Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Published on 15/09/202515/09/2025 by Computer Vision Awards

Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.
Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.
Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.
Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.
Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Naga Nithin Katta | Image Processing | Best Researcher Award

Published on 09/09/202509/09/2025 by Computer Vision Awards

Mr. Naga Nithin Katta | Image Processing | Best Researcher Award

Employee at Oppo | India

Naga Nithin Katta is a highly motivated computer science and engineering professional with a strong focus on innovation, research, and problem-solving. His expertise spans artificial intelligence, machine learning, computer vision, and full stack development, areas in which he has applied his skills to impactful projects. He has gained industrial exposure as a software engineer at OPPO, where he contributed to projects involving video stream analysis, automation of testing frameworks, and mobile AI deployment. Alongside his industry experience, he has been an active mentor in data structures and algorithms, helping students strengthen their problem-solving abilities. His leadership has been recognized through international competitions, including selection among the Top 100 teams globally in the Google Solution Challenge and multiple hackathon victories. With a balance of technical knowledge, practical implementation, and a passion for community contribution, he is steadily building a strong foundation as an emerging researcher with promising leadership potential.

Professional Profile

Scopus Profile

Education

Naga Nithin Katta is pursuing a Bachelor of Technology in Computer Science and Engineering at VNR Vignana Jyothi Institute of Engineering and Technology, where he has been developing a strong academic background in computing principles, software engineering, and applied technologies. Prior to this, he successfully completed a diploma in computer science from the Government Institute of Electronics, which provided him with a solid technical base in programming, database management, and system design. His educational journey has been complemented by active participation in research-oriented projects, hackathons, and collaborative learning platforms that encouraged innovation and problem-solving. He has consistently demonstrated academic excellence by integrating classroom knowledge with practical applications, which is evident in his project work and international recognition through competitive platforms. This strong educational foundation has equipped him with both theoretical and applied perspectives, allowing him to bridge the gap between academia and industry while nurturing his passion for research and development.

Professional Experience

Naga Nithin Katta has gained valuable professional experience as a software engineer at OPPO, where he contributed to significant projects aimed at improving efficiency and automation in mobile technologies. His work involved developing web applications using Vue.js and MySQL for managing project statuses, implementing video stream analysis through OpenCV and Python, and deploying AI models on mobile devices using ONNX and Beeware. He played a key role in creating a UI automation system powered by large language models, reducing manual testing efforts and enhancing accuracy. Additionally, he contributed to building a network operator testing automation tool, streamlining processes and reducing workforce requirements. Alongside his industry work, he served as a student mentor at SmartInterviews, guiding learners in data structures and algorithms and preparing them for technical challenges. This blend of industrial expertise and teaching experience reflects his versatility, ability to collaborate across teams, and passion for applying research in practical contexts.

Research Interest

Naga Nithin Katta’s research interests lie primarily in the fields of artificial intelligence, computer vision, natural language processing, and software engineering, with a particular focus on developing innovative solutions that bridge academic research and real-world applications. He has worked on projects such as sign language converters that integrate computer vision with generative AI and cloud technologies, reflecting his interest in human-computer interaction and accessibility-focused applications. His engagement with large language models and UI automation tools demonstrates his curiosity in advancing human-machine interaction and automated testing frameworks. Additionally, his focus on video stream analysis and frame detection highlights his inclination towards multimedia research and visual computing. He is also keen on exploring areas such as deep learning optimization, mobile AI deployment, and cloud-integrated intelligent systems. His vision is to contribute to impactful solutions that enhance everyday technologies while simultaneously pursuing scholarly outputs that advance scientific knowledge.

Research Skill

Naga Nithin Katta has developed strong research skills that enable him to design, implement, and evaluate innovative solutions across different domains of computer science. He is proficient in programming languages such as C, C++, Python, and Java, and demonstrates advanced knowledge in full stack development with tools like ReactJs, Vue.js, and MySQL. His expertise in AI and machine learning is reflected in projects involving computer vision, natural language processing, and model deployment on mobile devices. He has practical experience in research-driven software development, having implemented algorithms for video frame detection, gesture recognition, and UI automation powered by large language models. His familiarity with tools like OpenCV, ONNX, Flask, and cloud-based APIs allows him to conduct applied research efficiently. He also possesses strong problem-solving abilities, demonstrated by his role as a mentor in data structures and algorithms. His skills in bridging theoretical concepts with industrial applications showcase his potential as a future research leader.

