Ming Dong
- Artificial Intelligence top 10%
- Information Systems top 10%
- Sociology and Political Science
- Statistical and Nonlinear Physics top 10%
- Signal Processing
- Co-authors
- Bolong ZhengGuohui LiQuoc Viet Hung NguyenAlexander KotovApril Idalski CarconeHan SuFarshad FotouhiShiyong Lu
- Topics
- Topic Modeling (6 papers)Misinformation and Its Impacts (3 papers)Biomedical Text Mining and Ontologies (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Image ProcessingIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Ming Dong
15 papers receiving 251 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 155
- Information Systems 74
- Sociology and Political Science 73
- Statistical and Nonlinear Physics 63
- Signal Processing 33
Countries citing papers authored by Ming Dong
This map shows the geographic impact of Ming Dong'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 Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Dong more than expected).
Fields of papers citing papers by Ming Dong
This network shows the impact of papers produced by Ming Dong. 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 Dong. The network helps show where Ming Dong may publish in the future.
Co-authorship network of co-authors of Ming Dong
This figure shows the co-authorship network connecting the top 25 collaborators of Ming Dong. A scholar is included among the top collaborators of Ming Dong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Dong. Ming Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 9 | |
| 8 | 13 | |
| 9 | 15 | |
| 10 | 6 | |
| 11 | 68 | |
| 12 | 18 | |
| 13 | 43 | |
| 14 | 37 | |
| 15 | Interpretable Probabilistic Latent Variable Models for Automatic Annotation of Clinical Text. | 9 |
| 16 | 0 | |
| 17 | 7 | |
| 18 | The Semantic Web: Opportunities and Challenges for Next-Generation Web Applications | 37 |
About Ming Dong
Ming Dong is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 18 papers that have together received 268 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Misinformation and Its Impacts (3 papers) and Biomedical Text Mining and Ontologies (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (63 citations), Artificial Intelligence (155 citations) and Information Systems (74 citations). Ming Dong has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Bolong Zheng, Guohui Li, Quoc Viet Hung Nguyen, Alexander Kotov, April Idalski Carcone, Han Su, Farshad Fotouhi, Shiyong Lu, Sylvie Naar‐King and Sylvie Naar. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and IEEE Transactions on Neural Networks and Learning Systems.
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.