Hussien M.Hassan | AI and Automation in Architecture | Best Researcher Award

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Dr. Hussien M.Hassan | AI and Automation in Architecture | Best Researcher Award

Associate Professor | Port Said University | Egypt

Dr. Hussien M. Hassan is an Associate Professor at the Faculty of Engineering, Port Said University, specializing in Naval Architecture and Marine Hydrodynamics. With a Ph.D. in Ship Hydrodynamics, he has developed extensive expertise in Computational Fluid Dynamics (CFD), artificial intelligence applications in ship design, and green marine technologies. His research primarily focuses on hydrodynamic optimization, sustainable ship geometry, marine bio-mimetics, and the integration of AI for energy-efficient maritime systems. Dr. Hassan has led and contributed to numerous funded research projects, including initiatives on smart solar desalination and innovative ventilation systems for climate resilience. His scholarly output includes several high-impact publications in international journals such as Marine Systems & Ocean Technology and Journal of Ocean Engineering and Marine Energy. Beyond academia, he has demonstrated entrepreneurial leadership as CEO of multiple marine and engineering ventures. He is also the author of a technical book on Artcam software and has delivered seminars on emerging maritime technologies. Recognized for his contributions to marine innovation and education, Dr. Hassan actively engages with global research communities, serving as a reviewer and collaborator in multidisciplinary engineering forums. His research impact includes 5 citations, 3 publications, and an h-index of 2.

Profiles: Scopus | Google Scholar

Featured Publications

1. Hassan H.M., Elsakka M.M., Refaat A., Zhang H., Yin Z., Ahmed A., A comparative study on the hydrodynamic performance of traditional and closed-loop marine propellers. Marine Systems & Ocean Technology, 2025, 20(3), 34.

2. Mosaad M.A., Gafaary M.M., Yehia W., Hassan H.M., On the design of X-bow for ship energy efficiency. Influence of EEDI on Ship Design & Operation, London, UK, 2017, 22(11).

3. Hassan H.M., Elsakka M.M., Refaat A., Amer A.E., Rizk R.Y., Optimal design of container ships geometry based on artificial intelligence techniques to reduce greenhouse gases emissions. International Work-Conference on Bioinformatics and Biomedical Engineering, 2023, 3.

4. Hassan H.M., Elsakka M.M., Moustafa M.M., On the comparative hydrodynamic analysis of conventional and innovative closed-loop marine propellers, 2024, 2.

5. Mosaad M., Improving ship wave resistance by optimal bulb configuration. SYLWAN, 2020, 164(11), 1–14.

Dr. Hussien M. Hassan’s work advances sustainable maritime innovation by integrating artificial intelligence and hydrodynamic optimization to enhance ship energy efficiency and reduce environmental impact. His research contributes to the global shift toward greener marine technologies, fostering progress in both academic and industrial applications of smart ship design.

Ramazan Yasar | AI and Automation in Architecture | Pioneer Researcher Award

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Assoc. Prof. Dr. Ramazan Yasar | AI and Automation in Architecture | Pioneer Researcher Award

Lecturer | Ankara University | Turkey

Assoc. Prof. Dr. Ramazan Yasar is a faculty member in the Department of Artificial Intelligence and Data Engineering at Ankara University, specializing in artificial intelligence, cryptography, algorithms, graph theory, big data technologies, machine learning, neutrosophic and fuzzy logic systems, data science, and natural language processing. He has served in progressive academic roles, including long-term instructional and research positions, and has contributed to institutional development through editorial leadership as Managing Editor of the Hacettepe Journal of Mathematics and Statistics. His work spans advanced mathematical structures, module theory, algebraic systems, and computational intelligence, reflected in numerous peer-reviewed publications in respected international journals. He has collaborated on projects exploring generalized extending conditions, exact submodules, annihilator conditions, rough groups, and intuitionistic fuzzy group-based algebraic models, demonstrating sustained contributions to theoretical mathematics and emerging intelligent technologies. His academic journey includes recognitions, editorial responsibilities, professional memberships, and active participation in international research platforms, supporting his commitment to advancing interdisciplinary scholarship. His research impact includes 23 citations, 11 publications, and an h-index of 3.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Yasar R., Tercan A., When some complement of an exact submodule is a direct summand. Commun. Algebra, 2021, 49(10), 4304–4312.

2. Yasar R., C11-modules via left exact preradicals. Turk. J. Math., 2021, 45(4), 1757–1766.

3. Tercan A., Yasar R., Yücel C.C., Goldie extending property on the class of exact submodules. Commun. Algebra, 2022, 50(4), 1363–1371.

4. Tercan A., Yasar R., Weak FI-extending modules with ACC or DCC on essential submodules. Kyungpook Math. J., 2021, 61(2), 239–248.

5. Birkenmeier G.F., Kilic N., Mutlu F.T., Tastan E., Tercan A., Yasar R., Connections between Baer annihilator conditions and extending conditions for nearrings and rings. J. Algebra Appl., 2024, 2650050.

Ramazan Yasar’s research advances the theoretical foundations of algebra and intelligent systems, strengthening the bridge between mathematical structures and modern computational technologies. His contributions support the development of more reliable, explainable, and secure AI frameworks, offering long-term value to scientific innovation and emerging digital industries. Through sustained scholarly impact, he contributes to a global ecosystem that depends on rigorous mathematical reasoning for next-generation technological progress.

Suparna Biswas | AI and Automation in Architecture | Best Researcher Award

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Dr. Suparna Biswas | AI and Automation in Architecture | Best Researcher Award

Associate Professor | Guru Nanak Institute of Technology | India

Dr. Suparna Biswas is Associate Professor in the Department of Electronics and Communication Engineering and Controller of Examinations at Guru Nanak Institute of Technology, with expertise in image processing, signal processing, control systems, and machine learning. She earned her PhD in Engineering from IIEST Shibpur after completing her M.Tech in Control Systems at Jadavpur University and B.Tech in Electronics and Communication Engineering at Kalyani Government Engineering College. With extensive academic and administrative experience, she has advanced through faculty roles and taken on leadership responsibilities such as coordinating NAAC and AQAR committees, serving as convener of faculty development programs and national conferences, and securing competitive research funding. Her prolific research record includes more than 60 publications in SCI, Scopus, and WoS indexed journals and conferences, authorship of books, and contributions to patents, with recent works addressing vision transformers, signal analysis, and AI applications in healthcare. She has guided PhD and postgraduate scholars and consistently contributed to advancing academic innovation. Recognized with multiple honors including Best Paper Awards at international conferences, the JIS Samman for patent publications, and the Most Promising Academician award, she also serves as reviewer for reputed SCI and Scopus journals and holds editorial board memberships. A corporate member of the Institution of Engineers (India) and an active member of FOSET, she has also completed certifications in machine learning, artificial intelligence, and data science. Dr. Biswas’s academic and research contributions underscore her leadership in engineering education and her commitment to advancing multidisciplinary innovation. Her Scopus profile reflects 84 citations, 27 publications, and an h-index of 4.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Biswas S., Designing optimal Vision Transformer architecture using differential evolution for tomato leaf disease classification. Comput. Electron. Agric., 2025, 238, 110824.

2. Chattopadhyaya A., Biswas S., Rakshit S., Jana N.D., Mondal A., Statistical Signal Processing and Machine Learning Based Diagnosis of Arrhythmia. Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 2025, 17, 362–374.