; S. M. J. Mousavi; D. Alizadeh|10.1109/PEDSTC57673.2023.10087059|Z-source inverter;switched boost inverter;half-bridge inverter;switched inductor cell;Low voltage;Capacitors;Prototypes;Switches;High-voltage techniques;Voltage;Inverters|
|[Comparison and Performance Analysis of IPM, SynRM and Flux-intensifying IPM Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087055)|A. H. Amiri; R. Rouhani; S. E. Abdollahi; S. Hasanzadeh|10.1109/PEDSTC57673.2023.10087055|reluctance torque;reluctance;saliency ratio;IPM machines;Torque;Rotors;Permanent magnet motors;Harmonic analysis;Power electronics;Performance analysis;Finite element analysis|
|[Novel Symmetric Hybrid Multilevel Inverter with Reduced Switch Count and Low Blocked Voltage by Power Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087072)|M. Hamidi; M. Hamzeh|10.1109/PEDSTC57673.2023.10087072|low blocking voltage;new topology;reduced number of switches;symmetric multilevel inverter;Capacitors;Switches;Multilevel inverters;Power transistors;Mathematical models;Hybrid power systems;Topology|
|[Online Inductance Estimation of PM-Assisted Synchronous Reluctance Motor Using Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087136)|A. Karami-Shahnani; H. Dehghan-Niri; R. Nasiri-Zarandi; K. Abbaszadeh; M. S. Toulabi|10.1109/PEDSTC57673.2023.10087136|Artificial Neural Network (ANN);high-performance control;inductance estimation;online parameter estimation;Permanent Magnet Assisted Synchronous Reluctance Motor (PMASynRM);Inductance;Magnetic flux;Torque;Computational modeling;Rotors;Artificial neural networks;Predictive models|
|[Magnetizing Curve Identification of Induction Motor for Drive Application Considering the Inverter Nonlinearity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087123)|B. Nikmaram; M. S. Mousavi; M. Abbasi; R. Yousefi; M. Fazeli|10.1109/PEDSTC57673.2023.10087123|Magnetizing Curve;Inverter Nonlinearity;Induction Motor;Resistance;Inductance;Induction motors;Voltage measurement;Parameter estimation;Stators;Inverters|
|[A Comparative Study of E-type and C-type Hybrid Reluctance Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087001)|E. F. Farahani; M. Mirsalim|10.1109/PEDSTC57673.2023.10087001|Finite element method;enhanced torque;hybrid reluctance motor;permanent magnet;Couplings;Forging;Stator cores;Torque;Rotors;Switched reluctance motors;Topology|
|[Effect of Slot/Pole Combination on the Performance of Complementary-Rotor Doubly Salient Permanent Magnet Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087169)|M. Afrank; M. Amirkhani; M. Mirsalim|10.1109/PEDSTC57673.2023.10087169|Doubly salient PM motor;finite element analysis;high-torque density;permanent magnet;power factor;Reactive power;Torque;Software packages;Rotors;Power system harmonics;Stators;Permanent magnet motors|
|[An Improved High Step-up Non Isolated DC-DC Converter: Steady-State Analysis, Design and Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087003)|F. Falahi; H. Allahyari; E. Babaei|10.1109/PEDSTC57673.2023.10087003|Step-up DC-DC converter;Non-isolated;High voltage gain;Continuous input current;Low voltage;Analytical models;Voltage;DC-DC power converters;High-voltage techniques;Software;Semiconductor diodes|
|[An Isolated High-Efficient Impedance-Source AC-AC Converter with Extended Buck-Boost Operation for DVR Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087125)|I. Abdoli; A. Mosallanejad|10.1109/PEDSTC57673.2023.10087125|Ac-ac converter;high-gain;buck-boost;high-efficiency;voltage isolation;continuous current;safe commutation.;AC-AC converters;Snubbers;Switching loss;Prototypes;Voltage;Switches;Pulse width modulation|
|[Hardware-Based Fault-Tolerant Technique for Modular Multilevel Converter Based on Shifting Submodules between Legs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087096)|M. Khaleghi; A. H. A. Biglo|10.1109/PEDSTC57673.2023.10087096|Fault-Tolerant Technique;Hardware-Based Method;Modular Multilevel Converter;Neutral-Shift;Legged locomotion;Fault tolerance;Multilevel converters;Thyristors;Simulation;Fault tolerant systems;Voltage|
|[Virtual Impedance Compensator-based Control Strategy with Feedback Linearization for MMC-based HVDC System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087173)|E. Azimi; S. Hosseinnataj; A. Tavasoli; M. Mehrasa; J. Adabi|10.1109/PEDSTC57673.2023.10087173|MMC;Virtual compensator;circulating current;Input-Output Feedback Linearization (IOFL);Voltage balancing algorithm;HVDC transmission;System performance;Capacitors;Transfer functions;Stability analysis;Feedback linearization;Steady-state|
|[Bidirectional DISO DC-DC Converter Based on Lyapunov Control Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087050)|S. Hosseinnataj; M. Yazdani; M. Shekari; M. Jani; J. Adabi|10.1109/PEDSTC57673.2023.10087050|Bidirectional;DC-DC Converter;BDISO;Dual Input;Single Output;Buck;Boost and Lyapunov Control;Prototypes;DC-DC power converters;Stability analysis;Robustness;Power electronics;Mathematical models;Topology|
|[ZVT Interleaved High Step-Up Converter For Renewable Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087127)|B. Akhlaghi; H. Farzanehfard; D. Thiruvady; R. Faraji; F. Shiri|10.1109/PEDSTC57673.2023.10087127|High step-up (HSU) converter;interleaved converter;renewable energy (RE);soft switching (SS);zero voltage transition (ZVT);Renewable energy sources;Simulation;Soft switching;Mathematical analysis;Voltage;High-voltage techniques;Switches|
|[Ultra-high Step-up Soft Switched Quadratic DC-DC Converter with Continuous Input Current and Low Switch Voltage Stress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087159)|M. Hajilou; S. Gholami; H. Farzanehfard|10.1109/PEDSTC57673.2023.10087159|Quadratic converters;soft switching;low switch voltage stress;Inductance;Electric breakdown;Soft switching;Prototypes;Switches;DC-DC power converters;Clamps|
|[A Single-Switch Ultra-High Step-Up DC-DC Converter With Low Voltage Stress Based on Quadratic Y-Sources Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087094)|A. Masoud; M. Packnezhad; H. Farzanehfard|10.1109/PEDSTC57673.2023.10087094|Ultra high step-up converter;Y-source structure;Low switch voltage stress;Current ripple;Photovoltaic systems;Low voltage;Network topology;Simulation;Voltage;Switches;Power electronics|
|[Using a High frequency LC Resonant Inverter for Ultrasonic Cleaning Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087060)|Z. Asemi; R. Beiranvand|10.1109/PEDSTC57673.2023.10087060|LC resonant inverter;ultrasonic cleaner;zero voltage switching (ZVS);MOSFET;Simulation;Soft switching;Switching loss;Resonant frequency;Prototypes;Zero voltage switching|
|[An Improved Bidirectional Non-Isolated Dual-Input DC-DC Converter for Electric Vehicles Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087176)|S. Naeiji; S. E. Abdollahi; S. R. Abdollahi; B. Baigzadehnoe|10.1109/PEDSTC57673.2023.10087176|DC-DC Power Converter;Double-input;Electric vehicles (EVs);Bidirectional;Super-capacitor;Battery;Semiconductor device measurement;Power control;DC-DC power converters;Supercapacitors;Electric vehicles;Loss measurement;Regulation|
|[Integrated Dual Static Var Compensators to Reduce Railway Power Quality Conditioner Volt-ampere Rating in Co-phase V/v Traction System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087056)|M. Habibolahzadeh; A. Jalilian|10.1109/PEDSTC57673.2023.10087056|Volt-ampere rating reduction;railway static power quality conditioner;static VAR compensator;harmonics;filters;Reactive power;Passive filters;Impedance matching;Power quality;Harmonic analysis;Transformers;Rail transportation|
|[AC Fault Ride-Through Strategy for MMC-Based HVDC Systems with Short Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087160)|S. HosseiniKordkheili; M. Hamzeh|10.1109/PEDSTC57673.2023.10087160|fault ride through (FRT);high-voltage direct-current transmission (HVDC);modular multilevel converter (MMC);power system faults;DC voltage stability;Multilevel converters;HVDC transmission;Power quality;High-voltage techniques;Power system stability;Capacitance;Power markets|
|[Design of an In-Wheel Rotor Permanent Magnet Flux Switching Motor for Electric Bicycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087117)|A. Zia; S. Mirnikjoo; S. E. Abdollahi|10.1109/PEDSTC57673.2023.10087117|Flux Switching Machine;Electric Bicycle;Torque density;Permanent magnet (PM);Brushless;Magnetic flux;Torque;Sensitivity analysis;Rotors;Switches;Bicycles;Voltage|
|[Dual-Input, Single-Output Step-Up DC/DC Power Electronic Interface for Energy Harvesting Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087120)|S. Mashali; A. Y. Varjani|10.1109/PEDSTC57673.2023.10087120|boost;dual-input;energy harvesting;low-power;low-voltage;voltage multiplier cell;Low voltage;Simulation;Power electronics;Stability analysis;Steady-state;Energy harvesting;Voltage multipliers|
|[Family Of High Step-Up Multi-Input Converters With Continuous Battery Current Based On Three Port Boost Topology and Switched Capacitors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087103)|E. Meshkati; H. Farzanehfard|10.1109/PEDSTC57673.2023.10087103|multi-input converter;non-isolated high step-up converter;reduced voltage stress;switched capacitor technique;Capacitors;Prototypes;Switches;Voltage;Batteries;Topology;Semiconductor diodes|
|[Sensorless Speed Control of SPMSM Using Disturbance Rejection Predictive Functional Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087133)|A. Karami-Shahnani; H. Dehghan-Niri; K. Abbaszadeh; R. Nasiri-Zarandi; M. S. Toulabi|10.1109/PEDSTC57673.2023.10087133|Disturbance rejection;predictive functional control (PFC);sensorless control;speed control;Adaptation models;Adaptive systems;Uncertainty;Stability criteria;Velocity control;Mathematical models;Disturbance observers|
|[Magneto-Thermal Analysis of a Novel Excited Outer Rotor Flux-Switching PM Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087105)|A. Zarghani; S. M. Saghin; A. Ghaheri; E. Afjei; H. Torkaman|10.1109/PEDSTC57673.2023.10087105|flux switching machine;heat transfer;loss calculation;segmented rotor;thermal analysis;Temperature distribution;Thermal resistance;Windings;Rotors;Mathematical models;Thermal analysis;Topology|
|[Performance Optimization of Excited Outer Rotor Segmented-FSPM Motor Based on Taguchi Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087174)|S. M. Saghin; A. Ghaheri; M. Abolghasemi; E. Afjei|10.1109/PEDSTC57673.2023.10087174|flux switching;finite element analysis;optimization;segmented rotor;Taguchi method;Magnetic flux;Torque;Rotors;Stator windings;Switches;Permanent magnet motors;Topology|
|[A Soft Switching Three-phase Current-fed Bidirectional Converter with Low Input Current Ripple for energy storage system Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087083)|M. Soleimanifard; J. Mozayan|10.1109/PEDSTC57673.2023.10087083|Bidirectional converter;Energy storage system;Input current ripple;Renewable energy;Zero voltage switching;Industries;Renewable energy sources;Soft switching;Mathematical analysis;Switching loss;Switches;Zero voltage switching|
|[Multi-Port Dual-Active-Bridge DC-DC Converter for Bi-Polar DC Microgrid Application Using Buck-Boost Voltage Balancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087139)|A. A. Aghajani; M. Eldoromi; B. Nakhaei; A. A. M. Birjandi|10.1109/PEDSTC57673.2023.10087139|Bi-directional DC-DC converter;Bi-polar DC micro-grid;Dual active bridge (DAB);Multi-port DC-DC converter;voltage balancing;Photovoltaic systems;Renewable energy sources;Simulation;Modulation;Microgrids;Switches;Software|
|[Using Three-Dimensional Virtual Voltage Vector in Predictive Current Control of a Four-leg Inverter for Fixing Common-Mode Voltage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087076)|M. Akbari; S. A. Davari; R. Ghandehari; F. Flores-Bahamonde; J. Rodriguez|10.1109/PEDSTC57673.2023.10087076|Common-Mode Voltage (CMV);Three-dimensional Virtual Voltage Vectors (3-D-VVVs);3-D Remote Stat PWM (3-DRSPWM);3-D Predictive Current Control (3-DPCC);Current control;Total harmonic distortion;Solid modeling;Fluctuations;Voltage source inverters;Switches;Pulse width modulation|
|[A New 8/14 Two-Phase Switched Reluctance Motor with Improved Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087145)|G. Davarpanah; H. Shirzad; J. Faiz|10.1109/PEDSTC57673.2023.10087145|Electric machines;C-core;Switched reluctance motor;finite element method;Couplings;Stator cores;Torque;Magnetic cores;Rotors;Hysteresis motors;Switched reluctance motors|
|[A New Combination of Stator and Rotor Poles of Three-Phase Switched Reluctance Motor for Improved Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087057)|G. Davarpanah; H. Shirzad; J. Faiz|10.1109/PEDSTC57673.2023.10087057|Electric machines;Torque ripple;Switched reluctance motor;Finite Element Method;C-core;Magnetic flux;Torque;Core loss;Stator windings;Rotors;Switched reluctance motors;Finite element analysis|
|[Proposing a Low Frequency Control Method for Three-phase Four-leg Inverters to Feed Nonlinear and Unbalance Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087102)|S. Mohammadzadeh; H. Ghoreishy; M. Rezanejad|10.1109/PEDSTC57673.2023.10087102|Two-level four-leg inverter;Selective harmonic elimination (SHE);Selective harmonic mitigation (SHM);Legged locomotion;Maximum likelihood detection;Nonlinear filters;Harmonic analysis;Linear programming;Inverters;Voltage control|
|[Stability analysis of variable frequency control method of Soft Switching for Boost Converter with Wide Bandgap Semiconductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087093)|S. Norouzi; H. Ghoreishy; A. A. Ahmad; F. Tahami|10.1109/PEDSTC57673.2023.10087093|variable frequency control;Soft Switching;Boundary Conduction Mode;Boost Converter;Photonic band gap;Switching frequency;Soft switching;Process control;Switches;Stability analysis;Power electronics|
|[Extended Model of PM-Assisted Synchronous Reluctance Motor Including Torque Fluctuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087082)|H. Dehghan-Niri; A. Karami-Shahnani; K. Abbaszadeh; R. Nasiri-Zarandi; M. S. Toulabi|10.1109/PEDSTC57673.2023.10087082|Artificial neural network (ANN);cogging torque;PMaSynRM;torque ripple modeling;Backpropagation;Analytical models;Torque;Fluctuations;Computational modeling;Artificial neural networks;Mathematical models|
|[Partly Isolated Symmetrical Six-Phase Induction Motor Drive System Based on Conventional Three-Phase Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087161)|H. Aghaei; E. Babaei; M. Bagher Bannae Sharifian; I. Shoghli|10.1109/PEDSTC57673.2023.10087161|multi-phase induction motor;three-phase inverter;three-phase transformer;zero sequence currents;total harmonic distortion (THD);Induction motor drives;Total harmonic distortion;Costs;Software packages;Velocity control;Switches;Pulse width modulation|
|[A 24/28-Pole Hybrid Reluctance Motor with U-Core Stator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087047)|V. Mirzaei; M. Mirsalim|10.1109/PEDSTC57673.2023.10087047|Finite element method;high torque;hybrid reluctance motor;permanent magnets;Couplings;Forging;Magnetic flux;Torque;Switched reluctance motors;Stators;Power electronics|
|[Studying of Interlinking Converter for Appropriate Power Sharing in Hybrid AC/DC Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087171)|M. Eldoromi; A. Akbar Moti Birjandi; N. Mahdian Dehkordi|10.1109/PEDSTC57673.2023.10087171|Battery Energy Storage System;Hybrid Microgrid;Interlinking Converters;Power Sharing;System performance;Microgrids;Hybrid power systems;Software;Topology;Batteries;Software reliability|
|[Predictive Torque Control Using a Simplified Algorithm to Maximize Adhesion Force in Railway Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087100)|A. Hassanzadeh; A. Akbar Emarloo; B. Nikmaram; M. Fazeli|10.1109/PEDSTC57673.2023.10087100|Adhesion control;Railway Traction Motor Control;Prediction;Slip Control;Rails;Torque;Adhesives;Simulation;Torque control;Wheels;Propulsion|
|[A Rule-based Energy Management Strategy with Current Estimation for Controlling Grid Connected Hybrid Energy Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087163)|E. Farrokhi; P. Safari; H. Ghoreishy|10.1109/PEDSTC57673.2023.10087163|Energy management strategy (EMS);Hybrid energy storage system (HESS);Kalman filter;current estimation;Power measurement;Simulation;Estimation;Batteries;Kalman filters;State of charge;Resource management|
|[An Investigation on Selecting of Resonant Inverter Topologies for Efficient Wireless Power Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087165)|R. Kamali; S. Asghar Gholamian; S. Reza Abdollahi|10.1109/PEDSTC57673.2023.10087165|Wireless Power Transmission;WPT;Inverter;Resonant Converter;Impedance Matching Network;IMN;Wireless communication;Network topology;Impedance matching;Switching frequency;Zero voltage switching;Topology;Wireless power transmission|
|[A Quadratic High Gain DC-DC Converter with Continuous Input Current for PV Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087172)|H. Arvani; A. Sajjadi; M. KalamiAlhashem; M. Hamzeh|10.1109/PEDSTC57673.2023.10087172|Renewable Energy;Photovoltaic;DC-DC Converters;High-Gain Converters;Photovoltaic systems;Renewable energy sources;Switches;Voltage;High-voltage techniques;DC-DC power converters;Feature extraction|
|[A Modified MPPT-based Model Predictive Control for a Single-stage Modular Multilevel Converter in Grid-connected Photovoltaic Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087150)|E. Akbari; M. S. Shadlu|10.1109/PEDSTC57673.2023.10087150|Modular multilevel converter;Modified model predictive control;Photovoltaic system;Maximum power point tracking;Total harmonic distortion (THD);Photovoltaic systems;Maximum power point trackers;Multilevel converters;Total harmonic distortion;Radiation effects;Fluctuations;Simulation|
|[A Low-Ripple High-Voltage DC Power Supply Using a Switching Ripple Compensation Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087006)|A. Sheikhi Azizi; S. Kaboli|10.1109/PEDSTC57673.2023.10087006|High-voltage power supplies;high-voltage techniques;switching ripple compensation unit;ripple;Buck converters;Sensitivity;Power supplies;Voltage;High-voltage techniques;Switches;Mathematical models|
|[Fault Detection in Variable Air-gap Resolver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087148)|H. Lasjerdi; Z. Nasiri-Gheidari|10.1109/PEDSTC57673.2023.10087148|Dynamic Eccentricity (DE);Static Eccentricity (SE);Short Circuit (SC) Fault;Variable Reluctance (VR) Resolver;Mechanical sensors;Fault detection;Rotors;Wounds;Electrical fault detection;Sensor systems;Finite element analysis|
|[Flexible Control of the Interline DC Power Flow Controller Using Virtual Capacitor in VSC-HVDC Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087051)|M. Pourmirasghariyan; M. Pourmirasghariyan; S. S. H. Yazdi; S. Y. M. Mousavi; K. Rouzbehi; G. B. Gharehpetian|10.1109/PEDSTC57673.2023.10087051|Energy-based Control Scheme;HVDC Grids;Interline DC Power Flow Controllers (IDC-PFCs);Virtual Capacitor;Widening the Operational Area;Fluctuations;HVDC transmission;Capacitors;High-voltage techniques;Boosting;Mathematical models;Power conversion|
|[A Novel Approach for Improving the LCC Resonant Converter Efficiency Over Wide Load Variation Ranges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087068)|J. Shahsevani; R. Beiranvand|10.1109/PEDSTC57673.2023.10087068|Dc-dc power conversion;efficiency;Frequency modulation;LCC resonant converter;resonant conversion;variable inductor;Q-factor;Switching frequency;Simulation;Resonant frequency;Resonant converters;Logic gates;Load management|
|[Effects of Multi-phasing on the Performance of Flux-intensifying IPM Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087143)|A. H. Amiri; R. Rouhani; S. E. Abdollahi; S. Hasanzadeh|10.1109/PEDSTC57673.2023.10087143|multi-phase;three-phase;six-phase;IPM machines;flux-intensifying;fault-tolerant;Geometry;Inductance;Torque;Windings;Electric vehicles;Power electronics;Safety|
|[Collaborative Electric Vehicle Charging Strategy with Regenerative Braking Energy of Railway Systems in Park & Ride Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087071)|M. Golnargesi; H. J. Kaleybar; M. Brenna; D. Zaninelli|10.1109/PEDSTC57673.2023.10087071|Electric vehicle;electric railway;regenerative braking energy;supplementary charging;Substations;Simulation;Collaboration;Transportation;Electric vehicle charging;Rail transportation;Energy efficiency|
|[The Potential Impacts of Wireless Power Transfer on the Global Economy, Society, and Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087180)|S. M. Shakil; M. H. Rashid|10.1109/PEDSTC57673.2023.10087180|Wireless power transfer;Economic Impact;Global Impact and Social Impact;Economics;Wireless communication;Transmitters;Shape;Inductive charging;Wireless power transfer;Switches|
|[A DC Side Sensorless Single-Phase Shunt Active Power Filter with a Second Order Sliding Mode Control and Unbalance Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087062)|F. Bagheri; S. Biricik; H. Komurcugil|10.1109/PEDSTC57673.2023.10087062|Active power filter;super-twisting-sliding mode control;distorted grid voltage;Voltage measurement;Smoothing methods;Simulation;Capacitors;Active filters;Harmonic analysis;Voltage control|
|[Leakage Current Reduction in Four-leg Inverter Utilizing Three-Dimensional Virtual Voltage Vector-based Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087137)|M. Akbari; S. Alireza Davari; R. Ghandehari; F. Flores-Bahamonde; J. Rodriguez|10.1109/PEDSTC57673.2023.10087137|Common-Mode Voltage (CMV);Leakage Current (LC);Descrete Three-dimensional Virtual Voltage Vectors (3-D2-V3);3-D Predictive Current Control (3-DPCC);Current control;Total harmonic distortion;Solid modeling;Voltage fluctuations;Voltage source inverters;Switches;Transformers|
#### **2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)**
- DOI: 10.1109/CSPA57446.2023
- DATE: 3-4 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[The development of a Game for Cognitive Remediation Therapy (CRT) to Improve Attention Span and Memory Among Children with Learning Disabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087548)|R. Ramli; K. R. Purba|10.1109/CSPA57446.2023.10087548|Cognition;Interaction Design;Game Development;Cognitive Remediation Therapy (CRT);Learning disabilities;Graphics;Memory management;Medical treatment;Music;Games;Cathode ray tubes;Cognition|
|[Management of Raw Material Needs and Safety Stock Based on Data Forecast and System Dynamics Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087715)|T. Richard; L. Gozali; F. J. Daywin|10.1109/CSPA57446.2023.10087715|forecasting;material requirement planning;safety stock;system dynamics;productivity;Productivity;System dynamics;Finance;Companies;Predictive models;Raw materials;Safety|
|[Providing Unique Solution to Daubechies’s AM-M Oscillator Expansion, and its Limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087433)|F. Ishiyama|10.1109/CSPA57446.2023.10087433|Time-frequency analysis;mode decomposition;nonlinear oscillators;AM-FM oscillator expansion;Time-frequency analysis;Frequency modulation;Systematics;Time series analysis;Boundary conditions;Mathematical models;Safety|
|[Understanding User Behaviour with Web Session Clustering and User Engagement Metrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087488)|Z. -Y. Lim; L. -Y. Ong; M. -C. Leow; T. -W. Lee; Q. -M. Tay|10.1109/CSPA57446.2023.10087488|clustering;user engagement;web usage mining;web session clustering;Measurement;Navigation;Performance analysis|
|[Dragonfly Algorithm Strategy Parameters Analysis on Swarm Robot Multi-Target Search Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087728)|M. G. M. Hamami; Z. H. Ismail|10.1109/CSPA57446.2023.10087728|dragonfly algorithm;swarm intelligence;optimization;target search problem;swarm robotics;Swarm robotics;Benchmark testing;Search problems;Market research;Task analysis;Particle swarm optimization;Robots|
|[Color-assisted Multi-input Convolutional Neural Network for Cancer Classification on Mammogram Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087371)|N. F. Razali; I. S. Isa; S. N. Sulaiman; N. K. A. Karim; M. K. Osman|10.1109/CSPA57446.2023.10087371|multi-input;CNN;mammogram;breast cancer;classification;pseudo-color;Training;Solid modeling;Ultrasonic imaging;Computational modeling;Gray-scale;Mammography;Breast cancer|
|[Simulation Program for Modeling Temperature Distribution in a Food Dehydrator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087530)|E. H. Y. Lim; A. H. Tan; C. L. Cham|10.1109/CSPA57446.2023.10087530|dehydrators;optimization;simulation;software;temperature modeling;Temperature distribution;Uncertainty;Infrared heating;Predictive models;Mathematical models;Complexity theory;Optimization|
|[Statistical Assessment for Point Cloud Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087473)|A. F. Razali; M. F. M. Ariff; Z. Majid; H. A. Hamid|10.1109/CSPA57446.2023.10087473|Point cloud;3D model;hypothesis testing;terrestrial laser scanning;photogrammetry;Point cloud compression;Geometry;Solid modeling;Measurement errors;Three-dimensional displays;Soft sensors;Measurement uncertainty|
|[Investigation of Learning Rate for Directed Acyclic Graph Network Performance on Dysgraphia Handwriting Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087858)|S. A. Ramlan; I. S. Isa; M. K. Osman; A. P. Ismail; Z. H. C. Soh|10.1109/CSPA57446.2023.10087858|Deep Learning;DirectedAcyclic Graph Networks;Dysgraphia Handwriting;Learning Rate.;Training;Measurement;Directed acyclic graph;Convolution;Instruments;Psychology;Network architecture|
|[The Real-Time Monitoring of Air Quality Using IOT-Based Environment System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087557)|H. Hashim; M. N. Hazwan; P. S. M. Saad; Z. Harun|10.1109/CSPA57446.2023.10087557|Humidity sensor;Temperature sensor;Gas sensor;Wi-Fi module;Smartphone;Temperature sensors;Temperature measurement;Temperature distribution;Urban areas;Humidity;Air quality;Sensors|
|[Utilization of Augmented Reality in Assisting Surgical Needle Insertion Guidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087665)|M. -H. Lo; H. -L. Liu|10.1109/CSPA57446.2023.10087665|Surgical needle insertion guidance;Augmented reality;Pose estimation;Key points detection;Visualization;Three-dimensional displays;Biopsy;Surgery;Ultrasonography;Needles;Cameras|
|[Production and Capacity Planning as well as Inventory and Distribution Control in Snack Packaging Companies Using Open Source ERP Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087652)|V. A. Budiono; L. Gozali; I. W. Sukania|10.1109/CSPA57446.2023.10087652|Forecasting;Aggregate;Rough Cut Capacity Planning;safety stock;Material Requirement Planning;Capacity Requirement Planning;Distribution Requirement Planning;Enterprise Resource Planning;Costs;Aggregates;Companies;Production;Packaging;Raw materials;Planning|
|[Parameter-Replacement Functions for Stability-Guaranteed Variable Digital Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087739)|T. -B. Deng|10.1109/CSPA57446.2023.10087739|Variable filter;variable frequency response;recursive filter;stability;parameter-replacement (PR) function.;Filtering;Frequency-domain analysis;Measurement uncertainty;Transfer functions;Frequency response;Real-time systems;Digital filters|
|[EmHM: A Novel Hybrid Model for the Emotion Recognition based on EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087500)|R. Sharma; H. K. Meena|10.1109/CSPA57446.2023.10087500|Emotion recognition;DEAP dataset;CNN;LSTM;SEED datatset;EmHM;power spectral density (PSD);Emotion recognition;Analytical models;Predictive models;Brain modeling;Feature extraction;Electroencephalography;Entropy|
|[Manual and Automatic Feature Engineering in Digital Image Forgery Detection Algorithms: Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087398)|W. F. Mashaan; I. T. Ahmed|10.1109/CSPA57446.2023.10087398|Digital image forgery (DIF);Image splicing forgery detection (ISFD);passive approaches;Manualengineering features;Automatic-engineering features;Image recognition;Splicing;Digital images;Manuals;Transforms;Feature extraction;Forgery|
|[Refractive Index Measurement Based on Fibre Optics with Multiparameter Sensing Capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087706)|P. C. Bong; A. J. Y. Tan; H. S. Chua|10.1109/CSPA57446.2023.10087706|macrobending;multiparameter sensing;U-shaped fibre optic;refractive index sensing;integrated pixel response;Temperature sensors;Temperature measurement;Optical fibers;Optical fiber sensors;Sensitivity;Refractive index;Training data|
|[Cardiovascular Disease Prediction using Ensemble Learning Techniques: A Stacking Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087730)|Z. Rustamov; J. Rustamov; M. S. Sultana; J. Ywei; V. Balakrishnan; N. Zaki|10.1109/CSPA57446.2023.10087730|cardiovascular diseases;ensemble learning;machine learning;prediction;stacking ensemble;Heart;Data analysis;Machine learning algorithms;Stacking;Data preprocessing;Predictive models;Feature extraction|
|[Implementation of PRBS & RGS Perturbation Input Signals on Steam Temperature: Model Estimation and PID Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087748)|A. H. A. Nasir; N. Hambali; M. H. F. Rahiman|10.1109/CSPA57446.2023.10087748|Modelling;System Identification;Nonlinear Modelling;PID Control;Distillation Column;Steam Temperature;PI control;Software packages;Perturbation methods;Estimation;Mathematical models;Temperature control;System identification|
|[Towards Real-Time Graph Neural Network-Based 3D Object Detection for Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087809)|I. C. Ramadhan; B. R. Trilaksono; E. M. I. Hidayat|10.1109/CSPA57446.2023.10087809|Autonomous Vehicle;Lidar-based 3D object detection;Graph Neural Network;Point-GNN.;Degradation;Training;Solid modeling;Three-dimensional displays;Object detection;Benchmark testing;Graph neural networks|
|[LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087773)|P. Maniamfu; K. Kameyama|10.1109/CSPA57446.2023.10087773|COVID-19;Long Short-Term Memory;epidemiological model;policy pruning;COVID-19;Adaptation models;Pandemics;Infectious diseases;Stochastic processes;Predictive models;Data models|
|[Machine Learning Curriculums Generated by Classifier Ensembles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087822)|T. -J. Huang; K. Kameyama|10.1109/CSPA57446.2023.10087822|Image Classification;Machine Learning;Ensembles of Classifiers;Training;Face recognition;Stability criteria;Machine learning;Dogs;Classification algorithms;Task analysis|
|[CNN Model Compression by Merit-Based Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087390)|T. Morikawa; K. Kameyama|10.1109/CSPA57446.2023.10087390|Model compression;Distillation;Hint-based Training;Image classification;Training;Deep learning;Computational modeling;Convolutional neural networks;Task analysis|
|[Improvement of Trajectory Errors on Remote- Controlled Differential Drive Robot via Mobilebased GUI through Bluetooth Connection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087757)|Y. D. Dimalanta; B. Ching; L. M. Dalangin; A. A. Respicio; M. A. Topacio; M. M. Pangaliman; S. J. Dignadice; K. A. Felicilda; A. J. Bautista|10.1109/CSPA57446.2023.10087757|control system;Bluetooth app controller;joystick;trajectory error;Arduino;differential drive;robot motor speed;robot calibration;ODrive motor drive;logistic service robot.;Bluetooth;Navigation;Service robots;Wheels;Control systems;Trajectory;Mobile applications|
|[Detection of Bacterial Leaf Blight Disease Using RGB-Based Vegetation Indices and Fuzzy Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087429)|N. H. Aziz; R. H. Narashid; T. R. Razak; S. A. Anshah; N. Talib; Z. A. Latif; N. Hashim; K. Zainuddin|10.1109/CSPA57446.2023.10087429|UAV;RGB sensor;BLB disease;NGRDI;GLI;Fuzzy logic;Spectroradiometers;Microorganisms;Image recognition;Vegetation mapping;Production;Autonomous aerial vehicles|
|[Sensing the Presence of Benzotriazole Passivator in Thermally Aged Corrosive Transformer Insulating Oil Using UV-Vis Spectroscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087719)|N. I. H. Zulkefli; M. S. A. Khiar; S. A. Ghani|10.1109/CSPA57446.2023.10087719|BTA sensor;corrosive sulphur;passivator;thermally aged insulating oils;UV-vis spectroscopy;Spectroscopy;Oils;Wavelength measurement;Metals;Aging;Oil insulation;Insulation life|
|[The Effect of Ultrasonic Irradiation to Hematite Nanorod Arrays Properties for Humidity Sensor Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087704)|W. R. W. Ahmad; M. H. Mamat; A. S. Zoolfakar; Z. Khusaimi|10.1109/CSPA57446.2023.10087704|Hematite;Ultrasonic Irradiation;Structural Properties;Optical Properties;Humidity Sensor;Optical diffraction;X-ray scattering;Photonic band gap;Stimulated emission;Adaptive optics;Acoustics;Optical sensors|
|[A Review: Heart Disease Prediction in Machine Learning & Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087837)|W. A. W. A. Bakar; N. L. N. B. Josdi; M. B. Man; M. A. B. Zuhairi|10.1109/CSPA57446.2023.10087837|deep learning;heart disease;machine learning;prediction accuracy;Deep learning;Heart;Technological innovation;Machine learning algorithms;Predictive models;Prediction algorithms;Genetics|
|[Brightness Controlled Solar Powered Intelligent Street Light](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087420)|M. A. B. Iskandar; S. Saaidin; S. B. Kutty; M. Kassim|10.1109/CSPA57446.2023.10087420|Solar Power;Street Light;Artificial Intelligence;Power demand;Costs;Brightness;Lighting;Maintenance engineering;Light emitting diodes;Internet of Things|
|[Low-cost Soil Moisture and EC Sensor Design for Soil Salinity Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087387)|V. K. Bodasingi; B. Rao; H. K. Pillai|10.1109/CSPA57446.2023.10087387|Salinization;Moisture;EC;Frequency;Permittivity;Moisture measurement;Salinity (geophysical);Soil moisture;Moisture;Prototypes;Frequency measurement;Electrical resistance measurement|
|[Investigation of Different Classifiers for Stress Level Classification using PCA-Based Machine Learning Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087367)|M. R. B. Mazlan; A. S. B. A. Sukor; A. H. B. Adom; R. B. Jamaluddin; S. A. B. Awang|10.1109/CSPA57446.2023.10087367|mental health disorder;stress;machine learning;EEG;dimensionality reduction;Principal Component Analysis.;Machine learning algorithms;Human factors;Machine learning;Mental health;Multilayer perceptrons;Electroencephalography;Clinical diagnosis|
|[Smart Data for Sustainable Halal Supply Chain in Kuala Lumpur: A Proposal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087506)|N. N. A. Nizar; S. A. S. Z. Abidin; M. N. Taib|10.1109/CSPA57446.2023.10087506|smart data;halal supply chain;sustainability;traceability;Kuala Lumpur;Supply chain management;Supply chains;Urban areas;Transportation;Internet of Things;Sustainable development;Artificial intelligence|
|[Forecasting Harvest Yield with IoT-enabled Sensor Data of Malaysia Weather Conditions using Multiple Linear Regressions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087656)|Y. H. Tan; H. N. Chua; M. B. Jasser|10.1109/CSPA57446.2023.10087656|Data Mining;Multiple Linear Regressions;Machine Learning;Plant yield prediction;harvest prediction;Temperature sensors;Temperature measurement;Temperature distribution;Machine learning algorithms;Atmospheric modeling;Conductivity;Predictive models|
|[Semi-decoupled Tuning Gain PI Controller for Motor Speed Control Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087481)|C. H. Lai; C. L. Hoo|10.1109/CSPA57446.2023.10087481|PID controller;anti-windup;steady-state integral;semi-decoupling tuning gains;motor speed control;PI control;Windup;Velocity control;Loading;Steady-state;Time factors;PD control|
|[Potential Application of Brainwaves to Optimise Evacuation Wayfinding Performance During Fires](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087615)|F. Z. Othman; M. N. Taib; M. F. Shaari; Y. Zaiki|10.1109/CSPA57446.2023.10087615|wayfinding performance;evacuation performance;fire evacuation;human behaviour in fire (HBiF);neuro-architecture;electroencephalogram (EEG);Performance evaluation;Correlation;Cognitive processes;Buildings;Artificial neural networks;Signal processing;Feature extraction|
|[Implementation of Underwater Image Enhancement for Corrosion Pipeline Inspection (UIECPI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087382)|S. Q. Syamsul Amri; A. S. Abdul Ghani; M. A. S. Kamarul Baharin|10.1109/CSPA57446.2023.10087382|underwater image;image inspection;color correction;thresholding image;filtration color image;Image segmentation;Corrosion;Oils;Computational modeling;Pipelines;Transportation;Inspection|
|[Effect of Climate Change using Predictive Models with Remote Sensing Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087637)|M. S. Bhumika; N. Momaya; R. Nandan; K. Suhas; S. Tripathi|10.1109/CSPA57446.2023.10087637|climate change;auto-regressive;non-stationarity;Temperature sensors;Climate change;Precipitation;Biological system modeling;Land surface;Predictive models;Land surface temperature|
|[Estimating the Un-sampled pH Value via Neighbouring Points Using Multi-Layer Neural Network - Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087388)|M. A. A. Aziz; M. F. Abas; M. A. H. Ali; N. M. Saad; M. H. M. Ariff; M. K. A. A. Bashrin|10.1109/CSPA57446.2023.10087388|Fitness function;Genetic Algorithm;Multi-layer Neural Network;pH estimation;Root Mean Square Error;Backpropagation;Mean square error methods;Genetic algorithms;Multi-layer neural network|
|[Distribution Feeder Reconfiguration with Distributed Generation Using Backward/Forward Sweep Power Flow – Grey Wolf Optimizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087738)|S. M. F. S. Drus; N. M. Saad; M. F. Abas; S. Ab-Ghani; N. Jaalam; A. Ali|10.1109/CSPA57446.2023.10087738|Distributed Generation;Feeder Reconfiguration;Optimal Placement & Sizing;Power Loss Minimization;Voltage Profile Improvement;Reactive power;Computational modeling;Voltage;Distribution networks;Market research;Linear programming;Distributed power generation|
|[Universal Robust Vehicle Identification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087726)|L. J. S. Alfonso; T. J. F. Benitez; C. J. O. Cabalquinto; F. J. T. Perez; J. Yang; E. L. U. Ambata|10.1109/CSPA57446.2023.10087726|image processing;object detection;Python;YOLOv5.;Training;Machine learning algorithms;Image color analysis;Neural networks;Object detection;Manuals;Feature extraction|
|[Performance Analysis of Secure MQTT Communication Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087603)|M. Z. Ahmad; A. R. Adenan; M. S. Rohmad; Y. M. Yussoff|10.1109/CSPA57446.2023.10087603|MQTT;IoT;TLS in MQTT;Encryption MQTT;Protocols;Authentication;Passwords;Telecommunication traffic;Performance analysis;Cryptography;Servers|
|[Detection of White Stem Borer Disease in Coffee Plantation using Autonomous Multi Terrain Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087558)|L. S. Geddam; A. Mungara; K. Kapavari; K. Jayarama; S. Tripathi|10.1109/CSPA57446.2023.10087558|White stem borer;Object detection;Deep Learning;Transfer Learning;Elevation mapping;Multi-Terrain Robot;Training;Navigation;Robot vision systems;Pipelines;Production;Real-time systems;State estimation|
|[Rover Wheel Assistive Grouser Angle of Attack Effects on Traction Force in Soft Terrain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087721)|I. N. A. C. Aziz; A. N. Ibrahim; I. Basri; Y. Fukuoka|10.1109/CSPA57446.2023.10087721|robotics;assistive grouser;robot mobility;tractive performance;soft terrain;Force measurement;Shape;Force;Wheels;Robots|
|[Automatic Detection of Asynchrony Levels of Mechanically Ventilated Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087410)|N. S. Damanhuri; I. K. N. A. Bakar; N. S. M. Sauki; N. A. Othman; Y. S. Chiew; B. C. C. Meng|10.1109/CSPA57446.2023.10087410|mechanical ventilation;ARDS;asynchrony event;spontaneous breathing;graphical user interface (GUI);Measurement;Ventilators;Hospitals;Lung;Estimation;Ventilation;Indexes|
|[Analysis and Monitoring Energy Consumption in Basic Electric Bills](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087759)|A. B. A. Aziz; S. A. H. B. Amirrudin; L. B. Raya|10.1109/CSPA57446.2023.10087759|ESP32 Wi-Fi module;Blynk application;energy consumption;Energy consumption;Home appliances;Power demand;Smart homes;Control systems;Liquid crystal displays;Behavioral sciences|
|[Design and Analysis of Compliant Continuum Robots for Suturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087421)|N. Nishkala; V. Paliwal; N. K. B|10.1109/CSPA57446.2023.10087421|Continuum robots;compliant mechanism;medical robot;static structural analysis;geometric parametric study;Vibrations;Solid modeling;Analytical models;Parametric study;Benchmark testing;Manipulators;Stability analysis|
|[Manufacturing Execution System of Bluetooth Speaker Pen Holder Assembly Line based on Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087378)|Y. Qiuping; A. A. Hernandez|10.1109/CSPA57446.2023.10087378|manufacturing execution system;genetic algorithm;production scheduling;bluetooth speaker;Job shop scheduling;Bluetooth;Production;Manuals;Dynamic scheduling;Scheduling;Manufacturing|
|[Data Analysis on Instruction Delivery Technology: Determining the Factors Influencing the System Adaptation of Learning Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087859)|J. d. J. Alfante; E. T. Castro; N. P. Romano; R. F. R. J. C. Odchigue|10.1109/CSPA57446.2023.10087859|FSUU Learn;LMS;UTAUT;Training;COVID-19;Learning management systems;Analytical models;Electronic learning;Data analysis;Pandemics|
|[Computational Evaluation of Complexity Parameters of Machining and Manufacturing Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087624)|L. -. JiaLin; A. A. Hernandez|10.1109/CSPA57446.2023.10087624|machining;manufacturing system;complexity.;Layout;Machining;Production;Organizations;Manuals;Mathematical models;Software|
|[Improvement of Image Resolution using the Deconvolution Technique in Phase Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087579)|G. Park; J. Seo; M. Kim; J. Joo; J. Koh|10.1109/CSPA57446.2023.10087579|phase array;beamforming;deconvolution;image resolution;Phased arrays;Image resolution;Deconvolution;Radar detection;Radar tracking;Radar equipment;Radar antennas|
|[Prediction of Ultraviolet Corrosion Levels of High Density Polyethylene Using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087881)|J. Seo; G. Park; M. Kim; J. Joo; J. Koh|10.1109/CSPA57446.2023.10087881|high-density polyethylene;ultra violet Absorber;artificial intelligence;carbon black;Training;Radiation effects;Polyethylene;Corrosion;Neural networks;Time measurement;Plastics|
|[Study on Cannabidiol and Pulse Electric Field on Breast Cancer Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087551)|M. M. A. Jamil; N. A. A. Rahman; M. N. Adon; R. Ambar; F. Javid; M. Youseffi; S. Saidin|10.1109/CSPA57446.2023.10087551|Breast cancer cell;CBD;therapeutic;electroporation;Cancer treatment;Epigenetics;Electroporation;Breast cancer;Gastrointestinal tract;Permeability;Compounds|
|[Critical Success Factors of Operational Excellence in Software Quality Assurance: Best Practices for Integrated Change Control Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087552)|W. Y. Wong; T. H. Sam; C. W. Too; A. A. Khin|10.1109/CSPA57446.2023.10087552|Integrated Change Control Management;Operational Excellences;Software Quality Assurance;Control;PLC and SDLC;Software Quality Assurance Plan;Industries;Leadership;Costs;Project management;Software quality;Companies;Delays|
|[Smart IoT-Based Aquarium Monitoring System on Anabas Testudineus Habitat using NodeMcu and Blynk Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087383)|A. R. M. Soleh; N. Sulaiman; M. Kassim|10.1109/CSPA57446.2023.10087383|Smart Aquarium;Internet of Things (IoT);monitoring system;pH sensor;NodeMCU;Blynk;Temperature sensors;Temperature measurement;Water quality;Fish;Light emitting diodes;Real-time systems;Liquid crystal displays|
#### **2023 Annual Reliability and Maintainability Symposium (RAMS)**
- DOI: 10.1109/RAMS51473.2023
- DATE: 23-26 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Risk Assessment Using Information Entropy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088238)|P. Franklin|10.1109/RAMS51473.2023.10088238|Risk Assessment;Information Entropy;Maximum Entropy;Measurement;Schedules;Uncertainty;Costs;Time series analysis;Supply chains;Entropy|
|[Quantum Inference for Reliability Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088259)|G. San Martí Silva; E. López Droguett|10.1109/RAMS51473.2023.10088259|Quantum Computing;Quantum Inference;Bayesian Networks;Knowledge engineering;Quantum computing;Quantum algorithm;Monte Carlo methods;Random access memory;Logic gates;Probabilistic logic|
|[Reliability Assessment of Cross-Strapped Redundant Systems Considering Unit Level Failure Propagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088283)|W. Huang; R. Andrada; D. Borja|10.1109/RAMS51473.2023.10088283|System reliability;cross-strapped redundancy;failure propagation;exponential distribution;acceleration factor;Electric potential;Random access memory;Voltage;High-voltage techniques;Predictive models;Reliability engineering;Integrated circuit reliability|
|[Reliability Assurance for AI Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088197)|J. C. Blood; N. W. Herbert; M. R. Wayne|10.1109/RAMS51473.2023.10088197|AI reliability;Training;Pipelines;Random access memory;Life testing;Life estimation;Human factors;Maintenance engineering|
|[Democratizing AI for Condition-Based Maintenance Leveraging Probabilistic Programming for Symbolic Reasoning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088272)|K. Lu; S. Cvijic; D. Dewhurst; J. Gorman; R. Hyland; J. Templin|10.1109/RAMS51473.2023.10088272|artificial intelligence;condition-based maintenance;probabilistic programming;symbolic reasoning;Random access memory;Maintenance engineering;Programming;Probabilistic logic;Cognition;Reliability;Artificial intelligence|
|[An Assurance Case for the DoD Ethical Principles of Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088273)|B. D. Werner; B. J. Schumeg; T. M. Mills; E. V. Velilla|10.1109/RAMS51473.2023.10088273|Artificial Intelligence;Machine Learning;Assurance Case;Ethical Principles;TEVV;Reliability;Safety;Ethics;Technological innovation;Random access memory;Documentation;US Department of Defense;Reliability;Artificial intelligence|
|[Roadmap Development to Reduce Risk Associated with the Deployment of Artificial Intelligence Enabled Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088201)|B. D. Werner; B. J. Schumeg; T. M. Mills; D. M. Bott|10.1109/RAMS51473.2023.10088201|Artificial Intelligence;Machine Learning;safe;certification;challenges;roadmap;Materiel Release;assurance;test;Random access memory;US Department of Defense;Safety;Stakeholders;Reliability;Artificial intelligence;National security|
|[NASA Should Not Use the Traditional One- or Two-Fault Tolerance Rules to Design for Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088227)|H. W. Jones|10.1109/RAMS51473.2023.10088227|fault tolerance;redundancy;reliability;Space missions;Redundancy;Fault tolerant systems;NASA;Reliability engineering;Safety;Sensors|
|[Managing Risk for the James Webb Space Telescope Deployment Mechanisms: Enabling First Light](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088242)|P. Kalia; J. Evans; M. Menzel; H. A. Kilic|10.1109/RAMS51473.2023.10088242|JWST Risk;eCICP;FMEA;MRD;NEA;Space vehicles;Schedules;Video on demand;Space missions;Random access memory;Telescopes;Extraterrestrial measurements|
|[A Decision-Making Framework for the KC-46A Maintenance Program](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088176)|K. E. Blond; A. L. Clark; T. H. Bradley|10.1109/RAMS51473.2023.10088176|decision support;enterprise systems engineering;reliability and maintainability analysis;United States Air Force;Surveillance;Decision making;Random access memory;FAA;Maintenance engineering;Inspection;Reliability engineering|
|[Mission Targets Oriented Quantitative Quality Assurance of Space Parts: To Estimate the Reliability Quantitatively and Cost-effectively](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088215)|H. Liu|10.1109/RAMS51473.2023.10088215|space pars;reliability;quality assurance;mission targets oriented;quantitatively;and cost-effectively;Quality assurance;Costs;Data analysis;Space missions;Random access memory;Prototypes;Reliability engineering|
|[A Design for Availability Process Framework with Field Data and Web-based Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088264)|O. van der Burgt; I. Anthony Okaro|10.1109/RAMS51473.2023.10088264|Availability;Reliability & Maintainability applications in Health care;Analytical models;Costs;Statistical analysis;Medical services;Predictive models;Maintenance engineering;Reliability engineering|
|[How Digital Transformation Drives Predictive Maintenance to Optimize System Readiness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088194)|C. Conrad; J. Dekeyrel; C. Grounds; E. Schmidtberg|10.1109/RAMS51473.2023.10088194|Maintenance;Predictive Maintenance;Digital Transformation;Costs;Digital transformation;Taxonomy;Transforms;Time factors;Stakeholders;Predictive analytics|
|[Revisiting the Definition of Supportability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088270)|G. Pedro; J. Franqui|10.1109/RAMS51473.2023.10088270|reliability;maintainability;supportability;product support;readiness;availability;Measurement;Analytical models;Focusing;Random access memory;Production;Maintenance engineering;Predictive models|
|[Effects of Aggregate vs Fully Randomized Sampling Plans in Lot Acceptance in the Presence of Autocorrelation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088276)|M. Menon|10.1109/RAMS51473.2023.10088276|Monte Carlo;Statistical Quality Control;Operating Characteristic Curve;OC Curve;Monte Carlo methods;Aggregates;Random access memory;Production;Reliability engineering;Autocorrelation|
|[Recommended Approaches for Representing Reliability Margin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088216)|M. W. Daniels|10.1109/RAMS51473.2023.10088216|Availability;R&M Management;Risk Analysis and Management;Random access memory;Probability;Systems engineering and theory;Reliability|
|[A Suite of Analyses Used to Lower Risk and Maximize Mission Success of Space Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088261)|J. Soliman|10.1109/RAMS51473.2023.10088261|space;reliability;analyses;tools;low-risk;success;Schedules;Text analysis;Limiting;Space missions;Random access memory;Propulsion;Reliability engineering|
|[Multi-Stage Product Family Design for Reliability with Remanufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088245)|X. Liu; A. K. Mishra; C. Hu; P. Wang|10.1109/RAMS51473.2023.10088245|stochastic optimization;product family;remanufacturing;reliability;Warranties;Costs;Uncertainty;Decision making;Stochastic processes;Random access memory;Reliability engineering|
|[Reliability & Maintainability Strategies For Repairable Systems In Rail Transportation Fleets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088267)|A. R. Alencar|10.1109/RAMS51473.2023.10088267|Reliability;Maintainability;Safety;Repairable Components;Subway;Rail;Transportation Fleets;Rails;Atmospheric modeling;Decision making;Maintenance engineering;Reliability engineering;Rail transportation;Safety|
|[Validation of Recurrent Failures Prediction Model Based on Underlying Distribution Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088249)|A. Yevkin; V. Krivtsov|10.1109/RAMS51473.2023.10088249|recurrent events;stochastic process;underlying distribution function;expected number of failures;Parameter estimation;Inverse problems;Random access memory;Estimation;Predictive models;Data models;Reliability|
|[Machine Learning-Driven RAM Analysis Using Multi-variate Sensor Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088229)|G. Gugaratshan; D. Barthlow; D. Lingenfelser; B. Thumati|10.1109/RAMS51473.2023.10088229|Reliability;machine learning;multivariate analysis;diagnostics;prognostics;sensor data;outlier detection;Temperature sensors;Machine learning algorithms;Biological system modeling;Random access memory;Maintenance engineering;Data models;Real-time systems|
|[PCA-based Monitoring of Power Plant Vibration Signal by Discrete Wavelet Decomposition Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088271)|B. N. Oguejiofor; K. Seo|10.1109/RAMS51473.2023.10088271|power plant vibration;discrete wavelet decomposition;principal component analysis;Vibrations;Random access memory;Transforms;Wavelet analysis;Discrete wavelet transforms;Reliability;Inductors|
|[Reliability Demonstration Based on the Results of a Super Extended Life Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088206)|A. Kleyner; D. Elmore|10.1109/RAMS51473.2023.10088206|Accelerated testing;life data analysis;back casting;Weibull distribution;Life data analysis;Data analysis;Uncertainty;Monte Carlo methods;Sociology;Statistical distributions;Random access memory;Life estimation|
|[Real-Time Fault-Tolerant Computing with Machine Learning Enhancements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088209)|M. -L. Yin; H. Aroush|10.1109/RAMS51473.2023.10088209|fault tolerance;machine learning;real-time systems;Learning systems;Fault tolerance;Fault tolerant systems;Random access memory;Machine learning;Real-time systems|
|[Towards Compliance to Safety Objectives Using Data Curation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088181)|H. Herencia-Zapana; D. Russell; D. Prince; K. Siu; P. Cuddihy|10.1109/RAMS51473.2023.10088181|safety analysis;software development process;design assurance level;DO-178C objectives;evidence curation;Data visualization;Random access memory;Software;Data models;Safety;Reliability;Python|
|[A Case Study in Obtaining Freedom from Interference in a Mixed-ASIL Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088192)|A. Arena; F. Tronci; I. M. Savino; G. Dini|10.1109/RAMS51473.2023.10088192|Functional Safety;AUTOSAR;Performance Evaluation;ISO 26262;Source coding;Redundancy;Power system protection;Random access memory;Interference;Data structures;Safety|
|[Innovative Solution on the De-Orbital Reliability Calculation for Low-Earth Orbit Satellites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088203)|B. Caldwell; B. McGraw|10.1109/RAMS51473.2023.10088203|De-Orbital Reliability;Space Vehicle Reliability;Space vehicles;Systematics;ISO Standards;Space missions;Low earth orbit satellites;Probability;Mathematical models|
|[Avoiding System Failures with Event Interval Probability – 737 MAX Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088220)|J. B. Smith|10.1109/RAMS51473.2023.10088220|Poisson;null hypothesis;Monte Carlo;risk assessment;reliability distributions;event interval probability;Economics;Degradation;Monte Carlo methods;Data analysis;Grounding;Random access memory;Tail|
|[Taming the Unwieldly Data Beast: Applying Big Data Methods to Guarantee and Maintain High Quality Reliable Data in Smart Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088256)|C. Nogradi; L. Goff|10.1109/RAMS51473.2023.10088256|Big data;IoT;Smart Manufacturing;CPPS;Veracity;Production systems;Ecosystems;Decision making;Random access memory;Big Data;Reliability;Smart manufacturing|
|[Computational Enhancements to the Mahalanobis-Taguchi System to Improve Fault Detection and Diagnostics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088240)|K. Scott; D. Kakde; S. Peredriy; A. Chaudhuri|10.1109/RAMS51473.2023.10088240|Mahalanobis-Taguchi System;Anomaly Detection;Fault Detection;Interpretable Results;Orthogonal Array;Fault diagnosis;Training;Correlation;Fault detection;Training data;Anomaly detection;Standards|
|[A Generative Reinforcement Learning Framework for Predictive Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088246)|E. Skordilis; R. Moghaddass; M. T. Farhat|10.1109/RAMS51473.2023.10088246|predictive analytics;variational autoencoders;reinforcement learning;remaining useful life;Training;System dynamics;Estimation;Random access memory;Reinforcement learning;Probabilistic logic;Numerical models|
|[Learning from Product Warranty Field Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088234)|E. B. Araujo|10.1109/RAMS51473.2023.10088234|Product warranty;failure modes;reliability improvement;life data analysis;Warranties;Data analysis;Failure analysis;Random access memory;Production;Management information systems;Reliability engineering|
|[Improve Warranty Failures in Original Equipment Manufacturing via Design for Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088196)|V. Hegde|10.1109/RAMS51473.2023.10088196|Six Sigma;Design for Reliability;FMEA;Reliability Growth;Burn-in;Project Management;Warranties;Industries;Costs;Design methodology;Random access memory;Companies;Reliability engineering|
|[A Robust Warranty Data Analysis Method Using Data Science Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088226)|N. Annadurai|10.1109/RAMS51473.2023.10088226|warranty data analysis;data science;data visualization;Warranties;Random access memory;Data visualization;Data science;Real-time systems;Reliability|
|[Kick-off Your Reliability Program with a Requirements Failure Modes and Effects Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088237)|J. Franqui; S. Newell|10.1109/RAMS51473.2023.10088237|Failure Modes and Effects Analysis;Requirements Analysis;Risk Mitigation;Analytical models;Databases;Random access memory;Reliability engineering;Risk management;System analysis and design|
|[A Component FMECA Development Methodology to Support the DO-254 Compliance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088233)|Y. Yu; E. Ireri; B. Schmidt; V. Swope|10.1109/RAMS51473.2023.10088233|FMECA;DO-254;SAE ARP4754A;SAE ARP4760;Random access memory;Hardware;Reliability;Certification|
|[Software FMEA and the Common Defect Enumeration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088208)|A. M. Neufelder|10.1109/RAMS51473.2023.10088208|Software FMEA;Defects;Costs;Random access memory;Standardization;Software;Software reliability;Testing|
|[Testability Design and Testability Rating for Better Built In Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088236)|J. DiCesare|10.1109/RAMS51473.2023.10088236|Testability;Critical Fault;FMECA;Fault Detection;Fault Isolation;Built-In Test;MBSE;Bridges;Fault detection;Systems architecture;Random access memory;Reliability engineering;Prognostics and health management;Best practices|
|[Streamlining Classical RCM Using a Digitized Model-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088266)|M. Cutajar; H. -B. Kim|10.1109/RAMS51473.2023.10088266|Reliability Centered Maintenance;Model-based Engineering;Digital Risk Twin;Digital Threads;Industries;Analytical models;Schedules;Costs;Standards organizations;Random access memory;Organizations|
|[The Digital Risk Twin – Enabling Model-based RAMS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088269)|A. C. Thorn; P. Conroy; D. Chan; C. Stecki|10.1109/RAMS51473.2023.10088269|Digital Risk Twin;Model-based RAMS;System Safety Analysis;Safety Assessment;Risk Assessment;Knowledge engineering;Terminology;Digital transformation;Standards organizations;Random access memory;Organizations;Reliability engineering|
|[Model Based Sustainment for Asset Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088248)|D. Khambhati; N. Plawecki; K. Crooks|10.1109/RAMS51473.2023.10088248|asset tracking;Model Based Sustainment;configuration control;Analytical models;Bills of materials;Random access memory;Maintenance engineering;Predictive models;Data models;US Department of Defense|
|[Optimizing Operations and Logistics Support Using Opus Evo](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088186)|G. Solveling; J. Verbanick|10.1109/RAMS51473.2023.10088186|Heuristic Optimization;Tactical Logistic Planning;Costs;Transportation;Random access memory;Organizations;Maintenance engineering;Planning;Discrete event simulation|
|[Dynamic Multilevel Redundancy Allocation Optimization Under Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088213)|A. E. Baladeh; S. Taghipour|10.1109/RAMS51473.2023.10088213|redundancy allocation;optimization;uncertainty;stochastic programming;scenario planning;working conditions;Employee welfare;Uncertainty;System performance;Redundancy;Stochastic processes;Maintenance engineering;Numerical models|
|[Optimal Multi-type Component Reassignment Design Under Internal Degradation and External Shocks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088280)|L. J Z; L. C Y; X. M; K. W|10.1109/RAMS51473.2023.10088280|component assignment;internal degradation;external shocks;Wiener process;Markov chain;Degradation;Electric shock;Heuristic algorithms;Random access memory;Reliability;Optimization;Preventive maintenance|
|[An Exploratory Study on Stochastic Reliability Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088279)|C. Ling; J. Lei; W. Kuo|10.1109/RAMS51473.2023.10088279|redundancy allocation;interval;degradation;min-max regret;robust;Degradation;Uncertainty;Redundancy;Random access memory;Random variables;Reliability;Resource management|
|[A Study on Heatsink Cooling Fan Lifetime Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088247)|B. Ghosh; J. Jijun Cao; J. Zhu|10.1109/RAMS51473.2023.10088247|Fan Reliability;Bearing Failure;Corrosion;Weibull Distribution;Lubrication;Over Current;Shafts;Fans;Shape;Lubricants;Failure analysis;Random access memory;Reliability engineering|
|[Using HALT to Navigate Supplier Disruptions with Limited Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088250)|N. Doertenbach|10.1109/RAMS51473.2023.10088250|HALT;HASS;Highly Accelerated Life Testing;supply chain disruptions;Navigation;Supply chains;Force;Prototypes;Life testing;Random access memory;Life estimation|
|[A Novel ADT Approach for Partial Discharge in Electrical Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088263)|P. Mell; M. Dazer; C. He; M. Beltle|10.1109/RAMS51473.2023.10088263|degradation test;accelerated test;e-mobility;lifetime model;partial discharge;Degradation;Partial discharges;Analytical models;Systematics;Random access memory;Life estimation;Discharges (electric)|
|[Need for AI in Transformer Diagnostics and Prognostics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088199)|S. Cvijic; N. Gupta; S. Lux|10.1109/RAMS51473.2023.10088199|artificial intelligence;Bayesian inference;health monitoring;machine learning;probabilistic programming;Temperature distribution;Machine learning;Oil insulation;Maintenance engineering;Probabilistic logic;Power transformers;Reliability|
|[Evolving Maintenance Practices Into Guided Decision Assistance Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088262)|D. Hoffman; S. Fecteau|10.1109/RAMS51473.2023.10088262|Maintenance;Troubleshooting;AI/ML;Availability;Job shop scheduling;Random access memory;Maintenance engineering;Information age;Manufacturing;Reliability;Task analysis|
|[Experimental Fault Detection & Diagnostics Using Virtual Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088258)|K. Sreeram; K. Muralikrishna; A. Gitapathi; S. Sampath|10.1109/RAMS51473.2023.10088258|Fault Detection;Diagnostics;Virtual validation;Productivity;Procurement;Fault detection;Random access memory;Manuals;Maintenance engineering;Real-time systems|
|[Open Dependability Exchange Metamodel: A Format to Exchange Safety Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088190)|M. Zeller; I. Sorokos; J. Reich; R. Adler; D. Schneider|10.1109/RAMS51473.2023.10088190|safety;dependability;data exchange;metamodel;openness;Analytical models;Supply chains;Random access memory;Organizations;Markov processes;Hazards;Safety|
|[Integrating Fault Tree Analysis with System Theoretic Process Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088187)|J. E. Weglian; J. Riley; M. Gibson|10.1109/RAMS51473.2023.10088187|System Theoretic Process Analysis;STPA;Fault Tree;FTA;Risk;Digital I&C;Fault diagnosis;Instruments;Process control;Random access memory;Reliability theory;Control systems;Reliability engineering|
|[Recommendations to Improve Quality of Safety Indicators in the Railway Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088188)|B. Ebrahimi; N. Henderson|10.1109/RAMS51473.2023.10088188|railway industry;safety indicators;accident precursors;Industries;Rails;Costs;Databases;Railway accidents;Standards organizations;Maintenance engineering|
|[A Hybrid Evolutionary CNN-LSTM Model for Prognostics of C-MAPSS Aircraft Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088251)|P. Khumprom; A. Davila-Frias; D. Grewell; D. Buakum|10.1109/RAMS51473.2023.10088251|Prognostics and Health Management;Aircraft engines;C-MAPSS;Deep Learning;CNN;LSTM;Training;Deep learning;Atmospheric modeling;Computational modeling;Neural networks;Predictive models;Feature extraction|
|[Online Large Signal EIS To Predict The LFP Cell State of Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088223)|M. Köder; M. Loos; T. Winter; M. Glaser|10.1109/RAMS51473.2023.10088223|Accelerated Life Testing;Large Signal EIS;Life Data Analysis;LiFePO4;Online Prediction Method;Temperature measurement;Life estimation;Aging;Predictive models;Battery charge measurement;Time measurement;Batteries|
|[A Methodology for Maintenance Analysis and Modeling Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088232)|I. A. Okaro; O. van der Burgt|10.1109/RAMS51473.2023.10088232|R&M Applications in Health Care;Maintenance Models and Methodologies;Deep learning;Analytical models;Costs;Semantics;Transforms;Maintenance engineering;Feature extraction|
|[Data Science Knowledge and Skills That Reliability Engineers Need: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088219)|A. Jordan; D. Berleant|10.1109/RAMS51473.2023.10088219|Reliability Engineering Practitioners;Data Science;Survey;Knowledge engineering;Training;Analytical models;Random access memory;Data visualization;Data science;Predictive models|
|[NASA’s Safety, Reliability, and Mission Assurance Digital Future](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088205)|A. DiVenti; M. Forsbacka; K. Rainbolt; S. Cornford; M. Feather|10.1109/RAMS51473.2023.10088205|Assurance-Case;Authoritative Source of Truth;Digital Transformation;Digital Twin;Safety & Mission Success;Systems Modeling Language;Training;Standards organizations;Random access memory;Transforms;Ontologies;Data models;Real-time systems|
|[A Strategic Mission Engineering Process to Select Prognostics and Predictive Maintenance Data Standards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088204)|J. B. Kroculick|10.1109/RAMS51473.2023.10088204|Prognostics and Health Management;Condition-Based Maintenance Plus;Predictive and Prognostic Maintenance;Technical Standards;C4ISR/EW Modular Suite of Standards;Common Operating Environment;Pipelines;Random access memory;Transforms;Open systems;Reliability;Prognostics and health management;Standards|
|[VirtualWorx™: Transforming Maintenance Concepts through Augmented Reality Collaboration Capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088179)|R. Nepal; R. J. Pavlovich; C. E. Guilherme|10.1109/RAMS51473.2023.10088179|Advanced Visualization;Augmented Reality Remote Collaboration;Technical Support;Operations & Sustainment;COVID-19;Costs;Collaboration;Maintenance engineering;Streaming media;Software;Virtual private networks|
|[Customization of HRA Technique for UAV Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088218)|S. B. Boosnur; M. Sarma|10.1109/RAMS51473.2023.10088218|Human Reliability;CREAM standard;Aviation;Error analysis;Random access memory;Autonomous aerial vehicles;Safety;Reliability;Task analysis;Standards|
|[Digital Availability Twin – Targeted Risk Mitigation from Design to Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088191)|S. Hilton; J. Langton; P. Conroy; C. Stecki|10.1109/RAMS51473.2023.10088191|Digital Availability Twin;Model-based risk analysis;availability;serviceability;sustainability;maintenance design;Industries;Feedback loop;Digital transformation;Random access memory;Transforms;Maintenance engineering;Systems engineering and theory|
|[R&M Digital Transformation of a Conventional DoD Contractor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088198)|J. Brown|10.1109/RAMS51473.2023.10088198|Reliability;Digital Engineering;Digital transformation;Random access memory;Data integration;Reliability engineering;Software;US Department of Defense;Software reliability|
|[Process-Driven Versus Model-Based Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088182)|M. Nilson|10.1109/RAMS51473.2023.10088182|Design Failure Modes and Effects Analysis;process-driven method;model-based method;Analytical models;Design methodology;Computer architecture;Maintenance engineering;Libraries;Software reliability;Complexity theory|
|[Stochastic Constituents for the Probabilistic Metric of Random Hardware Failures in ISO 26262](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088228)|S. Atsushi|10.1109/RAMS51473.2023.10088228|Continuous-time Markov chain;ISO 26262;Periodic inspections and repairs;PMHF;Stochastic constituents;Measurement;ISO Standards;Road vehicles;Random access memory;Markov processes;Probabilistic logic;Mathematical models|
|[Sensitivity Analysis on Reliability Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088211)|V. Hegde|10.1109/RAMS51473.2023.10088211|Six Sigma;Design for Reliability;FMEA;Reliability Growth;Reliability Demonstration;Burn-in;Project Management;Design engineering;Sensitivity analysis;Random access memory;Reliability engineering;Robustness;Mathematical models;Product safety|
|[Increase Effectiveness of Reliability Tools with the Role of Reliability Czar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088195)|A. P. Bahret|10.1109/RAMS51473.2023.10088195|Reliability Engineering;Warranties;Industries;Costs;Random access memory;Companies;Reliability engineering;Throughput|
|[Trust Loss Effects Analysis Method for Zero Trust Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088265)|D. L. Van Bossuyt; N. Papakonstantinou; B. Hale; R. Arlitt|10.1109/RAMS51473.2023.10088265|FMEA;FMECA;Zero Trust;STRIDE;Safety;Security;Resilience;Systematics;Merging;Random access memory;Focusing;Reconnaissance;Zero Trust;Risk management|
|[Reliability Analysis of Metalorganic Chemical Vapor Deposition Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088183)|M. Geng; H. Meng; W. Yao; X. Liu|10.1109/RAMS51473.2023.10088183|FMEA;TOPSIS;MOCVDs;Semiconductor materials;Pipelines;Random access memory;Production;Valves;Safety;Reliability|
|[Application of FMEA in Developing Design and Reliability Verification Plan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088221)|P. Shrivastava|10.1109/RAMS51473.2023.10088221|FMEA;Reliability Test;Acceleration Factor;Design Verification Test;Failure Mechanism;Temperature sensors;Systematics;System performance;Failure analysis;Process control;Reliability engineering;Reservoirs|
|[How to Compare Sets of Repair Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088210)|W. B. Nelson|10.1109/RAMS51473.2023.10088210|compare samples;nonhomogeneous Poisson process;nonparametric estimation;recurrent repairs;Data analysis;Costs;Random access memory;Manuals;Maintenance engineering;Reliability;Automobiles|
|[Dynamic Maintenance for a Large Scale Identical Parallel Manufacturing Systems Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088200)|M. Salmani; F. Azizi; H. Rasay; F. Naderkhani|10.1109/RAMS51473.2023.10088200|Dynamic Maintenance;Manufacturing Systems;Reinforcement Learning;Costs;Q-learning;Decision making;Random access memory;Maintenance engineering;Markov processes;Systems engineering and theory|
|[A Decision-making Framework for Repair vs Replacement of a Multi-Component System Subject to Environmental Shocks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088255)|F. Safaei; S. Taghipour|10.1109/RAMS51473.2023.10088255|Decision-making framework;Environmental shock;Maintenance model;Repair time limit;System’s profit;Costs;Sensitivity analysis;Electric shock;Decision making;Statistical distributions;Random access memory;Maintenance engineering|
|[An Optimal Maintenance Spare Parts Prediction Model and Its Complex Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088217)|H. Wang; B. Hart|10.1109/RAMS51473.2023.10088217|spare parts;spares availability;operational availability;M/M/c queue;queueing theory;digital engineering;Costs;Computational modeling;Stochastic processes;Predictive models;Maintenance engineering;Numerical models;Software reliability|
|[Practical Approach for Predicting Reliability of Handheld Devices Based on "Field Stress- Strength" Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088184)|A. Qasaimeh; E. Kosiba|10.1109/RAMS51473.2023.10088184|Display/Touch panel reliability;stress-strength analysis;Reliability prediction;ALT;Warranties;Random access memory;Life estimation;Predictive models;Strain measurement;Reliability engineering;Libraries|
|[Integrating Reliability Engineering with Model Based Systems Engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088275)|M. W. Daniels; K. Pierre|10.1109/RAMS51473.2023.10088275|MBSE;Digital Tapestry;Reliability Modeling;R&M Management;Industries;Analytical models;Adaptation models;Systems architecture;Reliability engineering;Mathematical models;Hardware|
|[Containerizing Fault Detection and Fault Isolation: A Pathway To Prognostics And Health Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088207)|S. Fecteau; C. Nogradi; R. McCarthy|10.1109/RAMS51473.2023.10088207|Containers;Fault Detection;Fault Isolation;Industries;Fault detection;Digital transformation;Random access memory;Maintenance engineering;Software;Reliability|
|[Optimal Release Policy for Covariate Software Reliability Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088282)|E. Yawlui; P. Silva; V. Nagaraju; L. Fiondella|10.1109/RAMS51473.2023.10088282|optimal time to release;covariate software reliability model;optimal effort allocation;Costs;Random access memory;Software;Hazards;Software reliability;Resource management;Testing|
|[Data Analysis and Pattern Recognition for Software Anomalies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088230)|L. Meshkat; Y. Shi|10.1109/RAMS51473.2023.10088230|NASA;problem failure reports;data analysis;anomalies;Measurement;Data analysis;NASA;Buildings;Random access memory;Predictive models;Software|
|[A Stochastic Petri Net Model of Continuous Integration and Continuous Delivery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088212)|S. Bhadra; B. Das; P. Silva; V. Nagaraju; L. Fiondella|10.1109/RAMS51473.2023.10088212|CI/CD pipeline;SPN model;DevOps;performance analysis;sensitivity analysis;Analytical models;Sensitivity analysis;Pipelines;Petri nets;Stochastic processes;Software systems;Product delivery|
|[Test Design for Combining Tests at Multiple Product Levels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088231)|J. Zhang|10.1109/RAMS51473.2023.10088231|Accelerated life test;test design;multiple tests;product level;Performance evaluation;Design methodology;Random access memory;Life estimation;Reliability engineering;Stress|
|[Variability Of Fatigue Simulation Predictions For Automotive Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088243)|E. Czerlunczakiewicz; M. Majerczak; M. Bonato|10.1109/RAMS51473.2023.10088243|Variability;Fatigue Simulation;Accelerated Life Tests;Validation Specifications;Automotive;Heat Exchangers;Vibrations;Heating systems;Predictive models;Fatigue;Numerical models;Rubber;Iterative methods|
|[Root Cause and Reliability Predictions of Failed Multilayer Ceramic Capacitors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088281)|F. Chen; C. Bartosz|10.1109/RAMS51473.2023.10088281|ceramic capacitors;temperature-humidity-bias acceleration;drop shock;capacitor reliability;Temperature measurement;Electric shock;Surface mount technology;Life estimation;Humidity;Ceramic capacitors;Reliability engineering|
|[ANN-based Failure Modeling of T-56 Engine Turbine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088178)|N. A. Qattan; A. M. Al-Bahi; B. Kada|10.1109/RAMS51473.2023.10088178|reliability;neural network;back propagation;radial basis functions;multilayer perceptron;Atmospheric modeling;Random access memory;Artificial neural networks;Maintenance engineering;Predictive models;Software;Software reliability|
|[Bayesian Network for Reliability Predictions of Automotive Battery Cooling System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088278)|G. Sharma; M. Bonato; M. Krishnamoorthy|10.1109/RAMS51473.2023.10088278|Battery Coolers;PPM Calculation;Bayesian Networks;warranty Period Extension;Dependability;Conditional Reliability;Warranties;Vibrations;Frequency modulation;Calculators;Cooling;Vehicle safety;Batteries|
|[Multi-Fidelity Modeling and Reliability Analysis of Off-Shore Production Wells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088274)|B. Hamdan; P. Wang|10.1109/RAMS51473.2023.10088274|Bayesian Optimization;Dynamic Bayesian Networks;Multi-Fidelity Optimization;Gaussian Process Regression;Soft sensors;Random access memory;Production;Signal processing;Probabilistic logic;Real-time systems;Bayes methods|
|[Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088222)|B. Cheng; M. Potter|10.1109/RAMS51473.2023.10088222|Neural Network;Cox-Weibull;Bayesian;Weibull;Reliability Modeling;Measurement;System testing;Monte Carlo methods;Weapons;Neural networks;Predictive models;Bayes methods|
|[Multi-sensor Corrosion Growth Modeling with Latent Variables Using Hierarchical Clustering and Vector Autoregression Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088257)|A. A. Alqarni; P. K. Huynh; O. P. Yadav; T. Q. Le; Y. Huang|10.1109/RAMS51473.2023.10088257|corrosion growth models;clustering;oil refinery;time-series forecasting;latent variables;Reactive power;Corrosion;Time series analysis;Clustering algorithms;Predictive models;Prediction algorithms;Data models|
|[Toward Closed Form Formulas for System Reliability and Confidence Quantification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088214)|Z. C. Huang|10.1109/RAMS51473.2023.10088214|System Reliability;Confidence Level;Component Reliability;Closed Form Formulas;Reliability Prediction;Monte Carlo methods;Autonomous systems;Computational modeling;Random access memory;Estimation;Gaussian distribution;Safety|
|[Reliability Modeling of 12V Batteries Used in Multiple Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088235)|P. Fanelli; C. Sidoti; M. Thomas|10.1109/RAMS51473.2023.10088235|Reliability;Weibull Analysis;Failure Modes;Variety;Maintenance;Analytical models;Statistical analysis;Random access memory;Failure analysis;Data models;Batteries;Reliability|
|[Rail Track Maintenance Strategy Considering Competitive Failure Modes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088239)|B. Rahimikelarijani; M. Hamidi|10.1109/RAMS51473.2023.10088239|Competing failures;intervention level;Gamma Process;Perfect maintenance;Rail Geometry failures;Geometry;Rails;Degradation;Costs;Sensitivity analysis;Railway accidents;Random access memory|
|[Quantum-Enhanced Reliability Assessment of Power Networks in Response to Wildfire Events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088260)|G. S. M. Silva; T. Parhizkar; H. T. Nguyen; E. L. Droguett|10.1109/RAMS51473.2023.10088260|Power network;Vulnerability;Wildfire;Extreme events;Quantum computing;Quantum Bayesian Inference;Sociology;Fires;Random access memory;Probabilistic logic;Reliability engineering;Bayes methods;Quantum circuit|
|[A Framework for Selection of Random Inspection Routes for Power Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088224)|A. C. Netto; C. A. Murad; G. F. M. de Souza; A. J. Silva; S. I. Nabeta|10.1109/RAMS51473.2023.10088224|Random Inspection;FMEA;Maintenance;Task selection;RPN;Degradation;Time-frequency analysis;Inspection;Valves;Real-time systems;Random processes;Reliability|
|[Remaining Useful Life of Corroded Piping Based on Bayesian Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088252)|G. F. M. de Souza; E. G. Veruz; M. M. Miguel|10.1109/RAMS51473.2023.10088252|Corrosion rate;Integrity;Bayesian Network;NORSOK;Costs;Corrosion;Heuristic algorithms;Time series analysis;Random access memory;Inspection;Maintenance engineering|
|[Autonomous Vehicles - Trust, Safety and Security Cases: The Complete Picture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088202)|T. Myklebust; T. Stålhane; G. D. Jenssen|10.1109/RAMS51473.2023.10088202|Safety case;cybersecurity case;trust case;safety;autonomous;public;Standards organizations;Random access memory;Organizations;Safety;Reliability;Computer security;Artificial intelligence|
|[Automated Driving Systems Operating as Mobility as a Service: Operational Risks and SAE J3016 Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088244)|M. A. Ramos; C. Correa Jullian; J. McCullough; J. Ma; A. Mosleh|10.1109/RAMS51473.2023.10088244|Automated Driving Systems;Mobility as a Service;DDT Fallback;Operational Safety;J3016;Random access memory;Solids;Safety;Behavioral sciences;Reliability;Standards;Remote monitoring|
|[Reliability Estimation Using Long Short-Term Memory Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088225)|A. Davila-Frias; P. Khumprom; O. P. Yadav|10.1109/RAMS51473.2023.10088225|Long Short-Term Memory Networks;Reliability;Degradation Data;Degradation;Deep learning;Recurrent neural networks;Estimation;Random access memory;Data models;Reliability|
|[A Gaussian Process Model with Indirect Health Indicators for Battery Prognosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088180)|Y. Li; S. Kohtz; P. Wang|10.1109/RAMS51473.2023.10088180|Machine Learning;SOH Estimation;Gaussian Process Regression;Neural Network;Lithium-ion batteries;Machine learning;Gaussian processes;Voltage;Predictive models;Feature extraction;Lithium batteries|
|[Physics-Constrained Machine Learning for Reliability-Based Design Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088268)|Y. Xu; P. Wang|10.1109/RAMS51473.2023.10088268|Physics-constrained machine learning;GP-based model;Missing data;Partially observed information;Reliability-based design optimization;System performance;Estimation;Random access memory;Machine learning;Predictive models;Reliability engineering;Product design|
|[Applying Machine Learning Methods to Improve All-Terminal Network Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088254)|J. Azucena; F. Hashemian; H. Liao; E. Pohl|10.1109/RAMS51473.2023.10088254|reliability models;reliability growth analysis;deep reinforcement learning;Deep learning;Uncertainty;Neural networks;Stochastic processes;Random access memory;Reinforcement learning;Reliability engineering|
|[Regression and Monte Carlo Approach to Lithium-Ion Battery Capacity Degradation Modeling and Prediction for Heating Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088185)|L. Pangeni; T. Cimprich|10.1109/RAMS51473.2023.10088185|Monte Carlo;regression model;degradation;ALT;customer usage;battery;Degradation;Warranties;Lithium-ion batteries;Heating systems;Extrapolation;Monte Carlo methods;Predictive models|
|[A Physics of Failure, Kinetic Simulation Model for Reliability of RRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088177)|L. Huang; A. Mosleh|10.1109/RAMS51473.2023.10088177|Physics of Failure;RRAM;Kinetic Monte Carlo Simulation;Reliability Model;Endurance;Retention;Degradation;Analytical models;Adaptation models;Materials reliability;Voltage;Switches;Mathematical models|
|[Probabilistic Physics of Failure Modeling of Non-metallic Pipelines in Oil and Gas Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088253)|T. M. Stewart; A. Mosleh|10.1109/RAMS51473.2023.10088253|composite;environmental degradation;failure prediction model;Degradation;Resistance;Fluids;Oils;Pipelines;Predictive models;Mechanical factors|
|[A Physics-informed Latent Variables of Corrosion Growth in Oil and Gas Pipelines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10088241)|P. K. Huynh; A. A. Alqarni; O. P. Yadav; T. Q. Le|10.1109/RAMS51473.2023.10088241|physics-informed corrosion growth model;latent variable model for time series;oil and gas pipelines;Corrosion;Oils;Pipelines;Time series analysis;Stochastic processes;Predictive models;Soil|
#### **2023 8th International Conference on Technology and Energy Management (ICTEM)**
- DOI: 10.1109/ICTEM56862.2023
- DATE: 8-9 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A hybrid CPV/T system equipped with compound parabolic concentrator and Nano-Pcmfor optimal electricity generation and hot water](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083841)|M. Gharzi; A. Arabhosseini; Z. Gholami; M. Pakchi|10.1109/ICTEM56862.2023.10083841|Cooling;Hybrid CPV/T system;Nano-PCM;Concentrated photovoltaic;Efficiency;Phase change materials;Photovoltaic systems;Water;Temperature;Cooling;Short-circuit currents;Thermal management;Hybrid power systems|
|[Development Planning Policies for Renewable Electricity Generation in Competition with Fossil Electricity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084141)|A. Meydani|10.1109/ICTEM56862.2023.10084141|Renewable Energy;Electricity Generation;Fossil Fuel;Sustainable Development;Energy Policies;Industries;Renewable energy sources;Costs;Economic indicators;Production;Fossil fuels;Planning|
|[Analysis and Comparison of Reactive Power Optimization Using Improved Genetic Algorithm and Improved Quantum Particle Swarm Algorithm in an Active Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083893)|H. Amiri|10.1109/ICTEM56862.2023.10083893|distribution network;improved genetic algorithm;improved quantum particle swarm algorithm;reactive power optimization;Reactive power;Sociology;Distribution networks;Voltage;Particle swarm optimization;Statistics;Optimization|
|[Investigating the Impacts of Electric Vehicles on Iran's Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083993)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; B. Sabery; A. B. Bork Abad|10.1109/ICTEM56862.2023.10083993|Iran's Distribution Network;Electric Vehicles;Electric Vehicles Charging Stations;Home Charging;Renewable energy sources;Transportation;Distribution networks;Production;Charging stations;Electric vehicles;Fossil fuels|
|[Studying the Implementation of the Smartening Road Map of Iran's Electricity Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083717)|P. Zare; R. Zare; H. Ghadimi; B. Sabery; I. F. Davoudkhani; A. B. B. Abad|10.1109/ICTEM56862.2023.10083717|Smart Electricity Grid;Iran's Electricity Distribution Network;road map;Smartening;National Smart Metering Plan;FAHAM;Industries;Productivity;Electric potential;Power supplies;Roads;Distribution networks;Smart grids|
|[The Superiority of Turbulent Flow of Water-based Optimization for Speed Control of Brushless DC Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084323)|S. R. Mousavi Aghdam; P. Zare; A. Babaei; R. Mohajery|10.1109/ICTEM56862.2023.10084323|Brushless Direct Current Motors;Speed Control;PI Controller;Turbulent Flow of Water-based Optimization Algorithm;nan|
|[Investigating the Impact of Distributed Generation on the Retail Price of Electricity Market in Iran's Electricity Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084194)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; B. Sabery; A. B. Bork Abad|10.1109/ICTEM56862.2023.10084194|Iran's Electricity Distribution Network;Renewable Energy Sources;Iranian Electricity Market;Customers;Retail;Distributed Generation;Increasing Block Tariff Model;nan|
|[Intelligent Load Frequency Control in a Deregulated Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084072)|A. Sina; D. Kaur|10.1109/ICTEM56862.2023.10084072|Multi-Source;Evaluation Algorithm;Fuzzy PID;Optimization Controller;Deregulated Power System;Uncertainty;Heuristic algorithms;Power system dynamics;Transfer functions;Power system stability;Control systems;Hybrid power systems|
|[The Study Impact of Restructuring on Efficiency of Iran's Electricity Distribution And Transmission Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083558)|P. Zare; H. Ghadimi; R. Zare; A. B. Bork Abad; B. Sabery; I. F. Davoudkhani|10.1109/ICTEM56862.2023.10083558|Iran's Electricity Distribution Network;Iran's Electricity Transmission Network;Iranian Electricity Market;Customers;Retail;Iran Electricity Market;Restructuring Iran's Electricity Industry;Industries;Productivity;Open Access;Privatization;Electricity supply industry;Monopoly;Power system reliability|
|[Protection of the Blinding Area in Active Distribution Network by Multi-Function Relays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084126)|M. Zareein; J. S. Farkhani|10.1109/ICTEM56862.2023.10084126|Blind Protection Areas;Multifunction Relay Protection;OCR;Thermal Overload;DGs;Sensitivity;Optical character recognition;Protective relaying;Distribution networks;Generators;Delays;Power systems|
|[Sustainable development through the establishment of zero-carbon villages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083921)|H. Yousefi; A. Rahmani; M. Montazeri|10.1109/ICTEM56862.2023.10083921|Renewable energy;zero-carbon village;sustainable development;Remote area;Solar energy;Renewable energy sources;Costs;Sensitivity analysis;Urban areas;Generators;Software;Wind turbines|
|[IoT-based Office Buildings Energy Management with Distributed Edge Computing Capability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083681)|M. N. Moghadam; M. Abapour|10.1109/ICTEM56862.2023.10083681|Building Energy Management;Internet of Things;Internet of Energy;Edge Computing;Cloud Computing;Cloud computing;Urban areas;Companies;Control systems;Real-time systems;Delays;Servers|
|[A creative way to teach and learn power electronic laboratory equipment in renewable systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084122)|Z. Shahrouei; R. Gavagsaz-Ghoachani; M. Phattanasak|10.1109/ICTEM56862.2023.10084122|hybrid;circuit components;renewable energy;education;Renewable energy sources;Art;Animals;Education;Fuel cells;Power electronics;Behavioral sciences|
|[Wind Generators Ferroresonance Overvoltage Protection Methods: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083847)|M. Hesami; M. Bigdeli; M. A. Fatemi; N. Shafaghatian|10.1109/ICTEM56862.2023.10083847|Ferroresonance;Overvoltage;Protection;Suppression;Wind Generator;Inhibitors;Ferroresonance;Generators;Voltage control;Transient analysis;Energy management|
|[Optimum Operation of Grid-Independent Microgrid Considering Load Effect on Lifetime Characteristic of Battery Energy Storage System Using Dwarf Mongoose Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084271)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; B. Sabery; A. B. Bork Abad|10.1109/ICTEM56862.2023.10084271|Microgrid;battery life;Dwarf Mongoose Optimization Algorithm;optimization;nan|
|[Preventing Frequency Instability Using Large-Scale Photovoltaic Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083661)|S. Yari; H. Khoshkhoo; K. Rouzbehi; S. M. A. Emran|10.1109/ICTEM56862.2023.10083661|frequency stability;special protection system;power system stability;large-scale photovoltaic power plants;Photovoltaic systems;Time-frequency analysis;Power system dynamics;Software algorithms;Power system stability;Stability analysis;Software|
|[Efficient Multi-Objective Optimization for Analyzing Lifetime Characteristics of Battery Energy Storage System in a Standalone Microgrid Considering Resource and Load Limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083666)|P. Zare; I. F. Davoudkhani; R. Mohajery; R. Zare; H. Ghadimi; M. Ebtehaj|10.1109/ICTEM56862.2023.10083666|Microgrid;Energy Storage;Prairie Dog Optimization Algorithm;Battery Life;Load Limit;Resource Limitations;Renewable energy sources;Climate change;Costs;Microgrids;Production;Dogs;Generators|
|[Investigating green chemistry and the effect of nanotechnology on the environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084268)|N. Abdirad; A. Kamran-Pirzaman|10.1109/ICTEM56862.2023.10084268|Green chemistry;Environment;Nanotechnology;nan|
|[Photovoltaic Power Forecasting With an Ensemble Multi-Input Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084013)|F. Dehghan; M. P. Moghaddam; M. Imani|10.1109/ICTEM56862.2023.10084013|Bidirectional long short-term memory networks;Convolution neural networks;Deep learning;PV power forecasting;Deep learning;Photovoltaic systems;Three-dimensional displays;Convolution;Atmospheric modeling;Power system stability;Feature extraction|
|[Application of Artificial Neural Network in predicting building's energy consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084336)|R. Zahedi; A. Aslani; A. Gitifar; O. N. Farahani; H. Yousefi|10.1109/ICTEM56862.2023.10084336|Energy Consumption Modeling;Neural Network;Residential Buildings;Energy Efficiency;Forecasting;nan|
|[Pave the Way for Hydrogen-Ready Smart Energy Hubs in Deep Renewable Energy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083890)|M. L. Imeni; M. S. Ghazizadeh|10.1109/ICTEM56862.2023.10083890|energy hub;hydrogen energy;energy market;power-to-hydrogen;Renewable energy sources;Uncertainty;Job shop scheduling;Wind speed;Systems operation;Hydrogen;Stochastic processes|
|[Prediction of Electric Vehicle's Annual Accessibility to Chargers for Providing Ancillary Services Using an Efficient Random Forest Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083916)|S. N. Jahromi|10.1109/ICTEM56862.2023.10083916|Electric Vehicle;Supervised Machine Learning;Random Forest;Game Theory;Frequency Containment Reserve;Schedules;Uncertainty;Forestry;Predictive models;Electric vehicles;Smart grids;Power systems|
|[Comparison of effective greenhouse gases and global warming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083954)|M. Tavassoli; A. Kamran-Pirzaman|10.1109/ICTEM56862.2023.10083954|greenhouse gases;greenhouse effect;climate change;global warming;Earth;Gases;Temperature;Greenhouse effect;Green products;Legislation;Global warming|
|[A Probabilistic Approach for Scheduling of Wind-Energy-Integrated Power Systems Incorporating Dynamic Lines and Transformers Rating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084303)|M. Akhlaghi; Z. Moravej; A. Bagheri|10.1109/ICTEM56862.2023.10084303|Wind energy;Power system smart operation;Optimal power flow;DLR;DTR;Uncertainty;nan|
|[Using a Multi-Functional Inverter to Connect the Distributed Generation Source to the Network with Purpose of Reducing Harmonic and Network Imbalance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084004)|N. S. Ghiasi; M. Forouzanfar; M. Babaei; M. B. Sanjareh; S. M. Sadegh Ghiasi|10.1109/ICTEM56862.2023.10084004|Multi Functional Inverter;Imbalancing;Resonance control;Harmonics;Renewable energy sources;Power quality;Production;Microgrids;Harmonic analysis;Active filters;Inverters|
|[Wind Speed Forecasting and Probability Distribution Analysis Using Measured Data at Weather Stations in Iran](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084298)|S. Ghazanfari-Rad; Z. Riazi|10.1109/ICTEM56862.2023.10084298|Wind Energy;Statistical Analysis;Prediction;ARMA model;Probability Distribution Function;nan|
|[A Survey of Renewable Energy Approaches in Cloud Data Centers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083820)|S. Ghazanfari-Rad; S. Ebneyousef|10.1109/ICTEM56862.2023.10083820|Cloud Computing;Data Center;Renewable Energy;Data centers;Cloud computing;Renewable energy sources;Power demand;Pollution;Greenhouse effect;Taxonomy|
|[The Superiority of Coronavirus Optimization Algorithm for Optimal Designing of Photovoltaic/Wind/Fuel Cell Hybrid System Considering Cost Minimization Approach to Improve Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083526)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; B. Sabery; A. B. Bork Abad|10.1109/ICTEM56862.2023.10083526|Renewable Energy Sources;Distributed Generation Resources;Hybrid System;Reliability;Coronavirus Optimization Algorithm (COVIDOA);Photovoltaic;Wind Turbine;Fuel Cell;nan|
|[Multi-Objective Coordinated Optimal Allocation of Distributed Generation and D-STATCOM in Electrical Distribution Networks Using Ebola Optimization Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084121)|P. Zare; I. F. Davoudkhani; R. Mohajery; R. Zare; H. Ghadimi; M. Ebtehaj|10.1109/ICTEM56862.2023.10084121|Electricity Distribution Networks;Distributed Generation Sources;Ebola Optimization Search Algorithm;D-STATCOM;Costs;Distribution networks;Voltage;Production;Automatic voltage control;Stability analysis;Resource management|
|[Optimal Energy Management of a Residential Microgrid Considering the Range Anxiety Factor, Uncertainties, and Time-of-Use Tariff Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083653)|E. Akbari; M. S. Shadlu|10.1109/ICTEM56862.2023.10083653|Optimal Energy Management;Residential Microgrid;Range Anxiety Factor;Time-of-Use Tariff Schemes;Uncertainty;Costs;Anxiety disorders;Tariffs;Pricing;Microgrids;Real-time systems|
|[Maiden Application of Zebra Optimization Algorithm for Design PIDN-TIDF Controller for Frequency Control in Offshore Fixed Platforms Microgrid in the Presence of Tidal Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083612)|P. Zare; I. F. Davoudkhani; R. Zare; H. Ghadimi; R. Mohajeri; A. Babaei|10.1109/ICTEM56862.2023.10083612|Offshore Fixed Platforms Microgrid;Tidal Energy;Tidal Energy Converter System;Renewable Energy;Frequency Controller;PIDN-TIDF Controller;Zebra Optimization Algorithm;Fluctuations;Uncertainty;Simulation;Software algorithms;Stability criteria;Microgrids;Control systems|
|[A 37-Level Switched-Capacitor Boost Inverter With Reduced Blocking Voltage on Semiconductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084270)|A. Ghelichi; K. Varesi; V. Dargahi|10.1109/ICTEM56862.2023.10084270|Blocking voltage;boosting factor;multilevel inverter;reduced component;switched-capacitor;self-balancing;nan|
|[Comparing Three Separate Discrete Algorithms for Generation Maintenance Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084330)|S. Soltani; M. J. Kouhanjani|10.1109/ICTEM56862.2023.10084330|GMS;Power system;Preventive maintenance scheduling;Discrete optimization algorithms;Energy in industries;nan|
|[Disease Diagnosis and Data Protection System for IoT-based Wearable Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083543)|E. H. Khah; M. Golsorkhtabaramiri|10.1109/ICTEM56862.2023.10083543|Support Vector Regression (SVR);disease diagnosis;Machine learning;Emprical Mode Decompoition;Wearable Sensor Network;Support vector machines;Training;Empirical mode decomposition;Organizations;Safety;Medical diagnosis;Forecasting|
|[An Overview of Rooftop Photovoltaic Power Plant Development Process in Iran](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083740)|M. Tafazoli|10.1109/ICTEM56862.2023.10083740|Renewable energy;Photovoltaic;Rooftop power plants;Feed in tariff;Iran;Photovoltaic systems;Renewable energy sources;Costs;Temperature;Tariffs;Sea measurements;Production|
|[Mathematical modeling of magneto-electro-elastic energy harvesters considering nonlinearities in curvature and inertia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083816)|J. Khaghanifard; A. R. Askari; M. Taghizadeh|10.1109/ICTEM56862.2023.10083816|Energy harvesting;Functionally graded magneto-electro-elastic materials;Curvature and Inertial nonlinearities;Method of multiple time scales;Vibrations;Magnetic resonance;Harmonic analysis;Mathematical models;Behavioral sciences;Numerical models;Magnetic analysis|
|[A Review of Energy-efficient QoS-aware Composition in the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083692)|A. Mokabberi; A. Iranmehr; M. Golsorkhtabaramiri|10.1109/ICTEM56862.2023.10083692|IoT service;IoT service composition;Energy-efficiency;Quality of Service;Scalability;Lightning;Quality of service;Throughput;Load management;Energy efficiency;Internet of Things|
|[Investigating the increase of plutonium extraction from heavy water reactors1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083876)|M. A. Afroozi|10.1109/ICTEM56862.2023.10083876|plutonium;heavy water;reactor;heavy water reactor;Nuclear weapons;Uranium;Production;Neutrons;Plutonium;Fuels;Inductors|
|[The Performance of Electric and Hybrid Cars and the Effect of Their Use in the Environmental Cycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084171)|A. Meydani; A. Meidani; S. Shahablavasani|10.1109/ICTEM56862.2023.10084171|Hybrid car;Carbon Dioxide;Environment;Fuel consumption;Combustion Engine;Renewable energy sources;Urban areas;Air pollution;Safety;Automobiles;Hybrid electric vehicles;Reliability|
|[Clean Energy's Role in Power Plant Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084297)|A. Meydani; A. Meidani; S. Shahablavasani|10.1109/ICTEM56862.2023.10084297|Sustainable development;Renewable energy;Power plant development;Electricity generation;Fossil fuel;Industries;Renewable energy sources;Electric potential;Economic indicators;Solar energy;Fuels;Security|
|[Intelligent Control of a Domestic Solar Water Heating System with Thermal Storage Using Fuzzy Logic-Modified Model Predictive Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084188)|E. Akbari; M. S. Shadlu|10.1109/ICTEM56862.2023.10084188|Domestic solar water heating system;Thermal storage tank;Modified model predictive control;Fuzzy logic control;nan|
|[Application of clean energies in agricultural greenhouses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084219)|A. Arabhosseini|10.1109/ICTEM56862.2023.10084219|Agriculture;Clean Energy;Cooling;Greenhouse;Heating;Renewable Energy;nan|
|[Energy Analysis of Molten-Salt Storage Integrated with Air-Based Brayton Cycle: Case Study of a Wind Farm in Denmark](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084166)|H. R. Rahbari; M. Mandø|10.1109/ICTEM56862.2023.10084166|Wind Farms;Molten Salt Storage;Air-Based Brayton Cycle;District Heating;nan|
|[Application of Optical Wireless Communications in IoT Devices of Smart Grids within Smart Sustainable Cities: With Hybrid Perspectives to Metaverse & Quantum IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083835)|A. Safari; H. Kharrati|10.1109/ICTEM56862.2023.10083835|Artificial Neural Networks (ANN);Optical Wireless Communication (OWC);Quantum Neural Networks (QNN);Smart Grids of Smart Sustainable City;Wireless communication;Integrated optics;Metaverse;Urban areas;Optical computing;Optical fiber networks;Data transfer|
|[Maximum Power Point Tracking in a Photovoltaic System by Optimized Fractional Nonlinear Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083639)|A. Veisi; H. Delavari; F. Shanaghi|10.1109/ICTEM56862.2023.10083639|Fractional Calculus;Sliding Mode Controller;Back-stepping algorithm;Maximum Power Point Tracking;Perturbation and Observation;Ant Colony Optimization;Photovoltaic System;Maximum power point trackers;Photovoltaic systems;Productivity;Photovoltaic cells;Robustness;Global warming;Temperature control|
|[A Novel Common Grounded Type $1-\varphi$ Five-Level Boost PV Inverter with Reduced Device Count](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084101)|J. F. Ardashir; H. V. Ghadim; D. Heydari; J. Hu|10.1109/ICTEM56862.2023.10084101|Voltage boost;Five-level inverter;Leakage current;Switched capacitor;Multi-level inverter;Reactive power;Simulation;Capacitors;Prototypes;Switches;Multilevel inverters;Inverters|
|[Loss Reduction of Distribution Network by Optimal Reconfiguration and Capacitor Placement Using Cuckoo and Cultural Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083643)|M. Najjarpour; B. Tousi|10.1109/ICTEM56862.2023.10083643|Reconfiguration;Optimization;Capacitor Placement;Cuckoo Algorithm;Cultural Algorithm;Distribution Network;Network topology;Capacitors;Metaheuristics;Distribution networks;Voltage;Topology;Cultural differences|
|[Game- Theoretic Based Energy Sharing Strategy in Microgrid Considering Influence of Neighbors, Social Personality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084102)|M. B. Moradi; M. S. Ghazizadeh|10.1109/ICTEM56862.2023.10084102|Residential microgrid;Social behavior;Game theory;Energy management;Energy sharing;Costs;Simulation;Decision making;Microgrids;Companies;Behavioral sciences;Energy management|
|[Optimal Siting and Sizing of Distributed Generation Under Uncertainties Using Point Estimate Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083857)|A. Ashoornezhad; Q. Asadi; R. Saberi; H. Falaghi|10.1109/ICTEM56862.2023.10083857|distribution network;distributed generation;point estimate method;probabilistic load flow;Photovoltaic systems;Renewable energy sources;Uncertainty;Voltage;Evolutionary computation;Probabilistic logic;Distributed power generation|
|[Traffic-Constrained Multiobjective Placement of PEV Parking Lots with Flexible Charging Control in Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084146)|M. Mohammadilandi; E. Shalooyi|10.1109/ICTEM56862.2023.10084146|energy loss;reliability;electrical vehicle;traffic flow;Vehicle-to-grid;Sensitivity analysis;Systems operation;Urban areas;Power distribution;Distribution networks;Power industry|
|[An Improved H6-Type Single-Phase PV Inverter with Suppressed Leakage Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083868)|M. Farahani; M. -A. Shamsi-Nejad|10.1109/ICTEM56862.2023.10083868|Grid-tied photovoltaic (PV) plant;transformerless structure;IH6-D inverter;common-mode (CM) voltage;leakage current elimination;reactive power;Photovoltaic systems;Switching frequency;Transformers;Inverters;Software;Topology;Leakage currents|
|[An Optimized H6-Type Single-Phase PV Inverter with Bi-Directional Quasi-Diode Clamping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083649)|M. Farahani; M. -A. Shamsi-Nejad|10.1109/ICTEM56862.2023.10083649|Common-mode voltage (CMV);grid-tied;leakage current;transformerless inverter (TLI);bi-directional quasi-diode clamping (BQDC);parasitic capacitance;Reactive power;MOSFET;Electric potential;Bidirectional control;Transformers;Inverters;Topology|
|[A Levelized Feed-in Tariff to Integrate Battery into the PV-Connected System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083508)|A. Ashoornezhad; R. Saberi; Q. Asadi; H. Falaghi|10.1109/ICTEM56862.2023.10083508|Feed-in Tariff;residential investors;battery and photovoltaic systems;economic analysis;nan|
|[Optimal Placement of PEV Parking Lots with Flexible Charging Control in Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083585)|M. Mohammadilandi; E. Shalooyi|10.1109/ICTEM56862.2023.10083585|energy loss;reliability;electrical vehicle;distribution system;Plug-in electric vehicles;Vehicle-to-grid;Systems operation;Urban areas;Power distribution;Power industry;State of charge|
|[Thermal performance enhancement of microchannel heat sinks with a decreasing-height bifurcation plate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083775)|M. Fathi; M. M. Heyhat; M. Z. Targhi|10.1109/ICTEM56862.2023.10083775|Bifurcated microchannel heat sink;Decreasing-height bifurcation plate;Thermal enhancement;Micro-electronics cooling;Data centers;Temperature distribution;Power demand;Fluids;Heat pumps;Bifurcation;Energy efficiency|
|[Decentralized Passivity-based control of two distributed generation units in DC microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084246)|M. Afkar; R. Gavagsaz-Ghoachani; M. Phattanasak; S. Pierfederici|10.1109/ICTEM56862.2023.10084246|DC microgrid;decentralized method;Passivity control;droop control;nan|
|[Losses calculation of a two-input Boost Converter for Renewable Energy Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083975)|M. Afkar; M. A. Razmjou; R. Gavagsaz-Ghoachani; M. Phattanasak; S. Pierfederici|10.1109/ICTEM56862.2023.10083975|Three-level boost converter;Loss calculation;Voltage imbalance;Renewable energy;nan|
|[Integrated Scheduling and Bidding Strategy for Virtual Power Plants Based on Locational Flexibility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083720)|S. Afzali; R. Zamani; M. P. Moghaddam; M. K. Sheikh-El-Eslami|10.1109/ICTEM56862.2023.10083720|Flexibility market;flexibility service provider;locational flexibility;virtual power plant;Uncertainty;Numerical analysis;Systems operation;Stochastic processes;Optimal scheduling;Virtual power plants;Programming|
|[Optimal Scheduling of Active Distribution Networks Considering Dynamic Transformer Rating Under High Penetration of Renewable Energies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083734)|N. Osali|10.1109/ICTEM56862.2023.10083734|Wind energy;Photovoltaic units;DTR;LOL;Optimal dispatch;ADN;Renewable energy sources;Costs;Wind energy;Loading;Optimal scheduling;Voltage;Smart grids|
|[An Ultra Step-Up Non-Pulsating Input-Current DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083854)|K. Varesi; H. Rouin|10.1109/ICTEM56862.2023.10083854|Common-ground;DC-DC converter;non-pulsating input-current;ultra-step-up;voltage stress;Maximum power point trackers;Renewable energy sources;Low voltage;Prototypes;DC-DC power converters;Switches;Control systems|
|[Resiliency-Oriented Planning of Smart City Energy Infrastructure, Considering Energy Hubs, Based on Prioritized Critical Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084210)|A. Fallahsabet; M. Nozarian; A. Fereidunian|10.1109/ICTEM56862.2023.10084210|Resiliency;Energy hub;Smart city energy infrastructure;Critical load;nan|
|[Optimal Service Restoration with Repair Crew and Mobile Power Source Scheduling: A Step Towards a Smarter Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084137)|Q. Asadi; A. Ashoornezhad; H. Falaghi; M. Ramezani|10.1109/ICTEM56862.2023.10084137|Service restoration (SR);distribution network;repair crews;mobile power source (MPS);smart grid;Renewable energy sources;Uncertainty;Switches;Optimal scheduling;Distribution networks;Microgrids;Maintenance engineering|
|[Long- and Short-term Prediction of Bitcoin Energy Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084150)|A. Ghadertootoonchi; M. Bararzadeh; M. Fani|10.1109/ICTEM56862.2023.10084150|Blockchain;Bitcoin energy consumption;Hash rate;Machine learning;Bitcoin energy consumption prediction;Energy consumption;Recurrent neural networks;Neurons;Estimation;Bitcoin;Proof of Work;Market research|
|[Assessing the effect of biomass generation technologies on a hybrid AC/DC microgrid resilience enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084069)|S. A. Hosseini; M. E. Parast; M. Bagheri-Sanjareh; M. H. Nazari; S. H. Hosseinian|10.1109/ICTEM56862.2023.10084069|ACIDC microgrids;resilience strengthen;scenario-based optimization;minimax regret;biomass technology;Measurement;Uncertainty;Biological system modeling;Stochastic processes;Microgrids;Linear programming;Combustion|
|[Short-Term Forecasting of Wind Turbine-Generated Power in the Presence Of Actuator Fault](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084233)|H. Habibzadeh; B. M. Javidi; S. Motarabbesoun; M. Dinparast; H. Kharrati|10.1109/ICTEM56862.2023.10084233|energy management system;power forecasting;wind turbine;renewable energy;MLP Neural Network;nan|
|[Combination of Porous Layer and Jet impingement in an Annular Heat sink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083794)|M. Mashhadi Keshtiban; M. Zabetian Targi; M. M. Heyhat|10.1109/ICTEM56862.2023.10083794|Heat Sink;Porous Material;Heat Transfer;Energy Management;Fluids;Shape;Metal foam;Fluid flow;Hydraulic systems;Thermal management;Behavioral sciences|
|[Power Factor Correction of Parallel-Connected Boost Converter Utilizing a Fuzzy Logic-Based Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083763)|A. Farhadi; S. Mohammadi; S. A. Hosseini; M. M. Shahbazi; M. H. Moradi|10.1109/ICTEM56862.2023.10083763|Power Factor Correction (PFC);Fuzzy Logic Controller;continuous conduction mode (CCM);Boost Converter;traditional controllers;Proportional-Integral (PI);Fuzzy logic;Reactive power;Software packages;Electromagnetic interference;Transforms;Power factor correction;Solids|
|[A comprehensive study of optimal demand management for a distributed network with the EV charging stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084262)|M. H. Norouzi; M. Gholami; R. Noroozian|10.1109/ICTEM56862.2023.10084262|Electric vehicles (EVs);Green energy;Genetic algorithm (GA);Vehicle to grid(V2G);Distributed generations (DGs);nan|
|[Asymmetric resonance phenomenon in transmission lines equipped with compensation reactors and study about destructive effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083967)|M. Khaleghi; M. H. Norouzi|10.1109/ICTEM56862.2023.10083967|Overvoltage;Asymmetric Resonance;Shunt Reactor;Compensation Lines;Transposed Lines;Ferranti Effect;Passive Fault Introduction;Fault diagnosis;Shunts (electrical);Power transmission lines;Transmission line matrix methods;Resonant frequency;Conductors;Inductors|
|[Optimal Operation of Reconfigurable Active Distribution Networks Aiming at Resiliency Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083918)|S. Behzadi; A. Bagheri; A. Rabiee|10.1109/ICTEM56862.2023.10083918|ADN;Resiliency;DG;ESS;Reconfiguration;Grid formation;Energy loss;Systems operation;Optimal scheduling;Load shedding;Programming;Minimization;Mathematical models;Power system reliability|
|[P2P Strategy for Energy Cost Reduction in Multi-Energy Hubs Considering Uncertainty and Flexibility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083531)|S. Nojavan; E. Valipour|10.1109/ICTEM56862.2023.10083531|Peer-to-peer strategy;Load shiftin;Distributed energy resources;Uncertainty modeling;nan|
|[Improvement in LFC Performance of Dual Area Thermal Hydro System with Territory Control of TCPS and Redox Flow Battery Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083711)|N. A. Kumar; M. K. Kumar; B. S. Goud; C. N. S. Kalyan; H. Shahinzadeh; A. Hafezimagham|10.1109/ICTEM56862.2023.10083711|DATH system;Water cycle technique;TIDN controller;10%SLP;TCPS-RFBs strategy;Thyristors;System dynamics;Phase shifters;Redox;Control systems;Stability analysis;Batteries|
|[Reliable Operation of V2G-Equipped Parking Lots Based on Probabilistic Mobility Patterns of Plug-in Hybrid Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084190)|H. Shahinzadeh; J. Moradi; A. Hafezimagham; G. B. Gharehpetian; M. Benbouzid; S. M. Muyeen|10.1109/ICTEM56862.2023.10084190|Parking Lots;Plug-in Hybrid Electric Vehicles (PHEV);Smart grids;Vehicle-to-Grid (V2G);Mobility Patterns;Electricity Markets;Data Analytics;Probabilistic;Vehicle-to-grid;Costs;Distribution networks;Probabilistic logic;Data models;Batteries;Reliability|
|[Two-dimensional hexagonal sheet of TiO2: a promising candidate for use as anode material in Li-ion batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083862)|H. A. Eivari|10.1109/ICTEM56862.2023.10083862|Li battery;energy capacity;adsorption energy;safety;two dimensional hexagonal sheet;Lithium-ion batteries;Adsorption;Lithium batteries;Electric vehicles;Safety;Titanium dioxide;Anodes|
|[Optimal Energy Operation in DC Microgrids Including Hydro-Pumped Storage in the presence Demand Response Program](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083898)|S. Nojavan; A. Attar|10.1109/ICTEM56862.2023.10083898|Microgrid;Optimal Energy Operation;Hydro-Pumped Storage;uncertainty;Demand Response Program;Renewable energy sources;Costs;Uncertainty;Microgrids;Probabilistic logic;Software;Planning|
|[Restructured High-Gain DC-DC Converter with Improved Reliability and Reduced Failure-Rate of Filter-Capacitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084055)|M. K. Eslamloo; H. Damiri; K. Varesi|10.1109/ICTEM56862.2023.10084055|dc-dc converter;failure-rate;filter-capacitor;high-gain;reliability;Analytical models;Simulation;Capacitors;DC-DC power converters;Boosting;Capacitance;Steady-state|
|[PV-Fed Transformer-Less Five-Level Grid-Tied Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083895)|A. Zeinaly; K. Varesi; J. F. Ardashir|10.1109/ICTEM56862.2023.10083895|Grid-tied;multi-level inverter;switched-capacitor;transformer-less;Analytical models;Total harmonic distortion;Capacitors;Prototypes;Switches;Boosting;Transformers|
|[Cost optimization for hydrogen integration in energy portfolio based on water-energy and hydrogen nexus in economic zones; case study: Chabahar free zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083521)|E. Hasannezhad; H. Moqtaderi|10.1109/ICTEM56862.2023.10083521|Hybrid Energy Systems;Hydrogen Storage;Hydrogen Production;Optimization;Power to Gas;Renewable Energy;Sustainable Development of the Industrial Zone;nan|
|[Effect of PV roof coverage on the lighting availability, heating, and cooling demands for a Venlo greenhouse in Tehran](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083896)|A. Solaimanian; R. Roshandel|10.1109/ICTEM56862.2023.10083896|Greenhouse energy demand;Greenhouse energy system;Photovoltaic panels;solar energy;Renewable energy sources;Cooling;Lighting;Greenhouses;Production;Solar panels;Solar heating|
|[Assessment of Cyber Security in Renewable Electricity Market Considering System Reliability Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083662)|D. T. Rizi; M. H. Nazari; M. Fani; S. H. Hosseinian|10.1109/ICTEM56862.2023.10083662|Cyber security;reliability;data science;power market;machine learning;Machine learning algorithms;Neural networks;Wind power generation;Electricity supply industry;Software;Security;Reliability|
|[Optimal Energy Management of a Parking Lot in the Presence of Renewable Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084143)|M. Ghahramani; M. Abapour|10.1109/ICTEM56862.2023.10084143|Intelligent parking lot;Local generation sources;Electric vehicle;Demand response program;Renewable energy sources;Costs;Pollution;Greenhouse effect;Microgrids;Production;Electric vehicles|
|[Modified Cuckoo Optimization Algorithm for Frequency Regulation of Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083797)|A. F. Abriz; A. A. Ghavifekr; M. Soltaninejad; A. Tavana; A. Safari; S. Ziamanesh|10.1109/ICTEM56862.2023.10083797|Microgrids;Cuckoo Optimization Algorithm;Frequency Regulation;PID Controllers;Stability Margin;Renewable energy sources;Uncertainty;Simulation;Microgrids;Evolutionary computation;Cost function;Stability analysis|
|[Simultaneous investigation of the increase in organic load of sediment using spirulina algae powder and increase in catholyte conductivity on sediment microbial fuel cell performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083897)|H. Soleimani; M. Rahimnejad; M. Mashkour|10.1109/ICTEM56862.2023.10083897|Sediment microbial fuel cell;Power density;Spirulina algae powder;Conductivity of the catholyte;Power system measurements;Powders;Density measurement;Algae;Fuel cells;Conductivity;Sediments|
|[Local Peer-to-Peer Energy Trading Evaluation in Micro-Grids with Centralized Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084074)|A. Aminlou; M. M. Hayati; K. Zare|10.1109/ICTEM56862.2023.10084074|Transactive energy;Peer-to-peer;Battery energy storage;Blockchain;RES;Centralized;Energy market;CO2 emission;Renewable energy sources;Costs;Power transmission lines;Energy resources;Systems operation;Simulation;Propagation losses|
|[Optimal Design of 20 KW Grid-Connected Solar Power Plant for Maximizing Solar Radiation in Tabriz utilizing PV syst Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084263)|E. Soltani; M. M. Hayati; A. Aminlou; K. Zare|10.1109/ICTEM56862.2023.10084263|solar energy;optimum angle;photovoltaic system;Pvsyst;nan|
|[A Two-Stage Stochastic Optimization Scheduling Approach for Integrating Renewable Energy Sources and Deferrable Demand in the Spinning Reserve Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084163)|M. M. Hayati; A. Aminlou; K. Zare; M. Abapour|10.1109/ICTEM56862.2023.10084163|stochastic scheduling;renewable energy sources;load aggregator;energy storage system;spinning reserve market;deferrable demand;Renewable energy sources;Costs;Heuristic algorithms;Stochastic processes;Pricing;Probability density function;Real-time systems|
#### **2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)**
- DOI: 10.1109/OSMSES58477.2023
- DATE: 27-29 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[The DeMaDs Open Source Modeling Framework for Power System Malfunction Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089746)|D. Fellner; T. I. Strasser; W. Kastner|10.1109/OSMSES58477.2023.10089746|Data-driven approach;malfunction detection;modeling and simulation;electric power systems;smart grids;nan|
|[Reduced-Order Synchronous Generator Modelling for Real- Time Simulation using Shifted Frequency Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089718)|J. Dinkelbach; M. Moraga; A. Monti|10.1109/OSMSES58477.2023.10089718|Reduced-order modelling;interfacing techniques;synchronous generator;voltage-behind-reactance;real-time simulation;Shifted Frequency Analysis;nan|
|[FlexPlan.jl - An open-source Julia tool for holistic transmission and distribution grid planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089624)|M. Rossini; H. Ergun; M. Rossi|10.1109/OSMSES58477.2023.10089624|Holistic planning;mixed-integer optimisation;transmission grid planning;distribution grid planning;demand flexibility;storage;hvdc;nan|
|[PyAPI-RTS: A Python-API for RSCAD Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089671)|M. Weber; J. Enzinger; H. K. Çakmak; U. Kühnapfel; V. Hagenmeyer|10.1109/OSMSES58477.2023.10089671|Real-time Simulation;Workflow Automation;Python;nan|
|[Open Source Simulation Software pandapower and pandapipes: Recent Developments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089685)|R. Bolgaryn; G. Banerjee; S. Meinecke; H. Maschke; F. Marten; M. Richter; Z. Liu; P. Lytaev; B. Alfakhouri; J. Kisse; D. Lohmeier|10.1109/OSMSES58477.2023.10089685|power system modeling;power system protection;grid calculation;automation;short circuit calculation;temperature-dependent power flow;FACTS devices;gas grid modeling;district heating;nan|
|[Introducing PROOF - A PROcess Orchestration Framework for the Automation of Computational Scientific Workflows and Co-Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089680)|J. Liu; M. Chlosta; N. Schaber; J. Sidler; R. Lutz; T. Schlachter; V. Hagenmeyer|10.1109/OSMSES58477.2023.10089680|Scientific Workflow;Co-Simulation Framework;Automation;Smart grids;Modular Couplings;Energy System;nan|
|[Energy Systems Test Case Discovery Enabled by Test Case Profile and Repository](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089649)|P. Raussi; J. Kamsamrong; A. Paspatis; K. Heussen; T. A. Zerihun; E. Widl; F. P. Andrén; J. H. Kazmi; T. I. Strasser; F. Castro; L. Pellegrino|10.1109/OSMSES58477.2023.10089649|Energy system;test cases;test case discovery;test case profile;test case repository;nan|
|[Market integration analysis of heat recovery under the EMB3Rs platform: An industrial park case in Greece](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089644)|A. S. Faria; T. Soares; G. Goumas; A. Abotzios; J. M. Cunha; M. Silva|10.1109/OSMSES58477.2023.10089644|Thermal Market;P2P;EMB3Rs Platform;District Heating;nan|
|[DigitalEnergyTestbed: An Open Testbed Prototype for Integrated Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089634)|E. Widl; A. Sporr; R. -R. Schmidt; T. Natiesta; N. Marx|10.1109/OSMSES58477.2023.10089634|integrated energy system;testing;validation;hardware-in-the-loop;simulation;open source;nan|
|[Load and generation time series for German federal states: Static vs. dynamic regionalization factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089686)|M. Sundblad; T. Fürmann; A. Weidlich; M. Schäfer|10.1109/OSMSES58477.2023.10089686|energy system data;regionalization factors;generation time series;load time series;nan|
|[Rapid development and execution of complex agent-based energy system simulations: the FAME framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10089770)|U. J. Frey; C. Schimeczek; M. Deissenroth-Uhrig; B. Fuchs; K. Nienhaus|10.1109/OSMSES58477.2023.10089770|agent-based modelling;framework;simulation;energy system;nan|
#### **2023 International Conference on Power, Instrumentation, Energy and Control (PIECON)**
- DOI: 10.1109/PIECON56912.2023
- DATE: 10-12 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Three-Phase Dynamic AC Braking of Induction Motors by Discontinuous Phase-Controlled Switching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085833)|M. S. Jamil Asghar|10.1109/PIECON56912.2023.10085833|Induction motors;AC Dynamic braking;three-phase AC regulators;speed control;transient negative torque;discontinuous phase controlled switching (DPC);Torque;Regulators;Stator windings;Rotors;Switches;Control systems;Software|
|[Simulation Analysis Of Conventional Building To Convert It Into Net Zero Energy Building](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085769)|Y. Kumar; D. T. Thakur; D. S. Saha|10.1109/PIECON56912.2023.10085769|Building Energy Modelling;eQUEST;Energy Saving;Energy Performance Index;Analytical models;Renewable energy sources;Energy consumption;Instruments;Buildings;Energy measurement;Carbon dioxide|
|[Optimized Electric vehicle Charging and discharging with sporadic Renewable energy source](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085780)|S. Sharma; I. Ali|10.1109/PIECON56912.2023.10085780|Electric Vehicle;Vehicle-to grid;Grid-to-vehicle;Genetic algorithm;Photovoltaic;Vehicle-to-grid;Renewable energy sources;Smoothing methods;Stochastic processes;Microgrids;Scheduling;Real-time systems|
|[Robust and potential applications of memristors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085896)|Z. Mustaqueem; A. Quaiyum Ansari; M. W. Akram|10.1109/PIECON56912.2023.10085896|memristor;SRAM cell;Logic circuits;Oscillators;Applications;Electric potential;Nonvolatile memory;Scalability;Instruments;Memristors;Titanium;Market research|
|[Distribution System Reliability, Voltage Profile, and Power Losses Affected by EVCS Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085866)|V. K. Chaudhary; K. Sahay; M. K. Singh|10.1109/PIECON56912.2023.10085866|EV charging stations;Radial distribution network;charging strategy;power losses;reliability;voltage profiles.;Greenhouse effect;Transportation industry;Transportation;Programming;Electric vehicle charging;Power system reliability;Random processes|
|[Real-Time Simulation of Battery ElectricVehiclewith PI Controller Tuned by Particle Swarm Optimization (PSO) algorithm using OPAL-RT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085822)|H. Naz; M. Jamil; S. Kirmani|10.1109/PIECON56912.2023.10085822|Battery ElectricVehicle;PMSM;Particle Swarm Optimization;Real Time (OP4510) Simulator;OPAL-RT;PI control;Software packages;Heuristic algorithms;Electric vehicles;Mathematical models;Real-time systems;Batteries|
|[Solution of the Integro-Differential Equations related to Computational electromagnetics using Haar Wavelets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085874)|A. Iqbal; A. Q. Ansari; A. Kumar|10.1109/PIECON56912.2023.10085874|Poisson’s equation;Laplace equation;Wavelet;Haar Wavelet;Electric potential;Systematics;Laplace equations;Partial differential equations;Instruments;Electromagnetic scattering;Capacitors|
|[Analysing the Voltage Sensitivity in the Distribution Grid in Terms of Charging and Discharging of Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085794)|N. Kardam; S. K|10.1109/PIECON56912.2023.10085794|Distribution networks;Electric vehicle;SoC;Photovoltaic system;Renewable energy sources;Sensitivity;Instruments;Production;Discharges (electric);Power systems;State of charge|
|[Genetic Algorithm Based SPV System with Cascaded H-Bridge Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085864)|A. F. Minai; A. A. Khan; M. A. Siddiqui; F. I. Bakhsh; M. A. Hussain; R. K. Pachauri|10.1109/PIECON56912.2023.10085864|Genetic algorithm;maximum power;Cascaded H-bridge multilevel inverter;Total harmonic distortion;Renewable energy sources;Reactive power;Instruments;Switching loss;Pulse width modulation;Multilevel inverters|
|[Multi-Stage Binary Classification Technique for Incipient Fault Diagnosis of Oil Immersed Power Transformers Based on ANFIS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085900)|B. Jan; O. Rahman; S. Parveen; S. A. Khan|10.1109/PIECON56912.2023.10085900|Incipient faults;Transformers;DGA;Binary classification;ANFIS;Partial discharges;Sensitivity;Oils;Instruments;IEC;Dissolved gas analysis;Oil insulation|
|[A Simple Fringing Field Impedance Sensor to Measure the Quality of Toned Milk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085768)|A. Salamat; T. Islam; S. Sohail|10.1109/PIECON56912.2023.10085768|impedance;fringing;milk;sensor;Water;Surface impedance;Dairy products;Surface contamination;Capacitance;Water pollution;Sensors|
|[Performance analysis of various deep learning techniques for brain tumor classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085796)|P. Jaidka; S. Jain|10.1109/PIECON56912.2023.10085796|Brain Tumor;ensemble learning;Neural network;Logistic regression;SVM;Deep learning;Magnetic resonance imaging;Instruments;Manuals;Feature extraction;Performance analysis;Classification algorithms|
|[Ultra-Wide band Radar System Assembly across the Obstacle for Human Vital Signs Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085820)|M. Verma; M. Khan|10.1109/PIECON56912.2023.10085820|Ultrawideband;RADAR;NLOS;Doppler Sensor;Location awareness;Power system measurements;Source separation;Radar detection;Throughput;Time measurement;Ultra wideband radar|
|[Windoku Based Solar PV Array One Time Reconfiguration for Maximum Power Enhancement under Partial Shading Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085907)|K. Khatri; M. M. Shadab|10.1109/PIECON56912.2023.10085907|Fill Factor;Maximum Power Point;Partial Shading Condition;Power Loss;Analytical models;Power measurement;Fill factor (solar cell);Instruments;Mathematical analysis;Bridge circuits;Benchmark testing|
|[Adaptive integral velocity sliding mode control approach-based optimal AGWO-IMC-PID controller design and LFC for LSPS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085790)|R. Singh; J. Kumar; J. Singh; A. Singh|10.1109/PIECON56912.2023.10085790|Support Value;Adaptive Grey Wolf Optimization;Reduced Order Model (ROM);Load Frequency Control (LFC);power system;and IMC-PID Controller Design.;Adaptation models;Adaptive systems;Simulation;Computational modeling;Stability criteria;Power system stability;Approximation methods|
|[Control and Mitigation of Harmonics and Power Filtering in Photovoltaic Grid Tied System Using MFI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085875)|A. W. B. Barnawi; M. S. Arafath; J. K. Bhutto; M. A. Muqeet; A. A. A. Mohammed; M. S. Habeeb; A. A. Aljohani|10.1109/PIECON56912.2023.10085875|P-V system;MFIs;Phase Locked Loop;Active Power filters;Grid system;Photovoltaic systems;Filtering;Switching frequency;Harmonic analysis;Active filters;Control systems;Inverters|
|[Optimal Allocation of V2G Stations in a Microgrid Environment: Demand Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085813)|S. Kirmani; W. U. H. Paul; M. B. Bhat; I. Akhtar; A. S. Siddiqui|10.1109/PIECON56912.2023.10085813|Vehicle to grid;electric vehicles;microgrid;renewable energy integration;energy storage system;Vehicle-to-grid;Renewable energy sources;Wind;Supply and demand;Wind power generation;Wind turbines;Resource management|
|[Advancement and Control of Power Quality using Hybrid GoA-FPA approach for Grid Tied PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085767)|A. W. B. Barnawi; M. S. Arafath; J. K. Bhutto; A. S. Shaik; A. A. A. M. A. Jan; M. S. Habeeb; A. R. A. Alharbi|10.1109/PIECON56912.2023.10085767|Fractional Order-PI Controller;optimization Algorithms;Grid Connected PV;Harmonic Mitigation;Power Quality.;Radiation effects;Power quality;Solar energy;Power system harmonics;Harmonic analysis;Inverters;Planning|
|[3D-Block Partitioning Embedded Coding for Hyperspectral Image Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085841)|H. Chandra; S. Bajpai|10.1109/PIECON56912.2023.10085841|Hyperspectral Image *;Set Partitioned Hyperspectral Image Compression *;Wavelet Transform *;Coding Complexity *;Array Structure;Image sensors;Wavelet transforms;Image coding;Instruments;Memory management;Transform coding;Parallel processing|
|[A Review on Non-Intrusive Load Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085808)|P. K. Sonwani; M. Swarnkar; G. Singh; A. Soni; A. Swarnkar; K. R. Niazi|10.1109/PIECON56912.2023.10085808|Intrusive load monitoring;non-intrusive load monitoring;load disaggregation;Load monitoring;Event detection;Instruments;Hardware;Sensors;Data mining|
|[Economic feasibility of SPV integrated EV Charging Infrastructure for NIT Srinagar Institutional Campus in J&K, India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085893)|N. A. Ganie; Z. H. Rather; Z. Farooq; A. Rahman|10.1109/PIECON56912.2023.10085893|Electric-Vehicle charging infrastructure (EVCI);Solar photovoltaic (SPV);Distributed generation;Helioscope;HOMER;Economic feasibility;Photovoltaic systems;Renewable energy sources;Pollution;Instruments;Maintenance engineering;Market research;Electric vehicle charging|
|[Power Quality Improvement in Transmission Line by using DPFC-ZSI Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085884)|P. Roy; P. Pal|10.1109/PIECON56912.2023.10085884|ZSI;DPFC;DPFC-ZSI;3-Phase Converter;PSCAD;Economics;Reactive power;Power transmission lines;Power quality;Flexible AC transmission systems;Inverters;Topology|
|[ANN-Tuned PID Controller for LFC Investigation in Two-Area Interconnected System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085793)|R. K. Singh; V. Verma|10.1109/PIECON56912.2023.10085793|ANN;LFC;OVPL;PID Controller;PI control;Power system dynamics;Process control;Artificial neural networks;Interconnected systems;Superluminescent diodes;Regulation|
|[Deterministic approach-based energy management of smart microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085904)|N. Singh; P. Paliwal|10.1109/PIECON56912.2023.10085904|Community microgrids;demand response;energy pool;deterministic optimization;Renewable energy sources;Costs;Smart cities;Microgrids;Batteries;Smart grids;State of charge|
|[Symbiotic Organisms Search Algorithm to Optimally Allocate EV Charging Station in Radial Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085761)|P. Das; D. Das; A. R. Bhowmik; P. Das|10.1109/PIECON56912.2023.10085761|Active Power Loss Minimization;Electric Vehicles;Electric Vehicle Charging Station;Particle Swarm optimization;Radial Distribution Network;Symbiotic Organisms Search;Symbiosis;Transportation;Distribution networks;Search problems;Organisms;Electric vehicle charging;Power systems|
|[Design of Missile Roll Autopilot based on Quantitative Feedback Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085891)|J. V. Jacob; M. M. Gade; R. Jeyasenthil; Samran|10.1109/PIECON56912.2023.10085891|Non-linear control;QFT;Robust control;Disturbance rejection;Control in missiles;Missiles;Adaptation models;Attitude control;Instruments;Frequency-domain analysis;Design methodology;Stability analysis|
|[IOT Based Smart Wheelchair for Elderly Healthcare Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085814)|R. A. Dar; S. Khatoon; B. Saleem; H. Khan|10.1109/PIECON56912.2023.10085814|internet of things (IoT);smart wheelchair;Arduino;Healthcare Monitoring;sensors;Temperature sensors;Temperature measurement;Cloud computing;Technological innovation;Wheelchairs;Medical services;Sensor systems|
|[Design of HERIC Topology Based grid-tied Single Phase Transformerless Photovoltaic Inverter to Minimize Leakage Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085753)|M. A. Alam; S. V. A. V. Prasad; D. M. Asim|10.1109/PIECON56912.2023.10085753|Common mode voltage;PV inverter;leakage current;H4 topology;H5 topology;HERIC topology;Photovoltaic systems;Renewable energy sources;Modulation;Phase transformers;Inverters;Topology;Leakage currents|
|[Variable Parameter Based New Control Approach for Three- Phase Four Wire Grid-Tied PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085823)|M. Ibrahim; T. Khan; F. I. Bakhsh|10.1109/PIECON56912.2023.10085823|Adaptive control;Grid-integration;PV system;Inverter;Three-phase four-wire;Software packages;Instruments;Wires;Power quality;Control systems;Inverters;Steady-state|
|[Comparative Analysis Of Solar Fed DC-DC Converter Controlled With Different Types Of MPPT Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085886)|F. Khan; A. Tariq|10.1109/PIECON56912.2023.10085886|Photovoltaic (PV);Maximum Power Point Tracking (MPPT);Incremental conductance (INC);Fuzzy Logic Controller (FLC);Non-Isolated DC-DC converters;Maximum power point trackers;Fuzzy logic;Photovoltaic systems;Radiation effects;Temperature;DC-DC power converters;Topology|
|[Design and Study of Cylindrical Quad Electrode for Testing Water Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085782)|S. A. Ali; S. Hussain; M. Shakir; M. R. Mahboob; T. Islam|10.1109/PIECON56912.2023.10085782|Conductivity;Water Quality;Cylindrical Structure;quad Electrodes;Electrodes;Power measurement;Instruments;Water quality;Conductivity;Conductivity measurement;Software|
|[Jaya optimization-based approximation of LTI systems using stability equations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085851)|S. Gehlaut; D. Kumar; C. N. Singh; A. K. Gupta|10.1109/PIECON56912.2023.10085851|Integral square error;Jaya optimization;reduced order model;stability equation;Linear systems;Time-frequency analysis;Instruments;Stability criteria;Integral equations;Mathematical models;Numerical models|
|[A review of Market Based Economic Dispatch in India for uniform electricity pricing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085854)|R. Gupta; P. Jain|10.1109/PIECON56912.2023.10085854|Indian power sector;Market Based Economic Dispatch;Bilateral Contract Settlement;Central Electricity Regulatory Commission;Power Exchanges;Electric potential;Costs;Dams;Instruments;Pricing;Companies;Power markets|
|[Active Disturbance Rejection Control for Time Varying Disturbances: Comparative Study on a DC-DC Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085861)|F. Haider; A. Ali|10.1109/PIECON56912.2023.10085861|Active disturbance rejection control (ADRC);cascade extended state observer (CESO);generalized proportional integral observer (GPIO);Robust control;Uncertainty;Observers;Attenuation;Load management;Voltage control;Vehicle dynamics|
|[A Practical PHEV Charging Policy to Improve Performance of Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085850)|D. Khandelwal; S. Bhowmick; S. Painuli; R. Saha|10.1109/PIECON56912.2023.10085850|charging policies;daily load profile;distribution system;EVs;NHTS;Instruments;Power quality;Power distribution;Transportation;Internal combustion engines;Distribution networks;Power system stability|
|[Reduction of Bit Error Rate (BER) and Mean Square Error (MSE) in MIMO-OFDM System Using SUI and ETU Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085723)|M. Khan; S. Akbari; K. I. Sherwani|10.1109/PIECON56912.2023.10085723|MIMO-OFDM;ICI;SUI;ETU;TDS-OFDM;BER;MSE;IEEE 802.16;Wireless communication;OFDM;Bit error rate;Modulation;Interference;Time-varying channels;Channel models|
|[Design and Implementation of Autonomous Underwater Vehicles’ Software Stack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085802)|D. Singh; K. Masood; N. Jamshed; Y. Farooq; Y. Hasan; H. Ahmad|10.1109/PIECON56912.2023.10085802|Jetson Xavier NX;Autonomous Underwater Vehicle;Pixhawk;YOLO;Proportional-Integral-Derivative;Pressure sensors;Autonomous underwater vehicles;Machine learning algorithms;Navigation;Software algorithms;Full stack;Object detection|
|[Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085847)|J. Ahmad; C. -H. Lin; M. S. Khan; H. -D. Liu; M. Fahad|10.1109/PIECON56912.2023.10085847|DC/DC;High-Frequency Transformer;Electrical Isolation;nan|
|[Autonomous Underwater Vehicles’ Control System Design Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085754)|M. H. Zamir; A. Parveen; M. H. Akhtar; A. Jalil|10.1109/PIECON56912.2023.10085754|Control System;Autonomous Underwater Vehicle;Geo science;Proportional-Integral-Derivative;Autonomous underwater vehicles;Costs;Oceans;Instruments;Control systems;Reliability engineering;Genetics|
|[The influence of Aging on the Evolved Gases in a Liquid-Immersed In-Service Transformer: A DGA Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085760)|M. Singh; V. Jindal|10.1109/PIECON56912.2023.10085760|cellulosic insulation;dissolved gas analysis;insulating oil;power transformer;Oils;Systems operation;Cellulose;Transforms;Oil insulation;Aging;Dissolved gas analysis|
|[Overview of EV Charging Smart Control Architecture-Recent Developments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085827)|B. Faujdar; R. K. Pandey|10.1109/PIECON56912.2023.10085827|Electric Vehicle (EV);Renewable Energy (RE);Energy Storage Units (ESU);Photovoltaic (PV) System;EV Charging Infrastructure (EVCI);Smart Control Architecture for EV (SCAEV);State of Charge (SoC);Energy Storage System (ESS);Power supplies;Instruments;Power control;Transportation;Electric vehicle charging;Real-time systems;Reliability|
|[Optimal Deceleration Zone of EV with an Improved Parallel Regenerative Braking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085812)|S. Mondal; A. K. Nandi|10.1109/PIECON56912.2023.10085812|driving strategy;optimal deceleration zone;regenerative braking;multi-objective optimization;Systematics;Instruments;Software algorithms;Minimization;Software;Table lookup;State of charge|
|[Parameter Extraction Of Organic Solar Cell: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085878)|M. Uvais; A. J. Ansari; M. Asim|10.1109/PIECON56912.2023.10085878|Organic Solar Cell;Parameter Extraction;Classical Methods;Innovative Evolutionary Methods;Photovoltaic systems;Resistance;Costs;Pollution;Photovoltaic cells;Production;Solar energy|
|[Maximum Power Point Tracking (MPPT) Control of Grid Connected Solar-Wind Energy Conversion System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085842)|D. S. Yadav; J. Ali|10.1109/PIECON56912.2023.10085842|PMSG;Wind turbine;PV Array;Grid;MPPT;DC/DC Boost Converter;Maximum power point trackers;Fluctuations;Voltage fluctuations;Wind energy;Wind speed;Energy conversion;Synchronous generators|
|[Performance Analysis and Loss Estimation of an AC-DC PFC Topologies of an EV Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085786)|S. Gupta; V. V. Kumar|10.1109/PIECON56912.2023.10085786|ac-dc converter;power factor correction (PFC) topologies;boost rectifier circuit;EV chargers;Performance evaluation;Reactive power;Total harmonic distortion;Software packages;Simulation;Switching loss;Switches|
|[Performance Analysis of 400 kWp Rooftop Solar Plant at Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur using PVsyst](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085828)|T. Rawat; J. Singh; S. Sharma|10.1109/PIECON56912.2023.10085828|Solar;renewable;PVsyst;grid-connected;Analytical models;Technology management;Simulation;Instruments;Production;Software;Inverters|
|[PVsyst Based Comparative Analysis of 1 MW Solar Plant in Jaipur and Dehradun](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085836)|T. Rawat; J. Singh; S. Sharma|10.1109/PIECON56912.2023.10085836|Solar;renewable;PVsyst;grid-connected;Photovoltaic systems;Economics;Analytical models;Simulation;Instruments;Production;Solar energy|
|[High Voltage Motor Current Switching tests: It’s Motive & Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085817)|K. S. Kumar; D. Sahoo; Y. Agrawal; M. S. Takkher|10.1109/PIECON56912.2023.10085817|surges;motor switching;inductive circuit;etc;Insulation;Induction motors;Windings;Surge protection;Switches;High-voltage techniques;Surges|
|[Virtual HIL Simulation of Grid-tied PV Inverter with Hysteresis Current Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085771)|N. Kumar; S. M. Tripathi; V. K. Govil|10.1109/PIECON56912.2023.10085771|EN 50530 standard;Boost converter;Grid-tied inverter;MPPT(P&O);VHIL simulation environment;unity power factor operation;Current control;Reactive power;PI control;Instruments;Power system dynamics;Inverters;Power grids|
|[Design and Development of a High Gain DC-DC Converter Suitable for Renewable Energy Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085871)|S. Ahmad; A. S. Anees; J. Bashir|10.1109/PIECON56912.2023.10085871|High gain dc-dc converter;PWM;Continuous current;Solar PV system;Renewable energy sources;Prototypes;Switches;High-voltage techniques;Pulse width modulation;Control systems;Topology|
|[Low Cost Microcontroller based Experimental Setup to Study Sinusoidal Pulse Width Modulation for Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085863)|B. Sakthisudhursun; S. Muralidharan|10.1109/PIECON56912.2023.10085863|Single Carrier;SPWM;Multilevel Inverter;PWM;Multiplexing;Microcontrollers;Simulation;Switches;Pulse width modulation;Logic gates;Multilevel inverters|
|[A Hybrid Approach for Forecasting the Technical Anomalies in Sensor-based Water Quality Distribution Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085799)|K. Chinnakkaruppan; K. Krishnamoorthy; S. Agniraj|10.1109/PIECON56912.2023.10085799|anomalous data;drinking water quality distribution;forecasting;unsupervised machine learning;Wireless communication;Performance evaluation;Wireless sensor networks;Visualization;Machine learning algorithms;Instruments;Water quality|
|[DC Connected Solar Plus Storage Systems: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085778)|K. H. Aftab; M. Y. Yaseen; M. Asim|10.1109/PIECON56912.2023.10085778|Solar PV Plus storage;DC-Connected Battery;AC-Connected Battery;Dispatch.;Instruments;Software;Inverters;Batteries;Behavioral sciences;Solar panels;System analysis and design|
|[Design & Analysis of PV Interface Hybrid Two-Stage Transformerless Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085783)|V. Singh; S. Pattnaik; V. Anand; M. Dhananjaya|10.1109/PIECON56912.2023.10085783|Two-stage converter;Inverters;Common Mode Voltage;DC link;Network topology;Software packages;Switches;Transformers;Control systems;Inverters;Robustness|
|[Image Captioning Using Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085724)|A. Singh; A. Shah; P. Kumar; H. Chaudhary; A. Sharma; A. Chaudhary; A. Dixit|10.1109/PIECON56912.2023.10085724|LSTM;CNN;TensorFlow;Neural Networks;Deep Learning;Recurrent Neural Network;VGG-16;Deep learning;Image recognition;Instruments;Neural networks;Data models;Grammar;Convolutional neural networks|
|[A Review of Bidirectional DC-DC,DC-AC Converters for V2G and G2V Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085877)|M. K. Chaitanya; A. S. Vinayak; B. J. G. Pavan; N. N. Lakshmi; K. Sandeep|10.1109/PIECON56912.2023.10085877|Bidirectional DC-DC;DC-AC converters;V2G operations;fast charging system;switching devices;operation modes;filters;Industries;Performance evaluation;Power system measurements;Vehicle-to-grid;Density measurement;Photonic band gap;Capacitors|
|[System Profit Assessment of a Solar-Integrated Deregulated Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085889)|M. Vemula; S. Dawn; A. Machagiri; S. L. Potipireddi; B. R. Bobbili|10.1109/PIECON56912.2023.10085889|nan;Renewable energy sources;Computer languages;Costs;Instruments;Electricity supply industry;Power systems;Quadratic programming|
|[Review On Design and Performance of the Medical Ventilators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085815)|H. Gaur; S. Yadav; Shashikant; A. Shahin; M. Shahid|10.1109/PIECON56912.2023.10085815|Smart ventilator;Arduino Uno;Ambu bag;Pressure sensor;Temperature sensor;Servo Motor;COVID-19;Pistons;Ventilators;State feedback;Pulmonary diseases;Sensor systems;Pressure measurement|
|[Mango Leaf disease Classification using deep learning Hybrid Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085869)|S. Jain; P. Jaidka|10.1109/PIECON56912.2023.10085869|Mango leaf disease;ensemble learning;Support Vector machine;stochastic gradient descent;Deep learning;Plant diseases;Image segmentation;Image databases;Instruments;Support vector machine classification;Hybrid power systems|
|[Effect of Unbalanced Grid Voltage Conditions on The Performance of a Variable Frequency Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085818)|M. M. Khan; Imdadullah; M. Bilal|10.1109/PIECON56912.2023.10085818|Asynchronous interconnection;Doubly Fed Induction Machine (DFIM);Variable Frequency Transformer (VFT);Voltage sag;Unbalanced grid;Reactive power;Voltage fluctuations;HVDC transmission;System performance;Simulation;Power quality;Transformers|
|[Identification of Strong and Weak Bus and Optimal Bus Coupling by L —Index Coefficients For Voltage Regulation in Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085862)|A. K. Karn; S. Hameed; M. Sarfraz|10.1109/PIECON56912.2023.10085862|voltage variation;reactive power;L – index;Y matrices;strong and weak bus;Couplings;Reactive power;Analytical models;Computational modeling;Loading;Numerical models;Indexes|
|[Order Reduction of Fixed Coefficient System Using Optimization Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085765)|A. K. Kovvuru; P. D. Dewangan|10.1109/PIECON56912.2023.10085765|Transfer Function;Grey Wolf optimization;Particle Swarm optimization;and Reduced Order Modeling;Instruments;Reduced order systems;Time factors;Particle swarm optimization;Time-domain analysis;Optimization|
|[Advancements in Security of Internet of Things Using Blockchains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085894)|S. U. Haq; A. M. Abbas|10.1109/PIECON56912.2023.10085894|Blockchain technology;IoT;edge computing;security;Privacy;Instruments;Computer architecture;Decentralized applications;Blockchains;Consensus protocol;Security|
|[Solar-Powered Wireless Charging Station for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085752)|S. Tummapudi; T. Mohammed; R. K. Peetala; S. Chilla; P. P. Bollavarapu|10.1109/PIECON56912.2023.10085752|Electric Vehicle;Wireless Power transmission;dynamic wireless charging;Atmega328p controller with LCD;Wireless communication;Inductive charging;Wires;Switches;Solar energy;Charging stations;Electric vehicles|
|[Design of a cost-effective IoT based Battery Management System for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085853)|M. H. Rehman; A. Alam; A. Q. Ansari|10.1109/PIECON56912.2023.10085853|Battery Management System;Internet of Things;Li-ion Battery;Voltage sensor;Current sensor;Temperature Sensor;Temperature sensors;Temperature measurement;Battery management systems;Electric vehicles;Real-time systems;Liquid crystal displays;Internet of Things|
|[Multi-Objective Optimal Siting and Sizing of Distributed Generation Units by Grid-Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085882)|R. A. Lone; S. J. Iqbal; A. S. Anees|10.1109/PIECON56912.2023.10085882|Renewable Energy Sources;Voltage Imbalance;Distributed Generation;Optimal Location and Sizing;Distribution Network;Industries;Renewable energy sources;Limiting;Instruments;Voltage;Linear programming;Distributed power generation|
|[Hybrid Machine Learning Algorithms for Optimal Diagnosis of Heart Disease with Feature Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085781)|G. N. Ahmad; H. Fatima; Shafiullah; M. Haris|10.1109/PIECON56912.2023.10085781|Machine Learning;Datasets;Heart Disease;DTC;AdaBoost;XGBC;K-NN;GBC;RFC;Heart;Training;Support vector machines;Machine learning algorithms;Instruments;Receivers;Prediction algorithms|
|[Design and Simulation of Stair Shape Planar Antenna Using Vias Technique for Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085855)|M. Khan; Zahreeuddin; L. Kumari; A. Salhan; M. M. Sani|10.1109/PIECON56912.2023.10085855|Patch Antenna;MBAN;Reflector;On body;biomedical signals;Stair shape;Vias;Shape;Biological system modeling;Patch antennas;Transmitting antennas;Phantoms;Bandwidth;Stairs|
|[Optimization of PID controller based on various tuning methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085805)|S. Singh; V. Singh; A. Rani; J. Yadav|10.1109/PIECON56912.2023.10085805|Ziegler Nichols;Chien-Hrones-Reswick;Astrom-Hagglund;model identification;First Order Plus Time Delay (FOPDT);2-DOF PID controller;Genetic Algorithm;Tuning of PID controller;Software packages;Delay effects;Instruments;Process control;Delays;Tuning;Optimization|
|[AVOA-based PID+IDF controller for frequency control of isolated hybrid thermal power system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085725)|T. Veerendar; D. Kumar|10.1109/PIECON56912.2023.10085725|Load frequency control;PID+IDF controller;African Vultures optimization algorithm;Hybrid power system.;Uncertain systems;Wind speed;Wind power generation;Hybrid power systems;Robustness;Thermal loading;PD control|
|[A Simple Bio-Instrumentation Platform for Vital-Sign Estimation Using MagnetoPleythsmography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085840)|K. Jishnu; C. S. Anoop|10.1109/PIECON56912.2023.10085840|Biomedical Instrumentation;Giant Magneto Resistance;Heart rate;Magnetoplethysmography;Precision envelop estimation;Respiration Rate;Vital sign monitoring;Heart rate;Instruments;Estimation;Prototypes;Packaging;Sensor systems;Hardware|
|[Observer based state feedback controller design of a DC servo motor using identified motor model: an experimental study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085764)|S. Shafi; P. S. Hamid; S. A. Nahvi; M. H. Koul; M. A. Bazaz|10.1109/PIECON56912.2023.10085764|Luenberger Observer;State feedback control;System identification;State feedback;Trajectory tracking;Transfer functions;Process control;Position control;Observers;DC motors|
|[Stimulus of Magnetic Flux in the Surface Vicinity of Electric Motors on Human Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085829)|S. S. Hooli; A. Vadde; G. R. Kadambi; K. Manickavasagam|10.1109/PIECON56912.2023.10085829|Finite Element Analysis (FEA);Finite Element Method (FEM);Squirrel Cage Induction Motor (SCIM);Leakage Flux;Flux Density;Magnetic flux;Induction motors;Brushless DC motors;Electromagnetic scattering;Magnetic domains;Transmission line measurements;Magnetic heads|
|[Optimal Power Point Detection in Dynamic Partial Shading of PV Systems Using Darts Game Optimizer Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085785)|M. Patil; K. C. Archana; A. Gopagoni|10.1109/PIECON56912.2023.10085785|Darts Game optimization;particle swarm optimization;perturb and observe (P&O);Boost converter;Partial Shading;Production systems;Heuristic algorithms;Atmospheric modeling;Simulation;Games;Solar energy;Regulation|
|[Design and Performance Evaluation of Three-Phase Grid-Tied Solar Power Generation System Using STF based Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085865)|R. Singh; V. K. Yadav; M. Singh|10.1109/PIECON56912.2023.10085865|Renewable power generation;Photovoltaic system;Maximum power point tracking;Power quality;Self-tuning filter;Photovoltaic systems;Analytical models;Adaptation models;Power quality;Power factor correction;Harmonic analysis;Synchronization|
|[A Novel Reconfiguration Approach To Reduce Power Loss In Photovoltaic Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085816)|T. A. T. Kambo; C. Saiprakash; A. Mohapatra; P. Samal; B. Nayak; S. Samal|10.1109/PIECON56912.2023.10085816|Partial Shading Conditions;Solar Photovoltaic;Global Maximum PowerPoint;Configurations;Shading Loss;Mismatch power Loss;Multiprotocol label switching;Fill factor (solar cell);Instruments;Impurities;Layout;Solar radiation;Energy harvesting|
|[Design and Dynamic Response of FOGI-FLL Based Controller in Grid Tied Solar PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085766)|S. Singh; J. N. Rai|10.1109/PIECON56912.2023.10085766|Grid Connected PV System;FOGI-FLL;Synchronization;Power Quality;Frequency locked loops;Maximum power point trackers;Costs;Power quality;Harmonic analysis;Synchronization;Voltage control|
|[Active Disturbance Rejection Control of angular position of a DC servo motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085795)|S. Shafi; P. S. Hamid; S. A. Nahvi; M. H. Koul; M. A. Bazaz|10.1109/PIECON56912.2023.10085795|Active Disturbance Rejection Control (ADRC);Extended State Observer (ESO);DC motor control;disturbance rejection;model independent control;Robust control;Uncertainty;PI control;Position control;Observers;DC motors;Mathematical models|
|[Pid Controller Tuning In Magnetic Levitation System Using Giza Pyramid Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085826)|V. Sharma; R. Bhagat; N. Kumar|10.1109/PIECON56912.2023.10085826|Genetic Algorithm (GA);Giza Pyramids Construction (GPC) algorithm;Magnetic Levitation System (MLS);Proportional Integral Derivative (PID) Controller;Particle Swarm Optimization (PSO);Heuristic algorithms;Transfer functions;Mathematical models;Steady-state;Trajectory;Particle swarm optimization;Time-domain analysis|
|[Fault detection and fault-tolerant operation of a seven-level inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085776)|A. M. Naqvi; P. Tripathi; S. P. Singh|10.1109/PIECON56912.2023.10085776|Multilevel inverter;total harmonic distortion;fault-tolerant;voltage level;switches;Fault diagnosis;Fault tolerance;Total harmonic distortion;Event detection;Simulation;Fault detection;Fault tolerant systems|
|[Power Quality Enhancement and Coordination Control of a Microgrid System connected to a Grid using Hybrid Optimization Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085750)|M. Gupta; R. K. Viral; P. M. Tiwari; A. Srivastava|10.1109/PIECON56912.2023.10085750|Stability Enhancement;Micro Grid (MG);Wind Turbine (WT);Elephant Herd Optimization (EHO);Cuckoo Search Algorithm (CS);Particle Swarm Optimization (PSO);Artificial Bee Colony (ABC) Optimization;Power quality;Power system stability;Search problems;Stability analysis;Hybrid power systems;Wind turbines;Particle swarm optimization|
|[Simulation, Design and Modeling of Lead-Free Double Halide Perovskite Solar Cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085788)|S. Sharma; R. K. Pachauri; R. Mavi; Shashikant; A. F. Minai|10.1109/PIECON56912.2023.10085788|lead-free perovskite solar cell (PSCs);double halide lead-free PSCs;wide bandgap halides (WBH);electron transport layer (ETL);hole transport layer (HTL);Analytical models;Fill factor (solar cell);Photonic band gap;Photovoltaic cells;Short-circuit currents;Lead;Perovskites|
|[Lunar Habitat Wastewater Subsystem Power and Water Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085811)|D. Saha; N. Bazmohammadi; A. Lashab; A. Lashab; J. M. Guerrero|10.1109/PIECON56912.2023.10085811|Space microgrids;lunar base;energy management system;water management;Shackleton crater;Power demand;Water storage;Power system management;Moon;Mathematical models;Storage tanks;Wastewater|
|[Performance Investigation of Bit Error Rate using mostly utilized Modulation Schemes in RoF system for the Next Generation Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085789)|B. Tamrakar; K. Singh; G. Jha; T. Garg; S. Goel; A. Sharma; S. Shukla; Y. Verma|10.1109/PIECON56912.2023.10085789|Bit Error Rate (BER);Signal to Noise Ratio (SNR);Binary Phase Shift Keying (BPSK);Quadrature Phase Shift Keying (QPSK);Quadrature Amplitude Modulation (QAM);Photo Diode (PD);Laser Diode (LD).;Instruments;Bit error rate;Modulation;Optical fiber networks;Amplitude modulation;Mathematical models;Binary phase shift keying|
|[Identification and Recognition of face and number Plate for Autonomous and Secure Car Parking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085910)|A. Kakran; Anchal; Chirag; H. Chaudhary; P. Kumar; A. Sharma; H. Singh|10.1109/PIECON56912.2023.10085910|Face identification;Security;Microcontroller;Alex Net algorithm;Automation;Embedded systems;Face recognition;Cameras;Automobiles;Security;Synchronization;Object recognition|
|[Design of Network Rejoin Policy in Multi-UAVs Flying Ad-hoc Network using Finite Time Convergent Position Control Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085905)|S. Gupta; J. K. Mohanta; S. Samanta|10.1109/PIECON56912.2023.10085905|Multi-UAV system;FANET;Self-recovery;sliding mode control;path tracking control;Performance evaluation;Surveillance;Wildlife;Stability criteria;Position control;Autonomous aerial vehicles;Ad hoc networks|
|[Complementary Su-Do-Ku Puzzle based PV Array Reconfiguration to Enhance the Global Maximum Power under Partial Shaded Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085839)|M. Saeem; S. A. Kazmi; M. Ahmad; R. K. Pachauri|10.1109/PIECON56912.2023.10085839|Solar photovoltaic system;global maximum power;power loss;complementary Su-Do-Ku;partial shading conditions;Fill factor (solar cell);Short-circuit currents;Instruments;Bridge circuits;Production;Topology;Matlab|
|[Design of PID Controller using Artificial Neural Network for Step-up Power Converter in Photovoltaic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085911)|S. Gupta; J. K. Mohanta|10.1109/PIECON56912.2023.10085911|DC-DC Converter;Artificial Neural Network;PID control;Boost Converter;Renewable Energy;Photovoltaic systems;Maximum power point trackers;Renewable energy sources;Neural networks;Process control;Color;Solar energy|
|[A Review on Computer-assisted Techniques to Analyze Histopathological Images of the Breast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085880)|S. Nagdeote; S. Prabhu|10.1109/PIECON56912.2023.10085880|Breast WSI;BRCA;CAT;Feature extraction;Stain Normalization;HIPA;Pathology;Image segmentation;Image analysis;Shape;Instruments;Optimization methods;Feature extraction|
|[Physicochemical characterisation of lignocellulosic biomass for the identification of potential candidacy towards alternative renewable energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085857)|B. Rabi Prasad; P. Suman; G. Ghosh; R. K. Padhi|10.1109/PIECON56912.2023.10085857|Biofuel;cellulose;hemicellulose;lignin and bioenergy;Moisture;Crops;Cellulose;Ash;Production;Biomass;Sulfur|
|[Intelligent Controller Design for Motion Control of Smart Wheelchair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085867)|S. A. Wani; I. Nasiruddin; M. Shahid; S. Khatoon|10.1109/PIECON56912.2023.10085867|Smart wheelchair;Cerebral palsy;Fuzzy logic controller (FLC);Speed control;Modeling;MATLAB simulation;Fuzzy logic;Wheelchairs;Velocity control;Transfer functions;Prototypes;Mathematical models;Software|
|[Analysis of SAPF based on p-q and SRF Theory for Different Supply and Load Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085722)|A. V. Barva; Arkdev|10.1109/PIECON56912.2023.10085722|Current Harmonic;p-q Theory;SRF Theory;Shunt Active Power Filter (SAPF);Total harmonic distortion;Reactive power;Power filters;Passive filters;Power quality;Harmonic analysis;Active filters|
|[A Novel Seven-Level Switched Capacitor Multilevel Inverter Topology with Common Ground Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085748)|I. Sajid; J. Sajid; M. A. Azad; A. Sarwar|10.1109/PIECON56912.2023.10085748|Multilevel inverter;switched capacitor;nearest level control (NLC);common ground configuration;voltage gain.;Renewable energy sources;Capacitors;Switches;Harmonic analysis;Boosting;Transformers;Topology|
|[Sensitivity Analysis of Generic PEMFC to Operating Temperature and Load Variations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085846)|K. Anand; A. P. Mittal; B. Kumar|10.1109/PIECON56912.2023.10085846|Efficiency Analysis;Fuel Cell Performance;Hydrogen;PEMFC;Sensitivity Analysis;Temperature Effect;Temperature sensors;Analytical models;Sensitivity analysis;Software packages;Instruments;Load management;Mathematical models|
|[Field Control Grid Connected DFIG Turbine System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085726)|F. Nasim; S. Khatoon; Ibraheem; M. Shahid|10.1109/PIECON56912.2023.10085726|Doubly fed Induction Generator;Grid side converter;DC bus voltage;Pulse Width Modulation;Field Oriented Control;etc.;Reactive power;Renewable energy sources;Wind speed;Doubly fed induction generators;Wind power generation;Pulse width modulation;Mathematical models|
|[Deep Learning-Based Approach for State-of-Health Estimation of Lithium-Ion Battery in the Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085757)|A. Niraula; J. G. Singh|10.1109/PIECON56912.2023.10085757|Battery’s health degradation factors;Battery’s State-of-Health estimation;data-driven methods for SoH estimation;Second age battery application;Support vector machines;Lithium-ion batteries;Neural networks;Estimation;Battery management systems;Electric vehicles;Data models|
|[Impact Assesment of Microgrid Towards Achieving Carbon Neutrality: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085800)|F. Danish; M. F. Shamsi; A. Kumar; Prashant; A. S. Siddiqui; M. Sarwar|10.1109/PIECON56912.2023.10085800|Carbon footprint;renewables;DERs;microgrid;Heating systems;Renewable energy sources;Power demand;Pollution;Microgrids;Generators;Topology|
|[Design of Adaptive Fuzzy PID Control Framework for 3-Axis Platform Stabilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085775)|S. Dey; T. K. S. Kumar; S. Ashok; S. K. Shome|10.1109/PIECON56912.2023.10085775|Adaptive fuzzy;Fuzzy PID;Stabilization loop;Three-axis gimbal;Inertial stabilized Platform;Missiles;Adaptation models;PI control;Adaptive systems;Target tracking;Uncertainty;Systems modeling|
|[Effect of Inter-Turn Short Circuit Fault on Performance Parameters of Permanent Magnet Synchronous Motor using Finite Element Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085899)|V. R. Aduru; V. K. Jonnalagadda; A. Sireesha|10.1109/PIECON56912.2023.10085899|PMSM;FEA;ITSC;Fault Detection;Analysis;Torque;Sensitivity;Windings;Velocity control;Permanent magnet motors;Synchronous motors;Stability analysis|
|[Design of Adaptive Network Based Fuzzy Inference PID Control Methodology For 3 Degree of Freedom Gimbal Stabilized Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085825)|S. Dey; T. K. S. Kumar; S. Ashok; S. K. Shome|10.1109/PIECON56912.2023.10085825|Adaptive network based fuzzy inference system;Artificial Neural network;Adaptive fuzzy;Fuzzy PID;Stabilization loop;Three-axis gimbal;Fuzzy logic;Missiles;Tracking loops;PI control;Adaptive systems;Target tracking;Steady-state|
|[A Performance Comparison Study of Hybrid Electric Vehicle Between Type-1 And Interval Type-2.0 FLC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085834)|Y. Shekhar; A. U. Ahmad|10.1109/PIECON56912.2023.10085834|PI controller;FLC;Interval T2.0-FLC;Electrical Power train;Super-capacitor;Fuel Cell;Electric Vehicle;Hybrid Energy Storage System;Permanent Magnet Synchronous Motor;Torque;Fuel storage;Fuel cells;Transportation;Supercapacitors;Software;Batteries|
|[Optimization Scheme for Power Transmission in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085758)|A. K. Yadav; A. Sharma; A. Yadav; A. Vijayvargiya; A. Saxena; R. Kumar|10.1109/PIECON56912.2023.10085758|Wireless Sensor Network;Optimization algorithm;Power transmission;artificial ecosystem optimizer.;Wireless communication;Wireless sensor networks;Analytical models;Ecosystems;Clustering algorithms;Power transmission;Sensors|
|[Benefits of Demand Response with Electric Vehicles in Smart Grid: A Case Study of Pattaya City, Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085819)|T. Chanraksa; J. G. Singh|10.1109/PIECON56912.2023.10085819|demand response from controllable loads;demand response from the electric vehicle;demand response from the air conditioner;smart grid project in Pattaya city;Analytical models;Atmospheric modeling;Urban areas;Pricing;Electric vehicles;Demand response;Software|
|[Biomedical Signals Classification with Transformer Based Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085908)|S. Kumar; Y. U. Khan|10.1109/PIECON56912.2023.10085908|Seizure;Fast Fourier Transform;Transformer;Bonn Dataset;Electroencephalogram;Graphics;Deep learning;Fast Fourier transforms;Instruments;Pattern classification;Inspection;Feature extraction|
|[Simultaneous Tapping of AC and DC Power and their Independent Control from Composite AC–DC Power Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085876)|O. Rahman; S. Hameed; S. Parveen|10.1109/PIECON56912.2023.10085876|Composite AC-DC power transmission;DC power tapping;HVDC;Power transmission lines;Voltage source inverters;HVDC transmission;Sociology;Power system stability;Stability analysis;Software|
|[Smart Helmet and Bike Tracking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085870)|Smriti; V. Tiwari; R. Srivastava; M. S. Sharma|10.1109/PIECON56912.2023.10085870|MQ3 sensor;FSR sensor;GSM modem;GPS modem;Smart Helmet;Microcontroller;Head;Road accidents;Instruments;Sociology;Switches;Modems;Safety|
|[Investigating the Impact of Atmospheric Factors on Solar PV Panel Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085772)|Sameera; M. Tariq; M. Rihan|10.1109/PIECON56912.2023.10085772|Photovoltaic;Solar irradiance;Atmospheric factors;Clearness index;Ambient Temperature;Tilt angle;Renewable energy sources;Temperature;Pollution;Instruments;Urban areas;Solar energy;Voltage|
|[Techniques for optimal Placement of Electric Vehicle Charging Stations: A review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085887)|S. A. Parah; M. Jamil|10.1109/PIECON56912.2023.10085887|charging station;distributed generations electric vehicles;electric vehicle charging station;Greenhouse effect;Instruments;Charging stations;Linear programming;Electric vehicle charging;Hybrid power systems;Global warming|
|[Green Technology Based Space Heating Solutions for Cold Weather Condition: A Case Study of Kashmir Valley](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085784)|A. S. Anees; S. Ahmad; R. A. Lone|10.1109/PIECON56912.2023.10085784|Renewable energy source;Solar based Passive space heating;Ground Source Heat system;Ground Heat Exchanger;Power transmission lines;Costs;Green products;Fossil fuels;Power system reliability;Reliability;Solar heating|
|[Optimal BESS Compensator Design for Fast Frequency Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085831)|S. Nema; A. Mathur; V. Prakash; H. Pandžić|10.1109/PIECON56912.2023.10085831|Ancillary services;BESS;FFR;LFC;optimization;Time-frequency analysis;Renewable energy sources;Numerical analysis;System performance;Simulation;Power system dynamics;Metaheuristics|
|[Power Quality Disturbances Detection using Multi-Resolution Analysis Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085892)|N. Singh; A. Mazhar; M. A. Ansari; A. Mishra; M. Tripathy; A. Verma|10.1109/PIECON56912.2023.10085892|MultiResolutionAnalysis(MRA);Daubechies(db);Discrete wavelet transform(DWT);Power Quality Eruptions;Wavelet transforms;Location awareness;Time-frequency analysis;Wavelet domain;Power quality;Voltage;Power system stability|
|[Integrated SPV-Battery BLDC Motor Drive Powered By Interleaved Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085901)|R. Padhi; B. P. Behera; K. B. Mohanty; P. Daramukala|10.1109/PIECON56912.2023.10085901|Solar Photo Voltaic Module;Interleaved Boost converter (IBC);MPPT;Incremental Conductance MPPT(INC);Brushless DC Motor (BLDC);Photovoltaic systems;Maximum power point trackers;Motor drives;Brushless DC motors;Atmospheric modeling;Heuristic algorithms;Mathematical models|
|[Starting Inrush Current Mitigation during Reswitching of Three-Phase Induction Motors by Discontinuous Phase-Controlled Switching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085843)|M. S. Jamil Asghar|10.1109/PIECON56912.2023.10085843|Induction motors;soft-switching;three-phase AC regulators;speed control;transient negative torque;discontinuous phase controlled switching (DPC);Thyristors;Induction motors;Regulators;Torque;Soft switching;Rotors;Switches|
|[Harmonics Analysis of Six-Phase Induction Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085909)|M. R. Khan; M. N. Akhter; M. Sartaj|10.1109/PIECON56912.2023.10085909|Decoupling transformation;inverter;MATLAB Simulink;six-phase induction motor;Induction motors;Torque;Rotors;Switches;Stators;Propulsion;Harmonic analysis|
|[Analysis of Three-Phase to Five-Phase System Under Unbalance condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085848)|A. Riyaz; S. J. Singh; S. Kumar; M. Asim; A. R. Ansari|10.1109/PIECON56912.2023.10085848|Multiphase Transformer;Passive phase transformation;Winding Connection;symmetrical components;sequence networks;Reactive power;Analytical models;Instruments;Predictive models;Transformers;Integrated circuit modeling|
|[Placement of FCS Considering Power Loss, Land Cost, and EV Population](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085898)|F. Ahmad; I. Ashraf; A. Iqbal|10.1109/PIECON56912.2023.10085898|Fast charging station;Electric vehicle;Distribution system;optimization;Algorithm;Costs;Roads;Sociology;Distribution networks;Voltage;Charging stations;Indexes|
|[Power conditioning from EVs using bidirectional chargers: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085838)|H. Fatima; M. T. Altaf; M. Jamil|10.1109/PIECON56912.2023.10085838|reactive power compensation;ancillary services;voltage regulation;peak load shaving;harmonics;filter compensator;Power conditioning;Plug-in electric vehicles;Reactive power;Vehicle-to-grid;Instruments;Bidirectional control;Transformers|
|[Single-Stage & Single-Phase Grid Integrated Solar PV System Fed with MPPT Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085751)|M. F. Rashid; A. Azhar; S. A. Khan; S. Afzal; I. Ashraf|10.1109/PIECON56912.2023.10085751|Solar Photovoltaic (SPV);Renewable Energy Sources (RESs);Maximum Power point Tracking (MPPT);Grid Synchronization;Voltage Source Inverter (VSI).;Maximum power point trackers;Photovoltaic systems;Voltage source inverters;Switching loss;Transformers;Steady-state;Synchronization|
|[A Fault Tolerant Multilevel Inverter for Off-grid Solar Photovoltaic Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085787)|H. Rehman; A. Sarwar; M. Tariq; A. I. Sarwat|10.1109/PIECON56912.2023.10085787|fault tolerant multilevel inverter;multilevel inverter;reliability;fault tolerant capability;fault reconfiguration;PV systems;Photovoltaic systems;Simulation;Fault tolerant systems;Switches;Multilevel inverters;Control systems;Topology|
|[Comparison of Conventional, Modern and Intelligent Control Techniques on UAV Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085902)|S. Qadir; S. Khatoon; M. Shahid|10.1109/PIECON56912.2023.10085902|UAV;Quadrotor;Modeling;Control algorithms;Surveillance;Instruments;Autonomous aerial vehicles;Safety;Robots;Quadrotors;Resilience|
|[An optimization model using soft computing with the randomised response technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085835)|T. A. Tarray; Z. A. Ganie|10.1109/PIECON56912.2023.10085835|stratified random sample;optimal allocation;sensitive attribute;unrelated randomized response approach;Costs;Computational modeling;Instruments;Programming;Numerical models;Resource management;Optimization|
|[A Study on Demand Response Potential from Load Profiles of Smart Household Appliances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085856)|A. Mathur; S. Nema; S. Gupta; V. Prakash; H. Pandžić|10.1109/PIECON56912.2023.10085856|Demand Response;Frequency Response;Smart Homes;Smart Grids;Smart Appliances;Home appliances;Water heating;Smart homes;Oral communication;Demand response;Power system reliability;Washing machines|
|[Hybrid Variable Frequency PWM Methods For Seven Level Asymmetrical Inverter (7LAI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085791)|V. Arun; S. Prabhu; A. A. Stonier|10.1109/PIECON56912.2023.10085791|AVFMLI;APOD;Binary Source;PD;VFPWM;Measurement;Frequency modulation;Simulation;Instruments;Pulse width modulation;Harmonic analysis;Inverters|
|[Power Quality Enhancement in Grid-Connected Renewable Energy Sources Using MC-UPQC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085824)|G. Vineeth; J. L. Aishwarya; B. Sowmya; B. S. Rani; M. Narasimha|10.1109/PIECON56912.2023.10085824|PQ;RES;MC-UPQC;VSC;Renewable energy sources;Wind energy;Power quality;Switches;Solar energy;Power grids;Solar system|
|[A double switch high gain quadratic boost converter with low voltage stress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085777)|A. Alvi; S. Khan; A. A. Siddiqui; K. U. Rehman; S. Khan; M. Hamza; M. Zaid|10.1109/PIECON56912.2023.10085777|High gain;Voltage Multiplier Cell (VMC);Boost Converter;Renewable energy sources;Low voltage;Instruments;DC-DC power converters;Software;Topology;Steady-state|
|[Performance Investigations on Synchronous Reluctance Motor for Automotive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085860)|S. Prabhu; V. Arun; M. Balaji; V. Kalaimagal; A. Manikandan; V. Chandrasekar|10.1109/PIECON56912.2023.10085860|flux barrier;layers;torque ripple;transportation systems;Meters;Torque;Automotive applications;Traction motors;Finite element analysis;Behavioral sciences;Torque measurement|
|[Impact of Laminating Core Materials on Switched Reluctance Motor for Automotive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085806)|S. Prabhu; V. Kalaimagal; V. Arun; A. Manikandan; M. Balaji; V. Chandrasekar|10.1109/PIECON56912.2023.10085806|switched reluctance motor;finite element;laminating material;torque ripple;Torque;Rotors;Switches;Switched reluctance motors;Transformer cores;Transformers;Permanent magnet motors|
|[Current Feedback Operational Amplifier Based Active Filter Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085912)|R. Bhagat; M. Kumari; N. K. Jha; R. Yadav|10.1109/PIECON56912.2023.10085912|Active Filters;Current Feedback Operational Amplifier;AD844;Band-pass filters;Integrated circuits;Operational amplifiers;Matched filters;Circuit topology;Simulation;Low-pass filters|
|[Smart Grid Security by Embedding Cryptography Hardware Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085792)|N. Kumar; V. M. Mishra; A. Kumar|10.1109/PIECON56912.2023.10085792|Smart grid security;Embedded chip;AES algorithm;Key size;Cryptographic Simulation;Software algorithms;Software;Real-time systems;Smart grids;Encryption;Table lookup;Software reliability|
|[Fast Charging and Vehicle to Home Battery Management System Using MATLAB Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085852)|A. Ahmad; A. Kumar; P. M. Tiwari|10.1109/PIECON56912.2023.10085852|State of charge;Battery management system;Balancing;Electronic control unit;Driving;Charging;Plant;Vehicle-to-grid;Software packages;Instruments;Battery management systems;Electric vehicles;Control systems;Batteries|
|[Power Transmission Optimization Using Synchronous Condenser Incorporated with Hybrid Particle Swarm Pattern Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085755)|R. Karangan; A. Arief; R. S. Sadjad; M. B. Nappu|10.1109/PIECON56912.2023.10085755|power optimization;synchronous condenser;artificial algorithm;MATLAB;DigSILENT;Stability criteria;Power transmission;Power system stability;Propagation losses;Power grids;Particle swarm optimization;Voltage control|
|[Techno-Economic and Business aspects of Long-term Hydrogen Storage for Low Carbon Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085868)|T. Venkata Balaji; A. Firdous; P. Mathuria; R. Bhakar|10.1109/PIECON56912.2023.10085868|hydrogen storage;energy arbitrage;seasonal storage;grid flexibility;storage business models;Renewable energy sources;Hydrogen storage;Biological system modeling;Scalability;Systems operation;Hydrogen;Power systems|
|[Dynamic Performance Assessment of Power System Interconnected via Asynchronous Transmission Link in Reorganized Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085759)|R. N. Mishra; N. Kumar|10.1109/PIECON56912.2023.10085759|asynchronous transmission link;automatic generation control (AGC);dynamic stability;optimal AGC regulators (OARs);redox flow batteries (RFB);Regulators;Software packages;Power system dynamics;Stability criteria;Automatic generation control;Power system stability;Redox|
|[An Improved High Gain Single DC-Source MLI Circuit with Reduced Switch Count and Cost Factor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085845)|K. Mohammad; M. S. Alam; M. Tahir; M. S. B. Arif; H. Rahat; S. M. Ayob|10.1109/PIECON56912.2023.10085845|Switched Capacitor (S.C.);Nearest Level Control;Total harmonics distortion (THD);Total Standing Voltage (TSV);Cost Factor (C.F);Capacitors;Loading;Switches;Control systems;Boosting;Capacitive sensors;Topology|
|[Frequency control of two region two-unit systems with HVDC link and SMES](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085873)|A. Biswal; P. Dwivedi; S. Bose|10.1109/PIECON56912.2023.10085873|Differential Evolution (DE) algorithm;High Voltage Direct Current (HVDC);Proportional Integral Derivative (PID);Regulators;HVDC transmission;System performance;Wind power generation;Software;Power grids;Mathematical models|
|[Privacy Preservation Using Random Forest For Healthcare Data In Case Of Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085763)|S. Kumar; A. J. Devi|10.1109/PIECON56912.2023.10085763|Privacy Preservation;Random Forest;Healthcare;Smart City;Data privacy;Privacy;Smart cities;Medical services;Strategic planning;Needles;Safety|
|[Influence of Laminating Core Materials on Internal Permanent Magnet Motor for Locomotive Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085821)|S. Prabhu; V. Arun; M. Balaji; V. Kalaimagal; A. Manikandan; V. Chandrasekar|10.1109/PIECON56912.2023.10085821|internal permanent magnet motor;torque ripple;finite element analysis;Vibrations;Torque;Magnetic cores;Propulsion;Permanent magnet motors;Permanent magnets;Finite element analysis|
|[Interval Optimization Technique Based Multi-Objective Scheduling of Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085895)|A. Kharra; R. Tiwari; J. Singh; T. Rawat|10.1109/PIECON56912.2023.10085895|Electric Vehicles;Multi-Objective;Grey Wolf optimization;Interval optimization;TOPSIS;Uncertainty;Costs;Instruments;Electric vehicles;Optimization;Load modeling|
|[Impact of Electronic Health Record Use on Length of Stay Management in a General Hospital in Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085762)|A. Aljuhani; M. Nour; M. Hussain|10.1109/PIECON56912.2023.10085762|Length of stay (LOS);Electronic Health Record (EHR);Hospital Management (HM);Healthcare Technology (HCT);Hospitals;Instruments;Surgery;Machine learning;Predictive models;Regression analysis;Electronic medical records|
|[Hybrid Solar PV System for Electric Vehicles Battery Charging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085883)|Z. Firdouse; S. M. Ashraf; K. Rahman; H. Rehman|10.1109/PIECON56912.2023.10085883|Electric Vehicles;Solar energy;Hybrid solar PV system;Reactive power compensation;EV battery charging;Reactive power;Analytical models;Total harmonic distortion;Silver;Solar energy;Charging stations;Active filters|
|[Comparative Performance Analysis of Mamdani and Sugeno Fuzzy Controller using Application on 2DoF Ball Balancer System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085809)|N. Nikita; B. Bhushan; A. Chaudhary|10.1109/PIECON56912.2023.10085809|ball balancer system;PID Controller;Fuzzy Systems;Sugeno Fuzzy;Mamdani Fuzzy;Fuzzy logic;Transient response;Software packages;Delay effects;Transfer functions;Control systems;Mathematical models|
|[Intelligent Residential Energy Management via Fuzzy Logic and Smart Load Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085804)|S. Iqbal; M. Sarfraz; A. S. Allahloh; A. Nabi|10.1109/PIECON56912.2023.10085804|energy management;fuzzy logic;load profile;local power generation;peak demand;smart home;Fuzzy logic;Renewable energy sources;Energy consumption;Wind;Costs;Power demand;HVAC|
|[Comparative Analysis of SPWM Inverter Fed Five and Three Phase Induction Motor Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085837)|S. Sharma; D. Chatterjee|10.1109/PIECON56912.2023.10085837|five phase induction motor drives;SPWM;magnetomotive force;stationary reference frame;torque analysis;Induction motor drives;Analytical models;Torque;Voltage;DC motors;Mathematical models;Inverters|
|[Statistical Techniques for Prediction of Breakdown Voltage of Liquid Dielectrics used in Power Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085859)|S. A. Khan; M. Tariq; M. Farhan; Z. A. Khan|10.1109/PIECON56912.2023.10085859|Transformer insulating oil;Nanofluids;Weibull distribution;Normal distribution;Ac Breakdown voltage;Statistical analysis;Nanomaterials;Electric breakdown;Oils;Oil insulation;Gaussian distribution;Dielectrics;Weibull distribution;Dielectric liquids|
|[Comparative Analysis of Solar PV Array Under Mismatch Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085803)|S. Kumar; M. S. Ahmad; M. F. Jalil; A. Tariq; S. Kirmani|10.1109/PIECON56912.2023.10085803|Photovoltaic (PV) array reconfigurations;Total Cross Tied (TCT);Honey-Comb (HC);Series Parallel (SP);Bridge-Link (BL);and Su-Do-Ku;Global maximum power;Fill Factor.;Renewable energy sources;Radiation effects;Fill factor (solar cell);Short-circuit currents;Simulation;Topology;Power systems|
|[Finite Element Analysis on Interior Permanent Magnet Machine for Propulsion System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085801)|S. Prabhu; V. Arun; M. Balaji; V. Kalaimagal; A. Manikandan; V. Chandrasekar|10.1109/PIECON56912.2023.10085801|internal permanent magnet machine;finite element analysis;Permanent magnet machines;Torque;Transportation;Permanent magnet motors;Permanent magnets;Finite element analysis;Numerical models|
|[Review: Lane Detection for Autonomous Vehicles Using Image Processing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085756)|Tanviruzzama; S. Mehfuz|10.1109/PIECON56912.2023.10085756|Image Preprocessing;Canny Edge;Hough Transform;Spiking Neural Network;Kalman Filter;Particle Filter;Smoothing methods;Lane detection;Image edge detection;Neural networks;Transforms;Machine learning;Gray-scale|
|[Effect of Exciton Dissociation and Recombination Losses in Perovskite Photovoltaic Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085749)|M. Amir; M. P. Singh; I. Masood|10.1109/PIECON56912.2023.10085749|Perovskite photovoltaic cell (PPV);n-i-p structure;exciton dissociation;recombination losses;power conversion efficiency (PCE);simulation;OghmaNano.;Performance evaluation;Photovoltaic systems;Excitons;Photovoltaic cells;Short-circuit currents;Instruments;Radiative recombination|
|[Capacitive fringing sensor based on PCB for the detection of moisture content in grain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085773)|G. Aswani; T. Islam|10.1109/PIECON56912.2023.10085773|Capacitive fringing sensor;food grains;Moisture content;Meters;Spirals;Power measurement;Moisture measurement;Moisture;Capacitance;Capacitive sensors|
|[Performance analysis of Linear Quadratic Regulator for DC-DC Converter Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085906)|S. Kumari; M. M. Rayguru; S. Upadhyaya|10.1109/PIECON56912.2023.10085906|small signal analysis;Linear Quadratic Regulator;discrete time systems;state space models;Analytical models;Regulators;Buck converters;Software packages;Transfer functions;Control systems;Performance analysis|
|[Power Quality Improvement using Hopfield Neural Network in Grid Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085879)|P. Bansal; P. Bharati; S. Jeswani; A. Arora|10.1109/PIECON56912.2023.10085879|DSTATCOM;HNN;SRFT;STF;Clarke’s Transformation;Park’s Transformation;Measurement;Software packages;Software algorithms;Power quality;Hopfield neural networks;Harmonic analysis;Power harmonic filters|
|[Scheduling of Hydrothermal and Renewable Energy Systems Using Enhanced PSO Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085779)|P. Mundotiya; P. Mathuria; H. P. Tiwari; V. K. Sharma|10.1109/PIECON56912.2023.10085779|Hydrothermal;renewable energy;scheduling;soft computing;heuristic search;evolutionary computing;Schedules;Renewable energy sources;Pollution;Processor scheduling;Wind farms;Scheduling;Power systems|
|[Analysis on Misc Type Permanent Magnet Synchronous Reluctance Machine for Transportation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085747)|S. Prabhu; V. Arun; M. Balaji; V. Kalaimagal; A. Manikandan; V. Chandrasekar|10.1109/PIECON56912.2023.10085747|Torque ripple;Heat generation;dissipation and cooling techniques;Vibrations;Forging;Torque;Rotors;Estimation;Transportation;Permanent magnet motors|
|[Evaluation and performance analysis of electric spring with critical and non-critical loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085849)|J. R.Rokde; A. G.Thosar|10.1109/PIECON56912.2023.10085849|Electric spring;Reactive power management;Voltage regulation;Renewable energy sources;Smart load;Renewable energy sources;Reactive power;Simulation;Power grids;Performance analysis;Impedance;Voltage control|
|[A Robust R Peak Recognition Procedure of a cardiac Signal using Modified Db 20 Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085881)|P. Tripathi; M. A. Ansari; T. K. Gandhi; R. Mehrotra; C. Singh; A. Singh; S. Chauhan|10.1109/PIECON56912.2023.10085881|ECG signal;R-Peaks;Wavelet Transform;healthcare applications;filters;arrythmia;Shape;Databases;Instruments;Signal processing algorithms;Electrocardiography;Signal processing;Filtering algorithms|
|[Design and Analysis of Rotor Technology for Electric Vehicle Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085890)|P. Vinay; S. K. Suresh; V. D. Ganesh; T. V. Krishna; A. S. Ganesh; C. Saiprakash|10.1109/PIECON56912.2023.10085890|Rotor Topology;dual winding technology;electrical motor;electrical generator;Induction motors;Torque;Windings;Rotors;Prototypes;Switched reluctance motors;Permanent magnet motors|
|[Enhancing AC Breakdown Voltage of Insulating Oils Incorporating Al₂O₃ Nanoparticles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085888)|A. Ali; A. A. Khan; O. Rahman|10.1109/PIECON56912.2023.10085888|Nanofluid;Breakdown voltage;Synthetic ester oil;Transformer;Electrodes;Vegetable oils;Shape;Oil insulation;Nanofluidics;Minerals;Dielectrics|
|[Scenarios and Modified Interval Based Wind Power Uncertainty Modelling for Decision Making in Electricity Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085797)|S. Gupta; B. B. Sharma; V. Prakash; S. Chawda; K. C. Sharma|10.1109/PIECON56912.2023.10085797|Decision making;Interval forecast;Stochastic model;Uncertainty modelling;Wind power;Uncertainty;Computational modeling;Wind speed;Decision making;Stochastic processes;Wind power generation;Predictive models|
|[An improved Alternate-Odd-Even (AOE) reconfiguration technique for performance enhancement of SPV arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085798)|M. T. S. Beg; K. P. S. Rana; V. Kumar|10.1109/PIECON56912.2023.10085798|Photovoltaic (PV);partial shading scenarios;TCT;shading patterns;reconfiguration technique;performance metrics.;Measurement;Fill factor (solar cell);Instruments;Voltage;Topology;Performance analysis|
|[Analysis of Different Layers Thicknesses on the Performance of Organic Solar Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085830)|I. Masood; M. P. Singh; M. Amir|10.1109/PIECON56912.2023.10085830|Organic solar cells (OSCs);Bulk hetero-junction (BHJ);Layers thicknesses;carrier mobilities and OghmaNano software;Performance evaluation;Photovoltaic cells;Short-circuit currents;Instruments;Current-voltage characteristics;Charge carrier processes;Power conversion|
|[Motor Winding Temperature Estimation for Thermal Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085897)|A. Chauhan; K. K. P. Nidubrolu; A. Ghube|10.1109/PIECON56912.2023.10085897|temperature sensors;thermal protection;accuracy;winding temperature;Temperature sensors;Temperature measurement;Temperature distribution;Costs;Windings;Estimation;Temperature control|
|[Smart Energy Management Techniques of Microgrid: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085903)|P. Gupta; M. A. Ansari; I. Ali|10.1109/PIECON56912.2023.10085903|energy management;microgrid;optimization;techniques;Renewable energy sources;Wireless networks;Process control;Microgrids;Production;Supervisory control;Smart grids|
|[Reliability Analysis of Power System Employed by High Power Consumption Data Center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085721)|J. Piloto; R. Ribeiro; R. Sarker; M. Tariq; A. I. Sarwat|10.1109/PIECON56912.2023.10085721|Reliability;Monte Carlo;Markov Chain;distribution;Data Center;Power Consumption.;Data centers;Monte Carlo methods;Power demand;Instruments;Markov processes;Systems engineering and theory;Solids|
|[Performance Investigation of Reaching Laws based Sliding Mode Control in Spacecraft Attitude Anti-Unwinding Stabilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085770)|P. M. Tiwari|10.1109/PIECON56912.2023.10085770|nan;Space vehicles;Limiting;Attitude control;Simulation;Instruments;Stability analysis;Robustness|
|[A Transformerless High Gain DC-DC Converter with Two Inductor Structure for DC Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085844)|S. Athikkal; G. S|10.1109/PIECON56912.2023.10085844|Power converter;better voltage conversion ratio;comparative analysis;switch voltage stress;Electric potential;Microgrids;DC-DC power converters;Transformers;Mathematical models;Topology;Voltage control|
|[EV Charging Station Placement using Nature-Inspired Optimisation Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085885)|F. Ahmad; L. Al-Fagih; S. A. Qadir; M. Khalid|10.1109/PIECON56912.2023.10085885|Climate change;carbon emissions;electric vehicles;optimal placement;optimization;Instruments;Transportation;Distribution networks;Electric vehicle charging;Hybrid power systems;Planning;Optimization|
|[Reliability Analysis of a 19-Level Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085872)|S. Ahmad; M. Sharma; R. K. Baghel; V. Verma; M. Tariq|10.1109/PIECON56912.2023.10085872|multilevel inverter;reliability analysis;Performance evaluation;Fault tolerance;Semiconductor devices;Instruments;Fault tolerant systems;Pulse width modulation;Logic gates|
|[Analysis of Synchronous Reluctance Motor using ANSYS Maxwell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085832)|A. Yildiz; M. Polat|10.1109/PIECON56912.2023.10085832|ANSYS Maxwell;Finite Element Method;motor design;synchronous reluctance motor;Analytical models;Induction motors;Instruments;Software;Energy efficiency;Finite element analysis;Torque measurement|
|[Investigating and predicting the dielectric performance of non-edible Natural Ester using LSTM-based deep learning model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085810)|R. A. Raj; S. Murugesan; R. Sarathi; S. D|10.1109/PIECON56912.2023.10085810|LSTM;deep learning studies;natural ester;dielectric breakdown voltage;survival;Deep learning;Breakdown voltage;Solid modeling;Voltage measurement;Oils;Predictive models;Mathematical models|
|[Reliability Assessment of Distribution System With Distributed Generation: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085774)|A. Kumar; Prashant; D. Kumar; F. Danish; M. Sarwar; R. Singh|10.1109/PIECON56912.2023.10085774|DGs;Renewable energy resources;ETAP;carbon emissions;distribution system;reliability indices;Renewable energy sources;Analytical models;Instruments;Carbon dioxide;Software;Global warming;Power system reliability|
|[ZigBee Communication in WSN with Mesh Configured Routers and Hardware Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085807)|Ompal; V. M. Mishra; A. Kumar|10.1109/PIECON56912.2023.10085807|ZigBee Communication;Embedded chip;Wireless Sensor Networks (WSN);Xilinx ISE 14.7;Wireless communication;IEEE 802.15 Standard;Wireless sensor networks;Scalability;Zigbee;Data transfer;Hardware|
|[An optimal approach of bidirectional multiwavelength conversion based on XGM in MQW-SOA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085858)|Parashuram; C. Kumar|10.1109/PIECON56912.2023.10085858|Wavelength converter (WC);cross gain modulation (XGM);multi-quantum-well semiconductor optical amplifier (MQW-SOA);Quality Factor (Q);and Bit Error Rate (BER);Semiconductor optical amplifiers;Optical polarization;Stimulated emission;Bit error rate;Optical wavelength conversion;Optical pumping;Adaptive optics|
#### **2023 Argentine Conference on Electronics (CAE)**
- DOI: 10.1109/CAE56623.2023
- DATE: 9-10 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Compressive Sensing Mapping System for Spatial Characterization of Photovoltaic Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086971)|S. A. Cerezo; M. A. Córdoba; F. P. Quintián|10.1109/CAE56623.2023.10086971|Compressive sensing;photovoltaic;light beam induced current (LBIC) measurement;Walsh-Hadamard basis;DCT basis;Photovoltaic systems;Current measurement;Measurement by laser beam;Surface emitting lasers;Quality control;Production;Silicon|
|[Crest factor optimization of multi-tone signals generated for low-temperature sensor readout](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086985)|L. H. Arnaldi|10.1109/CAE56623.2023.10086985|Crest factor;cryogenic detector;multi-frequency signal;optimization;CMB;Multiplexing;Microwave technology;Inductance;Array signal processing;Heuristic algorithms;Detectors;Cryogenics|
|[Implementation of a 4-Parallel 128-Point Radix-8 FFT Processor via Folding Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086984)|K. H. Viglianco; D. R. Garcia; J. J. W. Kunst|10.1109/CAE56623.2023.10086984|Parallel-Pipelined Architecture;Fast Fourier Transform (FFT);Radix-8;VLSI Implementation;FreePDK45;Register minimization technique;CMOS 45nm;Quantization (signal);Power demand;Fast Fourier transforms;Very large scale integration;Minimization;CMOS technology;Registers|
|[Towards a Low-Cost Readout System for Arrays of Cryogenic Detectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086976)|K. J. Ramos; L. H. Arnaldi; L. Tosi|10.1109/CAE56623.2023.10086976|Electronic Readout;Kinetic Inductance Detectors;Microwave Resonators;Red Pitaya;Inductance;Resonant frequency;Detectors;Microwave theory and techniques;Kinetic theory;Frequency division multiplexing;Task analysis|
|[Nano-Ampere Area-Efficient Current Reference Based on Temperature-Controlled Pseudo-Resistor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087014)|I. Bruni; F. Olivera; A. Petraglia|10.1109/CAE56623.2023.10087014|CTAT behavior;CMOS current reference;PTAT behavior;pseudo-resistor;temperature-aware design;ultra-low power (ULP);Resistors;Temperature distribution;Simulation;Logic gates;CMOS process;Silicon;Regulation|
|[An FPGA RF PWM Modulator for ISM Bands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086975)|J. I. Morales; F. Chierchie; P. S. Mandolesi; E. E. Paolini|10.1109/CAE56623.2023.10086975|Pulse-width modulation (PWM);all-digital transmitter (ADT);field-programmable gate array (FPGA);radio frequency (RF);internet of things (IOT);Radio frequency;Time-frequency analysis;Frequency modulation;Quantization (signal);Baseband;Radio transmitters;Pulse width modulation|
|[Automated Monitoring and Control System for Greenhouses Using Modern Speech Processing Through Mathematical-Based Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086980)|M. C. Moreno; L. D. Quintero; N. A. Torres|10.1109/CAE56623.2023.10086980|Automation;Greenhouse;Voice Signal;Rosenberg Model;Fourier Transform;Arduino;Bluetooth;Temperature sensors;Fourier transforms;Bluetooth;Automation;Green products;Prototypes;Speech recognition|
|[A physics-based numerical modeling of total ionizing dose effects in CMOS integrated circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086970)|M. V. Cassani; L. S. Salomone; S. Carbonetto; E. Redin; A. Faigón; M. Garcia-Inza|10.1109/CAE56623.2023.10086970|MOSFETs;radiation effects;solid-state detectors;Semiconductor device modeling;MOSFET;Predictive models;Numerical models;CMOS integrated circuits;Transistors;Total ionizing dose|
|[Seismic Motion Detection and Classification Methodology for Buildings Using DFT and SVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087012)|E. A. Taypicahuana Loza; A. C. Nieto; S. G. Huaman Bustamante|10.1109/CAE56623.2023.10087012|accelerometer;resonance in building;structural health monitoring;early earthquake warning;Support vector machines;Vibrations;Buildings;Earthquakes;Discrete Fourier transforms;Prototypes;Sensor systems|
|[A Programmable Gain Dynamic Residue Amplifier in 65nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087007)|M. Germano; Á. F. Bocco; B. T. Reyes|10.1109/CAE56623.2023.10087007|Dynamic Residue Amplifier;Programmable Gain;SAR-pipelined;ADC;Semiconductor device modeling;Fabrication;Power demand;Layout;Distortion;CMOS technology|
|[Study of Feasibility of Image Compression with Wavelets over Hexagonal Pixel Arrays using a Custom Photodetector Integrated Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086986)|T. S. Ferreyra; F. P. Quintian; N. Calarco|10.1109/CAE56623.2023.10086986|image compression;hexagonal image;wavelets;Wavelet transforms;Integrated circuits;Image coding;Digital images;Transform coding;Photodetectors;Standards|
|[Study of Silicon Photomultipliers in Low Earth Orbit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086977)|L. Finazzi; G. A. Sanca; F. G. Marlasca; M. Barella; F. Izraelevitch; P. Levy; F. Golmar|10.1109/CAE56623.2023.10086977|Silicon Photomultipliers;Space Mission;Hardware;Small Satellites;Photomultipliers;Temperature sensors;Temperature measurement;Low earth orbit satellites;Lighting;Electronic components;Collaboration|
|[Printed electronics: a low-cost alternative to prototyping in the academic field](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086979)|G. Maroli; O. A. Aymonino; A. Oliva; J. Gak; P. Julián; F. Palumbo|10.1109/CAE56623.2023.10086979|silver inks;printed electronics;flexible electronics;inkjet printing;Silver;Flexible printed circuits;Costs;Rapid prototyping;Discharges (electric);Ink jet printing;Flexible electronics|
|[High Performance Amplifier in 130nm CMOS Technology using an Open Source Design Flow for 10Gbase-T Ethernet Transceivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087008)|F. Tolosa; E. Dri; Á. F. Bocco; B. T. Reyes|10.1109/CAE56623.2023.10087008|High Performance Amplifier;Skywater 130nm PDK;IEEE 802.3an Ethernet;Open Source Hardware;Integrated circuit synthesis;Total harmonic distortion;IEEE 802.3 Standard;CMOS technology;Transceivers;EPON;Open source hardware|
|[Thermal neutron detector for mixed neutron and gamma beams using a commercial, boron-coated, CMOS image sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087015)|J. Lipovetzky; F. A. Bessia; J. Blostein; M. Pérez; M. S. Haro; J. Longhino; I. Sidelnik; M. G. Berisso|10.1109/CAE56623.2023.10087015|CMOS image sensor;thermal neutrons;ionizing radiation;Neutron capture therapy;Electric potential;Ionizing radiation;Sodium;Detectors;Neutrons;Thermal sensors|
|[Controlled Magnetic Field Assisted Electrospinning for Experiments on Energy Harvesting Polymers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087010)|J. A. González Sánchez; A. La Santa Maldonado; Y. M. Díaz; L. C. Rosario; O. P. Oliveras; J. A. Diazgranados Jimenez|10.1109/CAE56623.2023.10087010|IoT;Electrospinning;Electronics;Ferroelectricity;Nanoparticles;Scanning electron microscopy;Magnetic sensors;Printed circuits;Zinc oxide;Iron;Sensor systems|
|[Integrated Three-Level Flying Capacitor DC-DC Buck Converter for CubeSat Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10087013)|J. Marin; J. Gak; A. Cortes; N. Calarco; A. Oliva; E. Lindstrom; M. Miguez; A. Falcón; N. Osterman; C. A. Rojas|10.1109/CAE56623.2023.10087013|DC-DC;power MOSFETs;CubeSat;Three-level Flying Capacitor Converter;Semiconductor device modeling;MOSFET;Buck converters;Switching frequency;Capacitors;Switches;Silicon|
|[Noise analysis of MIDNA Skipper-CCD readout ASIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086978)|F. A. Bessia; T. England; H. Sun; D. Braga; M. S. Haro; J. Lipovetzky; J. Estrada; F. Fahim|10.1109/CAE56623.2023.10086978|ASIC;Charge-Coupled Device;Noise;Dark Matter;Charge coupled devices;Performance evaluation;Operational amplifiers;Integrated circuits;Dark matter;Signal processing;Preamplifiers|
|[Monte-Carlo Analysis of Different High-Z Coating Layers as X-rays Detective Quantum Efficiency Intensifiers for Silicon Detectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086973)|N. Martín; M. Sofo-Haro; M. Pérez; J. Lipovetzky; M. Valente|10.1109/CAE56623.2023.10086973|Particle detector;X-rays;Silicon detectors;Micrometers;Monte Carlo methods;Radiation detectors;Gamma-rays;Detectors;X-rays;Gadolinium|
|[A 0.5-V 10-bit Asynchronous SAR ADC with Monotonic Switching for Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086972)|M. C. Rodrigues; J. L. J. Brum; A. G. Girardi; L. C. Severo; P. C. C. de Aguirre|10.1109/CAE56623.2023.10086972|nan;Wireless communication;Semiconductor device modeling;Protocols;Power demand;Wearable computers;Switches;Oxidation|
|[A 915-MHz RF-EH with Varactor-Based Adaptive Impedance Matching for ULV Batteryless Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086983)|T. C. De-Oliveira; A. G. Girardi; P. C. C. de Aguirre; L. Compassi-Severo|10.1109/CAE56623.2023.10086983|ultra-low voltage;impedance matching;RF energy harvesting;battery-less devices;Radio frequency;Sensitivity;Impedance matching;RF signals;Voltage;Frequency conversion;CMOS process|
|[Stratospheric balloon earth observation gathered imagery classification through deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086981)|C. Conchari; F. Ticona; M. Molina; J. Nina; M. Mamani; K. Vidaurre; F. Díaz|10.1109/CAE56623.2023.10086981|stratospheric balloon;image classification;deep learning;embedded device;Earth;Deep learning;Satellites;Costs;Instruments;Terrestrial atmosphere;Imaging|
|[Validation Analysis of a Distributed Battery Management System Implementation : Invited Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086974)|F. Quiñones; J. Goinhex; N. Calarco|10.1109/CAE56623.2023.10086974|Distributed BMS;Remaining Discharge Time prediction;Reduced Order Electrochemical Model;Extended Kalman Filter;State of Charge estimation;Heuristic algorithms;Battery management systems;Voltage;Prediction algorithms;Discharges (electric);Hardware;Batteries|
#### **2023 7th International Conference on Computing Methodologies and Communication (ICCMC)**
- DOI: 10.1109/ICCMC56507.2023
- DATE: 23-25 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Review on Adoption of Green Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083789)|C. Sailesh; V. S. Praneeth; S. S. Koushik; V. G. N. Sai; N. Vurukonda; V. K. Burugari|10.1109/ICCMC56507.2023.10083789|Green Computing;Cloud Computing;Load Balancing;SaaS;IaaS;PaaS;Carbon Footprints;Industries;Computers;Cloud computing;Pollution;Green products;Green computing;Electronic waste|
|[Detection of Stress by Machine Learning in IT Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083884)|M. P. D; H. H. S; B. R. B; H. M; J. K. P|10.1109/ICCMC56507.2023.10083884|Stress prediction;Boosting;Bagging;Decision Trees;Healthcare;Machine Learning;nan|
|[Deepfake Detection using Inception-ResNet-V2 Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083584)|R. R. Rajalaxmi; S. P. P; R. A. M; P. S; D. P; G. E|10.1109/ICCMC56507.2023.10083584|Deep Learning;Deepfake;CNN;Inception ResNet-V2;Deep learning;Deepfakes;Privacy;Social networking (online);Face recognition;Software algorithms;Neural networks|
|[Modified Firefly algorithm for Optimal Test Case Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083726)|P. Harris|10.1109/ICCMC56507.2023.10083726|Software Testing;Metaheuristic;Test Cases;Control Flow Graph;Firefly algorithm;Fitness;Artificial Bee Colony;nan|
|[IPL Data Analysis and Visualization for Team Selection and Profit Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083736)|S. G; A. Swaminathan; J. B. J; S. R; L. Nelson|10.1109/ICCMC56507.2023.10083736|Data Analysis;Data Cleaning;Indian Premier League;Prediction;Pandas;Numpy;Matplotlib;Seaborn;Analytical models;Scientific computing;Computational modeling;Data visualization;Machine learning;Data science;Probability|
|[Prognosis of Breast Cancer Cells using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083845)|C. T. Kalaivani; A. A. Jayapraba; R. Theerthika; M. Sivaranjini; E. Monisha|10.1109/ICCMC56507.2023.10083845|Breast cancer;Image processing;Algorithm;Segmentation;Adaptive mean filter;Classifier;Image segmentation;Neural networks;Clustering algorithms;Manuals;Machine learning;Needles;Breast cancer|
|[Predicting the Driver Variants and Mutations in Lung Cancer Genome using Transcriptional Regulation Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084125)|S. Chereddy; I. V. Srisurya; H. Bogineni; P. K. R; B. M. G|10.1109/ICCMC56507.2023.10084125|Graph mining;Transcriptional regulatory networks;Driver-gene;Integration-matrix;Network;Databases;Lung cancer;Genomics;Biological systems;Prediction algorithms;Regulation;Data models|
|[A Extensive Survey on Sign Language Recognition Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084111)|T. S. S; M. R. I|10.1109/ICCMC56507.2023.10084111|Gesture Recognition;Sign Language Recognition;Deep Learning;Continuous Sign;Hidden Markov Model;Computational modeling;Natural languages;Sociology;Gesture recognition;Assistive technologies;Markov processes;Statistics|
|[Enhanced Image-based Histopathology Lung Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084247)|D. Nannapaneni; V. R. S. V. Saikam; R. Siddu; V. M. Challapalli; V. Rachapudi|10.1109/ICCMC56507.2023.10084247|Image detection;Histopathology images;Lung cancer;Imaging;Deep learning;Bronchoscopy;Histopathology;Computed tomography;Computational modeling;Lung cancer;Medical services|
|[Sentiment Analysis on Amazon Product Reviews using LSTM and Naive Bayes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084052)|M. S. Meghana; D. Abhijith; S. Aysha; P. K. Kollu|10.1109/ICCMC56507.2023.10084052|Machine learning;Long short-term memory;Sentiment analysis;Natural language processing;Amazon product reviews;Naive Bayes;Deep Learning;Sentiment analysis;Supervised learning;Electronic commerce;Long short term memory|
|[Non-Invasive Detection of Coronary Artery Disease Symptoms using Cardiocare Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084255)|N. S. Garapati; K. Seelam; S. Jasti; T. Kasani; S. S. Palanki|10.1109/ICCMC56507.2023.10084255|Coronary artery disease;Coronary artery disease diagnosis;Heart rate sensor;Bluetooth module;Ischemic heart disease;Cardio care app;Heart;Fluctuations;Heart beat;Instruments;Medical services;Arteries;Smart phones|
|[A Review on Various Image-Recognition Types and their Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084285)|V. S. V. D. Prasad; I. M. Reddy; R. K. Sasank; S. D. T. Vinnakota; N. L. T|10.1109/ICCMC56507.2023.10084285|Text recognition;Efficient indexing;Retrieval system;Deep learning;Optical character recognition (OCR);Hybrid technology;Region-based methods;Texture-based methods;Deep learning;Text recognition;Databases;Image edge detection;Optical character recognition;Writing;Data mining|
|[Performance Analysis of Machine Learning Algorithm for the Credit Risk Analysis in the Banking Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083580)|S. K. Hegde; R. Hegde; K. P. R; S. S. S; A. V. G. A. Marthanda; K. Logu|10.1109/ICCMC56507.2023.10083580|Bank;Loan Prediction;Machine Learning;Python;Streamlit;Analytical models;Machine learning algorithms;Costs;Computational modeling;Banking;Performance analysis;Risk analysis|
|[A Comparative Study on Job Recommendation using Classification Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083728)|S. Sindhu; K. Shanmukh; S. Haq Munadi; J. Surya Kiran|10.1109/ICCMC56507.2023.10083728|Job Recommendation;Machine Learning;Clustering;rating;Support vector machines;Machine learning algorithms;Heuristic algorithms;Linear regression;Clustering algorithms;Natural language processing;Classification algorithms|
|[A Real Time System to Analyze Patient's Health Condition using Second Layer Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084046)|D. E; P. K; P. V; R. M|10.1109/ICCMC56507.2023.10084046|Fog Computing;Sensor;Health Monitoring;Cloud layers;Data transfer;Data storage and Analysis;Temperature sensors;Heart rate;Wireless communication;Cloud computing;Wireless sensor networks;Medical services;Real-time systems|
|[Developing a YOLO based Object Detection Application using OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084075)|M. Ponika; K. Jahnavi; P. S. V. S. Sridhar; K. Veena|10.1109/ICCMC56507.2023.10084075|YOLO;Object Detection;Convolutional Neural Network;OpenCV;Machine learning algorithms;Neural networks;Object detection;Prediction algorithms;Search problems;Real-time systems;Object recognition|
|[Identification of Voting Patterns using Clustering Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083748)|D. K. Minhas; A. Malik; S. K. Dubey|10.1109/ICCMC56507.2023.10083748|Clustering;Data Analytics;Dendrogram;Pattern;Analysis;Databases;Clustering methods;Sociology;Clustering algorithms;Statistics|
|[Human Disease Prediction based on Symptoms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083702)|U. K. Kommineni; D. P. P. Kowthavarapu; G. S. M. Polukonda; K. C. Yelavarti|10.1109/ICCMC56507.2023.10083702|Random Forest;Diagnosis of disease;Prediction;Machine Learning Algorithm;Database;nan|
|[Covid-19 Detection and Classification using Transfer learning with XGboost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083788)|K. Kumaran; M. Mohana; P. M. Lavanya; G. K. U; D. k. N|10.1109/ICCMC56507.2023.10083788|nan;nan|
|[An Integrated Wireless Aquaculture Monitoring and Feed Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084236)|K. S. Prasad; A. Sumalatha; J. V. K. S. C. Rao; V. V. V. Prasad|10.1109/ICCMC56507.2023.10084236|Aqua culture;Aqua Pond management;Automatic feed management;Integrated shrimp farming;Water quality Monitoring;Temperature sensors;Wireless communication;Wireless sensor networks;Water quality;Production;Mobile applications;Feeds|
|[A Survey of Forensic Applications using Digital Image Processing: Image Improvement Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084079)|D. Manaswini; S. S. V. Ghanta; T. L. Manogna; L. N. K. Kolakalapudi; G. K. Chaitanya; A. D. Kumar|10.1109/ICCMC56507.2023.10084079|Image processing;Image enhancement;Histogram equalization;Forensics;Digital images;Imaging;Software;Software reliability;Safety;Standards|
|[K-Means Clustering Algorithm for Crop Leaf Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083881)|T. S. Dharma Reddy Vanukuri; B. A. Mutthavarapu; K. B. V. Brahma Rao; S. T. Vadaranam; S. Shaik; S. H. K. RAJU|10.1109/ICCMC56507.2023.10083881|Image Preprocessing;Feature Extraction;Training;Machine Learning;Model;Histogram;Decision Making;nan|
|[An Automated Segregation and Storage of Onions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083846)|B. C. Sri; P. Korapati; S. N. Sai; C. H. Krishna; J. S. Mallela|10.1109/ICCMC56507.2023.10083846|Vertical storage;Arduino UNO;Infrared sensor (IR sensor);Digital Humidity and Temperature sensor (DHT11);Humidifier;ThingView;Temperature sensors;Hair;Fluctuations;Oils;Buildings;Moisture;Humidity|
|[Machine Learning based Ideal Job Role Fit and Career Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084315)|S. K. A. S; S. S; A. S. M; S. K. S|10.1109/ICCMC56507.2023.10084315|Job Recommendation;Machine Learning;Graduate interests;Content-Based Filtering;Filtering;Engineering profession;Education;Machine learning;Companies;Manuals;Recommender systems|
|[A Natural Language Processing System for Truth Detection and Text Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083948)|H. P. Rohith; K. Sooda; K. B. Rai; D. B. Srinivas|10.1109/ICCMC56507.2023.10083948|Artificial Intelligence;Natural Language Processing;Fake News;Text Summarizing;nan|
|[Comparative Analysis on Aspect-based Sentiment using BERT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084294)|A. Tiwari; K. Tewari; S. Dawar; A. Singh; N. Rathee|10.1109/ICCMC56507.2023.10084294|Bidirectional Encoder Representations from Transformers;Aspect-Based Sentiment Analysis;Sentiment Analysis;Training;Sentiment analysis;Analytical models;Computational modeling;Bit error rate;Transformers;Task analysis|
|[Forensic Art with Image Recognition and Brain Computing Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084031)|J. Suganthi; S. Sivaranjani; M. Hariharan|10.1109/ICCMC56507.2023.10084031|BCI (Brain Computing Interface) Head Band;CNN (Convolutional Neural Network) Algorithm;Victim Face;EEG (Electroencephalogram) Signal;Art;Databases;Face recognition;Forensics;Electroencephalography;Software;Reliability|
|[Adaptive Domestic Waste Segregation using Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083805)|K. B. J; N. C; R. S; S. R. A; Jaswanth; H. V. K|10.1109/ICCMC56507.2023.10083805|Smart aid;visually challenged;sensors;Arduino;Machine learning algorithms;Image color analysis;Machine learning;Object detection;Classification algorithms;Recycling;Pattern recognition|
|[Low Power Design of Edge Detector using Static Segmented Approximate Multipliers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083930)|K. Sivanandam; R. Jagadheesh; S. N. Kavi Bharath; S. Manoj|10.1109/ICCMC56507.2023.10083930|Truncation multiplier;Segmentation;NAND gate;Verilog HDL;Error correction;nan|
|[Human Action Detection using EfficientNetB3 Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083926)|K. S. Gill; A. Sharma; V. Anand; K. Sharma; R. Gupta|10.1109/ICCMC56507.2023.10083926|Convolutional Neural Network;Human Action Detection;Adam Optimizer;Visualization;Efficient Net Model;nan|
|[Smart Wearable Device to Prevent Accidents Caused by Medical Emergencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084266)|E. M. Kumar; A. Zahoor; C. S. L. Sruthi; K. Vaishnavi; V. R. Chowdary|10.1109/ICCMC56507.2023.10084266|Arduino UNO;Arduino NANO;Global System for Mobile Communication;GSM;Temperature sensors;Temperature measurement;Wearable computers;Computational modeling;Epilepsy;Biomedical monitoring|
|[Streamlining Medicine Delivery through Automation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084238)|D. Praneetha; S. Kranthi; G. Aashritha; C. Deepthi; N. Sushma|10.1109/ICCMC56507.2023.10084238|Medicine;Delivery;Online;Hypertext Preprocessor (PHP);My Structured Query Language (MYSQL);Cascading Style Sheets (CSS);Web Application;E-Commerce;Drugs;Structured Query Language;Toxicology;Costs;Automation;Computational modeling;Government|
|[Robotic Process Automation for Automating Business Processes: A use case](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083859)|V. Bhardwaj; Virender; M. Kumar; D. Thakur; V. Lamba|10.1109/ICCMC56507.2023.10083859|RPA;Business Processes;Automation Anywhere;Software BOT;nan|
|[Smart Energy Meter Monitoring using RS485](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083637)|S. S. P; V. K. N; M. M; P. S; M. K. B|10.1109/ICCMC56507.2023.10083637|ConzervEM6438H Multifunction meter;RS485 serial communication;Modbus software;Meters;Wireless communication;Voltage measurement;Power measurement;Current measurement;Energy measurement;Software|
|[Retinal Glaucoma Detection from Digital Fundus Images using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083712)|S. S; D. V. Babu|10.1109/ICCMC56507.2023.10083712|Ophthalmology;CNN;Deep Learning;Glaucoma;Optical losses;Optical filters;Deep learning;Image segmentation;Biomedical optical imaging;Neural networks;Optical computing|
|[Electric Quad Bike with Hybrid Charging Mode for Physically Challenged](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084320)|S. Sujitha; M. R. K. Reddy; H. R; A. M. Urs; K. P|10.1109/ICCMC56507.2023.10084320|Switched Reluctance Motor;Controller;Brakes;Quad Bike;Technological innovation;Sociology;Wheels;Electric variables measurement;Switches;Reliability engineering;Safety|
|[A Composite Technique for Creating Contemporary MRS using Association Rule Mining & CF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084205)|M. Amanatulla; M. S. Rao; P. Hemalathareddy; K. Pavani|10.1109/ICCMC56507.2023.10084205|Machine Learning;Movie Recommendation System;Association Rule Mining;Collaborative Filtering;Collaborative filtering;System performance;Computer architecture;Motion pictures;Behavioral sciences;Data mining;Reliability|
|[A Trust Prediction Mechanism in Edge Communications using Optimized Support Vector Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083686)|N. C. Gowda; B. M. A|10.1109/ICCMC56507.2023.10083686|Fog Computing;Edge Communications System;Trust Management;Support Vector Regression;Support vector machines;Performance evaluation;Computational modeling;Education;Transportation;Medical services;Machine learning|
|[Multi-Task Cascaded Convolutional Neural Networks (MTCNN) based Driver Drowsiness Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084076)|K. K. Suhas; M. S. Prakash; Y. Venkatesh; P. M. Latha|10.1109/ICCMC56507.2023.10084076|Open CV;MTCNN;Blink rate;Alarm;Eye ratio;Visualization;Road accidents;Multitasking;Convolutional neural networks;Monitoring;Injuries;Faces|
|[Thyroid Disease Prediction using Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083592)|V. V. Priya; R. Subashini; S. H. Priya|10.1109/ICCMC56507.2023.10083592|Machine Learning;Thyroid;Health History;Data Preprocessing;Accuracy;Dimensionality Reduction;Support vector machines;Machine learning algorithms;Prediction algorithms;Biochemistry;Random forests;Medical diagnostic imaging;Thyroid|
|[Face and Fingerprint based Secured ATM Authentication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083739)|M. Mohana; R. T; S. K; S. K; N. Ananthi; K. Kumaran|10.1109/ICCMC56507.2023.10083739|Fingerprint recognition;Face recognition;Automated teller machine;Red tacton;Wireless communication;Industries;Face recognition;Authentication;Fingerprint recognition;Hardware;Pins|
|[Exploring the Applications, Challenges, and Issues of Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083940)|V. A. Pulikonda; D. Vanukuru; M. S. Navali; B. Motepalli; K. Swathi|10.1109/ICCMC56507.2023.10083940|Machine Learning;Sentiment analysis;Natural language processing;Classification;Product reviews;nan|
|[Studies on Anomaly Detection Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083885)|M. H. Krishna; N. K; G. Charmitha; T. Vignesh; V. Ch; S. Kuchibhotla|10.1109/ICCMC56507.2023.10083885|Isolation Forest;Anomaly Detection;Unsupervised;Decision Tree;Outliers;nan|
|[Navigating the Healthcare Landscape with Recommendation Systems: A Survey of Current Applications and Potential Impact](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083785)|D. D; T. Dhiliphan Rajkumar|10.1109/ICCMC56507.2023.10083785|Recommendation Systems;Content-Based Filtering;Collaborative Filtering;Electronic Health Records;Telemedicine;Privacy;Machine learning algorithms;Navigation;Telemedicine;Scalability;Precision medicine;Medical services|
|[Multiple Object Recognition from Smart Document Images using YOLOv5s](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084220)|B. N. B. J; U. G. S; M. Jose|10.1109/ICCMC56507.2023.10084220|Deep Learning;Object Detection;Recognition;YOLOv5s;Identity proof;Image quality;Image recognition;Text recognition;Convolution;Government;Graphics processing units;Object detection|
|[Comparison of Machine Learning Techniques for Prediction of Diabetes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084244)|G. M; V. Asha; A. Prasad; S. K. P; R. C|10.1109/ICCMC56507.2023.10084244|Diabetes mellitus;hyperglycemia;neural networks;PCA;RMR;Training;Radio frequency;Neural networks;Prediction algorithms;Diabetes;Glucose;Decision trees|
|[Hand Detection and Gesture Recognition in Complex Backgrounds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084181)|S. Gnanapriya; K. Rahimunnisa; M. Sowmiya; P. Deepika; S. P. R. Kamala|10.1109/ICCMC56507.2023.10084181|Convolutional Neural Networks;Hand detection;Segmentation;Mixture of Gaussian Model 2;real-time;Background Elimination;Feature Extraction;Gesture recognition;Performance evaluation;Image segmentation;Computational modeling;Semantics;Gesture recognition;Computer architecture;Feature extraction|
|[A Comparative Study on Specialization Courses Recommendation Through E-Learning using Classification Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083579)|M. Varun; M. Jyothi Raditya Reddy; B. K. Megashyam; J. Surya Kiran|10.1109/ICCMC56507.2023.10083579|Recommendation;Interest;Machine Learning Algorithms;Suggestions;Hybrid Recommender system;E-learning;personalization;Training;Technological innovation;Electronic learning;Machine learning algorithms;Social networking (online);Collaborative filtering;Prediction algorithms|
|[Automatic Billing Trolley for an Enhanced Supermarket using RFID](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083555)|P. A; V. A; N. K. C; S. R; K. K|10.1109/ICCMC56507.2023.10083555|Automatic billing;Radio Frequency Identification (RFID);Arduino;Global System for Mobile communication (GSM);Matrix Keypad;Microcontrollers;Pricing;User interfaces;Mobile communication;User experience;Software;Liquid crystal displays|
|[Air Pollution Detection using Resnet-50](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083837)|L. Vuyyuru; S. Nalluri; J. Vempatapu; R. b. Thopuri|10.1109/ICCMC56507.2023.10083837|Pollution;Particulate matter;Air quality Index;Convolutional Neural Network;ResNet- 50;MobileNet;Atmospheric modeling;Computational modeling;Urban areas;Sensor systems;Hardware;Sensors;Indexes|
|[A Plausible RNN-LSTM based Profession Recommendation System by Predicting Human Personality Types on Social Media Forums](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083557)|M. R. V V R; S. N; M. Gadiraju; S. S. Reddy; S. Bonthu; R. R. Kurada|10.1109/ICCMC56507.2023.10083557|Recurrent Neural Network;Long Short Term Memory;Machine Learning;Deep Learning;Myers-Brigg Type Indicator;Profession Recommendation;Personality Type;Training;Support vector machines;Learning systems;Recurrent neural networks;Social networking (online);Blogs;Data models|
|[Gesture-based Control of a Bionic Arm – A Prototype Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084175)|N. Pushpalatha; S. Batcha; V. Yesvanthkrishna; S. Rathinamala; B. P. Devi; G. V|10.1109/ICCMC56507.2023.10084175|Artificial Intelligence;Robotic;Arm;Brain Computer Interface;Gesture control;Wireless communication;Human computer interaction;Wireless sensor networks;Prototypes;Production;Arms;Manipulators|
|[Detection of Lung Cancer using VGG-16](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084192)|P. H. S. Prasad; N. M. V. S. Daswanth; C. V. S. P. Kumar; N. Yeeramally; V. M. Mohan; T. Satish|10.1109/ICCMC56507.2023.10084192|Visual Geometry Group16 (VGG16);Convolutional Neural Network;Deep learning;Machine learning;Deep learning;Geometry;Visualization;Lung cancer;Training data;Data models;Classification algorithms|
|[Smart Device to Check Harmful Chemicals in Fruits and Vegetables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084204)|S. R; A. K. K; R. P; N. K. S; K. P|10.1109/ICCMC56507.2023.10084204|Ototoxic;Proteins;Internet of Things;Chemicals;Proteins;Ultrasonic imaging;Liquid crystal displays;Pollution measurement;Safety;Minerals;Smart devices|
|[Integral Clustering and spectral Feature Modeling for Sketch-based Image Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083826)|K. D. Prasad; K. Manjunathachari; M. N. G. Prasad|10.1109/ICCMC56507.2023.10083826|Sketch-Based Image Retrieval (SBIR);Spectral Feature;Clustering;Relative gain;Computational modeling;Image retrieval;Image representation;Search problems;Delays;Task analysis|
|[Product Negotiation in E-Commerce Website using Chatbot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083745)|P. D. Sree; M. R. Kokkiligadda; J. Teja; Y. Sandeep|10.1109/ICCMC56507.2023.10083745|Artificial Intelligence;chatbot;intents;entities;Natural Language Processing;Classification;text extraction;smart negotiation;Costs;IEEE merchandise;Customer satisfaction;Chatbots;Electronic commerce;Standards;Business|
|[An Innovative Deep Learning Framework for Healthcare Cost Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083524)|C. H. Nikhitha Sravani; R. Singamaneni; K. Praneetha; J. l. Yenigalla|10.1109/ICCMC56507.2023.10083524|Deep learning framework;keras;machine learning algorithms;expenditure prediction;Flask and MinMaxScaler;Deep learning;Costs;Computational modeling;Medical services;Predictive models;Prediction algorithms;Data models|
|[License Plate Recognition using a Sequential Model and OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083767)|H. Sambana; P. K; H. S. Chagarlamudi; A. Bhagavatula|10.1109/ICCMC56507.2023.10083767|Keras;TensorFlow;Segmentation;License plate;Authorization;Training;Image segmentation;Databases;Computational modeling;Roads;Logic gates|
|[Student Eye Gaze Tracking and Attention Analysis System using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083874)|S. Shilaskar; S. Bhatlawande; T. Gadad; S. Ghulaxe; R. Gaikwad|10.1109/ICCMC56507.2023.10083874|Attention analysis;Computer Vision;Gazetracking;Haar Cascade;Pupil-detection;nan|
|[Fuzzy KNN Implementation for Early Parkinson's Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083522)|S. Chandrasekaran; V. Dutt; N. Vyas; A. Anand|10.1109/ICCMC56507.2023.10083522|Neural networks;Prediction;Algorithms;Models;Accuracy;Convolutional Neural Network (CNN);Deep learning;Recurrent neural networks;Parkinson's disease;Computational modeling;Brain modeling;Decision trees;Convolutional neural networks|
|[Plant Classification based on Grey Wolf Optimizer based Support Vector Machine (GOS) Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083535)|P. Keerthika; R. M. Devi; S. J. S. Prasad; R. Venkatesan; H. Gunasekaran; K. Sudha|10.1109/ICCMC56507.2023.10083535|Grey Wolf Optimizer;Support Vector Machine (SVM);Plant Classification;Industries;Plant diseases;Shape;Image color analysis;Support vector machine classification;Feature extraction;Genetics|
|[Deep Residual Learning for Lung Cancer Nodules Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083783)|M. Sangeetha; R. M. Devi; H. Gunasekaran; R. Venkatesan; K. Ramalakshmi; P. Murugesan|10.1109/ICCMC56507.2023.10083783|computed tomography;Deep Learning;Lung Cancer;computed tomography scan;Deep learning;Computed tomography;Microprocessors;Computational modeling;Neural networks;Lung cancer;Lung|
|[A Survey on Data Augmentation Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084010)|K. Nanthini; D. Sivabalaselvamani; K. Chitra; P. Gokul; S. KavinKumar; S. Kishore|10.1109/ICCMC56507.2023.10084010|nan;Training;Deep learning;Solid modeling;Computer vision;Computational modeling;Training data;Data models|
|[Sentimental Analysis on Twitter Data using Supervised Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084278)|C. Susmitha; L. Nikhil; L. Akhil; M. Kavitha; V. S. N. Reddy; K. Shailaja|10.1109/ICCMC56507.2023.10084278|Accuracy;F1-score;precision;recall;tweets;sentimental analysis;Support vector machines;Measurement;Machine learning algorithms;Data analysis;Social networking (online);Blogs;Machine learning|
|[Classification for Crop Pest on U-SegNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083888)|A. A. Rani; K. L. Prasanna; M. Shaikhul Ashraf; A. Kumar Dey; M. A. Ala Walid; D. R. K. Saikanth|10.1109/ICCMC56507.2023.10083888|Deep learning;Crop;UNET;ResNet;U-SegNet;nan|
|[A Novel Trust Value Based Mobile Ad hoc Networks (MANETs) Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084006)|S. R; J. V|10.1109/ICCMC56507.2023.10084006|Trust;Trust Model;Security;MANET;Trustworthy;Wireless communication;Measurement;Navigation;Computational modeling;Memory management;Routing;Routing protocols|
|[Artificial Butterfly Optimization based Cluster Head Selection with Energy Efficient Data Aggregation model for Heterogeneous WSN Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083550)|S. Venkatasubramanian; R. Vijay; S. HariPrasath|10.1109/ICCMC56507.2023.10083550|Artificial butterfly optimization;Cluster Head;Multiple Mobile Sinks;Energy Consumption;Wireless Sensor Network;Wireless sensor networks;Energy consumption;Protocols;Simulation;Forestry;Throughput;Energy efficiency|
|[Privacy and Security of Healthcare Data in Cloud based on the Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083822)|A. Agarwal; R. Joshi; H. Arora; R. Kaushik|10.1109/ICCMC56507.2023.10083822|Cloud;Server;Healthcare;Blockchain;Security;Machine Learning (ML);Cloud computing;Technological innovation;Java;Data privacy;Medical services;Developing countries;Blockchains|
|[A Comprehensive Survey on Recent Advances in 5G Networks and Mobile Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083901)|G. Pavan; V. K. R. N. Reddy; A. R. Prasad; R. R. Chintala; N. R. Sai|10.1109/ICCMC56507.2023.10083901|Radiation;Millimeter waves;5G;Electromagnetic Radiation;Massive Multiple Input and Multiple Output;nan|
|[Submarine Communication for Monitoring Diver's Health using Li-Fi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083996)|K. K; P. N; R. N; R. S. C; S. G|10.1109/ICCMC56507.2023.10083996|Light Fidelity;Underwater communication and Health Monitoring System;Water;Temperature sensors;Temperature measurement;Costs;Frequency-domain analysis;Medical services;Water conservation|
|[Wireless Fuel Measurement System using UWB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083552)|Krishnaveni; M. Prabhu; Paveena; Priyanga; P. N; K. N|10.1109/ICCMC56507.2023.10083552|Wireless sensor network;Wireless sensor network Fuel management;Ultra-Wide Band based wireless communication;Wiring;IEEE 802.15 Standard;Wireless sensor networks;Current measurement;Ultra wideband communication;Prototypes;Main-secondary|
|[VLSI Design of Majority Logic based Wallace Tree Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083704)|J. S; M. M; K. Kumar M; H. Krithik Roshan S|10.1109/ICCMC56507.2023.10083704|Majority Gates;Wallace Tree Multiplier;V-HDL;nan|
|[Extended Finite State Machine based Fault Tolerance in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084221)|D. Lekha; A. S. M; S. V. Lakshmi; A. B. H|10.1109/ICCMC56507.2023.10084221|Finite state machine;Wireless sensor network;Fault tolerance;Fault detection ratio;False negative ratio;Fault diagnosis;Wireless sensor networks;Fault tolerance;Fault detection;Computational modeling;Fault tolerant systems;Automata|
|[Communication of Confidential Documents Through Email using Hybrid and Key-Exchange Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083629)|K. B. S; A. K. D; M. T; M. M|10.1109/ICCMC56507.2023.10083629|Advanced Encryption Standard;Rivest-S hamir-ADLEMAN;Polybius square cipher;Ciphertext attack;Email security;Cryptography in email;Pretty Good PRIVACY protocol;Ciphers;Privacy;Analytical models;Protocols;Software algorithms;Receivers;Software|
|[House Price Prediction using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084197)|S. Sharma; D. Arora; G. Shankar; P. Sharma; V. Motwani|10.1109/ICCMC56507.2023.10084197|Machine Learning (ML);House Price Prediction;Regression Techniques;ML Algorithm;Radio frequency;Histograms;Machine learning algorithms;Linear regression;Predictive models;Prediction algorithms;Boosting|
|[Multicast Routing Technique for Augmenting Routing Efficiency in Mobile WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083815)|K. Anand; M. Rajkumar; S. MS; S. Radhika|10.1109/ICCMC56507.2023.10083815|Mobile wireless sensor network;Multicast routing;Node mobility;Link stability;Network Simulation;Routing load minimization;Wireless sensor networks;Unicast;Receivers;Multicast communication;Routing;Throughput;Stability analysis|
|[Extending the Lifespan of Wireless Sensor Networks using Graph Theory Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084135)|K. Janakiram; P. J. Reginald|10.1109/ICCMC56507.2023.10084135|LEACH Protocol;Graph Theory;Sensor Node;Wireless Sensor Networks (WSNs);Network Lifetime;Wireless sensor networks;Energy consumption;Protocols;Simulation;Scalability;Systems architecture;Spread spectrum communication|
|[Cognitive Fire Alert Framework for Realizing Fire in Unfriendly Environment using Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083891)|A. Sathiya; A. K. Velmurugan; P. Santhuja; G. Indra|10.1109/ICCMC56507.2023.10083891|Random forest;Cognitive fire recognition;Fire alarm framework;Wireless sensor networks;Machine learning;nan|
|[Analysis of Hybrid Inverter with Solar Battery Charging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084245)|V. Kalandhar; A. V. Reddy; G. Y. Tejasree; G. Udith; R. G. Charan; N. A|10.1109/ICCMC56507.2023.10084245|Hybrid inverter;Uninterrupted power supply (UPS);Power quality fluctuations;Critical load;Power supplies;Power quality;Estimation;Solar energy;Inverters;Hybrid power systems;Batteries|
|[Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084040)|B. P. Kumar; J. Umamageswaran; A. V. Kalpana; R. Dhanalakshmi|10.1109/ICCMC56507.2023.10084040|Sensor node mobility;Trust manager;Security algorithm;Mobile wireless sensor networks;Received signal strength;Trustable routing;Supportive account;Wireless communication;Wireless sensor networks;Computational modeling;Routing;Throughput;Behavioral sciences;Delays|
|[A Review on mmWave Antennas for Wireless Cellular Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084024)|A. Rawat; D. Yadav; M. Tiwari|10.1109/ICCMC56507.2023.10084024|Millimeter Wave;Wireless Communication;Antenna;5G;Cellular Wireless Communication;Wireless communication;Costs;5G mobile communication;Millimeter wave technology;Interference;Mobile antennas;Data communication|
|[Body Area Wireless Sensor Network Communication with High Data Rate using Defected Ground Plane Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084258)|S. P. Rajan; R. Sripathi; D. R. Suryaraj; G. Thirumal|10.1109/ICCMC56507.2023.10084258|Body Area Wireless Sensor Networks;Flame Retardant 4 substrate;Defective ground Antennas;Wireless Local Area Network;Wireless communication;Temperature sensors;Wireless sensor networks;Wireless LAN;Temperature;Transmitting antennas;Bandwidth|
|[Vivaldi Antenna Implementation for Rescue and See-Through-Wall Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083992)|K. C T; A. K. Shrivastav; R. R; M. Harika; M. K. Priya; M. B. Sree|10.1109/ICCMC56507.2023.10083992|nan;Antenna measurements;Voltage measurement;Vivaldi antennas;Bandwidth;Receivers;Ultra wideband antennas;Real-time systems|
|[Design and Analysis of Fractal Antenna for Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084218)|K. S. Vupputuri; S. Kamani; S. A. Sale; Y. R. Reddy; J. P. Sudabathula|10.1109/ICCMC56507.2023.10084218|Fractal;Reflection coefficient;Return loss;Broadband;Wireless LAN;Dielectric constant;Bandwidth;Voltage;WiMAX;Fractals;High frequency|
|[Development of Diagonally Positioned MIMO Antenna with Improved Isolation and ReS Mitigation for Multiband Uses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083589)|M. Bindhu; Gomathi; S. S; A. Rajak; M. Ibrahmin|10.1109/ICCMC56507.2023.10083589|nan;Microwave antennas;Antenna measurements;Transmitting antennas;WiMAX;Reflector antennas;Telecommunications;Reflection coefficient|
|[Smart Grid Peer-to-Peer Exchanging Energy System using Block Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084032)|P. N. Kumar; B. Selvakumar; V. R; K. Rajkumar; K. K. Kumar; A. S. Kamaraja|10.1109/ICCMC56507.2023.10084032|Block chain;Renewable Energy Sources (RES);Transact energy management (TEM);P2P energy trading;Smart Contracts (SCs);Transactive energy;Scalability;Smart contracts;Memory;Production;Real-time systems;Blockchains|
|[Trust based Reputation Framework for Data Center Security in Cloud Computing Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084093)|P. S; R. S. Ponmagal|10.1109/ICCMC56507.2023.10084093|Cloud Computing;Security;Framework;Trust Degree;Reputation;Data Center;Cloud computing;Data centers;Costs;Simulation;Computational modeling;Delays;Servers|
|[Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084033)|N. A. S. Vinoth; M. Sindhuja; A. M. Nancy; R. R. Devi|10.1109/ICCMC56507.2023.10084033|Cloud Service Provider (CSP);Identity-based broadcast encryption (IBBE);Conditional Proxy Re-Encryption (CPRE);Cloud computing;Data privacy;Computational modeling;Distributed databases;Data transfer;Real-time systems;Data models|
|[Securing Sharable Electronic Health Records on Cloud Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083743)|D. B. Srinivas; D. K. M; R. H. P; L. H|10.1109/ICCMC56507.2023.10083743|CloudStorage;EHR;Firebase;Cloud storage;Cloud computing;Electric potential;Information sharing;Authentication;Organizations;Data breach;Encryption|
|[Statistical Analysis of Design Aspects on Various Graph Embedding Learning Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083741)|M. A. Madhuri; T. Uma Devi|10.1109/ICCMC56507.2023.10083741|Machine learning;Deep learning;static graphs;dynamic graphs;nan|
|[Secure Mobile ID Architecture on Android Devices based on Trust Zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083903)|K. K. Kamal; S. Gupta; P. Joshi; M. Kapoor|10.1109/ICCMC56507.2023.10083903|Software Vulnerabilities;Trusted Execution Environment (TEE);Android Application;Mobile System Security;nan|
|[A Competent Medical Image Steganography using Improved Optimization Algorithm with Huffman Encoding Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083698)|B. Ramapriya; Y. Kalpana|10.1109/ICCMC56507.2023.10083698|Medical Image Steganography;DT-CWT (Dual Tree Complex Wavelet Transform);SSOA (Salp Swarm Optimization Algorithm);Double Matrix XOR encoding;Huffman Coding;Compression;nan|
|[Malware Detection and Analysis using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083809)|M. Sirigiri; D. Sirigiri; R. Aishwarya; R. Yogitha|10.1109/ICCMC56507.2023.10083809|Computer malware;Trojan;Keyloggers;Port forwarding;Social Engineering;Pre-installation detection;post-installation detection;nan|
|[A Blockchain Application for the Verification of Academic Information and Scalable Certification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083848)|C. D. Pradeep; M. Ashislh; R. Aishwarya; R. Yogitha|10.1109/ICCMC56507.2023.10083848|Blockchain;Certificate validation;Fake certificates;Data integrity;Companies;Software systems;Forgery;Blockchains;Stakeholders;Reliability|
|[A Survey on Modern Innovative Secured Transport Layer Protocols on Recent Advances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084044)|V. S; R. R; S. Selvan; S. S. S; S. K; S. R|10.1109/ICCMC56507.2023.10084044|Transmission Control Protocol;User Datagram Protocol;Quick User Datagram Protocol Internet Connections;Stream Control Transmission Protocol;Datagram Congestion Control Protocol;Congestion Control;Protocols;Correlation;Telecommunication traffic;Communications technology;Internet;Servers|
|[DDoS Attack Detection on Cloud Computing Services using Algorithms of Machine Learning: Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083549)|C. Sathvika; V. Satwika; Y. Sruthi; M. Geethika; S. Bulla; S. K|10.1109/ICCMC56507.2023.10083549|Distributed denial of service attack;Cloud Computing;Machine learning;Intrusion Detection;Support vector machines;Cloud computing;Machine learning algorithms;Heuristic algorithms;Organizations;Denial-of-service attack;Feature extraction|
|[Peer to Peer Credentials Sharing using RSA and Session Token Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083606)|K. M. Gupta; K. S. Reddy; L. Vineeth; R. L. Kumar; G. Suryanarayana|10.1109/ICCMC56507.2023.10083606|Cryptography;Authentication;Single-sign-on;Caching;OAuth;Privacy;Authentication;Internet;Encryption;Task analysis|
|[A Hybrid Gradient Boost Model for Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084018)|R. Vaishali|10.1109/ICCMC56507.2023.10084018|Extreme Gradient Boosting;Hyperparameter tuning;Intrusion Detection System;Light Gradient Boost Machine;Machine Learning;Network attacks;Optimization;Training;Machine learning algorithms;Computational modeling;Intrusion detection;Reconnaissance;Predictive models;Reliability|
|[Cloud Security and Privacy Preservation - A Comprehensive Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083706)|P. Mukthasree; L. Pavani; G. Nikhila; K. S. Adarsh; N. Srinivasu|10.1109/ICCMC56507.2023.10083706|Cloud Computing;Database;Servers;Storage;Security;Threats;Cloud computing;Data centers;Data privacy;Costs;Databases;Data security;Software|
|[Evaluation of Adversarial Attacks and Detection on Transfer Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084164)|D. Vyas; V. V. Kapadia|10.1109/ICCMC56507.2023.10084164|Adversarial Attack;Transfer Learning Model;Feature-Map;VGGNet19;AlexNet;ResNet50;Training;Resistance;Error analysis;Computational modeling;Perturbation methods;Transfer learning;Closed box|
|[Pancreatic Cancer Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083716)|N. Vardhani; G. Gayathri; K. Leela; T. Bhavya; Y. D. Sravani|10.1109/ICCMC56507.2023.10083716|Pancreatic cancer;Pancreas;Deep learning;CNN algorithm;nan|
|[A Novel Fault Detection Model for Intrusion Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083541)|S. Valli; S. M; K. Shreya; H. Pitchiah; P. S. Manoharan|10.1109/ICCMC56507.2023.10083541|Fault detector;at89c52 microcontroller;feeder;control room;alarming unit;HT12E- “Holtek Encoder 12-bit”;HT12D- “Holtek Decoder 12-bit”;Voltage regulator IC7805;Optocoupler;rescuer;Regulators;Animals;Fault detection;Intrusion detection;Maintenance engineering;Electric fences;Control systems|
|[Secure Cloud Storage using a Digest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083802)|K. Sajeeth; S. Mahajan; A. Pathak; R. Saudagar; S. Shiravale|10.1109/ICCMC56507.2023.10083802|cloud;MD5;cryptography;encryption;Employee welfare;Cloud computing;Protocols;Databases;Standards organizations;Organizations;Modems|
|[Detection Methods for Distributed Denial of Services (DDOS) Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083689)|S. A. Raashid; T. Nimmagadda; C. P. Sai; A. Gandhi; K. C|10.1109/ICCMC56507.2023.10083689|Distributed Denial of Service;Packets;Servers;nan|
|[A Raspberry Pi based Smart Security Patrol Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083908)|S. Joy; R. L. Paulraj; P. M; S. M; S. Goudar; R. S|10.1109/ICCMC56507.2023.10083908|Mobile robots;Robotic Security Systems;Patrolling Robot;Sensor-Based Crime Detection;Raspberry Pi;nan|
|[Network Intrusion Detection using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083673)|V. Sujatha; K. L. Prasanna; K. Niharika; V. Charishma; K. B. Sai|10.1109/ICCMC56507.2023.10083673|Deep learning;neural networks;Network intrusion detection;NSL-KDD;reinforcement learning;deep q-learning;Epsilon Greedy;Artificial Intelligence;Training;Deep learning;Q-learning;Machine learning algorithms;Computational modeling;Network intrusion detection;Predictive models|
|[Hybrid Cryptography for Secure File Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084073)|C. Susmitha; S. Srineeharika; K. S. Laasya; S. K. Kannaiah; S. Bulla|10.1109/ICCMC56507.2023.10084073|Rivest;Shamir;Adleman Algorithm;Data Encryption Standard Algorithm;Blowfish;Key Generation;Authentication;Encryption;Decryption;Cryptography Algorithms;Data Security;Cloud computing;Elliptic curve cryptography;Solids;Virtual private networks;Cryptography;Security;Servers|
|[High Security Alert System for Industrial Atmospheric Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083512)|N. Bhavana; M. R. Ranga; A. G. Krishna; N. Manisha; D. Rithin|10.1109/ICCMC56507.2023.10083512|temperature sensor;humidity monitoring;Raspberry Pi;Internet of Things (IOT);Temperature sensors;Temperature measurement;GSM;Wiring;Wireless sensor networks;Temperature distribution;Temperature dependence|
|[VLSI Implementation of Triple-DES Block Cipher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083953)|S. Subaselvi; C. Mytheesh; R. Sanjay; G. Parithi malavan; S. D. Ragunath|10.1109/ICCMC56507.2023.10083953|DES;DNA;Triple DES;Cryptography;VLSI Design;nan|
|[Smart Communication System for Human Life Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083828)|M. S; S. G; Y. K. M; Y. S|10.1109/ICCMC56507.2023.10083828|DL;SSD;Helmet mobile net algorithm;CNN (Convolutional Neural Network);HOG;YOLO;Deep learning;Head;Gears;Transportation;Footwear;Safety;Security|
|[Intelligent Virtual Laboratory Development and Implementation using the RASA Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083701)|R. Josphineleela; S. Kaliappan; N. L; U. M. Bhatt|10.1109/ICCMC56507.2023.10083701|Artificial intelligence laboratory;Energy;Sustainable development;Virtual C;Chat using RASA;Science;Training;Electronic learning;Virtual assistants;Memory management;Virtual environments;Production;Machine learning|
|[CMOS Schmitt Trigger Circuit and Oscillator Design: The Impact of NBTI Degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084272)|A. Vijay; C. Duari; L. Garg; A. K. Singh|10.1109/ICCMC56507.2023.10084272|NBTI(Negative bias temperature instability);Schmitt Trigger;aging;CMOS;Oscillator;MOSRA;SPICE;Degradation;Negative bias temperature instability;Semiconductor device modeling;Circuit optimization;Thermal variables control;MOSFET circuits;SPICE|
|[Error Level Analysis and Deep Learning For Detecting Image Forgeries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084286)|D. Agrawal; H. Makwana; S. S. Dave; S. Degadwala; V. Desai|10.1109/ICCMC56507.2023.10084286|image forensic;deep learning;image forgery;convolutional neural network;error level analysis;Training;Deep learning;Analytical models;Image coding;Social networking (online);Computer architecture;Metadata|
|[Investigation on Privacy Hazards in Social Area Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083987)|A. Joyce; D. M. D. Preethi|10.1109/ICCMC56507.2023.10083987|Privacy;hazards;Taxonomy;Koobface worm;Performance;nan|
|[Data Security Analysis in Cloud Computing Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083547)|M. N. Durga; V. Babu; M. J. Chandra; A. S. Naga Raja; S. Kavitha; P. P. Mallik; A. V. Praveen Krishna|10.1109/ICCMC56507.2023.10083547|Cloud;Data Storage;Data Security;Cloud Based Enterprise System;Data Encryption;Privacy;Security;Cloud computing;Protocols;Target tracking;Data analysis;Computer hacking;Databases;Instruments|
|[Rapid Chaotic-based Image Encryption by Combining Chaotic and Non-Chaotic Scrambling Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084012)|S. M. R; U. Perumal; U. S. B; D. Komarasamy|10.1109/ICCMC56507.2023.10084012|Image Encryption;Chaos Theory;Scrambling Function;Chaotic Maps;Pre-encrypted image;Measurement;Chaotic communication;Social networking (online);Linearity;Transforms;Encryption;Logistics|
|[Obfuscation Technique to Protect the Hardware IP Piracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084185)|R. Seetharaman; A. Deepa; K. Mythily; V. Vidhya; S. Sunilkumar; P. Thirukumaran|10.1109/ICCMC56507.2023.10084185|Counterfeiting;Piracy;Reverse engineering;Obfuscation;Integrated circuits;TV;Reverse engineering;Logic gates;Benchmark testing;Encryption;IP networks|
|[An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083747)|M. Surya; S. Manohar|10.1109/ICCMC56507.2023.10083747|Blockchain;Cloud Architectures;Decentralized;Internet of Things (IoT);Security;Smart agriculture;Supply chain management;Costs;Supply chains;Transforms;Blockchains;Smart grids|
|[Real-Time Examining Bots based on the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083529)|A. Shiraz; R. Chouhan; R. Aishwarya|10.1109/ICCMC56507.2023.10083529|Surveillance;IoT (Internet of Things);ATmega2560;ESP32-CAM;Sensors;Industries;Rockets;Surveillance;Chatbots;Real-time systems;Safety;Machinery|
|[IoT based Weather, Soil, Earthquake, and Air Pollution Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083932)|Y. Pal; S. Nagendram; M. Saleh Al Ansari; K. Singh; L. A. A. Gracious; P. Patil|10.1109/ICCMC56507.2023.10083932|Weather Condition;soil;air quality;earthquake;environmental monitoring;Internet Of Things (IoT);flood monitoring;nan|
|[Hybrid Evolutionary Algorithm with Energy Efficient Cluster Head to Improve Performance Metrics on the IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083708)|J. D. Lal; T. Balachander; T. S. Karthik; S. Ariawan; P. M S; M. Tiwari|10.1109/ICCMC56507.2023.10083708|Internet of Things;Clustering;Energy efficiency;Evolutionary algorithm;Fitness value;nan|
|[An Efficient IoT based Smart Vehicle Parking Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083977)|J. Immanuel; B. Bersha; M. Boomadevi; N. Soundiraraj; K. L. Narayanan; R. S. Krishnan|10.1109/ICCMC56507.2023.10083977|IoT;RFID reader;Arduino mega;WiFi module;DC motor;nan|
|[Intelligent Crypto Mining Vehicle using AI Processor with IOT Payment System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083520)|M. Libina; V. Gnanadesigan|10.1109/ICCMC56507.2023.10083520|Crypto vehicle;Mining;IoT (Internet of Things);AI (Artificial Intelligent);Bitcoin;Computers;Transportation;Bitcoin;User experience;Blockchains;Workstations;History|
|[Recommender System for E-Commerce Application based on Deep Collaborative Conjunctive Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083923)|K. Kumaran; G. Saranya; V. Subhaa; S. S. Krishna; V. S. Prakash|10.1109/ICCMC56507.2023.10083923|recommender system;deep learning;DCCR;multilayered perceptron;auto-encoder;nan|
|[IoT Wearable Breast Temperature Assessment System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083511)|B. Ashreetha; D. G. V; H. Anandaram; N. B. A; N. Gupta; B. K. Verma|10.1109/ICCMC56507.2023.10083511|Abnormalities;Cancer;Detection;Internet of Things;Temperature;Sensor;Temperature sensors;Performance evaluation;Temperature measurement;Temperature distribution;Wearable computers;Microprocessors;Computer architecture|
|[An Android Application for Smart Garbage Monitoring System using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084308)|J. S; J. Ch; P. M; C. K. V; U. B|10.1109/ICCMC56507.2023.10084308|Android studio;Google Cloud;Firebase;Arduino nano board;Moisture sensor;Touch sensor;Waste management;Cloud computing;Operating systems;Green products;Urban areas;Registers;Mobile applications|
|[Hybrid Rule based Classification of Attacks in Internet of Things (IoT) Intrusion Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083504)|R. Alexander; K. P. M. Kumar|10.1109/ICCMC56507.2023.10083504|Traffic Classification;Internet of Things (IoT);Rule Classifier;Attacks;Wireless communication;Computational modeling;Intrusion detection;Machine learning;Telecommunication traffic;Predictive models;Data models|
|[Detection of Ripe and Raw Tomatoes using Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084228)|C. N. Vanitha; S. Malathy; S. A. Krishna; M. Manikantan; P. D. Kumar; M. G. Cibi|10.1109/ICCMC56507.2023.10084228|Internet of Things;Ripe and Raw tomato;Natural illumination;Sensors;Color;Agriculture;Sensors;Internet of Things;Servomotors|
|[First Aid and Emergency Assistance Robot for Individuals at Home using IoT and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083934)|M. Dias; H. Aloj; N. Ninan; D. Koshti; S. Kamoji|10.1109/ICCMC56507.2023.10083934|Emergency assistance Robot;Distress and scream detection;First aid robot;Search and rescue robot;Smart IoT robot;Human detection;Robot for elder people;nan|
|[Student Tracking System in School Bus using Face Recognition and IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084071)|S. Swathi; A. N. J; V. L. S; R. R|10.1109/ICCMC56507.2023.10084071|IoT;Face Recognition;GPS;Schedules;Text recognition;Face recognition;Cameras;Real-time systems;Delays;Safety|
|[IoT based Modern Agriculture Buffer Stock System AAF-Availability Accessibility Feasibility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084174)|P. N; S. D. K; S. O. S; S. Jabeera; S. R; M. R|10.1109/ICCMC56507.2023.10084174|Buffer Stock;Warehouse;Transportation;IoT;Information System;Data Deliberating Mechanism;Agricultural Buffer System;Government;Transportation;Crops;Production;Humidity;Computer architecture;Communication channels|
|[Energy Optimization and Dynamic Adaptive Secure Routing for MANET and Sensor Network in IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083519)|J. V. Gripsy; A. Jayanthiladevi|10.1109/ICCMC56507.2023.10083519|MASNET;Dynamic Adaptive secure Routing;Location Aided Routing;Sensor Network;Transverse Wave Rectangular with Dynamic Adaptive Secure Routing;Wireless communication;Wireless sensor networks;Adaptive systems;Simulation;Routing;Routing protocols;Mobile nodes|
|[Design of Real-Time Automatic Drainage Cleaning and Monitoring System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084257)|Y. Nandini; K. V. Lakshmi; T. I. S. Srujan; M. Yasheswi; K. S. Jagadish|10.1109/ICCMC56507.2023.10084257|sewage;Node micro-controller unit;Programmable Logic Controller;communication;Internet of Things;Waste materials;Roads;Solids;Robot sensing systems;Minimization;Cleaning;Real-time systems|
|[Pragmatic Evaluation of IoV based Cluster Formation Models for Efficient Routing Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084224)|S. R. Suryawanshi; P. Gupta|10.1109/ICCMC56507.2023.10084224|Internet of Vehicles (IoV);Routing;Clustering Methods;Energy;Machine Learning;Delay;Scalability;Complexity;Throughput;Measurement;Scalability;Biological system modeling;Computational modeling;Routing;Throughput;Energy efficiency|
|[Person Re-Identification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083656)|A. N; A. N. L; A. M; A. G|10.1109/ICCMC56507.2023.10083656|ResNet50;Person re-identification;Accuracy;image processing;MATLAB;Training;Deep learning;Computational modeling;Lighting;Cameras;Object recognition;Task analysis|
|[Analysis of Air Pollution in a Smart City Infrastructure using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083863)|P. P; P. K. G P; M. kumar E M; M. M; C. S. M|10.1109/ICCMC56507.2023.10083863|IOT;Air Pollution;RFID;Node MCU;Temperature measurement;Temperature sensors;Atmospheric measurements;Smart cities;Real-time systems;Pollution measurement;Security|
|[A Review on Ongoing Medical Care Observing Framework for Cardiovascular Patients Utilizing IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084082)|M. L. Shiny; N. Ramrao; K. Murugan|10.1109/ICCMC56507.2023.10084082|e-Health;Remote Diagnosis;Wearable devices;Wearable computers;Medical services;Organizations;Mental health;Sensors;Internet of Things;Security|
|[Arduino-based Railway Line Tracking System for Mitigating Animal Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083688)|K. S. V. S. K. D. Prakash; V. R. Aduru; R. H. Vardhan Manikanta; G. N. Sai Sri; S. Afthab|10.1109/ICCMC56507.2023.10083688|Arduino;Ultrasonic sensor;Passive Infrared Sensor (PIR);Indian Regional Navigation Satellite System (IRNSS);PIEZO buzzer;Satellites;Tracking;Wildlife;Cows;Satellite navigation systems;Rail transportation;Acoustics|
|[Enhancing Intrusion Detection in IoT Botnets using Novel Pay-Offs and Matching Coin Game Comparing with Dominant Game Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083562)|A. T. P|10.1109/ICCMC56507.2023.10083562|Game Strategy;Intrusion Detection System;Novel Pay-offs and matching coin Game Method;Dominant Game Strategy Method;Botnets;Botnet;Intrusion detection;Games;Machine learning;Nash equilibrium;Denial-of-service attack|
|[Advance Computing in IoT based High-Security Smart Bank Locker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084110)|B. Khokher; M. B. Savadatti; A. Kumar; T. V. M. Nikhil; P. Raj; A. V. Thakre|10.1109/ICCMC56507.2023.10084110|Bank Locker Security System;Internet Of Things;Wi-Fi module;Sensors;Blynk App;Microcontrollers;Safety;Security;Reliability;Intelligent sensors|
|[Embedded Sensor and IoT Technology based Substation Monitoring and Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083654)|R. Krishnaswamy; K. Radhika; V. G. Hamsaveni; G. Susila|10.1109/ICCMC56507.2023.10083654|Substation monitoring;Power;Transient;Internet of everything;Substations;Temperature;High-voltage techniques;Inspection;Transformers;Control systems;Hardware|
|[Smart Health Monitoring System for Coma Patients using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084196)|S. K; S. K; Y. M. G; T. P|10.1109/ICCMC56507.2023.10084196|Variable Sensors;Network Connectivity;Internet of Things;GSM Module;Coma Patients;Temperature measurement;Temperature sensors;Temperature distribution;Patient monitoring;Smart healthcare;Real-time systems;Reliability|
|[Drowsiness Detection for Drivers using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084081)|S. S S; K. M N; S. M; K. K; A. M|10.1109/ICCMC56507.2023.10084081|Sleep detection;Raspberry Pi;L298N Motor Drive;Rotary Encoder;Vibrations;Sleep;Face recognition;Wheels;Switches;Vibration measurement;Recording|
|[Machine Learning & Internet of Things in Plant Disease Detection: A comprehensive Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083972)|S. Pareek; A. Kumar; S. Degadwala|10.1109/ICCMC56507.2023.10083972|Internet of things;Machine Learning;Support Vector Machine;Navier Bayer's;Decision Tree;Random Forest;K-nearest Neighbor;Support vector machines;Smart agriculture;Productivity;Plant diseases;Sociology;Machine learning;Real-time systems|
|[Efficient Intensity Bedded Sonata Wiles System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084287)|M. Kathiravan; S. Manohar; R. Jayanthi; R. Dheepthi; R. V. Sekhar; N. Bharathiraja|10.1109/ICCMC56507.2023.10084287|Emotion Recognition;Facial Expression;Convolutional Neural Network;Haar cascade classifier;Emotion recognition;Webcams;Face recognition;Music;Streaming media;Software;Real-time systems|
|[A Medical Model Built on Machine Learning to Evaluating the Relationship between the Depression and Living Standards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083677)|S. PragnaSri; V. Ramana Gupta Nallagattla; S. Ajajunnisa; A. K; P. B. Phanindra; S. Parvathaneni|10.1109/ICCMC56507.2023.10083677|Depression;Living Standards;Health care;Supervised Learning (Naive Bayes;MCSVMPP;KNN;Voting Classifier);Unsupervised Learning (K-Means);Support vector machines;Q-factor;Machine learning algorithms;Computational modeling;Medical services;Machine learning;Mental health|
|[Environment Friendly based Industry Safety System with IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083604)|P. Anandan; G. Suresh; N. Anbuselvan; N. Kumaratharan; K. Sasikala|10.1109/ICCMC56507.2023.10083604|Monitoring;Power;Alert;Global System for Mobile communication;Safety;Temperature sensors;Temperature measurement;Microcontrollers;Manuals;Sensor systems;Sensors;Safety|
|[Exploring Innovative IoT Solutions for Automated Battery Condition Detection in Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083663)|S. S. Leksmi; K. K. Kumar; F. A. Jeffrey Vaz; M. Yuvarani|10.1109/ICCMC56507.2023.10083663|Lead-acid (Pb) battery;Electric Vehicle (EV) battery monitoring;Wireless Fidelity (Wi-Fi) Module;sensors;Relay;Temperature measurement;Temperature sensors;Performance evaluation;Renewable energy sources;Lead;Electric vehicles;Batteries|
|[IoT based Smart Agriculture for the Detection of Plant Decay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084312)|C. V. Kumar; K. Saritha; M. V. S. S. Reddy; H. A. Pai|10.1109/ICCMC56507.2023.10084312|IoT;Automation;Sensor;Algorithm;Energy;Sustainable development;Temperature sensors;Smart agriculture;Image color analysis;Plants (biology);Crops;Sensor systems;Internet of Things|
|[Development of IoT based Health Monitoring System for Disables using Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084026)|R. Josphineleela; M. Jyothi; L. Natrayan; A. Kaviarasu; M. Sharma|10.1109/ICCMC56507.2023.10084026|Internet of Things (IoT);Energy;monitoring system;disable patients;mobile application;Sustainable development;microcontroller;Temperature sensors;Temperature measurement;Hypertension;Microcontrollers;Mobile handsets;Software;Sensors|
|[Development of Healthcare Architecture based on Cloud Technology and IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084029)|L. L. P. Kumar; C. Ahalya; S. Kaliappan; A. P. H|10.1109/ICCMC56507.2023.10084029|Internet of Things;Energy;Healthcare;cloud Technology;Sustainable development;Network architecture;Wireless communication;Training;Cloud computing;Wireless sensor networks;Urban areas;Computer architecture;Network architecture|
|[Employing IoT and Machine Learning to Minimize Industrial Structure Resource Utilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083959)|A. Rajalingam; G. Charulatha; K. Machap; R. Kumudham; M. R. Prabhu|10.1109/ICCMC56507.2023.10083959|Machine Learning;power efficiency;Internet of Things;sensors;transmission power;Long Range Radio;nan|
|[Development of Machine Learning Framework for the Protection of IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083950)|R. Mohandas; N. Sivapriya; A. S. Rao; K. Radhakrishna; M. B. Sahaai|10.1109/ICCMC56507.2023.10083950|Machine Learning;Internet of Things Devices;Classification;Random Forest;Security;nan|
|[Internet of Things (IoT) Feedback System using Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083989)|P. G; R. B; M. Ayyannan; R. B. C; K. D. T; N. S. Kumar|10.1109/ICCMC56507.2023.10083989|Feedback System;IoT;Raspberry Pi;Industries;Documentation;Organizations;Maintenance engineering;Logic gates;Software;Product development|
|[A Roadmap Toward Prospects for IoT Enabled 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084183)|G. Singh; J. Singh; D. Mitra; C. Prabha|10.1109/ICCMC56507.2023.10084183|Architecture of 5G IoT;Technologies Enablers over 5G-IoT Era;Challenges and Issues of IoT-5G;Intelligent networks;5G mobile communication;Roads;Employment;Transforms;Network architecture;Market research|
|[Development of Weight System Embedded with Tracking System using Arduino UNO Rev3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084100)|Y. Sangeetha; P. S. Sashank; C. V. Satyanarayana; M. Geethika|10.1109/ICCMC56507.2023.10084100|Asset;Weight System;Arduino UNO REV3;GSM;Location;GSM;Prototypes;Mobile handsets;Internet|
|[The Hustlee Credit Card Fraud Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084063)|S. S. Velicheti; A. S. H. Pavan; B. T. Reddy; N. V. Srikala; R. Pranay; S. K. Kannaiah|10.1109/ICCMC56507.2023.10084063|Digital marketing;machine learning;PCA's isolation forest technique;logistic regression;decision tree;XGBoost;Naive Bayes;random forest;Machine learning algorithms;Soft sensors;Europe;Forestry;Credit cards;Fraud;Standards|
|[A Novel Energy Harvesting Logics using MEMS for IoT Assisted Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083545)|I. H. S; R. S. P; N. G; S. R. S|10.1109/ICCMC56507.2023.10083545|micro electro mechanical systems;internet of things;electromagnetic waves;vibrating;thermal;harvesting;Micromechanical devices;Wireless communication;Radio frequency;Wireless sensor networks;Green products;Electromagnetic scattering;Safety|
|[Reduced Device Count 9-Level Inverter for Standalone Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084282)|S. K. Ghanapuram; D. Sirimalla; V. Edla; S. K. Bykani; L. Utpalla|10.1109/ICCMC56507.2023.10084282|Multilevel inverter;Cascaded H-Bridge inverter;THD;Carrier Based PWM Technique;Asymmetrical Multilevel inverter;Costs;Switching loss;Switches;Pulse width modulation;Multilevel inverters;Control systems;Topology|
|[Case Study and Analysis to Improve Power Gating Feature in SoC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083980)|C. V. Reddy; G. K. Singh; D. Rawat; P. Vikas; S. Biswas; V. Gupta|10.1109/ICCMC56507.2023.10083980|Vmin Power gates;Power delivery network (PDN);Metal Insulated Metal (MIM) capacitors;Impedance profile;nan|
|[Grid-Connected Inverter Fed from PV Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083810)|V. Kumar; Y. M. aniteja Reddy; P. Meghana; M. Prashanth; J. Srilakshmi; K. Chenchireddy|10.1109/ICCMC56507.2023.10083810|Inverter;PV panel;Grid;Space vector pulse width modulation;PI control;Simulation;Switching loss;Voltage;Switches;Inverters|
|[15 Level Inverter for Stand-Alone Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083851)|N. Rajasekhar Varma; D. G; K. Tanuja; G. Avinash; K. S. Reddy; T. S. Teja|10.1109/ICCMC56507.2023.10083851|Inverters;MLI;PWM technique;harmonics;Industries;Reactive power;Voltage;Pulse width modulation;Multilevel inverters;Nonhomogeneous media|
|[Series Hybrid Vehicles by using Solar and Batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084176)|S. D. Ram; A. V. C. Cholleti; M. C. Rohit; S. Tharun|10.1109/ICCMC56507.2023.10084176|Electric Vehicle;Hybrid Vehicle;Battery;BLDC Motor;Industries;Earth;Gases;Pollution;Droughts;Solar energy;Batteries|
|[Distribution System Power Quality Improvement using IRP Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084162)|V. Kumar; K. Chenchireddy; K. R. Sreejyothi; T. Usha; A. Venkatasaireddy; B. Rakesh|10.1109/ICCMC56507.2023.10084162|Power quality;Active power filter;Harmonics;Voltage fluctuations;Reactive power;Reactive power;Power filters;Simulation;Wires;Power quality;Active filters;Harmonic analysis|
|[Certain Investigation of Filtered-OFDM in Visible Light Communications system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084283)|H. S. R. Hujijo; M. Ilyas; A. A. Qasim; U. Ullah|10.1109/ICCMC56507.2023.10084283|Filtered-OFDM;Visible Light Communication;DC-bias;QAM;Peak to Average Power Ratio;Bit Error Rate;Bit error rate;Modulation;Peak to average power ratio;Intensity modulation;Visible light communication|
|[Design and Implementation of Novel Reversible Full Adder using QCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084152)|R. Karwa; S. Singh; N. N; Y. Karekar; R. Seethur|10.1109/ICCMC56507.2023.10084152|Full width;Fixed width;Adder Tree (AT);approximate AT;Microprocessors;Layout;Computer architecture;Nonhomogeneous media;Compressors;Complexity theory;Delays|
|[Reduction of THD and Power Quality Improvement by using 48-pulse GTO-based UPFC in the Transmission Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083821)|K. R. Sreejyothi; S. Umesh; V. R. Kumar; K. Chenchireddy; Y. A. Sai; B. Nagarjun|10.1109/ICCMC56507.2023.10083821|UPFC;STATCOM;SSSC;SRF theory;PI controller;Total harmonic distortion;Reactive power;Power quality;Capacitors;Automatic voltage control;Steady-state;Phase locked loops|
|[Surveillance of Forest Areas and Detection of Unusual Exposures using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083641)|M. Arunkumar; B. P. Raj|10.1109/ICCMC56507.2023.10083641|Threats include unauthorized entry;poaching;trafficking;Radio frequency Identification;Global Positioning System;nan|
|[Design of Chevron Electrothermal Actuator for High Force Defense Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083925)|S. Ramya; S. P. Kumar; T. Aravind; U. Rasan; G. D. Ram; D. Lingaraja|10.1109/ICCMC56507.2023.10083925|Micro Electro Mechanical System;Micro-actuator;Electrothermal;Chevron;High force;nan|
|[Comprehensive Analysis of DG-TFET with Ferro Electric Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083683)|Y. Kadale; P. Singh; D. S. Yadav|10.1109/ICCMC56507.2023.10083683|Negative Capacitance;Ferro Electric Material;Subthreshold Swing;Energy Band;Electric Field;MOSFET;TFETs;Electronics industry;Logic gates;Capacitance;Permittivity|
|[Latency-Aware Frequency Scaling in Time-Triggered Network-on-Chip Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084313)|R. Nambinina; D. Onwuchekwa; R. Obermaisser|10.1109/ICCMC56507.2023.10084313|NoC;time-triggered;frequency scaling;multi-core architecture;power-saving;Time-frequency analysis;Schedules;Energy consumption;Power demand;System performance;Network-on-chip;Computer architecture|
|[Hybrid Variable Selection Approach to Analyse High Dimensional Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084227)|R. S. Subramanian; K. Sudha; K. V. Ramana; S. SivaKumar; R. Nithyanandhan; M. Nalini|10.1109/ICCMC56507.2023.10084227|Feature Selection;Filter;Wrapper;Hybrid;Naïve Bayes;Input variables;Machine learning;Feature extraction;Real-time systems;Computational efficiency;Noise measurement|
|[Execution of Convolution Coding Method for FPGA in Industrial Automation using VERILOG HDL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084251)|K. Venusamy; S. Kannadhasan; A. Vimalesh; P. Chandramohan; M. Shanmugam; K. Priyadarsini|10.1109/ICCMC56507.2023.10084251|FPGA;Convolution;Automation;FEC;Encoder;Decoder;Convolutional codes;Convolution;Forward error correction;Writing;Digital communication;Encoding;Registers|
|[Arduino based Smart Blind Stick for People with Vision Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083752)|P. Rajesh; R. Sairam; M. D. Kumar; P. K. Eswar; Y. Keerthi|10.1109/ICCMC56507.2023.10083752|Arduino Uno;Ultrasonic sensor;Infrared sensor;Soil moisture sensor;GPS (Global Positioning System);GSM (Global System for Mobile communication) module;Legged locomotion;GSM;Microcontrollers;Soil moisture;Moisture;Stairs;Sensor systems|
|[Bayesian Trust Prediction and EASI (Energy Accumulation with Swarm Intelligence) Routing Protocol for Securing WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083723)|N. Dhanalakshmi; M. S. Kumar; B. Indrani; R. Srinivasan|10.1109/ICCMC56507.2023.10083723|Wireless Sensor Network;Trust prediction;Swarm intelligence;Routing protocol;Bayesian model etc;nan|
|[Mobile based Automation of Electrical Appliances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083685)|D. D. Sri Perni; M. Suneetha; M. Kantamneni; R. V. Revalamadugu|10.1109/ICCMC56507.2023.10083685|Internet of Things;Microcontroller;Automation;Node MCU ESP8266 (Espressif Systems Model);Wiring;Wireless communication;Home appliances;Fans;Technological innovation;Automation;Internet of Things|
|[Enhancement of Double Gate Tunnel Field Effect Transistor Structures with Different Variable Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083660)|N. S. Tallapaneni; M. Venkatesan|10.1109/ICCMC56507.2023.10083660|Workfunction.Subthreshold Swing;Gate on Drain Overlap;Threshold Voltage;Performance evaluation;Analytical models;TFETs;Computational modeling;Computer architecture;Logic gates;Threshold voltage|
|[The Three-Tier Architecture of Federated Learning for Recommendation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084109)|V. M; S. Vemuru|10.1109/ICCMC56507.2023.10084109|Recommendation Systems;Federated Learning;Privacy;Security;Data privacy;Privacy;Analytical models;Federated learning;Data security;Distributed databases;Data models|
|[American Sign language Recognition using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084015)|A. Puchakayala; S. Nalla; P. K|10.1109/ICCMC56507.2023.10084015|Convolution Neural Network;You Only Look Once (YOLO);Sign Language;American Sign Language (ASL);Deep Learning;Deep learning;Image recognition;Convolution;Neural networks;Buildings;Gesture recognition;Assistive technologies|
|[Performance Investigations of PV Grid-Integrated Modular Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083587)|E. Parimalasundar; P. Sravanti; S. A. Razzak; P. N. Maruthi Abhigna; S. M. M. Zubair Hussain; R. K. Mandal|10.1109/ICCMC56507.2023.10083587|Efficiency;Multilevel inverter;Pulse width modulation;Reduced switches;THD;Photovoltaic systems;Semiconductor device modeling;Total harmonic distortion;Pulse width modulation;Multilevel inverters;Control systems;Root mean square|
|[Detection of COVID-19 using ResNet50, VGG19, MobileNet, and Forecasting; using Logistic Regression, Prophet, and SEIRD Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083564)|K. Jahnavi; V. S. Josthna Battu; N. S. Sandeep; R. Anitha; R. Deepika; K. B. Prakash|10.1109/ICCMC56507.2023.10083564|ResNet50;VGG19;MobileNet;SEIRD (Susceptible;Exposed;Infectious;Recovered;Dead) model;Logistic Regression;Prophet;COVID-19;Machine learning algorithms;X-rays;Predictive models;Convolutional neural networks;Forecasting;Artificial intelligence|
|[Performance Analysis of DC-DC Converter for Electric Vehicle Charging Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084154)|E. Parimalasundar; S. Maneesha; R. Hanish; P. M. Reddy; P. K. Harish; P. K. Rao|10.1109/ICCMC56507.2023.10084154|Boost converter;Charging;Duty ratio;Efficiency;PWM technique;Renewable energy sources;DC-DC power converters;Switches;Electric vehicle charging;Performance analysis;Batteries;Topology|
|[Scaling Object Detection to the Edge with YOLOv4, TensorFlow Lite](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084319)|R. S. Praneeth; K. C. S. Akash; B. K. Sree; P. I. Rani; A. Bhola|10.1109/ICCMC56507.2023.10084319|On-device object detection;You Only Look Once Version 4;Convolutional Neural Networks;TensorFlow Lite;Visualization;Quantization (signal);Image edge detection;Computational modeling;Scalability;Object detection;Computer architecture|
|[Employing Industry 4.0 to Supervise Soil Supplements and Estimate its Content](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083990)|V. S. D. Rekha; T. Haritha; S. R. Devi; P. Avinash; V. Srinivas|10.1109/ICCMC56507.2023.10083990|ESP8266;ThingView Free App;Colour Sensor;Soil Moisture Sensor;Temperature and Humidity Sensor;Temperature sensors;Temperature measurement;Crops;Moisture;Humidity;Soil;Sensors|
|[Rapid Transit Route Access Control for Bus and Ambulance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084342)|S. Shilaskar; C. Agrawal; P. Dalve; C. Bhandari; S. Daga; S. Bhatlawande|10.1109/ICCMC56507.2023.10084342|Vehicle Detection;Python;K-Nearest Neighbor;Open-CV;Image Classification;Smart City;Bus Route Transportation System;Histograms;Microcontrollers;Feature detection;Urban areas;Clustering algorithms;Logic gates;Hardware|
|[Matlab/Simulink Study and Implementation of Static Rotor Resistance Control of Wound Rotor Induction Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084299)|S. P. K; B. K; A. A|10.1109/ICCMC56507.2023.10084299|induction motor;speed control;chopper circuit;rotor resistance control;matlab;Resistance;Induction motors;Torque;Software packages;Computational modeling;Velocity control;Rotors|
|[A Study on Metaverse in Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083910)|A. Abraham; B. Suseelan; J. Mathew; P. Sabarinath; A. K|10.1109/ICCMC56507.2023.10083910|Metaverse;E-Learning Environment (ELEM);virtual learning environment (VLE);Edu-Metaverse ecosystem;nan|
|[An Improved Design of Low Power High Speed Configurable Logic Block using 90nm CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084130)|K. M. Daiyan; A. Mustakim; S. B. Abi|10.1109/ICCMC56507.2023.10084130|Field Programmable Gate Array (FPGA);Configurable Logic Block (CLB);Complementary metal-oxide semi-conductor (CMOS);Majority Function;Lookup Table (LUT);Electronic design automation (EDA);Process Technology;Static Random Access Memory (SRAM);Layout;Random access memory;Computer architecture;Logic gates;CMOS technology;Frequency measurement;Transistors|
|[Investigation of Renewable Energy Integration Challenges and Condition Monitoring Using Optimized Tree in Three Phase Grid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083636)|N. Shah; A. Haque; M. Amir; A. Kumar|10.1109/ICCMC56507.2023.10083636|LVRT;optimizable decision tree;grid codes;HVRT;decision tree;point of common coupling;Training;Renewable energy sources;Tropical cyclones;Machine learning;Voltage;Data models;Real-time systems|
|[An Improved System for Brain Pathology Classification using Hybrid Deep Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083870)|V. Sathya; S. Dhanabal|10.1109/ICCMC56507.2023.10083870|Brain tumor;deep learning;accuracy;AlexNet;transfer learning;higher-grade and Convolutional Neural Network;Deep learning;Machine learning algorithms;Error analysis;Magnetic resonance imaging;Computational modeling;Transfer learning;Speech recognition|
|[Hand Gesture Controlled Car using Bluetooth Modules and Accelerometer Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083843)|A. K. Dutta; J. S. G. Paul; S. Siddharth; K. G. Nitish; J. S. S. Sundar; P. S. Manoharan|10.1109/ICCMC56507.2023.10083843|Accelerometer;Car;Bluetooth Module;Motor Driver;Arduino UNO;Accelerometers;Wireless communication;Wide area networks;Radio frequency;Bluetooth;Transmitters;Wheelchairs|
|[Smart Garbage Monitoring System with Dynamic Programming Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084145)|N. Sugitha; M. K; H. M. K; D. G; P. K. S|10.1109/ICCMC56507.2023.10084145|Embedded systems;Wireless communication;Dynamic programming;Internet of Things;Low power systems;Waste management;Wireless sensor networks;Costs;Microcontrollers;Urban areas;Sociology;Mobile applications|
|[Optimized Controller Design for Induction Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083601)|S. K. Sunori; A. Bhakuni; P. Joshi; G. S. Jethi; P. Juneja|10.1109/ICCMC56507.2023.10083601|Induction motor;Speed control: FOPDT: Simulated Annealing;Genetic algorithm;Control system;Settling time;Induction motors;Simulated annealing;Control systems;Regulation;Mathematical models;Optimization;Genetic algorithms|
|[Development of Wireless Pulse Rate Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083675)|M. M. Tarafder; M. Tarequzzaman; M. A. Rahman|10.1109/ICCMC56507.2023.10083675|Android;Bluetooth;Hardware;Medical services;Monitoring;Pulse rate;Wireless network;Wireless sensor networks;Bluetooth;Heart beat;Pulse measurements;Wireless networks;Myocardium;Cardiovascular diseases|
|[Design of Bluetooth-based Solar Panel Cleaner Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084178)|R. Priya; K. S. Radha; M. Karthikeyan|10.1109/ICCMC56507.2023.10084178|Solar Power;Cleaner Robot;Bluetooth;Motor Driver;Solar Panel;Power Management;Renewable energy sources;Wheels;Solar energy;Programming;Cleaning;Mobile robots;Solar panels|
|[VLSI Design of Pipelined FFT Architecture for DSP Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083571)|S. Kumar T; N. K; R. R; R. r. S|10.1109/ICCMC56507.2023.10083571|The Shifter;Rotators;Pipelined register;Delay power analysis;Fast Fourier Transform and Inverse Fast Fourier Transform;Power demand;Fast Fourier transforms;Power quality;Computer architecture;Very large scale integration;Hardware design languages;Field programmable gate arrays|
|[Vector Control of an Induction Motor for Speed Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084248)|G. Satyanarayana; M. Karthikeyan; R. Mahalakshmi; T. Vandarkuzhali|10.1109/ICCMC56507.2023.10084248|Induction motor;Speed control;Vector control;Proportional integral controller;motor flux and torque;Pulse width modulation inverters;Induction motors;Torque;PI control;Velocity control;Rotors;Regulation|
|[Surveillance System for Aquaculture Farming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084016)|J. Suganthi; V. Rajasekar; S. Sivaranjani|10.1109/ICCMC56507.2023.10084016|Wi-Fi Module;Internet of Things (IoT);Sensors (pH;ammonia;Dissolved Oxygen (DO);temperature);Nitrate;Salt;Carbonates;Bi-Carbonates);Raspberry Pi;Temperature measurement;Temperature sensors;Temperature;Transportation;Water quality;Loss measurement;Internet of Things|
|[Design of Various Modulation Schemes using DDS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083652)|S. Jothimani; A. Narmadha; C. Santhiya; S. S. Priya|10.1109/ICCMC56507.2023.10083652|Phase shifting;sampling;Binary Phase Shift Keying;Binary Amplitude Shift Keying;Binary Frequency Shift Keying;Quadrature Phase Shift Keying;Digital Design Synthesizer;Synthesizers;Digital modulation;Hardware;Generators;Binary phase shift keying;Hardware design languages;Phase locked loops|
|[Design and Implementation of FPGA based Rescue Bot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083599)|K. SUDHAKAR; S. S. PIRAJIN; J. SHANMUGAPRIYAN; S. SUJEETH|10.1109/ICCMC56507.2023.10083599|Obstacle blocks;Obstacle avoidance;Opto-coupler;Passive infrared sensor;Radiofrequency;Direct current;Wireless communication;Wireless sensor networks;Robot vision systems;Vegetation;User interfaces;Cameras;Video surveillance|
|[Applying Deep Learning Methods on Spam Review Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083900)|M. Ramu; C. J. Raj; A. Nithish; C. Boggula; G. B. G; S. K. I. Goud|10.1109/ICCMC56507.2023.10083900|Spam;Spam reviews;Spam review detection;Deep learning;CNN;RNN;MLP;LSTM;amazon dataset;nan|
|[Integration of Programmable Logic Controller with Lab VIEW for Acquiring Data, Monitoring and Controlling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083850)|K. Sidhardha; V. N. Prudhvi Raj; J. M. Krishna; K. S. Vamsi; D. S. Hari|10.1109/ICCMC56507.2023.10083850|Totally Integrated Automation (TIA) portal;Open Platform Communications (OPC) Server;NI Lab VIEW;Real time;Automation;Data Logging and Supervisory Control (DSC) module;Multithreading;Process control;Supervisory control;Programming;Software;Real-time systems;Servers|
|[Automatic Attendance System based on FaceRecognition using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084017)|S. Sharmila; G. K. Nagasai; M. Sowmya; A. S. Prasanna; S. N. Sri; N. Meghana|10.1109/ICCMC56507.2023.10084017|OpenCV;operating system;NumPy;Cmake;face detection;extraction;recognition;verification using machine learning are also discussed;Portable computers;Webcams;Face recognition;Machine learning;Video surveillance;Software;Real-time systems|
|[Super Capacitor Integrated Battery System for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084005)|B. K; A. A; S. K. G|10.1109/ICCMC56507.2023.10084005|super-capacitor;Electric Vehicle (EV);Zero Current Switching (ZCS);Time-frequency analysis;Power distribution;Switches;Supercapacitors;Inverters;Batteries;Systems simulation|
|[Water Quality Analysis and Prediction using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083776)|D. Brindha; V. Puli; B. K. S. NVSS; V. S. Mittakandala; G. D. Nanneboina|10.1109/ICCMC56507.2023.10083776|Water Quality;Machine Learning;Random Forest regression;Decision Tree;Web User Interface (UI);Temperature measurement;Urban areas;Water quality;Conductivity;User interfaces;Water pollution;Pollution measurement|
|[Machine Learning Algorithms based Student Performance Prediction based on Previous Records](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084099)|S. Chandrahasa; S. M. S. Ganeshan; R. C. Maddineni; M. S. Divya; P. Tumuluru; B. Suneetha|10.1109/ICCMC56507.2023.10084099|Student grade prediction;Performance prediction;Classification;Machine learning algorithm;Bayesian classification;Probability;Machine learning algorithms;Computational modeling;Machine learning;Companies;Predictive models;Filtering algorithms;Prediction algorithms|
|[Air Quality Prediction and Classification using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083760)|D. Komarasamy; N. G. C; N. S; N. P. L; Mohanasaranya; K. K|10.1109/ICCMC56507.2023.10083760|Air Quality Index;Machine Learning;Atmospheric modeling;Air pollution;Prediction algorithms;Real-time systems;Nitrogen;Internet of Things;Time factors|
|[Spam Email Filtering using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083607)|D. Komarasamy; O. Duraisamy; M. S. S; S. Krishnamoorthy; S. Rajendran; D. M. K|10.1109/ICCMC56507.2023.10083607|Spam;Ham;Probability;Filtering Techniques;Classification Algorithms;Support vector machines;Industries;Machine learning algorithms;Filtering;Unsolicited e-mail;Neural networks;Knowledge based systems|
|[Detection of Cancer in Human Blood Sample using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083971)|C. Spandana; R. P. Kumar|10.1109/ICCMC56507.2023.10083971|Blood smear images;White Blood Cells;Blast Cells;Leukemia;Microscopic images;K-Mean clustering;Feature Extraction;nan|
|[Prognosis of Stroke using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084158)|K. S. R. S; B. Chandra; K. Kausalya; C. RM; G. R. V|10.1109/ICCMC56507.2023.10084158|Stroke Prediction;Random Forest;Regression;Machine learning;Training;Machine learning algorithms;Hospitals;Predictive models;Stroke (medical condition);Prediction algorithms;Classification algorithms|
|[Prediction of Insufficient Accuracy for Patients Length of Stay using Deep Belief Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084213)|C. V. Kumar; S. M. S; S. R|10.1109/ICCMC56507.2023.10084213|Machine Learning;Novel Deep Belief Network;Convolutional Neural Network;Health Care Analysis;Readmission;Clinical Prediction;Support vector machines;Machine learning algorithms;Current measurement;Neural networks;Medical services;Machine learning;Prediction algorithms|
|[Classification and Prediction of Diabetic Retinopathy Severity using Transfer Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083510)|J. Yogapriya; Dhanalakshmi|10.1109/ICCMC56507.2023.10083510|Diabetic retinopathy;CNN;VGG16;transfer learning;APTOS 2019;Visualization;Retinopathy;Computational modeling;Transfer learning;Blindness;Retina;Prediction algorithms|
|[Optimised Home Electricity Management using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084159)|M. Arunkumar; S. Devadharshini|10.1109/ICCMC56507.2023.10084159|Raspberry pi;Wi-Fi modem;relay;PHP;Internet of Things (IOT);Python;Machine learning;Home appliances;Protocols;Machine learning;User interfaces;Software;Mobile handsets;Internet of Things|
|[Machine Learning and Deep Learning Techniques on Accurate Risk Prediction of Coronary Heart Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083756)|M. S. Gangadhar; K. V. S. Sai; S. H. S. Kumar; K. A. Kumar; M. Kavitha; S. S. Aravinth|10.1109/ICCMC56507.2023.10083756|Deep learning;Coronary Heart Disease Prediction;Artificial Neural Networks;Machine Learning;Heart;Deep learning;Machine learning algorithms;Computational modeling;Transfer learning;Artificial neural networks;Predictive models|
|[Small Signal Analysis of Photovoltaic based Water-Pumping System Driven by Induction Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083929)|D. Vannurappa; G. P. R. Reddy; K. Deepak; Y. Hazarathaiah; S. S. Prasad; S. G. Malla|10.1109/ICCMC56507.2023.10083929|Photovoltaic;Water Pumping System;Induction Motor;Small Signal Analysis;nan|
|[Prediction of Healthy and Unhealthy Food Items using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084041)|N. P; T. S. R; S. M; D. B; K. S|10.1109/ICCMC56507.2023.10084041|Deep Learning;Traditional food;Fast foods;Healthy;Unhealthy;Classification;Training;Deep learning;Visualization;Solid modeling;Image recognition;Social networking (online);Internet|
|[Constructive Analysis on Prediction and Detection of Diabetic Retinopathy (DR) using Machine Learning Algorithms - A Generic Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083913)|C. Keerthana; P. Tejasree; M. V. Subba Rao; R. S. Sai Pavan Kumar; P. Yalla|10.1109/ICCMC56507.2023.10083913|Machine Learning;Diabetic retinopathy;Supervised and Unsupervised Learning Techniques;nan|
|[Comparison and Analysis of Various Machine Learning Algorithms for Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083509)|S. Kanamarlapudi; V. S. Yakkala; B. Gayathri; K. V. Nusimala; S. S. Aravinth; S. S|10.1109/ICCMC56507.2023.10083509|Machine learning;disease prediction;KNN;Naive Bayes;Decision Tree;Random Forest;Machine learning algorithms;Machine learning;Predictive models;Prediction algorithms;Data models;Medical diagnosis;Task analysis|
|[Optimising Face Recognition System using Contrastive Learning and Contrastive Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084086)|A. J. Kumar; A. Manvitha; B. Poojitha; M. Mahitha; K. Ashesh; K. V. D. Kiran|10.1109/ICCMC56507.2023.10084086|Contrastive learning;Contrastive loss;Face Recognition;Dimensionality Reduction;Self-supervised algorithm;Training;Image recognition;Face recognition;Computational modeling;Machine vision;Lighting;Machine learning|
|[Developing a Pre-Consultation System using Machine Learning for Medical Diagnostics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083792)|M. Likhitha; G. Kalyani; T. N. Vennela; D. M. Paul|10.1109/ICCMC56507.2023.10083792|Disease Prediction;Machine Learning;KNN;Decision Tree;Naïve Bayes;Random Forest;Machine learning algorithms;Predictive models;Prediction algorithms;Data models;Decision trees;Reliability;Prognostics and health management|
|[Study of Deep Learning Approaches for Diagnosing Covid-19 Disease using Chest CT Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083640)|A. S; A. L. R. P J|10.1109/ICCMC56507.2023.10083640|Deep Learning;Computed Tomography (CT)COVID-19;Diagnosis;Preprocessing;Segmentation and Classification;COVID-19;Deep learning;Histograms;Image segmentation;Computer viruses;Computed tomography;Computational modeling|
|[Detecting Phishing Websites using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083999)|M. Kathiravan; V. Rajasekar; S. J. Parvez; V. S. Durga; M. Meenakshi; S. Gowsalya|10.1109/ICCMC56507.2023.10083999|Phishing Website;Random Forest;Support Vector Machine;Naïve Bayes Classifier;Decision Tree;Uniform resource locators;Machine learning algorithms;Web services;Phishing;Support vector machine classification;Pressing;Machine learning|
|[A Statistical Machine Learning Approach to Optimize Workload in Cloud Data Centre](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083957)|P. Upadhyay; K. K. Sharma; R. Dwivedi; P. Jha|10.1109/ICCMC56507.2023.10083957|Cloud Computing;Data Centre;Machine Learning;Neural Network;Load Balancing;nan|
|[A Novel Deep Learning Approach for Number Plate Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084177)|K. M. N; S. S. S; S. E; S. D; D. R|10.1109/ICCMC56507.2023.10084177|OpenCV;Tensor Flow;Convolutional neural Network;Deep learning;Tensors;Roads;Sociology;Neural networks;Predictive models;Convolutional neural networks|
|[Systematic Review of Deep Learning Models for Dental Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083729)|U. K. S; P. J; K. S. R. S|10.1109/ICCMC56507.2023.10083729|Object detection;dental imaging;classification;segmentation;Deep learning;Image segmentation;Automation;Systematics;Computational modeling;Object detection;Radiology|
|[Novel Control Scheme for Wind based Standalone Hybrid Power Generation System under Faults on Three Phase Distribution Feeders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084134)|S. S. Prasad; K. Deepak; D. Vannurappa; Y. S. I. Priyadarshini; M. B. Lakshmi; S. G. Malla|10.1109/ICCMC56507.2023.10084134|PMSG based Wind energy conversion system;Standalone hybrid system;Fuel Cell;Electrolyzer;Faults;RTDS;Maximum power point trackers;Control systems;Inverters;Real-time systems;Wind turbines;Batteries;Reliability|
|[Design of Random Number Sequence Generator using Multi-Stage Feedback with Multi-Stage Ring Oscillator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083817)|S. VIMALNATH; H. R; H. P; H. S|10.1109/ICCMC56507.2023.10083817|TRNGs;MSFRO;BIST;Randomness;Ring oscillators;Jitter;Built-in self-test;Very large scale integration;Generators;Entropy;Phase locked loops|
|[Web Application-based Diabetes Prediction using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083583)|P. T. Siva Gurunathan; R. S; R. S; N. S|10.1109/ICCMC56507.2023.10083583|K-Nearest Neighbor;Random Forest;Diabetes prediction;Web application;Ensemble classifier;Machine learning algorithms;Tissue damage;Medical treatment;Predictive models;Prediction algorithms;Diabetes;Medical diagnosis|
|[A Study on Self-Configuring Intrusion Detection Model based on Hybridized Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084290)|S. A. Bajpai; A. B. Patankar|10.1109/ICCMC56507.2023.10084290|Machine learning;Intrusion detection;Journals;Motivation;Malicious attacks;Deep learning;Measurement;Adaptation models;Analytical models;Computational modeling;Intrusion detection;Hardware|
|[Recognition of Facial Stress System using Machine Learning with an Intelligent Alert System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083566)|S. K. Suba Raja; D. A. S S L; R. P. Kumar; J. Selvakumar|10.1109/ICCMC56507.2023.10083566|EEG Sensor;Facial Emotion;GSM Sensors;Fuzzy C-means;Image recognition;Psychology;Human factors;Games;Machine learning;Electroencephalography;Software|
|[Road Damage Detection using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083795)|P. S; S. M; V. S; S. N S|10.1109/ICCMC56507.2023.10083795|Road Damage detection;Deep learning;Region-based Convolutional Neural Network;Classification;Time series analysis;Neural networks;Linear algebra;Maintenance engineering;Feature extraction;Road safety;Planning|
|[Machine Learning based Landslide Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084226)|R. Anusuya; N. Anusha; V. Sujatha; R. Radhika; S. Iniyan|10.1109/ICCMC56507.2023.10084226|Machine learning;Safety;Message Queuing Telemetry Transport protocol;Sensors;Landslide;Vibrations;Accelerometers;Landslides;Humidity;Sensor systems;Real-time systems;Sensors|
|[Calorie Estimation of Food and Beverages using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083648)|P. G. A; S. S; Y. K M; P. Kumar M|10.1109/ICCMC56507.2023.10083648|Image Segmentation;Convolutional Neural Network (CNN) algorithm;Web application;Datasets;Training;Obesity;Neural networks;Estimation;Medical services;Multitasking;Software|
|[GUI based Heart using Disease Classification using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083667)|G. Revathy; P. M. Priya; K. Senthilnathan; P. Mythili; S. V. Haridharani|10.1109/ICCMC56507.2023.10083667|Heart disease classification;features selection;disease diagnosis;intelligent system;medical data analytics;FCMIM;Neural networks;Heart;Deep learning;Costs;Neural networks;Computer architecture;Feature extraction;Optimization|
|[Microarray based Disease Prediction using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083779)|P. M. Priya; S. Krishnan; G. Revathy; L. Kalaiselvi; M. T. Usha|10.1109/ICCMC56507.2023.10083779|Spatial Expectation Maximization;Microarray;Big data;KNN Algorithm;Deep learning;DNA;Lung;Biology;Spatial databases;Skin;Gene expression|
|[Prediction on Impact of Electronic Gadgets in Students Life using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084060)|S. K; D. B; K. S; S. S|10.1109/ICCMC56507.2023.10084060|Electronic gadgets;students;Machine learning algorithms;Academic performance;improved performance;Support vector machines;Performance evaluation;Machine learning algorithms;Speech recognition;Prediction algorithms;Decision trees;Task analysis|
|[Capacity Evaluation of MIMO System:with and without Successive Interference Cancellation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084170)|S. P; R. S. Miriyala; N. Matsa; N. V. L. K. A; S. Yallapu; V. R. Ch|10.1109/ICCMC56507.2023.10084170|Capacity;Optimization;Channel model;Decorrelator;Fading channel;Bit Error Rate;Matched Filter;Throughput;Multipath fading;Matched filters;Interference cancellation;Wireless networks;System performance;Simulation;Receivers;MIMO communication|
|[Medical Diagnosis of Human Heart Diseases with and without Hyperparameter tuning through Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084156)|K. K. Baseer; S. B. A. Nas; S. Dharani; S. Sravani; P. Yashwanth; P. Jyothirmai|10.1109/ICCMC56507.2023.10084156|Cardiac Disease;Gradient Boosting Classifier(GBC);Support Vector Machine(SVM);K Nearest Neighbor(KNN);Hyperparameter tuning;Heart;Radio frequency;Buildings;Support vector machine classification;Predictive models;Prediction algorithms;Task analysis|
|[Identification of an Animal Footprint with time prediction using Deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084050)|C. Kavitha; C. Hemanath; B. P. Raj; N. Sridevi; C. Hemalatha|10.1109/ICCMC56507.2023.10084050|Local Binary Pattern Histogram (LBPH);Principal Component Analysis (PCA);Support Vector Machine (SVM);neural networks;animal footprint recognition system;Training;Deep learning;Image segmentation;Animals;Neural networks;Feature extraction;Probabilistic logic|
|[Natural Language Processing using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083608)|K. S. Varshitha; C. G. Kumari; M. Hasvitha; S. Fiza; A. K; V. Rachapudi|10.1109/ICCMC56507.2023.10083608|Natural language processing;Convolutional neural network;Word Embedding;Word2Vec;Estimation error;Databases;Measurement uncertainty;Computer architecture;Benchmark testing;Natural language processing;Loss measurement|
|[Glaucoma Detection using Convolution Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083864)|P. V; N. M; S. M. U; T. T. V; M. CH; D. Y|10.1109/ICCMC56507.2023.10083864|Feature Extraction;Feature Selection;Deep Learning;Detection;Prediction of Glaucoma;Ultraviolet sources;Visual impairment;Surgery;Computer architecture;Feature extraction;Optical imaging;Data models|
|[Deep Neural Networks (DNN) based Sports Balls Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083750)|S. L. Srikar; P. S. Gowtham; D. S. Swetha; M. H. Sri; B. B. Kumar; S. Bulla|10.1109/ICCMC56507.2023.10083750|Image classification;Deep Learning;Deep learning;Training;Shape;Neural networks;Python;Image classification;Sports|
|[Fusion of Iris and Periocular Biometrics Authentication using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083982)|N. Prasanth; S. Nethi; N. Srinivasu; C. U. Sai Kiran; T. Ketineni; G. Pradeepini|10.1109/ICCMC56507.2023.10083982|Biometrics;Iris-Recognition;Deep-Learning;Image Processing;Convolutional Neural Network;nan|
|[Neural Machine Translation using Adam Optimised Generative Adversarial Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084034)|I. V. Srisurya; P. K. R|10.1109/ICCMC56507.2023.10084034|Encoder-Decoder;Linguistic features;Long Short-term memory;Recurrent Neural Networks;Training;Vocabulary;Sequential analysis;Neural networks;Linguistics;Generative adversarial networks;Transformers|
|[Fingerprint Sensor based Biometric Payment Cards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083680)|B. T. Dommaraju; D. S. Kondaveeti; S. Katta; V. N. Sai Akarsh Devanaboina; N. L. Sowjanya Cherukupalli|10.1109/ICCMC56507.2023.10083680|transaction;biometric;privacy;payment card;security;fraudulently;Industries;Privacy;Authentication;Companies;Fingerprint recognition;Silicon;Pins|
|[Design of Binary Neural Network Soft System for Pattern Detection using HDL Tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083574)|A. Chinchanikar; P. H. Chandankhede; A. Titarmare|10.1109/ICCMC56507.2023.10083574|Convolution Neural Network (CNN) or ConvNet);Binary neural network (BNN);Deep neural network (DNN) Field programmable Logic gate (FPGA);Look Up table (LUT);Graphic Processing Unit (GPU);Canadian Institute For Advance research (CIFAR);Compute unified Device Architecture (CUDA);Measurement;Convolution;Image processing;Synthesizers;Logic gates;Hardware;Real-time systems|
|[Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084191)|D. Bandopadhyay; R. Ghosh; R. Chatterjee; N. Das; B. Sadhukhan|10.1109/ICCMC56507.2023.10084191|Healthcare;Chatbot;Machine Learning;Pandemics;Hospitals;Computational modeling;Neural networks;Transportation;Speech recognition;Machine learning|
|[Review of Power Quality Issues in Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083904)|G. R; D. M; K. K; A. K. P; B. P. M; N. S. Kumar|10.1109/ICCMC56507.2023.10083904|Power Quality;Smart Grid;Microgrid;nan|
|[Diverse Convolutional Neural Network Models for Feature Extraction from Brain Tumor Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083973)|G. Malleswari; A. S. Reddy|10.1109/ICCMC56507.2023.10083973|Convolutional Neural Network;Brain Tumor;Feature Extraction;Segmentation;Accuracy;Image recognition;Computed tomography;Medical services;Feature extraction;Brain modeling;Surface texture;Convolutional neural networks|
|[Human Trap Detection using Convolution Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083927)|V. N; M. S; OmBhargava|10.1109/ICCMC56507.2023.10083927|Human Trap;Convolution Neural Networks;Natural Disaster;nan|
|[Railway Bridge Inspection using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083695)|L. N. Chennareddy; S. V. Gandabathula; V. V. Jasthi; F. Shaik|10.1109/ICCMC56507.2023.10083695|Remote sensing;OpenCv;Keras;TensorFlow;nan|
|[A Review on the Potential of AI Voice Assistants for Personalized and Adaptive Learning in Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084208)|D. K. Yadlapally; B. Vasireddy; M. Marimganti; T. Chowdary; C. Karthikeyan; T. Vignesh|10.1109/ICCMC56507.2023.10084208|Voice based Virtual Assistant;Natural Language Processing;Industries;Shape;Navigation;Virtual assistants;Speech recognition;Internet;Electronic mail|
|[Alzheimer's Disease (AD) Diagnosis from Brain MRI Image using Neural Network Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084001)|L. Ramesh; S. Raasika; P. S. P. Shree; B. Rithikashree|10.1109/ICCMC56507.2023.10084001|Alzheimer's condition;Magnetic Reasoning imaging;Deep learning;Brain disorder;Hypertension;Deep learning;Costs;Magnetic resonance imaging;Surgery;Brain modeling;Data models|
|[Image Segmentation Approaches to Detect Abnormalities in Brain MRI Images using CNN & U-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083935)|N. SrinivasaRao; G. R. Krishna; C. Raghu; K. Sasidhar|10.1109/ICCMC56507.2023.10083935|Machine Learning;Convolutional Neural Network;Magnetic Resonance Imaging;U-Net;nan|
|[An Efficient FPGA Implementation of the Multiplier-less LMS Adaptive Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084108)|K. S; S. G; S. M; Y. K|10.1109/ICCMC56507.2023.10084108|Least Mean Square (LMS);Field-Programmable Gate Array (FPGA);Low Power Consumption;Finite Impulse Response;Weight Updation;Performance evaluation;Power measurement;Noise reduction;Signal processing algorithms;Adaptive filters;Adaptive algorithms;Very large scale integration|
|[Implementation of Abnormal Event Detection using Automated Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084214)|M. Vaishnavi; J. Sowmya; M. Yaswanth; P. R. Maruvarasi|10.1109/ICCMC56507.2023.10084214|Internet of Things (IoT);Machine Learning;Object Detection;Security System;GAN;Event detection;Surveillance;Object detection;Cameras;Generative adversarial networks;Security;Internet of Things|
|[Design and Simulation of 16x16 Vedic Multiplier using Kogge-Stone Adder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083594)|Y. K; G. R; S. S; T. R. T; V. T|10.1109/ICCMC56507.2023.10083594|Vedic Mathematics;Urdhva Triyakbhyam;Kogge-Stone Adder;Vedic Multiplier;Digital systems;Image processing;Production;Computer architecture;Compressors;Real-time systems;Delays|
|[Gridlock Prediction using a Gated Recurrent Neural Network for Smart Transport Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084275)|B. U. Rani; S. S. Sulthana; P. K. Kumar; P. Bhavana|10.1109/ICCMC56507.2023.10084275|Traffic Forecasting;Recurrent Neural Network;Deep Learning;Recurrent neural networks;Computational modeling;Urban areas;Transportation;Predictive models;Traffic control;Logic gates|
|[Cloud Controlled Home Safety and Management Solution for Equipment Automation via Internet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083657)|D. V. S. Kishore; Y. M. Mohan Babu; B. Y M; K. Anitha; P. B. E. Prabhakar|10.1109/ICCMC56507.2023.10083657|Cloud;Home automation;Security;Internet of Things;Raspberry;Web portal;Home appliances;Automation;Protocols;Control systems;Safety;Servers;Security|
|[Software Effort Estimation using ANN (Back Propagation)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084264)|R. K. B. N; Y. Suresh|10.1109/ICCMC56507.2023.10084264|Artificial Neural Network;Software Effort Estimation;Machine Learning;Back propagation;Backpropagation;Computational modeling;Software algorithms;Estimation;Artificial neural networks;Machine learning;Predictive models|
|[Support Vector Machine (SVM) and Artificial Neural Networks (ANN) based Chronic Kidney Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083622)|V. C. R; V. Asha; A. Prasad; S. Das; S. Kumar; S. S. P|10.1109/ICCMC56507.2023.10083622|Chronic kidney disease;Chronic renal failure;Chronic kidney disease complication Machine learning;support vector machine;Artificial Neural Networks;Support vector machines;Computational modeling;Medical treatment;Artificial neural networks;Prediction algorithms;Chronic kidney disease;Data models|
|[A Comprehensive Analysis of Satellite Image Denoising using Earth Observation Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084128)|P. P; R. V V|10.1109/ICCMC56507.2023.10084128|CNN;Filtering;Image Denoising;Mean Square Error (MSE);Peak Signal to Noise Ratio (PSNR);Satellite Image;Measurement;Deep learning;Visualization;Satellites;PSNR;Image resolution;Noise reduction|
|[Semantic Segmentation using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084064)|B. V; M. S; S. B; J. Jeffin Gracewell|10.1109/ICCMC56507.2023.10084064|Self Driving Cars;Image Segmentation;Real Time Segmentation;Supervised Learning;Deep Learning;Auto Encoders;Convolutional Neural Networks (CNN);Semantic Network Architecture;Bottleneck Network Architecture;Data Generation;Deep learning;Visualization;Computational modeling;Semantic segmentation;Neural networks;Semantics;Cameras|
|[Fish Species Classifier for Allergic People using CNN Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084124)|P. S. V. Reddy; M. V. Krishna; R. Aishwarya; R. Yogitha; K. A. Kumar|10.1109/ICCMC56507.2023.10084124|Deep Learning;Fish Species;Allergies;Image Processing;Convolutional Neural Networks;Clinical immunology;Deep learning;Computer vision;Machine learning algorithms;Image analysis;Immunology;Food industry;Fish|
|[Prognosis of Idiopathic Parkinson's Disease using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084077)|M. Vemulapalli; R. P. Adimulam; B. S. Nikhitha|10.1109/ICCMC56507.2023.10084077|Idiopathic Parkinson's disease;Dopamine;Substantia nigra Heart;Brain;Tremors;rigidity;motor and non-motor symptoms;Convolutional Neural Network;Mobile net;Legged locomotion;Spirals;Parkinson's disease;Computational modeling;Wires;Midbrain;Paralysis|
|[Brain Tumor Detection using Mask RCNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083833)|V. Asha; S. P. Sreeja; B. Saju; P. Desai; K. M. Pavan; G. Kumari|10.1109/ICCMC56507.2023.10083833|Tumor Detection;Magnetic Resonance Imaging (MRI);Computed Tomography (CT);Image segmentation;Three-dimensional displays;Magnetic resonance imaging;Computed tomography;X-rays;Manuals;Size measurement|
|[State Estimation of a Utility-Scale Hybrid AC/DC Grid using a Decentralized Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083955)|K. Manikandan; R. S. Kumar; V. D|10.1109/ICCMC56507.2023.10083955|WLS Method;Decentralized Approach;DIgSILENT Software;Hybrid AC/DC System;nan|
|[Modular Adder Design based on Reversible Toffoli CLA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084302)|M. A; G. V; J. A. R; K. A|10.1109/ICCMC56507.2023.10084302|carry look-ahead adder;Toffoli gates;Power consumption;VLSI Design;Space vehicles;Reversible computing;Quantum computing;Costs;Production;Logic gates;Very large scale integration|
|[Convolutional Neural Network (CNN) based Blood Vessel Segmentation from Ocular Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083877)|V. Sujatha; B. S. Anitha; G. T. Rama; N. Niharika; A. Sahithi|10.1109/ICCMC56507.2023.10083877|Convolutional Neural Networks (CNN);Visual Geometry Group (VGG-16);Deep Learning;U-Net architecture;Generative Adversarial Networks (GAN);nan|
|[Auto Scaling Infrastructure with Monitoring Tools using Linux Server on Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083635)|S. S; S. C; D. K; P. L; R. M; N. C|10.1109/ICCMC56507.2023.10083635|Software as a Service;Service-Level Objective;Prometheus;Microservices;Monitoring tool;Quality of Service;Cloud computing;Costs;Linux;Software as a service;Microservice architectures;Quality of service;Planning|
|[Novel Deep CNN Model based Breast Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084229)|K. M; T. K; S. K|10.1109/ICCMC56507.2023.10084229|Breast Cancer;Deep Learning;Hyper-parameter;Mammogram;Random Search;Deep learning;Sensitivity;Computational modeling;Breast cancer;Mammography;Biomedical image processing;Optimization|
|[A Comparative Study of AI Algorithms for Anomaly-based Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084186)|V. P. Gandi; N. S. L. Jatla; G. Sadhineni; S. Geddamuri; G. K. Chaitanya; A. K. Velmurugan|10.1109/ICCMC56507.2023.10084186|Intrusion detection;Machine learning;Optimization algorithms;Support vector machines;Data privacy;Machine learning algorithms;Computer hacking;Neural networks;Intrusion detection;Network security|
|[Digital Clinical Diagnostic System for Lung Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083586)|D. Rawat; S. Sharma; S. Bhadula|10.1109/ICCMC56507.2023.10083586|Lung cancer;Digital clinical diagnostic system;Systematics;Lung cancer;Lung;Medical services;Real-time systems|
|[SupRes: Facial Image Upscaling Using Sparse Denoising Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083628)|M. Agrawal; M. A. Anwar; N. Saroha; A. Goel|10.1109/ICCMC56507.2023.10083628|Image Upscaling;Convolutional Neural Networks;Generative Adversarial Networks;Sparse Denoising Autoencoders;Image sensors;Image quality;Digital images;Noise reduction;Neural networks;Focusing;Deep architecture|
|[Twitter Sentiment Analysis using Enhanced TF-DIF Naive Bayes Classifier Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084106)|M. Sindhuja; K. S. Nitin; K. S. Devi|10.1109/ICCMC56507.2023.10084106|Twitter;Polarity;Naïve Bayes;Sentiment analysis;Sentiment analysis;Analytical models;Social networking (online);Computational modeling;Blogs;Machine learning;Naive Bayes methods|
|[A Hyperledger Fabric-based System Framework for Healthcare Data Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083620)|R. Saranya; A. Murugan|10.1109/ICCMC56507.2023.10083620|Blockchain;Crypto currency;Hyperledger Fabric;Healthcare;Smart contract;Distributed ledger;Hospitals;Smart contracts;Data collection;Fabrics;Blockchains;History|
|[Moodle Data Analysis For Effective Online Teaching And Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083697)|M. S. M. E.; H. S; H. K; J. K. R|10.1109/ICCMC56507.2023.10083697|Analysis;Moodle;Python code;Students' performance;Quiz activity;Easiness and difficulty of questions;Motivate Students;nan|
|[Analysis of Dental X-Ray Images for the Diagnosis and Classification of Oral Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083914)|D. Samiappan; J. R; N. S. K; N. K. N|10.1109/ICCMC56507.2023.10083914|Dental X-ray;teeth detection;tooth fractures;panoramic radiographies;semantic segmentation;CNN - Convolution Neural Network;nan|
|[Real-Time Translation of Sign Language for Speech Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084053)|A. D. Shetty; J. Shetty; K. K; Rakshitha; S. S. B|10.1109/ICCMC56507.2023.10084053|Sign Language Converter;Long-short Term Memory;Convolutional Neural Network;Natural Language Processing;Machine Learning;Bridges;Visualization;Text recognition;Computational modeling;Neural networks;Gesture recognition;Speech recognition|
|[Measuring the Performance of Clustered Image Data Sets using Image Proprietary](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083572)|D. Saravanan|10.1109/ICCMC56507.2023.10083572|Color values;Image extraction;Image value Comparison;Image content mining;Knowledge discovery;Integrated optics;Image color analysis;Current measurement;Image retrieval;Optical imaging;Data mining;Indexing|
#### **2023 6th Conference on Cloud and Internet of Things (CIoT)**
- DOI: 10.1109/CIoT57267.2023
- DATE: 20-22 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Urban Traffic Forecasting using Federated and Continual Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084875)|C. Lanza; E. Angelats; M. Miozzo; P. Dini|10.1109/CIoT57267.2023.10084875|Machine Learning;Edge computing;Continual Learning;Urban traffic forecasting;Smart Cities;Sustainability.;Training;Energy consumption;Smart cities;Computational modeling;Data models;Sensors;Peer-to-peer computing|
|[Socially Aware Multi-Resource Trading for IoT Applications in Smart Cities using Auction theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084895)|S. Ranjbaran; A. R. Jafari; N. Crespi|10.1109/CIoT57267.2023.10084895|Resource sharing;combinatorial auction;double auction;Internet of Things;fog computing;smart cities;Economics;Smart cities;Instruction sets;Computational modeling;Reliability theory;Dynamic scheduling;Sensors|
|[Dimensionality-reducing classifiers for Spanish winter maintenance of roadways](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084897)|D. M. Jiménez-Bravo; J. Bajo; E. Dopazo; J. F. De Paz; V. R. Q. Leithardt|10.1109/CIoT57267.2023.10084897|machine learning;PCA techniques;road maintenance;road monitoring;Training;Roads;Maintenance engineering;Predictive models;Data models;Safety;Sensors|
|[Estimation of the Cycling Cadence Using Low-Cost Inertial Sensors Mounted on a Bicycle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084907)|H. Mihaldinec; L. Klaić; H. Džapo|10.1109/CIoT57267.2023.10084907|wireless sensors;activity monitoring;inertial measurement unit;micro-mobility;cycling cadence measurement;Micromechanical devices;Wireless communication;Wireless sensor networks;Measurement units;Tracking;Roads;Estimation|
|[Introduction of a Cloud Computing Architecture for the Condition Monitoring of a Reconfigurable Battery System for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084883)|D. Karnehm; S. Pohlmann; A. Wiedenmann; M. Kuder; A. Neve|10.1109/CIoT57267.2023.10084883|Power Electronics;Reconfigurable Battery;Battery Management System (BMS);Automation;Cloud computing;Data analysis;Electric variables measurement;Data visualization;Computer architecture;Electric vehicles;Real-time systems|
|[An Adaptive Exponential Min Sum Decoding Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084886)|Z. Weijia; D. N. K. Jayakody|10.1109/CIoT57267.2023.10084886|LDPC Codes;Decoding Algorithms;Min Sum;Adaptive;Exponential;Internet of Things (IoTs);Wireless communication;Cloud computing;Simulation;Parity check codes;Hardware;Decoding;Internet of Things|
|[TinyZMQ++: A Privacy Preserving Content-based Publish/Subscribe IoT Middleware](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084896)|N. Ahmed|10.1109/CIoT57267.2023.10084896|publish and subscribe;micro services;docker containers;docker swarms;security and privacy;Raspberry Pi;Performance evaluation;Privacy;Cloud computing;Prototypes;Systems engineering and theory;Libraries;Encryption|
|[Zero-Knowledge and Identity-Based Authentication, Authorization, Access Control, and Key Exchange for Publish/Subscribe in Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084902)|I. Simsek|10.1109/CIoT57267.2023.10084902|Internet of Things;publish/subscribe;authentication;authorization;access control;key exchange;Authorization;Resistance;Cloud computing;Protocols;Authentication;Internet of Things;Cryptography|
|[Log message with JSON item count for root cause analysis in microservices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084901)|T. Koyama; T. Kushida|10.1109/CIoT57267.2023.10084901|Logging;Message format extension;Root cause analysis;Fault diagnosis;Root cause analysis;Cloud computing;Microservice architectures;Length measurement;Time measurement;Time factors|
|[Vulnerability and Threat Assessment Framework for Internet of Things Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084894)|M. Beyrouti; A. Lounis; B. Lussier; A. Bouadallah; A. E. Samhat|10.1109/CIoT57267.2023.10084894|Vulnerability Assessment;Threat Assessment;MITRE Corporation;NVD database;CAPEC Attack Patterns;Cloud computing;Databases;Medical services;Threat assessment;Internet of Things;Task analysis|
|[RINAsense: A prototype for implementing RINA networks in IoT environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084905)|D. Sarabia-Jácome; E. Grasa; M. Catalán|10.1109/CIoT57267.2023.10084905|IoT;RINA;Future Networks;Network Architecture;Network Protocol;Performance evaluation;Protocols;Packet loss;Prototypes;Zigbee;Logic gates;Delays|
|[UAV-based Scenario Builder and Physical Testing platform for Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084885)|A. Chilkunda; S. Nakama; V. Chilkunda; D. Pedro; J. P. Matos-Carvalho; L. Campos|10.1109/CIoT57267.2023.10084885|Autonomous Vehicles;Safety;Real-world Testbed;Cost effective;Driver Behaviour;Pedestrian Behaviour;UAV;Cloud computing;Costs;Roads;Autonomous aerial vehicles;Reproducibility of results;Safety;Internet of Things|
|[Two-Tier UAV-based Low Power Wide Area Networks: A Testbed and Experimentation Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084912)|S. Sobot; M. Lukic; D. Bortnik; V. Nikic; B. Lima; M. Beko; D. Vukobratovic|10.1109/CIoT57267.2023.10084912|NB-IoT;LoRa;UAV communications;coverage extension;Rural IoT;Base stations;Prototypes;Forestry;Data collection;Autonomous aerial vehicles;Radio links;Internet of Things|
|[A Hybrid LSTM-based Neural Network for Satellite-less UAV Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084873)|R. Santos∗‡; J. P. Matos-Carvalho∗; S. Tomic∗; M. Beko∗‡; S. D. Correia∗§|10.1109/CIoT57267.2023.10084873|Navigation;Long Short-Term Memory (LSTM);Unmanned Aerial Vehicle (UAV);Weighted Least Squares (WLS);Generalized Trust Region Sub-Problem (GTRS);Training;Machine learning algorithms;Neural networks;Machine learning;Predictive models;Satellite navigation systems;Autonomous aerial vehicles|
|[Network Virtualization and Slicing in UAV-enabled Future Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084908)|P. M. P. Payagalage; C. M. W. Basnayaka; D. N. K.Jayakody; A. Kumar|10.1109/CIoT57267.2023.10084908|Network slicing;UAV;MEC;ultra-reliable lowlatency communication;Network Virtualization;Network slicing;Wireless networks;Surgery;Wireless power transfer;Quality of service;Autonomous aerial vehicles;Smart grids|
|[Obtaining Accurate Bandwidth Estimations for the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084909)|S. Choy; B. Wong|10.1109/CIoT57267.2023.10084909|Bandwidth Estimation;Rate Control;Internet of Things;Measurement errors;Cloud computing;Systematics;Estimation;Bandwidth;Internet of Things;Noise measurement|
|[Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084906)|J. P. Matos-Carvalho; S. D. Correia; S. Tomic|10.1109/CIoT57267.2023.10084906|Artificial Intelligence;Embedded Computing;Fall Detection;LSTM;Machine Learning;Wearable Sensors;Sensitivity analysis;Hardware;Safety;Gyroscopes;Internet of Things;Older adults;Wearable sensors|
|[Skystrm: An Activity Monitoring System to Support Elderly Independence Through Smart Care Homes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084898)|I. Asghar; R. Ullah; M. G. Griffiths; G. Evans; J. Vermaak|10.1109/CIoT57267.2023.10084898|Activity recognition;remote monitoring;elderly care;internet of things;computer vision;Computer vision;Cloud computing;Tracking;Aging;Internet of Things;Older adults;Monitoring|
|[Feasibility analysis of an electrogastrography sensor for digestion detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084882)|P. A. Neves; N. J. Gonçalves; P. Varanda; J. Simões; F. Pires; N. M. Garcia; R. Costa; E. Zdravevski; I. M. Pires|10.1109/CIoT57267.2023.10084882|electrogastrography sensor;digestion detection;nutrition enhancement;system feasibility analysis;Wireless communication;Stomach;Watches;Data processing;Software;Batteries;Mobile applications|
|[EEG Channel Optimization for Wireless BMI-based Robot Interaction for Internet of Robotic Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084884)|S. Sugiyama; G. Pongthanisorn; S. Aya; G. Capi|10.1109/CIoT57267.2023.10084884|Brain-machine interface;Deep Learning;Internet of Robotic Things;EEG;Genetic Algorithm;Channel Optimization;Wireless communication;Deep learning;Redundancy;Electroencephalography;Brain-computer interfaces;Time factors;Internet of Things|
|[Internet of Things based Sensor System for Vertical Farming and Controlled Environment Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084913)|R. Ullah; I. Asghar; M. G. Griffiths; C. Stacey; W. Stiles; C. Whitelaw|10.1109/CIoT57267.2023.10084913|Controlled Environment Agriculture;Internet of Things;IoT;Vertical farming;Temperature sensors;Temperature measurement;Cloud computing;Costs;Temperature;Sensor systems;Temperature control|
|[Package Proposal for Data Pre-Processing for Machine Learning Applied to Precision Irrigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084899)|R. P. D. Santos; M. Beko; V. R. Q. Leithardt|10.1109/CIoT57267.2023.10084899|precision irrigation;internet of things;machine learning;predictive models;Irrigation;Cloud computing;Systematics;Scalability;Machine learning;Data models;Internet of Things|
|[Linearity-based Sensor Data Online Compression Methods for Environmental Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084892)|O. Väänänen; T. Hämäläinen|10.1109/CIoT57267.2023.10084892|compression algorithnb data compression;edge computing;Internet of Things;sensor data;Wireless communication;Temperature measurement;Wireless sensor networks;Energy consumption;Measurement uncertainty;Prediction algorithms;Internet of Things|
|[Enhancing the interoperability of heterogeneous hardware in the Industry: a Multi-Agent System Proposal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084891)|F. L. Alejano; D. H. d. l. Iglesia; M. R. Mateos; A. J. L. Rivero; V. R. Q. Leithardt|10.1109/CIoT57267.2023.10084891|Internet of Things;Modbus;Multi-Agent System;Industry 4.0;Industries;Lithium batteries;Second Life;Hardware;Proposals;Internet of Things;Standards|
|[Generative Pattern Dissemination for Collaborative Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084911)|M. Petersen; C. Hardegen; U. Buehler|10.1109/CIoT57267.2023.10084911|Collaborative Intrusion Detection;Data Dissemination;Generative Models;Traffic Classification;Network Flows;Data privacy;Cloud computing;Intrusion detection;Collaboration;Telecommunication traffic;Switches;Data models|
|[Design of a low-cost IoT device for the estimation of the state-of-health (SOH) of reused lithium batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084881)|D. H. D. L. Iglesia; F. L. Alejano; A. H. D. L. Iglesia; H. S. S. Blas; L. A. Silva; A. J. L. Rivero|10.1109/CIoT57267.2023.10084881|IoT;State of Health;Lithium Batteries;Circular Economy;State of Charge;Performance evaluation;Cloud computing;Costs;Instruments;Estimation;Lithium batteries;Energy efficiency|
|[Performance Analysis of Soft Information Relay Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084889)|K. Wickramarathna; D. N. K. Jayakody|10.1109/CIoT57267.2023.10084889|Cooperative Communication;Log Likelihood Ratio;Soft Information.;Analytical models;Computational modeling;Wireless networks;Bit error rate;Symbols;Gaussian distribution;Decoding|
|[NOMA-PLNC Based Visible Light Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084888)|S. Rajkumar; P. Tennakoon; D. N. K. Jayakody|10.1109/CIoT57267.2023.10084888|non-orthogonal multiple access;physical layer network coding and visible light communications.;Radio frequency;NOMA;Interference cancellation;Spectral efficiency;Wireless networks;Network coding;Physical layer|
|[CSMA/CA-based Random Access Control Suitable for Delay-Constrained Packet Transfer in Smart Factory Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084878)|H. Tode; K. Takemoto; Y. Tanigawa|10.1109/CIoT57267.2023.10084878|CSMA/ECA;Delay-Awareness;MAC Protocol;Smart Factory;WiFi;IoT;Wireless sensor networks;Wireless LAN;Protocols;Computer simulation;Prototypes;Jitter;Media Access Protocol|
|[Performance Evaluation of Vertical VLC Link in Mixed Water Mediums](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084904)|M. F. Ali; D. N. K. Jayakody; M. Beko; S. Correia|10.1109/CIoT57267.2023.10084904|Optical Wireless Communication (OWC);Strong Turbulence Channel Conditions;Underwater Wireless Communication (UWC);Vertical VLC Link;and Visible Light Communication (VLC).;Wireless communication;Analytical models;Simulation;Exponential distribution;Probability;Reservoirs;Transceivers|
|[Study of LoRaWAN Networks Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084880)|J. E. Rayess; K. Khawam; S. Lahoud; M. E. Helou; S. Martin|10.1109/CIoT57267.2023.10084880|LoRAWAN;Aloha type access;data link layer;reliability;Performance evaluation;Energy consumption;Interference cancellation;Memory management;Numerical simulation;Batteries;Numerical models|
|[Throughput Improvement for LoRaWAN Networks Considering IoT Applications Priority](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084887)|A. Loubany; S. Lahoud; A. E. Samhat; M. E. Helou|10.1109/CIoT57267.2023.10084887|Internet of Things;LPWAN;LoRaWAN;Spreading Factor;Priority;Cloud computing;Simulation;Quality of service;Throughput;Classification algorithms;Internet of Things;Reliability|
|[A Decentralized Web Service Infrastructure for the Interoperability of Applications in Multihop Dynamic Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084876)|L. Hogie|10.1109/CIoT57267.2023.10084876|Decentralised systems;overlay networks;middleware;edge;fog;IoT;Java;distributed computing;idawi;Computational modeling;Spread spectrum communication;Computer architecture;Dynamic scheduling;Mobile handsets;Service-oriented architecture;Internet of Things|
|[Performance Study of Disaster-Resilient Mesh Networking using NerveNet Wi-Fi and LoRa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084890)|M. -L. Tham; C. H. Lean; W. -S. Lim; R. -C. Leong; Y. Owada; M. M. Sein|10.1109/CIoT57267.2023.10084890|wireless mesh network;disaster resilient network;LoRa;database synchronization;Wireless communication;Web and internet services;Telecommunications;Information and communication technology;ITU;Synchronization;Data communication|
|[A Risk Assessment Study: Encircling Docker Container Assets on IaaS Cloud Computing Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084910)|M. H. Hersyah; M. D. Hossain; Y. Taenaka; Y. Kadobayashi|10.1109/CIoT57267.2023.10084910|Risk Assessment;docker container;Cloud Computing;Infrastructure-as-a-Service;Productivity;Cloud computing;Containers;NIST;Software;Topology;Risk management|
|[Probability-Based Crossover Genetic Algorithm for Task Scheduling in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084877)|S. A. Shamaa; W. Shi; G. Ankenmann|10.1109/CIoT57267.2023.10084877|Cloud;Task scheduling;Genetic Algorithm (GA);Makespan;Computational Time;Energy Consumption;Degree of Imbalance;Cloud computing;Energy consumption;Scheduling algorithms;Computational modeling;Dynamic scheduling;Probabilistic logic;Virtual machining|
|[IaC cloud testbed for secure ML based management of IoT services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084903)|T. Dimitrovski; T. Bergman; P. Zuraniewski|10.1109/CIoT57267.2023.10084903|IoT;ML;cloud computing;flexible resource allocation;autoencoder;contextual multi-armed bandit;Cloud computing;Botnet;Process control;Machine learning;Traffic control;Data collection;Malware|
#### **2023 15th International Conference on Knowledge and Smart Technology (KST)**
- DOI: 10.1109/KST57286.2023
- DATE: 21-24 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Real-time Evaluation of Food Acceptance From Facial Expressions Based on Exponential Decay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086796)|J. Han; A. K. Gopalakrishnan|10.1109/KST57286.2023.10086796|facial expressions recognition;partially occluded facial expressions recognition;occlusion detection;food acceptance estimation;Face recognition;Estimation;Object detection;Streaming media;Prediction algorithms;Real-time systems|
|[Design and Implementation of Fast and Secure SSO Authentication for Multi-Application Services Deployed in Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086933)|S. Fugkeaw; I. Langsanam; H. Saviphan|10.1109/KST57286.2023.10086933|Cloud-based SSO;Authentication;Multithread;Authorization;Authorization;Cloud computing;Authentication;Prototypes;Programming|
|[Skin Video-based Blood Pressure Approximation Using CHROM with LSTM-NN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086816)|C. Lumyong; N. Yodrabum; K. Winaikosol; T. Titijaroonroj|10.1109/KST57286.2023.10086816|blood pressure approximation;remote-Photoplethymography;long short-term memory;chrominance;Contacts;Photoplethysmography;Blood pressure;Skin;Physiology;Pressure measurement;Long short term memory|
|[Facial Emotion Detection for Thai Elderly People using YOLOv7](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086786)|T. Khajontantichaikun; S. Jaiyen; S. Yamsaengsung; P. Mongkolnam; T. Chirapornchai|10.1109/KST57286.2023.10086786|Facial Emotion Detection;Elderly;Artificial Intelligence;YOLOv7;Emotion recognition;Mental health;Aging;Older adults;Artificial intelligence|
|[PM2.5 Forecasting Model based on Linear and Non-linear Hybrid Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086907)|A. Banjongkan; N. Kerdprasop; A. Hirunyawanakul; K. Kerdprasop|10.1109/KST57286.2023.10086907|ANFIS;Hybrid Model;PM 2.5 ;Time-series;Adaptation models;Machine learning algorithms;Urban areas;Machine learning;Predictive models;Air pollution;Inference algorithms|
|[Multiple Words to Single Word Associations Using Masked Language Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086780)|Y. Soma; Y. Horiuchi; S. Kuroiwa|10.1109/KST57286.2023.10086780|Natural Language Processing;Word Association;Masked Language Models;Bit error rate;Predictive models;Lakes;Natural language processing;Task analysis|
|[Honeypot-Assisted Masquerade Detection with Character-Level Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086831)|R. Higuchi; H. Ochiai; H. Esaki|10.1109/KST57286.2023.10086831|Honeypot;Masquerade detection;Character-level CNN;Operating systems;Linux;Machine learning;Market research;Servers|
|[Traditional Vietnamese Herbal Medicine Image Recognition by CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086725)|T. N. Quoc; V. T. Hoang|10.1109/KST57286.2023.10086725|CNN;SOTAs;Tranditional herbal medicine;Support vector machines;Radio frequency;Computer vision;Image recognition;Transfer learning;Feature extraction;Classification algorithms|
|[zxCAPTCHA: New Security-Enhanced CAPTCHA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086931)|N. Dinh; T. Nguyen; V. Truong|10.1109/KST57286.2023.10086931|CAPTCHA;Security;Machine Learning;Cognitive;Deep learning;Neural networks;Security;Convolutional neural networks;Usability;CAPTCHAs|
|[WAFL-GAN: Wireless Ad Hoc Federated Learning for Distributed Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086811)|E. Tomiyama; H. Esaki; H. Ochiai|10.1109/KST57286.2023.10086811|Ad Hoc Network;Decentralized Federated Learning;Generative Adversarial Network;Machine Learning;Wireless communication;Knowledge engineering;Privacy;Federated learning;Aggregates;Generative adversarial networks;Ad hoc networks|
|[S-Edge: Smart Edge Computing Framework for Real-time Heterogeneous Vehicular Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086800)|A. Sachan; Y. Daultani; N. Kumar|10.1109/KST57286.2023.10086800|Edge Computing;Traffic Light Scheduling;Traffic Light Controller (TLC);Intelligent Transportation System (ITS);Smart City;Knowledge engineering;Analytical models;Smart cities;Processor scheduling;Roads;Computational modeling;Transportation|
|[Heart Rate Measurement on Smartphone using Cardiography: A Scoping Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086930)|T. Rahman; W. Krathu; C. Arpnikanondt|10.1109/KST57286.2023.10086930|heart rate (HR);measurement;cardiography;e-Health;mhealth;smartphone;contact;contact-free;Computer science;Heart beat;Medical services;Hardware;Heart rate measurement;Cardiography;Smart phones|
|[Heart Rate Measurement on PC and Phone using Facial Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086729)|T. Rahman; W. Krathu; C. Arpnikanondt|10.1109/KST57286.2023.10086729|heart rate (HR);measurement;non-contact;e-Health;facial video;Eulerian video magnification;Heart rate;Pulse oximeter;Fluctuations;Correlation;Heart beat;Photoplethysmography;Skin|
|[An Efficient Medical Records Access Control with Auditable Outsourced Encryption and Decryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086904)|S. Fugkeaw; L. Wirz; L. Hak|10.1109/KST57286.2023.10086904|IoT;CP-ABE;Blockchain;Fog Computing;Access control;Costs;Smart contracts;Authentication;Medical services;Encryption;Blockchains|
|[Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086905)|A. Pratap; N. Sardana; S. Utomo; A. John; P. Karthikeyan; P. -A. Hsiung|10.1109/KST57286.2023.10086905|additive manufacturing;deep learning;computer vision;explainable AI (XAI);Deep learning;Powders;Transfer learning;Surface morphology;Predictive models;Three-dimensional printing;Data models|
|[OWADIS: Rapid Discovery of OWASP10 Vulnerability based on Hybrid IDS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086878)|L. Wirz; A. Ketphet; N. Chiewnawintawat; R. Tanthanathewin; S. Fugkeaw|10.1109/KST57286.2023.10086878|IDS;Injection;HTTP Flooding;Session Hijacking;Cloud computing;Firewalls (computing);Intrusion detection;Telecommunication traffic;Throughput;Denial-of-service attack;Floods|
|[Hey Alexa … Examining Factors Influencing the Educational Use of AI-Enabled Voice Assistants During the COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086856)|R. Rohan; D. Pal; S. Funilkul|10.1109/KST57286.2023.10086856|artificial intelligence;online learning;pandemic;satisfaction;voice assistant;COVID-19;Learning management systems;Electronic learning;Pandemics;Virtual assistants;Psychology;Learning (artificial intelligence)|
|[A Dynamic and Efficient Crypto-Steganography System for Securing Multiple Files in Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086908)|S. Fugkeaw; N. Chinvorarat; N. Charnwutiwong; K. Chaisuwan; W. Pruktipinyopap|10.1109/KST57286.2023.10086908|Steganography;Cover image;Payload;Cloud;CP-ABE;Access control;Steganography;Heuristic algorithms;Encryption;Payloads|
|[Framework for Fine-grained Recognition of Retail Products from a Single Exemplar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086714)|R. Sakai; T. Kaneko; S. Shiraishi|10.1109/KST57286.2023.10086714|neural network-based image classification;fine-grained recognition;metric learning;data augmentation;retail products;Learning systems;Knowledge engineering;Image recognition;Target recognition;Lighting;Data collection;Extraterrestrial measurements|
|[Design and Development of Stress Monitoring System in Use Case: Twitter #Dek65](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086726)|N. Sukumthammarat; S. Nimma; P. Songmuang|10.1109/KST57286.2023.10086726|component;stress;entrance;sentiment;Visualization;Emotion recognition;Social networking (online);Blogs;Anxiety disorders;Education;Stakeholders|
|[Development of Low-cost and Robust IoT Field Station for Coffee Plantation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086759)|S. Siyang; T. Kerdcharoen|10.1109/KST57286.2023.10086759|Arabica coffee;IoT;Precision Agriculture;Monitoring System;Cloud computing;Agriculture;Servers;Monitoring;Farming;Meteorology|
|[Chinese Finger Sign Language Recognition Method with ResNet Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086825)|V. Chouvatut; B. Panyangam; J. Huang|10.1109/KST57286.2023.10086825|Chinese;sign language;recognition;transfer learning;ResNet;Deep learning;Image recognition;Fingers;Transfer learning;Gesture recognition;Assistive technologies;Convolutional neural networks|
|[Time and performance comparison on suicide detection using various feature engineering and machine learning models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086874)|K. Thongsi; N. Booncherd; P. Songmuang|10.1109/KST57286.2023.10086874|machine learning;suicide;natural language processing;word embedding;text classification;Measurement;Knowledge engineering;Deep learning;Sentiment analysis;Social networking (online);Text categorization;Neural networks|
|[Classifying Skin Cancer and Acne using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086873)|K. Vasudeva; S. Chandran|10.1109/KST57286.2023.10086873|Skin lesions;Acne;Benign skin Cancer;CNN;Visualization;Computer vision;Hospitals;Sociology;Developing countries;Skin;Lesions|
|[A GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086817)|P. Rattanawin; T. Pakinsee; P. Songmuang|10.1109/KST57286.2023.10086817|COVID-19;Chest X-rays;Medical Image Classification;Convolutional Neural Network;Deep Learning;COVID-19;Knowledge engineering;Pandemics;Pulmonary diseases;Lung;Medical diagnosis;Public healthcare|
|[LightPEN: Optimizing the Vulnerability Exposures for Lightweight Penetration Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086896)|S. Fugkeaw; L. Hak; N. Ploysopond; W. Apichonkit; S. Lahankaew|10.1109/KST57286.2023.10086896|penetration testing;vulnerability assessment;risks;code scanning;Codes;Security;Penetration testing|
