V Vivek
Impact in
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- Artificial Intelligence in Healthcare
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- AI in cancer detection
- Imbalanced Data Classification Techniques
- Machine Learning in Healthcare
Papers in
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- AI in cancer detection 4
- Anomaly Detection Techniques and Applications 2
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- IoT and Edge/Fog Computing 5
- Distributed and Parallel Computing Systems 4
- Co-authors
- T R Mahesh (19 shared papers)V. Vinoth Kumar (6 shared papers)Rajesh Natarajan (2 shared papers)V. Dhilip Kumar (1 shared paper)Junaid Asghar (1 shared paper)Suresh Guluwadi (1 shared paper)Veerma Ram (2 shared papers)V. Muthukumaran (1 shared paper)
- Journals
- Case Studies in Thermal Engineering (1 paper)IEEE Access (1 paper)Computational Intelligence and Neuroscience (1 paper)Scientific Reports (1 paper)Filomat (1 paper)
- Partner nations
- IndiaUnited StatesIraq
In The Last Decade
V Vivek
32 papers receiving 219 citations
Peers
Comparison fields: 5 of 74
- Health Information Management 51
- Artificial Intelligence 79
- Neurology 16
- Radiology, Nuclear Medicine and Imaging 37
- Information Systems 36
Countries citing papers authored by V Vivek
This map shows the geographic impact of V Vivek'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 V Vivek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V Vivek more than expected).
Fields of papers citing papers by V Vivek
This network shows the impact of papers produced by V Vivek. 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 V Vivek. The network helps show where V Vivek may publish in the future.
Co-authors
The 25 scholars most cited alongside V Vivek, 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 40 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 51 | |
| 2 | 2022 | 40 | |
| 3 | 2024 | 21 | |
| 4 | 2021 | 16 | |
| 5 | 2022 | 14 | |
| 6 | 2021 | 13 | |
| 7 | 2022 | 10 | |
| 8 | 2022 | 9 | |
| 9 | 2021 | 5 | |
| 10 | 2021 | 5 | |
| 11 | 2018 | 5 | |
| 12 | 2021 | 4 | |
| 13 | 2025 | 3 | |
| 14 | 2017 | 3 | |
| 15 | 2022 | 3 | |
| 16 | 2020 | 3 | |
| 17 | 2023 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2024 | 2 | |
| 20 | 2024 | 2 |
About V Vivek
V Vivek is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Health Information Management and Computer Vision and Pattern Recognition, having authored 40 papers that have together received 227 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (7 papers), Cloud Computing and Resource Management (6 papers), IoT and Edge/Fog Computing (5 papers), AI in cancer detection (4 papers), Distributed and Parallel Computing Systems (4 papers), COVID-19 diagnosis using AI (3 papers), Cloud Data Security Solutions (3 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Health Information Management (51 citations), Artificial Intelligence (79 citations), Neurology (16 citations), Radiology, Nuclear Medicine and Imaging (37 citations) and Information Systems (36 citations). V Vivek has collaborated with scholars based in India, United States and Iraq. Frequent co-authors include T R Mahesh, V. Vinoth Kumar, Rajesh Natarajan, V. Dhilip Kumar, Junaid Asghar, Suresh Guluwadi, Veerma Ram, V. Muthukumaran, R. Dhanasekaran and Bharadwaj Veeravalli. Their work appears in journals such as Case Studies in Thermal Engineering, IEEE Access, Computational Intelligence and Neuroscience, Scientific Reports and Filomat.
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.