Zeng Gao | Applications of Computer Vision | Research Excellence Award

Published on by Computer Vision Awards

Dr. Zeng Gao | Applications of Computer Vision | Research Excellence Award

Lecturer | Henan University of Technology | China 

Dr. Zeng Gao is a researcher at Henan University of Technology specializing in machine learning, image processing, and visual tracking. His work focuses on intelligent optimization–driven visual tracking and motion analysis, with influential contributions to abrupt and long-term tracking. He has published over 12 peer-reviewed papers in leading international journals and conferences, including IEEE Access, Expert Systems with Applications, Applied Soft Computing, Digital Signal Processing, and PRCV, accumulating 98 citations. He has participated in two National Natural Science Foundation of China projects and holds three granted invention patents. Dr. Gao actively collaborates with domestic and international institutions, serves as a reviewer for journals such as ACM TOMM and Digital Signal Processing, and contributes to advancing intelligent perception technologies with real-world societal impact.

 

Citation Metrics (Scopus)

200

150

100

50

0

Citations
98

Documents
12

h-index
7

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
      View Google Scholar Profile

Featured Publications


Visual tracking with levy flight grasshopper optimization algorithm.

– Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV  (2019). Cited By : 19

Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Published on by Computer Vision Awards

Mr. Abu Hanzala | Deep Learning for Computer Vision | Research Excellence Award

Research Assistant | Daffodil International University | Bangladesh

Mr. Abu Hanzala Daffodil International University, Dhaka, BangladeshHanzala, Abu is an emerging researcher specializing in artificial intelligence–driven medical image analysis, deep learning, and explainable healthcare systems. The researcher’s scholarly work focuses on developing robust hybrid and ensemble learning frameworks that integrate convolutional neural networks (CNNs), vision transformers (ViTs), graph neural networks (GNNs), transfer learning, self-supervised learning, and attention mechanisms for disease detection and classification.A key research achievement includes the publication of a peer-reviewed article in Array (2025) titled “A Hybrid Approach for Cervical Cancer Detection: Combining D-CNN, Transfer Learning, and Ensemble Models”, which demonstrates improved diagnostic accuracy using advanced ensemble strategies. In addition, the researcher has several manuscripts under peer review in high-impact international journals including Scientific Reports Neuroscience, IEEE Transactions on Medical Imaging, ACM Transactions on Computing for Healthcare, Discover Applied Science and Computers & Education: Artificial Intelligence. These studies address a wide range of clinically significant problems such as cervical, lung, and colorectal cancer, Alzheimer’s disease pneumonia neuromuscular disorders peripheral nerve disease and cerebral cortex pathology.The researcher has authored 5 scholarly documents receiving 5 citations, and currently holds an h-index of 2, reflecting a growing academic impact within the medical AI research community. International visibility is further strengthened through a peer-reviewed IEEE conference paper and an invited oral presentation at the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT 2024).Research collaborations span multidisciplinary teams involving computer scientists biomedical engineers and healthcare researchers. The societal impact of this work lies in advancing early disease detection reliable clinical decision support and explainable AI models contributing to scalable trustworthy and globally relevant healthcare technologies.

Profiles: Scopus | ResearchGate

Featured Publication

1. Hanzala, A., Akter, T., & Rahman, M. S. (2025). A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models. Cited By : 3

Mr. Abu Hanzala research advances global healthcare innovation by integrating reliable, explainable artificial intelligence with medical imaging to enable early disease detection and data-driven clinical decision support. This work bridges scientific rigor and real-world applicability, contributing to scalable, trustworthy AI solutions with meaningful societal and clinical impact.

Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Published on by Computer Vision Awards

Mr. Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Master of Engineering | Hubei Normal University | China

