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https://github.com/hiejulia/ai-project

AI projects that cover all posibilities
https://github.com/hiejulia/ai-project

ai algo algorithms deep-learning iot ml neural-network nlp openai-gym prolog reinforcement-learning smart-cities

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AI projects that cover all posibilities

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README 

        
# AI projects

Proof of concept of the state of the art AI with practical & research examples 

(with code demos)

- narrow AI 

- general AI 

- super AI 



## Collections of algorithms optimization 



+ Optimal path using BFS, DFS 

+ AI search algo 



## AI search algo 

- dijkstra search 

- heuristics 

- A* algo

    



## AI algorithm

+ Determined the optimal next move of a chessboard game using Minimax algorithm with Alpha-beta pruning

- The minimum cost transaction for a goal state 

- A sequence of transitions to a minimum cost goal 

- A minimum cost transaction for a minimum cost goal 



## AI in Finance 



## AI in Bioinformatics 



## AI in game 

- rule based system 

- Prolog 

    - `swipl`

    - `brew install swi-prolog`

    - prolog query 

    



- The min-max algorithm



## Edge AI 

- edge service 

    - smartphone 

    - devices 

    - microcontroller 

- openvm

- jevois 

- google edge TPU 

- movidius 

- nvidia jetson 

- UP AI Edge 

- Ultra96 

- TF Lite 

- utensor

- qualcomm neural processing SDK for AI 

- huawei NPU 



## AI use case 

- fraud detection 

- https://www.gurobi.com/

- integer linearn programming

    - https://www.gurobi.com/

    - https://www.ibm.com/analytics/cplex-optimizer



- robotics 

    - https://www.ros.org/



## AI in IIoT

- optimize logistics

- Electrical load forecasting

- Implementing a code to perform preventive maintenance based on aircraft engine sensors data



- 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.

- monitoring parameters/sensor

    - Vibration sensors mainly used to detect misalignment, imbalance, mechanical looseness, or wear on pumps and motors

    - Current/voltage sensors to measure the current and voltage supplied to an electric motor

    - Ultrasound analysis to detect leakage in pipe systems or tanks, or mechanical malfunctions of movable parts and faults in electrical equipment

    - Infrared thermography to identify temperature fluctuations

    - Sensors to detect liquid quality (for example in the case of wine sensors to detect the presence of different elements in the wine) 



- DL model: RNN, LSTM

- STLF using LSTM

    - dataset : https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption#

    - 2 LSTM and 1 connected layer 

    



## AI in Cybersecurity 

- Predictive model for credit card fraud detection

    - big data analytics to integrate information from different sources

    - ensemble learning

        - Use bagging and boosting algorithms 

        - Adaptive Boosting (AdaBoost)

        - gradient boosting algorithm

    - sampling techniques to rebalance datasets, thereby improving prediction accuracy

        - Oversampling with SMOTE

            - Synthetic Minority Over-sampling Technique (SMOTE)



- GANs - Attacks and defense 

    - forward propagation 

    - backpropagation 

- Feedforward neural networks (FFNNs)

- Recurrent neural network (RNNs)

    - network traffic analysis 

- Convolutional neural networks (CNNs)



- Spam detection

- Fraud detection algorithms

- Biometric authentication with facial recognition

- Classifying suspicious user activity

- User authentication with keystroke recognition

- Suspect fraud 

- Application security : 

    - attacks : SSRF, SQL injection, XSS, DDoS 

- Endpoint protection 

    - ransomware 

- Network protection 

    - intrusion detection system 

- Some tasks    

    - Predict : NN, DL 

    - Clustering 

- Multi Layer Perceptron 



- Using : 

    - https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.RandomUnderSampler.html 

    



