Investigating the Duality of Interpretability and Explainability in Machine Learning. - Moncef Garouani; Josiane Mothe; Ayah Barhrhouj; Julien Aligon (ICTAI, 2024)

Assessing the Impact of Temporal Data Aggregation on the Reliability of Predictive Machine Learning Models. - Ayah Barhrhouj; Bouchra Ananou; Mustapha Ouladsine (IDEAL (1), 2024)

Exploring Explainable Machine Learning for Enhanced Ship Performance Monitoring. - Ayah Barhrhouj; Bouchra Ananou; Mustapha Ouladsine (LOD (2), 2024)

Balancing Feature Selection, Model Accuracy, and Transparency in Maritime Machine Learning: A Trade-Off Analysis. - Ayah Barhrhouj; Bouchra Ananou; Mustapha Ouladsine (MED, 2025)

XStacking : An effective and inherently explainable framework for stacked ensemble learning. - Moncef Garouani; Ayah Barhrhouj; Olivier Teste (Inf. Fusion, 2025)

XStacking: Explanation-Guided Stacked Ensemble Learning. - Moncef Garouani; Ayah Barhrhouj; Olivier Teste (CoRR, 2025)