Mr. Xuewen Zhou is a developing researcher in medical signal processing, medical image segmentation, and intelligent optimization algorithms, with growing contributions to the fields of biomedical engineering and computational intelligence. Affiliated with Hubei Normal University, his research focuses on designing advanced fractional-order and optimization-driven neural network models to enhance the analysis of physiological signals such as ECG and EEG as well as dermatological image segmentation. With 5 scientific publications, 4 citations, and an h-index of 1, Dr. Zhou is steadily establishing a strong academic presence.Dr. Zhou’s notable achievements include the publication of multiple SCI-indexed journal papers and active participation in leading international conferences. His recent SCI Q2 paper Adaptive Fractional Order Pulse Coupled Neural Networks with Multi-Scale Optimization for Skin Image Segmentation introduces an innovative segmentation framework integrating fractional order optimization with pulse coupled neural networks. The method employs a novel entropy–edge fitness function significantly improving accuracy in skin lesion delineation.Another key contribution is the SCI Q2 paper Improved Sparrow Search Based on Temporal Convolutional Network for ECG Classification where Dr. Zhou explores hybrid fractional order algorithms to optimize ECG recognition. His work rigorously analyzes the influence of positive and negative fractional orders on optimization stability offering valuable insights into next-generation fractional learning systems.In the EI indexed China Automation Congress Dr. Zhou proposed an ECG classification model combining spatial–channel attention networks with an improved RIME optimization algorithm enhancing hyperparameter tuning for complex biomedical patterns. He also contributed to neuromorphic computing through the ICNC  paper on FRMAdam iTransformer KAN presenting a fractional order momentum optimizer for EEG and ECG prediction.Dr. Zhou maintains strong collaborations with researchers including Jiejie Chen Ping Jiang Xinrui Zhang Zhiwei Xiao and Zhigang Zeng contributing to interdisciplinary advancements across medical AI fractional order theory and neural computation. His research demonstrates meaningful societal impact by improving early disease detection supporting intelligent diagnostic tools and advancing clinical decision making technologies on a global scale.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1.Zhou, X., Chen, J., Jiang, P., Zhang, X., & Zeng, Z. (2026). Adaptive fractional-order pulse-coupled neural networks with multi-scale optimization for skin image segmentation. Biomedical Signal Processing and Control, (February 2026).

2.Zhou, X., Chen, J., Xiao, Z., Zhang, X., Jiang, P., & Zeng, Z. (2026). Improved sparrow search based on temporal convolutional network for ECG classification. Biomedical Signal Processing and Control, (February 2026).

3.Xiao, Z., Chen, J., Zhou, X., Wei, B., Jiang, P., & Zeng, Z. (2025). Monotonic convergence of adaptive Caputo fractional gradient descent for temporal convolutional networks. Neurocomputing, (December 2025).

4.Zhang, X., Chen, J., Zhou, X., & Jiang, P. (2024, December 13). FRMAdam-iTransformer KAN: A fractional order RMS momentum Adam optimized iTransformer with KAN for EEG and ECG prediction. In 2024 International Conference on Neuromorphic Computing (ICNC).

5.Zhou, X., Chen, J., Jiang, P., & Zhang, X. (2024, November 1). Electrocardiogram classification based on spatial-channel networks and optimization algorithms. In 2024 China Automation Congress (CAC).

Dr. Xuewen Zhou’s work advances science and society by developing fractional-order neural systems that significantly enhance the accuracy of biomedical signal and image analysis. His innovations support earlier disease detection, improved diagnostic reliability, and broader global access to intelligent healthcare technologies.

Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Published on by Computer Vision Awards

Dr. Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Lecturer | Aksaray University | Turkey

Dr. Şifa Gül Demiryürek is a researcher specializing in acoustics, dynamics, vibration control, nonlinear structures, and metamaterials, with a growing body of work that bridges fundamental mechanics and applied engineering. Her research focuses on low-frequency broadband vibration damping, nonlinear passive particle dampers, and metamaterial-inspired structures aimed at improving stability, efficiency, and durability in modern mechanical systems.She has authored 11 scientific documents, accumulating 19 citations with an h-index of 3, reflecting the emerging impact of her contributions. Her early work includes the experimental study of thermal-mixing phenomena in coaxial jets published in the Journal of Thermophysics and Heat Transfer demonstrating her multidisciplinary foundation in fluid–thermal interactions. Transitioning toward structural dynamics  her doctoral research at the University of Sheffield advanced the understanding of periodically arranged nonlinear particle dampers under low-amplitude excitation providing new insights into damping mechanisms critical for lightweight and high-performance structures.Dr. Demiryürek has collaborated with notable researchers such as A. Krynkin and J. Rongong contributing to recognized venues including DAGA, ACOUSTICS Proceedings, and the Institute of Acoustics. Her studies on metamaterial-based dampers and locally resonating structures highlight innovative strategies for vibration mitigation particularly in the low-frequency regime where traditional dampers are less effective. Her works further expand this direction with investigations on dynamic behavior of thermoplastics and material resonance considerations for wind turbine towers addressing contemporary engineering challenges related to sustainability and structural reliability.In addition to research publications she has contributed educational materials including Introduction to Metamaterials  supporting broader knowledge dissemination in emerging engineering domains. Her collaborations in applied mechanics such as the numerical evaluation of electric motorcycle chassis demonstrate a commitment to integrating theoretical advances into practical real-world applications.Through her focused work at the intersection of vibration engineering and metamaterial science Şifa Gül Demiryürek is contributing to next-generation solutions for safer quieter and more efficient mechanical systems with potential societal impact across manufacturing transportation renewable energy and advanced materials engineering.

