Mr. Suresha R | Computer Vision Awards | Excellence in Research Award

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Mr. Suresha R | Computer Vision Awards | Excellence in Research Award 

Mr. Suresha R | Computer Vision Awards | Amrita Vishwa Vidyapeetham | India

Mr. Suresha R. is a results-driven educator and technologist with over six years of combined experience in teaching computer science and academic leadership. He holds an M.Sc. in Computer Science and has qualified in UGC-NET and K-SET, while currently pursuing a Ph.D. Mr. Suresha R. has demonstrated expertise in curriculum design and research, particularly focusing on AI in autonomous solutions and computer vision applications. In his professional career, Mr. Suresha R. has served as an Assistant Professor at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus, and at SBRR Mahajana First Grade College, Mysuru, where he delivered advanced courses in Computer Vision, Digital Image Processing, Pattern Recognition, Computational Intelligence, Computer Graphics, Machine Learning, Exploratory Data Analysis, R Programming, Information Retrieval, Data Mining, Numerical Analysis, and Operations Research, consistently achieving high student satisfaction. His research interests encompass small traffic sign detection and recognition in challenging scenarios using computer vision and LiDAR-based techniques with ROS2 framework, deep learning-based vehicle detection and distance estimation for autonomous systems, motion blur image restoration, wild animal recognition through vocal analysis, and SVM-based medical image classification. Mr. Suresha . possesses strong research skills in Python, MATLAB, ROS2, machine learning, deep learning, image processing, and data analysis. He has successfully guided Bachelor’s and Master’s students in research projects, fostering innovation and academic growth. His academic contributions are recognized through multiple publications in prestigious journals and conferences, including IEEE Access, Procedia Computer Science, ICCCNT, CCEM, ICECAA, and INDIACom. Mr. Suresha . has a proven record of collaborating in interdisciplinary teams, effectively communicating complex technical concepts, and mentoring students to achieve excellence in research and practical applications. His dedication to lifelong learning and active engagement in both teaching and research demonstrates his commitment to advancing knowledge in computer science and autonomous systems. Throughout his career, Suresha  has received awards and recognitions for research excellence, contributing to the development of sustainable and intelligent solutions in the field of computer vision and AI. Overall, Mr. Suresha exemplifies a passionate and innovative professional, bridging theoretical foundations with applied research, and continues to make significant contributions to academia and technology

Professional Profiles: ORCID

Selected Publications 

  1. Suresha, R., Manohar, N., Ajay Kumar, G., & Singh, R. (2024). Recent advancement in small traffic sign detection: Approaches and dataset.

  2. Suresha, R., Manohar, N., & Jipeng, T. (2024). Two-stage traffic sign classification system.

  3. Sudharshan Duth, P., Manohar, N., Suresha, R., Priyanka, M., & Jipeng, T. (2024). Wild animal recognition: A vocal analysis.

  4. Suresha, R., Jayanth, R., & Shriharikoushik, M. A. (2023). Computer vision approach for motion blur image restoration system.

  5. Srinivasa, C., Suresha, R., Manohar, N., Dharun, G. K., Sheela, T., & Jipeng, T. (2023). Deep learning-based techniques for precise vehicle detection and distance estimation in autonomous systems.

  6. Suresha, R., Devika, K. M., & Prabhu, A. (2022). Support vector machine classifier based lung cancer recognition: A fusion approach.

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

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Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Brunel University of London | United Kingdom

Mr. Ahmet Serhat Yildiz is an emerging researcher in sensing technology with growing expertise in machine learning, deep learning, embedded systems, and multi-sensor fusion, demonstrating strong potential for advanced research roles and academic leadership. He is currently pursuing his PhD in Electronic and Computer Engineering at Brunel University London, where he focuses on real-time object detection, semantic 3D depth sensing, LiDAR–camera fusion, and intelligent autonomous perception systems, aligning closely with sensing applications in robotics, transportation, surveillance, and industrial automation. His academic foundation includes degrees in electronics, electrical engineering, business management, and extensive English language training, providing a multidisciplinary perspective that strengthens his analytical and communication abilities. His professional experience includes roles as a Graduate Teaching Assistant in digital design, embedded systems, and computer architecture, as well as serving as an IoT facilitator, where he mentored learners and contributed to community-oriented technology initiatives. Mr. AHMET SERHAT YILDIZ has developed notable research projects, including FPGA-based embedded game systems, PLC-controlled industrial automation setups, and biomedical sensing circuits for pulse wave velocity measurement, demonstrating strong hands-on engineering skills. His research portfolio includes Scopus-indexed publications on YOLO-based detection models, sensor fusion for autonomous vehicles, and real-time navigation using LiDAR and deep learning frameworks, reflecting his ability to integrate theory with practical sensing applications. His technical skills include Python, PyTorch, embedded C, FPGA development, digital circuit design, PLC programming, and multi-sensor signal processing, enabling him to contribute to both algorithmic and hardware-oriented research environments. His achievements include scholarly publications, increasing citation impact, and recognition through participation in international conferences and multidisciplinary research projects.