## AI in IoT 

- Self driving solution 

- Safe route parameter to trip planners 

- Apply CNN to parking lot 

- Apply SVM to safety on trip planning 

- Teaching MDP to find the safest route 

- Perform supervised and unsupervised machine learning for IoT data

- Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms

- Forecast time-series data using deep learning methods



- build smart systems for IoT



- monitor heart disease using ML 

- Smart home

- devices used in smart home

- AI in predicting human activity recognition



- set up RL-DL-CRLMM model

    - webcam images in real time 

    - CRL- CNN 

        - gap in parking lot 

        - SVM - optimizer 

        - MDP 

        - RL - DL - CRLMM find parking lot - available space 

        - Circular RL- DL - CRLMM 

            - CNN 

            - Markov decision process MDP 

            - CRLMM - recognize parkigng space in parking lot and send signal to self signal to self driving vehicle 

                - gaps, space between 2 objects 

                - context to establish whether this space between objects is positive or negative distance 



- IP camera : obtain right real time frames from webcam : lighting const, etc 

- Dataset : 

    - training set, test set 

- model trained : CNN Concept Strategy. py 

    - Classify parking lot : 

- Add SVM function to increase safety level 

    - avoid traffic 

    - read lat/long of datapoint in another table to convet back to GPS format 

    - sklearn

     

    - `make_blobs`



- classify



- IP camera 

    - Webcam can be tested 

    - webcam freeze a frame of a parking lot 



- Computer vision 

    - simulate frozen frame

    

- Run CRLMM 

    - Find parking space 

    - CRL-MM-IoT-SVM.py 



- decide how to get to the parking lot 

    - `crlmm == 1`

    - find a safe route to SDC -> activate SVM -> `safeSVM()` -> traffic graph 

    - send info to Google Maps -> script to read dataset that contains GPS coordinate for each datapoint in the SVM 





- Itinerary graph 

    

- Weight vector 

    - vertex weights (safest route) are updated after MDP 



- AI in heath care 

    - Heart_Disease_Prediction

        - dataset : https://archive.ics.uci.edu/ml/datasets/heart+Disease

            - 76 attributes 

        - SVC classifier & experiment with MLP classifier 



## AI in Robotics 

- 



### Data Access and Distributed Processing for IoT

- Hadoop's Distributed File System

- HDF5 

    -  PyArrow's filesystem interface for HDFS

- SQL, NoSQL

- 



- Dataset 

    - 9,568 data points collected from a combined cycle power plant (CCPP)

        - http://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant

    - Wine quality dataset

        - https://archive.ics.uci.edu/ml/datasets/Wine+Quality

    - Air quality data

        - https://www.kaggle.com/c/predict-impact-of-air-quality-on-death-rates



    



## ML algorithms 

### Image classification 

+ Build Nearest neighbour classifier for classifying different categories of images using K Means Clustering for effiency

+ Component analysis - histogram 

+ Classification feature 

+ Different distance measures for the nearest neighbour classifier was evaluated 



### Recommendation system 

+ Cluster algorithm - Reduce search space 

+ MapReduce to process large dataset 

+ ML model designed for content-based recommendation 

+ Cluster algorithm - reduce search space 

+ Leverage locality sensitive hashing LSH method to find similar users for a large dataset - 1GB 



- BM25 weighting

- Efficient nearest neighbor search

- matrix factorization

- https://github.com/benfred/implicit

- efficient nearest neighbor search: https://github.com/facebookresearch/faiss



## Image drawer program Mona Lisa 



## Deep learning 

- backpropagation

- gradient descent

- “skip connections”

- batch normalization 

- RNN : text, speech , time series data

- XOR

- multi layer, feed forward NN 



## Reinforcement learning 

- Building a learning agent 

- RL algorithms 

    - Markov process Hidden Markov Models (HMM)

    - Q Learning 

    - Temporal difference methods 

    - Monte Carlo methods 



### NLP & sentimental analysis with RNTN 

- Background on natural language processing (NLP) and sentiment analysis

- Core NLP: https://stanfordnlp.github.io/CoreNLP/

    - NLP processing such as sentence detection

    - word detection

    - part-of-speech tagging, named-entity recognition (finding names of people, places, dates, and so on), and sentiment analysis. 

    - Several NLP features, such as sentiment analysis, depend on prior processing including sentence detection, word detection, and part-of-speech tagging.

