Jay Kumar
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
Papers in
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- Data Stream Mining Techniques 7
- Text and Document Classification Technologies 5
- Privacy-Preserving Technologies in Data 5
- Topic Modeling 4
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- Blockchain Technology Applications and Security 4
- Co-authors
- Rajesh Kumar (13 shared papers)Wenyong Wang (4 shared papers)Abdullah Aman Khan (3 shared papers)Riaz Ullah Khan (5 shared papers)Ting Yang (1 shared paper)Xiaosong Zhang (3 shared papers)Salah Ud Din (8 shared papers)Rajesh Kumar (6 shared papers)
In The Last Decade
Jay Kumar
34 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 102
- Health Informatics 113
- Artificial Intelligence 582
- Signal Processing 184
- Information Systems 324
- Computer Networks and Communications 253
Countries citing papers authored by Jay Kumar
This map shows the geographic impact of Jay 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 Jay Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Kumar more than expected).
Fields of papers citing papers by Jay Kumar
This network shows the impact of papers produced by Jay 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 Jay Kumar. The network helps show where Jay Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay 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 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 337 | |
| 2 | 2020 | 110 | |
| 3 | 2019 | 96 | |
| 4 | 2022 | 89 | |
| 5 | 2020 | 60 | |
| 6 | 2018 | 45 | |
| 7 | 2022 | 39 | |
| 8 | 2021 | 31 | |
| 9 | 2021 | 30 | |
| 10 | 2023 | 27 | |
| 11 | 2020 | 23 | |
| 12 | 2024 | 22 | |
| 13 | 2018 | 22 | |
| 14 | 2017 | 21 | |
| 15 | 2022 | 16 | |
| 16 | 2023 | 15 | |
| 17 | 2023 | 14 | |
| 18 | 2021 | 12 | |
| 19 | 2020 | 10 | |
| 20 | 2019 | 7 |
About Jay Kumar
Jay Kumar is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications, having authored 38 papers that have together received 1.1k indexed citations. Recurring topics across this work include Data Stream Mining Techniques (7 papers), COVID-19 diagnosis using AI (5 papers), Text and Document Classification Technologies (5 papers), Privacy-Preserving Technologies in Data (5 papers), Network Security and Intrusion Detection (4 papers), Blockchain Technology Applications and Security (4 papers), Topic Modeling (4 papers) and Complex Network Analysis Techniques (3 papers). The work is most often cited by research in Health Informatics (113 citations), Artificial Intelligence (582 citations), Signal Processing (184 citations), Information Systems (324 citations) and Computer Networks and Communications (253 citations). Jay Kumar has collaborated with scholars based in China, Pakistan and Canada. Frequent co-authors include Rajesh Kumar, Wenyong Wang, Abdullah Aman Khan, Riaz Ullah Khan, Ting Yang, Xiaosong Zhang, Salah Ud Din, Rajesh Kumar, Zakria Zakria and Junming Shao. Their work appears in journals such as Information Sciences, IEEE Transactions on Cybernetics, IEEE Transactions on Systems Man and Cybernetics Systems, Computerized Medical Imaging and Graphics and Artificial Intelligence in Medicine.
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.