|[RANDES: A Ransomware Detection System based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086910)|T. Phuangtong; N. Jaroonchaipipat; N. Thanundonsuk; P. Sakda; S. Fugkeaw|10.1109/KST57286.2023.10086910|ransomware;cybercrime;deep learning;disassembling analysis;graph attention network;Deep learning;Knowledge engineering;Analytical models;Codes;Encryption;Ransomware;Computer crime|
|[Thai Dry-Evergreen Forest’s Biomass Estimation using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086748)|K. Puntumapon; A. Wuthithanakul; P. U. Recio; B. S. Vindevogel|10.1109/KST57286.2023.10086748|carbon credits;biomass;dry-evergreen forest;Satellites;Machine learning algorithms;Biological system modeling;Forestry;Predictive models;Prediction algorithms;Data models|
|[AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086917)|S. Utomo; A. John; A. Pratap; Z. -S. Jiang; P. Karthikeyan; P. -A. Hsiung|10.1109/KST57286.2023.10086917|explainable AI;trustworthy AI;air quality estimation;AI for social good;Deep learning;Pollution;Atmospheric modeling;Buildings;Estimation;Air quality;Artificial intelligence|
|[Information Security Awareness in Higher Education Institutes: A Work in Progress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086884)|R. Rohan; S. Funilkul; W. Chutimaskul; P. Kanthmanon; B. Papasratorn; D. Pal|10.1109/KST57286.2023.10086884|cybersecurity;expert interview;higher education institutes;information security awareness;Data privacy;Education;Information security;Human factors;Stakeholders;Interviews;Computer security|
|[Information Extraction from Indonesian Crime News with Named Entity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086789)|R. R. Sedik; A. Romadhony|10.1109/KST57286.2023.10086789|crime news extraction;Indonesian news;named entity recognition;Support vector machines;Gold;System performance;Tagging;Feature extraction;Data mining|
|[Real-Time Detection and Classification of Facial Emotions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086866)|T. Winyangkun; N. Vanitchanant; V. Chouvatut; B. Panyangam|10.1109/KST57286.2023.10086866|Facial emotion;classification;deep learning;CNN;motion;Deep learning;Emotion recognition;Histograms;Face recognition;Real-time systems;Classification algorithms;Convolutional neural networks|
|[Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10086784)|K. Thananukhun; S. Jaiyen; K. Jitkajornwanich; A. Hanskunatai|10.1109/KST57286.2023.10086784|question classification;question-answering system;natural language processing;Support vector machines;Knowledge engineering;Bit error rate;Artificial neural networks;Multilayer perceptrons;Chatbots;Transformers|
#### **2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)**
- DOI: 10.1109/ICECONF57129.2023
- DATE: 5-7 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel and Robust Sensing Technique under Cooperative Schemes of IOT Based Industrial WSN in Real Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083765)|V. V. R. S. P; B. Jaison; R. N. C; S. Ashok; S. S; R. T. Prabu|10.1109/ICECONF57129.2023.10083765|Security for IIoT networks;cooperative communication in IoT environments;wireless sensor networks in industry and finally;jamming;Industries;Wireless communication;Resistance;Measurement;Wireless sensor networks;Knowledge discovery;Real-time systems|
|[A Novel Hybridclustering Model Forwireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083615)|P. Vijayalakshmi; M. Saravanan; M. T. Rani; M. Ashok; R. Palaniappan; V. Nagaraj|10.1109/ICECONF57129.2023.10083615|Clustering;Energy optimization;K-means;LEACH;Wireless sensor network;Wireless sensor networks;Routing;Knowledge discovery;Routing protocols;Hybrid power systems;Energy efficiency;Concurrent engineering|
|[A Robust Task Organizing Approach on IoT Enabled Mobile Computing Paradigm Based on Multilayer Management Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084096)|T. D. Subha; N. C; N. R; T. K; P. Bhargavi; P. Vinitha|10.1109/ICECONF57129.2023.10084096|IoT;Task Scheduling;Energy Efficiency;Cloud Computing;Cloud computing;Processor scheduling;Nonhomogeneous media;Dynamic scheduling;Knowledge discovery;Mobile handsets;Sensors|
|[Multi-mode Summarization of Surveillance Videos using Supervised Learning techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083764)|R. M; A. Devi; D. MO|10.1109/ICECONF57129.2023.10083764|Object detection;Video summarization;Face detection;Clustering;Surveillance;Surveillance;Supervised learning;Organizations;Object detection;Observers;Media;Knowledge discovery|
|[An Energy-Efficient Cluster Routing Protocol for Wireless Networks Based on Blended Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084155)|D. K; L. Saravanan; P. Sharmila; R. Krishnan; S. M; A. G|10.1109/ICECONF57129.2023.10084155|WSN;Aquila optimization method;cluster head;mayfly;routing protocol;Wireless sensor networks;Energy consumption;Power demand;Wireless networks;Clustering algorithms;Routing;Knowledge discovery|
|[MRI Image Analysis for Brain Tumor Detection Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083560)|B. Ayshwarya; M. Dhanamalar; V. Kumar|10.1109/ICECONF57129.2023.10083560|Braintumors;Magnetic resonance imaging;Deep Convolutional Neural Network;training iterations;Training;Deep learning;Magnetic resonance imaging;Computational modeling;Computer architecture;Medical services;Brain modeling|
|[A Robust Network Connection Across Internet of Things Devices Inside the Room with Visible Light Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084326)|N. Banupriyal; S. G; R. Nandhini; A. M. Mathew; L. Saravanan; A. G|10.1109/ICECONF57129.2023.10084326|Proximity-aware device clustering;multi-access;edge computing;the Internet of Things;Performance evaluation;Ultrasonic imaging;Smart cities;Telecommunication traffic;Smart homes;Internet of Things;Reliability|
|[Adaptive FLAME based segmentation and classification for bone cancer detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083670)|A. George; B. Ayshwarya|10.1109/ICECONF57129.2023.10083670|bone cancer;classification;segmentation;fuzzy clustering;local approximation;Support vector machines;Image segmentation;Clustering algorithms;Bones;Feature extraction;Cancer detection;Probabilistic logic|
|[Medium Access Control Scheduling Policy Performance Evaluation in Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083659)|N. Banupriya; P. Jyothi; K. Malathy; R. Sureshkumar; D. Dhanush; G. Sajiv|10.1109/ICECONF57129.2023.10083659|Throughput guarantees;average end-to-end delays;scheduling;Cellular networks;Scheduling algorithms;Wireless networks;Throughput;Routing;Media Access Protocol;Knowledge discovery|
|[Enhancing Energy Level and Network Lifetime for Asynchronous Duty Cycled WSN with Expectation-Maximization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084142)|R. Purushothaman; R. Narmadha|10.1109/ICECONF57129.2023.10084142|Opportunistic Routing;Asynchronous Duty-Cycled Routing;Wireless Sensor Networks;Expectation Maximization Algorithm;Candidate Zone;Temperature sensors;Wireless sensor networks;Standards organizations;Stars;Organizations;Routing;Energy states|
|[Real-time scanning and tracking of health records through a centralized ecosystem based on Internet of Things and fog computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083790)|P. Vijayakumari; P. G. Kuppusamy; E. Kosalendra; K. Krishnamoorthi; S. Diwakaran|10.1109/ICECONF57129.2023.10083790|Contact-free temperature monitoring;Face picture capture;IoT;COVID-19 virus;Temperature sensors;Temperature measurement;Computer viruses;Statistical analysis;Ecosystems;Real-time systems;Vaccines|
|[A Novel and Robust Gait Recognition method based on Hybrid Learning Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083824)|Y. P; J. Mohana|10.1109/ICECONF57129.2023.10083824|Gait recognition;Hybrid Learning Classifier;Feature Extraction;Machine Learning;Gait biometrics;visual surveillance;Training;Legged locomotion;Tracking;Surveillance;Learning (artificial intelligence);Feature extraction;Knowledge discovery|
|[An Efficient and Intelligent Systems for Internet of Things Based Health Observance System for Covid 19 Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084066)|R. S. Vignesh; A. Kumar S; T. M. Amirthalakshmi; P. Delphy; J. R. Arunkumar; S. Kamatchi|10.1109/ICECONF57129.2023.10084066|Healthcare;Covid-19;CHMS;Internet of Things;Patient Health Monitoring System;Temperature sensors;Temperature measurement;Costs;Physical layer;Knowledge discovery;Sensors;Internet of Things|
|[A Novel and Effective Ensemble Machine Learning Model for Identifying Healthy and Rotten Fruits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083721)|A. K. S; H. M. L; S. V. G. V.; U. M.S; L. Kannagi; P. S. Bharathi|10.1109/ICECONF57129.2023.10083721|Machine learning;ResNet;SVM;Ensemble Machine Learning Technique;maturity categorization;quality identification;Support vector machines;Productivity;Food waste;Deep learning;Machine learning algorithms;Crops;Soil|
|[Various improvements of Multimodal imaging systems for detection of Age- Related Macular Degeneration during initial stage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084095)|A. A; P. Durgadevi|10.1109/ICECONF57129.2023.10084095|lipofuscin;choroidal neovascularization;retinal pigment epithelium;color fundus photography;imaging techniques based on optical coherence;Integrated optics;Technological innovation;Obesity;Shape;Pigments;Optical saturation;Retina|
|[A Novel Enhanced EfficientNet Model for Identification of Floating Debris on Marine Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083878)|S. Joseph; S. G; P. Jyothi; S. Ashok; D. Venkatesan; S. A. Shifani|10.1109/ICECONF57129.2023.10083878|Deep learning;radar target identification;oceanic clutter;picture categorization;Convolution;Computational modeling;Time series analysis;Knowledge discovery;Encoding;Concurrent engineering;Doppler radar|
|[An Effective Sensor Filtering Model for Mobile Devices Based on Long Short-Term Memory Technique in Sensor Archives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083544)|A. J. Gilda; S. G; S. Sivamrugan; S. Kumar. R; S. K; R. T. Prabu|10.1109/ICECONF57129.2023.10083544|LSTM;Monte Carlo;and a Sensor Data Registry System for Path Prediction;Monte Carlo methods;Filtering;Detectors;Predictive models;Knowledge discovery;Mobile handsets;Trajectory|
|[A Novel Orientation Approach in Artificial Intelligence for Mounting Robots Utilizing a Three-Dimensional Framework of the Broadcast Tower](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084216)|S. Niranjana; I. Chandra; G. Charulatha; S. Leopauline; C. Prathima; T. Geetha|10.1109/ICECONF57129.2023.10084216|3D Model;Mounting robot;transmission tower;sobel edge detection;Solid modeling;Three-dimensional displays;Service robots;Poles and towers;Maintenance engineering;Robot sensing systems;Climbing robots|
|[Robust technique using Novel Artificial Neural Network Classifier for detection and classification of Hepatitis C](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083668)|D. Sravanthi; J. Rani.D|10.1109/ICECONF57129.2023.10083668|Innovative Hepatitis C Detection;Artificial Neural Network Classifier;Support Vector Machine Classifier;Machine learning;Accuracy;Sensitivity;Specificity;Support vector machines;Sensitivity;Liver diseases;Support vector machine classification;Artificial neural networks;Sensitivity and specificity;Programming|
|[Dynamic Host Configuration Protocol Attacks and its Detection Using Python Scripts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084265)|P. Shrestha; T. D. Sherpa|10.1109/ICECONF57129.2023.10084265|DHCP;Python;Scapy;Detection;DHCP Starvation;Rouge DHCP;Starvation Detection;Rouge DHCP Detection;Network servers;Protocols;Network architecture;Knowledge discovery;Concurrent engineering;IP networks;Artificial intelligence|
|[Novel Method for classification of Hepatitis C Using Support Vector Machine Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083597)|D. Sravanthi; J. D|10.1109/ICECONF57129.2023.10083597|Novel Hepatitis C Detection;Support Vector Machine Classifier;K-Nearest Neighbor Classifier;Machine learning;Accuracy. Sensitivity;Specificity;Support vector machines;Sensitivity;Liver diseases;Support vector machine classification;Sensitivity and specificity;Knowledge discovery;Software|
|[Lack of Efficiency on Robotic Hectoring Recognition over Online Social Media Networks using Novel Support Vector Machine Algorithm Comparing Lasso Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083719)|P. S. Reddy; A. S. Vindhya|10.1109/ICECONF57129.2023.10083719|Cyber bullying;Detection;Tweets;Twitter;Novel Support Vector Machine;Lasso Algorithm;Social Media Sites;Regression;Support vector machines;Machine learning algorithms;Blogs;Cyberbullying;Machine learning;Media;Prediction algorithms|
|[Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine” and Comparing with “K Nearest Neighbour Algorithm”](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083960)|R. Jhansi; G. Uganya|10.1109/ICECONF57129.2023.10083960|Machine Learning;Innovative Speech Recognition;Covid-19 Detection;Support Vector Machine;K-Nearest Neighbour;Accuracy;Support vector machines;COVID-19;Statistical analysis;Speech recognition;Prediction algorithms;Knowledge discovery;Concurrent engineering|
|[Characterisation of Landuse / Landcover Changes and its Comparison in Vijayawada City using Artificial Neural Networks with Minimum Distance to Mean](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083912)|K. P. Venkat; V. L. Sivakumar|10.1109/ICECONF57129.2023.10083912|Land use;Land Cover;Novel Supervised Classification;Artificial Neural Networks;Minimum Distance to Mean;Satellite Images;Digital Image Processing;Image Classification;Earth;Artificial satellites;Change detection algorithms;Statistical analysis;Urban areas;Artificial neural networks;Software|
|[Improved Accuracy in Speech Recognition System for Detection of Covid-19 using K Nearest Neighbour and Comparing with Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083858)|R. Jhansi; G. Uganya|10.1109/ICECONF57129.2023.10083858|Machine Learning;Innovative Speech Recognition;Covid-19 Detection;KNN;ANN;COVID-19;Measurement;Artificial neural networks;Speech recognition;Knowledge discovery;Concurrent engineering;Artificial intelligence|
|[An Effective Analysis of Palm Print Detection Using SVM over ANN with Improved Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083943)|E. Apoorva; N. B. Devi|10.1109/ICECONF57129.2023.10083943|Artificial Neural Network;Authentication System;Biometric;Fingerprint;Novel Support Vector Machine;Palm Print;Support vector machines;Palmprint recognition;Support vector machine classification;Artificial neural networks;Machine learning;Prediction algorithms;Knowledge discovery|
|[An Innovation Success Prediction Model of Android Application Using Logistic Regression Over MLC in Combination with PCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084279)|A. Ranadheer; L. R. Parvathy|10.1109/ICECONF57129.2023.10084279|LR;MLC in combination with PCA;Android Application;Prediction;Accuracy;Machine learning;Technological innovation;Learning (artificial intelligence);Predictive models;Prediction algorithms;Knowledge discovery;Concurrent engineering;Classification algorithms|
|[Accurate Human Palm Recognition System in Cybercrime Analysis using Naive Bayes in comparison with Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083899)|A. Saisundar; D. T|10.1109/ICECONF57129.2023.10083899|Cybercrime Analysis;Detection;Machine Learning Novel Decision Tree;Naive Bayes;Palm Recognition;Machine learning algorithms;Machine learning;Knowledge discovery;Concurrent engineering;Naive Bayes methods;Decision trees;Computer crime|
|[An Evaluation of Performance of Change Detection of Land Use/Land Cover in Hyderabad city using Artificial Neural Network and Mahalanobis Classification to improve Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084090)|R. K. Appala; V. L. Sivakumar|10.1109/ICECONF57129.2023.10084090|Land use;Land cover;Digital Image Processing;Image classification;Innovative Artificial Neural Network;Mahalanobis Based Classification;Remote Sensing;Satellite;Hyderabad;Earth;Artificial satellites;Change detection algorithms;Statistical analysis;Urban areas;Artificial neural networks;Tail|
|[Lung Cancer Identification System to Improve the Accuracy Using Novel K Nearest Neighbour in Comparison with Logistic Regression Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084340)|Y. K. Kumar; R. Priyanka|10.1109/ICECONF57129.2023.10084340|Novel K Nearest Neighbour;LR Algorithm;Lung Cancer;Machine Learning Techniques;Lung cancer;Learning (artificial intelligence);Knowledge discovery;Concurrent engineering|
|[Analysis of Stock Market Value Prediction using Novel Long Short-Term Memory in Comparison with SVM for Increased Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083611)|N. J. Reddy; J. K|10.1109/ICECONF57129.2023.10083611|Machine Learning;NLSTM;RNN;Stock Market Prediction;SVM;Support vector machines;Machine learning algorithms;Machine learning;Predictive models;Prediction algorithms;Knowledge discovery;Concurrent engineering|
|[Anomaly Based Detection for Identifying R2L (Remote to Local) Attacks Using RNN-LSTM in Comparison with ANN for Reducing False Alarm Rate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084242)|B. Hemasree; D. N|10.1109/ICECONF57129.2023.10084242|Anomaly Detection;Artificial Neural Network;Deep Learning;False Alarm Rate;Novel Recurrent Neural Network-Long Short Term Memory;Remote to Local attacks;Recurrent neural networks;Prediction algorithms;Knowledge discovery;Concurrent engineering;Artificial intelligence;Standards;Anomaly detection|
|[Accurate Predictive model for Identifying At - Risk students at various Percentage of Course Length using Logistic Regression Compared with Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083684)|A. Yamini; K. S. Rekha|10.1109/ICECONF57129.2023.10083684|Gradient Boosting;Novel Logistic Regression;Accuracy;Dropouts;e-Learning and Machine Learning;Machine learning algorithms;Predictive models;Prediction algorithms;Knowledge discovery;Boosting;Concurrent engineering;Random forests|
|[Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083770)|P. S. Charan; G. Ramkumar|10.1109/ICECONF57129.2023.10083770|Mucormycosis;Black Fungus;Neural Network;Covid-19;SVM;CNNSVM;Support vector machines;COVID-19;Epidemics;Merging;Medical services;Learning (artificial intelligence);Feature extraction|
|[Novel Improved Communication Steadiness Routing for Wireless Sensor Network's Performance Analysis compared with Network Boundary Maintenance Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083655)|T. Anitha; S. Sridhar|10.1109/ICECONF57129.2023.10083655|Wireless Sensor Network;Novel Improved Communication Steadiness Routing;Cluster-Chain Mobile Agent Routing;Void Path Identification Algorithm;Packet Transmission;Energy Consumption;Measurement;Wireless communication;Energy consumption;Wireless sensor networks;Mobile agents;Energy measurement;Routing|
|[Implementation and health monitoring system of vehicle by Fog computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084038)|R. S. Rani; C. Raghavendra; K. S. Murthy; N. R. Reddy|10.1109/ICECONF57129.2023.10084038|nan;Wireless communication;Wireless sensor networks;Filtering;Knowledge discovery;Software;Sensor systems;Safety|
|[Scada energy management system under the distributed decimal of service attack using verification techniques by IIoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083924)|S. Sivakumar; R. Raffik; K. Kiran Kumar; B. Hazela|10.1109/ICECONF57129.2023.10083924|nan;SCADA systems;Smart grids;Power system reliability;Security;Reliability;Energy management systems;Standards|
|[Improved Accuracy for Identifying At-Risk Students at Different Percentage of Course Length using Logistic Regression Compared with K-Nearest Neighbour Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083749)|A. Yamini; K. S. Rekha|10.1109/ICECONF57129.2023.10083749|K-Nearest Neighbor;Novel Logistic Regression;Accuracy;e-Learning and Machine Learning;Computational modeling;Estimation;Machine learning;Knowledge discovery;Concurrent engineering;Task analysis;Logistics|
|[Analysis of a Wireless Sensor Network's Performance using Novel Improved Communication Steadiness Routing over Cluster-Chain Mobile Agent Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083976)|A. T; S. Sridhar|10.1109/ICECONF57129.2023.10083976|Wireless Sensor Network;Novel Improved Communication Steadiness Routing;Cluster-Chain Mobile Agent Routing;Void Path Identification Algorithm;Packet Transmission;Energy Consumption;Measurement;Wireless communication;Energy consumption;Wireless sensor networks;Mobile agents;Energy measurement;Routing|
|[Modified Convolutional Neural Network Architecture with XGBoost for Mucormycosis Detection and compare performance with XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084019)|P. V. Sai Charan; G. Ramkumar|10.1109/ICECONF57129.2023.10084019|Mucormycosis;Black Fungus;Neural Network;Covid-19;XGB;CNNXGB;COVID-19;Backpropagation;Epidemics;Neural networks;Learning (artificial intelligence);Knowledge discovery;Concurrent engineering|
|[Power Quality Enhancement in the Power Distribution System Using Seagull Optimization Algorithm Compared with Particle Swarm Optimization Algorithm by Increasing Voltage Stability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084212)|K. B. Gokulakrishnan; T. Yuvaraj|10.1109/ICECONF57129.2023.10084212|Novel Seagull Optimization Algorithm;Particle Swarm Optimization Algorithm;Power Loss;Voltage Stability;Power Quality;Radial Distribution System;Power Systems;Power quality;Power distribution;Voltage;Power system stability;Knowledge discovery;Boosting;Concurrent engineering|
|[Voltage Stability Improvement in Radial Distribution Network using Cat Swarm Algorithm Compared with Differential Evolution Algorithm by Reducing the Power Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084198)|K. B. G. Krishnan; T. Yuvaraj|10.1109/ICECONF57129.2023.10084198|Novel Cat Swarm Algorithm;Differential Evolution Algorithm;Power Loss;Voltage Stability;Radial Distribution System;Power Systems;Optimization methods;Voltage;Distribution networks;Knowledge discovery;Concurrent engineering;Artificial intelligence|
|[A Robust and Efficient Traffic Analysis for 5G Network Based on Hybrid LSTM comparing with XGBoost to Improve Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083633)|N. Deeban; P. S. Bharathi|10.1109/ICECONF57129.2023.10083633|Traffic analysis;LSTM;XGBoost;LXGB;Deep Learning;Machine Learning;5G Network;Wireless communication;Adaptation models;5G mobile communication;Telecommunication traffic;Quality of service;Predictive models;Prediction algorithms|
|[Deep Learning Based Network Traffic Analysis Using Modified Hybrid Methodology Comparing with SVM to Improve Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084206)|N. Deeban; P. S. Bharathi|10.1109/ICECONF57129.2023.10084206|Traffic analysis;ANN;SVM;Ensemble;ANNSVM;Deep Learning;Network Traffic;Support vector machines;Quality assurance;Terminology;Computational modeling;Telecommunication traffic;Knowledge discovery;Data models|
|[Particle Swarm Bacterial Foraging Optimization method for Enhanced digital image watermarking system for data security comparison with Genetic algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083811)|D. Pula; R. Puviarasi|10.1109/ICECONF57129.2023.10083811|Digital Watermarking;Wavelet transform;PSNR;MSE;Genetic algorithm;Novel Bacterial Foraging with Particle Swarm Optimization;Data Security;Microorganisms;PSNR;Digital images;Data security;Optimization methods;Watermarking;Knowledge discovery|
|[Enhanced Digital Image Watermarking System using Manta Ray Foraging Comparison with Bi-directional ELM for Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083744)|D. Pula; R. Puviarasi|10.1109/ICECONF57129.2023.10083744|Watermarking;Wavelet transform;PSNR;Bi-directional ELM;Novel Manta Ray Foraging Optimization;Data Security;PSNR;Databases;Digital images;Data security;Watermarking;Bidirectional control;Knowledge discovery|
|[A One-Dimensional Convolutional Neural Network and Long Short-Term Memory Model for Limb Movement Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083919)|Blessy; K. Neela; A. Rajalakshmi; A. Joseph; C. Muralidharan; A. G|10.1109/ICECONF57129.2023.10083919|ECG;deep learning;CNN;LSTM;periodic limb movement syndrome;Training;Measurement;Deep learning;Convolution;Electrocardiography;Knowledge discovery;Concurrent engineering|
|[An Efficient Image Based Mammogram Classification Framework Using Depth Wise Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083528)|T. D. Subha; D. S; G. S. Charan; D. D. E; P. I; C. C. Reddy|10.1109/ICECONF57129.2023.10083528|breast cancer;mammography;image processing;architectural distortion;CNN;Training;Location awareness;Computer vision;Image segmentation;Deformation;Noise reduction;Distortion|
|[Efficient Search Strategies in Selecting the Best Cluster Heads with Gray Wolf Optimization Based Clustering Technique in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084007)|V. Ramkumar; P. Jyothi; K. V. Karthikeyan; V. Senthilkumar; E. S. Reddy; R. T. Prabu|10.1109/ICECONF57129.2023.10084007|Network longevity;gray wolf optimization method;oppositional based learning;wireless sensor networks;clustering technique;Training;Wireless sensor networks;Power demand;Search problems;Knowledge discovery;Battery charge measurement;Batteries|
|[A Robust and Novel Hybrid Deep Learning based Lung Nodule Identification on CT Scan Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083700)|A. Sivakumar; L. S. S; M. Suganthy; B. Gunasundari; S. Sivamurugan; S. G|10.1109/ICECONF57129.2023.10083700|CT scan;DL;ML;computer-aided design;Hybrid Deep learning model;CNN;MRI;Deep learning;Computed tomography;Neural networks;Lung cancer;Medical treatment;Lung;Knowledge discovery|
|[A Safe and Reliable Digital Fingerprint Recognition Method for Internet of Things (IoT) Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083840)|S. Diwakaran; P. Vijayakumari; P. G. Kuppusamy; E. Kosalendra; K. Krishnamoorthi|10.1109/ICECONF57129.2023.10083840|Fingerprint Authentication;Nullify Fingerprint Model;Safe Authentication;IIoT;5G Network;5G mobile communication;Computational modeling;Biological system modeling;Authentication;Production;Fingerprint recognition;Reliability engineering|
|[A Robust and Efficient Computational Offloading and Task Scheduling Model in Mobile Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084293)|I. Chandra; K. V. Karthikeyan; R. V; S. K; M. Tamilselvi; J. R. Arunkumar|10.1109/ICECONF57129.2023.10084293|Competition for scarce resources;lightweight compute;energy-efficient mobile cloud computing;Wireless communication;Cloud computing;Energy consumption;Processor scheduling;Computational modeling;Mobile handsets;Computational efficiency|
|[Extracting Behavioral Characteristics of College Students Using Data Mining on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084276)|R. Meena; T. Kavitha; A. K. S; D. M. Mathew; R. Anusuya; G. Karthik|10.1109/ICECONF57129.2023.10084276|Data mining;Behavioral Characteristics Extraction Model;Big Data;Character analysis;Statistical analysis;Big Data;Solids;Market research;Knowledge discovery;Data models;Behavioral sciences|
|[Probability of a Device Failure using Support Vector Machine by comparing with Random Forest Algorithm to improve the accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083595)|D. Lokesh; F. R. J|10.1109/ICECONF57129.2023.10083595|Innovative Support Vector Machine;Random Forest;Machine Learning;Failure Devices;Dataset;Confusion Matrix;Support vector machines;Radio frequency;Training;Performance evaluation;Machine learning algorithms;Maintenance engineering;Knowledge discovery|
|[Design of Hearing Aid with Auto Tuning of Frequency on Comparison with IDFT Coefficients and Overlap Add Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084167)|M. Monisha; J. F. Roseline|10.1109/ICECONF57129.2023.10084167|Autotuning;Novel Frequency tuning;Redundancy filter;Hearing aid;Time domain;Frequency-domain analysis;Discrete Fourier transforms;Transfer functions;Transforms;Hearing aids;Knowledge discovery;Concurrent engineering|
|[Novel Detection of Water Quality Based on pH Level Data Obtained from Water Sources Using Node MCU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083596)|B. Naik; N. P. G. Bhavani|10.1109/ICECONF57129.2023.10083596|Environment;Node MCU;Novel detection;pH sensor;Water Quality;Wi-Fi;Performance evaluation;Water quality;Organizations;Knowledge discovery;Water pollution;Concurrent engineering;Error correction|
|[Accurate Precipitation Prediction using Deep Learning Neural Network Compared with Space Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083591)|D. V. Rayudu; J. F. Roseline|10.1109/ICECONF57129.2023.10083591|Space Vector Machine;Novel Deep Learning Neural Network;Rainfall Prediction;Machine learning;Forecasting;Regression;Support vector machines;Deep learning;Precipitation;Simulation;Neural networks;Weather forecasting;Prediction algorithms|
|[Accurate Weather Forecasting for Rainfall Prediction Using Artificial Neural Network Compared with Deep Learning Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084252)|D. V. Rayudu; J. F. Roseline|10.1109/ICECONF57129.2023.10084252|Artificial Neural Network (ANN);Deep Learning Neural Network (DNN);Rainfall Prediction;Neural Network;Weather Forecasting;Machine Learning;Deep learning;Precipitation;Error analysis;Weather forecasting;Artificial neural networks;Learning (artificial intelligence);Prediction algorithms|
|[New Crow Search Algorithm for Economic Load Dispatch Resolution vs. the Time-Proven BAT Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083563)|K. Sumanth; M. V. Priya|10.1109/ICECONF57129.2023.10083563|Crow Search Algorithm;Economic Load Dispatch;Novel Bat Algorithm;swarm intelligence;Emission Dispatch;Costs;Search problems;Knowledge discovery;Concurrent engineering;Artificial intelligence;Power generation|
|[Finding Solution for Practical Economic Load Dispatch Problem Using Dragonfly Algorithm in Comparison with Novel Bat Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083769)|K. Sumanth; M. V. Priya|10.1109/ICECONF57129.2023.10083769|Economic Load Dispatch Problem (ELDP);Dragonfly algorithm (DA);Novel BAT Algorithm (BA);Power system;Metaheuristic;Generation Cost;Costs;Knowledge discovery;Concurrent engineering;Artificial intelligence|
|[Performance Analysis of Mammogram Tumor Classification using Deep Belief Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083516)|M. S. Karthik; N. P. G. Bhavani|10.1109/ICECONF57129.2023.10083516|Novel Deep Belief Network (DBN);Decision Tree (DT);Mammogram;Thermal scan;Cancer;Malignant Tumor;Statistical analysis;Knowledge discovery;Mammography;Breast cancer;Classification algorithms;Performance analysis;Decision trees|
|[Hand Posture Recognition on Photographic Images using YOLO Algorithm in Comparison with Convolution Neural Network to Improve Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083945)|G. P. Raj; N. P. G. Bhavani|10.1109/ICECONF57129.2023.10083945|Novel YOLO Algorithm;Hand gesture recognition;Convolution Neural Network (CNN);Feature extraction;Pre-trained CNN;Sign Language;Image recognition;Convolution;Neural networks;Knowledge discovery;Concurrent engineering;Artificial intelligence|
|[Identify Facial Micro Expression Using Support Vector Machine Compared with Artificial Neural Network to Improve Recall Parameter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084307)|S. Soharika; N. P. G. Bhavani|10.1109/ICECONF57129.2023.10084307|Facial Recognition;Python;Emotions;Artificial Neural Network (ANN);Novel Support Vector Mechanism (SVM) Algorithm;Accuracy;Recall;Support vector machines;Emotion recognition;Face recognition;Software algorithms;Artificial neural networks;Prediction algorithms;Real-time systems|
|[An Automated System Based on Informational Value of Images for the Purpose of Classifying Plant Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083618)|B. Jeyapoornima; P. Suganthi; S. K; V. V. Chowdary; V. R. U. Sai; B. N. Venkat|10.1109/ICECONF57129.2023.10083618|Disease detection in plants;adaptive farming;single-shot learning;nuanced categorization;shared features across classes;Integrated circuits;Deep learning;Plant diseases;Image resolution;Costs;Databases;Knowledge discovery|
|[Fuzzy Evaluation Method on the Financing Efficiency of Small and Medium-Sized Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083731)|J. R; S. H. Krishna; M. G.M; S. Mohammed; K. B. Raj; G. Manoharan|10.1109/ICECONF57129.2023.10083731|Economy;Artificial Intelligence;Small-medium Entrepreneurs;financial health;Fuzzy logic;Adaptation models;Biological system modeling;Predictive models;Linguistics;Financial management;Artificial intelligence|
|[Implementation of Fog-IoT Framework to Deal with the Performance metrics in various IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083823)|T. D. Subha; K. S. Manasa; K. Akhila; N. S. Saranya; K. Tejaswini; K. Sannuthi|10.1109/ICECONF57129.2023.10083823|loT;Fog-IoT;Edge computing;fog computing;Distributed System;Performance evaluation;Wireless communication;Fluctuations;Density measurement;Maintenance engineering;Knowledge discovery;Concurrent engineering|
|[Maximum Energy Productivity for Concurrent Wireless Data and Power Shifting-Enabled IoT Network with Energy Coordination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084337)|S. Joseph; S. R; A. Royappa; A. D; G. D; K. V. Karthikeyan|10.1109/ICECONF57129.2023.10084337|SWIPT;IoT;energy consumption;transmission power;time changing;power collaboration;energy collaboration;Wireless communication;Productivity;Renewable energy sources;Collaboration;Quality of service;Knowledge discovery;Internet of Things|
|[Internet of Things for Green Smart City Application Based on Biotechnology Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083965)|N. Bhaskar; S. Ramana; G. M. Kumar|10.1109/ICECONF57129.2023.10083965|Internet of Things;Smart city;Health care;Bio Technology;Wireless communication;Cloud computing;Wireless sensor networks;Biotechnology;Electrocardiography;Sensor systems;Electroencephalography|
|[A Robust and Improved sparrow search algorithm for optimization in wireless sensor network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083538)|K. Krishnamoorthi; S. Diwakaran; P. Vijayakumari; P. G. Kuppusamy; E. Kosalendra|10.1109/ICECONF57129.2023.10083538|LHS;WSN;models;simulations;coverage optimization;SSA;Wireless sensor networks;Computational modeling;Sociology;Knowledge discovery;Stability analysis;Mathematical models;Particle swarm optimization|
|[Internet of Things Based Smart Home Security Analysis System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083624)|P. Srujana; K. R. Krishna; S. Madhavi; C. Sharma; N. Satheesh; G. Poshamallu|10.1109/ICECONF57129.2023.10083624|IOT;Internet Of Things;HomeSecurity;Smart Home;Healthcare Services;Planets;Sociology;Smart homes;Medical services;Passwords;Organizations;Sensor phenomena and characterization|
|[Application of Artificial Intelligence in E-Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084011)|S. Hari Krishna; S. S. Sargunam; N. Kulkarni; N. Nandal; V. Vidya Chellam; S. Praveenkumar|10.1109/ICECONF57129.2023.10084011|Artificial Intelligence;E-Marketing;Digital Marketing;Productivity;TV;Entertainment industry;Transforms;Predictive models;Real-time systems;Mobile handsets|
|[Early-Stage Timing Prediction in SoC Physical Design using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084105)|M. Kulkarni; J. K. Shareef Al-Safi; S. M. K Sukumar Reddy; S. B. G Tilak Babu; P. Kumar; P. P|10.1109/ICECONF57129.2023.10084105|C-MOS;Machine;IC;Automation;Electronic;Adaptation models;Schedules;Automation;Machine learning;Writing;Trajectory;Timing|
|[An efficient Intelligent Systems for Low-Power Consumption Zigbee-Based Wearable Device for Voice Data Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083856)|I. Chandra; G. Sowmiya; G. Charulatha; S. D; S. Gomathi; R. Anusuya|10.1109/ICECONF57129.2023.10083856|Zigbee;voice data;wireless sensor networks;microcontroller;media access control;Wi-Fi protected access;Wireless sensor networks;Home automation;Costs;Wireless networks;Wearable computers;Packet loss;Manufacturing|
|[Health Care and Management based on Block chain and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083757)|R. Bakh; V. Sarkar; J. P. Lovelin Auguskani; G. Poornima; M. N. Shetty; M. G. Raj|10.1109/ICECONF57129.2023.10083757|Machine Learning;Health Care;Block Chain;Technology;Telemedicine;Government;Medical treatment;Machine learning;Medical services;Knowledge discovery;Blockchains|
|[Assessment of Classification Techniques for Heart Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083836)|L. G; P. Sujatha|10.1109/ICECONF57129.2023.10083836|Heart Disease;Prediction;Decision Tree;K-Nearest Neighbor;Support Vector Machine;Linear Regression and Logistic Regression;Heart;Industries;Support vector machine classification;Machine learning;Medical services;Knowledge discovery;Task analysis|
|[A Sheltered CARMA Algorithm Using Cosseted Articulate For Implication Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083647)|R. C. Kalaiselvi; S. M. Vennila|10.1109/ICECONF57129.2023.10083647|AES (Advanced Encryption Standard);cryptography;decryption;encryption and CARMA (Modified Advanced Encryption Standard);Performance evaluation;Industries;Computational modeling;Knowledge discovery;Encryption;Discrete wavelet transforms;Safety|
|[Analysis of Visualization Tools for Team Sports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083551)|M. Gayathri; V. Kavitha|10.1109/ICECONF57129.2023.10083551|Sports analytics;Information Visualization;Team sports;Visual analytics;Heating systems;Data visualization;Radar;Knowledge discovery;Ice;Concurrent engineering;Trajectory|
|[Application of SMAP images in predicting Crops by using Decision Tree and Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083570)|V. Rohini; K. Meghana; R. K. Sowmya; K. S. Krishna; B. Srikrishna|10.1109/ICECONF57129.2023.10083570|Crop prediction;SMAP;and;Temperature measurement;Temperature sensors;Soil moisture;Crops;Vegetation mapping;Prediction algorithms;Indexes|
|[Privacy Preservation of Business Forecasting Using Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083676)|S. Shamreen; M. Madhavi; L. Priyanka; B. Saravani|10.1109/ICECONF57129.2023.10083676|Privacy preserving;Data privacy;Encryption;Decryption;Homomorphic Encryption;Training;Data privacy;Privacy;Computational modeling;Phishing;Machine learning;Organizations|
|[Graph Signal Estimation Based on Maximum Correntropy Criterion (The Work of A. Chandrasekar Was Supported by the MATRICS, SERB, India, Under Grant MTR/2021/000405)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084250)|A. Chandrasekar; S. Radhika|10.1109/ICECONF57129.2023.10084250|maximum correntropy criteria;adaptive filtering;mean square deviation;Graph estimation;impulsive noise;Resistance;Filtering;Simulation;Estimation;Filtering algorithms;Knowledge discovery;Reliability|
|[An Improved Deep Learning Framework Approach for Detecting the Psoriasis Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083536)|A. Singh; N. Kumar K C; M. A. Kumar; H. Singh Negi|10.1109/ICECONF57129.2023.10083536|psoriasis;deep learning;VGG 16;VGG16;inception v3;Geometry;Deep learning;Visualization;Feature extraction;Knowledge discovery;Skin;Concurrent engineering|
|[Sugarcane Yield and Price Prediction Using Forecasting Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084094)|V. Sneha; V. Bhavana|10.1109/ICECONF57129.2023.10084094|Multi Linear Regression Decision Tree Regressor;Lasso Regression;Adaboost Regressor;Random Forest;ARIMA model;Machine learning algorithms;Linear regression;Time series analysis;Crops;Predictive models;Knowledge discovery;History|
|[Prakruthi Sedhya: A Natural Farming Mobile App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084002)|T. S. J; R. r. J; E. r. E; Y. C. K|10.1109/ICECONF57129.2023.10084002|APCNF;Natural farming;crop care;weather fore- cast;government schemes;discussion forums;Training;Discussion forums;Government;Crops;Weather forecasting;Food security;Knowledge discovery|
|[Manual and Automated Web-based Testing of the NPTEL Portal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084058)|D. S. N; S. M. D A; G. J|10.1109/ICECONF57129.2023.10084058|selenium;white-box testing;black-box testing;performance testing;NPTEL;Software testing;Navigation;Source coding;Computer bugs;Closed box;Manuals;Logic gates|
|[Water Quality Prediction using Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084120)|S. Babu; B. B. Nagaleela; C. G. Karthik; L. N. Yepuri|10.1109/ICECONF57129.2023.10084120|Water Pollution;Random Forest;LSTM;Water Quality Index (WQI);Decision tree;Oxygen;Neural networks;Water quality;Water pollution;Data models;Pollution measurement;Naive Bayes methods|
|[A Novel Ensemble Classification Model for Plant Disease Detection Based on Leaf Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083902)|E. Saraswathi; J. FarithaBanu|10.1109/ICECONF57129.2023.10083902|ensemble classifier;plant disease detection;deep learning;classification;feature extraction;Deep learning;Plant diseases;Fluctuations;Databases;Biological system modeling;Crops;Predictive models|
|[Fundus Image Based DR Detection Using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084241)|T. Singh; A. Suresh|10.1109/ICECONF57129.2023.10084241|DR;FUNDUS image;image processing;DRIVE database;Image segmentation;Systematics;Retinopathy;Blood vessels;Speckle;Retina;Knowledge discovery|
|[A Novel Ai Framework for Personalisation and Customization of Product Prices through Bigdata Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083978)|S. D; A. K. Dubey; A. K. B; S. T. J; N. F; K. S. K|10.1109/ICECONF57129.2023.10083978|Application Programming Interface;Artificial Intelligence;Shopbot;Bigdata;Costs;Collaboration;Knowledge discovery;Chatbots;Concurrent engineering;Electronic commerce;Artificial intelligence|
|[Automated Brain Tumor Classification of Different Types of Tumors Using Convolutional Neural Network of Multi Label MRI Scans](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083600)|J. S. Raghav; A. N. Das; A. Suresh|10.1109/ICECONF57129.2023.10083600|Brain;Classification;MRI scans;Neural Network;Image;Brain Tumor;Predict;Training;Image segmentation;Magnetic resonance imaging;Feature extraction;Brain modeling;Timing;Reliability|
|[Evaluation of Biometric Classification and Authentication Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083610)|N. Umasankari; B. Muthukumar|10.1109/ICECONF57129.2023.10083610|Authentication;Biometric Image;Classification;Performance Metrics;Random Forest;Measurement;Machine learning algorithms;Biometrics (access control);Neural networks;Authentication;Multilayer perceptrons;Maintenance engineering|
|[Interactive Deep Neural Network for Aspect-Level Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083812)|R. Nareshkumar; K. Nimala|10.1109/ICECONF57129.2023.10083812|Opinion mining;deep learning;Sentiment analysis and Aspect extraction;Deep learning;Sentiment analysis;Neural networks;Natural languages;Writing;Knowledge discovery;Concurrent engineering|
|[Fake News Classification using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083678)|M. L. V.; K. Vijayakumar; S. D. P.; R. Gangadharan; D. Suresh|10.1109/ICECONF57129.2023.10083678|VNet;Social media;Social networks;Convolutional Neural networks;LSTM;Recurrent Neural Network;Elmo Embedding;Social networking (online);Neural networks;Transfer learning;Employment;Organizations;Games;Market research|
|[An Effective Deep Learning based Recommender System with user and item embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083578)|R. Nareshkumar; K. Agalya; A. Arunpandiyan; M. Vijayalakshmi; V. Ranjani; A. Ramya|10.1109/ICECONF57129.2023.10083578|user embedding;item embedding;Deep intelligence;dnn;recommendation;Deep learning;Neural networks;Measurement uncertainty;Stars;Predictive models;Prediction algorithms;Mathematical models|
|[Detecting Parkinson's disease from Speech signals using Boosting Ensemble Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083634)|P. Deepa; R. Khilar|10.1109/ICECONF57129.2023.10083634|Parkinson's disease;Speech Signal;Adaptive Boosting;Gradient Boosting;Light Gradient Boosting;XGradient Boosting;Training;Analytical models;Parkinson's disease;Sociology;Boosting;Acoustics;Recording|
|[XSS Attack Detection using Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083807)|G. S. Nilavarasan; T. Balachander|10.1109/ICECONF57129.2023.10083807|Cross-site scripting;network security;deep learning;XSS attacks;Deep learning;Cross-site scripting;Neural networks;Information security;Network security;Feature extraction;Knowledge discovery|
|[Image Manipulation Detection Using Man Tra-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083590)|V. R. R; T. R; G. Geetha|10.1109/ICECONF57129.2023.10083590|forgery;deepneural;ManTra-Net;localization;Location awareness;Deep learning;Image resolution;Neural networks;Self-supervised learning;Reliability engineering;Forgery|
|[Diagnosis of Depressive Disorder Based on Facial and Text-Based Features Using Effective Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083832)|K. Pathak; P. Gupta; K. Nimala|10.1109/ICECONF57129.2023.10083832|Depression;Sentiment Analysis;Natural Language Processing;Multimodal;Computers;Sentiment analysis;Training data;Depression;Knowledge discovery;Concurrent engineering;Data mining|
|[Emotion Detection in Instagram Social Media Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083724)|L. J. Sailesh; V. K. Kumar; K. Nimala; R. Nareshkumar|10.1109/ICECONF57129.2023.10083724|component;formatting;Convolutional neural network;Deep learning;Breast cancer;Emotion recognition;Social networking (online);Face recognition;Multimedia Web sites;Programming;Depression;Knowledge discovery|
|[Racial Slur detection using Natural Language Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084027)|V. Gupta; J. Godwin Ponsam; C. Vangavarapu|10.1109/ICECONF57129.2023.10084027|Machine learning;Deep Learning;Decision tree;Logistic Regression;NLTK;Confusion Matrix;TF-IDF Vectorization;Social networking (online);Law;Hate speech;Support vector machine classification;Machine learning;Writing;Software|
|[Panic Selling Analysis in Stock Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083696)|G. M. B; P. Ghosh; M. G. Geetha|10.1109/ICECONF57129.2023.10083696|Deep learning;Feature engineering;Forecasting;Stock market trend;Location awareness;Adaptation models;Statistical analysis;Companies;Predictive models;Market research;Knowledge discovery|
|[Data science: simulating and development of outcome based teaching method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083713)|K. Sridhar; G. Shinde; A. Chaurasia; A. R. N. R|10.1109/ICECONF57129.2023.10083713|nan;Visualization;Data privacy;Education;Decision making;Data science;Big Data;Knowledge discovery|
|[Baseline Modeling for Early Prediction of Loan Approval System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083650)|R. Priscilla; T. Siva; M. Karthi; K. Vijayakumar; R. Gangadharan|10.1109/ICECONF57129.2023.10083650|K-Fold Cross Validation;Baseline Modeling;prediction Model;Loans;Training;Machine learning algorithms;Profitability;Neural networks;Predictive models;Prediction algorithms;Data models|
|[Cursive Handwriting Recognition Using CNN with VGG-16](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083561)|A. A. Rangari; S. Das; R. D|10.1109/ICECONF57129.2023.10083561|CNN;TensorFlow;Deep Learning;Cursive Handwriting Recognition;Data Augmentation;Training;Image segmentation;Handwriting recognition;Text recognition;Writing;Feature extraction;Knowledge discovery|
|[Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083630)|N. Doshi; S. Bhavsar; D. Rajeswari; R. Srinivasan|10.1109/ICECONF57129.2023.10083630|CNN;boundaryconstraint;contextual regularization;Deep Learning;AOD-Net;Deep learning;Measurement;Autonomous systems;Atmospheric modeling;Estimation;Feature extraction;Knowledge discovery|
|[Customer Churn Prediction Using Machine Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083813)|R. Srinivasan; D. Rajeswari; G. Elangovan|10.1109/ICECONF57129.2023.10083813|Imbalance;Machine Learning;Customer Churn;Entity Extraction;Industries;Deep learning;Companies;Predictive models;Prediction algorithms;Knowledge discovery;Data models|
|[Assessing the impact of Eight EfficientNetB (0- 7) Models for Leukemia Categorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084025)|K. S. Gill; A. Sharma; V. Anand; R. Gupta|10.1109/ICECONF57129.2023.10084025|Leukemia;Blood Cell;EfficientNet;Classification;Biomedical;Deep learning;Pediatrics;Morphology;Cells (biology);Solids;Knowledge discovery;Concurrent engineering|
|[IoT Based Stormwater Quality Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084104)|K. Shetye; S. Gupta; K. Naidu; S. Keskar; P. Shahane; H. R.|10.1109/ICECONF57129.2023.10084104|Pollutants;Monitoring;Automation;Sensors;Resultants;Continuous Monitoring;Quality Management;Chemical sensors;Buildings;Water quality;Solids;Water pollution;Sensor systems;Stormwater|
|[Blockchain Open Source Tools: Ethereum and Hyperledger Fabric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084256)|S. S. D. Arigela; P. Voola|10.1109/ICECONF57129.2023.10084256|Blockchain;Open Source Tools;Cryptocurrencies;Ethereum Transactions;Hyperledger fabric;Privacy;Distributed ledger;Scalability;Smart contracts;Bitcoin;Proof of Work;Fabrics|
|[An Automatic Method for Identification of Cervical Cancer based on Multilayer Perceptron Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083694)|N. Meenakshisundaram; G. Ramkumar|10.1109/ICECONF57129.2023.10083694|Multiple-Layer Perceptron;a Boosted Decision Tree;a Random Forest;a Gradient Naive Bayes Classifier for detecting cervical cancer;preparing data using machine learning techniques. GBC;Neural networks;Switches;Forestry;Multilayer perceptrons;Knowledge discovery;Naive Bayes methods;Reliability|
|[A Novel and Effective method for Early Identification of Cervical Cancer based on Gradient Boosting Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083844)|N. Meenakshisundaram; G. Ramkumar|10.1109/ICECONF57129.2023.10083844|Cancer of the cervix;machine learning;decision tree;random forest;preprocessing data;gradient boosting classifier(GBC);Biopsy;Forestry;Data collection;Boosting;Knowledge discovery;Concurrent engineering;Decision trees|
|[Analysis of Breast Cancer Recognition in Histopathological Images using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084065)|S. G; R. G|10.1109/ICECONF57129.2023.10084065|Latent Dirichlet allocation;Naive Bayes;Histopathological Images;Deep Learning;DT;Convolutional Neural Networks;Breast Cancer;Protocols;Image recognition;Histopathology;Precision medicine;Imaging;Knowledge discovery;Breast cancer|
|[A Novel and Robust Breast Cancer classification based on Histopathological Images using Naive Bayes Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083855)|S. G; R. G|10.1109/ICECONF57129.2023.10083855|Machine learning;latent dirichlet allocation;naive bayes;histopathological images;breast cancer;Microscopy;Biopsy;Knowledge discovery;Boosting;Breast cancer;Concurrent engineering;Planning|
|[IoT Based Crowd Detection and Stampede Avoidance using Predictive Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084059)|H. R; M. Thanawala; S. Shukla; H. Upadhyay; S. Atharabbas; V. Chella|10.1109/ICECONF57129.2023.10084059|Stampede;Vibration sensor;Machine Learning;Dataset;Logistic Regression;Vibrations;Wiring;Tensile stress;Wires;Prototypes;Vibration measurement;Sensor systems|
|[VANET Real Safety Congestion Control Wireless Access in Vehicular Environment Using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083781)|A. Budholiya; A. B. Manwar|10.1109/ICECONF57129.2023.10083781|VANET;ITS;Vehicle to Vehicle;Wireless Access in Vehicular Environment;Privacy;Wireless Access in Vehicular Environments;Vehicular ad hoc networks;Entertainment industry;Knowledge discovery;Safety;Cryptography|
|[Comparative Analysis of Alzheimer's Disease Detection via MRI Scans Using Convolutional Neural Network and Vision Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084260)|P. Sherwani; P. Nandhakumar; P. Srivastava; J. Jagtap; V. Narvekar; H. R|10.1109/ICECONF57129.2023.10084260|Alzheimer's Disease(AD);Convolutional Neural Network (CNN);Vision Transformer(ViT);Deep Vision Transformer (DeepViT);Class Attention in Image Transformer (CaiT);MRI Scan;Training;Magnetic resonance imaging;Computational modeling;Neurons;Medical treatment;Transformers;Knowledge discovery|
|[Portable GPS Tracker with Standalone Quito App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083506)|A. Halder; V. Dhakan; Y. Sood; J. Singh; H. R; M. Singh|10.1109/ICECONF57129.2023.10083506|Portable Tracker;GPS;Application;Productivity;Bluetooth;Psychology;Search problems;Lithium;Knowledge discovery;Batteries|
|[A Comparison of Supervised Learning Algorithms to Prediction Heart Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084035)|K. P. Kumar; V. Rohini; J. Yadla; J. VNRaju|10.1109/ICECONF57129.2023.10084035|heart disease (HD);Prediction;Support Vector Machine(SVM);K-Nearest Neighbor (KNN);Multi-Layer Perception(MLP);Heart;Training;Machine learning algorithms;Neural networks;Supervised learning;Support vector machine classification;Prediction algorithms|
|[Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084281)|H. Vanam; J. R. R. R|10.1109/ICECONF57129.2023.10084281|Big Data;Sentiment Analysis;Twitter Data;Machine Learning;SPARK;Sentiment analysis;Analytical models;Social networking (online);Blogs;Cluster computing;Big Data;Feature extraction|
|[An Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083625)|S. G; K. Geetha; M. A; A. Arunarani; P. Samuel|10.1109/ICECONF57129.2023.10083625|WSN;Security;characteristics;Challenges;Nodes. Encoder;Decoder;Crytography;Wireless sensor networks;Protocols;Wireless networks;Security management;Manuals;Interference;Fault location|
|[Allocation of cloud resources based on prediction and performing auto-scaling of workload](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083865)|M. Jananee; K. Nimala|10.1109/ICECONF57129.2023.10083865|Cloud Computing;Auto-Scaling;ARIMA;LSTM;Cloud computing;Costs;Computational modeling;Simulation;Time series analysis;Quality of service;Web servers|
|[Breast Cancer Detection Mammogram Imagesusing Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083530)|S. V; G. Vadivu|10.1109/ICECONF57129.2023.10083530|Histopathology;Medical image processing;Convolutional neural network;Deep learning;Breast cancer;Deep learning;Machine learning algorithms;Cross-site scripting;Neurons;Transfer learning;Prediction algorithms;Feature extraction|
|[Price Prediction of Bitcoin and Ethereum - A Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084003)|S. B; T. I. T; T. J; T. Z. J; S. Ranjani; T. T. S|10.1109/ICECONF57129.2023.10084003|Cryptocurrency;machine learning;deep learning;bitcoin;ethereum;LSTM;CNN;GRU;stock market;neural networks;Online banking;Biological system modeling;Bitcoin;Learning (artificial intelligence);Predictive models;Logic gates;Knowledge discovery|
|[Predicting Anomalous and Consigning Apprise During Heists](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084114)|S. N. Bushra; S. A. Sibi; K. VijayaKumar; M. Niveditha|10.1109/ICECONF57129.2023.10084114|CNN;YOLO;SMTP;MobileNetV2;ResNet50;Face mask detection;object detection;Weapons;Surveillance;Government;Behavioral sciences;Safety;Security;Convolutional neural networks|
|[A Smart Framework for IoT Based Garbage Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083830)|A. Lunawat; V. Rastogi; A. Shrivastava; M. Singh; Ayush; H. R|10.1109/ICECONF57129.2023.10083830|Liquid Crystal Display (LCD) Display;Raspberry Pi (RPi);Ultrasonic Sensor;ThingSpeak;Internet of Things (IoT);Garbage Monitoring;Waste Collection;Economic indicators;Wireless networks;Urban areas;Liquid crystal displays;Acoustics;Timing;Internet of Things|
|[Defect detection and recognition of SS plate using deep autoencoder neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083834)|V. Elanangai; K. Vasanth|10.1109/ICECONF57129.2023.10083834|Deep auto encoder neural network (DAENN);Tamura features;Convolution Neural Network (CNN);Radio frequency;Training;Image recognition;Shape;Neural networks;Transforms;Feature extraction|
|[Hiring and Recruitment Process Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084133)|D. Sam; M. Ganesan; S. Ilavarasan; T. J. Victor|10.1109/ICECONF57129.2023.10084133|Machine learning;Smart Hiring E-Hiring;Smart Recruitment;Ranking;Machine learning algorithms;Resumes;Training data;Machine learning;Manuals;Knowledge discovery;Concurrent engineering|
|[Classification of Chromosomes to Diagnose Chromosomal Abnormalities using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083710)|S. Saranya; S. Lakshmi|10.1109/ICECONF57129.2023.10083710|Chromosome Karyotyping;deep structured learning;ConvNet (Convolutional NN);Support Vector networks (SVM);Measurement;Support vector machines;Machine learning algorithms;Medical services;Manuals;Genetics;Feature extraction|
|[Automated Detection of Breast Cancer using Artificial Intelligence Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083873)|B. J. J; A. S. S; S. Divakaran; S. Krishnakumar; H. R. J; P. G. Kanmani prince|10.1109/ICECONF57129.2023.10083873|Breast malignance;preprocessing;segmentation;Histopathological breast images;AlexNet;GoogLeNet;Deep learning;Image segmentation;Histopathology;Artificial neural networks;Medical services;Feature extraction;Breast cancer|
|[Neural Machine Translation Using Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083569)|D. Rose; K. Vijayakumar; D. Kirubakaran; R. Pugalenthi; G. Balayaswantasaichowdary|10.1109/ICECONF57129.2023.10083569|LSTM;Attention;Machine Translation;English-Hindi Translation;Training;Neural networks;Natural languages;Computer architecture;Knowledge discovery;Data models;Concurrent engineering|
|[Combined Application of Various Techniques for Personalized Job Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083944)|D. De; R. Dwivedi; N. Allwani|10.1109/ICECONF57129.2023.10083944|Job Recommendation;Tanimoto;Jaccard;City Block;Manhattan;Cosine;Orchini;similarity measures;vectorization;preprocessing;accuracy;Measurement;Urban areas;Employment;Organizations;Euclidean distance;Knowledge discovery;Concurrent engineering|
|[Empirical Copula based Naive Bayes Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083573)|T. Srivastava; D. De; P. Sharma; D. Sengupta|10.1109/ICECONF57129.2023.10083573|Bayesian model;Copula;Empirical Copula;Non-parametric;bivariate;multivariate;cumulative distribute function;joint distribution;Smoothing methods;Computational modeling;Predictive models;Hardware;Data models;Probability distribution;Naive Bayes methods|
|[Car Defect Detection Using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083542)|S. Durai; T. Sujithra; A. S. Charan; S. K. Pulyala; B. V. Satyam|10.1109/ICECONF57129.2023.10083542|Image Comparison;Grayscale Image;Color thresholding;in Range;CNN;Feature Extraction;Pooling;Training;Deep learning;Convolution;Neural networks;User interfaces;Prediction algorithms;Real-time systems|
|[Forest Fire Detection: A Comparative Analysis of Deep Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084329)|M. Karthi; R. Priscilla; S. G; N. Infantia C; A. G R; V. J|10.1109/ICECONF57129.2023.10084329|fire detection systems;YOLOV3;Computer Vision;Deep learning;Training;Technological innovation;Webcams;Smart cities;Predictive models;Convolutional neural networks|
|[SIPMS: IoT based Smart ICU Patient Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083603)|S. C; V. R; K. Sreelatha; S. V|10.1109/ICECONF57129.2023.10083603|Patient Monitoring System;Intensive Care Unit;Machine Learning;Artificial Intelligence;Embedded Systems;Patient monitoring;Cloud computing;Machine learning algorithms;Operating systems;Medical services;Prediction algorithms;Real-time systems|
|[Collecting Dataset for Machine Learning Using IoT: Based Electricity Consumption in Residential and Commercial Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084277)|D. Lingaraja; T. Aravind; S. P. Kumar; B. Vamsidharreddy; M. Rohith; T. P. Kumar|10.1109/ICECONF57129.2023.10084277|Electricity Consumption;Machine Learning;Smart Building;IoT;Residential Areas;Temperature measurement;Temperature sensors;Home appliances;Smart buildings;Machine learning algorithms;Machine learning;Turning|
|[Varoka-Chatbot: An Artificial Intelligence Based Desktop Partner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083961)|P. S. Varshita Reddy; T. P. Kalki; P. Roshini; S. Navaneethan|10.1109/ICECONF57129.2023.10083961|Intelligent Personal Assistants (IPA);Natural Language Processing (NLP);Virtual Personal Assistant (VPA);Artificial Intelligence (AI);Deep Neural Networks (DNN);Computers;Video on demand;Social networking (online);Virtual assistants;Neural networks;Natural language processing;Internet|
|[IoT-Enabled Intelligent Farming System with Integrated Tracking and Water Gathering Plan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084325)|M. Bhargav; N. P. Sai; N. Pavan; J. Gracewell|10.1109/ICECONF57129.2023.10084325|ESP8266;AM2302 soil moisture sensor;a relay;an Arduino Uno;IoT;Wireless sensor networks;Cloud computing;Technological innovation;Automation;Surveillance;Soil moisture;Internet of Things|
|[A Hybrid Model for Pupil Detection Using FPGA and CPU by Iterative Circle Fitting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084084)|K. Kumar.S; V. S; B. S|10.1109/ICECONF57129.2023.10084084|Pupil detection;FPGA;CPUI;hybrid model;iterative circle fitting;Analytical models;Biological system modeling;Surveillance;Fitting;Data models;Real-time systems;Security|
|[Development of Interactive Visual Recognition Assistant Bot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084243)|J. P. Kolengadan; S. J. Dsouza; M. M. Ramya|10.1109/ICECONF57129.2023.10084243|Voice Control;Personal Assistant;Person Recognition;Communication Interface;Natural Language Processing;Computer Vision;Visualization;Software architecture;Virtual assistants;Face recognition;Speech recognition;Assistive robots;Chatbots|
|[Coviguard - Intelligent shirt using Arduino for protection against covid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084022)|A. S. S; B. J. J; A. M. Kumar; S. Divakaran; K. S; S. A|10.1109/ICECONF57129.2023.10084022|Smart shirt;social distancing;SpO2;temperature;pulse rate;ThingSpeak;Temperature sensors;Temperature measurement;Meters;COVID-19;Human factors;Social factors;Internet of Things|
|[Conspectus of Techniques to Monitor and Control Diabetic Foot Ulcers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083968)|A. S. S; B. S. S; K. S|10.1109/ICECONF57129.2023.10083968|Diabetes mellitus;Diabetic foot ulcer;wound debridement. peripheral neuropathy;Economics;Medical treatment;Wounds;Knowledge discovery;Diabetes;Task analysis;Monitoring|
|[Machine Learning Techniques for the Prediction of Non-Communicable Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084301)|J. M. S. Lavanya; P. Subbulakshmi|10.1109/ICECONF57129.2023.10084301|Naive Bayes;Logistic Regression;Decision Tree;k-NN;Linear Regression;Support vector machines;Machine learning algorithms;Linear regression;Prediction algorithms;Classification algorithms;Random forests;Kidney|
|[Comprehensive Analysis on Drowsiness Detection of Drivers using Facial Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083687)|D. Samiappan; P. V. Ganesan; R. Subramanian; Y. Rajasekar|10.1109/ICECONF57129.2023.10083687|Diabetes mellitus;Diabetic foot ulcer;wound debridement. peripheral neuropathy;Sleep;Planets;Roads;Mouth;Cameras;Real-time systems;Monitoring|
|[Leaf Disease Identification using ResNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083963)|P. Sirenjeevi; J. M. Karthick; K. Agalya; R. Srikanth; T. Elangovan; R. Nareshkumar|10.1109/ICECONF57129.2023.10083963|convolutional neural network;CNN;deep learning;ResNet;leaf disease;Support vector machines;Deep learning;Image segmentation;Stars;Transforms;Knowledge discovery;Hardware|
|[A Smart Bin for Disposal of Infectious Medical Waste](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083671)|S. Divakaran; J. Bethanney Janney; M. Charan; S. Krishnakumar; B. Mathangi; K. Dhanalakshmi|10.1109/ICECONF57129.2023.10083671|Internet of Things;COVID Waste management;Sensors;Solar Piezo Hybrid Power;pathogens;COVID-19;Waste management;Pathogens;Pandemics;Sociology;Knowledge discovery;Hybrid power systems|
|[Brain tumor detection and Classification using VGG 16](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083866)|R. Sankaranarayaanan; M. S. Kumar; B. Chidhambararajan; P. Sirenjeevi|10.1109/ICECONF57129.2023.10083866|brain tumor;deep learning;convolutional neural network;CNN;VGG;Deep learning;Visualization;Magnetic resonance imaging;Transfer learning;Brain modeling;Convolutional neural networks;Medical diagnostic imaging|
|[The Adaptive Security of Cloud Information Management Encrypted with Cryptographic Network Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083532)|N. N; N. K. K. P; N. R. T|10.1109/ICECONF57129.2023.10083532|Cloud-based data storage;TPA;Data Security;Secure Network Coding;Cloud computing;Protocols;Prototypes;Network coding;Knowledge discovery;Servers;Reliability|
|[An analysis on Keylogger Attack and Detection based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083937)|Y. Balakrishnan; R. P N|10.1109/ICECONF57129.2023.10083937|keylogger;cyber-attack;Machine learning;prediction;Support vector machines;Telecommunication traffic;Organizations;Machine learning;Knowledge discovery;Malware;Hardware|
|[The Conceptualization and Implementation of a Patient Monitoring System Based on the Internet of Things for Use in Telemedicine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084203)|M. N; S. E; M. Safraz|10.1109/ICECONF57129.2023.10084203|nan;Training;Patient monitoring;Telemedicine;Sociology;Medical services;Knowledge discovery;Sensor systems|
|[A Novel Fabric Defect Detection Network in textile fabrics based on DLT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083970)|K. Ramakrishnan; P. G. Jayakumar; P. Saravanan; P. Sivakumar|10.1109/ICECONF57129.2023.10083970|Textile;CNN;Active contour;Histogram;textile fabric materials;Systematics;Quality assurance;Neural networks;Diamonds;Fabrics;Sparse matrices;Task analysis|
|[An Experimental model of Smart Digital Pills for real time in-body localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083540)|A. T; P. Dimple; P. Balu; P. Jude|10.1109/ICECONF57129.2023.10083540|Wireless Capsule Endoscopy;In-Body Localization;Drug Dosage Monitoring;Miniaturized Embedded Device;Smart medicine;Integrated circuits;Wireless communication;Wireless sensor networks;Stomach;Real-time systems;Time measurement;Safety|
|[Big Data analysis based intrusion detection in WSN with reduced features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083735)|D. N; S. P. D. M; S. S. V. A|10.1109/ICECONF57129.2023.10083735|DoS attacks;WSN - DS dataset;Feature selection;Reduced dataset;Big data analysis;Training;Wireless sensor networks;Time division multiple access;Intrusion detection;Big Data;Feature extraction;Complexity theory|
|[Advanced Semiconductor Classifiers Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083525)|O. G; K. M; P. S; P. S; A. R|10.1109/ICECONF57129.2023.10083525|Faster testing;Automation;Regression;Random Forest Classifier;ASC Advanced – semiconductor classification;Exploratory Data Analysis (EDA);Performance evaluation;Integrated circuits;Integrated optics;Optical microscopy;Microscopy;Optical device fabrication;Production|
|[Pilotcontamination analysis of Massive MIMO 5G networks based on HetNets weighted scheduling with reinforcement markov encoder model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084169)|T. Kanaparthi; R. S. Yarrabothu|10.1109/ICECONF57129.2023.10084169|MIMO;spectral efficiency;5G networks;heterogenous networks;deep learning;Analytical models;Spectral efficiency;5G mobile communication;Massive MIMO;Network analyzers;Quality of service;Markov processes|
|[Integrated Crow Lookup and Particle Optimization Algorithm-based Cluster Head Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083909)|P. V. Kumar; K. Venkatesh|10.1109/ICECONF57129.2023.10083909|wireless sensor networks;energy constraints;network lifespan optimization;Integrated Crow Look (ICL);Particle Optimization Technique (POT);Wireless communication;Wireless sensor networks;Protocols;Heuristic algorithms;Throughput;Knowledge discovery;Energy efficiency|
|[Analysis of Microneedle Using COMSOL for Automated Drug Delivery System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083674)|T. Archana; J. B; H. B. M; D. N; H. U|10.1109/ICECONF57129.2023.10083674|Microneedles;Microfluidics;Fea;Microstructures;Meters;Shape;Force;Knowledge discovery;Epidermis;Software;Finite element analysis|
|[Awake/Sleep Mechanism Using LEACH Protocol in WSN - An Energy Efficient Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083738)|R. M. D. Charaan; P. R. Therasa; M. Vasudevan; S. Karthi|10.1109/ICECONF57129.2023.10083738|Wireless Sensor Networks;LEACH;Sleep Mode;Energy Conservation;Wireless communication;Wireless sensor networks;Base stations;Protocols;Redundancy;Knowledge discovery;Energy efficiency|
|[Real Time Monitoring of Transformer for Industry using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083759)|G. Keerthiga; M. D. Reddy; P. V. Kumar; S. Gireesh|10.1109/ICECONF57129.2023.10083759|Substation;IoT;Overload;High temperature;Over voltage;Low voltage;Low oil level;Temperature sensors;Temperature measurement;Substations;Microcontrollers;Oils;Oil insulation;Transformers|
|[Divination of Air Quality Assessment using Ensembling Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083751)|P. William; D. N. Paithankar; P. M. Yawalkar; S. K. Korde; A. Rajendra; Pabale; D. S. Rakshe|10.1109/ICECONF57129.2023.10083751|IoT;Smart City;Air Quality Index (AQI);Data Mining;Apache Spark;Temperature sensors;Error analysis;Atmospheric modeling;Cluster computing;Boosting;Data models;Sparks|
|[Framework for Deployment of Smart Motor Starter using Android Automation Tool](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083946)|D. B. Pardeshi; A. K. Chaudhari; P. Thokal; R. S. Dighe; P. William|10.1109/ICECONF57129.2023.10083946|Agriculture;GSM;Irrigation system;Motor controlling;SMS;Smart agriculture;Automation;Crops;Control systems;Knowledge discovery;Software;Hardware|
|[An Efficient Classification of Gait Analysis Model using Modified Hybrid Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083892)|Y. P; J. Mohana|10.1109/ICECONF57129.2023.10083892|Gait recognition;ANN;Feature Extraction;Machine Learning;Gait biometrics;visual surveillance;SVM;Training;Support vector machines;Analytical models;Biological system modeling;Artificial neural networks;Knowledge discovery;Concurrent engineering|
|[Deployment of Framework for Charging Electric Vehicle based on Various Topologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084062)|P. S. Chobe; D. B. Padale; D. B. Pardeshi; N. M. Borawake; P. William|10.1109/ICECONF57129.2023.10084062|ultracapacitor;Battery Unit;DC/DC converter;EV;Schedules;Automation;Prototypes;Charging stations;Smart grids;Topology;Automobiles|
|[Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083691)|A. D. Kumar; T. Sasipraba|10.1109/ICECONF57129.2023.10083691|Diabetic Retinopathy;CNN;Machine Learning;Deep Learning;Image Processing;Image resolution;Machine learning algorithms;Retinopathy;Machine learning;Diabetes;Classification algorithms;Convolutional neural networks|
|[I-SPMS: IoT-Enabled Novel Smart Parking Management System with Load cell Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083800)|L. P; R. R. K; P. C; K. K|10.1109/ICECONF57129.2023.10083800|IoT;PMS;Sensors;RFID;Load cell deployment;Smart cities;Sociology;Transportation;Knowledge discovery;Regulation;Space exploration;Internet of Things|
|[An Effective Analysis to Reduce Hypertension with Adaptive Lifestyle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083791)|G. Sivakarthi; R. Perumalraja|10.1109/ICECONF57129.2023.10083791|Blood Pressure;fatal diseases;Blood Pressure fluctuation;Survey;life style factors;Hypertension;Fluctuations;Medical services;Blood pressure;Mobile applications;Pressure measurement;Biomedical monitoring|
|[Detecting Parkinson's Disease using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083581)|N. D. R; V. A; A. R. S; A. E; S. G|10.1109/ICECONF57129.2023.10083581|Parkinson;classification;machine learning;prediction;Training;Support vector machines;Parkinson's disease;Neurons;Supervised learning;Machine learning;Predictive models|
|[Horticulture Image Based Segmentation with Feature Selection Using U-ConVolNet with Boltzmann Machine Using Deep Learning Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084231)|D. A; S. D|10.1109/ICECONF57129.2023.10084231|horticulture images;segmentation;feature selection;U-ConVolNet;Boltzmann Machine;Deep learning;Image segmentation;Visualization;Smoothing methods;Horticulture;Feature extraction;Knowledge discovery|
|[Estimation of Deforestation Rate and Forest Land Use Land Cover Change Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083928)|M. Kalaiyarasi; S. Saravanan; B. R; S. Karthi; D. S. N. Malleswara Rao; D. N. Venkata Sireesha|10.1109/ICECONF57129.2023.10083928|Deforestation Rate;Land Cover;Change Detection;Business As Usual Scenario;Fuzzy K-means Clustering;Hydrology;Satellites;Estimation;Forestry;Knowledge discovery;Concurrent engineering;Compounds|
|[Crop Yield Identification Using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084304)|G. Yogapriya; R. S. Kumari; S. Suganthi; K. Agalya; T. Elangovan; D. S. Pandian|10.1109/ICECONF57129.2023.10084304|convolutional neural network;CNN;deep learning;ResNet;leaf disease;Deep learning;Training;Neural networks;Crops;Production;Predictive models;Prediction algorithms|
|[Facial Expression Recognition using Convolutional Neural Network and Haar Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083838)|A. D. S; S. R; A. A|10.1109/ICECONF57129.2023.10083838|Haar cascade model;convolution neural network;facial expression;face detection;feature extraction;human-computer interaction system;Human computer interaction;Emotion recognition;Face recognition;Neural networks;Mouth;Lighting;Knowledge discovery|
|[A Framework for Secure Cooperative Spectrum Sensing based with Blockchain and Deep Learning model in Cognitive Radio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083887)|N. Dewangan; A. Kumar; R. N. Patel|10.1109/ICECONF57129.2023.10083887|cognitive radio;blockchain;spectrum sensing;malicious user;Deep learning;Gold;Scalability;Knowledge discovery;Sensors;Blockchains;Cognitive radio|
|[Deep Learning based Lung Segmentation Prior for Robust COVID-19 Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083646)|T. Suvathi; A. Chandrasekar; K. P. Thanaraj|10.1109/ICECONF57129.2023.10083646|Lung CT scan images;COVID-19;Convolution neural network;Segmentation;Classification;Validation;COVID-19;Support vector machines;Performance evaluation;Image segmentation;Pandemics;Computed tomography;Lung|
|[Hybrid K-Means Clustering for Training Special Children using Utility Pattern Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083709)|J. Jeyachidra; T. Logesh; K. Nandhini; R. Krithiga|10.1109/ICECONF57129.2023.10083709|K-Means;Clustering;Grouping;Data Mining;Genetic Algorithm;Hybrid K-means;Accuracy;Training;Performance evaluation;Clustering methods;Clustering algorithms;Sensitivity and specificity;Knowledge discovery;Proposals|
|[A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning Information System Using IOT Based Traffic Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084039)|M. N; D. B. David|10.1109/ICECONF57129.2023.10084039|Intelligent Transport System;Machine Learning;CNN;LIDAR;VANET;Machine learning algorithms;Urban areas;Sociology;Neural networks;Machine learning;Traffic control;Internet of Things|
|[Disease Prediction Using Symptoms based on Machine Learning Algorithms and Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084030)|P. Hema; A. Darbha; N. Sunny; R. V. Naganjani|10.1109/ICECONF57129.2023.10084030|Naive Bayes;GUI;Decision trees;Machine learning;Random Forest;Chatbot;Machine learning algorithms;Hospitals;Navigation;Anxiety disorders;Predictive models;Chatbots;Decision trees|
|[Tracking of Food Waste in Food Supply Chain Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083755)|S. B; S. R; S. G. R; S. P. R. V; R. P. H; S. R|10.1109/ICECONF57129.2023.10083755|Landfills;Food Wastage;Food Supply Chain;Productive Usage;Food waste;Time series analysis;Machine learning;Predictive models;Prediction algorithms;Power capacitors;Security|
|[Robust Smart Face Recognition System Based on Integration of Local Binary Pattern (LBP), CNN and MTCNN for Attendance Registration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084103)|S. B; D. C. J. W. Wise; S. H. Annie Silviya; S. K. D; V. S. Sujan K; B. S|10.1109/ICECONF57129.2023.10084103|Attendance;Face Recognition;MTCNN;Face recognition;Authentication;Knowledge discovery;Concurrent engineering;Face detection;Artificial intelligence|
|[Enhancement of Recommendation Systems through Collaborative filtering to assure Customer Requirement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083984)|A. S. S H; S. Vinod; P. J; S. B; S. N. K; S. K. E|10.1109/ICECONF57129.2023.10083984|Collaborative and Content based Filtering;Hybrid Recommender System;Databases;Scalability;Collaboration;Turning;Knowledge discovery;Information filters;User experience|
|[Concurrent Social Media in Collaborative Media Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083780)|N. Raja|10.1109/ICECONF57129.2023.10083780|Concurrent social media;Social Media Learning;Collaborative Media Education;Learning through Social Networks;Social networking (online);Education;Collaboration;Media;Knowledge discovery;IEEE Fellows;Concurrent engineering|
|[Automated Fare Collection System for Public Transport using Intelligent IoT based System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083627)|K. S. Gill; A. Sharma; V. Anand; S. Gupta|10.1109/ICECONF57129.2023.10083627|Public Transport;Fare Collection;Tickets;Global Positioning System;Location;Radio Frequency Identification;Bus Passengers;Printing;Visual impairment;Conductors;Knowledge discovery;Sensor systems;Liquid crystal displays;Sensors|
|[Creation of a Drowning Prevention System for Kids Learning in Pool using Computational Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083598)|M. Pushpavalli; P. Abirami; P. Sivagami; M. Kavitha; C. Bhuvaneswari; W. A. Memala|10.1109/ICECONF57129.2023.10083598|Accidents;Swimming pool;Drowning;Prevention Eclipse Software;Pediatrics;Webcams;Surveillance;Software algorithms;Speech recognition;Conductivity;Watches|
|[Classification of Epileptic Seizures using Optimized TQWT and Hybrid Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084140)|V. P; R. V; C. S. C; A. V. R I; S. J. Sundar Rajasekar|10.1109/ICECONF57129.2023.10084140|Epilepsy;Stacked LSTM;Hybrid model;Scalograms;Continuous Bag of Words;Tunable Q Factor Wavelet Transform;Electroencephalogram;Wavelet transforms;Q-factor;Computational modeling;Signal processing algorithms;Predictive models;Brain modeling;Feature extraction|
|[A Machine Learning Approach to Segment the Customers of Online Sales Data for Better and Efficient Marketing Purposes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084339)|M. T; S. G; M. A|10.1109/ICECONF57129.2023.10084339|Machine Learning;Customer Segmentation;K-Means;Agglomerative;DBSCAN;Marketing;Machine learning algorithms;Clustering algorithms;Machine learning;Companies;Lead;Knowledge discovery;Concurrent engineering|
|[An Enhanced Optimal Design of a Phase Changing Material Based Photo Voltaic System using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084115)|P. L. G S; M. P. K; S. Sangeetha; T. S. Krishnan; T. Udhayakumar; M. Anusuya|10.1109/ICECONF57129.2023.10084115|Flux Fluctuations;PCM Thermal Energy;PID Control;Sigmoid Function;Heating systems;Phase change materials;Training;Backpropagation;Thermal engineering;Artificial neural networks;Thermal stability|
|[An Enhancement of Service Function Chaining in Metro Mobile Ad-Hoc Networks in 5G Communication Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084334)|K. C R; V. Kumara; N. S. Banu; S. Sumithra; S. R. Gopal; S. Mudradi|10.1109/ICECONF57129.2023.10084334|service function chaining;NTP;DNS;DHCP;SNMP;Voice over IP;command line interface;Service function chaining;Web pages;Telephony;Routing;Reliability engineering;Servers;IP networks|
|[An Innovation Development of Light Weight Deep Learning Algorithm for Smart Healthcare Neural Science Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083638)|M. Chithambarathanu; V. V. Kumar; P. Sreelekha; J. Samson Isaac; M. K. Ghumman; D. A. Subhahan|10.1109/ICECONF57129.2023.10083638|Healthcare Neural Science Management;Economic Relations;Medical Activities;Human Health;Computational Intelligence;Lightweight Deep Learning;Deep learning;Economics;Technological innovation;Uncertainty;Computational modeling;Medical services;Knowledge discovery|
|[Cost-Effective Communication in UDN in Indoor and Outdoor Environment via Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084080)|K. N. Karman; V. Velmurugan; K. M. Raju; T. Sajana; V. Vijayalakshmi; J. Dhanraj|10.1109/ICECONF57129.2023.10084080|Ultra Dense cloud Network;accurate;potential cost;cloud computing;cloud providers;cost effective communication;Costs;Mobility models;Machine learning;Knowledge discovery;Scheduling;Energy efficiency;Concurrent engineering|
|[An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083705)|G. Hariharan; P. Prasanth; P. A. Devarani; T. Sajana; I. C; A. Kumar|10.1109/ICECONF57129.2023.10083705|Small-Cell Lung Tumor;Therapeutic Approaches;Cytotoxic Chemotherapy Treatment;Clinical Trials;Edge Detection;Pattern Recognition Algorithm;Chemotherapy;Image edge detection;Lung cancer;Medical services;Clinical trials;Knowledge discovery;Concurrent engineering|
|[Machine Learning Designed identification on cervical cancers in patient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083718)|M. Sangeetha; G. Sasikala; K. Anitha; S. D. P. Ragavendiran; K. R. S. Kumar; M. Deivakani|10.1109/ICECONF57129.2023.10083718|Automated Diagnosis;Cervical Cancer;Bees Swarm Optimization;Computational modeling;Machine learning;Feature extraction;Knowledge discovery;Concurrent engineering;Particle swarm optimization;Cervical cancer|
|[Innovative Machine Learning method for the creation of Power-Maximizing Solar Trackers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083806)|Y. Mariappan; M. P. K; S. G. A; S. K; P. K. Inaniya; S. Vishwakarma|10.1109/ICECONF57129.2023.10083806|energy consumption;Power consumption;solar power;power generation;inverter;machine learning model;Power demand;Roads;Solar energy;Production;Predictive models;Inverters;Solar panels|
|[Supervised Machine Learning Strategy for detection of covid19 patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083602)|I. S. Hephzi Punithavathi; K. Deepa; C. P. Venkata Srinivasa Rao; S. R. Gopal; P. Rajasekar; A. Kumar|10.1109/ICECONF57129.2023.10083602|Covid19;Detection;Machine Learning;GLCM;Computerized Tomography;BPNN;COVID-19;Training;Support vector machines;Computed tomography;Computational modeling;Predictive models;Sensitivity and specificity|
|[The Effective Solar Panel Tracker to Obtain Maximum Energy Optimization by Using Innovative Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084249)|R. K. Kaushal; V. Velmurugan; S. Mahendran; A. P. M; B. Singh; J. A. Dhanraj|10.1109/ICECONF57129.2023.10084249|solar energy;thermal collector;machine learning;maximum energy optimization;DC power;AC power;Integrated optics;Heat pumps;Cogeneration;Optical saturation;Adaptive optics;Solar panels;Solar heating|
|[The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083906)|M. G; K. Sundaramoorthy; K. Maheswari; T. Sajana; M. S. C; A. Kumar|10.1109/ICECONF57129.2023.10083906|Machine learning;Blood samples;Fruits and Vegetables;Healthy;Innovative foods;Plasma;Quality;Machine learning algorithms;Shape;Measurement uncertainty;Machine learning;Pigments;Knowledge discovery;Plasmas|
#### **2023 International Conference on Artificial Intelligence and Smart Communication (AISC)**
- DOI: 10.1109/AISC56616.2023
- DATE: 27-29 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Analysis and Design of automatically generating for GPS Based Moving Object Tracking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085180)|S. P. Yadav; S. Zaidi; C. D. S. Nascimento; V. H. C. de Albuquerque; S. S. Chauhan|10.1109/AISC56616.2023.10085180|GPRS;GPS;Cost Effective;GSM;Tracking System;People Tracking System;Costs;Urban areas;Employment;Organizations;Planning;Servers;Object tracking|
|[Smart Grid Synchronization Techniques with PV Generating System and its Challenges: An Overview of Power Sector in India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085457)|B. Maurya; S. Sharma|10.1109/AISC56616.2023.10085457|Smart grid;Grid control;Grid reliability;Renewable energy;Conventional grid;Synchronization methods;Renewable energy sources;Government;Wind power generation;Smart grids;Power system reliability;Synchronization;Reliability|
|[Enterprise Architecture Frameworks for Security Establishment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085439)|K. Bhatia; S. K. Pandey; V. K. Singh|10.1109/AISC56616.2023.10085439|Business Transformation;Enterprise Architecture Framework;TOGAF;Zachman;COBIT;FEAF;IndEA;Information Technology;Security Reference Model;Costs;Firewalls (computing);Government;Intrusion detection;Computer architecture;Transforms;Security|
|[Variants of Naïve Bayes Algorithm for Hate Speech Detection in Text Documents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085511)|V. Vijay; P. Verma|10.1109/AISC56616.2023.10085511|Gaussian Naïve Bayes;hate speech;Bernoulli’s Naïve Bayes;Multinomial Naïve Bayes;Training;Machine learning algorithms;Social networking (online);Hate speech;Machine learning;Data models;Naive Bayes methods|
|[Electronics Epidermal Tattoo – An overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084950)|K. Srivastava; S. Choudhary; A. Sharma; A. Mishra|10.1109/AISC56616.2023.10084950|epidermal;electrode;GET;graphene;nanoparticles;artifacts;Performance evaluation;Mechanical sensors;Electric potential;Instruments;Graphene;Epidermis;Proposals|
|[COVID-19 detection on chest x-ray image using yolo based architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085498)|B. Patel; D. Kothadiya; R. Patel|10.1109/AISC56616.2023.10085498|COVID-19;segmentation;deep learning;YOLO;COVID-19;Deep learning;Computed tomography;Lung;Computer architecture;Data models;Lesions|
|[A Future Approach For Energy Harvesting In Trains Using Piezoelectricity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085562)|S. R. Majeed; A. Al-Thaedan; Z. Shakir; A. A. O. Shafy; R. Alsabah; A. Al-Sabbagh|10.1109/AISC56616.2023.10085562|Piezoelectricity;energy harvesting;renewable energy;green energy;Electric potential;Renewable energy sources;Piezoelectric materials;Piezoelectric transducers;Piezoelectricity;Rail transportation;Energy harvesting|
|[An Ensemble-based approach for assigning text to correct Harmonized system code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085512)|Shubham; A. Arya; S. Roy; S. Jonnala|10.1109/AISC56616.2023.10085512|HS code classification;Bert-Transformer;Knowledge-graph;AI based auditing;Codes;Computational modeling;Training data;Tariffs;Manuals;Companies;Data models|
|[American Sign Language Interpreter: A Bridge Between the Two Worlds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085066)|K. Sood; B. Navdiya; A. Hernandez|10.1109/AISC56616.2023.10085066|Computer Vision;Data Augmentation;American;Sign Language;Machine Learning;Naive Bayes;K-Nearest Neighbor;Logistic Regression;Random Forest;Bridges;Computer vision;Webcams;Gesture recognition;Forestry;Assistive technologies;Gray-scale|
|[Design and Simulation of Enhanced Bootstrapping technique based low power operational transconductance amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084993)|R. R. Kumar; K. Sharma|10.1109/AISC56616.2023.10084993|Biological acquisition systems;linearization techniques;figure of merit;operational transconductance amplifier;Performance evaluation;Total harmonic distortion;Power demand;Linearity;Low-pass filters;Voltage;Medical services|
|[Face Detection and Classification of Sleep State of Humans by Deep Learning Mechanisms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085589)|Z. Zhaoqi; B. V. D. Kumar|10.1109/AISC56616.2023.10085589|Deep learning;CNN model;Classification;sleep state;face recognition;back prorogation;Deep learning;Industries;Technological innovation;Image segmentation;Object detection;Medical services;Reliability|
|[Improving quantitative stock trading prediction based on MAD using Q-learning technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085634)|P. Zhao; S. Yin; B. V. D. Kumar; T. J. Yew|10.1109/AISC56616.2023.10085634|Median Absolute Deviation;Q-learning;Stock trading prediction;Reinforcement Learning;Training;Q-learning;Biological system modeling;Simulation;Time series analysis;Learning (artificial intelligence);Complex networks|
|[Functionality of Pharmacy Store Management System using Cascade Model and RSA Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085029)|J. Varshney; I. Varshney; B. Singh|10.1109/AISC56616.2023.10085029|Software;Inventory systems;Cascade Model;RSA Algorithm;Computational modeling;Manuals;Writing;Computer security;Artificial intelligence|
|[Study of Denial of Service Attack On AODV Routing Protocol in Mobile Ad-hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085132)|B. Patel; D. R. Patel|10.1109/AISC56616.2023.10085132|Mobile Ad-hoc Network;DOS;AODV;Manifolds;Routing protocols;Security;Data communication;Artificial intelligence;AODV;Wireless fidelity|
|[Optimal EKHO-IMC-PID controller design and LFC based on adaptive integral k-means control approach for LSPS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084998)|R. Singh; J. Kumar; J. Singh; A. Singh; K. Sharma|10.1109/AISC56616.2023.10084998|Enthalpy;Krill herd optimization;Reduced Order Model (ROM);Load Frequency Control (LFC);power system;IMC-PID Controller Design;Adaptation models;Uncertainty;Power system dynamics;Computer architecture;Power system stability;Stability analysis;Approximation methods|
|[A Microcontroller based Automated Waste Recycling Management System for SMEs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085182)|A. K. Nuhel; D. Paul; E. Hasan; A. Azad; F. F. Rafi; P. H. Roy|10.1109/AISC56616.2023.10085182|Waste Alert;Recycle;non-recycle;waste;Arduino;servo motor;Smart Dustbin;weight segregation;Weight measurement;Waste materials;Microcontrollers;Urban areas;Sociology;Metals;Recycling|
|[Novel Framework for Quality Crop Predictions Using Data Mining and Soft Computing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085117)|R. K. Maurya; H. Jain; T. K. Sharma; S. Sharma; M. Dublish|10.1109/AISC56616.2023.10085117|Agriculture;Crop Prediction;Economy;Soft Computing;Data Mining;Machine Learning;Neural Networks;Support vector machines;Sociology;Crops;Estimation;Machine learning;Soil;Feature extraction|
|[Design and Simulation of low power composite flipped follower based bulk driven Operational Transconductance Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085593)|R. R. Kumar; K. Sharma|10.1109/AISC56616.2023.10085593|Biological applications;flipped voltage follower;partial positive feedback;operational transconductance amplifier;Semiconductor device modeling;Sequential analysis;Biological system modeling;DNA;CMOS technology;Recording;Power dissipation|
|[LViT: Vision Transformer for Lung cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085230)|N. Malaviya; M. Rahevar; A. Virani; A. Ganatra; K. Bhuva|10.1109/AISC56616.2023.10085230|Lung cancer;classification;segmentation;Vision Transformer;Training;Image segmentation;Schedules;Head;Computed tomography;Computational modeling;Lung cancer|
|[Improved Healthcare System with Quantum Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085380)|S. Chandrasekaran; R. Agrawal; V. Dutt; N. Vyas|10.1109/AISC56616.2023.10085380|Medical diagnosis;imaging;machine learning;quantum computing;Drugs;Sequential analysis;Quantum computing;Medical services;Machine learning;Predictive models;Data systems|
|[Student Sentiment Analysis Using Various Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085018)|S. Chandrasekaran; V. Dutt; N. Vyas; R. Kumar|10.1109/AISC56616.2023.10085018|Lexicon;Sentiment;Feedback;SVM;Training;Sentiment analysis;Analytical models;Terminology;Engineering profession;Psychology;Machine learning|
|[Performance Analysis of Dual-HopaMixed RF/FSO Systemaover Nakagami-m/Fisher Snedecor Fading Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085239)|R. Rani; G. Kaur; S. Gautam; M. Lakshmanan; S. Kumar|10.1109/AISC56616.2023.10085239|Free-space optical communication;Fisher Snedecor distribution;Nakagami-m;outage probability;Radio frequency;Fading channels;Atmospheric modeling;System performance;Probability;Optical fiber communication;Power system reliability|
|[Facial Emotion-based Music Recommender System using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085294)|A. Srivastava; D. K. Srivastava; M. Shandilya|10.1109/AISC56616.2023.10085294|Convolutional Neural Network (CNN);Loss Function;Accuracy;Rectified Linear Unit (RLU);Emotion recognition;Mood;Face recognition;Medical treatment;Organizations;Human factors;Convolutional neural networks|
|[Design an Improved Model of Software Defect Prediction Model for Web Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085660)|A. Arya; S. K. Malik|10.1109/AISC56616.2023.10085660|Software Defect prediction Model;machine learning;software metrices;evaluation criteria;Analytical models;Costs;Software metrics;Object oriented modeling;Machine learning;Predictive models;Software|
|[Secure Framework for Cyber Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085548)|Bhawna; S. K. Malik|10.1109/AISC56616.2023.10085548|cryptography;cyber-attack;cyber security;steganography;visual cryptography;Steganography;Malware;Computer networks;Security;Artificial intelligence;Cyberattack|
|[Control Strategies for a Static VAR Compensator to Upgrade Voltage Stability and Harmonic Contamination in Grid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085135)|G. Shrivastava; S. Chandra|10.1109/AISC56616.2023.10085135|VAR (Reactive Volt-Amp);Flexible AC Transmission System (FACTS);Static VAR Compensator (SVC);SVC Controller;Thyristor Switched Capacitor(TSC);Thyristors;Harmonics;Thyristor Controlled Reactor(TCR);Reactive power;Voltage fluctuations;Fluctuations;Simulation;Static VAr compensators;Switches;Harmonic analysis|
|[Analysis of Obstruction Avoidance Assistants to Enhance the Mobility of Visually Impaired Person: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085416)|S. Tripathi; S. Singh; T. Tanya; S. Kapoor; Kirti; A. S. Saini|10.1109/AISC56616.2023.10085416|Blind;navigation assistant;travelling aid;vision and non-vision;Computer vision;Systematics;Navigation;Instruments;Focusing;Assistive technologies;Hardware|
|[Performance Study of Static and Dynamic WBAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085143)|P. Nehra; S. Goyal|10.1109/AISC56616.2023.10085143|Wireless Body Area Network;Ad hoc On-Demand Distance Vector routing;Destination Sequenced –Distance Vector routing;Dynamic Source Routing;Routing Protocol for Low Power and Lossy Network;Wireless communication;IEEE 802.15 Standard;Wireless sensor networks;Simulation;Body area networks;Routing;Routing protocols|
|[A Bibliometric Survey of Diabetic Retinopathy Research in the Last Decade](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084973)|P. Datta; A. Kumar; P. Das|10.1109/AISC56616.2023.10084973|diabetic retinopathy;trend analysis;bibliometric analysis;research;Retinopathy;Bibliometrics;Diabetes;Artificial intelligence|
|[Personality Analysis using Edge Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085010)|P. Upadhyay; M. Chhabra|10.1109/AISC56616.2023.10085010|Personality Judgement;PA (Personality Analysis);Physiognomy;Face Recognition;ED (Edge Detection);Shape;Image edge detection;Face recognition;Psychology;Feature extraction;Particle measurements;Interviews|
|[Energy-Efficient in Cognitive Radio network by optimizing sensing and transmission time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085206)|K. Sharma; M. Awasthi|10.1109/AISC56616.2023.10085206|Cognitive radio;cooperative communication;energy efficiency;Costs;Energy efficiency;Sensors;Classification algorithms;Cognitive radio;Artificial intelligence;Radio spectrum management|
|[Online Fraud Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085493)|D. Dhiman; A. Bisht; A. Kumari; D. H. Anandaram; S. Saxena; K. Joshi|10.1109/AISC56616.2023.10085493|Rule based approach;Machine Learning algorithms;convolutional neural networks;isolation forest;SVM;Local Outlier;Feedback mechanism;Training;Machine learning algorithms;Forestry;Machine learning;Predictive models;Prediction algorithms;Credit cards|
|[IoT Based Real Time Health Score Indicator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085632)|S. Narain Gupta; R. Bhardwaj; R. Dev; A. Kushwaha; G. Gupta; S. Kumari; U. Prajapati|10.1109/AISC56616.2023.10085632|Internet of things;Health score;Real-time;Microcontroller;Temperature measurement;Thermometers;Heart beat;Time measurement;Real-time systems;Blood pressure;Pressure measurement|
|[Power and Area Efficient Hybrid Memristor-CMOS based 2’s Complement FSM for High-Performance Computing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085331)|Shalini; K. Singh|10.1109/AISC56616.2023.10085331|Memristor;CMOS;FSM;2’s Complement;Flip Flop;MRL;Semiconductor device modeling;Program processors;Power demand;Computational modeling;Memristors;CMOS technology;Hybrid power systems|
|[Recommender System for Mobile Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085109)|R. Kumar; S. Joshi; C. Gupta; R. Aggarwal|10.1109/AISC56616.2023.10085109|Sentiment analysis;Recommendation system;Content Based Filtering;Cosine similarities;KNN;Sentiment analysis;Social networking (online);System performance;Blogs;Motion pictures;Real-time systems;Mobile applications|
|[Blockchain based Secure Communication in IoT Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085196)|A. Sharma; A. Chauidhary; D. P. Bhatt|10.1109/AISC56616.2023.10085196|IOT security;Blockchain;Decentralization;IoT security models;Privacy;Technological innovation;Protocols;Layout;Government;Blockchains;Safety|
|[Stroke Disease Detection and Prediction using Extreme Gradient Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085514)|R. Kuksal; M. Vaqur; A. Bhatt; H. Chander; K. Joshi|10.1109/AISC56616.2023.10085514|Transient Ischemic Attack;(TIA);Hemorrhagic;Mini-stroke;XGB;Pulmonary diseases;Data preprocessing;Stroke (medical condition);Bladder;Boosting;Encoding;Hemorrhaging|
|[Post Pandemic Cyber Attacks Impacts and Countermeasures: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084978)|S. Kumar; M. S. Gaur; P. Sagar Sharma; V. Sagar|10.1109/AISC56616.2023.10084978|Cyber Attacks;COVID-19;Security Threats;Social Engineering;Phishing attacks;Ransomware Attacks;COVID-19;Industries;Systematics;Pandemics;Government;Education;Medical services|
|[Rice Cultivation and Its Disease Classification in Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085072)|A. Kumar; B. Bhowmik|10.1109/AISC56616.2023.10085072|Rice Cultivation;Rice Diseases;ACPS;Agriculture Sector;Precision Agriculture;Productivity;Plant diseases;Buildings;Crops;Cyber-physical systems;Agriculture;History|
|[Quantum Machine Learning and Recent Advancements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085586)|M. T. D; B. Bhowmik|10.1109/AISC56616.2023.10085586|Classical and Quantum Computing;Quantum Machine Learning;Quantum Perceptron;Quantum Kernel;QSVM;Support vector machines;Quantum computing;Computational modeling;Machine learning;Artificial neural networks;Kernel|
|[IoT Systems and Battery-Based Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085426)|K. S. Sajja; B. Bhowmik|10.1109/AISC56616.2023.10085426|Internet of Things;IoT Applications;Energy Sources;Battery-Based IoT;Energy Harvesting;Energy consumption;Energy resources;Hospitals;Computer architecture;Supercapacitors;Software;Batteries|
|[A Thorough Review on Deep Learning Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085166)|P. Chhabra; D. S. Goyal|10.1109/AISC56616.2023.10085166|Deep Learning;Machine Learning;Convolutional neural networks;forward propagation;Object Detection;Deep learning;Knowledge engineering;Network topology;Image processing;Visual impairment;Neurons;Object detection|
|[Miniaturized Two-element MIMO Antenna with neutralization line and an asymmetric open slot for WLAN and IOT applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085167)|A. Kumar; C. Tyagi; D. Saxena; P. Jha|10.1109/AISC56616.2023.10085167|neutralized lines;MIMO Antenna;open-ended slot;Isolation enhancement;ECC;Wireless LAN;Slot antennas;Surface waves;Bandwidth;Internet of Things;Artificial intelligence;MIMO communication|
|[Two-element MIMO Antenna with High isolation based on Zigzag-shaped Slot on the ground for 5G and IOT applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085016)|A. Kumar; R. KumarVerma; D. Saxena; P. Sharma|10.1109/AISC56616.2023.10085016|MIMO Antenna;Slot in antenna;Isolation enhancement;ECC;Slot antennas;5G mobile communication;Microstrip antennas;Bandwidth;Radar antennas;Radar applications;Internet of Things|
|[Technical Approach Towards Elderly Right to Health and Well-Being](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085503)|S. Kathuria; P. Rawat; R. Singh; A. Gehlot; A. Kathuria; S. Pandey|10.1109/AISC56616.2023.10085503|IoT;sensors;elderly;rights;health;well-being;Virtual Reality (VR);Smart healthcare;Virtual reality;Aging;Information and communication technology;Internet of Things;Older adults;Artificial intelligence|
|[Imperative role of customer segmentation technique for customer retention using machine learning techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085487)|S. Koli; R. Singh; R. Mishra; P. Badhani|10.1109/AISC56616.2023.10085487|Customer Segmentation;Data Acquisition technique;Data Selection Approach;Customer Segmentation Techniques;Deep learning;Analytical models;Adaptation models;Consumer behavior;Databases;Market research;Feature extraction|
|[Voltage Controller Design for Photovoltaic Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085300)|N. Singh; J. Singh; M. A. Ansari; A. Verma; A. Mazhar|10.1109/AISC56616.2023.10085300|Inverter;Photovoltaic;PSIM;Current Control;Closed Loop;Photovoltaic systems;Power quality;Inverters;Temperature control;Solar radiation;Voltage control;Artificial intelligence|
|[Enhancement of Password Authentication System Using Vector (Graphical) Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085057)|K. Pandey; A. Singh; A. Anand; A. Kaushik; S. N. Gupta|10.1109/AISC56616.2023.10085057|Password Authentication;Graphical Password;Computer security;Persuasive Cued Click Point;Visualization;Technological innovation;Memory management;Authentication;Passwords;Mobile handsets;Malware|
|[A Comparative Analysis on 1D & 2D ECG applying Data Compression Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085223)|P. Tripathi; M. A. Ansari; T. Kumar Gandhi; R. Mehrotra; L. Layba; S. Gupta; M. Junaid|10.1109/AISC56616.2023.10085223|ECG;Data Compression;1D Domain;2D Domain;Compressed ratio;Telemedicine;Two dimensional displays;Measurement uncertainty;Data compression;Electrocardiography;Size measurement;Time measurement|
|[Robust Control of Integrated Renewable Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085508)|N. Singh; J. Singh; M. A. Ansari; A. Mishra; L. Layba; M. Junaid|10.1109/AISC56616.2023.10085508|PSO;PID;FUEL CELL;Renewable sources;Robust control;Renewable energy sources;Pollution;Software packages;Simulation;Urban areas;Power system stability|
|[Stability Control of Two-Area of Power System Using Integrator, Proportional Integral and Proportional Integral Derivative Controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085614)|A. Kumar Maurya; H. Khan; H. Ahuja|10.1109/AISC56616.2023.10085614|PID controller;Classical control technique;Artificial Intelligence method;Two-area system;Load frequency control;Time-frequency analysis;Regulators;Power transmission lines;Process control;Automatic generation control;Power system stability;Stability analysis|
|[A Review of Current Approaches to Automated Detection of Malignant Tumors Based on MR Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085438)|R. Dixit; M. K. Pal|10.1109/AISC56616.2023.10085438|Tumor;Segmentation;Classification;MRI;Image segmentation;Recurrent neural networks;Sensitivity;Magnetic resonance imaging;Malignant tumors;Feature extraction;Brain modeling|
|[Automatic irrigation system using solar panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084965)|J. Singh; P. Datta; N. Kumar; K. Sharma; R. Mehrotra; R. Singh|10.1109/AISC56616.2023.10084965|Solar panel;Sensors;Microcontroller;Irrigation;Soil moisture content;Irrigation;Geology;Soil moisture;Moisture;Manuals;Humidity;Water conservation|
|[A Strategic Metaheuristic Edge Server Placement Scheme for Energy Saving in Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084941)|C. Pandey; V. Tiwari; S. Pattanaik; D. Sinha Roy|10.1109/AISC56616.2023.10084941|Edge Server;Mobile Edge Computing;Particle Swarm Optimization;Multi-objective optimization;Energy consumption;Smart cities;Metaheuristics;Network architecture;Telecommunications;Servers;Time factors|
|[Right To Health of Differently-Abled People-Technology Intervention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085642)|S. Pandey; A. Gehlot; A. K. Dixit; S. Kathuria; R. Singh|10.1109/AISC56616.2023.10085642|Differently-abled people;IoT;Smart Homes;Sensors;Health;Solid modeling;Cloud computing;Smart healthcare;Virtual reality;Smart homes;Inspection;Internet of Things|
|[Web-based Pretrained Transformer Model for Scientific Paper Summarization (WPT-SPS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085409)|K. Girthana; S. Swamynathan; A. R. Nirupama; S. Sri Akshya; S. Adhithyan|10.1109/AISC56616.2023.10085409|Abstractive Summarization;Scientific paper;Transformers;Transfer learning;PEGASUS;Training;Deep learning;Art;Transfer learning;Transformers;Artificial intelligence|
|[Shorting pin and SRR loaded multi-band Antenna for 4G/5G/Wi-Fi/WiMAX and IOT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085352)|A. Kumar; D. Saxena; N. Sharma; A. Gankotiya|10.1109/AISC56616.2023.10085352|Multi-band Antenna;SRR;Shorting Pin;Partial Ground antenna;Bandwidth;WiMAX;Pins;Internet of Things;Impedance;Artificial intelligence;Loaded antennas|
|[Block-Chain Implementation in Industry 4.0: Critical Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085518)|NabiyevaNilufarMuratovna; TuychievaOdinaNabievna|10.1109/AISC56616.2023.10085518|Industry 4.0;block chain;advanced technology;Smart agriculture;Technological innovation;Standards organizations;Stability criteria;Organizations;Medical services;Computer architecture|
|[Technology Works as A Medicine for Crime Scene Evidence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085148)|S. Kathuria; P. Rawat; R. Singh; A. Gehlot; N. Kathuria; S. Pandey|10.1109/AISC56616.2023.10085148|AI;blockchain;victim;rights;legal;cloud computing;Cloud computing;Law;Face recognition;Digital forensics;Interference;Blockchains;Artificial intelligence|
|[Smart Devices Technology Based Homes for Differently Abled People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085549)|S. Pandey; A. Kumar Dixit; R. Singh; A. Gehlot; S. Kathuria; V. Pandey|10.1109/AISC56616.2023.10085549|Blockchain;Smart homes;AI;IoT;disabled people;Pediatrics;Cloud computing;Smart homes;Blockchains;Safety;Security;Sustainable development|
|[Artificial Intelligence-Based System for Advocate Assistance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084951)|S. Pandey; A. K. Dixit; R. Singh; A. Gehlot; N. Kathuria; S. Kathuria|10.1109/AISC56616.2023.10084951|Edge device;Advocate Assistance;AI;Client;Edge Computing;Performance evaluation;Industries;Technological innovation;Law;Databases;Legislation;Coherence|
|[Artificial Intelligence: Creation of Human Rights in Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085265)|S. Kathuria; P. Rawat; R. Singh; A. Gehlot; N. Kathuria; S. Pandey|10.1109/AISC56616.2023.10085265|human rights;AI;dignity;equality;freedom;solidarity;Geography;Economics;Computer science;Technological innovation;Ethics;Automation;Law|
|[Parasitic and Open slot-based Circular polarized Two-port Antenna for ISM applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085544)|A. Kumar; D. Saxena; P. Jha; N. Sharma|10.1109/AISC56616.2023.10085544|Two-port antenna;Isolation enhancement;CP antenna;Ism applications;Slot antennas;Shape;Bandwidth;Microstrip;Artificial intelligence;MIMO communication|
|[Remote Air Quality Sensing and Temperature Monitoring System using GSM for Smart City Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085138)|S. Pal; M. Bandyopadhyay; S. Chowdhury Kolay; S. Chattopadhyay|10.1109/AISC56616.2023.10085138|GSM modem;Temperature sensor;Air quality sensor;Notification SMS;Smart City Application;Temperature measurement;Temperature sensors;GSM;Temperature distribution;Smart cities;Real-time systems;Time measurement|
|[Assistive Technology Intervention in Dyslexia Disorder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085588)|R. Patnoorkar; S. Chaudhary; S. Pandey; P. Shekhar Pandey; R. Balyan; M. Kumar|10.1109/AISC56616.2023.10085588|Assistive technology;Dyslexia;Learning;Text-to-speech;Electronic learning;Education;Learning (artificial intelligence);Gaze tracking;Assistive technologies;Writing;Mathematics|
|[Review on Artificial Intelligence Role in Implementation of Goods and Services Tax(GST) and Future Scope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085030)|R. Kumar; R. Khannna Malholtra; C. N. Grover|10.1109/AISC56616.2023.10085030|Artificial Intelligence;GST;Taxation;assessment;Regulators;Instruments;Government;Finance;Predictive models;Developing countries;Fraud|
|[Role of Artificial Intelligence, Machine Learning, Deep Learning for Sericulture: A Technological Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085573)|P. Pal; D. Singh; R. Singh; A. Gehlot; S. V. Akram|10.1109/AISC56616.2023.10085573|Sericulture;silkworm;sustainable fabric;AI;Deep learning;Productivity;Sociology;Green products;Agriculture;Sustainable development;Statistics|
|[Machine Learning for Data Flow Processing in Learning Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085154)|F. Rahioui; M. A. T. Jouti; M. El Ghzaoui; P. Malik; S. Das; R. Singh|10.1109/AISC56616.2023.10085154|AI;Data Science;ML;MDP;linear regression;Knowledge engineering;Computers;Machine learning algorithms;Neurons;Symbols;Machine learning;Information processing|
|[Cotton Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084992)|K. Dakshinya; M. Roshitha; P. A. Raj; C. Anuradha|10.1109/AISC56616.2023.10084992|Convolution Neural Networks;Verticillium wilt;Alternaria spot;Cercosporin Leaf spot;Partial Differential Equations;Support Vector Machine;Support vector machines;Productivity;Plant diseases;Shape;Partial differential equations;Crops;Feature extraction|
|[Assessing the Influence of Big Data on Competition Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085471)|R. Nautiyal; R. S. Jha; R. Singh; A. Gehlot; S. Kathuria; N. Kathuria|10.1109/AISC56616.2023.10085471|Big data;Data;Digital markets;competitive market;business;Industries;Databases;Biological system modeling;Companies;Big Data;Regulation;Stakeholders|
|[A Strategic Alternative to Predicting Property Investment Value Through Random Forest and Gradial Enhancing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085127)|A. Sony; Aarju|10.1109/AISC56616.2023.10085127|Real Estate Analysis;Random-Forest;Linear Regression;Gradient Boosting;ML;SVM;LSSVM;and PLS;Gold;Fluctuations;Costs;Roads;Instruments;Layout;Artificial intelligence|
|[Difficulties and Potential Ulnerabilities in the IOT Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085640)|H. Singh; S. K. Mand|10.1109/AISC56616.2023.10085640|IoT;AWS;HTTP;SSL;Privacy and Cyber-Security;Cloud computing;Privacy;User interfaces;Regulation;Safety;Internet of Things;Security|
|[Reliable Block chain-Based Digital System of Voting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085374)|A. Raizada; B. Sharma|10.1109/AISC56616.2023.10085374|E-Voting;IoT;Healthcare;Blockchain;Visual Studio;Node.js;Security;Privacy and Identity Verification;Economics;Privacy;Electronic voting systems;Law;Distributed ledger;Medical services;Blockchains|
|[Static Excitatory Synapse with an Integrate Fire Neuron Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085652)|N. Shaik; P. Kumar Malik; S. Ravipati; S. Oduru; A. Munnangi; S. Boda; R. Singh|10.1109/AISC56616.2023.10085652|Neuromorphic Computing;synapse;neuron;Semiconductor device modeling;Power demand;Neuromorphic engineering;Neurons;Silicon;Real-time systems;Software|
|[Latest Developments in Smart Grid and Wireless Security Technologies Serve to Protect the Environment for Better Upcoming Generations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085582)|L. Goswami; N. Sharma|10.1109/AISC56616.2023.10085582|Smart Grid;Wireless Security;Deep Learning;Power generation;SEM and Regression Analysis;Power supplies;Architecture;Urban areas;Computer architecture;Mathematical models;Smart grids;Security|
|[The Analyzing of Role of Big Data in Security and General Privacy Problems through Correlation Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085049)|S. Gupta; H. Jeet Singh|10.1109/AISC56616.2023.10085049|Big Data;IoT;Correlation Analysis;SDN;MySQL;CSA;Security and Privacy;Procurement;Data privacy;Production;Big Data;Propulsion;Rendering (computer graphics);Safety|
|[An Intensive Technical Analysis of Smart Sporting Goods Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085621)|V. Kumar; A. Sony|10.1109/AISC56616.2023.10085621|Sport Industry;Smart Sport Accessories;IoT Devices;Smart Sport Analysis and Monitoring Report;Vibrations;Training;Shape;Blindness;Vibration measurement;Bones;Arthritis|
|[Predominance by a Founder Irregular Collection in Diagrams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084940)|A. Gupta; V. Saxena|10.1109/AISC56616.2023.10084940|Predominance;∪;CSRSD-Set;Nodes;RSD-Net and Co-Secure Regular Set;Artificial intelligence;Standards|
|[Difficulties and Opportunities There in Deployment of Renewable Energy Options into the AC Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085344)|D. Juneja; S. Goswami|10.1109/AISC56616.2023.10085344|RES;DV Photovoltaic System;SVM;TCSC;DVR;QD and Smart Grid;Photovoltaic systems;Renewable energy sources;Sociology;Transforms;Generators;Transceivers;Reliability|
|[Studying the Effect of an Online Realm Based on Wearable Technology on Learners' Understanding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085261)|Kirti; M. Ali Khan|10.1109/AISC56616.2023.10085261|ElectroAR;Smart Wearable;Student Database;SDK and MAR;Training;Technological innovation;Interactive systems;Crops;Mobile applications;Artificial intelligence;Augmented reality|
|[A Deep Analysis of Dilemma Devices which uses Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085570)|M. Gauri; M. P. Singh|10.1109/AISC56616.2023.10085570|Dilemma Device;ANN;NER;SPARQL;EMR;Neural Network and Voice Recognition;Industries;Neural networks;Surgery;Speech recognition;Medical services;Information age;Biology|
|[Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085077)|M. Kavita; H. Singh|10.1109/AISC56616.2023.10085077|NLP;ML;PasS;SVM;PCA;OLSR;MARS;CNN;LSI Engines;Visualization;Machine learning algorithms;Service robots;Text categorization;Natural languages;Transforms;Oral communication|
|[Groundwater Evaluation with Classification Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084926)|Y. Dogra; M. A. Khan|10.1109/AISC56616.2023.10084926|ML;SVM;Water Analysis;ANN;DNN;RBFNN;WPA;MLP;UNESCO Efforts;Liquids;Sociology;Soil;Classification algorithms;Rivers;Reliability;Task analysis|
|[A Thorough Study of Implications of Something Like the IoT Devices (Technology) Building More Design Data feed and on Local Variables Limits Imposed via Correlations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085365)|G. Kaur; R. Guputa|10.1109/AISC56616.2023.10085365|WSN;ML;CNN;MDE;model-driven processing;correlational analysis;and Rational Software Architect;Productivity;Wireless sensor networks;Artificial satellites;Correlation;Instruments;Computational modeling;XML|
|[An Assessment of Analysis Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085190)|A. Salaria; V. Balu|10.1109/AISC56616.2023.10085190|Cloud data Warehouse;Mining technique;K-D-D technique and P-P-D-M techique;Cloud computing;Data analysis;Supervised learning;Transforms;Organizations;Knowledge discovery;Information retrieval|
|[COVID-19 Effect on India's Business Utilizing Online, Blockchain, or AI Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085658)|H. S; B. J|10.1109/AISC56616.2023.10085658|Digital Marketing;Transformations;COVID-19;Pandemics;Buying Behavior;ERP System;IoT;AI;Telecommuting and Digital Transformation;COVID-19;Humanities;Planets;Human factors;Companies;Bitcoin;Social factors|
|[A Detailed study on Smart Vulnerability Scanning Stability and its Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085446)|E. Upadhyay; P. Savant|10.1109/AISC56616.2023.10085446|IoT;Reliability;Transformer;GSM/GPRS;Monitoring System;Sensor and ADC Converter;Renewable energy sources;Surveillance;Stability analysis;Batteries;Safety;Security;Reliability|
|[Image Classifications Methods Analysis with Differen Methods to for Identifying best Image Layout with High Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085356)|M. TR; V. V|10.1109/AISC56616.2023.10085356|virtual image order detection;spatial information;techniques for investigating maps;uniting by vegetation;Visualization;Image segmentation;Satellites;Image resolution;Layout;Vegetation mapping;Streaming media|
|[Design of application using API for the selection of music](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085603)|R. C. Tripathy; Chandramma|10.1109/AISC56616.2023.10085603|UI;AI;API;World Wide Web (WWW);HTTP;Web Application;Beat-up;Music Recommendation Analysis;Technological innovation;Operating systems;Music;Media;Mobile communication;Software;Servers|
|[Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085284)|R. Mehra; R. R|10.1109/AISC56616.2023.10085284|CNN;Re LU-Sigmoid;CUB200;Neural Network;Operating Languages and ReLU;Image color analysis;Logic gates;Birds;Feature extraction;Classification algorithms;Convolutional neural networks;Kernel|
|[Iot – Enabled Technologies for Sustainable Smart Agriculture and their Comprehensive Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085545)|S. S; R. R|10.1109/AISC56616.2023.10085545|Internet of Things (IoT) Applications;Smart Agriculture;Sustainable Agriculture;IoT Enabled Techniques;Smart agriculture;Technological innovation;Protocols;Government;Regulation;Internet of Things;Artificial intelligence|
|[Brain-Computer Interface Artifact Removal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084934)|G. S; S. Shetty|10.1109/AISC56616.2023.10084934|BC interface (BCI);Electroencephalogram (EEG);Artifacts;electroocculogram (EOG);electrocardiogram (ECG);electromyogram (EMG);Image resolution;Training data;Pattern classification;Low-pass filters;Electroencephalography;Electromyography;Brain-computer interfaces|
|[MRI techniques using image processing Brain Tumor Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085289)|M. T R; G. G|10.1109/AISC56616.2023.10085289|Brain Tumor;Brain MRI;Magnetic Resonance Imaging (MRI) and VGG19;Cooling;Magnetic resonance imaging;Image processing;Transfer learning;Manuals;Data collection;Task analysis|
|[A Hybrid technolgy using Machine learning and blockchain technology to prevent Covid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085526)|A. Kumar Pandey; V. Vekariya|10.1109/AISC56616.2023.10085526|Block chain (BC);Face recognition (FR);Single Shot Detection;Deep learning (DL);COVID-19;Tracking;Pandemics;Cellular phones;Human factors;Social factors;Vaccines|
|[Analysis of BERT Email Spam Classifier Against Adversarial Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085255)|A. Kushwaha; K. Dutta; V. Maheshwari|10.1109/AISC56616.2023.10085255|Spam emails;BERT Model;Adversarial Attacks;Analytical models;Unsolicited e-mail;Bit error rate;Natural languages;Artificial intelligence;Business|
|[Effective E-Learning Practices by Machine Learning and Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085391)|A. Naim; S. M. Alshawaf; P. Kumar Malik; R. Singh|10.1109/AISC56616.2023.10085391|Artificial Intelligence;Blackboard;E-Learning Deanship;Learning Management Services;Machine Learning;Adaptive learning;Electronic learning;Learning automata;Measurement uncertainty;Learning (artificial intelligence);Machine learning;Artificial intelligence|
|[Detection of Brick Kilns Using Multi-Spectral Bands of Sentinel-2 Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085085)|S. Imaduddin; Y. A. Khan; K. Mirza; B. K. Bhadra|10.1109/AISC56616.2023.10085085|Brick kiln;Remote Sensing;Sentinel Imagery;Spectral Bands;QGIS;Google Earth Engine;Machine Learning;Random Forest Classifier;Earth;Training;Kilns;Analytical models;Machine learning algorithms;Pollution control;Sensors|
|[A key-shaped ultra-wideband antenna for IoT applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085314)|Sneha; P. K. Malik; A. Gehlot|10.1109/AISC56616.2023.10085314|5G;Compact antenna;IoT;Patch antenna;Planar Antenna;Slotted antenna;Wireless communication;Polarization;Wireless sensor networks;Technological innovation;Patch antennas;Receiving antennas;Bandwidth|
|[ANN-based Non-Destructive Testing of Apples, using statistical and textured features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085524)|A. Toofani; H. Garg|10.1109/AISC56616.2023.10085524|Apple sorting;Histogram-based probability density;Discrete wavelet transform;Training;Computational modeling;Neural networks;Probability;Multilayer perceptrons;Feature extraction;Data models|
|[An Image Copy-Move Forgery Detection based on SURF and Fourier-Mellin Transforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085429)|R. Kumari; H. Garg|10.1109/AISC56616.2023.10085429|Copy-move;Forgery detection;CMFD;SURF;FMT;Image coding;Image resolution;Social networking (online);Splicing;Morphology;Transforms;Feature extraction|
|[An Approach for Enhancing Data Storage Capacity in Quick Response Code using Zip Compression Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085559)|C. Bhardwaj; H. Garg|10.1109/AISC56616.2023.10085559|QR code;Data compression;ZIP compression;Fault tolerance;Image color analysis;Fault tolerant systems;Memory;Information sharing;Data compression;QR codes|
|[Role of Machine Learning in Resource Usages and Security Challenges for Cloud Computing: Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085687)|U. Pandey; Tejveer; M. Rajput; R. Singh|10.1109/AISC56616.2023.10085687|nan;Training;Measurement;Data privacy;Maximum likelihood estimation;Cloud computing security;Intrusion detection;Machine learning|
|[Website Traffic Forecasting Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085005)|H. Nunnagoppula; K. Katragadda; M. Ramesh|10.1109/AISC56616.2023.10085005|Website traffic;Time series;Forecasting;Historical data;CNN;LSTM;Recurrent neural networks;Time series analysis;Decision making;Switches;Predictive models;Data models;Internet|
|[Hospital Resource Management -A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085061)|U. Pandey; Nidhi; R. I. Rahbar|10.1109/AISC56616.2023.10085061|Hospital Resource Allocation;Pandemic;Optimization Strategies;COVID-19;COVID-19;Hospitals;Resource management;Artificial intelligence;Faces|
|[Enhanced Anomaly Detection System for IOT Based on Improved Dynamic SBPSO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084980)|K. Chandhar; D. Pratap Singh; J. Alanya-Beltran; S. V. Akram; K. D; M. Tiwari|10.1109/AISC56616.2023.10084980|intrusion detection system;internet of things;anomaly detection;Privacy;Dynamics;Intrusion detection;Benchmark testing;Feature extraction;Search problems;Internet of Things|
|[IOT-Based Analysis for Effective Continuous Monitoring Prevent Fraudulent Intrusions in Finance and Banking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084920)|S. K. UmaMaheswaran; G. Singh; A. K. Dixit; S. C. Mc; M. K. Chakravarthi; D. P. Singh|10.1109/AISC56616.2023.10084920|Internet of Things (IoT);Malicious Attacks;Financial Collectors;Privacy;Target tracking;Planets;Computer hacking;Banking;Software;Internet of Things|
|[A Review of use of Artificial Intelligence on Cyber Security and the Fifth-Generation Cyber-attacks and its analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085175)|A. K. Dangi; K. Pant; J. Alanya-Beltran; N. Chakraborty; S. V. Akram; K. Balakrishna|10.1109/AISC56616.2023.10085175|Cybersecurity;artificial intelligence;machine learning;deep learning;cognitive science;and bio-inspired computing;Computer viruses;Phishing;Unsolicited e-mail;Cyberspace;Reinforcement learning;Artificial intelligence;Computer crime|
|[An implementation of Wireless Mesh Routing Protocol system against Dos attacks in IoT-based Assistance system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085415)|D. Yadav; R. Raman; D. Gangodkar; S. K. Joshi; B. Sreedevi; C. R. Prasad|10.1109/AISC56616.2023.10085415|Network layer attacks;denial of service;internet of things;e-Healthcare;wireless mesh network;routing protocol;and ambient assisted living;Resistance;Wireless communication;Ambient assisted living;Wireless mesh networks;Denial-of-service attack;Routing;Throughput|
|[A Survey on Patient Information Management System Using IOT and Cloud Computing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084969)|M. Goswami; F. D. Castro Dantas Sales; M. Sathe; M. K. Chakravarthi; R. Singh; D. Gangodkar|10.1109/AISC56616.2023.10084969|Edge Cloud Computing;Internate of Things (IoT);Patient Information Management;Cloud computing;Distributed databases;Data integration;Information management;Telecommunications;Internet of Things;Biomedical monitoring|
|[Image Processing with Intelligence System Using Sensing in Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085025)|S. Petikam; F. De Castro Dantas Sales; S. S.; J. L. A. Gonzáles; K. Joshi; B. Pant|10.1109/AISC56616.2023.10085025|Digital Image Processing;Artificial Intelligence;Internet of Things;Neural Networks;Deep Learning;Visualization;Data privacy;Smoothing methods;Image edge detection;Soft sensors;Image processing;Digital images|
|[Blockchain Integrated with Industrial IOT Towards Industry 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085226)|S. Tamilmani; T. Mohan; S. Jeyalakshmi; G. P. Shukla; A. Gehlot; S. K. Shukla|10.1109/AISC56616.2023.10085226|Blockchain;IoT;Industry 4.0;IIoT;Healthcare;Data Management;Food Trackability;M2M;P2P;Supply Chain;E-Commerce;Scalability;Market research;Software;Blockchains;Fourth Industrial Revolution;Cryptocurrency;Business|
|[Blockchain Applications for Security Issues and Challenges in IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085201)|A. K. Dangi; G. A. Pandurang; G. V. Bachhav; M. K. Chakravarthi; A. Gehlot; S. K. Shukla|10.1109/AISC56616.2023.10085201|Blockchain;Ledger technology;Internet of Things;Data Security;Information Technology;Industries;Wireless sensor networks;Privacy;Data privacy;Distributed ledger;Authentication;Blockchains|
|[Blockchain Implementation in Financial Sector and Cyber Security System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085045)|A. Y. A. B. Ahmad; S. S. Kumari; M. S; S. K. Guha; A. Gehlot; B. Pant|10.1109/AISC56616.2023.10085045|Block chain;Financial Sector;Cyber Security System;Technological innovation;Finance;Transforms;Blockchains;Security;Reliability;Computer crime|
|[Blockchain To Secure Cloud computing services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085060)|S. Goswami; D. Uike; S. Patil; Y. Thakur; S. V. Akram; K. Pant|10.1109/AISC56616.2023.10085060|Blockchain;Cloud Computing;Cyber Attacks;Secure Cloud Computing;Cloud computing;Privacy;Law;Power distribution;Finance;Blockchains;Peer-to-peer computing|
|[Blockchain Approach for Implementing Access Control In IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085452)|J. Panduro-Ramirez; M. Lourens; A. Gehlot; D. P. Singh; Y. Singh; D. J. Salunke|10.1109/AISC56616.2023.10085452|Block chain;IoT;ABAC;Consensus Algorithm;Key Management and Control Chain;Access control;Wireless communication;Privacy;Clustering algorithms;Vehicular ad hoc networks;Optical fiber networks;Blockchains|
|[A Novel based Hybrid smart System for detecting the cyber-attacks on IOT’s](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085521)|K. J. Gnanaselvi; S. K. Shukla; K. Kumar; A. Gehlot; T. Jaiswal; B. T. Geetha|10.1109/AISC56616.2023.10085521|Internet of Things;network;security;malware;vulnerability scanning;and fault detection;Fault diagnosis;Law;Databases;Fault detection;Stacking;Ecosystems;Intrusion detection|
|[A design of dynamic rate aware classified key for network security in wireless sensor network through optimized distributed secure routing protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085033)|A. Shameem; S. K. Shukla; M. Tiwari; D. Buddhi; Y. Singh; U. R|10.1109/AISC56616.2023.10085033|WMN;Secure routing;Classified key distributional scheme;Rate a ware routing;SRM;QoS Introduction;Wireless communication;Wireless sensor networks;Power transmission lines;Heuristic algorithms;Customer satisfaction;Network security;Routing|
|[Design of deep learning models for the identifications of harmful attack activities in IIOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085088)|S. RajBalaji; R. Raman; B. Pant; N. Rathour; B. R. Rajagopa; C. R. Prasad|10.1109/AISC56616.2023.10085088|Deep learning (DL);Industrial internet of things;Auto-encoder;Internet industrial control systems;Training;Deep learning;Network topology;Industrial control;Network intrusion detection;TCPIP;Feature extraction|
|[IoT Wireless Technology using lattice-based open source public-key NTRU cryptosystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085360)|T. L. Gudipalli; R. Raman; D. P. Singh; D. Singh; C. Venkateswarlu; J. L. A. Gonzáles|10.1109/AISC56616.2023.10085360|IoT Security;Wireless Fidelity (WI-Fi) Security;NTRU Cryptosystem;Encryption;Decryption;Wireless communication;Wireless sensor networks;Quantum computing;Protocols;Resists;Elliptic curve cryptography;Encryption|
|[A Comprehensive review on Data Mining Techniques in managing the Medical Data cloud and its security constraints with the maintained of the communication networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085161)|A. Uthiramoorthy; A. Bhardwaj; J. Singh; K. Pant; M. Tiwari; J. L. A. Gonzáles|10.1109/AISC56616.2023.10085161|Healthcare;different health issues prediction;smart technique;Industries;Ethics;Law;Medical services;Predictive models;Skin;Data mining|
|[Blockchain effect on E-commerce: A framework for key research areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085137)|N. K; J. Panduro-Ramirez; A. Gehlot; A. Barve; S. C. P; R. Ponnusamy|10.1109/AISC56616.2023.10085137|Blockchain framework;Distributed Ledger System;E- Commerce system;Data Mining;Data Encryption;Technological innovation;Law;Biological system modeling;Supply chains;Trustless services;Blockchains;Cryptocurrency|
|[Classification and Contrast of Supervised Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085338)|R. Raman; R. Shamim; S. V. Akram; L. Thakur; B. G. Pillai; R. Ponnusamy|10.1109/AISC56616.2023.10085338|Classifiers;Market Research;Deep Learning;Supervised Machine Learning;and Data Mining Techniques;Support vector machines;Machine learning algorithms;Supervised learning;Training data;Prediction algorithms;Time measurement;Classification algorithms|
|[Integration of Machine Learning Algorithms for E-Learning System Course Recommendation Based on Data Science](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085048)|K. K. Ramachandran; S. S. Phatak; S. V. Akram; V. Patidar; A. M. Raju; R. Ponnusamy|10.1109/AISC56616.2023.10085048|Classifier;Moodle;ADTree Classification;Apriori Pattern Classification;and Simple K-means;machinelearning algorithms;Learning management systems;Machine learning algorithms;Electronic learning;Databases;Clustering algorithms;Prediction algorithms;Classification algorithms|
|[Investigation of the Educational Performance on the Revolutionary Philosophical Electoral Online Learning Platform Centred on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085556)|K. K. Ramachandran; M. Ravichand; K. Joshi; V. Vekariya; D. Saini; R. Ponnusamy|10.1109/AISC56616.2023.10085556|Student performance;Deep learning;Evaluation methodologies;data mining for education;online platforms;Deep learning;Performance evaluation;Systematics;System performance;Data mining;Forecasting;Transient analysis|
|[Innovative Cyber Security Solutions Built on Block chain Technology for Industrial 5.0 Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085577)|K. K. Ramachandran; B. Nagarjuna; S. V. Akram; J. Bhalani; A. M. Raju; R. Ponnusamy|10.1109/AISC56616.2023.10085577|Internet ofThings (IoT);Blockchain;Industry 5.0;Cyber Security;Industrial Revolution;Artificial Intelligence;Industries;Supply chains;Ecosystems;Taxonomy;Medical services;Virtual reality;Fourth Industrial Revolution|
|[A Research of Wireless Effective Communication with Low Power Consumption for Internet of Things (IoT) Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084964)|R. Raman; J. Alanya-Beltran; S. V. Akram; S. Trivedi; S. Bothe; M. K. Chakravarthi|10.1109/AISC56616.2023.10084964|Wireless Low Power Consumption Modules;Power Saving Wireless Protocols;Wireless Sensor Networks;6LowPAN;Internet of Things;Wireless communication;Wireless sensor networks;Temperature distribution;Temperature dependence;Protocols;Power demand;Power system management|
|[Internet-Of-Things Wireless Communication with a Focus on the Protection of User Privacy and the Delivery of Relevant Facts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084975)|R. Raman; Z. Gupta; A. Gupta; S. V. Akram; D. Saini; J. Bhalani|10.1109/AISC56616.2023.10084975|Internet of Things (IoT);Cloud Services Wireless Communication;Wireless Sensor Network (WSN);Smart Sensors;Wireless communication;Privacy;Wireless sensor networks;Systematics;Urban areas;Transportation;Transforms|
|[Network Security Concerns for Designing Robotic Systems: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085453)|R. Raman; Z. Gupta; S. V. Akram; D. Lalit Thakur; B. G. Pillai; M. K. Chakravarthi|10.1109/AISC56616.2023.10085453|Artificial intelligence;autonomous robots;multi-robot systems;networking technologies;cyber-physical systems;and cyber security;Wireless communication;Wireless sensor networks;Collaboration;Systems architecture;Robot sensing systems;Control systems;Internet of Things|
|[An investigation on the role of artificial intelligence in scalable visual data analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085495)|R. Raman; D. Buddhi; G. Lakhera; Z. Gupta; A. Joshi; D. Saini|10.1109/AISC56616.2023.10085495|Distributed Computing;Parallel Computing;Big Data;Big Data Analytics;Artificial Intelligence (AI);Deep learning;Text mining;Visualization;Technological innovation;Data analysis;Systematics;Big Data|
|[Privacy Rating for Android Apps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085462)|P. K. Tiwari|10.1109/AISC56616.2023.10085462|android security;privacy;data security;permissions;ad libraries;Privacy;Data privacy;Costs;Static analysis;Companies;Libraries;Mobile applications|
|[Farmers' desire towards real-time implementation of IoT in agricultural productions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085639)|A. S. Kumar; V. Srinivasa|10.1109/AISC56616.2023.10085639|IoT adoption;Awareness;Challenges;Smart agriculture;Performance evaluation;Sociology;Production;Real-time systems;Internet of Things;Task analysis|
|[Analysis of Performance Metrics for Classification of Punjabi Poetry using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085197)|A. Kaushal; K. Dutta|10.1109/AISC56616.2023.10085197|LDA;TF-IDF;classification;performance met- rics;Support Vector Machine;Logistic Regression;Random Forest;Naive Bayes;Support vector machines;Training;Machine learning algorithms;Text categorization;Static VAr compensators;Writing;Tokenization|
|[A Review on Breast Cancer Detection for Histopathology Images Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085116)|E. Ramalakshmi; L. Gunisetti; L. Sumalatha|10.1109/AISC56616.2023.10085116|Breast cancer;detection;Deep learning;histopathology image;feature extraction;classification;Deep learning;Image analysis;Sensitivity;Histopathology;Prediction algorithms;Breast cancer;Classification algorithms|
|[Design and Simulation Analysis of 1 kW Hybrid Charging Station for Plug-In Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085159)|A. Singh; H. Kumar; H. Jedia; P. Thakur; P. Sahw|10.1109/AISC56616.2023.10085159|electric vehicle;charging station;hybrid;grid;solar;congestion;Plug-in electric vehicles;Renewable energy sources;Analytical models;Adaptation models;Simulation;Charging stations;Hybrid power systems|
|[Implementation of 28GHz Four Port MIMO Antenna with Funnel-Shaped Slot On Elliptical Patch And Five-Circular Slotted Common Shared Ground](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084925)|M. Sharma; M. Junedul Haque; R. Singh; P. K. Malik|10.1109/AISC56616.2023.10084925|28GHz MIMO;5G;funnel-shaped slot;five-circular slot;ECC4Port, DG4Port, TARC4Port, CCL4Port;Wireless communication;Slot antennas;5G mobile communication;Numerical analysis;Information and communication technology;Permittivity;Electromagnetics|
|[An Enhanced and Efficient approach towards text detection and extracting text in multi-oriented images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085454)|A. Shankhdhar; L. Daga; G. Jadaun|10.1109/AISC56616.2023.10085454|machine learning;OCR;filters;open CV;Training;Image edge detection;Image processing;Digital images;Optical character recognition;Neural networks;Brightness|
|[Face Recognition Application using MATLAB & Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085064)|S. Singh; S. Bharadwaj; G. Raj|10.1109/AISC56616.2023.10085064|MATLAB;Python;Eigenfaces algorithm;PCA;Photometric stereo;Geometric Relationships;Application Development;Image Processing;Face recognition;Buildings;Face detection;Reliability;Artificial intelligence;Matlab;Python|
|[Large Screen Wireless Notice Board with P10 Led Matrix Modules using LoRa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085490)|G. Raj; T. Mishra; T. Tegwani|10.1109/AISC56616.2023.10085490|LoRa Wi-Fi Module;Arduino Uno;Wireless Notice Board;P10 Led Matrix Modules;Wireless communication;Protocols;Hospitals;Prototypes;Modulation;Artificial intelligence;Wireless fidelity|
|[Code Reuse and Sustainable Testing-A Comparative Survey and Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085269)|P. Mukherjee; A. Kesharwani; J. Dua; S. K. Pandey|10.1109/AISC56616.2023.10085269|code reuse;sustainable testing;FLOSS;Software testing;Codes;Sociology;Organizations;Market research;Statistics;Artificial intelligence|
|[The Impact of Digitization on the Social Security Framework in India: An Analysis of Existing Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085527)|R. Shekhawat; S. Kumari|10.1109/AISC56616.2023.10085527|social security;digitization;government schemes;Data privacy;Costs;Sociology;Ecosystems;Safety;Pensions;Security|
|[A Real-Time Web Application for Road Accident Alert System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085225)|K. Kumar; R. Vashist; P. C. Vashist|10.1109/AISC56616.2023.10085225|Road accidents;fatal accidents;Web application;provide fast medical relief;alternative routes;Road accidents;Hospitals;Law enforcement;Roads;Area measurement;Real-time systems;Safety|
|[IoT based app controlled prepaid smart energy meter: the need of present time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085211)|P. C. Vashist; A. Tripathi; R. Vashist; T. K. Gupta|10.1109/AISC56616.2023.10085211|Smart Meter;IoT;Arduino;Meters;Power distribution;Electric variables measurement;Companies;Smart meters;Real-time systems;Artificial intelligence|
|[Prediction of Lung Cancer using Convolution Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085058)|A. Vij; K. S. Kaswan|10.1109/AISC56616.2023.10085058|Neural Networks;Deep Learning;Lung Cancer;VVG -16;Non-Small lung Cancer;Deep learning;Machine learning algorithms;Convolution;Computed tomography;Neural networks;Lung cancer;Prediction algorithms|
|[Anuvadak: Language System using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085373)|Y. Singh; P. Kumar; S. Goel; P. Garg; T. Srivastava; M. Bhardwaj|10.1109/AISC56616.2023.10085373|Machine Learning techniques;Natural language Processing;language;translation;Training;Computational modeling;Time series analysis;Machine learning;Receivers;Predictive models;Planning|
|[Automated system to detect social distancing among humans during covid pandemic situation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085364)|C. Rawal; P. Kumar; S. Goel; S. Jain; H. Pandey; V. Kumar|10.1109/AISC56616.2023.10085364|social distancing;alleged;ramification;adequacy;aggregation;Visualization;Ethics;Protocols;Pandemics;Human factors;Social factors;Security|
|[The Scripter: A Tool for Penetration Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085040)|J. Srivastava; P. K. Aggarwal; S. Goel; P. Shukla; S. Yadav; V. Jain|10.1109/AISC56616.2023.10085040|Cyber Security;Testing;Scripts;Education;Real-time systems;Internet;Fuels;Task analysis;Artificial intelligence;Cyberattack|
|[A Recommendation System to Envision Movie Ranking Using Collaborative Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085472)|M. Aggarwal; S. Goel; P. K. Aggarwal; T. Srivastava; A. Srivastava; V. Gupta; I. Ishani|10.1109/AISC56616.2023.10085472|Real-time;filtering;recommendation system;algorithms;Tracking;Databases;Collaborative filtering;Motion pictures;Information filters;Proposals;Artificial intelligence|
|[Fake News Detection Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085488)|G. Rawat; T. Pandey; T. Singh; S. Yadav; P. K. Aggarwal|10.1109/AISC56616.2023.10085488|Cyber Security;Testing;Scripts;Uniform resource locators;Machine learning algorithms;Social networking (online);Voting;Machine learning;Feature extraction;Data models|
|[Deep Learning Approaches for Human Gait Recognition: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085665)|V. Narayan; S. Awasthi; N. Fatima; M. Faiz; S. Srivastava|10.1109/AISC56616.2023.10085665|Gait Recognition;Deep Learning (DL);Convolutional Neural Network (CNN);Recurrent Neural Network (RNN);Capsule Networks;Autoencoders;Deep Belief Networks;Generative Adversarial Networks (GAN);Deep learning;Recurrent neural networks;Machine learning algorithms;Authentication;Learning (artificial intelligence);Generative adversarial networks;Market research|
|[FuzzyNet: Medical Image Classification based on GLCM Texture Feature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085348)|V. Narayan; P. K. Mall; S. Awasthi; S. Srivastava; A. Gupta|10.1109/AISC56616.2023.10085348|Fuzzy;GLCM;CAD;Medical Image;Radiography;Musculoskeletal system;Noise reduction;Delays;Medical diagnosis;X-ray imaging;Medical diagnostic imaging|
|[A Trustful Payment System for Crowdfunding using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085649)|K. Kumar; R. Vashist; P. C. Vashist|10.1109/AISC56616.2023.10085649|Blockchain;Smart Contracts;Crowd Funding;Trusted Payment System;Ethereum;Solidity;Technological innovation;Social networking (online);Smart contracts;Finance;Decentralized applications;Blockchains;Artificial intelligence|
|[Artificial Intelligence as a Tool for Enhanced Data Integrity and Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085250)|V. L. Priya; A. A; A. Chahar; A. A|10.1109/AISC56616.2023.10085250|AI-artificial intelligence;technology tools;technology survival;Data integrity;Data security;Data integrity;Data security;Big Data;Artificial intelligence;Business|
|[Prediction and Analysis of Heart Attack using Various Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085460)|O. Sharma|10.1109/AISC56616.2023.10085460|Deep Learning;Industry;Employability;SVM;Naïve Bayes;Accuracy;Industries;Analytical models;Machine learning algorithms;Cardiac arrest;Medical services;Machine learning;Electrocardiography|
|[Smart Glasses Embedded with Facial Recognition Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085337)|M. Kumar; B. Bharti; U. Chauhan|10.1109/AISC56616.2023.10085337|Text recognition;Raspberry pi;camera;speaker;ultrasonic sensor;smart devices;google text speech;data cable;power supply;Text recognition;Face recognition;Speech recognition;Cameras;Web servers;Software;System-on-chip|
|[Smart Kitchen using IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084932)|B. Bharti; M. Kumar; U. Chauhan|10.1109/AISC56616.2023.10084932|Internet of things;mobile application;sensors;kitchen- accidents;data transfer;integration;Temperature sensors;Temperature measurement;Home appliances;Cloud computing;Software;Hardware;Mobile applications|
|[Analysis of Decentralized Pharmaceutical Supply Chain: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085240)|P. Patel; H. Samantaray; R. Mansharamani; D. Vora; A. Goyal; A. Gupta|10.1109/AISC56616.2023.10085240|pharmaceutical;supply chains;blockchain;counterfeit drugs;Drugs;Systematics;Supply chain management;Scalability;Supply chains;Smart contracts;Blockchains|
|[The Application of Artificial Intelligence in Android Mobile Learning for the Special Education Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085531)|M. N. Nordin; M. Z. Mustafa; N. F. Mosbiran|10.1109/AISC56616.2023.10085531|AI;Mobilelearning;Mobile Induct Learning;Soft tools;Education;Mobile learning;Hardware;Artificial intelligence;Task analysis;Smart phones;Engines|
|[Successful Role of Data Science In Managing Covid-19 Battle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085065)|A. A. B. A. Rahman|10.1109/AISC56616.2023.10085065|Role of Data Science;Managing Covid-19 pandemic;Data Science (DS);Artificial-Intelligence (AI);Machine-Learning (ML);COVID-19;Technological innovation;Maximum likelihood estimation;Uncertainty;Pandemics;Machine learning;Data science|
|[Block Chain Technology Application to the Banking Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085346)|S. Alwi; I. N. B. Malan; S. H. Yusof; M. Mustafa|10.1109/AISC56616.2023.10085346|Block chain Applications;Block chain technology;Advantages of Block chain;Smart contracts;Banking;Features and Security Aspects of Block chain;Industries;Technological innovation;Regulators;Shape;Banking;Blockchains;Internet|
|[Development of Judgment Classification Models using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085124)|S. Ganti; M. Anirudh|10.1109/AISC56616.2023.10085124|Judgement Classification model;Random Forest;Sentiment Analysis;Support vector machines;Analytical models;Ethics;Machine learning algorithms;Computational modeling;Forestry;Classification algorithms|
|[A Survey on Firewall for cloud security with Anomaly detection in Firewall Policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085419)|D. Hakani|10.1109/AISC56616.2023.10085419|cloud;firewall rules;security anomaly policy;redundant rules;Cloud computing;Firewalls (computing);Law enforcement;Filtering;Cloud computing security;Redundancy;Network security|
|[Industrial IoT Condition Monitoring using Wireless IoT Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085613)|A. Bhaskar|10.1109/AISC56616.2023.10085613|Predictive Maintenance;IoT;LabView;Wireless communication;Vibrations;Temperature sensors;Wireless sensor networks;Systematics;Machine learning;Predictive models|
|[Artificial Intelligence Techniques: Smart Way to Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085216)|S. Davlyatov|10.1109/AISC56616.2023.10085216|Artificial Intelligence Techniques;Smart Grid;Machine Learning Applications;Systematics;Load forecasting;Process control;Power system stability;Smart grids;Safety;Security|
|[Discrete Wavelet Transform: A breakthrough in segmentation of CT scans for Intracranial Hemorrhages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085384)|H. M. Saifuddin; H. C. Vijayalakshmi; R. J|10.1109/AISC56616.2023.10085384|Discrete Wavelet Transform;Segmentation;Intracranial Haemorrhage;CT scans;Image segmentation;Head;Computed tomography;Coagulation;Wounds;Feature extraction;Discrete wavelet transforms|
|[Assessing the robustness of multi-criterial recommendation systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085184)|P. Tekmattla; S. T. Valluru|10.1109/AISC56616.2023.10085184|Multi-criteria recommendation systems;Similarity aggregation;Rating aggregation;Industries;Estimation;Robustness;Complexity theory;Task analysis;Artificial intelligence;Recommender systems|
|[Data Augmentation for Automated Essay Scoring using Transformer Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085523)|K. Gupta|10.1109/AISC56616.2023.10085523|Transfer Learning;Transformers;Automated Scoring;Transfer learning;Bit error rate;Transformers;Data models;Natural language processing;Artificial intelligence|
|[Fake Information Detection Using Deep Learning Methods: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085519)|P. Dhiman; A. Kaur; A. Bonkra|10.1109/AISC56616.2023.10085519|Deep learning techniques;disinformation;fake information detection;misinformation;Deep learning;Social networking (online);Transfer learning;Bit error rate;Random access memory;Feature extraction;Transformers|
|[A Survey on Malware Classification using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085625)|E. Vani; P. Prabhavathy|10.1109/AISC56616.2023.10085625|Malware;Privacy;Cybersecurity;Deep Learning;Deep learning;Privacy;Learning (artificial intelligence);Market research;Malware;Security;Information technology|
|[Scientific Landscape and the Road Ahead for Deep Learning: Apple Leaves Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085221)|A. Bonkra; P. K. Bhatt; A. Kaur; S. Kamboj|10.1109/AISC56616.2023.10085221|Apple leaves;Deep convolution neural network;Bibliometric;Deep Learning;Scopus;Cluster;Deep learning;Productivity;Plant diseases;Rain;Databases;Roads;Bibliometrics|
|[Image Caption using CNN in Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085162)|R. Kumar; G. Goel|10.1109/AISC56616.2023.10085162|Image Captioning;Recurrent Neural Network (RNN);Convolution Neural Network (CNN);Long Short Term Memory(LSTM) Neural Network;Deep Learning;Computer vision;Image recognition;Neural networks;Imaging;Learning (artificial intelligence);Closed captioning;Speech processing|
|[Analyzing the Impact of Feature Correlation on Classification Acuracy of Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085542)|S. Mishra; R. K. Pradhan|10.1109/AISC56616.2023.10085542|Feature Correlation;Machine Learning;Classification Accuracy;Breast Cancer;Support vector machines;Analytical models;Machine learning algorithms;Correlation;Machine learning;Predictive models;Feature extraction|
|[Emoji Prediction using Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085173)|L. K. Sagar; P. Shandil; A. Patel; S. Vishwakarma; S. Yadav|10.1109/AISC56616.2023.10085173|RNN;LSTM;Natural Language Processing (NLP);Sentiment analysis;Social networking (online);Mood;Supervised learning;Prediction algorithms;Data models;Reliability|
|[Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085654)|J. S. Dhatterwal; M. Singh Naruka; K. S. Kaswan|10.1109/AISC56616.2023.10085654|Particle Swarm Optimization;Multi-agent System;Surgical Intensive Care Unit;Ant Colony Optimization;Hospitals;Sociology;Semantics;Medical services;Medical diagnosis;Particle swarm optimization;Statistics|
|[Role of Information and Communication Technology in Rooms Divisions across Delhi NCR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085023)|J. Jyoti; M. Dash; K. S. Kaswan|10.1109/AISC56616.2023.10085023|Information Technology;Room Division;Hotel Industries;ICT;Industries;Training;Productivity;Scalability;Sociology;Globalization;Information and communication technology|
|[Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085002)|K. S. Kaswan; M. S. Naruka; J. S. Dhatterwal|10.1109/AISC56616.2023.10085002|Ofepisodic;Image Segmentation;Episodic Memory;Visual Soar;Soar Technology;Memory architecture;Learning (artificial intelligence);Encoding|
|[Clustering and Reinforcement Learning based Multi-Access Edge Computing in Ultra Dense Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085158)|V. N. Udupa; V. K. Tumuluru|10.1109/AISC56616.2023.10085158|Multi-access edge computing;ultra-dense network;deep reinforcement learning;Clustering;Base stations;Multi-access edge computing;Processor scheduling;Computational modeling;Neural networks;Reinforcement learning;Predictive models|
|[Analysis and Categorization of Emotet IoT Botnet Malware](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085302)|U. Garg; S. Kumar; M. Ghanshala|10.1109/AISC56616.2023.10085302|Internet of Things;Malware;Malware detection;Static analysis;Dynamic analysis;Emotet Malware;Military communication;Supply chain management;Smart cities;Static analysis;Malware;Real-time systems;Telecommunications|
|[Identification and Detection of Behavior Based Malware using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085168)|U. Garg; N. Sharma; M. Kumar; A. Singh|10.1109/AISC56616.2023.10085168|Malicious Program;Malware;Machine Learning;Classifiers Sentiment;Training;Support vector machines;Machine learning algorithms;Machine learning;Feature extraction;Malware;Classification algorithms|
|[Crime Analysis and Forecasting using Twitter Data in the Indian Context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085282)|M. Vivek; B. R. Prathap|10.1109/AISC56616.2023.10085282|Crimeanalysis;Machine Learning;Twitter;Social media;Crime prediction;Social networking (online);Law enforcement;Biological system modeling;Blogs;Time series analysis;Data visualization;Predictive models|
|[An Effective Model for Smartphone Based Pothole Classification and Admin Alerting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084985)|S. Vaddi; V. V. Maddi; Y. Sai Krishna Ramineni; L. S. Chalamalasetti|10.1109/AISC56616.2023.10084985|image classification;deep learning;transfer learning;flutter;Deep learning;Performance evaluation;Support vector machines;Training;Roads;Transfer learning;Sensors|
|[IOT based Smart Meter Using Node-Red](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084984)|V. M. Kumar Reddy; M. K. V; L. B. S; K. Nanda Kumar|10.1109/AISC56616.2023.10084984|Node-red;Arduino;Proteus;Costs;Power demand;Data visualization;Memory;Smart meters;Real-time systems;Software|
|[Using ARIMA and LSTM to Implement Stock Market Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085405)|A. Pandey; G. Singh; H. Hadiyuono; K. Mourya; M. J. Rasool|10.1109/AISC56616.2023.10085405|stock market;ARIMA;LSTM;stock prediction;finance;live data;Training;Recurrent neural networks;Time series analysis;Companies;Predictive models;Market research;Data models|
|[Performance Evaluation of Linearly Chirp Fiber Bragg Grating for Time Delay Analysis in Beam Steering for Enhance Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085177)|M. D. Nadeem; S. K. Raghuwanshi; P. S. Pandey|10.1109/AISC56616.2023.10085177|Phase array antenna;Photonics true time delay line;Fiber Bragg Grating;Phased arrays;Performance evaluation;Chirp;Delay effects;Simulation;Bandwidth;Delay lines|
|[Stock Market Prediction Approach: An Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085262)|M. Joshi; G. Goel|10.1109/AISC56616.2023.10085262|Stock market;machine learning;feature extraction;Feature extraction;Prediction algorithms;Classification algorithms;Data mining;Stock markets;Forecasting;Artificial intelligence|
|[Analyzing the Impact of Feature Correlation on Classification Acuracy of Machine Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085293)|S. Mishra; R. K. Pradhan|10.1109/AISC56616.2023.10085293|Feature Correlation;Machine Learning;Classification Accuracy;Breast Cancer;Support vector machines;Analytical models;Machine learning algorithms;Correlation;Machine learning;Predictive models;Feature extraction|
|[Multiclass Classification of Prostate Cancer Gleason Grades Groups Using Features of multi parametric-MRI (mp-MRI) Images by Applying Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085270)|I. S. Virk; R. Maini|10.1109/AISC56616.2023.10085270|multiclass classification;prostate cancer;Gleason grade groups;Support vector machines;Solid modeling;Magnetic resonance imaging;Machine learning;Receivers;Feature extraction;Lesions|
|[The Influence of Different Weighting Methods on MADM Ranking Techniques and Its Impact on Network Selection for Handover in HetNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085305)|A. K. Yadav; K. Singh|10.1109/AISC56616.2023.10085305|Handover;MADM;criteria weight;ping-pong effect;HetNet;Simulation;Handover;Entropy;Heterogeneous networks;Roaming;Mobile nodes;Artificial intelligence|
|[Study and Performance Comparison of Various MIMO Antenna Configurations Under Rayleigh Fading Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085325)|V. Gupta; P. Singla; P. Sharma|10.1109/AISC56616.2023.10085325|Antennas;SISO;SIMO;MISO;MIMO;5G etc;Wireless communication;Fading channels;Spectral efficiency;Simulation;Bandwidth;Throughput;Reliability|
|[Artificial Intelligence as a facilitator for Film Production Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085082)|H. Singh; K. Kaur; P. P. Singh|10.1109/AISC56616.2023.10085082|AI;Film;Film Production;Production process;Entertainment industry;Production;Motion pictures;Visual effects;Artificial intelligence;Faces|
|[Integration of IOT with Block Chain Technology for the Technology Advancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085279)|S. B; L. R; H. Bhaskar R; C. R. Prasad; A. Gehlot; D. Verma|10.1109/AISC56616.2023.10085279|Blockchain;Cloud Computing;Internet of Things;Cryptography;Data Sorage;Industries;Geography;Sociology;Memory;Bitcoin;Data collection;Robustness|
|[Wireless Communications Implementation Using Blockchain as Well as Distributed Type of IOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085249)|V. Gunturu; V. Bansal; M. Sathe; A. Kumar; A. Gehlot; B. Pant|10.1109/AISC56616.2023.10085249|Internet of Things;Blockchain;5g;Wireless communication;Mobile Edge Computing;Cryptography;Wireless communication;Measurement;Multi-access edge computing;Collaboration;Computer architecture;Financial management;Blockchains|
|[Integration of A-I in implementation of Wire-less Webbing: A detailed Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085628)|P. R. Kapula; B. Pant; B. Kanwer; D. Buddhi; K. V. D. Sagar; S. Sinthu|10.1109/AISC56616.2023.10085628|A-I integration;challenges;wire-less webbings.;Wireless communication;Wireless sensor networks;Computer architecture;Reconnaissance;Ubiquitous computing;Sensors;Internet of Things|
|[An Experimental Study of Sink Hole Attacks and Distributed Denial of Service (DDS) on IoT network based Healthcare Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085139)|R. Raman; B. Mandaloju; D. Singh; V. Tripathi; U. H. Maginmani; J. L. A. Gonzáles|10.1109/AISC56616.2023.10085139|Internet of things;Attack like DOS;Sinkhole attack;Ubiquitous computing;Standards organizations;Medical services;Telecommunication traffic;Organizations;Network architecture;Sensors;Safety|
|[Incorporating Deep Learning Methodologies into the Creation of Healthcare Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085651)|S. Khan; V. Ch; K. Sekaran; K. Joshi; C. K. Roy; M. Tiwari|10.1109/AISC56616.2023.10085651|Machine learning;AI in healthcare;artificial neural networks;CNN;LeNET CNN;Deep learning;Privacy;Telemedicine;Medical services;Big Data;History;Task analysis|
|[Heart Disease Prediction Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085584)|P. Singh; I. S. Virk|10.1109/AISC56616.2023.10085584|Cardiovascular Diseases;Machine Learning;K-Nearest Neighbor;Logistic Regression;Random Forest;Heart;Radio frequency;Machine learning algorithms;Medical services;Forestry;Predictive models;Prediction algorithms|
|[Black Friday Sales Prediction using Supervised Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084959)|S. Patil; O. Nankar; R. Agrawal; K. Sharma; S. Awasthi; N. Jha|10.1109/AISC56616.2023.10084959|Machine Learning;Classification;Regression;Black Friday;Computers;Face recognition;Computational modeling;Predictive models;Natural language processing;Data models;Random forests|
|[Load Optimization Accessions, Ramification the QoS in Software Defined Networking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085328)|P. S. Tiwna; J. Singh|10.1109/AISC56616.2023.10085328|SDN;Controller;Load Optimization;QoS;Switch;Migration.;Costs;Scalability;Reinforcement learning;Quality of service;Load management;Throughput;Software defined networking|
|[Design & Development of IOT based Vertical Farming Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085566)|K. Kundu; S. Sharma; B. Bhardwaj; R. Muddineni; A. Rai|10.1109/AISC56616.2023.10085566|Vertical Farming;Internet of Things;;Temperature;Microcontrollers;Crops;Prototypes;Soil;Sensor systems;Temperature control|
|[Multi-level authentication model with Political Dingo Optimizer-enabled ZFNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085133)|K. Singh; N. Singh|10.1109/AISC56616.2023.10085133|Zeiler and Fergus network (Z-FNet);Dingo Optimizer (DOX);Political Optimizer (PO).;Deep learning;Authorization;Authentication;Classification algorithms;Smart devices;Artificial intelligence;Optimization|
|[Analysis of Twitter Sentiment to Predict Financial Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085195)|A. Gupta; V. K. Tayal|10.1109/AISC56616.2023.10085195|Machine Learning;Natural Language Processing;RoBERTa-Large Model;Transformers Models;Financial Trends Detection;Financial Sentiments Detection;Financial Markets;Data Science;Computational Social Science;Semantic Science;Analytical models;Sentiment analysis;Social networking (online);Blogs;Semantics;Finance;Predictive models|
|[Mitigating Malware Attacks using Machine Learning: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085630)|M. Arse; K. Sharma; S. Bindewari; A. Tomar; H. Patil; N. Jha|10.1109/AISC56616.2023.10085630|Malware Attacks;Machine Learning;Deep Learning;Cyber Security;Machine learning algorithms;Automation;Computer hacking;Firewalls (computing);Intrusion detection;Data protection;Machine learning|
|[GNN Model Based On Node Classification Forecasting in Social Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085118)|A. K. Awasthi; A. K. Garov; M. Sharma; M. Sinha|10.1109/AISC56616.2023.10085118|ANN;GNNs;Forecasting;Social Networks;Machine Learning;Training;Deep learning;Social networking (online);Convolution;Roads;Artificial neural networks;Predictive models|
|[Presentation of futuristic Malarial Disease through a Hybrid Model of A.I. and Big data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085407)|A. K. Awasthi; M. Sharma; A. K. Garo; P. Chaudhary|10.1109/AISC56616.2023.10085407|Artificial intelligence;Neural Networks;LSTM;ARIMA-SARIMA;Pain;Neural networks;Time series analysis;Machine learning;Predictive models;Market research;Data models|
|[Automated Scene Text Detection Systems: An Imminent Progress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084958)|S. Singh; S. Kaur; S. Bhardwaj|10.1109/AISC56616.2023.10084958|Scene Text;Automatic Detection;Deep Learning;Navigation;etc;Computer vision;Text recognition;Navigation;Blindness;Benchmark testing;Cameras;Artificial intelligence|
|[Predicting Academic Performance of Students Using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085214)|N. Sharma; M. Sharma; U. Garg|10.1109/AISC56616.2023.10085214|KNN;Decision Tree;Random Forest;Academic Performance;Prediction;Education;Industries;Machine learning algorithms;Education;Forestry;Predictive models;Prediction algorithms;Internet|
|[IoT Based Sewage System Blockage and Water Accumulation Prevention System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085120)|A. Kaur; H. Wadhwa; S. Kamboj|10.1109/AISC56616.2023.10085120|Sewage system;blockage;pressurized sensor;IoT;smart cities;Pressure sensors;Smart cities;Buildings;Light emitting diodes;Water pollution;Mobile applications;Internet of Things|
|[Key Management Scheme for Cloud Integrated Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085554)|R. Walia; P. Garg|10.1109/AISC56616.2023.10085554|Diffie-Hellman;Dual Encryption;Attack Mitigation;Ad hoc On Demand Multipath Distance Vector;Routing Protocol for low power and lossy Network;Cloud computing;Protocols;Authentication;Throughput;Encryption;Safety;Internet of Things|
|[Sign Language Detection using LSTM Deep Learning Model and Media Pipe Holistic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085375)|M. Deshpande; V. Gokhale; A. Gharpure; A. Gore; H. Yadav; P. Kunekar; A. M. Sawant|10.1109/AISC56616.2023.10085375|Deep Learning;Image Processing;LSTM;Media pipe;NumPy;OpenCV;Python;Sign Language;TensorFlow;Deep learning;Image recognition;Statistical analysis;Data preprocessing;Gesture recognition;Media;Data models|
|[Breast Cancer Prediction: Impact of Stratified Sampling Approach on Classifier Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084954)|A. Sharma; A. Sharma; V. Bhattacharjee|10.1109/AISC56616.2023.10084954|K Nearest Neighbor Classifier;breast cancer;stratified sampling;accuracy;Representation learning;Analytical models;Machine learning algorithms;Breast tumors;Buildings;Lung cancer;Predictive models|
|[Performance Analysis of LoRa WAN in IoT at L band Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085530)|S. Sharma; P. Yadav; R. Tiwari|10.1109/AISC56616.2023.10085530|LoRa WAN;IoT;Outage Probability;Fading;path loss;Wide area networks;Fading channels;Meters;Power demand;Costs;Probability;Propagation losses|
|[Development of hybrid technology using Machine Learning and Block chain Technology to prevent from COVID 19 through the proper information gathering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085413)|J. J. M. A; R. Mishra; B. S. Alfurhood; P. Vats; S. Waghmare; K. Pant|10.1109/AISC56616.2023.10085413|Block chain Technology;Face (IMAGE) recognition;Artificial Intelligence (AI);COVID-19;Temperature sensors;Tracking;Databases;Mobile handsets;Blockchains;Vaccines|
|[Development of Critical Information Framework by Big Data Analytics and Artificial Intelligence to Prevent Cyber Attacks in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085465)|O. Bhimineni; S. G. Kulkarni; S. V. Joshi; S. Kadam; R. S. Sanap; B. Pant|10.1109/AISC56616.2023.10085465|Critical Information;Artificial Intelligence;Big Data Analytics;Cyber Attacks;WSN;Wireless sensor networks;Databases;Computer viruses;Big Data;Telecommunications;Safety;Artificial intelligence|
|[Significant effect of saving one unit of electricity and the role of saving energy by Non-Renewable energy in streetlight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085298)|S. Kanase; V. B. Mane; M. A. Suryawanshi; G. Kumbhar; V. Jamadar; P. P. Patil|10.1109/AISC56616.2023.10085298|Smart LED Street light;Solar;Arduino;Energy efficient;Street Lighting;Illumination;Technological innovation;Wind energy;Sodium;Lighting;Coal;Solar energy;Light emitting diodes|
|[The Role and Impact of Artificial Intelligence on Retail Business and its Developments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085624)|Neha; S. Mohanty; B. S. Alfurhood; R. Bakhare; S. Poongavanam; R. Khanna|10.1109/AISC56616.2023.10085624|Artificial intelligence;Value chain;Retail Technology;Business and Developments;Industries;Training;Supply and demand;Wearable computers;Supply chains;Transportation;Medical services|
|[Detecting breast cancer using machine learning algorithms: The efficient and accurate way](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085251)|S. Patro; B. V. Lakshmi; V. Sailaja; V. Sailaja; B. S. Panda; D. Verma|10.1109/AISC56616.2023.10085251|Breast cancer;Convolutional Neural Network;Decision tree;Random Forest and Machine Learning techniques;Support vector machines;Radio frequency;Machine learning algorithms;Forestry;Prediction algorithms;Breast cancer;Classification algorithms|
|[Trust aware secure energy efficient hybrid protocol for MANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085229)|A. Jha; V. Vivek; P. Gupta; R. Joshi; P. Singh; N. C. S. N. Iyengar|10.1109/AISC56616.2023.10085229|Hybrid Protocol;MANET;Energy Efficient;Trust Aware;Wireless communication;Privacy;Protocols;Quality of service;Routing;Ad hoc networks;Energy efficiency|
|[An implementation of virtual instruments for industries for the standardization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085547)|H. Kaur; C. Thacker; V. K. Singh; D. Sivashankar; P. P. Patil; K. S. Gill|10.1109/AISC56616.2023.10085547|Digital Control;Data Transfer;Instrumentation;Industrial Application;LabView;Measurements;Virtual Instruments;Solid modeling;Instruments;Virtual reality;Microcomputers;Media;Hardware;Software|
|[Smart Electricity Billing Management System Using Artificial Intelligent Based for the Implementation of Pre and Post Paid Tariffs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085534)|D. S. Hirolikar; E. G; K. Sharma; B. S. Alfurhood; D. Gangodkar; A. Kudale|10.1109/AISC56616.2023.10085534|Artificial Intelligence;Billing Management System;GSM Technology;Prepaid & Postpaid Tariffs;Smart Energy Meter;Meters;GSM;Wrist;Technological innovation;Government;Tariffs;Manuals|
|[The Way to Make Blind People Use the Email System: Voice Based Email Generating System Using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085638)|G. K. Rajput; S. Sharma; B. P. Dash; M. F. Ansari; P. Sharma; S. K. Shukla|10.1109/AISC56616.2023.10085638|Communication;Artificial Intelligence;blind people;Email;customer;Technological innovation;Blindness;Electronic mail;Cryptography;Artificial intelligence|
|[Efficient Task Scheduling in Cloud Environment Based On Dynamic Priority and Optimized Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085447)|G. M. Karthik; A. Gupta; S. Rajeshgupta; A. Jha; A. Sivasangari; B. P. Mishra|10.1109/AISC56616.2023.10085447|Task Scheduling;Preemptive Flow Manager;Cloud Computing;Allocation;Scheduling;Dynamic Multi-Level Task Scheduling (DMLTS);Cascade Shrink Priority (CSP);Cloud computing;Processor scheduling;Telecommunication traffic;Dynamic scheduling;Load management;Resource management;Internet of Things|
|[The Internet of Things Privacy is Provided Through Object Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085469)|S. Lahoti|10.1109/AISC56616.2023.10085469|Internet of Things (IoT);Cyber Security;Big Data;Wireless Sensor Network;Machine Learning (ML);Privacy;Traffic control;Turning;Real-time systems;Behavioral sciences;Internet of Things;Object recognition|
|[Analyzing the Computer Crimes Safety of IOT Networks using Traditional, Blockchain, and AI (Key-Security in Great Detail](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085591)|S. Pandey|10.1109/AISC56616.2023.10085591|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Identification;Biometric identification;Internet of Things (IoT);Power supplies;Authentication;Random access memory;Blockchains;Safety;Batteries;Internet of Things|
|[Integrated Blockchain and AI Research Infrastructure for IoT Based Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085063)|S. Bhatnagar|10.1109/AISC56616.2023.10085063|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Smartphones;Identification;Biometric identification;Internet of Things (IoT);Technological innovation;Scalability;Blockchains;Internet of Things;Security;Resource management;Reliability|
|[Blend CAC: Integration for the Blockchain for Distributed Potential Network Access for the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085656)|A. Malhotra|10.1109/AISC56616.2023.10085656|Blockchain;Blend CAC;Internet of Things (IoT);Smart Contract;Data Mining;Authorization;Computers;Smart cities;Smart contracts;Blockchains;Safety;Smart grids|
|[Cloud of Things and Blockchain Integration: Architecture, Applications, and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084991)|R. Mishra|10.1109/AISC56616.2023.10084991|Cloud of things;Blockchain;5G Technology;Internet of Things;cyber security;Internet of space things;Cloud computing;Privacy;Online banking;System performance;Scalability;Regulation;Blockchains|
|[Blockchain Technologies in 5g and Beyond Connections: A Review of the Taxonomy, Practice Area, Prospects, and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085682)|A. Aneja|10.1109/AISC56616.2023.10085682|Artificial intelligence;Blockchain;Cyber security;cyber-physical systems;Mobile Communication;Industries;Wireless communication;Privacy;5G mobile communication;Taxonomy;Memory;Blockchains|
|[Developing Verified Multidomain Connectivity using Distributed Blockchain for Mobile Edge of the Network in 5g and Even Beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084947)|D. Kundra|10.1109/AISC56616.2023.10084947|Mobile Edge Computing (MEC);Naive Bayes;Blockchain;Randomized Forest. 5G;Multiplexing;Privacy;5G mobile communication;Network topology;Collaboration;Transportation;Routing|
|[A Decentralized Cryptocurrency Infrastructure for Edge-Based IoMT Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085433)|A. Rathour|10.1109/AISC56616.2023.10085433|Data mining;Quality of Service(QoS);Cyber protection;crypto currencies;mobile network virtualization;Blockchain;Privacy;Hospitals;Smart contracts;Authentication;Computer architecture;Quality of service;Blockchains|
|[Combined computation interference and offloading control for mobile edge computing in wireless cellular networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085399)|S. S. Sarpal|10.1109/AISC56616.2023.10085399|Computation offloading;interference management;Mobile edge computing;resource allocation;small cell networks;Cellular networks;Multi-access edge computing;Wireless networks;Computational modeling;Computer architecture;Interference;Resource management|
|[Using Blockchain to Reduce Multi-Server Edge Computing Latencies for Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085285)|A. Bhalla|10.1109/AISC56616.2023.10085285|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Smartphones;Identification;Biometric identification;Microphone;and Audio;Training;Federated learning;Computational modeling;Supervised learning;Big Data;Robustness;Blockchains|
|[Offloading Collaborative Cloud-Edge-End Tasks Utilizing Mobile Edge of The Network Systems With Reduced Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085041)|R. Kumar|10.1109/AISC56616.2023.10085041|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Smartphones;Identification;Biometric identification;Microphone;and Audio;Multi-access edge computing;Simulation;Pipelines;Collaboration;Transforms;Mobile handsets;Resource management|
|[For Smartphone Occur under Normal Conditions with Data Protection, Distributed Edge Knowledge and Cryptocurrencies Should be Combined](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085141)|R. Bhardwaj|10.1109/AISC56616.2023.10085141|Blockchain;cryptocurrency;Sequencing minimum optimization techniques;Smartphones;Identification;authentication;Privacy;Memory;Data protection;Companies;Sensors;Cryptocurrency;Task analysis|
|[Block Chain Driven Marketing Resources in Mobile Edge Computing and System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085540)|P. Sharma|10.1109/AISC56616.2023.10085540|proof-of-reputation;device-to-device edge computing;the Proof-of-Work algorithm;Performance evaluation;Smart contracts;Computer architecture;Proof of Work;Real-time systems;Blockchains;Internet of Things|
|[A Blockchain-Based Incentive Mechanism for Crowdsensing Applications to Preserve Privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085322)|S. Kaur|10.1109/AISC56616.2023.10085322|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Smartphones;Identification;Biometric identification;Microphone;and Audio;Privacy;Costs;Crowdsensing;Forestry;Blockchains;Sensors;Security|
|[A Comprehensive Study of Blockchain for Federated Learning Toward Safe Distributed Machine Learning Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085557)|Rahul|10.1109/AISC56616.2023.10085557|Blockchain;reinforcement learning;deep learning;Privacy;Analytical models;Federated learning;Biological system modeling;Ecosystems;Systems architecture;System improvement|
|[Node-Aware Dynamic Weighting Methods for Federated Learning on the Blockchain to Improve Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085151)|A. Punia|10.1109/AISC56616.2023.10085151|Naive Bayes;Random Forest;Sequencing minimum optimization techniques;Smartphones;Identification;Biometric identification;Microphone;and Audio;Analytical models;Federated learning;Simulation;Stability analysis;Blockchains;Servers;Reliability|
|[IoT Administration Cybersecurity using Programmatic Monitoring and Pattern Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085587)|R. Thakur|10.1109/AISC56616.2023.10085587|IoT;blockchain;mobile edge computing;5G;Machine learning algorithms;Operating systems;Machine learning;Inspection;Prediction algorithms;Pattern recognition;Internet of Things|
|[Analysis of Fog Computing: An Integrated Internet of Things (IoT) Fog Cloud Infrastructure for Big Data Analytics and Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085681)|N. Raj|10.1109/AISC56616.2023.10085681|Internet of Things (IoT);cloud services;cyber espionage;difficulties;and remedies;Cloud computing;Virtual assistants;Soft sensors;Computer architecture;Big Data;Sensor systems;Internet of Things|
|[Coordinated Optimization Of Edge Computing and Unloading for Portable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085253)|N. Sharma|10.1109/AISC56616.2023.10085253|mobile edge computation;offloading;optimization;task allocation;and resource management;Cloud computing;Energy consumption;Base stations;5G mobile communication;Mobile handsets;Delays;Servers|
|[Nonparametric Keyed Hypothesis Tests Machine Learning Defense Against Poisoning Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085424)|R. Saini|10.1109/AISC56616.2023.10085424|ML;Cyber Security;Hypothesis test;Poisoning attack;Training;Toxicology;Machine learning;Generators;Cognition;Cryptography;Testing|
|[Analysis of IoT attack detection and Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085111)|K. Singh; N. Singh|10.1109/AISC56616.2023.10085111|Internet of Things (IoT);authentication;Attack detection;Threat model;Security Challenges;Analytical models;Intrusion detection;Authentication;Internet of Things;Security;Artificial intelligence;Cyberattack|
|[A 5G-mmWave Four-Port MIMO Antenna Including High Diversity Performance in Narrow Bandwidth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085655)|M. Sharma; M. Junedul Haque; G. P. Pandey; K. Sharma; D. Singh|10.1109/AISC56616.2023.10085655|28.0GHz MIMO;5G;Beveled Ground;Circular patch;EM Energy;Communication Technology;ECCFour Port;DGFour Port;TARCFour Port;CCLFour Port;Wireless communication;Slot antennas;5G mobile communication;Millimeter wave technology;Resonant frequency;Bandwidth;Impedance|
|[Deep Learning-based Advancement in Fuzzy Logic Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085358)|S. Kaul; S. Yadav; N. Tiwari; A. Singh|10.1109/AISC56616.2023.10085358|Fuzzy Logic Application;Fuzzy Rules (FRs);Adaptive Neuro-fuzzy Inference System (ANFIS);Hybrid systems;Neural Networks (NN);Neuro-fuzzy system (NFS);Deep Learning (Dl);Deep Learning Neuro Based Fuzzy System (DNFS) Artificial Intelligent (AI);Fuzzy logic;Deep learning;Operations research;Learning (artificial intelligence);Medical services;Artificial neural networks;System analysis and design|
|[Design of Dual-Band Pass Polarization Insensitive and Transparent Frequency Selective Surface for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085319)|Y. Solunke; D. G. Patanvariya; A. Kothari; K. Sharma; D. Singh|10.1109/AISC56616.2023.10085319|Transparent Frequency Selective Surface;Polarization Insensitive;Dual Band-Pass;Miniaturization;Geometry;Wireless communication;Frequency selective surfaces;Structural rings;Band-pass filters;Dual band;Resonant frequency|
|[Extractive Text Summarization of Indian Election Commission Manuals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085580)|S. Shukla; G. Ansari; D. Pandey; S. Mangesh; A. Gaur; V. N. Shukla|10.1109/AISC56616.2023.10085580|Automatic text summarization (ATS);TF-IDF;preprocessing text;cosine similarity;eliminating redundant sentences;Training;Voting;Semantics;Manuals;Linguistics;Portable document format;Artificial intelligence|
|[Explicability of Artificial Intelligence in Healthcare 5.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085222)|T. Soni; D. Gupta; M. Uppal; S. Juneja|10.1109/AISC56616.2023.10085222|healthcare;artificial intelligence;digital health;analysis;machine learning;Pathology;Bibliometrics;Surgery;Medical services;Companies;Stroke (medical condition);Radiology|
|[Complexity of Blockchain's Distributed Configuration to Rational User Behavior](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085277)|D. Paikaray; A. Chauhan|10.1109/AISC56616.2023.10085277|Crypto currency monitoring;Block chain;Security Network;Microwave integrated circuits;Production;Mobile handsets;Hardware;Blockchains;Safety;Sensors|
|[6G Massive Wireless Energy Transfer for Sustainable IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085215)|K. K. Pramanik; P. Kishori Shekokar|10.1109/AISC56616.2023.10085215|6G Technology;IoT;CSI;WET;MTC;6G mobile communication;Wireless communication;Procurement;Power transmission;Topology;Internet of Things;Channel state information|
|[A Comprehensive Analysis of 5G Security Core Technologies and Services: Conceptual Frameworks, Challenges, and Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085449)|A. K. Bhagat; J. Gandhi|10.1109/AISC56616.2023.10085449|5G Security;Blockchain;Artificial Intelligence;Communication Technologies;5G mobile communication;Wireless networks;Network security;Safety;Manufacturing;Blockchains;Smart grids|
|[Non-Coherent and Backscatter Communications: Towards Ultra-Massive Communication in 6G Wireless Connections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085275)|V. Vekariya; A. K. Pandey|10.1109/AISC56616.2023.10085275|IoT;mMTC;non-coherent;5th generation;6Generation;B5th generation;BsC. communications;6G mobile communication;Technological innovation;Costs;Wireless networks;Wires;Physical layer;Trajectory|
|[New areas and problems for 6G network security and privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085595)|D. Vekariya; A. K. Pandey|10.1109/AISC56616.2023.10085595|6G;Cyber security;Privacy preservation;Communication;6G mobile communication;Privacy;5G mobile communication;Focusing;Oral communication;Network security;Safety|
|[eMBB, URLLC, and mMTC 5G Wireless Network Slicing: A Communication-Theoretic View](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085623)|W. Patel; P. Tripathy|10.1109/AISC56616.2023.10085623|INDEX TERMS Machine2machine transmission;multiple access communication;NOMA;wireless technology are all terms used to describe 5G mobile technology;NOMA;Enhanced mobile broadband;Wireless networks;Network slicing;Ultra reliable low latency communication;Reliability engineering;Delays|
|[Providing Lower Latencies Assures on Slicing-Ready 5G Networks through Two-Level MAC Scheduler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085198)|J. Shah; A. Kumar|10.1109/AISC56616.2023.10085198|nan;Somatosensory;Sequential analysis;5G mobile communication;Network slicing;Ultra reliable low latency communication;Programming;Delays|
|[Using a Multifaceted, Network-Informed Methodology to Assess Data Sensitivity from Consumers IoT Gadgets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085075)|H. Shah; J. Sahoo|10.1109/AISC56616.2023.10085075|nan;Data privacy;TV;Sensitivity;Protocols;Telecommunication traffic;User interfaces;Encoding|
|[Multiple 5G systems using End-to-End Sectioning as a Solution with Allocation of resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085571)|G. Jethava; S. K. Singh|10.1109/AISC56616.2023.10085571|5G;eMBB;URLLC;mMTC;Network;Communication System;5G mobile communication;Scalability;Transportation;Computer architecture;Ultra reliable low latency communication;Control systems;Delays|
|[A Survey of ML for the Physical Layer in 5G and Future Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084939)|S. Parikh; A. K. Moharana|10.1109/AISC56616.2023.10084939|5G;IoT;machine learning;B5G;wireless networks;MIMO;Privacy;5G mobile communication;Wireless networks;Standards organizations;Physical layer;Real-time systems;Security|
|[Managing IoT Cyber-Security Using Programmable Telemetry and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085343)|K. Sutariya; K. K. Pramanik|10.1109/AISC56616.2023.10085343|IoT;system tracking;flow characteristics;ML;Performance evaluation;Protocols;Surveillance;Telecommunication traffic;Traffic control;Behavioral sciences;Telecommunications|
|[Internet of Things-Based System for Monitoring and Assessing Soil Nutrients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085098)|P. Sapra; A. Kumar|10.1109/AISC56616.2023.10085098|IoT Base Soil Nutrients Monitoring;Soil Monitoring System;Monitoring and Assessing;Soil;Sensor systems;Seeds (agriculture);Internet;Monitoring;Intelligent sensors;Fertilizers|
|[A Review on IOT as a Green Trade Economy Development Path for Ecological Sustainable Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085601)|A. Gandhi; K. Mishra|10.1109/AISC56616.2023.10085601|IoT in Green Trade;Green Trade Economy;Ecological Sustainable Development;IoT Enabled Energy Corporation;Gases;Limiting;Greenhouse effect;Green products;Government;Force;Production|
|[Environment Quality Assessment Web Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085252)|M. Verma; A. Kumar; M. Garg; S. Juneja|10.1109/AISC56616.2023.10085252|environmental;resources;dependent;techniques;factors;economic;Natural resources;Industries;Environmentally friendly manufacturing techniques;Wildlife;Soil;Water pollution;Quality assessment|
|[The Inception of Time Prudent Approach at Metro Stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085555)|G. Tripathi; I. Singh; S. Juneja|10.1109/AISC56616.2023.10085555|metro;card;travellers;passenger;station;Operating systems;Urban areas;Transportation;Artificial intelligence;Graphical user interfaces|
|[IoT enabled obstruction evasion Robots for enhancing the security of the Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085000)|S. Juneja; S. Mudgil; S. Saini; A. Sharma|10.1109/AISC56616.2023.10085000|Raspberry Pi;DTMF;IoT;Arduino;IR Sensor;Microcontrollers;Surveillance;Games;Robot sensing systems;Mobile handsets;Internet of Things;Security|
|[Using Machine Learning to Predict Housing Prices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085264)|H. Prakash; K. Kanaujia; S. Juneja|10.1109/AISC56616.2023.10085264|Property Prices;Prediction;Machine Learning;Real Estate;Linear Regression;Analytical models;Hospitals;Biological system modeling;Sociology;Linear regression;Machine learning;Predictive models|
|[Resource Management with Load Balancing Strategies in Fog-IoT Computing Environment: Trends, Challenges and Future directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085287)|S. Gupta; N. Singh|10.1109/AISC56616.2023.10085287|Fog Computing;IoT;Heuristics;Cloud Computing;Quality of service;Internet of Vehicles;Performance evaluation;Cloud computing;Home appliances;Load management;Market research;Internet of Things;Servers|
|[Experimental analysis of Disease Prediction using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084972)|Shivani; H. Vardhan; A. Gupta; D. Goswami; M. Zubair; L. Mangal|10.1109/AISC56616.2023.10084972|Logistic Regression;SVM;Decision Tree and KNN;Support vector machines;Machine learning algorithms;Costs;Medical services;Machine learning;Prediction algorithms;Medical diagnostic imaging|
|[Comparative Analysis of Different Algorithms in Link Prediction on Social Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085359)|A. Sharma; N. Aggarwal; H. Khatter; Saurabh; A. Tripathi; S. Awasthi|10.1109/AISC56616.2023.10085359|link prediction;social networks;recommendation;score;Fluctuations;Social networking (online);IEEE merchandise;Predictive models;Prediction algorithms;Indexes;Forecasting|
|[Smart Virological Modelling of YouTube Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085370)|H. Khatter; N. Aggrawal; V. Upadhyaya; M. Aggarwal; P. Gupta|10.1109/AISC56616.2023.10085370|YouTube;videos;popular;viral;promotional;content;subscribers;Video on demand;Social networking (online);Multimedia Web sites;Machine learning;Predictive models;Real-time systems;Task analysis|
|[Machine Learning Based Classification for Diabetic Retinopathy Detection using Retinal Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085442)|D. Diksha; G. Gagandeep|10.1109/AISC56616.2023.10085442|Diabetes;Diabetic Retinopathy;Machine Learning;Convolutional Neural Network;Deep Learning;Deep learning;Image recognition;Retinopathy;Blindness;Medical services;Learning (artificial intelligence);Retina|
|[In-Depth Analysis of Parkinson's Disease: A Comprehensive Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085219)|S. W. Akram; A. P. Siva Kumar|10.1109/AISC56616.2023.10085219|Non-Motor Symptoms (NMS);Amplitude of Low-Frequency Fluctuation (ALFF);Cerebrospinal Fluid (CSF);Dopamine Transporter (DAT);Essential Tremor (ET);Parkinson’s Disease (PD);MultiEdit Nearest Neighbour (MENN);Magnetic Resonance Imaging (MRI);Parkinson’s Progression Markers Initiative (PPMI);Grey Matter (GM);Progressive Supranuclear Palsy (PSP);Relevance vector regression (RVR);Blood Oxygen Level Dependent (BOLD);Multiple System Atrophy (MSA);Deep learning;Radiography;Thermometers;Parkinson's disease;Satellite broadcasting;Radiology;Feature extraction|
|[Factors Influencing Software Maintainability Prediction Using Soft Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085575)|A. Devi; S. Charaya; M. Maan|10.1109/AISC56616.2023.10085575|Software Maintainability;Soft Computing;Object-Oriented Metrics;Performance Criteria;Costs;Object oriented modeling;Computational modeling;Focusing;Predictive models;Maintenance engineering;Software|
|[Comparative Analysis of PET and MRI Image Processing in the C11-Raclopride PET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085677)|P. Faldu; A. K. Moharana|10.1109/AISC56616.2023.10085677|Raclopride;Neuroimaging;Movement disorders;PET/MRI;Magnetic resonance imaging;Sociology;Process control;Information processing;Streaming media;Particle measurements;Positron emission tomography|
|[Quantifiable Analysis of 18f-(+) DTBZ Picture: Comparison of PET Template & MRI Centred Image Investigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085140)|V. Patel; A. K. Pandey|10.1109/AISC56616.2023.10085140|PET;MRI;[18F] 9-fluoropropyl-(+)-dihydrotetrabenazine;Parkinson’s disease;Degradation;In vivo;Magnetic resonance imaging;Process control;Reliability;Artificial intelligence;Standards|
|[PET Imaging for Alzheimer's Neuroanatomical Disease Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085594)|H. Bhaidana; D. Paikaray|10.1109/AISC56616.2023.10085594|Alzheimer's disea;PET;β-amyloid;neuroanatomical;Florbetaben;Correlation coefficient;Software packages;Magnetic resonance imaging;Process control;Distance measurement;Medical diagnosis;Alzheimer's disease|
|[A Novel Dual Time-Point Unified Amyloid MRI/PET Data Analysis Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084979)|V. Vekariya; A. Kumari|10.1109/AISC56616.2023.10084979|MRI/PET;Quantification;tracers for amyloid;dual time points;Dementia;Alzheimer's disease (AD);Frontal lobe;Magnetic resonance imaging;Instruments;Medical treatment;Detectors;Information processing;Retina|
|[Network of Things-Based Smart Towns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085444)|K. K. Pramanik; S. Parikh|10.1109/AISC56616.2023.10085444|Trials and Test-beds for Smart Cities Integration of the sensor system;network design;administration of services;6lowPAN;EXI;CoAP;Technological innovation;Uncertainty;Protocols;Smart cities;Buildings;Regulation;Internet of Things|
|[Advanced Medical Nursing Care Unit for Emergency Healthcare Based on the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085394)|T. Patel; A. K. Pandey|10.1109/AISC56616.2023.10085394|IoT;global positioning system;Zigbee;Web server;LTE network technology;Smart medical care;Cloud computing;Telemedicine;Smart healthcare;Medical services;Sensor systems;Real-time systems;Safety|
|[A Deep Net Model for Covid-19 Detection Based on ROC Analysis Using Radiographs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085027)|A. Chauhan; A. K. Pandey|10.1109/AISC56616.2023.10085027|Convolutional Neural Network;Deep Transfer Learning;Pneumonia;Corona Virus;Chest X-ray Radiographs;COVID-19;Training;Analytical models;Sensitivity;Psychology;Convolutional neural networks;Diagnostic radiography|
|[Sustainable manufacturing integrated into cloud-based data analytics for e-commerce SMEs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085046)|S. Singhal; L. Ahuja; H. Monga|10.1109/AISC56616.2023.10085046|Sustainable manufacturing;Cloud computing;Industry 4.0;SMEs;Cloud computing;Data analysis;Redundancy;Transportation;Production;Fourth Industrial Revolution;Electronic commerce|
|[Designing of wideband patch antenna array for 5-6 GHz application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085560)|A. K. Dubey; V. Gupta|10.1109/AISC56616.2023.10085560|Patch antenna;field gain;VSWR;return loss etc;Performance evaluation;Industries;Microstrip antenna arrays;Patch antennas;Resonant frequency;Microstrip antennas;Topology|
|[Circularly Polarized Triangular-shaped Antenna and modified Inclined-shaped defected ground for Fixed Satellite Communication Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085650)|N. Sharma; A. Kumar; P. Jha; D. Saxena|10.1109/AISC56616.2023.10085650|Circular Polarization;Satellite Communication;defected ground;Axial ratio;Satellite antennas;Polarization;Satellites;Resonant frequency;Receiving antennas;Bandwidth;Impedance|
|[Fraud Detection in Financial Domain using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085181)|N. Pathak; S. Singhal|10.1109/AISC56616.2023.10085181|Machine Learning;random forest classifier;feature extraction;heatmap;confusion matrix;predictive analysis;Industries;Machine learning algorithms;Geology;Organizations;Machine learning;Forestry;Market research|
|[EHML: An Efficient Hybrid Machine Learning Model for Cyber Threat Forecasting in CPS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084987)|U. K. Lilhore; S. Simaiya; J. K. Sandhu; A. Baliyan; A. Garg|10.1109/AISC56616.2023.10084987|Cybercrime;Machine learning;Random forest;SVM;J-48;Classification method;Support vector machines;Analytical models;Supervised learning;Training data;Forestry;Predictive models;Data models|
|[Classification Techniques for Disease Detection in Plants: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085485)|A. Kaur; R. Chadha|10.1109/AISC56616.2023.10085485|Crop;Machine Learning;Deep Learning;Plant Disease Prediction;Productivity;Technological innovation;Plant diseases;Systematics;Sociology;Training data;Prediction algorithms|
|[The Future Era of Computing: Automatic Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085022)|S. Singhal; N. Pathak|10.1109/AISC56616.2023.10085022|Future computing;Quantum Computing;Technology Trends;Moore’s Law;Autonomic Computing;Semiconductor device modeling;Performance evaluation;Quantum computing;Quantum entanglement;Moore's Law;Personal digital devices;Transforms|
#### **2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)**
- DOI: 10.1109/ICAISC56366.2023
- DATE: 23-25 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Towards Green and Computing Approaches to Establish Intelligent Transportation Systems (ITS) in KSA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085538)|S. A. Almutairi|10.1109/ICAISC56366.2023.10085538|Distributed Computing- -Green Technology-Intelligent Transportation Systems;Internet of Things – Smart City.;Photovoltaic systems;Renewable energy sources;Technological innovation;Smart cities;Green products;Vehicular ad hoc networks;Distributed databases|
|[Intelligent Trash Bin for Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085313)|A. Mohamed; Y. ElSayed; H. Mahasneh; W. M. Rohouma; A. A. Otoom|10.1109/ICAISC56366.2023.10085313|Trash Monitor Levels;Combustible Gases Level;Smart Bin;Smart services in cities Cloud Dashboard;Waste management;Gases;Technological innovation;Smart cities;Ultrasonic variables measurement;Sociology;Safety|
|[Eco-Friendly IoT Solutions for Smart Cities Development: An Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085100)|N. A. El-Mawla; M. Badawy|10.1109/ICAISC56366.2023.10085100|IoT;Sustainable;Smart City;Systems;Productivity;Technological innovation;Climate change;Smart cities;Green products;Disaster management;Electronic waste|
|[Mapping the Scientific Landscape of Smart Buildings and Climate Change](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085598)|S. A. Ahmed; L. S. Abouelnaga; T. Brahimi|10.1109/ICAISC56366.2023.10085598|Smart Buildings;Climate Change;Bibliometric;Scopus;Smart Cities;Climate change;Technological innovation;Renewable energy sources;Smart buildings;Smart cities;Social sciences;Data visualization|
|[Smart and Secure IoT based Remote Real-Time Radiation Detection and Measurement System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085583)|K. Saleem; A. A. Alajroosh; R. Ouni; W. Mansoor; A. Gawanmeh|10.1109/ICAISC56366.2023.10085583|detection;internet of things (IoT);measurement;privacy;radiation;security;smart monitoring;Radiation monitoring;Cloud computing;Technological innovation;Smart cities;Radiation detectors;Redundancy;Real-time systems|
|[Potentials of semantic internet of things in smart cities: an overview and roadmap](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085121)|S. Nahhas|10.1109/ICAISC56366.2023.10085121|internet of things;smart cities;semantic web;linked data;Semantic Web;Technological innovation;Smart cities;Semantics;Ontologies;Feature extraction;Internet of Things|
|[Smart vegetable cutter for Smart Home](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085675)|S. Singhal; D. Mangal; R. Kumar; B. Sharma; I. B. Dhaou|10.1109/ICAISC56366.2023.10085675|Internet of Things (IoT);Real Time Systems;Home Automation;Wi-Fi;Raspberry Pi;Technological innovation;Costs;Smart cities;Smart homes;Medical services;Choppers (circuits);Cleaning|
|[Bayesian Optimization based Hyperparameter Tuning of Ensemble Regression Models in Smart City Air Quality Monitoring Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085504)|S. Das; A. Alzimami|10.1109/ICAISC56366.2023.10085504|air quality;smart city;pollution monitoring;ensemble regression;Bayesian optimization;hyper-parameter;Analytical models;Technological innovation;Smart cities;Atmospheric modeling;Computational modeling;Predictive models;Air quality|
|[Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085192)|L. A. Ajao; S. T. Apeh|10.1109/ICAISC56366.2023.10085192|Blockchain;5G network;Fog computing;Internet of things;Machine learning;Smart city;Machine learning algorithms;Smart cities;Markup languages;Intrusion detection;Computer architecture;Blockchains;Quality of experience|
|[Higher Education Model in Smart Cities: A case study in computer school](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085668)|T. Himdi|10.1109/ICAISC56366.2023.10085668|LMS;VCS;DLS;CMS;Learning Resources;SES;PLO;CLO;Technological innovation;Learning management systems;Electronic learning;Content management;Smart cities;Computational modeling;Libraries|
|[Telecom Churn Analysis using Machine Learning in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085183)|A. Sharma; P. Shukla; M. K. Gourisaria; B. Sharma; I. B. Dhaou|10.1109/ICAISC56366.2023.10085183|Churn;Telecommunication;Data digging or mining;Decision Tree;Logistic regression;Industries;Training;Support vector machines;Analytical models;Smart cities;Databases;Companies|
|[Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085688)|M. Kandpal; B. Sharma; R. K. Barik; S. Chowdhury; S. S. Patra; I. B. Dhaou|10.1109/ICAISC56366.2023.10085688|Smart Cities;Smart Watch;LSTM;RNN;Legged locomotion;Deep learning;Technological innovation;Recurrent neural networks;Smart cities;Medical services;Watches|
|[Detecting Available Parking Spaces in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085386)|H. M. Al-Barhamtoshy; K. K. Abdullah; M. K. Dauda; T. F. Himdi|10.1109/ICAISC56366.2023.10085386|Smart City;Smart Parking;Deep Learning;Smart living;Space vehicles;Technological innovation;Smart cities;Snow;Sociology;Transportation;Sensorless control|
|[A Comparison of Regression Techniques for Prediction of Air Quality in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085369)|K. D. Garg; M. Gupta; B. Sharma; I. B. Dhaou|10.1109/ICAISC56366.2023.10085369|Particulate Matter (PM2.5);Air Quality Index;Regression;Smart City;Random Forest;Deep learning;Technological innovation;Smart cities;Atmospheric modeling;Linear regression;Air pollution;Prediction algorithms|
|[Trends in Smart Healthcare Systems for Smart Cities Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085212)|M. A. Elhosseini; N. K. Gharaibeh; W. A. Abu-Ain|10.1109/ICAISC56366.2023.10085212|AI;Healthcare;Big Data;Systems;Technological innovation;Smart cities;Pandemics;Medical services;Big Data;Market research;Vaccines|
|[Unsafe and inefficient communication between automated buses and road users on public roads in Japan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085096)|M. Taima; T. Daimon|10.1109/ICAISC56366.2023.10085096|Automated vehicle (AV);Automated bus;Road safety;Field operation test (FOT);Japan;Technological innovation;Smart cities;Roads;Education;Cameras;Trajectory;Behavioral sciences|
|[Smart Buildings for Sustainable Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085629)|V. Bijlani|10.1109/ICAISC56366.2023.10085629|Sustainability;Smart buildings;urban planning;interoperability;intelligent Building Management Systems;building performance measurement;profit through purpose.;Energy consumption;Technological innovation;Smart buildings;Green buildings;Smart cities;Finance;Turning|
|[A Systematic Overview on Simulation of Connected Vehicles Infrastructure in Smart Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084942)|M. Gupta; I. B. Dhaou; K. D. Garg; B. Sharma|10.1109/ICAISC56366.2023.10084942|VANET Simulators;IoV Simulators;Connected Vehicles;Simulators;Wireless communication;Technological innovation;Connected vehicles;Systematics;Smart cities;Vehicular ad hoc networks;Transportation|
|[IoT-Based Smart Cities Beyond 2030: Enabling Technologies, Challenges, and Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085126)|W. Melibari; H. Baodhah; N. Akkari|10.1109/ICAISC56366.2023.10085126|Smart City;IoT;Sustainability;Technological innovation;Smart cities;Shape;Sociology;Market research;Internet of Things;Statistics|
|[Toward a Design of User’s Consent Tool for IoT-based Healthcare System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085034)|H. A. Albatati; M. F. Abulkhair; J. A. Clark|10.1109/ICAISC56366.2023.10085034|Privacy;Informed Consent;Consent design;IoT-based Healthcare;Technological innovation;Privacy;Ethics;Smart cities;Medical services;Internet of Things|
|[The Roles of Stakeholders in Internet of Things: A Theoretical Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085486)|L. S. Almalki; A. K. Alnahdi; T. F. Albalawi|10.1109/ICAISC56366.2023.10085486|Stakeholders;Internet of Things;Feature Selection;Soft Clustering;Semi-supervised;Theoretical Framework.;Technological innovation;Smart cities;Big Data;Feature extraction;Real-time systems;Stakeholders;Security|
|[Blockchain-enabled Device Authentication and Authorisation for Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084957)|R. Singh; S. Sturley; B. Sharma; I. B. Dhaou|10.1109/ICAISC56366.2023.10084957|Blockchain Technology;Internet of Things;Authentication;Confidentiality;Digital Certificate;Industry 4.0;Smart City;Authorization;Technological innovation;Smart cities;Supply chains;Authentication;Cameras;Blockchains|
|[Machine Learning Approach to Anomaly Detection Attacks Classification in IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085349)|A. A. Obaidli; D. Mansour; S. M. Abdulhamid; N. B. Halima; A. Al-Ghushami|10.1109/ICAISC56366.2023.10085349|nan;Support vector machines;Radio frequency;Technological innovation;Machine learning algorithms;Computer hacking;Software algorithms;Behavioral sciences|
|[Hybrid Sentiment Analysis Model with Majority Voting for Un-labeled Arabic Text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085303)|A. Alkabkabi; M. Taileb|10.1109/ICAISC56366.2023.10085303|Sentiment Analysis;Machine Learning;Ensemble Learning;Majority voting;Sentiment analysis;Analytical models;Technological innovation;Social networking (online);Smart cities;Machine learning;Data models|
|[Internet-of-Things in Emergency Services: Architecture, Applications, and Research Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085468)|S. Shamsudheen; G. Karthik; A. Anoop; P. Gobinathan|10.1109/ICAISC56366.2023.10085468|emergency services;internet of things;smart devices;smart emergency applications;Technological innovation;Power demand;Smart cities;Medical services;Emergency services;Internet of Things;Security|
|[Using Edge Computing framework with the Internet of Things for Intelligent Vertical Gardening](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085507)|A. Bhowmik; M. Sannigrahi; P. K. Dutta; S. Bandyopadhyay|10.1109/ICAISC56366.2023.10085507|Automated Gardening;Smart Buildings;AI/ML algorithms;Building Automation;Edge Computing;Temperature sensors;Temperature measurement;Semantic Web;Cloud computing;Technological innovation;Smart cities;Soil moisture|
|[Towards Edge Computing for 6G Internet of Everything: Challenges and Opportunities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085007)|A. Alawadhi; A. Almogahed; E. Azrag|10.1109/ICAISC56366.2023.10085007|6G;Internet of Everything;Edge computing;Internet of things;6G mobile communication;Cloud computing;Data centers;Technological innovation;Smart cities;Wireless networks;Smart transportation|
|[Sustainable Secure Internet of Things (SS-IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085280)|M. Badawy|10.1109/ICAISC56366.2023.10085280|IoT;sensors;security;SS-IoT;sustainability;Productivity;Technological innovation;Smart cities;Maintenance engineering;Market research;Energy efficiency;Internet of Things|
|[IIoT-Based Industry Transformation In Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085304)|W. Melibari; H. Baodhah; N. Akkari|10.1109/ICAISC56366.2023.10085304|4.0;Industry 5.0;IIoT;Industry Evolution;Vision 2030.;Industries;Technological innovation;Smart cities;Shape;Fourth Industrial Revolution;Fifth Industrial Revolution;Mining industry|
|[Industrial Internet of Things: A Cyber Security Perspective Investigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085080)|F. S. Al-Zahrani; N. Hassan|10.1109/ICAISC56366.2023.10085080|Industrial internet of things;security;architecture;threats;attacks;countermeasures;Technological innovation;Transportation;Computer architecture;System integration;Control systems;Fourth Industrial Revolution;Trajectory|
|[IoT-Fog Computing Sustainable System for Smart Cities: A Queueing-based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085238)|V. Goswami; B. Sharma; S. S. Patra; S. Chowdhury; R. K. Barik; I. B. Dhaou|10.1109/ICAISC56366.2023.10085238|smart cities;fog computing;VM breakdown;Queueing model;machine-repair problem;Cloud computing;Technological innovation;Smart cities;Quality of service;Maintenance engineering;Virtual machining;Steady-state|
|[Blood cells image segmentation and counting using deep transfer learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085605)|G. Aghiles; N. M. Lamine; T. Faycal; G. Djamel; Y. M. Riad|10.1109/ICAISC56366.2023.10085605|Blood cell segmentation;Blood cell counting;U-net;SegNet;deep learning;White blood cells;Image segmentation;Technological innovation;Image color analysis;Smart cities;Transfer learning;Watersheds|
|[Rechargeable Battery State Estimation Based on Adaptive-Rate Processing and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085242)|A. Alyoucef; S. M. Qaisar; M. Hafsi|10.1109/ICAISC56366.2023.10085242|Rechargeable battery;;Lithium-Ion;;Capacity estimation;Parameters acquisition;Machine learning;Regression;Evaluation measure;Lithium-ion batteries;Technological innovation;Smart cities;Service robots;Feature extraction;Prediction algorithms;Real-time systems|
|[Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085379)|R. Kumar; B. Sharma; S. Shekhar; I. B. Dhaou; S. Singhal|10.1109/ICAISC56366.2023.10085379|Pollution Detection;Linear Regression;Pollution Prediction;Machine Learning;Technological innovation;Smart cities;Atmospheric modeling;Predictive models;Air pollution;Real-time systems;Indexes|
|[Features Mining and Machine Learning for Home Appliance Identification by Processing Smart meter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085074)|R. A. Talib; S. M. Qaisar; H. Fatayerji; A. Waqar|10.1109/ICAISC56366.2023.10085074|Appliances elucidation;Features extraction;Machine learning;Smartmeter;Segmentation;Internet of Energy;Support vector machines;Home appliances;Technological innovation;Machine learning algorithms;Smart cities;Feature extraction;Load management|
|[GANN: A Hybrid Model for Permeability Prediction of Oil Reservoirs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085307)|M. Akhlaq; Z. Rasheed|10.1109/ICAISC56366.2023.10085307|Permeability;Prediction;Fuzzy Logic;Artificial Neural Networks;Genetic Algorithm;Hybrid Models;Sensor Data;Smart Cities;Electronic Methods;Correlation coefficient;Smart cities;Oils;Neural networks;Predictive models;Reservoirs;Permeability|
|[Breast Cancer Diagnosis Using a Machine Learning Model and Swarm Intelligence Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085393)|I. Gad; M. Elmezain; M. M. Alwateer; M. Almaliki; G. Elmarhomy; E. Atlam|10.1109/ICAISC56366.2023.10085393|Feature Selection;Machine Learning;Cancer Prediction;Pigeon Inspired Optimizer.;Training;Machine learning algorithms;Training data;Predictive models;Feature extraction;Data models;Breast cancer|
|[ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085467)|A. Alotaibi; T. Alafif; F. Alkhilaiwi; Y. Alatawi; H. Althobaiti; A. Alrefaei; Y. Hawsawi; T. Nguyen|10.1109/ICAISC56366.2023.10085467|breast cancer;vision transformer;histopathological images;image classification;computer-aided system;Solid modeling;Technological innovation;Smart cities;Histopathology;Computational modeling;Malignant tumors;Transformers|
|[Deep Learning to Predict At-Risk Students’ Achievement in a Preparatory-year English Courses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085097)|A. Al-Sulami; M. Al-Masre; N. Al-Malki|10.1109/ICAISC56366.2023.10085097|Artificial Neural Network;Educational Data Mining;Learning Management System;and At-risk Student;Deep learning;Training;Technological innovation;Learning management systems;Smart cities;Predictive models;Multilayer perceptrons|
|[BowlingDL: A Deep Learning-Based Bowling Players Pose Estimation and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085434)|N. F. Janbi; N. Almuaythir|10.1109/ICAISC56366.2023.10085434|Artificial Intelligent;Edge Intelligence;Mobile Intelligence;Deep Learning;Convolutional Neural Networks;Human Pose Estimation;Pose Classification;Smart Applications;Sports Analytics.;Training;Technological innovation;Pose estimation;Transportation;Medical services;Mobile applications;Augmented reality|
|[Dynamic Programing-based Green Speed Advisory System Design for Mixed Platooning Connected Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085392)|A. Özdemir; I. M. Koç|10.1109/ICAISC56366.2023.10085392|Connected Vehicles;Energy Management System;Well-to-Wheels Emissions;Electric Vehicles;Dynamic Programing;Platooning Vehicles;Technological innovation;Smart cities;Green products;Transportation;Optimization methods;Machine learning;Vehicle dynamics|
|[QuadSWARM: A Real-Time Autonomous Surveillance System using Multi-Quadcopter UAVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085441)|A. F. Moiz; H. M. Khawaja; Z. H. Khan; R. Sohail|10.1109/ICAISC56366.2023.10085441|smart cities;quadcopter;surveillance;swarm;unmanned aerial vehicle;Technological innovation;Smart cities;Surveillance;Streaming media;Real-time systems;Sensors;Time factors|
|[Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085320)|R. M. Wibowo; B. Fakieh; M. S. Ramzan; A. S. Alzahrani; M. Siddiqui; B. Alzahrani|10.1109/ICAISC56366.2023.10085320|Exploratory Data Analysis;Open Data;Election Voter;Kids Disability;Technological innovation;Data analysis;Pandemics;Voting;Data visualization;Big Data;Data models|
|[Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085350)|H. T. Himdi; F. Y. Assiri|10.1109/ICAISC56366.2023.10085350|Arabic textual analysis;Fake news detection;Machine learning classification model;Analytical models;Technological innovation;Freeware;Social networking (online);Smart cities;Machine learning;Metadata|
|[Hybrid grasshopper optimization algorithm with simulated annealing for feature selection using high dimensional dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085104)|B. Sahu; J. Ravindra; S. N. Mohanty; A. Panigrahi|10.1109/ICAISC56366.2023.10085104|Grasshopper optimization;mRMR;SVM;Wrapper model;Simulated Annealing;Support vector machines;Technological innovation;Smart cities;Computational modeling;Simulated annealing;Filtering algorithms;Feature extraction|
|[Towards Fake News Identification using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085321)|Y. Abdallah; N. Salhab; A. E. Falou|10.1109/ICAISC56366.2023.10085321|Fake news;Machine Learning;Natural Language Processing (NLP);Naïve Bayes;Logistic Regression;Classification;Technological innovation;Machine learning algorithms;Social networking (online);Smart cities;Roads;Machine learning;Natural language processing|
|[Using Data Mining Techniques To Enhance The Student Performance. A semantic review.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084963)|A. Alsulami; A. S. A. -M. Al-Ghamdi; M. Ragab|10.1109/ICAISC56366.2023.10084963|Data Mining;Educational Data Mining;Student Performance;Classification Techniques;Support vector machines;Technological innovation;Smart cities;Semantics;Prediction algorithms;Nonhomogeneous media;Feature extraction|
|[SDG-11.6.2 Indicator and Predictions of PM2.5 using LSTM Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085464)|S. Z. Mahfooz; A. Alhasani; A. Hassan|10.1109/ICAISC56366.2023.10085464|SDG-11.6.2 indicator;PM2.5;LSTM neural network;smart city;sustainable living;Technological innovation;Smart cities;Neural networks;Predictive models;Air quality;Data models;Reliability|
|[Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085014)|A. Khoudi; N. Yahiaoui; F. Rebahi|10.1109/ICAISC56366.2023.10085014|COVID-19;fake news detection;LSTM;BLSTM;NLP;BoW;Word2Vec;Bert;COVID-19;Deep learning;Training;Technological innovation;Social networking (online);Smart cities;Bit error rate|
|[Plagiarism Checker & Link Advisor using concepts of Levenshtein Distance Algorithm with Google Query Search - An Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085404)|D. A. Nandurkar; P. Ujjainkar; B. Miglani; A. Kanojiya|10.1109/ICAISC56366.2023.10085404|Natural Language Processing;Plagiarism Checker;Plagiarism Detector;Plagiarism Link Advisor;Levenshtein’s Algorithm;Google Query Search;BeautifulSoup;Streamlit;Technological innovation;Smart cities;Publishing;Plagiarism;Natural languages;Internet|
|[CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085039)|A. A. Haruna; I. A. Badi; L. J. Muhammad; A. Abuobieda; A. Altamimi|10.1109/ICAISC56366.2023.10085039|deep learning;CNN;LSTM;CNN-LSTM;apple disease;Deep learning;Performance evaluation;Technological innovation;Sensitivity;Smart cities;Training data;Classification algorithms|
|[Utilizing metabolite connectivity and G-CNN to detect gallbladder cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085286)|A. M. Obaid; M. Ksantini; A. Turki; A. Safaaldin; H. Bellaaj|10.1109/ICAISC56366.2023.10085286|Cancer;Diagnosis;Graph-Convolutional Neural Network;Gallbladder;Metabolic;Support vector machines;Radio frequency;Metabolomics;Technological innovation;Smart cities;Biological system modeling;Artificial neural networks|
|[Application of Content-Base Recommendation Algorithms on Mobile Travel Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085680)|H. Zacarias; G. Cangondo; L. Souza-Pereira; N. M. Garcia; B. Silva; N. Pombo|10.1109/ICAISC56366.2023.10085680|Recommendation System;Smart Cities;Content-Base filtering;Tourism;Economics;Technological innovation;Smart cities;Clustering algorithms;Filtering algorithms;Indexes;Cultural differences|
|[Twitter Sentimental Analysis using Machine Learning Approaches for SemeVal Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085107)|A. Imran; M. Fahim; A. Alzahrani; S. Fahim; K. M. A. Alheeti; S. U. Rehman|10.1109/ICAISC56366.2023.10085107|TF-IDF;Sentimental Analysis;Vectorization;Word Embedding;NLP;Training;Social networking (online);Terrorism;Blogs;Semantics;Training data;Machine learning|
|[Crowdsensing Technologies for Optimizing Passenger Flows in Public Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085515)|A. Plašilová; J. Procházka|10.1109/ICAISC56366.2023.10085515|smart city;crowdsensing;public transport;passenger;mobile crowdsensing;passive crowdsensing;MAC address;Wi-Fi;Technological innovation;Crowdsensing;Smart cities;Sensors;Planning;Reliability;Motion measurement|
|[Inferential Statistics and Visualization Techniques for Aspect Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085093)|N. Sharma; M. Mangla; M. Ishaque; S. N. Mohanty|10.1109/ICAISC56366.2023.10085093|Data Visualization;Hotel Booking;Demand;ANOVA;Surge;Python EDA;Seaborn;City Hotel;Resort Hotel;Exploratory Data Analysis;Technological innovation;Data privacy;Data analysis;Smart cities;Data visualization;Quality of service;Boosting|
|[Computer Vision-Based Military Tank Recognition Using Object Detection Technique: An application of the YOLO Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085552)|S. Ali; Abdullah; A. Athar; M. Ali; A. Hussain; H. -C. Kim|10.1109/ICAISC56366.2023.10085552|Computer vision;Military Tanks detection;YOLOv5;Object detection;Deep learning;Training;Technological innovation;Military computing;Smart cities;Computational modeling;Surveillance;Object detection|
|[Accidental Face Recognition and Detection Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085152)|A. Sharma; P. Agrawal; Krishan; B. Sharma; I. B. Dhaou|10.1109/ICAISC56366.2023.10085152|Machine Learning;Haar;Logistic regression;Face detection;Resistance;Technological innovation;Road accidents;Social networking (online);Smart cities;Face recognition;Roads|
|[Hybridization of Wavelet Decomposition and Machine Learning for Brain Waves based Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085288)|M. Ali; S. M. Qaisar; T. Anurulafchar|10.1109/ICAISC56366.2023.10085288|Electroencephalography (EEG);Machine learning;Emotion categorization;Wavelet Decomposition;Feature extraction;Emotion recognition;Emotion recognition;Technological innovation;Machine learning algorithms;Smart cities;Signal processing algorithms;Support vector machine classification;Signal processing|
|[A Survey - Soil Feature Analysis Using Clustering Techniques and Predict Various Crops in Madurai District](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085317)|D. Sakthipriya; T. Chandrakumar; B. Johnson; K. J. Prem|10.1109/ICAISC56366.2023.10085317|Soil varieties;Clustering Techniques;Crops;Nitrogen;Phosphorus;Potassium.;Productivity;Analytical models;Temperature distribution;Technological innovation;Smart cities;Crops;Soil|
|[Autonomous Vehicles: Security Challenges and Game theory-based Countermeasures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085301)|J. M. Qurashi; M. J. Ikram; K. Jambi; F. E. Eassa; M. Khemakhem|10.1109/ICAISC56366.2023.10085301|autonomous vehicle;cyber attack;game-theory;resilience;security;vulnerability;Technological innovation;Smart cities;Games;Cyber-physical systems;Reliability theory;Safety;Security|
|[Application of Advanced Deep Learning Techniques for Face Detection and Age Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085089)|R. A. AlQadi; M. Batouche|10.1109/ICAISC56366.2023.10085089|face recognition;age prediction;deep learning;convolutional neural network;King Abdullah bin Abdulaziz;Deep learning;Technological innovation;Image recognition;Smart cities;Face recognition;Estimation;Feature extraction|
|[Laptop Price Prediction Using Real Time Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085473)|C. L. Reddy; K. B. Reddy; G. R. Anil; S. N. Mohanty; A. Basit|10.1109/ICAISC56366.2023.10085473|Web scraping;Dataset;Multi-Linear Regression;Machine Learning;e-commerce;Support vector machines;Technological innovation;Portable computers;Smart cities;Pricing;Predictive models;Prediction algorithms|
|[Speech Recognition Android App with Dyslexia for Children Using AI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085189)|R. A. A. Helmi; M. G. M. Johar; V. K. Subamaniam|10.1109/ICAISC56366.2023.10085189|Speech Recognition;Voice Recognition Programming Interface (API);Dyslexia;Hidden (HMM);Dyslexic. Recognition;Application idden Markov Model;Voice activity detection;Technological innovation;Smart cities;Education;Hidden Markov models;Speech recognition;Programming|
|[Arabic Pilgrim Services Dataset: Creating and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085561)|H. M. Al-Barhamtoshy; H. T. Himdi; M. Alyahya|10.1109/ICAISC56366.2023.10085561|Arabic news;fake news;dataset;pilgrim;analysis;news classification;new country of origin;evaluation;Text mining;Technological innovation;Adaptation models;Smart cities;Search methods;Text categorization;Feature extraction|
|[DARIJA-C: towards a Moroccan DARIJA Speech recognition and speech-to-text Translation Corpus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085164)|M. Labied; A. Belangour; M. Banane|10.1109/ICAISC56366.2023.10085164|Moroccan Darija Speech corpus;Moroccan Arabic Dialect;Automatic speech recognition;Speech-to-Text Translation;DARIJA-C;Training;Crowdsourcing;Technological innovation;Smart cities;Buildings;Speech recognition;Recording|
|[Heart disease Prediction using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085522)|S. Ibrahim; N. Salhab; A. E. Falou|10.1109/ICAISC56366.2023.10085522|Heart Disease;Machine Learning;Classification;K-Nearest Neighbors (KNN);Decision Trees (DT);Random Forest (RF);Naïve Bayes (NB);Logistic Regression (LR);Gradient Boosting (GB);Heart;Training;Technological innovation;Machine learning algorithms;Smart cities;Predictive models;Prediction algorithms|
|[Arabic Speech Dialect Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085647)|M. Alrehaili; T. Alasmari; A. Aoalshutayri|10.1109/ICAISC56366.2023.10085647|Arabic Dialects;Convolutional Neural Networks;Dialect Identification;Automatic Dialect classification;Deep learning;Technological innovation;Smart cities;Speech enhancement;Feature extraction;Speech;Convolutional neural networks|
|[Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085673)|A. Riaz; M. J. Ikram; N. Asadullah|10.1109/ICAISC56366.2023.10085673|component;formatting;style;styling;insert (key words);Technological innovation;Systematics;Smart cities;Distributed ledger;Documentation;Forgery;Blockchains|
|[Online Phishing Detection Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085377)|R. A. A. Helmi; M. G. M. Johar; M. A. S. B. M. Hafiz|10.1109/ICAISC56366.2023.10085377|Phishing Detection;ML;Machine Learning;SVM.;Uniform resource locators;Support vector machines;Technological innovation;Portable computers;Machine learning algorithms;Smart cities;Phishing|
|[A Survey off Malware Forensics Analysis Techniques And Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085474)|S. Al-Sofyani; A. Alelayani; F. Al-zahrani; R. Monshi|10.1109/ICAISC56366.2023.10085474|Malware;forensics;detection;cyber threat.;Technological innovation;Smart cities;Forensics;Organizations;Malware|
|[Towards Privacy Preserving and Efficiency in Fog Selection for Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085094)|N. Alhwidi; N. Alqahtani; L. Almaiman; M. Rekik|10.1109/ICAISC56366.2023.10085094|Federated learning;Collaborative training;Heterogeneous privacy;Policy-based approach;Performance evaluation;Training;Threat modeling;Privacy;Technological innovation;Federated learning;Smart cities|
|[XSS Filter detection using Trust Region Policy Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085076)|B. Mondal; A. Banerjee; S. Gupta|10.1109/ICAISC56366.2023.10085076|Machine Learning;Reinforcement learning;Trust Region Policy Optimization;XSS Filter;Training;Cross-site scripting;Closed box;Web pages;Reinforcement learning;Information filters;Data models|
|[Process SDLC-GDPR: Towards the Development of Secure and Compliant Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085308)|M. B. Freitas; V. M. Araújo; J. P. Magalhães|10.1109/ICAISC56366.2023.10085308|GDPR;DPO;personal data;security by design;security by default;SDLC;Privacy;Technological innovation;Smart cities;Law;ISO Standards;Standards organizations;Process control|
|[An Enhanced Method for Detecting Attack in Collaborative Recommender System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085506)|R. A. Zayed; H. A. Hefny; L. F. Ibrahim; H. A. Salman|10.1109/ICAISC56366.2023.10085506|Robustness;Shilling Attack;Profile injection;Collaborative Recommender system;voting classifier;Information Filtering;supervised Learning;Machine Learning;Technological innovation;Video on demand;Web services;Smart cities;Supervised learning;Collaboration;Information services|
|[Document Provenance and Authentication through Authorship Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085330)|M. T. Zamir; M. A. Ayub; J. Khan; M. J. Ikram; N. Ahmad; K. Ahmad|10.1109/ICAISC56366.2023.10085330|Document Authentication;Provenance;BERT;RoBERTa;Text Classification;Late Fusion;Deep learning;Technological innovation;Machine learning algorithms;Smart cities;Text categorization;Authentication;Writing|
|[Novel Cybersecurity Issues in Smart Energy Communities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085312)|G. B. Gaggero; D. Piserà; P. Girdinio; F. Silvestro; M. Marchese|10.1109/ICAISC56366.2023.10085312|Energy Community;Cybersecurity;Smart Grid;Renewable energy sources;Technological innovation;Protocols;Smart cities;Law;Instruments;Power grids|
|[Awareness of Mobile Operating System Privacy Among Computer Science Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085581)|F. A. Alghamdi; W. S. AlAnazi; S. Snoussi|10.1109/ICAISC56366.2023.10085581|Privacy Awareness;Mobile Privacy;Digital Privacy;Computer science;Training;Privacy;Technological innovation;Social networking (online);Smart cities;Operating systems|
|[Next Generation Phishing Detection and Prevention System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085529)|S. Tyagi; D. R. K. Tyagi; D. P. K. Dutta; D. P. Dubey|10.1109/ICAISC56366.2023.10085529|Phishing;Phishing Email;Social Engineering Attacks;Machine Learning;Web Browser Plugin.;Uniform resource locators;Industries;Technological innovation;Phishing;Biological system modeling;Machine learning;Electronic mail|
|[Using an IPsec VPN to Secure The Network Communication in The Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085145)|M. H. A. Hamied|10.1109/ICAISC56366.2023.10085145|Authentication;Encryption;IKE;Integrity;IPsec VPN;Technological innovation;Protocols;Three-dimensional displays;Smart cities;Seals;Throughput;Virtual private networks|
|[Governance of Information Security and Its Role In Reducing the Risk of Electronic Accounting Information System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084976)|S. M. Alenazy; R. M. Alenazy; M. Ishaque|10.1109/ICAISC56366.2023.10084976|Electronic Accounting;Governance;Information Security;Training;Technological innovation;Smart cities;Shape;ISO Standards;Information security;Companies|
|[Blockchain-based System for Secure Storage and Sharing of Diabetics Healthcare Records](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085169)|M. Alharby|10.1109/ICAISC56366.2023.10085169|Diabetics healthcare records;Blockchain;Attribute-based encryption;Access control;Security;Data privacy;Technological innovation;Smart cities;Data integrity;Medical services;Diabetes;Blockchains|
|[Instructors Experience of using Cloud Computing Based Applications in Saudi Arabia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085435)|M. Gollapalli; B. AlQahtani; A. AlGhamdi; M. AlOtaibi|10.1109/ICAISC56366.2023.10085435|cloud computing;education;e-learning;distance education;Saudi Arabia;COVID-19;Cloud computing;Technological innovation;Pandemics;Smart cities;Computer viruses;Education|
|[Design and Performance Analysis of a Grid-Connected Solar Power System for Energy Efficient AR Building](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085131)|N. Salem; J. Asiri|10.1109/ICAISC56366.2023.10085131|energy production;azimuth angle;monocrystalline photovoltaic modules;polycrystalline photovoltaic modules;thin film photovoltaic modules;PVsyst software;KSA tariffs;Photovoltaic systems;Renewable energy sources;Azimuth;Oils;Buildings;Sociology;Tariffs|
|[Modelling and Evaluation of Renewable Integrated Grid-Connected Microgrid for Cost-Effective Energy Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085461)|M. M. Kamal; A. Asghar; I. Ashraf|10.1109/ICAISC56366.2023.10085461|renewable energy;energy management;microgrid;rural electrification;grid-connected;Photovoltaic systems;Renewable energy sources;Technological innovation;Costs;Greenhouse effect;Wind energy;Microgrids|
|[Autoresponder using Chatbot for Educational Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084956)|T. M. Alharethi|10.1109/ICAISC56366.2023.10084956|autoresponder;chatbot;telgram. bot;najran university;COVID-19;Technological innovation;Pandemics;Smart cities;Education;Chatbots;Corona|
|[A Short Review on Faster and More Reliable TCP Reassembly for High-Speed Networks in Deep Packet Inspection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085644)|S. Arshad; R. Zanib; A. Akram; T. Saeed|10.1109/ICAISC56366.2023.10085644|TCP reassembly in high-speed Networks;DPI;Deep Packet Inspection;and High-Speed Networks;Technological innovation;Target tracking;High-speed networks;Smart cities;Network intrusion detection;Inspection;IP networks|
|[Digital Twin for Advanced Automation of Future Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085428)|S. A. Khan; H. Z. U. Rehman; A. Waqar; Z. H. Khan; M. Hussain; U. Masud|10.1109/ICAISC56366.2023.10085428|smart grid;digital twin;fault detection;automation;Temperature sensors;Temperature distribution;Automation;Oils;Microgrids;Real-time systems;Smart grids|
|[An Optimized Half Wave Dipole Antenna for the Transmission of WiFi and Broadband Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085382)|S. Achimugu; S. Achimugu; L. A. Ajao; U. A. Usman|10.1109/ICAISC56366.2023.10085382|Broadband;Dipole antenna;Electromagnetic waves;Wireless communication;WiFi;Antenna measurements;Dipole antennas;Yagi-Uda antennas;Transmitting antennas;Adaptive arrays;Directive antennas;Loss measurement|
|[An Implementation of A Bus Following Model in an 802.11p Based Simulator in MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085008)|F. I. X. R. Gan; G. P. T. Mayuga; L. B. K. Lao; E. R. Magsino|10.1109/ICAISC56366.2023.10085008|VANET Toolbox;MATLAB;V2I Communications;Dedicated Short Range Communication (DSRC);IEEE 802.11p;Technological innovation;Smart cities;Mobility models;Communication systems;Roads;Layout;Vehicular ad hoc networks|
|[A Survey on IEEE 802.15.7 MAC Protocols for Visible Light Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085558)|A. Alawadhi; A. Almogahed; M. H. Omar|10.1109/ICAISC56366.2023.10085558|6G;IEEE 802.15.7;Superframe Structures;Visible Light Communication (VLC);MAC layer;IEEE 802.15 Standard;6G mobile communication;Measurement;Visualization;Technological innovation;Wireless networks;Full-duplex system|
|[Intelligent airport management system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085083)|M. A. Danah; F. Bourennani; A. S. M. Al-Shahrani|10.1109/ICAISC56366.2023.10085083|Smart Airport management;Artificiel Intelligence;metaheuristics;genetic algorithms;Technological innovation;Smart cities;Airports;Logistics;Genetic algorithms|
|[Lowering Weighted Average Cost of Generation by Optimizing Operating Time: A Study from Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085031)|H. O. A. Khan; A. Ahmad; N. Arshad; A. Nadeem|10.1109/ICAISC56366.2023.10085031|WACG;DSM;ToU pricing;emissions;Circular debt;Measurement;Renewable energy sources;Technological innovation;Costs;Smart cities;Tariffs;Smart meters|
|[Control of wind system based on DFIG using a Super Twisting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085260)|T. Mebkhouta; G. Amar; B. M. Lamine; K. Djaloul; D. B. Ouadeh; C. Youcef|10.1109/ICAISC56366.2023.10085260|Control;sliding mode;wind power;DFIG;WECS;Active and Reactive power;Wind energy generation;Reactive power;Technological innovation;Uncertainty;Smart cities;Doubly fed induction generators;Robustness|
|[A Game Theory-based Pricing Technique for Ridesharing Pairings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085412)|E. R. Magsino; G. R. C. Ching; F. M. M. Espiritu; K. D. Go|10.1109/ICAISC56366.2023.10085412|Ridesharing;Game Theory;Smart Cities;Passenger Pairing;Technological innovation;Costs;Smart cities;Pricing;Games;Automobiles;Public transportation|
|[Flash Flood Simulation for Assisting Children to Understand the Flood Disaster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085494)|N. K. Suraidi; N. S. C. Ismail; S. Mustapha|10.1109/ICAISC56366.2023.10085494|flash flood;simulation;flood awareness;assisting children;Solid modeling;Visualization;Technological innovation;Three-dimensional displays;Smart cities;Education;Virtual reality|
#### **2023 International Conference for Advancement in Technology (ICONAT)**
- DOI: 10.1109/ICONAT57137.2023
- DATE: 24-26 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Digital Image Forgery Detection Using Pre-Trained Xception Model as Feature Extractor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080127)|N. M. Pandiyan|10.1109/ICONAT57137.2023.10080127|Image Forgery Detection;Transfer Learning;Deep Learning;CNN;Feature Extraction;Xception;Deep learning;Social networking (online);Digital images;Feature extraction;Forgery;Software|
|[Review on Event Extraction for BioNLP with a Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080428)|V. V. Pattankar; P. Priyanga|10.1109/ICONAT57137.2023.10080428|Mining of biomedical texts;event extraction;natural language processing;and semantic parsing are some topics covered;Proteins;Drugs;Navigation;Biological system modeling;Taxonomy;Semantics;Natural languages|
|[Place Recognition Systems by Old Photos using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080180)|P. Ghulappanavar; H. Shanavas|10.1109/ICONAT57137.2023.10080180|Historical Images;Geo Labelled Images;Place Recognition;Support Vector Machine;Neural Network;Support vector machines;Image recognition;Machine learning algorithms;Databases;Internet;Classification algorithms;Engines|
|[Web-Page Content Classification on Entropy Classifiers using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080462)|S. A. Siddiqha; M. Islabudeen|10.1109/ICONAT57137.2023.10080462|Webpage classification;Knowledge Representation;Feature extraction;Classification;Frequency Occurrence;Training;Web pages;Knowledge representation;Feature extraction;World Wide Web;Entropy;Web sites|
|[Design and Performance Assessment of GaSb/Si Heterojunction Vertical TFET with Delta Doped Layer for Enhanced DC and AF/RF Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080244)|P. K. Bera; R. Kar; D. Mandal|10.1109/ICONAT57137.2023.10080244|Delta doped layer;Vertical TFET;Heterojunction;Tunneling;Ultra low power.;Performance evaluation;TFETs;Temperature;Photonic band gap;Heterojunctions;Tunneling;Threshold voltage|
|[Video Enhancement Algorithm using Pre-and Post-Processing for Compressed Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080711)|S. Nandyal; V. S.Patil|10.1109/ICONAT57137.2023.10080711|Edge Preserving Filter;Edge Protecting Filter;De-Blocking;H.264;H.265;Video Compression;Post-Processing;Pre-Processing.;Image coding;Image edge detection;Process control;Filtering algorithms;Video compression;Solids;Quality assessment|
|[Improvement in Controller Performance for Distillation Column Using IMC Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080246)|S. K. Sunori; K. A. Joshi; A. Mittal; D. Nainwal; F. Khan; P. Juneja|10.1109/ICONAT57137.2023.10080246|Distillation column;Control system;Ziegler-Nichols method;Fuzzy control;IMC;Transfer function;Peak overshoot;Fuzzy control;PI control;Liquids;Distillation equipment;Transfer functions;Mathematical models;Chemicals|
|[Energy Efficient D2D-mode-selection Based on Battery Life Constraint with A POMDP and Deep Q Learning-Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080449)|P. R. Teja; P. K. Mishra|10.1109/ICONAT57137.2023.10080449|POMDP;ADQRN1;D2D;Q learning;Performance evaluation;Transmitters;Quality of service;Markov processes;Probability;Batteries;Device-to-device communication|
|[Rain Effect on Satellite Communication’s Link Design in regions of P, N, and K with Logic design implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080160)|C. Nejat; Z. Z. Nazeri; N. Nejat; N. S. Nejat; A. R. Nejat|10.1109/ICONAT57137.2023.10080160|Satellite Communication;Rain Attenuation;QPSK DBS-TV;Ku-band transponders;Link budget design;Amazon Rainforest;Decoders;DFF;Multiplexer;Counters;Registers;Earth;Phase shift keying;Satellites;Rain;Transmitters;Downlink;Attenuation|
|[A Redundant Equation Method to Improve the Accuracy of Doppler Ranging Solution by Means of Frequency Shift Recurrence Equation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079991)|T. Yu|10.1109/ICONAT57137.2023.10079991|single station location;airborne passive positioning;Doppler shift;Doppler changing rate;ranging;frequency shift recursive;redundant equation;relative percentage error;Doppler shift;Distance measurement;Mathematical models|
|[Bidirectional DC-DC Converter with Multiport Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080202)|H. Gupta; A. Badhoutiya|10.1109/ICONAT57137.2023.10080202|Multi-Port Converters;Voltage Control;MIMO;DC-DC Converter;Topology;Low voltage;Magnetomechanical effects;DC-DC power converters;MIMO;Voltage control;Integrated circuit modeling;MIMO communication|
|[A Complex Multi Power Quality Disturbances detection and classification using MRA DWT and k-NN Classifier Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080821)|S. R. Kalluri; J. D. Kumar; S. R. K. Joga; V. Sudhakar; G. Jagadeesh; D. V. Kumar|10.1109/ICONAT57137.2023.10080821|power quality;multi resolution analysis;machine learning;power quality monitoring;wavelet transform;feature extraction;Power quality;Transforms;Harmonic analysis;Discrete wavelet transforms;Power systems;Noise measurement;Task analysis|
|[Life Cycle Analysis of Lithium-ion Batteries: An Assessment of Sustainability Impact](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080437)|M. Anil; G. Rejikumar|10.1109/ICONAT57137.2023.10080437|renewable energy integration;energy storage;environmental sustainability;life cycle analysis;lithium-ion battery;aluminium-air battery;redox flow battery;solid state lithium battery;Lithium-ion batteries;Renewable energy sources;Climate change;Atmosphere;Low-carbon economy;Vanadium;Water conservation|
|[“Churn Prediction in Telecommunication Industry”](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080425)|Y. Bharambe; P. Deshmukh; P. Karanjawane; D. Chaudhari; N. M. Ranjan|10.1109/ICONAT57137.2023.10080425|Churn Prediction;Machine Learning;Customer Churn;Big Data;Logistic Regression;XGBoost;SVM;Random Forest;Industries;Companies;Machine learning;Forestry;Communications technology;Telecommunications;Task analysis|
|[Novel Robust Payroll Management System For Micro, Small & Medium Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080240)|P. Kaur; S. Sharma; B. S. Kalyan|10.1109/ICONAT57137.2023.10080240|U.I - User Interface;P.M.S - Payroll Management System;A.P.I – Application program interface;Database Management System (DBMS);S.Q.L –Structured Query Language component;Java;Gears;Passwords;Manuals;Programming;Security;Database languages|
|[Anomaly Detection in Machine Failure Situation using CNN-AutoEncoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080018)|B. K. Sinha; S. Banerjee|10.1109/ICONAT57137.2023.10080018|Outlier Detection System;Long Short-Term Memory;Autoencoders;Neural Networks;Time-series;Automation;Electric breakdown;Oils;Organizations;Data science;Data models;Task analysis|
|[Machine Learning Approach for Estimation and Novel Design of Stroke Disease Predictions using Numerical and Categorical Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080722)|S. K. Satapathy; A. Patel; P. Yadav; Y. Thacker; D. Vaniya; D. Parmar|10.1109/ICONAT57137.2023.10080722|Stroke prediction;Stroke disease analysis;Machine learning;Support vector machines;Machine learning algorithms;Predictive models;Stroke (medical condition);Prediction algorithms;Brain modeling;Classification algorithms|
|[Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080059)|A. K. Behera; S. Dehuri; A. Ghosh|10.1109/ICONAT57137.2023.10080059|Fuzzy Associative Classification;Fuzzy Set;Genetic Algorithms;Multi-objective Genetic Algorithms;Radial Basis Functions Neural Networks;Rough Set and Surrogate-assisted Model;Search methods;Rough sets;Radial basis function networks;Genetics;Classification algorithms;Kernel;Genetic algorithms|
|[Design of Solar-Powered Electric Vehicle Charging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080468)|P. B. Suseela; C. V. K. Bhanu|10.1109/ICONAT57137.2023.10080468|charging stations;electric vehicles;renewable energy;solar system;storage system;Renewable energy sources;Costs;Employment;Government;Charging stations;Electric vehicle charging;Software|
|[Modified Scalar Controlled PWM Inverter Fed Induction Motor Drive with Output Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080681)|P. Mishra|10.1109/ICONAT57137.2023.10080681|Filter;Induction Motor;Inverter;Scalar Control;Induction motor drives;Pulse width modulation inverters;Torque;Magnetic separation;Simulation;Inverters;Steady-state|
|[Development and Analysis of Space-based Diffractive Optical Imaging Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080673)|R. Niu; S. Jiang; X. Zhi; J. Hu; W. Zhang; J. Gong|10.1109/ICONAT57137.2023.10080673|Diffractive membrane imaging system;space application;large aperture;light weight;Costs;Apertures;Optical imaging;Cameras;Microstructure;Substrates;Remote sensing|
|[Segmentation and Clustering of Time Series Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080820)|S. Tiwari|10.1109/ICONAT57137.2023.10080820|Classification;time series data;clustering;methodology;Machine learning algorithms;Costs;Correlation;Wind energy;Clustering methods;Time series analysis;Machine learning|
|[Development of an Elephant Detection and Repellent System based on EfficientDet-Lite Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079959)|S. Pemasinghe; P. K. W. Abeygunawardhana|10.1109/ICONAT57137.2023.10079959|Computer vision;Deep learning;Raspberry Pi;EfficientDet-Lite;Human-elephant conflict;Deep learning;Performance evaluation;Computer vision;Electric potential;Computational modeling;Computational efficiency;Gravity|
|[Question and Answering System For Investment Promotion Based on NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080255)|P. M. R. A. Panditharathna; S. Rajapaksha|10.1109/ICONAT57137.2023.10080255|Natural Processing Language(NLP);Deep Learning(DL);Machine Learning (ML);Natural Language Tool Kit(NLTK);Foreign Direct Investment(FDI);Industries;Deep learning;Manuals;Information retrieval;Chatbots;Generators;Standards|
|[Integument Neoplasm Detection using Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080189)|J. Yamini; R. R. Jalleda; N. Vennela; N. Padmavathi|10.1109/ICONAT57137.2023.10080189|Integument Neoplasm;HAM10000;CNN;Pooling;Sampling;Batch Normalization Melanoma;Benign;Vascular lesion;Deep learning;Convolution;Neural networks;Training data;Surgery;Melanoma;Lesions|
|[Forest Wildfire Detection and Forecasting Utilizing Machine Learning and Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080840)|S. N. Ghate; P. Sapkale; M. Mukhedkar|10.1109/ICONAT57137.2023.10080840|Image processing;Random Forest;Wildfires;Machine learning;Machine learning algorithms;Satellites;Wind speed;Image processing;Fires;Vegetation mapping;Forestry|
|[A Convolutional Neural Network Based Potato Leaf Diseases Detection Using Sequential Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080063)|C. C. Bonik; F. Akter; M. H. Rashid; A. Sattar|10.1109/ICONAT57137.2023.10080063|Potato leaf disease;Machine learning;Leaf disease detection;Image processing;Industries;Schedules;Crops;Production;Machine learning;Predictive models;Prediction algorithms|
|[Envisaging and Retaining College Workforce Attrition using Machine Learning and Ensemble Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080463)|A. Golande; V. Surwase; N. Patil; J. Bhandekar; J. Shinde|10.1109/ICONAT57137.2023.10080463|ML models;Deep learning models;Ensembles models;attrition prediction;employee attrition;Radio frequency;Analytical models;Focusing;Companies;Predictive models;Prediction algorithms;Depression|
|[Modeling and Simulation of Heterojunction Based Multilayer High-Speed Vertical CIGS Photodetector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080239)|G. Manzoor; K. K. Sharma; G. K. Bharti|10.1109/ICONAT57137.2023.10080239|Photodetector;micro-optical systems;CIGS;Photovoltaic systems;Performance evaluation;Sensitivity;Photovoltaic cells;Optical detectors;Numerical models;Photodiodes|
|[Image De-hazing techniques for Vision based applications - A survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080156)|S. Krishna B V; B. Rajalakshmi; U. Dhammini; M. K. Monika; C. Nethra; K. Ashok|10.1109/ICONAT57137.2023.10080156|Images;dehazing;datasets;dehazing techniques;computation;Visualization;Image recognition;Image color analysis;Transportation;Scattering;Video surveillance;Real-time systems|
|[Comparative Analysis of ST, ECRL and Static Logic Style at Different Process Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080818)|N. Arora; A. Sanadhya; A. Asati|10.1109/ICONAT57137.2023.10080818|ST;Adiabatic;Low power;ECRL;code converters;LT-Spice;Single power clock;Semiconductor device modeling;Power system measurements;Power demand;Density measurement;Very large scale integration;Reflective binary codes;Integrated circuit modeling|
|[Chronic Kidney Disease Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080709)|V. S. Pilli; K. Pamidi; P. E|10.1109/ICONAT57137.2023.10080709|Support Vector Classifier;Random Forest;Chisquare test;True Negative rate;Data Mining;Training;Machine learning algorithms;Support vector machine classification;Medical services;Forestry;Prediction algorithms;Chronic kidney disease|
|[Engagement in Video Graphic Online Learning Using the Emotional Dimensions in the Learning Context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080769)|B. Baranidharan; H. Bhandari; A. Tewari; I. Sachadeva; Abhinav|10.1109/ICONAT57137.2023.10080769|Facial Emotion Recognition;Convolutional Neural Network;DenseNET;VGG;Measurement;Graphics;Emotion recognition;Analytical models;Pandemics;Organizations;Predictive models|
|[Automatic System for Driver Drowsiness Detection System using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080067)|S. P. Ch; S. Guduru; V. Ronagala; H. Kuresan; S. Dhanalakshmi|10.1109/ICONAT57137.2023.10080067|Deep learning;Computer Vision;Transfer Learning;Inception v3;Open CV;Deep learning;Training;Technological innovation;Visualization;Computational modeling;Transfer learning;Sensors|
|[Adversarial Attacks and Defences for Skin Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080537)|J. Purohit; I. Shivhare; V. Jogani; S. Attari; S. Surtkar|10.1109/ICONAT57137.2023.10080537|Adversarial attack;Adversarial training;Fast Sign Gradient Method;Projected Gradient Descent;Skin cancer;Training;Industries;Medical services;Machine learning;Skin;Lesions;Convolutional neural networks|
|[Classification of Incomplete Data using Augmented MLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080793)|A. Bhattacharya; S. Bhose; S. J. Choudhury|10.1109/ICONAT57137.2023.10080793|Clustering;fuzzy-c-means;imputation;multi-view;missing data;Training;Data handling;Testing|
|[Design a System to Gain Optimum Solar Energy to Existing LV Distribution Transformer using Reactive Power Control Method in Sri Lankan PV Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080682)|U. S. Rahubadde; P. I. Eng. Widanapathirana; K. P. J. P. Eng Premathilaka; V. S. Galkissage; K. A. D. L. Ruwanga; J. G. K. Madusara; D. A. D. Weerakoon|10.1109/ICONAT57137.2023.10080682|Distribution Generation(DG);PV;Low voltage Network(LV);Volt/Var control;Matlab;OpenDSS;Reactive power;Analytical models;Renewable energy sources;Simulation;Solar energy;Transformers;Inverters|
|[Performance Analysis of Integrated Air-Ground-Underwater Hybrid RF/FSO Communication System Using HAPS Based Relaying](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080806)|R. Deka; S. Anees; I. D. R|10.1109/ICONAT57137.2023.10080806|Free Space Optics (FSO);High Altitude Platform (HAP);Underwater Wireless Optical Communication (UWOC);Decode and forward relaying;Radio frequency;Temperature distribution;Salinity (geophysical);Probability;Optical fiber communication;Power system reliability;Reliability|
|[Improved Food Traceability for Restaurant customers using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080182)|S. Dinde; S. Shirgave|10.1109/ICONAT57137.2023.10080182|Traceability;Food Supply chain;Blockchain;Distributed shared Ledger;Ethereum;Smart contract;Production systems;Distributed ledger;Data security;Supply chains;Smart contracts;QR codes;Blockchains|
|[Remotely Operated Animatronic Arm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080608)|R. Suryawanshi; O. Surase; K. Sulla; S. Chakrabarti; S. K. Prasad; N. Surjuse; S. Jagtap|10.1109/ICONAT57137.2023.10080608|Arduino UNO;Nrf24L01 transceiver module;Robotic arm;SG90 servo motor;Flex Sensors;Productivity;Industries;Animatronics;Robot sensing systems;Transceivers;Sensors;Safety|
|[System Control using Hand Gesture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080493)|G. Bhole; D. Bhingare; R. Bhise; S. Bhegade; S. Bhokare; A. Bhosle|10.1109/ICONAT57137.2023.10080493|Human Computer Interaction;OpenCV;Media pipe;Python;Virtual Mouse.;Human computer interaction;COVID-19;Webcams;Operating systems;Writing;Touch sensitive screens;Mice|
|[A Novel Triangular-Shaped Reconfigurable Dielectric Resonator Antenna for Microwave Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080557)|A. Azeez; L. Kumar|10.1109/ICONAT57137.2023.10080557|Dielectric Resonator Antenna (DRA);Frequency Reconfigurable antenna;Microwave antennas;Microwave integrated circuits;Slot antennas;Capacitors;Resonant frequency;Switches;Dielectric resonator antennas|
|[Multiband Quasi-Yagi Antenna without Airgap for 5G Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080397)|S. S. Shirabadagi; V. G. Kasabegoudar|10.1109/ICONAT57137.2023.10080397|Yagi-Uda;Antenna;BALUN;WLAN;5G;Front to back ratio;Wireless communication;5G mobile communication;Yagi-Uda antennas;Resonant frequency;Microstrip antennas;Mobile antennas;Antenna feeds|
|[NTFS: Introduction and Analysis from Forensics Point of View](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080271)|R. Hermon; U. Singh; B. Singh|10.1109/ICONAT57137.2023.10080271|New Technology File Systems (NTFS);WinHex;Active@ Disk Editor;Windows 10 and 11 Operating Systems;File systems;Operating systems;Forensics|
|[Performance Assessment of Deep Learning Algorithm Using xView Data Set](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080522)|A. Singh; M. J. Nene|10.1109/ICONAT57137.2023.10080522|Object detection;image recognition;multi-layer neural network;YOLO V7.;Deep learning;Satellites;Fluctuations;Military computing;Surveillance;Urban planning;Object detection|
|[Resolving Attached USBs: Analysis of Windows 11 Artifacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080101)|A. Budhrani; U. Singh; B. Singh|10.1109/ICONAT57137.2023.10080101|System;Software;Windows 11;Examination tools.;Image resolution;Operating systems;Forensics;Universal Serial Bus|
|[Improving the Reliability of Solar Panels by Hybrid Energy Storage and Energy Management based Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080272)|S. S. Laledia; S. Gupta|10.1109/ICONAT57137.2023.10080272|Solar PV;Fuel cell;Battery;Super capacitor;DC-DC converters;Energy Management system.;Maximum power point trackers;Renewable energy sources;Power system management;Microgrids;Supercapacitors;Power system reliability;State of charge|
|[Epilepsy Seizure Detection Using Optimised KNN Algorithm Based on EEG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080847)|A. Dogra; S. A. Dhondiyal; D. S. Rana|10.1109/ICONAT57137.2023.10080847|Epilepsy;EEG;Machine Learning;Discrete Wavelet Transform (DWT);KNN;Machine learning algorithms;Sleep;Wearable computers;Signal processing algorithms;Epilepsy;Sensitivity and specificity;Feature extraction|
|[Design of Modular Smart Electric Fence System with Fall-back Communication Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080302)|R. Vikram; K. M. Jayaraju; N. Anusha|10.1109/ICONAT57137.2023.10080302|Security;Smart Electric Fence;Fence Energizer;Communication controller;fault tolerant circuitry;Microcontrollers;High-voltage techniques;Electric fences;Safety;Systems simulation;Sparks;Security|
|[Research Article Summarizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080317)|D. Deshpande; R. Unawane; R. Tatiwar; A. Taware; A. Todkar; S. Munshi|10.1109/ICONAT57137.2023.10080317|Machine learning;read–aloud;research article;summarization tool;visually challenged;Machine learning;Software|
|[Design of a Cost-Effective Smart Mirror Using Raspberry Pi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080844)|A. Barbadekar; S. Bhake; S. Parekh|10.1109/ICONAT57137.2023.10080844|Raspberry Pi;Raspbian OS;Magic Mirror module;Smart Mirrors;Internet of things (IoT);Home Automation;Adaptation models;Technological innovation;Automation;Weather forecasting;Predictive models;Software;Hardware|
|[A Smart Building Design using Cyber-Physical System Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080712)|C. H. G. Bobis; C. M. Barrion; J. G. Villaluna; D. C. V. Onte; J. F. Peralta; R. G. B. Sangalang|10.1109/ICONAT57137.2023.10080712|cyber-physical systems;internet of things;modeling;smart building;HVAC;lighting;surveillance;MATLAB;CPT;Air conditioning;Wireless sensor networks;Smart buildings;HVAC;Surveillance;Atmospheric modeling;Lighting|
|[Design of an Exoskeleton Robot for Lower-Body Rehabilitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080501)|A. B. Mendoza; A. R. S. Austria; C. M. J. Maderazo; B. E. Alzate; L. M. Adia; A. L. Salvador; R. G. B. Sangalang|10.1109/ICONAT57137.2023.10080501|lower-body exoskeleton;SolidWorks;Simulink;Simscape Multibody;Stress Analysis;Legged locomotion;Visualization;Software packages;Exoskeletons;Spinal cord injury;Software measurement;Robots|
|[A Review on the Reliability Of AC/DC Composite Distributed Generation While Taking into Account Bidirectional Power Flow Through Interlinking Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080654)|P. D. Gawade; P. N. Korde|10.1109/ICONAT57137.2023.10080654|PV Array;battery;bidirectional interlinking converter;DG Set;hybrid micro-Grid;Renewable energy sources;Wind energy;Bidirectional power flow;Microgrids;Production;Power system stability;Stability analysis|
|[Reconfigurable Radar Antenna Design for UAV Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080058)|P. Mandal; C. S. Kashyap; L. P. Roy; S. K. Das|10.1109/ICONAT57137.2023.10080058|UAV;Directivity;Gain;Radiation Efficiency;Reconfigurable Radar Antenna Array.;Array signal processing;Surveillance;Sensor phenomena and characterization;Radar antennas;Autonomous aerial vehicles;Radar tracking;Software|
|[Performance Analysis of BLDC Motor using MATLAB Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080400)|Rupam; S. Marwaha|10.1109/ICONAT57137.2023.10080400|BLDC;Simulink;Speed control;MATLAB;Applications;etc;Semiconductor device modeling;Analytical models;Adaptation models;PI control;Brushless DC motors;Software packages;Permanent magnet motors|
|[Deep Learning based Target detection and Recognition using YOLO V5 algorithms from UAVs surveillance feeds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080677)|S. Kumar; C. Kumar|10.1109/ICONAT57137.2023.10080677|YOLO;COCO;GeOJSON;PR AuC;SGD and ADAM;Measurement;Deep learning;Image recognition;Target recognition;Surveillance;Decision making;Object detection|
|[Simulation of CPU Scheduling Algorithms for Efficient Execution of Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080113)|K. Vayadande; P. Sheth; D. Pawal; A. Pathak; K. Paralkar; S. Patil|10.1109/ICONAT57137.2023.10080113|CPU scheduling;FCFS algorithm;HRRN algorithm;LJF algorithm;LRJF algorithm;Priority scheduling;SJF algorithm;SRJF algorithm;Round Robin algorithm;Schedules;Scheduling;Central Processing Unit;Round robin|
|[Investigation of Urgent Service and Detoxification Requirements on 14 11kv Oil-Immersed Transformers Due to Transient Overvoltages Using Yateks Approach.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080597)|M. K. Ngwenyama; J. Nkosi; C. S. Makola|10.1109/ICONAT57137.2023.10080597|Detoxification;Dissolved gas analysis (DGA);Oil-immersed transformer (TX);Yateks approach.;Costs;Electric breakdown;Oils;Oil insulation;Surges;Transient analysis;Preventive maintenance|
|[Training Model on Electrical Safety and Energy Conservation to Socially-Backward Region School Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080576)|B. G. Mayanja; B. Rhodah; E. Namugeni; Y. Dhanalakshmi; T. T. P. Darshini; B. K. Reddy|10.1109/ICONAT57137.2023.10080576|electricity;safety;energy conservation;hazards;Training;Wind;Technological innovation;Renewable energy sources;Terminology;Electrical safety;Water conservation|
|[Application of Real-time Automatic Cartoon Style Generation from Live video](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080457)|G. M. Harshitha; Ramyashree; Vasudeva|10.1109/ICONAT57137.2023.10080457|Real time video;Animated videos;Generative Adversarial Network;UGATIT;Graphics;Art;Transforms;Streaming media;Generative adversarial networks;Turning;Cameras|
|[Two Element Microstrip-Fed Slot Loaded Millimeter Wave MIMO Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080826)|P. Tiwari; M. Kaushik; A. Shastri; V. Gahlaut|10.1109/ICONAT57137.2023.10080826|MIMO;Patch;5G;Millimeter wave;Wireless Technology;Group Delay;Wireless communication;Microwave antennas;Slot antennas;5G mobile communication;Millimeter wave technology;Microstrip antennas;Microstrip|
|[DDoS Attack Detection and Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080075)|B. Geluvaraj; S. Krishna B V; M. Umesh; V. Girish G; Y. Yaqoob|10.1109/ICONAT57137.2023.10080075|Tor Hammer;Weka;ML;SVM;DT;KNN;LR;DDoS;Support vector machines;Organizations;Denial-of-service attack;Classification algorithms;Servers;Task analysis|
|[Performance Evaluation of FFT through Adaptive Hold Logic (AHL) Booth Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080290)|B. V. Mahesh; T. Srivasarao|10.1109/ICONAT57137.2023.10080290|FFT (Fast Fourier Transform);Multiplier;Booth encoding and decoding;Radix-2;AHL;Adder;Memory Unit;Twiddle Factor;Control Unit;VHDL;Error probability;Simulation;Signal processing algorithms;Signal processing;Performance gain;Delays|
|[metaAR – AR/XR Shopping App using Unity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080651)|S. Patil; G. Gaikwad; S. Hiran; A. Ikhar; H. Jadhav|10.1109/ICONAT57137.2023.10080651|Augmented Reality;Metaverse;Virtual Shopping;Metaverse Shopping;Virtual Reality;Oculus;Military communication;Technological innovation;Metaverse;Education;Prototypes;Entertainment industry;Footwear|
|[Design of a Programmable, Compact C-Band RF and DC Multilayer Low-Power Driver Module for Troposcatter Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080420)|A. Kumar; P. Pai; K. Vadipilla; Y. Sandeep|10.1109/ICONAT57137.2023.10080420|Solid State Power Amplifier (SSPA);Intermodulation Distortion (IMD);Error Vector Magnitude (EVM);Automatic Level Control (ALC);Serial Peripheral Interface (SPI);kilowatts (kWs);Monitor and Control (M&C);Graphical User Interface (GUI);Quadrature Phase Shift Keying (QPSK);Radio frequency;Attenuators;Intermodulation distortion;C-band;Measurement uncertainty;Power amplifiers;Frequency conversion|
|[Decentralized Society: Student’s Soul Using Soulbound Tokens](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080658)|U. Tejashwin; S. J. Kennith; R. Manivel; K. C. Shruthi; M. Nirmala|10.1109/ICONAT57137.2023.10080658|Block chain;E-Wallet;Decentralized Society;Proof-of-stake;Soulbound Token;Web3;Data privacy;Law;Data security;Force;Certification|
|[Survey on Personality Detection for Recruitment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080507)|M. Nirmala; B. Rajalakshmi; S. Mandava; S. R. M. Sarvepally; M. Megana|10.1109/ICONAT57137.2023.10080507|Personality;MTBI;OCEAN;Resume;Social Media;Handwriting;Machine learning algorithms;Education;Organizations;Predictive models;Recruitment|
|[Cyber Security Enhancement with an Intelligent Android Prototype](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080464)|A. Salaria; C. Kumar; A. Mishra|10.1109/ICONAT57137.2023.10080464|Smartphone;Applications;Honey-trap;Technological innovation;Government;Employment;Prototypes;Data breach;Hazards;Mobile applications|
|[Technologies for Comprehensive Information Security in the IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080332)|V. Dankan Gowda; S. R. Kawale; K. Prasad; N. Anil Kumar; N. S. Reddy; B. Ashreetha|10.1109/ICONAT57137.2023.10080332|IoT;Security;Information;Smart city;Privacy issues and Block chain;Wireless sensor networks;Smart cities;Security management;Smart homes;Production facilities;Blockchains;Security|
|[Implementation of a Machine Learning-based Model for Cardiovascular Disease Post Exposure prophylaxis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080833)|V. Dankan Gowda; K. Prasad; N. Anil Kumar; S. Venkatakiran; B. Ashreetha; N. S. Reddy|10.1109/ICONAT57137.2023.10080833|Machine Learning;Cardiovascular;Prevention;ultrasound images and Control;Image segmentation;Thresholding (Imaging);Ultrasonic variables measurement;Atherosclerosis;Ultrasonography;Transforms;Lumen|
|[Benign and Malignant Skin Lesion Detection from Melanoma Skin Cancer Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080355)|S. Sharma; K. Guleria; S. Kumar; S. Tiwari|10.1109/ICONAT57137.2023.10080355|SqueezeNet;neural networks;skin cancer;deep learning;machine learning;Support vector machines;Pulmonary diseases;Biopsy;Artificial neural networks;Feature extraction;Skin;Lesions|
|[PEGASIS - LEACH: Energy Efficient Protocols With Route Optimised Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080381)|S. Chauhan; M. J. Nene|10.1109/ICONAT57137.2023.10080381|AMS Subject Classification: 42C15;94A12 Key Words: WSN;PEGASIS;Ant Colony optimization (ACO);Low Energy adaptive Clustering Hierarchy (LEACH) Grey Wolf Optimizer (GWO);Particle Swarm Optimizer (PSO);Measurement;Wireless sensor networks;Adaptation models;Stochastic processes;Mathematical models;Routing protocols;Energy efficiency|
|[On-board Charger Topology with PFC for Light Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080635)|M. R. Harikrishnan; P. Kanakasabapathy|10.1109/ICONAT57137.2023.10080635|PFC charger;on-board charger;Hybrid Electric Vehicle;Reconfigurable Power Converter;Total Harmonic Distortion;Active front end converter;Reactive power;Total harmonic distortion;Pollution;Software packages;Power quality;Rectifiers;Power system harmonics|
|[Design of a Novel Ultra-Low Power Time-Domain Temperature Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080249)|X. Pang; F. Yan; J. Guan; K. Sun; Z. Li; J. Liu|10.1109/ICONAT57137.2023.10080249|current generators;relaxation oscillator;ultra-low power;temperature sensor;subthreshold conduction;Temperature sensors;Temperature measurement;Voltage measurement;Power demand;Generators;Temperature control;Low-power electronics|
|[Convolutional Acceleration Algorithm Combining Loop Optimization and Automatic Scheduling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080410)|H. Liu; F. Deng|10.1109/ICONAT57137.2023.10080410|Convolution computation;Compile optimization;Loop optimization;Automatic scheduling;Deep learning;Processor scheduling;Convolution;Computational modeling;Layout;Neural networks;Parallel processing|
|[Plotplay: An Automated Data Visualization Website using Python and Plotly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079977)|Z. K. Mundargi; K. Patel; A. Patel; R. More; S. Pathrabe; S. Patil|10.1109/ICONAT57137.2023.10079977|Correlation;Data Visualization;Flask;Graphs;Plotly;Python and Plotly;Visualization Website;Knowledge engineering;Machine learning algorithms;Linear regression;Data visualization;Null value;Machine learning;Writing|
|[Criterion for Capacitive Interdigitated Electrode for Gas Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080414)|Shweta; S. Jadav; R. Tripathi|10.1109/ICONAT57137.2023.10080414|Interdigitated electrodes;capacitive sensor;metallization ratio;COMSOL Multiphysics;gas sensor;Electrodes;Analytical models;Sensitivity;Metallization;Simulation;Fingers;Capacitance|
|[A New Control strategy for Frequency Control using BESS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080259)|C. L. Prasanna; M. H. Latha; P. S. Lakshmi; A. S. L. K. Gopalamma|10.1109/ICONAT57137.2023.10080259|PI controller;Filter;Battery;Single area LFC;Time-frequency analysis;Generators;Steady-state;Batteries;Power systems;Time factors;Transient analysis|
|[Machine Learning Based Approach to Disinformation Detection Using Twitter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080790)|S. Yadav; C. Kumar|10.1109/ICONAT57137.2023.10080790|SMPs;NLP;BERT;W2V;Doc2Vec;TF-IDF;BoW;Sentiment analysis;Social networking (online);Blogs;Supervised learning;Machine learning;Feature extraction;Fake news|
|[Accurate Resistance Modeling of Cu-CNT ULSI Interconnects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080728)|A. Kumar|10.1109/ICONAT57137.2023.10080728|CNT;Cu-CNT;line resistance;on-chip interconnects;ULSI.;Resistance;Analytical models;Ultra large scale integration;Filling;Data models;System-on-chip|
|[Autonomous Drone Navigation using Monocular Camera and Light Weight Embedded System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080483)|R. H. Kumar; A. M. Vanjare; S. N. Omkar|10.1109/ICONAT57137.2023.10080483|Monocular vision;Autonomous navigation;Multi-threading;Midas;Program processors;Embedded systems;Laser radar;Navigation;Estimation;Cameras;Path planning|
|[Modeling and Simulation of Inverters for Electric Vehicle Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080144)|R. H; B. Bairwa; A. Banik; M. K. A|10.1109/ICONAT57137.2023.10080144|Electric;vehicle;ICE;Battery;Rectifier;Climate change;Pollution;Costs;Anxiety disorders;Transportation;Switches;Inverters|
|[Simulation-based Optimization for a Responsive, Resilient Supply Chain using Digital Twin and Usability Enhancement using NO-UI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080145)|K. Hanumanthaiah|10.1109/ICONAT57137.2023.10080145|Digital Twin;No-UI;Simulation;Optimization;Supply Chain Risk Management;SCRM;Supply Chain Digital Twin;Lead time Optimization;Human computer interaction;Supply chain management;Supply chains;Digital twins;Complexity theory;Usability;Optimization|
|[Design of Machine Learning-Based Malware Detection Methodologies in the Internet of Things Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080517)|P. V. Baviskar; G. Singh; V. N. Patil|10.1109/ICONAT57137.2023.10080517|Malware detection;feature extraction;machine learning;SVM;Random Forest;Multinomial Naive Bayes.;Performance evaluation;Smart TV;Machine learning;Forestry;Computer architecture;Logic gates;Malware|
|[An Evaluation of Techniques for Controlling and Monitoring Greenhouse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080566)|M. Ahirwar; R. N. Mahia|10.1109/ICONAT57137.2023.10080566|greenhouse;agriculture;intelligent control;hybrid control;fuzzy logic.;Temperature sensors;Mechanical sensors;Uncertainty;Green buildings;Heuristic algorithms;Crops;Humidity|
|[Design of Machine Learning-Based Malware Detection Techniques in Smartphone Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080819)|P. V. Baviskar; G. Singh; V. N. Patil|10.1109/ICONAT57137.2023.10080819|Malware detection;feature extraction;machine learning;SVM;Random Forest;Multinomial Naïve Bayes;Support vector machines;Radio frequency;Training;Supervised learning;Training data;Predictive models;Malware|
|[Design of Ultra High Frequency (UHF) based RFID Reader for Electronic Toll-gate application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080393)|A. M. Krishna; V. R; S. Srivastavas; N. Ranjan|10.1109/ICONAT57137.2023.10080393|RFID;UHF;RF Chip;Meters;Sensitivity;Voltage-controlled oscillators;Transmitting antennas;Receiving antennas;UHF measurements;UHF antennas|
|[Cyber Forensics Analysis of FPV-Non FPV Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080479)|A. Rathore; C. Kumar|10.1109/ICONAT57137.2023.10080479|Drones Forensics;Digital Forensics;FPV;Drugs;Electronic equipment;Forensics;Flight recording;Focusing;Streaming media;Feature extraction|
|[Development of Portable Tethered Vertical Profiler for Underwater Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080657)|S. L. Sequeira; E. S. Manish; Rakshith; P. Umesh; K. V. Gangadharan|10.1109/ICONAT57137.2023.10080657|Underwater vehicle;Open-source;Vertical Profiler;Water Quality;Temperature sensors;Ventilators;Ecosystems;Sea measurements;Water quality;Data collection;Water pollution|
|[Mathematical Modeling and Investigation on Faults In Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080232)|R. C. N; B. Bairwa; G. Raghavendra; M. K. A|10.1109/ICONAT57137.2023.10080232|Power system;faults;simulation;analysis;modeling;Analytical models;Power transmission lines;Software packages;Prototypes;Power transmission;Mathematical models;Circuit faults|
|[Deep Learning based Automated Wheat Disease Diagnosis System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080324)|P. R. Navale; Venkatesh; S. B. Basapur|10.1109/ICONAT57137.2023.10080324|Wheat disease;deep learning;vgg16;dataset;image processing;classification;segmentation;Deep learning;Visualization;Image recognition;Transfer learning;Crops;Forestry;Production facilities|
|[Optimal Scheduling of Residential Loads Using Binary Particle Swarm Optimization (BPSO) Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080137)|R. U. I. Disanayaka; K. T. M. U. Hemapala|10.1109/ICONAT57137.2023.10080137|Demand Side Management;Optimal Scheduling;Particle Swarm Optimization;Residential Loads;Time of Use;Home appliances;Costs;Tariffs;Power distribution;Optimal scheduling;Linear programming;Scheduling|
|[Smart Video Retrieval and Question Answering System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080460)|Likhith; M. Dubey; M. Dashora; V. Kusuma Shree; D. J. R|10.1109/ICONAT57137.2023.10080460|DistilBERT;RoBERTa;TF-IDF;RNN;CNN;Text Extraction;Semantic Index;Video on demand;Semantics;Computer architecture;Question answering (information retrieval);Indexing|
|[Axis Control of a Nonlinear Helicopter Model Using Intelligent Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080707)|A. Chaudhary|10.1109/ICONAT57137.2023.10080707|Air Vehicles;ANFIS;2-DOF Helicopter;intelligent controller;LQR;pitch angle;yaw angle;trajectory control;Helicopters;Rotors;Process control;Mathematical models;Trajectory;Time factors;Nonlinear systems|
|[A Novel Printed Monopole UWB Filtenna Using Semi-Circles and Square Patch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080646)|Vijay; L. Kumar|10.1109/ICONAT57137.2023.10080646|Transmission zero (TZ);Resonator;Ultra wideband (UWB) antenna;Microstrip Bandpass Filter (BPF);open triangular shape stub and Defected Ground Structure (DGS);Band-pass filters;Wireless communication;Microstrip filters;Resonator filters;Transmitting antennas;Microstrip antennas;Ultra wideband antennas|
|[Interpretable Lung Cancer Detection using Explainable AI Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080480)|M. S. Ahmed; K. N. Iqbal; M. G. R. Alam|10.1109/ICONAT57137.2023.10080480|Decision Tree;Logistic Regression;Explainable AI;Random Forest;SHAP;LIME;Deep learning;Atmospheric modeling;Lung cancer;Forestry;Predictive models;Naive Bayes methods;Reliability|
|[Pothole Detection System Using Object Detection through Dash Cam Video Feed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080856)|S. Sen; D. Chakraborty; B. Ghosh; B. D. Roy; K. Das; J. Anand; P. A. Maiti|10.1109/ICONAT57137.2023.10080856|Machine Learning;Image Processing;YOLO Algorithm;Deep Learning;Object Detection;Object Tracking;Convolutional Neural Network;CNN;Pothole;Road Safety;Training;Machine learning algorithms;Roads;Signal processing algorithms;Predictive models;Safety;Timing|
|[Broadband Proximity Coupled Antenna for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080545)|A. Gupta; V. K. Gupta; S. Bansal; A. Sharma|10.1109/ICONAT57137.2023.10080545|proximity coupled;IoT;wide bandwidth;V-shaped slot;parasitic radiator;Surface impedance;Surface waves;Bandwidth;Glass;Antenna feeds;Safety;Internet of Things|
|[Deep Learning in Early Prediction of Sepsis and Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080152)|S. K. Rout; B. Sahu; G. B. Regulwar; V. Kavididevi|10.1109/ICONAT57137.2023.10080152|Sepsis;SVM;LSTM;Infection;ICU patients;Machine Learning;Support vector machines;Deep learning;Training;Radio frequency;Recurrent neural networks;Pipelines;Predictive models|
|[PrePy - A Customize Library for Data Preprocessing in Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080134)|Z. Mundargi; S. Bhatti; A. Chandra; A. Kamble; B. Jiby; R. Arole|10.1109/ICONAT57137.2023.10080134|Data preprocessing;normalization;custom library;data cleaning;data extraction;Machine learning;Python;PCA;Z-Score;Support vector machines;Data preprocessing;Machine learning;Data science;Feature extraction;Libraries;Data models|
|[A Deep Learning Approach to Detect and Classification of Lung Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080801)|M. F. Khatun; M. R. Ajmain; M. Assaduzzaman|10.1109/ICONAT57137.2023.10080801|Lung Cancer;ResNet50;Deep Learning;Deep learning;Lung cancer;Lung;Benign tumors;Medical diagnostic imaging;Residual neural networks|
|[Optimum Tilt-Pitch Analysis and Design of 25kW Grid Connected Solar PV System using PVSyst Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080333)|T. Monu; S. Vadhera|10.1109/ICONAT57137.2023.10080333|Losses;PV Syst;Solar irradiation;Solar PV system;Renewable energy sources;Solar energy;Software;Performance analysis;Solar panels;System analysis and design;Optimization|
|[UI Design Method for Visualizing Sensor Data to Enhance User Understanding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080807)|M. Takeuchi; K. Yumita; R. Yoshioka|10.1109/ICONAT57137.2023.10080807|Human-Computer-Interaction;Knowledge Acquisition;Experience Design;Atmospheric measurements;Design methodology;Education;Data visualization;Switches;Particle measurements;Software|
|[A Comparative Study For Measuring The Quality of Dhaka City Transportation System: Survey Based](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080188)|M. A. R. Rejuan; S. S. Bandan; M. A. Rakib; M. Assaduzzaman|10.1109/ICONAT57137.2023.10080188|Bus Service;intercity Bus Service;Dhaka Bus Service;Comparative Study;Survey Analysis;Systematics;Urban areas;Sociology;Transportation;Internet;Statistics|
|[Car Sales Price Prediction using MLR, Random Forest and Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080025)|R. Chavare; R. Joshi; O. Wagh; A. Vaishale; A. Ingale|10.1109/ICONAT57137.2023.10080025|Regression;Multiple linear regression;Car details;prediction;Support vector machines;Training;Radio frequency;Linear regression;Prediction algorithms;Automobiles;Testing|
|[Bank Fixed Term Deposit analysis using Bayesian Logistic Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080695)|Z. K. Mundargi; A. Bodhankar; A. Mahajan; D. Prasad; S. Mahajan; R. Dhakalkar|10.1109/ICONAT57137.2023.10080695|Data Science;Bank Marketing;Fixed Term Deposit;Bayesian Logistic Regression;Machine Learning;Social networking (online);Insurance;Communication channels;Banking;Telephone sets;Bayes methods;History|
|[Study Of Electronic And Optical Properties Of Bulk And Monolayer Vanadium Di-Sulfide For Energy Storage Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080510)|A. K. Yadav; T. K. Jasil; S. K. Pandey|10.1109/ICONAT57137.2023.10080510|VS2;bulk;monolayer;DFT;band structure (key words);Crystal growth;Integrated optics;Performance evaluation;Temperature;Raman scattering;Crystals;Vanadium|
|[Sentiment Analysis of Online Customer Reviews for Handicraft Product using Machine Learning: A Case of Flipkart](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080169)|V. C. Kanakamedala; S. H. Singh; R. Talasani|10.1109/ICONAT57137.2023.10080169|online review;sentiment analysis;accuracy;Flipkart;Sentiment analysis;Machine learning algorithms;Databases;Support vector machine classification;Machine learning;Multilayer perceptrons;Classification algorithms|
|[Introduction of a New Tariff Structure for Electric Vehicle Owners in Sri Lanka to Promote Demand Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080559)|J. M. D. S. Jeewandara; R. U. I. Disanayaka; K. T. M. U. Hemapala; W. D. A. S. Wijayapala; I. D. Balahewa; A. U. Melagoda|10.1109/ICONAT57137.2023.10080559|Electric Vehicles;Time of Use Tariff;Residential Customers;Energy Consumption;Meters;Government;Tariffs;Transportation;Internal combustion engines;Electric vehicle charging;Demand response|
|[Face Mask and Body Temperature Scanning System for Covid-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080641)|S. Bamankar; P. Bhoir; S. Pednekar; G. Phadke|10.1109/ICONAT57137.2023.10080641|Covid-19;Face mask;CNN;MobileNet;Temperature sensors;COVID-19;Tensors;Semantics;Detectors;Streaming media;Feature extraction|
|[Development of Real Time Monitoring and Tracking System for Vehicles Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080069)|B. Bairwa; N. Vershini; V. K. Angadi; V. K. M|10.1109/ICONAT57137.2023.10080069|Tracking;Monitoring;System;Vehicle;GSM;Visualization;Java;Roads;Urban areas;Prototypes;Real-time systems|
|[Comparison of Different Machine Learning Algorithms for Predictive Maintenance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080334)|D. Dhanraj; A. Sharma; G. Kaur; S. Mishra; P. Naik; A. Singh|10.1109/ICONAT57137.2023.10080334|Predictive Maintenance;Machine Learning;Classification;Regression;Artificial Intelligence;Industries;Machine learning algorithms;Costs;Electric breakdown;Prediction algorithms;Manufacturing;Predictive maintenance|
|[Parameter Estimation of a Single Diode Model of a PV Panel using Gauss-Seidel Method: An Experimental Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080499)|S. S. Sakthivel; L. Gokulakrishnan; S. Arunachaleashwer; V. Lokesh; A. Venkadesan|10.1109/ICONAT57137.2023.10080499|Single Diode Model;PV System;Gauss-Seidel;parameters;Photovoltaic systems;Analytical models;Parameter estimation;Real-time systems;Hardware;Standards;Optimization|
|[Analysis and Optimization of Clustering-based Privacy Preservation using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080207)|C. N. Sowmyarani; L. G. Namya; G. K. Nidhi; P. Ramakanth Kumar|10.1109/ICONAT57137.2023.10080207|Data Anonymization;SAC Algorithm;Genetic Algorithm;Cost Of Degradation;k-Anonymity;Clustering;Deep learning;Data privacy;Machine learning algorithms;Clustering algorithms;Predictive models;Prediction algorithms;Information filtering|
|[An Aviation Industry Recommender System(AIRS) using K-nearest Neighbour and Cosine Similarity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080009)|Z. Mundargi; S. Mulay; D. Navale; V. Talnikar; A. Nawale; V. Sonkusale|10.1109/ICONAT57137.2023.10080009|K-Nearest Neighbour;Cosine Similarity;Label encoding;Pivot matrix;Machine Learning.;Atmospheric modeling;Data preprocessing;Quality of service;Predictive models;Prediction algorithms;Encoding;Safety|
|[An Enhanced Network Security using Machine Learning and Behavioral Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080157)|M. G. Haricharan; S. P. Govind; C. N. S. V. Kumar|10.1109/ICONAT57137.2023.10080157|Machine Learning;KDD;intrusion detection;neural network;support vector machine;feature selection;Eucledian;Support vector machines;Machine learning algorithms;Simulation;Supervised learning;Intrusion detection;Machine learning;Telecommunication traffic|
|[Ambient Air Quality Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080220)|K. Alekhya; P. D. Sravya; N. C. Naik; B. J. LakshmiNarayana|10.1109/ICONAT57137.2023.10080220|IoT;GSM Module;MQ135;MQ6;MQ2;MQ7;ATmega328P Microcontroller;Gases;Microcontrollers;Air pollution;Sensor systems;Real-time systems;Sensors;Pollution measurement|
|[PV System With Energy Monitoring to Enhance System Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080652)|M. Fulzele; S. Umathe|10.1109/ICONAT57137.2023.10080652|GUI;ACS712;MPPT;Wireless;Maximum power point trackers;Visualization;Cloud computing;Microcontrollers;Voltage;Time factors;Solar radiation|
|[Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual Text and Audio for Cultural Tourism Motivation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080862)|A. Chand; P. Agarwal; S. Sharma|10.1109/ICONAT57137.2023.10080862|Handwritten text recognition;Computer Vision;OCR;OpenCV;Tesseract;Google Text-to-Speech;googleTrans.;Handwriting recognition;Text recognition;Optical character recognition;Focusing;Production;Writing;Feature extraction|
|[Image Enhancement Technique for Underwater Image using Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080432)|A. Bedade; S. Kulkarni; B. Digey|10.1109/ICONAT57137.2023.10080432|underwater;hazy image;CLACHE;homomorphic filter;wavelet transform;Wavelet transforms;Image quality;Histograms;Statistical analysis;Image color analysis;Scattering;Wavelet analysis|
|[Breast Cancer Classification using CNN with Transfer Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080148)|C. R. Prasad; B. Arun; S. Amulya; P. Abboju; S. Kollem; S. Yalabaka|10.1109/ICONAT57137.2023.10080148|Breast cancer;CNN;transfer learning RESNET50;Inception V3;VGG19;Adam;SGDM;RMSProp;histopathological image;Analytical models;Transfer learning;Manuals;Breast cancer;Data models;Delays;Convolutional neural networks|
|[Skin Cancer Detection and Intensity Calculator using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080001)|A. Mankawade; A. Bodhankar; A. Mahajan; D. Prasad; S. Mahajan; R. Dhakalkar|10.1109/ICONAT57137.2023.10080001|Artificial Intelligence;Deep Neural Networks;Convolutional Neural Networks;Recurrent Neural Networks;Deep learning;Melanoma;Predictive models;Prediction algorithms;Skin;Classification algorithms;Convolutional neural networks|
|[Counterfeit Protection In Supplychain Using Blockchain: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080465)|M. A. Muzafar; A. Bhargav; A. Jha; P. Nand|10.1109/ICONAT57137.2023.10080465|Supply Chain;Blockchain;Hyperledger;Counterfeit;API etc;Distributed ledger;Supply chains;Buildings;Blockchains|
|[Estimation of Natural Frequency of Cantilever Beam using Harris Corner Detection Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080841)|L. Singh; A. Gupta|10.1109/ICONAT57137.2023.10080841|frequency estimation;computer vision;contactless measurement;digital image processing;Vibrations;Accelerometers;Sequences;Dynamics;Vibration measurement;Frequency estimation;Structural beams|
|[Empirical Wavelet Transform and Fano Equality Ratio Test-based Fault Detection Scheme for Inverter Dominated Autonomous Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080580)|A. Chandra; G. K. Singh; V. Pant|10.1109/ICONAT57137.2023.10080580|Empirical wavelet transform (EWT);Autonomous mode of operation (AMO);Fano equality ratio test (FERT);Distributed generation (DG);PV-system;Inverter-dominated autonomous microgrid;Wavelet transforms;Resistance;Fault diagnosis;Fault detection;Software algorithms;Microgrids;Inverters|
|[IoT Application using a Rectangular 2.4 GHz Microstrip Patch Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080448)|M. S. Rana; O. Islam; S. A. Shikha; M. Faisal|10.1109/ICONAT57137.2023.10080448|Internet of thing;wireless communication;Microstrip patch antenna;Wireless applications;Bandwidth;Wireless communication;Patch antennas;Transmitting antennas;Microstrip antennas;Bandwidth;Internet of Things;Microstrip|
|[Spotting the Power Quality Events Associated to Utility Distribution Network with Penetration of Wind Energy and Solar Energy Simultaneously](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080738)|S. Singh; A. Sharma; A. R. Garg; O. P. Mahela|10.1109/ICONAT57137.2023.10080738|Discrete Wavelet transform;Distribution network;Hilbert transform;Power quality event;Solar energy;Stockwell transform;Wind energy;Wind energy;Power quality;Solar energy;Transforms;Distribution networks;Software;Discrete wavelet transforms|
|[Animal Neuron Pattern Classification and Clustering using Self-Organizing Map (SOM) through Learning Rate and Minimal Cut](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080796)|P. S. Metkewar; P. R. Kaveri; A. Bokhare|10.1109/ICONAT57137.2023.10080796|Self-Organizing Map (SOM);learning rate;epoch;minimal cut;classification;Self-organizing feature maps;Animals;Databases;Computational modeling;Neurons;Pattern classification;Clustering algorithms|
|[One stride ahead Advancements in Dispersed Graph Coloring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080064)|P. S. Metkewar; A. Bokhare|10.1109/ICONAT57137.2023.10080064|Distributed symmetry breaking;maximal independent set;maximal matching;coloring;deterministic algorithms;randomized algorithms.;Industries;Target tracking;Message passing;Receivers;Organizations;Artificial neural networks;Data models|
|[Graphene Solar Photovoltaic Panel Assisted Electric Vehicle Charging Station: Design Aspect and Practical Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079968)|S. H. Mane; V. M. Panchade|10.1109/ICONAT57137.2023.10079968|Solar panel;MPPT controller PFC rectifier;Buck converter with PI controlled;PID controlled PLL(phase locked loop detector) EV battery;State-of-Charge(SOC);Battery Management System (BMS);Graphene Photovoltaic Pane (GPP)l.;Graphene;Transportation;Solar energy;Electric vehicle charging;Mathematical models;Data models;Batteries|
|[VLSI Implementation of Blanking Cover Pulse and CBCP module Propagation for External System Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080680)|R. Anurag; A. S. K. Nayani|10.1109/ICONAT57137.2023.10080680|Blanking Cover Pulse;Timing Parameters;Pulse Signals;ESM system;Input Trigger Pulses;Duty Cycle;Simulation;VHDL;Radar;Receivers;Very large scale integration;Blanking;Software;Hardware|
|[Energy Consumption Saving in 5G Network Based on Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080476)|D. Ghosh; S. H. Bharathi|10.1109/ICONAT57137.2023.10080476|5G;IIoT;3GPP;Energy Savings;PDCCH;Artificial Intelligence;Genetic Algorithm;Particle Swarm Optimization (PSO);Industries;Technological innovation;5G mobile communication;Urban areas;Machine learning;Energy efficiency;Telecommunications|
|[Design of Spy Robot with Wireless Night Vision Camera Using Android](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079953)|A. K. Bandani; A. Bollampally; S. Sahithi; R. Naik; N. Kumar; Goutham|10.1109/ICONAT57137.2023.10079953|Surveillance;Bluetooth;IoT Cloud;Night vision;Wireless communication;Bluetooth;Robot vision systems;Cameras;Software;Robots|
|[Classification of Human Activities using CNN with Principal Component Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080227)|C. R. Prasad; R. Bandi; D. Aashrith; A. Sampelly; M. S. Chand; S. Kollem|10.1109/ICONAT57137.2023.10080227|HAR;CNN model;Principal Component Analysis;Classification;Accuracy;Dimensionality reduction;Analytical models;Neural networks;Classification algorithms;Behavioral sciences;Convolutional neural networks;Object recognition|
|[Language Identification System: Employing ReLu for India’s Regional Languages (ReLu)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080570)|L. Saini|10.1109/ICONAT57137.2023.10080570|HMM;naïve Bayes;vectorization;Training;Correlation;Soft sensors;Neurons;Sociology;Hidden Markov models;Data models|
|[Automatic Boot Spray Machine: A Preventive Measure Against Corona Virus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080498)|D. Roy; M. Hossam-E-Haider|10.1109/ICONAT57137.2023.10080498|automatic boot spray machine;obstacle sensor;P-N-P transistor;COVID-19;Meters;Sensitivity;Surface contamination;Measurement uncertainty;Nose;Mouth|
|[Object Tracking Based on Background Subtraction and Kalman Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080170)|D. Roy; M. Hossam-E-Haider|10.1109/ICONAT57137.2023.10080170|object detection;object tracking;prediction;correction;background subtraction;kalman filter;Performance evaluation;Surveillance;Object detection;Streaming media;Real-time systems;Kalman filters;Object tracking|
|[Flying Squirrel Search Optimization based MPPT Controller for Stand-Alone PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080716)|A. Avasthi; R. Garg; P. Mahajan|10.1109/ICONAT57137.2023.10080716|solar photovoltaic;MPPT controller;fuzzy logic controller;FSSO;renewable Energy;Photovoltaic systems;Fuzzy logic;Radiation effects;Uncertainty;Real-time systems;Steady-state;Transient analysis|
|[Computational Time Complexity for Sorting Algorithm amalgamated with Quantum Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080217)|A. Dutta; J. Harshith; Y. Soni; A. Gupta; V. K. Gupta; A. Gupta|10.1109/ICONAT57137.2023.10080217|Quantum Sort;Gauss Error Function;Logarithmic Integral;Grover’s Search;Quantum computing;Upper bound;Quantum entanglement;Interference;Real-time systems;Time complexity;Informatics|
|[A Case Study:Spectrum Efficient Multiple Access Techniques for 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079976)|S. V. Patil; S. Kore; T. K. Harhare|10.1109/ICONAT57137.2023.10079976|Spectrum Efficiency;OMA;NOMA;NOMA;5G mobile communication;Simulation;Quality of service;Receivers;Media Access Protocol;Energy efficiency|
|[Detection and Estimation of Tree Canopy using Deep Learning and Sensor Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080785)|S. S.Patil; Y. M. Patil; S. B. Patil|10.1109/ICONAT57137.2023.10080785|Precision agriculture;Deep learning;Sensor fusion;Canopy detection & Estimation.;Deep learning;Training;Laser radar;Spraying;Estimation;Vegetation;Production|
|[A Survey on Improving Power System Dynamic Stability with a Single Machine Infinite Bus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080028)|Y. Kirange; P. Nema|10.1109/ICONAT57137.2023.10080028|Power System Stability (PSS);Single Machine Infinite Bus (SMIB);Conventional Power System Stabilizer(CPSS);Genetic Algorithm (GA);Heffron-Philips Model;Particle Swarm Optimization(PSO.;Uncertainty;Stability criteria;Power system dynamics;Power system stability;Generators;Power system reliability;Transient analysis|
|[Design and Implementation of 4-QAM Transceiver on FPGA using MATLAB/SIMULINK Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080452)|R. Muzammil|10.1109/ICONAT57137.2023.10080452|QAM;FEC;FPGA;ARM;Model-Based Design;Codes;VHDL;Forward error correction;Mathematical models;Transceivers;Encoding;Integrated circuit modeling|
|[Container-based Migration Technique for Fog Computing Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080771)|A. Bhardwaj; U. Gupta; I. Budhiraja; R. Chaudhary|10.1109/ICONAT57137.2023.10080771|Fog computing;Virtualization;Container migration;LXD.;Computer architecture;Maintenance engineering;Market research;Load management;Hardware;Energy efficiency;Delays|
|[Microgrid Design For EV Fast Charging Station Using Interleaved DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080139)|S. Vendoti; K. A. Swami; S. K. Govindaraju; D. V. S. S. Varma|10.1109/ICONAT57137.2023.10080139|Electric vehicles (EVs);Charging station;PV Solar System;Wind Energy System;MPPT Technique;Interleaved DC-DC Converter;Maximum power point trackers;Renewable energy sources;Wind energy;Simulation;DC-DC power converters;Microgrids;Electric vehicle charging|
|[Analytical Review of Machine Learning Techniques for Transmission Line Insulator Fault Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080667)|P. Yerunkar; P. Ramtekkar|10.1109/ICONAT57137.2023.10080667|Deep Leaning;Object Detection;Convolution Neural Network (CNN);Insulator;Fault Detection;Fault diagnosis;Analytical models;Power transmission lines;Image processing;Fault detection;Neural networks;Machine learning|
|[Cooja Simulator and Wireshark Traffic Capturing Are Used To Analyse User Datagram Protocol Communication For Low Power And Lossy Networks in IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080778)|A. Behal; J. K. Sandhu; G. Gupta|10.1109/ICONAT57137.2023.10080778|UDP;RPL;IoT;Spread spectrum communication;Routing;Routing protocols;Servers;Internet of Things;Task analysis;Standards|
|[Performance Evaluation of Automatic Suspicious Activity Detection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080627)|S. S. Gurav; V. V. Khandare|10.1109/ICONAT57137.2023.10080627|Surveillance monitoring;multiple features;suspicious activity;performance.;Performance evaluation;Machine learning algorithms;Automation;Surveillance;Machine learning;Feature extraction;Complexity theory|
|[Internet of Things based Rainwater Harvesting and Distribution Management System through Mobile Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080094)|A. Gomes; A. Shetty; C. Wilson; V. Sravani|10.1109/ICONAT57137.2023.10080094|scarcity of water;rainwater harvesting;sensors;Internet of Things;mobile application;Cloud computing;Buildings;Valves;Reservoirs;Information filters;Software;Hardware|
|[Learning Analytics Powered Teacher Facing Dashboard to Visualize, Analyze Students’ Academic Performance and give Key DL(Deep Learning) Supported Key Recommendations for Performance Improvement.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080832)|K. V. Deshpande; S. Asbe; A. Lugade; Y. More; D. Bhalerao; A. Partudkar|10.1109/ICONAT57137.2023.10080832|teacher facing dashboard;learning analytics;students;teachers;visualize;analyze;education field;machine learning;deep learning.;Deep learning;COVID-19;Government;Education;Data visualization|
|[AMulti-Stage Cloud Security for Cloud Datausing Amalgamate Data Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080583)|E. R. Kumar; S. S. S. Reddy; M. B. Reddy|10.1109/ICONAT57137.2023.10080583|Compound Encryption Based Algorithm;Compound Secure Storage;IDS;Cloud Security;Cloud computing;Cloud computing security;Merging;Encryption;Planning;Security;Servers|
|[Bankruptcy Prediction Using Machine Learning: A New Technological Approach to Prevent Corporate Bankruptcy Through Well Deployed Streamlit Based Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080089)|M. More; R. Panda; B. Bandgar; M. More|10.1109/ICONAT57137.2023.10080089|Machine learning(ML);model deployment;model building;SVM;KNN;Naive Bayes;CART;model accuracy;Bankruptcy;Support vector machines;Error analysis;Support vector machine classification;Machine learning;Organizations;Predictive models;Libraries|
|[Adaptive Cooling System for CVT of All Terrain Vehicles with Wireless Data Acquisition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080733)|A. Patil; P. Salke; O. Sangrulkar; M. Mitkari; M. V. Kulkarni|10.1109/ICONAT57137.2023.10080733|CVT;Gear Ratio;MCU;Heating systems;Wireless communication;Temperature distribution;Adaptive systems;Cooling;Gears;Pulleys|
|[An Exploration on Competent Video Processing Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080628)|P. R. P. Tammana; B. Penumutchi|10.1109/ICONAT57137.2023.10080628|Xilinx Vivado Video processing and Image Processing architecture;Xilinx System Generator;Matlab/Simulink;IIR filter;Measurement;Power demand;IIR filters;Computer architecture;Streaming media;Mathematical models;Software|
|[A Novel Approach to Record and Narrate the Summary of Conversation for Alzheimer Patient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080525)|M. H. S; S. Acharya; K. Suhas; S. Surya; T. Kumar P|10.1109/ICONAT57137.2023.10080525|Alzheimer;Dementia;application;Face detection;Summary;Speech recognition.;Oral communication;Assistive technologies;Face detection;Alzheimer's disease;Business|
|[Attacks on Social Media Networks and Prevention Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080106)|P. Pal; S. Ghosh; N. Kar|10.1109/ICONAT57137.2023.10080106|Social Media Networks;Categorisation of Social Media Attacks;Prevention Mechanisms;Privacy;Social networking (online);Computer hacking;Hazards;Security;Cyberattack|
|[Transformer Differential Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079990)|C. P. P. Cárdenas; E. A. G. González; F. A. Q. Palomeque|10.1109/ICONAT57137.2023.10079990|Differential Protection;Transformer;Schemes;Overcurrent Relays;Performance evaluation;Measurement errors;Magnetization;Software;Steady-state;Power transformers;Tap changers|
|[User Recognition Via Facial Parameters With Occlusion Using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080688)|M. D. Narlawar; R. D. J. D Pete|10.1109/ICONAT57137.2023.10080688|Convolutional Neural Network (CNN);Automated Teller Machines (ATMs);Machine Learning;Epochs (Iterations);Measurement;Face recognition;Surveillance;Neural networks;Authentication;Fraud;Convolutional neural networks|
|[Recognizing Face Features using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080632)|D. J. Sundaram; G. H. Sai; T. P. Reddy; C. U. Kumari|10.1109/ICONAT57137.2023.10080632|Convolutional Neural Networks (CNN);Facial shape;Skin tone;Expressions;SVM;Shape;Face recognition;Heuristic algorithms;Mouth;Nose;Feature extraction;Skin|
|[Frequency Control of Two Region Two-Unit Systems with PDF + (1+PI) Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079957)|A. Biswal; P. Dwivedi; S. Bose|10.1109/ICONAT57137.2023.10079957|Differential Evolution (DE) algorithm;Proportional Integral Derivative (PID);Tilted Integral Derivative (TID);Tilted Integral Derivative with Filter (TIDF);Power demand;Heuristic algorithms;Power system dynamics;Filtering algorithms;Load management;Complexity theory;Time factors|
|[Speech Emotion Recognition System using SVD algorithm with HMM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080093)|D. Sharma; A. P. Cheema; K. Koushik Reddy; C. Kusalanatha Reddy; G. Badri Ram; G. Avinash; P. K. Reddy|10.1109/ICONAT57137.2023.10080093|Hidden Markov model(HMM);Modelling;MATLAB;feature extraction;feature matching;classification;databases;Emotion recognition;Databases;Hidden Markov models;Speech recognition;Artificial neural networks;Feature extraction;Mathematical models|
|[Improved Security for Cloud Storage Using Elgamal Algorithms Authentication Key Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080619)|K. L. Narayanan; R. Naresh|10.1109/ICONAT57137.2023.10080619|key validation;cloud computing;cloud storage;security and privacy.;Cloud computing;Privacy;Data centers;Costs;Authentication;Encryption;Indexes|
|[Novel Nine-level Inverter Topology With Boosting Ability for Electric Vehicle Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080040)|K. Aditya; Y. Suresh; B. S. Naik; B. N. Rao; E. Karunakaran; R. D. Reddy|10.1109/ICONAT57137.2023.10080040|Electric Vehicle;self balancing;total harmonic distortion (THD);multilevel inverter;switched capacitor;Capacitors;Voltage;Switches;Pulse width modulation;Electric vehicles;Boosting;Multilevel inverters|
|[Comparison and Loss Analysis of Efficient Optical Routers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080535)|A. Sutar; S. Gaikwad; R. Bhadani; A. Dere; R. Domb; S. Malve|10.1109/ICONAT57137.2023.10080535|MRR;Optical routers;Optical network on chip;insertion loss (IL);Integrated optics;Optical losses;Optical interconnections;Power demand;Optical design;Multiprocessor interconnection;Random access memory|
|[Identifying Ship Types Using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080431)|S. Kantepalli; B. E. Reddy|10.1109/ICONAT57137.2023.10080431|Ship Detection;Deep Learning;Intelligent Transportation System;Deep learning;Training;Sociology;Transportation;Marine vehicles;Statistics|
|[CNN-Based Model for the HTTP Flood Attack Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080698)|P. Shorubiga; R. Shyam|10.1109/ICONAT57137.2023.10080698|Distributed Denial of Service;HTTP Flooding attack;1-D CNN;Layer seven DDoS attack;Training;Deep learning;Benchmark testing;Denial-of-service attack;Data models;Floods;Convolutional neural networks|
|[Cost-Effective and Retrofit Solution Control Algorithm of Renewable Fed DC Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080690)|B. K. R; S. Thambi; K. P|10.1109/ICONAT57137.2023.10080690|DC microgrid;Dual window;droop control;supercapacitor;ESS;DC bus signaling;piecewise droop;Renewable energy sources;Surge protection;Microgrids;Supercapacitors;Discharges (electric);Batteries;Wind turbines|
|[Solution of Reactive Power Dispatch problems using Enhanced Dwarf Mongoose Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080012)|B. K. Dora; S. Bhat; S. Halder; M. Sahoo|10.1109/ICONAT57137.2023.10080012|Dwarf Mongoose Optimization Algorithm;reactive power dispatch;symbiotic organism search;Metaheuristic Algorithm;Symbiosis;Reactive power;Statistical analysis;Metaheuristics;Voltage;Power system stability;Organisms|
|[Continuous Time LQR Based Position Control for an Electro-Hydraulic Actuation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080605)|A. K. Kumawat; R. Kumawat; R. Rout; M. Rawat|10.1109/ICONAT57137.2023.10080605|linear quadratic regulator;position control;proportional directional control valve;electro-hydraulic actuator.;Actuators;Mechatronics;Simulation;Process control;Position control;Valves;Mathematical models|
|[Speed Control of BLDC Motor Using PI Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080074)|B. Bairwa; M. Murari; M. Shahapur; K. M. R; M. F. Khan|10.1109/ICONAT57137.2023.10080074|BLDC;FPGA;PWM;PI Controller;PI control;Torque;Brushless DC motors;Service robots;Simulation;Velocity control;Permanent magnet motors|
|[Analytical Approach of EM Wave Propagation in Different Stacked Materials Encapsulated by Rectangular Waveguide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080779)|Sulistyaningsih; U. Khayam; A. Munir|10.1109/ICONAT57137.2023.10080779|electromagnetics (EM) wave;material;rectangular waveguide;reflection coefficient;transmission coefficient;wave propagation.;Correlation;Electromagnetic scattering;Rectangular waveguides;Conductivity;Reflection;Permeability;Reflection coefficient|
|[Deep Learning Model for Facial Kinship Verification Using Childhood Images*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080273)|M. Oruganti; T. Meenpal; S. Majumdar; S. R. N. Kusu|10.1109/ICONAT57137.2023.10080273|Facial images;Kinship verification;deep neural network;binary classification;parent-child images;childhood images;Deep learning;Computer vision;Art;Image databases;Neural networks;Feature extraction;Task analysis|
|[State of Charge Estimation of Lithium-ion Batteries for Electric Vehicle.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080458)|M. M. Naik; S. Koraddi; A. B. Raju|10.1109/ICONAT57137.2023.10080458|SOC;Battery Modelling;EKF;Filtering;Software packages;Battery management systems;Predictive models;Prediction algorithms;Mathematical models;State of charge|
|[Highly Sensitive Perovskite based Refractometric Sensor for Nitrogen Dioxide Gas Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080098)|K. K. Rana; A. Mishra; T. Srinivas|10.1109/ICONAT57137.2023.10080098|Refractive Index Sensor;Ring Resonator;Perovskite;Nitrogen Dioxide;Evanescent Field Ratio (EFR);Waveguide transitions;Sensitivity;Refractive index;Conductivity;Perovskites;Silicon;Sensors|
|[Computation Offloading in Mobile Edge Computing for Next Generation Networks: A deep reinforcement learning approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080439)|S. Dhiman; A. Chauhan; S. Kasuhal; H. Kumar|10.1109/ICONAT57137.2023.10080439|MEC;UAV;Computation Offloading;DDPG.;Training;Multi-access edge computing;Processor scheduling;5G mobile communication;Surgery;Reinforcement learning;Linear programming|
|[Stationary Human Target Detection Based on Millimeter Wave Radar in Complex Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080760)|Q. Li; M. Yang; Z. Wang; H. Chen|10.1109/ICONAT57137.2023.10080760|millimeter wave radar;fretting phase variation characteristics;multiple stationary human target;accurate location;Heart beat;Radar detection;Object detection;Millimeter wave radar;Distance measurement|
|[Healthcare Diagnostics Service Using Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080053)|K. Pereira; A. Parikh; P. Kumar; K. Devadkar|10.1109/ICONAT57137.2023.10080053|Federated Learning;Socket Programming;Neural Networks;Flower Client and Server;Training;Data privacy;Federated learning;Sockets;Transfer learning;Lung;Medical services|
|[Design of a New Single-Phase 15-Level Inverter with Minimized Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080215)|B. N. Rao; Y. Suresh; B. S. Naik; K. Aditya; E. Karunakaran; M. V. Kumar|10.1109/ICONAT57137.2023.10080215|Inverter;Multilevel Converter;Transformer;Power Quality;Renewable energy sources;Simulation;Voltage;Switches;Transformers;Multilevel inverters;Topology|
|[A New Algorithm for Automated LSS Targets’ Activities Measurement Based on a 24GHz CW RF Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080042)|D. Jayakumar; H. C. Kumawat; A. A. B. Raj|10.1109/ICONAT57137.2023.10080042|m-Doppler;Short Time Fourier Transform;Spectrogram;Wavelet Transform;Scalogram;Neighbouring Strength Value.;Radio frequency;Time-frequency analysis;Image resolution;Radar cross-sections;Software algorithms;Radar imaging;Software|
|[An Algorithmic Approach for Text Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080575)|A. Joshi; P. More; S. Shah; M. A. Sahitya|10.1109/ICONAT57137.2023.10080575|text;summarization;abstractive;extractive;paragraph;language;processing;Costs;Semantics;Personal digital devices;Syntactics;Software;Data mining;Task analysis|
|[Early Prediction of Maternal Health Risk Factors Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080700)|M. Assaduzzaman; A. A. Mamun; M. Z. Hasan|10.1109/ICONAT57137.2023.10080700|Random Forest;Maternal Health;Risk Factors;Machine Learning;Classification;Pregnancy;Pediatrics;Machine learning algorithms;Transportation;Medical services;Predictive models;Prediction algorithms|
|[Simulation and Analysis of MPPT-Based Solar Power Plant in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080445)|A. Aziz; S. Sazid; M. T. MuksituzzamanSSS; M. N. I. Rimon|10.1109/ICONAT57137.2023.10080445|MPPT controller;PV Panel;Constant Voltage;RETscreen;Inverter;Renewable energy sources;Analytical models;Costs;Buck converters;Voltage;Inverters;Software|
|[An Enhanced Student Attendance Monitoring System at Low Resolution and Various Angled Face Positions based on LBPH and CLAHE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080356)|M. Ramasane; S. Nadaf; K. Shah; P. Ramdasi; A. Golande|10.1109/ICONAT57137.2023.10080356|Computer Vision;Face Recognition;Feature Extraction;Image Processing;Low Resolution;LBPH;Training;Image resolution;Filtering;Databases;Face recognition;Noise reduction;Lightning|
|[A Comparative Study on Effects of Shading on a Solar Photovoltaic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080679)|A. K. Sahu; S. Gupta|10.1109/ICONAT57137.2023.10080679|Solar irradiance;partial shading;PV array;bypass diode;MPP;Sea surface;Temperature;System performance;Sociology;Solar panels;Solar heating;Statistics|
|[Dual Intercolumn Tied Configuration for Maximum Power Enhancement of Solar PV Array under Non-homogenous Solar Irradiations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079969)|T. A. T. Kambo; A. Mohapatra; B. Nayak; C. Saiprakash; S. Samal; P. Samal|10.1109/ICONAT57137.2023.10079969|Dual Intercolumn Tied;Partial Shading Conditions;Solar Photovoltaic;Global Maximum PowerPoint;Configurations;Shading Loss;Mismatch power Loss;Wiring;Photovoltaic systems;Renewable energy sources;Radiation effects;Software packages;Layout;Solar energy|
|[Analysis of Low-Delay in 64-bit Vedic multiplier based MAC unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080612)|E. V. Babu; S. Talasila; N. Divya; S. S. Deva; C. Vani|10.1109/ICONAT57137.2023.10080612|Delay;MAC unit;Verilog code;Xilinx tool;Modelsim;Program processors;Codes;Logic gates;Delays;Hardware design languages;Adders;Standards|
|[Performance Analysis of MIMO ACO OFDM VLC System under Ambient Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080631)|J. Pradhan; K. V. Kiran; S. K. Das|10.1109/ICONAT57137.2023.10080631|ACO OFDM;BER;MIMO;OOK;VLC;Wireless communication;Analytical models;OFDM;Interference;Hardware;Data models;Security|
|[Hate Speech Detection Network Using LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080786)|C. Lala; P. Dwivedi|10.1109/ICONAT57137.2023.10080786|LSTM;RNN;CNN;Natural Language Processing;Deep Learning;Training;Video games;Social networking (online);Blogs;Hate speech;Predictive models;Transformers|
|[Optimal Load Shedding Allocation for Preserving Frequency Stability in a Large-Scale PV Integrated Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080349)|F. Hossain; A. Jawad; Nahid-Al-Masood|10.1109/ICONAT57137.2023.10080349|solar photovoltaic systems;load shedding;renewable energy penetration;particle swarm optimization;frequency response;Photovoltaic systems;Measurement;Stability criteria;Load shedding;Power system stability;Generators;Frequency response|
|[ShEnc: A Versatile Secure Multi-Party Data Sharing Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080762)|Y. Namiki; A. Nakamura|10.1109/ICONAT57137.2023.10080762|public key cryptography;end-to-end encryption;multi-party data sharing;Performance evaluation;Communication channels;Fingerprint recognition;Elliptic curve cryptography;Encryption;Internet;Servers|
|[Biometric as Secure Authentication for Virtual Reality Environment: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080713)|C. H. Heruatmadja; Meyliana; A. N. Hidayanto; H. Prabowo|10.1109/ICONAT57137.2023.10080713|behavioral biometric;physiological biometric;authentication;virtual reality;security;Support vector machines;Solid modeling;Biometrics (access control);Biological system modeling;Veins;Authentication;Virtual reality|
|[Interactive Educational Device for the Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080314)|V. Agarwal; V. Keertana; I. Krishna; M. S. P; P. Y. J|10.1109/ICONAT57137.2023.10080314|Braille cell;micro servo;refreshable braille display;text to braille;braille keyboard;Braille;Costs;Keyboards;Blindness;Data models|
|[Design of a Novel Charge Pump based Current Starved Ring Oscillator with Reduced Phase Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080315)|M. Z. Jahangir; C. S. Paidimarry|10.1109/ICONAT57137.2023.10080315|Ring Oscillator;ADPLL;Charge pump;Phase Noise;Current-Starved Ring VCO;Ring oscillators;Phase noise;Semiconductor device modeling;Charge pumps;Voltage-controlled oscillators;CMOS technology;Integrated circuit modeling|
|[Wikipedia Vandalism Predictor Using Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080000)|M. Abhyankar; Y. Brid; P. Nair; S. Dholay|10.1109/ICONAT57137.2023.10080000|wikipedia vandalism;senitment analysis;bert model;Support vector machines;Analytical models;Social networking (online);Encyclopedias;Predictive models;Probability;Data models|
|[Bullet Hole Detection in a Military Domain Using Mask R-CNN and ResNet-50](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080859)|T. Ahmed; S. Rahman; A. A. Mahmud; M. A. Razzak; D. N. Sharmin|10.1109/ICONAT57137.2023.10080859|deep learning;machine learning;computer vision;bullet hole detection;Deep learning;Training;Measurement;Image segmentation;Rain;Weapons;Wind speed|
|[Inverse Design of SiN based Wavelength Demultiplexer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080706)|P. Kumar; F. Mihret; A. Mishra; E. S. Shivaleela; T. Srinivas|10.1109/ICONAT57137.2023.10080706|Inverse Design;WDM;SiN;Optimized Photonics;Performance evaluation;Inverse problems;Channel spacing;Crosstalk;Silicon nitride;Photonics|
|[Classification of the Speech Signal of Parkinson's Patient using Optimized Ensemble Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080133)|S. K. Mohapatra; R. Ram; S. Alamuru; M. N. Mohanty|10.1109/ICONAT57137.2023.10080133|Parkinson's;Speech;Classification;Stacked;Ensemble;Gradient Boosting;Support vector machines;Legged locomotion;Deep learning;Parkinson's disease;Neural networks;Boosting;Recording|
|[Descriptive Analysis of the Cloud Computing Services and Deployment Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080749)|T. Kaur; S. Kamboj|10.1109/ICONAT57137.2023.10080749|Cloud computing;evolution;deployment and service model;security issues.;Productivity;Cloud computing;Technological innovation;Databases;Computational modeling;Virtual environments;Parallel processing|
|[DaNI – The Fire Extinguisher Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080331)|P. S. Raju; C. Sindhu; C. Ajay; B. Srikanth|10.1109/ICONAT57137.2023.10080331|firefighting robot;flame sensor;DaNI(sbRIO);LabVIEW;obstacle avoidance;Fire extinguishers;Embedded systems;Service robots;Fires;Robot sensing systems;Real-time systems;Collision avoidance|
|[Layout Design and Analysis of UWB Gaussian Pulse Transmitter Using 90nm CMOS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080033)|M. A. Islam; M. Ariful; A. S. Jasir; A. A. Pratyay|10.1109/ICONAT57137.2023.10080033|BPSK modulator;Gaussian 2nd order pulse generator;UWB transmitter;Voltage Controlled Ring Oscillator.;Ring oscillators;Frequency modulation;Power demand;Transmitters;Layout;Binary phase shift keying;Transistors|
|[Layered Division Multiplexing in 5G NR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080542)|J. Shanbhag; S. Malarvizhi; S. Krithiga; S. V. Singh; R. Pushilal; S. S. Sinha; S. Mukherjee|10.1109/ICONAT57137.2023.10080542|Layered division multiplexing;5G;multi-layer;signal;modulation;power levels (key words);Radio frequency;System performance;Simulation;Receivers;Mathematical models;Robustness;Data communication|
|[Smart Public Transport Disinfection and Sterilization System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080107)|A. Mohammed; G. Sailaja; G. R. Kiran; K. Sasidhar; M. Sajid; B. Mariyam|10.1109/ICONAT57137.2023.10080107|Disinfection and sterilisation process;Mistspraying chemicals and illuminating with UV-Light;Thermoelectric-Peltier modules;HEPAfilter;Heating systems;Technological innovation;Costs;Microcontrollers;Vents;Organizations;Regulation|
|[A Graph Theory Based Post-Event Degraded State Resiliency Index for Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080825)|R. Choudhuri; D. Roy; M. Mandal; D. Bose; A. Paul; C. K. Chanda|10.1109/ICONAT57137.2023.10080825|Power system resilience;resilience index;graph theory;eigenvector centrality;cyber-attack;Complex networks;IEEE Standards;Graph theory;Power systems;Indexes;Cyberattack;Resilience|
|[Vision-based Driver’s Seat Belt Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080147)|J. Madake; S. Yadav; S. Singh; S. Bhatlawande; S. Shilaskar|10.1109/ICONAT57137.2023.10080147|BRIEF descriptor;Canny edge detector;FAST key point detector;Seatbelt detection;Location awareness;Heuristic algorithms;Image edge detection;Lighting;Pareto optimization;Feature extraction;Robustness|
|[CHESS AI: Machine learning and Minimax based Chess Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080746)|J. Madake; C. Deotale; G. Charde; S. Bhatlawande|10.1109/ICONAT57137.2023.10080746|Chess engine;Machine learning;Minimax;Alpha-Beta pruning;Machine learning algorithms;Law;Machine learning;Probability;Hardware;Engines|
|[Low-Cost Wearable Device to Provide an Effective Solution for Contact Tracing and Logistics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080852)|V. Joseph; S. K. S; S. Haridas|10.1109/ICONAT57137.2023.10080852|contact tracing;logistics;privacy;wearable band;pandemic;COVID-19;Privacy;Pandemics;Wearable computers;Human factors;Social factors;Task analysis|
|[Multi-Lingual Hybrid Chatbot for Empowering Rural Women Self-Help Groups in India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080347)|S. Anand; M. Karthikeya; A. M. Abhishek Sai; O. Balamurali|10.1109/ICONAT57137.2023.10080347|Chatbots;women’s community;neural network;CNN;rule-based;mobile application;Customer services;User interfaces;Chatbots;Software|
|[Safety Helmet Detection: A Comparative Analysis Using YOLOv4, YOLOv5, and YOLOv7](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080723)|S. Chourasia; R. Bhojane; L. Heda|10.1109/ICONAT57137.2023.10080723|Human Detection;Object detection;YOLOv4;YOLOv5;YOLOv7;Analytical models;Image segmentation;Head;Object detection;Manuals;Inspection;Real-time systems|
|[Early Detection of Glaucoma using CNN Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080853)|A. Balpande; D. Moundekar; R. Kothari; A. Ashtikar; D. D. M. K. Shelke|10.1109/ICONAT57137.2023.10080853|optic cup;cup to disk ratio;glaucoma;optic disk;segmentation;Optical fibers;Integrated optics;Measurement;Training;Image segmentation;Biomedical optical imaging;Computer architecture|
|[Dog Breed Classification using Inception-ResNet-V2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080065)|S. Manivannan; N. Venkateswaran|10.1109/ICONAT57137.2023.10080065|Dog breed classification;deep learning;artificial intelligence;Inception-ResNet-V2;CNN architecture;Training;Transfer learning;Dogs;Predictive models;Robustness;Artificial intelligence;Stress|
|[Arm-Volume Measurement for Early Lymphedema Detection using Kinect Sensor in Post-Surgical Oncology Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079948)|S. Noble; R. K. Pathinarupothi; G. Uma; D. K. Vijaykumar|10.1109/ICONAT57137.2023.10079948|Kinect IR Sensor;Lymphedema;voxelization;lymph nodes;Legged locomotion;Pathology;Volume measurement;Image processing;Medical treatment;Oncology;Infrared sensors|
|[Fixed Frequency Current Control-based Boost Rectifier Analysis for Single Phase Solid State Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080301)|D. Patel; A. Desai; N. Chothani|10.1109/ICONAT57137.2023.10080301|Solid State Transformer (SST);Boost Rectifier;Fixed Frequency Current Control;Control Strategy;Bridge Topology;Current control;Switching frequency;Rectifiers;Switching loss;Switches;Oil insulation;Transformers|
|[Assessment of Moisture Content in XLPE Insulation of Power Cable Using Dissipation Factor Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080804)|A. Jamshed; N. Haque|10.1109/ICONAT57137.2023.10080804|Frequency domain spectroscopy;XPLE;Dissipation factor;impedance analyzer;Temperature measurement;Spectroscopy;Polyethylene;Power measurement;Power cables;Moisture measurement;Frequency-domain analysis|
|[A Programmable,Multimode Operational 3U-VPX Based Digital Transceiver & Processing Module For CIT-MKXIIA IFF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080650)|N. K. Singh; C. Fouziya; V. Kumar; T. Venkatamuni|10.1109/ICONAT57137.2023.10080650|Identification of friend and foe (IFF);Interrogator and transponder (CIT);Digital Transceiver;RF ADC and DAC;Mode S;Mode 5 and SoC;Minimum shift keying (MSK);Signal processing;Data processing;Transceivers;Hardware;Delays;Transponders;Synchronization|
|[Data security and Integrity in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080440)|M. Z. Hasan; M. Z. Hussain; Z. Mubarak; A. A. Siddiqui; A. M. Qureshi; I. Ismail|10.1109/ICONAT57137.2023.10080440|Cloud Security;Data Security;Data Integrity;Data Protection;Cloud Computing;Information Technology;Trade Secrets;Shared Technology;Industries;Costs;Firewalls (computing);Data security;Data integrity;Cloud computing security;Data protection|
|[SVM-based Abnormal Operation Identification Method for Electric Heating Collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079996)|A. Liang; S. Zeng; Y. Ding; X. Li; F. He; Q. Li|10.1109/ICONAT57137.2023.10079996|Electric heating;synergy;SVM;Recognition of anomalies;Data driven;Fault diagnosis;Support vector machines;Photovoltaic systems;Heat pumps;Collaboration;Data models;Stability analysis|
|[Open Dots: Securely Connecting Like-Minded People Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080277)|P. Rachana; B. Rajlakshmi; P. V. Ajay; G. A. Achuth; K. Ranganath|10.1109/ICONAT57137.2023.10080277|interest;suggest;culture;practice;face to face;Semantic Web;Machine learning algorithms;Machine learning;Predictive models;Streaming media;Blockchains;Web sites|
|[A Feed-Forward and Back Propagation Neural Network Approach for Identifying Network Anomalies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080784)|A. Prashanthi; R. R. Reddy|10.1109/ICONAT57137.2023.10080784|network anomaly detection system (NADS);CICIDS2017;deep learning;convolution neural network (CNN);Backpropagation;Industries;Deep learning;Smart buildings;Neural networks;Sociology;Security|
|[Sensorless Control of a BLDC Motor using Back-EMF Detection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079963)|D. S. Sawant; Y. S. Rao; R. R. Sawant|10.1109/ICONAT57137.2023.10079963|Back-EMF;ZCD;Brushless DC Motor;Sensorless Control.;Motor drives;Costs;Brushless DC motors;Software packages;Commutation;Sensorless control;Detectors|
|[Providing End-to-End QoS over Mobile and Adhoc Networks using Dynamic Source Routing (DSR) Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080251)|N. Kishore Cv; V. Kumar H|10.1109/ICONAT57137.2023.10080251|Dynamic Source Routing (DSR);QoS;Mobile Network;Routing and Ad-hoc Network;Energy consumption;Costs;Network topology;Quality of service;Routing;Mobile communication;Routing protocols|
|[Disseminating the Process of Hurricane Path Prediction using Multilayer Perceptron and Support Vector Machine upon Varied Kernel Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080280)|I. Karn; S. T. Manikandan; A. S. Abdullah|10.1109/ICONAT57137.2023.10080280|Hurricane;predictive algorithms;Multilayer Perceptron;Support Vector Machine;temporal data;Support vector machines;Time series analysis;Data visualization;Multilayer perceptrons;Prediction algorithms;Hurricanes;History|
|[Are Women Employees in Engineering Institutions Suffer from the Stress: An Investigation Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080369)|T. S. Rani; L. Prathiba; M. S. A. Basha; V. Khangembam; M. M. Sucharitha|10.1109/ICONAT57137.2023.10080369|Stress management;Faculty;Females;and workplace;Uncertainty;Face recognition;Employment;Atmosphere;Task analysis;Engineering education;Stress|
|[Efficient Clock-less PCIe endpoint](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080754)|A. Suresh; S. Shyama; S. Srivastava; N. Ranjan|10.1109/ICONAT57137.2023.10080754|PCIe;Zynq;End point;VPX;DMA;Power demand;Debugging;Data transfer;Hardware;Resource management;IP networks;Complex systems|
|[Improved Artificial Potential Field Method for Motion-Planning of Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080077)|P. Paliwal|10.1109/ICONAT57137.2023.10080077|Autonomous;Potential Field;Motion Planning;Path optimization;Navigation;Simulation;Force;Path planning;Planning;Reliability;Fuels|
|[Machine Learning Applied to Speech Emotion Analysis for Depression Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080060)|B. S; D. S. Nayak; R. C. Dmello; A. Nayak; S. S. Bangera|10.1109/ICONAT57137.2023.10080060|Machine Learning;Support Vector Machine;Depression;Long Short-Term Memory.;Machine learning algorithms;Support vector machine classification;Medical treatment;Speech recognition;Machine learning;Depression;Feature extraction|
|[A Comparative Study on the Analytical Performance of AlAs and Silicon DGJLMOSFET in Terms of Gate Oxide Material and its Thickness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080459)|A. A. Pratyay; M. Ariful; A. S. Jasir; T. Nahar|10.1109/ICONAT57137.2023.10080459|Double Gate;MOSFET;Junction-less;SCEs;Electric Field Profile;I-V Characteristics. AlAs;Silicon;Performance evaluation;Fabrication;MOSFET;Dielectric constant;Logic gates;Silicon;High-k dielectric materials|
|[Performance Evaluation of Machine Learning Algorithms to Predict the Medication Prescription Errors in Intensive Care Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080289)|V. Pais; S. Rao; B. Muniyal|10.1109/ICONAT57137.2023.10080289|Intensive Care Unit(ICU);Medication error;Artificial Intelligence(AI);Performance evaluation;Drugs;Deep learning;Machine learning algorithms;Costs;Hospitals;Measurement uncertainty|
|[Optimal Design and Economic Analysis of Biogas/Diesel/PV Power System for Electrification of Palm Oil Factory and Surround Settlements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080365)|R. A. Pribadi; Syafii|10.1109/ICONAT57137.2023.10080365|Optimized design;Diesel-Photovoltaic-Biogas Hybrid;Economical and Environmentally compatible;Photovoltaic systems;Costs;Oils;Simulation;Wind power generation;Reliability engineering;Production facilities|
|[Breast Cancer Screening by Blood Test using miRNAs as Biomarkers- a Point of Care Engineering Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080408)|B. Salim; M. Vijayakumar; K. Kokilavani; A. T. C. John; C. D. K|10.1109/ICONAT57137.2023.10080408|Breast cancer;Screening;miRNA;diagnosis;Ethics;Costs;Point of care;Biomarkers;Breast cancer;Real-time systems;Mammography|
|[Performance Analysis of Novel Multilevel Inverter with Minimum Number of Switching Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080276)|T. A. Kumar; Y. Suresh; B. N. Rao; K. Aditya; B. S. Naik; E. Karunakaran|10.1109/ICONAT57137.2023.10080276|Pulsewidth modulation technique (PWM);Asymmetrical Multilevel Inverter;Total Hormonic Distortion(THD);Bidirectional converter;Renewable energy sources;Low voltage;Simulation;Voltage;Switches;Pulse width modulation;Multilevel inverters|
|[Exploiting the Joint Potential of Instance Segmentation and Semantic Segmentation in Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080167)|M. Usman; T. A. K; M. R. Ahmed; R. Gudodagi; N. K. T|10.1109/ICONAT57137.2023.10080167|Autonomous Vehicles;Deep Neural Networks;Instance segmentation;R-CNN;Semantic segmentation;Deep learning;Satellites;Semantic segmentation;Neural networks;Transportation;Autonomous automobiles;Automobiles|
|[State-Of-Charge Estimation using Extended Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080761)|R. Hiremath; S. Hulakund; V. R. Torgal; A. Patil; H. R. Patil; A. B. Raju|10.1109/ICONAT57137.2023.10080761|State-Of-Charge(SOC);Extended Kalman Filter;Li-ion Battery;model-based design;Battery Management system(BMS);Battery current rating.;Lithium-ion batteries;Voltage measurement;Parameter estimation;Estimation;Nonlinear dynamical systems;State of charge;Kalman filters|
|[Evaluating the Predictive Ability of the LightGBM Classifier for Assessing Customer Satisfaction in the Airline Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080120)|P. Kunekar; M. Deshpande; A. Gharpure; V. Gokhale; H. Yadav|10.1109/ICONAT57137.2023.10080120|Aviation;Airline industry;Customer satisfaction;Classification;Data mining;Inflight services;LightGBM;Support vector machines;Customer satisfaction;Classification algorithms;Wireless fidelity;Airline industry;Business|
|[Understanding the Behaviour of Android Ransomware Attacks with Real Smartphones Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080696)|A. Kumar; I. Sharma|10.1109/ICONAT57137.2023.10080696|Security;Android Malware;Android Ransomware;Cryptocurrency;Wannalocker Ransomware;Koler Ransomware;Data analysis;Operating systems;Network architecture;Data breach;Ransomware;Security;Cyberattack|
|[Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080586)|Y. Natarajan; G. Wadhwa; P. A. Ranganathan; K. Natarajan|10.1109/ICONAT57137.2023.10080586|building damage;machine learning;multi-source data;earthquake;regression;seismic building;Measurement;Machine learning algorithms;Buildings;Earthquakes;Predictive models;Prediction algorithms;Data models|
|[Long-term Power System Planning Considering Increased Electric Vehicle Penetration: A Systematic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080341)|S. Kumar; A. K. Verma; A. Jain; P. Mathuria; R. Bhakar|10.1109/ICONAT57137.2023.10080341|Generation expansion planning;Transmission generation co-optimization planning;Electric vehicle;Demand response;Electric vehicle demand curve;Power system model;Energy system model;Variable renewable energy;Renewable energy sources;Systematics;Roads;Predictive models;Electric vehicles;Reliability engineering;Planning|
|[Characteristics of Symmetrical Components During Unsymmetrical Faults in Distribution Network with PV-Inverter Connected](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080061)|Syafii; H. Yamashika; Adrianti|10.1109/ICONAT57137.2023.10080061|Distributed generation,;IEEE 13 nodes;Symmetrical components;SOGI-FLL inverter;Simulation;Distribution networks;Transformers;Inverters;Steady-state;Circuit faults;Impedance|
|[Performance Analysis and Numerical Modelling of Thinfilm sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080091)|K. Jayadeepthi; S. S. Raju; C. Sravani; K. N. Kishore; L. B. Nadh|10.1109/ICONAT57137.2023.10080091|FEM;COMSOL;VOC;MEMS;Thinfilm;Vibrations;Volatile organic compounds;Sensitivity;Torque;Biological system modeling;Thin film sensors;Structural engineering|
|[The Future of Transportation: A Review of Electric Vehicle Charging Management and Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080541)|S. Tripathi; P. K. Gurjar; C. P. Barala; P. Mathuria; R. Bhakar|10.1109/ICONAT57137.2023.10080541|Charging management;partial charging;priority reserved charging;navigation;credit mechanism;charging price.;Navigation;Roads;Transportation;Charging stations;Power systems;Delays;Smart charging|
|[Deep Deterministic Policy Gradient for Throughput Maximization in Energy Harvesting NOMA-Cognitive Radio Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080443)|L. Garg; S. Majumder; S. Chakravarty|10.1109/ICONAT57137.2023.10080443|NOMA;Cognitive Radio Network;DDPG;DQN;Solar energy harvester;Deep Reinforcement learning.;NOMA;Wireless sensor networks;Simulation;Solar energy;Reinforcement learning;Throughput;Batteries|
|[An Improved Method for the Data Cluster Based Feature Selection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080669)|K. Patidar; R. K. Gour; A. Dixit; M. Verma; A. K. Pal|10.1109/ICONAT57137.2023.10080669|SVM;LR;RF;Improved SVM;Heatmap;Dataset;Support vector machines;Heating systems;Machine learning algorithms;Clustering algorithms;Predictive models;Feature extraction;Prediction algorithms|
|[Implementation and Comparative Analysis of Static and Dynamic Load Balancing Algorithms in SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080430)|M. Shona; R. Sharma|10.1109/ICONAT57137.2023.10080430|Static Load Balancing;Dynamic Load Balancing;Software Defined Networking;Ryu controller;Mininet;Manifolds;Heuristic algorithms;Simulation;Packet loss;Load management;Throughput;Software|
|[Noise Margin analysis of Efficient CNTFET- based Standard Ternary Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080321)|K. Chauhan; S. Mittra; R. Sinha; D. Bansal|10.1109/ICONAT57137.2023.10080321|MVL;CNTFET;Noise Margin;Ternary inverters;Power demand;Power measurement;Reliability engineering;Inverters;Energy efficiency;Delays;CNTFETs|
|[A Comparative Study of Joint and Bolt Structures with and without Edge Detection using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080329)|T. S. Koundinya; S. Brinda; S. Nikhil; M. A. Thelapurath; R. Chinmayee; J. R. Munavalli|10.1109/ICONAT57137.2023.10080329|CNN;Joint and Bolt Structures;Edge Detection;Image Processing;Support vector machines;Computer vision;Histograms;Image edge detection;Neural networks;Transforms;Object detection|
|[Design of Highly Miniaturized Zero Order Resonator Antenna for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080088)|A. K. Varshney; B. S. Manoj; Y. Puneeth; S. Yuvaraj|10.1109/ICONAT57137.2023.10080088|Composite Right Left Hand Transmission Line;infinite wavelength;left-hand triad;meandered line;phase propagation constant;Zero Order Resonator;Wireless communication;Antenna measurements;Power transmission lines;Patch antennas;Resonant frequency;Transmission line measurements;Permittivity|
|[Battery Energy Storage Train Scheduling in Power System Considering Renewable Power Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080735)|K. M. Todakar; P. P. Gupta; V. Kalkhambkar|10.1109/ICONAT57137.2023.10080735|Autoregressive Integrated Moving Average Battery Energy Storage Train;Time Space Network;Wind Power Uncertainty;Renewable energy sources;Uncertainty;Processor scheduling;Biological system modeling;Stochastic processes;Transportation;Wind power generation|
|[Design, Loss Analysis and MFA Modeling of DAB-based Fast Charger for Bidirectional Grid to EV Power Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080578)|N. Naik; C. Vyjayanthi; C. Modi|10.1109/ICONAT57137.2023.10080578|Constant-Current Constant-Voltage;Electric Vehicle;Multi-Frequency Modeling;Vehicle-to-Grid.;Vehicle-to-grid;Computational modeling;Bridge circuits;Zero voltage switching;Mathematical models;Electric vehicle charging;Batteries|
|[Implementation of Smartphone Based Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080702)|S. Dupare; Y. Moundekar; P. Chandankhede|10.1109/ICONAT57137.2023.10080702|Activity recognition;sensors;smartphones;algorithms;Wireless Sensor Data Mining(WISDM);Support Vector Clustering(SVC);University of California Irvine(UCI);Accelerometers;Legged locomotion;Wearable computers;Stairs;Feature extraction;Labeling;Decision trees|
|[A Comparative Performance Analysis of OpenFlow based Network and Legacy Switching Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080718)|H. Kour; R. K. Jha|10.1109/ICONAT57137.2023.10080718|OpenFlow;OpenFlow controller;single topology;tree topology;custom topology.;Measurement;Protocols;Network topology;Simulation;Switches;Bandwidth;Control systems|
|[Optimal Coordination of Directional Over Current Relay Using Analytical and Swarm Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080380)|A. Karmakar; D. T. Santra; C. K. Chanda; S. Mahapatra|10.1109/ICONAT57137.2023.10080380|directional overcurrent relay;relay settings;plug setting;time multiplier setting;particle swarm optimization;firefly algorithm;Stability analysis;Iterative methods;Time factors;Relays;Particle swarm optimization;Plugs|
|[Customer Lifetime Value Prediction of an Insurance Company using Regression Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080805)|M. Surti; V. Shah; S. Bharti; R. Gupta|10.1109/ICONAT57137.2023.10080805|Customer Lifetime Value;Exploratory Data Analysis;Machine Learning;Feature Selection;Data analysis;Employment;Insurance;Machine learning;Companies;Predictive models;Prediction algorithms|
|[Incremental Learning of Handwritten Characters in EMNIST dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080724)|S. K. Dana|10.1109/ICONAT57137.2023.10080724|incremental learning;EMNIST;image classification;handwritten recognition;continual learning;Training;Target recognition;Memory management;Machine learning;Artificial neural networks;Mobile handsets;Character recognition|
|[Image Encryption based on Advanced Encryption Standard (AES)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080243)|S. Saudagar; M. Kulkarni; A. Giramkar; S. Godse; S. Gupta; G. Patil; S. Gunjal|10.1109/ICONAT57137.2023.10080243|Java;Cryptography;Swing;Image Encryption;GUI;AES Algorithm;Rijndael;Java;Heuristic algorithms;Software algorithms;Memory;Side-channel attacks;Software;Encryption|
|[Design of a Low-power Computational Unit using a Pipelined Vedic Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080520)|T. Mendez; S. G. Nayak|10.1109/ICONAT57137.2023.10080520|computational unit;low-power;ASIC;electronic design automation;Performance evaluation;Integrated circuit synthesis;Design automation;Power demand;Graphics processing units;Libraries;Delays|
|[Impact of Component Sensitivity on Biquad Equalizer based Plastic Optical Fiber Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080782)|R. D. Kamble; K. Appaiah|10.1109/ICONAT57137.2023.10080782|SI-POF;Plastic optical fiber;Equalizers;Sensitivity analysis;Bit error rate;Receivers;Optical fiber communication;Complexity theory|
|[Detecting Vehicular Networking Node Misbehaviour Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080114)|S. Saudagar; R. Ranawat|10.1109/ICONAT57137.2023.10080114|Smart Vehicles(SV);Road Side planted Device (RPD);Vehicular Network(VN);Machine Learning;Deep Learning;MVDS (misbehaving vehicular Detection System);Deep learning;Analytical models;Machine learning algorithms;Roads;Computer industry;Explosions;Data models|
|[Ultra-low Noise Figure and low Phase Noise compliant Compact Ku Band down Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080092)|N. S; A. S. S. Reddy; K. V|10.1109/ICONAT57137.2023.10080092|Ku-band Down-converter;Low Noise Figure;Frequency Synthesizer;Local Oscillator generation;Phase Locked Loop;Ku-band Local Oscillator;Frequency Conversion;Phase noise;Radio frequency;Noise figure;Synthesizers;Linearity;Bandwidth;Planning|
|[Performance Analysis of AlGaN/GaN FINFET for Different Temperatures, Gate Oxide dielectric’s and Work functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080795)|M. N. Reddy; D. K. Panda|10.1109/ICONAT57137.2023.10080795|AlGaN/GaN;Figure of Merit (FOM);Linearity;RF parameters;Subthreshold Swing (SS).;Measurement;Dielectric constant;Linearity;Logic gates;FinFETs;Wide band gap semiconductors;Performance analysis|
