Mingyue Zhang | Artificial Intelligence | Best Researcher Award

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Dr. Mingyue Zhang | Artificial Intelligence | Best Researcher Award

University of South China | China

Dr. Mingyue Zhang, Ph.D., is a dynamic researcher in computer vision, intelligent systems, and human–computer interaction, currently serving in the field of computer science with a focus on gesture recognition and lightweight deep learning models for edge devices. His research integrates advanced computer vision algorithms with human–machine collaboration, emphasizing intelligent gesture recognition for rehabilitation training, embedded AI, and Internet of Things (IoT)-enabled systems. Dr. Zhang has significantly contributed to developing efficient algorithms such as lightweight convolutional neural networks, adaptive Kalman filtering, and multi-sensor fusion frameworks, which enhance real-time performance in gesture estimation, object tracking, and assistive technologies. His innovative work bridges the gap between deep learning theory and practical deployment on embedded systems and mobile platforms. With over 25 publications in high-impact journals indexed in SCI and EI, including IEEE Internet of Things Journal, Expert Systems with Applications, IEEE Access, and Journal of Supercomputing, his research has achieved growing academic recognition. Dr. Zhang’s work has been widely cited across the global impact of his contributions in computer vision and artificial intelligence. His research is currently supported by the Hunan Provincial Department of Science and Technology, focusing on intelligent gesture recognition in rehabilitation. In recognition of his outstanding contributions to scientific innovation and scholarly excellence, Dr. Zhang is honored with the Best Researcher Award for his pioneering advancements in AI-driven human–computer interaction and lightweight network modeling.

Publication Profile

Orcid

Featured Publications

  • Jiang, C., Zhang, M., Wang, Y., & Zhang, A. (2025). AHMOT: Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking in IoT-Enabled Autonomous Vehicles. IEEE Internet of Things Journal.

  • Zhang, M., Zhou, Z., Tao, X., & Deng, M. (2023). Hand pose estimation based on fish skeleton CNN: Application in gesture recognition. Journal of Intelligent & Fuzzy Systems, 44, 8029–8042.

  • Zhang, M., Zhou, Z., Wang, T., & Zhou, W. (2023). A lightweight network deployed on ARM devices for hand gesture recognition. IEEE Access, 11, 45493–45503.

  • Zhang, M., Zhou, Z., & Deng, M. (2022). Cascaded hierarchical CNN for 2D hand pose estimation from a single color image. Multimedia Tools and Applications, 81, 25745–25763.

  • Zhang, M., & Zhou, Z. (2025). Speed-accuracy trade-off in lightweight-based hand pose estimation. The Journal of Supercomputing, 81, 1212

Jinping Xue | Artificial Intelligence | Best Researcher Award

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Ms. Jinping Xue | Artificial Intelligence | Best Researcher Award

Renergy Overseas Limited | China

Ms. Jinping Xue is an emerging researcher and engineer specializing in the integration of Artificial Intelligence with sustainable urban environments. Her research focuses on Smart Cities, Industrial AI, Edge Intelligence, and Environmental AI—fields that explore how data-driven intelligence can transform urban infrastructure into more adaptive, efficient, and sustainable systems. She has made significant contributions to the development of a privacy-preserving AI framework for smart city environmental monitoring, integrating federated learning with LSTM and genetic algorithms to achieve high pollution source traceability accuracy while maintaining data privacy. Her interdisciplinary expertise bridges environmental modeling, urban systems optimization, and AI-driven perception technologies, contributing to innovative solutions in sustainable city management and smart mobility. Xue’s scholarly work demonstrates a strong interest in the application of distributed sensing and federated learning for pollution source analysis, contributing to the broader goal of achieving clean, data-secure, and intelligent urban ecosystems. She has collaborated with experts in environmental sensing and intelligent perception to enhance the real-time adaptability of urban monitoring systems. Her publications, indexed in Scopus and Google Scholar, reflect her growing impact in the domains of computational intelligence and environmental systems. With research outputs recognized in peer-reviewed international journals such as Sensors, her work has begun to gain citations across the environmental AI research community. Her citation records are progressively expanding, with verified documentation and indexing available through Scopus and Google Scholar, and her h-index count demonstrates her early but impactful research trajectory. Through her interdisciplinary approach, Xue continues to push the boundaries of AI applications for sustainable development and smart city transformation.

Publication Profile

Google Scholar

Featured Publications

Xue, J., Hu, X., Liu, Q., Yin, C., Ni, P., & Bo, X. (2025). Air pollutant traceability based on federated learning of edge intelligent perception agents. Sensors, 25(19), 6119.