    - 85.4% accuracy for detecting positive/negative sentiment of sentences.

- Recursive neural tensor networks (RNTN) 

- twitter & reddit api 

- Data aggregation 

- Sentiment detector

    - libraries, hbc-core, JRAW, and Crux.



- Speech Recognizer

- transform audio signal 

- generate audio signal 

- synthesizing tones to generate music 

- extract speech features 

- recognize spoken words with Hidden Markov Model 



### CoreNLP processing pipeline 

- tokenization

- dependency tree

- annotations

-  part-of-speech tags



### Optimize running time 

+ Parallel processing and fault tolerance 

+ Optimize Map Reduce framework 

  + Support parallel processing 

  + Optimize scripts for map and reduce stage 

  

+ Distributed 



### Google Cloud AI Services 

- Cloud based machine learning 

- Cloud Vision API 

    - detect explicit content 

    - landmark detection 

    - optical character recognition 

    - face detection 

    - image attributes 

- Cloud Speech API 

- Cloud AutoML 

- Cloud TPU 

- Cloud ML engine

- Cloud natural language

    - syntax analysis

    - entity recognition 

    - sentiment analysis 

    - multi language 

    - integrated REST API 

- Cloud Speech API 

    - global vocab

    - streaming recognition 

    - word hints

    - real time / prerecorded audio support

    - noise robustness 

    - inappropriate content filtering 

- cloud translation API 

    -     



- cloud vidio inteligence 

    - label detection 

    - shot change detection 

    - video trans

    - explicit content detection 



#### project detection-gcloudvision 

- face detection 

- label detection 

- safe search detection 

- video inteligence api 

    - label, search video catalogues, distinguish scenes using shot detection 

    - content recommendation, content moderation, contextual ads, search media archives 

- cloud speech api 

    - streaming speech recognition 

    - audio to text with speech recognition 



- cloud NLP 

    - sentiment analysis 

    - entity analysis 

    - 

#### Tech stack 

- Java 

    - NN : (http://neuroph.sourceforge.net/index.html), Deeplearning4j

    - NLP : CoreNLP, OpenNLP

    - ML : JavaML, Weka, SMILE

    - ComputerVision : JavaCV

    - Tensorflow

        - on spark 

        - SparkDL 

        - PySpark 

       - keras 

- OpenAI gym   

- Open CV 

    

    



- Python 

- Prolog

- https://github.com/spotify/snakebite



- HDFS 



#### Resources / Ref 

+ https://www.cs.waikato.ac.nz/ml/weka/ 

- https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-performance-instances.html

- https://cloud.google.com/products/

- https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

- http://jevois.org/

- https://cloud.google.com/edge-tpu/

- https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/

- https://www.96boards.org/product/ultra96/ai/

- https://www.tensorflow.org/lite/

- http://docs.openmv.io/

- http://mpqa.cs.pitt.edu/opinionfinder/opinionfinder_2/

- https://en.wikipedia.org/wiki/AI_winter

- https://en.wikipedia.org/wiki/Computer_chess

- https://en.wikipedia.org/wiki/Watson_(computer)

- https://cloud.google.com/products/ai/

- https://stockfishchess.org/

- https://link.springer.com/chapter/10.1007%2F978-3-540-72079-9_10

- https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/

- https://prodi.gy/

- https://github.com/mnielsen/neural-networks-and-deep-learning

- https://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53

- http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

- https://www.arundo.com/

- https://www.canvass.io/

- https://c3.ai/

- https://www.uptake.com/



#### Applied Research paper/ Publication 

- Microsoft research 

    - Home Automation in the Wild: Challenges and Opportunities

- IBM research 

    - https://www.ibm.com/quantum-computing/



- Google Machine learning 

- Google research 

- Adaptive Machine Learning forCredit Card Fraud Detection(PhD thesis paper)



- Book 

    - Theory: Quantum Computation and Quantum Information: 10th Anniversary Edition, Michael Nielson, Isaac L. Chuang 

    - AI blueprints

    - AI by example 

    - AI with Python 

    - AI in finance