|[Decision Tree Based Multi-Terminal VSC-HVDC Transmission Line Protection Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080234)|A. Pragati; D. A. Gadanayak; T. Parida; M. Mishra|10.1109/ICONAT57137.2023.10080234|HVDC;fault;decision tree;transmission lines;classifier;Resistance;HVDC transmission;Fault detection;Simulation;Power system protection;Voltage;Feature extraction|
|[Self-excitation of Terahertz Plasmons in Graphene FETs Enabled by Transit-time Negative Dynamic Conductance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080743)|M. Ryzhii; V. Ryzhii; T. Otsuji; V. Mitin; M. S. Shur|10.1109/ICONAT57137.2023.10080743|terahertz;plasmon;graphene FET;negative dynamic conductance;self-excitation;Ring oscillators;Terahertz wave imaging;PIN photodiodes;Graphene;Field effect transistors;Logic gates;Tunneling|
|[LED Driving Topology for Efficiency Improvement of Solid state Luminaries Using Isolated Buck Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080855)|J. Bakliwal; D. V. Biradar; D. N. Narawade|10.1109/ICONAT57137.2023.10080855|LED driver topology;Efficiency improvement;Solid state Luminaries;Isolated buck converter;Wireless communication;Temperature dependence;Buck converters;Temperature;Switching frequency;Focusing;Light emitting diodes|
|[Non Inverting Buck Boost PFC Based AC and DC Ripple Free LED Driver using Luminaries Without Electrolytic Capacitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080505)|J. Bakliwal; D. V. Biradar; D. N. Narawade|10.1109/ICONAT57137.2023.10080505|LED driver topology;Efficiency improvement;Solid state Luminaries;Isolated buck converter Introduction;Smoothing methods;Capacitors;Prototypes;Power factor correction;Predictive models;Light emitting diodes;Harmonic analysis|
|[Spatial Statistics-based Participant Selection for Optimised Mobile Crowd-Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080362)|A. S. Sengar; S. Debnath|10.1109/ICONAT57137.2023.10080362|Mobile Crowd-Sensing;Radio Environment Map;Device-to-Device;Pollution;Wireless networks;Urban areas;Lattices;Weather forecasting;Sensor systems;Reliability|
|[Performance Evaluation of IoT-enabled WSN system With and Without DDoS Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080467)|K. S. Kumavat; J. Gomes|10.1109/ICONAT57137.2023.10080467|Attacks;DDoS;Detection;Internet of Things (IoT);sensor Nodes;threats.;Performance evaluation;Wireless sensor networks;Visualization;System performance;Throughput;Denial-of-service attack;Delays|
|[High Performance CMOS Voltage Level Shifters Design for Low Voltage Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079972)|A. Kapoor; C. S. Jha; A. Thapar; C. I. Kumar|10.1109/ICONAT57137.2023.10079972|Energy efficient Circuits;Low Power Design;Sub-threshold Level;Shifters;Voltage Level Shifter;Low voltage;Power demand;Voltage;CMOS technology;Frequency conversion;Energy efficiency;Delays|
|[Optimization of the Set of Regularized Solutions in the Coefficient Inverse Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080395)|L. V. Huyen; L. V. Chernenkaya|10.1109/ICONAT57137.2023.10080395|optimization;mathematical model;inverse problem Tikhonov’s regularization method;parameter recovery;Electrical engineering;Economics;Inverse problems;Oils;Refining;Ordinary differential equations;Mathematical models|
|[Wavelet and Machine learning based approach for Fault classification in AC Micro-grid system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080451)|G. Patel; T. Biswal; G. Dei; S. Mishra|10.1109/ICONAT57137.2023.10080451|Fault Classification;Microgrid;Discrete wavelet transform;Decision tree;Microgrids;Machine learning;Feature extraction;Software;Discrete wavelet transforms;Impedance;Decision trees|
|[Blockchain Powered Skill Verification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080848)|R. Govindwar; S. Didhate; S. Dalal; N. Musale; P. Shelke; R. Mirajkar; N. P. Sable|10.1109/ICONAT57137.2023.10080848|Blockchain;Ethereum;Wallet;IPFS;Decentralized Application;Skill Verification.;Manuals;Production facilities;Internet;Consensus protocol;Time complexity;Task analysis;Information technology|
|[Simulation and Comparison of Controller Strategy for Artificial Lighting for Indoor Lighting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080121)|L. Brinda; K. P. Vasant; D. Bhate; M. S. Deepak; G. G. Ganesh|10.1109/ICONAT57137.2023.10080121|Indoor Lighting System;Fuzzy Logic;Ant-Colony Optimization Algorithm;Ant-Lion Optimization Algorithm;Daylight Harvesting;Machine learning algorithms;Green buildings;Brightness;Light emitting diodes;Control systems;Lighting control;Artificial light|
|[Analysis of Factors Affecting the Development of Socio-Economic Systems of Vietnam Based on Combinatorial Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080736)|D. N. T. Thu; L. V. Chernenkaya|10.1109/ICONAT57137.2023.10080736|Fuzzy C-Means;Fuzzy Gap-statistic;Fuzzy clustering;quantity of socio-economic;economic of Viet Nam.;Clustering methods;Mathematical analysis;Decision making;Clustering algorithms;Probability;Resource management;Reliability|
|[Comparative Analysis of Terahertz Microstrip Patch Antenna with Single SRR and a Metasurface at the Ground Plane for the Medical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080151)|K. Singh; M. Dhayal; S. Dwivedi|10.1109/ICONAT57137.2023.10080151|directivity;efficiency;gain;radiation patterns;return loss;terahertz and VSWR;Microwave antennas;Microwave technology;Microwave integrated circuits;Patch antennas;Microstrip antennas;Medical services;Metasurfaces|
|[Text-Based Recommendation System for E-Commerce Apparel Stores](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080436)|U. C. De; B. B. Dash; T. M. Behera; T. Samant; S. Banerjee|10.1109/ICONAT57137.2023.10080436|Text Based Product Similarity;Bag of Words;Inverse Document Frequency;Term Frequency - Inverse Document Frequency;Information Retrieval;Extraterrestrial measurements;Real-time systems;Electronic commerce;Task analysis;Recommender systems|
|[An Assistive device for Alzheimer’s patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080787)|P. Roy; A. Kumble; P. H. M; A. Chandankeri; M. Anuradha|10.1109/ICONAT57137.2023.10080787|Alzheimer’s disease;face recognition;SMS alerts;scream detection;interactive;Performance evaluation;Face recognition;Machine learning;Brain cells;Assistive devices;Alzheimer's disease;Task analysis|
|[Kreeda: An Android Application for Searching and Organizing Sports Events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080602)|P. Rana; V. Semwal; D. Kalra|10.1109/ICONAT57137.2023.10080602|Mobile application;Android Studio;Firebase;Java;Event Organizer;Java;Social networking (online);Databases;Organizations;Scheduling;Mobile applications;Planning|
|[Miniaturization of S-Band Monopulse Antenna Feeding Network Using Substrate-Integrated-Waveguide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080850)|S. Herfiah; N. Rahayu; A. D. Prasetyo; A. Munir|10.1109/ICONAT57137.2023.10080850|feeding network;hybrid waveguide;miniaturization;monopulse antenna;S-band frequency;substrate integrated waveguide (SIW);Dielectric substrates;Insertion loss;Antenna feeds;Substrates|
|[Incorporation of Square-shaped Complimentary Split Ring Resonator for Characteristics Enhancement of SIW-based Bandpass Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080549)|N. Rahayu; S. Herfiah; M. F. Maulana; A. Munir|10.1109/ICONAT57137.2023.10080549|Bandpass Filter (BPF);Complimentary Split Ring Resonator (CSRR);Substrate Integrated Waveguide (SIW);square-shaped CSRR.;Band-pass filters;Split ring resonators;Resonator filters;Dielectric substrates;Resonant frequency;Bandwidth;Insertion loss|
|[Surveillance Algorithm for a Swarm of Self-Organized Aerial Unmanned Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080636)|S. Shivkumar; A. A. Nippun Kumaar|10.1109/ICONAT57137.2023.10080636|Unmanned Aerial Vehicles;Communication;Self Organization;Webots;Swarm Robots;Surveillance;Interference;Autonomous aerial vehicles;Complexity theory;Task analysis;Robots|
|[Survey on connecting to the decentralized storage using IPFS protocol with web 3 technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080423)|M. Vivek Anand; S. Mithun; L. S. Dhivya Shree; M. Ranjith|10.1109/ICONAT57137.2023.10080423|Blockchain;IPFS;Web3;Storage;Semantic Web;Protocols;Web 2.0;Aerospace electronics;Blockchains;Centralized control|
|[Design and Implementation of Wideband Patch Array Antenna for Unmanned Aerial Vehicle Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080492)|M. H. Fauzi; I. Kustiawan; N. F. A. Hakim; S. Herfiah; R. S. Asthan; A. Munir|10.1109/ICONAT57137.2023.10080492|Patch array antenna;proximity coupling;Unmanned Aerial Vehicle (UAV);UAV communication;wide bandwidth;Couplings;Patch antennas;Simulation;Dielectric substrates;Resonant frequency;Autonomous aerial vehicles;Broadband antennas|
|[Experimental Approach of SIW-based Filter Development Using Artificial Dielectric Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080524)|M. F. Maulana; A. Ikhyari; N. Rahayu; A. Munir|10.1109/ICONAT57137.2023.10080524|Artificial Dielectric Material (ADM);artificial materials;filter;Substrate Integrated Waveguide (SIW).;Surface waves;Permittivity measurement;Wires;Dielectric substrates;Dielectric materials;Prototypes;Conductors|
|[Short Term Load Forecasting based on Regression models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080359)|A. S. Mahajan; A. Shrivastav|10.1109/ICONAT57137.2023.10080359|STLF;SVM;RMSE;MSE;MAE;Decision Tree;Ensemble;Support vector machines;Training;Load forecasting;Stochastic processes;Machine learning;Mean square error methods;Predictive models|
|[A Technique for Early Identification of Network Separation to Enhance Frequency Resilience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080441)|M. F. I. Faruqui; K. I. Ahmed; A. Jawad; Nahid-Al-Masood|10.1109/ICONAT57137.2023.10080441|sustainable energy;LSTM;forecasting;blackout;ANN;frequency resilience;photovoltaic;wind;Renewable energy sources;Time series analysis;Power system stability;Predictive models;Synchronous generators;Stability analysis;Frequency measurement|
|[An Efficient Machine Learning Approach: Analysis of Supervised Machine Learning Methods to Forecast the Diamond Price](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080618)|M. S. A. Basha; P. M. Oveis; C. Prabavathi; M. B. Lakshmi; M. M. Sucharitha|10.1109/ICONAT57137.2023.10080618|Estimated values;Machine learning;Diamonds;Regression Models and Cross validation;Measurement;Training;Analytical models;Linear regression;Platinum;Diamonds;Prediction algorithms|
|[Design of Active Power Filter for Grid Connected WECS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080379)|P. L. Chavan; D. Gowda; S. K. Nayak|10.1109/ICONAT57137.2023.10080379|Active Filter;Renewable Energy;Wind Energy;Power filters;Reactive power;Passive filters;Power quality;Resonant frequency;Harmonic analysis;Active filters|
|[A GeO2-doped Silica Ring-core Bragg Fiber for OAM Mode Propagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079965)|A. Dyavangoudar; A. Saharia; G. Singh; Y. Ismail; A. V. Bourdine; M. Tiwari|10.1109/ICONAT57137.2023.10079965|orbital angular momentum;PCF;Finite element analysis;Silicon compounds;Orbital calculations;Refractive index;Propagation losses;Orbits;Finite element analysis;Nonlinear optics|
|[Hanowa Matrix based SLM Technique for PAPR Reduction in OFDM Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080797)|S. Prasad; S. Arun|10.1109/ICONAT57137.2023.10080797|5G systems;Modulation schemes;OFDM;Reduction techniques;PAPR Hanowa;SLM;Phase noise;Fading channels;5G mobile communication;Simulation;Modulation;Peak to average power ratio;Interference|
|[Color space analysis for improvement in rPPG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080102)|K. B. Jaiswal; M. Choubey; A. Mishra; D. Singh; T. Meenpal|10.1109/ICONAT57137.2023.10080102|rPPG;adaptive color space;heart rate;convolutional neural network;Adaptation models;Costs;Color;Aerospace electronics;Skin;Convolutional neural networks;Biomedical monitoring|
|[Mathematical Modeling and Analysis of Single-Diode, Double-Diode and Triple Diode based PV Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080741)|A. Kumar; R. Agarwal|10.1109/ICONAT57137.2023.10080741|solar cell;single-diode model;double-diode model;triple-diode model;ideality factor;irradiation;Resistance;Industries;Radiation effects;Analytical models;Temperature;Solar energy;Time division multiplexing|
|[Nanoscale CMOS Biasing Circuit for Analog Applications: The Impact of NBTI Degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080544)|A. Vijay; C. Duari; L. Garg; A. K. Singh|10.1109/ICONAT57137.2023.10080544|NBTI;aging;CMOS;analog;VLSI;MOSRA;SPICE;Negative bias temperature instability;Degradation;Semiconductor device modeling;MOSFET;Thermal variables control;Aging;Very large scale integration|
|[Flexible Eddy Current Braking Profile Employing Power Electronic Converter for Automotive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080611)|V. C. Magadal; M. Kappali|10.1109/ICONAT57137.2023.10080611|Eddy Current Braking;electrical braking systems;permanent magnets;electromagnets;Oils;Electronic components;Controllability;Permanent magnets;Mathematical models;Power electronics;Automobiles|
|[Garbage Monitoring Ultrasonic Sensor Development By Using Nodemcuesp8266](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079962)|S. V. S. Dinesh; K. Madala; T. Rakesh; M. Harshitha Priya; P. Ramesh Babu|10.1109/ICONAT57137.2023.10079962|IoT;NodeMCU;Waste Management;Ultrasonic Sensor;GSM;Costs;Microcontrollers;Sensor systems;Acoustics;Recycling;Monitoring|
|[Input Method to Support Communication of Intention of Impressions in an Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080851)|K. Yumita; M. Takeuchi; R. Yoshioka|10.1109/ICONAT57137.2023.10080851|Human-Computer Interaction;Knowledge Acquisition;Experience Design;Data Analysis;Visualization;Museums;User experience|
|[Eight-State Accuracy Prediction of Protein Secondary Structure using Ensembled Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080387)|C. S. Srushti; P. M. Prathibhavani; K. R. Venugopal|10.1109/ICONAT57137.2023.10080387|Proteins;Protein Secondary Structure Prediction;Deep Learning;Convolution Neural Network;Bidirectional Long Short Term Memory;Q8 Accuracy;Proteins;Three-dimensional displays;Computational modeling;Graphics processing units;Predictive models;Benchmark testing;Amino acids|
#### **2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)**
- DOI: 10.1109/ISACC56298.2023
- DATE: 3-4 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Single-hand Gesture Recognition of Manipuri Classical Dance of India based on Skeletonization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083889)|M. Devi; A. Chakraborty; A. Roy; D. Majumder|10.1109/ISACC56298.2023.10083889|Gestures recognition;Single-hand gestures;Manipuri classical dance of India;Dataset;Skeletization technique;Humanities;Gesture recognition;Linguistics;Feature extraction;Task analysis;Intelligent systems|
|[AO2DS: A Method of Auxiliary Operational Decision-making Based on System Dynamics Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084273)|Z. Ren; X. Ren; L. Ren|10.1109/ISACC56298.2023.10084273|System Dynamics Simulation;Battle Decision;StarCraft II;System dynamics;Computational modeling;Simulation;Decision making;Games;Libraries;Explosions|
|[Brain Tumor Classification using Deep Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083818)|A. Srivastava; A. Khare; A. Kushwaha|10.1109/ISACC56298.2023.10083818|Discrete Wavelet Transform;Local Binary Pattern;Convolutional Neural Network;ResNet;Deep learning;Training;Magnetic resonance imaging;Computer architecture;Transforms;Discrete wavelet transforms;Clinical diagnosis|
|[An efficient charging scheduling scheme to enhance the wireless rechargeable sensor networks' lifespan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084182)|S. M. A. Rahaman; M. Azharuddin|10.1109/ISACC56298.2023.10084182|WRSN;Scheduling;Wireless charging;Mobile charger;priority;Wireless communication;Wireless sensor networks;Schedules;Scheduling algorithms;Simulation;Wireless power transfer;Timing|
|[Security Enhancement using Vectoring, Cryptography and Steganography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084078)|M. Mittal; S. Gupta; P. Kumar Keserwani; M. Chandra Govil|10.1109/ISACC56298.2023.10084078|Steganography;Cryptography;Steganalysis;Se-curity;Image;Steganography;Costs;Public key;Receivers;Robustness;Encryption;Complexity theory|
|[Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084049)|S. V. R. Nair; S. K. Saha|10.1109/ISACC56298.2023.10084049|Cyber Security;Information Systems;Healthcare;Bibliometric Analysis;Citation Analysis;Deep learning;Smart cities;Computational modeling;Bibliometrics;Medical services;Organizations;Time factors|
|[The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084284)|M. M. Nair; G. H. Sai; P. V; H. Sriraman|10.1109/ISACC56298.2023.10084284|Visualization;COVID;Dashboard;Object Detection;RCNN;DNN;COVID-19;Visualization;Machine learning algorithms;Pandemics;Data visualization;Object detection;Human factors|
|[A Hybrid Programming Course recommendation system using Fuzzy Logic and xDeepFM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083853)|S. Ghosh|10.1109/ISACC56298.2023.10083853|Fuzzy logic;Mamdani;Recommendation system;Fuzzy Rules;Membership Function;xDeepFM;Fuzzy logic;Engineering profession;Search problems;Intelligent systems;Recommender systems;Programming profession|
|[Surface Electromyography based Hand Gesture Signal Classification using 1D CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083548)|K. S; J. P. Sahoo; S. Ari|10.1109/ISACC56298.2023.10083548|sEMG;1D CNN;NinaPro;Training;Deep learning;Computational modeling;Pattern classification;Gesture recognition;Feature extraction;Real-time systems|
|[Transcription of Indian Classical Music using Convolutional Recurrent Neural Network and CTC Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083872)|Y. Singh; Y. Gupta; S. Patar; A. Saraswat; A. Biswas|10.1109/ISACC56298.2023.10083872|Indian classical music;Transcription;Deep Learning;STFT;CTC;Recurrent neural networks;Convolution;Error analysis;Annotations;Computational modeling;Licenses;Complexity theory|
|[Modeling a Hybrid Stemmer for Kokborok](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084267)|P. Debbarma; P. Hrangkhawl; S. Debbarma|10.1109/ISACC56298.2023.10084267|Kokborok Language;Natural Language Processing;Stemming Algorithm;Hybrid Model;Computational modeling;Information retrieval;Natural language processing;Data mining;Intelligent systems|
|[Answer Selection Using Interactive Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084083)|V. Venkataraman; S. J. Nirmala|10.1109/ISACC56298.2023.10084083|Answer Selection;Self-attention;Coattention;Attentive Pooling;Measurement;Deep learning;Computational modeling;Question answering (information retrieval);Encoding;Proposals;Task analysis|
|[Processing IoT Sensor Fire Dataset Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084317)|S. Suklabaidya; I. Das|10.1109/ISACC56298.2023.10084317|IoT;SM;Naïve Bayes;KNN;Random Forest;Support vector machines;Machine learning algorithms;Error analysis;Smart homes;Prediction algorithms;Classification algorithms;Safety|
|[Deep Learning-Based Transfer Learning for the Detection of Leukemia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084138)|J. Sheet; C. Ghosh; B. K. Das|10.1109/ISACC56298.2023.10084138|ALL;Deep learning;CNN;Medical imaging;Mobilenet V2;Sensitivity;Microscopy;Computational modeling;Transfer learning;Merging;Predictive models;Convolutional neural networks|
|[Learning Techniques for Depression Detection: A Comparative Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083576)|A. Das; M. K. Debbarama; R. K. Barik|10.1109/ISACC56298.2023.10083576|depression;detection;machine learning;artificial intelligence;Support vector machines;Mood;Neural networks;Bit error rate;Depression;Chatbots;Feature extraction|
|[Automated Nuclei Analysis from Digital Histopathology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083938)|B. Roy; P. Sarkar; M. Gupta|10.1109/ISACC56298.2023.10083938|Histopathology;Nuclei;Threshold;8 neighborhood;Contour;Cancer;Training;Image segmentation;Visualization;Thresholding (Imaging);Histopathology;Shape;Medical treatment|
|[Performance Analysis of an ANN-based model for Breast Cancer Classification using Wisconsin Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083642)|U. P. Srivastava; V. Vaidehi; T. K. Koirala; P. Ghosal|10.1109/ISACC56298.2023.10083642|Breast Cancer;Classification;Benign Tumor;Malignant Tumor;Wisconsin;Measurement;Analytical models;Lymphatic system;Computational modeling;Artificial neural networks;Forestry;Market research|
|[Review of Various Machine Learning and Deep Learning Techniques for Audio Visual Automatic Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084209)|A. Choudhury; P. Roy; S. Bandyopadhyay|10.1109/ISACC56298.2023.10084209|audio visual speech recognition;feature extraction;deep learning;Deep learning;Visualization;Neural networks;Speech recognition;Speech enhancement;Feature extraction;Acoustics|
|[Image Super Resolution Based on Machine Learning for Enhancing Quality Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084087)|O. James; T. R. Singh; T. R. Singh|10.1109/ISACC56298.2023.10084087|High-resolution Photograph;Image Upscaling;Machine Learning;CNN;Deep learning;Portable computers;Filtering;Superresolution;Convolutional neural networks;Intelligent systems;Smart phones|
|[Hate Speech in Social Networks and Detection using Machine Learning Based Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084222)|C. Paul|10.1109/ISACC56298.2023.10084222|Machine Learning;Social Media;Hate Speech;Natural Language Processing;Support vector machines;Machine learning algorithms;Social networking (online);Hate speech;Blogs;User-generated content;Artificial neural networks|
|[Applying a machine learning model to forecast the risks to children's online privacy and security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084054)|R. M. S. Rahman Pir; M. F. Rabbi; M. J. Islam|10.1109/ISACC56298.2023.10084054|Privacy;Security;Prediction;Children;Digital;Privacy;Machine learning algorithms;Computational modeling;Personal digital devices;Human factors;Predictive models;Prediction algorithms|
|[A review on recent techniques developed for energy conservation in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083645)|H. Basumatary; R. Chowdhuri; M. K. Deb Barma|10.1109/ISACC56298.2023.10083645|Cluster Head;Energy Efficiency;Sink Node;Wireless Sensor Networks;Wireless communication;Wireless sensor networks;Power supplies;Military computing;Routing;Energy efficiency;Routing protocols|
|[AsCul: Annotated Dataset and a Deep Learning based framework for Assamese Cultural Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084269)|M. P. Lahkar; A. Gogoi; P. Choudhury|10.1109/ISACC56298.2023.10084269|Deep Learning;Object Detection;Dataset creation;Instruments;Music;Object detection;Streaming media;Prediction algorithms;Real-time systems;Cultural differences|
|[Comparative analysis of detecting over-claim permissions from android apps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084321)|R. J. Majethiya; M. Shah|10.1109/ISACC56298.2023.10084321|Android;Permission;Privacy;Dangerous;Over-claim;Semantic Analysis;Performance evaluation;Data privacy;Privacy;Operating systems;Semantics;Malware;Mobile security|
|[COVID-19 detection using lightweight CNN architecture on chest X-ray images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084139)|S. M. Farin; M. S. Islam Prottasha; S. M. S. Reza|10.1109/ISACC56298.2023.10084139|Covid detection;image processing;deep learning;convolutional neural network;CNN;COVID-19;Training;Computer architecture;Real-time systems;Convolutional neural networks;Polymers;X-ray imaging|
|[Java Based Software Robot for Automatic E-mail Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083905)|R. Singh; S. K. Upadhyay; N. Sagar|10.1109/ISACC56298.2023.10083905|Automate Ad Campaign;Conference;Promotional Messages And Formal Meetings;Java;Organizations;Software;Electronic mail;Task analysis;Organizational aspects;Intelligent systems|
|[Survey on Methodological Model of IoT in Digital Forensic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083537)|R. Roy; D. J|10.1109/ISACC56298.2023.10083537|IoT;Digital Forensic;Forensic in IoT;Sensors;Performance evaluation;Solid modeling;Face recognition;Digital forensics;Organizations;Data models;Internet of Things|
|[Traffic Sign Recognition System using Ensemble based Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083886)|D. Dey; S. Majumder; A. Halder|10.1109/ISACC56298.2023.10083886|Traffic Sign Recognition;Deep Learning;CNN;ResNet50;GoogLeNet;Ensemble Learning;Deep learning;Training;Image recognition;Computational modeling;Neural networks;Predictive models;Convolutional neural networks|
|[Mitigating Cold Start Problem in Recommendation Systems via Transfer Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084296)|M. Jangid; R. Kumar|10.1109/ISACC56298.2023.10084296|Cold Start Problem;transfer learning;Feature Extraction;Measurement;Analytical models;Social networking (online);Computational modeling;Transfer learning;Electronic commerce;Task analysis|
|[Cyberbullying Detection using Deep Learning Techniques on Bangla Facebook Comments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083690)|S. B. Shanto; M. J. Islam; M. A. Samad|10.1109/ISACC56298.2023.10083690|Cyberbullying;Bangla Sentiment Analysis;Deep Learning;GRU;LSTM;Bangla Face-book Comments;Deep learning;Computational modeling;Web and internet services;Data preprocessing;Cyberbullying;Predictive models;Logic gates|
|[Pre-trained Word Embeddings In Deep Multi-label Personality Classification Of YouTube Transliterations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084047)|M. A. Teli; M. A. Chachoo|10.1109/ISACC56298.2023.10084047|Personality;NLP;Multi-label;YouTube;Word-Embeddings;Deep Learning;Human computer interaction;Machine learning algorithms;Video on demand;Recurrent neural networks;Psychology;Machine learning;Semisupervised learning|
|[Fake news detection using social media data for Khasi language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083518)|S. Warjri; P. Pakray; S. A. Lyngdoh; A. K. Maji|10.1109/ISACC56298.2023.10083518|Fake news;Detecting Fake news;Natural Language Processing;News security;Text News Classification;Khasi fake news detection;Machine Learning;Khasi language;COVID-19;Knowledge engineering;Social networking (online);Pandemics;Decision trees;Data mining;Fake news|
|[Power of kernel functions, its benefits, and limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084201)|R. Indu; S. C. Dimri|10.1109/ISACC56298.2023.10084201|Kernel Functions;Support Vector Machines;Input Space;Feature Space;Vectors;Support vector machines;Machine learning algorithms;Data analysis;Supervised learning;Linearity;Machine learning;Kernel|
|[A Critical Study of Biometrics and Their Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083801)|M. Shekhar; R. Patgiri; A. K. Trivedi; P. Dhar|10.1109/ISACC56298.2023.10083801|Fingerprint;Gait;Unimodel;Multimodel;Biometric Fusion;Authorization;Biometrics (access control);Error analysis;Forensics;Fingerprint recognition;Safety;Security|
|[Beauty of Blockchain Technology in Healthcare Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084089)|P. Pattnayak; A. Mohanty; T. Das; S. Patnaik|10.1109/ISACC56298.2023.10084089|Blockchain;Hyperledger;Health policy;Corruption tolerance;Health insurance;EHR;Industries;Costs;Government;Insurance;Medical services;Open systems;Blockchains|
|[Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084331)|S. Chatterjee; R. K. Chatterjee; R. T. Goswami|10.1109/ISACC56298.2023.10084331|COVID-19 Pneumonia;Medical Image;CT scan;Segmentation;Ground glass opacity;Mathematical morphology;COVID-19;Image segmentation;Sensitivity;Computed tomography;Pulmonary diseases;Morphology;Lung|
|[Various Approaches to perceptual image hashing systems-A Survey*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083762)|M. Roy; D. M. Thounaojam; S. Pal|10.1109/ISACC56298.2023.10083762|Perceptual image hashing;Content-based hashing;Image authentication;Tamper detection;Measurement;Filtering;Authentication;Watermarking;Robustness;Computational efficiency;Time complexity|
|[Enhanced Channel-Wise Homomorphic Encryption for Image Inference based on Pairwise Activation Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084149)|Tanuja; R. Kumar|10.1109/ISACC56298.2023.10084149|Convolutional Neural Network(CNN);privacy-preserving;pairwise activation functions;Channel-wise homomorphic encryption;privacy-preserving machine learning (PPML);Fully-Homomorphic Encryption (FHE);privacy-preserving deep learning;Deep learning;Data privacy;Channel hot electron injection;Mathematical models;Data models;Convolutional neural networks;Homomorphic encryption|
|[A Comparative Analysis on Diabetic Retinopathy using Deep Learning and Nature based Optimization Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083804)|K. S. Gorde; A. A. Gurjar|10.1109/ISACC56298.2023.10083804|Machine learning;deep learning;diabetic retinopathy;inceptionV3;densenet-169;nature-based optimisation algorithms;Deep learning;Wavelet transforms;Retinopathy;Computational modeling;Neural networks;Metaheuristics;Visual impairment|
|[Exploring Stationarity and Fractality in Stock Market Time-series](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084056)|D. Batabyal; D. Bandopadhyay; B. Sadhukhan; N. Das; S. Mukherjee|10.1109/ISACC56298.2023.10084056|Stock Market;Hurst Parameter;Wavelet Variance Analysis (WVA);Finite Variance Scaling Method (FVSM);Smoothed Pseudo Wigner Ville Distribution (SPWVD);ADF (Augmented Dickey Fuller);KPSS (Kwiatkowski-Phillips-Schmidt-Shin);Industries;Time series analysis;Market research;Fractals;Behavioral sciences;Stock markets;Intelligent systems|
|[Efficient Semantic Image Editing with Feature Shared Spatially-Adaptive Normalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083951)|N. Halagatti; B. M C; A. N. Jadhav; S. P. Chermala; P. Desai; V. R. Peddigari; R. Sheshadri|10.1109/ISACC56298.2023.10083951|SESAME;GAN Optimization;Semantic Image Editing;Visualization;Image synthesis;Semantics;Redundancy;Graphics processing units;Streaming media;Generative adversarial networks|
|[Estimation of Position of Lathe Tool Using Edge Detection Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084225)|S. Kumari; C. Pathak; S. Vashist; P. K. Mahapatra|10.1109/ISACC56298.2023.10084225|Lathe tool;positioning;dimensions;edge detection;Economics;Micrometers;Thresholding (Imaging);Image edge detection;Lighting;Estimation;Optimized production technology|
|[Depressive and Non-depressive Tweets Classification using a Sequential Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083981)|A. Agrawal; S. Dey; G. C. Jana|10.1109/ISACC56298.2023.10083981|Depression Detection;Sequential Deep Learning Model;ID-Convolution;LSTM;Social Media;Deep learning;Social networking (online);Computational modeling;Mental health;Media;Depression;Intelligent systems|
|[Jellyfish Search Algorithm based Optimal Routing Protocol for Energy Efficient Data Aggregation in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084023)|S. K; S. D. K; J. Suganthi; D. T|10.1109/ISACC56298.2023.10084023|Wireless Sensor Networks;Jellyfish Search Algorithm;Routing Protocol;Data Aggregation;Wireless communication;Wireless sensor networks;Energy consumption;Voting;Simulation;Clustering algorithms;Data aggregation|
|[The Role of Deep Learning in Diagnosis and Grading of Diabetic Retinopathy: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084051)|S. Minz; S. Seth; S. Tiwari; R. Katarya|10.1109/ISACC56298.2023.10084051|Deep Learning;Diabetic Retinopathy;Fundoscopic Images;Healthcare;Machine Learning;Deep learning;Retinopathy;Law;Transfer learning;Manuals;Object detection;Retina|
|[An Efficient Consensus Algorithm for Blockchain-Based Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083761)|K. Pratim Kalita; D. Boro; D. Kumar Bhattacharyya|10.1109/ISACC56298.2023.10083761|blockchain;consensus algorithm;federated learning;data sharing;Training;Federated learning;Computational modeling;Consensus algorithm;Organizations;Data models;Blockchains|
|[Performance analysis of Sentiment Classification using Optimized Kernel Extreme Learning Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083939)|D. N. S. B. Kavitha; M. V. Subbarao|10.1109/ISACC56298.2023.10083939|Sentiment Analysis;Emotion Recognition;Text Analysis;Knowledge-Based approaches;KELM;SSA;SVM;Ensemble classifiers;Training;Support vector machines;Sentiment analysis;Extreme learning machines;Social networking (online);Real-time systems;Kernel|
#### **2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)**
- DOI: 10.1109/ACCTHPA57160.2023
- DATE: 20-21 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Comprehensive Study on E-learning Environments for Deaf or Hard of Hearing Learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083367)|A. Poly; P. K. Nizar Banu|10.1109/ACCTHPA57160.2023.10083367|e-learning;adaptive e-learning;personalization;d/DHH;machine learning;nan|
|[Comparative Analysis of Quantum Computing Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083363)|A. P. Nirmala; V. Asha; B. Saju; S. C. Murali; D. P. Jacob; B. J. Mathew|10.1109/ACCTHPA57160.2023.10083363|nan;nan|
|[Using machine learning to comprehend and forecast Post-COVID-19 pharmaceutical sales](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083359)|S. Mahendher; S. Singhal; J. Devaraj; P. Markandey|10.1109/ACCTHPA57160.2023.10083359|Pre-COVID;pharmaceuticals;Symptoms;Non-communicable diseases;Post-COVID;nan|
|[Personalized Self Adaptive Internet-of-Things Enabled Sustainable Healthcare Architecture for Digital Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083335)|S. Bhadula; S. Sharma|10.1109/ACCTHPA57160.2023.10083335|Personalized Self Adaptive;Internet-of-Things;Sustainable Healthcare Architecture;Digital Transformation;nan|
|[Deep Learning Model to Defend against Covert Channel Attacks in the SDN Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083336)|M. A. Kumar; A. H. Pai; J. Agarwal; S. Christa; G. M. S. Prasad; S. Saifi|10.1109/ACCTHPA57160.2023.10083336|Attacks;Bandwidth;Controller;Control plane;Classification;Data plane;Deep learning;Flooding;SDN;TCP;nan|
|[A Brief Study on Security Preserved Data Aggregation Approaches in WSN s](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083341)|A. Paul; S. E. Roslin|10.1109/ACCTHPA57160.2023.10083341|Data aggregation;Wireless sensor networks power saving;data security;nan|
|[Performance Analysis and Classification of Class Imbalanced Dataset Using Complement Naive Bayes Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083369)|B. Marapelli; S. Kadiyala; C. S. Potluri|10.1109/ACCTHPA57160.2023.10083369|Imbalanced Dataset;complement naive bayes;naïve bayes;Inherent;Hadoop;class;attributes;nan|
|[Human Action Recognition using Computer Vision and Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083351)|S. Ganta; D. S. Desu; A. Golla; M. A. Kumar|10.1109/ACCTHPA57160.2023.10083351|Video Processing;Long Term Recurrent Convolutional Network (LRCN);Convolution LSTM(ConvLSTM);Convolutional Neural Network;Long Short-Term Memory (LSTM);nan|
|[W3BnNr: An Automated tool for information gathering, vulnerability scanning, attacking and reporting for injection attacks on web application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083380)|M. Muralidharan; K. B. Babu; G. Sujatha|10.1109/ACCTHPA57160.2023.10083380|Web application;Security vulnerability;Injection;Scanner;nan|
|[Review on Automated Leaf Disease Prediction Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083382)|S. Jamal; J. E. Judith|10.1109/ACCTHPA57160.2023.10083382|Plant diseases;Leaf Disease Classification;Machine Learning;Deep Learning;Convolutional Neural Networks;nan|
|[Study on Book Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083372)|V. K. Kavitha; S. Koteeswaran|10.1109/ACCTHPA57160.2023.10083372|Recommendation system;Machine learning;Collaborative filtering;nan|
|[A Rule Based Secure Network System - Prevents Log4jshell and SSH Intrusions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083350)|M. A. S. Arsalan; S. Suryaraman; G. Sujatha|10.1109/ACCTHPA57160.2023.10083350|Honeypot;Log4j;Log4jshell;Firewall;SSH (Secure Shell);Header fields;HTTP (Hypertext Transfer Protocol) Requests;RCE (Remote Code Execution);Alert System;nan|
|[Diagnosis of Mammographic Images for Breast Cancer Detection using FF-CSO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083387)|R. V. Pawar; S. Saraf; U. Dixit; A. S. Jadhav|10.1109/ACCTHPA57160.2023.10083387|LBP;Active contour;DBN;breast cancer;Mammographic;nan|
|[Design and Analysis of PMDC Motor for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083384)|V. Chalawadi; S. P. Amminabhavi; A. S. Jadhav; R. Pawar; M. S. Kanamadi; R. S. Patil|10.1109/ACCTHPA57160.2023.10083384|Motion;dynamic;PMDC motor;electric vehicle;nan|
|[An Advanced Adaptive Neuro-Fuzzy Inference System for Classifying Alzheimer's Disease Stages From SMRI Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083347)|M. Emmanuel; J. Jabez|10.1109/ACCTHPA57160.2023.10083347|Alzheimer;MRI;Neural Network;AANFIS;KNN;EFKNN;nan|
|[Multimodal MR Brain Image Segmentation using Hybrid Optimization Techniques for Earlier Prognosis of Tumor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083352)|B. Karun; T. A. Prasath; M. P. Rajasekaran; M. Rakhee|10.1109/ACCTHPA57160.2023.10083352|Fast and Robust Fuzzy C-Means (FRFCM);Social Spider Optimization (SSO);Medical Image Segmentation;Glioma;Magnetic resonance imaging (MRI);Machine Learning;nan|
|[Certain investigations on Computed Tomography based imaging for identification of lung cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083360)|N. Vijayan; J. Kuruvilla|10.1109/ACCTHPA57160.2023.10083360|Pre-processing;Segmentation;Feature extraction;Classification;Performance metrics;CT scan;nan|
|[Blockchain Technology as a Solution for Vulnerabilities in Internet of Things-A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083337)|M. Rakhee; S. Elayidom|10.1109/ACCTHPA57160.2023.10083337|Internet of Things (IoT);Blockchain;Smart home;Smart Agriculture;Hyperledger Fabric;IOTA;Ethereum;nan|
|[Robust Air Quality Prediction Based on Regression and XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083379)|A. A. Varghese; J. Krishnadas; A. M. Antony|10.1109/ACCTHPA57160.2023.10083379|Time series model;Expectation Maximization algorithm;Regression models;Performance metrics;XGBoost;nan|
|[A Novel Method for Concrete Crack Detection Using Image Processing Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083343)|T. Davies; R. S. Kumar; A. M. Antony|10.1109/ACCTHPA57160.2023.10083343|DCNN;DDS;DIP;ResNet50;VGG16;nan|
|[Dark Channel Prior based Single Image Dehazing of Daylight Captures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083371)|A. P. Ajith; K. Vidyamol; B. R. Devassy; P. Manju|10.1109/ACCTHPA57160.2023.10083371|atmospheric haze;dehazing;distortions;Guided Variance Weighted Average filter;SSIM;PSNR;nan|
|[Evaluation of Optimizers for Predicting Epilepsy Seizures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083346)|P. Manju; B. R. Devassy; K. Vidyamol; A. P. Ajith|10.1109/ACCTHPA57160.2023.10083346|deep learning;CNN;Optimizers;EEG signal;epileptic seizure;seizure prediction;nan|
|[Detection of Credit Card Fraud Using Resampling and Boosting Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083376)|S. Jose; D. Devassy; A. M. Antony|10.1109/ACCTHPA57160.2023.10083376|SMOTE;over-sampling;Extra Tree;Gradient Boost;Decision Tree;Random Forest;AdaBoost;nan|
|[Drowsiness Sensing System for Driver Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083366)|P. A. Colin; S. G. Colaco; A. Deepansh; R. I. Melron; V. Deekshith|10.1109/ACCTHPA57160.2023.10083366|eye blink sensor;Microcontroller;drowsy driving;nan|
|[Natural Disaster Detection Using Social Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083339)|H. Krishnan; A. Roy; A. K. Menon; D. S; H. M. Babu|10.1109/ACCTHPA57160.2023.10083339|Disaster Detection;Machine learning;Deep Learning;nan|
|[Stress Prediction Using Enhanced Feature Selection and KNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083348)|A. S. Maria; R. Sunder; A. M. Antony|10.1109/ACCTHPA57160.2023.10083348|EEG;ECG;HRV;Machine Learning Model;KNN classifier;Data Filtering;Forward Filtering;Backward Filtering;Enhanced Feature Selection;nan|
|[Deep learning architectures for Brain Tumor detection: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083385)|D. K. S; M. T. I|10.1109/ACCTHPA57160.2023.10083385|Deep learning (DL);Transfer Learning (TL);Reinforcement Learning (RL);Evolutionary Algorithms (EA) Convolutional neural networks (CNN);Medical Images;classification;nan|
|[A Comparative Analysis and Study of a Fast Parallel CNN Based Deepfake Video Detection Model with Feature Selection (FPC-DFM)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083340)|A. Das; L. Sebastian|10.1109/ACCTHPA57160.2023.10083340|Deepfake video detection;Convolutional neural network (CNN);Principal Component Analysis;Support vector machine (SVM);nan|
|[Intention to Use eSanjeevani-OPD App: An Empirical Investigation Using Health Belief Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083374)|S. Kaladharan; M. Dhanya; R. Drisya; M. B. Raj|10.1109/ACCTHPA57160.2023.10083374|eSanjeevani;health belief model;telehealth;digital health interventions;nan|
|[Design of a Smart Gateway for Network/Device aware transmissions in M2M Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083365)|S. Sreeraj; D. Harikrishnan|10.1109/ACCTHPA57160.2023.10083365|M2M;Use case;IoT;SDO;OneM2M;Gateway;Communication;nan|
|[Importance of CNN in the Classification of Remote Sensing Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083375)|V. Kurian; V. Jacob|10.1109/ACCTHPA57160.2023.10083375|CNN;classification of images;remote sensing dataset;measures for evaluation;nan|
|[Cyber Complaint Automation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083354)|A. V. Prabhu; M. Jefiya; J. D. Joseph; T. Sunny; C. M. Abraham|10.1109/ACCTHPA57160.2023.10083354|Text Classification;Complaints;nan|
|[Social IoT Security and Privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083356)|Maniveena; Kalaiselvi|10.1109/ACCTHPA57160.2023.10083356|Internet of things;SIoT;security challenges;privacy;cyber threats;nan|
|[Spatial Intuitionistic Fuzzy C-means with Calcifications enhancement based on Nonsubsampled Shearlet Transform to detect Masses and Microcalcifications from MLO Mammograms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083338)|C. Sarada; K. V. Lakshmi; M. Padmavathamma|10.1109/ACCTHPA57160.2023.10083338|Medical image segmentation;Breast cancer;Mediolateral oblique(MLO) Mammogram;Masses and Microcalcifications identification;Spatial Intutionstic Fuzzy C-means(SIFCM);non-subsampled shearlet transform (NSST);Morphological area gradient;Image Enhancement;nan|
|[Semantic Image Style Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083358)|S. B. Stalin; J. E. Judith; C. D. Jegan|10.1109/ACCTHPA57160.2023.10083358|nan;nan|
|[IoT Based Heart attack Detection and Heart Rate Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083383)|T. A. Prasath; M. M. Arif; S. Srinivasan; A. Muthumanojkumar; M. Sushmitha; S. Sankaran|10.1109/ACCTHPA57160.2023.10083383|Internet of things;Arduino Uno;Pulse sensor;ECG sensor;Node MCU;nan|
|[Mobile Application Based Seed Counting Analysis Using Deep-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083344)|D. Devasena; Y. Dharshan; B. Sharmila; S. Aarthi; S. Preethi; M. Shuruthi|10.1109/ACCTHPA57160.2023.10083344|Seed Morphometry;Optical Sensing;Seed Texture;Canny Edge Detector;Fully Convolutional Network (FCN);U-Net;Fully Convolutional Regression Network (FCRN);nan|
|[Real-Time Sign Language Recognition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083349)|S. Sen; S. Narang; P. Gouthaman|10.1109/ACCTHPA57160.2023.10083349|nan;nan|
|[Efficiency Improvement in Smart grid using Enhanced Chaotic Crow Search Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083368)|S. Shiny; M. M. Beno; C. R. E. Selvan Rex|10.1109/ACCTHPA57160.2023.10083368|Enhanced Chaotic Crow Search Optimization (ECCSO);Voltage control;Solar PV System;Smart Grid;nan|
|[A Detailed Study on Adversarial Attacks and Defense Mechanisms on Various Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083378)|K. V. Priya; P. J. Dinesh|10.1109/ACCTHPA57160.2023.10083378|Deep Learning;Adversarial Learning;Medical Image Classification;nan|
|[Comparison and Analysis of CNN Based Algorithms for Plant Disease Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083342)|R. Rattan; J. O. Shiney|10.1109/ACCTHPA57160.2023.10083342|Convolutional neural network (CNN);plant disease detection;village plant disease dataset;nan|
|[Single Target Visual Object Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083357)|S. Kumar; S. K. Singh|10.1109/ACCTHPA57160.2023.10083357|Neural network;Visual object tracking;Bounding boxes;Attribute analysis;nan|
|[Comparative Analysis of the Machine and Deep Learning Classifier for Dementia Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083361)|A. Goel; M. Lal; A. Narendra Javadekar|10.1109/ACCTHPA57160.2023.10083361|neurodegenerative sickness;Alzheimer's disease;dementia;machine learning;deep learning;nan|
|[Smart Identification of Nutrient Based pH for an NFT Hydroponic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083370)|M. L. Anitha; G. S. Gowda; L. Tejaswini; P. Prokshith; A. P. S. Gupta|10.1109/ACCTHPA57160.2023.10083370|Hydroponics;NFT;Internet of Things;holy basil;Wi-Fi Module;nan|
|[Driver Drowsiness Detection From Multiple Facial Features Using Mobile Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083386)|J. K. John; J. Jose; D. Cyriac; H. A; A. K. Prince|10.1109/ACCTHPA57160.2023.10083386|nan;nan|
|[Energy Efficient Data Routing protocol in CLOUD Assisted MANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083377)|A. Bajulunisha; M. Surya; T. Azhagesvaran|10.1109/ACCTHPA57160.2023.10083377|Cloud Assisted MANET Architecture;Energy Based Data Routing;Packet Delivery ratio;Throughput;Average End to End delay;nan|
|[Large Data Block Transmission In VANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10083381)|A. Pullanatt; A. Anitha|10.1109/ACCTHPA57160.2023.10083381|VANET;communication;big chunks of data block communication;ad hoc network;MB-OFDM UWB;nan|
#### **2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)**
- DOI: 10.1109/ICCECE51049.2023
- DATE: 20-21 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Investigation of Hybrid Power Performance with Solar Module & Wind Turbine in MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085455)|M. S. Islam; S. Rahman; S. Chowdhury|10.1109/ICCECE51049.2023.10085455|Solar;Wind Turbine;MPPT;Converter;Software packages;Wind energy;Green products;Hybrid power systems;Mathematical models;Reflection;Fossil fuels|
|[Human Interaction-Free Object Localization in a Scene](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085125)|S. Moitra; S. Biswas|10.1109/ICCECE51049.2023.10085125|Object localization;Non-Maximum Suppression;Confidence threshold;IoU threshold;Regression;Bias-Variance Trade-Off;R2 score;Chi-squared test;Location awareness;Analytical models;Adaptation models;Object detection;Task analysis;Elbow|
|[Ensemble Learning And its Application in Spam Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085378)|A. Ghosh; R. Das; S. Dey; G. Mahapatra|10.1109/ICCECE51049.2023.10085378|spam detection;classification;support vector machine;random forest;decision tree;multinomial naïve bayes;ensemble learning;Support vector machines;Forestry;Electronic mail;Ensemble learning;Decision trees;Postal services;Testing|
|[Optimal Design of (α + β)-Order Butterworth Filter and Its Realization Using RLβCα Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085113)|S. Mahata; R. R. De Maity|10.1109/ICCECE51049.2023.10085113|fractional Bruton transformation;fractional order Butterworth filter;fractional order circuits;generalized impedance converter;improved particle swarm optimization;Transfer functions;Low-pass filters;Filtering algorithms;Approximation algorithms;SPICE;Stability analysis;Impedance|
|[A Review of Multi-Band Reflectarray Antenna Designs with Mutual Coupling Considerations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085232)|V. S; K. B J|10.1109/ICCECE51049.2023.10085232|Linear polarization;mutual coupling;polygon;reflectarray;single layer;unit cell;Phased arrays;Military communication;Mutual coupling;Satellite antennas;Key performance indicator;Reflector antennas;Military satellites|
|[Design And Development of Cost-Effective Automatic Solar Panel Cleaning System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085667)|A. M. Zaki Khayyat; A. Hamad Alharthi; F. L. Almohammadi; Z. Saad Almarwani; T. Shawely; M. E. Dessouki; Y. Mobarak; N. Kannan|10.1109/ICCECE51049.2023.10085667|Solar Panel;Soiling Effect;Temperature;I-V curve;P-V curve;Natural dust;Performance evaluation;Costs;Microcontrollers;Prototypes;DC motors;Cleaning;Water pumps|
|[Comparative Analysis of MIMO Multiuser Signal Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084997)|S. Dey; S. Bhowmick|10.1109/ICCECE51049.2023.10084997|Multi-user MIMO;CI;BD;Transmit Mechanism;Base stations;Spatial diversity;Precoding;Receiving antennas;Interference;Bandwidth;Space division multiplexing|
|[Model Based Test Framework for verification of Flight Control Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085332)|C. Singh; J. Shivamurthy; A. Garg|10.1109/ICCECE51049.2023.10085332|Light Combat Aircraft (LCA);Flight Control System (FCS);Digital Flight Control Computer (DFCC);Onboard Flight Program (OFP);Model Based Testing (MBT);Control Laws (CLAW);Modified Condition/Decision Coverage (MC/DC);Degree of Freedom (DOF);Codes;Atmospheric modeling;Software algorithms;Software;Autopilot;Safety;Task analysis|
|[A Fast-Converging Radial Basis Function Neural Network-Based MPPT Controller for Static and Dynamic Variations in Solar Irradiation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085281)|C. Venkateswararao; K. A. Naik|10.1109/ICCECE51049.2023.10085281|convergence;maximum power point;neural network;partial shading condition;radial basis function;Maximum power point trackers;Training;Radiation effects;Voltage;Radial basis function networks;Steady-state;Encryption|
|[Diabetic Retinopathy - An Ensemble Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085532)|A. Rastogi; T. Z. Rizvi; D. Deeba Kanan|10.1109/ICCECE51049.2023.10085532|Diabetes;Diabetic Retinopathy;Ensemble Approach;Transfer Learning;APTOS-19;Machine Learning;Classification Algorithm;K Nearest Neighbors;Training;Retinopathy;Transfer learning;Neural networks;Graphics processing units;Retina;Diabetes|
|[A Microcontroller Based FIR Filter With Dynamic Stabilization of Sampling Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085266)|S. Banerjee; B. Bhattacharyya; S. Munshi|10.1109/ICCECE51049.2023.10085266|Real-time FIR filter;Dynamic stabilization;Hamming window;ATmega2560;Microcontroller;Time-frequency analysis;Finite impulse response filters;Microcontrollers;Filtering algorithms;Dynamic scheduling;Real-time systems;Time measurement|
|[An Autonomous Assistance Robot for Multi-Purpose Medical Applications Using ROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085585)|P. Kadam; P. Padalkar; A. Mohite; S. Mirajgave; S. Gudadhe|10.1109/ICCECE51049.2023.10085585|SLAM;Gazebo;URDF;Navigation Stack;AMCL;Odometry;Google Cartographer;RViz;Visualization;Simultaneous localization and mapping;Robot vision systems;Transportation;Organizations;Assistive robots;Real-time systems|
|[Effect of barrier variabilities on the strain propagation and 2DEG profile of GaN/AlGaN HEMT heterostructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085316)|P. Kumar; J. Saha|10.1109/ICCECE51049.2023.10085316|GaN;Heterostructure;HEMT;Strain;2DEG;AlGaN double channel;Performance evaluation;HEMTs;Market research;Wide band gap semiconductors;MODFETs;Electric fields;Aluminum gallium nitride|
|[Analyzing and Addressing Data-driven Fairness Issues in Machine Learning Models used for Societal Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085470)|V. S. Pendyala; H. Kim|10.1109/ICCECE51049.2023.10085470|Machine Learning;Model Fairness;Bias;Fairness Metrics;Class Imbalance;Cohen’s Kappa statistic;Majority Weighted Minority Oversampling Technique;Weight measurement;Analytical models;Machine learning algorithms;Machine learning;Receivers;Predictive models;Data models|
|[Analysis of a Hysteresis Current Control DC–DC Buck Converter Suitable for Wide Range of Operating Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085385)|R. Ghosh; K. Dasgupta; S. P. Ghoshal|10.1109/ICCECE51049.2023.10085385|Hysteresis current control;dc–dc converters;buck power converter;energy optimization;light load;Current control;Buck converters;Capacitors;Switches;Stability analysis;Software;Transient analysis|
|[Envy Prediction from Users’ Photos using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085092)|M. A. K. Raiaan; A. Al Mamun; M. A. Islam; M. E. Ali; M. S. H. Mukta|10.1109/ICCECE51049.2023.10085092|Envy;BeMaS;Deep Learning;Convolutional Neural Network;Social networking (online);Neural networks;Predictive models;Behavioral sciences;Convolutional neural networks|
|[Predicting Gender from Human or Non-human Social Media Profile Photos by using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085525)|S. Sakib; N. M. Fahad; M. A. K. Raiaan; M. A. Rahman; A. Al Mamun; S. Islam; M. S. H. Mukta|10.1109/ICCECE51049.2023.10085525|Social Media;Gender;Deep Learning;Convolutional Neural Network;Transfer Learning;Social networking (online);Animals;Transfer learning;Multimedia Web sites;Blogs;Predictive models;Behavioral sciences|
|[Detection and Identification of Rice Pests Using Memory Efficient Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084936)|Z. H. Nayem; I. Jahan; A. A. Rakib; S. Mia|10.1109/ICCECE51049.2023.10084936|Rice Pests;Pests Detection;Convolutional Neural Network;Machine Learning;Training;Microorganisms;Image recognition;Sociology;Memory management;Production;Machine learning|
|[Network Traffic Classification Using Supervised Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084931)|M. R. Choudhury; M. N; P. Acharjee; A. T. George|10.1109/ICCECE51049.2023.10084931|Traffic classification;supervised learning;ma- chine learning;hyper-parameter tuning;decision tree;random forest;Radio frequency;Training;Supervised learning;Telecommunication traffic;Solids;Data models;Robustness|
|[Multiplexing of Infrared Images Using Periodic Optical Carrier Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085234)|B. Dutta Choudhuri Das; A. Saha|10.1109/ICCECE51049.2023.10085234|Fourier transform;Amplitude grating;PSNR;SSIM;Corelation coefficients;Integrated optics;Image coding;Image resolution;Satellites;Frequency-domain analysis;Ultraviolet sources;Optical imaging|
|[A Novel Low-Complexity Power-Efficient Design of Standard Ternary Logic Gates using CNTFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085528)|A. Paul; B. Pradhan|10.1109/ICCECE51049.2023.10085528|Ternary Logic;PTL;STI;STNAND;STNOR;STXOR;CNTFET;Simulation;Logic gates;Multivalued logic;Inverters;CNTFETs;Complexity theory;Transistors|
|[Real-Time Emotional Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084955)|A. Savva; V. Stylianou|10.1109/ICCECE51049.2023.10084955|Machine learning;Emotion analysis;Facial expressions;Machine learning;Real-time systems|
|[TiO2 Thick film Gas sensor for Detection H2S Gas Using ANN and Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085220)|A. Gupta; S. K. Dargar; A. Dargar|10.1109/ICCECE51049.2023.10085220|ANN;Thick film gas sensor;sensitivity;Training;Temperature measurement;Sensitivity;Heuristic algorithms;Thick film sensors;Thick films;Predictive models|
|[Satellite Wi-Fi Terminal for Post-Disaster Emergency Communication Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085637)|L. K. Kapoor; S. Ashish|10.1109/ICCECE51049.2023.10085637|Mobile Satellite Services;Disaster Management;Emergency Communication;GSM;Graphics;Satellites;Sociology;Universal Serial Bus;Routing;Statistics|
|[A New Search Algorithm for Calculating the Maximum Loadability of a Transmission System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085209)|J. Piri; G. Bandyopadhyay; M. Sengupta|10.1109/ICCECE51049.2023.10085209|Transmission System;Maximum Loadability;Line-loadability limit;Angle Stability Limit;DC Load Flow;Government;Companies;Power system stability;Linear programming;Generators;Planning;Optimization|
|[Development of a Laboratory Prototype of a Three Phase Three Bus Transmission System Emulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085657)|J. Piri; G. Bandyopadhyay; M. Sengupta|10.1109/ICCECE51049.2023.10085657|Transmission System Emulator;Charging VAR;Voltage Regulation;π-Modelling;ABCD-parameters;Power transmission lines;Simulation;Laboratories;Prototypes;Conductors;Regulation;Power systems|
|[Improvement of Transient Stability in Power System Using Rotating Disc Type Passive Magnetic Fault Current Limiter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085619)|A. Kumar Mondal; T. Santra|10.1109/ICCECE51049.2023.10085619|PMFCL;Transient Stability;SMIB;Variable Impedance;Power transmission lines;Power system stability;Stability analysis;Circuit stability;Finite element analysis;Impedance;Circuit faults|
|[Prediction of Recurrence in Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085448)|S. Bhattacharjee; B. Saha; S. Saha|10.1109/ICCECE51049.2023.10085448|lung cancer;cancer recurrence;gene expression;feature selection;machine learning;support vector machine;multi layer perceptron;random forest;Support vector machines;Machine learning algorithms;Lung cancer;Predictive models;Prediction algorithms;Feature extraction;Data models|
|[Power Spectral Density, Higuchi’s Fractal Dimension and Detrended Fluctuation Analysis of sEMG at Varying Weights](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085445)|S. Kumar Das; N. Das; M. Chakraborty|10.1109/ICCECE51049.2023.10085445|Surface Electromyogram (sEMG);Power Spectral Density (PSD);HFD (Higuchi’s Fractal Dimension);Fractal Dimension;Nonlinear;Detrended Fluctuation Analysis (DFA);Weight measurement;Fluctuations;Power measurement;Time series analysis;Muscles;Market research;Fractals|
|[Employee attrition prediction for imbalanced data using genetic algorithm-based parameter optimization of XGB Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085402)|K. Konar; S. Das; S. Das|10.1109/ICCECE51049.2023.10085402|Machine learning;Imbalanced classification;XGBoost;Genetic Algorithm;Training;Productivity;Adaptation models;Organizations;Predictive models;Prediction algorithms;Performance analysis|
|[Network Throughput Improvement in Wi-Fi 6 over Wi-Fi 5: A Comparative Performance Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085684)|D. Bandyopadhyay; S. De; S. Hom Roy; D. Biswas; M. Bhose; R. Karmakar|10.1109/ICCECE51049.2023.10085684|Network throughput;Wi-Fi 5;Wi-Fi 6;IEEE 802.11ax;IEEE 802.11ac;Protocols;Spectral efficiency;IEEE 802.11ax Standard;Bandwidth;Throughput;Downlink;Performance analysis|
|[A New hybrid Feature selection-Classification model to Improve Cancer Sample Classification Accuracy in Microarray Gene Expression Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085390)|R. Bandyopadhyay; A. Das Sharma; B. Dasgupta; A. Ghosh; C. Das; S. Bose|10.1109/ICCECE51049.2023.10085390|DNA Microarray Technology;Gene expression data;Feature selection;Mutual information;TLBO;Classification;Bagging;Machine learning;Medical services;Classification algorithms;Gene expression;Prognostics and health management;Bagging;Optimization|
|[Building a Classification Model based on Feature Engineering for the Prediction of Wine Quality by Employing Supervised Machine Learning and Ensemble Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085272)|M. Nandan; H. Raj Gupta; M. Mondal|10.1109/ICCECE51049.2023.10085272|Supervised machine learning;wine quality;classification;ensemble learning;feature engineering;Industries;Heart;Correlation;Buildings;Predictive models;Software;Ensemble learning|
|[Analysis and Processing of Spatial Remote Sensing Multispectral Imagery using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085536)|O. Soufi; F. Z. Belouadha|10.1109/ICCECE51049.2023.10085536|Deep learning;multispectral satellite images;spatial remote sensing;segmentation;classification;object detection;Deep learning;Analytical models;Satellites;Protocols;Systematics;Computational modeling;Semantic segmentation|
|[Performance Analysis of Three-Phase Cascaded Hbridge Multi Level Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085108)|D. Prasad; R. P. Singh; A. Islam; A. Roy; R. Roy; S. Mukherjee|10.1109/ICCECE51049.2023.10085108|Energy conversion;Inverter;Multi-level;THD Performance;Renewable energy;Total harmonic distortion;Analytical models;System performance;Simulation;Modulation;Multilevel inverters;Transformers|
|[Early Screening of Valvular Heart Disease Prediction using CNN-based Mobile Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085513)|T. S. Roy; J. K. Roy; N. Mandal|10.1109/ICCECE51049.2023.10085513|Deep Learning;Mobile Network;Feature Extraction;Classification;Acoustic Stethoscope;PCG Signal;Heart;Deep learning;Total harmonic distortion;Training data;Feature extraction;Software;Signal analysis|
|[Retinal and Semantic Segmentation of Diabetic Retinopathy Images Using MobileNetV3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085191)|M. Prajapati; S. K. Baliarsingh; J. Hota; P. P. Dev; S. Das|10.1109/ICCECE51049.2023.10085191|Diabetic Retinopathy;Classification;Deep Learning;CNN;Retinal Segmentation;Retinopathy;Semantic segmentation;Merging;Neural networks;Blindness;Retina;Diabetes|
|[Optimized Novel DC to DC Converter for PV Fed Grid Tied EV Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10084962)|T. Kumar; C. A. Rajan|10.1109/ICCECE51049.2023.10084962|Electric Vehicles (EVs);RES (Renewable Energy sources);DC-DC Bi-directional Boost-Zeta converter;Firefly Algorithm (FFA) and PI controller;Renewable energy sources;Reactive power;PI control;Switching loss;DC-DC power converters;Bidirectional control;Electric vehicle charging|
|[Flop Resistance Controlled Circulating Current Minimization of Parallel Quadratic Step-Up Converter in DCMicro grid Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085233)|S. Hema; Y. Sukhi; R. Suguna|10.1109/ICCECE51049.2023.10085233|Quadratic Step-up converter;Parallel Quadratic step up converter;Circulating Current Minimization;PI Controller;Flop Resistance Control;Resistance;Industries;Power cables;Switches;Microgrids;Control systems;Minimization|
|[Investigation on Efficient Machine Learning Algorithm for DDoS Attack Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085248)|R. S. Devi; R. Bharathi; P. K. Kumar|10.1109/ICCECE51049.2023.10085248|DoS;IOT;DDoS;K-Nearest Neighbour;Support Vector Machine (SVM);Machine learning algorithms;Stochastic processes;Support vector machine classification;Intrusion detection;Telecommunication traffic;Machine learning;Denial-of-service attack|
|[Seven Level CHB Multilevel Inverter based STATCOM using Decoupled Control & DC Voltage Balancing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085627)|V. Bharadwaj; S. Panda; S. Kundu; S. Banerjee|10.1109/ICCECE51049.2023.10085627|CHB multilevel inverter;DC voltage control;decoupling control;PSPWM;STATCOM;Reactive power;Capacitors;Loading;Logic gates;Multilevel inverters;Automatic voltage control;Hardware|
|[Effectiveness of Feature Collaboration in Speaker Identification for Voice Biometrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085318)|A. Das; L. P. Roy; S. Kumar Das|10.1109/ICCECE51049.2023.10085318|Voice biometric;Speaker Identification;Feature Collaboration;Training;Biometrics (access control);Biological system modeling;Collaboration;Feature extraction;Mathematical models;Object recognition|
|[Prediction of Idiopathic Recurrent Spontaneous Miscarriage using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085363)|D. Sherpa; R. D. Abhijit; I. Mitra; D. Dhar; S. Sharma; P. Chakraborty; K. Chaudhury|10.1109/ICCECE51049.2023.10085363|IRSM;Raman spectroscopy;machine learning;SVM;DT;XGBoost;CNN;AdaBoost;RF;GB;ANN;PCA;OPLS-DA;Support vector machines;Proteins;Machine learning algorithms;Raman scattering;Prediction algorithms;Lipidomics;Convolutional neural networks|
|[Relative Study on Performance analysis of DMFET and DMTFET based Transducers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10085414)|S. Dhar; B. Maji; S. Das; M. Das; S. J. Mukhopadhyay|10.1109/ICCECE51049.2023.10085414|Dielectric modulation;FET;TFET;Drain current Sensitivity;Transducers;Sensitivity;TFETs;Modulation;Dielectrics;Performance analysis;Biosensors|
#### **2023 Conference on Information Communications Technology and Society (ICTAS)**
- DOI: 10.1109/ICTAS56421.2023
- DATE: 8-9 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Performance Analysis of High Order Close-In Path Loss Model at 28 and 38 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082746)|T. T. Oladimeji; P. Kumar; M. K. Elmezughi|10.1109/ICTAS56421.2023.10082746|5G technology;antenna;indoor environment;mm wave;path loss;polarization;propagation;receiver;transmitter;Wireless communication;Fading channels;Analytical models;5G mobile communication;Millimeter wave technology;Predictive models;Propagation losses|
|[New Validation of a Cybersecurity Model to Audit the Cybersecurity Program in a Canadian Higher Education Institution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082731)|R. Sabillon; J. R. Bermejo Higuera|10.1109/ICTAS56421.2023.10082731|cybersecurity;cybersecurity audit;cybersecurity audit model;cybersecurity assurance;cybersecurity controls;cybersecurity domains;cybersecurity maturity assessment;cyber readiness;cybersecurity scorecard;cybersecurity domain criticality;Training;Standards organizations;Organizations;Mathematical models;Regulation;Planning;Time factors|
|[Smart Home IoT Cybersecurity Survey: A Systematic Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082751)|L. Ayavaca-Vallejo; D. Avila-Pesantez|10.1109/ICTAS56421.2023.10082751|Cyber attacks;Cybersecurity;Smart Home Internet of Things (SHIoT);Systematic Mapping;Privacy;Systematics;Authentication;Smart homes;Passwords;Encryption;Internet of Things|
|[Rule-based Entity Recognition for Forensic Timeline](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082742)|H. Studiawan; M. F. Hasan; B. A. Pratomo|10.1109/ICTAS56421.2023.10082742|digital forensics;log2timeline plaso;forensic timeline;named entity recognition;Digital forensics;Buildings;Communications technology;Standards|
|[A Framework for the Adoption of Emerging Technologies to Reduce Under-Five Mortality in Zimbabwe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082723)|J. Batani; M. S. Maharaj|10.1109/ICTAS56421.2023.10082723|emerging technology;under-five mortality;digital health;digital paediatrics;SDG 3;Drugs;Pediatrics;Philosophical considerations;Roads;Supply chains;Government;Electronic healthcare|
|[IT-Aided Forecasting Model for Malaria Spread for the Developing World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082725)|E. Tuyishimire; C. P. Mukamakuza; A. Mbituyumuremy; T. X. Brown; D. Iradukunda; O. Phuti; H. R. Mary|10.1109/ICTAS56421.2023.10082725|Malaria;digital;epidemic;mixed infections;reinforcement;Analytical models;Monte Carlo methods;Digital systems;Surface waves;Surveillance;Predictive models;Mathematical models|
|[User Expectations and Continuance Intention of mHealth among Community Health Workers in Malawi *](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082721)|D. F. Malanga; W. Chigona|10.1109/ICTAS56421.2023.10082721|user expectations;mHealth;Continuance intention;Malawi;Africa;Training;Seminars;Productivity;Measurement;Manuals;Developing countries;Mobile handsets|
|[Infant Iris Biometric Recognition System: Can the iris be used for a secure infant recognition system?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082729)|N. Nelufule; A. de Kock|10.1109/ICTAS56421.2023.10082729|Infant Biometrics;Iris Recognition;Infant Iris Segmentation;Image Quality Assessment;Pediatrics;Image segmentation;Cameras;Communications technology;Quality assessment;Iris recognition|
|[Biometric Recognition of Infants Using Fingerprints: Can the infant fingerprint be used for secure authentication?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082749)|N. Nelufule; Y. Moolla; S. Ntshangase; A. de Kock|10.1109/ICTAS56421.2023.10082749|Infant Fingerprint;Infant Biometric;Fingerprint Biometric;Authentication;Performance evaluation;Pediatrics;Error analysis;Prototypes;Fingerprint recognition;Communications technology;Registers|
|[Toward Hidden Data Detection via Local Features Optimization in Spatial Domain Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082736)|N. J. de La Croix; T. Ahmad|10.1109/ICTAS56421.2023.10082736|Data hiding;Steganalysis;Convolutional neural network;Spatial domain images;Network infrastructure;Training;Digital images;Watermarking;Feature extraction;Stability analysis;Data models;Convolutional neural networks|
|[Computer Vision-based Applications in Modern Cars for safety purposes: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082722)|L. Nkuzo; M. Sibiya; E. Markus|10.1109/ICTAS56421.2023.10082722|ADAS;Computer vision;Lane detection;Traffic sign detection;Pedestrian detection;Safety;Law enforcement;Deep learning;Systematics;Road accidents;Protocols;Fatigue;Safety;Internet|
|[Exploring factors that affect Business Process Management (BPM) adoption in South African State-Owned Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082735)|L. P. T. Salamntu; F. Makoza|10.1109/ICTAS56421.2023.10082735|Business Process Management;BPM adoption;South Africa;State-Owned Entreprises;Leadership;Bibliographies;Focusing;Communications technology;Business process management;Business|
|[Impact of anxiety on students' behavioural intention to use business simulation games](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082738)|F. Petersen|10.1109/ICTAS56421.2023.10082738|Business simulation games (BSGs);Unified Theory of Acceptance and Use of Technology (UTAUT) model;students' behavioural intention;anxiety;Training;Anxiety disorders;Sociology;Games;Mathematical models;Statistics;Business|
|[Designing A Frugal Inspection Robot for Detecting In-Pipe Leaks in The Oil And Gas Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082747)|Z. Mthimkhulu; H. Adebanjo; T. T. Adeliyi|10.1109/ICTAS56421.2023.10082747|Robotics;pipelines;in-pipe leaks;inspection technology;mobile robot;Legged locomotion;Costs;Service robots;Oils;Pipelines;Prototypes;Inspection|
|[Application of machine learning techniques for predicting child mortality and identifying associated risk factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082734)|E. Mbunge; S. G. Fashoto; B. Muchemwa; R. C. Millham; G. Chemhaka; M. N. Sibiya; T. Dzinamarira; J. Buwerimwe|10.1109/ICTAS56421.2023.10082734|Child mortality;Under-five mortality;machine learning;Prediction;Zimbabwe;Pregnancy;Pediatrics;Machine learning algorithms;Pulmonary diseases;Predictive models;Decision trees;Indexes|
|[Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082744)|E. Mbunge; M. N. Sibiya; S. Takavarasha; R. C. Millham; G. Chemhaka; B. Muchemwa; T. Dzinamarira|10.1109/ICTAS56421.2023.10082744|Diarrhoea;Ensemble methods;Children;class imbalance;machine learning;Prediction;Zimbabwe;Pediatrics;Machine learning algorithms;Computational modeling;Developing countries;Predictive models;Prediction algorithms;Classification algorithms|
|[Analysis of machine learning methods to determine the best data analysis method for diabetes prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082727)|N. Sihlangu; R. C. Millham|10.1109/ICTAS56421.2023.10082727|Diabetes prediction;machine learning;Orange data mining tool;PIMA Indian Diabetes Dataset;Support vector machines;Data analysis;Stochastic processes;Machine learning;Predictive models;Software;Diabetes|
|[Analysis of SD-WAN Packets using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082743)|I. D. Emmanuel; N. Linge; S. Hill|10.1109/ICTAS56421.2023.10082743|GNS3;SD-WAN;SDN;Python;Wide area networks;Machine learning algorithms;Databases;Software algorithms;Network architecture;Throughput;Real-time systems|
|[Analysing Channel Surfing Behaviour of IPTV Subscribers Using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082748)|T. T. Adeliyi; A. Singh; O. J. Aroba|10.1109/ICTAS56421.2023.10082748|Channel surfing;IPTV;machine learning;subscriber's behaviour;television;Training;Performance evaluation;Analytical models;TV;Biological system modeling;Stacking;Machine learning|
|[Analysing University at-Risk Students in a Virtual Learning Environment using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082752)|D. Naidoo; T. T. Adeliyi|10.1109/ICTAS56421.2023.10082752|Students at risk;machine learning;virtual learning environment;AdaBoost;Performance evaluation;Training;Support vector machines;Machine learning algorithms;Electronic learning;Error analysis;Prediction algorithms|
|[Effect of hyperparameter tuning on classical machine learning models in detecting potholes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082724)|S. L. Govender; S. Joseph; A. Singh|10.1109/ICTAS56421.2023.10082724|Hyperparameter tuning;Machine Learning;Pothole detection;Support vector machines;Machine learning algorithms;Runtime;Roads;Machine learning;Watermarking;Communications technology|
|[Enhancing Traffic Simulations Analysis Efficacy using Multiperspective Heterogeneous Toolset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082745)|S. T. Rakkesh; A. R. Weerasinghe; R. A. C. Ranasinghe|10.1109/ICTAS56421.2023.10082745|traffic simulation;macroscopic;microscopic;Analytical models;Simulation;Microscopy;Urban areas;Sociology;Traffic control;Communications technology|
|[A study on farmers' perceptions about the scope of the Kisan Suvidha App in improving agricultural sustainability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082741)|M. J. Bisheko; R. G|10.1109/ICTAS56421.2023.10082741|qualitative research;agricultural information;agricultural sustainability;mobile technology;rural community;Instruments;Communications technology;Agriculture;Mobile applications;Sustainable development;Interviews|
|[The Adoption of an Intelligent Waste Collection System in a Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082750)|O. J. Aroba; T. Xulu; N. N. Msani; T. T. Mohlakoana; E. E. Ndlovu; S. M. Mthethwa|10.1109/ICTAS56421.2023.10082750|Technology;Smart city;Higher education;waste management;Waste management;Waste materials;Visualization;Technological innovation;Smart cities;Urban areas;Sociology|
|[Developing a comprehensive evaluation questionnaire for university FAQ administration chatbots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082753)|L. Essop; A. Singh; J. Wing|10.1109/ICTAS56421.2023.10082753|Frequently asked questions chatbot;university administration;evaluation;anthropomorphism;usability;user experience;Instruments;MIMICs;Oral communication;User interfaces;Chatbots;Market research;User experience|
#### **2023 IEEE Texas Power and Energy Conference (TPEC)**
- DOI: 10.1109/TPEC56611.2023
- DATE: 13-14 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Inter-area Oscillations caused by Cyber Attacks and their Countermeasures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078562)|Z. Gao; T. R. B. Kushal; M. Illindala; J. Wang|10.1109/TPEC56611.2023.10078562|Time-delay attack;Pade Approximation;Man-in-the-Middle Attack;Vulnerability Evaluation;Inter-area Oscillation;Measurement;Monte Carlo methods;Intrusion detection;Power system stability;Smart grids;Risk management;Security|
|[A Bayesian Measure For Predicting Outages in Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078641)|R. M. Ferrand; A. E. Vela|10.1109/TPEC56611.2023.10078641|power outage;power system;distribution system;outage location;computer pinging;radial system;loop system;Bayesian statistics;Naive Bayes Classifier;Power measurement;Power distribution;Probabilistic logic;Power system reliability;Bayes methods;IP networks;Task analysis|
|[Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078568)|S. Badakhshan; R. A. Jacob; B. Li; J. Zhang|10.1109/TPEC56611.2023.10078568|Intentional Islanding;Grid Resilience;Reinforcement Learning;OpenAI Gym;PSS/E;Adaptation models;Islanding;Power system dynamics;Switches;Reinforcement learning;Power system stability;Control systems|
|[Categorical Databases for Mathematical Formalization of AC Optimal Power Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078526)|M. Barati|10.1109/TPEC56611.2023.10078526|ACOPF;Category;Compositional;Database;Functor;Computer languages;Databases;Computational modeling;Complex networks;Mathematical models;Data models;Power systems|
|[A Novel Hybrid MPPT Technique for PSC using Weighted Average Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078495)|K. Buch; C. Bhatt; U. Patel; P. N. Tekwani|10.1109/TPEC56611.2023.10078495|Global MPP;Hybrid MPPT;Weighted Average;Fibonacci series;Actuator Scheduling;Maximum power point trackers;Photovoltaic systems;Actuators;Numerical analysis;Simulation;Scheduling;Hybrid power systems|
|[Feasibility Analysis of Ice Thermal Energy Storage for a Chiller Plant Facility in Mediterranean Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078506)|P. R. Jackson; C. Venugopal; P. Kramer; A. Eiden|10.1109/TPEC56611.2023.10078506|Demand shift;incentives;peak shaving;Mediterranean climate;rate structures;Costs;Government;Companies;Ice;Reliability;Thermal energy;Stress|
|[H∞ Robust Control of a Grid-Connected Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078550)|A. Bamigbade|10.1109/TPEC56611.2023.10078550|Disturbance rejection;robust control;inverter;Robust control;Reactive power;PI control;Fluctuations;Simulation;Inverters;Robustness|
|[Social Network Power System Alarm Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078588)|J. Bizarro; C. Newman; W. Jang|10.1109/TPEC56611.2023.10078588|threat detection;power system;social media;Twitter;machine learning;Time-frequency analysis;Machine learning algorithms;Social networking (online);Blogs;Training data;Machine learning;Alarm systems|
|[A Method for Measuring the Power System Operation Modes in a Heavy Renewable Power Penetration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078452)|U. T. Salman; H. A. Al-Harathi; S. Adetona|10.1109/TPEC56611.2023.10078452|Data Analysis;Power System Operation;Power System Stability;Renewable Energy;Operation Modes;Clustering.;Dimensionality reduction;Renewable energy sources;Power measurement;Power distribution;Power system stability;Wind power generation;Power grids|
|[Automatic Generation Control Under Single Time-Delay Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078699)|Z. Gao; C. Moya; J. Wang; M. Illindala|10.1109/TPEC56611.2023.10078699|Time-delay attack;Automatic Generation Control;Cyber threat;Heuristic algorithms;Automatic generation control;Robustness;Mathematical models;Eigenvalues and eigenfunctions;Delays;Smart grids|
|[Solid State Condenser (SSC) - A New FACTS Device for Grid Inertia Support](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078567)|H. S. Rizi; Z. Chen; E. Nazerian; W. Xu; A. Q. Huang|10.1109/TPEC56611.2023.10078567|Grid forming;super capacitor;energy storage;inertia;FACTS;Reactive power;Time-frequency analysis;Simulation;Power system stability;Inverters;Stability analysis;Hardware|
|[Resilience-Enabling Load Flexibility and Resource Adequacy Investment in Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078500)|S. Yankson; S. M. Safayet Ullah; S. Ebrahimi; F. Ferdowsi; K. A. Ritter; T. Chambers|10.1109/TPEC56611.2023.10078500|Resilience;microgrids;resource adequacy (RA);energy planning;distributed energy resources (DERs);flexible load;Planing;Power distribution;Microgrids;Planning;Safety;Power system reliability;Resource management|
|[Large-signal Stability Analysis of Inverter-based Microgrids via Sum of Squares Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078586)|H. Hosseinpour; M. MansourLakouraj; M. Ben-Idris; H. Livani|10.1109/TPEC56611.2023.10078586|Inverter-based resources;large-signal stability;Lyapunov function;the sum of squares method;the domain of attraction;Analytical models;Computational modeling;Microgrids;Power system stability;Stability analysis;Inverters;State-space methods|
|[Volt/VAR Support and Demand Response Co-Optimization in Distribution Systems with Adaptive Droop Control of Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078554)|M. MansourLakouraj; H. Hosseinpour; H. Livani; M. Benidris; M. S. Fadali|10.1109/TPEC56611.2023.10078554|Two-step droop control;Three-phase distribution grids;Probabilistic Vol/VAR support;Demand response;Costs;Adaptive systems;Uncertainty;Stochastic processes;Minimization;Inverters;Demand response|
|[Undergraduate Research in Transmission Tower Physical Design for Synthetic Electric Grid Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078640)|J. W. Xia; A. B. Birchfield; J. Snodgrass|10.1109/TPEC56611.2023.10078640|Transmission tower;transmission line parameters;phase separation;physical configuration;synthetic grid;Limiting;Poles and towers;Estimation;Power grids;Research and development;Physical design|
|[A Common Automation Framework for Cyber-Physical Power System Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078723)|E. Cope; H. Huang; A. Sahu; K. Davis|10.1109/TPEC56611.2023.10078723|modeling;simulation;automation;virtualization;Automation;Power grids;Communication networks;Energy management systems;Cyberattack|
|[Impact of Current Limiting Reactor on Bulk Power Network – A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078670)|A. Shah|10.1109/TPEC56611.2023.10078670|CLR;rotor angle;steady-state;transient stability;short circuit;voltage drop;Reactive power;Limiting;Substations;Voltage;Power system stability;Stability analysis;Steady-state|
|[Smart bidirectional charging for frequency support of a low-inertia vehicle-to-grid system in presence of energy storage systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078605)|M. Mehrasa; H. Salehfar; D. F. Selvaraj; S. I. Ahmed|10.1109/TPEC56611.2023.10078605|electric vehicles;energy storage system;low-inertia grid;vehicle-to-grid;frequency control;Vehicle-to-grid;Simulation;Linear programming;Control systems;State of charge;Frequency control;Optimization|
|[Robust control strategy for a high-power off-board EV charger connected to grid-tied critical loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078516)|M. Mehrasa; D. F. Selvaraj; H. Salehfar|10.1109/TPEC56611.2023.10078516|electric vehicle;off-board charger;bidirectional DC/AC converter;dual active bridge converter;robust DC-link voltage;Robust control;Voltage measurement;Power system dynamics;Bridge circuits;Control systems;Robustness;Steady-state|
|[Preliminary Analysis of the Potential Impact of Electric Vehicle Fleets on Large Power System Inertia Floor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078590)|O. Oshinkoya; J. -O. Baek; J. Kao; A. Birchfield|10.1109/TPEC56611.2023.10078590|Inertia;electric vehicles fleets;clean school bus. batteries;frequency regulation;frequency nadir;UFLS.;Electric potential;Simulation;Power system dynamics;Load shedding;Electric vehicles;Generators;Vehicle dynamics|
|[Spatiotemporal Impact of Electric Vehicles in Mitigating Damages from Destructive Storms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078519)|J. K. Jung; F. Safdarian; J. L. Wert; D. Wallison; T. J. Overbye|10.1109/TPEC56611.2023.10078519|Vehicle-to-grid;winter storms;load shedding;power outages;grid reliability;renewable energy;Storms;Load shedding;Electric vehicles;Spatiotemporal phenomena;Reliability;Resilience|
|[Wind Resource Drought Identification Methodology for Improving Electric Grid Resiliency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078708)|J. L. Wert; F. Safdarian; A. Gonce; T. Chen; D. Cyr; T. J. Overbye|10.1109/TPEC56611.2023.10078708|renewable generation;weather;wind drought;power systems planning;Wind energy generation;Wind;Renewable energy sources;Wind speed;Droughts;Wind power generation;Propagation losses|
|[Cyber-Physical Power System Layers: Classification, Characterization, and Interactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078602)|M. Abdelmalak; N. Bhusal; M. Gautam; M. Benidris|10.1109/TPEC56611.2023.10078602|Cyber-Physical Power Systems;Resilience;real-time simulation;Analytical models;Computational modeling;Physical layer;Power grids;Power system reliability;Reliability;Resilience|
|[Properties of Geomagnetic Disturbances and How they Might Affect Power Systems: An Analysis of Past Geomagnetic Disturbances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078619)|J. Griffin; B. Kruse; M. S. Bitar; J. Snodgrass; K. Davis; T. Overbye|10.1109/TPEC56611.2023.10078619|nan;Geomagnetic storms;Power systems;Testing;Resilience|
|[Phase Unbalance Impacts on Black-Start Service Restoration of Distribution Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078630)|A. Heidari-Akhijahani; K. L. Butler-Purry|10.1109/TPEC56611.2023.10078630|Black-start restoration;frequency stability;microgrid;unbalanced distribution network;Optimization methods;Distribution networks;Microgrids;Power system stability;Stability analysis;Mathematical models;Generators|
|[Polynomial Fitting and Analysis of Geomagnetic Disturbance Impacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078446)|R. Guthrie; K. Davis|10.1109/TPEC56611.2023.10078446|Geomagnetic disturbances;Geomagnetically induced current;Polynomial fitting;Latitude;Longitude;Analytical models;Fluctuations;Storms;Shape;Magnetometers;Distributed databases;Mathematical models|
|[A Novel technique for Power sharing and Synchronization of Distributed Generators in an Islanded AC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078466)|B. K. Dey|10.1109/TPEC56611.2023.10078466|Islanded AC microgrid;Synchronization;VSIs;Reactive power control;Reactive power;Power control;Microgrids;Voltage;IEEE Standards;Generators|
|[Distributed Energy Resources: A Review, Modeling, and Cyber-Physical Potential of Solar and Wind Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078505)|L. A. Homoud; K. Davis|10.1109/TPEC56611.2023.10078505|Cyber-physical systems;distributed energy resources;photovoltaic systems;solar power;wind power;Wind energy generation;Wind;Renewable energy sources;Wind energy;Voltage;Wind power generation;Distributed power generation|
|[Undergraduate Research on Adding Relay Models and Generator Capability Curves to Synthetic Electric Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078672)|S. E. Hurt; J. Snodgrass; T. J. Overbye|10.1109/TPEC56611.2023.10078672|Power Systems;Synthetic Networks;Line Distance Relay;Time-Overcurrent Relay;Generator Capability Curve;Reactive power;Heuristic algorithms;Power system protection;Power system dynamics;Protective relaying;Data models;Generators|
|[Coordinated Security Measures for Industrial IoT Against Eavesdropping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078577)|A. Farraj|10.1109/TPEC56611.2023.10078577|nan;Wireless communication;Limiting;Buildings;Ecosystems;Receivers;Intellectual property;Security|
|[Cooperative Transmission Strategy for Industrial IoT Against Interference Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078488)|A. Farraj|10.1109/TPEC56611.2023.10078488|nan;Measurement;Numerical analysis;Cooperative communication;Information security;Focusing;Interference;Probability|
|[Soft-Trust Based Architecture for NextG IIoT/IoET Security, Authentication and Authorization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078555)|N. Garcia; E. Hammad; A. Farraj|10.1109/TPEC56611.2023.10078555|IoT;trust;security;feasible;scalable architecture;Blockchain;multi-tier architecture;smart contracts;Authorization;Scalability;Power system dynamics;Authentication;Massive machine type communications;Blockchains;Security|
|[Microgrid Optimal Energy Scheduling with Risk Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078597)|A. Siddique; C. Zhao; X. Li|10.1109/TPEC56611.2023.10078597|Battery degradation;Conditional value at risk;Day-ahead scheduling;Energy management system;Microgrid;Risk management;Optimization;Photovoltaic systems;Renewable energy sources;Storms;Microgrids;Hurricanes;Batteries;Risk management|
|[Techno-economic Feasibility of A Trust and Grid-aware Coordination Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078675)|J. A. Dominguez; A. Parrado-Duque; O. D. Montoya; N. Henao; J. Campillo; K. Agbossou|10.1109/TPEC56611.2023.10078675|Blockchain;Demand Response;Distributed Energy Management;Electric Thermal Storage;Power Flow;Economics;Peak to average power ratio;Voltage;Pricing;Games;Power grids;Blockchains|
|[Impact Analysis of DoS attacks on Different MAS Control architectures in Cyber-Physical Testbed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078661)|K. Katuri; H. T. Nguyen; E. Anagnostou|10.1109/TPEC56611.2023.10078661|Centralized control;Cyber-Physical testbed;Multi Agent System;Raspberry Pi;Hardware-in-the-Loop;Decentralized control;Computer architecture;Microgrids;Power system stability;Physical layer;Real-time systems;Security|
|[Designing a Solar/Wind Hybrid Power System for Charging Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078494)|A. Balal; R. Barnwal; B. Yogi|10.1109/TPEC56611.2023.10078494|Wind turbine;Solar photovoltaic system;Electric vehicles;Renewable energy sources;Costs;Wind energy;Simulation;Charging stations;Wind farms;Hybrid power systems|
|[Clustering of electricity price: an application to the Italian electricity market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078632)|M. H. Imani; M. T. Andani; H. T. Andani|10.1109/TPEC56611.2023.10078632|Clustering methods;Electricity price;Fuzzy C-Means;Hierarchical algorithm;K-Means;PUN;Clustering methods;Clustering algorithms;Prediction algorithms;Electricity supply industry;Indexes|
|[Multi-Agent Energy Management Strategy for Multi-Microgrids Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078538)|M. S. Hossain; C. Enyioha|10.1109/TPEC56611.2023.10078538|multi-agent;microgrid;reinforcement learning;proximal policy optimization;energy management system;Costs;Simulation;Tariffs;Microgrids;Reinforcement learning;Real-time systems;Power markets|
|[Application of Surge Arrester in Limiting Voltage Stress at DC Breaker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078558)|S. Sen; S. Mehraeen; M. Moghbeli|10.1109/TPEC56611.2023.10078558|DC Grid;DC Circuit Breakers (DCCBs);Surge Arrester;Arresters;Circuit breakers;Voltage;Surges;Integrated circuit modeling;Inductors;Voltage control|
|[A study on a new sensorless control method for an induction motor using a non-linear speed observer and hybrid V/f control method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078673)|S. Kim; I. Yoon; C. Hong; J. Ko; S. Oh|10.1109/TPEC56611.2023.10078673|induction motor control;V/f control;scalar control;sensorless;Hybrid V/f;nonlinear observer;flux observer;Magnetic flux;Fans;Induction motors;Torque;Velocity control;Rotors;Observers|
|[P2P Energy Exchanges for Lowering the Hydrogen Production Cost Using Realistic Electrolyzer Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078649)|H. Haggi; W. Sun; P. Brooker; J. M. Fenton|10.1109/TPEC56611.2023.10078649|Distributed energy resources (DERs);Electrolyzer;Energy Decarbonization;Hydrogen Cost;Peer-to-Peer Energy Exchanges;Variable Electrolyzer Conversion Efficiency;Renewable energy sources;Costs;Sensitivity analysis;Simulation;Hydrogen;Transportation;Production|
|[A Genetic Algorithm-Based Power System Volt/Var Optimization Applied to Transmission Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078585)|H. Li; G. Joshi; M. Mazhari; T. Kopp|10.1109/TPEC56611.2023.10078585|nan;Renewable energy sources;Reactive power control;Reactive power;High-voltage techniques;Generators;Planning;Complexity theory|
|[On the Lifetime Emissions of Conventional, Hybrid and Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078609)|Z. Hu; R. T. Mehrjardi; M. Ehsani|10.1109/TPEC56611.2023.10078609|electric vehicle;hybrid electric vehicle;lifetime emissions;vehicle performance;dynamic programming optimization;Integrated circuits;Sensitivity;Fasteners;Programming;Batteries;Hybrid electric vehicles;State of charge|
|[Energy Efficiency and Economic Analysis For School Buildings in Jordan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078523)|A. Abuelrub; L. Al-Hourani; E. Abu-AlDarabi; C. Singh|10.1109/TPEC56611.2023.10078523|energy efficiency;levelized cost of energy;PV system;renewable energy;school building;simple payback period;Photovoltaic systems;Renewable energy sources;Costs;Pollution;Simulation;Buildings;Light emitting diodes|
|[Evaluating a real-time model decoupling compensation approach for developing scalable, high-fidelity microgrid models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078472)|R. Jinsiwale; M. Maharjan; T. Becejac; A. Ashok|10.1109/TPEC56611.2023.10078472|microgrids;modeling;real-time;scaling;Adaptation models;Renewable energy sources;Machine control;Microgrids;Real-time systems;Inverters;Distributed power generation|
|[Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078599)|C. Zhao; X. Li; Y. Yao|10.1109/TPEC56611.2023.10078599|Battery Aging Test;Battery Degradation Models;Battery Energy Storage System;Energy Management System;Lithium-ion Batteries;Renewable Energy Sources;Degradation;Lithium-ion batteries;Analytical models;Renewable energy sources;Temperature;Fluctuations;Aging|
|[A New Index based on Power Splitting Indices for Predicting Proper Time of Controlled Islanding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078684)|H. Davarikia; F. Znidi; M. Barati; H. Rathore|10.1109/TPEC56611.2023.10078684|Coherent groups of generators;controlled islanding;synchronization coefficients;power splitting indices;Degradation;Islanding;Control systems;Generators;Data models;Indexes;Synchronization|
|[Robust Switching Control of DC-DC Boost Converter for EV Charging Stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078484)|S. Ahmad; R. P. C. de Souza; P. Kergus; Z. Kader; S. Caux|10.1109/TPEC56611.2023.10078484|DC-DC boost converter;switching control;electric vehicle charging;parameter estimation;measurement noise;Photovoltaic systems;Perturbation methods;Switching frequency;Simulation;Switches;Control systems;Stability analysis|
|[Measuring and Analyzing Effects of HEMP Simulation on Synthetic Power Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078638)|C. L. May; A. K. Barnes; J. E. Tabarez; A. Mate; E. M. Nelson; R. Guttromson|10.1109/TPEC56611.2023.10078638|electromagnetic pulse;power grid visualization;contingency simulation;cascading failures;severity index.;Electric potential;Visualization;Uncertainty;Power measurement;Correlation;Storms;Contingency management|
|[Bi-Level Transactive Coordination of Energy Management Systems in a Community](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078524)|F. Etedadi; S. Kelouwani; F. Laurencelle; N. Henao; K. Agbossou; F. Amara|10.1109/TPEC56611.2023.10078524|Home energy management;Demand response;Households coordination;Transactive Energy;Smart grid;Transactive energy;Energy consumption;Costs;Power system dynamics;Transformers;Demand response;Energy management systems|
|[Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078507)|N. Le; A. P. Leos; J. Henriquez; A. P. Ngo; H. T. Nguyen|10.1109/TPEC56611.2023.10078507|Energy storage;arbitrage and regulation services;linear programming;COVID-19 effect.;COVID-19;Pandemics;Urban areas;Vanadium;Low-carbon economy;Redox;Lead|
|[Improved Phasor Characterization of Power System Transients Using Adaptive Spectral Adjustment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078553)|C. P. Qian|10.1109/TPEC56611.2023.10078553|Discrete Fourier Transform (DFT);phasor estimation;power system measurement;real-time operation;spectral leakage;Power measurement;Heuristic algorithms;Simulation;Discrete Fourier transforms;Power system dynamics;Power system harmonics;Real-time systems|
|[Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078635)|M. Mahdavi; K. Schmitt; S. Bayne; M. Chamana|10.1109/TPEC56611.2023.10078635|Distribution networks;power quality;system reconfiguration;voltage-dependent loads;Voltage fluctuations;Fluctuations;Network topology;Computational modeling;Metaheuristics;Power distribution;Distribution networks|
|[Impact of Harmonics of Distributed Generators on the Harmonic Profiling of Distribution Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078476)|S. Rahman; I. A. Khan; A. Iqbal|10.1109/TPEC56611.2023.10078476|Total harmonic distortion;decoupled harmonic power flow analysis;distributed generators;source harmonics;IEEE 33-bus system;Total harmonic distortion;Simulation;Voltage;Distribution networks;Harmonic analysis;Active filters;Generators|
|[Review of Isolated DC-DC Converters for Applications in Data Center Power Delivery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078490)|S. Rahman; H. Shehada; I. A. Khan|10.1109/TPEC56611.2023.10078490|DC-DC Converter;soft-switching;resonant converter;high-frequency transformer;wide-bandgap devices.;Data centers;Power system measurements;Electric potential;Density measurement;DC-DC power converters;Transformers;Power system reliability|
|[Role of Consumption Pattern in Optimal Allocation of Distributed Generators in Electric Power and Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078503)|M. Mahdavi; F. Jurado; K. Schmitt; M. Chamana; S. Bayne|10.1109/TPEC56611.2023.10078503|Precise evaluation;distributed generation;load profile;renewable energy;Energy loss;Renewable energy sources;Power demand;Power distribution;Companies;Load management;Minimization|
|[An Efficient Model for Optimal Allocation of Renewable Energy Sources in Distribution Networks with Variable Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078511)|M. Mahdavi; K. Schmitt; S. Bayne; M. Chamana|10.1109/TPEC56611.2023.10078511|Distributed generation;energy losses;power distribution systems;load variations.;Energy loss;Renewable energy sources;Power demand;Costs;Propagation losses;Power systems;Numerical models|
|[Identifying Factors Contributing to Poor Performance of Near-Real-Time Power Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078697)|G. Cakir; K. Crawford; M. Baran; V. Cecchi; B. Chowdhury; O. Adeosun; M. Thomas; C. D. Chacko|10.1109/TPEC56611.2023.10078697|Distribution Management System (DMS);Logistic Regression;Bus Load Allocation (BLA);Machine Learning;Estimation;Real-time systems;Resource management;Load flow;Logistics|
|[Data Trading in Smart Grids: Future Research Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078634)|S. Gangopadhyay; S. Chakraborty; S. Das|10.1109/TPEC56611.2023.10078634|Data markets;data trading;smart grids;Data privacy;Volume measurement;Data security;Data integrity;Pricing;Smart grids;Surges|
|[Necessity of Joint Resource and Transmission Expansion Planning in Presence of System and Policy Uncertainties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078637)|M. Mehrtash; B. F. Hobbs; R. Mahroo|10.1109/TPEC56611.2023.10078637|Transmission expansion planning;generation expansion planning;joint resource and transmission expansion;co-optimization;policy and demand uncertainties;Economics;Industries;Uncertainty;Planning;Power systems;Numerical models|
|[Energy Theft Detection Using the Wasserstein Distance on Residuals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078584)|E. Altamimi; A. Al-Ali; Q. M. Malluhi; A. K. Al-Ali|10.1109/TPEC56611.2023.10078584|Energy theft;Non-technical losses;Information distance;Wasserstein distance;Short-term load forecasting;Meters;Training;Load forecasting;Computational modeling;Neural networks;Predictive models;Smart grids|
|[Sizing and efficiency models for the conceptual design of electric powertrains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078514)|F. Saemi; M. Benedict|10.1109/TPEC56611.2023.10078514|brushless DC motor;controller;inverter;battery;sizing;efficiency;Shafts;Torque;Brushless DC motors;Propellers;Computational modeling;Voltage;Mechanical power transmission|
|[Techno-Economical Assessment of MVAC and MVDC Collector Systems for Offshore Wind Farms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078627)|Y. Chen; S. Grijalva; L. Graber|10.1109/TPEC56611.2023.10078627|Offshore wind farm;electrical collector system;techno-economic analysis;efficiency;reliability;Costs;Uncertainty;Wind speed;Stars;Wind power generation;Wind farms;Probabilistic logic|
|[SOC-aware Primary Frequency Control of Low-inertia Power Systems with Battery Energy Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078647)|Z. Afshar; H. Rahmanei; I. Bhogaraju; M. Farasat|10.1109/TPEC56611.2023.10078647|Battery Energy Storage;Load Frequency Control;Primary Frequency Control;State of Charge Control;Stability criteria;Power system dynamics;Power system stability;Real-time systems;Mathematical models;Batteries;State-space methods|
|[Improved NaS Battery State of Charge Estimation by Means of Temporal Fusion Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078625)|A. H. Almarzooqi; M. O. Alhusin; I. P. Nikolakakos; A. Husnain; H. M. Albeshr|10.1109/TPEC56611.2023.10078625|Deep learning;deep neural network 3457;state-of-charge sd estimation;sodium sulfur (NaS) battery;Renewable energy sources;Sodium;Estimation;SCADA systems;Transformers;Stability analysis;Batteries|
|[Co-optimizing Behind-The-Meter Resources under Net Metering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078677)|A. S. Alahmed; L. Tong|10.1109/TPEC56611.2023.10078677|demand response;distributed energy resources;energy storage;home energy management;net metering;Renewable energy sources;Schedules;Nanoelectromechanical systems;Tariffs;Data models;Distributed power generation|
|[Design of a Three-Phase Inductive Power Transfer Coil with Interphase Mutual Inductance Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078680)|Y. Chen; H. A. Toliyat|10.1109/TPEC56611.2023.10078680|three-phase;WPT;IPT;interphase mutual inductance;coil design;Ferrites;Inductance;Simulation;Wireless power transfer;Integrated circuit modeling|
|[Angular Stability Analysis of Parallel Connected Grid-following PV Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078668)|R. Mishan|10.1109/TPEC56611.2023.10078668|Distributed Generators (DG);Grid Following inverters;Angular Stability;Multi-inverter PV;Couplings;Voltage;Power system stability;Inverters;Stability analysis;Generators;Power grids|
|[A High-Frequency AC-Linked Active Multicell Balancing Topology for Series-Connected Batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078694)|X. Yang; Q. Ma; A. Q. Huang|10.1109/TPEC56611.2023.10078694|Battery management system(BMS);Battary equalizer;Multicell balancing;DC-DC power converters;Equalizers;Current measurement;Battery management systems;Controllability;Hardware;Battery charge measurement;Topology|
|[Short-Term Dynamic Voltage Stability Status Estimation Using Multilayer Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078583)|M. Massaoudi; S. S. Refaat; A. Ghrayeb; H. Abu-Rub|10.1109/TPEC56611.2023.10078583|Classification;data analytics;machine learning;Short-Term Voltage Stability (STVS).;Voltage measurement;Stability criteria;Power system dynamics;Contingency management;Power system stability;Predictive models;Alarm systems|
|[Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer for Interval Prediction of Voltage Margin Stability Index in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078442)|M. Massaoudi; S. S. Refaat; A. Ghrayeb; H. Abu-Rub|10.1109/TPEC56611.2023.10078442|Deep learning;grid stability prediction;gated recurrent unit;bidirectional mechanism;interval prediction;Deep learning;Uncertainty;Stability criteria;Voltage;Power system stability;Logic gates;Prediction algorithms|
|[MV Propulsion Drive using Solid State Transformer (SST) Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078474)|H. Patel; S. Bhawal; K. Hatua|10.1109/TPEC56611.2023.10078474|Cascaded H-Bridge (CHB);High Frequency Induction Motor Drives (HF-IMDs);Propulsion Drives;Solid State Transformer (SST);Induction motor drives;Frequency modulation;Costs;Silicon carbide;Switching frequency;Switching loss;Switches|
|[A Constant Frequency Based Torque Ripple Free Three level NPC Brushless Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078712)|R. A. Raj; M. P. Shreelakshmi; S. George|10.1109/TPEC56611.2023.10078712|Multilevel inverter;Commutation torque ripple;BLDC motor;Constant switching frequency;Three level inverter;Torque;Brushless DC motors;Switching frequency;Switches;Hysteresis motors;Stators;Control systems|
|[Novel Functional Community Detection in Networked Smart Grid Systems-Based Improved Louvain Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078573)|M. E. Eddin; M. Massaoudi; H. Abu-Rub; M. Shadmand; M. Abdallah|10.1109/TPEC56611.2023.10078573|Community detection;electrical coupling strength;grid partition;intrusion detection;Louvain algorithm;Couplings;Simulation;Decentralized control;Clustering algorithms;Power transmission;Partitioning algorithms;Smart grids|
|[Capacitor Optimization for EV and Renewable DG Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078522)|M. Ghofrani; C. Hsu; M. Majidi|10.1109/TPEC56611.2023.10078522|Capacitors;Distributed power generation;Electric vehicles;optimization method;Smart grids;Renewable energy sources;Costs;Uncertainty;Simulation;Capacitors;Loading;Optimization methods|
|[Synthetic Geomagnetic Field Data Creation for Geomagnetic Disturbance Studies using Time-series Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078544)|A. Zhang; P. Dehghanian; M. Stevens; J. Snodgrass; T. Overbye|10.1109/TPEC56611.2023.10078544|Geomagnetic Disturbances;Magnetometer;Geomagnetic Field;Geomagnetically Induced Current;Machine Learning;Neural Networks;Generative Adversarial Networks;Analytical models;Fluctuations;Power system dynamics;Data visualization;Machine learning;Generative adversarial networks;Geomagnetic storms|
|[OUC Gardenia Grid Integration Laboratory: Overview and implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078633)|H. Panamtash; Q. Z. Sun; R. York; P. Brooker; J. Kramer|10.1109/TPEC56611.2023.10078633|Solar Power;Microgrid;Control;Battery;Photovoltaic (PV);renewable energy sources (RES);electric vehicle (EV);energy storage system (ESS);Industries;Smoothing methods;Protocols;Urban areas;Solar energy;Pricing;Microgrids|
|[Design and Development of Hybrid Power Conditioning System for Microgrid Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078721)|P. Brijesh; A. G. Lal; P. Anoop; V. Syam; A. Joseph|10.1109/TPEC56611.2023.10078721|DAB - Dual Active Bridge;PCS- Power Conditioning System;PV - Photovoltaic;DG - Distributed Generation;PMU-Phasor Measurement Unit;IMM- Intelligent Microgrid Manager;SiC - Silicon Carbide.;Power conditioning;Photovoltaic systems;Renewable energy sources;Power measurement;Silicon carbide;Microgrids;DC-DC power converters|
|[Open-Platform Sensor Node for Agrivoltaics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078620)|S. AlYasjeen; N. Elbeheiry; S. Shukri; R. S. Balog|10.1109/TPEC56611.2023.10078620|Smart Farming;Advanced Agriculture;Agrivoltaics;Internet of Things (IoT);Remote Monitoring;Sensors;Temperature sensors;Smart agriculture;Temperature measurement;Actuators;Machine learning algorithms;Soil moisture;Production|
|[Top-Down Control Design Strategy for Electric Power Grid EMP (E3) Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078616)|T. J. Donnelly; D. G. Wilson; R. D. Robinett; W. W. Weaver|10.1109/TPEC56611.2023.10078616|High Altitude Electromagnetic Pulse (HEMP);Geomagnetically Induced Currents (GIC);saturating transformer;optimal control;Electric potential;Power system dynamics;Optimal control;Transformers;Linear programming;EMP radiation effects;Power grids|
|[Rational Approximation of a Three-Phase Photovoltaic System via TD-VF and NL-VF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078565)|J. E. Tamayo; A. Ramirez; J. M. Ramirez|10.1109/TPEC56611.2023.10078565|Frequency domain synthesis;nonlinear systems;photovoltaic generators;Photovoltaic systems;Convolution;Frequency-domain analysis;Fitting;Transfer functions;Generators;Robustness|
|[Dynamic Transmission and Distribution Analysis for Asymmetrical Networks in the Presence of Distribution-Connected DER Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078461)|M. Moghbeli; M. M. Rezvani; S. Mehraeen; M. Peterson; T. Field|10.1109/TPEC56611.2023.10078461|Dynamic Analysis;Distributed Energy Resource (DER);DER_A Control Mechanism;Asymmetrical Network;Reactive power;Substations;Power system dynamics;Dynamic scheduling;Synchronous generators;Distributed power generation;Power system reliability|
|[Optimal Placement of Electric Vehicle Charging Stations: A Case Study in Jordan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078486)|A. Bashaireh; D. Obeidat; A. A. Almehizia; L. Shalalfeh|10.1109/TPEC56611.2023.10078486|electric vehicles;distribution system;charger placement;particle swarm optimization;EV charging;Power transmission lines;Limiting;Loading;Distribution networks;Transformers;Propagation losses;Electric vehicle charging|
|[SST-Based Marine Shipboard System to Achieve Improved Performance & Easy Renewable Energy Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078485)|I. Ullah; S. Rahman; I. A. Khan|10.1109/TPEC56611.2023.10078485|Power quality;marine shipboard;renewable energy system;solid-state transformer;Performance evaluation;Renewable energy sources;Voltage fluctuations;Power quality;Power system harmonics;Transformers;Harmonic analysis|
|[An Analysis of DMS Power Flow Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078468)|O. Adeosun; M. Thomas; G. Cakir; K. Crawford; V. Cecchi; B. Chowdhury; M. Baran; C. D. Chacko|10.1109/TPEC56611.2023.10078468|distribution power flow;distribution network;distribution management system;near real-time power flow;Measurement;Substations;Decision making;Real-time systems;Performance analysis;Distributed power generation;Reliability|
|[Optimal Coordination of Directional Overcurrent Relays using Numerical Iterative Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078545)|O. Adeosun; V. Cecchi|10.1109/TPEC56611.2023.10078545|protection coordination;optimization;distributed generation;Network topology;Distribution networks;Real-time systems;Power grids;Security;Reliability;Iterative methods|
|[A Peer-to-Peer Reputation-based Mechanism to Enhance Microgrids’ Power Exchange Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078463)|A. Loni; S. Asadi; M. Nazari-Heris|10.1109/TPEC56611.2023.10078463|Microgrid;Coalitional group;Reputation;Power exchange;Renewable energy sources;Costs;Simulation;Stability criteria;Collaboration;Microgrids;Power markets|
|[Impact of Grounding Conditions on Power Electronic Interfaces in a DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078450)|M. Naghizadeh; E. Farjah; T. Ghanbari; E. Muljadi|10.1109/TPEC56611.2023.10078450|DC microgrid;Grounding arrangement;Ground Faults;Two-level voltage source converter;Overcurrent transient;Overvoltage transient;Resistance;Grounding;Voltage;Microgrids;Power electronics;Mathematical models;Real-time systems|
|[Voltage Balancing Using Continuously Variable Series Reactor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078529)|M. Hayerikhiyavi; A. Dimitrovski|10.1109/TPEC56611.2023.10078529|Continuously Variable Series Reactor;Magnetic Amplifier;Gyrator-Capacitor model;voltage balancing;Magnetic flux;Systems operation;Magnetic cores;Windings;Phase control;Voltage control;Integrated circuit modeling|
|[Robust System Reconfiguration in the Presence of Uncertain Loads and Renewable Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078654)|M. Mahdavi; K. Schmitt; S. Bayne; M. Chamana|10.1109/TPEC56611.2023.10078654|Renewable energy;robust reconfiguration;time-varying loads;uncertain generation;Renewable energy sources;Energy loss;Uncertainty;Computational modeling;Distribution networks;Robustness;Topology|
|[Coalitional Game Theory in Power Systems: Applications, Challenges, and Future Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078455)|M. Gautam; M. Benidris|10.1109/TPEC56611.2023.10078455|Coalitional game theory;energy systems;nucleolus;power systems;Shapley value;Bibliographies;Games;Power systems;Behavioral sciences;Game theory|
|[Defense-in-Depth Framework for Power Transmission System against Cyber-Induced Substation Outages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078481)|K. Khanna; G. Ravikumar; M. Govindarasu|10.1109/TPEC56611.2023.10078481|Cybersecurity;planning;power system analysis;real-time contingency analysis;risk assessment;Substations;Contingency management;Power transmission;Cyber-physical systems;Real-time systems;Power grids;Power system reliability|
|[Reliability of Electric Vehicle Integrated Systems under Battery-Exchange and Plug-in Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078710)|D. Pandit; N. Nguyen|10.1109/TPEC56611.2023.10078710|Battery exchange (BE) mode;Controlled charging;Electric vehicles;Plug-in (PE) mode;Reliability;nan|
|[Deep Reinforcement Learning Framework for Short-Term Voltage Stability Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078572)|M. Sarwar; A. R. R. Matavalam; V. Ajjarapu|10.1109/TPEC56611.2023.10078572|Hybrid PV plants;Short term voltage stability;Deep Reinforcement Learning (DRL);Deep learning;Reactive power;Power system dynamics;Stability criteria;Reinforcement learning;Power system stability;Numerical simulation|
|[Quantum-Enhanced DC Optimal Power Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078612)|F. Amani; R. Mahroo; A. Kargarian|10.1109/TPEC56611.2023.10078612|Quantum computing;DC optimal power flow;Harrow-Hassidim-Lloyd algorithm;HHL Initialization;quantum computing error;Computers;Quantum computing;Signal processing algorithms;Signal processing;Quantum state;Mathematical models;Newton method|
|[A Tutorial on Identification of Subsynchronous Mode Frequencies in Power Transmission Systems using Parametric and Non-parametric Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078569)|S. A. Dorado-Rojas; Z. Wang|10.1109/TPEC56611.2023.10078569|nan;Power system dynamics;Estimation;Power transmission;Tutorials;Frequency estimation;Inverters;Transient analysis|
#### **2023 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD)**
- DOI: 10.1109/MNE3SD57078.2023
- DATE: 23-25 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Contribution to the construction of an ontology for the phytosanitary surveillance of cotton in Côte d'Ivoire](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079897)|T. K. N'Guessan Narcisse; M. Sadouanouan; K. Appoh; K. Malanno; B. K. Kra Norbert; O. O. Germain|10.1109/MNE3SD57078.2023.10079897|plant health surveillance;cotton;conceptual model;ontology;NeOn;UML;Systematics;Semantic search;Surveillance;Unified modeling language;OWL;Ontologies;Software|
|[Lightweight and robust MQTT protocol authentication model suitable for connected portals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079830)|E. Toé; D. F. Somé; T. Yélémou|10.1109/MNE3SD57078.2023.10079830|Internet of Things;MQTT;authentication;one-time password;Smarthome;Hash functions;Protocols;Authentication;Passwords;Smart homes;Resists;Robustness|
|[Application of Machine Learning in Software Quality: a Mini-review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079800)|O. Bombiri; P. Poda; T. F. Ouedraogo|10.1109/MNE3SD57078.2023.10079800|Machine Learning;software quality;defect prediction;maintainability prediction;Machine learning algorithms;Software algorithms;Software quality;Machine learning;Predictive models;Maintenance engineering;Prediction algorithms|
|[Passive Remote Sensing Studies of a Phantom Insect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079763)|M. Sougoti; S. Z. Kam; S. Zongo; A. Bere|10.1109/MNE3SD57078.2023.10079763|insect;passive LIDAR;quadrant photodiode;remote sensing;Laser radar;Insects;Phantoms;Power system harmonics;Telescopes;Harmonic analysis;Robot sensing systems|
|[Commercial microwave link networks for rainfall monitoring in Burkina Faso: First results from a dense network in Ouagadougou](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079165)|M. Djibo; C. Chwala; W. Y. S. B. Ouedraogo; A. Doumounia; S. R. Sanou; M. Sawadogo; H. Kunstmann; F. Zougmoré|10.1109/MNE3SD57078.2023.10079165|Attenuation;Microwave link;Rain rate;Signal;Telecommunications;nan|
|[Performance Evaluation of Fault Detection Algorithm Applied on a Photovoltaic Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079773)|O. W. Compaore; G. Hoblos; Z. Koalaga|10.1109/MNE3SD57078.2023.10079773|ROC;AVC;DCS;ARR;FDI;PVG;SHM;sensor fault;monitoring;diagnostic;nan|
#### **2023 15th International Conference on Computer Research and Development (ICCRD)**
- DOI: 10.1109/ICCRD56364.2023
- DATE: 10-12 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Enviroment Representations with Bisimulation Metrics for Hierarchical Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080162)|C. Zhang|10.1109/ICCRD56364.2023.10080162|Hierarchical reinforcement learning;Bisimulation Metricsng;Enviroment Representations;Training;Measurement;Reinforcement learning;Feature extraction;Task analysis;Research and development|
|[Crime Prediction Linked to Geographical Location with Periodic Features for Societal Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080235)|O. A. Esan; I. O. Osunmakinde|10.1109/ICCRD56364.2023.10080235|crime;link prediction;ensemble;machine learning;Law enforcement;Computational modeling;Decision making;Manuals;Machine learning;Predictive models;Personnel|
|[Sentence-BERT and k-means Based Clustering Technology for Scientific and Technical Literature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080830)|B. Yin; M. Zhao; L. Guo; L. Qiao|10.1109/ICCRD56364.2023.10080830|BERT;k-means;short text clustering;Bibliographies;Clustering algorithms;Data visualization;Feature extraction;Visual databases;Data mining;Research and development|
|[Differential Privacy Machine Learning Based on Attention Residual Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080444)|Y. Cui; J. Xu; M. Lian|10.1109/ICCRD56364.2023.10080444|deep learning;differential privacy;noise;residual networks;attention mechanism;Training;Differential privacy;Machine learning algorithms;Stochastic processes;Machine learning;Research and development;Residual neural networks|
|[An Energy and Memory Efficient Speaker Verification System Based on Binary Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080708)|Y. Lei; D. Kong; K. Xu; Z. Ren|10.1109/ICCRD56364.2023.10080708|speaker verification;binary neural network;deep learning;Degradation;Performance evaluation;Deep learning;Costs;Computational modeling;Neural networks;Memory management|
|[Named Entity Recognition Based on BERT-BiLSTM-SPAN in Low Resource Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080054)|M. Weng; W. Zhang|10.1109/ICCRD56364.2023.10080054|named entity recognition;BERT;BiLSTM;data augmentation;adversarial training;Training;Computational modeling;Bit error rate;Transfer learning;Merging;Knowledge graphs;Data models|
|[Enhancing the Feature Learning with Phonetic Control Module for Speaker Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080357)|Z. Zhang; W. Guo|10.1109/ICCRD56364.2023.10080357|speaker verification;residual network;multi-task learning;phonetic control module;Representation learning;Phonetics;NIST;Multitasking;Classification algorithms;Task analysis;Research and development|
|[Adaptive Recognition of Aircraft Cable Brackets Based on Improved Mask R-CNN and Synthetic Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080201)|J. Hu; G. Zhao; W. Xiao; J. Zou|10.1109/ICCRD56364.2023.10080201|intelligent manufacturing;aircraft assembly;visual inspection;object detection;synthetic dataset;Training;Adaptation models;Atmospheric modeling;Feature extraction;Aircraft manufacture;Aircraft;Aircraft propulsion|
|[Latency Minimization for Intelligent Reflecting Surface-Assisted Cloud-Edge Collaborative Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080403)|W. Li; J. Zhang; D. Guan; B. Cui; Z. Zheng; G. Feng; H. Wang; L. Zhang|10.1109/ICCRD56364.2023.10080403|Intelligent reflecting surface;cloud-edge Collaborative computing;latency;nan|
|[Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079983)|J. Yu; D. Huang; J. Li; W. Li; X. Wang; X. Shi|10.1109/ICCRD56364.2023.10079983|SURF;parallel pipeline;FPGA;real-time registration;feature extraction;Power demand;Computational modeling;Pipelines;Computer architecture;Gray-scale;Feature extraction;Real-time systems|
|[A High-Precision Intelligent Retrieval Algorithm for Bill of Quantities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080766)|R. Xie; J. Zhang|10.1109/ICCRD56364.2023.10080766|bill of quantities;Text similarity;TF-IDF;elastic search;Databases;Heuristic algorithms;NoSQL databases;Search engines;Feature extraction;Approximation algorithms;Market research|
|[Number and Classes of Rotations on Juggling Sequence Rotation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080407)|J. A. Erho; B. R. Japheth; J. I. Consul; P. O. Asagba|10.1109/ICCRD56364.2023.10080407|juggling array rotation;GCD-based sequence rotation;juggling algorithm;array circular shifting;properties of juggling algorithm;Software algorithms;Cache memory;Mathematical models;Software;Hardware;Pattern recognition;Behavioral sciences|
|[A Hybrid Approach for the Circular Knapsack Packing Problem with Rectangular Items](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080834)|Q. Luo; Y. Rao; P. Yang|10.1109/ICCRD56364.2023.10080834|biased genetic algorithm;knapsack packing;rectangular packing;Layout;Metaheuristics;Transforms;Benchmark testing;Encoding;Decoding;Biological cells|
|[Personalized Microblog Recommendation System Integrates Following and Reposting Relationship](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080318)|L. Wang; J. Wei; Y. Lu; Y. Zhu; L. Deng; J. Wu|10.1109/ICCRD56364.2023.10080318|microblog recommendation;following relationship;reposting relationship;LightGCN;Computational modeling;Collaborative filtering;Blogs;Convolutional neural networks;Recommender systems;Research and development|
|[MathBlock: Performing Complex Mathematical Operations on Synthetic Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080594)|P. Karamolegkos; A. Kiourtis; A. Karabetian; K. Voulgaris; Y. Poulakis; A. Mavrogiorgou; M. Filippakis; D. Kyriazis|10.1109/ICCRD56364.2023.10080594|mathematical operations;batch data;synthetic data;big data;microservices;math block;graph parsing algorithm;Medical services;Companies;Programming;Behavioral sciences;Reliability;Research and development;Synthetic data|
|[Research on Intelligent Scheduling Algorithm Based on Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080165)|X. Yan; J. Zhao; J. Han|10.1109/ICCRD56364.2023.10080165|cloud computing;genetic algorithm;particle swarm optimization;simulated annealing algorithm;Cloud computing;Scheduling algorithms;Heuristic algorithms;Neural networks;Parallel processing;Information age;Grid computing|
|[Harmonic Retrieval and Simulation of Aircraft Engine Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080582)|Y. Yang; S. Dai; X. Lei|10.1109/ICCRD56364.2023.10080582|flight simulation;aerodynamic;harmonic noise;high-order cumulant;harmonic retrieval;cyclic statistics;Time-frequency analysis;Maximum likelihood estimation;Atmospheric modeling;Simulation;Aerospace simulation;Harmonic analysis;Frequency estimation|
|[Aedes Aegypti Egg Classification Model Using Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080822)|C. R. Gumiran; A. C. Fajardo; R. P. Medina; M. S. Dao; J. M. Gumiran|10.1109/ICCRD56364.2023.10080822|aedes aegypti;egg classification;morphological feature;support vector machine or SVM;Shape;Computational modeling;Support vector machine classification;Color;Feature extraction;Classification algorithms;Viruses (medical)|
|[The Optimization of COVID-19 Detection Based on DesNet Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080527)|L. Mingchen; F. Jikun; C. Zhongxin; T. T. Toe|10.1109/ICCRD56364.2023.10080527|Transfer Learning;DesNet;Data Augmentation;SMOTE;Mixup;CutMix;Few-shot learning;CNN;COVID-19;Training;Pandemics;Pulmonary diseases;Computational modeling;Transfer learning;Data models|
|[Noninvasive Blood Pressure Prediction Based on Dual Encoder U-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080810)|Q. Sun; P. Chen; J. Zhang; Y. Xia; B. Wang|10.1109/ICCRD56364.2023.10080810|Blood Pressure;Photoplethysmogram;Hypertension;Computational modeling;Wearable computers;Semantics;MIMICs;Predictive models;Feature extraction|
|[A Proposed Model for Enhancing the Performance of Health Care Services in Smart Cities Using Hybrid Optimization Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080010)|A. M. Ali; A. -E. F. Hegazy; A. Dahroug; K. M. Hassan|10.1109/ICCRD56364.2023.10080010|HCS;IOT;cloud computing;task scheduling;hybrid optimization algorithm;smart cities intelligent applications;Cloud computing;Processor scheduling;Smart cities;Computational modeling;Simulation;Medical services;Scheduling|
|[DOSP: Distributed Computing-Based Operating Systems Labs Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080655)|H. Huang; L. Song|10.1109/ICCRD56364.2023.10080655|Distributed System;Online Judge;Operating System;Computer Science Education;Load Balancing;Pain;Scalability;Education;Manuals;Real-time systems;Virtual machining;Hardware|
|[Multi-view Discriminative Fusion on Canonical Correlation Analysis in Event Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080508)|X. Li; L. Zhang; Y. Zhang|10.1109/ICCRD56364.2023.10080508|Multi-view learning;Event analysis;Linear discriminant analysis;Canonical correlation analysis;Correlation;Semantics;Computer architecture;Benchmark testing;Linear discriminant analysis;Research and development|
|[A Novel Ray Tracing Method Based on Unity Scriptable Render Pipeline and DirectX Raytracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079997)|H. Wen|10.1109/ICCRD56364.2023.10079997|Computer Graphics;Ray Tracing;Path Finding;Render Pipeline;Geometry;Visualization;Monte Carlo methods;Pipelines;Production;Ray tracing;Sampling methods|
|[Noise-Based and Class-Based Curriculum Learning for Image Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080241)|Y. Li; E. Urtans|10.1109/ICCRD56364.2023.10080241|Deep Learning;Curriculum Learning;Image Classification;Training;Learning systems;Complexity theory;Noise measurement;Research and development;Testing|
|[A Transfer Learning Method for Covid-19 and Pneumonia Diagnosis Based on Chest Radiograph Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080197)|Y. Sun; L. Guo; T. -T. Teoh|10.1109/ICCRD56364.2023.10080197|Artificial Intelligence;Deep Learning;Transfer Learning;Image Processing;Pneumonia diagnosis;COVID-19;Sensitivity;Pulmonary diseases;Transfer learning;Lung;Feature extraction;Diagnostic radiography|
|[Augmentation of Alzheimer Images Base on Visual Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080298)|H. Xin; Z. Penggeng; Z. Beitian; Y. Shuxiang; T. T. Toe|10.1109/ICCRD56364.2023.10080298|Data Argumentation;Mixup;Overfitting;Image Classification;Visual Transform;Training;Visualization;Computational modeling;Training data;Transforms;Transformers;Data models|
|[A Lightweight Transfer Learning-Based Model for Building Classification in Aerial Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080643)|J. Herman; R. Zewail; T. Ogawa; S. ElSagheer|10.1109/ICCRD56364.2023.10080643|building classification;transfer learning;Computational modeling;Buildings;Urban planning;Transfer learning;Memory management;Mobile handsets;Iterative methods|
|[Learning a Good Representation for Metric-based Few-shot Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080350)|D. Liu; L. Bai; T. Yu; A. Zhang|10.1109/ICCRD56364.2023.10080350|artificial intelligence;image classification;few-shot learning;metric-based few-shot learning;Training;Learning systems;Transfer learning;Learning (artificial intelligence);Benchmark testing;Research and development|
|[Multi-stage Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080857)|T. Fang; J. Dong; C. Liang; J. Li|10.1109/ICCRD56364.2023.10080857|action recognition;graph convolutional networks;multi-stage temporal convolutional network;Convolution;Biological system modeling;Directed graphs;Feature extraction;Skeleton;Kinetic theory;Behavioral sciences|
|[3D Face Reconstruction Based on Fine-Grained Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080250)|J. Li; Z. Pan; M. Zhang; X. Liu|10.1109/ICCRD56364.2023.10080250|3D face reconstruction;deep neural network;self-supervised learning;residual learning;Training;Solid modeling;Three-dimensional displays;Supervised learning;Self-supervised learning;User experience;Task analysis|
|[Algorithm for Analyzing Rotating Images Based on the Fourier-Galois Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080084)|D. K. Matrassulova; S. B. Kabdushev; A. S. Bakirov; I. E. Suleimenov|10.1109/ICCRD56364.2023.10080084|Galois fields;Fourier-Galois transform;spectrum;rotating objects;rotation frequency;ultrasound;viscometry;multivalued logic;circuitry;nan|
|[The Latest Progress in Human Pose Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080205)|S. Zhang; X. Hu|10.1109/ICCRD56364.2023.10080205|pose estimation;deep learning;computer vision;Legged locomotion;Three-dimensional displays;Tracking;Annotations;Pose estimation;Video sequences;Symbols|
|[Deep Learning of Color Constancy Based on Object Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080343)|H. -y. Zhang; Y. Fang; J. -h. Wu; W. -z. Wang; N. -y. Zou|10.1109/ICCRD56364.2023.10080343|computational color constancy;object recognition;deep learning;Deep learning;Training;Image recognition;Image color analysis;Lighting;Predictive models;Prediction algorithms|
|[Research on End-to-End Continuous Sign Language Sentence Recognition Based on Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080216)|S. Jiang; Y. Liu; H. Jia; P. Lin; Z. He; L. Chen|10.1109/ICCRD56364.2023.10080216|Clip4clip;continuous sign language statement;deaf-mute;transformer;Visualization;Gesture recognition;Organizations;Assistive technologies;Transformers;Feature extraction;Data models|
|[The Data and Images of Natural Disaster News Report Based on Artificial Intelligence Technology Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080745)|C. Wang; R. Peng|10.1109/ICCRD56364.2023.10080745|artificial intelligence;natural disaster;data;images;Machine learning algorithms;Image recognition;Computational modeling;Crawlers;Machine learning;Writing;Data models|
|[Blockchain-based Distributed Identity Cryptography Key Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080490)|Y. Yu; Z. Li; Y. Tu; Y. Yuan; Y. Li; Z. Pang|10.1109/ICCRD56364.2023.10080490|identity-based cryptography;SM9;key generation;distributed computation;blockchain;Identity-based encryption;Bandwidth;Blockchains;Usability;Distributed management;Computer security;Research and development|
|[Technology-Based Spin-offs in Romania: Evaluation of Their Performance in Different Development Stages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080518)|L. M. Mihali|10.1109/ICCRD56364.2023.10080518|technology transfer;technology-based spin-offs;performance;Economics;Codes;Profitability;Databases;Europe;Companies;Research and development|
|[Decentralized Attribute-Based Access Control with Attribute Revocation and Outsourced Decryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080306)|F. Yang; H. Cui; J. Jing|10.1109/ICCRD56364.2023.10080306|Ciphertext-policy attribute-based encryption;Attribute-level revocation;Outsourced decryption;nan|
|[SZLS-GPSR: UAV Geographic Location Routing Protocol Based on Link Stability of Communication Safe Zone](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080861)|Y. Zhou; Z. MI; H. Wang; Y. Lu; Y. Tian|10.1109/ICCRD56364.2023.10080861|GPSR;FANET;communication safe zone;link stability;greedy forwarding;peripheral forwarding;Simulation;Redundancy;Length measurement;Routing;Stability analysis;Routing protocols;Topology|
|[Joint Backup Controller Placement and Routing for Low Invasive Flow Statistics Collection in SDNs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079939)|P. Xi; G. Zhao; H. Xu; L. Huang|10.1109/ICCRD56364.2023.10079939|software defined networks;flow statistics collection;backup controller placement;control link routing;Performance evaluation;Costs;Simulation;Process control;Bandwidth;Routing;Power capacitors|
|[Fully Homomorphic Encryption Accelerator Using DSP Embedded Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080450)|S. Hashim; M. Benaissa|10.1109/ICCRD56364.2023.10080450|fully homomorphic encryption;integer-based;digital signal processing;Cloud computing;Toy manufacturing industry;Public key;Digital signal processing;Throughput;Real-time systems;Homomorphic encryption|
|[Architecture Design of Flash Sale System Based on Apache Dubbo Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080753)|T. Chen; H. Suo|10.1109/ICCRD56364.2023.10080753|devops;software communication framework;Distributed architecture;Apache Dubbo;Netty;Flash buying system;Concurrent computing;Data centers;Protocols;System performance;Computer architecture;Throughput;Servers|
|[Research on Ship Wake Detection Method Based on Composite Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080122)|H. Xing; Y. Wang; J. Han; J. Bai|10.1109/ICCRD56364.2023.10080122|ship wake detection;Canny edge detection;probabilistic Hough transform;deep learning;neural network;Satellites;Image edge detection;Oceans;Neural networks;Object detection;Transforms;Probabilistic logic|
|[Design of Highly Reliable Fault Backtracking Calculation for Space Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080675)|J. Yu; D. Huang; J. Li; W. Li; X. Wang; X. Shi|10.1109/ICCRD56364.2023.10080675|highly reliable;fault recovery;FPGA;SPARC CPU;Backtracking;Fault detection;Process control;Computer architecture;Logic gates;Reliability engineering;Manipulators|
|[Medical Hardware for Stimulation and Metrology of the Heart: Electrocardiogram and Pacemaker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080176)|Z. Ran; H. Yu|10.1109/ICCRD56364.2023.10080176|signal acquisition;action potential;signal detection;pacemaker;ECG;Heart;Pacemakers;Metrology;Electrocardiography;Physiology;Hardware;Research and development|
|[Design and Implementation of a Two-Car Tracking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080429)|Y. Shang; Q. Yang; Y. Ma; J. Gao; Y. Zhu; M. Yuan|10.1109/ICCRD56364.2023.10080429|MCU MSP430F5529;tracking;two cars;ultrasonic;Bluetooth technology;Motor drives;Bluetooth;Microcontrollers;Debugging;Inspection;Distance measurement;Acoustics|
|[Research and Implementation of Space Launching Mission Support Simulation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10080623)|X. Liu; F. Wu; J. Wang; Y. Liu; A. Zhang; M. Wang|10.1109/ICCRD56364.2023.10080623|space launching mission;ground operations and tests;overall technical support;training simulation;directing;adjusting and control;training evaluation;Training;Analytical models;Space missions;Computational modeling;Aerospace electronics;Market research;Personnel|
#### **2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)**
- DOI: 10.1109/ITNEC56291.2023
- DATE: 24-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Dynamic Balance Calibration Algorithm based on Adaptive Temperature Adjustment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082177)|Z. Zhao; A. Wang; H. Sun|10.1109/ITNEC56291.2023.10082177|semiconductor dehumidifier;temperature and humidity self feedback dynamic balance;optimization objective function;dynamic balance calibrator;Semiconductor device modeling;Temperature distribution;Heuristic algorithms;Switchgear;Software algorithms;Humidity;Switches|
|[Adaptive update of UAV multi-target tracking based on Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082104)|H. Shen; Z. Huang; H. Wang; H. Yu|10.1109/ITNEC56291.2023.10082104|UAV;multi-target tracking;Transformer;Target tracking;Video tracking;Heuristic algorithms;Benchmark testing;Transformers;Autonomous aerial vehicles;Real-time systems|
|[Research on Clustering of Glass Cultural Relics Based on Weathering Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082601)|J. Zhao; Y. Xu; Y. Tao; Y. Zhao|10.1109/ITNEC56291.2023.10082601|Classification rule;Random forest;Kmeans++;Fuzzy c-means clustering;Sensitivity;Stability criteria;Glass;Lead;Cultural differences;Indexes;Potassium|
|[Research on The Transformation and Development of K9 Education and Training Institutions Under Xuzhou Double Reduction Policy based on Data Mining Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082585)|J. Zhao; S. Sun; W. Yu; Q. Shen|10.1109/ITNEC56291.2023.10082585|Data mining;Statistical analysis;Transformation of education and training institutions;Double reduction policy;Training;Industries;Text mining;Automation;Statistical analysis;Urban areas;Transforms|
|[Research on Power Optical cable network Fault Location Based on Fiber Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082617)|Q. Guo; P. Xie; X. Guo; Q. Zhang|10.1109/ITNEC56291.2023.10082617|fault location;optical cable network;fiber grating;optical fiber coding;Optical fibers;Power cables;Optical fiber cables;Optical fiber networks;Fault location;Encoding;Communication cables|
|[Research on Intelligent Mobile Edge Computing and Task Unloading Method of UAV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082507)|S. Zheng; X. Zhong; B. Huang; S. Zhuang|10.1109/ITNEC56291.2023.10082507|UAV;mobile edge computing;Stackelberg game;task unloading;Energy consumption;Multi-access edge computing;Numerical analysis;Games;Pricing;Nash equilibrium;Delays|
|[Multivariate Time Series Anomaly Detection: a Hybrid Method Based on GRU-SAE and GAIN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082407)|H. Xu; Y. Lou|10.1109/ITNEC56291.2023.10082407|anomaly detection;multivariate time series;GRU;SAE;GAIN;missing value imputation;Performance evaluation;Fault diagnosis;Automation;Fault detection;Time series analysis;Pipelines;Filling|
|[Reactive Power Optimization of Photovoltaic Power Grid based on Improved Imperialist Competitive Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082613)|R. Zeng; Z. Li; J. Cheng|10.1109/ITNEC56291.2023.10082613|imperialist competitive algorithm;particle swarm optimization;photovoltaic power generation;reactive power optimization;reactive quadric accurate moment;Photovoltaic systems;Reactive power;Benchmark testing;Power grids;Partitioning algorithms;Power system reliability;Reliability|
|[Defect Detection in Masks Based on Variation and Template Matching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082213)|W. Yuan; Y. Zhou; C. Luo|10.1109/ITNEC56291.2023.10082213|defect detection;mask;template matching;variation;point spread function;TV;Automation;Frequency-domain analysis;Lithography;Estimation;Interference;Task analysis|
|[Research of current control strategies for doubly-fed wind power generation system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082390)|M. Abulizi; C. Zhang; L. Xie|10.1109/ITNEC56291.2023.10082390|Current control;comparison;doubly-fed induction generator;wind energy;simulation;Current control;Analytical models;Adaptation models;Simulation;Power system dynamics;Rotors;Doubly fed induction generators|
|[Analysis and Optimization of Needle Selecting Electromagnet for Computer Jacquard Circular Knitting Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082610)|H. Gu; J. Liu; J. Xia; Y. Lu; M. Cui|10.1109/ITNEC56291.2023.10082610|needle selecting electromagnet;ansoftmaxwell software;finite element analysis;dynamic response;Magnetic cores;Dynamics;Needles;Electromagnets;Software;Permanent magnets;Weaving|
|[Control and Optimization of Distributed Large-scale Multi-station System Based on Model Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082330)|X. Zhang; J. Fang; J. Zou; Q. Chen; H. Huang; S. Chen; S. Wang|10.1109/ITNEC56291.2023.10082330|Large -scale multi-station system;hybrid AC/DC;distribution network;Photovoltaic systems;Predictive models;Prediction algorithms;Hybrid power systems;Stability analysis;Real-time systems;Steady-state|
|[Intelligent Scheduling Method of Optical Transmission Network Based on Digital Twin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082353)|J. Hao; J. Bai; G. Zhao; K. Yin; M. Jin; Y. Liu; Y. Ren|10.1109/ITNEC56291.2023.10082353|Digital twin network;Optical transmission network;DRL;Network topology generation;Optical fibers;Adaptation models;Network topology;Heuristic algorithms;Optical fiber networks;Maintenance engineering;Dynamic scheduling|
|[An End to End joint entity recognition and relationship extraction based on multi-domain Chinese corpus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082634)|Y. Zhang; Z. Liu; X. Ding|10.1109/ITNEC56291.2023.10082634|named entity recognition;relation extraction;ALBERT;BiLSTM;pre-trained model;Training;Automation;Annotations;Transformers;Nonhomogeneous media;Information retrieval;Probability distribution|
|[Fine-Grained Egocentric Action Recognition with Multi-Modal Unsupervised Domain Adaptation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082267)|X. Liu; T. Lei; P. Jiang|10.1109/ITNEC56291.2023.10082267|Fine-Grained Egocentric Action;Action Recognition;Unsupervised Domain Adaptation;Multi-Modal Learning;Training;Adaptation models;Automation;Supervised learning;Transformers;Adversarial machine learning;Encoding|
|[A Coordinated Optimization Method for Source-Load-Storage in Renewable Power System Considering Overcharge/Overdischarge Cost of Stored Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082520)|T. Guo; L. Huang; H. Guo|10.1109/ITNEC56291.2023.10082520|Renewable power system;coordinated optimization;source-load-storage coordinated;overcharge/overdischarge cost;Renewable energy sources;Wind;Costs;Optimization methods;Regulation;Demand response;Power systems|
|[Deployment for Balanced and Efficient 5G Slice Based on VIKOR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082403)|Y. Liang; J. Qiu; F. Hu; S. Li|10.1109/ITNEC56291.2023.10082403|5G network slices;slices deployment;workload balancing;complex network theory;VIKOR;Costs;5G mobile communication;Heuristic algorithms;Network slicing;Complex networks;Broadcasting;Dynamic scheduling|
|[Planning study for a high security enterprise wireless local area network (WLAN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082532)|C. Zhao; J. Xue; G. Yu; L. Qi|10.1109/ITNEC56291.2023.10082532|digital technology;wireless network;WLAN channel;information technology;Resistance;Wireless networks;Market research;Remote working;Planning;Security;Reliability|
|[Research on Prediction Model of Flight Departure Runway](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082471)|H. Qisong; W. Zixuan; Y. Wen; C. Guoqiang; Z. Yun|10.1109/ITNEC56291.2023.10082471|flight plan;take-off runway;intelligent decision support;big data;machine learning;Software design;Atmospheric modeling;Neural networks;Predictive models;Prediction algorithms;Airports;Data models|
|[Research on Classification of Wild Mushroom Based on Feature Fusion and Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082401)|X. Li; L. Guo; J. Yang; F. Ge; P. Yu|10.1109/ITNEC56291.2023.10082401|Image classification;fine-grained;feature fusion;attention mechanism;depthwise convolution;transfer learning;Training;Automation;Convolution;Shape;Fuses;Computational modeling;Transfer learning|
|[Cross-well Lithology Identification based on Dynamic Adversarial Adaptation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082699)|L. Sun; C. Yuan; K. Li; J. Zhu; Z. Li|10.1109/ITNEC56291.2023.10082699|Cross-well Lithology Identification;Unsupervised Domain Adaptation;Adversarial Learning;Semantic Segmentation;Fully Convolutional Network;Correlation;Automation;Semantic segmentation;Geology;Manuals;Feature extraction;Reservoirs|
|[Monitoring system of plant growth environment temperature based on LabVIEW and BP neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082281)|X. Cui; L. Tian; M. Li; H. Han; S. Hou; C. Shang|10.1109/ITNEC56291.2023.10082281|Plant electrical signals;LabVIEW;Wavelet denoising;BP network;Mixed programming with MATLAB;Monitoring System;Temperature sensors;Computational modeling;Plants (biology);Neural networks;Programming;Mathematical models;Eigenvalues and eigenfunctions|
|[Path Planning Algorithm of Mobile Robot Based on Improved Q-learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082134)|F. Qian; K. Du; H. Wang; T. Chen; X. Meng; S. Wang; B. Lu|10.1109/ITNEC56291.2023.10082134|Reinforcement Learning;Path Planning;CQL;IQL;Q-learning;Heuristic algorithms;Simulation;Power system stability;Path planning;Stability analysis;Mobile robots|
|[Implementation of a Safe and Efficient Point Multiplication for SM2 Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082311)|M. Li; N. Li; H. Liu; S. Cheng; X. Hu; J. Li|10.1109/ITNEC56291.2023.10082311|information security;SM2 algorithm;point multiplication;hardware implementation;Coordinate measuring machines;Information security;Resists;Logic gates;Libraries;Hardware;Communications technology|
|[Traffic sign detection algorithm based on improved YOLOv4](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082518)|H. Wang; J. Yang; F. Ge; L. Guo; P. Yu|10.1109/ITNEC56291.2023.10082518|Traffic sign detection;YOLOv4;depth separable convolution;PANet-A;CBAM attention module;CCTSDB;Image recognition;Automation;Convolution;Computational modeling;Semantics;Object detection;Feature extraction|
|[Application of Fuzzy Neural Networks for Intelligent Irrigation of Wheat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082703)|F. Ming; J. Mou; Y. Cui|10.1109/ITNEC56291.2023.10082703|neural networks;fuzzy control;intelligent irrigation;intelligent control;plant water saving;Irrigation;Fuzzy control;Software packages;Simulation;Soil moisture;Neural networks;Fuzzy neural networks|
|[Design and implementation of intelligent curtain control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082317)|L. Shen; G. Zhang; D. Luo; J. Zhang; Z. Pei; Y. Fang|10.1109/ITNEC56291.2023.10082317|Automatic adaptation;The experience of health intelligence;APP remote control;Tracking;Lighting;Production;Control systems;Motion pictures;Software;Mobile handsets|
|[Researched on Fault Location of Aircraft Distribution Lines Based on the Petri Net and Precise Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082577)|J. Zhang; L. Jiang; F. Wang; H. Xu|10.1109/ITNEC56291.2023.10082577|Petri net;fine integration method;aircraft distribution network;fault location;Power transmission lines;Simulation;Petri nets;Power distribution;Distribution networks;Fault location;Mathematical models|
|[Grey Predictive Control of the Generator Governor System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082251)|T. Ji; M. Wang; F. Wang; M. Zhou|10.1109/ITNEC56291.2023.10082251|Generator governing system;Grey prediction;GM(1,1);PID control;PI control;Power system stability;Predictive models;Prediction theory;Generators;Regulation;Stability analysis|
|[AIS data-based probabilistic ship route prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082574)|C. Wang; M. Zhu; O. Osen; H. Zhang; G. Li|10.1109/ITNEC56291.2023.10082574|AIS data;DTW;HDBSCAN;Route prediction;Collision avoidance;Uncertainty;Navigation;Heuristic algorithms;Clustering algorithms;Probabilistic logic;Prediction algorithms;Trajectory|
|[Application of Gaussian Filtering Three-Frame Difference Method in Moving Target Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082121)|S. Yin; X. Yue; W. Xu; S. Su; X. Liu|10.1109/ITNEC56291.2023.10082121|component;moving object;three-frame difference method;Gaussian model;Image segmentation;Adaptation models;Automation;Filtering;Object detection;Robustness;Noise robustness|
|[Research on PID Parameter Tuning Method of Brushless DC Motor with ITAE Index](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082641)|W. Xu; X. Yue; S. Yin; H. Wang; S. Zhang|10.1109/ITNEC56291.2023.10082641|brushless DC motor;proportional-integral-differential (polyethylene) controller;integration of time times absolute error (ITAE);Polyethylene;PI control;Brushless DC motors;Simulation;Transfer functions;Robustness;Indexes|
|[Adaptive Tracking Control of Space Continuum Robots based on a State Observer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082341)|X. Qiu; Z. Cai; H. Peng|10.1109/ITNEC56291.2023.10082341|tracking control;space continuum robots;state observers;adaptive neural networks;Adaptation models;Automation;Trajectory tracking;Simulation;Neural networks;Observers;Aerospace electronics|
|[Fault Prediction Method Based on Improved Bidirectional Long Short-Term Memory Combined with Sample Entropy for Battery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081968)|S. Ren; J. Sun; X. Zhang; Y. Liu; D. Wang|10.1109/ITNEC56291.2023.10081968|electric vehicles;fault prediction;whale optimization algorithm;bidirectional long short-term memory;attention mechanism;sample entropy;Fault diagnosis;Time series analysis;Prediction methods;Prediction algorithms;Electric vehicles;Entropy;Whale optimization algorithms|
|[A Review of Heterogeneous Wireless Network Selection Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082485)|D. Xiao; H. Lin; B. Wang|10.1109/ITNEC56291.2023.10082485|maritime battlefield;heterogeneous wireless network;network selection algorithm;decision parameters;optimal target network;Analytical models;Automation;Wireless networks;Market research;Heterogeneous networks;Classification algorithms;Reliability|
|[CS Optimization Iterative Thresholding Algorithm For Tobacco Pest Image Denoising](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082263)|C. Li; J. Wen; L. Yang; G. Yang; H. Zhang; J. Zhang|10.1109/ITNEC56291.2023.10082263|compressed sensing;tobacco pest images;optimization adaptive threshold;cycle spinning;denoising;Insects;Noise reduction;Iterative algorithms;Sensors;Noise measurement;Spinning;Image reconstruction|
|[Design of Rail Surface Defect Detection System Based on LabVIEW Machine Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082124)|H. Han; L. Tian; M. Li; X. Cui; C. Shang; S. Hou|10.1109/ITNEC56291.2023.10082124|LabVIEW;Machine vision;Rail workpiece;Surface defect detection;Rails;Image segmentation;Surface morphology;Object segmentation;Programming;Software;Real-time systems|
|[Research on Strategy for Direct Wind Power Supply to Industrial Consumers based on Big Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082265)|D. Wang; C. Liu; J. Chen; K. Su; Y. Shi|10.1109/ITNEC56291.2023.10082265|wind power;industrial consumer;direct power supply;big data;Economics;Databases;Clustering algorithms;Companies;Wind power generation;Big Data;Wind farms|
|[Enhanced Lyapunov Optimization of MEC Offload for Energy Harvesting Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082218)|W. Wang; L. Qiu; J. Yao; Z. Li; J. Shi|10.1109/ITNEC56291.2023.10082218|Mobile edge computing;computing offload;energy harvest-ing;Lyapunov optimization;Energy consumption;Costs;Multi-access edge computing;Heuristic algorithms;Smart grids;Batteries;Quality of experience|
|[Analysis of Factor Affecting SAR Ship Target Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082321)|Q. Huang; Z. Gao; H. Liu; J. Ma; L. Qu; G. Wang|10.1109/ITNEC56291.2023.10082321|deep learning;SAR;target detection;dataset;Image resolution;Automation;Imaging;Object detection;Radar imaging;Radar polarimetry;Marine vehicles|
|[Dual channel Chinese sentiment analysis of characters and words based on deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082296)|S. Zhao; F. Yang|10.1109/ITNEC56291.2023.10082296|sentiment analysis;deep learning;attention mechanism;dual channel;Deep learning;Sentiment analysis;Analytical models;Vocabulary;Convolution;Semantics;Feature extraction|
|[A Query Optimization Method for Blockchain-Based Traceability System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082162)|H. Qin; Y. Si; C. Xue; X. Li; Z. Jiang|10.1109/ITNEC56291.2023.10082162|Blockchain;Traceability system;Query;Merkle tree;Chinese herbal medicine;Supply chain management;Automation;Query processing;Blockchains;Indexes|
|[Research on Electromagnetic Simulation of the Electromagnetic Heating Roller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082474)|Y. Tang; Y. Wang; S. Hong; X. Li; S. Qi|10.1109/ITNEC56291.2023.10082474|electromagnetic induction heating technology;maxwell equations;electromagnetic field theory;faraday's law of electromagnetic induction;Resistance;Electromagnetic heating;Simulation;Oils;Electromagnetic field theory;Mathematical models;Software|
|[Research on Source Code Static Detection Method Based on Android Application Particularity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081998)|J. Zhang; Y. Xu; Y. Shi; A. Wu|10.1109/ITNEC56291.2023.10081998|Android application;source code security detection;context sensitivity;Java;Codes;Sensitivity analysis;Source coding;Operating systems;Optimization methods;Mobile applications|
|[Security research of power system based on Blockchain and 5G Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082196)|W. Wang; F. You; J. Zhu|10.1109/ITNEC56291.2023.10082196|5G network;blockchain;cyber security;data security;power system;Data privacy;5G mobile communication;Ecosystems;Process control;Information security;System integration;Production|
|[Optimization Strategy of Equipment Condition Maintenance Considering Service Age Retreat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082445)|X. Niu; L. Zhang; X. Li; X. Ai; J. Chang|10.1109/ITNEC56291.2023.10082445|electric power equipment;condition maintenance strategy;service age retreat;failure rate;benefit;Costs;Substations;Automation;Power supplies;Maintenance engineering;Probability;Reliability|
|[Spatiotemporal Causal Discovery Graph Convolutional Networks for Multivariate Time Series Forecasting of Industrial Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082527)|S. Zheng; K. Hao; X. Shi; X. Cai; L. Chen|10.1109/ITNEC56291.2023.10082527|Causal discovery algorithm;graph neural network;gated temporal convolution;multivariate time series forecasting;Convolution;Time series analysis;Process control;Predictive models;Logic gates;Prediction algorithms;Spatiotemporal phenomena|
|[Numerical Analysis and Process Parameter Optimization of Impact Descaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082056)|S. Wang; F. Peng; X. Wang; M. Liang|10.1109/ITNEC56291.2023.10082056|numerical analysis;control strategy;process parameter optimization;modeling;Energy consumption;Strips;Projectiles;Process control;Energy efficiency;Behavioral sciences;Steel|
|[Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082214)|G. Yang; Z. Li; Z. Yang; S. Cui|10.1109/ITNEC56291.2023.10082214|Small Sample Generation;Generative Adversarial Network;Photoresist Defect;Image coding;Automation;Image synthesis;Resists;Manuals;Generative adversarial networks;Coatings|
|[Microwave Imaging System Based on Synthetic Aperture Radar Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082576)|Y. Liao; Z. Zhou; F. Tian|10.1109/ITNEC56291.2023.10082576|microwave holography;Synthetic Aperture Radar;three-dimensional imaging;Microwave technology;Shape;Radar imaging;Radar polarimetry;Scattering parameters;Mechanical products;Permittivity|
|[Track Circuit Fault Diagnosis based on APSO-GMM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082174)|M. Zhao; F. Liu; B. Sun; Q. Meng|10.1109/ITNEC56291.2023.10082174|track circuit;fault diagnosis;gaussian mixture model;adaptive;particle swarm optimization algorithm;Fault diagnosis;Adaptation models;Automation;Adaptive systems;Stability analysis;Circuit stability;Circuit faults|
|[Turnout fault diagnosis based on adaptive threshold feature extraction and SVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082027)|Q. Meng; B. Wu; X. Yan; B. Sun; M. Zhao|10.1109/ITNEC56291.2023.10082027|turnout;fault diagnosis;feature extraction;SVM;Shuffled Frog Leaping Algorithm;Fault diagnosis;Support vector machines;Adaptation models;Automation;Feature extraction;Data models;Data mining|
|[Research on Synchronous Fuzzy Control of High Flow Servo Valve Based on Cross Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082143)|K. Zhou; B. Shi; R. Deng; H. Zhang|10.1109/ITNEC56291.2023.10082143|electro-hydraulic servo valve;Synchronous control;Cross coupling;Fuzzy control;Couplings;Fuzzy control;Simulation;Valves;Manufacturing;Synchronization;High frequency|
|[A physical oriented method for fuel cell system modeling and simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082659)|T. Zhang; S. Ma; S. He|10.1109/ITNEC56291.2023.10082659|Fuel Cell System;PEMFC;Modelica;Simulation;Control;Protons;Employee welfare;Control design;Fuel cells;Systems modeling;Libraries;Energy efficiency|
|[Aerial Reconfigurable Intelligent Surface-Aided Wireless Communications Against the Moving Jamming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082170)|Q. Su; G. Li; Y. Huang; X. Song; C. Liu; M. Guo|10.1109/ITNEC56291.2023.10082170|Aerial reconfigurable intelligent surface;antijamming communication;hierarchical optimization;Manifolds;Wireless communication;Automation;Simulation;Optimization methods;Reliability engineering;Reflection|
|[An Electric Field Probe for Wide-Band Intense Radiation Field Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081956)|J. Chai; Z. Zhou|10.1109/ITNEC56291.2023.10081956|intense radiation field;intensity measurement;electric field probe;calibration;antenna factor;Antenna measurements;Impedance measurement;Dipole antennas;Optical variables measurement;Optical fiber communication;Impedance;Probes|
|[Research on the new mode of "Main-Branch-End" cargo transportation under UAV distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082354)|L. Peng; H. Li; L. Li|10.1109/ITNEC56291.2023.10082354|large-scale network;survivability association;set pair analysis theory;complex system;Job shop scheduling;Automation;Transportation industry;Transportation;Airports;Aircraft;Aerospace control|
|[Event detection model based on graph attention network and graph convolutional network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082472)|J. Xintao; C. Zhishan; C. Guoyi|10.1109/ITNEC56291.2023.10082472|event detection;graph attention network;graph convolutional network;dependency parsing;BERT;attention mechanism;Event detection;Redundancy;Bit error rate;Syntactics;Feature extraction;Information filters;Complexity theory|
|[Calculation and risk assessment of transmission line wind deflection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082551)|Z. Zhang; G. Zhang; G. Tian; F. Gong; B. Li; X. Dai|10.1109/ITNEC56291.2023.10082551|Wind deflection flashover;BP neural network;AHP-entropy method;risk level assessment;Power transmission lines;Wind speed;Neural networks;Humidity;Flashover;Prediction algorithms;Environmental factors|
|[Fusion with GCN and SE-ResNeXt Network for Aspect Based Multimodal Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082618)|J. Zhao; F. Yang|10.1109/ITNEC56291.2023.10082618|aspect based multimodal sentiment analysis;SE-ResNeXt101;attention mechanism;graph convolution network;Visualization;Sentiment analysis;Convolution;Mood;Fuses;Computational modeling;Graphics processing units|
|[Research on Braking Energy Recovery Performance Detection of New Energy Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082679)|W. Wang; F. Qu; W. Li; P. Wang; J. Cai|10.1109/ITNEC56291.2023.10082679|New Energy Vehicles;Braking energy recovery;Performance testing;Setting of working condition;Employee welfare;Automation;Gears;Switches;Electric vehicles;Stability analysis;Sensors|
|[Gait Recognition based on Region Segmentation and Non-local Feature Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082363)|S. Jin; H. Yang; H. Zhang; J. Wei|10.1109/ITNEC56291.2023.10082363|gait recognition;motion region segmentation;convolutional neural network;feature extraction;non-local feature extraction;Legged locomotion;Deep learning;Correlation;Clothing;Feature extraction;Robustness;Convolutional neural networks|
|[Model Checking for Scheduling on Flight Decks of Aircraft Carriers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082464)|W. Zhu; Y. Wang|10.1109/ITNEC56291.2023.10082464|model checking;interval temporal logic;scheduling;flight deck;aircraft carrier;Automation;Scheduling algorithms;Atmospheric modeling;Model checking;Scheduling;Aircraft;Task analysis|
|[Ranging Model and Algorithm Based on Monocular Vision for Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082072)|R. Zheng; Y. Li; A. Wang|10.1109/ITNEC56291.2023.10082072|monocular vision;camera geometric;deep learning;automatic drive;distance measurement;Geometry;Deep learning;Measurement errors;Roads;Measurement uncertainty;Cameras;Distance measurement|
|[Attention Augmented Pedestrian Detection on Fisheye Cameras](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082540)|A. Wang; Y. Li; R. Zheng|10.1109/ITNEC56291.2023.10082540|pedestrian detection;attention mechanism;convolutional neural network;fisheye cameras;Adaptation models;Automation;Transfer learning;Cameras;Task analysis;Kernel;Autonomous vehicles|
|[Prediction and Analysis of Customer Churn of Automobile Dealers Based on BI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082554)|D. Zhang; C. Zhang; C. Zheng|10.1109/ITNEC56291.2023.10082554|customer churn;EDA;XGBoost;Decision Tree;BI/DA;Analytical models;Machine learning algorithms;Data visualization;Machine learning;Predictive models;Prediction algorithms;Loss measurement|
|[A Ku Band Broadband Energy Selective Surface Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082488)|X. Liu; S. Tang; J. Ren|10.1109/ITNEC56291.2023.10082488|energy selective surface;high-power microwave;shielding effectiveness;Microwave measurement;Microwave technology;Automation;PIN photodiodes;Lighting;Insertion loss;Propagation losses|
|[Policy Analysis and Operation Rules Suggestion of Big Cargo Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082611)|L. Li; H. Li; L. Peng|10.1109/ITNEC56291.2023.10082611|big cargo drones;branch airport logistics;policy analysis;operation rules;integrated operation;Costs;Contingency management;Distribution networks;Airports;Regulation;Proposals;Aircraft|
|[Simulation Analysis of Stability Operation of Hunan Power Grid Supported by Energy Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082501)|L. Wang; Y. Xie; K. Qin; H. Yu; H. Dai|10.1109/ITNEC56291.2023.10082501|energy storage;electromechanical transient model;reactive power support;transient voltage stability;Reactive power;Low voltage;Analytical models;Power system stability;Power grids;Stability analysis;Security|
|[Research on A New Unified Power Quality Comprehensive Management Device for Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082607)|P. Jiang; Y. Liu; Z. Li; L. Wang; Y. Gao; F. Jing|10.1109/ITNEC56291.2023.10082607|UPQC;harmonic suppression;instantaneous Fourier;three-phase four-wire;nan|
|[Research on Multi-pose Face Recognition based on Block Occlusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082671)|J. Shi; Y. Xiu; G. Tang|10.1109/ITNEC56291.2023.10082671|Multi-pose;Face Recognition;Occlusion;Block;Automation;Databases;Convolution;Face recognition;Neural networks;Power system stability;Feature extraction|
|[Research on the Inertia Identification of AC Servo System Based on Asynchronous Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082238)|Z. Huang; W. Song|10.1109/ITNEC56291.2023.10082238|Asynchronous motor;Vector control;Moment of inertia;Parameter identification;Parameter tuning;Torque;Heuristic algorithms;AC motors;Production facilities;Mathematical models;Steady-state;Servomotors|
|[A Webservice real-time data transmission technology based on c#](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082609)|C. Gong; Q. Meng; S. Jing; C. Zhao; G. Zhao|10.1109/ITNEC56291.2023.10082609|Real-time data transmission;.NET;Webservice;Codes;Automation;Databases;System performance;Web pages;Data collection;Real-time systems|
|[Location-Aware Resource Allocation for Multi-UAV Aided Multi-Cell NOMA Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082164)|Y. Sun; H. Shao; P. Yuan; J. Cai|10.1109/ITNEC56291.2023.10082164|UAV;NOMA;User pairing;Power allocation;Location optimization;Measurement;NOMA;Base stations;Simulation;Collaboration;Games;Downlink|
|[Application research of meteorological virtual private network security remote access technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082333)|Y. Xie; C. Zhang; X. He; J. Tian|10.1109/ITNEC56291.2023.10082333|VPN;mobile office;secure desktop;remote access;Maintenance engineering;Remote working;Software;Virtual private networks;Software reliability;Safety;Security|
|[Design of Anti-ship Missile Guidance Law with Attack Time Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082525)|Y. Cui; Y. Liu; J. Chang|10.1109/ITNEC56291.2023.10082525|residual time error;guidance law;dynamic inverse;time constraint;Missiles;Automation;Control design;Simulation;Dynamics;Lead;Missile guidance|
|[Design of Smoke-temperature Composite Detector Based on HWD32](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082156)|H. Zhang; H. Zhang; N. Yin|10.1109/ITNEC56291.2023.10082156|composite detector;HWD32;smoke-temperature composite;GB4715;GB4716;Temperature sensors;Temperature distribution;Software algorithms;Detectors;Feature extraction;Hazards;Software|
|[Comparative Analysis of 420kN Cylindrical and Conical Head Suspension Porcelain Insulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082606)|H. Wu; J. Zhang; J. Huang; Y. Pan; T. Qiu; J. Yin; S. Wang|10.1109/ITNEC56291.2023.10082606|Cylindrical head;Conical head;Suspension porcelain insulator;FEM analysis;Suspensions (mechanical systems);Porcelain;Simulation;Insulators;Stability analysis;Finite element analysis;Steady-state|
|[Economic evaluation of customer side energy storage based on hybrid leapfrog particle swarm optimization algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082456)|K. Wei; J. Wang|10.1109/ITNEC56291.2023.10082456|Customer side energy storage;Economy;Peak cutting and valley filling;Dynamic electricity price;Costs;Substations;Sodium;Batteries;Planning;Sulfur;Particle swarm optimization|
|[Optimization Design and Performance Research of Electronic Equipment Cooling System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082583)|W. Li|10.1109/ITNEC56291.2023.10082583|Electronic equipment;Cooling down system;Cycle of evaporation;Optimization calculation;performance research;Electronic equipment;Cooling;Electronics cooling;Atmospheric modeling;Design methodology;Water heating;Refrigeration|
|[Review on FPGA-Based Accelerators in Deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082175)|Y. Wu|10.1109/ITNEC56291.2023.10082175|deep learning;field programmable gate array (FPGA);hardware accelerator;CNN;Deep learning;Automation;Convolution;Parallel processing;Logic gates;Market research;Convolutional neural networks|
|[Dynamic Task Allocation for Heterogeneous Multi-Robot System under Communication Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082491)|J. Li; Z. Cai; M. Li; W. Huang; Y. Zhang|10.1109/ITNEC56291.2023.10082491|multi-robot system;task allocation;grouping;communication constraints;Uncertainty;Automation;Adaptive systems;Robot kinematics;Heuristic algorithms;Dynamic scheduling;Resource management|
|[Research on Camera Calibration Method Based on Homography Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082094)|Y. Luo; Z. Li; S. Luo|10.1109/ITNEC56291.2023.10082094|camera calibration;corner detection;homography matrix;tessellation grid;planimetric inspection;Automation;Production;Manuals;Glass;Inspection;Cameras;Mobile handsets|
|[A Home Isolated Flyback LED Lamp Driver Circuit Design Supporting Radar Sensor Control Dimming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082648)|L. Chen; F. Liu; Q. Chen|10.1109/ITNEC56291.2023.10082648|flyback;light emitting diode (LED);lamp driver;constant current;pulse width modulation (PWM);Current control;Reactive power;LED lamps;Electric shock;Radar;Pulse width modulation;Safety|
|[Research on Rolling Bearing Fault Diagnosis based on MVMD Energy Entropy and ELM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082233)|L. Chen; Q. Chen; F. Liu|10.1109/ITNEC56291.2023.10082233|rolling bearing;MVMD;energy entropy;ELM;Vibrations;Fault diagnosis;Analytical models;Extreme learning machines;Rolling bearings;Vibration measurement;Entropy|
|[A Contrast-Enhanced Graph Neural Network Recommendation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082290)|J. Liu; X. Cai; Q. Zhou|10.1109/ITNEC56291.2023.10082290|recommendation algorithm;graph neural network;collaborative filtering;contrastive learning;personalized recommendation;Training;Technological innovation;Convolution;Interference;Filtering algorithms;Information filters;Graph neural networks|
|[Building Contour Optimization Method based on V-System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082312)|Y. Liang; W. Chen|10.1109/ITNEC56291.2023.10082312|V-System;building extraction;remote sensing image;Canny operator;ploygon fitting;Shape;Image edge detection;Buildings;Fitting;Optimization methods;Feature extraction;Sensors|
|[Classification and Analysis of Common Data Sets of Hyperspectral Remote Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082421)|L. Chen; F. Liu; Q. Chen|10.1109/ITNEC56291.2023.10082421|hyperspectral remote sensing;spectral information features;classification;classifier;Training;Automation;Simulation;Interference;Feature extraction;Hyperspectral imaging|
|[Study on Construction of Radio Monitoring System Based on Cloud-Edge-Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082687)|N. Peng; X. Zhang|10.1109/ITNEC56291.2023.10082687|Radio monitoring;Cloud-Edge-Device;Framework;Construction of system;Cloud computing;Technological innovation;Automation;Computer architecture;Reliability engineering;Planning;Task analysis|
|[A Study of Customer Satisfaction in Milk Tea Stores Based on Online Comment Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082667)|R. Huang; S. Mao; Y. Chen|10.1109/ITNEC56291.2023.10082667|online comments;customer safisfaction;milk tea store;Dairy products;Information resources;Dictionaries;Automation;Customer satisfaction;Internet;Reliability|
|[Similarity Calculation of Propeller under Medium Speed Flight Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082701)|S. Yang|10.1109/ITNEC56291.2023.10082701|propeller;similarity calculation;tip induced losses;experiment design;Reynolds effect;Correlation;Automation;Propellers;Wind tunnels;Blades;Integral equations;Rotors|
|[Research on Entity recognition of Chinese place Names based on BERT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082135)|B. Zhang; W. Wang; H. Deng|10.1109/ITNEC56291.2023.10082135|named entity recognition;BERT;Bidirectional long and short term memory network;Conditional random field;Training;Bit error rate;Semantics;Neural networks;Feature extraction;Data models;Character recognition|
|[Microphone Array-Based Sound Source Localization And Tracking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082682)|Y. Bao; Q. Wang; K. Shen; Y. Wang|10.1109/ITNEC56291.2023.10082682|Microphone Array;Embedded;Particle swarm algorithm;Electric steering;Location awareness;PI control;Gears;Process control;Estimation;Delay estimation;Microphone arrays|
|[Research on Chinese named Entity Recognition based on RoBERTa and word fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082000)|W. Wang; B. Zhang; X. Zhu; H. Deng|10.1109/ITNEC56291.2023.10082000|RoBERTa;Word fusion;Chinese electronic medical record;Self-built dictionary;Named entity recognition;Deep learning;Chaos;Dictionaries;Automation;Decision making;Internet;Task analysis|
|[Operation safety analysis of substation main wiring based on new risk assessment model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082349)|Y. Wang; J. Chen; Z. Tang; Z. An; Z. Lu|10.1109/ITNEC56291.2023.10082349|substation;system operation safety index;regional power grid;load classification;optimal load shedding;Wiring;Analytical models;Substations;Systems operation;Power grids;Safety;Risk management|
|[Kubernetes-based Scripted Remote Sensing Process Service Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082221)|L. Zhou; M. Liu; W. Yang; Z. Liu; H. Zhao; H. Han|10.1109/ITNEC56291.2023.10082221|remote sensing data;scripting;process construction;container technology;Kubernetes;Satellites;Codes;Microservice architectures;Containers;Dynamic scheduling;Hardware;Real-time systems|
|[Study on monitoring and self-healing method of AC tampering fault](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082523)|D. Li; J. Yang; Q. Wang; S. Zhang; T. Pan|10.1109/ITNEC56291.2023.10082523|Dc system;Communication jumping in;Monitoring;self-healing;Wiring;Substations;Power supplies;Maintenance engineering;Power grids;Safety;Circuit faults|
|[The early warning model of HFMD which is implemented by the multivariable deep learning neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082278)|Y. Lai; P. Liu; F. Yang; H. Duan; F. Li; W. Ge; Z. Li|10.1109/ITNEC56291.2023.10082278|large-scale network;BiLSTM;The prediction of the disease;Analytical models;Temperature;Precipitation;Meteorological factors;Infectious diseases;Neural networks;Mouth|
|[Abnormal Traffic Detection Technology of Power IOT Terminal Based on PCA and OCSVM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082380)|Z. Minjie; Z. Yilian|10.1109/ITNEC56291.2023.10082380|Abnormal traffic detection;PCA;OCSVM;Power IOT terminal;Training;Machine learning algorithms;Simulation;Telecommunication traffic;Feature extraction;Smart meters;Libraries|
|[Two-Path Motion Excitation for Action Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082209)|C. Lin; Q. Wang; X. Han|10.1109/ITNEC56291.2023.10082209|action recognition;motion excitation;CNN;Solid modeling;Three-dimensional displays;Automation;Convolution;Computational modeling;Aggregates;Time series analysis|
|[An Improved Feature-Based Visual Slam Using Semantic Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082109)|S. Ma; H. Liang; H. Wang; T. Xu|10.1109/ITNEC56291.2023.10082109|feature matching;loop closure detection;YOLOV5;ORB-SLAM2;Training;Visualization;Simultaneous localization and mapping;Semantics;Pose estimation;Neural networks;Object detection|
|[A Rolling Bearing Fault Diagnosis Method based on improved MFE and WOA-PNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082690)|Z. Luo; G. Zhu; X. Dong; H. Tan; J. Li|10.1109/ITNEC56291.2023.10082690|rolling bearing fault diagnosis;variational mode decomposition;multi-scale fuzzy entropy;whale optimization algorithm;probability neural network;Fault diagnosis;Uncertainty;Smoothing methods;Neural networks;Rolling bearings;Estimation;Probabilistic logic|
|[A Kind of Syntax Parsing Algorithm Based on The Recursive Subroutines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082665)|H. Luo|10.1109/ITNEC56291.2023.10082665|natural language processing;syntax parsing algorithms;recursive subroutines;LL(1) grammar;Automation;Algorithms;Natural languages;Process control;Syntactics;Grammar|
|[Research on APF Based on Adaptive Analysis Instantaneous Reactive Power Harmonic Detection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082334)|J. Yi; Y. Gao; M. Wang; J. Zhu; J. Ma; W. Shi; H. Wang|10.1109/ITNEC56291.2023.10082334|active power filter;instantaneous reactive power;adaptive analysis;simulation;Reactive power;Harmonics suppression;Automation;Simulation;Low-pass filters;Adaptive filters;Active filters|
|[Modelling and Dynamic Simulation of Palletizing Robot System Based on Multibody](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082624)|Z. Zhuo; Z. Cheng; D. Zhenjie; F. Haijie|10.1109/ITNEC56291.2023.10082624|palletizing robot;kinematics;multibody simulation;dynamics modeling;Adaptation models;Analytical models;Visualization;Heuristic algorithms;Dynamics;Planning;Kinetic theory|
|[Research to Three-box Stair Sweeping Robot Based on Guideway Lifting Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082683)|W. Xu; X. Tian; B. Ma; T. Gao; D. Wen; X. Ma; J. Liu|10.1109/ITNEC56291.2023.10082683|Guideway lifting mechanism;Three box structure;Mecanum wheel;Stair cleaning robot;Industries;Service robots;Wheels;Manuals;Stairs;Robot sensing systems;Infrared sensors|
|[Default Risk Assessment of Internet Financial Enterprises Based on Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082169)|Y. Qiu|10.1109/ITNEC56291.2023.10082169|heterogeneous graph neural network;graph mining;default risk assessment;deep learning;Social networking (online);Time series analysis;Companies;Feature extraction;Graph neural networks;Data models;Internet|
|[Active IRS -Assisted Resource Allocation for MISO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082383)|X. Liu; H. Liu; Z. Li; D. Liu; H. Zhang; R. Wang; H. Yao; Y. Shao|10.1109/ITNEC56291.2023.10082383|Active IRS;Beamforming;QoS;Bilinear transformation;Internal approximation;Wireless communication;Array signal processing;Simulation;Quality of service;MISO communication;Approximation algorithms;Reflection|
|[One Self-securing Information Transmission Scheme Based on QAM Constellation Hopping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082580)|G. Zang; Y. Lv|10.1109/ITNEC56291.2023.10082580|information security;QAM modulation;modulation mapping;Wireless communication;Frequency modulation;Quadrature amplitude modulation;System performance;Information security;Information processing;Spread spectrum communication|
|[A Design of Direct RF Power Injection Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082655)|M. Yang; G. Wang; Y. Xiao|10.1109/ITNEC56291.2023.10082655|IEC 62132;direct RF power injection method;Couplings;Radio frequency;Integrated circuits;Power measurement;Wires;Insertion loss;Copper|
|[Simulation and Experiment of Field-to-Cable Coupling Terminal Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082718)|Y. Zheng; S. Jing; F. Tian|10.1109/ITNEC56291.2023.10082718|field-to-cable coupling;CST simulation;GTEM cell;measurement;Couplings;Analytical models;Coaxial cables;Magnetic field measurement;Wires;Software;Impedance|
|[Near-field calculation method of array antenna based on element pattern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082044)|C. Shen; X. Zhou; F. Tian|10.1109/ITNEC56291.2023.10082044|array antennas;near field;central array element;3D simulation;superposition;Solid modeling;Adaptation models;Three-dimensional displays;Computational modeling;Memory management;Layout;Adaptive arrays|
|[Virtualization Solution for Fast Storage Based on RoCE+P2P Technology in Heterogeneous Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082113)|H. Liu; B. Chen; J. Li; Y. Yang|10.1109/ITNEC56291.2023.10082113|RDMA;RoCE;P2P;virtualization solution;fast storage;heterogeneous resource pool;Data centers;Automation;High performance computing;Medical treatment;Explosives;Servers;Virtualization|
|[Analysis and design of a weak charge signal amplifier circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082158)|S. Wei; Q. Shao|10.1109/ITNEC56291.2023.10082158|piezoelectric force sensor;charge amplifier circuit;circuit simulation and experiment;analog filter;Performance evaluation;Operational amplifiers;Sensitivity;Capacitors;Reliability theory;Signal generators;Passband|
|[Research on Collocation Situation Analysis and Control Optimization of the BeiDou GEO Satellites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081971)|S. Lei; L. Quanjun; W. Hao; X. Keqiang|10.1109/ITNEC56291.2023.10081971|Collocation of Satellites;Situation Analysis;Orbit Maintenance;Optimized Control;Analytical models;Satellites;Simulation;Predictive models;Maintenance engineering;Prediction algorithms;Market research|
|[Research and Prospect of Cyber-Attacks Prediction Technology for New Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081983)|J. Liang; H. Zhu; B. Zhang; L. Liu; X. Liu; H. Lin; J. Tian; Q. Chen|10.1109/ITNEC56291.2023.10081983|New power system;defense-in-depth system;attack prediction;cyberattack;Neural networks;Prediction methods;Real-time systems;Power systems;Behavioral sciences;Steady-state;Power system reliability|
|[A Scheme of Secure Location Privacy Sharing in Mobile Crowd Sensing Application Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082223)|S. YanKang; W. Zhu; Y. Zhi-Wen|10.1109/ITNEC56291.2023.10082223|Mobile Crowd Sensing;Location Privacy Protection;Scheme of Blakley Secret Sharing Threshold;Privacy;Automation;Publishing;Resists;Ad hoc networks;Sensors;Cryptography|
|[Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082614)|H. Cui; Z. Su; L. Zhang; L. Bai|10.1109/ITNEC56291.2023.10082614|Dunhuang Mural images;Label Propagation;Active Learning Strategy;Semi-supervised Model;Technological innovation;Transfer learning;Supervised learning;Semisupervised learning;Predictive models;Feature extraction;Classification algorithms|
|[Reliability Analysis of Power Grid Security and Stability Control System under Dual Carbon Background](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082071)|M. Liu|10.1109/ITNEC56291.2023.10082071|double carbon;power grid;security and stability control system;reliability;Green products;Power system stability;Markov processes;Control systems;Stability analysis;Power grids;Hardware|
|[Bearing fault diagnosis method based on data augmentation and MCNN-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082598)|M. Hu; C. Wang; C. Zhuang; Y. Wang|10.1109/ITNEC56291.2023.10082598|rolling bearing;fault diagnosis;data augmentation;convolutional neural network;long short-term memory;Fault diagnosis;Training;Vibrations;Frequency-domain analysis;Rolling bearings;Feature extraction;Data models|
|[Spatio-temporal prediction of crime based on Data Mining and LSTM network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081985)|N. Jiang; K. Miao; Y. Chai; D. Lu; J. Wu|10.1109/ITNEC56291.2023.10081985|Big data;Spatio-temporal prediction;LSTM model;Data mining;Allocation of police force;Analytical models;Law enforcement;Time series analysis;Predictive models;Prediction algorithms;Market research;Data models|
|[RT-SLAM:Real-Time Visual Dynamic Object Tracking SLAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082331)|T. Ye; G. Zhao|10.1109/ITNEC56291.2023.10082331|Visual SLAM;dynamic environments;object tracking;real-time;Location awareness;Visualization;Simultaneous localization and mapping;System performance;Motion estimation;Dynamics;Semantics|
|[Research on Multi-objective Course Scheduling Method in Colleges based on Epidemic Prevention and Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082461)|P. Wang; J. Huang|10.1109/ITNEC56291.2023.10082461|Multi-objective;Genetic Algorithm;Epidemic Prevention and Control;Course Scheduling;Epidemics;Automation;Sociology;Minimization;Scheduling;Encoding;Personnel|
|[Feasibility Verification of Fusion Navigation Based on Roadside Light Pole Recognition Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082700)|J. Huang; P. Wang; Z. Ji; S. Xiao; C. Jia; X. Wang|10.1109/ITNEC56291.2023.10082700|recognition algorithm;iterative search;navigation path generation;convergence verification;baseline;Intelligent networks;Navigation;Machine vision;Electric variables measurement;Cameras;Electric vehicles;Iterative algorithms|
|[Smart grid edge fault detection architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082592)|X. Yang; X. Yu; H. Hou; Z. Tan; F. Wu|10.1109/ITNEC56291.2023.10082592|smart grid;edge computing;neural network;resource allocation;Fault diagnosis;Image edge detection;Fault detection;Computer architecture;Interference;Real-time systems;Smart grids|
|[Design of RISC-V heterogeneous multi-core SOC architecture for edge computing for power applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082372)|F. Wu; J. Tian; G. Yang; H. Gui; Y. Luo|10.1109/ITNEC56291.2023.10082372|smart grid;artificial intelligence;edge calculation;smart chip;Energy consumption;Multicore processing;Computer architecture;Hardware;Smart grids;System-on-chip;Security|
|[Efficient Asymmetric Encryption Scheme based on Elliptic Encryption Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082706)|X. Yang; X. Yu; H. Hou; Z. Tan; F. Wu|10.1109/ITNEC56291.2023.10082706|asymmetric encryption;authenticated encryption;elliptic curve encryption;Analytical models;Automation;Elliptic curves;Authentication;Encryption;Smart grids|
|[Analysis of Transmission Characteristics of Loosely Coupled Transformer with S-S Compensation Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082165)|Z. Xie; J. Wu; K. Zhang; X. Lu; Z. Xu; B. Jin|10.1109/ITNEC56291.2023.10082165|WPT;S-S compensation structure;transmission characteristics;parameter calculation;Automation;Capacitors;Transformers;Capacitance;Iron;Wireless power transmission;Impedance|
|[A Maximum Power Point Criterion of MPPT and its Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082306)|L. Liu; S. Liu|10.1109/ITNEC56291.2023.10082306|Solar Cell;MPPT control;Port voltage;new algorithm;nan|
|[Location Privacy Protection and Location Verification Mechanism of Vehicle in VANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082547)|W. Tong; X. Feng|10.1109/ITNEC56291.2023.10082547|VANET;vehicle location;privacy protection mechanism;Space vehicles;Analytical models;Privacy;Data privacy;Distributed databases;Big Data;Telematics|
|[Reference Signal Reconstruction for GPS-Based Passive Radar Based on Software Radio Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082494)|H. Zheng; S. Cheng; M. Zhang|10.1109/ITNEC56291.2023.10082494|passive radar;reference signal reconstruction;software radio;GPS;Passive radar;Correlation;Automation;Satellite broadcasting;Object detection;Signal reconstruction;Global Positioning System|
|[A model order reduction method considering the delay feature of wind power when participating in frequency regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082548)|W. Cheng; Z. Li; Y. Li; Y. Chu; H. Xie; B. Li; P. Tang; D. Peng|10.1109/ITNEC56291.2023.10082548|DFIG;Wind power frequency modulation;Timedelay characteristics;Model order reduction;Analytical models;Time-frequency analysis;Automatic frequency control;Frequency modulation;Wind power generation;Reduced order systems;Delays|
|[A novel power gird frequency prediction method considering the start-up threshold of wind power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081957)|B. Li; W. Cheng; Z. Li; Y. Li; H. Xie; Y. Chu; P. Tang; D. Peng|10.1109/ITNEC56291.2023.10081957|Primary frequency regulation;Start-up threshold;Frequency nadir;Steady-state frequency;Automatic frequency control;Automation;Sensitivity analysis;Biological system modeling;Wind power generation;Predictive models;Taylor series|
|[A novel voltage control strategy for distributed PV based on reactor and energy storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082468)|P. Tang; W. Cheng; D. Peng; Z. Li; Y. Li; Y. Chu; B. Li; N. Li|10.1109/ITNEC56291.2023.10082468|distributed;photovoltaic;grid;voltage;Photovoltaic systems;Shunts (electrical);Voltage fluctuations;Fluctuations;Power quality;Voltage control;Inductors|
|[Mechanism analysis and control strategy of paralled SVGs to avoid reactive power circulation at the same station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082639)|Y. Liu; Z. Li; W. Cheng; P. Tang; Y. Li; Y. Chu; N. Li; C. Wang; M. Qi|10.1109/ITNEC56291.2023.10082639|SVG;Reactive Power Circulation;New Energy;Economics;Reactive power;Analytical models;Power measurement;Automation;Biological system modeling;Wind power generation|
|[Coefficient correction model for wind power considering its delay feature and during its participation in frequency regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082573)|D. Peng; Y. Li; W. Cheng; Z. Li; B. Li; Y. Chu; P. Tang; N. Li|10.1109/ITNEC56291.2023.10082573|doubly-fed wind turbine;wind power frequency regulation;delay characteristics;model step-down;Automatic frequency control;Analytical models;Automation;Wind power generation;Frequency response;Delays;Wind turbines|
|[Mechanism and countermeasures of reactive power circulation of paralleled SVGs located at T-connected renewable energy stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082216)|Z. Li; W. Cheng; B. Li; P. Tang; Y. Li; D. Peng; N. Li; C. Wang|10.1109/ITNEC56291.2023.10082216|SVG;reactive power circulation;new energy;Economics;Reactive power;Renewable energy sources;Power measurement;Automation;Simulation;Energy measurement|
|[Automatic Identification of Seismic Signal Based on Entropy Feature and Genetic Algorithm Optimized BP Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082096)|C. Pang; C. Li; L. Ma; Y. Jiang; J. Chen; W. Ma; Y. Xiang|10.1109/ITNEC56291.2023.10082096|seismic type identification;genetic algorithm;back propagation neural network;distributed entropy;fuzzy entropy;sample entropy;initial weights;initial thresholds;Seismic measurements;Neural networks;Earthquakes;Sociology;Feature extraction;Entropy;Seismic waves|
|[Analysis and Prevention of Abnormal Shutdown Caused by Switch Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082242)|Z. Qi; Z. Du; G. Zhou|10.1109/ITNEC56291.2023.10082242|Safety protection system;BYPASS;Root switch;Unit trip;Measurement units;Automation;Intrusion detection;Switches;Companies;Control systems;Safety|
|[Static-dynamic Performance Analysis of Flexible Power Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082669)|L. Weiguo; Q. Guangyao; J. Yanjiao; L. Lu; Y. Zhichang; T. Aihong; W. Qian|10.1109/ITNEC56291.2023.10082669|wind power integration;flexible power transformer;topological structure;flexible voltage control;Analytical models;Fluctuations;Voltage fluctuations;Wind speed;Simulation;Wind power generation;Mathematical models|
|[Overview of Cartoon Face Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082673)|X. Shen; S. Lei; J. Liu|10.1109/ITNEC56291.2023.10082673|face cartoonization;unsupervised learning;deep learning;generative adversarial network;Training;Art;Image synthesis;Entertainment industry;Games;Production;Animation|
|[High-performance Network Traffic Classification Based on Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082049)|B. Pang; Y. Fu; S. Ren; Y. Jia|10.1109/ITNEC56291.2023.10082049|Traffic classification;Graph Neural Network;Deep Learning;Representation Learning;Deep learning;Automation;Telecommunication traffic;Predictive models;Network security;Feature extraction;Graph neural networks|
|[Multi-domain Network Intrusion Detection Based on Attention-based Bidirectional LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081953)|X. Wang|10.1109/ITNEC56291.2023.10081953|anomaly detection;attention mechanism;bidirectional LSTM;multi-domain learning;Encapsulation;Natural languages;Network intrusion detection;Machine learning;Telecommunication traffic;Virtual private networks;Trojan horses|
|[Application of UPF disaster tolerant networking in power private network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082484)|H. Xie; S. Du; Q. Zhang; Y. Yang; H. Wang|10.1109/ITNEC56291.2023.10082484|Private 5G;Edge UPF;UPF Disaster Recovery;Pool Nework;Wireless communication;Resistance;Automation;5G mobile communication;Conferences;Reliability;Investment|
|[A 5G terminal authentication method considering end-to-end electrical service security protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082678)|W. Hao; C. Qixiang; W. Hui; Z. Xiaoxi; L. Yuxiang; D. Yawen|10.1109/ITNEC56291.2023.10082678|Digital authentication;multi-factor authentication identifier;replay attack;Digital power grid;5G security;Wireless communication;Technological innovation;5G mobile communication;Authentication;Resists;Logic gates;Time measurement|
|[Research on deterministic service quality guarantee for 5G network slice in power grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082638)|L. Yuxiang; X. Hui; C. Julong; W. Hongyan; W. Hui; D. Yawen|10.1109/ITNEC56291.2023.10082638|Service function chain;swarm intelligent optimization;electrical 5G network slice;collaborative scheduling of heterogeneous resources;Cross layer design;5G mobile communication;Service function chaining;Network slicing;Quality of service;Routing;Power grids|
|[Research and Design of Wireless Charging System for Inspection Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082297)|Z. Kou; Z. Kong; G. Jing; N. Wang; F. Xin; X. Cui|10.1109/ITNEC56291.2023.10082297|Wireless charging;Patrol robot;Magnetic coupling type;Power transmission;nan|
|[Mean-gradient based two-dimensional histogram image segmentation algorithm in polar coordinate system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082347)|T. Shen; Y. Wei; L. Liu|10.1109/ITNEC56291.2023.10082347|image segmentation;two-dimensional histogram;mean-gradient;noise processing;Image segmentation;Histograms;Automation;Two dimensional displays;Process control;Interference;Entropy|
|[Research on Target Attitude Stability of Space Robot after Collision Based on Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082332)|Y. Qu; X. Yu; J. Wang; L. Huang|10.1109/ITNEC56291.2023.10082332|Space Robot;Simulink;Attitude Stability;Collision;Satellites;Torque;Software packages;Interference;Aerospace electronics;Manipulators;Stability analysis|
|[Locking and Twisting Effect Simulation in ADAMS of Space Robot to Target Satellite Based on Uniform Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082236)|L. Huang; X. Yu; J. Wang; X. Li; Y. Qu|10.1109/ITNEC56291.2023.10082236|Space Robot;ADAMS;Uniform Design;Locking and Twisting Effect;Space vehicles;Analytical models;Satellites;Automation;Attitude control;Aerospace electronics;Manipulators|
|[Research on Shape Reconstruction of Deep-Sea Riser Based on Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082119)|Q. Xu; L. Liang; L. Zhong|10.1109/ITNEC56291.2023.10082119|Deep-sea riser;Shape reconstruction;Neural network;Piggyback pipeline;nan|
|[Portable intelligent anti-loss monitoring equipment for the elderly](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082409)|M. Wang; S. Wang; Y. Deng; Z. Xie|10.1109/ITNEC56291.2023.10082409|Anti-wandering;STM32;Voice recognition;Location;distress;Heart rate;Temperature sensors;Temperature measurement;Temperature distribution;Speech recognition;Time measurement;Safety|
|[Deep Retinex image enhancement algorithm under weak Light Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082369)|J. -l. Zhao; Z. -q. Chen; H. -y. Jiang; Q. Zhang|10.1109/ITNEC56291.2023.10082369|The neural network;Image enhancement;CBAM;Bilinear interpolation;Interpolation;Image color analysis;Merging;Lighting;Interference;Distortion;Visual effects|
|[Research on Online Learning Achievement Prediction Based on Dynamic Bayesian Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082280)|J. Zheng; Y. Liu|10.1109/ITNEC56291.2023.10082280|Dynamic Bayesian network;HMM;EM;Online learning;Performance prediction;Heuristic algorithms;Decision making;Hidden Markov models;Reinforcement learning;Bidirectional control;Predictive models;Gaussian distribution|
|[NPPlanner: A Tool for National Park Planning at Scale Form Plant Diversity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082047)|Z. Hu; Z. Meng; X. Yi; X. Wang; L. Zhao; J. Ye; Z. Chen|10.1109/ITNEC56291.2023.10082047|National Park planning;Biodiversity conservation;WebGIS;Visualization;Fungi;Soft sensors;Diversity reception;Microservice architectures;Data visualization;Reliability engineering;Planning|
|[Research on Target Detection Algorithm for Complex Scenes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082670)|C. Yang; H. Fan; H. Zhu|10.1109/ITNEC56291.2023.10082670|target detection;complex scenes;deep learning;yolov5;Training;Deep learning;Computer vision;Automation;Target recognition;Object detection;Prediction algorithms|
|[KNN data filling method under incomplete fuzzy soft environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082215)|Y. Han; X. Ma|10.1109/ITNEC56291.2023.10082215|Fuzzy soft sets;incomplete information;data filling;KNN;Uncertainty;Automation;Decision making;Information representation;Data collection;Filling|
|[Research on Location and Capacity Determination Method of DG in Rural Distribution Network Based on BFOA Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082168)|M. Jiang; M. Ren; Y. Li; S. Chen; X. Su; C. Tang; J. Peng|10.1109/ITNEC56291.2023.10082168|Rural distribution network;Voltage quality;Network loss;Distributed photovoltaic;Bacterial foraging optimization algorithm;Capacity Planning;Photovoltaic systems;Low voltage;Costs;Sensitivity;Power supplies;Distribution networks;Voltage|
|[Research and Design on Architecture for Big Data Platform in Power Grid Dispatching and Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082357)|Q. Feng; F. Di; R. Ye; L. Xie; Y. Wang; L. Tao; Y. Huang; D. Li; C. Feng|10.1109/ITNEC56291.2023.10082357|Big data platform;dispatching and control cloud;heterogeneous data storage;unified data warehouse;data flow;Cloud computing;Architecture;Soft sensors;Computer architecture;Standardization;Big Data;Control systems|
|[A pulse rate prediction method in artifacts segments based on Elman neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081947)|M. Yang; A. An; B. Gong; Y. Zhou; Y. Chou|10.1109/ITNEC56291.2023.10081947|Elman neural network;pulse rate prediction;artifact segments;arterial blood pressure;time series;Error analysis;Databases;Time series analysis;Neural networks;Interference;Predictive models;Prediction algorithms|
|[Design and evaluation methodology for landing and taxiing lighting system in civil transport aircraft](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082653)|X. Haibo|10.1109/ITNEC56291.2023.10082653|civil transport aircraft;landing and taxiing lighting system;design;evaluation;Visualization;Design methodology;Lighting;Process control;Real-time systems;Safety;Aircraft|
|[POI Recommendation Algorithm Based on Geographical Information Matrix Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082387)|J. Jian; Y. Shi|10.1109/ITNEC56291.2023.10082387|social network;geographic information;matrix decomposition;point of interest recommendation;Analytical models;Automation;Social networking (online);Collaborative filtering;Estimation;Linear programming;Data models|
|[Research and Design of Power Big Data Desensitization System Based on K-means++ Clustering Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082046)|Z. Zhao; Y. Ji; W. Zheng; H. Xie; Y. Lian|10.1109/ITNEC56291.2023.10082046|K-means++ clustering;power big data;data desensitization;data leakage prevention technology;Economics;Concurrent computing;Data privacy;Clustering algorithms;Power system stability;Load management;Power grids|
|[Prediction Model of Over Entertainment based on Decision Tree - Taking Fire Control Related Government Tiktok as an Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082183)|S. Sun; Z. Xu; N. Wang|10.1109/ITNEC56291.2023.10082183|over entertainment;decision tree;government media;government Tiktok;Automation;Government;Entertainment industry;Predictive models;Media;Market research;Prediction algorithms|
|[Research on Depth Estimation Method of Single Aerial Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082076)|W. Gao; L. Pu; L. Li; F. Deng; L. Bie|10.1109/ITNEC56291.2023.10082076|depth estimation;aerial image;fully convolutional residual networks;laplacian pyramid-based depth residuals;deep learning;Automation;Image edge detection;Estimation;Data mining;Indexes|
|[Study on Optimization of Takeout Delivery Route in the Crowdsourcing Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082480)|S. Xia; H. Jiang|10.1109/ITNEC56291.2023.10082480|takeout delivery;crowdsourcing model;Genetic Algorithm;route optimization;Crowdsourcing;Costs;Automation;Heuristic algorithms;Mathematical models;Real-time systems;Time factors|
|[Energy Efficiency Optimization of Multi-user Switching and Scheduling in EH-SBS System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082509)|Y. Cao; K. Xiao|10.1109/ITNEC56291.2023.10082509|energy harvesting;small base station;user switching;energy efficiency;Base stations;Renewable energy sources;Simulation;Switches;Handover;Energy efficiency;Energy harvesting|
|[Evaluation Method of Main Transformer Capacity Expansion Scheme in Power Supply Area of 500kV Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082241)|G. Yang; X. Su; Y. Xie; P. Lu; F. Hou|10.1109/ITNEC56291.2023.10082241|substation;main transformer capacity;short-circuit;security margin;expansion scheme;Substations;Power transmission lines;Power supplies;Short-circuit currents;Transformers;Power grids;Generators|
|[Sunspot time series prediction based on EMD and LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082553)|Z. Li; Q. Zhang; Z. Li|10.1109/ITNEC56291.2023.10082553|Sunspot;EMD algorithm;LSTM model;Predictions;Empirical mode decomposition;Simulation;Time series analysis;Neural networks;Predictive models;Prediction algorithms;Data processing|
|[Error Analysis of an Airborne Dispenser Based on Monte-Carlo Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082619)|C. Tian; Z. Feng; T. Zhao; J. Che; X. Ge|10.1109/ITNEC56291.2023.10082619|dispenser;ballistic characteristics;drop point dispersion;error analysis;Analytical models;Monte Carlo methods;Automation;Error analysis;Projectiles;Atmospheric modeling;Mathematical models|
|[An Adaptive Shunt Model for Steel Defect Detection based on YOLOX](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082274)|S. Liu; M. Jia|10.1109/ITNEC56291.2023.10082274|surface defect detection;object detection;feature fusion;label assignment;Training;Location awareness;Adaptation models;Computational modeling;Heuristic algorithms;Surface fitting;Classification algorithms|
|[Multi-scale fusion-based expression recognition algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082269)|Y. Zhang; Y. Shi; Z. Sun|10.1109/ITNEC56291.2023.10082269|emotion recognition;VGGNet19;Xception;Dropout;loss function;Technological innovation;Emotion recognition;Face recognition;Image edge detection;Education;Transportation;Network architecture|
|[Adaptability Analysis of Directional Element in Photovoltaic-Connected Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082600)|Q. Huang; K. Li; Y. Li; P. Geng; Y. Sun; G. Zhang; R. Fan; A. Wang|10.1109/ITNEC56291.2023.10082600|photovoltaic;fault characteristic;protective relaying;directional element;Photovoltaic systems;Structural rings;Analytical models;Adaptation models;Power supplies;Simulation;Distribution networks|
|[An Active Power Filter Design Method based on Improved Immune Genetic Algorithm for Voltage Drop Detection and Compensation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082486)|C. Zhou; H. Zou; M. Zhu; H. Li|10.1109/ITNEC56291.2023.10082486|UPQC;Micro grid;Improved immune genetic algorithm;Voltage drop detection and compensation;PI control;Voltage fluctuations;Power quality;Power system dynamics;Microgrids;Search problems;Real-time systems|
|[Path Planning of Mobile Robots Based on the Fusion of an Improved A* Algorithm and a Dynamic Window Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082080)|J. Kong; J. Cheng|10.1109/ITNEC56291.2023.10082080|improved A* algorithm;dynamic window approach;path planning;mobile robot;real-time obstacle avoidance;Heuristic algorithms;Fitting;Cooperative systems;Turning;Search problems;Path planning;Real-time systems|
|[A hybrid signal source signal statistics and localisation algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082539)|H. Jiang; Z. Zhou|10.1109/ITNEC56291.2023.10082539|Mixed signal sources;Signal sources number estimation;Parameter estimation;Near-field signal versus far-field signal sources;Parameter estimation;Automation;Estimation;Pattern classification;Classification algorithms;Multiple signal classification|
|[Research on the Influence of Hydrodynamic Analysis to Dynamic Modeling of Underwater Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082093)|J. Zhong; C. Gao; Y. Tian; M. Zhang|10.1109/ITNEC56291.2023.10082093|underwater manipulator;dynamic model;hydrodynamics analysis;strip theory;Analytical models;Strips;Automation;Hydrodynamics;Task analysis;Manipulator dynamics|
|[An Improved Prediction Method of Transformer Oil Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082389)|Y. Cao|10.1109/ITNEC56291.2023.10082389|C-Prophet;Multi dimension prediction;Modal decomposition;Seasonal factor;Temperature;Oils;Windings;Sociology;Oil insulation;Predictive models;Prediction algorithms|
|[Improved blood cell detection method based on YOLOv5 algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082206)|M. Huang; B. Wang; J. Wan; C. Zhou|10.1109/ITNEC56291.2023.10082206|Blood cell test;Yolov5s algorithm;bidirectional feature pyramid;Attention mechanism;Automation;Adhesives;Object detection;Feature extraction;Neck;Detection algorithms;Blood|
|[Stability analysis of a transformer free hybrid active power filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082663)|Y. Shi; X. Xia; H. Zhu; J. Luo|10.1109/ITNEC56291.2023.10082663|no transformer;hybrid active power filter;control strategy;stability;Inductance;Power system stability;Active filters;Transformers;Harmonic analysis;Stability analysis;Hybrid power systems|
|[VTFL: A Blockchain Based Vehicular Trustworthy Federated Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082698)|A. Li; X. Chang; J. Ma; S. Sun; Y. Yu|10.1109/ITNEC56291.2023.10082698|Federated learning;blockchain;poisoning attacks;consensus mechanism;Training;Federated learning;Computer architecture;Resists;Data models;Robustness;Consensus protocol|
|[A Short-term Load Forecasting Method under Dual Network for Forty-eight moments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082247)|J. Zhao|10.1109/ITNEC56291.2023.10082247|CNN-LSTM;Load prediction;Depth;Space-time;Automation;Load forecasting;Predictive models;Load modeling|
|[Research on Simulation Evaluation Technology of Naval Gun Confrontation Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082108)|W. Zhao; J. Han|10.1109/ITNEC56291.2023.10082108|naval gun;confrontation training;missing distance;evaluation;Training;Automation;Projectiles;Iterative methods;Marine vehicles;Optimization;Dispersion|
|[Lighter and Faster Face Mask Detection Method Based on YOLOv5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082188)|L. Shuangyan; G. Huayong|10.1109/ITNEC56291.2023.10082188|face mask detection;ShuffleNet;GhostNet;Yolov5;CBAM;deep learning;Training;Face recognition;Image edge detection;Predictive models;Prediction algorithms;Data models;Real-time systems|
|[Design of Intelligent Low-power Micro Vibration Gating System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082230)|H. Gu; L. Zhang; X. Yin|10.1109/ITNEC56291.2023.10082230|intelligent control;micro-vibration;gating control;knock frequency;Vibrations;Bluetooth;EPROM;Passwords;Organic light emitting diodes;Logic gates;Control systems|
|[Region Portrait Segmentation Method based on boundary Search Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082289)|J. Shi; Q. Xu|10.1109/ITNEC56291.2023.10082289|Boundary pixels;Region;Portrait segmentation;Search network;Image segmentation;Automation;Annotations;Image edge detection;Simulation;Classification algorithms;Complexity theory|
|[Smartwatch Sales Forecast Based on CNN-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082702)|L. Yan|10.1109/ITNEC56291.2023.10082702|Sales forecast;smartwatch;CNN-LSTM;feature extraction;Automation;Neural networks;Predictive models;Data models;Planning;Internet;Electronic commerce|
|[Design and Implementation of the Ship Alarm Management System for Chief Engineer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082225)|G. Li; H. Zhao; M. Ma; Y. Zhao; P. Zhang|10.1109/ITNEC56291.2023.10082225|alarm management system;alarm filter;alarm association processor;alarm sequence;chief engineer;Correlation;Databases;Process control;Information filters;Real-time systems;Floods;Data mining|
|[Research on energy control strategy of four-wheel drive hybrid electric vehicle based on dynamic programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081950)|W. Wang; F. Qu; W. Li; P. Wang; J. Cai|10.1109/ITNEC56291.2023.10081950|Hybrid electric vehicle;Energy management;Dynamic programming;Four-wheel drive;Modeling simulation;Employee welfare;Heuristic algorithms;Simulation;Optimal control;Fuel economy;Mathematical models;Dynamic programming|
|[Sensing Characteristics Analysis of Dual-beam Optical Trap-based Optomechanical Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082630)|D. Liang; H. Yu; D. Zhao; L. Zou|10.1109/ITNEC56291.2023.10082630|dual-beam optical trap;accelerometer;light trapping force;accuracy;bandwidth;Accelerometers;Temperature measurement;Temperature sensors;Manufacturing processes;Temperature;Bandwidth;Optical variables measurement|
|[Traffic accident prediction method for Jiangsu section of the Yangtze River](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082253)|S. Zhang; S. Tao; Z. Ding|10.1109/ITNEC56291.2023.10082253|the Yangtze River;the Jiangsu section;navigation safety;Support vector machine;predict;Support vector machines;Automation;Navigation;Predictive models;Data models;Rivers;Safety|
|[A wearable, real-time sEMG gesture classifier based on E-tattoo and CDF-CNN for prosthetic control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082190)|C. Xu; X. Qu; H. Liang; D. Chen|10.1109/ITNEC56291.2023.10082190|sEMG;Deep Learning;Flexible electronic tattoo electrode;prosthetic;control system;Electrodes;Automation;Gesture recognition;Feature extraction;Control systems;Skin;Real-time systems|
|[Research on Fusion Location Positioning Technology in Power Business](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081965)|R. Zhao|10.1109/ITNEC56291.2023.10081965|power industry;Beidou technology;5G network;networking;fusion location positioning;Meters;Satellites;5G mobile communication;Production;Satellite navigation systems;Power industry;Safety|
|[CFD Calculation of Transonic Flow Around Airfoil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082199)|M. Tang; G. Tan|10.1109/ITNEC56291.2023.10082199|Transonic velocity;Flow around;Y +;Turbulence model;Experimental data;Adaptation models;Shock waves;Atmospheric modeling;Electric shock;Numerical simulation;Data models;Mathematical models|
|[Trajectory Angles Evaluation for the Flight Vehicle via Wavelet Transforms and Similarity Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082717)|K. Li; M. Qi; R. Cao|10.1109/ITNEC56291.2023.10082717|Trajectory angles;Wavelet transform;Lagrange polynomial interpolation;correlation coefficient;similarity measure;nan|
|[An optimization scheme to improve the write performance of PCM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082138)|D. Chang; S. Yan; W. Tan; D. He|10.1109/ITNEC56291.2023.10082138|Phase Change Memory;Non-volatile Memory;Write performance;Reliability;nan|
|[Detection of Low SNR Random Noise Signal Based on Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082030)|Z. Zhang; S. Li; J. Yao|10.1109/ITNEC56291.2023.10082030|wavelet transform;low signal-to-noise ratio;noise reduction;signal process;nan|
|[A Fractional-Order Digital Filter Implementation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082557)|R. He; H. Pu; J. Ding|10.1109/ITNEC56291.2023.10082557|fractional-order filter;digital filter;inverse Fourier transform;convolution;Fourier transforms;Automation;Convolution;Software packages;Differential operators;Information filters;Data processing|
|[Research on Dynamic Characteristics of Hydraulic Pump Slipper With Wear Based on Operational Modal Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082366)|M. Hao; S. Liu; G. Liu; H. Pan|10.1109/ITNEC56291.2023.10082366|axial piston pump;dynamic characteristic;operational modal analysis;slipper wear;feature extraction;Pistons;Fault diagnosis;Time-frequency analysis;Modal analysis;Hydraulic systems;Harmonic analysis;Feature extraction|
|[Adaptive Two-step Binary Exponential Backoff Strategy for Random Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082043)|Y. Li; Z. Lv; Z. Fan; H. Zhang|10.1109/ITNEC56291.2023.10082043|random access;backoff algorithm;window oscillation;adaptive;Adaptation models;Analytical models;Automation;Heuristic algorithms;Process control;Markov processes;Numerical simulation|
|[Detection of Rice Seed Hole Seeding Amount Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082466)|X. Deng; S. Liang|10.1109/ITNEC56291.2023.10082466|Deep Learning;Object Detection;YOLO Algorithm;Seed’s quantity;nan|
|[Analysis of Industrial Internet identification in Electric power industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082069)|W. Xie; S. Lu; J. Wu; X. Fang; J. Shao; J. Huang|10.1109/ITNEC56291.2023.10082069|Industrial Internet;Identify compatibility;Collaborative interaction;Internet of Things;nan|
|[Fusion Prediction of Aero-engine Performance Parameters based on Cubature Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082037)|J. Xiong; C. Liu|10.1109/ITNEC56291.2023.10082037|engine performance parameters;data fusion;state estimation;UKF;CKF;nan|
|[Collaborator Recommendation Based on Multiple Information Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082304)|D. Zhao; H. Qin|10.1109/ITNEC56291.2023.10082304|heterogeneous information networks;neural networks;collaborator recommendation;Recommend systems;nan|
|[A Multi-Feature Base Multi-Adaptation Lidar Odometry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082662)|G. Junjie; L. Fu; L. Jun; C. Jie|10.1109/ITNEC56291.2023.10082662|lidar odometry;sparse features;multi-feature;multi-adaptation;nan|
|[Radio Tomographic Imaging Localization Based on Transformer Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082228)|Z. Lu; H. Liu; X. Zhang|10.1109/ITNEC56291.2023.10082228|Radio tomographic imaging;wireless sensor network;Transformer;deep learning;nan|
|[Drought-tolerant crop disease identification based on attention mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082310)|R. Wang; L. Wu|10.1109/ITNEC56291.2023.10082310|drought-tolerant crops;deep learning;disease detection;migration learning;residual neural networks;attention mechanisms;nan|
|[Estimation of Benchmark Period for PRF Jitter Signal Based on Cumulative Histogram](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082153)|C. Heng; Z. Ying-Bo; W. Jin-Feng|10.1109/ITNEC56291.2023.10082153|PRF jitter radar;Sparse observation sequence with noise;MEA;cumulative histogram;Histograms;Automation;Radar measurements;Navigation;Estimation;Radar;Jitter|
|[Millimeter wave radar denoising and obstacle detection in highly dynamic railway environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082313)|Y. Zhao; Y. He; Y. Que; Y. Wang|10.1109/ITNEC56291.2023.10082313|object detection;Kalman filtering;millimeter wave radar;highly dynamic environment;denoise;Vibrations;Target recognition;Surface waves;Dynamics;Radar detection;Object detection;Millimeter wave radar|
|[Design of Multi-source Remote Sensing Image Fusion Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082152)|X. Wu; D. Qin|10.1109/ITNEC56291.2023.10082152|Remote Sensing Satellite;Multi-source Image Fusion;Fusion Framework;Edge Computing;Technical requirements;Deep learning;Satellites;Neural networks;Feature extraction;Market research;Hybrid power systems|
|[Research on the Speed and Accuracy of Full Port Scanning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082257)|J. Zhao; L. Yang; C. Zhang; J. Zhang|10.1109/ITNEC56291.2023.10082257|network attack;port scanning;sampling;Costs;Automation;Penetration testing|
|[Fuzzy Adaptive PI Circulating Current Suppressing Control for MMC-HVDC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082254)|W. Chao; J. Huang; C. Deng; L. Dai|10.1109/ITNEC56291.2023.10082254|MMC-HVDC;Fuzzy Adaptive PI;Circulating Current Suppressing;Adaptation models;Multilevel converters;PI control;HVDC transmission;High-voltage techniques;Mathematical models;Stability analysis|
|[Industrial Internet identification code compatibility method for power industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082435)|W. Xie; S. Lu; L. Zhang; X. He; R. Gu; Y. Lu|10.1109/ITNEC56291.2023.10082435|Industrial Internet;Identify compatibility;coding rules;Internet of Things;nan|
|[Research on QRS wave detection algorithm of ECG system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082483)|K. Ning; J. Qin; W. Song; G. Li; S. Wang|10.1109/ITNEC56291.2023.10082483|QRSwave;Cardiac pretreatment;Adaptive differential threshold;R-wave error detection compensation algorithm;Restraining period;nan|
|[Research on Container Edge Detection Algorithm Based on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082707)|K. Ning; S. Wang; W. Song; J. Qin|10.1109/ITNEC56291.2023.10082707|edge detection;Cannyalgorithm;Container images;FPGA;nan|
|[UAV swarm positioning and scheduling method from the perspective of pure orientation passive positioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082189)|W. Zhang; W. Zhang; X. Wang|10.1109/ITNEC56291.2023.10082189|Pure azimuthpassive positioning;UAV cluster scheduling;alternate flashing;adaptive scheduling;Computer science;Job shop scheduling;Costs;Automation;Azimuth;Processor scheduling;Sensors|
|[Research on weight function distribution method under special boundary based on electrical characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082346)|X. Li; X. Yao|10.1109/ITNEC56291.2023.10082346|Electrical characteristics;Induced potential;Weight function;Electrodes;Weight measurement;Conductivity;Flowmeters;Pollution measurement;Electron tubes;Electromagnetics|
|[The Combat Effectiveness Analysis of System Based on Complex Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082186)|L. Zhihuan; J. Yiguo; Z. Huifeng; W. Hongyan|10.1109/ITNEC56291.2023.10082186|complex network;combat system;Combat effectiveness;strike rate;Command and control systems;Atmospheric modeling;Information sharing;Complex networks;Reconnaissance;Mathematical models;Robustness|
|[Improved military equipment identification algorithm based on YOLOv5 framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081984)|H. Wang; J. Han|10.1109/ITNEC56291.2023.10081984|Military equipment target;Detection;C3CMix;YOLO v5;Activation function;Automation;Target recognition;Object detection;Real-time systems;Detection algorithms;Military equipment|
|[E-nose System using CNN and Abstract Odor Map to Classify Meat Freshness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082128)|X. Ren; Y. Wang; Y. Huang; D. Sun; L. Xu; F. Wu|10.1109/ITNEC56291.2023.10082128|Time Series Feature;electronic nose;MEMS gas sensor array;abstract odor map;CNN;meat freshness;Micromechanical devices;Time series analysis;Data acquisition;Printed circuits;Feature extraction;Electronic noses;Steady-state|
|[Proportional Integral Observer Design for a Class of Uncertain Nonlinear Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082528)|N. Gao; J. Luo; J. Tang; L. Geng; F. Huang; H. Huang|10.1109/ITNEC56291.2023.10082528|PIO;Uncertain;Nonlinear;LMI;nan|
|[MBFFNet:Multi-branch feature fusion network for underwater image restoration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082374)|H. Luo; G. Zhang; X. Zhang|10.1109/ITNEC56291.2023.10082374|Underwater image restoration;multi branch;feature fusion;UIEBD-snow;Image color analysis;Oceans;Impurities;Feature extraction;Spatial databases;Image restoration;Data mining|
|[Adaptively Weighted Balanced Feature Pyramid for Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082343)|J. Jiao; H. Qin|10.1109/ITNEC56291.2023.10082343|object detection;feature enhancement;feature pyramid;FCOS;Adaptation models;Computer vision;Automation;Fuses;Computational modeling;Object detection;Feature extraction|
|[Joint Entity Relation Extraction based on Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082180)|L. Xu; F. Yang|10.1109/ITNEC56291.2023.10082180|joint entity relation extraction;attention mechanism;graph neural networks;nan|
|[Research and application of key protection technologies for power industrial control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082524)|P. Yan; S. Fei; X. Ma; F. Sai; J. Jingus; W. Ma; J. Chen|10.1109/ITNEC56291.2023.10082524|Electric power monitoring;network security;joint prevention and control;fixed value management;Technological innovation;Process control;Production control;Network security;Maintenance engineering;Power grids;Real-time systems|
|[Research on Development Mode of Electronic Information System Driven by Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082379)|J. Xiao|10.1109/ITNEC56291.2023.10082379|open software architecture;constructed design;model driven;electronic information system;Analytical models;Technological innovation;Software design;Software architecture;Computer architecture;Software;Design tools|
|[Measurement errors introduced by plane heater in steady-state thermal conductivity measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081978)|Z. Cao; H. Yin; J. Yang|10.1109/ITNEC56291.2023.10081978|Plane heater;Temperature nonuniformity;Finite element analysis;Sensor;Temperature measurement;Coils;Temperature sensors;Measurement errors;Thermal resistance;Surface resistance;Measurement uncertainty|
|[A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082211)|H. Xia; S. Sun; X. Liu; J. Zhou; H. Wang; T. Chen|10.1109/ITNEC56291.2023.10082211|tissue oxygen saturation;stress classification;convolutional neural network;feature fusion;Dimensionality reduction;Automation;Anxiety disorders;Feature extraction;Physiology;Stress|
|[Osteoporosis risk prediction method based on relational network and GNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082316)|Z. Wang; Y. Li; Y. Xu|10.1109/ITNEC56291.2023.10082316|machine learning;network analysis;graph neural networks;osteoporosis;Osteoporosis;Analytical models;Sensitivity;Automation;Machine learning;Predictive models;Graph neural networks|
|[Study on Application of Multi-source Data Fusion Method in Environmental Control of Enclosed Layer House](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082074)|H. Li; M. Li; K. Zhan; P. Guo; X. Liu; X. Yang; Z. Ma|10.1109/ITNEC56291.2023.10082074|Layer house;Multi-source data fusion technology;The adaptive weighted fusion algorithm;D-S evidence theory algorithm;Automation;Adaptive systems;Evidence theory;Decision making;Data integration;Sensor fusion;Control systems|
|[Economic Benefit Post-evaluation for Technical Revamping of Power Grid Projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082631)|Y. Liu; H. Zhang; T. Li; M. Wang|10.1109/ITNEC56291.2023.10082631|technical revamping of the power network projects;innovative economic benefit post evaluation;method of before and after comparison;theory of whole life cycle costs;Analytical models;Costs;Automation;Reliability theory;Power grids;Indexes;Standards|
|[Bearing Remaining Useful Life Prediction Based on AE-BiLSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082350)|J. Liu; Z. Yang; R. Wang; S. Liu|10.1109/ITNEC56291.2023.10082350|remaining useful life;AutoEncoder;Bi-LSTM;rolling bearing;Degradation;Vibrations;Frequency-domain analysis;Noise reduction;Rolling bearings;Predictive models;Vibration measurement|
|[A Study of Early Warning Method for Diabetic Foot Based on Improved ConvNeXt Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082057)|J. Shan; X. Zhang; L. Gao; Z. Ma; Y. Sun; Y. Xu|10.1109/ITNEC56291.2023.10082057|diabetic foot;early warning;infrared thermography;image segmentation;deep learning;Image segmentation;Temperature distribution;Fuses;Infrared heating;Medical services;Color;Diabetes|
|[I-UNeXt: A Skin Lesion Segmentation Network Based on Inception and UNeXt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082025)|B. Luo; Y. Shu; Y. Nie; D. Chang; Y. Pan; H. Shi|10.1109/ITNEC56291.2023.10082025|Skin lesion segmentation;Inception;UNeXt;Feature extraction;Image segmentation;Convolution;Feature extraction;Skin;Planning;Lesions;Data mining|
|[Tooth-marked Tongue Recognition based on Wavelet Transform and Feature Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082166)|W. Tan; D. Chang; J. Li; D. He|10.1109/ITNEC56291.2023.10082166|Convolutional neural network (CNN);wavelet transform;feature fusion coding;tooth-marked tongue;Wavelet transforms;Training;Epidemics;Tongue;Image coding;Lighting;Feature extraction|
|[Optimization of Generator Unit Restoration Paths Based on PD-NSGA-II Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082512)|L. Ji; J. Tu; W. Sun; J. He; L. Li; X. Zhang|10.1109/ITNEC56291.2023.10082512|power system restoration;restoration paths optimization;PD-NSGA-II algorithm;Power transmission lines;Automation;Heuristic algorithms;Power system dynamics;Decision making;Optimization methods;Data processing|
|[GRU Deep Residual Network for Time Series Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082454)|F. Zhu; H. Wang; Y. Zhang|10.1109/ITNEC56291.2023.10082454|Convolutional Neural Network;GRU;ResNet structures;Time Series Classification;nan|
|[Design of Intelligent Power Supply Management System for Seismic Monitoring Station Based on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082086)|Y. Hu; H. Mu; D. Ding; P. Wang; D. Cheng|10.1109/ITNEC56291.2023.10082086|Intelligent power supply system;Remote monitoring;Cloud platform;Internet of things;Automatic control;Landline;Observatories;Power supplies;Power system management;Instruments;Lightning;Maintenance engineering|
|[Detection and estimation of Micro-motion target based on MM-CBMeMBer filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081987)|X. Yuntao; Z. Song|10.1109/ITNEC56291.2023.10081987|MM-CBMeMBer filter;micro-Doppler;vrotating targets;parameter estimation;nan|
|[Classification of Microsatellite Instability Status in Slide-level Annotated Colorectal Tumors by Weakly Supervised Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082276)|X. Yuan; J. Ruan; J. Yue|10.1109/ITNEC56291.2023.10082276|microsatellite instability;weak supervision;vcluster constraint;attention;Deep learning;Adaptation models;Costs;Small satellites;Clustering algorithms;Data models;Classification algorithms|
|[Multi-agent Reinforcement Learning with Multi-head Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082248)|K. Ni; J. Chen; J. Wang; B. Liu; T. Lei|10.1109/ITNEC56291.2023.10082248|multi-agent;reinforcement learning;attention;layer normalization;Training;Automation;Redundancy;Decision making;Reinforcement learning;Complexity theory;Task analysis|
|[DCTNet: A Hybrid Model of CNN and Dilated Contextual Transformer for Medical Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082385)|X. Pan; J. Xiong|10.1109/ITNEC56291.2023.10082385|transformer;contextual;image segmentation;hybrid model;Measurement;Image segmentation;Transformers;Feature extraction;Planning;Convolutional neural networks;Medical diagnosis|
|[Research and design of professional farmer learning service platform based on cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082201)|J. Shi; J. Zhang; Z. Luan; Q. Lu; J. Li|10.1109/ITNEC56291.2023.10082201|Intelligent Agriculture;Cloud Computing Platform;Big Data;Multi-tenant and multi-organization;nan|
|[Research on electromagnetic flowmeter measurement of non-full pipe flow based on numerical analysis method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082192)|X. Li; Z. Zhang|10.1109/ITNEC56291.2023.10082192|Partial pipe;Flow;Measurement;nan|
|[Edge Detection Algorithm based on Difference of Gaussian for Visual Prosthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082704)|F. Guo; J. Duan; S. Huang; Y. Xiao; R. Chu|10.1109/ITNEC56291.2023.10082704|edge detection;difference of Gaussian;visual prosthesis;Visualization;Image edge detection;Software algorithms;Visual prosthesis;Dogs;Hardware;Software|
|[Intelligent product design study of contact scene disinfection in post-epidemic era](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082115)|T. Yang; J. Fu; J. Yang|10.1109/ITNEC56291.2023.10082115|post-epidemic era;intelligent product design;disinfection;automatic control;Epidemics;Automation;Shape;Product design;Viruses (medical);Optimization|
|[Simulation analysis of energy saving potential of electric vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082587)|F. Qu; W. Wang; W. Li|10.1109/ITNEC56291.2023.10082587|Electric vehicles;Energy saving potential;Simulation;optimization;Resistance;Employee welfare;Economics;Analytical models;Energy consumption;Electric potential;Energy conservation|
|[Risk status assessment of transmission lines based on big data platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082079)|L. Tang; Y. Lin; C. Hu; L. Ma|10.1109/ITNEC56291.2023.10082079|risk status assessment;big data platform;transmission line;Power transmission lines;Computational modeling;Simulation;Big Data;Maintenance engineering;Power system stability;Data models|
|[Network Adaptive Node Acceleration Method based on Pruning Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082413)|Y. Yang|10.1109/ITNEC56291.2023.10082413|Network security;Data analysis;Artificial intelligence;Data security;Neural networks;Mathematical modeling;Analytical models;Adaptive systems;Automation;Memory management;Telecommunication traffic;Parallel processing;Network security|
|[End-to-end BEV Perception Via Homography Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082203)|P. Zhang; G. Li; C. Liu; J. Ma|10.1109/ITNEC56291.2023.10082203|bird’s eye view;automatic driving;algorithm;convolutional neural network;Industries;Three-dimensional displays;Roads;Neural networks;Estimation;Sensor phenomena and characterization;Network architecture|
|[Optimization and Research of Suspicious Object Detection Algorithm in X-ray Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082660)|R. Wang; Y. Shi; M. Cai|10.1109/ITNEC56291.2023.10082660|X-ray imaging;convolutional neural network;object detection;multi-scale feature fusion;attention;Training;Fuses;Semantics;Object detection;Inspection;Feature extraction;Security|
|[Electronic Nose System implemented on ZYNQ Platform for Fruits Freshness Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082522)|Y. Huang; X. Ren; Y. Wang; D. Sun; L. Xu; F. Wu|10.1109/ITNEC56291.2023.10082522|electronic nose;dimensionality reduction;feature extraction;Mann-Kendall trend test;Dimensionality reduction;Analytical models;Feature extraction;Electronic noses;Market research;Hardware;Software|
|[DeepFFMS: A Parallel Model for Advertise Click-Through Rate Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082073)|M. Cai; Y. Shi; Z. Hou|10.1109/ITNEC56291.2023.10082073|CTR;recommend;deep learing;DeepFFMS;nan|
|[Neuronal Morphology Classification based on Improved Residual Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082045)|Y. Wei; F. He; Y. Qian; F. Feng|10.1109/ITNEC56291.2023.10082045|neuronal morphology classification;residual network;feature reconstruction;deep learning;nan|
|[Identification of SSH Honeypots Using Machine Learning Techniques Based on Multi-Fingerprinting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082467)|Y. -J. Zhang; W. -J. Liu; K. -N. Guo; Y. -M. Kang|10.1109/ITNEC56291.2023.10082467|SSH honeypots;Honeypot fingerprinting;Random Forest;Protocols;Automation;Forestry;Fingerprint recognition;Feature extraction;Internet;Classification algorithms|
|[An Assessment Model of Digital Literacy for the Students in Vocational Education Based on Principal Component Analysis in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082530)|F. Peng; M. Guo; C. Zheng; S. Wang; X. Wang; M. Xu|10.1109/ITNEC56291.2023.10082530|Digital Literacy;Assessment;Vocational Education;Principal Component Analysis;nan|
|[Research and Application of Music Personalized Recommendation System Based on Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082503)|Z. Shu; Q. Shen; T. Zeng|10.1109/ITNEC56291.2023.10082503|randomforest algorithm;personalized recommendation;music;nan|
|[Research and Optimization of Summary Extraction Method Based on RoBERTa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082534)|W. Yu; S. Li; Z. Chen|10.1109/ITNEC56291.2023.10082534|Extraction summary;RoBERTa;Natural Language Processing;Neural Network;Correlation;Automation;Semantics;Process control;Coherence;Feature extraction;Transformers|
|[Carbon Sequestration-oriented Forest Management Plans](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082340)|J. Wu; J. Shi; D. Song; S. Zheng; S. Chen|10.1109/ITNEC56291.2023.10082340|APH;Fuzzy clustering;Dynamic differential equations;Climate change;Fuzzy systems;Clustering methods;Differential equations;Forests;Carbon footprint;Decision making|
|[Travel Path Processing and Visual Analysis of Key Personnel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082141)|Y. Liu|10.1109/ITNEC56291.2023.10082141|Infectious Diseases;trajectory data;Data processing;Data visualization;Visualization;Infectious diseases;Data visualization;Process control;Collaboration;Stability analysis;Trajectory|
|[A Method of Safety Risk Analysis for the lower waterways of the Yangtze River](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082299)|S. Zhang; S. Tao; Z. Ding|10.1109/ITNEC56291.2023.10082299|Waterway;traffic accident;5M model;factor analysis;risk analysis;Ranking (statistics);Analytical models;Statistical analysis;Navigation;Media;Safety;Rivers|
|[Research on Deployment Method of Service function Chain based on Network function Virtualization in Distribution communication Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082364)|B. Xia; C. Li; Z. Zhou; J. Liu|10.1109/ITNEC56291.2023.10082364|service function chain;network function virtualization;heuristic algorithm;Distribution communication network;Costs;Service function chaining;Computational modeling;Maintenance engineering;Software;Hardware;Network function virtualization|
|[Version Control Method of Power Dispatching Automatic Control Software based on Ladder Aggregation Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082459)|Z. Wei; L. Li; Y. Yu; X. Qi; X. Song; Y. Li|10.1109/ITNEC56291.2023.10082459|version control;ladder aggregation encryption;power Dispatching Automatic control software;consistency check;Codes;Reliability engineering;Software;Dispatching;Encryption;Software reliability;Power system reliability|
|[Establishment of the Evaluation Model Based on Dynamic Fuzzy Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082411)|L. Shi; H. Zhu|10.1109/ITNEC56291.2023.10082411|Dynamic Fuzzy;Evaluation Model;Quality of Classroom Teaching;Analytical models;Automation;Computational modeling;Education;Data models|
|[Research on Cross-site Scripting Attack Detection Technology Based on Few-shot Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082596)|D. Lu; L. Liu|10.1109/ITNEC56291.2023.10082596|cross-site scripting attack;few-shot learning;machine learning;virtual sample generation;convolution neural network;Machine learning algorithms;Automation;Cross-site scripting;Perturbation methods;Neural networks;Machine learning;Big Data|
|[Anomaly Detection For Autonomous Driving Public Transports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082709)|Y. Ye; P. Li|10.1109/ITNEC56291.2023.10082709|Anomaly Detection;Autonomous Driving;VAE;Deconvolution;Automation;Brain modeling;Real-time systems;Data models;Safety;Anomaly detection|
|[Intelligent Simulation of Water Environmental Pollutant Flux in River Basins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082452)|Y. Wang; Z. Jin; J. Ma; Y. Li; P. Run; H. Cui|10.1109/ITNEC56291.2023.10082452|pollutant flux;water quality;river basin;intelligent simulation;hybrid algorithm;Simulation;Neural networks;Water quality;Data models;Water pollution;Rivers;Pollution measurement|
|[Optimization of AODV Routing Protocol in Emergency Communication Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082575)|S. Dong|10.1109/ITNEC56291.2023.10082575|Natural Disaster;Emergency Communication;MANET;AODV;Backup Route;Automation;Law enforcement;Simulation;Routing;Routing protocols;Communication networks;Task analysis|
|[A Configurable On-Chip Spike Encoding Network Based on Dual-Mode Integrate & Fire Neurons](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081991)|Z. Zhong; Y. Tuo; H. Wang; T. Wang; J. He; S. Chen; M. Tian; C. Shi|10.1109/ITNEC56291.2023.10081991|neuromorphic hardware;spike encoding network;configurable circuit;on-chip encoding;Training;Neuromorphics;Neurons;Prototypes;Encoding;Hardware;Software|
|[A Cloud-side Collaboration Model-based Offloading Method for Power Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082462)|H. Xie; S. Du; Y. Zhang; K. Shuai; Q. Zhang|10.1109/ITNEC56291.2023.10082462|MEC;5G;DRL;PIoT;cooperative computing;Adaptation models;Processor scheduling;Wireless networks;Computational modeling;System performance;Collaboration;Mobile handsets|
|[Study on Charge Parameter Effects to Gun Interior Ballistic Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082656)|P. He; L. Li; J. Zhu; L. Yan|10.1109/ITNEC56291.2023.10082656|Gun;Propellant;Charge parameters;Interior ballistic performance;Temperature;Automation;Shape;Projectiles;Propulsion;Numerical models;Safety|
|[Deep Learning-driven Fast Planning of Informative Sensing for Environmental Field Reconstruction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082021)|K. Wang; S. Yang; Q. Cheng; T. Li|10.1109/ITNEC56291.2023.10082021|Informative sensing;online planning;mutual information;field mapping;Deep learning;Sensor placement;Neural networks;Process control;Network architecture;Reliability engineering;Planning|
|[Research on Classification of Scoliosis Based on Pulse Signal under Dynamic Pressure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082026)|Y. Shang; Q. Li; J. Yang; Z. Dai; X. Wang; X. Zhang|10.1109/ITNEC56291.2023.10082026|pulse signal;scoliosis;preprocessing;feature extraction;XGBoost;Support vector machines;Performance evaluation;Wrist;Micromechanical devices;Frequency-domain analysis;Scoliosis;Manuals|
|[Research on wheeled pipe robot based on fractional order PID control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082148)|M. Jiang; H. Xiong; C. He|10.1109/ITNEC56291.2023.10082148|wheeled pipe robot;fractional order PID control;robustness;PI control;Fluctuations;Robot control;Pipelines;Wheels;Interference;Robustness|
|[A Fast Antenna Measure Method Without Microwave Chamber](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082560)|N. Qi; F. Yang; X. Kang; D. Xiong; R. Wang; Z. Fu; W. Qi|10.1109/ITNEC56291.2023.10082560|Antenna Measurement;Antenna Parameters Test;Microwave Chamber;Power Amplifier;Antenna measurements;Microwave measurement;Microwave antennas;Power measurement;Power amplifiers;Microwave theory and techniques;Propagation losses|
|[Deep Learning Based Fusion Models for Sensitive Information Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082481)|S. Wang; L. Huang; Z. Wang; H. Ren|10.1109/ITNEC56291.2023.10082481|Sensitive information;Fusion model;Deep learning;Content identification;Deep learning;Training;Analytical models;Automation;Semantics;Neural networks;Internet|
|[Adaptive Compensation for Downlink Broadcast Signaling in the Narrow-beam Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082324)|Q. Qu; B. Zhou; W. Yu; Z. Bu|10.1109/ITNEC56291.2023.10082324|narrow-beam communication;circular-beam broadcast;spread spectrum coding;link budget;Adaptive systems;Automation;Unicast;Communication systems;Transmitting antennas;Optimization methods;Downlink|
|[An active LF RFID positioning method against metal interference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082191)|X. Ma; S. Chen; Q. Wang; Z. Chen|10.1109/ITNEC56291.2023.10082191|low frequency;received signal strength indicator;probability distribution;high precision;indoor positioning;Active RFID tags;Metals;Interference;Production;Inspection;Distance measurement;Probability distribution|
|[An Agile Quadrotor Motion Planning Method for Dynamic Target Following Flight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082365)|L. Zou; Z. Wang|10.1109/ITNEC56291.2023.10082365|motion planning;quadrotor;trajetory;target following;Target tracking;Trajectory planning;Surveillance;Focusing;Trajectory;Planning;Sensors|
|[Research on Surface Defect Detection of the Connecting Rod Based on Machine Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081974)|Z. Bin; M. Shaoxiong|10.1109/ITNEC56291.2023.10081974|machine vision;connecting rod;surface defect;CCD camera;image processing;defect detection;Charge coupled devices;Image segmentation;Computational modeling;Production;Cameras;Real-time systems;Hardware|
|[Research on high-precision lightweight speech recognition model with small training set in Multi-person conversation scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082545)|H. Jiang; Z. Chen; Q. Zhang; J. Zhao|10.1109/ITNEC56291.2023.10082545|speech recognition;Multi-person conversation;Fitting;Densely connected feedforward convolutional networks;light weight;Training;Text recognition;Speech recognition;Oral communication;Feature extraction;Prediction algorithms;Data models|
|[Virtual Reality Products that Foster Aesthetic Creativity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081988)|S. Ma; J. Fu; J. Yang|10.1109/ITNEC56291.2023.10081988|virtual reality technology;aesthetic creativity;educational products;product design;Automation;Education;Sociology;Virtual reality;Product design;IEEE educational products;Statistics|
|[Feedback Aggregation Based Retransmission Scheme for Multi-Hop Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082591)|Q. Liu; B. Zhou; W. Yu; Z. Bu|10.1109/ITNEC56291.2023.10082591|Multi-hop network;retransmission;end-to-end delay;ARQ based protocol;Protocols;Automation;Wireless networks;Simulation;Spread spectrum communication;Delays;Reliability|
|[Blockchain Technology based Metaverse Development Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082490)|X. Zhang|10.1109/ITNEC56291.2023.10082490|Metaverse;web3.0;VR/AR development and application;smart contract;Economics;Automation;Metaverse;Smart contracts;Virtual reality;Organizations;Decentralized applications|
|[Input-output efficiency evaluation of power supply station based on hybrid DEA method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082036)|X. Wang; H. Zhang; T. Li; M. Wang|10.1109/ITNEC56291.2023.10082036|hybrid data envelopment analysis;input-output;grid enterprise;efficiency evaluation;Analytical models;Costs;Automation;Power supplies;Companies;Power grids;Hybrid power systems|
|[Bayesian Optimization Machine Learning Models for True and Fake News Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082424)|G. Zhao; S. Song; H. Lin; W. Jiang|10.1109/ITNEC56291.2023.10082424|Bayesian;machine learning;hyperparameter;KNN;Random Forest;GBDT;Machine learning algorithms;Monte Carlo methods;Optimization methods;Machine learning;Forestry;Bayes methods;Task analysis|
|[Development of online monitoring system for cylinder pressure of marine low-speed engine based on virtual instrument](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082082)|W. Zhou; L. Hu; Y. Yu|10.1109/ITNEC56291.2023.10082082|marine low-speed engine;cylinder pressure;data processing;online monitoring;virtual instrumentation;Instruments;Diesel engines;Process control;Combustion;Real-time systems;Performance analysis;Data mining|
|[Feature-based identification method of MCU firmware IO interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082017)|Y. Sun; W. Dong|10.1109/ITNEC56291.2023.10082017|emulation;Interface identification;Analytical models;Automation;Source coding;Registers;Character recognition;Object recognition;Security|
|[Design of predictive controller for Networked Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082208)|F. Chen; X. Zhou|10.1109/ITNEC56291.2023.10082208|Communication delays;Data loss;Networked control systems (NCSs);Predictive controller;Degradation;Sufficient conditions;Automation;Networked control systems;System performance;Propagation losses;Stability analysis|
|[Dwtformer: Wavelet decomposition Transformer with 2D Variation for Long-Term Series Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082078)|Y. Cao; X. Zhao|10.1109/ITNEC56291.2023.10082078|time series forecasting;time series decomposition;transformer;deep learning;Representation learning;Time-frequency analysis;Tensors;Time series analysis;Predictive models;Network architecture;Transformers|
|[De-Biasing user conformity bias and item popularity bias in Group recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082689)|J. Jia; T. Shang; L. Li; S. Chen|10.1109/ITNEC56291.2023.10082689|Conformity bias;Popularity bias;Fairness;Group recommendation;Automation;Behavioral sciences;Recommender systems|
|[Recognition and detection technology for abnormal flow of rebound type remote control Trojan in power monitoring system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082482)|F. Sai; X. Wang; X. Yu; P. Yan; W. Ma|10.1109/ITNEC56291.2023.10082482|machine learning;Abnormal traffic detection;Rebound remote control Trojan;network traffic monitoring algorithm;Training;Machine learning algorithms;Production;Telecommunication traffic;Feature extraction;Safety;Security|
|[Depression Detection Based on Facial Expression, Audio and Gait](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082163)|Z. Dai; Q. Li; Y. Shang; X. Wang|10.1109/ITNEC56291.2023.10082163|Depression Detection;Multi-modal;Deep learning;Costs;Automation;Pipelines;Mental health;Depression;Data models|
|[Anomaly Detection of Credit Data based on Sparse Subspace Clustering Undersampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082623)|R. Sun; L. Wang; J. Tang; B. Bi|10.1109/ITNEC56291.2023.10082623|Outlier detection;Sparse subspace clustering;Undersampling;Unbalanced data set;Credit data;Automation;Clustering algorithms;Finance;Predictive models;Data models;Classification algorithms;Behavioral sciences|
|[Learning Representation for Clustering via Dual Correlation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082396)|T. Zhang; M. Hu|10.1109/ITNEC56291.2023.10082396|Contrastive learning;deep cluster;density clustering;self-supervised learning;unsupervised learning;Correlation;Automation;Clustering algorithms;Self-supervised learning;Data models|
|[An Intrusion Detection Feature Selection Method Based on Improved Mutual Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082305)|Z. Qi; J. Fei; J. Wang; X. Li|10.1109/ITNEC56291.2023.10082305|feature selection;correlation coefficient;conditional mutual information;intrusion detection;nan|
|[Smart Integrated MANET-DTN Scheme for Network Adaptation Enhancement in Emergency Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082308)|J. Zhang; C. Liu; T. Zheng|10.1109/ITNEC56291.2023.10082308|emergency communication;MANET;DTN;routing scheme;smart collaborative network;Automation;Adaptive systems;Network topology;Federated learning;Routing;Ad hoc networks;Topology|
|[A System-level Electromagnetic Characteristics Knowledge Graph Based on Knowledge Reasoning Applied to Electromagnetic Compatibility Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082065)|Y. Zhang; X. Tang; J. Cao|10.1109/ITNEC56291.2023.10082065|electromagnetic compatibility;OWL;SWRL;Neo4j;knowledge reasoning;Analytical models;Logic programming;Knowledge graphs;Electromagnetic compatibility;Cognition;Data models;Electromagnetic spectrum|
|[Design of Handheld PTZ Stabilizer Based on Arduino](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082408)|H. Hua|10.1109/ITNEC56291.2023.10082408|three-axis stabilized PTZ;MPU 6050;PID control;Kalman Filter;Arduino Uno development board;nan|
|[Research on Agile Modeling Approach for Power System Control Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082710)|M. Huang; X. Yang; X. Wen; Y. Ma|10.1109/ITNEC56291.2023.10082710|power system;control equipment;transfer function block diagram;image recognition;agile modeling;nan|
|[Research on product haptic experience design under visual expectation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082031)|G. Li; J. Fu; J. Yang|10.1109/ITNEC56291.2023.10082031|Haptic experience;Visual expectation;Material;Product design;Visualization;Materials science and technology;Automation;Computational modeling;Rendering (computer graphics);Product design;Haptic interfaces|
|[Automatic Construction of EV Industry Chain Knowledge Graph based on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082562)|Y. Jia|10.1109/ITNEC56291.2023.10082562|knowledge graph;big data;industry chain;electric vehicle;Industries;Text recognition;Filtering;Semantics;Knowledge graphs;Big Data;Ontologies|
|[A Structural Information Aided Method for Intelligent Detection of Power Line Targets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082674)|H. Zhencang; J. Renjie; L. Dong|10.1109/ITNEC56291.2023.10082674|Power liner detection;Information on structural features;Relational suggestion network;nan|
|[Detection Method of Helmet Wearing Based on UAV Images and Yolov7](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082536)|H. Bian; Y. Liu; L. Shi; Z. Lin; M. Huang; J. Zhang; G. Weng; C. Zhang; M. Gao|10.1109/ITNEC56291.2023.10082536|Safety helmet detection;Yolov7;UAV images;Target detection;Power line safety inspection;nan|
|[Optimization Research on The Relevant Algorithm of Density-based Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082277)|J. Xiao|10.1109/ITNEC56291.2023.10082277|data mining;cluster analysis;density-based clustering;nan|
|[Design of a Highly Integrated Series-Parallel Calibration Network for Phased Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082622)|P. Li; X. Zhang; J. Liu; B. Che|10.1109/ITNEC56291.2023.10082622|calibration network;phased array;stripline;highly integrated;Couplings;Phased arrays;Connectors;Automation;Stripline;Metals;Conductors|
|[Classification of Galaxy Morphology Based on Multi-Channel Deep Residual Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082415)|Y. He; Y. Zhang; S. Chen; Y. Hu|10.1109/ITNEC56291.2023.10082415|multi-channel;deep learning;residual network;classification of galaxies;Deep learning;Training;Convolution;Neural networks;Morphology;Feature extraction;Robustness|
|[ERBFV-Net: An Emotion Recognizer Based on Face Video for MAHNOB-HCI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082535)|H. Kuang; J. Liu; X. Ma; X. Liu|10.1109/ITNEC56291.2023.10082535|video;emotion recognition;Action Units;aroused emotion;Emotion recognition;Automation;Face recognition;Feature extraction;Nonhomogeneous media;Data mining;Kernel|
|[A Method of Predicting Continuous Blood Pressure by PPG Signal Based on Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082184)|X. Liu; M. Shao; X. Ma; H. Kuang|10.1109/ITNEC56291.2023.10082184|PPG Signals;blood pressure;Convolutional Neural Network;BHS;Performance evaluation;Wearable computers;MIMICs;Neural networks;Predictive models;Prediction algorithms;Photoplethysmography|
|[Research on Recognition for Subway Track Based on Canny Edge Detection and Hough Transformation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082085)|F. Peng; S. Wang; X. Wang; X. Yang; Y. Shen|10.1109/ITNEC56291.2023.10082085|Recognition;Subway Track;Canny Edge Detection;Hough Transformation;Rails;Training;Image edge detection;Transforms;Feature extraction;Information filters;Safety|
|[Simulation Analysis of Harmonic and Spurious Emission Characteristics of Array Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081967)|K. Yin; X. Zhou|10.1109/ITNEC56291.2023.10081967|array antenna;harmonics;CST;RE103;Phased arrays;Analytical models;Directive antennas;Harmonic analysis;Radar antennas;Brain modeling;Arrays|
|[Learning Quality Analysis System for the Second Degree Education Based On K-means Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082181)|J. Li; X. Feng; D. Jiang; F. Zhu|10.1109/ITNEC56291.2023.10082181|data mining;k-means;learning quality analysis system;second-degree education;Automation;Software algorithms;Education;Data mining;Elbow;Software engineering|
|[Design of Smart Home Control System Based on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082440)|Y. Wang; C. Chen; W. Zhang|10.1109/ITNEC56291.2023.10082440|smart home;Internet of things;microcontroller;wireless communication;Wireless communication;Wireless sensor networks;TV;Microcontrollers;Web pages;Smart homes;Control systems|
|[Spontaneous transport of nanodroplets in 2D nanochannels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082500)|J. Yang; Y. Wang; K. Bi|10.1109/ITNEC56291.2023.10082500|Directional transport;2D nanochannel;Nanodroplet;Strain gradient;Microelectromechanical systems;Heating systems;Automation;Simulation;Chemical engineering;Behavioral sciences;Nanostructured materials|
|[Image Denoising Algorithm Based on Multi-Scale Fusion and Adaptive Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082492)|Z. Su; S. Han; K. Ning; W. Song; G. Li|10.1109/ITNEC56291.2023.10082492|Image blur;Multiscale feature fusion;Residual networks;Training;Connectors;PSNR;Image representation;Feature extraction;Generative adversarial networks;Cameras|
|[An Automatic Configuration Framework for Cloud-Network Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082647)|B. Chen; H. Yao; H. Chen; Y. Wang; B. Shen; C. Shen; P. Yang|10.1109/ITNEC56291.2023.10082647|cloud-network infrastructure;automatic configuration;orchestration;Restful API;Automation;Manuals;Telecommunications;Task analysis;Business|
|[An Image Semantic Segmentation Method with Improved Network Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081999)|J. Shi; Q. Xu|10.1109/ITNEC56291.2023.10081999|Image semantic segmentation;Pixel;Classification accuracy;Improved network;Edge profile;Computer vision;Databases;Semantic segmentation;Image edge detection;Roads;Semantics;Object detection|
|[More nodes in fake news participate in multiple networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082060)|Z. Zhao; M. Zhao; Y. Yan; T. T. Lu; X. Zhu; L. Yang|10.1109/ITNEC56291.2023.10082060|fake news propagation;online social network;multi-networks;self-loops;Automation;Social networking (online);Fake news|
|[Dynamic reactive power optimization considering load uncertainty and period optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082300)|R. Zhang; S. Xiao; Y. Rao; P. Tao; W. Guo|10.1109/ITNEC56291.2023.10082300|Uncertainty;Time Division;Reactive Power Optimization;Genetic Algorithm;nan|
|[A Household Multimodal Physiological Signals Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082658)|P. Guo; Q. Li; Y. Zhu; H. Hejiaojiao; J. Yang; C. Zhang; J. Gao; X. Wang|10.1109/ITNEC56291.2023.10082658|household;multimodal;physiological signals;monitoring system;health management;Digital control;Cloud computing;Automation;Hospitals;Feature extraction;Sensors;Biomedical monitoring|
|[BIMFM: A Bidimensional Intrinsic Mode Functions Mixup Strategy for Thermal Imagery Data Augmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082298)|D. Yang; B. Zhang; Y. Wang; B. Hu; J. Huang|10.1109/ITNEC56291.2023.10082298|infrared target recognition;two-dimensional empirical mode decomposition;data augmentation;Night vision;Deep learning;Image recognition;Empirical mode decomposition;Target recognition;Thermal decomposition;Weather forecasting|
|[A Hybrid Variable Neighborhood Search Algorithm on Routing and Scheduling for Truck-Assisted Multi-Drone Parcel Delivery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082608)|M. Cai; H. Qian|10.1109/ITNEC56291.2023.10082608|Drone;Traveling Salesman Problem;Hybrid Variable Neighborhood Search;Mixed Integer Linear Program;nan|
|[Improving the semantic segmentation algorithm of DeepLabv3+](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082715)|X. Jia; T. Shen; Y. He|10.1109/ITNEC56291.2023.10082715|DeepLabv3+;dense convolutional pooling pyramids;MSCA attention mechanism;Mobilenetv3;NAM attention mechanism;Convolutional codes;Automation;Semantic segmentation;Feature extraction;Encoding;Decoding;Complexity theory|
|[Design and Experiment of Visual Feedback Control in Tomato Picking Bionic Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082621)|Q. Wang; D. Kong; X. Xie; X. Yu; H. Xie; X. Bai|10.1109/ITNEC56291.2023.10082621|bionic manipulator;deep learning;tomato picking;visual feedback;Image sensors;Visualization;Biological system modeling;Crops;Manipulators;Robot sensing systems;Control systems|
|[Research on Intelligence Mining of Illegal Underground Internet Production on Anonymous Network : Taking Personal Information Trading as an Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081964)|G. Chen; G. Chen; D. Wu; Q. Liu; L. Zhang|10.1109/ITNEC56291.2023.10081964|anonymous network;illegal underground Internet production;intelligence minning;crawler;Industries;Graphical models;Dark Web;Law enforcement;Crawlers;Semantics;Production|
|[Design of a novel sensory neuromorphic circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082147)|N. Chen; K. Peng; J. Su; Y. Qin|10.1109/ITNEC56291.2023.10082147|Sensory neurons;Neuromorphic circuit;LIF model;Memristor;Automation;Neuromorphics;Firing;Biological system modeling;Neurons;Memristors;Integrated circuit modeling|
|[Construction of guidance graph in blended learning based on knowledge point extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082051)|P. Jiang; S. Lu; Z. Gu; Y. Dai|10.1109/ITNEC56291.2023.10082051|guidance graph;blended learning;Frequent Pattern Tree;knowledge point extraction;Knowledge engineering;Automation;Navigation;Education;Cognition;Computer networks;Hybrid learning|
|[Automatic Recognition of Tram Tracks Based on Machine Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082097)|F. Peng; S. Wang; X. Wang; X. Yang; Y. Shen|10.1109/ITNEC56291.2023.10082097|Automatic Recognition;Tram Track;Canny Edge Detection;Machine Vision;Rails;Training;Image edge detection;Urban areas;Transforms;Feature extraction;Safety|
|[Research on high resolution and high frame rate image de-distortion algorithm based on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082176)|Y. Zeng; F. Xu; Y. Gong; S. Huang|10.1109/ITNEC56291.2023.10082176|FPGA;distortion correction;high speed processing;hardware acceleration;Interpolation;Image resolution;Process control;Rate distortion theory;Logic gates;Distortion;Cameras|
|[Researching on human abnormal behavior recognition algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082144)|L. Yan|10.1109/ITNEC56291.2023.10082144|improved 3D convolution residual network;optical flow characteristics;abnormal behavior recognition;Three-dimensional displays;Convolution;Surveillance;Feature extraction;Behavioral sciences;Character recognition;Data mining|
|[A Survey on Identification of Critical Nodes in Ad Hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081952)|J. Zhao; Z. Fang; J. Ge; J. Huang; Z. Niu|10.1109/ITNEC56291.2023.10081952|critical nodes;identification;Ad Hoc network;network topology;Automation;Network topology;Ad hoc networks;Topology;Monitoring|
|[Research on the design of cognitive intelligent furniture model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082182)|D. Yan; M. Chang; S. Chen; Y. Wang|10.1109/ITNEC56291.2023.10082182|smart furniture;deep learning;SPSS analysis;smart sofa;monitoring;Deep learning;Analytical models;Vocabulary;Statistical analysis;Atmospheric modeling;Data models;Blood pressure|
|[Study on Rapid Optical Measurement Method and Device of Hemoglobin Concentration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082378)|H. Yan; Z. Xiong; G. Yang; S. Li|10.1109/ITNEC56291.2023.10082378|hemoglobin;concentration measurement;spectral analysis;microcuvette;scattering compensation;Pollution;Correlation;Optical design;Optical variables measurement;Adaptive optics;Iron;Pollution measurement|
|[Algorithm Negative Effects and the Governance in Digital Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082059)|Z. Jin|10.1109/ITNEC56291.2023.10082059|Platform;Bayesian prediction;Artificial neural network prediction;Algorithm governance;Big Data;Supply and demand;Buildings;Closed box;Artificial neural networks;Predictive models;Prediction algorithms;Bayes methods|
|[Performance Analysis of Sonar Target Detection Based on Vector Hydrophone Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10081955)|Z. Chen; S. Sun; Z. Liu; H. Li; Y. Wang|10.1109/ITNEC56291.2023.10081955|vector array;detection performance;beam width;port/starboard discrimination;nan|
|[A Survey of Digital Literacy Requirements for Students in Vocational Education based on Likert Scale using the R Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082499)|F. Peng; S. Wang; X. Wang|10.1109/ITNEC56291.2023.10082499|Digital Literacy;Vocational Education;Likert Scale;R Language;R language;Industries;Histograms;Automation;Education;Regression analysis;Manufacturing|
|[Optimization of shield construction parameters through composite strata based on tunneling performance prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082107)|M. Hu; M. Lu|10.1109/ITNEC56291.2023.10082107|tunneling performance;composite strata;construction parameter;XGBoost;optimization;Performance evaluation;Earth;Fluctuations;Tunneling;Predictive models;Soil;Rocks|
|[A community discovery algorithm based on local extension of high-order triangle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082578)|G. Pengju; C. Mei; W. Youshuai; Z. Hongyu|10.1109/ITNEC56291.2023.10082578|Complex Networks;Community Detection;Higher-Order Triangles;Local expansion;Centrality measure;Automation;Complex networks;Detection algorithms|
|[Optimal Allocation of Regional Defense Resources Based on POS Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082586)|H. Ni; L. Hou; B. Hou; X. Zhao; Z. Chen|10.1109/ITNEC56291.2023.10082586|Water Demand Forecasting;POS Optimization Algorithm;Resource Optimization;Optimization Model;Automation;Urban areas;Decision making;Water conservation;Predictive models;Complexity theory;Resource management|
|[Computer Communication Network Delay and Traffic Distribution Based on Neural Network Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082431)|Y. Zhang|10.1109/ITNEC56291.2023.10082431|Neural Network Technology;Traffic Distribution;Network Delay;Load Balancing;Wireless communication;Costs;Neural networks;Prediction algorithms;Load management;Heterogeneous networks;Delays|
|[Ant Algorithm Based on Internet of Things in Image Recognition System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082625)|H. Yu; Y. Wang; J. Song|10.1109/ITNEC56291.2023.10082625|Internet of Things;Ant Algorithm;Image Recognition;Recognition System;Drugs;Biomedical equipment;Image recognition;Databases;Process control;Feature extraction;Linear programming|
|[Research on optimization of K-means Algorithm Based on Spark](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10082476)|M. Han|10.1109/ITNEC56291.2023.10082476|Data mining;K-means algorithm;Spark;Parallelization;File systems;Heuristic algorithms;Clustering algorithms;Stability analysis;Classification algorithms;Sparks;Data mining|
#### **2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)**
- DOI: 10.1109/ICPECA56706.2023
- DATE: 29-31 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Image Defogging Based on Joint Contrast Enhancement and Multi-scale Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076167)|J. Nie; H. Wu; S. Yao|10.1109/ICPECA56706.2023.10076167|Image dehazing;multi-angle contrast enhancement;multi-exposure fusion;structural patch decomposition;night-time scene;nan|
|[Face tracking by fusing convolutional neural networks and particle filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075786)|Z. Zhang; S. Lin; H. Xu|10.1109/ICPECA56706.2023.10075786|Convolutional neural network;feature extraction;face tracking;particle filtering;target tracking;nan|
|[Construction of LC Filter Integrated Circuit and Analysis of Control System Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075962)|M. Jiang; R. Bi; Z. Xue|10.1109/ICPECA56706.2023.10075962|integrated circuit;square spiral inductor;LC filter;single-chip microcomputer control system;nan|
|[Proposal and Design of Filtering 0°/180° Phase Shifter for Modern Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075803)|K. Xu; C. Hao|10.1109/ICPECA56706.2023.10075803|wave filter;phase shifter;SPDT switch;nan|
|[Privacy Preserved Federated Learning for Skin Cancer Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075862)|Y. Li; Y. He; Y. Fu; S. Shan|10.1109/ICPECA56706.2023.10075862|CNN;Federated Learning;neural network;machine learning;Privacy Protection;Data Silo;nan|
|[Research on the Control under the Participation of Heat Storage Device and Fast Cut Back Control Strategies in Isolated Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075988)|J. Li; S. Wang; Z. Wang; R. Si; X. Liu; Z. Yao; C. Zhou; B. Sun|10.1109/ICPECA56706.2023.10075988|heat storage device;FCB;isolated grid;nan|
|[The Pacific Wave Advanced Network Backbone: An Emulation Approach Under IPv6](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075846)|J. -I. Castillo-Velázquez; I. Varela-Sánchez; Y. Buendia-Gomez; M. -K. Huerta|10.1109/ICPECA56706.2023.10075846|Advanced Networks;IPV6;backbone;routing;nan|
|[Large-scale energy storage battery technology participates in the application of AGC frequency modulation in thermal power plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076231)|Z. Wang; J. Li; S. Wang; R. Si; X. Liu; Z. Yao; C. Zhou; Y. Song|10.1109/ICPECA56706.2023.10076231|AGC;Thermal power unit;frequency modulation;Energy storage;nan|
|[Improved application of SCR flue gas denitrification control system in power plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075996)|Z. Wang; J. Li; R. Si; C. Zhou; Y. Song; X. Li|10.1109/ICPECA56706.2023.10075996|SCR;Thermal power unit;Flue gas denitrification;Control;nan|
|[Intelligent Monitoring and Analysis System for Boiler Combustion Based on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075698)|W. Jiang; Z. Song; S. Lu; D. Zhang; K. Wang|10.1109/ICPECA56706.2023.10075698|boiler;wall temperature;real-time monitoring;burning;optimization;nan|
|[Research on the platform of metrological inspection calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075995)|J. Sun; S. Wang; X. Kong; B. Tan; X. Hu; J. Ying|10.1109/ICPECA56706.2023.10075995|metrological inspection;calibration services;calculation unit;formula automation;nan|
|[Inverter fault diagnosis algorithm based on midpoint voltage deviation polarity and topology reconstruction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076209)|Y. Li; X. Cheng; Z. Yang; Z. Huang; M. Ke|10.1109/ICPECA56706.2023.10076209|three-phase inverter;fault diagnosis;midpoint voltage;pulse transformation;nan|
|[Multimodal actuator for legged robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075924)|J. Shi; W. Jiang; Y. Han; W. Zhu|10.1109/ICPECA56706.2023.10075924|Multimodal elastic actuator;Brake clutch device;Multimodal switching;Legged robot;nan|
|[Integrated and Optimal Scheduling Model for Microgrids with Multiple VSG Types](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075904)|J. Sun; M. Zhang; J. Huang|10.1109/ICPECA56706.2023.10075904|virtual synchronisation machine;microgrid;carbon trading;optimised scheduling;model building;nan|
|[Next Point-of-Interest Recommendation for Cold-start Users with Spatial-temporal Meta-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075890)|S. Zhang; J. Guo; C. Liu; Z. Li; R. Li|10.1109/ICPECA56706.2023.10075890|Point-of-interest recommendation;Few-shot learning;Cold-start;Meta-learning;nan|
|[An Ultra-Low Power Fast-Transient Response Capacitor-Less Low-Dropout Regulator (LDO)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076062)|L. Yu; X. Zhao; W. Yu|10.1109/ICPECA56706.2023.10076062|Low-dropout regulator (LDO);Ultra-low power;Transient-enhancement;nan|
|[The Influence of Axial Flux Leakage from Motor Coils on the Magnetic Bearing and Shielding Measures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076080)|D. Wang; H. Li; Y. Le|10.1109/ICPECA56706.2023.10076080|magnetic levitation motor;magnetic bearing;magnetic shielding;finite element analysis;nan|
|[A Novel Attitude Feature Extraction Method for Multi-IMU Based Fall Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075997)|X. Chai; B. -G. Lee; M. Pike; R. Wu; X. Wu|10.1109/ICPECA56706.2023.10075997|Inertial Measurement Unit (IMU);Fall Detection System (FDS);Machine-Learning;Feature Extraction;nan|
|[A Common-Mode Voltage Suppression Oriented Modulation Method for Modular Multilevel Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076106)|B. Chen; S. Yang; R. Yang; Z. Zhang; X. Shen; Y. Tang; P. Wang|10.1109/ICPECA56706.2023.10076106|modular multilevel converter (MMC);nearest level modulation (NLM);common-mode voltage;nan|
|[Chance-constrained Energy Management Strategy for Micro-grids Intra-day Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075930)|H. Cui; W. Xia|10.1109/ICPECA56706.2023.10075930|intra-day energy management;model predictive control;chance constraint;micro-grid dispatch;nan|
|[An Improved Support Vector Machine Attack Detection Algorithm for Industry Controls System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076096)|S. Cui; D. Feng|10.1109/ICPECA56706.2023.10076096|Industrial control systems;attack detection;support vector machine;feature selection;dynamic;nan|
|[Influence of the Solid Heat Storage Electric Boiler on Optimal Operation of a Combined Heat and Power Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076195)|Y. Wang; Y. Zhang; Y. Zhou|10.1109/ICPECA56706.2023.10076195|Solid Heat Storage Electric Boiler (SHSEB);Extraction condensing unit;Operational flexibility;Optimal operation;nan|
|[Research on Frequency Modulation Capability of Motor Generator Pair Driven via Photovoltaic Power Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075939)|Z. Qi; L. Zhang|10.1109/ICPECA56706.2023.10075939|frequency modulation;system inertia;photovoltaic power generation;droop control;nan|
|[Research on the Control Strategy of Microgrid Inverter Based on Adaptive Virtual Synchronous Generator System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076177)|Z. Qi|10.1109/ICPECA56706.2023.10076177|virtual synchronous generator;adaptive control method;voltage source converter;droop control;nan|
|[FRTB-Fast Blockchain architecture based on block data and data protection system’s file partition tables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076131)|Y. Xu; Y. Wang; T. Li|10.1109/ICPECA56706.2023.10076131|FRTB;Blockchain;Disaster Recovery;File Allocation Table;FAT;nan|
|[Research on Reactive Power Impact and Optimization of Photovoltaic Power Distribution Network Based on the Concept of Power Flow Direction Change](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075863)|L. Yuan; Z. Liu; D. Li|10.1109/ICPECA56706.2023.10075863|Distributed photovoltaics;Power Factor;Distributed Photovoltaics;Trend Change;Reactive Power Compensation;nan|
|[Lightweight skeletal key point extraction algorithm based on TransPose network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075852)|W. Zeng; W. Li; Y. Li|10.1109/ICPECA56706.2023.10075852|lightweight network;transformer structure;self-attention mechanism;skeletal key points;nan|
|[An improved YOLOX method for surface defect detection of steel strips](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075827)|X. Wang; K. Zhuang|10.1109/ICPECA56706.2023.10075827|Surface defect detection;YOLOX;Coordinate attention block;Varifocal Loss;CIoU;nan|
|[Forecast of State of Charge and Adaptive Voltage for Lithium-ion Batteries Using Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075738)|Z. Li; X. Zhang; G. Li; Y. Zhang; Y. Liang; M. Chen|10.1109/ICPECA56706.2023.10075738|lithium-ion battery;state of charge (SOC);parameter identification;radial basis function (RBF);backtracking search algorithm (BSA);nan|
|[Capacitor-Current PID Feedback Control Method for Grid-Connected Inverter to Improve System Stability and Harmonic Rejection Ability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075961)|C. Xie; J. Wang; J. Xie; Y. Guo; X. Liu|10.1109/ICPECA56706.2023.10075961|grid-connected inverter;weak grid;harmonic rejection;stability;capacitor-current PID feedback;nan|
|[Object detection of double-sided copper laminates based on YOLOv5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075704)|Q. Wu; C. Wang; Y. Han; Q. Kang; J. Li; X. Lu|10.1109/ICPECA56706.2023.10075704|object detection;double-sided copper clad;counting;YOLOv5;image processing;nan|
|[Edge detection and processing method of plant root image based on joint wavelet transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075820)|P. Zhao; L. Wang|10.1109/ICPECA56706.2023.10075820|Plant Roots;Edge Detection;Wavelet Analysis;Image Recognition;nan|
|[A Frequency Control Strategy of Large Grid with Energy Storage Based on Multi-Agent Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076079)|M. Zhang; D. Shao; X. Pan; J. Fu; Y. Wang; J. Yang|10.1109/ICPECA56706.2023.10076079|large-scale energy storage;frequency control;multi-agent algorithm;DDPG;nan|
|[Research on automatic extraction method of quiet zone index in anechoic chamber](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076196)|Y. Liu; C. Fan; Y. Shi; W. Yu|10.1109/ICPECA56706.2023.10076196|anechoic chamber;quiet zone;region growth;three-dimensional;nan|
|[Constructing the Evaluation Index System of Chinese-Portuguese Machine Translation using the Delphi and Analytic Hierarchy Process Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075920)|Y. Sun; L. M. Hoi; S. Kei Im|10.1109/ICPECA56706.2023.10075920|Chinese-Portuguese Machine Translation;Evaluation Index;Delphi Method;Analytic Hierarchy Process;nan|
|[An Automatic Generation Method of Patent Specification Abstract Based on "Extraction- Abstraction "Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076029)|C. Zhu; X. Zheng; W. Feng|10.1109/ICPECA56706.2023.10076029|summary generation technology;patent specification;DGCNN model;NEZHA model;UniLM model;nan|
|[The calibration method of the external rotation axis for multi-line LIDAR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075938)|X. Feng; X. Yu; Y. He; W. Yang|10.1109/ICPECA56706.2023.10075938|external rotation axis;multi-line LIDAR;rotating LIDAR device;iterative optimization;rotation axis calibration;nan|
|[A Dependency Links Removal Strategy to Improve Robustness of Cyber-Physical Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075940)|S. Yao; Q. Zhang; X. Liu; X. Yang; G. Song; J. Chen|10.1109/ICPECA56706.2023.10075940|smart grid;cyber-physical power system;cascading failure;robustness;dependency links removal;nan|
|[Refined Modeling and Simulation Method of Raw Signal Returns for Space-based Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076012)|Z. Wang; X. Xi; G. Liao; L. Lan; H. Chen|10.1109/ICPECA56706.2023.10076012|space-based radar;modeling of clutter;terrain fluctuation;target scattering characteristic;the earth’s rotation;nan|
|[A High Resolution DOA Estimation Based on Random Array in Underwater Multipath Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075882)|X. Shen; T. Lu; H. Zhang; K. Zhao; B. Wei; Y. Dong|10.1109/ICPECA56706.2023.10075882|DOA estimation;BPDN;multipath effects;random spacing array;nan|
|[Modeling of Coherent Evolution of Ocean Turbulence Based on Time-dependent Intrinsic Correlation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075883)|H. Zhang; B. Mao; H. Yang; J. Zhang|10.1109/ICPECA56706.2023.10075883|turbulence evolution;signal decomposition;coherent structure;matrix profiler;modeling;nan|
|[An Underwater Wireless Sensor Network Localization Algorithm Based on Salp Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075931)|K. Zhao; T. Lu; H. Zhang; B. Wei; X. Shen; H. Zhang|10.1109/ICPECA56706.2023.10075931|underwater wireless sensor networks;node localization;salp swarm algorithm;sound speed correction;nan|
|[Nonuniform Doppler Shift Estimation for Fast Moving Underwater Acoustic Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075950)|B. Wei; T. Lu; H. Zhang; X. Shen; K. Zhao; C. Ye|10.1109/ICPECA56706.2023.10075950|hydroacoustic;doppler estimation;non-uniform;dynamically adjust;adaptive;nan|
|[Data Augmentation for building QA Systems based on Object Models with Star Schema](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076240)|L. M. Hoi; W. Ke; S. K. Im|10.1109/ICPECA56706.2023.10076240|data augmentation;data transformation;star schema;natural language query;QA System;nan|
|[Thermal Analysis of IGBT Module in Two-Level Neutral Point Inverter Using PSIM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076006)|M. Aydin; E. Beşer|10.1109/ICPECA56706.2023.10076006|Two level inverter;IGBT;PSIM;thermal losses;thermal temperature;nan|
|[An Anomaly Detection Approach of Part-of-Speech Log Sequence via Population Based Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075876)|X. Zhang; J. Zhang; J. Yang; F. Lin; C. Wang; L. Chang; D. Li|10.1109/ICPECA56706.2023.10075876|log sequential anomaly detection;Population Based Training;natural language processing;nan|
|[Parameter Identification of PMSMs Considering VSI Nonlinearity with Coupled Adaline NNs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075809)|E. Brescia; P. Vergallo; I. Celik|10.1109/ICPECA56706.2023.10075809|Adaline neural network;inverter nonlinearity;parameter identification;permanent magnet synchronous machines;nan|
|[Identification of VSI Nonlinearity in IoT-Embedded PMSM Drives Using FFT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075893)|E. Brescia; P. Vergallo; I. Celik|10.1109/ICPECA56706.2023.10075893|Fast Fourier transform (FFT);nonlinearity compensation;parameter identification;permanent magnet synchronous motor (PMSM);voltage source inverter (VSI);nan|
|[Combining spatial attention and cross-layer bilinear pooling for fine-grained image classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075984)|G. Xian; R. Tao; G. Chen|10.1109/ICPECA56706.2023.10075984|fine-grained image classification;spatial attention;bilinear pooling;feature fusion;nan|
|[Optimal Dispatchable Space Considering Aggregation of EV Swapping and Charging Stations to Participate in Demand Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075923)|N. Zou; J. Li; Q. Yang; M. Zhang; Z. Wang; H. Yu; W. Li|10.1109/ICPECA56706.2023.10075923|battery swapping and charging station;aggregator;dispatchable space;demand response;nan|
|[Extracting the Extreme Mode Operation of Coal Mills Using the Gaussian Mixture Model Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076110)|M. D. L. Sekhoto; A. Fujii|10.1109/ICPECA56706.2023.10076110|Coal mills;unsupervised machine learning;Gaussian Mixture Model;Davies-Bouldin;Clustering methods;nan|
|[Research on power entity recognition technology base on BiLSTM-CRF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075849)|B. Gao; X. Xia; Z. Ji; S. Zhang; Z. Yan; H. Luo; J. Ma|10.1109/ICPECA56706.2023.10075849|recognition;BiLSTM-CRF;power entity;nan|
|[Intelligent Home IoT Intrusion Detection System Based on RISC-V](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076248)|Q. Liang; S. Xie; B. Cai|10.1109/ICPECA56706.2023.10076248|RISC-V;IDS;Smart home;Internet of Things;nan|
|[Research on Motion Simulation of Stacking Robot Workstation Based on RobotStudio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075799)|W. Gui; F. Zhou; F. Tang|10.1109/ICPECA56706.2023.10075799|palletizing robot;Offline simulation;optimal design;nan|
|[Prediction of Concrete Strength Based on Random Forest and Gradient Boosting Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075839)|J. Zhu; S. Fang; Z. Yang; Y. Qin; H. Chen|10.1109/ICPECA56706.2023.10075839|High-performance concrete;Compressive strength;Data Prediction;Random Forest;Gradient Boosting Decision Tree;nan|
|[A Stochastic Resource Management Scheme for Multi-Energy Group Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075755)|R. Ding; R. Sun; Y. Geng; Y. Yao; E. Zhao; L. Xu|10.1109/ICPECA56706.2023.10075755|resource management scheme;energy hub;group control system;leader-follower;nan|
|[PalmMatchDB: An On-Device Contactless Palmprint Recognition Corpus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076097)|D. W. Alausa; E. Adetiba; J. A. Badejo; I. E. A. Davidson; K. T. Akindeji; O. Obiyemi; A. Abayomi|10.1109/ICPECA56706.2023.10076097|Palmprint Recognition;Image;Preprocess;Segmentation;Alignment;nan|
|[Test and verification of electric field characteristics in seawater based on ship scaled model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076244)|Y. Huan; Z. Liu; M. Shi; Z. Jiang; C. Xu; S. Li|10.1109/ICPECA56706.2023.10076244|Test and verification;electric field;scaled model;nan|
|[Failure Mechanism Analysis of High Resistance Grounding Fault Protection in Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076169)|Z. Li; S. Yu; Y. Xu; S. Zhao; Y. Su|10.1109/ICPECA56706.2023.10076169|high resistance grounding fault;three-phase asymmetry;protection failure;fault perception;nan|
|[Research on the Optimization of Underwater Vehicle Dynamic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075832)|S. Wan; Q. Huang; J. Ouyang|10.1109/ICPECA56706.2023.10075832|underwater vehicle;power system;dynamic water detection;ROV;fluid resistance;viscous resistance;nan|
|[A Multi-Modal Knowledge Graph of Intersection Traffic State](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075695)|J. Ning; Z. Chunjun; L. Yingqiu; X. Yanxin; Z. Jianbo|10.1109/ICPECA56706.2023.10075695|Vehicle Image Features;Intersection Text Features;Multi-Modal;Knowledge Graph;Information Alignment;nan|
|[Design and Research of High Resolution Satellite Image Data Receiving and Processing System Based on RS Error Correction Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076132)|T. Yan; X. Lu; Y. He|10.1109/ICPECA56706.2023.10076132|RS error correction coding;Satellite imagery;Image data receiving and processing;high resolution;nan|
|[WTLA: Time Series Prediction Based on Wind Turbine Oil Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075807)|Y. Wang; P. Lyu; J. Lan; G. Qin; J. Wen; Y. Jin; S. Huang; M. Liu|10.1109/ICPECA56706.2023.10075807|Time Series Forecasting;Wind Turbine;LSTM;Data Preprocessing;nan|
|[ISAR Image Registration Based on Normalized Correlation Coefficient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076123)|L. Wu; L. Zhao|10.1109/ICPECA56706.2023.10076123|ISAR images;normalized correlation coefficient;image registration;nan|
|[AttRSeq: Attack story reconstruction via sequence mining on causal graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075886)|F. Zhang; R. Dai; X. Ma|10.1109/ICPECA56706.2023.10075886|Attack story reconstruction;Causal graph;Sequence mining;nan|
|[Image-based Apple Disease Detection Based on Residual Neural Network and Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075910)|X. Song; V. Y. Mariano|10.1109/ICPECA56706.2023.10075910|ResNet50;Disease detection of fruit trees;Transfer learning;nan|
|[A Printing Defect Recognition Method Based on Class-imbalanced Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075748)|J. Li; J. Pan; Q. Zhang|10.1109/ICPECA56706.2023.10075748|printing defect;defect recognition;class-imbalanced learning;deep learning;nan|
|[Anomaly Detection Method for Connecting Bolts of Generator Rotor Coils Based on Local Enhancement and Regional Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075934)|H. Yin; K. Sun; S. Chen; X. Huang; Z. Sun|10.1109/ICPECA56706.2023.10075934|image processing;real-time detection;visual inspection;bolt shadow extraction algorithm;nan|
|[An upper monitor system design for security packet processing testing based on IEC 60870-5-104](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075899)|Q. Shengbo; L. Jiangchao; Z. Jing; B. Xuesong; F. Lili|10.1109/ICPECA56706.2023.10075899|feeder terminal unit;IEC 60870-5-104;security packet;asynchronous communication;nan|
|[Analysis of Electromagnetic Interference and Restraining Measures of Brushless DC Motor Drive System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076028)|Z. Sun; L. Zhong; X. Cheng; J. Guo|10.1109/ICPECA56706.2023.10076028|Brushless DC motor;Electromagnetic compatibility;Differential mode noise;Common mode noise;EMI suppression;nan|
|[Control Strategy Analysis of Brushless DC Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076158)|Z. Sun; L. Zhong; X. Cheng; J. Guo|10.1109/ICPECA56706.2023.10076158|DC brushless motor;Torque ripple;PID control algorithm;Predictive control algorithm;Synovial control algorithm;nan|
|[Parameter Identification of IoT-Embedded PMSMs Using an AWS Cloud-Computing Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076004)|P. Vergallo; P. Serafino; D. Cascella|10.1109/ICPECA56706.2023.10076004|Cloud computing;edge computing;parameter identification;permanent magnet synchronous motor;internet of things;nan|
|[Design and Implementation of StateGrid Blockchain-Based Regulation Platform of Source-Grid-Load-Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076037)|X. Wang; R. Peng; J. Du; J. Jia; A. Hu; Y. Zhu; L. Pei|10.1109/ICPECA56706.2023.10076037|source-grid-load-storage;blockchain;consensus mechanism;smart contract;nan|
|[Optimal Design and Simulation of DC Copper Bus support](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075800)|Y. Xiao|10.1109/ICPECA56706.2023.10075800|electrolytic refining;DC copper bus;support spacing;simulation;nan|
|[Research on the Visualization Technology of Diesel Engine Acoustic State Based on Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075965)|B. Feng; Z. Wang; H. Wu; Z. Zhu; J. Wang; G. Wang|10.1109/ICPECA56706.2023.10075965|diesel engine;augmented reality;acoustic status;visualization;nan|
|[Advance in 3D printing defect detection technology based on deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075837)|Y. Su; H. Guan; X. Wang; G. Qi; B. Lei|10.1109/ICPECA56706.2023.10075837|Defect detection;Additive manufacturing;3D printing;Deep learning;nan|
|[Dynamic analysis of the fifth order Chua circuit memristor system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076075)|P. Liu; X. Zhou; H. Xu|10.1109/ICPECA56706.2023.10076075|Chua circuit;Smooth memristor;Chaotic dynamics research;Chaotic secure communication;nan|
|[A Corporate Public Opinion Classification Method Based on Multi-teacher Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076112)|C. Xiaoxiao; X. Wenjue; L. Weiping|10.1109/ICPECA56706.2023.10076112|corporate public opinion classification;pretrained large-scale model;knowledge distillation;multi-teacher distillation;model compression;nan|
|[Application of LSTM Neural Network Based on Grid Search Optimization in ThinkPHP Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075980)|L. Shan; M. Shiming; L. Quansheng|10.1109/ICPECA56706.2023.10075980|LSTM recurrent neural network;performance prediction;machine learning;ThinkPHP training platform;nan|
|[Build a real-time building segmentation network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076210)|Z. Chen; Z. Wang; S. Luo|10.1109/ICPECA56706.2023.10076210|Unet;image segmentation;remote sensing image;nan|
|[Research on New Terminal Name Card Technology Based on Core Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076251)|W. Xuan; Z. Liuqi|10.1109/ICPECA56706.2023.10076251|5G;terminal card;AS application server;IMS;nan|
|[Structure Design and Simulation of Robot Arm for Confined Space Inner Roadway Repair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075861)|H. Chenglin|10.1109/ICPECA56706.2023.10075861|multi-functional mechanical arm;Mechanical structure;Static analysis;modal analysis;nan|
|[Research and development of VR based substation operation and maintenance simulation system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075851)|M. Wang; F. Gao; H. Zhang; X. Zhao; C. Zhao|10.1109/ICPECA56706.2023.10075851|VR technology;Immersion;Gesture interaction;Behavior guidance;nan|
|[The design and application of multi-specialty electric power simulation training system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075761)|Y. He; C. Zhao; H. Yin; X. Zhao; F. Gao; Y. Fan|10.1109/ICPECA56706.2023.10075761|dispatchers;training simulation system;Internet+;intelligent learning platform;nan|
|[Heterogeneous Embedded Resource Management under Space-based Edge Computing Environment with Kubernetes Device Plugin](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076221)|X. Wang; X. Yang; W. Ye; L. Yan; S. Cao|10.1109/ICPECA56706.2023.10076221|Kubernetes;edge systems;device plugin;container;nan|
|[Public Opinion Forecast of Mobile Network for Targeted Poverty Alleviation Based on GA Elman Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075716)|W. Yaoqing|10.1109/ICPECA56706.2023.10075716|GA elman model;targeted poverty alleviation;mobile network;public opinion;nan|
|[Design and Implementation of the Automatic Sorting System Based on PLC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075963)|L. Huimin|10.1109/ICPECA56706.2023.10075963|automatic sorting system;automatic control;PLC;nan|
|[Research on Method of Eliminating Spectral Background Signal of Water Quality Monitoring Based on Dynamic Reference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076219)|W. Kanglin; L. Chengsong; S. Fengping; Z. Shangqian|10.1109/ICPECA56706.2023.10076219|spectral water quality monitoring;dynamic reference;background interference;applied analysis;nan|
|[Semantic segmentation of vehicle vision based on two-branch Enet network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076001)|S. Miao; Y. Du; X. Lu; H. Guan|10.1109/ICPECA56706.2023.10076001|semantic segmentation;attention mechanism;two branch;nan|
|[Application of Fuzzy Clustering Algorithm in Predictive Control of Resource Optimal Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076071)|W. Chi; C. Zheng; M. Zhang; M. Liu|10.1109/ICPECA56706.2023.10076071|Fuzzy clustering;Data matrix;Data normalization;PCM algorithm;Feature matching;Resource allocation;nan|
|[Research on Intelligent Food Processors Supervision System for Smart City Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075793)|H. Xu; X. Yuan; Y. Sun; C. He; X. Yi|10.1109/ICPECA56706.2023.10075793|Smart city;Food security;Foreign object detection;YOLOv5;nan|
|[A novel smart substation secondary circuit simulation training system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075715)|X. Sun; W. Zheng; G. Huo; Y. Lian|10.1109/ICPECA56706.2023.10075715|secondary circuit;teaching case;graphical visualization;model monitoring;drawing recognition;fault tracking;smart substation;nan|
|[Design of near-infrared photoelectric thin film devices based on Macleod software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076226)|Y. Zhao|10.1109/ICPECA56706.2023.10076226|Optoelectronic devices;near infrared band;admittance function;Macleod software;nan|
|[An Effective Incentive Mechanism for Individual Data Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076130)|Y. Wu|10.1109/ICPECA56706.2023.10076130|data sharing;privacy protection;incentive mechanism;personalized local differential privacy;nan|
|[Research on the Life Cycle Carbon Emission Model of Wind-Solar-Storage Hybrid Power Generation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075705)|J. Li; N. Ma; S. Li; Y. Xing; B. Miao|10.1109/ICPECA56706.2023.10075705|Wind-Solar-Storage Hybrid Power Generation System;Life Cycle Assessment (LCA);Carbon dioxide;Direct carbon emissions;Indirect carbon emissions;nan|
|[Semantic similarity detection based on knowledge augmentation for short text](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075781)|W. Xu; G. Hao; X. Wang; W. Tang|10.1109/ICPECA56706.2023.10075781|semantic similarity;HowNet;attention;BERT;nan|
|[An Improved Cartoon Character Avatar Generation Algorithm Based on GAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075919)|Z. Xie|10.1109/ICPECA56706.2023.10075919|Image generation;Wasserstein distance;Adversarial loss;Mode collapse;nan|
|[A theory of causal inference based on additive noise model and its application to radiological security projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075833)|H. Jiahan; W. Yaping; Z. Hua|10.1109/ICPECA56706.2023.10075833|Radioactive substances;BDS;additive measurement noise;target tracking;nan|
|[Optimization of Permanent Magnet Synchronous Motor Speed and Rotor Position Detection Based on High Frequency Pulsation Voltage Injection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076203)|K. Hu; X. Shen|10.1109/ICPECA56706.2023.10076203|Permanent magnet synchronous motor;high frequency pulsation voltage injection method;speed detection;position detection;nan|
|[Research on Semantic Segmentation of Airborne LiDAR Point Cloud Based on Spatial Position Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075770)|Z. Tian; T. Guo; Y. Li|10.1109/ICPECA56706.2023.10075770|semantic segmentation;attention mechanism;point cloud;deep learning network;nan|
|[Design of cross-metal wireless power supply resonance tracking circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076035)|L. Qi; H. Jing; Z. Zhenhua|10.1109/ICPECA56706.2023.10076035|wireless power supply;across the metal;standing wave;resonance tracking;nan|
|[Specific Emitter Identification Based on CNN via Variational Mode Decomposition and Bimodal Feature Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075742)|J. Su; H. Liu; L. Yang|10.1109/ICPECA56706.2023.10075742|Specific emitter identification;RF fingerprint;variational mode decomposition;convolutional neural network;bimodal feature fusion;nan|
|[A study on acoustic emission signal processing of wood damage and fracture based on VMD and sample entropy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075948)|Z. Jiawen; L. Junqiu; Z. Qinghui; J. Xiaoyan; L. Zengquan|10.1109/ICPECA56706.2023.10075948|wood;sound emission;VMD;particle swarm algorithm;sample entropy;arrangement entropy;nan|
|[Short-term electricity sales forecasting model based on wavelet decomposition and LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075688)|L. Wang; F. Wu; B. Li; X. Xu; P. Wang; F. Liang|10.1109/ICPECA56706.2023.10075688|Electricity sales forecasting;wavelet decomposition;long and short-term memory neural networks;nonparametric generalized autoregressive conditional heteroskedasticity model;nan|
|[Yoga action recognition based on STF-ResNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076099)|Y. Wanjun; C. Chong; C. Rui|10.1109/ICPECA56706.2023.10076099|yoga action recognition;two-stream networks;residual network;spatial-temporal features mixing;convolutional block attention module;nan|
|[Research on turn-based war chess game based on reinforcement learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075872)|Y. Chen; L. Bai; Y. Tan; Y. Liu; H. Nan|10.1109/ICPECA56706.2023.10075872|turn-based war chess game;Reinforcement learning;Monte Carlo search tree;Deep neural network;nan|
|[Design of edge computing gateway for textile machine remote monitoring system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075844)|H. Yang; X. Guo; Y. Liu|10.1109/ICPECA56706.2023.10075844|edge computing gateway;textile machine;remote monitoring system;nan|
|[Research on Multi-Target Tracking Technology Based on Particle Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075812)|Y. Zou; Y. Shu; Q. Zhang; X. Wan; Z. Shu|10.1109/ICPECA56706.2023.10075812|Particle Filter Technology;Convolutional Neural Network(CNN);Softmax Classifier;Tracking State Update;Multi-target Tracking;nan|
|[Construction of CNN model based on hard-assigned coding of image features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075740)|B. Zhang; H. Tian; Z. Huang; X. Wan; Z. Shu|10.1109/ICPECA56706.2023.10075740|Image Feature Vector;Hard-assignment Coding(HC);Supervised learning Dictionary;Convolutional Neural Networks (CNN);nan|
|[Design of Facial Expression Recognition Algorithm Based on CNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075779)|Y. Luo; J. Wu; Z. Zhang; H. Zhao; Z. Shu|10.1109/ICPECA56706.2023.10075779|Facial expression recognition;Facial expression features;Convolution activation function;Classified loss function;Convolutional Neural Networks(CNN);nan|
|[Construction of Image Recognition Model Based on Second-Order Local Feature Aggregation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076105)|G. Min; Y. Shu; Q. Zhang; H. Zhao; Z. Shu|10.1109/ICPECA56706.2023.10076105|Image Feature Encoding;Net Vector of Locally Aggregated Descriptors;Reconstruction Residual Weight;Second-order Image Feature Vector;nan|
|[Explore Effect of Connection Radius on Cascading Robustness of Spatial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075864)|P. Yang|10.1109/ICPECA56706.2023.10075864|Cascading failures;spatial network;cascading model;connection radius;nan|
|[Research and system architecture design of dispatching data chain technology for load regulation and control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075656)|H. Wang; X. Li; J. Xuan; Q. Guo; L. Zhao; K. Yang|10.1109/ICPECA56706.2023.10075656|load regulation and control;Dispatching data chain;Consensus mechanism;identity authentication;System Architecture;nan|
|[Fault current suppression method of MMC-HVDC DC side based on edge devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075885)|H. Ze; W. Jianxin|10.1109/ICPECA56706.2023.10075885|Flexible HVDC transmission system;DC fault;current suppression strategy;Edge of computing;nan|
|[Study on breakdown characteristics of BOPP film under multi-frequency field strength](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075982)|F. Yan; W. Yang; H. He; T. Yin|10.1109/ICPECA56706.2023.10075982|BOPP;Capacitor;electric field strength;operating voltage;breakdown;nan|
|[Algorithm Research and Application of MCNP Coupling Point Kernel Integration on γ Biological Shielding Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075815)|Y. Chen; Y. Xu; S. Li; R. Wu; X. Li; J. Hu|10.1109/ICPECA56706.2023.10075815|MCNP;point kernel integration;coupling;estimate;accurate calculate;computational efficiency;nan|
|[Research on Large-scale Ship 3D Design Method Based on Remote Cross Domain Collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076207)|W. Pan; D. Yang; F. Li; S. Li; Z. Cheng; J. Zhang|10.1109/ICPECA56706.2023.10076207|collaborative design;large-scale ship;3D design;remote cross domain;data transmission;nan|
|[GIS Switch State Detection Based on Coupling Current and Heterogeneous Information Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076128)|K. Hu; W. Cui; M. Chen; Q. Shen; S. Li|10.1109/ICPECA56706.2023.10076128|Coupling capacitance current;particle swarm optimization;CNN;GIS on/off;IMF energy entropy;recursive network;nan|
|[THERP-CREAM Prediction Method for Human Failure Probability for Air Traffic Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075991)|R. Tan; X. Gan; T. Yang; S. Li|10.1109/ICPECA56706.2023.10075991|Human error;THERP;CREAM;Air traffic control;Failure probability;nan|
|[Fault Diagnosis of Gearbox Oil Temperature Exceeding the Limit of Wind Turbine Generator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076008)|Z. Qin|10.1109/ICPECA56706.2023.10076008|Gearbox;fault;diagnosis;data processing;network;nan|
|[Cable Fault on-Line Monitoring Based on Transient Traveling Wave Signal Analysis Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076201)|W. Li; W. Zhou; L. Fu; T. Yuan|10.1109/ICPECA56706.2023.10076201|traveling wave signal;cable fault;monitoring;power;nan|
|[Application of Transient Traveling Wave Technology in Fault Location and Condition Monitoring of Collecting Line in Wind Farm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076252)|S. Zhang; W. Zhou; L. Fu; T. Yuan|10.1109/ICPECA56706.2023.10076252|transient traveling wave;troubleshooting;monitoring;power;line fault;nan|
|[PV panel fault detection based on improved ResNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076069)|L. Wu; C. Zhao; Z. Ding; X. Zhang; Y. Wang; F. Sun; A. He|10.1109/ICPECA56706.2023.10076069|Photovoltaic hot spot;deep residual network;fault identification;nan|
|[Global Target Tracking Algorithm Based on Improved Twin Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076133)|L. Wanjun; M. Qinglai|10.1109/ICPECA56706.2023.10076133|twin neural network;target tracking;backbone network;regional proposal network;region convolution neural network;nan|
|[Semi-supervised Spatial Spectral Local Discriminant Analysis for Hyperspectral Image Feature Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076070)|L. Huanhuan; H. Yucheng; Z. Hui|10.1109/ICPECA56706.2023.10076070|hyperspectral image;semi-supervision;spatial spectrum;discriminant analysis;feature extraction;feature classification;nan|
|[Hybrid Recommendation Algorithm Combining Content and AHP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076215)|W. Guang; W. Zhenbo|10.1109/ICPECA56706.2023.10076215|user profile;content-based;analytic hierarchy process;collaborative filtering;hybrid recommendation;nan|
|[Hybrid Recommendation Algorithm Combining Project Importance and Prediction Score](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075881)|W. Yonggui; Z. Jundong|10.1109/ICPECA56706.2023.10075881|recommendation algorithm;personal rank;slope one;item importance;prediction score;nan|
|[A Recommendation Algorithm Incorporating Moth-Flame Optimization Algorithm and Fuzzy Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075973)|X. Shen; Y. Sun|10.1109/ICPECA56706.2023.10075973|moth-flame optimization algorithm;fuzzy c-means clustering;combination similarity;collaborative filtering;hybrid recommendation;nan|
|[Research on Optimal Model of Weapon Firing Firepower Distribution and Its Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075729)|J. Du; L. Du; H. Hu|10.1109/ICPECA56706.2023.10075729|firepower allocation;Damage value;Optimization model;nan|
|[The design and implementation of the all-in-one machine for killing and sorting in the small and medium-sized logistics center](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075764)|Y. Zeng; Y. Zhou; R. Peng; G. Huang|10.1109/ICPECA56706.2023.10075764|disinfection;deep learning;Internet of Things technology;all-in-one machine;epidemic prevention;nan|
|[M-element spread spectrum multi-user detection algorithm based on interference cancellation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075956)|J. Lv; P. Qin; K. Liu|10.1109/ICPECA56706.2023.10075956|M-element spread spectrum;Multiple users;Interference cancellation;Multiple access interference;nan|
|[Steel Types Association Analysis in Traffic Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076020)|Z. Yang; S. Fang; Y. Qin; J. Zhu; H. Chen|10.1109/ICPECA56706.2023.10076020|Apriori algorithm;data engineering;frequent itemset;association rules;nan|
|[Measurement and Analysis of the Transient Electric Field Generated by the Operation of the Disconnector in a 330 kV Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075971)|X. Kong; K. Liu; Y. Li; Y. Zhang; J. Xiong; K. Guo; Z. Zheng|10.1109/ICPECA56706.2023.10075971|condition monitoring;disconnector;electric field;electromagnetic compatibility;substation;nan|
|[Research on the Replacement Strategy of Steel Manufacturers Based on the K-means Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075874)|J. Chen; Z. Yang; Y. Qin; S. Fang; H. Chen|10.1109/ICPECA56706.2023.10075874|data engineering;steel material K-means clustering algorithm;Apriori algorithm;nan|
|[Application Analysis of FlexE Technology in the New Type of Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075953)|S. Qian; Z. Chen; J. Huang; J. Cao; Y. He; W. Liu; H. Xing; X. Jin|10.1109/ICPECA56706.2023.10075953|new type of power system;FlexE;distribution communication network;diversified busines;nan|
|[Design and Implementation of Smart Curtains](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076146)|N. Shaowei; Z. Minling; Z. Zhiwei; L. Xia; L. Zifei; K. Yuhui|10.1109/ICPECA56706.2023.10076146|Smart curtain;Wireless remote control;The Arduino;Smart home;nan|
|[Development of non-contact detection device for coal moisture based on microwave transmission method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076125)|R. Chai; X. Kong; X. Wang|10.1109/ICPECA56706.2023.10076125|contactless detection;dielectric constant;microwave attenuation;moisture content;nan|
|[Injection Pulse Source Using Linear Magnetic Induction Voltage Superposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075699)|L. Cheng; Y. Wu; Y. Wang|10.1109/ICPECA56706.2023.10075699|injection pulse source;MOSFET;pulse output;electromagnetic interference;nan|
|[Real-time flight path conflict detection method based on space box](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076243)|Z. Li; Y. Chen; R. Wang; J. Pan|10.1109/ICPECA56706.2023.10076243|Conflict detection;Air traffic control;Space box;Distributed Computing;nan|
|[Feature extraction and analysis of speech signal based on fractional Fourier transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076245)|Y. Zhai|10.1109/ICPECA56706.2023.10076245|Fractional Fourier transform;Speech signal;Feature extraction;nan|
|[Research on intelligent parking management system based on embedded sensor technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076211)|L. Zheng; S. Ding; J. Kong|10.1109/ICPECA56706.2023.10076211|Parking lot management system;Embedded sensing technology;Traffic flow statistics;Front and rear end separation development;Cloud service platform design;nan|
|[A Method for Improving Transient Response of the Source Measure Unit Based on PID+LPF Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076250)|Y. Liang; X. Liu|10.1109/ICPECA56706.2023.10076250|source measure unit;NI SourceAdapt technology;transient response;PID+LPF controller;FPGA;nan|
|[Borehole Trajectory Optimization System Based on Snake Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075804)|X. Wang; R. Cheng; G. Zhao; W. Zheng|10.1109/ICPECA56706.2023.10075804|Snake optimization algorithm;multi-objective optimization;spatial margin;variation coefficient method;wellbore trajectory;nan|
|[Data Processing Optimization of Power Grid Dispatching Control Cloud Based on Edge-Cloud Collaborative Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076154)|L. Sheng; C. Wang; Y. Liu; N. Zhang; Z. Wang|10.1109/ICPECA56706.2023.10076154|cloud computing;edge computing;edge-cloud collaborative computing;SDN;nan|
|[Research on Risk Warning Technology of Electric Power Backbone Communication Network Based on Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076205)|N. Zhang; H. Liu; L. Sheng; L. Wu; Z. Wang|10.1109/ICPECA56706.2023.10076205|Risk Warning;Communication;Knowledge Graph;nan|
|[New incomplete data imputation based on k-nearest neighbor type framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075766)|H. Zheng; T. Huang|10.1109/ICPECA56706.2023.10075766|machine learning;incomplete data;data imputation;classification;nan|
|[Generation Expansion Planning Model Considering Offshore Wind Power and Rotational Inertia Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075977)|W. Luo; H. Gao; Y. Zhang|10.1109/ICPECA56706.2023.10075977|generation expansion planning;offshore wind power;rotational inertia;receiving-end system;scenario analysis;nan|
|[Selecting a Suitable Portable X-ray Equipment Based on the Non-destructive Testing of Crimped Power Fittings on Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075870)|H. Ni; J. Chu; S. Huang; J. Zhou; Y. Wang; W. Zhu|10.1109/ICPECA56706.2023.10075870|portable equipment;power fittings;non-destructive testing;X-ray;nan|
|[Electricity load forecasting based on long and short-term memory neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075854)|B. Yang; K. Song; J. Qin|10.1109/ICPECA56706.2023.10075854|electric load;lstm;short-term forecasting;matlab;nan|
|[MSDB-based CNN architecture for image dehazing in driverless cars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076095)|Q. Wu; J. Liu; M. Feng|10.1109/ICPECA56706.2023.10076095|multi-scale dense block (MSDB);convolutional neural network;image reconstruction;single image dehazing;nan|
|[Back-projection Residual Low-light Image Enhancement Network with Color Correction Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075845)|K. Chen; C. Cao|10.1109/ICPECA56706.2023.10075845|Low-light image enhancement;Deep learning;Color correction model;nan|
|[Short-term wind power prediction based on EMD-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076173)|Z. Zhou; S. Sun; Y. Gao|10.1109/ICPECA56706.2023.10076173|Wind power prediction;empirical mode decomposition;long short-term memory;neural network;prediction accuracy;nan|
|[Detecting System of High Voltage Voltage Transformer Based on DSP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076227)|Y. Li; M. Wang; X. Wang; R. Gao|10.1109/ICPECA56706.2023.10076227|Digital Signal Processor;High Voltage Voltage Transformer;Detection System;On-Site Verification;nan|
|[Based on the LDA - RBF Electric Power Information Network Security Situational Awareness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075692)|H. Hu|10.1109/ICPECA56706.2023.10075692|Electric power information network;RBF neural network;Linear discriminant analysis;Security situation awareness;nan|
|[Performance Analysis of Convolutional Neural Networks and Multilayer Perceptron in Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076041)|S. Guan|10.1109/ICPECA56706.2023.10076041|Generative Adversarial Networks;Convolutional Neural Networks;Multilayer Perceptron;Deep Learning;nan|
|[Research on Image Positioning Design of Bluetooth Circuit Breaker Detection Pipeline](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076059)|X. Zhao; Z. Zhang; Z. Chen; Z. Jing; L. Guo|10.1109/ICPECA56706.2023.10076059|Bluetooth Circuit Breaker;Image Positioning;Trip Screw;Hough;nan|
|[Review of Research on Running Condition Monitoring of High Voltage Cables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075700)|J. Han; Y. Zhou; C. Yang; W. Zheng; X. Lu; Y. Wang; J. Han; L. Geng|10.1109/ICPECA56706.2023.10075700|High voltage cable;power supply reliability;running condition monitoring;PD detection;temperature detection;ground current detection;nan|
|[Bilstm Personalised-Style Poetry Generation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075878)|Z. Sun; Z. Zhang; M. Zhang|10.1109/ICPECA56706.2023.10075878|Poetry Generation;NLP;personalized generation;nan|
|[Research of Chinese relation extraction based on BERT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075925)|Z. Song; L. Wan|10.1109/ICPECA56706.2023.10075925|relation extraction;BERT;BiLSTM;CRF;nan|
|[DES-Co-RSA: A Hybrid Encryption Algorithm Based on DES and RSA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075771)|J. Zhao|10.1109/ICPECA56706.2023.10075771|DES algorithm;RSA algorithm;Hybrid algorithm;nan|
|[A Time Series Data Classification Method Based on Basis Function Expansion and Bagging Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075869)|J. Pang|10.1109/ICPECA56706.2023.10075869|time series classification;basis function expansion;Bagging framework;nan|
|[Detection of Pneumonia Based on ResNet Improved by Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076216)|Z. Zhou; Y. Liu; Q. Wang; T. T. Toe|10.1109/ICPECA56706.2023.10076216|Covid-19;X-ray;ResNet;CBAM;Attention Mechanism;nan|
|[Research on BERT-based Text2SQL Multi-task Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075969)|L. Xusheng; A. Yeteng; L. Jingxian; Z. Huimin; Z. Yumeng; L. Min; Z. Wei; H. Wei; S. Liangfei; L. Huiqin|10.1109/ICPECA56706.2023.10075969|Text2SQL;NL2SQL;Deep learning;Multi-task learning;nan|
|[UAV formation optimization model based on ant colony algorithm and particle swarm optimization algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075717)|Y. Yang; Y. Zhang; Q. Feng; L. Yang; H. Zhang|10.1109/ICPECA56706.2023.10075717|ant colony algorithm;Particle swarm optimization algorithm;Information simulation;Deviation adjustment;Analog signal;nan|
|[Super short-term wind speed prediction based on CEEMD decomposition and BILSTM-Transformer model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076025)|B. Jiang; Y. Liu; H. Xie|10.1109/ICPECA56706.2023.10076025|CEEMD;Transformer;BILSTM;Wind speed prediction;Copula Theory;nan|
|[Improved ViT-Based Fine-grained Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075978)|S. He; X. Zheng; H. He|10.1109/ICPECA56706.2023.10075978|fine-grained classification;progressive training;vision transformer;self-distillation;nan|
|[Research on Digital Engineering Modeling System under Computer Virtual Reality Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076149)|L. Zhou|10.1109/ICPECA56706.2023.10076149|computer;virtual reality;digital engineering;CNC machine tool modeling;system simulation;nan|
|[Research on pedestrian collision test method based on depth learning algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075754)|W. Peng; Z. Sun; T. Bi; Y. Pei|10.1109/ICPECA56706.2023.10075754|Deep learning;collision;strategy;pedestrian;nan|
|[Bionic, Fin-propelled Underwater Multi-legged Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075778)|H. Li|10.1109/ICPECA56706.2023.10075778|Underwater;Quadrupled;Bionic;Fin-propelled;Garbage picking;nan|
|[A novel approach for obstacle avoidance maneuverer controls to a 49ton autonomous drive heavy-duty truck in GCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075663)|T. Ye; D. Liu; J. Wei; Y. Lin|10.1109/ICPECA56706.2023.10075663|heavy duty truck;obstacle avoidance;automotive drive;lateral path planning;nan|
|[Computer Artificial Intelligence IT Technology Data Transmission Center Construction System Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076014)|Y. Rui|10.1109/ICPECA56706.2023.10076014|computer;artificial intelligence;IT technical data;data transmission;reliability;nan|
|[A Machine Learning Approach for Anomaly Detection in Power Mixing Equipment Intelligent Bearing Fault Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076055)|C. Fang; Q. Wang; B. Huang|10.1109/ICPECA56706.2023.10076055|Power Mixing Equipment;Gear Bearings;Fault Diagnosis;Machine Learning algorithms;nan|
|[Multi-closed-loop Noise Model of MEMS Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076168)|W. Nie; B. Jiang; Q. Fan; Y. Su|10.1109/ICPECA56706.2023.10076168|MEMS gyroscope;noise model;mode matching;nan|
|[Automatic identification technology and realization of main and side lobe tracking of shipborne measurement and control radar based on digital guidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075900)|X. Fan; J. Hu; Y. Liu; X. Zhang|10.1109/ICPECA56706.2023.10075900|main and side lobe tracking;shipborne measurement and control radar;nan|
|[Multi-Scale Pedestrian Detection Algorithm Based on YOLOv3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075937)|J. Chen; S. Li; G. Zhang|10.1109/ICPECA56706.2023.10075937|pedestrian detection;YOLOv3;CBAM;CSPNet;nan|
|[Research on Raman Spectroscopy in Geology, Rock and Mineral Physics Optical Exploration and Detection Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075810)|L. Shi; H. Chi; Y. Yu; W. Wang|10.1109/ICPECA56706.2023.10075810|Keywords—raman spectroscopy;geology;rock and mineralization;physics;exploration;nan|
|[Fault diagnosis method for substation grounding network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076077)|H. Wu; X. Wang; W. Zhou; X. Zhang|10.1109/ICPECA56706.2023.10076077|Keywords—substation;grounding network;fault diagnosis;nan|
|[Intelligent Control System of Construction Machinery Based on PLC and Machine Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075918)|J. Zhua; L. Guob; L. Sunc|10.1109/ICPECA56706.2023.10075918|Machine Vision;PLC Technology;Construction Machinery;Intelligent Control System;nan|
|[Research on natural disaster target change detection method based on deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076044)|Y. Yin|10.1109/ICPECA56706.2023.10076044|Remote Sensing Recognition;Faster-RCNN;Fast CNN;Region Proposal Networks;nan|
|[Research on time series based on improved LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076103)|Y. Qiao; K. Xu; K. Zhou|10.1109/ICPECA56706.2023.10076103|LSTM;CNN;Mixed model;Attention mechanism;nan|
|[Research on architectural CAD-aided design based on computer 3D reconstruction technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075927)|H. Du; J. Zhu|10.1109/ICPECA56706.2023.10075927|Computer-aided design;3D reconstruction;CAD parametric template;industrial building structure;nan|
|[End-to-end efficient cascade license plate recognition system in unconstrained scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076063)|W. Peng; Z. Hu; T. Liang|10.1109/ICPECA56706.2023.10076063|license plate detection;license plate recognition;convolutional neural network;attention mechanism;nan|
|[Simulation analysis of spatial electromagnetic field of transformer core considering nonlinear B-H curve](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076147)|X. Gou; Z. Huang; Z. Zhao|10.1109/ICPECA56706.2023.10076147|iron core;spatial electromagnetic field;nonlinear;E-transformer;nan|
|[A Study of Stroke Prevalence Prediction Based on Random Forest Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076060)|X. Shan; Y. Chen; Z. Qiao|10.1109/ICPECA56706.2023.10076060|Random forest algorithm;support vector machine;logistic regression;brain stroke;nan|
|[Design of service robot control system for epidemic prevention and control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076061)|X. Zhang; Y. Chen; W. Ma|10.1109/ICPECA56706.2023.10076061|mobile robot;Path planning and obstacle avoidance;Three degrees of freedom;facial identification;nan|
|[Research on pure azimuth passive positioning of UAV based on least squares method and grid search method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076066)|Y. Zhang|10.1109/ICPECA56706.2023.10076066|grid search method;least squares method;UAV pure azimuth passive positioning;nan|
|[Two dimensional wave power analysis based on Simulink simulation and particle swarm optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076199)|H. Lv; Y. Mou; Z. Li; S. Zhao|10.1109/ICPECA56706.2023.10076199|Particle swarm algorithm;simulink;Runge-Kutta algorithm;Lagrange's equation;nan|
|[UAV cluster adjustment strategy based on passive pure azimuth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075853)|H. Huang; W. Chen|10.1109/ICPECA56706.2023.10075853|triangulation positioning method;two-point intersection positioning method;adjustment strategy;UAV;nan|
|[A new improved clustering method of incomplete data based on K-nearest neighbors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076121)|T. Hao; Q. Shi; Z. Sun|10.1109/ICPECA56706.2023.10076121|Incomplete data;imputation;fuzzy C-means clustering (FCM);K-nearest neighbors(KNN);missing value;nan|
|[Research on Bearing Fault Diagnosis Based on SPWVD and Grid Optimization CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076015)|Y. Yan; J. Xing; M. Xie|10.1109/ICPECA56706.2023.10076015|Bearing fault diagnosis;grid method;SPWVD;CNN;nan|
|[Timely Rendering Algorithm of Virtualization System Based on CUDA for Smart Scenarios of Power Grid Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076225)|C. Wang; Y. Yan; B. Chen; X. Chen; X. Chen; N. Zhang|10.1109/ICPECA56706.2023.10076225|CPU and CUDA;Power Grid Infrastructure;Smart Scene;Virtualization System;Rendering Algorithm;nan|
|[Research and Development of Grid Infrastructure Intelligent Monitoring Platform Based on Distributed Real-time Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076134)|Y. Yan; C. Wang; N. Zhang; B. Chen; X. Chen; X. Chen|10.1109/ICPECA56706.2023.10076134|Distributed real-time sensor network;Wisdom of power grid infrastructure;Development of monitoring platform;nan|
|[Intelligent Recognition Method for Transformer Acoustic Signal Based on MVF Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075796)|H. Cao; L. Ling; C. Wei; S. Hu; W. Cai; J. Xiao; W. Peng|10.1109/ICPECA56706.2023.10075796|transformer;noise;MVF;nan|
|[Structure Design and Analysis on Crane Boom Detection Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075868)|J. Mao; W. Zhou; S. Chai; S. Zhu|10.1109/ICPECA56706.2023.10075868|crane boom;defect detection;detection device;finite-element simulation;nan|
|[A Smoking Detection Algorithm Based on Improved YOLOV5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076129)|C. Wang; T. Zheng; F. Sun; H. Liu|10.1109/ICPECA56706.2023.10076129|Smoking detection;YOLOV5;target detection;nan|
|[Traffic Sign Detection Based on YOLO v3](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075795)|X. Zhang|10.1109/ICPECA56706.2023.10075795|object detection;YOLO v3;R-CNN;traffic sign;nan|
|[Solving the point cloud registration based on deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076094)|C. Hu; W. Xu; J. Yuan|10.1109/ICPECA56706.2023.10076094|Point cloud registration;self-supervised learning technique;neural networks;nan|
|[Unscented Kalman Filter and Its Implementation in Digital Image Correlation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075979)|C. Qian|10.1109/ICPECA56706.2023.10075979|Unscented Kalman Filter;automatic recognition;digital image correlation;nan|
|[Path planning optimization strategy of mobile robot in two-dimensional fixed environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075843)|Y. Wang|10.1109/ICPECA56706.2023.10075843|path planning;mobile robot;SOGT;nan|
|[Mobile Robot Path Planning Based on Triangular Clip Ant Colony Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076068)|Z. Xu|10.1109/ICPECA56706.2023.10076068|Triangular clip ant colony algorithm;the path planning;the guiding factor;the inducing factor;nan|
|[Residual Life Prediction of Rolling Bearings Based on Multi-source Domain Sub-domain Adaptive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076186)|L. Tang; L. Hu; C. Cui; K. Wu; W. Zou; X. Zeng; Z. Zhang; P. Lan; J. Li; Y. Pu|10.1109/ICPECA56706.2023.10076186|Rolling bearing;Remaining service life;Multi-source domain;Subdomain adaptation;nan|
|[CNN-MLP-based transformer digital twin model construction and fault diagnosis and condition evaluation analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075826)|R. Wu; P. Zhu; X. Qin|10.1109/ICPECA56706.2023.10075826|Oil-immersed power transformers;CNN-MLP;fault diagnosis;digital twin;nan|
|[Research on Detection Algorithms of Weak Electromagnetic Spectrum Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076138)|Y. Zuo; C. Cao; Y. Zhu|10.1109/ICPECA56706.2023.10076138|detection algorithms;weak signal;nan|
|[Pure Azimuthal Passive Positioning in UAV Formation Flight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076190)|Y. He; Y. Duan; W. Huang|10.1109/ICPECA56706.2023.10076190|pure orientation positioning;passive positioning;drone formation;geometric analysis method;nan|
|[Research on Optimal Design of Wireless Sensor Network with Particle Swarm Optimization and Improved Firefly Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075859)|R. Li; D. Gong; Y. Tan|10.1109/ICPECA56706.2023.10075859|wireless sensor network;PGSO;global optimality;probabilistic measurement model;coverage optimization;nan|
|[Optimization of LEACH Routing Protocol Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075732)|S. Chen; Y. Chen; Y. Huang; W. Wei|10.1109/ICPECA56706.2023.10075732|LEACH protocol;cluster head;dead nodes;wireless sensor networks;distance threshold;nan|
|[Research on Wireless Sensor Networks Coverage Based on Fruit Fly Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076217)|J. Cheng; Y. Fang; N. Jiang|10.1109/ICPECA56706.2023.10076217|WSN;Fruit Fly Optimization Algorithm;coverage optimization;node energy;nan|
|[Apple bagging system based on machine vision and multi-factor discrimination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076067)|P. Wei; W. Sun; S. Cao; J. Liu; F. Kong|10.1109/ICPECA56706.2023.10076067|Apple bagging;Machine vision;Convolutional Neural Network;nan|
|[Research on modular charging systems based on digital twins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075975)|Q. Dong; Z. Xu; J. Wang; X. Yang; L. Fan; W. Li|10.1109/ICPECA56706.2023.10075975|digital twin;modular flexible charge;offline programming;secondary development;nan|
|[Optimization Model of Industrial Engineering Neural Network Algorithm Based on Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075823)|B. Li; Y. Cheng|10.1109/ICPECA56706.2023.10075823|Artificial Intelligence;Industrial Engineering;Motor Rotor Failure;Neural Network Algorithm;nan|
|[Research on Target Detection and Grabbing Positioning Method Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075802)|M. Jinzhuo; T. Chenghua; G. Cong; J. Pei; Z. Hongjian|10.1109/ICPECA56706.2023.10075802|Fast histogram equalization;yolov4;target detection;Eye to Hand;local coordinate system;nan|
|[A Lightweight Face Recognition Algorithm for Internet of Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076187)|R. Xie; Q. Zhang; Z. Du; C. Li|10.1109/ICPECA56706.2023.10076187|Internet of Vehicles;Lightweight;Depth characteristics;Face recognition;nan|
|[Optimization Method of Backbone Power Grid under Extreme Disaster Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075697)|H. Gao; Y. Zhang|10.1109/ICPECA56706.2023.10075697|extreme disaster condition;backbone power grid;system recovery;grey wolf optimizer (GWO);nan|
|[UAV Intelligent Forest Inspection System Based on Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076000)|D. Wu; X. Yuan; L. Guan|10.1109/ICPECA56706.2023.10076000|Deep learning;UAV inspection;Data cleaning;light-weight;nan|
|[Deep Learning-Based Retrieval Algorithms for Recommendation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076102)|S. Zhao; Z. Huang; H. Guo; X. Xiao; R. Ye|10.1109/ICPECA56706.2023.10076102|recommendation systems;retrieval layer;deep learning;candidate products;users’ interests;nan|
|[Research on Biometric Identification Recognition Method of Radial Artery Combined with Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076048)|W. Wen; J. Li; Q. Yang; S. Li|10.1109/ICPECA56706.2023.10076048|radial artery;biometric identification;empirical modal decomposition;convolutional neural networks;support vector machines;nan|
|[Machine Learning Based Method for Transient Stability Assessment of Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075791)|A. He|10.1109/ICPECA56706.2023.10075791|machine learning;XGBoost;transient stability assessment;Pearson correlation analysis;nan|
|[Comparison of CNN Models in Non-small Lung Cancer Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075772)|H. Xu|10.1109/ICPECA56706.2023.10075772|Non-small cell lung cancer;Convolutional Neural Network;Medical Diagnosis;ResNet;nan|
|[Parallel Quadrature Index Modulation with Transmit Diversity in Large MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076182)|X. Wang; J. Zong; G. Deng; S. Cao|10.1109/ICPECA56706.2023.10076182|Parallel quadrature index modulation;diversity gain;spectral efficiency;bit error rate;nan|
|[Research on Python Crawler Search System Based on Computer Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075835)|Y. Wang|10.1109/ICPECA56706.2023.10075835|computer;big data;python;crawler search system;web crawler;the data collection;nan|
|[An Improved Object Detection Method Based on NAO Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076034)|F. Sun; C. Wang; T. Zheng; H. Liu|10.1109/ICPECA56706.2023.10076034|NAO;YOLOv5;Object Detection;nan|
|[Data Encryption Algorithm Based on Chaos Sequence in Computer Network Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076072)|R. Zhu|10.1109/ICPECA56706.2023.10076072|Chaotic Sequence;Data Encryption;Encryption Algorithm;Network Security;nan|
|[Ultra-wideband (UWB) localization problem under signal interference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076118)|Y. Zhang; Z. Bai; Z. Yang|10.1109/ICPECA56706.2023.10076118|KNN classifier;crest search;nonlinear least squares fitting;spatial error compensation;measurement error compensation;nan|
|[Machine Learning Based Network Attacks Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075818)|Y. Che|10.1109/ICPECA56706.2023.10075818|Network attacks;machine learning;Naïve Bayes;SVM;nan|
|[A Review on 3D Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075760)|C. Wang|10.1109/ICPECA56706.2023.10075760|Convolutional Neural Network;3D CNN;image classification;video analysis;nan|
|[Design and Implementation of Indoor Patrol Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075725)|Z. Sun; X. Cai; L. Mao; J. Yao|10.1109/ICPECA56706.2023.10075725|PatrolRobot;SLAM;Control System;Autonomous Navigation;nan|
|[Online Estimation of Residual Electrical Lifetime for PV Low Voltage Circuit Breaker Based Grey Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075888)|C. Lu; C. Wang; J. Xie|10.1109/ICPECA56706.2023.10075888|photovoltaic;circuit breaker;residual electrical lifetime;online estimation strategy;grey model;exponential function;nan|
|[A Monocular SLAM System Based on the ORB Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075945)|S. Shao|10.1109/ICPECA56706.2023.10075945|visual SLAM;ORB-SLAM;monocular SLAM;nan|
|[Design and implementation of a personal loan default prediction platform based on LightGBM model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076254)|S. Fan|10.1109/ICPECA56706.2023.10076254|Personal loan defaults;prediction platform;LightGBM model;nan|
|[Research on image classification algorithm of domestic garbage based on deep learning method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075708)|Y. Pan|10.1109/ICPECA56706.2023.10075708|Image classification of domestic garbage;Deep learning methods;Unbalanced dataset;nan|
|[Breast Cancer Detection Using ResNet with Hyperparameter Tuning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076197)|J. Mu|10.1109/ICPECA56706.2023.10076197|Breast Cancer;Convolutional Neural Network;Residual Neural Network;Deep Learning;Hyperparameter Tuning;Histopathological Images;Binary Classification;nan|
|[Research on cold chain logistics distribution path optimization based on improved ant colony algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075989)|R. Wang|10.1109/ICPECA56706.2023.10075989|Improved ant colony algorithm;Distribution route optimization;cold chain logistics;nan|
|[Design and Optimization of Deep Immune Network Algorithm for Smart Grid Fault Data Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075911)|W. Zhou|10.1109/ICPECA56706.2023.10075911|deep learning;Immune algorithm;deep immune network;smart grid fault data analysis;nan|
|[Exploration and Application of Transformer Substation Automation Data Channel Based on UHF Wireless Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076172)|M. Hua|10.1109/ICPECA56706.2023.10076172|UHF wireless technology;smart grid;COFDM;wireless data channel;nan|
|[Research on Network Information Security Perception Technology Based on Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075750)|Y. Zhang|10.1109/ICPECA56706.2023.10075750|Big data;Information security;Honey net;Perception system;nan|
|[Practical research on lane recognition and driving state monitoring method based on computer vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075752)|Z. He|10.1109/ICPECA56706.2023.10075752|Computer Vision;OpenCV;Driving Status Monitoring;Lane Recognition;nan|
|[Algorithm optimization of multi-source heterogeneous big data in the context of cloud computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076235)|Y. Liu|10.1109/ICPECA56706.2023.10076235|heterogeneous data;cloud computing;big data;data source;nan|
|[A Blockchain Covert Communication Method Based on Voting Contract](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076114)|J. Du; L. Li; X. Xiong; Y. Zheng|10.1109/ICPECA56706.2023.10076114|covert communication channel;blockchain;Ethereum;voting contract;security;nan|
|[Self-supervised Image Classification Using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075949)|S. Kong|10.1109/ICPECA56706.2023.10075949|Representation learning;ResNet;SimCLR;K-means;Clustering;nan|
|[A Skin Cancer Detection System Based on CNN with Hair Removal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076164)|Z. Li|10.1109/ICPECA56706.2023.10076164|skin cancer;convolutional neural network;deep learning;nan|
|[A more accurate mask detection algorithm based on Nao robot platform and YOLOv7](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076188)|X. Duan; H. Chen; H. Lou; L. Bi; Y. Zhang; H. Liu|10.1109/ICPECA56706.2023.10076188|YOLOv7;Mask detection;COVID-19;Feature extraction;nan|
|[End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volume](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075875)|Q. Chen; C. Poullis|10.1109/ICPECA56706.2023.10075875|Deep Learning;Structure-from-Motion;Multi-View-Stereo;3D Reconstruction;nan|
|[A comparative study of machine vision-based rail foreign object intrusion detection models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075769)|X. Cai; X. Ding|10.1109/ICPECA56706.2023.10075769|Target detection;machine vision;edge detection;foreign object detection;nan|
|[Research on Enterprise Information Transmission System under Computer Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076101)|X. Lu|10.1109/ICPECA56706.2023.10076101|computer;big data;enterprise information system;system transmission;data storage;nan|
|[Breeding model of Bumblebee based on linear regression and BP neural network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075884)|X. Xiao|10.1109/ICPECA56706.2023.10075884|Bumblebee reproduction;BP neural network;prediction;nan|
|[Application of Cloud Theory in Evaluation of Driving Skills of Armored Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075840)|X. Li; Y. Yao; X. Yang|10.1109/ICPECA56706.2023.10075840|cloud theory;Driving skills;Assessment;application;nan|
|[Design and Application Research of Bank Customer Portrait System Based on Big Data Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076232)|C. Huan|10.1109/ICPECA56706.2023.10076232|big data;banks;customer portrait;portrait system;nan|
|[To study the sandstorm’s influence on high voltage transmission network based on the weather characteristics in northwestern area in China](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075915)|G. Dai|10.1109/ICPECA56706.2023.10075915|sand dust;flashover voltage;flat plate model;corona discharge;high voltage transmission line;insulator;nan|
|[Research on the Dynamic System Faults of Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075926)|C. Ma|10.1109/ICPECA56706.2023.10075926|electric cars;dynamic system;power battery;electric machinery;cooling system;nan|
|[Research on Distributed Phase Modifier Configuration Method for Short Circuit Ratio Requirements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075902)|Z. Zhao|10.1109/ICPECA56706.2023.10075902|renewable energy multi-site short-circuit ratio;synchronous modulator;distributed;optimal configuration;PSD-BPA;nan|
|[Analysis of High Gain DC-DC Converters for DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075947)|Y. Zhang|10.1109/ICPECA56706.2023.10075947|DC Microgrid;Boost Converter;High-Gain DC-DC Converter;Voltage gain;number of components;comparative method;nan|
|[Research on electricity theft identification methods using big data and large dimensional random matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076204)|M. Liu|10.1109/ICPECA56706.2023.10076204|Big data;electricity theft;large dimensional random matrix;time series;nan|
|[Research on Electronic Information Control System Based on Computer ARM Embedded Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076180)|C. Huiguo; G. Yanxiang|10.1109/ICPECA56706.2023.10076180|computer;ARM embedded;electronic information;video surveillance system;nan|
|[Comprehensive Analysis for Central Lithium-ion Batteries: Security Concerns and Recycle Strategies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075880)|F. Mo|10.1109/ICPECA56706.2023.10075880|battery;lithium-ion;security;recycle;nan|
|[Research on Signal Enhancement Method of Magnetic Flux Leakage Testing Simulation for Strain Clamp](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076003)|Z. Ma; Z. Xu; T. Zhao; J. Li; B. Kang; G. Ding; Z. Wang; X. Yang|10.1109/ICPECA56706.2023.10076003|Strain clamp;Magnetic flux leakage testing;Magnetic flux gather structure;Corresponding relationship;nan|
|[Flux-weakening Control of PMSM Based on Model Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076087)|W. Wang; C. Xiao; Y. Liu; Y. Wan|10.1109/ICPECA56706.2023.10076087|permanent magnet synchronous motor;vector control;flux-weakening control;model predictive control;incremental model;nan|
|[A multi-objective optimization model of robot path planning under different scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076142)|H. Ren; Y. Shi; Y. Qiao|10.1109/ICPECA56706.2023.10076142|Scenarios analysis;Path planning;Mobile robot;Optimization model;NSGAII;nan|
|[Design and Strategy Research of Primary Frequency Modulation Monitoring and Assessment System for Thermal Power Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075952)|C. Liu; Z. Xu; T. Zhao; J. Li; B. Kang; G. Ding; Z. Wang|10.1109/ICPECA56706.2023.10075952|thermal power unit;PFM;dynamic monitoring;FM assessment;nan|
|[Research on pedestrian trajectory prediction by GAN model based on LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076086)|H. Guan; P. Guo|10.1109/ICPECA56706.2023.10076086|Pedestrian trajectory prediction;GAN;LSTM;attention mechanism;nan|
|[Research on Computer Automation Information System Based on Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075785)|Y. Li; X. Yu|10.1109/ICPECA56706.2023.10075785|computer;artificial intelligence;automation technology;computer security;dynamic information flow tracking;nan|
|[Research on Information Database Construction System Based on Computer Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075834)|X. Yu; Y. Li|10.1109/ICPECA56706.2023.10075834|computer;large number;informatization;database construction;the middleware;integrated system;heterogeneous data;nan|
|[Exploration and Classification of Materials using Tactile Sensors and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075762)|Y. Lin|10.1109/ICPECA56706.2023.10075762|Machine Learning;Mechanical Haptics;Deep Learning;Tactile Sensors;nan|
|[Construction of a New Generation of Information Technology Remote Collaborative Office Efficiency Improvement Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076053)|L. Yunshan|10.1109/ICPECA56706.2023.10076053|computer;Information technology;Telecommuting;Collaborative working;The system model;nan|
|[A Method for Image Recognition and Processing of Windmill Contour](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075999)|Z. Sun; Y. Gao; L. Mao; S. Sun; J. Yao|10.1109/ICPECA56706.2023.10075999|Windmill;gallery recognition;image processing;nan|
|[Research on Optimization of Distributed Photovoltaic Grid Connection Scheme Based on New Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075719)|J. Zhuohan; X. Yudong; L. Shuncheng; P. Jian; O. Guang; L. Zuyuan|10.1109/ICPECA56706.2023.10075719|Power system;photovoltaic grid connection;optimization;power flow calculation;communication design;nan|
