Xingsi Xue Editor at IgMin Research
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Biography
Dr. Xingsi Xue is an Associate Professor at the Center for Information Development and Management at Fujian University of Technology, China. He earned his B.S. degree in Software Engineering from Fuzhou University in 2004, followed by an M.S. degree in Computer Application Technology from Renmin University of China in 2009, and a Ph.D. in Computer Application Technology from Xidian University in 2014.
Dr. Xue's research interests include Knowledge and Data Engineering, Intelligent Computation, Machine Learning, and Semantic Web Technology. He has been recognized as one of the World’s Top 2% Scientists for 2020 and 2021, a distinction published by Stanford University and Elsevier BV. His contributions to the academic community include over 200 academic papers in prestigious journals and conferences, and he has received several awards, including the ACM Xi’an Rising Star Award in 2017 and multiple Best Paper Awards.
In addition to his research, Dr. Xue serves as an Associate Editor for several journals, including Journal of Intelligent & Fuzzy Systems and Heliyon. He has also held roles as a Program Committee Chair and Member for numerous international conferences, further contributing to the fields of artificial intelligence and data engineering.
Research Interest
Data Engineering, Intelligent Computation, Machine Learning, and Semantic Web Technology
Editor
Work Details
Doctor
Fujian University of Technology
School of Computer Science and Mathematics
China
Contribution by Topic Area
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Research Articles
- Solar Energy Resource Potentials of the City of Arkadag
- Biomimetic Synthesis of Calcium Carbonate in Bile in the presence of Amino Acids
- Utilising Phytoremediation in Green Technologies: Exploring Natural Means of Environmental Clean-up
- The Influence of Low Pesticide Doses on Fusarium Molds
- Exploring Upper Limb Kinematics in Limited Vision Conditions: Preliminary Insights from 3D Motion Analysis and IMU Data
- From Traditionalism to Algorithms: Embracing Artificial Intelligence for Effective University Teaching and Learning
