Subrato Bharati
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Health Informatics top 1%
- Computer Networks and Communications top 10%
- Health Information Management top 2%
- Co-authors
- Prajoy PodderM. Rubaiyat Hossain MondalV. B. Surya PrasathMohammad Atikur RahmanUtku KöseMd Junayed HasanDang N. H. ThanhFarzin Piltan
- Topics
- COVID-19 diagnosis using AI (17 papers)AI in cancer detection (11 papers)Radiomics and Machine Learning in Medical Imaging (8 papers)
- Partner nations
- BangladeshCanadaUnited States
In The Last Decade
Subrato Bharati
41 papers receiving 827 citations
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 411
- Radiology, Nuclear Medicine and Imaging 290
- Health Informatics 122
- Computer Networks and Communications 99
- Health Information Management 80
Countries citing papers authored by Subrato Bharati
This map shows the geographic impact of Subrato Bharati'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 Subrato Bharati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subrato Bharati more than expected).
Fields of papers citing papers by Subrato Bharati
This network shows the impact of papers produced by Subrato Bharati. 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 Subrato Bharati. The network helps show where Subrato Bharati may publish in the future.
Co-authorship network of co-authors of Subrato Bharati
This figure shows the co-authorship network connecting the top 25 collaborators of Subrato Bharati. A scholar is included among the top collaborators of Subrato Bharati 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 Subrato Bharati. Subrato Bharati 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 | 1 | |
| 3 | 2 | |
| 4 | 14 | |
| 5 | 36 | |
| 6 | 2 | |
| 7 | BUSINESS INTELLIGENCE AND INDUSTRY 5.0 | 2 |
| 8 | Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review | 1 |
| 9 | 43 | |
| 10 | 59 | |
| 11 | 27 | |
| 12 | HOW ARTIFICIAL INTELLIGENCE IMPACTS BUSINESSES IN THE PERIOD OF PANDEMICS | 3 |
| 13 | Disease Detection from Lung X-ray Images based on Hybrid Deep Learning | 2 |
| 14 | 20 | |
| 15 | 65 | |
| 16 | 11 | |
| 17 | 3 | |
| 18 | 35 | |
| 19 | 10 | |
| 20 | 4 |
About Subrato Bharati
Subrato Bharati is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Modeling and Simulation, having authored 43 papers that have together received 887 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (17 papers), AI in cancer detection (11 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). The work is most often cited by research in Health Informatics (122 citations), Health Information Management (80 citations) and Artificial Intelligence (411 citations). Subrato Bharati has collaborated with scholars based in Bangladesh, Canada and United States. Frequent co-authors include Prajoy Podder, M. Rubaiyat Hossain Mondal, V. B. Surya Prasath, Mohammad Atikur Rahman, Utku Köse, Md Junayed Hasan, Dang N. H. Thanh, Farzin Piltan, Aditya Khamparia and Deepak Kumar Jain. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Sensors.
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.