Yan Fu
Impact in
- Genetics top 10%
- Glioma Diagnosis and Treatment
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- Scheduling and Optimization Algorithms
- Manufacturing Process and Optimization
- Digital Transformation in Industry
Papers in
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- Advanced Neural Network Applications 3
- Visual Attention and Saliency Detection 3
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- Robot Manipulation and Learning 5
- Co-authors
- Linjun Shou (2 shared papers)Daxin Jiang (2 shared papers)Ming Gong (2 shared papers)Fei Yuan (1 shared paper)Jian Pei (1 shared paper)Petr Kavan (1 shared paper)A. Cseh (1 shared paper)William Shapiro (1 shared paper)
- Journals
- Journal of Medical Internet Research (2 papers)IEEE Access (2 papers)Engineering Failure Analysis (2 papers)Applied Sciences (2 papers)Materials Characterization (1 paper)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Yan Fu
46 papers receiving 543 citations
Peers
Comparison fields: 5 of 111
- Genetics 95
- Industrial and Manufacturing Engineering 94
- Computer Vision and Pattern Recognition 90
- Rehabilitation 23
- Health Informatics 4
Countries citing papers authored by Yan Fu
This map shows the geographic impact of Yan Fu'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 Yan Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan Fu more than expected).
Fields of papers citing papers by Yan Fu
This network shows the impact of papers produced by Yan Fu. 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 Yan Fu. The network helps show where Yan Fu may publish in the future.
Co-authors
The 25 scholars most cited alongside Yan Fu, 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 47 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 141 | |
| 2 | 2021 | 85 | |
| 3 | 2020 | 31 | |
| 4 | 2019 | 23 | |
| 5 | 2019 | 22 | |
| 6 | 2022 | 21 | |
| 7 | 2008 | 20 | |
| 8 | 2020 | 19 | |
| 9 | 2016 | 18 | |
| 10 | 2021 | 17 | |
| 11 | 2020 | 12 | |
| 12 | 2021 | 11 | |
| 13 | 2018 | 10 | |
| 14 | 2017 | 10 | |
| 15 | 2022 | 9 | |
| 16 | 2009 | 9 | |
| 17 | 2016 | 9 | |
| 18 | 2018 | 8 | |
| 19 | 2016 | 7 | |
| 20 | 2021 | 6 |
About Yan Fu
Yan Fu is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Artificial Intelligence, Social Psychology and Mechanical Engineering, having authored 47 papers that have together received 555 indexed citations. Recurring topics across this work include Human-Automation Interaction and Safety (5 papers), Robot Manipulation and Learning (5 papers), Manufacturing Process and Optimization (4 papers), Energy Efficiency and Management (4 papers), Advanced Neural Network Applications (3 papers), Visual Attention and Saliency Detection (3 papers), Soft Robotics and Applications (3 papers) and AI-based Problem Solving and Planning (3 papers). The work is most often cited by research in Genetics (95 citations), Industrial and Manufacturing Engineering (94 citations), Computer Vision and Pattern Recognition (90 citations), Rehabilitation (23 citations) and Health Informatics (4 citations). Yan Fu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Linjun Shou, Daxin Jiang, Ming Gong, Fei Yuan, Jian Pei, Petr Kavan, A. Cseh, William Shapiro, David A. Reardon and David D. Eisenstat. Their work appears in journals such as Journal of Medical Internet Research, IEEE Access, Engineering Failure Analysis, Applied Sciences and Materials Characterization.
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.