Fun Ye
Impact in
- Artificial Intelligence top 5%
- Advanced Clustering Algorithms Research
- Metaheuristic Optimization Algorithms Research
- Neural Networks and Applications
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- Advanced Data Compression Techniques
Papers in
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- Wireless Networks and Protocols 3
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- Advanced Clustering Algorithms Research 3
- Neural Networks and Applications 1
- Co-authors
- Ching‐Yi Chen (4 shared papers)Ching‐Yi Chen (2 shared papers)Hsuan-Ming Feng (4 shared papers)Shiann‐Tsong Sheu (3 shared papers)Jenhui Chen (3 shared papers)Jen‐Shiun Chiang (3 shared papers)Chih‐Hsien Hsia (2 shared papers)Ying-Tung Hsiao (1 shared paper)
- Journals
- Expert Systems with Applications (1 paper)Journal of marine science and technology (1 paper)Cybernetics & Systems (2 papers)Journal of Applied Science and Engineering (2 papers)International Conference on Machine Learning and Cybernetics (1 paper)
- Partner nations
- Taiwan
In The Last Decade
Fun Ye
14 papers receiving 382 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 254
- Computer Vision and Pattern Recognition 52
- Computer Networks and Communications 42
- Information Systems 19
- Health Information Management 6
Countries citing papers authored by Fun Ye
This map shows the geographic impact of Fun Ye's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fun Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fun Ye more than expected).
Fields of papers citing papers by Fun Ye
This network shows the impact of papers produced by Fun Ye. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fun Ye. The network helps show where Fun Ye may publish in the future.
Co-authors
The 9 scholars most cited alongside Fun Ye, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 223 | |
| 2 | 2005 | 72 | |
| 3 | 2005 | 47 | |
| 4 | 2003 | 24 | |
| 5 | 2003 | 11 | |
| 6 | 2006 | 11 | |
| 7 | 2007 | 10 | |
| 8 | 2004 | 5 | |
| 9 | 2009 | 5 | |
| 10 | 2003 | 4 | |
| 11 | 2007 | 4 | |
| 12 | 2009 | 3 | |
| 13 | 2002 | 1 | |
| 14 | 2008 | 1 |
About Fun Ye
Fun Ye is a scholar working on Computer Networks and Communications, Artificial Intelligence, Computer Vision and Pattern Recognition, Architecture and Aerospace Engineering, having authored 14 papers that have together received 421 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (3 papers), Wireless Networks and Protocols (3 papers), Robotics and Sensor-Based Localization (2 papers), Advanced Data Compression Techniques (2 papers), Advanced Neural Network Applications (1 paper), Educational Technology and E-Learning (1 paper), Engineering Education and Pedagogy (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (254 citations), Computer Vision and Pattern Recognition (52 citations), Computer Networks and Communications (42 citations), Information Systems (19 citations) and Health Information Management (6 citations). Fun Ye has collaborated with scholars based in Taiwan. Frequent co-authors include Ching‐Yi Chen, Ching‐Yi Chen, Hsuan-Ming Feng, Shiann‐Tsong Sheu, Jenhui Chen, Jen‐Shiun Chiang, Chih‐Hsien Hsia, Ying-Tung Hsiao and Chun-Wen Chen. Their work appears in journals such as Expert Systems with Applications, Journal of marine science and technology, Cybernetics & Systems, Journal of Applied Science and Engineering and International Conference on Machine Learning and Cybernetics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.