Xingsi Xue 編集者 at IgMin Research
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バイオグラフィー
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.
研究の興味
Data Engineering, Intelligent Computation, Machine Learning, and Semantic Web Technology
Editor
仕事内容
Doctor
Fujian University of Technology
School of Computer Science and Mathematics
China
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研究論文
- Efficient Room Temperature Ethanol Vapor Sensing by Unique Fractal Features of Tin Oxide
- Modeling of an Electric-fired Brick Oven, Directly Heated
- Physical Activity and Lifestyle of Female Students of the Faculty of Health Sciences University of Applied Sciences in Tarnów (Poland)
- The Antioxidant and Antidepressant Properties of Dietary Proteins Derived from Egg and Bean Extracts and Their Acute Toxicity: A Journey from Nutrition to Pharmacognosy
- Current Oscillations and Resonances in Nanocrystals of Narrow-gap Semiconductors
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
