Anima Naik
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 2%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering top 10%
- Co-authors
- Suresh Chandra SatapathyK. ParvathiAjith AbrahamAmira S. AshourNilanjan DeyM. Ramakrishna MurtyJ. V. R. MurthyP. V. G. D. Prasad Reddy
- Topics
- Metaheuristic Optimization Algorithms Research (18 papers)Advanced Multi-Objective Optimization Algorithms (13 papers)Evolutionary Algorithms and Applications (9 papers)
In The Last Decade
Anima Naik
24 papers receiving 672 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 428
- Computational Theory and Mathematics 210
- Electrical and Electronic Engineering 147
- Computer Vision and Pattern Recognition 83
- Control and Systems Engineering 79
Countries citing papers authored by Anima Naik
This map shows the geographic impact of Anima Naik'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 Anima Naik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anima Naik more than expected).
Fields of papers citing papers by Anima Naik
This network shows the impact of papers produced by Anima Naik. 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 Anima Naik. The network helps show where Anima Naik may publish in the future.
Co-authorship network of co-authors of Anima Naik
This figure shows the co-authorship network connecting the top 25 collaborators of Anima Naik. A scholar is included among the top collaborators of Anima Naik 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 Anima Naik. Anima Naik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 5 | |
| 9 | 12 | |
| 10 | 8 | |
| 11 | 64 | |
| 12 | 193 | |
| 13 | 54 | |
| 14 | 15 | |
| 15 | 82 | |
| 16 | 8 | |
| 17 | 54 | |
| 18 | 15 | |
| 19 | 10 | |
| 20 | 16 |
About Anima Naik
Anima Naik is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Health Information Management, having authored 25 papers that have together received 705 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (18 papers), Advanced Multi-Objective Optimization Algorithms (13 papers) and Evolutionary Algorithms and Applications (9 papers). The work is most often cited by research in Artificial Intelligence (428 citations), Computational Theory and Mathematics (210 citations) and Industrial and Manufacturing Engineering (49 citations). Anima Naik has collaborated with scholars based in India, Sweden and Egypt. Frequent co-authors include Suresh Chandra Satapathy, K. Parvathi, Ajith Abraham, Amira S. Ashour, Nilanjan Dey, M. Ramakrishna Murty, J. V. R. Murthy, P. V. G. D. Prasad Reddy, Ajaya Kumar Parida and Jnyana Ranjan Mohanty. Their work appears in journals such as Scientific Reports, Applied Soft Computing 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.