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. Meir Marmor | Image Analysis Awards | Best Researcher Award

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Dr. Meir Marmor | Image Analysis Awards | Best Researcher Award

Dr. Meir Marmor , UCSF, United States

Dr. Meir Tibrin Marmor is a Professor of Clinical Orthopaedic Surgery at the University of California, San Francisco (UCSF), specializing in orthopaedic trauma and joint replacement. He earned his M.D. from the Israel Institute of Technology with cum laude distinction and completed his orthopaedic residency in Israel before advancing his training through AO and clinical fellowships in trauma and joint replacement surgery in Germany and the U.S. Since 2010, he has served as a trauma surgeon across several Level I and II centers in Northern California, with a primary clinical role at Zuckerberg San Francisco General Hospital (ZSFG), where he leads the Orthopaedic “Blue” Service, the Geriatric Orthopaedic Trauma Service, and the OTI Digital Science Laboratory. Dr. Marmor’s research focuses on musculoskeletal trauma in vulnerable and geriatric populations, as well as surgical education, data science, and digital health technology. He has received numerous awards, including the OTA’s Kathy Cramer Young Clinicians Research Award and multiple best paper and poster recognitions. An active member of global orthopedic societies, he also chairs the OTA Artificial Intelligence Task Force and contributes extensively to academic publishing and international presentations.

Professional Profile:

SCOPUS

Summary of Suitability – Dr. Meir Tibrin Marmor for Best Researcher Award

Dr. Meir Tibrin Marmor is a distinguished clinician-scientist in orthopaedic trauma surgery with over two decades of training, research, and clinical practice in high-impact academic and clinical environments. Currently a Professor of Clinical Orthopaedic Surgery at the University of California, San Francisco (UCSF), he combines surgical excellence with robust contributions to medical research and technology.

🎓 Education

  • 📘 B.Sc. in Medical Sciences – Israel Institute of Technology, Haifa (1988–1992) cum laude

  • 🩺 M.D. in Medicine – Ruth and Bruce Rappaport Faculty of Medicine, Israel Institute of Technology (1992–1996) cum laude

  • 🏥 Orthopaedic Surgery Residency – Tel-Aviv Medical Center & Barzilai Medical Center, Israel (2000–2008)

  • 🌍 AO Fellowship in Orthopaedic Trauma – Saarland University Hospital, Germany (2008)

  • 🔬 Research Fellowship – UCSF, Orthopaedic Trauma (2008–2009)

  • 🩻 Clinical Fellowship – UCSF, Orthopaedic Trauma Surgery (2009–2010)

  • 🦴 Joint Replacement Fellowship – Joint Replacement Institute, Los Angeles (2017–2018)

  • 💻 Master’s in Information and Data Science (MIDS) – UC Berkeley (2022–2024)

🧑‍⚕️ Work Experience

  • 👨‍🏫 Professor of Clinical Orthopaedic Surgery – UCSF, Step 1 (Current)

  • 🏥 Orthopaedic Trauma Surgeon at:

    • Zuckerberg San Francisco General Hospital (ZSFG)

    • Enloe Medical Center, Chico, CA (2012–2014)

    • Regional Medical Center of San Jose (2013–2022)

  • 👨‍🔬 Research and Medical Director Roles:

    • Clinical Research Director – UCSF @ Regional Medical Center

    • Medical Director – Biomechanics Testing Facility @ UCSF

    • Director – Geriatric Orthopaedic Trauma Service & OTI Digital Science Lab @ ZSFG

    • Chief – Orthopaedic “Blue” Service @ ZSFG

🏆 Honors and Awards

  • 🎖️ B.Sc. Medical Sciences – cum laude (1992)

  • 🎖️ M.D. – cum laude (1997)

  • 🪖 Operational Performance Citation – Lebanon Front, IDF (1999)

  • 💰 Fellowship Scholarships – American Physicians Fellowship & Israeli Medical Association (2008)

  • 🖼️ “Best Poster” Award – OTA 25th Annual Meeting, San Diego (2009)

  • 📜 Best Paper Nomination – CAINE Conference (2014)

  • 💡 Kathy Cramer Young Clinicians Research Development Award – OTA (2015)

  • 🏅 Howard Rosen Table Instructor Award – AO Trauma North America (2018)

Publication Top Notes:

Revisiting the OTA-OFC: a systematic review of open fracture classification studies since 2010

Artificial intelligence: international perspectives on critical issues

The Impact of National Orthopaedic Fracture Registries: A Systematic Review

Patient Recruitment Characteristics for Wearable-Sensor-Based Outcome Assessment in Trauma Surgery

A scoping review and critical appraisal of orthopaedic trauma research using the American College of Surgeons National Trauma Data Bank

Worldwide research trends concerning operative competence in orthopedics: A bibliometric and visualization study

Does the CDC Surgical Wound Classification adequately predict postoperative infection in lower extremity fracture surgery?

