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Anomaly prediction in mobile networks : A data driven approach for machine learning algorithm selection., , , and . NOMS, page 1-7. IEEE, (2020)A Hybrid machine learning based model for congestion prediction in mobile networks., , , and . PIMRC, page 583-588. IEEE, (2022)Méthodes avancées pour une ingénierie WLAN multi-standards. (Advanced methods for multi-standard WLAN engineering).. INSA de Rennes, France, (2007)On Selected V2X Technology Components and Enablers from the 5GCAR Project., , , , , , , , , and . BMSB, page 1-5. IEEE, (2018)Anomaly Prevision in Radio Access Networks Using Functional Data Analysis., , and . GLOBECOM, page 1-6. IEEE, (2017)Model-based co-clustering for functional data., , and . Neurocomputing, (2018)Robustness Analysis of Hybrid Machine Learning Model for Anomaly Forecasting in Radio Access Networks., , , and . ISCC, page 1104-1109. IEEE, (2023)Co-clustering for binary and functional data., , and . Commun. Stat. Simul. Comput., 51 (9): 4845-4866 (2022)Multicast and Broadcast Enablers for High-Performing Cellular V2X Systems., , , , , , , , , and 4 other author(s). IEEE Trans. Broadcast., 65 (2): 454-463 (2019)Portability of Hybrid machine learning based model for anomaly forecasting in mobile networks., , , and . VTC Fall, page 1-7. IEEE, (2023)