Xulei Cao | Computer Science | Research Excellence Award

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Mr. Xulei Cao | Computer Science | Research Excellence Award

University of Science and Technology of China | China

Mr. Xulei Cao research centers on advancing intelligent communication systems, large-scale machine learning, and adaptive networked environments, with a primary emphasis on vehicular ad hoc networks (VANETs), device–edge–cloud collaboration, and large language models. His work explores street-centric and microtopology-based routing strategies to address the challenges of dynamic mobility, frequent topology changes, and complex urban communication environments, proposing opportunistic routing protocols that leverage link correlation to enhance reliability, reduce packet loss, and optimize end-to-end performance. He has contributed to routing solutions grounded in urban road structure awareness, improving scalability and robustness in dense vehicular networks and supporting next-generation intelligent transportation systems. In parallel, his research extends into intelligent computing frameworks that integrate device, edge, and cloud layers to enable efficient distributed learning, resource-aware decision-making, and latency-sensitive AI applications. He also investigates algorithmic innovation within large language models, emphasizing scalability, deployment efficiency, and real-world applicability. Additionally, his work on biometric recognition, including palmprint feature extraction and direction coding, demonstrates expertise in pattern recognition and vision-based authentication systems. Supported by growing scholarly recognition, his work has been cited 212 times overall, including 101 citations since 2020, with an h-index of 3 and an i10-index of 2, underscoring the increasing impact and relevance of his contributions to networking, artificial intelligence, and intelligent mobility research.

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Featured Publications

 

Abdalilah Alhalangy | Computer Science | Innovative Research Award

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Assoc. Prof. Dr. Abdalilah Alhalangy | Computer Science | Innovative Research Award

Qassim university | Saudi Arabia

Assoc. Prof. Dr. Abdalilah Alhalangy, Ph.D., is an Associate Professor in Computer Engineering at Qassim University, Kingdom of Saudi Arabia, specializing in advanced areas of artificial intelligence, machine learning, intelligent systems, and cybersecurity. His research spans deep learning, ensemble methods, neural networks, computer vision, wireless networks, cloud computing, big data analytics, robotics, augmented reality, mobile applications, image and video analysis, GIS, and e-learning systems. He has a particular focus on artificial neural networks, wavelet neural networks, fuzzy logic, evolutionary algorithms, and computational intelligence, applied to enhancing the security and functional performance of intelligent systems. Dr. Al-Halangy has published 6 documents cited by 59 Scopus-indexed papers, achieving a Scopus h-index of 3 and an i10-index of 2 on Google Scholar, with a total of 131 citations. His work has earned recognition in fields ranging from Arabic speech emotion recognition and fake account detection in mobile networks to generative AI-driven cybersecurity systems and the evaluation of e-learning effectiveness. Dr. Al-Halangy’s research is characterized by its innovative integration of AI techniques to solve complex real-world problems, positioning him as a leading contributor to modern computing challenges. He has received accolades including the Innovative Research Award for his contributions to the development of secure, intelligent, and efficient computational systems. His work continues to impact both academic research and practical applications, advancing the state of intelligent and adaptive technologies globally.

Publication Profile

Scopus | Orcid | Google Scholar

Featured Publications

  • Alhalangy, A., & AbdAlgane, M. (2023). Exploring the impact of AI on the EFL context: A case study of Saudi universities.

  • Alhalangy, A. (2024). Deep learning, ensemble and supervised machine learning for Arabic speech emotion recognition. Engineering, Technology & Applied Science Research, 14, 1-10.

  • Hassan, A., & Alhalangy, G. I. A. (2023). Fake accounts identification in mobile communication networks based on machine learning. SSRN.

  • Alhalangy, A., Elhadi, O. A. M., & Mohamed, E. H. G. (2025). E-learning effectiveness and efficiency in Kassala and Gedaref universities: An IS-impact evaluation. UtilitasMathematica, 122(2), 1301-1317.

  • Alhalangy, A. (2025). Generative AI-driven information system for behavioral detection of zero-day cyber attacks. UtilitasMathematica, 122(2), 1194-1210.

