Dr. Fan Yang | Machine learning | Best Researcher Award

Dr. Fan Yang | Qinghai Normal University | China

Dr. Fan Yang, Ph.D., is an Associate Professor in the School of Computer Science at Qinghai Normal University, recognized for his expanding contributions to human–machine systems and artificial intelligence. He has developed a strong academic profile with multiple peer-reviewed publications in high-impact journals and internationally respected conferences, reflecting his growing influence in intelligent interaction and adaptive computational technologies. His background includes advanced training in computer science with a research emphasis on intelligent human–machine collaboration and adaptive AI modeling. In his current role, he teaches core subjects in artificial intelligence and interactive systems while supervising graduate research and contributing to national and provincial research initiatives. His research interests span intelligent interaction, AI-driven decision technologies, adaptive computational models, and integrated human–machine environments, with a focus on connecting machine intelligence to real-world human behavior. His early achievements, impactful research output, and contributions to cutting-edge AI technologies have earned him recognition within the research community and position him as a competitive candidate for prestigious research awards.

Profile: ORCID

Featured Publications

Yang, F. “Adaptive human–machine interaction using deep attention models.” IEEE Transactions on Human–Machine Systems. — Cited by 12.

Yang, F. “Multi-agent reinforcement learning for human-centered AI.” ACM CHI Conference. — Cited by 8.

Yang, F. “Cognitive-driven robot collaboration under dynamic environments.” Robotics and Autonomous Systems. — Cited by 15.

Yang, F. “Real-time interaction modeling using hybrid deep networks.” Neurocomputing. — Cited by 20.

Yang, F. “Intelligent behavior prediction in human–machine teams.” IEEE ICMLA Conference. — Cited by 5.

Assoc. Prof. Dr. Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

Associate Professor | National Chin-Yi University of Technology | Taiwan

Chia-Hung Lai, Ph.D., is an interdisciplinary researcher whose work bridges intelligent automation, smart manufacturing, and advanced sensing technologies to enhance industrial reliability and technical education. His research integrates deep learning, machine vision, nondestructive testing, and engineering information security, with notable contributions to welding automation, gear defect detection, tool-breakage prediction, and secure engineering data transmission. He has developed innovative AI-driven diagnostic systems using convolutional neural networks, symmetrized dot patterns, discrete wavelet transforms, and current-sensing analytics, enabling high-precision detection of defects in manufacturing processes. His studies also explore VR/AR-based learning systems, reflecting his commitment to advancing industry-aligned technical education through immersive and intelligent technologies. In addition, he has contributed to environmentally sustainable engineering through deep learning approaches for monitoring emissions in industrial operations. His work in information security demonstrates a unique blend of engineering design and cybersecurity through novel applications of steganography in CAD environments. With multiple publications in SCIE-indexed journals and recognition through awards and competitive achievements, he has established a strong research footprint across automation, sensing, and applied AI. He actively contributes to the scholarly community through reviewing roles and by leading numerous industry–academic collaborative projects focused on intelligent systems, advanced diagnostics, and smart manufacturing innovation.

Profiles: Scopus | Google Scholar

Featured Publications

Chien, Y.-C., Wu, T. T., Lai, C.-H., & Huang, Y.-M. (2022). Investigation of the influence of artificial intelligence markup language-based LINE ChatBot in contextual English learning. Frontiers in Psychology, 13, 785752.

Lai, C.-H., Liu, M.-C., Liu, C.-J., & Huang, Y.-M. (2016). Using positive visual stimuli to lighten the online learning experience through in-class questioning. International Review of Research in Open and Distributed Learning, 17(1), 23–41.

Huang, Y.-M., Liu, M.-C., Lai, C.-H., & Liu, C.-J. (2017). Using humorous images to lighten the learning experience through questioning in class. British Journal of Educational Technology, 48(3), 878–896.

Liu, C.-J., Huang, C.-F., Liu, M.-C., Chien, Y.-C., Lai, C.-H., & Huang, Y.-M. (2015). Does gender influence emotions resulting from positive applause feedback in self-assessment testing? Evidence from neuroscience. Journal of Educational Technology & Society, 18(1), 337–350.

Lai, C.-H., Wu, T. E., Huang, S.-H., & Huang, Y.-M. (2020). Developing a virtual learning tool for industrial high schools’ welding course. Procedia Computer Science, 172, 696–700.

Liu, M.-C., Lai, C.-H., Su, Y.-N., Huang, S.-H., Chien, Y.-C., & Huang, Y.-M., & Hwang, J. P. (2015). Learning with great care: The adoption of the multi-sensor technology in education. In Sensing technology: Current status and future trends III (pp. 223–242).

Lai, C.-H., & Yang, H.-C. (2016). Theoretical investigation of a planar rack cutter with variable diametral pitch. Arabian Journal for Science and Engineering, 41(5), 1585–1594.