Yu Zhou | Medical Image Analysis | Best Researcher Award

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Dr. Yu Zhou | Medical Image Analysis | Best Researcher Award

Lecturer | Henan University of Science and Technology | China

Dr. Yu Zhou is an emerging researcher in the intersecting domains of medical imaging, neuroscience, and artificial intelligence, recognized for advancing computational approaches that improve the understanding and diagnosis of neurological disorders. With 10 published research documents, 98 citations, an h-index of 7, and an i10-index of 6, his scholarly contributions reflect both productivity and growing international influence. His research has led to notable advancements in diffusion MRI analysis, white-matter connectivity modeling, and machine-learning-driven diagnostic frameworks, particularly within mild cognitive impairment (MCI), juvenile myoclonic epilepsy (JME), and neurobehavioral disorders.Yu Zhou’s most cited works demonstrate strong expertise in fiber-specific white matter analysis, CNN-based transfer learning, and automated classification systems, with contributions published in respected venues such as Cerebral Cortex, Frontiers in Aging Neuroscience, Frontiers in Neuroscience, and Journal of Neural Engineering. His research extends beyond human neuroscience to impactful cross-disciplinary applications, including AI-driven acoustic-based detection systems for livestock estrus identification, showcasing versatility and methodological depth.He has served as principal investigator for two provincial projects, participated in four additional provincial projects and one national project, and contributed to one consultancy/industry initiative, indicating growing leadership in funded research. His innovative capabilities are further evidenced by one granted patent and four patents under review, underscoring his commitment to translational and societally relevant technological development. With collaborations established across computational neuroscience and AI imaging research groups, he continues to contribute to global scientific networks.Yu Zhou’s ongoing work focuses on building interpretable deep-learning models, advancing multimodal data fusion for clinical diagnostics, and developing AI-assisted neuroimaging biomarkers for early disease identification. These contributions hold significant promise for clinical decision support, early-stage neurological assessment, and precision medicine applications. With increasing publication momentum and expanding collaborative research engagements, he is positioned to generate deeper scientific impact and contribute to the evolution of intelligent medical imaging and computational neuroscience.

Profiles:  Googlescholar | ResearchGate

Featured Publications

1.Zhou, Y., Si, X., Chen, Y., Chao, Y., Lin, C. P., Li, S., Zhang, X., Ming, D., & Li, Q. (2022). Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment. Cerebral Cortex, 32(15), 3159–3174. Cited By : 23

2.Si, X., Zhang, X., Zhou, Y., Sun, Y., Jin, W., Yin, S., Zhao, X., Li, Q., & Ming, D. (2020). Automated detection of juvenile myoclonic epilepsy using CNN-based transfer learning in diffusion MRI. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. Cited By : 18

3.Zhou, Y., Si, X., Chao, Y. P., Chen, Y., Lin, C. P., Li, S., Zhang, X., Sun, Y., & Ming, D. (2022). Automated classification of mild cognitive impairment by machine learning with hippocampus-related white matter network. Frontiers in Aging Neuroscience, 14, 866230.Cited By : 13

4.Wang, J., Si, Y., Wang, J., Li, X., Zhao, K., Liu, B., & Zhou, Y. (2023). Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag. Computers and Electronics in Agriculture, 210, 107949.Cited By : 13

5.Wang, J., Chen, H., Wang, J., Zhao, K., Li, X., Liu, B., & Zhou, Y. (2023). Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag. animal, 17(6), 100811.Cited By : 12

Dr. Yu Zhou’s work advances global healthcare innovation by integrating medical imaging, neuroscience, and artificial intelligence to enable earlier, more accurate detection of neurological disorders. His research drives the development of interpretable, data-driven diagnostic tools that strengthen clinical decision-making and support precision medicine. Through cross-disciplinary innovation, he envisions AI-empowered neuroimaging solutions that improve patient outcomes and transform future healthcare systems.

