Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

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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

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

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 “Distinguished Teacher” recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles: ORCID  

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. 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.

Aljaz Hojski | Vision Sensing | Best Researcher

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Dr. Aljaz Hojski | Vision Sensing | Best Researcher

Dr. Aljaz Hojski | Vision Sensing | Cadre doctor at Universitätspital Basel | Switzerland

Dr. Aljaz Hojski is a highly respected thoracic surgeon and clinical researcher, currently affiliated with Universitätspital Basel. With a strong focus on surgical innovation and patient-centered care, his contributions in minimally invasive thoracic procedures and oncological surgery have gained widespread recognition across academic and clinical communities. His medical background is complemented by an extensive portfolio of scientific publications, collaborative research initiatives, and active peer-review responsibilities in high-impact journals. A committed academician and practicing consultant, Dr. Hojski is known for bridging the gap between clinical application and evidence-based research, especially in lung cancer management, thoracic trauma, and postoperative pain optimization.

Academic Profile:

ORCID

Scopus

Education:

Dr. Hojski obtained his foundational medical education at the University of Ljubljana, where he developed a keen interest in thoracic medicine and surgical procedures. His education included comprehensive training in general medicine, with progressive specialization in thoracic surgery during his clinical rotations and postgraduate residency programs. Throughout his academic journey, he emphasized scientific inquiry alongside clinical excellence, engaging in laboratory-based research and hospital-based surgical trials. This dual focus on science and surgery established a strong platform for his later contributions to applied clinical research and international collaborations in minimally invasive thoracic techniques.

Experience:

Dr. Hojski currently serves in a senior consultant role within the Department of Thoracic Surgery at Universitätspital Basel, a leading center for cardiothoracic care and research in Europe. He is actively involved in surgical planning, patient care, and mentoring junior clinicians. In addition to his clinical duties, he contributes to institutional and multicenter research protocols aimed at improving perioperative outcomes and refining surgical strategies. His professional experience spans diverse domains including advanced thoracoscopic resections, surgical pain management, and postoperative complication risk stratification. Dr. Hojski’s extensive collaborations with multidisciplinary teams, including radiologists, anesthesiologists, and oncologists, have enabled the successful translation of academic research into clinical best practices.

Research Interest:

Dr. Hojski’s primary research interests include thoracic oncology surgery, 3D imaging and surgical planning, postoperative pain control strategies, and risk prediction in lung resection patients. He has been an investigator and co-investigator on several funded research projects focused on optimizing pain therapy following minimally invasive lung operations, and the development of advanced imaging tools for segmental lung function assessment. His research also extends into clinical outcome analysis, where he contributes to developing predictive models for surgical complications and evaluating the effectiveness of new procedural technologies. His interdisciplinary approach enables him to align clinical insight with scientific rigor in solving real-world surgical challenges.

Awards:

Dr. Hojski has been nominated for several recognitions in the field of medical science and thoracic surgery, reflecting his continued impact on both clinical advancement and scientific contribution. His research output and leadership have earned him invitations to present at international symposia, while his peer-reviewed publications and service as a reviewer demonstrate his influence in academic publishing. He remains committed to excellence in both operative care and medical scholarship, making him a compelling nominee for awards that celebrate high-impact contributions to science and medicine.

Selected Publications:

  • Estimating Postoperative Lung Function Using Three-Dimensional Segmental HRCT-Reconstruction: A Retrospective Pilot Study on Right Upper Lobe Resections, 2025, 60 citations

  • Perioperative Intravenous Lidocaine in Thoracoscopic Surgery for Improved Postoperative Pain Control: A Randomized, Placebo-Controlled, Double-Blind, Superiority Trial, 2024, 85 citations

  • Planning Thoracoscopic Segmentectomies with 3-Dimensional Reconstruction Software Improves Outcomes, 2025, 45 citations

  • A Risk Score to Predict Postoperative Complications in Patients with Resectable Non-Small Cell Lung Cancer, 2025, 50 citations

Conclusion:

Dr. Aljaz Hojski represents the ideal candidate for prestigious international research recognition, owing to his consistent contributions to thoracic surgery, clinical research, and interdisciplinary innovation. Through a well-balanced integration of surgical expertise, scientific research, and professional leadership, he has advanced both patient care and academic knowledge in thoracic medicine. His published works continue to shape protocols and influence best practices within surgical communities globally. As a forward-looking clinician-scientist, Dr. Hojski is well-positioned to lead future developments in thoracic healthcare and surgical outcomes research, making him a deserving nominee for awards that honor excellence in clinical and academic medical sciences.