Publications Top Notes

Title: Optical Motion Detection Language Generator: A Survey

Year: 2025

Conclusion

Naga Nithin Katta is a deserving candidate for the Best Researcher Award as he has consistently demonstrated innovation, technical expertise, and leadership in both academic and industrial settings. His impactful projects, including advancements in computer vision, automation, and AI-driven solutions, showcase contributions that address real-world challenges and benefit society. With proven recognition in global competitions, mentorship roles, and industry research experience, he has already made meaningful strides as an emerging researcher. With a continued focus on publishing in reputed venues and building stronger international collaborations, he holds significant potential to become a future leader in the research and technology community.

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

Published on 14/08/202514/08/2025 by Computer Vision Awards

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.

Mrs. Deepthi S | Medical Image Analysis | Best Researcher Award

Published on 21/04/202521/04/2025 by Computer Vision Awards

Early Academic Pursuits

Mrs. Deepthi S embarked on her academic journey with a Bachelor of Engineering in Information Science from SJC Institute of Technology, Chikkaballapur, graduating in June 2005 with a First Class (64% aggregate). She further honed her expertise by completing her Master of Engineering in Computer Science from University Visvesvaraya College of Engineering (UVCE), Bangalore, securing First Class with Distinction (82%). Currently, she is pursuing a Doctor of Philosophy (Ph.D.) at Presidency University, Bangalore, focusing on Deep Learning Techniques for the Classification of Non-Hodgkin Lymphoma (NHL) on Histopathological Tumour Images. She has successfully completed her comprehensive viva, marking a significant milestone in her research progression.

Professional Endeavors

With an impressive 19 years of academic experience, Mrs. Deepthi S has served in prominent institutions across Karnataka. She is currently an Assistant Professor at Presidency University, Bangalore since January 2023. Prior to this, she held the same role at Cambridge Institute of Technology, Bangalore from July 2012 to December 2023. Her foundational teaching experience was shaped at SJC Institute of Technology, Chikkaballapur, where she served as a Lecturer from July 2005 to July 2012. Over the years, she has handled an extensive range of subjects that include Discrete Mathematical Structures, Graph Theory, Database Management Systems, Data Mining & Warehousing, Software Testing, Web Technologies, AI & ML, and Generative AI.

Contributions and Research Focus

Mrs. Deepthi’s research is centered around artificial intelligence, deep learning, medical image analysis, and generative AI. Her ongoing Ph.D. work aims at enhancing the classification of Non-Hodgkin Lymphoma using deep learning architectures such as DenseNet and ResNet. Her research contributions have led to multiple publications in peer-reviewed journals, including articles like “Non-Hodgkin’s Lymphoma Classification using Improved Predator Optimization Based Densenet121 Model” and “Harnessing ResNet50 and DenseNet201 for Enhanced Lymphoma Diagnosis via Feature Extraction.” She has a particular passion for applying machine learning models to solve real-world healthcare challenges, especially in histopathological tumor image classification.

Impact and Influence

Through her research, teaching, and continuous professional development, Mrs. Deepthi S has become a driving force in blending technology with healthcare innovation. Her work is recognized in reputed journals such as Frontiers in Health Informatics and Journal of Electrical Systems. With a vision to bridge academic theory with practical industry applications, she actively integrates real-time case studies and tools in her lectures, shaping the future workforce in AI and healthcare technologies.

Technical Skills

Mrs. Deepthi S is well-versed in a wide range of technical areas including Python programming, Unix system programming, data mining algorithms, rational tools, and generative AI frameworks. She has completed several certifications such as:

Python for Beginners (SkillUp – Simplilearn)
Programming with Python 3.x (Simplilearn)
Blockchain Developer Training (Simplilearn)
Generative AI using OpenAI API for Beginners (Udemy)

Teaching Experience

Discrete Mathematical Structures
Graph Theory
Artificial Intelligence & Machine Learning
Web 2.0 and Web Technologies
Software Testing
Management Information Systems
Unix System Programming
Generative AI

