![]() | Tota, V., Mehuys, A., Vansnick, T., Amel, O., Chahbar, F., Mahmoudi, L., Mahmoudi, S., Briganti, G., Ris, L., & Mahmoudi, S. (01 September 2025). SUSTAINED COGNITIVE DECLINE IN MULTIPLE SCLEROSIS: INVESTIGATING THE ROLE OF WHITE MATTER LESION LOAD USING AN AI-DRIVEN BRAIN IMAGING - APPROACH. Psychiatria Danubina, 37 (1), 321-329. doi:10.1212/WNL.0b013e31824528c9 Peer Reviewed verified by ORBi |
![]() | Farinella, E., Papakonstantinou, D., Koliakos, N., Maréchal, M.-T., Poras, M., Pau, L., Amel, O., Mahmoudi, S., & Briganti, G. (2025). Integrating Machine Learning and Dynamic Digital Follow-up for Enhanced Prediction of Postoperative Complications in Bariatric Surgery. Obesity Surgery. doi:10.1007/s11695-025-07894-6 Peer Reviewed verified by ORBi |
![]() | Amel, O., Siebert, X., & Mahmoudi, S. (12 June 2024). Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites. Electronics (Switzerland), 13 (12), 2294. doi:10.3390/electronics13122294 Peer reviewed |
Stassin, S., ENGLEBERT, A., Amel, O., Siebert, X., & Mahmoudi, S. (29 May 2024). Explaining Through Multimodal Transformer Input Sampling [Paper presentation]. Infortech Day 2024, Mons, Belgium. Editorial reviewed |
Amel, O., Mahmoudi, S., & Siebert, X. (29 May 2024). Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites [Paper presentation]. Infortech Day 2024, Mons, Belgium. Editorial reviewed |
![]() | Ouardirhi, Z.* , Amel, O., Zbakh, M., & Mahmoudi, S. (28 February 2024). FuDensityNet: Fusion-Based Density-Enhanced Network for Occlusion Handling. Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Volume 3, 3, 632-639. doi:10.5220/0012425400003660 Peer reviewed |
Stassin, S., Amel, O., Siebert, X., & Mahmoudi, S. (24 May 2023). Best Approaches for Customs Fraud Detection [Paper presentation]. Infortech Day. |
![]() | Mahmoudi, S., Amel, O., Stassin, S., Liagre, M., Benkedadra, M., & Mancas, M. (May 2023). A Review and Comparative Study of Explainable Deep Learning Models Applied on Action Recognition in Real Time. Electronics, 12 (9), 2027. doi:10.3390/electronics12092027 Peer Reviewed verified by ORBi |
![]() | Amel, O., Stassin, S., Mahmoudi, S., & Siebert, X. (2023). Multimodal Approach for Harmonized System Code Prediction. In ESANN 2023 proceedings. doi:10.14428/esann/2023.ES2023-165 Peer reviewed |
![]() | Stassin, S., Amel, O., Mahmoudi, S., & Siebert, X. (2023). Similarity versus Supervision: Best Approaches for HS Code Prediction. ESANN. doi:10.14428/esann/2023.ES2023-163 Peer reviewed |
![]() | Amel, O., Mahmoudi, S., Siebert, X., & Hadjila fethallah. (24 August 2022). Multimodal learning for action recognition and customs fraud detection [Poster presentation]. deep learning indaba 2022, Tunis, Tunisia. Editorial reviewed |
![]() | Amel, O., Mahmoudi, S., & Siebert, X. (28 July 2022). Multimodal learning for customs fraud detection and action recognition [Paper presentation]. Deeplearn summer school 2022. Editorial reviewed |
Stassin, S.* , Amel, O., Mahmoudi, S., & Siebert, X. (30 March 2022). Artificial Intelligence and Natural Language Processing for Customs Fraud Prediction [Paper presentation]. InforTech Day, Mons, Belgium. Editorial reviewed |
Amel, O., Mahmoudi, S., Siebert, X., & Stassin, S. (30 March 2022). Review of multimodal approaches [Paper presentation]. Infortech day on Data science, Mons, Belgium. |