  | Lerat, J.-S. (2025). Conception et déploiement distribué de modèles IA pour des applications de vision par ordinateur et d’industrie 4.0 : Auto DIST-Framework [Doctoral thesis, UMONS - Université de Mons]. ORBi UMONS-University of Mons. https://orbi.umons.ac.be/handle/20.500.12907/53655 |
  | Mahmoudi, S., Gloesener, M., Benkedadra, M., & Lerat, J.-S. (2025). Edge AI System for Real-Time and Explainable Forest Fire Detection Using Compressed Deep Learning Models. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 3, 847-854. doi:10.5220/0013382500003912  Peer reviewed |
  | Lerat, J.-S., & Mahmoudi, S. (2025). Scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations. In Lecture Notes in Networks and Systems. Switzerland: Springer Nature Switzerland. doi:10.1007/978-3-031-78698-3_10 Peer reviewed |
 | Lerat, J.-S., & Mahmoudi, S. (2023). Scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations. 2023 IEEE 6th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). doi:10.1007/978-3-031-78698-3_10 Peer reviewed |
 | Lerat, J.-S., & Mahmoudi, S. (2023). Architecture to Distribute Deep Learning Models on Containers and Virtual Machines for Industry 4.0*. IEEE Cloud Computing. doi:10.1109/cloudtech58737.2023.10366111 Peer Reviewed verified by ORBi |
 | Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (2023). Single Node Deep Learning Frameworks: Comparative study and CPU/GPU performance analysis. Concurrency and Computation: Practice and Experience. Peer Reviewed verified by ORBi |
 | Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (06 September 2022). Apprentissage profond distribué : d’un seul nœud vers plusieurs nœuds de calcul [Poster presentation]. Mardi des chercheurs, Mons, Belgium. |
 | Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (2022). Distributed Deep Learning: From Single-Node to Multi-Node Architecture. Electronics. doi:10.3390/electronics11101525 Peer Reviewed verified by ORBi |
 | Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (22 November 2021). Single node deep learning frameworks: Comparative study and CPU/GPU performance analysis. Concurrency and Computation: Practice and Experience, 35 (14). Peer Reviewed verified by ORBi |
Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (27 July 2021). Deep Learning Frameworks: Performances Analysis [Paper presentation]. DeepLearn, Las Palmas de Gran Canaria, Spain. |
 | Lerat, J.-S., Mahmoudi, S., & Mahmoudi, S. (30 March 2021). Comparaison des frameworks d'apprentissage profond sur un seul noeud de calcul [Poster presentation]. Mardi des Chercheurs, Virtual Meeting, Unknown/unspecified. |
 | Faust, K., Lima-Mendez, G., Lerat, J.-S., Sathirapongsasuti, J. F., Knight, R., Huttenhower, C., Lenaerts, T., & Raes, J. (2015). Cross-biome comparison of microbial association networks. Frontiers in Microbiology. doi:10.3389/fmicb.2015.01200 Peer Reviewed verified by ORBi |
 | Lerat, J.-S., Han, T. A., & Lenaerts, T. (2013). Evolution of common-pool resources and social welfare in structured populations [Paper presentation]. International Joint Conference on Artificial Intelligence, Beijing, China. |