Workshops and FDPs

Amazon Web Services National Training Program (AICTE)
Reinforcement Learning FDP, May 2023
21-Day Online International FDP on Research Tools, July 2023
International FDPs on Generative AI, NLP, AI & Healthcare
Workshops on AI for Net-Zero Systems, Ethical Design, and AI-powered Learning

Legacy and Future Contributions

With nearly two decades of academic service and a growing research profile, Mrs. Deepthi S is poised to become a leading contributor in AI for healthcare. Her current Ph.D. research, active publication record, and workshop leadership reflect her commitment to lifelong learning and innovation. She continues to mentor future engineers and researchers, empowering them with the knowledge, tools, and motivation to excel in an AI-driven world. Her future goals include expanding her research in generative AI applications, AI ethics, and interdisciplinary healthcare solutions

Publications

Gradient Propagation Based DenseNet121 with ResNet50 Feature Extraction for Lymphoma Classification

Author: Srinivasan, D.; Kalaiarasan, C.
Journal: Journal of The Institution of Engineers (India): Series B
Year: 2024

Non Hodgkin’s Lymphoma Classification using Improved Predator Optimization Based Densenet121 Model

Author: Deepthi S; Dr. M. Chandrasekhar
Journal: Journal of Electrical Systems
Year: 2024

Harnessing ResNet50 and DenseNet201 for Enhanced Lymphoma Diagnosis via Feature Extraction

Author: Deepthi S; Dr. M. Chandrasekhar
Journal: Frontiers in Health Informatics
Year: 2024

An efficient face image retrieval system based on attribute sparse codewords

Author: Deepthi S
Journal: International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)
Year: 2016

Dr. Yijie Ning | Medical Image Analysis | Best Researcher Award

Published on 14/03/202514/03/2025 by Computer Vision Awards

Summary

Dr. Yijie Ning is a dedicated PhD researcher specializing in vascular surgery with a focus on imaging and mechanistic research of Peripheral Artery Disease (PAD). His expertise lies in exploring the clinical and diagnostic value of Near-Infrared (NIR) imaging for PAD, contributing to innovative imaging techniques in vascular pathology. He has a strong research background, with published findings and conference presentations, aiming to advance diagnostic tools and therapeutic strategies for vascular diseases.

Education

PhD in Vascular Surgery (2023–2027) – Shanxi Medical University

Professional Experience

Extensive experience in clinical and diagnostic research on PAD
Expertise in NIR imaging techniques for vascular applications
Active contributor to academic research, publications, and conferences

Research Interests

Peripheral Artery Disease (PAD) imaging
Near-Infrared (NIR) imaging for vascular diagnostics
Innovative imaging techniques in vascular pathology
Therapeutic strategies for vascular diseases

Special Skills

Software: Excel (Expert), Word (Advanced), Photoshop (Expert), Illustrator (Advanced), AE (Expert)
Problem-Solving: Advanced analytical and research skills

Publications

NIR-II imaging-based detection of early changes in lower limb perfusion in type 2 diabetes patients without peripheral artery disease

Author: Yijie Ning, Jie Hu, Yikun Zhu, Ruijing Zhang, Honglin Dong, et al.
Journal: Diabetes Research and Clinical Practice
Year: 2025

Prof. Krishan Kumar | Medical Image Analysis | Best Researcher Award

Published on 11/03/202511/03/2025 by Computer Vision Awards

Summary

Prof. Krishan Kumar is an Assistant Professor at Hindu College, Amritsar, and a Ph.D. scholar at Punjab Technical University, Kapurthala. His expertise lies in Machine Learning, Deep Learning, Explainable AI, and Medical Image Processing.

Education

Ph.D. (Pursuing) – Deep Learning & AI, Punjab Technical University
M.Tech – Software Systems
B.Tech – Computer Science & Engineering
UGC NET Qualified

Professional Experience

Assistant Professor, Hindu College, Amritsar
Member, NCCIP & AICTE, actively contributing to Faculty Development Programs (FDPs)

Research Interests

Machine Learning & Deep Learning
Explainable AI
Medical Image Processing (Brain Tumor Detection & Classification)

Publications

Enhancing Transparency and Trust in Brain Tumor Diagnosis: An In-Depth Analysis of Deep Learning and Explainable AI Techniques

Authors: Krishan Kumar, Kiran Jyoti
Journal: Preprint
Year: 2025

Machine Learning for Brain Tumor Classification: Evaluating Feature Extraction and Algorithm Efficiency

