Mr. Sina Rezaei | Computer Vision | Research Excellence Award

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Mr. Sina Rezaei | Computer Vision | Research Excellence Award

Engineer | University of Tehran | Iran

Mr. Sina Rezaei Nafar is a researcher in photogrammetry, computer vision, and geomatics engineering, with a strong focus on 3D reconstruction, semantic segmentation, and AI-driven analysis of spatial data. His work integrates spherical and fisheye camera systems, low-cost imaging sensors, and machine learning models to enhance point cloud accuracy, urban mapping, and cultural heritage documentation. His research has been published in high-impact international journals, including Sensors, addressing camera calibration, network design, and point cloud quality assessment. According to Google Scholar, his scholarly output includes multiple indexed documents with 12 citations and an h-index of 2, reflecting growing academic impact across photogrammetry and computer vision research.

Citation Metrics (Google Scholar)

15

10

5

0

Citations
12
i10-index
0
h-index
2
🟦 Citations    🟥 i10-index    🟩 h-index

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Featured Publications

Mr. Ayush Roy | Computer Vision | Young Researcher Award

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Mr. Ayush Roy | Computer Vision | Young Researcher Award

PhD, University at Buffalo, United States

Ayush Roy is an emerging researcher and innovator in the field of Electrical Engineering with a deep interest in AI, computer vision, and biomedical image analysis. Currently pursuing his B.E. at Jadavpur University, he has demonstrated exceptional potential through interdisciplinary research, AI-driven solutions, and impactful contributions to both academia and real-world applications. With multiple international publications and recognitions, Ayush is a dynamic force in the intersection of deep learning, signal processing, and intelligent systems.

Publication Profile

Google Scholar

🎓 Education Background

Ayush Roy is a final-year undergraduate student at Jadavpur University, West Bengal, India, enrolled in the Bachelor of Engineering (Electrical) program with an SGPA of 8.1/10 (2020–2024). He completed his schooling from Bhartiya Vidya Bhavan, West Bengal under the CBSE board, scoring 90.6% in Class 12 and a perfect CGPA of 10 in Class 10.

💼 Professional Experience

Ayush’s research journey began at Jadavpur University, working under renowned professors in Audio Signal Processing, Reinforcement Learning, and Image Segmentation. As a research intern at the Indian Statistical Institute, he contributed to dataset development and text detection models. He furthered his research as an intern at the University of Malaya on transformer-based networks and at IISc Bangalore on CLIP for image quality assessment. His work integrates deep learning models like YOLO, Swin Transformer, UNet, and CLIP with novel architectures and real-world applications.

🏆 Awards and Honors

Ayush has earned several accolades such as the Most Innovative Solution award at Hack-a-Web by NIT Bhopal (2021), 3rd Prize at FrostHack, IIT Mandi (2022), Top 10 in Cloud Community Hackday by GDG Cloud, and became a Finalist in both the IEEE R10 Robotics Competition and 404 Resolved hackathon at IIT Delhi.

🔬 Research Focus

His primary research areas include computer vision, medical image segmentation, scene text detection, and real-time AI systems. He is especially focused on lightweight models, attention mechanisms, domain adaptation, and hybrid approaches combining deep learning and signal processing. He has created multiple datasets for benchmarking including those for drone license plate detection, underwater text, water meter digit recognition, and circuit component recognition.

📌 Conclusion

Ayush Roy stands as a committed and creative researcher, blending electrical engineering fundamentals with cutting-edge AI methodologies. His work not only adds value to academic literature but also paves the way for practical, socially impactful AI systems. With an impressive early-career portfolio, Ayush continues to show immense promise for future contributions to science and technology.

