Prof. Chong Hyun Lee | Deep Learning | Best Researcher Award

Published on 28/10/202528/10/2025 by Global Mechanics Awards

Prof. Chong Hyun Lee | Deep Learning | Best Researcher Award

Professor | Jeju National University | South Korea

Dr. Chong Hyun Lee is a distinguished researcher and academic in the fields of electronics, power transfer systems, ocean system engineering, and applied signal processing, with a prolific research record and recognized scientific excellence. As a long-standing IEEE member, Dr. Lee has made significant contributions to the advancement of electronic and communication technologies through innovation, leadership, and collaborative research. His work has been recognized with prestigious honors, including the Best Science and Technology Paper Award from the Korean Federation of Science and Technology Societies (KOFST) and the Best Paper Award in Ocean System Engineering from the Institute of Electronics and Information Engineers in South Korea, reflecting his high academic impact and technical expertise. Dr. Lee’s research encompasses a wide range of advanced topics, including compact impedance matching circuits for wideband power transfer in Janus Helmholtz transducers, weakly supervised graph neural networks for line spectrum extraction, and DC power boosting circuits for triboelectric nanogenerators with high intrinsic impedance and multi-harmonic output. His interdisciplinary work bridges marine engineering, electronics, and nanogenerator technologies, offering practical applications in sustainable energy systems, signal processing, and sensor innovation.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Lee, C. H., & co-authors. (2024). DC power boosting circuit for freestanding-sliding triboelectric nanogenerators with high intrinsic impedance and multi-harmonic output. Advanced Materials Technologies.

Lee, C. H., & co-authors. (2024). Passive sonar signal classification using graph neural network based on image patch. Journal of the Acoustical Society of Korea.

Lee, C. H., & co-authors. (2023). Encoding and decoding-based normalizing flows for image anomaly localization. Electronics Letters.

Lee, C. H., & co-authors. (2023). Abnormal sonar signal detection using recurrent neural network and vector quantization. Journal of the Acoustical Society of Korea.

Ali Alyatimi | Deep Learning | Best Research Article Award

Published on 18/10/202518/10/2025 by Global Mechanics Awards

Mr. Ali Alyatimi | Deep Learning | Best Research Article Award

Associate Professor | The University of Sydney | Australia

Mr. Ali Alyatimiis a dedicated researcher and academic professional currently pursuing a PhD in Computer Science at the University of Sydney, specializing in deep learning and multi-omics data integration. With a solid interdisciplinary background in computer science, engineering technology, and artificial intelligence, Mr. Ali Alyatimi brings extensive experience in both research and teaching across data science, machine learning, and computer vision domains. His academic foundation includes advanced study in computational methods, database systems, and data-driven modeling, supported by expertise in programming languages such as Python, R, SQL, and MATLAB. His research focuses on developing multi-view deep learning frameworks that enhance biological data fusion and computational biology analysis, supervised by Dr. Vera Chung and Dr. Ali Anaissi. Before commencing doctoral research, he served as a lecturer and trainer in the Department of Computer Science at the College of Technology, Jizan, where he taught programming, database design, and computer network courses, guiding students in developing innovative, database-driven web applications using PHP and MySQL. His earlier research at the University of New England involved the design and implementation of illumination invariance techniques to improve weed detection in pastures using object detection models with YOLOv4 and TensorFlow, demonstrating the practical potential of deep learning in precision agriculture. Alongside research, he actively contributes to academic development through tutoring undergraduate courses in data science and computational methods at the University of Sydney. His current work integrates artificial intelligence, bioinformatics, and data fusion, aiming to advance computational modeling in healthcare and life sciences.

Profile: Google Scholar

Featured Publications:

Author(s). (Year). Improving weeds detection in pastures using illumination invariance techniques.

VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

Published on 17/10/202517/10/2025 by Global Mechanics Awards

Dr. VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

Professor and HOD | CVR College of Engineering | India

Dr. A. Vani Vathsala is a distinguished academician and researcher in the field of Computer Science and Engineering, currently serving as a Professor and Head of the Department of Computer Science and Engineering at CVR College of Engineering, Hyderabad. With a rich background spanning both academia and industry, including a foundational professional stint at Tata Consultancy Services, Dr. Vathsala has built a career marked by innovation, leadership, and impactful research. She holds a Ph.D. in Computer Science from the University of Hyderabad, preceded by a Master of Technology in Computer Science from the same university and a Bachelor’s degree in Computer Science and Engineering from Nagarjuna University. Her research portfolio reflects expertise in artificial intelligence, cloud computing, machine learning, data security, and sustainable computing systems. She has published extensively in Scopus and SCI-indexed journals, contributing to emerging technologies that bridge healthcare, security, and IoT-driven solutions. Her notable publications include Early Diagnosis for the Bacterial Infections for the Patients under the Medical Intervention of Automated Peritoneal Dialysis Using Machine Learning Techniques, A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model, Energy-Efficient and Sustainable Cluster-Based Routing in IoT-Based WSNs Using Metaheuristic Optimization, and Enhanced Real-Time Surveillance and Suspect Identification Using CNN-LSTM Based Body Language Analysis.

