Seok-Woo Jang | Computer Vision | Best Researcher Award

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Prof. Seok-Woo Jang | Computer Vision | Best Researcher Award

Associate Professor at Anyang University South Korea

Dr. Seok-Woo Jang is an Associate Professor in the Department of Software at Anyang University, Korea. With extensive experience in computer science and software engineering, he has contributed significantly to the fields of image processing, artificial intelligence, and human-computer interaction. His research spans biometrics, computer vision, and information security. Over the years, he has actively participated in numerous research projects and published widely in internationally recognized journals. Dr. Jang’s academic journey and professional experience highlight his dedication to advancing technology through innovative research and education.

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Education

Dr. Seok-Woo Jang obtained his Ph.D. in Computer Science from Soongsil University, Seoul, Korea, in 2000. His doctoral dissertation focused on “Shot Transition Detection by Compensating Camera Operations,” showcasing his early expertise in image and video processing. He completed his Master’s degree in Computer Science from the same institution in 1997, researching velocity extraction of moving objects through cluster analysis. His academic foundation was laid with a Bachelor’s degree in Computer Science from Soongsil University in 1995.

Experience

Dr. Jang’s professional career spans over two decades in academia and research. He has been a Professor at Anyang University since 2009, contributing to software education and research. Prior to that, he was a Research Professor at Sungkyunkwan University from 2008 to 2009. His industry and research experience include roles as a Senior Researcher at the Korea Institute of Construction Technology and a Principal Researcher at the Institute of Industrial Technology Research at Soongsil University. He has also conducted post-doctoral research at the University of Massachusetts, Boston, and the University of North Carolina at Charlotte. His teaching experience includes lecturing at Soongsil University and Sungkyul University.

Research Interests

Dr. Jang’s research focuses on multiple domains, including 2D/3D image processing, human-computer interaction, biometrics, information security, and pattern recognition. He is particularly interested in digital video data indexing, computer vision, object tracking, and image surveillance. His work also extends to developing innovative techniques for harmful content detection and deep learning-based solutions in software engineering and AI-driven image analysis.

Awards

Dr. Jang has received numerous awards for his contributions to research and academia. He was awarded the Best Paper Award at the International Conference on Small and Medium Business in 2018 for his work on harmful content extraction using learning algorithms. In 2016, he received the Best Researcher Award at Anyang University. He also won the Best Paper Award at the International Conference on Digital Policy and Management in 2013 for his work on dynamic camera switching. His achievements have been recognized internationally, including being listed in Marquis Who’s Who in the World in 2008.

Publications

Dr. Jang has authored numerous peer-reviewed publications. Some of his notable works include:

“Detection of Ventricular Fibrillation Using Wavelet Transform and Phase Space Reconstruction from ECG Signals” – Journal of Mechanics in Medicine and Biology, 2021.

“Pupil Detection and Gaze Tracking Using a Deformable Template” – Multimedia Tools and Applications, 2020.

“Robust Hand Pose Estimation Using Visual Sensor in IoT Environment” – The Journal of Supercomputing, 2019.

“Harmful Content Detection Based on Cascaded Adaptive Boosting” – Journal of Sensors, 2018.

“A Monitoring Method of Semiconductor Manufacturing Processes Using Internet of Things-based Big Data Analysis” – International Journal of Distributed Sensor Networks, 2017.

“Learning-based Detection of Harmful Data in Mobile Devices” – Mobile Information Systems, 2016.

“An Adaptive Camera-Selection Algorithm to Acquire Higher-Quality Images” – Cluster Computing, 2015.

Conclusion

Dr. Seok-Woo Jang is a highly deserving candidate for the Best Researcher Award. His extensive academic credentials, innovative research projects, influential publications, and numerous awards establish him as a leading researcher in his field. His contributions to computer vision, biometrics, and artificial intelligence continue to push the boundaries of technology, making a lasting impact on both academia and industry.

Usman Ahmad | Computer Vision | Best Researcher Award

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Mr Usman Ahmad | Computer Vision | Best Researcher Award

Zhengzhou University, China

Usman Ahmad is a dedicated researcher and data scientist specializing in computer vision and deep learning. Currently pursuing a Ph.D. in Electrical and Information Engineering at Zhengzhou University, China, his research focuses on small aerial object detection using advanced deep learning models. With a Master’s degree in Electrical and Computer Engineering from South China University of Technology, Usman has developed expertise in CNN-based neural networks for small object detection, a critical area in applications like autonomous driving and remote sensing. His professional journey includes roles as a freelance data scientist, site engineer, and visiting faculty member, showcasing his versatility in both academia and industry. Usman’s work has been published in prestigious journals like IEEE Geoscience and Remote Sensing Letters, reflecting his contributions to the field. Passionate about innovation, he continues to push the boundaries of object detection technologies.