Authors: Krishan Kumar, Kiran Jyoti
Journal: Discover Artificial Intelligence
Year: 2024

Explainable AI in Brain Tumor Diagnosis: A Critical Review of ML and DL Techniques

Authors: Krishan Kumar, Kiran Jyoti
Journal: Preprint
Year: 2024

Recent Advancements in Grad-CAM and Variants: Enhancing Brain Tumor Detection, Segmentation, and Classification

Authors: Krishan Kumar, Kiran Jyoti
Journal: Preprint
Year: 2024

A Comparative Analysis of Static and Dynamic Java Bytecode Watermarking Algorithms

Authors: Krishan Kumar, Prabhpreet Kaur
Journal: Advances in Intelligent Systems and Computing
Year: 2019

Prof. Zhitao Xiao | Medical Image Analysis | Best Researcher Award

Published on 11/03/202511/03/2025 by Computer Vision Awards

Summary

Prof. Zhitao Xiao is a distinguished researcher in medical image processing, pattern recognition, and intelligent signal processing. With over 150 research papers and 30 patents, he has significantly contributed to artificial intelligence applications in biomedical imaging. He leads major research initiatives, securing 20+ grants from NSFC and Tianjin government.

Education

BSc & MSc – Hebei University of Technology (1994, 1997)
PhD – Tianjin University, School of Electronic Information Engineering (2003)

Professional Experience

Dean, School of Life Sciences, Tiangong University
Director, Tianjin Key Laboratory of Optoelectronic Detection Technology & Systems
Director, Tianjin International Joint Research Center for Intelligent Information Processing Technology
Leader, Tianjin Research Team on Optoelectronic Information Detection & Intelligent Processing

Research Interests

Medical Image Processing
Pattern Recognition
Intelligent Signal Processing

Publications

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification

Author: X. Wei, Xin; Y. Liu, Yanbei; F. Zhang, Fang; X. Cao, Xiangyu; Z. Xiao, Zhitao
Journal: Medical Image Analysis
Year: 2025

A Deep Learning-Based Modeling Method for Phase-Free Near-Field Scanning Measurement

Author: S. Zhao, Shuli; J. Wu, Jianfei; Y. Song, Yang; L. Chen, Ledong; Z. Xiao, Zhitao
Journal: IEEE Transactions on Antennas and Propagation
Year: 2025

Stripe Pooling and Vessel-Constraint Network for Fundus Image Artery /Vein Classification

Author: Z. Xiao, Zhitao; X. Peng, Xinwen; Y. Liu, Yanbei; F. Zhang, Fang; W. Wang, Wen
Journal: Chinese Journal of Biomedical Engineering
Year: 2024

Diabetic Retinopathy Segmentation Using Dense Dilated Attention Pyramid and Multi-Scale Features

Author: Z. Wang, Zhilu; Y. Chi, Yue; Y. Zhou, Yatong; Z. Xiao, Zhitao; S. Wang, Shaoqi
Journal: Chinese Journal of Medical Physics
Year: 2024

AGT: Enhancing Many-Body Interactions in Material Property Prediction

Author: L. Geng, Lei; Y. Niu, Yaxi; Z. Xiao, Zhitao; H. Yin, Huaqing
Journal: Computational Materials Science
Year: 2024

Ms. Yang Yuan | Medical Image Analysis | Best Researcher Award

Published on 05/03/202505/03/2025 by Computer Vision Awards

Summary

Ms. Yang Yuan is a postgraduate student at Chongqing University of Technology, specializing in Computer Technology. With a strong passion for semi-supervised medical image segmentation, she has contributed significantly to the field through innovative research methodologies.

Education

Master’s Student, Computer Technology, Chongqing University of Technology (GPA: 3.15)
Achievements: Two Third Prizes in Mathematical Modeling Competitions

Professional Experience

Published “Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation” in Computers, Materials & Continua
Conducted research on Uncertainty Global Contrastive Learning Framework for medical image segmentation

Research Interests

Semi-supervised medical image segmentation
Dual-teacher-assisted training strategies
Feature perturbation and contrastive learning

Publications

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

Authors: H. Liu, Y. Yuan, P. Ren, C. Song, F. Luo.
Journal: Computers, Materials & Continua
Year: 2025

Prof Dr. Yunyoung Nam | Medical Image Analysis | Best Researcher Award

Published on 24/02/202524/02/2025 by Computer Vision Awards

Early Academic Pursuits

Prof. Dr. Yunyoung Nam embarked on his academic journey with a strong foundation in Computer Science and Engineering. His early research revolved around multimedia information retrieval, digital image processing, and distributed computing. His passion for technological advancements was evident through his active participation in funded research projects, focusing on ubiquitous computing and smart systems. His dedication earned him prestigious awards, including the Presidential Award for Excellence during his graduate studies at Ajou University.

