Xbest: Generative AI towards Inference to the Best Explanation
Status:
The purpose of this project – XBest – is to improve the transparency of AI systems by developing a novel theoretical framework to achieve Inference to the Best Explanation (IBE) for eXplainable AI (XAI).
| First Name | Last Name | Title |
|---|---|---|
| Mobyen Uddin | Ahmed | Professor |
| Shahina | Begum | Professor |
| Shaibal | Barua | Senior Lecturer |
Enhancing Industrial AI Usability Through Human-AI Interaction (Jul 2026) Marcus Hammarström , Liam Burberry Gahm , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Emmanuel Weiten , Daniel Aurel 28th International Conference on Computer and Information Technology (ICCIT25)
An End-to-End Explainable Fault Prediction Pipeline for Embedded Test Systems (Jul 2026) Md Motaher Hossain Bhuiyan, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum 28th International Conference on Computer and Information Technology (ICCIT25)
Explainable Quantum Machine Learning Concepts for Trajectory Optimization in Air Traffic Management (May 2026) Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed, Henri de Boutray , Christophe Hurter International Conference on Modern Artificial Intelligence and Data Science Systems (MAIDSS26)
Quantum Machine Learning for Optimisation: A Domain Focused Survey (May 2026) Surya Teja Darbhamalla, Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed International Conference on Modern Artificial Intelligence and Data Science Systems (MAIDSS26)
Hybrid Neuro-Fuzzy Approach for Transparent Anomaly Detection in Mining Equipment (Mar 2026) ANDIA SHKARPA , Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2026)
Mechanistic Interpretability of ReLU Neural Networks Through Piecewise-Affine Mapping (Jan 2026) Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum Machine Learning (ML)
