Dongheon Lee | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dongheon Lee | Computer Science and Artificial Intelligence | Best Researcher Award

Seoul National University College of Medicine | South Korea

Prof. Dongheon Lee is a researcher in medical image analysis and artificial intelligence, with a strong focus on deep learning–based clinical decision support systems. His work spans medical imaging, computer vision, bio signal analysis, and intelligent healthcare systems, addressing real-world diagnostic and interventional challenges. According to Scopus, he has authored 29 peer-reviewed publications, received 489 citations, and holds an h-index of 11, reflecting consistent scholarly impact. His research outputs appear in high-impact journals such as Radiology, Gastroenterology, Radiology: Artificial Intelligence, IEEE Journal of Biomedical and Health Informatics, and Computers in Biology and Medicine. His work demonstrates translational relevance, integrating methodological innovation with clinical applicability across radiology, endoscopy, and surgical intelligence.

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Featured Publications


Evaluation of surgical skills during robotic surgery by deep learning-based multiple surgical instrument tracking

– D. Lee, HW Yu, H Kwon, et al. · Journal of Clinical Medicine, 2020 · Cited by 117


CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules

– H Kim, D. Lee, WS Cho, et al. · European Radiology, 2020 · Cited by 60


Deep learning to optimize candidate selection for lung cancer CT screening

– JH Lee, D. Lee, MT Lu, et al. · Radiology, 2022 · Cited by 36

Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

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Mrs. Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mrs. Gulnaz Zakariya is a researcher specializing in Machine Learning, Data Science, and Geospatial Analytics, with strong interdisciplinary contributions spanning FinTech and remote sensing. Her research focuses on the development of intelligent data-driven models for credit risk assessment, antifraud systems, and decision-support frameworks using machine learning and deep learning techniques. She has also made notable contributions to object-based image analysis and spatial change detection, particularly in the study of informal settlements using satellite imagery. Her work bridges applied artificial intelligence, geoinformatics, and financial technology, emphasizing model robustness, interpretability, and real-world deployment. She has published in peer-reviewed international journals and conferences indexed in major scholarly databases, contributing to innovation at the intersection of data science, spatial analytics, and applied AI-driven solutions.

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Featured Publications


Innovative Credit Scoring and Sales Accounting Solutions for SMEs in Kazakhstan

– Gulnaz Zakariya, Olzhas Akylbekov, Aiman Moldagulova,· FinTech, 2025


Object-based change detection of informal settlements

– P. Hofmann, G. Bekkarnayeva · JURSE 2017 (IEEE)

Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

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Dr. Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

North China University of Science and Technology | China

Dr. Zhiqiang Wang is a dynamic researcher specializing in intelligent control, industrial process optimization, and data-driven modeling, with a particular emphasis on complex mineral processing systems such as copper flotation. His work integrates advanced control theory, artificial intelligence, subspace identification, transfer learning, and machine vision to enhance stability, efficiency, and real-time decision-making within large-scale industrial environments. He has contributed notable innovations in intelligent optimization strategies, expert-knowledge-assisted control frameworks, flotation-foam image analysis for process evaluation, and adaptive control algorithms designed for systems with nonlinear, highly variable operating conditions. His research extends across intelligent manufacturing, big-data-driven industrial analytics, and cross-disciplinary automation technologies, advancing next-generation solutions for process monitoring, fault diagnosis, and operational optimization. He has been actively involved in major national research initiatives focusing on full-process industrial evaluation, intelligent control system development, and 5G-enabled optimization technologies for metallurgy and beneficiation. According to Scopus, Wang has authored 35 indexed publications, accumulated 403 citations from 348 citing documents, and achieved an h-index of 9, reflecting his growing academic influence in industrial automation and intelligent systems engineering. His publications in leading journals including ISA Transactions, IEEE Transactions on Instrumentation and Measurement, Canadian Journal of Chemical Engineering, and the International Journal of Control, Automation and Systems demonstrate his contributions to advancing data-driven modeling, feature engineering, and real-time operational control. Through sustained collaboration, innovative methodologies, and impactful research, Wang continues to advance the field of intelligent industrial process control and smart manufacturing technologies.

