Junfeng Hu
Impact in
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- Artificial Intelligence in Healthcare
Papers in
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- Natural Language Processing Techniques 7
- Topic Modeling 6
- Semantic Web and Ontologies 4
- Advanced Graph Neural Networks 3
- Text Readability and Simplification 2
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- Advanced Neural Network Applications 3
- Co-authors
- Rachel Rudinger (1 shared paper)Benjamin Van Durme (1 shared paper)Matt Post (1 shared paper)Xiaohui Duan (1 shared paper)Roger Zimmermann (5 shared papers)Shan Xu (1 shared paper)Tiangang Zhu (1 shared paper)Zhen Zhang (1 shared paper)
In The Last Decade
Junfeng Hu
27 papers receiving 231 citations
Peers
Comparison fields: 5 of 60
- Health Information Management 38
- Medical Laboratory Technology 7
- Artificial Intelligence 125
- Health Informatics 4
- Signal Processing 24
Countries citing papers authored by Junfeng Hu
This map shows the geographic impact of Junfeng Hu'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 Junfeng Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junfeng Hu more than expected).
Fields of papers citing papers by Junfeng Hu
This network shows the impact of papers produced by Junfeng Hu. 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 Junfeng Hu. The network helps show where Junfeng Hu may publish in the future.
Co-authors
The 25 scholars most cited alongside Junfeng Hu, 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 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 49 | |
| 2 | 2019 | 45 | |
| 3 | 2024 | 28 | |
| 4 | 2019 | 19 | |
| 5 | 2024 | 13 | |
| 6 | 2020 | 13 | |
| 7 | 2020 | 8 | |
| 8 | 2023 | 8 | |
| 9 | 2019 | 6 | |
| 10 | 2024 | 6 | |
| 11 | 2018 | 6 | |
| 12 | 2023 | 6 | |
| 13 | 2023 | 6 | |
| 14 | 2015 | 5 | |
| 15 | 2024 | 5 | |
| 16 | 2021 | 4 | |
| 17 | 2014 | 3 | |
| 18 | 2025 | 3 | |
| 19 | 2011 | 2 | |
| 20 | 2016 | 2 |
About Junfeng Hu
Junfeng Hu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Building and Construction, Molecular Biology and Management Information Systems, having authored 32 papers that have together received 248 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (6 papers), Semantic Web and Ontologies (4 papers), Advanced Neural Network Applications (3 papers), Advanced Graph Neural Networks (3 papers), Business Process Modeling and Analysis (2 papers), Traffic Prediction and Management Techniques (2 papers) and Text Readability and Simplification (2 papers). The work is most often cited by research in Health Information Management (38 citations), Medical Laboratory Technology (7 citations), Artificial Intelligence (125 citations), Health Informatics (4 citations) and Signal Processing (24 citations). Junfeng Hu has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Rachel Rudinger, Benjamin Van Durme, Matt Post, Xiaohui Duan, Roger Zimmermann, Shan Xu, Tiangang Zhu, Zhen Zhang, Yuxuan Liang and Bryan Hooi. Their work appears in journals such as Complexity, European Heart Journal, BMC Genomics, Information Sciences and Sustainability.
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.