Ph.D. Student | Beijing Technology and Business University | China
Dr. Xiangfeng Bu, a researcher in computer science and system science at Beijing Technology and Business University, specializes in complex system modeling, remote sensing, artificial intelligence, and predictive modeling. He holds a Ph.D. in System Science (Complex System Modeling), an M.S. in Computer Technology, a B.S. in Computer Science and Technology, and an Associate Degree in Software Technology. His professional experience spans smart home hardware systems, greenhouse automation, and graduate leadership roles, including conference organization and academic affairs management. Dr. Bu’s research contributions include influential publications on lithium-ion battery fault prediction, deep reinforcement learning, harmful algal bloom detection, and multi-scale forest fire detection, alongside patents and software copyrights in environmental modeling and fire detection systems. His work integrates machine learning techniques such as XGBoost, LightGBM, and deep neural networks with applications in environmental sustainability, healthcare diagnostics, and intelligent systems. Recognized for academic excellence and leadership, he has received honors including Provincial Outstanding Student, Provincial Outstanding Graduate, and multiple scholarships, and has distinguished himself in national and international innovation competitions. Active in both research and academic service, Dr. Bu demonstrates a strong commitment to advancing interdisciplinary applications of artificial intelligence, making him a highly deserving candidate for this award. He has 61 citations by 61 documents, 4 documents, and an h-index of 2.
Profile: Scopus | ORCID
Featured Publications
1. Bu, X., Wang, L., Wang, X., Xu, J., Zhao, Z., Yu, J., Bai, Y., Zhang, H., & Sun, Q. (2025). A deep dual 3Q learning model incorporating nonlinear greedy factors. 2025 IEEE 2nd International Conference on Deep Learning and Computer Vision (DLCV).
2. Xie, M., Su, C., Bu, X., Yang, C., & Chen, B. (2025). A fault prediction method for lithium-ion batteries by fusing internal and external features with stacked integration models. Journal of The Electrochemical Society.
3. Bu, X. (2023). A harmful algal bloom detection model combining moderate resolution imaging spectroradiometer multi-factor and meteorological heterogeneous data. Sustainability, 15(21), 15386.
4. Zhang, L., Wang, M., Ding, Y., & Bu, X. (2023). MS-FRCNN: A multi-scale faster RCNN model for small target forest fire detection. Forests, 14(3), 616.