Profile: Scopus

Featured Publications

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2025). Subspace identification method-based setpoints tracking control and its applications to the column cleaning process. ISA Transactions.

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2023). Timeliness and stability-based operation optimization for copper flotation industrial process. IEEE Transactions on Instrumentation and Measurement.

  • Wang, Z., Peng, F., Li, Q., & He, D. (2023). State evaluation of copper flotation process based on transfer learning and a layered and blocked framework. Canadian Journal of Chemical Engineering.

  • Wang, Z., Zhang, X., & He, D. (2023). Dynamic global feature extraction and importance-correlation selection for the prediction of concentrate copper grade and recovery rate. Canadian Journal of Chemical Engineering.

  • Wang, Z., He, D., Zhang, Q., & Shi, J. (2020). Observer-based finite-time model reference adaptive state tracking control with actuator saturation. International Journal of Control, Automation and Systems.

Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Ozgur Tonkal | Computer Science and Artificial Intelligence | Best Researcher Award

Samsun University | Turkey

Dr. Ozgur Tonkal is a distinguished academician and researcher at Samsun University, specializing in cybersecurity, Software-Defined Networks (SDN), and AI-driven threat detection. He earned his Ph.D. in Computer Engineering from Gazi University in 2022, where his doctoral research introduced an autonomous intrusion detection and mitigation model for SDN, providing adaptive and traffic-aware defense against volumetric attacks. Building on this foundation, he developed a multimodal spam email detection framework that integrates Distil BERT embeddings with structural features, achieving 99.62% accuracy and exposing concept drift vulnerabilities across eras of spam. Dr. Ozgur Tonkal has contributed significantly to both academic research and practical applications through international journal articles, conference papers, and book chapters that advance explainable and continually learning cybersecurity systems. His completed research includes DDoS detection using machine learning and neighborhood component analysis, while ongoing projects focus on adaptive spam detection, IoT traffic analysis, and explainable deep learning models for robust network security. Beyond academia, he serves as ISO/IEC 27001:2022 Lead Auditor, coordinator of a university Cyber Incident Response Team, and technical advisor for the Ministry of Education International Robotics Competition, demonstrating his ability to translate research into real-world solutions. Notable publications include studies on multimodal spam detection, AI-based dementia diagnosis, and DDoS attack detection in SDN. Dr. Ozgur Tonkal exemplifies excellence in research, innovation, and the practical implementation of cybersecurity solutions.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn | Staff Page

Featured Publications

  • Tonkal, Ö., Polat, H., Başaran, E., Cömert, Z., & Kocaoglu, R. (2021). Machine learning approach equipped with neighbourhood component analysis for DDoS attack detection in software-defined networking. Electronics, 10(1227), 1–18. Cited by 128.

  • Tonkal, Ö., & Polat, H. (2021). Traffic classification and comparative analysis with machine learning algorithms in software-defined networks. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 9(1), 1–12. Cited by 15.

  • Sertkaya, M. E., Ergen, B., Türkoğlu, M., & Tonkal, Ö. (2024). Accurate diagnosis of dementia and Alzheimer’s with deep network approach based on multi‐channel feature extraction and selection. International Journal of Imaging Systems and Technology, 34(3), e23079. Cited by 4.

  • Ouhsousou, S., & Tonkal, Ö. (2024). Analysis of global language dynamics: A cross-cultural examination of the most spoken languages and perceived learning ease. 8th International Artificial Intelligence and Data Processing Symposium, 1–6. Cited by 1.

  • Selimdaroğlu, Y., Yusuf, & Tonkal, Ö. (2025). Acil durum çağrı merkezi uygulamalarında kullanıcı memnuniyeti ve performans analizi: 112 örneği. International Journal of Advances in Engineering and Pure Sciences, 37(2), 45–60.

Xiaogang Song | Computer Science | Best Researcher Award

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Prof. Xiaogang Song | Computer Science | Best Researcher Award

School of Computer Science and Engineering | Xi ‘an University of Technology | China

Dr. Xiaogang Song is an Associate Professor at the School of Computer Science and Engineering, Xi’an University of Technology. He earned his Ph.D. from Northwestern Polytechnical University and is a member of IEEE. His research focuses on computer vision and the autonomous navigation of unmanned systems. Throughout his career, Dr. Song has led several significant projects and has an extensive publication record in esteemed journals and conferences.

