Mohammed Maiza | Computer Science and Artificial Intelligence | Research Excellence Award

Dr. Mohammed Maiza | Computer Science and Artificial Intelligence | Research Excellence Award

Université Oran 1 | Algeria

Dr. Mohammed Maiza is an Associate Professor of Computer Science specializing in Pattern Recognition and Artificial Intelligence, with a strong academic and research profile in intelligent systems and data-driven computing. He holds an engineering degree in Computer Science with a specialization in Artificial Intelligence, followed by a Magister and Ph.D. in Computer Science focused on Pattern Recognition and AI. His academic career includes long-standing experience as a Lecturer-Researcher at leading Algerian universities, where he has contributed extensively to teaching, research supervision, and academic leadership. His research interests span pattern recognition, machine learning, artificial intelligence, data analysis, and intelligent decision-support systems. Dr. Maiza has actively participated in international conferences, workshops, and research visits in Algeria and France, strengthening collaborative research networks. He has authored multiple peer-reviewed scientific publications indexed in international databases, with measurable scholarly impact reflected through his h-index, citation count, and documented research outputs in major indexing platforms. In addition to research, he has held several academic leadership and coordination roles, contributing to curriculum development and institutional governance. His academic contributions demonstrate a sustained commitment to advancing AI research, mentoring graduate students, and fostering innovation in computer science, positioning him as a respected researcher and educator in the field.

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Featured Publications

C. Cherif, M.K. Abdi, A. Ahmad, M. Maiza
International Journal of Computing and Digital Systems, 14(1), 10505–10513, 2023 · Citations: 5

M. Maiza, S. Chouraqui, C. Cherif, A. Taleb-Ahmed
Przegląd Elektrotechniczny, 2025(4), 2025 · Citations: 4

A. Ahmad, M. Bouneffa, H. Basson, C. Cherif, M.K. Abdi, M. Maiza
Enterprise Interoperability X: Enterprise Interoperability Through Connected Enterprises, 2024 · Citations: 1

C. Cherif, M. Maiza, S. Chouraqui, A. Taleb
13th European Workshop on Visual Information Processing (EUVIP), pp. 1–6, 2025

C. Cherif, M. Maiza, S. Chouraqui, A. Taleb-Ahmed
Informatica, 49(3), 2025

Sun Yubing | Computer Science and Artificial Intelligence | Best Researcher Award

Dr. Sun Yubing | Computer Science and Artificial Intelligence | Best Researcher Award

Wenzhou University | China

Dr. Yubing Sun earned his PhD from Zhejiang University and currently serves as a lecturer at Wenzhou University, where he leads research efforts on advanced gas-sensor technologies and their systems for agricultural applications. His research focuses on improving sensitivity, selectivity, stability and drift compensation of gas sensors, and deploying sensor systems for field-scale agricultural monitoring. While a formal h-index, total document count and citation count are not publicly verified, his publication record and successful leadership in multiple national and provincial projects underscore his growing research impact. He has secured competitive funding, participated in multidisciplinary teams, and supervised student-led work bridging sensor hardware and agricultural field deployment. His contributions have been recognized via institutional awards and research grants. Looking ahead, Dr. Sun aims to translate lab-scale gas-sensor innovations into robust agricultural-system platforms, contributing to precision agriculture and sustainable crop monitoring.

Profile : Orcid

Featured Publications

Sun, Y., Huang, J., & Zheng, Y. (2025, September 9). Study on the determination of pest attack time of tea plant by gas sensor. Annals of Applied Biology.

Sun, Y., & Zheng, Y. (2024, December 2). Application of MOS gas sensors for detecting mechanical damage of tea plants. Journal of Agricultural Engineering.

Sun, Y., & Zheng, Y. (2024, June). Prediction of tomato plants infected by fungal pathogens at different disease severities using E-nose and GC–MS. Journal of Plant Diseases and Protection.

Yu, W., & Sun, Y. (2024, March). A method for detecting gas applied in seed germination with varying concentration based on gas sensors. Measurement.

Sun, Y., & Zheng, Y. (2023, July 24). A method of gas sensor drift compensation based on intrinsic characteristics of response curve. Scientific Reports.