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

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

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 Β 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:Β ORCID

Selected PublicationsΒ 

  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.

Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Ms. Priyanka Manchegowda | Computer Vision | Amrita Vishwa Vidyapeetham | India

Ms. Priyanka Manchegowda is a results-driven Assistant Professor and researcher, currently pursuing her Ph.D., with over 12 years of combined experience in teaching computer science, academic leadership, and curriculum development. She holds an M.Sc. in Computer Science from Pooja Bhagavat Memorial Mahajana Post Graduate Centre, affiliated with the University of Mysore, Mysuru, India, and has actively contributed to higher education by delivering advanced courses in Exploratory Data Analysis using Python, Digital Image Processing, Design and Analysis of Algorithms, Data Structures, Problem Solving and Programming, Operations Research, Numerical Analysis, Statistical Techniques, Programming in C/C++, and Database Management Systems, consistently achieving strong student satisfaction. Professionally, she has served as an Assistant Professor at SBRR Mahajana First Grade College, Mysuru, from 2013 to 2020, and currently at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus since 2020, where she also contributes as a member of the Board of Studies and has developed curricula in alignment with university standards. In addition to teaching, she has guided Bachelor’s and Master’s students on research projects, focusing her research on computer vision-based human age estimation tailored for Indian medico-legal scenarios, demonstrating expertise in analytical methods, quantitative aptitude, image processing, and programming with Python, C, and C++, alongside database management using MS SQL Server and tools such as MATLAB and Anaconda. Ms. Manchegowda has actively contributed to institutional initiatives and student development, serving as the SWAYAM MOOC Nodal Officer, and as convener for the Rotaract Club and SARANTHA, while also engaging in faculty evaluations for the Internal Quality Assurance Cell (IQAC). She brings strong leadership, teamwork, administrative, and communication skills, alongside a commitment to lifelong learning and academic engagement. Her professional recognition includes citations in Scopus with an h-index reflecting the impact of her scholarly contributions.

Professional Profiles: ORCID | Scopus

Selected Publications

  • Priyanka, M., Divyashree, M., & Madhu, V. (2022). Computer Vision-Based Approach for Estimating Age and Gender using Wrist X-Ray Images.

  • Priyanka, M., Sreekumar, S., & Arsh, S. (2022). Detection of Covid-19 from the Chest X-Ray Images: A Comparison Study between CNN and Resnet-50.

Prof. Zhang Wenli | Computer Vision | Excellence in Research Award

Prof. Zhang Wenli | Computer Vision | Excellence in Research AwardΒ 

Prof. Zhang Wenli | Computer Vision | Beijing University of Technology | China

Dr. Wenli Zhang is a distinguished scholar and innovative technology leader currently serving as a Professor in the Faculty of Information Technology at Beijing University of Technology, recognized for impactful contributions in signal and information processing, artificial intelligence, computer vision, 3D point cloud processing, unmanned aerial vehicle inspection technology, and brain-computer interfaces, positioning Dr. Wenli Zhang as a key figure advancing intelligent sensing and human-machine interaction research in China and globally. Building a strong academic foundation through advanced studies in computer science and informatics in both China and Japan, Dr. Wenli Zhang earned a Ph.D. in Engineering from the University of Tokyo, where a passion for applied research and innovation in intelligent systems was further strengthened. Prior to joining academia in China, Dr. Wenli Zhang developed extensive industrial innovation experience as Chief Researcher at Panasonic Corporation’s Tokyo Research Institute, driving real-world AI and vision-based solutions for next-generation automated applications. In her current role, Dr. Wenli Zhang leads interdisciplinary research that spans multiple sectors including smart agriculture, UAV-based intelligent inspection, and medical rehabilitation, effectively bridging fundamental theories with emerging societal needs and technological transformation. With strong collaboration networks and a commitment to promoting scientific excellence, Dr. Wenli Zhang serves actively in influential professional roles, including council member of the Beijing Interdisciplinary Science Society and committee member of the Innovation Engineering Branch of China Creative Studies Institute, contributing leadership within China’s innovation and engineering communities. Skilled in advanced algorithm development, intelligent visual perception, sensor network data fusion, and neural signal decoding, Dr. Wenli Zhang empowers her research team to develop practical systems that enhance automation, sustainability, and accessibility across industries. Her exceptional commitment to teaching and mentorship has earned her the prestigious β€œDistinguished Teacher” recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles:Β ORCIDΒ Β 

Selected Publications:

  • Jiang, K., Guo, W., & Zhang, W. (2025). Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. Sensors.

