tnpa-generalizability
IST'21 & SANER'22: Semantic-Preserving Program Transformations
https://github.com/bemanproject/infra-workflows
Support CI for beman projects by providing reusable GitHub Actions workflow files
https://github.com/benekenobi/colordna
A Go command-line tool that colorizes DNA/RNA sequences and quality scores for better visualization in the terminal.
poster-annotation-tool
Graduation project, a poster annotation tool for poster layout generation.
device-gateway
Device Gateway: Translates OpenQASM3 instructions into commands compatible with various quantum control software platforms.
https://github.com/bibiko219/sts-net
Efficient Distillation Involved Simulated Two Stream Network for Action recognition
https://github.com/beerda/lfl
linguistic fuzzy logic algorithms: mining for linguistic fuzzy association rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE)
explainable_heart_disease_prediction_using_ensemble-quantum_ml
An ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where Quantum Support Vector Classifier is used as the base classifier. Furthermore, in order to make the model's outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) were applied and compared with classical machine learning classifiers such as Support Vector Classifier (SVC), and Artificial Neural Network (ANN).