|[Early Stage Fire Warning of Substations Based on Concentration Prediction of Thermal Ionization Particle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075741)|B. Zhang; Y. Bai; H. Liang|10.1109/ICPECA56706.2023.10075741|substation;fire warning;thermal ionization particle;neural network;nan|
|[Image Style Transfer–A Critical Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075728)|Y. Zhang|10.1109/ICPECA56706.2023.10075728|image transfer;style transfer;neural network;nan|
|[Research on the Construction of Network Security Situational Awareness Platform for Logistics System Using Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075746)|R. Gong|10.1109/ICPECA56706.2023.10075746|big data;logistics system;network security;situational awareness;nan|
|[A BP Neural Network-based Approach to Intelligent Computer Image Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075917)|J. Ze|10.1109/ICPECA56706.2023.10075917|BP neural networks;GA & BP neural networks;computers;image recognition;nan|
|[Structure Design and Motion Study of Transformable Hexapod Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076143)|W. Wang|10.1109/ICPECA56706.2023.10076143|hexapod robot;deformable;motion;flexibility;stability;nan|
|[Tesla Model S Induction Motor Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075690)|C. -H. Chao|10.1109/ICPECA56706.2023.10075690|Tesla Model S;induction motor;simulation;nan|
|[Research Control a Smart Car Based on Chinese Voice Recognition Technology through the Raspberry Pi 4B Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076162)|Q. Wu|10.1109/ICPECA56706.2023.10076162|Chinese speech recognition;LD3320 speech chip;Voice control module;Programming with Python;nan|
|[Data-driven prediction of SOC during discharge of Li-ion batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076065)|Z. Zheng; Y. Qin; D. Cai|10.1109/ICPECA56706.2023.10076065|lithium battery;state of charge;CBAM;LSTM;nan|
|[Convolutional neural network-based ASIL rating method for automotive functional safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075790)|Y. Qin; Q. Liu; J. Hao; H. Li; D. Cai|10.1109/ICPECA56706.2023.10075790|functional safety;HARA;deep learning;ASIL rating;nan|
|[Research on the Innovation System of Computer Artificial Intelligence Technology in Museum Financial Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075850)|S. Wang|10.1109/ICPECA56706.2023.10075850|computer;Artificial intelligence;The museum;Financial management system;nan|
|[Research on Power Stability Control Technology of Light Source for Steel Ball Surface Defect Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076016)|D. Gao; G. Li; M. Li|10.1109/ICPECA56706.2023.10076016|steel ball surface defect detection;semiconductor laser;feedback control;temperature control;optical isolator;nan|
|[Application of PlayMaker-based visual programming in VR mobile game production](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075974)|L. Jianing; W. Yongsheng; L. Xu|10.1109/ICPECA56706.2023.10075974|Unity3D game engine;virtual reality;Playmaker;nan|
|[Research on the Influence of Distributed Alternating Current/Direct Current Hybrid Power Supply on Power Quality of Distribution Network and Its Reliability Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076151)|L. Xiaoming; Z. Yang; M. Fufeng; G. Xinzhi|10.1109/ICPECA56706.2023.10076151|Distributed Alternating current and Direct current power supply;distribution network;power quality;reliability evaluation;nan|
|[Research on AC/DC Hybrid Distribution Network System Based on Distributed Energy Access Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075665)|W. Yu; D. Zhi; L. Zongfeng; G. Xinzhi|10.1109/ICPECA56706.2023.10075665|Grid-connected distributed energy;AC and DC distribution network;energy storage system;Power supply reliability;nan|
|[Two-Level Hierarchical Reliability Evaluation Method for Distribution System Considering Distributed Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076230)|Y. Chang; Y. Wei; K. Xiaoyi; L. Heng; Q. Minzhong|10.1109/ICPECA56706.2023.10076230|distributed power supply;distribution network reliability;operator side;load side;two-level assessment;nan|
|[Grid-connected Debugging Method of Digital Power Grid Based on LR Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075829)|J. Zhao|10.1109/ICPECA56706.2023.10075829|logistic regression;digital grid;grid connection;grid connection debugging;nan|
|[Multi Vehicle Queue Forming Control Based on Multi-agent Swarm Motion Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075903)|J. Yang; Y. Jia; Y. Wang; S. Wang; Y. Ding; Z. Ma|10.1109/ICPECA56706.2023.10075903|Olfati-Saber;Multi vehicle queue;Sports strategy;Intelligent connected vehicle;nan|
|[Application and Analysis of Convolutional Neural Networks and Vision Transformer Models in Fruit Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076218)|S. Liu|10.1109/ICPECA56706.2023.10076218|Neural Network;Vision Transformer;fruit recognition;nan|
|[Stock Market Forecasting Method Based on LSTM Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076100)|X. Lei|10.1109/ICPECA56706.2023.10076100|LSTM Neural Network;Stock market;model prediction;nan|
|[Intelligent Detection of Unauthorized Buildings Based on UAV and Computer Vision Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075743)|M. Wang|10.1109/ICPECA56706.2023.10075743|drones;BeiDou technology;computer vision;RRT algorithm;target recognition algorithm;nan|
|[Research on quantitative evaluation method of demand response of new load park based on real-time data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075847)|C. Liu|10.1109/ICPECA56706.2023.10075847|New load park;Confidence Capacity;Data Center;nan|
|[Design of automatic terminal device parameter acquisition based on single chip microcomputer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075759)|Q. Wang; Z. Wen; S. Zhu|10.1109/ICPECA56706.2023.10075759|Electricity parameter acquisition;Equipment status monitoring;RN8302;nan|
|[Research on Automatic Control and Regulation System of Central Air Conditioning Based on Computer Automatic Control Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076127)|Z. Wang|10.1109/ICPECA56706.2023.10076127|computer;load forecasting;fuzzy control;central air conditioning;control and regulation systems;nan|
|[Power equipment parameter acquisition system based on single chip microcomputer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075831)|Q. Wang; W. Cui; S. Zhu|10.1109/ICPECA56706.2023.10075831|Power parameter acquisition;intelligent monitoring;wireless transmission;STM32;nan|
|[Sewer biogas monitoring and recycling system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076214)|R. Xie; F. Xia|10.1109/ICPECA56706.2023.10076214|internet of things;energy saving and environmental protection;methane;nan|
|[Research on Network Information Security Service Model Based on User Requirements under Artificial Intelligence Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075946)|X. Li|10.1109/ICPECA56706.2023.10075946|artificial intelligence;user requirements;network information;the security services;nan|
|[Research on Automatic Driving Vehicle Auxiliary System Based on Computer intelligent Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075905)|S. Zhou|10.1109/ICPECA56706.2023.10075905|intelligent network;car networking;automatic driving;vehicle driving system;nan|
|[Automatic Spraying of Insulator Persistent Room Temperature Vulcanized Coating Based on Digital Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076036)|Y. Zuo; Y. Cai; X. Li|10.1109/ICPECA56706.2023.10076036|pollution flashover;PRTV;image processing;paint spraying;nan|
|[Optimal Allocation and Operation of Combined Heat and Power Microgrid Including Phase Change Heat Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076163)|M. Zhao; Q. Zhang; C. Chen|10.1109/ICPECA56706.2023.10076163|Genetic Algorithm;combined heat and power microgrid;phase change heat storage;thermoelectric boiler;nan|
|[Research on Public Opinion Data Collection of Electric Power Information Based on Web Crawler Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075714)|W. Fan; W. Cheng; Y. Shang; J. Li|10.1109/ICPECA56706.2023.10075714|public opinion of electric power;data acquisition;crawler technology;topic vector;nan|
|[Optimal Allocation of Demand Response and Energy Storage Load in Agricultural Systems Considering Distributed PV Power Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076213)|H. He; Y. Tao; Z. Li; Y. Pan; J. Zhang; F. Zuo|10.1109/ICPECA56706.2023.10076213|PV power consumption;Battery energy storage;Demand response strategy;nan|
|[A Measurement System of Transient Overvoltage of Secondary Equipment Port During Digital Substation Switch Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076005)|B. Zhang; X. Meng; Y. Huang; J. Qu; L. Guo; S. Fang|10.1109/ICPECA56706.2023.10076005|switch operation;secondary equipment port;electromagnetic interference voltage;measurement system;nan|
|[Research on Design of Advanced Marine Scientific Survey Vessel Based on Computer Data Engineering and Intelligent Information System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075824)|N. Wang; X. Hou; L. Ma; H. Zhang; Z. Zhang|10.1109/ICPECA56706.2023.10075824|Data engineering;fishery environment;Marine Fishery Comprehensive Scientific Survey Vessel;computer system;nan|
|[Optimized SVM based on improved whale algorithm for EEG signal pattern classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076019)|J. Peng; L. Liu|10.1109/ICPECA56706.2023.10076019|Motor imagery EEG;SVM;WOA;Nonlinear time varying factor;nan|
|[Research on the Construction and Application of Power Grid Information Model Based on Information Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076107)|L. Qi; X. Sun; S. Zhang; Y. Zhang|10.1109/ICPECA56706.2023.10076107|multi-source information fusion;Multi-layer diagnostic model;Fault diagnosis;Feature reduction;distortion;nan|
|[Research and Implementation of 3D Visualization Technology Based on GIM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075713)|S. Zhang; Y. Zhang; L. Qi; X. Sun|10.1109/ICPECA56706.2023.10075713|GIM model;Three-dimensional visualization;Smart grid;Parametric modeling;nan|
|[Constructing Safe Flight Corridors for Quadrotor Navigation in Cluttered 3-D Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076054)|Z. Hua|10.1109/ICPECA56706.2023.10076054|quadrotor;path-planning;SFC;nan|
|[Walking problem of bipedal humanoid robot: comparison between model-based and learning-based method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075792)|Y. Chen|10.1109/ICPECA56706.2023.10075792|bipedal humanoid robot;PR-SRL algorithm;walking problem;nan|
|[Research on Wireless Network System Based on Computer Mobile Communication Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076135)|C. Yumeng; G. Yanxiang; C. Huiguo; Z. Chao|10.1109/ICPECA56706.2023.10076135|computer;Mobile communication;Wireless network system;Dynamic programming;The data collection;nan|
|[Analysis of Acoustic Signal in Switchgear Based on VMD Decomposition Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076084)|Y. Ying; C. Hao; H. Tao; W. Cai|10.1109/ICPECA56706.2023.10076084|switchgear;acoustic signal;VMD;nan|
|[Test and Analysis of Acoustic and Vibration Characteristics of Transformer in Winding and Iron Core Loose](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075954)|C. Hao; D. Xinyu; L. Ling; W. Cai|10.1109/ICPECA56706.2023.10075954|transformer;acoustic;vibration;winding loose;iron core loose;nan|
|[Improved BP neural network-based subsurface displacement prediction method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075712)|J. Li; H. Chen; H. Zhou|10.1109/ICPECA56706.2023.10075712|subsurface displacement;BP neural network;seagull optimization algorithm;nan|
|[Research on Base Station Location Based on K-Means Clustering and Simulated Annealing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076136)|Y. Zhang; H. Gao; H. Zheng; R. Li; Y. Ma|10.1109/ICPECA56706.2023.10076136|K-Means clustering simulated;annealing algorithm;0-1 planning;Monte Carlo simulations;nan|
|[Design of three-dimensional warehouse access material control system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075721)|Q. Yuan; Y. Han|10.1109/ICPECA56706.2023.10075721|Three dimensional warehouse;Servo control;PLC control;nan|
|[Electrical lighting design and intelligent control of a industry-education integration building through load calculation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075935)|Y. Qingmin; C. Peikun|10.1109/ICPECA56706.2023.10075935|electrical design;lighting and socket systems;Power supply and distribution system;nan|
|[Research on the Application of Automatization Technology of Human Large Data Communication in Automobile Manufacturing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076042)|S. Song; C. Guo; G. Song; N. Tang|10.1109/ICPECA56706.2023.10076042|automobile manufacturing;Robot;Big data communication;Communication automation;Communication system;nan|
|[Design of home burglar alarm based on MCU and PIR sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076018)|Q. Yuan; W. Zhang|10.1109/ICPECA56706.2023.10076018|MCU;PIR sensors;LCD1602;nan|
|[Research on the Casing System of Big Data Information Security Management and Control Platform of Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075805)|D. Chenxi; S. Xuan’an; Z. Qingyao; T. Yongfei; K. Hongyu|10.1109/ICPECA56706.2023.10075805|power system;big data;power information;security control platform;information security;terminal architecture;nan|
|[Analysis of the Influence of Single Line Configuration of Auxiliary Engine on Coal Power Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075860)|L. Cheng; X. Zhang; Z. Zhang; J. Li; Y. Zhang; J. Xu|10.1109/ICPECA56706.2023.10075860|coal power unit;double row auxiliary engine;single column configuration;reliability;nan|
|[Research on Accurate Timing System of Dispatching Automation Communication under Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075970)|C. Guo; S. Song; G. Song; C. Huang|10.1109/ICPECA56706.2023.10075970|smart grid;Scheduling automation;Communication;Clock synchronization;IEEE1588;nan|
|[Design of Sports Online Multimedia Resource Sharing System Based on Data Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076082)|J. Xu; L. Zhu|10.1109/ICPECA56706.2023.10076082|data clustering;Sports online educational resources;Resource sharing;Data mining;Information management system;Data management;nan|
|[Soundprint Feature Analysis of Main Transformers in a 500kV Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076202)|X. Duan; H. Cao; L. Ling; W. Cai|10.1109/ICPECA56706.2023.10076202|transformer;soundprint analysis;substation;nan|
|[Research on Noise Control Technology of DC Fast Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075727)|S. Hu; J. Zhou; W. Chen; Q. Tang; H. Cao; L. Lu|10.1109/ICPECA56706.2023.10075727|DC fast charging station;noise;control technology;nan|
|[Detection of electricity theft based on ISSA-DELM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076155)|L. Dong; W. Hu; S. Lin; Z. Zheng; Q. Wu|10.1109/ICPECA56706.2023.10076155|Sparrow search algorithm;deep extreme learning machine;electric theft detection;nan|
|[Electricity charge anomaly detection based on PCA-IK-means](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076038)|J. Zhuang; M. Dong; J. Lin; M. Liu; K. Lin|10.1109/ICPECA56706.2023.10076038|principal component analysis;K-means;density peak clustering;nan|
|[Abnormal electricity identification based on intelligent clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075816)|G. Shang; K. Chen; Y. Zheng; Y. Lin; W. Lin|10.1109/ICPECA56706.2023.10075816|Abnormal power consumption;support vector machine;ant lion optimization algorithm;nan|
|[Design and Implementation of YueXiang Aviation Information Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075894)|Q. Yuan; W. Zhao|10.1109/ICPECA56706.2023.10075894|car rental management;MySQL database;SSM technology;nan|
|[Dual Reciprocal Sensing Prediction Method of Underground 3D Displacement Based on RBF-MLP Neural Network Combination Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076092)|H. Chen; F. Wang; R. Yu; H. Zhou; J. Li|10.1109/ICPECA56706.2023.10076092|Three-dimensional underground displacement;Dual reciprocal inductance voltage;RBF-MLP;nan|
|[Raspberry Pi 4B-based cloud-based robot design and demonstration platform construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076246)|Y. Wei; S. Hong; M. Ma; J. Xie; Z. Lu; X. Zheng|10.1109/ICPECA56706.2023.10076246|Raspberry Pi 4B;cloud-based robot;teaching platform;robot interaction;nan|
|[Research on Prediction Model of Photovoltaic Power Generation Based on Nonlinear Regression Based on BP Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075822)|L. Zhiwen|10.1109/ICPECA56706.2023.10075822|BP Neural Network;Nonlinear Regression Analysis;Power Generation Forecast;Photovoltaic Power Generation;nan|
|[Research on Integrated Energy System Data Set Monitoring System Based on RPA Process Automation Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075858)|C. Zhan|10.1109/ICPECA56706.2023.10075858|RPA;Integrated Energy;System Monitoring;Data Analysis;Automation;nan|
|[Research on SDN Network Structure Optimization System Based on Computer 5G Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075758)|S. Xiaowei; W. Jun|10.1109/ICPECA56706.2023.10075758|computer;5g technology;SDN architecture;Network data;System architecture;nan|
|[Distribution system reliability assessment method considering integrated energy system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076113)|Z. Yantian; Y. Cheng; L. Hanyin|10.1109/ICPECA56706.2023.10076113|integrated energy system;load reduction;distribution system reliability;analysis target cascading;Markov chain Monte Carlo;nan|
|[Research on Intelligent Energy Management of Fuel Cell Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076239)|T. Yang; S. Wei; Z. Wang; J. Li; P. Yu|10.1109/ICPECA56706.2023.10076239|hydrogen consumption;the ECMS algorithm;equivalent factor optimization;nan|
|[Research on Innovation Governance Intellectual Property Management System under the Integration of Digital and Intelligent Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075718)|L. Huang|10.1109/ICPECA56706.2023.10075718|intelligent system;intellectual property management;crowdsourcing test;information system;nan|
|[Research of Digital Image Processing Technology of Photoelectric Theodolite’s Target Based on MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076208)|K. Li; L. Zhao; R. Xu; H. Zhou; X. Liu; F. Qian; X. Sun|10.1109/ICPECA56706.2023.10076208|Digital image processing;Pseudocolor;K-means clustering;Feature;nan|
|[Markov Process Based Reliability Study of Private Network System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075908)|Q. Li; J. Liu; Y. Huang; Z. Liu; W. He; L. Zhou|10.1109/ICPECA56706.2023.10075908|Markov;reliability;network system;nan|
|[Design of Dual-link Shared GRE over IPSec VPN on P2MP Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075987)|W. He; Y. Zhao; Z. Liu; Q. Li; B. Yu; H. Zhang|10.1109/ICPECA56706.2023.10075987|GRE over IPSec;P2MP network;dual-link sharing;one-way communication;nan|
|[Research on contour extraction and edge detection of sugarcane image based on MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076152)|M. Chen; J. Wang; L. Zhu; H. Deng; H. Wu; Z. Zhang|10.1109/ICPECA56706.2023.10076152|image processing;contour extraction;edge detection;operator;nan|
|[An Efficient Face Recognition Method Based on CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076242)|X. He; F. Ding|10.1109/ICPECA56706.2023.10076242|Face recognition;convolutional neural network;artificial intelligence;computer vision;nan|
|[Research on interior office comfort system combined with two factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075710)|L. Zhang; X. Zhang; L. Yang|10.1109/ICPECA56706.2023.10075710|Comfort Model;Machine Learning;nan|
|[Lightning overvoltage protection of 35kV collector lines in wind farms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075751)|J. Guo; R. Liu; Z. Jing|10.1109/ICPECA56706.2023.10075751|Lightning overvoltage;Lightning protection line;Cable;Grounding;nan|
|[Autonomous Detection Principle of Mission Payload of a Ground Armored Mobile Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076153)|H. Ye; X. Ma; X. Liu; Z. Ma; W. Chen; Y. Wang; K. Qiu|10.1109/ICPECA56706.2023.10076153|ground armored mobile platform;mission payload;stepping detection;reduction of false-alarm rate;nan|
|[Multiuser Cyclic Shift-Aided RA Code for Gaussian Multiple Access Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076191)|M. He; M. Zhu; X. Teng; H. Song; Z. Hu; H. Wang|10.1109/ICPECA56706.2023.10076191|Cyclic shift;RA code;Gaussian MAC;Multiuser decoding;IDMA;nan|
|[Application of millimeter-wave radar in giant-magnetostrictive brake by wire environment perception system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076178)|X. Jia; Y. Zhao; C. Chu; C. Run; Z. Gan|10.1109/ICPECA56706.2023.10076178|giant-magnetostriction material;intelligent vehicles;millimeter-wave radar;sensor signal processing;Kalman filter;nan|
|[Interpretation of Remote Sensing Data of Pipeline Geohazards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075936)|Y. Tang; M. Sun; D. Du; C. Liu; B. Tang|10.1109/ICPECA56706.2023.10075936|Pipeline;geohazard;landslide;deformation;InSAR;nan|
|[Study on applicability of electromagnetic eddy current internal inspection for gas field gathering and transmission pipeline](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075955)|C. Li; M. He; Y. Lei|10.1109/ICPECA56706.2023.10075955|Internal corrosion;electromagnetic eddy current;gas field gathering and transmission pipeline;internal inspection;field test;nan|
|[Research on composite grounding method of neutral point in distribution network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075867)|J. Li; Z. Guo; X. Chen; D. Chen|10.1109/ICPECA56706.2023.10075867|neutral grounding mode;composite grounding mode;arc suppression coil and small resistance;nan|
|[The Prediction of SOH and Capacity for Battery Based on Charging Voltage Curve during Equalizing Charge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075703)|C. He; Q. Li; B. Feng|10.1109/ICPECA56706.2023.10075703|battery;state of health;capacity prediction;nan|
#### **2023 25th International Conference on Advanced Communication Technology (ICACT)**
- DOI: 10.23919/ICACT56868.2023
- DATE: 19-22 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Proactive Rank Adaptation Method Using Probabilistic Interference Arrival Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079263)|T. Matsumuro; K. Yano; T. Sakano|10.23919/ICACT56868.2023.10079263|MIMO;rank adaptation;MMSE weight;adaptive array;probabilistic interference condition;Channel capacity;Estimation;Channel estimation;Adaptive arrays;Interference;Traffic control;Probabilistic logic|
|[MUSIC Spectrum Based Interference Detection and Localization for mmWave RIS-MIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079677)|Y. Hou; K. Yano; N. Suga; S. Denno; T. Sakano|10.23919/ICACT56868.2023.10079677|nan;nan|
|[ILLUMINATE - VIsibLe Light CommUnication enabled SMart Indoor lightiNg And control SysTEm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079560)|G. Dhivya; K. Hariharan; P. Poonguzhali; M. Vaibhav; S. Lokeshwar; S. Bhattacharya|10.23919/ICACT56868.2023.10079560|Green Communication;Lighting Control;Line of Sight;Occupancy Control;Visible Light Communication;nan|
|[Enhanced Deep Residual Shrinkage Network Based Channel Estimation in RIS Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079658)|Y. Ma; Z. Bai; B. He; J. Zhao; A. Mohamed; K. Pang; K. Kwak|10.23919/ICACT56868.2023.10079658|channel estimation;reconfigurable intelligent surface;EDRSN;denoising block;nan|
|[Smart Mirror Activated by User’s Face Recognition with Simulation of Artificial Intelligence Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079381)|B. -Y. Lu; J. Liu; Z. Wang; H. Li; J. He; X. Wen; P. Chen; J. Chen; W. Lai; C. Huang|10.23919/ICACT56868.2023.10079381|Loongson;RT thread;real-time operating system;OpenMV;wireless transmission;AI;ANFIS;nan|
|[A Horizontal Federated-Learning Model for Detecting Abnormal Traffic Generated by Malware in IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079624)|P. Hao Do; T. Duc Le; V. Vishnevsky; A. Berezkin; R. Kirichek|10.23919/ICACT56868.2023.10079624|IoT;abnormal traffics;malware detection;federated learning;AI model;nan|
|[A Terminal Trust Assessment Method Based on Consensus Trust Aggregation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079276)|S. Cheng; J. Zhang; L. Han|10.23919/ICACT56868.2023.10079276|trust assessment;consensus;power mobile network;terminal node;Energy loss;Filtering;Simulation;Resists;Data models;Security;Data communication|
|[Dimensional Feature Reduction for Detecting Botnet Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079359)|M. A. R. Putra; T. Ahmad; D. P. Hostiadi|10.23919/ICACT56868.2023.10079359|botnet detection;intrusion detection system;network infrastructure;network security;dimensional reduction;Radio frequency;Performance evaluation;Analytical models;Machine learning algorithms;Botnet;Feature extraction;Data models|
|[Fuzzy PID Controlled Temperature in Phototherapy Incubator for Infant Jaundice Treatment: A Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079548)|J. He; X. Wen; J. Cai; B. -Y. Lu; H. Zheng; X. Cheng; Y. Chen|10.23919/ICACT56868.2023.10079548|newborn infant;Fuzzy-PID controller;blue light optical therapy;neonatal jaundice;treatment;computer simulation;Image motion analysis;Computer vision;Pediatrics;Process control;Phase control;Optical variables control;Mathematical models|
|[Denoising CNN Based Channel Estimation for Vehicular OTFS Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079625)|B. He; Z. Bai; Y. Ma; H. Xu; A. Mohamed; Y. Yang; K. Kwak|10.23919/ICACT56868.2023.10079625|OTFS;vehicular communication;channel estimation;OMP;Time-frequency analysis;Communication systems;Simulation;Noise reduction;Channel estimation;Feature extraction;Robustness|
|[Activation functions for deep learning: an application for rare attack detection in wireless local area network (WLAN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079326)|V. -T. Vu; T. Q. B. Thi; H. -S. Gan; V. -V. Vu; D. M. Quang; V. T. Duc; D. -L. Pham|10.23919/ICACT56868.2023.10079326|WLAN;Deep learning;Intrusion Detection System;Rare Attack;activation function;Deep learning;Wireless LAN;Neural networks;Government;Data models;Communications technology;Computer crime|
|[Intent Classification of Users Conversation using BERT for Conversational Dialogue System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079261)|S. Chakraborty; K. Yul Ohm; H. Jeon; D. Hyun Kim; H. Jae Jin|10.23919/ICACT56868.2023.10079261|Owner’s Manual;Car;NLP;NLU;Chatbot;BERT;Industries;Social networking (online);Bit error rate;Blogs;Manuals;Production;Oral communication|
|[Development of Cost-Effective Wi-Fi 6 SISO/MIMO Vector Signal Generator and Analyzer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079516)|J. -K. Hwang; C. -M. Chen|10.23919/ICACT56868.2023.10079516|VSG/VSA;Wi-Fi 6;Software Defined Radio;MIMO;Cost-effective;Wireless communication;Instruments;IEEE 802.11ax Standard;Computer architecture;Signal generators;Software;Transceivers|
|[NB-IoT NTN Band-Edge Attenuation/EVM Tradeoff with Real-System Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079567)|C. -F. Li; J. -K. Hwang; C. Ma|10.23919/ICACT56868.2023.10079567|Narrowband Internet-of-Things;Non-Terrestrial Network;Satellite;Geostationary Orbit;Spectral Edge;Error Vector Magnitude;Real-system;Satellites;Filtering;OFDM;Prototypes;Attenuation;Orbits;3GPP|
|[Multi-User Dynamic Spectrum Access Based on LRQ Deep Reinforcement Learning Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079537)|Y. Li; Y. Wang; Y. Li; B. Shen|10.23919/ICACT56868.2023.10079537|Dynamic spectrum access;Deep reinforcement learning;Heterogeneous;ResNet;LSTM;Deep learning;Training;Heuristic algorithms;Simulation;Dynamic spectrum access;Interference;Prediction algorithms|
|[Large MIMO Channel Estimation Study Based on Independent Component Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079390)|Y. Takahashi; T. Ikegami|10.23919/ICACT56868.2023.10079390|Large-scale MIMO;Channel Estimation;Independent Component Analysis;Blind Signal Separation;Source separation;Precoding;Transmitting antennas;Channel estimation;Receiving antennas;Estimation;Independent component analysis|
|[NLoS-VICINITY: A Non-Line of Sight Approach for Visible LIght Communication based INdoor PosITioning SYstem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079479)|D. G; H. K; S. Bhattacharya; P. P; V. M; L. S|10.23919/ICACT56868.2023.10079479|Indoor Positioning System (IPS);Optical Camera Communication (OCC);Optical Wireless Communication (OWC);Non-Line of Sight (N-LoS);Visible Light Communication (VLC);Radio frequency;Wireless communication;Costs;Satellite broadcasting;Radio transmitters;Lighting;Optical transmitters|
|[Tracking Risks from Multi-path TDoA-based Localization in Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079488)|L. -H. Nguyen; V. -L. Nguyen; Y. -H. Liu|10.23919/ICACT56868.2023.10079488|Wireless security;User tracking;Signal-based tracking;Signal surveillance;Wireless communication;Phased arrays;Privacy;Target tracking;Simultaneous localization and mapping;Surveillance;Buildings|
|[Hierarchical DP-K Anonymous Data Publishing Model Based on Binary Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079551)|Y. Xia; T. Zhao; Y. Lv; Y. Li; R. Yang|10.23919/ICACT56868.2023.10079551|K-Anonymous;differential privacy;clustering;binary tree;privacy protection;Data privacy;Differential privacy;Sensitivity;Publishing;Clustering algorithms;Binary trees;Data models|
|[Intrusion Detection System for AI Box Based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079610)|J. -L. Chen; Z. -Z. Chen; Y. -S. Chang; C. -I. Li; T. -I. Kao; Y. -T. Lin; Y. -Y. Xiao; J. -F. Qiu|10.23919/ICACT56868.2023.10079610|Artificial Intelligence;Information Security;Machine Learning;IoT Device Security;HistGradient Boosting Classifier Algorithm;Confidentiality of Data Transfer;Packet Capture Analysis;Feature Selection;Machine learning algorithms;Buildings;Network intrusion detection;Telecommunication traffic;Predictive models;Feature extraction;Boosting|
|[Development of Security Target for Router Based on ENISA Common Criteria Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079357)|J. -L. Chen; J. -C. Hsu; C. Ahmadi; B. T. Atmaja; C. -C. Lin; S. -H. Wang; S. -Y. Lin|10.23919/ICACT56868.2023.10079357|Common Criteria;ENISA;Network device;Router;Security target;Performance evaluation;ISO;IEC;Software;Regulation;Information and communication technology;Security|
|[Distributed Spatial Transformer for Object Tracking in Multi-Camera](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079540)|S. -K. Im; K. -H. Chan|10.23919/ICACT56868.2023.10079540|STN;Distribution;Mulit-View;Person Tracking;Target tracking;Manuals;Cameras;Transformers;Video surveillance;Real-time systems;Personnel|
|[Density peak clustering evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079561)|V. -V. Vu; B. Yoon; D. -L. Pham; H. -Q. Do; H. -M. Nguyen; T. -C. Dao; T. -H. -Y. Nguyen; D. -V. Tran; T. -H. -L. Nguyen; V. -T. Vu|10.23919/ICACT56868.2023.10079561|Density based clustering;density peak clustering;density estimation;nan|
|[Evolving Interest for Information Diffusion Prediction on Social Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079436)|Y. Liu; J. Gao; Z. Zhao; H. Wu; Z. Yue; J. Li|10.23919/ICACT56868.2023.10079436|social network;information diffusion;cascade prediction;neural network;evolving interest;Social networking (online);Microscopy;Prediction algorithms;Communications technology;Behavioral sciences|
|[A Deep Learning-Based Real-Time Video Object Contextualizing and Archiving System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079454)|D. -L. Pham; B. Yoon; V. -V. Vu; J. -C. Kim; S. -E. Ahn; J. -H. Chang; H. Yoo; K. Sun; K. -S. Kim; K. P. Kim|10.23919/ICACT56868.2023.10079454|Video object contextualizing;CCTV Cameras;Deep learning-based systems;Video archiving systems;Surveillance;Urban areas;Streaming media;Big Data;Video compression;Cameras;Search problems|
|[Multi-Agent Deep Reinforcement Learning for D2D-assisted MEC system with Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079275)|X. Mi; H. He|10.23919/ICACT56868.2023.10079275|MEC;D2D communication;multi-agent reinforcement learning;energy harvesting;task offloading;Performance evaluation;Time-frequency analysis;Heuristic algorithms;Reinforcement learning;Wireless power transfer;Telecommunication traffic;Device-to-device communication|
|[A Density-Based RSU Deployment and Optimization Heuristic Method for Vehicular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079515)|Y. Feng; N. Ge; T. Xiang|10.23919/ICACT56868.2023.10079515|Vehicular Communications;Network Design;Network Optimization;Heuristic Method;Density-Based Solution;nan|
|[An Adaptive User Scheduling Algorithm for 6G Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079558)|R. Chataut; R. Akl; U. K. Dey|10.23919/ICACT56868.2023.10079558|Massive MIMO;5G;6G;user scheduling;sumrate;fairness;6G mobile communication;Wireless communication;Adaptation models;Adaptive systems;Scheduling algorithms;Massive MIMO;Throughput|
|[Joint Resource Allocation and Task Offloading for Hybrid NOMA-assisted MEC Network with Network Slicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079379)|Y. Zhang; H. Zhang; Y. Li; Y. Zhang; S. Yuan|10.23919/ICACT56868.2023.10079379|MEC;hybrid NOMA;network slicing;DRL;swap matching;NOMA;Energy consumption;Q-learning;Simulation;Delays;Complexity theory;Resource management|
|[5G NR based initial access procedure simulation environment implementation including system information using ns-3 simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079674)|S. Hong; D. Kim; J. Moon; K. Lee; I. Joe|10.23919/ICACT56868.2023.10079674|5g NR;Initial acess;ns-3 simulation;RRC protocol;System information blocks;Base stations;Analytical models;Protocols;Codes;5G mobile communication;Communications technology;3GPP|
|[Analysis of DNS Graph of Phishing Websites Using Digital Certificates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079566)|Y. Ishida; M. Hanada; A. Waseda; M. W. Kim|10.23919/ICACT56868.2023.10079566|Phishing;DNS;DNS Graph;Security;Measurement;Phishing;Time series analysis;Complex networks;Communications technology;Encryption;IP networks|
|[An Overview of HTTPS and DNSSEC Services Adoption in Higher Education Institutions in Brazil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079449)|J. Barreto; H. Almeida; P. Pinto|10.23919/ICACT56868.2023.10079449|DNSSEC;HTTPS;Higher Education Institutions (HEI);Security Headers;Performance evaluation;Protocols;Education;Organizations;Internet;Encryption;Security|
|[Security Assist Mechanisms for Industrial Control Systems with Authentication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079611)|C. -W. Tsou; Y. -W. Ma; Y. -H. Tu; J. -L. Chen|10.23919/ICACT56868.2023.10079611|Industry control system;security;central control;device to device (D2D);hybrid;Industries;Protocols;Industrial control;Authentication;Control systems;Communications technology;Device-to-device communication|
|[An Intelligent Cyber Threat Classification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079405)|M. -L. Liao; C. -L. Yu; Y. -C. Lai; S. -P. Chiu; J. -L. Chen|10.23919/ICACT56868.2023.10079405|TF-IDF Technology;Honeypot;K-means Algorithm;MITRE ATT&CK;Cyber Threat;nan|
|[Lightweight Group Key Establishment for Reducing Memory Overhead](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079388)|S. Agustini; Wirawan; G. Hendrantoro|10.23919/ICACT56868.2023.10079388|Group key establishment;WSN;Lucas polynomial;memory overhead;information security;key distribution;nan|
|[Estimation of Power Generation and Consumption based on eXplainable Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079678)|S. Shin; H. Yang|10.23919/ICACT56868.2023.10079678|Green gas;SHAP;XAI;LSTM;LGBM;Renewable energy sources;Uncertainty;Employment;Climate change;Predictive models;Energy efficiency;Time measurement|
|[AI Model to Improve HR Decision-Making with Machine Learning Predictions Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079282)|A. H. Fomude; C. Yang; G. K. Agordzo; A. V. Serwah; L. Abangbila|10.23919/ICACT56868.2023.10079282|AI model;HR decision-making;predictions;machine-learning;algorithm;Technological innovation;Machine learning algorithms;Data analysis;Decision making;Machine learning;Companies;Predictive models|
|[Chinese ASR and NER Improvement Based on Whisper Fine-Tuning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079686)|H. Yang; M. Zhang; S. Tao; M. Ma; Y. Qin|10.23919/ICACT56868.2023.10079686|Chinese ASR;Chinese NER;Whisper finetuning;Convolution;Speech recognition;Multitasking;Transformers;Communications technology;Task analysis|
|[Autoencoder based framework for drone RF signal classification and novelty detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079363)|S. Basak; S. Rajendran; S. Pollin; B. Scheers|10.23919/ICACT56868.2023.10079363|Deep neural networks;Unsupervised learning;UAV;RF signals;Pattern classification;Detectors;Autonomous aerial vehicles;Communications technology;Classification algorithms;Anomaly detection|
|[Multi-source DNN task offloading strategy based on in-network computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079646)|L. Hu; Y. Chai; Q. Li; W. Li; Y. Zhang|10.23919/ICACT56868.2023.10079646|in-network computing;edge-cloud computing;task offloading;particle swarm optimization;Cloud computing;Costs;Computational modeling;Bandwidth;Quality of service;Routing;Task analysis|
|[Review of New Data Center Network Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079689)|W. Xue; Z. Han; X. Du|10.23919/ICACT56868.2023.10079689|Cloud computing;Network structure;Data center network (DCN);Wireless communication;Wiring;Data centers;Cloud computing;Horn antennas;Transmitting antennas;Switches|
|[Bayesian Constrained Optimization of IEEE 802.11 VANET for Safety Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079511)|S. Ding; X. Ma|10.23919/ICACT56868.2023.10079511|Optimization;Bayesian Optimization;Ad hoc networks;Quality of Service;Safety;Analytical models;Computational modeling;Vehicular ad hoc networks;Quality of service;Real-time systems;Bayes methods;Safety|
|[Study of Cluster-Based D2D Communication in Next Generation Mobile Network for the Post-Disaster Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079499)|S. Ahmed|10.23919/ICACT56868.2023.10079499|D2D communication;Cluster Head;Disaster network;5G;5G mobile communication;Switches;Spread spectrum communication;Quality of service;Probability;Routing;Mobile handsets|
|[An Intelligence Defense System with SNORT Rules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079506)|Y. -C. Lai; C. -L. Yu; M. -L. Liao; Y. -S. Lin; Y. -C. Chang; J. -L. Chen|10.23919/ICACT56868.2023.10079506|MITRE ATT&CK;K-means;PCRE Regular Expression;Snort Rule;Data analysis;Firewalls (computing);Feature extraction;Real-time systems;Communications technology;Classification algorithms;Behavioral sciences|
|[A Blockchain based Security Information and Event Monitoring Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079550)|S. N; C. G|10.23919/ICACT56868.2023.10079550|Blockchain;Security Assurance Policy;Continuous Monitoring;Root cause analysis;Cloud computing security;Organizations;Data collection;Data models;Communications technology;Blockchains|
|[Analysis of Olympus DAO: a popular DeFi model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079319)|A. Song; E. Seo; H. Kim|10.23919/ICACT56868.2023.10079319|Olympus DAO;Decentralized Finance;DeFi;Blockchain;OHM;Ethereum;Smart contract;Analytical models;Codes;Smart contracts;Systems architecture;Decentralized applications;Communications technology;Cryptocurrency|
|[Development of Indicator with Interactive Visualization System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079668)|M. Ida|10.23919/ICACT56868.2023.10079668|financial indicator;correlation;principal component analysis;clustering;visualization;business intelligence;Decision support systems;Dimensionality reduction;Correlation;Education;Decision making;Data visualization;Organizations|
|[A Study of AI-based Harbor Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079320)|D. Shon; J. Kim; T. H. Yoon; W. -S. Jung; D. S. Yoo|10.23919/ICACT56868.2023.10079320|Harbor surveillance;Artificial intelligent;Object detection;Anomaly situation determination;Image recognition;Surveillance;Seaports;Computer crashes;Communications technology;Object recognition;Marine vehicles|
|[Time-frequency Analysis and Convolutional Neural Network Based Fuze Jamming Signal Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079346)|J. Yang; Z. Bai; J. Hu; Y. Yang; Z. Xian; X. Hao; K. Kwak|10.23919/ICACT56868.2023.10079346|fuze;CNN;STFT;image;accuracy;Time-frequency analysis;Image recognition;Simulation;Detectors;Interference;Convolutional neural networks;Jamming|
|[TeacherSim: Cross-lingual Machine Translation Evaluation with Monolingual Embedding as Teacher](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079300)|H. Yang; M. Zhang; S. Tao; M. Ma; Y. Qin; D. Wei|10.23919/ICACT56868.2023.10079300|Machine Translation Evaluation;Knowledge Distillation;Monolingual Embedding;Semantics;Estimation;Transformers;Laser modes;Feature extraction;Communications technology;Machine translation|
|[On Concatenated Coding Scheme for High-Speed Ethernet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079248)|N. Tang; Y. S. Han; H. Ren|10.23919/ICACT56868.2023.10079248|forward error correction;concatenated coding scheme;Reed-Solomon codes;soft-decision decoding;performance analysis;Reed-Solomon codes;Performance evaluation;Codes;Computer simulation;Forward error correction;Encoding;Decoding|
|[QoS-aware Resource Allocation for Healthcare Data Transmission using D2D Communication in NB-IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079299)|N. Sultana; F. Huq; M. A. Razzaque; M. M. Rahman|10.23919/ICACT56868.2023.10079299|Device to Device communication;Resource Allocation;Health Monitoring;NB-IoT;Performance evaluation;Simulation;Medical services;Quality of service;Throughput;Device-to-device communication;Delays|
|[A Study on Connectivity Evaluation Among Peer Groups in Pure P2P Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079579)|Y. Naito; T. Uemura; T. Hoshiai|10.23919/ICACT56868.2023.10079579|P2P;Pure model;Cluster model;Performance evaluation;Peer groups’ connectivity;Performance evaluation;Computer simulation;Computational modeling;Communications technology;Peer-to-peer computing|
|[A Survey of Data Center Network Topology Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079437)|M. Zhao; Z. Han; X. Du|10.23919/ICACT56868.2023.10079437|Data center network;Structural features;Topology;Scalability;Fault tolerance;Data centers;Fault tolerance;Scalability;Fault tolerant systems;Switches;Bandwidth;Network architecture|
|[Damage Detection and Safety Diagnosis for Immovable Cultural Assets Using Deep Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079559)|S. -Y. Lee; H. -H. Cho|10.23919/ICACT56868.2023.10079559|Deep Learning;Cultural Heritage;Machine Learning;Anomaly Detection;Displacement detection;Deep learning;Training;Databases;Predictive models;Logic gates;Robustness;Safety|
|[Accelerating path tracing rendering with Multi-GPU in Blender cycles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079514)|J. Chen; L. Chen; Z. Yu|10.23919/ICACT56868.2023.10079514|Multi-GPU;Rendering;Path-Tracing;Blender Cycles;Performance Optimization;Metaverse;Scalability;Heuristic algorithms;Graphics processing units;Parallel processing;Rendering (computer graphics);Real-time systems|
|[Study on standardization for Interoperable Metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079642)|W. Hyun|10.23919/ICACT56868.2023.10079642|metaverse;standardization;interoperability;interoperable metaverse;interaction model;Economics;Analytical models;Technological innovation;Metaverse;Ecosystems;Force;Companies|
|[DC Nanogrid using IEC 61850](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079288)|G. Kang; J. Park; S. Shin; H. Yang|10.23919/ICACT56868.2023.10079288|DC nanogrid;IEC61850;USB Type-C;RESTful;EMS;Low voltage;Predictive models;Power grids;Energy efficiency;Hardware;Data models;IEC Standards|
|[Applications and Possible Challenges of Healthcare Metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079314)|A. Athar; S. M. Ali; M. A. I. Mozumder; S. Ali; H. -C. Kim|10.23919/ICACT56868.2023.10079314|AR;VR;Healthcare;Metaverse;Industries;Metaverse;Pandemics;Telemedicine;Medical services;Organizations;Mental health|
|[Hybrid Personalized Book Recommender System Based on Big Data Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079457)|F. Liu; S. P. R. Asaithambi; R. Venkatraman|10.23919/ICACT56868.2023.10079457|Hybrid recommender algorithm;Big data framework;Collaborative filtering;Alternating Least Squares (ALS);PySpark;Electronic publishing;Collaborative filtering;Cluster computing;Big Data;Filtering algorithms;Market research;Communications technology|
|[Multi-Modal Deep Learning for the Thickness Prediction of Blood Clot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079491)|J. Moon; S. Ahn; M. G. Joo; H. W. Baac; J. Shin|10.23919/ICACT56868.2023.10079491|Multiclass Classification;Blood Clot;Thickness Prediction;Multi-Modal Deep Learning;Deep learning;Ultrasonic imaging;Ultrasonic variables measurement;Coagulation;Bandwidth;Feature extraction;Data models|
|[Dynamic Neural Network Accelerator for Multispectral detection Based on FPGA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079367)|X. Wang; L. Zhao; W. Wu; X. Jin|10.23919/ICACT56868.2023.10079367|Multispectral detection;FPGA-based accelerator;Dynamic Neural Network;Corss-layer scheduling;Cross layer design;Adaptation models;Runtime;Processor scheduling;Computational modeling;Neural networks;Memory management|
|[Graphed-based K-Means and Shortest Distance Tree for the Construction of Elderly Safe Corridor Accident and Prevention Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079584)|Y. -J. Sua; Z. -K. Wua; Y. -J. Sub; Y. -S. Linb; Y. -C. Changa|10.23919/ICACT56868.2023.10079584|elderly care;weighted K-means;movement patterns;shortest path tree;police force arrangement;safety corridors;Law enforcement;Search methods;Force;Sociology;Data models;Safety;Trajectory|
|[Quality of Service Aware Order Allocation for Inter-Regional Online Food Delivery Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079492)|F. Huq; N. Sultana; M. A. Razzaque|10.23919/ICACT56868.2023.10079492|Crowdsourcing;Online Food Delivery;Order Allocation;Service Time Minimization;Inter-Region Delivery;Costs;Focusing;Quality of service;Communications technology;Resource management;Optimization|
|[Performance Analysis of Virtual Learning System: A Case Study of ANGKASA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079602)|S. D. Arisandi; N. Selviandro; K. A. Laksitowening; D. S. Kusumo|10.23919/ICACT56868.2023.10079602|Performance Analysis;E-Learning System;Apache Benchmarking;Virtualization;Computers;Cloud computing;Electronic learning;Costs;Containers;Benchmark testing;Media|
|[A Design of Data Interaction Interface Based on DDS for UAV-Borne SAR Distributed Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079570)|F. Tang; H. Yang; H. Li; Z. Feng; P. Gong|10.23919/ICACT56868.2023.10079570|UAV-borne SAR;distributed simulation;QualNet;DDS;data interaction interface;Distributed databases;Publish-subscribe;Reconnaissance;Throughput;Data models;Real-time systems;Radar polarimetry|
|[Redesign of Indonesia E-commerce Online Review using User Information Behavior and Build-Learn-Measure Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079552)|D. S. Kusumo; D. Noviyanti; A. Gandhi; I. M. Karo Karo; S. -C. Haw; P. Naveen|10.23919/ICACT56868.2023.10079552|online review;user information behavior;initial usability testing;build-measure-learn;Heating systems;Information filters;Particle measurements;Filtering theory;Behavioral sciences;Electronic commerce;Usability|
|[Time-Series Load Data Analysis for User Power Profiling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079478)|M. D. Firoozjaei; M. Kim; D. Alhadidi|10.23919/ICACT56868.2023.10079478|Power profiling;Privacy awareness;Dynamic time warping (DTW);Smart grid;Power demand;Heuristic algorithms;Clustering algorithms;Feature extraction;Smart meters;Data models;Smart grids|
|[Medical Education, Training and Treatment Using XR in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079321)|S. M. Ali; S. Aich; A. Athar; H. -C. Kim|10.23919/ICACT56868.2023.10079321|Extended Reality (XR);Augmented Reality (A.R.);Virtual Reality (V.R.);Mixed Reality (M.R.) Education;Training;Treatment;Training;Industries;Pandemics;Extended reality;Mixed reality;Medical services;Communications technology|
|[Review of Internet of Things security protocols – A Bibliometric Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079641)|G. Mwansa; N. Mabanza|10.23919/ICACT56868.2023.10079641|Security Protocols;Internet of Things;Cloud-based technologies;Cybersecurity;Bibliometric analysis;Protocols;Databases;Bibliometrics;Africa;Market research;Fourth Industrial Revolution;Internet of Things|
|[Elastic Auto-Scaling Architecture in Telco Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079575)|D. S. Cao; D. Tam Nguyen; X. C. Nguyen; V. Thuyet Tran; H. B. Nguyen; K. Thuan Lang; V. T. Nguyen; N. Lam Dao; T. T. Pham; N. Son Cao; D. H. Chu; P. Hung Nguyen; C. D. Pham; D. Hai Nguyen|10.23919/ICACT56868.2023.10079575|Auto-scaling;Scalability;Telecom;Telco Cloud;Cloud computing;Cloud-native;Cloud computing;Costs;Scalability;Clustering algorithms;Computer architecture;Virtual machining;Communications technology|
|[The metaverse applications for the finance industry, its challenges, and an approach for the metaverse finance industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079695)|M. Ariful Islam Mozumder; A. Tagne Poupi Theodore; A. Athar; H. -C. Kim|10.23919/ICACT56868.2023.10079695|Metaverse;finance;banking;artificial intelligence;blockchain;digital twins;nan|
|[SCN-SAM: A Modified Self-Cure Network for Facial Expression Recognition Under Face Masks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079406)|Q. Wu; K. Hamada; M. Arai|10.23919/ICACT56868.2023.10079406|computer vision;facial expression recognition;occlusion;face mask;SCN-SAM;nan|
|[Linear Leakage: Better Robustness for Spiking Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079519)|J. Che; J. Cao; S. Feng; J. Chen; Y. Wang|10.23919/ICACT56868.2023.10079519|Spiking neural networks;Spatio-temporal backpropagation;Leaky-integrate-fire neuron;Robustness;Noise attacks;nan|
|[Improving Embodied Instruction Following with Deterministic Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079273)|D. Kim; Y. -J. Lee|10.23919/ICACT56868.2023.10079273|Embodied Instruction Following;FILM;ALFRED benchmark;Embodied AI;Visualization;Navigation;Semantic segmentation;Semantics;Decision making;Reinforcement learning;Benchmark testing|
|[Cloud and Edge Computing Based Movable 3D Dynamic Image Recognition and Analysis Layer System for Remote Biological Laboratory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079325)|M. -S. Jian; Y. -Z. Luo; P. -W. Wang; T. -Y. Lai|10.23919/ICACT56868.2023.10079325|Artificial Intelligence;Image Recognition;Edge Computing;Cloud Computing;Unmanned System;nan|
|[Blockchain System for Trustless Healthcare Data Sharing with Hyperledger Fabric in Action](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079423)|M. M. Sheeraz; M. A. I. Mozumder; M. O. Khan; M. U. Abid; M. -I. Joo; H. -C. Kim|10.23919/ICACT56868.2023.10079423|medical data;medical image;security;privacy;data sharing;blockchain;hyperledger fabric;Data privacy;Privacy;Distributed ledger;Smart contracts;Organizations;Medical services;Fabrics|
|[Simulation of Stator Current Signal Fault Characteristics of Induction Motor based on ANSYS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079588)|A. Rahman; A. I. Badhon|10.23919/ICACT56868.2023.10079588|Finite Element;Induction Motor;Electromagnetic Field;ANSYS;Load Torque;Coil Pitch;Gear System;Signal Frequency;Fault Diagnosis;Induction motors;Torque;Gears;Rotors;Voltage;Stators;Communications technology|
|[Scheduling Memory Access Optimization for HBM Based on CLOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079513)|S. Xue; H. Liang; Q. Wu; X. Jin|10.23919/ICACT56868.2023.10079513|HBM;CLOS;access schedule;FPGA;BFS;Memory architecture;Bandwidth;Switches;Throughput;Scheduling;Communications technology;Parallel algorithms|
|[Path Planning for Cellular-connected UAV using Heuristic Algorithm and Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079278)|J. Bao; Y. Yang; Y. Wang; X. Yang; Z. Du|10.23919/ICACT56868.2023.10079278|UAV;Cellular-connected UAV;path planning;Heuristic Algorithm;Reinforcement Learning;Travel Salesman Problem;Heuristic algorithms;Reinforcement learning;Simulated annealing;Autonomous aerial vehicles;Path planning;Real-time systems;Communications technology|
|[Field Testing of HEVC based Terrestrial UHD 3D Broadcast in ATSC 3.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079389)|Y. Choi; S. -H. Kim; H. Choi; S. Jung|10.23919/ICACT56868.2023.10079389|ATSC 3.0;stereoscopic 3D video;UHD 3D TV;HEVC;2D-3D simulcast;broadcasting field test;Three-dimensional displays;Protocols;Stereo image processing;Virtual reality;Media;Broadcasting;Encoding|
|[Technological Roadmap of the Future Trend of Metaverse based on IoT, Blockchain, and AI Techniques in Metaverse Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079464)|M. A. Islam Mozumder; A. Athar; T. P. Theodore Armand; M. M. Sheeraz; S. M. Imtiyaj Uddin; H. -C. Kim|10.23919/ICACT56868.2023.10079464|Metaverse education;Artificial intelligence;Mixed reality;Metaverse;Virtual Education;COVID-19;Learning systems;Industries;Metaverse;Education;Virtual reality;Educational technology|
|[Neural Network based Transceiver for Non-Coherent OFDM Optical Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079446)|A. Ibrahim; A. Elsheikh; A. M. Abdelsalam; J. Prat|10.23919/ICACT56868.2023.10079446|ACO-OFDM;DCO-OFDM;Deep neural network;Supervised learning;Optical modulation;Integrated optics;Wireless communication;OFDM;Optical computing;Artificial neural networks;Optical receivers;Transceivers|
|[A Markov Analytical Model between MANET Single-path and Multi-path Load Balancing Routing Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079374)|L. Xia; Y. Yan; D. Meng; Z. Li; X. Xia|10.23919/ICACT56868.2023.10079374|Analytical model;Energy consumption;Stability;Throughput;Delay;Wireless communication;Analytical models;Energy consumption;Simulation;Markov processes;Throughput;Routing protocols|
|[Deep Learning Model Protection using Negative Correlation-based Watermarking with Best Embedding Regions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079421)|S. Kakikura; H. Kang; K. Iwamura|10.23919/ICACT56868.2023.10079421|Copyright protection;deep learning model protection;digital watermarking;Training;Deep learning;Data analysis;Neural networks;Watermarking;Intellectual property;Robustness|
|[Android Malware Detection: Feature Update Using Incremental Learning Approach: Further Investigation of UFILA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079298)|Z. Sawadogo; J. -M. Dembele; G. Mendy; S. Ouya|10.23919/ICACT56868.2023.10079298|imbalanced dataset;Android malware detection;Malware classification;Artificial intelligence;Machine learning;Measurement;Heuristic algorithms;Machine learning;Feature extraction;Malware;Communications technology;Classification algorithms|
|[Android malware detection: An in-depth investigation of the impact of the use of imbalance datasets on the efficiency of machine learning models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079245)|Z. Sawadogo; J. -M. Dembele; G. Mendy; S. Ouya|10.23919/ICACT56868.2023.10079245|imbalanced dataset;Android malware detection;Malware classification;Artificial intelligence;Machine learning;Measurement;Training;Performance evaluation;Machine learning algorithms;Databases;Machine learning;Prediction algorithms|
|[Basic Developing Environment of Microcontroller-based Monitoring System for Physiological Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079583)|M. Xie; R. Wu; S. Liu; A. Lin; M. Liu; P. -A. Lin; G. Lei; J. Liu; J. Lu; B. -Y. Lu|10.23919/ICACT56868.2023.10079583|physiological signals;cloud;computation;Bluetooth communication;microcontroller;Temperature measurement;Temperature distribution;Clouds;Neurons;Thermistors;Communications technology;Web sites|
#### **2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)**
- DOI: 10.1109/ISGTMiddleEast56437.2023
- DATE: 12-15 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[UAE - Performance Review of the 935 MW PV Solar Power Plant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078493)|A. Taha; Z. Al-Abedeen; E. Al-Zarooni|10.1109/ISGTMiddleEast56437.2023.10078493|Photovoltaic (PV);Solar Power;Ramp-Rate;Power Fluctuation;Energy Storage.;Photovoltaic systems;Weather forecasting;Production;Solar energy;Smart grids;Spinning;Security|
|[Dual Current Control of Inverter-Based Resources for Enhanced Resilience and Complying Grid Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078537)|A. A. Aboelnaga; M. A. Azzouz; A. S. A. Awad; H. F. Sindi|10.1109/ISGTMiddleEast56437.2023.10078537|Phase selection;dual current control;fault resilience;fault type identification.;nan|
|[Analysis and Verification of Islanding Detection Techniques for Grid-integrated PV Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078615)|M. Alsabban; O. Bertozzi; S. Ahmed|10.1109/ISGTMiddleEast56437.2023.10078615|Islanding Detection Techniques;Inverter-based Technologies;Grid-integrated PV Systems;False Islanding Detection.;nan|
|[Use of Battery Systems for VAR Support in Con Edison Network Distribution Substation via Bi-Directional Smart Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078469)|M. K. Kamaludeen; Y. Esa; K. Zafar; E. Nyemah; A. A. A. Mohamed; T. Ibrahim; S. Odie|10.1109/ISGTMiddleEast56437.2023.10078469|Battery Energy Storage System (BESS);Distributed Energy Resources (DERs);Degradation of Battery State of Charge (SoC);Reactive Power (VARs);Smart Inverter;Total Harmonic Distortion (THD).;nan|
|[Small Signal Stability Analysis of DFIG Integrated Power System Considering PLL Dynamics Under Different Grid Strengths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078449)|B. Sahu; B. P. Padhy|10.1109/ISGTMiddleEast56437.2023.10078449|PLL;grid-connected DFIG;low-frequency electromechanical oscillations;modal analysis;power grid strengths;nan|
|[A novel robust back propagation neural networkdual extended Kalman filter model for state-ofcharge and state-of-health co-estimation of lithiumion batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078467)|S. Jin; X. Yang; C. Wang; S. Wang; D. -I. Store|10.1109/ISGTMiddleEast56437.2023.10078467|lithium-ion battery;second-order RC equivalent circuit model;BP neural network;dual extended Kalman filter;state of charge;state of health;nan|
|[Centralized Multi-objective Framework for Smart EV Charging in Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078604)|P. V. Dahiwale; Z. H. Rather|10.1109/ISGTMiddleEast56437.2023.10078604|Centralized strategy;charging cost;EV charging;load leveling;smart charging;time-of-use tariff;nan|
|[Renewable Energy Share Assessment of Electric Vehicles Based on Marginal Contribution to Integrating Renewable Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078587)|Y. Wei; S. Fan; J. Xiao; G. He|10.1109/ISGTMiddleEast56437.2023.10078587|Renewable energy integration;electric vehicle;marginal contribution;demand-side management;nan|
|[Calculating the Maximum Penetration of Electric Vehicles in Distribution Networks with Renewable Energy and V2G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078520)|H. Thomas; H. Sun; B. Kazemtabrizi|10.1109/ISGTMiddleEast56437.2023.10078520|nan;Vehicle-to-grid;Renewable energy sources;Low voltage;Monte Carlo methods;Distribution networks;Batteries;Distributed power generation|
|[Allocation of Multi-Type Charging Equipment in Electric Vehicles Parking Lots Considering Avoidable Capacity Cost of Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078696)|A. A. Al-Obaidi; H. E. Z. Farag; H. Memon; A. Asif|10.1109/ISGTMiddleEast56437.2023.10078696|nan;nan|
|[A Modified Version of the IEEE 39-bus Test System for the Day-Ahead Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078548)|G. Tricarico; R. Wagle; M. Dicorato; G. Forte; F. Gonzalez-Longatt; J. L. Rueda|10.1109/ISGTMiddleEast56437.2023.10078548|Day-Ahead Market;IEEE 39-bus system;DIgSILENT PowerFactory;Renewable Energy Resources.;nan|
|[Demand-side management via game theory considering distributed energy storage and generation with discomfort as objective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078594)|M. K. Mishra; K. A. Jaafari; A. A. Sumaiti; S. K. Parida|10.1109/ISGTMiddleEast56437.2023.10078594|Demand-side management;energy storage;energy generation;game theory;discomfort cost;nan|
|[Concise Definition of the Overcurrent Protection System for CIGRE European Medium Voltage Benchmark Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078621)|L. N. H. Pham; R. Wagle; F. Gonzalez-Longatt|10.1109/ISGTMiddleEast56437.2023.10078621|CIGRE MV benchmark;distribution networks;overcurrent protection;protection systems;nan|
|[A New Three-Phase Bridgeless Interleaved Rectifier based EV On-board Fast Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078454)|R. Kushwaha; V. Khadkikar; B. Zahawi|10.1109/ISGTMiddleEast56437.2023.10078454|Three-phase interleaving;On-board charger;bridgeless Zeta converter;DCM;Power Factor Correction;Power system measurements;Rectifiers;Power factor correction;Sensors;Smart grids;Semiconductor diodes;Magnetics|
|[Optimal Location of Fast Electric Vehicle Charging Stations on the Transportation and Active Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078655)|M. Madboly; H. H. Zeineldin; T. El-Fouly; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078655|Renewable energy;flexible loads;distributed generation;electric vehicles;transportation network;power system;optimization;Sioux Falls;nan|
|[Battery Swapping Policy Review: An Indian and International Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078543)|S. Tripathi; C. P. Barala; P. Mathuria; R. Bhakar|10.1109/ISGTMiddleEast56437.2023.10078543|Electric Vehicles;Battery Swapping Standards;EV transition;BS policies;nan|
|[Monte Carlo BEV Users Simulation to Assess the Charging Stations Usage in Highway](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078706)|D. Martini; M. Longo; F. Foiadelli|10.1109/ISGTMiddleEast56437.2023.10078706|highway;BEV;behavior;charging stations;Monte Carlo;nan|
|[Artificial Neural Network for High-Impedance-Fault Detection in DC Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078693)|I. Grcić; H. Pandžić|10.1109/ISGTMiddleEast56437.2023.10078693|DC microgrid;fault detection;recurrent neural network;high-impedance fault;nan|
|[Participation of DERs at Transmission Level: FERC Order No.2222 and TSO-DSO Coordination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078662)|R. R. Trivedi; R. Vijay; S. Sharma; P. Mathuria; R. Bhakar|10.1109/ISGTMiddleEast56437.2023.10078662|Aggregation;distributed energy resources;FERC Order No. 2222;TSO-DSO Coordination;nan|
|[Differential Evolution for Peak Demand Aware Economical Sizing of Photovoltaic Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078657)|N. B. Awshan; A. Alharbi; A. Habib|10.1109/ISGTMiddleEast56437.2023.10078657|Photovoltaic Sizing;Multi-objective Optimization;Differential Evolution;Economics;Photovoltaic systems;Renewable energy sources;Azimuth;Smart grids;Indexes;Optimization|
|[Is dynamic energy sharing really necessary? The case study of collective renewable self-consumers in Croatia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078631)|M. Gržanić; T. Capuder|10.1109/ISGTMiddleEast56437.2023.10078631|collective self-consumption;jointly acting renewable self-consumers;prosumer;self-supply customer;solar power plant;nan|
|[Optimal Economic Dispatch of Power System Networks with Renewable Energy Sources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078642)|A. M. Mohammad; O. A. Alghamdi; A. A. Alhusssainy; M. Rawa; A. M. Abusorrah; Y. A. Al-Turki|10.1109/ISGTMiddleEast56437.2023.10078642|Renewable Energy Sources;PV;Wind Turbines;Battery Storage;HOMER Grid;Hybrid System;nan|
|[Optimum Unit Commitment Considering Renewable Energy Sources Using Differential Evolution Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078509)|A. Alshalawi; H. Alharthi|10.1109/ISGTMiddleEast56437.2023.10078509|Economic Dispatch;renewable energy sources;unit commitment problem;nan|
|[Tuning PID Controller Parameters of Automatic Voltage Regulator (AVR) Using Particle Swarm Optimization : A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078497)|S. Alghamdi; A. B. Wazir; H. H. H. Awaji; A. A. Alhussainy; H. F. Sindi; M. Rawa|10.1109/ISGTMiddleEast56437.2023.10078497|Automatic Voltage Regulator (AVR);Proportional integral derivative (PID) controller;Particle swarm optimization (PSO);Computational Intelligence (CI);Artificial Intelligence (AI);nan|
|[A Power Quality Analysis Comparison between Single and Double PV stages of a Voltage Source Converter based on a d-q frame-tied grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078482)|F. Almalki; A. Alsulami; A. A. Alhusssainy; S. Alghamdi; A. H. Milyani; M. Rawa; Y. A. Al-Turki|10.1109/ISGTMiddleEast56437.2023.10078482|Maximum power point (MPPT);Photovoltaic (PV);Active Power (P);Reactive Power (Q);nan|
|[A Simple Overcurrent Scheme for Distribution Networks with High Penetration of Inverter-Based Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078690)|A. Alhnani|10.1109/ISGTMiddleEast56437.2023.10078690|IBG;renewable energy;inverter;distribution networks;overcurrent;instantaneous;fault;nan|
|[Fuzzy logic based Vehicle to Grid Controller for Voltage Regulation in Distribution Network with Solar-PV Penetration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078445)|S. Shekhar; P. Arya; H. Kumar; G. S. Chawda; A. G. Shaik|10.1109/ISGTMiddleEast56437.2023.10078445|Electric Vehicles;Fuzzy Logic;Solar-PV;Synchronous Reference Frame;V2G Controller;Voltage Regulation;nan|
|[Stabilization of a Utility-Scale Grid-Connected PV System with Reduced DC-Link Capacitance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078531)|M. A. K. Magableh; A. Radwan; Y. A. -R. I. Mohamed|10.1109/ISGTMiddleEast56437.2023.10078531|DC-link Stabilization;dynamic photovoltaic resistance;active damping;small-signal analysis;nan|
|[Evolution of Load Redistribution Attack in Cyber Physical Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078512)|R. Haridas; S. Sharma; R. Bhakar; P. Mathuria|10.1109/ISGTMiddleEast56437.2023.10078512|Load Redistribution Attack;Attack Modelling;Cyber-Security;Cyber-Physical Power System;Smart Grid;Economics;Data integrity;Taxonomy;Smart grids;Power systems;Security;Cyberattack|
|[False Data Injection Detection using H Infinity Filter in an Automatic Generation Controlled Two Area Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078579)|S. Beura; B. P. Padhy|10.1109/ISGTMiddleEast56437.2023.10078579|Load frequency control (LFC);process noise;h infinity flltering;TCPIIP communication;and real-time simulation.;nan|
|[Impact of Integrating Renewable Energy Systems on the Smart Grid-Transportation Nexus Operation under Electric Vehicle cyber-attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078591)|O. A. A. Isawi; K. A. A. Jaafari; A. S. A. Sumaiti|10.1109/ISGTMiddleEast56437.2023.10078591|cyber security;smart grid;electric vehicles;nexus;renewable energy;nan|
|[Evaluation of the Usage of Edge Computing and LoRa for the Control of Electric Vehicle Charging in the Low Voltage Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078593)|B. Steinhagen; T. Jungh; M. Hesse; U. Rückert; L. Quakernack; M. Kelker; J. Haubrock|10.1109/ISGTMiddleEast56437.2023.10078593|Smart grids;Edge computing;Electric vehicle charging;nan|
|[Assessment of Parametric and KDE Statistical Models for Wind Turbine Energy Yield](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078667)|M. Wahbah; E. -F. Tarek H.M.; B. Zahawi|10.1109/ISGTMiddleEast56437.2023.10078667|Kernel density;wind energy;probability density estimation;wind speed models.;nan|
|[An Online Degradation Condition Evaluation Method for Solar Photovoltaic Panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078559)|X. Dai; K. H. A. Hosani; A. A. Sumanti; M. E. Moursi; H. Wang|10.1109/ISGTMiddleEast56437.2023.10078559|solar PV panels;condition monitoring;impedance spectroscopy;feature fingerprint intervals;Degradation;Condition monitoring;Photovoltaic systems;Spectroscopy;Parameter estimation;Fingerprint recognition;Smart grids|
|[Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078513)|L. Yu; P. Wang; Z. Chen; D. Li; N. Li; R. Cherkaoui|10.1109/ISGTMiddleEast56437.2023.10078513|Electricity market;Transmission line;Bidding strategies;Distributed optimization.;Power demand;Heuristic algorithms;ISO;Consensus algorithm;Dynamic scheduling;Power markets;Smart grids|
|[A Method for Quantifying Battery’s Fast Charging Ability and Application to Different Lithium-Ion Chemistries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078498)|V. Bobanac; H. Pandžić|10.1109/ISGTMiddleEast56437.2023.10078498|Lithium-ion batteries;fast charging;experimental research;nan|
|[Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078477)|A. Jaradat; H. Lutfiyya; A. Haque|10.1109/ISGTMiddleEast56437.2023.10078477|Demand Response (DR);Load Profile Clustering;Dynamic Time Warping (DTW);DBSCAN;Smart Home Energy Management Systems (SHEMS);Smart Grid;Smart Homes.;nan|
|[Cascaded Controller for Electric Vehicle Utilization to Improve Frequency Regulation of Microgrid Considering Communication Delay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078692)|A. Latif; A. Al-Durra; T. H. M. El-Fouly; H. Zeineldin; E. El-Saadany; S. Alghamdi; H. Sindi|10.1109/ISGTMiddleEast56437.2023.10078692|Aggregated electric vehicle;cascaded controller;frequency regulation;skill optimization technique;solar photovoltaic system;nan|
|[Fault Ride-Through in Grid-Connected DC Microgrid to Improve Microgrid and Utility Grid Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078453)|V. E. Wissa; A. E. Badawy; A. El-Guindy|10.1109/ISGTMiddleEast56437.2023.10078453|dc microgrid;fault ride-through;short-circuit fault;fault current limiter.;nan|
|[Voltage Disturbances Compensation Associated with A Smooth Angle Transition Using Dynamic Voltage Restorer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078614)|A. M. Abdelemam; H. H. Zeineldin; A. Al-Durra; V. Khadkikar; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078614|Dynamic Voltage Restorer (DVR);Voltage sag;Phase jump mitigation;nan|
|[Peer-to-Peer Energy Trading Among Networked Microgrids Considering the Complementary Nature of Wind and PV Solar Energy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078483)|D. Michon; T. M. Masaud|10.1109/ISGTMiddleEast56437.2023.10078483|P2P Trading;Networked Microgrids;PV Solar and Wind Energy;nan|
|[Establishing Security Controls For Blockchain Technology In P2P Energy Trading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078508)|A. Alahbabi; K. Alshehhi; A. Albloushi; K. Shuaib|10.1109/ISGTMiddleEast56437.2023.10078508|Blockchain;Security Control;DLT;peer-to-peer;Energy;Standards;Trading;Renewable energy sources;Supply chain management;Pandemics;Oils;Information security;Blockchains;Smart grids|
|[Autonomous Peer-to-Peer Energy Trading in Networked Microgrids: A Distributed Deep Reinforcement Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078444)|M. Foroughi; S. Maharjan; Y. Zhang; F. Eliassen|10.1109/ISGTMiddleEast56437.2023.10078444|Networked microgrids;peer-to-peer trading;partially observable Markov game;distributed deep reinforcement learning;sensitivity analysis;nan|
|[Bidding Strategies for HT Consumers in Open Access Energy Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078499)|V. Cherala; V. A. Agawane; P. K. Yemula|10.1109/ISGTMiddleEast56437.2023.10078499|Electricity market participation;Participation strategy;Market bidding;Open Access;HT Consumer;nan|
|[Peer-to-Peer Energy Trading Negotiation Based on Cost Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078521)|S. Jhajhriya; S. Sharma; R. Bhakar; P. Mathuria|10.1109/ISGTMiddleEast56437.2023.10078521|Distributed Energy Resources (DERs);negotiation;P2P energy market;prosumer;transaction fee.;nan|
|[Electric Load Probability Density Estimation using Root-Transformed Local Linear Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078504)|B. B. Elhouty; S. F. Feng; T. H. M. El-Fouly; B. Zahawi|10.1109/ISGTMiddleEast56437.2023.10078504|Kernel density estimwtion;load distribution models;nonparametric regression;probability density estimation;nan|
|[Discrete analysis Theory and Calculation Method of Carbon Flow Distribution in Power System Considering the Contribution of New Energy Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078669)|P. Ji; H. Zhou; Y. Hu; G. He|10.1109/ISGTMiddleEast56437.2023.10078669|net-zero carbon evolution;carbon flow distribution;spatiotemporal coupling;discrete analysis;renewable energy consumption;nan|
|[Experiences in a Cyber-Physical Co-Simulation Testbed Development for a Smart-er Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078722)|R. Wagle; L. N. H. Pham; G. Tricarico; P. Sharma; J. L. Rueda; F. Gonzalez-Longatt|10.1109/ISGTMiddleEast56437.2023.10078722|Cyber-physical co-simulation testbed;Real-time control;Real-time simulator;Smart distribution networks;nan|
|[Modelling electricity demand in Karachi: historical trends, determinants, and future scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078525)|H. Kazmi; F. Mehmood|10.1109/ISGTMiddleEast56437.2023.10078525|Pakistan;Karachi;electricity demand;modelling;climate and weather;Smart grids;Climate change;Electricity supply demand;Asia;Meteorology|
|[Smart Meter Data Analytics for Building Monitoring System: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078592)|P. Rajesh; V. Cherala; P. K. Yemula|10.1109/ISGTMiddleEast56437.2023.10078592|Solar power plants;rooftop solar;nan|
|[Offline Parameter Estimation of a Fractional-Order Buck Converter Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078636)|A. M. AbdelAty; A. Al-Durra; H. Zeineldin; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078636|Fractional-order model;Buck converter;Continuous conduction mode;cuckoo search optimizer;nan|
|[Optimal Placement of Phase Shifting Transformer for Power Flow Control Using Linear Search Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078515)|S. K. Singh; K. S. Swarup|10.1109/ISGTMiddleEast56437.2023.10078515|Contingency Analysis;Flexible Alternating Current Transmission System (FACTS);Linear search;Newton Raphson;Phase Shifting Transformer.;Reactive power;Processor scheduling;Contingency management;Loading;Power transmission;Smart grids;Power system reliability|
|[Optimal Placement and Sizing of Capacitor Banks in Radial Distribution Systems Using the Whale Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078650)|A. Osama; H. H. Zeineldin; E. -F. Tarek H.M.; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078650|Capacitor Bank;Optimal Capacitor Placement;Radial Distribution Networks (RDN);Whale optimization Algorithm (WOA).;nan|
|[Design of Wide Area Damping Controller Based on Clustering of Inter-Area Oscillations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078566)|A. Prakash; M. S. E. Moursi; S. K. Parida; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078566|Low-frequency oscillation;Inter-area oscillation;Wide-area damping control;power system stabilizer;operating point uncertainties;nan|
|[A DMD-based Method for Assessing Small-signal and Transient Stability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078561)|Y. J. Isbeih; M. S. E. Moursi; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078561|Dynamic mode decomposition (DMD);small-signal and transient stability assessment.;nan|
|[A Design of Partial Shading Detection Method and Global Power Point Searching Technique for Grid- Connected PV System Operating Under Partial Shading Condition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078492)|W. A. Abri; R. A. Abri; H. Yousef; A. Al-Hinai|10.1109/ISGTMiddleEast56437.2023.10078492|Partial Shading;Characteristics Curve Tracing;Global Maximum Power Point Tracker;Bald Eagle Search Method;nan|
|[Evaluation of Forced Oscillation Detection Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078535)|D. Bergman; R. Eriksson; M. G. Alavijh|10.1109/ISGTMiddleEast56437.2023.10078535|forced oscillations;electromechanical dynamics;mode estimation;spectral analysis;power system monitoring;synchrophasor measurements;nan|
|[Lossless Compression of Synchro-Waveform Measurements for Smart Grid Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078713)|W. Qiu; H. Yin; Y. Wu; C. Chen; C. Zeng; L. Zhan; Y. Liu|10.1109/ISGTMiddleEast56437.2023.10078713|Online lossless compression;synchro-waveform measurement;smart grid monitoring;high-speed data streaming;nan|
|[Protection Relay based Industrial Motors Pro-Active Asset Management Solution and Case studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078671)|B. Pamulaparthy; B. Atef; A. Sachdev; M. Bonner; M. Kumar|10.1109/ISGTMiddleEast56437.2023.10078671|motor;failure;signature;analysis;condition;asset;management;relay;nan|
|[An Autonomous Address Translation Scheme to Integrate Modbus with IEEE 802.15.4 Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078688)|S. Gupta; S. Gupta; A. Gupta; A. Shukla|10.1109/ISGTMiddleEast56437.2023.10078688|CADA system;PLC;Modbus;IEEE 802.15.4;UART;RS-485;XBee;nan|
|[Blockchain-enabled Local Market for Sharing Storage in Energy Communities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078460)|D. Thomas; E. Kotsakis; I. Kounelis; A. D. Paola; G. Fulli|10.1109/ISGTMiddleEast56437.2023.10078460|blockchain;energy storage;flexibility;physical storage rights;nan|
|[Optimal Design of Electrolysis Hydrogen Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078682)|A. F. El-Hamalawy; H. E. Farag; A. Asif|10.1109/ISGTMiddleEast56437.2023.10078682|green hydrogen plants;electrolysis;design;optimization;nan|
|[Optimal Operation of Energy Hubs in an Auction-Based Peer-To-Peer Energy Trading Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078546)|K. Rowe; G. Mokryani; K. Cooke; F. Campean; T. Chambers|10.1109/ISGTMiddleEast56437.2023.10078546|Multi-carrier energy systems;energy hubs;integrated natural gas and electricity network;bi-level optimization;peer-to-peer;energy trading;uncertainty;Costs;Stochastic processes;Distribution networks;Peer-to-peer computing;Smart grids;Mixed integer linear programming;Natural gas|
|[Hydrogen as a Sustainable Mobility Fuel: A Global Perspective and Opportunities for India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078470)|R. Agarwal; A. Firdous; P. Mathuria; R. Bhakar; V. S. Pareek; H. Tiwari|10.1109/ISGTMiddleEast56437.2023.10078470|Fuel cell electric vehicles;hydrogen;hydrogen mobility;hydrogen-powered transportation;Greenhouse effect;Hydrogen;Government;Transportation;Internal combustion engines;Smart grids;Safety|
|[Adaptive Virtual Inertia and Damping for Frequency Stability Enhancement using A Seamless Compensator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078465)|S. E. Sati; A. Al-Durra; H. Zeineldin; T. H. M. El-Fouly; E. F. El-Saadany|10.1109/ISGTMiddleEast56437.2023.10078465|adaptive virtual inertia and damping;BESS;frequency control;frequency oscillation damping;lead compensator;PSO;renewable energy resources.;nan|
#### **2023 27th International Conference on Information Technology (IT)**
- DOI: 10.1109/IT57431.2023
- DATE: 15-18 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Machine Learning-Based Framework for Detecting Credit Card Anomalies and Fraud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078528)|M. A. Alamri; M. A. Ykhlef|10.1109/IT57431.2023.10078528|credit card;anomaly detection;fraud detection;sampling techniques;machine learning;optimization;nan|
|[The use of GeoGebra software to improve teaching in the field of marine electrical engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078581)|I. Knežević; M. Krčum; T. Dlabač; A. Gudelj|10.1109/IT57431.2023.10078581|nan;nan|
|[A Systems-oriented Approach to Medical Education Incorporates Virtual Patients and Gamification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078624)|M. N. R. Khan; K. Lippert; R. J. Cloutier|10.1109/IT57431.2023.10078624|VP;game-based learning;system engineering;nan|
|[Improved LOS Guidance Law for Path Following of Underactuated USV with Sideslip Compensation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078645)|L. Ašanin; L. Martinović; Ž. Zečević; M. Bibuli; R. Ferretti; M. Caccia|10.1109/IT57431.2023.10078645|USV;Guidance;Path Following;Line-of-Sight (LOS);Predictor-based;Sideslip Compensation;nan|
|[Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078724)|I. Čavor; S. Djukanović|10.1109/IT57431.2023.10078724|nan;nan|
|[Applications Impressed Current Cathodic Protection of the Ship Hull](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078679)|L. Mrdović; Š. Ivošević|10.1109/IT57431.2023.10078679|nan;nan|
|[Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078518)|A. Cvijetić; S. Djukanović; A. Peruničić|10.1109/IT57431.2023.10078518|nan;nan|
|[Automatic Water Distribution System Using Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078653)|A. Ndagi; C. Z. Kertész|10.1109/IT57431.2023.10078653|nan;nan|
|[Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078639)|A. Peruničić; S. Djukanović; A. Cvijetić|10.1109/IT57431.2023.10078639|nan;nan|
|[Single Beacon-Based AUV Navigation: A Comparative Study of Kalman Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078685)|U. Janković; L. Martinović; Ž. Zečević|10.1109/IT57431.2023.10078685|navigation;single beacon;UKF;EKF;nan|
|[A Blockchain-Based Approach to Management of University Diploma Authenticity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078715)|B. Jovović; T. Popović; S. Šandi; Z. Djikanović|10.1109/IT57431.2023.10078715|blockchain;education;credential verification;university diploma;nan|
|[Assistive Technical Communication of Montenegrin eServices: a Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078664)|A. Simon; L. Laković; P. Kovačević; P. A. Kara; I. Ognjanović; R. Šendelj; C. Reich; M. Roganović; J. Mantas; L. Bokor|10.1109/IT57431.2023.10078664|nan;nan|
|[Evaluation of the Montenegrin Academic Digital Innovation Hub](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078596)|L. Laković; P. A. Kara; I. Ognjanović; L. Bokor; R. Šendelj; C. Reich; M. Roganović; E. Zoulias; J. Eraković; T. Radusinović; N. Rakočević; A. Simon|10.1109/IT57431.2023.10078596|nan;nan|
|[Authenticated Key Exchange in Underwater Acoustic Sensor Networks based on Implicit Certificates: Performance Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078623)|B. Krivokapic; S. Tomovic; I. Radusinovic|10.1109/IT57431.2023.10078623|nan;nan|
|[Comparative analysis of TG FinFET and GAA FinFET in 3 nm technology node](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078551)|L. Abdelaziz; B. Khaled; G. Mustapha|10.1109/IT57431.2023.10078551|nan;nan|
|[Reconstruction of Sparse Graph Signals from Reduced Sets of Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078603)|M. Brajović; I. Stanković; M. Daković; L. Stanković|10.1109/IT57431.2023.10078603|nan;nan|
|[Effectiveness of Using OWASP TOP 10 as AppSec Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078626)|T. Petranović; N. Žarić|10.1109/IT57431.2023.10078626|nan;nan|
|[A Sentiment Analysis to Understand the Role of Twitter Towards Sustainable Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078580)|C. C. Cerasi; Y. S. Balcioglu; A. Kilic; F. Huseynov; P. Rasti|10.1109/IT57431.2023.10078580|nan;nan|
|[LSTM Encoder Decoder Based Text Highlight Abstraction Method Using Summaries Extracted by PageRank](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078652)|T. G. Altundogan; M. Karakose|10.1109/IT57431.2023.10078652|Abstractive Summarization;Pagerank;Extractive Summarization;Encoder Decoder;Attention;nan|
|[Applying OptaPlanner in the implementation of doctors’ schedule of duty hours](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078560)|A. Kljajić; N. Bakić; D. Savić; V. Stanojević|10.1109/IT57431.2023.10078560|metaheuristics;simulated annealing;OptaPlanner tool;schedule-solving problem;nan|
|[Degradation of the Recoloring Specific Degree Protan CVD Image From inserted Watermark](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078714)|Z. N. Milivojević; B. Prlinčević|10.1109/IT57431.2023.10078714|nan;nan|
|[User Behavior Analysis of Short-form Video Prolonging Usage towards Purchase Intention on social media using SEM Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078656)|F. Jingga; R. R. Wiryawan; M. Rahmanita; F. Z. Adani|10.1109/IT57431.2023.10078656|addiction;purchase intention;short-form video;user behavior;technology;nan|
|[Yolov5 Based Fault Detection Approach in Railway Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078564)|M. Yilmazer; M. Karakose|10.1109/IT57431.2023.10078564|deep learning;railway fault detection;autonomous drone;nan|
|[A Monitoring System for Postural Sway Stabilization Using Tactile Stimulation Near the Auricles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078716)|M. Tadokoro; T. Shibanoki; H. Tonooka|10.1109/IT57431.2023.10078716|nan;nan|
|[QR Code Encryption for improving Bank information and Confidentiality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078457)|F. L. Malallah; A. I. Abduljabbar; B. T. Shareef; A. O. Al-Janaby|10.1109/IT57431.2023.10078457|nan;nan|
|[FIB Analysis of Corrosion Effects on Shape Memory Alloys in Marine Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078686)|N. Pudar; Š. Ivošević|10.1109/IT57431.2023.10078686|nan;nan|
|[Analysis and Development of the Model for Google Assistant and Amazon Alexa Voice Assistants Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078705)|B. Savić; M. Milić; S. Vlajić|10.1109/IT57431.2023.10078705|nan;nan|
|[Software for image analysis and inspection of optical lens](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078533)|D. Pepedzhiev; V. Hristov|10.1109/IT57431.2023.10078533|nan;nan|
|[Performance Analysis of an Underwater Acoustic Communication System Based on DCSK Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078575)|L. Lazović; A. Jovanović; V. Rubežić|10.1109/IT57431.2023.10078575|nan;nan|
|[Blockchain Principles and Energy Consumption Concerns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078571)|S. Bauk|10.1109/IT57431.2023.10078571|nan;nan|
|[Proposal for Improved Navigation Safety of Non Solas Vessels by Combining TSS And IoT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078689)|I. Stanovčić; R. Bošnjak; M. Radonjić; B. Krstajić|10.1109/IT57431.2023.10078689|nan;nan|
|[Binary Watermark Application in Color Image Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078501)|Z. S. Veličković; D. R. Blagojević; M. Z. Veličković|10.1109/IT57431.2023.10078501|nan;nan|
|[A Maturity Model of Digital Transformation in Supply Chains: A Multi-dimensional Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078510)|S. Tiss; M. Orellano|10.1109/IT57431.2023.10078510|Maturity model;digital transformation;maturity assessment;supply chain 4.0;nan|
|[The importance of information system for seafarers in Montenegro](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078618)|V. Kapetanović; M. Krčum; I. Petrović; T. Dlabač|10.1109/IT57431.2023.10078618|nan;nan|
|[A Quantum-Classical Hybrid Classifier Using Multi-Encoding Method for Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078617)|N. F. Bar; H. Yetis; M. Karakose|10.1109/IT57431.2023.10078617|quantum computing;quantum machine learning;variational quantum circuit;image classifier;nan|
|[Extreme Gradient Boosting based Anomaly detection for Kubernetes Orchestration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078576)|A. Yolchuyev|10.1109/IT57431.2023.10078576|Cloud computing;Container Orchestration;Kubernetes;Artificial Intelligence;eXtreme Gradient Boosting;nan|
|[Disease Prediction Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078464)|I. Jovovic; D. Babic; T. Popovic; S. Cakic; I. Katnic|10.1109/IT57431.2023.10078464|nan;nan|
|[Advanced Mission Critical Communication in Maritime Search and Rescue Actions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078557)|Z. Paladin; E. Fountoulakis; Ž. Lukšić; N. Kapidani; D. Ribar; G. Boustras|10.1109/IT57431.2023.10078557|nan;nan|
|[Optimization of ETA using information technologies in order to minimize the negative impacts of anchoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078440)|B. Drašković; R. Bošnjak|10.1109/IT57431.2023.10078440|nan;nan|
#### **2023 IEEE 7th Global Electromagnetic Compatibility Conference (GEMCCON)**
- DOI: 10.1109/GEMCCON57842.2023
- DATE: 19-20 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Trade-off of Losses, Voltage Drop, and Harmonics in Cable Selection of Remote Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078223)|I. Sulaeman; M. I. Sudrajat; N. Moonen; J. Popovic; F. Leferink|10.1109/GEMCCON57842.2023.10078223|Long cable;lumped elements;power losses;voltage drop;voltage harmonics;Resistance;Inductance;Focusing;Voltage;Microgrids;Harmonic analysis;Capacitance|
|[Cage-in-Cage Method for EMI Gaskets Pressure-Dependent RF Characteristics Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078188)|K. Sieczkarek; A. Mackowiak; T. Warzynski; B. Nagorny; M. Rokossowski; R. Szczepanski|10.1109/GEMCCON57842.2023.10078188|EMI;EMC;gaskets;comb generator;Faraday cage;anechoic chamber;nan|
|[Susceptibility of Current Transformer Measurements Due to Pulsed Currents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078204)|B. t. Havel; L. Bolzonella; T. Hartman; N. Moonen; F. Leferink|10.1109/GEMCCON57842.2023.10078204|Current sensing;current transformer;electromagnetic interference;non-linear;pulsed currents;Current transformers;Pulse measurements;Current measurement;Electromagnetic interference;Switches;Electromagnetic compatibility;Energy efficiency|
|[Concentration of Multi-Point Measurement Data for DC-150 kHz EMI Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078228)|A. Matthee; N. Moonen; F. Leferink|10.1109/GEMCCON57842.2023.10078228|Electromagnetic interference;Micro-grids;Inrush-current;Power electronic devices;Power measurement;Electromagnetic interference;Electromagnetic compatibility;Power electronics;Frequency measurement;Power system reliability;Behavioral sciences|
|[Investigation of Anti-Aliasing Filter Performance in Low-End Oscilloscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078181)|P. Koch; N. Moonen|10.1109/GEMCCON57842.2023.10078181|Anti-Aliasing filter (AAF);Digital Signal Processing (DSP);Measurement systems;Performance evaluation;Oscilloscopes;Signal processing;Electromagnetic compatibility;Filtering theory;Frequency response;Frequency measurement|
|[Common Mode Reduction by the Method of Interleaved Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078190)|P. Koch; C. E. S. Feloups; N. Moonen; F. Leferink|10.1109/GEMCCON57842.2023.10078190|Common mode (CM);differential mode (DM);electromagnetic interference (EMI);forward converter;interleaved converters;Electromagnetic interference;Switches;Electromagnetic compatibility;Safety;Behavioral sciences;Parasitic capacitance|
|[Predict Channel Performance Using S-parameter Extrapolation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078186)|S. S. Chen; B. Holden; A. Tajali; F. De Paulis|10.1109/GEMCCON57842.2023.10078186|S-parameter extrapolation;polynomial;semiLog;fitted insertion loss;112 Gbps;nan|
|[A Multi-wire Dynamic Encoding Chiplet High-speed Transmission Interface - CNRZ-5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078216)|S. S. Chen; B. Holden; A. Tajali; F. de Paulis|10.1109/GEMCCON57842.2023.10078216|Chiplet;CNRZ-5;code weight;EqualSignal;UCIe;BoW;112 Gbps;336 Gbps;Codes;Heuristic algorithms;Simulation;Modulation;Electromagnetic compatibility;Encoding;Data communication|
|[Characterising Transient Radio Frequency Interference from Motors for the Square Kilometre Array Radio Telescopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078182)|A. J. Otto; A. R. Botha; P. S. van der Merwe; T. Nkawu|10.1109/GEMCCON57842.2023.10078182|broadband;electromagnetic compatibility;EMC;electromagnetic interference;EMI;pulsed;transient;radio frequency interference;SKA;radio telescope;Radio astronomy;AC motors;Receivers;Telescopes;Generators;Transient analysis;Servomotors|
|[Freeware Calculator for Designing Yagi-Uda Antenna in the 162 MHz AIS Receiver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078219)|I. M. O. Widyantara; I. G. A. K. D. D. Hartawan; N. Pramaita; I. P. Ardana; A. A. Pramudiawati|10.1109/GEMCCON57842.2023.10078219|Yagi-Uda Antenna;Directional antenna;Freeware Calculator;AIS;Antenna measurements;Freeware;Calculators;Yagi-Uda antennas;Receiving antennas;Directional antennas;Directive antennas|
|[Influence of High-Quality Installation and Grounding on the Operation of Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078224)|R. Botnarevscaia; B. Puylaert; T. Serdiuk; F. Leferink|10.1109/GEMCCON57842.2023.10078224|common mode;differential mode;metal box;electromagnetic interferences (EMI);line filter housing;power line filter;Grounding;Wires;Electromagnetic interference;Metals;Crosstalk;Electromagnetic compatibility|
|[Test Methods for Performance of Protective Devices Excited by Conducted Transient Electromagnetic Disturbance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078220)|Y. -y. Wu; Y. -z. Xie; H. Cao; N. Dong; Y. -p. Ge|10.1109/GEMCCON57842.2023.10078220|Conducted transient electromagnetic disturbance;protective devices;response behavior;test method;Performance evaluation;Power transmission lines;Pulse measurements;Electromagnetic scattering;High-voltage techniques;Transmission line measurements;Transient analysis|
|[Hybrid Energy Storage System in Microgrid to Improve Power Quality in Indonesia's Remote Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078206)|E. A. Arifin; I. Sulaeman; N. Moonen; J. Popović|10.1109/GEMCCON57842.2023.10078206|remote area;distributed generators;battery;supercapacitor;power quality;Software packages;Power quality;Microgrids;Supercapacitors;Electromagnetic compatibility;Hybrid power systems;Mathematical models|
|[An Ultrathin Broadband Polarization Conversion Metasurface with Large-Range Angular Stability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078195)|Han-Cao; Y. -z. Xie; Y. -y. Wu; Z. -t. Li; Yan-Wang|10.1109/GEMCCON57842.2023.10078195|Polarization conversion metasurface;ultrathin;large-range angular stability;broadband;Polarization;Electric potential;Metasurfaces;Frequency conversion;Electromagnetic compatibility;Distance measurement;Broadband communication|
|[Choice of the Parameters of an EMI Monitoring System for an AC Traction Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078179)|V. Havryliuk; R. Nibaruta|10.1109/GEMCCON57842.2023.10078179|electromagnetic interference;electromagnetic compatibility;traction current harmonics;Electromagnetic interference;Harmonic analysis;Electromagnetic compatibility;Electromagnetics;Monitoring;Standards|
|[On-site Radiated Emissions Result Visualization using Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078183)|D. Pokotilov; R. Vogt-Ardatjew; F. Leferink|10.1109/GEMCCON57842.2023.10078183|Magnetic field measurements;augmented reality;time-domain;electromagnetic compatibility;radiated EM field visualization;Visualization;Magnetic field measurement;Electromagnetic interference;Area measurement;Electromagnetic compatibility;Real-time systems;Magnetic fields|
|[Stochastic Approach to Modelling Emissions of Multiple Power Electronic Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078210)|E. Ballukja; I. Aitbar; K. Niewiadomski; D. W. P. Thomas; M. Sumner; R. Smolenski|10.1109/GEMCCON57842.2023.10078210|Electromagnetic Compatibility;Polynomial Chaos;Power Electronic Converters;Delay effects;Stochastic processes;Switches;Tail;Power system harmonics;Harmonic analysis;Electromagnetic compatibility|
|[State of the Art of Near-Field Scanning: Contemporary Standards and Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078194)|S. Voskresenskyi; I. Aitbar; E. Ballukja; K. Niewiadomski; D. W. P. Thomas; S. Greedy|10.1109/GEMCCON57842.2023.10078194|EMC;Near-Field;Far-Field;time domain;frequency domain;standards;compliance;Technical requirements;Surface reconstruction;Surface waves;Software;Calibration;Noise measurement;Finite wordlength effects|
|[Technologies for Interoperability in Microgrids for Energy Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078200)|A. Suryani; I. Sulaeman; J. Popovic; N. Moonen; F. Leferink|10.1109/GEMCCON57842.2023.10078200|energy access;interoperability;microgrid;photovoltaic;Photovoltaic systems;Industries;Scalability;Microgrids;Electromagnetic compatibility;Electricity supply industry;Smart grids|
|[Methodical Vulnerability Assessment for Electronic Equipment Based on Fisher Discriminant and Gaussian Process Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078217)|X. Wang; Y. Chen; Z. Wang; Y. Xie|10.1109/GEMCCON57842.2023.10078217|electromagnetic effect evaluation;Fisher discrimination;classification index;Gaussian process regression;non-parametric model;application;Electronic equipment;Training data;Gaussian processes;Predictive models;Electromagnetic compatibility;Data models;Electromagnetics|
|[A Method for Extracting Plausible Images From EM Leakage Measured at Low Sampling Rates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078185)|T. Kitazawa; H. Kubo; Y. Hayashi|10.1109/GEMCCON57842.2023.10078185|nan;Performance evaluation;Surface reconstruction;Surface waves;Instruments;Jitter;Software;Timing|
|[Comparison of Great Britain and Ukraine railway systems based on their EMC capability and electrification systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078193)|H. H. Adhena; T. Serdiuk; D. Thomas; S. Greedy|10.1109/GEMCCON57842.2023.10078193|traction system;EMC;EMI;signalling system;Electromagnetic interference;Europe;Electromagnetic compatibility;Railway electrification;Telecommunications;Axles|
|[Evaluation of Revision in MIL-STD-461 for Regulatory Guidance on Nuclear Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078214)|J. Park; J. Choo|10.1109/GEMCCON57842.2023.10078214|electromagnetic interference;nuclear power plant;nuclear safety;Military standards;Electromagnetic interference;Electromagnetic compatibility;Regulation;Safety;Electromagnetics;Power generation|
|[Electromagnetic Environment Measurement Procedure for a Moving Car](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078197)|R. Aba; V. Gkatsi; R. Vogt-Ardatjew; F. Leferink|10.1109/GEMCCON57842.2023.10078197|automotive;electromagnetic environment;risk-basedEMC;Couplings;Time-frequency analysis;Statistical analysis;Reverberation chambers;Electromagnetic interference;Complexity theory;Automobiles|
|[Identification of the Failed Component in a Malfunctioning Passive Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078227)|I. Struzhko; R. Vogt-Ardatjew; F. Leferink|10.1109/GEMCCON57842.2023.10078227|insertion loss measurements;low-pass filter;Monte Carlo;simulation;Degradation;Passive filters;Statistical analysis;Low-pass filters;Insertion loss;Numerical simulation;Loss measurement|
|[Measurement of Wireless on Body Propagation Characteristics from e-Health Monitoring Wearable Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078213)|H. D. Prananto; A. N. Bakti; W. Ardiatna; I. Sukma; D. Mandaris|10.1109/GEMCCON57842.2023.10078213|Body Propagation;wearable medical device;S11;radiation pattern;path loss;Antenna measurements;Wireless communication;Performance evaluation;Medical devices;Biological system modeling;Wearable computers;Propagation losses|
|[Detection of EMI Issues Caused by Differential-Mode Voltages on an Electric Scooter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078199)|V. Gkatsi; R. Vogt-Ardatjew; F. Leferink|10.1109/GEMCCON57842.2023.10078199|coupling;DPI;EMC;scooter;VNA;Couplings;Voltage measurement;Correlation;Electromagnetic interference;Motorcycles;Resonant frequency;Electromagnetic compatibility|
|[The Effect of Ship Mains Supply Frequency Transient on UPS-based Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078201)|M. I. Sudrajat; N. Moonen; H. Bergsma; F. Leferink|10.1109/GEMCCON57842.2023.10078201|commercial off-the-shelf;uninterruptible power supply;amplitude modulation;frequency transient;Frequency modulation;Electromagnetic interference;Power quality;Measurement techniques;Power system harmonics;Amplitude modulation;Harmonic analysis|
|[Analysis of Electrified Systems and Electromagnetic Interferences on the Railways](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078191)|T. Serdiuk; K. Serdiuk; V. Profatylov; H. H. Adhena; D. Thomas; S. Greedy|10.1109/GEMCCON57842.2023.10078191|electromagnetic compatibility (EMC);track circuits (TC);electromagnetic interference (EMI);harmonics;Power transmission lines;Electromagnetic interference;Pressing;Electromagnetic compatibility;Transmission line measurements;Transformers;Rail transportation|
|[Universal Energy Access: at the Intersection of Power Electronics, EMC, Philosophy, and Social Sciences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078187)|M. Appelman; N. Moonen; J. Popovic|10.1109/GEMCCON57842.2023.10078187|Electromagnetic Compatibility;Energy Access;Feminist Philosophy;Science and Technology Studies;Philosophy of Technology;Power Electronics;Philosophical considerations;Social groups;Social sciences;Africa;Electromagnetic compatibility;Power electronics;Electromagnetics|
|[On the Importance of Power Quality in Solar Home Systems for Energy Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078203)|M. Appelman; B. t. Have; N. Moonen; J. Popovic|10.1109/GEMCCON57842.2023.10078203|DC;Energy Access;EMC;Multi-tier Frame-work;Power Quality;Solar Home System;nan|
|[Conducted Emission of a DC Motor Speed Drive: an Approach on Risk Assessment for Ship Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078192)|M. I. Sudrajat; M. A. Wibisono; N. Moonen; F. Leferink|10.1109/GEMCCON57842.2023.10078192|switching-based motor speed drive;conducted emission;behavioral model;Computational modeling;Electromagnetic interference;Switches;Predictive models;DC motors;Mathematical models;Behavioral sciences|
|[Simulation of Conducted Emissions From Power Converters Using Leading Switching Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078225)|A. Madi; F. Vieira; P. Lezynski; S. Koj; R. Smolenski|10.1109/GEMCCON57842.2023.10078225|Conducted emissions;electric vehicle;IGBT;MOSFET;power converter;wide-bandgap switches;MOSFET;Silicon carbide;Switching frequency;Interference;Predictive models;SPICE;Inverters|
|[Positioning Uncertainty of Near-Field Probes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078196)|T. Monopoli; X. Wu; F. Grassi; S. A. Pignari; K. -F. J. Wolf|10.1109/GEMCCON57842.2023.10078196|Microstrip;Near-field probe;Uncertainty;Uncertainty;Monte Carlo methods;Electromagnetic compatibility;Microstrip;Probes|
|[Influence of Terminal Units on the Radiation Properties on Ethernet Cables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078218)|L. Illiano; X. Liu; F. Grassi; S. A. Pignari|10.1109/GEMCCON57842.2023.10078218|Ethernet;termination;common mode;unshielded twisted wire pairs;Modal analysis;Wires;Focusing;Ethernet;Electromagnetic compatibility;Network interfaces;Standards|
|[The Need for EMI Risk Management in MRI Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078222)|S. R. Veléz; M. J. A. M. van Helvoort; R. Vogt-Ardatjew; F. Leferink|10.1109/GEMCCON57842.2023.10078222|MRI;EMC;EMI;Intended Environment;Rule-based;Risk-based;Install-based Equipment;Databases;Magnetic resonance imaging;Electromagnetic interference;Focusing;Electromagnetic compatibility;Safety;Risk management|
|[Distance Characteristics of Power Absorption Ratio of the Skin Based on Sommerfeld's Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078184)|K. Sato; Y. Kamimura|10.1109/GEMCCON57842.2023.10078184|RF protection guidelines;analytical solution;skin tissue;power absorption;Radio frequency;Absorption;Antenna theory;Electromagnetic compatibility;Skin;Numerical models|
|[Design and Simulation of Quadratic Curve Discone Antenna for Medical Interference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078205)|H. D. Prananto; D. Mandaris; A. Munir|10.1109/GEMCCON57842.2023.10078205|discone antenna;electromagnetic interference (EMI);medical device;quadratic curve;nan|
|[A Wideband Spearhead-Shaped Patch Antenna Evolved from Spline-Based Oval Geometry as Sensor in EMC/EMI Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078207)|A. D. Prasetyo; D. Hamdani; A. Munir|10.1109/GEMCCON57842.2023.10078207|Electromagnetic compatibility (EMC)/electromagnetic interference (EMI) sensor;spline;wideband antenna;Antenna measurements;Shape;Shape measurement;Patch antennas;Electromagnetic interference;Electromagnetic compatibility;Particle measurements|
|[Analytical Approach of EM Wave Absorber Characteristics Based on Constitutive Material Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078180)|B. Syihabuddin; M. R. Effendi; A. Munir|10.1109/GEMCCON57842.2023.10078180|EM wave absorber;frequency response;square patch;permittivity;permeability;conductivity;Simulation;Electromagnetic scattering;Conductivity;Electromagnetic compatibility;Boundary conditions;Frequency response;Permeability|
|[Design of Nanosecond-level Transient Electric Field Sensor and its Application in HVDC Converter Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078208)|X. Ni; P. Qiu; B. Zhang; Y. Song; L. Song; S. He|10.1109/GEMCCON57842.2023.10078208|conditioning circuit;MMC-HVDC system;monopole electrically small rod antenna;nanosecond-level E-field measurement;radiation E-field;Antenna measurements;Power measurement;Pulse measurements;HVDC transmission;Valves;Power electronics;Velocity measurement|
|[Estimating The Differential Mode Emission of a Boost Converter Based On The Input Capacitor Series Resistance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078212)|Yoppy; D. Mandaris; A. N. Bakti; H. W. Nugroho; Y. Yudhistira; D. Hamdani|10.1109/GEMCCON57842.2023.10078212|input filter;capacitor;conducted emission;complex impedance;equivalent series resistance;Resistance;Capacitors;Estimation;DC-DC power converters;Electromagnetic compatibility;Electrical resistance measurement|
|[The Use of a Magneto-Dielectric Absorber to Balance the Asymmetrical Line Feeding a Symmetrical Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078226)|I. Mori; A. E. Sowa|10.1109/GEMCCON57842.2023.10078226|magneto-dielectric absorber;coaxial cable;balanced and unbalanced lines;far-field pattern;Coaxial cables;Dipole antennas;Antenna feeds;Electromagnetic compatibility;Magnetic analysis;Task analysis;Antenna radiation patterns|
|[Reduction of Electromagnetic Coupling between Patch Antennas Using Meander Line Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078189)|Zulfi; A. Munir|10.1109/GEMCCON57842.2023.10078189|electromagnetic coupling;meander line (ML) structure;patch antenna;Couplings;Patch antennas;Simulation;Resonant frequency;Bandwidth;Electromagnetic compatibility;Distance measurement|
|[Control of Circularly-polarized Radiation on Ring Array Antenna for EMC Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078221)|M. R. Effendi; R. S. Asthan; A. Munir|10.1109/GEMCCON57842.2023.10078221|electromagnetic compatibility (EMC);circularly-polarized radiation;ring array antenna;Simulation;Bandwidth;Electromagnetic compatibility;Antenna arrays;Antenna radiation patterns|
#### **2023 SICE International Symposium on Control Systems (SICE ISCS)**
- DOI: 10.23919/SICEISCS57194.2023
- DATE: 9-11 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Image-based Visual-Servoing for Air-to-Air Drone Tracking & Following with Model Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079208)|W. K. Chan; S. Srigrarom|10.23919/SICEISCS57194.2023.10079208|image-based visual servoing (IBVS);instantaneous velocity vector;target tracking;target following;model predictive control;air-to-air;drone;unmanned aerial vehicle (UAV);nan|
|[Vehicle Pitch Angle Estimation by a Triaxial Accelerometer with a Wheel Speed Sensor and an Altimeter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079201)|T. Wada; S. Kobayashi; S. Takamoto; A. Nishikawa; Y. Sumiyoshi; W. Tsujita|10.23919/SICEISCS57194.2023.10079201|Vehicle;Pitch angle;Estimation;nan|
|[Vehicle Dynamics Trajectory Planning: Minimum Violation Planning Modifications Reducing Computational Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079204)|D. Vosahlik; P. Turnovec; J. Pekar; M. Bohac; T. Hanis|10.23919/SICEISCS57194.2023.10079204|nan;nan|
|[System level modeling and closed loop control for a droplet-based micro-actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079205)|A. M. Kopp; P. Conrad; M. Hoffmann; H. Ament|10.23919/SICEISCS57194.2023.10079205|system identification;neural network;closed loop control;micro-actuator;nan|
|[Control of a Smart Active Flexible Needle for Therapeutic procedures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079211)|S. Hans; J. K. Goyal; A. Sachan; P. Bansal; S. Soni; M. Djemai|10.23919/SICEISCS57194.2023.10079211|nan;nan|
|[Shoe-type device to estimate body balance focusing on the relationship between CoM and BoS estimated from CoP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079197)|Y. Ishii; K. Date; T. Itami; J. Yoneyama|10.23919/SICEISCS57194.2023.10079197|Center of mass;Center of pressure;Body balance;Shoe-type device;nan|
|[Nearest Neighbor Search-Based Modification of RRI Data with Premature Atrial Contraction and Premature Ventricular Contraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079199)|S. Chen; S. Kato; K. Fujiwara; M. Kano|10.23919/SICEISCS57194.2023.10079199|Heart rate variability;RRI data;machine learning;nearest neighbor search algorithm;nan|
|[Structural Modeling and Intervention of Gene Regulatory Networks for Ultra-Early Disease Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079209)|R. Naga; M. Inoue|10.23919/SICEISCS57194.2023.10079209|Ultra-Early Disease Prevention;Gene Regulatory Networks;Network Structural Modeling;Model-Based Control;Robust Control;nan|
|[Relation between disturbance observer and model error compensator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079207)|K. Shikada; N. Sebe|10.23919/SICEISCS57194.2023.10079207|disturbance observer;model error compensator;robust control;servo system.;nan|
|[Neural Continuous-Time Markov Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079202)|M. Reeves; H. S. Bhat|10.23919/SICEISCS57194.2023.10079202|System Identification;Continuous-Time Markov Chains;Neural Networks;Parameter Estimation;nan|
|[Force Control of a 1-DoF Cable Robot Using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079206)|M. Hamann; J. Brandl; C. Ament|10.23919/SICEISCS57194.2023.10079206|Force Control;Reinforcement Learning;DDPG;Cable Robot;nan|
|[Mixed Compositional Pattern-Producing Network-NeuroEvolution of Augmenting Topologies Method for the Locomotion Control of a Snake-Like Modular Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079203)|Y. -L. Song; W. -Y. Chiu; S. H. Manoharan|10.23919/SICEISCS57194.2023.10079203|Multiobjective genetic algorithm;neuroevolution algorithm;serpentine locomotion;snake-like modular robot;nan|
|[Novel Auto-Tuning PD-Fuzzy Control of Current Harmonics to Reduce Losses in Motor Drive Systems Excited by SiC-MOSFET Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079198)|N. G. M. Thao; T. D. Do; D. -K. Ngo; K. Fujisaki|10.23919/SICEISCS57194.2023.10079198|PD-fuzzy control;current harmonics control;SiC-based motor drive;motor copper loss;inverter loss;nan|
|[Distributed Traffic Signal Control with Fairness Using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079200)|S. Shirasaka; N. Kodama; T. Harada|10.23919/SICEISCS57194.2023.10079200|Traffic Signal Control;Fairness;Deep Reinforcement Learning;Cooperation;nan|
|[Robust H∞ Guaranteed Cost Controller Design for Islanded Microgrids: An LMI Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10079210)|B. Lee; J. Kim; J. Ban|10.23919/SICEISCS57194.2023.10079210|linear matrix inequality;optimal control;power systems;renewable energy sources;robust control;nan|
#### **2023 17th International Conference on the Experience of Designing and Application of CAD Systems (CADSM)**
- DOI: 10.1109/CADSM58174.2023
- DATE: 22-25 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Designing the Topology of Microelectromechanical Systems by Machine Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076504)|M. Lobur; A. Zdobytskyi; U. Marikutsa; N. Muliak|10.1109/CADSM58174.2023.10076504|optimization;design;metamaterial;machine learning;micro speaker;nan|
|[Development of a Remote-Control System for a Mobile Vibration-Driven Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076517)|V. Korendiy; O. Kachur; R. Predko|10.1109/CADSM58174.2023.10076517|vibration-driven locomotion system;mobile robotics;Arduino hardware;Bluetooth;Android application;nan|
|[Models of Recurrent Distributions Statistical Averaging for Electronic Components with Fluctuations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076523)|P. Kosobutskyy; V. Oksentyuk|10.1109/CADSM58174.2023.10076523|model;mean;variance;random discrete numbers;Fibonacci numbers;Horadam numbers;Jacobsthal number;distribution probability;Jacobian matrices;Solid modeling;Fluctuations;Electronic components;Mean square error methods;Root mean square;Dispersion|
|[Inverse-Dynamic Neural Controller Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076494)|M. Nakonechnyi; O. Ivakhiv; Y. Hirniak; Y. Nakonechnyi; O. Viter|10.1109/CADSM58174.2023.10076494|dynamic;controller;simulation;PID-law;adaptation;tracking;Training;Adaptation models;Solid modeling;Adaptive systems;Heuristic algorithms;Systems operation;Process control|
|[A Method of Creating Virtual Pixels in Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076534)|M. Slonov; O. Maryliv; S. Pisnenko; H. Samarets; K. Rubel; D. Trehubov|10.1109/CADSM58174.2023.10076534|scanning;snapshots;matrix;virtual pixel;Visualization;Digital images;Documentation;Optical imaging;Size measurement;Hardware;Loss measurement|
|[The Computer Modeling of the Thermal Agent Hydrodynamics Through the Alcohol Distillery Stillage Stationary Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076496)|O. Ivashchuk; R. Chyzhovych; V. Atamanyuk; Z. Hnativ|10.1109/CADSM58174.2023.10076496|alcohol distillery stillage;filtration drying;hydrodynamics;computer simulation;CFD;Resistance;Solid modeling;Computational modeling;Atmospheric modeling;Hydraulic systems;Containers;Hydrodynamics|
|[Software-Defined Multi-Access Edge/Cloud Computing for 5G/6G Time-Critical Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076524)|M. Seliuchenko; M. Beshley; O. Shpur; N. Seliuchenko; M. Klymash; H. Beshley|10.1109/CADSM58174.2023.10076524|Multiple Access Edge Computing;Cloud Computing;5G;6G;QoS;SDN;Solid modeling;Computational modeling;Quality of service;Computer architecture;Virtual reality;Containers;Hardware|
|[Method for Estimating the Topological Structure of Self-Organized Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076498)|M. Klymash; M. Kaidan; B. Strykhalyuk; Y. Pyrih; Y. Pyrih|10.1109/CADSM58174.2023.10076498|selg-organized network;design;topological structure;compactness of set;nan|
|[A New Hybrid Method for Predicting Recommendations for Collaborative Recommender Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076527)|M. Lobur; Y. Stekh; R. Holovatskyy; M. Kamiska|10.1109/CADSM58174.2023.10076527|collaborative filtering;recommendation prediction;categorical clustering;group recommendations;nan|
|[Software-Defined Transmitter to Support Automatic Dependent Surveillance-Broadcast](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076507)|I. Ostroumov; O. Kutsenko|10.1109/CADSM58174.2023.10076507|Software-defined transmitter;ADS-B;software;digital data transmission;Mode 1090 ES;civil aviation;air traffic;nan|
|[QoS-Coordinated Adaptive Spectrum Management Method for Coexistence 5G-U and Wi-Fi Networks with Short-Term Channel Failures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076520)|M. Beshley; V. Kochan; H. Beshley; M. Medvetskyi; I. Kahalo; Y. Shkoropad|10.1109/CADSM58174.2023.10076520|5G on Unlicensed Band;Wi-Fi;QoS;MSE;queuing algorithm;short-term channel failures;nan|
|[Deductive Matrix Synthesis for Fault Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076506)|W. Gharibi; V. Hahanov; D. Devadze; A. V. Hacimahmud; S. Chumachenko; Z. Davitadze; E. Litvinova; I. Hahanov|10.1109/CADSM58174.2023.10076506|truth table;vector form of logic;active matrix;fault simulation;test generation;nan|
|[Pedestrain Detection Based on Improved CSP Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076538)|Y. Xu; X. Zong; O. Kochan|10.1109/CADSM58174.2023.10076538|Center and scale prediction;Attentional Mechanism;Non-maximum suppression;Training;Resistance;Solid modeling;Semantics;Detectors;Feature extraction;Task analysis|
|[Investigation of Response from the Micro Objects of Complex Shape Irradiated by Acoustic Wave](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076510)|M. Andriychuk; M. Melnyk; M. Orynchak|10.1109/CADSM58174.2023.10076510|smasll inclusion;asymptotic approach;analytical-numerical solution;illuminationn characteristic;numerical modeling;nan|
|[Outdoor Positioning for Industrial Workplace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076493)|A. Lebedieva-Dychko; G. Shilo|10.1109/CADSM58174.2023.10076493|positioning system;industrial workplace;safety improvement;RFID;GPS;WiFi;Scalability;Employment;Metals;Switches;Electromagnetic radiation;Control systems;Real-time systems|
|[A Software Complex for Researching Algorithms for Working with Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076530)|V. Karkulovskyy; R. Kryvyy; N. Jaworskyi; A. Bezkostyi|10.1109/CADSM58174.2023.10076530|Software Complex;Algorithms for Working with Graphs;Problem of Finding the Shortest Path;Training;Shortest path problem;Software algorithms;Memory management;Software;Information technology|
|[Hydrogen Adsorption in Porous Silicon: Simulation and Control Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076503)|V. Skryshevsky; A. Manilov; I. Ivanov|10.1109/CADSM58174.2023.10076503|porous silicon;hydrogen storage;simulation;adsorption;electrophysical properties;nan|
|[The Technique of Modelling and Statistical Analysis of Energy Consumption in 5G Multi-Tier Radio Access Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076531)|O. Kochan; M. Beshley; H. Beshley; M. Levkiv|10.1109/CADSM58174.2023.10076531|Statistical modeling;5G multi-tier network;small cells;energy consumption;QoS;Energy consumption;Base stations;Solid modeling;Analytical models;5G mobile communication;Statistical analysis;Switches|
|[Modeling of Wireless Sensor Network Based on Functioning Parameters of Unevenly Distributed Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076512)|O. Belej; N. Nestor; I. Artyshchuk; N. Spas|10.1109/CADSM58174.2023.10076512|information system;wireless sensor network;network model;multimodal distribution of nodes;simulation model;graph vertices;nan|
|[Approach for Automated Designing Robust Systems for Stabilizing Data Measuring Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076501)|O. Sushchenko; Y. Bezkorovainyi; O. Salyuk; A. Kovalenko|10.1109/CADSM58174.2023.10076501|computer-aided design;robust stabilization system;automated design procedure;simulation;mathematical model;nan|
|[Feature Selection and Parameter Optimization of Optimized Extreme Learning Machine for Motor Fault Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076499)|D. Wu|10.1109/CADSM58174.2023.10076499|Motor Fault Detection;Whale optimization algorithm;Simulated annealing;Feature Selection;nan|
|[Exploring Multimodal Data Approach in Natural Language Processing Based on Speech Recognition Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076532)|B. Oleh; F. Ihor; R. Zoriana|10.1109/CADSM58174.2023.10076532|Multimodal data;Natural Language Processing;Speech Recognition;Machine Learning;Deep Learning;Solid modeling;Sentiment analysis;Terminology;Software algorithms;Speech recognition;Feature extraction;Software|
|[Analysis of Proactive Models of Fault-Tolerant Routing under Load Balancing and Border Routers Availability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076525)|O. Lemeshko; O. Yeremenko; A. Mersni; M. Yevdokymenko; M. Persikov; A. Kruhlova|10.1109/CADSM58174.2023.10076525|Network resilience;border router;availability;fault-tolerant routing;Traffic Engineering;nan|
|[Meteorological Information Access and Decision-Making for UAS Flight Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076518)|M. Ivanytskyi; Y. Znakovska; Y. Averyanova|10.1109/CADSM58174.2023.10076518|UAS;weather elements;weather hazards;weather-related risks;risk analysis;meteorological information;software;mobile application;meteorological support;nan|
|[Physics-Informed Neural Network for Modeling the Process of Heat-and-Mass Transfer Based on the Apparatus of Fractional Derivatives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076540)|Y. Sokolovskyy; T. Samotii; I. Kroshnyy|10.1109/CADSM58174.2023.10076540|anisotropic heat-and-mass transfer;the Grünwald-Letnikov fractional derivatives;finite difference method;sequential learning;physics-based fractal neural networks;Temperature sensors;Solid modeling;Neural networks;Moisture;Network architecture;Mathematical models;Fractals|
|[Deadlock Recovery for Flexible Manufacturing Systems with Exhaustive Exploration of the Reachability Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076500)|I. Grobelna; A. Karatkevich|10.1109/CADSM58174.2023.10076500|automation and control;deadlock recovery;manufacturing systems;production systems;Petri nets;nan|
|[Microstrip Resonator on Stub and Section](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076526)|E. Nelin; Y. Nepochatykh|10.1109/CADSM58174.2023.10076526|resonator;open-circuited stub;short-circuited stub;transmission response;transmission line model;characteristic impedance;input impedance;nan|
|[Floating-Point Number Scalar Product Hardware Implementation for Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076502)|I. Tsmots; V. Rabyk; V. Teslyuk; Y. Opotyak|10.1109/CADSM58174.2023.10076502|neuro-like network;dot product;macro product table;floating point numbers;FPGA EP3C16F484C6;simulation;timing diagrams;nan|
|[Development of an Intelligent Forecasting Unit for the Protection Device Against Leakage Currents in Electric Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076495)|V. Gerasymenko; N. Maiborodina; V. Kozyrskyi; O. Kovalov|10.1109/CADSM58174.2023.10076495|leakage current;technological parameters;theory of time series forecasting;neural network;Training;Solid modeling;Neural networks;Time series analysis;Predictive models;Data models;Leakage currents|
|[Control System of Mobile Platform Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076522)|V. Mazur; S. Panchak|10.1109/CADSM58174.2023.10076522|robotic mobile platform;manipulator;control system;positioning of containers;Robot kinematics;Loading;Automata;Containers;Manipulators;Control systems;Electromagnets|
|[Academic Performance Prediction Model Based on Educational Similarity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076516)|Y. Wang; Y. OuYang; M. Levkiv|10.1109/CADSM58174.2023.10076516|home education;academic performance prediction;graph neural networks;nan|
|[Sentaurus TCAD Model for Thin Layer Sample Used in Van Der Pauw Hall Mobility Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076537)|J. Woźny; L. Bruno|10.1109/CADSM58174.2023.10076537|TCAD;thin layer;Hall mobility;van der Pauw ZnO;Si;Silicon compounds;Solid modeling;II-VI semiconductor materials;Zinc oxide;Silicon;Hall effect;Substrates|
|[Parallel Algorithm for Numerical Modeling of Anisotropic Heat and Mass Transfer in Fractal Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076515)|Y. Sokolovskyy; V. Yarkun; M. Levkovych|10.1109/CADSM58174.2023.10076515|heat and mass transfer;media with a fractal structure;derivatives of fractional order;parallel algorithm;algorithm acceleration;nan|
|[Simulation of Slowwave Spiral Structures Based on Analytical Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076509)|R. Politanskyi; O. Malanchuk; M. Vistak|10.1109/CADSM58174.2023.10076509|decelerated electromagnetic waves;cylindrical functions;transverse wavenumber;longitudinal wavenumber;nan|
|[Development of Heat Detector Based on Fuzzy Logic Using Arduino Board Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076536)|A. Kushnir; B. Kopchak; V. Oksentyuk|10.1109/CADSM58174.2023.10076536|fire detection system;heat detector;intelligent fire detector;fuzzy logic;Heating systems;Fuzzy logic;Solid modeling;Computer languages;Software packages;Microcontrollers;Detectors|
|[Formation of Bandpass Response by Orthogonal Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076505)|E. Nelin; Y. Nepochatykh|10.1109/CADSM58174.2023.10076505|bandpass filter;resonator;transmission line;open-circuited stub;short-circuited stub;nan|
|[SDN-based Internet of Video Things Platform Enabling Real-Time Edge/Cloud Video Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076539)|O. Kochan; M. Beshley; H. Beshley; Y. Shkoropad; I. Ivanochko; N. Seliuchenko|10.1109/CADSM58174.2023.10076539|Internet of Video Things;Software-defined Networking;Cloud Computing;Edge Computing;Quality of Service;Cloud computing;Visual analytics;Heuristic algorithms;Image edge detection;Software algorithms;Streaming media;Throughput|
|[Vibration Oscillations Modeling for Printed Boards of Machine Control Units during Their Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076529)|D. Rebot; V. Topilnytskyy; T. Stefanovych; S. Shcherbovskykh|10.1109/CADSM58174.2023.10076529|vibration;oscillations;printed circuit boards;mathematical modeling;amplitude;Vibrations;Solid modeling;Machine control;Printed circuits;Mathematical models;Data models;Integrated circuit modeling|
|[Bidirectional Linkage Robot Digital Twin System Based on ROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076497)|Z. Wang; Y. OuYang; O. Kochan|10.1109/CADSM58174.2023.10076497|robot;digital twin;Unreal Engine 4;motion planning;real-time monitoring;Couplings;Training;Service robots;Real-time systems;Digital twins;Synchronization;Time factors|
|[Model of Large Sparse Datasets Processing Efficiency in IIOT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076508)|M. Klymash; M. Kyryk; Y. Pyrih; O. Hordiichuk-Bublivska; T. Andrukhiv|10.1109/CADSM58174.2023.10076508|big data;IIoT;Funk SVD;recommender systems;nan|
|[Application of Global Optimization Toolbox for Identification of Parameters of Interval Nonlinear Models of Static Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076513)|M. Dyvak; V. Manzhula; A. Pukas; T. Dyvak; V. V. Manzhula|10.1109/CADSM58174.2023.10076513|interval analysis;interval nonlinear model;static system;parametric identification;multidimensional optimization;objective function;convergence;nan|
|[Matrix Approach to Numerical Modeling of Heat-and-Moisture Transfer Processes in a Medium with a Fractal Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076519)|Y. Sokolovskyy; M. Levkovych; M. Mysyk|10.1109/CADSM58174.2023.10076519|Riesz derivative;discretization;fractal structure;matrix equations;nan|
|[Automatic Diagnosis of Diabetic Retinopathy Based on EfficientNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076511)|W. Liu; Z. Zhao; M. Levkiv|10.1109/CADSM58174.2023.10076511|Diabetic retinopathy;Deep learning;Attention mechanism;Image classification;nan|
|[Video-based Concrete Road Damage Assessment Using JetRacer Kit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076528)|R. Mysiuk; I. Mysiuk; G. Pawlowski; V. Yuzevych; M. Yasinskyi; Y. Tyrkalo|10.1109/CADSM58174.2023.10076528|robotics;image filters;defects;concrete;computer vision;Visualization;Roads;Robot vision systems;Moisture;Filtering algorithms;Streaming media;Software|
|[Traffic Flow Prediction Model Based on Temporal Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076492)|C. Zhao|10.1109/CADSM58174.2023.10076492|component;formatting;style;styling;insert;Solid modeling;Correlation;Roads;Predictive models;Logic gates;Data models;Convolutional neural networks|
#### **2023 11th International Winter Conference on Brain-Computer Interface (BCI)**
- DOI: 10.1109/BCI57258.2023
- DATE: 20-22 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Interpreting Deep Learning Models for Multi-modal Neuroimaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078502)|K. . -R. Müller; S. M. Hofmann|10.1109/BCI57258.2023.10078502|nan;nan|
|[Feature Selection Based on Layer-Wise Relevance Propagation for EEG-based MI classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078676)|H. Nam; J. -M. Kim; T. -E. Kam|10.1109/BCI57258.2023.10078676|Brain-Computer Interface;Electroencephalo-gram;Motor Imagery;Feature Selection;Layer-Wise Relevance Propagation;nan|
|[Interpretability of Hybrid Feature Using Graph Neural Networks from Mental Arithmetic Based EEG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078648)|M. -K. Jung; H. Kim; S. Lee; J. B. Kim; D. -J. Kim|10.1109/BCI57258.2023.10078648|brain computer interface;mental arithmetic task;electroencephalography;conncetivity;graph neural networks;explainable artificial intelligence;nan|
|[Explainable Deep Learning for Brain-Computer Interfaces through Layerwise Relevance Propagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078678)|V. Mun; B. Abibullaev|10.1109/BCI57258.2023.10078678|Brain-Computer Interfaces;Event-Related Potentials (ERP);Explainable AI;complexity reduction;deep learning;pruning;EEG analysis;nan|
|[Correlation between Neurophysiological Measures of Consciousness and BCI Performance in a Locked-in Patient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078703)|T. Settgast; F. Zilio; A. Kübler; G. Northoff|10.1109/BCI57258.2023.10078703|BCI;consciousness;criticality;complexity;ALS;locked-in;nan|
|[Effects of Input Neuron Mapping Coordinates in Spiking Neural Network on the Motor Imagery EEG Signals Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078628)|G. Zhan; H. Su; P. Wang; L. Niu; J. Bin; W. Mu; X. Zhang; H. Jiang; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078628|SNN;BCI;input neuron mapping;EEG;nan|
|[Cognitive-switch detection for un-cued SSVEP BCI speller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078622)|H. Kim; K. Won; M. Ahn; S. C. Jun|10.1109/BCI57258.2023.10078622|Brain-computer interface(BCI);steady-state visual evoked potential (SSVEP);stimulus detection;cognitive switch.;nan|
|[Sleep-CMKD: Self-Attention CNN/Transformer Cross-Model Knowledge Distillation for Automatic Sleep Staging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078646)|H. Kim; M. Kim; W. Chung|10.1109/BCI57258.2023.10078646|Automatic Sleep Staging;Electroencephalogram (EEG);Transformer;Knowledge Distillation;nan|
|[Development of Personalized Sleep Induction System based on Mental States](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078552)|Y. -S. Kweon; G. -H. Shin; H. -G. Kwak|10.1109/BCI57258.2023.10078552|sleep;brain-computer interface;electroencephalogram;nan|
|[Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078448)|L. Wang; J. Wang; B. Wen; W. Mu; L. Liu; J. Han; L. Zhang; J. Jia; X. Kang|10.1109/BCI57258.2023.10078448|brain computer interface;motor imagery;EEG;GoogLeNet.;nan|
|[Hierarchical Transformer for Brain Computer Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078473)|P. Deny; K. W. Choi|10.1109/BCI57258.2023.10078473|Motor imagery (MI);brain-computer interface (BCI);electroencephalogram (EEG);hierarchical transformer;nan|
|[Advances in Imaging of Neural Oscillations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078491)|S. S. Nagarajan|10.1109/BCI57258.2023.10078491|EEG;MEG;source reconstruction;Bayesian inference;Spectral graph modeling;Alzheimer’s disease;nan|
|[Implantable Brain-Computer Interface Based On Printing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078643)|L. Liu; B. Wen; M. Wang; A. Wang; J. Zhang; Y. Zhang; S. Le; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078643|Printed electronics;implantable brain-computer interface;electrodes;optopoles;nan|
|[Light Radiation Simulation of Micro-LED Array for the Optical Neural Interface Targeted Cerebral Cortex in Rhesus Monkey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078610)|L. Liu; W. Zhang; B. Wen; M. Wang; A. Wang; J. Zhang; Y. Zhang; S. Le; H. Jiang; R. Zhai; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078610|Optogenetics;micro-LED;neural interface;light simulation;cerebral cortex;nan|
|[Authentication System Based on Event-related Potentials Using AR Glasses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078487)|H. Jang; S. Park; J. Woo; J. Ha; L. Kim|10.1109/BCI57258.2023.10078487|Biometric authentication;Event-related potentials;Rapid Serial Visual Presentation;Human photographs;Augmented reality;nan|
|[S2S-StarGAN: Signal-to-Signal Translation Method based on StarGAN to Generate Artificial EEG for SSVEP-based Brain-Computer Interfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078582)|J. Kwon; J. Hwang; C. -H. Im|10.1109/BCI57258.2023.10078582|SSVEP;BCI;EEG;signal-to-signal translation;StarGAN;nan|
|[Reference Bank Multi-Feature Extraction for EEG-Based Concentration Discrimination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078480)|J. W. Choi; J. Choi; S. Jo|10.1109/BCI57258.2023.10078480|brain-computer interface (BCI);electroencephalogram (EEG);concentration discrimination;deep learning;nan|
|[Reservoir Splitting method for EEG-based Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078629)|Anubhav; K. Fujiwara|10.1109/BCI57258.2023.10078629|EEG;Emotion Recognition;Reservoir Computing;Echo State Networks;nan|
|[Towards Brain-based Interface for Communication and Control by Skin Touch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078458)|M. -K. Kim; J. -H. Cho; H. -B. Shin; S. -W. Lee|10.1109/BCI57258.2023.10078458|brain-computer interface;human-computer interaction;deep learning;touch and gestural inputs;nan|
|[The Effect of Wireless Communication Interference on Wireless BCI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078549)|D. -U. Kim; J. Lee; M. Kim; J. Kim; J. Lee; D. Heo; S. -P. Kim|10.1109/BCI57258.2023.10078549|Wireless BCI;EEG;Bluetooth;WiFi;nan|
|[Revealing interpretable object representations from human visual cortex and artificial neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078606)|M. Hebart|10.1109/BCI57258.2023.10078606|representation learning;object representations;similarity;interpretability;behavior;fMRI;deep learning;nan|
|[On the Effect of Size and Contrast of the SSVEP Visual Stimuations on Classification Accuracy and User-Friendliness in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078459)|H. Si-Mohammed; C. Holz; A. Wilson; H. Gamper; A. K. Lee; D. Emmanouilidou; E. Cutrell; I. Tashev|10.1109/BCI57258.2023.10078459|component;formatting;style;styling;insert;nan|
|[Source-free Subject Adaptation for EEG-based Visual Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078570)|P. Lee; S. Jeon; S. Hwang; M. Shin; H. Byun|10.1109/BCI57258.2023.10078570|Brain-computer interface;Electroencephalography;EEG-based visual recognition;Source-free subject adaptation;Deep learning;nan|
|[High Accuracy Silent Speech BCI Using Compact Deep Learning Model for Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078589)|N. Kobayashi; T. Nemoto; T. Morooka|10.1109/BCI57258.2023.10078589|silent speech;brain-computer interface (BCI);EEG;long short-term memory (LSTM);EEGNet;nursing care support;QoL;nan|
|[Direct Cortical Stimulation for inducing Artificial Speech Perception: A Preliminary Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078541)|Y. Hong; J. S. Kim; C. K. Chung|10.1109/BCI57258.2023.10078541|direct cortical stimulation;language perception;auditory hallucination;semantic hallucination;visual hallucination;electrocorticography;nan|
|[Speech Synthesis from Brain Signals Based on Generative Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078574)|Y. -E. Lee; S. -H. Kim; S. -H. Lee; J. -S. Lee; S. Kim; S. -W. Lee|10.1109/BCI57258.2023.10078574|computer interface;speech synthesis;generative model;nan|
|[BrainGate: An Intracortical Brain-Computer Interface for the Restoration of Communication and Functional Independence for People with Paralysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078547)|D. B. Rubin; L. R. Hochberg|10.1109/BCI57258.2023.10078547|Brain Computer Interface;Paralysis;Neuroengineering;Neuroprostheses;nan|
|[A Channel Selection Method for Motor Imagery EEG Based on Fisher Score of OVR-CSP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078658)|W. Mu; J. Wang; L. Wang; P. Wang; J. Han; L. Niu; J. Bin; L. Liu; J. Zhang; J. Jia; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078658|channel selection;EEG;motor imagery;brain computer interface;CSP;nan|
|[Distribution of Electrical Stimulation Current of a Minimally Invasive Endovascular Stent-Electrode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078540)|B. Wen; J. Zhang; M. Wang; L. Liu; Y. Zhang; A. Wang; L. Wang; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078540|Endovascular;implantable brain-computer interface;stent electrode array;electrical stimulation;nan|
|[Decoding Action Planning of three-dimensional Movements Using Electrocorticographic signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078701)|Y. J. Yang; J. S. Kim; C. K. Chung|10.1109/BCI57258.2023.10078701|Brain-machine interface;Motor imagery;Action planning;Electrocorticography;nan|
|[Generative Adversarial Networks for Electroencephalogram Signal Analysis: A Mini Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078666)|J. Wang; W. Mu; A. Wang; L. Wang; J. Han; P. Wang; L. Niu; J. Bin; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078666|Generative adversarial network (GAN);braincomputer interface (BCI);electroencephalography (EEG);data augmentation;nan|
|[Automatic Sleep Stage Classification Method based on Transformer-in-Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078607)|M. Kim; K. Jung; W. Chung|10.1109/BCI57258.2023.10078607|Automatic Sleep Staging;Electroencephalogram (EEG);Transformer-in-Transformer (TNT);nan|
|[Epoch-level and Sequence-level Multi-Head Self-Attention-based Sleep Stage Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078700)|K. Jung; M. Kim; W. Chung|10.1109/BCI57258.2023.10078700|Brain-Computer Interface (BCI);Electroencephalogram (EEG);Sleep Stage Classification;Transformer.;nan|
|[Multi-class Motor Imagery Classification using Multi-class SVM with Multi-band Riemannian Tangent Space Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078711)|J. Shin; W. Chung|10.1109/BCI57258.2023.10078711|Brain-Computer Interface (BCI);Electroencephalogram (EEG);Motor Imagery (MI);Multi-class support vector machine (SVM);Multi-band;Riemannian tangent space mapping;nan|
|[Changes in Power and Information Flow in Resting-state EEG by Working Memory Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078717)|G. -H. Shin; Y. -S. Kweon; H. -G. Kwak|10.1109/BCI57258.2023.10078717|electroencephalogram;working memory;restingstate;power spectral density;phase transfer entropy;nan|
|[Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078702)|B. -H. Lee; J. -H. Cho; B. -H. Kwon|10.1109/BCI57258.2023.10078702|brain-computer interface;electroencephalogram;knowledge distillation;deep learning;nan|
|[Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078695)|B. -H. Kwon; B. -H. Lee; J. -H. Cho|10.1109/BCI57258.2023.10078695|brain–computer interface;visual imagery;robotic arm control;nan|
|[Target-centered Subject Transfer Framework for EEG Data Augmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078578)|K. Yin; B. -H. Lee; B. -H. Kwon; J. -H. Cho|10.1109/BCI57258.2023.10078578|Data augmentation;brain-computer interface;generative adversarial network;electroencephalogram;nan|
|[Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078681)|D. -H. Lee; S. -J. Kim; Y. -W. Choi|10.1109/BCI57258.2023.10078681|brain–computer interface;electroencephalogram;abnormal mental states;flight environment;nan|
|[CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078539)|S. -J. Kim; D. -H. Lee; Y. -W. Choi|10.1109/BCI57258.2023.10078539|computer interface;electroencephalogram;data augmentation;motor imagery;nan|
|[Investigation of Stealing Passwords Stored in Human Brain Using Hemodynamic Responses Measured by fNIRS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078475)|S. Bak; J. Park; J. Jeong|10.1109/BCI57258.2023.10078475|Digit Passwords;fNIRS;Neuro Crimes;Neuro Security;Personal Identification Numbers (PINs);nan|
|[Removing EOG Artifacts from the Resting State EEG Signal of Methamphetamine Addicts by ICA Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078556)|G. Zhan; H. Su; P. Wang; L. Niu; J. Bin; W. Mu; X. Zhang; H. Jiang; L. Zhang; X. Kang|10.1109/BCI57258.2023.10078556|EEG;Artifact removal;Independent component analysis (ICA);EOG;nan|
|[Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078534)|D. -Y. Kim; D. -K. Han; H. -B. Shin|10.1109/BCI57258.2023.10078534|Brain-computer interface;Electroencephalogram;Driver drowsiness classification;Domain generalization;Manifoldlevel augmentation;nan|
|[Learning-based Sleep Quality Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078644)|S. Jeong; E. Jeon; S. Noh; J. Lee; H. Kim; S. Kim; H. -I. Suk|10.1109/BCI57258.2023.10078644|Sleep Staging Analysis;Machine Learning;Hidden Markov Model;Similarity Measure;nan|
|[Subject-Independent Classification of Brain Signals using Skip Connections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078601)|S. Kim; J. -W. Lee; Y. -E. Lee; S. -H. Lee|10.1109/BCI57258.2023.10078601|brain–computer interface;deep learning;electroencephalography;speech processing;nan|
|[Channel-Aware Self-Supervised Learning for EEG-based BCI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078451)|S. Jo; J. Jeon; S. Jeong; H. -I. Suk|10.1109/BCI57258.2023.10078451|Self-Supervised Learning;Electroencephalogram;Sleep Staging Classification;Seizure Detection;nan|
|[Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078532)|H. -G. Kwak; Y. -S. Kweon; G. -H. Shin|10.1109/BCI57258.2023.10078532|sleep stage scoring;siamese network;transformer;electroencephalogram;self-knowledge distillation;nan|
|[Subject-Transfer with Subject-Specific Fine-Tuning Based on Multi-Model CNN for Motor Imagery Brain-Computer Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078479)|J. -H. Jeong; D. -J. Sung; K. -T. Kim; S. J. Lee; D. -J. Kim; H. Kim|10.1109/BCI57258.2023.10078479|Subject-Transfer;Motor Imagery;EEG;Brain-Computer interface;Convolutional Neural Network;nan|
|[EEG-Based Multioutput Classification of Sleep Stage and Apnea Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078663)|D. Jo; C. -H. Lee; H. Kim; H. Kim; J. B. Kim; D. -J. Kim|10.1109/BCI57258.2023.10078663|Multioutput classification;sleep stage;apnea;deep learning;EEG;nan|
|[Towards Developing Novel and Intuitive Brain-based Control Interface via On-skin Input](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078660)|H. -B. Shin; M. -K. Kim; J. -H. Cho|10.1109/BCI57258.2023.10078660|Brain-computer interface (BCI);touch and gestural inputs;electroencephalogram;deep learning;nan|
|[Effect of Grasping Speed During Wearable Robotic Glove-Based Motor Imagery Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078478)|C. Hwang; J. W. Choi; S. Jo|10.1109/BCI57258.2023.10078478|electroencephalogram (EEG);wearable robotic glove;rehabilitation;motor imagery;event-related desynchronization (ERD);nan|
|[Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078687)|J. -H. Cho; B. -H. Kwon; B. -H. Lee|10.1109/BCI57258.2023.10078687|brain–computer interface;motor execution;motor imagery;hand grasping;electroencephalogram;deep learning;nan|
|[Subject-Aware User State Classification with Deep Learning Models: An Exploratory Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078542)|H. Kwon; J. Choi; J. W. Choi; S. Jo|10.1109/BCI57258.2023.10078542|Brain-computer interface;Electroencephalography;Speech imagery;Visual imagery;Subject independent;nan|
|[Towards Neural Decoding of Imagined Speech based on Spoken Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078707)|S. -H. Lee; Y. -E. Lee; S. Kim; B. -K. Ko; S. -W. Lee|10.1109/BCI57258.2023.10078707|brain-computer interface;imagined speech;speech recognition;spoken speech;visual imagery;nan|
|[Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078683)|D. -K. Han; G. -D. Jang; D. -Y. Kim|10.1109/BCI57258.2023.10078683|Brain-computer interface;electroencephalography;motor imagery;domain generalization;open-set recognition;nan|
|[Hybrid approach of SSVEP and EEG-based eye-gaze tracking for enhancing BCI performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078517)|Y. Han; S. Park; J. Ha; L. Kim|10.1109/BCI57258.2023.10078517|Steady-state visual evoked potential;eye-gaze tracking;brain-computer interface;nan|
#### **2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)**
- DOI: 10.1109/AICAPS57044.2023
- DATE: 1-3 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Deep Learning based Approach to Stock Market Price Prediction using Technical indicators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074445)|N. Parida; B. K. Balabantaray; R. Nayak; J. K. Rout|10.1109/AICAPS57044.2023.10074445|CNN;KELM;LSTM;technical indicators;Deep learning;Extreme learning machines;Computational modeling;Predictive models;Feature extraction;Matrices;Stock markets|
|[AI Powered Screening Aid for Dyslexic Children in Tamil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074214)|K. Banumathi; G. SudhaSadasivam; B. BanuRekha; N. Shahana; K. Vaishnavi|10.1109/AICAPS57044.2023.10074214|Dyslexia;Screening;Smartphone Application;Regional Language;Tamil;Training;Visualization;Error analysis;Education;Sociology;Standardization;Writing|
|[Automatic feedback captions for eye-tracker based online assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074484)|U. Kumar; A. J; C. K R|10.1109/AICAPS57044.2023.10074484|Deep Learning;Eye-Gaze;T-5;Supervised Learning;NLP;Deep learning;Automation;Tracking;Computational modeling;Behavioral sciences|
|[Diagnosis of Middle Ear Diseases using Deep Learning Paradigm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074505)|D. K. Tayal; N. Srivastava; U. Singh|10.1109/AICAPS57044.2023.10074505|Deep learning;Convolutional Neural Network;Machine learning;Otitis media;Artificial Neural Network;Deep learning;Antibiotics;Neural networks;Ear;Auditory system;Medical services;Media|
|[Enhancing Immersive User Experience Quality of StudoBot Telepresence Robots with Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074544)|K. N. Rajanikanth; M. Rehab Sait; S. R. Kashi|10.1109/AICAPS57044.2023.10074544|Immersive User Experience;Spatial Presence;Social Presence;Telepresence Robot;Mobile Robotic Platform;Robot Operating Modes;Reinforcement Learning;Q-Learning;SysML System Model;Training;Telepresence;Q-learning;Pandemics;Heuristic algorithms;System performance;User experience|
|[Feature Selection using Enhanced Nature Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074104)|D. K. Tayal; N. Srivastava; Neha|10.1109/AICAPS57044.2023.10074104|Nature-inspired algorithm (NIOA);Feature selection (FS);Harris hawk Optimization (HHO);Visual Geometry Group (VGG);Feature Extraction (FE);Geometry;Visualization;Neural networks;Sociology;Feature extraction;Iron;Classification algorithms|
|[Feature Selection using Generalized Linear Model for Machine Learning-based Sepsis Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074523)|M. Ashikur Rahman; A. AbuBakar Ibrahim; A. Tumian|10.1109/AICAPS57044.2023.10074523|Sepsis prediction;Machine Learning;Feature selection;Generalized linear model;Radio frequency;Computational modeling;MIMICs;Artificial neural networks;Receivers;Predictive models;Sepsis|
|[Guided Cost Learning for Lunar Lander Environment Using Human Demonstrated Expert Trajectories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074283)|D. Dharrao; S. Gite; R. Walambe|10.1109/AICAPS57044.2023.10074283|Inverse Reinforcement Learning;Guided cost learning;Lunar Lander;Reinforce;Space vehicles;Training;Costs;Moon;Reinforcement learning;Classification algorithms;Trajectory|
|[Hybrid Perception Analysis of World Leaders in Reddit using Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074005)|V. R. Sekar; T. K. R. Kannan; S. N; P. Vijay|10.1109/AICAPS57044.2023.10074005|Perception Analysis;Natural Language Processing;Sentimental Analysis;Reddit;Sentiment analysis;Social networking (online);Data models;Classification algorithms;Artificial intelligence;Speech processing|
|[Machine Learning Based Patient Classification In Emergency Department](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074003)|M. Shahul; P. K. P|10.1109/AICAPS57044.2023.10074003|machine learning;classification;patients;risk level;healthcare;triage;Industries;Support vector machines;Oxygen;Machine learning algorithms;Hospitals;Pandemics;Delay effects|
|[Malayalam Handwritten Character Recognition Using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074586)|B. Jose; K. P. Pushpalatha|10.1109/AICAPS57044.2023.10074586|Inception;Residual Connections;Deep Learning;Deep Convolution Neural Network(DCNN);Malayalam Handwritten Character Recognition(MHCR);Transfer Learning(TL);Pre-trained Model;Training;Handwriting recognition;Convolution;Computational modeling;Transfer learning;Computer architecture;Feature extraction|
|[Measuring the Effectiveness of LDA-Based Clustering for Social Media Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074399)|A. Khan; R. Ali|10.1109/AICAPS57044.2023.10074399|Reddit;latent dirichlet allocation;topic modeling;mental health disorders;bigrams;trigrams;social media mining;Productivity;Social networking (online);Mood;Computational modeling;Mental health;Medical services;Media|
|[Opinion Detection in Hinglish News Reporting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074121)|Ananya; R. Kaushal|10.1109/AICAPS57044.2023.10074121|Opinion Detection;Hinglish;Code-mixing;Biases;Machine learning algorithms;Machine learning;Journalism;Task analysis|
|[Patients’ Medical History Summarizer using NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074336)|D. Dharrao; A. M. Bongale; V. Kadalaskar; U. Singh; T. Singharoy|10.1109/AICAPS57044.2023.10074336|Natural Language Processing;Extractive summarization;Abstractive Summarization;Natural Entity Recognition;Negation Detection;Machine learning algorithms;Medical services;Natural language processing;History;Medical diagnostic imaging|
|[Prediction of Autism and Dyslexia Using Machine Learning and Clinical Data Balancing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074161)|S. Shilaskar; S. Bhatlawande; S. Deshmukh; H. Dhande|10.1109/AICAPS57044.2023.10074161|autism;dyslexia;clinical data;machine learning;classification algorithms;prediction;Support vector machines;Computers;Autism;Education;Predictive models;Data models;Decision trees|
|[Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network & Supervised Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074510)|M. Pal; R. Prasad|10.1109/AICAPS57044.2023.10074510|Machine Learning;Sentiment Analysis;Sarcasm Detection;Natural Language Processing;Long short-term memory;Support vector machines;Radio frequency;Sentiment analysis;Social networking (online);Computational modeling;Neural networks;Motion pictures|
|[Stiffness Analysis for the Prediction of Fake News through Online Digital Networks in India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074518)|G. Sreeraag; P. G. Shynu|10.1109/AICAPS57044.2023.10074518|Social network;fake news;misinformation;SIR model;stiffness ratio;COVID-19;Analytical models;Social networking (online);Pandemics;Computational modeling;Government;Predictive models|
|[An Optimal Differential Evolution Based XGB Classifier for IoMT malware classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074030)|D. L; C. R|10.1109/AICAPS57044.2023.10074030|Internet of Medical Things (IoMT);XGBoost;Hyperparameter tuning;Medical devices;Internet of Medical Things;Boosting;Malware;Real-time systems;Classification algorithms;Servers|
|[Blockchain based Secure Erlang Server for Request based Group Communication over XMPP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074294)|J. C I; M. Vivekanandan; P. K. Premkamal; R. R|10.1109/AICAPS57044.2023.10074294|Erlang;Concurrency;XMPP;Python;Messaging;Light-weighted servers;XML;Servers;File-sharing;Group-chatting;Concurrent computing;Protocols;XML;Real-time systems;Blockchains;Telecommunications;Servers|
|[ChainHire: A Privacy-Preserving and Transparent Job Search Portal Using an Enterprise-Level Permissioned Blockchain Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074582)|S. Ghosh; R. Islam; A. Jaman; A. Bose; A. Roy|10.1109/AICAPS57044.2023.10074582|Hyperledger Fabric;Blockchain;Job Search Platform;Permissioned Blockchain;Distributed ledger;Databases;Smart contracts;Employment;Prototypes;Fabrics;Blockchains|
|[DeepHyperv: A deep neural network based virtual memory analysis for malware detection at hypervisor-layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074347)|A. Gaur; A. Singh; A. Nautiyal; G. Kothari; P. Mishra; A. Jha|10.1109/AICAPS57044.2023.10074347|Malware detection;Hypervisor;Intrusion Detection System;Deep Neural Network;Deep Memory Introspection;Deep learning;Virtual machine monitors;Neural networks;Feature extraction;Malware;Hardware;Behavioral sciences|
|[Detecting Macro less and Anti-evasive Malware in Malspam Word Attachments Using Anergy Scoring Methodology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074267)|S. S. Ramaswami; G. Swain|10.1109/AICAPS57044.2023.10074267|Malspam;Macro malware;Emotet;Trickbot;Threat actors;Evasive malware;Anti-evasive;Computational modeling;Buildings;Entertainment industry;Malware;Real-time systems;Business|
|[Malware Detection using Dynamic Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074588)|A. V; V. P; V. G. Menon; A. K. E R; A. Shilesh; A. Viswam; A. Shafiq|10.1109/AICAPS57044.2023.10074588|malware analysis;cuckoo sandbox;bidirectional long short-term memory;dynamic analysis;Machine learning algorithms;Computational modeling;Feature extraction;Malware;Libraries;Numerical models;Security|
|[Reducing the Effects of DDos Attacks in Software Defined Networks Using Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074273)|K. Radha.; R. Parameswari|10.1109/AICAPS57044.2023.10074273|Network;Software-defined networking (SDN);Denial of Service (DDoS);Quality of Service(QoS);Traffic;Thresholds;Measurement;Cloud computing;Quality of service;Computer architecture;Programming;Denial-of-service attack;Throughput|
|[Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074490)|N. K. Chahar; K. P. Singh; M. Hussain|10.1109/AICAPS57044.2023.10074490|Micropayment;E-Commerce;Two-factor Authentication;Double Payment Problems;Micropayments;Internet;Security;Electronic commerce;Business|
|[Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074211)|A. D. Joshi; R. Bhattacharyay; G. Luhadia; V. V|10.1109/AICAPS57044.2023.10074211|Seam Carving;Image Processing;Parallel Processing;Multiprocessing;Multithreading;Multithreading|
|[Student performance prediction in e-learning system and evaluating effectiveness of online courses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074504)|B. P. P; C. Kurian|10.1109/AICAPS57044.2023.10074504|Virtual Learning Environment(VLE);Massive Open Online Courses (MOOCs);Learning Management System(LMS);Artificial Neural Networks(ANN);Decision Tree (DT);Classification and Regression Tree (CART);COVID-19;Electronic learning;Pandemics;Education;Machine learning;Predictive models;Regression tree analysis|
|[A Semi-Supervised GAN Architecture for Video Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074051)|P. Ghadekar; D. Khanwelkar; N. Soni; H. More; J. Rajani; C. Vaswani|10.1109/AICAPS57044.2023.10074051|Discriminator;Generative Adversarial Network;Semi-Supervised Learning;Video Classification;Roads;Supervised learning;Computer architecture;Semisupervised learning;Generative adversarial networks;Feature extraction;Generators|
|[Covid-19 crowd detection and alert system using image processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074221)|N. Lodha; H. Singh Gahlaut|10.1109/AICAPS57044.2023.10074221|IOT;UbiBots;image processing;vectorization;machine learning;COVID-19;Protocols;Webcams;Image processing;Human factors;Object detection;Social factors|
|[Deep Learning-Based Brain Tumor Classification Prototype Using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074201)|B. Saju; L. Thomas; F. Varghese; A. Prasad; N. Tressa|10.1109/AICAPS57044.2023.10074201|Brain tumor;Glioma;Meningioma;Pituitary RESTNET 5.0;Transfer Learning Model;Analytical models;Machine learning algorithms;Computational modeling;Transfer learning;Brain modeling;Data models;Tumors|
|[Detection of Brain Tumor Using Image Processing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074053)|V. R. Prabha K; R. Gujjarlapudi; S. Ravi; Y. Satuluri; C. Nekkanti; R. P|10.1109/AICAPS57044.2023.10074053|Filter;Image subtraction;Morphology;Threshold;Tumor;Image segmentation;Magnetic resonance imaging;Tumors;Diseases|
|[Evaluation of Dilated CNN for Hand Gesture Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074389)|Y. Altaf; A. Wahid|10.1109/AICAPS57044.2023.10074389|Dilated Convolution;CNN;Gesture Classification;Sign Language Recognition;Deep Learning;Computer architecture;Benchmark testing;Feature extraction;Convolutional neural networks;Standards|
|[Hierarchical vision transformer model for polyp segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074447)|G. S.; G. C.; V. Vinod|10.1109/AICAPS57044.2023.10074447|Hierarchical vision transformer;convolutional decoder;polyp segmentation;colonoscopy;Deep learning;Image segmentation;Visualization;Image analysis;Computer architecture;Transformers;Decoding|
|[Identification of Tuberculosis Bacilli from Bright Field Microscopic Sputum Smear Images using U-Net and Random Forest Classification Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074198)|G. K; V. S|10.1109/AICAPS57044.2023.10074198|Tuberculosis;Microscopy;Bacilli;U-Net;Random Forest;Image segmentation;Microorganisms;Tuberculosis;Microscopy;Lung;Forestry;Network architecture|
|[Impact of Stain Normalisation Technique on Deep Learning based Nuclei Segmentation in Histopathological Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074363)|K. Vaishnani; B. Gohel; A. Hati|10.1109/AICAPS57044.2023.10074363|Histopatholodical image;Nuclei segmentation;U-net;H&E stain normalisation;Deep learning;Image segmentation;Pathology;Analytical models;Image color analysis;Computational modeling;Morphology|
|[Intelligent Papilledema Detector (IPD)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074229)|P. Thiagarajan; S. M|10.1109/AICAPS57044.2023.10074229|Papilledema;Pseudopapilledema;Retinal fundus;Artificial intelligence;Neural networks;EfficientNet;Measurement;Training;Training data;Detectors;Medical services;Retina;Market research|
|[Kidney Disease Detection from CT Images using a customized CNN model and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074314)|M. S. Hossain; S. M. Nazmul Hassan; M. Al-Amin; M. N. Rahaman; R. Hossain; M. I. Hossain|10.1109/AICAPS57044.2023.10074314|Watershed Algorithm;ResNet50;EANet;CKD;CNN;Deep learning;Image segmentation;Computed tomography;Computational modeling;Neural networks;Watersheds;Kidney|
|[Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074562)|S. R. B. S; M. G; E. Sherly|10.1109/AICAPS57044.2023.10074562|Segmentation;receiver operating characteristic curve;spatial Fuzzy C-Means;Clustering;Probabilistic Neural Network;Discrete Wavelet Transform;Computed Tomography;Image segmentation;Computed tomography;Clustering algorithms;Watersheds;X-rays;Feature extraction;Probabilistic logic|
|[Labeled Hands in Wild](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074180)|A. Kalra; A. Salunke; P. Majali; P. Bhandiwad; K. Chachadi; S. Kamath; S. Jana; R. Joshi|10.1109/AICAPS57044.2023.10074180|nan;Geometry;Human computer interaction;Three-dimensional displays;Annotations;Pose estimation;Lighting;Manuals|
|[MSCAUNet-3D: Multiscale Spatial Channel Attention 3D-UNet for Lung Carcinoma Segmentation on CT Image](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074322)|S. Poonkodi; M. Kanchana|10.1109/AICAPS57044.2023.10074322|3D-UNet;Segmentation;Attention Mechanism;Lung Carcinoma;Multi-Scale Attention;Image segmentation;Adaptation models;Solid modeling;Computed tomography;Computational modeling;Volume measurement;Lung|
|[VR for automobile customization and its feedback analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074233)|P. Ghadekar; K. Jhanwar; A. Karpe; T. Shetty; A. Sivanandan; P. Khushalani|10.1109/AICAPS57044.2023.10074233|Oculus quest 2;VR;Text analysis;NLTK;Scikit learn;Analytical models;Text analysis;Pandemics;Computational modeling;Companies;Human factors;Drives|
|[Wind profiler Doppler power spectrum segmentation using U-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074415)|B. P. Thampy; J. M. V; A. Kottayil|10.1109/AICAPS57044.2023.10074415|Wind profiler radar;Doppler power spectrum;Segmentation;U-Net;Deep learning;Performance evaluation;Motion segmentation;Atmospheric modeling;Computational modeling;Estimation;Doppler radar|
|[A New Clustering Approach based on Trust and Rat Swarm Algorithm for WSN Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074379)|W. Osamy; A. Salim; A. A. Ali; A. M. Khedr|10.1109/AICAPS57044.2023.10074379|Wireless Sensor Network;RSO Algorithm;Trust;IoT;Wireless communication;Measurement;Wireless sensor networks;Simulation;Clustering algorithms;Rats;Routing|
|[Analysis of Plantar Pressure to detect Foot Abnormalities among various subjects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074000)|D. Saha; S. Prabhu; A. Thapliyal; M. M. M. Pai|10.1109/AICAPS57044.2023.10074000|Plantar Pressure;RSscan;pedobarography;Pathology;Estimation;Machine learning;Programming;Diabetes;Pressure measurement;Stress|
|[Design of IoT based hybrid Red LED VLC-fiber communication system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074488)|M. Kumari|10.1109/AICAPS57044.2023.10074488|Optical wireless communication (OWC);Visible light communication (VLC);Light emitting diode (LED);Internet of things (IoT);Wireless communication;Optical fibers;Costs;Stimulated emission;Light emitting diodes;Internet of Things;Visible light communication|
|[Gradient Enhanced Regressive Multivariate Artificial Fish Swarm Optimized Data Collection for IoT-Enabled WSN in Smart Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074386)|S. R. S; R. R. Mostafa; M. E. Bannany; A. M. Khedr|10.1109/AICAPS57044.2023.10074386|WSN-IoT;data collection;Gradient-enhanced broken-stick regression;Multivariate Artificial Fish Swarm Optimization;Wireless sensor networks;Energy consumption;Bandwidth;Data collection;Fish;Throughput;Energy efficiency|
|[IoT Based Smart Irrigation and Farm Protection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074143)|S. N. Shilaskar; S. S. Bhatlawande; J. B. Deshmukh; S. A. Dehankar|10.1109/AICAPS57044.2023.10074143|smart irrigation;NodeMCU;soil moisture sensor;water pump;animals;Irrigation;Animals;Soil moisture;Crops;Production;Monsoons;Real-time systems|
|[Smart Irrigation Management System for Precision Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074171)|D. V. Sarath Chandra; G. Kaur; M. Bhattacharya|10.1109/AICAPS57044.2023.10074171|Sensors;Internet of Things (IoT);smart;precision agricultureSensors;precision agriculture;Smart agriculture;Productivity;Temperature sensors;Temperature measurement;Irrigation;Temperature distribution;Computational modeling|
|[Smart River Water Quality and Level Monitoring: a Hybrid Neural Network Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074495)|C. C; G. T. S; R. B; D. R. A; D. S; K. K|10.1109/AICAPS57044.2023.10074495|deep neural network;water quality monitoring;level monitoring;river water;water parameters;Temperature measurement;Measurement;Water quality;Production;Feature extraction;Rivers;Convolutional neural networks|
|[Statistical Modelling of Massive MIMO Channel at FR2 Frequency Bands for B5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074158)|S. D. L; R. Dilli; K. M|10.1109/AICAPS57044.2023.10074158|Wireless communication;B5G;5G NR;mmWave;6G;FR2 and FR1 frequency bands;channel model;NYUSIM;Power delay profiles;massive MIMO;Wireless communication;6G mobile communication;Atmospheric modeling;Receiving antennas;Massive MIMO;Software;Behavioral sciences|
#### **2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)**
- DOI: 10.1109/ICCT56969.2023
- DATE: 19-20 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Impersonated Human Speech Chatbot with Adaptive Frequency Spectrum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076120)|G. Chettiar; A. Shukla; P. Nalwaya; K. Sethi; S. Prakash|10.1109/ICCT56969.2023.10076120|Adaptive Personalized Dataset;NLP Chatbot;Sentiment Analysis;Speech-To-Text;Text-To-Speech;Training;Adaptation models;Vocabulary;Technological innovation;Vocoders;Oral communication;Chatbots|
|[An Emerging Detection Design Adopting Two-Keying Technique in SAC-OCDMA-Based MDW Code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076193)|H. M. R. Al-Khafaji; S. A. Aljunid; A. Rathi|10.1109/ICCT56969.2023.10076193|SAC-OCDMA;TKSD;MDW code;MUI;PIIN;Integrated optics;Codes;Interference;Network security;Feature extraction;Optical receivers;Encoding|
|[Comparative Analysis of ISP-Perf and TEMs in Mobile Broadband QoS Metrics Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076198)|S. I. Orakwue; H. M. R. Al-Khafaji; A. Rathi|10.1109/ICCT56969.2023.10076198|ISP-Perf;TEMs;QoS;Broadband;Performance metrics;Hospitals;Measurement uncertainty;3G mobile communication;Quality of service;Modems;Downlink;Broadband communication|
|[Precision Monitoring of Health-Care Using Big Data and Java from Social Networking and Wearable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075744)|R. Goel; S. C. J|10.1109/ICCT56969.2023.10075744|Health;Patient;Machine learning;Data mining;Java;Java;Wireless sensor networks;Social networking (online);Wearable computers;Semantics;Medical services;Ontologies|
|[Self-assess Momentary Mood in Mobile Devices: a Case Study with Mature Female Participants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075891)|C. Senette; M. C. Buzzi; M. T. Paratore|10.1109/ICCT56969.2023.10075891|momentary mood;mobile devices;well-being Apps;design;input methods;Target tracking;Mood;Image color analysis;Sociology;Wheels;User interfaces;Tag clouds|
|[A Novel Approach to Object Detection: Object Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076212)|M. Singh|10.1109/ICCT56969.2023.10076212|Object Detection;Deep Learning;Convolutional Neural Networks;Computers;Computational modeling;Neural networks;Focusing;Object detection;Predictive models;Search problems|
|[A Review on Unconstrained Real-Time Rotation-Invariant Face Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076222)|S. L. Agrwal; S. K. Sharma; V. Kant|10.1109/ICCT56969.2023.10076222|Face detection;Rotation and pose invariant face detection;Deep learning;Databases;Face recognition;Computer architecture;Benchmark testing;Feature extraction;Real-time systems;Magnetic heads|
|[A Case Study to Analyze the Impact of Social Media on Video Game Sales](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076200)|K. Malvankar; E. Fallon; P. Connolly; K. Flanagan|10.1109/ICCT56969.2023.10076200|Sentiment Analysis;Social Media;Twitter;Data Analysis;Video games;Data analysis;Social networking (online);Blogs;Entertainment industry;Media;Market research|
|[A Comparative Study on Deep Learning Techniques for Bird Species Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075901)|S. V. S. Kumar; H. K. Kondaveerti|10.1109/ICCT56969.2023.10075901|Bird species recognition;Convolutional neural network;Data augmentation;Deep learning;Image classification;Training;Deep learning;Shape;Image color analysis;Computational modeling;Manuals;Birds|
|[Trends in Research Topic Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075871)|S. Lakhanpal; A. Gupta|10.1109/ICCT56969.2023.10075871|text mining;research trend analysis;phrase sense disambiguation;Data analysis;Market research|
|[A Systematic Review of Task Offloading & Load Balancing Methods in a Fog Computing Environment: Major Highlights & Research Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075966)|G. Goel; A. K. Chaturvedi|10.1109/ICCT56969.2023.10075966|Fog Computing;Job offloading;Load Balancing;Quality of Service;Multi-Objective Optimization;Performance evaluation;Analytical models;Systematics;Computational modeling;Load management;Throughput;Time factors|
|[State-Of- The-Art and Research Challenges in Task Scheduling and Resource Allocation Methods for Cloud-Fog Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076030)|P. K. Mishra; A. K. Chaturvedi|10.1109/ICCT56969.2023.10076030|Resource Allocation;Task Scheduling;Execution Time;Cost;Deadline;Fog computing;Cloud computing;Processor scheduling;Dynamic scheduling;Resource management;Task analysis;Edge computing|
|[A Comparative Study of Wireless Communication Protocols for use in Smart Farming Framework Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075696)|S. Bhatia; Z. A. Jaffery; S. Mehfuz|10.1109/ICCT56969.2023.10075696|Internet of things;Wireless Communication protocols;Smart farming;Agriculture;LoRa WAN;Smart agriculture;Wide area networks;Productivity;Wireless sensor networks;Protocols;Zigbee;WiMAX|
|[Polar Code Trellis Decoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076185)|J. Sodha|10.1109/ICCT56969.2023.10076185|Polar codes;Code Trellis;Viterbi;BPSK;AWGN;Phase shift keying;Codes;Symbols;Euclidean distance;Prediction algorithms;Throughput;Encoding|
|[Automatic Seat Identification System in Smart Transport using IoT and Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075664)|S. Bhatia; D. Gautam; S. Kumar; S. Verma|10.1109/ICCT56969.2023.10075664|Internet of things;Automatic Seat Identification;Public Transport;Vehicles;Image processing;Smart Transportation System;Webcams;Face recognition;Digital images;Logic gates;Real-time systems;Software;Servers|
|[Decentralized Identity Management System using the amalgamation of Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076117)|S. Gupta; A. K. Bairwa; S. S. Kushwaha; S. Joshi|10.1109/ICCT56969.2023.10076117|Blockchain;IoT;AES;SHA-256;IPFS;Data privacy;Sociology;Smart contracts;Public key;Privacy breach;Blockchains;Encryption|
|[Analysis of the Effect of Adversarial Training in Defending EfficientNet-B0 Model from DeepFool Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075774)|A. M. A.; B. M.; S. D. Dunston; M. A. R. V.|10.1109/ICCT56969.2023.10075774|Deep Learning;EfficientNet-B0 model;Adversarial Attack;DeepFool;Adversarial Training;Training;Deep learning;Analytical models;Computational modeling;Microprocessors;Computed tomography;Lung|
|[Counterfeit Drug Prevention in Pharma Supply Chain using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076043)|R. K. Jha; P. Alam; N. Priyadarshi; M. A. Ghazi; M. S. Bhargavi|10.1109/ICCT56969.2023.10076043|Blockchain;Hyperledger fabric;Smart Contract;Pharma Supply Chain;Drugs;Distributed ledger;Supply chains;Forestry;Fabrics;Blockchains;Safety|
|[Optimal Predictive Maintenance Technique for Manufacturing Semiconductors using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075658)|D. Pradeep; B. V. Vardhan; S. Raiak; I. Muniraj; K. Elumalai; S. Chinnadurai|10.1109/ICCT56969.2023.10075658|Machine Learning(ML) Techniques;Logistic Regression;Random Forest Classifier;Support Vector Machine;Decision Tree Classifier;Extreme Gradient Boost and Neural Networks(NN);Training;Productivity;Semiconductor device modeling;Buildings;Feature extraction;Manufacturing;Numerical models|
|[Received Signal Strength and Optimized Support Vector Machine based Sybil Attack Detection Scheme in Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075848)|R. Sriranjani; N. Hemavathi; A. Parvathy; B. Salini; L. Nandhini|10.1109/ICCT56969.2023.10075848|Sybil attack;smart grid;machine learning;support vector machine;received signal strength;performance indices;Support vector machines;Training;Sensitivity;Machine learning algorithms;Receivers;Machine learning;Mathematical models|
|[Key Cryptographic Methods in the Cloud: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076165)|V. Yadav; M. Kumar|10.1109/ICCT56969.2023.10076165|Cloud Computing;Cryptography;Security;AES;DES;Triple DES;Blow Fish;RC4;RSA;ECC;Industries;Cloud computing;Data privacy;Ciphers;Costs;Elliptic curve cryptography;Boosting|
|[A Hybrid Cryptography Approach Using Symmetric, Asymmetric and DNA Based Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076124)|V. Yadav; M. Kumar|10.1109/ICCT56969.2023.10076124|cloud computing;cryptography;AES;RSA;DNA;security;Cloud computing;Ciphers;Databases;DNA;Encryption;Complexity theory;Safety|
|[IHDNA: Identical Hybrid Deep Neural Networks for Alzheimer's Detection using MRI Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075912)|A. P. Singh; N. Upadhyay; V. G. Shankar; B. Devi|10.1109/ICCT56969.2023.10075912|ADNI Dataset;Deep Learning;MRI;Neurodegenerative Disorder;Transfer Learning;Identical Hybrid Neural Networks;VGG16;Neuroimaging;Deep learning;Costs;Magnetic resonance imaging;Computational modeling;Pipelines;Feature extraction|
|[Automated Pneumonia Detection using deep features in chest X-ray images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076157)|T. Ouleddroun; A. Ellahyani; M. El Ansari|10.1109/ICCT56969.2023.10076157|Pneumonia;Deep Learning;Convolutional Neu-ral Network;Histograms;Pulmonary diseases;Lung;Machine learning;Adaptive equalizers;Streaming media;Feature extraction|
|[Seismic Lithology Interpretation using Attention based Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075964)|V. C. Dodda; L. Kuruguntla; S. Razak; A. Mandpura; S. Chinnadurai; K. Elumalai|10.1109/ICCT56969.2023.10075964|Deep learning;Lithology prediction;seismic data;Interpretation;Deep learning;Training;Measurement;Time-frequency analysis;Continuous wavelet transforms;Geology;Noise reduction|
|[Secure Beamforming for Intelligent Reflecting Surface Assisted MISO Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075734)|R. Kaur; B. Bansal|10.1109/ICCT56969.2023.10075734|Physical layer security (PLS);secrecy capacity;intelligent reflecting surface;maximum ratio transmission (MRT);zero-forcing (ZF) beamforming;Wireless communication;Measurement;Array signal processing;Simulation;Transmitting antennas;Line-of-sight propagation;MISO communication|
|[Seamless Integration of DevOps Tools for Provisioning Automation of the IoT Application on Multi-Infrastructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075814)|H. E. Solayman; R. P. Qasha|10.1109/ICCT56969.2023.10075814|Provisioning;Orchestration;DevOps;CI/CD;TOSCA;Cloud;Edge;Cloud computing;Systematics;System performance;Pipelines;Production;Sensors;Internet of Things|
|[Bibliometric Analysis of High-Performance Athletes During Covid-19 Pandemic: From Citation Mapping to Research Agenda](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076183)|C. Páez-Quinde; A. C. Orellana-Páez; D. Reyes-Bedoya; W. Llerena-Llerena|10.1109/ICCT56969.2023.10076183|Bibliometric analysis;athletes;Covid-19;high performance;health 4.0;scientific production;co-citations;COVID-19;Biomechanics;Systematics;Pandemics;Databases;Bibliometrics;Production|
|[Rapport of Counterfeit Profiles in Social Networking using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076017)|M. A. Jyothi; T. Sridevi; K. Rajani; U. Channabasava; S. Bethu|10.1109/ICCT56969.2023.10076017|Data Science;fake profiles;fake recognition;identity fraudulence;social media;Automation;Social networking (online);Computational modeling;Psychology;Machine learning;Microcomputers;Media|
|[Individual Performance Model for E-Learning in University](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075959)|L. Susanti; D. P. Alamsyah; N. K. Hikmawati|10.1109/ICCT56969.2023.10075959|E-Learning;Individual Performance;Continuance Intention;Satisfaction;Student;Electronic learning;Correlation;Pandemics;Behavioral sciences;Mediation;Testing|
|[Effectiveness of Anti-Spoofing Protocols for Email Authentication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076098)|P. D. Chauhan; A. M. Shah|10.1109/ICCT56969.2023.10076098|Spoofing;email;SPF;DMARC;DKIM;SMTP;Protocols;Authentication;Security|
|[A Survey on Detection of Fraudulent Credit Card Transactions Using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076122)|A. N. Ahmed; R. Saini|10.1109/ICCT56969.2023.10076122|AI;Fraud Detection;Credit Card;Machine learning;Algorithms;Data Imbalance;Support vector machines;Machine learning algorithms;Databases;Neural networks;Organizations;Credit cards;Data models|
|[Extracting potential Travel time information from raw GPS data and Evaluating the Performance of Public transit - a case study in Kandy, Sri Lanka](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075789)|S. Ratneswaran; U. Thayasivam|10.1109/ICCT56969.2023.10075789|Travel time;GPS data;public transit;performance metrics;Measurement;Schedules;Statistical analysis;Transforms;Receivers;Real-time systems;Geospatial analysis|
|[Analysing the Need for 5G Networks based on Smartphone Market Penetration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075753)|C. Panse; A. Chaskar|10.1109/ICCT56969.2023.10075753|5G;Smartphone;Transmission;IOT;LTE;Wireless communication;Cloud computing;5G mobile communication;Virtual reality;Machine learning;Market research;Telecommunications|
|[Lung Disease Classification Using Deep Learning Models from Chest X-ray Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075968)|S. Sultana; A. Pramanik; M. S. Rahman|10.1109/ICCT56969.2023.10075968|Lung Disease;COVID-19;Pneumonia;Tubercu-losis;X-ray Image;COVID-19;Deep learning;Tuberculosis;Pulmonary diseases;Lung;Medical services;Convolutional neural networks|
|[IoT & Vision Based Environment Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076137)|Y. B. Jonnala; L. S. Talluru; S. Kapuganti; J. Joshi|10.1109/ICCT56969.2023.10076137|Artificial Intelligence;Vision-Based Application;IoT;AIoT;Ardunio;Sensors;Air Quality Index;ppm;Gases;Surveillance;Organizations;Hardware;Software;Sensor systems;Sensors|
|[Secured Environmental Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075967)|L. S. Talluru; S. Kapuganti; Y. B. Jonnala; J. Joshi|10.1109/ICCT56969.2023.10075967|Internet of Things;Security;Challenges;Limitations;Industries;Smart cities;Pollution control;Smart homes;Real-time systems;Safety;Smart transportation|
|[A Compact Symmetrically Inverted Slotted T-Shaped Patch Antenna for Tri-band Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075877)|S. Goswami; S. K. Mandal; S. Banerjee|10.1109/ICCT56969.2023.10075877|Inverted T-shaped patch;tri-band;modified ground plan;microstrip antenna;antenna gain;Antenna measurements;Wireless LAN;Slot antennas;Spaceborne radar;Resonant frequency;Bandwidth;Microstrip antennas|
|[HIREX: A Heterogeneous Interoperable Blockchain Solution For Hiring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076241)|R. Amin; A. Islam; D. H. Tanvir; R. I. Arif|10.1109/ICCT56969.2023.10076241|heterogeneous interoperability;heterogeneous blockchain;hiring system;Hyperledger fabric;Ethereum;Cross chain;Distributed ledger;User interfaces;Fabrics;Forgery;Blockchains;Safety;Interoperability|
|[A Detailed Investigation of Wireless Sensor Network Energy Harvesting Schemes to Maximize Lifetime of Sensor Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075842)|H. S. Dhillon; K. Kumar; P. Chawla|10.1109/ICCT56969.2023.10075842|Energy harvesting;Wireless sensor networks;Schemes;Sensor nodes;NS2;Wireless communication;Wireless sensor networks;Energy consumption;Power demand;Throughput;Batteries;Computational efficiency|
|[A Novel Technique to Detect URL Phishing based on Feature Count](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075943)|V. Dantwala; R. Lakhani; N. Shekokar|10.1109/ICCT56969.2023.10075943|URL phishing;neural network;machine learning model;feature extraction;Uniform resource locators;Machine learning algorithms;Phishing;Computational modeling;Artificial neural networks;Feature extraction;Classification algorithms|
|[Systems & Methods for Generation Of Electrical Power From A Sludge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075972)|N. N. Das; A. Rai; A. Singh; K. Kundu|10.1109/ICCT56969.2023.10075972|Sludge;electron;electricity;fuel cell;sewage Plant;Meters;Renewable energy sources;Microorganisms;Limiting;Pandemics;Photovoltaic cells;Wind turbines|
|[Open-Source Software Security Challenges and Policies for Cloud Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076194)|S. Gupta; S. Vadlamudi|10.1109/ICCT56969.2023.10076194|Open Source;Enterprise Security;Open-Source Security Polices;Cloud Security;Industries;Cloud computing;Technological innovation;Codes;Supply chains;Collaboration;Libraries|
|[Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076090)|N. Babbar; A. Kumar; V. Kuma Verma|10.1109/ICCT56969.2023.10076090|Long Short-Term Memory (LSTM);Convolutional Neural Network (CNN);Recurrent Neural Network (RNN);Training;Temperature distribution;Statistical analysis;Crops;Moisture;Soil;Nitrogen|
|[Analysis based on Different Optimization Algorithms for Landslide Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075907)|L. L; G. A. S. Saroja|10.1109/ICCT56969.2023.10075907|Landslide Identification;Deep Residual network;Angular distance;Data augmentation;deep learning;Training;Landslides;Adaptation models;Machine learning algorithms;Sensitivity;Databases;Biological system modeling|
|[Voice-Based Interaction for an Aging Population: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075801)|S. Pednekar; P. Dhirawani; R. Shah; N. Shekokar; K. Ghag|10.1109/ICCT56969.2023.10075801|Older Adults;Human-Computer Interaction;Literature Review;Voice User Interfaces;Voice Assistants;Privacy;Systematics;Sociology;Smart homes;Speech recognition;Market research;Older adults|
|[Detecting Hate Speech in Hindi in Online Social Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075749)|A. Sharma; R. Kaushal|10.1109/ICCT56969.2023.10075749|Hate speech;Multilingual;Code-mixing;NLP;Speech coding;Hate speech;Blogs;Cyberbullying;Oral communication;Writing;Natural language processing|
|[A Robust Neural Network Based Short Time Electricity Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075993)|A. Pandey; M. Pandey|10.1109/ICCT56969.2023.10075993|Load forecasting;Price Prediction;Polak-Rlbière-Polyak;MATLAB;Neural Network and Market clear price (MCP) etc;Training;Load forecasting;Computational modeling;Predictive models;Prediction algorithms;Root mean square;Task analysis|
|[A Comparison of Neural Networks and Machine Learning Methods for Prediction of Heart Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076174)|O. S. Ghongade; S. K. S. Reddy; S. Tokala; K. Hajarathaiah; M. K. Enduri; S. Anamalamudi|10.1109/ICCT56969.2023.10076174|Machine leaning algorithms;ML performance models;Neural Networks;Heart;Support vector machines;Machine learning algorithms;Predictive models;Prediction algorithms;Medical diagnosis;Predictive analytics|
|[Semantic Segmentation of Dental Caries using Improved Deeplab V3Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075992)|M. Kaki; S. Gunnam; S. Dhanavath; P. K. Gorantla; R. Saripineni|10.1109/ICCT56969.2023.10075992|Semantic segmentation;Dental caries;Deeplabv3;Computer-aided diagnosis;Deep learning;Pain;Semantic segmentation;Semantics;Neural networks;Medical services;Attenuation|
|[An Approach against Vampire Attack for Successful Transmission in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076140)|V. Juneja; S. Kumar Dinkar|10.1109/ICCT56969.2023.10076140|Vampire Attack;Wireless Sensor Network;Energy Consumption;Battery life;DoS;Fuzzy logic;Wireless sensor networks;Energy consumption;Telecommunication traffic;Probabilistic logic;Approximation algorithms;Feature extraction|
|[Identification and Screening of Novel ACE Inhibitors using Computational Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075941)|M. M. Mishra; P. Kumar|10.1109/ICCT56969.2023.10075941|acetylcholinesterase;cholinergic neurons;neocortex;neurofibrillary tangles (NFT);Donepezil;5;6-dimeth oxy-2-(piperidin-4-ylmethyl)-2;3-dihydroinden-l-one;Drugs;Enzymes;Neurotransmitters;Inhibitors;Neurons;Rats;Skin|
|[Content based Video Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075906)|H. Shrimali; R. Saxena; Kavita|10.1109/ICCT56969.2023.10075906|Recommendation system;Content based recommendation;YouTube recommendation;Computer aided instruction;Video on demand;Electronic learning;Semantics;Natural languages;Recommender systems;Videos|
|[Development of Secure IoT Ecosystems for Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076119)|P. Shirgur; S. Chaurasia|10.1109/ICCT56969.2023.10076119|IoT;Microcomputers;MQTT;personalized healthcare;OWASP ISVS;secure health care platform;Industries;Economics;Cloud computing;Ecosystems;Medical services;Behavioral sciences;Internet of Things|
|[A Review On: Autism Spectrum Disorder Detection by Machine Learning Using Small Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076139)|K. Garg; N. N. Das; G. Aggrawal|10.1109/ICCT56969.2023.10076139|ASD;Machine Leaning for ASD;Deep Learning For ASD;Autism spectrum disorder;Computers;Deep learning;Autism;Pediatrics;Machine learning algorithms;DNA;Feature extraction|
|[Analysing Shifts in Perceptions of Indians during COVID-19 Pandemic by Mining Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075775)|R. Saxena; M. Jadeja|10.1109/ICCT56969.2023.10075775|COVID-19;Corona Virus;Twitter India;Sentiment Analysis;COVID-19;Visualization;Social networking (online);Pandemics;Computer viruses;Mood;Blogs|
|[A Study on Smart Homes And Grids Under IoT Components](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076093)|N. Jakhar; R. Nandal; K. Joshi; A. Noonia|10.1109/ICCT56969.2023.10076093|Smart homes;the Internet of things;smart grids;Energy Management;Roads;Smart homes;Ontologies;Turning;Smart grids;Safety;Internet of Things|
|[Review of GPS-GSM Based Intelligent Speed Assistance Systems: Development and Research Opportunities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076115)|A. Ya'u Gital; M. Abdulhamid; M. Abdulhameed; F. U. Zambuk; M. Nehemiah; M. A. Lawal; Z. I. Yakubu|10.1109/ICCT56969.2023.10076115|Intelligent Transport Systems;Intelligent Speed Assistance;GPS;GSM;GSM;Tracking;Roads;Surveillance;Velocity control;Traffic control;Real-time systems|
|[Real Time Driver Drowsiness Detecion using Transfer learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075913)|N. Gupta; F. Khan; B. Saini|10.1109/ICCT56969.2023.10075913|Drowsiness detection;CNN;Transfer Learning;DenseNet201;Deep learning;Computational modeling;Transfer learning;Predictive models;Cameras;Real-time systems;Object recognition|
#### 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)
- DOI: 10.1109/ICAIS56108.2023
- DATE: 2-4 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Home Monitoring System using Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073847)|R. A; M. K; V. E; S. P|10.1109/ICAIS56108.2023.10073847|Internet of Things;smart kitchen;server;sensor;Android application;HTTP;MQTT;Temperature sensors;Temperature measurement;Operating systems;Prototypes;Sensor systems;Servers;Internet of Things|
|[Wearable Device based Fall Prediction and Alert Mechanism for Aged People using IoT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073853)|D. V. Babu; S. Ramya|10.1109/ICAIS56108.2023.10073853|Patient Monitoring;Wearable Fall Detection;Internet of Things (IoT);Micro Electro-Mechanical System (MEMS) Sensor;Cloud Gateway;Temperature sensors;Temperature measurement;Micromechanical devices;Medical services;Aging;Sensors;Fall detection|
|[Comparative Analysis of Crop Diseases Detection Using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073831)|P. Jha; D. Dembla; W. Dubey|10.1109/ICAIS56108.2023.10073831|ML;Deep Learning;Crop;Plant;Crop Diseases;Agriculture;Farmer;Deep learning;Machine learning algorithms;Stacking;Sociology;Crops;Feature extraction;Classification algorithms|
|[Malpractice Detection in Online Proctoring using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073664)|I. D. Raga Priya; P. Sree Ramya; M. V. Vamshi; B. Chandana; M. K. Rao|10.1109/ICAIS56108.2023.10073664|Virtual Proctoring;Histogram of Oriented Gradients;Convolutional Neural Network;Support Vector Machine;Generative Adversarial Networks;Machine Learning;Facial Recognition;Facial Detection;Face Landmark estimation;Open Face;Support vector machines;Industries;Tracking;Face recognition;Image processing;Manuals;Magnetic heads|
|[Pneumonia Detection and Classification using Hybrid Convolution Neural Network and Machine Learning Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073927)|M. A; A. N; R. Sk; Y. P. V; S. Sk; L. S. K. D|10.1109/ICAIS56108.2023.10073927|Hybrid Convolutional Neural Network;Chest X-rays;Support Vector Machine;Microorganisms;Convolution;Pulmonary diseases;Neural networks;Support vector machine classification;Lung;Medical services|
|[Application of Machine Learning to Generate a Contingency Ranking for Power System Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073836)|P. Venkatesh; N. Visali|10.1109/ICAIS56108.2023.10073836|Machine Learning;Power System Security;STATCOM;Unified Power Flow Controller (UPFC);Power transmission lines;Contingency management;Machine learning;Power system stability;Load management;Generators;Stability analysis|
|[Job Recommendation System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073757)|S. Gadegaonkar; D. Lakhwani; S. Marwaha; P. A. Salunke|10.1109/ICAIS56108.2023.10073757|Machine Learning;App Development;Android;Content-Based Filtering;Material Design;Kotlin;Jetpack Compose;Bridges;Databases;Machine learning;Companies;Filtering algorithms;Recommender systems|
|[IoT based Smoke Detection with Air Temperature and Air Humidity; High Accuracy with Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073920)|S. Kumaran; A. S; S. V; S. T|10.1109/ICAIS56108.2023.10073920|Internet of Things (IoT);Smoke Detection;Machine Learning (ML);Home Automation;Temperature sensors;Temperature measurement;Cloud computing;Wireless sensor networks;Home automation;Humidity;Real-time systems|
|[Electricity Price Prediction using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073777)|K. L. Chowdary; C. N. Krishna; K. S. Manaswini; B. Jithendra|10.1109/ICAIS56108.2023.10073777|Electricity;Price Prediction;Machine learning;Regressors;Mean Absolute Error;Support vector machines;Measurement;Machine learning algorithms;Artificial neural networks;Production;Predictive models;Prediction algorithms|
|[Work Visa Analysis using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073837)|P. B. Aakash Sai Raj; J. Piri; S. B. Eluri; S. R. S|10.1109/ICAIS56108.2023.10073837|H1B Visa Prediction;Random Forest algorithm;Synthetic Minority Oversampling Technique (SMOTE);Imbalanced Dataset;Machine learning algorithms;Costs;Law;Employment;Machine learning;Forestry;Data science|
|[A Machine Learning based Accurate Localization Technique for 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073924)|P. C. S; H. Nishat; D. B; R. P; P. B. A|10.1109/ICAIS56108.2023.10073924|Time Difference of Arrival;Channel State Information;Heterogeneous network;Real dynamic Network;Q learning model;Location awareness;Training;Machine learning algorithms;Q-learning;5G mobile communication;Time difference of arrival;Simulation|
|[Addictive Disorder Susceptibility Prediction Using Machine Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073701)|A. Prasad; V. Asha; A. P. Nirmala; M. S.; M. Kumar; S. P. Sreeja|10.1109/ICAIS56108.2023.10073701|Addictive Disorder;Drug;Machine Learning;Neural Networks;Predictive Analysis;Training;Machine learning algorithms;Addiction;Training data;Machine learning;Predictive models;Reliability|
|[IoT based Smart Car Parking System with the Help of Sensors Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073729)|R. M; R. A; R. A; S. J. D|10.1109/ICAIS56108.2023.10073729|Car;Parking;Automation;IR sensor;IOT;Rails;Multiplexing;Roads;Government;Sensor systems;Automobiles;Internet of Things|
|[Review of Artificial Intelligence methods for detecting cancer in medical image processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073817)|P. Sarkar; A. Saha|10.1109/ICAIS56108.2023.10073817|Cancer;Pre-processing;Segmentation;Classification;Image segmentation;Sociology;Lung cancer;Liver;Medical services;Artificial intelligence;Statistics|
|[Detection of Salient Objects in a Video using a Hybrid Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073838)|M. Indirani; C. Anitha; S. Goswami; K. Baranitharan; S. Govindaraju; M. R|10.1109/ICAIS56108.2023.10073838|Computer Vision;Convolution networks;Deep Learning;Firefly algorithm;Object detection;Artificial intelligence;Recurrent neural networks;Terminology;Computational modeling;Benchmark testing;Spatiotemporal phenomena;Convolutional neural networks;Video recording|
|[Melanoma Boundaries Detection Techniques using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073936)|K. J. Velmurugan; P. Srinivasan; A. Gayathri; S. Yuvarani|10.1109/ICAIS56108.2023.10073936|Boundary detection;Dermoscopic images;Melanoma;Machine learning;Skin lesion segmentation;Support vector machine;Random Forest;Decision tree;Support vector machines;Radio frequency;Sensitivity;Image recognition;Melanoma;Skin;Power capacitors|
|[ANN Based Static Var Compensator For Improved Power System Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073700)|R. L. Kapse; V. K. Chandrakar|10.1109/ICAIS56108.2023.10073700|SVC;PI;Damping of oscillation;ANN;Power system security;Static VAr compensators;Power system stability;Nonhomogeneous media;Stability analysis;Power system security;Feeds;Time factors|
|[Chief Remote Officer Role in COVID-19 for Work Sustainability and Use of Artificial Intelligence (AI)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073923)|S. Tayal; K. Rajagopal; V. Mahajan|10.1109/ICAIS56108.2023.10073923|Chief Remote Officer (CRO);Chief Remote lead;Head of Remote;Director of Remote Work;Strategic Human Resource Management;Work from home;Changing Leadership;Artificial Intelligence;Work sustainability;Sustainable development goal SDG 8;Decent work and economic growth;COVID-19 Pandemic;COVID-19;Industries;Employee welfare;Pandemics;Standards organizations;Organizations;Standardization|
|[Analysis of Liver Tumor Segmentation using Deep ResUNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073676)|L. Krishnakumari; R. Ramalakshmi; V. Srirenganachiyar; K. Ragavan; K. Ramalakshmi|10.1109/ICAIS56108.2023.10073676|ResUNet;Two cascaded Convolutional Neural Networks(CNNs);Computed Tomograph.y;Liver cancer;Computed tomography;Liver;Physiology;Convolutional neural networks;Reliability;Artificial intelligence|
|[Artificial Intelligence Enabled Diagnostic Digital Cytopathology System for Cervical Intraepithelial Neoplasia Detection: Advantages and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073850)|A. Kapruwan; S. Sharma; H. R. Goyal|10.1109/ICAIS56108.2023.10073850|Artificial intelligence;Digital cytopathology;Cervical Intraepithelial Neoplasia (CIN);Chemotherapy;Surgery;Psychology;Hazards;Artificial intelligence;Cervical cancer;Neoplasms|
|[Multiple Linear Regression Algorithm-based Car Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073882)|R. Swarnkar; R. Sawant; H. R; S. P|10.1109/ICAIS56108.2023.10073882|Machine Learning algorithms;MLR;Support Vector Machine;Decision Tree;Random Forest;car-price prediction;Support vector machines;Visualization;Machine learning algorithms;Redundancy;Predictive models;Prediction algorithms;Software|
|[Energy Efficient Data Management in Health Care](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073796)|H. V; L. J; S. R. A; N. Divya Bharathi; S. Upadhyay; V. R|10.1109/ICAIS56108.2023.10073796|Network simulator;energy consumption;hop to hop loss;energy efficient routing;WSN;Wireless sensor networks;Packet loss;Medical services;Telecommunication traffic;Routing;Energy efficiency;Routing protocols|
|[Advanced Data Mining Enabled Robust Sentiment Analysis on E-Commerce Product Reviews and Recommendation Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073782)|B. Shanthini; N. Subalakshmi|10.1109/ICAIS56108.2023.10073782|Sentiment analysis;Product recommendation;Product reviews;E-commerce;Deep learning;Analytical models;Sentiment analysis;Computational modeling;Learning (artificial intelligence);Data models;Classification algorithms;Electronic commerce|
|[Energy Harvesters based on MEMS for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073733)|G. D. Ram; T. Aravind; C. V. T. Reddy; B. S. Kumar; C. Sailokesh|10.1109/ICAIS56108.2023.10073733|Microelectromechanical system;Piezoelectric;Power harvester;IoT;Micromechanical devices;Vibrations;Wireless communication;Q-factor;Wireless sensor networks;Process control;Production|
|[Indic Language Question Answering: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073689)|D. Kolhatkar; D. Verma|10.1109/ICAIS56108.2023.10073689|natural language processing;Indic questionanswering;question-answering;multilingual question-answering;datasets;Measurement;Bridges;Current measurement;Bit error rate;Asia;Benchmark testing;Question answering (information retrieval)|
|[Image Segmentation based Imperative Feature Subset Model for Detection of Vehicle Number Plate using K Nearest Neighbor Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073848)|V. Pavani; K. Divya; V. V. Likhitha; G. S. Mounika; K. S. Harshitha|10.1109/ICAIS56108.2023.10073848|Image Segmentation;Feature Subset;Vehicle Number Plate Recognition;Image Processing;Optical Character Recognition;K Nearest Neighbor;Image segmentation;Image recognition;Government;Buildings;Licenses;Gray-scale;Feature extraction|
|[Email Spam: A New Strategy of Screening Spam Emails using Natural Language Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073758)|G. V. Sesha Sai Krishna Vineeth; M. Leela Venkata Sai; M. U. Mahesh; M. Varun; S. Shanmugapriya|10.1109/ICAIS56108.2023.10073758|Email Spam;Classification;Machine Learning Algorithms;NLTK – Natural Language Tool Kit;Support vector machines;Machine learning algorithms;Automation;Unsolicited e-mail;Manuals;Machine learning;Learning (artificial intelligence)|
|[Analysis of Various Image Segmentation Techniques on Retinal OCT Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073657)|T. M. Sheeba; S. Albert Antony Raj; M. Anand|10.1109/ICAIS56108.2023.10073657|Clustering;Edge detection;Image segmentation;Region-based;Threshold;Image segmentation;PSNR;Wiener filters;Image edge detection;Clustering methods;Mean square error methods;Speckle|
|[An Overview and Application of Deep Convolutional Neural Networks for Medical Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073857)|S. Patel|10.1109/ICAIS56108.2023.10073857|Deep Learning;Convolutional Neural Network;Medical Image Segmentation;U-Net;Fully Convolutional Network;Deep learning;Image segmentation;Computer vision;Ultrasonic imaging;Computational modeling;Benchmark testing;Convolutional neural networks|
|[Design Process for Adaptive Spraying of Pesticides Based on Mutual Plant Health Detection and Monitoring: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073695)|D. Shende; N. Wyawahare; L. Thakare; R. Agrawal|10.1109/ICAIS56108.2023.10073695|IoT;L293D Driver;Water level Sensor;Proximity sensor;IR sensor;Esp32 & Raspberry pi Controller;Cloud;Plants (biology);Sociology;Crops;Spraying;Soil;Robot sensing systems;Agriculture|
|[Automated Text-based Depression Detection using Hybrid ConvLSTM and Bi-LSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073683)|N. Firoz; O. G. Beresteneva; A. S. Vladimirovich; M. S. Tahsin; F. Tafannum|10.1109/ICAIS56108.2023.10073683|Depression Detection;Deep Learning;Machine Learning;Accuracy;Prediction;Deep learning;Buildings;Mental health;Predictive models;Depression;Natural language processing;Hybrid power systems|
|[Basal Cell Carcinoma Prediction in Pigmented Skin Infection using Intelligent Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073849)|S. P. R. K.V; A. K.S|10.1109/ICAIS56108.2023.10073849|Skin Cancer;Image Pre-processing;Feature extraction;Classification and Machine Learning Models;SVM;Naïve Bayes;Ada booster;Photography;Image segmentation;Machine learning algorithms;Melanoma;Prediction algorithms;Feature extraction;Skin|
|[Smart Cost-Effective Shopping System using Radio Frequency Identification Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073829)|N. M. Lindsay; S. Sunder. R; N. Karthy; A. Krishnan|10.1109/ICAIS56108.2023.10073829|Smart shopping;Web servers;Payment Gateway;IOT;Wireless communication;Wireless sensor networks;Heuristic algorithms;Software algorithms;Web pages;Logic gates;Software|
|[Multimodal Approaches based on Fake News Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073839)|B. S. Reddy; A. P. Siva Kumar|10.1109/ICAIS56108.2023.10073839|Machine Learning (ML);Natural Language Toolkit (NLTK);Optical Character Recognition (OCR);Multilayer Perceptron (MLP);Naïve Bayes(NB);Support vector machine(SVM);Voting Classifier(VC);Economics;Visualization;Optical character recognition;Support vector machine classification;Machine learning;Organizations;Predictive models|
|[Detecting the Plant Species using Deep-Convolutional Neural Network (D-CNN) with Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073798)|K. S. Jogi; R. V. H. Prasad|10.1109/ICAIS56108.2023.10073798|Plant Species;Deep Learning (DL);Automated Systems;nan|
|[A Brief Survey on Feature Extraction Models for Brain Tumor Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073722)|M. Janapati; D. S. Akhtar|10.1109/ICAIS56108.2023.10073722|Brain Tumor;Feature Extraction;Deep Learning;Clustering;Medical Diagnosis;Deep learning;Image segmentation;Machine learning algorithms;Magnetic resonance imaging;Medical services;Feature extraction;Prediction algorithms|
|[Query Processing Over RelationalCross Model in Uncertain and Probabilistic Databases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073827)|V. V. Kheradkar; S. K. Shirgave|10.1109/ICAIS56108.2023.10073827|Certain and Uncertain data;Uncertain and Probabilistic Database (UPDB);Lineage;Query processing;Null value;Probabilistic logic;Data models;Artificial intelligence|
|[A Comparative Review on Approaches of Aspect Level Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073770)|G. V. R; S. T|10.1109/ICAIS56108.2023.10073770|Machine learning;Deep learning;Knowledge bases;Aspect level sentiment analysis;Deep learning;Sentiment analysis;Analytical models;Social networking (online);Knowledge based systems;Mental health;Linguistics|
|[A Review: Comprehensive and Systematic Analysis of Medical Image Segmentation Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073734)|A. A. Shaik; S. Siddi Gopal Lingamallu; V. R. Bezzam; S. Harsha Tiruveedula; S. S. Imambi; S. V. Gangashetty|10.1109/ICAIS56108.2023.10073734|Segmentation;Tumor detection;Classification;Image analysis;Noise reduction;Training;Fault diagnosis;Image segmentation;Image analysis;Systematics;Noise reduction;Dogs|
|[Maintaining the Integrity of Online Exams using Computer Vision and Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073899)|N. K. K; N. Guthikonda; M. A. Abdullah; S. Sagar Imambi; V. M. Mohan|10.1109/ICAIS56108.2023.10073899|online learning;efficient methods;validity;online tests;multiple-choice exams (MCQs);computer vision;picture pre-processing;cheating;facial recognition algorithms;image segmentation;integrity;privacy concerns;trustworthy algorithms;Computer vision;Privacy;Ethics;Image segmentation;Image resolution;Face recognition;Employment|
|[A Novel Deep Belief Network with Butterfly Optimization Algorithm for the Classification of Paddy Leaf Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073684)|U. Lathamaheswari; J. Jebathangam|10.1109/ICAIS56108.2023.10073684|Image Augmentation;Image Processing;Generative Adversarial Network;Paddy Leaf;Deep learning;Machine learning algorithms;Metaheuristics;Real-time systems;Classification algorithms;Internet of Things;Task analysis|
|[Automated Hybrid Emergency Medical Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073724)|J. Jayachitra; M. Arunkumar; S. S. Hameed; S. Pradeesh|10.1109/ICAIS56108.2023.10073724|Ultra Wide Band (UWB);Arduino;Control Traffic Signals;Reducing Traffic Congestion;Global Positioning System (GPS);Emergency Green Signal;Vibrations;Hospitals;Sociology;Traffic control;Time factors;Security;Statistics|
|[Voice E-Mail Synced with Gmail for Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073879)|T. S. Roy; N. Namratha; T. Y. J. Naga Malleswari|10.1109/ICAIS56108.2023.10073879|IVR;converter for speech to text;convert text to speech;voicemail for email;visually impaired individuals;Visualization;Visual impairment;Sociology;Speech recognition;Blindness;Software;Electronic mail|
|[Bio-Inspired Feature Selection Techniques for Sentiment Analysis – Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073715)|S. L. Vemula; N. Rathee|10.1109/ICAIS56108.2023.10073715|Sentiment classification;Feature selection;Optimization;Swarm intelligence;Review;Sentiment analysis;Machine learning algorithms;Systematics;Text categorization;Neural networks;Robustness;Real-time systems|
|[Detecting Cyber Bullying on Twitter using Support Vector Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073658)|P. Dedeepya; P. Sowmya; T. D. Saketh; P. Sruthi; P. Abhijit; S. P. Praveen|10.1109/ICAIS56108.2023.10073658|Computer learning;classifiers;natural language processing;support vector machines;and Twitter;Support vector machines;Bridges;Social networking (online);Blogs;Cyberbullying;Machine learning;Software|
|[Internet of Smart Things for Smart Healthcare and Safety Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073692)|K. Ganesh; K. Parimala; P. Raveesha; A. Samal; M. L. LN; A. Verma|10.1109/ICAIS56108.2023.10073692|IoT;Healthcare system;Safety Management;IoT devices;nan|
|[Knowledge Blended Open Domain Visual Question Answering using Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073911)|D. Koshti; A. Gupta; M. Kalla|10.1109/ICAIS56108.2023.10073911|Image question answering;Bidirectional Encoder Representation from Transformers;Visual question answering;ConceptNet;Knowledge visual question answering;Visualization;Knowledge based systems;Natural languages;Transformers;Question answering (information retrieval);Commonsense reasoning|
|[3D Brain Tumor Segmentation with U-Net Network using Public Kaggle Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073895)|S. Sujatha; T. S. Reddy|10.1109/ICAIS56108.2023.10073895|Convolutional Neural Network;U-Net architecture;3D volumes;Brain Tumor (Gliomas);segmentation;Image segmentation;Three-dimensional displays;Magnetic resonance imaging;Random access memory;Brain modeling;Mathematical models;Software|
|[U-NET and RCNN Ensembled Satellite Object Detection and Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073720)|G. Subhashini; A. Devi; C. Niroshini Infantia; M. Karthi; G. Raghul; S. A. Shankar|10.1109/ICAIS56108.2023.10073720|Satellite Imagery;GPS Technology;automation;RCNN;RPN;and U-NET;Deep learning;Image segmentation;Satellites;Machine learning algorithms;Automation;Storms;Weather forecasting|
|[Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073788)|M. Jeyakarthic; R. Ramesh|10.1109/ICAIS56108.2023.10073788|Credit risk assessment;Credit scoring;Genetic programming;Dynamic Bayesian network;Data classification;Heuristic algorithms;Decision making;Genetic programming;Organizations;Data models;Bayes methods;Dynamic programming|
|[Improved Water Strider Optimization with Deep Learning based Image Classification for Wireless Capsule Endoscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073766)|M. Amirthalingam; R. Ponnusamy|10.1109/ICAIS56108.2023.10073766|Wireless capsule endoscopy;Image classification;Deep learning;Parameter tuning;Artificial intelligence;Wireless communication;Deep learning;Solid modeling;Feature extraction;Classification algorithms;Artificial intelligence;Tuning|
|[Smart Urban Traffic Management System using Energy Efficient Optimized Path Discovery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073840)|P. Agarwal; S. Sharma|10.1109/ICAIS56108.2023.10073840|IoT;SUTMS;Path optimization;Energy efficiency;Optimized path;Roads;Sociology;Telecommunication traffic;Energy efficiency;Real-time systems;Path planning;Statistics|
|[Emotion Recognition using Deep Stacked Autoencoder with Softmax Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073937)|M. Mohana; P. Subashini|10.1109/ICAIS56108.2023.10073937|Facial Emotion Recognition (FER);Stacked Autoencoder;Softmax Classifier;Deep Learning;Computer Vision;Training;Deep learning;Emotion recognition;Face recognition;Psychology;Human-robot interaction;Feature extraction|
|[A Comprehensive Review on Continent-based Plant Recognition and Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073913)|R. R; S. A|10.1109/ICAIS56108.2023.10073913|Mobile Applications;Plantnet;Plantix;Plant Dataset;Plant Identification;Systematics;Costs;Ecosystems;Focusing;Vegetation;Hazards;Mobile applications|
|[Intelligent Crop Recommendation with Yield Prediction using Dragonfly Algorithm based Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073744)|P. S. S. Gopi; M. Karthikeyan|10.1109/ICAIS56108.2023.10073744|Agriculture;Deep learning;Crop recommendation;Yield prediction;Dragonfly algorithm;Deep learning;Productivity;Recurrent neural networks;Machine learning algorithms;Computational modeling;Crops;Predictive models|
|[Early detection of Diabetes using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073861)|A. H. Ataya|10.1109/ICAIS56108.2023.10073861|Artificial Intelligence;Machine Learning;Diabetes;Disease Detection;Healthcare;Support vector machines;Pregnancy;Performance evaluation;Predictive models;Prediction algorithms;Diabetes;Classification algorithms|
|[Cloud IoT based Poultry Environment Analysis System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073851)|K. S. Dhanalakshmi; J. S. Jeyanathan; K. C. Anjineyulu; K. M. Babu; M. Mahendra; N. P. Reddy|10.1109/ICAIS56108.2023.10073851|Poultry Monitoring;Temperature;Humidity;Light Intensity;Temperature sensors;Temperature measurement;Temperature;Production;Humidity;Mobile handsets;Sensors|
|[Hybrid Artificial Ecosystem Optimization Algorithm based on Search Manager Framework for Big Data Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073919)|P. Sarao; M. Milind; G. N. P. V. Babu; R. Rameshbhai Savaliya; M. Devi; M. Tiwari|10.1109/ICAIS56108.2023.10073919|Hybrid Artificial Ecosystem Optimization Algorithm based on the Search Manager Framework (HAEOA-SMF);Big Data;Big Data Optimization Problem (BDOP);search manager;evolutionary algorithm (EA);Biological system modeling;Sociology;Ecosystems;Metaheuristics;Big Data;Linear programming;Search problems|
|[A Combined Architecture of Image Processing Techniques and Deep Neural Network for the Classification of Corn Plant Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073762)|R. K. Vh; T. R|10.1109/ICAIS56108.2023.10073762|Corn;Disease;Convolutional Neural Network;Histogram of Oriented Gradients;Image Processing;Non-Local Means;Unsupervised Wiener filter;Otsu’s method;Canny Edge detection;Entropy;Morphology;Deep learning;Plant diseases;Image segmentation;Histograms;Wiener filters;Image edge detection;Neural networks|
|[HOG and Cloud Computing based Face Recognition for Attendance Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073909)|S. V; K. R; J. C K|10.1109/ICAIS56108.2023.10073909|Cloud Firestore;Encoding faces;Face Recognition;Flutter.;Computers;Cloud computing;Face recognition;Surveillance;Process control;Lighting;Manuals|
|[Analysis of Face Feature Recognition using MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073828)|M. K; A. B; R. Balaji; D. T|10.1109/ICAIS56108.2023.10073828|Matlab;Fisherface;Eigen faces;biometrics;Image recognition;Image color analysis;Face recognition;Neural networks;Skin;Software;Task analysis|
|[Reducing Interference and Antenna Array thinning using Nature Inspired Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073866)|D. Ramesh Varma; M. Venkata Subbarao; G. Challa Ram; D. Girish Kumar|10.1109/ICAIS56108.2023.10073866|Interference direction;Antenna Array;optimization algorithm;Wireless communication;Power demand;Array signal processing;Interference;Planar arrays;Directive antennas;Artificial intelligence|
|[A Comparison of Different Learning Algorithms for Wildlife Detection and Classification in Animal Conservation Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073833)|M. Mangaleswaran; M. Azhagiri|10.1109/ICAIS56108.2023.10073833|Deep Learning;Animal Detection;Convolutional Neural Network;Thermal Images;Classification.;Deep learning;Time-frequency analysis;Tracking;Roads;Wildlife;Video sequences;Taxonomy|
|[A Review on Metaverse and Immersive Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073666)|L. Soni; A. Kaur; A. Sharma|10.1109/ICAIS56108.2023.10073666|nan;Knowledge engineering;Metaverse;Market research;Blockchains;Next generation networking|
|[Real Time Object Distance and Dimension Measurement using Deep Learning and OpenCV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073888)|B. M U; H. Raghuram; Mohana|10.1109/ICAIS56108.2023.10073888|OpenCV;Deep Learning;Computer vision;YOLO;R-CNN;Dimension Measurement.;Deep learning;Webcams;Image edge detection;Detectors;Streaming media;Feature extraction;Real-time systems|
|[Facial Skin Analysis for Detection of Dark Circles and Acne](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073775)|K. M K; K. Meda Ravi; U. V|10.1109/ICAIS56108.2023.10073775|Acne vulgaris;Dark circles;Periorbital hyper-pigmentation;Otsu’s thresholding;Deep Learning;Classification.;Deep learning;Measurement;Visualization;Computer vision;Skin;Artificial intelligence|
|[Performance Analysis of Object Detection Algorithms for Waste Segregation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073863)|N. Singh; P. Sastry; N. B S; A. Sinha; U. V|10.1109/ICAIS56108.2023.10073863|Object Detection;Waste Segregation;Trash Detection;Deep Learning;Training;Measurement;Urban areas;Object detection;Learning (artificial intelligence);Performance analysis;Task analysis|
|[The Role of Internet of Things in Developing Competitive Healthcare Devices: A Case Study in the Digital Healthcare Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073802)|R. Yamaganti; P. N. S. Jyothi; S. U. Manjari|10.1109/ICAIS56108.2023.10073802|Digital Health care industry;Competitive healthcare Industry;Internet of Things;Industries;Schedules;Taxonomy;Switches;Market research;Product development;Electronic healthcare|
|[Community Detection using Unsupervised Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073881)|A. Mittal; A. Goel|10.1109/ICAIS56108.2023.10073881|Community Detection;Louvain Algorithm;K-means Clustering;Gaussian Mixture Model;Social networking (online);Clustering algorithms;Transportation;Mixture models;Genetics;Artificial intelligence;Unsupervised learning|
|[Recognition of Tuberculosis on Medical X-Ray Images Utilizing MobileNet Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073728)|S. I. Hossain; S. Alam Nipu; M. R. Hasan|10.1109/ICAIS56108.2023.10073728|Tuberculosis;Mycobacterium Tuberculosis;Prediction;Detection;Artificial Intelligence;Supervised Machine Learning;Transfer learning;Lungs Segmentation.;Radiography;Microorganisms;Tuberculosis;Transfer learning;Neural networks;Organizations;Nonhomogeneous media|
|[A Review of Multiple Prognosticate Techniques for Parkinson's Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073808)|R. K. G; V. Ratna Prabha K; V. K; T. J; J. S; R. P|10.1109/ICAIS56108.2023.10073808|ConvNet;Deep learning;Internet of Things;Machine learning;Neurodegenerative disorder;Parkinson’s ailment;Transfer Learning;Degradation;Deep learning;Neurotransmitters;Parkinson's disease;Transfer learning;Learning (artificial intelligence);Internet of Things|
|[Energy Management based on K-Nearest Neighbour Approach in Residential Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073859)|K. S. Radha; R. Priya; K. Jeevitha|10.1109/ICAIS56108.2023.10073859|Energy Management;Support Vector Machine;Residential Application;Home Energy Management System;Classification Algorithm;Support vector machines;Energy consumption;Home appliances;Costs;Prototypes;Machine learning;Planning|
|[Hybrid CNNLBP using Facial Emotion Recognition based on Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073918)|S. Depuru; K. Vaishnavi; B. Manogna; K. J. Sri; A. Preethi; C. Priyanka|10.1109/ICAIS56108.2023.10073918|Deep learning;CNNLBP (Convolution neural network local binary pattren);FER (Facial Emotoion Recognition);Fusion CNN;Machine learning;Deep learning;Training;Emotion recognition;Machine learning algorithms;Face recognition;Software;Convolutional neural networks|
|[An Integrated Usage of Bidirectional LSTM and Computer-based Cognitive Attention to Categorize Speech Stutters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073818)|K. Basak; V. Sharma; S. R. Kv; N. Mishra|10.1109/ICAIS56108.2023.10073818|Stutter Classification;Mel-spectrogram;Convolutional Neural Network;Bidirectional Long Short-Term Memory;Attention;Deep learning;Annotations;Computational modeling;Feature extraction;Speech processing;Artificial intelligence|
|[Acquiring Metacognitive Reading Technique through Web 2.0 Application – An Empirical study with ESL Learners](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073783)|D. Hethesia; S. Mercy Gnana Gandhi|10.1109/ICAIS56108.2023.10073783|Metacognition;Online Tools;Reading Skills;Web2.0;English as Second Language (ESL)Learners;Training;Web 2.0;Collaboration;Recording;Artificial intelligence|
|[Evaluating the Performance of YOLO V5 for Electronic Device Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073671)|T. S. Naga Venkata Satya Sirisha; N. Venkata Sai Mada; S. Haritha; P. Tumuluru; V. Rachapudi|10.1109/ICAIS56108.2023.10073671|Device classification;Detection;Classification;Computer Vision;Machine Learning;Training;Performance evaluation;Support vector machines;Lighting;Object detection;Learning (artificial intelligence);Motion pictures|
|[Particle Swarm Optimization based Detection of Diabetic Retinopathy using a Novel Deep CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073926)|B. Revathi; S. K. K. Elizabeth; P. Nagaraj; S. S. Birunda; D. Nithya|10.1109/ICAIS56108.2023.10073926|Classification;Particle Swarm Optimization;Convolutional Neural Network;Retinopathy;Image processing;Neural networks;Optimization methods;Blood vessels;Retina;Diabetes|
|[Deep Learning-based Hybrid Model for Severity Prediction of Leaf Smut Sugarcane Infection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073663)|V. Tanwar; S. Lamba; B. Sharma|10.1109/ICAIS56108.2023.10073663|Convolutional Neural Network and Support Vector Network (CNN-SVM) hybrid;Leaf smut;Sugarcane Diseases;Crop Disease Severity;Support vector machines;Deep learning;Costs;Crops;Predictive models;Feature extraction;Data models|
|[Smart Home Messenger Notifications System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073780)|M. R. Sai; K. K. Teja; V. P. Sasank; M. Kavitha; S. S. Aravinth|10.1109/ICAIS56108.2023.10073780|Internet of Things;Smart Home;NodeMCU;Messaging API;Arduino;Freeware;Java;Codes;Lighting;Smart homes;Internet of Things;Security|
|[Arduino based Smart Water Management System for Water Loss Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073707)|N. Mannam; K. A. Shaik; K. Pranitha; M. B. Sri; N. Praveen Vemulapalli|10.1109/ICAIS56108.2023.10073707|Arduino;Water Sensor;Ultrasonic sensor;Water leakage;Natural resources;Shape;Pipelines;Water conservation;Valves;Sensor systems;Water pumps|
|[Modeling and Simulation of Selfish Mining Attacks in Blockchain Network using Evolutionary Game Theory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073670)|K. R; K. M. Pitchai|10.1109/ICAIS56108.2023.10073670|Evolutionary game theory;Selfish Mining;Blockchain networks;Technological innovation;Solid modeling;Computational modeling;Games;Reliability theory;Solids;Blockchains|
|[Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073898)|V. A. Mohammed; M. A. Mohammed; M. A. Mohammed; J. Logeshwaran; N. Jiwani|10.1109/ICAIS56108.2023.10073898|Heart rate;Autism;Spectrum Disorder (ASD);Blockage;Birth;Congenital;Disability;Autism;Pediatrics;Pain;Autonomic nervous system;Surgery;Human factors;Muscles|
|[Yolo for Detecting Plant Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073875)|C. L. K R; P. B; S. G; N. J. J; G. T; M. Hashim|10.1109/ICAIS56108.2023.10073875|Plant Disease;You Only Look Once (YOLO V3);Object detection;Grid Cell;Plant diseases;Analytical models;Pathology;Neural networks;Crops;Object detection;Machine learning|
|[Area Reduction AES Algorithm in Hardware Trojan Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073929)|M. S; S. S. S. Priya; R. Naveenkumar; N. A|10.1109/ICAIS56108.2023.10073929|hardware trojan;IP security;AES algorithm;Integrated circuits and Design security;Power demand;Heuristic algorithms;Mission critical systems;Turning;Prediction algorithms;Hardware;Encryption|
|[Development of an Android Application for Tracking Post COVID Symptoms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073752)|S. Sameer; J. Pande; S. Devi|10.1109/ICAIS56108.2023.10073752|Android application;Post COVID symptoms;Post COVID complications;Comorbidities;COVID-19;Hypertension;Hospitals;Databases;Internet;Diabetes;Older adults|
|[Smart Navigation Aid for Visually Impaired Person using a Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073922)|V. K N; B. S. Ullal; P. Gogoi; K. Singh; A. Biswas; K. Yash|10.1109/ICAIS56108.2023.10073922|Unfamiliar Condition;Smart Navigation;Hindrance;Legged locomotion;Deep learning;Costs;Navigation;Roads;Belts;Real-time systems|
|[Modelling of Optimal Quantum Neural Network for DDoS Attack Classification in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073673)|C. Murugesh; S. Murugan|10.1109/ICAIS56108.2023.10073673|Distributed Denial of Service (DDoS) attacks;Wireless sensor networks;Security;Machine learning;Spotted hyena optimizer;Wireless sensor networks;Surveillance;Neural networks;Smart homes;Benchmark testing;Traffic control;Denial-of-service attack|
|[A Comparative Analysis of HDL and HLS for Developing CNN Accelerators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073746)|S. Srilakshmi; G. L. Madhumati|10.1109/ICAIS56108.2023.10073746|CNN;Accelerator;HDL;Xilinx Vitis HLS;Runtime;Convolution;Neural networks;Computer architecture;Programming;High level synthesis;Hardware design languages|
|[Stationary Wavelet Transform with Modified Grey Wolf Optimizer for Digital Image Watermarking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073889)|R. Parthiban; S. Manikandan|10.1109/ICAIS56108.2023.10073889|Digital image watermarking;Security;Grey wolf optimizer;Embedding process;Wavelet transforms;Image resolution;Digital images;Frequency-domain analysis;Watermarking;Benchmark testing;Image decomposition|
|[Internet of Things based Natural Disaster Detection and Personalized Notification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073873)|M. Varadharajan; S. Balaji; V. Ezhilarasan; A. Gowthaman|10.1109/ICAIS56108.2023.10073873|Internet of Things;Intelligent systems;Decision making;Disaster detection;Sensor array;Satellites;Organizations;Alarm systems;Wounds;Sensors;Planning;Internet of Things|
|[Abnormal Activity Recognition on Surveillance: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073703)|V. Pogadadanda; S. Shaik; G. V. S. Neeraj; H. V. Siralam; I. T. Joseph S; K. B. V. B. Rao|10.1109/ICAIS56108.2023.10073703|Abnormal activity;Smart surveillance;Abnormal event detection;Human activity;Surveillance;Anomaly detection;Deep learning;Machine learning algorithms;Surveillance;Activity recognition;Cameras;Safety;Behavioral sciences|
|[Pneumonia Detection in Chest X-Ray Images by using Resnet-50 Deep Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073748)|V. Kadali; B. S. Pudi; K. A. Shaik; A. Janjam; J. Javvadi|10.1109/ICAIS56108.2023.10073748|Pneumonia;Chest X-ray images;Convolution Neural Network (CNN);Deep learning;Computer-aided diagnostics;Resnet-50;Pulmonary diseases;Computational modeling;Transfer learning;Neural networks;Convolutional neural networks;Proposals;X-ray imaging|
|[Decoding and Analysing Consumer Feedback for Companies and Goods using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073801)|M. R. Joel; V. Ebenezer; K. A. Jeyaraj; M. Navaneethakrishnan; R. Arunadevi; D. R. Jenifa|10.1109/ICAIS56108.2023.10073801|Text mining;Natural Language Processing (NLP);crawling;Support Vector Machine (SVM);Vectorization;Stemming;Support vector machines;Companies;Boosting;Decoding;Personnel;Decision trees;Business|
|[IoT based Lithium-Ion Battery Monitoring System in Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073696)|V. G. G; A. N; J. S; V. N D|10.1109/ICAIS56108.2023.10073696|Battery;Electric vehicle;Microcontroller-based circuit;liquid cooling system;IoT monitoring system;Temperature sensors;Lithium-ion batteries;Temperature measurement;Temperature;Liquid cooling;Microcontrollers;Electric vehicles|
|[Convergence Analysis of Music Technology: From Audio Digital Watermarking to Denoising Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073797)|L. Zhan|10.1109/ICAIS56108.2023.10073797|Signal Denoising Algorithm;Convergence Analysis;Music Technology;Audio Digital Watermarking;Technological innovation;Noise reduction;Watermarking;Wavelet analysis;Robustness;Multiple signal classification;Safety|
|[3D Game Design Aided by Multi-Dimensional Collision Detection Algorithm with GPU Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073717)|Z. Wang; D. Pan; B. Di|10.1109/ICAIS56108.2023.10073717|3D game design;collision detection algorithm;multi-dimensional information;GPU optimization;Solid modeling;Three-dimensional displays;Computational modeling;Memory management;Graphics processing units;Games;Hardware|
|[Intelligent Navigation Informatization based On Signal Enhancement Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073902)|S. Fan; L. Huang|10.1109/ICAIS56108.2023.10073902|Intelligent navigation;informatization model;signal enhancement;algorithm design;signal processing;Wireless communication;Adaptation models;Satellites;Simulation;Interference;Satellite navigation systems;Downlink|
|[Realization of Halcon Image Segmentation Algorithm in Machine Vision for Complex Scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073925)|L. Ke|10.1109/ICAIS56108.2023.10073925|HALCON;Image Segmentation;Algorithm Design;Machine Vision;Complex Scenarios;Training;Support vector machines;Image segmentation;Clustering algorithms;Object segmentation;Real-time systems;Complexity theory|
|[A Stable Cloud Storage Algorithm for Online Interaction Effect Data based on HarmonyOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073704)|Y. Liu|10.1109/ICAIS56108.2023.10073704|HarmonyOS;cloud storage;stable algorithm;online interaction;data storage;data security;Performance evaluation;Cloud computing;Maintenance engineering;Load management;Routing protocols;Robustness;Peer-to-peer computing|
|[A Nighttime Monitoring System for Combined Structural Stability based on Infrared Image Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073660)|J. Li; S. Guo; W. Li|10.1109/ICAIS56108.2023.10073660|Infrared image recognition;nighttime monitoring system;video system;combined structural feature;image features;Image segmentation;Analytical models;Image recognition;Feature extraction;Structural engineering;Security;Task analysis|
|[Detection and Security in Falls with IoT Server](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073845)|C. Kavitha; N. Sridevi; D. Dhivagar|10.1109/ICAIS56108.2023.10073845|Anti-drowning;Arduino kits;Internet of Things;Lifeguard alert;Transmitter strap;Training;Sociology;Psychology;Servers;Security;Reliability;Heart rate variability|
|[A Novel Path Planning Method for Aerial UAV based on Improved Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073792)|H. Liu|10.1109/ICAIS56108.2023.10073792|Genetic algorithm;path planning method;aerial UAV;intelligent control;algorithm optimization;Pipelines;Autonomous aerial vehicles;Control systems;Path planning;Planning;Complexity theory;Collision avoidance|
|[Hi-C Data Resolution Improvement Method based on Ensemble Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073678)|Z. Ai; H. Wu|10.1109/ICAIS56108.2023.10073678|Ensemble Learning;Hi-C Data;Data Resolution;Algorithm Design;Sequential analysis;Systematics;Stacking;Standardization;Predictive models;Robustness;Ensemble learning|
|[Smart Design Module Optimization of Intelligent Instruments Considering Cloud Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073880)|H. Liu|10.1109/ICAIS56108.2023.10073880|Cloud Storage;HART Protocol;Smart Design;Module Optimization;Intelligent Instruments;Cloud computing;Automation;Protocols;Embedded systems;Instruments;Memory;Control systems|
|[Optimal Feedback Loop Algorithm for Automatic Control of Ultrasonic Gas Flowmeter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073731)|H. Liu|10.1109/ICAIS56108.2023.10073731|Optimal Feedback;Loop Algorithm;Automatic Control;Ultrasonic Gas Flowmeter;Motion planning;Feedback loop;Visualization;Ultrasonic variables measurement;Fluid flow;Flowmeters;Acoustics|
|[Wind Speed Prediction Using Sentinel-1 OCN Products](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073800)|K. Gupta; S. Shukla; S. Pasari; S. Sheoran|10.1109/ICAIS56108.2023.10073800|Sentinel-1;Wind-speed prediction;Time-series analysis;Satellites;Wind energy;Wind speed;Oceans;Time series analysis;Sea measurements;Wind farms|
|[An Attention based Recurrent Neural Network Model for Link Quality based Path Selection in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073934)|S. K; N. Rayen; J. L. T; S. Kumar E|10.1109/ICAIS56108.2023.10073934|Quality of Service;Recurrent Neural Network;Inductive Matrix Completion;Attention Mechanism;Encoder Decoder Architecture;Wireless communication;Wireless sensor networks;Recurrent neural networks;Scalability;Predictive models;Energy efficiency;Delays|
|[Regional GIS-based Location Map with 3-D Projection for Multistoried Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073811)|S. Ramamoorthy; R. Gupta; A. Singh|10.1109/ICAIS56108.2023.10073811|Dijkstra’s Algorithm;Shortest distance;3-Dimensional projection;Geographic Information System (GIS);Latitude and Longitude;Georeferencing;Navigation;Buildings;Layout;Mobile applications;Artificial intelligence;Information systems|
|[Prediction of Blood Lactate Levels in Children after Cardiac Surgery using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073941)|H. K. Hussain; A. Ahmad; M. A. Adam; T. Kiruthiga; K. Gupta|10.1109/ICAIS56108.2023.10073941|Lactate;Enzyme;Acid;Blood tests;Lactate Dehydrogenate;Biochemical;Cardiac;Surgery;Machine learning;Location awareness;Pathology;Enzymes;Machine learning algorithms;Large Hadron Collider;Surgery;Liver|
|[Approach towards Weather Prediction Model for Aggrotech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073904)|G. Sharma; S. K. Dubey|10.1109/ICAIS56108.2023.10073904|Agriculture;Rainfall;Agrotech;Artificial Intelligence;Linear Regression;Machine learning algorithms;Linear regression;Crops;Weather forecasting;Machine learning;Predictive models;Soil|
|[A Comprehensive Study of Different Security Features in eBanking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073735)|N. P. Hemanth Kumar Mothukuri; A. Rakesh; P. Y. Babu; U. Kiran; B. G; B. B. Hazarika|10.1109/ICAIS56108.2023.10073735|E-banking;Cyber fraud;Banking security;Voice recognition;Blockchain;Data encryption;Protocols;Limiting;Online banking;Phishing;Government;Speech recognition;Passwords|
|[Design and Development of Automatic Lie Detector using Arduino](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073694)|I. D. R; M. Shibili; S. C V; V. V. Prasad; N. C; N. K|10.1109/ICAIS56108.2023.10073694|Lie Detection;Temperature;Skin Resistance;Heart Rate;Temperature Sensor;Polygraph;Temperature sensors;Heart rate;Law enforcement;Detectors;Light emitting diodes;Physiology;Skin|
|[Detecting the Human Activities of Aging People using Restricted Boltzmann Machines with Deep Learning Technique in IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073665)|M. Manimaran; A. Sasi Kumar; N. V. S. Natteshan; K. Baranitharan; R. Mahaveerakannan; K. Sudhakar|10.1109/ICAIS56108.2023.10073665|Elderly People;Deep Learning Model;Deep Q-Network;Internet of Healthcare Things;Deep learning;Performance evaluation;Schedules;Tracking;Medical services;Stairs;Safety|
|[Design and Analysis of CNN based Residue Number System for Performance Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073805)|J. R. J; S. S. C; B. L. R; D. S|10.1109/ICAIS56108.2023.10073805|Convolutional Neural Network (CNN);Residue Number System (RNS);Mixed Radix Conversion (MRC);Prewitt operator;Choice of Moduli;nan|
|[Investigation of Intelligent Methodologies in Prediction of Depression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073713)|M. Chintapalli; N. Kanugula; L. Nagisetti; A. V. V; I. T. J. S; G. Bindu|10.1109/ICAIS56108.2023.10073713|Depression;Stress;Chatbot;Deep Learning;Machine Learning;nan|
|[Predicting the Impact of Road Conditions on Battery Health Via Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073732)|R. A. Nair; D. K. Suresh; R. D|10.1109/ICAIS56108.2023.10073732|Battery Health;Road Condition;Image classification;Convolutional Neural Network;Deep Learning;Visual Geometry Group Neural Network;Electric Vehicle (EV);Transfer Learning;Geometry;Temperature;Roads;Transfer learning;Artificial neural networks;Learning (artificial intelligence);Predictive models|
|[Smart Attendance System with and Without Mask using Face Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073686)|T. Charishma; K. Pranathi; T. Niharika|10.1109/ICAIS56108.2023.10073686|Authentication;Biometric;Face recognition;Identification;Local Binary Pattern Histogram;OpenCV;Smart attendance;Spreadsheet;Tkinter;Visualization;Histograms;Face recognition;Biological system modeling;Education;Registers;Recording|
|[Privacy Protection Against Reverse Image Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073803)|R. Pratik; R. Sendhil|10.1109/ICAIS56108.2023.10073803|Privacy protection;image recognition;metadata;cloaking;reverse search;google lens;Privacy;Image recognition;Social networking (online);Face recognition;Multimedia Web sites;Organizations;Metadata|
|[Fused Image Classification using Pre-trained Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073743)|M. S. Patil; P. B. Mane|10.1109/ICAIS56108.2023.10073743|Image Fusion;Deep Convolutional Neural Network (Deep CNN);Image Classification;Image Quality Assessment;Training;Image quality;Transforms;Feature extraction;Discrete wavelet transforms;Classification algorithms;Quality assessment|
|[Crypto Analysis with Modified Diffie–Hellman Key Exchange based Sensor Node Security Improvement in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073749)|J. Rangarajan; K. Suriyakrishnaan; V. Meenakshi; M. Tholkapiyan|10.1109/ICAIS56108.2023.10073749|Modified Diffie–Hellman key exchange;Man-in-the-middle attack;Wireless sensor network;Data security;Encryption;and Decryption;Wireless communication;Vibrations;Temperature sensors;Wireless sensor networks;Analytical models;Receivers;Routing|
|[Analyzing Land Cover Changes over Landsat-7 Data using Google Earth Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073795)|A. Srivastava; S. Biswas|10.1109/ICAIS56108.2023.10073795|Classification;Change Detection;Landsat;Machine Learning;Earth;Artificial satellites;Urban areas;Sociology;Vegetation mapping;Indexes;Statistics|
|[Network based Learning Platform Application Model for Enhancement of Realtime Working Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073739)|R. Shanmuga Priya; S. Shanmugavadivel; M. Sakthi Muthusamy Sivaraja; M. Shifani Francis|10.1109/ICAIS56108.2023.10073739|User Datagram Protocol;File Transfer Protocol;Simple Mail Transfer Protocol;Reliable;Learning Platform;Privacy;Protocols;Uncertainty;Education;Permission;Streaming media;Real-time systems|
|[Medical Image Encryption using Enhanced Rivest Shamir Adleman Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073869)|Y. Vijaya Lakshmi; K. Naveena; M. Ramya; N. Pravallika; T. Sindhu; V. Namitha|10.1109/ICAIS56108.2023.10073869|Encryption;Decryption;Public key cryptography;Private Key cryptography;RSA;Privacy;Computer hacking;Software algorithms;Government;Data protection;Non-repudiation;Software|
|[Dimensionality Reduction with DLMNN Technique for Handling Secure Medical Data in Healthcare-IoT Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073679)|M. Lalithambigai; V. Kalpana; A. Sasi Kumar; J. Uthayakumar; J. Santhosh; R. Mahaveerakannan|10.1109/ICAIS56108.2023.10073679|Heart Disease;Deep Learning Modified Neural Network;Internet of things;Dimensionality Reduction;Principal Component Analysis;Secure Transmission;Heart;Dimensionality reduction;Medical services;Data transfer;Data models;Encryption;Classification algorithms|
|[Improved Dynamic Response of Harmonic Controlled Grid-Connected PV using FOPID](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073656)|R. Senthil Kumar; S. Prabakaran; S. Sentamil Selvan|10.1109/ICAIS56108.2023.10073656|FOPID Controller;Voltage Harmonics;Active Filters;PV Systems;Voltage source inverters;Simulation;Switches;Harmonic analysis;Active filters;Steady-state;Voltage control|
|[Analysing the Accuracy of Detecting Phishing Websites using Ensemble Methods in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073834)|S. Menaka; J. Harshika; S. Philip; R. John; N. Bharathiraja; S. Murugesan|10.1109/ICAIS56108.2023.10073834|Phishing identification;Random Forest;Extreme Gradient Boost;Ensemble Learning;Machine Learning;Phishing;Learning (artificial intelligence);Forestry;Feature extraction;Malware;Electronic mail;Ensemble learning|
|[ANN based Bridgeless Landsman Converter Design for Electric Vehicle Power Factor Correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073855)|S. Vendoti; R. Manikanta Swamy; T. Sai Saran Jyothi; B. Varun|10.1109/ICAIS56108.2023.10073855|Bridgeless Landsman Converter;Artificial Neural Network;PWM generator;Hysteresis controller;PI controller;Switching frequency;Transportation;Voltage;Power factor correction;Pulse width modulation;Electric vehicles;Batteries|
|[Machine Learning Techniques for Weather based Crop Yield Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073740)|K. Lohitha Reddy; A. P. Siva Kumar|10.1109/ICAIS56108.2023.10073740|Crop yield prediction (CYP);Machine learning (ML);Decision Tree Classifier (DTC);Random Forest Classifier (RFC);Gradient Boosting (GB);Decision Tree Regression (DTR);Random Forest Regression (RFR);Temperature;Sociology;Crops;Weather forecasting;Forestry;Boosting;Agriculture|
|[Prediction of Infectious Diseases based on Age and Gender using OCSVM-CDT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073793)|K. Indhumathi; K. S. Kumar|10.1109/ICAIS56108.2023.10073793|Age-wise distribution;Gender-wise distribution;OCSVM-CDT (One Class Support Vector Machine- Chi square Automatic Interaction Detection Decision Tree);Machine learning and ailments;Support vector machines;Measurement;Infectious diseases;Pulmonary diseases;Neural networks;Machine learning;Medical services|
|[Electric Load Disaggregation using Machine Learning Approach on IEDL Dataset: a Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073822)|D. Rohit Chavan; D. S. More|10.1109/ICAIS56108.2023.10073822|Machine Learning;Non-Intrusive Load Monitoring;Random Forest;Load Disaggregation;Deep learning;Analytical models;Energy resources;Forestry;Feature extraction;Power grids;Random forests|
|[Real Time Object Detection using Neural Networks: A Comprehensive Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073826)|G. Singh; S. Tiwari; J. Singh|10.1109/ICAIS56108.2023.10073826|Object Detection;Neural Networks;Deep Learning;Region Proposals;Deep learning;Computer vision;Convolution;Neural networks;Object detection;Real-time systems;Security|
|[Cloud based Landslide Detection and Alerting Nearby People by using IoT Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073773)|B. Gopi; J. Premalatha; R. Kalaivani; D. Ravikumar|10.1109/ICAIS56108.2023.10073773|Landslide detection;Cloud server;Internet of Things;Message Queuing Telemetry Transport;Disaster management;Wireless communication;Vibrations;Landslides;Wireless sensor networks;Rain;Protocols;Earthquakes|
|[Inventory Waste Management with Augmented Analytics for Finished Goods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073862)|S. Sam Plamoottil; B. Kunden; A. Yadav; T. Mohanty|10.1109/ICAIS56108.2023.10073862|Supply Chain;Finished Goods Waste;Stales;Damages;Descriptive;Diagnostic;Predictive;Prescriptive;Augmented Analytics;Dashboard;Waste management;Industries;Market research;Predictive analytics;Artificial intelligence|
|[A Review of Cyber Security in Cryptography: Services, Attacks, and Key Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073747)|C. Prabha; N. Sharma; J. Singh; A. Sharma; A. Mittal|10.1109/ICAIS56108.2023.10073747|Algorithms;Cipher-text;Cryptography;Plain text;Security;Industries;Hash functions;Encryption;Cryptography;Servers;Artificial intelligence;Cyberattack|
|[Power Centric Learning Models for the Prediction of Heart Rate using IoT Enabled Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073824)|Y. M. Manu; A. P. Jaya Krishna; K. Gopala Krishnan; B. Vasavi|10.1109/ICAIS56108.2023.10073824|Multiple Regression;Recurrent Neural Network;Arduino;Sensor;Power Consumption;and Prediction Accuracy;Heart beat;Machine learning;Cardiac arrest;Predictive models;Hybrid power systems;Data models;Internet of Things|
|[Design and Implementation of Common EV Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073755)|K. V; M. K. R; N. V. K; H. S|10.1109/ICAIS56108.2023.10073755|conventional vehicles;electric vehicles;multi-tap changing transformer;charging stations;Technological innovation;Costs;TV;Prototypes;Charging stations;Transformers;Electric vehicle charging|
|[Identification of Counterfeit Indian Currency Note using Image Processing and Machine Learning Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073787)|V. Sharan; A. Kaur; P. Singh|10.1109/ICAIS56108.2023.10073787|Counterfeit Currency;Features Extraction;Accuracy;Classification;Printing;Wavelet transforms;Machine learning algorithms;Shape;Support vector machine classification;Feature extraction;Classification algorithms|
|[Machine Learning Framework for Breast Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073765)|S. Gudelly; S. Jaiswal; R. A. Borgalli|10.1109/ICAIS56108.2023.10073765|Breast cancer;Tumor;Convolution neural networks;Machine learning;Machine learning algorithms;Convolution;Neural networks;Melanoma;Learning (artificial intelligence);Bones;Breast cancer|
|[Health Information Broadcast Distributed Pattern Association based on Estimated Volume](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073672)|R. Dhanalakshmi; A. V. Kalpana; J. Umamageswaran; B. P. Kumar|10.1109/ICAIS56108.2023.10073672|Data mining;Pattern recognition;Data density;Histogram;Information spreading;Histograms;Neural networks;Redundancy;Approximation algorithms;Data models;Classification algorithms;Data mining|
|[Secure and Delicate Cloud Storage Network Access Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073794)|R. G; S. Noordeen; S. Kavitha|10.1109/ICAIS56108.2023.10073794|Cloud Computing;Access Management;Server;Data Sharing;Throughput;Sensitivity;Performance evaluation;Cloud computing;Data security;Logic gates;Regulation;Peer-to-peer computing;Encryption|
|[Quality of Service based Selfish Node Detection in Mobile Ad-Hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073884)|B. M. Kannan; G. Vadivel; C. Viswanathan; P. K. K. Reddy|10.1109/ICAIS56108.2023.10073884|Quality of Service;Routing;Mobile ad hoc network;Selfish node detection;Packet loss Ratio;Packet delay Ratio;remaining energy ratio;Wireless networks;Packet loss;Quality of service;Spread spectrum communication;Throughput;Ad hoc networks;Delays|
|[Single Shot Detector Algorithms for Hardware Design on FPGAs for Fast Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073846)|T. Bernatin; A. S. A. Nisha; G. Sundari; V. J. K. Kishoresonti|10.1109/ICAIS56108.2023.10073846|CNN;Object detection;FPGA;SRAM;YOLOv2;Power demand;Heuristic algorithms;Object detection;Detectors;Mathematical models;Parallel architectures;Object recognition|
|[Cloud based Intelligent Accident Proof Helmet and Detect State of Intoxication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073737)|R. Bhowmick; S. Dutta; V. Roy; B. Kundu; S. Dutta; D. Saha|10.1109/ICAIS56108.2023.10073737|Microcontroller;Alcohol Sensor;Global Positioning System;Global System for Mobile Communication;Location Tracking;GSM;Cloud computing;Head;Bluetooth;Microcontrollers;Wires;Motorcycles|
|[Bi-LSTM and Conventional Classifiers for Email Spam Filtering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073776)|C. M. Shaik; N. M. Penumaka; S. K. Abbireddy; V. Kumar; S. S. Aravinth|10.1109/ICAIS56108.2023.10073776|Machine Learning;Naive Bayes classifier;k- Nearest Neighbor;Support Vector Machine (SVM);Term Frequency;Bi-Directional Long Short Term Memory;Long Short Term Memory;Sigmoid;Deep learning;Filtering;Computer hacking;Unsolicited e-mail;Weapons;Support vector machine classification;Naive Bayes methods|
|[Automatic Panel Board with Protection System in Agri-Irrigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073710)|R. V; K. S; K. K; J. S; K. S|10.1109/ICAIS56108.2023.10073710|Watering system;Automation in agriculture;Microcontroller;Microcontrollers;Profitability;Prototypes;Moisture;Maintenance engineering;Storage tanks;Task analysis|
|[Wireless Patient Health Monitoring System using Internet of Things and Mobile device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073725)|J. S. Gonal; M. M. Patil; A. S. Jadhav; R. V. Pawar|10.1109/ICAIS56108.2023.10073725|Wireless;Sensors;Arduino;Health Monitoring System;Online;Mobile device;Internet of Things;Wireless communication;Temperature measurement;Temperature sensors;Wireless sensor networks;Patient monitoring;Temperature distribution;Hospitals|
|[Traffic Prediction with Network Slicing in 5G: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073876)|D. Kulkarni; M. Venkatesan; A. V. Kulkarni|10.1109/ICAIS56108.2023.10073876|Traffic Prediction;Network Slicing;5G;Resource Management;Deep learning;Performance evaluation;5G mobile communication;Network slicing;Symbols;Network analyzers;Virtual reality|
|[Machine Learning based Real Time Water Quality Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073761)|J. Priskilla Angel Rani; R. Nivasini; C. Yesubai Rubavathi; P. Jona|10.1109/ICAIS56108.2023.10073761|National Water Information System;Mean Squared Error;Root Mean Squared Error;Artificial Neural Network;Urban areas;Water quality;Artificial neural networks;Predictive models;Watches;Water pollution;Stakeholders|
|[All-purpose Health Monitoring Belt using HIoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073938)|M. S. Chakka; M. M. Reddy; S. S. N. Reddy; J. S. Rudrapogu; S. Thatavarti; V. M. Mohan|10.1109/ICAIS56108.2023.10073938|Internet of Things;Healthcare Internet of Things;Cloud Computing;Sensors;Machine Learning;Support vector machines;Visualization;Machine learning algorithms;Medical services;Machine learning;Belts;Prediction algorithms|
|[Earthquake Prediction using Long Short Term Memory on Spatio-Temporally Segmented Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073687)|A. Sonthalia; S. Pasari; S. Devi|10.1109/ICAIS56108.2023.10073687|Earthquake prediction;LSTM;Spatio-temporal analysis;Instruments;Earthquakes;Neural networks;Machine learning;Predictive models;Aerospace electronics;Data models|
|[Image Captioning with CNN and LSTM using Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073841)|S. Bethu; N. S. Chandra|10.1109/ICAIS56108.2023.10073841|Image Captioning;Long Short Term Memory;Machine Learning;Deep learning;Visualization;Recurrent neural networks;Learning (artificial intelligence);Network architecture;Software;Natural language processing|
|[High Gain Sheppard Taylor Fed EV in a Grid Connected PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073760)|C. L; S. K. S; S. K P; C. J|10.1109/ICAIS56108.2023.10073760|Sheppard Taylor converter;Electric vehicle (EV);Solar Photovoltaic (PV);Voltage source inverter (VSI);Continuous Voltage Mode (CVM);Induction motors;PI control;Voltage source inverters;System performance;Transportation;Switching loss;Electric vehicles|
|[Ingredients to Recipe: A YOLO-based Object Detector and Recommendation System via Clustering Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073769)|M. Swain; A. R. Manyatha; A. S. Dinesh; G. S. Sampatrao; M. Soni|10.1109/ICAIS56108.2023.10073769|Computer Vision;Recommendation;Object Labeling;K-means clustering;Energy loss;Fluctuations;Engineering profession;Object detection;Detectors;User experience;Real-time systems|
|[Object Detection and Video Analyser for the Visually Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073662)|A. S. Alva; R. Nayana; N. Raza; G. S. Sampatrao; K. B. S. Reddy|10.1109/ICAIS56108.2023.10073662|Video description;Object detection;Distance estimation;Visually impaired;Training;Visualization;Shape;Visual impairment;Sociology;Streaming media;Feature extraction|
|[A Secured Method for Authentication of Data Security using Public Key Cryptography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073781)|S. D. Vara Prasad; J. Kavitha; R. K. Yarava; A. Nagaraju; G. C. Babu; P. Gopala Krishna|10.1109/ICAIS56108.2023.10073781|Data Security;encryption techniques;cryptography;Privacy;Cloud computing;Aggregates;Simulation;Scalability;Authentication;Information sharing;Media|
|[Location based Context Awareness with Multipoint Transmission by ELAML Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073864)|S. R. Prasath; K. Gokulakrishnan; M. Subramanian; N. Mohan|10.1109/ICAIS56108.2023.10073864|5G;mmWave communication;machine learning;navigational network locators;Enhanced Location Awareness Machine Learning;Location awareness;Base stations;Wireless sensor networks;Machine learning algorithms;Wireless networks;Machine learning;Context awareness|
|[An Efficient Artificial Bee Colony based Optimized Model for Load Prediction in IoT Enabled Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073810)|J. Manju; R. B. Manjula; R. Dash|10.1109/ICAIS56108.2023.10073810|Internet of Things;Load Prediction;S mart Grid;Power Consumption;Artificial Bee Colony;Optimization;Load forecasting;Metaheuristics;Predictive models;Smart grids;Numerical models;Planning;Behavioral sciences|
|[A Hybrid Deep Learning based Classification of Brain Lesion Classification in CT Image using Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073907)|R. S. Priya; K. L. Narayanan; B. V. Nirmala; R. S. Krishnan|10.1109/ICAIS56108.2023.10073907|CT image;Gabor filter;K-means clustering;Fuzzy logic;Gray Level Co-occurrence Matrix;Convolutional Neural Network;Particle Swarm Optimization;Deep learning;Image segmentation;Computed tomography;Adaptive filters;Training data;Stroke (medical condition);Convolutional neural networks|
|[Management in Industrial Sectors using Neuro-Fuzzy Controller and Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073714)|D. Sabapathi; Y. S. Pawar; S. Patnaik; E. Sivanantham; D. K. Prabhu; N. B. Prakash|10.1109/ICAIS56108.2023.10073714|Load forecasting;load scheduling;demand side management;priority scheduling;communication infrastructure;neuro-fuzzy controller;deep learning;Deep learning;Productivity;Schedules;Job shop scheduling;Demand side management;Load forecasting;Learning (artificial intelligence)|
|[Detection of Early Fault in Power Electronic Converters through Machine Learning and Data Mining Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073820)|P. K. V; K. Gowrishankar; E. Sivanantham; K. S. Rao; N. Kiran; A. Vimal|10.1109/ICAIS56108.2023.10073820|Power electronic system;electromagnetic interference;harmonics;fault detection;machine learning;data mining techniques;Electromagnetic interference;Rectifiers;Machine learning;Power system harmonics;Harmonic analysis;Power electronics;Circuit faults|
|[Prediction of Future Market Share of Cryptocurrencies using Real-Time Investments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073844)|A. Sa; B. D. Reddy; S. Aishwarya|10.1109/ICAIS56108.2023.10073844|Cryptocurrency;decision-making;investment;regulation;Industries;Uncertainty;Profitability;Decision making;Regulation;Real-time systems;Cryptocurrency|
|[A Systematic Review on Beamforming Aided Channel Estimation Techniques for MIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073835)|L. R. Reddy; B. D. Mathews|10.1109/ICAIS56108.2023.10073835|Beamforming;channel estimation;massive multiple input multiple output;Wireless communication;Systematics;Power demand;Array signal processing;Channel estimation;Power amplifiers;Massive MIMO|
|[Systematic Review on on-Air Hand Doodle System for the Purpose of Authentication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073858)|S. D; C. V; S. Adiga; S. G; P. M|10.1109/ICAIS56108.2023.10073858|CNN (Convolution Neural Network);Kalman Filter;hand gesture recognition;Doodle;deep learning;Air writing;wearable sensors;DNST (Differentiable Spatial to Numerical Transform);Bi-LSTM (Bidirectional Long Short-Term Memory);LMC (Leap motion Controller);DTW (Dynamic Time Warping);RNN (Recurrent Neural Network);TA-RNNs (Time-Aligned Recurrent Neural Network);Handwriting recognition;Recurrent neural networks;Three-dimensional displays;Authentication;Gesture recognition;Writing;Hardware|
|[Automatic Detection and Classification of Diabetic Retinopathy using Modified UNET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073897)|N. P; M. Kandasamy; R. Shanmugam; S. K. A|10.1109/ICAIS56108.2023.10073897|Automated Diabetic Retinopathy;Deep Neural Networks;UNet Topology;Deep learning;Cataracts;Network topology;Retinopathy;Visual impairment;Neural networks;Retina|
|[Rainfall Prediction using Machine Learning Techniques – A Comparative Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073697)|S. Sivanantham; P. H. Kumar; S. N. Vardhan; S. C. Kumar; T. A. Kumar; T. Pradeep|10.1109/ICAIS56108.2023.10073697|Prediction;Machine Learning;Supervised Learning;SVM;Decision Tree;Random Forest;Regression;Classification;Neural Networks;Support vector machines;Machine learning algorithms;Rain;Forestry;Prediction algorithms;Agriculture;Classification algorithms|
|[Analysis Application of Big Data-based Analysis of Network Security and Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073823)|K. Hussain; S. Sah; B. Seth; N. Fatima Rizvi; B. V. Febiyola Justin|10.1109/ICAIS56108.2023.10073823|Privacy;Big data;Network security;Intelligence analysis;Technological innovation;Computational modeling;Big Data;Network security;Market research;Computer networks;Research initiatives|
|[Comparative Study of Blockchain Technology in Supply Chain Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073892)|K. R. Pardeshi|10.1109/ICAIS56108.2023.10073892|Supply Chain management;Blockchain technology;Features;Types of Blockchain;Hyperledger;Ethereum;Supply chain management;Supply chains;QR codes;Organizations;Blockchains;Internet;Security|
|[Analysis of the Effect of IoT-based Wearables in Healthcare Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073791)|S. Junaid Parvez; S. Salman Babu; S. Farook; P. Baraneedharan|10.1109/ICAIS56108.2023.10073791|Healthcare;IOT;Cloud computing;cloud security;Cloud computing;Technological innovation;Costs;Wearable computers;Medical services;Big Data;Internet of Things|
|[Heuristics based Segmentation of Left Ventricle in Cardiac MR Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073912)|G. R; S. K. Rani K; R. M; A. S; D. B; A. L|10.1109/ICAIS56108.2023.10073912|Ventricle;Magnetic Resonance Imaging;Segmentation;Convolutional Neural Network;Thresholding;Heart;Image segmentation;Thresholding (Imaging);Sensitivity;Shape;Magnetic resonance imaging;Loss measurement|
|[An Assessment of Object Detection in Thermal (Infrared) Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073709)|K. P; K. N|10.1109/ICAIS56108.2023.10073709|Keywords - computer vision;object detection;YOLO;Thermal images;Image segmentation;Tracking;Surveillance;Motion segmentation;Wildlife;Object detection;Detectors|
|[Integrated IoT System for Automatic Dust Cleaning of Solar Panels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073675)|R. Balamurugan; A. A. Kumar; A. Kalaimaran; V. Sathish|10.1109/ICAIS56108.2023.10073675|Solar Panel;IoT;Arduino UNO;LDR Sensor;Renewable energy sources;Cloud computing;Voltage;Solar energy;Market research;Cleaning;Solar panels|
|[Data Mining in Education: A Review of Current Practices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073932)|S. S. Mangalapalli; S. Venkata Pavan Kumar Munnangi; M. A. Mulpuri; S. Snigdha Reddy Goguladinne; S. Kodukula; S. Thatavarthi|10.1109/ICAIS56108.2023.10073932|Educational Data Mining;Educational Data Sets;Concurrent Methods in Data Mining;Data Mining Algorithms;Industries;Cloud computing;Law enforcement;Forensics;Education;Medical services;Planning|
|[Analysis of Panoramic Images using Deep Learning For Dental Disease Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073939)|M. Mallick; M. K. A; S. Govindaraju; A. S. Kumar; M. Kandasamy; P. Anitha|10.1109/ICAIS56108.2023.10073939|Dental analysis;Panoramic radiography;X-Ray Image;Image enhancements;Deep learning;Deep learning;Lacquers;Visualization;Laplace equations;Teeth;X-rays;Learning (artificial intelligence)|
|[A Study on Election Prediction using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073693)|K. Myilvahanan; Y. P; S. Pasha; M. Ismail; V. Tharun|10.1109/ICAIS56108.2023.10073693|Election Result Prediction;Prediction of election results;KNN Algorithm;Feature Engineering;Training;Testing models;Resistance;Social networking (online);Voting;Supervised learning;Prototypes;Machine learning;Predictive models|
|[Deep Learning based Automatic Radiology Report Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073691)|M. A. Kumar; M. Panitini; S. Vemulapalli; M. J. N. V. Sai|10.1109/ICAIS56108.2023.10073691|Chest x-rays;Radiologist;Encoder;Decoder;Attention;Bilingual Evaluation Understudy;Chexnet;Deep learning;Heart;Lung;Radiology;Turning;Feature extraction;Decoding|
|[Solar Powered Bluetooth Controlled Grass Cutting Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073789)|S. Athipatla; B. Kandati; S. Kanipaku; D. Kalluri|10.1109/ICAIS56108.2023.10073789|Solar panel;Arduino uno;Glass cutting robot;Motor driver relay;Wireless communication;Bluetooth;Protocols;DC motors;Liquid crystal displays;Batteries;Solar panels|
|[A Cloud based Improved File Handling and Duplicate Removal using MD5](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073786)|M. Kathiravan; R. Logeshwari; S. Pavithra; M. Meenakshi; V. Sathya Durga; M. Vijayakumar|10.1109/ICAIS56108.2023.10073786|De-Duplication;Checksum;Cloud computing;MD5;Hash Function;Databases;Artificial intelligence|
|[Smart clustering attack detection system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073716)|R. Vinod Kumar; G. Jayasri; M. M. A; E. Vidya|10.1109/ICAIS56108.2023.10073716|Wireless communication;Wireless sensor networks;Cluster attacks;Network Security;Intrusion detection;Wireless communication;Wireless sensor networks;Cloud computing;Heuristic algorithms;Intrusion detection;Clustering algorithms;Synchronization|
|[IoT based Early Flood Detection, Destruction Avoidance and Automated Dam Gate Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073890)|J. Sampson; R. Nandakishore Reddy; M. Venkata Janardhan Reddy; M. Chandra Narayana; R. Varun Kumar|10.1109/ICAIS56108.2023.10073890|Flood Prediction;Flood Prevention;Sensors;Wireless Communication Technology (Wi-Fi);ESP8266 Micro controller;Cloud;Wireless communication;Dams;Ultrasonic variables measurement;Transmitters;Receivers;Logic gates;Control systems|
|[Evaluation of Wireless Sensor Networks Module using IoT Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073799)|L. Anand; P. S; J. Seetha; R. Juliana; P. Naveen Kumar; G. Parasa|10.1109/ICAIS56108.2023.10073799|WSN;WAN;U200b touch networks;Data Security;Temperature sensors;Wide area networks;Vibrations;Temperature measurement;Wireless sensor networks;Wireless networks;Transceivers|
|[A Novel Machine Learning Technique for Predicting Road Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073778)|S. Alagarsamy; P. Nagaraj; B. Srikanth; C. V. Krishna; G. Bharath; S. S. Kalyan|10.1109/ICAIS56108.2023.10073778|Random Forest;Classification;Road accidents;Road accidents;Machine learning algorithms;Instruments;Government;Machine learning;Forestry;Predictive models|
|[A Study on Deep Learning based Classification and Identification of offensive memes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073933)|K. Myilvahanan; S. B; T. Raj; C. Attanti; S. Sahay|10.1109/ICAIS56108.2023.10073933|Deep Learning;Classification;Bidirectional Long Short Term Memory (Bi-LSTM);Recurrent Neural Network (RNN);Deep learning;Sentiment analysis;Recurrent neural networks;Social networking (online);Bit error rate;Syntactics;Transformers|
|[Remote Sensing Image Classification with a Few Labeled Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073806)|Y. Harshitha; B. L. N. P. Kumar; M. V. Aparna; A. V. Kishore|10.1109/ICAIS56108.2023.10073806|Principal Component Analysis;factor Analysis;Deep learning;CNN;Hyperspectral image classification (HSIC);Machine learning algorithms;Convolution;Neural networks;Machine learning;Geospatial analysis;Classification algorithms;Data mining|
|[A Scalable Network Intrusion Detection System using Bi-LSTM and CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073719)|S. S. Kanumalli; L. K; R. A; S. P; T. M|10.1109/ICAIS56108.2023.10073719|Network Intrusion;Cyber Attacks;Classification;Decision Trees;Intrusion Detection System (IDS);Deep Learning;Recurrent Neural Networks (RNN);Convolutional Neural Networks (CNN);Bi-Directional LSTM (Bi-LSTM);Ensemble Models;Measurement;Training;Support vector machines;Protocols;Neural networks;Network intrusion detection;Telecommunication traffic|
|[Comparative Study of IDS against DDOS Attack using Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073681)|P. Karthik; V. Vaddadi; L. P. Vakkapatla; L. Sridharala; S. Bulla; S. Kavitha|10.1109/ICAIS56108.2023.10073681|Machine Learning;Distributed Denial of Service (DDOS);K-Nearest Neighbor;Random Forest;Decision Tree;Industries;Support vector machines;Machine learning algorithms;Rain;Telecommunication traffic;Semisupervised learning;Feature extraction|
|[VLSI Implementation of Coordinate Rotation Based Design Methodology using Verilog HDL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073928)|Chetana; S. K P|10.1109/ICAIS56108.2023.10073928|CORDIC- Coordinate rotation digital computer;FPGA- Field programmable gate array;Digital computers;Power demand;Computer architecture;Very large scale integration;Throughput;Hardware;Timing|
|[Agriculture-based Automation with Recommendation Systems based on AI Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073768)|P. R; D. R; P. A|10.1109/ICAIS56108.2023.10073768|Machine Learning;Deep Learning;Artificial Intelligence;Support vector machines;Automation;Supply chains;Crops;Soil;Recommender systems;Monitoring|
|[MDC-Net:Intelligent Malware Detection and Classification using Extreme Learning Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073874)|V. S. K. Reddy; I. Nagaraju; M. Gayatri; R. C. R; D. P; R. P|10.1109/ICAIS56108.2023.10073874|Security breaches;malware detection;machine learning framework;convolution neural networks;Measurement;Machine learning algorithms;Extreme learning machines;Neural networks;Symbols;Feature extraction;Malware|
|[Design of Two -Wheeler Hybrid Electric Vehicle using Series Parallel Configuration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073870)|S. S. Yeole; S. Vasant Kolhe; R. R. Bibave; V. Shivaji Chavan; B. B. Kadam; V. Sakharam Bodhe|10.1109/ICAIS56108.2023.10073870|Hybrid;Electrical vehicle;Battery;Regenerative Braking;Pollution;Oils;Hybrid electric vehicles;Automobiles;Integrated circuit modeling;Artificial intelligence;Engines|
|[Carbon Neutrality Approaches for IoT-Enabled Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073921)|S. Benedict|10.1109/ICAIS56108.2023.10073921|Carbon Neutrality;Energy;IoT;Cloud;Finance;Medical services;Carbon neutral;Agriculture;Sensors;Energy harvesting;Sustainable development|
|[Accident Emergency Alert System using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073901)|N. Bhatia; Y. Dixit; K. M. Balamurugan|10.1109/ICAIS56108.2023.10073901|accident;alert system;emergency;accident alert based on computer vision;Computer vision;Road accidents;Surveillance;Transportation;Estimation;Trajectory;Sensors|
|[Implementation of a Smart Meter with Independent Load Monitoring using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073741)|A. Jagadeeshwaran; M. Vetrivel; C. A. Vaithilingam; B. Karthikeyan; A. Sheela|10.1109/ICAIS56108.2023.10073741|Smart Meter;Internet of Things;Voltage and current measurement;Meters;Load monitoring;Cloud computing;Current measurement;Voltage transformers;Switches;Smart meters|
|[Cybersecurity Threats, Detection Methods, and Prevention Strategies in Smart Grid: Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073843)|K. D. Kumar; M. A. Jawale; M. Sujith; D. B. Pardeshi|10.1109/ICAIS56108.2023.10073843|Smart grid;Cybersecurity;sensors;communication;Privacy;Solids;Sensor systems;Smart grids;Power systems;Communication networks;Computer crime|
|[QoS Design using Mmwave Backhaul Solution for Utilising Underutilised 5G Bandwidth In GHz Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073756)|M. Kandasamy; N. Yuvaraj; R. A. Raja; N. V. Kousik; M. R. Tf; A. S. Kumar|10.1109/ICAIS56108.2023.10073756|QoS Design;bandwidth utilization;packet scheduler;5G Transmission;5G mobile communication;Bandwidth;Quality of service;Instant messaging;Motion pictures;Software;Browsers|
|[Performance Analysis of Spatial Domain Image Steganography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073940)|G. V; I. G; J. V. R; S. S|10.1109/ICAIS56108.2023.10073940|steganography;spatial domain;Least Significant Bit substitution;stego image;cover image;edge detection;canny;sobel;peak signal-to-noise ratio;Image quality;Steganography;Visualization;PSNR;Image edge detection;Resists;Media|
|[Heterogeneous Fog Computing Implementation for Internet of Things Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073699)|P. Jenifer; J. Angela Jennifa Sujana; M. Sharon Nisha; B. Benita|10.1109/ICAIS56108.2023.10073699|Quality of Experience;Internet of Things;Fog computing;low latency;low energy;Power demand;Metaheuristics;Random access memory;Internet of Things;Quality of experience;Time factors;Security|
|[Artificial Intelligence based Self-Driving Car using Robotic Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073726)|S. Sujitha; S. Pyari; W. Y. Jhansipriya; Y. Roopeswar Reddy; R. Vinod Kumar; P. Ravi Nandan|10.1109/ICAIS56108.2023.10073726|Object detection;Vehicle route;Lane detection;Self-driving vehicle;Robotic Kit;Raspberry PI;OpenCV;Technological innovation;Shape;Transforms;Software;Regulation;Security;Artificial intelligence|
|[A Hybrid-Layered Framework for Detection and Diagnosis of Alzheimer’s Disease (AD) from Fundus Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073930)|V. Srilakshmi; A. Anumolu; M. A. Safali; V. S. Parvathi|10.1109/ICAIS56108.2023.10073930|Alzheimer’s disease (AD);Machine Learning (ML);retina;DL;Fundus images;Training;Measurement;Manuals;Learning (artificial intelligence);Prediction algorithms;Retina;Optical imaging|
|[Frequency-based Drone Detection using I-Hawk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073668)|C. C. Sai; C. B. Gowtham; S. S. Imambi; M. Sravani; M. Vivek; S. Bulla|10.1109/ICAIS56108.2023.10073668|Unmanned aerial vehicles;accelerometer;image processing;Heating systems;Privacy;Terrorism;Surveillance;Cameras;Autonomous aerial vehicles;Biology|
|[IoT Alert Reflexion of Forbidden Deforestation Regions with Drone observation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073774)|S. K. Saravanan; T. Krishna Kumar; D. Udaya Suriya Rajkumar; R. Krishnamoorthy; R. Narayana Rao; R. Thiagarajan|10.1109/ICAIS56108.2023.10073774|IoT;Drones;Remote Sensing;GPS;Deforestation;nan|
|[Investigation of Security Concerns and Solutions for the Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073669)|G. Janani Alias Pandeeswari; D. M. D. Preethi|10.1109/ICAIS56108.2023.10073669|cybersecurity;IoT;KDD-CUP;nan|
|[A Survey on Evolution of Video Summarization Techniques in the Era of Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073688)|P. Vaghela; A. Makwana|10.1109/ICAIS56108.2023.10073688|BoI Model;CCTV;Artificial Intelligence (AI);Long Short-Term Memory (LSTM);Unmanned Aerial Vehicles (UAV);Reinforcement Learning (RL);MPEG;Deep Learning;Convolutional Neural Networks;Recurrent Neural Networks;Deep learning;Surveillance;Neural networks;Memory management;Education;Entertainment industry;Medical services|
|[Utilizing the Random Forest Algorithm to Enhance Alzheimer’s disease Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073852)|C. Kaur; T. Panda; S. Panda; A. Rahman Mohammed Al Ansari; M. Nivetha; B. Kiran Bala|10.1109/ICAIS56108.2023.10073852|Alzheimer’s disease;Random Forest;Magnetic resonance imaging dataset;nan|
|[Sentiment Analysis of Movie Review using Hybrid Optimization with Convolutional Neural Network in English Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073750)|V. M. Tidake; N. Mazumdar; A. Suresh Kumar; B. Nageswara Rao; G. Fatma; I. Infant Raj; B. Kiran Bala|10.1109/ICAIS56108.2023.10073750|Movie review;English language;sentiment analysis;convolutional neural network;sentiment classification;Sentiment analysis;Analytical models;Databases;Social networking (online);Motion pictures;Feature extraction;Internet|
|[A Low-Energy System for IoT-based Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073908)|A. S; S. V M; S. M. Aasif; S. Y. Adithya|10.1109/ICAIS56108.2023.10073908|monitoring;automation;networks;antenna;Smart agriculture;Wireless sensor networks;Technological innovation;Simulation;Organizations;Internet of Things;Data communication|
|[Post-Pandemic Air Quality Monitoring System using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073718)|A. P. Murdan; M. U. -R. Jeetun|10.1109/ICAIS56108.2023.10073718|indoor air quality;sensor;alerting system;Internet of things;Atmospheric measurements;Pandemics;Conferences;Air quality;Air pollution;Real-time systems;Liquid crystal displays|
|[Arduino based Dual Axis Smart Solar Tracking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073860)|M. Karthik; R. Vishnu; M. Vigneshwar; M. Logaeshwar|10.1109/ICAIS56108.2023.10073860|Dual Axis;Renewable;Solar Panels;Tracking;Energy;Performance evaluation;Renewable energy sources;Tracking;Planets;Transforms;Solar energy;Electromagnetic radiation|
|[Design of Reconfigurable Reversible Low Power Adders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073896)|S. T; S. S; M. P; S. Padmapriya; M. Jagadeeswari; V. Anbumani|10.1109/ICAIS56108.2023.10073896|Field Programmable Gate Array (FPGA);Error Tolerant Adder (ETA);Toffoli gate;Reversible gates;Power Optimization;Reconfigurable;Low Power Adder(LPA);Embedded systems;Films;Design methodology;Logic gates;Artificial intelligence;Adders;Field programmable gate arrays|
|[Exploiting Reliable Power from Independent PV-Battery Systems by Combining Improved INC with Power Management Circuitry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073825)|K. K. Kumar; M. W. Iruthayarajan; A. S. Kamaraja|10.1109/ICAIS56108.2023.10073825|Solar PV;Lithium Battery;Improved Incremental Conductance algorithm;Power converter;State of Charge;Power management circuitry;Maximum power point trackers;Fluctuations;Power system management;Lighting;Light emitting diodes;Batteries;Voltage control|
|[Investigating the Performance of a Dual-Axis Solar Tracking System in a Tropical Climate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073903)|A. Khurwolah; V. Oree|10.1109/ICAIS56108.2023.10073903|Solar tracking;Climate;Design;PV panel;Dual-axis;Mechanical sensors;Economics;Energy consumption;Tracking;Microcontrollers;Clouds;Prototypes|
|[Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073753)|S. P; S. K. Ravichandran; Rakshitha|10.1109/ICAIS56108.2023.10073753|Predictive maintenance;Industry 4.0;Artificial Intelligence;CBM;PHM;Manufacturing industries;Productivity;Production systems;Systematics;Companies;Life estimation;Market research|
|[Smart Grid based Mitigation of Carbon Dioxide Emissions in Various Sectors -A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073772)|N. Nimagalu; N. M. Reddy; V. H. Reddy; K. Deepa; V. Sailaja|10.1109/ICAIS56108.2023.10073772|Sustainability;Carbon dioxide;Greenhouse gases;Renewable energy;Smart grid;Pre-cooling;Impacts;Mitigation;Photovoltaic systems;Gases;Renewable energy sources;Greenhouse effect;Oils;Green products;Transportation|
|[Modeling and Simulation of H-Bridge Motor Driven Electric Vehicle using Matlab/Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073813)|V. Sabarish; C. V. D. Dhayalan; M. Sriraman; P. S. Babu|10.1109/ICAIS56108.2023.10073813|Electric vehicle;H-bridge converter;MATLAB;DC link voltage;Battery;SoC;Costs;Mathematical models;Fossil fuels;Batteries;Reliability;Automobiles;Integrated circuit modeling|
|[EEG Signal Classification using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073767)|S. D; N. S; L. S; R. S|10.1109/ICAIS56108.2023.10073767|EEG Signal;Convolutional neural network;ConvNet;Classifications of Signals;Machine Learning;Fast Fourier Transform;Deep learning;Training;Image segmentation;Time-frequency analysis;Neural networks;Pattern classification;Brain modeling|
|[Improved Performance of Canny Edge Detection in Low-Light Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073915)|P. S. Dadi; S. G; T. T; L. Nelson; S. R|10.1109/ICAIS56108.2023.10073915|Edge detection;Canny edge algorithm;Facial Expressions;Active Vision;Surveillance;Mobile Robots;Slam;Visualization;Simultaneous localization and mapping;Navigation;Image edge detection;Streaming media;Real-time systems;Mobile robots|
|[Rectangular Microstrip Patch Array Antenna for Short Wave Radio Band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073819)|P. M; B. V. L. K; C. R; S. Vairaprakash; N. K; P. V|10.1109/ICAIS56108.2023.10073819|Rectangular Microstrip Patch;Antenna Array;Advance Design System 2009 Simulator;Microstrip center feed;Microstrip antenna arrays;Patch antennas;Resonant frequency;Microstrip antennas;Antenna feeds;Dielectric resonator antennas;Software|
|[Analysis of Current Smart Wearable Trends using Internet of Medical Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073832)|P. A.; B. Seth; G. Ramachandran|10.1109/ICAIS56108.2023.10073832|Digital devices;Human Activity;Health Systems;Sensor;Medical applications;Wearable technologies;Wireless;Smart textiles;Wearable computers;Cellular phones;Machine learning;Smart homes;Market research;Real-time systems|
|[FPGA Implementation of RO-PUF using Chaotic Maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073917)|A. Azhaganantham; K. M; G. T. S; J. S|10.1109/ICAIS56108.2023.10073917|Tent map;Bernoulli shift map;Ring oscillator;PUF;Statistical tests;Sensitivity;Information security;Resists;NIST;Physical unclonable function;Generators;Table lookup|
|[Photovoltaic System based Interleaved Converter for Grid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073708)|K. M; J. N. L; J. P. R; S. P|10.1109/ICAIS56108.2023.10073708|Photovoltaic system;Maximum energy;Direct current to Direct current converter;grid system;high gain;Photovoltaic systems;Software packages;Switches;Solar energy;Inverters;Energy harvesting;Voltage control|
|[Implementation of RFID based material Identification System in Super Markets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073931)|R. Pandurangan; G. N; P. R; S. P|10.1109/ICAIS56108.2023.10073931|Embedded systems;Internet of Things (IoT);Radio frequency identification;Raspberry Pi;Automated billing system;Radio frequency;Industries;Cloud computing;Memory;Manuals;Machine learning;Internet of Things|
|[Multiport Converter based Solar PV System using Flyback Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073790)|S. Franklin; K. Gunasekaran; K. Rajesh; J. S. Priya|10.1109/ICAIS56108.2023.10073790|Multiport converter;Flyback converter;Solar photo voltaic;Bidirectional port;Battery;Maximum power point trackers;Renewable energy sources;Buck converters;Hybrid power systems;Power electronics;Batteries;Steady-state|
|[An Efficient Text based Classification using Neural Networks and Long Short-Term Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073886)|S. S. S. Srinivas; D. S. Harshith; V. Rachapudi|10.1109/ICAIS56108.2023.10073886|Word Embedding;Neural Networks;LSTM;Training;Support vector machines;Sentiment analysis;Recurrent neural networks;Text categorization;Benchmark testing;Real-time systems|
|[Improved Segmentation of Neurodegenerative Disease using Deep Auto Encoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073905)|B. Mahalakshmi; A. T. Raja|10.1109/ICAIS56108.2023.10073905|Segmentation;Neurodegenerative Disease;Deep Auto Encoder (DAE);Image segmentation;Correlation;Databases;Feature extraction;Brain modeling;Fats;Indexes|
|[An Efficient Prototype for PV based Water Pumping Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073705)|K. N; A. Ravichandran; S. Sahoo|10.1109/ICAIS56108.2023.10073705|Proportional Integral;Matlab simulation;Simulink;Zeto converter;Cuk converter;Reactive power;Voltage measurement;Switching loss;Rotors;Prototypes;Switches;Stators|
|[Classification of Emotions using EEG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073677)|P. Gourabathuni; R. S. Pothineni; K. C. Yelavarti|10.1109/ICAIS56108.2023.10073677|Emotions;EEG-Electroencephalography Signals;Classification;LSTM-Long Short-Term Memory;GRU-Gated Neural Network;DNN-Deep Neural Network;Affective computing;Pattern classification;Feature extraction;Electroencephalography;Real-time systems;Data models;Classification algorithms|
|[Generate Secure Transactions between Clients using a Three-Phase Password Generation Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073871)|D. Saravanan|10.1109/ICAIS56108.2023.10073871|Text password;Authentication;User validity;System recognition;System attack;Graphical password;Dictionaries;Computer hacking;Text recognition;Image color analysis;Biometrics (access control);Authentication;Passwords|
|[Hybrid Energy Sources using Current Fed Inverter for High Gain in Single Phase to AC Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073877)|S. K; J. S; H. M; R. R|10.1109/ICAIS56108.2023.10073877|Wind;Fuel cell (FC);Current Fed Inverter (CFI);Proportional Integral (PI) and Pulse Width Modulation (PWM);Renewable energy sources;Switches;High-voltage techniques;Pulse width modulation;IEEE Standards;Inverters;Performance analysis|
|[IoT based Automatic Smart Black Box System to Detect Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073763)|S. C; K. Avinash; K. Manjunadh; K. Jyothish|10.1109/ICAIS56108.2023.10073763|Black Box;Internet of Things (IoT);Accidents;Electronic equipment;Flight recording;Control systems;Safety;Automobiles;Internet of Things;Traffic congestion|
|[A Novel Sensing System to Detect the Overflow of Septic Tanks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073674)|P. Sivakumar; R. Rajalakshmi; A. K. T; R. Nandakishore Reddy; G. Mounika; K. S. Kumar|10.1109/ICAIS56108.2023.10073674|septic tank;gas sensor;wireless fluid sensor;public health;Methane;Gases;Infectious diseases;Maintenance engineering;Water pollution;Sensors;Public healthcare|
|[Design of Power and Delay Efficient Fault Tolerant Adder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073682)|K. C; V. K. Perumal; M. Vivek Kumar; J. Muralidharan|10.1109/ICAIS56108.2023.10073682|ETA;Adder;Fault-tolerance;Carry;Low power;Fault tolerance;Energy consumption;Power demand;Delay effects;Fault tolerant systems;Buildings;Very large scale integration|
|[Different Methods to Remove Baseline Wandering Noise from the ECG Signal: A Research Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073914)|K. K; S. Latha|10.1109/ICAIS56108.2023.10073914|Electrocardiography;Baseline Wandering noise;Adaptive filters;Linear/Non-Linear filters;Heart;Power measurement;Electrocardiography;Skin;Low-frequency noise;Recording;Power harmonic filters|
|[Optimal Placement of Static Phase Shifter and STATCOM to Enhance Performance of the Electrical Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073815)|R. B. Magadum; A. V. Deshapande; S. N. Dodamani; V. G. Mathad; S. D. Hirekodi; M. Hegde|10.1109/ICAIS56108.2023.10073815|STATCOM;Static Phase Shifter;Load flow analysis;Power loss;T&D infrastructure and Voltage profile;Electric potential;Phase shifters;Sociology;Transportation;Surge protection;Automatic voltage control;Stability analysis|
|[Intelligent Control system for BLDC Motor Driven Solar Water Pumping System using Wode Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073723)|A. Saravanan; M. Rekha; S. T. Subha; D. Arulanantham; S. Baskaran; S. Gomathi|10.1109/ICAIS56108.2023.10073723|E Bike;BLDC Motor Controller;Throttle Sensors;Electric Motor;Maximum power point trackers;Photovoltaic systems;Renewable energy sources;Brushless DC motors;Snubbers;Topology;Steady-state|
|[3-Phase 7-Level Diode Clamped Inverter for Standalone Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073661)|K. Chenchireddy; V. Kumar; N. S. Shankar; P. Vamshi; S. Tulasi; K. Radhika|10.1109/ICAIS56108.2023.10073661|Inverter;MLI;DC MLI;PWM technique;THD;Legged locomotion;Switches;Pulse width modulation;Multilevel inverters;Circuit synthesis;Artificial intelligence|
|[Footsteps Based Sustainable Energy Generation and Consumption System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073702)|R. P; M. Jayanthi; A. M A; A. Dharmik Sai Reddy; D. M. Reddy; G. Pavan Kalyan Reddy|10.1109/ICAIS56108.2023.10073702|Locomotion;Radio-Frequency Identification;Aperiodic;Piezo-Electric Sensor;Ground Reaction Force.;Cellular phones;Roads;Force;Lighting;Lattices;Switches;Task analysis|
|[Fertilizer Sensing and Solar based RTC Water Pumping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073812)|A. J. Lakshmi; V. Krishnaveni; G. Vinuthna; A. L. Goud|10.1109/ICAIS56108.2023.10073812|Solar Panel;Soil Moisture Sensor;PH sensor;RTC(Real time clock);Irrigation;Soil measurements;Moisture measurement;Soil moisture;Moisture;Water conservation;Water pollution|
|[Implementation of PFC Bridgeless-Zeta Converter with FOPID using Improved PSO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073764)|K. Durgadevi; C. Amali; R. Karthik|10.1109/ICAIS56108.2023.10073764|Power Factor Correction (PFC);Discontinuous Conduction Mode (DCM);DC-DC Converter;Particle Swarm Optimization (PSO);Fractional Order PID Controller (FOPID);Voltage fluctuations;Simulation;Bridge circuits;Power factor correction;Power system reliability;Topology;Particle swarm optimization|
|[A Systematic Review of the Soil Fertility Monitoring and Organic Farming Techniques for an Improved Crop Yield](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073868)|S. V. Gaikar; M. S. Zambare; A. D. Shaligram|10.1109/ICAIS56108.2023.10073868|Agriculture;Crop Yield;Soil Fertility;Machine Learning;Crop Recommendation;Training;Systematics;Crops;Estimation;Production;Soil;Predictive models|
|[Automated Arrhythmia Classification using Harris Hawks Optimization with Deep Learning Model on IoT Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073736)|S. Jagadeesh; M. Shrimali; P. S. Prasad; K. Maheswari; A. Narendrakumar; K. R. G|10.1109/ICAIS56108.2023.10073736|Arrhythmia classification;Internet of Things;Harris Hawks optimization;Deep learning;electrocardiogram (ECG) Signals;nan|
|[Computation of Electric Field Distribution on 110 kV Polymer Insulator for Different Corona Ring Position](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073745)|T. Sukumar; B. Vigneshwaran|10.1109/ICAIS56108.2023.10073745|Composite insulator;Electric stress;Finite element method;Corona Ring;Electric potential;Power transmission lines;Pollution;Insulators;Conductors;Corona;Real-time systems|
|[Enhancing Reliability in Multi-Path Mobile Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073667)|J. Visumathi; S. Gurusubramani; S. K. Mouleeswaran; N. Sammeta|10.1109/ICAIS56108.2023.10073667|Mobile Wireless Sensor Network;Multi-Path;Genetic Algorithm;load minimization;throughput improvement;Wireless sensor networks;Machine learning algorithms;Simulation;Packet loss;Routing;Throughput;Delays|
|[Rule-based Routing in the Cognitive Radio Network for the Data Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073809)|A. S; S. D|10.1109/ICAIS56108.2023.10073809|Cognitive Radio;Rule-based;Routing;Weighted Channel;Resource Sharing;Cross layer design;Adaptation models;Routing;Throughput;Data models;Delays;Cognitive radio|
|[Using the Cooperative Nodes for Diminishing the Packet Drops in Mobile WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073906)|V. Elangovan; D. R. C. H; A. Arul Prasath; G. Padmapriya|10.1109/ICAIS56108.2023.10073906|Diminishing The Packet Drops;Cooperative Nodes;Sensor Node Movement;Simulation Results;Mobile Wireless Sensor Network;Wireless sensor networks;Network topology;Wireless networks;Simulation;Packet loss;Throughput;Routing|
|[Received Signal Strength based Proficient Clustering in Mobile Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073842)|G. Suresh; P. Anandan; N. Kumaratharan; N. Anbuselvan|10.1109/ICAIS56108.2023.10073842|Clustering;Received Signal Strength;Mobile Wireless Sensor network;link life time;Cluster Head;Wireless communication;Wireless sensor networks;Simulation;Redundancy;Buildings;Organizations;Bandwidth|
|[Concealing the Data using Cryptography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073878)|P. L. Sri; C. Nanda Krishna; A. D. Sai; S. Roshini|10.1109/ICAIS56108.2023.10073878|Cryptography;ASCII (American Standard Code for Information Interchange);Advanced Encryption Standard;Costs;Codes;Maintenance engineering;Encryption;Artificial intelligence;Standards|
|[Video Frame based Prompt Compression Model with Steganography for Secure Data Transmission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073883)|V. L. Narayana; K. S. N. Malleswari; M. Divyanjali; S. Nandini; G. Purnima|10.1109/ICAIS56108.2023.10073883|Compress;Encryption;Decryption;Video Steganography;Data hiding;Steganography;Codes;Pressing;Motion pictures;Safety;Data communication;Security|
|[Boosted Hybrid Privacy Preserving Data Mining (BHPPDM) Technique to Increase Privacy and Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073804)|K. N. Prasanthi; C. Sekhara Rao Mvp; S. B. Pallapothu|10.1109/ICAIS56108.2023.10073804|privacy enabled data mining;geometric data perturbation;k-anonymization;L diversity;Data privacy;Privacy;Perturbation methods;Organizations;Medical services;Machine learning;Information filtering|
|[Detection of DDoS Attacks using Artificial Gorilla Troops Optimizer based Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073935)|S. Govindaraju; R. Metia; P. Girija; K. Baranitharan; M. Indirani; M. R|10.1109/ICAIS56108.2023.10073935|Distributed Denial of Service attacks;Long Short Term Memory;Auto-encoder;Software-Defined Network;Internet of Things;Deep learning;Training;Runtime;Time series analysis;Denial-of-service attack;Feature extraction;Classification algorithms|
|[Digital Signature and Private Key Cryptography Mechanisms for Enhancing Node Authentication in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073784)|K. Suriyakrishnaan; N. Anusha; V. Sujatha; R. Radhika|10.1109/ICAIS56108.2023.10073784|Digital signature and Private Key Cryptography;Authentication;Eavesdropping Attack;Wireless Sensor Networks;Eavesdropping attack detection;Multiple Attack Detection;Wireless communication;Wireless sensor networks;Simulation;Vents;Authentication;Routing;Delays|
|[Review on Design of Smart Domestic Farming based On Internet of Things (IOT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073779)|M. Pillewan; R. Agrawal; N. Wyawahare; L. Thakare|10.1109/ICAIS56108.2023.10073779|Internet of Things (IOT);Real Time Clock (RTC);Light Dependent Resistor (LDR);Sensors;Raspberry pi;GSM;DHT 11;Temperature sensors;Temperature measurement;Animals;Production;Soil;Agriculture;Internet of Things|
|[A Study on Digital Signature in Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073680)|C. M. Nalayini; Jeevaakatiravan; P. V. Imogen; J. M. Sahana|10.1109/ICAIS56108.2023.10073680|Digital Signature Algorithm;Block chain;Elliptic Curve Digital Signature Algorithm;Private Key;Public key;Verification;Validation;Wireless communication;Wireless sensor networks;Protocols;Elliptic curves;Hospitals;Databases;Public key|
|[Secure Storage of Land Records and Implementation of Land Registration using Ethereum Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073887)|G. Kusuma; C. Rupa; S. Reshma; G. Rochana|10.1109/ICAIS56108.2023.10073887|Blockchain;Land Registration;Web application;Transfer ownership;Verification system;Maintenance engineering;Blockchains;Fraud;Artificial intelligence;Secure storage|
|[Study of Enhancing Usage of Data Visualization in Cyber Security- Quick, Efficient, and Complete](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073891)|B. V. Vikas; N. S. Karthikeya; G. S. Chandu; G. Raja; N. R. Sai|10.1109/ICAIS56108.2023.10073891|Data Visualization (dataviz);Hacking and Cyber Threats;Cyber data log;Data privacy;Digital forensics;Data visualization;Data breach;Data science;Market research;Safety|
|[A Detailed Study on Preventing the Malicious URLs from Cyber Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073865)|S. Adnaan; k. Swathi; G. Tejasri; A. V. Praveen Krishna; M. G. Sai Reddy|10.1109/ICAIS56108.2023.10073865|URLs (Uniform Resource Locator);Phishing;Machine learning (ML);Deep learning (DL);Cyber Attacks;Deep learning;Uniform resource locators;Phishing;Learning (artificial intelligence);Software;Electronic mail;Browsers|
|[Implementation of a Secured Document Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073854)|V. R. Koppurkar; S. Ramisetty; V. Solapure|10.1109/ICAIS56108.2023.10073854|Encryption;Decryption;Rivest Shamir Adleman;Machine Learning;Ciphers;Machine learning algorithms;Machine learning;Learning (artificial intelligence);Media;Classification algorithms;Sorting|
|[An Analysis of Various Cyber Threat Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073771)|B. K; S. T; S. S. V|10.1109/ICAIS56108.2023.10073771|Cyber security;Threat;STRIDE;PASTA;OCTAVE;Threat modeling;Organizations;Software systems;Hazards;Security;Computer crime;Artificial intelligence|
|[Advanced Encryption Standard-128 bit Design Flow for ZYNQ-7 ZC702 Evaluation Board](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073759)|B. M. Nisha; V. Nithya; J. Selvakumar|10.1109/ICAIS56108.2023.10073759|Hardware security;Field Programmable Gate Array;ZYNQ family board;Top-Down design;Advanced Encryption Standard;Atmospheric measurements;Programmable logic arrays;Logic gates;Particle measurements;Encryption;Table lookup;Registers|
|[Issues in Cloud Load balancing Fault-Tolerance: Review and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073738)|V. Siruvoru; S. Aparna|10.1109/ICAIS56108.2023.10073738|Load-balancing;Fault-Tolerance;Static load-balancing algorithm;Dynamic load- balancing algorithm;Virtual Machines;Cloud-Computing;Resistance;Fault tolerance;Fault tolerant systems;Load management;Time factors;Task analysis;Artificial intelligence|
|[Pre Encryption Data Hiding Techniques using Reserving Room Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073751)|J. Deepthi; T. Venu Gopal|10.1109/ICAIS56108.2023.10073751|Insert visible watermark;reversible data hiding;pixel prediction;image encryption;Watermarking;Prediction methods;Media;Encryption;Image restoration;Error correction codes;Data mining|
|[A Study on Data Security and Privacy Issues in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073721)|K. N. Gottipati; N. Peddisetty; S. Pothireddy; G. Botta; P. Yellamma; G. Swain|10.1109/ICAIS56108.2023.10073721|Data security;Cloud computing;Encryptions Privacy;Algorithms;Computers;Cloud computing;Data privacy;Costs;Cloud computing security;Memory;Cryptography|
|[An IoT-based Approach to Air Circulation within a Specific Room Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073742)|M. K. Gopal; A. V; A. I. Hegde; A. Jaiswal|10.1109/ICAIS56108.2023.10073742|Room Air Quality;Pollutants;Sensor;Dust;Atmosphere;Microcontroller Unit;Internet of things;Temperature;Controller;Access point;Server;Temperature sensors;Fuzzy logic;Degradation;Gases;Temperature distribution;Filtration;Humidity|
|[Proving Ownership of Privacy-Protected Cloud Storage Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073830)|S. T. Chadalawada; K. Mandadi; J. Machupally; V. I. Sena Reddy; B. T. Reddy; A. V. Praveen Krishna|10.1109/ICAIS56108.2023.10073830|Merkle trees (Binary hash trees);PoW;deduplication;Message locked encryption;Convergent key encryption;Hash functions;Cloud computing;Costs;Encryption;Servers;Artificial intelligence;Standards|
|[Large Data Processing for Cloud Service Collaborative Authenticity Computing Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073900)|S. R. Pujar; R. V. Patil; V. Sharma S; S. M. S|10.1109/ICAIS56108.2023.10073900|Cloud computing;Authenticity;Data process;Quality of service;Resource matching;Cloud computing;Collaboration;Computer architecture;Data processing;Virtual machining;Real-time systems;Safety|
|[Cloud based Location Tracking and Controlling Parameters System Implementation for Armed Forces in the War field](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073894)|S. Prabagar; C. S. Reddy; C. Ravindra Murthy; G. H. Kumar|10.1109/ICAIS56108.2023.10073894|Cloud Computing;Healthcare;Internet of Things;Wireless Sensor Network.;Temperature sensors;Temperature measurement;Cloud computing;Heart beat;Cameras;Real-time systems;Blood pressure|
|[Glaucoma Detection using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073754)|V. C R; V. Asha; U. M; T. M|10.1109/ICAIS56108.2023.10073754|Glaucoma disease;Fundus pictures;Convolutional Neural Network (CNN) types.;Integrated optics;Optical losses;Visualization;Transfer learning;Optical computing;Optical imaging;Convolutional neural networks|
|[Control of Software-Defined Networks of Unmanned Aerial Vehicles using Distributed Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073872)|S. Hauider Abbas; M. Guru Vimal Kumar; L. D; G. G; S. V. S; A. Deepak|10.1109/ICAIS56108.2023.10073872|Unmanned Aerial Vehicles (UAVs);cost-effective;Machine Learning (ML);crowd management.;Deep learning;Training;Wireless communication;Surveillance;Virtual environments;Learning (artificial intelligence);Autonomous aerial vehicles|
|[Detection of Stages of Cervical Cancer using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073730)|P. Chatterjee|10.1109/ICAIS56108.2023.10073730|Cervical Cancer;Pap Smear Images;Deep Learning;Image Preprocessing;Feature Extraction.;Deep learning;Image segmentation;Neural networks;Prediction algorithms;Cancer detection;Feature extraction;Biomedical image processing|
|[Disease Prediction and Classification using Intelligent Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073698)|P. Mohana Priya|10.1109/ICAIS56108.2023.10073698|Cardio vascular disease;Machine learning;Prediction;Classification;Artificial intelligence;Heart failure;Heart;Veins;Training data;Support vector machine classification;Prediction algorithms;Classification algorithms;Thrombosis|
|[An Efficient Machine Learning Classification model for Credit Approval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073706)|D. M. V. Rajesh; A. Lakshmanarao; D. C. Gupta|10.1109/ICAIS56108.2023.10073706|Bank Loan;Credit Approval;Machine Learning;Random Forest;Logistic Regression.;Analytical models;Waste materials;Profitability;Predictive models;Market research;Data models;History|
|[Effective Location-based Recommendation Systems for Holiday using RBM Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073856)|R. Jayaraman; K. C; A. Sahaya Anselin Nisha; K. Somasundaram; N. Naga Saranya; V. B. D|10.1109/ICAIS56108.2023.10073856|Restricted Boltzmann Machine (RBM);Attraction;Mean Absolute Error (MAE).;Machine learning algorithms;Machine learning;Approximation algorithms;Prediction algorithms;Planning;Unsupervised learning;Recommender systems|
|[Transfer Learning for Rice Leaf Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073711)|S. C. Gopi; H. Kishan Kondaveeti|10.1109/ICAIS56108.2023.10073711|Image Classification;Deep Learning;Convolutional Neural Networks(CNN);Transfer Learning;Pre-trained models;Rice Leaf Disease Detection;Deep learning;Analytical models;Plant diseases;Microorganisms;Transfer learning;Sociology;Learning (artificial intelligence)|
|[IoT based Performance and Power Monitoring System for Autolooms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073893)|M. N; S. L; S. T; S. M|10.1109/ICAIS56108.2023.10073893|Voltage sensor;Current sensor;Micro-controller unit;Internet of things;Productivity;Performance evaluation;Automation;Sociology;Voltage;Web servers;Weaving|
|[Adaptive Multi-Variate Deep Learning Model for Music Hit Prediction: A Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073785)|D. N. Raju; A. Thilagavathy; B. Ekshitha; N. Kedari; G. H. Naidu; V. G. Reddy|10.1109/ICAIS56108.2023.10073785|Deep learning;Prediction;music;Machine learning;Multi-variate model;Deep learning;Adaptation models;Systematics;Neural networks;Metadata;Predictive models;Feature extraction|
|[Smart Payment Fraud Detection using QML – A Major Challenge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073712)|C. G. Tekkali; K. Natarajan|10.1109/ICAIS56108.2023.10073712|Quantum computing;Quantum Machine Learning;Payment fraud detection;Quantum computing;Phishing;Neural networks;Quantum mechanics;Machine learning;Credit cards;Fraud|
|[Traffic Sign Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073690)|R. S. Pothineni; S. Inampudi; L. Y. Gudavalli; T. Lakshmi Surekha|10.1109/ICAIS56108.2023.10073690|Traffic signs;Data pre-processing;CNN-Convolutional Neural Networks;LeNet-5;Deep learning;Road accidents;Safety;Autonomous automobiles;Convolutional neural networks;Security;Artificial intelligence|
|[Development of High-Quality Crops using Optimized Machine Learning in Smart Agriculture Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073727)|G. Venkatakotireddy; C. Lakshminatha Reddy; R. R; J. Prabhakaran; T. Nivethitha; M. R|10.1109/ICAIS56108.2023.10073727|Agriculture;Machine Learning;Hyper-parameter;Paddy Crop;Sustainability;Support Vector Model.;Support vector machines;Smart agriculture;Training;Sociology;Crops;Machine learning;Statistics|
|[Smart Vision Software Application using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073814)|S. Alagarsamy; P. Deepak; L. M; T. G. Reddy; M. Kedareswari; A. Senthil Kumar|10.1109/ICAIS56108.2023.10073814|Convolutional Neural Network;Computer vision;Hyper Text Markup Language;Computer vision;Computational modeling;Roads;Markup languages;Object detection;Streaming media;Real-time systems|
|[Air Pollution Prediction using Supervised Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073821)|P. O; B. N; P. K; M. D; K. M; V. K. M|10.1109/ICAIS56108.2023.10073821|Pollution;Machine Learning;Prediction;Artificial Intelligence;Forecast;Statistical analysis;Atmospheric modeling;Weather forecasting;Transportation;Predictive models;Air pollution;Decision trees|
|[Forest Fire Detection using CNN-RF and CNN-XGBOOST Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073910)|U. R; S. P. S; R. Ganesan; U. S|10.1109/ICAIS56108.2023.10073910|Convolutional Neural Network;Random Forest;Feature Extraction;Classification Metrics;Support Vector Machine;Gradient Boosted Decision Tree;Visual Geometry Group (VGG-16);Support vector machines;Deep learning;Machine learning algorithms;Clustering algorithms;Forestry;Feature extraction;Data models|
|[Machine Learning Pose Detection Kit Implementation in Taspen Android Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073816)|A. H. Habbe; I. Farida Adi Prawira; I. Muda; R. M. Hasibuan; N. R. Dhumale|10.1109/ICAIS56108.2023.10073816|machine learning;live detection;android;Authentication;Machine learning;Time measurement;Pensions;Internet;Task analysis|
|[Multi-Class Pneumonia Classification Using Transfer Deep Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073807)|S. Degadwala; D. Vyas; D. D. Pandya; H. Dave|10.1109/ICAIS56108.2023.10073807|Pneumonia;X-ray;Convolution Neural Network (CNN);Dense Net;VGGNet;ResNet;Inception Net;Radiography;Deep learning;Analytical models;Pediatrics;Microorganisms;Liquids;Pulmonary diseases|
|[An Effective Machine Learning Techniques to Detect Parkinson's Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073685)|N. SrinivasaRao; D. Anusha; U. Mayuri; S. Eswar|10.1109/ICAIS56108.2023.10073685|Machine Learning;Random Forest;eXtreme Gradient Boosting;Linear Regression;Support vector machines;Neurological diseases;Spirals;Machine learning algorithms;Parkinson's disease;Current measurement;Classification algorithms|
#### **2023 5th International Conference on Power, Control & Embedded Systems (ICPCES)**
- DOI: 10.1109/ICPCES57104.2023
- DATE: 6-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Single-Band Ultrathin Polarization-insensitive Metamaterial Absorber for X-Band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075763)|N. Singh; R. Dhara; S. Paul|10.1109/ICPCES57104.2023.10075763|Absorber;polarization;potential;radiolocation;radio-determination;Embedded systems;Absorption;Shape;Satellite broadcasting;Radio navigation;Satellite navigation systems;Metamaterials|
|[On Sliding Mode based Event-Trigger Control of a Micro Arial Robot for Perching on Vertical Outdoor Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076045)|S. Gupta; S. Samanta|10.1109/ICPCES57104.2023.10076045|Event-Trigger control;Micro arial robot;Perching on wall;Terminal sliding mode control;Manifolds;Robust control;Autonomous aerial vehicles;Real-time systems;Resource management;Environmental monitoring;Robots|
|[Anti-windup Design for Uncertain Systems based on Guaranteed Cost Control Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076171)|S. Verma; A. Srivastava; R. Negi|10.1109/ICPCES57104.2023.10076171|Anti-windup;guaranteed cost control;uncertainty;Asymptotic stability;Uncertain systems;Costs;Embedded systems;Stability criteria;Convex functions;Performance analysis|
|[High Efficient Quality of Video Compression using Variational Autoencoders in Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075897)|M. Khadir; M. F. Hashmi|10.1109/ICPCES57104.2023.10075897|HEVC;VAEs;Adaptive Compression;AVC;Deep learning;Training;Adaptation models;Three-dimensional displays;Semantics;Video compression;Encoding|
|[Critical Clearing Time Revisited in an Inverter Dominated Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075983)|G. K; M. K. Jena; A. K. Moharana|10.1109/ICPCES57104.2023.10075983|Critical Clearing Time;Inertia Distribution Index;Power System Stability;Renewable Energy Sources;Wind Turbine Generator;Renewable energy sources;System dynamics;Power system transients;Stability criteria;Power system stability;Inverters;Generators|
|[BigSync: Big Data Analytics For Synchrophasors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075932)|T. K. K; G. K; H. Anand; M. K. Jena; S. Das; V. Bharti; M. Rupesh; S. Baskey; G. Nithin|10.1109/ICPCES57104.2023.10075932|Data analytics;phasor measurement unit;software solution;wide area measurement system;Fault diagnosis;Area measurement;Power system stability;Big Data;Software;Phasor measurement units;Stability analysis|
|[Design of IoT Gateway and DSM Implementation in an Educational Institution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076181)|A. C. V. Dharssini; S. C. Raja; G. R. Hemanth; P. Venkatesh|10.1109/ICPCES57104.2023.10076181|Smart energy meters;MQTT-based data transmission;time series classification;Demand trend;Demand side management;Meters;Schedules;Demand side management;Time series analysis;Logic gates;Feature extraction;Smart meters|
|[Techno-Economic Analysis of RES based Islanded Microgrid for Different Locations at Tamilnadu](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076056)|N. J; C. R. S; J. D. F. Josephin; A. J. K|10.1109/ICPCES57104.2023.10076056|Solar System;Wind System;Battery System;Diesel system;HOMER Software;Techno-economic analysis;Wind energy generation;Renewable energy sources;Costs;Wind speed;Production;Hybrid power systems;Generators|
|[Resilience Analysis of Mesh-Grid Integrated Distribution Network using Bayes' Theorem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076022)|Sonal; D. Ghosh|10.1109/ICPCES57104.2023.10076022|Active distribution network (ADN);mesh-grid;distributed generation (DG);Bayesian network (BN);reconfiguration;resilience;Uncertainty;Wind speed;Distribution networks;Reliability theory;Reliability engineering;Power system reliability;Distributed power generation|
|[Image-based Foreign Object Detection using YOLO v7 Algorithm for Electric Vehicle Wireless Charging Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075892)|Y. A. Khan; S. Imaduddin; A. Ahmad; Y. Rafat|10.1109/ICPCES57104.2023.10075892|Electric Vehicles;Wireless Charging;Deep Learning;YOLO;Transportation;Foreign Object Detection;Deep learning;Embedded systems;Inductive charging;Green products;Object detection;Wireless power transfer;Fasteners|
|[Performance Investigation of Graded channel and Dual-Metal Gate-Stack DG MOSFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075985)|S. Verma; M. K. Rai; S. Kumar; S. Rai|10.1109/ICPCES57104.2023.10075985|DGMOSFET;Graded Channel;Work function;Dual-Metal Gate-Stack;Performance evaluation;Semiconductor device modeling;Radio frequency;MOSFET;Transducers;Sensitivity;Metals|
|[Feasibility Analysis of Hybrid Renewable Energy Systems in a Commercial Building by Enhancing Renewable Resources using HOMER Software](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076249)|V. S. Kumar; S. Parameswari; S. C. Raja; A. C. V. Dharssini|10.1109/ICPCES57104.2023.10076249|Hybrid Energy System (HES);Renewable integration;Supply chain management;HOMER software;Techno-economic analysis;Smart grid;Renewable energy sources;Costs;Buildings;Tariffs;Switches;Software;Real-time systems|
|[Low Power Architecture of 4-bit Odd Parity Generator/Checker Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075976)|A. R. Siddharth; Urvashi; G. Khanna|10.1109/ICPCES57104.2023.10075976|MGDI;CPL;DPL;CPLH;DPLH;XNOR Gate;Semiconductor device modeling;Embedded systems;Logic gates;Very large scale integration;Hybrid power systems;Power dissipation;Delays|
|[Impact of Variable Fault Impedances during Symmetrical Fault in Active Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076039)|T. Sikdar; R. Koner; D. Mondal; N. Sen; S. B. Raha|10.1109/ICPCES57104.2023.10076039|Symmetrical fault;Fault Impedance;Bolted Fault;Active distribution network;Fault parameters;Power World Simulator;Impedance measurement;Embedded systems;Generators;Distributed power generation;Impedance;Active distribution networks|
|[Optimization of Wind-Hydro Hybrid Energy System for Stability Analysis by Using Different Types of Controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076091)|A. Nigam; K. K. Sharma|10.1109/ICPCES57104.2023.10076091|Wind turbine;hydro energy;P&O;PI;PD;PID;optimization technique;Renewable energy sources;Wind energy;Stability criteria;Power system stability;Hybrid power systems;Wind turbines;State of charge|
|[Deep Neural Network models for classification of significant attributes to predict Pre-Diabetes Mellitus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076156)|B. Singh; J. Yadav; M. Singh; A. Rani|10.1109/ICPCES57104.2023.10076156|Deep neural network;Pre-Diabetes Mellitus;Machine learning;Cross validation;Early stopping;Deep learning;Wearable computers;Neural networks;Predictive models;Photoplethysmography;Particle measurements;Physiology|
|[Mitigation Law against Time-delay based Cyber Intrusion Uncertainty in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075780)|S. A. Khan; K. Ojha; S. Prasad|10.1109/ICPCES57104.2023.10075780|Time delays;Frequency regulation;Sliding mode control;Uncertainty;Delay effects;Power system dynamics;Power system stability;Time measurement;Trajectory;Delays|
|[Mitigation of False Data based Cyber Intrusion Uncertainty in Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076023)|A. Kumar; A. Anand; S. Prasad|10.1109/ICPCES57104.2023.10076023|False information or data injection (FDI);Frequency regulation (LFC);linear quadratic regulator (LQR);Asymptotic stability;Regulators;Uncertainty;Power system dynamics;Power system stability;Stability analysis;Trajectory|
|[Design of Low Power and High Noise Immunity Schmitt Triggers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076031)|S. Singh; S. K. Gupta|10.1109/ICPCES57104.2023.10076031|Schmitt trigger;Hysteresis;self-bias;stacking;Noise immunity;MOSFET;Embedded systems;CMOS technology;Hysteresis|
|[Impact of PLL Dynamics on Frequency Stability in Low Inertia Trending Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075797)|M. R. Chowdhury; A. R. Galib; A. M. Jobayer; S. Hossain; M. N. Isalm; M. S. Islam|10.1109/ICPCES57104.2023.10075797|phase-locked loop (PLL);second-order generalized integrator (SOGI);synchronous reference frame PLL (SRF-PLL);digital signal processors (DSP);low inertia power system;Analytical models;Time-frequency analysis;Simulation;Power quality;Power system stability;Stability analysis;Phase locked loops|
|[Analysis on Bus Voltage with Optimal Placement and Size of PV System for Different Solar Radiation and Load Changes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075922)|A. Yadav; N. Kishor; R. Negi|10.1109/ICPCES57104.2023.10075922|Photovoltaic (PV);voltage violation;voltage stability;voltage index;electricity and curtailment cost;Photovoltaic systems;Reactive power;Renewable energy sources;Low voltage;Costs;Sensitivity;Embedded systems|
|[Study and Design of DC-DC LLC Full Bridge Converter for Electric Vehicle Charging Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076234)|M. Israr; P. Samuel|10.1109/ICPCES57104.2023.10076234|ZVS;ZCS;LLC;efficiency;resonant converter;soft-Switching;Soft switching;Bridge circuits;Resonant frequency;Rectifiers;DC-DC power converters;Zero voltage switching;Frequency conversion|
|[Analysis and Modelling of Multilevel Inverter for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076233)|S. Ranjan; M. V. Naik|10.1109/ICPCES57104.2023.10076233|Multilevel inverter (MLI);total standing voltage(TSV);total harmonic distortion (THD);maximum power point tracking (MPPT);electric vehicles (EV);photo voltaic (PV);cost function (CF);switched capacitor (SC);phase disposition pulse width modulation (PD-PWM);nine level switched capacitor inverter (9-LSCI);Capacitors;Switches;Multilevel inverters;Electric vehicles;Topology;Voltage control;Integrated circuit reliability|
|[Tariff-aware Power Management Scheme for Economic Operation of Electric Vehicle Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075808)|I. Chandra; P. Bharti; N. K. Singh; P. Samuel|10.1109/ICPCES57104.2023.10075808|Electric vehicle;Photovoltaic system;Grid cost coefficient;Energy management system;Charging station;Photovoltaic systems;Software packages;Power supplies;Power system management;Neural networks;Tariffs;Pricing|
|[Closed Loop Control of Brushless DC Motor by Various Controllers for Lightweight EVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076051)|S. Jain; M. Israr; P. Samuel|10.1109/ICPCES57104.2023.10076051|Electric Vehicles;BLDC motor;torque ripple;close loop control;Motor drives;PI control;Torque;Brushless DC motors;Velocity control;Hysteresis motors;DC motors|
|[Modelling and Analysis of a PFC Based EV Battery Charger Using Cuk-SEPIC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075768)|R. Prajapati; M. V. Naik|10.1109/ICPCES57104.2023.10075768|Battery charger;discontinuous mode (DCM);power quality (PQ);electric vehicle (EV);single-ended primary-inductor converter (SEPIC) converter;bridgeless (BL);Battery chargers;Analytical models;Embedded systems;Merging;Power factor correction;Electric vehicles;Topology|
|[Design and Analysis of Isolated Bidirectional Converter with Center Tapped Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076176)|V. Kumar; M. V. Naik|10.1109/ICPCES57104.2023.10076176|Zero voltage switching (ZVS);Low voltage (LV);Electromagnetic interference (EMI);LLC dc-dc transformer (LLC-DCX);Low voltage;Electromagnetic interference;Voltage;Switches;DC-DC power converters;Zero voltage switching;Transformers|
|[Analysis of Three Area Interconnected Wind-integrated Multiterminal HVDC Network under AC and DC Faults](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076108)|K. Rana; N. Kishor; R. Negi; M. Biswal|10.1109/ICPCES57104.2023.10076108|Multiterminal Network;High Voltage Transmission;AC-DC grid;Voltage Source Converter;AC Faults;DC Faults;Performance evaluation;Analytical models;HVDC transmission;Simulation;Mathematical models;Power electronics;Power system reliability|
|[Novel Current Based Unit Protection Scheme for Transmission Line Connected to The Inverter Based DG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076046)|S. Gangolu; S. Sarangi|10.1109/ICPCES57104.2023.10076046|Phase Current Ratio;Line to ground fault;Current differential relay;Alpha plane characteristics;Resistance;Sensitivity;Power transmission lines;Fault detection;Computational modeling;Inverters;Security|
|[Harmonic Compensation Method for Low-voltage Small-scale Islanded Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076010)|P. Lakshmanan|10.1109/ICPCES57104.2023.10076010|Distributed generation;harmonic impedance;islanded mode;microgrid;harmonic compensation;Low voltage;Embedded systems;Simulation;Estimation;Microgrids;Observers;Harmonic analysis|
|[An empirical study of PMU data based power system event detection techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076009)|S. K. M. Shaikh; M. K|10.1109/ICPCES57104.2023.10076009|Classification;machine learning;PMU and power system events;Measurement;Event detection;Capacitors;Taxonomy;Power control;Switches;Microgrids|
|[Experimental Study of a Soft Pneumatic Actuator for the Application of Robotic Gripper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10076021)|N. Gariya; P. Kumar; M. Makkar|10.1109/ICPCES57104.2023.10076021|Soft actuator;Soft materials;Gripper;Soft robotics;Pneumatic actuators;Medical devices;Shape;Medical services;Bending;Soft robotics;Pneumatic systems|
|[Bond Graph Model-based Control of a Wheeled Mobile Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075817)|J. Joshi; P. Kumar; A. R. Manral; M. Makkar|10.1109/ICPCES57104.2023.10075817|mobile robots;modeling;bond graph;control;Analytical models;Actuators;Uncertainty;Simulation;Wheels;Transportation;Symbols|
|[Wavelet Based Enhanced Fault Detection Scheme for A Distribution System Embedded with Electric Vehicle Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075986)|A. Gupta; S. Sarangi; A. K. Singh|10.1109/ICPCES57104.2023.10075986|Electric Vehicle;Charging station;Distribution system;Protection;Continuous wavelet transform;Teager energy;Resistance;Substations;Fault detection;Simulation;Charging stations;Electric vehicle charging;Topology|
|[A Seven-Level Switched Capacitor-Based RSC-MLI Topology with Suppressed Inrush Currents for Grid-Connected Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075773)|P. K. Yadav; H. P. Vemuganti; M. Biswal|10.1109/ICPCES57104.2023.10075773|Reduced switch count (RSC);Multilevel inverters (MLI);Reduced carrier;Switched capacitor (SC);Series parallel switched sources (SSPS);pulse width modulation (PWM);Inrush current suppression;Software packages;Simulation;Capacitors;Redundancy;Switches;Voltage;Production|
#### **2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)**
- DOI: 10.1109/ICPADS56603.2023
- DATE: 10-12 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[MHMC: Real-Time Hand Motion Capture Using Millimeter-Wave Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078014)|F. Deng; H. Zhou; P. Xia; Z. Wang; X. Wang; X. -Y. Li|10.1109/ICPADS56603.2022.00009|nan;Privacy;Neural networks;Lighting;Millimeter wave radar;Motion capture;Real-time systems;User experience|
|[Traffic Processing and Fingerprint Generation for Smart Home Device Event](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077967)|Y. Yao; J. Hou; S. Zhang; Z. Xu; X. -Y. Li|10.1109/ICPADS56603.2022.00010|smart home;traffic processing;event fingerprint;Performance evaluation;Wide area networks;Passive networks;Fluctuations;Smart homes;Machine learning;Interference|
|[FedCS: Communication-Efficient Federated Learning with Compressive Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078005)|Y. Liu; S. Chang; Y. Liu|10.1109/ICPADS56603.2022.00011|federated learning;communication-efficient;compressive sensing;dictionary learning;Training;Adaptation models;Image coding;Costs;Federated learning;Servers;Task analysis|
|[Improved DQN-Based Computation Offloading Algorithm in MEC Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077965)|Z. Zhao; H. Cheng; X. Xu|10.1109/ICPADS56603.2022.00012|Mobile Edge Computing;Computation Offloading;Reinforcement Learning;Deep Q-Learning Network;Greedy algorithms;Energy consumption;Technological innovation;Costs;Q-learning;Heuristic algorithms;System performance|
|[Fingersound: A Low-cost and Deployable Authentication System with Fingertip Sliding Sound](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077983)|Z. Shu; Z. Wang; G. Yang; C. Zang; Z. Ma; F. Lin; K. Ren|10.1109/ICPADS56603.2022.00013|Biometrics;Embedded system;Machine learning;User authentication;Image sensors;Time-frequency analysis;Embedded systems;Authentication;Fingerprint recognition;Feature extraction;Mobile handsets|
|[A Distributed Method to Form UAV Swarm based on Moncular Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077993)|Y. Jia; M. Chen; Y. Gao; H. Wang|10.1109/ICPADS56603.2022.00014|UA Vswarm;monocular vision;distributed control model;target detection pruning algorithm;vision-based orientation estimation;Wireless communication;Visualization;Satellites;Prototypes;Estimation;Object detection;Satellite navigation systems|
|[Time-Frequency Analysis-Based Transient Harmonic Feature Extraction for Load Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077924)|P. Xia; H. Zhou; S. Jiang; F. Deng; Z. Liu; X. -Y. Li|10.1109/ICPADS56603.2022.00015|non-intrusive load monitoring (NILM);feature extraction;transient state;harmonic;time-frequency analysis;Load monitoring;Home appliances;Time-frequency analysis;Transforms;Harmonic analysis;Feature extraction;Libraries|
|[Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077954)|Z. Luo; X. Zheng; B. Wang; Q. Meng; H. Cui; X. Guo; L. Liu|10.1109/ICPADS56603.2022.00016|IIoT;Computing Network Collaboration;Edge Computing;Fog Computing;Time-Sensitive Networking;Job shop scheduling;Processor scheduling;Collaboration;Computer architecture;Dynamic scheduling;Delays;Reliability|
|[An Improved Spray And Wait Algorithm Based on the Node Social Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078013)|J. Cui; S. Gong; Y. Chang; Z. Chen; H. Chen; Y. Yang|10.1109/ICPADS56603.2022.00017|delay-tolerant network;social tree;propagation capacity;Simulation;Prediction algorithms;Delays;Relays|
|[CACam: Consecutive Angular Measurements with Camera on Smartphone for Distance Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078004)|Y. Zhu; X. Zhu|10.1109/ICPADS56603.2022.00018|ranging;camera;sensor;outdoor;Geometric modeling;Cameras;Search problems;Distance measurement;Hardware;Gyroscopes;Smart phones|
|[AudioWrite: A Handwriting Recognition System Using Acoustic Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077961)|L. Wang; J. Zhang; Y. Li; H. Wang|10.1109/ICPADS56603.2022.00019|acoustic sensing;handwriting recognition;generative adversarial network;Training;Handwriting recognition;Time-frequency analysis;Computational modeling;Generative adversarial networks;Feature extraction;Real-time systems|
|[Melanlysis: A mobile deep learning approach for early detection of skin cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077979)|S. A. Arani; Y. Zhang; M. T. Rahman; H. Yang|10.1109/ICPADS56603.2022.00020|Melanlysis;Melanoma;Dermoscopic image;Mobile deep learning;TensorFlow Lite;Smart Health;Deep learning;Training;Memory management;Melanoma;Skin;Mobile handsets;Libraries|
|[FedDGIC: Reliable and Efficient Asynchronous Federated Learning with Gradient Compensation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077936)|Z. Xie; J. Jiang; R. Chen; Z. Qu; H. Liu|10.1109/ICPADS56603.2022.00021|Asynchronous federated learning;node dropout;model convergence;dynamic grouping;gradient compensation;Training;Performance evaluation;Federated learning;Heuristic algorithms;Data models;Reliability;Convergence|
|[A Blind Message Dissemination Method for OUSN using Unmanned Underwater Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077984)|Y. Zhou; L. Liu|10.1109/ICPADS56603.2022.00022|Opportunistic underwater sensor network;unmanned underwater vehicle;message dissemination;underwater spy-robot;Autonomous underwater vehicles;Wireless sensor networks;Analytical models;Simulation;Surveillance;Distributed databases;Communication channels|
|[LoRa-based contactless long-range respiration classification system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077910)|J. Luo; R. Zhou; Y. Cheng|10.1109/ICPADS56603.2022.00023|Bayesian Classifier;LoRa;Respiration State Detection;Training;Frequency-domain analysis;Linear regression;Medical services;Interference;Feature extraction;Robustness|
|[RPCRS: Human Activity Recognition Using Millimeter Wave Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077977)|T. Huang; G. Liu; S. Li; J. Liu|10.1109/ICPADS56603.2022.00024|Human Activity Recognition;Millimeter Wave Radar;Point Cloud;Data Augmentation;Neural Network;Point cloud compression;Training;Correlation;Shape;Distributed databases;Millimeter wave radar;Data collection|
|[Causal Ordering in the Presence of Byzantine Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077934)|A. Misra; A. D. Kshemkalyani|10.1109/ICPADS56603.2022.00025|Byzantine fault-tolerance;Causal Order;Causality;Asynchronous message-passing;Unicast;Multicast;Fault tolerance;Upper bound;Multicast algorithms;Fault tolerant systems;Semantics;Faces|
|[CD-DAA-MD: A Cross-domain DAA Scheme with Mimic Defense for Internet of Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077991)|L. Chen; Y. Miao; C. Yu; S. Liu|10.1109/ICPADS56603.2022.00026|Internet of Vehicles;Trusted computing;Mimic defense;Direct anonymous attestation;Cross-domain attestation;Protocols;Simulation;Redundancy;Authentication;Internet;Security;Vehicle dynamics|
|[Nuwa: A Receiver-driven Congestion Control Framework to Achieve High-throughput and Controlled Delay over Dynamic Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077899)|G. Gong; X. Jiang; Y. Xie; G. Jin; J. Zhang; H. Chen|10.1109/ICPADS56603.2022.00027|TCP;receiver side;congestion control;Degradation;Wireless networks;Heuristic algorithms;Bandwidth;Receivers;Throughput;Delays|
|[Relationship between g-extra Connectivity and g-restricted Connectivity in Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077909)|Y. Wang; X. Sun; W. Fan; B. Cheng; L. Xu; J. Fan|10.1109/ICPADS56603.2022.00028|data center network;multiprocessor network;g-restricted connectivity;g-extra connectivity;Data centers;Fault tolerance;Fault tolerant systems;Servers;Task analysis|
|[Optimizing Resource Allocation in Pipeline Parallelism for Distributed DNN Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077955)|Y. Duan; J. Wu|10.1109/ICPADS56603.2022.00029|DNN training;pipeline parallelism;machine learning;model partition;resource allocation;Training;Performance evaluation;Privacy;Computational modeling;Simulation;Pipelines;Parallel processing|
|[Tunable Causal Consistency: Specification and Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077925)|X. Jiang; H. Wei; Y. Huang|10.1109/ICPADS56603.2022.00030|Causal Consistency;Tunable Consistency;Session Guarantees;Protocols;Prototypes;Throughput;Low latency communication|
|[Improving Energy Efficiency of Permissioned Blockchains Using FPGAs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077932)|N. Santoso; H. Javaid|10.1109/ICPADS56603.2022.00031|Energy-efficient-blockchains,-Hyperledger-Fabric,-FPGA-accelerators;Distributed ledger;Throughput;Energy efficiency;Software;Fabrics;Peer-to-peer computing;Blockchains|
|[Blockchain-based Health Data Sharing for Continuous Disease Surveillance in Smart Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077940)|S. Amofa; X. Lin; Q. Xia; H. Xia; J. Gao|10.1109/ICPADS56603.2022.00032|Health Data Sharing;Data Protection;Privacy;Blockchain;Data Control;Disease Surveillance.;Privacy;Data privacy;Pandemics;Surveillance;Urban areas;Smart contracts;Data aggregation|
|[UltraBD: Backdoor Attack against Automatic Speaker Verification Systems via Adversarial Ultrasound](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078010)|J. Ze; X. Li; Y. Cheng; X. Ji; W. Xu|10.1109/ICPADS56603.2022.00033|backdoor attack;automatic speaker verification;adversarial ultrasound.;Training;Ultrasonic imaging;Frequency modulation;Baseband;Limiting;User interfaces;Robustness|
|[G-SM3: High-Performance Implementation of GPU-based SM3 Hash Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077957)|J. Dong; S. Lu; P. Zhang; F. Zheng; F. Xiao|10.1109/ICPADS56603.2022.00034|SM3;Graphic Processing Unit;CUDA;Cryptographic Engineering;Performance evaluation;Hash functions;Instruction sets;Graphics processing units;Parallel processing;Throughput;Blockchains|
|[Hebo System: Trusted Copyright Authorization in Computer Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077959)|C. Zeng; F. Liang; S. Yixuan; P. Xiaohui|10.1109/ICPADS56603.2022.00035|Distributed Ledger Technology;Digital Rights Management;Blockchain;Copyrights Authorization Model;Authorization;Sufficient conditions;Costs;Computational modeling;Forestry;Copyright protection;Non-repudiation|
|[Efficient-HotStuff: A BFT Blockchain Consensus with Higher Efficiency and Stronger Robustness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077968)|D. Zhai; J. Wang; J. Liu; T. Liu; W. Niu|10.1109/ICPADS56603.2022.00036|Byzantine fault tolerance;consensus;blockchain;throughput;Fault tolerance;Voting;Fault tolerant systems;Throughput;Robustness;Consensus protocol;Behavioral sciences|
|[An Efficient Fully Homomorphic Encryption Sorting Algorithm Using Addition Over TFHE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077981)|C. Wang; J. Chen; X. Zhang; H. Cheng|10.1109/ICPADS56603.2022.00037|TFHE;Bitwise Comparison;Bitwise Swap;Homomorphic Addition;Sorting;Data privacy;Privacy;Costs;Logic gates;Libraries;Servers;Homomorphic encryption|
|[Blockchain-based Privacy-preserving Authentication Key Agreement Protocol for Industrial Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077964)|X. Su; Y. Xie; H. Wang; H. Wang|10.1109/ICPADS56603.2022.00038|Authentication Key Agreement (AKA);Elliptic Curve Cryptography (ECC);Blockchain;Industrial Wireless Sensor Networks (IWSNs);Wireless communication;Wireless sensor networks;Costs;Smart contracts;Authentication;Physical unclonable function;Elliptic curve cryptography|
|[G-PPG: A Gesture-related PPG-based Two-Factor Authentication for Wearable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077974)|J. Pan; X. Zhou; Z. Zhang; X. Ji; H. Chen|10.1109/ICPADS56603.2022.00039|Wearable Security;Internet of Things;Twofactor Authentication;PPG sensors;Heart beat;Wearable computers;Authentication;Sensor phenomena and characterization;Feature extraction;Photoplethysmography;Particle measurements|
|[Secure and Efficient Anonymous Authentication Key Agreement Scheme for Smart Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077914)|X. Su; Y. Xie; H. Wang; W. Liu; D. Shui|10.1109/ICPADS56603.2022.00040|Key Agreement;Mutual Authentication;Realtime data access;Physical Unclonable Function (PUF);Smart Industry;Industries;Wireless sensor networks;Costs;Authentication;Process control;Resists;Physical unclonable function|
|[An Improved Least-square based Jammer Localization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077943)|R. Tong; Y. Du; H. Liu; Y. Chen|10.1109/ICPADS56603.2022.00041|Jammer localization;Stochastic point process;Least-Square;Location awareness;Wireless networks;Simulation;Estimation;Stochastic processes;Auditory system;Mathematical models|
|[Blockchain Data Transaction with Leakage Tracing Based on Digital Fingerprint](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077921)|B. Wang; Y. Zhang; B. Niu|10.1109/ICPADS56603.2022.00042|Data transaction;Blockchain;Digital fingerprint;Smart contract;Leakage tracing.;Data security;Smart contracts;Systems architecture;Fingerprint recognition;Decentralized applications;Data models;Blockchains|
|[Modeling Cross-blockchain Process Using Queueing Theory: The Case of Cosmos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077908)|O. Wu; S. Li; Y. Wang; H. Li; H. Zhang|10.1109/ICPADS56603.2022.00043|Blockchain;Cross-blockchain;Relays;Performance modeling;Queueing theory;Simulation;Performance evaluation;Stability criteria;Markov processes;Mathematical models;Blockchains;Time factors;Indexes|
|[Stealing Secrecy from Outside: A Novel Gradient Inversion Attack in Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077902)|C. Zhang; H. Liang; Y. Li; T. Wu; L. Zhu; W. Zhang|10.1109/ICPADS56603.2022.00044|gradient inversion;grey-box attack;federated learning;Image resolution;Federated learning;Data models;Security;Image reconstruction;Finite difference methods|
|[Multi-channel Walk Embedding Based Ethereum Phishing Scam Detection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078011)|J. Li; H. Li; N. Cheng; W. Zhang; Y. Xu; H. Zhang; J. Li|10.1109/ICPADS56603.2022.00045|Ethereum;transaction network;multi-channel walk;network embedding;phishing detection;Visualization;Machine learning algorithms;Phishing;Computational modeling;Machine learning;Feature extraction;Natural language processing|
|[FLForest: Byzantine-robust Federated Learning through Isolated Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077947)|T. Wang; B. Zhao; L. Fang|10.1109/ICPADS56603.2022.00046|Federated learning;Byzantine-robust;Robustness rule;Dimensionality reduction;Adaptation models;Federated learning;Forestry;Predictive models;Robustness;Spatiotemporal phenomena|
|[Enhancing Security of Certificate Authorities by Blockchain-based Domain Transparency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077930)|Q. Xiong; Y. Zhang; J. Li; F. Tong|10.1109/ICPADS56603.2022.00047|blockchain;public key infrastructure;certificate transparency;domain transparency;Performance evaluation;Distributed ledger;Public key;Prototypes;Cyberspace;Resists;Fabrics|
|[A Cross Data Center Access Control Model by Constructing GAS on Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077987)|S. Xu; C. Zhang; L. Wang; S. Zhang; W. Shao|10.1109/ICPADS56603.2022.00048|Blockchain;GAS;ABAC;Cross Data Center;Access Control;Access control;Data centers;Analytical models;Computational modeling;Data security;Semantics;Smart contracts|
|[Towards A Scalable and Privacy-Preserving Blockchain-based European Parking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077982)|J. K. Brittain; W. -Y. Chiu; W. Meng|10.1109/ICPADS56603.2022.00049|Blockchain Technology;Decentralized System;Parking System;Hyperledger Fabric;GDPR;Distributed ledger;System performance;Urban areas;Europe;Throughput;Regulation;Fabrics|
|[Authenticated Selective Disclosure of Credentials in Hybrid-Storage Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077994)|R. Tian; L. Kong; B. Zhang; X. Li; Q. Li|10.1109/ICPADS56603.2022.00050|blockchain;erasure coding;Merkle B-tree;selective disclosure;verifiable credential;Privacy;Data privacy;Costs;Education;Digital representation;Medical services;Maintenance engineering|
|[TSHN: A Trajectory Similarity Hybrid Networks for Dummy Trajectory Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077969)|Y. Li; X. Li; S. Shang; X. Zhang|10.1109/ICPADS56603.2022.00051|Dummy trajectory identification;Neural networks;location privacy;mobility feature;individual feature.;nan|
|[UTIO: Universal, Targeted, Imperceptible and Over-the-air Audio Adversarial Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078009)|C. Zhao; Z. Li; H. Ding; W. Xi|10.1109/ICPADS56603.2022.00052|Voice control systems;Adversarial Example;Speech Recognition;Machine Learning;Psychoacoustics;Deep learning;Perturbation methods;Neural networks;Prototypes;Speech recognition;Resists|
|[Optimization of Ultrasonic Respiratory Signals based on Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078002)|J. Chen; Z. Dang; L. Li; F. Dang|10.1109/ICPADS56603.2022.00053|ultrasonic perception;piezoelectric sensing;supervised learning;signal optimization;Ultrasonic imaging;Supervised learning;Acoustics;Mobile handsets;Sensors;Reliability;Biomedical monitoring|
|[HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077890)|J. Yu; Y. -E. Sun; H. Huang; Y. Du; G. Gao; H. Xu; S. Chen|10.1109/ICPADS56603.2022.00054|Traffic measurement;Heavy hitters;Stream processing algorithms;Art;High-speed networks;Measurement uncertainty;Telecommunication traffic;Size measurement;Frequency estimation;Internet|
|[ERP: An Efficient Rewrite Scheme to Improve the Inline Deduplication Restore Performance in Backup Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077920)|W. Liu; Y. Lu; C. Wu; J. Li; M. Guo|10.1109/ICPADS56603.2022.00055|Deduplication;Read Amplification;Rewrite Algorithm;Container;Data Restore;Costs;Redundancy;Containers;Writing|
|[eSwin-UNet: A Collaborative Model for Industrial Surface Defect Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077951)|H. Cui; T. Xing; J. Ren; Y. Chen; Z. Yu; B. Guo; X. Guo|10.1109/ICPADS56603.2022.00056|Deep Learning;U-Net;Transformer;Industrial Equipment;Defect Detection;Training;Semantics;Collaboration;Production;Inspection;Feature extraction;Transformers|
|[Fine-Grained Battery-Swap Order Prediction Using Spatio-Temporal Data Via GAT Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078006)|X. Pan; P. Yang; X. Fan; G. Zhang; P. Pan; L. Ye; Y. Wu; D. Shu|10.1109/ICPADS56603.2022.00057|Order Prediction;Spatio-Temporal Correlation Data;Graph Fusion;Graph Attention Network;Solid modeling;Analytical models;Correlation;Fuses;Buildings;Predictive models;Graph neural networks|
|[Online Large-scale Garbage Collection Scheduling: A Divide-and-conquer Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077978)|Y. Bian; H. Zhu; Z. Lou|10.1109/ICPADS56603.2022.00058|Large-scale garbage collection problem;capacitated vehicle routing problem;agglomerative hierarchical clustering algorithm;Costs;Vehicle routing;Urban areas;Clustering algorithms;Scheduling;Complexity theory;Indexes|
|[Talking Head Generation for Media Interaction System with Feature Disentanglement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077905)|L. Zhang; Q. Chen; Z. Liu|10.1109/ICPADS56603.2022.00059|talking head generation;feature disentanglement;feature fusion;media interaction system;Analytical models;Redundancy;Media;Synchronization;Task analysis;Faces|
|[Context-Adaptive Online Reinforcement Learning for Multi-view Video Summarization on Mobile Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077980)|J. Hao; S. Liu; B. Guo; Y. Ding; Z. Yu|10.1109/ICPADS56603.2022.00060|multi-view video summarization;reinforcement learning;context-adaptive;Training;Representation learning;Performance evaluation;Correlation;Data acquisition;Reinforcement learning;Cameras|
|[OutletGuarder: Detecting DarkSide Ransomware by Power Factor Correction Signals in an Electrical Outlet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077889)|S. Zou; J. Zhang; S. Jiang; Y. Cheng; X. Ji; W. Xu|10.1109/ICPADS56603.2022.00061|Side channel monitoring;ransomware detection;power factor correction signal;Economics;Power demand;Biological system modeling;Computational modeling;Organizations;Power factor correction;Robustness|
|[Persistent Monitoring for Points of Interests with Different Priorities Using Multiple UAVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077933)|Q. Guo; W. Xu; J. Peng; H. Li; Z. Xiang|10.1109/ICPADS56603.2022.00062|flying tour scheduling;data collection with UAVs;weighted monitoring latency minimization;heuristic algorithm.;Heuristic algorithms;Minimization;Autonomous aerial vehicles;Scheduling;Task analysis;Monitoring|
|[WMDRS: Workload-Aware Performance Model Based Multi-Task Dynamic-Quota Real-Time Scheduling for Neural Processing Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077953)|C. Liu; Y. Yao; Y. Dang; G. Yang; W. Jia; X. Tian; X. Zhou|10.1109/ICPADS56603.2022.00063|real-time scheduling;NPU performance model;embedded system;preemptive scheduling;Embedded systems;Scheduling algorithms;Computational modeling;Atmospheric modeling;Prototypes;Predictive models;Multitasking|
|[Influence of beta and source packet rate on electromagnetic nanocommunications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077960)|F. Hoteit; E. Dedu; W. K. G. Seah; D. Dhoutaut|10.1109/ICPADS56603.2022.00064|Congestion;Routing;Nanonetwork;Dense network;Scalability;Wireless networks;Symbols;Nanoscale devices;Nanocommunication (telecommunication);Electromagnetics|
|[Dependency-Aware Traffic Management for Configuring On-demand in Service Meshes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077918)|L. Wang; X. Li; N. Wang; H. Li; X. Qin; J. Wu|10.1109/ICPADS56603.2022.00065|Service Mesh;Traffic Management;ConFigure On-demand;Cost Reduction;Measurement;Heuristic algorithms;Process control;Distributed databases;Clustering algorithms;Closed box;Aerospace electronics|
|[Efficient Dynamic Binary Translation with Accumulative Persistent Code Caching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077985)|H. Lin; Y. Dong; W. Chi; Z. Wu; H. Zeng|10.1109/ICPADS56603.2022.00066|Dynamic Binary;Software Code Cache;Code Optimization;Codes;Runtime;Prototypes;Optimization methods;Binary codes|
