Samia ZAOUI | AI Advancements | Women Researcher Award

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Mrs. Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | Mohammed VI Foundation of Health and Sciences | Morocco

Dr. Samia Zaoui, based in Rabat, Morocco, is a multidisciplinary researcher and project leader bridging artificial intelligence, aeronautics, and healthcare systems innovation. She is currently pursuing her Ph.D. in Computer Science Engineering (AI & Aeronautics) at the Higher Institute of Aeronautics and Space (ISAE-SUPAERO) and INP Toulouse, France. Her research focuses on the application of AI technologies for supply chain resilience, predictive modeling, and sustainable industrial systems, with a strong emphasis on pharmaceutical and healthcare logistics. Dr. Zaoui has an extensive background in strategic project development, digital transformation, and industrial management, having held leadership roles at the Mohammed VI Foundation of Health and Sciences and the Cheikh Zaid Foundation. She has spearheaded projects in sports medicine innovation, pharmaceutical manufacturing, and medical technology transfer, fostering collaborations with international organizations such as WHO, LCIF, and Smile Train. Her scientific contributions include several peer-reviewed publications in international journals, such as the Global Journal of Flexible Systems Management and Production Planning & Control, covering topics like AI-driven supply chain viability, sustainability in Industry 5.0, and pharmaceutical risk prediction using machine learning. Dr. Zaoui’s research integrates AI-based decision systems with aeronautical and industrial engineering principles, contributing to global efforts in intelligent, resilient, and sustainable supply networks. She also actively participates in international technology exhibitions and collaborative industrial initiatives across Europe, Asia, and Africa.

Profile: Google Scholar

Featured Publications

Zaoui, S., Foguem, C., Tchuente, D., Fosso-Wamba, S., & Kamsu-Foguem, B. (2023). The viability of supply chains with interpretable learning systems: The case of COVID-19 vaccine deliveries. Global Journal of Flexible Systems Management, 24(4), 633–657. https://doi.org/10.1007/s40171-023-00357-w

Zaoui, S., Foguem, C., Tchuente, D., & Kamsu-Foguem, B. (2025). The application of artificial intelligence technologies in the resilience and the viability of supply chains: A systematic literature review. Production Planning & Control, 1–18.

Zaoui, H., Zaoui, S., Kamsu-Foguem, B., & Tchuente, D. (2024). Sustainability: The main pillar of Industry 5.0. Oklahoma International Publishing (OkIP) Books. https://doi.org/10.55432/978-1-6692-0007-9_13

StEER – Structural Engineering Extreme Event Reconnaissance. (2024). Hualien City, Taiwan Earthquake: Preliminary Virtual Reconnaissance Report (PVRR). https://doi.org/10.17603/ds2-0d2z-9682

Keyi Chen | AI Advancements | Best Researcher Award

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Mr. Keyi Chen Jihua Laboratory | AI Advancements | Best Researcher Award

Mr. Keyi Chen | Jihua Laboratory | China

Keyi Chen is a dedicated research engineer at Jihua Laboratory, Foshan, Guangdong Province, China. He obtained his MSc in Crop Informatics from Huazhong Agricultural University , where he built a strong foundation in computational modeling and artificial intelligence applications. His research primarily focuses on deep learning algorithms, particularly their integration into computer-based recognition systems and intelligent environmental analysis. He has completed three research projects and participated in one industry consultancy project, demonstrating both academic and applied innovation. His current research explores AI-driven recognition of marine microalgae, an essential area for assessing aquatic ecological health. In this domain, Chen developed a ResNeXt-50-based multi-expert network with an exponential feature compression mechanism that effectively mitigates class imbalance issues. Evaluated on the WHIO-Plankton dataset, his model achieved a state-of-the-art performance with an average precision and average recall , outperforming existing baselines. The system’s low inference latency demonstrates high real-time feasibility. His contributions provide a robust framework for marine microalgae recognition, supporting environmental monitoring and marine life science research. With Citations by 38 documents, 3 publications, and an h-index of 2, Chen has established himself as a rising researcher in applied AI and computational biology. His ongoing innovations signify impactful potential in environmental intelligence, sustainable technology, and bioinformatics applications.

Profile: Scopus | Orcid

Featured Publications

Chen, K., Cui, S., Zhong, J., & Wang, Q. (2025). MicroalgaeNet: Enhancing recognition of long-tailed marine microalgae images through multi-expert networks and feature compression. Algal Research, 92, 104333. https://doi.org/10.1016/j.algal.2025.104333

Song, P., Chen, K., Zhu, L., Yang, M., Ji, C., Xiao, A., Jia, H., Zhang, J., & Yang, W. (2022). An improved cascade R-CNN and RGB-D camera-based method for dynamic cotton top bud recognition and localization in the field. Computers and Electronics in Agriculture, 202, 107442. https://doi.org/10.1016/j.compag.2022.107442