Longfei Yue | AI Advancements | Best Researcher Award

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Dr. Longfei Yue | AI Advancements | Best Researcher Award

Dr. Longfei Yue | NUE | China

Longfei Yue is an influential researcher in the fields of unmanned aerial vehicles (UAVs), reinforcement learning, multi-agent systems, and intelligent autonomous control. His work focuses on advancing next-generation autonomous flight technologies, with major contributions to cooperative decision-making, swarm intelligence, guidance laws, and mission-planning strategies for aerial and aerospace systems. He has produced an extensive body of work, reflected through a strong publication record and impactful citation metrics. His research outputs include dozens of journal articles and conference papers, spanning high-quality platforms such as international aeronautical and aerospace journals, IEEE publications, machine learning proceedings, and multidisciplinary scientific journals. His citation 300, H-index 11, and 286 publication data highlight the growing influence and visibility of his contributions in the global research community. Yue’s research emphasizes cutting-edge reinforcement learning approaches such as hierarchical learning, multi-agent reinforcement learning, soft actor-critic frameworks, and constrained learning techniques. These methods are applied to challenging aerospace scenarios including exoatmospheric evasion, missile guidance, cooperative multi-target tracking, aerial confrontation strategies, dual-UAV reconnaissance, and intelligent route planning for UAV swarms. His studies integrate autonomy, control theory, optimization, and machine learning to develop efficient, safe, and robust decision-making mechanisms for complex flight environments. His work also extends to the development of unsupervised learning techniques for grouping aerial swarms and dynamic policy learning for combat maneuvering. Many of his publications have received substantial citations, demonstrating wide academic and practical relevance. Beyond UAVs, Yue has collaborated on interdisciplinary studies in applied sciences, psychology, medical engineering, and data-driven modeling, further broadening his research impact. Overall, Longfei Yue’s research significantly advances autonomous aerial systems, cooperative robotics, and intelligent control engineering. His contributions play a pivotal role in shaping the future of UAV autonomy, multi-agent intelligence, and high-level aerospace decision-making technologies.

Profile: Scopus

Featured Publications

Collaborative energy-saving path planning of unmanned surface vehicle cluster based on multi-head attention mechanism and multi-agent deep reinforcement learning. (2025). Engineering Applications of Artificial Intelligence.

 CAP planning method based on elliptic fitting of optimal detection routes. (2025). Beijing Hangkong Hangtian Daxue Xuebao (Journal of Beijing University of Aeronautics and Astronautics).

Exoatmospheric evasion guidance law with total energy limit via constrained reinforcement learning. (2024). International Journal of Aeronautical and Space Sciences.

Marwan | AI Advancements | Excellence in Research

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Dr. Marwan | AI Advancements | Excellence in Research

Dr. Marwan | King Faisal University | Saudi Arabia

The applicant is an accomplished scholar in Computer Information Systems with specialized expertise in Artificial Intelligence and Data Science, supported by extensive experience in academia, research, and innovation. Over more than a decade of university-level teaching and research, the applicant has contributed significantly to advancing intelligent systems, machine learning applications, IoT security, biomedical imaging, and pattern recognition. Their doctoral work introduced a novel model for Arabic handwritten text recognition, forming the foundation for a strong research career in AI-driven language and image processing technologies. The applicant has authored and co-authored numerous impactful, refereed journal publications in well-recognized international outlets such as Sustainability, IJACSA, Traitement du Signal, and the Journal of Ayub Medical College. Research contributions span cancer therapy enhancement, anomaly detection, cephalometric landmark detection, multiple sclerosis classification, industrial IoT security, biometric iris recognition, palm disease classification, and Arabic word recognition. Several works have been indexed in Scopus and other reputable databases, with active collaborations involving interdisciplinary and multinational research teams. Beyond publications, the applicant has secured competitive research funding, including a grant supporting the development of a weighted-voting IoT security model targeting BASHLITE and Mirai cyberattacks. Ongoing research activities include hybrid deep learning systems for intrusion detection, medical image analysis, human nail disease diagnosis, music-brain interactions, and predicate-logic-based machine translation. The applicant has demonstrated strong academic service through extensive peer-reviewing, participation in scientific councils, and membership in research committees. Their professional development includes participation in conferences, training programs, and workshops focused on e-learning, scientific publishing, and advanced teaching strategies. Recognized for excellence, the applicant has received awards such as the Distinguished Scientific Research Award and the Outstanding International Publishing Award, reflecting sustained research quality and global scholarly impact. Their overall portfolio showcases a balanced blend of innovative research, academic leadership, and contributions to the AI and data science community.

Profiles: Scopus 

Featured Publications

Siddiqi, M. H., Alhwaiti, Y., Elaiwat, S., & Abu-Zanona, M. (2024). Dynamic healing process analysis: Image morphing with warping technique for nose and esophagus studies. The International Arab Journal of Information Technology, 21(3). (Accepted December 26, 2023).

 

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

Muhammad Arshad | AI Advancements | Best Researcher Award

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Dr. Muhammad Arshad | AI Advancements | Best Researcher Award

Dr. Muhammad Arshad | Yeez Consultants, Pakistan

Dr. Muhammad Zeshan Arshad is a distinguished data scientist and academic with a Ph.D. in Statistics from the University of Agriculture, Faisalabad, specializing in mathematical statistics and advanced probability distributions. His expertise lies in machine learning, time complexity analysis, and predictive modeling, with applications spanning public health, engineering, and environmental sciences. With peer-reviewed publications, numerous ongoing collaborative projects, and experience in both academic and applied research settings, Dr. Arshad contributes significantly to interdisciplinary data-driven solutions. He is currently serving as a Data Scientist at Yeez Consultants and has held teaching positions at several renowned institutions in Pakistan.