Shibaprasad Sen
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Media Technology top 5%
- Computer Networks and Communications
- Co-authors
- Ram SarkarSomnath ChatterjeeRishav PramanikKaushik RoySeyedali MirjaliliDmitrii KaplunJoão Paulo PapaLuis A. de Souza
- Topics
- Handwritten Text Recognition Techniques (11 papers)Vehicle License Plate Recognition (8 papers)Natural Language Processing Techniques (4 papers)
- Partner nations
- IndiaUnited StatesGermany
In The Last Decade
Shibaprasad Sen
26 papers receiving 500 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 258
- Computer Vision and Pattern Recognition 179
- Radiology, Nuclear Medicine and Imaging 167
- Media Technology 63
- Computer Networks and Communications 45
Countries citing papers authored by Shibaprasad Sen
This map shows the geographic impact of Shibaprasad Sen'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 Shibaprasad Sen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shibaprasad Sen more than expected).
Fields of papers citing papers by Shibaprasad Sen
This network shows the impact of papers produced by Shibaprasad Sen. 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 Shibaprasad Sen. The network helps show where Shibaprasad Sen may publish in the future.
Co-authorship network of co-authors of Shibaprasad Sen
This figure shows the co-authorship network connecting the top 25 collaborators of Shibaprasad Sen. A scholar is included among the top collaborators of Shibaprasad Sen 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 Shibaprasad Sen. Shibaprasad Sen 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 | 3 | |
| 3 | 1 | |
| 4 | 19 | |
| 5 | 1 | |
| 6 | 20 | |
| 7 | 5 | |
| 8 | 85 | |
| 9 | 13 | |
| 10 | 2 | |
| 11 | 21 | |
| 12 | 34 | |
| 13 | 0 | |
| 14 | 4 | |
| 15 | 74 | |
| 16 | 27 | |
| 17 | 40 | |
| 18 | 53 | |
| 19 | 10 | |
| 20 | 5 |
About Shibaprasad Sen
Shibaprasad Sen is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 28 papers that have together received 512 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (11 papers), Vehicle License Plate Recognition (8 papers) and Natural Language Processing Techniques (4 papers). The work is most often cited by research in Health Informatics (14 citations), Computer Vision and Pattern Recognition (179 citations) and Artificial Intelligence (258 citations). Shibaprasad Sen has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Ram Sarkar, Somnath Chatterjee, Rishav Pramanik, Kaushik Roy, Seyedali Mirjalili, Dmitrii Kaplun, João Paulo Papa, Luis A. de Souza, Aleksandr Sinitca and Friedhelm Schwenker. Their work appears in journals such as Scientific Reports, Expert Systems with Applications and Pattern Recognition Letters.
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.