Ming Du
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Graph Theory and Algorithms
Papers in
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- Graph Theory and Algorithms 8
- Advanced Image and Video Retrieval Techniques 3
- Multimodal Machine Learning Applications 3
- Advanced Neural Network Applications 2
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- Topic Modeling 6
- Machine Learning in Healthcare 3
- Co-authors
- Xin Shane Li (1 shared paper)Xuejiao Yang (1 shared paper)Xin Feng (1 shared paper)Bo Xu (11 shared papers)Sufen Wang (5 shared papers)Xiaoqiang Liu (1 shared paper)Hui Song (6 shared papers)Tao Li (1 shared paper)
- Journals
- IEEE Access (4 papers)Interactive Learning Environments (2 papers)IEEE Transactions on Knowledge and Data Engineering (1 paper)Applied Sciences (1 paper)Electronics (1 paper)
- Partner nations
- ChinaHong KongUnited Kingdom
In The Last Decade
Ming Du
28 papers receiving 326 citations
Peers
Comparison fields: 5 of 77
- Computer Vision and Pattern Recognition 151
- Health Informatics 9
- Hardware and Architecture 28
- Industrial and Manufacturing Engineering 19
- Artificial Intelligence 63
Countries citing papers authored by Ming Du
This map shows the geographic impact of Ming Du'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 Ming Du with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Du more than expected).
Fields of papers citing papers by Ming Du
This network shows the impact of papers produced by Ming Du. 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 Ming Du. The network helps show where Ming Du may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Du, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 251 | |
| 2 | 2019 | 13 | |
| 3 | 2018 | 11 | |
| 4 | 2015 | 9 | |
| 5 | 2020 | 8 | |
| 6 | 2023 | 8 | |
| 7 | 2019 | 6 | |
| 8 | 2023 | 6 | |
| 9 | 2021 | 4 | |
| 10 | 2009 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2022 | 3 | |
| 13 | 2023 | 3 | |
| 14 | 2020 | 2 | |
| 15 | 2023 | 2 | |
| 16 | 2008 | 1 | |
| 17 | 2009 | 1 | |
| 18 | 2011 | 1 | |
| 19 | 2020 | 1 | |
| 20 | 2021 | 1 |
About Ming Du
Ming Du is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Computer Science Applications and Information Systems, having authored 34 papers that have together received 345 indexed citations. Recurring topics across this work include Graph Theory and Algorithms (8 papers), Topic Modeling (6 papers), Advanced Image and Video Retrieval Techniques (3 papers), Multimodal Machine Learning Applications (3 papers), Machine Learning in Healthcare (3 papers), Biomedical Text Mining and Ontologies (2 papers), Advanced Neural Network Applications (2 papers) and Online and Blended Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (151 citations), Health Informatics (9 citations), Hardware and Architecture (28 citations), Industrial and Manufacturing Engineering (19 citations) and Artificial Intelligence (63 citations). Ming Du has collaborated with scholars based in China, Hong Kong and United Kingdom. Frequent co-authors include Xin Shane Li, Xuejiao Yang, Xin Feng, Bo Xu, Sufen Wang, Xiaoqiang Liu, Hui Song, Tao Li, Jiajin Le and Baiyan Li. Their work appears in journals such as IEEE Access, Interactive Learning Environments, IEEE Transactions on Knowledge and Data Engineering, Applied Sciences and Electronics.
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.