Latifa OUKHELLOU
Directrice du laboratoire GRETTIA Directrice de Recherche
Marne-la-Vallée
Bâtiment: Building: Bienvenüe
14-20 Boulevard Newton$Champs-sur-Marne$77447 Marne-la-Vallée Cedex 2
Bureau: Office: C110
Latifa OUKHELLOU
Directrice du laboratoire GRETTIA Directrice de Recherche
Latifa Oukhellou (HDR 2010, dr 1997), est directrice de recherche à l'Université Gustave Eiffel. Actuellement, elle est direrctrice du laboratoire GRETTIA. Elle était auparavant maître de conférence à l’Université Paris-Est Créteil entre 1998 et 2011. Ses activités de recherche concernent l'apprentissage statistique et la fusion de données pour l’analyse des données urbaines (mobilité, smart grids) et pour le diagnostic des systèmes de transport. Elle est co-auteure de plusieurs publications internationales (https://scholar.google.com/citations?user=8w9-fAQAAAAJ&hl=fr). Elle assure la responsabilité de plusieurs projets dont certains en coordination : ANR MOBITIC (Coord. 2020-2023, Mesurer les présences et les mobilités des personnes à l'aide des TIC), FLUX-Data (Resp., partenariat RATP, 2019-2022, Data sciences pour analyser les flux des usagers de TC), PIA IVA (Resp., 2018-2021, Information Voyageur Augmentée), FUI AWACS (Resp., 2015-2019 Airside Watch for Amelioration of Capacity and Safety), Predit Mobilletic (Coord., 2013-2016) sur l’analyse de l'intermodalité par les données de mobilité billettiques : le cas Rennais, ANR DIADEM (Coord., 2013-2015) sur le diagnostic dynamique et la maintenance prévisionnelle de systèmes embarqués sur Train.
Mes dernières références
My latest references
Publications en revues internationales 2010-2019
• M. Leyli-Abadi, A. Samé, L. Oukhellou, N. Cheifetz, P. Mandel, C. Féliers, O. Chesneau (2019). Mixture of Joint Nonhomogeneous Markov Chains to Cluster and Model Water Consumption Behavior Sequences. ACM Trans. Intell. Syst. Technol. 10(6): 71:1-71:21.
• M. Heinrich, S. Meunier, A. Samé, L. Queval, A. Darga, L. Oukhellou, B. Multon (2020). Detection of cleaning interventions on photovoltaic modules with machine learning. Applied Energy, Elsevier, 2020, 263, pp.114642.
• N. Khoury, F. Attal, Y. Amirat, L. Oukhellou, S. Mohammed (2019). Data-Driven Based Approach to Aid Parkinson's Disease Diagnosis. Sensors 19(2): 242.
• F. Attal, A. Boubezoul, A. Samé, L. Oukhellou, S. Espié (2018). Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches. IEEE Transactions on Intelligent Transportation Systems, pages : 1-12, DOI: 10.1109/TITS.2018.2797065, Ed IEEE.
• K. Safi, S. Mohammed, F. Attal, Y. Amirat, L. Oukhellou, M. Khalil, J-M. Gracies, E. Hutin (2018). Automatic Segmentation of Stabilometric Signals Using Hidden Markov Model Regression, IEEE Transactions on Automation Science and Engineering, 15(2): 545-555, Ed. IEEE.
• C. Richer, E. Côme, M-K. El Mahrsi et L. Oukhellou (2018). Intermodal mobility analysis with smart-card data. Spatio-temporal analysis of the bus-metro network of Rennes metropole, European Journal of Geography, 10(10), DOI : 10.4000/cybergeo.29132.
• F-N. Melzi, A. Samé, M-H Zayani, L. Oukhellou (2017). A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors, Energies, 10(10), 1446; Ed. MDPI. doi.org/10.3390/en10101446.
• A-S. Briand, E. Côme, M. Trépanier, L. Oukhellou (2017). Analyzing year-to-year changes in public transport passenger behaviour using smart card data, Transportation Research Part C, Pages : 274 - 289, Elsevier, DOI: 0.1016/j.trc.2017.03.021.
• D. Benouioua, D. Candusso, F. Harel, L. Oukhellou (2017). Multifractal Analysis of Stack Voltage Based on Wavelet Leaders: A New Tool for PEMFC Diagnosis, Fuel Cells 17 (2), 217-224, Elsevier.
• M. K. El Mahrsi, E. Côme, L. Oukhellou, M. Verleysen (2017). Clustering Smart Card Data for Urban Mobility Analysis, IEEE Transactions on Intelligent Transportation Systems Pages : 1 - 17, DOI: 10.1109/TITS.2016.2600515.
• A-S. Briand, M. K. El Mahrsi, E. Côme, L. Oukhellou (2016). A mixture model clustering approach for temporal passenger pattern characterization in public transport, International Journal of Data Science and Analytics Volume 1, Issue 1, pp 37–50, 2016, Springer.
• D. Benouioua, D. Candusso, F. Harel, L. Oukhellou (2016). The dynamic multifractality in PEMFC stack voltage signal as a tool for the aging monitoring, International Journal of Hydrogen Energy, Elsevier, DOI : 10.1016/j.ijhydene.2016.04.033.
• M-R. Senouci, A. Mellouk, N. Aït Saadi, L. Oukhellou (2016) Fusion-based Surveillance WSN Deployment using Dempster-Shafer Theory. Journal of Network and Computer Applications, 64, pp 154-166, Ed. Elsevier. (SJR, Q1, Impact factor : 2.3)
• F. Attal, M. Samer, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, Y. Amira (2015) Physical Human Activity Recognition Using Wearable Sensors. Sensors 15(12): 31314-31338, MDPI. (SJR, Engineering and Q2, Impact factor : 2.03)
• F. Attal, A. Boubezoul, L. Oukhellou, S. Espié (2015) Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning Framework. IEEE Transactions on Intelligent Transportation Systems 16(1): 475-487
• P-A. Laharotte, R. Billot, E. Côme, L. Oukhellou, A. Nantes, N-E El Faouzi (2015) Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network. IEEE Transactions on Intelligent Transportation Systems 16(3): 1439-1448 .
• M-R. Senouci, A. Mellouk, L. Oukhellou, A. Aissani (2015) WSNs deployment framework based on the theory of belief functions. Computer Networks 88: 12-26
• M-R. Senouci, A. Mellouk, M-A. Senouci, L. Oukhellou (2014) Belief functions in telecommunications and network technologies: an overview. Annales des Télécommunications, Springer, 2014, DOI : 10.1007/s12243-014-0425-8, 69(3-4), pp 135-145
• D. Benouioua, Djedjiga D. Candusso, F. Harel, L. Oukhellou (2014) Fuel cell diagnosis method based on multifractal analysis of stack voltage signal, International Journal of Hydrogen Energy, Volume 39, Issue 5, 2014, pp 2236-2245, Ed. Elsevier. DOI : 10.1016/j.ijhydene.2013.11.066
• A. Randriamanamihaga, E. Côme, L. Oukhellou, G. Govaert, (2014). Clustering the Vélib' Dynamic Origin/Destination flows using a family of Poisson Mixture Models, Neurocomputing, Ed. Elsevier, DOI : 10.1016/j.neucom.2014.01.050
• M. Samer, A. Samé, L. Oukhellou, K. Kong, W. Huo, Y. Amirat (2014). Recognition of gait cycle phases using wearable sensors, Robotics and Autonomous Systems, Ed. Elsevier, ISSN 0921-8890, doi.org/10.1016/j.robot.2014.10.012.
• E. Côme, L. Oukhellou, (2014). Model-based count series clustering for Bike-sharing system usage mining, a case study with the Vélib system of Paris, ACM Transactions on Intelligent Systems and Technology (TIST). 5(3). Ed. ACM
• F. Chamroukhi, S. Mohammed, D. Trabelsi, L. Oukhellou, Y. Amirat (2013). Joint segmentation of multivariate time series with hidden process regression for human activity recognition, Neurocomputing, 120, pp. 633-644, Ed. Elsevier. DOI : 10.1016/j.neucom.2013.04.003 Ed. Elsevier
• D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou, Y. Amirat (2013). An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression, IEEE Transactions on Automation Science and Engineering, 10(3), pp. 829-335, Ed. IEEE, DOI : 10.1109/TASE.2013.2256349
• M.R. Senouci, A. Mellouk, L. Oukhellou, A. Aissani (2012). An Evidence-based Sensor Coverage Model. Accepté pour publication dans la revue IEEE Communications letters. Ed. IEEE
• Z.L. Cherfi, L. Oukhellou, E. Côme , T. Denoeux, P. Aknin, (2012) Partially supervised Independent Factor Analysis usingsoft labels elicited from multiple experts: Application to railway track circuit diagnosis, Soft Computing, Vol. 16, no5, pp. 741-754, 2012. Ed. Elsevier
• E. Côme, L. Oukhellou, T. Denoeux, P. Aknin, (2012, online Avril 2011) Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints, Pattern Analysis and Applications. Vol. 15, no3, pp. 313-326, Ed. Springer
• R. Onanena, L. Oukhellou, D. Candusso, F.Harel, D. Hissel, P. Aknin, (2010) Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time, International Journal of Hydrogen Energy, Vol. 36, no2, pp. 1730-1739. Ed. Elsevier
• R. Onanena, L. Oukhellou, D. Candusso, A. Samé, D. Hissel, P. Aknin, (2010) Estimation of fuel cell operating time for predictive maintenance strategies, International Journal of Hydrogen Energy, Vol. 35, no15, pp. 8022-8029. Ed. Elsevier
• L. Oukhellou, A. Debiolles, P. Aknin, T. Denœux, (2010) Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion, Engineering Applications of Artificial Intelligence, Vol, 23, pp117–128. Ed. Elsevier