Hossein Ghaffarian | Machine Learning | Editorial Board Member

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Dr. Hossein Ghaffarian | Machine Learning | Editorial Board Member 

Assistant Professor | Arak University | Iran

Dr. Hossein Ghaffarian is a distinguished researcher and faculty member in the Department of Computer Engineering at Arak University, Iran, recognized for his expertise in computer networks, intelligent transportation systems (ITS), data mining, and applied artificial intelligence. His academic contributions encompass both theoretical and applied dimensions of wired and wireless network architectures, network security, and quality of service optimization. Dr. Ghaffarian’s scholarly work demonstrates a strong interdisciplinary orientation, bridging computer systems architecture with real-world applications in vehicular ad hoc networks (VANETs), indoor localization, and cloud-based network solutions. He has served in multiple academic and professional capacities, including as IT and Product Manager at Sanaat Yar Afzar Iranian and consultant for Iran’s Ministry of Education and the Electrical Industry Data Committee (Tavanir). His innovative research has earned national recognition, including a Best Paper Award at the IEEE International Conference on Internet of Things and Applications. Dr. Ghaffarian has also contributed to key industrial and governmental projects, such as developing WAN solutions for electrical industries and designing cloud-based monitoring systems. His research achievements are further complemented by his active engagement in academic translation and technical education, with works such as Python Numpy for Beginners and Python Pandas for Beginners (Farsi editions). Dr. Hossein Ghaffarian’s academic impact is reflected in his international research visibility, with 82 citations by 81 documents, 21 publications, and an h-index of 4, underscoring his growing influence in computer engineering and artificial intelligence research.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Ghaffarian, H., Fathy, M., & Soryani, M. (2012). Vehicular ad hoc networks enabled traffic controller for removing traffic lights in isolated intersections based on integer linear programming. IET Intelligent Transport Systems, 6(2), 115–123. Citations: 52

  2. Farahani, B. J., Ghaffarian, H., & Fathy, M. (2009). A fuzzy based priority approach in mobile sensor network coverage. International Journal of Recent Trends in Engineering, 2(1), 138. Citations: 19

  3. Rashvand, H. F., & Chao, H. C. (2013). Dynamic ad hoc networks. Institution of Engineering and Technology. Citations: 18

  4. Parvin, H., Minaei-Bidgoli, B., & Ghaffarian, H. (2011). An innovative feature selection using fuzzy entropy. In International Symposium on Neural Networks (pp. 576–585). Citations: 16

  5. Keramatpour, A., Nikanjam, A., & Ghaffarian, H. (2017). Deployment of wireless intrusion detection systems to provide the most possible coverage in wireless sensor networks without infrastructures. Wireless Personal Communications, 96(3), 3965–3978. Citations: 15

Zhizhong Xing | Artificial Intelligence | Best Innovation Award

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Dr. Zhizhong Xing | Artificial Intelligence | Best Innovation Award

University Teacher | Kunming Medical University | China

Dr. Zhizhong Xing, Ph.D., is a distinguished provincial-level Xingdian Young Talent and high-level recruited scholar at Kunming Medical University, widely recognized for his impactful contributions at the intersection of artificial intelligence, deep learning, smart education, and rehabilitation medicine. He obtained his doctoral degree in a technical discipline that laid the foundation for his expertise in AI-driven systems, intelligent sensing, and advanced computational modeling. Professionally, he has accumulated significant experience as principal investigator and collaborator on multiple prestigious projects, including the National Key R&D Program of China, the National Natural Science Foundation, and the Provincial Natural Science Foundation, reflecting both leadership and team-driven research capacity. His research interests center on the development of graph-based deep learning algorithms, point cloud analysis, and multi-source data fusion for applications in education technology, healthcare rehabilitation, and coal resource management under carbon peak initiatives. He has cultivated advanced research skills in 3D deep learning, human–computer interaction, laser point cloud segmentation, and biosensor-enhanced modeling, enabling translational advances across engineering, medicine, and education. Dr. Xing has published over 40 high-level academic papers, including more than 30 indexed by SCI, featured in CAS Tier 1 journals and IEEE Transactions, with several ranked as ESI Global Top 1% Highly Cited and Top 1‰ Hot Papers, reaching a total cumulative impact factor exceeding 111.8. His international visibility is further underscored by ongoing submissions to elite journals such as Nature Communications. Among his awards and honors are the Excellent Achievement Award for Scientific and Technological Research in Higher Education and the Provincial Science and Technology Award (Second Prize). He also serves as a reviewer for leading SCI journals and is a member of Sigma Xi, The Scientific Research Honor Society. His career reflects not only scholarly excellence but also commitment to advancing global collaborations, mentoring, and applied innovation. 286 Citations by 216 documents | 28 Documents | 10 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Xing, Z., Zhao, S., Guo, W., Meng, F., Guo, X., Wang, S., & He, H. (2023). Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model. Energy, 285, 128771. Cited by: 68

  2. Wu, Y., Zhao, S., Xing, Z., Wei, Z., Li, Y., & Li, Y. (2023). Detection of foreign objects intrusion into transmission lines using diverse generation model. IEEE Transactions on Power Delivery, 38(5), 3551–3560. Cited by: 37

  3. Xing, Z., Zhao, S., Guo, W., Guo, X., & Wang, Y. (2021). Processing laser point cloud in fully mechanized mining face based on DGCNN. ISPRS International Journal of Geo-Information, 10(7), 482. Cited by: 29

  4. Xing, Z., Ma, G., Wang, L., Yang, L., Guo, X., & Chen, S. (2025). Towards visual interaction: Hand segmentation by combining 3D graph deep learning and laser point cloud for intelligent rehabilitation. IEEE Internet of Things Journal, 12, 21328–21338. Cited by: 25

  5. Xing, Z., Meng, Z., Zheng, G., Ma, G., Yang, L., Guo, X., Tan, L., Jiang, Y., & Wu, H. (2025). Intelligent rehabilitation in an aging population: Empowering human–machine interaction for hand function rehabilitation through 3D deep learning and point cloud. Frontiers in Computational Neuroscience, 19, 1543643