Swati Vipsita
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics
- Computer Networks and Communications
- Co-authors
- Santos Kumar BaliarsinghSambit BakshiKhan MuhammadSantanu Kumar RathWeiping DingSomula RamasubbareddyRabindra K. BarikRakesh Kumar Lenka
- Topics
- Gene expression and cancer classification (16 papers)Evolutionary Algorithms and Applications (14 papers)Machine Learning in Bioinformatics (11 papers)
- Journals
- Applied Soft ComputingComputer Methods and Programs in BiomedicineNeural Computing and Applications
- Partner nations
- IndiaSouth KoreaAustralia
In The Last Decade
Swati Vipsita
34 papers receiving 328 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 191
- Molecular Biology 138
- Computer Vision and Pattern Recognition 63
- Computational Theory and Mathematics 32
- Computer Networks and Communications 27
Countries citing papers authored by Swati Vipsita
This map shows the geographic impact of Swati Vipsita'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 Swati Vipsita with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Swati Vipsita more than expected).
Fields of papers citing papers by Swati Vipsita
This network shows the impact of papers produced by Swati Vipsita. 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 Swati Vipsita. The network helps show where Swati Vipsita may publish in the future.
Co-authorship network of co-authors of Swati Vipsita
This figure shows the co-authorship network connecting the top 25 collaborators of Swati Vipsita. A scholar is included among the top collaborators of Swati Vipsita 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 Swati Vipsita. Swati Vipsita is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 9 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 26 | |
| 7 | 2 | |
| 8 | 51 | |
| 9 | 41 | |
| 10 | 3 | |
| 11 | 48 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 2 | |
| 15 | 3 | |
| 16 | 2 | |
| 17 | 4 | |
| 18 | 11 | |
| 19 | 3 | |
| 20 | 4 |
About Swati Vipsita
Swati Vipsita is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology, having authored 38 papers that have together received 349 indexed citations. Recurring topics across this work include Gene expression and cancer classification (16 papers), Evolutionary Algorithms and Applications (14 papers) and Machine Learning in Bioinformatics (11 papers). The work is most often cited by research in Artificial Intelligence (191 citations), Health Information Management (20 citations) and Computational Mathematics (2 citations). Swati Vipsita has collaborated with scholars based in India, South Korea and Australia. Frequent co-authors include Santos Kumar Baliarsingh, Sambit Bakshi, Khan Muhammad, Santanu Kumar Rath, Weiping Ding, Somula Ramasubbareddy, Rabindra K. Barik, Rakesh Kumar Lenka, Puspanjali Mohapatra and Amir H. Gandomi. Their work appears in journals such as Applied Soft Computing, Computer Methods and Programs in Biomedicine 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.