Professional Profile

Google Scholar

Education 🎓

Usman Ahmad holds a Ph.D. in Electrical and Information Engineering from Zhengzhou University, China (2022–present), focusing on small aerial object detection through deep learning. He earned his M.S. in Electrical and Computer Engineering from South China University of Technology (2018–2020), where he specialized in small object detection using CNN-based networks, achieving a final grade of 3.4/4. His thesis, Small Object Detection Through CNN-Based Network, addressed challenges in detecting small-scale images in high-resolution and remote sensing applications. Earlier, he completed his B.S. in Electrical Engineering from the National University of Computer and Emerging Sciences (FAST-NUCES), Islamabad (2010–2014), with a thesis on Prepaid Energy Meter, which tackled issues like power theft and billing inefficiencies. His academic journey reflects a strong foundation in electrical engineering and a progressive shift toward cutting-edge AI and computer vision research.

Experience  💼

Usman Ahmad has a diverse professional background. As a Freelance Data Scientist (2020–present), he developed CNN-based models for small image detection and OpenCV machine learning algorithms with 82% efficiency. From 2015 to 2018, he served as a Site Engineer at China State Construction Engineering Corporation Ltd, contributing to the New Islamabad International Airport Project, where he managed MEP systems and HVAC electrical parameters. He also worked as a Visiting Faculty Member at the University College of Engineering & Technology, Sargodha (2014–2015), teaching electrical and computer engineering while managing labs and exams. His technical expertise spans deep learning, computer vision, and electrical systems, making him a versatile professional in both academic and industrial settings. His hands-on experience in large-scale construction projects and AI-driven solutions highlights his adaptability and problem-solving skills.

Awards and Honors  �

Usman Ahmad’s contributions to the field of computer vision and deep learning have earned him recognition in prestigious journals. His research on infrared small target detection, published in IEEE Geoscience and Remote Sensing Letters (2022), has been cited 42 times, showcasing its impact. He co-authored MCDNet: An Infrared Small Target Detection Network Using Multi-Criteria Decision and Adaptive Labeling Strategy (2024), published in IEEE Transactions on Geoscience and Remote Sensing, which has already garnered citations. His innovative work on GAN-integrated feature pyramid networks for small aerial object detection further underscores his expertise. While specific awards are not listed, his consistent publication record in high-impact journals and active contributions to advancing object detection technologies highlight his academic excellence and dedication to the field.

Research Focus 🔍

Usman Ahmad’s research centers on small object detection using deep learning models, particularly in high-resolution and remote sensing applications. His work addresses the challenges of detecting small-scale objects, such as distant vehicles in autonomous driving or tiny structures in satellite imagery. He has developed advanced CNN-based networks and GAN-integrated feature pyramid networks to improve detection accuracy and speed. His research also explores infrared small target detection, employing innovative strategies like adaptive labeling and multi-criteria decision-making. By focusing on practical applications like self-driving cars, remote sensing, and surveillance, Usman aims to bridge the gap between theoretical advancements and real-world solutions. His contributions have been published in leading journals, reflecting his commitment to pushing the boundaries of computer vision and AI.

Publication Top Notes📚

  1. Infrared small target detection network with generate label and feature mapping – IEEE Geoscience and Remote Sensing Letters (2022)
  2. MCDNet: An Infrared Small Target Detection Network Using Multi-Criteria Decision and Adaptive Labeling Strategy – IEEE Transactions on Geoscience and Remote Sensing (2024)
  3. Small Aerial Object Detection through GAN-Integrated Feature Pyramid Networks – In Progress

Conclusion 🌟

Usman Ahmad is a highly skilled researcher and data scientist with a strong background in electrical engineering and deep learning. His expertise in small object detection, particularly through CNN-based and GAN-integrated models, has made significant contributions to computer vision and remote sensing. With a robust academic foundation, diverse professional experience, and a growing list of impactful publications, Usman continues to drive innovation in AI and its real-world applications. His dedication to solving complex problems and advancing technology makes him a valuable asset to the field.

AZIZI ABDULLAH | Computer Vision | Best Paper Award

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Assoc. Prof. Dr AZIZI ABDULLAH | Computer Vision | Best Paper Award

Researcher, Universiti Kebangsaan Malaysia, Malaysia

Azizi Abdullah is an esteemed academic and researcher, currently serving as an Associate Professor at Universiti Kebangsaan Malaysia. He holds a Ph.D. in Computer Vision from Utrecht University, The Netherlands. With over two decades of experience in the fields of machine learning, computer vision, and robotics, Dr. Abdullah is recognized for his contributions to medical applications, particularly breast cancer classification and object recognition. He has authored several influential research papers and is an expert in Simultaneous Localization and Mapping (SLAM). Dr. Abdullah is passionate about advancing AI and deep learning techniques, with a focus on applications in autonomous vehicles and medical image analysis.

