Sunil Kumar Mishra
- Artificial Intelligence top 10%
- Information Systems
- Computer Networks and Communications
- Physiology
- Molecular Biology
- Co-authors
- Vinay K. ChaudhriBruce PorterPeter E. ClarkKen BarkerKiran Kumar RavulakolluRavinder GoswamiNandita GuptaAndrés Rodríguez
- Topics
- Semantic Web and Ontologies (9 papers)AI-based Problem Solving and Planning (5 papers)Topic Modeling (4 papers)
- Journals
- The Journal of Clinical Endocrinology & MetabolismArchives of Gerontology and GeriatricsAI Magazine
- Partner nations
- IndiaUnited StatesAustralia
In The Last Decade
Sunil Kumar Mishra
19 papers receiving 263 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 177
- Information Systems 40
- Computer Networks and Communications 30
- Physiology 30
- Molecular Biology 29
Countries citing papers authored by Sunil Kumar Mishra
This map shows the geographic impact of Sunil Kumar Mishra'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 Sunil Kumar Mishra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunil Kumar Mishra more than expected).
Fields of papers citing papers by Sunil Kumar Mishra
This network shows the impact of papers produced by Sunil Kumar Mishra. 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 Sunil Kumar Mishra. The network helps show where Sunil Kumar Mishra may publish in the future.
Co-authorship network of co-authors of Sunil Kumar Mishra
This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Kumar Mishra. A scholar is included among the top collaborators of Sunil Kumar Mishra 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 Sunil Kumar Mishra. Sunil Kumar Mishra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 27 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 43 | |
| 8 | 6 | |
| 9 | 41 | |
| 10 | 14 | |
| 11 | 21 | |
| 12 | 3 | |
| 13 | A question-answering system for AP chemistry: assessing KR&R technologies | 24 |
| 14 | A Knowledge Acquisition Tool for Course of Action Analysis | 26 |
| 15 | 8 | |
| 16 | 15 | |
| 17 | Experimental Evaluation of Subject Matter Expert-oriented Knowledge Base Authoring Tools | 5 |
| 18 | 3 | |
| 19 | 48 | |
| 20 | Dual-Crosshatch Disk Array: A Highly Reliable Hybrid-RAID Architecture. | 2 |
About Sunil Kumar Mishra
Sunil Kumar Mishra is a scholar working on Artificial Intelligence, Behavioral Neuroscience and Software, having authored 20 papers that have together received 307 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (9 papers), AI-based Problem Solving and Planning (5 papers) and Topic Modeling (4 papers). The work is most often cited by research in Artificial Intelligence (177 citations), Hepatology (20 citations) and Information Systems and Management (13 citations). Sunil Kumar Mishra has collaborated with scholars based in India, United States and Australia. Frequent co-authors include Vinay K. Chaudhri, Bruce Porter, Peter E. Clark, Ken Barker, Kiran Kumar Ravulakollu, Ravinder Goswami, Nandita Gupta, Andrés Rodríguez, Yolanda Gil and Aaron Spaulding. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, Archives of Gerontology and Geriatrics and AI Magazine.
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.