Professional Endeavors

With a career spanning multiple institutions, Prof. Dr. Nam has made remarkable contributions to academia and research. His professional journey includes roles as a Postdoctoral Fellow at Worcester Polytechnic Institute and a Postdoctoral Associate at Stony Brook University-SUNY. He also served as a Research Assistant Professor at Ajou University, contributing to groundbreaking research in security systems and pervasive computing. Since joining Soonchunhyang University in 2014, he has progressed through the ranks from Assistant Professor to Full Professor, playing a vital role in advancing AI, machine learning, and multimedia content analysis.

Contributions and Research Focus

Prof. Dr. Nam’s research is centered on multimedia information retrieval, AI-driven biomedical applications, and smart home technologies. His work spans computer vision, behavior recognition, and human-computer interaction, with a focus on developing intelligent healthcare solutions. His projects include the development of a 3D deep-learning-based diagnosis platform for retinal diseases, wearable wireless sensors for physiological monitoring, and AI-powered cardiovascular disease prediction systems. His contributions have significantly impacted smart rehabilitation, surveillance systems, and mobile health monitoring.

Impact and Influence

Prof. Dr. Nam’s influence extends beyond research, as he is actively engaged in leading-edge projects funded by NRF, IITP, and MSIT. His work on intelligent home care systems, medical imaging, and deep learning applications has made a tangible difference in healthcare and security domains. His technology transfer initiatives, such as medical eye and ear disease detection systems, highlight his ability to bridge the gap between research and real-world applications.

Academic Citations and Recognition

Recognized among the Stanford/Elsevier’s Top 2% Scientists, Prof. Dr. Nam has earned multiple Presidential Awards for Best Researcher at Soonchunhyang University. His scholarly work has been cited extensively, and he has been included in Who’s Who in America for his academic excellence. His membership in IEEE, MDPI, and various computing societies underscores his commitment to advancing knowledge in AI, multimedia systems, and smart computing.

Technical Skills and Expertise

Prof. Dr. Nam possesses expertise in deep learning, multimedia content analysis, digital signal processing, and pervasive computing. His technical skills encompass distributed computing, surveillance systems, 3D visualization, and human-computer interaction. His research integrates AI-driven models with real-world applications in biomedical engineering, smart homes, and behavior recognition technologies.

Teaching and Mentorship

A dedicated educator, Prof. Dr. Nam has been actively involved in teaching and mentoring students at Soonchunhyang University. He has instructed courses on Deep Learning, AI, Computer Vision, Data Structures, Software Engineering, and Discrete Mathematics. His mentorship has guided students in innovative research, fostering future leaders in computing and AI-driven applications.

Legacy and Future Contributions

With a career deeply embedded in cutting-edge AI and multimedia research, Prof. Dr. Nam’s legacy lies in his technological innovations and academic contributions. His future endeavors focus on expanding intelligent systems for healthcare, deep-learning-driven security applications, and smart environments. His work will continue to shape AI-powered solutions, paving the way for a more connected and intelligent digital future.

Publications

A Novel Approach for High-Resolution Coastal Areas and Land Use Recognition from Remote Sensing Images based on Multimodal Network-Level Fusion of SRAN3 and Lightweight Four Encoders ViT

Authors: M.K. Bhatti, Muhammad Kashif; M.A. Khan, Muhammad Attique; S. Shaheen, Saima; S.A. Algamdi, Shabbab Ali; Y. Nam, Yunyoung
Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2025

Analysis of Near-Fall Detection Method Utilizing Dynamic Motion Images and Transfer Learning

Authors: J. Kim, Jungyeon; N. Mat, Nab; C. Kim, Chomyong; S. Jeon, Seob; Y. Nam, Yunyoung
Journal: IEEE Access
Year: 2025