📚 Top Publication Notes 

AWGUNet: Attention-aided Wavelet Guided U-net for nuclei segmentation in histopathology images

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 2 articles (Google Scholar)

A Wavelet Guided Attention Module for Skin Cancer Classification

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 1 article (Google Scholar)

A New Lightweight Attention-based Model for Emotion Recognition Using Distorted Social Media Images

Year: 2023

Journal/Conference: ACPR 2023

Cited By: 3 articles

Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

Year: 2023

Conference: ICDAR WML 2023

Cited By: 1 article

A Lightweight Script Independent Scene Text Style Transfer Network

Year: 2024

Journal: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Cited By: 1 article

Identification and Classification of Human Mental Stress using Physiological Data

Year: 2022

Conference: IEEE CATCON 2022

Cited By: 4 articles

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 2 articles

An Attention-based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 1 article

DAU-Net: Dual Attention-aided U-Net for Segmenting Tumor Region in Breast Ultrasound Images

Year: 2023

Journal: PLOS ONE

Cited By: 6 articles

 

 

Thittaporn Ganokratanaa | Computer Vision | Best Researcher Award

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Assist. Prof. Dr. Thittaporn Ganokratanaa | Computer Vision | Best Researcher Award

Lecturer at King Mongkut’s University of Technology Thonburi, Thailand

Dr. Thittaporn Ganokratanaa is an Assistant Professor in the Applied Computer Science Programme at King Mongkut’s University of Technology Thonburi. She is a dynamic academic leader involved in national and international committees including IEEE and AIAT. She actively advises innovation projects and engages in AI policy shaping in Thailand. With a strong academic and research background, she contributes significantly to the fields of artificial intelligence and multimedia signal processing. Dr. Thittaporn is widely recognized for her innovative spirit, mentorship, and leadership in applied research and education.

Publication Profile🌏📚

Academic Background🎓

Dr. Thittaporn holds a Ph.D. in Electrical Engineering with a focus on Multimedia and Signal Processing from Chulalongkorn University, with research collaboration at the University of Trento, Italy. She earned her M.Eng. from Chulalongkorn University with a GPA of 3.92 and her B.Sc. in Media Technology with first-class honors and a gold medal from KMUTT. Her academic journey is marked by multiple prestigious scholarships and fellowships, reflecting her academic excellence and commitment to research in AI, signal processing, and biomedical technology.

Professional Experience📊

Dr. Thittaporn currently serves as an Assistant Professor at KMUTT and holds several key leadership roles including Secretary of the IEEE Thailand Section and committee positions in IEEE MGA, CQC, and AIAT. She has contributed to national AI advisory committees and has served as advisor to several award-winning student innovation projects. Her career is defined by interdisciplinary collaboration, global engagement, and dedication to advancing computer science and AI education. She actively participates in conferences, policy development, and technical review roles in the academic and governmental sectors.

Awards and Honors🏆🥇

Dr. Thittaporn has received numerous prestigious awards, including the Grand Prize and Gold Medal at JDIE2024, multiple National Research Council of Thailand innovation awards, and Best Presentation at CSoNet 2024. She has been awarded both nationally and internationally for her innovative projects such as robotic prosthetics and AI-driven healthcare solutions. Her mentorship has led to student accolades at events like NSC and CommTECH. Recognized by organizations like UNOOSA and NUS, her work continues to drive excellence in AI research and technological innovation

Research Focus🔬

Dr. Thittaporn’s research interests span artificial intelligence, video anomaly detection, computer vision, human-computer interaction, multimedia signal processing, and the Internet of Things. She focuses on applying machine learning to solve real-world problems in healthcare, education, and smart technologies. Her projects include intelligent assistive devices, AI-powered learning platforms, and robotic systems. She integrates innovation with societal impact, aiming to bridge research and practical applications. Her interdisciplinary approach and global collaborations support her goal of creating technology that is ethical, inclusive, and transformative.

Publication Top Notes📊

Unsupervised anomaly detection and localization based on deep spatiotemporal translation network
citation: 123
year: 2020

Video anomaly detection using deep residual-spatiotemporal translation network
citation: 39
year: 2022

Iot system design for agro-tourism
citation: 33
year: 2021

Development of a process to enhance the reimbursement efficiency with OCR and ontology for financial documents
citation: 32
year: 2022

Voice-activated assistance for the elderly: Integrating speech recognition and IoT
citation: 20
year: 2024

Sorting red and green chilies by digital image processing
citation: 19
year: 2023

Smart agricultural greenhouses for earthworm farming
citation: 19
year: 2023

Pillow for detecting snoring with embedded techniques for elderly people with snoring problems
citation: 16
year: 2023

