Deepak Ranjan Nayak
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 1%
- Neurology top 1%
- Media Technology top 2%
- Co-authors
- Banshidhar MajhiRatnakar DashYudong ZhangRam Bilas PachoriShuihua WangUtkarsh SinhaSoumya Ranjan NayakVaibhav Arora
- Topics
- Brain Tumor Detection and Classification (28 papers)Machine Learning and ELM (19 papers)AI in cancer detection (12 papers)
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsIEEE Transactions on Fuzzy Systems
- Partner nations
- IndiaUnited KingdomChina
In The Last Decade
Deepak Ranjan Nayak
69 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 908
- Radiology, Nuclear Medicine and Imaging 760
- Computer Vision and Pattern Recognition 723
- Neurology 540
- Media Technology 150
Countries citing papers authored by Deepak Ranjan Nayak
This map shows the geographic impact of Deepak Ranjan Nayak'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 Deepak Ranjan Nayak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Ranjan Nayak more than expected).
Fields of papers citing papers by Deepak Ranjan Nayak
This network shows the impact of papers produced by Deepak Ranjan Nayak. 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 Deepak Ranjan Nayak. The network helps show where Deepak Ranjan Nayak may publish in the future.
Co-authorship network of co-authors of Deepak Ranjan Nayak
This figure shows the co-authorship network connecting the top 25 collaborators of Deepak Ranjan Nayak. A scholar is included among the top collaborators of Deepak Ranjan Nayak 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 Deepak Ranjan Nayak. Deepak Ranjan Nayak 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 | 8 | |
| 4 | 0 | |
| 5 | 16 | |
| 6 | 4 | |
| 7 | 11 | |
| 8 | 43 | |
| 9 | 43 | |
| 10 | 5 | |
| 11 | 28 | |
| 12 | 2 | |
| 13 | 188 | |
| 14 | 27 | |
| 15 | 1 | |
| 16 | Sinonasal - Type Hemangiopericytoma of Nasal Cavity: A Rare Neoplasm- Case Report with a Brief Review of Literature | 3 |
| 17 | 20 | |
| 18 | 1 | |
| 19 | 57 | |
| 20 | 2 |
About Deepak Ranjan Nayak
Deepak Ranjan Nayak is a scholar working on Neurology, Computer Vision and Pattern Recognition and Urban Studies, having authored 74 papers that have together received 2.0k indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (28 papers), Machine Learning and ELM (19 papers) and AI in cancer detection (12 papers). The work is most often cited by research in Neurology (540 citations), Health Informatics (65 citations) and Computer Vision and Pattern Recognition (723 citations). Deepak Ranjan Nayak has collaborated with scholars based in India, United Kingdom and China. Frequent co-authors include Banshidhar Majhi, Ratnakar Dash, Yudong Zhang, Ram Bilas Pachori, Shuihua Wang, Utkarsh Sinha, Soumya Ranjan Nayak, Vaibhav Arora, Xin Zhang and David S. Guttery. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Transactions on Fuzzy Systems.
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.