Self-supervised classification of dynamic obstacles using the temporal information provided by videos. - Sid Ali Hamideche; Florent Chiaroni; Mohamed-Cherif Rahal (CoRR, 2019)

Indoor-Outdoor Detection using Time Series Classification and User Behavioral Cognition. - Sid Ali Hamideche; Marie-Line Alberi-Morel; Kamal Deep Singh; César Viho (WMNC, 2022)

Improving User Environment Detection Using Context-Aware Multi-Task Deep Learning in Mobile Networks. - Marie-Line Alberi-Morel; Illyyne Saffar; Kamal Deep Singh; Sid Ali Hamideche; César Viho (IEEE Trans. Cogn. Commun. Netw., 2022)

Federated Representation Learning for Indoor-Outdoor Detection in Beyond 5G Networks. - Sid Ali Hamideche; Marie-Line Alberi-Morel; Kamal Deep Singh; César Viho (GLOBECOM (Workshops), 2023)

Classification with Synthetic Radio Data for Real-life Environment Sensing. - Soumeya Kaada; Sid Ali Hamideche; Chloe Daems; Marie-Line Alberi-Morel (VTC2023-Spring, 2023)

User Environment Detection Using Long Short-Term Memory Autoencoder. - Karthika Satheesh; Kamal Deep Singh; Sid Ali Hamideche; Marie-Line Alberi-Morel; César Viho (ISNCC, 2024)

Machine learning for mobile network user behavior detection in 5G networks and beyond : environment and application classification. (Apprentissage automatique appliqué à l'identification du comportement des utilisateurs de réseaux mobiles 5G et au-delà : classification de l'environnement et de l'application). - Sid Ali Hamideche (2024)

Federated Representation Learning for Encrypted Application Type Classification in Beyond 5G RAN. - Sid Ali Hamideche; Marie-Line Alberi-Morel; Kamal Singh; César Viho (CCNC, 2025)