@article{bacha_investigating_2025,

title = {Investigating food pairing hypothesis based on deep learning: Case of Algerian cuisine},

author = {S. Bacha and M. S. Allili and T. Kerbedj and R. Chahboub},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214793354&doi=10.1016%2fj.ijgfs.2024.101098&partnerID=40&md5=b2548861f182c4ea1820fceec7003f82},

doi = {10.1016/j.ijgfs.2024.101098},

issn = {1878450X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Gastronomy and Food Science},

volume = {39},

abstract = {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.},

note = {Publisher: AZTI-Tecnalia},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bouafia_ses-renet_2025,

title = {SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions},

author = {Y. Bouafia and M. S. Allili and L. Hebbache and L. Guezouli},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208562795&doi=10.1016%2fj.image.2024.117223&partnerID=40&md5=322b79f8d78045395efcabcbf86c6e1c},

doi = {10.1016/j.image.2024.117223},

issn = {09235965 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Signal Processing: Image Communication},

volume = {130},

abstract = {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},

note = {Publisher: Elsevier B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}