N. Komal Kumar
Impact in
- Health Information Management top 0.5%
- Artificial Intelligence in Healthcare
- Artificial Intelligence top 10%
- Machine Learning in Healthcare
- AI in cancer detection
- Imbalanced Data Classification Techniques
Papers in
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- Artificial Intelligence in Healthcare 14
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- Machine Learning in Healthcare 4
- AI in cancer detection 2
- Co-authors
- Tsehay Admassu Assegie (9 shared papers)R Prasanna Kumar (1 shared paper)Sangeetha Ganesan (1 shared paper)Priyesh Patel (1 shared paper)Ayodeji Olalekan Salau (1 shared paper)M. V. Rama Krishna (1 shared paper)S. Siva Sathya (1 shared paper)
In The Last Decade
N. Komal Kumar
22 papers receiving 297 citations
Peers
Comparison fields: 5 of 81
- Health Information Management 161
- Artificial Intelligence 154
- Medical Laboratory Technology 5
- Radiology, Nuclear Medicine and Imaging 47
- Health Informatics 3
Countries citing papers authored by N. Komal Kumar
This map shows the geographic impact of N. Komal 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 N. Komal Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites N. Komal Kumar more than expected).
Fields of papers citing papers by N. Komal Kumar
This network shows the impact of papers produced by N. Komal 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 N. Komal Kumar. The network helps show where N. Komal Kumar may publish in the future.
Co-authors
The 7 scholars most cited alongside N. Komal Kumar, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 55 | |
| 2 | 2019 | 38 | |
| 3 | 2022 | 36 | |
| 4 | 2018 | 26 | |
| 5 | 2022 | 24 | |
| 6 | 2019 | 19 | |
| 7 | 2022 | 18 | |
| 8 | 2019 | 18 | |
| 9 | 2020 | 16 | |
| 10 | 2020 | 16 | |
| 11 | 2023 | 13 | |
| 12 | 2020 | 10 | |
| 13 | 2022 | 9 | |
| 14 | 2018 | 9 | |
| 15 | 2020 | 4 | |
| 16 | 2020 | 3 | |
| 17 | 2023 | 2 | |
| 18 | 2020 | 2 | |
| 19 | 2024 | 1 | |
| 20 | 2020 | 1 |
About N. Komal Kumar
N. Komal Kumar is a scholar working on Health Information Management, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications and Molecular Biology, having authored 27 papers that have together received 322 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (14 papers), Machine Learning in Healthcare (4 papers), COVID-19 diagnosis using AI (3 papers), AI in cancer detection (2 papers), Diabetes Management and Research (1 paper), Microgrid Control and Optimization (1 paper), Gene expression and cancer classification (1 paper) and COVID-19 epidemiological studies (1 paper). The work is most often cited by research in Health Information Management (161 citations), Artificial Intelligence (154 citations), Medical Laboratory Technology (5 citations), Radiology, Nuclear Medicine and Imaging (47 citations) and Health Informatics (3 citations). N. Komal Kumar has collaborated with scholars based in India, Ethiopia and Italy. Frequent co-authors include Tsehay Admassu Assegie, R Prasanna Kumar, Sangeetha Ganesan, Priyesh Patel, Ayodeji Olalekan Salau, M. V. Rama Krishna and S. Siva Sathya. Their work appears in journals such as MethodsX, IAES International Journal of Artificial Intelligence, Bulletin of Electrical Engineering and Informatics, Journal of Computational and Theoretical Nanoscience and International journal of intelligent engineering and 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.