Profile: Google Scholar

Featured Publications:

Vathsala, A. V., & Mohanty, H. (2014). A survey on checkpointing web services. Proceedings of the 6th International Workshop on Principles of Engineering,

Vathsala, A. V., & Mohanty, H. (2012). Using HMM for predicting response time of web services. Proceedings of the CUBE International Information Technology Conference, 520–525,

Atluri, V. V., & Mohanty, H. (2014). Web service response time prediction using HMM and Bayesian network. In Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014,

Swathi, V., Kumar, G. S., & Vathsala, A. V. (2023). Cloud service selection system approach based on QoS model: A systematic review. International Journal on Recent and Innovation Trends in Computing and Communication,

Vathsala, A. V. (2011). Optimal call based checkpointing for orchestrated web services. International Journal of Computer Applications, 975(8887),

Archana, M., Kavitha, S., & Vathsala, A. (2023). Auto deep learning-based automated surveillance technique to recognize the activities in the cyber-physical system. International Journal on Recent and Innovation Trends in Computing and Communication,

Swathi, V., Akhilesh, D., Senthil Kumar, G., & Vathsala, A. V. (2021). Real-time driver distraction detection using OpenCV and machine learning algorithms. In Smart Computing Techniques and Applications: Proceedings of the Fourth International Conference,

Dynhora Danheyda Ramirez Ochoa | Inteligencia de enjambre | Best Researcher Award

Published on 17/10/2025 by Global Mechanics Awards

Dr. Dynhora Danheyda Ramirez Ochoa | Inteligencia de enjambre | Best Researcher Award

Universidad Tecnológica de Chihuahua | Mexico

Dynhora Danheyda Ramírez Ochoa is a distinguished full-time professor at Universidad Tecnológica de Chihuahua, specializing in automation, data storage and analysis, project management, and digital innovation. With a Doctorate in Technology, a Master’s in Computer Systems Engineering, and a Bachelor’s in Computer Systems with a Hardware specialization, she has cultivated extensive expertise in multidisciplinary academic and technological environments. Since beginning her tenure at the university, she has advised over seventy students, facilitated seventeen graduations, and coordinated the development and monitoring of curricula for the Information Technologies and Digital Innovation program. Her leadership spans chairing and serving on committees for ethics, research, software, and tutoring, and she actively oversees STEM mentorship initiatives through the MC3T project. Dynhora has contributed to national and international conferences, focusing on swarm intelligence, decision-making processes, and emerging technologies, while publishing in specialized journals. Her dedication to fostering collaborative research teams, integrating immersive technologies, and promoting educational innovation reflects her commitment to developing both academic excellence and applied technological solutions, positioning her as an exemplary candidate for the Best Researcher Award.

Profile: Google Scholar

Featured Publications:

Ramírez-Ochoa, D. D., Pérez-Domínguez, L. A., Martínez-Gómez, E. A., & others. PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review. Symmetry, 14(3), 455.

Ramírez-Ochoa, D. D., Pérez-Domínguez, L., & Martínez-Gómez, E. A. Supplier selection process based on CODAS method using q-rung orthopair fuzzy information. In q-Rung Orthopair Fuzzy Sets: Theory and Applications (pp. 219–240).

Royo-León, M., Hedley, D., Gal, E., Katz, N., Nitzan, M. W., Schreuer, N., & others. eWPQ: Development of a Computer-Based Work Performance Review Applications for Persons with Autism Spectrum Disorder. Revista ELECTRO, 40(1), 101–1.

Ramírez-Ochoa, D. D., Villagran-Vizcarra, D. C., & Zamarrón, V. T. TIC e IA, el futuro de la educación: mejorando el acceso y la efectividad del aprendizaje. RECIE. Revista Electrónica Científica de Investigación Educativa, 8, e2309–e2309.

Ramírez-Ochoa, D. D., Pérez-Domínguez, L. A., & Gómez-Martínez, E. Diseño de herramienta para la toma de decisiones avanzada con inteligencia de enjambre en un ambiente multicriterio: 6CP23-10. Memorias Científicas y Tecnológicas, 2(3), 16–17.

Pérez-Domínguez, L. A., Ramírez-Ochoa, D. D., & Luviano-Cruz, D. Implementing Sensible Algorithmic Decisions in Manufacturing. Applied Sciences, 15(16), 8885.

Cortés, T. J. G., Ramírez-Ochoa, D. D., & Ortega, E. C. P. Inspirando vocaciones científicas: estrategias de mentoría para niñas en STEM. In Transformando la educación: pensamiento computacional y robótica como …

Pérez-Domínguez, L. A., Luviano-Cruz, D., Hernández-Hernández, J. I., & Ramírez-Ochoa, D. D. An Integrated Approach to Decision-Making that Maximizes the Plastic Injection Molding Process. In Systems and Decision Processes in Management, Innovation and Sustainability