Profiles: Googlescholar | Scopus | ORCID

Featured Publications

1.Demiryürek, S. G., Kok, B., Varol, Y., Ayhan, H., & Oztop, H. F. (2018). Experimental investigation of thermal-mixing phenomena of a coaxial jet with cylindrical obstacles. Journal of Thermophysics and Heat Transfer, 32(2), 273–283. Cited By: 5

2. Demiryürek, S. G. (2022). Periodically arranged nonlinear passive particle dampers under low-amplitude excitation (Doctoral research, University of Sheffield). Cited By: 3

3. Demiryürek, S. G., & Krynkin, A. (2021). Low-frequency broadband vibration damping using the nonlinear damper with metamaterial properties. In DAGA 2021 Conference Proceedings (pp. 94–96). Cited By: 3

4.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2020). Modelling of nonlinear dampers under low-amplitude vibration. In ACOUSTICS 2020 Proceedings. Cited By: 3

5.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2019). Non-linear metamaterial structures: Array of particle dampers. Universitätsbibliothek der RWTH Aachen. Cited By: 3

Dr. Şifa Gül Demiryürek’s research advances next-generation vibration damping and metamaterial technologies, enabling safer, quieter, and more efficient mechanical systems across industries. Her contributions support innovation in sustainable engineering from wind energy structures to lightweight transportation strengthening global efforts toward resilient, high-performance designs.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Published on by Computer Vision Awards

Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Benito Farina | Spatio-Temporal CV | Best Researcher Award

Published on by Computer Vision Awards

Mr. Benito Farina | Spatio-Temporal CV | Best Researcher Award

Researcher | Universidad Politecnica de Madrid | Spain

Benito Farina is a dedicated researcher in artificial intelligence, machine learning, and biomedical engineering with a strong focus on medical imaging, cancer screening, and predictive modeling. He completed his bachelor’s and master’s degrees in Biomedical Engineering with highest honors at Università degli Studi di Napoli Federico II, where his theses explored machine learning for breast cancer histopathology and deep learning models for lung nodule malignancy detection. He pursued his doctoral studies in Electrical Engineering at Universidad Politécnica de Madrid, graduating with distinction for his research on spatio-temporal image analysis methods to enhance lung cancer screening and therapy response prediction. Professionally, he gained extensive experience as a Junior Research Scientist at Universidad Politécnica de Madrid, where he developed AI-based medical imaging datasets, implemented advanced models including CNNs, RNNs, and transformers, and explored generative models and explainable AI for clinical applications. He later joined the Centro de Investigación Biomédica en Red as a Research Scientist, leading projects in medical image segmentation, classification, and interpretability, managing GPU-based deployments, and contributing to international collaborations and grant proposals. His international exposure includes visiting scientist positions at Harvard University’s Brigham and Women’s Hospital, where he worked on image harmonization techniques to improve consistency in multi-center datasets. His research interests lie in artificial intelligence for healthcare, medical image processing, radiomics, generative models, self-supervised learning, and explainable AI with a vision of translating computational tools into clinical practice. Throughout his career, he has guided undergraduate and master’s students, actively contributed to competitive AI challenges, and engaged in cultural leadership as Vice-President of a community association promoting cultural heritage and development. He has presented his research at reputed conferences, published in indexed journals, and continues to expand his academic contributions through collaborative projects. His research skills include proficiency in Python, R, MATLAB, TensorFlow, PyTorch, and Keras, expertise in GPU cluster computing, dataset development, model deployment with Docker, and technical documentation with LaTeX. Fluent in Italian, Spanish, and English, he thrives in multicultural academic environments and has demonstrated both technical excellence and leadership capabilities. Benito has earned academic distinctions for his outstanding performance in higher education and doctoral research, reflecting his commitment to excellence. With strong foundations in artificial intelligence and biomedical engineering, he aspires to drive advancements in precision medicine, foster global collaborations, and translate AI innovations into impactful healthcare solutions.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Farina, B., Guerra, A. D. R., Bermejo-Peláez, D., Miras, C. P., Peral, A. A., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., Muñoz-Barrutia, A., & others. (2021). Delta-radiomics signature for prediction of survival in advanced NSCLC patients treated with immunotherapy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 886–890). IEEE.

Farina, B., Benito, R. C., Montalvo-García, D., Bermejo-Peláez, D., Maceiras, L. S., & others. (2025). Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification. Computers in Biology and Medicine, 196, 110813.

Ramos-Guerra, A. D., Farina, B., Rubio Pérez, J., Vilalta-Lacarra, A., & others. (2025). Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data. Cancer Immunology, Immunotherapy, 74(4), 120.

Seijo, L., Bermejo-Peláez, D., Gil-Bazo, I., Farina, B., Domine, M., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Bolaños, M. C., Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., & others. (2020). Design and implementation of predictive models based on radiomics to assess response to immunotherapy in non-small-cell lung cancer. In XXXVIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica.

Osman Yildirim | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey 

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. Çanakkale Onsekiz Mart Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 4(1), 804959.

Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Associate Prof. in ERI at Electronics Research Institute, Egypt

Dr. Mohamed Ahmed Hebaishy is a distinguished researcher with extensive expertise in biometrics, iris recognition, image processing, computer vision, and satellite imaging. He has made remarkable contributions through his work in human identification systems, advanced image representation, and security technologies. His career spans academia, research institutions, and international collaborations, combining theoretical innovation with real-world applications in areas such as space research and remote sensing. He has published in reputed journals and conferences, including IEEE and Springer platforms, and actively engages in research that bridges science and technology. Beyond his research output, he has held significant leadership roles, mentored graduate students, and reviewed research projects for universities and conferences. His diverse professional experiences, strong academic foundation, and continuous pursuit of impactful research highlight his commitment to advancing scientific knowledge and addressing global challenges, making him a valuable contributor to the academic and research community.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Mohamed Ahmed Hebaishy completed his Bachelor of Science in Electronic Engineering with a focus on automatic control and measurements at Menoufia University, where he built a strong foundation in control systems and electronics. He later pursued a Master of Science degree in Electronics and Communication at Cairo University, with his thesis centered on developing a fuzzy controller for flexible joint manipulators, reflecting his early focus on control and automation. His academic journey culminated in earning a Doctor of Philosophy in Information Technology from Vladimir State University in the Russian Federation, specializing in control system analysis and data processing. His doctoral thesis focused on using iris image processing in human identification systems, marking the beginning of his long-term contributions to the field of biometrics. Through these academic achievements, he has combined expertise in engineering, computing, and data-driven technologies, equipping him with the knowledge and skills to contribute meaningfully to interdisciplinary research.

Professional Experience

Dr. Mohamed Ahmed Hebaishy has built a rich professional career across academia and research institutions, holding positions that span lecturer, assistant professor, and department head roles. He has served as a researcher at the Electronics Research Institute, contributing to significant projects in informatics and computer science. His work extended to leadership in national space programs, where he played a key role in satellite image processing and payload command systems for EgyptSat missions. He also gained international academic experience as an assistant professor at Shaqra University in Saudi Arabia, where he later became head of the computer science department. His contributions include guiding research projects, supervising theses, and leading academic initiatives. Additionally, he has been a reviewer for major universities and scientific conferences, reflecting his involvement in shaping the academic community. His experience demonstrates a balance of teaching, research, and leadership, making him a well-rounded academic and professional.

Research Interest

Dr. Mohamed Ahmed Hebaishy’s research interests lie at the intersection of biometrics, image processing, computer vision, and artificial intelligence, with a strong emphasis on human identification systems and security technologies. He has worked extensively on iris recognition, exploring innovative approaches to enhance accuracy and efficiency in biometric applications. His interests also extend to satellite imaging and remote sensing, where he has contributed to projects in national space programs, including the development of image processing systems for EgyptSat satellites. In recent years, his focus has broadened to include advanced methods in pattern recognition, machine learning, and computer-aided automation systems. He is also engaged in applied research addressing real-world challenges such as waste sorting, wireless communication, and medical applications of imaging. His diverse interests reflect a commitment to advancing cutting-edge technologies that improve security, automation, and sustainability, while also fostering new interdisciplinary pathways in computer science and engineering.

Award and Honor

Throughout his career, Dr. Mohamed Ahmed Hebaishy has received recognition for his contributions to research, teaching, and leadership within the fields of biometrics, image processing, and space technology. His involvement in the EgyptSat satellite programs and ITIDA-funded security projects demonstrated his ability to translate research into impactful applications, earning him acknowledgment within the scientific community. He has also been invited as a reviewer for universities, research conferences, and scientific committees, reflecting trust in his expertise and judgment. His leadership as head of the computer science department at Shaqra University further highlights his role in shaping academic excellence and guiding student development. While his curriculum vitae does not list specific awards, his record of sustained contributions, successful project leadership, and active engagement in international research platforms stands as a form of recognition in itself. His ongoing publications in reputed journals further strengthen his professional standing as a dedicated and accomplished researcher.

Research Skill

Dr. Mohamed Ahmed Hebaishy possesses a broad set of research skills that reflect his deep expertise in both theoretical and applied aspects of computer science and engineering. He is skilled in biometric system design, with specialization in iris recognition, image processing algorithms, and human identification technologies. His technical capabilities extend to satellite image analysis, data processing, and control systems, where he has led projects involving payload command systems for national space programs. He is proficient in developing and applying advanced algorithms, including fuzzy logic, wavelet transforms, and optimization techniques, to solve complex research problems. His experience also covers interdisciplinary areas such as wireless communication systems, security applications, and automated testing tools. Beyond technical expertise, he has strong skills in project leadership, academic supervision, and research collaboration, enabling him to contribute effectively to both academic and applied research communities. His skill set demonstrates adaptability, innovation, and problem-solving ability.