Mortality, perioperative complications and surgical timelines in hip fracture patients: Comparison of the Spanish with the non-Spanish Cohort of the HIP ATTACK-1 trial

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

Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award

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Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award 

Ms. Saleha Kamal, Air University, Pakistan

Saleha Kamal is an accomplished AI and Computer Vision professional based in Rawalpindi, Pakistan, with expertise in image processing, silhouette detection, segmentation, and feature classification. She is currently pursuing an MS in Computer Science at Air University, Islamabad, Pakistan (2023-2025). Saleha’s research focuses on human interaction analysis and the development of advanced algorithms for computer vision tasks. Her work has been published in esteemed international conferences, including IEEE ICECT 2024 and IEEE ICET 2024, showcasing her innovative contributions to multi-feature descriptors and composite feature-based classifiers for human interaction recognition.

Professional Profile:

GOOGLE SCHOLAR

Suitability of Saleha Kamal for the Best Researcher Award

Saleha Kamal demonstrates exceptional potential and achievements in AI, machine learning, and computer vision research, making her a compelling candidate for the Best Researcher Award. Her dedication to advancing knowledge in human interaction recognition, along with her technical and academic accomplishments, positions her as a rising star in the research community.

Education 🎓

  • MS in Computer Science (2023 – 2025)
    Air University, Islamabad, Pakistan

Work and Research Experience 💼

  • Research Experience
    • Co-authored research papers published in international conferences:
      • “Multi-Feature Descriptors for Human Interaction Recognition in Outdoor Environments” – IEEE ICECT, 2024.
      • “A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier” – IEEE ICET, 2024.

Achievements and Certifications 🏆

  • Published research in prestigious IEEE conferences.
  • Certifications:
    • Advanced Computer Vision with TensorFlow – Coursera, 2023.
    • Machine Learning Specialization – Coursera, 2023.

Publication Top Notes:

A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier

CITED:8

CITED:6

Assist Prof Dr. Nikolay Kiktev | Pattern Recognition Award | Best Researcher Award

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Assist Prof Dr. Nikolay Kiktev | Pattern Recognition Award | Best Researcher Award

Assist Prof Dr. Nikolay Kiktev, National Univercity of Life and Environemental of Ukraine, Ukraine

Nikolay Kiktev (Mykola Kiktiev), born on December 17, 1972, in Donetsk, Ukraine, is a Candidate of Technical Sciences and an Associate Professor. He earned a specialist diploma in systems engineering from Donetsk State Technical University in 1994 and later obtained his Candidate of Sciences degree in 2011 in “Automation of Management Processes” from the National University of Food Technologies in Kiev. With extensive experience in academia and industry, he has held positions in software engineering, project management for educational computerization, and automation systems development for various industries. Currently, he serves as an Associate Professor at Taras Shevchenko National University of Kyiv, teaching and supervising students in fields such as automation, data analytics, and intelligent systems. He has authored 120 scientific works, holds two patents, and has an h-index of 12 in Scopus. His research focuses on automated information-management systems, particularly in agriculture, chemical, and electrochemical industries.

Professional Profile:

Suitability Summary:

He holds a Candidate of Sciences degree in “Automation of Management Processes,” a well-respected technical qualification in his field. His academic journey in automation systems and his associate professor role further enhance his expertise.

Education:

  1. Specialist Diploma in Systems Engineering
    • Field of Study: Automated Information Processing and Control Systems
    • Year of Graduation: 1994
    • Institution: Donetsk State Technical University, Ukraine
  2. Candidate of Technical Sciences (Ph.D. equivalent) in Automation of Management Processes
    • Specialty: 05.13.07 – Automation of Management Processes
    • Defense Year: 2011
    • Institution: National University of Food Technologies, Kyiv, Ukraine
  3. Certificate of Associate Professor
    • Department: Automation and Robotic Systems
    • Year Awarded: 2020

Work Experience:

  1. Software Engineer
    • Organization: Association of Defense Industry Enterprises of the Donetsk Region “Temp”
    • Duration: 1993–1995
    • Responsibilities:
      • Developed and operated software for company activities, including product accounting databases and economic information processing.
      • Created communication systems between enterprises within the association.
      • Maintained computer equipment.
  2. Graduate Researcher
    • Institution: Donetsk State Technical University
    • Duration: 1995–1999
    • Field: Automation of Technological Processes
    • Contributions:
      • Participated in a state-funded research project titled “Development of an Environmentally Friendly Technological Process for Obtaining Carbonates for Premixes in Animal Feed” (1994–1998).
      • Conducted patent searches and analytical reviews of carbonate production methods and control systems.
      • Planned and executed experiments; installed experimental research setups.
      • Developed a new technological process and improved existing control methods.
      • Created algorithms and software for process automation.
      • Obtained a patent for the invention “Method for the Electrochemical Production of Carbon Dioxide Salts of Metal D-Elements.”
  3. Assistant Lecturer
    • Institution: Donetsk National Technical University
    • Departments: Electronic Engineering; Computational Mathematics and Programming
    • Duration: 1999–2002
    • Additional Roles:
      • Part-time lecturer at various institutions, including the Donetsk Institute of Railway Transport, Donetsk Institute of Psychology and Entrepreneurship, and Donetsk Lyceum “College.”

Publication top Notes:

CITED:26
CITED:22
CITED:21
CITED:20
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