Divya Nimma | Applications of Computer Vision | Women Researcher Award

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Assist. Prof. Dr. Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assistant Professor | Arkansas Tech University | United States

Dr. Divya Nimma is an accomplished researcher and Assistant Professor at Arkansas Tech University, specializing in Computer Vision, Artificial Intelligence, Image Processing, and Machine Learning. With a strong interdisciplinary footprint, she has contributed extensively to domains spanning environmental monitoring, healthcare analytics, intelligent transportation cybersecurity and immersive technologies. She has published 46 scholarly works and accumulated over 326 citations, with an h-index of 10 and i10-index of 10, underscoring her growing global research influence.Dr. Nimma’s research portfolio reflects a commitment to developing intelligent systems for real-world impact. Her notable contributions include climate-responsive modeling of freshwater ecosystems remote sensing–based marine life assessment for food security transformer-driven object detection , and advanced deep learning frameworks for image forensics and semantic segmentation. She has led and co-authored high-impact studies published in Scientific Reports IEEE Transactions Alexandria Engineering Journal Desalination and Water Treatment Remote Sensing in Earth Systems Sciences and other reputed journals.Her collaborative research spans international teams across the United States  Europe the Middle East  and Asia. Significant works include attention-based models for real-time surveillance explainable AI pipelines for fingerprint recognition IoT-enabled energy management for EV charging predictive maintenance in Industry 4.0 and multisource wearable data analytics for human activity recognition.Dr. Nimma has also made influential contributions to biomedical informatics including cancer detection using optimized deep learning osteoporosis classification and non-invasive brain stimulation–based sleep stage modeling. Additionally her research extends to precision agriculture integrating drone imagery AI and consumer electronics to enhance crop optimization and sustainability.Committed to societal and technological advancement Dr. Nimma’s work demonstrates a unique synthesis of deep learning innovation domain-driven applications and cross-disciplinary collaboration positioning her as a rising scholar and impactful global contributor in modern AI-driven intelligent systems.

Profiles:  Scopus | ORCID | Googlescholar

Featured Publications

1. Nimma, D., Devi, O. R., Laishram, B., Ramesh, J. V. N., Boddupalli, S., Ayyasamy, R., et al. (2025). Implications of climate change on freshwater ecosystems and their biodiversity. Desalination and Water Treatment, 321, 100889. Cited By : 42

2. Srikanth, G., Nimma, D., Lalitha, R. V. S., Jangir, P., Kumari, N. V. S., & Arpita. (2025). Food security-based marine life ecosystem for polar region conditioning: Remote sensing analysis with machine learning model. Remote Sensing in Earth Systems Sciences, 8(1), 65–73. Cited By : 36

3. Nimma, D., Nimma, R., Rajendar, & Uddagiri. (2024). Image processing in augmented reality (AR) and virtual reality (VR). International Journal on Recent and Innovation Trends in Computing and Communication. Cited By : 27

4. Nimma, D., & Zhou, Z. (2024). IntelPVT: Intelligent patch-based pyramid vision transformers for object detection and classification. International Journal of Machine Learning and Cybernetics, 15(5), 1767–1778. Cited By : 25

5. Nimma, D., Nimma, R., & Uddagiri, A. (2024). Advanced image forensics: Detecting and reconstructing manipulated images with deep learning. International Journal of Intelligent Systems and Applications in Engineering.
Cited By : 24

Dr. Divya Nimma’s research advances intelligent vision systems that enhance environmental sustainability, healthcare diagnostics, and smart transportation. Her work integrates AI with real-world applications, driving scientific innovation that strengthens societal resilience and global technological progress.

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

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Dr. P. Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Associate Professor | SRM Institute of Science and Technology  | India 

Dr. P. Nagaraj is an esteemed Associate Professor at the SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. With research expertise spanning Artificial Intelligence, Data Science, Data Analytics, Machine Learning, and Recommender Systems, he has made substantial contributions to intelligent computing and healthcare analytics. His innovative work focuses on applying deep learning, fuzzy inference, and explainable AI (XAI) techniques to real-world challenges in medical diagnosis, cybersecurity, and sustainable automation.Dr. Nagaraj has an impressive research portfolio, with over 208 indexed publications, 2,736 citations, and an h-index of 32, reflecting the global relevance and scholarly influence of his work. His notable publications include advancements in diabetes prediction, brain tumor classification, Alzheimer’s disease analysis, and cyberattack detection using AI-driven frameworks. His studies on distributed denial-of-service (DDoS) detection, IoT-based healthcare systems, and intelligent recommendation models have been widely cited and applied across multiple interdisciplinary domains.In recognition of his outstanding research, Dr. Nagaraj has been consecutively listed among the World’s Top 2% Scientists (2023–2025), highlighting his sustained impact in computer science and data-driven innovation. He is also a two-time recipient of the prestigious India AI Fellowship (Ministry of Electronics and Information Technology, MeitY), each worth ₹1 Lakh, for his pioneering projects titled AgriTech of Next-Gen Automation for Sustainable Crop Production and A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN.Through collaborations with national and international scholars, Dr. Nagaraj continues to advance the frontier of intelligent data analytics for societal benefit. His research contributes significantly to sustainable digital transformation, healthcare improvement, and agricultural innovation, positioning him as a leading figure in India’s AI research landscape and a global advocate for technology-driven social progress.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of distributed denial of service attacks in SDN using machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–6). IEEE. Cited By : 158