|[iCheck: Leveraging RDMA and Malleability for Application-Level Checkpointing in HPC Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077923)|J. John; I. D. N. Araya; M. Gerndt|10.1109/ICPADS56603.2022.00067|Fault Tolerance;Adaptive Checkpointing;RDMA;Malleable Checkpointing;MPI;Checkpointing;Jacobian matrices;Heating systems;Fault tolerance;File systems;Fault tolerant systems;Supercomputers|
|[MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077903)|P. Qiao; Z. He; R. Li; J. Jiang; Y. Dou; D. Li|10.1109/ICPADS56603.2022.00068|deep reinforcement learning;deep neural networks;MLPerf;heterogeneous architecture;large-scale parallel computing;Training;Deep learning;Neural networks;Memory management;Reinforcement learning;Parallel processing;Programming|
|[Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077986)|Z. Du; Y. Guan; T. Guan; D. Niu; N. Tan; X. Yu; H. Zheng; J. Meng; X. Yan; Y. Xie|10.1109/ICPADS56603.2022.00069|Sparse matrix multiplication;SpGEMM;predicting output structure;nonzero structure;size estimation;Costs;Correlation;Memory management;Estimation;Prediction methods;Libraries;Sparse matrices|
|[A Two-stage Algorithm Based on Prediction and Search for Maxk-Truss Decomposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077952)|J. Tang; L. Liu; J. Jia; L. Wu; X. Wang; J. Huang|10.1109/ICPADS56603.2022.00070|k-truss;k-core;triangle counting;truss decomposition;Technological innovation;Data analysis;Social networking (online);Heuristic algorithms;Image edge detection;Prediction algorithms;Hardware|
|[GraphGRU: A Graph Neural Network Model for Resource Prediction in Microservice Cluster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077915)|H. He; L. Su; K. Ye|10.1109/ICPADS56603.2022.00071|Cloud computing;resource prediction;graph neural network;deep learning;Deep learning;Training;Recurrent neural networks;Heuristic algorithms;Microservice architectures;Clustering algorithms;Production|
|[Associativity-Aware Transaction Processing Optimization for Web Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077956)|Q. Yu; J. Zhou|10.1109/ICPADS56603.2022.00072|Database Systems;Concurrency Control;Web Applications;Semantics;Distributed databases;System recovery;Throughput;Control systems;Database systems;Concurrency control|
|[DGEMM Optimization Oriented to ARM SVE Instruction Set Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077998)|Y. Wei; L. Deng; S. Sun; S. Li; L. Shen|10.1109/ICPADS56603.2022.00073|DGEMM;OpenBLAS;SVE;vector registers;assembly kernel;Reduced instruction set computing;Multicore processing;Memory management;Mathematical models;Libraries;Partitioning algorithms;Registers|
|[DFBuffer: High-performance data forwarding software optimized for single-process I/O scenarios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077945)|X. He; W. Xiao; X. Deng; Q. Chen; B. Yang; Z. Chen|10.1109/ICPADS56603.2022.00074|supercomputing;storage;data forwarding;buffer;latency;bandwidth;Spectral efficiency;Scalability;Prototypes;Bandwidth;Computer architecture;Writing;Software|
|[Long-life Sensitive Modulo Scheduling with Adaptive Loop Expansion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077927)|H. Zhong; Z. Liu|10.1109/ICPADS56603.2022.00075|Software pipelining;ILP;loop unrolling;longlived variable;register requirement;Adaptive scheduling;Processor scheduling;Software;Registers;Resource management;VLIW;Iterative methods|
|[Dynamic Adaptive Checkpoint Mechanism for Streaming Applications Based on Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077898)|Z. Zhang; T. Liu; Y. Shu; S. Chen; X. Liu|10.1109/ICPADS56603.2022.00076|stream processing;fault-tolerance;checkpoint interval;reinforcement learning;Flink;Fault tolerance;Adaptation models;Costs;Q-learning;Heuristic algorithms;Fault tolerant systems;Distributed databases|
|[A Transformable NVMeoF Queue Design for Better Differentiating Read and Write Request Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077922)|W. Gu; X. Xie; W. Zhang; D. Dong|10.1109/ICPADS56603.2022.00077|NVMe-over-TCP;Differentiated Scheme;RNoT;Protocols;Linux;Pipelines;TCPIP;Switches;Throughput;Software|
|[Reinforcement Learning based Scheduling Optimization Mechanism on Switches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077912)|X. Lu; X. Wang; J. Jia; X. Wang; M. Huang|10.1109/ICPADS56603.2022.00078|scheduling;fair queueing;bandwidth allocation;Markov decision process;reinforcement learning;Schedules;Q-learning;Prototypes;Packet loss;Markov processes;Channel allocation;Throughput|
|[Heterogeneous Federated Learning for Balancing Job Completion Time and Model Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077941)|R. Zhou; R. Wang; J. Yu; B. Li; Y. Li|10.1109/ICPADS56603.2022.00079|Federated Learning;Client Selection;Time and Accuracy Balancing;Training;Performance evaluation;Technological innovation;Computer aided instruction;Federated learning;Distance learning;Computational modeling|
|[Pallas: Optimizing Userspace TCP Stack for Short-Lived Connections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077963)|H. Lin; W. Li; W. Qu|10.1109/ICPADS56603.2022.00080|Low latency;High throughput;User-level TCP stack;Short-lived connection;Packet scheduler;Schedules;Scheduling algorithms;Scalability;Linux;Prototypes;Switches;Scheduling|
|[A Heat-Recirculation-Aware Data Placement Strategy towards Data Centers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077900)|Z. Zhong; Y. Deng; J. Li|10.1109/ICPADS56603.2022.00081|Storage Datacenter;Data Placement;Energy Consumption;Thermal-Aware;Heating systems;Measurement;Energy consumption;Data centers;Cloud computing;Cooling;Throughput|
|[Service Level Objective Adaptive Energy Efficiency Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077892)|C. Song; F. Jiang; X. Liang; N. Ahuja; M. J. Kumar|10.1109/ICPADS56603.2022.00082|Microservices;Service Mesh;Per-Core P-States;Service Level Objective (SLO);Service Level Indicator (SLI);Energy Efficiency;Carbon Reduction;Sustainability;Adaptive Control;Core Frequency Scaling;Energy consumption;Program processors;Costs;Power system management;Memory management;Microservice architectures;Tail|
|[Efficient Control of Unscheduled Packets for Credit-based Proactive Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077944)|X. He; W. Li; S. Zhang; K. Li|10.1109/ICPADS56603.2022.00083|Protocols;Data Center Network (DCN);Credit Reservation;Latency and Throughput;Heart;Degradation;Bandwidth;Switches;Tail;Aerospace electronics;Throughput|
|[FLEET: Fluid Layout of End to End Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077897)|M. Bharde; K. Nayak|10.1109/ICPADS56603.2022.00084|full-stack-visibility,-cross-stack-dependency-mapping,-configuration-discovery,-compute-storage-topology,-infrastructure-monitoring;Fluids;Layout;Software as a service;Weaving;Hardware;Topology;Virtualization|
|[SNIP: Southbound Message Delivery with In-network Pruning in Clouds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077995)|Y. Yu; G. Zhao; H. Xu; H. Tu; L. Luo; L. Xie|10.1109/ICPADS56603.2022.00085|Southbound Message Delivery;Message Queue;Programmable Switch;In-network Pruning;Data centers;Simulation;Computational modeling;Redundancy;Process control;Switches;Approximation algorithms|
|[A Novel Discrete Bi-objective Optimization Method for Virtual Machine Placement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077917)|Y. Feng; C. Li; B. Tu|10.1109/ICPADS56603.2022.00086|Virtual machine placement;Energy efficiency;PM performance;Data center;Degradation;Energy consumption;Cloud computing;Costs;Optimization methods;Telecommunication traffic;Prediction algorithms|
|[Fairness Scheduling for Tasks with Different Real-time Level on Heterogeneous Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077904)|S. Shao; S. Gu; B. Sun; E. H. . -M. Sha; Q. Zhuge|10.1109/ICPADS56603.2022.00087|dynamic scheduling;heterogeneous systems;real-time;Schedules;Scheduling algorithms;Heuristic algorithms;Dynamic scheduling;Real-time systems;Resource management;Task analysis|
|[SepJoin: A Distributed Stream Join System with Low Latency and High Throughput](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078008)|Q. Wang; D. Zuo; Z. Zhang; S. Chen; T. Liu|10.1109/ICPADS56603.2022.00088|big data;distributed stream join system;queuing theory;partitioning scheme;Analytical models;System performance;Throughput;Real-time systems;Indexes;Low latency communication;Queueing analysis|
|[Balancing Load: An Adaptive Traffic Management Scheme for Microservices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077895)|J. Zhou; X. Li; Q. Wang; X. Qin; W. Miao; J. Tian|10.1109/ICPADS56603.2022.00089|Service Mesh;Traffic management;Real-Time;Load balancing;Istio;Automation;Source coding;Scalability;Microservice architectures;Elasticity;Load management;Real-time systems|
|[Towards Fast and Energy-Efficient Offloading for Vehicular Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077972)|M. Su; C. Cao; M. Dai; J. Li; Y. Li|10.1109/ICPADS56603.2022.00090|Vehicular edge computing;resource allocation;computation offloading;multi-objective optimization;Energy consumption;Quality of service;Energy efficiency;Delays;Trajectory;Complexity theory;Task analysis|
|[Rewriting Deep Learning Models for Maximizing Edge TPU Utilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077989)|K. -F. Chen; D. -Y. Hong|10.1109/ICPADS56603.2022.00091|MLIR;Edge TPU;deep learning;inference;Deep learning;Transforms;Internet;Servers|
|[Multi-Resource Scheduling for Multiple Service Function Chains with Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077893)|R. He; B. Ren; J. Xie; D. Guo; L. Zhao|10.1109/ICPADS56603.2022.00092|Service Function Chain;Deep Reinforcement Learning;Scheduling;Deep learning;Service function chaining;Scheduling algorithms;NP-hard problem;Reinforcement learning;Benchmark testing;Software|
|[A Nginx-based Dynamic Feedback Load Balancing Algorithm With Adaptive Heartbeat Detecting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077891)|G. Hao; Z. Qiongbing; L. Xuan; C. Junchao|10.1109/ICPADS56603.2022.00093|Load balancing;Nginx;heartbeat detection mechanism;dynamic feedback;Adaptation models;Adaptive systems;Heart beat;Heuristic algorithms;Clustering algorithms;Quality of service;Load management|
|[Flet-Edge: A Full Life-cycle Evaluation Tool for deep learning framework on the Edge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077926)|X. Jiang; X. Zhang; X. Peng|10.1109/ICPADS56603.2022.00094|evaluation tool;edge-devices;deep learning frameworks;Deep learning;Training;Runtime;Programming;Hardware;Software;Complexity theory|
|[Decentralized Federated Learning with Data Feature Transmission and Neighbor Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077906)|W. Lou; Y. Xu; H. Xu; Y. Liao|10.1109/ICPADS56603.2022.00095|Edge Computing;Neighbor Selection;Decentralized Federated Learning;Data Feature Transmission.;Training;Privacy;Costs;Federated learning;Computational modeling;Data models;Peer-to-peer computing|
|[Secure and Efficient Cloud Ciphertext Deduplication Based on SGX](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077929)|L. Li; G. Qin; P. Liu; C. Hu; S. Guo|10.1109/ICPADS56603.2022.00096|Cloud storage;Deduplication;Encrypted data;Semantic security;SGX;Cloud computing;Semantics;Distributed databases;Containers;Encryption;Outsourcing;Servers|
|[A Container Pre-copy Migration Method Based on Dirty Page Prediction and Compression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077919)|Y. Lu; Y. Jiang|10.1109/ICPADS56603.2022.00097|container live migration;pre-copy;dirty page prediction;page compression;Cloud computing;Containers;Predictive models;Prediction algorithms;Data communication|
|[Prototyping of Low-Cost Configurable Sparse Neural Processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078001)|B. Wu; W. Furtner; B. Waschneck; C. Mayr|10.1109/ICPADS56603.2022.00098|low-cost;configurable;neural processing unit;mixed-precision;sparsity;Deep learning;Runtime;Quantization (signal);Program processors;Neural networks;Random access memory;Prototypes|
|[SA-DDQN: Self-Attention Mechanism Based DDQN for SFC Deployment in NFV/MEC-Enabled Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078012)|Z. Wang; S. Guo; G. Liu|10.1109/ICPADS56603.2022.00099|Mobile edge computing;network function virtualization;SFC deployment;deep reinforcement learning;self-attention mechanism.;Deep learning;Service function chaining;Heuristic algorithms;Simulation;Bandwidth;Reinforcement learning;Real-time systems|
|[On Optimizing Traffic Imbalance in Large-scale Block-based Cloud Storage: Trace Analysis and Algorithm Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077931)|H. Mao; Y. Li; W. Zhu; F. Li; Y. Xu|10.1109/ICPADS56603.2022.00100|nan;Cloud computing;Analytical models;Costs;Clustering algorithms;Memory;Production;Computer architecture|
|[DiTing: Edge Assisted Real-time ID-aware Visual Interaction for Multi-user Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077939)|X. Cai; Z. Yang; L. Dong; Q. Ma; X. Miao; Z. Liu|10.1109/ICPADS56603.2022.00101|nan;Visualization;Technological innovation;Image edge detection;Pose estimation;Prototypes;Games;Real-time systems|
|[Subgraph Sampling for Inductive Sparse Cloud Services QoS Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077976)|J. Xu; Z. Xia; Y. Li; Y. Zeng; Z. Liu|10.1109/ICPADS56603.2022.00102|Collaborative Filtering;Cloud Service;Graph Neural Network;QoS Prediction;Fluctuations;Collaborative filtering;Time series analysis;Memory management;Collaboration;Quality of service;Predictive models|
|[OXDP: Offloading XDP to SmartNIC for Accelerating Packet Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077996)|F. Wang; G. Zhao; Q. Zhang; H. Xu; W. Yue; L. Xie|10.1109/ICPADS56603.2022.00103|XDP;Hardware Offloading;SmartNIC;Virtual Network;Runtime environment;High-speed networks;Throughput;Hardware;Software;Delays;Software reliability|
|[Maxwell’s Demon in Tail-tolerant, Resource-efficient Serverless Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077958)|H. Zhang; W. Huang; L. Zhao; K. Li|10.1109/ICPADS56603.2022.00104|nan;Thermodynamics;Uncertainty;Buildings;Serverless computing;Tail;Reinforcement learning;Behavioral sciences|
|[Spatio-Temporal Data Anomaly Detection Using 3G-Net in IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077988)|S. Zhang; J. Chen; X. Chen; Q. Jiang; H. Huang|10.1109/ICPADS56603.2022.00105|Anomaly Detection;Spatio-Temporal Data;IoT;Training;Deep learning;3G mobile communication;Feature extraction;Sensor systems;Internet of Things;Smart devices|
|[Distributed Exact Structural Clustering on Large Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077913)|J. Zhou; C. Rong; D. Liu; Z. Chai|10.1109/ICPADS56603.2022.00106|Structural Clustering;Distributed Computing;Spark;Distributed processing;Costs;Social networking (online);Scalability;Clustering algorithms;Focusing;Cyberspace|
|[FedDyn: A dynamic and efficient federated distillation approach on Recommender System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077950)|C. Jin; X. Chen; Y. Gu; Q. Li|10.1109/ICPADS56603.2022.00107|Distributed Machine Learning;Federated Learning;Recommender System;Knowledge Distillation;Differential privacy;Federated learning;Perturbation methods;Multimedia Web sites;Distributed databases;Data models;Reliability|
|[Improving Confidence of Uncertain Knowledge Graphs by Crowdsourcing with Limited Budget](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077971)|H. Zhang; W. Huang; C. Xu; Z. Yang; H. Zhou; F. Qi; C. Zhang; K. Wu|10.1109/ICPADS56603.2022.00108|crowdsourcing;knowledge graph;uncertain data;Crowdsourcing;Uncertainty;Data integrity;Distributed databases;Knowledge graphs;Probabilistic logic;Medical diagnosis|
|[Automatic Operator Performance Tumng in a Machine Learning System on Edge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078003)|P. Xu; X. Chang; J. Zhao; C. H. Liu|10.1109/ICPADS56603.2022.00109|optimization;automatic tuning;convolution;machine learning system;Performance evaluation;Deep learning;Convolution;Memory management;Libraries;Hardware;Software|
|[Accelerating Federated Learning with Two-phase Gradient Adjustment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10078007)|J. Wang; Y. Mao; X. He; T. Zhou; J. Wu; J. Wu|10.1109/ICPADS56603.2022.00110|Federated Learning;Client Drift;Cloud-Edge;Distributed optimization;Fast Convergence Rate;Training;Weight control;Federated learning;Surveillance;Distributed databases;Internet of Things;Oscillators|
|[A Variable Sliding Window Algorithm Based on Concept Drift for Frequent Pattern Mining Over Data Streams*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077997)|Y. Yin; P. Li; J. Chen|10.1109/ICPADS56603.2022.00111|data stream mining;variable sliding window;concept drift;frequent patterns;Heuristic algorithms;Distributed databases;Data models;Data mining;Electronic commerce;Biomedical monitoring;Monitoring|
|[STEGNN: Spatial-Temporal Embedding Graph Neural Networks for Road Network Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077938)|J. Si; X. Gan; T. Xiao; B. Yang; D. Dong; Z. Pang|10.1109/ICPADS56603.2022.00112|graph neural network;traffic flow forecasting;spatial-temporal graph;graph convolution;Correlation;Convolution;Roads;Time series analysis;Predictive models;Feature extraction;Data models|
|[A Continuous Encoder-Decoder Method for Spatial-Temporal Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077966)|L. Liu; Y. Shen; H. Qi|10.1109/ICPADS56603.2022.00113|spatial-temporal forecasting;neural controlled differential equations;fast weight programmers;graph neural networks;Message passing;Stacking;Redundancy;Differential equations;Mathematical models;Graph neural networks;Trajectory|
|[Knowledge-Enhanced Learning for KG Embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077970)|H. Zhang; Z. Chen; J. Nie; D. Jiang; L. Fan; K. Wu|10.1109/ICPADS56603.2022.00114|knowledge graph embedding;data enhancement;relation pattern;Training;Crowdsourcing;Semantics;Training data;Knowledge graphs;Predictive models;Data models|
|[Improving Federated Learning on Heterogeneous Data via Serial Pipeline Training and Global Knowledge Regularization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077894)|Y. Luo; T. Lu; S. Chang; B. Wang|10.1109/ICPADS56603.2022.00115|federated learning;knowledge distillation;data heterogeneity;Training;Data privacy;Federated learning;Computational modeling;Pipelines;Distributed databases;Data models|
|[CoupHM: Task Scheduling Using Gradient Based Optimization for Human-Machine Computing Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077975)|H. Wang; Z. Ren; Z. Yu; Y. Zhang; J. Liu; H. Cui|10.1109/ICPADS56603.2022.00116|Human-machine computing;Task scheduling;Gradient based optimization;Quality of service;nan|
|[Tereis: A Package-Based Scheduling in Deep Learning Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077992)|C. Chen; S. Wang; Y. Chen; J. Han|10.1109/ICPADS56603.2022.00117|Schedule;Deep learning;Package;GPU cluster;Time prediction;Training;Deep learning;Sensitivity;Computational modeling;Graphics processing units;Training data;Packaging|
|[WAIR: Watermark Attack on Image Retrieval Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077928)|Z. Duan; X. Zheng; P. Du; L. Liu; H. Ma|10.1109/ICPADS56603.2022.00118|Watermark attack;image retrieval system;evolutionary algorithm;Electronic equipment;Flight recording;Image retrieval;Web and internet services;Closed box;Watermarking;Evolutionary computation|
|[CluFL: Cluster-driven Weighted FL Model Aggregation Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077949)|H. Shen; J. Li; K. Wei; P. Xia; S. Tian; M. Ding; Z. Li|10.1109/ICPADS56603.2022.00119|Federated Learning;Non-IID;Spectral Clustering.;Training;Data privacy;Costs;Federated learning;Neural networks;Clustering algorithms;Distributed databases|
|[Category-aware Graph Neural Network for Session-based Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077896)|R. Chen; P. Ma; Y. Zhu; Q. Chen|10.1109/ICPADS56603.2022.00120|Recommender Systems;Graph Neural Networks;Session-based Recommendation;Fuses;Graph neural networks|
|[Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077946)|W. Duan; X. He; L. Zhou; L. Thiele; H. Rao|10.1109/ICPADS56603.2022.00121|hypernetworks;time series forecasting;distribution shift;Training;Adaptation models;Time series analysis;Predictive models;Benchmark testing;Air quality;Distance measurement|
|[DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077901)|R. Zhang; Y. Wang; H. Zhou; B. Li; H. Xu|10.1109/ICPADS56603.2022.00122|Parameter Selection;Data Stream;Outlier Detection;Bayesian optimization;Training;Fault diagnosis;Machine learning algorithms;Memory management;Gaussian processes;Detectors;Bayes methods|
|[Accelerating Convolutional Neural Networks via Inter-operator Scheduling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10077907)|Y. You; P. Liu; D. -Y. Hong; J. -J. Wu; W. -C. Hsu|10.1109/ICPADS56603.2022.00123|Machine-learning;Convolution-neural-networks;Inter-operator-parallelism;Schedules;Tensors;Processor scheduling;Heuristic algorithms;Graphics processing units;Parallel processing;Hardware|
#### **2023 International Multi-disciplinary Conference in Emerging Research Trends (IMCERT)**
- DOI: 10.1109/IMCERT57083.2023
- DATE: 4-5 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Performance Metrics of RAKE Receivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075244)|A. Faraz|10.1109/IMCERT57083.2023.10075244|delay in signals;time shifting;Bayesian belief networks;modulation;redundant systems;demodulation of signals;oscillators;receivers;transmitters;Fading channels;Performance evaluation;Wireless communication;Transmitters;Statistical analysis;Receivers;Correlators|
|[Assessment of Advanced Artificial Intelligence Techniques for Flood Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075119)|M. Waqas; S. Bonnet; U. H. Wannasing; P. T. Hlaing; H. A. Lin; S. Hashim|10.1109/IMCERT57083.2023.10075119|Climate Change;Weather;Artificial Intelligence;Modeling;Support vector machines;Adaptation models;Biological system modeling;Computational modeling;Predictive models;Floods;Risk management|
|[Comparative Analysis of Deep Learning Methods in the Realm of Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075107)|C. Lal; Z. Nasir|10.1109/IMCERT57083.2023.10075107|deep learning;Neural Networks;Natural languageprocessing;text classification;Sentiment analysis;Deep learning;Training;Sentiment analysis;Analytical models;Recurrent neural networks;Bit error rate;Text categorization|
|[Examining Malware Patterns in Android Platform using Sufficient Input Subset (SIS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075203)|F. Nazir; M. U. S. Khan; N. Khan; A. Fayyaz|10.1109/IMCERT57083.2023.10075203|SIS;machine learning;black box model;black box prediction;black box explain-ability;Visualization;Machine learning algorithms;Operating systems;Decision making;Predictive models;Market research;Malware|
|[Temperature prediction by gene expression programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075127)|B. Chansawang; M. Waqas; U. H. Wanasing; P. T. Hlaing; H. A. Lin; R. Ali|10.1109/IMCERT57083.2023.10075127|Climate Change;Weather;Artificial Intelligence;Modeling;Temperature measurement;Support vector machines;Precipitation;Weather forecasting;Programming;Predictive models;Gene expression|
|[Prediction of IMDB Movie Score & Movie Success By Using The Facebook](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075189)|I. Sindhu.; F. Shamsi|10.1109/IMCERT57083.2023.10075189|Sentiment Analysis;Facebook;Movie;SVM;IMDB Score;Linear Regression;Support vector machines;Industries;Sentiment analysis;Video on demand;Social networking (online);Linear regression;Sociology|
|[Impact of Computer Gaming Addiction on Body Mass Index - A Quantitative Investigation about Sukkur City of Pakistan](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075233)|F. Shamsi; I. Sindhu; S. Fatima|10.1109/IMCERT57083.2023.10075233|computer gaming addiction;BMI Body Mass Index;Chi-Square;Sukkur;nan|
|[Semantic Social Searching-An Ontology Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075145)|I. Sindhu; F. Shamsi|10.1109/IMCERT57083.2023.10075145|component;formatting;style;styling;insert;Social networking (online);Semantics;Search engines;Ontologies;Market research;Information filters;User experience|
|[Design and Development of Die Sink Electrical Discharge Machine for Melting Point and Removal Rate of Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075169)|M. Waqas; Z. Ali; A. Hussain; M. Waqas|10.1109/IMCERT57083.2023.10075169|Micro-EDM;Die Sink EDM;Material Removal Rate;Lathe machine;Milling Machine;Flow System;Surface Roughness;Electrodes;Dies;Surface discharges;Machining;Surface roughness;Discharges (electric);Rough surfaces|
|[Investigation of MPC for MIMO system in presence of both input and output constraints with relative parametric variation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075329)|S. Ahmad; S. Aslam; S. Khalid; U. Shabbir; M. Qaiser|10.1109/IMCERT57083.2023.10075329|Constraints;DC motor;MIMO;MPC;Parametric variation;Quadcopter;Simulink;nan|
|[Implementation of SVPWM based Multilevel Three Phase Inverter to Reduce THD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075245)|M. Shakir; S. Ahmad; S. Hannan; B. Ahmad; S. Aslam; M. Rizwan|10.1109/IMCERT57083.2023.10075245|Harmonics;Multilevel Inverters;Space Vector Pulse Width Modulation;Switching losses;Total Harmonic Distortion;Space vector pulse width modulation;Total harmonic distortion;Soft switching;Switching loss;Switches;Multilevel inverters;Market research|
|[Skin Cancer Prediction using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075313)|T. Irfan; A. Rauf; M. J. Iqbal|10.1109/IMCERT57083.2023.10075313|Deep learning;CNN;Skin Cancer;Melanoma;Detection;Diagnosis;nan|
|[Magnetic Anamoly-Based Detection of a Submarine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075316)|A. Ashraf; T. Abbas; A. Ejaz|10.1109/IMCERT57083.2023.10075316|MAD;aircraft;submarine;magnetic interference field;COMSOL Multiphysics;OBF;detection;nan|
|[Design and Control of a Bionic Leg](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075334)|S. Masroor; M. Arsalan; S. G. Khan; S. H. Shah; M. S. Alam; A. Imran|10.1109/IMCERT57083.2023.10075334|Prosthetic leg;Adaptive control;Bionic leg control;nan|
|[Rain Predictive Model using Machine learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075275)|M. S. Muneer; S. M. Nabeel Mustafa; S. S. Zehra; H. Maqsood|10.1109/IMCERT57083.2023.10075275|Rain prediction;Machine Learning;Prediction techniques;Climate Change;nan|
|[Customer Segmentation using Machine learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075194)|S. M. Nabeel Mustafa; A. Akhtar; J. T. Peter Noronha; M. Salman; M. A. Baig|10.1109/IMCERT57083.2023.10075194|E-commerce;Machine learning;customer segmentation;Data mining;Support vector machines;Static VAr compensators;Production;Boosting;Market research;Classification algorithms;Electronic commerce|
|[Hybrid Approach using Extreme Gradient Boosting (XGBoost) and Evolutionary Algorithm for Cancer Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075236)|M. T. Ashraf; I. Hamid; Q. Nawaz; H. Ali|10.1109/IMCERT57083.2023.10075236|XGBoost;Feature selection;GCO;Classification;evolutionary algorithm;Employee welfare;Data analysis;Lung;Evolutionary computation;Organizations;Boosting;Market research|
|[MIMO Antenna for C-band Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075193)|M. A. Arshad; M. Zahid; Y. Amin; S. S. Jaffer|10.1109/IMCERT57083.2023.10075193|MIMO;Patch Antenna;C-Band;WLAN;IoT;nan|
|[$5\times 5$ MIMO Antennas for Future 5G mm-Wave Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075274)|A. Samad; M. Zahid; A. Sultan; Y. Amin; S. Shoaib|10.1109/IMCERT57083.2023.10075274|MIMO;FCC;spectral bands;5G;CST;nan|
|[Multi-feature Integration with Adaptive Learning Based Correlation Filter for Visual Object Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075225)|M. Masood; G. Raja|10.1109/IMCERT57083.2023.10075225|visual object tracking;feature fusion;peak-to-sidelobe ratio;adaptive learning;occlusion;Adaptive learning;Visualization;Target tracking;Correlation;Neural networks;Market research;Robustness|
|[A Real-time Sequence Based Human Activity Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075257)|W. Iqrar; A. Shahzad; W. Hameed; M. Z. Abidien|10.1109/IMCERT57083.2023.10075257|CNN-LSTM;intelligent surveillance system;real-time activity classification;sequence-based detection;suspicious activity detection;Video on demand;Hospitals;Surveillance;Neural networks;Streaming media;Market research;Real-time systems|
|[Fake Reviews Classification using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075156)|S. Ashraf; F. Rehman; H. Sharif; H. Kim; H. Arshad; H. Manzoor|10.1109/IMCERT57083.2023.10075156|Convolutional networks;Deep learning in NLP;Ensemble models;Fake reviews detection;Information retrieval;Social behavior;nan|
|[Network Attack Detection in IoT using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075102)|M. J. Gul; M. K. -u. -R. Raazi Syed|10.1109/IMCERT57083.2023.10075102|Network Attack Detection;Malware Detection;Malware in IoT;Machine Learning Classifiers;Aposemat IoT-23 dataset;nan|
|[Analyzing Doppler Effects in Millimeter Wave VANET Communications Using BCH Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075280)|A. Ahmed; H. Rasheed|10.1109/IMCERT57083.2023.10075280|BCH coding;Millimeter wave;Reliability;Doppler Effect;VANET;Relative velocity;nan|
|[MOOCs and their contribution to the continuous development of high school teachers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075306)|D. R. Tacca Huamán; M. A. Alva Rodriguez; R. C. Cordero; H. A. Córdova-Berona; D. G. Franco Canaval; A. L. Tacca Huamán|10.1109/IMCERT57083.2023.10075306|MOOC;professional training;teachers;high school;virtual classes;nan|
|[An Assessment on Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10075113)|E. A. Ahmed; H. A. Ahmed|10.1109/IMCERT57083.2023.10075113|Internet of things;Sensors;Devices;Technologies;Applications;Performance evaluation;Protocols;Corporate acquisitions;Market research;Internet of Things;Security;Reliability|
#### **2022 19th China International Forum on Solid State Lighting & 2022 8th International Forum on Wide Bandgap Semiconductors (SSLCHINA: IFWS)**
- DOI: 10.1109/SSLChinaIFWS57942.2023
- DATE: 7-10 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Demonstration of an 1200V/20A 4H-SiC Multi-Step Trenched Junction Barrier Schottky Diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071062)|J. Yuan; F. Guo; K. Wang; N. Liu; C. Wu; B. Yang|10.1109/SSLChinaIFWS57942.2023.10071062|nan;Structural rings;Performance evaluation;Schottky diodes;Photonic band gap;Prototypes;Voltage;Numerical simulation|
|[4H-SiC Trench-gate MOSFET with JTE termination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071072)|Z. Zhu; N. Ren; H. Xu; L. Liu; K. Sheng|10.1109/SSLChinaIFWS57942.2023.10071072|nan;Fabrication;Semiconductor device modeling;MOSFET;Semiconductor device measurement;Ion implantation;Photonic band gap;Current measurement|
|[Review of Avalanche Tolerance of Silicon Carbide Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071040)|Y. Jiao; J. Q. Zhang; P. Liu|10.1109/SSLChinaIFWS57942.2023.10071040|nan;MOSFET;Silicon carbide;Photonic band gap;Failure analysis;Switches;Thermal conductivity;Market research|
|[Development of SiC Superjunction MOSFET: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071020)|Y. Duan; Y. -L. Zhang; J. Q. Zhang; P. Liu|10.1109/SSLChinaIFWS57942.2023.10071020|nan;Fabrication;Performance evaluation;MOSFET;Silicon carbide;Photonic band gap;Switching loss;Strategic planning|
|[The Investigation of 1200V SiC MOSFET Radiation Ruggedness with Different Channel Lengths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071136)|H. Xu; N. Ren; Z. Chen; Z. Re; X. Wan; J. Cheng; K. Sheng|10.1109/SSLChinaIFWS57942.2023.10071136|nan;MOSFET;Silicon carbide;Photonic band gap;Failure analysis;Logic gates;Threshold voltage;Leakage currents|
|[Experimental Investigations on Short-Circuit Capability of a New Structure Planar SiC MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070963)|C. Lin; N. Ren; H. Xu; Z. Zhu; K. Sheng|10.1109/SSLChinaIFWS57942.2023.10070963|nan;Electrodes;MOSFET;Solid modeling;Three-dimensional displays;Rapid thermal processing;Silicon carbide;Voltage|
|[Simulation Investigation of Trenched Silicon Carbide Super-junction Schottky Diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071049)|M. Su; Y. Sun; Q. Cui; B. Li; L. Jiang; J. Tang; C. Zeng; Z. Zeng; B. Zhang|10.1109/SSLChinaIFWS57942.2023.10071049|nan;Performance evaluation;Schottky diodes;Silicon carbide;Photonic band gap;Simulation;Structural engineering;Finite element analysis|
|[Research on the technique of accurately measuring thermal resistance of SiC MOSFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070926)|A. Liu; G. Zhang; T. Liu; S. Li; T. Zhang; R. Huang; S. Bai; S. Xu; W. Sun|10.1109/SSLChinaIFWS57942.2023.10070926|nan;MOSFET;Silicon carbide;Thermal resistance;Logic gates;Threshold voltage;Thermal analysis;Thermal stresses|
|[Electrical characterization of HfO2/4H-SiC and HfO2/Si MOS structures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071123)|X. -R. Wang; J. Zhang; H. -P. Ma; Q. -C. Zhang|10.1109/SSLChinaIFWS57942.2023.10071123|High-k dieletric;atomic layer deposition (ALD);current-voltage (I-V);capacitance-voltage (C-V);interfacial defect density (Dit);Silicon carbide;Capacitance-voltage characteristics;MOS capacitors;Logic gates;Silicon;Hafnium compounds;Dielectrics|
|[A decrease in TDDB Lifetime of Commercial SiC power MOSFETs Under Repetitive Unclamped Inductive Switching Stresses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070979)|S. Yang; H. Feng; X. Yu; X. Lin|10.1109/SSLChinaIFWS57942.2023.10070979|nan;Degradation;Resistance;Silicon carbide;Switches;Logic gates;Hot carriers;Threshold voltage|
|[1200 V 14 mΩ SiC MOSFET with Low Specific On-Resistance of 3.3 mΩ cm2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071073)|Y. -L. Zhang; W. -H. Shi; G. -Y. Lei; Q. J. Zhang|10.1109/SSLChinaIFWS57942.2023.10071073|nan;Insulated gate bipolar transistors;MOSFET;Temperature;Silicon carbide;Switching frequency;Switching loss;Voltage|
|[Design and Performance Evaluation of a Short-circuit Protection Scheme for SiC MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071001)|B. Zheng; Y. Fang; G. Zhang; J. Zhang|10.1109/SSLChinaIFWS57942.2023.10071001|nan;MOSFET;Semiconductor device measurement;Voltage measurement;Silicon carbide;Short-circuit currents;Logic gates;Time measurement|
|[High performance 1200V SiC MOSFET platform with AEC-Q101 certificate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070927)|W. Shi; Q. Jon Zhang|10.1109/SSLChinaIFWS57942.2023.10070927|SiC MOSFET;4H-SiC;AEC-Q101;Platform;nan|
|[Two-photon process in F treated AlGaN/GaN heterojunction HEMT device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071084)|Y. Ma; C. Chen; Y. Hong; Y. Cai|10.1109/SSLChinaIFWS57942.2023.10071084|nan;nan|
|[A Comparative Study on P-GaN HEMTs with Schottky/Ohmic Gate Contacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071028)|W. Lu; K. Ren; Y. An; Z. Wu; L. Yin; J. Zhang|10.1109/SSLChinaIFWS57942.2023.10071028|nan;nan|
|[Design and simulation of GaN devices and integrated circuits using ASM compact HEMT model for mixed-signal applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071042)|S. Lu; K. Chen; J. Liu; P. Cui; A. Li; W. Liu|10.1109/SSLChinaIFWS57942.2023.10071042|nan;nan|
|[Quasi-vertical GaN SBD device structure and parameter optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070951)|X. Xiucheng; C. Haijuan; Q. Yalong; L. Jing; G. Weiling|10.1109/SSLChinaIFWS57942.2023.10070951|GaN;Quasi vertical;GaN SBD;terminal structure;breakdown voltage;nan|
|[Design and Simulation of Quasi-Vertical GaN Based Junction Barrier Schottky Diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071124)|Y. Qin; H. Cheng; W. Guo; A. Fang; J. Li; H. Guo|10.1109/SSLChinaIFWS57942.2023.10071124|GaN;Quasi-vertical;Junction Barrier Schottky Diode,JBS, breakdown voltage, On resistance;Performance evaluation;Resistance;Fabrication;Schottky diodes;Photonic band gap;Schottky barriers;Semiconductor process modeling|
|[Non-contact sensing system based on GaN optoelectronic chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070952)|M. Bai; L. Wang; C. Zhang; Y. Wang|10.1109/SSLChinaIFWS57942.2023.10070952|nan;nan|
|[A new lateral AlGaN/GaN Schottky barrier diode combining with floating metal rings and P-guard rings for high breakdown voltage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071065)|K. Liu; C. Wang; X. Zheng; X. Ma; A. Li; Y. Zhao; Y. He; Y. Hao|10.1109/SSLChinaIFWS57942.2023.10071065|nan;nan|
|[Analysis on abnormal forward conduction properties in GaN based JBS diode by TCAD simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070928)|Q. Zhang; Q. Wu; Y. Wang; Q. Qiu; Z. Liu; J. Zhang; C. Li; Y. Zhou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10070928|nan;nan|
|[Dynamic-QGD of Enhancement-mode AlGaN/GaN MIS-HEMTs Induced by Bulk/Interface States in SiNx Passivation Dielectric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071109)|Y. Yao; Q. Jiang; S. Huang; X. Wang; H. Jin; X. Dai; J. Fan; H. Yin; K. Wei; X. Liu|10.1109/SSLChinaIFWS57942.2023.10071109|nan;Semiconductor device measurement;Voltage measurement;Capacitance-voltage characteristics;Switches;Logic gates;Dielectric measurement;Dielectrics|
|[Simulation and Characterization Studies of GaN-Based Quasi-Vertical MIS SBD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070950)|J. Li; H. Cheng; Y. Qin; X. Xu; W. Guo|10.1109/SSLChinaIFWS57942.2023.10070950|nan;Insulation;Photonic band gap;Simulation;Proximity effects;Modulation;Dielectrics;Structural engineering|
|[A High Gain P-L-S Band Power Amplifier Based On IPD technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071012)|R. Yu; Y. Wang; Y. Hu; J. Yin; J. Mo; C. Wang; Q. Wang; G. Yan; T. Ni|10.1109/SSLChinaIFWS57942.2023.10071012|nan;Semiconductor device measurement;Power measurement;Broadband amplifiers;Photonic band gap;Power amplifiers;Gain measurement;Size measurement|
|[Partial etched AlGaN layer on p-GaN HEMT with gate field plate and drain field plate for channel temperature reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071069)|S. Wu; L. Yin; K. Ren; J. Zhang|10.1109/SSLChinaIFWS57942.2023.10071069|nan;Temperature distribution;Analytical models;Thermal resistance;HEMTs;Logic gates;Etching;Wide band gap semiconductors|
|[Study on High-Linearity Devices Based on Double-Channel AlGaN/GaN FinFET Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071092)|A. Li; C. Wang; X. Zheng; X. Ma; Y. He; K. Liu; Y. Zhao; Y. Hao|10.1109/SSLChinaIFWS57942.2023.10071092|nan;nan|
|[A Simulation Study of Multi-Channel AlInN/GaN Schottky Barrier Diodes and Experimental Comparison with Low On-resistance of 1.9 Ω•mm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070934)|Y. Li; A. Li; C. Wang|10.1109/SSLChinaIFWS57942.2023.10070934|nan;nan|
|[The surface and interface evolution of graphene under air exposure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070961)|P. Wang; L. Sun; X. Zhang; X. Guo; F. Yu; X. Zhao|10.1109/SSLChinaIFWS57942.2023.10070961|nan;nan|
|[LOW Inductance High Power IGBT Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070948)|L. Zhao; Y. Jia; X. Zhou; Z. Wang|10.1109/SSLChinaIFWS57942.2023.10070948|nan;Insulated gate bipolar transistors;Inductance;Wires;Bridge circuits;Multichip modules;Switches;Voltage|
|[Thermal characteristics analysis and optimization of 3D-Stacked Memory Packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071101)|F. Cao; W. Yang; M. Yun; D. Yang|10.1109/SSLChinaIFWS57942.2023.10071101|nan;Sealing materials;Temperature;Power demand;Thermal resistance;Design methodology;Packaging;Thermal analysis|
|[A Prediction for Allowable Maximum Turn-on Speed of SiC Power Module by A Correlation Between Turn-on Transient Waveforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070936)|M. Zhang; Q. Guo; H. Wang; N. Ren; K. Sheng|10.1109/SSLChinaIFWS57942.2023.10070936|nan;Correlation;Silicon carbide;Photonic band gap;Multichip modules;Switches;Voltage;Logic gates|
|[Multiphysics Simulation for Silicon Carbide Power Module Design Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071048)|L. Hu; G. Xiaochuan; P. Haijiang|10.1109/SSLChinaIFWS57942.2023.10071048|nan;Silicon carbide;Photonic band gap;Computational modeling;Thermomechanical processes;Multichip modules;Thermal conductivity;Software|
|[Analysis and Research on Application Failure of Automotive Microchip (MCU)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071090)|L. Yuqing; F. Tao; Y. Weiqiao; P. Jintao|10.1109/SSLChinaIFWS57942.2023.10071090|nan;Analytical models;Microscopy;High-voltage techniques;Reliability engineering;Product design;Pins;Automobiles|
|[Reduction of threading screw dislocations in 4H-SiC crystals by physical vapor transport growth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070954)|G. Hu; G. Zhong; X. Yang; X. Chen; X. Xie; G. Yu; X. Hu; X. Xu|10.1109/SSLChinaIFWS57942.2023.10070954|nan;Atomic force microscopy;Silicon carbide;Photonic band gap;Stacking;Surface morphology;Morphology;Crystals|
|[Femtosecond laser modification of silicon carbide to improve the materials removal efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071060)|Z. Chi; P. Chen; F. Qin|10.1109/SSLChinaIFWS57942.2023.10071060|nan;nan|
|[Study on Uniformity of Epitaxial Graphene on 6-inch 4H-SiC Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071008)|C. Shao; X. Li; G. Zhong; X. Chen; X. Xu; X. Hu; W. Yu; X. Yang; X. Xie; H. Chen|10.1109/SSLChinaIFWS57942.2023.10071008|nan;nan|
|[Epitaxial lateral overgrowth of GaN using hexagonal patterned PECVD and wet transferred graphene masks by MOCVD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071045)|J. Tao; Y. Xu; J. Li; X. Cai; Y. Wang; G. Wang; B. Cao; K. Xu|10.1109/SSLChinaIFWS57942.2023.10071045|nan;nan|
|[Influence of patterned graphene mask on nucleation behavior of GaN by MOCVD](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071116)|J. Li; Y. Xu; J. Tao; X. Cai; Y. Wang; G. Wang; B. Cao; K. Xu|10.1109/SSLChinaIFWS57942.2023.10071116|nan;Strips;Photonic band gap;Graphene;Market research;Behavioral sciences;Epitaxial growth;Dielectrics|
|[A Surface Potential Based Compact Model for β-Ga2O3 Power MOSFETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070932)|K. Zhou; X. Zhou; S. Miao; Q. He; W. Hao; J. Du; G. Xu; S. Long|10.1109/SSLChinaIFWS57942.2023.10070932|β-Ga2O3 power MOSFETs;surface potential model;Pao-Sah’s dual integral;TCAD simulation;Semiconductor device modeling;MOSFET;Temperature dependence;Photonic band gap;Computational modeling;Mathematical models;Data models|
|[Performance enhancement of AlGaN deep-ultraviolet laser diodes with linear variation quantum barriers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071071)|P. Zhang; G. Zhong; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071071|nan;Performance evaluation;Photonic band gap;Radiative recombination;Threshold voltage;Wide band gap semiconductors;Diode lasers;Aluminum gallium nitride|
|[Performance optimization of deep ultraviolet laser diodes with superlattice hole blocking layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071131)|L. Jia; P. Zhang; M. Yin; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071131|nan;Photonic band gap;Superlattices;Radiative recombination;Optical superlattices;Diode lasers;Wide band gap semiconductors;Aluminum gallium nitride|
|[Deep-ultraviolet Laser Diode Characterization Improvement by Inverted Trapezoidal Hole Blocking Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071119)|Y. Xu; M. Yin; P. Zhang; A. Zhang; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071119|nan;Performance evaluation;Stimulated emission;Semiconductor lasers;Photonic band gap;Power lasers;Radiative recombination;Threshold current|
|[Improvement of Optoelectronic Characteristics of Deep-ultraviolet Laser Diode with an Optimal Thickness of Electron Blocking Layer and Waveguide Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071002)|C. Zhang; Y. Xu; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071002|nan;Stimulated emission;Waveguide lasers;Electron optics;Software;Diode lasers;Wide band gap semiconductors;Semiconductor waveguides|
|[Performance improvement in AlGaN-based ultraviolet light diodes with superlattice hole reservoir layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071030)|X. Sang; M. Yin; Y. Xu; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071030|nan;Performance evaluation;Semiconductor lasers;Power lasers;Superlattices;Lattices;Charge carrier processes;Threshold current|
|[First principles studies for electronic structure of β-Ga2O3 and GaAs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070929)|R. Yang; J. Zhang; H. -P. Ma; Q. -C. Zhang|10.1109/SSLChinaIFWS57942.2023.10070929|nan;Band structures;Photonic band gap;Gallium arsenide;Discrete Fourier transforms;Radiative recombination;Heterojunctions;Predictive models|
|[Investigation of Lateral Gallium Oxide MOSFET with Discrete Field Plate by Calibrated Numerical Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070995)|L. Wang; M. Zhang; Z. Liu; L. Yang; S. Li; W. Tang; Y. Guo|10.1109/SSLChinaIFWS57942.2023.10070995|nan;Resistance;MOSFET;Gallium;Photonic band gap;Numerical simulation;Numerical models;Structural engineering|
|[Machine learning assisted early anomaly detection of LEDs with spectral power distribution modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071010)|M. Liu; M. S. Ibrahim; M. Wen; S. Li; A. Wang; G. Zhang; J. Fan|10.1109/SSLChinaIFWS57942.2023.10071010|White LEDs;Spectral power distribution;Anomaly detection;Principal component analysis;K-nearest neighbor;Degradation;Photonic band gap;Power distribution;Lumen;Feature extraction;Light emitting diodes;Reliability|
|[Design and optical characteristics simulation of LED plant lighting source](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071112)|D. Li; W. Guo; A. Fang|10.1109/SSLChinaIFWS57942.2023.10071112|nan;Analytical models;Optical design;Photonic band gap;Simulation;Lighting;Ray tracing;Light emitting diodes|
|[Design and performance tuning of luminescent materials for Micro LED](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071067)|Y. Wang; X. Wang; D. Wang; T. Li; T. Seto|10.1109/SSLChinaIFWS57942.2023.10071067|nan;Toxicology;Quantum dots;Phosphors;Brightness;Luminescence;Doping;Perovskites|
|[High Efficiency Nitride Green LEDs with Built-in PL Color Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071052)|C. H. Yan; Y. H. Du; W. Sun; A. L. Yang; D. W. Nie; B. B. Shen; C. Y. Yu|10.1109/SSLChinaIFWS57942.2023.10071052|nan;Photonic band gap;Lighting;Color;Radiative recombination;Photoluminescence;Light emitting diodes;Electroluminescence|
|[High-voltage Linear LED for Three-phase AC Power Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070980)|X. Zhang; B. Wang; S. Li; H. Guo; R. Yue; Y. Cai|10.1109/SSLChinaIFWS57942.2023.10070980|nan;Wiring;Reactive power;Voltage fluctuations;Fluctuations;Costs;Stimulated emission;Lighting|
|[Recent Research Progress on Colour Quality of Lighting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070988)|L. Jia; W. Chen; Y. Liu; X. Deng; Q. Liu|10.1109/SSLChinaIFWS57942.2023.10070988|nan;Semiconductor device modeling;Visualization;Image color analysis;Photonic band gap;Lighting;Predictive models;Data models|
|[Thermal design of circular VCSEL arrays for optical output performance improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071014)|G. -X. Fan; D. -Y. Jin; W. -R. Zhang; X. Lei; Y. -X. Zhou; Y. -Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071014|nan;Temperature distribution;Optical design;Photonic band gap;Rollover;Layout;Numerical models;Vertical cavity surface emitting lasers|
|[Wearable laser Doppler based on VCSEL and its application in human blood flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070969)|H. Zhang; J. Cui; X. Xing; J. Xu|10.1109/SSLChinaIFWS57942.2023.10070969|nan;Heart;Stimulated emission;Photonic band gap;Myocardium;Optical sensors;Older adults;Vertical cavity surface emitting lasers|
|[Optimization of thickness in hole blocking layer of AlGaN-based deep ultraviolet laser diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071110)|M. Yin; A. Zhang; Y. Xu; F. Wang; J. J. Liou; Y. Liu|10.1109/SSLChinaIFWS57942.2023.10071110|nan;Semiconductor lasers;Photonic band gap;Charge carrier processes;Software;Wide band gap semiconductors;Diode lasers;Optimization|
|[Influence of the spectrum of all-day LED lighting on human daytime and nighttime performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071053)|N. Li; S. Zhou; W. Miao; C. Dai; A. H. Shah; Y. Lin|10.1109/SSLChinaIFWS57942.2023.10071053|nan;Photonic band gap;Lighting;Light emitting diodes;Rhythm|
|[Lighting Intervention with Different Colors on Emotion: A Bayesian Network Meta-Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071137)|L. Zhou; T. Fu; W. Miao; Y. Lin|10.1109/SSLChinaIFWS57942.2023.10071137|nan;Semiconductor device modeling;Image color analysis;Photonic band gap;Lighting;Visual effects;Size measurement;Software|
|[Reliability and Lifetime of a Novel 222-nm KrCl Excilamp](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070975)|F. Zhao; Y. Zeng; H. Wu; J. Chen; G. Yang; L. Zhen; B. Shieh; S. W. R. Lee|10.1109/SSLChinaIFWS57942.2023.10070975|nan;Temperature;Filtering;Power supplies;Photonic band gap;Aging;Lifetime estimation;Epidermis|
|[On-road projection symbols for future vehicles: A survey study for Chinese roads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071038)|X. Sun; A. H. Shah; J. Ao; W. Miao; Y. Lin; W. Chen|10.1109/SSLChinaIFWS57942.2023.10071038|eHMI;On-road projections;DMD;feelings of safety;autonomous vehicles;Industries;Symbols;Road safety;Physiology;Automobiles;Synchronization;Vehicle dynamics|
|[Optimize Screen CCTs in 3000K and 5000K Ambient CCTs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071046)|S. Zeng; W. Hao; Y. Guo; G. Li; W. Li; C. Chen; Z. Zhang; D. Qu; S. Zhang; X. Qin; J. Cai|10.1109/SSLChinaIFWS57942.2023.10071046|Indoor CCT;Market Smartphone;Average CCT Level;Automatic Screen CCT;Ocular Discomfort;Visualization;Photonic band gap;Transfer functions;Modulation;Lighting;Indexes;Task analysis|
|[Visual comfort of coloured background display stimuli under the dark environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071138)|Y. Liu; Y. Zhu; M. R. Luo|10.1109/SSLChinaIFWS57942.2023.10071138|nan;Semiconductor device modeling;Visualization;Image color analysis;Photonic band gap;Predictive models;Observers;Logistics|
|[Research on the Optimization of the Lighting Environment in the public space of the Polar Cruise ship "Greg Mortimer"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070987)|J. Minyu; G. Wenjing; Y. Xiu|10.1109/SSLChinaIFWS57942.2023.10070987|Lighting Evaluation;Polar Cruise Ship;Cruise Interior;Lighting Luminous Environment;Semiconductor device measurement;Photonic band gap;Image color analysis;Current measurement;Brightness;Lighting;Space exploration|
|[Analysis and research on color parameter drift and thermal properties of packaged light source applied in education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070933)|R. Yin; X. Song; X. Fang; J. Zhao; S. Wang; Z. Sun|10.1109/SSLChinaIFWS57942.2023.10070933|nan;Phosphors;Education;Lighting;Color;Luminescence;Light emitting diodes;Stability analysis|
|[The research on circadian rhythm parameters testing of lighting quality in classrooms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071118)|B. Li; L. Gu|10.1109/SSLChinaIFWS57942.2023.10071118|circadian rhythm;testing;lighting quality;classroom;Photonic band gap;Lighting;Circadian rhythm;Proposals;Standards;Testing|
|[Impact of blue light irradiation on the viability of four types of human cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070982)|W. Zhang; Y. Zhao; J. Dong|10.1109/SSLChinaIFWS57942.2023.10070982|nan;Radiation effects;Pathogens;Photonic band gap;Medical treatment;DNA;Pigments;Optogenetics|
|[Design and application of dermoscope based on polarization imaging and uniform illumination](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071107)|J. Xu; J. Cui|10.1109/SSLChinaIFWS57942.2023.10071107|nan;Photonic band gap;Telemedicine;Lighting;Imaging;Morphology;Pigments;Epidermis|
|[A new duplex optical communication scheme based on quantum well diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071096)|K. Fu; L. Wang; Y. Wang|10.1109/SSLChinaIFWS57942.2023.10071096|nan;Costs;Photonic band gap;Receivers;Optical fiber communication;Transceivers;Software;Semiconductor diodes|
|[Compact fluorescence detection system based on self- filtering illumination GaN-based quantum well diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071134)|J. Fu; J. Yan; Z. Ye; Y. Wang; Y. Wang|10.1109/SSLChinaIFWS57942.2023.10071134|nan;Optical filters;Temperature measurement;Fluorescence;Light emitting diodes;Sensors;Semiconductor diodes;Distributed Bragg reflectors|
|[Strain related degradation of highly tensile-strained multi-quantum well laser diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071021)|B. Chen; C. Cao; X. Che; Y. Fang|10.1109/SSLChinaIFWS57942.2023.10071021|nan;Degradation;Temperature;Epitaxial layers;Semiconductor device reliability;Diode lasers;Behavioral sciences;Quantum well devices|
|[A low-power, high-gain operational amplifier for the preamplification circuit of cooled infrared sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070942)|Y. Luo; F. Wang; K. Liu; A. Guo|10.1109/SSLChinaIFWS57942.2023.10070942|nan;Operational amplifiers;Semiconductor device modeling;Infrared detectors;Power demand;Imaging;Detectors;Preamplifiers|
|[Design of low-noise multi-channel active surface electromyography acquisition equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071075)|N. Jiang; A. Guo; Q. Lei; Z. Wang|10.1109/SSLChinaIFWS57942.2023.10071075|nan;Electrodes;Legged locomotion;Photonic band gap;Noise reduction;Interference;Muscles;Electromyography|
|[A Large Field-of-view (FOV) Visible Laser Light Communication System Reaching 180° with Silicon Photomultiplier Receiver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071103)|C. Ma; Y. Hou; L. Zha; S. Lin; H. Jia; C. Shen|10.1109/SSLChinaIFWS57942.2023.10071103|nan;Photomultipliers;Wireless communication;Transmitters;Semiconductor lasers;Photonic band gap;Lighting;Receivers|
|[Lowering Power Consumption for Organic Light-Emitting Diodes Full-Screen Display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071114)|Z. Xiong; H. Liu; P. Zhu|10.1109/SSLChinaIFWS57942.2023.10071114|nan;Power demand;Photonic band gap;Optimization methods;Voltage;Organic light emitting diodes;Data models;Integrated circuit modeling|
|[Simulation Study on the Size Effect and Transient Characteristics of Micro-LEDs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070998)|Z. Wu; K. Ren; Y. An; L. Yin; X. Lu; A. Guo; J. Zhang|10.1109/SSLChinaIFWS57942.2023.10070998|nan;Photonic band gap;Dry etching;Process control;Switches;Radiative recombination;Light emitting diodes;Energy states|
|[Research on monolithic integrated driver of Micro LED](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071104)|F. Aoqi; G. Weiling; X. Hao; L. Jixin|10.1109/SSLChinaIFWS57942.2023.10071104|Micro LED;monolithic integrated;GaN;MOSFET;Power demand;Photonic band gap;Monolithic integrated circuits;Metals;Light emitting diodes;Silicon|
|[Enhanced Performance of a Metal-insulator-semiconductor Structured p/n-Electrode for InGaN-based Green LEDs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071078)|H. Wenjun; M. Xiangyu; L. Zhaojun|10.1109/SSLChinaIFWS57942.2023.10071078|nan;Photonic band gap;Atomic layer deposition;Current-voltage characteristics;Tunneling;Light emitting diodes;Insulators;MIS devices|
|[Size-Independent Current Density-Voltage Characteristic of Micro-LEDs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070937)|H. Yang; W. -J. Huang; Y. -H. Lin; M. -Y. Zhang-Hu; Z. -J. Liu|10.1109/SSLChinaIFWS57942.2023.10070937|Micro-LEDs Fabrication;size effect;external quantum efficiency (EQE);Performance evaluation;Electric potential;Photonic band gap;Metals;Voltage;Contact resistance;Leakage currents|
|[Thermodynamic simulation of 6×6 Micro-LED array in flip-chip bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070985)|H. Zhou; X. Ji; L. Yin; J. Zhang|10.1109/SSLChinaIFWS57942.2023.10070985|nan;Thermodynamics;Deformation;Shape;Simulation;Software;Silicon;Flip-chip devices|
|[Junction temperature of 260 nm AlGaN UVC micro-LEDs under different irradiated Ta fluences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071139)|Z. Zhao; X. Shan; S. Zhu; Z. Qian; X. Cui; S. Zhang; L. Wang; P. Tian|10.1109/SSLChinaIFWS57942.2023.10071139|nan;Temperature measurement;Degradation;Radiation effects;Temperature;Voltage measurement;Ions;Light emitting diodes|
|[Transfer printing quantum dots for full-color micro-LED display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070974)|Y. Li; X. Shan; Z. Wang; S. Zhu; X. Cui; G. Huang; Y. Mei; P. Tian|10.1109/SSLChinaIFWS57942.2023.10070974|nan;Printing;Photonic band gap;Quantum dots;Glass;Color;Distributed Bragg reflectors;Substrates|
|[Large Dynamic Range Real-time White Balance Control in Laser Display](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071068)|Z. Jianying; B. Yong; S. Minyuan; G. Weinan; Y. Yuan; Z. Shuo|10.1109/SSLChinaIFWS57942.2023.10071068|nan;Temperature sensors;Temperature distribution;TV;Power lasers;Dynamic range;Control systems;Real-time systems|
|[Near ultraviolet light modulator based on InGaN/AlGaN MQW diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071126)|J. Yan; L. Fang; Z. Sun; H. Zhang; P. Liu; Y. Wang|10.1109/SSLChinaIFWS57942.2023.10071126|nan;Stimulated emission;Optical device fabrication;Modulation;Light emitting diodes;Semiconductor diodes;Optical modulation;Quantum well devices|
|[Ag-based hollow reflective electrode on thin p-GaN layer for improving the light output efficiency of AlGaN-based deep-ultraviolet light-emitting diodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070972)|S. Pan; K. Chen; Y. Guo; Z. Liu; Y. Zhou; R. Zhang; Y. Zheng|10.1109/SSLChinaIFWS57942.2023.10070972|nan;Electrodes;Photonic band gap;Light emitting diodes;Wide band gap semiconductors;Ohmic contacts;Optical reflection;Mirrors|
|[Critical aspects of deep-UV LED design and operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071057)|M. Yanlin; K. Bulashevich|10.1109/SSLChinaIFWS57942.2023.10071057|nan;Electrodes;Resistance;Reflectivity;Thermal factors;Absorption;Voltage;Light emitting diodes|
#### **2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)**
- DOI: 10.1109/HPCA56546.2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071102)|M. Yoo; J. Song; J. Lee; N. Kim; Y. Kim; J. Lee|10.1109/HPCA56546.2023.10071102|Graph Convolutional Networks;Sparsity;Compressed Format;Accelerators;Degradation;Microarchitecture;Costs;Neural networks;Computer architecture;Network architecture;Market research|
|[PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070931)|S. Li; H. Yang; C. W. Wong; V. J. Sorger; P. Gupta|10.1109/HPCA56546.2023.10070931|nan;nan|
|[INCA: Input-stationary Dataflow at Outside-the-box Thinking about Deep Learning Accelerators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070992)|B. Kim; S. Li; H. Li|10.1109/HPCA56546.2023.10070992|nan;nan|
|[GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070983)|R. Hwang; M. Kang; J. Lee; D. Kam; Y. Lee; M. Rhu|10.1109/HPCA56546.2023.10070983|nan;Casting;Software algorithms;Computer architecture;Parallel processing;Hardware;Energy efficiency;Software|
|[Logical/Physical Topology-Aware Collective Communication in Deep Learning Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071117)|J. Sanghoon; H. Son; J. Kim|10.1109/HPCA56546.2023.10071117|nan;nan|
|[Sibia: Signed Bit-slice Architecture for Dense DNN Acceleration with Slice-level Sparsity Exploitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071031)|D. Im; G. Park; Z. Li; J. Ryu; H. -J. Yoo|10.1109/HPCA56546.2023.10071031|Hardware accelerator;deep neural network;binary representation;bit-slice;sparsity;output speculation;non-ReLU;Point cloud compression;Neural networks;Computer architecture;Bandwidth;Network-on-chip;Throughput;Hardware|
|[AstriFlash A Flash-Based System for Online Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070955)|S. Gupta; Y. Oh; L. Yan; M. Sutherland; A. Bhattacharjee; B. Falsafi; P. Hsu|10.1109/HPCA56546.2023.10070955|nan;Costs;Memory management;Random access memory;Tail;Switches;Throughput;Servers|
|[Thoth: Bridging the Gap Between Persistently Secure Memories and Memory Interfaces of Emerging NVMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070991)|X. Han; J. Tuck; A. Awad|10.1109/HPCA56546.2023.10070991|Persistent Memory;Security Metadata;Secure NVM;Nonvolatile memory;Memory modules;Metadata;System recovery;Elliptic curve cryptography;Software;Error correction codes|
|[Multi-Granularity Shadow Paging with NVM Write Optimization for Crash-Consistent Memory-Mapped I/O](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071009)|H. Du; Q. Li; R. Pan; T. -W. Kuo; C. J. Xue|10.1109/HPCA56546.2023.10071009|nan;nan|
|[MGC: Multiple-Gray-Code for 3D NAND Flash based High-Density SSDs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070946)|Y. Lv; L. Shi; Q. Li; C. Gao; Y. Song; L. Luo; Y. Zhang|10.1109/HPCA56546.2023.10070946|Storage System;3D NAND Flash;Gray-Code;Reliability;Performance;Performance evaluation;Three-dimensional displays;Runtime;Programming;Writing;Encoding;Parity check codes|
|[Baryon: Efficient Hybrid Memory Management with Compression and Sub-Blocking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071115)|Y. Li; M. Gao|10.1109/HPCA56546.2023.10071115|Hybrid memory;compression;sub-blocking;Costs;Nonvolatile memory;Memory management;Layout;Systems architecture;Data compression;Bandwidth|
|[Root Crash Consistency of SGX-style Integrity Trees in Secure Non-Volatile Memory Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071003)|J. Huang; Y. Hua|10.1109/HPCA56546.2023.10071003|nan;Nonvolatile memory;Data integrity;Computer architecture;System recovery;Computer crashes;System-on-chip;Low latency communication|
|[ACIC: Admission-Controlled Instruction Cache](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071033)|Y. Wang; C. -H. Chang; A. Sivasubramaniam; N. Soundararajan|10.1109/HPCA56546.2023.10071033|nan;Pollution;Codes;Optimized production technology;Computer architecture;Prediction algorithms;Libraries|
|[Compression-Aware and Performance-Efficient Insertion Policies for Long-Lasting Hybrid LLCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070968)|C. Escuin; A. A. Khan; P. Ibáñez; T. Monreal; J. Castrillon; V. Viñals|10.1109/HPCA56546.2023.10070968|nan;Performance evaluation;Fault tolerance;Runtime;Nonvolatile memory;Fault tolerant systems;Random access memory;Hardware|
|[NOMAD: Enabling Non-blocking OS-managed DRAM Cache via Tag-Data Decoupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071016)|Y. Kim; H. Kim; W. J. Song|10.1109/HPCA56546.2023.10071016|nan;nan|
|[Safety Hints for HTM Capacity Abort Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071113)|A. Jain; D. K. Kadiyala; A. Daglis|10.1109/HPCA56546.2023.10071113|nan;nan|
|[iCache: An Importance-Sampling-Informed Cache for Accelerating I/O-Bound DNN Model Training](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070964)|W. Chen; S. He; Y. Xu; X. Zhang; S. Yang; S. Hu; X. -H. Sun; G. Chen|10.1109/HPCA56546.2023.10070964|nan;nan|
|[Are Randomized Caches Truly Random? Formal Analysis of Randomized-Partitioned Caches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071041)|A. Chakraborty; S. Bhattacharya; S. Saha; D. Mukhopadhyay|10.1109/HPCA56546.2023.10071041|nan;nan|
|[HIRAC: A Hierarchical Accelerator with Sorting-based Packing for SpGEMMs in DNN Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070977)|H. Shabani; A. Singh; B. Youhana; X. Guo|10.1109/HPCA56546.2023.10070977|nan;Runtime;Multiprocessor interconnection;System performance;Neural networks;Parallel processing;Sparse matrices;Matrix converters|
|[VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071058)|G. Jeong; S. Damani; A. R. Bambhaniya; E. Qin; C. J. Hughes; S. Subramoney; H. Kim; T. Krishna|10.1109/HPCA56546.2023.10071058|nan;nan|
|[ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071027)|H. You; Z. Sun; H. Shi; Z. Yu; Y. Zhao; Y. Zhang; C. Li; B. Li; Y. Lin|10.1109/HPCA56546.2023.10071027|nan;nan|
|[Leveraging Domain Information for the Efficient Automated Design of Deep Learning Accelerators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071095)|C. Sakhuja; Z. Shi; C. Lin|10.1109/HPCA56546.2023.10071095|nan;nan|
|[DIMM-Link: Enabling Efficient Inter-DIMM Communication for Near-Memory Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071005)|Z. Zhou; C. Li; F. Yang; G. Suny|10.1109/HPCA56546.2023.10071005|nan;nan|
|[AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070947)|M. Luo; W. Xiong; G. Lee; Y. Li; X. Yang; A. Zhang; Y. Tian; H. -H. S. Lee; G. E. Suh|10.1109/HPCA56546.2023.10070947|nan;nan|
|[SHADOW: Preventing Row Hammer in DRAM with Intra-Subarray Row Shuffling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070966)|M. Wi; J. Park; S. Ko; M. J. Kim; N. Sung Kim; E. Lee; J. H. Ahn|10.1109/HPCA56546.2023.10070966|nan;Microarchitecture;Random access memory;Computer architecture;Probabilistic logic;Pattern analysis;Computer security;Optimization|
|[Efficient Distributed Secure Memory with Migratable Merkle Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071130)|E. Feng; D. Du; Y. Xia; H. Chen|10.1109/HPCA56546.2023.10071130|nan;Protocols;Program processors;Memory management;Prototypes;Metadata;Data transfer;Software|
|[AB-ORAM: Constructing Adjustable Buckets for Space Reduction in Ring ORAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071064)|M. Raoufi; J. Yang; X. Tang; Y. Zhang|10.1109/HPCA56546.2023.10071064|Oblivious RAM;ORAM;security;memory access pattern;space efficiency;Degradation;Memory management;Random access memory;Bandwidth;Compaction;Resource management;Optimization|
|[Scalable and Secure Row-Swap: Efficient and Safe Row Hammer Mitigation in Memory Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070999)|J. Woo; G. Saileshwar; P. J. Nair|10.1109/HPCA56546.2023.10070999|nan;nan|
|[Post0-VR: Enabling Universal Realistic Rendering for Modern VR via Exploiting Architectural Similarity and Data Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071097)|Y. Wen; C. Xie; S. L. Song; X. Fu|10.1109/HPCA56546.2023.10071097|nan;nan|
|[ParallelNN: A Parallel Octree-based Nearest Neighbor Search Accelerator for 3D Point Clouds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070940)|F. Chen; R. Ying; J. Xue; F. Wen; P. Liu|10.1109/HPCA56546.2023.10070940|nan;Point cloud compression;Three-dimensional displays;Octrees;Graphics processing units;Bandwidth;Parallel processing;Search problems|
|[ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071081)|J. Dass; S. Wu; H. Shi; C. Li; Z. Ye; Z. Wang; Y. Lin|10.1109/HPCA56546.2023.10071081|nan;nan|
|[CTA: Hardware-Software Co-design for Compressed Token Attention Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070997)|H. Wang; H. Xu; Y. Wang; Y. Han|10.1109/HPCA56546.2023.10070997|nan;nan|
|[HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071047)|P. Dong; M. Sun; A. Lu; Y. Xie; K. Liu; Z. Kong; X. Meng; Z. Li; X. Lin; Z. Fang; Y. Wang|10.1109/HPCA56546.2023.10071047|Vision Transformer;FPGA Accelerator;Hardware and Software Co-design;Data-level Sparsity;nan|
|[Trans-FW: Short Circuiting Page Table Walk in Multi-GPU Systems via Remote Forwarding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071054)|B. Li; J. Yin; A. Holey; Y. Zhang; J. Yang; X. Tang|10.1109/HPCA56546.2023.10071054|multi-GPU;page fault;page table walk;nan|
|[Ah-Q: Quantifying and Handling the Interference within a Datacenter from a System Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071128)|Y. Liu; X. Deng; J. Zhou; M. Chen; Y. Bao|10.1109/HPCA56546.2023.10071128|nan;Concurrent computing;Interference;Computer architecture;Tail;Quality of service;Dynamic scheduling;User experience|
|[Market Mechanism-Based User-in-the-Loop Scalable Power Oversubscription for HPC Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071006)|M. R. Hossen; K. Ahmed; M. A. Islam|10.1109/HPCA56546.2023.10071006|nan;Power demand;Costs;High performance computing;Prototypes;Computer architecture|
|[Rambda: RDMA-driven Acceleration Framework for Memory-intensive µs-scale Datacenter Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071127)|Y. Yuan; J. Huang; Y. Sun; T. Wang; J. Nelson; D. R. K. Ports; Y. Wang; R. Wang; C. Tai; N. S. Kim|10.1109/HPCA56546.2023.10071127|cache-coherent interconnects and accelerators;RDMA;heterogeneous and disaggregated memory;datacenters;nan|
|[FinePack: Transparently Improving the Efficiency of Fine-Grained Transfers in Multi-GPU Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070949)|H. Muthukrishnan; D. Lustig; O. Villa; T. Wenisch; D. Nellans|10.1109/HPCA56546.2023.10070949|nan;Performance evaluation;Technological innovation;Protocols;Scalability;Memory management;Graphics processing units;Programming|
|[Mitigating GPU Core Partitioning Performance Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070957)|A. Barnes; F. Shen; T. G. Rogers|10.1109/HPCA56546.2023.10070957|GPU;Scheduling;Register File;Bank Conflict;Pathology;Scheduling algorithms;Databases;Instruction sets;Query processing;Graphics processing units;Computer architecture|
|[Plutus: Bandwidth-Efficient Memory Security for GPUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071100)|R. Abdullah; H. Zhou; A. Awad|10.1109/HPCA56546.2023.10071100|nan;Trusted computing;Authentication;Graphics processing units;Bandwidth;Organizations;Metadata;Throughput|
|[MPress: Democratizing Billion-Scale Model Training on Multi-GPU Servers via Memory-Saving Inter-Operator Parallelism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071077)|Q. Zhou; H. Wang; X. Yu; C. Li; Y. Bai; F. Yan; Y. Xu|10.1109/HPCA56546.2023.10071077|Inter-Operator Parallelism;DNN Training;Swap;Recomputation;Training;Performance evaluation;Tensors;Costs;Computational modeling;Graphics processing units;Parallel processing|
|[DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071098)|L. Mei; K. Goetschalckx; A. Symons; M. Verhelst|10.1109/HPCA56546.2023.10071098|nan;nan|
|[CEGMA: Coordinated Elastic Graph Matching Acceleration for Graph Matching Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070956)|Y. Dai; Y. Zhang; X. Tang|10.1109/HPCA56546.2023.10070956|Graph Matching Networks;Graph Neural Networks;Accelerator;Matched filters;Fuses;Computational modeling;Neural networks;Memory management;Graphics processing units;Software|
|[ISOSceles: Accelerating Sparse CNNs through Inter-Layer Pipelining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071080)|Y. Yang; J. S. Emer; D. Sanchez|10.1109/HPCA56546.2023.10071080|nan;nan|
|[OptimStore: In-Storage Optimization of Large Scale DNNs with On-Die Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071024)|J. Kim; M. Kang; Y. Han; Y. -G. Kim; L. -S. Kim|10.1109/HPCA56546.2023.10071024|nan;nan|
|[KRISP: Enabling Kernel-wise RIght-sizing for Spatial Partitioned GPU Inference Servers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071121)|M. Chow; A. Jahanshahi; D. Wong|10.1109/HPCA56546.2023.10071121|GPU Inference Server;Compute Unit Masking;GPU Spatial Partitioning;Training;Graphics processing units;Machine learning;Computer architecture;Throughput;Servers;Kernel|
|[MERCURY: Accelerating DNN Training By Exploiting Input Similarity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071051)|V. Janfaza; K. Weston; M. Razavi; S. Mandal; F. Mahmud; A. Hilty; A. Muzahid|10.1109/HPCA56546.2023.10071051|nan;Training;Deep learning;Quantization (signal);Computational modeling;Impedance matching;Neural networks;Computer architecture|
|[Silo: Speculative Hardware Logging for Atomic Durability in Persistent Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071034)|M. Zhang; Y. Hua|10.1109/HPCA56546.2023.10071034|nan;nan|
|[Reconciling Selective Logging and Hardware Persistent Memory Transaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071088)|C. Ye; Y. Xu; X. Shen; Y. Sha; X. Liao; H. Jin; Y. Solihin|10.1109/HPCA56546.2023.10071088|nan;nan|
|[SecPB: Architectures for Secure Non-Volatile Memory with Battery-Backed Persist Buffers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071082)|A. Freij; H. Zhou; Y. Solihin|10.1109/HPCA56546.2023.10071082|nan;nan|
|[EVE: Ephemeral Vector Engines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071074)|K. Al-Hawaj; T. Ta; N. Cebry; S. Agwa; O. Afuye; E. Hall; C. Golden; A. B. Apsel; C. Batten|10.1109/HPCA56546.2023.10071074|nan;nan|
|[On Consistency for Bulk-Bitwise Processing-in-Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071007)|B. Perach; R. Ronen; S. Kvatinsky|10.1109/HPCA56546.2023.10071007|nan;Databases;Computational modeling;Computer architecture;Benchmark testing;Software;Hardware;Standards|
|[Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071089)|M. Orenes-Vera; E. Tureci; D. Wentzlaff; M. Martonosi|10.1109/HPCA56546.2023.10071089|Distributed;scalable;parallel;data-local;near-memory;architecture;sparse;graph;NoC;network;bandwidth;nan|
|[HyQSAT: A Hybrid Approach for 3-SAT Problems by Integrating Quantum Annealer with CDCL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071022)|S. Tan; M. Yu; A. Python; Y. Shang; T. Li; L. Lu; J. Yin|10.1109/HPCA56546.2023.10071022|nan;nan|
|[Duet: Creating Harmony between Processors and Embedded FPGAs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070989)|A. Li; A. Ning; D. Wentzlaff|10.1109/HPCA56546.2023.10070989|nan;Program processors;Computer architecture;Bandwidth;Benchmark testing;Software;Registers;Task analysis|
|[Co-Designed Architectures for Modular Superconducting Quantum Computers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071036)|E. McKinney; M. Xia; C. Zhou; P. Lu; M. Hatridge; A. K. Jones|10.1109/HPCA56546.2023.10071036|nan;nan|
|[A Pulse Generation Framework with Augmented Program-aware Basis Gates and Criticality Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070990)|Y. Chen; Y. Jin; F. Hua; A. Hayes; A. Li; Y. Shi; E. Z. Zhang|10.1109/HPCA56546.2023.10070990|nan;Circuit optimization;Program processors;Databases;Optimal control;Transforms;Computer architecture;Logic gates|
|[The Imitation Game: Leveraging CopyCats for Robust Native Gate Selection in NISQ Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071025)|P. Das; E. Kessler; Y. Shi|10.1109/HPCA56546.2023.10071025|Quantum;Gate nativization;NISQ Compilation;nan|
|[eNODE: Energy-Efficient and Low-Latency Edge Inference and Training of Neural ODEs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070935)|J. Zhu; Y. Tao; Z. Zhang|10.1109/HPCA56546.2023.10070935|nan;nan|
|[SpecFaaS: Accelerating Serverless Applications with Speculative Function Execution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071120)|J. Stojkovic; T. Xu; H. Franke; J. Torrellas|10.1109/HPCA56546.2023.10071120|Cloud computing;Serverless computing;Function-as-a-Service;nan|
|[MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071035)|S. Kim; H. Genc; V. V. Nikiforov; K. Asanović; B. Nikolić; Y. S. Shao|10.1109/HPCA56546.2023.10071035|nan;nan|
|[Know Your Enemy To Save Cloud Energy: Energy-Performance Characterization of Machine Learning Serving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070943)|J. Yu; J. Kim; E. Seo|10.1109/HPCA56546.2023.10070943|nan;nan|
|[Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070939)|D. Masouros; C. Pinto; M. Gazzetti; S. Xydis; D. Soudris|10.1109/HPCA56546.2023.10070939|Memory disaggregation;Data placement;Deep Learning;Cloud Infrastructures;Orchestration;Interference;Deep learning;Memory management;Interference;Hardware;Resource management|
|[Poseidon: Practical Homomorphic Encryption Accelerator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070984)|Y. Yang; H. Zhang; S. Fan; H. Lu; M. Zhang; X. Li|10.1109/HPCA56546.2023.10070984|nan;Data privacy;Graphics processing units;Bandwidth;Benchmark testing;Parallel processing;Explosions;System-on-chip|
|[FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070953)|R. Agrawal; L. de Castro; G. Yang; C. Juvekar; R. Yazicigil; A. Chandrakasan; V. Vaikuntanathan; A. Joshi|10.1109/HPCA56546.2023.10070953|nan;Training;Costs;Memory management;Graphics processing units;Bandwidth;System-on-chip;Cryptography|
|[FxHENN: FPGA-based acceleration framework for homomorphic encrypted CNN inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071133)|Y. Zhu; X. Wang; L. Ju; S. Guo|10.1109/HPCA56546.2023.10071133|fully homomorphic encryption;convolution neural network;FPGA acceleration;high-level synthesis;design space exploration;Performance evaluation;Data privacy;Computational modeling;Memory management;Space exploration;System-on-chip;Resource management|
|[D-Shield: Enabling Processor-side Encryption and Integrity Verification for Secure NVMe Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070924)|M. H. I. Chowdhuryy; M. Jung; F. Yao; A. Awad|10.1109/HPCA56546.2023.10070924|nan;Protocols;System performance;Data protection;Metadata;Throughput;Hardware;Encryption|
|[TensorFHE: Achieving Practical Computation on Encrypted Data Using GPGPU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071017)|S. Fan; Z. Wang; W. Xu; R. Hou; D. Meng; M. Zhang|10.1109/HPCA56546.2023.10071017|nan;Cloud computing;Software algorithms;Graphics processing units;Computer architecture;Parallel processing;Software;Servers|
|[AVGI: Microarchitecture-Driven, Fast and Accurate Vulnerability Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071105)|G. Papadimitriou; D. Gizopoulos|10.1109/HPCA56546.2023.10071105|microarchitecture;reliability;hardware/software interface;microprocessors;microarchitecture-level fault injection;nan|
|[Realizing Extreme Endurance Through Fault-aware Wear Leveling and Improved Tolerance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071093)|J. Zhang; C. Wang; Z. Zhu; D. Kline; A. K. Jones; H. Yang; Y. Wang|10.1109/HPCA56546.2023.10071093|nan;Phase change materials;Runtime;Microprocessors;Redundancy;Crosstalk;Random access memory;Computer architecture|
|[ESD: An ECC-assisted and Selective Deduplication for Encrypted Non-Volatile Main Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071011)|C. Du; S. Wu; J. Wu; B. Mao; S. Wang|10.1109/HPCA56546.2023.10071011|nan;nan|
|[A Systematic Study of DDR4 DRAM Faults in the Field](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071066)|M. V. Beigi; Y. Cao; S. Gurumurthi; C. Recchia; A. Walton; V. Sridharan|10.1109/HPCA56546.2023.10071066|nan;Industries;Systematics;Random access memory;Computer architecture;Reliability engineering;Error correction codes;Reliability|
|[High Performance and Power Efficient Accelerator for Cloud Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070941)|J. Yao; H. Zhou; Y. Zhang; Y. Li; C. Feng; S. Chen; J. Chen; Y. Wang; Q. Hu|10.1109/HPCA56546.2023.10070941|nan;nan|
|[LightTrader: A Standalone High-Frequency Trading System with Deep Learning Inference Accelerators and Proactive Scheduler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070930)|S. Yoo; H. Kim; J. Kim; S. Park; J. -Y. Kim; J. Oh|10.1109/HPCA56546.2023.10070930|nan;Scheduling algorithms;Pipelines;AI accelerators;Computer architecture;Throughput;Prediction algorithms;Dynamic scheduling|
|[BM-Store: A Transparent and High-performance Local Storage Architecture for Bare-metal Clouds Enabling Large-scale Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071029)|Y. Chen; J. Xu; C. Wei; Y. Wang; X. Yuan; Y. Zhang; X. Yu; Y. Chen; Z. Wang; S. He; W. Chen|10.1109/HPCA56546.2023.10071029|nan;nan|
|[Turbo: SmartNIC-enabled Dynamic Load Balancing of µs-scale RPCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071135)|H. Seyedroudbari; S. Vanavasam; A. Daglis|10.1109/HPCA56546.2023.10071135|nan;nan|
|[A Scalable Methodology for Designing Efficient Interconnection Network of Chiplets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070981)|Y. Feng; D. Xiang; K. Ma|10.1109/HPCA56546.2023.10070981|Chiplet;network-on-chip;interconnection network;adaptive routing;deadlock-free;nan|
|[VVQ: Virtualizing Virtual Channel for Cost-Efficient Protocol Deadlock Avoidance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071059)|H. Kasan; J. Kim|10.1109/HPCA56546.2023.10071059|nan;Out of order;Protocols;Costs;Organizations;Tail;System recovery;Throughput|
|[Mix-GEMM: An efficient HW-SW Architecture for Mixed-Precision Quantized Deep Neural Networks Inference on Edge Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071076)|E. Reggiani; A. Pappalardo; M. Doblas; M. Moreto; M. Olivieri; O. S. Unsal; A. Cristal|10.1109/HPCA56546.2023.10071076|nan;Performance evaluation;Deep learning;Training;Neural networks;Computer architecture;Energy efficiency;Computational efficiency|
|[FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071015)|R. Sarkar; S. Abi-Karam; Y. He; L. Sathidevi; C. Hao|10.1109/HPCA56546.2023.10071015|nan;nan|
|[Chimera: An Analytical Optimizing Framework for Effective Compute-intensive Operators Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071018)|S. Zheng; S. Chen; P. Song; R. Chen; X. Li; S. Yan; D. Lin; J. Leng; Y. Liang|10.1109/HPCA56546.2023.10071018|nan;Analytical models;Tensors;Convolution;Computational modeling;Graphics processing units;Machine learning;Bandwidth|
|[Securator: A Fast and Secure Neural Processing Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071091)|N. Shrivastava; S. R. Sarangi|10.1109/HPCA56546.2023.10071091|nan;nan|
|[Tensor Movement Orchestration in Multi-GPU Training Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071043)|S. -F. Lin; Y. -J. Chen; H. -Y. Cheng; C. -L. Yang|10.1109/HPCA56546.2023.10071043|nan;nan|
|[A Storage-Effective BTB Organization for Servers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070938)|T. Asheim; B. Grot; R. Kumar|10.1109/HPCA56546.2023.10070938|nan;nan|
|[HoPP: Hardware-Software Co-Designed Page Prefetching for Disaggregated Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070986)|H. Li; K. Liu; T. Liang; Z. Li; T. Lu; H. Yuan; Y. Xia; Y. Bao; M. Chen; Y. Shan|10.1109/HPCA56546.2023.10070986|nan;Software design;Prefetching;Software algorithms;Semantics;Prototypes;Real-time systems;Hardware|
|[Speculative Register Reclamation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071122)|S. Mehta|10.1109/HPCA56546.2023.10071122|nan;Power demand;Microarchitecture;Source coding;Random access memory;Computer architecture;Registers;Proposals|
|[SnakeByte: A TLB Design with Adaptive and Recursive Page Merging in GPUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071063)|J. Lee; J. M. Lee; Y. Oh; W. J. Song; W. W. Ro|10.1109/HPCA56546.2023.10071063|GPU;virtual memory;address translation;TLB;Merging;Memory management;Graphics processing units;Virtualization|
|[CARE: A Concurrency-Aware Enhanced Lightweight Cache Management Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071125)|X. Lu; R. Wang; X. -H. Sun|10.1109/HPCA56546.2023.10071125|nan;nan|
|[Memory-Efficient Hashed Page Tables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071061)|J. Stojkovic; N. Mantri; D. Skarlatos; T. Xu; J. Torrellas|10.1109/HPCA56546.2023.10071061|Virtual memory;Page tables;Hashed page tables;Costs;Scalability;Memory management;Resource management|
|[NvWa: Enhancing Sequence Alignment Accelerator Throughput via Hardware Scheduling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070978)|Y. Li; X. Li; R. Gao; W. Liu; G. Tan|10.1109/HPCA56546.2023.10070978|nan;nan|
|[Efficient Supernet Training Using Path Parallelism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071099)|Y. Xu; L. Cheng; X. Cai; X. Ma; W. Chen; L. Zhang; Y. Wang|10.1109/HPCA56546.2023.10071099|nan;Training;Correlation;Computational modeling;Neural networks;Merging;Graphics processing units;Computer architecture|
|[Phloem: Automatic Acceleration of Irregular Applications with Fine-Grain Pipeline Parallelism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071026)|Q. M. Nguyen; D. Sanchez|10.1109/HPCA56546.2023.10071026|nan;Codes;Pipelines;Computer architecture;Transforms;Linear algebra;Parallel processing;Programming|
|[CHOPPER: A Compiler Infrastructure for Programmable Bit-serial SIMD Processing Using Memory in DRAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10071070)|X. Peng; Y. Wang; M. -C. Yang|10.1109/HPCA56546.2023.10071070|nan;Codes;Program processors;Sensitivity;Random access memory;Computer architecture;Choppers (circuits);Programming|
|[VAQUERO: A Scratchpad-based Vector Accelerator for Query Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070958)|J. Pavón; I. V. Valdivieso; J. Marimon; R. Figueras; F. Moll; O. Unsal; M. Valero; A. Cristal|10.1109/HPCA56546.2023.10070958|Vector Computing;Scratchpad Memory;Database Accelerator;PostgreSQL;MonetDB;nan|
#### **2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)**
- DOI: 10.1109/ICAIIC57133.2023
- DATE: 20-23 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Combination of Multi-Branch CNN and Feature Rearrangement for Down Syndrome Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067118)|N. H. Phung; C. T. Nguyen; T. K. Tran; T. T. H. Truong; D. C. Tran; T. T. Nguyen; D. H. Do|10.1109/ICAIIC57133.2023.10067118|Multi-branch cnn;down syndrome prediction;feature rearrangement;first trimester screening;prenatal screening;biochemical marker;ultrasound marker;Deep learning;Correlation;Predictive models;Data models;Testing|
|[Smart platform for Blood Management in Healthcare using AI/ML Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067054)|W. Benelmir; A. Hemmak; B. Senouci|10.1109/ICAIIC57133.2023.10067054|Blood Supply Chain;Blood Bank Management;Machine Learning Algorithms;Time Series Forecasting Models;Machine learning algorithms;Uncertainty;Machine learning;Production;Organizations;Predictive models;Prediction algorithms|
|[Filter Validation for Detecting Outliers of Photoplethysmograph Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067110)|T. C. Amri; R. Sarno; R. Abdillah; F. A. Haq; A. F. Septiyanto; D. Sunaryono|10.1109/ICAIIC57133.2023.10067110|Moving average filter;Outlier;Photoplethysmograph;Signal filter;Slope;Z-score;Cardiovascular system;Filtration;Volume measurement;Data integrity;Linear regression;Information filters;Skin|
|[Twitter User Sentiments Analysis: Health System Cyberattacks Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067026)|M. Abusaqer; M. Benaoumeur Senouci; K. Magel|10.1109/ICAIIC57133.2023.10067026|Sentiment analysis;cyberattacks;ransomware;healthcare;security breach;Sentiment analysis;Epidemics;Data analysis;Social networking (online);Hospitals;Blogs;Motion pictures|
|[Question Answering Chatbots for Biomedical Research Using Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066979)|E. Xygi; A. D. Andriopoulos; D. A. Koutsomitropoulos|10.1109/ICAIIC57133.2023.10066979|chatbot;natural language processing;word embeddings;BERT;Count Vectorizer;Training;Measurement;Biological system modeling;Bit error rate;Chatbots;Transformers;Question answering (information retrieval)|
|[A Study on the Fatigue Evaluation Platform using Bio-signal based on Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067117)|J. -S. Kim; D. M. Lee|10.1109/ICAIIC57133.2023.10067117|Artificial Neural Network;Electroencephalogram;Electromyography;random forest;Support Vector Machine;Training;Support vector machines;Artificial neural networks;Forestry;Fatigue;Electromyography;Electroencephalography|
|[Understanding Consumer Advertising via Audio Analytics of Sports Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067125)|S. J. Barnes; W. Wang|10.1109/ICAIIC57133.2023.10067125|Audio analytics;voice analytics;machine learning;music tempo;sports;commercial;Analytical models;Video on demand;Data analysis;Machine learning;Data models;Advertising;Sports|
|[CWSI-based Smart Irrigation System Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066983)|K. Il Ko; M. Hun Lee; H. Yoe|10.1109/ICAIIC57133.2023.10066983|open-field;smart irrigation system;drone;CWIS;IoT;Irrigation;Temperature distribution;Soil moisture;Crops;Cameras;Temperature control;Timing|
|[A Blockchain-based Decision Support System for E-commerce Order Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067036)|G. T. S. Ho; Y. M. Tang; H. Y. Lam; V. Tang|10.1109/ICAIIC57133.2023.10067036|E-commerce;logistic industries;Blockchain;Machine learning;Industries;Analytical models;Heuristic algorithms;Supply chains;Predictive models;Real-time systems;Blockchains|
|[Development of Timun Mas Game Platformer for Increasing Generation Z Interest to Indonesian Folklore](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067133)|A. B. Putra; A. K. Nurkusuma; G. J. Khawarga; Meiliana; M. Fajar|10.1109/ICAIIC57133.2023.10067133|Game;Platformer;Unity;Folk Tale;Generation Z;Games;User experience;Artificial intelligence;Engines;Testing|
|[Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067088)|R. F. Pamungkas; I. B. K. Yoga Utama; M. M. Faridh; M. M. Alam; B. Chung; Y. M. Jang|10.1109/ICAIIC57133.2023.10067088|Forecasting;GCN;LSTM;Renewable Energy;Deep learning;Renewable energy sources;Uncertainty;Solar energy;Hybrid power systems;Spatial databases;Spatiotemporal phenomena|
|[An Overview of 3GPP Release 17 & 18 Advancements in the Context of V2X Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067121)|M. M. Saad; M. A. Tariq; J. Seo; D. Kim|10.1109/ICAIIC57133.2023.10067121|NR-V2X;5G-Advanced;Re-evaluation mechanism;NR-V2X Mode 2;Resource Allocation;Performance evaluation;Industries;Technological innovation;Learning (artificial intelligence);Energy efficiency;3GPP;Resource management|
|[A Flexible Fall Detection Framework Based on Object Detection and Motion Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066990)|D. Ros; R. Dai|10.1109/ICAIIC57133.2023.10066990|fall detection;vision-based;image processing;object detection;motion analysis;Tracking;Surveillance;Wearable computers;Object detection;Cameras;Fall detection;Motion analysis|
|[Automatic Pest Image Acquisition System Hardware Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067130)|H. O. Choe; M. Hun Lee; H. Yoe|10.1109/ICAIIC57133.2023.10067130|Pest;Automatic collector;Image analysis;Data;Solar system;Systematics;Image resolution;Insects;Crops;Manuals;Learning (artificial intelligence);Cameras|
|[Dimensionality reduction as a non-cooperative game](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067075)|H. Honda; P. Dinh; P. T. Thao; Y. Tabata; B. D. Anh|10.1109/ICAIIC57133.2023.10067075|Dimensionality reduction;non-cooperative game;Nash equilibrium;Dimensionality reduction;Games;Pareto optimization;Nash equilibrium;Functional analysis;Behavioral sciences;Artificial intelligence|
|[Predicting Human Activity with LSTM Face Detection on Server Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066981)|A. Nurenie; Y. Heryadi; Lukas; W. Suparta; Y. Arifin|10.1109/ICAIIC57133.2023.10066981|Predicting;Human Activity;LSTM;Face Detection;Server Surveillance System;Training;Surveillance;Time series analysis;Programming;Behavioral sciences;Servers;Face detection|
|[Differential Image-based Fast and Compatible Convolutional Layers for Multi-core Processors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066972)|S. Hong; D. Park|10.1109/ICAIIC57133.2023.10066972|Convolutional neural networks;Convolution techniques;Deep learning;Fast convolution;Multicore processors;Visualization;Convolution;Multicore processing;Memory management;Transforms;Parallel processing;Convolutional neural networks|
|[Masked Face Images Based Gender Classification using Hybrid Bat Algorithm Optimized Bagging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067008)|S. A. Sari; W. F. A. Maki|10.1109/ICAIIC57133.2023.10067008|gender classification;Gray Level Co-occurrence Matrix(GLCM);Bagging;Hybrid Bat Algorithm(HBA);face mask;Biometrics (access control);Forensics;Feature extraction;Real-time systems;Classification algorithms;Security;Faces|
|[Combination of Reinforcement and Deep Learning for EEG Channel Optimization on Brain-Machine Interface Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066973)|G. Pongthanisorn; A. Shirai; S. Sugiyama; G. Capi|10.1109/ICAIIC57133.2023.10066973|Reinforcement learning;Q-learning;Convolutional Neural Network;Classification;Optimal channel selection;Electroencephalography;Brain-machine interface;Deep learning;Training;Q-learning;Scalp;Electroencephalography;Brain-computer interfaces;Decoding|
|[Tree-Based Ensemble Models and Algorithms for Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067006)|J. Tsiligaridis|10.1109/ICAIIC57133.2023.10067006|Decision Trees;Ensemble Methods;Data Mining;Support vector machines;Machine learning algorithms;Predictive models;Prediction algorithms;Boosting;Classification algorithms;Ensemble learning|
|[Semantic Similarity-based Visual Reasoning without Language Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067104)|C. Choi; H. Lim; H. Jang; J. Park; E. Kim; K. Lim|10.1109/ICAIIC57133.2023.10067104|Visual Reasoning;Inference;Image similarity;Deep Learning;Training;Deep learning;Visualization;Semantics;Training data;Transformers;Cognition|
|[A Performance Efficient Approach of Global Training in Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066985)|D. M. S. Bhatti; H. Nam|10.1109/ICAIIC57133.2023.10066985|Federated learning;heterogeneous networks;deep learning;Training;Privacy;Data privacy;Costs;Federated learning;Distributed databases;Learning (artificial intelligence)|
|[Generalized Spatio-Temporal Adaptive Normalization Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067068)|N. Kumar; A. Narang|10.1109/ICAIIC57133.2023.10067068|nan;Deep learning;Coherence;Activity recognition;Task analysis;Artificial intelligence;Videos|
|[Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067135)|S. Sim; D. Kim; S. C. Jeong|10.1109/ICAIIC57133.2023.10067135|Time-Series Prediction;Temporal Attention Gate Network;Temporal Filter;Univariate Time-Series Data;Deep learning;Limiting;Microprocessors;Decision making;Logic gates;Network architecture;Data science|
|[Channel Access Control Instead of Random Backoff Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067055)|T. Imanaka; M. Ohta; M. Taromaru|10.1109/ICAIIC57133.2023.10067055|reinforcement learning;Multi-Armed Bandit (MAB);CSMA/CA;Backoff;Access control;Wireless communication;Wireless LAN;Simulation;Reinforcement learning;Artificial intelligence;Frequency control|
|[5D Spectrum Database](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067018)|H. Nakajo; Y. Itayama; S. Matsuo; T. Fujii|10.1109/ICAIIC57133.2023.10067018|Spectrum database;radio propagation;radio map;dynamic spectrum sharing;Antenna measurements;Wireless communication;Three-dimensional displays;Databases;Frequency-domain analysis;Satellite broadcasting;Size measurement|
|[A Review on Unmanned Aerial Vehicle-based Networks and Satellite-based Networks with RSMA: Research Challenges and Future Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067007)|C. M. Ho; D. S. Lakew; A. -T. Tran; C. Lee; D. T. Hua; S. Cho|10.1109/ICAIIC57133.2023.10067007|UAV-based system;Satellite-based system;RSMA;6G mobile communication;Satellites;5G mobile communication;Wireless networks;Focusing;Interference;Autonomous aerial vehicles|
|[Evaluation of Deterministic Routing on 100-cores Mesh Wireless NoC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067074)|A. Lit; J. S. Joshima; S. Suhaili; N. Rajaee; S. K. Sahari; R. Sapawi|10.1109/ICAIIC57133.2023.10067074|Wireless network-on-chip;radio hub;mm-wave;deterministic routing algorithm;Wireless communication;Wiring;Energy consumption;Network-on-chip;Benchmark testing;Routing;Throughput|
|[Integration of Reconfigurable Intelligent Surface and Visible Light Communication Systems for 5G and Beyond Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067001)|S. R. Hasan; M. Z. Chowdhury; M. Saiam; Y. M. Jang|10.1109/ICAIIC57133.2023.10067001|LED;photodetector;reconfigurable intelligent surface;visible light communication;Radio frequency;Indoor communication;5G mobile communication;Channel capacity;Telecommunication traffic;Network security;Energy efficiency|
|[Interference-Robust OFDM Communication System using Weight Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067021)|K. Vanin; H. -G. Ryu; A. R. Safin; O. V. Kravchenko|10.1109/ICAIIC57133.2023.10067021|spectral concentrated interference;OFDM;weight functions;Kravchenko window;Wireless communication;OFDM;System performance;Computer simulation;Buildings;Interference;Encoding|
|[The optimum number of users using sequential Power Allocation on PD-NOMA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066962)|H. Vidyaningtyas; A. Kurniawan; Iskandar; A. A. Pramudita; D. M. Saputri|10.1109/ICAIIC57133.2023.10066962|PDNOMA;power allocation;SePA;SIC;NOMA;Interference cancellation;Simulation;Receivers;Bandwidth;Downlink;Resource management|
|[Parallel Corpus Curation for Filipino Text-to-SQL Semantic Parsing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066976)|C. J. Borjal; M. Visperas; A. J. Adoptante; M. T. Abia; J. K. Catapang; E. Peramo|10.1109/ICAIIC57133.2023.10066976|relational databases;deep learning;semantic parsing;code-switching;Part-of-Speech Tagging;GPT-3;Deep learning;Structured Query Language;Annotations;Semantics;Relational databases;Oral communication;Tagging|
|[A Deep Learning-Based Coyote Detection System Using Audio Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067023)|H. Jung; B. Kwon; Y. Kim; Y. Lee; J. Park; G. Pegg; Y. M. Wang; A. H. Smith|10.1109/ICAIIC57133.2023.10067023|audio classification;image classification;feature extraction;spectrogram features;machito learning;Analytical models;Education;Crops;Machine learning;Learning (artificial intelligence);Feature extraction;Data models|
|[GNN Link Prediction for Time-Triggered Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066960)|C. Lua; Y. Zhang; O. Hekal; D. Onwuchekwa; R. Obermaisser|10.1109/ICAIIC57133.2023.10066960|Graph neural networks;link prediction;time-triggered systems;scheduling;Schedules;Processor scheduling;Predictive models;Prediction algorithms;Graph neural networks;Scheduling;Topology|
|[Multi-Spectral Fusion using Generative Adversarial Networks for UAV Detection of Wild Fires](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067042)|T. Kacker; A. Perrusquia; W. Guo|10.1109/ICAIIC57133.2023.10067042|fire detection;deep learning;UAV;drone;GAN;Image segmentation;Visualization;Biological system modeling;Fires;Lighting;Infrared image sensors;Image representation|
|[A Method of PV Power Generation Forecasting using Constrained Transformer Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067014)|D. H. Tran; V. L. Nguyen; I. B. Krishna Yoga Utama; H. Nguyen; B. Chung; Y. M. Jang|10.1109/ICAIIC57133.2023.10067014|time-series forecasting;transformer neural network;edge computing;Photovoltaic systems;Training;Deep learning;Machine learning algorithms;Computational modeling;Predictive models;Transformers|
|[Early Product Cost Estimation by Intelligent Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067092)|R. Lackes; J. Sengewald|10.1109/ICAIIC57133.2023.10067092|price prediction;cost prediction;neural networks;early cost estimation;machine learning;Costs;Machine learning algorithms;Neural networks;Estimation;Pricing;Machine learning;Product design|
|[Exploiting TAS schemes to Enhance the PHY-security in Cooperative NOMA Networks: A Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067050)|Y. Pramitarini; R. H. Yoga Perdana; K. Shim; B. An|10.1109/ICAIIC57133.2023.10067050|Cooperative non-orthogonal multiple access (NOMA);deep learning;physical layer security;transmit antenna selection;Deep learning;NOMA;Channel capacity;Transmitting antennas;Receiving antennas;Channel estimation;Benchmark testing|
|[A Natural Language Understanding Approach Toward Extraction of Specifications from Request for Proposals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067032)|B. K. Saha; L. Haab; D. Tandur|10.1109/ICAIIC57133.2023.10067032|Artificial Intelligence;Natural Language Under-standing;Bid Engineering;Request for Proposals;Data Model;Networks;Pipelines;Data models;Natural language processing;Fourth Industrial Revolution;Proposals;Data mining;Artificial intelligence|
|[Predicting Indoor PM2.5 Concentration using LSTM-BNN in Edge Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067057)|I. B. K. Y. Utama; D. H. Tran; R. F. Pamungkas; B. Chung; Y. M. Jang|10.1109/ICAIIC57133.2023.10067057|LSTM;bayesian;PM2.5;IoT;prediction;Measurement;Correlation;Time series analysis;Predictive models;Data models;Forecasting;Artificial intelligence|
|[Text Preprocessing Approaches in CNN for Disaster Reports Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067109)|A. O. Arisha; Hazriani; Y. Wabula|10.1109/ICAIIC57133.2023.10067109|Text Preprocessing;CNN;Disaster;automatic;semi-automatic;Training data;Data models;Cleaning;Artificial intelligence;Convergence|
|[Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067012)|I. Ahmad; S. Shin|10.1109/ICAIIC57133.2023.10067012|deep learning;noise-based augmentation;medical image analysis;Deep learning;Image coding;Image resolution;Image analysis;Transform coding;Distortion;Data models|
|[Effect of Optimization Techniques on Feedback Alignment Learning of Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067047)|S. Lee; H. Park|10.1109/ICAIIC57133.2023.10067047|Error backpropagation;Biological plausibility;Feedback alignment;Weight transport problem;Random backward weight;Optimization algorithm;Backpropagation;Heuristic algorithms;Biological system modeling;Backpropagation algorithms;Learning (artificial intelligence);Benchmark testing;Brain modeling|
|[Multi Task Learning: A Survey and Future Directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067098)|T. Lee; J. Seok|10.1109/ICAIIC57133.2023.10067098|recommendation systems;multi-task learning;conversion rate prediction;sample selection bias;data sparsity;Industries;Predictive models;Multitasking;Amplitude modulation;Data models;Delays;Electronic commerce|
|[3D Human Pose Estimation Using Blazepose and Direct Linear Transform (DLT) for Joint Angle Measurement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066978)|I. M. Hakim; H. Zakaria; K. Muslim; S. I. Ihsani|10.1109/ICAIIC57133.2023.10066978|Joint angle;Human pose estimation;Markerless;Push-up;Direct linear transform;System testing;Three-dimensional displays;Pose estimation;Measurement uncertainty;Surgery;Shoulder;Transforms|
|[RNN-based Interference Suppression Method for CS radar: Simulation and Experimental Evaluations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067132)|R. Koizumi; X. Wang; M. Umehira; S. Takeda; R. Sun|10.1109/ICAIIC57133.2023.10067132|automotive CS radar;wideband interference;interference suppression;RNN;simulation and experiment;Interference suppression;Training;Deep learning;Recurrent neural networks;Radar;Software;Wideband|
|[Training SNNs Low Latency Utilizing Batch Normalization Through Time and Iterative Initialization Retraining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067096)|T. D. Tran; H. H. Hoang|10.1109/ICAIIC57133.2023.10067096|spiking neural network;one time step;batch normalization through time;low latency;neuromorphic computing;Training;Costs;Neuromorphics;Neurons;Membrane potentials;Hardware;Energy efficiency|
|[Dual Policy-Based TD-Learning for Model Predictive Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067037)|C. -H. Ji; H. -B. Choi; J. -S. Heo; J. -B. Kim; H. -K. Lim; Y. -H. Han|10.1109/ICAIIC57133.2023.10067037|Deep RL;Model-based RL;Control;Training;Computational modeling;Predictive models;Prediction algorithms;Computational efficiency;Task analysis;Artificial intelligence|
|[A Digital Twins Model for Analyzing and Simulating Cold Chain Risks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067025)|H. Y. Lam; V. Tang; G. T. S. Ho|10.1109/ICAIIC57133.2023.10067025|Cold chain;digital twins;risk analysis;temperature-sensitive-products;Temperature sensors;Temperature measurement;Analytical models;Virtual environments;Transportation;Predictive models;Digital twins|
|[Representation Learning for Wafer Pattern Recognition in Semiconductor Manufacturing Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067020)|S. Song; J. -G. Back|10.1109/ICAIIC57133.2023.10067020|Semiconductor manufacturing process;Wafer pattern recognition;volumetric representation learning;structure generator;pseudo-rendering;Semiconductor device modeling;Representation learning;Productivity;Solid modeling;Scanning electron microscopy;Three-dimensional displays;Costs|
|[Pallet Detection and Distance Estimation with YOLO and Fiducial Marker Algorithm in Industrial Forklift Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066999)|E. S. Kesuma; P. H. Rusmin; D. A. Maharani|10.1109/ICAIIC57133.2023.10066999|Fiducial Marker;YOLOv5;real-time detection;Robot kinematics;Linear regression;Transportation;Cameras;Fiducial markers;Real-time systems;Distance measurement|
|[Crossover Methods Comparison in Flood Evacuation Route Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067101)|M. A. Nur; Hazriani; N. K. Nur|10.1109/ICAIIC57133.2023.10067101|optimization;evacuation route;flood;Makassar;genetic algorithm;Roads;Sociology;Wheels;Transportation;Routing;Floods;Statistics|
|[Exploiting Secure Multihop Transmission in Underlying Cognitive Radio Networks: Analysis and Deep Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066997)|K. Shim; B. An|10.1109/ICAIIC57133.2023.10066997|Cognitive Radio;Physical Layer Security;Deep Neural Network;Deep learning;Analytical models;Closed-form solutions;System performance;Neural networks;Spread spectrum communication;Probability|
|[Digital Twin and Ontology based DDoS Attack Detection in a Smart-Factory 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067049)|V. V. Gowripeddi; S. GVK; J. Bapat; D. Das|10.1109/ICAIIC57133.2023.10067049|Smart Factory;Industry 4.0;Digital Twin;Internet of Things (IoT);Distributed Denial of Service (DDoS);Prototypes;Intrusion detection;Systems architecture;Quality of service;Denial-of-service attack;Throughput;Fourth Industrial Revolution|
|[Research Opportunity of Insider Threat Detection based on Machine Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067010)|N. T. Moekthi Prajitno; H. Hadiyanto; A. F. Rochim|10.1109/ICAIIC57133.2023.10067010|insider threat;machine learning;detection;Systematics;Machine learning algorithms;Bibliographies;Machine learning;Planning;Information technology|
|[Optimal Tree Bayesian for the Characterization of Ciphered Network Communication Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067102)|L. A. Chijioke Ahakonye; C. I. Nwakanma; M. A. Paramartha Putra; A. Gohil; J. M. Lee; D. -S. Kim|10.1109/ICAIIC57133.2023.10067102|AI;Bayesian Optimization;Communication;Encoded Network Traffic;Machine Learning;Support vector machines;Costs;Computational modeling;Neural networks;Telecommunication traffic;Learning (artificial intelligence);Bayes methods|
|[Clustering of Photoplethysmography Data Signals for Developing Noise Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066966)|R. Abdillah; R. Sarno; T. Choirul Amri; F. Atoil Haq; K. Rossa Sungkono; D. Sunaryono|10.1109/ICAIIC57133.2023.10066966|photoplethysmography;signal processing;clustering;noise filtering;k-means;exponential filter;Smoothing methods;Simulation;Clustering methods;Fingers;Machine learning;Photoplethysmography;Information filters|
|[Cross-Corpus Disparity of Parkinson's Voice Datasets Observed on Control Group Distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066982)|N. D. Pah; V. Indrawati; D. K. Kumar|10.1109/ICAIIC57133.2023.10066982|Parkinson's Disease;voice features;sustained phoneme;statistical analysis;Parkinson's disease;Databases;Computational modeling;Statistical distributions;Machine learning;Feature extraction;Harmonic analysis|
|[Feature Selection of Photoplethysmograph Data in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067116)|F. A. Haq; R. Sarno; R. Abdillah; T. C. Amri; A. F. Septiyanto; K. R. Sungkono|10.1109/ICAIIC57133.2023.10067116|Feature Selection;PPG;Fast Forward Selection;Sequential Input Selection Algorithm;Machine learning algorithms;Machine learning;Feature extraction;Photoplethysmography;Random forests;Medical diagnostic imaging;Blood|
|[Cracked Tongue Recognition Based on CNN with Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067035)|J. Hong; J. L. Korea; H. Tae|10.1109/ICAIIC57133.2023.10067035|image segmentation;tongue image analysis;deep learning;transfer learning;VGG-16;Image segmentation;Tongue;Shape;Transfer learning;Medical services;Learning (artificial intelligence);Data models|
|[Bad Sitting Posture Detection and Alerting System using EMG Sensors and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067076)|R. Laidi; L. Khelladi; M. Kessaissia; L. Ouandjli|10.1109/ICAIIC57133.2023.10067076|Posture monitoring;smart health;machine learning;Internet of Things;Support vector machines;Training;Machine learning algorithms;Sensor systems;Electromyography;Sensors;Mobile applications|
|[Time Series Anomaly Detection Using Contrastive Learning based One-Class Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067089)|Y. Lee; Y. Byun; J. -G. Baek|10.1109/ICAIIC57133.2023.10067089|Contrastive Learning;One-Class Classification;Time Series Augmentation;Unsupervised Learning;Learning systems;Manufacturing processes;Time series analysis;Learning (artificial intelligence);Linear programming;Feature extraction;Data models|
|[Automatic 3D Digital Dental Landmark Based on Point Transformation Weight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067081)|S. Triarjo; R. Sarno; S. C. Hidayati; G. Sihaj|10.1109/ICAIIC57133.2023.10067081|Deep learning;Weighted;Tooth Landmark;Automation;Deep learning;Solid modeling;Three-dimensional displays;Pipelines;Teeth;Data models;Dentistry|
|[PUFDid: PUF-based Drone IDentifier and Its Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067041)|J. W. Byun|10.1109/ICAIIC57133.2023.10067041|communication identifer;drone identifer;puf;puf-based drone identifer;drone identification;authentication;Privacy;Protocols;Physical unclonable function;Security;Artificial intelligence;Drones|
|[Deep Learning based Channel Estimation for Full-Duplex Backscatter Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066967)|C. Y. Jung; J. M. Kang; D. I. Kim|10.1109/ICAIIC57133.2023.10066967|Beamforming;channel estimation (CE);deep learning (DL);full-duplex backscatter communication;internet of things (IoT);wireless-powered sensor networks (WPSN);Deep learning;Power demand;Communication systems;Channel estimation;Full-duplex system;Downlink;Internet of Things|
|[A Mini Literature Review on Integrating Cybersecurity for Business Continuity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067127)|S. Altaha; M. M. Hafizur Rahman|10.1109/ICAIIC57133.2023.10067127|Cybersecurity;Cyber-Attacks;Business Continuity;Government;Companies;Data breach;Planning;Business continuity;Risk management;Computer security|
|[AI-Based Intrusion Detection System for Secure AI BOX Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066986)|J. -L. Chen; Z. -Z. Chen; Y. -S. Chang; C. -I. Li; T. -I. Kao; Y. -T. Lin; Y. -Y. Xiao; J. -F. Qiu|10.1109/ICAIIC57133.2023.10066986|Information Security;Machine Learning;IoT Device Security;Extremely Randomized Trees;Packet Analysis;Yocto Project;Operating systems;Intrusion detection;Machine learning;Feature extraction;Data models;Production facilities;Paralysis|
|[The Use of Background Features, Template Synthesis and Deep Neural Networks in Document Forgery Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067120)|M. Hamido; A. Mohialdin; A. Atia|10.1109/ICAIIC57133.2023.10067120|Document;forgery;detection;genuine;CNN;background;subtraction;forged;splicing;copy-move;Industries;Fabrication;Deep learning;Neural networks;Feature extraction;Distortion;Forgery|
|[A Reinforcement Learning Assisted Relative Distance based MAC in Vehicular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067126)|Y. Deng; Y. -J. Choi|10.1109/ICAIIC57133.2023.10067126|Vehicular Networking;TDMA;Merging Collision;Reinforcement Learning;Machine learning algorithms;Heuristic algorithms;Merging;Vehicular ad hoc networks;Reinforcement learning;Topology;Resource management|
|[Deep Reinforcement Learning-based Building Energy Management using Electric Vehicles for Demand Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066975)|D. Kang; S. Yoont; H. Lim|10.1109/ICAIIC57133.2023.10066975|nan;Costs;Simulation;Quality of service;Electric vehicles;Power grids;Demand response;Stability analysis|
|[Recent Studies on Deep Reinforcement Learning in RIS-UAV Communication Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067052)|T. -H. Nguyen; H. Park; L. Park|10.1109/ICAIIC57133.2023.10067052|5G/6G network;aerial access network;deep reinforcement learning;UAV;RIS;wireless communication;Deep learning;Array signal processing;Wireless networks;Reinforcement learning;Autonomous aerial vehicles;Communication networks;Vehicle dynamics|
|[A Systematic Literature Review of Virtual Reality Implementation in Sports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067095)|E. G. Malachi; R. Tunggara; Y. Cahyadi; Meiliana; M. Fajar|10.1109/ICAIIC57133.2023.10067095|virtual reality;sport;Systematic literature view. Training;Exercise;Training;Productivity;Systematics;Databases;Bibliographies;Psychology;Virtual reality|
|[Deep Single Shot Musical Instrument Identification using Scalograms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066992)|D. Chatterjee; A. Dutta; D. Sil; A. Chandra|10.1109/ICAIIC57133.2023.10066992|Audio excerpts;scalogram;one-shot learning;convolutional Siamese network;nan|
|[On Modern Text-to-SQL Semantic Parsing Methodologies for Natural Language Interface to Databases: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067134)|M. Visperas; A. J. Adoptante; C. J. Borjal; M. T. Abia; J. K. Catapang; E. Peramo|10.1109/ICAIIC57133.2023.10067134|text-to-SQL;natural language query;natural language processing;semantic parsing;NLIDB;Structured Query Language;Databases;Semantics;Natural languages;Syntactics;Benchmark testing;Decoding|
|[FIDGAN: A Generative Adversarial Network with An Inception Distance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066964)|J. Lee; M. Lee|10.1109/ICAIIC57133.2023.10066964|generative adversarial network;Fréchet Inception distance;image generation;sample generation;generative models;Training;Measurement;Adaptation models;Image synthesis;Computational modeling;Generative adversarial networks;Generators|
|[Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067106)|B. Tri Wibowo; D. Nurjanah; H. Nurrahmi|10.1109/ICAIIC57133.2023.10067106|Identification;Misogyny;Fine-tuning;BERT;IndoBert;Video on demand;Social networking (online);Bit error rate;Text categorization;Multimedia Web sites;Hate speech;Buildings|
|[ConvELM: Exploiting Extreme Learning Machine on Convolutional Neural Network for Age Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067115)|I. Apuandi; E. Rachmawati; G. Kosala|10.1109/ICAIIC57133.2023.10067115|age estimation;age classification;convolutional neural network;extreme learning machine;backpropagation;Training;Backpropagation;Extreme learning machines;Estimation;Computer architecture;Learning (artificial intelligence);Computational efficiency|
|[Captioning Remote Sensing Images Using Transformer Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067039)|W. Nanal; M. Hajiarbabi|10.1109/ICAIIC57133.2023.10067039|nan;Measurement;Training;Computational modeling;Bit error rate;Computer architecture;Predictive models;Transformers|
|[Faster Few-Shot Face Image Generation With Features of Specific Group Using Pivotal Tuning Inversion and PCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067122)|Y. Kato; M. Mikawa; M. Fujisawa|10.1109/ICAIIC57133.2023.10067122|few-shot image generation;GAN inversion;pre-trained model;pivotal tuning;principal component analysis;Training;Analytical models;Image synthesis;Generative adversarial networks;Feature extraction;Generators;Artificial intelligence|
|[An Integrated Approach to Near-duplicate Image Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067005)|H. Yang; H. Park|10.1109/ICAIIC57133.2023.10067005|deep learning features;feature integration;near-duplicate image detection;image recommendation system;Performance evaluation;Deep learning;Visualization;Redundancy;Self-supervised learning;Feature extraction;Object recognition|
|[Learning the Protein Language Model of SARS-CoV-2 Spike Proteins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067040)|P. V. Llanes; G. Solano; M. J. Pontiveros|10.1109/ICAIIC57133.2023.10067040|SARS-CoV-2;spike proteins;sequence mutations;COVID-19;language modelling;recurrent neural network;Leiden clustering algorithm;viral escape;Proteins;Recurrent neural networks;Pandemics;Surveillance;Semantics;Clustering algorithms;Learning (artificial intelligence)|
|[Machine Learning Implementation in Lung Cancer Prediction - A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067128)|J. Oentoro; R. Prahastya; R. Pratama; M. S. Kom; M. Fajar|10.1109/ICAIIC57133.2023.10067128|Lung;Cancer Prediction;Machine Learning;Deep Learning;Systematic Review;Support vector machines;Training;Solid modeling;Systematics;Three-dimensional displays;Bibliographies;Lung cancer|
|[XGBoost Calibration Considering Feature Importance for Noninvasive HbA1c Estimation Using PPG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067013)|M. S. Turja; T. -H. Kwon; H. Kim; K. -D. Kim|10.1109/ICAIIC57133.2023.10067013|diabetes;glycated hemoglobin;HbA1c;noninvasive;Training;Monte Carlo methods;Estimation;Feature extraction;Glucose;Diabetes;Calibration|
|[Aspect-Based Sentiment Analysis with Semi-Supervised Approach on Taiwan Social Distancing App User Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067048)|U. Nuha; C. -H. Lin|10.1109/ICAIIC57133.2023.10067048|Sentiment analysis;semi-supervised;lexicon-based;BERT;aspect-based sentiment;Performance evaluation;Sentiment analysis;Annotations;Bit error rate;Human factors;Multilayer perceptrons;Transformers|
|[Unique Functions for the Stability-Guarantee of Variable Digital Filters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066998)|T. -B. Deng|10.1109/ICAIIC57133.2023.10066998|Frequency response;stability;unity-bounded function;variable filter;variable frequency response;Frequency-domain analysis;Transfer functions;Information filters;Stability analysis;Mathematical models;Frequency response;Digital filters|
|[2D Fluid Flows Prediction Based on U-Net Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066980)|A. Teguh Prihatno; H. Kang; C. Woo Choi; T. -T. -H. Le; S. Heo; M. Kim; H. Kim|10.1109/ICAIIC57133.2023.10066980|CFD;Laminar Flow;Deep Learning;Convolutional Neural Networks;U-Net Architecture;Learning systems;Deep learning;Computational fluid dynamics;Computational modeling;Atmospheric modeling;Memory management;Fluid flow|
|[Real-time object detection using a domain-based transfer learning method for resource-constrained edge devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067064)|D. Kim; S. Lee; N. -M. Sung; C. Choe|10.1109/ICAIIC57133.2023.10067064|Object detection;YOLOv7;transfer learning;Deep learning;Training;Image edge detection;Computational modeling;Transfer learning;Urban areas;Object detection|
|[Radar Fault Detection via Camera-Radar Branches Learning Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067071)|D. Ning; D. S. Han|10.1109/ICAIIC57133.2023.10067071|Anomaly Detection;Radar Cross Section(RCS);Convolutional Neural Network(CNN);Radar cross-sections;Navigation;Radar detection;Cameras;Real-time systems;Generators;Safety|
|[Energy and Entropy based Intelligence Metric for Performance Estimation in DNNs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067093)|B. Kartal; B. B. Üstündağ|10.1109/ICAIIC57133.2023.10067093|entropy;intelligence;predictability;performance;hyper-parameter optimization;Training;Energy consumption;Estimation;Artificial neural networks;Entropy;Data models;Organisms|
|[TFE-NET: Time and Feature focus Embedding Network for Multivariate-to-Multivariate Time Series Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066984)|S. Roh; Y. Jung; J. -G. Baek|10.1109/ICAIIC57133.2023.10066984|Multivariate to multivariate time series forecasting;feature dependency;temporal dependency;deep learning;Power demand;Time series analysis;Linearity;Transportation;Finance;Learning (artificial intelligence);Predictive models|
|[Interaction-based Fault Detection and Classification Using Randomly Masked 1D Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067129)|J. Kim; S. Song; W. Y. Hwang; J. -G. Baek|10.1109/ICAIIC57133.2023.10067129|interaction;fault detection and classification;randomly masked;1-dimensional convolutional neural network;Analytical models;Manufacturing processes;Machine learning algorithms;Convolution;Fault detection;Time series analysis;Feature extraction|
|[Exploring Normalization Techniques in Neural Networks for Bitcoin Candlestick Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067086)|S. Simtharakao; D. Sutivong|10.1109/ICAIIC57133.2023.10067086|Neural networks;Normalization;Cryptocurrency price prediction;Candlestick;Recurrent neural networks;Time series analysis;Bitcoin;Predictive models;Logic gates;Prediction algorithms;Forecasting|
|[Graph Neural Networks to Enable Scalable MAC for Massive MIMO Wireless Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067031)|O. Orhan; V. N. Swamy; M. Rahman; H. Nikopour; S. Talwar|10.1109/ICAIIC57133.2023.10067031|Open Radio Access Networks;Graph Neural Networks;Massive MIMO;MAC User Scheduling;Wireless networks;Massive MIMO;Feature extraction;Graph neural networks;Inference algorithms;Scheduling;Complexity theory|
|[Performance Evaluation of Training ResNet Receiver Under Mobility for 5G NR Wireless Communication Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067016)|A. Irawan|10.1109/ICAIIC57133.2023.10067016|5G NR;ResNet;deep learning;mobility;OFDM;Training;Wireless communication;Transmitters;5G mobile communication;Bit error rate;Receivers;Robustness|
|[Federated Learning of Wireless Network Experience Anomalies Using Consumer Sentiment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067061)|W. Guo; B. Jin; S. C. Sun; Y. Wu; W. Qi; J. Zhang|10.1109/ICAIIC57133.2023.10067061|federated learning;wireless network;quality of experience;sentiment analysis;social media;Social networking (online);Federated learning;Wireless networks;Distributed databases;Media;Data transfer;Real-time systems|
|[Early-Exiting DNN MIMO Detector Design with Fast Signal Estimation Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067114)|D. -T. Hua; D. Lee; A. T. Tran; D. S. Lakew; Q. T. Do; S. Cho|10.1109/ICAIIC57133.2023.10067114|Multiple input multiple output;deep neural network;deep convolutional neural network;signal detector;early-exiting model;Computational modeling;Wireless networks;Neural networks;Estimation;Detectors;Computer architecture;Receivers|
|[Kidney Diseases Detection Based on Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067085)|Q. Rui; L. Sinuo; T. T. Toe; B. Brister|10.1109/ICAIIC57133.2023.10067085|CNN;Kidney;Python;image classification;machine learning;Training;Stochastic processes;Medical services;Classification algorithms;Convolutional neural networks;Kidney;Medical diagnostic imaging|
|[A Mobile Application for Obesity Early Diagnosis Using CNN-based Thermogram Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066987)|H. Leo; K. Saddami; R. Roslidar; R. Muharar; K. Munadi; F. Arnia|10.1109/ICAIIC57133.2023.10066987|Obesity;Deep Learning;CNN;Thermal Imaging;Performance evaluation;Obesity;Sensitivity;Costs;Computational modeling;Transfer learning;Neural networks|
|[Interpretable Anomaly Detection for Lung Sounds Using Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067072)|R. Wakamoto; S. Mabu|10.1109/ICAIIC57133.2023.10067072|lung sounds;deep learning;anomaly detection;topology;Deep learning;Time series analysis;Lung;Data visualization;Predictive models;Feature extraction;Topology|
|[Suspicious Activity Trigger System using YOLOv6 Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066970)|S. Awang; M. Q. Rafiqi Rokei; J. Sulaiman|10.1109/ICAIIC57133.2023.10066970|deep learning;object detection;machine learning;Deep learning;Limiting;Tracking;Surveillance;Logic gates;Cameras;Behavioral sciences|
|[Data Pipeline Design for Dangerous Driving Behavior Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067059)|H. Jo; S. Yeom; H. Chen; K. Kim|10.1109/ICAIIC57133.2023.10067059|Artificial intelligence;Data science;Data pipeline;Soft sensors;Pipelines;Loading;Cameras;Data models;Regulation;Behavioral sciences|
|[Instruction-based March Test Pattern Generation Scheme for At-Speed Test Cost Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067073)|S. Park; G. Lee; J. Shin; S. Lee; Y. -w. Lee|10.1109/ICAIIC57133.2023.10067073|automated test equipment;pattern generator;memory test;at-speed testing;test cost reduction;Performance evaluation;Costs;Memory management;Generators;Manufacturing;Complexity theory;Test pattern generators|
|[Performance Analysis of Machine Learning Algorithms with Clustering Protocol in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067019)|R. Gantassi; Z. Masood; S. Lim; Q. A. Sias; Y. Choi|10.1109/ICAIIC57133.2023.10067019|Clustering protocol;Machine Learning;Quality of Service;Wireless Sensor Networks;Measurement;Wireless sensor networks;Machine learning algorithms;Protocols;Clustering algorithms;Quality of service;Machine learning|
|[Urban Traffic Density Estimation from Vehicle-mounted Camera for Real-time Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066969)|H. Cho; Y. Yoon; J. Kim; H. Yeo|10.1109/ICAIIC57133.2023.10066969|Traffic Density Estimation;Vehicle-mounted Camera;Real Time Application;Computer Vision;Deep Learning;Roads;Vehicle detection;Urban areas;Vehicle driving;Traffic control;Cameras;Real-time systems|
|[Cyber Security in Fog Computing Using Blockchain: A Mini Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066994)|N. A. Alhumam; N. S. Alyemni; M. M. Hafizur Rahman|10.1109/ICAIIC57133.2023.10066994|Fog Computing;Cloud Computing;Edge Computing;Security;Blockchain;Cloud computing;Systematics;Bibliographies;Scalability;Intrusion detection;Switches;Blockchains|
|[A Mini Literature Review on Challenges and Opportunity in Threat Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067080)|M. A. Althamir; J. Z. Boodai; M. M. Hafizur Rahman|10.1109/ICAIIC57133.2023.10067080|Cybersecurity;threat actors;threat intelligence;vulnerability;tactic;techniques;procedures;indicators of compromise;feeds;Bibliographies;Cyberspace;Malware;Artificial intelligence;Monitoring;Cyberattack|
|[An End-to-End Convolutional Recurrent Neural Network with Multi-Source Data Fusion for Sleep Stage Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066965)|T. I. Toma; S. Choi|10.1109/ICAIIC57133.2023.10066965|Sleep stage classification;convolutional neural network;recurrent neural network;multi-source data fusion;Electrooculography;Recurrent neural networks;Sleep;Data integration;Benchmark testing;Brain modeling;Feature extraction|
|[Biometric in Cyber Security: A Mini Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067017)|E. A. Debas; R. S. Alajlan; M. M. Hafizur Rahman|10.1109/ICAIIC57133.2023.10067017|cybersecurity;biometric;biometric identification;authentication;security;Systematics;Biometrics (access control);Face recognition;Authentication;Banking;Fingerprint recognition;Mobile handsets|
|[Detection of Morphological Characteristics of Atrial Fibrillation Using Semantic Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067119)|K. Jo; H. Kim; J. Choi; Y. K. Kim; L. -M. Hwang; S. -R. Lee; E. -K. Choi; H. S. Song|10.1109/ICAIIC57133.2023.10067119|electrocardiography;atrial fibrillation;fibrillation wave;deep learning;semantic segmentation;Location awareness;Shape;Event detection;Semantic segmentation;Semantics;Atrial fibrillation;Electrocardiography|
|[An OAM Classification Technique using CNN Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067022)|S. Arya; Y. H. Chung|10.1109/ICAIIC57133.2023.10067022|Achievable capacity;Game theory;Poisson channel;Nash bargaining;optical communications;Orbital calculations;Convolution;Simulation;Neural networks;Decoding;Noise measurement;Indexes|
|[Time-triggered Network Interface Extension for the Versal Network-an-Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067077)|D. Onwuchekwa; J. Paulachan; R. Nambinina; R. Obermaisser|10.1109/ICAIIC57133.2023.10067077|Network-on-Chip;Time-Triggered Systems;Latency;Jitter;Versal NoC;Microprocessors;Quality of service;Jitter;Routing;System-on-chip;Network interfaces;IP networks|
|[Radar Signal Abnormal Point Classification based on Camera-Radar Sensor Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067112)|H. Seo; D. S. Han|10.1109/ICAIIC57133.2023.10067112|Radar;RCS;deep learning;classification;sensor fusion;Deep learning;Radar cross-sections;Radar imaging;Sensor fusion;Cameras;Data models;Generators|
|[Fast Verilog Simulation using Tel-based Verification Code Generation for Dynamically Reloading from Pre-Simulation Snapshot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066996)|Y. Lee; D. Park|10.1109/ICAIIC57133.2023.10066996|Pulggable Verification;Tcl-based verification code;Fast simulation Methode;Codes;Source coding;Booting;Complexity theory;Iterative methods;Hardware design languages;Artificial intelligence|
|[Coordinated autonomous networks for remote synchronized video services with the autonomous mobility robots - prelude implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067079)|H. H. Yamamoto; M. Iwashita; N. Kondo; L. Wong; Y. Kaneta; K. Urano; T. Yonezawa; N. Kawaguchi|10.1109/ICAIIC57133.2023.10067079|autonomous network system;autonomous mobility robot;360 degree display;bi-directional CDN;synchronized failure information;Couplings;Robot kinematics;Bidirectional control;Streaming media;Synchronization;Eye protection;Artificial intelligence|
|[Design and Implementation of Decentralized TDMA for Low Power IoT Devices Based on Desynchronization of Nonlinear Oscillators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067015)|T. Osada; H. Yasuda; A. Li; S. -J. Kim; M. Hasegawa|10.1109/ICAIIC57133.2023.10067015|desynchronization;Kumamoto model;nonlinear oscillator;medium access control;time division multiple access;low power IoT device;Performance evaluation;Time division multiple access;Protocols;Error analysis;Numerical simulation;Media Access Protocol;Timing|
|[Sequential Rasterized Image-based Trajectory Prediction Deep-Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067033)|C. Lee; D. S. Han|10.1109/ICAIIC57133.2023.10067033|autonomous vehicle;trajectory forecasting;trajectory prediction;Deep learning;Training;Roads;Predictive models;Feature extraction;Mathematical models;Data models|
|[Rethinking bank branch closure strategies through omni-channel usage data analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066991)|M. G. Kim; S. A. Kang; M. H. Ryu|10.1109/ICAIIC57133.2023.10066991|Omni-Channel;ANOVA;Regression Analysis;Customer data;Data analysis;Banking;Behavioral sciences;Regression analysis;Internet;Artificial intelligence;Analysis of variance|
|[PCR Radar-Based Counting System for Packaged Objects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066968)|J. Cho; S. Yang; J. Kim|10.1109/ICAIIC57133.2023.10066968|Radar;Object counting;Deep Neural Network;Liquids;Ultrasonic variables measurement;Radar measurements;Pulse measurements;Neural networks;Inspection;Radar signal processing|
|[Strategic Use of Online Review of Mobile App](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067058)|S. Kim; M. H. Ryu|10.1109/ICAIIC57133.2023.10067058|Text Mining;Mobile Banking;Online Review;IPA;Text mining;Banking;Mobile applications;Internet;Artificial intelligence|
|[Multivariate Time Series Anomaly Detection via Temporal Encoder with Normalizing Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067087)|J. Moon; S. Song; J. -G. Baek|10.1109/ICAIIC57133.2023.10067087|Anomaly detection;long short term memory;normalizing flow;smart factory;Manufacturing processes;Correlation;Time series analysis;Real-time systems;Production facilities;Data models;Noise measurement|
|[Spectrum Sensing Mechanism For Congnitive Radio using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066974)|P. K. Shah; D. Sultane; P. Singh|10.1109/ICAIIC57133.2023.10066974|Signal processing feature;Spectrum Sensing;Congnitive Radio;Generalized Gaussian Noise;Deep learning;Frequency modulation;Gaussian noise;Neural networks;Signal processing;Data models;Entropy|
|[Robustness of Deep Learning enabled IoT Applications Utilizing Higher Order QAM in OFDM Image Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067100)|N. Islam; I. Ahmad; S. Shin|10.1109/ICAIIC57133.2023.10067100|OFDM System;QAM Modulation;Deep learning;Deep learning;Quadrature amplitude modulation;OFDM;Image communication;Bit error rate;Symbols;Throughput|
|[Multi-network based MAC Protocol Identification with Decision Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067028)|A. R. Seraponzo; Q. Guo; S. Peng|10.1109/ICAIIC57133.2023.10067028|MAC protocol identification;Deep learning;Multi-network;Decision fusion;Deep learning;Wireless communication;Military communication;Fuses;Neural networks;Logic gates;Media Access Protocol|
|[Performance Analysis of Beam-Tracking Technique for IRS-assisted Cellular Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067097)|Y. J. Kim; Y. S. Cho|10.1109/ICAIIC57133.2023.10067097|Beam-tracking;intelligent reflecting surface (IRS);cellular system;Base stations;Planar arrays;Trajectory;Performance analysis;Millimeter wave communication;Artificial intelligence|
|[Deep Learning-based Spectral Efficiency Maximization in Massive MIMO-NOMA Systems with STAR-RIS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067078)|R. H. Yoga Perdana; T. -V. Nguyen; Y. Pramitarini; K. Shim; B. An|10.1109/ICAIIC57133.2023.10067078|Deep learning neural networks;massive MIMO;NOMA;non-convex optimization;phase shift;power allocation;spectral efficiency;STAR-RIS;Deep learning;Base stations;Spectral efficiency;Simulation;Massive MIMO;Reflection;Real-time systems|
|[Unscented Kalman Filter-based Beam Tracking in NR MIMO System using Hybrid Beamforming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067030)|Y. Sim; S. Sin; J. Cho; K. Kim; S. Moon; I. Hwang|10.1109/ICAIIC57133.2023.10067030|Beam tracking;hybrid beamforming;multiple-input multiple-output;orthogonal frequency diversity multiplexing;unscented Kalman filter;Wireless communication;Multiplexing;Array signal processing;Tracking;5G mobile communication;Millimeter wave technology;Autonomous aerial vehicles|
|[Simultaneously Transmitting and Reflecting-Reconfigurable Intelligent Surfaces with Hardware Impairment and Phase Error](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067009)|W. Khalid; M. A. Ur Rehman; T. Van Chien; H. Yu|10.1109/ICAIIC57133.2023.10067009|STAR-RIS;NOMA;Transceiver hardware im-pairment;Phase error;Ergodic rates;Fading channels;NOMA;Performance gain;Downlink;Hardware;Transceivers;Behavioral sciences|
|[Analysis of Resource Usage Management Plan for Federated Learning in Hybrid Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067124)|S. Oh; H. Shin; M. Hahn; J. Kim|10.1109/ICAIIC57133.2023.10067124|federated learning;cloud system;hybrid cloud;docker;resource management;Measurement;Cloud computing;Federated learning;Data security;Market research;Data models;Artificial intelligence|
|[Power Distribution Adjustment for Rate-Splitting Performance Improvement in MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066961)|D. M. Saputri; A. Kurniawan; M. S. Arifianto; A. A. Pramudita; H. Vidyaningtyas|10.1109/ICAIIC57133.2023.10066961|RSMA;Power Distribution;MIMO;Imperfect CSIT;NOMA;Spectral efficiency;Transmitters;Wireless networks;Simulation;Power distribution;Interference|
|[Data Accuracy Pattern-based Transmission Period Control Algorithm for IoT networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067002)|J. Han; G. H. Lee; H. Park; J. K. Choi|10.1109/ICAIIC57133.2023.10067002|Transmission period control;K-means clustering;Internet of Things;energy consumption;particle swarm optimization;Performance evaluation;Energy consumption;Adaptation models;Time series analysis;Clustering algorithms;Mathematical models;Sensors|
|[Development of AI Educational Datasets Library Using Synthetic Dataset Generation Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067000)|S. k. Kim; K. Kim; T. Kim|10.1109/ICAIIC57133.2023.10067000|Artificial Intelligence Education;Synthetic data;Teaching and learning materials;Python Library;Education;Learning (artificial intelligence);Libraries;Artificial intelligence;Synthetic data|
|[An End-to-End Motion Planner Using Sensor Fusion for Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067069)|N. T. Hoai Thu; D. Seog Han|10.1109/ICAIIC57133.2023.10067069|Autonomous vehicles;Motion planning;Sensor fusion;End-to-end deep learning;Training;Deep learning;Laser radar;Semantic segmentation;Sensor fusion;Transformers;Cameras|
|[Reinforcement Learning for Predicting Traffic Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067034)|I. Cho; P. K. Rajendran; T. Kim; D. Har|10.1109/ICAIIC57133.2023.10067034|accident anticipation;reinforcement learning;Measurement;Adaptation models;Supervised learning;Reinforcement learning;Predictive models;Safety;Task analysis|
|[Kick-motion Training with DQN in AI Soccer Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067011)|B. Park; J. Lee; T. Kim; D. Har|10.1109/ICAIIC57133.2023.10067011|Reinforcement learning (RL);Deep Q-Network (DQN);AI soccer;Curse of dimensionality (COD);Coordinate transformation matrix (CTM);Training;Robot kinematics;Software algorithms;Training data;Reinforcement learning;Software;Artificial intelligence|
|[Inter-Protocol Fairness Evaluation of DQN-based Congestion Control Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067107)|S. -J. Seo; Y. -Z. Cho|10.1109/ICAIIC57133.2023.10067107|Deep Q Network;TCP congestion control;Protocols;Throughput;Artificial intelligence|
|[Implementation of WiFi Communication on Multi UAV for Leader-Follower Trajectory based on ROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067024)|P. Anggraeni; H. Khoirunnisa; M. N. Rizal; M. F. Alfadhila|10.1109/ICAIIC57133.2023.10067024|ROS;Multi-Master;multi quadcopter;Raspberry Pi;System testing;Delay effects;Autonomous aerial vehicles;Real-time systems;Workstations;Trajectory;Data communication|
|[3D Object Localization Using Time of Flight And Angle of Arrival](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067084)|H. Sohn; D. Yi; J. Seo; G. Zhu; J. Lim; S. C. Kim|10.1109/ICAIIC57133.2023.10067084|Localization;Time of Flight;Angle of Arrival;Location awareness;Three-dimensional displays;Ultrasonic imaging;Coordinate measuring machines;Ultrasonic variables measurement;Error analysis;Measurement uncertainty|
|[Double Deep Q-Learning based Backhaul Spectrum Allocation in Integrated Access and Backhaul Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067029)|J. Park; H. Jin; J. Joo; G. Choi; S. C. Kim|10.1109/ICAIIC57133.2023.10067029|mmWave;Integrated Access and Backhaul;Reinforcement Learning;Spectrum Allocation;Wireless communication;Q-learning;Costs;Simulation;Network architecture;Dynamic scheduling;Resource management|
|[Bi-directional Visual Geo-localization-based Cross-Domain Matching between Digital Twin and Real World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066977)|S. Yang; S. Kim; J. Kim|10.1109/ICAIIC57133.2023.10066977|digital twin;visual geo-Localization;smart city;IoT;Visualization;Satellites;Smart cities;Image matching;Bidirectional control;Digital twins;Internet of Things|
|[A Review on AI-Enabled Content Caching in Vehicular Edge Caching and Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067094)|A. Masood; D. Q. Tuan; D. S. Lakew; N. -N. Dao; S. Cho|10.1109/ICAIIC57133.2023.10067094|Vehicular network;vehicular edge caching;artificial intelligence;machine learning;Machine learning;Learning (artificial intelligence);Servers;Optimization|
|[A Review on AI-Driven Aerial Access Networks: Challenges and Open Research Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067056)|D. S. Lakew; A. -T. Tran; A. Masood; N. -N. Dao; S. Cho|10.1109/ICAIIC57133.2023.10067056|Aerial access network;UAV;HAP;edge computing;reinforcement learning;deep reinforcement learning;Base stations;Wireless networks;Taxonomy;Reinforcement learning;Computational efficiency;Resource management;Artificial intelligence|
|[Philippine National Elections 2022: Voter Preferences and Topics of Discussion on Twitter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067082)|R. E. Demillo; G. Solano; N. Oco|10.1109/ICAIIC57133.2023.10067082|supervised learning;sentiment analysis;biterm topic modeling;elections;twitter;Support vector machines;Analytical models;Sentiment analysis;Social networking (online);Voting;Blogs;Focusing|
|[Preliminary Work on Automatic ARXML Generation for SOME/IP in AUTOSAR Classic Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067065)|K. Kyung; H. Kim; J. Cho|10.1109/ICAIIC57133.2023.10067065|AUTOSAR;ARXML;SOME/IP;Franca IDL;Industries;Protocols;Ethernet;Bandwidth;Writing;Network architecture;Market research|
|[A Machine Learning Approach to Predict Customer Churn of a Delivery Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067108)|Q. Liu; Q. Chen; S. -J. Lee|10.1109/ICAIIC57133.2023.10067108|machine learning;customer churn;delivery platform;prediction;Industries;COVID-19;Machine learning algorithms;Costs;Machine learning;Companies;Prediction algorithms|
|[Development of MIMO Scheme-based Optical Camera Communication System using Deep Learning method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067044)|V. L. Nguyen; D. H. Tran; H. Nguyen; B. Chung; Y. M. Jang|10.1109/ICAIIC57133.2023.10067044|deep learning;optical camera communication;multiple-input multiple-output;Deep learning;Wireless communication;Integrated optics;Cameras;Light emitting diodes;Adaptive optics;Internet of Things|
|[Design and Implementation of RS-OFDM scheme for Optical Camera Communication based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067103)|H. Nguyen; D. H. Tran; V. L. Nguyen; B. Chung; Y. M. Jang|10.1109/ICAIIC57133.2023.10067103|RS-OFDM;Optical Camera Communication;Deep Learning;Deep learning;Integrated optics;Wireless communication;OFDM;Transportation;Optical computing;Cameras|
|[A Deep Learning-Based SAR Ship Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067131)|C. Yu; Y. Shin|10.1109/ICAIIC57133.2023.10067131|Synthetic aperture radar;remote sensing;ship detection;deep learning;YOLOv7;Sea measurements;Scattering;Object detection;Transformers;Radar polarimetry;Sensors;Marine vehicles|
|[cGAN Model-Based Radio Frequency Interference Mitigation for Radio Astronomy Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066995)|I. Helmy; W. Choi|10.1109/ICAIIC57133.2023.10066995|RFI mitigation;cGAN;radio astronomy;Deep learning;Radio astronomy;Interference;Generative adversarial networks;Data models;Artificial intelligence;Radiofrequency interference|
|[A New Approach to Lidar and Camera Fusion for Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066963)|S. Bae; D. Han; S. Park|10.1109/ICAIIC57133.2023.10066963|Autonomous Driving;Camera;LiDAR;Laser radar;Object detection;Detectors;Sensor fusion;Cameras;Feature extraction;Cognitive systems|
|[Visualization Algorithm for Cargo Stowage Optimization of Vehicle Carriers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067046)|J. Yeon Kim; Y. -J. Kang; S. Hyun Ha; S. Chan Jeong|10.1109/ICAIIC57133.2023.10067046|visualization;vehicle carrier;CMD;Space vehicles;Visualization;Loading;Layout;Symbols;Optimization methods;Filling|
|[Cost-Effective Peak Shaving Strategy Based on Clustering and XGBoost Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067091)|S. Lim; R. Gantassi; Y. Choi|10.1109/ICAIIC57133.2023.10067091|clustering;cost minimization;K-means;machine learning;peak shaving;XGBoost;Costs;Machine learning algorithms;Load forecasting;Simulation;Buildings;Clustering algorithms;Predictive models|
|[Design of poultry farm disease detection system based on K-Nearest Neighbor Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067067)|S. J. Kim; H. Yoe; M. H. Lee|10.1109/ICAIIC57133.2023.10067067|Samrt Agriculture;Poultry;IoT;KNN;Mobile Service);Machine learning algorithms;Data analysis;Infectious diseases;Influenza;Production;Machine learning;Agriculture|
|[Vehicle Lateral Motion Modeling Using Data-Driven Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067099)|C. Park; C. Jeong; C. Mook Kang; W. Kim; Y. Seop Son|10.1109/ICAIIC57133.2023.10067099|Dynamic mode decomposition;modeling;vehicle lateral dynamics;Fast Fourier Transform;Analytical models;Fourier transforms;System dynamics;Control design;Dynamics;Process control;Mathematical models|
|[Indoor Space Flow Analysis Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067105)|C. Woo Choi; H. Eun Kang; Y. Young Hong; Y. Su Kim; G. Bo Kim; A. Teguh Prihatno; J. Hyun Ji; S. Do Hong; H. W. Kim|10.1109/ICAIIC57133.2023.10067105|Deep Learning;CFD(Computational Fluid Dynamics);UNet;Transformer;TransUNet;Air Conditioner;Deep learning;Analytical models;Solid modeling;Three-dimensional displays;Fluids;Costs;Computational fluid dynamics|
|[Illegal 3D Content Distribution Tracking System based on DNN Forensic Watermarking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067060)|J. Park; J. Kim; J. Seo; S. Kim; J. -H. Lee|10.1109/ICAIIC57133.2023.10067060|Forensic Watermark;AI;Copyright;Training;Industries;Three-dimensional displays;Metaverse;Forensics;Watermarking;Rendering (computer graphics)|
|[Enhanced-feature pyramid network for semantic segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067062)|V. T. Quyen; J. H. Lee; M. Y. Kim|10.1109/ICAIIC57133.2023.10067062|Semantic segmentation;feature pyramid network;multiscale prediction;real-time application;Semantic segmentation;Semantics;Computer architecture;Object detection;Feature extraction;Hardware;Proposals|
|[Analysis of Teaching and Learning Environment for Data Science and AI Education (Focused on 2022 Revised Curriculum)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067051)|S. ki Kim; T. Kim; K. Kim|10.1109/ICAIIC57133.2023.10067051|Data Science;Artificial intelligence;Teaching and Learning Environment;Programming;Dataset;Education;Focusing;Learning (artificial intelligence);Data science;Big Data;Market research;Programming profession|
|[Contextual Dependency-aware Graph Convolutional Network for Extracting Entity Relations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067083)|J. Liao; Y. Du|10.1109/ICAIIC57133.2023.10067083|Relation Extraction;Relative position;Dependency tree;Graph convolutional network;Attention mechanism;Deep learning;Adaptation models;Semantics;Linguistics;Predictive models;Distortion;Graph neural networks|
|[An Automatic Vehicle Speed Control System with Consideration of Various Uncertainties*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067045)|P. S. Kim; S. Y. Kim|10.1109/ICAIIC57133.2023.10067045|PID Controller;Kalman Filter;Automatic Vehicle Speed Control;Disturbance;System Variation;Feedback Sensor Noise;Degradation;Uncertainty;Computer simulation;Velocity control;Process control;Control systems;Information filters|
|[Study of validation methods for augmented data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067113)|J. -J. Jung; K. -W. Kim|10.1109/ICAIIC57133.2023.10067113|data augmentation;validation;biased distribution;embedding vector;Learning (artificial intelligence)|
|[Federated Learning-Enabled Digital Twin for Smart Additive Manufacturing Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067043)|M. A. Paramartha Putra; S. M. Rachmawati; R. N. Alief; L. A. Chijioke Ahakonye; A. Gohil; D. -S. Kim; J. -M. Lee|10.1109/ICAIIC57133.2023.10067043|Digital twin;federated learning;smart additive manufacturing;Training;Industries;Analytical models;Solid modeling;Three-dimensional displays;Federated learning;Fault detection|
|[Preliminary Design for Development of Detachable Test Automation System Based on AUTOSAR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067063)|S. Lee; J. Kwak; J. Cho|10.1109/ICAIIC57133.2023.10067063|AUTOSAR;Test system;Blackbox test;Test Automation;Application Software Layer;Automation;Production;Complexity theory;Artificial intelligence;Testing;Embedded software;Automotive engineering|
|[Proposal of Docker and Kubernetes Direction through the Event Timeline of Kubernetes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066988)|S. Woo; J. -H. Lee|10.1109/ICAIIC57133.2023.10066988|Kubernetes;Docker;Event Timeline;Standards organizations;Companies;Containers;Proposals;Artificial intelligence|
|[Deep Learning-based Human Vehicle Interface for Smart Golf Cart](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067123)|M. W. Yoo; C. H. Lee; D. S. Han|10.1109/ICAIIC57133.2023.10067123|deep learning;object detection;classification;Deep learning;Image recognition;Object detection;Detectors;Classification algorithms;Artificial intelligence;Wearable sensors|
|[A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067053)|B. Kwon; Y. Kim; H. Lee|10.1109/ICAIIC57133.2023.10067053|path loss modeling;data augmentation;CNN;5G;Deep learning;Base stations;Convolution;Communication systems;Neural networks;Millimeter wave technology;Training data|
|[Implementation of Single and Multi Linear Regression for Prediction of Energy Consumption based on Previous Data of Energy Production](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066989)|Q. A. Sias; S. Lim; R. Gantassi; Y. Choi|10.1109/ICAIIC57133.2023.10066989|energy demand;energy supply;MVR;SLR;Energy consumption;Correlation;Input variables;Oils;Linear regression;Production;Predictive models|
|[A Review on Rate-Splitting Multiple Access-Assisted Downlink Networks: Energy Optimizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067070)|A. -T. Tran; D. S. Lakew; D. T. Hua; Q. T. Do; N. -N. Dao; S. Cho|10.1109/ICAIIC57133.2023.10067070|RSMA;weighted power consumption;energy efficiency;downlink;1-layer RSMA;RS-CMD;Weight measurement;Power measurement;Power demand;Transmitters;Energy measurement;Bandwidth;Downlink|
|[Introduction of Optimization Algorithm for Adaptive Gradient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066993)|M. Lamine; S. -C. Kim|10.1109/ICAIIC57133.2023.10066993|Machine Learning;Optimization Methods;Gradient Decent;Adaptive Algorithms;AdaDelta;Adam;Adapg;Measurement;Adaptive learning;Machine learning algorithms;Source coding;Optimization methods;Machine learning|
|[Analysis of Recent IIoT Security Technology Trends in a Smart Factory Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067004)|J. Kim; J. Park; J. -H. Lee|10.1109/ICAIIC57133.2023.10067004|Smart Factory;Industrial IoT;Security;Manufacturing industries;Standardization;Market research;Production facilities;Blockchains;Security;Artificial intelligence|
|[FLB2: Layer 2 Blockchain Implementation Scheme on Federated Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067038)|R. N. Alief; M. A. Paramartha Putra; A. Gohil; J. -M. Lee; D. -S. Kim|10.1109/ICAIIC57133.2023.10067038|Blockchain;Ethereum;Federated Learning;Optimism;Training;Federated learning;Scalability;Simulation;Training data;Data models;Blockchains|
|[AI in Classroom: Group Score Prediction System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067066)|Y. C. Kim; P. Agarwal|10.1109/ICAIIC57133.2023.10067066|group learning;knowledge tracing;convolution neural network;learning path optimization;Learning management systems;Convolution;Education;Neural networks;Ecosystems;Learning (artificial intelligence);Predictive models|
|[Deep Learning-based Ultra Short Baseline Underwater Positioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067090)|H. Lee; K. Kim; T. Chung; H. Ko|10.1109/ICAIIC57133.2023.10067090|Deep learning;USBL;Positioning;Underwater;Direction-of-arrival estimation;Computer simulation;Position measurement;Delays;Artificial intelligence;Intelligent sensors;Signal to noise ratio|
|[Preventive Maintenance Techniques through Learning-based Remaining Useful Lifetime Prediction in IoT Sensor Networks: The Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067003)|D. Lee; Y. Jeon; J. Oh; C. Lee; T. Ha; S. Cho|10.1109/ICAIIC57133.2023.10067003|Remaining Useful Life;Prognostics and Health Management;Internet of Things;RUL;PHM;IoT;Industries;Atmospheric modeling;Prognostics and health management;Marine vehicles;Artificial intelligence;Aircraft;Preventive maintenance|
|[Remaining Useful Life Prediction Using an Ensemble Learning-Based Network for a Belt Conveyor System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066971)|J. Jo; Z. Kim; Y. -J. Suh|10.1109/ICAIIC57133.2023.10066971|Remaining useful life prediction;ensemble learning;belt conveyor system;Vibrations;Industries;Pulleys;Buildings;Production;Predictive models;Belts|
|[Optimization of Cloud Computing Workload Prediction Model with Domain-based Feature Selection Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066959)|C. Lee; M. Song; K. Min; E. Ha; J. Lee; W. Kim|10.1109/ICAIIC57133.2023.10066959|Cloud Computing;Workload Prediction;Deep Learning;Long Short-Term Memory;Cloud computing;Data centers;Root cause analysis;Reactive power;Computational modeling;Predictive models;Feature extraction|
|[Intelligent Task Offloading Method using Deep Q-Network for Collaborative Edge Computing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067111)|J. Youn|10.1109/ICAIIC57133.2023.10067111|edge computing;offloading;Deep Q-network;Deep learning;Computational modeling;Reinforcement learning;Markov processes;Servers;Resource management;Task analysis|
#### **2023 International Conference on Computing, Networking and Communications (ICNC)**
- DOI: 10.1109/ICNC57223.2023
- DATE: 20-22 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Decomposition Models for the Routing and Slot Provisioning Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074088)|B. Jaumard; A. Mohammed; Q. A. Nguyen|10.1109/ICNC57223.2023.10074088|Optical Networks;Routing and Slot Assignment;Column Generation.;nan|
|[Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074279)|R. Kaewpuang; M. Xu; D. Niyato; H. Yu; Z. Xiong; X. S. Shen|10.1109/ICNC57223.2023.10074279|Federated learning;quantum key distribution;adaptive resource allocation;stochastic programming;Adaptation models;Costs;Uncertainty;Adaptive systems;Federated learning;Computer architecture;Routing|
|[COVID-19 Fake News Detector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074216)|J. Liu; M. Chen|10.1109/ICNC57223.2023.10074216|COVID-19;deep learning;fake news detection;machine learning;COVID-19;Deep learning;Adaptation models;Machine learning algorithms;Social networking (online);Detectors;Data models|
|[Estimation of Cellular Wireless User Coordinates via Channel Charting and MUSIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074200)|A. Aly; E. Ayanoglu|10.1109/ICNC57223.2023.10074200|Channel charting;user equipment (UE);channel state information (CSI);MUSIC;PCA;SM;AE;Wireless communication;Base stations;Linear regression;Channel estimation;Estimation;Multiple signal classification;Complexity theory|
|[Evolutionary Algorithm with Phenotype Diversity for Virtual Network Embedding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074545)|T. Otoshi; M. Murata|10.1109/ICNC57223.2023.10074545|Novelty Search;Attractor Selection;Virtual Network Embedding;Local Competition;Evolutionary Algorithm;nan|
|[Deep Reinforcement Learning Based Resource Allocation for 5G V2V Groupcast Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074102)|S. -H. Wu; R. -H. Hwang; C. -Y. Wang; C. -H. Chou|10.1109/ICNC57223.2023.10074102|V2V;Platoon;Groupcast;Deep Reinforcement Learning (DRL);resource allocation;nan|
|[A Refined Energy Optimization Model for Edge Computing with Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074377)|X. Liu; C. Huang; W. Xu|10.1109/ICNC57223.2023.10074377|Index Terms-Edge intelligence;machine learning;mobile edge learning;offloading;nan|
|[LEO Satellite Communication in IoRT: a Virtual MISO Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074403)|X. Liu|10.1109/ICNC57223.2023.10074403|Hoyt fading;Internet of remote things;low Earth orbit satellite communication;nan|
|[Towards Programmable Networking With CoopNet: A Horizontal Parallel Multipath Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074094)|R. C. Voicu; Y. Chang|10.1109/ICNC57223.2023.10074094|Heterogeneous Networks;Device to Device;Network Architecture;Communication;Spectrum Efficiency;Multipath;Machine Learning;RNN;FFNN;Infrastructure as a Service;nan|
|[Shallow- and Deep- fake Image Manipulation Localization Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074246)|J. Zhang; H. Tohidypour; Y. Wang; P. Nasiopoulos|10.1109/ICNC57223.2023.10074246|Image manipulation;Manipulation localization;shallowfakes;deepfakes;Location awareness;Deep learning;Deepfakes;Codes;Task analysis;Artificial intelligence|
|[Static Virtual Network Mapping With Advance Reservation In Elastic Optical Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074419)|J. Zhao; V. Kohirkar; P. Nigade; R. Kalkunte; L. Posham; S. Subramaniam|10.1109/ICNC57223.2023.10074419|elastic optical networks;virtual network mapping;advance reservation;Integrated optics;Time-frequency analysis;Network topology;Simulation;Bandwidth;Optical fiber networks;Integer linear programming|
|[Study on High Availability and Fault Tolerance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074557)|N. K. K. Kit; M. Aibin|10.1109/ICNC57223.2023.10074557|availability;cloud computing;docker;nan|
|[Using Diversity to Evolve More Secure and Efficient Virtual Local Area Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074594)|A. J. Aizpurúa; E. W. Fulp; D. Cañas|10.1109/ICNC57223.2023.10074594|security;VLAN;access control;genetic algorithms;critical infrastructure;nan|
|[On False Data Injection Attack against Building Automation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074353)|M. Cash; C. Morales-Gonzalez; S. Wang; X. Jin; A. Parlato; J. Zhu; Q. Z. Sun; X. Fu|10.1109/ICNC57223.2023.10074353|nan;Support vector machines;Temperature sensors;Costs;Protocols;Feature extraction;Time measurement;Probability distribution|
|[Implementing Virtual Network Functions in Named Data Networking and Web 3.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074018)|P. Fang; T. Wolf|10.1109/ICNC57223.2023.10074018|named data networking;virtual network function;network protocol;simulation;Web 3.0.;Semantic Web;Prototypes;Telecommunication traffic;Inspection;Load management|
|[Real-Time Search and Rescue using Remotely Piloted Aircraft System with Frame Dropping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074217)|R. Sharma; M. Aibin|10.1109/ICNC57223.2023.10074217|rpas;uav;computing;Computational modeling;Atmospheric modeling;Graphics processing units;Thermal sensors;Streaming media;Search problems;Real-time systems|
|[Time-Topology Routing in 3D Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074122)|T. Korikawa; C. Takasaki; K. Hattori; H. Oowada|10.1109/ICNC57223.2023.10074122|Time-topology routing;beyond 5G/6G;3D networks;non-terrestrial networks;mobile networks;Space vehicles;Base stations;Three-dimensional displays;Satellites;Network architecture;Routing;Topology|
|[AI-based Cyber Event OSINT via Twitter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074187)|D. Dale; K. McClanahan; Q. Li|10.1109/ICNC57223.2023.10074187|Cybersecurity;OSINT;AI;Social networking (online);Blogs;Pipelines;Natural languages;Information filters;Malware;Data mining|
|[Client-Transparent and Self-Managed MQTT Broker Federation at the Application Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074556)|J. F. d. L. Machado; M. A. Spohn; L. Z. Granville|10.1109/ICNC57223.2023.10074556|nan;nan|
|[DIRS: Dynamic Initial Rate Setting in Congestion Control for Disaggregated Storage Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074382)|X. Zhang; A. Yang; D. Jia; L. Wang; M. Bayati; P. Subedi; X. Yao; B. Sheng; N. Mi|10.1109/ICNC57223.2023.10074382|nan;Data centers;Protocols;Network topology;Heuristic algorithms;Low latency communication|
|[Hierarchical Bayesian Attractor Model for Dynamic Task Allocation in Edge-Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073977)|T. Otoshi; M. Murata; H. Shimonishi; T. Shimokawa|10.1109/ICNC57223.2023.10073977|Bayesian Attractor Model;Hierarchical Model;Intention;Edge-Cloud Computing;Power demand;Computational modeling;Decision making;MIMICs;Switches;Brain modeling;Bayes methods|
|[A load balancing method using load factor on unsplittable flow edges in information and communication networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074451)|D. Shimizu; N. Shinomiya|10.1109/ICNC57223.2023.10074451|Network flow problem;Load balancing;Graph theory;nan|
|[Is Active IRS Useful for mmWave Wireless Networks or Not?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074428)|J. Jalali; A. Khalili; A. Rezaei; J. Famaey|10.1109/ICNC57223.2023.10074428|Intelligent reflecting surface (IRS);millimeter-wave (mmWave);weighted minimum mean square error (WMMSE);successive convex approximation (SCA).;Array signal processing;Surface waves;Wireless networks;System performance;Simulation;Scattering;MISO communication|
|[Traffic Behavior-based Device Type Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074041)|C. Takasaki; T. Korikawa; K. Hattori; H. Ohwada|10.1109/ICNC57223.2023.10074041|Device type classification;traffic behavior analysis;machine learning;deep learning;6G mobile communication;Deep learning;Analytical models;5G mobile communication;Databases;Time series analysis;Standardization|
|[Membership Management in Collaborative Intrusion Detection Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073989)|C. Ezelu; U. Buehler|10.1109/ICNC57223.2023.10073989|Collaborative Intrusion Detection Systems;CIDS Membership Management;Trust Management;Collaboration;Intrusion detection;Data structures;Trust management|
|[On the Placement of Edge Servers in Mobile Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074304)|H. Liu; S. Wang; H. Huang; Q. Ye|10.1109/ICNC57223.2023.10074304|Edge Server Placement;Mobile Edge Computing;Clustering;Heuristic Schemes;Measurement;Multi-access edge computing;Heuristic algorithms;Machine learning;Games;Minimization;Mobile handsets|
|[Impact of Grammar on Language Model Comprehension](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074239)|K. Ameri; M. Hempel; H. Sharif; J. Lopez; K. Perumalla|10.1109/ICNC57223.2023.10074239|Natural Language Processing;Transfer Learning;Transformers;BERT;Part of Speech;Grammar Enriched;Deep learning;Computational modeling;Syntactics;Transformers;Natural language processing;Magnetic heads;Grammar|
|[Enabling PeerCloud in Vehicular Networks: Feasibility and Reliability of Vehicle-to-Vehicle Offloading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074311)|X. Zhang|10.1109/ICNC57223.2023.10074311|nan;Social networking (online);Vehicular ad hoc networks;Feature extraction;Reliability engineering;Robustness;Delays;Servers|
|[Distributed UAV Swarm Placement Optimization for Compressive Sensing based Target Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074263)|Y. -C. Wang; D. Cabric|10.1109/ICNC57223.2023.10074263|Unmanned aerial vehicle (UAV);compressive sensing;MIMO radar;localization;nan|
|[ECEA Based Joint Circular Design of Reconfigurable Intelligent Surfaces with Drone During Disaster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074153)|S. Newaz; X. Liu|10.1109/ICNC57223.2023.10074153|Drone Base Station (DBS);Reconfigurable Intelligent Surfaces (RIS);Coverage Extension;Power Loss;Disaster;Base stations;Analytical models;Costs;Computational modeling;Satellite broadcasting;Interference;Communication networks|
|[Ergodic Capacity of the Cloud Radio Access Network: A General Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074371)|X. Liu|10.1109/ICNC57223.2023.10074371|Ergodic capacity;cloud radio access network;Meijer‘s G-functions;nan|
|[Wake-up Control for Energy-efficient Identifications of Multiple Emission Sources in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074459)|J. Shiraishi; H. Yomo|10.1109/ICNC57223.2023.10074459|nan;nan|
|[A Novel Multivariate and Accurate Detection Scheme for Electricity Theft Attacks in Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074440)|A. A. Abdellatif; A. Amer; K. Shaban; A. Massoud|10.1109/ICNC57223.2023.10074440|Machine learning;electricity-theft detection;cyberattacks;anomaly detection;advanced metering infrastructure;nan|
|[Wiggle: Physical Challenge-Response Verification of Vehicle Platooning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074426)|C. Dickey; C. Smith; Q. Johnson; J. Li; Z. Xu; L. Lazos; M. Li|10.1109/ICNC57223.2023.10074426|Vehicular security;access control;authentication.;Resistance;Access control;Protocols;Perturbation methods;Vehicular ad hoc networks;Trajectory|
|[Performance Analysis of Fixed Broadband Wireless Access in mmWave Band in 5G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074306)|S. Banerjee; S. P. Gochhayat; S. Shetty|10.1109/ICNC57223.2023.10074306|mmWave;5G;Online classification;Multihead LSTM;Wireless communication;Training;Solid modeling;5G mobile communication;Machine learning;Optical fiber networks;Real-time systems|
|[Two-Stage Online Reinforcement Learning based Distributed Optimal Resource Allocation for Multiple RIS-assisted Mobile Ad-Hoc Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074134)|Y. Zhang; H. Xu|10.1109/ICNC57223.2023.10074134|Reconfigurable intelligent surfaces;RIS phase shift;energy efficiency;RIS selection;Reinforcement Learning;Wireless communication;Uncertainty;Power demand;Power control;Reinforcement learning;Numerical simulation;Real-time systems|
|[Emergency Surgical Scheduling Model Based on Moth-flame Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074256)|C. Huang; S. Ye; S. Shuai; M. Wei; Y. Zhou; A. Aibin; M. Aibin|10.1109/ICNC57223.2023.10074256|cloud computing;moth-flame algorithm;scheduling;nan|
|[Trust and Rewards in a Two-Tier Consensus Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074168)|H. Qushtom; J. Mišić; V. B. Mišić; X. Chang|10.1109/ICNC57223.2023.10074168|Blockchain;Internet-of-Things;Practical Byzantine Fault Tolerance (PBFT);Fault tolerance;Computational modeling;Fault tolerant systems;Decision making;Consensus algorithm;Computer architecture;Markov processes|
|[Early Rumor Detection in Social Media Based on Graph Convolutional Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074242)|N. R. Thota; X. Sun; J. Dai|10.1109/ICNC57223.2023.10074242|rumor detection;pattern matching;GCN;structure reconstruction;Representation learning;Analytical models;Social networking (online);Heuristic algorithms;Predictive models;Feature extraction;Real-time systems|
|[Network Anomaly Detection Using a Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074111)|P. Kisanga; I. Woungang; I. Traore; G. H. S. Carvalho|10.1109/ICNC57223.2023.10074111|Anomaly detection;intrusion prevention system;intrusion detection systems;Activity and Event Network (AEN);Graph neural network (GNN);datasets;Graph convolutional network (GCN);nan|
|[Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074355)|V. -H. Bui; F. Chang; W. Su; M. Wang; Y. L. Murphey; F. L. D. Silva; C. Huang; L. Xue; R. Glatt|10.1109/ICNC57223.2023.10074355|deep reinforcement learning;deep neural networks;optimal parameters design;optimization;power converters.;nan|
|[Byzantine-Resilient Federated Learning With Differential Privacy Using Online Mirror Descent](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074494)|O. T. Odeyomi; G. Zaruba|10.1109/ICNC57223.2023.10074494|Byzantine client;federated learning;differential privacy;regret;online mirror descent;nan|
|[Malicious Model Detection for Federated Learning Empowered Energy Storage Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074032)|X. Wang; Y. Chen; O. A. Dobre|10.1109/ICNC57223.2023.10074032|climate change;renewable energy;federated learning;autoencoder;energy storage system;Renewable energy sources;Data privacy;Federated learning;Computational modeling;Carbon dioxide;Batteries;Monitoring|
|[Neural Network based Unsupervised Face and Mask Detection in Surveillance Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074565)|A. R. Ani; S. Saheel; T. Ahmed; M. F. Uddin|10.1109/ICNC57223.2023.10074565|nan;nan|
|[Network Services Management using Programmable Data Planes for Visual Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074183)|A. E. Morel; P. Calyam; C. Qu; D. Gafurov; C. Wang; K. Thareja; A. Mandal; E. Lyons; M. Zink; G. Papadimitriou; E. Deelman|10.1109/ICNC57223.2023.10074183|Programmable Data Planes;In-band Network Telemetry;Video Delivery;Traffic Engineering;Visualization;Cloud computing;Magnetic resonance imaging;Packet loss;Switches;Streaming media;Throughput|
|[Transmission-Cost Minimization for Packet-level Coding on Multi-path Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074036)|W. Mao; S. -P. Yeh; J. Zhu; H. Nikopour; S. Talwar|10.1109/ICNC57223.2023.10074036|packet-level coding;URLLC;multi-path network;heterogeneous networks;optimization;Costs;Wireless networks;Heuristic algorithms;Redundancy;Ultra reliable low latency communication;Minimization;Approximation algorithms|
|[Joint Task and Flow Scheduling for Time-Triggered and Strict-Priority Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074288)|A. Arestova; L. Maile; N. Halikulov; K. -S. Hielscher; R. German|10.1109/ICNC57223.2023.10074288|Cause-Effect-Chains;Real-Time;Time-Aware Shaper;Strict Priority Scheduling;Network Calculus;Schedules;Runtime;Ethernet;Scheduling;Calculus;Real-time systems;Hardware|
|[Evaluating Weather Influence on User Participation in a Crowd-sensing Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074590)|I. F. Ribeiro; V. F. Calmon; T. H. Silva; C. A. S. Santos; V. Mota|10.1109/ICNC57223.2023.10074590|Urban computing;Crowdsensing;Traffic alerts;nan|
|[AI-based RF-Fingerprinting Framework and Implementation using Software-Defined Radios](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074023)|H. Kulhandjian; E. Batz; E. Garcia; S. Vega; S. Velma; M. Kulhandjian; C. D’Amours; B. Kantarci; T. Mukherjee|10.1109/ICNC57223.2023.10074023|RF-Fingerprinting;machine learning;and software-defined radios.;Radio frequency;Wireless communication;Accesslists;Transfer learning;Radio transmitters;Fingerprint recognition;Physical layer|
|[Evaluation of Different Time Series Forecasting Models for 5G V2V Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074369)|J. Liu; C. -T. Huang|10.1109/ICNC57223.2023.10074369|5G;V2V Network;Forecasting Model;Machine Learning;nan|
|[A Hybrid Delay-aware Approach Towards UAV Flight Data Anomaly Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074138)|M. Jia; A. Raja; J. Yuan|10.1109/ICNC57223.2023.10074138|nan;Machine learning;Predictive models;Cyber-physical systems;Autonomous aerial vehicles;Reliability engineering;Delays;Security|
|[Adversarial Technique Validation & Defense Selection Using Attack Graph & ATT&CK Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074241)|M. A. Haque; S. Shetty; C. A. Kamhoua; K. Gold|10.1109/ICNC57223.2023.10074241|Cybersecurity;networked system;attack graph;ATT&CK;TF-IDF;adverasial techniques;mitigations;Bit error rate;Transformers;Security;Computer crime|
|[Virtual Curtain: A Communicative Fine-grained Privacy Control Framework for Augmented Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074372)|A. Shrestha; Y. Hou; M. Long; J. Yuan|10.1109/ICNC57223.2023.10074372|augmented reality;privacy;machine learning;Performance evaluation;Privacy;Visualization;System performance;Pipelines;Prototypes;Cameras|
|[Mobile Sensing Cluster with Orbiting Mutant for Indistinguishable Events in Noisy Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074114)|N. Fujiyama; S. Izuhara; S. Nishigami; E. Nii; H. Yomo; Y. Takizawa|10.1109/ICNC57223.2023.10074114|Swarm Intelligence;Wireless Sensor Network;Particle Swarm optimization;Autonomous Mobile Systmes;Temperature sensors;Degradation;Adaptation models;Computational modeling;Robot sensing systems;Autonomous aerial vehicles;Orbits|
|[Analysis of Job Completion Time in Vehicular Cloud Under Concurrent Task Execution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074524)|C. Tran; M. Mehmet-Ali|10.1109/ICNC57223.2023.10074524|Vehicular clouds;Edge computing;Job completion time;Stochastic processes;nan|
|[Simulation-based Performance Analysis of an NR-U and WiFi Coexistence System in the Presence of Hidden Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074338)|Q. Ren; J. Zheng|10.1109/ICNC57223.2023.10074338|NR-U;WiFi;coexistence system;hidden node;performance analysis;Analytical models;Simulation;System performance;Throughput;Sensors;Delays;Performance analysis|
|[Using Blockchain for Decentralized Artificial Intelligence with Data Privacy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074247)|A. S. Masurkar; X. Sun; J. Dai|10.1109/ICNC57223.2023.10074247|Artificial intelligence;Blockchain;Data privacy;Training;Data privacy;Federated learning;Computational modeling;Smart contracts;Prototypes;Data models|
|[A Novel Information-Directed Tree-Search Algorithm for RIS Phase Optimization in Massive MIMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074174)|I. Z. Ahmed; H. Sadjadpour; S. Yousefi|10.1109/ICNC57223.2023.10074174|nan;Phase measurement;Wireless networks;Line-of-sight propagation;Symbols;Receivers;Throughput;Transceivers|
|[Augmenting Campus Wireless Architectures with SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074202)|W. Brockelsby; R. Dutta|10.1109/ICNC57223.2023.10074202|802.11 wireless networking;software-defined networking;cybersecurity;Wireless networks;Mission critical systems;Computer architecture;Traffic control;Internet of Things;Software defined networking;IEEE 802.11 Standard|
|[perMAC: Perturbation-based MAC for Dense Wireless Networks with Periodic Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074191)|J. Deng; P. -N. Chen; Y. S. Han|10.1109/ICNC57223.2023.10074191|nan;Performance evaluation;Wireless networks;Roads;Perturbation methods;Throughput|
|[A Generative Adversarial Network Based Tone Mapping Operator for 4K HDR Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074176)|J. Zhang; Y. Wang; H. Tohidypour; M. T. Pourazad; P. Nasiopoulos|10.1109/ICNC57223.2023.10074176|High dynamic range (HDR);Standard dynamic range (SDR);Tone mapping operator (TMO);Deep learning;Deep learning;Training;Codes;Image color analysis;Brightness;Computer architecture;Generative adversarial networks|
|[Warehouse Deployment: A Comparative Measurement Study of Commercial Wi-Fi and CBRS Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074584)|V. Sathya; L. Zhang; M. Goyal; M. Yavuz|10.1109/ICNC57223.2023.10074584|nan;nan|
|[The Hysteresis Effect of Momentum Spillover in Asset Pricing via Spatial-Temporal Graph Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074006)|C. He; Q. Li; R. Cheng; J. Wang; J. Tan|10.1109/ICNC57223.2023.10074006|Asset Pricing;Stock Prediction;Graph Neural Network;Spatial-Temporal Learning.;Shape;Finance;Pricing;Graph neural networks;Data models;Hysteresis|
|[Localization System Architecture for Enhanced Positioning in Industry 4.0 Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074001)|R. Vasist; V. Sark; M. Goodarzi; J. Gutiérrez; E. Grass|10.1109/ICNC57223.2023.10074001|Localization system architecture;localization server;enhanced positioning;Location awareness;Wireless communication;Millimeter wave technology;Merging;Systems architecture;Computer architecture;Position measurement|
|[ABP vs. OTAA activation of LoRa devices: an Experimental Study in a Rural Context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074553)|A. M. D. Rocha; M. A. D. Oliveira; P. José F. M.; G. G. H. Cavalheiro|10.1109/ICNC57223.2023.10074553|Internet of Things (IoT);Sensors;LoRa;ABP;OTAA;Activation;nan|
|[Channel Allocation Algorithm Of Wi-Fi](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074461)|J. Wang; X. Zhong; S. Zhou|10.1109/ICNC57223.2023.10074461|primary channel;bandwidth;channel bonding;adjacent channel interference;chload;noise;nan|
|[SPA: A Scalable Pedestrian-awareness Application using NDN over CV2X](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074070)|P. Podder; A. Neishaboori; S. D. Gupta; A. Afanasyev|10.1109/ICNC57223.2023.10074070|Vehicular Communication;NDN;CV2X;5G;Scalability;Computational modeling;Semantics;Information-centric networking;Computer architecture;Hazards;Communications technology|
|[Evaluating Generative Adversarial Networks: A Topological Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074146)|N. Alipourjeddi; A. Miri|10.1109/ICNC57223.2023.10074146|Generative Adversarial Networks;Evaluation metrics;Topological data analysis;Persistent homology;Measurement;Manifolds;Training;Analytical models;Network topology;Computational modeling;Generative adversarial networks|
|[Dynamic Spectrum Access in Non-stationary Environments: A DRL-LSTM Integrated Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074454)|M. Feng; W. Zhang; M. Krunz|10.1109/ICNC57223.2023.10074454|Dynamic spectrum access;non-stationary environment;hidden-mode Markov Decision Process;deep reinforcement learning;long short-term memory;nan|
|[Pedestrian Detection and Avoidance at Night Using Multiple Sensors and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074081)|H. Kulhandjian; J. Barron; M. Tamiyasu; M. Thompson; M. Kulhandjian|10.1109/ICNC57223.2023.10074081|Machine learning;pedestrian detection;accident prevention;intelligent transportation systems;Training;Visualization;Machine learning algorithms;Radar detection;Radar;Data collection;Streaming media|
|[Blockchain-based Data Quality Assessment to Improve Distributed Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074543)|Y. Du; Z. Wang; C. Leung; L. Victor C.M.|10.1109/ICNC57223.2023.10074543|Blockchain;distributed machine learning;data quality;edge computing;Internet of things;nan|
|[Consensus algorithms for Opt-in/Opt-out and proximity marketing context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074503)|F. Chahal; H. Fouchal; D. Gaiti|10.1109/ICNC57223.2023.10074503|Consensus algorithms;Paxos;Raft;PBFT;PoW;PoS;Opt-in;Opt-out;Proximity marketing.;Technological innovation;Backtracking;Data security;Process control;Consensus algorithm;Companies;Regulation|
|[Reinforcement Learning based Optimal Dynamic Resource Allocation for RIS-aided MIMO Wireless Network with Hardware Limitations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074116)|Y. Zhang; L. Qian; A. Eroglu; B. Yang; H. Xu|10.1109/ICNC57223.2023.10074116|Reconfigurable intelligent surfaces;RIS phase shift;energy efficiency;hardware limitation;Uncertainty;Power demand;Wireless networks;Heuristic algorithms;Power control;Reinforcement learning;Dynamic scheduling|
|[Hyperbolic Routing for Cache Privacy in Information Centric Networking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074285)|S. Cannan; A. Jones; R. Simon|10.1109/ICNC57223.2023.10074285|nan;nan|
|[Cloud Security Requirement Based Threat Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074275)|A. Taha; A. Lawall; N. Suri|10.1109/ICNC57223.2023.10074275|Threat Analysis;Cloud Security;Service’s dependencies;Systematics;Cloud computing security;Manuals;Security|
|[Secrecy Performance and Power Allocation for Cooperative Vehicular Relaying Networks in the Presence of Interference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074258)|M. G. A. E. Ghafour; A. H. A. El-Malek; M. Abo-Zahhad|10.1109/ICNC57223.2023.10074258|Physical-layer security;cooperative relaying networks;interference modelling;secrecy outage probability;double Nakagami-m fading channels;power allocation.;Fading channels;Closed-form solutions;Computational modeling;Power distribution;Interference;Probability;Power system reliability|
|[Activity Detection for Grant-Free NOMA in Massive IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074280)|M. Mehrabi; M. Mohammadkarimi; M. Ardakani|10.1109/ICNC57223.2023.10074280|Activity detection;IoT;deep learning;NOMA;massive MIMO;Performance evaluation;Deep learning;NOMA;Base stations;Bandwidth;Internet of Things;Convolutional neural networks|
|[On an Integrated Security Framework for Defense Against Various DDoS Attacks in SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074226)|H. Wu; A. Hou; W. Nie; C. Wu|10.1109/ICNC57223.2023.10074226|Software-Defined Networking;high-rate DDoS attack;low-rate DDoS attack;Slow-TCAM attack;attack defense;Associative memory;Denial-of-service attack;Security;Ensemble learning;Software defined networking;Computer crime|
|[Implementation of Real-Time Adversarial Attacks on DNN-based Modulation Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074421)|E. Shtaiwi; A. R. Hussein; A. Khawar; A. Alkhateeb; A. Abdelhadi; Z. Han|10.1109/ICNC57223.2023.10074421|Modulation classifications;DNN-based classifier;FSGM;USRPs;SDR.;nan|
|[SEANAC: Schema Enforced Automation of Name-based Access Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073994)|P. Podder; A. Afanasyev|10.1109/ICNC57223.2023.10073994|Named Data Networking;Access Control Policy;Name-based Access Control;Access control;Automation;Encryption|
|[VM consolidation considering cores, memory, load for energy-constrained data centers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074410)|M. Ali; I. Andriyanova; J. David|10.1109/ICNC57223.2023.10074410|Hardware-based VM placement;energy-constrained data center.;Data centers;Energy consumption;Renewable energy sources;Power demand;Random access memory;Load management;Virtual machining|
|[Wrapper-Based Federated Feature Selection for IoT Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074296)|A. Mahanipour; H. Khamfroush|10.1109/ICNC57223.2023.10074296|Feature selection;Federated Learning;Internet-of-Things;Machine Learning;Learning systems;Costs;Smart cities;Computational modeling;Feature extraction;Internet of Things;Servers|
|[A Machine Learning Approach for the Detection of Injection Attacks on ADS-B Messaging Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074232)|J. Price; H. O. Slimane; K. A. Shamaileh; V. Devabhaktuni; N. Kaabouch|10.1109/ICNC57223.2023.10074232|Automatic dependent surveillance-broadcast (ADS-B);federal aviation administration (FAA);machine learning (ML);message injection;national airspace system (NAS);Measurement;Analytical models;Atmospheric modeling;Computational modeling;Machine learning;Predictive models;Feature extraction|
|[Blockchain-enabled Efficient and Secure Federated Learning in IoT and Edge Computing Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074277)|R. A. Mallah; D. López; T. Halabi|10.1109/ICNC57223.2023.10074277|Federated learning;Blockchain-based security;Internet of Things;behavior monitoring;model optimization;Training;Performance evaluation;Federated learning;Blockchains;Behavioral sciences;Internet of Things;Task analysis|
|[Uplink-to-Downlink Covariance Matrix Estimation in ULA-equipped FDD Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074252)|S. Bameri; K. Almahrog; R. H. Gohary; A. El-Keyi; Y. Ahmed|10.1109/ICNC57223.2023.10074252|nan;Estimation error;Upper bound;Sensitivity;Massive MIMO;Numerical simulation;Downlink;Linear antenna arrays|
|[DAWN-Sim: A Distributed Algorithm Simulator for Wireless Ad-hoc Networks in Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074218)|M. Tosun; U. C. Cabuk; O. Dagdeviren; Y. Ozturk|10.1109/ICNC57223.2023.10074218|IoT;MANET;Python;simulator;SimPy;WSN;Wireless communication;Wireless sensor networks;Software algorithms;Education;Ad hoc networks;Software;Hardware|
|[A-C: An NDN-based Blockchain Network With Erasure Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074192)|R. Wang; L. Njilla; S. Yu|10.1109/ICNC57223.2023.10074192|Blockchain;Information-Centric Network;Named-Data Network;Bandwidth optimization;Efficiency;Security;Privacy;Protocols;Spectral efficiency;Redundancy;Data dissemination;Probabilistic logic;Encoding;Blockchains|
|[A Reservation-based Adaptive MAC Protocol for OFDM Physical Layers in Underwater Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074103)|S. Falleni; T. Melodia; S. Basagni|10.1109/ICNC57223.2023.10074103|nan;nan|
|[A Simple UDP-Based Web Server on a Bare PC with 64-bit Multicore Processors: Design and Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074453)|N. Ordouie; R. K. Karne; A. L. Wijesinha; N. Soundararajan|10.1109/ICNC57223.2023.10074453|Bare PC;Bare Machine Computing;64-bit processor;multicore processor;Web server;UDP;Protocols;Program processors;Multicore processing;Scalability;Buildings;Ethernet;Load management|
|[Hensel’s Compression-Based Dimensionality Reduction Approach for Privacy Protection in Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074197)|A. E. Ouadrhiri; A. Abdelhadi; P. H. Phung|10.1109/ICNC57223.2023.10074197|Federated learning;privacy protection;differential privacy;dimensionality reduction;Hensel’s compression;Training;Dimensionality reduction;Deep learning;Privacy;Differential privacy;Federated learning;Neural networks|
|[Flick me once and I know it’s you! Flicking-based Implicit Authentication for Smartwatch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074521)|Y. Li; F. Ferreira; M. Xie|10.1109/ICNC57223.2023.10074521|nan;nan|
|[SGChain: Blockchain Platform for Availability Attack Mitigation in Smart Grid Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074093)|R. L. Neupane; P. Bhandari; P. Calyam; R. Mitra|10.1109/ICNC57223.2023.10074093|Smart Grid; Smart Metering;Permissioned Blockchain; Availability Attacks;Impact Mitigation;Smart Grid;Smart Metering, Permissioned Blockchain;Availability Attacks, Impact Mitigation;nan|
|[Enable Computation in the Network for Driver Assistance with Object Notification and Augmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074318)|L. Dong; R. Li|10.1109/ICNC57223.2023.10074318|computation in the network;driver assistance;IP packet extension;augmentation;central cloud;mobile edge computing;New IP;contract;metadata;action;future Internet;Cloud computing;Bandwidth;Object detection;Context awareness;Image capture;Real-time systems;Safety|
|[Iterative Symbol Decision Schemes for CP-OTFS on Static Multipath Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074022)|S. -Y. Tsai; W. -C. Chen; C. -D. Chung|10.1109/ICNC57223.2023.10074022|Orthogonal time frequency space modulation;iterative decision-feedback hybrid equalization;message passing algorithm;static multipath channels.;nan|
|[Deep learning inference time guarantee in near future edge computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074015)|K. Genda|10.1109/ICNC57223.2023.10074015|Edge computing;Deep learning;Inference time;Partitioning;Queueing model;Deep learning;Adaptation models;Image edge detection;Computational modeling;Simulation;Real-time systems;Delays|
|[CMRCV: Causal Modeling to Localize Failed Equipment by Representative Nodes and Contribution Values](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074021)|Y. Matsuo; Y. Nakano; K. Watanabe|10.1109/ICNC57223.2023.10074021|Root Cause Analysis;Localizing Failed Equipment;Bayesian Network;Contribution Values;Aggregates;Data models;Information and communication technology;Topology;Data mining|
|[Secure Anonymous Acknowledgments in a Delay-Tolerant Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074097)|E. Biagioni|10.1109/ICNC57223.2023.10074097|nan;Protocols;Receivers;Encryption;Reliability|
|[A Zero-Trust Framework for Industrial Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074295)|A. Atieh; P. Nanda; M. Mohanty|10.1109/ICNC57223.2023.10074295|Zero-Trust;Industrial Control Systems (ICS);Industrial Internet of Things (IIoT);Internet of Things (loT);Critical Infrastructures;Defence-in-Depth;Biological system modeling;Industrial control;Ecosystems;Data models;Complexity theory;Security;Integrated circuit modeling|
|[Towards Instant Clustering Approach for Federated Learning Client Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074237)|S. Arisdakessian; O. A. Wahab; A. Mourad; H. Otrok|10.1109/ICNC57223.2023.10074237|Federated Learning;Client Selection;Heterogeneity in Federated Learning;Clustering;Training;Privacy;Federated learning;Data integrity;Clustering algorithms;Real-time systems;Data models|
|[A Two-stage Trunk Reservation Control Method corresponding to General Call Priority using Switch Outside Disaster Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074541)|Y. Arii; K. Yamaoka; K. -I. Baba|10.1109/ICNC57223.2023.10074541|Emergency telecommunications;Trunk reservation control;Call blocking probability;Accommodation ratio of general call;Congestion control;nan|
|[Verification of a method for latent interest estimation based on user behavior analysis and POI attributes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074037)|T. Omura; F. Dollack; P. Siriaraya; D. Li; K. Tanaka; Y. Kawai; S. Nakajima|10.1109/ICNC57223.2023.10074037|Advertisement Recommender system;latent interest analysis;user behavior in physical space;Geotagged SNS;Training;Estimation;Predictive models;Real-time systems;Data models;Behavioral sciences;Internet|
|[Effectiveness and predictability of in-network storage cache for Scientific Workflows](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074058)|C. Sim; K. Wu; A. Sim; I. Monga; C. Guok; F. Würthwein; D. Davila; H. Newman; J. Balcas|10.1109/ICNC57223.2023.10074058|in-network caching;data throughput;transfer performance;data access trends;nan|
|[A Deep Q-Learning Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074301)|S. Devaraju; A. Ihler; S. Kumar|10.1109/ICNC57223.2023.10074301|Airborne network;UAV network;area coverage;network connectivity;reinforcement learning;deep Q-learning;Training;Q-learning;Mobility models;Databases;Surveillance;Computational modeling;Atmospheric modeling|
|[Using Artificial Intelligence and IoT Solution for Forest Fire Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074289)|G. Pettorru; M. Fadda; R. Girau; M. Sole; M. Anedda; D. Giusto|10.1109/ICNC57223.2023.10074289|Sensors and Actuator Systems;Internet of Things;Internet of Everywhere;and Edge Computing;Machine Learning;Deep Learning and AI in CE;Forestry;Vegetation;Sensor systems;Real-time systems;Social Internet of Things;Telecommunications;Artificial intelligence|
|[Preliminary Analysis of Dietary Management Support Method for Improving the Symptoms in Irritable Bowel Syndrome](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073984)|T. Yamanaka; D. Li; S. Nakajima|10.1109/ICNC57223.2023.10073984|Irritable Bowel Syndrome;IBS;machine learning;data mining;health care;Measurement;Support vector machines;Stomach;Intestines;Training data;Medical treatment;Predictive models|
|[TPMWallet: Towards Blockchain Hardware Wallet using Trusted Platform Module in IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074126)|W. -Y. Chiu; W. Meng; W. Li|10.1109/ICNC57223.2023.10074126|Trusted Platform Module;Internet of Things;Blockchain Technology;Hardware Wallet;Smart Contract;Performance evaluation;Smart contracts;Hardware;Software;Blockchains;Fraud;Internet of Things|
|[Prioritizing transaction delivery in Ethereum network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074528)|S. N. Mighan; J. Mišić; V. B. Mišić|10.1109/ICNC57223.2023.10074528|nan;nan|
|[Energy-Aware and Fair Multi-User Multi-Task Computation Offloading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074189)|V. Latzko; O. Lhamo; M. Mehrabi; C. Vielhaus; F. H. P. Fitzek|10.1109/ICNC57223.2023.10074189|nan;5G mobile communication;Simulation;Benchmark testing;Multitasking;Mobile handsets;Device-to-device communication;Central Processing Unit|
|[Towards Secure Communications in Heterogeneous Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074323)|J. H. Nguyen; W. Liao; W. Yu|10.1109/ICNC57223.2023.10074323|Internet of Things;Security;Heterogeneous Networks;CoAP.;nan|
|[A privacy awareness framework for NFT avatars in the metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074107)|D. Zelenyanszki; Z. Hóu; K. Biswas; V. Muthukkumarasamy|10.1109/ICNC57223.2023.10074107|metaverse;NFT;avatar;privacy;blockchain;ML;nan|
|[Optimal Codes for Distributed Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074312)|J. Ren; J. Li; T. Li|10.1109/ICNC57223.2023.10074312|Distributed data storage;regeneration code;optimal storage;adversarial networks;Codes;Costs;Data security;Simulation;Memory;Distributed databases;Maintenance engineering|
|[IP Transformation Initiatives to Generate Scalable Functional Verification Collaterals for Smart Reusability and Reduced Effort for Sign-off](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074542)|S. Bhattacherjee; D. Pal|10.1109/ICNC57223.2023.10074542|Scalable-Verification-Collateral;Perl-Template-Toolkit;ML-Plug-in;IP-SoC-Verification;IP-Validation-Scalability.;Fuzzy logic;Codes;Machine learning;Complexity theory;IP networks;Iterative methods;Standards|
|[Can We Have a Better System than OFDM?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074195)|T. Li; J. Deng; J. Ren|10.1109/ICNC57223.2023.10074195|OFDM;peak-to-average power ratio;IFFT-Relocated OFDM;jamming;secure precoding;Spectral efficiency;Transmitters;Modulation;Peak to average power ratio;Receivers;Transceivers;Robustness|
|[An edge computing-based monitoring framework for situation-aware embedded real-time systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074096)|N. Islam; A. Azim|10.1109/ICNC57223.2023.10074096|nan;nan|
|[Deep Learning Based Malapps Detection in Android Powered Mobile Cyber-Physical System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074208)|M. I. Sayed; S. Saha; A. Haque|10.1109/ICNC57223.2023.10074208|Android;deep learning;malware;malapps;mobile cyber-physical system;Deep learning;Performance evaluation;Costs;Codes;Operating systems;Static analysis;Cyber-physical systems|
|[On Line Secure Elements: Deploying High Security Keystores and Personal HSMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074066)|P. Urien|10.1109/ICNC57223.2023.10074066|Secure Element;IOSE;Security;TLS;Uniform resource locators;Performance evaluation;Phase shift keying;Authentication;TCPIP;Hardware;Servers|
|[Guiding Interactive Film With Emotion-Profiling Chatbots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074513)|C. L. Ma; H. Wang; M. Wang|10.1109/ICNC57223.2023.10074513|interactive film;generative language models;chatbots;multimodal emotion recognition;nan|
|[AFFIRM: Privacy-by-Design Blockchain for Mobility Data in Web3 using Information Centric Fog Networks with Collaborative Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074160)|J. A. Khan; K. Ozbay|10.1109/ICNC57223.2023.10074160|Blockchain;Web3;ICN;Fog Computing;IoT;Privacy preservation.;Data privacy;Connected vehicles;Federated learning;Urban areas;Information-centric networking;Computer architecture;Blockchains|
|[Metaverse-based education service adoption and preference study using conjoint analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074583)|J. Lee; S. Jang|10.1109/ICNC57223.2023.10074583|Metaverse;virtual reality;education;conjoint analysis;Application;nan|
|[Hybrid Quantum Machine learning using Quantum Integrated Cloud Architecture (QICA)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074394)|S. Fadli; B. S. Rawal; A. Mentges|10.1109/ICNC57223.2023.10074394|Gates-based quantum computing;NISQ algorithms;Quantum machine learning;quantum error correction;Quantum integrated cloud platforms;Training;Cloud computing;Quantum computing;Machine learning algorithms;Architecture;Neural networks;Quantum mechanics|
|[Flying Ad Hoc Coverage Area Mobility Model for Post-Disaster Area based on Two Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074120)|I. N. Azmi; Y. M. Yussoff; N. M. Thamrin; F. H. K. Zaman; N. M. Tahir|10.1109/ICNC57223.2023.10074120|Drones;search and rescue;disaster area;network throughput;Simulation;Packet loss;Throughput;Ad hoc networks;Delays;Safety;Quality assessment|
|[Optimization Framework for Green Networking (Invited Paper)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074563)|C. Westphal; A. Clemm|10.1109/ICNC57223.2023.10074563|Green IP;sustainability;energy effiency;carbon footprint;networking protocols;green management layer;optimization framework;nan|
|[5G NR-Light at Millimeter Waves: Design Guidelines for Mid-Market IoT Use Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074354)|M. Pagin; T. Zugno; M. Giordani; L. -A. Dufrene; Q. Lampin; M. Zorzi|10.1109/ICNC57223.2023.10074354|NR-Light;REDCAP;Internet of Things (IoT);3GPP;performance evaluation;use cases;technology enablers.;Performance evaluation;5G mobile communication;Throughput;Video surveillance;Distance measurement;Complexity theory;3GPP|
|[From Screenplay to Screen: A Natural Language Processing Approach to Animated Film Making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10074526)|K. Jorgensen; H. Wang; M. Wang|10.1109/ICNC57223.2023.10074526|interactive media;natural language processing;automatic character placement;screenplay subject labeling;nan|
#### **2023 IEEE International Solid- State Circuits Conference (ISSCC)**
- DOI: 10.1109/ISSCC42615.2023
- DATE: 19-23 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Reflections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067797)|L. C. Fujino|10.1109/ISSCC42615.2023.10067797|nan;nan|
|[Foreword: Building on 70 Years of Innovation in Solid-State Circuit Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067454)|P. Wambacq|10.1109/ISSCC42615.2023.10067454|nan;nan|
|[1.1 Innovation For the Next Decade of Compute Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067810)|L. Su; S. Naffziger|10.1109/ISSCC42615.2023.10067810|nan;Industries;Performance evaluation;Technological innovation;Renewable energy sources;Software algorithms;Packaging;Energy efficiency|
|[1.2 Shape the World with Mixed-Signal Integrated Circuits - Past, Present, and Future](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067478)|A. Matsuzawa|10.1109/ISSCC42615.2023.10067478|nan;Shape;DVD;Wireless networks;Optical recording;Personal digital devices;Signal processing;CMOS technology|
|[1.3 EU Ships Act Drives Pan-European Full-Stack Innovation Partnerships](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067442)|J. De Boeck; J. -R. Lèquepeys; C. Kutter|10.1109/ISSCC42615.2023.10067442|nan;Symbiosis;Radio frequency;Technological innovation;Semiconductor device measurement;Supply chains;Europe;Companies|
|[1.4 5G Drives Exponential Increase in Processing Needs Across all Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067359)|E. Ekudden|10.1109/ISSCC42615.2023.10067359|nan;nan|
|[“Zen 4”: The AMD 5nm 5.7GHz x86-64 Microprocessor Core](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067540)|B. Munger; K. Wilcox; J. Sniderman; C. Tung; B. Johnson; R. Schreiber; C. Henrion; K. Gillespie; T. Burd; H. Fair; D. Johnson; J. White; S. McLelland; S. Bakke; J. Olson; R. McCracken; M. Pickett; A. Horiuchi; H. Nguyen; T. H. Jackson|10.1109/ISSCC42615.2023.10067540|nan;Microprocessors;Collaboration;FinFETs;Transistors;Next generation networking;Physical design|
|[2.2 A 5G Mobile Gaming-Centric SoC with High-Performance Thermal Management in 4nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067271)|B. -J. Huang; A. Tsai; L. Hsieh; K. Chang; C. . -J. Tsai; J. -M. Chen; E. J. -W. Fang; S. S. . -Y. Hsueh; J. Ciao; B. Chen; C. Chang; P. Kao; E. Wang; H. H. Chen; H. Mair; S. -A. Hwang|10.1109/ISSCC42615.2023.10067271|nan;Temperature sensors;Temperature measurement;Temperature distribution;5G mobile communication;Benchmark testing;Thermal management;FinFETs|
|[Amorphica: 4-Replica 512 Fully Connected Spin 336MHz Metamorphic Annealer with Programmable Optimization Strategy and Compressed-Spin-Transfer Multi-Chip Extension](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067504)|K. Kawamura; J. Yu; D. Okonogi; S. Jimbo; G. Inoue; A. Hyodo; Á. L. García-Anas; K. Ando; B. H. Fukushima-Kimura; R. Yasudo; T. Van Chu; M. Motomura|10.1109/ISSCC42615.2023.10067504|nan;Couplings;Annealing;Quantum annealing;Energy states;Topology;Planning;Optimization|
|[A Fully Integrated End-to-End Genome Analysis Accelerator for Next-Generation Sequencing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067532)|Y. -L. Chen; C. -H. Yang; Y. -C. Wu; C. -H. Lee; W. -C. Chen; L. -Y. Lin; N. -S. Chang; C. -P. Lin; C. -S. Chen; J. -H. Hung; C. -H. Yang|10.1109/ISSCC42615.2023.10067532|nan;Sequential analysis;Data analysis;Pipelines;DNA;Genomics;Medical diagnosis;Computational complexity|
|[2.5 A 28nm 142mW Motion-Control SoC for Autonomous Mobile Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067700)|I. -T. Lin; Z. -S. Fu; W. -C. Chen; L. -Y. Lin; N. -S. Chang; C. -P. Lin; C. -S. Chen; C. -H. Yang|10.1109/ISSCC42615.2023.10067700|nan;Navigation;Trajectory;Mobile robots;Time factors;Motion control;Task analysis;Trajectory optimization|
|[VISTA: A 704mW 4K-UHD CNN Processor for Video and Image Spatial/Temporal Interpolation Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067857)|K. -P. Lin; J. -H. Liu; J. -Y. Wu; H. -C. Liao; C. -T. Huang|10.1109/ISSCC42615.2023.10067857|nan;Interpolation;Frequency modulation;Convolution;Processor scheduling;Superresolution;Streaming media;Convolutional neural networks|
|[2.7 MetaVRain: A 133mW Real-Time Hyper-Realistic 3D-NeRF Processor with 1D-2D Hybrid-Neural Engines for Metaverse on Mobile Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067447)|D. Han; J. Ryu; S. Kim; S. Kim; H. -J. Yoo|10.1109/ISSCC42615.2023.10067447|nan;Training;Solid modeling;Three-dimensional displays;Simultaneous localization and mapping;Metaverse;Computational modeling;Wearable computers|
|[3.1 A 120.9dB DR, -111.2dB THD+N Digital-Input Capacitively-Coupled Chopper Class-D Audio Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067400)|H. Zhang; M. Berkhout; K. A. A. Makinwa; Q. Fan|10.1109/ISSCC42615.2023.10067400|nan;Intermodulation distortion;Sensitivity;Costs;Prototypes;Jitter;Choppers (circuits)|
|[3.2 A Chopper-Stabilized Amplifier with a Relaxed Fill-In Technique and 22.6pA Input Current](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067656)|T. Roollers; J. H. Huijsing; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067656|nan;Intermodulation distortion;Linearity;Choppers (circuits);Frequency conversion;Delays;Floors;Clocks|
|[Bandpass Filter and Oscillator ICs with THD < -140dBc at 10Vppd for Testing High-Resolution ADCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067771)|S. Sarkar; R. Agarwal; N. Krishnapura|10.1109/ISSCC42615.2023.10067771|nan;Band-pass filters;Integrated circuits;Feedback loop;Negative feedback;Distortion;Harmonic analysis;Topology|
|[A 0.01 mm2 10MHz RC Frequency Reference with a 1-Point On-Chip-Trimmed Inaccuracy of $\boldsymbol{\pm 0.28\%}$ from $\boldsymbol{-45^{\mathrm{o}}\mathrm{C}}$ to $\boldsymbol{125^{\mathrm{o}}\mathrm{C}}$ in 0.18μm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067530)|X. An; S. Pan; H. Jiang; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067530|nan;Resistors;Frequency modulation;Crystals;CMOS technology;System-on-chip;Oscillators|
|[A $1.4\mu$ W/MHz 100MHz RC Oscillator with $\pm$ 1030ppm Inaccuracy from $-40^{\circ}\mathrm{C}$ to $85^{\circ}\mathrm{C}$ After Accelerated Aging for 500 Hours at $125^{\circ}\mathrm{C}$](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067729)|K. -S. Park; N. Pal; Y. Li; R. Xia; T. Wang; A. Abdelrahman; P. K. Hanumolu|10.1109/ISSCC42615.2023.10067729|nan;Resistors;Micromechanical devices;Accelerated aging;Prototypes;Crystals;Circuit stability;Behavioral sciences|
|[3.6 A 12/13.56MHz Crystal Oscillator with Binary-Search-Assisted Two-Step Injection Achieving 5.0nJ Startup Energy and 45.8μs Startup Time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067401)|H. Li; K. -M. Lei; P. -I. Mak; R. P. Martins|10.1109/ISSCC42615.2023.10067401|nan;Limiting;Resonant frequency;Crystals;Steady-state;Phase locked loops;Oscillators|
|[3.7 A 16MHz X0 with 17.5μs Startup Time Under 104ppm-ΔF Injection Using Automatic Phase-Error Correction Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067675)|Z. Cai; X. Wang; Z. Wang; Y. Yin; W. Zhang; T. Xu; Y. Guo|10.1109/ISSCC42615.2023.10067675|nan;Power demand;Chirp;Crystals;Voltage;System-on-chip;Synchronization;Phase locked loops|
|[3.8 A 0.954nW 32kHz Crystal Oscillator in 22nm CMOS with Gm-C-Based Current Injection Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067462)|Y. Zhang; Y. You; W. Ren; X. Xu; L. Shen; J. Ru; R. Huang; L. Ye|10.1109/ISSCC42615.2023.10067462|nan;Resistance;Energy loss;Power demand;Crystals;Jitter;Inverters;Energy efficiency|
|[A 0.5-to-400MHz Programmable BAW Oscillator with Fractional Output Divider Achieving 4ppm Frequency Stability over Temperature and <95fs Jitter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067511)|S. Mukherjee; Y. Darwhekar; J. Janardhanan; P. Mirajkar; R. Reddy; H. Ramesh; B. Bahr; J. Chand; U. Meda; B. Haroun; S. Karantha; E. Yen; K. Martin; D. Gan; A. Sijelmassi; S. Aniruddhan|10.1109/ISSCC42615.2023.10067511|nan;Temperature;Costs;Resonant frequency;Jitter;Frequency conversion;Circuit stability;Phase locked loops|
|[4.1 A 16GHz, $41\text{kHz}_{\text{rms}}$ Frequency Error, Background-Calibrated, Duty-Cycled FMCW Charge-Pump PLL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067404)|P. T. Renukaswamy; K. Vaesen; N. Markulic; V. Derudder; D. -W. Park; P. Wambacq; J. Craninckx|10.1109/ISSCC42615.2023.10067404|nan;Charge pumps;Chirp;Linearity;Bandwidth;Medical services;Doppler radar;Sensors|
|[A 135fsrms-Jitter 0.6-to-7.7GHz LO Generator Using a Single LC-VCO-Based Subsampling PLL and a Ring-Oscillator-Based Sub-Integer-N Frequency Multiplier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067748)|Y. Jo; J. Kim; Y. Shin; C. Hwang; H. Park; J. Choi|10.1109/ISSCC42615.2023.10067748|nan;Phase noise;Frequency modulation;Quadrature amplitude modulation;5G mobile communication;Jitter;Frequency conversion;Generators|
|[4.3 A 76.7fs-lntegrated-Jitter and −71.9dBc In-Band Fractional-Spur Bang-Bang Digital PLL Based on an Inverse-Constant-Slope DTC and FCW Subtractive Dithering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067719)|S. M. Dartizio; F. Tesolin; G. Castoro; F. Buccoleri; L. Lanzoni; M. Resson; D. Cherniak; L. Bertuless; C. Samor; A. L. Lacaita; S. Levantino|10.1109/ISSCC42615.2023.10067719|nan;Wireless communication;Wireless sensor networks;Sensitivity;Quantization (signal);Linearity;Voltage;Radar|
|[A 32kHz-Reference 2.4GHz Fractional-N Nonuniform Oversampling PLL with Gain-Boosted PD and Loop-Gain Calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067516)|J. Qiu; W. Wang; Z. Sun; B. Liu; Y. Zhang; D. Xu; H. Huang; A. A. Fadila; Z. Liu; W. Madany; Y. Xiong; A. Shirane; K. Okada|10.1109/ISSCC42615.2023.10067516|nan;Wireless communication;Power demand;Bandwidth;Voltage;Detectors;Jitter;Calibration|
|[4.5 A 9.25GHz Digital PLL with Fractional-Spur Cancellation Based on a Multi-DTC Topology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067351)|G. Castoro; S. M. Dartizio; F. Tesolin; F. Buccoleri; M. Rossoni; D. Cherniak; L. Bertulessi; C. Samori; A. L. Lacaita; S. Levantino|10.1109/ISSCC42615.2023.10067351|nan;nan|
|[4.6 A $47\text{fs}_{\text{rms}}$-Jitter and 26.6mW 103.5GHz PLL with Power-Gating Injection-Locked Frequency-Multiplier-Based Phase Detector and Extended Loop Bandwidth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067293)|J. Bang; J. Kim; S. Jung; S. Park; J. Choi|10.1109/ISSCC42615.2023.10067293|nan;Phase noise;Frequency synthesizers;Frequency modulation;Voltage-controlled oscillators;Bandwidth;Phase frequency detectors;Detectors|
|[4.7 A O.4V-VDD 2.25-to-2.75GHz ULV-SS-PLL Achieving 236.6fsrms Jitter, −253.8dB Jitter-Power FoM, and −76.1dBc Reference Spur](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067638)|Z. Zhang; X. Shen; Z. Zhang; G. Li; N. Qi; J. Liu; Y. Chen; N. Wu; L. Liu|10.1109/ISSCC42615.2023.10067638|nan;Phase noise;Degradation;Low voltage;Power demand;Voltage-controlled oscillators;Voltage;Detectors|
|[A 3-Wafer-Stacked Hybrid 15MPixel CIS + 1 MPixel EVS with 4.6GEvent/s Readout, In-Pixel TDC and On-Chip ISP and ESP Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067476)|M. Guo; S. Chen; Z. Gao; W. Yang; P. Bartkovjak; Q. Qin; X. Hu; D. Zhou; M. Uchiyama; S. Fukuoka; C. Xu; H. Ebihara; A. Wang; P. Jiang; B. Jiang; B. Mu; H. Chen; J. Yang; T. Dai; A. Suess; Y. Kudo|10.1109/ISSCC42615.2023.10067476|nan;Three-dimensional displays;Vision sensors;CMOS image sensors;System-on-chip;Object tracking;Low latency communication|
|[1.22μm 35.6Mpixel RGB Hybrid Event-Based Vision Sensor with 4.88μm-Pitch Event Pixels and up to 10K Event Frame Rate by Adaptive Control on Event Sparsity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067520)|K. Kodama; Y. Sato; Y. Yorikado; R. Berner; K. Mizoguchi; T. Miyazaki; M. Tsukamoto; Y. Matoba; H. Shinozaki; A. Niwa; T. Yamaguchi; C. Brandli; H. Wakabayashi; Y. Oike|10.1109/ISSCC42615.2023.10067520|nan;Image sensors;Photography;Sensitivity;Power demand;Sensor phenomena and characterization;Vision sensors;Packaging|
|[A 2.97μm-Pitch Event-Based Vision Sensor with Shared Pixel Front-End Circuitry and Low-Noise Intensity Readout Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067566)|A. Niwa; F. Mochizuki; R. Berner; T. Maruyarma; T. Terano; K. Takamiya; Y. Kimura; K. Mizoguchi; T. Miyazaki; S. Kaizu; H. Takahashi; A. Suzuki; C. Brandli; H. Wakabayashi; Y. Oike|10.1109/ISSCC42615.2023.10067566|nan;Event detection;Vision sensors;Throughput;High dynamic range;Time factors;Synchronization;Proposals|
|[A 0.64μm 4-Photodiode 1.28μm 50Mpixel CMOS Image Sensor with 0.98e- Temporal Noise and 20Ke- Full-Well Capacity Employing Quarter-Ring Source-Follower](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067732)|H. Kim; Y. H. Kim; S. Moon; H. Kim; B. Yoo; J. Park; S. Kim; J. -M. Koo; S. Seo; H. J. Shin; Y. Choi; J. Kim; K. Kim; J. -H. Seo; S. Lim; T. Jung; H. Park; S. Jung; J. Ko; K. Lee; J. Ahn; J. Yim|10.1109/ISSCC42615.2023.10067732|nan;Image quality;Image resolution;CMOS image sensors;Cameras;Mobile handsets;Signal to noise ratio;Diffusion tensor imaging|
|[A 16.4kPixel 3.08-to-3.86THz Digital Real-Time CMOS Image Sensor with 73dB Dynamic Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067620)|M. Liu; Z. Cai; S. Zhou; M. -K. Law; J. Liu; J. Ma; N. Wu; L. Liu|10.1109/ISSCC42615.2023.10067620|nan;Terahertz wave imaging;Sensitivity;Image resolution;Voltage-controlled oscillators;CMOS image sensors;Silicon;System-on-chip|
|[5.6 A 400 $\times$ 200 600fps 117.7dB-DR SPAD X-Ray Detector with Seamless Global Shutter and Time-Encoded Extrapolation Counter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067344)|B. Park; B. Ahn; H. -S. Choi; J. Jeong; K. Hwang; T. Kim; M. -J. Lee; Y. Chae|10.1109/ISSCC42615.2023.10067344|nan;Extrapolation;X-ray detectors;Radiation detectors;Detectors;Switches;Real-time systems;X-ray imaging|
|[55pW/pixel Peak Power Imager with Near-Sensor Novelty/Edge Detection and DC-DC Converter-Less MPPT for Purely Harvested Sensor Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067486)|K. A. Ahmed; H. Okuhara; M. Alioto|10.1109/ISSCC42615.2023.10067486|nan;Measurement;Costs;Image resolution;Photovoltaic cells;Neural networks;Vision sensors;Low-power electronics|
|[Dual-Port CMOS Image Sensor with Regression-Based HDR Flux-to-Digital Conversion and 80ns Rapid-Update Pixel-Wise Exposure Coding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067708)|R. Gulve; R. Rangel; A. Barman; D. Nguyen; M. Wei; M. A. Sakr; X. Sun; D. B. Lindell; K. N. Kutulakos; R. Genov|10.1109/ISSCC42615.2023.10067708|nan;Image coding;Costs;Lighting;Cameras;Software;Encoding;Sensors|
|[6.1 A 112Gb/s Serial Link Transceiver With 3-tap FFE and 18-tap DFE Receiver for up to 43dB Insertion Loss Channel in 7nm FinFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067657)|B. Zhang; A. Vasani; A. Sinha; A. Nilchi; H. Tong; L. Rao; K. Khanoyan; H. Hatamkhani; X. Yang; X. Meng; A. Wong; J. Kim; P. Jing; Y. Sun; A. Nazemi; D. Liu; A. Brewster; J. Cao; A. Momtaz|10.1109/ISSCC42615.2023.10067657|nan;Data centers;Transmitters;Social networking (online);Receivers;Insertion loss;Streaming media;FinFETs|
|[A 4.63pJ/b 112Gb/s DSP-Based PAM-4 Transceiver for a Large-Scale Switch in 5nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067613)|H. Park; M. Abdullatif; E. Chen; A. Elmallah; Q. Nehal; M. Gandara; T. -B. Liu; A. Khashaba; J. Lee; C. -Y. Kuan; D. Hamachandran; R. -B. Sun; A. Atharav; Y. Chun; M. Zhang; D. -F. Weng; C. -H. Tsai; C. -H. Chang; C. -S. Peng; S. -T. Hsu; T. Ali|10.1109/ISSCC42615.2023.10067613|nan;Optical losses;Optical fibers;Power demand;Optical switches;Repeaters;Transceivers;Large scale integration|
|[6.3 A 0.43pJ/b 200Gb/s 5-Tap Delay-Line-Based Receiver FFE with Low-Frequency Equalization in 28nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067348)|B. Ye; G. Wu; W. Gai; K. Sheng; Y. He|10.1109/ISSCC42615.2023.10067348|nan;nan|
|[A 4nm 32Gb/s 8Tb/s/mm Die-to-Die Chiplet Using NRZ Single-Ended Transceiver With Equalization Schemes And Training Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067477)|K. Seong; D. Park; G. Bae; H. Lee; Y. Suh; W. Oh; H. Lee; J. Kim; T. Lee; G. Mo; S. Jung; D. Choi; B. -J. Yoo; S. Park; H. -G. Rhew; J. Shin|10.1109/ISSCC42615.2023.10067477|nan;Training;Power demand;Transmitters;Bandwidth;Tail;Transceivers;Decision feedback equalizers|
|[A 37.8dB Channel Loss 0.6μs Lock Time CDR with Flash Frequency Acquisition in 5nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067754)|C. -K. Kao; S. -C. Hung; T. -H. Yeh; C. -Y. Hsiao|10.1109/ISSCC42615.2023.10067754|nan;Time-frequency analysis;Transmitters;Time series analysis;Receivers;Phase frequency detectors;Detectors;Propagation losses|
|[A 0.83pJ/b 52Gb/s PAM-4 Baud-Rate CDR with Pattern-Based Phase Detector for Short-Reach Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067541)|S. Park; Y. Choi; J. Sim; J. Choi; H. Park; Y. Kwon; C. Kim|10.1109/ISSCC42615.2023.10067541|nan;Power demand;Frequency modulation;Detectors;Receivers;Transceivers;Energy efficiency;Registers|
|[6.7 A 128Gb/s PAM-4 Transmitter with Programmable-Width Pulse Generator and Pattern-Dependent Pre-Emphasis in 28nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067407)|K. Sheng; W. Gai; Z. Feng; H. Niu; B. Ye; H. Zhou|10.1109/ISSCC42615.2023.10067407|nan;Pulse generation;Transmitters;Equalizers;Symbols;Modulation;Interference;Bandwidth|
|[6.8 A 100Gb/s 1.6Vppd PAM-8 Transmitter with High-Swing $\mathbf{3+1}$ Hybrid FFE Taps in 40nm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067452)|J. Yang; E. Song; S. Hong; D. Lee; S. Lee; H. Im; T. Shin; J. Han|10.1109/ISSCC42615.2023.10067452|nan;Power demand;Transmitters;Modulation;Dynamic range;Transceivers;Energy efficiency;Timing|
|[A 22nm 832Kb Hybrid-Domain Floating-Point SRAM In-Memory-Compute Macro with 16.2-70.2TFLOPS/W for High-Accuracy AI-Edge Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067527)|P. -C. Wu; J. -W. Su; L. -Y. Hong; J. -S. Ren; C. -H. Chien; H. -Y. Chen; C. -E. Ke; H. -M. Hsiao; S. -H. Li; S. -S. Sheu; W. -C. Lo; S. -C. Chang; C. -C. Lo; R. -S. Liu; C. -C. Hsieh; K. -T. Tang; M. -F. Chang|10.1109/ISSCC42615.2023.10067527|nan;Power demand;Neural networks;Parallel processing;SRAM cells;Routing;Software;Transistors|
|[A 28nm 64-kb 31.6-TFLOPS/W Digital-Domain Floating-Point-Computing-Unit and Double-Bit 6T-SRAM Computing-in-Memory Macro for Floating-Point CNNs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067260)|A. Guo; X. Si; X. Chen; F. Dong; X. Pu; D. Li; Y. Zhou; L. Ren; Y. Xue; X. Dong; H. Gao; Y. Zhang; J. Zhang; Y. Kong; T. Xiong; B. Wang; H. Cai; W. Shan; J. Yang|10.1109/ISSCC42615.2023.10067260|nan;Training;Image segmentation;In-memory computing;Energy efficiency;System-on-chip;Task analysis;Artificial intelligence|
|[7.3 A 28nm 38-to-102-TOPS/W 8b Multiply-Less Approximate Digital SRAM Compute-In-Memory Macro for Neural-Network Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067305)|Y. He; H. Diao; C. Tang; W. Jia; X. Tang; Y. Wang; J. Yue; X. Li; H. Yang; H. Jia; Y. Liu|10.1109/ISSCC42615.2023.10067305|nan;Degradation;Design methodology;Layout;Random access memory;Computer architecture;Artificial neural networks;Throughput|
|[A 4nm 6163-TOPS/W/b $\mathbf{4790-TOPS/mm^{2}/b}$ SRAM Based Digital-Computing-in-Memory Macro Supporting Bit-Width Flexibility and Simultaneous MAC and Weight Update](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067555)|H. Mori; W. -C. Zhao; C. -E. Lee; C. -F. Lee; Y. -H. Hsu; C. -K. Chuang; T. Hashizume; H. -C. Tung; Y. -Y. Liu; S. -R. Wu; K. Akarvardar; T. -L. Chou; H. Fujiwara; Y. Wang; Y. -D. Chih; Y. -H. Chen; H. -J. Liao; T. -Y. J. Chang|10.1109/ISSCC42615.2023.10067555|nan;Quantization (signal);Pipelines;Random access memory;Voltage;Throughput;Energy efficiency;Topology|
|[A 28nm Horizontal-Weight-Shift and Vertical-feature-Shift-Based Separate-WL 6T-SRAM Computation-in-Memory Unit-Macro for Edge Depthwise Neural-Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067526)|B. Wang; C. Xue; Z. Feng; Z. Zhang; H. Liu; L. Ren; X. Li; A. Yin; T. Xiong; Y. Xue; S. He; Y. Kong; Y. Zhou; A. Guo; X. Si; J. Yang|10.1109/ISSCC42615.2023.10067526|nan;Convolution;Neural networks;Random access memory;Energy efficiency;Common Information Model (computing);Computational efficiency;Power dissipation|
|[7.6 A 70.85-86.27TOPS/W PVT-Insensitive 8b Word-Wise ACIM with Post-Processing Relaxation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067335)|S. -E. Hsieh; C. -H. Wei; C. -X. Xue; H. -W. Lin; W. -H. Tu; E. -J. Chang; K. -T. Yang; P. -H. Chen; W. -N. Liao; L. L. Low; C. -D. Lee; A. -C. Lu; J. Liang; C. -C. Cheng; T. -H. Kang|10.1109/ISSCC42615.2023.10067335|nan;Training;Limiting;Linearity;Detectors;Learning (artificial intelligence);Inference algorithms;Energy efficiency|
|[7.7 CV-CIM: A 28nm XOR-Derived Similarity-Aware Computation-in-Memory for Cost-Volume Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067720)|Z. Yue; Y. Wang; H. Wang; Y. Wang; R. Guo; L. Tang; L. Liu; S. Wei; Y. Hu; S. Yin|10.1109/ISSCC42615.2023.10067720|nan;Image resolution;Loading;Parallel processing;Cost function;Common Information Model (computing);Real-time systems;Stereo vision|
|[7.8 A 22nm Delta-Sigma Computing-In-Memory (Δ∑CIM) SRAM Macro with Near-Zero-Mean Outputs and LSB-First ADCs Achieving 21.38TOPS/W for 8b-MAC Edge AI Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067289)|P. Chen; M. Wu; W. Zhao; J. Cui; Z. Wang; Y. Zhang; Q. Wang; J. Ru; L. Shen; T. Jia; Y. Ma; L. Ye; R. Huang|10.1109/ISSCC42615.2023.10067289|nan;Deep learning;Energy consumption;Surveillance;Neural networks;Random access memory;Market research;Common Information Model (computing)|
|[CTLE-Ising:A 1440-Spin Continuous-Time Latch-Based isling Machine with One-Shot Fully-Parallel Spin Updates Featuring Equalization of Spin States](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067622)|J. Bae; W. Oh; J. Koo; B. Kim|10.1109/ISSCC42615.2023.10067622|nan;Power demand;Qubit;CMOS technology;Generators;Behavioral sciences;Oscillators;Optimization|
|[8.1 An 11.5-to-14.3GHz 192.8dBc/Hz FoM at 1MHz Offset Dual-Core Enhanced Class-F VCO with Common-Mode-Noise Self-Cancellation and Isolation Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067672)|Q. Wu; W. Deng; H. Jia; H. Liu; S. Zhang; Z. Wang; B. Chi|10.1109/ISSCC42615.2023.10067672|nan;Phase noise;Resistance;Inductance;Power demand;Costs;Voltage-controlled oscillators;Resonator filters|
|[8.2 A 22.4-to-26.8GHz Dual-Path-Synchronized Quad-Core Oscillator Achieving −138dBc/Hz PN and 193.3dBc/Hz FoM at 10MHz Offset from 25.8GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067277)|X. Zhan; J. Yin; P. -I. Mak; R. P. Martins|10.1109/ISSCC42615.2023.10067277|nan;Wireless communication;Resistance;Phase noise;Spirals;Transformer cores;Transformers;Synchronization|
|[8.3 A 28GHz Scalable Inter-Core-Shaping Multi-Core Oscillator with DM/CM-Configured Coupling Achieving 193.3dBc/Hz FoM and 205.5dBc/Hz FoMA at 1MHz Offset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067826)|Y. Shu; Z. Deng; X. Luo|10.1109/ISSCC42615.2023.10067826|nan;Couplings;Phase noise;Degradation;Mutual coupling;Inductance;Multicore processing;Capacitors|
|[8.4 An 83.3-to-104.7GHz Harmonic-Extraction VCO Incorporating Multi-Resonance, Multi-Core, and Multi-Mode (3M) Techniques Achieving -124dBc/Hz Absolute PN and 190.7dBc/Hz $\text{FoM}_{\mathrm{T}}$](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067832)|H. Guo; Y. Chen; Y. Huang; P. -I. Mak; R. P. Martins|10.1109/ISSCC42615.2023.10067832|nan;Couplings;Sensitivity;Multicore processing;Voltage-controlled oscillators;Magnetic cores;Spaceborne radar;Switches|
|[9.1 D1: A 7nm ML Training Processor with Wave Clock Distribution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067658)|T. C. Fischer; A. K. Nivarti; R. Ramachandran; R. Bharti; D. Carson; A. Lawrendra; V. Mudgal; V. Santhosh; S. Shukla; T. -C. Tsai|10.1109/ISSCC42615.2023.10067658|nan;Training;Modular construction;Chip scale packaging;Clocks|
|[A 1mW Always-on Computer Vision Deep Learning Neural Decision Processor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067588)|D. Garrett; Y. S. Park; S. Kim; J. Sharma; W. Huang; M. Shaghaghi; V. Parthasarathy; S. Gibellini; S. Bailey; M. Moturi; P. Vorenkamp; K. Busch; J. Holleman; B. Javid; A. Yousefi; M. Judy; A. Gupta|10.1109/ISSCC42615.2023.10067588|nan;Deep learning;Performance evaluation;Image sensors;Computer vision;Neural networks;Modulation;Cameras|
|[9.3 NVLink-C2C: A Coherent Off Package Chip-to-Chip Interconnect with 40Gbps/pin Single-ended Signaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067395)|Y. Wei; Y. C. Huang; H. Tang; N. Sankaran; I. Chadha; D. Dai; O. Oluwole; V. Balan; E. Lee|10.1109/ISSCC42615.2023.10067395|nan;Integrated circuit interconnections;Graphics processing units;Central Processing Unit;Low latency communication;Artificial intelligence|
|[9.4 An In-depth Look at the Intel IPU E2000](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067333)|N. Sundar; B. Burres; Y. Li; D. Minturn; B. Johnson; N. Jain|10.1109/ISSCC42615.2023.10067333|nan;Technological innovation;Cloud computing;Pipelines;Production;Software;Distance measurement;Telemetry|
|[A 1.8GHz 12b Pre-Sampling Pipelined ADC with Reference Buffer and OP Power Relaxations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067258)|S. -E. Hsieh; T. -C. Wu; C. -C. Hou|10.1109/ISSCC42615.2023.10067258|nan;Radio frequency;Receivers;Energy efficiency;Calibration;Registers;Complexity theory;Circuit stability|
|[10.2 A Single-Channel 2.6GS/s 10b Dynamic Pipelined ADC with Time-Assisted Residue Generation Scheme Achieving Intrinsic PVT Robustness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067822)|J. Hao; M. Zhang; Y. Zhang; S. Liu; Z. Zhu; Y. Zhu; C. -H. Chan; R. P. Martins|10.1109/ISSCC42615.2023.10067822|nan;Quantization (signal);Pipelines;Energy resolution;Linearity;Modulation;Receivers;Robustness|
|[10.3 A Single-Channel 12b 2GS/s PVT-Robust Pipelined ADC with Critically Damped Ring Amplifier and Time-Domain Quantizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067687)|Y. Cao; M. Zhang; Y. Zhu; C. -H. Chan; R. P. Martins|10.1109/ISSCC42615.2023.10067687|nan;Costs;Topology;Calibration;Time-domain analysis;Convergence|
|[10.4 A Rail-to-Rail 12MS 91.3dB SNDR 94.1dB DR Two-Step SAR ADC with Integrated Input Buffer Using Predictive Level-Shifting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067703)|M. Li; C. Y. Lee; A. ElShater; Y. Miyahara; K. Sobue; K. Tomioka; U. -K. Moon|10.1109/ISSCC42615.2023.10067703|nan;Power supplies;Linearity;Bandwidth;Drives;Capacitance;Thermal noise;Registers|
|[10.5 A 25MHz-BW 77.2dB-SNDR 2nd-Order Gain-Error-Shaping and NS Pipelined SAR ADC Based on a Quantization-Prediction-Unrolled Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067438)|H. Zhang; Y. Zhu; C. -H. Chan; R. P. Martins|10.1109/ISSCC42615.2023.10067438|nan;Multi-stage noise shaping;Quantization (signal);Shape;Filtering;Transfer functions;Voltage;Germanium|
|[10.6 A 150kHz-BW 15-ENOB Incremental Zoom ADC with Skipped Sampling and Single Buffer Embedded Noise-Shaping SAR Quantizer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067696)|Z. Wang; L. Jie; Z. Kong; M. Zhan; Y. Zhong; Y. Wang; X. Tang|10.1109/ISSCC42615.2023.10067696|nan;Multiplexing;Quantization (signal);Energy resolution;Bandwidth;System integration;Energy efficiency;Noise shaping|
|[10.7 A Single-Channel 70dB-SNDR 100MHz-BW 4th-Order Noise-Shaping Pipeline SAR ADC with Residue Amplifier Error Shaping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067689)|Y. Zhang; J. Hao; S. Liu; Z. Zhu; Y. Zhu; C. -h. Chan; R. P. Martins|10.1109/ISSCC42615.2023.10067689|nan;Wireless communication;Sigma-delta modulation;Pipelines;Tin;Energy efficiency;Noise shaping;Registers|
|[11.1 A Scalable Heterogeneous Integrated Two-Stage Vertical Power-Delivery Architecture for High-Performance Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067315)|C. Hardy; H. Pham; M. M. Jatlaoui; F. Voiron; T. Xie; P. -H. Chen; S. Jha; P. Mercier; H. -P. Le|10.1109/ISSCC42615.2023.10067315|nan;Rails;Program processors;Regulators;High performance computing;Multichip modules;Thermal conductivity;Mobile handsets|
|[11.2 A 12V-to-1V Quad-Output Switched-Capacitor Buck Converter with Shared DC Capacitors Achieving 90.4% Peak Efficiency and 48mA/mm3 Power Density at 85% Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067463)|T. Hu; M. Huang; Y. Lu; R. P. Martins|10.1109/ISSCC42615.2023.10067463|nan;Power system measurements;Buck converters;Regulators;Density measurement;Switching frequency;Switching loss;Switches|
|[11.3 A 1.8W High-Frequency SIMO Converter Featuring Digital Sensor-Less Computational Zero-Current Operation and Non-Linear Duty-Boost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067637)|S. Kim; H. K. Krlshnarnurthy; S. Sofer; S. Weng; S. Wolf; A. Ravi; K. Ravichandran; O. Degani; J. W. Tschanz; V. De|10.1109/ISSCC42615.2023.10067637|nan;Time-frequency analysis;Buck converters;Power system management;Capacitors;Frequency conversion;Regulation;Inductors|
|[11.4 A Double Step-Down Dual-Output Converter with Cross Regulation of 0.025mV/mA and Improved Current Balance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067843)|W. -C. Hung; C. -W. Chen; Y. -W. Huang; A. Chen; Z. -Y. Yang; K. -H. Chen; K. -L. Zhenq; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai; W. -C. Huang|10.1109/ISSCC42615.2023.10067843|nan;Power system measurements;Density measurement;Energy exchange;Capacitors;Voltage;Switches;Regulation|
|[11.5 A 21W 94.8%-Efficient Reconfigurable Single-Inductor Multi-Stage Hybrid DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067316)|C. Hardy; H. -P. Le|10.1109/ISSCC42615.2023.10067316|nan;Voltage;DC-DC power converters;Batteries;Inductors;Video recording;Load flow|
|[A 42W Reconfigurable Bidirectional Power Delivery Voltage-Regulating Cable](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067491)|Z. Tong; J. Huang; Y. Lu; R. P. Martins|10.1109/ISSCC42615.2023.10067491|nan;Heating systems;Thermal factors;Protocols;Switches;Universal Serial Bus;Regulation;Topology|
|[11.7 A Wide 0.1-to-10 Conversion-Ratio Symmetric Hybrid Buck-Boost Converter for USB PD Bidirectional Conversion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067408)|C. Lin; C. -S. Hung; S. -Y. Li; Y. -T. Hsu; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai|10.1109/ISSCC42615.2023.10067408|nan;Transient response;Power transmission;Voltage;Bidirectional control;High-voltage techniques;Switches;Universal Serial Bus|
|[11.8 A 5A 94.5% Peak Efficiency 9~16V-to-1V Dual-Path Series-Capacitor Converter with Full Duty Range and Low V.A Metric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067802)|X. Yang; L. Zhao; M. Zhao; Z. Tan; Y. Ding; W. Li; W. Qu|10.1109/ISSCC42615.2023.10067802|nan;Measurement;Transient response;Power demand;Capacitors;Switching loss;Voltage;Steady-state|
|[11.9 A Compact 12V-to-1V 91.8% Peak Efficiency Hybrid Resonant Switched-Capacitor Parallel Inductor (ReSC-PL) Buck Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067303)|G. Cai; Y. Lu; R. P. Martins|10.1109/ISSCC42615.2023.10067303|nan;Buck converters;Switching frequency;Voltage;Switches;Topology;Inductors;Current density|
|[11.10 A 12V-lnput 1V-1.8V-Output 93.7% Peak Efficiency Dual-Inductor Quad-Path Hybrid DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067710)|W. -L. Zeng; G. Cai; C. -F. Lee; C. -S. Lam; Y. Lu; S. -W. Sin; R. P. Martins|10.1109/ISSCC42615.2023.10067710|nan;Industries;Portable computers;Capacitors;Voltage;High-voltage techniques;Energy efficiency;Topology|
|[A O.96pJ/b 7 × 50Gb/s-per-Fiber WDM Receiver with Stacked 7nm CMOS and 45nm Silicon Photonic Dies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067617)|M. Raj; C. Xie; A. Bekele; A. Chou; W. Zhang; Y. Cao; J. W. Kim; N. Narang; H. Zhao; Y. Wang; K. H. Tan; W. Lin; J. Im; D. Mahashin; S. Asuncion; P. Upadhyaya; Y. Frans|10.1109/ISSCC42615.2023.10067617|nan;Meters;High performance computing;Graphics processing units;Receivers;Machine learning;Wavelength division multiplexing;Silicon photonics|
|[A 7 pA/$\surd\text{Hz}$ Asymmetric Differential TIA for 100Gb/s PAM-4 links with −14dBm Optical Sensitivity in 16nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067483)|K. Lakshmikumar; A. Kurylak; R. K. Nandwana; B. Das; J. Pampanin; M. Brubaker; P. K. Hanumolu|10.1109/ISSCC42615.2023.10067483|nan;Resistors;Regulators;Power demand;Capacitors;Bandwidth;Receivers;Regulation|
|[12.3 A Carrier-Phase-Recovery Loop for a 3.2pJ/b 24Gb/s QPSK Coherent Optical Receiver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067602)|A. E. Abdelrahman; M. G. Ahmed; M. A. Khalil; M. B. Younis; K. -S. Park; P. K. Hanumolu|10.1109/ISSCC42615.2023.10067602|nan;Optical filters;Phase noise;Demultiplexing;Phase shift keying;Power demand;Optical polarization;Spectral efficiency|
|[Crystalline Oxide Semiconductor-based 3D Bank Memory System for Endpoint Artificial Intelligence with Multiple Neural Networks Facilitating Context Switching and Power Gating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067603)|Y. Yakubo; K. Furutani; K. Toyotaka; H. Katagiri; M. Fujita; M. Kozuma; Y. Ando; Y. Kurokawa; T. Nakura; S. Yamazaki|10.1109/ISSCC42615.2023.10067603|nan;Three-dimensional displays;Stacking;Switches;Artificial neural networks;Silicon;Central Processing Unit;Transistors|
|[A 47nW Mixed-Signal Voice Activity Detector (VAD) Featuring a Non-Volatile Capacitor-ROM, a Short-Time CNN Feature Extractor and an RNN Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067728)|J. Lin; K. -F. Un; W. -H. Yu; P. -I. Mak; R. P. Martins|10.1109/ISSCC42615.2023.10067728|nan;Voice activity detection;Memory management;Filter banks;Speech recognition;Detectors;Switches;Feature extraction|
|[13.3 A Triturated Sensing System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067416)|N. Miura; K. Naruse; J. Shiomi; Y. Midoh; T. Hirose; T. Okidono; T. Miki; M. Nagata|10.1109/ISSCC42615.2023.10067416|nan;Wireless communication;Radio frequency;Wireless sensor networks;Scalability;Acoustics;Explosives;System-on-chip|
|[13.4 A Self-Programming PUF Harvesting the High-Energy Plasma During Fabrication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067576)|K. Naruse; T. Ueda; J. Shiomi; Y. Midoh; N. Miura|10.1109/ISSCC42615.2023.10067576|nan;Integrated circuits;Fabrication;Instruments;Metals;Logic gates;Ions;Etching|
|[13.5 Subtractive Photonic Waveguide-Coupled Photodetectors in 180nm Bulk CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067724)|C. Ives; A. Hajimiri|10.1109/ISSCC42615.2023.10067724|nan;Optical losses;Optical interconnections;Silicon-on-insulator;Logic gates;CMOS process;Silicon photonics;Photodetectors|
|[A Silicon Photonic Reconfigurable Optical Analog Processor (SiROAP) with a 4x4 Optical Mesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067633)|M. J. Shawon; V. Saxena|10.1109/ISSCC42615.2023.10067633|nan;Integrated optics;Semiconductor device modeling;Optical device fabrication;Packaging;Silicon photonics;Optical crosstalk;Solid state circuits|
|[14.1 A Fractional-N Digital MDLL with Injection-Error Scrambling and Background Third-Order DTC Delay Equalizer Achieving −67dBc Fractional Spur](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067342)|Q. Zhang; H. -C. Cheng; S. Su; M. S. -W. Chen|10.1109/ISSCC42615.2023.10067342|nan;Phase noise;Equalizers;Error analysis;Prototypes;Error compensation;Jitter;Delays|
|[A 10-to-300MHz Fractional Output Divider with -80dBc Worst-Case Fractional Spurs Using Auxiliary-PLL-Based Background 0th/1st/2nd-Order DTC INL Calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067785)|Y. Yang; W. Deng; A. Yan; H. Jia; J. Gong; Z. Wang; B. Chi|10.1109/ISSCC42615.2023.10067785|nan;Time-frequency analysis;Power system management;Microprocessors;Memory management;Silicon;Calibration;System-on-chip|
|[14.3 A Digital Low-Dropout (LDO) Linear Regulator with Adaptive Transfer Function Featuring 125A/mm2 Power Density and Autonomous Bypass Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067368)|M. Zelikson; K. Luria; L. Gil; Y. Brown; V. Goldenbeg; D. Kasif; E. Hlees; A. Vinichuk|10.1109/ISSCC42615.2023.10067368|nan;Power system measurements;Density measurement;Transfer functions;Computer architecture;Delays;Reliability;Transient analysis|
|[14.4 A Monolithic 26A/mm2Imax, 88.5% Peak-Efficiency Continuously Scalable Conversion-Ratio Switched-Capacitor DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067583)|N. Butzen; H. Krishnarnurthy; Z. Ahmed; S. Weng; K. Ravichandran; M. Zelikson; J. Tschanz; J. Douglas|10.1109/ISSCC42615.2023.10067583|nan;Regulators;Switches;Resonant converters;Silicon;Topology;Voltage control;Current density|
|[15.1 A Self-Powered SoC with Distributed Cooperative Energy Harvesting and Multi-Chip Power Management for System-in-Fiber](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067265)|X. Liu; D. S. Truesdell; O. Faruqe; L. Parameswaran; M. Rickley; A. Kopanski; L. Cantley; A. Coon; M. Bernasconi; T. Wang; B. H. Calhoun|10.1109/ISSCC42615.2023.10067265|nan;Temperature sensors;Temperature distribution;Power demand;Power system management;Sodium;Polymer fibers;Sensors|
|[15.2 A 2.19µW Self-Powered SoC with Integrated Multimodal Energy Harvesting, Dual-Channel up to −92dBm WRX and Energy-Aware Subsystem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067337)|C. J. Lukas; F. B. Yahya; K. -K. Huang; J. Boley; D. S. Truesdell; J. Breiholz; A. Wokhlu; K. Craig; J. K. Brown; A. Fitting; W. Moore; A. Shih; A. Wang; A. Gravel; D. D. Wentzloff; B. H. Calhoun|10.1109/ISSCC42615.2023.10067337|nan;Sensitivity;Power demand;System performance;Receivers;Data collection;Batteries;Energy harvesting|
|[15.3 A 33kDMIPS 6.4W Vehicle Communication Gateway Processor Achieving 10Gbps/W Network Routing, 40ms CAN Bus Start-Up and 1.4mW Standby Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067585)|K. Shimada; K. Sano; K. Fukuoka; H. Morita; M. Daito; T. Kamei; H. Hamasaki; Y. Shimazaki|10.1109/ISSCC42615.2023.10067585|nan;Connected vehicles;Ethernet;Logic gates;Routing;Battery charge measurement;Safety;Batteries|
|[A 28nm 68MOPS 0.18\mu\mathrm{J}/\text{Op}$ Paillier Homomorphic Encryption Processor with Bit-Serial Sparse Ciphertext Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067522)|G. Shi; Z. Tan; D. Cao; J. Cai; W. Zhang; Y. Wu; K. Ma|10.1109/ISSCC42615.2023.10067522|nan;Heart;Cloud computing;Privacy;Costs;Scalability;Throughput;Servers|
|[15.5 A 100Gbps Fault-Injection Attack Resistant AES-256 Engine with 99.1-to-99.99% Error Coverage in Intel 4 CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067715)|R. Kumar; A. Varna; C. Tokunaga; S. Taneja; V. De; S. Mathew|10.1109/ISSCC42615.2023.10067715|nan;Resistance;Semiconductor lasers;Measurement uncertainty;Measurement by laser beam;Sensor phenomena and characterization;Registers;Circuit faults|
|[16.1 MuITCIM: A 28nm $2.24 \mu\mathrm{J}$/Token Attention-Token-Bit Hybrid Sparse Digital CIM-Based Accelerator for Multimodal Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067842)|F. Tu; Z. Wu; Y. Wang; W. Wu; L. Liu; Y. Hu; S. Wei; S. Yin|10.1109/ISSCC42615.2023.10067842|nan;Runtime;Computational modeling;Natural languages;Image retrieval;Switches;Transformers;Common Information Model (computing)|
|[16.2 A 28nm 53.8TOPS/W 8b Sparse Transformer Accelerator with In-Memory Butterfly Zero Skipper for Unstructured-Pruned NN and CIM-Based Local-Attention-Reusable Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067360)|S. Liu; P. Li; J. Zhang; Y. Wang; H. Zhu; W. Jiang; S. Tang; C. Chen; Q. Liu; M. Liu|10.1109/ISSCC42615.2023.10067360|nan;nan|
|[A 28nm 16.9-300TOPS/W Computing-in-Memory Processor Supporting Floating-Point NN Inference/Training with Intensive-CIM Sparse-Digital Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067779)|J. Yue; C. He; Z. Wang; Z. Cong; Y. He; M. Zhou; W. Sun; X. Li; C. Dou; F. Zhang; H. Yang; Y. Liu; M. Liu|10.1109/ISSCC42615.2023.10067779|nan;Tail;Computer architecture;Energy efficiency;Common Information Model (computing)|
|[16.4 TensorCIM: A 28nm 3.7nJ/Gather and 8.3TFLOPS/W FP32 Digital-CIM Tensor Processor for MCM-CIM-Based Beyond-NN Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067285)|F. Tu; Y. Wang; Z. Wu; W. Wu; L. Liu; Y. Hu; S. Wei; S. Yin|10.1109/ISSCC42615.2023.10067285|nan;Deep learning;Tensors;Program processors;Algebra;Computational modeling;Computer architecture;Bandwidth|
|[16.5 DynaPlasia: An eDRAM In-Memory-Computing-Based Reconfigurable Spatial Accelerator with Triple-Mode Cell for Dynamic Resource Switching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067352)|S. Kim; Z. Li; S. Um; W. Jo; S. Ha; J. Lee; S. Kim; D. Han; H. -J. Yoo|10.1109/ISSCC42615.2023.10067352|nan;nan|
|[A Nonvolatile Al-Edge Processor with 4MB SLC-MLC Hybrid-Mode ReRAM Compute-in-Memory Macro and 51.4-251TOPS/W](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067610)|W. -H. Huang; T. -H. Wen; J. -M. Hung; W. -S. Khwa; Y. -C. Lo; C. -J. Jhang; H. -H. Hsu; Y. -H. Chin; Y. -C. Chen; C. -C. Lo; R. -S. Liu; K. -T. Tang; C. -C. Hsieh; Y. -D. Chih; T. -Y. Chang; M. -F. Chang|10.1109/ISSCC42615.2023.10067610|nan;Program processors;Nonvolatile memory;Random access memory;Energy efficiency;Common Information Model (computing);System-on-chip;Task analysis|
|[16.7 A 40-310TOPS/W SRAM-Based All-Digital Up to 4b In-Memory Computing Multi-Tiled NN Accelerator in FD-SOI 18nm for Deep-Learning Edge Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067422)|G. Desoli; N. Chawla; T. Boesch; M. Avodhyawasi; H. Rawat; H. Chawla; V. Abhijith; P. Zambotti; A. Sharma; C. Cappetta; M. Rossi; A. De Vita; F. Girardi|10.1109/ISSCC42615.2023.10067422|nan;Phase change materials;Tensors;Scalability;Random access memory;In-memory computing;Safety;Artificial intelligence|
|[17.1 A 2x-lnterleaved 9b 2.8G8S/s 5b/cycle SAR ADC with Linearized Configurable V2T Buffer Achieving >50dB SNDR at 3GHz Input](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067627)|H. Zhao; M. Zhang; Y. Zhu; C. -H. Chan; R. P. Martins|10.1109/ISSCC42615.2023.10067627|nan;Costs;Prototypes;Linearity;Voltage;Rendering (computer graphics);Hardware;Registers|
|[An 8b 1.0-to-1.25GS/s 0.7-to-0.8V Single-Stage Time-Based Gated-Ring-Oscillator ADC with $2\times$ Interpolating Sense-Amplifier-Latches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067745)|A. S. Yonar; P. A. Francese; M. Brändli; M. Kossel; M. Prathapan; T. Morf; A. Ruffino; T. Jang|10.1109/ISSCC42615.2023.10067745|nan;Technological innovation;Interpolation;Spectral efficiency;Layout;Modulation;Logic gates;CMOS technology|
|[17.3 A 14b 16GS/s Time-Interleaving Oirect-RF Synthesis OAe with T-OEM Achieving -70dBc IM3 up to 7.8GHz in 7nm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067459)|W. -H. Tseng; W. Lin; C. -W. Hsu; C. -Y. Huang; Y. -S. Lin; H. -Y. Huang; H. Chen; S. -H. Liao; K. -D. Chen; J. Strange; G. Manganaro|10.1109/ISSCC42615.2023.10067459|nan;Wireless communication;Radio frequency;Power demand;Temperature;RF signals;Linearity;System integration|
|[A 750mW 24GS/s 12b Time-Interleaved ADC for Direct RF Sampling in Modern Wireless Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067793)|S. S. Kumar; M. Kudo; V. Cretu; A. Morineau; A. Matsuda; M. Yoshida; M. Marutani; A. H. Maniyar; J. Kumar|10.1109/ISSCC42615.2023.10067793|nan;Radio frequency;Base stations;Power demand;Filtering;Wireless networks;Modulation;Bandwidth|
|[17.5 A 10mW 10-ENOB 1GS/s Ring-Amp-Based Pipelined TI-SAR ADC with Split MDAC and Switched Reference Decoupling Capacitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067475)|M. Zhan; L. Jie; N. Sun|10.1109/ISSCC42615.2023.10067475|nan;Wireless communication;Pipelines;Capacitors;Switches;Telephone sets;Timing;Standards|
|[A 7b 4.5GS/s 4× Interleaved SAR ADC with Fully On-Chip Background Timing Skew Calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067573)|Y. -H. Wang; S. -J. Chang|10.1109/ISSCC42615.2023.10067573|nan;Wireless communication;Power demand;Voltage;Timing;Calibration;Registers;System-on-chip|
|[A 3mW 2.7GS/s 8b Subranging ADC with Multiple-Reference-Reference-Embedded Comparators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067498)|J. -C. Wang; T. -H. Kuo|10.1109/ISSCC42615.2023.10067498|nan;Resistors;Wideband|
|[17.8 A Single-Channel 10GS/s 8b>36.4d8 SNDR Time-Domain ADC Featuring Loop-Unrolled Asynchronous Successive Approximation in 28nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067397)|O. Chen; Y. Liang; C. C. Boon; O. Liu|10.1109/ISSCC42615.2023.10067397|nan;Quantization (signal);Pipelines;Prototypes;Linearity;Throughput;Delays;Synchronization|
|[18.1 A W-Band Transceiver Array with 2.4GHz LO Synchronization Enabling Full Scalability for FMCW Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067317)|J. Zhang; A. Singhvi; S. S. Ahmed; A. Arbabian|10.1109/ISSCC42615.2023.10067317|nan;Image resolution;Ultraviolet sources;Scalability;Distribution networks;Radar imaging;Transceivers;System-on-chip|
|[18.2 A 128Gb/s 1.95pJ/b D-Band Receiver with Integrated PLL and ADC in 22nm FinFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067439)|A. Agrawal; A. Whitcombe; W. Shin; R. Bhat; S. Kundu; P. Sagazio; H. Chandrakumar; T. Brown; B. Carlton; C. Hull; S. Callender; S. Pellerano|10.1109/ISSCC42615.2023.10067439|nan;Radio frequency;Wireless communication;Art;Receivers;Bandwidth;FinFETs;Dielectrics|
|[71-to-89GHz 12Gb/s Double-Edge-Triggered Quadrature RFDAC with LO Leakage Suppression Achieving 20.5dBm Peak Output Power and 20.4% System Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067731)|B. Yang; Z. Deng; H. J. Qian; X. Luo|10.1109/ISSCC42615.2023.10067731|nan;Wireless communication;Radio frequency;Power amplifiers;Systems architecture;FCC;Bandwidth;Telecommunications|
|[18.4 A $4\times 4$ 607GHz Harmonic Injection-Locked Receiver Array Achieving $4.4\text{pW}/\surd\text{Hz}$ NEP in 28nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067847)|A. De Vroede; P. Reynaert|10.1109/ISSCC42615.2023.10067847|nan;Sensitivity;Optical mixing;Detectors;Quality control;Optical detectors;Optical imaging;Optical receivers|
|[19.1 A 300MHz-BW, 27-to-38dBm In-Band OIP3 sub-7GHz Receiver for 5G Local Area Base Station Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067266)|M. A. Montazerolghaem; L. C. N. de Vreede; M. Babaie|10.1109/ISSCC42615.2023.10067266|nan;Radio frequency;Base stations;Frequency modulation;Baseband;5G mobile communication;Linearity;Receivers|
|[19.2 An Interferer-Tolerant Harmonic-Resilient Receiver with >+10dBm 3rd-Harmonic Blocker P1dB for 5G NR Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067574)|S. Araei; S. Mohin; N. Reiskarimian|10.1109/ISSCC42615.2023.10067574|nan;Linearity;Receivers;Harmonic analysis;New Radio;Wideband;Radiofrequency interference|
|[A 2.95mW/element Ka-band CMOS Phased-Array Receiver Utilizing On-Chip Distributed Radiation Sensors in Low-Earth-Orbit Small Satellite Constellation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067629)|X. Fu; D. You; X. Wang; M. Ide; Y. Zhang; J. Sakamaki; A. A. Fadila; Z. Li; Y. Wang; J. Sudo; M. Higaki; S. Inoue; T. Eishima; T. Tomura; J. Pang; H. Sakai; K. Okada; A. Shirane|10.1109/ISSCC42615.2023.10067629|nan;Degradation;Satellite antennas;Small satellites;Low earth orbit satellites;Receivers;Sensor phenomena and characterization;System-on-chip|
|[19.4 A Small-Satellite-Mounted 256-Element Ka-Band CMOS Phased-Array Transmitter Achieving 63.8dBm EIRP Under 26.6W Power Consumption Using Single/Dual Circular Polarization Active Coupler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067451)|D. You; X. Fu; X. Wang; Y. Gao; W. Wang; J. Sakamaki; H. Herdian; S. Kato; M. Ide; Y. Zhang; A. A. Fadila; Z. Li; C. Wang; Y. Wang; J. Sudo; M. Higaki; N. Kawaguchi; M. Nitta; S. Inoue; T. Eishima; T. Tomura; J. Pang; H. Sakai; K. Okada; A. Shirane|10.1109/ISSCC42615.2023.10067451|nan;Space vehicles;Polarization;Power demand;Transmitters;Small satellites;Low earth orbit satellites;Throughput|
|[20.1 A High Common-Mode Transient Immunity GaN-on-SOI Gate Driver for High dV/dt SiC Power Switch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067394)|S. -Y. Li; W. -C. Hung; T. -W. Wang; Y. -T. Hsu; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai|10.1109/ISSCC42615.2023.10067394|nan;MOSFET;Silicon carbide;High-voltage techniques;Switches;Logic gates;Gate drivers;Transient analysis|
|[A Condition-Adaptive △f3-EMI Control GaN Switching Regulator With Modulation Frequency Envelope Tracking For Full-Spectrum Automotive CISPR 25 Compliance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067594)|L. Du; D. Yan; D. B. Ma|10.1109/ISSCC42615.2023.10067594|nan;Integrated circuits;Space vehicles;Power system measurements;Density measurement;Passive filters;Electromagnetic interference;Active circuits|
|[20.3 A GaN Gate Driver with On-chip Adaptive On-time Controller and Negative Current Slope Detector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067355)|S. -Y. Lin; S. -Y. Lin; S. -H. Hung; T. -W. Wang; C. -H. Li; C. -L. Go; S. -C. Huang; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai|10.1109/ISSCC42615.2023.10067355|nan;nan|
|[20.4 Multiple-Phase Accelerated Current Control in Bidirectional Energy Transfer of Automotive High-Voltage and Low-Voltage Batteries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067291)|T. -W. Wang; S. -Y. Li; S. -H. Hung; T. -Y. Wu; C. -Y. Chen; P. -J. Chiu; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; T. -Y. Tsai; S. -R. Lin|10.1109/ISSCC42615.2023.10067291|nan;Low voltage;Laser radar;Power supplies;High-voltage techniques;Sensor systems;Batteries;Sensors|
|[21.1 A 65nm CMOS Living-Cell Dynamic Fluorescence Sensor with 1.05fA Sensitivity at 600/700nm Wavelengths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067325)|F. Aghimand; C. Hu; S. Sharma; K. K. Pochana; R. M. Murray; A. Emami|10.1109/ISSCC42615.2023.10067325|nan;Optical filters;Proteins;Biomedical optical imaging;Sensitivity;Fluorescence;Biomarkers;System-on-chip|
|[21.2 A $\boldsymbol{22\mu \mathrm{W}}$ Peak Power Multimodal Electrochemical Sensor Interface IC for Bioreactor Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067298)|Q. Lin; W. Sijbers; C. Avdikou; D. Gomez; D. Biswas; S. Sneha; A. Malissovas; B. Tacca; N. Van Helleputte|10.1109/ISSCC42615.2023.10067298|nan;Temperature measurement;Temperature sensors;Integrated circuits;Wireless sensor networks;Bioreactors;Amperometric sensors;Sensors|
|[A CMOS Multi-Functional Biosensor Array for Rapid Low-Concentration Analyte Detection with On-Chip DEP-Assisted Active Enrichment and Manipulation with No External Electrodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067525)|D. Lee; D. Jung; F. Jiang; G. V. Junek; J. Park; H. Liu; Y. Kong; Y. Kim; J. Wang; H. Wang|10.1109/ISSCC42615.2023.10067525|nan;Electrodes;Transducers;Biomedical optical imaging;Stimulated emission;Biology;System-on-chip;Biosensors|
|[A 263GHz 32-Channel EPR-on-a-Chip Injection-Locked VCO-Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067623)|A. Chu; M. Kern; K. Khan; K. LiPS; J. Anders|10.1109/ISSCC42615.2023.10067623|nan;Polarization;Sensitivity;Resonant frequency;Metals;Nuclear magnetic resonance;Frequency measurement;Magnetic fields|
|[21.5 An LTE-Harvesting BLE-to-WiFi Backscattering Chip for Single-Device RFID-Like Interrogation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067815)|S. -K. Kuo; M. Dunna; H. Lu; A. Agarwal; D. Bharadia; P. P. Mercier|10.1109/ISSCC42615.2023.10067815|nan;Performance evaluation;Costs;RF signals;Modulation;Maintenance engineering;Transceivers;Sensors|
|[ASIL-D Compliant Battery Monitoring IC with High Measurement Accuracy and Robust Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067607)|J. -K. Lee; S. Woo; W. Jeong; K. -S. Oh; D. Kim; Y. Ko; J. -Y. Jeon; J. Lee; Y. -S. Son; S. -G. Lee; K. Kwon|10.1109/ISSCC42615.2023.10067607|nan;Temperature measurement;Temperature sensors;Integrated circuits;Voltage measurement;Current measurement;Battery charge measurement;Batteries|
|[22.1 A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067643)|F. Conti; D. Rossi; G. Paulin; A. Garofalo; A. Di Mauro; G. Rutishauer; G. m. Ottavi; M. Eggimann; H. Okuhara; V. Huard; O. Montfort; L. Jure; N. Exibard; P. Gouedo; M. Louvat; E. Botte; L. Benini|10.1109/ISSCC42615.2023.10067643|nan;Adaptive systems;Neural networks;Medical services;Threshold voltage;Hardware;Generators;System-on-chip|
|[A 28nm 2D/3D Unified Sparse Convolution Accelerator with Block-Wise Neighbor Searcher for Large-Scaled Voxel-Based Point Cloud Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067644)|W. Sun; X. Feng; C. Tang; S. Fan; Y. Yang; J. Yue; H. Yang; Y. Liu|10.1109/ISSCC42615.2023.10067644|nan;Point cloud compression;Visualization;Three-dimensional displays;Convolution;Navigation;Memory management;Virtual reality|
|[A 127.8TOPS/W Arbitrarily Quantized 1-to-8b Scalable-Precision Accelerator for General-Purpose Deep Learning with Reduction of Storage, Logic and Latency Waste](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067615)|S. Moon; H. -G. Mun; H. Son; J. -Y. Sim|10.1109/ISSCC42615.2023.10067615|nan;Deep learning;Quantization (signal);RLC circuits;Bandwidth;Market research;Encoding;Complexity theory|
|[A 28nm 11.2TOPS/W Hardware-Utilization-Aware Neural-Network Accelerator with Dynamic Dataflow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067774)|C. -Y. Du; C. -F. Tsai; W. -C. Chen; L. -Y. Lin; N. -S. Chang; C. -P. Lin; C. -S. Chen; C. -H. Yang|10.1109/ISSCC42615.2023.10067774|nan;Deep learning;Convolution;Shape;Neural networks;Parallel processing;Benchmark testing;Energy efficiency|
|[C-DNN: A 24.5-85.8TOPS/W Complementary-Deep-Neural-Network Processor with Heterogeneous CNN/SNN Core Architecture and Forward-Gradient-Based Sparsity Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067497)|S. Kim; S. Kim; S. Hong; S. Kim; D. Han; H. -J. Yoo|10.1109/ISSCC42615.2023.10067497|nan;Training;Energy consumption;Power demand;Neurons;Computer architecture;Energy efficiency;Biology|
|[22.6 ANP-I: A 28nm 1.5pJ/SOP Asynchronous Spiking Neural Network Processor Enabling Sub-O.1 μJ/Sample On-Chip Learning for Edge-AI Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067650)|J. Zhang; D. Huo; J. Zhang; C. Qian; Q. Liu; L. Pan; Z. Wang; N. Qiao; K. -T. Tang; H. Chen|10.1109/ISSCC42615.2023.10067650|nan;Training;Energy consumption;Program processors;Memory management;Energy efficiency;System-on-chip;Synchronization|
|[22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learning-Based Visual Object Processing Unit Supporting Multi-Scale Semantic Feature Extraction for Mobile Object Detection/Tracking Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067704)|Y. Gong; T. Zhang; H. Guo; X. Liu; J. Zheng; H. Wu; C. Jia; L. Que; L. Zhou; L. Chang; J. Zhou|10.1109/ISSCC42615.2023.10067704|nan;Visualization;Program processors;Semantics;Redundancy;Computer architecture;Streaming media;Feature extraction|
|[22.8 A0.81 mm2 740μW Real-Time Speech Enhancement Processor Using Multiplier-Less PE Arrays for Hearing Aids in 28nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067646)|S. Park; S. Lee; J. Park; H. -S. Choi; D. Jeon|10.1109/ISSCC42615.2023.10067646|nan;Computational modeling;Wearable computers;Neural networks;Auditory system;Speech enhancement;Hearing aids;Routing|
|[22.9 A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication and Fine-Grained Power Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067817)|T. Tambe; J. Zhang; C. Hooper; T. Jia; P. N. Whatmough; J. Zuckerman; M. C. D. Santos; E. J. Loscalzo; D. Giri; K. Shepard; L. Carloni; A. Rush; D. Brooks; G. -Y. Wei|10.1109/ISSCC42615.2023.10067817|nan;Solid modeling;Computational modeling;Heuristic algorithms;Virtual assistants;Power system management;Transformers;Natural language processing|
|[23.1 A 7.9fJ/Conversion-Step and 37.12aFrms Pipelined-SAR Capacitance-to-Digital Converter with kT/C Noise Cancellation and Incomplete-Settling-Based Correlated Level Shifting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067383)|J. Gao; L. Shen; H. Li; S. Ye; J. Li; X. Xu; J. Cui; Y. Gao; R. Huang; L. Ye|10.1109/ISSCC42615.2023.10067383|nan;nan|
|[23.2 A 40A Shunt-Based Current Sensor with ±0.2% Gain Error from −40°C to 125°C and Self-Calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067304)|Z. Tang; N. G. Toth; R. Zamparette; T. Nezuka; Y. Furuta; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067304|nan;Temperature sensors;Resistance;Temperature distribution;Art;Automotive applications;Metals;Energy efficiency|
|[23.3 A 51A Hybrid Magnetic Current Sensor with a Dual Differential DC Servo Loop and 43mArms Resolution in a 5MHz Bandwidth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067677)|A. Jouvaeian; Q. Fan; M. Motz; U. Ausserlechner; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067677|nan;Coils;Art;Magnetic sensors;Energy resolution;Switched mode power supplies;Bandwidth;Energy efficiency|
|[A Closed-Loop 12bit CMOS-Integrated Stress Sensor System with 4bit Adjustable Sensitivity from 178 to 11 kPa/LSB at up to 22.5kS/s and 5bit Dynamic Range Adjustment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067788)|K. Allinger; M. KuhI|10.1109/ISSCC42615.2023.10067788|nan;Sensitivity;Dynamic range;Robot sensing systems;Sensor systems;Silicon;Stress;Piezoresistance|
|[23.5 A Sub-1V 810nW Capacitively-Biased BJT-Based Temperature Sensor with an Inaccuracy of ±0.15°C (3σ) from −55°C to 125°C](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067695)|Z. Tang; S. Pan; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067695|nan;Temperature sensors;Capacitive sensors;Sensors;Calibration|
|[A 2.98pJ/conversion 0.0023mm2 Dynamic Temperature Sensor with Fully On-Chip Corrections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067789)|Y. Shen; H. Li; E. Cantatore; P. Harpe|10.1109/ISSCC42615.2023.10067789|nan;Temperature sensors;Resistors;Systematics;Prototypes;System-on-chip;Sensors;Environmental monitoring|
|[23.7 A BJT-Based Temperature Sensor with $\pm 0.1^{\circ}\mathrm{C}(3\sigma)$ Inaccuracy from -55°C to 125°C and a 0.85pJ.K2 Resolution FoM Using Continuous-Time Readout](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067457)|N. G. Toth; Z. Tang; T. Someya; S. Pan; K. A. A. Makinwa|10.1109/ISSCC42615.2023.10067457|nan;Temperature sensors;Temperature distribution;Costs;Thermal resistance;Energy resolution;Energy efficiency|
|[24.1 A 0.64-to-0.69THz Beam-Steerable Coherent Source with 9.1dBm Radiated Power and 30.8dBm Lensless EIRP in 65nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067455)|L. Gao; C. H. Chan|10.1109/ISSCC42615.2023.10067455|nan;Terahertz wave imaging;Spectroscopy;Array signal processing;Thermal lensing;Radar imaging;Silicon;Topology|
|[24.2 A 264-to-287GHz, −2.5dBm Output Power, and −92dBc/Hz 1MHz-Phase-Noise CMOS Signal Source Adopting a 75fsrms Jitter D-Band Cascaded Sub-Sampling PLL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067713)|B. -T. Moon; S. -G. Lee; J. Choi|10.1109/ISSCC42615.2023.10067713|nan;Phase noise;Sensitivity;Phase modulation;Power amplifiers;Receivers;Transceivers;Phase locked loops|
|[24.3 A 200-to-350GHz SiGe BiCMOS Frequency Doubler with Slotline-Based Mode-Decoupling Harmonic-Tuning Technique Achieving 1.1-to-4.7dBm Output Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067586)|S. Li; X. Li; H. Wu; W. Chen|10.1109/ISSCC42615.2023.10067586|nan;Varactors;Terahertz wave imaging;Spectroscopy;Bandwidth;Harmonic analysis;Broadband communication;Transistors|
|[A 4.1 W Quadrature Doherty Digital Power Amplifier with 33.6% Peak PAE in 28nm Bulk CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067809)|J. Li; Y. Yin; H. Chen; J. Lin; Y. Li; X. Jia; Z. Hu; X. Zhang; H. Xu|10.1109/ISSCC42615.2023.10067809|nan;Semiconductor device modeling;5G mobile communication;Transmitters;Quadrature amplitude modulation;Power amplifiers;Modulation;System integration|
|[25.2 A 19.7-to-43.8GHz Power Amplifier with Broadband Linearization Technique in 28nm Bulk CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067840)|W. Zeng; L. Gao; N. Sun; H. Xu; Q. Xue; X. Zhang|10.1109/ISSCC42615.2023.10067840|nan;Radio frequency;5G mobile communication;Broadband amplifiers;Quadrature amplitude modulation;Wireless networks;Peak to average power ratio;Throughput|
|[A 4.8dB NF, 70-to-86GHz Deep-Noise-Canceling LNA Using Asymmetric Compensation Transformer and 4-to-1 Hybrid-Phase Combiner in 40nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067549)|C. Han; J. Zhou; Z. Deng; Y. Shu; X. Luo|10.1109/ISSCC42615.2023.10067549|nan;Wireless communication;Noise figure;Millimeter wave transistors;Low-noise amplifiers;Millimeter wave circuits;Receivers;Transformers|
|[25.4 A 4b RFDAC at 8GS/s for FMCW Chirps with 4GHz Bandwidth in $\boldsymbol{10\mu} \mathbf{s}$](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067341)|S. K. Sireesh; S. H. Abkenar; N. Christotters; C. Wagner; T. Brandt; A. Stelzer|10.1109/ISSCC42615.2023.10067341|nan;Frequency synthesizers;Frequency modulation;Chirp;Phase modulation;Bandwidth;Radar;Transceivers|
|[A 1-to-5GHz All-Passive Frequency-Translational 4th-Order N-path Filter with Low-Power Clock Boosting for High Linearity and Relaxed $\mathrm{P}_{\text{dc}}$-Frequency Trade-Off](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067862)|A. Nagulu; M. Yi; Y. Zhuang; S. Garikapati; H. Krishnaswamy|10.1109/ISSCC42615.2023.10067862|nan;Radio frequency;Measurement;Maximum likelihood detection;Technological innovation;Power demand;Nonlinear filters;System-on-chip|
|[26.1 A Source-Driver IC Including Power-Switching Fast-Slew-Rate Buffer and 8Gb/s Effective 3-Tap DFE Receiver Achieving 4.9mV DVRMS and 17V/ps Slew Rate for 8K Displays and Beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067592)|K. Ryu; J. -Y. Jeong; J. -P. Lim; K. -H. Lee; K. Kim; Y. Kwon; S. Yoo; S. Kim; H. -W. Lim; J. -Y. Lee|10.1109/ISSCC42615.2023.10067592|nan;Integrated circuits;TV;Receivers;Voltage;Switches;Throughput;Decision feedback equalizers|
|[26.2 Virtual Rotating Gesture Recognizable Touch Readout IC for 1.26” Circular Touch Screen Panel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067584)|S. Ko; J. Lee; J. Ham; B. So; D. Cho; H. Kim; B. Kim; W. Sim; G. Youm|10.1109/ISSCC42615.2023.10067584|nan;Integrated circuits;Three-dimensional displays;Wearable computers;Fingers;Two dimensional displays;Tablet computers;Touch sensitive screens|
|[26.3 A 45.8dB-SNR 120fps 100pF-Load Self-Capacitance Touch-Screen Controller with Enhanced In-Band Common Noise Immunity Using Noise Antenna Reference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067374)|S. Byun; H. Lee; T. -G. Song; J. Lee; J. Baek; G. Ha; S. Baek; Y. Kim; W. Jung; H. -W. Lim; S. Kim; J. -Y. Lee|10.1109/ISSCC42615.2023.10067374|nan;Encapsulation;Discrete Fourier transforms;Interference;Bandwidth;Touch sensitive screens;Dynamic range;Organic light emitting diodes|
|[Some Recent Progress in Bioelectronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067546)|J. Rogers|10.1109/ISSCC42615.2023.10067546|nan;Wireless communication;Wireless sensor networks;Heart beat;Wires;Surgery;Pacemakers;Bioelectric phenomena|
|[The Tall Thin Molecular Programmer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067743)|E. Winfree|10.1109/ISSCC42615.2023.10067743|nan;Computers;Technological innovation;Chemistry;Systematics;DNA;Organisms;Complexity theory|
|[The Promise of 2-D Materials for Scaled Digital and Analog Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067567)|D. Verreck; P. Wambacq; M. Van De Put; Z. Ahmed; Q. Smets; A. Afzalian; R. Duflou; X. Wu; G. Mirabelli; R. Chen; I. Asselberghs; G. S. Kar|10.1109/ISSCC42615.2023.10067567|nan;Two dimensional displays;Scattering;Logic gates;FinFETs;Silicon;Nanoscale devices;Transistors|
|[27.4 Inverse Designed, Densely Integrated Classical and Quantum Photonics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067466)|J. Vuckovic; G. H. Ahn; K. Van Gasse; M. Guidry; H. Kwon; J. Lu; D. Lukin; A. Piggott; N. Sapra; L. Su; J. Skarda; R. Trivedi; D. Vercruysse; A. White; J. Yang; K. Yang|10.1109/ISSCC42615.2023.10067466|nan;Manuals;Libraries;Energy efficiency;Manufacturing;Complexity theory;Optoelectronic and photonic sensors;Tuning|
|[A 1.67Tb, 5b/Cell Flash Memory Fabricated in 192-Layer Floating Gate 3D-NAND Technology and Featuring a 23.3Gb/mm2 Bit Density](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067616)|A. Khakifirooz; E. Anaya; S. Balasubrahrmanyam; G. Bennett; D. Castro; J. Egler; K. Fan; R. Ferdous; K. Ganapathi; O. Guzman; C. W. Ha; R. Haque; V. Harish; M. Jalalifar; O. W. Jungroth; S. -T. Kang; G. Karbasian; J. -Y. Kim; S. Li; A. S. Madraswala; S. Maddukuri; A. Mohammed; S. Mookiah; S. Nagabhushan; B. Ngo; D. Patel; S. K. Poosarla; N. V. Prabhu; C. Quiroga; S. Rajwade; A. Rahman; J. Shah; R. S. Shenoy; E. T. Menson; A. Tankasala; S. K. Thirumala; S. Upadhyay; K. Upadhyayula; A. Velasco; N. K. B. Vemula; B. Venkataramaiah; J. Zhou; B. M. Pathak; P. Kalavade|10.1109/ISSCC42615.2023.10067616|nan;Costs;Nonvolatile memory;Flash memories|
|[28.2 A High-Performance 1Tb 3b/Cell 3D-NAND Flash with a 194MB/s Write Throughput on over 300 Layers $\mathsf{i}$](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067666)|B. Kim; S. Lee; B. Hah; K. Park; Y. Park; K. Jo; Y. Noh; H. Seol; H. Lee; J. Shin; S. Choi; Y. Jung; S. Ahn; Y. Park; S. Oh; M. Kim; S. Kim; H. Park; T. Lee; H. Won; M. Kim; C. Koo; Y. Choi; S. Choi; S. Park; D. Youn; J. Lim; W. Park; H. Hur; K. Kwean; H. Choi; W. Jeong; S. Chung; J. Choi; S. Cha|10.1109/ISSCC42615.2023.10067666|nan;Resistance;Quality of service;Throughput;Flash memories|
|[A 4nm 16Gb/s/pin Single-Ended PAM4 Parallel Transceiver with Switching-Jitter Compensation and Transmitter Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067738)|J. Jin; S. -M. Lee; K. Min; S. Ju; J. Lim; H. Chae; K. Kang; Y. Hong; Y. Jeong; S. -H. Kim; J. Lee; J. Kim|10.1109/ISSCC42615.2023.10067738|nan;Modulation;Low-pass filters;Switches;Throughput;Transceivers;X reality;Optical signal processing|
|[A 4nm 1.15TB/s HBM3 Interface with Resistor-Tuned Offset-Calibration and In-Situ Margin-Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067736)|K. Chae; J. Park; J. Song; B. Koo; J. Oh; S. Yi; W. Lee; D. Kim; T. Yeo; K. Kang; S. Park; E. Kim; S. Jung; S. Park; S. Park; M. Noh; H. Rhew; J. Shin|10.1109/ISSCC42615.2023.10067736|nan;Limiting;High performance computing;Random access memory;Process control;Integrated circuit interconnections;Machine learning;Bandwidth|
|[28.5 A 900µW, 1-4GHz Input-Jitter-Filtering Digital-PLL-Based 25%-Duty-Cycle Quadrature-Clock Generator for Ultra-Low-Power Clock Distribution in High-Speed DRAM Interfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067283)|Y. Shin; Y. Jo; J. Kim; J. Lee; J. Kim; J. Choi|10.1109/ISSCC42615.2023.10067283|nan;Power demand;Random access memory;Jitter;Generators;Timing;Standards;Clocks|
|[A 32Gb/s/pin 0.51 pJ/b Single-Ended Resistor-less Impedance-Matched Transmitter with a T-Coil-Based Edge-Boosting Equalizer in 40nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067552)|J. -H. Park; H. Lee; H. Cho; S. Lee; K. -H. Lee; H. -G. Ko; D. -K. Jeong|10.1109/ISSCC42615.2023.10067552|nan;Resistors;Power demand;Transmitters;Equalizers;Impedance matching;Random access memory;Bandwidth|
|[28.7 A 1.1V 6.4Gb/s/pin 24-Gb DDR5 SDRAM with a Highly-Accurate Duty Corrector and NBTI-Tolerant DLL](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067651)|D. Kwon; H. S. Jeong; J. Choi; W. Kim; J. W. Kim; J. Yoon; J. Choi; S. Lee; H. N. Rie; J. -i. Lee; J. Lee; T. Jang; J. Kim; S. Kang; J. Shin; Y. Loh; C. Y. Lee; J. Woo; H. Yu; C. Bae; R. Oh; Y. -s. Sohn; C. Yoo; J. Lee|10.1109/ISSCC42615.2023.10067651|nan;Negative bias temperature instability;Charge pumps;Sensitivity;Power demand;Transmitters;Thermal variables control;Low-pass filters|
|[A 1.1V 16Gb DDR5 DRAM with Probabilistic-Aggressor Tracking, Refresh-Management Functionality, Per-Row Hammer Tracking, a Multi-Step Precharge, and Core-Bias Modulation for Security and Reliability Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067805)|W. Kim; C. Jung; S. Yoo; D. Hong; J. Hwang; J. Yoon; O. Jung; J. Choi; S. Hyun; M. Kang; S. Lee; D. Kim; S. Ku; D. Choi; N. Joo; S. Yoon; J. Noh; B. Go; C. Kim; S. Hwang; M. Hwang; S. -M. Yi; H. Kim; S. Heo; Y. Jang; K. Jang; S. Chu; Y. Oh; K. Kim; J. Kim; S. Kim; J. Hwang; S. Park; J. Lee; I. Jeong; J. Cho; J. Kim|10.1109/ISSCC42615.2023.10067805|nan;High performance computing;Memory management;Random access memory;Modulation;Failure analysis;Probabilistic logic;Security|
|[29.1 A 32.5mW Mixed-Signal Processing-in-Memory-Based k-SAT Solver in 65nm CMOS with 74.0% Solvability for 3D-Variable 126-Clause 3-SAT Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067570)|D. Kim; N. M. Rahman; S. Mukhopadhyay|10.1109/ISSCC42615.2023.10067570|nan;Recurrent neural networks;Limiting;Network topology;Supply chains;Lattices;Simulated annealing;Problem-solving|
|[29.2 Snap-SAT: A One-Shot Energy-Performance-Aware All-Digital Compute-in-Memory Solver for Large-Scale Hard Boolean Satisfiability Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067380)|S. Xie; M. Yang; S. A. Lanham; Y. Wang; M. Wang; S. Oruganti; J. P. Kulkarni|10.1109/ISSCC42615.2023.10067380|nan;nan|
|[29.3 An 8.09TOPS/W Neural Engine Leveraging Bit-Sparsified Sign-Magnitude Multiplications and Dual Adder Trees](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067269)|H. An; Y. Chen; Z. Fan; Q. Zhang; P. Abillama; H. -S. Kim; D. Blaauw; D. Sylvester|10.1109/ISSCC42615.2023.10067269|nan;Energy consumption;Costs;Computational modeling;Artificial neural networks;Logic gates;Energy efficiency;Distance measurement|
|[29.4 Wafer-Level Stacking of High-Density Capacitors to Enhance the Performance of a Large Multicore Processor for Machine Learning Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067282)|S. Felix; S. Morton; S. Stacey; J. Walsh|10.1109/ISSCC42615.2023.10067282|nan;Multicore processing;Capacitors;Stacking;Random access memory;Voltage;Machine learning;Packaging|
|[A 73.53TOPS/W 14.74TOPS Heterogeneous RRAM In-Memory and SRAM Near-Memory SoC for Hybrid Frame and Event-Based Target Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067544)|M. Chang; A. S. Lele; S. D. Spetalnick; B. Crafton; S. Konno; Z. Wan; A. Bhat; W. -S. Khwa; Y. -D. Chih; M. -F. Chang; A. Raychowdhury|10.1109/ISSCC42615.2023.10067544|nan;Target tracking;Pipelines;Random access memory;Vision sensors;Cameras;Throughput;Autonomous aerial vehicles|
|[A $1.5\mu\mathrm{W}$ End-to-End Keyword Spotting SoC with Content-Adaptive Frame Sub-Sampling and Fast-Settling Analog Frontend](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067808)|J. -H. SeoI; H. Yang; R. Rothe; Z. Fan; Q. Zhang; H. -S. Kim; D. Blaauw; D. Sylvester|10.1109/ISSCC42615.2023.10067808|nan;Resistors;Deep learning;Power demand;Processor scheduling;Switching frequency;Artificial neural networks;Switches|
|[29.7 CCSA: A 394TOPS/W Mixed-Signal GPS Accelerator with Charge-Based Correlation Computing for Signal Acquisition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067275)|J. Li; W. He; B. Zhang; L. Qi; G. He; M. Seok|10.1109/ISSCC42615.2023.10067275|nan;Performance evaluation;Correlation;Satellites;Power demand;Voltage;Receivers;Throughput|
|[A Sub-0.8pJ/b 16.3Gbps/mm2 Universal Soft-Detection Decoder Using ORBGRAND in 40nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067519)|A. Riaz; A. Yasar; F. Ercan; W. An; J. Ngo; K. Galligan; M. Medard; K. R. Duffy; R. T. Yazicigl|10.1109/ISSCC42615.2023.10067519|nan;Power demand;Voltage;Throughput;Hardware;Reconfigurable architectures;Resource management;Reliability|
|[An 8T eNVSRAM Macro in 22nm FDSOI Standard Logic with Simultaneous Full-Array Data Restore for Secure IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067521)|S. Nouri; S. S. Iyer|10.1109/ISSCC42615.2023.10067521|nan;Phase change materials;Fabrication;Energy consumption;Manufacturing processes;Nonvolatile memory;Memory management;Silicon-on-insulator|
|[30.1 A Scalable N-Step Equal Split SSHI Piezoelectric Energy Harvesting Circuit Achieving 1170% Power Extraction Improvement and 22nA Quiescent Current with a $\mathbf{1\mu{H}-{to}-10\mu H}$ Low Q Inductor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067389)|Y. -W. Jeong; S. -J. Lee; J. -H. Kim; M. -J. Cho; H. -S. Kim; S. -U. Shin|10.1109/ISSCC42615.2023.10067389|nan;Q-factor;Micromechanical devices;Capacitors;Piezoelectric transducers;Rectifiers;Switches;Mechanical energy|
|[30.2 A 93.2%-Efficiency Multi-Input Bipolar Energy Harvester with $17.9\times$ MPPT Loss Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067272)|Z. -Y. Yang; A. Chen; C. -W. Chen; W. -C. Hung; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai|10.1109/ISSCC42615.2023.10067272|nan;Maximum power point trackers;Radio frequency;Photovoltaic systems;Power system measurements;Voltage;Transformers;Generators|
|[30.3 A Bias-Flip Rectifier with a Duty-Cycle-Based MPPT Algorithm for Piezoelectric Energy Harvesting with 98% Peak MPPT Efficiency and 738% Energy-Extraction Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067284)|X. Yue; S. Javvaji; Z. Tang; K. A. A. Makinwa; S. Du|10.1109/ISSCC42615.2023.10067284|nan;Maximum power point trackers;Voltage measurement;Piezoelectric transducers;Rectifiers;Switches;Sensors;Synchronization|
|[A 3. 7V-to-1kV Chip-Cascaded Switched-Capacitor Converter with Auxiliary Boost Achieving > 96{\%}$ Reactive Power Efficiency for Electrostatic Drive Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067796)|Y. Li; B. Mabetha; J. T. Stauth|10.1109/ISSCC42615.2023.10067796|nan;Reactive power;Actuators;Dielectric losses;High-voltage techniques;Haptic interfaces;Batteries;Electrostatics|
|[30.5 A 95.3% 5V-to-32V Wide Range 3-Level Current Mode Boost Converter with Fully State-based Phase Selection Achieving Simultaneous High-Speed $\mathbf{V}_{\text{CF}}$ Balancing and Smooth Transition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067321)|S. -J. Lee; Y. -W. Jeong; M. -J. Cho; J. -H. Kim; H. -S. Kim; J. -S. Bang; S. -U. Shin|10.1109/ISSCC42615.2023.10067321|nan;Resistance;Solid state drives;Capacitors;Surge protection;Calibration;Sensors;Transistors|
|[30.6 A 98.6%-Peak-Efficiency 1.47A/mm2-Current-Density Buck-Boost Converter with Always Reduced Conduction Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067471)|J. Jin; Y. Zhou; C. Chen; X. Han; W. Xu; L. Cheng|10.1109/ISSCC42615.2023.10067471|nan;Radio frequency;Switching loss;Power amplifiers;Voltage;Topology;Batteries;Transistors|
|[A Continuously Scalable-Conversion-Ratio SC Converter with Reconfigurable VCF Step for High Efficiency over an Extended VCR Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067756)|Y. Wang; M. Huang; Y. Lu; R. P. Martins|10.1109/ISSCC42615.2023.10067756|nan;Rails;Maximum power point trackers;Costs;Capacitors;Voltage;Switches;DC-DC power converters|
|[30.8 3D Wireless Power Transfer with Noise Cancellation Technique for −62dB Noise Suppression and 90.1% Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067347)|F. Huang; H. -Y. Tsai; C. -Y. Huang; Y. -C. Luo; C. -H. Li; S. -C. Huang; Y. -H. Kao; K. -H. Chen; K. -L. Zheng; Y. -H. Lin; S. -R. Lin; T. -Y. Tsai|10.1109/ISSCC42615.2023.10067347|nan;nan|
|[30.9 A 90%-Efficiency 40.68MHz Single-Stage Dual-Output Regulating Rectifier with ZVS and Synchronous PFM Control for Wireless Powering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067331)|Z. Luo; J. Liu; H. Lee|10.1109/ISSCC42615.2023.10067331|nan;Wireless communication;Rails;Rectifiers;Resonant frequency;Receivers;Wireless power transfer;Zero voltage switching|
|[30.10 Single-Chip Qi-Compliant 40W Wireless-Power-Transmission Controller using RMS Coil Current Sensing and Adaptive ZVS for 4dB EMI and up to 1.7% Efficiency Improvements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067848)|F. Neri; G. Mehas; F. Di Fazio; G. Figliozzi; J. Menart; M. Augustyniak; T. Acar; A. Bavisi|10.1109/ISSCC42615.2023.10067848|nan;Wireless communication;Wireless sensor networks;Embedded systems;Electromagnetic interference;Consumer products;Electronic components;Zero voltage switching|
|[A Quadrature Uncertain-IF IR-UWB Transceiver with Twin-OOK Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067737)|B. Wang; W. Rhee; Z. Wang|10.1109/ISSCC42615.2023.10067737|nan;Baseband;Sensitivity;Receivers;Interference;Transceivers;Distance measurement;Energy efficiency|
|[31.2 A Fully Integrated IEEE 802.15.4/4z-Compliant 6.5-to-8GHz UWB System-on-Chip RF Transceiver Supporting Precision Positioning in a CMOS 28nm Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067364)|W. Kim; H. -G. Seok; G. Lee; S. Kim; J. -K. Lee; C. Kim; W. Kim; W. Jung; Y. Cho; S. Bae; J. Cho; H. Na; B. Kang; H. Han; H. Son; C. Ahn; H. Kang; S. Jung; H. Sung; Y. Kim; D. Kim; D. Kim; J. -S. Paek; S. Oh; J. Lee; S. Kwak; J. Kim|10.1109/ISSCC42615.2023.10067364|nan;Radio frequency;IEEE 802.15 Standard;System-in-package;Transmitters;Voltage;Transceivers;Phasor measurement units|
|[A 1.8Gb/s, 2.3pJ/bit, Crystal-Less IR-UWB Transmitter for Neural Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067667)|J. Lei; X. Liu; W. Song; H. Huang; X. Ma; J. Wei; M. Zhang|10.1109/ISSCC42615.2023.10067667|nan;Wireless communication;Power system measurements;Power demand;Transmitters;Phase modulation;Throughput;Recording|
|[31.4 A 128-Channel 2mmx2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission Through Multi-Carrier Orthogonal Backscatter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067688)|C. Yang; Z. Zhang; L. Zhang; Y. Zhang; Z. Li; Y. Luo; G. Pan; B. Zhao|10.1109/ISSCC42615.2023.10067688|nan;Wireless communication;Power demand;Implants;Wireless power transfer;Throughput;Real-time systems;Recording|
|[A Passive Bidirectional BLETag Demonstrating Battery-Free Communication in Tablet/Smartphone-to-Tag, Tag-to-Tablet/Smartphone, and Tag-to-Tag Modes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067538)|Z. Chang; Q. Xiao; W. Wang; Y. Luo; B. Zhao|10.1109/ISSCC42615.2023.10067538|nan;Wireless communication;Meters;Hardware;Transceivers;Batteries;Standards;Smart phones|
|[A ULP Long-Range Active-RF Tag with Automatic Antenna-Interface Calibration Achieving 20.5% TX Efficiency at -22dBm EIRP, and -60.4dBm Sensitivity at 17.8nW RX Power](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067628)|Z. Yang; J. Yin; W. -H. Yu; H. Zhang; P. -I. Mak; R. P. Martins|10.1109/ISSCC42615.2023.10067628|nan;Sensitivity;Transmitters;Capacitors;Resonant frequency;Transceivers;Batteries;Topology|
|[31.7 A 0.7-to-2.5GHz Sliding Digital-IF Quadrature Digital Transmitter Achieving >40% System Efficiency for Multi-Mode NB-IoT/BLE Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067825)|C. Hu; D. Zheng; Y. Yin; J. Lin; Y. Li; W. Li; H. Xu|10.1109/ISSCC42615.2023.10067825|nan;Meters;Power demand;Transmitters;Phase modulation;Power amplifiers;Signal processing;Synchronization|
|[31.8 A 0.4-to-0.95GHz Distributed N-Path Noise-Cancelling Ultra-Low-Power RX with Integrated Passives Achieving −85dBm/100kb/s Sensitivity, −41dB SIR and 174dB RX FoM in 22nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067338)|H. Bialek; M. Johnston; A. Natarajan|10.1109/ISSCC42615.2023.10067338|nan;Geography;Power demand;Sensitivity;Frequency modulation;Linearity;Noise cancellation;Robustness|
|[32.1 A Behind-The-Ear Patch-Type Mental Healthcare Integrated Interface with 275-Fold Input Impedance Boosting and Adaptive Multimodal Compensation Capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067723)|H. Kim; M. Kim; K. Lee; S. Cho; C. S. Park; S. Song; D. S. Keum; D. P. Jang; J. J. Kim|10.1109/ISSCC42615.2023.10067723|nan;Electrodes;Scalp;Prototypes;Medical services;Electrocardiography;Electroencephalography;Skin|
|[32.2 A Stimulus-Scattering-Free Pixel-Sharing Sub-Retinal Prosthesis SoC with 35.8dB Dynamic Range Time-Based Photodiode Sensing and Per-Pixel Dynamic Voltage Scaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067387)|K. Eom; M. Park; H. -S. Lee; S. -B. Ku; N. Kim; S. Cha; Y. S. Goo; S. Kim; S. -W. Kim; H. -M. Lee|10.1109/ISSCC42615.2023.10067387|nan;nan|
|[A 1V 136.6dB-DR 4kHz-BW $\Delta\Sigma$ Current-to-Digital Converter with a Truncation-Noise-Shaped Baseline-Servo-Loop in 0.18\mu\mathrm{m}$ CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067537)|T. Seol; S. Lee; G. Kim; S. Kim; E. Kim; S. Baik; J. Kung; J. -W. Choi; A. K. George; J. Lee|10.1109/ISSCC42615.2023.10067537|nan;Voltage-controlled oscillators;Current measurement;Bandwidth;Photoplethysmography;Energy efficiency;Sensors;Power capacitors|
|[32.4 A 1V-Supply $1.85\mathrm{V}_{\text{PP}}$ -Input-Range 1kHz-BW 181.9dB-FOMDR179.4dB-FOMSNDR 2nd-Order Noise-Shaping SAR-ADC with Enhanced Input Impedance in 0.18μm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067844)|G. Kim; S. Lee; T. Seol; S. Baik; Y. Shin; G. Kim; J. -H. Yoon; A. K. George; J. Lee|10.1109/ISSCC42615.2023.10067844|nan;Electrooculography;Tracking;Wearable computers;Electrocardiography;Energy efficiency;Electromyography;Electroencephalography|
|[A 384-Channel Online-Spike-Sorting IC Using Unsupervised Geo-OSort Clustering and Achieving 0.0013mm2/Ch and $1.78\mu \text{W/ch}$](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067264)|Y. Chen; B. Tacca; Y. Chen; D. Biswas; G. Gielen; F. Catthoor; M. Verhelst; C. M. Lopez|10.1109/ISSCC42615.2023.10067264|nan;Training;Limiting;Software algorithms;Neurons;Clustering algorithms;Software;Real-time systems|
|[SciCNN: A 0-Shot-Retraining Patient-Independent Epilepsy-Tracking SoC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067518)|C. -W. Tsai; R. Jiang; L. Zhang; M. Zhang; L. Wu; J. Guo; Z. Yan; J. Yoo|10.1109/ISSCC42615.2023.10067518|nan;Support vector machines;Training;Energy consumption;Databases;Firing;Feature extraction;Electroencephalography|
|[Fascicle-Selective Bidirectional Peripheral Nerve Interface IC with 173dB FOM Noise-Shaping SAR ADCs and 1.38pJ/b Frequency-Multiplying Current-Ripple Radio Transmitter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067626)|J. Xu; J. S. Filho; S. Nag; L. Long; C. Tejeiro; E. Hwang; G. O'Leary; Y. Huang; M. Kanchwala; M. Abdolrazzaghi; C. Tang; P. Liu; Y. Sui; X. Liu; G. Eleftheriades; J. Zariffa; R. Genov|10.1109/ISSCC42615.2023.10067626|nan;Wireless communication;Peripheral nervous system;Wireless sensor networks;Medical conditions;Radio transmitters;Medical treatment;Machine learning|
|[33.1 A 16nm 32Mb Embedded STT-MRAM with a 6ns Read-Access Time, a 1M-Cycle Write Endurance, 20-Year Retention at 150°C and MTJ-OTP Solutions for Magnetic Immunity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067837)|P. -H. Lee; C. -F. Lee; Y. -C. Shih; H. -J. Lin; Y. -A. Chang; C. -H. Lu; Y. -L. Chen; C. -P. Lo; C. -C. Chen; C. -H. Kuo; T. -L. Chou; C. -Y. Wang; J. J. Wu; R. Wang; H. Chuang; Y. Wang; Y. -D. Chih; T. -Y. J. Chang|10.1109/ISSCC42615.2023.10067837|nan;Semiconductor device measurement;Automotive applications;Magnetic domains;Interference;Phase change random access memory;Time measurement;Sensors|
|[A 22nm 8Mb STT-MRAM Near-Memory-Computing Macro with 8b-Precision and 46.4-160.1TOPS/W for Edge-AI Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067563)|Y. -C. Chiu; W. -S. Khwa; C. -Y. Li; F. -L. Hsieh; Y. -A. Chien; G. -Y. Lin; P. -J. Chen; T. -H. Pan; D. -Q. You; F. -Y. Chen; A. Lee; C. -C. Lo; R. -S. Liu; C. -C. Hsieh; K. -T. Tang; Y. -D. Chih; T. -Y. Chang; M. -F. Chang|10.1109/ISSCC42615.2023.10067563|nan;Energy consumption;Memory management;Artificial neural networks;Bandwidth;Market research;Hardware;Energy efficiency|
|[A 9Mb HZO-Based Embedded FeRAM with 1012-Cycle Endurance and 5/7ns Read/Write using ECC-Assisted Data Refresh and Offset-Canceled Sense Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067752)|J. Yang; Q. Luo; X. Xue; H. Jiang; Q. Wu; Z. Han; Y. Cao; Y. Han; C. Dou; H. Lv; Q. Liu; M. LiU|10.1109/ISSCC42615.2023.10067752|nan;Temperature measurement;Voltage measurement;Nonvolatile memory;Ferroelectric films;Scalability;Capacitors;Random access memory|
|[33.4 A 28nm 2Mb STT-MRAM Computing-in-Memory Macro with a Refined Bit-Cell and 22.4 - 41.5TOPS/W for AI Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067339)|H. Cai; Z. Bian; Y. Hou; Y. Zhou; J. -l. Cui; Y. Guo; X. Tian; B. Liu; X. Si; Z. Wang; J. Yang; W. Shan|10.1109/ISSCC42615.2023.10067339|nan;Nonvolatile memory;Neural networks;Common Information Model (computing);Foundries;Transistors;Magnetoresistance;Junctions|
|[34.1 THz Cryo-CMOS Backscatter Transceiver: A Contactless 4 Kelvin-300 Kelvin Data Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067445)|J. Wang; M. I. Ibrahim; I. B. Harris; N. M. Monroe; M. I. Wasiq Khan; X. Yi; D. R. Englund; R. Han|10.1109/ISSCC42615.2023.10067445|nan;Heating systems;Superconducting cables;Computers;Program processors;Qubit;Optical fiber cables;Cryogenics|
|[34.2 A 28-nm Bulk-CMOS IC for Full Control of a Superconducting Quantum Processor Unit-Cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067292)|J. Yoo; Z. Chen; F. Arute; S. Montazeri; M. Szalay; C. Erickson; E. Jeffrey; R. Fatemi; M. Giustina; M. Ansmann; E. Lucero; J. Kelly; J. C. Bardin|10.1109/ISSCC42615.2023.10067292|nan;Radio frequency;Computers;Superconducting integrated circuits;Baseband;Qubit;Fault tolerant systems;Process control|
|[A Polar-Modulation-Based Cryogenic Qubit State Controller in 28nm Bulk CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067495)|Y. Guo; Y. Li; W. Huang; S. Tan; Q. Liu; T. Li; N. Deng; Z. Wang; Y. Zheng; H. Jiang|10.1109/ISSCC42615.2023.10067495|nan;Radio frequency;Temperature distribution;Power demand;Qubit;Modulation;Cryogenics;Temperature control|
|[34.4 A Cryogenic Controller IC for Superconducting Qubits with DRAG Pulse Generation by Direct Synthesis without Using Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067671)|K. Kang; D. Minn; J. Lee; H. -J. Song; M. Lee; J. -Y. Sim|10.1109/ISSCC42615.2023.10067671|nan;Microwave integrated circuits;Superconducting integrated circuits;Shape;Scalability;Qubit;Logic gates;Microwave circuits|
|[34.5 A Calibration-Free 12.8-16.5GHz Cryogenic CMOS VCO with 202dBc/Hz FoM for Classic-Quantum Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067803)|G. Zhang; H. Lin; C. Wang|10.1109/ISSCC42615.2023.10067803|nan;Voltage-controlled oscillators;Qubit;Resonant frequency;Cryogenics;Reflectometry;Thermal noise;Frequency measurement|
#### **2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)**
- DOI: 10.1109/ISGT51731.2023
- DATE: 16-19 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Graph-Theoretic Partitioning for Differential Zone Protection in an Islanded Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066434)|F. Alsaeidi; C. -C. Liu; L. -A. Lee|10.1109/ISGT51731.2023.10066434|Micrigrid;Microgrid Protection;Differential Protection;Graph Theory;Graph Partitioning Algorithm;Electric potential;Costs;Microgrids;Power system reliability;Partitioning algorithms;Smart grids;Reliability|
|[Short-term Load Forecasting with Distributed Long Short-Term Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066368)|Y. Dong; Y. Chen; X. Zhao; X. Huang|10.1109/ISGT51731.2023.10066368|short-term load forecasting;long short term memory;distributed learning;consensus;multi-agent system;Training;Privacy;Load forecasting;Distance learning;Employment;Smart meters;Demand response|
|[Dynamic behavior of combined 100% IBR transmission and distribution networks with grid-forming and grid-following inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066356)|A. Quedan; W. Wang; D. Ramasubramanian; E. Farantatos; S. Asgarpoor|10.1109/ISGT51731.2023.10066356|Aggregated model;Current limiter;Grid-following inverter;Grid-forming inverter;Inverter-based resources;Power distribution network;Transmission network;Virtual oscillator control;Computational modeling;Power system dynamics;Distribution networks;Dynamic scheduling;Inverters;Behavioral sciences;Planning|
|[A Framework for Model Validation and Calibration of Microgrid Components Using PMU Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066408)|S. Biswas; J. Follum; F. Tuffner; T. Wall|10.1109/ISGT51731.2023.10066408|microgrid;model validation;grid-forming inverter;PMU data;Reactive power;Microgrids;Current distribution;Inverters;Data models;Phasor measurement units;Calibration|
|[Power Hardware-in-the-Loop Interfacing of Grid-Forming Inverter for Microgrid Islanding Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066453)|A. A. Ceceña|10.1109/ISGT51731.2023.10066453|Grid-forming inverters;hardware-in-the-loop;real-time simulation;microgrids;stability;Renewable energy sources;Short-circuit currents;Microgrids;Power system stability;Inverters;Stability analysis;Smart grids|
|[Adaptive Power Oscillation Damping Control via VSC- HVDC for the Great Britain Power Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066435)|Y. Dong; Y. Zhao; K. Alshuaibi; C. Zhang; Y. Liu; L. Zhu; E. Farantatos; K. Sun; B. Marshall; M. Rahman; O. Adeuyi; S. Marshall; I. L. Cowan|10.1109/ISGT51731.2023.10066435|adaptive control;power oscillation damping;RTDS;VSC-HVDC;Damping;Adaptation models;Renewable energy sources;HVDC transmission;High-voltage techniques;Real-time systems;Smart grids|
|[Real-Time Optimization of Microgrid Energy Management Using Double Deep Q-Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066355)|S. B. Rokh; R. Zhang; J. Ravishankar; H. Saberi; J. Fletcher|10.1109/ISGT51731.2023.10066355|DDQN;Energy Management Systems;MINLP Optimization;Reinforcement learning;Uncertainty;Microgrids;Benchmark testing;Mathematical models;Real-time systems;Data models;Robustness|
|[Autonomous Operation of a Mixed-Source Microgrid as a Remote Field-Oriented Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066343)|M. L. Little; P. W. Lehn|10.1109/ISGT51731.2023.10066343|Microgrid;Active Stabilization;Synchronous Drives;Autonomous Operation;Hardware in the Loop Simulation;Heuristic algorithms;Capacitors;Microgrids;Frequency conversion;Real-time systems;Power conversion;Synchronization|
|[A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066446)|S. H. Haghshenas; M. A. Hasnat; M. Naeini|10.1109/ISGT51731.2023.10066446|nan;Location awareness;Analytical models;Sensitivity analysis;Computational modeling;Message passing;Graph neural networks;Data models|
|[Power System Event Detection Using the Energy Detector: A Performance Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066444)|A. J. Wilson; A. R. Ekti; Y. Liu|10.1109/ISGT51731.2023.10066444|Transient;harmonics;event detection;sensor measurement error;Event detection;Detectors;Switches;Microgrids;Wind farms;Smart grids;Power systems|
|[Cost-Optimization for Win-Win P2P Energy Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066367)|R. Duvignau; V. Gulisano; M. Papatriantafilou|10.1109/ISGT51731.2023.10066367|P2P energy sharing;win-win energy policies;energy communities;peak-demand;cost-optimization;Adaptation models;Analytical models;Renewable energy sources;Energy resources;Sensitivity analysis;Companies;Peer-to-peer computing|
|[An Innovative Architecture for Monitoring and Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066397)|K. J. Lutz|10.1109/ISGT51731.2023.10066397|Computing Platforms;Management Applications;Operations Support Systems;Utility Operations;Computer architecture;Standardization;Malware;Telecommunications;Smart grids;Security;Monitoring|
|[Determination of the Optimal Air Gap of an HFCT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066365)|M. Fritsch; M. Wolter|10.1109/ISGT51731.2023.10066365|air gap;current transformer;current measurement;high-frequency;HFCT;partial discharges;saturation;Ferrites;Sensitivity;Power measurement;Power cables;Current measurement;Air gaps;Transformer cores|
|[A Unified Metric for Fast Frequency Response in Low-Inertia Power Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066382)|X. Cui; S. Dong; A. Hoke; J. Tan|10.1109/ISGT51731.2023.10066382|fast frequency response;metrics;low-inertia grid;effective inertia;frequency stability;Measurement;Schedules;Renewable energy sources;Systematics;Power system dynamics;Power system stability;Frequency response|
|[Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066364)|D. Pylorof; H. E. Garcia; R. Bhattarai|10.1109/ISGT51731.2023.10066364|Photovoltaic systems;Power grids;Risk analysis;Probabilistic graphical models;Bayes methods;Machine learning;Photovoltaic systems;Graphical models;Sensitivity analysis;Scalability;Neural networks;Statistical learning;Bayes methods|
|[Framework of real-time grid stabilization technology for suppression of sub-synchronous oscillations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066381)|R. Pandey; D. Kato; T. Yoshihara; T. Ito; S. Ohara|10.1109/ISGT51731.2023.10066381|Inverter-based resources;Sub-synchronous control interaction;Sub-synchronous resonance;Sub-synchronous oscillations;Renewable energy sources;Wind;Time-frequency analysis;Power transmission lines;Stability criteria;Wind farms;Real-time systems|
|[SDAG: Blockchain-enabled Model for Secure Data Awareness in Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066338)|A. S. Sani; D. Yuan; Z. Y. Dong|10.1109/ISGT51731.2023.10066338|data awareness;security;blockchain;smart grids;key exchange;Smart contracts;Data models;Smart grids;Cryptography|
|[Valuation of Distributed Wind Turbines Providing Multiple Market Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066377)|B. A. Bhatti; A. P. Reiman; D. S. Boff; S. E. Barrows; A. C. Orrell|10.1109/ISGT51731.2023.10066377|Distributed energy resources;optimization methods;power markets;valuation;wind turbines;Sensitivity analysis;Wind speed;Regulation;Wind turbines;Smart grids;Spinning;Reliability|
|[High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066369)|A. Cooper; A. Bretas; S. Meyn; N. G. Bretas|10.1109/ISGT51731.2023.10066369|High impedance fault;Fault detection techniques;State Estimation;Parameter error detection;Wide Area Measurements;Measurement errors;Power measurement;Voltage measurement;Fault detection;Current measurement;Measurement uncertainty;Impedance|
|[An Efficient Voltage Compensation and SoC-based Power Management in DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066409)|M. S. Alam; F. S. Al-Ismail; M. A. Abido|10.1109/ISGT51731.2023.10066409|DC microgrids;battery;state of charge;power management;voltage control;converter;Power system management;Simulation;Microgrids;Electrostatic discharges;Steady-state;Smart grids;State of charge|
|[Future Distribution Power Flow Scenario Generation Method Using Generative Adversarial Network Considering Correlation Between DERs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066337)|H. Ichinomiya; K. Kawabe; S. Chaitusaney|10.1109/ISGT51731.2023.10066337|-distributed energy resources;generative adversarial network;distribution grid planning;scenario generation;Correlation;Distribution networks;Predictive models;Generative adversarial networks;Data models;Planning;Smart grids|
|[Deep Reinforcement Learning for Distribution System Cyber Attack Defense with DERs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066375)|A. Selim; J. Zhao; F. Ding; F. Miao; S. -Y. Park|10.1109/ISGT51731.2023.10066375|Cyber attack;Active distribution systems;Renewable generation;Deep reinforcement learning;Deep learning;Training;Scalability;Reinforcement learning;Control systems;Robustness;Topology|
|[Joint Sparse Estimation of Sensitivity Distribution Factors and Power Flows in Low-Observable Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066366)|A. Akrami; H. Mohsenian-Rad|10.1109/ISGT51731.2023.10066366|Sensitivity distribution factor;injection shift factor;power flow estimation;sparse recovery;low-observability;power distribution systems;computation;Power measurement;Sensitivity;Atmospheric measurements;Power distribution;Estimation;Particle measurements;Feature extraction|
|[Edge-Computing Based Dynamic Anomaly Detection for Transmission Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066432)|X. Wang; D. Shi; G. Xu; F. Wang|10.1109/ISGT51731.2023.10066432|Computer vision;dynamic anomaly detection;motion detection;object detection;Three-dimensional displays;Tracking;Heuristic algorithms;Image edge detection;Surveillance;Power transmission;Object detection|
|[Power Management in Smart Buildings Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066398)|Z. Rostmnezhad; L. Dessaint|10.1109/ISGT51731.2023.10066398|Power management system;reinforcement learning;thermal energy storage;battery energy storage;Schedules;Smart buildings;Q-learning;Power demand;Power system management;Metaheuristics;Markov processes|
|[DP-AMI-FL: Secure Framework for Machine Learning-based AMI Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066415)|A. S. M. Tayeen; M. Biswal; S. Misra|10.1109/ISGT51731.2023.10066415|Smart meter data analytics;federated machine learning;short-term load forecasting;user privacy;advanced metering infrastructure;Privacy;Recurrent neural networks;Federated learning;Load forecasting;Smart meters;Data models;Smart grids|
|[Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066387)|B. Mitra; A. Singhal; S. Kundu; J. P. Ogle|10.1109/ISGT51731.2023.10066387|eddy current;harmonics;power transformer;PV;THD;Degradation;Electric potential;Loading;Power system harmonics;Transformers;Harmonic analysis;Power electronics|
|[Comparison of the Combined Deep Learning Methods for Load Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066449)|A. Unlu; M. Peña; Z. Wang|10.1109/ISGT51731.2023.10066449|Load Forecasting;Electricity Load;Combined Deep Learning;CNN;RNN;LSTM;GRU;Deep learning;Analytical models;Load forecasting;Biological system modeling;Predictive models;Planning;Convolutional neural networks|
|[High Performance Computing Reinforcement Learning Framework for Power System Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066416)|I. Damjanović; I. Pavić; M. Brčić; R. Jerčić|10.1109/ISGT51731.2023.10066416|High-performance computing;power system control;reinforcement learning;Training;High performance computing;Scalability;Power system control;Clustering algorithms;Reinforcement learning;Computer architecture|
|[Characterizing HVDC Transmission Flexibility under Extreme Operating Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066347)|Q. Nguyen; P. Etingov; M. A. Elizondo|10.1109/ISGT51731.2023.10066347|nan;Heating systems;HVDC transmission;Earthquakes;High-voltage techniques;Control systems;Power electronics;Distance measurement|
|[Analysis of Nonconvexities for Pricing Inertial Response in Electricity Markets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066386)|L. Viola; C. Sagastizábal; M. R. Hesamzadeh; D. Dotta; M. J. Rider|10.1109/ISGT51731.2023.10066386|unit commitment;inertial response;nonconvex pricing;convex-hull pricing;inverter-based generation;Economics;Pricing;Electricity supply industry;Smart grids;Computational efficiency;Security|
|[A Price-Based Strategy to Coordinate Electric Springs for Demand Side Management in Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066461)|D. A. Quijano Rodezno; M. Vahid-Ghavidel; M. S. Javadi; A. P. Feltrin; J. Catalão|10.1109/ISGT51731.2023.10066461|nan;Schedules;Renewable energy sources;Demand side management;Water heating;Microgrids;Smart grids;Power systems|
|[Efficient Optimal Power Flow Flexibility Assessment: A Machine Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066430)|W. Pan; C. Zhao; L. Fan; S. Huang|10.1109/ISGT51731.2023.10066430|Flexibility Assessment;Gaussian RBF Kernel SVM;Optimal Power Flow;Machine Learning;Support vector machines;Nonlinear equations;Uncertainty;Computational modeling;Simulation;Machine learning;Power systems|
|[Reinforcement-learning-based Smart Water Heater Control: An Actual Deployment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066373)|K. Amasyali; K. Kurte; H. Zandi; J. Munk|10.1109/ISGT51731.2023.10066373|Demand response (DR);model-free control;reinforcement learning (RL);deep learning;electric water heaters;Training;Costs;Water heating;Reinforcement learning;Pricing;Demand response;Smart grids|
|[High-Impedance Non-Linear Fault Detection via Eigenvalue Analysis with low PMU Sampling Rates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066424)|G. Paramo; A. Bretas; S. Meyn|10.1109/ISGT51731.2023.10066424|High impedance faults;non-linear faults;arcing faults;power system state estimation;power system protection;power system monitoring;eigenvalue estimation;fault detection;phasor measurement unit;wide-area measurement systems;Sensitivity;Measurement units;Impedance measurement;Fault detection;Energy measurement;Eigenvalues and eigenfunctions;Phasor measurement units|
|[On the Resilience of Distributed Consensus Control to Multiplicative Cyberattacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066410)|G. Raman; K. Liao|10.1109/ISGT51731.2023.10066410|cyberattacks;distributed consensus control;small-signal modeling;stability analysis;Reactive power;Consensus control;Power system stability;Market research;Frequency conversion;Stability analysis;Smart grids|
|[Inertia — Small-signal Stability Nexus in Grid-forming Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066385)|G. Raman; G. Raman|10.1109/ISGT51731.2023.10066385|decentralized;grid-forming inverters;inertia;renewables;small-signal stability;Renewable energy sources;Simulation;Stability criteria;Power system stability;Inverters;Synchronous generators;Generators|
|[Protection and Communication Model of Intelligent Electronic Devices to Investigate Security Threats](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066371)|M. F. Elrawy; E. Tekki; L. Hadjidemetriou; C. Laoudias; M. K. Michael|10.1109/ISGT51731.2023.10066371|cybersecurity;digital substation;GOOSE protocol;IEC 61850;protective relays;Performance evaluation;Substations;Protocols;Protective relaying;Mathematical models;Software;Behavioral sciences|
|[Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066417)|D. Pylorof; H. E. Garcia|10.1109/ISGT51731.2023.10066417|Photovoltaic systems;Analytics;Machine learning;Uncertainty quantification;Bayesian methods;Photovoltaic systems;Uncertainty;Neural networks;Estimation;Sensor phenomena and characterization;Harmonic analysis;Bayes methods|
|[Economic and ecological evaluation of sustainable heat generators in an existing industrial heat supply structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066399)|M. Alexander; K. Ekrem; S. Alexander|10.1109/ISGT51731.2023.10066399|Economic evaluation;defossilization;heat generators;industrial infrastructure;hybrid operation;Economics;Couplings;Thermal factors;Heat pumps;Boilers;Generators;Biomass|
|[Impact of Open Communication Networks on Load Frequency Control with Plug-In Electric Vehicles By Cyber-Physical Dynamic Co-simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066380)|W. Wang; M. Cai; X. Fang; C. Irwin|10.1109/ISGT51731.2023.10066380|Communication;electric vehicle;frequency regulation;transmission-and-distribution-and-communication dynamic co-simulation;smart charging;Renewable energy sources;Computational modeling;Power system dynamics;Transportation;Packet loss;Power system stability;Delays|
|[Vulnerability Analysis of Virtual Power Plant Voltage Support under Denial-of-Service Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066361)|J. Chen; J. Yan; H. Du; M. Debbabi; M. Kassouf|10.1109/ISGT51731.2023.10066361|Cybersecurity;denial-of-service;virtual power plant;distributed energy resources;voltage support;Electric potential;Stability criteria;Voltage;Virtual power plants;Power system stability;Denial-of-service attack;Smart grids|
|[Evaluating the Risk of Enabling Energy Storage Systems to Provide Multiple Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066439)|J. R. Theisen; A. Bose; M. Mukherjee; M. Diedesch; J. Gibson|10.1109/ISGT51731.2023.10066439|Battery energy storage systems;multi-service;optimization;regulation;uncertainty;Renewable energy sources;Uncertainty;Systematics;Stacking;Stochastic processes;Batteries;Smart grids|
|[Resilience Assessment Framework For Distribution Systems Performance Under Extreme Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066427)|S. Poudel; M. Mukherjee; R. A. Jinsiwale; S. Hanif|10.1109/ISGT51731.2023.10066427|Smart grids;power distribution;system modeling;resilience;outage;Measurement;Databases;System performance;Taxonomy;Switches;Predictive models;Smart grids|
|[Optimal Dispatch for A Microgrid with Distributed Generations and EV Charging Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066388)|S. S. Shuvo; M. M. Islam|10.1109/ISGT51731.2023.10066388|Economic Dispatch;LSTM;Optimization Techniques;Microgrid;Genetic Algorithm;PSO;Renewable energy sources;Pricing;Microgrids;Cost function;Real-time systems;Electric vehicle charging;Smart grids|
|[Realization of Intelligent-Inspection Functions of UAV in Transmission Grids Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066450)|T. -C. Lin; Z. -S. Ding; B. Simachew; Y. -C. Shih; C. -Y. Zhang|10.1109/ISGT51731.2023.10066450|UAV inspection;insulator dirt;transmission tower;image recognition;machine learning;artificial intelligence;Vibrations;Resistance;Poles and towers;Inspection;Autonomous aerial vehicles;Insulators;Smart grids|
|[Fast Traveling Wave Detection and Identification Method for Power Distribution Systems Using the Discrete Wavelet Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066438)|M. Jimenez-Aparicio; M. J. Reno; J. Hernandez-Alvidrez|10.1109/ISGT51731.2023.10066438|Fault detection;Traveling Waves;Power System Protection;Discrete Wavelet Transform;Random Forest;Fault diagnosis;Voltage measurement;Capacitors;Power distribution;Transformers;Discrete wavelet transforms;Time factors|
|[Phasor data correction and transmission system state estimation under Man-in-the-Middle attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066426)|A. Tharzeen; B. Natarajan; B. Srinivasan|10.1109/ISGT51731.2023.10066426|Unordered sensing;man-in-the-middle attack;EM algorithm;state estimation;Meters;Actuators;Data integrity;Phasor measurement units;Sensors;Smart grids;Communication networks|
|[Sizing Energy Storage Systems to Mitigate Variability of Renewable Generation for Grid Stability using Inverse Uncertainty Propagation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066448)|H. Choi; R. Elliott|10.1109/ISGT51731.2023.10066448|Inverse uncertainty propagation;renewable;energy storage system;Renewable energy sources;Uncertainty;Power system stability;Smart grids;Random variables;Batteries;Security|
|[A Fast Microprocessor-Based Traveling Wave Fault Detection System for Electrical Power Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066370)|A. Y. Montoya; M. Jimenez-Aparicio; J. Hernandez-Alvidrez; M. J. Reno|10.1109/ISGT51731.2023.10066370|Power Systems Protection;Real-Time Simulation;Traveling Wave Relays;Digital Signal Processing;Fault detection;System performance;Signal processing algorithms;Digital signal processing;Transforms;Electrical fault detection;Feature extraction|
|[Tuning Phase Lock Loop Controller of Grid Following Inverters by Reinforcement Learning to Support Networked Microgrid Operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066360)|T. L. Vu; A. Singhal; K. Schneider; W. Du|10.1109/ISGT51731.2023.10066360|Networked microgrids;inverters;smart grid;reinforcement learning;System performance;Simulation;Microgrids;Reinforcement learning;Electric variables;Inverters;Generators|
|[Analysis of the Impacts of Inverter Based Resources on the Impedance Matrix of Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066423)|D. R. Kunwar; S. Brahma|10.1109/ISGT51731.2023.10066423|Bus impedance matrix;fault;inverter;short circuit analysis;Thevenin impedance;Voltage measurement;Impedance measurement;Current measurement;Synchronous generators;Smart grids;Impedance;Circuit faults|
|[An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066402)|H. Kim; K. Ye; H. P. Lee; R. Hu; N. Lu; D. Wu; P. Rehm|10.1109/ISGT51731.2023.10066402|HVAC system;Independent component analysis;Non-intrusive load monitoring;Smart meter data;Meters;Temperature sensors;Temperature measurement;HVAC;Temperature;Computational modeling;Ventilation|
|[Aggregation of Solar and Type 4 Wind Farms for Short Circuit Studies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066433)|T. Patel; S. Brahma; M. J. Reno|10.1109/ISGT51731.2023.10066433|fault;inverter;short circuit analysis;solar farm;wind farm;Computational modeling;EMTP;Switches;Wind farms;Transformers;Inverters;Wind turbines|
|[A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066396)|S. Islam; I. Zografopoulos; M. T. Hossain; S. Badsha; C. Konstantinou|10.1109/ISGT51731.2023.10066396|Smart grid;automation;energy system;DQN;edge computing;fine grained classification;false state injection;6G mobile communication;Costs;Toxicology;Image edge detection;Reinforcement learning;Dynamic scheduling;Smart meters|
|[Large-Scale Cascading Failure Mitigation in Power Systems via Typed-Graphlets Partitioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066436)|R. Atat; M. Ismail; K. R. Davis; E. Serpedin|10.1109/ISGT51731.2023.10066436|Power grid;typed-graphlets;constrained clus-tering;Benders decomposition;cascading failures;Bridges;Power system protection;Smart grids;Power system faults|
|[Collaborative and Autonomous Black Start: Theory and Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066451)|C. Klauber; H. Burroughs; A. Zhou|10.1109/ISGT51731.2023.10066451|power system restoration;power system reliability;black start;renewable energy;energy storage;distributed power generation;collaborative autonomy;Collaboration;Process control;Microgrids;Reliability theory;Hazards;Robustness;Power system reliability|
|[Deep Reinforcement Learning based HVAC Control for Reducing Carbon Footprint of Buildings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066358)|K. Kurte; K. Amasyali; J. Munk; H. Zandi|10.1109/ISGT51731.2023.10066358|carbon emission;deep reinforcement learning;HVAC control;Deep learning;Energy consumption;HVAC;Buildings;Water heating;Carbon dioxide;Reinforcement learning|
|[Modeling Adequacy of Droop-Controlled Grid-Forming Converters for Transient Studies: Singular Perturbation Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066392)|A. Yogarathnam; L. Karunaratne; N. R. Chaudhuri; M. Yue|10.1109/ISGT51731.2023.10066392|Grid-forming converter;droop-control;dc current limit;singular perturbation analysis;model reduction;Analytical models;Perturbation methods;Reduced order systems;Mathematical models;Synchronous generators;Smart grids;Transient analysis|
|[Development of a State-Space Average Nanogrid Model for DC Microgrid Power Management Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066354)|R. Darbali-Zamora; A. R. R. Dow; F. Palacios; J. D. Flicker; D. Bauer|10.1109/ISGT51731.2023.10066354|photovoltaics;DC microgrid;renewable energy;energy storage systems;Photovoltaic systems;Renewable energy sources;Power demand;Simulation;Microgrids;Data models;Smart grids|
|[An Advanced Voltage Regulation Control for Distributed Wind Turbine Generators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066390)|S. T. Ojetola; R. Darbali-Zamora; F. Wilches-Bernal|10.1109/ISGT51731.2023.10066390|wind turbine generators;distributed wind;controls;voltage regulation;stability;Reactive power;Simulation;Transfer functions;Generators;Stability analysis;Wind turbines;System identification|
|[Smart Sampling-based Scenario Selection for Real Power System Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066458)|X. Sun; S. Datta; Z. J. Hou; X. Li; Q. Huang; R. Huang; Y. Chen|10.1109/ISGT51731.2023.10066458|Generator Commitment Pattern;Hierarchical Latin Hypercube Sampling;Scenario Selection;Measurement;Deep learning;Power measurement;Power system dynamics;Reinforcement learning;Hypercubes;Feature extraction|
|[Robust Distributed Finite-time Secondary Frequency Control of Islanded AC Microgrids With Event-Triggered Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066379)|Z. Wang; H. Li; H. He; Y. Sun|10.1109/ISGT51731.2023.10066379|Robust frequency regulation;distributed finite-time control;event-triggered control;islanded microgrids;Microgrids;Power system stability;Numerical simulation;Stability analysis;Smart grids;Behavioral sciences;Communication networks|
|[Flexibility Requirements for Energy Systems with Renewable Generation under Forecast Uncertainties](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066352)|S. Bhattacharya; T. Ramachandran; B. Mitra; A. Somani|10.1109/ISGT51731.2023.10066352|Renewable Energy;Energy Flexibility;Flexibility Capacity;Demand and Generation Uncertainty;Optimal Dispatch;Renewable energy sources;Costs;Systems operation;Forecast uncertainty;Smart grids;Optimization;Cost accounting|
|[Adaptive Control of Grid Forming Inverters for System Black Start](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066422)|O. Dutta; T. Chen; D. Ramasubramanian; E. Farantatos|10.1109/ISGT51731.2023.10066422|Adaptive control;Black-starting;Frequency recovery;Grid-forming inverter;Voltage stability;Current control;Induction motors;Power system stability;Transformers;Inverters;Stability analysis;Power system reliability|
|[Demand Estimation of Net Metered Loads for Microgrid Restoration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066395)|A. K. Jain; C. -C. Liu; K. P. Schneider; F. K. Tuffner; D. Ton|10.1109/ISGT51731.2023.10066395|Demand Forecasting;Microgrids;Power system restoration;Time series analysis;Meters;Correlation;Time series analysis;Demand forecasting;Estimation;Microgrids;Smart grids|
|[Uninterruptible Power Supply: An Optimal Passivity-Based Control Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066406)|A. Bakeer; M. Alhasheem; H. M. Elhelw|10.1109/ISGT51731.2023.10066406|Passivity-based control (PBC);optimization;tuning;uninterruptible power supply (UPS);Damping;Total harmonic distortion;Control design;Voltage source inverters;Power supplies;Simulation;Smart grids|
|[Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066445)|A. Sundararajan; M. Olama; M. Ferrari; B. Ollis; Y. Chen; G. Liu|10.1109/ISGT51731.2023.10066445|univariate forecasting;time series;extreme weather;no real-time data;blind forecast;Photovoltaic systems;Adaptation models;Temperature distribution;Weather forecasting;Predictive models;Real-time systems;Data models|
|[NILMEV: Electric Vehicle disaggregation for residential customer energy efficiency incentives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066441)|C. Mariño; G. Cossio; P. Massaferro; M. Di Martino; A. Gómez; A. Fernandez|10.1109/ISGT51731.2023.10066441|NILM;Electric Vehicles;load disaggregation;Deep learning;Renewable energy sources;Power demand;Machine learning algorithms;Neural networks;Water heating;Electric vehicles|
|[A Hydrogen Load Modeling Method for Integrated Hydrogen Energy System Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066443)|X. Zhao; Y. Yao; W. Liu; R. Jain; C. Zhao|10.1109/ISGT51731.2023.10066443|Integrated hydrogen energy system;renewable electricity;grid service;hydrogen load simulation;profitability;Renewable energy sources;Hydrogen storage;Costs;Profitability;Hydrogen;Stacking;Planning|
|[Machine Learning based Occupant Behavior Prediction in Smart Building to Improve Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066411)|N. Fatehi; A. Politis; L. Lin; M. Stobby; M. H. Nazari|10.1109/ISGT51731.2023.10066411|Deep neural network;Energy consumption;Internet of things;GRU;LSTM;Occupancy prediction;Smart building;Training;Performance evaluation;Energy consumption;Smart buildings;Neural networks;Predictive models;Windows|
|[Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066357)|J. Gui; H. Lei; T. R. McJunkin; B. K. Johnson|10.1109/ISGT51731.2023.10066357|Frequency;grid-forming inverter;resilience;renewable generation;under-frequency load shedding;Measurement;Fault tolerance;Simulation;Load shedding;Power system stability;Inverters;Stability analysis|
|[Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066460)|X. Ma; D. Wu; A. Crawford|10.1109/ISGT51731.2023.10066460|Battery energy storage systems;load forecast uncertainties;model predictive control;optimal dispatch;Degradation;Uncertainty;Costs;Load forecasting;Urban areas;Benchmark testing;Batteries|
|[Comparison of Wide-Area and Local Power Oscillation Damping Control Through Inverter-Based Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066362)|Y. Zhao; K. M. Alshuaibi; X. Jia; C. Zhang; Y. Liu; D. Ramasubramanian; L. Zhu; E. Farantatos|10.1109/ISGT51731.2023.10066362|Inverter-based resources (IBRs);low-frequency oscillation;measurement-driven model;phasor measurement unit (PMU);power oscillation damping (POD);Damping;Renewable energy sources;Simulation;Modulation;Power system stability;Synchronous generators;Smart grids|
|[Power Flow Optimization Redesign for Transient Stability Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066339)|R. Chakraborty; A. Chakrabortty; D. Osipov; J. H. Chow|10.1109/ISGT51731.2023.10066339|Inverter-based resources;optimal power flow;stability index;transient stability;wind farms;Stability criteria;Power system dynamics;Transfer functions;Power system stability;Wind farms;Minimization;Transient analysis|
|[Fraud Detection Using Event Logs with LSTM and Gradient Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066346)|E. Acevedo; P. Massaferro; A. Fernández; A. Martins; G. Caudullo|10.1109/ISGT51731.2023.10066346|nan;Energy consumption;Recurrent neural networks;Costs;Time series analysis;Energy resolution;Feature extraction;Particle measurements|
|[Predictive Control of TN-DN Boundary Bus Voltages with Long-Term Stability Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066454)|F. Escobar; G. Valverde|10.1109/ISGT51731.2023.10066454|Ancillary services;distributed energy resources;model predictive control;transmission and distribution;TSO-DSO coordination;voltage stability;Voltage measurement;Substations;Power system stability;Transformers;Stability analysis;Data models;Voltage control|
|[Integrating Learning and Physics based Computation for Fast Online Transient Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066348)|J. Li; Y. Zhao; M. Yue|10.1109/ISGT51731.2023.10066348|nan;Training;Power system dynamics;Contingency management;Generators;Mathematical models;Trajectory;Smart grids|
|[A Resilience Quantitative Framework for Wide Area Damping Control Against Cyberattacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066437)|A. Rahiminejad; O. Duman; M. Ghafouri; R. Atallah; A. Mohammadi; M. Debbabi|10.1109/ISGT51731.2023.10066437|Resilience;wide area damping control;stability;cyber-physical system;cyberattack;Wide area networks;Damping;Loading;Probability;Power system stability;Phasor measurement units;Smart grids|
|[Online Data-Driven Detection of Phase Changes in Evolving Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066455)|B. D. Peña; L. Blakely; M. J. Reno|10.1109/ISGT51731.2023.10066455|distribution system;model calibration;change point detection;event detection;phase identification;online algorithm;Measurement;Meters;Renewable energy sources;Monte Carlo methods;Clustering algorithms;Voltage;Prediction algorithms|
|[Diverse Effects of Dynamic Wireless Power Transfer Roadway In-Motion Electric Vehicle Charging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066429)|T. M. Newbolt; P. Mandal; H. Wang; R. Zane|10.1109/ISGT51731.2023.10066429|Battery energy storage system;distributed energy resource;dynamic wireless power transfer;electric vehicles charging;power distribution;nan|
|[Real-Time Model-Adaptive Relaying Applied to Microgrid Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066421)|M. Ferrari; T. Smith; N. Shepard; A. Sundararajan; D. Herron; E. Piesciorovsky; I. Snyder; B. Ollis; J. Hambrick; C. Sticht; M. Marshall|10.1109/ISGT51731.2023.10066421|Adaptive relay;microgrid protection;Adaptation models;Sensitivity;Short-circuit currents;Microgrids;Voltage;Real-time systems;Smart grids|
|[Reinforcement Learning-based Electricity Market Vulnerability Analysis under Cyber-Topology Attack](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066378)|A. Dey; M. V. Salapaka|10.1109/ISGT51731.2023.10066378|Cyber-security;electricity market;locational marginal price;reinforcement learning;topology attack;Costs;Network topology;Reinforcement learning;Power system stability;Electricity supply industry;Real-time systems;Stability analysis|
|[A Supervised Early Attack Detection Mechanism for Smart Grid Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066351)|A. Salehpour; I. Al-Anbagi; K. -C. Yow; X. Cheng|10.1109/ISGT51731.2023.10066351|Cascading failures;cyber-attacks;failure propagation;interconnection networks;machine learning;smart grid;Support vector machines;Training;Measurement errors;Machine learning algorithms;Power system protection;Machine learning;Real-time systems|
|[Analysis of Internal Energy in GFL-MMCs and a Decoupled Energy Control Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066400)|P. Huang; L. Vanfretti|10.1109/ISGT51731.2023.10066400|modular multilevel converter;energy-controlled MMC;energy balancing;decoupled control;internal dynamics;Multilevel converters;Simulation;HVDC transmission;Power control;Power system stability;Stability analysis;Steady-state|
|[VAPOR: A Novel Approach to Power Forecasting in a Photovoltaic Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066452)|A. Xu|10.1109/ISGT51731.2023.10066452|attention;convolutional neural network;energy generation forecast;microgrid;predictive models;Photovoltaic systems;Deep learning;Adaptation models;Renewable energy sources;Simultaneous localization and mapping;Liquids;Profitability|
|[Hardware Fingerprinting of Phasor Measurement Units for Data Provenance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066419)|M. A. Baig; M. N. Aman|10.1109/ISGT51731.2023.10066419|Data Provenance;Proxy Attacks;Phasor Measurement Unit;Wireless communication;Training;Fingerprint recognition;Phasor measurement units;Hardware;Virtual private networks;Smart grids|
|[Development and Stability Analysis of Representative Future High Renewable Penetration Power Grid Models for California](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066404)|S. Samanta; S. Debnath; N. R. Chaudhuri|10.1109/ISGT51731.2023.10066404|nan;Renewable energy sources;Analytical models;Databases;Power system dynamics;Power system stability;Power grids;Stability analysis|
|[Comparison of Electromagnetic Transient and Phasor Dynamic Simulations: Implications for Inverter Dominated Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066384)|R. W. Kenyon; B. Wang; A. Hoke; J. Tan; B. M. Hodge|10.1109/ISGT51731.2023.10066384|electromagnetic transient-domain;inverter based resources;phasor-domain;power system simulation;Computational modeling;Power system dynamics;Voltage;Power system stability;Dynamic scheduling;Stability analysis;Systems simulation|
|[ANN-based Dynamic Frequency Regulation of PV-based Hybrid Microgrid system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066418)|A. Debnath; S. Roy; A. Stevenson; T. O. Olowu; A. Sarwat|10.1109/ISGT51731.2023.10066418|Microgrid;Artificial Neural Network;Virtual Inertia;Frequency Regulation;Photovoltaic Hybrid System;Simulation;Power system dynamics;Microgrids;Machine learning;Power system stability;Stability analysis;Hybrid power systems|
|[Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066393)|B. Rout; G. Saraswat; B. Natarajan|10.1109/ISGT51731.2023.10066393|Network partition;Spectral clustering;Decentralized state estimation (DSE);Compressive sensing;Power distribution network;alternating direction method of multipliers (ADMM);Simulation;Clustering algorithms;Power distribution;Distribution networks;Real-time systems;Sensors;Partitioning algorithms|
|[Improving Situational Awareness in Power Grids of Developing Countries: A Case Study of the Nigerian Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066359)|A. Umunnakwe; A. Alimi; K. R. Davis; K. L. Butler-Purry|10.1109/ISGT51731.2023.10066359|Electric power grid;situational awareness;developing countries;Nigeria;cost-effective;rolling horizon asset integration;cyber-physical Nigerian grid;Sensor placement;Developing countries;Phasor measurement units;Smart grids;Power system reliability;Reliability;Stakeholders|
|[Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066344)|A. Bretas; D. Wang; O. Vasios; J. Ogle|10.1109/ISGT51731.2023.10066344|Formal model;power system protection;automatic load shedding;real-time measurements;phasor measurement units;Measurement units;Decision making;Distributed databases;Load shedding;Predictive models;Linear programming;Phasor measurement units|
|[Contribution of Blockchain Technology In Energy to the Climate Change Efforts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066374)|U. Cali; U. Halden; M. Kuzlu; M. Pasetti; S. N. G. Gourisetti; S. Chandler; F. Rahimi; C. Lima|10.1109/ISGT51731.2023.10066374|Blockchain;Distributed Ledger Technology;Climate Change;Sustainability;Standardization;Energy Systems;Renewable energy sources;Climate change;Distributed ledger;Green products;Transportation;Bitcoin;Air pollution|
|[Event-Based Dynamic Response Modeling of Large Behind-the-Meter Solar Farms: A Data-Driven Method Based on Real-World Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066394)|P. Khaledian; A. Shahsavari; H. Mohsenian-Rad|10.1109/ISGT51731.2023.10066394|Dynamic response;solar farm;data-driven model;micro-PMU measurements;model library;practical study;Power measurement;Energy measurement;Libraries;Data models;Behavioral sciences;Smart grids;Planning|
|[Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066391)|J. Sawant; G. -S. Seo; F. Ding|10.1109/ISGT51731.2023.10066391|Inverter collective black start;grid-forming inverter;inverter-based resource;negative-sequence control;phase current limiter;system restoration;Fault tolerance;Electric potential;Power system dynamics;Transformers;Inverters;Smart grids;Power system restoration|
|[Modeling and Demonstrating the Effect of Human Decisions on the Distribution Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066376)|S. C. Doumen; J. Hönen; P. Nguyen; J. L. Hurink; B. Zwart; K. Kok|10.1109/ISGT51731.2023.10066376|Human Decisions;Behavior;Electricity Market;Demand-Side Management;Distribution Grid;Demand side management;Heat pumps;Biological system modeling;Electricity supply industry;Social factors;Smart grids;Distributed power generation|
|[Calculating PV Hosting Capacity in Low-Voltage Secondary Networks Using Only Smart Meter Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066372)|J. A. Azzolini; M. J. Reno; J. Yusuf; S. Talkington; S. Grijalva|10.1109/ISGT51731.2023.10066372|advanced metering infrastructure (AMI);data-driven analysis;hosting capacity analysis;smart meter data;Photovoltaic systems;Meters;Low voltage;Computational modeling;Measurement uncertainty;Size measurement;Smart meters|
|[Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066341)|T. L. Vu; S. Mukherjee; V. Adetola|10.1109/ISGT51731.2023.10066341|Smart grid;resiliency;networked microgrids;consensus algorithms;Distributed databases;Microgrids;Consensus algorithm;Control systems;Data models;Smart grids;Power system reliability|
|[SM-DIV: A Smart Metering Distributed privacy framework with Integrity Verification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066363)|G. Wagh; S. Mishra|10.1109/ISGT51731.2023.10066363|customer privacy;spatio-temporal aggregation;smart metering;integrity;Threat modeling;Privacy;Data privacy;Distributed databases;Load management;Smart meters;Real-time systems|
|[Impact Analysis of Future Electric Vehicles Using Model of Real Distribution Feeders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066428)|J. Xie; M. C. W. Kintner-Meyer; S. Sridhar; X. Chen; T. E. McDermott; W. Du; K. P. Schneider; H. Wang; J. C. Bedoya; K. Mo; M. Ramesh|10.1109/ISGT51731.2023.10066428|Electric vehicles;modeling and simulation;power distribution system;power flow analysis;Analytical models;Estimation;Voltage;Companies;Electric vehicles;Data models;Planning|
|[A Quantitative Approach for Convergence Analysis of a Singularly Perturbed Inverter-Based Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066431)|A. Nayak; M. M. Rayguru; S. Mishra; M. J. Hossain|10.1109/ISGT51731.2023.10066431|nan;Sufficient conditions;Uncertainty;Loading;Microgrids;Reduced order systems;Trajectory;Smart grids|
|[Feasibility of 100% Renewable-Energy-Powered Microgrids Serving Remote Communities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066456)|A. F. Bastos; R. D. Trevizan|10.1109/ISGT51731.2023.10066456|battery energy storage system;microgrid;re-mote community;renewable energy;solar photovoltaic;Photovoltaic systems;Underwater cables;Renewable energy sources;Costs;Microgrids;Generators;Fossil fuels|
|[Detection and Classification of Single Line to Ground Faults in Unbalanced Islanded Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066407)|P. T. Mana; M. Miranbeigi; J. Benzaquen; D. M. Divan|10.1109/ISGT51731.2023.10066407|machine learning;support vector machines;multiresolution analysis;MRA;wavelet transform;Support vector machines;Time-frequency analysis;Induction motors;Microgrids;Transforms;Feature extraction;Entropy|
|[Random Forest Meta Learner for Generating Pseudo-Measurements in Active Distribution Power Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066389)|S. Radhoush; T. Vannoy; B. M. Whitaker; H. Nehrir|10.1109/ISGT51731.2023.10066389|Active Distribution Network;Base Learner;Random Forest;Pseudo-Measurements;State Estimation;Weight measurement;Training;Neural networks;Measurement uncertainty;Forestry;IEEE Standards;Distributed power generation|
|[Reliability Challenges and Improvement Strategies in Puerto Rico](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066349)|A. B. Nassif; M. Lelic; D. Kushner; A. Paaso|10.1109/ISGT51731.2023.10066349|CMI;Critical facilities;distribution planning;distribution reliability;SAIDI;SAIFI;Measurement;Power measurement;Vegetation;Hurricanes;Power system reliability;Smart grids;Reliability|
|[State Estimation for Unbalanced Three-Phase AC Microgrids Based on Mathematical Programming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066353)|B. A. A. Acurio; D. E. C. Barragán; J. C. López; F. Grijalva; J. C. Rodríguez; L. C. Pereira da Silva|10.1109/ISGT51731.2023.10066353|Microgrids;state estimation;nonlinear programming problem;unbalanced three-phase AC network;Computational modeling;Measurement uncertainty;Microgrids;Data processing;Mathematical models;Topology;Steady-state|
|[Evidence of Residential Demand Flexibility in a 46 Townhome Neighborhood](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066425)|E. Tsybina; C. Winstead; J. Hill; H. Zandi; T. Kuruganti|10.1109/ISGT51731.2023.10066425|Demand response;load flexibility;smart grid;prosumers;distributed energy resources;derating;cycling;event duration;communication delays;latency;Productivity;Load management;Demand response;Remote working;Smart grids;Delays|
|[A Corrective Scheme to Prevent Adverse Dynamic Interaction of Grid-forming Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066342)|M. F. Umar; M. Hosseinzadehtaher; M. B. Shadmand; H. Livani; M. Ben-Idris|10.1109/ISGT51731.2023.10066342|grid-forming inverter;virtual synchronous generator;homogeneous dynamic response;transient stability;Transient response;Reactive power;Fluctuations;Dynamics;Force;Power system stability;Inverters|
|[Advancing Energy Equity Considerations in Distribution Systems Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066350)|A. K. Bharati; A. Singhal; R. Jinsiwale; K. Kazimierczuk; J. Yoshimura; B. Tarekegne|10.1109/ISGT51731.2023.10066350|energy justice;energy equity;distribution systems planning;hosting capacity;DER adoption;Photovoltaic systems;Measurement;Taxonomy;Sociology;Planning;Smart grids;Power systems|
|[Short-Term Load Forecasting using Conditionally Restricted Boltzman Machine Optimized by Modified Grasshopper Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066413)|M. Zulfiqar; M. B. Rasheed; M. D. R-Moreno|10.1109/ISGT51731.2023.10066413|Conditionally restricted Boltzmann;Modified grasshopper optimization algorithm;Kernel principal component analysis;Load forecasting;Benchmark testing;Predictive models;Indexes;Time factors;Forecasting;Optimization|
|[The Distributed Grid Sensing Services (DGSS) platform: A scalable sensor dissemination network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066403)|D. J. Sebastian-Cardenas; M. Touhiduzzaman; A. Singh; J. Kolln; J. Ogle|10.1109/ISGT51731.2023.10066403|Sensor Dissemination Networks;Extended Grid State;Distributed networks;Operating systems;Ecosystems;Distributed databases;Sensor systems;Real-time systems;Sensors;Smart grids|
|[Small Signal Stability Analysis of Dispatchable Virtual Oscillator Controlled Inverters with Adaptive Virtual Inertia Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066457)|S. A. Aghdam; M. Agamy|10.1109/ISGT51731.2023.10066457|Adaptive virtual inertia control;dispatchable virtual oscillator control;small signal stability analysis;Adaptive systems;System dynamics;System performance;Power system stability;Stability analysis;Inverters;Eigenvalues and eigenfunctions|
|[Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066442)|S. Dahale; S. Munikoti; B. Natarajan; R. Yang|10.1109/ISGT51731.2023.10066442|Smart distribution system;Multi time-scale measurements;Neural ordinary differential equations;variational autoencoder;Simulation;Aggregates;Energy measurement;Bandwidth;Ordinary differential equations;Real-time systems;Smart grids|
|[Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066447)|G. Saraswat; B. Lundstrom; M. V. Salapaka|10.1109/ISGT51731.2023.10066447|Nonintrusive load monitoring (NILM);multiclass classification;regression;power prediction;feature extraction;smart buildings;grid-interactive;smart grid;Load monitoring;Time-frequency analysis;Power demand;Costs;Buildings;Machine learning;Hybrid power systems|
|[Investigating Multi-Microgrid Black Start Methods Using Grid-Forming Inverters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066412)|E. Fix; A. Banerjee; U. Muenz; G. -S. Seo|10.1109/ISGT51731.2023.10066412|black start;inverter-based resources;microgrid;bottom-up restoration;grid-forming inverter;synchrobreaker;Loading;Microgrids;Inverters;Smart grids;Synchronization;Next generation networking;Load modeling|
|[Coordinated Operations of Hydrogen and Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066336)|M. Li; S. Wang; L. Fan; Z. Han|10.1109/ISGT51731.2023.10066336|power distribution network;hydrogen storage;hydrogen transportation;renewable energy;Renewable energy sources;Power transmission lines;Costs;Simulation;Hydrogen;Pipelines;Power distribution|
|[Study of Communication Boundaries of Primal-Dual-Based Distributed Energy Resource Management Systems (DERMS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066440)|J. Comden; J. Wang; A. Bernstein|10.1109/ISGT51731.2023.10066440|nan;Industries;Upper bound;Energy resolution;Packet loss;Numerical simulation;Distributed power generation;Smart grids|
|[Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066405)|O. Yurdakul; F. Qiu; S. Albayrak|10.1109/ISGT51731.2023.10066405|contextual stochastic optimization;unit commitment;ensemble learning;Radio frequency;Training;Costs;Uncertainty;Stochastic processes;Weather forecasting;Predictive models|
|[Performance Evaluation of Next-Generation Grid Automation and Controls with High PV Penetration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066420)|J. Wang; H. Padullaparti; S. Veda; I. Mendoza; S. Tiwari; M. Baggu|10.1109/ISGT51731.2023.10066420|Advanced Distribution Management System (ADMS);Distributed energy resource management system (DERMS);power hardware-in-the-loop (PHIL);voltage regulation;Performance evaluation;Photovoltaic systems;Reactive power;Automation;Substations;Energy resources;Distribution networks|
|[An Alternative Compensation Mechanism for Demand-Side Flexibility Considering Low and Medium Income (LMI) Participants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066459)|A. Abbas; B. Chowdhury|10.1109/ISGT51731.2023.10066459|demand side management;flexibility;compen-sation;low and medium-income (LMI) customers;game theory;Additives;Data models;Smart grids;Resource management|
|[A Hybrid Optimization and Deep Learning Algorithm for Cyber-resilient DER Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066345)|M. Panahazari; M. Koscak; J. Zhang; D. Hou; J. Wang; D. W. Gao|10.1109/ISGT51731.2023.10066345|cyber-resilient algorithm;distributed energy resources (DERs);DER control;LSTM;Deep learning;Deep learning;Voltage measurement;Virtual power plants;Predictive models;Smart grids;Security;Voltage control|
|[A 5G Enabled Adaptive Computing Workflow for Greener Power Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066383)|Y. Chen; L. Wang; X. Fan; D. Wang; J. Ogle|10.1109/ISGT51731.2023.10066383|5G technology;integrated workflow;edge computing;cloud computing;GPU computing;Wireless communication;Cloud computing;Renewable energy sources;5G mobile communication;Green products;Graphics processing units;User experience|
|[Restoration Modeling and Optimization of Hybrid Overhead-Underground Power Distribution Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066340)|Y. N. Belaid; Y. Fang; Z. Zeng; P. Coudray; A. Barros|10.1109/ISGT51731.2023.10066340|Smart Grid;Resilience;Overhead and Under-ground Networks;Radiality;Optimization;Communication;Simulation;Stochastic processes;Power distribution;Smart grids;Stakeholders;Optimization;Monitoring|
|[An Unsupervised Similarity-based Method for Estimating Behind-the-Meter Solar Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066401)|K. Pu; Y. Zhao|10.1109/ISGT51731.2023.10066401|nan;Temperature measurement;Temperature sensors;Temperature distribution;Systems operation;Supervised learning;Estimation;Smart meters|
|[End-to-End Performance Evaluation of R-SV / R-GOOSE Messages for Wide Area Protection and Control Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066414)|S. M. S. Hussain; M. M. Roomi; D. Mashima; E. -C. Chang|10.1109/ISGT51731.2023.10066414|IEC 61850-90-5;Performance evaluation;computational delay;communication delay;End-to-End Delay;Performance evaluation;Computational modeling;Area measurement;Phasor measurement units;Delays;Smart grids;Communication networks|
#### **2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)**
- DOI: 10.1109/ICIN56760.2023
- DATE: 6-9 March 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Collaborative and Distributed Learning-Based Solution to Autonomously Plan Computer Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073505)|D. Monaco; A. Sacco; E. Alberti; G. Marchetto; F. Esposito|10.1109/ICIN56760.2023.10073505|software-defined networking;distributed learning;reinforcement learning;Technological innovation;Computer aided instruction;Network topology;Distance learning;Design methodology;Conferences;Collaboration|
|[Efficient Optimization of Actor-Critic Learning for Constrained Resource Orchestration in RAN with Network Slicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073489)|H. K. Janjua; I. de Miguel; R. J. Durán Barroso; Ó. G. de Dios; J. C. Aguado; N. M. Álvarez; P. Fernández; R. M. Lorenzo|10.1109/ICIN56760.2023.10073489|Network Slicing;Resource Orchestration;Reinforcement Learning;Constrained Optimization;Technological innovation;Network slicing;Ecosystems;Quality of service;Reinforcement learning;Linear programming;Resource management|
|[A data infrastructure for heterogeneous telemetry adaptation. Application to Netflow-based cryptojacking detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073490)|A. A. Moreno-Sancho; A. Pastor; I. D. Martinez-Casanueva; D. González-Sánchez; L. B. Triana|10.1109/ICIN56760.2023.10073490|Netflow;YANG;Data modelling;Data Normalization;Data Aggregation;Supply chain;Cryptojacking;Technological innovation;Supply chains;Semantics;Telecommunication traffic;Machine learning;Data aggregation;Data models|
|[DCnet: Evaluation Of A New Data Center Network Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073473)|B. Hardin; D. Comer; A. Rastegarnia|10.1109/ICIN56760.2023.10073473|Software Defined Networking (SDN);Data center networks;Emulations;Mininet;ONOS;iPerf;Data centers;Technological innovation;Network architecture;Throughput;Load management;Software;Steady-state|
|[Soteria: An Approach for Detecting Multi-Institution Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073491)|S. Zabarah; O. Naman; M. A. Salahuddin; R. Boutaba; S. Al-Kiswany|10.1109/ICIN56760.2023.10073491|nan;Technological innovation;Cloud computing;Conferences;Pipelines;Production;Machine learning;Data processing|
|[Service-based Federated Deep Reinforcement Learning for Anomaly Detection in Fog Ecosystems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073495)|M. Al-Naday; M. Reed; V. Dobre; S. Toor; B. Volckaert; F. De Turck|10.1109/ICIN56760.2023.10073495|cyber security;federated deep reinforcement learning;Deep Q-Learning;anomaly detection;cloud-to-edge continuum;fog computing;Deep learning;Training;Cloud computing;Technological innovation;Costs;Q-learning;Sensitivity|
|[Troubleshooting Enhancement with Automated Slow-Start Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073485)|Z. Tlaiss; I. Hamchaoui; I. Amigo; A. Ferrieux; S. Vaton|10.1109/ICIN56760.2023.10073485|troubleshooting;active measurement;passive measurement;Congestion Control algorithm;Slow-Start;Degradation;Technological innovation;Cloud computing;Conferences;Delays;Reliability;Task analysis|
|[Distributed Congestion Control Method for Sending Safety Messages to Vehicles at a Set Target Distance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073494)|K. Takahashi; S. Shioda|10.1109/ICIN56760.2023.10073494|V2X;distributed congestion control;safety message;cooperative awareness message;stochastic geometry;Geometry;Technological innovation;Cloud computing;Density measurement;Conferences;Channel estimation;Safety|