Publications Top Notes

Title: A comparative study of QTP and load runner automated testing tools and their contributions to software project scenario
Authors: M Imran, M Hebaishy, AS Alotaibi
Year: 2016
Citation: 12

Title: Road extraction from high resolution satellite images by morphological direction filtering and length filtering
Authors: TM Talal, MI Dessouky, A El-Sayed, M Hebaishy, FA El-Samie
Year: 2008
Citation: 12

Title: Increasing the Efficiency of Iris Recognition Systems by Using Multi-Channel Frequencies of Gabor Filter
Authors: AS Alotaibi, MA Hebaishy
Year: 2014
Citation: 7

Title: Extraction of roads from high-resolution satellite images with the discrete wavelet transform
Authors: TM Talal, A El-Sayed, M Hebaishy, MI Dessouky, SA Alshebeili
Year: 2013
Citation: 4

Title: Optimized Daugman’s algorithm for iris localization
Authors: MA Hebaishy
Year: 2008
Citation: 4

Title: Sibs: A sparse encoder utilizing self-inspired bases for efficient image representation
Authors: AN Omara, MA Hebaishy, MS Abdallah, YI Cho
Year: 2024
Citation: 3

Title: Poster: Optimized Daugman’s algorithm for iris localization
Authors: M Hebaishy
Year: 2008
Citation: 3

Title: Fast Fingerprint Identification based on the DoG Filter
Authors: MA Hebaishy, FA Syam
Year: 2025

Title: S-shaped patch antenna array for automotive applications in X-band for wireless communications
Authors: MA Hebaishy
Year: 2024

Title: Building an automatic waste sorting system with controller based wireless sensor smart segregation system
Authors: MA Hebaishy
Year: 2024

Title: Security system based on human iris
Authors: HS Ahmed, MA Hebaishy
Year: 2014

Title: Attitude determination for geostationary satellite using optimized real time image registration algorithm
Authors: AE OA Elsayed, A Farrag, M Hebaishy
Year: 2009

Title: Texture analysis of the human iris for high authentication
Authors: MA Hebaishy, BV Gerkov
Year: 2002

Title: Using phase demodulator for encoding iris
Authors: AS Alotaibi, MA Hebaishy

Conclusion

Dr. Mohamed Ahmed Hebaishy is highly deserving of the Best Researcher Award for his significant contributions to biometrics, image processing, and satellite imaging, which have advanced both scientific understanding and practical applications in security and space research. His extensive academic career, impactful publications, leadership roles, and dedication to mentoring students highlight his commitment to advancing knowledge and fostering innovation. With his proven expertise and strong foundation in applied research, he is well positioned to continue driving advancements in computer vision, human identification systems, and international collaborations, further strengthening his role as a leader in research and society.

Jong-Hyun Kim | Applied Visual Computing | Best Researcher Award

Published on by Computer Vision Awards

Prof. Jong-Hyun Kim | Applied Visual Computing | Best Researcher Award

Associate Professor at Inha University, South Korea

Prof. Jong-Hyun Kim is an Associate Professor at the College of Software and Convergence, Department of Artificial Intelligence, Design Technology at Inha University, with a joint appointment at the Graduate School of Electrical and Computer Engineering. He is a distinguished researcher with expertise spanning computer graphics, visual effects, physically based simulation, physics engines, artificial intelligence, VR/AR, geometry processing, and GPU optimization. His career bridges academia and industry, having led and participated in numerous national research projects and industry collaborations in areas such as digital twin technology, immersive simulation systems, and AI convergence. With an impressive record of award-winning publications in reputed conferences and journals indexed in IEEE and Scopus, he has contributed significantly to advancing emerging technologies. His leadership in collaborative initiatives and dedication to innovative research continue to strengthen his impact on both scientific communities and practical applications.

Professional Profile 

ORCID Profile

Education

Prof. Jong-Hyun Kim completed his Ph.D. in Computer Science and Engineering from Korea University, following his master’s degree and bachelor’s degree in the same field from Korea University and Sejong University, respectively. His academic journey reflects a strong foundation in both theoretical and applied aspects of computer science, equipping him with advanced skills in simulation, visualization, and artificial intelligence. His studies covered a broad spectrum of technical disciplines, from physics-based modeling and geometry processing to interactive graphics and human-computer interaction. The rigorous academic training at prestigious institutions provided him with the expertise to excel in interdisciplinary research and to address complex computational challenges. This solid educational background has enabled him to integrate advanced computing techniques with creative technological solutions, laying the groundwork for his influential research contributions in academia and his ability to collaborate effectively with industry partners on innovative projects.