2.Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. Cited By : 100

3.Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1–6). IEEE. Cited By : 85

4.Nagaraj, P., Deepalakshmi, P., & Romany, F. M. (2021). Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 2789–2802. Cited By : 79

.5.Nagaraj, P., & Deepalakshmi, P. (2020). A framework for e-healthcare management service using recommender system. Electronic Government, an International Journal, 16(1–2), 84–100. Cited By : 70

Dr. P. Nagaraj’s research advances global innovation by integrating artificial intelligence and data analytics to address critical challenges in healthcare, agriculture, and cybersecurity. His vision is to harness intelligent automation and explainable AI to create sustainable, data-driven solutions that enhance human well-being, industrial efficiency, and societal resilience.

Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

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Mr. Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

University of Crete Medical School | Greece

Dimitrios Theodoropoulos is a researcher and AI specialist in medical imaging with expertise in machine learning, deep learning, computer vision, and artificial intelligence applications in healthcare, particularly in radiology and diabetic retinopathy analysis. He is currently pursuing a PhD in Artificial Intelligence in Medical Imaging at the University of Crete, Medical School, building on a master’s degree in computer engineering from Hellenic Mediterranean University and a bachelor’s degree in physics with specialization in microelectronics from the University of Crete, complemented by training as a radiology assistant. Alongside his academic path, he has worked extensively as a radiographer in MRI, CT, X-ray, mammography, DEXA, and EEG imaging, effectively integrating research with clinical practice. He has served as a visiting research fellow at FORTH-CBML and collaborated with institutions such as the Athens Neurotraining Center and Alexandra Hospital, bridging advanced AI research with healthcare innovation. His research focuses on the development of machine learning and deep learning algorithms for classification, segmentation, and detection tasks in medical imaging, with emphasis on diabetic retinopathy, intensive care monitoring, and noninvasive intracranial pressure assessment, while also extending to areas such as pollen analysis. He has published widely in Scopus-indexed journals and conferences, presented at international congresses and academic symposiums, and delivered guest lectures at the National and Kapodistrian University of Athens. Proficient in Python, MATLAB, TensorFlow, PyTorch, Scikit-learn, Linux, and Docker, he has advanced expertise in data preprocessing, model optimization, and AI-driven biomedical solutions. With certifications in Python programming, machine learning, and deep learning, combined with memberships in the Hellenic Artificial Intelligence Society and the Union of Greek Physicists, he demonstrates a rare integration of technical, clinical, and analytical skills, enabling him to advance scientific progress while contributing to patient-centered healthcare innovation.

Profile: Google Scholar | Scopus Profile

Featured Publications

Tsiknakis N., Theodoropoulos D., Manikis G., Ktistakis E., Boutsora O., et al., Deep learning for diabetic retinopathy detection and classification based on fundus images: A review, Comput. Biol. Med., 135, 104599.

Chatziadam P., Dimitriadis A., Gikas S., Logothetis I., Michalodimitrakis M., Theodoropoulos D., et al., TwiFly: A data analysis framework for Twitter, Information, 11(5), 247.

Theodoropoulos D., Karabetsos D.A., Antonios V., Efrosini P., Karantanas A., et al., The current status of noninvasive intracranial pressure monitoring: A literature review, Clin. Neurol. Neurosurg., 108209.