Professional Profiles: ORCID | Google Scholar

Featured Publications 

  1. Alkandary, K., Yildiz, A. S., & Meng, H. (2025). A comparative study of YOLO series (v3–v10) with DeepSORT and StrongSORT: A real-time tracking performance study. Electronics.

  2. Tunali, M. M., Yildiz, A., & Çakar, T. (2022). Steel surface defect classification via deep learning. International Conference on Computer Science and Engineering (UBMK).

  3. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). Real-time object detection and distance measurement enhanced with semantic 3D depth sensing using camera–LiDAR fusion. Applied Sciences.

  4. Tunali, M. M., Sayar, A., Aslan, Y., Mutlu, İ., & Çakar, T., including Yildiz, A. (2023). Enhancing quality control in plastic injection production: Deep learning-based detection and classification of defects. International Conference on Computer Science and Engineering (UBMK).

  5. Yıldız, A., Mişe, P., Çakar, T., Terzibaşıoğlu, A. M., & Öke, D. (2023). Spine posture detection for office workers with hybrid machine learning. International Conference on Computer Science and Engineering (UBMK).

  6. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). YOLOv8–LiDAR fusion: Increasing range resolution based on image-guided sparse depth fusion in self-driving vehicles. Lecture Notes in Networks and Systems.

  7. Yildiz, A. S., Meng, H., & Swash, M. R. (2024). A multi-sensor fusion approach to real-time bird’s-eye view navigation: YOLOv8 and LiDAR integration for autonomous systems. Korkut Ata Scientific Research Conference Proceedings.

Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award

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Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award 

Dr. Zhiwei Zhang, AVIC Manufacturing Technology Institute, China

Zhiwei Zhang, is a research engineer specializing in aviation manufacturing technology in China. He holds a bachelor’s and master’s degree in Automation from Shenyang Ligong University and earned his Ph.D. in Instrument Science and Technology from Yanshan University. His research focuses on digital radiographic and industrial CT nondestructive testing, computer vision, and ensemble learning algorithms for additive manufacturing. He has published seven SCI-indexed research papers and holds two authorized patents. Zhiwei Zhang also serves as a reviewer for the Journal of Computational Methods in Sciences and Engineering, reflecting his active contribution to the academic and industrial research community.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Zhiwei Zhang

Zhiwei Zhang, a highly skilled research engineer in aviation manufacturing technology, has demonstrated outstanding contributions in the fields of nondestructive testing, computer vision, and ensemble learning for additive manufacturing. His innovative research integrates cutting-edge technologies like digital radiography, industrial CT, and machine learning, addressing critical challenges in the aerospace industry.

🎓 Education

  • 🏫 Bachelor’s Degree in Automation – Shenyang Ligong University

  • 🎓 Master’s Degree in Automation – Shenyang Ligong University

  • 🧪 Ph.D. in Instrument Science and Technology – Yanshan University

💼 Work Experience

  • 👨‍🔧 Research Engineer – Specializing in aviation manufacturing technology in China

  • 🔬 Focus areas include:

    • Digital radiographic and industrial CT nondestructive testing

    • Computer vision

    • Ensemble learning algorithms for additive manufacturing

🏆 Achievements

  • 📄 Published 7 SCI-indexed research papers in high-impact journals

  • 🧾 Granted 2 authorized patents

  • 🧑‍⚖️ Reviewer for the Journal of Computational Methods in Sciences and Engineering

🎖️ Awards & Honors

  • 🏅 Recognized for contributions in nondestructive testing and AI applications in manufacturing
    (Note: Specific award titles not mentioned; can be added if provided.)

