{"id":23064433,"url":"https://github.com/hiejulia/ai-project","last_synced_at":"2025-04-28T16:16:50.603Z","repository":{"id":39940101,"uuid":"239288459","full_name":"hiejulia/AI-project","owner":"hiejulia","description":"AI projects that cover all posibilities","archived":false,"fork":false,"pushed_at":"2025-02-11T16:54:22.000Z","size":14982,"stargazers_count":5,"open_issues_count":3,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-28T16:16:42.602Z","etag":null,"topics":["ai","algo","algorithms","deep-learning","iot","ml","neural-network","nlp","openai-gym","prolog","reinforcement-learning","smart-cities"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hiejulia.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-02-09T10:54:05.000Z","updated_at":"2025-02-11T16:54:27.000Z","dependencies_parsed_at":"2025-02-08T21:54:07.864Z","dependency_job_id":null,"html_url":"https://github.com/hiejulia/AI-project","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hiejulia%2FAI-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hiejulia%2FAI-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hiejulia%2FAI-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hiejulia%2FAI-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hiejulia","download_url":"https://codeload.github.com/hiejulia/AI-project/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251342725,"owners_count":21574245,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","algo","algorithms","deep-learning","iot","ml","neural-network","nlp","openai-gym","prolog","reinforcement-learning","smart-cities"],"created_at":"2024-12-16T04:17:59.667Z","updated_at":"2025-04-28T16:16:50.593Z","avatar_url":"https://github.com/hiejulia.png","language":"Jupyter Notebook","funding_links":["https://www.buymeacoffee.com/hientech"],"categories":[],"sub_categories":[],"readme":"\u003ca href=\"https://www.buymeacoffee.com/hientech\" target=\"_blank\"\u003e\u003cimg src=\"https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee\" alt=\"Buy Me A Coffee\"\u003e\u003c/a\u003e\n  \u003cbr\u003e\n# AI projects\nProof of concept of the state of the art AI with practical \u0026 research examples \n(with code demos)\n- narrow AI \n- general AI \n- super AI \n\n\n## Collections of algorithms optimization \n\n+ Optimal path using BFS, DFS \n+ AI search algo \n\n\n## AI search algo \n- dijkstra search \n- heuristics \n- A* algo\n    \n\n\n## AI algorithm\n+ Determined the optimal next move of a chessboard game using Minimax algorithm with Alpha-beta pruning\n- The minimum cost transaction for a goal state \n- A sequence of transitions to a minimum cost goal \n- A minimum cost transaction for a minimum cost goal \n\n\n\n## AI in Finance \n\n\n\n## AI in Bioinformatics \n\n\n\n## AI in game \n- rule based system \n- Prolog \n    - `swipl`\n    - `brew install swi-prolog`\n    - prolog query \n    \u003ca href=\"https://imgur.com/cEGMZb0\"\u003e\u003cimg src=\"https://i.imgur.com/cEGMZb0.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n\n\n- The min-max algorithm\n\n\n## Edge AI \n- edge service \n    - smartphone \n    - devices \n    - microcontroller \n- openvm\n- jevois \n- google edge TPU \n- movidius \n- nvidia jetson \n- UP AI Edge \n- Ultra96 \n- TF Lite \n- utensor\n- qualcomm neural processing SDK for AI \n- huawei NPU \n\n## AI use case \n- fraud detection \n- https://www.gurobi.com/\n- integer linearn programming\n    - https://www.gurobi.com/\n    - https://www.ibm.com/analytics/cplex-optimizer\n\n- robotics \n    - https://www.ros.org/\n\n\n## AI in IIoT\n- optimize logistics\n- Electrical load forecasting\n- Implementing a code to perform preventive maintenance based on aircraft engine sensors data\n\n- deploy machine-to-machine (M2M) and machine-to-human (M2H) communication, along with AI-powered analytical algorithms, enabling predictive maintenance, that predict the breakdown before it occurs using past data.