Professional Experience

Prof. Jong-Hyun Kim currently serves as an Associate Professor at Inha University, having previously held the same position at Kangnam University. He has also served as a lecturer and teaching fellow at Korea University, contributing to the development of academic programs and mentoring students in advanced computing topics. Before his academic career, he worked extensively in the industry as a senior research engineer and research engineer at multiple companies, gaining hands-on experience in simulation technologies, visual effects, and interactive systems. His professional trajectory reflects a balance between academic scholarship and practical application, with roles that involved designing innovative solutions, leading research teams, and collaborating on both government-funded and industry-driven projects. His combined academic and industrial experience has strengthened his expertise in bridging theoretical research with real-world implementation, enhancing his ability to deliver impactful outcomes in both educational and technological domains.

Research Interest

Prof. Jong-Hyun Kim’s research interests cover a broad and interdisciplinary range of topics, including computer graphics, visual effects, physically based simulation, physics engines, and game physics. He actively explores artificial intelligence techniques for scientific visualization, geometry processing, image processing, and immersive VR/AR experiences. His work often focuses on GPU optimization to achieve real-time performance in complex simulations, enabling practical applications in gaming, virtual reality, and industrial simulations. Additionally, he is interested in human-computer interaction, particularly in developing intuitive interfaces for creative expression and realistic virtual environments. His projects integrate physics-based modeling with AI-driven approaches to address challenges in simulation accuracy, interactivity, and scalability. By combining deep technical expertise with creativity, his research aims to advance the capabilities of simulation and visualization technologies, making them more efficient, accessible, and adaptable for diverse fields ranging from entertainment and education to engineering and healthcare.

Award and Honor

Prof. Jong-Hyun Kim has received numerous awards and honors recognizing his excellence in research, innovation, and academic contributions. His accolades include multiple Best Paper Awards from prestigious conferences such as those organized by the Korea Society of Computer and Information and the Korean Association of Data Science, acknowledging his groundbreaking work in simulations, VR frameworks, AI-driven modeling, and GPU optimization. He has been honored by the Ministry of Science and ICT and the Korean Ministry of Education for his creative and impactful research ideas. His achievements extend beyond academia, with awards recognizing his leadership in industry-academic cooperation and excellence in teaching. These recognitions reflect his sustained contributions to advancing cutting-edge technologies, fostering collaboration between academia and industry, and mentoring future innovators. His consistent recognition at national and professional levels underscores his influence in both research and education, and his ongoing commitment to delivering impactful technological advancements.

Research Skill

Prof. Jong-Hyun Kim possesses advanced research skills in multiple technical domains, including physically based simulation, visual effects, GPU optimization, and complex animation systems. He is proficient in designing real-time interactive environments, implementing physics engines, and integrating artificial intelligence into simulation and visualization frameworks. His expertise includes scientific visualization, geometry processing, VR/AR development, and image processing, enabling him to create innovative solutions that merge creativity with computational precision. He has extensive experience managing large-scale research projects funded by national agencies and industry partners, demonstrating strong project management, team leadership, and cross-disciplinary collaboration skills. His technical abilities are complemented by his capacity to translate theoretical models into practical applications across entertainment, engineering, and scientific research. By combining analytical thinking, problem-solving, and creative design, he continues to push the boundaries of simulation and visualization technologies, contributing significantly to both academic advancements and industry innovation.

Publications Top Notes

Title: A Geometric Approach to Efficient Modeling and Rendering of Opaque Ice With Directional Air Bubbles
Authors: Jong-Hyun Kim
Year: 2025

Title: Advanced GPU Techniques for Dynamic Remeshing and Self-Collision Handling in Real-Time Cloth Tearing
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: Improved Air Mesh Refinement for Accurate Strand-Solid and Self-Collision Handling
Authors: Jong-Hyun Kim
Year: 2025

Title: Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams
Authors: Jong-Hyun Kim
Year: 2025

Title: Porous Models for Enhanced Representation of Saturated Curly Hairs: Simulation and Learning
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: A 3D Visual Tool for Analyzing Changes in Hair Volume and Length Caused by Medications
Authors: Jong‐Hyun Kim; Jung Lee; Seungbin Kwon; Minji Jo; Yunjin Hwang; In‐Sook An
Year: 2025

Title: Numerical Dispersed Flow Simulation of Fire-Flake Particle Dynamics and Its Learning Representation
Authors: Jong-Hyun Kim; Jung Lee
Year: 2025

Title: Unified GPU Framework for Simulating Wave Turbulence, Diffusion, and Wrinkling in Fluid-Cloth Interaction
Authors: Eun Su Park; Juyong Lee; In Kyu Park; Jong-Hyun Kim
Year: 2025

Title: Scalable and Rapid Nearest Neighbor Particle Search Using Adaptive Disk Sector
Authors: Jong-Hyun Kim; Shaofeng Xu; Jung Lee
Year: 2025

