Divya Nimma
Impact in
Papers in
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- AI in cancer detection 4
- Imbalanced Data Classification Techniques 4
- Anomaly Detection Techniques and Applications 3
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- Advanced Neural Network Applications 3
- Co-authors
- Janjhyam Venkata Naga Ramesh (19 shared papers)Arpita Arpita (1 shared paper)Nikul Kumari (1 shared paper)Vineet Tirth (1 shared paper)Pradeep Jangir (6 shared papers)Zhaoxian Zhou (2 shared papers)Mukesh Soni (7 shared papers)Ismail Keshta (4 shared papers)
- Journals
- IEEE Transactions on Consumer Electronics (7 papers)IEEE Transactions on Intelligent Transportation Systems (3 papers)Journal of Thermal Biology (2 papers)Alexandria Engineering Journal (2 papers)International Journal of Machine Learning and Cybernetics (2 papers)
- Partner nations
- United StatesIndiaSaudi Arabia
In The Last Decade
Divya Nimma
38 papers receiving 130 citations
Peers
Comparison fields: 5 of 72
- Energy Engineering and Power Technology 4
- Health Informatics 1
- Orthopedics and Sports Medicine 6
- Computer Vision and Pattern Recognition 15
- Aging 1
Countries citing papers authored by Divya Nimma
This map shows the geographic impact of Divya Nimma'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 Divya Nimma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Divya Nimma more than expected).
Fields of papers citing papers by Divya Nimma
This network shows the impact of papers produced by Divya Nimma. 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 Divya Nimma. The network helps show where Divya Nimma may publish in the future.
Co-authors
The 25 scholars most cited alongside Divya Nimma, 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 61 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 29 | |
| 2 | 2024 | 29 | |
| 3 | 2025 | 10 | |
| 4 | 2025 | 7 | |
| 5 | 2025 | 6 | |
| 6 | 2023 | 3 | |
| 7 | 2025 | 3 | |
| 8 | 2025 | 2 | |
| 9 | 2024 | 2 | |
| 10 | 2024 | 2 | |
| 11 | 2025 | 2 | |
| 12 | 2025 | 2 | |
| 13 | 2025 | 2 | |
| 14 | 2025 | 2 | |
| 15 | 2024 | 2 | |
| 16 | 2024 | 2 | |
| 17 | 2025 | 2 | |
| 18 | 2025 | 2 | |
| 19 | 2025 | 2 | |
| 20 | 2025 | 2 |
About Divya Nimma
Divya Nimma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Information Systems and Building and Construction, having authored 61 papers that have together received 131 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Imbalanced Data Classification Techniques (4 papers), Customer churn and segmentation (4 papers), Blockchain Technology Applications and Security (3 papers), Anomaly Detection Techniques and Applications (3 papers), Advanced Neural Network Applications (3 papers), IoT and Edge/Fog Computing (3 papers) and Traffic Prediction and Management Techniques (3 papers). The work is most often cited by research in Energy Engineering and Power Technology (4 citations), Health Informatics (1 citation), Orthopedics and Sports Medicine (6 citations), Computer Vision and Pattern Recognition (15 citations) and Aging (1 citation). Divya Nimma has collaborated with scholars based in United States, India and Saudi Arabia. Frequent co-authors include Janjhyam Venkata Naga Ramesh, Arpita Arpita, Nikul Kumari, Vineet Tirth, Pradeep Jangir, Zhaoxian Zhou, Mukesh Soni, Ismail Keshta, Rahul Pradhan and Haewon Byeon. Their work appears in journals such as IEEE Transactions on Consumer Electronics, IEEE Transactions on Intelligent Transportation Systems, Journal of Thermal Biology, Alexandria Engineering Journal and International Journal of Machine Learning and Cybernetics.
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.