Etudiant : ESSALEH ABDELMOGHIT

Filière : Machine Learning Avancé et Intelligence Multimédia (MLAIM)

Encadrant : Pr. BENLAHBIB ABDESSAMAD

Annèe : 2026

Résumé : This study examines the migration of foraminifera under sediment discharge conditions, utilizing observations from microtomography (micro-CT scan). The methodology involves collecting and analyzing data from sediment cores to produce usable images, followed by the application of machine learning and deep learning techniques to detect these microorganisms. These approaches provide insights into the environmental impacts on these marine bioindicators, offering a deeper understanding of their behavior in response to sediment disturbances. The results highlight promising advancements in automated detection, though further refinements are needed to enhance accuracy, with potential improvements including enriched datasets and multidimensional analysis