Title: Depth-of-Field Region Detection and Recognition From a Single Image Using Adaptively Sampled Learning Representation
Authors: Jong-Hyun Kim; Youngbin Kim
Year: 2024

Title: Motion Generation and Analyzing the User’s Arm Muscles via Leap Motion and Its Data-Driven Representations
Authors: Jong-Hyun Kim; Jung Lee; Youngbin Kim
Year: 2024

Title: Visualization of Vector Fields from Density Data Using Moving Least Squares Based on Monte Carlo Method
Authors: Jong-Hyun Kim
Year: 2024

Title: Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
Authors: Jun Yeong Kim; Chang Geun Song; Jung Lee; Jong-Hyun Kim; Jong Wan Lee; Sun-Jeong Kim
Year: 2024

Title: Efficient and Stable Generation of High-Resolution Hair and Fur With ConvNet Using Adaptive Strand Geometry Images
Authors: Jong-Hyun Kim; Jung Lee
Year: 2023

Conclusion

Prof. Jong-Hyun Kim is highly deserving of the Best Researcher Award for his outstanding contributions to cutting-edge research in computer graphics, AI-driven simulation, and immersive technologies, as well as his significant role in bridging academia and industry through impactful collaborative projects. His innovative work has advanced both scientific understanding and practical applications, benefiting diverse sectors and inspiring the next generation of researchers. With a proven track record of excellence, leadership, and innovation, he holds strong potential to make even greater contributions to research and society in the future.

Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

Published on by Computer Vision Awards

Dr . Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

Professor at National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, China

Associate Professor He Rui is a prominent academic and researcher at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. With a specialized focus on advanced braking systems, autonomous driving technologies, and driver behavior analysis, he stands at the forefront of intelligent vehicle systems research. His career is marked by a strong integration of theoretical innovation and practical application, demonstrated through leadership in national and industrial research projects and the development of multiple patented technologies. Dr. He has published over 30 scholarly articles and holds more than 20 invention patents, showcasing a high level of scientific productivity and innovation. His guidance as a doctoral supervisor also reflects his deep commitment to nurturing future researchers in the field. Acknowledged for his contributions to visual perception, trajectory planning, and chassis-by-wire control, Dr. He Rui continues to drive transformative advancements in the evolving landscape of automotive engineering and intelligent mobility.

Professional Profile 

Education🎓

He Rui holds a robust academic background rooted in mechanical and automotive engineering, having pursued his higher education at esteemed institutions in China. He completed his undergraduate studies in vehicle engineering, laying a strong foundation in dynamics, control, and systems integration. He later obtained his Master’s and Doctoral degrees in automotive engineering, with a research focus on intelligent vehicle systems, including sensor-based perception and integrated chassis control. His doctoral work, in particular, explored advanced concepts in vehicle dynamics and control algorithms tailored to autonomous systems. Throughout his academic journey, Dr. He acquired a deep understanding of interdisciplinary technologies involving mechanical systems, computer vision, and artificial intelligence. His education reflects a well-rounded and comprehensive training that blends traditional automotive knowledge with emerging technologies, effectively preparing him to lead innovative research in smart mobility. His continuous pursuit of knowledge and research excellence positions him as a key figure in the automotive academic community.

Professional Experience📝

Dr. He Rui currently serves as an Associate Professor and doctoral supervisor at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. He has actively led and contributed to various research projects funded by the National Natural Science Foundation of China and major automotive companies such as Dongfeng Motor and SAIC Motor. His portfolio includes pivotal roles in projects related to chassis control, autonomous intelligent driving systems, and integrated modeling methods for electric vehicles. These engagements have enabled him to bridge academic research with industrial implementation. His career demonstrates a commitment to pushing the boundaries of automotive control technologies, especially in intelligent perception and driver-vehicle interaction. In addition to research, Dr. He plays a significant role in mentoring postgraduate students, contributing to curriculum development, and fostering interdisciplinary collaborations. His professional path reflects a balance of theoretical advancement and practical application in the field of intelligent automotive systems.

Research Interest🔎

Dr. He Rui’s research interests lie at the intersection of automotive engineering and intelligent systems. He is primarily focused on the development of advanced chassis-by-wire systems, visual perception for autonomous driving, and analysis of driver behavior for improved human-vehicle interaction. His work explores how artificial intelligence, computer vision, and dynamic control strategies can be integrated into vehicle systems to enhance safety, efficiency, and driving experience. He is particularly interested in intelligent trajectory planning and how vehicles can autonomously adapt to real-world driving conditions using data-driven models. Another major research thrust involves understanding and modeling driver behavior under extreme conditions, such as tire blowouts or sudden braking, to improve control algorithms. These diverse interests underscore his commitment to solving critical challenges in the transition toward intelligent and autonomous mobility. Dr. He’s multidisciplinary approach has led to impactful research that supports both theoretical exploration and real-world implementation.

