Ali Ismail-Fawaz
Présentation
I am an Associate Professor in Computer Science at the Universite de Haute-Alsace, lecturer at ENSISA and researcher at IRIMAS institute in the MSD team, specializing in time series analysis. My research explores time series machine learning, focusing on tasks such as classification, regression, clustering, and generation, with a particular specialty in deep learning. The applications of my work span human motion sequences, remote sensing, and medical domains. For more details, visit my website: https://hadifawaz1999.github.io/
Publications
Publications
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Establishing a unified evaluation framework for human motion generation: A comparative analysis of metricsComputer Vision and Image Understanding, 2025, 254, pp.104337. ⟨10.1016/j.cviu.2025.104337⟩ |
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Look into the LITE in deep learning for time series classificationInternational Journal of Data Science and Analytics, 2025, pp.1--21. ⟨10.1007/s41060-024-00708-5⟩ |
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Enhancing Time Series Classification with Diversity-Driven Neural Network EnsemblesIEEE International Joint Conference on Neural Networks (IJCNN), Jun 2025, Rome, France. pp.1-10, ⟨10.1109/IJCNN64981.2025.11228372⟩ |
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Finding Foundation Models for Time Series Classification with a PreText TaskPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) - International Workshop on Temporal Analytics, May 2024, Taipei, Taiwan. pp.123 - 135, ⟨10.1007/978-981-97-2650-9_10⟩ |
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A Supervised Variational Auto-encoder for Human Motion Generation Using Convolutional Neural NetworksInternational Conference on Pattern Recognition and Artificial Intelligence (ICPRAI), Jul 2024, Jeju Island, South Korea. pp.166 - 181, ⟨10.1007/978-981-97-8705-0_11⟩ |
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Weighted Average of Human Motion Sequences for Improving Rehabilitation AssessmentECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD), Sep 2024, Vilnius, Lithuania. pp.131 - 146, ⟨10.1007/978-3-031-77066-1_8⟩ |
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Enhancing Time Series Classification with Self-Supervised LearningInternational Conference on Agents and Artificial Intelligence (ICAART), Feb 2023, Lisbon, France. pp.40-47, ⟨10.5220/0011611300003393⟩ |
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LITE: Light Inception with boosTing tEchniques for Time Series ClassificationIEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2023, Thessaloniki, France. pp.1-10, ⟨10.1109/dsaa60987.2023.10302569⟩ |
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Apprentissage en Profondeur pour la Classification des Séries Temporelles à l'aide de Nouveaux Filtres de Convolution Créés ManuellementORASIS 2023, Laboratoire LIS, UMR 7020, May 2023, Carqueiranne, France |
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Deep Learning For Time Series Classification Using New Hand-Crafted Convolution FiltersIEEE International Conference on Big Data (Big Data), Dec 2022, Osaka, Japan. pp.972-981, ⟨10.1109/bigdata55660.2022.10020496⟩ |