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 |
Integrating XGBoost, SHAP, and DiCE for Prescriptive Maintenance of Undercarriage Sprockets (Feb 2027) LIBAN Mohamed AHMAD , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Daniel Aurel , JONATHAN WRIGHT , Emmanuel Weiten 9th Artificial Intelligence and Cloud Computing Conference (AICCC2026)
Unmasking Novel IoT Threats through Attack Disjoint Feature Attribution and Transparent Machine Learning (Feb 2027) DANJELA KUÇI , Mobyen Uddin Ahmed, Shahid Raza 9th Artificial Intelligence and Cloud Computing Conference (AICCC2026)
Evaluating Explainable Hybrid Intrusion Detection Models Under ZeroDay Conditions (Sep 2026) Sumayyamol Mukkil Muhammed Ismail , Mobyen Uddin Ahmed, Shahina Begum Cybersecurity Symmetry: Encryption, AI, and Attack Patterns (CSEAIAP)
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)