Real-Time Credit Card Fraud Detection Surveillance System
citation: 16
year: 2023

Conclusion🌏

Dr. Thittaporn Ganokratanaa is an outstanding candidate for the Best Researcher Award, with a strong track record in artificial intelligence, computer vision, multimedia signal processing, and human-computer interaction. Her academic excellence—evident from her Ph.D. in Electrical Engineering with international collaboration and multiple scholarships—pairs seamlessly with her innovation-driven research, reflected in numerous national and international awards, including from NRCT and JDIE. She actively contributes to impactful real-world applications, such as AI-assisted healthcare technologies and smart systems. Her leadership roles in IEEE Thailand, the AI Association of Thailand, and advisory committees for national AI policy underscore her influence in both academia and policy. Additionally, her mentorship of award-winning student projects highlights her dedication to shaping future researchers. Overall, Dr. Thittaporn exemplifies the qualities of a top-tier researcher with global impact, national relevance, and visionary leadership.

 

 

Ojonugwa Ejiga Peter | Computer Vision | Best Researcher Award

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Mr. Ojonugwa Ejiga Peter | Computer Vision | Best Researcher Award

Morgan State University, United States

Ojonugwa Oluwafemi Ejiga Peter is an emerging AI researcher specializing in computer vision and Generative AI. He is currently pursuing a Master’s degree in Advanced Computing at Morgan State University, Baltimore, where he also serves as a Research Assistant. His academic journey began at the Federal University of Technology, Minna, where he earned a Bachelor of Technology in Computer Science. Ejiga has contributed to cutting-edge research in AI-driven medical imaging and educational technology, supported by prestigious NSF grants. His work explores synthetic medical image generation, AI-based classification models, and ethical AI applications in healthcare and education.

Publication Profile

🎓 Education

Ejiga Peter has built a strong academic foundation in computer science and advanced computing. He earned a Diploma (OND) in Computer Science from Kogi State Polytechnic, Lokoja (2014), followed by a B.Tech in Computer Science from the Federal University of Technology, Minna (2019). Currently, he is pursuing an MSc in Advanced Computing at Morgan State University, Baltimore, where he focuses on AI and computer vision applications.

💼 Experience

Ejiga Peter has gained significant research and academic experience throughout his career. Since September 2023, he has been working as a Research Assistant in Advanced Computing at Morgan State University. Previously, he served as a Graduate Assistant in Civil Engineering at the same university. His work includes contributing to NSF-funded projects, such as deep learning-based medical image captioning and experiment-centered pedagogy for STEM education. His expertise in AI and machine learning has allowed him to explore innovative solutions in healthcare and education.

🏆 Awards and Honors

Ejiga’s research excellence has been recognized through prestigious NSF grants, enabling him to work on high-impact projects in AI-driven medical imaging and STEM education. Additionally, he is a member of the American Society for Engineering Education (ASEE), further showcasing his commitment to academic and professional growth in engineering and computing.

🔬 Research Focus

Ejiga Peter’s research lies at the intersection of computer vision, Generative AI, and AI applications in healthcare and education. His work includes developing synthetic medical images for AI training, exploring AI-driven medical image classification and segmentation, and addressing ethical concerns in AI deployment. His involvement in AI-powered educational innovations aims to improve student learning through hands-on, experiment-centered pedagogy.

🔚 Conclusion

Ojonugwa Oluwafemi Ejiga Peter is a dedicated AI researcher pushing the boundaries of computer vision and Generative AI in healthcare and education. His academic achievements, research contributions, and prestigious NSF-funded projects position him as a rising star in artificial intelligence. Through innovative AI solutions, he aims to revolutionize medical imaging and STEM education, making a lasting impact on both fields. 🚀

📚 Publications

Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation  (Algorithms, 2025) 🔗 DOI: 10.3390/a18030155

Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA (CEUR Workshop Proceedings, 2024) 🔗 Scopus EID: 2-s2.0-85201627982

Comparative Study of Digital Electronics Learning: Using PCB versus Traditional Methods in an Experiment-Centered Pedagogy (ECP) Approach for Engineering Students (ASEE Annual Conference Proceedings, 2024) 🔗 Scopus EID: 2-s2.0-85202046456