Award and Honor🏆

While specific awards and honors have not been listed in the profile, Dr. He Rui’s achievements speak to a high level of professional recognition. His leadership in multiple nationally funded research projects and industry collaborations with top automotive manufacturers such as Dongfeng and SAIC reflect his esteemed status in the field. He has authored more than 30 peer-reviewed papers and holds over 20 invention patents, demonstrating consistent innovation and contribution to automotive technology. His position as a doctoral supervisor and associate professor at a prestigious institution like Jilin University further reinforces his academic credibility. It’s highly likely that he has received institutional accolades, commendations from industry partners, and recognition for his research outputs. These accomplishments collectively underscore a career marked by excellence, leadership, and a strong impact on the advancement of intelligent vehicle systems. Further formal honors would only enhance an already distinguished academic and research profile.

Research Skill🔬

Dr. He Rui possesses an impressive set of research skills that span across automotive engineering, intelligent control systems, and artificial intelligence. His expertise in chassis-by-wire technologies allows him to design and develop next-generation braking and steering systems with high reliability and precision. He has strong capabilities in computer vision and sensor fusion, which are essential for enabling autonomous vehicle perception. Dr. He is also proficient in developing and applying advanced control algorithms for vehicle trajectory planning, especially under uncertain or complex driving conditions. He excels in integrating experimental testing with simulation environments, supporting both theoretical research and applied development. His skills include modeling driver behavior using machine learning techniques and incorporating it into vehicle control strategies. Furthermore, he has proven experience in leading large-scale research projects, writing scientific publications, and filing patents. These comprehensive research abilities make him a valuable contributor to the evolution of intelligent transportation technologies.

Conclusion💡

He Rui is a highly suitable candidate for the Best Researcher Award, particularly in fields such as automotive innovation, autonomous systems, and intelligent control technologies. His project leadership, prolific output, and patent record strongly support his candidacy. With further emphasis on international exposure and societal narratives, his profile would be even more competitive at global award levels.

Publications Top Noted✍

  • Title: Research on vehicle trajectory prediction methods in dense and heterogeneous urban traffic
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi
    Year: 2025
    Citation: Transportation Letters, DOI: 10.1080/19427867.2024.2403818

  • Title: Research on Vehicle Trajectory Prediction Methods in Urban Main Road Scenarios
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi, Ning Sun
    Year: 2024
    Citation: IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/tits.2024.3419037

  • Title: A skip feature enhanced multi-source fusion framework for switch state detection
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yongshuai Zhi
    Year: 2024
    Citation: International Journal of Rail Transportation, DOI: 10.1080/23248378.2024.2372729

  • Title: Decision-making of active collision avoidance system based on comprehensive evaluation method of dangerous scenarios
    Authors: Rui He, Zhiwei Meng, Sumin Zhang, Zhi Yang, Yongshuai Zhi, Jiaxiang Qin
    Year: 2024
    Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070221137398

  • Title: IDPNet: a light-weight network and its variants for human pose estimation
    Authors: Huan Liu, Jian Wu, Rui He
    Year: 2024
    Citation: The Journal of Supercomputing, DOI: 10.1007/s11227-023-05691-5

  • Title: Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition
    Authors: Huan Liu, Jian Wu, Haokai Ma, Yuqi Yan, Rui He
    Year: 2024
    Citation: Multimedia Tools and Applications, DOI: 10.1007/s11042-023-15778-z

  • Title: LEES-Net: Fast, lightweight unsupervised curve estimation network for low-light image enhancement and exposure suppression
    Authors: Xuanhe Li, Rui He, Jian Wu, Hu Yan, Xianfeng Chen
    Year: 2023
    Citation: Displays, DOI: 10.1016/j.displa.2023.102550

  • Title: GIVA: Interaction-aware trajectory prediction based on GRU-Improved VGG-Attention Mechanism model for autonomous vehicles
    Authors: Zhiwei Meng, Rui He, Jiaming Wu, Sumin Zhang, Ri Bai, Yongshuai Zhi
    Year: 2023
    Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070231207669

  • Title: Center point to pose: Multiple views 3D human pose estimation for multi-person
    Authors: Huan Liu, Jian Wu, Rui He
    Year: 2022
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0274450

  • Title: Monocular Vision SLAM Research for Parking Environment with Low Light
    Authors: Sumin Zhang, Yongshuai Zhi, Shouyi Lu, Ze Lin, Rui He
    Year: 2022
    Citation: International Journal of Automotive Technology, DOI: 10.1007/s12239-022-0063-5

  • Title: Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection
    Authors: Guojun Wang, Jian Wu, Rui He, Bin Tian
    Year: 2021
    Citation: IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/jas.2020.1003414