Feiyue Ye
Impact in
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- Face and Expression Recognition
- Video Surveillance and Tracking Methods
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Media Technology top 10%
- Remote-Sensing Image Classification
Papers in
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- Cognitive Computing and Networks 8
- Topic Modeling 7
- Advanced Text Analysis Techniques 7
- Semantic Web and Ontologies 5
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- Web Data Mining and Analysis 8
- Data Mining Algorithms and Applications 6
- Recommender Systems and Techniques 5
- Co-authors
- Honghui Fan (9 shared papers)Shengwei Tian (2 shared papers)Zhiyin Wang (1 shared paper)Long Yu (2 shared papers)Jianli Ding (1 shared paper)Jun Kong (1 shared paper)Zhenqiu Shu (5 shared papers)Xiao‐Jun Wu (4 shared papers)
In The Last Decade
Feiyue Ye
51 papers receiving 320 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 122
- Media Technology 52
- Environmental Engineering 51
- Artificial Intelligence 106
- Global and Planetary Change 64
Countries citing papers authored by Feiyue Ye
This map shows the geographic impact of Feiyue 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 Feiyue Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feiyue Ye more than expected).
Fields of papers citing papers by Feiyue Ye
This network shows the impact of papers produced by Feiyue 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 Feiyue Ye. The network helps show where Feiyue Ye may publish in the future.
Co-authors
The 25 scholars most cited alongside Feiyue 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
Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 84 | |
| 2 | 2018 | 36 | |
| 3 | 2017 | 29 | |
| 4 | 2013 | 18 | |
| 5 | 2018 | 15 | |
| 6 | 2013 | 14 | |
| 7 | 2020 | 13 | |
| 8 | 2011 | 12 | |
| 9 | 2016 | 10 | |
| 10 | 2005 | 10 | |
| 11 | 2017 | 8 | |
| 12 | 2011 | 8 | |
| 13 | 2011 | 7 | |
| 14 | 2011 | 7 | |
| 15 | 2014 | 5 | |
| 16 | 2018 | 5 | |
| 17 | 2011 | 5 | |
| 18 | 2010 | 4 | |
| 19 | 2019 | 4 | |
| 20 | 2019 | 4 |
About Feiyue Ye
Feiyue Ye is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Media Technology, having authored 55 papers that have together received 357 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (9 papers), Cognitive Computing and Networks (8 papers), Web Data Mining and Analysis (8 papers), Topic Modeling (7 papers), Advanced Text Analysis Techniques (7 papers), Data Mining Algorithms and Applications (6 papers), Semantic Web and Ontologies (5 papers) and Recommender Systems and Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (122 citations), Media Technology (52 citations), Environmental Engineering (51 citations), Artificial Intelligence (106 citations) and Global and Planetary Change (64 citations). Feiyue Ye has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Honghui Fan, Shengwei Tian, Zhiyin Wang, Long Yu, Jianli Ding, Jun Kong, Zhenqiu Shu, Xiao‐Jun Wu, Qimei Chen and Xiangfeng Luo. Their work appears in journals such as Future Internet, International Journal of Advancements in Computing Technology, IEEE Transactions on Systems Man and Cybernetics Systems, Applied Intelligence and PLoS ONE.
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.