\n- monitoring parameters/sensor\n    - Vibration sensors mainly used to detect misalignment, imbalance, mechanical looseness, or wear on pumps and motors\n    - Current/voltage sensors to measure the current and voltage supplied to an electric motor\n    - Ultrasound analysis to detect leakage in pipe systems or tanks, or mechanical malfunctions of movable parts and faults in electrical equipment\n    - Infrared thermography to identify temperature fluctuations\n    - Sensors to detect liquid quality (for example in the case of wine sensors to detect the presence of different elements in the wine) \n\n\n- DL model: RNN, LSTM\n- STLF using LSTM\n    - dataset : https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption#\n    - 2 LSTM and 1 connected layer \n    \n\n## AI in Cybersecurity \n- Predictive model for credit card fraud detection\n    - big data analytics to integrate information from different sources\n    - ensemble learning\n        - Use bagging and boosting algorithms \n        - Adaptive Boosting (AdaBoost)\n        - gradient boosting algorithm\n    - sampling techniques to rebalance datasets, thereby improving prediction accuracy\n        - Oversampling with SMOTE\n            - Synthetic Minority Over-sampling Technique (SMOTE)\n\n- GANs - Attacks and defense \n    - forward propagation \n    - backpropagation \n- Feedforward neural networks (FFNNs)\n- Recurrent neural network (RNNs)\n    - network traffic analysis \n- Convolutional neural networks (CNNs)\n\n\n\n- Spam detection\n- Fraud detection algorithms\n- Biometric authentication with facial recognition\n- Classifying suspicious user activity\n- User authentication with keystroke recognition\n- Suspect fraud \n- Application security : \n    - attacks : SSRF, SQL injection, XSS, DDoS \n- Endpoint protection \n    - ransomware \n- Network protection \n    - intrusion detection system \n- Some tasks    \n    - Predict : NN, DL \n    - Clustering \n- Multi Layer Perceptron \n\n\n\n- Using : \n    - https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.RandomUnderSampler.html \n    \n\n\n\n## AI in IoT \n- Self driving solution \n- Safe route parameter to trip planners \n- Apply CNN to parking lot \n- Apply SVM to safety on trip planning \n- Teaching MDP to find the safest route \n- Perform supervised and unsupervised machine learning for IoT data\n- Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms\n- Forecast time-series data using deep learning methods\n\n- build smart systems for IoT\n\n- monitor heart disease using ML \n- Smart home\n- devices used in smart home\n- AI in predicting human activity recognition\n\n\n\n- set up RL-DL-CRLMM model\n    - webcam images in real time \n    - CRL- CNN \n        - gap in parking lot \n        - SVM - optimizer \n        - MDP \n        - RL - DL - CRLMM find parking lot - available space \n        - Circular RL- DL - CRLMM \n            - CNN \n            - Markov decision process MDP \n            - CRLMM - recognize parkigng space in parking lot and send signal to self signal to self driving vehicle \n                - gaps, space between 2 objects \n                - context to establish whether this space between objects is positive or negative distance \n\n\n- IP camera : obtain right real time frames from webcam : lighting const, etc \n- Dataset : \n    - training set, test set \n- model trained : CNN Concept Strategy. py \n    - Classify parking lot : \n- Add SVM function to increase safety level \n    - avoid traffic \n    - read lat/long of datapoint in another table to convet back to GPS format \n    - sklearn\n    \u003ca href=\"https://imgur.com/LShCcXm\"\u003e\u003cimg src=\"https://i.imgur.com/LShCcXm.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e \n    - `make_blobs`\n\n- classify\n\n\n- IP camera \n    - Webcam can be tested \n    - webcam freeze a frame of a parking lot \n\n- Computer vision \n    - simulate frozen frame\n    \u003ca href=\"https://imgur.com/afQSh2z\"\u003e\u003cimg src=\"https://i.imgur.com/afQSh2z.