Vipin Kumar
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Health Information Management top 2%
- Co-authors
- Michael SteinbachGyörgy SimonSonajharia MinzPranjul YadavBasant SubbaEmmanuel S. PilliSumit PundirRifaqat Ali
- Topics
- Face and Expression Recognition (9 papers)Machine Learning and Data Classification (7 papers)Machine Learning in Healthcare (5 papers)
- Partner nations
- IndiaUnited StatesChina
In The Last Decade
Vipin Kumar
34 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 143
- Artificial Intelligence 597
- Computer Vision and Pattern Recognition 173
- Information Systems 156
- Computer Networks and Communications 121
- Health Information Management 102
Countries citing papers authored by Vipin Kumar
This map shows the geographic impact of Vipin Kumar'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 Vipin Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vipin Kumar more than expected).
Fields of papers citing papers by Vipin Kumar
This network shows the impact of papers produced by Vipin Kumar. 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 Vipin Kumar. The network helps show where Vipin Kumar may publish in the future.
Co-authorship network of co-authors of Vipin Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Vipin Kumar. A scholar is included among the top collaborators of Vipin Kumar 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 Vipin Kumar. Vipin Kumar 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 | 0 | |
| 4 | 11 | |
| 5 | 11 | |
| 6 | 14 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 39 | |
| 10 | 13 | |
| 11 | 58 | |
| 12 | 6 | |
| 13 | 29 | |
| 14 | 41 | |
| 15 | Feature Selection: A literature Reviewbreakdown → | 437 |
| 16 | 2 | |
| 17 | 2 | |
| 18 | 12 | |
| 19 | Learning classifier models for predicting rare phenomena | 8 |
| 20 | 23 |
About Vipin Kumar
Vipin Kumar is a scholar working on Artificial Intelligence, Health Information Management and Computer Vision and Pattern Recognition, having authored 37 papers that have together received 1.1k indexed citations. Recurring topics across this work include Face and Expression Recognition (9 papers), Machine Learning and Data Classification (7 papers) and Machine Learning in Healthcare (5 papers). The work is most often cited by research in Health Information Management (102 citations), Artificial Intelligence (597 citations) and Health Informatics (14 citations). Vipin Kumar has collaborated with scholars based in India, United States and China. Frequent co-authors include Michael Steinbach, György Simon, Sonajharia Minz, Pranjul Yadav, Basant Subba, Emmanuel S. Pilli, Sumit Pundir, Rifaqat Ali, Pawan Kumar Sharma and Eui-Hong Han. Their work appears in journals such as ACM Computing Surveys, Computer Networks and Neural Computing and Applications.
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.