Jamal Zraqou | Machine Learning | Research Excellence Award

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Assoc. Prof. Dr. Jamal Zraqou | Machine Learning | Research Excellence Award

Associate Professor | University of Petra | Jordan

Assoc. Prof. Dr. Jamal S. Zraqou is an active researcher with demonstrated contributions across data-driven engineering, machine learning, cybersecurity, and digital transformation. He has authored 45 scholarly documents indexed in Scopus, accumulating 202 citations with an h-index of 9, reflecting consistent academic impact. His recent work addresses optimization techniques for engineering design, advanced machine learning methods for phishing detection, cybersecurity vulnerability analysis, and the strategic role of business intelligence in digital transformation. Dr. Zraqou has collaborated with a broad international network of over 60 co-authors, highlighting interdisciplinary and cross-sector engagement. His research supports practical problem-solving in engineering systems, information security, and decision intelligence, contributing to improved technological resilience, safer digital environments, and enhanced organizational competitiveness at societal and industrial levels.

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Featured Publications

Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

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Dr. Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

Senior Lecturer | Universiti Sains Malaysia | Malaysia

Dr. Nur Intan Raihana Ruhaiyem is a highly accomplished researcher and Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia, with notable expertise in computational biology, image processing, data visualization, and artificial intelligence applications. Her research spans deep learning, computer vision, and biomedical informatics, focusing on developing intelligent systems that enhance healthcare diagnostics, cultural heritage preservation, and data-driven decision-making. She has authored over 50 scholarly publications in reputable international journals and conferences, including IEEE Access, Biomedical Signal Processing and Control, Intelligence-Based Medicine, Diagnostics (Basel), Image and Vision Computing, and Scientific Reports. Her works have collectively garnered more than 230 citations and an h-index of 7, underscoring her growing impact in the computational and data science research community. Recent contributions such as the development of Mamba-based UNet architectures for medical image segmentation and hybrid restoration models for historical murals reflect her capacity to integrate advanced AI models into multidisciplinary domains. Dr. Ruhaiyem’s collaborative research extends internationally, with partnerships involving scholars from Australia, China, and the broader ASEAN region. Her role as a technical committee member for several prominent conferences—such as the International Visual Informatics Conference and Soft Computing in Data Science—demonstrates her leadership in promoting innovation and research excellence in data science and visual analytics. A Certified Professional Trainer recognized by Malaysia’s Human Resources Development Fund, she has also played a key role in professional education, serving as a lead instructor for national Data Science Certification programs. Through her research, mentorship, and active academic engagement, Dr. Ruhaiyem contributes significantly to advancing digital transformation, fostering analytical literacy, and bridging computational intelligence with societal needs.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Younis, H. A., Ruhaiyem, N. I. R., Ghaban, W., Gazem, N. A., & Nasser, M. (2023). A systematic literature review on the applications of robots and natural language processing in education. Electronics, 12(13), 2864. Citations: 75

2. Salisu, S., Ruhaiyem, N. I. R., Eisa, T. A. E., Nasser, M., Saeed, F., & Younis, H. A. (2023). Motion capture technologies for ergonomics: A systematic literature review. Diagnostics, 13(15), 2593. Citations: 63

3. Goni, M. R., Ruhaiyem, N. I. R., Mustapha, M., Achuthan, A., & Nassir, C. M. N. C. M. (2022). Brain vessel segmentation using deep learning—A review. IEEE Access, 10, 111322–111336. Citations: 42

4. Yang, J., & Ruhaiyem, N. I. R. (2024). Review of deep learning-based image inpainting techniques. IEEE Access, 12, 138441–138482. Citations: 17

5. Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., & others. (2023). Multimodal age and gender estimation for adaptive human-robot interaction: A systematic literature review. Processes, 11(5), 1488. Citations: 16

Rong Wang | Artificial Intelligence | Best Researcher Award

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Mrs. Rong Wang | Artificial Intelligence | Best Researcher Award

Postdoc | University of Tuebingen | Germany

Mrs. Rong Wang is a postdoctoral researcher at the Eberhard Karls University of Tübingen, Germany, specializing in computational linguistics and the evaluation and optimization of large language models (LLMs). She holds an M.Sc. in Computational Linguistics (NLP) from the University of Stuttgart (Grade: 1.7, 2024) and a Ph.D. in Digital Humanities from Zhejiang University, China (2016). Her interdisciplinary academic background bridges computer science, linguistics, and AI-driven humanities research, reflecting her ability to apply quantitative and symbolic methods to linguistic and cognitive studies. Professionally, she has served as a Postdoctoral Fellow at the University of Tübingen, AI Engineer at Telus International Digital AI, AGI Engineer Intern at Deepseek AI, Data Scientist at DEKRA GmbH, and Assistant Professor of Linguistics at Hangzhou Dianzi University. Her research focuses on language model evaluation metrics, neural-symbolic reasoning, multimodal semantics, and automated linguistic assessment. She has contributed to projects on enhancing spatial reasoning in LLMs, multi-agent AI systems, and personality recognition models, alongside authoring several publications on machine learning applications in cognitive linguistics and NLP evaluation. Technically proficient in Python, R, JavaScript, and SQL, she is experienced with frameworks such as LangChain, Autogen, Hugging Face, and PyTorch, and cloud platforms including Azure ML and AWS SageMaker. Her certifications include Azure Certified Data Scientist Associate and AWS Certified AI Practitioner. Mrs. Wang is fluent in English, German, and Chinese, with working knowledge of Japanese, and is recognized for her strong teamwork, communication, and leadership abilities. Her recent works have appeared in Data Intelligence, Psychology Methods, and TMLR, demonstrating her innovative contributions to the AI and NLP research community. (0 Citations ; 2 Documents ; 0 h-index.)