 

CHANDRASEKHAR C | Image Processing | Lifetime achievement Award

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Dr. CHANDRASEKHAR C | Image Processing | Lifetime achievement Award

PRINCIPAL, PARVETE, India

🎓 Dr. C. Chandrasekhar is an esteemed academician and researcher specializing in Electronics and Communication Engineering. With over two decades of rich experience in teaching, research, and industrial roles, he has significantly contributed to advancing low-power VLSI designs, MEMS technology, and image processing techniques. Currently, he serves as the In-charge Principal at SVEC and Professor in the Department of ECE. He is a Fellow of the Institution of Engineers (India) and has led several impactful research projects funded by prestigious organizations.

Publication Profile

Education

📘 Dr. Chandrasekhar holds a Ph.D. in Electronics and Communication Engineering from Sri Venkateswara University, Tirupati (2014), where his thesis focused on Discrete Wavelet Transform-based image fusion and compression for micro air vehicle applications. He earned an M.E. in Digital Electronics from BVB College of Engineering, Karnataka (2003), and a B.E. in Instrumentation Technology from Govt. BDT College of Engineering, Karnataka (1994), showcasing his strong academic foundation in electronics and instrumentation.

Experience

💼 Dr. Chandrasekhar has an impressive career trajectory, ranging from industrial roles to academic leadership positions. He served as an Engineer at Hindustan Aeronautics Limited, Bangalore, and has held several academic roles, including Associate Professor, Head of Department, and Professor at institutions like NBKR Institute of Science & Technology and SVCET, Chittoor. His teaching portfolio includes courses such as Microprocessors, Digital Signal Processing, MEMS, and Embedded Systems.

Awards and Honors

🏆 Dr. Chandrasekhar is the recipient of numerous grants, including the DST-SEED/TIDE grant for profiling skill development activities and a DSIR-PRISM-funded project for developing security gadgets for pilgrims. His achievements underline his dedication to addressing societal and technological challenges through research.

Research Focus

🔬 Dr. Chandrasekhar’s research interests encompass low-power VLSI architectures for 2D/3D DWT-IDWT, MEMS technology, and advanced image compression, registration, and fusion techniques. His work is characterized by its emphasis on practical applications, particularly in vehicular communications, cognitive radio, and micro air vehicle systems.

Conclusion

🌟 Dr. C. Chandrasekhar stands out as a visionary educator and researcher committed to the growth of electronics and communication engineering. With a legacy of impactful projects, scholarly contributions, and innovative designs, he continues to inspire the next generation of engineers and researchers.

Publications

Pass-Transistor-Enabled Split Input Voltage Level Shifter for Ultra-Low-Power Applications, Micromachines, 2025.

Varactor Tunable Compact MIMO Antenna with Reconfigurable Multi-Band Operating and Notching for Cognitive Radio Applications, Wireless, Antenna and Microwave Symposium WAMS, 2024.

A Miniaturized-Slotted Planar MIMO Antenna with Switchable Configuration for Dual-/Triple-Band Notches, AIP Advances, 2024.

A Triple Band Pattern Reconfigurable Planar Antenna for 5G Applications, Frequenz, 2022.

A Study and Review on Frequency Band Notch Characteristics in Reconfigurable MIMO-UWB Antennas, Wireless Personal Communications, 2021.

Compact Quad Band Radiator for Wireless Applications, Lecture Notes in Electrical Engineering, 2021.

Micro Organic Photo Detector Characterization using Data Acquisition, TEST Engineering Management, 2020.

Review of 2D/3D DWT-IDWT VLSI Architectures for Image Compression, International Journal of Signal and Imaging Systems Engineering, 2014.

Analysis of Fractional Frequency Reuse (FFR) over Classical Reuse Scheme in 4G (LTE) Cellular Network, Advances in Intelligent Systems and Computing, 2012.

A Conformal Multi-Band MIMO Antenna for Vehicular Communications, Journal Article, year not specified.

 

Jihyeon Ryu | Computer Science | Best Researcher Award

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Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

Profile

Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.