T. S. Subashini
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Pulmonary and Respiratory Medicine
- Biomedical Engineering
- Co-authors
- S. PalanivelV. RamalingamN. BalajiNatarajan ChidambaramA. AhilanR. SrinivasanA. SureshR. Jebakumar
- Topics
- Digital Media Forensic Detection (12 papers)Medical Image Segmentation Techniques (9 papers)AI in cancer detection (9 papers)
- Cited by
- Computer Vision and Pattern RecognitionHealth Information ManagementArtificial Intelligence
- Journals
- Expert Systems with ApplicationsComputer Vision and Image UnderstandingEngineering Science and Technology an International Journal
- Partner nations
- India
In The Last Decade
T. S. Subashini
39 papers receiving 521 citations
Peers
Comparison fields: 5 of 89
- Computer Vision and Pattern Recognition 256
- Artificial Intelligence 232
- Radiology, Nuclear Medicine and Imaging 125
- Pulmonary and Respiratory Medicine 61
- Biomedical Engineering 54
Countries citing papers authored by T. S. Subashini
This map shows the geographic impact of T. S. Subashini'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 T. S. Subashini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. S. Subashini more than expected).
Fields of papers citing papers by T. S. Subashini
This network shows the impact of papers produced by T. S. Subashini. 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 T. S. Subashini. The network helps show where T. S. Subashini may publish in the future.
Co-authorship network of co-authors of T. S. Subashini
This figure shows the co-authorship network connecting the top 25 collaborators of T. S. Subashini. A scholar is included among the top collaborators of T. S. Subashini 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 T. S. Subashini. T. S. Subashini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 10 | |
| 4 | 3 | |
| 5 | 21 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 30 | |
| 9 | 10 | |
| 10 | 39 | |
| 11 | 1 | |
| 12 | 34 | |
| 13 | 17 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 4 | |
| 17 | 3 | |
| 18 | A Novel Approach to Improve Invisibleness and Robustness of a Digital Watermark in CopyrightProtection | 2 |
| 19 | 1 | |
| 20 | 81 |
About T. S. Subashini
T. S. Subashini is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Media Technology, having authored 43 papers that have together received 585 indexed citations. Recurring topics across this work include Digital Media Forensic Detection (12 papers), Medical Image Segmentation Techniques (9 papers) and AI in cancer detection (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (256 citations), Health Information Management (34 citations) and Artificial Intelligence (232 citations). T. S. Subashini has collaborated with scholars based in India. Frequent co-authors include S. Palanivel, V. Ramalingam, N. Balaji, Natarajan Chidambaram, A. Ahilan, R. Srinivasan, A. Suresh, R. Jebakumar, S. Velmurugan and M. Bharathi. Their work appears in journals such as Expert Systems with Applications, Computer Vision and Image Understanding and Engineering Science and Technology an International Journal.
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.