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