Manas Ranjan Prusty
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Plant Science
- Molecular Biology
- Computer Vision and Pattern Recognition
- Co-authors
- K. VelusamyManoj KumarManish K. PandeyAbhishek BohraPrashant Kumar SinghRajeev K. VarshneyBaozhu GuoA. Balasundaram
- Topics
- AI in cancer detection (6 papers)COVID-19 diagnosis using AI (4 papers)Digital Imaging for Blood Diseases (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Access
- Partner nations
- IndiaUnited StatesIsrael
In The Last Decade
Manas Ranjan Prusty
29 papers receiving 442 citations
Peers
Comparison fields: 5 of 104
- Artificial Intelligence 145
- Radiology, Nuclear Medicine and Imaging 96
- Plant Science 96
- Molecular Biology 62
- Computer Vision and Pattern Recognition 58
Countries citing papers authored by Manas Ranjan Prusty
This map shows the geographic impact of Manas Ranjan Prusty'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 Manas Ranjan Prusty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manas Ranjan Prusty more than expected).
Fields of papers citing papers by Manas Ranjan Prusty
This network shows the impact of papers produced by Manas Ranjan Prusty. 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 Manas Ranjan Prusty. The network helps show where Manas Ranjan Prusty may publish in the future.
Co-authorship network of co-authors of Manas Ranjan Prusty
This figure shows the co-authorship network connecting the top 25 collaborators of Manas Ranjan Prusty. A scholar is included among the top collaborators of Manas Ranjan Prusty 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 Manas Ranjan Prusty. Manas Ranjan Prusty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 6 | |
| 5 | 8 | |
| 6 | 8 | |
| 7 | 0 | |
| 8 | 84 | |
| 9 | 31 | |
| 10 | 0 | |
| 11 | 27 | |
| 12 | 9 | |
| 13 | 2 | |
| 14 | 30 | |
| 15 | 36 | |
| 16 | 18 | |
| 17 | 39 | |
| 18 | 55 | |
| 19 | 12 | |
| 20 | 18 |
About Manas Ranjan Prusty
Manas Ranjan Prusty is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 32 papers that have together received 458 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), COVID-19 diagnosis using AI (4 papers) and Digital Imaging for Blood Diseases (3 papers). The work is most often cited by research in Health Informatics (8 citations), Health Information Management (23 citations) and Artificial Intelligence (145 citations). Manas Ranjan Prusty has collaborated with scholars based in India, United States and Israel. Frequent co-authors include K. Velusamy, Manoj Kumar, Manish K. Pandey, Abhishek Bohra, Prashant Kumar Singh, Rajeev K. Varshney, Baozhu Guo, A. Balasundaram, Shaurya Gupta and Anmol Gupta. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.
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.