Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.

A course project on explainable AI

Research on AutoML and Explainability．

This repository is the code basis for the paper titled "Balancing Privacy and Explainability in Federated Learning"

This project poses a new methodology for assessing and improving sequential concept bottleneck models (CBMs). The research undertaken in this project builds upon the model proposed by Grange et al., of which I was one of the co-authors.

Code for evaluating saliency maps with classification metrics.

CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.

Repository for ReVel framework to Measure Local-Linear Explanationsfor Black-Box Models

ConsisXAI is an implementation of a technique to evaluate global machine learning explainability (XAI) methods based on feature subset consistency

Semantic Meaningfulness: Evaluating counterfactual approaches for real world plausibility

Open and extensible benchmark for XAI methods

Explainable and trustworthy AI Course

Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".

CoSy: Evaluating Textual Explanations

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems