Mohammed HINDAWI
- Laboratoire d'Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires (LINEACT)
Présentation
An enseignant chercheur en informatique à l'école d'ingénieur CESI Lyon. Après un doctorat en apprentissage en automatique à l'INSA de Lyon, j'ai fait des postdocs à l'université de Paris (Paris Nord), à l'INSA de Rennes, des postes ATERs à l'université de Paris (Panthéon-Assas), et un poste de maitre des conférences à l'université ZIRVE en Turquie.
Mes domaines d'intérêts sont : Apprentissage automatique, réduction de dimensions, systèmes d'information, génie logiciel
Publications
Publications
DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall DetectionIEEE Sensors Journal, 2025, pp.1-1. ⟨10.1109/JSEN.2025.3620575⟩ |
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Impact Detection in Fall Events: Leveraging Spatio-temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeleton DataJournal of Healthcare Informatics Research, 2025, ⟨10.1007/s41666-025-00215-7⟩ |
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Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selectionKnowledge and Information Systems (KAIS), 2015, 45 (3), pp.1-25. ⟨10.1007/s10115-015-0901-0⟩ |
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Efficient semi-supervised feature selection: Constraint, Relevance and Redundancy.IEEE Transactions on Knowledge and Data Engineering, 2014, 5, 26, pp.1131-1143. ⟨10.1109/TKDE.2013.86⟩ |
Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Sep 2023, Poitiers, France. pp.1-6, ⟨10.1109/MMSP59012.2023.10337682⟩ |
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Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor DataMMSP, Sep 2023, Poitiers, France |
Weighting-based approach for semi-supervised feature selectionInternational Conference on Neural Information Processing, Nov 2015, Istanbul, Turkey |
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Efficient semi-supervised feature selection by an ensemble approachInternational Workshop on Complex Machine Learning Problems with Ensemble Methods COPEM@ECML/PKDD'13, Sep 2013, Prague, Czech Republic. pp.41-55 |
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Local-To-Global Semi-Supervised Feature selectionACM International Conference on Information and Knowledge Management (CIKM 2013), Oct 2013, San Fransisco CA, United States. pp.2159-2168, ⟨10.1145/2505515.2505542⟩ |
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Une approche embedded pour la sélection de variables en mode semi-superviséSociété Française de Classification (SFC), Oct 2012, Marseille, France. pp.13 |
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Constrained Laplacian Score for semi-supervised feature selectionECML/PKDD. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. pp.204-218, ⟨10.1007/978-3-642-23780-5_23⟩ |
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Un score Laplacien sous contraintes pour la sélection de variables en mode semi-superviséJournées Fouille de Données Complexes et de Grands Graphes, Jun 2011, Paris, France. pp.1-15 |
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Description and Implementation of a UML Style GuideWorkshop Quality in Modeling, Sep 2008, France. pp.291-302 |
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Specifying consistent subsets of UMLEducator symposium (co-located with Models'08), Sep 2008, Toulouse, France. pp.26-38 |
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Description and Implementation of a Style Guide for UMLQuality in Modeling (co-located with MODELS'08), Sep 2008, Toulouse, France. pp.31-45 |
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Constraint Selection-Based Semi-supervised Feature Selection.Cook, Diane J. and Pei, Jian and 0010, Wei Wang and Zaïane, Osmar R. and Wu, Xindong. ICDM, Dec 2011, Vancouver,BC, Canada. IEEE, pp.1080-1085, 2011, ⟨10.1109/ICDM.2011.42⟩ |
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Feature selection for semi-supervised data analysis in decisional information systemsArtificial Intelligence [cs.AI]. INSA de Lyon, 2013. English. ⟨NNT : 2013ISAL0015⟩ |