|[Troubleshooting Distributed Network Emulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073480)|H. ElBouanani; C. Barakat; W. Dabbous; T. Turletti|10.1109/ICIN56760.2023.10073480|network emulation;passive delay measurement;network tomography;Technological innovation;Cloud computing;Conferences;Emulation;Tomography;Numerical simulation;Delays|
|[Implementation of 5G Experimentation Environment for Accelerated Development of Mobile Media Services and Network Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073512)|H. Khalili; S. Kahvazadeh; J. F. Pajo; R. Frizzell; A. Castelli; M. Tolan; I. Markopoulos; R. Ribback; E. Erikstad; D. Nicholon; K. Ramantas; L. Christofi; C. Verikoukis|10.1109/ICIN56760.2023.10073512|5G;Cloud-Native;Testbeds;Experimentation tools;Network Applications;Media services;Cloud computing;Technological innovation;Uncertainty;5G mobile communication;Production;Life estimation;Virtual reality|
|[Toward migration to SDN: Generating SDN Forwarding Rules by Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073500)|S. Youssef; O. Rysavy|10.1109/ICIN56760.2023.10073500|Decision tree;Software Defined Networks;Data Mining;Netflow;Flow Table;Rule Generation;nan|
|[In-Network Video Quality Adaption using Packet Trimming at the Edge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073496)|S. Clayman; M. Tuker; E. Karakış; M. Sayıt|10.1109/ICIN56760.2023.10073496|Edge Computing;High-speed Packet Processors;Packet Trimming;Traffic Engineering;SVC Video;Video coding;Transport protocols;Technological innovation;Static VAr compensators;Receivers;Streaming media;Reliability engineering|
|[How Organic Networking meets 6G Campus Network Management Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073499)|M. -I. Corici; F. Eichhorn; V. Gowtham; T. Magedanz; E. -R. Modroiu; F. Schreiner|10.1109/ICIN56760.2023.10073499|6G;Network Management;Autonomic NetworkManagement;AI-/ML-based Network Automation;Intent-based Networking;Organic Networking;6G mobile communication;Technological innovation;Cloud computing;Automation;Conferences;Market research;Optimization|
|[A Novel Telemetry Compression Method for Enhancing Network Resilience](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073476)|P. Martinez-Julia; V. P. Kafle; H. Asaeda|10.1109/ICIN56760.2023.10073476|nan;Technological innovation;Cloud computing;Conferences;Buildings;Bandwidth;Digital twins;Telemetry|
|[On the Performance of Consensus Mechanisms in Privacy-Enabled Decentralized Peer-to-Peer Renewable Energy Marketplace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073510)|R. -V. Tkachuk; D. Ilie; R. Robert; V. Kebande; K. Tutschku|10.1109/ICIN56760.2023.10073510|Renewable Energy Marketplace;Blockchain Technology;Peer-To-Peer Energy Trading;Hyperledger Besu;Data Privacy;Renewable energy sources;Fault tolerance;Technological innovation;Distributed ledger;Fault tolerant systems;Smart contracts;Throughput|
|[User Profile and Mobile Number Portability for Beyond 5G: Blockchain-based Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073486)|F. Ghaffari; E. Bertin; N. Crespi|10.1109/ICIN56760.2023.10073486|User profile management;subscription;Mobile number porting;IPFS;Blockchain;smart contract;Technological innovation;Cloud computing;Scalability;Conferences;Smart contracts;Number portability;Switches|
|[Accountable and Distributed Industrial Control Systems with Autonomous Contracts : OCN-DLT: Industry Operations and Control Networks with Distributed Ledger Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073481)|K. Makhijani; T. Faisal|10.1109/ICIN56760.2023.10073481|Smart Contracts;New IP;DLT;High-Precision Communication;HPC-Contract;Operation & Control Networks (OCN);and Control Systems;Integrated circuits;Technological innovation;Distributed ledger;Industrial control;Smart contracts;Quality of service;Production|
|[Real-time Pipeline Reconfiguration of P4 Programmable Switches to Efficiently Detect and Mitigate DDoS Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073501)|A. A. Sadi; M. Savi; D. Berardi; A. Melis; M. Prandini; F. Callegati|10.1109/ICIN56760.2023.10073501|nan;Technological innovation;Cloud computing;Conferences;Pipelines;Denial-of-service attack;Real-time systems;Computer crime|
|[Federated Learning for Distributed NWDAF Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073493)|P. Rajabzadeh; A. Outtagarts|10.1109/ICIN56760.2023.10073493|Machine Learning;5G;Federated Learning;NWDAF;Distributed Data;Technological innovation;Data analysis;5G mobile communication;Federated learning;Conferences;Distributed databases;Parallel processing|
|[PCANT: Programmable Capture and Analysis of Network Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073484)|R. Varloot; L. Noirie|10.1109/ICIN56760.2023.10073484|Traffic monitoring;programmability;Knowledge engineering;Technological innovation;Data acquisition;Focusing;Telecommunication traffic;Manuals;Feature extraction|
|[DisPOSE: Demonstrating Opportunistic Multi-tenant Fog Orchestration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073508)|V. Verdot; P. Peloso; M. Boussard; R. Douville; R. Varloot|10.1109/ICIN56760.2023.10073508|fog computing;decentralized orchestration;Software Defined Networking (SDN);Internet of Things;6G;Technological innovation;Conferences;Collaboration;Computer architecture;Production facilities;Information and communication technology;Internet of Things|
|[Decentralized incentive-based DDoS mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073483)|S. Bitan; A. Molkho; A. Dankner|10.1109/ICIN56760.2023.10073483|DDoS attack;DRDoS attack;Permission based blockchain;nan|
|[RAINSTORM: Reconnaissance SDR Platform1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073511)|I. Palamà; A. Paci; M. Ferri; L. Valeriani; G. Bianchi|10.1109/ICIN56760.2023.10073511|nan;Technological innovation;Cloud computing;Navigation;Conferences;Reconnaissance;Downlink;Real-time systems|
|[5GShell: a plug-and-play framework for automating the deployment of 5G cellular networks1](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073497)|F. Mancini; L. Tamiano; G. Bianchi|10.1109/ICIN56760.2023.10073497|5G;automation;deployment;testing;Cellular networks;Technological innovation;Cloud computing;Automation;5G mobile communication;Conferences;Security|
|[Decentralized Serverless IoT Dataflow Architecture for the Cloud-to-Edge Continuum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073502)|J. J. L. Escobar; F. Gil-Castiñeira; R. P. Díaz Redondo|10.1109/ICIN56760.2023.10073502|Cloud-to-Edge Continuum;IoT Framework;Serverless (FaaS) Dataflow Computing;Decentralized Publish-Subscribe;Zenoh;WebAssembly (WASM);nan|
|[Customizable Cloud-Native Infrastructure for Private 5G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073479)|Q. Zhao; S. Ranganath; S. Feng; G. Li; S. J. Li; Z. Shi; B. Ding; J. Gao|10.1109/ICIN56760.2023.10073479|Private 5G;Edge Computing;reference solutions;Open Source;cloud native;containerization;accelerator multiplexing;lifecycle management;performance;Multiplexing;Wireless communication;Cloud computing;Technological innovation;5G mobile communication;Smart cities;Metals|
|[Low-latency remote-offloading system for accelerator offloading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073506)|S. Saito; K. Fujimoto; A. Shiraga|10.1109/ICIN56760.2023.10073506|accelerator;network;offloading;low latency;realtime system;vRAN;Technological innovation;Protocols;Graphics processing units;Switches;Real-time systems;Servers;Low latency communication|
|[Archipelago: A Hybrid Multi-Node Campus SDN Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073492)|W. Brockelsby; R. Dutta|10.1109/ICIN56760.2023.10073492|Network;SDN;Policy;Cybersecurity;Technological innovation;Cloud computing;Conferences;Computer architecture;Computer security|
|[Enhancement in Multus CNI for DPDK Applications in the Cloud Native Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073475)|A. Ayub; M. Ishaq; M. Munir|10.1109/ICIN56760.2023.10073475|Networking;CNI Plugins;Multus CNI;Userspace CNI;Cloud computing;Technological innovation;Protocols;Conferences;Ecosystems;Switches;Containers|
|[Real-time DDoS Attack Defense System in SDN Using LSSOM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073509)|S. Liu; H. Fukuda; P. Leger|10.1109/ICIN56760.2023.10073509|Software-Defined Networking;Distributed Denial of Service;Machine Learning;Linear Discriminant Analysis;Real-time System;Self-organizing feature maps;Technological innovation;Scalability;Conferences;Machine learning;Network architecture;Denial-of-service attack|
|[Internet of Things over Beam-Hopping-Based Non-terrestrial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073482)|Y. Chen; F. Zhao; R. Yang; C. Kong; R. Li; J. Wang|10.1109/ICIN56760.2023.10073482|5G;6G;Non-terrestrial Network;IoT;satellite communication;beam-hopping;6G mobile communication;Technological innovation;Satellites;5G mobile communication;Transmitters;Numerical analysis;Internet of Things|
|[Open Radio Access Network challenges for Next Generation Mobile Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073507)|N. Aryal; E. Bertin; N. Crespi|10.1109/ICIN56760.2023.10073507|Disaggregation;Open RAN;ORAN;Challenges;Technological innovation;Software architecture;Ecosystems;Hardware;Software;Next generation networking;Interoperability|
|[A 6G RAN-Core Control Plane Convergence Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10073488)|M. Corici; F. Eichhorn; E. Troudt; F. Schreiner; T. Magedanz|10.1109/ICIN56760.2023.10073488|6G;RAN-Core convergence;O-RAN;Core Network;6G mobile communication;Technological innovation;Cloud computing;5G mobile communication;Conferences;Convergence|
#### **2023 National Conference on Communications (NCC)**
- DOI: 10.1109/NCC56989.2023
- DATE: 23-26 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Lightweight Deep Residual Attention Network for Single Image Super Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067881)|Inderjeet; J. S. Sahambi|10.1109/NCC56989.2023.10067881|Super-Resolution;Residual Network;Attention Network;Interpolation;Image coding;Optical character recognition;Superresolution;Deep architecture;Predictive models;Convolutional neural networks|
|[Optimal Data Transfer Over RIS-Assisted Edge Networks Using Coordinated 3D Beamforming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067936)|S. Tripathi; O. J. Pandey; R. M. Hegde|10.1109/NCC56989.2023.10067936|Coordinated 3D beamforming;reconfigurable intelligent surfaces (RISs);power management;throughput;Wireless communication;Three-dimensional displays;Array signal processing;Transmitting antennas;Interference;Throughput;Data transfer|
|[Analysis of THz wireless systems in Rician fading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067906)|S. Khadanga; P. Sudharsan; v. Satyakumar; S. Yoon|10.1109/NCC56989.2023.10067906|Terahertz;Rician fading;Coverage probability;Ergodic capacity;Probability of Error;Misalignment;Fading channels;Radio frequency;Measurement;Wireless networks;Rician channels;Line-of-sight propagation;Probability density function|
|[A heterogeneous stacking ensemble-based security framework for detecting phishing attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068026)|B. Subba|10.1109/NCC56989.2023.10068026|Phishing attacks;stacking ensemble-based classifier;PhishTank & UNB datasets;Uniform resource locators;Training;Phishing;Stacking;Benchmark testing;Feature extraction;Security|
|[Machine Learning Decoder for 5G NR PUCCH Format 0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067950)|A. K. Yerrapragada; J. K. S; A. Gautam; R. K. Ganti|10.1109/NCC56989.2023.10067950|nan;Wireless communication;5G mobile communication;Neural networks;Machine learning;Throughput;Radio links;Real-time systems|
|[Compact printed super wideband MIMO antenna with polarization diversity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068050)|A. K. Chaudhary; M. Manohar|10.1109/NCC56989.2023.10068050|ECC;Equivalent circuit;Polarization diversity;MIMO;SWB;TARC;Correlation coefficient;Military communication;Space communications;Microstrip antennas;Radar imaging;Radar antennas;Envelope detectors|
|[Power Allocation in a Cell-Free MIMO System using Reinforcement Learning-Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067961)|S. Chakraborty; B. R. Manoj|10.1109/NCC56989.2023.10067961|Cell-free;downlink;expected SARSA;max-product SINR;Q-learning;reinforcement learning (RL);SARSA;Q-learning;Computational modeling;Interference;Real-time systems;Numerical models;Resource management;Computational complexity|
|[Time-Frequency Domain Modified Vision Transformer Model for Detection of Atrial Fibrillation using Multi-lead ECG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068090)|H. Manda; S. Dash; R. K. Tripathy|10.1109/NCC56989.2023.10068090|Atrial fibrillation;Multi-lead ECG;WSST;Transformer;Accuracy;nan|
|[Joint Compensation of TX/RX IQ Imbalance and Channel parameters for OTFS Systems under Timing Offset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067967)|S. G. Neelam; P. R. Sahu|10.1109/NCC56989.2023.10067967|IQ imbalance;OTFS;OFDM;MFGS;Channel estimation;nan|
|[Hovering UAV-Based FSO Communications with DF Relaying: A Performance Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068006)|D. Singh; C. S. S. Reddy; R. Swaminathan|10.1109/NCC56989.2023.10068006|DF relaying;ergodic capacity;free space optics (FSO);hovering unmanned-aerial-vehicles (UAVs);generalized distribution;outage probability;average symbol error rate;Fluctuations;Closed-form solutions;Error analysis;Atmospheric modeling;Symbols;Probability;Diversity methods|
|[A Novel Shared-Aperture Dual-Band (n78/n257) Reconfigurable Antenna for 5G Mobile Terminals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067934)|S. Tiwari; A. K. Singh; A. Dubey|10.1109/NCC56989.2023.10067934|Dual-band;Millimeter-wave (mm-wave);Reconfigurable antenna;Shared-aperture;Sub-6 GHz;Wireless communication;Microstrip antenna arrays;Wireless LAN;5G mobile communication;PIN photodiodes;Dual band;Switches|
|[Rate Adaptation For Low Latency Real-Time Video Streaming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067883)|R. Doreswamy; A. G. Colaco; V. Sevani; P. Patil; H. Tyagi|10.1109/NCC56989.2023.10067883|Real-time video;stochastic gradient descent;Channel capacity;Wireless networks;Process control;Streaming media;Markov processes;Real-time systems;Quality assessment|
|[Limitations of the Perceptual Deadband Approach for Haptic Data Compression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067964)|M. Chhabaria; G. Hemanth; A. Bhardwaj|10.1109/NCC56989.2023.10067964|Haptic data;Perceptual deadband;Just noticeable difference;Weber fraction;nan|
|[Hybrid Decode- and Amplify-and-Forward Protocol for NOMA-Enabled Power Line Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067935)|R. Ramesh; S. Gurugopinath|10.1109/NCC56989.2023.10067935|Cooperative communication;hybrid decode-and amplify-and-forward relay;non-orthogonal multiple access;outage performance;power line communication;NOMA;Protocols;Monte Carlo methods;Interference cancellation;Power line communications;Symbols;Probability|
|[Tensor-Domain Machine Learning Based Cardiac Diseases Detection Using 12-lead ECG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068027)|C. Chauhan; R. K. Tripathy; M. Agrawal|10.1109/NCC56989.2023.10068027|12-lead ECG;Third-order tensor;Tensor domain features;Cardiac Diseases;Wavelet transforms;Tensors;Three-dimensional displays;Cardiac disease;Resists;Medical services;Electrocardiography|
|[Study on Machine Learning Models for IPv6 Address Lookup in Large Block Lists](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068091)|N. K. Radke; S. S. Tomar; A. Rajan|10.1109/NCC56989.2023.10068091|IPv6;firewall;block lists;machine learning;“Trie”;IPv6 address lookup;nan|
|[Sniper Localization using Acoustic Signal Processing based on Time of Arrivals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067971)|R. Rakesh; G. Routray; P. Dwivedi; R. M. Hegde|10.1109/NCC56989.2023.10067971|Shock waves;muzzle blast;GCC-PHAT;time delays;trajectory;localization;nan|
|[A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068032)|A. Biswas; A. P. Goutham; S. Pateriya; D. S. Jadav; S. Mulleti; V. M. Gadre|10.1109/NCC56989.2023.10068032|ear biometrics;hair occlusions;learnable Scat-terNet;wavelets;one-shot learning;Hair;Training;Measurement;Degradation;Biometrics (access control);Simulation;Scattering|
|[Stationary Wavelet Transform Based Detection of Aortic Stenosis Using Seismocardiogram Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068062)|M. J. Singh; S. Das; L. N. Sharma; S. Dandapat|10.1109/NCC56989.2023.10068062|Seismocardiogram (SCG);Stationary Wavelet Transform (SWT);Random Forest (RF);Aortic Stenosis (AS);Wavelet transforms;Heart;Sensitivity;Databases;Medical services;Forestry;Feature extraction|
|[On Secret Sharing Schemes from Nonlinear Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067953)|D. Agrawal; S. Das; S. Krishanaswamy|10.1109/NCC56989.2023.10067953|Secret sharing scheme;Access structure;Nordstrom-Robinson code;Privacy;Semantics;Resists;Error correction codes;Error correction;Blockchains;Cryptography|
|[Bit Error Rate Analysis of Double IRS Assisted Communication System Under Transceiver Hardware Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067984)|C. Kumar; A. Kumar; S. Kashyap|10.1109/NCC56989.2023.10067984|nan;nan|
|[Machine Learning Based Power Control for a Secondary Cell-Free Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067923)|R. Sarvendranath; K. Paul|10.1109/NCC56989.2023.10067923|Cell-free;spectrum sharing;energy efficiency;power control;machine learning;deep neural network.;Wireless communication;Computational modeling;Neural networks;Transmitting antennas;Power control;Training data;Complexity theory|
|[Wavelength Dependent Performance of Spatial Light Modulator-based OAM Beam Generator and Detector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067873)|A. M. Shukla; S. Gupta|10.1109/NCC56989.2023.10067873|Free space optical Communication;Orbital Angular Momentum Communication;Wavelength Division Multiplexing;Q measurement;C-band;Wavelength measurement;Optical variables measurement;Wavelength division multiplexing;Extraterrestrial measurements;Optical fiber communication|
|[Parturition Hindi Speech Dataset for Automatic Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067891)|V. Bansal; T. Thishyan Raj; N. Ravi; S. Korde; J. Kalra; S. Murugesan; B. Ramkrishnan; A. Gore; V. Arora|10.1109/NCC56989.2023.10067891|Automatic speech recognition;speech dataset;limited vocabulary;digital healthcare;Adaptation models;Vocabulary;Pediatrics;Sociology;Medical services;Real-time systems;Recording|
|[On Performance of Wireless-Powered FD Relaying Network with Imperfect SIC and Hardware Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068077)|D. Kumar; P. K. Singya; V. Bhatia|10.1109/NCC56989.2023.10068077|Energy harvesting (EH);hardware impairments (HIs);imperfect self-interference cancellation (SIC);residual self-interference (RSI);time switching (TS);nan|
|[Radio Tomographic Imaging with Input Sensor Location Uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068087)|A. Mishra; U. K. Sahoo; S. Maiti|10.1109/NCC56989.2023.10068087|Radio tomographic imaging;received signal strength;spatial loss field;stochastic robust approximation;nan|
|[Does Spatial Location of The Electrodes in EEG Matter for Tracking the Brain States?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067919)|R. Dev; S. Kumar; T. K. Gandhi|10.1109/NCC56989.2023.10067919|electroencephalography;eigenspace;10-20 protocol.;Electrodes;Location awareness;Systematics;Protocols;Scalp;Electroencephalography;Mathematical models|
|[A Random-key Based Second-level Encryption for Reversible Data Hiding in Encrypted Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067978)|S. Panchikkil; V. R. Malpeddi; V. M. Manikandan|10.1109/NCC56989.2023.10067978|Image encryption;Reversible data hiding;Data extraction and image recovery;Information hiding;nan|
|[Communication-Constrained Distributed Mean Estimation of Log-Concave Distributions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067942)|R. Kumar; S. Vatedka|10.1109/NCC56989.2023.10067942|nan;Upper bound;Protocols;Estimation;Gaussian distribution;Random variables;Servers|
|[Analysis and Classification of Electroglottography Signals for the Detection of Speech Disorders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067972)|D. Kumar; U. Satija; P. Kumar|10.1109/NCC56989.2023.10067972|EGG signals;feature extraction;feature selection;machine learning;Support vector machines;Pathology;Machine learning algorithms;Databases;Cepstral analysis;Redundancy;Neural networks|
|[Short Packet Communication over 2-user Non-orthogonal Multiple Access Channel with Confidential Message](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068105)|U. Somalatha; P. Mohapatra|10.1109/NCC56989.2023.10068105|NOMA;throughput;finite block-length codes;and SIC;nan|
|[A BBL-Net based OFDM Signal Detection in the Presence of RF Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067999)|S. Anand; A. K. Singh; P. Kumar|10.1109/NCC56989.2023.10067999|OFDM;signal detection;channel estimation;RNN;RF impairments;Radio frequency;Training;OFDM;Simulation;Channel estimation;Symbols;Receivers|
|[Opportunistic Beamforming based Hybrid Analog-Digital Multi-User mmWave Communications: Design and Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068084)|K. Verma; C. R. Murthy|10.1109/NCC56989.2023.10068084|MmWave;opportunistic beamforming;Multi-User diversity;Average throughput;Rate based Scheduling;nan|
|[Pattern and Frequency Re-configurability with Liquid DRAs: Bi-directional and Uni-Directional Radiation Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068045)|U. K. Sarma; R. S. Kshetrimayum|10.1109/NCC56989.2023.10068045|Cylindrical ring dielectric resonator antenna (CRDRA);dielectric resonator (DR);liquid dielectric resonator (LDR);Wireless LAN;Liquids;Roads;Resonant frequency;Bidirectional control;Transforms;Directive antennas|
|[A Fast Dictionary Learning Algorithm for CSI Feedback in Massive MIMO FDD Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067888)|P. K. Gadamsetty; K. V. S. Hari|10.1109/NCC56989.2023.10067888|K-SVD;channel state information (CSI);compressive sensing (CS);dictionary design;massive MIMO;Simulation;Discrete Fourier transforms;Channel estimation;Machine learning;Massive MIMO;Market research;MIMO|
|[Quantum Network Coding and Distribution of Maximally Entangled States in Measurement-Based Quantum Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067927)|C. Pandey; S. Gupta; R. R. Das; A. Raina|10.1109/NCC56989.2023.10067927|Quantum network coding;Entanglement distribution;Measurement-based quantum computing;Unicast;Multicast;Protocols;Unicast;Transmitters;Quantum entanglement;Receivers;Network coding;Throughput|
|[Improved Epoch Based Prosody Modification by Zero Frequency Filtering of Gabor Filtered Telephonic Speech](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068021)|M. R. Rajeswari; D. Govind; S. V. Gangashetty; A. K. Dubey|10.1109/NCC56989.2023.10068021|Epochs;Telephonic speech;Gabor windows;multi-scale product;Degradation;Filtering;Estimation;Channel estimation;Speech enhancement;Telephone sets;Frequency estimation|
|[On age of information for remote control of Markov decision processes over multiple access channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068042)|M. Mubarak; B. S. Vineeth|10.1109/NCC56989.2023.10068042|Remote control;Markov Decision Processes;Age of Information;Multiple access channels;nan|
|[Long-Term Temporal Audio Source Localization using SH-CRNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067916)|P. Dwivedi; S. B. Hazare; G. Routray; R. M. Hegde|10.1109/NCC56989.2023.10067916|source localization;spherical harmonics decomposition;convolutional recurrent neural network;direction of arrival estimation;spherical harmonics domain.;Location awareness;Training;Direction-of-arrival estimation;Recurrent neural networks;Convolution;Estimation;Harmonic analysis|
|[On the Optimal Tradeoff of Age of Information and Transmission Power for Point-to-Point Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067998)|S. A. K; V. B. S; C. R. Murthy|10.1109/NCC56989.2023.10067998|Age of information;Transmission power;Optimal tradeoff;Semi-Markov decision process;nan|
|[On the Performance of Generalized Spatial-Index Modulation Based Orthogonal Time Frequency Space System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068109)|K. D. Rajpoot; P. Maheswaran|10.1109/NCC56989.2023.10068109|Orthogonal time frequency space (OTFS);Index Modulation (IM);Generalized spatial modulation (GSM);performance analysis;nan|
|[A Two-Tier Deep Neural Network Detector for Two-User Rate-Splitting Multiple Access Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068068)|K. R. Spoorthy; S. B. Hudalimath; S. Medatati; V. Sinchana; A. K. Kowshik; S. Gurugopinath|10.1109/NCC56989.2023.10068068|Deep learning (DL);long short-term memory (LSTM);non-orthogonal multiple access (NOMA);rate-splitting multiple access (RSMA);two-tier neural network;nan|
|[Ergodic Sum Rate Analysis of STAR-RIS-Aided NOMA Network with Imperfect SIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067997)|A. K. Pandey; A. Bansal|10.1109/NCC56989.2023.10067997|Ergodic sum rate;non-orthogonal multiple access;simultaneous transmitting and reflecting reconfigurable intelligent surface;successive interference cancellation;nan|
|[On the Performance of Communication in a Vehicular Network with Platooned Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068039)|P. Rangesh; K. Pandey; A. K. Gupta|10.1109/NCC56989.2023.10068039|Platooned vehicular network;Coverage;Meta distribution;Stochastic geometry;nan|
|[A non-linear source-filter based vocoder with prosody control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067968)|P. Giridhar; G. Ramesh; K. S. R. Murty|10.1109/NCC56989.2023.10067968|Speech reconstruction;source-filter model;neural vocoder;prosody control;multi-head attention;nan|
|[RIS-assisted User Pairing NOMA System for THz Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068095)|M. H. Kumar; S. Sharma; K. Deka; M. K. Sharma|10.1109/NCC56989.2023.10068095|Terahertz communication;reconfigurable intelligent surface (RIS);sum-rate and BER;user pairing NOMA;nan|
|[Breaking the Unit Throughput Barrier in Random Access Protocol Based Distributed Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068073)|A. Kumar; P. Hegde; R. Vaze; A. Alloum; C. Adjih|10.1109/NCC56989.2023.10068073|nan;nan|
|[RF Metasurface Based ‘Add-Ons’ for Boosting Signal-To-Noise Ratio of 1.5T MRI Scans](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067982)|J. Gupta; P. Das; T. Bhowmik; R. Bhattacharjee; D. Sikdar|10.1109/NCC56989.2023.10067982|Magnetic resonance imaging;RF metasuface based ‘add-ons’;signal-to-noise ratio;specific absorption rate;nan|
|[Robust Manipulation Parameter Estimation Scheme for Post-JPEG Compressed Images using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067901)|V. Kadha; S. K. Das|10.1109/NCC56989.2023.10067901|Digital image forensics;manipulation parameter estimation;post-JPEG compression;forgery detection.;Q-factor;Visualization;Image coding;Parameter estimation;Forensics;Transform coding;Detectors|
|[Photonic Generation of Multiband V and Cross Chirps using a Dual-Drive Mach Zehnder Modulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067902)|R. Dhawan; R. Parihar; A. Choudhary|10.1109/NCC56989.2023.10067902|microwave photonics;dual or cross chirp;range resolution;ambiguity function;Radio frequency;Mach-Zehnder interferometers;Chirp;Modulation;Radar;Bandwidth;Microwave communication|
|[Millimeter Wave MPA using Metamaterial-Substrate Antenna Array for Gain Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068072)|P. Mishra; R. Komatineni; K. D. Kulat|10.1109/NCC56989.2023.10068072|Microstrip Patch Antenna (MPA);Split Ring Resonator (SRR);Metamaterials;Superstrate;Millimeter Waves;CST;HFSS;nan|
|[Adversarial Robustness via Class Partitioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067966)|R. G. Tiwari; A. Thangaraj|10.1109/NCC56989.2023.10067966|Adversarial Attacks;Defense;Diversity;Ensemble Neural Networks;nan|
|[Impact of Sinusoidal Co-channel CW Interference on the Spectral Efficiency of OTFS Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068080)|M. Ubadah; S. K. Mohammed|10.1109/NCC56989.2023.10068080|OTFS;High mobility;Doppler domain resolution;CW interference;Sinusoidal;nan|
|[Feature Space Perturbation for Transferable Adversarial Examples in Image Forensics Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068064)|R. Rangslang; P. K. Bora|10.1109/NCC56989.2023.10068064|image forensics;manipulation detection;adversarial examples;transferability;feature space perturbation;hierarchical feature space;nan|
|[Comparative Study of Dynamic TDD with Full-Duplex in Cell-Free Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068116)|A. Chowdhury; C. R. Murthy|10.1109/NCC56989.2023.10068116|Cell-free massive MIMO;full-duplex;dynamic TDD;nan|
|[Challenges in spoken language diarization in code-switched scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068088)|J. Mishra; S. R. Mahadeva Prasanna|10.1109/NCC56989.2023.10068088|Code-switched speech;spoken language diarization;speaker diarization;language change detection;posteriors;nan|
|[Optically Controlled Digital Metasurface for Radar Cross-Section Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068115)|C. Dhote; A. Singh|10.1109/NCC56989.2023.10068115|Beam Splitting;Digital Coding;Optical Light;Metasurface;PIN Diode;RCS;Wireless communication;Phased arrays;Radar cross-sections;Laser radar;Voltage;Metasurfaces;Encoding|
|[On the Effect of Phase Error on Physical Layer Security of RIS-Aided NOMA Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068041)|Y. Khan; A. Dubey|10.1109/NCC56989.2023.10068041|Non-orthogonal multiple access;phase error;physical layer security;reconfigurable intelligent surfaces;secrecy outage probability;nan|
|[Average BER Analysis of NOMA Systems under TWDP fading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068065)|P. Kumar; K. Dhaka|10.1109/NCC56989.2023.10068065|Average bit-error rate (BER);NOMA;TWDP fading;5G and Beyond;nan|
|[Receiver for Asynchronous Distributed Transmission over AWGN Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067946)|A. Ahmad; Sonalika; S. Agarwal|10.1109/NCC56989.2023.10067946|Asynchronous distributed beamforming;receiver design;Generalized likelihood ratio test;carrier frequency offset;AWGN channels;Transmitters;Array signal processing;Bit error rate;Receivers;Synchronization|
|[Localization of Nanomachine with Correlated Observations in Biological Nanonetworks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067924)|A. Kumar; S. Kumar|10.1109/NCC56989.2023.10067924|Biological nanonetwork;bio-nanomachine;localization;correlated observation;inter-symbol interference (ISI);Location awareness;Maximum likelihood estimation;Transmitters;Symbols;Interference;Receivers;Detectors|
|[Multi-User mmWave Massive-MIMO Hybrid Beamforming: A Quantize Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068081)|S. Kumar; R. Mahapatra; A. Singh|10.1109/NCC56989.2023.10068081|Quantized Deep Learning;mmWave-MIMO;hybrid beamforming;multi user MIMO;spectral efficiency;nan|
|[Investigation Of Data Augmentation Techniques For Bi-LSTM Based Direct Speech To Speech Translation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067896)|L. Arya; A. Agarwal; S. R. Mahadeva Prasanna|10.1109/NCC56989.2023.10067896|Speech-To-Speech Translation (S2ST);Voice Conversion (VC);Bidirectional Long Short Term Memory (Bi-LSTM);data Augmentation;Deep learning;Measurement;Perturbation methods;Training data;Distortion;Robustness;Task analysis|
|[Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067994)|M. Ashhad; U. Goenka; A. Jagetia; P. Akhtari; S. K. Ambat; M. Samuel|10.1109/NCC56989.2023.10067994|Acoustic Traffic Monitoring (ATM);Signal Processing;Audio Classification;Machine Learning;CNN;Privacy;Computer vision;Roads;Lighting;Cameras;Acoustics;Task analysis|
|[OTSM-SCMA: Code-Domain NOMA based Orthogonal Time Sequency Multiplexing Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067992)|A. Thomas; N. Sangeeta; K. Deka; S. Sharma; A. Rajesh|10.1109/NCC56989.2023.10067992|OTSM;delay-sequency (DS);delay-Doppler (DD);SCMA;OTFS;nan|
|[ConvPlant-Net: A Convolutional Neural Network based Architecture for Leaf Disease Detection in Smart Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067920)|S. D. Deb; R. K. Jha; S. Kumar|10.1109/NCC56989.2023.10067920|Leaf Disease;CNN;Deep Learning;Deep learning;Smart agriculture;Plant diseases;Plants (biology);Crops;Feature extraction;Mobile handsets|
|[Information Loss Minimization in FANET-Aided Rechargeable IoT Networks Using Q-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067958)|A. Singh; Y. Dixit; R. M. Hegde|10.1109/NCC56989.2023.10067958|FANET;Multi-objective routing;Q-learning;Unmanned Aerial vehicles;Wireless Power Transfer;Base stations;Systematics;Surveillance;Simulation;Routing;Autonomous aerial vehicles;Internet of Things|
|[On Secondary Structure Avoiding DNA codes with Reversible and Reversible-Complement Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067900)|K. G. Benerjee; A. Banerjee|10.1109/NCC56989.2023.10067900|DNA data storage;DNA computing;DNA codes;Codes;DNA;Memory;Reed-Muller codes;Periodic structures;DNA computing|
|[Learning 6DoF Grasp-poses in Occluded Scenes Using RGB and 3D Point Cloud Modalities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068034)|A. D. Mathur; S. Bhadang; P. Raj; L. Behera; T. Sandhan|10.1109/NCC56989.2023.10068034|automated fruit plucking;grasp-pose;point cloud;data annotation;data augmentation;deep learning;Point cloud compression;Training;Deep learning;Three-dimensional displays;Annotations;Pose estimation;Training data|
|[Performance Analysis of Smart Grid Network with Energy Harvesting over Mixed RF/PLC Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067970)|H. K. Sahu; A. K. Padhan; S. G. Dontamsetti; P. R. Sahu|10.1109/NCC56989.2023.10067970|Smart grid (SG);Nakagami m channel;Power line communication (PLC);Energy Harvesting;Average bit error probability (ABEP);nan|
|[2-Stage Convolutional Neural Network for Breast Cancer Detection from Ultrasound Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067925)|S. D. Deb; A. Abhishek; R. K. Jha|10.1109/NCC56989.2023.10067925|Breast Cancer;Ultrasound Images;CNN;Visualization;Image segmentation;Ultrasonic imaging;Design automation;Feature extraction;Breast cancer;Robustness|
|[Modeling of Channel Asymmetry in HAP-based Optical Wireless Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068106)|N. Tiwari; S. De; S. Dharmaraja|10.1109/NCC56989.2023.10068106|Free-space optical communication;channel asymmetry;anisotropic turbulence;HAP-to-ground communication;nan|
|[Meditative State Classification Using Neuronal Multi-IMF Band Power and Complexity Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067947)|S. Singh; V. Gupta; T. K. Reddy; L. Behera; S. Samanta|10.1109/NCC56989.2023.10067947|Meditation;EEG;Empirical Mode Decomposition (EMD);Intrinsic Mode Functions (IMFs);Feature Extraction;Sample Entropy (SE);Hurst Exponent (HE);Band Power (BP) Classification;Machine Learning (ML);Chaotic communication;Time series analysis;Feature extraction;Electroencephalography;Physiology;Entropy;Complexity theory|
|[Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068074)|N. Kumar; T. Sandhan|10.1109/NCC56989.2023.10068074|Malignant;Convolutional Neural Network (CNN);Skip Connection;Dermoscopic images;Skin Cancer detection;Cancer classification;nan|
|[Zero-Error Communication with an Influencer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068030)|R. Deori; A. A. Kulkarni|10.1109/NCC56989.2023.10068030|nan;nan|
|[PPG-based End-to-End Framework for Cuffless Blood Pressure Estimation Using CNN-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067922)|S. Gupta; A. Singh; A. Sharma|10.1109/NCC56989.2023.10067922|PPG;CNN;LSTM;Automatic feature extraction.;Estimation;Feature extraction;Blood pressure;Pressure measurement;Convolutional neural networks;Biomedical monitoring;Monitoring|
|[Effective WBC Segmentation Using Hybrid Loss](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067937)|A. Abhishek; S. D. Deb; R. K. Jha; R. Sinha; K. Jha|10.1109/NCC56989.2023.10067937|White Blood Cell Segmentation;Data Imbalance;Hybrid Loss;White blood cells;Image segmentation;Microscopy;Focusing;Feature extraction;Task analysis;Diseases|
|[Variable Step-size Zero Attractor LMS based Channel Estimator for Millimeter Wave Hybrid MIMO System with Hardware Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067962)|V. B. Shukla; V. Bhatia|10.1109/NCC56989.2023.10067962|Hardware impairments;mmWave hybrid MIMO;channel estimation;sparse recovery;SBL;BCS;nan|
|[Frequency Projection: A Review and its application in channel modelling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10067981)|C. Ramanathan; S. Y. Desai; S. Banerjee; K. Giridhar|10.1109/NCC56989.2023.10067981|Frequency Projection;out-of-band information;Sub 6GHz information;FDD downlink beamforming;multi-band communication;nan|
|[Effect of Polarization on RF Signal Transmission over Two-Ray Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068000)|S. Sharma; C. Ganguly; S. De|10.1109/NCC56989.2023.10068000|Polarization interference;reflector permittivity;RF energy transfer;short-range wireless communication;two-ray propagation channel;Wireless communication;Transmitters;RF signals;Receivers;Reflection;Permittivity|
#### **2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)**
- DOI: 10.1109/ICREST57604.2023
- DATE: 7-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Predicting Mushroom Edibility with Effective Classification and Efficient Feature Selection Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070049)|M. S. Morshed; F. Bin Ashraf; M. U. Islam; M. S. R. Shafi|10.1109/ICREST57604.2023.10070049|feature selection;machine learning;intelligent farming;agriculture;classification;nan|
|[IoT Based Seat Reservation System for University Bus with Wireless Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070092)|M. E. Alam; M. A. Kader; J. B. Saba; F. Sultana; S. Farid; M. N. Arefin|10.1109/ICREST57604.2023.10070092|IoT;microcontroller;Bluetooth;Transport Management;Blynk;nan|
|[Performance Enhancement of Conventional Design of 4-Bit Carry Look-Ahead Adder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070089)|U. B. Joy; A. Chakraborty; S. Sen; A. Das; P. Biswas; A. Tasnim|10.1109/ICREST57604.2023.10070089|adder;carry look-ahead;AND gate;XOR gate;gate diffusion input;nan|
|[Two-Bit Magnitude Comparator Design Using Gate Diffusion Input Technique and Static CMOS Logic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070047)|U. B. Joy; A. Chakraborty; P. Biswas; A. Das; S. Sen; A. Tasnim|10.1109/ICREST57604.2023.10070047|magnitude comparator;gate diffusion input technique;static CMOS logic;high performance;nan|
|[Design and Implementation of IoT-Based Load Monitoring and Outage Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070034)|A. M. Anwar; M. R. Hazari; M. A. Mannan|10.1109/ICREST57604.2023.10070034|Electrical power system;microcontroller;wi-fi module;GSM module;remote data monitoring;outage management system;nan|
|[A Comprehensive Study of Camouflaged Object Detection Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070074)|K. Bin Khair; S. Jahir; M. Ibrahim; D. Karmaker|10.1109/ICREST57604.2023.10070074|Deep Learning;Transfer Learning;TensorFlow;Camouflage;Object Detection;Architecture;Accuracy;Model;VGG16;nan|
|[Fuzzy Logic-Based Design Optimization and Economic Planning of a Microgrid for a Residential Community in Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070044)|S. Hasan; M. R. Hazari; M. A. Mannan|10.1109/ICREST57604.2023.10070044|Fuzzy logic;Load modeling;Microgrid;Power system economics;Solar energy;Wind energy;nan|
|[Performance Analysis of the AVR Using An Artificial Neural Network and Genetic Algorithm Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070076)|N. Goswami; M. R. Habib; A. H. Shatil; K. F. Ahmed|10.1109/ICREST57604.2023.10070076|Automatic Voltage Regulator system;Proportional Integral Derivative controller;Artificial Neural Network;Genetic Algorithm;Optimization;nan|
|[Electrical Impedance Measurement Technique to Determine the Impedance of a Volume Conductor with Embedded Object](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070027)|M. Mobarak|10.1109/ICREST57604.2023.10070027|Tetrapolar (4-electrode) impedance measurements (TPIM);Focused Impedance Method (FIM);COMSOL Multiphysics;Volume conductor;nan|
|[Design and Implementation of Embedded Sensor Network for an Automated Radio Telescope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070051)|K. Meah; D. Hake; D. Babcock; E. Bosse; B. Nelson|10.1109/ICREST57604.2023.10070051|Embedded sensor network;Radio telescope;Super loop;Bluetooth;nan|
|[Design Of an Off-Grid Solar-Wind-Bio Hybrid Power Generation For Remote Areas Of Chapainawabgonj District In Bangladesh Using Homer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070032)|M. K. Saifullah; R. Halder; S. Afroz; A. H. Shatil; K. F. Ahmed|10.1109/ICREST57604.2023.10070032|Solar;Wind;Biomass;Fuel;Load;Cost;hybrid;COE;NPC;nan|
|[Development of a Facial Recognition Pantograph Drawing Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070086)|D. Giles; P. Glenn; T. Burnell; S. Flynn; R. Noorani|10.1109/ICREST57604.2023.10070086|nan;nan|
|[An Integrated Approach of MCDM Methods and Machine Learning Algorithms for Employees' Churn Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070079)|S. J. Chowdhury; M. I. Mahi; S. A. Saimon; A. N. Urme; R. H. Nabil|10.1109/ICREST57604.2023.10070079|MCDM;AHP;TOPSIS;Machine Learning;Random Forest;nan|
|[ReChain-A Blockchain Network for Review and Rating System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070038)|R. Ahmed; M. S. Islam; S. Osmani; M. A. Shakil; M. Mazid-Ul-Haque; M. Al-Amin|10.1109/ICREST57604.2023.10070038|blockchain;ethereum;bitcoin;rechain;decentralization;distributed ledger;proof-of-work;nan|
|[Design and Analysis of IoT-Based Adaptive Microgrid System including Renewable Energy Sources for Decentralized Zones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070093)|M. E. Deowan; A. K. Nuhel; M. M. Sazid; R. T. Meghla; I. Haider; M. R. Hazari|10.1109/ICREST57604.2023.10070093|Microgrid;IoT;Power Monitoring;Fault analysis;Zonal fault;Relay;Busbar;Mobile application;nan|
|[Drone-Based Real- Time Air Pollution Monitoring for Low-Access Areas by Developing Mobile-Smart Sensing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070050)|I. A. Limon; A. D. Hossain; K. F. I. Faruque; M. R. Uddin; M. Hasan|10.1109/ICREST57604.2023.10070050|Microcontroller;Remote Monitoring;Drone;Pollution;Air;Parts per Million;Quality;IoT;nan|
|[An IoT Based Smart Vault Security and Monitoring System with Zero UI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070057)|M. H. C. Joy; M. M. A. Karim; A. H. Choudhury; M. Razin; S. N. M. Ahmed|10.1109/ICREST57604.2023.10070057|Internet of Things;Automated Safety Vault;Smart Security System;Triple Layered Defense Mechanism;Password Verification;Electronic Lock;Security Features;Zero User Interface (UI);Touchless Technology;nan|
|[Blockchain Based Secured Refugee Identity Management by Using the Assistance Smart Contract](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070069)|A. Habib; T. Refat; M. T. Ahad|10.1109/ICREST57604.2023.10070069|Refugee identity;Identity management;Smart contract;nan|
|[Design and Implementation of IoT-Based Smart Energy Meter to Augment Residential Energy Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070068)|M. A. H. S. Midul; S. H. Pranta; A. S. I. Biddut; S. I. Siam; M. R. Hazari; M. A. Mannan|10.1109/ICREST57604.2023.10070068|Smart Energy Meter (SEM);Arduino (Microcontroller);Global System for Mobile (GSM);Short Message Service (SMS);Internet of Things (IoT);Prepaid Energy Meter;WiFi Module;Overload & Electricity Theft;nan|
|[Wastewater Microbial Fuel Cell Stack with the Ability of Driving Low Power Electronic Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070094)|U. B. Joy; M. M. Uddin; N. U. A. Khan; M. H. Banna; S. H. Tushar; K. M. S. Kabir|10.1109/ICREST57604.2023.10070094|microbial fuel cell;wastewater;bacteria;double chamber micorbial fuel cell;membrane-less microbial fuel cell;nan|
|[Software Design, Development, and Implementation for an Automated Radio Telescope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070030)|T. Franks; D. Hake; D. Babcock; K. Meah; T. Ullery; D. McHugh; D. Herr|10.1109/ICREST57604.2023.10070030|Software development;Radio telescope;Control room;Mobile application;Simulation;Laboratory testing;nan|
|[Effect of Pressure on VTe2/NbTe2 Heterostructure in Photonic Applications: A First-Principles Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070090)|S. M. T. -S. Afrid; R. Kundu|10.1109/ICREST57604.2023.10070090|TMD;DFT;Photonics;VTe2/NbTe2;Het-erostructure;Pressure;DOS;Magnetization;Stability;nan|
|[Analysis of a Grid Tie Inverter for attaining the Maximum Power Factor and Minimum Total Harmonic Distortion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070058)|S. M. Nayeem; Z. H. Anik; N. R. Nadi; K. M. Salim|10.1109/ICREST57604.2023.10070058|Grid Tie Inverter;Power Factor;Total Harmonic distortion;Photovoltaic system;nan|
|[Feasibility Analysis of Off-Grid Hybrid Renewable Energy for Rohingya Refugee in Bhasan Char](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070066)|N. A. Nuha; M. T. S. Injam; N. Chowdhury|10.1109/ICREST57604.2023.10070066|Remote area;renewable sources;HOMER;hybrid energy system;NPC;COE;nan|
|[Performance Analysis of Load Frequency Control for Power Plants Using Different Optimization Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070029)|N. Goswami; A. H. M. Shatil|10.1109/ICREST57604.2023.10070029|Load Frequency Control;Particle Swarm Optimization;Genetic Algorithm;Adaptive Neuro-Fuzzy Inference System;Governor;Turbine;Load;nan|
|[Intelligent Software Bug Prediction: An Empirical Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070026)|M. A. Jahangir; M. A. Tajwar; W. Marma; M. S. Islam|10.1109/ICREST57604.2023.10070026|Bug;Software;Machine Learning;Deep Learning;Code Based Metrics;Prediction;PROMISE Dataset;nan|
|[Computational fluid dynamics (CFD) analysis of thermoelectric generator for Regenerative Braking of the Hybrid Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070036)|M. Z. U. Saif; M. R. Ahmed; M. A. Hanif; F. Tasnim; C. A. Hossain|10.1109/ICREST57604.2023.10070036|Thermoelectric Generator;Hybrid Electric Vehicle;Ansys;Computational Fluid Dynamics;regenerative braking;wasted recover;nan|
|[A Methodology for Implementing Demand Side Management in a Community Having Both Residential and Commercial Customers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070043)|M. K. Islam; S. J. Hamim; S. Dhar; T. Aziz|10.1109/ICREST57604.2023.10070043|DSM;Load shifting;Peak clipping;Peak to average ratio (PAR);Residential load;Commercial load;Hybrid load;Shiftable loads;Non shiftable loads;nan|
|[Design and Implementation of Smart Wheelchair with Advanced Control Interfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070067)|S. M. Shifa; T. M. Mridul; S. S. Rafat; M. H. Monjur; M. R. Islam; M. K. Hassan|10.1109/ICREST57604.2023.10070067|GPS;Voice control;Bluetooth;Channel transmitter;nan|
|[Brain tumor detection by Kapton Polyimide based on-body patch antenna in K band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070083)|S. H. Eshan; R. R. Hasan; A. Al Mamun Sarker; S. Zabin; R. T. H. Tusher; M. A. Rahman|10.1109/ICREST57604.2023.10070083|Brain tumor antenna;Brain Tumor Detection Patch Antenna;Tumor Affected Brain;Kapton Polyimide;Biomedical Antenna;nan|
|[Analysis of Layered Shielding for Capacitive Wireless Power Transfer Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070059)|M. Rahman; K. Ahmed; M. I. I. Nuhin; M. T. Ali|10.1109/ICREST57604.2023.10070059|capacitive power transfer;shielding;shielding mate-rials;electric field;specific absorption rate;nan|
|[A Simulation of a Robot Operating System Based Autonomous Wheelchair with Web Based HMI Using Rosbridge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070046)|M. T. Islam; I. R. Hameem; S. Saha; M. J. R. Chowdhury; M. E. Deowan|10.1109/ICREST57604.2023.10070046|Robot Operating System;Gazebo;Autonomous Navigation;Cloud Control;rosbridge;nan|
|[ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070078)|M. S. Nafiz; S. S. Das; M. K. Morol; A. Al Juabir; D. Nandi|10.1109/ICREST57604.2023.10070078|Deep learning;CNN;Waste detection;Waste management system;GSM notification;Circular economy;nan|
|[IoT-Based Automatic Shed System to Prevent Unwanted Rain for Growing Crops](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070052)|M. S. Reza; M. I. Hossen; S. Afrose; K. M. Niloy; M. S. Kabir|10.1109/ICREST57604.2023.10070052|Umbrella Shaped;Rack-Pinion;NodeMCU;Arduino Uno;Callback Function;IoT;nan|
|[Different Converter Integration and Performance Assessment of a Multi-Stage Transformer Less Grid Tie Inverter Using Thin Film PV Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070062)|M. I. I. Sakib; M. R. Uddin; K. F. I. Faruque; A. A. Mamun; K. M. Salim|10.1109/ICREST57604.2023.10070062|grid-tie inverter;converters;transformer less;PV;MPPT;high-efficiency;nan|
|[Designing of an Autonomous System for Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070070)|T. M. Mridul; A. R. Sagor; F. A. Mridha; M. M. H. Bhuiyan; M. J. Hasan; M. A. Rahman|10.1109/ICREST57604.2023.10070070|Autonomous;Raspberry PI;Arduino Uno;Ultrasonic Sensor;Camera;LoRa Communication;nan|
|[IoT Based Air Quality and Noise Pollution Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070039)|A. Datta; M. M. Islam; M. S. Hassan; K. B. Aka; I. Ahamed; A. Ahmed|10.1109/ICREST57604.2023.10070039|IoT;Air and Sound;Noise Pollution;Climate;Sensors;Air Pollution;Air Quality;nan|
|[Thermogram-based Regions With Convolutional Neural Network (RCNN) and Facial Biometrics for Safe Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070063)|S. Sarkar; T. Bin Khayer; N. H. Kisan; M. N. Uddin|10.1109/ICREST57604.2023.10070063|Regions with convolutional neural network (RCNN);Thermogram;Facial biometric;Immobilizer;YOLOv3;YOLOv5;Safe driving;Assistive technology;nan|
|[Exploring High-Level Neural Networks Architectures for Efficient Spiking Neural Networks Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070080)|R. Islam; P. Majurski; J. Kwon; S. R. S. K. Tummala|10.1109/ICREST57604.2023.10070080|Artificial neural network (ANN);spiking neural network (SNN);convolutional neural network (CNN);ANN-to-SNN conversion;nan|
|[Design and Implementation of a Low-cost Solar Charged Portable Disinfectant Chamber](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070060)|R. Rofique; M. Y. A. Alif; S. S. Himu; F. Z. Naima; C. A. Hossain|10.1109/ICREST57604.2023.10070060|Confusion Matrix;Co-Lab Software;Training Loss and Accuracy;Cost-Efficient;Polycrystalline Solar Panel;Convolutional Layer;Environmentally Friendly;Receiver Operator Characteristic (ROC) Curve;nan|
|[Solar Based Smart Water Pump Control with Turbidity and pH Measuring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070048)|S. H. Shovo; S. Karmarker|10.1109/ICREST57604.2023.10070048|Arduino Mega;Light Depending Resistor (LDR);Liquid Crystal Display (LCD);Linear Actuator;Charge Controller;pH Sensor;Turbidity Sensor;NTU (Nephelometric turbidity unit);GSM module;Filter;nan|
|[Success History Moth Flow Optimization for Multi-Goal Generation Dispatching with Nonlinear Cost Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070071)|M. K. Alam; M. H. Sulaiman; M. S. Sayem; M. M. A. Ringku; S. Imtiaz; R. Khan|10.1109/ICREST57604.2023.10070071|Combined Economic Emission Dispatch (CEED);Success History Moth Flow Optimization (SHMFO);Grew Wolf Optimization;Valve-point loading;nan|
|[Lead-free (Na0.5Bi0.5)0.45Ba0.40Sr0.15TiO3ceramic: Synthesis, structure and polarization response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070077)|M. A. Islam; M. A. Zubair|10.1109/ICREST57604.2023.10070077|lead-free materials;ceramics;structure;bismuth titanates;NB-BST;nan|
|[Surface Damage Detection of Line Insulators Using Deep Learning Algorithms to Avoid Insulation Failure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070065)|K. M. Rayhan; S. D. Roy; M. F. H. Sadid; K. F. Ahmed; A. H. Shatil|10.1109/ICREST57604.2023.10070065|insulator;damage detection;discoloring detection;CNN;YOLOv4;CSPDarkNet53;nan|
|[An Affordable Solution for the Rural Farmers for Irrigation Purpose Including Hybrid Power Source using Solar and Biogas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070085)|J. Hasan; A. I. Anik; S. S. Mishu; I. Mohajan; C. A. Hossain; M. R. Hazari; K. Shikdar|10.1109/ICREST57604.2023.10070085|Biogas;Solar panel;Renewable energy;Farmers;Hybrid;Controller;nan|
|[Design and Implementation of Regulated Oxygenation Based Ventilation System with Feedback Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070056)|R. Faiz; N. N. Alam; M. A. Arafi; A. Saha; M. H. Imam|10.1109/ICREST57604.2023.10070056|Ventilation;SpO2;COVID-19;Health monitoring;IoT;nan|
|[Detection of Myocardial Infarction Using Hybrid CNN-LSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070055)|M. Firoz; R. Faiz; N. N. Alam; M. H. Imam|10.1109/ICREST57604.2023.10070055|Myocardial infarction;CNN-LSTM;ECG 15 lead;nan|
|[Solar Powered Smart Irrigation System Based on Internet of Things (IoT) Using Microcontroller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070088)|S. Niloy; F. H. Sumona; M. H. Khan; M. Z. Islam; S. Ahmad; S. Howlader|10.1109/ICREST57604.2023.10070088|Irrigation;IoT;Microcontroller;Moisture;PV;nan|
|[Total Harmonic Distortion Reduction in a Power Grid: A UPQC Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070091)|B. B. Pathik; S. Islam; N. M. Akram; M. S. Zaman; M. A. Al-Nahian|10.1109/ICREST57604.2023.10070091|Power quality improvement;UPQC;harmonic distortion reduction;nan|
|[Human Lung Non-Invasive Anomaly Detection through UWB Microwave Imaging and Diagnosis of COVID-19: A Possible Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070028)|A. K. Nuhel; M. E. Deowan; M. M. Sazid; N. Sakib; M. E. Rahman; M. Al Amin|10.1109/ICREST57604.2023.10070028|Microwave Imaging;lung model;Lung damage;Covid-19;nan|
|[Techno-Economic Feasibility Analysis of Hybrid System in Bangladesh - A Case Study for Higher Learning Institution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070053)|A. Shafee; M. R. Uddin; U. Dev; S. Ahmad; S. Howlader; M. Z. Islam; M. M. Hossain; A. J. Khan|10.1109/ICREST57604.2023.10070053|HOMER;hybrid system;NPC;PV;COE;nan|
|[Development of High-Performance Single Inductor Quadratic Multilevel DC-DC Step-Up Converter with MPPT Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070035)|K. Akter; S. M. A. Motakabber; A. H. M. Z. Alam; S. H. B. Yusoff|10.1109/ICREST57604.2023.10070035|Quadratic Multilevel Boost Converter (QMBC);MPPT;Voltage Gain;nan|
|[Analytical Comparison of the Impact of Si and GaAs as Materials in Designing 3D Density Gradient Nanowire MOSFET for Low Power Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070082)|S. F. Nazreen; M. T. Ali|10.1109/ICREST57604.2023.10070082|semiconductor;nanowire;density-gradient;drift-diffusion;material;drain current;threshold voltage;transconductance;nan|
|[An IoT Based Smart Grid: Peer-to-peer Energy Trading for Electric Vehicles Using M2M Communication Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070042)|A. Noor; M. S. Ratul; A. I. Ahmed; H. Hassain; A. Ahmed|10.1109/ICREST57604.2023.10070042|Machine to machine;Internet of Thing;Message Queuing Telemetry Transport;Smart Grid;Peer to peer;National Grid;nan|
|[Study of Different Gate Materials on Performance of Si Based MOSFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070064)|A. M. Mizan; S. F. Nazreen; S. Ashraf; A. Z. M. T. Kabir; J. M. Gomes; S. Karmoker; N. S. Nabil; M. Kabiruzzaman|10.1109/ICREST57604.2023.10070064|Gate material;Oxide permittivity;metal work function;Silicon die oxide;Germanium oxide;Aluminum oxide;nan|
|[DESIGN AND DEVELOPMENT OF ROBO MEDICAL ASSISTANT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070041)|M. M. Hasan; S. Ray; S. Sarkar; M. L. Akter; M. A. Shawon|10.1109/ICREST57604.2023.10070041|Medical Robot;IoT;Non-invasive Diagnosis;COVID-19;Voice Controlled motion;Patient Data Management;nan|
|[A Smart Helmet: Ensuring Safety of Bike Riders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070031)|J. F. Riya; M. T. H. Chowdhury; S. A. Usha; S. Howlader; S. Ahmad; M. Z. Islam|10.1109/ICREST57604.2023.10070031|Arduino Uno;Smart Helmet;GPS;GSM;Solar;Accident;Safety;nan|
|[Action Recognition Based Real-time Bangla Sign Language Detection and Sentence Formation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070072)|S. K. Akash; D. Chakraborty; M. M. Kaushik; B. S. Babu; M. S. R. Zishan|10.1109/ICREST57604.2023.10070072|Pose Estimation;LSTM;Bangla Sign Language;MediaPipe;Action recognition;nan|
|[Smart System To Monitor and Control Transformer Health Condition in Sub-Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070073)|S. Dash; S. Das; M. B. Billah; B. Das; I. Ahamed; A. Ahmed|10.1109/ICREST57604.2023.10070073|Substation;Transformer;Control;Monitoring;IoT;Automation;nan|
|[Comparative Simulation of GaAs and AlGaAs Based On Triple Barriers-Resonant Tunneling Diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070061)|R. Paul; L. Mannan; T. A. Meem; M. M. Hasan|10.1109/ICREST57604.2023.10070061|Resonant Tunneling Diode (RTD);Triple Barrier Resonant Tunneling Diode (TBRTD);Negative Differential Region (NDR);nan|
|[Prospect of Hybrid Power Generation in Nijhum Dwip Utilizing Renewable Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070084)|M. S. Miah; I. A. Choudhury; M. S. Reza; M. A. Al Joha; S. Islam; M. F. Islam|10.1109/ICREST57604.2023.10070084|HOMER;Hybrid system;Solar Energy;Wind Energy;nan|
|[Design of Industrial Robotic Arm For Surgical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070037)|H. R. Chowdhury; S. Hossain; M. H. Imam|10.1109/ICREST57604.2023.10070037|Healthcare Robotics;Robotic Surgeries;Robots-Assisted Surgeries;intuitive surgical;laparoscopic surgery;nan|
|[Smart Wheelchair for COVID-19 Patients with Mobile Application Based Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070087)|A. Z. M. T. Kabir; A. M. Mizar; P. K. Saha; M. G. Sarowar; R. Saha; I. Ahmad; M. S. R. Zishan|10.1109/ICREST57604.2023.10070087|COVID-19;Smart Wheel Chair;IoT System;NodeMCU;Firebase;Health Monitoring;COVID-19 Patient;Mobile Application;nan|
|[Isolation Forest Based Anomaly Detection and Fault Localization for Solar PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070033)|S. Kabir; A. Shufian; M. S. R. Zishan|10.1109/ICREST57604.2023.10070033|Anomaly Detection;Rule-Based Fault Detection;Isolation Forest;Solar PV;Outlier;Resilient;nan|
|[Design and Implementation of Smart Drainage System for Bangladesh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070054)|M. S. Alam; S. Sitara; A. S. Irin; A. T. Anu; A. Ahmed; M. S. R. Zishan|10.1109/ICREST57604.2023.10070054|GSM module;IoT based monitoring;Automated Control;nan|
|[Toward a Transfer Learning Approach to Detect Face Mask Type in Real-time](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070040)|T. Ibnath; A. Dey|10.1109/ICREST57604.2023.10070040|Face mask;Convolutional Neural Network;Transfer learning;Covid-19;Real-time;nan|
|[Design and Implementation of a Smart Wind Turbine with Yaw Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070075)|S. B. Abhi; M. I. Hossain; R. H. Suny; F. T. Zahura; M. R. Hazari; E. Jahan; M. A. Mannan|10.1109/ICREST57604.2023.10070075|SCIG;yaw mechanism;gear ratio;wind turbine;wind direction sensor;stepper motor;nan|
|[Design and Development of E-Waste Monitoring, Segregation and Recycling System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070081)|M. S. Roy; M. N. I. Lusan; M. A. R. Khan; M. P. Khan; A. Ahmed; M. S. R. Zishan|10.1109/ICREST57604.2023.10070081|E-waste detection;sorting and segregation;website system;shredding process;conveyor;metal detector sensors;nan|
|[Design and Implementation of Solar PV Operated E-Power Tiller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070045)|A. R. Sagor; T. M. Mridul; M. M. H. Bhuiyan; F. A. Mridha; M. R. Hazari; M. A. Rahman|10.1109/ICREST57604.2023.10070045|Solar;Controller;BLDC motor;Cultivator teeth;charging station;nan|
#### **2023 IEEE 17th International Conference on Semantic Computing (ICSC)**
- DOI: 10.1109/ICSC56153.2023
- DATE: 1-3 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Retrieving Users’ Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066699)|M. Anschütz; T. Eder; G. Groh|10.1109/ICSC56153.2023.00008|Image retrieval;Flickr;multimodal;Opinion mining;Social media analysis;Sentiment analysis;Social networking (online);Pipelines;Semantics;Image retrieval;User-generated content;Buildings|
|[Coherence based Document Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066754)|A. Thielmann; C. Weisser; T. Kneib; B. Säfken|10.1109/ICSC56153.2023.00009|Document Clustering;Topic Modeling;Coherence Scores;Hierarchical Clustering;Topic Extraction;Measurement;Analytical models;Computational modeling;Semantics;Clustering algorithms;Coherence;Transformers|
|[SentiMap: Domain-Adaptive Geo-Spatial Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066769)|E. Veltmeijer; C. Gerritsen|10.1109/ICSC56153.2023.00010|Sentiment analysis;location estimation;domain-specificity;Sentiment analysis;Uncertainty;Bit error rate;Urban areas;Semantics;Estimation;Manuals|
|[Conditional Cross Correlation Network for Video Question Answering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066584)|K. Ouenniche; R. Tapu; T. Zaharia|10.1109/ICSC56153.2023.00011|video question answering;multimodal learning;cross-correlation;Visualization;Fuses;Semantics;Pipelines;Natural languages;Memory management;Transformer cores|
|[Does Noise Really Matter? Investigation into the Influence of Noisy Labels on Bert-Based Question Answering System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066742)|D. Alexandrov; A. Zakharova; N. Butakov|10.1109/ICSC56153.2023.00012|question-answering system;BERT;noisy data;noise simulation;Training;Computational modeling;Semantics;Data models;Robustness;Question answering (information retrieval);Noise measurement|
|[Scaling Large RDF Archives To Very Long Histories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066565)|O. Pelgrin; R. Taelman; L. Galárraga; K. Hose|10.1109/ICSC56153.2023.00013|Semantic Web;RDF;Indexing;Storage;Archiving;Versioning;Runtime;Semantics;Knowledge based systems;Machine learning;Benchmark testing;Resource description framework;Encoding|
|[Robust Zero-Shot Intent Detection via Contrastive Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066631)|M. H. Maqbool; F. A. Khan; A. B. Siddique; H. Foroosh|10.1109/ICSC56153.2023.00014|Intent detection;zero shot;transfer learning;contrastive learning;Training;Representation learning;Vocabulary;Sensitivity;Transfer learning;Supervised learning;Semantics|
|[Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic Forgetting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066722)|G. Mantione-Holmes; J. Leo; J. Kalita|10.1109/ICSC56153.2023.00015|Incremental Learning;Deep Learning;NLP;Training;Semantics;Merging;Psychology;Artificial neural networks;Machine learning;Logic gates|
|[OntoSeg - Segmentation of Large Volumetric Datasets Using Semantic Knowledge](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066660)|T. Lang; T. Sauer; T. Wittenberg; S. Gerth; N. Uhlmann|10.1109/ICSC56153.2023.00016|Big data and semantics;computed tomography;image processing;segmentation;semantic technology;Location awareness;Image segmentation;Image analysis;Computed tomography;Semantics;Ontologies;Big Data|
|[An Approach for Fusing Two Training-Datasets with Partially Overlapping Classes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066669)|J. Niemeijer; F. Battistella; G. Srinivas; A. Leich|10.1109/ICSC56153.2023.00017|Dataset Fusion;Traffic Sign detection;Deep Learning;Uncertainty Quantification;Pseudo-Labeling;Training;Uncertainty;Annotations;Roads;Semantics;Training data;Object detection|
|[Visual Enhancement and Semantic Segmentation of Murine Tissue Scans with Pulsed THz Spectroscopy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066617)|H. Liu; N. Vohra; K. Bailey; M. El-Shenawee; A. Nelson|10.1109/ICSC56153.2023.00018|deep learning;image translation;semantic segmentation;pulsed terahertz imaging;breast cancer imaging;Training;Terahertz wave imaging;Visualization;Spectroscopy;Semantic segmentation;Semantics;Pipelines|
|[Semantic Validation and Interpretability of Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066637)|S. Reynolds; A. Butka; B. Butka|10.1109/ICSC56153.2023.00019|artificial intelligence;object detection;explainability;interpretability;logical validation;Computer vision;Computational modeling;Semantics;Decision making;Object detection;Data models;System implementation|
|[FinKG: A Core Financial Knowledge Graph for Financial Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066552)|N. Kertkeidkachorn; R. Nararatwong; Z. Xu; R. Ichise|10.1109/ICSC56153.2023.00020|Financial Knowledge Graph;Ontology;Financial Analysis;Databases;Computational modeling;Semantics;Knowledge graphs;Ontologies;Predictive models;Stakeholders|
|[Modeling Recommendation and Project Deployment Automation for Semantic Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066721)|H. Ferguson; D. Yu; M. Kritzler|10.1109/ICSC56153.2023.00021|semantic project automation;semantic knowledge graph recommender;ontology recommender;semantic knowledge graph;ontology education tool;semantic graph education tool;semantic knowledge graph decision support;ontology accessibility;industry adoption of semantics;Industries;Automation;Semantics;Knowledge graphs;Ontologies;Data models;Trajectory|
|[Ontology Mediated Document Retrieval for Exploratory Big Data Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066567)|A. Kulkarni; C. Ramanathan; V. E. Venugopal|10.1109/ICSC56153.2023.00022|Document Retrieval;Ontology;Querying Heterogeneous Data;Ranking;Clustering;Couplings;Data analysis;Soft sensors;Semantics;Government;Memory;Ontologies|
|[Learning from the Evidence: Impact Evaluations, Ontology and Policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066824)|M. Murtagh-White; P. J. Wall; D. O’Sullivan|10.1109/ICSC56153.2023.00023|Knowledge Graphs;Ontologies;Meta-Science;International Development;Economics;Filtering;Social sciences;Semantics;OWL;Banking;Ontologies|
|[Improving Zero-Shot Inference with Unsupervised Key-Sentences Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066630)|L. F. A. O. Pellicer; A. H. R. Costa|10.1109/ICSC56153.2023.00024|Zero-Shot;Text Labelling;Unsupervised Keyword;Long Text;Embedding Ranking;Training;Measurement;Computational modeling;Text categorization;Semantics;Redundancy;Machine learning|
|[Automatic Identification of Chinese Paired Discourse Connectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066811)|N. F. Costa; Y. Cheng; T. C. Muermans; B. Hanel; L. Kosseim|10.1109/ICSC56153.2023.00025|Discourse Connectives;Corpus Creation;Chinese Discourse Treebank;Machine Learning;Hypothesis Testing;Computational modeling;Semantics;Machine learning;Testing|
|[Text-Defend: Detecting Adversarial Examples using Local Outlier Factor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066588)|M. Omar; G. Sukthankar|10.1109/ICSC56153.2023.00026|adversarial learning;anomaly detection;text classification;Training;Sentiment analysis;Social networking (online);Semantics;Training data;Computer architecture;Transformers|
|[Conceptual Attention in StyleGAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066830)|K. Suekane; S. Haji; H. Sano; T. Takagi|10.1109/ICSC56153.2023.00027|attention;concept;StyleGan;Deep learning;Image synthesis;Semantics;Data visualization;Transformers;Cognition;Noise measurement|
|[A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066561)|J. Wu; I. G. Harris; H. Zhao; G. Ling|10.1109/ICSC56153.2023.00028|Slot filling;Intent detection;Graph Convolutional LSTM;Recurrent neural networks;Computational modeling;Semantics;Focusing;Oral communication;Feature extraction;Filling|
|[Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066628)|M. Boyapati; R. Aygun|10.1109/ICSC56153.2023.00029|Graph Variable Clustering;Credit Default Prediction;Machine learning algorithms;Runtime;Semantics;Neural networks;Clustering algorithms;Machine learning;Organizations|
|[Machine Learning Based Specularity Detection Techniques To Enhance Indoor Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066705)|R. Kardan; S. S. Roy; A. Elsaharti; J. Neubert|10.1109/ICSC56153.2023.00030|Specular detection;Specularity;Highlight detection;Machine learning;YOLO;YOLOv4;YOLOv4-tiny;DeepLabv3;nan|
|[Dynamic User Understanding and Interaction in Cultural Heritage Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066570)|I. Infantino; G. Pilato; G. Vitale|10.1109/ICSC56153.2023.00031|Cultural Heritage;Conversational Agents;CPT;Semantics;Cultural differences|
|[Ensemble deep learning with HuBERT for speech emotion recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066578)|J. Yang|10.1109/ICSC56153.2023.00032|transformer;ensemble model;HuBERT;speech emotion recognition;Deep learning;Emotion recognition;Computational modeling;Semantics;Speech recognition;Network architecture;Transformers|
|[Transformers, Tables and Frame Semantics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066761)|M. Ramirez; A. Bogatu; N. W. Paton; A. Freitas|10.1109/ICSC56153.2023.00033|structured data;deep learning;natural language processing;transformers;data lakes;table understanding;semantics;Adaptation models;Analytical models;Semantics;Syntactics;Linguistics;Transformers;Data models|
|[Chaos to Clarity with Semantic Inferencing for Python Source Code Snippets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066765)|A. Stein; S. Mancoridis|10.1109/ICSC56153.2023.00034|natural language;structured data;deep learning;semantic inference;source code analysis;Measurement;Analytical models;Codes;Filtering;Source coding;Computational modeling;Semantics|
|[Genealogical Relationship Extraction from Unstructured Text Using Fine-Tuned Transformer Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066823)|C. Parrolivelli; L. Stanchev|10.1109/ICSC56153.2023.00035|relationship extraction;genealogical documents;coreference resolution;nan|
|[Exploratory Inference Chain: Exploratorily Chaining Multi-hop Inferences with Large Language Models for Question-Answering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066644)|S. Haji; K. Suekane; H. Sano; T. Takagi|10.1109/ICSC56153.2023.00036|Multi-hop Inference;Large Language Models;Neuro-Symbolic;Logical Reasoning;Cognitive processes;Computational modeling;Semantics;Task analysis|
|[Filtering Recommender System for Semantic Model Refinement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066798)|A. Paulus; A. Burgdorf; A. Pomp; T. Meisen|10.1109/ICSC56153.2023.00037|semantic modeling;semantic refinement;recommendation;graph neural network;knowledge graph;Computational modeling;Semantics;Training data;Manuals;Predictive models;Ontologies;Data models|
|[Attribute Enhancement using Aligned Entities between Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066750)|R. F. Munne; R. Ichise|10.1109/ICSC56153.2023.00038|Knowledge Graph;Attribute;Enhancement;Alignment;Knowledge engineering;Semantics;Knowledge graphs;Graph neural networks;Cognition|
|[NatUKE: A Benchmark for Natural Product Knowledge Extraction from Academic Literature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066602)|P. V. Do Carmo; E. Marx; R. Marcacini; M. Valli; J. V. Silva e Silva; A. Pilon|10.1109/ICSC56153.2023.00039|knowledge extraction;natural products;knowledge graphs;Training;Semantics;Pipelines;Knowledge graphs;Benchmark testing;Robustness;Topology|
|[ExPAD: An Explainable Distributed Automatic Anomaly Detection Framework over Large KGs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066741)|F. B. Moghaddam; J. Lehmann; H. Jabeen|10.1109/ICSC56153.2023.00040|RDF;Explainable Anomaly Detection;Big Data;Distributed Computing;SANSA;Decision Tree;Publishing;Scalability;Semantics;Knowledge graphs;Feature extraction;Resource description framework;Decision trees|
|[ABox Enrichment Through Semantic Data Recognition in Industrial Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066654)|S. Bauer; C. Merlo; Z. Boussaada|10.1109/ICSC56153.2023.00041|ontology;interoperability;Internet of Things;Deep learning;Data privacy;Semantics;Manuals;Companies;Ontologies;Security|
|[A Proactive and Generalizable Conflict Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066629)|A. Enayet; G. Sukthankar|10.1109/ICSC56153.2023.00042|Generalizability;Resource Scarcity;Conflict Prediction;Multiparty Dialogue;Adaptation models;Technological innovation;Semantics;Machine learning;Syntactics;Predictive models;Feature extraction|
|[Towards an Ontological Framework for Integrating Domain Expert Knowledge with Random Forest Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066787)|S. Beden; A. Beckmann|10.1109/ICSC56153.2023.00043|Ontology;Semantic Technologies;Reasoning;Random Forest;Industry 4.0;Steel;Radio frequency;Semantics;Machine learning;Forestry;Manufacturing;Steel;Predictive maintenance|
|[Spiking Convolutional Vision Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066559)|S. Talafha; B. Rekabdar; C. Mousas; C. Ekenna|10.1109/ICSC56153.2023.00044|SNN;Transformer;Computer Vision;nan|
|[Sentiment Analysis for Women in STEM using Twitter and Transfer Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066709)|S. Fouad; E. Alkooheji|10.1109/ICSC56153.2023.00045|Women In STEM;Machine Learning;Deep Learning;Transformers;Twitter;Sentiment Analysis;Natural Language Processing;Deep learning;Sentiment analysis;Social networking (online);Engineering profession;Biological system modeling;Blogs;Bit error rate|
|[Assessing the Effects of Lemmatisation and Spell Checking on Sentiment Analysis of Online Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066648)|J. Kavanagh; K. Greenhow; A. Jordanous|10.1109/ICSC56153.2023.00046|Natural Language Processing;Language parsing and understanding;Text analysis;Web text analysis;Sentiment analysis;Sentiment analysis;Statistical analysis;Computational modeling;Current measurement;Semantics;Pipelines;Mental health|
|[Deterministic bibliometric disambiguation challenges in company names](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066642)|A. Belz; A. Graddy-Reed; F. Shweta; A. Giga; S. M. Murali|10.1109/ICSC56153.2023.00047|disambiguation;names;patents;NLP;bibliometric;NASA;SBIR;nan|
|[Validating Security Requirement Specifications through the use of a Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066597)|L. V. Haar; S. Reynolds; T. Procko; O. Ochoa|10.1109/ICSC56153.2023.00048|Entity Linking;Knowledge Graphs;Requirement Specifications;Requirements Validation;Software Security;Industries;Software design;Semantics;Knowledge graphs;Turning;Software;Security|
|[Ontology Modeling for Probabilistic Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066702)|H. Freedman; N. Abolhassani; J. Metzger; S. Paul|10.1109/ICSC56153.2023.00049|nan;Uncertainty;Semantics;Knowledge graphs;Ontologies;Probabilistic logic;Cognition;Probability distribution|
|[Assessing Bias on Entity Retrieval Models through Conjunctive Fallacies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066693)|E. Marx|10.1109/ICSC56153.2023.00050|information bias;entity retrieval;information theory;Semantics;Machine learning;Benchmark testing;Information retrieval;Probabilistic logic|
|[XR4DRAMA Knowledge Graph: A Knowledge Graph for Disaster Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066601)|A. Vassiliades; S. Symeonidis; S. Diplaris; G. Tzanetis; S. Vrochidis; N. Bassiliades; I. Kompatsiaris|10.1109/ICSC56153.2023.00051|Knowledge Graphs;Disaster Management;Points of Interest;POI Management Mechanism;Decision support systems;Visualization;Semantics;Decision making;Knowledge graphs;Disaster management;Data collection|
|[Unsupervised Estimation of Subjective Content Descriptions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066743)|M. Bender; T. Braun; R. Möller; M. Gehrke|10.1109/ICSC56153.2023.00052|nan;Computational modeling;Semantics;Estimation;Collaboration;Coherence;Information retrieval;Probability distribution|
|[Unsupervised Relation Extraction with Sentence level Distributional Semantics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066672)|M. Ali; M. Saleem; A. -C. N. Ngomo|10.1109/ICSC56153.2023.00053|Natural Language Processing, Sentence Encoding, Unsupervised Relation Extraction;Semantics;Training data;Feature extraction;Encoding;Data mining;Context modeling|
|[Explaining BERT model decisions for near-duplicate news article detection based on named entity recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066612)|A. S. Novo; F. Gedikli|10.1109/ICSC56153.2023.00054|Near-Duplicate Detection;News Articles;Explainability;BERT;SHAP;Deep learning;Measurement;Bit error rate;Semantics;Search engines;Data models;Libraries|
|[A Novel Link Prediction Method for Multiplex Networks with Incomplete Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066568)|J. Luo; J. Yu; Z. Liu; Y. Liu|10.1109/ICSC56153.2023.00055|multiplex networks;link prediction;network collapse;Multiplexing;Computer science;Epidemics;Social networking (online);Semantics;Prediction methods;Network analyzers|
|[Criminal Investigation with Augmented Ontology and Link Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066762)|T. Ugai|10.1109/ICSC56153.2023.00056|Knowledge Graph;Ontology;Wikidata;Link Prediction;Semantics;Knowledge graphs;Ontologies;Cognition|
|[Criminal Deduction Using Similarity Analysis Between Mystery Stories](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066652)|S. Hattori; A. Fujii|10.1109/ICSC56153.2023.00057|BERT;BERTscore;Natural Language Processing;Similarity;Bit error rate;Semantics;Focusing;Knowledge graphs;Cognition|
|[Supporting the construction of mystery novel knowledge graphs using BERT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066633)|K. Hasegawa; A. Fujii|10.1109/ICSC56153.2023.00058|BERT;classification;Natural Language Processing;Casting;Computational modeling;Bit error rate;Semantics;Data visualization;Knowledge graphs;Natural language processing|
|[Towards reasoning over knowledge graphs under aleatoric and epistemic uncertainty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066758)|L. Kunitomo-Jacquin; K. Fukuda|10.1109/ICSC56153.2023.00059|Knowledge Graph;Belief Functions;Treatment of Uncertainties;Adaptation models;Evidence theory;Computational modeling;Semantics;Knowledge graphs;Cognition|
|[Deciphering the code of the novel "The Dancing Men"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066791)|K. Murakami; A. Kishida; H. Ito; S. Matsumoto; K. Takamatsu|10.1109/ICSC56153.2023.00060|integer linear programming;image processing;simulation;Wasserstein distance;Graphics;Ciphers;Codes;Semantics;Cognition;Mathematical programming|
|[A Method to Constract a Masked Knowlege Graph Model using Transformer for Knowledge Graph Reasoning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066745)|R. Kaneda; M. Okada; N. Mori|10.1109/ICSC56153.2023.00061|Knowledge Graph;Transformer;Masked learning;Computational modeling;Semantics;Bit error rate;Estimation;Knowledge graphs;Machine learning;Predictive models|
|[Towards a Knowledge Graph for the Visual Recognition of Skin Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066683)|P. Dogoulis; A. Vassiliades; N. Bassiliades|10.1109/ICSC56153.2023.00062|Knowledge Graph;Data Integration;Computer Vision;Decision Support System;Skin Cancer;Decision support systems;Visualization;Computer vision;Semantics;Data integration;Medical services;Knowledge graphs|
|[Gun Violence Tracker Using Semantic Data Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066718)|U. Chhetri; S. Kapoor; S. K. Sivaprasad; R. Rao Thakkalapelli; M. Kohli; S. Bansal|10.1109/ICSC56153.2023.00063|Semantic Data;Ontology engineering;Data Integration;Social media data;RDF;nan|
|[What metadata is needed for semantic and data mappings?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066727)|S. Alzahrani; D. O’Sullivan|10.1109/ICSC56153.2023.00064|Mapping;Alignment;Metadata Model;Knowledge Representation;Annotation;Social networking (online);Annotations;Linked data;Semantics;Metadata;Resource description framework;Quality assessment|
|[An Interdisciplinary Approach to Misinformation and Concept Drift in Historical Cannabis Tweets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066734)|J. Turner; M. McDonald; H. Hu|10.1109/ICSC56153.2023.00065|cannabidiol;Tweets;concept drift;machine learning;misinformation;media theory;Analytical models;Multiple sclerosis;Correlation;Social networking (online);Semantics;Epilepsy;Oral communication|
|[A Novel Framework for Constructing Multimodal Knowledge Graph from MuSe-CaR Video Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066613)|A. Usmani; S. H. Alsamhi; J. Breslin; E. Curry|10.1109/ICSC56153.2023.00066|Multimodal Knowledge Graph;MuSe-CAR;Multimodal Queries;Knowledge Graph;Autonomous Multi-modal System;Industries;Video reviews;Semantics;Buildings;Knowledge graphs;Streaming media;Feature extraction|
|[Information Retrieval from Facebook for Social Network Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066569)|F. Persia; D. D’Auria|10.1109/ICSC56153.2023.00067|Information Retrieval;Social Network Analysis;Facebook;Complex Event Processing;Users’ Interactions;Analytical models;Social networking (online);Semantics;Blogs;Video surveillance;Business process management;Data mining|
|[Smart Monitoring Program for Selective Laser Melting 3D Printing Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066835)|D. J. S. Agron; D. -S. Kim; J. -M. Lee; M. Almendrala|10.1109/ICSC56153.2023.00069|nan;Training;Machine learning algorithms;Three-dimensional displays;Fault detection;Lasers;Machine learning;Logic gates|
#### **2023 International Microwave and Antenna Symposium (IMAS)**
- DOI: 10.1109/IMAS55807.2023
- DATE: 7-9 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Microwave-Based Technique for Measuring Glucose Levels in Aqueous Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066913)|Y. Mahnashi; K. K. Qureshi; A. Al-Shehri; H. Attia|10.1109/IMAS55807.2023.10066913|Non-invasive glucose monitoring;glucose detection using RF sensors;biomedical applications;biosensors;Microwave measurement;Microwave antennas;Radio frequency;Antenna measurements;Sensitivity;Voltage measurement;Microwave theory and techniques|
|[Design of Wideband, Low Profile Quasi-Rectangular Planar Surface Wave Launcher Fed by CPW Slot for 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066902)|A. Alshehry|10.1109/IMAS55807.2023.10066902|Surface-wave antenna;Co-planar wave guide;E-field;Surface impedance;Microwave antennas;Antenna measurements;Microwave measurement;Wireless communication;Surface impedance;Surface waves;5G mobile communication|
|[Geometrical Concepts for Phase-Shifting in Circularly-Polarized Reflector Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066906)|M. Terada|10.1109/IMAS55807.2023.10066906|Reflector antennas;Polarization;Phased arrays;Numerical analysis;Communication systems;Phased arrays;Optical interferometry;Antenna theory;Numerical analysis;Phase shifting interferometry;Reflector antennas;Microwave theory and techniques|
|[Polarization Reconfigurable Intelligent Metasurface for X- & Ku-band Applications in Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066931)|M. Sumaid; A. G. Hassan; N. Shoaib; S. Nikolaou|10.1109/IMAS55807.2023.10066931|Electromagnetic (EM);ultra-wide band (UWB) polarization Reconfigurable Intelligent Metasurface (PRIM);Microwave antennas;Polarization;PIN photodiodes;Switches;Metasurfaces;Stability analysis;Distance measurement|
|[A Reconfigurable Cosecant-Squared/ Pencil Beam Antenna Array for Radar Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066936)|A. Alieldin; A. M. Eid; A. A. Salama; A. M. El-Akhdar|10.1109/IMAS55807.2023.10066936|antenna array;cosecant-squared;reconfigurable antenna;pencil beam;pseudo inverse synthesis;Phased arrays;Radar;Transforms;Radar antennas;Antenna feeds;Microwave theory and techniques;Microwave antenna arrays|
|[A Miniaturized Tag Antenna for UHF RFID Metallic Objects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066903)|R. Xu; Z. Shen|10.1109/IMAS55807.2023.10066903|Tag antenna;radio frequency identification (RFID);ultrahigh frequency (UHF);metal platform;Microwave antennas;Surface impedance;Semiconductor device measurement;Dipole antennas;Resonant frequency;UHF measurements;UHF antennas|
|[A Printed Helical Circular Polarization Antenna for GNSS Anti-Attacking System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066881)|M. Akmal; A. Alieldin; A. R. Eldamak|10.1109/IMAS55807.2023.10066881|Anti-attacking;circular polarization;directive antenna;helical antenna;printed;GNSS;Microwave antennas;Global navigation satellite system;Satellite antennas;Polarization;Helical antennas;Costs;Shape|
|[COMSOL solutions to EMI hardening of UAVs against lightning strikes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066923)|M. S. H. Al Salameh; B. A. M. Musa|10.1109/IMAS55807.2023.10066923|UAV;lightning;interference;EMC;COMSOL;Microwave antennas;Time-frequency analysis;Computational modeling;Electromagnetic interference;Lightning;Nose;Autonomous aerial vehicles|
|[Newly Designed Antenna Platform for Transient Array Radio Telescope (TART)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066914)|S. Ghosh; R. Human; S. Kuja; O. Smirnov; T. Molteno; P. Okouma|10.1109/IMAS55807.2023.10066914|educational tool;radio interferometry;instrumentation;GPS receivers;radio imaging;astronomy;satellite;Microwave antennas;Radio astronomy;Layout;Receiving antennas;Laser beams;Structural beams;Steel|
|[T-ray Photoconductive Antenna Design for Biomedical Imaging Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066897)|R. Han; A. Abohmra; S. Nourinovin; H. Abbas; J. Ponciano; L. Shi; A. Alomainy; M. Imran; Q. H. Abbasi|10.1109/IMAS55807.2023.10066897|Terahertz;Spiral antenna;Photoconductive antenna;Antenna lens;Microwave antennas;Antenna measurements;Microwave measurement;Spirals;Gallium arsenide;Silicon;Antennas|
|[Wearable Wideband Printed Monopole Antenna System for Electromagnetic Field Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066949)|P. Falcão; C. Peixeiro|10.1109/IMAS55807.2023.10066949|printed monopoles;wideband antennas;dual-linearly polarized antennas;wearable antennas;electromagnetic field evaluation;Microwave antennas;Torso;Estimation;Phantoms;Numerical simulation;Safety;Numerical models|
|[A Circularly Polarized Quadrifilar Helix Antenna With Steerable Beam](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066934)|W. Sun; X. Chen; L. Qian|10.1109/IMAS55807.2023.10066934|Circular polarization;beam steering;quadrifilar helix antenna;phase control;Antenna measurements;Microwave antennas;Microwave measurement;Wireless communication;Helical antennas;Wires;Bandwidth|
|[Inkjet Printed Passive RFID Sensor for Wetness Detection of Diapers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066932)|A. Sharif; S. Liaqat; K. Arshad; K. Assaleh; N. Ramzan|10.1109/IMAS55807.2023.10066932|Passive RFID Sensor;Wetness Detection;Internet of things (IoT);UHF RFID tag;Microwave antennas;Pediatrics;Bandwidth;Conductivity;Distance measurement;Received signal strength indicator;Slabs|
|[Enforcing Spectral Continuity of Complex Dielectric Permittivity Values For RFM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066892)|I. Dilman; M. N. Akinci; C. Aydinalp; S. Joof; T. Yilmaz; M. Cayoren|10.1109/IMAS55807.2023.10066892|Complex dielectric permittivity;open-ended coaxial probes;microwave material characterization;Microwave antennas;Liquids;Permittivity measurement;Microwave theory and techniques;Reflection coefficient;Dielectrics;Numerical models|
|[Quadruple-Mode Wideband Bandpass Filter Using Symmetric Structure in Single Cylindrical Cavity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066884)|M. H. Elfeshawy; Y. A. Zaghloul; H. F. Hammad|10.1109/IMAS55807.2023.10066884|Multi-mode cavity filter;multimode resonator;metal cavity;cylindrical cavity;perturbation;quadruple-mode;wideband bandpass filter;Band-pass filters;Microwave antennas;Perturbation methods;Resonator filters;Aluminum;Resonant frequency;Scattering parameters|
|[Linearly Polarized Dipole Antenna and Antenna Array for LoRa Base-Station Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066927)|F. Al-Seba'ey; R. Elkhosht; H. Hammad|10.1109/IMAS55807.2023.10066927|LoRa;ISM-Band;radio;antenna;array;Dipole antennas;Directive antennas;Microwave antenna arrays;Linear antenna arrays;Microstrip;Gain;Antenna radiation patterns|
|[Circularly Polarized Folded Reflectarray Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066894)|S. A. M. Soliman; E. M. Eldesouki; A. M. Attiya; A. P. Freundorfer; Y. M. M. Antar|10.1109/IMAS55807.2023.10066894|folded antenna;reflect array;satellite communication;circular polarized;Wireless communication;Polarization;Dipole antennas;Transmitting antennas;Reflector antennas;Antenna feeds;Microwave antenna arrays|
|[Design of a Compact Asymmetric Orthomode Transducer for 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066890)|O. Wadah; I. A. Eshrah; M. A. M. Hassan|10.1109/IMAS55807.2023.10066890|Compact orthomode transducer (OMT);Asymmetric OMT;5G over-the-air (OTA) antenna measurements;Antenna measurements;Microwave measurement;Microwave antennas;Transducers;5G mobile communication;Bandwidth;Machining|
|[E-Nose for Odor Detection of Humanoid Robots Based on Micro Opto-Mechatronics Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066889)|A. R. Ali; M. Algohary; M. Wael; J. Magdy|10.1109/IMAS55807.2023.10066889|electronic nose;odor detection;whispering gallery mode;humanoid robots;bionics;Microwave antennas;Sensitivity;Olfactory;Humanoid robots;Prototypes;Whispering gallery modes;Robot sensing systems|
|[Tunable Non-Reciprocal Phase Shifter and Spin-Coated Ferrites for Adaptive Microwave Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066899)|K. Srinivasan; A. El-Ghazaly|10.1109/IMAS55807.2023.10066899|ferromagnetic resonance;tunable phase shifter;yttrium iron garnet;tunable microwave device;serrated coplanar waveguide;Microwave antennas;Ferrites;Transmission line matrix methods;Phase shifters;Microwave devices;Thick films;Microwave circuits|
|[Highly Sensitive Terahertz Electromagnetically Induced Transparency-like Metasurface for Refractive Index Biosensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066946)|T. Pires; S. Nourinovin; H. Abbas; M. A. Imran; A. Alomainy; Q. H. Abbasi|10.1109/IMAS55807.2023.10066946|THz Metasurface;Biosensor;Electromagnetically Induced Transparency;Microwave antennas;Q-factor;Sensitivity;Refractive index;Metasurfaces;Market research;Sensors|
|[Uplink Performance Analysis of Multiple Relays Hybrid Satellite-Terrestrial Cooperative Networks employing Amplify-and-Forward DS-CDMA Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066939)|A. H. Swalem; J. V. M. Halim; H. El Hennawy|10.1109/IMAS55807.2023.10066939|Uplink;Outage Probability;Amplify-and-forward (AF);Ergodic Capacity;Direct Sequence Code-division multiple-access (DS-CDMA);Land Mobile Satellite (LMS) channel;Microwave antennas;Fading channels;Satellites;System performance;Probability;Power system reliability;Performance analysis|
|[Optimization of Choke Antenna Aperture Radiation Pattern](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066947)|I. N. Alquaydheb; S. Alfawaz; S. Ghayouraneh; A. G. Avval; S. El-Ghazaly|10.1109/IMAS55807.2023.10066947|waveguide;choke;current distribution;Microwave antennas;Analytical models;Satellite antennas;Spaceborne radar;Radar antennas;Aperture antennas;Finite element analysis|
|[Temperature-Controlled Microwave Heating System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066920)|S. Alfawaz; I. N. Alquaydheb; S. Ghayouraneh; A. G. Avval; S. El-Ghazaly|10.1109/IMAS55807.2023.10066920|microwave heating;temperature controlled;IR sensor;Temperature sensors;Microwave antennas;Electromagnetic heating;Temperature distribution;Microwave theory and techniques;Control systems;Generators|
|[Planar Array Radiation Pattern Synthesis Using Relative Elements Axial Rotation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066917)|A. M. Mahfouz; S. I. Shams; M. Elsaadany; A. A. Kishk|10.1109/IMAS55807.2023.10066917|planar array;array factor;radar applications;flat-top beam;cosecant beam;Dipole antennas;Surveillance;Planar arrays;Radar;Solids;Radar antennas;Microwave antenna arrays|
|[A Dualband Rectenna Design for RF Energy Scavenging using a Modified Yagi-Uda Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066948)|R. Elkhosht; H. Hammad|10.1109/IMAS55807.2023.10066948|dual band;rectenna;energy harvesting;Yagi-Uda;broadband;rectifier;matching network;Microwave antennas;Wireless sensor networks;Rectennas;Yagi-Uda antennas;Dual band;Rectifiers;Voltage|
|[Indirect Electrical-Control Through Heating of a GeTe Phase Change Switch and Its Application to Reflexion Type Phase Shifting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066891)|A. Naoui; B. Reig; E. Perret; M. El-Chaar; F. Podevin|10.1109/IMAS55807.2023.10066891|RF-switches;Phase change material (PCM);GeTe;Branch-line coupler;RTPS phase shifters;Phase change materials;Microwave antennas;Resistance;Radio frequency;Simulation;Phase shifters;RF signals|
|[Accurate Vegetation Models with Low Computational Complexity for Ray Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066883)|E. Aksoy; H. Khan; Y. Chen; L. Raschkowski; L. Thiele; S. Stanczak|10.1109/IMAS55807.2023.10066883|Ray Tracing Simulations;Vegetation;Attenuation;Propagation;Microwave antennas;Wireless sensor networks;Temperature;5G mobile communication;Computational modeling;Vegetation mapping;Vegetation|
|[A New Real Frequency Line Segment Technique to Assess the Gain Bandwidth Limitations for the Double Matching Problems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066921)|B. S. Yarman; S. Kilinc|10.1109/IMAS55807.2023.10066921|Broadband Matching;Single Matching;Double Matching;Real Frequency Technique;Real Frequency Line Segment Technique for Single Matching Problems;Phased arrays;Transmitters;Receiving antennas;Bandwidth;Microwave communication;Microwave theory and techniques;Radar antennas|
|[Low-Profile off-Body Wearable Antenna for Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066886)|R. Rabhi; H. Akbari-Chelaresi; M. Olaimat; A. Gharsallah; O. M. Ramahi|10.1109/IMAS55807.2023.10066886|low-profile antenna;off body antenna;meandered antenna;biomedical applications;wireless communications;Microwave antennas;Wireless communication;Microwave measurement;Microwave communication;Body area networks;Extraterrestrial measurements;Sensors|
|[Design of a Wideband Circularly Polarized Orthomode Transducer for 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066910)|A. M. Refaey; I. A. Eshrah; M. A. Moharram Hassan|10.1109/IMAS55807.2023.10066910|Wideband orthomode transducer (OMT);Septum;Circular polarization;Dual polarization;5G over-the-air (OTA) antenna measurements;Antenna measurements;Microwave measurement;Microwave antennas;Solid modeling;Transducers;5G mobile communication;Solids|
|[Highly Sensitive Bi-Transmission Line-based Sensors for Liquid Characterization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066907)|M. M. Y. R. Riad; M. A. Ateyya; A. R. Eldamak; A. M. E. Safwat|10.1109/IMAS55807.2023.10066907|Cross-junction;microwave sensor;defected ground structures;liquid characterization;Microwave antennas;Liquids;Sensitivity;Prototypes;Sensor phenomena and characterization;Predictive models;Transmission line measurements|
|[Analysis of 2-D PEC Cylindrical Scatterers Using a Hybrid RAS-MoM Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066925)|A. M. H. Eissa; I. A. Eshrah; M. A. M. Hassan|10.1109/IMAS55807.2023.10066925|Computational electromagnetics;hybrid methods;method of moments (MoM);random auxiliary sources (RAS);Microwave antennas;Geometry;Computational modeling;Electromagnetic scattering;Microwave theory and techniques;Electromagnetics;Method of moments|
|[Millimeter-Wave Planar Antenna Array for Radar and Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066912)|Y. Al-Alem; S. M. Sifat; Y. M. M. Antar; A. A. Kishk|10.1109/IMAS55807.2023.10066912|High gain antenna structures;planar antenna arrays;circularly polarized antennas;Microstrip antenna arrays;Costs;Planar arrays;Packaging;Millimeter wave radar;Antenna feeds;Radar antennas|
|[Computerized Tomography with Low-Frequency Electromagnetic Radiation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066933)|S. H. Mirjahanmardi; S. Ba Raean; H. Akbari-Chelaresi; V. Nayyeri; O. M. Ramahi|10.1109/IMAS55807.2023.10066933|Computerized Tomography;Radon Transforms;Imaging;Biomedical Engineering;Electromagnetic Radiation;Fourier Slice Theorem;Microwave antennas;Image resolution;Computed tomography;Energy resolution;Electromagnetic scattering;Transforms;Electromagnetic radiation|
|[Performance Enhancement of W-Band Radar Through Front End Reconfiguration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066887)|L. Butts; S. Becker; A. Mantooth; S. El-Ghazaly|10.1109/IMAS55807.2023.10066887|W-Band;FMCW;front-end;millimeter-wave;Microwave measurement;Antenna measurements;Radar measurements;Power amplifiers;Radar detection;Radar;Insertion loss|
|[Self-Isolated Multiple-Input-Multiple-Output Antenna for mm-Wave Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066905)|O. Sokunbi; H. Attia; A. Hamza; A. Shamim; A. A. Kishk|10.1109/IMAS55807.2023.10066905|mm-wave antenna;MIMO;self-decoupled antenna;Microwave antennas;Wireless communication;Surface waves;Millimeter wave technology;Transmitting antennas;Bandwidth;Impedance|
|[Capacity Enhanced High Throughput Satellite - Coded-Beams resource management strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066901)|E. S. Abass; J. V. M. Halim; H. E. Hennawy|10.1109/IMAS55807.2023.10066901|High throughput satellites;Tera Hertz communication;Multi-beam satellites;Coded-Beams HTS;CDMA Satellites;Microwave antennas;Satellite antennas;Satellites;Codes;System performance;Throughput;Complexity theory|
|[High Sensitive Measurement Sensor for Industrial Hydraulic Cylinder Stroke Based on Fabry-Pérot Optical Interferometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066938)|A. R. Ali; M. Tarek; M. Lokma; N. Eid; T. S. Eldin|10.1109/IMAS55807.2023.10066938|cylinder stroke;optical resonance;sensors;Fabry-Pérot etalon;optical interferometer;Pistons;Microwave antennas;Optical interferometry;Costs;Hydraulic systems;Optical variables measurement;Optical sensors|
|[Tri-Ridged Waveguide Orthomode Transducer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066918)|Z. du Toit; F. T. T. Mokhupuki; D. I. L. de Villiers|10.1109/IMAS55807.2023.10066918|orthomode transducer;tri-ridged waveguide;Microwave antennas;Transducers;Waveguide components;Horn antennas;Buildings;Bandwidth;Antenna feeds|
|[Fully Integrable BiCMOS Classical Rat-Race Coupler Based on Coplanar Striplines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066879)|S. R. Zahran; L. Boccia; F. Podevin; G. Amendola; P. Ferrari|10.1109/IMAS55807.2023.10066879|BiCMOS;Coplanar Stripline;millimeter-wave;rat-race coupler;Microwave antennas;Q-factor;Strips;Stripline;Simulation;Couplers;Insertion loss|
|[Design of CPW-Fed Dual-Band Four-Element MIMO Microstrip Patch Antenna for WLAN/WiMAX Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066915)|I. E. Lamri; A. Mansoul|10.1109/IMAS55807.2023.10066915|Dual Band Antennas;MIMO Antenna;MIMO Metrics;CPW-Fed;WLAN/ WiMAX;Microwave antennas;Antenna measurements;Wireless LAN;Dual band;Prototypes;Microstrip antennas;WiMAX|
|[Compact Circularly Polarized Implantable Antenna With Wide Axial-Ratio Bandwidth for biomedical applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066885)|R. Rabhi; A. Gharsallah|10.1109/IMAS55807.2023.10066885|implantable antenna;circular polarization (CP);Axial ratio (AR);specific absorption rate (SAR);ISM band;Microwave antennas;Phantoms;Bandwidth;Implants;Specific absorption rate;Regulation;Safety|
|[A New Enhanced Design of broadband Gysel Power Divider and Combiner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066898)|M. A. Ahmed; H. N. Kheirallah; A. S. Eltrass; A. S. I. Amar; A. Almslmany|10.1109/IMAS55807.2023.10066898|Wilkinson Power Divider (WPD);Gysel Power Combiner/Divider (GPCD);Microstrip Line;Wideband;Microwave antennas;Power dividers;Microstrip filters;Wavelength measurement;Simulation;Transmission line measurements;Microwave amplifiers|
|[On the Aperture Size of Digitally Coded Metasurfaces for Beam Steering Applications using Anomalous Reflection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066942)|R. Malleboina; J. C. Dash; A. Lemma; D. Sarkar|10.1109/IMAS55807.2023.10066942|Digital Metasurface;Beam Steering;Aperture Size;Anomalous Reflection;Passive Metasurface;Microwave antennas;Analytical models;Beam steering;Apertures;Metasurfaces;Microwave theory and techniques;Reflector antennas|
|[A Dual-Port Bessel Beam Launcher for Microwave Wireless Power Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066895)|G. R; C. Saha; Y. M. M. Antar|10.1109/IMAS55807.2023.10066895|Bessel beam;Dual port antenna;Wireless Power Transfer (WPT);Microwave antennas;Wireless power transfer;Finite element analysis;Feeds;Probes;Electric fields|
|[UAV Assisted IoT Geo-positioning Solution Employing Low-Cost Bluetooth Enabled Tags](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066940)|R. M. Bilal; Z. Akhter; N. Alsahli; M. Abdel-Aal; A. Shamim|10.1109/IMAS55807.2023.10066940|Bluetooth node tracking;Bluetooth UAV tracking;shopping trolley tracking;infrastructure less tracking;Microwave antennas;Atmospheric modeling;Buildings;Security;Bluetooth Low Energy;Carbon footprint;Testing|
|[Simple Beam Switching Cylindrical Dielectric Resonator Antenna Using Helix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066944)|A. K. Pandey; S. K. Pathak|10.1109/IMAS55807.2023.10066944|Beam Switching;CDRA;Helix;Microwave measurement;Microwave antennas;Helical antennas;Switches;Bandwidth;Directive antennas;Dielectric resonator antennas|
|[Low Profile Corrugated Horn for Minimum Side lobe Levels based on PRGW](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066945)|M. Gadelrab; S. I. Shams; A. R. Sebak|10.1109/IMAS55807.2023.10066945|Millimeter-wave;Corrugated Horn;PRGW;High Gain;6G & beyond communication;Microwave antennas;Wireless communication;Horn antennas;Millimeter wave technology;Microwave communication;Manufacturing;Millimeter wave communication|
|[New Wideband Antenna Arrays with Low Sidelobe Based on Space Filling Curves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066930)|S. E. El-Khamy; H. F. EL-Sayed; A. S. Eltrass|10.1109/IMAS55807.2023.10066930|Wideband;Space-filling curves;Iterated Function System (IFS);Grating lobes;Side-Lobe Level (SLL);Wireless communication;Microwave technology;Planar arrays;Filling;Microwave antenna arrays;Broadband antennas;Gratings|
|[Experimental Investigation on the Performance of Angle-of-Arrival-based Asset Localization in a Warehouse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066896)|S. Abu-Sardanah; J. Loher; Y. Guarisma; D. Dobre; K. Soni; O. Ramahi; G. Shaker|10.1109/IMAS55807.2023.10066896|AOA;indoor positioning;asset tracking;Location awareness;Microwave antennas;Microwave technology;Indoor navigation;Organizations;Topology;Sensors|
|[A Multi-Megawatt Range, Dual-Band Waveguide Antenna System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066919)|A. Banelli; N. AlEissaee; M. Almansoori; H. Alyahyaee; F. Vega; C. Kasmi|10.1109/IMAS55807.2023.10066919|HPEM;waveguide;corrugated horn;combiner;Microwave antennas;Horn antennas;Dual band;Electromagnetic waveguides;Three-dimensional printing;Numerical simulation;Microwave theory and techniques|
|[An 8-bit 3.5 GS/s Current Steering DAC for Wireless Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066935)|M. Taha; K. M. Morsi; A. Naguib|10.1109/IMAS55807.2023.10066935|Digital to Analog Converters (DAC);current steering (CS);device mismatch;spurious free dynamic range (SFDR);Wireless communication;Microwave antennas;Transmitters;Digital-analog conversion;Linearity;Bandwidth;Dynamic range|
|[A CPW-Fed Antenna Array with MIMO and Phased Array Operations for Sub-6-GHz 5G Smartphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066908)|N. O. Parchin; A. S. I. Amar; M. Alibakhshikenari; H. El-Hennawy; M. Darwish|10.1109/IMAS55807.2023.10066908|5G;CPW-fed antenna;MIMO;phased array;ring resonators;smartphone applications;sub 6 GHz;Phased arrays;5G mobile communication;Resonant frequency;Bandwidth;Microwave communication;Microwave antenna arrays;Scattering parameters|
|[Wide-Scan Phased Array Antenna Design for Broadband 5G Cellular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066922)|A. S. I. Amar; N. O. Parchin; M. Alibakhshikenari; H. El-Hennawy; M. Darwish|10.1109/IMAS55807.2023.10066922|5G networks;modified dipole resonator;smartphone applications;wideband phased array;Phased arrays;Cellular networks;5G mobile communication;Dipole antennas;Bandwidth;Microwave antenna arrays;Broadband communication|
|[Design of a monopulse array antenna with low sidelobe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066926)|L. N. Thai; L. T. Anh; N. H. Son; L. T. Hang|10.1109/IMAS55807.2023.10066926|array antenna;monopulse comparator;unequal power divider;vivaldi;microstrip patch;Power dividers;Patch antennas;Vivaldi antennas;Bandwidth;Frequency conversion;Microwave antenna arrays;Impedance|
|[A 1.5 KW L-Band All GaN High-Efficiency Solid State Power Amplifier for Pulsed Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066929)|A. M. E. Abounemra; N. O. Parchin; A. M. El-Tager; M. Mahdi; M. Darwish|10.1109/IMAS55807.2023.10066929|Power Amplifiers;SSPA;High Efficiency;L-Band;Pulsed;Microwave antennas;Radio frequency;Semiconductor device measurement;Power supplies;Power amplifiers;Transistors;Gallium nitride|
|[Design and Fabrication a W-Shape Form Dual-Band Flexible Antenna For Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066924)|S. Y. A. Fatah; F. Taher; M. T. Haweel; H. Al Hamadi; K. H. Mohamadien; M. F. A. Sree|10.1109/IMAS55807.2023.10066924|Microstrip;Fabrication;Biomedical applications;W-Shape;Microwave antennas;Slot antennas;Permittivity measurement;Capacitors;Dual band;Microstrip antennas;Bandwidth|
|[Design of a Ka-Band LNA Based on 150 nm GaN-on-Si Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066882)|A. M. E. Abounemra|10.1109/IMAS55807.2023.10066882|GaN on Si;low noise amplifier (LNA);Ka-band;wideband;Microwave antennas;Wireless communication;Performance evaluation;Noise figure;Linearity;Microwave circuits;Silicon|
|[An Ultra-Low Phase Noise Low-Power 10-GHz LC VCO with High-Q Common-Mode Harmonic Resonance for 5G Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066937)|Y. Ehab; A. Naguib; H. N. Ahmed|10.1109/IMAS55807.2023.10066937|Low phase noise;Low power;Common-mode resonance;Switched Varactor Bank;Voltage biased;Cross coupled;LC VCO;Microwave;RFIC;5G;Phase noise;Varactors;Radio frequency;Microwave antennas;5G mobile communication;Voltage-controlled oscillators;Harmonic analysis|
|[A Microwave Passive Topology Based on Simultaneous Injection-Locking and Injection-Pulling for Passive Indoor Sensing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066911)|D. V. Q. Rodrigues; C. Li|10.1109/IMAS55807.2023.10066911|injection-locking;injection-pulling;oscillators;passive sensing;radar;vital signs monitoring;wireless sensing;Radio frequency;Microwave antennas;Baseband;Microwave communication;Microwave theory and techniques;Microwave oscillators;Sensors|
|[Development of a Multi-Functional Remote Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066893)|C. J. Bauder; A. -K. Moadi; P. T. Theilmann; A. E. Fathy|10.1109/IMAS55807.2023.10066893|radar;camera;telemedicine;health;remote;non-contact;estimation;Temperature measurement;Microwave antennas;Temperature sensors;Telemedicine;Radar;Radar tracking;Blood pressure|
|[Distance Measurement System Based on Mode-Locked Laser](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066928)|O. Terra; H. M. Hussein; H. Kotb|10.1109/IMAS55807.2023.10066928|passively Mode-locked lasers;distance measurement in air;soliton mode-locking;Microwave measurement;Antenna measurements;Microwave antennas;Uncertainty;Laser mode locking;Measurement uncertainty;Measurement by laser beam|
|[RCS Reduction Using Reconfigurable Chessboard Coding Plasma-Based Dielectric Resonator Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066941)|S. H. Zainud-Deen; H. A. E. -A. Malhat; A. S. Zainud-Deen; M. M. Badawy|10.1109/IMAS55807.2023.10066941|RCS Reduction;Chessboard arrangement;Microwave antennas;Radar cross-sections;Surface waves;Resonant frequency;Bandwidth;Voltage;Encoding|
|[An Inductor-less Current-Reuse CS LNA with Resistive-Feedback for Low-Noise Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066880)|A. H. Mahmoud; A. H. Ismail|10.1109/IMAS55807.2023.10066880|Common-source;current-reuse;inverter-based amplifier;LNA;low noise;low power;UWB;Microwave antennas;Power demand;Impedance matching;Loading;Microwave amplifiers;Wideband;Gain|
#### **2023 Global Conference on Wireless and Optical Technologies (GCWOT)**
- DOI: 10.1109/GCWOT57803.2023
- DATE: 24-27 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[E-Education Application Using Flutter: Concepts and Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064660)|S. Khan; R. Usman; W. Haider; S. Murtaza Haider; A. Lal; A. Q. Kohari|10.1109/GCWOT57803.2023.10064660|Qualitative Approach;IEEE;Times New Roman;Flowchart;Peer-Tutoring;Home Tutor Application;Tutors’ Gate;Flutter Project;Hybrid Application;and How Education was affected by COVID-19;Overcoming high illiteracy rates in Pakistan;withstand inflation;COVID-19;Wireless communication;Training;Electronic learning;Shape;Pandemics;Optical feedback|
|[Performance Analysis & Latency Calculation Of Cooperative Communication Protocols](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064681)|N. Akram; A. Rafique; M. Abbasi; A. Hasan; S. R. Ali Zaidi|10.1109/GCWOT57803.2023.10064681|communication protocols;latency;MIMO systems;diversity;wireless network;antenna;probability;Protocols;Cooperative communication;Wireless networks;Prototypes;Probability;Throughput;Power system reliability|
|[Estimation of Chromatic Dispersion by means of Innovative Rectification Element](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064659)|M. Abbasi; A. Rafique; N. Akram; A. Akram|10.1109/GCWOT57803.2023.10064659|Chromatic Dispersion (CD);Mode Field Diameter (MFD);Optical-Time-Domain-Reflectometer (OTDR);Single Mode Fiber (SMF);Backscattered;Optical fibers;Wireless communication;Chromatic dispersion;Wavelength measurement;Optical distortion;Optical variables measurement;Distortion|
|[Warming Trends over the Major Urban Centers of Pakistan Due to Climate Change](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064649)|Q. Haris Uddin; S. Syed Muzzamil Hussain|10.1109/GCWOT57803.2023.10064649|Climate change;temperature trends;SDG 13;urban centers;global temperature;Wireless communication;Climate change;Urban areas;Sociology;Time series analysis;Water heating;Developing countries|
|[Smart Water Level Monitoring System Using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064661)|S. Parveen; S. M. Nabeel Mustafa; S. Soomro; S. Bano|10.1109/GCWOT57803.2023.10064661|Water shortage;Internet of Things (IoT);monitoring;smart system;android application;Wireless communication;Wireless sensor networks;Ultrasonic variables measurement;Optical switches;Water conservation;Acoustics;Mobile applications|
|[Low-Cost 3D Holographic Display With Gesture Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064651)|S. Bibi; A. Imran; S. Saud; M. K. Maheshwari; B. Shankar Chowdhry; W. Ejaz|10.1109/GCWOT57803.2023.10064651|Hologram;Augmented Reality;Gesture Control;Wireless communication;Three-dimensional displays;Costs;Portable computers;Education;Glass;Holography|
|[An Energy-Efficient Communication Protocol for Power-Constrained IoT Networks: A Deep Reinforcement Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064678)|S. A. Ullah; S. Muhammad Khalid; U. A. Korai; A. Ullah|10.1109/GCWOT57803.2023.10064678|Next-generation Industrial Internet-of-Things (IIoT);non-orthogonal multiple access (NOMA);deep reinforcement learning (DRL);combined experience replay deep deterministic policy gradient (CER-DDPG);Optical losses;NOMA;Protocols;Simulation;Reinforcement learning;Throughput;Energy efficiency|
|[Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064671)|A. Afroz; R. Zia; S. Noor; A. O. Garcia; S. Shams; S. Mughal|10.1109/GCWOT57803.2023.10064671|Convolutional neural network;deep learning;image classification;LeNet-5;melanoma skin cancer;python;Training;Wireless communication;Training data;Melanoma;Manuals;Skin;Data models|
|[Task Scheduling in Fog Computing: Parameters, Simulators and Open Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064652)|M. Saad; R. I. Qureshi; A. U. Rehman|10.1109/GCWOT57803.2023.10064652|Fog Computing (FC);Task Scheduling;Internet of Things (IoT);Quality of Service (QoS);Wireless communication;Cloud computing;Scheduling algorithms;Computational modeling;Bibliographies;Quality of service;Dynamic scheduling|
|[Comparative Analysis Of Intrusion Detection Systems in SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064664)|R. Shams; D. O. Suri; F. Hanif; P. Otero|10.1109/GCWOT57803.2023.10064664|Software Defined Networks (SDN);Intrusion Detection;Snort;Suricata;Industries;Wireless communication;Prototypes;Network intrusion detection;Production;Optical fiber networks;Hardware|
|[A Simultaneous Wireless Power and Data Transfer System for an SAE J2954 compliant EV Charger](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064648)|I. Casaucao; A. Triviño|10.1109/GCWOT57803.2023.10064648|SWPDT;coil;data transfer;EV;inductive charging;modulation;demodulation;Wireless communication;Coils;Vehicle-to-grid;Frequency modulation;Prototypes;Transformers;Optical transmitters|
|[Design and Development of a Portable Spectrophotometer for Glass Characterization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064680)|R. Chueca; S. Andrés; R. Alcain; C. Heras; I. Salinas|10.1109/GCWOT57803.2023.10064680|nanometric coatings;spectrophotometer;normal incidence;glazing;Wireless communication;Three-dimensional displays;Prototypes;Glass;Optical variables measurement;Reflection;Optical receivers|
|[Linear Regression Based Crop Suggestive System for Local Pakistani Farmers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064668)|B. Das; S. M. Ali; M. Z. Shaikh; A. F. Chandio; M. A. Rahu; J. K. Pabani; M. Ur Rehman Khalil|10.1109/GCWOT57803.2023.10064668|Crop Suggestive System;Machine Learning;Algorithms;Soil;Wireless communication;Linear regression;Crops;Production;Machine learning;Soil;Raw materials|
|[Applications of Machine Learning in Medicine: Current Trends and Prospects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064665)|M. Aamir; S. Bazai; U. A. Bhatti; Z. A. Dayo; J. Liu; K. Zhang|10.1109/GCWOT57803.2023.10064665|machine learning;healthcare;medicine recommendation;deep learning;Industries;Wireless communication;Sociology;Machine learning;Medical services;Big Data;Predictive models|
|[Availability of Free Space Optical links of Hyderabad Pakistan using Climate Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064675)|M. Ehsan; U. A. Korai; A. W. Memon; A. Ullah; B. Muneer; A. A. Memon|10.1109/GCWOT57803.2023.10064675|Free space optics;fog attenuation;5G Networks;electromagnetic wave propagation;Wireless communication;Wavelength measurement;Optical propagation;Urban areas;Optical attenuators;Probability;Optical fiber networks|
|[A Survey on the CEM Life Cycle and their Test Sites Limitations & Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064670)|B. Fayyaz; M. Zubair; E. N. Baro|10.1109/GCWOT57803.2023.10064670|consumer electronic product (CEP);original equipment manufacturer (OEM);contract electronics manufacturer (CEM);designing for manufacturing (DFM);design for testability (DFT);Wireless communication;Automation;Surveillance;Power transmission;Companies;Production;Hardware|
|[Analyzing the Classification Performance of DenseNet121 on Pre-processsed MIAS Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064663)|T. Mubeen; Zain-Ul-Abidin; M. H. Shahbaz; P. O. Roth; M. A. L. Nieto|10.1109/GCWOT57803.2023.10064663|Breast Cancer;Mammograms;Deep Neural Networks Convolutional neural network;Segmentation;Ultrasounds;MRI;DDSM;Cancer Detection;Classification;Computer Aided Detection;pre-processing;MIAS;Noise;Wireless communication;Training;Solid modeling;Ultrasonic imaging;Three-dimensional displays;Sensitivity;Neural networks|
|[Expression Detection Of Autistic Children Using CNN Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064653)|A. J. Syed; D. J. Durrani; N. Shahid; W. Khan; A. Muhammad|10.1109/GCWOT57803.2023.10064653|Autism;CNN;HaarCascade;Confusion Matrix;Wireless communication;Autism;Pediatrics;Wireless sensor networks;Machine learning algorithms;Object detection;Feature extraction|
|[A Deep Learning Approach To Recognizing Emotions Through Facial Expressions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064654)|R. Kumar|10.1109/GCWOT57803.2023.10064654|Facial Expression;Computer Vision;Convolutional Neural Networks Deep Learning;Deep learning;Industries;Wireless communication;Emotion recognition;Webcams;Face recognition;Optical computing|
|[Early Prediction of Diabetes Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064682)|B. Rathi; F. Madeira|10.1109/GCWOT57803.2023.10064682|Machine learning;KNN;Diabetes Predictions;Wireless communication;Machine learning algorithms;Machine learning;Predictive models;Prediction algorithms;Diabetes;Classification algorithms|
|[Recursive Neural Network Based Degradation Trend Estimation for Efficient Maintenance of Aerial Bundled Cables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064677)|S. M. U. Talha; S. M. Ali; S. S. Ahmed; A. Ali; T. Mairaj|10.1109/GCWOT57803.2023.10064677|Aerial Bundled Cables;degradation estimation;Condition Monitoring;Recurrent neural network (RNN);Degradation;Wireless communication;Recurrent neural networks;Power cables;Urban areas;Sea measurements;Predictive models|
|[Comparative Evaluation of Acoustic Channel Characteristics for Reliable Design of Underwater Acoustic Sensor Networks (UASN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064662)|M. U. Khan; M. Aamir; R. Shams; P. Otero; U. Jilani; F. Saleem|10.1109/GCWOT57803.2023.10064662|Acoustic Channel;Underwater Communication;Propagation Delay;Transmission Loss;Spreading Loss;Noise;SNR;nan|
|[Performance Analysis of Industry 4.0 and Small and Medium Enterprises (SMEs) for Financial Sustainability Using Strategic Planning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064655)|S. Lohana; U. K. Rashid; S. M. Zabri; Z. U. Rehman|10.1109/GCWOT57803.2023.10064655|Financial sustainability;SME Performance;Industry 4.0;Wireless communication;Finance;Companies;Strategic planning;Stability analysis;Fourth Industrial Revolution;Performance analysis|
|[Intelligent Car Parking System Using WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064656)|S. A. Ratti; N. Pirzada; S. M. A. Shah; A. Naveed|10.1109/GCWOT57803.2023.10064656|Wireless Body Area Network;Smart Payment Parking System;File transfer protocol;Internet of Things;Wireless communication;Space vehicles;Wireless sensor networks;Base stations;Aerospace electronics;Mobile applications;Automobiles|
|[The Impact of Fog computing in the IoT World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064669)|F. Sohail; H. Haider; N. Ismat|10.1109/GCWOT57803.2023.10064669|Cloud Computing;Internet of Things;Fog computing;IoT Applications;IoT services;IoT and Fog framework;Wireless communication;Cloud computing;Wireless sensor networks;Privacy;Computational modeling;Market research;Internet of Things|
|[Review on Three-Factor Authorization based on different IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064673)|M. Mairaj; M. S. A. Khan; D. -e. -S. Agha; F. Qazi|10.1109/GCWOT57803.2023.10064673|Internet of things (IoT);Three factors Authentication;Security;Multi-Gateway;Threats Attacks;Wireless sensor networks;Protocols;Multi-factor authentication;Smart buildings;Wireless networks;Smart homes;Internet of Things|
|[Prototype Model of Voice Activated Wheelchair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064667)|S. M. Omair; S. A. Syed; M. M. Khan; A. Shaikh; R. Ismail; H. Shah; Z. Irfan|10.1109/GCWOT57803.2023.10064667|Wheelchair;Voice controlled wheelchair;home automation system;Automatic wheelchair;Microphone;Disability;Wireless communication;Wireless sensor networks;Technological innovation;Wheelchairs;Optical switches;Prototypes;Speech recognition|
|[Identification of Humans in a Disaster using Radio Frequency Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064672)|S. I. Hassan; M. Y. I. Zia; P. Otero|10.1109/GCWOT57803.2023.10064672|disaster;human lives;radio frequency;robot;building collapse;Wireless communication;Laser radar;Earthquakes;Buildings;Transmitting antennas;Receiving antennas;Radar detection|
|[IoT – Assets Taxonomy, Threats Assessment and Potential Solutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064657)|S. Zardari; N. Nisar; Z. Fatima; L. L. Dhirani|10.1109/GCWOT57803.2023.10064657|Internet of Things;Security;Privacy;Threats;Wireless communication;Industries;Privacy;Taxonomy;Threat assessment;Internet of Things;Stakeholders|
|[Lung Cancer Detection using Artificial Neural Network on Android](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064658)|A. Jawaid; J. Hafeez; S. Khan; A. Ur Rehman|10.1109/GCWOT57803.2023.10064658|Lung Cancer Detection;Artificial Neural Network;Image Processing;Staging;Android;Wireless communication;Training;Wireless sensor networks;Sensitivity;Operating systems;Computed tomography;Lung cancer|
#### **2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)**
- DOI: 10.1109/ICITIIT57246.2023
- DATE: 11-12 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068658)|K. Khan; R. Katarya|10.1109/ICITIIT57246.2023.10068658|ASD;Artificial intelligence;Machine learning;Supervised learning;Autism;Medical treatment;Machine learning;Market research;Medical tests;Biology;Data mining|
|[Improvement in Breakdown Voltage of Junctionless Power Transistor with Ga2O3 RESURF region](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068623)|M. V. R.; K. S. Nikhil|10.1109/ICITIIT57246.2023.10068623|Ga2O3;Higher breakdown voltage;Enhancement mode;RESURF;Asymmetric gate;Junction less;MOSFET;Low voltage;Photonic band gap;Surface resistance;Logic gates;Power transistors;Market research|
|[Mutable Blockchain for Identity Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068675)|S. Jadon; D. A. Bhat; S. R; Y. TB; P. HB|10.1109/ICITIIT57246.2023.10068675|Identity Management;Mutable Blockchain;Chameleon hashing;Consensus;Tokens;Databases;Authentication;Market research;Blockchains;Data mining;Information technology|
|[Recognition of Characters using PCE based Convolutional LSTM Networks from Palaeographic Writings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068679)|S. Ezhilarasi; P. UmaMaheswari; S. Raghavi|10.1109/ICITIIT57246.2023.10068679|Character Recognition;Patch Chunk Extraction;PCE;CNN;LSTM;Paleographic writings;Image recognition;Lighting;Writing;Feature extraction;Rocks;Market research;Character recognition|
|[A Truncated SVD Framework for Online Hate Speech Detection on the ETHOS Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068574)|A. Chhabra; D. K. Vishwakarma|10.1109/ICITIIT57246.2023.10068574|Hate Speech;Machine Learning;SVD;Binary-label Classification;TF-IDF;Support vector machines;Machine learning algorithms;Social networking (online);Hate speech;Information sharing;Market research;Information technology|
|[Herbs Ailment Diagnosis using AI Techniques for Sustainable Innovation in Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068575)|S. D; J. D. R. J; A. H. S; C. D. R; D. M. B; K. A. Y|10.1109/ICITIIT57246.2023.10068575|deep learning;convolution neural network;artificial intelligence;Deep learning;Technological innovation;Economic indicators;Plants (biology);Image processing;Switches;Predictive models|
|[Leveraging YOLOv7 for Plant Disease Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068590)|S. Vaidya; S. Kavthekar; A. Joshi|10.1109/ICITIIT57246.2023.10068590|Plant Disease Detection;Convolutional Neural Networks;Object Detection;YOLOv7;PlantDoc dataset;Training;Plant diseases;Satellites;Economic indicators;Computational modeling;Object detection;Market research|
|[Muzzle Based Identification of Cattle Using KAZE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068662)|K. Kaushik; D. J. Reddy; R. Raman|10.1109/ICITIIT57246.2023.10068662|Visual Animal Biometrics;Muzzle point;Cattle Recognition;Computer Vision;SIFT;BRISK;ORB;KAZE;AKAZE;Feature Extraction;Visualization;Animals;Databases;Pressing;Cows;Feature extraction;Vaccines|
|[CISER: Customized Institute Specific Search Engine for Retrieving Research Papers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068620)|S. Sankar; H. Muslihuddeen; S. Ostwal; P. Sathvika; A. K. Madasamy|10.1109/ICITIIT57246.2023.10068620|Search Engine;Cosine similarity;Semantic Similarity;BERT Model;Django;Semantics;Search engines;Market research;Information technology;Faces|
|[A Weighted Ensemble Model for Prediction of Dengue Occurrence in North India (Chandigarh)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068636)|K. Shashvat; A. Kaur|10.1109/ICITIIT57246.2023.10068636|Regression;Dengue;Weighted ensemble;Prediction;Support vector machines;Analytical models;Time series analysis;Neural networks;Predictive models;Real-time systems;Data models|
|[Universal Adversarial Perturbation Attack on the Inception-Resnet-v1 model and the Effectiveness of Adversarial Retraining as a Suitable Defense Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068722)|R. Senthil; L. Ravishankar; S. D. Dunston; M. A. R. V.|10.1109/ICITIIT57246.2023.10068722|deep convolutional neural networks;Inception-ResNet-v1;universal adversarial perturbation attack;adversarial retraining;COVID-19;Analytical models;Perturbation methods;Computed tomography;Lung;Retina;Market research|
|[Feature Drift Detection using Overlapping Window and Mann-Whitney U Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068710)|J. K. T; S. S; S. A|10.1109/ICITIIT57246.2023.10068710|Streaming Data;Feature Drift;Mann-Whitney U Test;Drift detection;Feature extraction;Market research;Proposals;Data mining;Task analysis;Information technology|
|[Analysis of User Experience on The Government Application of Indonesian Higher Education Institutional Information Systems Using Usability Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068674)|F. Qonita; M. F. Budiman; V. M. Sari; N. Limantara|10.1109/ICITIIT57246.2023.10068674|Usability;Usability Testing;System Usability Scale;Efficiency;Effectiveness;Satisfaction;Concurrent Think Aloud;Navigation;Layout;Education;Market research;User experience;Proposals;Usability|
|[A Rear-view Aid System For Vehicles Based on Panoramic Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068600)|M. S; B. D. N|10.1109/ICITIIT57246.2023.10068600|panoramic vision;gradient;blending;Streaming media;Market research;Information technology;Vehicles|
|[Web Scrapping Tools and Techniques: A Brief Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068666)|R. R. N. R; N. R. S; V. M.|10.1109/ICITIIT57246.2023.10068666|web scraping;Beautifulsoup;Selenium;Scrapy;Survey;performance evaluation;statistical validation;Java;Web pages;C++ languages;Search engines;Market research;Libraries;Software|
|[Friend Recommendation System Using Map-Reduce and Spark: A Comparison Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068723)|A. M. A. Sai; G. Sahil; B. S. S. Nadh; K. L. S. Eswar; N. K. S; K. S. R. B. Prakash; A. D. Mahesh|10.1109/ICITIIT57246.2023.10068723|Map-Reduce;Spark;Recommendation System;Hadoop;Social networking (online);Distributed databases;Data collection;Market research;Sparks;Information technology;Recommender systems|
|[Optimal hardware implementation for end-to-end CNN-based classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068601)|S. G. Aydin; H. S. Bilge|10.1109/ICITIIT57246.2023.10068601|Arbitrary precision fixed-point;convolutional neural network;field-programmable gate array;high-level synthesis;Computer vision;Computer architecture;Parallel processing;Hardware;Real-time systems;Central Processing Unit;Convolutional neural networks|
|[IoT-enabled Contactless Doorbell with Facial Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068625)|G. Rodrigo; D. De Silva|10.1109/ICITIIT57246.2023.10068625|Smart Doorbell;Masked Face Recognition;Convolutional Neural Network;Deep Learning;IoT;Deep learning;COVID-19;Training;Face recognition;Passwords;Feature extraction;Mobile applications|
|[Blockchain based Secure Data Storage Verification Algorithm for Smart City Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068638)|M. Vivekanandan; P. K. Premkamal; C. I. Johnpaul; S. E. Ebinazer|10.1109/ICITIIT57246.2023.10068638|Blockchain;Security;Integrity;data consistency;smart city;Machine learning algorithms;Smart cities;Distributed ledger;Data integrity;Soft sensors;Memory;Machine learning|
|[Exposing the Vulnerabilities of Deep Learning Models in News Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068577)|A. Bajaj; D. K. Vishwakarma|10.1109/ICITIIT57246.2023.10068577|Adversarial Attack;News Classification;(Natural Language Processing) NLP;Semantic Similarity;Vulnerability;Transformers;Deep learning;Analytical models;Bit error rate;Semantics;Text categorization;Transformers;Robustness|
|[Forecasting of Satellite Based Carbon-Monoxide Time-Series Data Using a Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068609)|A. Verma; V. Ranga; D. K. Vishwakarma|10.1109/ICITIIT57246.2023.10068609|Carbon-Monoxide;Earth Engine;Sentinel 5p;RMS;LSTM;Deep learning;Gases;Satellites;Time series analysis;Air pollution;Market research;Carbon monoxide|
|[Visual Based Malware Clustering Using Convolution Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068670)|S. B. Kadam; V. Abhijith; P. A. Sreelekha|10.1109/ICITIIT57246.2023.10068670|malware classification;internet of things;machine learning;cnn;Malware;Internet of Things;Training;Computational modeling;Computer viruses;Codes;Monitoring|
|[Residential Load Forecasting based on Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068706)|K. S. Sudheera; S. R; T. R; V. M. M; A. G. Kumar|10.1109/ICITIIT57246.2023.10068706|Short Term Load Forecasting (STLF);Deep Neural Networks (DNN);Long-Short Term Memory (LSTM);Convolution Neural Networks (CNN);Hybrid Model;CNN-LSTM;Measurement;Deep learning;Energy consumption;Load forecasting;Neural networks;Predictive models;Convolutional neural networks|
|[Design of a Crop Disease Detection Model using Multi-parametric Bio-inspired Feature Representation and Ensemble Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068649)|S. A. Lohi; C. Bhatt|10.1109/ICITIIT57246.2023.10068649|Crop;Disease;Machine;Learning;SVM;KNN;MLP;LR;RF;Accuracy;Precision;Recall;AUC;Support vector machines;Adaptation models;Image segmentation;Shape;Image color analysis;Biological system modeling;Crops|
|[Attention-based Model for Multi-modal sentiment recognition using Text-Image Pairs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068626)|A. Pandey; D. K. Vishwakarma|10.1109/ICITIIT57246.2023.10068626|multi-modal sentiment recognition (MSR);Sentiment recognition (SR);Convolution block attention module (CBAM);BERT;Bidirectional LSTM;Convolution;Bit error rate;Benchmark testing;Predictive models;Market research;Task analysis;Information technology|
|[Machine Learning based framework for Drone Detection and Identification using RF signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068637)|K. N. Inani; K. S. Sangwan; Dhiraj|10.1109/ICITIIT57246.2023.10068637|Drone;Detection;Identification;Machine Learning;Deep Learning;feature extraction;Deep learning;Training;Machine learning algorithms;RF signals;Feature extraction;Data mining;Time-domain analysis|
|[Expert System Techniques in Intelligent Diagnostic Digital Cytopathology System for Cervical Intraepithelial Neoplasia Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068718)|A. Kapruwan; S. Sharma; H. R. Goyal|10.1109/ICITIIT57246.2023.10068718|Expert system (ES);Hybrid Expert system;Cervical intraepithelial neoplasia cancer (CIN-C);Knowledge Engineering (K.E.) Languages;Market research;Medical diagnosis;Expert systems;Information technology;Neoplasms;Medical diagnostic imaging;Faces|
|[Sales Application Solution for Small Medium Enterprise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068688)|Ferdianto; L. S. Sanjaya; Titan; E. Halim; D. W. Sukmaningsih; A. Effendi; Y. Lie|10.1109/ICITIIT57246.2023.10068688|Point-of-Sales;Point-of-Sales information system;sales data processing;Companies;Documentation;Inventory management;Market research;Recording;Resource management;Information technology|
|[Factors influenced user in Using Streaming Music Applications Using the TAM Method: Technology Acceptance Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068571)|F. Jingga; Z. H. Fitria; J. Alfi; A. D. Kusumaiati|10.1109/ICITIIT57246.2023.10068571|music streaming;structural equation modelling;behavioural intention;technology acceptance model;Industries;Technology acceptance model;Social networking (online);Customer satisfaction;Market research;Mathematical models;Data models|
|[A Three-Dimensional Approach for Stock Prediction Using AI/ML Algorithms: A Review & Comparison](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068584)|S. S. Raju; M. Srikanth; K. Guravaiah; P. Pandiyaan; B. Teja; K. S. Tarun|10.1109/ICITIIT57246.2023.10068584|Stock Prediction;Artificial Intelligence;Machine Learning;Technical Analysis;Fundamental Analysis;Sentimental Analysis;Globalization;Companies;Prediction algorithms;Market research;Stock markets;Information technology;Portfolios|
|[The integration of Blockchain and AI for Web 3.0: A security Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068672)|A. Suryavanshi; A. G; M. B. T. N; R. M; A. H. N|10.1109/ICITIIT57246.2023.10068672|Web 3.0;Blockchain;Security;Internet;Data Transparency;Semantic Web;Data privacy;Merging;Transforms;User interfaces;Decentralized applications;User experience|
|[Tiny Face Presence Detector using Hybrid Binary Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068573)|M. Chandna; P. Bhatia; S. -P. Singh; S. Suneja|10.1109/ICITIIT57246.2023.10068573|Quantization;Artificial Neural Networks;Deep Learning;Binary Neural Networks;Deep learning;Energy consumption;Neural networks;Memory management;Detectors;Market research;Mobile handsets|
|[Approaches for Plant Leaf Classification: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068650)|S. Kunjachan; K. S|10.1109/ICITIIT57246.2023.10068650|Deep learning;Machine learning;Image recognition;Leaf classification;Convolutional neural network;Plant diseases;Shape;Image color analysis;Neural networks;Morphology;Lighting;Market research|
|[Integrated Healthcare Monitoring System using Wireless Body Area Networks and Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068616)|M. Ezhilarasi; A. Kumar; M. Shanmugapriya; A. Ghanshala; A. Gupta|10.1109/ICITIIT57246.2023.10068616|Body sensors;Cloud Storage;Disease;Fog computing;Latency;IoT;Medical Diagnosis;Wireless sensors;Industries;Computational modeling;Medical services;Computer architecture;Real-time systems;Internet of Things;Time factors|
|[An Enhanced Hybrid Scheme for IP Traceback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068579)|S. A; A. D. CS; V. M|10.1109/ICITIIT57246.2023.10068579|network security;DoS/DDoS attacks;hybrid IP traceback;spoofing;packet marking;packet logging;Denial-of-service attack;Market research;Internet;IP networks;Information technology|
|[Rechain: A Secured Blockchain-Based Digital Medical Health Record Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068707)|N. Nautiyal; P. Agarwal; S. Sharma|10.1109/ICITIIT57246.2023.10068707|Blockchain;Ethereum;Web3;Medical records;Interplanetary File System;secure storage;smart contracts;Uncertainty;Smart contracts;Memory;Authentication;Medical services;Market research;Blockchains|
|[Blockchain Based Record Management System in Hospitals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068685)|A. Vernekar; A. Sakhare; P. Bhapkar; S. Jadhav; R. B. Adhao|10.1109/ICITIIT57246.2023.10068685|blockchain;record management;smart contract;Industries;Data privacy;Hospitals;Data breach;Market research;Blockchains;Information technology|
|[Phishing Perception and Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068585)|A. N. P; H. V. V; S. P. H|10.1109/ICITIIT57246.2023.10068585|Phishing;Natural Language processing;Machine Learning;Web Scraping;Uniform resource locators;Histograms;Machine learning algorithms;Phishing;Machine learning;Prediction algorithms;Market research|
|[Agricultural Food supply chain Traceability using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068564)|S. Rajput; A. Jadhav; J. Gadge; D. Tilani; V. Dalgade|10.1109/ICITIIT57246.2023.10068564|Blockchain;Traceability;Interplanetary File System (IPFS);Smart Contracts;Radio Frequency Identification (RFID);Supply Chain;Scalability;Supply chains;Sociology;Smart contracts;Systems architecture;Sensor systems;Blockchains|
|[Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068711)|S. A. Kahate; A. D. Raut|10.1109/ICITIIT57246.2023.10068711|Cyber-attacks;LSTM;CNN;Bullying;Social Media Analysis;Deep learning;Training;Analytical models;Sentiment analysis;Cyberbullying;Feature extraction;Real-time systems|
|[Machine Learning techniques for identifying Cyberbullying on digital networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068647)|N. Gayathri; P. R; R. k. A; M. R|10.1109/ICITIIT57246.2023.10068647|Cyberbullying;Opinion Examination;Optimization in social media;Support vector machines;Neural networks;Cyberbullying;Entertainment industry;Standardization;Linguistics;Market research|
|[Pedestrian Direction Estimation: An Approach via Perspective Distortion Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068588)|S. B. V. S; R. Raman|10.1109/ICITIIT57246.2023.10068588|Pedestrian Detection;Monocular Vision;Vanishing Point;Centroid Displacement;Perspective Distortion;Legged locomotion;Video sequences;Estimation;Clustering algorithms;Distortion;Market research;Information technology|
|[Self-Driving Car: Simulation of Highly Automated Vehicle Technology using Convolution Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068691)|M. Mallikarjuna; A. Bhosle|10.1109/ICITIIT57246.2023.10068691|Self-Driving Car Simulation;Deep CNN;Computer Vision;Image Augmentation;Machine Learning;Training;Road accidents;Navigation;Wheels;Robustness;Autonomous automobiles;Safety|
|[NFT Application for Music Industry using Blockchain Smart contracts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068684)|T. Tharun; A. Vamshi; R. Eswari|10.1109/ICITIIT57246.2023.10068684|Blockchain;copyright;Non-Fungible Tokens(NFT);Public ledger;Smart Contract;streams;Industries;Semantic Web;Smart contracts;Music;Market research;Nonfungible tokens;Reliability|
|[An Overview of Different Types of Recommendations Systems - A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068631)|P. Duraisamy; S. Yuvaraj; Y. Natarajan; V. Niranjani|10.1109/ICITIIT57246.2023.10068631|Recommendation System;Social-Media;Machine Learning Algorithms;Support vector machines;Machine learning algorithms;Social networking (online);Collaborative filtering;Linear regression;Market research;Recommender systems|
|[A study of the effectiveness of the Profile Closeness Attack on the Sicilian Mafia Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10068719)|D. P. B; T. P. Johnson; K. Balakrishnan|10.1109/ICITIIT57246.2023.10068719|Central Attacks;Vulnerability;Profile Close-ness;Sicilian Mafia;Social networking (online);Terrorism;Market research;Information technology|
#### **2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)**
- DOI: 10.1109/SCEECS57921.2023
- DATE: 18-19 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Generalized Immutable Ledger (GILED) using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062983)|H. Bari; N. Patel|10.1109/SCEECS57921.2023.10062983|add block;block chain;generalised ledger;generate database;hash;immutable ledger;lock ledger/record;organization;unlock ledger/record;verify ledger;Computer science;Databases;Government;Blockchains;Task analysis;Faces;Audio-visual systems|
|[Smart IR Drop Reduction technique using Pegasus flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062999)|S. Saurabh; S. G. Dastidar; A. Singh; T. Kumar Gupta|10.1109/SCEECS57921.2023.10062999|IR Drop;Design sign off;Performance evaluation;Computer science;Runtime;Metals;Timing;Optimization;Antennas|
|[FIS Based Fault Identification and Classification in IEEE RTS96 System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063132)|M. Kochar; A. Ekka; A. Yadav|10.1109/SCEECS57921.2023.10063132|IEEE RTS96 system;FIS-Fuzzy Inference System;Fault Identification;Fault diagnosis;Resistance;Fuzzy logic;Computer science;Power transmission lines;Current measurement;Computational modeling|
|[Solar Power Monitoring and Forecasting System using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062972)|A. A. Alanqar; V. Chitturi|10.1109/SCEECS57921.2023.10062972|solar power;monitoring;internet of things;neural network;forecasting;Training data;Predictive models;Prediction algorithms;Mathematical models;Real-time systems;Planning;Solar panels|
|[Design And Development of BLDC Motor Drive For Solar-PV Irrigation System Using MATLAB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063109)|M. Kumar; P. Deosarkar; R. N. Mahanty|10.1109/SCEECS57921.2023.10063109|PV array;VSI;DC-DC converter;MATLAB;Speed Control;Smooth Starting;PWM;BLDC motor;Irrigation;Renewable energy sources;Regulators;Solar energy;Pulse width modulation;Water pumps;Software|
|[Third Eye – A Smart Wearable Glass With Deep Learning Technology Powered With Artificial Intelligence for Visually Impaired People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061824)|U. R; H. P. S.; R. N. G.; N. A|10.1109/SCEECS57921.2023.10061824|Machine Learning;Yolo Algorithm;Speech Recognition;Image Recognition;Pysttx3;Deep learning;Computer science;Machine learning algorithms;Navigation;Glass;Detectors;Artificial intelligence|
|[Grid Integrated Bidirectional EV Battery Charger in V2G-G2V Mode With Improved Power Quality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062990)|A. Dixit; P. Swarnkar; R. K. Nema|10.1109/SCEECS57921.2023.10062990|On board chargers (OBC);DC-DC converter;Buck;Boost;THD;V2G;G2V;V2L;Vehicle-to-grid;Reactive power;DC-DC power converters;Active filters;Mathematical models;Batteries;Topology|
|[Efficient Soil Condition Monitoring with IoT Enabled Intelligent Farming Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063050)|K. Sudharson; B. Alekhya; G. Abinaya; C. Rohini; S. Arthi; D. Dhinakaran|10.1109/SCEECS57921.2023.10063050|Smart Farming;Sensing Technique;Cloud Storage;Soil Monitoring;Data Analytics;Temperature sensors;Temperature measurement;Smart agriculture;Strips;Temperature;Surveillance;Crops|
|[A Review on Dam Water Level Alerting System Using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063071)|P. Dudhe; G. Magarde; M. Raj; A. Karemore; J. Wankhede|10.1109/SCEECS57921.2023.10063071|Internet of things;Cloud database;water level;Dam;Wireless Technology;Global System for Mobile Communication;Sensor;GSM;Water;Industries;Dams;Software as a service;Maintenance engineering;Reservoirs|
|[Deep Learning Approach for Sign Language Recognition Using DenseNet201 with Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063044)|Y. Altaf; A. Wahid; M. M. Kirmani|10.1109/SCEECS57921.2023.10063044|Transfer Learning;DenseNet201;Deep Learning;Sign Language Recognition;Multilayer DenseNets;Image recognition;Deep learning;Computer science;Image recognition;Computational modeling;Transfer learning;Gesture recognition;Computer architecture|
|[Modelling Proton Exchange Membrane Fuel Cell for Power Generation Using Multi-Stage Power Conversion System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063048)|U. Mitra; A. Arya; R. Dwivedi; S. Gupta; P. Paliwal; S. Tomar|10.1109/SCEECS57921.2023.10063048|Proton Exchange Membrane Fuel Cell (PEMFC);PID controller;Polarization curve;Power conditioning unit (PCU);Distribution generation (DG);Protons;Power conditioning;Software packages;Fuel cells;DC-DC power converters;Mathematical models;Inverters|
|[Seven-Level Single-Phase Reduced Device Count Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063035)|D. K. Patel; N. K. Dewangan; D. Kumar; A. Rathore|10.1109/SCEECS57921.2023.10063035|Asymmetrical configuration;Dc-voltage source;Multi-level inverter (MLI);Reduced device count;Total harmonic distortion (THD);Performance evaluation;Costs;Software packages;Voltage;Switches;Multilevel inverters;Topology|
|[Linear l-intersection Pairs of Monomial and Decreasing Monomial Cartesian Codes with Application to EAQEC Codes *](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063066)|M. A. Hossain; R. Bandi|10.1109/SCEECS57921.2023.10063066|LCP;Monomial Code;l-intersection;EAQEC codes;Computer science;Codes;Monte Carlo methods;Quantum entanglement;Galois fields|
|[Government Infra Assessment System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063094)|R. Thakur; A. Gadbail; A. Gomkale; T. Jambhulkar; H. Bhujade; A. Charpe|10.1109/SCEECS57921.2023.10063094|nan;Computer science;Databases;Government;Software|
|[Carbon Nano tube based Thermoelectric generator and Graphite Nanoparticle based supercapacitor for smart wearable sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063002)|B. R. Gunawardana|10.1109/SCEECS57921.2023.10063002|Thermoelectric Generator;Super Capacitor;Carbon Nanotube;Graphene Nano particles;health diagnostics;sensors;glucose monitoring;carbon nanoparticle wearables;Temperature sensors;Temperature measurement;Wearable computers;Graphite;Voltage;Supercapacitors;Generators|
|[Sarcasm Detection Analysis – Comparative Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063107)|C. Vaidya; A. Gupta; A. Shastrakar; A. Kathane; K. Atmande; K. Iyer|10.1109/SCEECS57921.2023.10063107|Affront;Sarcasm detection;Rule-Based Approach;Machine Learning;Dataset;Sentiment Analysis;SVM;Decision Tree;Random Forest;Data Pre-Processing;Comparative Analysis;Support vector machines;Computer science;Social networking (online);Computational modeling;Forestry;Decision trees;Random forests|
|[Speed Regulation of Stepping Motor Using Metaheuristic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063041)|S. Sachan; A. Narwaria; P. Swarnkar|10.1109/SCEECS57921.2023.10063041|stepping motor;actuators;PID controller;genetic algorithm (GA);PI control;Metaheuristics;Regulation;Steady-state;PD control;Security;Transient analysis|
|[A Review on Battery Technologies and Its Challenges in Electrical Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063067)|D. Mehar; R. K. Singh; A. K. Gupta|10.1109/SCEECS57921.2023.10063067|Electric Vehicle;Battery Technology;Charging station & battery charging;Computer science;Smart cities;Greenhouse effect;Environmentally friendly manufacturing techniques;Transportation;Production;Electric vehicle charging|
|[Raisin Grain Classification Using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063039)|O. Sahin|10.1109/SCEECS57921.2023.10063039|Raisin;Raisin Dataset;Machine Learning;Random Forest;Feature Selection;Computer science;Computational modeling;Forestry;Feature extraction;Random forests|
|[Smart Contract-Based Land Registration System Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063068)|R. K. Yadav; R. Dabare; M. Ghyar; S. Bhongade; M. Gautam|10.1109/SCEECS57921.2023.10063068|Decentralized;Distributed ledger;Cryptographic Algorithm;Blocks;Immutable;Computers;Computer science;Authorization;Distributed ledger;Government;User interfaces;Lead|
|[Arduino Based Bluetooth Voice-Controlled Robot Car and Obstacle Detector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063092)|R. Sissodia; M. S. Rauthan; V. Barthwal|10.1109/SCEECS57921.2023.10063092|Arduino Uno;L298N;Bluetooth (HC-05);Ultrasonic sensor (HC-SR04);Bluetooth;Microcontrollers;Wires;Robot sensing systems;Software;Acoustics;Autonomous automobiles|
|[Random Key Generation based Double Image Encryption System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063011)|H. Singh; C. Gupta|10.1109/SCEECS57921.2023.10063011|Encryption;Data Encryption Standard;integrity;authenticity;cryptography;Computer science;Histograms;Ciphers;Wireless networks;Encryption;Internet;Standards|
|[Online Training and Placement System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063051)|G. Jewani; S. Sahare; T. Kamble; R. Kathalkar; A. Unhale|10.1109/SCEECS57921.2023.10063051|Smart Phones;Android Application;Android Studio;Sublime Text Editor;Training;Computer science;Employment;Companies;Documentation;Logic gates;Resource management|
|[Simulation and Performance Analysis of a Fuel Cell Hybrid Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061819)|U. Mitra; A. Arya; A. Khan; N. Javara; S. K. Gawre; M. Arya|10.1109/SCEECS57921.2023.10061819|ultra-capacitor;proton exchange membrane fuel cell (PEMFC);split phase induction motor;boost converter;Torque;Induction motors;Software packages;Fuel cells;Voltage;Permanent magnet motors;Synchronous motors|
|[On Parametric Picture Fuzzy Information Measure in Pattern Recognition Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063129)|A. A. Tiwari; H. Dhumras; R. K. Bajaj|10.1109/SCEECS57921.2023.10063129|Picture Fuzzy Set;Distance Measure;Information Measure;Pattern Recognition;Computer science;Fuzzy sets;Decision making;Electric variables measurement;Data science;Pattern recognition;Medical diagnosis|
|[Performance Comparison of Multilevel and 2-level Inverters for High Voltage E-drive Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063049)|A. Misra; K. Srikanth; B. L. Narasimharaju|10.1109/SCEECS57921.2023.10063049|Multilevel inverters;electric vehicle;THD;PWM;Inverter losses;e-drive efficiency;BEV;Performance parameters;Fault tolerance;Costs;Simulation;Fault tolerant systems;Electromagnetic interference;Switches;High-voltage techniques|
|[Design And Control of Solar-Battery Fed PMSM Drive For LEVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063001)|M. Kumar; P. Deosarkar; N. Tayade; S. Yenare|10.1109/SCEECS57921.2023.10063001|SPV array;VSI;PMSM;LEV;MATLAB;INC MPPT;Torque;Life estimation;Permanent magnet motors;Synchronous motors;Mathematical models;Robustness;Batteries|
|[Teeth Gap and Position Recognition System with Intraoral Scanner Images Using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063102)|R. Sathya; N. N. Anirudh; H. Ganeshram|10.1109/SCEECS57921.2023.10063102|Teeth detection;position;space between adjacent teeth;K-Means;Euclidean;R-CNN;Computer science;Image recognition;Clustering algorithms;Teeth;Euclidean distance;Facial muscles;Planning|
|[A Review On College Enquiry Chatbot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063053)|G. Sambhe; S. Awaze; S. Bobade; S. Bhagwatkar; P. Bhoyar|10.1109/SCEECS57921.2023.10063053|ELIZA;Provide information;query;inquiry;college inquiry chatbot;Computer science;Technological innovation;Databases;Chatbots|
|[On Medical Diagnosis Problem Utilizing Parametric Neutrosophic Discriminant Measure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063138)|Muskan; H. Dhumras; V. Shukla; R. K. Bajaj|10.1109/SCEECS57921.2023.10063138|Neutrosophic Set;Discriminant Measure;Medical Diagnosis;Parametric Information Measures;Computer science;Current measurement;Decision making;Numerical models;Medical diagnosis|
|[A State-of-Art Review on Applications of Machine Learning Based Approaches on DSM Programs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063027)|A. Shrivastava; P. Paliwal|10.1109/SCEECS57921.2023.10063027|demand response;microgrids;prosumers;demand side management;Analytical models;Renewable energy sources;Machine learning algorithms;Machine learning;Predictive models;Data models;Software|
|[Design of metasurface radome with large angle stability in Ku-band](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061826)|Q. Wang; J. Liang; R. Li|10.1109/SCEECS57921.2023.10061826|Ku-band antennas;metasurface radome;high transmission;large angular incidence;Microwave antennas;Satellite antennas;Dipole antennas;Simulation;Transmitting antennas;Insertion loss;Microwave communication|
|[Multi band FSS for 5G Signal Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062970)|Y. Ni; Q. Xiong; S. Zhang; Y. Lou|10.1109/SCEECS57921.2023.10062970|5G;frequency selective surface (FSS);multi band;Frequency selective surfaces;Structural rings;Computer science;Frequency modulation;5G mobile communication;Frequency-domain analysis;Filtering theory|
|[Machine learning based approach to predict cardiovascular diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063059)|R. Garg; R. Srivastava; V. Rathore; S. K. Gautam; J. S. Kumar|10.1109/SCEECS57921.2023.10063059|Cardiovascular Diseases;Logistic Regression;CNN (Convolutional Neural Network);Computer science;Neurons;Machine learning;Predictive models;Electrocardiography;Data models;Glucose|
|[A Focusing Meta-surface with High Transmittance and Low Profile for Millimeter-wave Lens Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063038)|K. Zheng; Q. Xiong; Y. Luo|10.1109/SCEECS57921.2023.10063038|Focusing;Meta-Surface;Lens Antenna;Plane Wave;High Gain;Horn antennas;Transmitting antennas;Focusing;Radar detection;Metals;Radar antennas;Millimeter wave communication|
|[A Comparative Study of Five Level Reduced Switch Count Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063012)|J. Chouhan; A. Ojha; P. Swarnkar|10.1109/SCEECS57921.2023.10063012|Cascaded H-Bridge MLI;Reduced Switch Topology;PWM methods;Switching losses;Computational modeling;Switching frequency;Switching loss;Switches;Pulse width modulation;Multilevel inverters;Control systems|
|[Hand Sign Language Detection - Using Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063060)|N. Titarmare; C. Vaidya; R. Meshram; A. Dongre; P. Jawale; N. Bambale; O. Awachaat|10.1109/SCEECS57921.2023.10063060|Deep Learning;Deep Neural Network;Hand Sign language;ANN;Machine Learning;Website;Wix;Deep learning;Earth;Computer science;Computational modeling;Neural networks;Symbols;Gesture recognition|
|[A Multi-layer Planar Beamfocusing Lens for 5G Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063137)|Q. Xiong; S. Zhang; Y. Ni; Y. Luo|10.1109/SCEECS57921.2023.10063137|Lens;Metasurface;High transmission magnitude;Phased arrays;Computer science;Communication systems;5G mobile communication;Computational modeling;Electromagnetic scattering;Dielectric substrates|
|[A Survey of A-Star Algorithm Family for Motion Planning of Autonomous Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063063)|P. Paliwal|10.1109/SCEECS57921.2023.10063063|Motion Planning;A-star Algorithm;Path Optimization;Autonomous Vehicles;Computer science;Heuristic algorithms;Path planning;Planning;Task analysis;Autonomous vehicles|
|[The Sensitivity Analysis of Metasurface-based Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063016)|G. Yang; Y. Luo|10.1109/SCEECS57921.2023.10063016|microwave sensor;permittivity;metasurface;sensitivity;Analytical models;Sensitivity analysis;Electromagnetic scattering;Media;Metasurfaces;Surface structures;Sensors|
|[Auto Detection of Personal Protective Equipment on Human](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062962)|C. Vaidya; P. Yelure; S. Gonnade; R. Mohitkar; T. Burande; U. Sonkawade|10.1109/SCEECS57921.2023.10062962|YOLO;Dataset;AI;CNN;Model;Features;Training;Testing;Annotations;Labeling;Deep Learning;Object Detection;Personal protective equipment;Hair;Computer science;Computer viruses;Hospitals;Surgery;Object detection|
|[Design and Simulation Of Energy Selective Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063013)|Y. Chen; X. T. Li; H. Sen Wang; M. X. Tan|10.1109/SCEECS57921.2023.10063013|energy selective surface;adaptive protection;strong electromagnetic protection;Computer science;Limiting;Electronic equipment;C-band;Simulation;Computational modeling;Electromagnetics|
|[High-lens metasurface focusing vortex current beams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063104)|H. Wang; Q. Yang; Z. Sun|10.1109/SCEECS57921.2023.10063104|Terahertz;focusing;vortex;beam scanning;metalens;meta materials;Computer science;Electric potential;Costs;Beams;Focusing;Production;Metasurfaces|
|[Decentralized File Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062977)|C. Vaidya; K. Takalkar; A. Ghosekar; S. Nimgade; V. Ghode|10.1109/SCEECS57921.2023.10062977|Decentralized file sharing;peer-to-peer;decentralized;centralized;authentication;accuracy;security;Gnutella;Fast Tra ck;BitTorrent;user friendly;messaging;duplication;latency;bandwidth;centralized directory;query flooding;Computers;Privacy;Protocols;Scalability;Authentication;Computer architecture;Permission|
|[Comparative study of cell balancing techniques for battery module performance optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063087)|S. Shrivastava; P. Swarnkar|10.1109/SCEECS57921.2023.10063087|cell balancing;SOC;performance optimization;energy storage;Electric potential;Simulation;Capacitors;Switches;Electric vehicles;Safety;Batteries|
|[A Review On Strategies Of Electrical Drive Utilization In Solar Powered Airplane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063127)|A. Shukla; S. Nema|10.1109/SCEECS57921.2023.10063127|PV cells;MPPT;DC-DC converters;Energy Flow System;VSI;Electrical drives;propulsion system;Airplanes;Brushless motors;Bibliographies;Atmospheric modeling;Velocity control;Drives;Aerospace electronics|
|[A Review of Metamaterial Absorber and its Absorption Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062996)|A. Verma; O. Meena|10.1109/SCEECS57921.2023.10062996|communication;electromagnetic (EM);metamaterials;metamaterial absorbers (MAs);ANSYS HFSS;Fabrication;Computer science;Costs;Absorption;Metamaterials;Communications technology;Complexity theory|
|[Design of LCL Filter For Grid Connected Three Phase Three Level Inverter to Meet IEEE 519 Standards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061817)|A. Ranjan; D. Giribabu|10.1109/SCEECS57921.2023.10061817|Three-level neutral point clamped (NPC) inverter;IEEE 519 standards;Sinusoidal pulse width modulation (SPWM);LCL Filter;Damping resistor;Resistors;Damping;Simulation;Voltage;Switches;Harmonic analysis;Inverters|
|[Performance Analysis of Two-Stage Micro-Inverter under Different Pulse Modulation Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063056)|A. K. Sahu; R. N. Patel; L. K. Sahu|10.1109/SCEECS57921.2023.10063056|Solar Photovoltaic (PV);SPWM;PSPWM;PDPWM;PODPWM;APODPWM;THD;Total harmonic distortion;Phase modulation;Power quality;Voltage;Switches;Pulse width modulation;Multilevel inverters|
|[AI Based Segmentation Technique to Identify Abnormality in MRI Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063040)|P. Prasad; A. Mishra|10.1109/SCEECS57921.2023.10063040|Convolutional Wavelet Network;Brain MRI segmentation;Artificial Intelligence;Abnormality detection;AI based segmentation accuracy;dice similarity coefficient;Computer science;Image segmentation;Magnetic resonance imaging;Computational modeling;Filtering algorithms;Brain modeling;Classification algorithms|
|[A Threat modeling approach to analyze and mitigate WhatsApp attacks: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062984)|P. K. Patidar; D. S. Tomar; R. K. Pateriya; Y. K. Sharma|10.1109/SCEECS57921.2023.10062984|WhatsApp;Vulnerabilities;Security Threat Modelling;Database Security;Backup;Cryptography;Threat modeling;Freeware;Databases;Computer bugs;Organizations;Security;Cryptography|
|[Grid Integrated Electrical Vehicle Bi-Directional Charging Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063043)|R. Mandloi; J. Rajender; M. Dubey|10.1109/SCEECS57921.2023.10063043|EV battery;buck-boost converter;energy storage device;Computer science;Vehicle-to-grid;Power quality;Bidirectional control;DC-DC power converters;Software;Electric vehicle charging|
|[Detecting Fake Profiles on Social Networks: A Systematic Investigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063046)|R. Bhambulkar; S. Choudhary; A. Pimpalkar|10.1109/SCEECS57921.2023.10063046|Fake Accounts;Machine Learning;Deep Learning;Social Media;Feature Extraction;Deep learning;Support vector machines;Measurement;Machine learning algorithms;Systematics;Recurrent neural networks;Social networking (online)|
|[Application of linear optimization technique to design a fixed coefficient multiplierless sample rate conversion filter for multiple-of-three decimation factor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063018)|D. Gautam; K. Khare; B. P. Shrivastava|10.1109/SCEECS57921.2023.10063018|Sample rate conversion;Cascaded integrator comb;Decimation;linear programming optimization (LPO);Maximum likelihood detection;Interpolation;Nonlinear filters;Programming;Attenuation;Linear programming;Comb filters|
|[Thirteen Level Inverter with Low Switch Count and Self Balancing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063020)|G. K. Yadav; S. C. Gupta; M. K. Kirar|10.1109/SCEECS57921.2023.10063020|Multilevel inverter;switched capacitor;Self-balancing;reduced switch count;Renewable energy sources;Bridge circuits;Voltage;Switches;High-voltage techniques;Inverters;Semiconductor diodes|
|[Hybrid and Classical Models of Recommendation Systems- A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063125)|M. M. ul Haque; B. Kotaiah|10.1109/SCEECS57921.2023.10063125|Collaborative Filtering;Clustering;Content-Based Filtering;Hybrid Filtering;Ontology;Recommender systems;Sequential pattern mining;Computer science;Electric potential;Computational modeling;Collaboration;Information retrieval;Recommender systems;Videos|
|[ANN based Solar MPPT for BLDC Motor load using Bidirectional Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061821)|P. Mandre; B. Somanna; S. Gupta; S. Nema|10.1109/SCEECS57921.2023.10061821|ANN-based MPPT;bidirectional converter;battery;BLDC;Maximum power point trackers;System performance;Voltage;Artificial neural networks;Switches;Electric vehicles;Control systems|
|[Design and implementation of a Voice Controlled chessboard using an X-Y Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063112)|H. S. Panuganti; A. Shaik; S. M. Padmaja; G. S. Thota; P. V. Saranya; J. R. Balaji; T. Poolla|10.1109/SCEECS57921.2023.10063112|Voice recognition;automated chess board;XY Plotter Mechanism;Computer science;Microcontrollers;Prototypes;Games;Speech recognition;Software;Hardware|
|[Review on BIST architectures of DRAMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063015)|M. Lukka; S. Nakhate; S. G. Dastidar|10.1109/SCEECS57921.2023.10063015|Built-in-self-test;test;high bandwidth memory (HBM);channel-based DRAM;DRAM chips;VRT;March test;retention faults;BIST;Computer science;Temperature;Fault detection;Microprocessors;Random access memory;Computer architecture;Bandwidth|
|[A review on direct torque control strategies in induction motor drives for electric vehicle applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063141)|S. Sharma; B. Singh; A. Datar|10.1109/SCEECS57921.2023.10063141|Electric vehicle (EV);Space vector modulation (SVM);Artificial neural network (ANN);Internal combustion engine (ICE);Direct torque control (DTC);Industries;Torque;Sensitivity;Switching frequency;Torque control;Transportation;Modulation|
|[Bulk-driven inverter configuration and its application for implementing ring oscillator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063005)|M. Javed; R. Gupta; S. Sharma|10.1109/SCEECS57921.2023.10063005|bulk-driven;bulk-driven FGMOS;bulk-driven QFGMOS;inverters;ring oscillators;Ring oscillators;MOSFET;Logic gates;SPICE;CMOS technology;Inverters;Threshold voltage|
|[Smart Attendance System Using Biometric and GPS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062969)|M. D. Vinay; M. H. Kumar; B. Hemanth; D. Singh Tomar|10.1109/SCEECS57921.2023.10062969|Student Attendance;QR code;Fingerprint;GPS;Android;Computer science;Costs;Aggregates;QR codes;Fingerprint recognition;Software;Global Positioning System|
|[Enhanced low dimensional MOSFETs with variation of high K dielectric materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062976)|A. P. Singh; R. K. Baghel; S. Tirkey|10.1109/SCEECS57921.2023.10062976|ON Current (ION);OFF Current (IOFF);Short Channel effects (SECs);Drain induced barrier lowering (DBIL);Subthreshold slope (SS);Performance evaluation;MOSFET;Simulation;Switches;Silicon nitride;Logic gates;Ions|
|[Quantile Regression Comprehensive in Machine Learning: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063026)|V. K. Patidar; R. Wadhvani; S. Shukla; M. Gupta; M. Gyanchandani|10.1109/SCEECS57921.2023.10063026|Quantile regression;Linear regression;Heteroscedasticity;Time series data;Risk evaluation;Computational modeling;Biological system modeling;Time series analysis;Linear regression;Tail;Machine learning;Data models|
|[A Review on Identification of Number Plate and Wrong Way Vehicles Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063054)|N. Shelke; S. Jadhav; M. Doifode; Y. Umate; R. Patil; N. Harinkhede|10.1109/SCEECS57921.2023.10063054|Vehicle number plate detection;Computer vision;Image processing;Vehicle number extraction;Roads;Object detection;Rail transportation;Real-time systems;Automobiles;Jamming;License plate recognition|
|[Design Approaches and Performance Analysis of Electric Vehicle using MATLAB/Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063009)|D. K. Kohar; A. K. Pati; S. Nanda|10.1109/SCEECS57921.2023.10063009|Electric Vehicle;MATLAB/ SIMULINK;Drive Cycle;State Of Charge (SOC);Regenerative Braking System (RBS);Battery Management System (BMS);Performance evaluation;Industries;Analytical models;Software packages;Electric vehicles;Mathematical models;Performance analysis|
|[Real Time Door Unlocking System using Facial Biometrics based on IoT and Python](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063142)|M. Y. S. Krishna; A. Arya; S. Ansari; S. Awasya; J. Sushakar; N. Uikey|10.1109/SCEECS57921.2023.10063142|Internet of Things (IoT);artificial intelligence (A.I);facial recognition;local binary pattern histogram (LBPH);security;smart home;Training;Histograms;Three-dimensional displays;Face recognition;Cameras;Real-time systems;Internet of Things|
|[Comparative Analysis of Emitter and Collector Coupling for Synchronization of Chaotic Colpitts Oscillators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063024)|M. Sharma; B. Saxena; S. Shandilya|10.1109/SCEECS57921.2023.10063024|Linear expressions;Chaotic Colpitts;oscillators;MATLAB;Couplings;Computer science;Correlation;Measurement uncertainty;Generators;Synchronization;Surges|
|[Sub-10-nm Tunnel Field-Effect Transistor with Schottky Drain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062994)|Y. Liu; Y. Wang; C. Shan; L. -Y. Ou-Yang; S. -R. Wang; P. -T. Ruan; G. -J. Xu|10.1109/SCEECS57921.2023.10062994|Schottky;SD-TFET;silicide;TFET scaling;Integrated circuits;Computer science;TFETs;Metals;Switches;Energy efficiency;Transistors|
|[Deep Learning Approaches for Plant Disease Detection: A Comparative Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063036)|M. Agarwal; A. Kotecha; A. Deolalikar; R. Kalia; R. K. Yadav; A. Thomas|10.1109/SCEECS57921.2023.10063036|Convolutional Neural Networks (CNN);Deep Learning;Disease Detection & Classification;Industries;Deep learning;Plant diseases;Image recognition;Image color analysis;Databases;Biological system modeling|
|[A Comprehensive Analysis of Interaction Based Segmentation Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063111)|G. S. Tomar; H. K. Soni|10.1109/SCEECS57921.2023.10063111|image processing;segmentation;clustering;convolutional neural network;FPR;TPR;IOU;MIoU and F score;Deep learning;Computer science;Image segmentation;Satellites;Semantic segmentation;Image edge detection;Object detection|
|[A Survey on AR-Based Digitization for Smart Education System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061816)|K. Sudharson; R. R. Arvindan; M. Balashunmugham|10.1109/SCEECS57921.2023.10061816|Smart Education;Augmented Reality;Digitization;3-Dimensional Object;Interactive Learning;Computer science;Psychology;Educational technology;Augmented reality|
|[A Survey on Reader's Society: New Social Network of Book Swapping Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063065)|S. K; A. P. H; S. S|10.1109/SCEECS57921.2023.10063065|Collaborative Learning;Book Swapping;loT;Recommendation System;Learning Platform;Computer science;Costs;Social networking (online);Sociology;Collaboration;Libraries;User experience|
|[Recommendation System for Crops Integrating with Specific soil parameters by Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063029)|N. Bhatt; S. Varma|10.1109/SCEECS57921.2023.10063029|Crop Recommendation;Decision Tree;Naive Bayes;SVM;Logistic Regression;RF;XGBoost;Support vector machines;Radio frequency;Productivity;Crops;Soil;Agriculture;Data mining|
|[Agriculture Advisory Using ML in Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063100)|B. Potbhare; S. Khandate; T. Ingole; S. Manohare; N. Kanoje; R. Talekar|10.1109/SCEECS57921.2023.10063100|Agro Advisory;Precision Agriculture;Wireless Internet;Cloud computing;Irrigation;Processor scheduling;Sociology;Crops;Production;Real-time systems|
|[Evaluation of Deep Learning Methods and Face Detection Framework with Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063030)|G. V. N; V. Jeyakumar; A. S. R|10.1109/SCEECS57921.2023.10063030|Artificial intelligence;Deep learning;Face detection;Features extraction;Machine learning;Deep learning;Error analysis;Computational modeling;Learning (artificial intelligence);Hafnium;Feature extraction;Real-time systems|
|[Analysis and Methodology to Reduce Aggregate Technical & Commercial Losses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063091)|R. Kumar; A. Rathore; A. Kumar; D. Kumar|10.1109/SCEECS57921.2023.10063091|aggregate technical Losses;commercial losses;power distribution system;SBPDCL;DISCOM;Economics;Computer science;Aggregates;Power distribution;Companies;Reliability|
|[Security and Privacy in Wireless Sensor Networks Using Intrusion Detection Models to Detect DDOS and Drdos Attacks: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063057)|K. Chaitanya; S. Narayanan|10.1109/SCEECS57921.2023.10063057|Wireless Sensor Networks;Digitalization;Distributed Denial of Service;Distributed Reflective Denial of Service;Attack detection;Cyber Attack;Intrusion Detection;Wireless communication;Wireless sensor networks;Analytical models;Computational modeling;Refining;Traffic control;Denial-of-service attack|
|[A Secure Electronic Medical Record Authorization System in Clouds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061820)|H. Zade; R. Khedekar; A. Gwalani; C. Borkar; A. Bokade; V. Katare|10.1109/SCEECS57921.2023.10061820|Digital fitness facts (ERRs);Medical;healthcare;cloud;authentication;Authorization;Cloud computing;Government;Prototypes;Medical services;Safety;History|
|[Deepfake Face Detection Using Deep InceptionNet Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063128)|P. Theerthagiri; G. b. Nagaladinne|10.1109/SCEECS57921.2023.10063128|Deepfake;Inception net;CNN(Convolutional Neural Network);Vision Transformers;Measurement;Deep learning;Computer science;Deepfakes;Computer architecture;Transformers;Recording|
|[A Comprehensive Review of the Occlusion Identification Based Gait Recognition Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063052)|B. Kumari; P. J. Bharti|10.1109/SCEECS57921.2023.10063052|Gait Recognition Mechanism;Occluded Gait Patterns;Multi-Person Gait (MPG);Curve Fitting and Skeletal;Legged locomotion;Face recognition;Biological system modeling;Fingerprint recognition;Security;Curve fitting;Splines (mathematics)|
|[Med Assistant Android Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063061)|H. Turkar; S. Tiwari; A. Mate; A. Naranje; J. Jaulkar; K. Hadke|10.1109/SCEECS57921.2023.10063061|MVC;MVP;MVVM;Android;Web Architecture;Computer science;Wearable computers;Cellular phones;Avatars;Computer architecture;Mobile applications;Internet|
|[Machine Learning Based Intrusion Detection Scheme to Detect Replay Attacks in Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063021)|R. Sriranjani; B. K. M; P. A. K; M. Saleem; N. Hemavathi; A. Parvathy|10.1109/SCEECS57921.2023.10063021|Smart grid;Internet of Things;Cyber Security;Replay attack;Machine learning;Support vector machines;Machine learning algorithms;Computational modeling;Zigbee;Intrusion detection;Mathematical models;Smart grids|
|[On Banking Site Selection Problem Utilizing Novel Picture Fuzzy Discriminant Measure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063117)|E. Sharma; H. Dhumras; R. K. Bajaj|10.1109/SCEECS57921.2023.10063117|Picture fuzzy set;Discriminant Measure;Multi-criteria decision-making;Computer science;Decision making;Electric variables measurement;Banking|
|[A Novel Optical Encryption Strategy using Virtual Digital Holography and Fractional Fourier Based Key Hiding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063101)|R. Tiwari; M. Yadav|10.1109/SCEECS57921.2023.10063101|Simulation Optics;Encryption;Holography;Optical Phase;Fractional Fourier Transform;Fourier transforms;Computational modeling;Holography;Optical imaging;Holographic optical components;Adaptive optics;Encryption|
|[Novel Deep Neural Network for Suspicious Activity Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063130)|A. M. Bhugul; V. S. Gulhane|10.1109/SCEECS57921.2023.10063130|Neural Network;time complexity;YOLO v3;YOLO v4;YOLO v5;Suspicious Activity;Deep learning;Weapons;Heuristic algorithms;Terrorism;Surveillance;Neural networks;Systems architecture|
|[Segregation of Solid Municipal Waste Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063126)|A. Pandey; B. Khator; D. Agrawal; D. Halim; J. S. Kumar|10.1109/SCEECS57921.2023.10063126|CNN;Neural Networks;Epochs;ReLU;Machine Learning;Waste Segregation;Classification;DenseNet;Deep Learning;Training;Waste materials;Machine learning algorithms;Computational modeling;Stochastic processes;Solids;Data models|
|[An Overview of the Regenerative Braking Technique and Energy Storage Systems in Electric, Hybrid, and Plug-In Hybrid Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063062)|A. Raghuwanshi; A. Ojha|10.1109/SCEECS57921.2023.10063062|Regenerative braking;Powertrain;vehicle dynamics;Braking force distribution;Induction motors;Brushless DC motors;Force;Dynamics;Permanent magnet motors;Batteries;Topology|
|[Mirchi Crop Yield Prediction based on Soil and Environmental Characteristics using modified RNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063004)|C. Kosaraju; C. Nama; Y. Deepthi; C. Ramanjamma; P. Chandrakala|10.1109/SCEECS57921.2023.10063004|Crop Yield Prediction;NPK sensor;RNN;Recurrent Neural Network (RNN);Soil Characteristics;Chilli crop;Mirchi crop;Economic indicators;Wires;Software algorithms;Crops;Soil;Predictive models;Agriculture|
|[Using The Cooja Simulator, Analysing The Routing Protocol (RPL) For Low Power And Lossy Networks In IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061823)|A. Behal; J. K. Sandhu; G. Gupta|10.1109/SCEECS57921.2023.10061823|DIO(DODAG Information Object);DODAG(Destination Oriented Directed Acyclic Graph);RPL(Routing Protocol for LLN);LLN(low power and lossy networks);IOT (Internet of Things);Temperature measurement;Computer science;Operating systems;Routing;Routing protocols;Internet of Things|
|[EV Battery Charging using DAB DC-DC Converter with EPS and DPS modulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063090)|M. Mishra; I. Sarkar|10.1109/SCEECS57921.2023.10063090|Dual active bridge (DAB);extended phase shift (EPS);dual phase shift (DPS);constant current (CC);constant voltage (CV);Phase modulation;Simulation;Bridge circuits;DC-DC power converters;Zero voltage switching;Electric vehicle charging;Batteries|
|[Data Hide Security Using Image Object](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063023)|P. Tiwari; T. Sahare; H. Mahajan; I. Kanojiya; D. Talekar; P. Lohe|10.1109/SCEECS57921.2023.10063023|Cryptography;steganography;clustering;virtual photo;k-means;encryption;Resistance;Steganography;Image coding;Switches;Games;Media;Encryption|
|[Secure-e-Share: Data leakage Detection and Prevention with Secured Cloud Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063119)|A. Jaiswal; V. Purohit; V. Jhawar; Y. Jadhav; K. Borhade|10.1109/SCEECS57921.2023.10063119|Python;MongoDB-Atlas;HTML;CSS;JavaScript;GitHub;Computer science;Cloud computing;Data privacy;Firewalls (computing);Databases;Organizations;Safety|
|[Monitoring Health of IoT Equipped 3-Phase Induction Motor using Interactive Dashboard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061814)|C. Bhandarkar; S. Deshmukh; K. Yeole; R. Dalvi; S. Kapse; P. Shete|10.1109/SCEECS57921.2023.10061814|Arduino uno;3-Phase Induction AC motors;Real-time;Interactive Dashboard;Industries;Temperature sensors;Temperature measurement;Computer science;Induction motors;Electric breakdown;Real-time systems|
|[HoneyTrack: An improved honeypot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063105)|A. Auti; S. Pagar; V. Mishra; J. Makwana; S. Borade|10.1109/SCEECS57921.2023.10063105|Honeypot;Data Visualization;IntrusionDetection System;Network Security;Firewall;Computer hacking;Data visualization;Passwords;Planning;Servers;Ransomware;Security|
|[Predicting the Prices of the Used Cars using Machine Learning for Resale](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063133)|B. Hemendiran; P. N. Renjith|10.1109/SCEECS57921.2023.10063133|Machine learning;forecast(predict);Random Forest;Decision Tree;Extra Tree Regressor;Bagging Regressor;Accuracy;Machine learning algorithms;Pricing;Predictive models;Automobiles;Reliability;Task analysis;Regression tree analysis|
|[Bending Stability Analysis of Flexible Polymer based Temperature Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061825)|K. Tripathy; M. Bhattacharjee|10.1109/SCEECS57921.2023.10061825|Temperature sensor;Flexible and printed sensor;PEDOT;PSS;Bending deformation;Temperature sensors;Resistance;Encapsulation;Sensitivity;Deformation;Bending;Robot sensing systems|
|[Superpixel based Image Colorization with Automated Reference Image Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061822)|V. U; G. V|10.1109/SCEECS57921.2023.10061822|Color by example;Superpixel;Texture descriptors;Deep learning;Measurement;Image quality;Histograms;Image color analysis;Receivers;Manuals|
|[Deep Learning Technique to generate lip-sync for live 2-D Animation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063097)|A. Soni; J. Deshmukh; A. Shende; R. Gawande; P. Agatkar; K. Chilbule|10.1109/SCEECS57921.2023.10063097|LSTM;2D - Animation;deep learning;lips synchronized;Deep learning;Social networking (online);Lips;Pipelines;Two dimensional displays;Education;Entertainment industry|
|[Music Streaming Using Blockchain - Blockics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062986)|A. K. Soni; A. Padiya; B. Patel; S. H. Raza; K. Malviya|10.1109/SCEECS57921.2023.10062986|Blockchain;NFTs;Music Streaming;Smart Contract;Virtual currency;artists;Computer science;Online banking;Government;Smart contracts;Medical services;Bitcoin;Streaming media|
|[Reconfiguration of the Distribution Network using a Whale Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062989)|A. Solanki; S. Mehroliya; A. Arya; S. Tomar; K. Nikum|10.1109/SCEECS57921.2023.10062989|Power Distribution System;Reconfiguration;Whale Optimization;Network topology;Heuristic algorithms;Power distribution;Voltage;Distribution networks;Whale optimization algorithms;Power system reliability|
|[Improved Soft Actor-Critic: Reducing Bias and Estimation Error for Fast Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063058)|M. Shil; G. N. Pillai; M. K. Gupta|10.1109/SCEECS57921.2023.10063058|Deep reinforcement learning;soft actor critic;actor critic;dual actor network;delayed updates;Training;Computer science;Estimation error;Costs;Simulation;Benchmark testing;Mathematical models|
|[Priority Based Task Scheduling in Cloud Integrated WOBAN Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063047)|M. Verma; U. R. Bhatt; R. Upadhyay; V. Bhat|10.1109/SCEECS57921.2023.10063047|WOBAN;priorities;scheduling;CIW;cloudlets;Computer science;Schedules;Processor scheduling;Wireless networks;Optical fiber networks;Scheduling;Broadband communication|
|[Driver Drowsiness Detection and Alerting Model for Minimizing Road Accidents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063113)|R. Sathya; D. S. S. Harsha; M. G. Krishna; G. P. S. Reddy; P. S. Arhith|10.1109/SCEECS57921.2023.10063113|Image Processing;Drowsiness Detection;Arduino;Eye Aspect Ratio (EAR);Electrodes;Vibrations;Sleep;Software algorithms;Ear;Vibration measurement;Software|
|[A Brief Review on Triple Active Bridge DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063031)|S. Mukherjee; I. Sarkar|10.1109/SCEECS57921.2023.10063031|Isolated DC-DC Converter;Triple Active Bridge;Duty Cycle Control;Decoupled Power Flow Control;Computer science;Windings;Bridge circuits;DC-DC power converters;Zero voltage switching;Frequency conversion;High-frequency transformers|
|[A Smart IoT System for Accident Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062980)|R. Sathya; B. A. Reddy; P. Jaswanth; C. S. Ram; T. S. Swami|10.1109/SCEECS57921.2023.10062980|nan;Computer science;Transmitters;Contacts;Organizations;Bicycles;Cameras;Safety|
|[Covid Wave Prediction using SARIMA Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062979)|R. Sathya; L. P. Steve; N. Narayan; A. Upadhye; M. Aravindharaj|10.1109/SCEECS57921.2023.10062979|Covid '19 Prediction;SARIMA;Unsupervised;COVID-19;Machine learning algorithms;Hospitals;Government;Predictive models;Prediction algorithms;Classification algorithms|
|[Car Hiring System using Web Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063118)|R. Sathya; G. S. Reddy; G. R. Teja; V. P. S. S. Iyengar; P. Kumar|10.1109/SCEECS57921.2023.10063118|Car Renting;Web development;user;Car Bookings;Technological innovation;Costs;Databases;Software;Automobiles;Stress;Contracts|
|[Pothole Detection Using YOLOv3 Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063116)|R. Sathya; B. Saleena; B. Prakash|10.1109/SCEECS57921.2023.10063116|You Only Look Once (YOLO);Convolutional Neural Network (CNN);real time object detection;pothole;accuracy;mAP;Heart;Analytical models;Shape;Roads;Computational modeling;Neural networks;Object detection|
|[An Analytical Study of Various Pothole Detection and Prevention Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063088)|R. Sathya; B. Saleena; N. N. Anirudh|10.1109/SCEECS57921.2023.10063088|Vision method;Vibration method;Stereo vision;Image processing;Pothole detection;Road accidents;Storms;Local government;Roads;Sociology;Sensors;Safety|
|[A TKF Based Adaptive Filtering for ECG Signal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063022)|P. Talwar; K. Cecil|10.1109/SCEECS57921.2023.10063022|Three-iteration of Kalman adaptive Filter (TKF);Customized Otsu Thresholding;Empirical Mode Decomposition;Electrocardiogram;Least Mean Square;SNR;Filtering;Noise reduction;Adaptive filters;Switches;Electrocardiography;Kalman filters;Noise measurement|
|[A Novel Miniaturized Triple-Band Monopole Antenna for 2.4/5.2 GHz WLAN and 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063034)|A. B. N. Surendra; I. Srihari; P. Akash; B. Yaswanth; M. V. Pullarao; K. d. Ayinala|10.1109/SCEECS57921.2023.10063034|Compact;Triple-band;Meander slot;Monopole antenna;WLAN;5G communication;Wireless communication;Wireless LAN;Slot antennas;5G mobile communication;Resonant frequency;Microstrip antennas;Bandwidth|
|[Intelligent transformation of 6G network by incorporating Expert Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063099)|T. Chauhan; R. P. N.|10.1109/SCEECS57921.2023.10063099|Expert Systems;6G;Performance Analysis;ADHOC;Mobile Network;6G mobile communication;5G mobile communication;Wireless networks;Stability criteria;Speech recognition;User experience;Resource management|
|[Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062985)|S. B; R. Priyadarshini; S. Bhattacharjee; S. K. S; P. U; K. V. Gangadharan; S. K. Ghosh|10.1109/SCEECS57921.2023.10062985|Water salinity prediction;Remote sensing;Spatio-temporal analysis;Machine Learning;Landsat 8;SMAP;Salinity (geophysical);Computational modeling;Ultraviolet sources;Sociology;Sea measurements;Predictive models;Statistics|
|[Data Driven UX/UI design for reproductive health tracker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063134)|A. Upadhyay; M. Shekhawat; R. Manhas|10.1109/SCEECS57921.2023.10063134|UX/UI;Human-computer Interaction (HCI);data analysis;website;persona;design;Human computer interaction;Computer science;Data analysis;Target tracking;Image color analysis;Prototypes;Market research|
|[Incubation of a Metaheuristic Searching Approach for Intrusion Detection into A New Constrained Crow Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063120)|K. Rai; M. Kamble|10.1109/SCEECS57921.2023.10063120|Heuristic algorithms;Swarm Intelligence (SI);Meta-Heuristic;Crow Search Algorithm (CSA);ANFIS;Machine learning (ML);deep Learning (DL);Computer science;Heuristic algorithms;Metaheuristics;Intrusion detection;Search problems;Feature extraction;Classification algorithms|
|[Real time seatbelt detection using YOLO deep learning model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063114)|A. Upadhyay; B. Sutrave; A. Singh|10.1109/SCEECS57921.2023.10063114|Seatbelt detection;YOLO;Supervised Learning;Deep Learning;Training;Visualization;Belts;Feature extraction;Real-time systems;Classification algorithms;Safety|
|[Acoustic Power Distribution Analysis in Different Human Tissues for Bioimplant Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062978)|A. Siddiqui; S. Das; M. Bhattacharjee|10.1109/SCEECS57921.2023.10062978|bio-implant;piezoelectric transducer;power intensity;acoustic pressure;acoustic wave;Wrist;Transmitters;Power distribution;Phantoms;Voltage;Muscles;Bones|
|[Ant Species Recognition using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062974)|A. Upadhyay; V. Prajapati; A. Shinde|10.1109/SCEECS57921.2023.10062974|Ant species;Convolution Neural Network;Insect detection;Classification;Image processing;Training;Convolution;Computational modeling;Biological system modeling;Pipelines;Neural networks;Object detection|
|[A Review on Strategies for Hybrid DC/AC Microgrid Power Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063106)|P. Garhe; S. C. Gupta; G. K. Yadav|10.1109/SCEECS57921.2023.10063106|hybrid DC/AC microgrid;power management;interlinking converter (ILC);renewable energy sources;standalone operation;grid-connected operation;distributed energy resources(DERs);Power system management;Microgrids;Power system stability;Hybrid power systems;Regulation;Generators;Topology|
|[A Study on Creation of Industry 5.0: New Innovations using big data through artificial intelligence, Internet of Things and next-origination technology policy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063069)|M. Yadav; A. Vardhan; A. S. Chauhan; S. Saini|10.1109/SCEECS57921.2023.10063069|Industrial Revolution;Big data;Artificial Intelligence (AI);IoT;Industry5.0;Industries;Technological innovation;Service robots;Smart healthcare;Production;Solids;Fourth Industrial Revolution|
|[Gait Assessment using Optimized Machine Learning and Feature Selection Algorithm for identifies Parkinson's Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062964)|N. Singh; P. Tripathi|10.1109/SCEECS57921.2023.10062964|Parkinson's disease;sensor;Motor disorder;gait;Machine Learning;features selection;optimization;Legged locomotion;Support vector machines;Machine learning algorithms;Parkinson's disease;Predictive models;Prediction algorithms;Feature extraction|
|[VI Based Hemodynamic Monitor Using Labview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063135)|R. D. Manoj; G. Bagyalakshmi; V. Gomathi; V. Valarmathi|10.1109/SCEECS57921.2023.10063135|virtual instrumentation;systolic parameters;electrocardiograph;Photoplethysmogram;arrhythmia;LabVIEW;Temperature measurement;Temperature sensors;Temperature distribution;Arrhythmia;Instruments;Prototypes;Electrocardiography|
|[Aspect Based Neural Recommender Using Adaptive Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063025)|A. Bhojwani; V. Jolly; S. Goel; A. K. M|10.1109/SCEECS57921.2023.10063025|Aspect-Based Recommendation System;aspects;Neural co-attention;amazon datasets;web-scraping;Computer science;Adaptation models;Adaptive systems;Computational modeling;Information processing;Predictive models;Motion pictures|
|[A PV-Powered Microcontroller-Based Agricultural Robot Utilizing GSM Technology for Crop Harvesting and Plant Watering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062995)|A. K. Nuhel; M. M. Sazid; D. Paul; E. Hasan; P. H. Roy; F. P. Sinojiya|10.1109/SCEECS57921.2023.10062995|GSM;sonar sensor;microcontroller;robot;GSM;Costs;Sonar applications;Spraying;Robot sensing systems;Turning;Solar panels|
|[Connection of Big Data Analytics & Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063008)|R. Soni; A. Baghel; S. Paliya; R. Mamtani; L. Gupta|10.1109/SCEECS57921.2023.10063008|Big data;artificial intelligence;design considerations;parallel processing;image processing;Computer science;Data centers;Learning (artificial intelligence);Big Data;Parallel processing;Market research;Hardware|
|[REVIEW OF BIG DATA ANALYTICS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063140)|R. Soni; A. Baghel; S. Paliya; L. Gupta|10.1109/SCEECS57921.2023.10063140|decision-making;social networks;analytical techniques;transactions;mechanisms;Industries;Computer science;Supply chain management;Social networking (online);Decision making;Medical services;Big Data|
|[Apply machine learning and image processing to detect plant diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063139)|S. Chourasiya; A. A. Gadpale; P. S. Thethi; P. D. Nagdeve; P. Wakode; M. Indorkar|10.1109/SCEECS57921.2023.10063139|nan;Productivity;Wireless sensor networks;Plant diseases;Crops;Sensors;Internet of Things;Reliability|
|[A System for Medical Record Using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063042)|M. Mulchandani; P. Samrit; S. Wakode; P. Tahlyani; I. Ansari; M. Sheikh|10.1109/SCEECS57921.2023.10063042|nan;Industries;Hospitals;Instruments;Focusing;Encoding;Blockchains;Information management|
|[Steady-State Analysis of Dual Active Bridge Converter with Single Phase Shift and Dual Phase Shift Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062987)|D. C. Pandey; P. K. Behera; M. Pattnaik|10.1109/SCEECS57921.2023.10062987|Dual active bridge converter;Dual-phase shift;Isolated converter;Single-phase shift modulation;Phase modulation;Simulation;Switching frequency;Soft switching;Modulation;Bridge circuits;Bidirectional power flow|
|[A Comparative Analysis of Two-phase and Three-phase Interleaved Bidirectional DC-DC Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063095)|R. Kumar; P. K. Behera; M. Pattnaik|10.1109/SCEECS57921.2023.10063095|Battery energy storage;Bidirectional DC-DC converter;Current sharing;Interleaved Converter;Vehicle-to-grid;Power system measurements;Bidirectional power flow;DC-DC power converters;Voltage;Power markets;Topology|
|[A Comprehensive survey on Real Time human pose estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063000)|A. P. Datir; S. S. Funde; N. T. Bhore; S. B. Gawande; P. Dhade; P. Nehete|10.1109/SCEECS57921.2023.10063000|Smart Healthcare;Human Pose Estimation;deep neural network;2D and 3D pose;Evaluation metric;Dataset;Single and multi-person pose estimation;openpose;depth images;Deep-pose;action recognition;superpixels;Part Affinity Fields;Bone-Based Pose Decomposition;Bernoulli heatmap;Deep learning;Three-dimensional displays;Pose estimation;Graphics processing units;Real-time systems;Skeleton;Security|
|[Music Genre Classification Based on Song Titles with Long Short-Term Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063028)|O. Sahin|10.1109/SCEECS57921.2023.10063028|Deep Learning;Music Genre;LSTM;Dropout;Song Titles;Deep learning;Computer science;Task analysis;Long short term memory|
|[A Planar Dual-Band 4-Element MIMO Configuration for WLAN and Sub-6 GHz 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063032)|I. Srihari; A. B. N. Surendra; P. Akash; B. Yaswanth; M. v. Pullarao; K. d. Ayinala|10.1109/SCEECS57921.2023.10063032|Dual-band;Isolation;Multiple-Input Multiple-Output (MIMO);Monopole antenna;WLAN;5G communication;Wireless communication;Wireless LAN;Mutual coupling;Slot antennas;5G mobile communication;Dual band;Bandwidth|
|[Job Applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063010)|A. Pimpalkar; A. Lalwani; R. Chaudhari; M. Inshall; M. Dalwani; T. Saluja|10.1109/SCEECS57921.2023.10063010|Resume parser;Unstructured written language;Text Mining;Natural Language Processing;Machine Learning;Machine learning algorithms;Semantic search;Resumes;Machine learning;Syntactics;Natural language processing;Software|
|[Building Intelligent Embodied AI Agents for Asking Clarifying Questions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063017)|D. Lakhtaria; R. Chhabra; R. Taparia; A. K. M|10.1109/SCEECS57921.2023.10063017|nan;Computer science;Knowledge based systems;Buildings;Virtual environments;Games;Intelligent agents;Task analysis|
|[Novel Four-Stage Comparator with High Speed and Low Kickback Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063006)|S. N. Shende; A. Singh Yadav|10.1109/SCEECS57921.2023.10063006|Comparator;slew rate;regeneration;low kick-back;Computer science;Costs;Simulation;Energy resolution;Voltage;Noise cancellation;Preamplifiers|
|[Hyperspectral Image Classification using Machine Learning Techniques - A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062963)|S. Pattem; S. Thatavarti|10.1109/SCEECS57921.2023.10062963|Hyperspectral Images;Image Classification;Deep Learning Techniques;Image Transformations;Deep learning;Training;Three-dimensional displays;Transforms;Feature extraction;Classification algorithms;Optimization|
|[Security Analysis of Open Source SDN (ODL and ONOS) Controllers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063108)|P. Ohri; S. G. Neogi; S. K. Muttoo|10.1109/SCEECS57921.2023.10063108|ODL;ONOS;Defense4All;Wireshark;Hping3;Computer science;Operating systems;Computer architecture;Denial-of-service attack;Security;Software defined networking|
|[Comparison of different levels of cascaded H bridge multilevel inverter using PSPWM technique for EV applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062982)|V. Kumar; P. Kumari; N. Kumar|10.1109/SCEECS57921.2023.10062982|Multi Level Inverter (MLI);pulse width modulation (PWM) technique;Total Harmonic Distortion (THD);fast Fourier transform (FFT) Analysis;Power system measurements;Computational modeling;Bridge circuits;Switches;Pulse width modulation;Multilevel inverters;Nonhomogeneous media|
|[Optical Performance Enhancement of GaAsBi/P3HT Hybrid Solar CellIncorporatingMetallic Nanoparticlesinthe Absorber Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063103)|D. Roy; D. P. Samajdar; A. Biswas|10.1109/SCEECS57921.2023.10063103|nan;Nanoparticles;Electrodes;Gold;Photovoltaic cells;Indium tin oxide;Lighting;Geometrical optics|
|[Automatic Classification of Multi-Class Skin Lesions Dermoscopy Images Using an Efficient Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062981)|A. Kumar; A. Vishwakarma; V. Bajaj|10.1109/SCEECS57921.2023.10062981|Dermoscopy image;data augmentation;median filter;pre-trained network;skin lesions;Computer science;Lung;Manuals;Skin;Colon;Convolutional neural networks;Lesions|
|[A Comparative Analysis of the Feature Selection Process Using Deep Learning Methods for Arrhythmia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063070)|S. Janbhasha; S. N. Bhavanam|10.1109/SCEECS57921.2023.10063070|Arrhythmias;ECG;Feature selection;Deep learning;Classification;Accuracy;Heart;Deep learning;Sensitivity;Heart beat;Arrhythmia;Computational modeling;Neural networks|
|[Optical Performance Analysis of GaAs Thin Film Solar Cells with different Anti-Reflection Coatings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063124)|S. Singh; D. P. Samajdar; K. Dutta|10.1109/SCEECS57921.2023.10063124|GaAs;Absorptance;Optical JSC;COMSOL Multiphysics;Antireflection Coatings;Optical losses;II-VI semiconductor materials;Absorption;Photovoltaic cells;Optical recording;Gallium arsenide;Zinc oxide|
|[Empirical Analysis of Income Prediction Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062992)|J. Vemulapati; A. Bayyana; S. H. Bathula; S. Tokala; K. Hajarathaiah; M. K. Enduri|10.1109/SCEECS57921.2023.10062992|Deep Learning;Long Short-Term Memory;Stacked LSTM;Bi-Directional LSTM;Convolutional LSTM;Deep learning;Computer science;Costs;Computational modeling;Memory architecture;Insurance;Data models|
|[Digital Image Watermarking and Its Applications: A Detailed Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063033)|S. Kashifa; S. Tangeda; U. K. Sree; V. M. Manikandan|10.1109/SCEECS57921.2023.10063033|Digital watermarking;Copyright Protection;Watermark extraction;Data authentication;Computer science;Electric potential;Digital images;Authentication;Information security;Watermarking;Copyright protection|
|[Determination of Battery Life Using State of Health Data and Linear Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063037)|A. Banthia; S. Paliwal; V. Patle; M. S. Ghole; P. Paliwal|10.1109/SCEECS57921.2023.10063037|state of health;linear regression;battery life;lead acid battery;Performance evaluation;Renewable energy sources;Temperature;Computational modeling;Linear regression;Predictive models;Data models|
|[Fault Detection in Transmission Line Using ANN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063045)|S. Kumari; A. Mishra; A. Singhal; V. Dahiya; M. Gupta; S. K. Gawre|10.1109/SCEECS57921.2023.10063045|Artificial Neural Network;Transmission line;Line-Line fault;Line-Ground fault;Line-Line-Ground fault;Fault Detection;MATLAB;Power transmission lines;Fault detection;Artificial neural networks;Production;Electrical fault detection;Transmission line measurements;Power systems|
|[Modified Boost Converter-Based Speed Control of BLDC Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062973)|S. K. Sahare; D. Suresh|10.1109/SCEECS57921.2023.10062973|BLDC;High gain converter;Boost fed converter;Computer science;Electron traps;Torque;Brushless DC motors;Voltage source inverters;Velocity control;Rotors|
|[A Survey on Diagnosing Pulmonary Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063115)|A. S. Rao; S. Shamitha; S. R. G. Tarun; S. U. Priya; V. R. B. Prasad|10.1109/SCEECS57921.2023.10063115|Chest X-Ray;Computed Tomography Scan;Covid;Lung Cancer;Tuberculosis;Pneumonia;Pandemics;Pulmonary diseases;Sociology;Lung;Lung cancer;Tomography;Task analysis|
|[An Improved Indexing Technique for Tribal Art Retrieval System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061818)|K. Mane; U. Shrawankar|10.1109/SCEECS57921.2023.10061818|Big data;content based Indexing;content based retrieval;machine learning;deep learning;tribal art;tribal painting;image preprocessing;feature extraction;Deep learning;Computer science;Art;Keyword search;Finishing;Indexing|
|[A Comparative Analysis of Low Power FINFET SRAM Cells on Different Technology Node with Variable Number of Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063123)|K. Kishor; L. Gupta|10.1109/SCEECS57921.2023.10063123|FinFET;Read/Write Static Noise Margin (RSNM/WSNM);Short Channel Effects (SCE);delay;CMOS;Power demand;Logic gates;FinFETs;SRAM cells;CMOS technology;Stability analysis;Power dissipation|
|[Reduction in Harmonics at load Side of Dynamic Voltage Restorer at Different Dip Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062960)|S. Patel; A. Rathore; N. P. Patidar|10.1109/SCEECS57921.2023.10062960|Space Vector Pulse Width Modulation;Dynamic Voltage Restorer;Space Transformations;Voltage dip;THD Calculations;Space vector pulse width modulation;Performance evaluation;Total harmonic distortion;Voltage fluctuations;Power quality;Harmonic analysis;Mathematical models|
|[An efficient LGBM based DDoS attack Detection Approach for SD-IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063003)|P. Chauhan; M. Atulkar|10.1109/SCEECS57921.2023.10063003|Software-defined Internet of Things (SD-IoT);distributed denial of service (DDoS);attack detection system;Ryu;Machine Leaning;Training;Support vector machines;Computer science;Computer architecture;Denial-of-service attack;Time measurement;Internet of Things|
|[An Efficient Technique for Improving Trust and Privacy in Blockchain as a Service (BaaS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063014)|S. V. Shinge; U. Shrawankar|10.1109/SCEECS57921.2023.10063014|Trust;Privacy;Blockchain as a Service;Cloud Computing;Blockchain Technology;Computer science;Cloud computing;Privacy;Data privacy;Computational modeling;Data integrity;Consensus algorithm|
|[A Review on Fuel Cell and Different Controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062988)|R. Kumar; S. Patel; N. P. Patidar; S. K. Patel|10.1109/SCEECS57921.2023.10062988|Fuel Cell;PEMFC;Standalone;Fuzzy logic controller;Cogeneration;Electrolyte;Back-up system;Starvation;Buck-Boost Inverter;Distributed Generator;Supercapacitor;PID Controller;Thermal management of electronics;Computer science;Renewable energy sources;Costs;Fuel cells;Converters;Control systems|
|[A Survey on Homomorphic Encryption for Biometrics Template Security Based on Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062968)|P. Chitrapu; H. K. Kalluri|10.1109/SCEECS57921.2023.10062968|Biometrics;Fully Homomorphic Encryption;CKKS Encryption Scheme;Computer science;Biometrics (access control);Computational modeling;Biological system modeling;Face recognition;Authentication;Machine learning|
|[A Hybrid Intelligent Control for DSCC Based Modular STATCOM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062971)|R. Sharma; V. H. Makwana|10.1109/SCEECS57921.2023.10062971|STATCOM;Power Quality;Voltage sag;Modular Multilevel Inverter;Distributed generation;THD;Resistance;Reactive power;Uncertainty;Simulation;Power quality;Automatic voltage control;Robustness|
|[MR Brain 2D image Tumor and Cyst Classification Approach: an Empirical Analogy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063064)|S. K. Panda; R. C. Barik|10.1109/SCEECS57921.2023.10063064|Brain Tumor;Cyst;Classification;Magnetic Resonance Imaging (MRI);Logistics Regression;Support Vector Machine (SVM);k-nearest neighbour (KNN);Random Forests;Decission Tree;Naive Bayes;Training;Support vector machine classification;Imaging;Forestry;Brain modeling;Classification algorithms;Random forests|
|[Data Ensemble Model for Prediction of Oxygen Content in Gas fired Boiler for Efficient Combustion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062991)|K. G. S. Sharma; S. Bhusnur|10.1109/SCEECS57921.2023.10062991|BFG Gas fired Boiler;O2 prediction;ANN-CNN Ensemble Learning;Python PiTorch Framework O2 Prediction;Deep learning;Temperature sensors;Analytical models;Atmospheric modeling;Toxic chemicals;Predictive models;Boilers|
|[Oral Cancer Detection Using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062993)|R. Kumar Yadav; P. Ujjainkar; R. Moriwal|10.1109/SCEECS57921.2023.10062993|Machine Learning;CNN;Oral cancer;ABC;FPSO;Deep learning;Support vector machines;Technological innovation;Transfer learning;Mouth;Feature extraction;Convolutional neural networks|
|[Smart Car Parking System using Arduino](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063121)|M. Dixit; A. Priya; G. Haldiya; A. Priya; B. Kumar|10.1109/SCEECS57921.2023.10063121|LED;IR Sensors;Arduino Atmega328p;Servo Motor;I2CModule;Space vehicles;Logic gates;Aerospace electronics;Turning;Liquid crystal displays;Sensor systems;Automobiles|
|[Various Channel Coding Schemes for 5G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062998)|A. Muskan; T. Raj; T. Nisar; M. Abbas; S. Naz; U. Tiwari|10.1109/SCEECS57921.2023.10062998|Coding Schemes;Polar;LDPC;Turbo;5G telecommunication;Wireless communication;Codes;Systematics;Convolution;Simulation;Turbo codes;Parity check codes|
|[Energy Meter and Power Theft Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062967)|R. Andore; S. S. Kulkarni; A. G. Thosar|10.1109/SCEECS57921.2023.10062967|Power theft;Wi-Fi module;Energy Meter;Monitoring;Meters;Computer science;Databases;Monitoring;Wireless fidelity|
|[Facial Image Super-Resolution with CNN, “A Review”](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063110)|A. Sharma; B. P. Srivastava; P. N. Shankar|10.1109/SCEECS57921.2023.10063110|nan;Resistance;Performance evaluation;Deep learning;Superresolution;Neural networks;Information filters;Convolutional neural networks|
|[A Review on Heartbeat Classification for Arrhythmia Detection Using ECG signal Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063143)|P. K. Tyagi; N. Rathore; D. Agrawal|10.1109/SCEECS57921.2023.10063143|ECG Signal;Arrhythmia;Machine learning;RR Interval;classification;Training;Arrhythmia;Wearable computers;Noise reduction;Cardiac disease;Electrocardiography;Real-time systems|
|[Semi-automatic Analysis of cells in honeybee comb images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063122)|N. Rathore; P. K. Tyagi; D. Agrawal|10.1109/SCEECS57921.2023.10063122|Honeybee;Image Processing;CLAHE;Circle Hough transform;Computer science;Visualization;Image segmentation;Image edge detection;Digital images;Estimation;Transforms|
|[A Review on Graphene Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062965)|A. P. Singh; P. N. Shankar; R. Baghel; S. Tirkey|10.1109/SCEECS57921.2023.10062965|Graphene Transistor (GFET);Graphene Nano-Ribbon (GNR);Mono-vacancy (MV);Stone Wale (SW) defect;Zigzag-Armchair-Zigzag (Z-A-Z) Graphene Structure;Fabrication;Electric potential;Graphene;Field effect transistors;Voltage;Conductivity;Silicon|
|[Analysis of Linux Server Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062997)|B. Sachdeva; A. Kushwaha; A. Kumar; A. Tiwari|10.1109/SCEECS57921.2023.10062997|Linux;Performance Tuning;Enterprise Linux;Networking;Linux;Memory management;Standardization;Acoustic measurements;Acoustics;Servers;Kernel|
|[Design A Voice App Controlled IoT Based Water Tank System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063007)|M. Madhuri; B. Sridhar; K. Anusha; K. Siddardha|10.1109/SCEECS57921.2023.10063007|NodeMCU ESP8266;4- channel Relay module;ultra sonic sensor;buzzer;Arduino UNO software;voice recognition module;WI-FI;Cloud computing;User interfaces;Valves;Control systems;Sensor systems;Storage tanks;Internet of Things|
|[Design an IoT Based Highway Toll Plaza System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063136)|T. Prathyusha; B. Sridhar; T. Jagadeesh; Y. S. Deepika|10.1109/SCEECS57921.2023.10063136|Internet of things;Cloud;Arduino UNO;RFID Module;IR Sensors;Keypad;LCD Display;Costs;Roads;Video surveillance;Safety;Delays;Sensors;Internet of Things|
|[Cyber Physical Security of a Smart Grid: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062966)|U. Khare; A. Malviya; S. Kumar Gawre; A. Arya|10.1109/SCEECS57921.2023.10062966|Cyber physical systems (CPS). Sensors;Smart grids;Attack. Security system;Sensor systems;Real-time systems;Smart grids;Security;Trojan horses;Reliability;Spyware|
|[Comparing HTTP And COAP For IoT Low-power and Lossy Networks Using The Cooja Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062975)|A. Behal; J. K. Sandhu; G. Gupta|10.1109/SCEECS57921.2023.10062975|COAP;RPL;LLN;IOT;WSN;Protocols;Soft sensors;Semantics;Force;Market research;Delays;Internet of Things|
|[Performance improvement of SEIG based WECS using Artificial Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063131)|N. Kumar; G. Dyanamina|10.1109/SCEECS57921.2023.10063131|SASEIG;SAWECS;SPWM;SVM;Back-to-Back converter;Computer science;Reactive power;Torque;Modulation;Artificial neural networks;Aerospace electronics;Generators|
|[Piezoelectric Based Power Generation For Portable Charging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063055)|S. Sahu; V. K. Verma; G. Yadav; A. Hema; R. Arya; S. K. Gawre; R. Verma|10.1109/SCEECS57921.2023.10063055|piezoelectric material;supercapacitor;rectifier;micro controller;energy harvesting;Vibrations;Transducers;Urban areas;Transforms;Shock absorbers;Sensor systems;Safety|
|[Recent Advancement in Autonomous Vehicle and Driver Assistance Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063089)|A. Mishra; J. Purohit; M. Nizam; S. K. Gawre|10.1109/SCEECS57921.2023.10063089|Autonomous Vehicles (AVs);Human-Driven Vehicles (HVs);Markov Decision Process (MDP);Artificial Intelligence (AI);Advance Driving & Driver Assistance Systems (ADDAS);Deep Learning (DL);Smart Transportation Robots (STR);Deep learning;Road accidents;Markov processes;Safety;Internet of Things;Autonomous vehicles;Traffic congestion|
|[IoT and Machine Learning based Standalone Health Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063098)|S. Mishra; A. Raj; R. Dubey; D. Tewari; P. Dubey; S. K. Gawre; J. Purohit|10.1109/SCEECS57921.2023.10063098|Telehealth;ESP32;surveil;IoT;Solar PV system;Machine learning;Heart rate;Computer science;Epidemics;Prototypes;Detectors;Machine learning;Monitoring|
|[Decoupled Control of Rotor Side Power Electronic Converter for Grid Connected DFIG Based Wind Energy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063093)|A. A. Ansari; G. Dyanamina; A. A. Ansari|10.1109/SCEECS57921.2023.10063093|DFIG;Renewable Energy;Wind energy system;Rotor side converter;Torque;Windings;Doubly fed induction generators;Rotors;Stator windings;Wind power generation;Power electronics|
|[An extensive critique on Quality Checking of Natural Ester Based Oil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061815)|D. Sarathkumar; R. A. Raj; P. Sujeet; S. Sukitha; J. Sumithra; R. Muralidharan|10.1109/SCEECS57921.2023.10061815|Free Fatty Acid (FFA);Per Oxide Value (POV);Quality;Edible Oil;Chemical properties;Measurement;Vegetable oils;Costs;Safety management;Production;Oxidation;Chemicals|
|[IOT Based Surveillance Camera with GPS Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062961)|L. J. Baptist Andrews; S. D; R. A. Raj|10.1109/SCEECS57921.2023.10062961|Raspberry Pi;GPS;Surveillance;Internet of things (IOT);Computer science;Surveillance;Urban areas;Organizations;Cameras;Internet of Things;Global Positioning System|
|[A Strategy to Reduce the Importation of Petroleum Oils in Vietnam by Transforming Rice Bran Waste to Vegetable Oil-Based Transformer Liquid Insulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063096)|R. A. Raj; S. Murugesan; D. Sarathkumar; V. S. Kumar|10.1109/SCEECS57921.2023.10063096|Vegetable oil;Insulating fluid;Transformer oil;Petroleum oils;Liquid insulation;Breakdown voltage;Ageing;Additives;Viscosity;Vegetable oils;Liquids;Purification;Lubricants;Oil insulation;Oxidation|
|[An Extensive Critique on Microgrid Control Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10063019)|S. D; R. A. Raj; S. K. V|10.1109/SCEECS57921.2023.10063019|Microgrid;Smart Grid;Power Systems;Distributed Energy Sources;Centralized Control;Decentralized Control;Renewable energy sources;Reactive power;Decentralized control;Microgrids;Real-time systems;Smart grids;Topology|
#### **2023 IEEE International Conference on Big Data and Smart Computing (BigComp)**
- DOI: 10.1109/BigComp57234.2023
- DATE: 13-16 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066795)|K. Mun; B. Cha; J. Lee; J. Kim; H. Jo|10.1109/BigComp57234.2023.00010|Sports Prediction;Logistic Regression;Baseball Analysis;Sabermetrics;Siamese Network;Industries;Analytical models;Neural networks;Games;Machine learning;Organizations;Predictive models|
|[System Optimization of Data Analytics Platforms using Compute Express Link (CXL) Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066614)|S. Ryu; S. Kim; J. Jun; D. Moon; K. Lee; J. Choi; S. Kim; H. Kim; L. Kim; W. H. Choi; M. Nam; D. Hwang; H. Roh; Y. Joo|10.1109/BigComp57234.2023.00011|CXL;Memory Expansion;Memory Solution;Apache Spark;Shuffle;In-memory DBMS;Data Analytics Platform;Performance evaluation;Data analysis;Memory management;Prototypes;Physical layer;Software;Sparks|
|[Self-supervised learning for climate downscaling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066728)|K. Singh; C. Jeong; S. Park; A. N. Babur; E. Zeller; M. Cha|10.1109/BigComp57234.2023.00012|Earth system models;Climate simulation;Super-resolution;Self-supervised learning;Self-supervised learning;Climate change;Modeling;Earth;Computer simulation|
|[Classification of Event Sequences Based on Temporal Relation Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066551)|K. Cheng|10.1109/BigComp57234.2023.00052|feature extraction;sequence classification;temporal database;symbolic time interval;sequential pattern mining;machine learning;Training;Databases;Time series analysis;Semantics;Symbols;Syntactics;Data science|
|[A Machine Learning Approach to Government Business Process Re-engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066589)|A. Riyadi; M. Kovacs; U. Serdült; V. Kryssanov|10.1109/BigComp57234.2023.00013|government;public sector;business process reengineering;machine learning;Local government;Finance;Manuals;Big Data;Semisupervised learning;Data models;Business process re-engineering|
|[CLIP: Train Faster with Less Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066625)|M. A. Khan; R. Hamila; H. Menouar|10.1109/BigComp57234.2023.00014|Convergence;crowd counting;curriculum learning;data-centric;pruning.;Training;Deep learning;Estimation;Computer architecture;Big Data;Data models;Iterative methods|
|[Pairs Trading Strategy Optimization Using Proximal Policy Optimization Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066541)|Y. -F. Chen; W. -Y. Shih; H. -C. Lai; H. -C. Chang; J. -L. Huang|10.1109/BigComp57234.2023.00015|Deep reinforcement learning;pairs trading;proximal policy optimization;quantitative trading;Training;Deep learning;Dairy products;Reinforcement learning;Big Data;Market research;Stock markets|
|[An Auditable and Efficient Prepaid Scheme with Privacy Preservation in Smart Grids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066834)|Z. Sui; J. Li|10.1109/BigComp57234.2023.00016|smart grid;anonymity;privacy;cybersecurity;Privacy;Costs;Law enforcement;Computational modeling;Maintenance engineering;Smart meters;Energy efficiency|
|[Federated Learning with Intermediate Representation Regularization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066639)|Y. L. Tun; C. M. Thwal; Y. M. Park; S. -B. Park; C. S. Hong|10.1109/BigComp57234.2023.00017|federated learning;representation;data heterogeneity;distributed;Training;Performance evaluation;Data privacy;Limiting;Codes;Federated learning;Computational modeling|
|[Lazy Node-Dropping Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066557)|J. Lee; M. J. Kim; H. Shin|10.1109/BigComp57234.2023.00018|Neural Networks;Autoencoder;Dimensionality Reduction;Dimension Estimation;Training;Dimensionality reduction;Computational modeling;Estimation;Batch production systems;Big Data;Complexity theory|
|[Feed-forward Design vs. Mimic Learning for Interpretable Deep Models: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066749)|J. -G. Park; D. Seit; K. Tokayev|10.1109/BigComp57234.2023.00019|Interpretable ML;Simulatability;Knowledge Distillation;Mimic Learning;DNNs;Feed-forward MLP;LDA;Measurement;Deep learning;Embedded systems;Costs;Computational modeling;Neural networks;Finance|
|[A GPU-based tensor decomposition method for large-scale tensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066634)|J. Lee; K. -W. Chon; M. -S. Kim|10.1109/BigComp57234.2023.00020|Scalable Tucker decomposition;GPU;Largescale tensor;Tensor partitioning;Analytical models;Tensors;Scalability;Memory management;Graphics processing units;Big Data;Data models|
|[A BERT-enhanced Graph Neural Network for Knowledge Base Population](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066725)|H. Lim; M. -S. Kim|10.1109/BigComp57234.2023.00021|deep learning;language model;graph neural network;Computational modeling;Knowledge based systems;Sociology;Bit error rate;Big Data;Graph neural networks;Data models|
|[LFP: Layer Wise Feature Perturbation based Graph Neural Network for Link Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066651)|M. G. Morshed; Y. -K. Lee|10.1109/BigComp57234.2023.00022|Graph Neural Network;Link prediction;Feature Extraction;Edge perturbation;Perturbation methods;Computational modeling;Predictive models;Big Data;Graph neural networks;Data models;Data mining|
|[Inductive Graph-based Knowledge Tracing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066831)|D. Han; D. Kim; K. Han; M. Y. Yi|10.1109/BigComp57234.2023.00023|knowledge tracing;graph-based knowledge tracing;IGMC;IGKT;rating prediction;Knowledge engineering;Deep learning;Adaptation models;Target tracking;Recurrent neural networks;Pandemics;Predictive models|
|[Fast Integration for Multiple Graphs with Neumann Approximation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066805)|T. Yun; M. J. Kim; H. Shin|10.1109/BigComp57234.2023.00024|graph-based semi-supervised learning;graph integration;maximum likelihood estimation;Neumann series;Maximum likelihood estimation;Laplace equations;Machine learning;Semisupervised learning;Big Data;Data models;Noise measurement|
|[JIDECA: Jointly Improved Deep Embedded Clustering for Android activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066814)|S. Choi; H. -T. Seo; Y. -S. Han|10.1109/BigComp57234.2023.00025|Keywords-activity clustering;DEC;IDEC;JIDECA;Rico dataset;Deep learning;Clustering methods;Neural networks;Big Data|
|[Evaluating Mitigation Approaches for Adversarial Attacks in Crowdwork](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066801)|C. G. Harris|10.1109/BigComp57234.2023.00026|adversarial attacks;crowdsourcing;quality assurance;assessment techniques;Amazon Mechanical Turk;Crowdsourcing;Collaboration;Big Data;Task analysis;Monitoring|
|[Outlier-aware Cross-Market Product Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066665)|H. Kang; D. Lee; H. Cho|10.1109/BigComp57234.2023.00027|recommendation, outlier, meta learning;Adaptation models;Computational modeling;Neural networks;Big Data;Benchmark testing|
|[Edge-Cloud Collaboration Architecture for Efficient Web-Based Cognitive Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066821)|Z. Wang; I. -Y. Ko|10.1109/BigComp57234.2023.00028|edge-cloud collaboration;deep neural network;web application;cognitive service;Visualization;Web services;Image edge detection;Computational modeling;Collaboration;Computer architecture;Time factors|
|[Machine learning-based prediction for 30-day unplanned readmission in all-types cancer patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066724)|H. Jung; H. W. Park; Y. Kim; Y. Hwangbo|10.1109/BigComp57234.2023.00029|Unplanned readmission;Cancer;Machine learning;Prediction model;Hospitals;Computational modeling;Machine learning;Predictive models;Data warehouses;Big Data;Data models|
|[Prediction of Chemotherapy-Induced Neutropenia using Machine Learning in Cancer Patients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066616)|Y. Kim; Y. Lee; H. W. Park; H. Jung; Y. Hwangbo; H. S. Cha|10.1109/BigComp57234.2023.00030|chemotherapy-induced neutropenia;cytotoxic drugs;cancer;machine learning;predictive models;Chemotherapy;Federated learning;Neural networks;Time series analysis;Lung cancer;Predictive models;Data models|
|[An ECG Beat Classification Method using Multi-kernel ResNet with Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066657)|S. Chon; K. -W. Ha; S. Park; S. Jung|10.1109/BigComp57234.2023.00031|ECG;Beat classification;Transformer;Multikernel ResNet;Position embedding;Transfer learning;Heart rate;Deep learning;Arrhythmia;Electrocardiography;Transformers;Feature extraction;Data models|
|[One year mortality prediction in heart failure using feature selection and missing value imputation in deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066800)|T. Kim; M. Kim; H. W. Lee; G. Song|10.1109/BigComp57234.2023.00032|heart failure;death;KAMIR;missing value;feature selection;machine learning;deep learning;Deep learning;Heart;Drugs;Current measurement;Data preprocessing;Myocardium;Big Data|
|[Transformer-Based Gene Scoring Model for Extracting Representative Characteristic of Central Dogma Process to Prioritize Pathogenic Genes Applying Breast Cancer Multi-omics Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066686)|J. H. Jhee; M. -Y. Song; B. G. Kim; H. Shin; S. Y. Lee|10.1109/BigComp57234.2023.00033|Multi-omics;data integration;breast cancer;gene scoring;deep learning;Proteins;Analytical models;Computational modeling;Biological system modeling;Predictive models;Transformer cores;Transformers|
|[Actionable Suggestions in Support of Rehospitalization Risk Predicted by Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066585)|G. Confortola; M. Takata; N. Yokoi; M. Egi|10.1109/BigComp57234.2023.00034|Artificial Intelligence;Actionable Suggestions;Risk Reduction;MIMIC-III;Costs;Statistical analysis;Hospitals;MIMICs;Decision making;Probability;Big Data|
|[Neural Classification of Terrestrial Biomes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066562)|V. Shen; D. -K. Kim; E. Zeller; M. Cha|10.1109/BigComp57234.2023.00035|classification;biome;climate change;Climate change;Classification;Vegetation mapping;Predictive models;Convolutional neural networks;Meteorology;Machine learning;Ecosystems;Biomes|
|[What rather than how : A DMR topic modeling analysis of news coverage on the British Museum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066775)|M. Kim; H. Lee; S. H. Lee; J. H. Kim|10.1109/BigComp57234.2023.00036|DMR;Topic modeling;Agenda-setting theory;Journalism;British Museum;Big Data Analysis;Analytical models;Social networking (online);Law;Government;Entertainment industry;Media;Metadata|
|[Speech Recognition for Turkic Languages Using Cross-Lingual Transfer Learning from Kazakh](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066768)|D. Orel; R. Yeshpanov; H. A. Varol|10.1109/BigComp57234.2023.00037|automatic speech recognition;cross-lingual transfer learning;deep learning;Turkic languages;Codes;Error analysis;Speech coding;Computational modeling;Transfer learning;Buildings;Training data|
|[Dialogue Response Evaluation Model with Conversational Feature Sensitive Negative Sampling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066731)|D. Kang; J. Bak|10.1109/BigComp57234.2023.00038|Evaluation;Negative Sampling;Conversation;Dialogue;Language Model;Measurement;Correlation;Computational modeling;Oral communication;Coherence;Big Data;Data models|
|[Automatic Rule Definition for Pattern-Based Text Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066726)|M. Kuriu; I. Mendonça; M. Aritsugi|10.1109/BigComp57234.2023.00039|aspect classification;sentiment analysis;pattern matching;Text mining;Sentiment analysis;Machine learning;Big Data;Pattern matching|
|[Attention Masking for Improved Near Out-of-Distribution Image Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066676)|M. Sim; J. Lee; H. -J. Choi|10.1109/BigComp57234.2023.00040|out-of-distribution detection;image classification;vision transformer;quantifying attention map;Training;Deep learning;Weight measurement;Sentiment analysis;Computational modeling;Benchmark testing;Transformers|
|[Enhancing Image Representation in Conditional Image Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066667)|J. Shim; E. Kim; H. Kim; E. Hwang|10.1109/BigComp57234.2023.00041|Generative Model;Conditional Image Synthesis;Image Representation;Normalization Layer;Edge Detection;Image quality;Shape;Image synthesis;Computational modeling;Image edge detection;Image representation;Big Data|
|[AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066796)|A. Kuzdeuov; D. Koishigarina; H. A. Varol|10.1109/BigComp57234.2023.00042|face detection;input-agnostic models;datacentric AI;deep learning;Training;Animals;Face recognition;Source coding;Neural networks;Detectors;Data models|
|[A Comparative Study on Bengali Speech Sentiment Analysis Based on Audio Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066609)|A. C. Shruti; R. H. Rifat; M. Kamal; M. G. R. Alam|10.1109/BigComp57234.2023.00043|Bangla Sentiment Analysis;Machine Learning;CNN;SHAP;MFCC;KNN;AdaBoost;Random Forest;Explanable AI;LSTM;Bi-LSTM;Training;Radio frequency;Computers;Sentiment analysis;Analytical models;Computational modeling;Machine learning|
|[Text Augmentation Based on Integrated Gradients Attribute Score for Aspect-based Sentiment Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066737)|N. Santoso; I. Mendonça; M. Aritsugi|10.1109/BigComp57234.2023.00044|aspect-based sentiment analysis;data augmentation;feature importance;integrated gradients;Deep learning;Sentiment analysis;Analytical models;Computer vision;Text analysis;Diversity reception;Training data|
|[MMTS: Multimodal Teacher-Student learning for One-Shot Human Action Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066809)|J. Lee; M. Sim; H. -J. Choi|10.1109/BigComp57234.2023.00045|human action recognition;skeleton;keypoints;one-shot;metric learning;teacher-student networks;Learning systems;Measurement;Visualization;Protocols;Surveillance;Streaming media;Big Data|
|[Deep Learning-based AOI System for Detecting Component Marks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066828)|Y. -M. Chang; T. -L. Lin; H. -C. Chi; W. -K. Lin|10.1109/BigComp57234.2023.00046|surface mount technology (SMT);printing circuit board (PCB);printing circuit board assembly (PCBA);styling;automated optical inspection (AOI);convolutional neural networks (CNN).;Training;Printing;Brightness;Surface mount technology;Production;Optical fiber networks;Optical imaging|
|[Jobicon:A Job Icon Dataset for Concept Recognition by Understanding Compositionality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066714)|S. Park; H. Park; H. Kim; Y. Yoo; M. Song|10.1109/BigComp57234.2023.00047|compositional learning;icon;multimodality;Image retrieval;Big Data;Standards;Testing|
|[Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066697)|Y. Park; M. J. Kim; W. S. Park|10.1109/BigComp57234.2023.00048|Anomaly detection;Noise reduction;Road safety;Sound event extraction;Fourier transforms;Roads;Friction;Computational modeling;Tires;Robustness;Task analysis|
|[MENDEL: Time series anomaly detection using transfer learning for industrial control systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066595)|J. Park; B. Kim; H. Kim|10.1109/BigComp57234.2023.00049|industrial control systems (ICS);anomaly detection;transfer learning;feature mapping;Integrated circuits;Training;Analytical models;Computational modeling;Industrial control;Transfer learning;Time series analysis|
|[Temporal Convolutional Network-Based Time-Series Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066661)|H. Min; J. -G. Lee|10.1109/BigComp57234.2023.00050|Time series;segmentation;temporal clustering;unsupervised learning;Time series analysis;Big Data;Convolutional neural networks;Optimization|
|[Predicting Next Application Most Likely Used with Word Embedding and Time-Series Data Encoding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066817)|T. Song; G. Gweon|10.1109/BigComp57234.2023.00051|Next App Most Likely Used;Word Embedding;Time-series Data Encoding;Convolutional Neural Network;Mobile;Learning systems;Costs;Convolution;Buildings;Transfer learning;Time series analysis;Predictive models|
|[DCA: A Dual-layer Cache Architecture for Providing High Lookup Performance in KV Stores](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066752)|J. Lee; Y. Song; Y. I. Eom|10.1109/BigComp57234.2023.00053|key-value store;cache architecture;RocksDB;LRU cache;Degradation;Concurrent computing;Instruction sets;Computer architecture;Big Data|
|[Federated Learning Framework for Blockchain based on Second-Order Precision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066556)|W. Song; T. Yan|10.1109/BigComp57234.2023.00054|Federated learning;second-order precision;data privacy;blockchain;data heterogeneity.;Data privacy;Costs;Federated learning;Computational modeling;Collaboration;Computer architecture;Big Data|
|[Identification of Hot data and Caching strategy for Industrial Big Data Based on Temperature Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066767)|Y. Wang; J. Zhao; Q. Zhou; X. Xiong; H. Zhang; C. Chen|10.1109/BigComp57234.2023.00055|identification;hot data and cold data;temperature model;industrial big data;Heating systems;Adaptation models;Temperature distribution;Cooling;Computational modeling;Heuristic algorithms;Big Data|
|[Transformer-Based Flood Detection Using Multiclass Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066794)|J. -C. Park; D. -G. Kim; J. -R. Yang; K. -S. Kang|10.1109/BigComp57234.2023.00056|Segmentation;Change Detection;Flood;Road;Building;APLS;Measurement;Economics;Roads;Earthquakes;Predictive models;Transformers;Prediction algorithms|
|[NFTeller: Dual Centric Visual Analytics of NFT Transactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066688)|Y. Cao; X. Yang; M. Xia; H. Liu; K. Shigyo; W. Zeng; F. Cheng; Y. Wang; Q. Yu; H. Qu|10.1109/BigComp57234.2023.00057|Non-fungible tokens (NFTs);Blockchain;Visual analytics;Correlation;Social networking (online);Visual analytics;Whales;Collaboration;Big Data;Nonfungible tokens|
|[Needs and Challenges of Personal Data Visualisations in Mobile Health Apps: User Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066583)|Y. A. Alshehhi; B. Philip; M. Abdelrazek; A. Bonti|10.1109/BigComp57234.2023.00058|data visualisation;mHealth apps;user experience;Data visualization;Big Data;Complexity theory;Guidelines|
|[Dealloc FTL: Efficiently Managing Temporary Files on SSDs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066605)|J. Kim; S. Lee|10.1109/BigComp57234.2023.00059|temporary file;SSD;garbage collection;FTL;hash join;sort merge join;deallocate;Computer architecture;Big Data applications;Transient analysis|
|[MTFT: Multi-Tenant Fair Throttling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066566)|I. Song; S. -W. Lee|10.1109/BigComp57234.2023.00060|Datacenters;Operating Systems;I/O;Containers;Degradation;Data centers;Linux;Bandwidth;Quality of service;Containers;Big Data|
|[Improving Generation of Sentiment Commonsense by Bias Mitigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066681)|J. Lee; J. Kim|10.1109/BigComp57234.2023.00061|Commonsense;Sentiment;Bias;EDA;UDA;Comets;Tail;Knowledge graphs;Big Data;Transformers;Natural language processing;Task analysis|
|[A SimSiam-based Generalized Model Training Technique for Classification of ECG from Heterogeneous Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066680)|K. -W. Ha; S. Park; S. Chon; J. -K. Kim; S. Jung|10.1109/BigComp57234.2023.00062|SimSiam network;Contrastive learning;ECG;deep learning;ECG classification;Performance evaluation;Training;Hospitals;Arrhythmia;Electrocardiography;Big Data|
|[TCAC-GAN: Synthetic Trajectory Generation Model Using Auxiliary Classifier Generative Adversarial Networks for Improved Protection of Trajectory Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066580)|J. Shin; Y. Song; J. Ahn; T. Lee; D. -H. Im|10.1109/BigComp57234.2023.00063|generative adversarial networks;ACGAN;trajectory data;synthetic trajectory generation;privacy protection;Data privacy;Privacy;Social networking (online);Publishing;Computational modeling;Big Data;Generative adversarial networks|
|[Randomizing Hypergraphs Preserving Two-mode Clustering Coefficient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066677)|R. Miyashita; K. Nakajima; M. Fukuda; K. Shudo|10.1109/BigComp57234.2023.00064|hypergraph;clustering coefficient;generative model;Computational modeling;Big Data;Data structures;Data models|
|[DAG-GCN: Directed Acyclic Causal Graph Discovery from Real World Data using Graph Convolutional Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066790)|S. Park; J. Kim|10.1109/BigComp57234.2023.00065|Graph Neural Networks;Graph Representation Learning;Directed Acyclic Graphs;DAG;Causal Discovery;Causal Structure Learning;Directed acyclic graph;Art;Optimization methods;Big Data;Data models;Graph neural networks;Convolutional neural networks|
|[A Method for Estimating Online Chess Game Player Ratings with Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066746)|H. Yamada; N. Kishi; M. Oguchi; M. Nakano|10.1109/BigComp57234.2023.00066|Decision Tree;Chess log;player rating prediction;Training;Analytical models;Automation;Computational modeling;Games;Big Data;Decision trees|
|[Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066546)|C. -C. Lee; S. Lee; M. H. Song; S. Lee; J. -Y. Kim|10.1109/BigComp57234.2023.00067|ADR;classification;pharmacovigilance;concept;pipeline;SNS;Drugs;Recurrent neural networks;Social networking (online);Filtering;Computational modeling;Blogs;Big Data|
|[Offline RL oriented Functions Design for Dynamic Power Management on CNN Workloads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066779)|J. -G. Park|10.1109/BigComp57234.2023.00068|Offline (Batch) RL;Deep Learning;CNNs;DPM;DVFS;Integrated GPUs;Training;Deep learning;Costs;Power system management;Graphics processing units;Programming;Big Data|
|[pmmeter: A Microbenchmark for Understanding Synchronization Cost on Persistent Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066607)|H. Yoshioka; Y. Hayamizu; K. Goda; M. Kitsuregawa|10.1109/BigComp57234.2023.00069|Persistent memory;performance measurement;microbenchmarking tool;Performance evaluation;Industries;Costs;Source coding;Programming;Big Data;Synchronization|
|[System Design for determining the optimal pig shipping time using prediction model based on big data learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066712)|J. W. Jang; J. H. Lee; G. P. Nam; S. H. Lee|10.1109/BigComp57234.2023.00070|big data learning, shipping of pig, prediction model, shipping timing, Convolutional Neural network;Dairy products;Computational modeling;Big Data;Predictive models;Breast;Data models;Feeds|
|[SmartEye: Detecting COVID-19 Misinformation on Twitter for Mitigating Public Health Risk](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066716)|J. Liu; R. Gong; W. Zhou|10.1109/BigComp57234.2023.00071|misinformation;COVID-19;pandemic;public health;social media;COVID-19;Deep learning;Social networking (online);Blogs;Big Data;Fake news;Public healthcare|
|[Knowledge Map Automatic Update System Using Graph Convolutional Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066723)|H. -H. Huang; N. -F. Huang; J. -W. Tzeng; X. -M. Dong; H. -Y. Kao; T. -W. Lin|10.1109/BigComp57234.2023.00072|Graph;Graph Convolutional Network;Knowledge Map;MOOCs;Learning Analysis;Learning Assistant;Knowledge engineering;Measurement;Electronic learning;Computational modeling;Predictive models;Big Data;Prediction algorithms|
|[Comparison of Three Korean Sentiment/Emotion Word Dic tionaries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066733)|H. Cho|10.1109/BigComp57234.2023.00073|Korean sentiment dictionary;multi-label Korean emotion word dictionary;KOSAC sentiment lexicon;KNU Korean sentiment lexicon;24 emotions;Sentiment analysis;Humanities;Dictionaries;Training data;Big Data;Motion pictures;Task analysis|
|[Health24: Health-related Data Collection from Wearable and Mobile Devices in Everyday Lives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066622)|G. Ji; C. Msigwa; D. Bernard; G. Lee; J. Woo; J. Yun|10.1109/BigComp57234.2023.00074|Health-related data;lifelog;wearables;smartphone;everyday life;Wearable Health Monitoring Systems;Telemedicine;Medical services;Watches;Information and communication technology;Servers;Smart phones|
|[Rethinking TPC-C Characteristic: At Logical Write Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066577)|K. -S. Lee; S. -W. Lee|10.1109/BigComp57234.2023.00075|buffer management policy;flash-memory SSD;database storage engine;MySQL;Databases;Big Data;Benchmark testing|
|[Extended Abstract: Predicting the Morality of a Character Using Character-Centric Embeddings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066592)|S. Y. Bae; E. C. Kim; Y. G. Cheong|10.1109/BigComp57234.2023.00076|Morals;Story Character Embedding;Masked Language Modeling;Ethics;Computational modeling;Bit error rate;Big Data;Data models;Natural language processing;Artificial intelligence|
|[Feature Engineering and Selection for Predicting Foreign Exchange Rates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066646)|H. Kim; H. Yun; Y. Lee; M. S. Kim|10.1109/BigComp57234.2023.00077|nan;Radio frequency;Exchange rates;Computational modeling;Predictive models;Big Data;Feature extraction;Robustness|
|[Pairwise Application Log Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066820)|B. Stuike; Y. Amannejad|10.1109/BigComp57234.2023.00078|application identification;pairwise classification;machine learning;big data applications;Training;Computational modeling;Training data;Big Data applications;Labeling|
|[Auto-Labeling of Anomalies on Access Logs and Pairwise Comparison-based Validation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066694)|J. Moon; H. -Y. Kwon|10.1109/BigComp57234.2023.00079|Unlabeled access logs;Auto-labeling;Labeling validation;Annotations;Semisupervised learning;Big Data;Labeling|
|[Fast Prediction for Suspect Candidates from Criminal Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066739)|J. H. Jhee; M. J. Kim; M. Park; J. Yeon; Y. Kwak; H. Shin|10.1109/BigComp57234.2023.00080|criminal data;crime network;suspect candidate prediction;machine learning;graph-based semi-supervised learning;Machine learning algorithms;Law enforcement;Machine learning;Semisupervised learning;Big Data;Benchmark testing;Prediction algorithms|
|[A prognostic model for classification of COVID-19 severity based on clinical and laboratory testing data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066574)|J. -B. Lee; S. C. Kim; J. -H. Lee; H. -Y. Jo|10.1109/BigComp57234.2023.00081|COVID-19;machine learning;clinical data;laboratory testing data;prognostic model;COVID-19;Proteins;Sensitivity;Infectious diseases;Computational modeling;Predictive models;Turning|
|[Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066732)|J. Kim; H. Shin|10.1109/BigComp57234.2023.00082|feature extraction;multimodality;prediction;data embedding;thyroid cancer;Artificial Intelligence in Medicine;Pathology;Computational modeling;Machine learning;Big Data;Feature extraction;Data models;Biochemistry|
|[Latent Inter-relation Augment between Crimes and Criminals using Factorization Machines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066719)|J. Yeon; J. H. Jhee; H. Shin|10.1109/BigComp57234.2023.00083|factorization machines;inter-relation augmentation;crime network;graph-based semi-supervised learning;heterogeneous network;Systematics;Law enforcement;Big Data|
|[Feature-based Map Merging with Mesh Networks in Swarm Robot Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066674)|I. Jang; J. Park; H. Lee|10.1109/BigComp57234.2023.00084|Small robot system;SLAM;Map merge;Feature extraction;Swarm robot;Mesh network;Mesh networks;Merging;Swarm robotics;Big Data;Robot sensing systems;Feature extraction;Real-time systems|
|[Implementaion of 3D Collaborative Object Detection Systems using RGB-D Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066704)|S. Jun; J. Baek; S. Do; C. Lee|10.1109/BigComp57234.2023.00085|artificial;intelligence;collaborative;object;military;detection;defense;RGB-D;battlefield;Three-dimensional displays;Collaboration;Object detection;Big Data;Sensor systems;Artificial intelligence;Intelligent sensors|
|[Battlefield Situation Awareness Model Using Convolutional LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066590)|P. -S. Kim; S. -Y. Hyun; Y. -G. Ha|10.1109/BigComp57234.2023.00086|Machine Learning;Battlefield Situation Awareness;LSTM;CNN;Training;Recurrent neural networks;Computational modeling;Time series analysis;Machine learning;Predictive models;Feature extraction|
|[Leveraging Semantics in Appearance based Loop Closure Detection for Long-Term Visual SLAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066673)|S. Arshad; G. -W. Kim|10.1109/BigComp57234.2023.00087|visual loop closure detection;semantic labels;long term autonomy;visual navigation.;Visualization;Image segmentation;Simultaneous localization and mapping;Navigation;Databases;Computational modeling;Semantics|
|[Causal Analytic Process for Mobile Health Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066663)|G. Jung; S. Park; U. Lee; E. -Y. Ma; H. Kim|10.1109/BigComp57234.2023.00088|Causal Inference;Mobile Data;Human Behavior;Big Data;Mobile handsets;Behavioral sciences;Mobile applications|
|[Investigating Causality in Mobile Health Data through Deep Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066832)|E. -Y. Ma; H. Kim; U. Lee|10.1109/BigComp57234.2023.00089|causal inference;mobile health;digital therapeutics;Deep learning;Computational modeling;Time series analysis;Big Data;Data models;Sensors;Standards|
|[Can Eye Gaze Improve Emotional State Detection on Off the Shelf Smart Devices Jiwan Kim Doyoung Lee Jaeho Kim](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066649)|J. Kim; D. Lee; J. Kim; I. Oakley|10.1109/BigComp57234.2023.00090|digital phenotyping;smartphones;gaze;User-generated content;Sociology;Multimedia Web sites;Tactile sensors;Reliability engineering;Robustness;Sensors|
|[WildStress: exploring rich situational contexts for stress detection in the wild](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066803)|K. Toshnazarov; T. H. Lee; Y. Noh|10.1109/BigComp57234.2023.00091|stress;context;smartwatch;smartphone;mHealth;ubiquitous computing;Human factors;Big Data;Physiology;Task analysis|
|[Designing a Personalized Stress Management System for Call Center Workers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066645)|K. Lee; H. Lim; S. Ahn; T. Kim; H. Hong|10.1109/BigComp57234.2023.00092|Mental health;mobile;stress management;coping;interventions;recommender;quantifying;goal-setting;Conferences;Employment;Data visualization;Human factors;Mental health;Big Data;Stress measurement|
|[Data-driven Digital Therapeutics Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066700)|U. Lee; G. Jung; S. Park; E. -Y. Ma; H. Kim; Y. Lee; Y. Noh|10.1109/BigComp57234.2023.00093|Digital Therapeutics;DTx Data Analysis;Proactive Engagement Management;Employee welfare;Wearable Health Monitoring Systems;Systematics;Medical devices;Transforms;Software;Human in the loop|
|[Contactless Vital Signs Tracking with mmWave RADAR in Realtime](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066788)|M. Salman; Y. Noh|10.1109/BigComp57234.2023.00094|mmWave;Vital Signs;Heart Rate (HR);Breathing Rate;FMCW;Radar measurements;Heart beat;Radar;Radar tracking;Real-time systems;Frequency measurement;Millimeter wave communication|
|[Data Processing Pipeline of Short-Term Depression Detection with Large-Scale Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066563)|Y. Lee; Y. Noh; U. Lee|10.1109/BigComp57234.2023.00095|Large-scale Data Processing;Short-Term Depression Detection;Positive Computing;Mobile Computing;Mood;Computational modeling;Mental disorders;Pipelines;Depression;Data processing;Ubiquitous computing|
|[Measuring Device-Specific Physical Activity Trackability in Multi-Device Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066772)|S. Park; E. Park; P. H. Lee; U. Lee|10.1109/BigComp57234.2023.00096|Wearables;Smartphones;Physical Activity;Device Coverage;Wearable computers;Big Data;Smart phones|
|[Deep Learning Mental Health Dialogue System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066740)|L. Brocki; G. C. Dyer; A. Gładka; N. C. Chung|10.1109/BigComp57234.2023.00097|Deep Learning;Artificial Intelligence;Transformers;Mental Health;Chatbot;Dialogue System;Deep learning;Employee welfare;Costs;Mental health;Transformer cores;Big Data;Transformers|
|[Multilayer CARU Model for Text Summarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066632)|S. -K. Im; K. -H. Chan|10.1109/BigComp57234.2023.00098|CARU;Summarization;NLP;Selector;Encode-Decode Neural Network;Training;Measurement;Design methodology;Semantics;Neural networks;Big Data;Feature extraction|
|[A Literature Review on AWS-Based Cloud Computing: A Case in South Korea](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066781)|B. Lee; J. Oh; W. Shon; J. Moon|10.1109/BigComp57234.2023.00099|Amazon Web Services (AWS);big data processing;cloud computing;distributed system;Computers;Cloud computing;Web services;Smart cities;Standards organizations;Transforms;Big Data|
|[Extracting Time Information from Korean Documents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066547)|S. -D. Lee; Y. -S. Jeong|10.1109/BigComp57234.2023.00100|time information extraction;joint-task learning;timex3;event;Korean text;Computational modeling;Computer architecture;Writing;Big Data;Data models;Data mining;Labeling|
|[Can CLIP Share Image in Dialogue?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066624)|Y. -J. Lee; H. -J. Choi|10.1109/BigComp57234.2023.00101|multi-modal dialogue;CLIP;robustness;imagesharing behavior;Oral communication;Linguistics;Big Data;Robustness;Behavioral sciences;History|
|[Towards an Effective Over-The-Top Platform Service: A Machine Learning Approach for Box Office Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066777)|S. Leem; J. Oh; J. Moon|10.1109/BigComp57234.2023.00102|box office analysis;data reduction;data clustering;ensemble learning;explainable artificial intelligence;Manifolds;Analytical models;Input variables;Data preprocessing;Production;Predictive models;Motion pictures|
|[Memory-based Consultation System for Personalized Conversations Using Temporal Contexts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066759)|C. -G. Lim; H. -J. Choi|10.1109/BigComp57234.2023.00103|personalized conversation;consultation system;episodic memory;temporal context;Employee welfare;Oral communication;Transforms;Big Data;Natural language processing;History;Data mining|
|[Nearest Neighbor Search using Metric-Preserving Function for Retrieval-based Dialogue System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10066773)|J. Lim; E. Joo; H. -J. Choi|10.1109/BigComp57234.2023.00104|Nearest Neighbor Search;Retrieval-based Dialogue System;Metric-Preserving Function;Metric Tree;Measurement;Navigation;Nearest neighbor methods;Big Data;Extraterrestrial measurements;Context modeling|
#### **2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)**
- DOI: 10.1109/CCNC51644.2023
- DATE: 8-11 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Communication-Aware Flight Algorithm for UAVs in Delay-Tolerant Aerial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059783)|H. Asano; H. Okada; C. Ben Naila; M. Katayama|10.1109/CCNC51644.2023.10059783|unmanned aerial vehicle;delay-tolerant network;flight algorithm;particle swarm optimization;Simulation;Autonomous aerial vehicles;Particle swarm optimization;Optimization|
|[An OpenVPN-Based Interconnection in Multi-Clouds with Windows and Linux nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059709)|S. Jarrous-Holtrup; S. Gorlatch; M. Dey; F. Schamel|10.1109/CCNC51644.2023.10059709|Cloud Computing;Networking;Multi-Cloud;OpenVPN;Secure Communication;Cloud computing;Three-dimensional displays;Electronic learning;Linux;Computer architecture;Rendering (computer graphics);Real-time systems|
|[Edge-Assisted Multi-User 360-Degree Video Delivery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059914)|T. Okamoto; T. Ishioka; R. Shiina; T. Fukui; H. Ono; T. Fujiwara; T. Fujihashi; S. Saruwatari; T. Watanabe|10.1109/CCNC51644.2023.10059914|VR;Edge Server;Hybrid multicast and unicast;JPEG-XS;Headphones;Degradation;Unicast;Virtual reality;Quality assessment;Servers;Video recording|
|[Robot-Network Co-optimization Using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060010)|H. Shinmiya; T. Motoo; T. Fujihashi; R. Kudo; K. Takahashi; T. Murakami; T. Watanabe; S. Saruwatari|10.1109/CCNC51644.2023.10060010|Robot;Deep Reinforcement Learning;Deep learning;Wireless LAN;Wireless networks;Transportation;Reinforcement learning;Computer architecture;Throughput|
|[Deep Reinforcement Learning Model Design and Transmission for Network Delay Compensation in 3D Online Shooting Game](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060183)|S. Akama; T. Motoo; T. Ishioka; T. Fujihashi; S. Saruwatari; T. Watanabe|10.1109/CCNC51644.2023.10060183|Delay Compensation;DRL Networks;Model Transmission;Deep learning;Solid modeling;Three-dimensional displays;Games;Reinforcement learning;Predictive models;Probabilistic logic|
|[Optimal Rotated QPSK Constellation for a Semi-Orthogonal Multiple Access Visible Light Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060582)|S. K. Sahoo; S. P. Dash; S. Joshi; D. Ghose|10.1109/CCNC51644.2023.10060582|Quadrature-phase shift keying (QPSK);semi-orthogonal multiple access (SOMA);symbol error probability (SEP);visible light communication (VLC);Phase shift keying;Closed-form solutions;Transmitters;Error probability;Symbols;Receivers;Light emitting diodes|
|[Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060466)|Y. Sahraoui; L. De Lucia; C. A. Kerrache; A. M. Vegni; M. Amadeo; A. Korichi|10.1109/CCNC51644.2023.10060466|Contact Tracing;Community Detection;COVID-19;Online Social Networks;COVID-19;Performance evaluation;Privacy;Target tracking;Social networking (online);Infectious diseases;Vaccines|
|[LF-WD: Device-Free Walking Direction Estimation with Low-Frequency CSI Acquisition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060404)|K. Tamai; T. Kanda; S. Kondo; K. Yamamoto; N. Kato; S. Taki; T. Negishi|10.1109/CCNC51644.2023.10060404|nan;nan|
|[Demonstration Of Performance For Low Cost Personal HSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060586)|P. Urien|10.1109/CCNC51644.2023.10060586|Personal HSM;TLS;Secure Element;Security;Costs;Microcontrollers;Elliptic curves;Software;Hardware;Internet;Servers|
|[Joint Resource Allocation and Link Adaptation for Ultra-Reliable and Low-Latency Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060392)|M. A. Hossen; T. X. Vu; V. -D. Nguyen; S. Chatzinotas; B. Ottersten|10.1109/CCNC51644.2023.10060392|Hybrid automatic repeat request (HARQ);link adaptation;modulation and coding scheme (MCS);resource allocation;ultra-reliable and low latency communication (URLLC);System performance;Ultra reliable low latency communication;Programming;Throughput;Encoding;Resource management;Iterative methods|
|[Optimizing Vehicle-to-Edge Mapping with Load Balancing for Attack-Resilience in IoV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060632)|A. Talpur; M. Gurusamy|10.1109/CCNC51644.2023.10060632|Internet of vehicles;resilience;attack;service availability;vehicle-to-edge mapping;edge network;nan|
|[Trusted Platform for Disruptive Vehicular Ad Hoc Networks using Distributed Ledger Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060255)|A. Lindgren; A. Ghafoor|10.1109/CCNC51644.2023.10060255|VANET;blockchains;distributed ledger;Technical requirements;Protocols;Distributed ledger;Vehicular ad hoc networks;Data collection;Sensor systems and applications;Road safety|
|[What WiFi Probe Requests can tell you](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060447)|R. Rusca; F. Sansoldo; C. Casetti; P. Giaccone|10.1109/CCNC51644.2023.10060447|Probe request;Passive sniffing;WiFi;People counting;MAC randomization;Portable computers;Operating systems;Urban areas;Standardization;Communication system security;Security;Probes|
|[Optimal Auction for Effective Energy Management for UAV-assisted Metaverse Synchronization System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059864)|N. C. Luong; L. K. Chau; N. D. Duy Anh; N. H. Sang; S. Feng; V. -D. Nguyen; D. Niyato; D. I. Kim|10.1109/CCNC51644.2023.10059864|Digital twin;energy trading;Metaverse;optimal auction;deep learning;Integrated circuits;Metaverse;Energy resources;Simulation;Inductive charging;Digital twins;Synchronization|
|[User Authentication by Fusion of Mouse Dynamics and Widget Interactions: Two Experiments with PayPal and Facebook](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059968)|S. Khan; D. Hou|10.1109/CCNC51644.2023.10059968|nan;Social networking (online);Fuses;Error analysis;Data security;Data protection;Authentication;Data breach|
|[Extremely High-accuracy Automatic Following between Mobilities Using Wireless Two-way Interferometry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059978)|R. Isogai; S. Yasuda; N. Shiga; Y. Shoji|10.1109/CCNC51644.2023.10059978|automatic following;millimeter-precision ranging;wireless two-way interferometry;interference;mobility;Wireless communication;Fading channels;Wireless sensor networks;Smoothing methods;Distance measurement;Sensors;Microwave filters|
|[Performance Analysis of Harvest-then-Access Protocol for Wireless Powered Communication Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060293)|A. Iwaki; K. Sanada; H. Hatano; K. Mori|10.1109/CCNC51644.2023.10060293|Wireless Powered Communication Networks (WPCNs);Wireless Power Transfer;Queuing theory;Energy queuing model;Wireless communication;Analytical models;Protocols;Simulation;Information processing;Throughput;Performance analysis|
|[Search for Unknown Events in Blind Zone using Multiple Autonomous Mobile Systems with Mobile Sensing Cluster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059994)|S. Izuhara; S. Nishigami; N. Fujiyama; E. Nii; H. Yomo; Y. Takizawa|10.1109/CCNC51644.2023.10059994|nan;Wireless communication;Spirals;Sensors;Task analysis|
|[Improvement of accommodation efficiency by TAS scheduling considering jitter caused by transmission period](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060149)|H. Abe; H. Kawata; T. Kubo; N. Yasuhara; Y. Kawakami; S. Yoshihara; T. Yoshida|10.1109/CCNC51644.2023.10060149|Time-sensitive Networking (TSN);Time-Aware Shaper (TAS);Service Provider Network;Wide Area Network (WAN);real-time communication;Multiplexing;Schedules;Job shop scheduling;Wireless networks;Jitter;Logic gates;Delays|
|[Design of Artificial Noise for Physical Layer Security in Visible Light Systems With Clipping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060251)|T. V. Pham; S. Hranilovic; S. Ishihara|10.1109/CCNC51644.2023.10060251|VLC;physical layer security;artificial noise;clipping distortion;Simulation;Nonlinear distortion;Interference;Physical layer security;Light emitting diodes;Security;Visible light communication|
|[The testing framework for Vehicular Edge Computing and Communications on the Smart Highway](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060332)|T. Verschoor; V. Charpentier; N. Slamnik-Kriještorac; J. Marquez-Barja|10.1109/CCNC51644.2023.10060332|Measurement tooling;V2X;ITS-G5;LTE-V2X PC5;5G;LTE-V2X Uu;Vehicular Networks;Edge computing;MEC;VEC;Road transportation;Cloud computing;Vehicular and wireless technologies;Road side unit;Packet loss;Throughput;Communications technology|
|[SDN Framework for QoS provisioning and latency guarantee in 5G and beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059714)|S. MESSAOUDI; A. Ksentini; C. BONNET|10.1109/CCNC51644.2023.10059714|nan;5G mobile communication;Quality of service;Telecommunication traffic;Real-time systems;Open source hardware;Low latency communication;Software defined networking|
|[Adaptive NDN, DTN and NoD Deployment in Smart-City Networks Using SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060803)|V. Demiroglou; L. Mamatas; V. Tsaoussidis|10.1109/CCNC51644.2023.10060803|Information-Centric Networking;Named Data Networking;Delay-Tolerant Networking;Software-Defined Networks;Smart Cities;Adaptive systems;Protocols;Smart cities;Line-of-sight propagation;Delays;Internet of Things;Reliability|
|[Analysis of Novel Mouse Dynamics Dataset with Repeat Sessions: Helpful Observations for Tackling Session-Replay Bot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060083)|S. Sadeghpour; N. Vlajic|10.1109/CCNC51644.2023.10060083|Behavioral biometrics;Mouse dynamics;Feature learning;Convolutional neural network;Clustering algorithms;Biometrics (access control);Heuristic algorithms;Banking;Image representation;Chatbots;Mice;Trajectory|
|[Compressing Model before Federated Learning by Transferrable Surrogate Lottery Ticket](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060578)|T. Tanimura; M. Takase|10.1109/CCNC51644.2023.10060578|Machine Learning;Federated Learning;Model Pruning;Lottery Ticket Hypothesis;Performance evaluation;Training;Costs;Federated learning;Servers;Task analysis|
|[Evaluating Offloading Scalability Using a Multi-language Approach on Cellular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060426)|F. De Matos; P. A. L. Rego; F. Trinta|10.1109/CCNC51644.2023.10060426|Offloading;Multi-Language;Mobile Cloud Computing;Scalability;Cellular Networks;Performance evaluation;Cellular networks;Java;Computer languages;Scalability;Mobile handsets;Servers|
|[AppDAS: An Application QoS-Aware Distributed Antenna Selection for 5G Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059796)|T. Onishi; E. Takahashi; Y. Nishikawa; S. Maruyama|10.1109/CCNC51644.2023.10059796|Distributed antenna system (DAS);Antenna selection;Deep reinforcement learning (DRL);5G;6G;Industrial application;Measurement;5G mobile communication;Spatial diversity;Reinforcement learning;Quality of service;Throughput;Stability analysis|
|[Delay Tolerable Precaching Scheme in Content-Centric Vehicular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060765)|Y. Nam; H. Choi; Y. Shin; D. Mugerwa; E. Lee|10.1109/CCNC51644.2023.10060765|Content-Centric Vehicular Networks;Content Precaching;Tolerable Delay Time;Optimization;nan|
|[Physical Layer Security in Untrusted Lossy Decode-and-Forward Relay Networks with Finite Blocklength](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060369)|S. Qian|10.1109/CCNC51644.2023.10060369|physical layer security;untrusted relay;finite blocklength;reliable-and-secure probability;lossy decode-and-forward;Measurement;Relay networks (telecommunication);Physical layer security;Reliability;Security;Resource management;Signal to noise ratio|
|[Spatial model for capturing size and shape of object from point cloud data for robot vision system with LIDAR sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060006)|K. Suzuki; R. Shinkuma; N. Nakamura; G. Trovato|10.1109/CCNC51644.2023.10060006|robot vision system;LIDAR sensor;point cloud;spatial model;Point cloud compression;Visualization;Laser radar;Shape;Robot vision systems;Prototypes;Benchmark testing|
|[Proportionally Fair Resource Allocation in SD-RAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060479)|F. Mehmeti; W. Kellerer|10.1109/CCNC51644.2023.10060479|SD-RAN;5G;Proportional fairness;Measurement;Cellular networks;Base stations;5G mobile communication;Throughput;Resource management;Software radio|
|[Joint α-Fair Allocation of RAN and Computing Resources to Vehicular Users with URLLC Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060840)|V. T. Haider; F. Mehmeti; A. Cantarero; W. Kellerer|10.1109/CCNC51644.2023.10060840|5G;Vehicular networks;URLLC;α-fairness;5G mobile communication;Wireless networks;Ultra reliable low latency communication;Minimax techniques;Downlink;Complexity theory;Resource management|
|[On the Decentralization of Health Systems for Data Availability: a DLT-based Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059701)|G. Bigini; M. Zichichi; E. Lattanzi; S. Ferretti; G. D'Angelo|10.1109/CCNC51644.2023.10059701|Distributed Ledger Technology;Smart Contracts;Health Data;Distributed Storage;Social Networks;Technological innovation;Distributed ledger;Social networking (online);Scalability;Smart contracts;Memory;Market research|
|[An Online Multi-dimensional Knapsack Approach for Slice Admission Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060460)|J. Ajayi; A. Di Maio; T. Braun; D. Xenakis|10.1109/CCNC51644.2023.10060460|Network Slicing;Admission Control;Online Algorithms;Mobile Networks;Economics;Uncertainty;Monte Carlo methods;Simulation;Network slicing;Admission control;Resource management|
|[Impact Evaluation of Driving Style on Electric Vehicle Battery based on Field Testing Result](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060822)|K. S. Chou; D. Aguiari; R. Tse; S. -K. Tang; G. Pau|10.1109/CCNC51644.2023.10060822|Electric Vehicle;SOH;Driving Behaviour;Lithium Battery;Battery Aging;Temperature sensors;Temperature measurement;Aging;Electric vehicles;Batteries;Behavioral sciences;Brakes|
|[A virtualized testbed for IoT: Scalability for swarm application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060090)|W. T. Pereira; L. C. C. De Biase; G. Fedrecheski; M. K. Zuffo|10.1109/CCNC51644.2023.10060090|IoT;testbed;virtualization;tests;container;scalability;network emulation;Performance evaluation;Scalability;Surveillance;Emulation;Containers;Software;Heterogeneous networks|
|[I See What You're Watching on Your Streaming Service: Fast Identification of DASH Encrypted Network Traces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060390)|M. Björklund; M. Julin; P. Antonsson; A. Stenwreth; M. Åkvist; T. Hjalmarsson; R. Duvignau|10.1109/CCNC51644.2023.10060390|privacy;DASH;video streaming;traffic analysis;wireless networks;SVT Play;Support vector machines;Privacy;Protocols;Prototypes;Telecommunication traffic;Fingerprint recognition;Streaming media|
|[Programmable Software-Defined Testbed for Visible Light UAV Networks: Architecture Design and Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060808)|Y. Zhang; N. Cen|10.1109/CCNC51644.2023.10060808|Visible Light Communication;Unmanned Aerial Vehicles;Wireless Networking;nan|
|[Joint Altitude and Beamwidth Optimization for UAV-Powered Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060122)|H. -H. Choi; J. -R. Lee|10.1109/CCNC51644.2023.10060122|UAV communication;wireless power transfer;wireless sensor network;directional antenna;altitude optimization;Wireless communication;Wireless sensor networks;Numerical analysis;Simulation;Mathematical analysis;Directional antennas;Data collection|
|[Entropy based DDoS Detection in Software Defined Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059949)|G. Fioravanti; M. G. Spina; F. De Rango|10.1109/CCNC51644.2023.10059949|Software Defined Networks;DoS;DDoS;Security;Phase measurement;Computer architecture;Size measurement;Denial-of-service attack;Entropy;Security;Computer crime|
|[Supporting Path Planning in LoRa-based UAVs for dynamic Coverage for IoT devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060525)|F. De Rango; D. Stumpo|10.1109/CCNC51644.2023.10060525|LoRa;IoT;UAV;intelligent path;dynamic coverage;Performance evaluation;Wireless communication;Power demand;Packet loss;Autonomous aerial vehicles;Path planning;Internet of Things|
|[Outlier Detection in IoT data for Elderly Care in Smart Homes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060085)|Z. K. Shahid; S. Saguna; C. Åhlund|10.1109/CCNC51644.2023.10060085|Internet-of- things;healthcare;ambient assisted living;anomaly detection;unsupervised learning;Privacy;Sociology;Smart homes;Medical services;Machine learning;Motion detection;Older adults|
|[A Q-learning-based Multipath Scheduler for Data Transmission Optimization in Heterogeneous Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060683)|T. T. Nguyen; M. H. Vu; P. L. Nguyen; P. T. Do; K. Nguyen|10.1109/CCNC51644.2023.10060683|Q-learning;MPQUIC;multipath scheduler;heterogeneous networks;dynamicity;Transport protocols;Schedules;5G mobile communication;Wireless networks;TCPIP;Mobile handsets;Heterogeneous networks|
|[Economics of Semantic Communication in Metaverse: An Auction Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060647)|Z. Q. Liew; H. Du; W. Y. Bryan Lim; Z. Xiong; D. Niyato; H. Yu|10.1109/CCNC51644.2023.10060647|Metaverse;incentive mechanism;semantic communication;age of information;Measurement;Metaverse;Data integrity;Semantics;Streaming media;Information filters;Data models|
|[Real-Time Hash Aggregation for Blockchain System With 3D Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060594)|K. Hirai; K. Akiyama; R. Shinkuma; A. Mine|10.1109/CCNC51644.2023.10060594|image sensor network;blockchain network;LIDAR;hash aggregation;smart monitoring;Laser radar;Three-dimensional displays;Smart cities;Benchmark testing;Time measurement;Real-time systems;Blockchains|
|[A Provably Secure and Efficient 5G-AKA Authentication Protocol using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059918)|A. K. Yadav; A. Braeken; M. Misra; M. Liyange|10.1109/CCNC51644.2023.10059918|5G-AKA;Authentication;ECC;Cloud Computing;Internet of Things (IoT);ROR logic;Network Security;Privacy;Protocols;Costs;Smart contracts;Authentication;Mobile communication;Blockchains|
|[A modular and mesh-capable LoRa based Content Transfer Protocol for Environmental Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060496)|B. Arratia; P. García-Guillamón; C. T. Calafate; J. -C. Cano; J. M. Cecilia; P. Manzoni|10.1109/CCNC51644.2023.10060496|LoRa;IoT;Mesh;Environmental Intelligence;Green Connectivity;Temperature measurement;Performance evaluation;Energy consumption;Temperature distribution;Sea measurements;Water quality;Time measurement|
|[Energy-Efficient Resource Scheduling Using X-CNN and CD-SBO for SDN based MEC Enabled IoV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060646)|S. Safavat; D. B. Rawat|10.1109/CCNC51644.2023.10060646|Internet of Vehicles (IoV);SDN;K-Means Algorithm;MEC and Satin Bowerbird Optimization (SBO);nan|
|[Detection of DGA-based Malware Communications from DoH Traffic Using Machine Learning Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059835)|R. Mitsuhashi; Y. Jin; K. Iida; T. Shinagawa; Y. Takai|10.1109/CCNC51644.2023.10059835|DNS over HTTPS (DoH);Hierarchical network traffic classification;Gradient Boosting Decision Tree (GBDT);Regularized Greedy Forest (RGF);Domain Generation Algorithm (DGA);DGA-based malware;Privacy;Protocols;Machine learning algorithms;Operating systems;Telecommunication traffic;Boosting;Malware|
|[Evaluation of Source Data Selection for DTL Based CSI Feedback Method in FDD Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060176)|M. Inoue; T. Ohtsuki; K. Yamamoto; G. Gui|10.1109/CCNC51644.2023.10060176|Deep transfer learning (DTL);downlink CSI;limited feedback;FDD;massive MIMO;source data selection;Measurement;Computational modeling;Transfer learning;Line-of-sight propagation;Massive MIMO;Downlink;Data models|
|[SPinS-FL: Communication-Efficient Federated Subnetwork Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060698)|M. Tsutsui; S. Takamaeda-Yamazaki|10.1109/CCNC51644.2023.10060698|federated learning;the Edge-Popup algorithm;deep learning;IoT;nan|
|[Optimal dynamic power allocation based on multiuser cooperative mobility for energy efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060580)|J. Xie; T. Hirai; Y. Gao; T. Murase|10.1109/CCNC51644.2023.10060580|Energy efficiency;MANETs;dynamic power allocation;multiuser cooperative mobility;Lyapunov optimization;Costs;Heuristic algorithms;Quality of service;Dynamic scheduling;Energy efficiency;Batteries;Resource management|
|[A Real-time Object Detection for WiFi CSI-based Multiple Human Activity Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059647)|I. Elujide; J. Li; A. Shiran; S. Zhou; Y. Liu|10.1109/CCNC51644.2023.10059647|WiFi;channel state information;activity recognition;deep learning;object detection framework;Deep learning;Training;Image segmentation;Continuous wavelet transforms;Wavelet domain;Object detection;Streaming media|
|[Power-efficient Antenna Switching and Beamforming Design for Multi-User SWIPT with Non-Linear Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059879)|J. Jalali; A. Khalili; A. Rezaei; J. Famaey; W. Saad|10.1109/CCNC51644.2023.10059879|nan;Array signal processing;Simulation;Receiving antennas;Programming;Energy efficiency;Iterative methods;Energy harvesting|
|[Dynamic Offloading for Compute Adaptive Jobs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059859)|A. Bari; G. De Veciana; K. Johnsson; A. Pyattaev|10.1109/CCNC51644.2023.10059859|Computation offloading;Upper bound;Performance evaluation;Wireless communication;Adaptation models;Adaptive systems;Upper bound;Computational modeling;Admission control|
|[Mobility Adaptive Data Rate Based on Kalman Filter for LoRa-Empowered IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060330)|A. Farhad; G. -R. Kwon; J. -Y. Pyun|10.1109/CCNC51644.2023.10060330|The adaptive data rate;Internet of Things;LoRaWAN;Spreading Factor;Kalman Filter;Resource Allocation;Energy consumption;Adaptive systems;Simulation;Packet loss;Dynamic scheduling;Internet of Things;Kalman filters|
|[LinUCB-Based Handover Algorithm for Throughput Maximization in Heterogeneous Cellular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060709)|Y. -S. Chen; Z. -H. Huang; M. -J. Tsai|10.1109/CCNC51644.2023.10060709|handover;linear upper confidence bound;heterogeneous network;Cellular networks;Base stations;Uncertainty;Spectral efficiency;Artificial neural networks;Bandwidth;Handover|
|[MHND: Multi-Homing Network Design Model for Delay Sensitive Distributed Processing Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059846)|A. Kawabata; B. C. Chatterjee; E. Oki|10.1109/CCNC51644.2023.10059846|Delay sensitive service;network design;availability;distributed computing;conservative synchronization;Degradation;Distributed processing;Costs;Mission critical systems;Integer linear programming;Delays;Numerical models|
|[Watch From Sky: Machine-Learning-Based Multi-UAV Network for Predictive Police Surveillance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060624)|R. Sugano; R. Shinkuma; T. Nishio; N. B. Mandayam|10.1109/CCNC51644.2023.10060624|nan;Performance evaluation;Law enforcement;Surveillance;Computational modeling;Reinforcement learning;Autonomous aerial vehicles;Dispatching|
|[Pilot Power Allocation Scheme for User-Centric Cell-free Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059832)|M. Sarker; A. O. Fapojuwo|10.1109/CCNC51644.2023.10059832|Massive MIMO;cell-free massive MIMO;centralized control;pilot power allocation;B5G;Training;Estimation error;Spectral efficiency;Scalability;Heuristic algorithms;Massive MIMO;MIMO|
|[Disaggregated Micro Data Center: Resource Allocation Considering Impact of Network on Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059790)|A. Ikoma; Y. Ohsita; M. Murata|10.1109/CCNC51644.2023.10059790|Micro data center;Resource disaggregation;Resource allocation;Multi-core optical fiber;Data centers;Network topology;Resource management|
|[Towards a Reliable Hierarchical Android Malware Detection Through Image-based CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060381)|J. Geremias; E. K. Viegas; A. O. Santin; A. Britto; P. Horchulhack|10.1109/CCNC51644.2023.10060381|Android Malware;CNN;Hierarchical Classification;Source coding;Focusing;Malware;Convolutional neural networks;Reliability;Proposals;Task analysis|
|[RADTEC: Re-authentication of IoT Devices with Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059777)|K. Gupta; N. Ghose; B. Wang|10.1109/CCNC51644.2023.10059777|IoT security;authentication;device type classification;machine learning;cross layer analysis;Performance evaluation;Privacy;Home appliances;Protocols;Authentication;Machine learning;Data models|
|[Hide & Seek: Traffic Matrix Completion and Inference Using Hidden Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060329)|A. Sacco; F. Esposito; G. Marchetto|10.1109/CCNC51644.2023.10060329|traffic matrix;machine learning;inference;Hidden Markov models;Estimation;Telecommunication traffic;Data collection;Prediction algorithms;Inference algorithms;Encoding|
|[A New Scheduling Algorithm for Time-varying MIMO Channels with a Channel Aging Metric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059856)|M. Hong; I. Hwang; J. Heo; D. Hong|10.1109/CCNC51644.2023.10059856|massive MIMO;scheduling;user selection;fast fading;channel aging;time-varying channel;channel correlation;Multiplexing;Measurement;Simulation;Channel estimation;Interference;Aging;Time-varying channels|
|[Low Complexity Implementation of Symbol-level Precoding in Multi-user MISO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060084)|J. Lee; C. G. Kang|10.1109/CCNC51644.2023.10060084|symbol-level precoding(SLP);constructive interference gain;constructive interference optimization(CIO);minimum mean square error (MMSE);hybrid beamforming;Phase shift keying;Array signal processing;Precoding;Modulation;Interference;Performance gain;Computational complexity|
|[An Adaptive MBSFN Resource Allocation Algorithm for Multicast and Unicast Traffic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060040)|I. Khalid; M. Girmay; V. Maglogiannis; D. Naudts; A. Shahid; I. Moerman|10.1109/CCNC51644.2023.10060040|LTE;eMBMS;MBSFN;multicast traffic;dynamic resource allocation;unicast traffic;SDR;Digital multimedia broadcasting;Multicast algorithms;Unicast;Software algorithms;Streaming media;Throughput;Resource management|
|[Client Tuned Federated Learning for RSSI-based Indoor Localisation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059919)|J. Paulavičius; P. Carnelli; R. Piechocki; A. Khan|10.1109/CCNC51644.2023.10059919|federated learning;localisation;fingerprinting;e-health;Federated learning;Fingerprint recognition;Benchmark testing;Received signal strength indicator|
|[Deep Reinforcement Learning Based Beamforming Codebook Design for RIS-aided mmWave Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060056)|A. Abdallah; A. Celik; M. M. Mansour; A. M. Eltawil|10.1109/CCNC51644.2023.10060056|nan;Training;Wireless communication;Deep learning;Array signal processing;Simulation;Discrete Fourier transforms;Reinforcement learning|
|[Two-Tier Anomaly Detection for an Internet of Things Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059733)|S. Narayanan; S. Uludag|10.1109/CCNC51644.2023.10059733|nan;Measurement;Computational modeling;Simulation;Clustering algorithms;Data models;Classification algorithms;Internet of Things|
|[A Systematic Constellation Design for BICM Systems With Geometric Shaping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060754)|E. Kurihara; H. Ochiai|10.1109/CCNC51644.2023.10060754|nan;Systematics;Quadrature amplitude modulation;Simulation;Peak to average power ratio;Gaussian distribution;Turbo codes;Labeling|
|[NPRA: A Novel Predictive Resource Allocation Mechanism for Next Generation Network Slicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060670)|W. Binghui; N. V. Abhishek; A. PC; M. Gurusamy|10.1109/CCNC51644.2023.10060670|Long Short Term Memory;Resource Prediction;Network Slicing;Resource Allocation;5G mobile communication;Network slicing;Heuristic algorithms;Predictive models;Prediction algorithms;Data models;Resource management|
|[5G RRC Protocol and Stack Vulnerabilities Detection via Listen-and-Learn](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059624)|J. Yang; Y. Wang; T. X. Tran; Y. Pan|10.1109/CCNC51644.2023.10059624|Fuzz Testing;Vulnerabilities Detection;RRC Protocols;5G Stack;LSTM;Protocols;Automation;Systematics;5G mobile communication;Scalability;Fuzzing;Real-time systems|
|[Adaptive Priority Control Method for ABR Streaming to Reduce Congestion Levels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059760)|T. Ozu; H. Aoki; A. Hasegawa; H. Yokoyama|10.1109/CCNC51644.2023.10059760|Priority control;QoS;Adaptive bitrate streaming;MPEG-DASH;Adaptation models;Heuristic algorithms;Bit rate;Transform coding;Packet loss;Streaming media;Mobile communication|
|[Ensuring Content Integrity and Confidentiality in Information-Centric Secure Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060672)|H. H. Hlaing; H. Asaeda|10.1109/CCNC51644.2023.10060672|Information-centric network;in-network caching;content integrity;confidentiality;nan|
|[LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060415)|I. Mavromatis; A. Sanchez-Mompo; F. Raimondo; J. Pope; M. Bullo; I. Weeks; V. Kumar; P. Carnelli; G. Oikonomou; T. Spyridopoulos; A. Khan|10.1109/CCNC51644.2023.10060415|Data Drift;IoT;Drift Detector;Resource-Constrained;Ensemble Learning;Performance evaluation;Data privacy;Scalability;Data integrity;Distributed databases;Detectors;Internet of Things|
|[Assessment of MU-MIMO schemes with cylindrical arrays under 3GPP 3D channel model for B5G networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060861)|D. G. Riviello; R. Tuninato; R. Garello|10.1109/CCNC51644.2023.10060861|MU-MIMO;cylindrical arrays;planar arrays;precoding;mmWave;B5G;nan|
|[Deep Reinforcement Learning-based Uplink Power Control in Cell-Free Massive MIMO](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059615)|X. Zhang; M. Kaneko; V. An Le; Y. Ji|10.1109/CCNC51644.2023.10059615|Cell-free massive MIMO;DDPG;power control;Power control;Quality of service;Massive MIMO;Aerospace electronics;Minimax techniques;Benchmark testing;MIMO|
|[BASICS: A Multi-Blockchain Approach for Securing VM Migration in Joint-Cloud Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060260)|P. B. Velloso; D. C. Morales; T. M. T. Nguyen; G. Pujolle|10.1109/CCNC51644.2023.10060260|nan;Cloud computing;System performance;Scalability;Maintenance engineering;Virtual machining;Delays;Blockchains|
|[A Generative Adversarial Network-based Attack for Audio-based Condition Monitoring Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060644)|A. R. Ba Nabila; E. K. Viegas; A. Almahmoud; W. T. Lunardi|10.1109/CCNC51644.2023.10060644|Generative Adversarial Networks;Adversarial Example;Audio Generation;Condition monitoring;Propellers;Production;Machine learning;Generative adversarial networks;Proposals;Reliability|
|[Compressed Client Selection for Efficient Communication in Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059659)|A. H. Mohamed; N. R. G. Assumpçáo; C. A. Astudillo; A. M. de Souza; L. F. Bittencourt; L. A. Villas|10.1109/CCNC51644.2023.10059659|Federated Learning;Communication;Machine Learning;Training;Performance evaluation;Machine learning algorithms;Federated learning;Fitting;Multilayer perceptrons;Particle measurements|
|[Coalitional Game-Theoretical Approach to Coinvestment with Application to Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060093)|R. Patanè; A. Araldo; T. Chahed; D. Kiedanski; D. Kofman|10.1109/CCNC51644.2023.10060093|Coinvestment;Multi-tenancy;Edge computing;Coalitional game theory;Shapley;Costs;Sensitivity;Games;Telecommunication traffic;Streaming media;Solids;Stakeholders|
|[RRP: A Reliable Reinforcement Learning Based Routing Protocol for Wireless Medical Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060225)|M. S. Hajar; H. Kalutarage; M. O. Al-Kadri|10.1109/CCNC51644.2023.10060225|Routing;Reinforcement Learning;Trust Management;Blackhole;Selective Forwarding;Sinkhole;On-off;Wireless communication;Wireless sensor networks;Medical services;Routing;Routing protocols;Robustness;Communication system security|
|[Lightweight, Dynamic and Energy Efficient Security Mechanism for constrained IoT devices using CoAP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059854)|M. G. Spina; F. De Rango|10.1109/CCNC51644.2023.10059854|IoT Security;CoAP;DTLS;lightweight security;dynamic security;energy;Performance evaluation;Privacy;Protocols;Dynamic scheduling;Energy efficiency;Security;Internet of Things|
|[Enabling Proportionally Fair Mobility Management in 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060784)|A. Prado; F. Stöckeler; F. Mehmeti; W. Kellerer|10.1109/CCNC51644.2023.10060784|Proportional fairness;handovers;mobility;deep reinforcement learning;Deep learning;5G mobile communication;Reinforcement learning;Handover;Radio links;User experience;Complexity theory|
|[A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060584)|M. Raeiszadeh; A. Saleem; A. Ebrahimzadeh; R. H. Glitho; J. Eker; R. A. F. Mini|10.1109/CCNC51644.2023.10060584|Anomaly Detection;Deep Learning;Real-time Analytics;LSTM;Streaming Data;Measurement;Deep learning;Predictive models;Performance gain;Real-time systems;Data models;Fourth Industrial Revolution|
|[Maximizing Network Throughput Using SD-RAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059833)|F. Mehmeti; A. Papa; W. Kellerer|10.1109/CCNC51644.2023.10059833|SD-RAN;5G;Performance optimization;Cellular networks;Measurement;Base stations;5G mobile communication;Throughput;Resource management;Optimization|
|[Detection and Localization of DDoS Attack During Inter-Slice Handover in 5G Network Slicing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060402)|H. Bisht; M. Patra; S. Kumar|10.1109/CCNC51644.2023.10060402|Network Slicing;Inter-Slice Handover;DDoS attack;nan|
|[The Role of Augmented Reality and the Internet of Things in the Management of University Cultural Heritage. A Case Study: The Prat Collection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060549)|A. A. R. Batista; S. M. Labrada; M. S. Suárez; M. Á. Z. Barbán; C. M. P. Rondón; J. Marquez-Barja|10.1109/CCNC51644.2023.10060549|Augmented Reality;Internet of Things;Management of Cultural Heritage;University Museum;Memory management;Education;Energy measurement;Data collection;Museums;Internet of Things;Cultural differences|
|[Meta-transfer Learning for Massive MIMO Channel Estimation for Millimeter-Wave Outdoor Vehicular Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060092)|B. Tolba; A. H. Abd El-Malek; M. Abo-Zahhad; M. Elsabrouty|10.1109/CCNC51644.2023.10060092|Channel estimation;massive MIMO;meta-learning;outdoor environment;millimeter-wave vehicular environment;Deep learning;Training;Simulation;Millimeter wave technology;Transfer learning;Transmitting antennas;Channel estimation|
|[Optimized CNN Auto-Generator Using GA With Stopping Criterion: Design and a Use Case](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060860)|C. Montes; T. Morehouse; D. Kasilingam; R. Zhou|10.1109/CCNC51644.2023.10060860|optimization;CNN;automatic modulation recognition;AMR;AMC;hyperparameter;neural architecture search;genetic algorithm;Training;Neural networks;Modulation;Optimization methods;Network architecture;Hyperparameter optimization;Computational efficiency|
|[Pair-Less Bluetooth for Touchless Interaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059727)|V. Figueroa; J. Kristanto; A. Nahapetian|10.1109/CCNC51644.2023.10059727|Pair-less Bluetooth;wireless signals;mobile computing;human-computer interaction;touchless interaction;Wireless communication;Strips;Bluetooth;Transmitters;Pandemics;Space technology;Receivers|
|[Performance Benchmarking of the QUIC Transport Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060785)|B. V. Da Cunha; X. Li; W. Wilson; K. Harfoush|10.1109/CCNC51644.2023.10060785|Hypertext Transfer Protocol (HTTP) versions 2/3;QUIC;Transport Protocols;Transport protocols;Degradation;Force;Switches;Benchmark testing;Internet;Task analysis|
|[Tiny but Mighty: Embedded Machine Learning for Indoor Wireless Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060797)|B. Jones; U. Raza; A. Khan|10.1109/CCNC51644.2023.10060797|Indoor positioning;Embedded machine learning;Localization;Location awareness;Wireless communication;Performance evaluation;Costs;Heuristic algorithms;Machine learning;Brain modeling|
|[Collusion-resistant. Lightweight and Privacy-preserving Authentication Protocol for IoV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060548)|W. Lalouani; M. Younis|10.1109/CCNC51644.2023.10060548|Authentication;Physically Unclonable Functions;IoV;Security;Collusion resistance;Privacy preservation;nan|
|[Twitter as Passive Sensor to Understand How COVID-19 Pandemic Affected Human Mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060726)|M. Furini; M. Montangero|10.1109/CCNC51644.2023.10060726|Human mobility;Twitter;passive sensors;psycho-linguistic analysis;COVID-19;Social networking (online);Pandemics;Urban areas;Blogs;Telephone sets;Social factors|
|[Understanding users music listening habits for time and activity sensitive customized playlists](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060462)|M. Furini; M. Montangero|10.1109/CCNC51644.2023.10060462|Recommendation systems;music playlists;music listening habits;Spotify;Music;Auditory system;History;Standards;Testing|
|[Privacy-Preserving Honeypot-Based Detector in Smart Grid Networks: A New Design for Quality-Assurance and Fair Incentives Federated Learning Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060393)|A. Albaseer; M. Abdallah|10.1109/CCNC51644.2023.10060393|Smart Grid Networks;Honeypot;DL-based detector;Privacy-preserving;Incentive mechanism;Federated Learning;Training;Machine learning algorithms;Federated learning;Detectors;Data models;Smart grids;Security|
|[Divide and Cache: A Novel Control Plane Framework for Private 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059999)|T. Park; H. Lee; H. Kim; S. Han; T. Kim; S. Pack|10.1109/CCNC51644.2023.10059999|Private 5G;control plane;cache;free5GC;open-source software testbed;Costs;5G mobile communication;Statistical analysis;Dynamic scheduling;Noise measurement;3GPP;Open source software|
|[Multi-UAV Assisted Network Coverage Optimization for Rescue Operations using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060414)|O. S. Oubbati; H. Badis; A. Rachedi; A. Lakas; P. Lorenz|10.1109/CCNC51644.2023.10060414|Reinforcement Learning;Disaster relief;UAV;Emergency networks;Trajectory optimization;Coverage;Deep learning;Energy consumption;Reinforcement learning;Throughput;Mobile communication;Emergency services;Downlink|
|[Encryption-based Security in Wearable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059824)|A. Budzik; G. Srivastava; M. Baza|10.1109/CCNC51644.2023.10059824|wearable devices;security;encryption;Privacy;Wearable computers;Cryptography|
|[IEEE 802.1AS Precision Time Protocol Full Hardware Prototyping for Industrial IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059981)|N. Sakaguchi; R. Kawate; Y. Nagai|10.1109/CCNC51644.2023.10059981|IEEE 802.1AS;precision time protocol;time-sensitive networking;time synchronization;Performance evaluation;Protocols;Fluctuations;Prototypes;Hardware;Software;Synchronization|
|[MUSK-DQN: Multi-UBS Selective-K Deep Q-Network for Maximizing Energy-Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059845)|S. Lee; H. Lee|10.1109/CCNC51644.2023.10059845|Unmanned aerial vehicle (UAV);deep Q-network (DQN);UAV-base station (UBS);UBS control;energy efficiency maximization;Cellular networks;6G mobile communication;Wireless communication;Reinforcement learning;Autonomous aerial vehicles;Energy efficiency;Batteries|
|[BloodHound: Early Detection and Identification of Jamming at the PHY-layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059878)|S. Alhazbi; S. Sciancalepore; G. Oligeri|10.1109/CCNC51644.2023.10059878|Jamming Detection;Machine Learning for Security;Mobile Security;Training;Measurement uncertainty;Packet loss;Receivers;Radio links;Robustness;Hardware|
|[Information-Centric Wireless Sensor Networks for Smart-City-as-a Service: Concept Proposal, Testbed Development, and Fundamental Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060577)|S. Mori|10.1109/CCNC51644.2023.10060577|Information-centric wireless sensor networks;Smart-city-as-a-service;Ecosystem;Wireless communication;Performance evaluation;Wireless sensor networks;Ecosystems;Prototypes;Reliability;Proposals|
|[zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059601)|Y. Tao; Y. Jiang; P. Lin; M. Tsukada; H. Esaki|10.1109/CCNC51644.2023.10059601|Cooperative Intelligent Transportation Systems;Cooperative Perception;Data Authenticity;Self-proving Data Verification;Security Privacy and Trust;Economics;Data privacy;Privacy;Biological system modeling;Computational modeling;Vehicular ad hoc networks;Transportation|
|[Should Large ISPs Apply the Same Settlement-Free Peering Policies To Both ISPs and CDNs?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060728)|A. Nikkhah; S. Jordan|10.1109/CCNC51644.2023.10060728|Internet Interconnection;Net Neutrality;Peering Policies;Costs;Web and internet services;Internet|
|[Recognition-aware Bitrate Allocation for AI-Enabled Remote Video Surveillance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059877)|F. Beye; Y. Shinohara; H. Itsumi; K. Nihei|10.1109/CCNC51644.2023.10059877|nan;Wireless networks;Bit rate;Bandwidth;Streaming media;Video compression;Cameras;Video surveillance|
|[Impact of Virtual Collisions on the Performance of IEEE 802.11ad EDCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059765)|M. P. R. S. Kiran|10.1109/CCNC51644.2023.10059765|IEEE 802.11ad;Virtual collisions;Millimeter-wave communications;Directional channel access;Wireless communication;Analytical models;Monte Carlo methods;Millimeter wave technology;Markov processes;Throughput;Mathematical models|
|[Performance Evaluation of Quantum-Resistant TLS for Consumer IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060762)|J. Bozhko; Y. Hanna; R. Harrilal-Parchment; S. Tonyali; K. Akkaya|10.1109/CCNC51644.2023.10060762|Post-Quantum Cryptography;Key Encapsulation Mechanism;TLS;IoT;IP Over Bluetooth;Wireless communication;Performance evaluation;Encapsulation;Heuristic algorithms;Resists;Internet of Things;Cryptography|
|[Q-FiRM: Fidelity-based Rate Maximizing Routes for Quantum Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060707)|K. Li; V. Chaudhary; S. G. Sanchez; K. R. Chowdhury|10.1109/CCNC51644.2023.10060707|Quantum networks;quantum repeaters;quantum routing;quantum communication;entanglement;Measurement;Transmitters;Quantum entanglement;Simulation;Qubit;Receivers;Quantum state|
|[Addressing the Unbounded Latency of Best-Effort Device-to-Device Communication with Low Earth Orbit Satellite Support](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059685)|M. Franke; F. Klingler; C. Sommer|10.1109/CCNC51644.2023.10059685|nan;Performance evaluation;Satellites;Protocols;Computer simulation;Computer network reliability;Low earth orbit satellites;Throughput|
|[RAP-G: Reliability-aware service placement using genetic algorithm for deep edge computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060108)|A. KACI; S. Ait-Chellouche; Y. Hadjadj-Aoul; M. Bagaa|10.1109/CCNC51644.2023.10060108|Edge Computing;Artificial Intelligence;Ultra-Reliable Low Latency Communications;Service Orchestration;Image edge detection;Image processing;Turning;Genetics;Reliability;Low latency communication;Genetic algorithms|
|[Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060026)|M. A. Habib; H. Zhou; P. E. Iturria-Rivera; M. Elsayed; M. Bavand; R. Gaigalas; S. Furr; M. Erol-Kantarci|10.1109/CCNC51644.2023.10060026|Multi-RAT;traffic steering;reinforcement learning;Q-learning;5G mobile communication;Heuristic algorithms;Quality of service;Throughput;Load management;User experience|
|[MobiDeep: Mobile DeepFake Detection through Machine Learning-based Corneal-Specular Backscattering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059841)|M. Mohzary; K. J. Almalki; B. -Y. Choi; S. Song|10.1109/CCNC51644.2023.10059841|DeepFake;Corneal-Specular Backscattering;Deep learning;Deepfakes;Visualization;Shape;Image color analysis;Neural networks;Focusing|
|[Texting and Driving Recognition leveraging the Front Camera of Smartphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060838)|F. Montori; M. Spallone; L. Bedogni|10.1109/CCNC51644.2023.10060838|image recognition;object detection;internet of things;Image recognition;Cameras;Prediction algorithms;Belts;Classification algorithms;Security;Automobiles|
|[DroneNet: Crowd Density Estimation using Self-ONNs for Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059904)|M. A. Khan; H. Menouar; R. Hamila|10.1109/CCNC51644.2023.10059904|CNN;crowd counting;density estimation;drones;self-ONNs;Performance evaluation;Deep learning;Computational modeling;Neural networks;Estimation;Benchmark testing;Video surveillance|
|[Reconfigurable Intelligent Surface aided Wireless Powered Mobile Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059797)|Y. Lu; J. Zhao|10.1109/CCNC51644.2023.10059797|Reconfigurable intelligent surface;mobile edge computing;wireless power transfer;Wireless communication;Energy consumption;Multi-access edge computing;Simulation;Wireless power transfer;Mobile handsets;Servers|
|[Arousal effects on Fitness-to-Drive assessment: algorithms and experiments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060261)|M. Andruccioli; M. Mengozzi; R. Presta; S. Mirri; R. Girau|10.1109/CCNC51644.2023.10060261|Drivers' behaviour;Driving simulation;Arousal Monitoring;Experimental evaluations;Temperature sensors;Temperature measurement;Visualization;Temperature distribution;Heart beat;Distance measurement;Indexes|
|[Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059966)|N. F. Cheng; T. Pamuklu; M. Erol-Kantarci|10.1109/CCNC51644.2023.10059966|network slicing;O-RAN;CU-DU;functional split;bandwidth optimization;RL;Q-learning;Q-learning;Enhanced mobile broadband;Network slicing;Bandwidth;Quality of service;Ultra reliable low latency communication;Throughput|
|[An ML-driven framework for edge orchestration in a vehicular NFV MANO environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060659)|N. Slamnik-Kriještorac; M. C. Botero; L. Cominardi; S. Latré; J. M. Marquez-Barja|10.1109/CCNC51644.2023.10060659|management and orchestration;NFV;MEC;ML;zenoh;testbeds;experimentation;vehicular services;Road transportation;Machine learning algorithms;Key performance indicator;Quality of service;Machine learning;Network function virtualization;Reliability|
|[Autonomic Faulty Node Replacement in UAV-Assisted Wireless Sensor Networks: a Test-bed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059801)|L. Montecchiari; A. Trotta; L. Bononi; M. Di Felice; E. Natalizio|10.1109/CCNC51644.2023.10059801|Unmanned Aerial Vehicles (UAVs);Wireless Sensor Networks (WSNs);Resilient communications;Test-bed;Wireless sensor networks;Systems operation;Robot kinematics;Spread spectrum communication;Data collection;Robot sensing systems;Feature extraction|
|[A Novel Array Antenna-Based GNSS Spoofing Detection and Mitigation Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060423)|Y. -S. Lee; J. S. Yeom; B. C. Jung|10.1109/CCNC51644.2023.10060423|Global navigation satellite system (GNSS);Anti-spoofing;Spoofing detection;Spoofing mitigation;Global navigation satellite system;Satellite antennas;Direction-of-arrival estimation;Satellites;Array signal processing;Computer simulation;Receiving antennas|
|[Machine Learning based Thermal Prediction for Energy-efficient Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060079)|I. Nisce; X. Jiang; S. P. Vishnu|10.1109/CCNC51644.2023.10060079|Machine Learning;Thermal Prediction;Energy-efficiency Data Center;Temperature distribution;Data centers;Machine learning algorithms;Computational modeling;Machine learning;Predictive models;Thermal management|
|[Public-attention-based Adversarial Attack on Traffic Sign Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060485)|L. Chi; M. Msahli; G. Memmi; H. Qiu|10.1109/CCNC51644.2023.10060485|Adversarial attack;attention heat map;trans-ferability;deep neural networks;traffic sign recognition;Heating systems;Deep learning;Perturbation methods;Neural networks;Closed box;Pattern recognition;Autonomous vehicles|
|[A LoRa-mesh based system for marine Social IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060829)|T. Patriti; S. Mirri; R. Girau|10.1109/CCNC51644.2023.10060829|marine;boats;social internet of things;LoRa;mesh;Temperature measurement;Mesh networks;Current measurement;Boats;Sea measurements;Size measurement;Social Internet of Things|
|[Version++: Cryptocurrency Blockchain Handshaking With Software Assurance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059985)|A. Sarker; S. Wuthier; J. Kim; J. Kim; S. -Y. Chang|10.1109/CCNC51644.2023.10059985|Bitcoin;Software Assurance;Permissionless;Distributed;Merkle Tree;Bitcoin Core;Trusted computing;Upper bound;Prototypes;Bitcoin;Computer architecture;Software;Virtual machining|
|[Random Activation Control for Priority AoI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060321)|H. Xie; Y. Hu; S. -W. Jeon; H. Jin|10.1109/CCNC51644.2023.10060321|Age of Information;priority;random access;online estimation;Measurement;System performance;Estimation;Throughput;Information age;Internet of Things|
|[Neural Filter Design for Frequency Selective Channel Equalization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059823)|W. Lee; S. Park; D. Kim; J. Kang|10.1109/CCNC51644.2023.10059823|Channel equalization;frequency-selective channel;neural filter;Training;Maximum likelihood detection;Equalizers;Neural networks;Symbols;Nonlinear filters;Receivers|
|[Towards Incorporating a Possibility of Zero-day Attacks Into Security Risk Metrics: Work in Progress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060371)|V. Marbukh|10.1109/CCNC51644.2023.10060371|Security risk metrics;zero-day exploits;attack graph;robust risk metrics;Entropic Value at Risk;Measurement;Probabilistic logic;Bayes methods;Security|
|[Estimation of physical activities of people in offices from time-series point-cloud data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060097)|K. Kizawa; R. Shinkuma; G. Trovato|10.1109/CCNC51644.2023.10060097|physical activity in office;light detection and ranging;time-series point-cloud data;Point cloud compression;Laser radar;Computational modeling;Estimation;Data models;Edge computing|
|[Joint Optimal Placement and Dynamic Resource Allocation for multi-UAV Enhanced Reconfigurable Intelligent Surface Assisted Wireless Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059677)|Y. Zhang; L. Qian; H. Xu|10.1109/CCNC51644.2023.10059677|Reconfigurable intelligent surfaces;Unmanned aerial vehicles;dynamical channel;Reinforcement Learning;Q-learning;Heuristic algorithms;Wireless networks;Urban areas;Clustering algorithms;Autonomous aerial vehicles;Dynamic scheduling|
|[Agent-based Simulation for Placement and Pricing of 5G Network Slices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059952)|J. Shakya; C. Ghribi; M. Chopin; L. Merghem-Boulahia|10.1109/CCNC51644.2023.10059952|Agent Based Simulation;5G and beyond Networks;5G Network Slicing;Slice Placement and Pricing;5G mobile communication;Network slicing;Decision making;Pricing;Quality of service;Production;Resource management|
|[Training of Perceptual Image Denoising Network with Weighted Sum of IQA Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059768)|T. Miyata|10.1109/CCNC51644.2023.10059768|image denoising;CNN;perceptual quality;IQA;Video coding;Training;Measurement;Image quality;Deep learning;Smoothing methods;Noise reduction|
|[Adaptive and Lightweight Cyber-Attack Detection in Modern Automotive Cyber-Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060377)|Y. Baek; S. -H. Park|10.1109/CCNC51644.2023.10060377|controller area network;message authentication;chaos map;cryptographic hash function;Adaptive systems;Protocols;Production;Cyber-physical systems;Real-time systems;Space exploration;Reliability|
|[A Stealthy False Command Injection Attack on Modbus based SCADA Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059804)|W. Alsabbagh; S. Amogbonjaye; D. Urrego; P. Langendörfer|10.1109/CCNC51644.2023.10059804|SCADA;PLCs;ICSs;Modbus Protocol;Cyber-attacks;Command Injection Attacks;Performance evaluation;Protocols;Databases;Data acquisition;SCADA systems;Process control;Industrial plants|
|[Impact of Quantization Noise on CNN-based Joint Source-Channel Coding and Modulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060488)|K. Matsumoto; Y. Inoue; Y. Hara-Azumi; K. Maruta; Y. Nakayama; D. Hisano|10.1109/CCNC51644.2023.10060488|deep learning;image coding;Joint source-channel coding;quantization;Training;Image quality;Quantization (signal);Image coding;PSNR;Image communication;Symbols|
|[Advanced MAB Schemes for WiGig-Aided Aerial Mounted RIS Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060437)|S. Hashima; K. Hatano; E. M. Mohamed|10.1109/CCNC51644.2023.10060437|nan;Knowledge engineering;Base stations;Trajectory planning;Simulation;Wireless networks;Heuristic algorithms;Benchmark testing|
|[A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060210)|R. Amoroso; L. Pappone; F. Esposito|10.1109/CCNC51644.2023.10060210|traffic estimation;super resolution;deep learning;federated learning;Interpolation;Federated learning;Statistical analysis;Scalability;Computational modeling;Superresolution;Neural networks|
|[UNR-IDD: Intrusion Detection Dataset using Network Port Statistics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059640)|T. Das; O. A. Hamdan; R. M. Shukla; S. Sengupta; E. Arslan|10.1109/CCNC51644.2023.10059640|Computer networks;network intrusion detection;machine learning;dataset;Computer hacking;Network intrusion detection;Tail;Machine learning|
|[Multilevel PAM with ANN Equalization for an RC-LED SI-POF System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059694)|I. N. O. Osahon; S. Rajbhandari; A. Ihsan; J. Tang; W. O. Popoola|10.1109/CCNC51644.2023.10059694|Equalization;artificial neural network;multi-layer perceptron;pulse amplitude modulation;plastic optical fiber communication;resonant cavity light emitting diode;Plastic optical fiber;Bit rate;Artificial neural networks;Optical distortion;Light emitting diodes;Decision feedback equalizers;Optical transmitters|
|[Transfer Learning Based Efficient Traffic Prediction with Limited Training Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060745)|S. Saha; A. Haque; G. Sidebottom|10.1109/CCNC51644.2023.10060745|deep sequence model;internet traffic;IP traffic prediction;ISP;transfer learning;Training;Recurrent neural networks;Transfer learning;Training data;Telecommunication traffic;Predictive models;Self-organizing networks|
|[TLS-Monitor: A Monitor for TLS Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059989)|D. G. Berbecaru; G. Petraglia|10.1109/CCNC51644.2023.10059989|network security;TLS attacks;cybersecurity;Resistance;Protocols;Codes;Heart beat;Malware;Servers;Security|
|[Optimal VNF Scheduling for Minimizing Duration of QoS Degradation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060258)|M. Iwamoto; A. Suzuki; M. Kobayashi|10.1109/CCNC51644.2023.10060258|Virtual network function;Service function chaining;Scheduling;Optimization;Degradation;Schedules;Processor scheduling;Service function chaining;Simulation;Stochastic processes;Quality of service|
|[On-the-Fly Edge Transcoding for Interactive VR](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060308)|A. Casparsen; F. Chiariotti; J. J. Nielsen|10.1109/CCNC51644.2023.10060308|nan;Cloud computing;Base stations;Philosophical considerations;Head-mounted displays;Transcoding;Virtual reality;Resists|
|[Enabling Efficient Data Transport for ICN-based In-Network Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059800)|Y. Hayamizu; K. Matsuzono; K. Kenji; H. Asaeda|10.1109/CCNC51644.2023.10059800|nan;Information-centric networking;Telecommunication traffic;Task analysis|
|[SHARQ: Scheduled HARQ for Time- and Loss-Rate-Sensitive Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060294)|K. Vogelgesang; P. G. Pereira; T. Herfet|10.1109/CCNC51644.2023.10060294|Hybrid Automatic Repeat reQuest;Transport Protocols;Schedules;Cross layer design;Protocols;Codes;Redundancy;Maintenance engineering;Error correction|
|[Radio Resource Allocation by controlling Number of MIMO Layers per Subband for Fronthaul-limited Shared Radio Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059884)|T. Oyama; T. Kobayashi; Y. Wen; T. Seyama; T. Dateki|10.1109/CCNC51644.2023.10059884|5G;NR;RU Sharing;PF Utility;O-RAN;RIC;Radio Resource Optimization;MIMO;Radio frequency;5G mobile communication;Interference;Bandwidth;Throughput;Scheduling;Explosions|
|[AP Connection Method Considering Interference for Maximizing System Throughput Using Potential Game](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060520)|Y. Kato; J. Xie; T. Murase; S. Miyata|10.1109/CCNC51644.2023.10060520|AP connection method;Optimal position;Throughput;Potential game;User moving;Interference;Costs;Numerical analysis;Interference;Games;Throughput;Behavioral sciences;Numerical models|
|[DFT-s-OFDM for sub-THz Transmission - Tracking and Compensation of Phase Noise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060597)|Y. Bello; J. -B. Doré; D. Demmer|10.1109/CCNC51644.2023.10060597|nan;Phase noise;Wireless communication;Interpolation;Modulation;Estimation;Bandwidth;Filtering algorithms|
|[Cache-Aided Networks with Shared Caches and Correlated Content under Non-Uniform Demands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060380)|B. Merikhi; M. R. Soleymani|10.1109/CCNC51644.2023.10060380|Shared Caches;Content placement;delivery rate;Coded caching;Source coding;Simulation;Libraries;Decoding;Data mining;Servers|
|[Blockage Prediction Using Exhaustive Beam-Pair Scan in mmWave Networks: An Experimental Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060352)|I. Yonemura; T. Kanda; R. Hanahara; K. Yamamoto; T. Arai; S. Wai; T. Iwakuni; D. Uchida; N. Kita|10.1109/CCNC51644.2023.10060352|nan;6G mobile communication;Millimeter wave technology;Line-of-sight propagation;Predictive models;Millimeter wave communication|
|[Leveraging 5G to Enable Automated Barge Control: 5G-Blueprint Perspectives and Insights](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060113)|N. Slamnik-Kriještorac; W. Vandenberghe; N. Masoudi-Dione; S. Van Staeyen; L. Xiangyu; R. Kusumakar; J. M. Marquez-Barja|10.1109/CCNC51644.2023.10060113|5G;automated ships;teleoperation;shipping operations;5G ports;real-life experimentation environments;Production systems;Automation;5G mobile communication;Boats;Ultra reliable low latency communication;Control systems;Safety|
|[PRADA: Practical Access Point Deployment Algorithm for Cell-Free Industrial IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060457)|K. -H. Lee; H. -W. Lee; H. Lee; B. C. Jung|10.1109/CCNC51644.2023.10060457|Industrial Internet-of-things (IIoT);access point deployment;cell-free massive multiple-input multiple-output (CF -mMIMO);macro-diversity;minimum set cover problem;Wireless communication;Search methods;Simulation;Quality of service;MIMO;Planning;Reliability|
|[Measurement of Attenuated LoRa Propagation in Sandy Loam](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060076)|C. Paolini; S. Komeylian; R. Raghav; M. Sarkar|10.1109/CCNC51644.2023.10060076|smart agriculture;LoRa;wireless underground sensor networks;WUSN;Wireless communication;Wireless sensor networks;Soil measurements;Moisture;Soil;Dielectric loss measurement;Attenuation measurement|
|[Bluetooth Low Energy with Software-Defined Radio: Proof-of-Concept and Performance Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060476)|A. Casparsen; J. I. Christensen; P. Antoniou; M. J. Remy; I. Leyva-Mayorga; G. C. Madueño; J. J. Nielsen|10.1109/CCNC51644.2023.10060476|Bluetooth Low Energy (BLE);Proof of Concept (PoC);Software-Defined Radio (SDR);Timing;Protocols;Systematics;Software design;Logic gates;Signal generators;Software;Timing|
|[A Structured Approach to Insider Threat Monitoring for Offensive Security Teams](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060017)|A. Al Sadi; D. Berardi; F. Callegati; A. Melis; M. Prandini; L. Tolomei|10.1109/CCNC51644.2023.10060017|Secure Infrastructure;Penetration Testing;Insider Threat;IaC;Operating systems;Government;Companies;Behavioral sciences;Security;Virtualization;Monitoring|
|[A Comprehensive Study on Efficient and Accurate Machine Learning-Based Malicious PE Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060214)|O. Barut; T. Zhang; Y. Luo; P. Li|10.1109/CCNC51644.2023.10060214|Malware Analysis;Ransomware Detection;Machine Learning;Feature Engineering;Databases;Feature extraction;Ransomware;Time factors|
|[Shade Mapping: Using Mobile Devices to Find Shade from the Sun in Neighborhoods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060722)|P. -C. Liu; A. Nahapetian|10.1109/CCNC51644.2023.10060722|Light sensor;mobile computing;shade;nan|
|[An Adaptive Slotted-Contention-Based MAC Protocol for Ad-hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060507)|Y. Hu; H. Xie; S. -W. Jeon; H. Jin|10.1109/CCNC51644.2023.10060507|Ad-hoc network;minislot;online control;Bayesian estimation;nan|
|[Search for Unknown Events in Blind Zone using Multiple Autonomous Mobile Systems with Mobile Sensing Cluster](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060259)|S. Izuhara; S. Nishigami; N. Fujiyama; E. Nii; H. Yomo; Y. Takizawa|10.1109/CCNC51644.2023.10060259|nan;Wireless communication;Spirals;Sensors;Task analysis|
|[Design and Evaluation of an Application-Oriented Data-Centric Communication Framework for Emerging Cyber-Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060823)|M. Kaur; A. Razi; L. Cheng; R. Amin; J. Martin|10.1109/CCNC51644.2023.10060823|nan;nan|
|[Cost-efficient blockchain application to secure data transmission in heterogeneous FANETs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060385)|D. P. Fernandes; J. B. Machado; S. Givigi|10.1109/CCNC51644.2023.10060385|IoV;UAV;FANET;blockchain;security;cybersecurity;5G;Hyperledger;Sawtooth;PoET;Mininet;Containernet;Network;Ad-hoc;Fault tolerance;Energy consumption;Distributed ledger;Fault tolerant systems;Consensus algorithm;Computer crashes;Ad hoc networks|
|[Exploring Pseudo-Analog Video Transmission for Edge-AI Vision Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059990)|J. Yamada; K. Suto|10.1109/CCNC51644.2023.10059990|Pseudo-Analog Video Transmission;Edge-AI;Object Detection;Wireless communication;Image resolution;Image edge detection;Object detection;Video compression;Streaming media;Real-time systems|
|[Overlapping Vehicular Micro Clouds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060277)|S. Ucar; T. Higuchi; O. Altintas|10.1109/CCNC51644.2023.10060277|connected vehicles;vehicular micro cloud;overlapping micro cloud members;Degradation;Connected vehicles;Simulation;Switches;Control systems;Servers;Task analysis|
|[Full Software-Defined Factory Networks by Industrial Ethernet Protocol Softwarization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059822)|Y. Koyasako; T. Suzuki; T. Hatano; T. Shimada; T. Yoshida|10.1109/CCNC51644.2023.10059822|Software Defined Networking;Motion Control;Industrial Automation;Industrial Ethernet Protocol;Motion planning;Protocols;Ethernet;Computer architecture;Production facilities;Software;Real-time systems|
|[Automation of spatial calibration for heterogeneous multi-LIDAR network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060607)|K. Teraoka; K. Azuma; R. Shinkuma; G. Trovato|10.1109/CCNC51644.2023.10060607|spatial calibration;LIDAR sensor;sensor network;point cloud data;Laser radar;Automation;Sensor systems;Distance measurement;Sensors;Calibration|
|[Enabling Block Transmission on Backoff-based Opportunistic Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059637)|E. Hosonuma; Y. Nishiyama; K. Sezaki; T. Miyoshi; T. Yamazaki|10.1109/CCNC51644.2023.10059637|ad hoc networks;opportunistic routing;block transmission;path diversity;link utilization;Computer simulation;Focusing;Routing;Routing protocols;Ad hoc networks;Spatiotemporal phenomena|
|[Novel Federated Learning by Aerial-Assisted Protocol for Efficiency Enhancement in Beyond 5G Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059852)|T. Vrind; L. Pathak; D. Das|10.1109/CCNC51644.2023.10059852|aerial communication;federated learning;Wireless communication;Analytical models;Protocols;Federated learning;5G mobile communication;Computational modeling;Bandwidth|
|[Strictly Prioritized Multihop Transmission of High-Priority Data Composed of Multiple Frames in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060331)|K. Yoshitomi; Y. Tanigawa; D. Kondo; H. Tode|10.1109/CCNC51644.2023.10060331|nan;Wireless sensor networks;Computer simulation;Spread spectrum communication;Media Access Protocol;Delays|
|[On Multihop vs. End-to-End Transport](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059916)|K. Vogelgesang; T. Herfet|10.1109/CCNC51644.2023.10059916|Latency- and Resilience-Awareness;Adaptive Error Coding;Channel Capacity;Transport-Layer;Error analysis;Channel capacity;Redundancy;Spread spectrum communication;Telecommunication traffic;Routing protocols;IP networks|
|[An Analytical Model to Quantify the Effect of Handover and Cell Density on SINR in Emerging Cellular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060458)|S. M. Asad Zaidi; M. Manalastas; A. Imran|10.1109/CCNC51644.2023.10060458|nan;Degradation;Cellular networks;Analytical models;Interference;Handover;Downlink;Mobile handsets|
|[Low-Luminance Space Division Multiplexing With Spatial 4-PPM Correlation for Smartphone Screen to Camera Uplink Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060461)|A. Kawade; W. Chujo; K. Kobayashi|10.1109/CCNC51644.2023.10060461|visible light communication;optical camera communication;screen camera communication;space division multiplexing;4-PPM;inverted 4-PPM;Correlation coefficient;Correlation;Bit error rate;Modulation;Cameras;Space division multiplexing;Uplink|
|[Coverage Analysis of Spectrum-Shared Directional Networks: Exclusion Zone and Antenna Radiation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059973)|H. Kim; J. S. Yeom; J. M. Kim; B. C. Jung|10.1109/CCNC51644.2023.10059973|Spectrum sharing;coverage probability;directional antenna;stochastic geometry;exclusion zone;Geometry;Fading channels;Wireless networks;Stochastic processes;Directional antennas;Receivers;Directive antennas|
|[An Application of DAG-based Distributed Ledger to Manage Content Whereabouts for Beyond 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060703)|Y. Watanabe; Y. Shoji|10.1109/CCNC51644.2023.10060703|Blockchain;distributed ledger;directed acyclic graph;store-carry-forward technique;beyond 5G;Resistance;Multiplexing;Distributed ledger;5G mobile communication;Computer simulation;Spread spectrum communication;Censorship|
|[Decentralized Q-learning based Optimal Placement and Transmit Power Control in Multi-TUAV Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060188)|S. Lim; H. Lee|10.1109/CCNC51644.2023.10060188|Tethered unmanned aerial vehicle-enabled base station;multi-agent decentralized Q-learning;optimal placement and transmit power control;individual rate maximization;Wireless communication;Q-learning;Three-dimensional displays;Power control;Line-of-sight propagation;Downlink;Batteries|
|[Characteristics of Wireless Signal Propagation Inside Robotic Limbs employing Rotary Waveguide Joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059963)|M. Kokuryo; G. Konishi; M. Saito; S. Shimamoto|10.1109/CCNC51644.2023.10059963|Robot;Waveguide;Wireless-communication;Signal-propagation;HFSS;Wireless communication;Legged locomotion;Simulation;Wires;Rectangular waveguides;Electromagnetic waveguides;Interference|
|[Real-Time Cleaning Activity Support System using Accelerometer and Audio Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059713)|R. Yaegashi; M. Mikuriya; F. Ogino; Y. Nakayama|10.1109/CCNC51644.2023.10059713|Activity recognition;Machine learning;Accelerometer sensor;Monitoring;Accelerometers;Machine learning;Robot sensing systems;Cleaning;Real-time systems;Sensors;Monitoring|
|[Deep-learning-based Estimation of Radio-quality Deterioration Causes for 5G Industrial Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060500)|Y. Nishikawa; S. Maruyama; E. Takahashi; T. Onishi|10.1109/CCNC51644.2023.10060500|5G;radio quality deterioration;shadowing;fading;root cause identification;deep learning;nan|
|[An approach for efficient vehicular tracking in internet of vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060508)|M. Ahman; A. Rechache; A. Kaci; O. Annad; A. Nace; S. A. Chellouche|10.1109/CCNC51644.2023.10060508|Internet of Vehicles;Mobililty;Quality of Service;Tracking loops;Vehicular ad hoc networks;Quality of service;Bandwidth;Routing;Routing protocols;Delays|
|[Active Intrusion Detection with Periodical Probing for IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059684)|R. Yamamoto; T. Ohtani; S. Ohzahata|10.1109/CCNC51644.2023.10059684|IoT;Security;Active probing;Machine learning;Performance evaluation;Privacy;Costs;Intrusion detection;Focusing;Logic gates;Software|
|[Towards the Creation of Interdisciplinary Consumer-Oriented Security Metrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060733)|G. Gori; A. Melis; D. Berardi; M. Prandini; A. A. Sadi; F. Callegati|10.1109/CCNC51644.2023.10060733|Security metrics;Usable Security;Standardization;IoT;CPS;Measurement;Cyber-physical systems;Internet of Things;Computer security;Usability;Information systems|
|[An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060443)|V. Holubenko; P. Silva; C. Bento|10.1109/CCNC51644.2023.10060443|Intrusion Detection System;Federated AI;Machine Learning;Internet of Things;Security;Privacy;Training;Privacy;Machine learning algorithms;Federated learning;Neural networks;Intrusion detection;Real-time systems|
|[Analysis of the Impact of Homomorphic Algorithm on Offloading of Mobile Application Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060199)|F. A. A. Gomes; F. de Matos; P. Rego; F. Trinta|10.1109/CCNC51644.2023.10060199|Encryption;Homomorphic;Mobile Device;Offloading;Performance evaluation;User experience;Mobile applications;Task analysis;Homomorphic encryption|
|[Set Ranking-Based Precaching Protocol in Vehicular Ad hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060617)|Y. Nam; H. Choi; J. Bang; Y. Shin; E. Lee|10.1109/CCNC51644.2023.10060617|vehicular ad hoc networks;content precaching;multiple precaching vehicles;Protocols;Simulation;Roads;Vehicular ad hoc networks;Mathematical models;Delays;Backhaul networks|
|[Leveraging on the Synergy between Visible Light Communication (VLC) and Radio Frequency (RF) to Enhance Intelligent Transport Systems (ITS)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060613)|C. Bammens; N. Slamnik-Kriještorac†; V. Charpentier; J. M. Marquez-Barja|10.1109/CCNC51644.2023.10060613|V2V;Intelligent Transportation Systems;C-V2X;Dedicated Short Range Communication;Vehicular Visible Light Communication;Autonomous Driving;Platooning;Cooperative adaptive cruise control;Radio frequency;Vehicular ad hoc networks;Transportation;Tail;Light emitting diodes;Time factors;Vehicle-to-everything|
|[Cellular-V2X QoS Adaptive Distributed Congestion Control: A Deep Q Network Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059812)|W. Yang; B. Jeon; C. Mun; H. -S. Jo|10.1109/CCNC51644.2023.10059812|C-V2X;distributed congestion control;deep reinforcement learning;QoS;Training;Adaptive systems;Simulation;Vehicular ad hoc networks;Quality of service;Autonomous vehicles;Periodic structures|
|[Reducing Redundant Transmissions for Message Broadcast in Vehicular Ad Hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060804)|Y. -T. Wang; M. -H. Tsai; A. Matsubayashi|10.1109/CCNC51644.2023.10060804|message broadcast;push-pull algorithm;recording lists;redundant transmissions;VANET;Vehicular ad hoc networks;Recording;Automobiles|
|[The Ugly Truth of Realistic Perception in Vehicular Simulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060697)|F. Hawlader; R. Frank|10.1109/CCNC51644.2023.10060697|Vehicular Simulation;Perception;Sensor;Sensors;Complexity theory;Reliability;Autonomous vehicles|
|[Efficient Aerial Relaying Station Path Planning for Emergency Event-based Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060300)|V. -L. Nguyen; L. -H. Nguyen; R. -H. Hwang; J. -J. Kuo; P. -C. Lin|10.1109/CCNC51644.2023.10060300|nan;Emergency services;Medical services;Telemedicine;Dispatching;Vehicle dynamics;Cellular networks|
|[Real Time Facial Recognition and Tracking System Using Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059664)|A. Melkumyan; K. Mkrtchyan|10.1109/CCNC51644.2023.10059664|Facial Detection & Recognition;Drone;Tracking;Target tracking;Law enforcement;Face recognition;System performance;Streaming media;Real-time systems;Face detection|
|[A Study Real-Time WLAN Sensing System Using Channel State Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060556)|R. Isshiki; K. Tsuji; Y. Nagao; M. Kurosaki; B. Sai; H. Ochi|10.1109/CCNC51644.2023.10060556|Channel State Information;Wireless Sensing;Singular Value Decomposition;Receivers;Wireless access points;Real-time systems;Sensors;Indoor environment;Delays;Channel state information|
|[Experimental Evaluation of Rate Adaptation using Deep-Q-Network in IEEE 802.11 WLAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060609)|M. H. Bintang Pratama; T. Nakashima; Y. Nagao; M. Kurosaki; H. Ochi|10.1109/CCNC51644.2023.10060609|Rate adaptation;DQN;FPGA;ns-3;Adaptation models;Simulation;Throughput;Real-time systems;Field programmable gate arrays;Wireless fidelity;IEEE 802.11 Standard|
|[A Peer-to-Peer Group Conversation System Based on Location and Direction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060596)|T. Yamazaki; W. Amishiro; T. Miyoshi; T. Asaka|10.1109/CCNC51644.2023.10060596|peer-to-peer;voice conversation;location;direction;Oral communication;User experience;Peer-to-peer computing;Usability|
|[A Data Transfer Method Combining Erasure-coding with Cumulative Acknowledgment for Lossy Optical Packet Switching Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060721)|M. Ono; Y. Seki; Y. Hashimoto; Y. Tanigawa; Y. Hirota; H. Tode|10.1109/CCNC51644.2023.10060721|Optical Packet Switching;Forward Error Correction;Erasure-coding;Cumulative Acknowledgment;nan|
|[Towards a centralized security architecture for SOME/IP automotive services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059950)|H. Khemissa; P. Urien|10.1109/CCNC51644.2023.10059950|CAVs;SOME/IP;ECU;security;authentication;data confidentiality;Hash functions;Data centers;Connected vehicles;Codes;Computer architecture;IP networks;Cryptography|
|[On Potential Risks of “Natural” Hybrid Load Balancing in Large-Scale Clouds: Work in Progress](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060156)|V. Marbukh|10.1109/CCNC51644.2023.10060156|Cloud model;hybrid (static/dynamic) load balancing;throughput region;queuing performance;Economics;Cloud computing;Computational modeling;Load management;Real-time systems;Reliability;Low latency communication|
|[Computing Node Selection Method Based on Proactive Grasping of Computational Performance in End Cloud Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060095)|T. Ueda; D. Kondo; Y. Tanigawa; H. Tode|10.1109/CCNC51644.2023.10060095|End Cloud;Distributed Computing;Computing Resources;Distributed processing;Computational modeling;Grasping;Computer applications;Task analysis|
|[Edge system for providing blind-spot information using multi-LIDAR network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059689)|J. Negishi; K. Azuma; R. Shinkuma; G. Trovato|10.1109/CCNC51644.2023.10059689|smart monitoring;3D sensing;LIDAR;point cloud;spatial feature;blind spot;Prototypes;Feature extraction;Spatial databases;Sensors;Data mining;Monitoring|
|[Utilizing Smartphones for Blind Spot Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060770)|T. T. Sarı; M. K. Assoy; G. Seçinti|10.1109/CCNC51644.2023.10060770|Blind Spot Detection Systems;Ultra-Wide Band Communications;LiDAR;nan|
|[AoA Estimation for High Accuracy BLE Positioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059866)|Y. Yamami; S. Tang|10.1109/CCNC51644.2023.10059866|BLE positioning;angle-of-arrival;CTE;MUSIC;Power demand;Linear regression;Robot control;Receiving antennas;Switches;Mobile handsets;Multiple signal classification|
|[Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059612)|A. Kachroo; S. P. Chinnapalli|10.1109/CCNC51644.2023.10059612|probabilistic modeling;Bayesian networks;wireless sensing;WiFi;uncertainty quantification;machine learning;Wireless communication;Wireless sensor networks;Uncertainty;Machine learning;Predictive models;Millimeter wave radar;Probabilistic logic|
|[Cooperative Local Distributed Machine Learning Considering Communication Latency and Power Consumption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060678)|S. Ono; T. Yamazaki; T. Miyoshi; Y. Nishiyama; K. Sezaki|10.1109/CCNC51644.2023.10060678|Distributed Machine learning;Green computing;Power consumption;Communication latency;nan|
|[A Domain Adaptation-based Detector for Cooperative Spectrum Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060702)|L. Li; J. Li; Y. He; L. Slayton|10.1109/CCNC51644.2023.10060702|Cooperative spectrum sensing;domain adaptation;adaptiveness;robustness;Manifolds;Simulation;Training data;Modulation;Detectors;Machine learning;Feature extraction|
|[F2MKD: Fog-enabled Federated Learning with Mutual Knowledge Distillation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059910)|Y. Yamasaki; H. Takase|10.1109/CCNC51644.2023.10059910|federated learning;fog computing;knowledge distillation;Knowledge engineering;Adaptation models;Computer aided instruction;Data privacy;Federated learning;Distance learning;Computational modeling|
|[Machine Learning-Based Handover Failure Prediction Model for Handover Success Rate Improvement in 5G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060203)|M. Manalastas; M. U. Bin Farooq; S. M. A. Zaidi; A. Ijaz; W. Raza; A. Imran|10.1109/CCNC51644.2023.10060203|Inter-Frequency Handover;Handover Failure Prediction;Machine Learning Classifiers;Base stations;5G mobile communication;Machine learning;Handover;Predictive models|
|[Efficient Conditional Handover Algorithm in 5G with Blockages using Recurrent Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059816)|Z. -H. Huang; Y. -S. Chen; M. -J. Tsai|10.1109/CCNC51644.2023.10059816|nan;Recurrent neural networks;5G mobile communication;Handover;Radio links;Prediction algorithms;Classification algorithms;3GPP|
|[Analysis of Open Set Deep Neural Network Variants towards Classification of Known and Unknown Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059665)|S. K. Kompella; S. Kompella|10.1109/CCNC51644.2023.10059665|Open Set;Closed Set;Convolutional Neural Network;Residual Network;Long-Short Term Memory;LSTM Network;Deep learning;5G mobile communication;Neural networks;Pattern classification;Cognitive radio;Electromagnetics|
|[Deep Learning aided Energy Efficient Band Assignment in Multiband Heterogeneous Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060279)|B. Soni; S. Govindasamy; D. K. Patel|10.1109/CCNC51644.2023.10060279|Deep learning;Band assignment;Sub-6 GHz;mmWave;Power consumption;Energy efficiency;Radio frequency;Deep learning;Power demand;Switches;Energy efficiency;Heterogeneous networks;Batteries|
|[Simple Reinforcement Learning based Contention Windows Adjustment for IEEE 802.11 Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059960)|K. Sanada; H. Hatano; K. Mori|10.1109/CCNC51644.2023.10059960|IEEE 802.11;Reinforcement-learning;Multi-armed bandit (MAB);Distribute coordination function (DCF);Q-learning;Wireless communication;Performance evaluation;Computer simulation;Reinforcement learning;IEEE 802.11 Standard|
|[Optimization of CNN-based Federated Learning for Cyber-Physical Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059840)|A. K. Abasi; M. Aloqaily; B. Ouni; M. Hamdi|10.1109/CCNC51644.2023.10059840|Cyber-physical Systems (CPS);Federated Learning (FL);CNN Hyper-parameter;Honey Badger Algorithm (HBA);Sleep Apnea (SA);Training;Federated learning;Image edge detection;Wearable computers;Distributed databases;Medical services;Cyber-physical systems|
|[Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060680)|H. Shimomura; Y. Koda; T. Kanda; K. Yamamoto; T. Nishio; A. Taya|10.1109/CCNC51644.2023.10060680|nan;nan|
|[A Testbed for a Controller Area Network Communication Protocol in Automobiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059608)|D. Oladimeji; R. Amar; S. Narasimha; C. Varol|10.1109/CCNC51644.2023.10059608|CAN bus;CAN high;CAN low;CAN nodes;CAN testbed;CAN security;Wireless communication;Protocols;Navigation;Hardware;Decoding;Automobiles;Security|
|[Deep Learning based 2D Symbol Detection for Display-Camera Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060131)|Y. Sasaki; K. Maruta; S. Kojima; D. Hisano; Y. Nakayama|10.1109/CCNC51644.2023.10060131|nan;Deep learning;Codes;Image edge detection;Symbols;Object detection;Data mining|
|[Towards Trusted and Accountable Win-Win SDWN Platform for Trading Wi-Fi Network Access](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060540)|M. H. Eiza; A. Raschellà; M. Mackay; Q. Shi; F. Bouhafs|10.1109/CCNC51644.2023.10060540|Blockchain;Radio Access Network (RAN);Software Defined Network (SDN);Spectrum Sharing;Wi-Fi;nan|
|[A Service Consolidation Approach for Edge-Vehicle Network using Multi-agent Decision-making Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059927)|M. S. Mekala; H. Zhang; J. H. Park; H. -Y. Jung|10.1109/CCNC51644.2023.10059927|Edge computing;RSU consolidation;Cyber-physical systems;resource-weight factor;Service offloading policy;RASC approach;Q-learning;Simulation;Decision making;Quality of service;Reinforcement learning;Linear programming;Heterogeneous networks;Reliability|
|[Secure and Efficient Data Integrity Verification Scheme for Cloud Data Storage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059690)|N. Garg; A. Nehra; M. Baza; N. Kumar|10.1109/CCNC51644.2023.10059690|cloud storage;data integrity;schnorr signarture;third party auditing;Cloud computing;Handwriting recognition;Costs;Data integrity;Memory;Maintenance engineering;Computational efficiency|
|[Non-contact Blood Pressure Prediction Employing Microwave Reflection based on Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060068)|H. Ochi; J. Liu; S. Shimamoto|10.1109/CCNC51644.2023.10060068|non-contact;pulse;blood pressure;augmentation index;acceleration pulse waveform;microwave;machine learning;Wrist;Network analyzers;Machine learning;Microwave theory and techniques;Blood pressure;Reflection;Synchronization|
|[Traffic Matrix Completion by Weighted Tensor Nuclear Norm Minimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060087)|T. Miyata|10.1109/CCNC51644.2023.10060087|Traffic matrices;tensor completion;non-convex optimization;ADMM;Measurement errors;Tensors;Estimation;Network analyzers;Minimization;Computational efficiency;Task analysis|
|[Secure Federated Learning: An Evaluation of Homomorphic Encrypted Network Traffic Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060116)|S. P. Sanon; R. Reddy; C. Lipps; H. D. Schotten|10.1109/CCNC51644.2023.10060116|Federated Learning;Homomorphic Encryption;Secure Multi-Party computation;Wireless communication;Privacy;Federated learning;Collaboration;Telecommunication traffic;Companies;Multi-party computation|
|[Edge system with multi-LIDAR sensor network for tracking micro-mobility vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059769)|T. Kudo; K. Azuma; R. Shinkuma; G. Trovato|10.1109/CCNC51644.2023.10059769|LIDAR;point cloud;sensor network;micro-mobility;object detection;smart city;Cloud computing;Laser radar;Image edge detection;Urban areas;Machine learning;Data models|
|[Version++ Protocol Demonstration for Cryptocurrency Blockchain Handshaking with Software Assurance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059971)|A. Sarker; S. Wuthier; J. Kim; J. Kim; S. -Y. Chang|10.1109/CCNC51644.2023.10059971|Cryptocurrency;Blockchain;Bitcoin;Software Assurance;Permissionless;Distributed;Merkle Tree;Protocols;Prototypes;Bitcoin;Software;Blockchains;Behavioral sciences|
|[Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060554)|I. Mavromatis; A. Khan|10.1109/CCNC51644.2023.10060554|Data Drift;IoT;Drift Detector;Resource-Constrained;Ensemble Learning;Data privacy;Data integrity;Internet of Things|
|[Online Federated Learning based Object Detection across Autonomous Vehicles in a Virtual World](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060782)|S. Dai; S. M. Iftekharul Alam; R. Balakrishnan; K. Lee; S. Banerjee; N. Himayat|10.1109/CCNC51644.2023.10060782|Federated Learning;realistic dataset;streaming data;automatic annotation;object detection;Training;Federated learning;Mobile agents;Collaboration;Object detection;Benchmark testing;Data models|
|[Vision-based Swing Trajectory Estimation using RGBD Camera](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060571)|D. Nakajima; M. Mikuriya; F. Ogino; Y. Nakayama|10.1109/CCNC51644.2023.10060571|Pose estimation;activity recognition;computer vision;Training;Pose estimation;Grasping;Cameras;Cleaning;Trajectory|
|[CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060805)|Y. Lu; M. Zhang; J. Hou; D. Li; M. Moghaddassian; A. Leon-Garcia|10.1109/CCNC51644.2023.10060805|nan;COVID-19;Pandemics;Pipelines;Lighting;Aerospace electronics;Data collection;Real-time systems|
|[An Unsupervised Learning Approach for Smart Home Operational Policy Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059897)|S. P. Challa; R. Iqbal; S. Liu|10.1109/CCNC51644.2023.10059897|human behavior pattern;pattern mining;policy generation;user preferences;Scalability;Supervised learning;Smart homes;User experience;Behavioral sciences;Internet of Things;Task analysis|
|[Experiment of Multi-UAV Full-Duplex System Equipped with Directional Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060015)|T. Yu; K. Kajiwara; K. Araki; K. Sakaguchi|10.1109/CCNC51644.2023.10060015|experiment;multi-UAV;UAV communication;full-duplex;Communication systems;System performance;Prototypes;Full-duplex system;Directional antennas;Downlink;Autonomous aerial vehicles|
|[Optimal Power and Position Control for UAV-assisted JCR Networks: Multi-Agent Q-Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060439)|J. M. Park; H. Lee; H. Yu|10.1109/CCNC51644.2023.10060439|JCR;UAV-assisted network;multi-agent Q-learning;Wireless communication;Measurement;Q-learning;Position control;Estimation;Radar;Downlink|
|[Information Freshness-Oriented Relay Selection in Two-Way Relay Networks: A Multi-Armed Bandit Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059719)|G. Chen; T. -T. Chan; H. Pan; K. -H. Ho|10.1109/CCNC51644.2023.10059719|nan;Wireless communication;Protocols;Heuristic algorithms;Simulation;Relay networks (telecommunication);Interference;Network coding|
|[Quantum-based Offloading Strategy for Intelligent Vehicle Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059954)|M. S. Mekala; H. Zhang; J. H. Park; H. -Y. Jung|10.1109/CCNC51644.2023.10059954|Edge computing;RSU consolidation;resource-weight factor;Service offloading policy;Q-learning;Costs;Decision making;Qubit;Quantum mechanics;Reinforcement learning;Reliability theory;Probability|
|[Blockchain framework for managing machine-learning models for 3D object detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060161)|Y. Tsuruta; K. Akiyama; R. Shinkuma; A. Mine|10.1109/CCNC51644.2023.10060161|blockchain;smart monitoring;machine learning model;Solid modeling;Three-dimensional displays;Smart cities;Object detection;Machine learning;Mathematical models;Time measurement|
|[SALEM: Service Fairness in Wireless Mesh Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060742)|H. Silva; N. Godinho; B. Sousa|10.1109/CCNC51644.2023.10060742|Software-Defined Networks;Fairness;Wireless Mesh Networks;Multi-objective Optimisation;K-shortest path;nan|
|[Depth of Field Blur Effect Considering Convergence Distance in Virtual Reality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060608)|Y. Kawabata; M. Bandai|10.1109/CCNC51644.2023.10060608|depth of field blur;convergence distance;eye tracking;photorealism;virtual reality;Photorealism;Portable computers;Head-mounted displays;Virtual reality;Resists;Filling;Calibration|
|[Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060275)|H. Du; S. Thudumu; S. Singh; S. Barnett; I. Logothetis; R. Vasa; K. Mouzakis|10.1109/CCNC51644.2023.10060275|Decentralized Federated Learning;Image Classification;Multi-Agent Systems;Privacy;Federated learning;Information security;Finance;Medical services;Artificial intelligence;Task analysis|
|[A Testbed for Evaluating Performance and Cybersecurity Implications of IEC-61850 GOOSE Hardware Implementations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060534)|M. Boeding; M. Hempel; H. Sharif; J. Lopez; K. Perumalla|10.1109/CCNC51644.2023.10060534|ICS;GOOSE;IEC-61850;Cybersecurity;Testbed;nan|
|[Open-Source Testbeds for Integrating Time-Sensitive Networking with 5G and beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060159)|S. Senk; H. K. Nazari; H. -H. Liu; G. T. Nguyen; F. H. P. Fitzek|10.1109/CCNC51644.2023.10060159|UAVs;cellular communication;Time-Sensitive Networking;5G and beyond;testbed;Quality-of-Service;Wireless communication;Industries;5G mobile communication;Buildings;Ultra reliable low latency communication;Autonomous aerial vehicles;Hardware|
|[Implementation of the Data Conversion Function for Wireless Environments for Beyond 5G Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060497)|K. Tokugawa; J. Nakazato; H. Matsuo; K. Kubota; K. Sakaguchi|10.1109/CCNC51644.2023.10060497|Beyond 5G;6G;Digital Twin;Proof-of-Concepts;Measurement;Outdoor Field;Wireless communication;Navigation;Diversity reception;Cyberspace;Data visualization;Quality of service;Digital twins|
|[Prototype of edge sensing and computing system with multi-LIDAR network for autonomous micro-mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060477)|M. Wago; K. Akiyama; R. Shinkuma; G. Trovato; K. Nihei; T. Iwai|10.1109/CCNC51644.2023.10060477|nan;Laser radar;Prototypes;Sensors;Autonomous vehicles;Edge computing|
|[What is needed for campus networks to embrace 5G?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060712)|F. Panken; M. van den Akker|10.1109/CCNC51644.2023.10060712|research and education community;mobile private network;cellular indoor communication;5G;Wi-Fi;Optical fibers;Integrated optics;Cellular networks;Baseband;Education;Buildings;Optical network units|
|[BACKWARD: A Victim-Centric DDoS Detection and Mitigation Scheme in Programmable Data Plane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059724)|S. Oh; S. Han; H. Lee; S. Pack|10.1109/CCNC51644.2023.10059724|Network Security;Programmable Data Planes;P4;DDoS Attack;Denial-of-service attack;Computer crime|
|[AI-Empowered Database Management Platform for New Materials Discovery for Consumer Electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060412)|T. Kim; J. Lee; J. Song; D. Lee; J. -C. Na; S. -I. Yang; K. -J. Park; Y. -J. Yoo; J. Lee; W. -Y. Shin|10.1109/CCNC51644.2023.10060412|nan;Databases;Metals;Consumer electronics|
|[Automated Battery Power Fade Estimation for Fast Charge and Discharge Operations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060391)|E. Zarfati; L. Bedogni|10.1109/CCNC51644.2023.10060391|battery;state of health;power fade;maintenance;data science;causal machine learning;Performance evaluation;Machine learning;Maintenance engineering;Discharges (electric);Batteries;Fourth Industrial Revolution;Time factors|
