Hafiz Muhammad Raza ur Rehman | Data Science | Best Researcher Award 

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Assist. Prof. Dr. Hafiz Muhammad Raza ur Rehman | Data Science | Best Researcher Award 

Assist. Prof. Dr. Hafiz Muhammad Raza ur Rehman | Yeungnam University | South Korea

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Early Academic Pursuits

Dr. Hafiz Muhammad Raza ur Rehman began his academic journey with a strong foundation in information and communication engineering, culminating in a PhD from Yeungnam University, Korea. His doctoral research laid the groundwork for his later contributions in machine learning, multi-agent reinforcement learning (MARL), and data-science. His academic excellence and early engagement with algorithmic design and optimization established his trajectory as a dedicated researcher and educator in computational intelligence.

Professional Endeavors

Following his doctoral studies, Dr. Raza ur Rehman pursued a postdoctoral research position in Korea, focusing on sensor calibration for autonomous vehicles (AVs). Over 5.5 months, he conducted high-level interdisciplinary work aimed at improving the precision and reliability of AV sensor systems. He also gained substantial teaching experience 9 months as an Assistant Professor where he taught undergraduate and graduate courses in machine learning, deep learning, reinforcement learning, and data-science. In addition, his collaboration with the Electronics and Telecommunications Research Institute (ETRI), Korea, on a US Air Force–funded project, exemplified his ability to contribute to large-scale international research efforts.

Contributions and Research Focus

Dr. Raza ur Rehman’s research portfolio reflects a deep commitment to innovation and interdisciplinary integration. His primary focus areas include multi-agent reinforcement learning (MARL), autonomous vehicle systems, natural language processing (NLP), and optimization algorithms. He has authored a patent centered on MARL techniques and published several impactful journal and conference papers. Key publications include “QsOD: MARL-based QMIX with Grey Wolf Optimization” and “Prediction-Based Model for Chemical Compounds.” Moreover, he has presented research such as “Camera Calibration with CNN” at IEEE conferences and six additional papers at Korean academic venues. His current research extends to seven articles under review in internationally reputed journals, reinforcing his commitment to advancing data-science and intelligent systems.

Impact and Influence

Dr. Raza ur Rehman’s interdisciplinary research bridges theory and application spanning from algorithmic optimization to real-world technological integration. His MARL-related patent and publications contribute significantly to the growing body of knowledge in intelligent agent systems. By integrating data-science with advanced computational models, his work influences emerging fields such as autonomous navigation, machine learning-based control systems, and intelligent automation. As a mentor, he continues to inspire students through hands-on projects, fostering innovation and critical thinking in the next generation of engineers and researchers.

Academic Cites

His scholarly output includes publications in peer-reviewed international journals, conference presentations, and ongoing submissions to high-impact outlets. The QsOD study and the chemical compound prediction model have attracted interest in computational optimization and artificial intelligence research circles. His IEEE presentation on CNN-based camera calibration further strengthened his academic visibility and recognition within the AI research community.

Legacy and Future Contributions

Looking ahead, Dr. Hafiz Muhammad Raza ur Rehman aims to expand his research on multi-agent reinforcement learning, autonomous systems, and optimization-driven AI architectures. His future work is poised to contribute substantially to global research in data-science, particularly in developing adaptive, intelligent algorithms for complex real-world problems. Through continued teaching, mentorship, and publication, he aspires to leave a lasting legacy in both academia and applied research bridging the gap between theoretical innovation and practical technological advancement.

Featured Publications

Raza, S. N., ur Rehman, H. M., Lee, S. G., & Choi, G. S. (2019). Artificial intelligence-based camera calibration. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 32. IEEE.