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n- Run CRLMM \n    - Find parking space \n    - CRL-MM-IoT-SVM.py \n\n- decide how to get to the parking lot \n    - `crlmm == 1`\n    - find a safe route to SDC -\u003e activate SVM -\u003e `safeSVM()` -\u003e traffic graph \n    - send info to Google Maps -\u003e script to read dataset that contains GPS coordinate for each datapoint in the SVM \n\u003ca href=\"https://imgur.com/BGsMWVj\"\u003e\u003cimg src=\"https://i.imgur.com/BGsMWVj.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://imgur.com/FwxQRQs\"\u003e\u003cimg src=\"https://i.imgur.com/FwxQRQs.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n- Itinerary graph \n    \n- Weight vector \n    - vertex weights (safest route) are updated after MDP \n\n\n\n- AI in heath care \n    - Heart_Disease_Prediction\n        - dataset : https://archive.ics.uci.edu/ml/datasets/heart+Disease\n            - 76 attributes \n        - SVC classifier \u0026 experiment with MLP classifier \n\n\n## AI in Robotics \n- \n\n\n\n### Data Access and Distributed Processing for IoT\n- Hadoop's Distributed File System\n- HDF5 \n    -  PyArrow's filesystem interface for HDFS\n- SQL, NoSQL\n- \n\n- Dataset \n    - 9,568 data points collected from a combined cycle power plant (CCPP)\n        - http://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant\n    - Wine quality dataset\n        - https://archive.ics.uci.edu/ml/datasets/Wine+Quality\n    - Air quality data\n        - https://www.kaggle.com/c/predict-impact-of-air-quality-on-death-rates\n\n\n\n    \n\n\n## ML algorithms \n### Image classification \n+ Build Nearest neighbour classifier for classifying different categories of images using K Means Clustering for effiency\n+ Component analysis - histogram \n+ Classification feature \n+ Different distance measures for the nearest neighbour classifier was evaluated \n\n### Recommendation system \n+ Cluster algorithm - Reduce search space \n+ MapReduce to process large dataset \n+ ML model designed for content-based recommendation \n+ Cluster algorithm - reduce search space \n+ Leverage locality sensitive hashing LSH method to find similar users for a large dataset - 1GB \n\n- BM25 weighting\n- Efficient nearest neighbor search\n- matrix factorization\n- https://github.com/benfred/implicit\n- efficient nearest neighbor search: https://github.com/facebookresearch/faiss\n\n## Image drawer program Mona Lisa \n\n\n\n## Deep learning \n- backpropagation\n- gradient descent\n- “skip connections”\n- batch normalization \n- RNN : text, speech , time series data\n- XOR\n- multi layer, feed forward NN \n\n\n\n## Reinforcement learning \n- Building a learning agent \n- RL algorithms \n    - Markov process Hidden Markov Models (HMM)\n    - Q Learning \n    - Temporal difference methods \n    - Monte Carlo methods \n\n\n\n\n\n### NLP \u0026 sentimental analysis with RNTN \n- Background on natural language processing (NLP) and sentiment analysis\n- Core NLP: https://stanfordnlp.github.io/CoreNLP/\n    - NLP processing such as sentence detection\n    - word detection\n    - part-of-speech tagging, named-entity recognition (finding names of people, places, dates, and so on), and sentiment analysis. \n    - Several NLP features, such as sentiment analysis, depend on prior processing including sentence detection, word detection, and part-of-speech tagging.\n    - 85.4% accuracy for detecting positive/negative sentiment of sentences.\n- Recursive neural tensor networks (RNTN) \n- twitter \u0026 reddit api \n- Data aggregation \n- Sentiment detector\n    - libraries, hbc-core, JRAW, and Crux.