|[A Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060165)|S. Devaraju; A. Ihler; S. Kumar|10.1109/CCNC51644.2023.10060165|Airborne network;UAV network;search and rescue;network connectivity;pheromone model;Mobility models;Atmospheric modeling;Surveillance;Spread spectrum communication;Network architecture;Reliability engineering|
|[An Unsupervised Detection Approach for Location Attacks in Satellite-Based Navigation Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060239)|M. A. Messous; E. Ferreyra; E. R. Ikwu; Y. Unno; H. Kojima|10.1109/CCNC51644.2023.10060239|Anomaly detection;vehicle security;GPS spoofing;GPS jamming;unsupervised classification;K-means;Location awareness;Connected vehicles;Event detection;Trajectory;Sensors;Security;Proposals|
|[Non-Parallel Voice Conversion Using Cycle-Consistent Adversarial Networks with Self-Supervised Representations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060510)|C. Chun; Y. H. Lee; G. W. Lee; M. Jeon; H. K. Kim|10.1109/CCNC51644.2023.10060510|Voice conversion;generative adversarial networks;CycleGAN-VC;self-supervised learning;wav2vec;Analytical models;Linguistics;Feature extraction|
|[A Low-Complexity Subarray-Based UCCA for Robust LoS MIMO Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060740)|M. Oh; Y. -S. Lee; B. C. Jung|10.1109/CCNC51644.2023.10060740|6G communications;line-of-sight MIMO channel;terahertz band;uniform circular array (UCA);subarray;6G mobile communication;Computer simulation;Transmitting antennas;Line-of-sight propagation;Receiving antennas;Computer architecture;Robustness|
|[Accessible Wayfinding for the Visually Impaired through Sustainable Smartphone Based Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059763)|M. Franco; O. Gaggi; S. E. Merzougui; C. E. Palazzi|10.1109/CCNC51644.2023.10059763|accessibility;mobile;smartphone;visually impaired;Energy consumption;Urban areas;Sensors;Internet;Automobiles;Smart phones;Public transportation|
|[Complementary Data Transmission Control With Collision Avoidance for Efficient Retention of Large-size Spatio-Temporal Data*](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060769)|H. Kaneyasu; D. Nobayashi; K. Tsukamoto; T. Ikenaga; M. Lee|10.1109/CCNC51644.2023.10060769|Vehicular Networks;Local Production and Consumption of Data;Spatio-Temporal Data Retention;Simulation;Packet loss;Production;Real-time systems;Data models;Data communication;Internet of Things|
|[Time series prediction in IoT: a comparative study of federated versus centralized learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060467)|L. F. Da Costa; L. S. Furtado; P. H. G. Rocha; P. A. L. Rego; F. A. M. Trinta|10.1109/CCNC51644.2023.10060467|Centralized Learning;Federated Learning;LSTM;Time Series;Training;Performance evaluation;Federated learning;Time series analysis;Predictive models;Prediction algorithms;Sensors|
|[Magnetic MIMO for Brain Treatment and Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060658)|G. Park; G. Na; J. Lee; C. -B. Chae|10.1109/CCNC51644.2023.10060658|nan;Radio frequency;Neurological diseases;Transcranial magnetic stimulation;Phase shifters;Depression;Real-time systems;Magnetic fields|
|[Improvements of IoT Waveform for High Doppler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059811)|P. Kim; S. Jung; J. -G. Ryu|10.1109/CCNC51644.2023.10059811|High Dopper effect;IoT waveform;Lora;Synchronization;Convolutional codes;Satellites;Low earth orbit satellites;Encoding;Complexity theory;Doppler effect;Synchronization|
|[Realizing Physical Layer Security with common off-the-shelf WiFi equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059888)|S. A. Hoseini; F. den Hartog; F. Bouhafs|10.1109/CCNC51644.2023.10059888|Mobile and wireless security;physical layer security;wireless systems security;Wireless networks;Computer architecture;Physical layer security;Security;Communication system security;Wireless fidelity;Open source software|
|[Security, Trust, and Privacy Management Framework in Cyber-Physical Systems using Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060483)|D. Das; S. Banerjee; P. Chatterjee; U. Ghosh; U. Biswas; W. Mansoor|10.1109/CCNC51644.2023.10060483|Blockchain Applications;Cyber-Physical Systems;Blockchain-enabled CPS;Cyber Security;STP-aware CPS;Privacy;Data privacy;Connected vehicles;Data security;Cyber-physical systems;Blockchains;Peer-to-peer computing|
|[Designing Accessible Urban AR Experiences for Digital Humanities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060236)|F. Pittarello; A. Carrieri; T. Pellegrini; A. Volo|10.1109/CCNC51644.2023.10060236|accessibility;augmented reality;digital humanities;urban environment;UX;Humanities;Cognitive load;Augmented reality|
|[Window-type and AR Glass-type Transparent Antenna Systems for B5G/6G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059609)|S. -H. Park; C. -K. Park; H. Yoo; B. Kim; C. -B. Chae|10.1109/CCNC51644.2023.10059609|Transparent antenna;AR glass;invisible antenna;in-building networks;software-defined radio;6G mobile communication;Base stations;5G mobile communication;Millimeter wave communication;Antennas;Augmented reality;Network systems|
|[Building an SDVN Framework for RSU-Centric Cooperative Perception with Heterogeneous V2X](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060139)|Z. Li; T. Yu; T. Suzuki; K. Sakaguchi|10.1109/CCNC51644.2023.10060139|RSU;cooperative perception;V2X;SDVN;Power demand;Connected vehicles;Dynamic scheduling;Road safety;Dedicated short range communication;Millimeter wave communication;Vehicle dynamics|
|[A Reliability Audit Mechanism based on Multi-layered Blockchain for Spatio-Temporal Data Retention System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059819)|J. Ueda; K. Tsukamoto; H. Yamamoto; D. Nobayashi; T. Ikenaga; M. Lee|10.1109/CCNC51644.2023.10059819|Multi-layered Blockchain;STD-RS;Reliability audit;Blockchains;Behavioral sciences;Reliability;History;Global Positioning System|
|[Integration of a Smart Bed Infrastructure with Hospital Information Systems using Fast Health Interoperability Resources: *A case study of the Wireless biOmonitoring stickers and smart bed architecture: toWards Untethered Patients (WoW) R&D Project](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060813)|D. Portugal; J. N. Faria; M. Domingues; L. Gaspar|10.1109/CCNC51644.2023.10060813|nan;Wireless communication;Wireless sensor networks;Hospitals;Computer architecture;Logic gates;Writing;Security|
|[LIFT the AV: Location InFerence aTtack on Autonomous Vehicle Camera Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060796)|O. Adeboye; A. Abdullahi; T. Dargahi; M. Babaie; M. Saraee|10.1109/CCNC51644.2023.10060796|Autonomous Vehicle;Location Inference Attack;Privacy;Generative model;cyber-physical systems;Meters;Data privacy;Image matching;Geology;Benchmark testing;Cameras;Generative adversarial networks|
|[Improving Iterative Interference Replica Subtraction based Precoding for Massive MIMO Systems by Partial Zeroization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059872)|T. Suzuki; S. Berra; K. Maruta; O. Muta|10.1109/CCNC51644.2023.10059872|nan;Interference cancellation;Precoding;Simulation;Receiving antennas;Massive MIMO;Iterative methods;Computational complexity|
|[Accessibility for Virtual Tours: on Designing a Prototype for People with Visual Impairments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060297)|S. Bertani; V. Rubano; S. Mirri; C. Prandi|10.1109/CCNC51644.2023.10060297|Virtual tours;Accessibility;Navigation;People with Visual Impairments;COVID-19;Three-dimensional displays;Pandemics;Navigation;Visual impairment;Virtual environments;Prototypes|
|[Modeling and Evaluation of the Internet of Things Communication Protocols in Security Constrained Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059643)|C. Helbig; S. Otoum; Y. Jararweh|10.1109/CCNC51644.2023.10059643|Modeling and Simulation;Internet of Things;Edge Networks;Cloud Networks;Communication Protocols;Measurement;Protocols;Data security;Internet of Things|
|[Performance Evaluation on the Impact of Bottleneck Link Buffer Size Under Different Congestion Control Algorithm Competition in QUIC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060682)|N. Uchida; D. Nobayashi; T. Ikenaga; D. Cavendish|10.1109/CCNC51644.2023.10060682|Congestion Control;BBR;CUBIC;Transport protocols;Performance evaluation;Bandwidth|
|[Real-time Implementation of Semi-active Reconfigurable Intelligent Surfaces for mmWave and Sub-THz Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060036)|D. Jun; Y. Youn; C. Lee; M. Hwang; W. Hong; C. -B. Chae|10.1109/CCNC51644.2023.10060036|reconfigurable intelligent surface;beyond fifth-generation (B5G);millimeter-wave communication;sub-terahertz communication;meta-device;prototype;Fabrication;Array signal processing;Prototypes;Real-time systems;Energy efficiency;Proposals;Millimeter wave communication|
|[D2D Communication-Based Salvage Transmission Scheme for Communication Disturbance in 5G Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060351)|M. Saito; L. Jiang; P. Zhenni; S. Shimamoto|10.1109/CCNC51644.2023.10060351|Device-to-Device communication (D2D);5G;Mobile communication networks;Wi-Fi Direct;IEEE 802.11;Cellular networks;Base stations;Protocols;5G mobile communication;Simulation;Device-to-device communication;IEEE 802.11 Standard|
|[Level of Detail-based 3D Space Point Cloud Streaming and its Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060202)|Y. Tagashira; Y. Chujo; K. Kanai; C. Nakatsuka; K. Unno; J. Katto|10.1109/CCNC51644.2023.10060202|point cloud;tile-based streaming;Point cloud compression;Performance evaluation;Three-dimensional displays;Laser radar;Optimization methods;Quality control;Aerospace electronics|
|[Optimizing wireless network throughput under the condition of Physical Layer Security using Software-Defined Networking enabled collaboration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060341)|R. Ranji; U. Javed; B. Boltjes; F. Bouhafs; F. Den Hartog|10.1109/CCNC51644.2023.10060341|Mobile and wireless security;physical layer security;wireless systems security;spectrum trading;Learning systems;Wireless networks;Physical layer security;Throughput;Discrete event simulation;Communication system security;Security|
|[Cloud-based Web of Things: A Telemedicine Use Case](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060344)|L. D'Agati; Z. Benomar; F. Longo; G. Merlino; A. Puliafito|10.1109/CCNC51644.2023.10060344|Internet of Things;Cloud computing;Web of Things;REST;Cyber Physical Systems;OpenStack;Web services;Telemedicine;HATEOAS;Cloud computing;Actuators;Protocols;Embedded systems;Telemedicine;Sensor systems;Internet of Things|
|[Performance Evaluation of Vehicular Wireless Communications in Terahertz Bands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060248)|S. Satche; D. B. Rawat|10.1109/CCNC51644.2023.10060248|Terahertz spectrum band;mobility;Random Walk and orientation of an end user;tracking;sinuosity;Wireless communication;Performance evaluation;Behavioral sciences;Antennas|
#### **2023 5th Australian Microwave Symposium (AMS)**
- DOI: 10.1109/AMS57822.2023
- DATE: 16-17 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Liquid Crystal Tunable Stripline Phase Shifter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062318)|H. Zhou; L. Guo; A. Abbosh|10.1109/AMS57822.2023.10062318|Stripline;liquid crystal;delay line phase shifter;high figure of merit;Stripline;Phase shifters;Insertion loss;Delay lines;Distance measurement;Liquid crystal devices;Reflection coefficient|
|[X-Band GaN Stacked-FET Power Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062320)|D. J. Niven; S. J. Mahon; M. C. Heimlich|10.1109/AMS57822.2023.10062320|GaN;stacked-FET;Microwave measurement;Power amplifiers;Scattering parameters;Microwave amplifiers;Power generation|
|[An 82 - 98 GHz medium power amplifier in a 0.1-µm GaAs pHEMT process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062311)|J. Mihaljevic; S. Chakraborty; S. J. Mahon; M. Heimlich|10.1109/AMS57822.2023.10062311|Power amplifier;W-band;GaAs;Power system measurements;Power measurement;Density measurement;Gallium arsenide;PHEMTs;Power amplifiers;Gain measurement|
|[SiGe Direct Up-converter with above 30 dB Single Sideband Suppression from 18 to 39 GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062312)|L. E. Milner; S. Chakraborty|10.1109/AMS57822.2023.10062312|SiGe;pcells;image-reject;upconverter;broad-band transmitter;SiGe layout;Q measurement;Layout;Bandwidth;Amplitude modulation;Frequency conversion;Transformers;Signal generators|
|[A Compact Power Divider/Combiner Using A Defected Microstrip Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062334)|N. Weerathunge; S. Chakraborty; S. J. Mahon|10.1109/AMS57822.2023.10062334|Wilkinson power divider;power combiner;defected microstrip structure (DMS);monolithic microwave integrated circuit (MMIC);Power dividers;Microwave integrated circuits;Bandwidth;Insertion loss;Microwave circuits;Microstrip|
|[Optimization of Triaxial Horns: A Simultaneous S/X/Ka Example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062335)|C. Granet; M. Olszewska-Placha|10.1109/AMS57822.2023.10062335|Antenna;horn;feed;reflector;S/X/Ka;LEO satellite;multiband;triaxial;body-of-revolution;optimization;corrugated;Microwave antennas;Satellite antennas;Codes;Horn antennas;Optimization;Matlab|
|[A 15-Watt Ka-band power amplifier in 0.15 µm Gallium Nitride process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062326)|S. Chakraborty; L. E. Milner; S. J. Mahon; B. Wu; M. C. Heimlich|10.1109/AMS57822.2023.10062326|Power amplifier;Gallium nitride;Ka-band;MMIC;Microwave measurement;Power measurement;Silicon carbide;Power amplifiers;Microwave amplifiers;Gallium nitride;Power generation|
|[Modelling Coaxial SHORT and OPEN Calibration Standards](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062343)|D. C. X. Ung|10.1109/AMS57822.2023.10062343|offset coxial standard;EM simulation;Simulation;Microwave theory and techniques;Conductors;Mathematical models;Software;Reflection coefficient;Calibration|
|[Wideband Multi-Port Multi-Mode 3D-Printed Cylindrical Dielectric Resonator Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062330)|T. Ma; N. Nguyen-Trong; Q. Hung Dang; C. Fumeaux|10.1109/AMS57822.2023.10062330|3D-printing;dielectric resonator antennas;MIMO;polarization diversity;pattern diversity;Couplings;Antenna measurements;Microwave antennas;Slot antennas;Antenna feeds;Dielectric resonator antennas;Dielectrics|
|[Phase Cancellation for Signal Decoupling Between Overlapped RF Paths](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062328)|Y. Fu; K. Y. Chan; T. Deng; R. Ramer|10.1109/AMS57822.2023.10062328|additive manufacturing;directional coupler;phase cancellation;rectangular waveguide (RWG);signal decoupling;Radio frequency;Microwave measurement;K-band;Optical device fabrication;Prototypes;Rectangular waveguides;Three-dimensional printing|
|[Single-Ended-to-Balanced Hybrid Coupler for In-Band Full-Duplex Transceivers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062317)|H. Zhu; Y. Jay Guo|10.1109/AMS57822.2023.10062317|balanced;common-mode suppression;electrical balance duplexers;hybrid couplers;in-band full-duplex;single-ended-to-balanced;wideband;Microwave measurement;Prototypes;Couplers;Full-duplex system;Transformers;Microwave circuits;Transceivers|
|[The reflectance of sepia melanin at THz frequencies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062337)|N. Foroughimehr; Z. Vilagosh; A. W. Wood|10.1109/AMS57822.2023.10062337|Melanin;THz;ATR;Synchrotron;Sepia;Reflectivity;Absorption;Ink;Dielectrics;Biological materials|
|[Near-Field Metallic Metasurfaces for Enhancing Antenna Capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062315)|F. Ahmed; K. Singh; K. P. Esselle; D. Thalakotuna|10.1109/AMS57822.2023.10062315|Beam-steering;high-gain;metasurface;Antenna measurements;Phased arrays;Microwave measurement;Three-dimensional displays;Metasurfaces;Dielectric measurement;Microwave antenna arrays|
|[Frequency-Reconfigurable UHF Wearable Textile Antenna for RFID Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062314)|Q. H. Dang; N. Nguyen-Trong; S. J. Chen; C. Fumeaux|10.1109/AMS57822.2023.10062314|Frequency-reconfigurable antennas;UHF antennas;textile antennas;wearable antennas;Microwave antennas;Microwave measurement;Antenna measurements;Tuning;Antenna radiation patterns;Radiofrequency identification|
|[Theoretical RF Design of the New ESA DSA4 35m-Diameter Antenna in Australia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062323)|P. Regnier; J. . -J. Herren; L. Bru; L. Foucaud; C. Granet; P. Droll; F. Concaro; P. M. Besso|10.1109/AMS57822.2023.10062323|Antenna;reflector;X/K/Ka;beam-waveguide;European Space Agency;deep space antenna;Microwave antennas;Radio frequency;Antenna theory;K-band;Australia;Deep-space communications|
|[Design of a current controlling circuit integrated with a power amplifier in gallium nitride process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062333)|B. Wu; S. Chakraborty; S. J. Mahon; M. Heimlich|10.1109/AMS57822.2023.10062333|Gallium nitride;power amplifier;differential amplifier;bandwidth;Operational amplifiers;Gallium;Power amplifiers;Process control;Bandwidth;Microwave circuits;Differential amplifiers|
|[Microwave Imaging Using Cascaded Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062327)|F. Xue; L. Guo; A. Abbosh|10.1109/AMS57822.2023.10062327|Microwave imaging;Deep learning;Training;Shape;Neural networks;Microwave theory and techniques;Classification algorithms;Convolutional neural networks;Permittivity|
|[Varactor-Based 360° Differential Phase Shifter Pair](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062325)|Y. Yuan; S. Jammy Chen; C. Fumeaux|10.1109/AMS57822.2023.10062325|Phase shifter pair;differential mode;full 360°phase shift;low loss;Microwave antennas;Microwave measurement;Antenna measurements;Varactors;Phase measurement;Phase shifters;Microwave devices|
|[Low-Profile Dual-Polarization Patch Antenna for Mm-Wave Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062338)|Z. Yin; Y. Yang|10.1109/AMS57822.2023.10062338|Low-profile;dual-polarized;characteristic mode analysis;millimeter-wave;Microwave antennas;Couplings;Patch antennas;Bandwidth;Scattering parameters;Characteristic mode analysis;Broadband communication|
|[Time-Domain Performance of a Directional Wearable UWB Antenna under Bending](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062324)|P. B. Samal; S. J. Chen; C. Fumeaux|10.1109/AMS57822.2023.10062324|Flexible antennas;wearable antennas;wireless body area network (WBAN);ultrawideband (UWB) antennas;time-domain performance;system fidelity factor;Antenna measurements;Microwave antennas;Microwave measurement;Pulse measurements;Transmitting antennas;Receiving antennas;Bending|
|[A 40-60 GHz driver amplifier implemented in 0.1 µm Gallium Arsenide process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062313)|Q. Toan Chau; S. Chakraborty; N. T. Weerathunge|10.1109/AMS57822.2023.10062313|driver amplifier;gallium arsenide;sub-harmonic mixer;W-band;Semiconductor device measurement;Local oscillators;Broadband amplifiers;Gallium arsenide;PHEMTs;Power amplifiers;Gain measurement|
|[Compact Polarization Agile Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062345)|N. Lawrence; M. Fisher; J. Brittain|10.1109/AMS57822.2023.10062345|antenna;circular polarization;dual sense;satellite communications;Microwave antennas;Antenna measurements;Multiplexing;Polarization;Orbital calculations;Satellites;Microwave communication|
|[GPS Antenna with Perpendicular Quad-elements for Autonomous Vehicle’s Shark Fin Aerial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062331)|I. Saleem; M. Ali Babar Abbasi; D. Zelenchuk; S. Muzahir Abbas; S. Mukhopadhyay|10.1109/AMS57822.2023.10062331|GPS L1;GPS L2;microstrip antenna;perpendicular quad-elements;autonomous vehicle;shark fin aerial;Microwave antennas;Substrates;Tuning;Global Positioning System|
|[Compact Broadband Terahertz Filter Based on Effective Medium](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062329)|L. Chen; W. Gao; C. Fumeaux; W. Withayachumnankul|10.1109/AMS57822.2023.10062329|Terahertz;broadband filter;Bragg grating;effective-medium;slot waveguide;Bragg gratings;Wireless communication;Terahertz materials;Sensor systems;Broadband communication;Sensors;Passband|
|[A Huygens Source Quasi-End-Fire Button Antenna](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062321)|X. Yin; S. Jammy Chen; C. Fumeaux|10.1109/AMS57822.2023.10062321|Button antenna;end-fire radiation;Huygens source;Antenna measurements;Wireless communication;Microwave antennas;Dipole antennas;Prototypes;Magnetic noise;Body area networks|
|[High-Efficiency Multi-Beam GRIN Lens with 2-D Wide-Angle Coverages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062340)|L. Song; P. Qin; T. Zhang; J. Du; Y. Jay Guo|10.1109/AMS57822.2023.10062340|GRIN lens;high efficiency;multi-beam;Microstrip antenna arrays;Refractive index;Prototypes;Microstrip antennas;Bandwidth;Apertures;Microwave theory and techniques|
|[An Electromagnetically Transparent Dipole for Cross-Band Scattering and Coupling Suppression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062336)|S. Sun; C. Ding; Y. Jay Guo|10.1109/AMS57822.2023.10062336|coupling suppression;dual-band antenna array;isolation;pattern distortion;scattering suppression;Couplings;Dipole antennas;Electromagnetic scattering;Loading;Dual band;Microwave antenna arrays;Loaded antennas|
|[A Multi-Beam Antenna Based on Modulated Metasurface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062342)|Y. Wen; P. -Y. Qin; Y. Jay Guo|10.1109/AMS57822.2023.10062342|Holographic reactance surface;multibeam antennas;modulated metasurface;Microwave measurement;Microwave antennas;Antenna measurements;Surface impedance;Prototypes;Position measurement;Metasurfaces|
|[Resonance Behavior of an Obliquely-Oriented Conducting Wire Object Above a Halfspace](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062332)|S. Li; C. Hargrave; H. -S. Lui|10.1109/AMS57822.2023.10062332|Singularity Expansion Method;Resonance-based Radar Target Recognition;Transient Electromagnetic Scattering;Target recognition;Spaceborne radar;Wires;Radar scattering;Microwave theory and techniques;Dielectrics;Behavioral sciences|
#### **2023 International Conference on Electronics, Information, and Communication (ICEIC)**
- DOI: 10.1109/ICEIC57457.2023
- 5-8 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Lightweighted FPGA Implementation of Even-Odd-Buffered Active Noise Canceller with On-Chip Convolution Acceleration Units](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049949)|S. Park; D. Park|10.1109/ICEIC57457.2023.10049949|Noise cancelling;Even-Odd-buffer;FFT;Adaptive algorithm;Power demand;Convolution;Adaptive algorithms;Noise cancellation;Acoustics;Delays;System-on-chip|
|[Hybrid Beamforming-based Beam Tracking using UKF in Massive MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049914)|Y. Sim; S. Sin; J. Cho; K. Kim; S. Moon; I. Hwang|10.1109/ICEIC57457.2023.10049914|beam tracking;hybrid beamforming;unscented Kalman filter;Deep learning;Wireless communication;Array signal processing;Computational modeling;Neural networks;Millimeter wave technology;Massive MIMO|
|[Data Collection Framework Using a Lightweight Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049886)|K. Tulkinbekov; D. -H. Kim|10.1109/ICEIC57457.2023.10049886|Blockchain;Data Collection;Security;Privacy;Reliability;Protocols;Databases;Data collection;Blockchains;Reliability;Servers|
|[Nanospeckle Illumination Microscopy of Extracellular Vesicles on Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049929)|H. Lee; H. Yoo; G. Myeong Seo; K. Kang; S. Ah Lee; K. -A. Toh; J. Hwan Sung; D. Kim|10.1109/ICEIC57457.2023.10049929|nanospeckle;microscopy;structured illumination;exosome;biochip;Image resolution;Diffraction;Lighting;Biochips;Plasmons;Light fields;Electron microscopy|
|[Post-layout simulation automation based on Reinforcement Learning using Schematic to Layout sync module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049861)|J. Jeong; H. Kim; T. -H. Kim; E. Cheon|10.1109/ICEIC57457.2023.10049861|Post-layout simulation;design automation;Reinforcement learning;Q-learning;circuit optimization;Automation;Monte Carlo methods;Layout;Reinforcement learning;Manuals;Circuit synthesis;Synchronization|
|[IDS-Extract:Downsizing Deep Learning Model For Question and Answering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049915)|Z. Guo; S. Kavuri; J. Lee; M. Lee|10.1109/ICEIC57457.2023.10049915|question-answering;dense passage retrieval;Integration Gradient;SBERT;Training;Performance evaluation;Adaptation models;Semantics;Training data;Data models;Question answering (information retrieval)|
|[Dual-Mode Waveguide Cavity Filters and Diplexer With Fractional Bandwidths Around 0.2%](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049947)|J. Lee; S. -W. Jeong; B. Lee; M. Lee; J. Lee|10.1109/ICEIC57457.2023.10049947|Diplexer;filter;multiplexer;Radio frequency;Circulators;Receivers;Insertion loss;Propagation losses;Orbits;Frequency measurement|
|[AI Feedback Architecture of Video Surveillance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049874)|T. Kim|10.1109/ICEIC57457.2023.10049874|Video surveillance;intrusion detection;lifelong-learning;domain adaptation;Learning systems;Adaptation models;Computational modeling;Visual analytics;Heuristic algorithms;Object detection;Video surveillance|
|[A Design Approach for 5G-NR Radio Planning Using Both FR1 and FR2 on Any Selected Outdoor and Indoor Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049901)|S. Chinkhong; P. Kaewplung|10.1109/ICEIC57457.2023.10049901|Sub-6GHz (FR1);mmWave (FR2);5G-NR radio planning;Service data rate;Total path loss;Coverage area;6G mobile communication;Base stations;5G mobile communication;Planning;Millimeter wave communication;Low latency communication|
|[Deep Reinforcement Learning for Semi-Active Suspension: A Feasibility Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049850)|S. R. Kim; C. Kim; S. Shin; S. -W. Kim|10.1109/ICEIC57457.2023.10049850|nan;Deep learning;Damping;Vibrations;Roads;Friction;Force;Reinforcement learning|
|[Hardware-Based Isolation Technique to Guarantee Availability of Security Controls in a Gateway for Industrial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049871)|H. Choi; H. Kwon; J. Lee; Y. Lee; K. Kim|10.1109/ICEIC57457.2023.10049871|TrustZone;Industrial Network;Smart Grid;Availability;Degradation;Protocols;Law;Logic gates;Fuzzing;Aerospace electronics;Security|
|[Intelligent Microcontroller Using Runtime Coefficient Update Techniques for Disturbance Robust Output Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049975)|J. Jung; D. Park|10.1109/ICEIC57457.2023.10049975|Automatic control;PID control;Intelligent microcontroller;Motor drives;Runtime;PI control;Microcontrollers;DC motors;Real-time systems;PD control|
|[A Study on the Assistive System for Safe Elevator Get on of Wheelchair Users with Upper Limb Disability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049877)|D. Kim; W. -Y. Lee; J. -W. Shin; E. -H. Lee|10.1109/ICEIC57457.2023.10049877|electric wheelchair;elevator;safety;autonomous;laser range scanner;Laser radar;Error analysis;Wheelchairs;Elevators;Path planning;Safety;Planning|
|[A Fundamental Study on a System for Estimating the Intention to Change a User Control Method by Measuring the Pressure inside the Transfemoral prosthetic Socket](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049956)|N. -Y. Park; S. -H. Eom; J. -H. Ryu; E. -H. Lee|10.1109/ICEIC57457.2023.10049956|prosthetic leg;sockets;velostat;switching locomotion mode;pressure sensor;direction of movement;Legged locomotion;Pressure sensors;Shape;Sockets;Sensor systems;Pressure measurement;Prosthetics|
|[Quality Assurance of Autonomous Vehicle’s Sensor Data based on Classifying Level of Difficulty](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049897)|K. Kim; S. Lee; H. Kim|10.1109/ICEIC57457.2023.10049897|Dataset Quality;Autonomous Vehicle;Quality Assurance;Level of Difficulty;Training;Deep learning;Quality assurance;Data models;Classification algorithms;Artificial intelligence|
|[A Study on the Application of OpenPose for the Prevention of Collision of VR HMD Wearers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049936)|D. Woo; G. Byeon; E. Song; S. Yu|10.1109/ICEIC57457.2023.10049936|Virtual Reality;Extended Reality;Unity;OpenPose;Safety;Pose estimation;Resists;Cameras;Libraries;Safety;Behavioral sciences;Engines|
|[Acquisition of High-Quality Image by Using Maximum Correntropy Criterion Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049878)|H. -S. Jang|10.1109/ICEIC57457.2023.10049878|Shape from Focus (SFF);jitter noise;focus curve;Maximum Correntropy Criterion Kalman Filter (MCC-KF);Optical filters;Vibrations;Smart agriculture;Three-dimensional displays;Shape;Filtering;Computational modeling|
|[Empirical Analysis of Side-Channel Attack Resistance of HLS-designed AES Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049904)|T. Mizuno; H. Nishikawa; X. Kong; H. Tomiyama|10.1109/ICEIC57457.2023.10049904|AES;side-channel attack;high-level synthesis;T-test;Resistance;Performance evaluation;Correlation;Power demand;Costs;Side-channel attacks;Hardware|
|[Ambulance Dispatch Scheme based on Maximum Flow Minimum Cost models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049923)|J. Lee; J. Park; E. Park|10.1109/ICEIC57457.2023.10049923|ambulance dispatch;maximum cost minimum flow;flow graph model;distance and criticality;Measurement;Biomedical equipment;Costs;Smart cities;Prototypes;Cost function;Time factors|
|[Multi-Head Convolutional Neural Network Compression based on High-Order Principal Component Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049909)|T. Kim; Y. Na; S. Park|10.1109/ICEIC57457.2023.10049909|Bayesian optimization;convolutional neural network;high-order principal component analysis;multi-task learning;neural network compression;Degradation;Computer vision;Neural network compression;Computer architecture;Multitasking;Bayes methods;Convolutional neural networks|
|[Quantizing Heading Change Angles for Enhanced Indoor Pedestrian Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049973)|J. Seol Son; S. Baek; J. Kim|10.1109/ICEIC57457.2023.10049973|Pedestrian Dead Reckoning;Quantizing Heading Change Angle;Indoor Inertial Navigation;Dead reckoning;Quantization (signal);Inertial sensors;Indoor navigation;Estimation;Inertial navigation;Indoor environment|
|[Design of Low Drop Out Regulator with High Robustness ESD Protection Circuit Using Current Buffer Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049925)|S. W. Kwon; S. G. Jeong; K. Y. Lee; Y. S. Koo|10.1109/ICEIC57457.2023.10049925|LDO regulator;overshoot;undershoot;ESD surge;ESD protection circuit;LDO regulator with ESD protection circuit;Integrated circuits;Regulators;Voltage measurement;Power measurement;Capacitors;Electrostatic discharges;Robustness|
|[An Enhanced Beam Index Detection Scheme Robust to Timing Offset in a 5G Software Modem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049855)|D. Lim; J. Kim|10.1109/ICEIC57457.2023.10049855|nan;5G mobile communication;Software algorithms;Symbols;Modems;Search problems;Software;Real-time systems|
|[LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049863)|D. Jung; J. -K. Cho; Y. Jung; S. Shin; S. -W. Kim|10.1109/ICEIC57457.2023.10049863|nan;Point cloud compression;Convolutional codes;Laser radar;Estimation;Predictive models;Convolutional neural networks;Robots|
|[Interference Mitigation between Remote Base Stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049866)|S. Ku; K. Lee; C. Lee|10.1109/ICEIC57457.2023.10049866|5G NR;TDD;atmospheric duct;remote interference;aggressor;victim;interference alignment;Base stations;5G mobile communication;Ducts;Null space;Interference;Uplink;MIMO communication|
|[An Automated Synthesis Framework for Fast Evaluation of Maximum Operating Frequency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049896)|B. Y. Kong; H. Yoo; Y. Lee|10.1109/ICEIC57457.2023.10049896|clock frequency;maximum operating frequency;synthesis;very large scale integration (VLSI);Frequency synthesizers;Time-frequency analysis;Systematics;Manuals;Very large scale integration;Timing;Clocks|
|[High Performance 3.3KV 4H-SiC MOSFET with a Floating Island and Hetero Junction Diode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049864)|J. Na; K. Kim|10.1109/ICEIC57457.2023.10049864|4H-SiC;MOSFET;floating island;body diode;heterojunction diode;switching loss;Performance evaluation;MOSFET;Electric breakdown;Switching loss;Multichip modules;High-voltage techniques;Switches|
|[Stable Quantization-Aware Training with Adaptive Gradient Clipping](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049939)|J. Park; S. Lee; B. -C. Song|10.1109/ICEIC57457.2023.10049939|nan;Training;Degradation|
|[Analysis of performance difference when using knowledge distillation of efficient CNN-based super-resolution algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049942)|M. Yoon; S. Lee; B. -C. Song|10.1109/ICEIC57457.2023.10049942|Super resolution;Knowledge distillation;Costs;Superresolution|
|[Frequency-Varying Doppler Shift Effect on Wideband Orthogonal Time-Frequency Space Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049980)|H. -G. Lee; J. Kim; J. Joung; J. Choi|10.1109/ICEIC57457.2023.10049980|Doppler shift;wideband communications;OTFS;mobile communications;Doppler shift;Degradation;Time-frequency analysis;Simulation;Aerospace electronics;Delays;Wideband|
|[An investigation into time-varying characteristics of multivariate time series in Grassmann classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049926)|B. H. Nuriye; B. Oh|10.1109/ICEIC57457.2023.10049926|Multivariate Time Series;Linear Dynamical System;Grassmannian manifold;Manifolds;Correlation;Time series analysis;Benchmark testing;Data models;Task analysis;Dynamical systems|
|[Hardware Design of Intrusion Detection System for Automotive CAN Bus Using Random Forest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049883)|D. Lee; C. Han; S. Lee|10.1109/ICEIC57457.2023.10049883|Controller Area Network (CAN);Hacking;Cybersecurity;Intrusion Detection System (IDS);Node Exclusion System (NES);Random Forest;Machine Learning;Protocols;Intrusion detection;Forestry;Hardware;Reliability;Hamming distances;Hardware design languages|
|[Korean Tokenization for Beam Search Rescoring in Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049934)|K. Shim; H. Bae; W. Sung|10.1109/ICEIC57457.2023.10049934|speech recognition;Korean;tokenization;beam search;rescoring;language model;Error analysis;Oral communication;Tokenization;Decoding;Automatic speech recognition|
|[An Analysis of CMOS Latched Comparators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049873)|S. Yeom; T. Sim; J. Han|10.1109/ICEIC57457.2023.10049873|CMOS;latched comparator;slicer;kickback noise;clock-to-Q delay;Semiconductor device modeling;Latches;Power demand;Circuits and systems;Simulation;Voltage;CMOS process|
|[Automation methodology of local defocus monitoring on fixed chips in semiconductor fabrication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049963)|T. Shin; S. Ok; K. Lee; S. Hwang|10.1109/ICEIC57457.2023.10049963|local defocus monitoring;wafer backside;early detection;Fabrication;Productivity;Automation;Surface contamination;Lithography;Process control;Semiconductor device manufacture|
|[UCR-SSL: Uncertainty-Based Consistency Regularization for Semi-Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049938)|S. Lee; H. Kim; D. Chun|10.1109/ICEIC57457.2023.10049938|Convolutional Neural Network;Semi-Supervised Object Detection;Consistency Regularization;Uncertainty;Matched filters;Uncertainty;Head;Annotations;Filtering;Neural networks;Semisupervised learning|
|[AGT: Channel Pruning Using Adaptive Gradient Training for Accelerating Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049943)|N. J. Kim; H. Kim|10.1109/ICEIC57457.2023.10049943|Convolutional Neural Network;Pruning;Channel Pruning;Adaptive Gradient Training;Training;Degradation;Backpropagation;Adaptation models;Adaptive systems;Memory management;Taylor series|
|[Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049894)|J. Jang; H. Kim; H. Lee|10.1109/ICEIC57457.2023.10049894|Memory access pattern;Processing-in-Memory;Near data processing;Convolutional Neural Networks;Network training;Training;Analytical models;Computational modeling;Memory management;Training data;Data models;Convolutional neural networks|
|[DWT+DWT: Deep Learning Domain Generalization Techniques Using Discrete Wavelet Transform with Deep Whitening Transform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049902)|J. Shin; H. Kim|10.1109/ICEIC57457.2023.10049902|domain generalization;frequency domain;discrete wavelet transform;deep whitening transform;Deep learning;Wavelet domain;Sensitivity;Image color analysis;Semantic segmentation;Feature extraction;Discrete wavelet transforms|
|[Whole-body Human Mesh Reconstruction with Transformer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049916)|J. Park; S. -j. Kang|10.1109/ICEIC57457.2023.10049916|whole-body human mesh reconstruction;transformer;Three-dimensional displays;Image resolution;Correlation;Reconstruction algorithms;Transformers;Image reconstruction;Faces|
|[Impacts of Clock Constraints on Side-Channel Leakage of HLS-designed AES Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049959)|Y. Miura; T. Mizuno; H. Nishikawa; X. Kong; H. Tomiyama|10.1109/ICEIC57457.2023.10049959|Clock constraints;AES;side-channel attack;high-level synthesis;T-test;Resistance;Measurement;Energy consumption;Correlation;Side-channel attacks;Security;Internet of Things|
|[InvHDR: Inverse Tone Mapping With Invertible Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049952)|J. Kim; S. -J. Kang|10.1109/ICEIC57457.2023.10049952|High Dynamic Range Imaging;Invertible Neural Network;Single Image HDR Imaging;Fuses;Neural networks;Cameras;High dynamic range;Sensors|
|[Vehicle wheel edge detection using ToF sensor for low cost of vehicle specification measurement system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049860)|G. -Y. Kim; S. -H. Eom; C. -W. Lee; W. -S. Kim; E. -H. Lee|10.1109/ICEIC57457.2023.10049860|Autonomous robot valet parking system;Robotic parking management system;Parking robot;Car body measurement;Costs;Ultrasonic variables measurement;Image edge detection;Urban areas;Wheels;Position measurement;Robot sensing systems|
|[A segment-wise extraction of multivariate time-series features for Grassmann clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049970)|S. Heo; B. H. Nuriye; B. Oh|10.1109/ICEIC57457.2023.10049970|Multivariate Time-Series;Feature Extraction;Clustering;Grassmann Manifold;Manifolds;Feature extraction;Data mining;Principal component analysis|
|[QoS-Aware UAV-BS Deployment Optimization Based on Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049907)|H. Lee; C. Eom; C. Lee|10.1109/ICEIC57457.2023.10049907|UAV-BS deployment;quality-of-service (QoS);Reinforcement learning (RL);deep Q-network (DQN);Base stations;Simulation;Quality of service;Reinforcement learning;Linear programming;Autonomous aerial vehicles;Optimization|
|[Multi-view stereo with recurrent neural networks for spatio-temporal consistent depth maps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049937)|H. Son; S. -j. Kang|10.1109/ICEIC57457.2023.10049937|multi-view stereo;video depth estimation;convolutional recurrent neural networks;Training;Deep learning;Recurrent neural networks;Estimation;Decoding;Convolutional neural networks|
|[Analysis on the Neural Network-aided Satellite Resource Allocation Schemes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049977)|G. Jo; S. Chan; S. Kim; D. Oh|10.1109/ICEIC57457.2023.10049977|satellite;resource allocation;neural network;Satellites;Simulation;Linear regression;Low earth orbit satellites;Estimation;Artificial neural networks;Bandwidth|
|[Module Implementation and Simulation of Timing Constraint Check Function of I2C Protocol Using Verilog](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049891)|J. -C. Lee; T. -O. Kim; J. -H. Chae|10.1109/ICEIC57457.2023.10049891|I2C;Verilog;timing parameter;error detection;Protocols;Stability analysis;Timing;Circuit stability;Behavioral sciences;Hardware design languages;Integrated circuit modeling|
|[Deep learning classification of focal liver lesions with contrast-enhanced ultrasound from arterial phase recordings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049872)|N. Kim; W. J. Lee; H. -J. Lee|10.1109/ICEIC57457.2023.10049872|hepatocellular carcinoma (HCC);focal nodular hyperplasia (FNH);contrast-enhanced ultrasound (CEUS);deep neural network (DNN);Deep learning;Ultrasonic imaging;Liver;Recording;Lesions;Convolutional neural networks;Portals|
|[Robust Lane Tracking for Harsh Driving Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049885)|S. -H. Lee; H. -J. Kwon; S. -H. Lee|10.1109/ICEIC57457.2023.10049885|lane detection;Multi-scale Retinex (MSR);Kalman filter;bilateral filter;noise reduction;Industries;Rain;Lane detection;Lane departure warning systems;Information filters;Safety;Engines|
|[A 56-to-110 dB Gain Programmable Gain Amplifier with Second-Order Band Pass Filter for Ultrasonic Sensor Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049884)|J. Kang; S. Park; S. Kim; J. Rhee|10.1109/ICEIC57457.2023.10049884|Low-noise circuit design;variable gain amplifier (VGA);programmable gain amplifier (PGA);ultrasonic sensor receiver;DC offset cancellation;Band-pass filters;Resistors;Filtering;Capacitors;Acoustics;Sensor systems;Voltage control|
|[A 133.3 dB Dynamic Range Pulse Oximeter Front-End with Low-Noise Area-Efficient Offset Cancellation Current DAC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049972)|S. Park; W. Kim; S. Kim; J. Rhee|10.1109/ICEIC57457.2023.10049972|Analog front-end (AFE);pulse oximeter;current digital-to-analog converter (DAC);area-efficiency;wide dynamic range (DR);Integrated circuits;Pulse oximeter;Current mirrors;Digital-analog conversion;Dynamic range;Light emitting diodes;Transistors|
|[Domain adaptation from posteroanterior to anteroposterior X-ray radiograph classification via deep neural converter with label recycling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049919)|D. Park; M. Kim; H. Kim; J. Lee; S. Y. Chun|10.1109/ICEIC57457.2023.10049919|domain adaptation;X-ray radiograph;antero-posterior;posteroanterior;classification;Radiography;Deep learning;Protocols;Neural networks;Recycling;Labeling;Task analysis|
|[Digital handwriting correction using deep learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049853)|D. -H. Kim; H. -J. Na; S. -H. Jeong; N. -Y. Kim; K. Kong|10.1109/ICEIC57457.2023.10049853|CNN;OCR;Spell-check;handwrite;font;Deep learning;Optical character recognition;Keyboards;Writing;Information age;Convolutional neural networks|
|[A Study on Edge Computing-Based Microservices Architecture Supporting IoT Device Management and Artificial Intelligence Inference](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049892)|T. -G. Kwon; K. Ro|10.1109/ICEIC57457.2023.10049892|Edge Computing;IoT;Microservice Architecture;Cloud computing;Data security;Microservice architectures;Computer architecture;Data processing;Delays;Internet of Things|
|[The Effect of Electrode Distance on the Voltage Distribution during Non-invasive Vagus Nerve Stimulation – a Preliminary Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049951)|K. Shin; Y. M. Bae; H. -S. Park; D. -G. Kang; M. Kang|10.1109/ICEIC57457.2023.10049951|cervical vagus nerve;electrode distance;biological tissue;electrical stimulation;Electrodes;Voltage measurement;Medical devices;Computer simulation;Wires;Electrical stimulation;Lead|
|[Transistor Sizing Scheme for DICE-Based Radiation-Resilient Latches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049983)|J. -J. Park; Y. -M. Kang; G. -H. Kim; I. -J. Chang; J. Kim|10.1109/ICEIC57457.2023.10049983|double-node upset (DNU);radiation-hardened latch;DICE;single-node upset (SNU);soft error;Latches;Systematics;Single event upsets;Focusing;Very large scale integration;Inverters;Transistors|
|[Fast Virtual Keyboard Typing Using Vowel Hand Gesture Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049889)|S. -J. Park; T. -H. Lee; V. Munasinghe; T. -S. Kim; H. -J. Lee|10.1109/ICEIC57457.2023.10049889|Hand Gestures;Virtual Keyboard;Vowel Gestures;Augmented Reality;Virtual Reality;Layout;Keyboards;Gesture recognition;Switches;Nonhomogeneous media|
|[Regression Model-based VCO Design Optimization Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049908)|J. -W. Hyun; J. -W. Nam|10.1109/ICEIC57457.2023.10049908|analog circuit design automation;ocean script;machine learning;ANN;voltage-controlled-oscillator;Semiconductor device modeling;Design automation;Machine learning algorithms;Voltage-controlled oscillators;Supervised learning;Voltage;Analog circuits|
|[Multi-Camera-Based Product Recognition for Inventory Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049880)|V. Munasinghe; T. -H. Lee; H. -J. Lee; T. Sung Kim; J. -S. Kim|10.1109/ICEIC57457.2023.10049880|convolutional neural network;deep learning;checkout-free store;object recognition;multi-camera;Deep learning;Object detection;Detectors;Inventory management;Cameras;Object recognition;Convolutional neural networks|
|[Eight-bit Quantization for Super-Resolution Networks and Its Hardware Implementation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049948)|X. T. Nguyen; Y. Ko; T. N. Nguyen; H. -J. Lee; K. Lee|10.1109/ICEIC57457.2023.10049948|8-bit quantization;SR networks;SR accelerator;Degradation;Quantization (signal);Costs;Superresolution;Hardware;Libraries;Hardware design languages|
|[IoT Based Intelligent Greenhouse Farming Technology with Low Cost and Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049868)|K. Lee; M. J. Islam; H. Lee; W. Kim; S. -K. Lee; B. Kim|10.1109/ICEIC57457.2023.10049868|smart farm;sensors;greenhouse;solar;Smart agriculture;Protocols;Microcontrollers;Green products;Microcomputers;Energy efficiency;Power systems|
|[Multi-Droplet Routing based on a Shape-Dependent Velocity Model on MEDA Biochips](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049887)|C. Shiro; H. Nishikawa; X. Kong; H. Tomiyama; S. Yamashita|10.1109/ICEIC57457.2023.10049887|MEDA;Mathematical Programming Problem;Droplet Routing;Industries;Shape;Simulation;Biological system modeling;Scalability;Heuristic algorithms;Programming|
|[VITON-Based CT Image Registration for Medical Surgery Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049879)|J. Xu; T. -H. Lee; V. Munasinghe; H. -J. Lee; T. S. Kim|10.1109/ICEIC57457.2023.10049879|High Resolution Virtual Try-on;Medical Image Registration;Computed Tomography (CT);Image resolution;Computed tomography;Wearable computers;Surgery;Distortion;Cameras;Registers|
|[Diagnose Label Errors for 3D Object Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049969)|J. Wang; R. Wang; T. S. Kim; J. -S. Kim; H. -J. Lee|10.1109/ICEIC57457.2023.10049969|3D dataset analysis;3D object classification;noisy labels;Analytical models;Solid modeling;Three-dimensional displays;Protocols;Shape;Semantics;Data models|
|[Time-Based Performance Analysis of Narrowband Internet of Things (NB-IoT) for Particulate Matter Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049930)|T. Inthasuth; P. Uarchoojitt; W. Boonsong; N. Kaewthong|10.1109/ICEIC57457.2023.10049930|PM2.5 monitoring;Wireless sensor node;NB-IoT;and IoT cloud platform;Cloud computing;Receivers;Time measurement;Real-time systems;Delays;Internet of Things;Data communication|
|[The Communication Link Analysis of ZigBee Mesh Networks Using Received Signal Strength Indicator (RSSI) for the Agricultural Slope Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049953)|P. Uarchoojitt; S. Pothongkham; T. Kongnarong; W. Boonsong; C. Samakee; T. Inthasuth|10.1109/ICEIC57457.2023.10049953|Highland agriculture;slope area;RSSI;ZigBee;and Wireless communication;Wireless communication;Smart agriculture;Protocols;Transmitters;Zigbee;Receivers;Repeaters|
|[A New 3-D PCA Regression Method for Manifold Dimension Reduction with Image Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049946)|K. Min Lee; C. -H. Lin|10.1109/ICEIC57457.2023.10049946|Regression Manifold;3-D PCA;Autoencoder;Image Enhancement;Manifolds;Deep learning;Dimensionality reduction;Performance evaluation;Image analysis;Pollution;Feature extraction|
|[CNN-based Hand Gesture Recognition for Contactless Elevator Button Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049968)|Taeho-Lee; V. Munasinghe; Y. -M. Li; T. Sung Kim; H. -J. Lee|10.1109/ICEIC57457.2023.10049968|Convolutional neural networks;deep learning;hand gesture recognition;contactless elevator control;Jetson GPU board;COVID-19;Deep learning;Computer viruses;Pandemics;Graphics processing units;Gesture recognition;Control systems|
|[Development and Accuracy Analysis of Embedded Based Instrumentation Toward ZigBee and NB-IoT Networks for Efficiency Energy Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049870)|W. Anupan; S. Nunsong; B. Yotsavip; W. Boonsong; S. Somwong; T. Inthasuth|10.1109/ICEIC57457.2023.10049870|WSN;IoT;ZigBee;NB-IoT;and smart home;Wireless communication;Wireless sensor networks;Instruments;Zigbee;Smart homes;Particle measurements;Real-time systems|
|[Detection Performance Analysis based on EEG Signal for Visual BCI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049806)|K. Lee; G. H. Ko; C. H. Lee; Y. S. Jeong|10.1109/ICEIC57457.2023.10049806|Visual BCI;Classification;Takens' delay embedding;Visualization;Feature extraction;Electroencephalography;Brain-computer interfaces;Delays;Classification algorithms;Steady-state|
|[ReRAM Switching Performance based on Single-Walled Carbon Nanotubes Wire Electrode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049931)|D. Jang; B. Jo; Y. L. Kim; M. -W. Kwon|10.1109/ICEIC57457.2023.10049931|nan;Electrodes;Performance evaluation;Voltage measurement;Neuromorphics;Wires;Switches;Carbon nanotubes|
|[Fabrication of Piezoelectric Nanogenerators Based on Spin Coating of PVDF/ZnO Composite Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049960)|M. J. Islam; H. Lee; K. Lee; C. Cho; B. Kim|10.1109/ICEIC57457.2023.10049960|spin coating;piezoelectric;nanogenerator;Nanoparticles;Scanning electron microscopy;X-ray scattering;Nanocomposites;Wearable computers;X-ray diffraction;Zinc oxide|
|[Radiation-Hardened Processing-In-Memory Crossbar Array With Hybrid Synapse Devices for Space Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049920)|S. -U. Kang; J. -W. Han; M. -S. Choo|10.1109/ICEIC57457.2023.10049920|processing in memory (PIM);multilayer perceptron (MLP);neural network;radiation-hardened;bit error;static-random access memory (SRAM);most significant bit (MSB);Performance evaluation;Degradation;Radiation effects;Neural networks;Random access memory;Stochastic processes;Parallel processing|
|[Machine Learning-based Signal-to-Noise Ratio Estimation using Amplitude Frequency Vector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049849)|J. -Y. Ahn; H. Wang|10.1109/ICEIC57457.2023.10049849|SNR estimation;machine learning;Maximum likelihood estimation;Histograms;Neural networks;Symbols;Machine learning;Frequency conversion;Frequency estimation|
|[Efficient Hardware Acceleration of Chinese Remainder Theorem for Fully Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049928)|H. Kim; S. -C. Park|10.1109/ICEIC57457.2023.10049928|fully homomorphic encryption (FHE);chinese remainder theorem (CRT);recursive arithmetic operation;large arithmetic word size (LAWS);hardware implementation;Surface acoustic waves;Cathode ray tubes;Hardware;Table lookup;Cryptography;Homomorphic encryption;Field programmable gate arrays|
|[Direct Phase Control in Digital Phase-Locked Loop Mitigating Loop Delay Effect inside Digital Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049900)|I. -W. Jang; M. -S. Choo|10.1109/ICEIC57457.2023.10049900|digital phase-locked loop (DPLL);bang bang;phase and frequency detector (PFD);digital loop filter (DLF);loop delay;Frequency synthesizers;Temperature distribution;Detectors;Jitter;Stability analysis;Delays;Autocorrelation|
|[Wireless event-based kill-switch for safe and autonomous UAS operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049917)|J. Uddin; M. Ahad; A. Hil Kafi|10.1109/ICEIC57457.2023.10049917|Wireless Communication;Kill-switch;UAS;Drone;Intelligent;Circuit;Robotics;Algorithm;Wireless communication;Authorization;Decision making;Behavioral sciences;Communication system security;Drones|
|[Quantitative Analysis of Various 2D CNN Structures based on Dataflow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049910)|S. Lee; J. Park; J. Kim; Y. Hwang; S. Choi; H. Yoo|10.1109/ICEIC57457.2023.10049910|convolutional neural networks;convolutional architecture;dataflow;Statistical analysis;Convolution;Neural networks;Hardware;Energy efficiency;Convolutional neural networks;Kernel|
|[GPU Acceleration of Chinese Remainder Theorem for Fully Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049852)|Y. Oh; S. -C. Park; J. -C. Na; D. K. Kim|10.1109/ICEIC57457.2023.10049852|Fully homomorphic encryption;CRT;GPU implementation;Privacy preserving;Accelerating FHE;Instruction sets;Graphics processing units;Information security;Cathode ray tubes;Parallel processing;Data processing;Computational efficiency|
|[A Study of Optimization Algorithm based on Non-negative Matrix Factorization for Face Age Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049921)|M. Zhang; D. M. Lee|10.1109/ICEIC57457.2023.10049921|face age prediction;non-negative matrix factorization;K-L divergence;support vector machines;Support vector machines;Dimensionality reduction;Face recognition;Prediction algorithms;Classification algorithms;Kernel;Optimization|
|[Practical Analysis of Xilinx FPGAs' Bitstream Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049905)|E. Lee; S. Choi; J. Park; S. Shin; H. Yoo|10.1109/ICEIC57457.2023.10049905|Xilinx FPGA;Bitstream encryption;Xilinx design suite;Adaptive arrays;Logic gates;Encryption;Data mining;Field programmable gate arrays|
|[One-Shot Face Reenactment with 2D Facial Landmark Conditional Normalizing Flow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049848)|D. Han; T. H. Kim|10.1109/ICEIC57457.2023.10049848|face reenactment;pose transfer;image generation;normalizing flow;Training;Image synthesis;Pipelines;Noise measurement;Task analysis;Faces|
|[Compact Design of 60-GHz Wilkinson Power Dividers in a 65-nm CMOS Process for Monolithic Microwave Integrated Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049935)|H. Bao; S. Lam|10.1109/ICEIC57457.2023.10049935|Wilkinson power divider;power combiner;monolithic microwave integrated circuits (MMICs);CMOS transmission line;millimeter-wave CMOS;mm-wave ICs;Power dividers;Power transmission lines;Three-dimensional displays;Insertion loss;Microwave communication;MMICs;Microwave circuits|
|[Multispectral Palm-vein Fusion for User Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049882)|J. Lee; J. Kim; D. Kim; S. Ah Lee; J. Hwan Sung; K. -A. Toh|10.1109/ICEIC57457.2023.10049882|Palm-vein recognition;Feature level fusion;Residual learning;Databases;Convolution;Stacking;Neural networks;Data preprocessing;Feature extraction|
|[Estimation of Graphene Homogeneity Using Electrical Impedance Tomography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049856)|M. Jeon; A. Kumar Khambampati; F. Alberto Solano Sanchez; K. -R. Kwon; K. Youn Kim|10.1109/ICEIC57457.2023.10049856|Electrical impedance tomography;Graphene;Homogeneity;DNN;Deep learning;Fabrication;Electrical impedance tomography;Surface impedance;Voltage measurement;Graphene;Neural networks|
|[Spatially Adaptive Image Deblurring Module Based on Dilated Deformable Convolutions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049875)|H. -B. Yang; S. -J. Cho; S. -J. Ko|10.1109/ICEIC57457.2023.10049875|Image deblurring;neural network;image processing;Representation learning;Adaptation models;Feature extraction;Image restoration;Kernel|
|[Query Similarity of Various Linguistic Levels for Hybridized Conversational Agents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049903)|S. -E. Kim; C. S. Hong; S. -B. Park|10.1109/ICEIC57457.2023.10049903|conversational agent;query similarity;lexical feature;syntactic feature;Databases;Manuals;Search engines;Linguistics|
|[Full Diversity Low-Density Parity-Check (LDPC) Codes for Block-Fading Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049965)|R. Kadel; G. Lechner|10.1109/ICEIC57457.2023.10049965|LDPC;full-diversity;block-fading channel;Message passing;Parity check codes;Decoding;Channel models|
|[Research on the Indoor Environment Positioning Algorithm Using Sensor Fusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049976)|S. Jang; B. An; S. Yoon; K. Lim|10.1109/ICEIC57457.2023.10049976|sensor fusion;ultra-wide band;inertial measurement unit;autonomous driving system;Measurement units;Inertial navigation;Sensor fusion;Position measurement;Indoor environment;Trajectory;Safety|
|[Establishment of High Precision Positioning Verification Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049888)|B. An; S. Jang; S. Yoon; K. Lim|10.1109/ICEIC57457.2023.10049888|High Precision;LiDAR;ultra-wide band;GPS;SLAM;autonomous driving system;Wireless communication;Real-time systems;Automobiles|
|[A Study on V2I based Cooperative Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049918)|J. Jang; J. Baek; K. Lim; Y. Ro; S. Yoon; S. Jang|10.1109/ICEIC57457.2023.10049918|V2I;5G-V2X;Cooperative Driving;Machine-to-machine communications;Roads;Surveillance;Vehicle-to-infrastructure;Road side unit;Merging;Cameras|
|[Application on Parking Guidance System Using Surveillance Camera](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049876)|J. Baek; J. Jang; D. Shin; K. Lim; S. Yoon; S. Jang|10.1109/ICEIC57457.2023.10049876|V2I;AVP;Smart Infrastructure;Surveillance;Cameras;Autonomous vehicles;Intelligent sensors;Global Positioning System|
|[An Efficient NPU-Aware Filter Pruning in Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049954)|S. Lee; K. Kim; J. Kwak; E. Lee; S. -S. Lee|10.1109/ICEIC57457.2023.10049954|NPU-aware;filter pruning;convolutional neural network;Training;Neural networks;Aerospace electronics;Hardware;Computational efficiency;Convolutional neural networks;Artificial intelligence|
|[Reflectance-based Code Optimization for Motion Deblurring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049961)|J. Lee; B. Jeon|10.1109/ICEIC57457.2023.10049961|coded exposure photography;code optimization;motion deblurring;Poisson noise;Reflectivity;Photography;Codes;Image color analysis;Brightness;Light emitting diodes;Colored noise|
|[Improving Corruption Robustness with Random Erasing in the Frequency Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049881)|H. Hwang; K. Lee; H. -J. Lee|10.1109/ICEIC57457.2023.10049881|Data Augmentation;Deep Learning;Robustness;Frequency Domain;Discrete Fourier Transform;Deep learning;Frequency-domain analysis;Discrete Fourier transforms;Neural networks;Robustness;Data models;Convolutional neural networks|
|[Class Abstraction and Upcasting for Self-evolving Digital Twin System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049945)|Y. Lee; S. Kim; K. Yoon|10.1109/ICEIC57457.2023.10049945|Digital Twin;Digital Thing;Abstraction;Upcasting;Inheritance;Industries;Smart cities;Sensor systems and applications;Software;Production facilities;Digital twins;Manufacturing|
|[CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049941)|J. -Y. Kim; S. -H. Lee|10.1109/ICEIC57457.2023.10049941|speech emotion recognition;audio classification;coordinate information;Training;Emotion recognition;Time-frequency analysis;Speech recognition;Transformers;Convolutional neural networks;Spectrogram|
|[Privacy-Preserving Federated Learning in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049966)|S. Moon; W. Hee Lee|10.1109/ICEIC57457.2023.10049966|Artificial intelligence;federated learning;privacy-preserving;healthcare;COVID-19;Data privacy;Privacy;Federated learning;Buildings;Collaboration;Medical services|
|[Trigger-based Blocking Mechanism for Access to Email-derived Phishing URLs with User Alert](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049906)|Y. Jin; M. Tomoishi; N. Yamai|10.1109/ICEIC57457.2023.10049906|trigger-based;email-derived phishing URL;DNS;RPZ;Response Policy Zone;Uniform resource locators;Performance evaluation;Phishing;Web and internet services;Prototypes;Organizations;Electronic mail|
|[Fine-Grained Data Augmentation using Generative Adversarial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049982)|S. -H. Kim; C. Park; M. -H. Choi; S. -J. Yang; K. Lee; H. -J. Lee|10.1109/ICEIC57457.2023.10049982|Convolutional Neural Network(CNN);Classification;Small Dataset;Data Augmentation;Training;Deep learning;Electric potential;Shape;Superresolution;Neural networks;Object detection|
|[Photoplethysmogram(PPG) and Phonocardiogram(PCG) integrated circuits for multi-mode health monitoring system on the chest](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049854)|M. Park; J. J. Kim|10.1109/ICEIC57457.2023.10049854|Photoplethysmogram (PPG);Phonocardiogram (PCG);Vascular Transit Time (VTT);blood pressure;peak detector;noise shaping;chopper-stabilization;Integrated circuits;Quantization (signal);Prototypes;Detectors;Noise shaping;High dynamic range;Biomedical monitoring|
|[High Dynamic Range Image Recovery by Use of Lens Flare Events Detection Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049895)|B. Chang; H. Ryu; H. -J. Lee|10.1109/ICEIC57457.2023.10049895|Event camera;high dynamic range;lens flare;Gaussian distribution;image reconstruction;Surface reconstruction;Three-dimensional displays;Heuristic algorithms;Cameras;High dynamic range;Detection algorithms;Voltage control|
|[Material map generation using hyper-spectral NIR images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049950)|D. -K. Han; J. -W. Ha; J. -O. Kim|10.1109/ICEIC57457.2023.10049950|NIR hyper-spectral image, surface material classification, 3D convolution, material map;Three-dimensional displays;Convolution;Silicon;Fabrics;Object recognition;Data mining|
|[Deep-Clustering Based Plant Disease Segmentation Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049898)|S. -E. Lee; S. -H. Lee; J. -O. Kim|10.1109/ICEIC57457.2023.10049898|nan;Image segmentation;Plant diseases;Clustering methods|
|[A survey on Transformers for long sentences and its application to medical notes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049932)|J. Jeon; J. Kim|10.1109/ICEIC57457.2023.10049932|Sparse attention;Transformer;EHR;NLP;Pain;Computational modeling;Predictive models;Transformers;Task analysis;Computational complexity;Electronic medical records|
|[A Survey on Causal Inference in Image Captioning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049869)|J. Kim; J. Kim|10.1109/ICEIC57457.2023.10049869|Image Captioning;Causal inference;Data bias;Vision-Language task;Integrated circuits;Training;Correlation;History;Task analysis;Integrated circuit modeling|
|[A Survey of Visual Commonsense Generation Task](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049859)|J. Park; J. Kim|10.1109/ICEIC57457.2023.10049859|deep learning;visual commonsense generation;survey;introduction;Visualization;Question answering (information retrieval);Cognition;Data mining;Task analysis|
|[A Survey on Attention mechanism in NLP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049971)|N. Zhang; J. Kim|10.1109/ICEIC57457.2023.10049971|Attention mechanism;NLP;Systematics;Neural networks;Focusing;Natural language processing;Classification algorithms;Resource management;Task analysis|
|[Performance Analysis of Self-biasing Technique for Differential RF-DC Rectifier in IoT Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049944)|B. C. Terence Teo; W. Cong Lim; X. Y. Lim; V. Navaneethan; C. B. Tan; N. Utomo; L. Siek|10.1109/ICEIC57457.2023.10049944|CMOS Rectifier;Energy harvesting;Radio Frequency;Self-biasing;Wireless power transfer (WPT);Semiconductor device modeling;Performance evaluation;Sensitivity;Rectifiers;Pins;Performance analysis;Power conversion|
|[A Review on Current-Steering DAC Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049912)|X. Y. Lim; W. C. Lim; B. C. T. Teo; V. Navaneethan; C. B. Tan; N. Utomo; L. Siek; A. Alvandpour|10.1109/ICEIC57457.2023.10049912|Current-steering;DAC;Codes;Simulation;Layout;Jitter;Calibration;Behavioral sciences;Impedance|
|[A Simple Reconfigurable FSS Structure for Antenna Beam Steering Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049862)|P. Danuor; Y. -B. Jung|10.1109/ICEIC57457.2023.10049862|Beam steering;frequency selective surface (FSS);monopole antenna;reconfigurable antenna;Frequency selective surfaces;Beam steering;PIN photodiodes;Transmitting antennas;Electromagnetic scattering;Reflector antennas;Antenna radiation patterns|
|[Verilator-based Fast Verification Methodology for BLE MAC Hardware](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049940)|E. Ham; Y. Jeon; J. Lim; J. -H. Kim|10.1109/ICEIC57457.2023.10049940|Verification Methodology;Verilator;Hardware design;Costs;Design methodology;Production;C++ languages;Media Access Protocol;Market research;Hardware|
|[Chiplet Heterogeneous-Integration AI Processor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049867)|Y. Kwon; J. Han; Y. P. Cho; J. Kim; J. Chung; J. Choi; S. Park; I. Kim; H. Kwon; J. Kim; H. Kim; W. Jeon; Y. Jeon; M. Cho; M. Choi|10.1109/ICEIC57457.2023.10049867|chiplets;AI;processor;interposer;heterogeneous integration;2.5D integration;3D integration;Costs;Computer architecture;Reliability engineering;Explosions;Stability analysis;Thermal analysis;Performance analysis|
|[2.5D Large-Scale Interposer Bonding Process Verification using Daisy-Chain for PIM Heterogeneous Integration Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049851)|S. Park; Y. -G. Kim; Y. -D. Jeon; M. -H. Cho; J. Han; Y. Kwon|10.1109/ICEIC57457.2023.10049851|2.5D integration technology;daisy chain;bonding process verification;large-scale interposer;PIM heterogeneous integration platform;Costs;Bonding processes;Wires;Multichip modules;Bandwidth;Pins;Substrates|
|[Improved Estimation Method for Effect of DBD (Dielectric Barrier Discharge) Plasma on RCS (Radar Cross Section) Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049958)|J. Jeong; C. Cho; Y. Lee; J. Ha; J. Yim; S. You; M. Choi|10.1109/ICEIC57457.2023.10049958|dielectric-barrier-discharge;FSS;plasma;radar cross section;X-band;Electrodes;Radar cross-sections;Deformation;Estimation;Discharges (electric);Plasmas|
|[High-Level AMBA Monitoring Platform for SoC Architecture Exploration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049893)|J. Lim; Y. Jeon; E. Ham; J. -H. Kim|10.1109/ICEIC57457.2023.10049893|AMBA;SoC Platform;Verilator;Hardware design;On-chip interconnect;Power demand;C++ languages;Traffic control;Modems;Hardware;System-on-chip;Central Processing Unit|
|[Hardware-Software Co-Design of AES-CCM for Bluetooth LE Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049857)|Y. Jeon; E. Ham; J. Lim; J. -H. Kim|10.1109/ICEIC57457.2023.10049857|Hardware-Software Co-Design;AES;AES-CCM;Bluetooth LE Security;Bluetooth;Computer architecture;Throughput;Software;Hardware;Encryption;Computational efficiency|
|[A Unified framework based on Large-scale Momentum Contrastive learning for Text-Video Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049967)|E. Kim; N. Lee; Y. -S. Cho|10.1109/ICEIC57457.2023.10049967|nan;Training;Representation learning;Computational modeling;Semantics;Focusing;Streaming media;Transformers|
|[Please Be Nice: A Deep Learning Based Approach to Content Moderation of Internet Memes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049865)|G. Macrayo; W. Casiño; J. Dalangin; J. Gabriel Gahoy; A. Christian Reyes; C. Vitto; M. Abisado; S. Lor Huyo-a; G. Avelino Sampedro|10.1109/ICEIC57457.2023.10049865|deep learning;meme;multimodal detection;Deep learning;Visualization;Systematics;Filtering;Hate speech;Symbols;Cyberbullying|
|[Ano Raw: A Deep Learning Based Approach to Transliterating the Filipino Sign Language](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049890)|M. Allen Cabutaje; K. Ang Brondial; A. Franchesca Obillo; M. Abisado; S. Lor Huyo-a; G. Avelino Sampedro|10.1109/ICEIC57457.2023.10049890|Filipino sign language;convolutional neural network;deep learning;Training;Deep learning;Image recognition;Gesture recognition;Auditory system;Assistive technologies;Predictive models|
|[Classification of the Type of Brain Tumor in MRI Using Xception Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049979)|R. Cobilla; J. Carlo Dichoso; A. B. Miñon; A. Kate Pascual; M. Abisado; S. L. Huyo-a; G. Avelino Sampedro|10.1109/ICEIC57457.2023.10049979|Brain Tumor;Convolutional Neural Networks;Magnetic Resonance Imaging;Deep Learning;Xception Model;Deep learning;Visualization;Image recognition;Magnetic resonance imaging;Transfer learning;Brain modeling;Complexity theory|
|[Smart Commuting: Exploring Machine Learning Approaches to Understanding the Metro Rail Transit System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049933)|J. Empino; J. A. Junsay; M. G. Verzon; M. Abisado; S. L. Huyo-a; G. A. Sampedro|10.1109/ICEIC57457.2023.10049933|Light GBM;Gradient Boosting;Extreme Random Trees;Time Series Forecasting;Rails;Training;Time series analysis;Transportation;Predictive models;Boosting;Data models|
|[EyeRis: Visual Image Recognition using Machine Learning for the Visually-Impaired](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049927)|A. M. Eugenio; M. J. Patulot; L. J. Seguiro; A. N. Tuazon; S. L. Huyo-a; M. Abisado; G. A. Sampedro|10.1109/ICEIC57457.2023.10049927|Machine Learning;Visual Image Recognition;Convolutional Neural Networks;Visualization;Image recognition;Machine learning algorithms;Software algorithms;Neural networks;Software;Real-time systems|
|[Automatic Conversation Turn-Taking Segmentation in Semantic Facet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049858)|D. Jung; Y. -S. Cho|10.1109/ICEIC57457.2023.10049858|Turn-taking Segmentation;Live Text Stream;Token Classification;Semantics;Data preprocessing;Oral communication;Predictive models;Linguistics;Natural language processing;Task analysis|
|[Machine Learning Approaches to Facial Recognition: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049964)|R. F. F. Paguirigan; M. B. B. Camero; M. A. Equias; M. Abisado; G. A. Sampedro|10.1109/ICEIC57457.2023.10049964|artificial neural network;support vector machine;Eigenface;Gabor Wavelet;Support vector machines;Solid modeling;Three-dimensional displays;Face recognition;Hidden Markov models;Lighting;Artificial neural networks|
|[Development of an Automated Floating Water Filter Device Using Hollow Fibers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049913)|J. Ng; M. Abisado; J. J. Bofill; G. A. Sampedro|10.1109/ICEIC57457.2023.10049913|Hollow Fiber;Floating Water Filter Device;Level Sensor;Hall Effect Sensor;Optical fiber sensors;Irrigation;Metals;Water quality;Manuals;Maintenance engineering;Information filters|
|[Design and Comparative analysis of 6T and 7T SRAM Cells for Improved TREAD and TWRITE Noise Margins](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049962)|P. Tiwari; R. Jarial; G. Kumar|10.1109/ICEIC57457.2023.10049962|Component;Noise Margin;SRAM;Bit Line;Couplings;MOSFET;Capacitors;Cache memory;SRAM cells;Inverters;Transistors|
|[A Review on Few-shot Learning for Medical Image Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049899)|Y. Kim; D. Kang; Y. Mok; S. Kwon; J. Paik|10.1109/ICEIC57457.2023.10049899|Meta-learning;few-shot learning;medical image;semantic segmentation;MRI;Industries;Image segmentation;Semantic segmentation;Magnetic resonance imaging;Training data;Linear programming;Task analysis|
|[Image Enhancement for High-Resolution Visual Contents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049957)|H. Lim; J. Lee; H. Kim; H. Oh; J. Paik|10.1109/ICEIC57457.2023.10049957|image enhancement;neural network;visual contents;Degradation;Visualization;Image color analysis;Neural networks;Imaging;Distortion;Environmental factors|
|[BLM-DTO: Bandit Learning and Matching based Distributed Task Offloading in Fog Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049981)|H. Tran-Dang; D. -S. Kim|10.1109/ICEIC57457.2023.10049981|Fog network;multi-armed bandit;thompson sampling;task offloading;distributed algorithm;stable matching;Computational modeling;Simulation;Heuristic algorithms;Games;Delays;Task analysis;Game theory|
|[Three-Dimensional Residual Dense Network For High Dynamic Range Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049974)|J. Lee; H. Lim; H. Choi; J. Paik|10.1109/ICEIC57457.2023.10049974|high dynamic ragne (HDR);convolutional neural network (CNN);three-dimensional (3D) filter;Learning systems;Three-dimensional displays;Neural networks;Merging;Imaging;Information filters;High dynamic range|
|[Human Group Clustering in a Crowded Public Place Using Multiple Object Detection and Tracking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049978)|D. Kang; Y. Mok; Y. Kim; S. Kwon; J. Paik|10.1109/ICEIC57457.2023.10049978|Object Detection;Multi-Object Tracking;Visual Surveillance;Group Clustering;Social groups;Pipelines;Clustering algorithms;Detectors;Object detection;Real-time systems;Behavioral sciences|
|[Image Stitching Method for Surround View Image without Seamline](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049955)|J. Choi; H. Lim; S. Yun; M. Shin; J. Paik|10.1109/ICEIC57457.2023.10049955|Surround view;Seamline estimation;Image stitching;Estimation;Feature extraction;Distortion;Cameras;Image stitching|
|[Efficient Pavement Crack Detection in Drone Images using Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049911)|H. Kim; J. Sung; M. Kim; C. Park; J. Paik|10.1109/ICEIC57457.2023.10049911|crack detection;sigmoid fusion module;YOLOX;Deep learning;Shape;Neural networks;Drones|
|[Audio-to-Facial Landmarks Generator for Talking Face Video Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049847)|D. Jeong; I. Lee; J. Paik|10.1109/ICEIC57457.2023.10049847|Talking Head Generation;Video Generation;Lips;Transformers;Feature extraction;Inference algorithms;Generators;Synchronization;Data mining|
|[Person Re-identification Method Using Text Description Through CLIP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049924)|K. Kim; M. -J. Kim; H. Kim; S. Park; J. Paik|10.1109/ICEIC57457.2023.10049924|Person Re-identification;Multi-modal learning;Text based person search;Knowledge engineering;Linguistics|
|[Learning-based Light Field View Synthesis Using Multiplane Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049922)|S. Yun; J. Jang; J. Paik|10.1109/ICEIC57457.2023.10049922|light field;reconstruction;angular resolution;multiplane image;Image resolution;Three-dimensional displays;Image synthesis;Image color analysis;Transforms;Cameras;Light fields|
#### **2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)**
- DOI: 10.1109/MIGARS57353.2023
- DATE: 27-29 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Deep Learning based Fusion of LiDAR Point Cloud and Multispectral Imagery for Crop Classification Sensitive to Nitrogen Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064497)|J. Reji; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064497|LiDAR point cloud;multispectral imagery;data fusion;nitrogen levels;deep learning;CNN;nan|
|[Subpixel Level Discrimination of Vegetable Crops in a Complex Landscape Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064602)|C. V. S. S. Manohar Kumar; R. R. Nidamanuri; V. K. Dadhwal|10.1109/MIGARS57353.2023.10064602|hyperspectral remote sensing;spectral library;spectral unmixing;abundance estimation;nan|
|[An Improved Water Body Segmentation from Satellite Images using MSAA-Net](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064508)|M. S. Guru Prasad; J. Agarwal; S. Christa; H. Aditya Pai; M. A. Kumar; A. Kukreti|10.1109/MIGARS57353.2023.10064508|satellite image analysis;water bodies;Convolutional neural network;multi-feature extraction;Training;Water;Analytical models;Satellites;Semantic segmentation;Weather forecasting;Vegetation mapping|
|[Cloud Detection from AWiFS Imagery using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064610)|S. Singhal; L. James; A. R. V. G; S. C. V; M. K. S; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064610|Cloud detection;Remote Sensing;Advance Wide Field Sensor (AWiFS);Reflectance;Segmentation;U-Net;Jaccard Index;Attention.;nan|
|[Analyzing the variations in the water surface area of Taleqan Dam of Iran using ground-based and satellite observations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064509)|A. Tayfehrostami; A. Abedini; B. Tajfirooz|10.1109/MIGARS57353.2023.10064509|Taleqan Dam;Water Surface Area;Sentinel-2 MSI;Google Earth Engine platform;nan|
|[Seedable Conditions of Clouds Using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064525)|R. K. Roy; A. Bhattacharya; A. Priamvada; B. P. Shukla|10.1109/MIGARS57353.2023.10064525|Cloud Seeding;Unsupervised Machine Learning;Clustering;nan|
|[Near-Real-Time Detection of Craters: A YOLO v5 Based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064529)|S. Chatterjee; S. Chakraborty; A. Nath; P. R. Chowdhury; B. Deshmukh|10.1109/MIGARS57353.2023.10064529|Crater detection;CNN;YOLO;Mars;Space vehicles;Mars;Interplanetary exploration;Inertial navigation;Reconnaissance;Position measurement;Real-time systems|
|[ESize and Power of Hypothesis Test Based on Geodesic Distances Induced by h-ϕ Entropies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064539)|E. L. Ensslin; A. C. Frery|10.1109/MIGARS57353.2023.10064539|Edge detection;geodesic distances;h-ϕ entropies;hypothesis tests;speckle;Synthetic Aperture Radar (SAR);nan|
|[Improved Landslide Susceptibility mapping using statistical MLR model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064594)|K. C. Niraj; A. Singh; D. P. Shukla|10.1109/MIGARS57353.2023.10064594|Landslide Susceptibility Map (LSM);multiple linear regression (MLR) model;NDVI;Causative factors;AUC;nan|
|[Endmember Extraction without prior Number of Endmembers using Barycentric Abundances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064624)|G. S. Chetia; B. P. Devi|10.1109/MIGARS57353.2023.10064624|Hyperspectral Unmixing;Endmember Extraction;Abundance;Spatial Features;nan|
|[Deep learning classification of desert-fringe vegetation patterns: Comparison of input layers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064548)|M. Cohen; M. Shoshany|10.1109/MIGARS57353.2023.10064548|Deep learning;Natural patterns. Classification;Pre-processing;Deep learning;Image color analysis;Image edge detection;Vegetation mapping;Morphology;Classification algorithms;Remote sensing|
|[Tracing dynamics of groundwater potential zones for a watershed in Pune, India using geo-spatial technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064498)|S. Patil; N. Mohite; S. Palve|10.1109/MIGARS57353.2023.10064498|groundwater;potential;remote sensing;geomorphology;geospatial technique;Satellites;Costs;Knowledge based systems;Soil;Sensors;Planning;Water resources|
|[OOCS and Attention based Remote Sensing Classifications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064606)|S. K. Singaraju; V. Ghanta; M. Pal|10.1109/MIGARS57353.2023.10064606|CNN architectures;Remote Sensing Images;Attention Models;OOCS;nan|
|[Multi-Scenario Target Detection using Neural Networks on Hyperspectral Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064621)|J. Chilakamarri; R. R. Nidamanuri; P. Murugan|10.1109/MIGARS57353.2023.10064621|Machine Learning;Convolution Neural Network;Target detection;Hyperspectral Imagery;Remote Sensing;nan|
|[Lunar Landing Site Selection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064571)|D. Darlan; O. S. Ajani; R. Mallipeddi|10.1109/MIGARS57353.2023.10064571|Landing Site Selection;Clustering.;nan|
|[A Novel Approach for SAR to Optical Image Registration using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064578)|L. James; R. R. Nidamanuri; S. Murali Krishnan; R. Anjaneyulu; C. Srinivas|10.1109/MIGARS57353.2023.10064578|Synthetic Aperture Radar;optical;Generator;Discriminator;Conditional Generative Adversarial Network;Fast Fourier Transform correlation;nan|
|[LoRa-Based Communication System for Monitoring Water Quality of Lakes and Reservoirs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064590)|R. Pandey; N. K. Sharma; M. Saharia|10.1109/MIGARS57353.2023.10064590|Lake Monitoring;Water Quality;LoRaWAN;Temperature sensors;Temperature measurement;Wide area networks;Water quality;Logic gates;Lakes;Reservoirs|
|[Neural networks and satellite images-based shrub tundra landscape study: phenomena with fuzzy geometric and categorical boundaries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064585)|A. Derkacheva; G. V. Frost; K. Ermokhina; H. Epstein|10.1109/MIGARS57353.2023.10064585|Earth remote sensing;Convolutional Neural Network;tundra landscape;ecology;Training;Satellites;Image recognition;Instruments;Neural networks;Rocks;Convolutional neural networks|
|[Classification of Horticultural Crops in High Resolution Multispectral Imagery Using Deep Learning Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064521)|A. Palaparthi; A. M. Ramiya; H. Ram; D. D. Mishra|10.1109/MIGARS57353.2023.10064521|multispectral satellite imagery;deep learning;CNN.;Deep learning;Image resolution;Satellites;Sociology;Crops;Network architecture;Task analysis|
|[An Iteration-Based Methodology to Cross Compare Volume Matched Indian Ground Radar Reflectivity Observations Against Space Radar](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064527)|A. Sharma; S. R. Kannan|10.1109/MIGARS57353.2023.10064527|ground radar;space radar;radar reflectivity;volume matching methodology.;nan|
|[New scoring model for remote working hubs analysis and decision making](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064492)|M. H. S. Jahromi; M. Yazdi; A. Dehghani; M. Rasouli|10.1109/MIGARS57353.2023.10064492|remote working hubs;nearest neighbour classification;Decision making;amenity scoring;Analytical models;Smart cities;Decision making;Remote working;Data models;Remote sensing;Open data|
|[UAV Based Multispectral Image Processing Framework: A Band Combination Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064514)|S. K. Das; P. Bose; K. Patra; S. Chakravarty|10.1109/MIGARS57353.2023.10064514|Multispectral Image Processing;Orthomosaic Generation;Reflectance Calibration;MODIS NDVI;Band Combinatorics.;Water;Image processing;Vegetation mapping;Cameras;Autonomous aerial vehicles;Mathematics;Electromagnetic spectrum|
|[Segmentation of Watery Low Land Area using Hyperspectral Imaging Technique: A Comparative Study with PPI, N-FINDR, ATGP, and FIPPI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064564)|G. Bej; T. Dey; A. Pal; T. Sutradhar; A. Akuli; A. Ghosh|10.1109/MIGARS57353.2023.10064564|Endmember detection;abundance map;segmentation;performance analysis;hyperspectral imaging technology;nan|
|[Comparative retrieval approaches for concentrated Suspended Particulate Matter (CSPM) detection in the Ganges using Sentinel-2 and Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064535)|K. C. A. Kumar; S. Bandopadhyay; U. Garai; M. Das; R. D. Das|10.1109/MIGARS57353.2023.10064535|Suspended Particulate Matter;Sentinel-2;Cloud Computing;COVID-19;Ganges.;COVID-19;Reflectivity;Cloud computing;Satellites;Computational modeling;Government;Rivers|
|[A Robust Estimation Method for Automatic Registration of Remote Sensing Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064601)|D. S. Pankaj; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064601|nan;nan|
|[Enhanced IHS Pan-sharpening using K-Means Segmentation Guided Adaptive Intensity Histogram Matching and CLAHE Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064530)|S. Kundu; I. Misra; S. M. Moorthi; D. Dhar|10.1109/MIGARS57353.2023.10064530|K-Means Segmentation;Adaptive Intensity;Histogram Matching;CLAHE;IHS;Pan-sharpening;Measurement;Image quality;Image segmentation;Histograms;Visualization;Satellites;Transforms|
|[Satellite image super-resolution for forest localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064552)|E. Lymperopoulos; P. Tzouveli; S. Kollias|10.1109/MIGARS57353.2023.10064552|Deep Learning Networks;Forest Localization;Semantic Segmentation;Super Resolution;Training;Deep learning;Satellites;Superresolution;Forestry;Transformers;Feature extraction|
|[Sensitivity of Temperature Perturbation to Precipitation: A Parametric Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064516)|P. Mishra; D. S. R. Kannan|10.1109/MIGARS57353.2023.10064516|Perturbation;Convection;TRMM PR Temperature;Urban Heat Island: Weather;Atmospheric boundary layer;Temperature sensors;Heating systems;Thermodynamics;Temperature distribution;Precipitation;Perturbation methods;Urban areas|
|[Combined Optical and SAR remote sensing for LULC mapping of Imphal valley using Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064582)|P. Gupta; D. P. Shukla|10.1109/MIGARS57353.2023.10064582|Synthetic Aperture Radar (SAR);Land Use Land Cover (LULC)Mapping;Loktak Lake;Machine Learning (ML) algorithm;Google Earth Engine (GEE);Earth;Biomedical optical imaging;Machine learning algorithms;Forestry;Optical imaging;Adaptive optics;Classification algorithms|
|[Mapping a Landslide Event on Puthumala, Kerala, India using SAR Interferometry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064499)|A. Jacob; K. A. Prasad; S. Manickam; K. Balasubramani; R. Vyshnav|10.1109/MIGARS57353.2023.10064499|Landslide;SAR Inteferometry;PS InSAR;Sentinel data;StaMPSlMTI;Landslides;Deformation;Time series analysis;Time measurement;Terrain factors;Synthetic aperture radar interferometry;Time factors|
|[A Comparative Study of Spatial Interpolation Methods for CMIP6 Monthly Historical and Future Hydro-climatic Datasets for Indian Region](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064528)|M. Shah; A. Thakkar; H. Shastri|10.1109/MIGARS57353.2023.10064528|GCM;RCM;CMIP6;comparative;interpolation;Performance evaluation;Interpolation;Climate change;Graphical models;Precipitation;Spatial resolution;Standards|
|[Target Detection in Airborne Hyperspectral Imagery and its Sensitivity to Different Atmospheric Correction Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064518)|C. V. S. S. Manohar Kumar; S. S. Jha; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064518|Spectral unmixing;target detection;atmospheric correction;6S;FLAASH;QUAC;Reflectivity;Sensitivity;Atmospheric modeling;Computational modeling;Object detection;Mathematical models;Libraries|
|[Satellite-based Optical Water Type Classification of Inland Waters Bodies of India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064493)|Gujrati; Ashwin; Jha; V. Bhushan; Nidamanuri; R. Rao; R. P. Singh.|10.1109/MIGARS57353.2023.10064493|Remote Sensing;Inland waters;Optical water type;eutrophication;nan|
|[Agricultural Field Boundary Delineation From Multi-Temporal IRS P-6 LISS IV Images Using Multi-Task Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064490)|B. Chaudhury; A. S. Sahadevan; P. Mitra|10.1109/MIGARS57353.2023.10064490|Agriculture Field Delineation;Image Edge Analysis;Multi-Task Network;Directional Filters;Optical filters;Deep learning;Training;Satellites;Image edge detection;Multitasking;Robustness|
|[Orthorectification For Multi Detector High Resolution Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064542)|M. Suresh Kumar; D. S. Rao; T. Radhika; M. Manju Sarma|10.1109/MIGARS57353.2023.10064542|PAN;Multi-scale;Hierarchical;Image Matching;IRS-1C;IRS 1D;Ortho-rectification;Image resolution;Systematics;Image matching;Detectors;Cameras;Usability;Standards|
|[A NDVI Based Approach To Detect The Landslides By Using Google Earth Engine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064592)|M. Vishnu Vardhan; S. Harish Kumar; S. Mohan Kumar; S. Kundapura|10.1109/MIGARS57353.2023.10064592|Landslides;NDVI;Google Earth Engine;Synthetic aperture radar (SAR);Sentinel etc;Earth;Landslides;Monsoons;Optical imaging;Terrain factors;Internet;Optical sensors|
|[AI assisted pothole detection and depth estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064547)|E. Ranyal; A. Sadhu; K. Jain|10.1109/MIGARS57353.2023.10064547|pothole detection;pothole depth estimation;pavement health monitoring;3D point cloud;CNN;RetinaNet;structure-from-motion;Point cloud compression;Solid modeling;Three-dimensional displays;Structure from motion;Estimation;Road safety;Real-time systems|
|[Landslide susceptibility mapping using XGBoost machine learning method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064496)|S. Badola; V. N. Mishra; S. Parkash|10.1109/MIGARS57353.2023.10064496|Landslide Potential Index;Landslide Risk;XGBoost algorithm;receiver operating characteristic-Area under the curve.;nan|
|[Agricultural Crop Hyperspectral Image Classification using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064595)|V. K. Munipalle; U. R. Nelakuditi; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064595|Deep Learning;VGGNet;ResNet;Hyperspectral image classification;Transfer learning;nan|
|[SDAT: An Open Source Tool for Processing, Analysis and Simulation of Spectroradiometer Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064549)|A. S. Sahadevan; R. B. Lyngdoh; P. Nidhin; P. S. Rathore; D. Putrevu|10.1109/MIGARS57353.2023.10064549|Spectroradiometer;VNIR-SWIR spectra;Machine Learning;Python;Simulation;nan|
|[Seasonal Analysis of GroundWater recharge using GIS and Remote sensing techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064600)|C. M. Lukose; A. K. R; R. T|10.1109/MIGARS57353.2023.10064600|Average evapotranspiration;precipitation;runoff;Wetspass;groundwater recharge.;nan|
|[Influence of Atmospheric Correction Models on the Discriminatrion of Crops using Airborne Hyperspectral Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064534)|F. T. Jose; C. V. S. S. Manohar Kumar; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064534|hyperspectral;atmospheric correction models;crop classification;nan|
|[Enhancing Plant Area Index Retrieval Using Gaussian Process Regression from Dual-Polarimetric SAR Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064591)|S. S. Ghosh; N. Bhogapurapu; A. Bhattacharya; S. Homayouni|10.1109/MIGARS57353.2023.10064591|Gaussian Process Regression (GPR);Plant Area Index (PAI);Sentinel-l;Pseudo Entropy Parameter (Hc);SMAPVEX16-MB;nan|
|[Machine learning-based mapping of pyroxene on global lunar surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064622)|A. Majumdar; S. Purohit; U. Mall|10.1109/MIGARS57353.2023.10064622|M3;remote sensing;hyperspectral data;machine learning;mineralogy;nan|
|[Improving SAR-based flood detection in arid regions using texture features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064526)|D. Ritushree; S. Garg; A. Dasgupta; S. Martinis; S. Selvakumaran; M. Motagh|10.1109/MIGARS57353.2023.10064526|Flood mapping;SAR;texture;Random Forest;nan|
|[A new change index for identification of flooding in fully polarimetric SAR data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064604)|S. Surampudi; V. Kumar|10.1109/MIGARS57353.2023.10064604|Change detection;fully polarimetric SAR;floods;indices;Scattering;Feature extraction;Behavioral sciences;Indexes;Floods;Synthetic aperture radar;Remote sensing|
|[Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064558)|S. Bandopadhyay; B. Das; A. C. Sánchez; S. P. Banerjee; B. P. Banerjee; S. Ghosh|10.1109/MIGARS57353.2023.10064558|Forest;LAI;Random Forest;Biophysical;India;nan|
|[Quality Assessment of UAV Data using Multiple RTK Reference Stations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064555)|C. H. Singh; A. Rai; Harshit; V. Mishra; S. K. P. Kushwaha; K. Jain|10.1109/MIGARS57353.2023.10064555|UAV (unmanned aerial vehicle);RTK (real-time kinematic);photogrammetry;mapping;DSM (digital surface model);nan|
|[Spatial assessment of soil erosion rate using remote sensing and GIS techniques in Mediterranean Watershed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064608)|S. E. Harche; M. Chikhaoui; N. Mustapha|10.1109/MIGARS57353.2023.10064608|Open access;KINEROS2;AGWA;Tleta Watershed;FAO;ESACCI;nan|
|[Mixed and Sub-Pixel Target Detection Using Space Borne Hyper-Spectral Imaging Data: Analysis and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064536)|C. Shekhar; A. Sharma; P. Preet; H. S. Negi; P. R. Chowdhury; P. K. Satyawali|10.1109/MIGARS57353.2023.10064536|Hyperspectral;HySIS;PRISMA;Spectro-radiometer;Atmospheric correction;Target detection;Reflectivity;Image quality;Data integrity;Imaging;Object detection;Performance analysis;Synchronization|
|[Mapping Canopy Height from ICESat-2 and Landsat-9 using Machine Learning in the Himalayan Corbett Tiger Reserve, India](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064540)|R. Gupta; L. K. Sharma|10.1109/MIGARS57353.2023.10064540|canopy height;random forest;support vector machine;LiDAR;ICESat-2;Support vector machines;Radio frequency;Earth;Training;Artificial satellites;Forestry;Root mean square|
|[Prediction of IRNSS User Position using Regression Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064576)|M. K. Karthan; P. N. Kumar|10.1109/MIGARS57353.2023.10064576|IRNSS;Regression;Prediction;RMSE;nan|
|[Machine Learning Based Modeling for Forest Aboveground Biomass Retrieval](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064607)|K. Kumari; S. Kumar|10.1109/MIGARS57353.2023.10064607|Forest Biomass;GEDI;Machine Learning;Support Vector Machine;Random Forest;nan|
|[Class Information-based Principal Component Analysis Algorithm for Improved Hyperspectral Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064597)|R. Sunkara; A. K. Singh; G. R. Kadambi|10.1109/MIGARS57353.2023.10064597|Hyperspectral image classification;Naive Bayes;Class Information-based Principal Component Analysis (CI-PCA).;nan|
|[Virtual Sample Generation Of Hyperspectral Mineral Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064561)|P. P. Yadav; A. Shetty; B. S. Raghavendra; A. V. Narasimhadhan|10.1109/MIGARS57353.2023.10064561|Hyperspectral data;Mineral signatures;virtual sample generation.;nan|
|[Crop Phenology Extraction Using Big Geospatial Datacube](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064579)|J. Vijaywargiya; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064579|Phenology parameters;wheat crop calendar;big geospatial data-cube;nan|
|[Does Google Earth CRS induce bias with increasing UTM zone number?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064588)|A. Pragada; K. S. Rajan|10.1109/MIGARS57353.2023.10064588|Google Earth;Sentine1-2B;UTM Zone;Relative Planimetric Accuracy;Reliability;High Precision Applications;nan|
|[Enhanced Efficient Image Dehazing for Onboard Satellite Imagery Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064620)|D. S. Sreekar; V. Vatsal; A. Kothandhapani; R. Rajagopalan|10.1109/MIGARS57353.2023.10064620|remote sensing;single image dehazing;onboard computing;nan|
|[Spectrally Optimized Feature Identification (SOFI): A Novel Band Selection Method for Hyperspectral Image Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064625)|R. Chugh; R. R. Nidamanuri; U. R. Nelakuditi; M. Dileep; A. Davood|10.1109/MIGARS57353.2023.10064625|Hyperspectral imaging;Optimal bands selection;Feature selection;Evolutionary optimization;nan|
|[DenseResSegnet: A Dense Residual Segnet for Road Detection Using Remote Sensing Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064603)|N. Akhtar; M. Mandloi|10.1109/MIGARS57353.2023.10064603|Road Detection;Remote Sensing;Segnet;Residual Learning;Dense connection;nan|
|[Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064623)|R. G. Negri; E. O. Andréa Luz; A. C. Frery; W. Casaca|10.1109/MIGARS57353.2023.10064623|Forest fires;spectral index;multitemporal;unsupervised mapping.;nan|
|[Assessment of Wheat Productivity Enhancement by Integrated Nutrient Management (INM) using Remote Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064574)|U. Shafi; R. Mumtaz; Z. Mahmood; M. D. M. Qureshi§; R. U. Khan¶; S. I. H. K. Tanveer|10.1109/MIGARS57353.2023.10064574|Wheat productivity;remote sensing;plant height;vegetation index;integrated nutrient management;nan|
|[Transfer Learning for Plant-level Crop Classification using Drone-based Hyperspectral Imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064501)|A. S. Sarma; R. R. Nidamanuri|10.1109/MIGARS57353.2023.10064501|transfer learning;structural similarity index;normalized spectral similarity score spectral information divergence;Transfer learning;Crops;Imaging;Soil;Sensors;Yield estimation;Time factors|
|[Spectral discrimination of vegetable crops using in situ hyperspectral data and reference to organic vegetables](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064553)|M. Kaushik; R. R. Nidamanuri; A. B; R. A. M|10.1109/MIGARS57353.2023.10064553|spectral signatures;hyperspectral imagery;machine learning;classification;organic crops;non-organic crops;Reflectivity;Systematics;Satellites;Stability criteria;Crops;Vegetation mapping;Risk management|
|[Multi-resolution remote sensing for the specieslevel classification of mangroves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10064566)|H. Sanam; A. K. Mathai; G. Lakshmanan|10.1109/MIGARS57353.2023.10064566|mangroves;object-based image analysis (OBIA);multi-resolution segmentation;Jeffries-Matusita distance;Random Forest classifier;Gray Level Co-occurrence Matrix;Gray Level Difference Vector.;|
#### **2023 31st Southern African Universities Power Engineering Conference (SAUPEC)**
- DOI: 10.1109/SAUPEC57889.2023
- 24-26 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Fault Location In Electrical Power Distribution Network Using Support Vector Regression and Wavelet Packet Transform Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057613)|K. Moloi; I. E. Davidson|10.1109/SAUPEC57889.2023.10057613|Fault location;Power system;Support vector regression;Wavelet packet transform;Support vector machines;Power engineering;Power distribution;Machine learning;Fault location;Feature extraction;Wavelet packets|
|[Parameter Estimation of a Distribution Transformer Model using Pseudo-Random Impulse Sequence Perturbation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057626)|D. M. Banks; J. C. Bekker; H. J. Vermeulen|10.1109/SAUPEC57889.2023.10057626|Distribution Transformer Model;Wideband Perturbation;Parameter Estimation;Runtime;Perturbation methods;Transfer functions;Resonant frequency;Voltage;Transformers;Frequency response|
|[The Cost Implications of Operating a Coal-Fired Power Plant in a Cycling Mode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057620)|P. T. Mzimba; K. E. Langerman; J. Calitz|10.1109/SAUPEC57889.2023.10057620|Cycling;base-load;coal-fired power plant;cost;boiler tube leaks;fuel oil;load following;Renewable energy sources;Costs;Uncertainty;Coal;Production;Maintenance engineering;Boilers|
|[An IEC 61850 standard-based Edge Computing algorithm to enhance communications in modern power systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057804)|A. Adam; M. Mnguni; M. Ratshitanga|10.1109/SAUPEC57889.2023.10057804|IEC 61850;Edge Computing;Communication;DERs;Smart Grid;Substations;Communication systems;Bandwidth;Telecommunication traffic;Logic gates;Internet of Things;Time factors|
|[Power System Transient Stability Considering Wind Power Generation and System Net-Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057633)|S. Ncwane; K. A. Folly|10.1109/SAUPEC57889.2023.10057633|probabilistic method;system net-load;transient stability;wind power generators;wind speed;Power engineering;Power measurement;Wind speed;Simulation;Loading;Power system stability;Wind power generation|
|[Modeling and Analysis of Multiple Capacitor Coupled Substations at Different Proximities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057698)|S. W. Nene; B. T. Abe; A. F. Nnachi|10.1109/SAUPEC57889.2023.10057698|Capacitor Coupled Substation;Conventional Rural Electrification Technologies;Un-conventional Rural Electrification;System Modeling;Analytical models;Adaptation models;Substations;Power transmission lines;Capacitors;Interference;Switches|
|[Distribution network planning practices based on the transition toward active distribution networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057843)|P. Jaglal; A. L. Marnewick; D. J. Van Vuuren|10.1109/SAUPEC57889.2023.10057843|Distribution network planning;practices;passive network;active network;impacts;Power engineering;Bibliographies;Sociology;Employment;Distribution networks;Planning;Power systems|
|[Progress In The Development of the Southern African Regional Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057883)|N. W. Ndlela; I. E. Davidson|10.1109/SAUPEC57889.2023.10057883|Grid Reliability;Power Exchange;Power Interconnections;Southern African Power Pool;SAPP challenges;Electric potential;Renewable energy sources;Supply and demand;Sociology;Africa;Production;Reliability engineering|
|[Analysis and Compensation of Power Systems with Harmonics and Unbalance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057672)|D. Oyedokun; P. Jankee; M. Soltanian; H. Chisepo|10.1109/SAUPEC57889.2023.10057672|Compensation;harmonics;orthogonality;reactive power;Reactive power;Power engineering;Power measurement;Power system harmonics;Harmonic analysis;Distortion;Harmonic distortion|
|[Multi-Nodal Energy Systems Modelling Scenarios of South Africa's Power Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057721)|R. Mehta; D. Oyedokun; B. Merven; R. Larmour|10.1109/SAUPEC57889.2023.10057721|Energy systems modelling;FlexTool;load shedding;power sector;renewable energy;South Africa;Climate change;Renewable energy sources;Africa;Energy managemnet;Power systems;Load modeling|
|[Techno-Economic Analysis of Solar-PV/Battery System for a Foundry Company](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057896)|T. Duma; B. Seteni; O. Dzobo|10.1109/SAUPEC57889.2023.10057896|Solar PV;battery;demand charges;peak shaving;energy cost saving;Renewable energy sources;Costs;Tariffs;Companies;Solar energy;Foundries;Stability analysis|
|[Optimal Placement of a Battery Energy Storage System (BESS) in a Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057659)|C. E. Okafor; K. A. Folly|10.1109/SAUPEC57889.2023.10057659|Battery energy storage system;Distribution Network;Power losses;Voltage deviations;Costs;Simulation;Voltage;Distribution networks;Wind power generation;Minimization;Linear programming|
|[A PV-Supplied Cooking Solution using a Hybrid Electric-Thermal Energy Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057833)|L. L. Chiloane; M. Aswat; Y. -C. J. Yen; W. A. Cronje|10.1109/SAUPEC57889.2023.10057833|Thermal Energy Storage;Storage Water Heater;Solar PV;Off-Grid Cooking;Renewable energy sources;Power engineering;Insulation;Heat engines;Water heating;Batteries;Thermal analysis|
|[A Distributed Standalone Solar PV and Battery Energy Storage System DC Microgrid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057645)|T. Makhanya; R. Sewsunker; N. Pillay|10.1109/SAUPEC57889.2023.10057645|DC microgrid;distributed generation;nano-grid;power sharing;Renewable energy sources;Power engineering;Low voltage;Microgrids;Load shedding;Batteries;Voltage control|
|[Satellite to Ground Communication Energy Storage Selection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057853)|S. A. Ntlela; I. E. Davidson|10.1109/SAUPEC57889.2023.10057853|Leo satellite power supply;flywheel;storage of power system;chemical battery;Temperature dependence;Temperature distribution;Power engineering;Satellite broadcasting;Low earth orbit satellites;Reliability engineering;Land surface temperature|
|[The Green Hydrogen as a Renewable Energy Source and Storage in the Transportation Sector of Germany](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057750)|A. Asiegbu; T. Kahn; A. Almaktoof|10.1109/SAUPEC57889.2023.10057750|hydrogen;renewable energy;energy storage;fossil;fuel;hybrid;energy mix;generation;Climate change;Hydrogen;Renewable energy sources;Energy storage;Fossill fuels;Hybrid power systems|
|[Potential of Abandoned Mine Infrastructure for Pumped Hydropower Energy Storage Implementation in South Africa](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057857)|U. O. Ikitde; I. E. Davidson; A. A. Adebiyi|10.1109/SAUPEC57889.2023.10057857|abandoned mines;energy storage;hydropower;renewable energy;underground water;mining;Electric potential;Renewable energy sources;Power engineering;Costs;Hydroelectric power generation;Fossil fuels;Power systems|
|[Investigating the Effect of Convection on the Rating of Buried Cables Using the Finite Element Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057890)|J. S. B. Mballa; J. J. Walker; I. K. Kyere|10.1109/SAUPEC57889.2023.10057890|finite element method;convection;buried cables;steady-state rating;Analytical models;Atmospheric modeling;Thermal resistance;Soil;Conductors;Land surface temperature;Finite element analysis|
|[Power Quality Classification Scheme For A Grid-Integrated Power Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057772)|K. Moloi; H. M. Langa|10.1109/SAUPEC57889.2023.10057772|Classification;Distributed generation;Power quality;Support vector machine;Training;Power quality;Power distribution;Voltage;Feature extraction;Hybrid power systems;Wavelet packets|
|[Speed Function MPPT Control of Self-Magnetised DC-Connected Wound Rotor Synchronous Wind Generator An Equivalent DC System Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057918)|M. J. Kamper; G. C. Garner; X. Kong|10.1109/SAUPEC57889.2023.10057918|maximum power point;DC grid;wound rotor;synchronous wind generator;Maximum power point trackers;Sensitivity;Wind energy;Wind speed;Rotors;DC-DC power converters;Wind power generation|
|[Analysis of a Hoist Motor Regenerated Energy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057628)|C. Namponya; I. E. Davidson; A. A. Adebiyi|10.1109/SAUPEC57889.2023.10057628|variable frequency drives (VFDs);supercapacitor;DC bus;bidirectional DC-DC converter;regenerated energy;dynamic braking;Resistors;Renewable energy sources;Cranes;Torque;Power system dynamics;DC-DC power converters;Supercapacitors|
|[Analysis of Large-scale Non-Overlap Winding Wound Rotor Synchronous Machine with Stator Slitting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057665)|U. Coetzee; K. S. Garner|10.1109/SAUPEC57889.2023.10057665|generator;harmonics;losses;stator slitting;wind;Stator cores;Torque;Magnetic cores;Core loss;Windings;Stator windings;Rotors|
|[Comparison Between A Three Level Inverter Synchronous Reluctance Machine and A Permanent Magnet Assisted Synchronous Reluctance Machine Drives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057903)|L. Masisi|10.1109/SAUPEC57889.2023.10057903|Three level inverter;neutral point (NP) voltage ripple;neutral-point-clamped (NPC);synchronous reluctance machine (SynRM);saliency;permanent magnet-assisted syn-chronous reluctance machine (PMaSynRM);Reactive power;Capacitors;Voltage;Reluctance machines;Inverters;Permanent magnets;Steady-state|
|[Design and Optimization of a Large Scale Grid Connected Wound Rotor Synchronous Machine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057887)|N. N. Siphepho; K. S. Garner|10.1109/SAUPEC57889.2023.10057887|design;grid-connected;load currents;multi-objective optimization;non-gradient;non-overlapping;wind energy;Renewable energy sources;Reactive power;Power system measurements;Wind energy;Rotors;Wounds;Torque measurement|
|[On the Study of Induction Motor Fault Identification using Support Vector Machine Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057836)|P. Zitha; B. A. Thango|10.1109/SAUPEC57889.2023.10057836|Induction motors;faults;support vector Machine (SVM);Support vector machines;Training;Induction motors;Costs;Torque;Prediction algorithms;Mathematical models|
|[Determining an Induction Machine's Torque-Speed Curve from an Audio Spectrogram Recorded During Run-Up](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057621)|J. Braid|10.1109/SAUPEC57889.2023.10057621|induction motor;torque-speed curve;spectrogram;curve-fitting;Power engineering;Torque;Shape;Rotors;Test equipment;Curve fitting;Spectrogram|
|[The Analytical Study of the Controlled Switching of an AC Vacuum Circuit Breaker for Fault Interruption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057859)|N. G. Hoosen; E. E. Ojo; N. M. Ijumba|10.1109/SAUPEC57889.2023.10057859|arcing;controlled switching;current zero;interruption phenomena;medium voltage;vacuum breaker;Circuit breakers;EMTP;Simulation;Voltage;Control systems;Circuit faults;Transient analysis|
|[Analytical Approach Towards Low Power Device (Differential Amplifier) Using DG MOSFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057691)|T. Tekisi; V. M. Srivastava|10.1109/SAUPEC57889.2023.10057691|Double-gate MOSFET;Differential amplifier;Device;Energy efficiency;Low power electronics;Microelectronics;Radio frequency;Operational amplifiers;MOSFET;Power engineering;Differential amplifiers;Frequency response;Gain|
|[Cryptocurrency Mining Powered by Renewable Energy Using a DC-DC Connection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057799)|P. Rorich; K. Moloi; T. F. Mazibuko; I. E. Davidson|10.1109/SAUPEC57889.2023.10057799|Buck converters;cryptocurrency mining;DC-DC connection;Graphics Processing Unit;solar power;Renewable energy sources;Power engineering;Software packages;Voltage;Solar energy;Cryptocurrency;Data mining|
|[Improvement of a Three-Phase Z-Source Inverter Performance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057891)|M. S. Perfect Ngongoma; I. E. Davidson; J. T. Agee|10.1109/SAUPEC57889.2023.10057891|% total harmonics distortion;boost factor;capacitor-boosted-Z-Source Inverter;current-source inverter;DC-link voltage;modulation index;switching components switching stress;voltage-source inverter;Z-source inverter;Total harmonic distortion;Switches;Pulse width modulation;Power system harmonics;Control systems;Inverters;Mathematical models|
|[Comparative Analysis Of Simple Boost, Constant Boost And Maximum Boost Pulse Width Modulation Schemes On A Three-Phase Z-Source Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057616)|M. S. P. Ngongoma; I. E. Davidson; J. T. Agee|10.1109/SAUPEC57889.2023.10057616|% total harmonics distortion;boost factor;modulation index;switching components switching stress;Z-source inverter Introduction;Space vector pulse width modulation;Measurement;Total harmonic distortion;Switches;Power system harmonics;Control systems;Inverters|
|[Experimental Identification of the Output Impedance Frequency Response of an Open-Loop Half-Bridge Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057764)|I. P. Gerber; F. M. Mwaniki; H. J. Vermeulen|10.1109/SAUPEC57889.2023.10057764|inverter;half-bridge;wideband;output impedance;frequency response;perturbation;Renewable energy sources;Frequency modulation;Perturbation methods;Stability criteria;Power system stability;Inverters;Frequency response|
|[An Assessment of some Power Converter Topology Evaluation Metrics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057746)|F. Letsoalo; I. Hofsajer|10.1109/SAUPEC57889.2023.10057746|DC-DC conversion;Processed power;Component stress factors;Topology;performance metric;RMS;Measurement;Power engineering;Systematics;Power electronics;Topology;Power conversion;Stress|
|[Optimization of Hybrid Power System for Cost Minimization: Case Study of a University Library](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057819)|K. T. Akindeji|10.1109/SAUPEC57889.2023.10057819|campus;HRES;PV;library;optimal;Renewable energy sources;Power engineering;Costs;Switches;Libraries;Hybrid power systems;Generators|
|[Calculating the Aerodynamic Drag Coefficient of a Toyota Avanza Car CAD Model using CFD Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057807)|N. Xavier|10.1109/SAUPEC57889.2023.10057807|CFD;CAD;Aerodynamic Drag;Aerodynamic Drag Coefficient;ICE;EV;Solid modeling;Power engineering;Analytical models;Computational fluid dynamics;Drag;Internal combustion engines;Aerodynamics|
|[The impact of GPS cleaning techniques on vehicle dynamics calculations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057951)|T. J. McBride; K. J. Nixon|10.1109/SAUPEC57889.2023.10057951|Vehicle Dynamics;On-Board Diagnostics;GPS Cleaning;Elevation Cleaning;Road Grade;Performance evaluation;Power engineering;Soft sensors;Receivers;Cleaning;Barometers;Vehicle dynamics|
|[High Speed Photography of Streamer Breakdown in Air Gaps Less than 15 cm Using HVDC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057636)|K. Moodley; A. Swanson|10.1109/SAUPEC57889.2023.10057636|High Speed Photography;Streamer Breakdown;Air;HVDC;Photography;Insulation;Electric breakdown;HVDC transmission;Air gaps;High-voltage techniques;Space charge|
|[Practical Study on the Lifetime Prediction of High Voltage Cross-Linked Polyethylene Cable (XLPE) using Thermal Aging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057603)|R. M. Mutepe; B. A. Thango; P. N. Bokoro|10.1109/SAUPEC57889.2023.10057603|cross-linked polyethene cable (XLPE);thermal aging;lifetime;degradation;Temperature;Power cables;Power cable insulation;High-voltage techniques;Mechanical cables;Distribution networks;Predictive models|
|[Testing of different materials for composite aircraft lightning protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057901)|S. Simelane; T. Magoro; H. Hunt; W. Hlungwana; L. Moloisane|10.1109/SAUPEC57889.2023.10057901|Composite Material;Lightning;Copper Mesh;Cold Spray;Coating;Temperature measurement;Temperature distribution;Power engineering;Composite materials;Lightning protection;Generators;Coatings|
|[Characterization of Cavity PD Under Impulse Voltage in Polyethylene Insulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057881)|T. V. Mphuti; C. Nyamupangedengu|10.1109/SAUPEC57889.2023.10057881|Cavity PD;Impulse voltage;Balanced PD detection circuit;Partial discharges;Insulation;Voltage measurement;Pulse measurements;Lightning;Tail;Switches|
|[Simulation of PD Characteristics of Closely Coupled Air Cavities Using MATLAB/SIMULINK](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057826)|M. Thekiso; C. Nyamupangedengu; I. K. Kyere|10.1109/SAUPEC57889.2023.10057826|Partial discharge;Double cavity PD;Partial discharges;Couplings;Insulation;Power engineering;Software packages;Atmospheric modeling;Solids|
|[Test Voltage Frequency Effects on Electric Field Profiles in MV Cable Joints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057817)|B. Sukdev; C. Nyamupangedengu|10.1109/SAUPEC57889.2023.10057817|0.1 Hz VLF;Electric Field Stress;MV XLPE;Power Cable Joint;FEM;Modelling;Permittivity;Conductivity;Analytical models;Power cables;Voltage;Finite element analysis;Integrated circuit modeling;Electric fields;Permittivity|
|[Country-Wide Evaluation of the South African Lightning Detection Network Location Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057765)|N. Tanase; T. Manape; H. G. Hunt|10.1109/SAUPEC57889.2023.10057765|Density-bar;Elevated-density area;Location accuracy;SALDN;Power engineering;Poles and towers;Lightning|
|[Breakdown Voltage of Natural Ester-Based Nanofluid: A comparison between anatase-TiO2 and rutile-TiO2 nanoparticles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057745)|M. Miya; C. Nyamupangedengu; K. Nixon; N. Moloto|10.1109/SAUPEC57889.2023.10057745|Nanofluids;TiO2;anatase;rutile;natural ester;AC breakdown voltage;Nanoparticles;Power engineering;Insulation;Liquids;Photonic band gap;Electric breakdown;Oils|
|[Johannesburg Lightning Nowcasting From Meteorological Data and Electric Field Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057947)|O. Marope; B. G. Tshabalala; C. Schumann; H. G. P. Hunt|10.1109/SAUPEC57889.2023.10057947|Recall;Precision;Random Forest;Logistic Regression;LSTM;Cloud-to-ground lightning;cloud-to-cloud lightning;Radio frequency;Analytical models;Sensitivity;Atmospheric modeling;Clouds;Urban areas;Lightning|
|[Significance of Nanoparticles on Electrical Breakdown Strength of Oil-Impregnated Paper Reinforced with Rutile-TiO2](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057888)|M. M. Katun; C. Gomes; C. Nynamupangedengu|10.1109/SAUPEC57889.2023.10057888|Transformer;oil-impregnated paper;nanocomposite kraft paper;kraft paper;fibre length;interaction zone;nanotechnology;nanoparticles;nanofiller;Nanoparticles;Insulation;Power engineering;Electric breakdown;Charge carriers;Cellulose;Surfactants|
|[Analysis of Solar Irradiation Impact on Grid-tied Photovoltaic Systems' Power Quality Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057766)|A. A. Adebiyi; I. E. Davidson|10.1109/SAUPEC57889.2023.10057766|PV system;power quality;solar irradiation;harmonics distortion;Photovoltaic systems;Radiation effects;Reactive power;Total harmonic distortion;Power measurement;Power quality;Solar energy|
|[Design and Application of the Passive Filters for Improved Power Quality in Stand-alone PV Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057777)|S. Dlamini; I. E. Davidson; A. A. Adebiyi|10.1109/SAUPEC57889.2023.10057777|PV System;passive filter;DC-AC converter;harmonic mitigation;total harmonic distortion (THD);Total harmonic distortion;Reactive power;Passive filters;Power quality;Voltage;Active filters;Harmonic analysis|
|[Analytical and Experimental Determination of Panel Generation Factor for Witbank Area](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057922)|K. P. Phukubje; A. F. Nnachi; A. O. Akumu|10.1109/SAUPEC57889.2023.10057922|Panel generation factor;PGF;Photovoltaic;solar design;Photovoltaic systems;Power engineering;Maintenance engineering;Meteorology|
|[Architecture of Renewable Energy System via Non-Terrestrial Communication Nodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057617)|A. A. Periola|10.1109/SAUPEC57889.2023.10057617|Cloud Data Centers;Multi-disciplinary Perspective;Renewable Energy;Future Grid;Underwater cables;Performance evaluation;Renewable energy sources;Data centers;Power engineering;Terrestrial atmosphere;Solar energy|
|[Optimum Reliability Integrated WEF Renewable Technology into the Eskom Distribution Grid, in the Eastern Cape Operating Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057681)|S. L. Matondolo; M. T. Kahn|10.1109/SAUPEC57889.2023.10057681|Renewable Energy (WEF);Load Forecast;Loadshedding/Energy Crisis;Pollution/Carbon Emissions;Cost v Sustainability (Just Transition);Renewable energy sources;Power engineering;Coal;Production;Distribution networks;Carbon dioxide;Reliability engineering|
|[An Economic Feasibility Study for Off-Grid Hybrid Renewable Energy Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057767)|E. I. Ogunwole; S. Krishnamurthy|10.1109/SAUPEC57889.2023.10057767|Economic feasibility study;sensitivity analysis;Renewable energy resources;solar PV;wind turbine;batteries;converters;cost optimization;grid-connected and islanded mode;Renewable energy sources;Wind;Costs;Systems architecture;Generators;Software;Wind turbines|
|[Transient Operation of a Hybrid Wind Farm With FSIG and PMSG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057726)|O. Apata; D. T. O. Oyedokun; P. N. Bokoro|10.1109/SAUPEC57889.2023.10057726|Fixed speed induction generator (FSIG);doubly fed induction generator (DFIG);hybrid wind farm;permanent magnet synchronous generator (PMSG);wind turbine (WT);Reactive power;Power engineering;Induction generators;Wind farms;Wind power generation;Hybrid power systems;Synchronous generators|
|[Network Integrated Power Architecture for Terrestrial and Modular Data Center Contexts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057618)|A. A. Periola|10.1109/SAUPEC57889.2023.10057618|Cloud Data Centers;Cyberphysical Networks;Renewable Energy;Future Grid;Performance evaluation;Data centers;Renewable energy sources;Technological innovation;Power engineering;Servers;Communication networks|
|[A Nexus for Dispatching of Ancillary Services of Emergency Reserves in South African Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057832)|N. Tshinavhe; M. Ratshitanga; K. Aboalez|10.1109/SAUPEC57889.2023.10057832|Ancillary services;voltage control;frequency regulation;operating reserves;South African electricity challenges;Variable renewable energy dispatch;Eskom;Renewable energy sources;Power engineering;Bibliographies;Buildings;Dispatching;Regulation;Power systems|
|[Artificial Intelligence Solution for Energy Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057850)|A. Yusuff; T. Mosetlhe|10.1109/SAUPEC57889.2023.10057850|Energy management;total final energy consumption;energy transportation;artificial intelligence;Industries;Economics;Power engineering;Energy management;Artificial intelligence|
|[Comparative Study of Deep Learning Techniques for Breakdown Prediction in Pump Sensor Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057611)|S. Chawanda; P. Olukanmi|10.1109/SAUPEC57889.2023.10057611|Anomaly detection;Predictive Maintenance;Long Short-Term Memory (LSTM);Temporal Convolutional Network (TCN);Multi-Layer Perceptron (MLP);Deep learning;Power engineering;Electric breakdown;Pumps;Predictive models;Data models;Sensor systems|
|[Using the Multilayer Perceptron (MLP) Model in Predicting the Patterns of Solar Irradiance at Several Time Intervals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057744)|C. N. Obiora; A. Ali; A. N. Hasan|10.1109/SAUPEC57889.2023.10057744|Deep Learning;Multilayer Perceptron (MLP));Support Vector Regression (SVR);Solar irradiance;Prediction;Time Intervals;Renewable energy sources;Power system protection;Stochastic processes;Solar energy;Predictive models;Multilayer perceptrons;Data models|
|[Study of Fault Detection on a 230kV Transmission Line Using Artificial Neural Network (ANN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057902)|Z. I. Sobhuza; B. A. Thango|10.1109/SAUPEC57889.2023.10057902|Southern African Development Community (SADC);transmission line;faults;artificial neural network (ANN);Levenberg-Marquardt (LM) algorithm;Training;Power transmission lines;Fault detection;Simulation;Neural networks;Transfer functions;Protective relaying|
|[Internet of Things (IoT) based Microgrid System for Optimal Scheduling: Case Study Kadoma-Zimbabwe](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057889)|O. C. P. Munemo; B. A. Thango; P. N. Bokoro|10.1109/SAUPEC57889.2023.10057889|Microgrid;Optimal scheduling;Internet of Things;Artificial Neural Network;Random Forest and Extreme Gradient Boosting;Machine learning algorithms;Decision making;Optimal scheduling;Microgrids;Artificial neural networks;Boosting;Prediction algorithms|
|[Development of Dissolved Gas Analysis-based Fault identification System using Machine Learning with Google Colab](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057713)|N. L. Z. Msomi; B. A. Thango|10.1109/SAUPEC57889.2023.10057713|SVM;DGA;methods;power transformers;faults;Support vector machines;IEC;Load shedding;Oil insulation;Dissolved gas analysis;Maintenance engineering;Internet|
|[AI Edge Processing - A Review of Distributed Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057624)|M. Sibanda; E. Bhero; J. Agee|10.1109/SAUPEC57889.2023.10057624|Distributed Embedded Systems;High Performance Embedded Computing;Embedded systems;Edge computing;Embedded computing;Power engineering;Data privacy;Machine learning;Real-time systems;Hardware;System software|
|[Performance Analysis of Cascode Configuration 3T Pixel Structure Detectors Using y-parameter Representation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057814)|J. Venter|10.1109/SAUPEC57889.2023.10057814|Image sensors;CMOS image sensors;integrated circuit modeling;analytical modeling;phototransistors;Temperature sensors;Resistance;MOSFET;Power engineering;Costs;Prototypes;Detectors|
|[The Use of Digital Image Processing for Investigating Vulture Streamer Lengths to better understand Power Line Fault Mechanisms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057676)|N. Parus; N. Mahatho; D. Matlou; A. Beutel; K. Durgapersad; C. Gomes|10.1109/SAUPEC57889.2023.10057676|power line faults;line performance;bird streamer;digital image processing;vulture;artificial intelligence;camera calibration;Power engineering;Power measurement;Digital images;Image processing;Streaming media;Length measurement;Cameras|
|[Distributed Hybrid Power-Sharing Control Strategy within Islanded Microgrids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057917)|G. W. Ndiwulu; M. Matalatala; A. K. Lusala; P. N. Bokoro|10.1109/SAUPEC57889.2023.10057917|AC microgrid;inverter;grid-forming;grid-following;power-sharing control;Power engineering;Simulation;Rotating machines;Microgrids;Power system stability;Inverters;Stability analysis|
|[Effects of Upside Risk on Microgrids' Reliability Considering the COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057664)|A. K. Onaolapo; E. E. Ojo|10.1109/SAUPEC57889.2023.10057664|upside risk;microgrids;reliability;COVID-19;pandemic;COVID-19;Photovoltaic systems;Power engineering;Energy resources;Systems operation;Microgrids;Production facilities|
|[A Modified Droop Control Technique for Accurate Power-Sharing of a Resilient Stand-Alone Micro-grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057668)|P. A. Gbadega; Y. Sun; K. T. Akindeji|10.1109/SAUPEC57889.2023.10057668|Micro-grid modeling;network-forming converter;transient stability;droop-based control;virtual impedance;MATLAB/Simulink;Renewable energy sources;Simulation;Microgrids;Power system stability;Stability analysis;Inverters;Reliability|
|[Using LSTM To Perform Load Modelling For Residential Demand Side Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057875)|K. N. Simani; Y. O. Genga; Y. -C. J. Yen|10.1109/SAUPEC57889.2023.10057875|load forecasting;LSTM;periodic signal;Monte Carlo simulation;electric water heater (EWH);Temperature measurement;Power measurement;Load forecasting;Sociology;Water heating;Stochastic processes;Machine learning|
|[Load Shedding to Load Hedging - Evaluating Load Shedding Mitigation Options for Residential Customers within eThekwini Municipality](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057880)|L. Moodliar; I. E. Davidson|10.1109/SAUPEC57889.2023.10057880|Battery energy storage;load shedding;techno-economic modelling;residential solar;Power engineering;Costs;Energy resources;Load shedding;Generators;Power grids;Batteries|
|[Improving Stability Through Adaptive Under-Frequency Load Shedding: the Zambian Scenario](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057735)|F. Kanjelesa; A. C. Mumba; K. Mulenga; C. Siame; P. N. Bokoro|10.1109/SAUPEC57889.2023.10057735|frequency stability;renewable energy sources;rate-of-change-of-frequency;under frequency load shedding;Renewable energy sources;Voltage measurement;Contingency management;Power system simulation;Load shedding;Power system stability;Predictive models|
|[Framework for ancillary services design for low inertia power systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057797)|A. O. Olasoji; D. T. O. Oyedokun|10.1109/SAUPEC57889.2023.10057797|electricity market;inertia;PFR;SCUC;MILP;Renewable energy sources;Time-frequency analysis;Uncertainty;Stochastic processes;Power system stability;Electricity supply industry;Frequency response|
|[Critical Assessment of the Feasibility of Shielding Overhead Medium Voltage Lines in South African Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057779)|C. Mathopo; A. Beutel; B. McLaren; W. D. Van Schalkwyk|10.1109/SAUPEC57889.2023.10057779|Basic insulation level;footing resistance;lightning;medium voltage lines;shield wires;soil ionization;soil resistivity;Insulation;Costs;Grounding;Wires;Lightning;Voltage;Medium voltage|
|[Integrated Monitoring and Control System Architecture for 11 kV Substation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057757)|D. E. Okojie; S. Mhlongo; U. B. Akuru|10.1109/SAUPEC57889.2023.10057757|control;communication;digital;integrated monitoring;protocols;substation;system architecture;Substations;Switchgear;Systems architecture;Reliability engineering;Control systems;Real-time systems;IEC Standards|
|[Modelling CSIR Long-term Electricity Least-cost Proposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057934)|U. B. Mudau; M. Senatla; A. F. Mulaba-Bafubiandi; J. Meyer|10.1109/SAUPEC57889.2023.10057934|energy demand;least cost electricity;solar photovoltaic;wind;Photovoltaic systems;Renewable energy sources;Power engineering;Costs;Biological system modeling;Urban areas;Planning|
|[Network Analysis and Compensation of Underground Cable Capacitive Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057823)|H. K. Chisepo; C. T. Gaunt|10.1109/SAUPEC57889.2023.10057823|distribution networks;underground cables;general power theory;power system;capacitive current;Analytical models;Shunts (electrical);Reactive power;Power engineering;Power cables;Network analyzers;Inverters|
|[Online Education Poverty: A Multidimensional Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057827)|M. R. Dangor; E. Trengove; M. Bekker|10.1109/SAUPEC57889.2023.10057827|energy;education;poverty index;emergency remote teaching;COVID-19;Power engineering;Pandemics;Education;Educational technology;Planning;Indexes|
|[Educating the Future Engineer Towards Realizing the Powering of the Future Internet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057695)|A. A. Periola; A. A. Alonge; K. A. Ogudo|10.1109/SAUPEC57889.2023.10057695|Cloud Data Center;Renewable Energy Sources;Future Internet;Future Engineer;Training;Measurement;Cloud computing;Data centers;Renewable energy sources;Power engineering;Power measurement|
|[Transformer Differential Protection System Testing for Scholarly Benefits Using RTDS Hardware-in-the-Loop Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10057606)|S. Nomandela; M. Mnguni; M. Ratshitanga; S. Ntshiba|10.1109/SAUPEC57889.2023.10057606|Power systems;power transformers;transformer protection;differential protection system;inrush currents;current transformer saturation;hardware-in-the-loop;System testing;Power engineering;Current transformers;Simulation;IEEE Standards;Power systems;Power transformers|
#### **2023 International Conference On Cyber Management And Engineering (CyMaEn)**
- DOI: 10.1109/CyMaEn57228.2023
- 26-27 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Multidimensional Approach of Corporate Sustainability Ranking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051050)|S. A. Alajaji; H. Al-Fadhel; B. Pérez-Gladish; H. Masri|10.1109/CyMaEn57228.2023.10051050|ESG rating;corporate sustainability assessment;ordered weighted aggregating operators;TOPSIS ranking;linguistic variables;Decision making;Linguistics;Reliability;Sustainable development;Investment|
|[Home Care Automation: Market Research, Industry Analysis, and Security Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051063)|A. Fayoumi; S. Sobati-Moghadam; A. Rajaiyan; C. Oxley; P. F. Montero; A. Safarian|10.1109/CyMaEn57228.2023.10051063|Internet of Things (IoT);market research;IoT security;care home;digital healthcare;Industries;Automation;Medical services;Market research;Security;Stakeholders;Object recognition|
|[Digitalization Of Public Vehicles Using On Board Diagnostic-II (OBD-II)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051080)|C. P. Purnomo; R. Munadi; Istikmal; A. Widodo; S. Kuntadi; R. H. Putra|10.1109/CyMaEn57228.2023.10051080|OBD-II;ECU;GPS;NPS;UT;Media;Automobiles;Time factors;Usability;Task analysis;Standards;Monitoring|
|[Classifying Cyberattacks on Financial Organizations Based on Publicly Available Deep Web Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050921)|M. J. Hossain; U. N. Jahan; R. H. Rifat; A. A. Rasel; M. A. Rahman|10.1109/CyMaEn57228.2023.10050921|Cyberattack;Cybersecurity;Fintech;Cyber Threat;Financial Scam;Deep Web;Financial organization;Training;Industries;Dark Web;Computer hacking;Databases;Ecosystems;Data visualization|
|[Gender Diversity and Firm Profitability, before and during the Covid-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051029)|T. Nur; Q. Darvin|10.1109/CyMaEn57228.2023.10051029|Gender diversity;Board of Director;Independent Director;COVID-19 PandemicIntroduction (Heading 1);COVID-19;Regulators;Profitability;Pandemics;Decision making;Companies;Market research|
|[Management of enterprise cyber security: A review of ISO/IEC 27001:2022](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051114)|M. Malatji|10.1109/CyMaEn57228.2023.10051114|cyber security;ISO/IEC 27001;information security;information systems;ISMS;NIST;Privacy;ISO Standards;Information security;NIST;Security;Internet of Things;IEC Standards|
|[An Ultra Lightweight Cipher Algorithm For IoT Devices and Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051100)|N. A. Mawla; H. K. Khafaji|10.1109/CyMaEn57228.2023.10051100|Drones;Internet of Things;Security;Attacks;Encryption;Logic gates;hexadecimal system;ASCII code;Correlation coefficient;Codes;Smart homes;Rate distortion theory;Logic gates;Entropy;Encoding|
|[Efficient MANETs Routing Algorithm to Mitigate Latency Based on AODV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050888)|E. Temesgen; A. O. Salau; E. Yitayal; S. L. Braide|10.1109/CyMaEn57228.2023.10050888|MANETs;Real-Time Applications;QoS routing;AOD V;Wireless communication;Simulation;Packet loss;Quality of service;Routing;Routing protocols;Mobile handsets|
|[Hospital Performance Measurement with the Balanced Scorecard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051067)|M. D. A. Reinaldy; J. N. Elquthb; G. A. Yudhistira; Qurtubi|10.1109/CyMaEn57228.2023.10051067|balanced scorecard;hospital performance;performance measurement;Measurement;Technological innovation;Fluctuations;Hospitals;Customer satisfaction;Companies;Sustainable development|
|[Dhouib-Matrix-TP1 Method to solve Fuzzy Transportation Problem Involving Heptagonal Fuzzy Numbers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050892)|S. Dhouib; T. Loukil; M. Kammoun; S. Dhouib|10.1109/CyMaEn57228.2023.10050892|Transportation Problem;Fuzzy Numbers;Heuristic;Dhouib-Matrix;Measurement;Costs;Transportation;Approximation algorithms;Approximation methods;Optimization;MODIS|
|[Bespoke Mitigation Framework for False Data Injection Attack-Induced Contingency Events](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050946)|S. Chan; P. Nopphawan|10.1109/CyMaEn57228.2023.10050946|Convex Relaxation;Decision Engineering (DE);Industry 5.0;Artificial Intelligence (AI);Supply Chain Vulnerability (SCV);Industrial Internet of Things (IIOT);Information and Communications Technology (ICT);Industrial Control System (ICS) Reliability;Power System State Estimation (PSSE);Bad Data Detector (BDD);Cyber Trust;Initial Contingency;Metaheuristic.;Continuous wavelet transforms;Storms;Contingency management;Supply chains;Detectors;Controllability;Risk management|
|[Optimization of Network Reconfiguration in the ULP Way Halim Distribution System, Bandar Lampung City Considering the Use of Nonlinear Loads](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050958)|M. D. Faraby; S. Sofyan; A. R. Idris; Usman; A. Fitriati; Isminarti|10.1109/CyMaEn57228.2023.10050958|Harmonic Distortion;Reconfiguration;tie switch;PSO%THDv;Network topology;System performance;Urban areas;Switches;Voltage;Power system harmonics;Harmonic analysis|
|[Effect of Digital Literacy and IT Service Quality on Tourists’ Visit Decision to Marine Attractions During the Covid-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050913)|R. I. P. Anom; R. Yasirandi; K. Ladkoom; Y. F. Dewantara; H. T. Gandhiwati|10.1109/CyMaEn57228.2023.10050913|Digital Literacy;IT Service Quality;Visit Decision;Marine Attractions;COVID-19;COVID-19;Pandemics;Social networking (online);Tourism industry;Reliability;Sustainable development;Information technology|
|[Recently Research on Social Media Influencers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051103)|Y. -J. Yang; C. -C. Wang|10.1109/CyMaEn57228.2023.10051103|social media;influencers;literature review;bibliometric analysis;Social networking (online);Databases;Market research|
|[Bespoke Weighting Schema and Sequence of Transformations for Enhanced Insight into Prospective False Command Injection Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051057)|S. Chan; P. Nopphawan|10.1109/CyMaEn57228.2023.10051057|Smart Grid;Cyber Physical Power Systems;Supply Chain Management;Supply Chain Vulnerabilities;Industry 4.0;Industry 5.0;Risks;Vulnerabilities;Reliability;Statistical Measures in Engineering Management;Heuristics;Metaheuristics;Optimization;Cyber Technologies;Data Analytics;Artificial Intelligence;Decision Systems and Decision Engineering;Weight measurement;Power measurement;Automation;Smart grids;Power system reliability;Task analysis;Particle swarm optimization|
|[Analytical Modelling and Performance Analysis of Resource Utilization Using Proactive Contention System in Highly Mobile Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050889)|Y. Kırsal|10.1109/CyMaEn57228.2023.10050889|analytical modelling;performance analysis;proactive contention;spectral expansion;Analytical models;System performance;Quality of service;Performance analysis;Resource management;Time factors;Queueing analysis|
|[Edge Machine Learning to Detect Malicious Activity in IoT Devices through System Calls and Traffic Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051091)|N. Pape; C. Mansour|10.1109/CyMaEn57228.2023.10051091|nan;Performance evaluation;Machine learning algorithms;Image edge detection;Computational modeling;Machine learning;Explosions;Internet of Things|
|[Failure Prediction for High Voltage Induction Motor using Artificial Neural Network (ANN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051019)|A. M. B. Zainol; N. R. A. B. Burhani|10.1109/CyMaEn57228.2023.10051019|big data analytics;artificial neural network;machine learning;predictive maintenance;high voltage induction motor;failure prediction.;Vibrations;Reactive power;Analytical models;Induction motors;Artificial neural networks;Machine learning;Voltage|
|[The Outlook of Non Fungible Tokens (NFTs): an alternative for academic manuscript ownership and scholarly publications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051033)|A. G. Zanjanab; N. Ahadi; G. Monametsi; S. Sorooshian; A. Taghipour|10.1109/CyMaEn57228.2023.10051033|Non-Fungible Tokens;Blockchain technology Academic publishing;Scientific research;Scientific integrity;Manuscript ownership;Resistance;Industries;Open Access;Copyright protection;Peer-to-peer computing;Fraud;Fourth Industrial Revolution|
|[Development of a Precision Agricultural Based Unmanned Aerial Vehicle for Pest Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051014)|O. L. Rominiyi; A. O. Salau; B. A. Adaramola; M. A. Ogunlade; T. O. Olanibi; F. A. Akintoye|10.1109/CyMaEn57228.2023.10051014|Unmanned aerial vehicle;unmanned aerial system;autopilot;agricultural drone;farm produce;quadcopter;aerial mapping;Satellites;Surveillance;Steering systems;Prototypes;Spraying;Autonomous aerial vehicles;Agriculture|
|[How to Evaluate Success Startups: Case of FinTech and Cybersecurity in the GCC Venture Capital Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051035)|Y. H. A. Rahma; A. Al-Alawi|10.1109/CyMaEn57228.2023.10051035|FinTech;Cybersecurity;Entrepreneur;Startups;Criteria;entrepreneurial finance;Law;Biological system modeling;Bibliographies;Government;Ecosystems;Decision making;Finance|
|[Analysis of Consumer Behavior in Online Shopping on Social Media](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051027)|S. L. Bong; A. B. Y. Adriawan; G. Kamilah; E. D. Madyatmadja|10.1109/CyMaEn57228.2023.10051027|Social Media;Online Shopping;Consumer Behavior;Freeware;Consumer behavior;Video on demand;Social networking (online);Sociology;Multimedia Web sites;Data collection|
|[Comparison of Machine Learning Algorithms for Prediction of Total Assets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051085)|S. Chaising; M. Syukur; P. Nithibandanseree|10.1109/CyMaEn57228.2023.10051085|Machine Learning;Total Assets;Gradient Boosted Trees;Prediction;Training;Radio frequency;Machine learning algorithms;Predictive models;Prediction algorithms;Discrete cosine transforms;Decision trees|
|[Machine Learning for Classification of IT Support Tickets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051041)|L. S. B. Pereira; R. Pizzio; S. Bonho; L. M. F. De Souza; A. C. A. Junior|10.1109/CyMaEn57228.2023.10051041|Deep Learning;Text Classification;Natural Language Processing;Neural Networks;IT Support Tickets;Automatic Ticket Classification;Codes;Prototypes;Machine learning;Data models|
|[Determinants of Customer Satisfaction with Self-Service Technology (SST): A Case Study of Mae Fah Luang University’s Library](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051048)|A. J. Aree; D. Kamhangwong; S. Wicha; S. Yamsa-Ard|10.1109/CyMaEn57228.2023.10051048|Self-Service Technology;Customer Satisfaction;Technology Adoption;Library;Technological innovation;Customer satisfaction;Libraries;Self-service|
|[The Prosecution of Cybercrimes in Nigeria: Challenges and Prospects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050896)|U. J. Idem; E. S. Olarinde; E. O. Anwana; A. T. Ogundele; M. A. Awodiran; M. -A. Omomen|10.1109/CyMaEn57228.2023.10050896|Criminal Trials and Prosecution;Cybercrimes;Challenges and Prospects;Enforcement;Theory of Technology-Enable Crime;Nigeria;Economics;Training;Law enforcement;Forensics;Government;Entertainment industry;Media|
|[Cybercrime Regulatory Agencies need urgent Reform to Protect Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050994)|U. J. Idem; E. S. Olarinde; N. G. Ikpeze; Anwana; O. Emem; A. T. Ogundele; M. A. Awodiran|10.1109/CyMaEn57228.2023.10050994|Cybercrimes;Regulatory Agencies;Cybercriminals;Routine Activity Theory;Nigeria;Training;Law enforcement;Terminology;Forensics;Cyberspace;Africa;Mice|
|[Green Supply Chain Management and Organizational Performance in Bahrain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051075)|A. Al-Maathidi; M. Al-Shammari|10.1109/CyMaEn57228.2023.10051075|Green supply chain management;Organizational performance;Kingdom of Bahrain;Supply chain management;Green products;Supply chains;Reverse logistics;Packaging;Sampling methods;Mathematical models|
|[Digital Delivery of Consulting Service for Data-Driven Manufacturing Using Industrial Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051060)|P. -H. Chueh; J. K. H. Wang|10.1109/CyMaEn57228.2023.10051060|Digital Delivery;Data-Driven Manufacturing;Industrial Internet of Things (IIoT);Manufacturing industries;Analytical models;Organizations;Media;Manufacturing;Industrial Internet of Things;Context modeling|
|[Digital Forensic Accounting and Cyber Fraud in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050992)|M. A. Awodiran; A. T. Ogundele; U. J. Idem; Anwana; O. Emem|10.1109/CyMaEn57228.2023.10050992|Digital forensics;cyberfraud;phishing Scam;advance fee fraud;credit card fraud;Data analysis;Correlation;Phishing;Digital forensics;Sociology;Government;Credit cards|
|[Digitally Designed Forensic Procedure a Panacea to Cyber Fraud Control in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050964)|A. T. Ogundele; M. A. Awodiran; U. J. Idem; Anwana; O. Emem|10.1109/CyMaEn57228.2023.10050964|Cyber fraud. Digitally Designed Procedure;EFCC;Forensic Procedure;Nigeria;Digital forensics;Government;Education;Fraud;Internet;Standards;Certification|
|[TripAdvisor Restaurant Customer Reviews Analysis in Bangkok, Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050971)|N. Pleerux; W. Boonpook; A. Chaiyo; A. Nardkulpat|10.1109/CyMaEn57228.2023.10050971|customer satisfaction;sentiment analysis;natural language processing;VADER;TripAdvisor;COVID-19;Sentiment analysis;Public relations;Pandemics;Social networking (online);Government;Customer satisfaction|
|[E-Learning Quality and Satisfaction of User](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051115)|S. M. Utomo; D. P. Alamsyah; N. A. Othman; I. Setyawati; H. Rohaeni|10.1109/CyMaEn57228.2023.10051115|Attitude;E-Learning Quality;Confirmation;Satisfaction;Electronic learning;Employment;Distributed databases;Behavioral sciences|
|[Non-Fungible Token (NFT) Games: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050961)|Y. -J. Yang; J. -L. Wang|10.1109/CyMaEn57228.2023.10050961|non-fungible token;NFT;cryptocurrency;blockchain;bibliometric analysis;Video games;Social networking (online);Databases;Bibliographies;Games;Market research;Libraries|
|[Faba Bean Disease Detection Using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051088)|A. O. Salau; B. T. Abeje; A. N. Faisal; T. T. Asfaw|10.1109/CyMaEn57228.2023.10051088|Deep Learning;Image Processing;Convolutional Neural Network;Faba Bean;Disease Detection;Training;Proteins;Deep learning;Productivity;Microorganisms;Transfer learning;Crops|
|[A Machine Learning Approach for Depression Screening in College Students Based on Non-Clinical Information](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051001)|P. Nison; P. Vuttipittayamongkol; P. Boonyapuk; K. Kemavuthanon|10.1109/CyMaEn57228.2023.10051001|depression;mental health screening;college students;machine learning;classification;imbalanced data;Training;Machine learning algorithms;Mental health;Predictive models;Depression;Prediction algorithms;Reliability|
|[The Legal Approach for Fighting Cybercrimes in Nigeria: Some Lessons from the United States and the United Kingdom](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050983)|U. J. Idem|10.1109/CyMaEn57228.2023.10050983|Cybercrime;Legal Perspective;Internet Business;Nigeria;United States;United Kingdom;Systematics;Law;Government;Cyberspace;Licenses;Fraud;Computer crime|
|[Vehicle Management System: Fuel, Mileage, Cost, and Maintenance Tracker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051089)|C. Kamolsin; W. Rungkul; C. Limpalasuk; F. Pensiri; C. Ratanavilisagul; P. Visutsak|10.1109/CyMaEn57228.2023.10051089|cost management system;web application;online reservation;car usage tracking;Costs;Portable computers;Pandemics;Unsolicited e-mail;Supply chains;Transportation;Maintenance engineering|
|[Empirical Review on the Use of Blockchain Technology in Supply Chain in the Sultanate of Oman](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051079)|A. Younas; R. W. Anwar|10.1109/CyMaEn57228.2023.10051079|Blockchain;supply chain;Oman 2040 vision;benefits;Challenges;Industries;Supply chain management;Costs;Supply chains;Globalization;Transforms;Market research|
|[Covid Impact Analysis on Small and Medium Sized French Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051040)|A. El-Qadi; M. Trocan; T. Frossard|10.1109/CyMaEn57228.2023.10051040|Covid-19;Small and Medium Enterprises;Default;Sector;COVID-19;Economics;Pandemics;Government;Ecosystems;Focusing;Companies|
|[Organizational Culture Analysis in the Implementation of Knowledge Management in Government Research Agency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051044)|D. I. N. Afra; I. Ramadhan; D. S. Rafianti; A. P. Prasetiyo; A. R. Syariati; L. Setyaningrum; A. Sutejo; T. Susanto|10.1109/CyMaEn57228.2023.10051044|organizational culture;knowledge management;OCAI;government research agency;Leadership;Instruments;Government;Force;Knowledge management;Teamwork|
|[Robotic Process Automation and Intelligent Automation Security Challenges: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050996)|Y. Al-Slais; M. Ali|10.1109/CyMaEn57228.2023.10050996|Robotic Process Automation;Intelligent Automation;cybersecurity;Blockchain;Artificial Intelligence;Intelligent automation;Systematics;Bibliographies;Government;Fraud;Fourth Industrial Revolution;Blockchains|
|[The Adoption of Automated Building Code Compliance Checking Systems in the Architecture, Engineering, and Construction Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050930)|E. A. Nama; A. Alalawi|10.1109/CyMaEn57228.2023.10050930|digital building permit;automation of compliance checking;ifcOWL ontology;BIM-based code compliance;AEC;Kingdom of Bahrain.;Solid modeling;Codes;Systematics;Law;Buildings;Switches;Syntactics|
|[Predicting Student Retention in Higher Education Using Data Mining Techniques: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051056)|W. M. Attiya; M. B. Shams|10.1109/CyMaEn57228.2023.10051056|Student retention;Higher Education;Drop out;Data mining techniques;Machine learning;Radio frequency;Schedules;Education;Predictive models;Prediction algorithms;Behavioral sciences;Timing|
|[The Application of Data Analytics to Career Choice Prediction: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051101)|S. Al-Dhari; A. I. Al-Alawi|10.1109/CyMaEn57228.2023.10051101|Career Guidance;Data Analytics;Prediction;Artificial Intelligence;Machine Learning;Data analysis;Machine learning algorithms;Engineering profession;Bibliographies;Random forests|
|[Fintech in the Fourth Industrial Revolution: Literature Review on Usage and Concerns of e-Wallet Payment Transactions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051026)|I. S. Abdulla; A. I. Al-Alawi|10.1109/CyMaEn57228.2023.10051026|Fintech;E-wallet;Cashless Payment;Fourth Industrial Revolution;Digital Transformation;COVID-19;Systematics;Online banking;Pandemics;Bibliographies;Human factors;Social factors|
|[Predicting Student’s Academic Performance Using Data Mining Methods: Review Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050962)|A. I. Al-Alawi; N. M. A. Alsubaiee|10.1109/CyMaEn57228.2023.10050962|Data Mining;Decision Tree;Students Performance;Academic Performance;Factors;Analytical models;Prediction algorithms;Data models;Data mining;Decision trees;Random forests|
|[Review of Data Mining Techniques in Performance Prediction for Medical Schools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050935)|J. K. Alanzi; A. I. Al-Alawi|10.1109/CyMaEn57228.2023.10050935|Data Mining Techniques;Attrition;Student Performance;Nomination;Peformance Prediction;Medical Schools;Education;Data mining|
|[Stock Market Prediction using Machine Learning Techniques: Literature Review Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050933)|A. I. Al-Alawi; Y. A. Alaali|10.1109/CyMaEn57228.2023.10050933|stocks;prediction;machine learning;banks;healthcare;Deep learning;Sentiment analysis;Machine learning algorithms;Bibliographies;Medical services;Predictive models;Logic gates|
|[Police Patrol Routes Optimization: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050899)|M. Thabet; M. Messaadia; A. K. Al-Khalifa|10.1109/CyMaEn57228.2023.10050899|(Optimization methods;Patrols routing;Machine learning;Scheduling);Systematics;Law enforcement;Bibliographies;Stochastic processes;Optimization methods;Predictive models;Mathematical models|
|[Analyzing the required skills and competencies in Industrial revolution 4.0 and 5.0: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050941)|A. Ghassoul; M. Messaadia|10.1109/CyMaEn57228.2023.10050941|skills;competencies;Industry 4.0;Industry 5.0;Training;Technological innovation;Systematics;Bibliographies;Employment;Big Data;Fourth Industrial Revolution|
|[Implementing Machine Learning in Optimizing Stock Portfolios: A review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051023)|M. Al-Muharraqi; M. Messaadia|10.1109/CyMaEn57228.2023.10051023|Machine learning;Stock return prediction;Portfolio optimization;Assets selection;Analytical models;Machine learning algorithms;Bibliographies;Machine learning;Predictive models;Internet;Stock markets|
|[A Joint Routing and Charging Management for Drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051066)|C. -J. Huang; K. -W. Hu; B. Z. Xie; H. -W. Cheng|10.1109/CyMaEn57228.2023.10051066|drone;route and charging planning;data mining;optimization;Renewable energy sources;Simulation;Inductive charging;Land transportation;Charging stations;Routing;Electric vehicles|
|[Artificial Intelligence Techniques for the Forecasting of Crude Oil Price: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050945)|N. A. Mohamed; M. Messaadia|10.1109/CyMaEn57228.2023.10050945|crude oil price (COP);neural networks (NN);oil price (OP);machine learning (ML);hybrid intelligent systems;artificial intelligence techniques (AIT);support vector machines (SVM).;Support vector machines;Oils;Bibliographies;Artificial neural networks;Media;Prediction algorithms;Market research|
|[Examine Riding Speed In School Zone Toward Traffic Safety Management Using License Plate Matching Technic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051052)|A. Thaomuen; A. Teycharaveenun; S. Kaewnongrong; A. Ruangsri; K. Ounkat; T. Arreeras|10.1109/CyMaEn57228.2023.10051052|Speed;License plate;Traffic safety;Delivery rider;School zone violation;Meters;Road accidents;Head;Roads;Safety management;Motorcycles;Cameras|
|[The Use of Data Mining Techniques to Predict Employee Performance: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050937)|T. M. Alsobaey; A. I. Al-Alawi|10.1109/CyMaEn57228.2023.10050937|employee performance;prediction;data mining techniques;naïve bayes;c4.5 algorithm;On the job training;Bibliographies;Employment;Organizations;Predictive models;Prediction algorithms;Size measurement|
|[Enhancing responsible production sustainability by utilizing Arena Simulation Empirical study in Al-Waha soft drink company](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050936)|A. Majeed; A. Hasan; K. G. Kadhim; I. Ibrahim|10.1109/CyMaEn57228.2023.10050936|simulation;responsible production;optimization;Analytical models;Adaptation models;Uncertainty;Statistical analysis;Companies;Production;Inventory management|
|[Green Energy of Handover Management in Seamless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050950)|C. Sapwatcharasakun; S. Kunarak; K. Prasertwong|10.1109/CyMaEn57228.2023.10050950|energy saving;femtocell;handover management;Macrocell;seamless networks;Energy consumption;Wireless networks;Simulation;Decision making;Handover;Throughput;Ubiquitous computing|
|[Exploring Artificial Intelligence (AI) Impact on Businesses: Perspectives from Big Data and Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051065)|S. Gopalkrishnan; A. Reddipogu|10.1109/CyMaEn57228.2023.10051065|Artificial Intelligence Experiences;Business Impact of AI;Data and Security;Big Data vs Small Data;Deep learning;Costs;Human intelligence;Education;Artificial neural networks;Big Data;Security|
|[Adaptive Thresholds for Task Offloading in IoT-Edge-Cloud Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051086)|O. M. Al-Tuhafi; E. H. Al-Hemiary|10.1109/CyMaEn57228.2023.10051086|task offloading;adaptive offloading;queueing model;cloud computing;edge computing;Performance evaluation;Adaptation models;Energy consumption;Adaptive systems;Computational modeling;Simulation;Heuristic algorithms|
|[User Association for Network Slicing-Enabled Heterogeneous Hybrid Wireless-Wireline Access Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050953)|F. Nizam; T. C. Chuah; Y. L. Lee|10.1109/CyMaEn57228.2023.10050953|Hybrid wireless-wireline access network;network slicing;user association;HetNets;convex optimization;Wireless communication;Base stations;Simulation;Quality of service;Bandwidth;Throughput;Load management|
|[Implementation of Digital Marketing in Micro, Small, and Medium Enterprises (MSMEs) in Indonesia During the COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050901)|M. Purnamasari; A. M. Herdina; R. D. Kumalasari; P. A. Purnama; N. J. Siregar|10.1109/CyMaEn57228.2023.10050901|Digital Marketing;Marketing;MSMEs;COVID-19;Freeware;Pandemics;Social networking (online);Brand management;Multimedia Web sites;Production|
|[Design of Trading Energy System Management Using Blockchain Hyperledger Fabric](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051046)|F. Triadi; Syafaruddin; A. Ahmad|10.1109/CyMaEn57228.2023.10051046|Hypeledger Fabric;Blockchain;microgrid;Hyperledger Caliper;Blockexplorer;validation;cryptography;Privacy;Distributed ledger;Computational modeling;Fabrics;Blockchains;Peer-to-peer computing;Servers|
|[Design Of Vehicle Routing Problem Improvements To Optimize Distance, Cost And Carriage Capacity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051096)|A. Parkhan; Y. Efendi; I. D. Widodo|10.1109/CyMaEn57228.2023.10051096|Vehicle Routing Problem;Nearest Neighbor;Local Search;Costs;Vehicle routing;Companies;Search problems;Sugar industry;Planning|
|[Analysis of Competitiveness and Halal Logistics of Small and Medium Industry in Beverage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051071)|F. Alhamudi; J. N. Elquthb; Qurtubi; I. P. Rachmadewi|10.1109/CyMaEn57228.2023.10051071|SMI;beverage industry;halal logistics;SOAR;marketing strategy;Economics;COVID-19;Pandemics;Government;Education;Beverage industry;Proposals|
|[The Requirements Analysis of a Smart Monitoring System Designed for the Rice Milling Process: A Case Study of the Agricultural Cooperative Sector in Chiang Rai](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051025)|M. Anan; D. Khamhangwong; S. Yamsa-Ard; P. Sureephong; S. Wicha|10.1109/CyMaEn57228.2023.10051025|IoT;A smart factory;Digital Twin;Productivity;Process monitoring;Industries;Digital transformation;Milling;Predictive models;Market research|
|[Implementation of Design Thinking in Marketing Strategy Development at Fajar Motor’s Small and Medium Enterprises](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050891)|R. F. Anasrul; W. Sutrisno|10.1109/CyMaEn57228.2023.10050891|Design Thinking;Digital Marketing Canvas;Marketing Strategy;Satisfy Customers;Turnover;Freeware;Visualization;Monte Carlo methods;Social networking (online);Multimedia Web sites;Prototypes;Quality assessment|
|[E-Word of Mouth and Restaurant-goers: an Empirical Study on the Influence of e-WOM on the Selection of Restaurants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050980)|M. Singh; A. Fatma; V. Bhatt; B. Sinha|10.1109/CyMaEn57228.2023.10050980|Customer Reviews;E-WoM;Online Offers;User-Friendly Websites;Virtual Tour;Restaurant;Electric potential;Pandemics;Urban areas;Force;Decision making;Mouth;Oral communication|
|[A Study on the Customer Buying Behaviour on E-Commerce Websites with Respect to Electronics during Covid-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051053)|A. Unni; S. Nand; V. Bhatt; B. Sinha|10.1109/CyMaEn57228.2023.10051053|E-Commerce;Buying Behaviour;Online Shopping;COVID-19;Pandemics;Education;Electronic commerce;Business|
|[Cybercrime Consciousness Among Undergraduate Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050982)|M. A. Awodiran; A. T. Ogundele; U. J. Idem; Anwana; O. Emem|10.1109/CyMaEn57228.2023.10050982|Cybercrime;undergraduate students;cybercrime consciousness;gender;Veins;Cyberspace;Safety;Computer crime|
|[Determination of SME Tax with Naïve Bayes Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050993)|Yunita; A. Herliana; D. P. Alamsyah; N. M. Wijanto|10.1109/CyMaEn57228.2023.10050993|SMEs Taxpayer;Naive Bayes Method;Website Classification System;Finance;Regulation;Naive Bayes methods;Planning;Task analysis;Business|
|[Mitigation Strategy of Work Accident Risks Using ANP Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050919)|R. Yanti; Qurtubi; M. Sugarindra|10.1109/CyMaEn57228.2023.10050919|ANP;Mitigation Strategy;Occupational Health and Safety (OHS);Work Accident;Training;Occupational safety;Companies;Human factors;Developing countries;Software;Risk management|
|[Industrial Field IoT Data Analysis Based on Efficient Data Collection (EDC) Toolkit: A Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050952)|D. Liu; J. K. H. Wang|10.1109/CyMaEn57228.2023.10050952|Efficient Data Collection (EDC);Industrial Internet of Things (IIoT);Data Analysis;Case Study;Cloud computing;Analytical models;Production;Data collection;Data models;Foundries;Sensors|
|[A Risk Assessment Methodology for Supply Chain Tracking Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051006)|D. Koutras; V. Malamas; P. Kotzanikolaou; T. Dasaklis|10.1109/CyMaEn57228.2023.10051006|Supply chain tracking systems;Risk Assessment;Operational requirements;Technical requirements;Target tracking;Supply chains;Refining;Transportation;Production;Risk management|
|[CXLSeg Dataset: Chest X-ray with Lung Segmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050951)|W. Nimalsiri; M. Hennayake; K. Rathnayake; T. D. Ambegoda; D. Meedeniya|10.1109/CyMaEn57228.2023.10050951|medical image segmentation;MIMIC-CXR;U-NET;SA-UNet;Shenzhen;Montgomery;Deep learning;Image segmentation;Visualization;Biological system modeling;MIMICs;Lung;Computer architecture|
|[Ground-level Post-Disaster Image Classification using DenseNet201 for Disaster Damage Assessment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050981)|A. D. J. Abadicio; K. E. E. Camota; K. A. F. Niosco; D. S. Hernandez; R. C. Belleza; R. A. Maaño; D. E. S. Oreta|10.1109/CyMaEn57228.2023.10050981|Artificial Intelligence;computer vision;convolutional neural network;disaster damage assessment;transfer learning;Training;Buildings;Learning (artificial intelligence);Artificial neural networks;Predictive models;Feature extraction;Emergency services|
|[A Product Review-Based Case Study Approach with Influencer Youth in an Urban Indonesian Context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050978)|I. Rahyadi; A. Azzahra; A. I. K|10.1109/CyMaEn57228.2023.10050978|Instagram;online environment;social media;influencers;product review;Technological innovation;Social networking (online);Multimedia Web sites;Oral communication;Media;Business|
|[Current practices, challenges, and opportunities for lifestyle-based market segmentation of older consumers in Thailand](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050965)|K. Yampray; S. Choemprayong; T. Taiphapoon|10.1109/CyMaEn57228.2023.10050965|Market segmentation;Lifestyle-based market segmentation;Qualitative study;Marketing tools;Older adults;Social networking (online);Sociology;Data collection;Telephone sets;Product development;Reliability;Older adults;Interviews;Statistics;Task analysis|
|[Instagram Feeds for Commercial Purpose Based on the Carousel Posts from Indonesian Marketplace Accounts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051073)|I. Rahyadi; M. Fransiska; V. Hardjantini; M. Lay; T. Y. Onasie|10.1109/CyMaEn57228.2023.10051073|instagram feeds;carousel;advertisements;interactive advertising;digital marketing.;Social networking (online);Multimedia Web sites;Companies;Feeds;Electronic commerce;Advertising;Videos|
|[Instagram Reels as a New Platform for Social Criticism Among Millennial](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051032)|I. Rahyadi; A. C. V. Drupadi; B. I. Pramesti; D. L. Karimah; K. Kinari|10.1109/CyMaEn57228.2023.10051032|Instagram;Politics;Social Critics;Reels;Social Media;Ethics;Social networking (online);Brand management;Multimedia Web sites;Sociology;Media;Behavioral sciences|
|[Embracing engagement, practice, and viral content: using TikTok to gain more TV audiences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050955)|I. Rahyadi; O. P. Dewi; J. A. Citra; P. A. Hapsari; Z. A. Rizki|10.1109/CyMaEn57228.2023.10050955|TikTok;TV station;engagement;content analysis;Visualization;TV;Social networking (online);Force;Entertainment industry;Media;Streaming media|
|[Analysis of Shopping Mall Tourist Satisfaction in Bangkok Using Word Cloud of Online Reviews](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051076)|K. Ladkoom; E. F. B. Tarigan; R. Yasirandi; N. A. Suwastika; R. I. P. Anom; M. A. Makky|10.1109/CyMaEn57228.2023.10051076|Online Reviews;Word Cloud;Tourist Satisfaction;Shopping Mall;Bangkok;Couplings;Pandemics;Urban areas;Tourism industry;Customer satisfaction;Developing countries;Tag clouds|
|[Artificial Intelligence in the Judiciary System of Saudi Arabia: A Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050929)|A. I. Al-Alawi; A. M. A-Lmansouri|10.1109/CyMaEn57228.2023.10050929|Artificial Intelligence (AI);Machine Learning (ML);Judicial System;Minimal human interaction;Text mining;Bibliographies;Government;Focusing;Medical services;Machine learning;Learning (artificial intelligence)|
|[Cybercrime Activities and the Emergence of Yahoo Boys in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051083)|A. T. Ogundele; M. A. Awodiran; U. J. Idem; E. O. Anwana|10.1109/CyMaEn57228.2023.10051083|Cybercrime;Yahoo Boys;Nigeria;Economics;Government;Employment;Security;Computer crime;Unemployment|
|[Prototype of the Solution with a View to Industrialization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050928)|S. E. Bellal; M. Sahnoun; M. A. Benatia; L. H. Mouss|10.1109/CyMaEn57228.2023.10050928|Wheelchair;Smart wheelchair;Smart module;Solid modeling;Three-dimensional displays;Costs;Navigation;Wheelchairs;Computational modeling;Prototypes|
|[Application of Big Data and Artificial Intelligence in Pilot Training: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050972)|M. H. Shaker; A. I. Al-Alawi|10.1109/CyMaEn57228.2023.10050972|aviation;big data;artificial intelligence;machine learning;pilot training;commercial airlines;Training;Technological innovation;Systematics;Bibliographies;Pipelines;Big Data;Real-time systems|
|[Cybercrime and its Negative Effects on Youth’s Development, the Economy and Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051047)|U. J. Idem; E. S. Olarinde|10.1109/CyMaEn57228.2023.10051047|Cybercrimes;Cybersecurity;Youth Development;Economy;Nigeria;Ethics;Local government;Employment;Stability criteria;Regulation;Hardware;Image restoration|
|[Proliferation of social media And Cybercrime Against Women and Girls in Nigeria](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051018)|A. O. Adenrian; O. H. Adeniran; A. T. Ogundele; M. T. Adedara|10.1109/CyMaEn57228.2023.10051018|social media;Cyber Violence;Women;Girls;Privacy;Freeware;Social networking (online);Education;Sociology;Security;Computer crime|
|[Educational Data Mining Utilization to Support the Admission Process in Higher Education Institutions: A Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051077)|A. I. Al-Alawi; M. A. A. Alfateh; A. M. Alrayes|10.1109/CyMaEn57228.2023.10051077|Admission Process;Data Mining;Machine Learning Algorithms;student enrollment;pre-admission;student success prediction;Support vector machines;Systematics;Bibliographies;Education;Predictive models;Prediction algorithms;Planning|
|[Mediation Model of E-Learning Satisfaction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051002)|O. I. B. Hariyanto; D. P. Alamsyah; N. A. Karim|10.1109/CyMaEn57228.2023.10051002|Student Continuance Intention;E-Learning;Quality;Satisfaction;Electronic learning;Correlation;Employment;Behavioral sciences;Mediation;Testing|
|[Analysis of Digital Branding Strategy on Hairen’s Instagram in Increasing Awareness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051087)|A. S. E. Savitri; A. Widita; G. S. Putri; A. M. C. Amalia|10.1109/CyMaEn57228.2023.10051087|brand awareness;digital branding;Instagram;haircare;Hairen;Hair;Pandemics;Social networking (online);Brand management;Multimedia Web sites;User-generated content;Entertainment industry|
|[Development of Pedal Driven Reciprocating Pump for Rural Usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051106)|O. L. Rominiyi; A. O. Salau; B. A. Adaramola; M. A. Ogunlade; O. P. Badaseraye; F. A. Akintoye|10.1109/CyMaEn57228.2023.10051106|Pump;pedal;crank;chain drive;Performance evaluation;Pistons;Pumps;Laboratories;Transforms;Materials reliability;Reliability engineering|
|[A Review of Machine Learning Methods For Predicting Churn in the Telecom Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051108)|F. A. Mohamed; A. K. Al-Khalifa|10.1109/CyMaEn57228.2023.10051108|Predicting Churn;Business Analytics;Machine Learning;Telecommunication;Machine learning algorithms;Databases;Optimization methods;Machine learning;Predictive models;Prediction algorithms;Telecommunications|
|[Potential Application of HR Analytics to Talent Management in the Public Sector: a Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051107)|E. Ali; H. Elias|10.1109/CyMaEn57228.2023.10051107|HR analytics;performance;public sector;talent management;literature review;Training;Leadership;Engineering profession;Databases;Bibliographies;Data visualization;Organizations|
|[Sustainable and Flexible Hub Location Problem](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050887)|M. A. Brahami; B. Bettayeb; M. Sahnoun|10.1109/CyMaEn57228.2023.10050887|Sustainable hub location;Flexible hub location;Multi-objective optimisation;Analytical models;Costs;Metaheuristics;Transportation;Pareto optimization;Numerical models;Behavioral sciences|
|[Context aware human machine interface for decision support](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051078)|M. Salima; S. M’Hammed; M. Messaadia; S. M. Benslimane|10.1109/CyMaEn57228.2023.10051078|Human machine interface;Adaptive HMI;Decision support;Context of use;Industries;Decision support systems;Context-aware services;Adaptation models;Bibliographies;Decision making;Production|
|[Exploring and visualizing the autonomous vehicle innovation system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050931)|I. Rouidjali; S. Negassi|10.1109/CyMaEn57228.2023.10050931|Autonomous vehicle;R&D collaboration;patents;social network analysis;Industries;Technological innovation;Patents;Social networking (online);Databases;Ecosystems;Data visualization|
|[Industrial Marketing Strategy Formulation Based on RFM-Data Analytics and Fuzzy Database](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10051008)|A. Parkhan; M. R. A. Purnomo|10.1109/CyMaEn57228.2023.10051008|customer behaviour;customer clustering;RFM;fuzzy database;fuzzy SQL;Databases;Companies;Data models;Customer profiles|
|[Distributed Air-Time Reduction In Multi-Hop LoRa Networks With Multiple Spreading Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050943)|C. Chimma; A. Phonphoem; A. Jansang; W. Tangtrongpairoj; C. Jaikaeo|10.1109/CyMaEn57228.2023.10050943|component;LoRa;Multi-Spreading Factor;Multi-Hop;Air-Time Reduction;Wireless communication;Performance evaluation;Smart cities;Network topology;Simulation;Modulation;Spread spectrum communication|
|[Decision Tree and Random Forest Models for Internal Audit Effectiveness Factors Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10050900)|M. M. Omar; M. Messaadia; A. M. Elmezughi|10.1109/CyMaEn57228.2023.10050900|Internal audit effectiveness;Digital transformation;COVID 19;Classifier;Adaptation models;Technological innovation;Pandemics;Digital transformation;Organizations;Forestry;Data models|
#### **2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)**
- DOI: 10.1109/ICSSIT55814.2023
- Date: 23-25, January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Novel Converter Design for Energy Management in Electrical Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061018)|G. Vasuki; M. Vinothini; K. K. Kumar|10.1109/ICSSIT55814.2023.10061018|Electric vehicle;Charging station;Bidirectional converter;Solar Photovoltaic;Photovoltaic systems;Performance evaluation;Bidirectional control;Supercapacitors;Electric vehicles;Batteries;Topology|
|[A Novel Hybrid Islanding Detection Method to Improve the Performance of PV Grid Connected System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061097)|B. Hariprasad; P. Bharat Kumar; P. Sujatha; G. Sreenivasan|10.1109/ICSSIT55814.2023.10061097|Active;Passive;Hybrid;Islanding Detection;Measurement;Renewable energy sources;Islanding;Bibliographies;Voltage;Inverters;Generators|
|[A Review on Optical Amplifiers for Future Optical Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060875)|M. Kumari|10.1109/ICSSIT55814.2023.10060875|Erbium-doped fibre amplifier (EDFA);Passive optical network (PON);Ramwn amplifier;Semiconductor optical amplifier (SOA);Optical fiber amplifiers;Semiconductor optical amplifiers;Wireless networks;Receivers;Optical fiber networks;Erbium-doped fiber amplifiers;Optical transmitters|
|[Throughput Performance of 802.11e for Different Versions of Wi-Fi Standard](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061124)|G. Gangaprasad; B. Seetha Ramanjaneyulu|10.1109/ICSSIT55814.2023.10061124|IEEE 802.11e;Data Rate;Throughput;Priority;Wireless LAN;Quality of Service.;Measurement;IEEE 802.11e Standard;Quality of service;Telecommunication traffic;Jitter;Throughput;Real-time systems|
|[An Adaptive-based Predicted Nodes Behavior Algorithm in Flying Ad Hoc Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060941)|O. Aruna; A. Sharma|10.1109/ICSSIT55814.2023.10060941|FANET;FSQoS;Predicted Weight;PFWGTR;QLF-MOR;Routing;Three-dimensional displays;Network topology;Simulation;Routing;Prediction algorithms;Throughput;Routing protocols|
|[Performance Analysis of Clustering Technique using LEACH in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061140)|R. Guruprasath; R. Nagarajan; S. Kannadhasan|10.1109/ICSSIT55814.2023.10061140|WSN;Throughput;Clustering;Energy Efficient;LEACH;Wireless sensor networks;Privacy;Data collection;Throughput;Routing;Energy efficiency;Routing protocols|
|[A Study of Routing Mechanisms in Vehicular Ad Hoc Networks for Reliability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060876)|H. A. Ahmed; D. Cheelu|10.1109/ICSSIT55814.2023.10060876|Vehicular Ad hoc Networks;Routing;Routing Protocols;Reliability;Delay;Packet Delivery Ratio;Throughput;Road side unit;Broadcasting;Routing;Throughput;Routing protocols;Real-time systems;Delays|
|[Optimizing Cluster Head Formation using Re-Election Algorithm in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060956)|R. V. Kumar; K. Babu; K. S. Ram; G. Karthikeyan|10.1109/ICSSIT55814.2023.10060956|Re-election algorithm;Cluster Head;Wireless sensor network (WSN);Sensor nodes;Wireless sensor networks;Heuristic algorithms;Voting;Clustering algorithms;Power system stability;Network architecture;Energy efficiency|
|[Improving Data Integrity for Gray Hole Attack Detection by using a Hash Signature Algorithm in WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061102)|S. Radhika; M. Srikanth; K. Anand; K. Saravanan; S. Sree Southry|10.1109/ICSSIT55814.2023.10061102|Gray hole attack detection;Wireless Sensor Network;packet size;principle communication count;hash signature algorithm;data integrity.;Wireless sensor networks;Data integrity;Simulation;Packet loss;Security;Data communication|
|[Dynamic Transmit Awake Feature based Forwarder Election in Mobile WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061101)|P. S. Juliet; A. Raman; R. Valarmathi; G. Brindha|10.1109/ICSSIT55814.2023.10061101|Dynamic Transmit Awake Feature;Mobile Wireless Sensor Networks;Received Signal Strength;Cuckoo search algorithm;Forwarder Election;Wireless communication;Wireless sensor networks;Voting;Heuristic algorithms;Simulation;Throughput;Routing|
|[Mobile Sink based Data-Gathering Method to Minimize the Network Delay](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061125)|K. Vanisree; P. Sivakumar; G. P. Reddy; X. S. A. Shiny|10.1109/ICSSIT55814.2023.10061125|Wireless ad-hoc networks;Lifetime;Cluster head;Energy efficiency;Minimum spanning tree;Mobile sink;Wireless communication;Wireless sensor networks;Packet loss;Quality of service;Throughput;Routing;Energy efficiency|
|[A Comparative Analysis of Machine and Deep Learning Classifiers for Intrusion Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061112)|P. L. S. Jayalaxmi; R. Saha; M. Alazab|10.1109/ICSSIT55814.2023.10061112|Internet of Thing;Industrial;Machine Learning;Deep Learning;Classifier.;Deep learning;Support vector machines;Analytical models;Intrusion detection;Real-time systems;Malware;Internet|
|[Design of Solar Energy Harvester for Smart Home Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060954)|D. Lingaraja; S. P. Kumar; T. Aravind; T. K. Srinivasan; G. D. Ram; S. Ramya|10.1109/ICSSIT55814.2023.10060954|Solar cell;Hole concentration;Smart home;Electric potential;Light intensity;Power system measurements;Power supplies;Photovoltaic cells;Smart homes;Voltage;Solar energy;Production|
|[Security Techniques in Unmanned Air Traffic Management System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061115)|P. Agarwal; S. Sharma; P. Matta|10.1109/ICSSIT55814.2023.10061115|UAV;Drones;Security;Privacy;Air Space Management system.;Wireless communication;Data privacy;Privacy;Space technology;Autonomous aerial vehicles;Security;Air traffic control|
|[A Key Generation Algorithm for Cryptographic Algorithms to Improve Key Complexity and Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060906)|B. Umapathy; G. Kalpana|10.1109/ICSSIT55814.2023.10060906|Cloud Storage Services;Cryptography;Security;Key Generation;Encryption Key;Symmetric Key;Multicore processing;Microprocessors;Software algorithms;Force;Passwords;Metadata;Prediction algorithms|
|[Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061153)|I. A. K. Shaikh; P. V. Krishna; S. G. Biswal; A. S. Kumar; S. Baranidharan; K. Singh|10.1109/ICSSIT55814.2023.10061153|Bayesian optimization;Autoencoder;Cyptocuwency;Predictive models;Machine learning;Training;Government policies;Online banking;Social networking (online);Legislation;Predictive models;Cryptocurrency|
|[An Empricial Study of Brute Force Attack on Wordpress Website](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060966)|P. G. Shah; J. Ayoade|10.1109/ICSSIT55814.2023.10060966|Brute force attack;Kali Linux;WordPress security scanner etc.;Computer hacking;Force;Authentication;Passwords;Web sites;Security;Australia|
|[Smart Patient Records using NLP and Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061111)|A. Dubey; K. Jain; K. Kalaiselvi|10.1109/ICSSIT55814.2023.10061111|NLP;NER;Extraction;POS;Text mining;Sentiment analysis;Social networking (online);Hospitals;Distributed ledger;Blockchains;Registers|
|[An Investigation on Distributed Denial of Service Attack in Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061128)|S. Nirenjena; D. S. Baskaran|10.1109/ICSSIT55814.2023.10061128|Edge Computing;Distributed Denial of service attack;Machine Learning;malware injection;mitigating DDoS attacks;Correlation analysis;data protection.;Industries;Memory;Machine learning;Side-channel attacks;Malware;Servers;Computer crime|
|[A Bitcoin Transaction Network using Cache based Pattern Matching Rules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061064)|G. R. Trivedi; J. V. Bolla; M. Sireesha|10.1109/ICSSIT55814.2023.10061064|Cryptocurrencies;bitcoin transaction network;pattern matching rules;cache memory;transaction processing time;Deep learning;Forensics;Symbols;Bitcoin;Fraud;Blockchains;Data mining|
|[Comprehensive Assessment of Reverse Social Engineering to Understand Social Engineering Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061054)|A. Bishnoi; Garv; S. Bishnoi; N. Gupta|10.1109/ICSSIT55814.2023.10061054|Social Engineering Attacks;Cyber Security;Phishing;Vishing;Scams;Spear phishing;Baiting;Training;Systematics;Computer hacking;Social networking (online);Web and internet services;Passwords;Network security|
|[An Efficient & Secure Approach under Multiple Attack Prone MANET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061008)|I. Nausheen; A. Upadhyay|10.1109/ICSSIT55814.2023.10061008|Mobile Ad-hoc Network (MANET);Security attacks;Routing protocols;AODV (Ad-hoc On-demand Distance Vector);Authorization;Protocols;Ad hoc networks;Batteries;Security;Mobile computing|
|[A Review on Cyber Security and Anomaly Detection Perspectives of Smart Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060871)|M. Ravinder; V. Kulkarni|10.1109/ICSSIT55814.2023.10060871|SG;anomaly;security;power consumption;and energy theft;Dimensionality reduction;Modems;Market research;Smart grids;Research initiatives;Critical infrastructure;Anomaly detection|
|[Optimal PID Control of a Buck Converter using MOBA and IMOBA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060931)|V. V. Pande; S. R. Mhatre; T. P. Nagarhalli|10.1109/ICSSIT55814.2023.10060931|PID controller;Buck converter;Multi-Objective Optimization;Bee algorithm;Improved Bee algorithm;Buck converters;PI control;Simulation;Aerospace electronics;PD control;Transient analysis|
|[An Artificial Intelligence Enabled Self Replication System Against Cyber Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061089)|R. Mokkapati; V. L. Dasari|10.1109/ICSSIT55814.2023.10061089|Artificial intelligence;Self-Replication;Cyber Threats;Parallel Programming;Cyber Security;Performance evaluation;Computers;COVID-19;Pandemics;Security;Medical diagnosis;Artificial intelligence|
|[Ensemble based Dimensionality Reduction for Intrusion Detection using Random Forest in Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060929)|S. Suhana; S. Karthic; N. Yuvaraj|10.1109/ICSSIT55814.2023.10060929|Intrusion detection systenb Dimensionality reduction;Random Forest Classifier;Linear Discriminant Analysis;Dimensionality reduction;Measurement;Wireless networks;Intrusion detection;Benchmark testing;Feature extraction;Real-time systems|
|[Machine Learning for Enhanced Cyber Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060896)|M. Vargheese; G. Nallasivan; D. D. N. Ponkumar; N. Ponnithish; P. K. Devi; M. Arun|10.1109/ICSSIT55814.2023.10060896|Cyber security;DOS attacks;probing;Computer Network;Cyber attacks;Machine learning algorithms;Heavily-tailed distribution;Intrusion detection;Focusing;Machine learning;Gaussian distribution;Reliability|
|[Improved Arbitrary Graphical Password Authentication for Web Application Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060964)|N. S. S. Chaluvadi; L. Chitteti; L. Challa; S. Srithar|10.1109/ICSSIT55814.2023.10060964|Graphical Authentication;Attacks;Validation;Guessing;Passwords.;Dictionaries;Force;Authentication;Passwords;Safety;Registers;Electronic mail|
|[Differentially Distributed Private Intelligence Security in Cybersecurity Infrastructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061092)|P. Jenifer; A. R. Geebin; P. Brundha; E. Manohar|10.1109/ICSSIT55814.2023.10061092|Critical Infrastructure;Machine Learning;Intrusion Detection Systems;Support vector machines;Automation;Machine learning algorithms;Heuristic algorithms;Transfer learning;Intrusion detection;Machine learning|
|[Protecting Bitcoins Frequency Count Against Double-Spend Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060988)|M. S. Nisha; G. Rajakumar; P. Jenifer; B. Benita|10.1109/ICSSIT55814.2023.10060988|Bitcoin;Cryptocurrency;Cyberattacks;P2P Network;Bitcoin;Observers;Size measurement;Proof of Work;Blockchains;Security;Computer crime|
|[Real-Time Cryptocurrency Calculator using an Application Programming Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061038)|S. Imran Hussain; R. Vigneshwaran; K. R. Sabarisrinivas; J. Sakthja|10.1109/ICSSIT55814.2023.10061038|cryptocurrency;blockchain;converter;secure hash algorithm;application programming interface;Costs;Calculators;Organizations;Programming;Real-time systems;Web servers;Cryptocurrency|
|[Identification of Fake Products using Blockchain Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060998)|N. T. Singh; Saurav; V. Sharma; A. Raizada; S. Sharma; N. Pathak|10.1109/ICSSIT55814.2023.10060998|Blockchain;Legitimate;Counterfeit;Decentralised;IoT;Costs;Bitcoin;Product design;Blockchains;Quality assessment;Monitoring|
|[Stock Price Prediction using Zero-Shot Sentiment Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060957)|L. R. Burra; B. P. Koppolu; B. D. Karthik; B. L. N. S. Priya; A. Prasanthi; P. Tumuluru|10.1109/ICSSIT55814.2023.10060957|Stock Price;Prediction;Zero-Shot Classification;Long Short-Term Memory;Sentiment Classification;Support vector machines;Sentiment analysis;Machine learning algorithms;Social networking (online);Blogs;Neural networks;Linear regression|
|[Sentiment Analysis of Product Reviews by using Naive Bayes and Vader Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061002)|Y. L. S. Krishna; P. Paramesh; Y. T. Kumar; A. Gopi|10.1109/ICSSIT55814.2023.10061002|NRC Emotion;Sentimental CNN;Naïve Indexing;Product Analysis;Sentiment analysis;Vocabulary;Analytical models;Social networking (online);Blogs;Tag clouds;Internet|
|[Impact of High-k Materials and Tube Numbers on CNFET Gate’s Performances](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060873)|C. Yalung; D. Tantraviwat; S. Uatrongjit; W. Yamwong|10.1109/ICSSIT55814.2023.10060873|Carbon nanotube field effect transistor (CNFET);high-k materials;propagation delay;genetic algorithm;R-method;Silicon compounds;Dielectric constant;Correlation;Logic gates;CNTFETs;Power dissipation;Electron tubes|
|[Football Player Substitution Analysis using NLP and Survival Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061118)|A. Bera; S. Joshi; A. M. Nair|10.1109/ICSSIT55814.2023.10061118|Survival Analysis;Kaplan-Meier Fitter;Natural language Processing;Sentimental analysis;team’s performance;player analysis;player comparison.;Analytical models;Mental health;Games;Natural language processing;Stability analysis;Hazards;Sports|
|[Activity Scheduling Method for Cloud-based Data Centers with Quality Performance Employing References Queues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061051)|G. Visalaxi; A. Muthukumaravel|10.1109/ICSSIT55814.2023.10061051|Orientation Queue established Cloud Facility Architecture;Active Election tool and Load Balancing;Cloud computing;Program processors;Instruments;Voting;Web and internet services;Computer architecture;Programming|
|[Review on Various Clustering Algorithm on Healthcare based Cloud Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061079)|M. L. Pushpalatha; R. Durga|10.1109/ICSSIT55814.2023.10061079|Clustering;data analysis;machine learning;healthcare data;Measurement;Machine learning algorithms;Pandemics;Stability criteria;Clustering algorithms;Medical services;Machine learning|
|[Blockchain and Cloud-based Technology in Automotive Supply Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060948)|S.Yasmin; G. S. Devi|10.1109/ICSSIT55814.2023.10060948|Blockchain Technology;Automotive sector;Supply Chain;Cloud services;Cryptography;Sustainability;Couplings;Customer services;Supply chains;Insurance;Production;Companies;Blockchains|
|[An Integrated Approach to Improve E-Healthcare System using Dynamic Cloud Computing Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060945)|S. S. Vellela; B. Venkateswara Reddy; K. K. Chaitanya; M. V. Rao|10.1109/ICSSIT55814.2023.10060945|Dynamic Cloud Computing;e-health systems;Electronic Medical Record (EMR);Measurement;Cloud computing;Data privacy;Government;Ubiquitous computing;Heterogeneous networks;Electronic healthcare|
|[Big Data Security: Detect and Prevent the Data from Attacks with Digital Forensic Tools](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060947)|S. Jilani; N. N. Kishore; N. N. Chand; R. D. Varma; G. Raja; P. V. Rao|10.1109/ICSSIT55814.2023.10060947|Big-data;Security;Cyber-attacks;Digital Forensics;Data security;Digital forensics;Big Data;Data science;Computer crime|
|[Data Security and Privacy Issues in Cloud Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060925)|K. Pavani; J. R. S. Sree; A. S. S. Rani; K. Rohini; T. P. Kumar; P. Yellamma|10.1109/ICSSIT55814.2023.10060925|Cloud computing;Cryptography;Symmetric encryption;Asymmetric encryption.;Cloud computing;Data privacy;Privacy;Protocols;Standards organizations;Organizations;Maintenance engineering|
|[A Study on Weather based Crop Prediction System using Big Data Analytics and Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060865)|S. Sahu; T. Daniya; R. Cristin|10.1109/ICSSIT55814.2023.10060865|Agriculture;Big Data Analysis;Visual Representation;K-Means Clustering;and Map Reduce;Temperature distribution;Three-dimensional displays;Wind speed;Crops;Production;Soil;Agriculture|
|[Global Identification Passport: A Unique Cloud based Passport Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061108)|A. R. Raja; T. Anuradha; S. Majji; T. R. Patnala; C. Sushama; D. Kapila|10.1109/ICSSIT55814.2023.10061108|unique passport model;Service Level Agreement;cloud-based passport model;Cloud computing;Technological innovation;Computational modeling;Terrorism;Logic gates;Virtual machining;Safety|
|[An Improved Scheduling Algorithm for Grey Wolf Fitness Task Enrichment with Cloud](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061152)|U. Punia; T. Batra; U. Jindal; B. N; R. G. Tiwari; P. K|10.1109/ICSSIT55814.2023.10061152|Virtualization;Cloud Computing;Fitness Function;S cheduling Algorithm;Technology;Research;Industries;Cloud computing;Technological innovation;Costs;Scheduling algorithms;Quality of service;Task analysis|
|[FSO based MPPT Algorithm for Maximizing Power Output in PV System under Partial Shading Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061009)|S. GayathriMonicka; D. Manimegalai; M. Karthikeyan|10.1109/ICSSIT55814.2023.10061009|Firebug Swarm Optimization;Grasshopper Optimization Algorithm;Grey Wolf Optimization Algorithm;and Particle Swarm Optimization Algorithm;maximum power point tracking;partial shading conditions;Maximum power point trackers;Photovoltaic systems;Analytical models;Optimization methods;Performance analysis;Particle swarm optimization;Matlab|
|[Software Re-Engineering using Cloud Computing Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060891)|A. R. Raja; K. R. Raghunandan; R. Dodmane; A. Dixit; M. S. Kumar; D. Kapila|10.1109/ICSSIT55814.2023.10060891|Cloud Computing;Software reengineering;web applications;Cloud computing challenges;Cloud computing;Visualization;Data analysis;Databases;Software architecture;Object oriented modeling;Quality of service|
|[Robust Extreme Learning Machine based Sentiment Analysis and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061017)|R. Gnanakumaran; D. Rohatgi; A. K. Sampath; N. Nagar; D. Amuthaguka; R. K. Gupta|10.1109/ICSSIT55814.2023.10061017|Sentiment analysis;Data classification;Machine learning;Data mining;Accuracy;Extreme Learning Machine (ELM);Adaptation models;Sentiment analysis;Analytical models;Extreme learning machines;Databases;Computational modeling;Optimization methods|
|[Stock Market Price Prediction Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061120)|S. Mohammed; S. H. Krishna; P. K. Mudalkar; N. Verma; P. Karthikeyan; A. S. Yadav|10.1109/ICSSIT55814.2023.10061120|Stock Market Price Prediction;Machine Learning;Analysis;Machine learning algorithms;Biological system modeling;Time series analysis;Machine learning;Predictive models;Market research;Prediction algorithms|
|[Theoretical Evaluation of Ensemble Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061139)|M. Shah; H. Kantawala; K. Gandhi; R. Patel; K. A. Patel; A. Kothari|10.1109/ICSSIT55814.2023.10061139|Ensemble Learning;Bagging;Boosting;Stacking;Machine Learning;Multiple Classifier;Blend of Experts;Stacking;Neural networks;Merging;Predictive models;Boosting;Ensemble learning;Data mining|
|[Symmetrized Feature Selection with Stacked Generalization based Machine Learning Algorithm for the Early Diagnosis of Chronic Diseases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061062)|S. K. Hegde; R. Hegde; V. Hombalimath; D. Palanikkumar; N. Patwari; V. Dankan Gowda|10.1109/ICSSIT55814.2023.10061062|Stacked generalization;Chronic Diseases;Genetic Algorithm;Metaheuristic;Machine Learning;Machine learning algorithms;Metaheuristics;Transfer learning;Stacking;Medical services;Predictive models;Feature extraction|
|[Machine Learning for Identification of Immedicable Renal Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061031)|A. Prasad; V. Asha; B. Saju; S. M. Manoj; P. Manoj Kumar; B. N. Ramesh|10.1109/ICSSIT55814.2023.10061031|Immedicable;Renal Disease;Machine Learning;Glomerular Filtration Rate;Training;Machine learning algorithms;Filtration;Neural networks;Decision making;Machine learning;Prediction algorithms|
|[Machine Learning based Plant Disease Identification by using Hybrid Naïve Bayes with Decision Tree Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061099)|N. K. Yetukuri; K. Maddali; N. Jayapandian|10.1109/ICSSIT55814.2023.10061099|Artificial Intelligence;Convoluted Neural Networks;Decision Tree;Machine Learning;Naïve Bayes;Plant diseases;Solid modeling;Machine learning algorithms;Shape;Redundancy;Turning;Agriculture|
|[Analysis on the Performance of Machine Learning Models for Forest Fire Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060870)|D. C. J. Kani; S. Saudia|10.1109/ICSSIT55814.2023.10060870|Forest Fire;Independent Features;Machine Learning Models;Radio frequency;Support vector machines;Analytical models;Moisture;Forestry;Receivers;Predictive models|
|[Deep Learning-based Speech Recognition Algorithm for a Bionic Arm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060985)|A. N. V. Pranav; R. C.; M. V. B. Kumar; A. K. Dash|10.1109/ICSSIT55814.2023.10060985|Bionic arm;Convolutional Neural Network;Mel Frequency Cepstral Coefficient;Arduino nano ble 33;Actuators;Torque;Cepstral analysis;Biological system modeling;Speech recognition;Arms;Libraries|
|[Comparative Analysis of Facial Forgery Detection using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060980)|R. R. Sekar; K. R. Anne|10.1109/ICSSIT55814.2023.10060980|Facial Anti-Spoofing;Face Presentation Attack;Face Forgery Detection;Deep Fake Detection;Deep learning;Training;Deepfakes;Three-dimensional displays;Databases;Face recognition;Forgery|
|[Design of Low-Power High-Gain Transimpedance Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060885)|R. R. Kumar; S. Sharma; K. Sharma; A. Sharma|10.1109/ICSSIT55814.2023.10060885|High-gain;Low-power;Transimpedance amplifer;Nano-current sensing;Optical fiber amplifiers;Temperature sensors;Spectroscopy;Microwave integrated circuits;Microwave communication;Microwave circuits;Optical fiber communication|
|[Internet of Things (IoT) and Machine Learning based Optimized Smart Irrigation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061080)|N. S. Royal; W. Parre; S. C. Bandarupalli; M. R. Achary; D. V. C. Jadala; D. M. Kavitha|10.1109/ICSSIT55814.2023.10061080|Internet of Things;Cloud Computing;Sensors;Machine learning.;Temperature sensors;Machine learning algorithms;Crops;Moisture;Production;Humidity;Machine learning|
|[Machine Learning Algorithms for Disease Prediction Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060987)|M. Govindaraj; V. Asha; B. Saju; M. Sagar; Rahul|10.1109/ICSSIT55814.2023.10060987|Disease Prediction;Symptoms;Challenging;medical sciences;Data mining.;Machine learning algorithms;Prototypes;Medical services;Machine learning;Data mining;Medical diagnostic imaging;Diseases|
|[An Improved Machine Learning Approach to Detect Real Time Face Mask](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060976)|V. Vipul; N. Manoj; L. S. Nair; S. Siji Rani|10.1109/ICSSIT55814.2023.10060976|Deep Learning;Face detection;Support Vector Machine;Neural Network;Support vector machines;Training;COVID-19;Machine learning algorithms;Protocols;Neural networks;Machine learning|
|[Weather Prediction Analysis using Classifiers and Regressors in Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061073)|C. B. S; B. Shreegagana; B. H. S; I. Karanth; A. R. K. P; G. S|10.1109/ICSSIT55814.2023.10061073|Weather Forecast;Machine Learning (ML);Prediction;Classification;Regression;Comparative study;Precipitation;Temperature;Biological system modeling;Wind speed;Weather forecasting;Forestry;Mathematical models|
|[User Recognition for App Rating using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061107)|J. Y; S. M; V. K. M; S. R; A. A. S|10.1109/ICSSIT55814.2023.10061107|YOLO;Convolutional Neural Network;Face Recognition;Open CV;Natural Language Processing;Machine learning algorithms;Webcams;Face recognition;Machine learning;Fingerprint recognition;Search engines;Real-time systems|
|[Application using Machine Learning to Promote Women’s Personal Health](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061126)|P. Arunkumar; K. Abarna; N. Nagamithra; G. Suweatha; P. Varssha|10.1109/ICSSIT55814.2023.10061126|menstrual irregularities;irregular periods;Invitro Fertilization;Polycystic Ovarian Disease.;Machine learning algorithms;Databases;Text categorization;Medical services;Programming;Chatbots;Mobile applications|
|[Design of a Machine Learning based Data Analytics Engine to Improve Decision Making Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061028)|Y. S. Pethe; P. R. Dandekar|10.1109/ICSSIT55814.2023.10061028|Grey Wolf optimization;Decision Making Process;Analytical Hierarchy Processing;Iterative Learning;Accuracy;Precision;Delay;Scenanos;Correlation;Image processing;Machine learning;Real-time systems;Complexity theory;Large-scale systems;Iterative methods|
|[Multiple Disease Prediction System using Machine Learning and Streamlit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060903)|L. D. Gopisetti; S. K. L. Kummera; S. R. Pattamsetti; S. Kuna; N. Parsi; H. P. Kodali|10.1109/ICSSIT55814.2023.10060903|Single user intetface;Diabetes;Heart disease;Chronic kidnev disease;Cancer;K Nearest Neighbor;Support Vector Machine;Decision Tree;Random Forel Logistic Regression;Gaussian naive bayes;Heart;Support vector machine classification;User interfaces;Predictive models;Chronic kidney disease;Diabetes;Classification algorithms|
|[ParPER: A Partitioned Prioritized Experience Replay in Multi-Agent Setting of Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061044)|T. Sarkar; S. Kalita|10.1109/ICSSIT55814.2023.10061044|Multi-Agent Reinforcement Learning (MARL);Multi-Agent Deep Deterministic Policy Gradient (MADDPG);Prioritized Experience Replay(PER);Partitioned PER(ParPER);Training;Reinforcement learning;Stability analysis|
|[DDOS Attack Detection with Machine Learning: A Systematic Mapping of Literature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060897)|S. Singh; M. Gupta; D. K. Sharma|10.1109/ICSSIT55814.2023.10060897|Cybersecunrity;DDOS;IOT;intrusion detection mechanisms;machine learning;SDN;Analytical models;Machine learning algorithms;Systematics;Neural networks;Telecommunication traffic;Denial-of-service attack;Throughput|
|[Implementation of a Pre-Crash Detection System using Node MCU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061016)|R. Balaji V; S. Sanjay; N. Sakthishree G; T. Sushruthan|10.1109/ICSSIT55814.2023.10061016|Internet of Things;Node Micro Controller Unit;Arduino;Integrated Development Environment;Velocity control;Automotive applications;Prototypes;Sonar;Alarm systems;Software;Internet|
|[Sentiment Analysis: Quantitative Evaluation of Machine Learning Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061130)|H. Singh; D. Srivastava|10.1109/ICSSIT55814.2023.10061130|Sentiment analysis;Machine learning;NLP;Opinion mining.;Support vector machines;Measurement;Sentiment analysis;Analytical models;Machine learning algorithms;Text categorization;Machine learning|
|[Machine Learning and Deep Learning based Intrusion Detection in Cloud Environment: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060868)|A. Vinolia; N. Kanya; V. N. Rajavarman|10.1109/ICSSIT55814.2023.10060868|Cloud computing;Intrusion Detection System (IDS);Deep learning;Machine learning;Soft computing;Data mining;Deep learning;Cloud computing;Computer hacking;Soft sensors;Fitting;Intrusion detection;Market research|
|[Building Accurate Machine Learning Models for Predicting the Habitability of Exo-Planets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061122)|C. Raminaidu; V. Priyadarshini; C. R. Swaroop; R. S. Shankar|10.1109/ICSSIT55814.2023.10061122|Machine Learning (ML);Exo-Planents;Stratified KFold;Data Mining (DM);Statistical Techniques (ST).;Support vector machines;Temperature dependence;Machine learning algorithms;Temperature;Planets;Buildings;Extrasolar planets|
|[Short Analysis of Machine Learning and Deep Learning Techniques used for Glaucoma Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060909)|E. Archana; S. Geetha; G. V. S. George|10.1109/ICSSIT55814.2023.10060909|Glaucoma Detection;Machine learning;Deep learning;Algonthmic Classification;Datasets;Petfomwnce Measures;Implementation Tools;Research Challenges with Future Research Directions;Deep learning;Optical fibers;Integrated optics;Visualization;Machine learning algorithms;Biomedical optical imaging;Current measurement|
|[Convolutional Neural Networks (CNN) based Brain Tumor Detection in MRI Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060968)|C. Rajeshkumar; K. R. Soundar; M. Sneha; S. S. Maheswari; M. S. Lakshmi; R. Priyanka|10.1109/ICSSIT55814.2023.10060968|Convolutional Neural Network;Magnetic Resonance Imaging;Malignant;Medical Practice;Deep learning;Training;Magnetic resonance imaging;Predictive models;Brain modeling;Convolutional neural networks;Task analysis|
|[A Performance Analysis of Machine Learning Algorithms for Malaria Parasite Detection using Microscopic Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061060)|T. K. Kundu; D. K. Anguraj|10.1109/ICSSIT55814.2023.10061060|Malaria disease;Plasmodium parasite;Conventional Microscopy;Peripheral blood smear;Different Machine Learning Algorithms Introduction;Machine learning algorithms;Tuberculosis;Microscopy;Sensitivity and specificity;Feature extraction;Performance analysis;Blood|
|[AMLGB-: Efficient Model for Skin Disease Detection and Classification using Adaptive Machine for Light Gradient Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060982)|A. M. Vidhyalakshmi; M. Kanchana|10.1109/ICSSIT55814.2023.10060982|Skin disease precision;Support Vector Machine;Deep Learning;Light Gradient Boosting Machine;Ensemble architecture;Adaptation models;Sensitivity;Computational modeling;Computer architecture;Transforms;Boosting;Skin|
|[Diabetes Prediction using Extreme Learning Machine: Application of Health Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061058)|S. N. B. Reddy; K. V. Narasimha Reddy; S. N. Tirumala Rao; K. S. M. V. Kumar|10.1109/ICSSIT55814.2023.10061058|Health informatics;Indian Diabetes dataset;Diabetes prediction;Extreme learning machine;Analytical models;Extreme learning machines;Symbols;Predictive models;Feature extraction;Data models;Diabetes|
|[Intrusion Detection System in Vehicular Network using Deep Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060872)|N. Karyemsetty; T. Syamsundarao; S. Venkata Kishore Babu; D. Suresh Kumar; J. Goddu; B. Samatha|10.1109/ICSSIT55814.2023.10060872|Deep learning;Intrusion Detection System Vehicular Network;V2V Communication;Deep learning;Training;Tensors;Neural networks;Intrusion detection;Safety;Vehicle dynamics|
|[A Review on Data Balancing Techniques and Machine Learning Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061154)|G. Parmar; R. Gupta; T. Bhatt; G. J. Sahani; B. Y. Panchal; H. Patel|10.1109/ICSSIT55814.2023.10061154|Data Mining;Machine Learning;Over Sampling;Under Sampling;Parallelization;Class Bias;Protected Attributes;Distributed databases;Machine learning;Data mining|
|[89nW Class AB Bulk Driven Quasi Floating Gate Two Stage Operational Transconductance Amplifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061065)|R. R. Kumar; K. Sharma; A. Sharma|10.1109/ICSSIT55814.2023.10061065|Biological signals;Bulk driven quasi floating gate;Figure of merit;Operational transconductance amplifier;Resistors;Total harmonic distortion;Low voltage;Power demand;Capacitors;CMOS technology;Biology|
|[Machine Learning Models for News Article Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061095)|B. Naseeba; N. P. Challa; A. Doppalapudi; S. Chirag; N. S. Nair|10.1109/ICSSIT55814.2023.10061095|Text Classification;Machine Learning;Categorization;Structured Data;Support vector machines;Text categorization;Supervised learning;Machine learning;Natural language processing;Data models;Labeling|
|[Adding Knock Code Technology as a Third Authentication Element to a Global Two-factor Authentication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060915)|M. N. Hossain; S. F. U. Zaman; M. S. Sayeed|10.1109/ICSSIT55814.2023.10060915|Data protection;network security;one-time passwords (OTPs);two-factor authentication (2FA);three-factor authentication (3FA);hacking;and knock codes (KNCs);Privacy;Codes;Authentication;Passwords;Mobile applications|
|[Animal Intrusion Detection using Deep Learning for Agricultural Fields](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060984)|A. K. Thomas; P. Poovizhi; M. Saravanan; K. Tharageswari|10.1109/ICSSIT55814.2023.10060984|Animal Intrusion;Invariant Feature Extraction;SlowFast Architecture;Internet of Things;Sensor Monitoring;Deep learning;Wireless communication;Wireless sensor networks;Animals;Intrusion detection;Feature extraction;Spatial databases|
|[Bankruptcy Prediction using Emperor Penguin Optimizer with Deep Learning Model on Qualitative Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060962)|V. Abrol; P. Singh; R. Subhashini; A. S. Kumar; K. Singh; B. R. Mannar|10.1109/ICSSIT55814.2023.10060962|Bankruptcy;Data classification;Emperor Penguin Optimizer;Long Short Term Memory(LSTM) model;Adam optimizer;Deep learning;Decision making;Predictive models;Benchmark testing;Prediction algorithms;Artificial intelligence;Bankruptcy|
|[Blockchain and Machine Learning Approaches for Credit Card Fraud Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060999)|A. Peter; K. Manoj; P. Kumar|10.1109/ICSSIT55814.2023.10060999|Blockchain;credit card;One Time Password;Money ransaction;SMS;security;Smart contracts;Machine learning;Transforms;Switches;Passwords;Credit cards;Blockchains|
|[Review on Malware Classification and Malware Detection Using Transfer Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061076)|V. Priya; A. Sathya Sofia|10.1109/ICSSIT55814.2023.10061076|Malware Detection Algorithms;Zero-Day Malware Detection;Grayscale Images;Transfer Learning;Malware Classification;Training;Computers;Transfer learning;Production;Gray-scale;Malware;Classification algorithms|
|[Detection and Diagnosis of Fetus Ultra Sound Images using Deep Learning Classification Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061088)|S. Saranya; V. Keerthiga|10.1109/ICSSIT55814.2023.10061088|Deep Learning;Gabor Transform;Down Syndrome (DS);Deep learning;Image segmentation;Sensitivity;Sonogram;Nose;Transforms;Feature extraction|
|[Computer Aided Diagnosis for Cervical Cancer Screening using Monarch Butterfly Optimization with Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060959)|C. Suguna; S. P. Balamurugan|10.1109/ICSSIT55814.2023.10060959|Cervical cancer;Transfer learning;Pap smear imwges;Deep learning;Monarch butterfly optimization;Adaptation models;Databases;Computational modeling;Feature extraction;Boosting;Classification algorithms;Optimization|
|[Grey Wolf Optimizer with Deep Learning based Short Term Traffic Forecasting in Smart City Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061127)|R. Jegadeesan; E. Vijayakrishna Rapaka; K. Himabindu; N. R. Behera; A. K. Shukla; A. K. Dangi|10.1109/ICSSIT55814.2023.10061127|Deep learning;ITS;Smart cities;Traffic flow prediction;Grey wolf optimizer;Deep learning;Measurement;Smart cities;Transportation;Predictive models;Prediction algorithms;Real-time systems|
|[Analysis of Emotion Detection of Images using Sentiment Analysis and Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060930)|Y. S. Deshmukh; N. S. Patankar; R. Chintamani; N. Shelke|10.1109/ICSSIT55814.2023.10060930|Emotion Detection;Sentiment Analysis;HAAR Classifier;Face Emotion Recognizer;Convolutional Neural Network;Cepstral Coefficient;Sequential Model;Deep learning;Visualization;Emotion recognition;Analytical models;Sentiment analysis;Machine learning algorithms;Architecture|
|[Comparison of Different Shapes MEMS based Piezo Electric Pressure Sensor for Smart Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061138)|S. S. Nath; D. Lingaraja; P. Aravind; P. Thangavel; P. Arun Kumar; G. D. Ram|10.1109/ICSSIT55814.2023.10061138|Pressure sensor;COMSOL Multi-physics;Piezoelectric sensor;Square;Circle;Triangle;Deflection.;Pressure sensors;Micromechanical devices;Industries;Shape;Software;Software reliability;Smart devices|
|[Multilingual Sentiment Analysis using Deep-Learning Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060993)|P. Dominic; N. Purushothaman; A. S. A. Kumar; A. Prabagaran; J. Angelin Blessy; A. John|10.1109/ICSSIT55814.2023.10060993|Sentiment analysis;Deep learning;Transformers;BERT;Text Summarization;Named Entity Recognition;Neural machine translation;Mbart;Tableau;social media analytics Text pre-processing;Sentiment analysis;Analytical models;Social networking (online);Oils;Government;Bit error rate;Machine learning|
|[Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061135)|C. Rajyalakshmi; K. LakshmiNadh; M. S. Reddy|10.1109/ICSSIT55814.2023.10061135|Speech emotion;facial emotion;convolutional neural network;Mel Frequency Cepstral Coefficient;speech emotion recognition;Emotion recognition;Visualization;Computational modeling;Predictive models;Ions;Feature extraction;Convolutional neural networks|
|[Machine Learning Framework for Women Safety Prediction using Decision Tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060997)|P. S. Sowmika; S. S. N. Rao; S. Rafi|10.1109/ICSSIT55814.2023.10060997|online social networking;natural language toolbox kit;term frequency-inverse document frequency;women safety prediction using decision tree;Training;Protocols;Social networking (online);Blogs;Urban areas;Feature extraction;Tokenization|
|[Comparative Analysis of Machine Learning Algorithms for Weather Prediction using Error Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061132)|N. Kaur; N. Singh|10.1109/ICSSIT55814.2023.10061132|Weather;Prediction;Linear Regression;Polynomial Regression;Cart;Random Forest;Forest Depth;Accuracy;Radio frequency;Machine learning algorithms;Weather forecasting;Null value;Predictive models;Data models;Cleaning|
|[Significance of Data Augmentation in Identifying Plant Diseases using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061007)|T. Bhargavi; D. Sumathi|10.1109/ICSSIT55814.2023.10061007|Data Augmentation;Plant diseases detection;Deep Learning;Residual network;Convolutional layer;Training;Deep learning;Plant diseases;Computational modeling;Crops;Production;Data models|
|[Spam SMS (or) Email Detection and Classification using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060908)|V. Dharani; D. Hegde; Mohana|10.1109/ICSSIT55814.2023.10060908|Spam SMS;Spam Email;Machine Learning;Naïve Bayes;Cyber Crime;Cyber Scam;Adaptation models;Visualization;Machine learning algorithms;Unsolicited e-mail;Passwords;Machine learning;Predictive models|
|[Artificial Rabbit Optimization with Improved Deep Learning Model for Plant Disease Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061156)|K. Jayaprakash; S. P. Balamurugan|10.1109/ICSSIT55814.2023.10061156|Agnculture;Plant disease recognition;Computer vision;Deep learning;Artificial Rabbit optimization;Deep learning;Plant diseases;Rabbits;Filtering;Pipelines;Metaheuristics;Crops|
|[A Survey on Machine Learning to Detect Creation of Fake Identities by Human vs. Bots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060939)|L. V; K. Manasa; P. S. Soundarya; R. Renuka; R. Subanandhana|10.1109/ICSSIT55814.2023.10060939|Fake account detection by humans;Detection by bots;Machine learning techniques.;Support vector machines;Social networking (online);Multimedia Web sites;Information sharing;Companies;Chatbots;Explosives|
|[Supply Chain for Safe & Timely Distribution of Medicines using Blockchain & Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061049)|Y. Shah; Y. Verma; U. Sharma; A. Sampat; V. Kulkarni|10.1109/ICSSIT55814.2023.10061049|Blockchain;Machine Learning;Pharmaceutical Supply Chain Management;Counterfeit Drugs;Drugs;Machine learning algorithms;Supply chains;Decision making;Medical services;Machine learning;Real-time systems|
|[Theoretical Evaluation of Machine Learning Approaches for Hotel Recommendation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061074)|S. N. Parikh; J. Shah; K. Sutaria; B. Vala|10.1109/ICSSIT55814.2023.10061074|Hotel Rating;Data Balancing;Recursive Feature Elimination (RFE);Review Analysis;Customer Satisfaction;Machine Learning Algorithm (SVM;Naive Bayes;KNN);Support vector machines;Machine learning algorithms;Urban areas;Entertainment industry;Information filters;Recommender systems;Random forests|
|[TCAD Analysis of Substrate Thickness of a 10nm Vertical Double Gate SOI n-Type MOSFET](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060965)|S. Varma; C. P. Chowdari; A. K. Panigrahi|10.1109/ICSSIT55814.2023.10060965|10nm Field Effect Transistor;Double Gate Transistor;Low Area;Analysis of Substrate Thickness;Semiconductor device modeling;Resistance;Silicon-on-insulator;Voltage;Logic gates;Software;Silicon|
|[A Machine Learning based Malware Classification Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060914)|S. Depuru; P. Hari; P. Suhaas; S. R. Basha; R. Girish; P. K. Raju|10.1109/ICSSIT55814.2023.10060914|Malware;Malware Classification;Machine learning;Convolutional Neural Network;Prediction;Training;Visualization;Computational modeling;Neural networks;Machine learning;Gray-scale;Malware|
|[Hotel Recommendation using Feature and Machine Learning Approaches: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061034)|A. Patel; N. Shah; V. Bakaraniya Parul; K. S. Suthar|10.1109/ICSSIT55814.2023.10061034|Hotel Rating;Data Balancing;Recursive Feature Elimination;Review Analysis;Customer Satisfaction;Machine Learning Algorithm (Support Vector Machine, Nave Bayes, K-Nearest Neighbor);Support vector machines;Crowdsourcing;Machine learning algorithms;Tourism industry;Stars;Entertainment industry;Information filters|
|[Forest Fire Detection and Temperature Monitoring Alert using IoT and Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061086)|V. Venkataramanan; G. Kavitha; M. R. Joel; J. Lenin|10.1109/ICSSIT55814.2023.10061086|Fire detection;Temperature Monitoring;GPRS Module;Machine learning algorithm;Data Analytics;IoT;Temperature sensors;Temperature measurement;Machine learning algorithms;Fires;Forestry;Machine learning;Real-time systems|
|[A Machine Learning Approach to Predict Skin Diseases and Treatment Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061150)|S. Murugan; S. R. Srividhya; S. P. Kumar; B. Rubini|10.1109/ICSSIT55814.2023.10061150|Skin disease prediction;K- Nearest Neighbour;Naive Bayes Algorithm;Pediatrics;Microorganisms;Machine learning;Aging;Prediction algorithms;Skin;Naive Bayes methods|
|[Analysis and Detection of Fake Profile Over Social Media using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061020)|M. Kathiravan; S. J. Parvez; R. Dheepthi; R. Jayanthi; S. Gowsalya; R. V. Sekhar|10.1109/ICSSIT55814.2023.10061020|Text Mining;Online Social Network;Fake Account Detection;Artificial Neutral Networks;Natural Language Processing;Analytical models;Social networking (online);Machine learning;Media;Ontologies;Chatbots;Behavioral sciences|
|[Artificial Intelligence based Electronics Engineering Software Application System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060898)|T. Kittappa; C. Sandhya; V. Krishnan; S. Sharma; D. Khubalkar; G. Ramachandran; T. Muthumanickam|10.1109/ICSSIT55814.2023.10060898|Application;Artificial Intelligence;Industry;Wireless network security;Manufacturing industries;5G mobile communication;Smart cities;Software algorithms;Network security;Software;Communications technology|
|[Medical Data Classification using a Gravitational Search Algorithm and Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060866)|P. D. K. Reddy; M. Umaselvi; D. Devarajan; S. Jha; Amuthaguka; R. K. Gupta|10.1109/ICSSIT55814.2023.10060866|Healthcare;Medical data classification;Deep Support Vector Machine (DSVM);Machine learning;parameter tuning;Support vector machines;Analytical models;Machine learning algorithms;Benchmark testing;Data models;Classification algorithms;Reliability|
|[Super Artificial Intelligence Medical Healthcare Services and Smart Wearable System based on IoT for Remote Health Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060928)|S. Elango; L. Manjunath; D. Prasad; T. Sheela; G. Ramachandran; S. Selvaraju|10.1109/ICSSIT55814.2023.10060928|Health monitoring;Telemedicine;Internet of Things;Wearable technology;Temperature sensors;Wearable computers;Medical services;Internet of Things;Artificial intelligence;Biomedical monitoring;Task analysis|
|[Age and Gender Detection to Detect the Manipulated Images using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060905)|B. S. Chowdary; V. N. Subhadra; S. Kavitha|10.1109/ICSSIT55814.2023.10060905|Convolutional Neural Network;OpenCV;Deep Convolution Neural Network;Principal Component Analysis;Supervised Machine learning;Java Database Connectivity;Convolution;Social networking (online);Supervised learning;Training data;Benchmark testing;Feature extraction;Convolutional neural networks|
|[Visual Impaired Assist Mobile App using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061033)|S. Suresh Kumar; B. Prasana; S. Pratheep Kumar|10.1109/ICSSIT55814.2023.10061033|Face recognition;Objects detection;SOS;Route Assist;Location;Visualization;Navigation;Face recognition;Object detection;Cameras;Feature extraction;Mobile applications|
|[An Automatic Solar Panel Performance Monitoring System and Load Control using IoT Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060886)|K. K. Kumar; F. A. J. Vaz; M. W. Iruthayarajan; A. S. Kamaraja|10.1109/ICSSIT55814.2023.10060886|Bolt IoT ESP8266;Battery Performance;Load Protection and Control;Bolt Cloud;Relay Driver;Voltage Regulator.;Temperature sensors;Temperature measurement;Voltage fluctuations;Fasteners;Time measurement;Solar panels;Relays|
|[Taxonomy on DSP Based Islanding Detection Techniques in Micro Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061078)|S. Veerababu; R. Francis; V. Srinivasarao|10.1109/ICSSIT55814.2023.10061078|Distributed Generation;Signal processing;Islanding detection;Power Quality;Renewable energy sources;Islanding;Voltage fluctuations;Voltage measurement;Taxonomy;Signal processing;Time measurement|
|[Smart Farming Monitoring Through Artificial Intelligence for Enhancement of Harvest Quality and Productivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060943)|Y. Y. S. Alawfi; G. R. D; M. Q. M. Almaawali|10.1109/ICSSIT55814.2023.10060943|Internet of Things;Artificial Intelligence;Harvest;Environment;Farming;Productivity;Temperature measurement;Smart agriculture;Economic indicators;Soil measurements;Machine learning;Soil|
|[Optimization of Quality Power Distribution System using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061066)|S. P. Awasthi; S. Singh; U. Pilania; M. Kumar; A. S. Sindhu; A. S. Sarika|10.1109/ICSSIT55814.2023.10061066|Photovoltaic (PV) Module;Maximum Power Point Tracker (MPPT) controller;Solar;simulation;Artificial Intelligence;Maximum power point trackers;Photovoltaic systems;Software packages;Power quality;Mathematical models;Behavioral sciences;Steady-state|
|[Game Theory based Artificial Player for Morabaraba Game](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060972)|M. Nkongolo|10.1109/ICSSIT55814.2023.10060972|Ludology;Game Theory;Knowledge Representation and Reasoning;Artificial Intelligence;Indigenous Knowledge Games;Computers;Computational modeling;Current measurement;Bibliographies;Games;Complexity theory;Game theory|
|[Fire Detection and Analysis in Video Surveillance Application using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061004)|C. C. Kiliroor; M. H. Venkatkumar; R. Rakesh|10.1109/ICSSIT55814.2023.10061004|Fire detection;fire localization;convolutional neural networks;fire disaster;fire region segmentation;Location awareness;Computer vision;Image edge detection;Computer network reliability;Estimation;Video surveillance;Convolutional neural networks|
|[Plant Disease Classification using CNN-LSTM Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061003)|E. A. Devi; S. Gopi; U. Padmavathi; S. R. Arumugam; S. P. Premnath; D. Muralitharan|10.1109/ICSSIT55814.2023.10061003|Plant Disease;Convolutional Neural Network (CNN);Long Short-Term Memory(LSTM);Deep Learning;Plant diseases;Transfer learning;Neural networks;Training data;Food security;Production;Feature extraction|
|[Hyper Automation and Augmented Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060938)|A. Mathew; H. Alex|10.1109/ICSSIT55814.2023.10060938|Hyper-Automation;Augmented Intelligence;Robotic Process Automation (RPA);Artificial Intelligence (AI);Artificial Neural Networks (ANN);Industries;Training;Ethics;Automation;Human intelligence;Decision making;Government|
|[Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061119)|S. G. P; K. N|10.1109/ICSSIT55814.2023.10061119|Cloud Computing;Named Entity Recognition;spaCy;Convolutional Neural Network;Random Forest Conditional Random Field;Deep learning;Training;Radio frequency;Databases;Data security;Forestry;Predictive models|
|[Neonatal Jaundice Identification Over the Face and Sclera using Graph Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060877)|N. Madhusundar.; R. Surendran.|10.1109/ICSSIT55814.2023.10060877|Computer Vision;Deep learning;Spatial and Spectral Graph Neural network;SPAGNN;SPEGNN;Deep learning;Pregnancy;Pediatrics;Computer vision;Image color analysis;Graph neural networks;Faces|
|[A Review Study of AI Enabled Computer Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060883)|M. Mirza; K. Venusamy; M. Akbar; S. Kannadhasan; A. Judice; S. Ganesh|10.1109/ICSSIT55814.2023.10060883|Network Quality of Service;security;network management;Deep learning models;computer network technology;Artificial Intelligence;Deep learning;Computational modeling;Heuristic algorithms;Neural networks;Network security;Routing;Computer networks|
|[Smart Attendance System using Dlib Pre-Trained Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060958)|D. Nivedhitha; G. Madhumitha; J. Janani Sri; S. Johnson; R. Priyanka Pramila; D. M. Divya|10.1109/ICSSIT55814.2023.10060958|Face Recognition;Support Vector Machine(SVM);Histogram of Oriented Gradients (HOG);Covid-19;Support vector machines;COVID-19;Histograms;Pandemics;Hospitals;Face recognition;Manuals|
|[Analysis of Silicon-on-Insulator based Planar Optical Waveguide](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061091)|A. Manissh; M. Choudhary; R. Rishi; A. A. Linsie|10.1109/ICSSIT55814.2023.10061091|Silicon-On-Insulator;Optical Waveguide;Modulator;Optical losses;Silicon compounds;Integrated optics;Optical interferometry;Optical polarization;Silicon-on-insulator;Optical variables control|
|[SQL Injection Detection using Machine Learning and Convolutional Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061019)|J. Misquitta; S. Asha|10.1109/ICSSIT55814.2023.10061019|SQL injection;CNN;Machine Learning;userinterface;Naive Bayes;neural networks;Support vector machines;Machine learning algorithms;Codes;Databases;SQL injection;User interfaces;Real-time systems|
|[A Network Monitoring Model based on Convolutional Neural Networks for Unbalanced Network Activity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060879)|A. Srinath Reddy; S. P. Praveen; G. Bhargav Ramudu; A. Bhanu Anish; A. Mahadev; D. Swapna|10.1109/ICSSIT55814.2023.10060879|Intrusion Detection System;Deep learning;Convolution Neural Networks;Cybersecurity;Linear Discriminant analysis;Protection;Data Security;False Positives;False Negatives;True Negative rate;Measurement;Machine learning algorithms;Face recognition;Current measurement;Optimization methods;Intrusion detection;Machine learning|
|[ResNet50-based Classification of Coffee Cherry Maturity using Deep-CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061006)|S. Raveena; R. Surendran|10.1109/ICSSIT55814.2023.10061006|Deep Convolution Neural Network (DCNN);Types of coffee Cherry;Transfer Learning;Stages of coffee Cherry;Image Processing;Order-Statistics Filters;Residual Network (ResNet50);Deep learning;Convolution;Transfer learning;Crops;Radial basis function networks;Manuals;Classification algorithms|
|[Usage of Machine Learning and Artificial Intelligence in Industry 4.0 and Banking Sector](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060952)|S. Balakrishna; V. Arulkumar; M. Srihari; C. Rohith|10.1109/ICSSIT55814.2023.10060952|Industry 4.0;Digital Economy;Banking 4.0;FinTech;Industries;Biological system modeling;Banking;Machine learning;Market research;Fourth Industrial Revolution;Security|
|[The Role of AI in “Oral Cancer” Detection: A Systematic Review and Future Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061090)|S. Ramya; R. I. Minu|10.1109/ICSSIT55814.2023.10061090|Artificial Intelligence;Machine Learning;Deep Learning;Non-Invasion;Artificial Neural Network;S upport Vector Machine;Deep learning;Support vector machines;Vocabulary;Systematics;Neural networks;Medical services;Cancer detection|
|[Fall Detection Technique for Older Individuals based on Deep Layered Neural Networks Embedded with Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061005)|J. Deepika; J. Akilandeswari|10.1109/ICSSIT55814.2023.10061005|Machine Learning;Convolutional Neural Network;Transfer Learning;Fall Detection;Accelerometers;Training;Time-frequency analysis;Computational modeling;Transfer learning;Wavelet analysis;Feature extraction|
|[An Optimal Bidirectional Gated Recurrent Neural Network Model for Crop Yield Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060890)|R. ThangaSelvi; M. Sathish|10.1109/ICSSIT55814.2023.10060890|Crop yield prediction;Deep learning;Machine learning;Forecasting;Parameter tuning;Recurrent neural networks;Biological system modeling;Crops;Predictive models;Logic gates;Benchmark testing;Soil|
|[Application of Bayesian Regularization ANN for the Classification of HLHS Anomaly Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060986)|D. Kavitha; R. Geetha|10.1109/ICSSIT55814.2023.10060986|Enhanced Perona Malik Filter;K-means clustering;Bayesian Regularization algorithm;Heart;Training;Ultrasonic imaging;Speckle;Fetus;Numerical models;Bayes methods|
|[Iris Recognition -A Multilayer Algorithm based: CNN and Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061083)|S. Sesha Vidhya; P. Priyadharshini; K. G. Shanthi; N. Pujasaini; G. Neeta; D. Reshmika|10.1109/ICSSIT55814.2023.10061083|Convolution Neural Networks (CNN);ALEXNET;Multilayer Convolution Neural Networks (MCNN);Deep Belief Network (DBN).;Visualization;Convolution;Databases;Biological system modeling;Simulation;Neural networks;Transfer learning|
|[Implementation of Kidney Stone Detection using CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061142)|K. Arunkumar.; S. Eshwar.; D. Mohammed Saleem.; C. Vamsikrishna|10.1109/ICSSIT55814.2023.10061142|Convolutional Neural Network (CNN);Region of Interest (ROI);kidney disease detection;Pathology;Sensitivity;Pain;Veins;Probabilistic logic;Particle measurements;Classification algorithms|
|[Delay Enhancement of Wallace Tree Multiplier with Binary to Excess-1 Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061043)|G. C. Ram; M. V. Subbarao; D. R. Varma; M. P. Kumar|10.1109/ICSSIT55814.2023.10061043|Array multiplier;Binary to Excess-1 code (BEC);Carry select adder (CSLA);Kogge Stone Adder (KSA);Ripple carry adder (RCA);Wallace multiplier (WM);Codes;Microprocessors;Memory management;Delays;Hardware design languages;Adders;Standards|
|[Gene Expression Analysis Using SVM And KNN Classifiers On Various Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061014)|K. S. Dhanush; S. Sudha|10.1109/ICSSIT55814.2023.10061014|Gene expression;classifier;prediction;accu racy;feature representation;Support vector machines;Training;Smoothing methods;Lung;Predictive models;Gene expression;Labeling|
|[Application of NARX Neural Network for Predicting Suitable crop for Cultivation- An Comparative analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060923)|T. Sivaranjani; S. P. Vimal|10.1109/ICSSIT55814.2023.10060923|Crop Prediction;NARX;ANN;LevenbergMarquardt algorithm;Machine Learning;Training;Productivity;Soil measurements;Crops;Soil;Predictive models;Prediction algorithms|
|[An Advanced Computerized Artificial Intelligent System for Assisting Real Life Feature Extraction using AI-NOVA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061087)|S. D. Vara Prasad; J. Kavitha; G. C. Babu; K. V. M. Mohan; P. G. Krishna|10.1109/ICSSIT55814.2023.10061087|Artificial intelligence;voice assistance;Exploratory Data Analysis;Performance evaluation;Data analysis;Video on demand;Personal voice assistants;Libraries;Task analysis;Intelligent systems|
|[Performance Analysis of Ischemic Stroke Lesion Segmentation in Brain MR Images using Histogram based Filter Enhanced FCM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061114)|S. K. Thiyagarajan; K. Murugan|10.1109/ICSSIT55814.2023.10061114|MR Magnetic Resonance;Diffusion Weighted Imaging (DWI);FCM Fuzzy C Means;FLuid Attenuated Inversion Recovery (FLAIR) MRI;HBFEFCM Histogram Based Filter Enhanced FCM;Ischemic Stroke Lesion;Morphological Reconstruction;MRI Magnetic Resonance Image;T1 MRI mode;T2 MRI mode;Image segmentation;Histograms;Sensitivity;Magnetic resonance imaging;Magnetic separation;Stroke (medical condition);Information filters|
|[Design of Abdominal Pain Reliever based upon the Principle of TENS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061052)|S. P. Pandey; S. Bansod; P. Pal; S. K. Singh|10.1109/ICSSIT55814.2023.10061052|Transcutaneous electrical nerve stimulation;Pulse width;Intensity;Pulse width modulation;Electrodes;Bluetooth;motor contraction;Abdominal;Non-invasive;Endorphins;Electrodes;Bluetooth;Pain;High-voltage techniques;Pulse width modulation;Mobile applications;Safety|
|[Object Detection Mechanism using Deep CNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060996)|R. K. Kandagatla; P. S. Sreenivasa Reddy; A. Katuri; K. S. S. Rama Reddy; D. Pavan Vamsi; G. Amulya|10.1109/ICSSIT55814.2023.10060996|Computer Vision;Occlusion;Deep Learning;Convolutional Neural Network;Deep learning;Webcams;Digital images;Computational modeling;Neural networks;Object detection;Streaming media|
|[Automated Leaf Disease Identification and Analysis in Precision Agriculture based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061149)|A. Pavithra; G. Kalpana|10.1109/ICSSIT55814.2023.10061149|nan;Deep learning;Plant diseases;Analytical models;Crops;Soil;Feature extraction;Boosting|
|[Abusive Comment Detection in Social Media with Bidirectional LSTM Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060887)|S. A. Agnes; A. A. Solomon; D. J. C. Tamilmaran|10.1109/ICSSIT55814.2023.10060887|abusive language detection;Biiffrectional longshort term memhoty;natural language processing;text classification;word embedding;Support vector machines;Radio frequency;Measurement;Deep learning;Instruments;Blogs;Cyberbullying|
|[SDDD: Stacked Ensemble Model for Driver Drowsiness Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060933)|P. Tumuluru; S. S. Kumar; N. Sunanda; J. S. Koduri; T. Ayyappa; K. Balasankar|10.1109/ICSSIT55814.2023.10060933|Deep learning;Driver Drowsiness;ShuffleNet;MobileNetV2;SqueezeNet;Light-Weight Network Models;Deep learning;Road accidents;Computational modeling;Computer architecture;Feature extraction;Hardware;Physiology|
|[Performance Analysis of Different Fatty Acid Vegetable Oil using Ranking Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061136)|P. A. Kandhan; N. B. Prakash; M. Bakrutheen|10.1109/ICSSIT55814.2023.10061136|Vegetable oils;Fatty acids;Ranking method;Breakdown voltage;Viscosity;Flash point;Pour point and Density;Viscosity;Vegetable oils;Renewable energy sources;Sociology;Minerals;Performance analysis;Dielectric liquids|
|[Augmented Reality for Advanced Home Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060904)|Pronika; D. Mudgal; G. Yati|10.1109/ICSSIT55814.2023.10060904|Augmented Reality;Internet of Things (IoT);Home Automation;Home Applications;Mixed Reality;Home appliances;Video games;Solid modeling;Home automation;Mixed reality;Switches;Internet of Things|
|[Weather Prediction using Long Short Term Memory (LSTM) model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061039)|K. Nitesh; Y. Abhiram; R. K. Teja; S. Kavitha|10.1109/ICSSIT55814.2023.10061039|Long Short-Term Memory;Empirical Model;Gated Recurrent Units (GRUs);Decomposition;Neural Network;Time-Series Data Prediction;Recurrent Neural Networks;Temperature measurement;Recurrent neural networks;Wind speed;Time series analysis;Weather forecasting;Predictive models;Data models|
|[Labelled Feature Dimensionality Reduction for Liver Disorder Classification in CT Liver Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060921)|V. Gavini; G. R. Jothi Lakshmi|10.1109/ICSSIT55814.2023.10060921|Feature Extraction;Feature Dimensionality Reduction;Feature Set;Liver Disorder;Feature Labeling;Convolution Neural Network.;Dimensionality reduction;Training;Liver cancer;Computer vision;Computed tomography;Neural networks;Liver|
|[Sparrow Search Optimizer for Constrained Engineering Optimal Designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060911)|Y. R. Vada; B. E. Oshiojum; A. B. Krishna; V. K. Kamboj|10.1109/ICSSIT55814.2023.10060911|Spawow Search Optimizer;Benchmarks;Engineeing Structural Designs;Artificial Intelligence;Memetics;Image segmentation;Image resolution;Gears;Globalization;Search problems;Hybrid power systems|
|[Effective Model of Detecting Driver’s Drowsiness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061133)|J. S. H. Reddy; T. Chandana; J. L. Navya; V. Rachapudi|10.1109/ICSSIT55814.2023.10061133|Artificial intelligence;Autonomous Vehicle Technology;Drowsiness Detection;Machine Learning;Deep Learning;Mood;Biological system modeling;Machine learning;Learning (artificial intelligence);Ear;Faces;Vehicles|
|[Resume Classification using Elite Bag-of-Words Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061036)|M. Sharma; G. Choudhary; S. Susan|10.1109/ICSSIT55814.2023.10061036|Bag-of-words;Elite keywords;Term frequency;Resume classification;Training;Engineering profession;Resumes;Semantics;Termination of employment;Feature extraction;Natural language processing|
|[Digital Gamification in Unified Payment Interface (UPI) towards Sustainable Development Goals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061109)|S. Tayal; K. Rajagopal; V. Mahajan|10.1109/ICSSIT55814.2023.10061109|Gamification;Unified Payment Interface (UPI);Application Programming Interface (API);Digital Communication Technology;Sustainable Development Goal 13;Sustainable Development Goal 17;Climate change;Global partnership;Paper Currency;Digital wallet;Digital transaction;Cross-border transactions;Mobile Payment Apps;Climate change;Programming;Sustainable development;Surges|
|[Augmented Random Search based Autonomous Driving System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061010)|M. Lakshmanan; G. S. Anandha Mala|10.1109/ICSSIT55814.2023.10061010|ARS;Deep Q-Learning;Deep-CNN;YOLOV3;CARLA;Training;Weight measurement;Urban areas;Supervised learning;Robot vision systems;Autonomous automobiles;Task analysis|
|[Identification of Road and Surrounding Obstacles using U-Net Architecture for Better Perception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060912)|R. SaiNikhil; S. G. Rao; P. V. P. Rao|10.1109/ICSSIT55814.2023.10060912|Convolutional Neural Networks;Semantic Segmentation;U-Net;Training;Tracking;Roads;Semantic segmentation;Semantics;Urban areas;Computer architecture|
|[Alleviating the Naive Bayes Assumption using Filter Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061030)|R. S. Subramanian; P. Girija; K. Sudha; J. Aswini; S. SivaKumar; N. V. S. Nattesan|10.1109/ICSSIT55814.2023.10061030|Naive Bayes;Conditional Independent;Feature Selection;Machine learning algorithms;Filtering algorithms;Feature extraction;Information filters;Real-time systems;Naive Bayes methods;Time complexity|
|[Designing the Prediction Protocol based on Sparrow Search Optimization Algorithm with Vibrational Auto Encoder in E-Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061100)|R. Saraswathi; D. Pavithra; S. Gokuldev; J. I. D; K. Tharageswari|10.1109/ICSSIT55814.2023.10061100|Sparrow Search Optimization (SSO);Vibrational Auto Encoder (VAE);Disease Prediction;Healthcare;Training;Prediction algorithms;Real-time systems;Classification algorithms;Safety;Data mining;Optimization|
|[A Low SAR Beam Steering Slotted Array Antenna for mmWave 5G Mobile Handsets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060874)|M. Manikandan; S. K. Lakshmi|10.1109/ICSSIT55814.2023.10060874|5G (Fifth Generation);millimeter-wave;beamsteering;slotted array;SAR (Specific Absorption Rate);Beam steering;5G mobile communication;Metals;Switches;Bandwidth;Mobile antennas;Mobile handsets|
|[A Survey on Recommendation Systems using Collaborative Filtering Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060889)|R. Prabakaran; J. Pradeepkandhasamy; M. Arun|10.1109/ICSSIT55814.2023.10060889|Big Data;Recommendation systems;Data Sparsity;Collaborative Filtering E-Commerce industries;User Preferences;Industries;Filtering;Collaborative filtering;Big Data applications;Distance measurement;Electronic commerce;Problem-solving|
|[Dynamic Grouping of Players and Analysis for Regional Tournaments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061151)|P. Parande; N. Sorthiya; M. Lanje; N. Dudhuke; P. Maidamwar|10.1109/ICSSIT55814.2023.10061151|auto pairing;competitiveness;local tournament;manual pairing;organizer;pairing system;participants;webbased solution;Fixtures;Manuals;Software;Standards|
|[CNN based Mood Detection using Facial Expression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060991)|L. S. K. Dasari; F. Baig; D. J. Sai; G. K. Michael; A. Laxman; A. Madhuri|10.1109/ICSSIT55814.2023.10060991|Artificial Intelligence;Convolutional Neural Network;Computer Vision;OpenCV;Facial Expression Recognition;Extended Cohn-Kanade;FER-13;Human computer interaction;Deep learning;Emotion recognition;Mood;Face recognition;Force;Artificial neural networks|
|[Suspicious Activity Detection using LRCN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061045)|B. V. V; V. Indhuja; M. V. Reddy; N. Nikhitha; P. Pramila|10.1109/ICSSIT55814.2023.10061045|Convolutional Neural Network (CNN);Keras;Long Short Term Memory(LSTM);Long Term Recurrent Neural Network(LRCN);Neural Networks;Tensorflow;Deep learning;Visualization;Recurrent neural networks;Tensors;Firing;Weapons;Predictive models|
|[Acute Lymboplastic Leukemia Detection Challenges and Systematic Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060916)|M. Abirami; G. V. S. George; D. Sam|10.1109/ICSSIT55814.2023.10060916|Acute Lymboplastic Leukaemia Detection;Computer-Aided Diagnosis;Machine or deep learning;Performance analysis;Implementation tools;Research gap and challenges;Measurement;Deep learning;Solid modeling;Systematics;Computational modeling;Bibliographies;Prediction algorithms|
|[Context based Emotion Recognition from Bengali Text using Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060927)|L. Ahmed; I. K. Polok; M. A. Islam; M. Akhtaruzzaman; M. S. H. Mukta; M. M. Rahman|10.1109/ICSSIT55814.2023.10060927|Emotion recognition;romanized Bangla;military dataset;context based;Bidirectional Encoder Representations from Transformers (BERT);natural language processing (NLP);Training;Emotion recognition;Technological innovation;Social networking (online);Bit error rate;Transformers;Tokenization|
|[Software Risk Ranking Assessment Model using Fuzzy Decision-Making Trial and Evaluation Laboratory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061105)|D. K. Suresh; M. V. Kavitha; M. S. Poonkodi|10.1109/ICSSIT55814.2023.10061105|Fuzzy Decision-Making Trial and Evaluation Laboratory;Risk Factor;Risk Assessment.;Decision making;Machine learning;Software;Risk management;Task analysis|
|[A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061146)|R. Venkatesan; A. Pandiaraj; M. Selvakumar|10.1109/ICSSIT55814.2023.10061146|Image Processing;Convolution Neural Network;Recurrent Neural Network;Image Segmentation;Visualization;Image segmentation;Recurrent neural networks;Transform coding;Wounds;Media;Image representation|
|[Detection of Land Cover Changes using Satellite Image Classification Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060992)|P. Pavan; B. S. S. Varma; K. Asish; M. Suneetha|10.1109/ICSSIT55814.2023.10060992|Remote Sensing;Change Detection;Image Classification;Image Segmentation;Object Based Image Classification;Land cover;Image segmentation;Satellites;Land surface;Remote sensing;Image classification|
|[Intelligent Analysis on Frameworks for Mobile App Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060902)|S. Gowri; C. Kanmani Pappa; T. Tamilvizhi; L. Nelson; R. Surendran|10.1109/ICSSIT55814.2023.10060902|MobileApplication Development;designed frameworks;React-Native;Flutter;Ionic;Prototypes;Passwords;Mobile communication;Mobile applications;Encryption;Security;Task analysis|
|[Bluetooth based Garage Door Opening System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061046)|C. K. Pappa; N. Ashokkumar; P. Nagarajan; K. Thandapani|10.1109/ICSSIT55814.2023.10061046|Voice command;Bluetooth;Android device;Bluetooth;Regulators;Automation;Pandemics;Gears;Cleaning;Proposals|
|[Student Performance Analysis with Ensemble Progressive Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060910)|B. Althaph; S. V. N. Sreenivasu; D. V. Reddy|10.1109/ICSSIT55814.2023.10060910|Machine learning;student performance;ensemble progressive prediction;performance estimation;accuracy;mean absolute error;Clustering methods;Education;Buildings;Machine learning;Predictive models;Benchmark testing;Data models|
|[An Enhanced Deep Learning Model for Handwritten Tamil Character Identification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060920)|R. Babitha Lincy; J. Jency Rubia; C. Sherin Shibi; M. Kavitha|10.1109/ICSSIT55814.2023.10060920|Adam Optimizer Algorithm;Deep learning;Enhanced Convolutional Neural Network;Support Vector Machine;Tamil Character Recognition.;Industries;Image segmentation;Machine learning algorithms;Image resolution;Optical character recognition;Support vector machine classification;Writing|
|[Medical Image Denoising using Convolutional Autoencoder with Shortcut Connections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061131)|M. Gupta; A. Goel; K. Goel; J. Kansal|10.1109/ICSSIT55814.2023.10061131|Medical Image Denoising;Convdutional Neural Nenvork;ConvolutionalAutoencoders;Shortcut Connections;Deep learning;Image analysis;Noise reduction;Medical diagnostic imaging;Image denoising;Cancer|
|[Implementation of Medical Insurance Price Prediction System using Regression Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060926)|V. Vijayalakshmi; A. Selvakumar; K. Panimalar|10.1109/ICSSIT55814.2023.10060926|Medical Insurance Cost;Regression Algonthms;Evaluation Mettices;Prediction;Support vector machines;Costs;Neural networks;Measurement uncertainty;Insurance;Manuals;Programming|
|[Pre-Processing Algorithms for Accurate Analysis of an Image: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060937)|Y. Satuluri; K. Venkata Ratna Prabha; J. S. Polavarapu; H. V. Battina; V. Kota; P. Ramesh|10.1109/ICSSIT55814.2023.10060937|Analysis;Database;Filtering Techniques;Gabor filter;Images;Interpolation;Kernels;Pre-Processing;Histograms;Interpolation;Convolution;Filtering;Image edge detection;Filtering algorithms;Adaptive equalizers|
|[Survey on Collaborative and Content-based Recommendation Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061072)|S. A. Thorat; G. Ashwini; M. Seema|10.1109/ICSSIT55814.2023.10061072|Recommendation System;Content-based;Collaborative Filtering;Research Challenges;Applications;Scalability;Collaborative filtering;Neural networks;Memory architecture;Collaboration;Medical services;Information filters|
|[Automatic Detection and Localization of Breast Cancer from Mammogram Imaging Modality using Modified Faster RCNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061000)|P. Kumar; K. Sangeetha; A. S. Praneeth; M. S. Madhav; T. Swaroopa; G. Rohit|10.1109/ICSSIT55814.2023.10061000|Breast cancer;Faster RCNN;CBIS-DDSM;Mammography;Location awareness;Performance evaluation;Solid modeling;Statistical analysis;Pain;Imaging;Medical services|
|[Evaluation of Wetland Transformations using HSV Color Model for Denduluru Region](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061110)|G. Chaitanya; J. R. H. Kumar; K. L. Sailaja; A. S. Chandra; B. M. Babu|10.1109/ICSSIT55814.2023.10061110|Wetlands;Computer Vision;OpenCV;Clustering;Image Segmentation;Colour Model;Hue Saturation Value.;Image color analysis;Statistical analysis;Data acquisition;Data models;Water resources;Wetlands;Aquaculture|
|[Optimizing Predictive Analytics Model (PAM) using Hyper-Parameter Tuning (HPT): A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061084)|N. Krishnadoss; R. Lokesh Kumar|10.1109/ICSSIT55814.2023.10061084|Big data analytics;hyper-parameter tuning;predictive analytics;Training;Analytical models;Biological system modeling;Predictive models;Prediction algorithms;Data models;Real-time systems|
|[Attention based Image Caption Generation (ABICG) using Encoder-Decoder Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061040)|U. Kulkarni; K. Tomar; M. Kalmat; R. Bandi; P. Jadhav; S. Meena|10.1109/ICSSIT55814.2023.10061040|Convolutional Neural Network (CNN);Recurrent Neural Network (RNN);Gated Recurrent Unit (GRU);Encoder;Decoder;Attention mechanism;Image captioning;Training;Vocabulary;Recurrent neural networks;Machine learning algorithms;Transformers;Natural language processing;Decoding|
|[Improved EMD Algorithm for Electrocardiogram Denoising and Feature Extraction for Detection of Cardiovascular Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061053)|S. Immaculate Joy; C. Venkatesh; B. Bhargav|10.1109/ICSSIT55814.2023.10061053|Electrocardiogram;Discrete Wavelet Transform;empirical mode decomposition;Dual Tree Complex Wavelet Transform;QRS complex;Heart;Empirical mode decomposition;Hospitals;Heart beat;Noise reduction;Mean square error methods;Electrocardiography|
|[Comparative Model of Offline Signature Verification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060946)|M. S. Sultana; S. Geethanjali; M. Ramesh|10.1109/ICSSIT55814.2023.10060946|Artificial Intelligence;Signature Verification;forgery detection;SSIM-Structural Similarity Index Measure CNN-Convolutional Neural Network.;Handwriting recognition;Computational modeling;Neural networks;Speech recognition;Manuals;Fingerprint recognition;Convolutional neural networks|
|[Investigating the Influence of Self-Driving Cars Accidents on the The Public Attitude: Evidence from Different Countries in Different Continents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061032)|K. Othman|10.1109/ICSSIT55814.2023.10061032|Self-Driving Cars;Public Acceptance;Trust;Concern;Demographic Analysis;Prior Experience;Hypothesis Testing;Resistance;Europe;Developing countries;Position measurement;Computer crashes;Autonomous automobiles;Accidents|
|[GITAAR-GIT based Abnormal Activity Recognition on UCF Crime Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061116)|U. Sirisha; B. S. Chandana|10.1109/ICSSIT55814.2023.10061116|University of central florida crime captioning dataset;abnormal activity recognition;Generative Image to text architecture;Deep learning;Attention mechanism;Deep learning;Computer science;Adaptation models;Image recognition;Text recognition;Activity recognition;Transformers|
|[Emotion Recognition of Online Learners for Smart Education Systems using Computational Intelligence: Review and Insight](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060940)|M. Jagadeesh; N. Sajeev; N. Nithish Kumar|10.1109/ICSSIT55814.2023.10060940|Online Learning;Emotional Analysis;Facial Expression Recognition;Learners Engagement;Learners Mood Assessment;Convolutional Neural Network;Deep learning;Deep learning;Emotion recognition;Mood;Production;Educational technology;Convolutional neural networks;Computational intelligence|
|[Speech Emotion Recognition using Mel Frequent Cepstral Coefficients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060950)|S. Sharmila; M. S. Lakshmi; G. S. Chowdary; P. Mounika; C. Srujana|10.1109/ICSSIT55814.2023.10060950|Convolutional Neural Networks;Multilayer Perceptron Classifier;and Emotional Speech Recognition;Emotion recognition;Cepstral analysis;Speech recognition;Reflection;Convolutional neural networks|
|[Fuzzy Hybrid Filtration based Rician Noise Removal and Classification of MRI Images without Segmentation using Deep Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061104)|N. J. Shafana; A. SenthilSelvi|10.1109/ICSSIT55814.2023.10061104|Neural network;Deep Autoencoder;Classification;Pre-processing;Fuzzy Hybrid filter;Brain tumor detection.;Wavelet transforms;Deep learning;Image segmentation;Filtration;Magnetic resonance imaging;Current measurement;Rician channels|
|[Fusion of Multimodal Textual and Visual Descriptors for Analyzing Disaster Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061061)|S. Kamoji; M. Kalla; C. Joshi|10.1109/ICSSIT55814.2023.10061061|Pretrained Models;DenseNet;BERT;Multimodal Fusion;Visualization;Social networking (online);Blogs;Bit error rate;Transfer learning;Feature extraction;Transformers|
|[A New Proposed Hybrid Page Replacement Algorithm (HPRA) in Real Time Systems.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060934)|P. Banerjee; V. Raj; K. Thakur; B. Kumar; M. K. Dehury|10.1109/ICSSIT55814.2023.10060934|Virtual Memory;Page;Hit Ratio Replacement;Fault Rate;Time-frequency analysis;Codes;Operating systems;Programming;Real-time systems|
|[Improved Multiphase PSO using Greedy Approach for Effective Population Size](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061081)|S. Rawat; M. K. Khandewal|10.1109/ICSSIT55814.2023.10061081|Swarm Intelligence (SI);Particle Swarm optimization (PSO);Greedy Approach;Sociology;Metaheuristics;Pressing;Market research;Stability analysis;Topology;Particle swarm optimization|
|[MedNet: A Segmentation Algorithm for Effective Lung cancer Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061117)|M. Divya; S. Sathya|10.1109/ICSSIT55814.2023.10061117|MedNet;Pre-processing;Segmentation;Deep Learning;Convolutional;Artificial Intelligence;Deep learning;Image segmentation;Analytical models;Error analysis;Computed tomography;Computational modeling;Lung cancer|
|[Design and Performance Analysis of NOMA_OFDM based MIMO System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060867)|C. Madhan; M. Pappa|10.1109/ICSSIT55814.2023.10060867|OMA;NOMA;TDMA;CDMA;LTE;Wireless communication;NOMA;Systematics;5G mobile communication;Computational modeling;Transmitting antennas;Receiving antennas|
|[Android Malware Detection Model using the Adaptive Swarm Optimization-based Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061042)|P. M. Shimpi; N. N. Pise|10.1109/ICSSIT55814.2023.10061042|Android mobile;Optimization;Neural Network;Attack detection;Security;Deep learning;Learning systems;Adaptation models;Sensitivity;Smart cities;Transportation;Artificial neural networks;Malware|
|[Various Methods in Texture Analysis and Classification Techniques used in Cervical Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060990)|S. Poojahsri; G. K. Prince; S. Prerana; S. Krishnakumar|10.1109/ICSSIT55814.2023.10060990|Cervical cancer;Machine learning;Deep learning;Texture analysis;Feature extraction;Deep learning;Magnetic resonance imaging;Computed tomography;Feature extraction;Cancer detection;Cervical cancer;Monitoring|
|[Automating the Home Appliances through Voice Commands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061123)|A. M. Kumar; B. V. K. Reddy; Y. N. Kumar; Y. S. Reddy; V. Gampala; S. Bulla|10.1109/ICSSIT55814.2023.10061123|Node Microcontroller Unit (Node MCU);If This Then That (IFTTT);Blynk App;Goggle Assistant;Internet of things;Home Automation;Wireless communication;Home appliances;Costs;Automation;Microcontrollers;Smart homes;Turning|
|[Analyzing the Accuracy of AI Techniques for Breast Cancer Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061075)|S. L. Naraparaju; N. S. N. Eerlapalli; N. Modem; R. Paidipati; C. Karthikeyan; K. Sathish Kumar|10.1109/ICSSIT55814.2023.10061075|Breast Cancer;Detection;Optimization;Machine Learning;Artificial Intelligence;Cybersecurity;Dataset;Female;Techniques;Algorithms;Classifications;Training;Training data;Prediction algorithms;Probabilistic logic;Breast cancer;Data models;Classification algorithms|
|[Chronic Kidney Disease Prediction using Data Pre-Processing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061025)|O. Aruna; S. Sameerunnisa.|10.1109/ICSSIT55814.2023.10061025|Chronic Kidney Disease;Data Pre-processing;Classification;Decision Tree Classifier;Random Forest Classifier.;Machine learning algorithms;Collaboration;Forestry;Filtering algorithms;Chronic kidney disease;Decision trees;Random forests|
|[Deep Analysis on Inductive and Resistive Loading Variations for Optimal Switching Loss Reduction in 3-Phase VSI Employing Hybrid Rider Group Search Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061067)|G. Rajeshkumar; P. S. Therese|10.1109/ICSSIT55814.2023.10061067|3-ΦVSI(Voltage Source Inverter);Inductive and Resistive Loading Variations;Total Harmonic Distortion;Hybrid Rider Group Search Algorithm.;Switching frequency;Computational modeling;Loading;Switching loss;Modulation;Minimization;Mathematical models|
|[Application of YOLOv3 in Lung Nodule Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061145)|P. M. Bruntha; D. Khanna; R. G; S. Deenu; A. R. Jesli Sharon; A. Karunya|10.1109/ICSSIT55814.2023.10061145|Lung Cancer;Lung Nodule;Pulmonary Nodule;YOLO;Lung Nodule Detection;Pulmonary Nodule Detection;Deep learning;Sensitivity;Computed tomography;Simulation;Neural networks;Lung;Lung cancer|
|[An Efficient Ensemble Approach for Hyperspectral Image Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061021)|K. P. Ram; B. L. N. Phaneendra Kumar; T. Bhargav|10.1109/ICSSIT55814.2023.10061021|Hyperspectral image classification;Principle Component Analysis;Factor Analysis;Expectation Maximization Principal Component Analysis;Albedo recovery;Support vector machine;Training;Measurement;Support vector machine classification;Feature extraction;Robustness;Minerals;Task analysis|
|[A Novel Framework for Fake News Detection using Double Layer BI-LSTM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061026)|A. R. Merryton; M. G. Augasta|10.1109/ICSSIT55814.2023.10061026|Natural Language Processing;Double Bi-LSTM;Fake News Detection;Deep Learning;Pre-processing;Vectorizer;Social networking (online);Collaboration;Feature extraction;Natural language processing;Internet;Fake news|
|[Stock Trend Prediction: A Comparative Study using Different Approaches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060936)|P. Khanpara; R. Kadam; K. Lavingia; S. Patel|10.1109/ICSSIT55814.2023.10060936|Stock Trend Prediction;LSTM;RNN;ANN;Deep learning;Time series analysis;Companies;Predictive models;Market research;Stock markets;Forecasting|
|[Series-Input-Connected Structure for Extended Duty Cycle in Forward Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061023)|S. Kumar; S. Sangwan|10.1109/ICSSIT55814.2023.10061023|Series-input;parallel-output;main module;auxiliary module;wide duty cycle;magnetization;demagnetization;forward converter;Switches;Voltage;Rendering (computer graphics);Batteries|
|[Impact of Adam, Adadelta, SGD on CNN for White Blood Cell Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061068)|R. Singh; A. Sharma; N. Sharma; R. Gupta|10.1109/ICSSIT55814.2023.10061068|White Blood Cell;Leukocytes;Deep Learning;Convolutional Neural Network (CNN);Classification;White blood cells;Deep learning;Measurement;Industries;Analytical models;Convolution;Microscopy|
|[A Conceptual Overview on Earlier Methodologies Focused on Stock Price Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061063)|R. Gnanavel; J. M. Gnanasekar|10.1109/ICSSIT55814.2023.10061063|Stock Price Prediction;Data Analytics;Machine Learning;Deep Learning;Deep learning;Economics;Machine learning algorithms;Statistical analysis;Prediction algorithms;Market research;Distance measurement|
|[Design and Implementation of Smart Glove for Visually Impaired People](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061070)|J. Jayachitra; S. Muthulakshmy; M. A. Kumar; K. Elavarasi|10.1109/ICSSIT55814.2023.10061070|Deep Neural Network;Micro-Vibrating Motors;Smart Glove;yolov5;Deep learning;Neural networks;Merging;Color;Cameras;Universal Serial Bus;Real-time systems|
|[Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061094)|N. Bharathiraja; K. Pradeepa; I. Jeya Sheela; G. Sudhakar; M. V. Kumar; G. Kaur|10.1109/ICSSIT55814.2023.10061094|Plant Leaf Disease;Drone;Image processing;Sensor;Internet of Things;Productivity;Surveillance;Instruments;Sensor systems;Sensors;Internet of Things;Servers|
|[Analysis on Cutting Down of Stock Market Outbreak of Covid-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061011)|C. Susmitha; L. Nikhil; M. Kavitha|10.1109/ICSSIT55814.2023.10061011|Covid-19;data science;matplotlib;seaborn;stock market;COVID-19;Data analysis;Profitability;Data visualization;Market research;Time measurement;Libraries|
|[Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060899)|R. Sundar; S. Ramadass; D. Meeha; B. Subramanian; S. Siva Shankar; G. Parasa|10.1109/ICSSIT55814.2023.10060899|machine-learning;peer pressure;polynomial regression;Machine learning algorithms;Correlation;Linear regression;Anxiety disorders;Lung cancer;Machine learning;Prediction algorithms|
|[Analysis of a Real Time Fire Detection and Intimation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061106)|R. Seetharaman; R. R. Sreeja; S. V. Dakshin; N. Nivetha; S. Gowsigan; M. Barath|10.1109/ICSSIT55814.2023.10061106|Fire detection;Microprocessor;Smoke sensor;Flame sensor;Temperature sensor;Buzzer;Industries;Temperature sensors;Heating systems;Image processing;Fires;Streaming media;Real-time systems|
|[A Novel Communication using WWSN with Intelligent Controller Unit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060977)|K. Elavarasi; S. Veeramani|10.1109/ICSSIT55814.2023.10060977|Water Sensor Networks (WSN);IR Transmitter;IR Receiver;Sonar waves;Sea surface;Wireless sensor networks;Transmitters;Surface waves;Receivers;Media;Surface roughness|
|[Prediction of Paddy Yield based on IoT Data using GRU Model in Lowland Coastal Regions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060935)|A. P. Gopi; V. Swathi; G. S. Harshitha; B. Swetha; N. Alekhya|10.1109/ICSSIT55814.2023.10060935|Paddy;deep learning;IoT;GRU algonthm;Temperature sensors;Training;Temperature measurement;Crops;Moisture;Humidity;Soil|
|[Brain Metastasis Tumor Detection using Image Segmentation and VGG16 Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061029)|S. Donepudi; S. C. Palagani; P. S. N. Pramod; Y. R. Kumar; S. Karthikeya; S. P. Praveen|10.1109/ICSSIT55814.2023.10061029|Brain Tumors;Visual Geometry Group 16;AlexNet;Googlenet;Image Segmentation;Image segmentation;Sensitivity;Microprocessors;Computer architecture;Sensitivity and specificity;Brain modeling;Data models|
|[Bio Sensor Technology Applications and Development in Process Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060884)|S. Walke; M. B. Mandake|10.1109/ICSSIT55814.2023.10060884|Industry;Electronics;Chips;circuits;Low power;Mechanical sensors;Industries;Sensitivity;Service robots;Robot sensing systems;Sensor systems;Regulation|
|[MPPT Extremum Seeking Control Algorithm for Standalone PV System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060869)|J. K. Sahu; S. K. Mishra; J. P. Patra|10.1109/ICSSIT55814.2023.10060869|Photo Voltaic;Extremum Seeking Control Algorithm;Maximum Power Point;Photovoltaic systems;Renewable energy sources;Electric potential;Simulation;Solar energy;Approximation algorithms;Mathematical models|
|[Fishermen and Fishing Boat Monitoring System using MIMO Technology and Database Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060951)|R. K. Ramesh; S. Ramesh|10.1109/ICSSIT55814.2023.10060951|Fishermen;Radio Frequency Identification;Multiple Input and Multiple Output;Database Management;Transceiver;Databases;Simulation;Boats;Sea measurements;QR codes;Valves;Repeaters|
|[Adaptive Speed Controller for Electric Vehicle using ADAS Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060973)|S. R V; P. G. A. Mani; N. Karthick|10.1109/ICSSIT55814.2023.10060973|Advanced Driver Assistant System (ADAS);Adaptive cruise control (ACC);Lane departure warning system;Software packages;Microcontrollers;Roads;Alarm systems;Electric vehicles;Safety;Vehicles|
|[Analysis of Alcoholic EEG Signal using Semantic Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060917)|A. Gopakumar; A. Shine; T. Anjali|10.1109/ICSSIT55814.2023.10060917|electroencephalogram;convolutional neural network;residual network 50;visual geometry group 16;support vector machine;cross validation;Deep learning;Computational modeling;Alcoholism;Support vector machine classification;Alcoholic beverages;Brain modeling;Electroencephalography|
|[Detection and Prediction of Landslide Vulnerability through Case Study using DInSAR Technique and U-net Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061077)|K. V. Vishnu Vardhan; V. H. S. S. Kaushik; K. L. Sailaja; P. R. Kumar|10.1109/ICSSIT55814.2023.10061077|Landslide detection;Differential Interferometric Synthetic Aperture Radar technique;U-net Model;Normalized Difference Vegetation Index;Remote Sensing;Digital Elevation Model;Sentinel Satellite;Landslides;Satellites;Deformation;Vegetation mapping;Predictive models;Data models;Sensors|
|[Designing Smart Room Using Cisco Packet Tracer Simulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061048)|B. Ratnala; T. Anuradha; V. Maddali; H. V. Chintalapudi|10.1109/ICSSIT55814.2023.10061048|Internet of Things;Sensors;Packet tracer;Automation;Seminars;Home appliances;Fans;Automation;Portable computers;Prototypes;Smart homes|
|[Particle Swarm Optimization and Genetic Algorithms for PID Controller Tuning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060892)|D. P. Mishra; U. Raut; A. P. Gaur; S. Swain; S. Chauhan|10.1109/ICSSIT55814.2023.10060892|Particle swarm optimization (PSO);proportional-integral-derivative (PID) tuning;Genetic algorithm(GA);and GAPSO are some of the terms used;Costs;Sociology;Time factors;PD control;Particle swarm optimization;Statistics;Tuning|
|[Efficiency and Computation of WiMAX OFDM using Millimeter Wave and Smart Antennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060978)|A. H. Malini; S. Mohith; B. S. Prasad; K. S. C. Kumar|10.1109/ICSSIT55814.2023.10060978|Alamouti;Bit error rate;Channel Capacity;Equalizer’s;Fourier transform;Harmonization;Modulation;Orthogonal Frequency Division Multiplexing (OFDM);Space Time Coding;Wi-Max.;Technological innovation;Transmitters;OFDM;Simulation;Bit error rate;WiMAX;Encoding|
|[Highly Efficient 2.45 GHz Rectifier Circuit for RF Energy Harvesting Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061055)|A. Pandey; A. Srivastava; A. Pandey; A. Sharma; R. Kumar|10.1109/ICSSIT55814.2023.10061055|Rectifier;Radio Frequency to Direct Current converter;Schottky diode;Radio frequency;Schottky diodes;Sensitivity;Rectennas;Rectifiers;Resonant frequency;Voltage|
|[A Battery Monitoring System based on IoT for Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061035)|P. Sasirekha; E. Sneka; B. Velmurugan; M. Sahul Hameed; P. Sivasankar|10.1109/ICSSIT55814.2023.10061035|Internet of Things;Electric Vehicles Battery;Grid;State of Charge;Message Queue Telemetry Transport (MQTT);Battery Management System;Temperature sensors;Temperature;Battery management systems;Estimation;Electric vehicles;Batteries;State of charge|
|[Analysis of Ethical Issues Associated with Wearable Medical Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061037)|S. Venkatachalam; T. Padmavathi; N. Vinodh; J. Thilagavathi; G. Joshi; G. Ramachandran; B. Rajasekaran|10.1109/ICSSIT55814.2023.10061037|Privacy;Health Care;Medical;Wearable device;Ethics;Privacy;Medical devices;Law;Wearable computers;Medical services;Stakeholders|
|[Sparse Long Short-Term Memory Approach for Energy-Efficient Adaptive Cluster Fuzzy-based Controller in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061129)|K. Ramanan; S. P. Ramesh; C. S. Kingsly; G. V. Rajkumar; D. D. N. Ponkumar; M. Vargheese|10.1109/ICSSIT55814.2023.10061129|Wireless Sensor Network;Cluster Heads;Sink Selection;First Node Dead;Half Nodes Dead;Last Node Dead;Wireless communication;Wireless sensor networks;Regulators;Protocols;Power demand;Packet loss;Telecommunication traffic|
|[Wireless-Powered Relaying Communication based on MIMO-OFDM: A Comprehensive Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060888)|T. Durgarao; T. J. Naga Lakshmi|10.1109/ICSSIT55814.2023.10060888|Multiple-Input-Multiple-Output;orthogonal frequency-division multiplexing;relaying communication;Wireless-powered network;energy recycle;spectral efficiency;power allocation.;Wireless communication;Spectral efficiency;OFDM;Transmitting antennas;Telephony;Frequency division multiplexing;Resource management|
|[Simulink & Modeling of Multi-Machine Systems with STATCOM/UPFC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060878)|S. Kaur; N. Kaur|10.1109/ICSSIT55814.2023.10060878|MATLABlSIMULINK;STATCOM;UPFC;mutimwchine;Analytical models;Power transmission lines;Software packages;Simulation;Power quality;Voltage;Power system stability|
|[Monitoring of Solar cell and Damage Detection Using LabVIEW](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060881)|M. Maheshwaran; G. Bagyalakshmi; R. Valarmathi; V. Gomathi|10.1109/ICSSIT55814.2023.10060881|LabVIEW;Arduino;LINX;Solar panel;Solar tracking;LabVIEW-Laboratoty Vinual Instrument Engineering Workbench;PWM-Pulse Width Modulation;Temperature sensors;Temperature measurement;Shafts;Resistance;Voltage measurement;Pulse width modulation;Real-time systems|
|[Green Technology and Sustainability: A Review and Future Research Agenda](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061015)|A. K. Singh; P. Tyagi; P. K. Tyagi; H. K. Chanchal; A. K. Singh; B. Arora|10.1109/ICSSIT55814.2023.10061015|Green technology;Sustainability;Environment;Bibliomettix;China;Manifolds;Data analysis;Soft sensors;Bibliometrics;Data visualization;Production;Visual databases|
|[Design and Implementation of Solar based Maximum Power Point Tracking using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060975)|B. R. Kumar; S. Srikanth; P. Sujeet; T. Swetha; P. Tanya|10.1109/ICSSIT55814.2023.10060975|Maximum Power Point Tracking;Artificial Neural Network;PhotoVoltaic panels;machine learning algorithm;Maximum power point trackers;Photovoltaic systems;Training;Machine learning algorithms;Photovoltaic cells;Artificial neural networks;Network architecture|
|[The Impact of Alteration of Superframe Duration on the Consumption of Energy in the IEEE 802.15.4 MAC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061143)|U. S. Pandey; G. Soni; S. K. Chandra|10.1109/ICSSIT55814.2023.10061143|Superframe Duration;Physiological sensors;IEEE 802.15.4 Medium Access Control (MAC);wake-up and sleep mode;IEEE 802.15 Standard;Wireless communication;Wireless sensor networks;Medical services;Throughput;Media Access Protocol;Sensor systems|
|[Power Generation of Wind-PV-Battery based Hybrid Energy System for Standalone AC Microgrid Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060963)|N. Arise; V. Bhoomika; N. A. Reddy; S. Harika; A. Koushik|10.1109/ICSSIT55814.2023.10060963|Hybrid energy system;PV(Solar Cell);Wind;Battery;Microgrid;Photovoltaic systems;Wind speed;Power system management;Energy resolution;Power transmission;Microgrids;Hybrid power systems|
|[Eigen Frequency Analysis of Single Axis 3D Comb Drive Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060960)|S. Ramya; S. P. Kumar; K. Kalaivani; G. D. Ram; D. Lingaraja|10.1109/ICSSIT55814.2023.10060960|Micro Electro Mechanical System;Accelerometer;Comb drive;Eigen frequency;Accelerometers;Micromechanical devices;Electric potential;Three-dimensional displays;Force;Fingers;Drives|
|[An Efficient and Robust Modified Hybrid Multipliers with Less Power and Better Speed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061082)|V. Gomathi; M. S. Varshan; V. Subramaniyan; S. U. Krishna|10.1109/ICSSIT55814.2023.10061082|Multipliers;low power consumption;hybrid addition;wallace trees;GDI;Integrated circuits;Voltage;Logic gates;Hybrid power systems;Hardware;Complexity theory;Central Processing Unit|
|[Importance of Phasor Measurement Unit in Modern Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060942)|N. Joshi; A. Khandelwal|10.1109/ICSSIT55814.2023.10060942|Phasor Measurement Unit;SCADA;Modem Power System;Power measurement;Measurement units;Memory;Power system stability;Modems;Phasor measurement units;Stability analysis|
|[Study on the Performance of Single Slope Solar Still using Marble Stone as Energy Storage Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060919)|S. Kumaravel.; M. Nagaraj; G. Bharathiraja.|10.1109/ICSSIT55814.2023.10060919|Single slope solar still;Marble stones;Desalination;PCM;Energy storage materials;Phase change materials;Sea measurements;Production;Material storage;Turning;Solar panels;Task analysis|
|[Smart Health Monitoring System in Intensive Care Unit using Bluetooth Low Energy and Message Queuing Telemetry Transport Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061050)|K. Ragavan; R. Ramalakshmi; V. SrirengaNachiyar; G. G. Priya; K. Jeyageetha|10.1109/ICSSIT55814.2023.10061050|Bluetooth Low Energy (BLE);Message Queuing Telemetry Transport (MQTT) protocol;health monitoring;Short Message Peer to Peer (SMPP) protocol;Intensive Care Unit (ICU);ZigBee;Temperature sensors;Smart healthcare;Sociology;Zigbee;Telemetry;Bluetooth Low Energy;Biomedical monitoring|
|[Automated Parasitic-Aware and optimization Tool for CMOS Active Phase Shifters Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060974)|N. Mansour; M. Elnozahi; H. Ragaai|10.1109/ICSSIT55814.2023.10060974|Automated Design;Optimizer Algorithims;Parasitic Design Aware;Active Phase Shifters;Phased Array Systems;Millimeter-Wave (Mm-wave);Quadrature Networks;Analog Adders;Phased arrays;Phase shifters;Layout;Integrated circuit interconnections;Estimation;CMOS technology;Generators|
|[A Design-Aware Parasitic-Aware Simulation-Based Tool for the Design of RF Amplifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060922)|R. A. Onsy; M. E. Nozahi; H. Ragai|10.1109/ICSSIT55814.2023.10060922|Automation;Design;optimization;Radio frequency Amplifiers;Simulation-based;Radio frequency;Semiconductor device modeling;Sensitivity;Design automation;Layout;Design tools;Optimization|
|[Implementation of PV Integrated Power Quality Enhancement and Performance Analysis of Battery Operated Electric Vehicle using Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060994)|M. Hariprabhu; M. Sarankumar; S. Sharansharvesh; S. M. Sivakumar; S. Sridharani|10.1109/ICSSIT55814.2023.10060994|Maximum Power Point Tracking;Photovoltaic panels;Boost converter;Electric vehicle;Active Power Filter;Total Harmonic Distortion;Maximum power point trackers;Total harmonic distortion;Renewable energy sources;Software packages;Simulation;Power quality;Voltage|
|[KPCA and SVR-based Cardiac Arrhythmia Classification on Electrocardiography Waves](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061047)|S. T.Sanamdikar; N. M. Karajanagi; A. V. Kulkarni; S. D. Kamble|10.1109/ICSSIT55814.2023.10061047|Arrhythmia detection;(PCA) Principal Component Analysis;(ECG) Electrocardiogram signal;(SVM) Support Vector Machine;and SVR (Super Vector Regression);Support vector machines;Heart;Pregnancy;Sensitivity;Arrhythmia;Low-pass filters;Electrocardiography|
|[Evolution of Electric Vehicle-A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061001)|R. Harikrishnan; P. Sivagami; M. Pushpavalli; G. Rajesh; P. Abirami; M. Ram|10.1109/ICSSIT55814.2023.10061001|EV-Electric vehicle;PHEV-Plugged in Hybrid Electric vehicle; SiC-Silicon Carbide;Ni-MH-Nickel Metal Hydride;IPM-interior permanent magnet synchronous motor;EV-Electric vehicle, PHEV-Plugged in Hybrid Electric vehicle;SiC- Silicon Carbide, Ni-MH- Nickel Metal Hydride, IPM-interior permanent magnet synchronous motor;Silicon carbide;Roads;Urban areas;Permanent magnet motors;Air pollution;Synchronous motors;Safety|
|[Design and Development of Thickness Measuring System for Copper Wire](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061093)|K. N. Baluprithviraj.; M. Dhanalakshmi.; D. Dharanidharan.; K. Gokulkrishnan.; M. Madhan Mohan.; S. Janarthanan.|10.1109/ICSSIT55814.2023.10061093|Electrical wiring;Copper wire;Thickness;Screw gauge;EMF;Light emitting;Wiring;Meters;Manufacturing industries;Wires;Windings;Manuals;Manufacturing|
|[SMMap: Soil Moisture Content Mapping/Estimation Using Synthetic Aperture Radar (SAR) Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060918)|M. Sriram; P. Ramesh Kumar; S. Hussain; K. L. Sailaja|10.1109/ICSSIT55814.2023.10060918|Soil Moisture Content (SMC);Polarization;Regression Convolution Neural Network;Radar Backscattering;Synthetic Aperture Radar (SAR);Water;Soil moisture;Moisture;Estimation;Crops;Radar imaging;Microwave theory and techniques|
|[Modeling and Simulation Analysis of Solar Charging Station for Electric Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061147)|N. Kaur; R. K. Bindal|10.1109/ICSSIT55814.2023.10061147|Electric Vehicle;Solar System;MATLAB Simulation.;Road transportation;Analytical models;Renewable energy sources;Charging stations;Electric vehicle charging;Automobiles;Matlab|
|[Rehabilitative Embedded Hand Glove for the Paralyzed](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061056)|K. Umapathy; D. K. Sri; G. Poojitha; A. S. Samvida; D. M. Sharma; S. B. S. Sairam|10.1109/ICSSIT55814.2023.10061056|Stroke;Rehabilitation;Embedded glove;Flex Sensors;Arduino;Servomotors;Microcontrollers;Robot kinematics;Muscles;Stroke (medical condition);Robot sensing systems;Electromyography;Sensors|
|[An Intelligent Hybrid Fuzzy PI controller for Performance Analysis of Permanent Magnet Synchronous Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061103)|N. Lakshmipriya; S. Ayyappan; R. Prabu; M. Hariprabhu|10.1109/ICSSIT55814.2023.10061103|Permanent Magnet Synchronous Motor;Slime Mould Algorithm;Fuzzy Proportional -Integral Controller;Back Propagated Spiking Neural Network;PI control;Neural networks;Stochastic processes;Process control;Permanent magnet motors;Reliability engineering;Regulation|
|[Intelligent Controller for Flyback Converter with 31-Level Inverter for Grid-Connected Hybrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060900)|D. R. Kishore; K. Ajay; D. Anil; G. Venkatesh|10.1109/ICSSIT55814.2023.10060900|Renewable energy;Multilevel;Flyback;PI;Fuzzy;Continuous and discontinuous conduction mode;Wind;Renewable energy sources;Wind energy;Control systems;Multilevel inverters;Hybrid power systems;Temperature control|
|[Design and Analysis of Prediction method for FBG based Humidity Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061013)|B. Raju; R. Kumar; S. Dhanalakshmi|10.1109/ICSSIT55814.2023.10061013|Prediction;Regression;Fibre Bragg Grating;Humidity Sensor;Machine learning.;Optical fibers;Integrated optics;Optical fiber sensors;Optical polarization;Wavelength measurement;Optical variables measurement;Fiber gratings|
|[Design and Simulation of Five Level Buck Boost Converter for Grid Connected Hybrid Power System using Matlab/Simulink](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061069)|J. Shri Harsha; N. Shamala; L. S. Kumar; R. Prakash|10.1109/ICSSIT55814.2023.10061069|voltage multiplier;voltage-lift switched inductor (VLSI) cell;multilevel buck-boost converter;PI control;Software packages;Switching frequency;Switches;Very large scale integration;Transformers;Topology|
|[Arduino based Zero Emission Residential Home for Sustainable Township](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061144)|S. Potdar; C. More; S. Bhingare; V. Mahajan; A. Girase; S. Gupta|10.1109/ICSSIT55814.2023.10061144|Zero Emission Home;Sustainable Home;Solar PV System;Rain;Government;Focusing;Alarm systems;Energy efficiency;Sustainable development;Power generation|
|[Advancements in PV Technology- Floating Photovoltaics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060971)|A. Badhoutiya|10.1109/ICSSIT55814.2023.10060971|Polyethylene floatation tanks;floating solar projects;Pontoon;Green energy;mooring structure;FPV (Floating Photovoltaic);floaters;TIC (total installed capacity);Technological innovation;Polyethylene;Sociology;Sea measurements;Solar energy;Rivers;Solar panels|
|[Design and Implementation of an Interoperable IoT Based Health Monitoring System for Diabetes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061057)|M. Bollimuntha; K. Murugan|10.1109/ICSSIT55814.2023.10061057|Internet of Things (IoT);Healthcare;chronic diseases;Arduino-UNO;cloud;Liquid Crystal Display (LCD) monitor;Temperature sensors;Temperature measurement;Medical services;Liquid crystal displays;Diabetes;Sensors;Internet of Things|
|[Multifactor Mutual Authentication of IoT Devices and Server](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061113)|N. Odyuo; S. Lodh; S. Walling|10.1109/ICSSIT55814.2023.10061113|Internet of Things (IoT);Rivest;Shamir;Adleman (RSA);Advanced Encryption Standard (AES);Nonce;Encryption;Decryption;Multi-factor authentication;Authentication;Data protection;Encryption;Sensors;Batteries;Internet of Things|
|[An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060907)|G. A. Senthil; P. Suganthi; R. Prabha; M. Madhumathi; S. Prabhu; S. Sridevi|10.1109/ICSSIT55814.2023.10060907|Internet of Things (IoT);Global System for Mobile Communications (GSM);Liquefied Petroleum Gas (LPG);Sensor Networks;Gas Sensor;Ultrasonic Sensor;Short Message Service (SMS);Global Positioning System (GPS);General Packet Radio Service (GPRS);Arduino;Smart Intelligence;Industries;GSM;Cloud computing;Vents;Fires;Sensor systems;Intelligent sensors|
|[IoT – based Electrolytic and Pulse Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060894)|N. V. D. Padmanabhuni; N. Neelima; S. Mogili; S. Dokku|10.1109/ICSSIT55814.2023.10060894|Load sensor;Pulse sensor;Saline;Amplifier;Global System for Mobile communication (GSM) module;GSM;Costs;Hospitals;Salinity (geophysical);Sociology;Mobile communication;Liquid crystal displays|
|[Edge and Android Application based Health Monitor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061024)|K. Umapathy; S. Chandramohan; D. Muthukumaran; M. Sivakumar; M. Vinoth; S. Selvakumar|10.1109/ICSSIT55814.2023.10061024|Server;Arudino;Edge;Internet of Things;Android;Temperature sensors;Temperature measurement;Heart rate;Temperature distribution;Computational modeling;Sensors;Servers|
|[An GMM Method in IoT Approach to Improve Energy Efficiency in Smart Building](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061096)|R. Raja; R. Saraswathi|10.1109/ICSSIT55814.2023.10061096|IoT;Smart Energy Systems;Transmission Network;TTS;Sensors;Actuators;Smart buildings;Protocols;Transmitters;Power system management;Layout;Water quality;Transforms|
|[IoT Cloud Systems: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060983)|G. A. Nagendran; R. J. S. Raj; C. Sharon Roji Priya; H. Singh|10.1109/ICSSIT55814.2023.10060983|IoT;Cloud Computing;Survey;Industries;Complexity theory;Internet of Things;Business|
|[Home Automation using Arduino through Voice Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061121)|C. Srinivasan; C. Sumanthkumar; C. Harsha Vardhan; C. Sai Chaitanya|10.1109/ICSSIT55814.2023.10061121|Home Automation;Internet of Things;Voice Control;Drugs;Schedules;Home automation;Portable computers;Hospitals;Organizations;Maintenance engineering|
|[Effective Billing Methodology in Large Scale Stores/Malls using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061137)|G. Srinadh; G. N. Kumar; G. Billgates; M. V. Sheela Devi|10.1109/ICSSIT55814.2023.10061137|Smart Trolley;QR/Barcode;Internet of Things;Home appliances;Sports equipment;Customer satisfaction|
|[A Top-View Hand Gesture Recognition System for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060969)|W. J. Wisener; J. D. Rodriguez; A. Ovando; C. Woolford; K. Patel|10.1109/ICSSIT55814.2023.10060969|Internet of Things;Neural Network;Deep Learning;Gesture Recognition;YOLOv7;Object Detection;Protocols;Deep architecture;Gesture recognition;Smart homes;Cooperative systems;Cameras;Software|
|[Low-cost Prototype for IoT-based Smart Monitoring through Telegram](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061155)|P. Samuda; K. Sivachandar; N. G. Praveena; C. Nithiya; D. Kamalesh; C. Lokesh|10.1109/ICSSIT55814.2023.10061155|Internet of Things;Infrared Sensors;Surveillance;Telegram;Smart Monitoring;ESP32 microcontroller;Low-cost;Vibrations;Surveillance;Prototypes;Cameras;Motion detection;Software;Security|
|[Arduino based Smart Controller for Footwear Mat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060932)|V. Lankipalli; A. Yogeswari; J. Archana; S. Sridevi; N. Sridhar; M. Pappa|10.1109/ICSSIT55814.2023.10060932|Air blower;ATmega328P Microcontroller;Relay coil;Steel Rod;Ultrasonic Sensor.;Hospitals;Power supplies;Microcontrollers;Gears;Oils;Footwear;Switches|
|[Analysis of Impacts of Various Masks on Human Health using IoT Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060901)|S. Vijayalakshmi; A. Paramasivam; R. Khanal; R. Kamar; L. Lyngdoh; N. M. Masoodhu Banu|10.1109/ICSSIT55814.2023.10060901|Face masks;Heart beat rate;Internet of things;Oxygen saturation;Pulse oximeters;Heart beat;Pulmonary diseases;Surgery;Sensor systems;Internet of Things;Respiratory system;Viruses (medical)|
|[Android based Smart System for Vehicle Garage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060944)|S. Alagarsamy; B. Deepak; S. M. Asif; V. M. Krishna; C. Tejesh; A. S. Kumar|10.1109/ICSSIT55814.2023.10060944|Service Providers for Vehicle Repair;Mechanic Finder;Vehicle Breakdown Service Stations;Electric breakdown;Software;Workstations;Internet;Automobiles|
|[An Automatic Water Flow Management System for Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060953)|S. Kalyanapu; A. R. Kandula; S. R. K. D. Movva; S. M. C. Potharlanka; P. Yakkala; M. Pittu|10.1109/ICSSIT55814.2023.10060953|Smart Irrigation System;Capacitive Soil Moisture Sensor;Solenoid Valves;Arduino;Digital Temperature and Humidity (DHT-II) Sensor;Soil Moisture Percentage;Irrigation;Temperature sensors;Temperature measurement;Irrigation;Schedules;Soil measurements;Soil moisture;Moisture|
|[Design of Various Controllers for Car Suspension System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060961)|S. K. Sunori; A. Mittal; M. Manu; P. Agarwal; S. Arora; P. Garia; P. Juneja|10.1109/ICSSIT55814.2023.10060961|Suspension system;Quad car model;Spring;Damper;Controller;CHR;SIMC;ANFIS;Setpoint;PI control;Roads;Friction;Electric shock;Tires;Shock absorbers;Mathematical models|
|[Advanced Smart Home and Office Automation using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061148)|M. Arunkumar; V. Archana; S. A. Devi; S. N. Sri|10.1109/ICSSIT55814.2023.10061148|Internet of Things;Global system for Monitoring;Voice-automated system;Smart Home;Home appliances;Wireless sensor networks;Bluetooth;Surveillance;Smart homes;Transforms;Speech recognition|
|[Smart Home Security System using IoT and ESP8266](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061059)|P. K. Sattaru; K. V. Burugula; R. Channagiri; S. Kavitha|10.1109/ICSSIT55814.2023.10061059|Smart home;Internet of Things (IoT);ESP8266 Microprocessor;Surveillance Camera;Urban areas;Sociology;Smart homes;Safety;Security;Reliability;Older adults|
|[Smart Manhole Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060880)|K. Ravi Kumar; K. Vijaya Lakshmi; G. Rohin Kumar; G. Jagan Mohan; P. Devi Vara Prasad|10.1109/ICSSIT55814.2023.10060880|Arduino Uno;Manhole cover;Internet of Things;Sensors;Thing-Speak;Gases;Roads;Maintenance engineering;Safety;Internet of Things;Monitoring|
|[Ultra-Low Power Microcontroller Architectures for the Internet of Things (IoT) devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060949)|I. Sultan; M. T. Banday|10.1109/ICSSIT55814.2023.10060949|ARM(Advanced RISC Machine)Processors;Internet of Things;Ultra-low Power Microcontrollers;Digital Processing;Sensing Subsystem.;Wireless communication;Integrated circuits;Wireless sensor networks;Power demand;Microcontrollers;Computer architecture;Market research|
|[An Advanced Analyzation of Ultrasonic Sound Detection and Ranging Method using Arduino](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060882)|T. K. Devi; Ritu; S. Polepaka; R. Rastogi; S. R. Wategaonkar; A. Achuthan|10.1109/ICSSIT55814.2023.10060882|Sonars;Navigation;2-Layered Screen;3-layered screen;Costs;Ultrasonic variables measurement;Instruments;Sonar;Acoustics;Distance measurement;Object recognition|
|[Crop Monitoring System with Water Moisture Level using Arduino](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060913)|K. R. Sowmia; S. Prithi; K. Vijay; I. Eugene Berna; B. Bhuvaneswaran|10.1109/ICSSIT55814.2023.10060913|IoT;farmer;crop;moisture;soil;Productivity;Soil measurements;Moisture measurement;Crops;Moisture;Soil;Water pumps|
|[Automation of Smart Home using Smart Phone via Google Assistant](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060979)|S. Imran Hussain; S. Deepalakshmi; R. J. Benilla; V. Charu Nivetha|10.1109/ICSSIT55814.2023.10060979|smart home;automation;smart phone;IF This Than That;embedded C;google assistant;voice commands;Industries;Wireless communication;Wireless sensor networks;Automation;Microcontrollers;Smart homes;Web servers|
|[Blood and Plasma Donation, Management System with Global Positioning System using FIREBASE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061022)|I. Vasavi; C. N. Krishna; K. Sathvika|10.1109/ICSSIT55814.2023.10061022|C MongoDB;ExpressJS;ReactJS and NodeJS Stack;Donor;Seeker;Global Positioning System;Firebase;Technological innovation;Law;Databases;Sociology;Plasmas;Reliability;Statistics|
|[Implementation of Smart Security Band for Women Safety](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061098)|M. Kalbande; Y. Gaidhani; S. Telrandhe; M. C. Paul|10.1109/ICSSIT55814.2023.10061098|Smart Security;Wearable Band;Women Safety;GSM;GPS;Radio frequency;Law enforcement;Electric shock;Pressing;Receivers;Modems;Transceivers|
|[IoT based Soil Monitoring and Control Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061141)|K. Sakthi; Y. Mohamed Sarim Zain; S. Manoj Shakthi Raj; M. Manickavasakar|10.1109/ICSSIT55814.2023.10061141|Agriculture;Internet of Things;Soil;Nitrogen;Phosphorus;and Potassium (NPK);Soil Moisture;Arduino UNO.;Irrigation;Temperature distribution;Soil moisture;Crops;Moisture;Weather forecasting;Humidity|
|[Design of Power, Control and Communication Module for Electric Vehicle Supply Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060967)|M. Sivadharani; D. S. A. E. Xavier|10.1109/ICSSIT55814.2023.10060967|Power Factor Correction with IBC Converter;Control Scheme;Boost Inductor;Current Partaking;Cuwent Ripple Reduction;Lyapunov-likelihood Control Method;AClDC Power Conversion;Switching Logic Scheme;Control performance;DClDC Power Conversion;Resonant Converter.;Inductance;Sockets;Power control;Soft switching;Switches;Resonant converters;Zero voltage switching|
|[Review on Healthcare Monitoring and Tracking Wristband for Elderly People using ESP-32](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061134)|U. Aakesh; Y. Rajasekaran; Sabarivani; T. Sudhakar|10.1109/ICSSIT55814.2023.10061134|Internet of Things;Microcontroller;Healthcare;Wearable Health Monitoring Systems;Medical services;Transforms;Feature extraction;Older adults;Biomedical monitoring;Task analysis|
|[Power Quality Improvement of Standalone Microgrid during Faults on Distribution Line with DSTATCOM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061071)|Y. I. P. Darshini; K. Deepak; U. Chaithanya; Y. Hazarathaiah; D. Vannurappa; D. S. G. Malla|10.1109/ICSSIT55814.2023.10061071|Voltage Control;Reactive Power and Unbalanced Load Compensation;Faults;DSTATCOM;Hydrogen Production;Maximum power point trackers;Reactive power;Voltage measurement;Torque;Wind energy;Microgrids;Generators|
|[A Fog Computing based Agriculture-IoT Framework for Detection of Alert Conditions and Effective Crop Protection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060995)|G. Singh; J. Singh|10.1109/ICSSIT55814.2023.10060995|Internet of Things;Sensors;IoT Ecosystem Architecture;Applications of IoT in Smart Agriculture;Alert Conditions Detection;Fog Computing;IoT based Fog Computing Framework;Smart agriculture;Cloud computing;Technological innovation;Smart cities;Crops;Sensors;Internet of Things|
|[Women Safety Night Patrolling IoT Robot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060955)|R. Mahalakshmi; M. Kavitha; B. Gopi; S. M. Kumar|10.1109/ICSSIT55814.2023.10060955|Intemet of Things;Night SunZeillance;Robot;Women Safety;Security;Technological innovation;Surveillance;Roads;Prototypes;Product development;Safety;Security|
|[Rider Safety System using IoT for Two-Wheelers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061027)|A. Ramu; J. Chandra; M. Parthive; S. Jayanth.|10.1109/ICSSIT55814.2023.10061027|Limit Switches;Gas Sensor;Eye-blink Sensor;Arduino;GSM module;GPS module;Radio frequency;Head;Transmitters;Sleep;Roads;Switches;Control systems|
|[Wireless IoT Security Management Enhancement and Optimization using Various Elements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060989)|C. S. Kingsly; N. George; N. Joseph; K. Johnpeter; S. K. Joseph; K. A. Mohamed Riyazudeen|10.1109/ICSSIT55814.2023.10060989|Internet of Things;Cloud Technology;Hybrid;Data Science;Wireless communication;Productivity;Costs;Security management;Dogs;Data science;Remote working|
|[IoT based Smart Communication System for Accident Prevention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060924)|N. Selvam; M. Sampath; J. S. Madhavan; T. Sharmitha; D. S. Mugesh|10.1109/ICSSIT55814.2023.10060924|motorcycle;smart helmet;fall detection;radio-frequency module;Head;Data analysis;Communication systems;Sensor systems;Real-time systems;Safety;Internet of Things|
|[Efficiency Enhancement of Solar Panel using IoT and Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060893)|D. Manimegalai; S. Winlin Agastiya; P. Chandrasekhar; S. Sivakumar|10.1109/ICSSIT55814.2023.10060893|Solar;Artificial Intelligence;Internet of Things;Photovoltaic modules (PV);Power Plant;Efficiency;Affordable Source;Electricity;Dust;Humidity;Temperature;Artificial Neural Network (ANN);Temperature sensors;Degradation;Temperature;Neural networks;Energy measurement;Solar energy;Cleaning|
|[Smart LPG Gas Leakage Detection and Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060970)|G. N. Sai; K. P. Sai; K. Ajay; P. Nuthakki|10.1109/ICSSIT55814.2023.10060970|Gas leakage detection;Internet of Things;Mobile Application;Industries;Cloud computing;Gases;Atmospheric measurements;Atmosphere;Containers;Loss measurement|
|[Sine Cosine Algorithm based Optimal Cluster Head Selection in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060895)|B. Pitchaimanickam; P. Muthuvel; R. Rajasekar|10.1109/ICSSIT55814.2023.10060895|Sine Cosine Algorithm;Cluster Head Selection;Energy Consumption;Network Lifespan;Wireless Sensor Networks;Wireless sensor networks;Base stations;Metaheuristics;Clustering algorithms;Energy measurement;Heterogeneous networks;Sensors|
#### **2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS)**
- DOI: 10.1109/MEMS49605.2023
- 15-19 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[From ETCH to EDGE AI: Opening New Horizons with Smart Sensor Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052196)|S. Finkbeiner|10.1109/MEMS49605.2023.10052196|nan;Micromechanical devices;Performance evaluation;Power demand;Artificial intelligence;Consumer electronics;Smart phones;Intelligent sensors|
|[Sub-300 Millivolt Operation in Nonvolatile 300 nm x 100 nm Phase Change Nanoelectromechanical Switch](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052605)|M. A. Masud; G. Piazza|10.1109/MEMS49605.2023.10052605|Phase Change Materials;NEM Relay;GeTe;Emerging Memory;Nonvolatility;Heating systems;Phase change materials;Micromechanical devices;Nanofabrication;Nonvolatile memory;Nanoelectromechanical systems;Switches|
|[A Fast and Energy-Efficient Nanoelectromechanical Non-Volatile Memory for In-Memory Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052346)|Y. -B. Lee; M. -H. Gang; P. -K. Choi; S. -H. Kim; T. -S. Kim; S. -Y. Lee; J. -B. Yoon|10.1109/MEMS49605.2023.10052346|Nanoelectromechanical (NEM) non-volatile memory;Fast speed;Energy-efficiency;CMOS-compatible;In-memory computing;Micromechanical devices;Nonvolatile memory;Nanoelectromechanical systems;Programming;Logic gates;In-memory computing;CMOS technology|
|[Towards Ultra-High Spatial Resolution Sensing of GHz Ultrasound Using Strain Modulation of Field Effect Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052433)|R. Sanghvi; J. Kuo; A. Ravi; A. La|10.1109/MEMS49605.2023.10052433|GHz imaging;ultrasonic;Fresnel focusing transducer;CMOS;strain modulation;piezoresistance;Frequency modulation;Transducers;Three-dimensional displays;Focusing;Bandwidth;Acoustics;Sensors|
|[A Tactile Sensor Array with a Monolithically Integrated Neural Network for Edge Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052560)|T. Lei; Y. Hu; M. Wong|10.1109/MEMS49605.2023.10052560|Tactile sensor array;artificial neural network;edge computation;dual-gate;metal-oxide;thin-film transistors;Training;Neuromorphics;Array signal processing;Tactile sensors;Artificial neural networks;Thin film transistors;Transistors|
|[Evaluation of Local and Internal Elasticity of Hydrogel Materials by Using Light-Driven Gel Actuator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052379)|H. Nakajima; Y. Koike; Y. Yokoyama; M. Hagiwara; T. Hayakawa|10.1109/MEMS49605.2023.10052379|Gel actuator;Light drive;PNIPAAm;Elasticity evaluation;Wiring;Micromechanical devices;Actuators;Force measurement;Deformation;Hydrogels;Microscopy|
|[3D Printed Miniaturized Soft Microswimmer for Multimodal 3D Air-Liquid Navigation and Manipulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052220)|D. Decanini; A. Harouri; A. Mizushima; B. Kim; Y. Mita; G. Hwang|10.1109/MEMS49605.2023.10052220|3D printing;Two-photon nanolithography;Soft microswimmer;Magnetic Actuation;Microfluidics;Motion planning;Visualization;Three-dimensional displays;Automation;Navigation;Tracking;Force|
|[Self-Driven Capillaric Viscometer for Direct or Cascaded Bar Graph Read-Out of Relative Sample Viscosity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052458)|D. Mak; R. C. Meffan; J. Menges; F. Dolamore; C. Fee; R. C. J. Dobson; V. Nock|10.1109/MEMS49605.2023.10052458|Capillaric Circuits;Co-flow Viscometer;Capillaric Field Effect Transistor;Point-of-care Testing;Viscosity;Temperature measurement;Visualization;Liquids;Rheology;Point of care;Blood|
|[A Flexible Biosensing Platform for High-Throughput Measurement of Cardiomyocyte Contractility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052252)|W. Dou; J. Maynes; Y. Sun|10.1109/MEMS49605.2023.10052252|hiPSC-CMs;Microdevice array;Contractility;Drug testing;Drugs;Force;Strain measurement;Rhythm;Capacitive sensors;Biomembranes;Biosensors|
|[Flexible Bi-directional Brain Computer Interface for Controlling Turning Behavior of Mice](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052363)|Y. Ye; Y. Tian; H. Wang; Q. Cheng; K. Zhang; X. Wang; C. Zhou; C. Xu; X. Wei; Z. Zhou; T. H. Tao; L. Sun|10.1109/MEMS49605.2023.10052363|Brain computer interface;Flexible probe;Turning behavior;Neuromodulation;Motor control;Electrodes;Neurons;Bidirectional control;Electrical stimulation;Turning;Mice;Brain-computer interfaces|
|[High Sensitivity Mems Z-Axis Accelerometer with In-Plane Differential Readout](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052375)|V. Zega; G. Gattere; M. Riani; F. Rizzini; A. Frangi|10.1109/MEMS49605.2023.10052375|MEMS;z-axis accelerometer;motion conversion;high-sensitivity;differential readout;comb fingers;Accelerometers;Micromechanical devices;Fabrication;Particle beams;Sensitivity;Numerical analysis;Fingers|
|[Two-Axis Electromagnetic Scanner Integrated with an Electrostatic XY-Stage Positioner](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052600)|Y. Okamoto; H. Okada; M. Ichiki|10.1109/MEMS49605.2023.10052600|MEMS Scanner;stabilizer;positioner;comb-drive actuator;electromagnetic actuator;Micromechanical devices;Lasers;Electromagnetic forces;Mirrors;Electromagnetics;Electrostatics;Electrostatic actuators|
|[MEMS Shock Absorbers Integrated with Al2O3-Reinforced, Mechanically Resilient Nanotube Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052484)|H. Lee; E. Jo; J. -I. Lee; J. Kim|10.1109/MEMS49605.2023.10052484|Shock absorber;alumina;carbon nanotube;mechanical shock;Micromechanical devices;Electric shock;Carbon nanotubes;Shock absorbers;Nanoscale devices;Kinetic energy;Reliability|
|[High-Inductance-Density MEMS 3D-solenoid Transformers with Inserted Thin-Film Ferrite Magnetic Core For On-Chip Integrated DC-DC Power Conversions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052574)|C. Chen; P. Pan; J. Gu; X. Li|10.1109/MEMS49605.2023.10052574|Power MEMS;Micro integrated transformers;PwrSoC;Isolation technology;MEMS-casting;Ferrite magnetic core;DC-DC power conversions;Micromechanical devices;Ferrites;Couplings;Inductance;Windings;DC-DC power converters;Transformer cores|
|[Micron-Sized Parylene-In-Oil Water Protection Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052444)|K. -M. Shang; H. Shen; N. Dai; D. Kong; T. Hsiai; Y. -C. Tai|10.1109/MEMS49605.2023.10052444|Parylene;Parylene-in-oil;Silicone oil;Adhesion;Corrosion;Medical devices;Implantable devices;Micromechanical devices;Medical devices;Adhesives;Oils;Morphology;Life estimation;Implants|
|[A Pipette Tip Integrated with a Capacitive Microsensor Fabricated by Combined 3D Printing and MEMS Process for Cell Detection and Transportation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052499)|S. Amaya; H. Sugiura; B. Turan; S. Kaneko; F. Arai|10.1109/MEMS49605.2023.10052499|Capacitive microsensor;3D printing;Cell Detection;Micromechanical devices;Performance evaluation;Fabrication;Electrodes;Transportation;Three-dimensional printing;Robot sensing systems|
|[Foldable Polymer Stent Integrated with Wireless Pressure Sensor for Blood Pressure Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052470)|N. -E. Oyunbaatar; D. -W. Lee|10.1109/MEMS49605.2023.10052470|Polymer stent;wireless sensor;LC type pressure sensor;Pressure sensors;Wireless communication;Micromechanical devices;Fabrication;Wireless sensor networks;Sensitivity;Three-dimensional displays|
|[A Dynamic Microarray Device for Selective Pairing and Electrofusion of Liposome](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052475)|S. Takamori; H. Mimura; T. Osaki; S. Takeuchi|10.1109/MEMS49605.2023.10052475|Giant Liposomes;Selective Pairing;Selective Electrofusion;Micro Electrodes;Performance evaluation;Micromechanical devices;Microelectrodes;Metals;Fluorescence;Photomicrography;Microfluidics|
|[Real-Time Functional Brain Mapping Based on High-Channel-Count, Ultra-Conformal Neural Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052566)|X. Wang; Z. Chen; J. Liang; X. Wei; L. Sun; M. Li; Z. Zhou; T. H. Tao|10.1109/MEMS49605.2023.10052566|Passive functional brain mapping;ECoG;Neural interface;Ultra-conformal contact;Electrodes;Micromechanical devices;Electric potential;Nose;Brain mapping;Eyelids;Dogs|
|[Acoustofluidics: Merging Acoustics and Fluid Mechanics for Biomedical Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052445)|T. J. Huang|10.1109/MEMS49605.2023.10052445|nan;Pulmonology;Technological innovation;Pathology;Fluids;Ultrasonic imaging;Shoulder;Medical services|
|[Silicon Carbide Reinforced Vertically Aligned Carbon Nanotube Composite for Harsh Environment Mems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052162)|J. Mo; S. Shankar; G. Zhang; S. Vollebregt|10.1109/MEMS49605.2023.10052162|SiC-CNT composite;HAR structures;harsh environment;thermal actuator;Micromechanical devices;Fabrication;Actuators;Silicon carbide;Micromachining;Carbon nanotubes;Task analysis|
|[A Reliable Release Method for A Back-End-Of-Line Nems Switch of A Monolithic Three-Dimensional Integrated Cmos-Nems Circuit](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052124)|T. -S. Kim; Y. -B. Lee; S. -Y. Lee; S. -J. Lee; J. -B. Yoon|10.1109/MEMS49605.2023.10052124|Reliable Release Method;Monolithic Three-Dimensional Integrated CMOS-NEMS Circuit;Aluminum Back-End-Of-Line NEMS Switch;Silicon compounds;Water;Nanoelectromechanical systems;Surface resistance;Switches;Etching;Surface roughness|
|[Increase of Expansion Rate and Direction Control of Microgel Actuators for Single Cell Manipulations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052310)|K. Nakano; H. Wada; Y. Yokoyama; T. Hayakawa|10.1109/MEMS49605.2023.10052310|Gel actuator;Cell manipulation;On-chip actuator;PNIPAAm;Lift-off process;Fabrication;Micromechanical devices;Actuators;Microorganisms;Deformation;Process control;Proposals|
|[Generalized-Accumulated-Temperature Parameter for Characteristic Prediction of Metal-Based Mems Cantilever](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052153)|Y. Zhang; J. Sun; H. Liu; Z. Liu|10.1109/MEMS49605.2023.10052153|MEMS;Metal Cantilever;Lifetime;Generalized-Accumulated-Temperature (GAT) Parameter;Micromechanical devices;Radio frequency;Performance evaluation;Temperature sensors;Temperature;Microswitches;Fitting|
|[Mems-Based Water Collection Condensation Particle Counter (WCCPC) Optimized for Multi-Point Monitoring of Airborne Nanoparticles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052381)|S. -J. Yoo; Y. -J. Kim|10.1109/MEMS49605.2023.10052381|Water collection condensation particle counter;Airborne nanoparticle;Multi-point monitoring;Nanoparticles;Performance evaluation;Micromechanical devices;Monitoring|
|[Reconstituting Fundamentals of Bacteria Mediated Cancer Therapy On A Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052432)|W. Lee; J. Park; D. Kang; S. Suh|10.1109/MEMS49605.2023.10052432|Organ-on-a-chip;Bacteria mediated cancer therapy;Tumor microenvironment;Micromechanical devices;Microorganisms;Medical treatment;Media;Mice;In vitro;Microfluidics|
|[3D Spatial Focal Control by Arrayed Optofluidic Prisms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052460)|C. -H. Lee; Y. Lee; S. -Y. Park|10.1109/MEMS49605.2023.10052460|Electrowetting;liquid prism;optofluidics;focal length;arrayed system;Photovoltaic systems;Three-dimensional displays;Liquids;Beam steering;Tracking;Fluid dynamics;Focusing|
|[High-Speed and Pinpoint Liquid Exchange on Microfluidic Chip using 3d Printed Double-Barreled Microprobe with Dual Pumps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052138)|X. Du; S. Kaneko; H. Maruyama; H. Sugiura; F. Arai|10.1109/MEMS49605.2023.10052138|Liquid exchange;Microfluidic chip;3D printed double-barreled microprobe;Dual pumps;Micromechanical devices;Liquids;Three-dimensional displays;Sensitivity;Force;Physiology;Time factors|
|[Design of a DNA Synthesis Chip for Data Storage with Ultra-High Throughput and Density Featuring Large-Scale Integrated Circuits and Microfluidic Confinement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052171)|N. Wang; S. Yang; D. Wang; Z. Cao; Y. Luo; J. Zhao|10.1109/MEMS49605.2023.10052171|DNA Data Storage;DNA Synthesis;Microfluidic;DRAM;Protons;Micromechanical devices;Liquids;DNA;Random access memory;Voltage;Throughput|
|[A Real-Time Wireless Calorimetric Flow Sensor System with a Wide Linear Range for Low-Cost Respiratory Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052555)|L. Huang; I. Izhar; X. Zhou; M. Fang; S. Huang; Y. -K. Lee; X. Pan; W. Xu|10.1109/MEMS49605.2023.10052555|Thermal flow sensor;respiratory monitoring;wide linear range;wireless sensing system;Wireless communication;Wireless sensor networks;Sensitivity;Medical services;Thermal sensors;Sensor systems;Real-time systems|
|[Advanced Thermophysical Properties Measurements Using Heater-Integrated Fluidic Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052242)|J. Ko; B. J. Lee; J. Lee|10.1109/MEMS49605.2023.10052242|Channel resonator;Simultaneous measurements;Heat capacity;Thermal conductivity;Heating systems;Temperature measurement;Liquids;Atmospheric measurements;Density measurement;Conductivity measurement;Conductivity|
|[A Miniaturized Transit-Time Ultrasonic Flowmeter Using PMUTS for Low-Flow Measurement in Small-Diameter Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052185)|Y. Gao; Z. Wu; M. Chen; L. Lou|10.1109/MEMS49605.2023.10052185|Aluminum nitride (AlN);PMUTs;Transit-time ultrasonic flowmeter (TTUF);Small-diameter;Ultrasonic transducers;Micromechanical devices;Sensitivity;Ultrasonic variables measurement;Fluid flow;Flowmeters;Mechanical variables measurement|
|[MEMS Differential Thermopiles for High-Sensitivity Hydrogen Gas Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052631)|H. Zhang; H. Jia; M. Li; P. Xu; X. Li|10.1109/MEMS49605.2023.10052631|Hydrogen sensor;MEMS thermopile;MIS process;High Sensitivity;Micromechanical devices;Sensitivity;Hydrogen;Platinum;Thermocouples;Combustion;Silicon|
|[Domain/Boundary Variation in Cantilever Array for Bandwidth Enhancement of PZT MEMS Microspeaker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052611)|S. -W. Chang; T. -C. Wei; S. -C. Lo; W. Fang|10.1109/MEMS49605.2023.10052611|MEMS;microspeaker;piezoelectric;acoustics;cantilever array;out-of-phase driving;wide bandwidth;Micromechanical devices;Vibrations;Phased arrays;Power measurement;Resonant frequency;Bandwidth;Ear|
|[On the Design of Piezoelectric MEMS Microspeaker with High Fidelity and Wide Bandwidth](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052253)|T. -C. Wei; Z. -S. Hu; S. -W. Chang; W. Fang|10.1109/MEMS49605.2023.10052253|Piezoelectric MEMS Microspeaker;PZT;acoustic devices;SPL;bandwidth;high fidelity;Micromechanical devices;Pistons;Actuators;Voltage measurement;Force;Bandwidth;Routing|
|[High-Performance Wafer-Scale Transfer-Free Graphene Microphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052360)|R. Pezone; G. Baglioni; P. M. Sarro; P. G. Steeneken; S. Vollebregt|10.1109/MEMS49605.2023.10052360|Graphene;Microphone;Membrane;Wafer-Scale;MEMS;Transfer-Free;Micromechanical devices;Sensitivity;Graphene;Hafnium;Ions;Silicon;Etching|
|[High-SPL and Low-Driving-Voltage pMUTs by Sputtered Potassium Sodium Niobate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052391)|F. Xia; Y. Peng; S. Pala; R. Arakawa; W. Yue; P. -C. Tsao; C. -M. Chen; H. Liu; M. Teng; J. H. Park; L. Lin|10.1109/MEMS49605.2023.10052391|Ultrasound;pMUT;acoustic pressure;low driving voltage;piezoelectric;KNN;Loudspeakers;Ultrasonic transducers;Time-frequency analysis;Sensitivity;Ultrasonic imaging;Sodium;Resonant frequency|
|[Epitaxial Pb(Zr,Ti)O3-Based Piezoelectric Micromachined Ultrasonic Transducer Fabricated on Silicon-On-Nothing (SON) Structure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052628)|T. Sekiguchi; S. Yoshida; Y. Kanamori; S. Tanaka|10.1109/MEMS49605.2023.10052628|Piezoelectric micro ultrasonic transducers (pMUTs);Silicon on Nothing (SON);Pb(Zr,Ti)O3 (PZT) monocrystalline thin films;silicon migration;hydrogen anneal;Ultrasonic transducers;Fabrication;Annealing;Piezoelectric materials;Hydrogen;Resonant frequency;Silicon|
|[Leveraging Semiconductor Ecosystems to MEMS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052342)|W. Fang; S. -S. Li; M. -H. Li|10.1109/MEMS49605.2023.10052342|CMOS;CMOS-MEMS;semiconductor;MEMS;sensors;actuators;transducers;piezoelectric;Micromechanical devices;Industries;Metaverse;Electronics industry;Ecosystems;Urban areas;Sociology|
|[Programmable Silicon Nitride Photonic Integrated Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052328)|H. Tian; A. G. Attanasio; A. Siddharth; A. Voloshin; V. Snigirev; G. Lihachev; A. Bancora; V. Shadymov; R. N. Wang; J. Riemensberger; T. J. Kippenberg; S. A. Bhave|10.1109/MEMS49605.2023.10052328|Piezoelectric actuator;Si3N4 microring resonator;Stress-optic effect;Acousto-optic modulator;Optical fibers;Optical fiber sensors;Actuators;Optical device fabrication;Optical resonators;Photonic integrated circuits;Silicon nitride|
|[Multifrequency Nanomechanical Mass Spectrometer Prototype for Measuring Viral Particles Using Optomechanical Disk Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052311)|O. Malvar; E. Gil-Santos; J. J. Ruz; E. Sentre-Arribas; A. Sanz-Jiménez; P. M. Kosaka; S. García-López; Á. S. Paulo; S. Sbarra; L. Waquier; I. Favero; M. Van Der Heiden; R. K. Altmann; D. Papanastasiou; D. Kounadis; I. Panagiotopoulos; J. Mingorance; M. Rodríguez-Tejedor; R. Delgado; M. Calleja; J. Tamayo|10.1109/MEMS49605.2023.10052311|Nanomechanical mass spectrometry;optomechanical disk resonators;microcantilevers;mass sensing;Nanoparticles;Bacteriophages;Gold;Visualization;Atmospheric measurements;Prototypes;Measurement by laser beam|
|[A Microfabricated Diamond Quantum Magnetometer with Picotesla Scale Sensitivity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052283)|F. Xie; Q. Liu; Y. Hu; L. Li; Z. Chen; J. Zhang; Y. Zhang; Y. Zhang; Y. Wang; J. Cheng; H. Chen; Z. Wu|10.1109/MEMS49605.2023.10052283|Diamond quantum magnetometer;nitrogen-vacancy (NV) centers;microfabrication;miniaturization;integration;portability;Sensitivity;Magnetometers;Magnetic resonance;Quantum mechanics;Diamonds;Fluorescence;Sensors|
|[A Non-Volatile Threshold Sensing System Using a Ferroelectric Hf0.5Zr0.5O2 Device and a LiNbO3 Microacoustic Resonator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052395)|O. Kaya; L. Colombo; B. Davaji; C. Cassella|10.1109/MEMS49605.2023.10052395|Ferroelectricity;Temperature Threshold Sensing;Hafnium Zirconium Oxide;Lithium Niobate;Acoustic Resonators;Varactors;Temperature sensors;Temperature measurement;Zirconium;Voltage measurement;Nonvolatile memory;Lithium niobate|
|[Resonant Confiners for Acoustic Loss Mitigation in Bulk Acoustic Wave Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052545)|J. Segovia-Fernandez; E. T. . -T. Yen|10.1109/MEMS49605.2023.10052545|BAW resonator;Q-factor;acoustic energy losses;antisymmetric mode;resonant confiners;Electrodes;Q-factor;Micromechanical devices;Shape;Metals;Resonant frequency;Finite element analysis|
|[High-Crystallinity 30% Scaln Enabling High Figure of Merit X-Band Microacoustic Resonators for Mid-Band 6G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052384)|G. Giribaldi; P. Simeoni; L. Colombo; M. Rinaldi|10.1109/MEMS49605.2023.10052384|MEMS;microacoustic resonators;CLMR;ScAlN;5G;6G;Performance evaluation;Q-factor;6G mobile communication;Micromechanical devices;Passive filters;Resonator filters;Resonant frequency|
|[Ferrite-Rod Antenna Driven Wireless Resoswitch Receiver](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052463)|K. H. Zheng; Q. Jin; C. T. . -C. Nguyen|10.1109/MEMS49605.2023.10052463|Resoswitch;wireless;receiver;antenna;bit rate;low power;resonator;micromechanical;quality factor;Wireless communication;Wireless sensor networks;Sensitivity;Bit rate;Receiving antennas;Adaptive arrays;Bandwidth|
|[Ultra-Wideband Mems Filters Using Localized Thinned 128° Y-Cut Thin-Film Lithium Niobate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052357)|J. Wu; S. Zhang; P. Zheng; L. Zhang; H. Yao; X. Fang; X. Tian; X. Zhao; T. Wu; X. Ou|10.1109/MEMS49605.2023.10052357|5G New Radio;MEMS;acoustic;resonators;filters;ultra-wideband;lithium niobate;A1 mode;Micromechanical devices;Couplings;Radio frequency;Lithium niobate;Resonator filters;Resonant frequency;Bandwidth|
|[Attractor Exchanger for Open-Loop Operation of Micromechanical Nonlinear Resonators Using Gap-Spacing Continuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052420)|C. -P. Tsai; W. -C. Li|10.1109/MEMS49605.2023.10052420|Attractor;gap-spacing continuation;parameter sweeping;vibro-impact resonator;Micromechanical devices;Actuators;Resonant frequency;Integrated circuit modeling;Hysteresis|
|[A CMOS-MEMS Ultrasensitive Thermometer Using Internal Resonance Iuduced Frequency Combs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052361)|T. -Y. Chen; C. -P. Tsai; W. -C. Li|10.1109/MEMS49605.2023.10052361|Frequency combs;internal resonance;CMOS-MEMS;temperature sensors;Mechanical sensors;Micromechanical devices;Temperature distribution;Temperature dependence;Frequency modulation;Thermometers;Sensitivity|
|[Atomically Thin NEMS Frequency Comb with Both Frequency Tunability and Reconfigurable Via Simultaneous 1:2 and 1:3 Mode Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052146)|B. Xu; J. Zhu; C. Jiao; J. Chen; Z. Wang|10.1109/MEMS49605.2023.10052146|Molybdenum disulfide (MoS2);internal resonance;nanoelectromechanical system (NEMS);mode coupling;frequency comb;two-dimensional (2D) materials;Micromechanical devices;Couplings;Nanoelectromechanical systems;Resonant frequency;Switches;Logic gates;Threshold voltage|
|[Instrumental Analysis of Advanced Catalysts Based on Resonant Microcantilevers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052599)|X. Li; P. Xu; Y. Chen; H. Yu; X. Li|10.1109/MEMS49605.2023.10052599|Resonant microcantilever;TPD;advanced catalysts;instrument;Temperature measurement;Heating systems;Instruments;Catalysts;Furnaces;Energy measurement;Resonant frequency|
|[A Multiplexed Bioaffinity Biosensing Patch for Point-of-Care Chronic Ulcer Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052565)|M. Sharifuzzaman; D. Kim; M. S. Reza; S. Jeong; H. S. Song; M. Abu Zahed; J. Y. Park|10.1109/MEMS49605.2023.10052565|Multiplexed biosensing patch;Microfluidic wound exudate collector;Antifouling coating;Chronic ulcer monitoring;Multiplexing;Temperature sensors;Temperature distribution;Sensitivity;Point of care;Wounds;Biosensors|
|[3-DoF Biohybrid Actuator with Multiple Skeletal Muscle Tissues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052336)|X. Ren; Y. Morimoto; S. Takeuchi|10.1109/MEMS49605.2023.10052336|Electrical stimulation;Muscle contraction;3D printing;Human myoblast;Hand-rolled structure;Micromechanical devices;Electrodes;Actuators;Animals;3-DOF;Muscles;Robots|
|[A Low Noise Microelectrode Array for Specific Cell Activity Modulation from Cell to Tissue](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052455)|B. Zhang; H. Yang; X. Wang; Z. Zhu; Z. He; W. Jiang; C. Tao; D. Zou; M. Li; Z. Zhou; L. Sun; T. H. Tao; X. Wei|10.1109/MEMS49605.2023.10052455|Microelectrode array;Specific modulation;Low noise;High signal to noise ratio;Microelectrodes;Target tracking;Neurons;Glass;Regulation;Recording;Spatial resolution|
|[Bionic Mechanical Hand Integrated with Artificial Olfactory Sensor Array for Enhanced Object Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052128)|J. Wang; X. Li; M. Liu; P. Zhang; T. H. Tao; N. Qin|10.1109/MEMS49605.2023.10052128|Object recognition;olfactory sensor array;bionic Drosophila algorithm;mechanical hand;MEMS;Micromechanical devices;Gases;Visualization;Target recognition;Shape;Olfactory;Robot sensing systems|
|[High Resolution Tactile Sensor for Measurement of a Complicated Tactile Feeling of "Shittori" With Moistness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052225)|G. Yamada; Y. Morita; K. Terao; F. Shimokawa; H. Takao|10.1109/MEMS49605.2023.10052225|Tactile sensors;Touch feeling;Haptics;Surface topography;Frictional forces;Micromechanical devices;Moisture measurement;Tactile sensors;Mechanical factors;Skin|
|[Pyramidal Structured Mxene/Ecoflex Composite-Based Toroidal Triboelectric Self-Powered Sensor for Human-Machine Interface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052622)|S. Zhang; S. S. Rana; G. Bahadur Pradhan; T. Bhatta; S. Jeong; J. Y. Park|10.1109/MEMS49605.2023.10052622|Toroidal TENG;self-powered sensor;human-machine interface;MXene/Ecoflex composite;Electrodes;Sensitivity;Nanocomposites;Fingers;Virtual reality;Three-dimensional printing;Fabrics|
|[Lig-Based Triaxial Tactile Sensor Utilizing Rotational Erection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052413)|R. Nakashima; N. Nakamura; T. G. Sano; E. Iwase; H. Takahashi|10.1109/MEMS49605.2023.10052413|Triaxial tactile sensor;Laser-induced graphene;Rotational erection system;Construction;Lasers;Force;Refining;Tactile sensors;Prototypes;Polyimides|
|[A Stretchable Strain-Insensitive Smart Glove for Simultaneous Detection of Pressure and Temperature](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052496)|S. Sharma; G. B. Pradhan; S. Jeong; J. Y. Park|10.1109/MEMS49605.2023.10052496|Smart glove;strain-insensitive;stretchable electronics;pressure sensor;temperature sensor;Temperature sensors;Fabrication;Electrodes;Temperature measurement;Sensitivity;Temperature;Lasers|
|[A Gesture Recognition Glove Assembled with Nanoforest-Integrated Infrared Thermopiles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052495)|M. Li; M. Shi; G. Chen; N. Zhou; H. Mao; C. Huang|10.1109/MEMS49605.2023.10052495|Gesture recognition;wearable;nanoforests;infrared thermopiles;Micromechanical devices;Sensitivity;Absorption;Imaging;Gesture recognition;Assistive technologies;Surface plasmons|
|[One Push Membrane Formation for Iterative Measurement of Ion Channel Activity on Arrayed Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052568)|H. Mimura; T. Osaki; S. Takamori; K. Nakao; S. Takeuchi|10.1109/MEMS49605.2023.10052568|Planar lipid bilayer;Artificial membrane;Ion channel activity measurement;Performance evaluation;Micromechanical devices;Semiconductor device measurement;Particle separators;Ions;Mechanical variables measurement;Lipidomics|
|[An Implantable Differential Sensor with Passive Wireless Interrogation for In-Situ Early Detection of Periprosthetic Joint Infection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052594)|J. Jiang; C. Napier; C. Choudhary; H. Claude Sagi; C. -Y. Lin; M. T. Archdeacon; T. Li|10.1109/MEMS49605.2023.10052594|Magnetoelastic sensor;implantable device;resonant;differential;bacterial detection;mass loading;viscosity;Wireless communication;Viscosity;Wireless sensor networks;Microorganisms;Sensitivity;Antibodies;Distance measurement|
|[Micromachined Piezoelectric Film-Based Flexible Electronics with Integration of Film-Self Temperature-Detecting Breath Sensor and Acetone Gas Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052614)|H. -Y. Yeh; G. -H. Feng|10.1109/MEMS49605.2023.10052614|Piezoelectric;wearable device;breath sensor;barium titanate;thermistor;acetone;Temperature sensors;Performance evaluation;Temperature distribution;Sensitivity;Thermistors;Polyimides;Sensors|
|[Flexible Tactile Sensing Array with High Spatial Density Based on Parylene Mems Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052487)|M. Zhang; Z. Wang; H. Xu; L. Chen; Y. Jin; W. Wang|10.1109/MEMS49605.2023.10052487|Flexible tactile sensing array;Parylene-filled trench;Silicon-based flexible electronics;Parylene MEMS;Micromechanical devices;Pressure sensors;Young's modulus;Mass production;Microfabrication;Linearity;Skin|
|[Silk-Enabled Foldable and Conformal Neural Interface with In-Plane Shielding for High-Quality Electrophysiological Recordings](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052264)|J. Liang; Z. Chen; X. Wang; F. Xu; X. Wei; L. Sun; M. Li; T. H. Tao; Z. Zhou|10.1109/MEMS49605.2023.10052264|Electrophysiological recordings;Conformal contact;In-plane shielding;Silk-enabled foldable;Micromechanical devices;Minimally invasive surgery;Shape;Metals;Interference;Surface fitting;Recording|
|[Materials Engineering for Chemical Sensing Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052498)|N. Kaur; D. Zappa; E. Comini|10.1109/MEMS49605.2023.10052498|Metal oxide;nickel oxide;tungsten oxide;zinc oxide;nanowire;heterostructures;chemical/gas sensor;Temperature sensors;Mechanical sensors;Gases;Sensitivity;Metals;Sensor systems;Oxidation|
|[On-Demand Preparation of Gas-Sensing Materials Guided by Resonant Cantilever-Based Thermogravimetric Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052606)|Y. Zhou; M. Li; Y. Chen; X. Li; P. Xu; X. Li|10.1109/MEMS49605.2023.10052606|Resonant microcantilever;thermogravimetric analysis;calcination;nanomaterial;Nanoparticles;Micromechanical devices;X-ray scattering;Sensitivity;Atmosphere;Morphology;Calcination|
|[An Intelligent Gas Analysis System Consisting of Sensors and a Neural Network Implemented Using Thin-Film Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052572)|Z. Liu; Y. Hu; G. E. Carranza; F. Wang; M. Wong|10.1109/MEMS49605.2023.10052572|Gas-sensor;thin-film transistor;indium-gallium-zinc oxide;artificial neural network;monolithic integration;Temperature sensors;Micromechanical devices;Buildings;Artificial neural networks;Thin film transistors;Gas detectors;Intelligent sensors|
|[Single-Layer-Electrode Temperature-Modulated SNO2 Gas Sensor Cell with Low Power Consumption for Discrimination of Food Odors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052255)|C. Xing; R. Liu; Y. Zhang; D. Xie; Y. Wang; Y. Huang; M. Mustafa; H. Zhang; Z. Shi; L. Xu; F. Wu|10.1109/MEMS49605.2023.10052255|gas sensor;temperature-modulated;process simplification;low power;Heating systems;Micromechanical devices;Temperature distribution;Gases;Power demand;Metals;Feature extraction|
|[Performance Enhanced Thermal Flow Sensor with Novel Dual-Heater Structure Using CMOS Compatible Fabrication Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052330)|Z. Liu; R. Wang; G. Yang; X. Zhang; R. Jiao; X. Li; J. Qi; H. Yu; H. Xie; X. Wang|10.1109/MEMS49605.2023.10052330|CMOS compatible;flow sensor;dual-heater;Fabrication;Sensitivity;Thermal sensors;Time measurement;Stability analysis;Time factors;Velocity measurement|
|[Local Metal Deposition on Hydrogels Using Micro-Plasma-Bubbles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052218)|H. Takahashi; Y. Yamashita; N. Tottori; S. Sakuma; Y. Yamanishi|10.1109/MEMS49605.2023.10052218|Hydrogel;Microbubble;Plasma;Metal deposition;Nanoparticle;Nanoparticles;Micromechanical devices;Gold;Hydrogels;Metals;Soft robotics;Ions|
|[Folding Method of Kirigami Structure with Folding Lines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052244)|N. Nakamura; E. Iwase|10.1109/MEMS49605.2023.10052244|Origami;Kirigami;Flexible device;Self-folding;Batch fabrication;Fabrication;Micromechanical devices;Shape;Deformation;Force;Fasteners;Bending|
|[Bubble-Assisted Re-Formation of Individual Lipid Bilayers in Arrayed Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052315)|I. Hashimoto; T. Osaki; H. Mimura; S. Takamori; N. Miki; S. Takeuchi|10.1109/MEMS49605.2023.10052315|Bilayer lipid membrane;Air bubble;Re-formation;Proteins;Micromechanical devices;Drugs;Lipidomics;Throughput;Biomembranes;Real-time systems|
|[Large-Scale Arrays of Tunable Monolayer MoS2 Nanoelectromechanical Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052324)|Z. Liu; L. Wang; P. Zhang; M. Xie; Y. Jia; Y. Chen; H. Jia; Z. Wang; R. Yang|10.1109/MEMS49605.2023.10052324|2D NEMS;MoS2 resonators;Large-scale resonator arrays;Frequency tuning;Scalable fabrication;Fabrication;Semiconductor device measurement;Two dimensional displays;Resonant frequency;Frequency measurement;Sulfur;Molybdenum|
|[Antifouling for Electrochemically Biosensing in Body Fluids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052511)|W. He; C. Zhou; Y. Lin; Y. Tian; L. Liu; Q. Zhang; X. Ye; T. Cui|10.1109/MEMS49605.2023.10052511|Antifouling;Nano-wrinkle;Electrochemical sensor;Dopamine;Shrinking technique;Serum;Proteins;Micromechanical devices;Electrodes;Electric potential;Fluids;Bovine;Nanobioscience|
|[Electro-Magnetic Sensor Mediated by Magnetic Biomolecules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052371)|Q. Cheng; Y. Ge; H. Mao; L. Zhou; J. Zhao|10.1109/MEMS49605.2023.10052371|magnetic-electrical sensor;Magnetic biomolecules;Side-gate configured;Graphene Electrolyte-gated Transistors;Electrodes;Proteins;Micromechanical devices;Navigation;Graphene;Scattering;Molecular biophysics|
|[Gas-Flow Device for Effective Dissolution of Gas-Phase Odorants Utilized for Biohybrid Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052624)|T. Nakane; T. Osaki; H. Mimura; S. Takamori; N. Miki; S. Takeuchi|10.1109/MEMS49605.2023.10052624|Biohybrid sensor;Cell-based sensor;Gas sensor;Olfactory receptor;Gas-phase odorant;Gas-flow channel;Micromechanical devices;Mechanical sensors;Sensitivity;Computational modeling;Olfactory;Sensor systems;Calibration|
|[Multiple Wells on A Cmos-Mea for Cell-Based Biohybrid Odorant Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052404)|Y. Lian; H. Oda; M. Nie; S. Takeuchi|10.1109/MEMS49605.2023.10052404|Biohybrid odorant sensor;CMOS-MEA;neuron cell;multiple wells;Micromechanical devices;Microelectrodes;Sensitivity;Olfactory;Feature extraction;Sensors;Biosensors|
|[The Integrated RGO/PEDOT:PSS-Modified Ultraflexible Microelectrodes Towards Long-Term Neurophysiological Signaling and Dopamine Sensitive Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052189)|X. Wang; H. Yang; B. Zhang; M. Li; L. Sun; Z. Zhou; T. H. Tao; X. Wei|10.1109/MEMS49605.2023.10052189|penetrating flexible probe;neural electrophysiological signal detection;dopamine detection;Performance evaluation;In vivo;Micrometers;Sensitivity;Neuroscience;Fluctuations;Accelerated aging|
|[Comparison of Selective Filtration of On-Chip Glomerulus Comprised of Organoid-Derived and Immortalized Podocytes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052159)|A. Tabuchi; K. Yabuuchi; Y. Sahara; M. Takasato; K. Fujimoto; R. Yokokawa|10.1109/MEMS49605.2023.10052159|hiPSCs;kidney organoid;filtration function;microphysiological systems (MPS);Micromechanical devices;Semiconductor device measurement;Filtration;Veins;Stem cells;Organisms;System-on-chip|
|[Controlling Firing Point of Microfiber-Shaped Hipsc-Derived Cardiac Tissue with Localized Electrical Stimulation Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052536)|A. Masuda; S. Itai; Y. Kurashina; S. Tohyama; H. Onoe|10.1109/MEMS49605.2023.10052536|iPSCs;Cardiac Tissue;Tissue Engineering;Electrical Stimulation;Microfiber;Cardiac Conduction;Electrodes;Cardiac tissue;Pathology;Stimulated emission;Firing;Arrhythmia;Electrical stimulation|
|[Developmental Phases of On-Chip Vasculogenesis Classified Using A Deep Learning Visual Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052548)|T. Irisa; H. Zhou; K. Fujimoto; R. Yokokawa|10.1109/MEMS49605.2023.10052548|Vasculogenesis;deep learning;morphology analysis;microvasculature-on-a-chip;Visualization;Analytical models;Supervised learning;Pathological processes;Morphology;Real-time systems;Physiology|
|[Hand-Driven Device for Preparation of Linearly Aligned Hydrogel Sheets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052320)|A. Kato; H. Oda; S. Takamori; H. Mimura; T. Osaki; N. Miki; S. Takeuchi|10.1109/MEMS49605.2023.10052320|Cell-based sensor;Insect olfactory receptor;Surface tension;Hydrogel sheet;Micromechanical devices;Mechanical sensors;Chemical sensors;Sensitivity;Hydrogels;Olfactory;Manuals|
|[Microfabrication and Characterization of Micro-Stereolithographically 3d Printed, and Double Metallized Bioplates with 3D Microelectrode Arrays for In-Vitro Analysis of Cardiac Organoids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052547)|J. M. Castro; I. Johnson; S. Rajaraman|10.1109/MEMS49605.2023.10052547|Micro-stereolithography;3D printing;Ink-casting;3D Microelectrode Array (MEA);Electrochemical Impedance Spectroscopy (EIS);High-Throughput Bioplates;Cardiac Organoids;Microelectrodes;Scanning electron microscopy;Three-dimensional displays;Metallization;Stimulated emission;Microfabrication;Three-dimensional printing|
|[Oil-Sealed Rgd-Modified Hydrogel Microwell Array with Size-Selective Permeation for Analysis on Exosomes from Single Cells](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052318)|C. Yamagata; S. Itai; Y. Kurashina; M. Asai; A. Hoshino; H. Onoe|10.1109/MEMS49605.2023.10052318|Exosome;Extracellular vesicle;Single-cell analysis;Microwell array;Alginate;Hydrogel;Arginine-glycine-aspartate (RGD) motif;Micromechanical devices;Hydrogels;Cells (biology);Fluorescence;Permeability;Diseases|
|[Picking Single Cells from 10 ML Sample Based on a Microfiltration- Lift Combination Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052221)|Q. Xu; Y. Wang; X. Ma; H. Li; Y. Xue; Y. Zhang; S. Dou; H. Wang; B. Li; W. Wang|10.1109/MEMS49605.2023.10052221|Single cell picking;Filtration;Laser-induced forward transfer (LIFT);Micromechanical devices;Microfiltration;Lasers;Receivers;Fluorescence;Cancer|
|[A Transfer Method for Embedding Conductive Fillers on the Surface of Multi-Scale Structures for 3D Flexible Conductors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052193)|D. Yoo; S. Kim; J. Hwang; J. Kim|10.1109/MEMS49605.2023.10052193|nan;Pressure sensors;Three-dimensional displays;Sensitivity;Surface resistance;Conductors;Robustness;Plastics|
|[Fabrication of High-Frequency 2D Flexible pMUT Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052246)|S. V. Joshi; S. Sadeghpour; M. Kraft|10.1109/MEMS49605.2023.10052246|Flexible Microsystems;Wearable Medical Devices;Ultrasound Transducers;PMUT;Piezoelectric Thin Films;PZT;Piezo-MEMS;Fabrication;Sensitivity;Resonant frequency;Polyimides;Bandwidth;Bending;Time measurement|
|[Flexible Silk-Based Graphene Bioelectronics for Wearable Multimodal Physiological Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052459)|S. Mirbakht; A. Golparvar; M. Umar; M. K. Yapici|10.1109/MEMS49605.2023.10052459|Electronic skin (e-skin);epidermal bioelectronics patch;continuous monitoring;flexible electronics;green electronics;reduced graphene oxide (rGO);silk protein;skin-like electronics;Proteins;Electrooculography;Graphene;Ink;Electrocardiography;Epidermis;Recording|
|[Highly Accurate Measurement of Contact Resistance Between Galinstan and Copper Using Transfer Length Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052543)|T. Sato; E. Iwase|10.1109/MEMS49605.2023.10052543|Liquid metal;Galinstan;Contact resistance;Transfer Length Method;Stretchable electronics;Wiring;Electrodes;Micromechanical devices;Liquids;Length measurement;Conductivity;Contact resistance|
|[Machine Learning Enabled Hind Foot Deformity Detection Using Individually Addressable Hybrid Pressure Sensor Matrix](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052134)|N. T. Beigh; F. Beigh; S. Naval; D. Mukherjee; D. Mallick|10.1109/MEMS49605.2023.10052134|Piezoelectric;Triboelectric;Pressure Sensor;Convolution Neural Network;Flexible Sensor;Pressure sensors;Sensitivity;Convolution;Neural networks;Crosstalk;Feature extraction;Nanoscale devices|
|[Multi-Mode E-Skin Integrating Capacitive-Piezoelectric Sensors for Static-Dynamic Mechanoresponse with Wide Sensing Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052349)|M. Yousuf; S. Kumar; D. S. Arya; M. Garg; K. Joshi; P. Singh|10.1109/MEMS49605.2023.10052349|Multimode Sensor;Mechanoreceptors;μ-Pyramidal;Static;Dynamic Stimuli;Somatosensory;Micromechanical devices;Electrodes;Sensitivity;Real-time systems;Capacitive sensors;Sensors|
|[Non-Invasive Instant Measurement of Arterial Stiffness Based on High-Density Flexible Sensor Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052429)|F. Wang; H. Yang; K. Sun; Y. Sun; X. Li|10.1109/MEMS49605.2023.10052429|Arterial stiffness measurement;high-density flexible sensor array;tactile perception;deep learning model;stiffness index;Deep learning;Micromechanical devices;Pressure sensors;Analytical models;Aging;Mechanical variables measurement;Indexes|
|[Suppression of Bioelectrical Noise Signals in Motion State by Low-Cost Micropillar Hydrogel Electrode](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052262)|G. Shen; N. Zhao; C. Jiang; Z. Wang; J. Liu|10.1109/MEMS49605.2023.10052262|Bioelectrical signals;MEMS technology;hydrogel electrode;micropillar arrays;Electrodes;Micromechanical devices;Electric potential;Costs;Correlation;Array signal processing;Hydrogels|
|[Ultra-Thin Mems Packaging Based on Auxetic Stretchable Structures for Applications in Wearable Electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052114)|D. Zymelka; T. Takeshita; Y. Takei; T. Kobayashi|10.1109/MEMS49605.2023.10052114|Auxetics;mechanical metamaterials;MEMS;packaging;printed electronics;stretchable electronics;Micromechanical devices;Auxetic materials;Wearable computers;Packaging;Metamaterials;Silicon;Substrates|
|[Ultralow Power Flexible Ocular Microsystem for Vergence and Distance Sensing Based on Passive Differential Magnetometry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052200)|A. Deshpande; M. U. Karkhanis; C. Ghosh; H. Kim; C. H. Mastrangelo|10.1109/MEMS49605.2023.10052200|Object distance sensing;vergence angle triangulation;magnetometry;smart contact lens;Micromechanical devices;Visualization;Magnetic field measurement;Magnetometers;Measurement uncertainty;Sensors;Magnetic fields|
|[Electrohydrodynamic Nebuliser (eNEB) for Direct Pulmonary Drug Delivery Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052181)|T. -H. Vu; L. N. Mai; T. -H. Nguyen; D. Tran; T. -K. Nguyen; T. Nguyen; J. Fastier-Woollel; C. -D. Tran; T. Dinh; H. -Q. Nguyen; D. V. Dao; V. Thanh Dau|10.1109/MEMS49605.2023.10052181|Ion wind;neutralisation;atomised plume;electrohydrodynamic;Lung cancer;Lung;Medical services;Propulsion;Ions;Drug delivery;Breast cancer|
|[Flexible Polymer Optical Waveguides for Integrated Optogenetic Brain Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052492)|J. A. Singer; T. Stramm; J. Fasel; O. Schween; A. Gelaeschus; A. Bahr; M. Kuhl|10.1109/MEMS49605.2023.10052492|MEMS;optogenetics;waveguide;polymer;brain implants;neural implants;integrated circuits;ASIC;biomedical;SPAD;µLED;thin-film;molding;Optical fibers;Optical fiber sensors;Biomedical optical imaging;Stimulated emission;Optical imaging;Sensor systems and applications;Optogenetics|
|[Highly Reproducible Tissue Positioning with Tapered Pillar Design in Engineered Heart Tissue Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052166)|C. L. Mummery; P. M. Sarro; M. Mastrangeli|10.1109/MEMS49605.2023.10052166|engineered heart tissue;heart-on-chip;organ-on-chip;microfabrication;Heart;Micromechanical devices;Force measurement;Self-assembly;Shape;Optical design;Force|
|[In Vitro Assembly of Muscle Rings and Bioprinted Hydrogel for Branching Tubular Tissue Constructs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052406)|T. Morita; B. Jo; M. Nie; S. Takeuchi|10.1109/MEMS49605.2023.10052406|Polyethylene glycol;Bioprinting;Circumferential cell alignment;Electrical stimulation;Stretch stimulation;A glass capillary-based transferring method;Fabrication;Structural rings;Drugs;Printing;Micromechanical devices;Polyethylene;Hydrogels|
|[Microelectrodes Fabricated by Vacuum Filling with Low Melting-Point Alloy for Muscle Tissue Stimulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052569)|T. Li; M. Nie; Y. Morimoto; S. Takeuchi|10.1109/MEMS49605.2023.10052569|Low-melting-point alloy;microelectrode;vacuum filling;biohybrid actuator;Micromechanical devices;Actuators;Microelectrodes;Gold;Power demand;Voltage;Muscles|
|[Optoelectronic Integrated Ultramicroelectrode for Optical Stimulation and Electrical Recording of Single-Cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052245)|Q. Xu; Y. Xi; Z. Du; L. Wang; T. Ruan; M. Xu; J. Cao; B. Yang; J. Liu|10.1109/MEMS49605.2023.10052245|Ultramicroelectrode;Optogenetics;Optoelectronic Integration;Single-Cell Stimulation;Optical fibers;Integrated optics;Performance evaluation;Micromechanical devices;Stimulated emission;Optical recording;Optical device fabrication|
|[Thermoforming of Parylene C to Form Helical Structures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052211)|B. L. Thielen; E. Meng|10.1109/MEMS49605.2023.10052211|Parylene C;thermoforming;three-dimensional;fabrication;Micromechanical devices;Geometry;Strips;Insulation;Three-dimensional displays;Shape;Films|
|[Fabrication of Biodegradable Soft Tissue-Mimicked Microelectrode Arrays for Implanted Neural Interfacing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052266)|W. -C. Huang; W. -L. Lei; C. -W. Peng|10.1109/MEMS49605.2023.10052266|Neural interfaces;Microelectrode arrays;Biodegradable electronics;Hydrogel electronics;Platinum;Transfer printing;Polylactic acid (PLA);Printing;Micromechanical devices;Insulation;Microelectrodes;Hydrogels;Biological tissues;Programmable logic arrays|
|[An Optimization of Perforation Design on a Piezoelectric-Based Smart Stent for Blood Pressure Monitoring and Low-Frequency Vibrational Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052623)|J. Y. Tan; S. Islam; Y. Li; A. Kim; J. ‘. Kim|10.1109/MEMS49605.2023.10052623|Smart Stent;piezoelectric;pressure sensor;low-frequency energy harvester;implantable medical device;Integrated circuits;Wireless communication;Vibrations;Wireless sensor networks;Sensitivity;Shape;Voltage|
|[Development of an Electrical-Stimulation-Induced Mechanomyogram Probe for Muscle Contraction Characteristics Evaluation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052156)|Y. Takei; T. Takeshita; D. Zymelka; T. Kobayashi|10.1109/MEMS49605.2023.10052156|Muscle;Mechanomyogram;Lead Zirconate Titanate;Ultrathin MEMS;Micromechanical devices;Electrodes;Electric variables measurement;Muscles;Electrical stimulation;Titanium compounds;Probes|
|[Dual-Frequency Piezoelectric Micromachined Ultrasonic Transducers for Fundamental and Harmonic Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052426)|Y. Zhai; W. Maqsood; Z. Da; N. Andrianov; Y. Zhang; M. Moridi; L. Wu|10.1109/MEMS49605.2023.10052426|Piezoelectric micromachined ultrasonic transducer (PMUT);dual frequency bands;harmonic imaging;Ultrasonic transducers;Micromechanical devices;Ultrasonic imaging;Image resolution;Imaging;Bandwidth;Harmonic analysis|
|[Fractal Microelectrodes Integrated With the Catheter for Low-Voltage Pulsed Field Ablation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052546)|M. Xu; M. Qin; Z. Song; W. Hong; Q. Xu; J. Cao; K. Tu; L. Wang; B. Yang; J. Liu|10.1109/MEMS49605.2023.10052546|Atrial Fibrillation;Catheter;Fractal Microelectrode;Pulsed Field Ablation;Performance evaluation;Low voltage;Microelectrodes;In vivo;Electrocardiography;Fractals;Recording|
|[Hierarchical Bonding Yield Test Structure for Flexible High Channel-Count Neural Probes Interfacing ASIC Chips](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052500)|M. C. Odenthal; V. Claar; O. Paul; P. Ruther|10.1109/MEMS49605.2023.10052500|Hybrid integration;flexible neural probes;flip-chip bonding;process development;Micromechanical devices;Gold;Semiconductor device measurement;Contacts;Force;Polyimides;Silicon|
|[Microwave-Induced Thermoacoustic Imaging Using Aluminum Nitride PMUT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052158)|Y. Wang; L. Zhang; J. Cai; B. Wang; Y. A. Gu; L. Lou; X. Wang; T. Wu|10.1109/MEMS49605.2023.10052158|Aluminum nitride;PMUT;thermoacoustic imaging;Ultrasonic transducers;Micromechanical devices;Microwave devices;Mean square error methods;Microwave theory and techniques;III-V semiconductor materials;Image reconstruction|
|[Needle-Free Drug Injection Using a Shock Wave Focusing System with the Function of Real-Time Microbubble-Based Distance Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052502)|Y. Ma; W. Huang; K. Ichikawa; Y. Yamanishi|10.1109/MEMS49605.2023.10052502|Electrically induced microbubbles;needle-free injector;shock wave focusing;drug delivery;Drugs;Micromechanical devices;Shock waves;Actuators;Focusing;Real-time systems;Distance measurement|