PROFESSIONAL PROFILE

Google Scholar

Orcid

STRENGTHS FOR THE AWARD

  1. Strong Academic Foundation: Azizi Abdullah holds a Ph.D. in Computer Vision from Utrecht University, The Netherlands, along with a Master’s in Software Engineering and a Bachelor’s in Computer Science. This strong academic background provides a solid foundation for his research endeavors.
  2. Proven Research Impact: Abdullah’s publications reflect significant contributions in computer vision, machine learning, and robotics, with a focus on real-world applications in areas such as medical diagnostics (e.g., breast cancer classification) and autonomous systems (e.g., autonomous vehicle systems and SLAM). The high citation count for his work highlights the widespread impact of his research in the academic community.
  3. Diverse Research Interests: His research spans several cutting-edge areas, including deep learning, AI, object recognition, and autonomous mobile robotics. This multidisciplinary approach is critical for advancing knowledge in these fields.
  4. Leadership and Experience: Having held academic positions from Lecturer to Associate Professor at Universiti Kebangsaan Malaysia, Abdullah has demonstrated leadership in both research and teaching, further underlining his ability to shape the future of the field.

AREAS FOR IMPROVEMENT

  1. Expansion of Collaborative Research: While Abdullah has published extensively, fostering collaborations with more international researchers could further broaden the scope and impact of his work.
  2. Interdisciplinary Applications: Although Abdullah’s research touches on multiple domains, additional focus on interdisciplinary applications, particularly in industries outside academia (e.g., healthcare, manufacturing), could maximize the practical application of his work.

EDUCATION

Dr. Azizi Abdullah earned his Ph.D. in Computer Vision from Utrecht University in 2010. He completed his Master of Software Engineering (MSE) at Universiti Malaya in 1999 and his Bachelor of Science in Computer Science from Universiti Kebangsaan Malaysia in 1996. His academic journey reflects his deep commitment to expanding his expertise in software engineering, artificial intelligence, and computer vision, which are central to his groundbreaking work in machine learning and robotics. His diverse academic background has laid the foundation for his successful career in both research and teaching.

EXPERIENCE

Dr. Abdullah’s academic career spans over two decades, beginning as a Research Assistant at Universiti Kebangsaan Malaysia in 1997. He served as a Tutor and Lecturer before being promoted to Senior Lecturer in 2010. His expertise and leadership earned him the title of Associate Professor in 2015. Throughout his career, Dr. Abdullah has made significant contributions to teaching and research, guiding numerous students in software engineering, computer vision, and AI. He has also played an active role in various academic and research initiatives, further enhancing the global impact of his work.

AWARDS AND HONORS

Dr. Abdullah’s exceptional research contributions have earned him recognition in the fields of computer vision, machine learning, and AI. His work on improving neural network performance and breast cancer classification using deep learning has received widespread acclaim. His research on object categorization and autonomous vehicles has been influential in both academic and industrial sectors. In addition to his numerous citations, Dr. Abdullah’s expertise continues to be acknowledged with awards for his outstanding contributions to technological advancements and the scientific community.

RESEARCH FOCUS

Dr. Abdullah’s research is primarily focused on the intersection of computer vision, machine learning, and robotics. His current work revolves around deep learning models, particularly their application in medical image analysis, such as breast cancer detection. He also explores object categorization and recognition using machine learning techniques. Another key area of his research is autonomous mobile robots, specifically Simultaneous Localization and Mapping (SLAM), which is integral to the development of autonomous systems. His interdisciplinary approach combines cutting-edge AI algorithms with practical applications in medical and robotics fields.

PUBLICATION TOP NOTES

  • Deep CNN model based on VGG16 for breast cancer classification 🏥
  • A linear model based on Kalman filter for improving neural network classification performance 🤖
  • Support vector machine approach to real-time inspection of biscuits on moving conveyor belt 🍪
  • Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading 🧬
  • Detection of leukemia in human blood sample based on microscopic images 🩸
  • Vision-based autonomous vehicle systems based on deep learning: A systematic literature review 🚗
  • Machine vision for crack inspection of biscuits featuring pyramid detection scheme 🍪
  • An ensemble of deep support vector machines for image categorization 🖼️
  • Spatial pyramids and two-layer stacking SVM classifiers for image categorization 🖼️
  • Fixed partitioning and salient points with MPEG-7 cluster correlograms for image categorization 🖼️

CONCLUSION

Azizi Abdullah is a highly deserving candidate for the “Best Researcher Award.” His strong academic qualifications, broad research interests, and impactful contributions to computer vision, AI, and robotics make him a standout figure in his field. While there is room to enhance the interdisciplinary reach and foster more international collaborations, his record of achievement in both theory and application positions him as an influential researcher poised to continue making significant advancements in his areas of expertise.