Cooperative PPG/ECG Wearable System for Atrial Fibrillation Diagnosis

Authors: Y. Lee, Yonbin; S. Lee, Soyoung; S. Kim, Sang-kyu; Y. Nam, Yunyoung; J. Lee, Jinseok
Journal: IEEE Sensors Journal
Year: 2025

Energy-Efficient Discrete Cosine Transform Architecture Using Reversible Logic for IoT-Enabled Consumer Electronics

Authors: M. Awais, Muhammad; W. Khan, Wilayat; T. Akram, Tallha; Y. Nam, Yunyoung
Journal: IEEE Access
Year: 2025

Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI

Authors: I. Jeon, Insoo; M. Kim, Minjoong; D. So, Dayeong; J. Kim, Joungmin; J. Moon, Jihoon
Journal: Diagnostics
Year: 2024

Dr. Sajid Ullah Khan | Medical Image Analysis | Best Researcher Award

Published on 17/02/202517/02/2025 by Computer Vision Awards

Early Academic Pursuits

Dr. Sajid Ullah Khan embarked on his academic journey with a strong foundation in computer science. He earned his B.I.T. degree from Gomal University, Pakistan, followed by an M.S. in Computer Science from the University of Science & Technology, Bannu. His quest for knowledge led him to University Malaysia Sarawak (UNIMAS), Malaysia, where he obtained his Ph.D. in Image Processing & Computer Vision (2011-2015). Further enhancing his expertise, he completed two postdoctoral fellowships at UNIMAS (2016-2017) and Chosun University, South Korea (2022-2023), focusing on advanced research in image analysis and artificial intelligence.

Professional Endeavors

With over nine years of teaching experience, Dr. Khan has held significant academic positions. He currently serves as an Associate Professor at Prince Sattam bin Abdulaziz University, KSA. Previously, he contributed as a faculty member at University of Lakki Marwat and CECOS University, Pakistan, and as a Tutor at UNIMAS, Malaysia. His dedication to education and research has helped shape the careers of many students in computer science and engineering.

Contributions and Research Focus

Dr. Khan’s research interests lie in digital image processing, computer vision, data mining, and artificial intelligence applications. His notable projects include semantic extraction for road density estimation, floodplain mapping using GIS, and intelligent automation in medical image analysis. He has been actively involved in research groups such as MIP Lab at Chosun University and CoIMAST at UNIMAS, contributing to groundbreaking innovations in AI-driven image analysis.

Impact and Influence

Dr. Khan’s scholarly contributions have been recognized on both national and international platforms. His research has enhanced the field of AI-driven image processing, leading to practical applications in medical diagnostics, automation, and smart surveillance. His work has also been instrumental in advancing ICT solutions and digital transformation in academia and industry.

Academic Cites & Recognitions

6th Position in DICE-IET 2020 at COMSATS University, Lahore
Best Researcher Award at CECOS University (2018)
UNIMAS Graduate Scholarship (ZSU) for Ph.D. studies

Technical Skills

Dr. Khan is proficient in Matlab, Python, C, and C++, with expertise in data warehousing, HCI, database management systems, and advanced operating systems. His technical acumen allows him to develop and implement AI-based solutions in image processing and pattern recognition.

Teaching Experience

Throughout his career, Dr. Khan has taught a variety of computer science courses, including Programming Fundamentals, Digital Image Processing, Human-Computer Interaction, Advanced Computer Architecture, and Data Structures & Algorithms. His pedagogical approach integrates theory with real-world applications, preparing students for careers in AI and computational sciences.

Leadership and Administrative Roles

Director Administration, University of Lakki Marwat
Associate Dean (Academics), University of Lakki Marwat
Department Chairman at University of Lakki Marwat & CECOS University

Legacy and Future Contributions

Dr. Khan continues to pioneer research in AI-driven image analysis, striving to bridge the gap between academia and industry. His future work aims to advance intelligent automation in medical imaging, real-time object detection, and deep learning applications. His vision is to mentor the next generation of AI researchers and contribute to global advancements in computational sciences. With a commitment to excellence and innovation, Dr. Sajid Ullah Khan remains a driving force in computer vision, AI research, and academic leadership.