|[New Wafer-Level Fabrication of Ultrathin Silicon Insertion Shuttles for Flexible Neural Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052581)|K. Sharma; C. Boehler; M. Asplund; O. Paul; P. Ruther|10.1109/MEMS49605.2023.10052581|Silicon thinning;grinding;silicon shuttles;ultrathin probes;polyimide probes;self-alignment;backgrinding liquid wax;ultrathin chips;Fabrication;Liquids;Semiconductor device reliability;Neural implants;Polyimides;Silicon;Metamaterials|
|[Real-Time Dynamic Lactate Detection in a Pipeline Using a Microsensing Needle for ICU Patient Monitoring Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052531)|Y. -S. Tang; T. -L. Yang; Y. -T. Cheng; H. -E. Tsai; Y. -S. Chen|10.1109/MEMS49605.2023.10052531|Non-enzymatic lactate sensor;needle-typed biosensor;Real-time detection;Dynamic environment;Micromechanical devices;Patient monitoring;Sensitivity;Pipelines;Lithography;Linearity;Needles|
|[Three-Dimensional Flexible Neural Opto-Electronic Array with Silk-Based Shuttle-Free Implantation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052219)|C. Gu; H. Yang; B. Zhang; Z. Zhou; L. Sun; M. Li; X. Wei; T. H. Tao|10.1109/MEMS49605.2023.10052219|3D Neural Opto-electronic Array;Silk fiber;Shuttle-free Implantation;Electrodes;Optical fiber losses;Three-dimensional displays;Stimulated emission;Optical recording;Neural circuits;Optogenetics|
|[A Microfluidic Biosensor for Rapid Detection of Covid-19](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052321)|S. A. Muhsin; Y. He; M. Al-Amidie; K. Sergovia; A. Abdullah; Y. Wang; O. Alkorjia; R. A. Hulsey; G. L. Hunter; Z. Erdal; R. J. Pletka; G. S. Hyleme; X. -F. Wan; M. Almasri|10.1109/MEMS49605.2023.10052321|Microfluidic channel;MEMS;biosensor;SARS-COV-2;dielectrophoresis;microfabrication;Electrodes;Antigens;Sensitivity;Focusing;Antibodies;Coronaviruses;Biosensors|
|[A Loop-Mediated Isothermal Amplification (Lamp)-Based Point-of-Care System for Rapid on-Site Clinical Detection of Sars-Cov-2 Viruses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052467)|T. Nguyen; A. C. Vinayaka; V. N. Huynh; Q. T. Linh; S. Z. Andreasen; M. Golabi; D. D. Bang; J. K. Møller; A. Wolff|10.1109/MEMS49605.2023.10052467|SARS-CoV-2;point-of-care;COVID-19;LAMP;limit of detection;open-source hardware;total internal reflection;TIR;COVID-19;Micromechanical devices;Pandemics;Point of care;Real-time systems;Polymers;Object recognition|
|[A Solar-Driven Wearable Multiplexed Bio-Sensing System For Noninvasive Healthcare Monitoring In Sweat](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052393)|J. Singh; B. Ning; P. Lee; L. Liu|10.1109/MEMS49605.2023.10052393|Electrochemical methods;Wearable and flexible sensors;Point of Care (POC);Mental health;Cortisol;Sweat;Glucose;pH monitoring;Multiplexing;Wireless communication;Temperature sensors;Wireless sensor networks;Wearable Health Monitoring Systems;Glucose;Biosensors|
|[High-Throughput Mass Measurement Of Single Bacterial Cells By Silicon Nitride Membrane Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052618)|A. Sanz-Jiménez; O. Malvar; J. J. Ruz; S. García-López; P. M. Kosaka; E. Gil-Santos; Á. Cano; D. Papanastasiou; D. Kounadis; E. Panagiotopoulos; J. Mingorance; M. Rodríguez-Tejedor; Á. S. Paulo; M. Calleja; J. Tamayo|10.1109/MEMS49605.2023.10052618|Nanomechanical mass spectrometry;high-throughput and high-precision membrane resonators;mass sensing;Koch & Schaechter model;bacteria growth stochasticity;Microorganisms;Sociology;Stochastic processes;Silicon nitride;Throughput;Size measurement;Time measurement|
|[Microfabricated Isothermal Eg-Fet Sensor For Lamp Mediated Crispr/Cas12a Detection Of Hepatitis C Virus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052240)|H. -Y. Ho; W. -S. Kao; P. Deval; L. -S. Yu; C. -H. Lin|10.1109/MEMS49605.2023.10052240|EG-FET;HCV;ITO;LAMP;CRISPR/cas12a;Sensitivity;Liver diseases;RNA;Indium tin oxide;Real-time systems;Sensors;Transistors|
|[Smart Electrode Array for Cochlear Implants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052348)|A. Itawi; S. Ghenna; G. Tourrel; S. Grondel; C. Plesse; T. M. G. Nguyen; F. Vidal; Y. Adagolodjo; L. Xun; G. Zheng; A. Kruszewski; C. Duriez; E. Cattan|10.1109/MEMS49605.2023.10052348|Cochlear electrode array;electronic conducting polymer;actuator;Electrodes;Cochlear implants;Friction;Wires;Surgery;Prototypes;Ear|
|[A Three-Dimensional Artificial Intestinal Tissue With A Crypt-Like Inner Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052541)|S. Tanaka; S. Itai; H. Onoe|10.1109/MEMS49605.2023.10052541|Intestine;Intestinal tissue;Organoid;Collagen gel tube;Cell culture;Electrolysis;Co-culture;Bacteria;Productivity;Micromechanical devices;Microorganisms;Shape;Biomimetics;Biological system modeling;Stem cells|
|[Tissue-Engineered Pennate Muscles On A Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052551)|M. Ito; Y. Morimoto; S. Takeuchi|10.1109/MEMS49605.2023.10052551|Tissue Engineering;Pennate Muscle;Muscle Fibers Orientation;Contractile Force;Micromechanical devices;Actuators;Microscopy;Force;Muscles;Optical fiber devices;Bars|
|[Weight Training Device to Promote Maturation in Skeletal Muscle Tissues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052280)|K. Motoi; B. Jo; Y. Morimoto; S. Takeuchi|10.1109/MEMS49605.2023.10052280|Tissue engineering;Electrical stimulation;Mechanical stimulation;Biofabrication;Myotube diameter;Myotube orientation;Training;Micromechanical devices;Fabrication;In vivo;Electric potential;Muscles;Electrical stimulation|
|[Microsystem Vibrating Mesh Atomizer With Integrated Microheater For High Viscosity Liquid Aerosol Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052232)|P. Sharma; I. R. Vazquez; N. Jackson|10.1109/MEMS49605.2023.10052232|Vibrating Mesh Atomizer;Aerosol;Microheater;Microelectromechanical Systems;Viscosity;Viscosity;Micromechanical devices;Heating systems;Drugs;Liquids;Aerosols;Three-dimensional printing|
|[Scalable Modular Measurement System For Continuous Blood Monitoring With Piezoelectric Mems Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052396)|M. Schneider; B. Kößl; S. Alasatri; I. A. M. Magnet; U. Schmid|10.1109/MEMS49605.2023.10052396|Piezoelectric MEMS;resonator;blood;continuous monitoring;Viscosity;Micromechanical devices;Q-factor;Fluid flow measurement;Pumps;Resonant frequency;Cleaning|
|[Silicon Compatible Process To Integrate Impedance Cytometry With Mechanical Characterization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052621)|Q. Rezard; F. A. Shaik; J. C. Gerbedoen; F. Cleri; D. Collard; C. Lagadec; M. C. Tarhan|10.1109/MEMS49605.2023.10052621|3D electrodes;impedance cytometry;single-cell analysis;microfabrication;Electrodes;Micromechanical devices;Mechanical sensors;Spectroscopy;Actuators;Three-dimensional displays;Silicon|
|[Sorting Of Extracellular Vesicles By Using Optically-Induced Dielectrophoresis On An Integrated Microfluidic Chip](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052228)|W. -J. Soong; C. -H. Wang; Y. -S. Chen; C. Chen; G. -B. Lee|10.1109/MEMS49605.2023.10052228|extracellular vesicles (EVs);microfluidics;size-based sorting;optically-induced dielectrophoresis;Micromechanical devices;Semiconductor device measurement;Biomedical optical imaging;Size measurement;Dielectrophoresis;Risk management;Extracellular|
|[A Reprogrammable Mem Switch Utilizing Controlled Contact Welding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052428)|T. K. Esatu; H. Kam; L. P. Tatum; X. Hu; U. Sikder; S. Almeida; J. Wu; T. -J. K. Liu|10.1109/MEMS49605.2023.10052428|Micro-electro-mechanical switches;ultra-low-power;reprogrammable;non-volatile;embedded memory;Resistance;Temperature;Nonvolatile memory;Microswitches;Welding;Contacts;Process control|
|[Micromechanical RSSI Based on Force Interaction Derived Tapping Bandwidth Variation in VIBRO-IMPACT Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052169)|Y. -H. Huang; H. -S. Zheng; C. -P. Tsai; W. -C. Li|10.1109/MEMS49605.2023.10052169|CMOS-MEMS;vibro-impact resonators;received signal strength indicator;indoor positioning;Micromechanical devices;Location awareness;Sensitivity;Power demand;Force;Bandwidth;Particle measurements|
|[Wake-Up IoT Wireless Sensing Node Based on a Low-G Threshold mems Inertial Switch with Reliable Contacts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052265)|S. Ghosh; D. J. Goh; Y. Koh; J. Sharma; W. Da Toh; W. Chen; Y. Zhang; E. Ng; A. Lal; J. E. . -Y. Lee|10.1109/MEMS49605.2023.10052265|Low-g MEMS inertial switch;Event-driven zero-power IoT systems;switch contact time;Titanium Nitride (TiN) contact;zero-power sensor switch fabrication platform;Micromechanical devices;Wireless communication;Fabrication;Wireless sensor networks;Contacts;Switches;Tin|
|[Artificial Intelligence (AI)-Enhanced E-Skin with Artificial Synapse Sensory Output for Humanoid Robotic Finger of Multimodal Perception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052121)|X. Guo; C. Lee|10.1109/MEMS49605.2023.10052121|E-skin;Artificial Synapse;Multimodality;Artificial Intelligence;Temperature sensors;Temperature measurement;Robot sensing systems;Feature extraction;Surface roughness;Skin;Surface texture|
|[Multi-Mems Differential Pressure Sensor Elements-Based Airflow Sensor with Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052249)|K. Haneda; K. Matsudaira; H. Takahashi|10.1109/MEMS49605.2023.10052249|Airflow sensor;Differential pressure sensor;Machine learning;Neural network;Micromechanical devices;Pressure sensors;Wind speed;Atmospheric modeling;Wind tunnels;Neural networks;Toy manufacturing industry|
|[Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052277)|F. Sui; W. Yue; Z. Zhang; R. Guo; L. Lin|10.1109/MEMS49605.2023.10052277|Artificial Intelligence;MEMS Design;Design Space Exploration;Deep Reinforcement Learning;Micromechanical devices;Deep learning;Training;Systematics;Design methodology;Resonant frequency;Reinforcement learning|
|[Fully Microelectromechanical Non-Volatile Memory Cell](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052290)|E. Worsey; M. K. Kulsreshath; Q. Tang; D. Pamunuwa|10.1109/MEMS49605.2023.10052290|MEMS;Microswitches;Nonvolatile memory;Radiation hardening (electronics);High-temperature;Micromechanical devices;Multiplexing;Nonvolatile memory;Microprocessors;Computer architecture;Writing;Relays|
|[Nonvolatile State Configuration of Nano-Watt Parametric ISING Spins Through Ferroelectric Hafnium Zirconium Oxide MEMS Varactors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052601)|N. Casilli; O. Kaya; T. Kaisar; B. Davaji; P. X. . -L. Feng; C. Cassella|10.1109/MEMS49605.2023.10052601|MEMS;Ising Machines;Parametric Oscillators;Ferroelectric Devices;Ferromagnetic Spins;Micromechanical devices;Varactors;Couplings;Zirconium;Phase measurement;Nonvolatile memory;Systems operation|
|[Physical Reservoir Computing Using Nonlinear MEMS Resonator Having High Memory Capacity at "Edge of Chaos"](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052286)|H. Takemura; T. Mizumoto; A. Banerjee; J. Hirotani; T. Tsuchiya|10.1109/MEMS49605.2023.10052286|Physical reservoir computing;electrostatic;nonlinear resonator;machine learning;silicon-on-insulator;Micromechanical devices;Chaos;Performance evaluation;Silicon-on-insulator;Resonant frequency;Machine learning;Reservoirs|
|[Programmable Ferroelectric HZO NEMS Mechanical Multiplier for in-Memory Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052285)|S. Jadhav; V. Gund; A. Lal|10.1109/MEMS49605.2023.10052285|HZO;ferroelectric material;in-memory computing;NEMS;programming;Micromechanical devices;Voltage measurement;Zirconium;Nanoelectromechanical systems;Resonant frequency;Hafnium;Drives|
|[Storing MEMS Interfaces Without Electrical Auxiliary Energy for Long-Time Monitoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052634)|M. Hoffmann; P. Schmitt; S. Wittemeier; F. Schaller; A. Shaporin; C. Stöckel; V. Geneiß; R. Forke; C. Hedayat; U. Hilleringmann; H. Kuhn; S. Zimmermann|10.1109/MEMS49605.2023.10052634|Passive sensors;condition monitoring;predictive maintenance;nanoionic memory;ratcheting mechanism;Micromechanical devices;Transducers;Energy measurement;Mechanical variables measurement;Recording;Mechanical energy;Task analysis|
|[A New Finding on Nonlinear Damping and Stiffness of Flexural Mode Capacitive MEMS Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052284)|H. -Y. Chen; M. -H. Li; S. -S. Li|10.1109/MEMS49605.2023.10052284|CMOS;MEMS;capacitive transduction;resonator;electromechanical coupling;nonlinearity;Damping;Micromechanical devices;Force measurement;Force;Electrostatic measurements;Resonant frequency;Behavioral sciences|
|[Exploiting Parametric Instability in Bistable MEMS Actuators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052469)|D. Platz; J. Fabian; E. Samm; M. Mortada; M. Schneider; U. Schmid|10.1109/MEMS49605.2023.10052469|Actuator;nonlinear dynamics;bistability;snap- through;parametric instability;parametric resonance;plates;Micromechanical devices;Actuators;Simulation;Piezoelectric devices;Stress|
|[First Prototype of Polymer Micromachined Flapping Wing Nano Air Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052580)|Rashmikant; R. Suetsugu; M. Onishi; D. Ishihara|10.1109/MEMS49605.2023.10052580|MEMS flyer;flapping wing nano air vehicle;insect-inspired;flight performance;fluid-structure interaction;polymer micromachining;Micromechanical devices;Performance evaluation;Insects;Force;Dynamics;Prototypes;Micromachining|
|[Iterative Learning Control for Quasi-Static MEMS Mirror with Switching Operation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052637)|M. Macho; H. W. Yoo; R. Schroedter; G. Schitter|10.1109/MEMS49605.2023.10052637|Iterative Learning Control;Quasi-Static MEMS Mirror;Electrostatic Actuation;Switching Operation;Micromechanical devices;Uncertainty;Frequency-domain analysis;Switches;Trajectory;Mirrors;Feedforward systems|
|[Mz Atomic Magnetometer Using A 3D Mems Glass Alkali Vapor Cell With Vertical Sidewalls](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052299)|J. Zhang; J. Zhang; W. Li; Z. Wang; J. Shang|10.1109/MEMS49605.2023.10052299|3D MEMS glass alkali vapor cell;vertical sidewalls;atomic magnetometer;multiple optical paths;Micromechanical devices;Three-dimensional displays;Sensitivity;Magnetometers;Atom optics;Optical design;Optical device fabrication|
|[On-Chip Heating Noise Suppression of 3d Chip-Scale Atomic Magnetometer Using Single-Layer Shifted Heater](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052410)|Z. Wang; J. Wu; J. Zhang; J. Shang|10.1109/MEMS49605.2023.10052410|3D MEMS atomic chip;single-layer on-chip heater;heating noise suppression;Heating systems;Micromechanical devices;Fabrication;Three-dimensional displays;Sensitivity;Costs;Magnetometers|
|[Labor-Saving Platform for Characterization of Membrane Proteins by Automated Monitoring and Data Reporting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052267)|K. Ogishi; T. Osaki; Y. Morimoto; S. Takeuchi|10.1109/MEMS49605.2023.10052267|Artificial cell membrane;Lipid bilayer;Nanopore;Ion channel recording;Conductance;Droplet contact method;Real-time;Automation;Restoration;Proteins;Histograms;Current measurement;Manuals;Lipidomics;Time measurement;Real-time systems|
|[Modelling Impact of Viscoelastic Properties of Die-Attach Material on the Bias Response of Resonant Inertial Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052540)|T. Miani; L. Gurung; G. Sobreviela-Falces; D. Young; C. Baker; A. A. Seshia|10.1109/MEMS49605.2023.10052540|Vibrating beam accelerometer;viscoelastic-induced bias;polymer-based die attach package;Accelerometers;Micromechanical devices;Temperature distribution;Solid modeling;Inertial sensors;Computational modeling;Predictive models|
|[CMOS-Embedded 3D Micro/Nanofluidics Employing Top-Down Beol Single-Step Wet-Etching Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052329)|W. -Y. Weng; H. -Y. Hou; Y. -J. Chao; S. -J. Liaw; J. -C. Chien|10.1109/MEMS49605.2023.10052329|CMOS;microfluidics;nanofluidics;top-down BEOL etching;cellular detection;resistive pulse sensing;Three-dimensional displays;Microorganisms;Metallization;Point of care;CMOS technology;Shift registers;Nanofluidics|
|[Implementation of a Monolithic SoC Environmental Sensing Hub Using CMOS-MEMS Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052584)|Y. -C. Lee; T. -L. Chien; C. -T. Fang; Y. Huang; W. -L. Sung; Y. -C. Chu; R. Chen; W. Fang|10.1109/MEMS49605.2023.10052584|Environment sensing hub;CMOS-MEMS;Gas sensor;Thermometer;Anemometer;Humidity sensor;Visible light sensor;Micromechanical devices;Semiconductor device measurement;Thermometers;Sensitivity;Fluid flow measurement;Wavelength measurement;Humidity|
|[Monolithically and Vertically Integrated Environmental Sensing Hub with Novel Air-Based Humidity Sensor Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052127)|T. -L. Chien; Y. Huang; F. Shih; W. Fang|10.1109/MEMS49605.2023.10052127|Environment sensing hub;CMOS-MEMS;Pressure sensor;Humidity sensor;Thermometer;Pressure sensors;Heating systems;Temperature measurement;Sensitivity;Thermometers;Humidity measurement;Humidity|
|[A Self-Corrected, Self-Cleaned MEMS and Suitable for Advanced Foundry Multi-Project Wafer (MPW)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052195)|S. Kumar; D. S. Arya; M. Garg; P. Singh|10.1109/MEMS49605.2023.10052195|MEMS;Residual Stress;Out-of-Plane Actuation;Refractory Transition Metal;Multi-Project Wafer;Micromechanical devices;Fabrication;Lasers;Process control;Production;Etching;Foundries|
|[Monolithic Integration of Humidity/Flow/Temperature Sensors As Environment Sensing Hub for Apparent-Temperature Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052523)|Y. -H. Li; T. -L. Chien; F. Shih; Y. Huang; W. Fang|10.1109/MEMS49605.2023.10052523|CMOS-MEMS;capacitive humidity sensor;hot-wire flow sensor;apparent temperature;Temperature sensors;Temperature measurement;Sensitivity;Monolithic integrated circuits;Humidity;Sensor phenomena and characterization;Capacitive sensors|
|[Piezoresistive Pressure Sensor with Monolithically Integrated Amplifier Based on Metal-Oxide Transistors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052421)|R. Shi; D. Lin; K. Chau; M. Wong|10.1109/MEMS49605.2023.10052421|IGZO TFT;MEMS;piezoresistive pressure sensor;monolithic integration;Pressure sensors;Micromechanical devices;Sensitivity;Loading;Monolithic integrated circuits;Differential amplifiers;Thin film transistors|
|[A Performance Enhancement Method for Thermopile Sensors Using a Chip Probe Test System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052327)|M. Shi; M. Li; Y. Ni; C. Zhang; N. Zhou; H. Mao; C. Huang|10.1109/MEMS49605.2023.10052327|Thermopile sensors;chip probe (CP) test;ink dots;optical absorption enhancement;Performance evaluation;Temperature sensors;Printing;Micromechanical devices;Temperature distribution;Absorption;Process control|
|[Characterizing Inductively-Coupled-Plasma Etching of Single Crystalline Lithium Tantalate for Micro-Acoustic Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052441)|Y. Majd; J. M. Castro; H. Mansoorzare; R. Abdolvand|10.1109/MEMS49605.2023.10052441|Lithium Tantalate;Etch characteristic;Etch rate;ICP-RIE;Selectivity;Micromechanical devices;Fabrication;Ions;Nickel;Etching;Plasmas;Resonators|
|[Robust Polycrystalline 3C-Sic-on-Si Heterostructures with Low CTE Mismatch up to 900 °C for MEMS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052144)|P. Moll; G. Pfusterschmied; U. Schmid|10.1109/MEMS49605.2023.10052144|3C-SiC;LPCVD;CTE mismatch;thermal stress;Micromechanical devices;Temperature measurement;Vibrations;Temperature sensors;Thermal expansion;Fluid flow;Silicon|
|[A 3D-Printed Functional MEMS Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052385)|S. Pagliano; D. E. Marschner; D. Maillard; N. Ehrmann; G. Stemme; S. Braun; L. G. Villanueva; F. Niklaus|10.1109/MEMS49605.2023.10052385|3D printing;two-photon polymerization;shadow masking;strain gauge transducer;Laser Doppler Vibrometer;custom MEMS;low-volume manufacturing;Micromechanical devices;Accelerometers;Three-dimensional displays;Transducers;Resonant frequency;Production;Three-dimensional printing|
|[A Full 3D Printing Method for Monolithic Integration of an Accelerometer and a Force Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052173)|G. Liu; C. Wang; K. Wang; Z. Jia; R. Luo; W. Ma|10.1109/MEMS49605.2023.10052173|Integrated 3D printing;monolithic integration;MEMS;accelerometer;force sensor;Micromechanical devices;Accelerometers;Mechanical sensors;Three-dimensional displays;Sensitivity;Monolithic integrated circuits;Three-dimensional printing|
|[Characterization of Vapor HF Sacrificial Etching Through Submicron Relase Holes for Wafer-Level Vacuum Packaging Based on Silicon Migration Seal](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052275)|T. Gong; Y. Suzuki; M. J. Khan; K. Hiller; S. Tanaka|10.1109/MEMS49605.2023.10052275|Vapor hydrogen fluoride etching;Silicon migration sealing;Through-hole etching;Wafer-level vacuum packaging;Micromechanical devices;Water;Vacuum systems;Catalysts;Seals;Packaging;Etching|
|[Damage Profile Modeling and Experiment of Silicon Carbide Substrates in Micro-Nano Structure Fabricated By Helium Focused ION Beam](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052589)|S. Gao; X. Chen; Q. Chen; Q. Li; Y. Xing|10.1109/MEMS49605.2023.10052589|helium focused ion beam;amorphous profiles;silicon carbide substrate;Micromechanical devices;Analytical models;Silicon carbide;Ions;Mathematical models;Silicon;Helium|
|[Liquid-Immersion Inclined-Rotated UV Lithography for Micro Suction Cup Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052567)|G. Kagawa; H. Takahashi|10.1109/MEMS49605.2023.10052567|KEYWORDS Suction cup;Inclined/rotated lithography;Liquid-immersion lithography;Micromechanical devices;Performance evaluation;Fabrication;Three-dimensional displays;Lithography;Force;Microstructure|
|[Parametric Amplification and Phononic Frequency Comb Generation in MoS2 Nanoelectromechanical Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052243)|S. M. E. H. Yousuf; Y. Wang; J. Lee; S. W. Shaw; P. X. . -L. Feng|10.1109/MEMS49605.2023.10052243|Parametric amplification;parametric resonator;gain;phononic frequency comb (PnFC);two-dimensional (2D) materials;quality (Q) factor;Couplings;Q-factor;Nanoelectromechanical systems;Buildings;Thermomechanical processes;Resonant frequency;Quantum mechanics|
|[Parylene-N as a high Temperature thin Film Piezoelectric Material](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052501)|N. Jackson; D. Kunwar|10.1109/MEMS49605.2023.10052501|KEYWORDS Parylene;Piezoelectric;Poling;Flexible Thin Film;MEMS;Piezoelectric films;Temperature;Annealing;Deformation;Microfabrication;Ions;Etching|
|[Silicon Carbide-on-Insulator Thermal-Piezoresistive Resonator for Harsh Environment Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052401)|B. Sun; J. Mo; H. Zhang; H. W. van Zeijl; W. D. van Driel; G. Zhang|10.1109/MEMS49605.2023.10052401|Silicon carbide-on-insulator;thermal-piezoresistive;resonator;harsh environment;Fabrication;Resistance;Micromechanical devices;Temperature;Current-voltage characteristics;Resonant frequency;Silicon|
|[Spin Coating of Highly Aligned Agcn Microwires Epitaxially Grown on 2d Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052297)|J. Ham; J. Lim; J. Lim; G. Jang; S. Y. Lee; D. Lim; S. Hong; W. C. Lee|10.1109/MEMS49605.2023.10052297|Crystallographic orientation;epitaxy;AgCN microwire;2D materials;spin coating;inorganic material;Micromechanical devices;Optical microscopy;Chemistry;Microscopy;Ethanol;Graphene;Crystals|
|[Suspended Two-Dimensional Material Membranes For Sensor Applications Fabricated With A High-Yield Transfer Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052595)|S. Lukas; I. Kraiem; M. Prechtl; O. Hartwig; A. Grundmann; H. Kalisch; S. Kataria; M. Heuken; A. Vescan; G. S. Duesberg; M. C. Lemme|10.1109/MEMS49605.2023.10052595|suspended 2D materials;graphene;PtSe2;MoS2;transition metal dichalcogenides;membrane-based sensors;Fabrication;Pressure sensors;Micromechanical devices;Graphene;Two dimensional displays;Metals;Resists|
|[TCF-Improved SH0 Mode Acoustic Resonators Based on 30°YX-LINBO3/SIO2 Membrane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052518)|S. Wu; Z. Wu; H. Qian; F. Bao; G. Tang; F. Xu; J. Zou|10.1109/MEMS49605.2023.10052518|Large coupling;lithium niobate;plate acoustic wave;shear horizontal;temperature stability;Temperature measurement;Resonant frequency;Acoustic measurements;Mechanical variables measurement;Stability analysis;Loss measurement;Frequency measurement|
|[Wafer Scale Multilayer Graphene Based Brain Probes by Spin-Spraying Methods for Magnetic Resonance Imaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052198)|K. Tu; Z. Guo; M. Xu; B. Yang; J. Liu|10.1109/MEMS49605.2023.10052198|Wafer Scale;Multilayer Graphene;Spin-spraying;Brain Probes;Magnetic Resonance Imaging;Semiconductor device modeling;Magnetic resonance imaging;Graphene;Microfabrication;Metals;Nonhomogeneous media;Recording|
|[3d Self-Aligned Fabrication of Suspended Nanowires by Crystallographic Nanolithography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052139)|E. J. W. Berenschot; Y. Pordeli; L. J. Kooijman; Y. L. Janssens; R. M. Tiggelaar; N. R. Tas|10.1109/MEMS49605.2023.10052139|Corner lithography;nanowires;templating;3D;silicon crystal;Fabrication;Micromechanical devices;Three-dimensional displays;Wires;Silicon nitride;Interference;Crystals|
|[A Simple Process for the Fabrication of Parallel-Plate Electrostatic MEMS Resonators by Gold Thermocompression Bonding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052563)|D. M. Juarez; F. Mathieu; G. Libaude; D. Bourrier; S. Charlot; L. Mazenq; V. Conédéra; L. Salvagnac; I. Dufour; L. Nicu; T. Leïchlé|10.1109/MEMS49605.2023.10052563|Micromechanical device;Cantilever;Parallel-plate capacitor;Resonant frequency;Q-factor;Packaging;Thermocompression;Micromechanical devices;Fabrication;Damping;Gold;Wet etching;Silicon carbide;Resonators|
|[Electromechanically Stable Interconnection between LIG and Thick Dam-Shaped Metallic Electrode via Stored AG Microparticle Solution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052528)|S. Park; Y. -K. Shin; M. -H. Seo|10.1109/MEMS49605.2023.10052528|Laser-induced graphene;Ag microparticle ink;dam-shaped electrode;Laser-induced graphene interconnection;Electrodes;Micromechanical devices;Visualization;Silver;Graphene;Ink;Inspection|
|[Free-Standing Membranes with Self-Assembled Nanopore Arrays for Tem Observation of Liquid Samples](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052473)|J. Lim; J. Ham; S. Jeon; Y. Bae; M. Kang; S. Y. Lee; J. Park; W. C. Lee|10.1109/MEMS49605.2023.10052473|AAO mask;reactive ion etching;liquid TEM;nanofabrication;in-situ electron microscopy;Fabrication;Liquids;Self-assembly;Transmission electron microscopy;Two dimensional displays;Silicon;Real-time systems|
|[Nonplanar Nanofabrication Via Interface Engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052141)|S. O. Spector; P. F. Satterthwaite; F. Niroui|10.1109/MEMS49605.2023.10052141|Nonplanar nanofabrication;Atomic layer deposition;Self-assembled molecular layer;Interface engineering;Nanoelectromechanical devices;Mechanical resonators;Fabrication;Self-assembly;Nanofabrication;Nanoelectromechanical systems;Nanoscale devices;Nanostructures;Resonators|
|[Wafer-Level Fabrication of Conformal Sub 10-NM Nanogaps](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052564)|S. Tope; S. Noh; H. Kim|10.1109/MEMS49605.2023.10052564|ALD calibration;lateral nano-gap;sub-10-nm;Exposure Mode;Deep trench coating;conformality;uniformity;Fabrication;Resistance;Micromechanical devices;Electrodes;Image resolution;Fluid flow;Surface treatment|
|[Mems Resonator Vacuum-Sealed by Silicon Migration and Hydrogen Outdiffusion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052449)|M. J. Khan; Y. Suzuki; T. Gong; T. Tsukamoto; S. Tanaka|10.1109/MEMS49605.2023.10052449|Resonator MEMS;Wafer-level Vacuum package;Silicon Migration Sealing (SMS);Q factor;Q-factor;Micromechanical devices;Encapsulation;Q measurement;Vacuum systems;Annealing;Hydrogen|
|[MEMS Thin-Film Vacuum Package Utilizing Glow Discharge Getter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052625)|V. Maharshi; M. Kumar; A. Agarwal; B. Mitra|10.1109/MEMS49605.2023.10052625|MEMS;alumina;porous;pirani;getter;hermiticity;Micromechanical devices;Temperature sensors;Temperature measurement;Electrodes;Voltage;Vacuum technology;Silicon|
|[LNOI Thin-Film Dual-Axis Resonant Micro-Mirror with E16 Torsional Actuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052626)|Y. Lu; K. Liu; Y. Wang; R. Nie; T. Wu|10.1109/MEMS49605.2023.10052626|Dual-axis;Micro-mirror;LNOI;Torsional actuation;Micromechanical devices;Mechanical sensors;Actuators;Lithium niobate;Resonant frequency;Process control;Sensor systems and applications|
|[A Piezoelectric MEMS Speaker with Stretchable Film Sealing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052314)|L. Xu; M. Sun; M. Zhang; C. Liu; X. Yang; W. Pang|10.1109/MEMS49605.2023.10052314|Piezoelectric;MEMS speaker;sound pressure level;acoustic short circuit;polydimethylsiloxane;Micromechanical devices;Damping;Actuators;Young's modulus;Sensitivity;Resonant frequency;Plastic films|
|[Broadband MEMS Speaker by Single-Way Multi-Resonance Array with Acoustic Damping Tuning: A Proof of Concept](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052570)|M. Sun; M. Zhang; C. Liu; W. Pang|10.1109/MEMS49605.2023.10052570|Piezoelectric;MEMS speaker;broadband;SPL;resonance synthesis;acoustic damping;Micromechanical devices;Damping;Actuators;Sensitivity;Resonant frequency;Bandwidth;Acoustic arrays|
|[Ionic Liquid Electrospray Thruster with Two-Stage Electrodes On Glass Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052351)|A. Nishimura; T. Tsuchiya; Y. Takao|10.1109/MEMS49605.2023.10052351|Electrospray thruster;ionic liquid;silicon emitter array;glass via hole;Electrodes;Liquids;Satellites;Glass;Voltage;Propulsion;Ions|
|[Monolithic Integration of PZT Actuation Units of Various Activated Resonances for Full-Range Mems Speaker Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052557)|H. -H. Cheng; S. -C. Lo; Y. -C. Chen; M. -C. Cheng; T. -C. Wei; M. Wu; W. Fang|10.1109/MEMS49605.2023.10052557|MEMS speaker array;piezoelectric;PZT;full range;Micromechanical devices;Electrodes;Voltage measurement;Bridge circuits;Monolithic integrated circuits;Micromachining;Ear|
|[Pull-in Voltage Reduction in Electrostatic Airgap Actuator Using 12 NM-Ultrathin Internal Dielectric Transduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052612)|S. K. Verma; B. Mitra|10.1109/MEMS49605.2023.10052612|MEMS Actuation;Pull-In Voltage;Hybrid Actuator;Internal Dielectric;Micromechanical devices;Transducers;Electrostatic measurements;Capacitance;Dielectric measurement;Dielectrics;Softening|
|[A Reverse Electrowetting-On-Dielectric (Rewod) Energy Harvester Using Nonwetting Gallium Coated Electrode And Ultrathin Gallium Oxide Shell As Dielectric Layer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052602)|J. Jeong; B. Suh; J. B. J. Lee|10.1109/MEMS49605.2023.10052602|REWOD;liquid metal;gallium oxide;energy harvest;Electrodes;Micromechanical devices;Gallium;Liquids;Nanoscale devices;Dielectrics;Energy harvesting|
|[Asymmetric Quad Leg Orthoplanar Spring for Wideband Piezoelectric Micro Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052512)|A. Mohammadi; S. Sadrafshari; A. Shokrani; C. R. Bowen|10.1109/MEMS49605.2023.10052512|Piezoelectric Energy Harvester;Micro-electromechancial Systems (MEMS);Orthoplanar Spring;Vibrations;Transducers;Microfabrication;Resonant frequency;Vibration measurement;Frequency measurement;Piezoelectric devices|
|[Evaluation of Thermoelectric Properties of Monolithically-Integrated Core-Shell Si Nanowire Bridges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052493)|A. Uesugi; S. Nishiyori; K. Sugano; Y. Isono|10.1109/MEMS49605.2023.10052493|Thermoelectric conversion;Seebeck coefficient;Silicon;Nanowire;Crystal growth;Monolithic integration;Temperature sensors;Temperature measurement;Electric potential;Voltage measurement;Temperature;Bridge circuits;Monolithic integrated circuits|
|[Glaze Tile-Inspired Liquid-Solid Power Generator for Continuous Water Flow Energy Harvesting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052174)|D. Nie; B. Lyu; Y. Hu; J. Zhang; Y. Fu; H. Chang; K. Tao|10.1109/MEMS49605.2023.10052174|Tile-inspired structure;micro water flow energy harvesting;liquid-solid contact electrification;electret energy harvesting;Water;Micromechanical devices;Pain;Mechanical variables measurement;Generators;Sensors;Glazes|
|[MEMS Cantilevered Energy Harvester with Tapered Thickness for Stress Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052148)|T. Yokota; K. Kanda; T. Fujita; K. Maenaka|10.1109/MEMS49605.2023.10052148|Uniform stress energy harvester;Series-connected piezoelectric film;Stress concentration relief;Fillet;Micro-loading effect;Thickness control;Piezoelectric films;Micromechanical devices;Three-dimensional displays;Tungsten;Stress control;Structural beams;Voltage control|
|[Tapered Helmholtz Resonator Wind Energy Harvester Driven by Aeroacoustics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052126)|C. Hua; L. Zhen; J. Liu; B. Yang|10.1109/MEMS49605.2023.10052126|Aeroacoustics;Wind Energy Harvester;Piezoelectric Circular Transducer;Tapered Helmholtz Resonator;Resistance;Performance evaluation;Micromechanical devices;Transducers;Wind energy;Resonant frequency;Voltage|
|[Andromeda: A Flexible MEMS Technology Platform for a Variety of Piezoelectrically Actuacted Micromirrors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052507)|I. Martini; A. Alessandri; M. Carminati; R. Carminati; P. Ferrarini; D. A. L. Gatti; R. Gianola; B. Lazarova; C. M. Lazzari; A. Nomellini; L. Oggioni; C. Pedrini; C. L. Prelini; R. Tacchini; M. Vimercati|10.1109/MEMS49605.2023.10052507|MEMS scanner;MEMS mirror;PZT;piezoelectric;AR/MR;LiDAR;Micromechanical devices;Laser radar;Piezoelectric materials;Resonant frequency;Manufacturing;Micromirrors|
|[Design of Butterfly Plate Piezoelectric Actuator with Dual Driving Electrodes for MEMS Micro-Mirror](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052250)|S. -H. Chen; S. -C. Liu; H. -C. Cheng; H. -Y. Lin; K. -C. Liang; M. Wu; W. Fang|10.1109/MEMS49605.2023.10052250|Piezoelectric MEMS Micro-mirror;PZT;Optical devices;AR-HUD;Micromechanical devices;Electrodes;Piezoelectric actuators;Bending;Boundary conditions;Residual stresses;Frequency measurement|
|[Fully-Flexible Micro-Scale Actuator Array with the Liquid-Gas Phase Change Materials](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052525)|S. Sim; K. Bae; J. Kim|10.1109/MEMS49605.2023.10052525|Phase change mechanism;micro-scale actuator;fully-flexible;MEMS fabrication process;Phased arrays;Phase change materials;Fabrication;Actuators;Liquids;Shape;Wearable computers|
|[A Novel Comb Design for Enhanced Power and Bandwidth in Electrostatic Mems Energy Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052590)|J. Li; H. Ouro-Koura; H. Arnow; A. Nowbahari; M. Galarza; M. Obispo; X. Tong; M. Azadmehr; M. M. Hella; J. A. Tichy; D. -A. Borca-Tasciuc|10.1109/MEMS49605.2023.10052590|ultralow frequency;energy harvester;comb design;up-conversion;Electrodes;Micromechanical devices;Fabrication;Time-frequency analysis;Capacitors;Bandwidth;Surfaces|
|[A Hybrid Nanogenerator-Driven Self-Powered Wearable Perspiration Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052366)|M. A. Zahed; S. M. S. Rana; M. Sharifuzzaman; S. Jeong; G. B. Pradhan; H. S. Song; J. Y. Park|10.1109/MEMS49605.2023.10052366|Self-Powered;Hybrid Nanogenerator;Wearable;Microfluidic;Biosensors;Temperature sensors;Wireless communication;Wireless sensor networks;User interfaces;Hybrid power systems;Biosensors;Triboelectricity|
|[A Monolithic Integrated and Transparent Microsystem Constructed by Using Amorphous InGaZnO Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052160)|B. Jia; C. Zhang; X. Huang|10.1109/MEMS49605.2023.10052160|Integrated microsystem;transparent;InGaZnO (IGZO);on-chip energy device;Micromechanical devices;Lithium-ion batteries;Fabrication;Performance evaluation;Glass;Photodetectors;Thin film transistors|
|[Flowing Water Enables Steerable Charge Distribution on Electret Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052517)|B. Lyu; J. Zhang; Y. Li; Y. Fu; H. Chang; W. Yuan; K. Tao|10.1109/MEMS49605.2023.10052517|Contact electrification;oxygen plasma treatment;electret;water energy harvesting;Micromechanical devices;Three-dimensional displays;Shape;Surface charging;Plasmas;Energy harvesting;Surface treatment|
|[Self-Powered Flexible Piezoelectret Array for Wearable Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052352)|H. Yang; R. M. R. Pinto; P. González; A. Ainla; M. Faraji; K. B. Vinayakumar|10.1109/MEMS49605.2023.10052352|Ferroelectret;pyroelectric;charging;flexible;wearable;sensor;Wrist;Pressure sensors;Micromechanical devices;Geometry;Shape;Piezoelectric effect;Force|
|[A Bulk-Type Pressure Sensor with Full-Bridge Implementation Enabled by Stress-Modifying Trenches](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052520)|D. Lin; M. Wong; K. Chau|10.1109/MEMS49605.2023.10052520|MEMS;pressure;sensor;piezoresistive;trench;stress-filtering;Pressure sensors;Resistors;Micromechanical devices;Sensitivity;Piezoresistive devices;Bridge circuits;Silicon|
|[A CMOS Compatible Micro Pirani Gauge with Structure Optimization for Performance Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052309)|R. Jiao; G. Yang; R. Wang; Y. Tang; Z. Liu; H. Xie; H. Yu; X. Wang|10.1109/MEMS49605.2023.10052309|Pirani gauge;FEM;CMOS compatible;Performance Enhancement;Performance evaluation;Fabrication;Micromechanical devices;Sensitivity;Fluid dynamics;Dry etching;Stability analysis|
|[A Thermal Airflow Sensor Based on Mn-Co-Ni-O Thin Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052597)|J. Wang; Y. Liu; Z. Zhu; C. Gao; Z. Yang; Y. Hao|10.1109/MEMS49605.2023.10052597|Thermal airflow sensor;Mn-Co-Ni-O thin film;high temperature coefficient of resistance;Temperature sensors;Magnetic films;Micromechanical devices;Sensitivity;Temperature;Thermal resistance;Platinum|
|[Highly Sensitive Wave Height Sensor with MEMS Piezoresistive Cantilever and Waterproof Membrane](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052152)|T. Hirayama; H. Takahashi|10.1109/MEMS49605.2023.10052152|Wave height sensor;piezoresistive cantilever;frequency response;Micromechanical devices;Sensitivity;Power measurement;Power demand;Atmospheric measurements;Volume measurement;Low-pass filters|
|[MEMS Capacitance Diaphragm Gauge With Two Sealed Reference Cavities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052333)|X. Han; J. Li; G. Li; Y. Feng|10.1109/MEMS49605.2023.10052333|capacitance;diaphragm gauge;vacuum;two cavities;Micromechanical devices;Sensitivity;Micromachining;Capacitance;Mechanical variables measurement;Stability analysis;Distance measurement|
|[Towards a Gas Independent Thermal Flow Meter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052491)|S. A. Kenari; R. J. Wiegerink; R. G. P. Sanders; J. C. Lotters|10.1109/MEMS49605.2023.10052491|Thermal flow sensor;thermal conductivity;calorimetric sensor;Wheatstone bridge;Temperature measurement;Semiconductor device measurement;Gases;Temperature distribution;Voltage measurement;Wires;Thermal sensors|
|[An Integrated Mems Device for in-Situ Four-Probe Electro-Mechanical Characterization of PT Nanobeam](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052503)|Y. Huang; M. Nie; B. Li; K. Yin; L. Sun|10.1109/MEMS49605.2023.10052503|Micro-electro-mechanical system;In-situ test;Electro-mechanical property;Pt nanobeam;Micromechanical devices;Couplings;Scanning electron microscopy;Actuators;Thermal sensors;Nanomaterials;Nanoscale devices|
|[Fingerlike Tactile Texture Integrated Sensor with Cold and Warm Sensations of Sub-MM Spatial Resolution](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052422)|N. Mise; M. Kozasa; K. Terao; F. Shimokawa; H. Takao|10.1109/MEMS49605.2023.10052422|tactile sensor ， piezo resistor ， surface shape ， friction force;heat flex;Temperature measurement;Temperature sensors;Heating systems;Shape;Friction;Piezoelectric transducers;Conductivity measurement|
|[Modified Beam Structures for Improved Resonant Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052235)|E. Ghaderi; B. Bahreyni|10.1109/MEMS49605.2023.10052235|Force Sensors;Resonant Sensors;Microbeam;Boundary Conditions;Sensitivity;Performance evaluation;Micromechanical devices;Sensitivity;Manufacturing processes;Simulation;Force;Boundary conditions|
|[Occlusal Paper-Based Flexible Pressure Sensor for in Situ Measuring Oral Occlusal Force](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052537)|W. Wang; X. Zhang; N. Zhao; J. Liu; B. Yang|10.1109/MEMS49605.2023.10052537|Paper-based Flexible Sensor;Occlusal Force;In Situ Measurement;Interdigital Electrode;Pressure sensors;Force measurement;Bluetooth;Voltage measurement;Atmospheric measurements;Force;Particle measurements|
|[Suction Cup Array Working Also as Tactile Sensor to Detect Cups Deformation Using KCF and CNN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052603)|T. Shiratori; J. Sakamoto; Y. Kumokita; M. Suzuki; T. Takahashi; S. Aoyagi|10.1109/MEMS49605.2023.10052603|Suction cup array;CNN;tactile sensor;nanoimprint;femtosecond laser processing;Three-dimensional displays;Deformation;Force;Tactile sensors;Laser modes;Cameras;Convolutional neural networks|
|[Vertical Integration of Force Transmission Structure on Capacitive Cmos-Mems Tactile Force Sensor for Sensitivity Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052308)|Y. Huang; Y. -L. Chen; S. Lin; F. Shih; Z. Hu; W. Fang|10.1109/MEMS49605.2023.10052308|Capacitive tactile force sensor;CMOS-MEMS;Vertical integration;Force transmission structure;Micromechanical devices;Sensitivity;Force;Glass;CMOS process;Force sensors;Polymers|
|[1-octadecanethiol Sam on CMOS-MEMS Gold-Plated Resonator via Dip-Cast for VOCS Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052527)|R. Perelló-Roig; J. Verd; S. Bota; B. Soberats; A. Costa; J. Segura|10.1109/MEMS49605.2023.10052527|MEMS resonators;VOCs;CMOS-MEMS;Lab-on- Chip;Self-Assembled Monolayer;Gas sensor;ENIG;Micromechanical devices;Gold;Electric potential;Resonant frequency;CMOS technology;Sensors;Medical diagnosis|
|[Application of Deep Learning Network for Humidity Compensation of Semiconductor Metal Oxide Gas Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052322)|M. Kang; I. Cho; N. Park|10.1109/MEMS49605.2023.10052322|Semiconductor metal oxide gas sensor;Deep learning;Humidity compensation;Deep learning;Micromechanical devices;Heating systems;Neural networks;Metals;Humidity;Real-time systems|
|[Development of Monolithic Micro-Led Gas Sensor Based E-Nose System for Real-Time, Selective Gas Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052155)|K. Lee; M. Kang; I. Park|10.1109/MEMS49605.2023.10052155|Micro-LED;electronic nose;ultra-low-power;light- activated gas sensor;convolutional neural network;Temperature sensors;Gold;Silver;Ultraviolet sources;Prediction algorithms;Light emitting diodes;Real-time systems|
|[Electronic-Nose: An Array of 16 MOS-Gas Sensors Integrated With Temperature and Moisture Sensing Capabilities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052638)|X. Yue; S. Wei; P. Zhang; Z. Zhou; T. H. Tao; N. Qin|10.1109/MEMS49605.2023.10052638|Gas Sensor Array;Electronic-nose;Long-term Stability;Micro Electromechanical System;Temperature sensors;Micromechanical devices;Temperature;Moisture;Sensor systems;Sensors;Electromechanical systems|
|[Enhancement of Sensitivity in Photonic Crystal Based Chemical Sensor Using Chemo-Mechanical Bilayer Effect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052505)|S. Lee; N. T. Banavathi; D. Kang; S. Kang; K. Cho; J. Kim; J. Park|10.1109/MEMS49605.2023.10052505|Volatile Organic Compounds;Photonic Crystals;Chemo-mechanical Bilayer Effects;Diffusion;Mechanical sensors;Chemical sensors;Sensitivity;Color;Photonic crystals;Nanostructures;Polymers|
|[Metal Ion Recognition Sensor Based on Resistive Switching Effect](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052388)|T. Kang; Y. Chen; G. Lin; S. Jin; L. Li; H. Sun; S. Hu; W. Wu|10.1109/MEMS49605.2023.10052388|Electrolyte-Oxide-Metal;Conductive filaments;Resistive switching;Ion sensing;Silicon compounds;Deep learning;Neural networks;Metals;Voltage;Switches;Electrolytes|
|[Multi-Hotspot MID-IR Nanoantennas with Matched Loss and High-Intensity Near-Field for Sub-ppm-Level Gas Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052260)|H. Zhou; Z. Ren; C. Xu; L. Xu; X. Guo; C. Lee|10.1109/MEMS49605.2023.10052260|multi-hotspot nanoantennas;metal-organic frameworks;gas detection;Optical losses;Micromechanical devices;Spectroscopy;Design methodology;Optimization methods;Plasmons;Optical coupling|
|[Palladium Based Mems Hydrogen Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052177)|M. Hoffmann; M. Wienecke; M. Lengert; M. H. Weidner; J. Heeg|10.1109/MEMS49605.2023.10052177|Hydrogen Sensor;Micro-Electro-Mechanical Sensor;Volume Change;Selectivity;Micromechanical devices;Temperature measurement;Methane;Temperature sensors;Sensitivity;Hydrogen;Switches|
|[Selective Discrimination of Ppb-level Vocs Using Mos Gas Sensor in Pulse-Heating Mode with the Modified Hill's Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052356)|G. Niu; Y. Zhuang; Y. Hu; Z. Liu; F. Wang|10.1109/MEMS49605.2023.10052356|Pulse-heating;MOSs gas sensor;the modified Hill’s model;ppb-level;selectivity;Temperature sensors;Resistance;Micromechanical devices;Gases;Analytical models;Ethanol;Data models|
|[Thermal Conductivity Detector (TCD)-Type Gas Sensor Based on the Suspended 1D Nanoheater for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052214)|W. Cho; J. -H. Kwak; T. Kim; H. Shin|10.1109/MEMS49605.2023.10052214|Thermal conductivity detector (TCD);Gas sensor;Pulse-width modulation;Suspended architecture;C-MEMS;Heating systems;Detectors;Thermal sensors;Packaging;Conductivity;Thermal conductivity;Internet of Things|
|[120 ppm Quality Factor Thermal Stability from -40 ℃ to +60℃ of a Dual-Axis MEMS Gyroscope Based on Joule Effect Dynamic Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052533)|J. Cui; Q. Zhao|10.1109/MEMS49605.2023.10052533|MEMS gyroscope;Quality factor tuning;Joule effect;Q-factor;Micromechanical devices;Couplings;Thermal factors;Negative feedback;Thermal resistance;Gyroscopes|
|[A Force-Balance Capacitive MEMS Gravimeter with Superior Response Time, Self-Noise and Drift](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052380)|L. Gao; F. Li; J. Zhang; B. Cai; W. Wu; L. Tu|10.1109/MEMS49605.2023.10052380|electromagnetic force-balance;MEMS gravimeter;capacitive displacement transducer;earth tide;dynamic response;Micromechanical devices;Seismic measurements;Transducers;Sensitivity;Time measurement;Time factors;Motion measurement|
|[A MEMS-Based Gravimeter for Simultaneous Vertical and Horizontal Earth Tides Measurements](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052577)|L. Yang; X. Xu; Q. Wang; J. Tian; Y. Fang; C. Zhao; W. Wu; F. Hu; L. Tu|10.1109/MEMS49605.2023.10052577|Micro-electromechanical system (MEMS);gravimeter;Vertical and horizontal gravity measurements;Earth tides;Micromechanical devices;Earth;Microelectromechanical systems;Sensitivity;Sensors;Tides;Springs|
|[A Novel Multiple-Folded Beam Disk Resonator for Maximizing the Thermoelastic Quality Factor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052522)|X. Sun; X. Zhou; L. Yu; K. He; X. Wu; D. Xiao|10.1109/MEMS49605.2023.10052522|Microelectromechanical systems (MEMS);quality factor;thermoelastic dissipation;anchor loss;disk resonator;Q-factor;Damping;Temperature measurement;Micromechanical devices;Microelectromechanical systems;Q measurement;Temperature|
|[A Time-Series Configuration Method of Mode Reversal in MEMS Gyroscopes Under Different Temperature-Varying Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052326)|L. Chen; T. Miao; Q. Li; P. Wang; J. Li; X. Wu; D. Xiao; X. Xi|10.1109/MEMS49605.2023.10052326|MEMS gyroscopes;Mode reversal;Timing configuration;Micromechanical devices;Fluctuations;Optimization methods;Real-time systems;Gyroscopes;Timing;Guidelines|
|[Acoustically Isolated MEMS Baw Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052331)|D. E. Serrano; A. Rahafrooz; D. Younkin; K. Nunan; M. Dalal; S. Pal; I. Jafri; S. Hiraoka|10.1109/MEMS49605.2023.10052331|MEMS;Gyroscope;Bulk-Acoustic Wave;Micromechanical devices;Semiconductor device modeling;Performance evaluation;Couplings;Solid modeling;Silicon-on-insulator;White noise|
|[Active Quality Factor Stabilization of MEMS Resonator Utilizing Electrical Dissipation Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052345)|Y. Zhao; Q. Shi; G. Xia; A. Qiu|10.1109/MEMS49605.2023.10052345|Quality factor;active stabilization;electrical dissipation;MEMS resonator;temperature variation;Q-factor;Micromechanical devices;Electrodes;Current control;Resonant frequency;Regulation;Impedance|
|[Demonstration of Gyro-Less North Finding Using a T-Shaped MEMS Differential Resonant Accelerometer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052125)|K. Masunishi; E. Ogawa; D. Ono; F. Miyazaki; H. Hiraga; K. Uchida; J. Ogawa; H. Murase; Y. Tomizawa|10.1109/MEMS49605.2023.10052125|MEMS;differential resonant accelerometer;T-shaped MEMS DRA;north finding;Coriolis force;centrifugal force;table rotation;microcontroller module;Micromechanical devices;Accelerometers;Performance evaluation;Electrodes;Design methodology;Force;Estimation|
|[Enhanced Stiffness Sensitivity in a Mode Localized Sensor Using Internal Resonance Actuation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052378)|J. Chen; H. Zhang; T. Tsukamoto; M. Kraft; S. Tanaka|10.1109/MEMS49605.2023.10052378|Mode localized sensors;Internal resonance;Enhanced stiffness sensitivity;Couplings;Micromechanical devices;Location awareness;Sensitivity;Perturbation methods;Resonant frequency;IP networks|
|[Modeling Stress Effects on Frequencies of a Mems Ring Gyroscope](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052439)|M. Hosseini-Pishrobat; B. E. Uzunoglu; E. Tatar|10.1109/MEMS49605.2023.10052439|Analytical modeling;Extensible ring;Ring gyroscope;Stress sensing;Micromechanical devices;Analytical models;Time-frequency analysis;Extensibility;Gyroscopes;Sensors;System-on-chip|
|[Rate Integrating Gyroscope Tuned by Focus Ion Beam Trimming and Independent CW/CCW Modes Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052147)|J. Chen; T. Tsukamoto; G. Langfelder; S. Tanaka|10.1109/MEMS49605.2023.10052147|Rate integrating gyroscope;Focus ion beam trimming;Mismatch compensation;Vibrations;Time-frequency analysis;Resonant frequency;Gyroscopes;Trajectory;Softening;Electrostatics|
|[Temperature Dependence of Quality Factors at High Frequencies in MEMS Gyroscopes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052368)|D. Schiwietz; E. M. Weig; P. Degenfeld-Schonburg|10.1109/MEMS49605.2023.10052368|MEMS gyroscopes;numerical modelling;thermoelastic damping;quality factors;Temperature measurement;Q-factor;Micromechanical devices;Damping;Temperature dependence;Temperature distribution;Simulation|
|[0.5MM×0.5MM 150KPA-Measure-Range High-Temperature Pressure Sensor with High-Performance and Low Fabrication-Cost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052462)|P. Li; W. Li; C. Chen; K. Sun; M. Liu; S. Wu; P. Pan; J. Wang; X. Li|10.1109/MEMS49605.2023.10052462|pressure sensor;high temperature;tiny size;single side;TUB process;Pressure sensors;Temperature sensors;Temperature measurement;Wafer bonding;Temperature distribution;Sensitivity;Micromachining|
|[Automatic Pico Laser Trimming System for Silicon MEMS Resonant Devices Based on Image Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052176)|Y. Liu; Q. Zhao; D. Zhang; J. Cui|10.1109/MEMS49605.2023.10052176|Silicon Resonator;Laser Trimming;Image Recognition;Micromechanical devices;Vibrations;Shape measurement;Lasers;Optical resonators;Measurement by laser beam;Size measurement|
|[Micromachining Fused Silica Micro Shell Resonator with Quartz Glass Mold by Thermal Reflow](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052274)|Z. Su; B. Luo; Q. Tang; L. Zhu; J. Shang|10.1109/MEMS49605.2023.10052274|Micro Shell Resonator;Fused Silica;Thermal Reflow;Resonant Frequency;Quality Factor;Silicon compounds;Q-factor;Micromechanical devices;Micrometers;Resonant frequency;Glass;Micromachining|
|[Wafer-Level Patterning of Tin Oxide Nanosheets for MEMS Gas Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052390)|M. Li; W. Luo; X. Liu; G. Niu; F. Wang|10.1109/MEMS49605.2023.10052390|SnO2 nanosheets;patterning;MEMS technology;ethanol sensors;Micromechanical devices;Ethanol;Lithography;Tin;Calcination;Reproducibility of results;Sensors|
|[Air Damping Effects on Different Modes of AlN-on-Si Microelectromechanical Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052273)|Y. Liu; S. M. Enamul Hoque Yousuf; A. Qamar; M. Rais-Zadeh; P. X. . -L. Feng|10.1109/MEMS49605.2023.10052273|Micromechanical resonator;air damping;aluminum nitride (AlN);piezoelectric resonator;quality (Q) factor;Damping;Q-factor;Micromechanical devices;Q measurement;Transducers;Resonant frequency;Frequency measurement|
|[A Novel Piezoresistive Pressure Sensor Based on Cr-doped V2O3 Thin Film](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052552)|M. Gidts; W. -F. Hsu; M. R. Payo; S. Kushwaha; C. Wang; F. Ceyssens; D. Reynaerts; J. -P. Locquet; M. Kraft|10.1109/MEMS49605.2023.10052552|Piezoresistive pressure sensor;transition metal oxides;thin film;phase transition;sapphire membrane;Pressure sensors;Temperature sensors;Micromechanical devices;Sensitivity;Piezoresistive devices;Metals;Voltage|
|[A Novel Feedthrough Cancellation Technique for Piezoelectric MEMS Resonant Sensors in Ionic Liquid Medium](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052319)|C. -Y. Wu; Z. -W. Lin; S. -S. Li|10.1109/MEMS49605.2023.10052319|TPoS;ionic liquid;conductive medium;feedthrough cancellation;MEMS resonant transducers;PZT/AlN resonator;physical/chemical/bio applications;mass sensor;Micromechanical devices;Phase noise;Performance evaluation;Liquids;Transducers;Resonant frequency;Sensors|
|[Characterization of Packaging Stress with a Capacitive Stress Sensor Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052529)|T. Veske; D. Erkan; E. Tatar|10.1109/MEMS49605.2023.10052529|Packaging stress;die-attach;capacitive stress sensor;Micromechanical devices;Temperature measurement;Temperature sensors;Packaging;System-on-chip;Stress;Substrates|
|[Millisecond-Level Pulse-Heating Sensing System for MEMS-based Gas Sensors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052316)|Y. Zhuang; G. Niu; L. Wu; F. Wang|10.1109/MEMS49605.2023.10052316|Gas Sensor;Pulse-heating;Fast Detection;Sensing System;Heating systems;Temperature sensors;Power demand;Semiconductor device reliability;Quality control;Gas detectors;Thermal stability|
|[Multiple Parameter Decoupling Using a Single Resonant MEMS Sensor via Blue Sideband Excitation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052550)|J. Xi; L. Xu; Y. Wang; F. Hu; C. Li; L. Wang; H. Liu; C. Wang; M. Kraft; C. Zhao|10.1109/MEMS49605.2023.10052550|Multiple parameter decoupling;blue-sideband excitation;micro-resonator;parametric resonance;Temperature sensors;Micromechanical devices;Temperature measurement;Vibrations;Electric potential;Fluctuations;Perturbation methods|
|[Diamond Nanowires Array Prepared by Annealing Nano-Crystalline Diamond in Air and Its Application in Field Emission](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052372)|Y. Wang; C. Lin; J. Zhang|10.1109/MEMS49605.2023.10052372|Diamond nanowires;nano-crystalline diamond;annealing;surface hydrogenation;field electron emission;field enhancement factor;Annealing;Films;Hydrogen;Diamonds;Micromachining;Iron;Nanowires|
|[Quantified Stress Relaxation in Carbon Nanotube Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052187)|M. Vollmann; C. Roman; M. Haluska; C. Hierold|10.1109/MEMS49605.2023.10052187|Carbon nanotube resonator;NEMS;stress relaxation;harmonic balancing;contact slipping;nonlinear spring constant;tuning range;Micromechanical devices;Resonant frequency;Carbon nanotubes;Springs;Stress|
|[Self-Referenced Temperature Sensors Based on Cascaded Silicon Ring Resonator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052234)|X. Zhu; M. You; Z. Lin; B. Yang; J. Liu|10.1109/MEMS49605.2023.10052234|Cascaded Micro-ring Resonator (CMRR);Titanium Dioxide (TiO2);Reference Ring;Sensing ring;Temperature Insensitive;Temperature sensors;Temperature measurement;Sensitivity;Claddings;Simulation;Silicon;Titanium dioxide|
|[A 0.35 MM2 System on Chip Level Detector Based on An Annular Pmut-On-Cmos Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052514)|E. Ledesma; I. Zamora; F. Torres; A. Uranga; N. Barniol|10.1109/MEMS49605.2023.10052514|MEMS-on-CMOS;PMUTs;ultrasound;fluid level sensor;evaporation rates;pulse-echo system;ring array;Ultrasonic transducers;Ultrasonic imaging;Sensitivity;Fluids;Ethanol;Ultrasonic transducer arrays;Resonant frequency|
|[An ALSCN PMUT-on-CMOS Sensor for Monitoring Fluids’ Density, Viscosity, Sound Velocity, and Compressibility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052334)|E. Ledesma; I. Zamora; J. Yanez; A. Uranga; N. Barniol|10.1109/MEMS49605.2023.10052334|PMUT-on-CMOS;AlScN;PMUT;fluid sensor;resonator;ultrasound system;pulse-echo system;acoustics;Viscosity;Ultrasonic transducers;Sensitivity;Liquids;Ultrasonic imaging;Transducers;Ultrasonic variables measurement|
|[Auto-Positioning and Haptic Stimulations via A 35 mm Square Pmut Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052452)|W. Yue; Y. Peng; H. Liu; F. Xia; F. Sui; S. Umezawa; S. Ikeuchi; Y. Aida; L. Lin|10.1109/MEMS49605.2023.10052452|Haptic Sensation;LN pMUT;Beamforming;Ultrasound;Ultrasonic transducers;Array signal processing;Ultrasonic variables measurement;Piezoelectric transducers;Ultrasonic transducer arrays;Robot sensing systems;Size measurement|
|[Body Force Based Droplet Ejection by GHz Acoustic Micro-Transducer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052168)|H. Zhang; Y. Zhou; M. Zhang; W. Guo; C. Sun; X. Duan; W. Pang|10.1109/MEMS49605.2023.10052168|Acoustic droplet ejection;GHz acoustic transducer;nozzle-free;body force;printing;Micromechanical devices;Vibrations;Bioprinting;Liquids;RF signals;Acoustics;Drug delivery|
|[Bone Conduction Pickup Based on Piezoelectric Micromachined Ultrasonic Transducers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052340)|C. Liu; X. Wang; Y. Xie; G. Wu|10.1109/MEMS49605.2023.10052340|Bone Conduction;Microphone;Sound Pickup;Piezoelectric Micromachined Ultrasonic Transducers (PMUTs);Microelectromechanical System (MEMS);Ultrasonic transducers;Micromechanical devices;Bones;Noise measurement;Signal to noise ratio;Noise level|
|[Breaking the Dead Zone Limitation of Pmuts Based on a Phase Shift of Driving Waveform with Window Function](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052477)|C. -Y. Liu; C. -Y. Chang; S. -S. Li|10.1109/MEMS49605.2023.10052477|Ring-down time reduction;axial resolution;PMUT;ultrasonic;dead zone;pulse-echo;time of flight;phase shift of driving waveform;window function;Electrodes;Ultrasonic transducers;Phase measurement;Sensitivity;Pulse measurements;Interference;Time measurement|
|[Drone-Mounted Low-Frequency pMUTS for > 6-Meter Rangefinder in Air](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052443)|H. Liu; Y. Peng; W. Yue; S. Umezawa; S. Ikeuchi; Y. Aida; C. Chen; P. Tsao; L. Lin|10.1109/MEMS49605.2023.10052443|MEMS;pMUT;rangefinder;ultrasound;drone-mounted devices;Semiconductor device measurement;Power demand;Ultrasonic variables measurement;Receivers;Attenuation;Transceivers;Real-time systems|
|[Mass Produced Micromachined Ultrasonic Time-Of-Flight Sensors Operating in Different Frequency Bands](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052367)|R. J. Przybyla; S. E. Shelton; C. Lee; B. E. Eovino; Q. Chau; M. H. Kline; O. I. Izyumin; D. A. Horsley|10.1109/MEMS49605.2023.10052367|Ultrasound;piezoelectric;piezoelectric micromachined ultrasound transducer (PMUT);ultrasonic transceiver;rangefinder;time-of-flight;Mechanical sensors;Ultrasonic transducers;Micromechanical devices;Time-frequency analysis;Mass production;Ultrasonic imaging;Transducers|
|[MEMS First-Order Bessel Beam Acoustic Transducer for Particle Trapping And Controllable Rotating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052542)|J. Li; Z. Sun; Y. Jia; T. Li; H. Lu; L. Zhao; H. Liu; S. Liu|10.1109/MEMS49605.2023.10052542|MEMS acoustic transducer;acoustic Bessel beam;soft lithography;PDMS;acoustic manipulation;BBAT;Micromechanical devices;Fabrication;Particle beams;Acoustic transducers;Spirals;Voltage;Resists|
|[Non-Invasive Carotid Artery Monitoring by Using Aluminum Nitride Pmut Close-Packed Arrays](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052418)|S. Wu; K. Liu; S. Shao; W. Li; Y. Chen; T. Wu; X. Li|10.1109/MEMS49605.2023.10052418|Artery monitoring;PMUT arrays;Pulse-echo;Pulsation wave;Portable medical ultrasonic system;Ultrasonic transducers;Ultrasonic imaging;Array signal processing;Skin;III-V semiconductor materials;Carotid arteries;Monitoring|
|[Non-Linear Behavioral Modeling of Capacitive MEMS Microphones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052386)|S. Anzinger; H. S. Wasisto; A. Basavanna; M. Fueldner; A. Dehé|10.1109/MEMS49605.2023.10052386|MEMS;Microphone;Verilog-A;Modeling;SNR;Micromechanical devices;Total harmonic distortion;Sensitivity;Key performance indicator;Behavioral sciences;Integrated circuit modeling;Hardware design languages|
|[Vortex-Beam Acoustic Transducer for Underwater Propulsion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052504)|J. Lee; K. S. Esfahani; E. S. Kim|10.1109/MEMS49605.2023.10052504|Acoustic transducer;Vortex beam;Acoustic propulsion;Self-focusing acoustic transducer;Micromechanical devices;Acoustic transducers;Force measurement;Force;Area measurement;Propulsion;Acoustic measurements|
|[Wideband and Highly Sensitive Micromachined PZT Film-Based Ultrasonic Microphone with Parylene Film and Flexible Helmholtz Resonator Enhancement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052534)|C. -H. Huang; G. -H. Feng|10.1109/MEMS49605.2023.10052534|Piezoelectric;micromachining;Helmholtz resonator;paylene;ultrasonic microphone;Micromechanical devices;Time-frequency analysis;Sensitivity;Titanium;Acoustics;Residual stresses;Structural beams|
|[Halbach-Array Magnetic Coil Arrangement on CMOS Chip for Sensitivity Enhancement of Inductive Tactile Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052312)|T. Chou; Z. -S. Hu; W. Fang|10.1109/MEMS49605.2023.10052312|Inductive sensing;tactile force sensor;CMOS chip;Halbach array;Coils;Magnetic flux leakage;Sensitivity;Magnetic field measurement;Spirals;Force;Magnetic fields|
|[ON-MEMS-CHIP Compact Temperature Sensor for Large-Volume, Low-Cost Sensor Calibration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052513)|P. Frigerio; A. Fagnani; V. Zega; G. Gattere; A. Frangi; G. Langfelder|10.1109/MEMS49605.2023.10052513|MEMS;multi-mode resonators;temperature sensors;temperature coefficient of frequency;Temperature sensors;Temperature measurement;Micromechanical devices;Mechanical sensors;Time-frequency analysis;Resonant frequency;Sensor systems|
|[Particulate Matter Sensor Based on Two-Stage Cascade Virtual Impactors and Thermophoretic Microheaters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052538)|K. -W. Choi; I. Kim; S. Chung; G. -B. Sung; S. -J. Yook|10.1109/MEMS49605.2023.10052538|Particulate matter sensor;Gravimetric sensor;Virtual impactor;Thermophoresis;Airborne particle;Micromechanical devices;Heating systems;Sensitivity;Atmospheric measurements;Surface acoustic waves;Aerosols;Reliability engineering|
|[A Microfluidic Oxygen Gradient Generator for the Study of Aerotropism in Hyphae of Oomycetes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052442)|A. Tayagui; Y. Sun; A. Garrill; V. Nock|10.1109/MEMS49605.2023.10052442|Oomycetes;Aerotropism;Hyphae;Oxygen Gradient;Oxygen Sensor;Lab-on-a-Chip;Micromechanical devices;Fungi;Oxygen;Color;Fluorescence;Mechanical variables measurement;Generators|
|[A Paper-Based Dual Aptamer Assay on an Integrated Microfluidic System for Detection of HNP 1 as a Biomarker for Periprosthetic Joint Infections](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052430)|R. Gandotra; F. -C. Kuo; M. S. Lee; G. -B. Lee|10.1109/MEMS49605.2023.10052430|Dual-aptamer assay;paper-based assay;microfluidics;HNP 1;PJI;Performance evaluation;Micromechanical devices;Costs;Peptides;Loading;Fluorescence;Biomembranes|
|[An Integrated Microfluidic Platform for Tumor Cell Separation and Fluorescence in Situ Hybridization at Single Cell Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052450)|S. Qiu; N. Li; Z. Wu; J. Zhao; H. Mao|10.1109/MEMS49605.2023.10052450|Fluorescence In Situ Hybridization (FISH);Single-cell Analysis;Circulating Tumor Cells (CTCs);Proteins;Micromechanical devices;Costs;Fluorescence;Biomarkers;Fish;Microfluidics|
|[Characterization of Oocyte Hardening Using a Microfluidic Aspiration-Assisted Electrical Impedance Spectroscopy System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052639)|Y. Cao; J. Floehr; U. Schnakenberg|10.1109/MEMS49605.2023.10052639|Microfluidics;lab-on-a-chip;oocyte;zona pellucida;electrical impedance spectroscopy;Young’s modulus;equivalent electrical circuits;Micromechanical devices;Spectroscopy;Analytical models;Impedance measurement;Orifices;Hydrodynamics;Impedance|
|[Double Pulse Irradiation of FS Laser for Enhancing the Performance of Precise Laser Sorting Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052362)|R. Kiya; Y. Rintaro; Y. Tanaka; Y. Yalikun; Y. Hsokawa|10.1109/MEMS49605.2023.10052362|Multiple pulse irradiation;femtosecond laser;high-throughput;cell sorter;Micromechanical devices;Radiation effects;Numerical analysis;Laser modes;Throughput;Biology;Sorting|
|[Droplet Based High Throughput Single-Sperm Cryopreservation Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052288)|N. Li; S. Qiu; Z. Wu; H. Mao|10.1109/MEMS49605.2023.10052288|Azoospermatism;Cryopreservation;Vitrification;Single Cell;Droplet Microfluidics;Resistance;Micromechanical devices;Fabrication;Subspace constraints;Loading;Media;Throughput|
|[Dual Ion-Selective Membrane Deposited Ion-Sensitive Field-Effect Transistor (DISM-ISFET) Integrating Whole Blood Processing Microchamber for In Situ Blood Ion Testing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052183)|X. -W. Chen; S. -R. Huang; N. -T. Huang|10.1109/MEMS49605.2023.10052183|blood ion concentration test;ion-selective membrane (ISM);ion-sensitive field-effect transistor (ISFET);Sensitivity;Ions;Feature extraction;Sensors;System-on-chip;Transistors;Blood|
|[Strong Microstreaming from a Pinned Oscillating Membrane and Application to Gas Exchange](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052276)|A. L. Mercader; S. Kwon Cho|10.1109/MEMS49605.2023.10052276|Acoustofluidics;Microstreaming;Artificial Lung;Liquids;Wavelength measurement;Lung;Acoustics;Frequency measurement;pH measurement;Oscillators|
|[Tunable Nanopore-Integrated Micro-/Nanofluidic Platform for Ion Transport Control in the Presence of Concentration and Temperature Gradients](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052241)|D. Seo; D. Kim; J. Lee; C. Wang; J. Park; T. Kim|10.1109/MEMS49605.2023.10052241|Nanofluidics;Neural Signaling;Diffusioosmosis;Ion transport;Nanofabrication;Nanopore/Nanostructure;Nanoparticles;Micromechanical devices;Self-assembly;Protocols;Medical services;Ions;Nanoscale devices|
|[Quantitative Assessment of Captured Magnetic Nanoparticles Using Self-Powered Magnetoelectric Platform for Biological Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052508)|P. Pathak; V. K. Yadav; S. Das; D. Mallick|10.1109/MEMS49605.2023.10052508|Self-powered;Magnetoelectric;Magnetic Nanoparticles;Photocurrent;Optothermal;Pyroelectric;Sensitivity;Magnetoelectric effects;Magnetic nanoparticles;Voltage;Drug delivery;Time factors;Labeling|
|[Real-Time Operation of Microcantilever-Based in-Plane Resonators Partially Immersed in a Microfluidic Sampler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052263)|J. Xu; E. Cao; M. Fahrbach; V. Agluschewitsch; A. Waag; E. Peiner|10.1109/MEMS49605.2023.10052263|Piezoresistive microcantilever;electrothermal actuation;liquid-phase detection;electromechanical amplitude modulation;Radio frequency;Liquids;Voltage;Amplitude modulation;Real-time systems;Sensors;Resonators|
|[Suspended Nanochannel Resonators Made by Nanoimprint and Gas Phase Deposition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052412)|M. Müller; J. Teuber; R. Nasri; F. T. Canals; N. Barniol; J. L. Sixto; X. Borrisé; F. Perez-Murano; I. Fernandez-Cuesta|10.1109/MEMS49605.2023.10052412|Nanofluidics;suspended nanochannels;resonators;ALD;nanoimprint;COMSOL;Micromechanical devices;Geometry;Fabrication;Damping;Resonant frequency;Predictive models;Nanoscale devices|
|[Developing an Extremely High Flow Rate Pneumatic Peristaltic Micropump for Blood Plasma Separation with Inertial Particle Focusing Technique from Fingertip Blood with Lancets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052216)|T. N. A. Vo; P. -C. Chen; P. -S. Chen|10.1109/MEMS49605.2023.10052216|Micropump;Inertial Microfluidics;Blood Plasma Separation;Micromechanical devices;Spirals;Focusing;Micropumps;Valves;Plasmas;System-on-chip|
|[Direct Patterning on Porous Surface Using Drop Impact Printing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052230)|B. S. Reddy; C. Dey Modak; R. Lathia; B. Agarwal; E. A. R; P. Sen|10.1109/MEMS49605.2023.10052230|Superhydrophobic Sieve;Droplet Impact;Printing;Mass loading;Printing;Micromechanical devices;Loading;Ink;Conductivity;Sensors;Printers|
|[Manufacturing 3d-Printed Paper Microfluidics Integrated With Ionization Mass-Spectrometry for Illicit Drugs Analysis and On-Chip Chromatography](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052397)|M. F. Zaki; P. -C. Chen; Y. -X. Wu; P. -S. Chen|10.1109/MEMS49605.2023.10052397|Paper-based microfluidic devices;stereolithography 3D printing;paper-spray ionization;chromatography;drug analysis;Drugs;Performance evaluation;Solvents;Three-dimensional displays;Ionization;Stability analysis;System-on-chip|
|[Detection Limits in Nanomechanical Mass Flow Sensing for Nanofluidics With Nanowire Open Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052471)|J. E. Escobar; J. Molina; E. Gil-Santos; J. J. Ruz; Ó. Malvar; P. M. Kosaka; J. Tamayo; Á. S. Paulo; M. Calleja|10.1109/MEMS49605.2023.10052471|Semiconductor nanowires;NEMS;nanomechanical resonators;nanofluidics;open fluidics;ionic liquids;Printing;Semiconductor device measurement;Liquids;Electric variables measurement;Reservoirs;Silicon;Sensor systems|
|[Controlling Particle Aggregation and Separation in Liquid on Membrane Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052179)|H. Zhang; H. Jia; X. Li|10.1109/MEMS49605.2023.10052179|membrane resonator;particle interaction;streaming flow;Chladni figure;Vibrations;Micromechanical devices;Couplings;Liquids;Switches;Fluid flow;Biomembranes|
|[Development of Boat Model Powered by Electro-Hydrodynamic Propulsion System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052157)|L. N. Mai; T. -K. Nguyen; T. H. Vu; T. Xuan Dinh; C. -D. Tran; H. -P. Phan; T. Dinh; T. Nguyen; N. -T. Nguyen; D. V. Dao; V. Thanh Dau|10.1109/MEMS49605.2023.10052157|Boat model;electro-hydrodynamic propulsion;ion wind;numerical simulations;thrust-to-power;Electrodes;Surveillance;Simulation;Wires;Boats;Propulsion;Ions|
|[Hemodynamic Analysis of Cardiomems: Adverse Hemodynamic Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052229)|Z. Liu; J. Han; X. Chen|10.1109/MEMS49605.2023.10052229|CardioMEMS;hemodynamics;computational fluid dynamics;implantable sensor;endovascular sensor;Micromechanical devices;In vivo;Medical devices;Perturbation methods;Prototypes;Atherosclerosis;Open systems|
|[Modal Quality Factor Inversion of Non-Slender Mems Resonators Between Gases and Liquids](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052411)|A. L. Gesing; T. Tran; D. Platz; U. Schmid|10.1109/MEMS49605.2023.10052411|MEMS;Fluid-structure interaction;Micro resonators;Plate theory;Stokes flow;Q-factor;Micromechanical devices;Geometry;Water;Damping;Gases;Fluid dynamics|
|[Classifying Cell Cycle By Electrical Properties Using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052482)|J. Wei; X. Xing|10.1109/MEMS49605.2023.10052482|cell cycle;machine learning;electrical characteristics;classification;Drugs;Support vector machines;Radio frequency;Micromechanical devices;Semiconductor device measurement;Phase measurement;Feature extraction|
|[High-Throughput Spherical Supraparticle Self-Assembly by Enhanced Evaporation of Colloidal Water Droplets Through Thin Film of Water-Soluble Oil](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052292)|W. Lee; J. Rhee; J. Kim|10.1109/MEMS49605.2023.10052292|Open-Microfluidics;Water-In-Oil Emulsification;Bottom-Up Fabrication;Nanoparticles;Micromechanical devices;Suspensions (mechanical systems);Self-assembly;Shape;Oils;Production|
|[In-Ice Polymerization for Functional Hydrogel Microbead with Flash Freezing Centrifugal Microfluidic Device](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052270)|T. Murayama; K. Yoshida; Y. Kurashina; H. Onoe|10.1109/MEMS49605.2023.10052270|Microgel beads;In-ice polymerization;Centrifugal force;Gelling by UV irradiation;Ethanol responsive hydrogel;Fabrication;Temperature measurement;Radiation effects;Shape;Hydrogels;Ethanol;Liquid nitrogen|
|[Temperature-Responsive Microcapsules Manufactured by Promoting Controlled Cloaking with the Help of Micro/Nanoparticles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052506)|R. Lathia; B. S. Reddy; C. Dey Modak; S. Nagpal; P. Sen|10.1109/MEMS49605.2023.10052506|Cloaking;Liquid infused surface;Capsule;Liquid Marbles;Micromechanical devices;Fabrication;Drugs;Cooling;Turning;Surface treatment;Chemicals|
|[Water Vitrification in a Microchannel at Low Cooling Rate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052613)|A. Sato; T. Hayashi; T. Ishida|10.1109/MEMS49605.2023.10052613|Cryopreservation;vitrification;microchannel;micro thermometer;Raman spectroscopy;Micromechanical devices;Spectroscopy;Thermometers;Cooling;Raman scattering;Vitrification;Crystals|
|[A Highly Sensitive 3-Axis Micro Search-Coil Magnetometer enabled by High-Density Through-Silicon-Via Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052451)|H. Tavakkoli; M. Duan; L. Qi; Izhar; X. Zhao; Y. -K. Lee|10.1109/MEMS49605.2023.10052451|3-axis search-coil magnetometer;MEMS;planar spiral inductor;through silicon via (TSV);Micromechanical devices;Fabrication;Sensitivity;Spirals;Three-dimensional displays;Magnetometers;Semiconductor device reliability|
|[Fully Integrated Back-Biased 3d Hall Sensor with Wafer-Level Integrated Permanent Micromagnets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052184)|B. Gojdka; D. Cichon; M. Stahl-Offergeld; D. Schröder; N. Clausen; C. Hedayat; H. -P. Hohe; T. Lisec|10.1109/MEMS49605.2023.10052184|Integrated micromagnets;magnetic bias;back bias;wafer-level integration;Hall sensor;magnetic field shaping;Micromechanical devices;Geometry;Three-dimensional displays;Magnetic field measurement;Wheels;Silicon;Magnetic fields|
|[A Large-Stroke Tip-Tilt-Piston Micromirror with Electromagnetic Actuators Based on Metallic Glass](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052335)|C. -H. Ou; N. V. Toan; T. Ono|10.1109/MEMS49605.2023.10052335|Micromirror;Electromagnetic;Metallic glass;Micro-interferometer;Performance evaluation;Micromechanical devices;Actuators;3-DOF;Glass;Robustness;Electromagnetics|
|[Arbitrary Shaped Backside Reinforcement for Two Dimensional Resonant Micromirrors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052389)|T. Sasaki; A. Piot; A. Lagosh; C. Fleury; M. Bainschab; Y. Zhai; M. Baumgart; S. Guerreiro; D. Holzmann; A. Travnik; M. Moridi|10.1109/MEMS49605.2023.10052389|Tow dimensional resonant micromirror;backside reinforcement;dynamic deformation;silicon on insulator wafer;piezoelectric actuator;Fabrication;Micromechanical devices;Actuators;Deformation;Surface stress;Design methodology;Insulators|
|[High Transmittance Metasurface Holograms Using Silicon Nitride](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052377)|M. Yamaguchi; H. Saito; S. Ikezawa; K. Iwami|10.1109/MEMS49605.2023.10052377|Hologram;Metasurface;Silicon Nitride;Holography;Micromechanical devices;Image color analysis;Silicon nitride;Metasurfaces;Motion pictures;Silicon;Reproducibility of results|
|[Multifunctional Optical Metasurface for Anomalous Reflection, Structural Color, and Surface Lattice Resonance](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052394)|L. Li; H. Sun; Y. Ouyang; S. Jin; T. Kang; Z. Qi; W. Wu|10.1109/MEMS49605.2023.10052394|multifunctional metasurface;anomalous reflection;structural color;surface lattice resonance;polarization multiplexing;Multiplexing;Q-factor;Lattices;Optical saturation;Metasurfaces;Optical variables control;Reflection|
|[Novel Wavefront-Splitting Interferometer for Ultra-Compact Broadband FT-IR Spectroscopy Extending to Visible Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052423)|B. Mortada; Y. M. Sabry; B. Saadany; T. Bourouina; D. Khalil|10.1109/MEMS49605.2023.10052423|Interferometer;Wavefront splitting;Fourier transform and Spectrometer;Micromechanical devices;Spectroscopy;Optical interferometry;Fourier transforms;Optical propagation;Silicon-on-insulator;Spatial resolution|
|[Piezoelectrically Actuated Micromirror with Dynamic Deformation Compensation Mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052289)|T. Sasaki; A. Piot; J. Pribošek; A. Lagosh; C. Fleury; M. Bainschab; Y. Zhai; M. Baumgart; S. Guerreiro; D. Holzmann; A. Travnik; M. Moridi|10.1109/MEMS49605.2023.10052289|Dynamic deformation;silicon on insulator wafer;piezoelectric actuator;compensation;Micromechanical devices;Semiconductor device modeling;Deformable models;Electric potential;Deformation;Shape;Piezoelectric actuators|
|[Resonant d33 Mode PZT Mems Mirror Excited with Directional Interdigitated Electrodes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052304)|P. Thakkar; A. Lagosh; T. Sasaki; M. Bainschab; J. Pribošek|10.1109/MEMS49605.2023.10052304|Interdigitated electrodes;mechanical amplification;1D PZT MEMS micromirror;d33 mode actuation;Electrodes;Q-factor;Micromechanical devices;Stimulated emission;Resonant frequency;Optical variables measurement;Optical sensors|
|[Resonant Piezoelectric Varifocal Mirror with on-Chip Integrated Diffractive Optics for Increased Frequency Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052247)|J. Pribošek; A. Lagosh; P. Thakkar; T. Sasaki; M. Bainschab|10.1109/MEMS49605.2023.10052247|Varifocal mirror;diffractive optics;piezo actuators;Integrated optics;Optical diffraction;Deformation;Optical device fabrication;Focusing;Resonant frequency;Optical imaging|
|[Unique Dispersion Relation for Plasmonic Photodetectors with Submicron Grating](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052298)|Y. Kaneda; M. Oshita; U. Yamaoka; S. Saito; T. Kan|10.1109/MEMS49605.2023.10052298|Spectroscopy;Plasmonics;Schottky photodetector;MEMS;Near-infrared;Micromechanical devices;Performance evaluation;Spectroscopy;Spectral shape;Wavelength measurement;Plasmons;Photodetectors|
|[Integration of a High Temperature Transition Metal Oxide NTC Thin Film in a Microbolometer for LWIR Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052556)|S. Risquez; S. Redolfi; C. Fleury; M. Wulf; A. Roshanghias; A. Piot; J. Streque; K. Schmoltner; T. D. Dao; M. Puff; M. Moridi|10.1109/MEMS49605.2023.10052556|Metal oxide thin film;negative temperature coefficient resistance thermistor;microbolometer;infrared detector micromachining;Temperature sensors;Micromechanical devices;Temperature;Thermal resistance;Metals;Thermistors;Silicon nitride|
|[Periodic Cavities on the IR-Absorber for Responsivity Enhancement of CMOS-MEMS Thermoelectric IR Sensor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052530)|Y. -C. Li; T. Chou; P. -S. Lin; Y. . -C. Huang; F. Shih; Y. . -A. Lin; D. -J. Yen; M. -F. Lai; W. Fang|10.1109/MEMS49605.2023.10052530|CMOS-MEMS;thermoelectric infrared sensor;IR-absorber;periodic cavities;responsivity;Temperature measurement;Temperature sensors;Thermoelectric materials;Temperature distribution;Absorption;Optical design;Wavelength measurement|
|[Ultra-Large Pixel Array Photothermal Transducer and its Thermal Performance Prediction Strategy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052516)|D. Li; J. Zhang; J. Xu; E. Peiner; Z. Li; X. Wang; S. Yang; Y. Gao|10.1109/MEMS49605.2023.10052516|Photothermal transducer;ultra-large pixel array;thermal performance predication strategy;Temperature measurement;Micromechanical devices;Transducers;Temperature;Simulation;Power lasers;Time measurement|
|[A CMOS-MEMS Beam Resonator with Q > 10,000](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052474)|T. -Y. Chen; W. -C. Li|10.1109/MEMS49605.2023.10052474|Quality factor enhancement;stress dilution;CC-beam resonators;CMOS-MEMS resonators;Q-factor;Micromechanical devices;Q measurement;Annealing;Atmospheric measurements;Particle measurements;Timing|
|[Generic Temperature Compensation Scheme for CMOS-MEMS Resonators Based on ARC-Beam Derived Electrical Stiffness Frequency Pulling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052231)|I. -C. Hsieh; H. -S. Zheng; C. -P. Tsai; T. -Y. Chen; W. -C. Li|10.1109/MEMS49605.2023.10052231|CMOS-MEMS resonator;temperature compensation;frequency pulling;electrical stiffness;Micromechanical devices;Electrodes;Temperature dependence;Resonant frequency;Predictive models;Topology;Resonators|
|[High-Q and Low-Motional Impedance Piezoelectric mems Resonator through Mechanical Mode Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052210)|L. Huang; Z. Feng; Y. Xiao; F. Sun; J. Xu|10.1109/MEMS49605.2023.10052210|Quality factor (Q);motional impedance;piezoelectric resonator;mode coupling;Micromechanical devices;Q-factor;Couplings;Q measurement;Intelligent vehicles;Silicon;Impedance|
|[Crosstalk-Free Large Aperture 2D Gimbal Micromirror](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052202)|B. Ghazinouri; S. He|10.1109/MEMS49605.2023.10052202|Scanning micromirror;Crosstalk-free micromirror;Electromagnetic actuation;LiDAR (Light Detection and Ranging);Vibrations;Coils;Laser radar;Crosstalk;Prototypes;Apertures;Distance measurement|
|[Inverse Interference Effect-Enhanced Ultrasensitive Sensing Via Mid-IR Nanoantennas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052376)|H. Zhou; D. Li; X. Guo; Z. Ren; C. Lee|10.1109/MEMS49605.2023.10052376|electromagnetically induced transparency;electromagnetically induced absorption;gas detection;Micromechanical devices;Fabrication;Sensitivity;Costs;Absorption;Interference;Plasmons|
|[Twisted and Contacted Au Micro-Rods 3d Chiral Metamaterials with Circular Dichroism Via an Absorptive Route in Long-Wavelength Infrared](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052199)|G. Furusawa; N. Kanda; R. Matsunaga; T. Kan|10.1109/MEMS49605.2023.10052199|Chiral metamaterial;optical region;microfabrication;Gold;Three-dimensional displays;Absorption;Surface waves;Shape;Wavelength measurement;Mechanical variables measurement|
|[3D Hybrid Acoustic Resonator with Coupled Frequency Responses of Surface Acoustic Wave and Bulk Acoustic Wave](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052578)|L. Zhang; S. Zhang; J. Wu; P. Zheng; H. Yao; Y. Chen; K. Huang; X. Zhao; M. Zhou; X. Ou|10.1109/MEMS49605.2023.10052578|Hybrid Acoustic Resonator;Lithium Niobate Thin Film on Conductive Silicon Carbide;Surface Acoustic Waves;Bulk Acoustic Waves;Quality Factor;Electromechanical Coupling Coefficient;Phase Velocity;Couplings;Wafer bonding;Three-dimensional displays;Surface acoustic waves;Resonant frequency;Imaging;Frequency response|
|[A C/Ku Dual-Band Reconfigurable Baw Filter Using Polarization Tuning in Layered Scaln](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052130)|D. Mo; S. Dabas; S. Rassay; R. Tabrizian|10.1109/MEMS49605.2023.10052130|Scandium Aluminum Nitride;ferroelectricity;intrinsic switchability;complementary switchable;periodically polled;BAW filter;super high frequency;Band-pass filters;Transducers;Program processors;Resonator filters;Dual band;Resonant frequency;Prototypes|
|[Acoustoelectric-Driven Frequency Mixing in Micromachined Lithium Niobate on Silicon Waveguides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052257)|H. Mansoorzare; R. Abdolvand|10.1109/MEMS49605.2023.10052257|Acoustoelectric;frequency mixing;Lamb waves;lithium niobate;piezoelectric;Couplings;Radio frequency;Transducers;Waveguide components;Lithium niobate;Acoustoelectric effects;RF signals|
|[Effect of Scandium Composition on the Phonon Scattering Lifetime of Aluminum Scandium Nitride Acoustic Wave Resonators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052227)|Y. Zheng; M. Park; C. Yuan; A. Ansari|10.1109/MEMS49605.2023.10052227|Aluminum Scandium Nitride;Quality Factor;Phonon Scattering;Lattice Defect;Anelastic Damping;Energy Loss Mechanism;Phonon Relaxation Time;Q-factor;Grain boundaries;Scattering;Phonons;Scandium;Surface roughness;Resonators|
|[Lithium Niobate Thin Film Based A1 Mode Resonators with Frequency up to 16 Ghz and Electromechanical Coupling Factor Near 35%](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052269)|R. Su; Z. Yu; S. Fu; H. Xu; S. Zhang; P. Liu; Y. Guo; C. Song; F. Zeng; F. Pan|10.1109/MEMS49605.2023.10052269|Lamb wave devices;high electromechanical coupling factor;high frequency;LiNbO3;Couplings;Surface waves;Lithium niobate;Resonator filters;Resonant frequency;Crystals;Acoustic measurements|
|[Sub-3 DB Insertion Loss Broadband Acoustic Delay Lines and High Fom Resonators in LiNbO3/SiO2/Si Functional Substrate](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052343)|C. -C. Yeh; C. -H. Tsai; G. -L. Wu; T. -H. Hsu; M. -H. Li|10.1109/MEMS49605.2023.10052343|MEMS;lithium niobite;acoustic delay lines;SAW resonators;and piezoelectric transducers;Gold;Electric potential;RF signals;Resonant frequency;Insertion loss;Delay lines;Propagation losses|
|[Suppression of Spurious Modes in Aluminum Nitride S1 Lamb Wave Resonators Using A Mechanical Soft-Contact Scheme](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052454)|S. -S. Tung; T. -H. Hsu; Y. Ho; Y. -H. Chen; Y. R. Pradeep; R. Chand; M. -H. Li|10.1109/MEMS49605.2023.10052454|Aluminum nitride (AlN);microelectromechanical system (MEMS);resonator;Lamb wave;mechanical soft contact;spurious mode;Couplings;Vibrations;Deformation;Energy dissipation;Acoustics;Resonators;III-V semiconductor materials|
|[Terahertz Reflective Metalens For Arbitrary Off-Axis Focusing With Large Depth of Focus](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052296)|J. Miao; Y. Liu; C. Lin; Z. Zhou; X. Yu|10.1109/MEMS49605.2023.10052296|Terahertz;reflective metalens;off-axis focusing;depth of focus;focusing efficiency;Micromechanical devices;Optical polarization;Optical device fabrication;Focusing;Optical resonators;Metals;Interference|
|[Towards a Better CMOS-MEMS Resoswitch Using Electroless Plating for Contact Engineering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052575)|T. -J. Liou; C. -P. Tsai; T. -Y. Chen; W. -C. Li|10.1109/MEMS49605.2023.10052575|Electroless nickel plating;contact materials;CMOS-MEMS resoswitches;reliability enhancement;Sensitivity;Surface waves;Tungsten;Voltage;Materials reliability;Plating;Reliability engineering|
#### **2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)**
- DOI: 10.1109/ISBP57705.2023
- DATE: 6-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Drug Resistance Testing Using Electrical Impedance Counting Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061299)|J. Huang; D. Zhang|10.1109/ISBP57705.2023.10061299|Electrical Impedance;broth dilution method;MIC;Coulter principle;CLSI;Microwave integrated circuits;Microorganisms;Sensitivity analysis;Antibiotics;Software algorithms;Software;Impedance|
|[Application of virtual reality technology in post-traumatic stress disorder](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061313)|J. Zhang; X. Shen|10.1109/ISBP57705.2023.10061313|VR;PTSD;application;exposure therapy;innovation;Computer science;Technological innovation;Mental disorders;Medical treatment;Virtual reality;Human factors|
|[ConvE-Bio: Knowledge Graph Embedding for Biomedical Relation Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061292)|X. Qu; Y. Cai|10.1109/ISBP57705.2023.10061292|convolution neural network;knowledge graph representation;bio-medical relation;machine learning;Proteins;Protein engineering;Biological system modeling;Computational modeling;Neural networks;Knowledge graphs;Machine learning|
|[Box-Behnken Designs for the Optimization of the Ethanol Extraction Process for Chuilian Jianpi Granules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061319)|Y. Wang; G. -J. Zhou; X. -W. Li|10.1109/ISBP57705.2023.10061319|Chuilian Jianpi granules;Extraction technology;Single factor test;Response surface method;Ethanol;Response surface methodology;Surface treatment;Optimization|
|[Improving Genome Compression Performance by Extending Reference Sequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061320)|X. Ma; J. Chen|10.1109/ISBP57705.2023.10061320|referential;MEM;reverse complementation;Micromechanical devices;Genomics;Bioinformatics;Compression algorithms|
|[Building Semantic Segmentation of High-resolution Remote Sensing Image Buildings Based on U-net Network Model Based on Pytorch Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061309)|X. Wu|10.1109/ISBP57705.2023.10061309|high resolution remote sensing images;U-net;deep learning;semantic-level image segmentation;building extraction;Deep learning;Image resolution;Image analysis;Semantic segmentation;Buildings;Transfer learning;Stability analysis|
|[Deep Learning-based Identification of DNA-N4 Methylcytosine Modification Sites](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061304)|X. Wu|10.1109/ISBP57705.2023.10061304|4mc;U-net;multi-headed attention mechanism;GRU networks;Adaptation models;Biological system modeling;Neural networks;DNA;Predictive models;Feature extraction;Data models|
|[A Deep Learning Method with Self-Attention Mechanism for Cross-Subject Sleep Stage Classification Based on EEG and EOG](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061318)|J. Huang; J. Qu|10.1109/ISBP57705.2023.10061318|BCI;EEG;EOG;LSTM;Self-Attention;Cross-Subject;Sleep stage;Deep learning;Electrooculography;Sleep;Time series analysis;Mission critical systems;Manuals;Feature extraction|
|[EEG Motion Classification Combining Graph Convolutional Network and Self-attentiion](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061298)|L. Chen; Y. Niu|10.1109/ISBP57705.2023.10061298|EEG signal;motor imagery;graph convolutional network;self-attentive mechanism;Image segmentation;Image recognition;Correlation;Motion segmentation;Time series analysis;Feature extraction;Electroencephalography|
|[Artificial Intelligence Algorithms in Biomedical Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061317)|Y. Song|10.1109/ISBP57705.2023.10061317|Artificial Intelligence;Machine Learning;Deep Learning;Transformer;Biomedical Applications;Industries;Deep learning;Drugs;Machine learning algorithms;Image analysis;Neural networks;Transformers|
|[Intelligent Compound Selection of Anti-cancer Drugs Based on Multi-Objective Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061321)|X. Liu; Z. Xu; G. Liu; L. Liu|10.1109/ISBP57705.2023.10061321|Intelligent compound selection of anti-cancer drugs;biological activity;pharmacokinetics and safety properties (ADMET);hybrid multi-objective optimization;Drugs;Toxicology;Monte Carlo methods;Biological system modeling;Pareto optimization;Genetics;Cancer drugs|
|[AI Technology for Anti-Aging: an Overview](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061311)|A. Huang; Y. Huo; Y. Zhong; W. Yang|10.1109/ISBP57705.2023.10061311|AI;anti-aging;aging clock;aging biomarker;AI drug discovery;Drugs;Deep learning;Three-dimensional displays;Sociology;Biomarkers;Market research;Artificial intelligence|
|[10-Hz Repetitive Transcranial Magnetic Stimulation over the Frontal Eye Field Modulates Feature-Based Attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061302)|N. Li; F. Yan; L. Yan; Y. Wu; B. Xiang|10.1109/ISBP57705.2023.10061302|component;frontal eye field;transcranial magnetic stimulation;feature-based attention;attention network test;Visualization;Brain;Transcranial magnetic stimulation;Color;Task analysis|
|[Research on the Application of Artificial Intelligence in the Development of Biomedicine and Oncology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061308)|H. -R. Wang; Y. -H. Jing|10.1109/ISBP57705.2023.10061308|Artificial intelligence;Biological medicine;Technical experimental application;Drugs;Hospitals;Education;Information processing;Lead;Oncology;Synthetic biology|
|[Classification and Processing of MIT-BIH Arrhythmia-Based on BP Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061303)|F. Mi; B. Li; X. Cheng; Y. Zhao; M. Li; J. Jing|10.1109/ISBP57705.2023.10061303|ECG;BP Neural Network;SVM;Classification;Support vector machines;Wavelet transforms;Heart;Databases;Arrhythmia;Neural networks;Electrocardiography|
|[Glucose Prediction Based on the Recurrent Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061295)|Y. Zhang|10.1109/ISBP57705.2023.10061295|Recurrent neural network;glucose prediction;SVM;NNPG;Support vector machines;Recurrent neural networks;Biological system modeling;Predictive models;Time measurement;Glucose;Convolutional neural networks|
|[U-Net multi-modality glioma MRIs segmentation combined with attention](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061312)|Y. Wang; X. Ye|10.1109/ISBP57705.2023.10061312|Glioma;Attention;U-Net;Segmentation;Deep learning;Image segmentation;Sensitivity;Magnetic resonance imaging;Malignant tumors;Manuals;Central nervous system|
|[Pacellation method based on brain cortical morphological aging trajectory in normal cohorts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061316)|J. Xia|10.1109/ISBP57705.2023.10061316|ageing trajectory;spectral clustering;parcellation;Correlation coefficient;Surface morphology;Morphology;Aging;Surface fitting;Trajectory;Diseases|
|[Hybrid Multistage Feature Selection Method and its Application in Chinese Medicine](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061301)|M. Liu; J. Du; Z. Li; J. Luo; B. Nie; M. Zhang|10.1109/ISBP57705.2023.10061301|component;approximate Markov blanket;Black widow algorithm;Lasso;Feature selection;TCM information;Pediatrics;Sociology;Clustering algorithms;Markov processes;Approximation algorithms;Feature extraction;Statistics|
|[Adaptive Noise-Reduction Algorithm for Diaphragm Electromyography Based on Linear Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061294)|L. Chen; Y. Xu; B. Li; H. Mo|10.1109/ISBP57705.2023.10061294|Diaphragm electromyography (EMGdi);electrocardiogram (ECG) interference;adaptive filter;linear prediction;Adaptive filters;Interference;Nonlinear filters;Electrocardiography;Filtering algorithms;Prediction algorithms;Electromyography|
|[ECG arrhythmias Classification with a Graph Bispectrum method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061314)|Y. Shiyilin; S. Jie; Y. Xin; C. Xin; W. Xingxing|10.1109/ISBP57705.2023.10061314|ECG Arrhythmias;Graph Fourier Transform;Graph Integral Bispectrum;Deep Neural Networks;Heart;Deep learning;Fourier transforms;Arrhythmia;Neural networks;Electrocardiography;Feature extraction|
|[Fused Residual Attention Dense Double-U Network Retinal Vessel Segmentation Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061315)|C. Zhu; X. Niu; L. Zuo; Z. Liu|10.1109/ISBP57705.2023.10061315|component;LSCD-UNet;LadderNet;scSE-Residual;Multi-module;Hybrid loss function;Sensitivity;Convolution;Feature extraction;Retinal vessels;Convergence|
|[High-efficiency drug design research based on virtual high-throughput screening](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061293)|H. Zhou|10.1109/ISBP57705.2023.10061293|Drug screening;High-throughput screening;CPU;Multi-core computing;Drugs;Proteins;Compounds;Task analysis;Optimization|
|[Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061307)|W. Tian; F. Li; Y. Liu; Z. Wang; T. Zhang|10.1109/ISBP57705.2023.10061307|UFP;pattern mining;Item-Wise Supports;Itemsets;Redundancy;Measurement uncertainty;Probabilistic logic;Explosions|
|[Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061306)|Z. Gao; T. Yu|10.1109/ISBP57705.2023.10061306|Medical image segmentation;Semi-supervised learning;Low confidence;Consistency;Class separation;Deep learning;Image segmentation;Semantic segmentation;Prototypes;Semisupervised learning;Predictive models;Labeling|
#### **2023 18th Wireless On-Demand Network Systems and Services Conference (WONS)**
- DOI: 10.23919/WONS57325.2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Vehicles or Pedestrians: On the gNB Placement in Ultradense Urban Areas](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062000)|G. Gemmi; M. Segata; L. Maccari|10.23919/WONS57325.2023.10062000|vehicular communication;5G;mmWave;gNB placement;Wireless communication;Base stations;5G mobile communication;Urban areas;Graphics processing units;Traffic control;High frequency|
|[Evaluating the Impact of Anchors Deployment for an AoA-based Indoor Localization System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061949)|F. Mavilia; P. Barsocchi; F. Furfari; M. Girolami|10.23919/WONS57325.2023.10061949|Bluetooth 5.1;Angle of Arrival;Indoor Localization;Proximity;Location awareness;Wireless communication;Bluetooth;Indoor environment;Network systems|
|[Visible light or infrared? Modulating LiFi for dual operation in the visible and infrared spectra](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061923)|D. F. Fonseca; M. S. Mir; B. G. Guzman; D. Giustiniano|10.23919/WONS57325.2023.10061923|nan;Wireless communication;Degradation;Costs;Modulation;Prototypes;Light fidelity;Infrared spectra|
|[Towards Hybrid Electronic-Mechanical Beamforming for IEEE 802.11ad](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062285)|A. Zubow; A. Memedi; F. Dressler|10.23919/WONS57325.2023.10062285|millimeter-wave;beamforming;802.11ad;Wireless communication;Array signal processing;Millimeter wave technology;Prototypes;Performance gain;Throughput;Millimeter wave communication|
|[Pitfalls in Measuring Ultra Low Power Energy Harvesting Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062282)|S. Pullwitt; L. Wolf|10.23919/WONS57325.2023.10062282|sensor network;real-world;evaluation;Wireless communication;Wireless sensor networks;Power measurement;Power demand;Scalability;Hardware;Time measurement|
|[ASAP: Adaptive and Scalable Microservice Provisioning for Edge-IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062045)|A. Samanta; F. Esposito; T. G. Nguyen|10.23919/WONS57325.2023.10062045|nan;Wireless communication;Adaptive systems;Multi-access edge computing;Mission critical systems;Microservice architectures;Prototypes;Throughput|
|[Radio-in-the-Loop Simulation Modeling for Energy-Efficient and Cognitive IoT in Smart Cities: A Cross-Layer Optimization Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062069)|S. Boehm; H. Koenig|10.23919/WONS57325.2023.10062069|Hardware-in-the-loop;radio-in-the-loop;simulation;emulation;wireless sensor networks;cross-layer optimization;cognitive radio;cognitive iot;Wireless sensor networks;Cross layer design;Protocols;Smart cities;Physical layer;Energy efficiency;Internet of Things|
|[Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061953)|F. Hawlader; F. Robinet; R. Frank|10.23919/WONS57325.2023.10061953|5G;Cloud/Edge Computing;Perception;C-V2X;Autonomous Driving;Wireless communication;Runtime;Computational modeling;Vehicle-to-infrastructure;Transform coding;Object detection;Real-time systems|
|[Enabling Cooperative Autonomous Driving through mmWave and Reconfigurable Intelligent Surfaces](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062109)|M. Segata; M. Lestas; P. Casari; T. Saeed; D. Tyrovolas; G. Karagiannidis; C. Liaskos|10.23919/WONS57325.2023.10062109|nan;Wireless communication;Protocols;Veins;Roads;Urban areas;Physical layer;Safety|
|[Evaluation of Open-Source Mobile Network Software Stacks: A Guide to Low-cost Deployment of 5G Testbeds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061896)|M. Chepkoech; N. Mombeshora; B. Malila; J. Mwangama|10.23919/WONS57325.2023.10061896|Testbed;4G;5G NSA;5G SA;Open-source;Software Defined Radio;Wireless communication;Performance evaluation;5G mobile communication;Quality of service;Medical services;Interconnected systems;Throughput|
|[Energy-Efficient Bootstrapping in Multi-hop Harvesting-Based Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062242)|N. Stricker; J. Hora; A. Gomez; L. Thiele|10.23919/WONS57325.2023.10062242|Indoor Energy Harvesting;Internet of Things;Multi-Hop Network;Synchronous Communication;Wireless communication;Limiting;Modulation;Spread spectrum communication;Frequency shift keying;Energy efficiency;Internet of Things|
|[Stop & Route: Periodic Data Offloading in UAV Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062043)|N. Bartolini; A. Coletta; F. Giorgi; G. Maselli; M. Prata; D. Silvestri|10.23919/WONS57325.2023.10062043|UAVs;Drones;routing;data offloading;FANETs;Wireless communication;Wireless sensor networks;Spread spectrum communication;Autonomous aerial vehicles;Routing;Ad hoc networks;Routing protocols|
|[5G Configured Grant Scheduling for 5G-TSN Integration for the Support of Industry 4.0](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062219)|A. Larrañaga; M. C. Lucas-Estañ; I. Martinez; J. Gozalvez|10.23919/WONS57325.2023.10062219|5G;5G-TSN integration;Deterministic;Low Latency;TSN;Configured Grant;scheduling;Industry 4.0;Wireless communication;Wireless sensor networks;Job shop scheduling;5G mobile communication;Service robots;Robot kinematics;Robot sensing systems|
|[Wi-Fi Throughput Estimation for Vehicle-to-Network Communication in Heterogeneous Wireless Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061940)|D. Teixeira; R. Meireles; A. Aguiar|10.23919/WONS57325.2023.10061940|Heterogeneous wireless networks;symbolic regression;vehicular offloading;throughput estimation;Computational modeling;Wireless networks;Estimation;Training data;Throughput;IEEE 802.11n Standard;Wireless fidelity|
|[Sensitivity Analysis of Ambient Backscattering Communications in Heavily Loaded Cellular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062021)|R. Biswas; J. Lempiäinen|10.23919/WONS57325.2023.10062021|Sensitivity;AmBC;SIR;IoT;LTE;5G;Wireless communication;Base stations;Wireless sensor networks;5G mobile communication;Urban areas;Interference;Long Term Evolution|
|[On Mitigating DIS Attacks in IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061962)|G. Aljufair; M. Mahyoub; A. S. Almazyad|10.23919/WONS57325.2023.10061962|IoT;LLNs;RPL;Security;DIS Attacks;Contiki;Wireless communication;Operating systems;Routing;Throughput;Routing protocols;Internet of Things;Security|
|[Using CTI Data to Understand Real World Cyberattacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061921)|M. Allegretta; G. Siracusano; R. Gonzalez; P. Vallina; M. Gramaglia|10.23919/WONS57325.2023.10061921|nan;Wireless communication;Forensics;Knowledge based systems;Collaboration;Market research;Malware;Cyber threat intelligence|
|[ML-based Network Pruning for Routing Data Overhead Reduction in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061999)|D. Andreoletti; C. Rottondi; F. Ezzeddine; O. Ayoub; S. Giordano|10.23919/WONS57325.2023.10061999|nan;Wireless communication;Wireless sensor networks;Machine learning algorithms;Routing;Routing protocols;Ad hoc networks;Sensors|
|[Interplay Between Priority Queues and Controlled Delay in Programmable Data Planes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062179)|T. V. Doan; T. Scheinert; O. Lhamo; J. A. Cabrera; F. H. P. Fitzek; G. T. Nguyen|10.23919/WONS57325.2023.10062179|Tactile Internet;Buffer Bloat;Active Queue Management;Congestion Control;P4;Tactile Internet;Wireless communication;Protocols;Packet loss;Bandwidth;Streaming media;Real-time systems|
|[Internet of Things for Hydrology: Potential and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10062193)|A. Zanella; S. Zubelzu; M. Bennis; M. Capuzzo; P. Tarolli|10.23919/WONS57325.2023.10062193|nan;Climate change;Internet of Things;Hydrology;Energy management;Water resources;Sustainable development|
|[6G Integrated Access and Backhaul Networks with Sub-Terahertz Links](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10061913)|A. A. Gargari; M. Pagin; M. Polese; M. Zorzi|10.23919/WONS57325.2023.10061913|Sub-THz Communication;IAB;Self-backhauling;Wireless Backhaul;6G;6G mobile communication;Performance evaluation;Wireless networks;Millimeter wave technology;Licenses;Throughput;Millimeter wave communication|
#### **2023 International Conference on Power Electronics and Energy (ICPEE)**
- DOI: 10.1109/ICPEE54198.2023
- 3-5 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Finite Element Method designing of Ionic current density and Electric field for Hybrid Transmission Lines and HVDC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060639)|P. Tanuja.; S. R. N; R. Sastry.R|10.1109/ICPEE54198.2023.10060639|Monopolar;COMSOL Multiphysics;FEM analysis;HVDC;Bipolar;Hybrid configuration;Any-pole.;Renewable energy sources;Power transmission lines;Systematics;HVDC transmission;Space charge;Ions;Conductors|
|[Islanding Detection of Inverter based Grid Tied Photovoltaic System in Microgrid using Maximum Power point Tracking Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060619)|B. Soreng; R. Pradhan; P. Jena|10.1109/ICPEE54198.2023.10060619|DG;Microgrid;GCPVS;IDM;MPPT;Maximum power point trackers;Photovoltaic systems;Renewable energy sources;Islanding;Voltage fluctuations;Power quality;Green products|
|[Two-Axis Modeling of Synchronous Reluctance Motor for Electric Four Wheeler](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060516)|M. P. Nikhila; V. P. Mini; R. Harikumar; N. Mayadevi|10.1109/ICPEE54198.2023.10060516|Electric vehicle;synchronous reluctance motor;Induction motors;Torque;Brushless DC motors;Propulsion;Permanent magnet motors;Electric vehicles;Mathematical models|
|[Reliability Study of Multi-Phase Coupled Inductor Based Boost Converter with Continuous Input/Output Currents](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060687)|B. T. Rao; D. De; D. Chattopadhyay|10.1109/ICPEE54198.2023.10060687|Mean time to failure (MTTF);maximum power point tracking (MPPT);boost converter;interleaved;photovoltaic systems;Markov reliability modeling;Temperature dependence;Analytical models;Temperature;Capacitors;Switches;Voltage;Markov processes|
|[Demand Side Frequency Control in Low Inertia Power System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060755)|O. Bosaletsi; W. Cronje; L. Masisi|10.1109/ICPEE54198.2023.10060755|demand side frequency response;low system inertia;rate of change of frequency;frequency nadir;Power system dynamics;Contingency management;Microgrids;Mathematical models;Frequency response;Power electronics;Behavioral sciences|
|[Local Energy System: A Comprehensive Review of Modelling, Tools and Pilot Projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060751)|S. Deb; D. Li; S. Sinha; P. Malik; G. Raina; J. Wang|10.1109/ICPEE54198.2023.10060751|Benefits;Modelling;LES;Pilot Project;Tools;Systematics;Costs;Metaheuristics;Power transmission;Power distribution;Low-carbon economy;Power electronics|
|[Control and Energy Management of DC Nano Grid- Connected Solar PV, Fuel cell and Battery Energy Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060628)|S. R. Karri; R. Gugulothu; N. Bhookya|10.1109/ICPEE54198.2023.10060628|Photovoltaic;Fuel cell;Nano grid;Energy Management System;Photovoltaic systems;Renewable energy sources;Power system management;Fuel cells;Power electronics;Batteries;Energy management systems|
|[Reduced CMV SVPWM Scheme for Three-Level Z-Source NPC Inverter for PV Grid Integration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060515)|S. K. Baksi; R. K. Behera|10.1109/ICPEE54198.2023.10060515|Neutral-Point-Clamped (NPC) Inverter;ZSource;Common-Mode-Voltage (CMV);Space-Vector PWM (SVPWM);Photovoltaic;Support vector machines;Space vector pulse width modulation;Renewable energy sources;Voltage;Switches;Boosting;Multilevel inverters|
|[Design of 15 kW, 440 V Three Phase Induction Motor for Electrical Vehicle Applications with Improved Efficiency and Wide Speed Range](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059829)|V. Avusula; S. S. Khamari; G. S. Rani; R. K. Behera|10.1109/ICPEE54198.2023.10059829|Induction motor;stator;rotor;winding;Geometry;Reactive power;Induction motors;Torque;Windings;Stator windings;Rotors|
|[Torque Ripple Reduction in Six-phase Induction Motor Drive using DTC Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059889)|A. Gauri; K. G. Sreeni; G. Shiny|10.1109/ICPEE54198.2023.10059889|direct torque control;multiphase;current harmonic;torque pulsations;Induction motor drives;Torque;Hysteresis motors;Stators;Control systems;Inverters;Steady-state|
|[Reliable Multilevel Inverter Topology using two DC Sources.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059912)|S. T. Kamble; H. B. Gobburi; V. B. Borghate|10.1109/ICPEE54198.2023.10059912|Multi-level Inverter Topology;Fault Tolerance;MATLAB Simulink.;Fault tolerance;Software packages;Fault tolerant systems;Switches;Logic gates;Multilevel inverters;Transformers|
|[Machine Learning based Cooperative Control of Photovoltaic Systems for Voltage Regulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059998)|C. N. Bhende; G. N. V. Mohan; Y. Raghuwanshi|10.1109/ICPEE54198.2023.10059998|Low voltage distribution network;PV system;voltage regulation;cooperative control;neural network based control;Photovoltaic systems;Reactive power control;Simulation;Artificial neural networks;Machine learning;Control systems;Inverters|
|[Lithium-Ion Battery Pack SOC Estimation using Optimized ECM Parameters and Kalman Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060088)|P. K. Aher; S. L. Patil; A. Gambhir; A. Mandhana; A. Deshpande; S. K. Pandey|10.1109/ICPEE54198.2023.10060088|Battery management system;battery modeling;extended Kalman filter;state estimation;state of charge;Resistance;Computational modeling;Estimation;Real-time systems;Mathematical models;Hardware;Batteries|
|[Solar Photo Voltaic Panel Interfaced Boost Converter with MPPT Algorithm at Various Climatic Conditions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060750)|L. Nanda; S. Mandal; A. Priya; A. Pradhan; C. Jena; B. Panda|10.1109/ICPEE54198.2023.10060750|PV Panel;MPPT;Boost converter;Solar irradiance;Photovoltaic systems;Radiation effects;Simulation;Software algorithms;Voltage;Solar energy;Mathematical models|
|[Performance Analysis of Multiphase Interleaved boost converter topologies for FCEV applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060558)|P. Garg; V. V. Kumar; S. Kumar|10.1109/ICPEE54198.2023.10060558|Fuel cell stack;Electric vehicle;dc-dc converter;interleaved topologies;Power system measurements;Motor drives;Simulation;Fuel cells;Switches;Power electronics;Topology|
|[Synchrosqueezed Wavelet transform Based Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060530)|D. Pattanaik; S. C. Swain; I. S. Samanta; U. K. Rout; R. Dash; K. Swain|10.1109/ICPEE54198.2023.10060530|Power Quality Disturbances;Artificial Neural Network;Wavelet synchro-squeezing transform;Convolutional Neural Network;Micro Grid;Renewable Energy Sources;GoogLeNet neural network;Innovation;Time-frequency analysis;Renewable energy sources;Continuous wavelet transforms;Power quality;Transfer learning;Microgrids;Wavelet analysis|
|[Energy Management for a RES-Powered DC Microgrid Under Variable Load](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060143)|M. C. Das; S. C. Swain; C. K. Nayak; R. Dash|10.1109/ICPEE54198.2023.10060143|Energy management;DC microgrid;Renewable energy;Energy storage;Microgrids;Energy management;Climate change;Energy storage;Renewable energy sources;Load modeling|
|[Hardware Simulator Design of Variable Speed PMSG for Robust Grid Interface using Pitch Angle and Voltage-Oriented Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060023)|P. Srivastav; K. Sandhya; K. Chatterjee|10.1109/ICPEE54198.2023.10060023|Wind energy conversion system;permanent magnet synchronous generator;grid side converter;renewable energy sources;Voltage oriented control;generator side converter;pitch angle control;Torque;Wind speed;Generators;Real-time systems;Picture archiving and communication systems;Inverters;Wind turbines|
|[SuDoKU based Reconfiguration Techniques to T-C-T PV Array for Enhancing the Maximum Power under Partial Shading Patterns](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060192)|E. G. Gummadi; S. Mikkili; S. Yedla|10.1109/ICPEE54198.2023.10060192|T-C-T configuration;SuDoKU reconfigurations;Partial shading patterns;Maximum power;mismatching Power Loss;number of Peak power points;Photovoltaic systems;Analytical models;Simulation;Power electronics;Mathematical models;Software tools;Matlab|
|[Design and Analysis of High Gain DC-DC Boost Converter for Grid Connected Solar Photovoltaic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060054)|M. R. Rasekh; P. K. Jamwal; V. Gali; M. J. Ahmadi|10.1109/ICPEE54198.2023.10060054|High gain DC-DC converter;solar photovoltaic (SPV);current harmonics;nonlinear load;MPPT;power quality;Photovoltaic systems;Radiation effects;Power quality;Switches;DC-DC power converters;Inverters;Software|
|[The Impact of Social Nudge on System Cost & Revenue Optimization in Local Electricity Market](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060025)|P. Mochi; K. S. Pandya|10.1109/ICPEE54198.2023.10060025|socioeconomic;energy economics;energy policy;intervention;energy market;optimization;Costs;Biological system modeling;Systems operation;Energy conservation;Social sciences;Electricity supply industry;Linear programming|
|[A Solid State CB topology for DC microgrid application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059970)|P. Roy; D. Barman; D. Chatterjee|10.1109/ICPEE54198.2023.10059970|DC microgrid;Solid State Circuit Breaker;Pole-to-pole fault;Thyristors;Renewable energy sources;Circuit breakers;Microgrids;Switches;Power electronics;Topology|
|[Enhanced Power Management and Control of a PVWind- BES Based Microgrid System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060536)|M. J. Ahmadi; P. K. Jamwal; V. Gali; M. R. Rasekh|10.1109/ICPEE54198.2023.10060536|Renewable Energy;Microgrid systems;DC-DC Boost Converter;Improved Power Management (IPM)Scheme;Grid Synchronization;Wind;Total harmonic distortion;Uncertainty;Wind energy;Power system management;Power quality;Microgrids|
|[Comparative Review and Finite Element Analysis of Energy Efficient Motors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059611)|P. Kannojia; K. A. Chinmaya|10.1109/ICPEE54198.2023.10059611|Energy efficiency;Permanent Magnet Synchronous Motor (PMSM);Brushless Direct current (BLDC) Motor;Switched and Synchronous Reluctance motors;Machine Design;Industries;Motor drives;Stators;Permanent magnet motors;DC motors;Energy efficiency;Finite element analysis|
|[Design, Sizing and Implementation of a Parallel Active Battery-Supercapacitor based Hybrid Energy Storage System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060100)|P. K. Behera; P. K. Piyush; M. Pattnaik|10.1109/ICPEE54198.2023.10060100|Battery;Bidirectional DC-DC converter;Hybrid energy storage system;Parallel-active topology;Sizing;Supercapacitor;Filtration;Microgrids;Load management;Power electronics;Batteries;Steady-state;Resource management|
|[PV-Grid Assisted Uninterruptible Power Supply System for a BLDC Motor Drive](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059754)|V. Gullipalli; P. K. Behera; M. Pattnaik|10.1109/ICPEE54198.2023.10059754|Brush-less Direct Current (BLDC) Motor;Grid Integration;PV-Grid Hybrid System;Proportional Resonant (PR) Control;Uninterruptible Power Supply;Radiation effects;Motor drives;Power supplies;Voltage source inverters;System performance;DC motors;Hybrid power systems|
|[Design and Analysis of Circular Coil Geometries for Wireless Power Transfer in Electric Vehicles The Effect of Multiple Coils at Primary and Secondary Sides](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060651)|A. Yadav; T. K. Bera|10.1109/ICPEE54198.2023.10060651|Electric Vehicles (EVs);Ansys Maxwell;Coil Geometry design;Coupling Co-efficient;Wireless Power Transfer(WPT).;Geometry;Couplings;Inductance;Wires;Air gaps;Magnetic resonance;Wireless power transfer|
|[Isolated Multi-Output Power Supply Based on Flyback Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060081)|P. K. Padhee; P. C. Sekhar|10.1109/ICPEE54198.2023.10060081|DC-DC Converter;Flyback Converter;Forward Converter;Isolated;Push-pull Converter;Renewable energy sources;Buck converters;Power supplies;Prototypes;Numerical simulation;Power electronics;Mathematical models|
|[A Combined S-transform and Ensemble of DT based Protection scheme for six-phase transmission line](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060048)|S. K. Shukla; M. Manohar; C. Patel; T. Tailor; A. Rathore|10.1109/ICPEE54198.2023.10060048|Six-phase transmission system;S-transform;Ensemble of tree;Ensemble tree based fault detector/classifier;Time-frequency analysis;Power transmission lines;Fault detection;Real-time systems;Power electronics;Reliability;Decision trees|
|[Autocorrelation Aided Islanding Detection Using bi-directional Long-short Type Memory Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059865)|A. Chakraborty; S. Chatterjee; R. Mandal|10.1109/ICPEE54198.2023.10059865|Autocorrelation;bidirectional long-short type memory network classification;distributed generation and islanding;Resistance;Islanding;Voltage;Bidirectional control;Predictive models;Feature extraction;Power system reliability|
|[Fourth-Order SEPIC Converter Order Diminution and Interval Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060018)|J. Kapoor; V. P. Meena; V. P. Singh|10.1109/ICPEE54198.2023.10060018|EPIC converter;Modified Routh table;State space averaging;Order diminution;Interval system;Stability equation method.;Transfer functions;Voltage;Switches;Aerospace electronics;Control systems;Mathematical models;Stability analysis|
|[Implementation of a novel Hebbian least mean square techniques to a Cascaded MLI based SAPF](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060045)|R. R. Behera; A. R. Dash; R. Patel; A. K. Panda|10.1109/ICPEE54198.2023.10060045|Shunt Active Power Filter (SAPF);cascaded MLI;coupled transformer;PS-PWM;Power quality;harmonics;Artificial Neural Network;Hebbian least mean square control technique (H-LMS);Reactive power;PI control;Simulation;Loading;Switches;Transformers;Power electronics|
|[Performance Analysis and Hardware Implementation of A Dual Input Triple Output DC-DC Converter for EV application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060793)|T. Mishra; R. K. Singh|10.1109/ICPEE54198.2023.10060793|electric vehicle;multi-input multi-output;power electronic converter;DC-DC converter;renewable energy;solar energy;Renewable energy sources;Switches;Packaging;Hybrid power systems;Hardware;Power electronics;Topology|
|[Perfectly Convergent Particle Swarm Optimization for Solving Combined Economic Emission Dispatch Problems with and without Valve Loading Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060438)|D. Kumar; N. K. Jain; N. Uma|10.1109/ICPEE54198.2023.10060438|combined economic emission dispatch;Perfectly convergent Particle swarm optimization;price penalty factor;non-smooth cost function;quadratic emission;Costs;Software algorithms;Valves;Propagation losses;Software;Resource management;Particle swarm optimization|
|[Machine Learning Based Peripheral Bus Fault Discrimination using Sequence Components in Radial Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060112)|S. Chattopadhyay; G. Humne; M. S. Alam; B. Roy; A. Bera; G. Bandyopadhyay|10.1109/ICPEE54198.2023.10060112|Distribution generation;Fault classification;Load flow analysis;Machine learning algorithm;Peripheral Bus;Phase sequence;Fault diagnosis;Learning systems;Machine learning algorithms;Network topology;Machine learning;Distribution networks;Power electronics|
|[Kurtosis-Skewness Scanning and Machine Learning-based Discrimination of Fault Location in Radial Power Distribution Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060396)|S. Chattopadhyay; B. Roy; A. Bera; G. Humne; M. S. Alam; G. Bandyopadhyay|10.1109/ICPEE54198.2023.10060396|Bus Fault;Fault Location;Statistical distribution;Wavelet decomposition;Wavelet coefficients.;Network topology;Machine learning;Fault location;Reliability engineering;Power electronics;Topology;Power systems|
|[Multi-device L-impedance CLD Cell DC-DC Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059830)|A. Abhishek; R. Patel; T. Roy; C. K. Panigrahi; V. Khadkikar|10.1109/ICPEE54198.2023.10059830|DC-DC boost converter;L-impedance;multidevice;high voltage gain;reduced switch voltage stress and Capacitor-Inductor-Diode (CLD) cell;Network topology;Voltage;High-voltage techniques;Power electronics;Topology;Inductors;Stress|
|[A Novel Technique Based on Crow Search Algorithm to Solve Optimal Control Problem of a Single-Link Rigid Robotic Manipulator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060674)|V. S. D. M. Sahu; P. Samal; C. K. Panigrahi|10.1109/ICPEE54198.2023.10060674|optimal control problem;crow search algorithm;single-link rigid manipulator;Metaheuristics;Optimal control;Manipulators;Search problems;Whale optimization algorithms;Power electronics;Performance analysis|
|[Dynamic Combined Economic Emission Load Dispatch Using Perfectly Convergent Particle Swarm Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060649)|D. Kumar; N. K. Jain; N. Uma|10.1109/ICPEE54198.2023.10060649|dynamic combined economic emission dispatch;Pefectly convergent Particle swarm optimization;price penalty factor;non-smooth costfunction;quadratic emission;Power transmission lines;Heuristic algorithms;Power system dynamics;Loading;Valves;Propagation losses;Linear programming|
|[Performance enhancement of induction motor drive using Type-1 NFC and Type-2 NFC based torque controller](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059847)|G. Durgasukumar; R. R. Prasad; S. R. Gorantla|10.1109/ICPEE54198.2023.10059847|Indirect vector control;Type-II neuro-fuzzy controller (T2NFC);Type-I neuro-fuzzy controller (T1NFC);Induction Motor Drive (IMD);PI Controller.;Induction motor drives;Motor drives;Torque;PI control;Simulation;Power electronics;Torque measurement|
|[Multi Objective Location of EVCS by CSO driven Heuristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060388)|S. Sachan|10.1109/ICPEE54198.2023.10060388|Electric Vehicle;Vehicle-Grid;Pattern modeling;Smart charging;Total harmonic distortion;Distribution networks;Voltage;Charging stations;Road safety;Power electronics;Power systems;Harmonic distortion|
|[Power Enhancement of Solar PV Array Under PSC by Puzzle Based Arrangement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060705)|A. K. Parida; A. Mohapatra; R. N. Das; C. Saiprakash; S. Samal; R. Patel|10.1109/ICPEE54198.2023.10060705|Partial shading;Puzzle-based arrangement (PBA);Mismatch loss;Fill Factor;Maximum Power;PV array reconfiguration.;Radiation effects;Fill factor (solar cell);Software packages;Power electronics;Performance analysis;Power generation;Matlab|
|[Controlled dynamic analysis of offshore wind turbines under random environmental conditions-An experimental investigations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060307)|R. Manikandan.|10.1109/ICPEE54198.2023.10060307|Offshore wind turbines;spar platform;dynamic analysis;back stepping control;sliding mode control.;Codes;Wind energy;Wind speed;Loading;Reflection;Power electronics;Wind turbines|
|[Application of Transfer Learning Approach for Diabetic Retinopathy Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060777)|N. Jiwani; K. Gupta; M. H. U. Sharif; R. Datta; F. Habib; N. Afreen|10.1109/ICPEE54198.2023.10060777|Diabetic Retinopathy;IDRiD Dataset;Inception V3;Transfer learning;VGGl 6;Deep learning;Solid modeling;Retinopathy;Transfer learning;Visual impairment;Blindness;Retina|
|[A priority-reservation queueing-based approach for Blockchain-assisted smart-grid system](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060850)|S. R. Mallick; V. Goswami; R. N. Dash; R. K. Lenka; S. Sharma; R. K. Barik|10.1109/ICPEE54198.2023.10060850|Smartgrid;blockchain;priority-reservation queueing;distributors;consumers;Access control;Analytical models;Power electronics;Smart grids;Blockchains;Queueing analysis|
|[Prediction of EV Energy consumption Using Random Forest And XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060798)|H. Rathore; H. K. Meena; P. Jain|10.1109/ICPEE54198.2023.10060798|Artificial Neural Network;Deep Neural network;Electric Vehicles (EVs);Machine Learning;Energy consumption;Social networking (online);Scheduling algorithms;Transportation;Artificial neural networks;Predictive models;Prediction algorithms|
|[Analysis of Torque Ripple in Open-End Winding Dual Inverter-Based BLDC Motor Drive System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059634)|N. Kumar; A. Das|10.1109/ICPEE54198.2023.10059634|Open-end winding BLDC motor;current commutation;torque ripple;dual inverter;robotics;and automation applications;Torque;Brushless DC motors;Software packages;Commutation;Windings;Voltage;Switches|
|[Model Predictive Control Based Maximum Power Point Tracking Algorithm for Solar PV Module](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059779)|Srishti; P. K. Padhy|10.1109/ICPEE54198.2023.10059779|DC-DC Boost Converter;InC;MPC;MPPT;Renewable Energy;Solar Energy;Solar PV;Maximum power point trackers;Software packages;Software algorithms;Prediction algorithms;Power electronics;Oscillators;Predictive control|
|[Optimal Tuning of Fractional Order PID Controller with Metaheuristic Algorithms for High Efficiency High Gain DC-DC Boost Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059937)|D. Patel; Srishti; P. K. Padhy|10.1109/ICPEE54198.2023.10059937|DC-DC Converter;PID controller;FOPID controller;GA-FOPID;PSO-FOPID;WOA-FOPID;GWO-FOPID;Power conditioning;Software packages;Photovoltaic cells;Metaheuristics;Whale optimization algorithms;Voltage control;Inductors|
|[Vanadium Redox Flow Battery System Power Loss Optimization: Genetic Algorithm based Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10059766)|N. Ra; A. Bhattacharjee|10.1109/ICPEE54198.2023.10059766|Vanadium Redox Flow Battery;System power loss;Genetic Algorithm;optimization;Electrolyte flow rate;Renewable energy sources;Vanadium;Redox;Electrolytes;Power systems;Batteries;Topology|
|[Design and Development of a New Soft-Switching Buck Converter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10060126)|S. Behera; S. K. Dash; M. K. Sahu; I. Sahu; S. Parida|10.1109/ICPEE54198.2023.10060126|Buck converter;Fast recovery devices (diode/Switch);Polypropylene capacitors;High frequency inductor;Zero-voltage switching (ZVS);Zero-current switching(ZCS);MATLAB(Simulink);Buck converters;Snubbers;Capacitors;Soft switching;Switching loss;Voltage;Switches|
#### **2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)**
- DOI: 10.1109/IDCIoT56793.2023
- 5-7 Jan. 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Vehicle Collision Avoidance System During Lane Change using Internet-of-Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053502)|A. Ranjan; S. Sharma; H. R. Goyal; N. Kumar K C|10.1109/IDCIoT56793.2023.10053502|Vehicular Collision;Internet of Things;Driving Behavior;Industries;Wheels;Information sharing;Cameras;Safety;Recording;Internet of Things|
|[RPL Protocol Enhancement using Artificial Neural Network (ANN) for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053540)|S. Kuwelkar; H. G. Virani|10.1109/IDCIoT56793.2023.10053540|IPv6;Low Power Lossy Networks;Routing protocol;Artificial Neural Network (ANN);Internet of Things (IoT);Measurement;Wireless sensor networks;Microcontrollers;Artificial neural networks;Routing;Routing protocols;Delays|
|[Psychology based Recommender System for Visual E-Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053419)|S. Manimozhi; J. Jeyachidra; G. Umamaheswari|10.1109/IDCIoT56793.2023.10053419|K-means;Clustering;Decision Tree;Collaborative Filtering;Visualization;Electronic learning;Filtering;Psychology;Decision trees;Internet of Things;Data mining|
|[Automated Crop Recommender System using Pattern Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053451)|J. S. Jeyanathan; B. Medha; G. T. V. Sai; R. B. Kumar; V. Sahu|10.1109/IDCIoT56793.2023.10053451|crop recommendation system;pattern classifiers;pH level;System Vector Machine (SVM);K-Nearest Neighbor (KNN);Random Forest (RF);Decision Tree (DT);Productivity;Green products;Crops;Support vector machine classification;Humidity;Forestry;Soil|
|[A Review on Autopilot using Neuro Evaluation of Augmenting Topologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053395)|K. C. Reddy; V. K. K; F. A. Mulla; G. N. H. Kumar; J. Prajwal; M. Gopal|10.1109/IDCIoT56793.2023.10053395|Deep Learning;Neural Network;Convolution Neural Network;Stimulator;Image Processing;Automated Driving;Training;Deep learning;Roads;Training data;Robustness;Software;Autonomous automobiles|
|[Analysis on Different Optimization Methods, Applications, and Categories of Optical Fiber Networks: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053521)|A. Dubey; H. Singh; S. Kaur|10.1109/IDCIoT56793.2023.10053521|Index Terms - Optical Fiber Networks;Data Transmission;GA (genetic algorithm);PSO (particle swarm optimization);and ACO (ant colony optimization) methods;Optical polarization;Optimization methods;Optical distortion;Quality of service;Optical fiber networks;Optical variables control;Fiber nonlinear optics|
|[Performance Analysis of Base and Meta Classifiers and the Prediction of Cardiovascular Disease using Ensemble Stacking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053464)|V. K. H M; S. D S|10.1109/IDCIoT56793.2023.10053464|Cardiovascular disease;ensemble;base classifiers;meta classifiers;stacking;Heart;Stacking;Organizations;Predictive models;Prediction algorithms;Performance analysis;Data mining|
|[Multi-Featured Movie Recommendation Using Knowledge Graph](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053435)|L. S. Nair; J. Cheriyan|10.1109/IDCIoT56793.2023.10053435|Movie Recommendation;Knowledge Graph;Machine Learning;Particle Filtering;Deep learning;Filtering;Knowledge graphs;Filtering algorithms;Motion pictures;Prediction algorithms;Internet of Things|
|[Lifting Wavelet Transform based FBMC for Visible Light Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053421)|V. G. Krishnan; J. Deepa; G. Vishnupriya; B. S. Gowri; S. Raja|10.1109/IDCIoT56793.2023.10053421|Lifting Wavelet Transform;Visible Light Communication;Filter Bank Multi-Carrier;Fast Fourier Transform;Pulse Amplitude Modulation;Phase shift keying;Prototypes;Peak to average power ratio;Amplitude modulation;Mathematical models;Low latency communication;Standards|
|[Survey on Various Techniques based on Voice Assistance for Blind](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053499)|D. P; H. Jain; M. H; M. B; A. V. Kulkarni|10.1109/IDCIoT56793.2023.10053499|Visually impaired;object detection;Text-to-speech;Voice Assistance;Deep Learning;Neural Networks;Single Shot Multi-Box Detector;Regions with Convolutional Neural Networks (R-CNN);Smart Eye;Raspberry Pi;Optical Character Recognition (OCR);Computer Vision;Text Extraction;Artificial Intelligence and Machine Learning (AIML);You Only Look Once (YOLO) Algorithm;Common Objects in Context (COCO);Mobile Net Single Shot Detector (SSD);Performance evaluation;Visualization;Technological innovation;Machine learning algorithms;Navigation;Face recognition;Speech recognition|
|[Mining Road Traffic Accident Data for Prediction of Accident Severity](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053409)|T. K. Bahiru; V. S. Manjula; T. B. Akele; E. A. Tesfaw; T. D. Belay|10.1109/IDCIoT56793.2023.10053409|Data mining;Traffic Accidents;Classification algorithms;Naïve Bayes classifier;J48 classifier;Support Vector Machine;Accident severity;Roads;Lighting;Transportation;Support vector machine classification;Predictive models;Classification algorithms;Data mining|
|[Advanced Optimized Counter based Hierarchal Model to Predict Cancer’s Disease from Cancer Patients Neurological Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053483)|K. Laxminarayanamma; R. V. Krishnaiah; P. Sammulal|10.1109/IDCIoT56793.2023.10053483|Cancer Patient’s Data Prediction;Contour Map Optimization;Remote Sensing;Neurological Features;Convolution Neural Networks;Multi Scale Feature Extraction;Neurological Feature Resolution;Location awareness;Semantics;Feature extraction;Sensors;Data mining;Object recognition;Convolutional neural networks|
|[Modeling of IoT based High-Speed Hybrid Fiber-Optical Wireless Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053482)|M. Kumari|10.1109/IDCIoT56793.2023.10053482|Optical Wireless Communication (OWC);Visible light communication (VLC);Light emitting diode (LED);Internet of things (IoT);Optical fibers;Wireless communication;Costs;System performance;Optical fiber cables;Optical fiber networks;Apertures|
|[Framework for Implementation of Personality Inventory Model on Natural Language Processing with Personality Traits Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053501)|P. William; Y. N; V. M. Tidake; S. Sumit Gondkar; C. R; K. Vengatesan|10.1109/IDCIoT56793.2023.10053501|Personality Prediction;Word Embedding Model;Machine Learning;Interview Answers;Personality Traits;Social groups;Computational modeling;Soft sensors;Psychology;Predictive models;Prediction algorithms;Natural language processing|
|[Weight based Load Balancing in Kubernetes using AWS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053466)|S. B; K. T; N. R. G; N. S; J. Noronha|10.1109/IDCIoT56793.2023.10053466|Devops;Cloud Computing;Amazon Web services;Canary deployment;Kubernetes;LoadBalancer;Linkerd;Flagger;Software algorithms;Telecommunication traffic;Production;Switches;Organizations;Containers;Load management|
|[Diagnostic Criteria for Depression based on Both Static and Dynamic Visual Features](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053450)|D. D. Pandya; A. Jadeja; S. Degadwala; D. Vyas|10.1109/IDCIoT56793.2023.10053450|Affective Computing;Machine Learning;Artificial Intelligence;Visual-based Depression Detection;BDI-II estimate;Dynamic and Static characteristics;Deep learning;Visualization;Heuristic algorithms;Dynamics;Feature extraction;Depression;Boosting|
|[Implementation of Motorist Weariness Detection System using a Conventional Object Recognition Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052783)|K. Gupta; S. Choubey; Y. N; P. William; V. T. N; C. P. Kale|10.1109/IDCIoT56793.2023.10052783|Drowsiness Detection;Eye Aspect Ratio;OpenCV;Face Detection;Buzzer Alert;Sleep;Face recognition;Wearable computers;Cameras;Real-time systems;Behavioral sciences;Safety|
|[Smart Travel Planner using Hybrid Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053424)|S. B. Dasari; V. Vandana; A. Bhharathee|10.1109/IDCIoT56793.2023.10053424|Web scraping;Clustering Algorithm;K-Means Algorithm;Gaussian Mixture Model Algorithm;Mixture models;Data models;Planning;Internet of Things;Data communication;Gaussian mixture model|
|[Voting Classifier on Ensemble Algorithms for Breast Cancer Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053498)|R. Uppara; S. Yadav; D. M. Kavitha|10.1109/IDCIoT56793.2023.10053498|Brest Cancer;Computer-Aided Design System;Healthcare;Machine Learning;Voting Classifier;Support vector machines;Solid modeling;Machine learning algorithms;Prediction algorithms;Boosting;Breast cancer;Classification algorithms|
|[Predicting Heart Failure using SMOTE-ENN-XGBoost](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053458)|S. Parthasarathy; V. Jayaraman; J. P. Princy R|10.1109/IDCIoT56793.2023.10053458|Heart failure;Machine learning;XGBoost;SMOTE-ENN;Healthcare;Industries;Radio frequency;Heart;Machine learning algorithms;Medical services;Prediction algorithms;Cardiovascular diseases|
|[Design of Shark Detection and Decoy Buoys](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053408)|A. Sebastian; P. R; G. K. S; A. R|10.1109/IDCIoT56793.2023.10053408|Shark Species Detection;Deep Learning;Decoy System;Predator alert;Biological system modeling;Wildlife;Sea measurements;Radar detection;Cameras;Pattern recognition;Behavioral sciences|
|[Motion Estimating Optical Flow for Action Recognition : (FARNEBACK, HORN SCHUNCK, LUCAS KANADE AND LUCAS-KANADE DERIVATIVE OF GAUSSIAN)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053515)|R. K. Bhogal; V. Devendran|10.1109/IDCIoT56793.2023.10053515|Optical Flow;Action Recognition;Videos;NTURGB+D;Motion Estimation;Video coding;Three-dimensional displays;Motion estimation;Brightness;Estimation;Internet of Things;Data communication|
|[An Adaptive Modelling of Neuro-Fuzzy and Craziness PSO Algorithm for DC Fault Protection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053557)|C. M; P. K. Dhal|10.1109/IDCIoT56793.2023.10053557|Fault Protection;Craziness based Particle Swarm Optimization (CRPSO);Artificial Neural Network & Fuzzy Logic;MMC (Modular Multi level Converter);Adaptation models;Simulation;Switches;Power system harmonics;Harmonic analysis;Circuit faults;Transient analysis|
|[Internet of Things (IoT) and Immersive Technology based Extended Reality in Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053546)|S. K. Routray; S. Mohanty; A. Javali; S. K. Routray|10.1109/IDCIoT56793.2023.10053546|Internet of Things (IoT);immersive technologies;immersive technologies in healthcare;IoT for healthcare;extended reality;extended reality in healthcare;Training;Extended reality;Surgery;Medical services;Sensors;Internet of Things;X reality|
|[Comparative Analysis of Classifiers in a Plant Recommendation System based on Environmental Factors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053489)|R. Mittal; S. Mandava; T. S. Shetty; H. Patel|10.1109/IDCIoT56793.2023.10053489|Classifiers;Comparative Analysis;Support Vector Machine;AdaBoost;Random Forest;Recommendation System;Training;Analytical models;Support vector machine classification;Predictive models;Prediction algorithms;Environmental factors;Classification algorithms|
|[E-Billing System using Smart Energy Meter for Domestic Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053452)|H. B. Thummar; J. M. Jangid; A. Patel|10.1109/IDCIoT56793.2023.10053452|ESP8266;Adafruit;Message Queuing Telemetry Transport (MQTT);Arduino;If This Then That (IFTTT);Internet of things (IoT);Secure Digital Card;Liquid Crystal Display (LCD);Meters;Cloud computing;Energy consumption;Protocols;Tariffs;Liquid crystal displays;Real-time systems|
|[MSCP based Rotor Faults and Mechanical Bearing Failure Identification in Induction Motor using Power Quality Analyzer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053456)|W. Rajan Babu; M. Sundaram; G. Muthuram; A. Khaja Najumudeen; M. Karthick; B. Chandramouli|10.1109/IDCIoT56793.2023.10053456|Polyphase Induction Motor (PIM);Rotor copper bars;Mechanical fault;Stator current monitoring;MSCP;Temperature sensors;Temperature measurement;Electromagnetic heating;Induction motors;Rotors;Stators;Circuit faults|
|[Non-Invasive Performance Investigation of Single-Phase Induction Motor under Rotor Fault and Mechanical Bearing Failure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053444)|G. Venkateswarlu; N. Hariharan; S. M. Ingale; S. N. Teli; W. Rajan Babu; C. Mohan Raj|10.1109/IDCIoT56793.2023.10053444|Single-Phase Induction Motor;Rotor copper bars;Mechanical fault;Stator current monitoring;Vibrations;Total harmonic distortion;Induction motors;Power quality;Rotors;Stators;Harmonic analysis|
|[Experimental Investigation of Biomass Gasifier Using HTAG Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053393)|K. Raj Thilak; D. Suganyadevi; M. Varatharaj; V. Muralidharan; C. Pavithra; S. Ashwinkarthik|10.1109/IDCIoT56793.2023.10053393|Updraft Gasifier;HTAG;Air Preheating;Heating systems;Temperature dependence;Pollution;Production;Biomass;Internet of Things;Fuels|
|[An Investigation on Battery Management System for Autonomous Electric Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053441)|K. R. Thilak; S. Ashwinkarthik; M. Varatharaj; V. Muralidharan; M. Vinosh; Y. Shinde|10.1109/IDCIoT56793.2023.10053441|Combustion Engines;Electric Vehicles;Very Large-Scale Integration (VLSI);Cloud computing;Battery management systems;Voltage;Transforms;Very large scale integration;Electric vehicles;Batteries|
|[Performance Analysis of Energy Efficient Single Phase Induction Motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053475)|R. Premkumar; Shanmugasundaram|10.1109/IDCIoT56793.2023.10053475|Constant speed application;energy efficiency;mono phase Induction Motor;Main or running coil;Auxiliary or starting coil;MATLAB Simulink;Shafts;Reactive power;Induction motors;Software packages;Windings;Loading;Energy conservation|
|[An Overview of Navigation Algorithms for Unmanned Aerial Vehicle](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053496)|R. A; S. P; V. E; S. P|10.1109/IDCIoT56793.2023.10053496|UAV;UV;GPS;INS;localization;mapping;path planning;algorithm;Space vehicles;Navigation;Heuristic algorithms;Decision making;Position measurement;Autonomous aerial vehicles;Path planning|
|[Modeling and Simulation of Photovoltaic Cell and Module for IoT applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053556)|D. Gupta; S. P. Koiry; P. Veerender; P. Jha; C. Sridevi; A. K. Chauhan|10.1109/IDCIoT56793.2023.10053556|Quite Universal Circuit Simulation (QUCS);Internet of Things (IoT);Photovoltaic cell and module;Modeling;Simulation;Shadowing effect;Photovoltaic systems;Radiation effects;Temperature;Circuit simulation;Simulation;Photovoltaic cells;Software|
|[Gaussian Approximation based WCDMA and OFDMA System Performance Investigation for Various Fading Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053401)|P. Singla; V. Gupta; R. Mittal; R. Kaur; J. Kaur|10.1109/IDCIoT56793.2023.10053401|AC-MIMO Radio;OFDMA;WCDMA system;Gaussian Approximation;BER;Fading Distributions;Nakagami distribution;Wireless communication;System performance;Bit error rate;Rician channels;Spread spectrum communication;Rayleigh channels|
|[Designing an Interactive Chatbot for Educational Assistance using Rasa Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053457)|X. Gonsalves; S. Deshmukh|10.1109/IDCIoT56793.2023.10053457|chatbots;query;enquiry;crce bot;institute website;easily navigate;answer in natural language;natural language processing;Knowledge engineering;Navigation;Natural languages;Chatbots;User experience;Servers;Object recognition|
|[Minimization of Power Losses in the Distribution System by Controlling Tap Changing Transformer using the PSO Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053479)|C. Srinivas; V. Bhargavi; N. S. Babu; P. Harika; P. Kranthi|10.1109/IDCIoT56793.2023.10053479|Charging Stations;Distributed Generation;Distribution Transformers;Electric Vehicles (EV);Radial Distribution System (RDS);Voltage Profile;Fans;Uncertainty;TV;Power demand;Software algorithms;Voltage;Transformers|
|[Minimization of Frequency Deviations in Multi-Area Power System with SSSC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053433)|N. M. Reddy; C. Srinivas; P. N. Sai Varsha; S. Srujana; N. Saipriya; R. S. Ganesh|10.1109/IDCIoT56793.2023.10053433|Frequency Deviation;Load Uncertainty;Multi-Area System;Tie-line Power;Settling Time;SSSC Controller;Time-frequency analysis;Uncertainty;Flexible AC transmission systems;Interconnected systems;Power system stability;Load management;Minimization|
|[A Comparative Study of Drone Forensic Tools and Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053530)|A. S. Mohammed; A. Abul Hasanaath; A. Moinuddeen; N. Mohammad|10.1109/IDCIoT56793.2023.10053530|Unmanned Aerial Vehicles;Drone Security;Machine Learning;Drone Forensics;Forensics;Surveillance;Cellular phones;Wildlife;Machine learning;Internet of Things;Data communication|
|[Building a Smart City: A Conceptual Approach to Real-Time Urban Flood Control System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053509)|A. M. Hingmire; P. R. Bhaladhare|10.1109/IDCIoT56793.2023.10053509|Urban Flood;Fuzzy logic;Water Network;Internet of Things (IoT);Smart City;Rain;Smart cities;Control systems;Real-time systems;Floods;Internet of Things;Monitoring|
|[Sparrow Search Optimization with Deep Belief Network based Wind Power Prediction Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053490)|D. A. M; P. B. Narayana; S. N. Kumar; A. Ala Walid; D. Jyoti Prasad Patra; B. S. Kumar|10.1109/IDCIoT56793.2023.10053490|Wind power;Predictive model;Sparrow search optimization;Deep learning;Parameter optimization;Renewable energy sources;Wind energy;Wind power generation;Predictive models;Prediction algorithms;Power grids;Safety|
|[Flood Surveillance using FPV drones](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053431)|N. V. Pragna; J. Srinivasan; G. M; A. J. Sri Vishnu; R. Polavarapu; A. Mohanty|10.1109/IDCIoT56793.2023.10053431|Electronic Speed Controller;Flight Controller;VTX (Video Transmitter);First-Person View (FPV) Camera;Surveillance;Streaming media;Real-time systems;Floods;Synchronization;Internet of Things;Data communication|
|[Automatic Alcohol Sensing and Vehicle Accident Detection System using GPS and GSM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053555)|L. K. Divi; N. Neelima; Y. S. K. Gudela; N. R. Palaparti|10.1109/IDCIoT56793.2023.10053555|Global System for Mobile Communication (GSM);Global Positioning System (GPS);Accelerometer Sensor;Vibration Sensor;Camera Module;GSM;Vibrations;Accelerometers;Emergency services;Cameras;Liquid crystal displays;Sensors|
|[Time-Domain Control Algorithms of DSTATCOM in a 3-Phase, 3-Wire Distribution System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053535)|K. Santhosh; K. Chenchireddy; P. Vaishnavi; A. Greeshmanth; V. M. Kumar; P. N. Reddy|10.1109/IDCIoT56793.2023.10053535|FACTS;DSTATCOM;SRFT;IRPT;Reactive power;Voltage source inverters;Flexible AC transmission systems;Power quality;Process control;STATCOM;Internet of Things|
|[Renewable Energy Source Fed Multilevel Inverter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053486)|A. Manjula; M. Palepu; N. Karnekanti; S. Gogireddy; C. K. Chiguru|10.1109/IDCIoT56793.2023.10053486|Single-phase inverter;hybrid energy sources;Arduino;renewable energy sources;windmill;PV cell;multilevel inverter;Renewable energy sources;Microcontrollers;Voltage;Switches;Oral communication;Pulse width modulation;Multilevel inverters|
|[Freshness Evaluation of Beef using MOS Based E-Nose](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053399)|B. V A; M. M. George; M. A. Sibichan; M. Raj; K. Prasad|10.1109/IDCIoT56793.2023.10053399|Electronic Nose;Sensors;Machine Learning;Beef;Support vector machines;Sensitivity;Machine learning algorithms;Nose;Training data;Voltage;Electronic noses|
|[Smart Energy Meter and Monitoring System using Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053541)|M. Karpagam; S. S. S; S. S; S. S|10.1109/IDCIoT56793.2023.10053541|Internet of Things (IoT);Arduino UNO;energy meter;LCD;current sensor;Meters;Software;Smart meters;Sensor systems;Liquid crystal displays;Internet of Things;Reliability|
|[A Review on Cloud Security and Its Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053520)|S. B. Mallisetty; G. A. Tripuramallu; K. Kamada; P. Devineni; S. Kavitha; A. V. P. Krishna|10.1109/IDCIoT56793.2023.10053520|Cloud Computing;Cloud Security;Privacy Protection;Security Threats;Firewalls (computing);Cloud computing security;Information security;Switches;Network architecture;Regulation;Product development|
|[A Comparative Survey on K-Means and Hierarchical Clustering in E-Commerce Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053472)|C. S. Reddy; N. S. K. Deepak Rao; A. Sisir; V. S. Srinivasa Raju; S. S. Aravinth|10.1109/IDCIoT56793.2023.10053472|K-mean Clustering;Hierarchical Clustering;Dataset Clustering;Agglomerative Clustering;Divisive Clustering;Uncertainty;Clustering algorithms;Psychology;Complexity theory;Electronic commerce;Reliability;Noise measurement|
|[Hybrid Approach for Weather Prediction in IoT Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053510)|R. C. Tiple; G. S. Rathod; K. Akant; P. Chandankhede; S. Yadav|10.1109/IDCIoT56793.2023.10053510|Weather Monitoring;Internet of Things;Optimization shuffled shepherd algorithm;Mayfly hybrid;Temperature sensors;Temperature measurement;Wireless sensor networks;Temperature distribution;Weather forecasting;Prediction algorithms;Real-time systems|
|[Hand Gesture-based Virtual Mouse using Open CV](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053488)|G. Jagnade; M. Ikar; N. Chaudhari; M. Chaware|10.1109/IDCIoT56793.2023.10053488|Software;Computer Vision;Media pipe;Virtual mouse;Pyttsx3;Voice Engine;Portable computers;Pandemics;Prototypes;Mouth;Virtual reality;Companies;Object detection|
|[IoT-based Automatic Weather Station](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053407)|V. Gaikwad; N. Kamtalwar; H. Karadbhajne; M. Karmarkar; H. Kendre; O. Ketkar|10.1109/IDCIoT56793.2023.10053407|Internet of Things;Smart City;Node microcontroller unit (MCU) Esp8266;DHT11: Digital temperature and humidity sensor;BMP 180: Barometric Pressure Sensor;and Rain Sensor FC-37;Temperature sensors;Temperature measurement;Rain;Atmospheric measurements;Humidity;Sensor systems;IP networks|
|[Dynamic Clustering Algorithm for Video Summarization on VSUMM Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053559)|V. R. T. Jaladanki; R. R. Duvvada; H. V. S. S. R. Badugu|10.1109/IDCIoT56793.2023.10053559|Video Summarization;Keyframe;Dynamic Clustering;Unsupervised learning;Deep Learning;Measurement;Error analysis;Heuristic algorithms;Supervised learning;Clustering algorithms;Benchmark testing;Information retrieval|
|[ML-based Java UI for Residence Predictor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053480)|S. Saudagar; M. Kulkarni; I. Raghvani; H. Hirkani; I. Bassan; P. Hole|10.1109/IDCIoT56793.2023.10053480|Machine Learning;Residence predictor;Java;UI;Java;Machine learning algorithms;Navigation;Urban areas;Stacking;Neural networks;Predictive models|
|[Data Integration and Transformation using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053513)|S. Pandey; A. K; M. R. Shaikh; D. Y. P; Y. Vishwakarma|10.1109/IDCIoT56793.2023.10053513|Data Integration;Data Transformation;Machine Learning;Feature Description;Rule-based machine learning;Domain Expert;Feedback loop;Solution design;Standards organizations;Warehousing;Data integration;Organizations;Manuals;Machine learning|
|[Fuzzy Logic based Clustering Approach in Heterogeneous WSN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053528)|S. D. Patil; P. S. Patil|10.1109/IDCIoT56793.2023.10053528|Fuzzy Logic;Low-Energy Adaptive Clustering Hierarchy;Wireless Sensor Network;Clustering;Fuzzy logic;Wireless sensor networks;Energy consumption;Protocols;Throughput;Data models;Topology|
|[File Encryption-Decryption using Java](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053514)|S. Saudagar; N. Kamtalwar; H. Karadbhajne; M. Karmarkar; H. Kendre; O. Ketkar|10.1109/IDCIoT56793.2023.10053514|Encryption;Decryption;Java;File;Blowfish;Caesar Cipher;Data Encryption Standard;Java;Protocols;XML;Public key;Media;Encryption;Virtual private networks|
|[Smart Energy Management in Classroom using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053500)|M. Challa; K. Shreya Reddy; M. Jacob; N. S. Varna|10.1109/IDCIoT56793.2023.10053500|IoT;Camera-Based Detection;Smart Classroom;Automation;Energy Conservation;Fans;Energy consumption;Automation;Energy conservation;Cameras;Internet of Things;Data communication|
|[Convolutional Neural Network based Digital Image Forensics using Random Forest and SVM Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053434)|M. S. Kaushik; A. B. Kandali|10.1109/IDCIoT56793.2023.10053434|Image Forensics;Convolutional Neural Network;Random Forest;Support Vector Machine;Support vector machines;Deep learning;Image coding;Social networking (online);Transform coding;Training data;Feature extraction|
|[Handy Non-Invasive Blood Glucose Estimator using Arduino and NodeMCU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053485)|S. K. G; S. V. S; S. S; S. D. P; S. R. P; V. P. M|10.1109/IDCIoT56793.2023.10053485|Metabolic pathological illness;Invasive;Non-Invasive;Diabetes;Blood Glucose;Cloud computing;Pathology;Embedded systems;Liquid crystal displays;Mobile handsets;Glucose;Servers|
|[A Novel Approach for Smart Battery Monitoring System in Electric Vehicles using Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053529)|C. R. Durga; J. Karthik; R. Dakshayani; S. Manikanta|10.1109/IDCIoT56793.2023.10053529|Thermal Runaway;Sensors;Internet of things;Notification;Dashboard;Mobile Application;Temperature sensors;Temperature measurement;Voltage fluctuations;Motorcycles;Electric vehicles;Batteries;Safety|
|[Skin Lesion Classification using Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053478)|B. P. Koppolu|10.1109/IDCIoT56793.2023.10053478|Disease Classification;Global Health;Skin Cancer;Image Classification;Transfer Learning;Training;Costs;Transfer learning;Sociology;Melanoma;Predictive models;Skin|
|[Real-Time Operating System for Multitasking Control in the Robotics and Automation Industry](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053493)|J. Sayyad; A. Jatti; K. Attarde; R. B. T; S. Deokar|10.1109/IDCIoT56793.2023.10053493|Real-Time Operating System;FreeRTOS;Espressif32 (ESP32);Software timers;Stepper motor;multitasking;Industries;Automation;Microcontrollers;Switches;Multitasking;Software;Real-time systems|
|[Implantable Smart Devices for Remote Health Monitoring to Detect Hypo/Hyperglycaemia](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053411)|R. Palaniappan; S. R; S. Surendran|10.1109/IDCIoT56793.2023.10053411|Hypoglycemia;Remote Health Monitoring;Continuous Glucose Monitoring;Internet of Medical Things;Smart Healthcare;Medical services;Software;Glucose;Recording;Biomedical monitoring;Springs;Smart devices|
|[Building Effective Features based on Automatic Learning for Smart Search](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053448)|D. -H. Pham|10.1109/IDCIoT56793.2023.10053448|product title data;feature based on dictionary;feature based on automatic learning;alliterative dictionary;brand alliterative dictionary;Dictionaries;Buildings;Feature extraction;Search problems;Frequency conversion;Natural language processing;Internet of Things|
|[An Intelligent Music Genre Classification Method with Feature Extraction based on Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053460)|K. Manikandan; G. Mathivanan|10.1109/IDCIoT56793.2023.10053460|Music genre;digital music genre;deep learning;training and learning process;Deep learning;Training;Music;Learning (artificial intelligence);Feature extraction;Prediction algorithms;Standards categories|
|[A Train with Automatic Functioning based on IoT with Solar Energy Source](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053403)|S. Padmakala; O. A. Alkawak; G. V. S. Reddy; S. K. Narendranathan; M. S. Muthuraman; S. Sendilvelan|10.1109/IDCIoT56793.2023.10053403|Renewable energy;carbon footprints;real time monitoring;Internet of Things (IoT);battery;Renewable energy sources;Job shop scheduling;Lighting;Solar energy;Rail transportation;Real-time systems;Internet of Things|
|[Artificial Neural Network to Predict Swinging of Lower Limb in Jumping Jack Exercise](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053468)|G. Nagaraj; B. A. Mir; B. Gomathy; L. S. R; A. Sengupta; S. S. Ahmad|10.1109/IDCIoT56793.2023.10053468|Motion capture system;EMG;lower limb kinematics;jumping jack;postural exercise analysis;Analytical models;Artificial neural networks;Predictive models;Muscles;Fatigue;Mathematical models;Motion capture|
|[Transformer Monitoring and Security System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053405)|M. Yuvaraju; S. Kumar; K. Singh; G. Nageswara Rao; B. J. Kumar; K. Vigneshwaran|10.1109/IDCIoT56793.2023.10053405|Internet of Things (IoT);Security;Transformer Monitoring;Power Systems;Temperature sensors;Temperature measurement;Temperature distribution;Oils;Oil insulation;Transformers;Real-time systems|
|[Detection and Classification of Early Stage Diabetic Retinopathy using Artificial Intelligence and Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053477)|C. Aravindan; R. Vasuki|10.1109/IDCIoT56793.2023.10053477|Diabetic retinopathy detection;artificial intelligence;earlier prediction;image processing;ophthalmologists;Training;Retinopathy;Image processing;Data visualization;Blood vessels;Retina;Diabetes|
|[AI-Powered Mobility Educational Application for Enhancing Student Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053423)|M. R. M; M. A. K. G; S. T; S. A|10.1109/IDCIoT56793.2023.10053423|Artificial Intelligence;Machine Learning;Naive Bayes;Probability;Seminars;Conferences;Organizations;Oral communication;Internet of Things;Data communication;Artificial intelligence|
|[A Machine Learning-Based Approach for Anomaly Detection for Secure Cloud Computing Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053518)|P. Parameswarappa; T. Shah; G. R. Lanke|10.1109/IDCIoT56793.2023.10053518|Cloud computing;cloud security Intrusion Detection System;Machine Learning-KNN;RF;DT;MLP;LR;Extra Tree and Gradient boosting;UNSW-NB-15;Training;Machine learning algorithms;Firewalls (computing);Cloud computing security;Intrusion detection;Machine learning;Security|
|[An Intrusion Detection System for MANET to Detect Gray Hole Attack using Fuzzy Logic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053550)|K. Bala; J. Paramesh; K. J. Elma; S. T. Santhanalakshmi|10.1109/IDCIoT56793.2023.10053550|Mobile Ad hoc network (MANET);wireless communication network;Intrusion Detection System;nodes;grey hole attack;fuzzy logic system;Fuzzy logic;Wireless communication;Wireless sensor networks;Intrusion detection;Security;Internet of Things;Data communication|
|[Novel Empirical Block Chain Ecosystem with Deep Neural Key Exchange Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053436)|S. S. Barani; R. Durga|10.1109/IDCIoT56793.2023.10053436|Authentication;Internet of Things;Key exchange;privacy preservation;Blockchain;Data protection;Authentication;Public key;Receivers;Trustless services;Computational efficiency;Blockchains|
|[Detecting Renal Disease using Meta-Classifiers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053551)|L. B; A. V; Y. A. N; H. P. R; S. S; A. S. S|10.1109/IDCIoT56793.2023.10053551|Renal Disease;Meta classifiers;Fusion Learning;Preprocessing;Accuracy;Medical services;Encoding;Reliability;Internet of Things;Data communication;Forecasting;Random forests|
|[Automated Hydroponics System to Study Nutrient Allocation and Plant Responses in a Controlled Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053508)|S. K. P; D. Menon; D. Sharma; A. Dadsena|10.1109/IDCIoT56793.2023.10053508|Hydroponics;IoT;Arduino;Node MCU;Smart Farming;Automated Agriculture;Mist/spray setup;Blynk;Plants (biology);Hydroponics;Production;Aerospace electronics;Seeds (agriculture);Resource management;Internet of Things|
|[A Methodology to Predict the Lung Cancer and its Adverse Effects on Patients from an Advanced Correlation Analysis Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053531)|T. A. S. Srinivas; M. M; N. Aparna; K. K. K; N. R. C; R. J|10.1109/IDCIoT56793.2023.10053531|Lung Cancer Detection;Correlation Analysis;Healthcare;Multiple Regression;Machine learning algorithms;Correlation;Linear regression;Anxiety disorders;Lung cancer;Machine learning;Prediction algorithms|
|[Hardware based Analysis of Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053400)|A. Karia; A. Patil; M. K. Apoorva; V. P; S. Pillay; B. R. Mohan|10.1109/IDCIoT56793.2023.10053400|Index Terms—Machine Learning;Deep Neural Networks (DNN);Deep Learning;Measurement;Deep learning;Neural networks;Parallel processing;Hardware;Libraries;Internet of Things|
|[Issues and Future Trends in IoT Security using Blockchain: A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053430)|S. Kumar; A. Vidhate|10.1109/IDCIoT56793.2023.10053430|Index Terms – Internet of Things;IoT;security;smart environment;privacy;cloud;blockchain;Privacy;Automation;Authentication;Market research;Blockchains;Internet of Things;Security|
|[IoT based Energy Efficient using Wireless Sensor Network Application to Smart Agriculture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053446)|R. Deepa; M. Sankar; R. R; C. Sankari; Venkatasubramanian; R. Kalaivani|10.1109/IDCIoT56793.2023.10053446|Energy;Precision Agriculture;Stability;Water;Smart agriculture;Wireless communication;Wireless sensor networks;Irrigation;Turning;Energy efficiency;Stability analysis|
|[Design and Development of Protected Services in Cloud Computing Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053533)|P. K. Purohit; A. Kumar; S. Degadwala|10.1109/IDCIoT56793.2023.10053533|Quantum bits;DNA-based encryption;Storage Security;cloud computing;Cloud computing;Technological innovation;Quantum computing;Software;Safety;Encryption;Blockchains|
|[Hierarchical Fuzzy Methodologies for Energy Efficient Routing Protocol for Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053474)|M. Prabha; M. Anbarasan; S. Sunithamani; M. K. Saranya|10.1109/IDCIoT56793.2023.10053474|K-means clustering;Fuzzy C-means algorithm;network life time;network Coverage;Energy Efficiency;Measurement;Wireless sensor networks;Wireless networks;Scalability;Clustering algorithms;Spread spectrum communication;Routing|
|[Prevention of Aflatoxin in Peanut Using Naive Bayes Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053416)|R. Subha; Suchithra|10.1109/IDCIoT56793.2023.10053416|Nematodes;Fungicides;K-Means;Naïve Bayes;Hierarchical Clustering;Fuzzy Clustering and Cultivars;Fungi;Schedules;Pathogens;Sociology;Crops;Production;Mobile handsets|
|[Modular Controller Based Intelligent System for Smart Autocleaning and Effective Usage of Utensils](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053549)|B. Hemananth; A. J. Sagaya Pushpam; G. Venkateswarlu; S. M. Balaganesan; P. K. Poonguzhali; S. Gokul|10.1109/IDCIoT56793.2023.10053549|Plastic usage;Metal Plates;Water Management;Plate Washing Machine and PLC;Protocols;Programmable logic devices;Metals;Water pollution;Software;Washing machines;Plastics|
|[MSCP Based Stator Fault Identification in Induction Motor Using Power Quality Analyzer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053524)|W. Rajan Babu; M. Sundaram; A. Kavithamani; S. Sam Karthik; N. Abinaya; V. Bharath Choudry|10.1109/IDCIoT56793.2023.10053524|Induction Motor (IM);stator turn to turn short or inter turn fault;Stator current pattern monitoring;MSCP;Fault diagnosis;Resistance;Induction motors;Power measurement;Current measurement;Power quality;Windings|
|[Machine Learning based Secure Data Transmission and Improvement in MANET through Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053398)|G. Dinesh; Y. S. Pawar; P. Dinshi; S. S; S. S. Husain; C. Venkata Krishna Reddy|10.1109/IDCIoT56793.2023.10053398|Mobile Ad hoc network (MANET);wireless communication system;heterogeneous network;selfish nodes;Internet of Things (IoT);machine learning;genetic algorithm;Wireless communication;Machine learning algorithms;Biological system modeling;Machine learning;Data communication;Internet of Things;Security|
|[Polycystic Ovary Syndrome Monitoring using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052781)|R. R. Vasavi; S. Priscilla Prathibha; H. Valiveti; S. Maringanti; A. Parsa|10.1109/IDCIoT56793.2023.10052781|Polycystic ovary syndrome;Machine Learning;Monitor their lifestyle;App Development;GSR Sensor;Heart;Machine learning;Skin;Blood pressure;Diabetes;Internet of Things;Data communication|
|[Arduino based Touchless Temperature Sensing Visitor Notification System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053438)|C. Pathania; J. Vijay; R. Mahalakshmi; V. Sailaja|10.1109/IDCIoT56793.2023.10053438|Monitoring;Temperature Sensors;Arduino NANO;Infrared Sensors;Proximity Sensors;COVID’19;Temperature sensors;COVID-19;Temperature measurement;Costs;Pandemics;Sensors;Behavioral sciences|
|[Design of a Ring Oscillator for IoT Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053537)|S. K. Dash; S. N. Mishra; A. Bakshi; J. R. Panda|10.1109/IDCIoT56793.2023.10053537|Inverter;Current starved inverter;oscillator;Ring oscillators;Time-frequency analysis;Power demand;Tagging;Inverters;Generators;Delays|
|[Smart Waste Management Scheme using IoT for Metropolitan Cities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053396)|R. Rathna|10.1109/IDCIoT56793.2023.10053396|Internet of Things;Garbage Collection;Sensors;Pyrolysis;Incinerator;etc;Waste management;Oils;Urban areas;Sociology;Plastics;Internet of Things;Proposals|
|[A Secure Design of Healthcare System with Blockchain and Internet of Things (IoT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053491)|S. Pandey; Vanshika; Anshul; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053491|Blockchain;Electronic Health Records (EHRs);Smart Contract;Peer to Peer(P2P) distributed ledger;Consensus;Immutable;Throughput;Internet of Things (IoT);Proof of Work (PoW);Proof of State (PoS);Proof of Authority (PoA);Hyper (HER) Ledger;Industries;Distributed ledger;Smart contracts;Medical services;Medical instruments;Blockchains;Internet of Things|
|[A Novel Composite Intrusion Detection System (CIDS) for Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053547)|S. K; V. Ravindran; R. P. Ponraj; S. Venkatasubramanian; K. S. Chandrasekaran; S. Ragunathan|10.1109/IDCIoT56793.2023.10053547|Wireless Sensor Networks;External Threats;Intrusion;adaptive networks;Training;Wireless sensor networks;Power demand;Wireless networks;Surveillance;Intrusion detection;Artificial neural networks|
|[Randomized Matrix-based Double-Key Cryptographic System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053402)|M. Malhotra; J. Rathod|10.1109/IDCIoT56793.2023.10053402|Cryptography;Encryption;Matrix-based;Caesar Cipher;One-time padding;Ciphers;Virtual assistants;Information security;Encoding;Encryption;Cryptography;Internet of Things|
|[Lightweight Cryptographic Approach to Address the Security Issues in Intelligent Applications: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053412)|R. S. M; P. R|10.1109/IDCIoT56793.2023.10053412|Elliptic Curve Cryptography (ECC);Digital Identity;Authentication;Cryptographic Method;Systematics;Smart cities;Ecosystems;Smart homes;Elliptic curve cryptography;Real-time systems;Internet of Things|
|[Rule-based Intrusion Detection System using Logical Analysis of Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053505)|A. Kumar; T. K. Das|10.1109/IDCIoT56793.2023.10053505|network security;machine learning;intrusion detection system;Logical analysis of data (LAD);Learning systems;Support vector machines;Machine learning algorithms;Portable computers;Intrusion detection;Real-time systems;Security|
|[Machine Learning Approaches in Cyber Attack Detection and Characterization in IoT enabled Cyber-Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053545)|S. Kantimahanthi; J. V. D. Prasad; S. Chanamolu; K. Kommaraju|10.1109/IDCIoT56793.2023.10053545|Deep learning;Cyber-attack detection;Deep-Neural-Networks(DNN) Cyber-attack attribution;Cyber-attacks;Cyber-physical systems;Industrial-Internet of Things (IIoT);Industrial Control System(ICS);One-vs-All classifiers;Decision Trees;Integrated circuits;Computational modeling;Pipelines;Neural networks;Cyber-physical systems;Internet of Things;Security|
|[Use of Blockchain Technology to Protect Privacy in Electronic Health Records- A Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053417)|M. F. Ansari; B. Dash; S. Swayamsiddha; G. Panda|10.1109/IDCIoT56793.2023.10053417|Electronic Health Records (EHRs);Privacy;Security;Blockchain;Cryptography;Decentralization;COVID-19;data network;e-Policy;Privacy;Pandemics;Urban areas;Smart contracts;Transportation;Smart homes;Blockchains|
|[Smart Security System using Shuffling Keypad with SOS System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053455)|H. V. Bajaj; A. Nijanth; A. A. Linsie; M. Saravana Sanjhay|10.1109/IDCIoT56793.2023.10053455|Lockers;shuffling keyboard;security;hostage situation;micro-controller;touch screen;SOS-Save Our Souls;Powders;Keyboards;Prototypes;Passwords;Organizations;Fingerprint recognition;Touch sensitive screens|
|[Importance of Logic Locking Attacks in Hardware Security](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052782)|A. S. V; N. M. Sivamangai; R. Naveenkumar; N. A|10.1109/IDCIoT56793.2023.10052782|Logic Locking;SAT attack;Anti-SAT attack;Hardware Security;Threat modeling;Knowledge engineering;Integrated circuits;Rendering (computer graphics);Foundries;Delays;Trojan horses|
|[Convolutional Neural Networks (CNN)-based Vehicle Crash Detection and Alert System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053471)|J. M; R. P; A. Kunjumon; M. Thamban; A. Roy|10.1109/IDCIoT56793.2023.10053471|Accident Detection;Road Safety;Live Road Rash Surveillance;Quick Emergency Response;Vehicle Plate Detection;Chaos;Law enforcement;Hospitals;Roads;Image edge detection;Emergency services;Convolutional neural networks|
|[LoRa - IoT based Industrial Automation Motor Speed Control Monitoring System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053525)|B. S. Kumar; S. Ramalingam; V. Divya; S. Amruthavarshini; S. Dhivyashree|10.1109/IDCIoT56793.2023.10053525|Wireless technologies;Industry 4.0;Data revolution;Wired infrastructure;Automation system;Industries;Process monitoring;Wireless sensor networks;Technological innovation;Wireless networks;Velocity control;Process control|
|[Blockchain-based Secure Land Registry System using Efficient Smart Contract](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053476)|A. L. Shrivastava; R. Kumar Dwivedi|10.1109/IDCIoT56793.2023.10053476|Blockchain;Ethereum;React;Smart Contract;Smart contracts;Web pages;Routing;Blockchains;Safety;Registers;Servers|
|[Blockchain in Indian Agriculture to Disrupt the Food Supply Chain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053406)|G. P. Singh; V. Singh Tomer; A. Pandey; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053406|Supply Chain;Blockchain;Decentralized;Immutable;Smart Contract;Consensus;Databases;Supply chains;Organizations;Blockchains;Fraud;Complexity theory;Servers|
|[Designing a Blockchain-Enabled Methodology for Secure Online Voting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053410)|S. Singh; A. Singh; S. Verma; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053410|Blockchain;Electronic Voting (e-voting);Electronic Voting Machine (EVM);Smart Contract;Secured-voting;Decentralized ledger;Privacy;Electronic voting systems;Distributed ledger;Blockchains;Internet of Things;Data communication;Electronic voting|
|[Secure Land Registration Management via Ethereum Blockchain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053494)|S. Dubey; D. Vinod; A. Gupta; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053494|Blockchain;Land Registration;Security;Consensus;Immutable;Decentralized;Manuals;Forgery;Consensus protocol;Reliability;Internet of Things;Data communication;Task analysis|
|[A Probabilistic Model Checking (PMC) Approach to Solve Security Issues in Digital Twin (DT)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053389)|E. Shaikh; N. Mohammad; A. Al-Ali; S. Muhammad|10.1109/IDCIoT56793.2023.10053389|Digital Twin;Smart Healthcare;Healthcare;Cybersecurity;Smart Cities;Urban areas;Medical services;Model checking;Probabilistic logic;Digital twins;Manufacturing;Security|
|[A Secure Data Sharing Framework with Blockchain in IoMT using Bald Eagle Search Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053538)|M. J. F L; S. StewartKirubakaran; G. J. L. Paulraj; I. J. Jebadurai; J. Jebadurai|10.1109/IDCIoT56793.2023.10053538|Internet of Medical Things (IoMT);Cloud computing (CC);Hashing function;Blockchain technology (BT);Bald eagle search optimization (BES);Privacy;Cloud computing;Standards organizations;Authentication;Internet of Medical Things;Blockchains;Sensors|
|[CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053425)|U. Sakthi; T. M. Chen; M. Sathiyanarayanan|10.1109/IDCIoT56793.2023.10053425|Dragonfly algorithm;Deep Belief Network;Suicidal Ideation;Social media content analysis;Hyperparameter Optimization;Sentiment analysis;Social networking (online);Predictive models;Media;Prediction algorithms;Classification algorithms;Behavioral sciences|
|[An Intrusion Detection Approach using Hierarchical Deep Learning-based Butterfly Optimization Algorithm in Big Data Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053504)|Manoranjithem; S. Dhanasekaran; A. Asokan; A. Kumar; C. Yamini; M. Tiwari|10.1109/IDCIoT56793.2023.10053504|Deep learning;Big data;Security;Intrusion detection;Hyperparameter tuning;Data analysis;Intrusion detection;Big Data;Benchmark testing;Feature extraction;Classification algorithms;Internet of Things|
|[Secure Communication for Unmanned Aerial Vehicles](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053437)|M. T; V. S|10.1109/IDCIoT56793.2023.10053437|UAV trajectory design;Secure communication;UAV protocols;Smart agents;Attacks;Wireless communication;Protocols;Autonomous aerial vehicles;Trajectory;Internet of Things;Data communication;Jamming|
|[Smart Home Security Monitoring System based on Face Recognition and Android Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053558)|S. Bhatlawande; S. Shilaskar; T. Gadad; S. Ghulaxe; R. Gaikwad|10.1109/IDCIoT56793.2023.10053558|Android Application;FaceNet;Face Recognition;Mediapipe;Security System;Program processors;Face recognition;Process control;Smart homes;Detectors;Switches;Security|
|[IoT based Smart Intravenous Fluids (IV) Drip Monitoring and Reverse Blood Flow Prevention System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053449)|V. Kumar M; R. Sundar. G; M. K; K. C; S. S|10.1109/IDCIoT56793.2023.10053449|Internet of Things;Electrolyte;Alert sensors;Automatic;Fluids;Hospitals;Manuals;Electrolytes;Internet of Things;Data communication;Monitoring|
|[Concept for Value Stream-Oriented Analyses of Event-based Data in Three Perspectives](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053516)|N. Scheder; T. Teriete; S. Eisl; M. Nausch; M. Böhm|10.1109/IDCIoT56793.2023.10053516|Value Stream Mapping;Data Visualization;Sustainability;Order Tracking;Resource Monitoring;Industry 4.0;Productivity;Industries;Analytical models;Data visualization;Real-time systems;Internet of Things;Sustainable development|
|[Development of Web Application for Accessing & Managing e - Resources](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053413)|A. Gothi; D. Mandal; K. Bangde; S. Ghogare; P. Rajarapollu|10.1109/IDCIoT56793.2023.10053413|e-Library;web development;e-resource management;digital metadata management;Multi-factor authentication;Pandemics;Education;Metadata;Libraries;Security;Web sites|
|[Development of a Face Recognition System for Registering Attendance of Students Wearing Mask](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053511)|V. G. Priya; M. A. Sri; S. Sunithamani; S. M. Roy|10.1109/IDCIoT56793.2023.10053511|Caffe model;CNN model;Haar Cascade classifier;Face detection;Recognition;Pandemics;Computational modeling;Face recognition;Data models;Real-time systems;Internet of Things;Face detection|
|[Hierarchical Analysis of Intelligent Fusion Data of College Training Theory based on Virtual Cloud Classroom](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053443)|Y. Li|10.1109/IDCIoT56793.2023.10053443|Virtual cloud classroom;hierarchical analysis;intelligent fusion;data mining;training theory;Training;Cloud computing;Analytical models;Distribution strategy;Data integration;Data models;Internet of Things|
|[Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053519)|L. R. M. T; N. Jayapandian|10.1109/IDCIoT56793.2023.10053519|Augmented Reality;Big data Processing;Data Hive Architecture;Edge computing;Metaverse;Virtual Reality;Cloud computing;Solid modeling;Metaverse;Social networking (online);Databases;Big Data;Data models|
|[A Novel Approach for Product Recommendation using XGBOOST](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053453)|B. Bhavana; J. Karthik; P. L. Kumari|10.1109/IDCIoT56793.2023.10053453|Extreme Gradient Boosting Algorithm;Extreme Gradient Boosting–Random Forest;Sentiment Analysis;Synthetic minority over-sampling technique;Feature extraction;Radio frequency;Sentiment analysis;Machine learning algorithms;Forestry;Boosting;Internet of Things;Decision trees|
|[Data Privacy Over Cloud Computing using Multi Party Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053427)|A. V. Kumar; K. Monica; K. Mandadi|10.1109/IDCIoT56793.2023.10053427|Data Mining;Cryptography;Encryption;Decryption;Data Security;Symmetric key block;Industries;Cloud computing;Data privacy;Technological innovation;Information filters;Multi-party computation;Internet of Things|
|[Review of Task Scheduling based on Different Parameters in Cloud Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053484)|K. Pradeep; K. Sharma; J. Edara|10.1109/IDCIoT56793.2023.10053484|Task scheduling;Clouds computing;Data centers;Scheduling algorithms;Urban areas;Quality of service;Scheduling;Internet of Things;Security|
|[Heuristics and Meta-Heuristics based Algorithms for Resource Optimization in Fog Computing Environment: A Comparative Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053388)|S. Gupta; N. Singh|10.1109/IDCIoT56793.2023.10053388|Heuristics;Metaheuristics;Fog Computing;Optimization;Measurement;Heuristic algorithms;Metaheuristics;Energy efficiency;Resource management;NP-complete problem;Internet of Things|
|[Image Security Utilizing Hybrid Model of Steganography and Asymmetric Cryptography Methods](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053432)|H. Arora; R. Agarwal; P. Sharma; G. Shankar; D. Arora|10.1109/IDCIoT56793.2023.10053432|Digital Image;Steganography;Cryptography;Encryption;Decryption;Hiding;Steganography;Cloud computing;Digital images;Communication channels;Data models;Internet of Things;Data communication|
|[Solar Powered Automated Harvesting Bot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053397)|Amisha; B. Kumari; Priya; S. Kumari; S. B. V|10.1109/IDCIoT56793.2023.10053397|Smart;Efficient;Automated Fruit Harvesting bot;Horticulture;Colour Detection;Line Tracking Robot;Job shop scheduling;Service robots;Tracking;Production;Robot sensing systems;Chatbots;Manipulators|
|[Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring System in Big Data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053418)|S. Ayoub; N. R. Behera; M. N. Raju; P. Singh; S. Praveena; R. K|10.1109/IDCIoT56793.2023.10053418|Big data;Medical imaging;Manta Ray Foraging Optimization (MRFO);Healthcare monitoring;Deep learning;Deep learning;Tuners;Medical services;Big Data;Feature extraction;Data models;Task analysis|
|[Hand Sign Recognition using Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053506)|H. S. S. K. Vezzu; S. Nalluri; K. Swetha; V. SaiKumar|10.1109/IDCIoT56793.2023.10053506|Image Processing;OpenCV;Machine Learning;Tensor flow Lite;Gesture Recognition;Flask;Keras;ASL;Performance evaluation;Image recognition;Image resolution;Image color analysis;Gesture recognition;Auditory system;Organizations|
|[A Novel Virtual Reality (VR) based Intelligent Guiding System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053542)|C. Fuxiong|10.1109/IDCIoT56793.2023.10053542|Image Processing;Virtual Reality;Intelligent Guiding;System Improvement;Programming Method;Human computer interaction;Systematics;Image processing;Virtual reality;Programming;Internet of Things;Data communication|
|[Intelligent Generation Algorithm for Multi-scene Virtual Images of Dynamic Pictures Based on Metaverse](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053548)|Y. Shengwen; K. Xi|10.1109/IDCIoT56793.2023.10053548|Metaverse;Generation Algorithm;Multi-scene;Virtual Images;Dynamic Pictures;Metaverse;Social networking (online);Heuristic algorithms;Streaming media;Aerodynamics;Generative adversarial networks;Market research|
|[Identification of Malignant Patterns in FNAC Digital Images of Thyroid Nodules through Cascaded Segmentation Stages](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053392)|B. Gopinath; N. Shanthi; R. Santhi|10.1109/IDCIoT56793.2023.10053392|benign;classification;cytology;feature extraction;pattern;segmentation;Support vector machines;Wavelet transforms;Image segmentation;Morphology;Feature extraction;Needles;Gabor filters|
|[Highway Collision Avoidance by Detection of Animal’s Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053391)|M. R; M. M; M. K; R. S; K. S. S; S. M. P|10.1109/IDCIoT56793.2023.10053391|Highway Collison;Deep learning;Convolutional neural network (CNN);Histogram of Oriented Gradients (HOG);Support Vector Machine;Road transportation;Support vector machines;Shape;Wildlife;Neural networks;Cameras;Feature extraction|
|[Circumvolution of Centre Pixel Algorithm in Pixel Value Differencing Steganography Model in the Spatial Domain](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053536)|K. S. Suresh; T. Kamalakannan|10.1109/IDCIoT56793.2023.10053536|Pixel Value Differencing -PVD;Least Significant Bit - LSB;Peak Signal Nosie Ratio - PSNR;Mean Squared Error - MSE;stego-image;Steganography;Computational modeling;Software algorithms;Receivers;Data models;Mathematical models;Software|
|[Forest Fire Classification and Detection in Aerial Images using Inception-V3 and SSD Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053522)|S. S. Jandhyala; R. R. Jalleda; D. M. Ravuri|10.1109/IDCIoT56793.2023.10053522|Wildfires;Convolutional Neural Network;Inception-V3;Single Shot Detector;Aerial Images;Heating systems;Sociology;Transfer learning;Green products;Fires;Forestry;Disaster management|
|[Enhancing Prediction of Cardiovascular Disease using Bagging Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053426)|H. S. Shaik; G. V. RajyaLakshmi; V. Alane; N. D. Kandimalla|10.1109/IDCIoT56793.2023.10053426|cardiovascular diseases;machine learning;Recursive Feature Elimination;InterQuartile Range (IQR) and ensemble methods;Support vector machines;Training data;Predictive models;Feature extraction;Prediction algorithms;Data models;Decision trees|
|[Automatic Certificate Verification using Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053544)|S. M; H. SK; Y. A. B; V. S. G. S; P. R. Kumar; K. L. Sailaja|10.1109/IDCIoT56793.2023.10053544|Image Augmentation;Yolo v3;Aspect Ratio;Thresholding;Optical Character Recognition (OCR) and Querying;Computer vision;Image recognition;Text recognition;Optical character recognition;Employment;Prototypes;Organizations|
|[Blockchain and Internet of Things (IoT) Enabled Smart E-Voting System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053462)|D. Kumar; R. Kumar Dwivedi|10.1109/IDCIoT56793.2023.10053462|Security;Blockchain;Consensus;Smart Contract;Merkle Tree;Hash;Nonce;IoT;Hash functions;Casting;Authentication;Blockchains;Encryption;Internet of Things;Security|
|[Remote Intelligent System for Monitoring and Control of Water Distribution Network Using Remote I/O Module for Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053492)|C. M. Raj; W. R. Babu; V. Gomathi; S. Bharath; G. V. Jagatap; S. S. Kumar|10.1109/IDCIoT56793.2023.10053492|Water management;PLC;SCADA;iOT and Remote I/O Module;Fault diagnosis;Smart cities;Pipelines;Government;Distribution networks;Flowmeters;Internet of Things|
|[Driver Safety System using Microcontroller and Image Processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053512)|S. Shilaskar; V. Patil; S. Pawar; A. Poke; R. Jambhulkar; S. Bhatlawande|10.1109/IDCIoT56793.2023.10053512|ATMega328p;Sensors;Drowsiness;Lane Detection;Image Processing;Road accidents;Microcontrollers;Sleep;Vehicle detection;Image processing;Roads;Lighting|
|[Automated Deep Learning with Wavelet Neural Network based Rice Plant Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053487)|V. Rekha; L. V. Reddy; S. V. Chaudhari; A. Gopi; C. Nithiya; S. Khaleel Ahamed|10.1109/IDCIoT56793.2023.10053487|Rice plant images;Disease diagnosis;Image classification;Convolution neural network;Machine learning;Deep learning;Solid modeling;Analytical models;Plant diseases;Image recognition;Plants (biology);Neural networks|
|[Employing Machine Learning to Identify Waste Characteristics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053527)|A. Shah; V. Patel; G. Usha|10.1109/IDCIoT56793.2023.10053527|Numpy;Pandas;Machine Learning;Matplotlib;Python;and Waste;Waste management;Government;Metals;Machine learning;Glass;Solids;Regulation|
|[Detection of Crime Scene Objects using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053440)|N. T. J; K. Thinakaran|10.1109/IDCIoT56793.2023.10053440|Object detection;Deep Learning;COLAB;CNN;Crime scene;Deep learning;Navigation;Surveillance;Lighting;Graphics processing units;Pressing;Cameras|
|[Customer Churn Analysis in Financial Domain using Deep Intelligence Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053473)|S. Kumar Hegde; R. Hegde; S. S. Nanda; G. Phatak; P. Hongal; D. G. V|10.1109/IDCIoT56793.2023.10053473|Predictive Model;Deep Neural Network;Customer Churn;Banking Sector;Industries;Deep learning;Analytical models;Machine learning algorithms;Profitability;Predictive models;Naive Bayes methods|
|[An Efficient Genetic Algorithm based Auto ML Approach for Classification and Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053442)|C. Spandana; I. V. Srisurya; S. Aasha Nandhini; R. P. Kumar; G. Bharathi Mohan; P. Srinivasan|10.1109/IDCIoT56793.2023.10053442|AutoML;Genetic Algorithm;Possum Dataset;Binary Classification;Regression;Image Classification;Fitness Function;Random Forest (RF);Radio frequency;Productivity;Machine learning;Forestry;Network architecture;Iterative methods;Internet of Things|
|[Leveraging Deep Learning to Spot Communities for Influence Maximization in Social Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053447)|S. Mishra; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053447|Social Networks;Influence Maximization;Network Embedding;Community Detection;Diffusion Model;Deep learning;Social networking (online);Clustering algorithms;Feature extraction;Data models;Internet of Things;Data communication|
|[A Real-Time Crowd Detection and Monitoring System using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053517)|P. Shrivastav; V. R. J|10.1109/IDCIoT56793.2023.10053517|Object Detection;Crowd Detection;Machine Learning;Histogram of Oriented Gradients (HOG);Support Vector Machine (SVM);Support vector machines;Pandemics;Human factors;Medical services;Machine learning;Real-time systems;Social factors|
|[Predictive Analytics for Black Friday Sales using Machine Learning Technique](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053454)|S. Alagarsamy; K. G. Varma; K. Harshitha; K. Hareesh; K. Varshini|10.1109/IDCIoT56793.2023.10053454|Xboost;Regression;Machine learning;Classification;Correlation;Machine learning;Predictive models;Behavioral sciences;Internet of Things;Data communication;Predictive analytics|
|[Edge Devices and Blockchain Integration in IoT System: A Novel Design Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053481)|T. Bisht; D. Dinesh; G. Usha; K. Gautam|10.1109/IDCIoT56793.2023.10053481|Blockchain;Internet of Things;Scalability;Consensus Optimization;Edge Computing;Interplanetary File System (IPFS);Proof of History;Scalable and Consensus-Optimized architecture for integrating Blockchain with Edge Computing (SC-IBEC Architecture);Scalability;Computer architecture;Blockchains;Cryptocurrency;Consensus protocol;Complexity theory;Timing|
|[A Predictive Analysis on CO2 Emissions in Automobiles using Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053539)|M. S. Manvitha; M. Vani Pujitha; N. H. Prasad; B. Yashitha Anju|10.1109/IDCIoT56793.2023.10053539|climatic change;predictive analysis;fuel consumption;carbon dioxide emissions;Regression;Random Forest Model;Machine Learning;Measurement;Analytical models;Linear regression;Sociology;Carbon dioxide;Predictive models;Automobiles|
|[A Data-Driven Probabilistic Machine Learning Study for Placement Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053523)|S. Bhoite; C. H. Patil; S. Thatte; V. J. Magar; P. Nikam|10.1109/IDCIoT56793.2023.10053523|Campus placement;Machine Learning;Deep Learning;Ensemble Learning;AdaBoost classifier;Training;Support vector machines;Analytical models;Machine learning algorithms;Computational modeling;Companies;Predictive models|
|[Smart Glove for Bi-lingual Sign Language Recognition using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053470)|D. Alosail; H. Aldolah; L. Alabdulwahab; A. Bashar; M. Khan|10.1109/IDCIoT56793.2023.10053470|Smart Glove;Machine Learning;Arabic Sign Language;American Sign Language;Flex sensors;Accelerometers;Support vector machines;Flexible printed circuits;Text recognition;Gesture recognition;Speech recognition;Assistive technologies|
|[Remora Optimization with Machine Learning Driven Churn Prediction for Business Improvement](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053554)|S. Kumar; P. Baruah; S. Kirubakaran; A. S. Kumar; K. Singh; M. V. J. Reddy|10.1109/IDCIoT56793.2023.10053554|Customer churn;Business;Predictive model;machine learning;Remora optimization algorithm;Customer relationship management;Insurance;Machine learning;Predictive models;Prediction algorithms;Telecommunications;Internet of Things|
|[Driver Drowsiness Monitoring and Detection using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053497)|P. Pandey; M. Sharma; P. Saxena; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053497|Machine Learning;Feature Extraction;Feature Normalization;Neural Networks;Long Short-Term Memory Network (LSTM);K-Nearest Neighbors (KNN);Location awareness;Image recognition;Road accidents;Face recognition;Machine learning;Real-time systems;Face detection|
|[Cloud Resources Forecasting based on Server Workload using ML Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053532)|T. S. Janjanam; K. S. Siram; P. K. Kollu|10.1109/IDCIoT56793.2023.10053532|Server workload;Time series;Cloud resources;Support Vector Regression;Queuing model;Cloud computing;Quality of service;Predictive models;Data models;Web servers;Resource management;Quality of experience|
|[Corneal Localization for Discerning Faces using Advanced Machine Learning Algorithms of GAN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052780)|A. Shinde; S. Watekar; S. Shekadar; A. Memon; S. Gharat|10.1109/IDCIoT56793.2023.10052780|Cornea;Generative Adversarial Networks;Style-GAN 2;Generative Adversarial Network (GAN) synthesized eyes;specular highlights;Training;Biometrics (access control);Computational modeling;Biological system modeling;Graphics processing units;Training data;Generative adversarial networks|
|[An Intelligent Video Surveillance System using Edge Computing based Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053404)|R. P. Singh; H. Srivastava; H. Gautam; R. Shukla; R. K. Dwivedi|10.1109/IDCIoT56793.2023.10053404|Video Surveillance;Deep Learning;Edge Computing;Object Detection;Multi-layer Intelligence Video Surveillance (MIVS);Deep learning;Performance evaluation;Solid modeling;Computational modeling;Streaming media;Video surveillance;Data models|
|[A Critical Survey on Real-Time Traffic Sign Recognition by using CNN Machine Learning Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053394)|M. V. K. Choda; S. V. Perla; B. Shaik; Y. T. A. Yelchuru; P. Yalla|10.1109/IDCIoT56793.2023.10053394|Real-Time Traffic Sign Recognition System (RTTSRS);Traffic surveillance;Support Vector Machine (SVM);Convolutional Neural Network (CNN);Accuracy;Support vector machines;Location awareness;Machine learning algorithms;Image recognition;Neural networks;Feature extraction;Real-time systems|
|[Predictive Monitoring of Learning Processes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052784)|G. Thiyagarajan; P. S|10.1109/IDCIoT56793.2023.10052784|Predictive Process Monitoring;Predictive Learning Analytics;Process Mining;Learning Processes;Process monitoring;Neural networks;Decision making;Real-time systems;Behavioral sciences;Internet of Things;Data communication|
|[Smart E-Locker System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053507)|K. R. Umadi; A. Aishwarya; J. E. Narendramath; I. S. M; P. P. Priya Dharishini|10.1109/IDCIoT56793.2023.10053507|Internet of Things;Radio-frequency Identification;Alarm;Ultrasonic sound sensor;Privacy;Cloud computing;Security;Internet of Things;Data communication;Radiofrequency identification|
|[Critical Investigation of COVID-19 using Machine Learning Algorithms - A Generic Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053414)|S. R. R. Jampana; T. Dama; N. Paleti; K. Sarabu; P. Yalla|10.1109/IDCIoT56793.2023.10053414|Covid-19;Machine Learning;Prediction;classification;data visualization;Random Forest;KNN;SVM;COVID-19;Support vector machines;Radio frequency;Machine learning algorithms;Pandemics;Data visualization;Forestry|
|[Machine Learning Solution for Police Functions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053461)|A. P; S. K. A. M; C. C. Vignesh; K. S|10.1109/IDCIoT56793.2023.10053461|Machine Learning;Decision Tree;Random Forest;Gaussian Naïve Bayes;Non-Linear Support Vector Machine;Police functions;Measurement;Support vector machines;Radio frequency;Machine learning algorithms;Law enforcement;Fitting;Urban areas|
|[Malicious URL Detection and Classification Analysis using Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053422)|U. S. D. R; A. Patil; Mohana|10.1109/IDCIoT56793.2023.10053422|URL Detection;Cybersecurity;Machine Learning;URL classification;Phishing;Benign;Defacement;Malware;Uniform resource locators;Training;Machine learning algorithms;Phishing;Unsolicited e-mail;Training data;Search engines;Malware;Random forests;Web search|
|[Detection and Analysis of Faults in Transformer using Machine Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052786)|A. Balan; S. T. L; M. P. V; K. Deepa|10.1109/IDCIoT56793.2023.10052786|Transformer;Fault;Machine Learning;Classifier;Overcurrent Relay;Time Series Plot;Magnetic Oil Gauge;Oils;Biological system modeling;Windings;Oil insulation;Transformers;Circuit faults;Decision trees|
|[Non - Contact Heart Rate Monitoring System using Deep Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053467)|A. H. Malini; S. G; S. K. G; S. G|10.1109/IDCIoT56793.2023.10053467|Heart Rate;ROI;CNN;Face detection;object tracking;Heart rate;Deep learning;Face recognition;Signal processing algorithms;Streaming media;Signal processing;Object tracking|
|[AI-Powered Student Assistance Chatbot](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053439)|A. Bhharathee; S. Vemuri; B. Bhavana; K. Nishitha|10.1109/IDCIoT56793.2023.10053439|Chatbot;Natural Language Understanding;WordPress;Botpress;WAMP (Windows-Apache-MySQL-PHP) server;Chatbots;Software;Servers;Internet of Things;Data communication|
|[Smart Dimensional Measurement and Material Transportation (SDMMT) System using Artificial Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053429)|C. M. Raj; W. R. Babu; K. Uday; R. Senthilkumar; V. Sudha; V. Anandhakumar|10.1109/IDCIoT56793.2023.10053429|Logistics;Volumetric charge;PLC;HMI;RV-2FB-Q robotic arm;Weight measurement;Service robots;Ultrasonic variables measurement;Volume measurement;Robot sensing systems;Manipulators;Sensor systems|
|[Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053387)|N. R. Behera; M. Umaselvi; D. Devarajan; B. J. Komathi; P. Parmar; R. Kumar Gupta|10.1109/IDCIoT56793.2023.10053387|Deep learning;Lymph disease;Data classification;Machine learning;Parameter tuning;Deep learning;Neural networks;Cells (biology);Data models;Feeds;Root mean square;Prognostics and health management|
|[Crevices Recognition on Asphalt Surfaces using Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053463)|M. Chinta; A. Likhita; Y. Aravapalli|10.1109/IDCIoT56793.2023.10053463|Crack Detection;Convolution Neural Network;Machine Learning;Road Safety;Structural panels;Urban areas;Public infrastructure;Machine learning;Inspection;Road safety;Safety|
|[Tuning Artificial Neural Network for Healthcare 4.0. by Sine Cosine Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053543)|N. Milutinovic; S. Cabarkapa; M. Zivkovic; M. Antonijevic; D. Mladenovic; N. Bacanin|10.1109/IDCIoT56793.2023.10053543|swarm intelligence;metaheuristic optimization;healthcare;sine cosine algorithm;multi-layer perceptron;Support vector machines;Cloud computing;Machine learning algorithms;Metaheuristics;Medical services;Artificial neural networks;Machine learning|
|[Artificial Neural Network (ANN) Enabled Weather Monitoring and Prediction System using IoT](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053534)|P. G. Krishna; K. Chandra Bhanu; S. A. Ahamed; M. Umesh Chandra; N. Prudhvi; N. Apoorva|10.1109/IDCIoT56793.2023.10053534|Artificial Neural Networks;Internet of Things (IoT);Weather monitoring;Prediction;ThingSpeak;Cloud;Cloud computing;Neurons;Artificial neural networks;Sensor systems;Sensors;Mobile applications;Internet of Things|
|[A Review of Convolutional Neural Network-based Approaches for Disease Detection in Plants](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053428)|B. Biswas; R. K. Yadav|10.1109/IDCIoT56793.2023.10053428|Artificial Intelligence(AI);Convolutional Neural Network(CNN);Deep Learning(DL);Machine Learning(ML);Plant Disease Detection;Transfer Learning;Deep learning;Plant diseases;Sociology;Learning (artificial intelligence);Convolutional neural networks;Internet of Things;Task analysis|
|[Artificial Intelligence in Children with Special Need Education](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053420)|C. H. Neeharika; Y. M. Riyazuddin|10.1109/IDCIoT56793.2023.10053420|Special need Education;Artificial Intelligent;Autism Spectrum Disorder;Machine Learning;Support vector machines;Pediatrics;Education;Employment;Functional magnetic resonance imaging;Feature extraction;Internet of Things|
|[Maximizing the Net Present Value of Resource-Constrained Project Scheduling Problems using Recurrent Neural Network with Genetic Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053390)|T. Phuntsho; T. Gonsalves|10.1109/IDCIoT56793.2023.10053390|Recurrent Neural Networks (RNN);net present value;genetic algorithm;project scheduling;capital budgeting;Recurrent neural networks;Transfer learning;Metaheuristics;Software algorithms;Computer architecture;Transformers;Software|
|[Detection and Classification of License Plate by Neural Network Classifier](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053459)|S. Chalnewad; A. Manjaramkar|10.1109/IDCIoT56793.2023.10053459|Preprocessing;Horizontal and Vertical Line detection;Neural Network Classifier;Image segmentation;Roads;Neural networks;Feature extraction;Real-time systems;Security;Internet of Things|
|[Identification of Hate Speech and Offensive Content using BI-GRU-LSTM-CNN Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053415)|S. Shubhang; S. Kumar; U. Jindal; A. Kumar; N. R. Roy|10.1109/IDCIoT56793.2023.10053415|Hate Speech;Natural Language Processing;Support Vector Machine;Deep Learning;Machine learning;Text Classification;Social-Media;Sentiment Analysis;Systematic Review;Bi-GRU-LSTM-CNN;Vietnamese;Social Media Text;Analytical models;Social networking (online);Hate speech;Government;Support vector machine classification;Media;Data models|
|[The Java Framework Construction of the Intelligent Information System of University Scientific Research](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052785)|D. Pang; Y. Bian; Y. Wang; X. Zhang; H. Qu|10.1109/IDCIoT56793.2023.10052785|Java;Framework Construction;Intelligent Information System;Scientific Research;Java;Structured Query Language;Databases;Social networking (online);Management information systems;Writing;Media|
|[Design of Combination Optimization Algorithm for the Development of AdaBoost New Media Networking Small and Medium-sized Enterprises Network Marketing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053552)|L. Lin|10.1109/IDCIoT56793.2023.10053552|Combination Optimization Algorithm;AdaBoost;New Media Networking;Small and Medium-sized Enterprises;Network Marketing;Visualization;Media;Linguistics;Ensemble learning;Labeling;Internet of Things;Data communication|
|[Development of Mobile Software for College Talent Acquisition using Achievement Database Mining](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053503)|Y. Wang|10.1109/IDCIoT56793.2023.10053503|Achievement database mining;mobile software;talent cultivation;production in colleges;data structure;Databases;Delay effects;Production;Organizations;Software;Data mining;Iterative methods|
|[Smart Vehicle Status Service Guarantee Framework Integrating Vehicle-Machine Smart Modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053469)|Y. Wang|10.1109/IDCIoT56793.2023.10053469|Smart Vehicle;Status Service;Guarantee Framework;Vehicle-Machine;Smart Modules;Systematics;Speech coding;Radio navigation;Speech recognition;Gesture recognition;Telematics;Market research|
|[Internet Public Opinion Mining and Data Classification Model for Rural Anti-Poverty in Minority Areas based on Social Capital Online Screening Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10053445)|P. Ye|10.1109/IDCIoT56793.2023.10053445|Internet Public;Opinion Mining;Data Classification;Rural Anti-Poverty;Social Capital Online Screening;Analytical models;Sentiment analysis;Area measurement;Sociology;Data models;Object recognition;Reliability|
#### **2023 Fourth International Symposium on 3D Power Electronics Integration and Manufacturing (3D-PEIM)**
- DOI: 10.1109/3D-PEIM55914.2023
- 1-3 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[NMOS/NLDMOS LSS dead-time minority carrier isolation optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052175)|G. Liu; O. Causse|10.1109/3D-PEIM55914.2023.10052175|Synchronous Step-down converters;isolation ring;MAAP;dead-time;channel conduction;Costs;Three-dimensional displays;Simulation;Silicon;Power electronics;Transistors;Reliability|
|[Laser-induced graphene supercapacitors on flex substrates for package-integrated power supply](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052332)|R. Banerjee; A. H. Chowdhury; P. S. Kumar; C. Wang; S. Goel; P. M. Raj|10.1109/3D-PEIM55914.2023.10052332|supercapacitors;graphene;laser-induced graphene;Power system measurements;Three-dimensional displays;Surface waves;Power supplies;Graphene;Surface emitting lasers;Supercapacitors|
|[Design Considerations for 48-V VRM: Architecture, Magnetics, and Performance Tradeoffs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052608)|M. Chen; S. Jiang; J. A. Cobos; B. Lehman|10.1109/3D-PEIM55914.2023.10052608|voltage regulation module (VRM);power architecture;magnetics;packaging;switched-capacitor;transformer;Power system measurements;Three-dimensional displays;Regulators;Computer architecture;Switches;Packaging;Transformers|
|[New Design Concepts for PCB-Integration Technology in Power Electronics reducing Circuit Parasitics to a Minimum](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052616)|R. Raßmann; J. Schnack; K. Gripp; U. Schümann|10.1109/3D-PEIM55914.2023.10052616|Silicon Carbide (SiC);3-D Layout Structures;Power Module;Parasitic Elements;Semiconductor device measurement;Three-dimensional displays;Power measurement;Silicon carbide;Wires;Printed circuits;Multichip modules|
|[Class I Multi-Layer Ceramic Capacitors (MLCCs) Performance as Wide Band Gap (WBG) Snubbers in Hard Switching Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052607)|A. Templeton; N. Reed; H. Hayes; J. Davis; J. Bultitude|10.1109/3D-PEIM55914.2023.10052607|Multi-Layer Ceramics Capacitors (MLCCs);Wide Band Gap (WBG);Snubber;Hard Switching;high dV/dt;Performance evaluation;Three-dimensional displays;Photonic band gap;Switching frequency;Snubbers;Switches;Ceramic capacitors|
|[Laminate-Embedded Multimodal Energy Harvester for Multilevel Power Supply](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052323)|J. A. Caripidis Troccola; S. Gupta; M. Carvalho; S. B. Venkatakrishnan; P. M. Raj; J. L. Volakis|10.1109/3D-PEIM55914.2023.10052323|Power harvesting;3D power packaging;laminate;embedding;RF;power;solar;Radio frequency;Technological innovation;Three-dimensional displays;Power supplies;Stacking;Voltage;Packaging|
|[Low-Frequency Power Telemetry Using Multiferroic Laminate Heterostructures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052239)|V. Jaiswal; P. Gaire; S. Bhardwaj; A. Y. Hassan; J. L. Volakis; P. M. Raj|10.1109/3D-PEIM55914.2023.10052239|Piezoelectric;magnetostrictive;multiferroic;magneto-electric coupling;Power telemetry;Radio frequency;Power system measurements;Three-dimensional displays;Magnetostriction;Magnetoelectric effects;Rectifiers;Wireless power transmission|
|[Power Systems on Chiplet: Inductor-Linked Multi-Output Switched-Capacitor Multi-Rail Power Delivery on Chiplets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052630)|M. Liao; D. H. Zhou; P. Wang; M. Chen|10.1109/3D-PEIM55914.2023.10052630|voltage regulation module (VRM);power architecture;magnetics;packaging;switched-capacitor;transformer;Rails;Power system measurements;Three-dimensional displays;Regulators;Switches;Computer architecture;High-voltage techniques|
|[Cold-sprayed aluminum capacitors on leadframes for 3D power packaging](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052633)|R. Banerjee; D. John; C. Zhang; A. Agarwal; P. Raj|10.1109/3D-PEIM55914.2023.10052633|capacitors;power packaging;power modules;cold-spray;aluminum;Process design;Three-dimensional displays;Capacitors;Aluminum;Surface morphology;Lead;Power electronics|
|[Reticular Graphene Reinforced Copper for Electromagnetic Shielding Application](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052591)|A. Nisar; G. A. Duhni; C. Zhang; P. M. Raj; A. Agarwal|10.1109/3D-PEIM55914.2023.10052591|Metal Matrix Composites (MMCs);Graphene Foam (GrF);Spark Plasma Sintering (SPS);Layered Structure;Interface;Electromagnetic (EM) Shielding;Performance evaluation;Electromagnetic heating;Three-dimensional displays;Graphene;Microwave theory and techniques;Power electronics;Object recognition|
|[Overview of Power Electronic Converters in Electric Vehicle Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052532)|S. M. S. H. Rafin; R. Islam; O. A. Mohammed|10.1109/3D-PEIM55914.2023.10052532|Power Converters;Electric Vehicles;Third Harmonic Injection;Multi-level Inverter;Three-dimensional displays;Rectifiers;Mechanical power transmission;DC-DC power converters;Power system harmonics;Electric vehicles;Harmonic analysis|
|[Wide Band Gap Semiconductor Devices for Power Electronic Converters](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052586)|S. M. S. H. Rafin; R. Ahmed; O. A. Mohammed|10.1109/3D-PEIM55914.2023.10052586|Power converters;Wide band gap (WBG);Smart grid;SiC;GaN;Performance evaluation;Three-dimensional displays;Silicon carbide;Photonic band gap;Switches;Silicon;Power semiconductor devices|
|[A Review of Power Electronic Converters for Electric Aircrafts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052535)|S. M. S. H. Rafin; M. A. Haque; R. Islam; O. A. Mohammed|10.1109/3D-PEIM55914.2023.10052535|Electric Aircraft;Power Electronic Converters;More Electric Aircraft;Electric Propulsion Aircraft;Airplanes;Three-dimensional displays;Voltage;System integration;Aerospace electronics;Power electronics;Safety|
|[Power Electronic Converters for Wind Power Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052364)|S. M. S. H. Rafin; R. Islam; O. A. Mohammed|10.1109/3D-PEIM55914.2023.10052364|Wind Power;DC-DC Converter;Matrix Converter;Multilevel converters;Three-dimensional displays;Wind energy;DC-DC power converters;Wind power generation;Power electronics;Wind turbines|
|[EMI Shielding Performance of Thin and Thick Graphene Films Placed Within Integrated Power Modules](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052133)|G. A. Duhni; J. L. Volakis; P. M. Raj|10.1109/3D-PEIM55914.2023.10052133|Graphene films/Shielding Effectiveness;PMIC;Power inductors;PIFA;Three-dimensional displays;Films;Scalability;Graphene;Electromagnetic interference;Conductivity;Inductors|
|[Inverter/converter power density and flexibility improvements through modularity and novel thermal management architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052206)|I. Byers; S. N. Wooters|10.1109/3D-PEIM55914.2023.10052206|Inverter;Converter;Power Density;Thermal Management;Silicon Carbide;Thermal management of electronics;Power system measurements;Technological innovation;Three-dimensional displays;Silicon carbide;Density measurement;Architecture|
|[Reliability Analysis of Wireless Power Transfer for Electric Vehicle Charging based on Continuous Markov Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052353)|M. Behnamfar; M. A. Taher; A. Polowsky; S. Roy; M. Tariq; A. Sarwat|10.1109/3D-PEIM55914.2023.10052353|Reliability;Markov process;Failure rate;Wireless power transfer;Electric vehicle (EV);Wireless communication;Three-dimensional displays;Rectifiers;Wireless power transfer;Markov processes;Inverters;Electric vehicle charging|
#### **2023 6th World Conference on Computing and Communication Technologies (WCCCT)**
- DOI: 10.1109/WCCCT56755.2023
- DATE: 6-8 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Reconfigurable Intelligent Surface Aided DFRC Vehicular Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052208)|J. Feng; P. Zhang; L. Huang; G. Qian|10.1109/WCCCT56755.2023.10052208|RIS;DFRC;Vehicular networks;N-LoS;Array signal processing;Vehicle-to-infrastructure;Simulation;Channel estimation;Radar detection;Radar;Downlink|
|[Stackelberg Game Based Resource Allocation Algorithm for Federated Learning in MEC Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052172)|X. Tang; Y. Wang; R. Huang; G. Chen; L. Wang|10.1109/WCCCT56755.2023.10052172|federated learning;incentive mechanism;mobile edge computing;resource allocation;Energy consumption;Federated learning;Computational modeling;Simulation;Games;Delays;Resource management|
|[Review of 5G MIMO Enhancement Technologies](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052403)|H. Xu; J. Xin; S. Xu; H. Zhang; S. Xiong|10.1109/WCCCT56755.2023.10052403|full power transmission;multi-TRP enhancement;MU-MIMO enhancement;multi-beam enhancement;5G mobile communication;Spectral efficiency;Wireless networks;Throughput;Resource management;Reliability;Uplink|
|[Sum Rate Maximization for UAV Assisted NOMA Backscatter Communication System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052259)|X. Gan; Y. Jiang; Y. Wang; D. Hong; Z. Wang|10.1109/WCCCT56755.2023.10052259|backscatter communication;unmanned aerial vehicle (UAV);non-orthogonal multiple access (NOMA);sum rate;NOMA;Processor scheduling;Simulation;Logic gates;Benchmark testing;Autonomous aerial vehicles;Scheduling|
|[Research on Key Technologies of High Precision Millimeter Wave Radar Ranging System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052457)|W. Gu; B. Feng; X. Wang; X. Zou|10.1109/WCCCT56755.2023.10052457|high precision measurement;millimeter wave radar;FMCW;digital signal processing;Antenna measurements;Millimeter wave measurements;Signal processing algorithms;Digital signal processing;Millimeter wave radar;Radar antennas;Distance measurement|
|[Routing Architecture Design for the Space-Ground Integrated Information Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052483)|X. Xu; C. Dong; J. Cai|10.1109/WCCCT56755.2023.10052483|space-ground integrated information network;routing architecture;border gateway protocol;autonomous system;interior gateway protocols;exterior gateway protocols;Satellites;Scalability;Computer architecture;Logic gates;Routing;Communications technology;Border Gateway Protocol|
|[Optimal Game Routing for UAV Adhoc Networks in Smart City](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052140)|Saifullah; Z. Ren; K. H. Mohammadani; W. Riaz|10.1109/WCCCT56755.2023.10052140|UAV ad-hoc network;centrality measure;game theory;internet of things;Smart cities;Wireless networks;Games;Benchmark testing;Autonomous aerial vehicles;Routing;Routing protocols|
|[IDEAL: Intent Driven Emerging Anti-congestion Router with Load-Balance for SDN](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052261)|J. Guo; C. Yang; F. Li; R. Dong; Y. Song; S. Kou|10.1109/WCCCT56755.2023.10052261|anti-congestion router;deep learning;intent-driven network;network management;Knowledge engineering;Switches;Manuals;Routing;Load management;Stability analysis;Routing protocols|
|[A Framework for Decoupling Overlay SDN and Computing Virtualization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052205)|B. Lin; L. Wu; Z. Huang; Y. Liu; Y. Li; Y. Fan|10.1109/WCCCT56755.2023.10052205|SDN;computing virtualization;decoupling;heterogeneous;interoperability;Cloud computing;Data centers;Telecommunication traffic;Virtual machining;Synchronization;Virtualization;Software defined networking|
|[A 2.4 GHz SiGe Envelope Tracking Power Amplifier for LTE Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052136)|Y. Jing; B. Zhu|10.1109/WCCCT56755.2023.10052136|RF power amplifier;envelope tracking;hybrid envelope amplifier;SiGe BiCMOS;Radio frequency;Power supplies;Power amplifiers;Modulation;Switches;Envelope detectors;Long Term Evolution|
|[High Angle Stability Frequency Selective Surface Design and Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052549)|L. Feng; Y. Zhen; P. Xiaoyu|10.1109/WCCCT56755.2023.10052549|frequency selective surface;equivalent circuit;bandwidth compensation;high angle stability;waveguide slot antenna;Frequency selective surfaces;Analytical models;Slot antennas;Radar cross-sections;Simulation;Electromagnetic waveguides;Stability analysis|
|[Multiple Flipping Strategy LISBF for LDPC Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052347)|Y. Sun; P. Huang; Y. Zhang; Z. Cheng|10.1109/WCCCT56755.2023.10052347|Low-density parity-check codes;bit flipping;flipping strategy;layered decoding;trapping set;Multiplexing;Simulation;Perturbation methods;Throughput;Hardware;Decoding;Complexity theory|
|[A Continue-Domain Sparse-Based Channel Estimation Method in Power Tunnel Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052431)|W. Liu; T. Liang; T. Guan; K. Zhang; W. Wang; D. Wang|10.1109/WCCCT56755.2023.10052431|power tunnel;channel estimation;grid-free method;orthogonal frequency division multiplexing;normalized mean squared error;bit error rate;Wireless communication;Minimization methods;Simulation;OFDM;Bit error rate;Channel estimation;Estimation|
|[Weight Discretized BP Algorithm Based on Synapse Transistor with Symmetric/Asymmetric Memory Curve](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052598)|S. Chen; L. Han; K. Sun; D. Luo; Y. -M. Wang; D. -L. Yu; Y. -T. Li|10.1109/WCCCT56755.2023.10052598|synaptic transistor;simulate;evaluate;discreteness;symmetry;asymmetry;non-linearity;BP neural network;Performance evaluation;Backpropagation;Hardware;Communications technology;Transistors;Character recognition;Artificial intelligence|
|[Power Allocation in Cell-Free mmWave Massive MIMO: Using Deep Deterministic Policy Gradient](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052585)|Y. Zhao; F. Zhang; Y. Gao; C. Fu|10.1109/WCCCT56755.2023.10052585|Cell-free massive MIMO;mmWave;deep deterministic policy gradient;power allocation;Simulation;Neural networks;Massive MIMO;Reinforcement learning;Mean square error methods;Minimax techniques;MIMO|
|[Joint Rate and Resource Allocation for Panoramic Videos over Future Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052627)|R. Deng|10.1109/WCCCT56755.2023.10052627|panoramic video;quality model;joint optimization;resource allocation;rate adaptation;Fluctuations;Simulation;Streaming media;Heterogeneous networks;Quality assessment;Delays;Resource management|
|[Timestamp Free Synchronization with Clock Skew Estimation in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052472)|S. Wang; L. Sun|10.1109/WCCCT56755.2023.10052472|time synchronization;wireless sensor networks;clock skew;one-way communication;timestamp-free;Wireless sensor networks;Maximum likelihood estimation;Power demand;Simulation;Spread spectrum communication;Delays;Synchronization|
|[Research on Intelligent Monitoring of Offshore Wind Turbine Environment Based on Wireless Transmission and Distributed Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052405)|Z. Wu; Y. Yao; S. Li; C. Yu; C. Chen; T. Huang|10.1109/WCCCT56755.2023.10052405|Intelligent monitoring;wireless transmission;distributed computing;Wireless communication;Performance evaluation;Analytical models;Computational modeling;Wind power generation;Probability;Mobile handsets|
|[RIS Relaying UAV-Aided WPCN for Throughput Maximization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052414)|X. Pan; Z. Zhang; H. Lin; J. Zhang|10.1109/WCCCT56755.2023.10052414|Reconfigurable intelligent surface (RIS);un-manned aerial vehicle (UAV);wireless powered communication network (WPCN);optimal placement;reflective beamforming;Wireless communication;Array signal processing;Performance gain;Autonomous aerial vehicles;Throughput;Approximation algorithms;Communications technology|
|[Distributed Phase Calibration for Massive OAM Backhauling in 5G IoT Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052446)|X. Wang; Y. Zhang; Z. Li|10.1109/WCCCT56755.2023.10052446|IOT;UCA;OAM;phase deviation calibration;signaling interaction;Orbital calculations;Costs;5G mobile communication;Simulation;Phase shifters;Communications technology;Mathematical models|
|[Intent-Driven Internet of Things: Architectures, Technology, and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052382)|L. Zhang; R. Dong; F. Li; J. Zhang; J. Zhang; C. Yang|10.1109/WCCCT56755.2023.10052382|intent-driven networks;internet of things;configuration and management;Industries;Human computer interaction;Configuration management;Computer architecture;User experience;Communications technology;Complexity theory|
|[A Method of Counteracting Main Lobe Deception Jamming with Frequency Diverse Array](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052279)|J. Yan; Y. Ju; S. Wang; X. Feng; J. Xiao; J. Luo|10.1109/WCCCT56755.2023.10052279|frequency diverse array;main lobe deception jamming;jamming nulling;transmitting antenna pattern;target identification;Phased arrays;Computer simulation;Radar;Radar antennas;Frequency diversity;Communications technology;Jamming|
|[A Low Noise and Spur Sub-sampling Phase Locked Loop Based on Clock System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052425)|Y. Yang; S. Song; M. Cen; P. Jiang; C. Cai|10.1109/WCCCT56755.2023.10052425|charge pump;phase locked loop;Sub-sampling phase locked loop;low noise;low reference spur;Phase noise;Semiconductor device modeling;Charge pumps;Power demand;Voltage-controlled oscillators;Simulation;Layout|
|[A Chinese Speech Recognition System Based on Binary Neural Network and Pre-processing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052123)|L. Guo; Y. Deng; L. Tang; R. Fan; B. Yan; Z. Xiao|10.1109/WCCCT56755.2023.10052123|voice activate detection;binary weights neural network;speech recognize;and edge compute;Power demand;Convolution;Error analysis;Computational modeling;Neural networks;Speech recognition;Real-time systems|
|[Online Distribution Method of Application Key Based on AKMA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052464)|J. Li; J. Wang; C. Huang|10.1109/WCCCT56755.2023.10052464|5G;AKMA;authentication;key management;key update;security;5G mobile communication;Authentication;Distributed databases;Passwords;Network security;Encryption;Security|
|[Deterministic Convergence of Backpropagation Algorithm with Cyclic Dropconnect for Linear Output Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052295)|J. Jing; Z. Wang; H. Zhang|10.1109/WCCCT56755.2023.10052295|deterministic convergence;backpropagation algorithm;DropConnect;linear output neural networks;Simulation;Neural networks;Backpropagation algorithms;Cost function;Communications technology;Convergence|
|[Towards A Strategy for Developing a Project Partner Recommendation System for University Course Projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052282)|C. Victor Obionwu; D. Singh Walia; T. Tiwari; T. Ghosh; D. Broneske; G. Saake|10.1109/WCCCT56755.2023.10052282|Recommendation Systems;Big Five;Collaboration in Teams;Industries;Government policies;Electric breakdown;Collaborative filtering;Bibliographies;Collaboration;Predictive models|
|[CIMAX-Compiler: An End-to-End ANN Compiler for Heterogeneous Computing-in-Memory Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052488)|C. Yang; Y. Wang; L. Wu; X. Qiu|10.1109/WCCCT56755.2023.10052488|compiler;computing-in-memory;artificial neural network;embedded system;Codes;Embedded systems;Computational modeling;Image edge detection;Artificial neural networks;Speech recognition;Hardware|
|[Semantic Relatedness: A Strategy for Plagiarism Detection in SQL Assignments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052438)|C. V. Obionwu; R. Kumar; S. Shantharam; D. Broneske; G. Saake|10.1109/WCCCT56755.2023.10052438|plagiarism;SQL;plagiarism detection;Computer science;Structured Query Language;Codes;Plagiarism;Semantics;Relational databases;Natural language processing|
|[The Analysis of Mobility Patterns during the COVID-19 Pandemic in Thailand Using Time Series Clustering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052489)|W. Supanich; S. Kulkarineetham; B. Vanishkorn|10.1109/WCCCT56755.2023.10052489|human mobility patterns;transportation behaviors;time series clustering;dynamic time warping;COVID-19 pandemic;COVID-19;Economics;Pandemics;Soft sensors;Time series analysis;Government;Market research|
|[A Study of Mental Health Self-Monitoring Based on the Combination of BERT and Low-Code Platform](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052392)|T. Chen; L. Song; H. Zhou; Y. Li; H. Wang; C. Kong|10.1109/WCCCT56755.2023.10052392|mental health;BERT;low-code;sentiment analysis;Training;Weight measurement;Analytical models;Sentiment analysis;Bit error rate;Training data;Mental health|
|[Automotive Mixed Criticality DAG Function Scheduling Optimization Based on Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052398)|T. Wang; Y. Zou; X. Zhang; J. Liu; J. Wu|10.1109/WCCCT56755.2023.10052398|mixed-criticality system;edge computing;weighted completion index;Weight measurement;Processor scheduling;Computational modeling;Optimization methods;Transportation;Real-time systems;Indexes|
|[High Performance and Hardware Efficient Stochastic Computing Elements for Deep Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052402)|S. Hu; K. Han; J. Hu|10.1109/WCCCT56755.2023.10052402|Stochastic Computing;Machine Leaning;Deep Neural Network;Deep learning;Neural networks;Hardware;Encoding;Communications technology|
|[Snapshot Ensemble One-Dimensional Convolutional Neural Networks for Ballistic Target Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052300)|Q. Xiang; X. Wang; J. Lai; Y. Song; J. He; L. Lei|10.1109/WCCCT56755.2023.10052300|ballistic missiles;target recognition;high-resolution range profile;convolutional neural networks;snapshot ensemble;stochastic gradient descent;Training;Annealing;Costs;Target recognition;Computational modeling;Predictive models;Computational efficiency|
|[Remote Traffic Light Detection and Recognition Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052610)|M. DeRong; T. ZhongMei|10.1109/WCCCT56755.2023.10052610|deep learning;target detection;traffic light detection;YOLOv5;Target recognition;Filtering;Roads;Object detection;Predictive models;Prediction algorithms;Mobile handsets|
|[Hazardous Behavior Identification Based on BIM and AutoML Applied to Prefabricated Construction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10052373)|N. Hou|10.1109/WCCCT56755.2023.10052373|AutoML;object identification;BIM;engineering supervision;engineering safety;Training;Solid modeling;Head;Three-dimensional displays;Safety;Behavioral sciences;Personnel|
#### **2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC)**
- 16-19 Jan. 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[A Fast Semi-Analytical Approach for Transient Electromigration Analysis of Interconnect Trees using Matrix Exponential](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044796)|P. Stoikos; G. Floros; D. Garyfallou; N. Evmorfonoulos; G. Stamoulis|nan|Electromigration;Matrix exponential method;Krylov subspace;Electromigration;Integrated circuit technology;Industries;Design automation;Integrated circuit interconnections;Mathematical models;Power grids|
|[Chiplet Placement for 2.5D IC with Sequence Pair Based Tree and Thermal Consideration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044783)|H. -W. Chiou; J. -H. Jiang; Y. -T. Chang; Y. -M. Lee; C. -W. Pan|nan|2.5D IC;chiplet placement;sequence pair;thermal;Integrated circuits;Design automation;Art;Asia|
|[An On-line Aging Detection and Tolerance Framework for Improving Reliability of STT-MRAMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044802)|Y. -G. Chen; P. -Y. Huang; J. -F. Li|nan|STT-MRAM;reliability enhancement;TDDB;aging detection mechanism;aging tolerance mechanism;Resistance;Analytical models;Aging;Reliability engineering;System-on-chip;Sensors;Reliability|
|[Automated Equivalence Checking Method for Majority based In-Memory Computing on ReRAM Crossbars](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044822)|A. Deb; K. Datta; M. Hassan; S. Shirinzadeh; R. Drechsler|nan|Boolean Satifiability (SAT);In-Memory Computing;ReRAM cross-bar;Verification;Fabrication;Systematics;Design automation;Design methodology;Manuals;Inspection;In-memory computing|
|[An Equivalence Checking Framework for Agile Hardware Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044842)|Y. Wang; F. Xie; Z. Yang; P. Cocchini; J. Yang|nan|Equivalence Checking;Halide;Agile Hardware;Formal Verification;Deep learning;Design automation;Image processing;Asia;Hardware;Hardware acceleration;Domain specific languages|
|[Towards High-Bandwidth-Utilization SpMV on FPGAs via Partial Vector Duplication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044864)|B. Liu; D. Liu|nan|SpMV;FPGA;Vector Duplication;Bandwidth Utilization;Design automation;Redundancy;Asia;Bandwidth;System-on-chip;Registers;Sparse matrices|
|[Safety-driven Interactive Planning for Neural Network-based Lane Changing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044807)|X. Liu; R. Jiao; B. Zheng; D. Liang; Q. Zhu|nan|autonomous driving;neural networks;human-robot interaction;Training;Design automation;Asia;Companies;Planning;Safety;Trajectory|
|[Safety-Aware Flexible Schedule Synthesis for Cyber-Physical Systems using Weakly-Hard Constraints](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044808)|S. Xu; B. Ghosh; C. Hobbs; P. S. Thiagarajan; S. Chakraborty|nan|nan;Schedules;Design automation;Processor scheduling;Autonomous systems;Process control;Cyber-physical systems;Control systems|
|[Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044851)|P. -C. Chen; X. Liu; C. -W. Lin; C. Huang; Q. Zhu|nan|Connected and autonomous vehicles;intersection management;mixed-traffic;Schedules;Connected vehicles;Regulators;Design automation;Asia;Linear programming;Dynamic programming|
|[Fully Automated Machine Learning Model Development for Analog Placement Quality Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044865)|C. -C. Chang; J. Pan; Z. Xie; Y. Li; Y. Lin; J. Hu; Y. Chen|nan|nan;Measurement;Design automation;Scalability;Layout;Machine learning;Voltage;Predictive models|
|[Efficient Hierarchical mm-Wave System Synthesis with Embedded Accurate Transformer and Balun Machine Learning Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044838)|F. Passes; N. Lourenço; L. Mendes; R. Martins; J. Vaz; N. Horta|nan|automated design;baluns;bottom-up methodologies;machine learning;multiobjective;transformers;mm-Wave circuits;Semiconductor device modeling;Performance evaluation;Transmitters;Surface waves;Baluns;Scalability;Machine learning|
|[APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors using DNN Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044833)|A. F. Budak; D. Smart; B. Swahn; D. Z. Pan|nan|nan;Deep learning;Design automation;Costs;Computational modeling;Neural networks;Optimization methods;Computer architecture|
|[ML to the Rescue: Reliability Estimation from Self-Heating and Aging in Transistors all the Way up Processors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044828)|H. Amrouch; F. Klemme|nan|Circuit reliability;transistor self-heating;transistor aging;machine learning;library characterization;CAD;Degradation;Maximum likelihood estimation;Three-dimensional displays;Design automation;Thermal resistance;Reliability engineering;Timing|
|[Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044847)|L. Alrahis; J. Knechtel; O. Sinanoglu|nan|nan;Integrated circuits;Training;Design automation;Computational modeling;Pipelines;Computer architecture;Reliability engineering|
|[Detection and Classification of Malicious Bitstreams for FPGAs In Cloud Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044810)|J. Chaudhuri; K. Chakrabarty|nan|nan;Design automation;Computational modeling;Feature extraction;Encryption;Security;Convolutional neural networks;Circuit faults|
|[Learning Based Spatial Power Characterization and Full-Chip Power Estimation for Commercial TPUs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044754)|J. Lu; J. Zhang; W. Jin; S. Sachdeva; S. X. . -D. Tan|nan|nan;Deep learning;Temperature measurement;Power system measurements;Power measurement;Density measurement;Neural networks;Estimation|
|[DECC: Differential ECC for Read Performance Optimization on High-Density NAND Flash Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044797)|Y. Song; Y. Lv; L. Shi|nan|ECC;LDPC;3D NAND Flash;Read Performance;Fault tolerance;Three-dimensional displays;Costs;Fault tolerant systems;Parity check codes;Encoding;Error correction codes|
|[Optimizing Data Layout for Racetrack Memory in Embedded Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044861)|P. Hui; E. H. . -M. Sha; Q. Zhuge; R. Xu; H. Wang|nan|racetrack memory;shift operation;hybrid SPM;data layout;Performance evaluation;Costs;Embedded systems;Design automation;Nonvolatile memory;Layout;Asia|
|[Exploring Architectural Implications to Boost Performance for in-NVM B+-tree](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044845)|Y. Hu; Q. Jiang; C. Wang|nan|B+-tree;VIPT Cache;Non-volatile Memory;Design automation;Nonvolatile memory;Asia;Computer architecture;Writing;Performance gain;Minimization|
|[An Efficient Near-Bank Processing Architecture for Personalized Recommendation System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044823)|Y. Yang; W. Yang; Q. Wang; N. Jing; J. Jiang; Z. Mao; W. Sheng|nan|Recommendation system;Near-memory processing;Mapping scheme;HMC;Data centers;Design automation;Computational modeling;Random access memory;Bandwidth;Computer architecture;Performance gain|
|[PAALM: Power Density Aware Approximate Logarithmic Multiplier Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044875)|S. Yu; S. X. . -D. Tan|nan|nan;Image quality;Power system measurements;Density measurement;Image processing;Error compensation;Switches;Reliability engineering|
|[Approximate Floating-Point FFT Design with Wide Precision-Range and High Energy Efficiency](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044764)|C. Wen; Y. Wu; X. Yin; C. Zhuo|nan|FFT;pipeline;optimization;approximate computing;top-down;Fast Fourier transforms;Design methodology;Pipelines;Signal processing algorithms;Digital signal processing;Approximation algorithms;Energy efficiency|
|[RUCA: RUntime Configurable Approximate Circuits with Self-Correcting Capability](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044766)|J. Ma; S. Reda|nan|nan;Runtime;Power demand;Scalability;Approximate computing;Benchmark testing;Market research;Delays|
|[Approximate Logic Synthesis by Genetic Algorithm with an Error Rate Guarantee](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044756)|C. -T. Lee; Y. -T. Li; Y. -C. Chen; C. -Y. Wang|nan|nan;Power demand;Design automation;Error analysis;Asia;Approximate computing;Benchmark testing;Minimization|
|[Depth-optimal Buffer and Splitter Insertion and Optimization in AQFP Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044832)|A. T. Calvino; G. De Micheli|nan|nan;Design automation;Costs;Superconducting logic circuits;Optimization methods;Logic gates;Benchmark testing;Scheduling|
|[Area-Driven FPGA Logic Synthesis Using Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044776)|G. Zhou; J. H. Anderson|nan|nan;Training;Design automation;Asia;Reinforcement learning;Benchmark testing;Inference algorithms;Classification algorithms|
|[Optimization of Reversible Logic Networks with Gate Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044757)|Y. -C. Chen; F. -J. Chao|nan|nan;Costs;Qubit;Optimization methods;Transforms;Logic gates;Benchmark testing;Minimization|
|[Iris: Automatic Generation of Efficient Data Layouts for High Bandwidth Utilization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044781)|S. Soldavini; D. Sciuto; C. Pilato|nan|nan;Iris;Design automation;Spectral efficiency;Processor scheduling;Layout;Bandwidth;Manuals|
|[ViraEye: An Energy-Efficient Stereo Vision Accelerator with Binary Neural Network in 55 nm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044825)|Y. Zhang; G. Chen; T. He; Q. Huang; K. Huang|nan|nan;Measurement;Image sensors;Design automation;Costs;Neural networks;Estimation;Energy efficiency|
|[A 1.2nJ/Classification Fully Synthesized All-Digital Asynchronous Wired-Logic Processor Using Quantized Non-linear Function Blocks in 0.18µm CMOS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044869)|R. Sumikawa; K. Shiba; A. Kosuge; M. Hamada; T. Kuroda|nan|nan;Energy consumption;Design automation;Neurons;Artificial neural networks;Hardware;Energy efficiency;Hardware design languages|
|[A Fully Synthesized 13.7µJ/prediction 88% Accuracy CIFAR-10 Single-Chip Data-Reusing Wired-Logic Processor Using Non-Linear Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044870)|Y. -C. Hsu; A. Kosuge; R. Sumikawa; K. Shiba; M. Hamada; T. Kuroda|nan|FPGA;CNN;wired-logic;neural network;Wiring;Design automation;Neurons;Memory management;Asia;Energy efficiency;Hardware|
|[A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting Digital and Analog Projects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044768)|K. . -E. Harabi; C. Turck; M. Drouhin; A. Renaudineau; T. Bersani--Veroni; D. Querlioz; T. Hirtzlin; E. Vianello; M. Bocquet; J. . -M. Portal|nan|nan;Design automation;Neuromorphics;Asia;Memristors;Prototypes;Writing;CMOS technology|
|[A Fully Synchronous Digital LDO with Built-In Adaptive Frequency Modulation and Implicit Dead-Zone Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044800)|S. Yamaguchi; M. Islam; T. Hisakado; O. Wada|nan|Digital LDO;Adaptive clocking;Dead-zone control;Frequency modulation;Design automation;Asia;Process control;Voltage control|
|[Demonstration of Order Statistics Based Flash ADC in a 65nm Process](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044784)|M. Islam; T. Kitamura; T. Hisakado; O. Wada|nan|Comparator;flash ADC;offset voltage;order statistics;Voltage measurement;Power demand;Design automation;Asia;Linearity;Calibration;Tuning|
|[A SAT Encoding for Optimal Clifford Circuit Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044774)|S. Schneider; L. Burgholzer; R. Wille|nan|nan;Performance evaluation;Quantum algorithm;Design automation;Qubit;Logic gates;Search problems;Encoding|
|[An SMT-Solver-based Synthesis of NNA-Compliant Quantum Circuits Consisting of CNOT, H and T Gates](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044836)|K. Seino; S. Yamashita|nan|Nearest Neighbor Architecture (NNA) restriction;SMT-Solver;T gate;Don't Care Condition;Design automation;Qubit;Asia;Optimization methods;Computer architecture;Transforms;Logic gates|
|[Compilation of Entangling Gates for High-Dimensional Quantum Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044834)|K. Mato; M. Ringbauer; S. Hillmich; R. Wille|nan|nan;Quantum system;Program processors;Quantum entanglement;Qubit;Logic gates;Licenses;Hardware|
|[WIT-Greedy: Hardware System Design of Weighted ITerative Greedy Decoder for Surface Code](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044818)|W. Liao; Y. Suzuki; T. Tanimoto; Y. Ueno; Y. Tokunaga|nan|QEC;surface code;decoder;non-identical weights;FPGA;Codes;Error analysis;Qubit;Hardware;Decoding;Delays;Table lookup|
|[Quantum Data Compression for Efficient Generation of Control Pulses](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044752)|D. Volya; P. Mishra|nan|nan;Dimensionality reduction;Quantum system;Quantum algorithm;Design automation;Optimal control;Logic gates;Real-time systems|
|[Toward Energy-Efficient Sparse Matrix-Vector Multiplication with Near STT-MRAM Computing Architecture](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044772)|Y. LI; H. ZHANG; X. WANG; H. CAI; Y. ZHANG; S. LV; R. LIU; W. ZHAO|nan|nan;Torque;Simulation;Memory architecture;Random access memory;Bandwidth;Throughput;Sparse matrices|
|[RIMAC: An Array-level ADC/DAC-Free ReRAM-Based In-Memory DNN Processor with Analog Cache and Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044806)|P. Chen; M. Wu; Y. Ma; L. Ye; R. Huang|nan|In-Memory Computing;AI Circuit Design;Design automation;Nonvolatile memory;Digital-analog conversion;Neural networks;Lattices;Switches;Reconstruction algorithms|
|[Crossbar-Aligned & Integer-Only Neural Network Compression for Efficient In-Memory Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044780)|S. Huai; D. Liu; X. Luo; H. Chen; W. Liu; R. Subramaniam|nan|In-memory computing;pruning;quantization;neural networks;Training;Learning systems;Quantization (signal);Power demand;Design automation;Computational modeling;Design methodology|
|[Discovering the In-Memory Kernels of 3D Dot-Product Engines](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044787)|M. R. Haq Rashed; S. K. Jha; R. Ewetz|nan|nan;Three-dimensional displays;Design automation;Scientific computing;Wires;Two dimensional displays;Resistive RAM;Metals|
|[RVComp: Analog Variation Compensation for RRAM-based In-Memory Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044798)|J. He; Y. Huang; M. Lastras; T. T. Ye; C. -Y. Tsui; K. -T. Cheng|nan|Resistive random access memory;reliability;analog compensation;Degradation;Design automation;Simulation;Resistive RAM;Neural networks;Memristors;Programming|
|[Rethink before Releasing your Model: ML Model Extraction Attack in EDA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044762)|C. -C. Chang; J. Pan; Z. Xie; J. Hu; Y. Chen|nan|nan;Degradation;Training;Design automation;Computational modeling;Training data;Data models;Security|
|[MacroRank: Ranking Macro Placement Solutions Leveraging Translation Equivariancy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044786)|Y. Chen; J. Mai; X. Gao; M. Zhang; Y. Lin|nan|nan;Correlation coefficient;Measurement;Design automation;Asia;Predictive models;Benchmark testing;Routing|
|[BufFormer: A Generative ML Framework for Scalable Buffering](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044843)|R. Liang; S. Nath; A. Rajaram; J. Hu; H. Ren|nan|buffer insertion;timing optimization;interconnect optimization;generative model;machine learning;Steiner trees;Training;Correlation coefficient;Design automation;Scalability;Asia;Graphics processing units|
|[Decoupling Capacitor Insertion Minimizing IR-Drop Violations and Routing DRVs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044831)|D. Hyun; Y. Jung; I. Cho; Y. Shin|nan|Decoupling capacitor;dynamic IR-drop;design rule violation;machine learning;Runtime;Design automation;Heuristic algorithms;Capacitors;Metals;Switches;Routing|
|[DPRoute: Deep Learning Framework for Package Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044844)|Y. -H. Yeh; S. Y. -H. Chen; H. -M. Chen; D. -Y. Tu; G. -Q. Fang; Y. -C. Kuo; P. -Y. Chen|nan|substrate routing;deep learning;multi-agent reinforcement learning;Deep learning;Schedules;Design automation;Heuristic algorithms;Wires;Asia;Reinforcement learning|
|[High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044748)|S. Yin; G. Dai; W. W. Xing|nan|Yield Analysis;Bayesian Optimization;Failure Probability;Computational modeling;Random access memory;Machine learning;Parallel processing;SPICE;Entropy;Yield estimation|
|[MIA-aware Detailed Placement and VT Reassignment for Leakage Power Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044805)|H. -C. Lin; S. -Y. Fang|nan|nan;Degradation;Runtime;Power demand;Design automation;Asia;Minimization;Threshold voltage|
|[SLOGAN: SDC probability estimation using structured graph attention network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044760)|J. Ma; S. Huang; Z. Duan; L. Tang; L. Wang|nan|nan;Costs;Design automation;Computational modeling;Semantics;Asia;Estimation;Predictive models|
|[Microarchitecture Power Modeling via Artificial Neural Network and Transfer Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044792)|J. Zhai; Y. Cai; B. Yu|nan|nan;Training;Microarchitecture;Program processors;Design automation;Transfer learning;Asia;Artificial neural networks|
|[MUGNoC: A Software-configured Multicast-Unicast-Gather NoC for Accelerating CNN Dataflows](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044867)|H. Chen; D. Liu; S. Li; S. Huai; X. Luo; W. Liu|nan|Network-on-chips;CNN Dataflow;Parallel multipath transmission;Design automation;Unicast;System performance;Traffic control;Boosting;Routing;Software|
|[COLAB: Collaborative and Efficient Processing of Replicated Cache Requests in GPU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044750)|B. -W. Cheng; E. -M. Huang; C. -H. Chao; W. -F. Sun; T. -T. Yeh; C. -Y. Lee|nan|nan;Design automation;Asia;Graphics processing units;Collaboration;Network-on-chip;Bandwidth;Throughput|
|[Mixed-Criticality with Integer Multiple WCETs and Dropping Relations: New Scheduling Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044755)|F. Reghenzani; W. Fornaciari|nan|mixed-criticality;real-time;scheduling;fault tolerance;Fault tolerance;Schedules;Design automation;Scheduling algorithms;Computational modeling;Fault tolerant systems;Numerical simulation|
|[An Exact Schedulability Analysis for Global Fixed-Priority Scheduling of the AER Task Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044794)|T. Thilakasiri; M. Becker|nan|schedulability analysis;multi-core;real-time systems;Analytical models;Design automation;Asia;Automata;Parallel processing;Real-time systems;Task analysis|
|[Skyrmion Vault: Maximizing Skyrmion Lifespan for Enabling Low-Power Skyrmion Racetrack Memory](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044770)|S. -W. Lu; S. -H. Chen; Y. -P. Liang; Y. -H. Chang; K. Wang; T. -Y. Chen; W. -K. Shih|nan|nan;Energy consumption;Design automation;Art;Nonvolatile memory;Energy conservation;Memory management;Energy resolution|
|[Parallel Incomplete LU Factorization Based Iterative Solver for Fixed-Structure Linear Equations in Circuit Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044751)|L. Li; Z. Liu; K. Liu; S. Shen; W. Yu|nan|Circuit simulation;incomplete LU factorization;iterative equation solver;parallel computing;Scheduling algorithms;Circuit simulation;Mathematical models;Scheduling;Nonlinear circuits;Matrices;Iterative algorithms|
|[Accelerated Capacitance Simulation of 3-D Structures With Considerable Amounts of General Floating Metals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044871)|J. Huang; W. Yu; M. Song; M. Yang|nan|Capacitance Simulation;Floating Metal;Floating Random Walk (FRW) Method;Network Reduction;Solid modeling;Systematics;Design automation;Computational modeling;Metals;Capacitance;Conductors|
|[On Automating Finger-Cap Array Synthesis with Optimal Parasitic Matching for Custom SAR ADC](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044857)|C. -Y. Chiang; C. -L. Hu; M. P. -H. Lin; Y. -S. Chung; S. -J. Jou; J. -T. Wu; S. -h. W. Chiang; C. -N. J. Liu; H. -M. Chen|nan|parasitic effect;capacitance matching;placement;routing;common-centroid;analog-to-digital converter;linear programming;Power demand;Layout;Capacitors;Wires;Manuals;Programming;Routing|
|[FPGANeedle: Precise Remote Fault Attacks from FPGA to CPU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044803)|M. Gross; J. Krautter; D. Gnad; M. Gruber; G. Sigl; M. Tahoori|nan|FPGA-SoC;on-chip fault attack;voltage drop;differential fault attack;Performance evaluation;Design automation;Linux;Voltage;Side-channel attacks;Data transfer;Libraries|
|[FPGA Based Countermeasures Against Side channel Attacks on Block Ciphers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044830)|D. Jayasinghe; B. Udugama; S. Parameswaran|nan|side-channel;power analysis attacks;remote power analysis;fault injection;countermeasures;Ciphers;Design automation;Asia;Side-channel attacks;Logic gates;Power dissipation;Circuit faults|
|[Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044799)|R. Liu; C. Wei; Y. Yang; W. Wang; H. Yang; Y. Liu|nan|fully-quantized network training;mixed-precision quantization;neural network training acceleration;Training;Energy consumption;Quantization (signal);Sensitivity;Power demand;Design automation;Neural networks|
|[Quantization Through Search: A Novel Scheme to Quantize Convolutional Neural Networks in Finite Weight Space](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044839)|Q. Lu; W. Jiang; X. Xu; J. Hu; Y. Shi|nan|Convolutional Neural Network;Quantization;Weight search;Training;Deep learning;Quantization (signal);Design automation;Neural networks;Asia;Search problems|
|[Multi-Wavelength Parallel Training and Quantization-Aware Tuning for WDM-Based Optical Convolutional Neural Networks Considering Wavelength-Relative Deviations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044849)|Y. Zhu; M. Liu; L. Xu; L. Wang; X. Xiao; S. Yu|nan|CNN accelerator;WDM;device deviations;quantization errors;Training;Quantization (signal);Neural networks;Optical computing;Parallel processing;Optical fiber networks;Complexity theory|
|[Semantic Guided Fine-grained Point Cloud Quantization Framework for 3D Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044761)|X. Feng; C. Tang; Z. Zhang; W. Sun; Y. Liu|nan|nan;Point cloud compression;Quantization (signal);Three-dimensional displays;Image coding;Semantics;Object detection;Dynamic range|
|[ReMeCo: Reliable Memristor-Based In-Memory Neuromorphic Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044809)|A. BanaGozar; S. H. H. Shadmehri; S. Stuijk; M. Kamal; A. Afzali-Kusha; H. Corporaal|nan|Reliability;Neural Networks;Computation In-Memory;Neuromorphic;Memristor;Redundancy;Stuck-at-fault;Process Variation;Performance evaluation;Sensitivity analysis;Neuromorphic engineering;Computational modeling;Neurons;Termination of employment;Artificial neural networks|
|[SyFAxO-GeN: Synthesizing FPGA-based Approximate Operators with Generative Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044817)|R. Ranjan; S. Ullah; S. S. Sahoo; A. Kumar|nan|Approximate Computing;Arithmetic Operator Design;Circuit Synthesis;AI-based Exploration;Training;Degradation;Privacy;Design automation;Asia;Computer architecture;Generative adversarial networks|
|[Approximating HW Accelerators through Partial Extractions onto shared Artificial Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044848)|P. Chowdhury; J. C. Godínez; B. C. Schafer|nan|nan;Energy consumption;Power demand;Design automation;Asia;Approximate computing;Artificial neural networks;Computer architecture|
|[DependableHD: A Hyperdimensional Learning Framework for Edge-oriented Voltage-scaled Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044853)|D. Liang; H. Awano; N. Miura; J. Shiomi|nan|Hyperdimensional Computing;Voltage Scaling;Memory Failure;Noise Injection;Margin Enhancement;Training;Low voltage;Energy consumption;Voltage;Very large scale integration;Robustness;Hardware|
|[EDDY: A Multi-Core BDD Package With Dynamic Memory Management and Reduced Fragmentation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044753)|R. Krauss; M. Goli; R. Drechsler|nan|Boolean functions;binary decision diagrams;model checking;memory management;parallel computing;Boolean functions;Design automation;Instruction sets;Memory management;Model checking;Benchmark testing;Hardware|
|[Exploiting Reversible Computing for Verification: Potential, Possible Paths, and Consequences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044829)|L. Burgholzer; R. Wille|nan|nan;Reversible computing;Design automation;Circuits and systems;Emulation;Asia;Model checking;Fuzzing|
|[Automatic Test Pattern Generation and Compaction for Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044793)|D. Moussa; M. Hefenbrock; C. Münch; M. Tahoori|nan|Deep Neural Networks;Fault injection;Functional Faults;Test pattern generation;Test compaction;Deep learning;Design automation;Heuristic algorithms;Fault detection;Neural networks;Neurons;Pattern clustering|
|[Wafer-Level Characteristic Variation Modeling Considering Systematic Discontinuous Effects](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044837)|T. Nagao; T. Nakamura; M. Kajiyama; M. Eiki; M. Inoue; M. Shintani|nan|Wafer-level spatial characteristic modeling;Process variation;Gaussian process regression;Semiconductor device modeling;Semiconductor device measurement;Systematics;Production;Predictive models;Large scale integration;Data models|
|[Hardware Security Primitives using Passive RRAM Crossbar Array: Novel TRNG and PUF Designs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044850)|S. Singh; F. Zahoor; G. Rajendran; S. Patkar; A. Chattopadhyay; F. Merchant|nan|Embedded systems security;PUF;TRNG;RRAM;Memristors;Hardware Security;Performance evaluation;Privacy;Switches;NIST;Physical unclonable function;SPICE;Generators|
|[Data Sanitization on eMMCs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044824)|A. Fukami; F. Regazzoni; Z. Geradts|nan|CCS CONCEPTS • Security and privacy → Security in hardware;security;digital forensics;data sanitization;data recovery;Performance evaluation;Design automation;Data integrity;Ecosystems;Personal digital devices;Media;Electronic waste|
|[Fundamentally Understanding and Solving RowHammer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044819)|O. Mutlu; A. Olgun; A. G. Yağlıkcı|nan|DRAM;Security;Vulnerability;Technology Scaling;Reliability;Safety;Errors;Memory Systems;Fault Attacks;RowHammer;Temperature sensors;Temperature distribution;Design automation;Process control;DRAM chips;Temperature control;Security|
|[Hardware-Software Codesign of DNN Accelerators using Approximate Posit Multipliers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044835)|T. Glint; K. Prasad; J. Dagli; K. Gandhi; A. Gupta; V. Patel; N. Shah; J. Mekie|nan|DNN accelerators;neural networks;co-design;Design automation;Costs;Memory management;Asia;Accelerator architectures;Artificial neural networks;Data transfer|
|[Reusing GEMM Hardware for Efficient Execution of Depthwise Separable Convolution on ASIC-based DNN Accelerators](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044816)|S. D. Manasi; A. A. Sorokin; S. Banerjee; S. M. Burns; S. S. Sapatnekar; A. Davare; D. A. Kirkpatrick|nan|CCS CONCEPTS • Hardware → Application specific integrated circuits;• Computing methodologies → Neural networks;depthwise convolution;lightweight CNN;deep learning accelerator;Deep learning;Design automation;Convolution;Asia;Graphics processing units;Performance gain;Hardware|
|[BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044789)|M. Askarihemmat; S. Wagner; O. Bilaniuk; Y. Hariri; Y. Savaria; J. -P. David|nan|neural networks;hardware acceleration;FPGA;low-precision;Codes;Quantization (signal);Computational modeling;Pipelines;Process control;Throughput;Generators|
|[Agile Hardware and Software Co-design for RISC-V-based Multi-precision Deep Learning Microprocessor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044863)|Z. He; A. Sheri; Q. Li; Q. Cheng; H. Yu|nan|nan;Deep learning;Design automation;Microprocessors;Neural networks;Network architecture;Throughput;Software|
|[Hardware Trojan Detection Using Shapley Ensemble Boosting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044790)|Z. Pan; P. Mishra|nan|nan;Training;Analytical models;Supply chains;Boosting;Feature extraction;Hardware;System-on-chip|
|[ASSURER: A PPA-friendly Security Closure Framework for Physical Design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044826)|G. Guo; H. You; Z. Tang; B. Li; C. Li; X. Zhang|nan|Hardware security closure;PPA-friendly;Physical design;Hardware Trojans;Probing attacks;Fault-Injection;Laser theory;Power demand;Layout;Very large scale integration;Routing;Timing;Partitioning algorithms|
|[Static Probability Analysis Guided RTL Hardware Trojan Test Generation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044876)|H. Wang; Q. Zhou; Y. Cai|nan|nan;Design automation;Asia;Hardware;Trojan horses;Test pattern generators;Security|
|[Hardware Trojan Detection and High-Precision Localization in NoC-based MPSoC using Machine learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044814)|H. Wang; B. Halak|nan|NoC;MPSoC;Hardware Security;Hardware Trojan;ANN;Location awareness;Machine learning algorithms;Protocols;Heuristic algorithms;Machine learning;Hardware;Software|
|[An Integrated Circuit Partitioning and TDM Assignment Optimization Framework for Multi-FPGA Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044749)|D. Zheng; E. F. Y. Young|nan|nan;Integrated circuits;Runtime;Design automation;System performance;Wires;Time division multiplexing;Partitioning algorithms|
|[A Robust FPGA Router with Concurrent Intra-CLB Rerouting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044840)|J. Wang; J. Mai; Z. Di; Y. Lin|nan|nan;Measurement;Runtime;Design automation;Merging;Switches;Benchmark testing;Routing|
|[Efficient Global Optimization for Large Scaled Ordered Escape Routing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044777)|C. Chen; D. Lin; R. Wei; Q. Liu; Z. Zhu; J. Chen|nan|nan;Wiring;Runtime;NP-hard problem;Heuristic algorithms;Integer linear programming;Routing;Path planning|
|[An Adaptive Partition Strategy of Galerkin Boundary Element Method for Capacitance Extraction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044855)|S. Wu; B. Xie; X. Li|nan|Galerkin method;boundary element method;boundary partition;capacitance extraction;Design automation;Design methodology;Wires;Very large scale integration;Capacitance;Mathematical models;Boundary-element methods|
|[Graph-Learning-Driven Path-Based Timing Analysis Results Predictor from Graph-Based Timing Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044841)|Y. Ye; T. Chen; Y. Gao; H. Yan; B. Yu; L. Shi|nan|nan;Learning systems;Runtime;Design automation;Costs;Asia;Timing;Delays|
|[Beyond von Neumann Era: Brain-inspired Hyperdimensional Computing to the Rescue](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044801)|H. Amrouch; P. R. Genssler; M. Imani; M. Issa; X. Jiao; W. Mohammad; G. Sepanta; R. Wang|nan|Brain-Inspired Computing;Computer Architecture;Technological innovation;Machine learning algorithms;Design automation;Computer architecture;Reinforcement learning;Hardware;Robustness|
|[System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044813)|R. Medina; J. Kein; G. Ansaloni; M. Zapater; S. Abadal; E. Alarcón; D. Atienza|nan|Multi-Chiplet Systems;On-Package Wireless Communication;Full System-level Simulation;DNNs;Wireless communication;Runtime;Protocols;Design automation;Power supplies;Wireless networks;Neural networks|
|[Efficient System-Level Design Space Exploration for High-level Synthesis using Pareto-Optimal Subspace Pruning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044804)|Y. Liao; T. Adegbija; R. Lysecky|nan|System-level optimization;design space exploration;high-level synthesis;subspace pruning;embedded system;Embedded systems;Design automation;Design methodology;Asia;Genetics;Hardware;Space exploration|
|[Automatic Generation of Complete Polynomial Interpolation Design Space for Hardware Architectures](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044827)|B. Orloski; S. Coward; T. Drane|nan|CCS CONCEPTS • Hardware → Circuit optimization;Datapath optimization;Arithmetic and datapath circuits;datapath design;elementary function;polynomial interpolation;Interpolation;Design automation;Scalability;Asia;Computer architecture;Parallel processing;Hardware|
|[SHarPen: SoC Security Verification by Hardware Penetration Test](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044868)|H. Al-Shaikh; A. Vafaei; M. M. M. Rahman; K. Z. Azar; F. Rahman; F. Farahmandi; M. Tehranipoor|nan|SoC Security Verification;Penetration Testing;BPSO;Cost Function;Design automation;Intellectual property;Computer architecture;Cost function;Hardware;Software;Mathematical models|
|[SecHLS: Enabling Security Awareness in High-Level Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044779)|S. Shi; N. Pundir; H. M. Kamali; M. Tehranipoor; F. Farahmandi|nan|High-Level Synthesis;Scheduling;Binding;Security Constraints;Information Leakage;Intermediate Representation (IR);Design automation;Asia;Benchmark testing;Scheduling;Complexity theory;Security;Resource management|
|[A Flexible ASIC-oriented Design for a Full NTRU Accelerator](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044860)|F. Antognazza; A. Barenghi; G. Pelosi; R. Susella|nan|Post-quantum cryptography;NTRU;Key Encapsulation Mechanism;Encapsulation;Design automation;Asia;Public key;Standardization;Libraries;Space exploration|
|[Robust Hyperdimensional Computing Against Cyber Attacks and Hardware Errors: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044775)|D. Ma; S. Zhang; X. Jiao|nan|nan;Deep learning;Systematics;Design automation;Neural networks;Medical services;Robustness;Hardware|
|[In-Memory Computing Accelerators for Emerging Learning Paradigms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044763)|D. Reis; A. F. Laguna; M. Niemier; X. S. Hu|nan|nan;Design automation;Computational modeling;Asia;Computer architecture;Machine learning;In-memory computing;Transformers|
|[Toward Fair and Efficient Hyperdimensional Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044815)|Y. Sheng; J. Yang; W. Jiang; L. Yang|nan|nan;Sensitivity;Machine learning algorithms;Memory management;Machine learning;Market research;Skin;Medical diagnosis|
|[Improving the Robustness and Efficiency of PIM-based Architecture by SW/HW Co-design](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044852)|X. Yang; S. Li; Q. Zheng; Y. Chen|nan|Processing-in-Memory;Hardware-Software Co-Design;Resistive Random Access Memory;Machine Learning;Transformer;Efficiency;Robustness;Variation;Training;Performance evaluation;Cross layer design;Costs;Computational modeling;Memory management;Robustness|
|[Hardware-Software Co-Design for On-Chip Learning in AI Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044873)|M. L. Varshika; A. K. Mishra; N. Kandasamy; A. Das|nan|Spiking Neural Network (SNN);Spike Timing Dependent Plasticity (STDP);FPGA;Neuromorphic Computing;Convolution;Neuromorphics;Computational modeling;Network-on-chip;Feature extraction;Hardware;System software|
|[Towards On-Chip Learning for low Latency Reasoning with End-to-End Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044859)|V. G. Castellana; N. B. Agostini; A. Limaye; V. Amatya; M. Minutoli; J. Manzano; A. Tumeo; S. Curzel; M. Fiorito; F. Ferrandi|nan|High Level Synthesis;Design Automation;Machine Learning;Neural Networks;Edge Computing;Transmission electron microscopy;Synthesizers;Microscopy;Focusing;Programming;System-on-chip;Space exploration|
|[Knowledge Distillation in Quantum Neural Network using Approximate Synthesis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044872)|M. Alam; S. Kundu; S. Ghosh|nan|nan;Training;Performance evaluation;Knowledge engineering;Numerical analysis;Neural networks;Logic gates;Approximation error|
|[NTGAT: A Graph Attention Network Accelerator with Runtime Node Tailoring](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044812)|W. Hou; K. Zhong; S. Zeng; G. Dai; H. Yang; Y. Wang|nan|Graph attention network;software-hardware codesign;Runtime;Pipelines;Graphics processing units;Graph neural networks;Inference algorithms;Energy efficiency;Hardware|
|[ALow-Bitwidth Integer-STBP Algorithm for Efficient Training and Inference of Spiking Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044759)|P. -Y. Tan; C. -W. Wu|nan|artificial intelligence (AI);back-propagation;direct-training algorithm;image classification;machine learning;neuromorphic computing;quantization;spiking neural network (SNN);Training;Design automation;Neuromorphics;Neural networks;Learning (artificial intelligence);Inference algorithms;Hardware|
|[TiC-SAT: Tightly-coupled Systolic Accelerator for Transformers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044769)|A. Amirshahi; J. A. Harrison Klein; G. Ansaloni; D. Atienza|nan|nan;Computational modeling;Bit error rate;Computer architecture;Organizations;Transformers;Systolic arrays;Software|
|[PMU-Leaker: Performance Monitor Unit-based Realization of Cache Side-Channel Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044791)|P. Qiu; Q. Gao; D. Wang; Y. Lyu; C. Wang; C. Liu; R. Sun; G. Qu|nan|performance monitor unit;side-channel attack;transient execution attack;hardware security;information leakage;Program processors;Portable computers;Side-channel attacks;Lakes;Phasor measurement units;Time measurement;Hardware|
|[EO-Shield: A Multi-function Protection Scheme against Side Channel and Focused Ion Beam Attacks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044788)|Y. Gao; Q. Zhang; H. Ma; J. He; Y. Zhao|nan|Active shield;electromagnetic side-channel;side-channel security;Resistance;Correlation;Metals;Prototypes;Side-channel attacks;Real-time systems;Integrated circuit modeling|
|[CompaSeC: A Compiler-assisted Security Countermeasure to Address Instruction Skip Fault Attacks on RISC-V](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044811)|J. Geier; L. Auer; D. Mueller-Gritschneder; U. Sharif; U. Schlichtmann|nan|Redundancy;Fault injection attack;Compiler;RISC-V;Fault tolerance;Runtime;Fault detection;Fault tolerant systems;Hardware;Software;Safety|
|[Trojan-D2: Post-Layout Design and Detection of Stealthy Hardware Trojans - a RISC-V Case Study](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044782)|S. Parvin; M. Goli; F. S. Torres; R. Drechsler|nan|Optical Probing;Hardware Trojan;LLSI;Hardware Secuirty;Semiconductor device measurement;Simulation;Layout;Optical device fabrication;Mission critical systems;Hardware;Optical design techniques|
|[Graph Partitioning Approach for Fast Quantum Circuit Simulation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044854)|J. Im; S. Kang|nan|nan;Computers;Design automation;Computational modeling;Qubit;Asia;Logic gates;Integrated circuit modeling|
|[A Robust Approach to Detecting Non-equivalent Quantum Circuits Using Specially Designed Stimuli](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044821)|H. -L. Liu; Y. -T. Li; Y. -C. Chen; C. -Y. Wang|nan|nan;Quantum system;Design automation;Asia;Benchmark testing;Time factors;Quantum circuit;Integrated circuit modeling|
|[Equivalence Checking of Parameterized Quantum Circuits: Verifying the Compilation of Variational Quantum Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044866)|T. Peham; L. Burgholzer; R. Wille|nan|nan;Quantum algorithm;Design automation;Asia;Computer architecture;Benchmark testing;Libraries;Hardware|
|[Software Tools for Decoding Quantum Low-Density Parity-Check Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044785)|L. Berent; L. Burgholzer; R. Wille|nan|nan;Runtime;Qubit;Quantum mechanics;Parity check codes;Hardware;Decoding;Error correction codes|
|[Enabling Scalable AI Computational Lithography with Physics-Inspired Models](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044771)|H. Yang; H. Ren|nan|nan;Runtime;Design automation;Computational modeling;Lithography;Neural networks;Machine learning;Semiconductor device manufacture|
|[Data-Driven Approaches for Process Simulation and Optical Proximity Correction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044862)|H. -C. Shao; C. -W. Lin; S. -Y. Fang|nan|nan;Semiconductor device modeling;Image segmentation;Runtime;Inverse problems;Lithography;Layout;Semiconductor device manufacture|
|[Mixed-Type Wafer Failure Pattern Recognition (Invited Paper)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044765)|H. Geng; Q. Sun; T. Chen; Q. Xu; T. -Y. Ho; B. Yu|nan|nan;Semiconductor device modeling;Art;Federated learning;Statistical distributions;Feature extraction;Foundries;Data models|
|[Accelerating Convolutional Neural Networks in Frequency Domain via Kernel-sharing Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044858)|B. Liu; H. Liang; J. Wu; X. Chen; P. Liu; Y. Han|nan|Acceleration;frequency-domain DNN architecture;Design automation;Fast Fourier transforms;Frequency-domain analysis;Asia;Computer architecture;Energy efficiency;Convolutional neural networks|
|[Mortar: Morphing the Bit Level Sparsity for General Purpose Deep Learning Acceleration](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044795)|Y. Gao; H. Li; K. Zhang; X. Yu; H. Lu|nan|deep learning accelerator;neural networks;bit-level sparsity;ACM Reference format;Deep learning;Training;Mortar;Superresolution;Software algorithms;Inference algorithms;Software|
|[Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044758)|Y. Zhang; A. K. Kamath; Q. Wu; Z. Fan; W. Chen; Z. Wang; S. Chang; S. Liu; C. Hao|nan|nan;Technological innovation;Solid modeling;Streaming media;Solids;Energy efficiency;Data models;Real-time systems|
|[Latent Weight-based Pruning for Small Binary Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044856)|T. Chen; N. Anderson; Y. Kim|nan|binary neural network;pruning;latent weight;Backpropagation;Analytical models;Energy consumption;Design automation;Sensitivity analysis;Computational modeling;Asia|
|[AutoFlex: Unified Evaluation and Design Framework for Flexible Hybrid Electronics](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044846)|T. Ma; Z. Deng; L. Shao|nan|Flexible hybrid electronics;design automation;flexible electronics;heterogeneous system design;hardware/software co-design;Technological innovation;System performance;Benchmark testing;Silicon;Sensor systems;Sensors;Internet of Things|
|[CNFET7: An Open Source Cell Library for 7-nm CNFET Technology](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044778)|C. Shi; S. Miwa; T. Yang; R. Shioya; H. Yamaki; H. Honda|nan|nan;Power demand;Microprocessors;Random access memory;Predictive models;SPICE;Libraries;CNTFETs|
|[A Global Optimization Algorithm for Buffer and Splitter Insertion in Adiabatic Quantum-Flux-Parametron Circuits](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044820)|R. Fu; M. Wang; Y. Kan; N. Yoshikawa; T. -Y. Ho; O. Chen|nan|superconducting electronics;AQFP;buffer and splitter insertion;Quantum computing;Logic circuits;Logic gates;Integer linear programming;Benchmark testing;Delays;Dynamic programming|
|[FLOW-3D: Flow-Based Computing on 3D Nanoscale Crossbars with Minimal Semiperimeter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044773)|S. Thijssen; S. K. Jha; R. Ewetz|nan|nan;Energy consumption;Three-dimensional displays;Design automation;Boolean functions;Wires;Asia;Metals|
#### **2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC)**
- DOI: 10.1109/ICAIC57335.2023
- DATE: 7-9 February 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Significance of artificial intelligence to deal with stress during the organisational changes](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044168)|Y. Aditya|10.1109/ICAIC57335.2023.10044168|Change management;stress management;workforce productivity;artificial intelligence;organizational change;Leadership;Automation;Force;Organizations;Mental health;Safety;Complexity theory|
|[Voltage hopping induced by bias injection attack against Kalman filter of BLDC motor](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044123)|Y. Boiko; I. Kiringa; T. Yeap|10.1109/ICAIC57335.2023.10044123|bias injection attack;Kalman filter;BLDC motor;control system;closed loop;P-controller;PID-controller;voltage hopping;distortion;Electric potential;Voltage measurement;Windings;Rotors;Voltage;Distortion;Angular velocity|
|[Long Short-Term Memory Networks for Monitoring Groundwater Contamination at the Hanford Site](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044121)|M. P. Murphy; H. Mazumdar; H. A. Gohel; H. P. Emerson; D. I. Kaplan|10.1109/ICAIC57335.2023.10044121|groundwater;contamination;prediction;machine learning;optimization;Adaptation models;Recurrent neural networks;Production;Chromium;Predictive models;Soil;Reservoirs|
|[On Phishing: Proposing a Traffic Behavior-Based Model to Detect, Prevent, and Classify Webpage Suspicious and Malicious Activities](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044171)|W. Fadheel; S. Carr; W. Al-Mawee|10.1109/ICAIC57335.2023.10044171|Phishing;Classification;Detecting;Prevention;Webpage Traffic Behavior;Webpage Lexical Host-Based;Contents Features;Support vector machines;Machine learning algorithms;Phishing;Large Hadron Collider;Feature extraction;Data models;Behavioral sciences|
|[Machine Learning and Sentiment Analysis for Predicting Environmental Lead Toxicity in Children at the ZIP Code Level](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044177)|S. Puppala; I. Hossain; S. Talukder|10.1109/ICAIC57335.2023.10044177|nan;Correlation;Machine learning algorithms;Codes;Toxicology;Social networking (online);Welding;Lead|
|[Penetration Testing for IoT Security: The Case Study of a Wireless IP Security CAM](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044176)|O. Almazrouei; P. Magalingam; M. Kamrul Hasan; M. Almehrzi; A. Alshamsi|10.1109/ICAIC57335.2023.10044176|Penetration testing;IoT;Vulnerability;Wireless communication;Privacy;Wireless sensor networks;Cameras;Software;Behavioral sciences;Security|
|[Seasonal Trend Assessment for Groundwater Contamination Detection and Monitoring using ARIMA Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044126)|M. A. Barajas; M. P. Murphy; L. C. Lasseter; G. I. Sunny; H. Mazumdar; H. A. Gohel; H. P. Emerson; D. I. Kaplan|10.1109/ICAIC57335.2023.10044126|Groundwater;Contamination;Prediction;ARIMA;Machine Learning;Optimization;Training;Visualization;Chromium;Predictive models;Market research;Data models;Reliability|
|[Blockchain Technology and Impacts on Potential Industries](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044170)|G. Othman Alandjani|10.1109/ICAIC57335.2023.10044170|blockchain;bitcoin;crypto-currency;IoT;industrial Internet of Things (IIoT);Industries;Technological innovation;Smart cities;Supply chains;Bitcoin;Watermarking;Media|
|[A Framework to Reconstruct Digital Forensics Evidence via Goal-Oriented Modeling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044180)|A. Ogundiran; H. Chi; J. Yan; R. Agada|10.1109/ICAIC57335.2023.10044180|reconstruction;digital forensic evidence;goal - oriented model;graph models;cybersecurity attacks;Analytical models;Digital forensics;Semantics;Personal digital devices;Computer security;Artificial intelligence|
|[Quantum Machine Learning for Network Intrusion Detection Systems, a Systematic Literature Review](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044125)|O. K. Nicesio; A. G. Leal; V. L. Gava|10.1109/ICAIC57335.2023.10044125|Cybersecurity;Intrusion Detection System;Quantum Machine Learning;Quantum Computing;Machine Learning;Training;Support vector machines;Machine learning algorithms;Systematics;Computational modeling;Bibliographies;Network intrusion detection|
|[How Private Blockchain Technology Secure IoT Data Record](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044178)|E. Anaam; M. K. Hasan; T. M. Ghazal; S. -C. Haw; H. M. Alzoubi; M. T. Alshurideh|10.1109/ICAIC57335.2023.10044178|Blockchain;IoT;Data Records;Security;Private Blockchain;Protocols;Web services;Scalability;Public key;Software;Blockchains;Recording|
|[Bearing Fault Diagnosis Based on Sparse Wavelet Decomposition and Sparse Graph Connection Using GraphSAGE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044173)|G. Zhu; X. Liu; L. Tan|10.1109/ICAIC57335.2023.10044173|Bearing Fault Diagnosis;Sparse Wavelet Decomposition;Sparse Connectivity Graph;GraphSAGE;Fault diagnosis;Vibrations;Deep learning;Time-frequency analysis;Time series analysis;Feature extraction;Frequency conversion|
|[Decision Tree Algorithm for Depression Diagnosis from Facial Images](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044179)|D. Choi; G. Zhang; S. Shin; J. Jung|10.1109/ICAIC57335.2023.10044179|Depression;Machine Learning;Decision Tree;Gold;Head;Databases;Hospitals;Neural networks;Receivers;Depression|
|[Cuff-less blood pressure estimation from ECG and PPG using CNN-LSTM algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044172)|G. Zhang; D. Choi; S. Shin; J. Jung|10.1109/ICAIC57335.2023.10044172|Blood pressure;CNN-LSTM;Cuff-less;ECG;PPG;Hypertension;Neural networks;Estimation;Medical services;Electrocardiography;Feature extraction;Classification algorithms|
|[EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044182)|J. Su; S. Nair; L. Popokh|10.1109/ICAIC57335.2023.10044182|Resource Allocation;NFV;Deep Learning;Reinforcement Learning Environments;Gym;Training;Deep learning;Reinforcement learning;Network function virtualization;Resource management;Reliability;Computer security|
|[Design of QR Based Smart Student Attendance System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044175)|S. V. Sai Harsith Reddy; G. R. Sekhar Raju; N. Jayanth; M. C. Sai; B. Pandey; G. G.; H. Gohel|10.1109/ICAIC57335.2023.10044175|QR code;Attendance system;Open CV;Smart Attendance;Pandemics;Atmospheric measurements;QR codes;Particle measurements;Libraries;Registers;Fraud|
|[Collaborative Differentially Private Federated Learning Framework for the Prediction of Diabetic Retinopathy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044122)|I. Hossain; S. Puppala; S. Talukder|10.1109/ICAIC57335.2023.10044122|nan;Deep learning;Training;Retinopathy;Federated learning;Collaboration;Medical services;Predictive models|
|[Suspicious Permissions Detection of Covid-19 Themed Malicious Android Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044167)|M. Sharma; A. Kaul|10.1109/ICAIC57335.2023.10044167|Covid-19;Android malware detection;Dangerous Permissions;Malware Analysis;COVID-19;Pandemics;Phishing;Static analysis;Feature extraction;Malware;Mobile applications|
|[Scrutinization of Saline Sea Utilizing a Water-Based Antenna for the Radio Communications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044124)|I. E. Elmutasima; I. I. Mohd; K. H. Bilal|10.1109/ICAIC57335.2023.10044124|Water Antenna;Salinity;Conductivity;Perforation;Propagation;Water;Wireless communication;Sea surface;Fluctuations;Salinity (geophysical);Government;Desalination|
|[Detection of Cardiac Tumors Using Parallel Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044174)|J. Saini; K. Kumar; B. Pandey; H. Gohel|10.1109/ICAIC57335.2023.10044174|Cardiac tumors;Parallel computing;MRI;Healthcare;Dataset;Support vector machine;Heart;Support vector machines;Computational modeling;Magnetic resonance imaging;Neurons;Gastroenterology;Parallel processing|
|[Utilization of Blockchain Technology In Human Resource Management](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044181)|E. Anaam; T. M. Ghazal; S. -C. Haw; H. M. Alzoubi; M. T. Alshurideh; A. A. Mamun|10.1109/ICAIC57335.2023.10044181|Blockchain;Human resource management Security;Data privacy;Privacy;Data privacy;Blockchains;Planning;Information management;Resource management;Computer security|
|[Supervised and Unsupervised Learning Techniques Utilizing Malware Datasets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10044169)|D. Smith; S. Khorsandroo; K. Roy|10.1109/ICAIC57335.2023.10044169|area under the curve-receiver operating characteristics (AUC-ROC);density-based spatial clustering of applications with noise (DBSCAN);Gaussian Mixture Model (GMM);hierarchical density-based spatial clustering of applications with noise (HDBSCAN);supervised machine learning;unsupervised machine learning;Machine learning algorithms;Clustering algorithms;Prediction algorithms;Feature extraction;Malware;Classification algorithms;Unsupervised learning|
#### **2023 International Conference on Information Networking (ICOIN)**
- DOI: 10.1109/ICOIN56518.2023
- DATE: 11-14 January 2023
|Name|Authors|DOI|Tags|
|-|-|-|-|
|[Height Pattern Estimation Method Using the Combination of Radio Map and 3D Map for Spectrum Sharing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048945)|S. Miyamoto; S. Yamada; T. Fujii|10.1109/ICOIN56518.2023.10048945|Spectrum sharing;Radio propagation;Radio map;Height pattern;Extrapolation;Three-dimensional displays;Radio transmitters;Estimation;Receivers;Interference;Radio propagation|
|[Beam Pattern Estimation of 5G Millimeter-Wave Base Station Based on Radio Map and Multi-Beam Antenna Model at 28GHz](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048917)|S. Matsuo; S. Miyamoto; H. Nakajo; T. Fujii|10.1109/ICOIN56518.2023.10048917|Radio Map;radio propagation;5G;beamforming;array antenna;Antenna measurements;Base stations;Solid modeling;Interpolation;Extrapolation;5G mobile communication;Millimeter wave technology|
|[Network-Assisted Full-Duplex Millimeter-Wave Cell-Free Massive MIMO with Localization-Aided Inter-User Channel Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048919)|S. Fukue; G. T. Freitas de Abreu; K. Ishibashi|10.1109/ICOIN56518.2023.10048919|Cell-free massive MIMO;Network-assisted full-duplex;mmWave;Channel estimation;Simulation;Millimeter wave technology;Channel estimation;Full-duplex system;Interference;Throughput;Propagation losses|
|[An Artificial Intelligence Framework for Holographic Beamforming: Coexistence of Holographic MIMO and Intelligent Omni-Surface](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048994)|A. Adhikary; M. S. Munir; A. Deb Raha; Y. Qiao; S. H. Hong; E. -N. Huh; C. S. Hong|10.1109/ICOIN56518.2023.10048994|Holographic MIMO;intelligent omni-surface;coexistence;holographic beamforming.;Wireless communication;Power demand;Array signal processing;Azimuth;Channel capacity;Simulation;Sensors|
|[A Design of Service Mesh Based 5G Core Network Using Cilium](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049044)|V. -B. Duong; Y. Kim|10.1109/ICOIN56518.2023.10049044|5G;5GC;Service Mesh;5G mobile communication;Containers;Security;Network interfaces;Observability;Secure storage|
|[Measurement of Sub-GHz Band LPWA Radiowave Propagation on Each Floor in Indoor Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048939)|T. Miyamoto; S. Narieda; T. Fujii; H. Naruse|10.1109/ICOIN56518.2023.10048939|LPWA communications;radiowave propagation;indoor environment;Buildings;Concrete;Indoor environment;Radiowave propagation;Floors|
|[Considerations on Tradeoff Between Downlink NOMA and Beamforming in Mobile Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048952)|K. Yoda; T. Yasaka; K. Miyashita; S. Suyama; H. Otsuka|10.1109/ICOIN56518.2023.10048952|mobile communication;5G;NOMA;beamforming;user throughput;system-level computer simulations;NOMA;Array signal processing;5G mobile communication;Spectral efficiency;Computer simulation;Downlink;Throughput|
|[Lower Bound for the Number of Accommodable End-devices in LPWAN with Multiple Interferences](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049040)|D. Kumamoto; S. Narieda; T. Fujii; H. Naruse|10.1109/ICOIN56518.2023.10049040|LPWAN;coverage probability;lower bound for the number of accommodable end devices.;Measurement;Wireless networks;Computer simulation;Interference;Low-power wide area networks|
|[Measurement-based Spectrum Database for Non-Terrestrial Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049042)|Y. Itayama; H. Nakajo; S. Matsuo; T. Fujii|10.1109/ICOIN56518.2023.10049042|Non-terrestrial networks;spectrum database;radio environment estimation;Degradation;Databases;Azimuth;Satellite broadcasting;Buildings;Estimation;Vegetation|
|[An Evaluation of Time-Series Anomaly Detection in Computer Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049051)|H. Nguyen; A. Hajisafi; A. Abdoli; S. H. Kim; C. Shahabi|10.1109/ICOIN56518.2023.10049051|Time-series Data Analysis;Anomaly Detection;Computer Networks;Deep Learning;Unsupervised Learning;Deep learning;Computational modeling;Time series analysis;Detectors;Computer networks;Data models;Labeling|
|[Performance Analysis of Uplink IM-OFDMA Systems in the Presence of CFO and Rx-IQI](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048914)|O. Alaca; S. Althunibat; S. Yarkan; S. L. Miller; K. A. Qaraqe|10.1109/ICOIN56518.2023.10048914|IM-OFDMA;OFDMA;index modulation;carrier frequency offset;IQ imbalance;Wireless communication;Spectral efficiency;OFDM;Mathematical analysis;Interference;Receivers;Distortion|
|[Selective Competition for NOMA-capable Devices Using RIS](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048932)|M. -S. Yoon; K. M. Kim; T. -J. Lee|10.1109/ICOIN56518.2023.10048932|Non-Orthogonal Multiple Access (NOMA);Medium Access Control (MAC);Reconfigurable Intelligent Surface (RIS);Internet of Things (IoT);Wireless Local Area Network (WLAN);Access control;NOMA;Wireless LAN;Protocols;Interference;Media Access Protocol;Throughput|
|[Channel Characterization at Sub-THz Band with Measurements and Ray Tracing in Indoor Case](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048904)|M. U. Sheikh; M. Ali; G. Carpintero; K. Ruttik; E. Mutafangwa; R. Jäntti|10.1109/ICOIN56518.2023.10048904|Measurements;simulations;power angular spectrum;ray tracing;terahertz;THz;sub-THz;indoor.;Power measurement;Measurement uncertainty;Ray tracing;Position measurement;Optical variables measurement;Reflection;Frequency measurement|
|[THL2H-Ex: An Improved Neighbor Discovery Approach for Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048965)|R. R. Suarez; A. Nayak|10.1109/ICOIN56518.2023.10048965|nan;Measurement;Wireless sensor networks;Protocols;Delays|
|[IEEE 802.11ac WLAN Analysis in 160Mhz Channel](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048903)|S. S. Kolahi; T. Shaheen; E. Aderson; R. Aljadani; A. Ceyddique|10.1109/ICOIN56518.2023.10048903|nan;Wireless LAN;Protocols;Bandwidth;Throughput;Size measurement;Time measurement;Peer-to-peer computing|
|[Topology Design for Data Center Networks Using Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048955)|H. Qi; Z. Shu; X. Chen|10.1109/ICOIN56518.2023.10048955|Low-latency Data Center Network Topology;Deep Reinforcement Learning;Multi-objective Learning;Deep learning;Measurement;Data centers;Network topology;Reinforcement learning;Throughput;Topology|
|[Design of a 3D Scene Reconstruction Network Robust to High-Frequency Areas Based on 2.5D Sketches and Encoders](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048963)|C. D. Lee; J. Yoo; K. -H. Park|10.1109/ICOIN56518.2023.10048963|3D scene reconstruction;edge image;surface normal image;swin transformer;ConvNeXt;TSDF volume;Image segmentation;Three-dimensional displays;Image edge detection;Object detection;Feature extraction;Transformers;Real-time systems|
|[VNDN-Fuzzy - A strategy to mitigate the forwarding interests broadcast storm problem in VNDN networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049030)|I. K. Barbosa Cunha; J. Celestino Junior; M. P. Fernandez; A. Patel; M. E. Monteiro|10.1109/ICOIN56518.2023.10049030|Veicular Ad Hoc Networks;Named Data Networking;Vehicular Named-Data Networking;Broadcast Storm;Fuzzy Logic;Measurement;Fuzzy logic;Analytical models;Storms;Vehicle-to-infrastructure;Urban areas;Vehicular ad hoc networks|
|[ETANet: An Efficient Triple-Attention Network for Salient Object Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048982)|T. -T. Ngo; E. -N. Huh; C. Seon Hong|10.1109/ICOIN56518.2023.10048982|salient object detection;receptive field block;attention mechanism;Training;Correlation;Fuses;Object detection;Computer architecture;Feature extraction;Task analysis|
|[Robust Federated Learning with Local Mixed Co-teaching](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049004)|G. F. Ejigu; S. Hoon Hong; C. S. Hong|10.1109/ICOIN56518.2023.10049004|federated learning;robust federated learning;noisy labels;Training;Knowledge engineering;Data privacy;Federated learning;Benchmark testing;Data models;Robustness|
|[Integrating Machine Learning for Network Threat Detection with SmartX Multi-Sec Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049012)|T. Farasat; M. A. Rathore; J. Kim|10.1109/ICOIN56518.2023.10049012|OF@TEIN playground;SmartX Multi-Sec Framework;Network Threat Detection;Machine Learning Techniques;Zero-Trust model;Image edge detection;Prototypes;Focusing;Telecommunication traffic;Denial-of-service attack;Zero Trust;Pattern recognition|
|[An Artificial Intelligent-Driven Semantic Communication Framework for Connected Autonomous Vehicular Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049005)|A. Deb Raha; M. Shirajum Munir; A. Adhikary; Y. Qiao; S. -B. Park; C. Seon Hong|10.1109/ICOIN56518.2023.10049005|semantic communication;reinforcement learning;HAP;auto encoder;vehicular network;Costs;Simulation;Semantics;Decision making;Learning (artificial intelligence);Sensors;Data mining|
|[Is Puzzle-Based CAPTCHA Secure Against Attacks Based on CNN?](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049032)|K. Terada; Y. Okabe; Y. Matsumoto|10.1109/ICOIN56518.2023.10049032|puzzle-based CAPTCHA;bot;CNN;Shape;Machine learning;Predictive models;Chatbots;Mice;Trajectory;Convolutional neural networks|
|[LSTM-based PdM Platform for Automobile SCU Inspection Equipment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048924)|S. H. Oh; J. Gon Kim|10.1109/ICOIN56518.2023.10048924|Predictive Maintenance (PdM);Artificial Intelligence (AI);Long-Short Term Memory (LSTM);Industrial Automation;Shift-by-wire Control Unit Inspection Equipment;Training data;Inspection;Predictive models;Data models;Real-time systems;Production facilities;Monitoring|
|[Coreset Construction for Extra Binomial Variation in Binomial Regression](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048948)|F. Zhang; N. Wichitaksorn; B. Chattopadhyay|10.1109/ICOIN56518.2023.10048948|Big Data;Coresets;Binomial Regression;Over-dispersion;Sensitivity;Scalability;Machine learning;Big Data|
|[Small Object Detection Technology Using Multi-Modal Data Based on Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049014)|C. -W. Park; Y. Seo; T. -J. Sun; G. -W. Lee; E. -N. Huh|10.1109/ICOIN56518.2023.10049014|Multi-Modal Data;Object Detection;Low Resource;Image quality;Deep learning;Computer vision;Web services;Object detection;Artificial intelligence|
|[RNN-based Text Summarization for Communication Cost Reduction: Toward a Semantic Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048944)|S. K. Dam; M. Shirajum Munir; A. D. Raha; A. Adhikary; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10048944|Text Summarization;Base Station;LSTM-RNN;Communication Cost Reduction;Deep learning;Base stations;Analytical models;Costs;Text analysis;Recurrent neural networks;Semantics|
|[The Emerged Artificial Intelligence Protocol for Hierarchical Information Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048942)|C. Wu; P. Bouvry|10.1109/ICOIN56518.2023.10048942|Artificial Intelligence;Protocol;Machine Learning Hierarchical Information Network;Decision Making;Layers;Adaptation models;Protocols;Machine learning algorithms;Human intelligence;Decision making;Buildings;Machine learning|
|[Machine Learning Assisted Approach for Water Leaks Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048954)|S. Badar; S. Labghough; A. Al-Abdulghani; E. Mohammed; O. Bouhali; K. A. Qaraqe|10.1109/ICOIN56518.2023.10048954|KNN;ANN;XGBoost;Gradient Boosting;Pressure sensors;Machine learning algorithms;Pipelines;Boosting;Prediction algorithms;Sensor systems;Leak detection|
|[A Heuristic Intrusion Detection Approach Using Deep Learning Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049024)|C. -s. Wu; S. Chen|10.1109/ICOIN56518.2023.10049024|IDS;DNN;neural net;web security;deep learning;Deep learning;Wireless networks;Neural networks;Intrusion detection;Network security;Feature extraction;Behavioral sciences|
|[Scene identification using visual semantic segmentation and supplementary classifier for resource-constrained edge systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048947)|C. Choe; S. Jung; N. -M. Sung; S. Lee|10.1109/ICOIN56518.2023.10048947|Scene identification;image segmentation;edge computing;Training;Visualization;Service robots;Image edge detection;Semantic segmentation;Computational modeling;Surveillance|
|[Advances in Distributed Load Orchestration for Vision Computing in 5G-MEC Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049022)|R. do Nascimento Boing; H. Vaz Sampaio; F. Koch; W. dos Reis Bezerra; R. Nolio Santa Cruz; C. B. Westphall|10.1109/ICOIN56518.2023.10049022|5G-MEC;Multi-access Edge Computing;Load Orchestration;Distributed Computing;Video Surveillance;Technological innovation;Multi-access edge computing;Instruction sets;Machine learning;Big Data;Network architecture;Video surveillance|
|[Offloading Visual SLAM Processing to the Edge: An Energy Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048921)|P. Sossalla; J. Hofer; C. Vielhaus; J. Rischke; F. H. P. Fitzek|10.1109/ICOIN56518.2023.10048921|nan;Location awareness;Visualization;Simultaneous localization and mapping;Power demand;Runtime;Bit rate;Servers|
|[Dynamic Edge Server Placement for Computation Offloading in Vehicular Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049001)|D. Nakrani; J. Khuman; R. N. Yadav|10.1109/ICOIN56518.2023.10049001|Edge Computing;Internet of vehicles;Computation offloading;Matching;Costs;Power demand;Smart cities;Heuristic algorithms;Real-time systems;Servers;Reliability|
|[Blockchain-Based Service Migration for Multi-Access Edge Computing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048906)|S. Ren; C. Lee|10.1109/ICOIN56518.2023.10048906|Multi-Access Computing (MEC);Blockchain;Service Migration;Asynchronous Advantage Actor-critic (A3C);Multi-access edge computing;Computational modeling;Simulation;Clustering algorithms;Markov processes;Real-time systems;Delays|
|[Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048957)|J. Park; K. Chung|10.1109/ICOIN56518.2023.10048957|Computation Offloading;Internet of Things;Reinforcement Learning;Mobile Edge Computing;Deep learning;Simulation;Collaboration;Reinforcement learning;Linear programming;Quality of experience;Task analysis|
|[Edge-assisted Attention-based Federated Learning for Multi-Step EVSE-enabled Prosumer Energy Demand Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048987)|L. Zou; C. M. Thwal; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10048987|Multi-step energy demand prediction;EVSE-enabled prosumer;attention mechanism;LSTM-based Seq2Seq model;federated learning;Training;Energy consumption;Federated learning;Image edge detection;Predictive models;Feature extraction;Prediction algorithms|
|[Accelerating convergence in wireless federated learning by sharing marginal data](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048937)|E. Seo; V. Pham; E. Elmroth|10.1109/ICOIN56518.2023.10048937|Edge computing;federated learning;data sharing;wireless mobile network;Wireless communication;Costs;Federated learning;Neural networks;Performance gain;Feature extraction;Data models|
|[Adaptive Streaming Scheme with Reinforcement Learning in Edge Computing Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048966)|J. Kang; K. Chung|10.1109/ICOIN56518.2023.10048966|Dynamic Adaptive Streaming over HTTP (DASH);Quality of Experience (QoE);Reinforcement Learning;Edge Computing;Degradation;Adaptive systems;Heuristic algorithms;Computational modeling;Neural networks;Collaboration;Reinforcement learning|
|[A Framework for Multi-Prototype Based Federated Learning: Towards the Edge Intelligence](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048999)|Y. Qiao; M. S. Munir; A. Adhikary; A. D. Raha; S. H. Hong; C. S. Hong|10.1109/ICOIN56518.2023.10048999|Distributed edge network;federated learning;multi-prototype;communication efficiency;Training;Weight measurement;Federated learning;Prototypes;Distributed databases;Inference algorithms;Data models|
|[Node-Centric Random Walk for Fast Index-Free Personalized PageRank](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049056)|K. Tsuchida; N. Matsumoto; K. Kaneko|10.1109/ICOIN56518.2023.10049056|Personalized PageRank;Random Walk;Index-Free;Monte Carlo methods;Aggregates;Focusing;Proposals;Bars|
|[The More The Merrier: Reconstruction of Twitter Firehose](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048898)|S. A. Alhosseini; C. Meinel|10.1109/ICOIN56518.2023.10048898|Social Network Data;Twitter API;Snowflake;Codes;Social networking (online);Blogs;User-generated content;Estimation;Market research;Servers|
|[Joint Association and Power Allocation for Data Collection in HAP-LEO-Assisted IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049035)|N. N. Ei; P. S. Aung; S. -B. Park; E. -N. Huh; C. S. Hong|10.1109/ICOIN56518.2023.10049035|Low earth orbit satellites;high altitude platforms;LEO satellite’s visibility time;whale optimization algorithm;Performance evaluation;Satellites;Low earth orbit satellites;Quality of service;Reinforcement learning;Orbits;Whale optimization algorithms|
|[Development of Activity Management System to Watch over Children](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048950)|I. Inoue; K. Yamamoto|10.1109/ICOIN56518.2023.10048950|Activity Management System;Web-Geographic Information Systems (Web-GIS);Management System for Watching Over Children Activity;Child-Related Crime Prevention;Local government;Urban areas;Organizations;Mobile handsets;Safety;Registers;Task analysis|
|[Traffic reduction for out-of-band network management over LPWA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048968)|K. Tanabe; G. Hasegawa; G. Kitagata|10.1109/ICOIN56518.2023.10048968|Compression;Out-of-band management;Performance evaluation;Costs;Codes;Buildings;Symbols;Prototypes;Numerical models|
|[Deep Reinforcement Learning Driven Aggregate Flow Entries Eviction in Software Defined Networking](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049020)|J. Zang; S. M. Raza; H. Choo; G. Byun; M. Kim|10.1109/ICOIN56518.2023.10049020|Software-Defined Networking;OpenFlow;Aggregate Flow Entry;Deep Reinforcement Learning;Deep Q-learning;Deep learning;Q-learning;Costs;Network topology;Aggregates;Software algorithms;Aerospace electronics|
|[A QoS-Aware Routing Mechanism for SDN-Based Integrated Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048989)|Y. Zhang; M. Cui; M. Abadeer; S. Gorlatch|10.1109/ICOIN56518.2023.10048989|Quality of Service (QoS);Routing Optimization;Software Defined Networking (SDN);Integrated Network Architecture;QoS-Aware Routing;Energy consumption;Adaptation models;Adaptive systems;Packet loss;Quality of service;Computer architecture;Network architecture|
|[Forecasting SDN End-to-End Latency Using Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048915)|Z. Ge; J. Hou; A. Nayak|10.1109/ICOIN56518.2023.10048915|GNN;SDN;End-to-end Delay Estimation;Radio frequency;Delay estimation;Telecommunication traffic;Predictive models;Data models;User experience;Software defined networking|
|[A Software-Defined Networks Approach for Cyber Physical Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049028)|J. Khrais; M. Al-Issa; R. Al-Omari; A. T. Al-Hammouri|10.1109/ICOIN56518.2023.10049028|Software Defined Networking;Cyber Physical Systems;Industrial Control Systems;OpenFlow;Mininet;MiniCPS;Quality of Service;Integrated circuits;Process control;Control systems;Feature extraction;Real-time systems;Delays;Behavioral sciences|
|[Optimizing Forensic Data Availability and Retention of SDN Forensic Logs by Using Bloom Filter](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048908)|V. Sharma; S. Rawat|10.1109/ICOIN56518.2023.10048908|SDN;forensics;bloom filter;retention;Forensics;Big Data;Information filters;Communications technology;Complexity theory;Software defined networking;Faces|
|[Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048935)|H. Dutta; A. Kumar Bhuyan; S. Biswas|10.1109/ICOIN56518.2023.10048935|Medium Access Control;Multi-Armed Bandit;Spectral Utilization;Wireless Sensor Networks;Internet-of-Things;Wireless communication;Wireless sensor networks;Time division multiple access;Schedules;Protocols;Bandwidth;Transceivers|
|[UAV Trajectory Planning For Improved Content Availability in Infrastructure-less Wireless Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048929)|A. K. Bhuyan; H. Dutta; S. Biswas|10.1109/ICOIN56518.2023.10048929|Post-disaster networks;Unmanned Aerial Vehicles;Zipf Distribution;Caching;Content Popularity;Content Distribution;Performance evaluation;Analytical models;Base stations;Trajectory planning;Wireless networks;Benchmark testing;Autonomous aerial vehicles|
|[Resource Allocation and User Association Using Reinforcement Learning via Curriculum in a Wireless Network with High User Mobility](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048927)|D. U. Kim; S. B. Park; C. S. Hong; E. N. Huh|10.1109/ICOIN56518.2023.10048927|wireless networks;user mobility;resource allocation;user association;deep reinforcement learning;curriculum learning;Learning systems;Deep learning;Enhanced mobile broadband;NP-hard problem;Wireless networks;Reinforcement learning;Handover|
|[Reinforcement Learning Approach for Resource Allocation in 5G HetNets](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049013)|F. Allagiotis; C. Bouras; V. Kokkinos; A. Gkamas; P. Pouyioutas|10.1109/ICOIN56518.2023.10049013|5G;Resource Allocation;HetNets;Reinforcement Learning;Deep Q-Learning;Macrocell networks;Deep learning;Q-learning;5G mobile communication;Quality of service;Interference;Heterogeneous networks|
|[Cache Node Placement Scheme Considering Maximum Traffic in Content-Centric Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048983)|P. Pavarangkoon; S. Nakajima; N. Kitsuwan|10.1109/ICOIN56518.2023.10048983|Content-centric networking;maximum traffic;optimization;Heuristic algorithms;Benchmark testing;Routing;Mathematical models;Numerical models|
|[Seamless and Efficient Resources Allocation in 6G Satellite Networks Servicing Remote User Equipments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049036)|S. Salman Hassan; Y. Min Park; K. Kim; S. Hoon Hong; C. Seon Hong|10.1109/ICOIN56518.2023.10049036|6G;satellite networks;resource allocation;remote user equipments;decomposition method;optimization theory;6G mobile communication;Satellites;Closed-form solutions;Power system management;Wireless networks;Bandwidth;Quality of service|
|[Kuramoto-Inspired Wireless Resource Allocation for Weighted Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048922)|K. Chhea; S. Muy; J. -R. Lee|10.1109/ICOIN56518.2023.10048922|Kuramoto model;fair resource allocation;weighted de-synchronization;Adaptation models;Wireless networks;Simulation;Scalability;Quality of service;Spread spectrum communication;Telecommunication traffic|
|[Robustness to Digital Power Adjustment in Transmit Power Allocation for Poor Conditioned LPWA End Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049053)|S. Narieda; T. Fujii|10.1109/ICOIN56518.2023.10049053|LPWA communications;poor conditioned end devices;digital transmit power adjustment;Performance evaluation;Attenuators;Computer simulation;Logic gates;Attenuation;Propagation losses;Robustness|
|[A Collaborative UAV Routing Algorithm for Time Sensitive Surveillance Tasks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048936)|Y. Yu; S. Lee|10.1109/ICOIN56518.2023.10048936|Cooperative unmanned aerial vehicle teams;Path planning;Predictive task assignment;Task allocation;Unmanned aerial vehicles;UAV;Surveillance;Motion segmentation;Vehicle routing;Collaboration;Reconnaissance;Autonomous aerial vehicles;Routing|
|[Scalable Channel Allocation in Downlink NOMA Using Parallel Array of Laser Chaos Decision-Maker](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048909)|M. Sugiyama; A. Li; M. Naruse; M. Hasegawa|10.1109/ICOIN56518.2023.10048909|Non-Orthogonal Multiple Access (NOMA);Throughput;Laser Chaos;Laser Chaos Decision-Maker;Channel Allocation;Chaos;Wireless communication;NOMA;Transmitters;Simulation;Scalability;Channel allocation|
|[Signal Strength Balanced Scheduling for Secure Ambient Backscatter Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049059)|Y. An; H. Park; W. Lee|10.1109/ICOIN56518.2023.10049059|Artificial Noise Injection;Ambient Backscatter Network;Collision Signal Decoding;Passive Eavesdropper;Physical-layer Security;Analytical models;Wireless networks;Simulation;Decoding;Security;Internet of Things;Backscatter|
|[Towards Generating Semi-Synthetic Datasets for Network Intrusion Detection System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048962)|N. -T. Nguyen; T. -N. Le; K. -H. Le-Minh; K. -H. Le|10.1109/ICOIN56518.2023.10048962|Intrusion detection system;synthetic data generation;generative adversarial networks;Training;Deep learning;Solid modeling;Network intrusion detection;Telecommunication traffic;Generative adversarial networks;Data models|
|[An Experimentation on CoAP Multi Factor Authentication Mechanism with Reputation for Internet of Things Constrained Devices and Low Power Wide Area Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048959)|W. D. R. Bezerra; R. D. N. Boing; C. A. de Souza; C. B. Westphall|10.1109/ICOIN56518.2023.10048959|security;authentication;network security;lpwan;Wide area networks;Multi-factor authentication;Protocols;Internet of Things;Security;Proposals|
|[Development of WLAN Topology Display System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048958)|O. Wongwirat; S. Chotiphan; T. Hongtong; N. Suttijumnong|10.1109/ICOIN56518.2023.10048958|Packet sniffer;WLAN;IEEE 802.11;network topology;and network monitoring;Wireless LAN;Network topology;Display systems;Prototypes;Software;Topology;Monitoring|
|[Relay Selection, Eavesdropper-Aware Relaying, PHY-Secrecy Capacity Analysis of Cooperative Wireless System over Hybrid Fading Channels](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049029)|T. Jain; S. Bitragunta; A. Bhatia|10.1109/ICOIN56518.2023.10049029|Cooperative relays;fading channels;selection;PHY security;secrecy capacity;secrecy efficiency;Fading channels;Wireless communication;Transmitters;Receivers;Probabilistic logic;Physical layer;Energy efficiency|
|[T-PASS: A Blockchain-based NFT Enabled Property Management and Exchange System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048973)|M. K. Hari; A. Agrawal; R. Bhatia; A. Bhatia; K. Tiwari|10.1109/ICOIN56518.2023.10048973|Blockchain;Smart Contracts;NFT;Ethereum;Property Exchange;Distributed ledger;Finance;Intellectual property;Games;Decentralized applications;Tokenization;Explosives|
|[Post Quantum Cryptography: A Review of Techniques, Challenges and Standardizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048976)|R. Bavdekar; E. Jayant Chopde; A. Agrawal; A. Bhatia; K. Tiwari|10.1109/ICOIN56518.2023.10048976|Post Quantum Cryptography;Quantum Computers;Shor’s Algorithm;NIST PQC Standardization;Computers;Performance evaluation;Quantum algorithm;Three-dimensional displays;Standardization;NIST;Cryptography|
|[Secure, Dynamic and Uncomplicated Licensing of Movies on a Blockchain Infrastructure](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049017)|J. Santos; I. Amorim; A. Ulisses; J. C. Lopes; V. Filipe|10.1109/ICOIN56518.2023.10049017|Blockchain;Smart contracts;Video licensing;Decentralized network;Media asset management;Costs;Video on demand;Redundancy;Prototypes;Licenses;Media;Blockchains|
|[Introduction to MITRE ATT&CK: Concepts and Use Cases](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048930)|S. B. Son; S. Park; H. Lee; Y. Kim; D. Kim; J. Kim|10.1109/ICOIN56518.2023.10048930|nan;Complex networks;Security;Cyberattack|
|[Android Malware Category and Family Classification Using Static Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049039)|C. -D. Nguyen; N. H. Khoa; K. N. -D. Doan; N. T. Cam|10.1109/ICOIN56518.2023.10049039|Android malware;CNN;Malware classification;Multi-class classification;Multi-category classifi-cation;Static analysis;Deep learning;Static analysis;Feature extraction;Malware|
|[Perceptual Encryption-based Privacy-Preserving Deep Learning for Medical Image Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048970)|I. Ahmad; S. Shin|10.1109/ICOIN56518.2023.10048970|privacy-preserving deep learning;EfficientNetV2;COVID-19;cloud-services;Deep learning;Cloud computing;Sensitivity;Image analysis;Organizations;Medical services;Encryption|
|[Towards Decentralized Autonomous Digital Signatures Using Smart Contracts](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049041)|K. Omote|10.1109/ICOIN56518.2023.10049041|nan;COVID-19;Privacy;Autonomous systems;Smart contracts;Blockchains;Digital signatures|
|[Privacy Data Protection Scheme of Industrial Field Equipment Based on Fully Homomorphic Encryption](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049058)|F. Xiao; J. Wang; M. Wei; C. Liu; V. Le; J. Xu|10.1109/ICOIN56518.2023.10049058|industrial field equipment;privacy data;fully homomorphic encryption;edge gateway;industrial cloud platform;Cloud computing;Sensitivity;Data security;Computational modeling;Data protection;Resists;Logic gates|
|[Generative Data Augmentation applied to Face Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049052)|M. Jabberi; A. Wali; A. M. Alimi|10.1109/ICOIN56518.2023.10049052|Face Recognition;Data augmentation;GANs;Deep CNNs;Pose variation;High Resolution;DCGAN;ESRGAN;Training;Face recognition;Pipelines;Superresolution;Neural networks;Generative adversarial networks;Robustness|
|[Privacy-Preserving Traffic Flow Prediction: A Split Learning Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048996)|N. -P. Tran; N. -N. Dao; Q. -T. Do; T. -V. Nguyen; S. Cho|10.1109/ICOIN56518.2023.10048996|traffic flow prediction;privacy-preserving;communication reduction;split learning;gated recurrent units;Training;Dimensionality reduction;Data privacy;Privacy;Neural networks;Government;Transportation|
|[Privacy Enhanced Federated Learning Utilizing Differential Privacy and Interplanetary File System](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049019)|H. Kim; I. Doh|10.1109/ICOIN56518.2023.10049019|Federated Learning;Differential Privacy;Interplanetary File System;IoT;Data Poisoning Attack;Deep learning;Privacy;Differential privacy;Federated learning;Generative adversarial networks;Safety;InterPlanetary File System|
|[Establishing Trustworthy Rational Friendships in Social Internet of Things](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048998)|R. U. Mustafa; A. McGibney; S. Rea|10.1109/ICOIN56518.2023.10048998|Social Internet of Things;Rational Friendships;Trustworthiness;Resource Management;Navigability;Friendship Selection and Management;Social networking (online);Computational modeling;Robustness;Social Internet of Things;Resource management;Load modeling|
|[Indirect Bluetooth Low Energy Connection Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048897)|O. Hujňák; K. Malinka; P. Hanáček|10.1109/ICOIN56518.2023.10048897|Bluetooth;Network Security;Monitoring;Intrusion Detection;Internet of Things;Wearable computers;Detectors;Metadata;Feature extraction;Internet of Things;Security;Bluetooth Low Energy|
|[IoT Gateways Network Communication Analysis](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049047)|J. Zbořil; O. Hujňák; K. Malinka|10.1109/ICOIN56518.2023.10049047|IoT;Internet of Things;IoT Gateways;Network Traffic Analysis;Attacks on IoT Devices;Telecommunication traffic;Logic gates;Fingerprint recognition;Safety;Internet of Things;Security;Monitoring|
|[Applying of Websocket and WebRTC for Video Calling in Telemedicine during COVID-19 Pandemic](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048899)|K. Namee; P. Sawatdee; N. Promsorn; S. Chinchua; A. Meny; S. Kulchan|10.1109/ICOIN56518.2023.10048899|Websocket;WebRTC;Telemedicine;Teleconsultation;Video Calling;COVID-19;Pandemics;Hospitals;Telemedicine;Memory management;Social factors;Uplink|
|[Characteristics of FRET-based Molecular Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049048)|T. Duong; S. Kwon|10.1109/ICOIN56518.2023.10049048|Molecular communication;FRET;probability;Analytical models;Energy exchange;Stochastic processes;Fluorescence;Molecular communication (telecommunication)|
|[UAV-Based Data Collection and Wireless Power Transfer System with Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048978)|J. Lee; S. Seo; H. Ko|10.1109/ICOIN56518.2023.10048978|Internet of Things (IoT);unmanned aerial vehicle (UAV);deep reinforcement learning (DRL);proximal policy optimization algorithm (PPO);energy;lifetime;Deep learning;Wireless communication;Wireless sensor networks;Reinforcement learning;Wireless power transfer;Data aggregation;Autonomous aerial vehicles|
|[ABEP Analysis of Coded and Uncoded Mixed RF/FSO Communication System SWIPT-based](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048988)|S. Labghough; F. Ayoub; F. E. Bouanani; M. Belkasmi; K. A. Qaraqe|10.1109/ICOIN56518.2023.10048988|Error probability;energy Harvesting;Málaga-ℳ fading;RF/FSO communication;Rayleigh fading;CSOC codes;Radio frequency;Error probability;Communication systems;Transmitters;Rayleigh channels;Switches;Encoding|
|[Secrecy Performance of Energy Harvesting Based D2D Communications in Spectrum-Sharing Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048975)|S. Kumar; S. Thakur; A. Singh|10.1109/ICOIN56518.2023.10048975|Cognitive radio;energy harvesting (EH);physical layer security;power outage probability;secrecy outage probability;relaying protocol;Protocols;Switches;Probability;Device-to-device communication;Power system reliability;Energy harvesting;Communication networks|
|[Security of Energy Harvesting Based D2D Communications in Cognitive Cellular Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048923)|K. S. Venkateswara Rao; A. Singh|10.1109/ICOIN56518.2023.10048923|Cognitive cellular networks;D2D communications;energy harvesting;secrecy outage probability;Cellular networks;Transmitters;Receivers;Probability;Device-to-device communication;Power system reliability;Performance analysis|
|[ResNet-TCN: A Joint Model for ECG Heartbeat Classification with High Accuracy](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048913)|S. Hu; R. Li; Q. Hu; G. Qiao|10.1109/ICOIN56518.2023.10048913|electrocardiogram (ECG);joint model;residual network (ResNet);temporal convolutional network (TCN);Heart;Sensitivity;Heart beat;Electrocardiography;Feature extraction;Data models;Spatial databases|
|[Histopathological Classification of Colorectal Polyps using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048925)|M. P. Paing; O. -S. Cho; J. -W. Cho|10.1109/ICOIN56518.2023.10048925|colorectal cancer;denseNet;mixup augmentation;cutout augmentation;label smoothing;rectified Adam;Deep learning;Adaptation models;Smoothing methods;Transfer learning;Estimation;Data models;Classification algorithms|
|[Link-Level Assessment of NOMA Aided Multi-hop DECT-2020 New Radio for mMTC Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048900)|S. Soni; R. Makkar; D. Rawal; N. Sharma|10.1109/ICOIN56518.2023.10048900|Decode-and-forward;digital enhanced cordless telecommunication-2020 new radio;massive machine-type communication;multi-hop;non-orthogonal multiple access;Wireless communication;NOMA;Spectral efficiency;Simulation;Bit error rate;Spread spectrum communication;ITU|
|[A New Chapter for Medical Image Generation: The Stable Diffusion Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049010)|L. X. Nguyen; P. Sone Aung; H. Q. Le; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10049010|Medical Image Generation;Diffusion Model;UNet architecture;CT scan of Covid-19;Training;COVID-19;Performance evaluation;Power demand;Image synthesis;Computational modeling;Data models|
|[Heimdall: Blockchain-Based Consent Management Framework](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048920)|F. M. V. Filho; B. L. De Alcantara Batista; J. C. Júnior; J. N. De Souza|10.1109/ICOIN56518.2023.10048920|Eletronic Health Record;Blockchain;Consent;Smart contract;GDPR;Cloud computing;Smart contracts;Medical services;Writing;Throughput;Data models;Blockchains|
|[Classification performance evaluation of latent vector in encoder-decoder model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048940)|K. Kang; C. Bae|10.1109/ICOIN56518.2023.10048940|Autoencoder;encoder;decoder;latent vector;unsupervised learning;Performance evaluation;Support vector machines;Analytical models;Supervised learning;Transforms;Data models;Decoding|
|[A Research on Low Latency Motion Control System using Real-time Scheduling in Edge Server](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048964)|D. Ko; J. Jeon; S. Kang|10.1109/ICOIN56518.2023.10048964|Real-time Linux;Low latency;Motion control;Edge computing system;Job shop scheduling;Processor scheduling;Service robots;Process control;Control systems;Real-time systems;Delays|
|[Noise Reduction Caused by External Events in Wireless Sensor Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049027)|T. T. Lai; J. Cho; M. Yoo|10.1109/ICOIN56518.2023.10049027|Wireless sensor network;noise reduction;weather effect;data reconstruction;CNN autoencoder;Wireless communication;Wireless sensor networks;Data privacy;Noise reduction;Reconstruction algorithms;Data transfer;Sensors|
|[Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048928)|C. M. Thwal; Y. L. Tun; K. Kim; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10048928|attentive aggregation;federated learning;multi-head self-attention;time series stock forecasting;transformer;Training;Data privacy;Technological innovation;Federated learning;Time series analysis;Transformers;Natural language processing|
|[Seamless and Intelligent Resource Allocation in 6G Maritime Networks Framework via Deep Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049050)|S. Salman Hassan; S. -B. Park; E. -N. Huh; C. Seon Hong|10.1109/ICOIN56518.2023.10049050|6G;satellite networks;resource allocation;marine users;deep reinforcement learning;6G mobile communication;Deep learning;Wireless communication;Satellites;Spectral efficiency;Power distribution;Power system stability|
|[Advanced Signal Processing of Photo-Excited Current Spectroscopy Based on Trap State Distribution for Photo-Sensor Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048911)|D. Kim; H. Lee; J. -H. Kwon; J. Park; B. Kim; E. -J. Kim|10.1109/ICOIN56518.2023.10048911|Density of states calculation;Photo-sensor;Signal processing;Semiconductor device modeling;Spectroscopy;Semiconductor device measurement;Voltage measurement;Photonic band gap;Current measurement;Signal processing|
|[Resource Allocation Reinforcement Learning for Quality of Service Maintenance in Cloud-Based Services](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048905)|D. Hong; D. Kim; O. J. Min; Y. Shin|10.1109/ICOIN56518.2023.10048905|Resource Allocation;Reinforcement Learning;Deep Q Network;Cloud Computing;Costs;Neural networks;Buildings;Reinforcement learning;Predictive models;Prediction algorithms;Hardware|
|[Segmentation of Cerebral Hemorrhage CT Images using Swin Transformer and HarDNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049057)|Z. Piao; Y. H. Gu; S. J. Yoo; M. Seong|10.1109/ICOIN56518.2023.10049057|HarDNet;Segmentation;Swin Transformer;Image segmentation;Three-dimensional displays;Computed tomography;Magnetic resonance imaging;Transformers;Data models;Hemorrhaging|
|[Layer-wise Knowledge Distillation for Cross-Device Federated Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049011)|H. Q. Le; L. X. Nguyen; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10049011|Federated Learning;Knowledge Distillation;Training;Performance evaluation;Degradation;Federated learning;Data models;Servers;Task analysis|
|[Vehicle Platooning Algorithm for Improving Following Control](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048990)|Y. Li; S. -C. Kim|10.1109/ICOIN56518.2023.10048990|Connected Autonomous Driving (CAD);Vehicle-to-vehicle(V2V);Queue;Vehicle Platooning;Multi-access Edge Computing (MEC);Packet loss;Bandwidth;Stability analysis;Real-time systems;Delays;Safety;Feeds|
|[A Review on Rate-Splitting Multiple Access-Assisted Downlink Networks: Rate Optimizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049025)|A. -T. Tran; D. S. Lakew; D. Thien Hua; Q. T. Do; N. -N. Dao; S. Cho|10.1109/ICOIN56518.2023.10049025|Rate-splitting multiple access;weighted sum-rate;maximum minimum rate;downlink;1-layer RSMA;RS-CMD;Measurement;NOMA;Transmitters;5G mobile communication;Bandwidth;Performance gain;Downlink|
|[Integrated Optimization in Training Process for Binary Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048969)|Q. Hieu Vo; S. Hoon Hong; L. -W. Kim; C. Seon Hong|10.1109/ICOIN56518.2023.10048969|Binary Neural Network;Deep Neural Network;Deep Learning;Machine Learning;Training;Degradation;Quantization (signal);Limiting;Neural networks;Memory management;Optimization methods|
|[Phase-Compensating-Circuit Design Utilizing Linear-Nonlinear Joint Optimizations](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048985)|T. -B. Deng|10.1109/ICOIN56518.2023.10048985|Phase-compensating circuit (PCC);all-pass PCC;phase approximation;linear-nonlinear optimization;Stability analysis;Circuit stability;Matrix converters;Integrated circuit modeling;Optimization|
|[AI BOX: Artificial intelligence-based autonomous abnormal network traffic response mechanism](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048979)|J. -L. Chen; Z. -Z. Chen; Y. -S. Chang; C. -I. Li; T. -I. Kao; Y. -T. Lin; Y. -Y. Xiao; J. -F. Qiu|10.1109/ICOIN56518.2023.10048979|Artificial Intelligence;Information Security;Machine Learning;IoT Device Security;Random Forest Algorithm;Confidentiality of Data Transfer;Packet Capture Analysis;Tracking;Information security;Telecommunication traffic;Machine learning;Predictive models;Feature extraction;Production facilities|
|[Semantic Communication for AR-based Services in 5G and Beyond](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049026)|T. Nguyen Dang; L. X. Nguyen; H. Q. Le; K. Kim; S. M. Ahsan Kazmi; S. -B. Park; E. -N. Huh; C. Seon Hong|10.1109/ICOIN56518.2023.10049026|Semantic communication;Augmented Reality;B5G;Multi-access edge computing;5G mobile communication;Wireless networks;Semantics;Neural networks;Symbols;Feature extraction|
|[The Performance of Graph Neural Network in Detecting Fake News from Social Media Feeds](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048961)|I. Islam Shovon; S. Shin|10.1109/ICOIN56518.2023.10048961|Fake News Detection;Graph Neural Network;Social Media;Data analysis;Analytical models;Social networking (online);Instruction sets;Machine learning;Writing;Graph neural networks;Natural language processing|
|[Hand Bone X-rays Segmentation and Congregation for Age Assessment using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048972)|K. Jung; T. Duc Nguyen; D. -T. Le; J. Bum; S. S. Woo; H. Choo|10.1109/ICOIN56518.2023.10048972|Medical Image Processing;Bone Age Assessment;Deep Learning;Segmentation;Congregation;Deep learning;Image segmentation;Pediatrics;Ionizing radiation;Medical services;Predictive models;Bones|
|[Spectral Efficiency Maximization for V2V Communication Underlaid Cellular Uplink Using Deep Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049008)|D. Ron; E. Han; J. -R. Lee|10.1109/ICOIN56518.2023.10049008|V2V communication;transmit power control;spectral efficiency;and deep neural networks;Deep learning;Transmitters;Neural networks;Vehicular ad hoc networks;Interference;Transceivers;Computational complexity|
|[Applying Deep Knowledge Tracing Model for University Students’ Programming Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048977)|H. -C. Hung; P. -H. Lee|10.1109/ICOIN56518.2023.10048977|Learning analysis;Educational data mining;Deep knowledge tracing;Programming education;Knowledge engineering;Learning management systems;Target tracking;Education;Focusing;Trajectory;Systems support|
|[A Data-plane Approach for Detecting Malware in IoT Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048918)|M. H. Gaikar; K. Haribabu|10.1109/ICOIN56518.2023.10048918|p4;data plane programming;machine learning;malware;malicious;Training;Measurement;Support vector machines;Botnet;Telecommunication traffic;Malware;Classification algorithms|
|[Cooperative User Relaying with RSMA for 6G Networks: Overview, Research Challenges and Future Trends](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048951)|C. M. Ho; T. Vi Nguyen; C. Lee; T. M. T. Nguyen; S. Cho|10.1109/ICOIN56518.2023.10048951|Cooperative User Relaying;RSMA;CRS;6G networks;6G mobile communication;Transmitters;Wireless networks;Interference;Tutorials;Market research;Transceivers|
|[Trajectory Optimization of Multiple Urban Air Mobility for Reliable Communications with Integrated Space-Air-Ground Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048981)|Y. M. Park; K. Kim; S. -B. Park; C. S. Hong|10.1109/ICOIN56518.2023.10048981|Urban air mobility;trajectory optimization;integrated network;reinforcement learning;Satellites;Smart cities;Simulation;Buildings;Complexity theory;Reliability;Trajectory optimization|
|[Implementation of Edge Servers on an Open 5G Core Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049000)|P. Vanichchanunt; I. Yamyuan; P. Sasithong; L. Wuttisittikulkij; S. Paripurana|10.1109/ICOIN56518.2023.10049000|Edge server;edge computing;core network;5G network;5G mobile communication;Delay effects;ETSI;Jitter;User experience;Time measurement;Delays|
|[A Long Distance Low Bandwidth Firmware Update process for LPWAN - Taking LoRaP2P+ as example](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048943)|D. H. Teck Khieng; Y. -Z. Xie; J. -C. Zhang; N. -F. Huang|10.1109/ICOIN56518.2023.10048943|Wireless;LPWAN;LoRaP2P;LoRaP2P+;Reliability;Automation Control System;FUOTA;Firmware Update;Meters;Irrigation;Scalability;Crops;Process control;Production;Robustness|
|[A PSK-based Multi-hop Authentication for Home Network and its Implementation Using PUCC Protocol](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049046)|T. Kato; N. Ishikawa|10.1109/ICOIN56518.2023.10049046|Overlay network;Home network;Network security;PUCC;IoT;M2M;Wireless communication;Protocols;Home automation;Authentication;Public key;Spread spectrum communication;Passwords|
|[UAVs Reformation Approach Based on Packet Loss in GPS-Denied Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049031)|I. Srisomboon; S. Lee|10.1109/ICOIN56518.2023.10049031|aerial surveillance;drone swarm formation;packet transmission;GPS-denied environments;Location awareness;Industries;Surveillance;Packet loss;Autonomous aerial vehicles;Real-time systems;Drones|
|[A Brief review on Network Identity-based Moving Target Defense](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048901)|N. Saputro|10.1109/ICOIN56518.2023.10048901|Moving Target Defense;Security;Network Identity;Protocols;TCPIP;Network architecture|
|[FedBeam: Federated learning based privacy preserved localization for mass-Beamforming in 5GB](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048980)|D. Sharma; A. Kumar; R. B. Battula|10.1109/ICOIN56518.2023.10048980|Beyond 5G (5GB);mass-Beamforming;Ultra-accurate localization;Channel State Information (CSI);Federated Learning (FL);Location awareness;Deep learning;Data privacy;Privacy;5G mobile communication;Federated learning;Massive MIMO|
|[Controlling and simulation system for hydraulic valve testing based on Qt](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049037)|X. Liu; C. Zhang|10.1109/ICOIN56518.2023.10049037|controlling system;simulation;Qt;hydraulic valve testing;Three-dimensional displays;Process control;Hydraulic systems;Life estimation;Valves;Control systems;Software|
|[ParaNet: A Single Blocked Network for Mobile Edge Computing Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048960)|S. Akhter; M. Imtiaz Hossain; M. Delowar Hossain; C. Seon Hong; E. -N. Huh|10.1109/ICOIN56518.2023.10048960|ParaNet;Knowledge Distillation;Mobile Edge Computing;Performance evaluation;Microservice architectures;Computer architecture;Parallel processing;Network architecture;Real-time systems;Time factors|
|[State of the Art Analysis of Resource Allocation Techniques in 5G MIMO Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049018)|C. Bouras; I. Caragiannis; A. Gkamas; N. Protopapas; T. Sardelis; K. Sgarbas|10.1109/ICOIN56518.2023.10049018|5G;MIMO;DUDe;Machine Learning;Game Theory;Resource Allocation;Industries;Machine learning algorithms;5G mobile communication;Machine learning;Throughput;MIMO;Resource management|
|[A Review on Satellite-Terrestrial Integrated Wireless Networks: Challenges and Open Research Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049009)|D. S. Lakew; A. -T. Tran; A. Masood; N. -N. Dao; S. Cho|10.1109/ICOIN56518.2023.10049009|LEO satellite;integrated networks;satellite networks;edge computing;aerial networks;wireless network;Wireless networks;Roads;Satellite broadcasting;Massive machine type communications;Low earth orbit satellites;Focusing;Network architecture|
|[A Survey on Mobile Edge Computing for Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048953)|P. Choi; J. Kwak|10.1109/ICOIN56518.2023.10048953|mobile edge computing;deep learning;resource allocation;Deep learning;Performance evaluation;Adaptation models;Multi-access edge computing;Computational modeling;Taxonomy;Optimization methods|
|[A Dynamic Scheduling Technique to Optimize Energy Consumption by Ductless-split ACs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048941)|K. Kaushik; P. Agrawal; V. Naik|10.1109/ICOIN56518.2023.10048941|IoT;smart buildings;cooling system;grid control;CPS;dynamic scheduling;Heating systems;Greedy algorithms;Energy consumption;Schedules;Cooling;Buildings;Dynamic scheduling|
|[A Study on the Derivation of Essential Security Elements through Analysis of Non-face-to-face Telehealth Service Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048931)|J. Jin; S. Park; I. Lee|10.1109/ICOIN56518.2023.10048931|Healthcare;Telehealth;Service Model;Security Elements;Non-face-to-face;Performance evaluation;Analytical models;Hospitals;Telemedicine;Surgery;Mobile handsets;Software|
|[Hybrid MAC for Military UAV Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048910)|G. S. Kim; H. Lee; C. Park; S. Jung; J. -H. Kim; J. Kim|10.1109/ICOIN56518.2023.10048910|nan;Military communication;Autonomous aerial vehicles;Throughput;Media Access Protocol;Manufacturing;Servers|
|[DCGit: Decentralized Internet Hosting for Software Development](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049006)|P. Gupta; K. Shah; A. Agrawal; R. Bhatia; A. Bhatia; K. Tiwari|10.1109/ICOIN56518.2023.10049006|Git;GitHub;Software Development;Ethereum;Smart Contract;Solidity;Blockchain;IPFS;Privacy;Scalability;Collaboration;Intellectual property;Software;Blockchains;Internet|
|[More general discussions on information transfer of the centralized network system with coupled oscillations and random matrices](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049054)|T. Hoshiyama; H. Shimoyama|10.1109/ICOIN56518.2023.10049054|random matrix;Laplacian matrix;sparse matrix;large-sized network;network dynamics;Eigenvalues and eigenfunctions;Sparse matrices;Oscillators;Network systems;Context modeling|
|[A Review on Reinforcement Learning enabled Cooperative Spectrum Sensing](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048946)|T. T. H. Pham; S. Cho|10.1109/ICOIN56518.2023.10048946|Cognitive Radio Networks (CRNs);Cooperative Spectrum Sensing (CSS);Reinforcement Learning (RL);Deep Reinforcement Learning (DRL);Fading channels;Reinforcement learning;Sensors;Cognitive radio;Radio spectrum management;Shadow mapping|
|[Multi-Person 3D Pose Estimation in Mobile Edge Computing Devices for Real-Time Applications](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049033)|M. Imtiaz Hossain; S. Akhter; M. Delowar Hossain; C. Seon Hong; E. -N. Huh|10.1109/ICOIN56518.2023.10049033|RRMPs;Lightweight Architecture;Depthwise Separable Convolutions;Pose Inference;3D Pose Estimations;Mobile Edge Computing;Residual Connection;Performance evaluation;Solid modeling;Three-dimensional displays;Multi-access edge computing;Motion estimation;Computational modeling;Pose estimation|
|[The study on TAVR Medical Twin Method Based on Real World Data(RWD)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049049)|S. -M. Hyun; K. Lee|10.1109/ICOIN56518.2023.10049049|TAVR;Medical Twin;Real World Data;Analytical models;Surgery;Virtual environments;Predictive models;Stroke (medical condition);Valves;Real-time systems|
|[GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048926)|J. Su Yoon; S. Moo Kang; S. Bae Park; C. Seon Hong|10.1109/ICOIN56518.2023.10048926|Federated learning;Fedavg;GNN;GDFed;Performance evaluation;Federated learning;Computational modeling;Computer architecture;Real-time systems;Graph neural networks;Delays|
|[A Study on the Cluster-wise Regression Model for Bead Width in the Automatic GMA Welding](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049016)|B. Ram Lee; W. Bin Oh; H. Hyoung Kim; Y. Jae Jeong; J. Seung Yoon; I. Soo Kim|10.1109/ICOIN56518.2023.10049016|GMA(Gas Metal Arc) welding process;Cluster-wise regression model;Intelligent model;Bead geometry;Welding quality;Welding;Clustering algorithms;Voltage;Production;Predictive models;Prediction algorithms;Regression analysis|
|[Techno-Economic Analysis of IoT Networks in 5G](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049023)|C. Bouras; C. Chatzigeorgiou; A. Kollia; P. Pouyioutas|10.1109/ICOIN56518.2023.10049023|IoT;CAPEX;OPEX;Femtocells;LoRa WAN networks;Wide area networks;Analytical models;Costs;Power demand;5G mobile communication;Scalability;Internet of Things|
|[Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049003)|H. Baek; Y. Jeong Anna Ha; M. Yoo; S. Jung; J. Kim|10.1109/ICOIN56518.2023.10049003|mmWave Radar;human gesture recognition;autonomous driving;deep learning;Deep learning;Roads;Noise reduction;Millimeter wave measurements;Radar imaging;Classification algorithms;Sensors|
|[Modern Trends in Quantum AI: Distributed and High-Definition Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048995)|J. Pyoung Kim; W. Joon Yun; H. Baek; J. Kim|10.1109/ICOIN56518.2023.10048995|nan;Deep learning;Computers;Computer aided instruction;Quantum computing;Distance learning;Computational modeling;Simulation|
|[Hands-up-go: Development of gas efficient Blockchain event DApp](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049034)|A. Kim; M. Essaid; J. Ryu; H. Ju|10.1109/ICOIN56518.2023.10049034|Ethereum;DApp;Gas;Event;Development;Costs;Smart contracts;Web pages;Decentralized applications;Blockchains;Fraud;Security|
|[A Review on Congestion Control for Internet of Deep Space Things Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048993)|A. Masood; T. Ha; D. Shumeye Lakew; N. -N. Dao; D. Thien Hua; G. Woraphonbenjakul; S. Cho|10.1109/ICOIN56518.2023.10048993|Congestion control;Internet of deep space things;Transport protocols;Space vehicles;Earth;Machine learning;Internet;Reliability;Deep-space communications|
|[Key generation and management method using AI generated Rubik’s cube states](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048916)|J. Jin; S. Park|10.1109/ICOIN56518.2023.10048916|Artificial intelligence;key generation;symmetric-key encryption method;cryptography;Information security;Web and internet services;Focusing;Receivers;Companies;Speech recognition;Encryption;Internet|
|[A Survey on Fuzzy Logic for Cluster Head Selection in Wireless Sensor Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049015)|G. Woraphonbenjakul; A. Masood; S. Cho|10.1109/ICOIN56518.2023.10049015|Wireless sensor network;clustering;fuzzy logic;network lifetime;Fuzzy logic;Wireless communication;Wireless sensor networks;Base stations;Clustering algorithms;Energy efficiency;Sensors|
|[A Review on Recent Approaches in mmWave UAV-aided Communication Networks and Open Issues](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049043)|Q. Tuan Do; D. Shumeye Lakew; A. Tien Tran; D. Thien Hua; S. Cho|10.1109/ICOIN56518.2023.10049043|UAV;mmWave;reinforcement learning;wireless communication;Learning systems;Wireless communication;Three-dimensional displays;Autonomous aerial vehicles;Market research;Communication networks;Millimeter wave communication|
|[The Application of Distributed Ledger Technology in Agribusiness](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048992)|Z. Chen|10.1109/ICOIN56518.2023.10048992|Distributed Ledger Technology (DLT);Agribusiness Industry;Food Supply Chain;Blockchain;Factor Variable;Information Data Security;Logistics Regression;Industries;Privacy;Distributed ledger;Supply chains;Project management;Companies;Regulation|
|[Hand Written Digits Recognition based on Concatenated LSTMs](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048934)|N. Kaneko; M. Ogawa|10.1109/ICOIN56518.2023.10048934|Contactless Operation;LSTM;Hand Tracking Sensor;Leap Motion Controller;COVID-19;Training;Adaptation models;Analytical models;Tracking;Training data;Data models|
|[Abnormal Client Detection Federated Learning Using Image Vectors](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048907)|J. Park; K. Tae Kim; S. -B. Park; C. Seon Hong|10.1109/ICOIN56518.2023.10048907|Federated Learning;Vector;Cosine Similarity;Training;Federated learning;Computational modeling;Distributed databases;Information sharing;Data models;Convergence|
|[Design and Analysis of 29 GHz Millimeter-waves Phased Array Antenna with Reduced Mutual Coupling](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048974)|A. Ahmad; D. -y. Choi; H. Jung Kim; Y. Hwang Lee; O. Mi Kyung; G. Ok Lee|10.1109/ICOIN56518.2023.10048974|MIMO antenna;ECC;Phased array;Diversity gain;s-parameter;Phased arrays;Mutual coupling;5G mobile communication;Mobile antennas;MIMO;Scattering parameters;Millimeter wave communication|
|[Deep Learning for 2D-MIMO scheme based on Optical Camera Communication](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049055)|H. Nguyen; V. L. Nguyen; D. H. Tran; Y. M. Jang|10.1109/ICOIN56518.2023.10049055|2D-MIMO;Optical Camera Communication;Deep Learning;Wireless communication;Integrated optics;Deep learning;Wires;Cameras;Light fidelity;Light emitting diodes|
|[A V2X Access Authorization Mechanism based on Decentralized ID (DID) and Verifiable Credentials (VC)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048986)|J. Lim; H. Oh; K. Sim; S. Kim; K. -H. Kim|10.1109/ICOIN56518.2023.10048986|Blockchain;V2X;SCMS;Authorization;DID;VC;Authorization;Data privacy;Buildings;Authentication;Lead;Reliability;Security|
|[A Practical HMM-Based Map-Matching Method for Pedestrian Navigation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049007)|S. Ma; H. Lee|10.1109/ICOIN56518.2023.10049007|pedestrian navigation;map-matching;hidden Markov model (HMM);map-matching initialization;Navigation;Current measurement;Roads;Urban areas;Hidden Markov models;Markov processes;Trajectory|
|[A Review on Matching-based Models for Distributed Computation Offloading in Fog-enabled IoT Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049002)|H. Tran-Dang; D. -S. Kim|10.1109/ICOIN56518.2023.10049002|Fog computing;Matching theory;Distributed algorithm;Computational offloading;Performance evaluation;Computational modeling;Distributed algorithms;Distributed computing;Computational complexity;Optimization;Edge computing|
|[Contents Delivering Network on Constellation Satellite using THz: Latency Minimization and Energy Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048912)|M. S. Kim; S. H. Hong; C. S. Hong|10.1109/ICOIN56518.2023.10048912|LEO satellite;THz Network;Intersatellite communication;Energy Optimization;Route Optimization;Airplanes;Mesh networks;Satellites;Low earth orbit satellites;Reinforcement learning;Routing;Minimization|
|[Computation Offloading Strategy Based on Multi-armed Bandit Learning in Microservice-enabled Vehicular Edge Computing Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048991)|M. D. Hossain; T. Sultana; S. Akhter; M. I. Hossain; G. -W. Lee; C. S. Hong; E. -N. Huh|10.1109/ICOIN56518.2023.10048991|vehicular edge computing;microservice;MAB theory;task offloading;vehicular networks;Road transportation;Simulation;Microservice architectures;Real-time systems;Delays;Computational efficiency;Proposals|
|[Sionna: Introduction to Embedded Open-Source Semantic Communication Platforms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048938)|J. -H. Lee; J. Kim|10.1109/ICOIN56518.2023.10048938|nan;Wireless communication;Protocols;Quadrature amplitude modulation;Simulation;Semantics;Symbols;Modulation|
|[Exaggerated Advertisement Inspection System for Judging the Suitability of Advertisements in Social Media Environment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048933)|Y. Park; Y. Kim; J. Mun; J. Choi; J. Choi; Y. Cho|10.1109/ICOIN56518.2023.10048933|social media advertisements;advertisement classification;language model;explainable artificial intelligence (XAI);health functional food;Analytical models;Social networking (online);Shape;Inspection;Writing;Data models;Artificial intelligence|
|[Abnormal human behavior detection based on VAE-LSTM hybrid model in WiFi CSI with PCA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048984)|Y. Kim; S. -C. Kim|10.1109/ICOIN56518.2023.10048984|LSTM;VAE;CNN;CSI;autoencoder;PCA;RNN;Smart City;IOT;Deep learning;Performance evaluation;Location awareness;Supervised learning;Data models;Behavioral sciences;Internet|
|[A Reference Architecture for Activities-as-Asset Distributed Ledger with Secure Private Computation](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10048971)|C. -F. Chiang; A. Tekeoglu; S. Sengupta; T. -C. Wei; A. Gregory; D. Kusukuntla|10.1109/ICOIN56518.2023.10048971|nan;Distributed ledger;Scalability;Ecosystems;Computer architecture;Planning;Blockchains;Contracts|
|[Multi-Keyword Based Information Routing in Peer-to-Peer Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10049045)|H. Tran; M. Miao; F. Bastani; I. -L. Yen|10.1109/ICOIN56518.2023.10049045|nan;Soft sensors;Query processing;Semantics;Software algorithms;Information-centric networking;Routing;Routing protocols|