\n\n- Speech Recognizer\n- transform audio signal \n- generate audio signal \n- synthesizing tones to generate music \n- extract speech features \n- recognize spoken words with Hidden Markov Model \n\n\n### CoreNLP processing pipeline \n- tokenization\n- dependency tree\n- annotations\n-  part-of-speech tags\n\n\n\n\n\n### Optimize running time \n+ Parallel processing and fault tolerance \n+ Optimize Map Reduce framework \n  + Support parallel processing \n  + Optimize scripts for map and reduce stage \n  \n+ Distributed \n\n\n### Google Cloud AI Services \n- Cloud based machine learning \n- Cloud Vision API \n    - detect explicit content \n    - landmark detection \n    - optical character recognition \n    - face detection \n    - image attributes \n- Cloud Speech API \n- Cloud AutoML \n- Cloud TPU \n- Cloud ML engine\n- Cloud natural language\n    - syntax analysis\n    - entity recognition \n    - sentiment analysis \n    - multi language \n    - integrated REST API \n- Cloud Speech API \n    - global vocab\n    - streaming recognition \n    - word hints\n    - real time / prerecorded audio support\n    - noise robustness \n    - inappropriate content filtering \n- cloud translation API \n    -     \n\n- cloud vidio inteligence \n    - label detection \n    - shot change detection \n    - video trans\n    - explicit content detection \n\n#### project detection-gcloudvision \n- face detection \n- label detection \n- safe search detection \n- video inteligence api \n    - label, search video catalogues, distinguish scenes using shot detection \n    - content recommendation, content moderation, contextual ads, search media archives \n- cloud speech api \n    - streaming speech recognition \n    - audio to text with speech recognition \n\n- cloud NLP \n    - sentiment analysis \n    - entity analysis \n    - \n#### Tech stack \n- Java \n    - NN : (http://neuroph.sourceforge.net/index.html), Deeplearning4j\n    - NLP : CoreNLP, OpenNLP\n    - ML : JavaML, Weka, SMILE\n    - ComputerVision : JavaCV\n    - Tensorflow\n        - on spark \n        - SparkDL \n        - PySpark \n       - keras \n- OpenAI gym   \n- Open CV \n    \u003ca href=\"https://imgur.com/RNAlNgS\"\u003e\u003cimg src=\"https://i.imgur.com/RNAlNgS.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://imgur.com/u6wDCNL\"\u003e\u003cimg src=\"https://i.imgur.com/u6wDCNL.png\" title=\"source: imgur.com\" /\u003e\u003c/a\u003e\n\n\n\n- Python \n- Prolog\n- https://github.com/spotify/snakebite\n\n- HDFS \n\n\n\n#### Resources / Ref \n+ https://www.cs.waikato.ac.nz/ml/weka/ \n- https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-performance-instances.html\n- https://cloud.google.com/products/\n- https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/\n- http://jevois.org/\n- https://cloud.google.com/edge-tpu/\n- https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/\n- https://www.96boards.org/product/ultra96/ai/\n- https://www.tensorflow.org/lite/\n- http://docs.openmv.io/\n- http://mpqa.cs.pitt.edu/opinionfinder/opinionfinder_2/\n- https://en.wikipedia.org/wiki/AI_winter\n- https://en.wikipedia.org/wiki/Computer_chess\n- https://en.wikipedia.org/wiki/Watson_(computer)\n- https://cloud.google.com/products/ai/\n- https://stockfishchess.org/\n- https://link.springer.com/chapter/10.1007%2F978-3-540-72079-9_10\n- https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/\n- https://prodi.gy/\n- https://github.com/mnielsen/neural-networks-and-deep-learning\n- https://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53\n- http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial\n- https://www.arundo.com/\n- https://www.canvass.io/\n- https://c3.ai/\n- https://www.uptake.com/\n\n#### Applied Research paper/ Publication \n- Microsoft research \n    - Home Automation in the Wild: Challenges and Opportunities\n- IBM research \n    - https://www.ibm.com/quantum-computing/\n\n- Google Machine learning \n- Google research \n- Adaptive Machine Learning forCredit Card Fraud Detection(PhD thesis paper)\n\n\n\n- Book \n    - Theory: Quantum Computation and Quantum Information: 10th Anniversary Edition, Michael Nielson, Isaac L. Chuang \n    - AI blueprints\n    - AI by example \n    - AI with Python \n    - AI in finance \n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhiejulia%2Fai-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhiejulia%2Fai-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhiejulia%2Fai-project/lists"}