V. Pattabiraman
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Plant Science
- Radiology, Nuclear Medicine and Imaging
- Information Systems
- Co-authors
- R. ParvathiIshita DasguptaThavavel VaiyapuriHaya AlaskarAbir HussainAkshay KumarR. SivakumarMangayarkarasi Ramaiah
- Topics
- Data Mining Algorithms and Applications (4 papers)Artificial Intelligence in Healthcare (4 papers)COVID-19 diagnosis using AI (4 papers)
- Partner nations
- IndiaUnited KingdomUnited Arab Emirates
In The Last Decade
V. Pattabiraman
22 papers receiving 313 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Computer Vision and Pattern Recognition 105
- Artificial Intelligence 86
- Plant Science 42
- Radiology, Nuclear Medicine and Imaging 41
- Information Systems 30
Countries citing papers authored by V. Pattabiraman
This map shows the geographic impact of V. Pattabiraman'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 V. Pattabiraman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. Pattabiraman more than expected).
Fields of papers citing papers by V. Pattabiraman
This network shows the impact of papers produced by V. Pattabiraman. 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 V. Pattabiraman. The network helps show where V. Pattabiraman may publish in the future.
Co-authorship network of co-authors of V. Pattabiraman
This figure shows the co-authorship network connecting the top 25 collaborators of V. Pattabiraman. A scholar is included among the top collaborators of V. Pattabiraman 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 V. Pattabiraman. V. Pattabiraman 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 22 | |
| 9 | 1 | |
| 10 | Comparative analysis of deep learning image detection algorithmsbreakdown → | 202 |
| 11 | 26 | |
| 12 | 9 | |
| 13 | 1 | |
| 14 | 32 | |
| 15 | 1 | |
| 16 | 4 | |
| 17 | 0 | |
| 18 | 2 | |
| 19 | 1 | |
| 20 | A new technique of embedding multigrain parallel HPRC in OR1200 a soft-core processor | 1 |
About V. Pattabiraman
V. Pattabiraman is a scholar working on Health Information Management, Neurology and Hardware and Architecture, having authored 32 papers that have together received 341 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (4 papers), Artificial Intelligence in Healthcare (4 papers) and COVID-19 diagnosis using AI (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (105 citations), Media Technology (26 citations) and Artificial Intelligence (86 citations). V. Pattabiraman has collaborated with scholars based in India, United Kingdom and United Arab Emirates. Frequent co-authors include R. Parvathi, Ishita Dasgupta, Thavavel Vaiyapuri, Haya Alaskar, Abir Hussain, Akshay Kumar, R. Sivakumar, Mangayarkarasi Ramaiah, Anoop Kumar Mishra and Meghna Manoj Nair. Their work appears in journals such as Scientific Reports, Applied Sciences and Biomedical Signal Processing and Control.
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.