Publication Top Notes:

A Hybrid Framework for Metal Artifact Suppression in CT Imaging of Metal Lattice Structures via Radon Transform and Attention-Based Super-Resolution Reconstruction

Complex Defects Detection of 3-D-Printed Lattice Structures: Accuracy and Scale Improvement in YOLO V7

A Prediction Model for Maximum Stress of Additive Manufacturing Lattice Structures Based on Voting-Cascading

Deep convolution IT2 fuzzy system with adaptive variable selection method for ultra-short-term wind speed prediction

An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure

An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress

Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures

Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN

A Hybrid Model Based on Jensen’s Inequality Theory for 3D Printed Lattice Structures Maximum Stress Prediction

Prof. Fengyun Cao | Computer Vision Awards | Excellence in Research Award

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Prof. Fengyun Cao | Computer Vision Awards | Excellence in Research Award 

Prof. Fengyun Cao, Hefei Normal University, China

Dr. Cao Fengyun is an Associate Professor and Master’s Supervisor at the School of Computer and Artificial Intelligence, Hefei Normal University, where she also serves as Director of the Department of Computer Science and Technology. Her primary research interests include digital image processing, computer vision, and artificial intelligence. Dr. Cao is a member of the Image Application and System Integration Committee of the Chinese Image and Graphics Society and serves on the young editorial board of the international journal INSTRUMENTATION. She is also a reviewer for numerous prestigious journals such as IEEE/CAA Journal of Automatica Sinica, Scientific Reports, and The Journal of Supercomputing. She currently holds the position of Vice President of Science and Technology at the Medical Artificial Intelligence Technology R&D Center, Hefei Innovation Institute. Over the years, she has led various funded research projects, including those focused on depth estimation, remote sensing, and smart control systems. Dr. Cao has authored several high-impact papers and holds 10 authorized invention patents, along with multiple software copyrights and integrated circuit layout designs. Her work has earned her accolades including the “Research Star” award and third prize in the Anhui Province Science and Technology Awards. She has also contributed to the development of local standards in smart systems and information monitoring.

Professional Profile:

SCOPUS

Summary of Suitability:

Dr. Cao Fengyun, an Associate Professor and Director of the Department of Computer Science and Technology at the School of Computer and Artificial Intelligence, Hefei Normal University, is a highly accomplished researcher with a proven track record in digital image processing, computer vision, and artificial intelligence. His outstanding contributions to both theoretical advancements and practical innovations make him an excellent candidate for the Excellence in Research Award.

🎓 Education & Work Experience

  • 👨‍🏫 Teaching Assistant
    School of Computer Science, Hefei Normal University
    📅 June 2013 – November 2017

  • 👨‍🏫 Lecturer
    School of Computer Science, Hefei Normal University
    📅 December 2017 – December 2022

  • 👩‍🏫 Associate Professor
    School of Computer and Artificial Intelligence, Hefei Normal University
    📅 January 2023 – Present

  • 🧠 Vice President of Science and Technology
    Medical AI Technology R&D Center, Hefei Innovation Institute
    📅 November 2024 – Present

🏆 Achievements

  • 📚 Research Areas:
    Digital Image Processing, Computer Vision, Artificial Intelligence

  • 🧪 Research Projects (Host):

    • 🔍 Magnetic Tile Surface Defect Detection (2024–2025)

    • 🤖 Monocular Image Depth Estimation using Deep CNN (2019–2020)

    • 🖼 Single Image Depth Restoration via Low-level Features

    • 🌩 Cloud Tech for Remote Sensing Image Thinning (2018–2019)

    • 🔧 Smart Fire Protection Water Supply System (2025)

    • 📡 High Performance Frequency Hopping Filter Development

    • ⚡ Intelligent Control System for Power Distribution Cabinet (2021)

    • 🔋 Smart-LW Charging Operation and Maintenance System

    • 🧠 Graph Neural Network Intelligent Computing System (Ranked 3rd)

    • 🌐 IoT Equipment Remote Upgrade System (2021)

  • 📄 Representative Papers:

    • ⚙ Electric Bike Testing Dataset – Alexandria Engineering Journal (2024, SCI Zone II TOP)

    • 🎯 YOLOv7-based Anti-target Detection – Traitement du Signal (2023, SCI)

    • 🧩 PCB Defect Recognition via Bi-directional Feature Extraction – Journal of Wuhan University of Technology

    • 🖌 Edge Blur Estimation for Depth Restoration – Journal of Computers

    • 🧠 Image Segmentation and Depth Recovery – Journal of Chinese Image and Graphics

  • 💡 Intellectual Property:

    • 🔬 Invention Patents: 10 (Ranked 1st to 8th) – covering intelligent factories, robotic arms, IoT, and image processing

    • 💻 Software Copyrights: 3 (First author)

    • 🧿 Integrated Circuit Layout Designs: 2 (One authored by him)

🥇 Awards & Honors

  • 🌟 HefeiNormal University Research Star, 2022

  • 🥉 Third Prize – Natural Science Award (Host), Hefei Normal University, 202X

  • 🥉 Third Prize – Anhui Province Science and Technology Award (Ranked 4th), 2021

  • 🏅 Excellence in Science & Technology Progress, Anhui Provincial Computer Society (1st Rank), 2021