Nagulapati, V. M., ur Rehman, H. M. R., Haider, J., Qyyum, M. A., Choi, G. S., & Lim, H. (2022). Hybrid machine learning-based model for solubilities prediction of various gases in deep eutectic solvent for rigorous process design of hydrogen purification. Separation and Purification Technology, 298, 121651.

ur Rehman, H. M. R., On, B. W., Ningombam, D. D., Yi, S., & Choi, G. S. (2021). QSOD: Hybrid policy gradient for deep multi-agent reinforcement learning. IEEE Access, 9, 129728–129741.

ur Rehman, H. M. R., Saleem, M., Jhandir, M. Z., & Hafiz, H. G. I. A. (2025). Detecting hate in diversity: A survey of multilingual code-mixed image and video analysis. Journal of Big Data, 12(1), Article 5.

Younas, R., ur Rehman, H. M. R., Lee, I., On, B. W., Yi, S., & Choi, G. S. (2025). Sa-MARL: Novel self-attention-based multi-agent reinforcement learning with stochastic gradient descent. IEEE Access, 13, Article 5.

Khan, N. U., & ur Rehman, H. M. R. (2025). Real time signal decoding in closed loop brain computer interface for cognitive modulation. Ubiquitous Technology Journal, 1(1), 32–39.

ur Rehman, H. M. R., Haider, S. A., Faisal, H., Yoo, K. Y., Jhandir, M. Z., & Choi, G. S. (2025). A novel framework for Saraiki script recognition using advanced machine learning models (YOLOv8 and CNN). IEEE Access, 13, Article 2.

Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

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Prof. Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

Prof. Konstantinos Diamantaras | International Hellenic University | Greece

Prof. Konstantinos Diamantaras is a Professor at the International Hellenic University, Department of Information & Electronic Engineering, and Vice Rector since 2023, holding a Beng from NTUA, Greece, and an MSc and PhD in Electrical Engineering from Princeton University. His research focuses on machine learning, signal processing, and augmented/virtual reality, with over 230 scientific publications and 79 journal articles indexed in SCI and Scopus, accumulating more than 7,300 citations on Google Scholar (h-index 30) and 3,027 citations on Scopus (h-index 23). He has authored four books, including Principal Component Neural Networks (1996) and Artificial Neural Networks (2007), and received the IEEE Best Paper Award in 1997 for Adaptive Principal Component Extraction (APEX). He leads multiple EU- and university-funded projects, such as Kids Radio Europe, METACHEF, Digital4all, and AI-based food recognition. His collaborations include Prof. S. Y. Kung (Princeton), Prof. Athina Petropulu (Rutgers), Prof. Tomas McKelvey (Chalmers), and partnerships with Alzheimer Hellas and the University of Alicante on NLP applications. He serves on editorial boards of Journal of Signal Processing Systems and Applied Sciences, contributing to advancements in deep learning, pattern recognition, biomedical informatics, adaptive signal processing, and educational technology. His work spans practical AI applications in health, digital learning, and immersive experiences, influencing both academic research and societal impact. He is an active IEEE member and IEEE Signal Processing Society participant, advancing knowledge in neural networks, computational intelligence, and multilingual natural language generation.

Profiles: Scopus | Orcid | Google Scholar | Staff Page

Featured Publications

Diamantaras, K. I., & Kung, S. Y. (1996). Principal component neural networks: Theory and applications. In Adaptive and learning systems for signal processing, communications, and control (p. 1694). Springer.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1–9

Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, A., & Sarigiannidis, G. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 69, 214–224.

Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022). Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Applied Sciences, 12(13), 6809.

Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., & Strintzis, M. (1998). ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International Journal of Medical Informatics, 52(1–3), 191–208.

Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P. (2019). Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 124, 292–303.

Kung, S. Y., & Diamantaras, K. I. (1990). A neural network learning algorithm for adaptive principal component extraction (APEX). In ICASSP-90. Acoustics, Speech, and Signal Processing (pp. 256–259).

Kung, S. Y., Diamantaras, K. I., & Taur, J. S. (1994). Adaptive principal component extraction (APEX) and applications. IEEE Transactions on Signal Processing, 42(5), 1202–1217.

Stamkopoulos, T., Diamantaras, K., Maglaveras, N., & Strintzis, M. (1998). ECG analysis using nonlinear PCA neural networks for ischemia detection. IEEE Transactions on Signal Processing, 46(11), 3058–3067.