Amit Kumar Das
Impact in
- Health Informatics top 10%
-
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- Text and Document Classification Technologies 4
- Metaheuristic Optimization Algorithms Research 2
-
- Face and Expression Recognition 6
- Co-authors
- Amlan Chakrabarti (10 shared papers)Saptarsi Goswami (15 shared papers)Sachin Agarwal (1 shared paper)Basabi Chakraborty (6 shared papers)Himadri Nath Saha (1 shared paper)Sruti Das Choudhury (1 shared paper)Soumya Sen (1 shared paper)Sahil Kumar (1 shared paper)
In The Last Decade
Amit Kumar Das
28 papers receiving 512 citations
Peers
Comparison fields: 5 of 108
- Health Informatics 26
- Radiology, Nuclear Medicine and Imaging 149
- Artificial Intelligence 245
- General Dentistry 8
- Computer Vision and Pattern Recognition 101
Countries citing papers authored by Amit Kumar Das
This map shows the geographic impact of Amit Kumar Das'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 Amit Kumar Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit Kumar Das more than expected).
Fields of papers citing papers by Amit Kumar Das
This network shows the impact of papers produced by Amit Kumar Das. 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 Amit Kumar Das. The network helps show where Amit Kumar Das may publish in the future.
Co-authors
The 25 scholars most cited alongside Amit Kumar Das, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 164 | |
| 2 | 2019 | 99 | |
| 3 | 2022 | 60 | |
| 4 | 2017 | 43 | |
| 5 | 2017 | 39 | |
| 6 | 2019 | 32 | |
| 7 | 2019 | 14 | |
| 8 | 2017 | 10 | |
| 9 | 2019 | 9 | |
| 10 | 2022 | 9 | |
| 11 | 2023 | 9 | |
| 12 | 2015 | 6 | |
| 13 | 2019 | 6 | |
| 14 | 2017 | 5 | |
| 15 | 2020 | 5 | |
| 16 | 2020 | 5 | |
| 17 | 2021 | 5 | |
| 18 | 2015 | 4 | |
| 19 | 2016 | 3 | |
| 20 | 2020 | 3 |
About Amit Kumar Das
Amit Kumar Das is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Health Information Management, Computer Networks and Communications and Orthopedics and Sports Medicine, having authored 28 papers that have together received 541 indexed citations. Recurring topics across this work include Face and Expression Recognition (6 papers), Text and Document Classification Technologies (4 papers), Artificial Intelligence in Healthcare (3 papers), Gene expression and cancer classification (3 papers), Machine Learning in Bioinformatics (2 papers), COVID-19 diagnosis using AI (2 papers), Metaheuristic Optimization Algorithms Research (2 papers) and UAV Applications and Optimization (2 papers). The work is most often cited by research in Health Informatics (26 citations), Radiology, Nuclear Medicine and Imaging (149 citations), Artificial Intelligence (245 citations), General Dentistry (8 citations) and Computer Vision and Pattern Recognition (101 citations). Amit Kumar Das has collaborated with scholars based in India, Japan and Nepal. Frequent co-authors include Amlan Chakrabarti, Saptarsi Goswami, Sachin Agarwal, Basabi Chakraborty, Himadri Nath Saha, Sruti Das Choudhury, Soumya Sen, Sahil Kumar, Jyotsna Kumar Mandal and Arindam Basak. Their work appears in journals such as Sadhana, Expert Systems with Applications, Pattern Analysis and Applications, Gastrointestinal Endoscopy and Education and Information Technologies.
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.