Publication Top Notes:

Optimization of the Pure Pursuit algorithm based on real-time error

Prof. Raziyeh Pourdarbani | Computer Vision Awards | Best Researcher Award

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Prof. Raziyeh Pourdarbani | Computer Vision Awards | Best Researcher Award 

Prof. Raziyeh Pourdarbani, University of Mohaghegh Ardabili, Iran

Dr. Razieh Pourdarbani is a professor in the Department of Biosystems Engineering at the Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabili, Iran. She holds a Ph.D. in Agricultural Mechanization Engineering from the University of Tabriz (2012), where her dissertation focused on sorting date fruits based on maturity stages using image processing. She also earned her M.Sc. (2009) and B.Sc. (2005) degrees in Agricultural Mechanization and Machinery Engineering, respectively, from the same university. Dr. Pourdarbani’s research specializes in precision agriculture, image processing, artificial intelligence, and machine vision. Her contributions to these fields aim to advance agricultural technologies and systems for improved efficiency and sustainability.

Professional Profile:

GOOGLE SCHOLAR

Suitability for the Research for Best Researcher Award

Razieh Pourdarbani, born on December 9, 1982, is a professor at the Department of Biosystems Engineering, University of Mohaghegh Ardabili, Iran. With a Ph.D. in Agricultural Mechanization Engineering from the University of Tabriz (2012), her dissertation on date fruit sorting using image processing laid the foundation for her expertise in precision agriculture and automation.

🎓 Education

  • Ph.D. in Agricultural Mechanization Engineering
    • Institution: University of Tabriz
    • Year: 2012
    • Dissertation: Sorting of Date Fruit Based on Maturity Stages Using Image Processing
  • M.Sc. in Agricultural Mechanization Engineering
    • Institution: University of Tabriz
    • Year: 2009
    • Thesis: Investigation on Apple Sorting Using Image Processing
  • B.Sc. in Agricultural Machinery Engineering
    • Institution: University of Tabriz
    • Year: 2005

🏢 Work Experience

  • Professor, Department of Biosystems Engineering
    • Institution: Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili
    • Role: Specializing in Precision Agriculture, Machine Vision, Image Processing, and Artificial Intelligence.

🏆 Achievements and Honors

  • 🎖 Recognized Expert in Precision Agriculture and Image Processing.
  • 🏅 Pioneer in integrating Artificial Intelligence and Machine Vision in agricultural mechanization.
  • 📚 Published numerous influential papers in the field of agricultural engineering and biosystems.
  • 🌟 Mentored students and researchers in advanced biosystems engineering applications.

Publication Top Notes:

CITED:83
CITED:66
CITED:54
CITED:39
CITED:38
CITED:37

Prof. Suk Chan Kim | Computer Vision Awards | Best Researcher Award

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Prof. Suk Chan Kim | Computer Vision Awards | Best Researcher Award 

Prof. Suk Chan Kim, Pusan National University, South Korea

Suk Chan Kim is a distinguished scholar in the field of Electrical and Electronics Engineering, specializing in wireless mobile communications, signal processing, mesh networks, IoT, underwater communications, and artificial intelligence. He currently serves as an Assistant Professor in the Department of Electronics Engineering at Pusan National University (PNU), Korea, where he has been contributing to academia since 2002. Dr. Kim earned his Ph.D. (2000) and M.S.E. (1995) in Electrical Engineering from the Korea Advanced Institute of Science & Technology (KAIST), following a B.S.E. degree with summa cum laude honors from PNU in 1993. His postdoctoral research at Princeton University (2000–2001) further honed his expertise in advanced engineering topics. He has also worked as a researcher at the Electronics and Telecommunications Research Institute (ETRI) in Korea and as a teaching and research assistant at KAIST.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Suk Chan Kim

Dr. Suk Chan Kim is a highly accomplished researcher with significant contributions in wireless mobile communications, signal processing, IoT, underwater communications, and artificial intelligence. Based on his profile, here are the key highlights supporting his candidacy for the “Research for Best Researcher Award”:

Education 🎓

  • Ph.D. in Electrical Engineering
    Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Korea, 2000
  • M.S.E. in Electrical Engineering
    KAIST, Daejeon, Korea, 1995
  • B.S.E. (Summa Cum Laude) in Electronics Engineering
    Pusan National University (PNU), Pusan, Korea, 1993