Profiles: Scopus | ResearchGate

Featured Publications

Wang, R., Sun, K., & Kuhn, J. (2024, Dec). Dspy-based neural-symbolic pipeline to enhance spatial reasoning in LLMs [Preprint]. arXiv. https://arxiv.org/abs/2411.18564

Wang, R., Sun, K., & Kuhn, J. (2024, Nov). A pipeline of neural-symbolic integration to enhance spatial reasoning in large language models [Preprint]. arXiv. https://arxiv.org/abs/2411.18564

Sun, K., & Wang, R. (2024, Oct). The roles of contextual semantic relevance metrics in human visual processing [Preprint]. arXiv. https://arxiv.org/abs/2410.09921

Wang, R., & Sun, K. (2024, Jul). A novel dependency framework for enhancing discourse data analysis [Preprint]. arXiv. https://arxiv.org/abs/2407.12473

Wang, R., & Sun, K. (2024, Jun). Continuous output personality detection models via mixed strategy training [Article]. arXiv. https://arxiv.org/abs/2406.16223

Zhizhong Xing | Artificial Intelligence | Best Innovation Award

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Dr. Zhizhong Xing | Artificial Intelligence | Best Innovation Award

University Teacher | Kunming Medical University | China

Dr. Zhizhong Xing, Ph.D., is a distinguished provincial-level Xingdian Young Talent and high-level recruited scholar at Kunming Medical University, widely recognized for his impactful contributions at the intersection of artificial intelligence, deep learning, smart education, and rehabilitation medicine. He obtained his doctoral degree in a technical discipline that laid the foundation for his expertise in AI-driven systems, intelligent sensing, and advanced computational modeling. Professionally, he has accumulated significant experience as principal investigator and collaborator on multiple prestigious projects, including the National Key R&D Program of China, the National Natural Science Foundation, and the Provincial Natural Science Foundation, reflecting both leadership and team-driven research capacity. His research interests center on the development of graph-based deep learning algorithms, point cloud analysis, and multi-source data fusion for applications in education technology, healthcare rehabilitation, and coal resource management under carbon peak initiatives. He has cultivated advanced research skills in 3D deep learning, human–computer interaction, laser point cloud segmentation, and biosensor-enhanced modeling, enabling translational advances across engineering, medicine, and education. Dr. Xing has published over 40 high-level academic papers, including more than 30 indexed by SCI, featured in CAS Tier 1 journals and IEEE Transactions, with several ranked as ESI Global Top 1% Highly Cited and Top 1‰ Hot Papers, reaching a total cumulative impact factor exceeding 111.8. His international visibility is further underscored by ongoing submissions to elite journals such as Nature Communications. Among his awards and honors are the Excellent Achievement Award for Scientific and Technological Research in Higher Education and the Provincial Science and Technology Award (Second Prize). He also serves as a reviewer for leading SCI journals and is a member of Sigma Xi, The Scientific Research Honor Society. His career reflects not only scholarly excellence but also commitment to advancing global collaborations, mentoring, and applied innovation. 286 Citations by 216 documents | 28 Documents | 10 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Xing, Z., Zhao, S., Guo, W., Meng, F., Guo, X., Wang, S., & He, H. (2023). Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model. Energy, 285, 128771. Cited by: 68

  2. Wu, Y., Zhao, S., Xing, Z., Wei, Z., Li, Y., & Li, Y. (2023). Detection of foreign objects intrusion into transmission lines using diverse generation model. IEEE Transactions on Power Delivery, 38(5), 3551–3560. Cited by: 37

  3. Xing, Z., Zhao, S., Guo, W., Guo, X., & Wang, Y. (2021). Processing laser point cloud in fully mechanized mining face based on DGCNN. ISPRS International Journal of Geo-Information, 10(7), 482. Cited by: 29

  4. Xing, Z., Ma, G., Wang, L., Yang, L., Guo, X., & Chen, S. (2025). Towards visual interaction: Hand segmentation by combining 3D graph deep learning and laser point cloud for intelligent rehabilitation. IEEE Internet of Things Journal, 12, 21328–21338. Cited by: 25

  5. Xing, Z., Meng, Z., Zheng, G., Ma, G., Yang, L., Guo, X., Tan, L., Jiang, Y., & Wu, H. (2025). Intelligent rehabilitation in an aging population: Empowering human–machine interaction for hand function rehabilitation through 3D deep learning and point cloud. Frontiers in Computational Neuroscience, 19, 1543643