Theodoropoulos D., Sifakis N., Manikis G., Papadourakis G., Armyras K., et al., Semantic segmentation of diabetic retinopathy lesions using deep learning, SN Comput. Sci., 6(7), 782.

Theodoropoulos D., Trivizakis E., Marias K., Xirouchaki N., Vakis A., et al., Predicting intracranial pressure levels: A deep learning approach using computed tomography brain scans, Neurosurgery, 10.1227.

Jong-Hyun Kim | Applied Visual Computing | Best Researcher Award

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Prof. Jong-Hyun Kim | Applied Visual Computing | Best Researcher Award

Associate Professor at Inha University, South Korea

Prof. Jong-Hyun Kim is an Associate Professor at the College of Software and Convergence, Department of Artificial Intelligence, Design Technology at Inha University, with a joint appointment at the Graduate School of Electrical and Computer Engineering. He is a distinguished researcher with expertise spanning computer graphics, visual effects, physically based simulation, physics engines, artificial intelligence, VR/AR, geometry processing, and GPU optimization. His career bridges academia and industry, having led and participated in numerous national research projects and industry collaborations in areas such as digital twin technology, immersive simulation systems, and AI convergence. With an impressive record of award-winning publications in reputed conferences and journals indexed in IEEE and Scopus, he has contributed significantly to advancing emerging technologies. His leadership in collaborative initiatives and dedication to innovative research continue to strengthen his impact on both scientific communities and practical applications.

Professional Profile 

ORCID Profile

Education

Prof. Jong-Hyun Kim completed his Ph.D. in Computer Science and Engineering from Korea University, following his master’s degree and bachelor’s degree in the same field from Korea University and Sejong University, respectively. His academic journey reflects a strong foundation in both theoretical and applied aspects of computer science, equipping him with advanced skills in simulation, visualization, and artificial intelligence. His studies covered a broad spectrum of technical disciplines, from physics-based modeling and geometry processing to interactive graphics and human-computer interaction. The rigorous academic training at prestigious institutions provided him with the expertise to excel in interdisciplinary research and to address complex computational challenges. This solid educational background has enabled him to integrate advanced computing techniques with creative technological solutions, laying the groundwork for his influential research contributions in academia and his ability to collaborate effectively with industry partners on innovative projects.

Professional Experience

Prof. Jong-Hyun Kim currently serves as an Associate Professor at Inha University, having previously held the same position at Kangnam University. He has also served as a lecturer and teaching fellow at Korea University, contributing to the development of academic programs and mentoring students in advanced computing topics. Before his academic career, he worked extensively in the industry as a senior research engineer and research engineer at multiple companies, gaining hands-on experience in simulation technologies, visual effects, and interactive systems. His professional trajectory reflects a balance between academic scholarship and practical application, with roles that involved designing innovative solutions, leading research teams, and collaborating on both government-funded and industry-driven projects. His combined academic and industrial experience has strengthened his expertise in bridging theoretical research with real-world implementation, enhancing his ability to deliver impactful outcomes in both educational and technological domains.

Research Interest

Prof. Jong-Hyun Kim’s research interests cover a broad and interdisciplinary range of topics, including computer graphics, visual effects, physically based simulation, physics engines, and game physics. He actively explores artificial intelligence techniques for scientific visualization, geometry processing, image processing, and immersive VR/AR experiences. His work often focuses on GPU optimization to achieve real-time performance in complex simulations, enabling practical applications in gaming, virtual reality, and industrial simulations. Additionally, he is interested in human-computer interaction, particularly in developing intuitive interfaces for creative expression and realistic virtual environments. His projects integrate physics-based modeling with AI-driven approaches to address challenges in simulation accuracy, interactivity, and scalability. By combining deep technical expertise with creativity, his research aims to advance the capabilities of simulation and visualization technologies, making them more efficient, accessible, and adaptable for diverse fields ranging from entertainment and education to engineering and healthcare.