Profile

Scopus

Orcid

Strengths for the Awards

  • Strong Academic Background – Dr. Xiaogang Song holds a Ph.D. from Northwestern Polytechnical University and serves as an Associate Professor and Associate Dean at the School of Computer Science and Engineering, Xi’an University of Technology.
  • Significant Research Contributions – His expertise in computer vision and autonomous navigation is demonstrated through extensive research, including national and provincial-level funded projects.
  • Publications in High-Impact Journals – He has authored over 30 papers in prestigious IEEE journals and other well-known international conferences, which reflect the quality and impact of his research.
  • Innovative Research Work – The development of the Spatial and Channel Enhanced Self-Attention Network (SCESN) and the Global Self-Attention Module (GSM) shows his contributions to advancing AI and machine learning.

Education 🎓

Dr. Song completed his doctoral studies at Northwestern Polytechnical University, where he specialized in areas that laid the foundation for his future research in computer vision and autonomous systems. His academic journey equipped him with the expertise to contribute significantly to these fields.

Experience 🏫

Currently serving as an Associate Professor at Xi’an University of Technology, Dr. Song has been instrumental in advancing research in computer science and engineering. His role involves both teaching and leading cutting-edge research projects, fostering innovation and knowledge dissemination within the academic community.

Research Interests On Computer Science🔍

Dr. Song’s research interests encompass:

  • Machine Learning
  • Multimodal Learning
  • Computer Vision

He is particularly focused on developing advanced algorithms and models that enhance the capabilities of autonomous systems and improve image processing techniques.

Awards 🏆

Dr. Song has been recognized for his contributions to the field, including:

  • Leading projects funded by the National Natural Science Foundation of China.
  • Securing grants from the National Key Research and Development Program of China.
  • Receiving support from the Key Research and Development Program of Shaanxi Province.

These accolades underscore his commitment to advancing research and innovation in computer science.

Publications 📚

  1. “Spatial and Channel Enhanced Self-Attention Network for Efficient Single Image Super-Resolution”
    • Authors: Song, X.; Tan, Y.; Pang, X.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  2. “Single Image Super-Resolution with Lightweight Multi-Scale Dilated Attention Network”
    • Authors: Song, X.; Pang, X.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  3. “Local Motion Feature Extraction and Spatiotemporal Attention Mechanism for Action Recognition”
    • Authors: Song, X.; Zhang, D.; Liang, L.; He, M.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  4. “Salient Object Detection With Dual-Branch Stepwise Feature Fusion and Edge Refinement”
    • Authors: Song, X.; Guo, F.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  5. “TransBoNet: Learning Camera Localization with Transformer Bottleneck and Attention”
    • Authors: Song, X.; Li, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 5
  6. “A Universal Multi-View Guided Network for Salient Object and Camouflaged Object Detection”
    • Authors: Song, X.; Zhang, P.; Lu, X.; Hei, X.; Liu, R.
    • Publication Year: 2024
    • Citations: 0
  7. “Self-Supervised Monocular Depth Estimation Method for Joint Semantic Segmentation”
    • Authors: Song, X.; Hu, H.; Ning, J.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  8. “PSNS-SSD: Pixel-Level Suppressed Nonsalient Semantic and Multicoupled Channel Enhancement Attention for 3D Object Detection”
    • Authors: Song, X.; Zhou, Z.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 1
  9. “Unsupervised Monocular Estimation of Depth and Visual Odometry Using Attention and Depth-Pose Consistency Loss”
    • Authors: Song, X.; Hu, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  10. “Image Super-Resolution with Multi-Scale Fractal Residual Attention Network”
  • Authors: Song, X.; Liu, W.; Liang, L.; Lu, X.; Hei, X.
  • Publication Year: 2023
  • Citations: 5

Conclusion 📝

Dr. Xiaogang Song is a distinguished scholar in computer science, with a focus on machine learning, multimodal learning, and computer vision. His extensive research, numerous publications, and leadership in significant projects highlight his dedication to advancing technology and contributing to the academic community.