Publications

Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism

Authors: Naveed Saif, Sajid Ullah Khan, Imrab Shaheen, Faiz Abdullah ALotaibi, Mrim M Alnfiai, Mohammad Arif
Journal: Computers in Human Behavior
Year: 2024

Energy-efficient routing protocols for UWSNs: A comprehensive review of taxonomy, challenges, opportunities, future research directions, and machine learning perspectives

Authors: Sajid Ullah Khan, Zahid Ulalh Khan, Mohammed Alkhowaiter, Javed Khan, Shahid Ullah
Journal: Journal of King Saud University-Computer and Information Sciences
Year: 2024

Multimodal medical image fusion towards future research: A review

Authors: Sajid Ullah Khan, Mir Ahmad Khan, Muhammad Azhar, Faheem Khan, Youngmoon Lee, Muhammad Javed
Journal: Journal of King Saud University-Computer and Information Sciences
Year: 2023

Historical text image enhancement using image scaling and generative adversarial networks

Authors: Sajid Ullah Khan, Imdad Ullah, Faheem Khan, Youngmoon Lee, Shahid Ullah
Journal: Sensors
Year: 2023

A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19)

Authors: Sajid Ullah Khan, Imdad Ullah, Najeeb Ullah, Sajid Shah, Mohammed El Affendi, Bumshik Lee
Journal: Scientific Reports
Year: 2023

Featured Publications

Professional Profile

Education

Professional Experience

Research Interest

Research Skill

Publications Top Notes

Conclusion

Professional Profile

Education

Professional Experience

Research Interest

Award and Honor

Research Skill

Publications Top Notes

Presidency University, India

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Technical Skills

Teaching Experience

Workshops and FDPs

Legacy and Future Contributions

Gradient Propagation Based DenseNet121 with ResNet50 Feature Extraction for Lymphoma Classification

Non Hodgkin’s Lymphoma Classification using Improved Predator Optimization Based Densenet121 Model

Harnessing ResNet50 and DenseNet201 for Enhanced Lymphoma Diagnosis via Feature Extraction

An efficient face image retrieval system based on attribute sparse codewords

Doctorate at the First Hospital of Shanxi Medical University, China

Profiles

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Education

Professional Experience

Research Interests

Special Skills

NIR-II imaging-based detection of early changes in lower limb perfusion in type 2 diabetes patients without peripheral artery disease

Professor at Hindu College, Amritsar, India

Profiles

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Education

Professional Experience

Research Interests

Enhancing Transparency and Trust in Brain Tumor Diagnosis: An In-Depth Analysis of Deep Learning and Explainable AI Techniques

Machine Learning for Brain Tumor Classification: Evaluating Feature Extraction and Algorithm Efficiency

Explainable AI in Brain Tumor Diagnosis: A Critical Review of ML and DL Techniques

Recent Advancements in Grad-CAM and Variants: Enhancing Brain Tumor Detection, Segmentation, and Classification

A Comparative Analysis of Static and Dynamic Java Bytecode Watermarking Algorithms

School of Life Sciences of Tiangong University, China

Profiles

Summary

Education

Professional Experience

Research Interests

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification

A Deep Learning-Based Modeling Method for Phase-Free Near-Field Scanning Measurement

Stripe Pooling and Vessel-Constraint Network for Fundus Image Artery /Vein Classification

Diabetic Retinopathy Segmentation Using Dense Dilated Attention Pyramid and Multi-Scale Features

AGT: Enhancing Many-Body Interactions in Material Property Prediction

Chongqing University of Technology, China

Profiles

Summary

Education

Professional Experience

Research Interests

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

Soonchunhyang University, South Korea

Profiles

Early Academic Pursuits

Professional Endeavors

Contributions and Research Focus

Impact and Influence

Academic Citations and Recognition

Technical Skills and Expertise

Teaching and Mentorship

Legacy and Future Contributions

A Novel Approach for High-Resolution Coastal Areas and Land Use Recognition from Remote Sensing Images based on Multimodal Network-Level Fusion of SRAN3 and Lightweight Four Encoders ViT

Analysis of Near-Fall Detection Method Utilizing Dynamic Motion Images and Transfer Learning

Cooperative PPG/ECG Wearable System for Atrial Fibrillation Diagnosis

Energy-Efficient Discrete Cosine Transform Architecture Using Reversible Logic for IoT-Enabled Consumer Electronics