Work Experience 💼

  • Assistant Professor
    Dept. of Electronics Engineering, Pusan National University, Korea (March 2002 – Present)
  • Postdoctoral Researcher
    Dept. of Electrical Engineering, Princeton University, USA (August 2000 – July 2001)
  • Researcher
    Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea (March 2000 – July 2000)
  • Teaching and Research Assistant
    Dept. of Electrical Engineering, KAIST, Daejeon, Korea (March 1993 – February 2000)

Awards & Honors 🏆

  • Grants for Postdoctoral Study Abroad
    Korea Science and Engineering Foundation (KOSEF), 2000 🌍
  • Grants for Young Scientists
    Korea Research Foundation (KRF), 1998 🧑‍🔬
  • Summa Cum Laude
    Pusan National University, Pusan, Korea, 1993 🎖️
  • Hyundai Asan Foundation Scholarship
    1992 🎓

Publication Top Notes:

ESFD-YOLOv8n: Early Smoke and Fire Detection Method Based on an Improved YOLOv8n Model

Enhancing automated strabismus classification with limited data: Data augmentation using StyleGAN2-ADA

Ultrasonic Based Outdoor Localization Using Threshold Crossing

DBPN-Based Uplink Channel Estimation for Multi-User MISO RIS System

Low-Complexity RIS Phase Error Estimation Method for RIS-Aided OFDM Systems

Spectrum Allocation Based on Deep Reinforcement Learning in mmWave Integrated Access and Backhaul Network

Ms. Maryam Moshrefizadeh | Computer Vision Awards | Best Researcher Award

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Ms. Maryam Moshrefizadeh | Computer Vision Awards | Best Researcher Award 

Ms. Maryam Moshrefizadeh, Siant Louis University, United States

Maryam Moshrefizadeh is a Ph.D. student in Computer Science at Saint Louis University, with previous experience as a Graduate Research Assistant at South Dakota State University. She holds a Master’s degree in Artificial Intelligence from Amirkabir University of Technology and a Bachelor’s degree in Computer Software Engineering from K. N. Toosi University of Technology, both in Tehran, Iran. Maryam’s research interests lie in computer vision, deep learning, and machine learning. Professionally, she has worked as an AI researcher and developer, including roles at DRNEXT.IR, Payesh24, and Cobenefit, where she contributed to the development of AI-driven platforms, machine learning models, and website functionality. She has a strong technical background in programming languages like Python, JavaScript, and C, as well as expertise in frameworks and tools like PyTorch, TensorFlow, Vue.js, and Docker. Maryam is fluent in English and Persian and is passionate about mountaineering, cycling, photography, and outdoor activities.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Maryam Moshrefizadeh is a promising and highly capable PhD student with extensive experience and research contributions in the field of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision. Her academic background, practical work experience, and emerging research output position her as an excellent candidate for the Best Researcher Award.

Education

🎓 Saint Louis University
Ph.D. | Graduate Research Assistant | Computer Science
📅 Jan 2024 ‑ Dec 2028 | St. Louis, MO, USA

🎓 South Dakota State University
Ph.D. | Graduate Research Assistant | Computer Science
📅 Aug 2022 ‑ Dec 2023 | Brookings, SD, USA

🎓 Amirkabir University of Technology (Polytechnic)
M.S. in Artificial Intelligence
📅 Jan 2014 ‑ Sept 2017 | Tehran, Iran

🎓 K. N. Toosi University of Technology
B.S. in Computer Software Engineering
📅 Sept 2009 ‑ Aug 2013 | Tehran, Iran

Work Experience

💼 DrNext.ir | Developer and AI Researcher
📅 Nov 2020 – Present
• Developed prescription writing notepad allowing doctors to type or use a pen 🖊️
• Implemented features for appointment scheduling and clinic reception handling 🗓️
• Worked in an agile team with Kanban, Scrum, Jira, and Git 🔧

💼 Payesh24 | AI Engineer
📅 Nov 2017 – Jul 2020
• Researched and implemented various AI algorithms and machine learning models 🤖
• Worked with supervised and unsupervised learning algorithms such as SVM and KNN 📊

💼 BeFine | Developer
📅 Apr 2006 – Feb 2009
• Developed and maintained website for diabetic products and information 💻
• Shared health tips and updates on diabetes 🩺

💼 Cobenefit Developer | Remote
📅 Oct 2021 – Present
• Develop and maintain websites using Vue.js, ES6, HTML5, CSS3, and SASS 🌐

Research Interests

🔍 Computer Vision | 🤖 Deep Learning | 📚 Machine Learning

Publication Top Notes

EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery

Multimodal Fusion of Heterogeneous Representations for Anomaly Classification in Satellite Imagery