Prof. Binghui Xu is a researcher whose recent work focuses on machine learning, data-driven modeling, and computational intelligence, with applications across remote sensing, chemical engineering, environmental systems, and digital risk management. His research advances intelligent algorithms for solving complex real-world problems, emphasizing improved accuracy, predictive performance, and system-level optimization. In remote sensing, he has developed enhanced convolutional neural network architectures to achieve higher-precision image classification, addressing challenges related to feature extraction and large-scale datasets. His studies on cloud-based platforms highlight their critical role in supporting cancer-related research by enabling scalable data management, computational analytics, and collaborative scientific workflows. Prof. Xu has also applied supervised learning techniques to chemical and energy engineering, particularly for accurately predicting hydrogen content in bio-oil—an essential factor in assessing biofuel quality. His contributions to communication networks include proposing a quality-of-service control model for overlay convergence transmission, aimed at improving reliability and efficiency. Recently, he has extended machine learning methods to environmental research through the computation of water activity in ionic liquid–based aqueous ternary systems. He has also developed early warning systems for public risk perception and explored data association rule mining using genetic optimization, alongside graph attention–based image classification models. Collectively, Prof. Xu’s work integrates artificial intelligence, optimization algorithms, and applied modeling to deliver innovative solutions across multiple engineering and technological domains.
Profile: Scopus
Featured Publications
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Xu, B. (2025). Machine learning-assisted computation of water activity for ionic liquid-based aqueous ternary elements. Desalination and Water Treatment, 10.
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Xu, B. (2021). Improved convolutional neural network in remote sensing image classification. Neural Computing & Applications, 7, 8169–8180.
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Xu, B., & Fengcheng. (2022). The roles of cloud-based systems on cancer-related studies: A systematic literature review. IEEE Access, 10, 64126–64145.
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Xu, B., Chen, T.-C., & Danial, A. (2021). Application of a supervised learning machine for accurate prognostication of hydrogen contents of bio-oil. International Journal of Chemical Engineering, 7.
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Xu, B., & Yan, W. (2020). A QoS control model in overlay convergence transmission. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 82(4), 103–118.