Weidong Ji | Computer Science | Editorial Board Member

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Dr. Weidong Ji | Computer Science | Editorial Board Member

Harbin Normal University | China

Dr. Weidong Ji is a distinguished Chinese scholar known for his innovative contributions to artificial intelligence in education, learning analytics, and intelligent recommendation technologies, with a strong research presence reflected across international journals and conferences. His work integrates machine learning, knowledge graphs, deep learning models, and cognitive theories to enhance personalized learning, online education systems, student engagement assessment, and data-driven educational decision-making. With a Scopus record of 39 documents, 102 citations from 100 citing documents, and an h-index of 6, Dr. Ji’s scholarly influence continues to grow as his recent works advance emerging domains such as quantum-constructivism-based knowledge tracing, lightweight human–computer interaction models, and user-preference-driven recommendation algorithms. Google Scholar metrics (if available) would further extend his citation visibility, demonstrating the expanding global use of his models in adaptive learning, course recommendations, and real-time student behavior analysis. He also contributes to the academic community through participation in peer-review processes and editorial activities that support the development of high-quality research in AI-driven educational technology. Dr. Ji’s recent publications showcase cutting-edge computational frameworks such as neural knowledge graph reasoning, lightweight vision models for engagement analytics, and personalized prediction architectures for sparse learning environments. His contributions position him as an emerging leader at the intersection of educational psychology and computational intelligence, emphasizing practical applicability, algorithmic efficiency, and innovative pedagogical design, demonstrating excellence aligned with this award category as an Editorial Board Member.

Publication Profile

Scopus

Featured Publications

  • Authors unavailable. (2025). Knowledge graph convolutional networks with user preferences for course recommendation. Scientific Reports.

  • Authors unavailable. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge Based Systems.

  • Authors unavailable. (2025). LightNet: A lightweight head pose estimation model for online education and its application to engagement assessment. Journal of King Saud University Computer and Information Sciences.

Jian-Gang Tang | Computational theory | Distinguished Scientist Award

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Prof. Dr. Jian-Gang Tang | Computational theory | Distinguished Scientist Award

Yili Normal University | China

Academic Background:

Professor Jian-Gang Tang holds dual doctoral degrees, one in Computer Science from the Polish Academy of Sciences and another in Pure Mathematics from Sichuan University. He has developed deep expertise across computability, complexity, computational theories including quantum and DNA computing, algebra, and topology. His scholarly output is extensive, with citations recorded in Scopus and Google Scholar reflecting the impact of his work. According to Scopus, he has a citation count of 38, with 45 documents contributing to his academic profile, resulting in a strong h-index that highlights his influence and the sustained relevance of his research contributions.

Research Focus:

Professor Tang’s research focuses on the interface of computational theory, algebra, and category theory. His work systematically investigates internal group objects, L-set and Ω-set categories, structural enhancement based on group categories, and applications of quasi-Hopf algebras, advancing the understanding of categorical structures in mathematics and their computational implications.

Work Experience:

Professor Tang has held prominent academic positions including Vice President of Yili Normal University, where he cultivated numerous researchers and educators. Post-retirement, he continues to contribute as a professor at Sichuan University Jinjiang College and Kashgar University. His career has been characterized by mentorship and institution-building, reflecting his commitment to nurturing talent and advancing mathematical education at multiple levels.

Key Contributions:

Professor Tang’s foundational work on internal group objects established conditions for completeness and cocompleteness transfer, bridging algebra, topology, and computation. He has made significant advancements in category algebra, lattice-valued structures, and quasi-Hopf algebras. His research has created theoretical frameworks with applications in homotopical algebra and duality theory, and he has mentored a generation of scholars who continue to contribute to these fields.

Awards & Recognition:

Professor Tang is recognized as a national-level expert and has been honored with the Special Government Allowance from the State Council. His work is widely acknowledged for its scholarly rigor and influence, establishing him as a distinguished leader in mathematics and computational theory. His recognition extends through numerous citations and collaborations with international researchers.

Professional Roles & Memberships:

He serves on the editorial boards of multiple international mathematics journals and acts as a reviewer for leading publications. Professor Tang is an active member of professional mathematical societies, contributing to the academic community through collaboration, peer review, and knowledge dissemination.

Profile: 

Scopus | Orcid

Featured Publications:

Tang, J., Maihemuti, N., Peng, J., Aisan, Y., & Song, A. (2025). Completeness and cocompleteness transfer for internal group objects with geometric obstructions. Mathematics.

Tang, J., Lin, F., Aishan, Y., Liu, J., & Peng, J. (2025). Research on group type theory and its functorial semantic models in category logic. PLOS One.

Tang, J., Chen, Q. (2025). Weak Hopf algebra structures on hybrid numbers. Symmetry.

Tang, J., Maihemuti, N., Peng, J., & Aisan, Y. (2025). Controlled and assisted cloning of two-, three- and four-qubit states with optimal quantum resources. International Journal of Quantum Information.

Tang, J., Lei, H., & Liu, J. (2025). Limits in D-module categories: Completeness and derived geometric extensions. AIMS Mathematics.

Impact Statement / Vision:

Professor Tang envisions advancing the integration of algebra, topology, and computation to foster innovative mathematical frameworks. Through mentorship, research, and interdisciplinary collaboration, he aims to inspire future generations of mathematicians and expand the boundaries of theoretical and applied mathematics globally.

Sara Dankir | Computer Science | Best Researcher Award

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