

de Recherche et d’Innovation
en Cybersécurité et Société
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Mohand Said Allili
Professeur
Université du Québec en Outaouais (UQO)
Département d'informatique et d'ingénierie
Mohand Said Allili est professeur titulaire au Département d'Informatique et d'Ingénierie de l'Université du Québec en Outaouais où il dirige le Laboratoire de recherche en Imagerie, Vision et Intelligence Artificielle (LARIVIA). Ses activités de recherche tournent autour de la vision artificielle, de l’apprentissage par ordinateur et le traitement de données multimédias, avec des applications dans l’analyse sémantique des images médicales et aériennes, et la cybersécurité.
Productions incluses dans la recherche:
AUT (Autres), BRE (Brevet), CAC (Publications arbitrées dans des actes de colloque), CNA (Communication non arbitrée), COC (Contribution à un ouvrage collectif), COF (Communication arbitrée), CRE, GRO, LIV (Livre), RAC (Revue avec comité de lecture), RAP (Rapport de recherche), RSC (Revue sans comité de lecture).
Année : 1975 à 2024
Publications sélectionnées
2025 |
Bacha, S.; Allili, M. S.; Kerbedj, T.; Chahboub, R. Investigating food pairing hypothesis based on deep learning: Case of Algerian cuisine Article de journal Dans: International Journal of Gastronomy and Food Science, vol. 39, 2025, ISSN: 1878450X (ISSN), (Publisher: AZTI-Tecnalia). @article{bacha_investigating_2025, Traditional cuisine is considered a core element of cultural identity. The choice of food can often be influenced by identity, culture, and geography. This work investigates the traditional Algerian cuisine by exploring the food pairing hypothesis, which stipulates that combined ingredients with common flavor compounds taste better than their counterpart. To gain insight into the ingredients compounds found in this cuisine, we analyze their characteristics using spectral clustering. Then, we propose a model based on LSTMs to test the food pairing hypothesis in the Algerian cuisine on a collected corpus. Our research shows that the Algerian cuisine has a negative food pairing tendency, which is consistent with the South European cuisine, suggesting broader regional culinary patterns. To the best of our knowledge, this is the first study to investigate the FPH in Algerian cuisine, contributing to a deeper understanding of the food pairing tendencies specific to this region and offering a comparative perspective with neighboring Mediterranean cuisines. © 2025 Elsevier B.V. |
Bouafia, Y.; Allili, M. S.; Hebbache, L.; Guezouli, L. SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions Article de journal Dans: Signal Processing: Image Communication, vol. 130, 2025, ISSN: 09235965 (ISSN), (Publisher: Elsevier B.V.). @article{bouafia_ses-renet_2025, Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-the-art methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications. © 2024 |
Lapointe, J. -F.; Allili, M. S.; Hammouche, N. Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone Article d'actes Dans: D., Harris; W.-C., Li; H., Krömker (Ed.): Lect. Notes Comput. Sci., p. 278–287, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303176823-1 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.). @inproceedings{lapointe_field_2025, Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
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