Surendra Thakur
Impact in
Papers in
-
- Sentiment Analysis and Opinion Mining 2
- Text and Document Classification Technologies 2
-
- Spam and Phishing Detection 3
- Co-authors
- Sibusiso Moyo (7 shared papers)Emmanuel Adetiba (18 shared papers)Stanley Chibuzor Onwubu (6 shared papers)Oludayo O. Olugbara (4 shared papers)Richard Millham (4 shared papers)Joke A. Badejo (2 shared papers)Kamal Kant Hiran (1 shared paper)M. Nawaz Sharif (1 shared paper)
- Journals
- IEEE Access (1 paper)Journal of Computer Information Systems (1 paper)IEEE Sensors Journal (1 paper)Informatics (1 paper)Journal of Computer Science (2 papers)
- Partner nations
- South AfricaNigeriaUnited Kingdom
In The Last Decade
Surendra Thakur
36 papers receiving 156 citations
Peers
Comparison fields: 5 of 87
- Health Informatics 4
- Life-span and Life-course Studies 2
- Modeling and Simulation 8
- Numerical Analysis 9
- Orthodontics 7
Countries citing papers authored by Surendra Thakur
This map shows the geographic impact of Surendra Thakur'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 Surendra Thakur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Surendra Thakur more than expected).
Fields of papers citing papers by Surendra Thakur
This network shows the impact of papers produced by Surendra Thakur. 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 Surendra Thakur. The network helps show where Surendra Thakur may publish in the future.
Co-authors
The 20 scholars most cited alongside Surendra Thakur, 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 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 17 | |
| 2 | 2021 | 16 | |
| 3 | 2014 | 10 | |
| 4 | 2023 | 9 | |
| 5 | 2022 | 9 | |
| 6 | 2015 | 8 | |
| 7 | 2022 | 8 | |
| 8 | 2020 | 7 | |
| 9 | 2019 | 7 | |
| 10 | 2022 | 7 | |
| 11 | 2019 | 7 | |
| 12 | 2021 | 6 | |
| 13 | 2022 | 6 | |
| 14 | 2021 | 4 | |
| 15 | 2019 | 4 | |
| 16 | 2023 | 3 | |
| 17 | 2022 | 3 | |
| 18 | 2022 | 3 | |
| 19 | 2014 | 3 | |
| 20 | 2021 | 3 |
About Surendra Thakur
Surendra Thakur is a scholar working on Artificial Intelligence, Information Systems, Electrical and Electronic Engineering, Sociology and Political Science and Computer Networks and Communications, having authored 45 papers that have together received 164 indexed citations. Recurring topics across this work include IoT-based Smart Home Systems (3 papers), Spam and Phishing Detection (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Misinformation and Its Impacts (3 papers), COVID-19 diagnosis using AI (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Cognitive Radio Networks and Spectrum Sensing (2 papers) and Text and Document Classification Technologies (2 papers). The work is most often cited by research in Health Informatics (4 citations), Life-span and Life-course Studies (2 citations), Modeling and Simulation (8 citations), Numerical Analysis (9 citations) and Orthodontics (7 citations). Surendra Thakur has collaborated with scholars based in South Africa, Nigeria and United Kingdom. Frequent co-authors include Sibusiso Moyo, Emmanuel Adetiba, Stanley Chibuzor Onwubu, Oludayo O. Olugbara, Richard Millham, Joke A. Badejo, Kamal Kant Hiran, M. Nawaz Sharif, Geoff Harris and Ruchi Doshi. Their work appears in journals such as IEEE Access, Journal of Computer Information Systems, IEEE Sensors Journal, Informatics and Journal of Computer Science.
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.