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1985_auto_imports_database

Prediction Model of Loss Payment Ratio of Motors, using 1985 Auto Import Database

2018-cs109a

Repository for CS109A Fall 2018

2018aicity_teamuw

The winning method in Track 1 and Track 3 at the 2nd AI City Challenge Workshop in CVPR 2018 - Official Implementation

2021-cs109a

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

absa-pytorch

Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现。

afinn

Sentiment Analysis in Javascript using the AFINN Lexicon

afinn-1

AFINN sentiment analysis in Python

age-and-sex-prediction-from-image---convolutional-neural-network-with-artificial-intelligence

Age and Sex Prediction from Image - Convolutional Neural Network with Artificial Intelligence

ahmed

ai-capstone-project-on-e-commerce-amazon-domain-

Project Task: Week 1 Class Imbalance Problem: 1. Perform an EDA on the dataset. a) See what a positive, negative, and neutral review looks like b) Check the class count for each class. It’s a class imbalance problem. 2. Convert the reviews in Tf-Idf score. 3. Run multinomial Naive Bayes classifier. Everything will be classified as positive because of the class imbalance. Project Task: Week 2 Tackling Class Imbalance Problem: Oversampling or undersampling can be used to tackle the class imbalance problem. In case of class imbalance criteria, use the following metrices for evaluating model performance: precision, recall, F1-score, AUC-ROC curve. Use F1-Score as the evaluation criteria for this project. Use Tree-based classifiers like Random Forest and XGBoost. Note: Tree-based classifiers work on two ideologies namely, Bagging or Boosting and have fine-tuning parameter which takes care of the imbalanced class. Project Task: Week 3 Model Selection: Apply multi-class SVM’s and neural nets. Use possible ensemble techniques like: XGboost + oversampled_multinomial_NB. Assign a score to the sentence sentiment (engineer a feature called sentiment score). Use this engineered feature in the model and check for improvements. Draw insights on the same. Project Task: Week 4 Applying LSTM: Use LSTM for the previous problem (use parameters of LSTM like top-word, embedding-length, Dropout, epochs, number of layers, etc.) Hint: Another variation of LSTM, GRU (Gated Recurrent Units) can be tried as well. 2. Compare the accuracy of neural nets with traditional ML based algorithms. 3. Find the best setting of LSTM (Neural Net) and GRU that can best classify the reviews as positive, negative, and neutral. Hint: Use techniques like Grid Search, Cross-Validation and Random Search Optional Tasks: Week 4 Topic Modeling: 1. Cluster similar reviews. Note: Some reviews may talk about the device as a gift-option. Other reviews may be about product looks and some may highlight about its battery and performance. Try naming the clusters. 2. Perform Topic Modeling Hint: Use scikit-learn provided Latent Dirchlette Allocation (LDA) and Non-Negative Matrix Factorization (NMF). Download the Data sets from here .

ai-cheatsheets

ai-ids-iot

AI IDS Application for IoT Dataset

airlinemaps

A tool for interactively visualizing airline maps, color-coded by either Airline or Hub. Try it out at https://saumikn.com/airlinemaps.

amazon-reviews-nlp

Sentiment analysis for Amazon review comments on various products and Subjective/ Objective Classification of the comments

amazonreviews_classification_using_sentimentanalysis

Data classification tool used to classify positive and negative amazon reviews by using sentiment analysis. Used Naive Bayes Classifier for classification process and increased the accuracy 5 - 10 % by using subjectivity lexicon during the classification process

an-xai-based-autism-detection-the-context-behind-the-detection

With the rapid growth of the Internet of Healthcare Things, a massive amount of data is generated by a broad variety of medical de- vices. Because of the complex relationship in large-scale healthcare data, researchers who bring a revolution in the healthcare industry embrace Artificial Intelligence (AI). In certain cases, it has been reported that

analysis-of-arabic-wikipedias

Learning From Arabic Corpora But Not Always From Arabic Speakers: A Case Study of the Arabic Wikipedia Editions.

analyticsanddatascience

Analytics and Data Science

anomaly-detection-in-networks-using-machine-learning

A thesis submitted for the degree of Master of Science in Computer Networks and Security

anomaly-detection-iot23

A research project of anomaly detection on dataset IoT-23

antcorpus.multisource.data

:books: ANT Corpus data files for multi-source news version.

appbuilder

Cross Platform Mobile Builder Tool

application

A dashboard that supports fleet managers and decision makers to gain insights into their automotive fleets and optimize them

arabic-ner

archnetsci

Code repository and Bookdown project for Online Companion to Network Science in Archaeology by Tom Brughmans and Matthew A. Peeples (Cambridge Manuals in Archaeology)

arglex

Arlex (Arguing Lexicon)

argument-graph-mining

Implementation of the Paper "Towards an Automated Argument Mining Pipeline to Transform Plain Text to Argument Graphs"

arz

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying a