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Amel Otmane

Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

Main Referenced Co-authors
MAHMOUDI, Sidi  (12)
SIEBERT, Xavier  (10)
STASSIN, Sédrick  (6)
BENKEDADRA, Mohamed  (1)
ENGLEBERT, Alexandre (1)
Main Referenced Keywords
Artificial Intelligence (3); deep learning (3); Multimodal learning (3); Computer Networks and Communications (2); Control and Systems Engineering (2);
Main Referenced Disciplines
Computer science (12)

Publications (total 12)

The most downloaded
142 downloads
Stassin, S., Amel, O., Mahmoudi, S., & Siebert, X. (2023). Similarity versus Supervision: Best Approaches for HS Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com. https://hdl.handle.net/20.500.12907/47896

The most cited

16 citations (Scopus®)

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 https://hdl.handle.net/20.500.12907/46538

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
* These authors have contributed equally to this work.

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

Stassin, S., Amel, O., Mahmoudi, S., & Siebert, X. (2023). Similarity versus Supervision: Best Approaches for HS Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com.
Peer reviewed

Amel, O., Stassin, S., Mahmoudi, S., & Siebert, X. (2023). Multimodal Approach for Harmonized System Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com.
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

Amel, O., Mahmoudi, S., Siebert, X., & Stassin, S. (30 March 2022). Review of multimodal approaches [Paper presentation]. Infortech day on Data science, Mons, Belgium.

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

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