Award and Honor

Prof. Jong-Hyun Kim has received numerous awards and honors recognizing his excellence in research, innovation, and academic contributions. His accolades include multiple Best Paper Awards from prestigious conferences such as those organized by the Korea Society of Computer and Information and the Korean Association of Data Science, acknowledging his groundbreaking work in simulations, VR frameworks, AI-driven modeling, and GPU optimization. He has been honored by the Ministry of Science and ICT and the Korean Ministry of Education for his creative and impactful research ideas. His achievements extend beyond academia, with awards recognizing his leadership in industry-academic cooperation and excellence in teaching. These recognitions reflect his sustained contributions to advancing cutting-edge technologies, fostering collaboration between academia and industry, and mentoring future innovators. His consistent recognition at national and professional levels underscores his influence in both research and education, and his ongoing commitment to delivering impactful technological advancements.

Research Skill

Prof. Jong-Hyun Kim possesses advanced research skills in multiple technical domains, including physically based simulation, visual effects, GPU optimization, and complex animation systems. He is proficient in designing real-time interactive environments, implementing physics engines, and integrating artificial intelligence into simulation and visualization frameworks. His expertise includes scientific visualization, geometry processing, VR/AR development, and image processing, enabling him to create innovative solutions that merge creativity with computational precision. He has extensive experience managing large-scale research projects funded by national agencies and industry partners, demonstrating strong project management, team leadership, and cross-disciplinary collaboration skills. His technical abilities are complemented by his capacity to translate theoretical models into practical applications across entertainment, engineering, and scientific research. By combining analytical thinking, problem-solving, and creative design, he continues to push the boundaries of simulation and visualization technologies, contributing significantly to both academic advancements and industry innovation.

Publications Top Notes

Title: A Geometric Approach to Efficient Modeling and Rendering of Opaque Ice With Directional Air Bubbles
Authors: Jong-Hyun Kim
Year: 2025

Title: Advanced GPU Techniques for Dynamic Remeshing and Self-Collision Handling in Real-Time Cloth Tearing
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: Improved Air Mesh Refinement for Accurate Strand-Solid and Self-Collision Handling
Authors: Jong-Hyun Kim
Year: 2025

Title: Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams
Authors: Jong-Hyun Kim
Year: 2025

Title: Porous Models for Enhanced Representation of Saturated Curly Hairs: Simulation and Learning
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: A 3D Visual Tool for Analyzing Changes in Hair Volume and Length Caused by Medications
Authors: Jong‐Hyun Kim; Jung Lee; Seungbin Kwon; Minji Jo; Yunjin Hwang; In‐Sook An
Year: 2025

Title: Numerical Dispersed Flow Simulation of Fire-Flake Particle Dynamics and Its Learning Representation
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: Unified GPU Framework for Simulating Wave Turbulence, Diffusion, and Wrinkling in Fluid-Cloth Interaction
Authors: Eun Su Park; Juyong Lee; In Kyu Park; Jong-Hyun Kim
Year: 2025

Title: Scalable and Rapid Nearest Neighbor Particle Search Using Adaptive Disk Sector
Authors: Jong-Hyun Kim; Shaofeng Xu; Jung Lee
Year: 2025

Title: Depth-of-Field Region Detection and Recognition From a Single Image Using Adaptively Sampled Learning Representation
Authors: Jong-Hyun Kim; Youngbin Kim
Year: 2024

Title: Motion Generation and Analyzing the User’s Arm Muscles via Leap Motion and Its Data-Driven Representations
Authors: Jong-Hyun Kim; Jung Lee; Youngbin Kim
Year: 2024

Title: Visualization of Vector Fields from Density Data Using Moving Least Squares Based on Monte Carlo Method
Authors: Jong-Hyun Kim
Year: 2024

Title: Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
Authors: Jun Yeong Kim; Chang Geun Song; Jung Lee; Jong-Hyun Kim; Jong Wan Lee; Sun-Jeong Kim
Year: 2024

Title: Efficient and Stable Generation of High-Resolution Hair and Fur With ConvNet Using Adaptive Strand Geometry Images
Authors: Jong-Hyun Kim; Jung Lee
Year: 2023

Conclusion

Prof. Jong-Hyun Kim is highly deserving of the Best Researcher Award for his outstanding contributions to cutting-edge research in computer graphics, AI-driven simulation, and immersive technologies, as well as his significant role in bridging academia and industry through impactful collaborative projects. His innovative work has advanced both scientific understanding and practical applications, benefiting diverse sectors and inspiring the next generation of researchers. With a proven track record of excellence, leadership, and innovation, he holds strong potential to make even greater contributions to research and society in the future.