  • Zhang, W., Peng, X., Bai, T., Wang, H., Takata, D., & Guo, W. (2024). A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees. Remote Sensing.

  • Zhang, W., Peng, X., Cui, G., Wang, H., Takata, D., & Guo, W. (2023). Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud. Drones.

  • Li, Y., Liu, B., & Zhang, W. (2024). Driving-Related Cognitive Abilities Prediction Based on Transformer’s Multimodal Fusion Framework. Sensors.

  • Pang, G., Liu, B., & Zhang, W. (2025). Cloud Rehabilitation System Based on Automatic sEMG Signal Processing. Book Chapter.

  • Zhai, R., Gao, Y., Li, G., Ding, Q., Zhang, Y., & Zhang, W. (2025). Control System for Rehabilitation Bionic Hand Based on Precise Control Algorithms.

  • Wang, Y., Pang, G., Liu, B., Li, Y., & Zhang, W. (2025). Gesture Recognition Method Based on Hybrid Classifier Under Non-ideal Conditions.

Mr. Wenqiang Hua | Image Processing | Best Researcher Award

Mr. Wenqiang Hua | Image Processing | Best Researcher AwardΒ 

Mr. Wenqiang Hua, Xi’an University of Posts and Telecommunications, China

Wenqiang Hua is a lecturer at the School of Computer Science, Xi’an University of Posts and Telecommunications, China, and a researcher at the Key Laboratory of Big Data and Intelligent Computing. He holds a Ph.D. in Electronic Circuit and System Artificial Intelligence from Xidian University. His research focuses on deep learning, image classification, and remote sensing image classification, particularly in PolSAR image analysis. Dr. Hua has authored numerous publications in top-tier journals, including IEEE Geoscience and Remote Sensing Letters and Knowledge-Based Systems. He has led multiple research projects, including a Youth Project funded by the Natural Science Foundation of China. Known for his extroverted and enthusiastic character, he actively engages in academic collaborations and seeks opportunities for Ph.D. co-supervisio

Professional Profile:

SCOPUS

ORCID

Summary of Suitability for Best Researcher Award – Wenqiang Hua

Wenqiang Hua is a highly qualified candidate for the Best Researcher Award, given his extensive contributions to deep learning, image classification, and remote sensing image analysis, particularly in Polarimetric Synthetic Aperture Radar (PolSAR) classification. His research is at the cutting edge of artificial intelligence and geospatial data processing, with a strong record of high-impact publications, funded research projects, and academic mentorship.

Dr. Wenqiang Hua πŸŽ“πŸ‘¨β€πŸ«

Education & Work Experience πŸ“šπŸ’Ό

  • Ph.D. in Electronic Circuit and System (Artificial Intelligence) (2013.09 – 2018.06)
    Xidian University, Xi’an, Shaanxi, China πŸŽ“

  • Lecturer (2018.06 – Present)
    School of Computer Science, Xi’an University of Posts and Telecommunications, China 🏫

Achievements & Contributions πŸ†πŸ”¬

  • Research Expertise: Deep Learning, Image Classification, Remote Sensing Image Classification πŸ€–πŸ“‘

  • Key Research Topics: PolSAR Image Classification, Semi-supervised Learning, Contrastive Learning, Feature Fusion, and Adversarial Networks πŸ”πŸŒ

  • Publications: πŸ“„βœοΈ

    • Published 12+ high-impact journal papers in top journals such as IEEE Geoscience and Remote Sensing Letters, Remote Sensing, and Knowledge-Based Systems πŸ…

    • Developed novel Semi-Supervised Hybrid Contrastive Learning & Deep Feature Fusion Networks for PolSAR image classification πŸ“Š

Awards & Honors πŸ…πŸŽ–οΈ

  • Principal Investigator for two major funded projects:

    • National Natural Science Foundation of China (NSFC) Youth Project (2020-2022) πŸ†

    • Special Scientific Research Project of Shaanxi Education Department (2019-2020) πŸŽ“

  • Recognized for significant contributions to remote sensing image classification and deep learning applications in PolSAR terrain analysis 🌟

Dr. Wenqiang Hua continues to advance big data and intelligent computing through his research at Xi’an University of Posts and Telecommunications, making impactful contributions to AI-driven remote sensing applications πŸš€πŸ“‘

PublicationΒ Top Notes:

Semi-supervised hybrid contrastive learning for PolSAR image classification

Multichannel semi-supervised active learning for PolSAR image classification

PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network

PolSAR Image Classification Based on Relation Network with SWANet

Attention-Based Multiscale Sequential Network for PolSAR Image Classification

Polarimetric SAR Image Classification Based on Ensemble Dual-Branch CNN and Superpixel Algorithm

Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

Mr. Adrian Barglazan, University “Lucian Blaga” Sibiu, Romania

Adrian Barglazan is a Senior Software Engineer at Cognizant Softvision, based in Sibiu, Romania, with a strong focus on continuous learning and growth in software development. He holds a Bachelor’s and Master’s degree in Computer Science from Lucian Blaga University of Sibiu, where he is also pursuing a Ph.D. with a research focus on media forensics. With over 15 years of professional experience, Adrian has worked in various roles, including software development, team leadership, and teaching. His expertise spans Microsoft-related technologies, agile development, clean code principles, and design patterns. Throughout his career, he has contributed to projects in cloud ERP systems, pharmaceutical software, and ERP applications, working with technologies such as C#, ASP.NET, JavaScript, React, and Azure. In addition to his industry work, Adrian has been a teaching assistant at Lucian Blaga University of Sibiu since 2011, specializing in data compression and DirectX. His interests extend to computer vision and machine learning, reflecting his passion for innovative and high-quality software solutions

Professional Profile:

ORCID

Suitability for Best Researcher Award – Adrian Barglazan

Adrian Barglazan demonstrates strong expertise in software development, computer vision, and media forensics, with a balance of industry experience and academic involvement. His Ph.D. research in media forensics, combined with over a decade of teaching experience in data compression and image processing, positions him as a knowledgeable professional in his field. However, for a Best Researcher Award, factors such as high-impact publications, patents, funded research projects, and citations play a crucial role. While Adrian has valuable technical contributions, his eligibility for this award would be strengthened by more peer-reviewed research publications and recognized contributions to the scientific community. Therefore, he is a strong candidate for an innovation or industry-academic impact award but may need further academic credentials to be fully competitive for a Best Researcher Award.

πŸŽ“ Education:

  • PhD in Computer Science (2019 – Present) πŸ“–πŸ”
    Lucian Blaga University of Sibiu – Focus on Media Forensics
  • Master’s Degree in Computer Science (2009 – 2011) πŸŽ“
    Lucian Blaga University of Sibiu
  • Bachelor’s Degree in Computer Science (2005 – 2009) πŸŽ“
    University “Lucian Blaga”, Faculty of Engineering “Hermann Oberth”, Sibiu

πŸ’Ό Work Experience:

πŸ”Ή Senior Software Engineer – Cognizant Softvision (Sept 2020 – Present)
πŸ“ Sibiu, Romania

  • Focus on Microsoft-related technologies, agile development, and clean code
  • Expertise in software architecture, development, testing, and mentoring

πŸ”Ή PhD Student & Teaching Assistant – Lucian Blaga University of Sibiu (Sept 2011 – Present)
πŸ“ Sibiu County, Romania

  • Research in Media Forensics πŸ”
  • Teaching Data Compression & DirectX to 4th-year students πŸŽ“
  • Covers key algorithms like Shannon, Huffman, LZ77, JPEG, MPEG

πŸ”Ή Software Developer – Visma (Apr 2017 – Sept 2020)
πŸ“ Sibiu County, Romania

  • Senior developer in cloud ERP Single Page Application (SPA) development β˜οΈπŸ’»
  • Technologies: C#, ASP.NET MVC, Azure SQL, React, TypeScript
  • Worked with Kanban methodology, CI/CD, and cross-country teams

πŸ”Ή Developer – iQuest Technologies (Sept 2011 – Apr 2017)
πŸ“ Sibiu County, Romania

  • Lead developer in Pharma sector projects πŸ’Š
  • Software architecture, risk management, and recruitment πŸ“‹

πŸ† Achievements, Awards & Honors:

🌟 PhD Researcher in Media Forensics πŸ“ΈπŸ”¬
🌟 Senior Software Engineer with over 17 years of experience in the software industry πŸ’»
🌟 Specializes in Microsoft technologies, Agile development, and Clean Code principles ⚑
🌟 Mentor & Teacher – educating future developers on Data Compression & DirectX πŸŽ“
🌟 Experienced in cloud-based ERP systems, software architecture, and machine learning β˜οΈπŸ€–
🌟 Contributor to recruitment & technical interviews in multiple companies πŸ…

PublicationΒ Top Notes:

Wavelet Based Inpainting Detection

Enhanced Wavelet Scattering Network for Image Inpainting Detection

Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation

Image Inpainting Forgery Detection: A Review

Image Inpainting Forgery Detection: A Review