Channabasava Chola
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- Digital Imaging for Blood Diseases 2
- Artificial Intelligence top 10%
- AI in cancer detection 3
- Text and Document Classification Technologies 3
- Advanced Text Analysis Techniques 2
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- COVID-19 diagnosis using AI 4
- Radiomics and Machine Learning in Medical Imaging 2
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- Spam and Phishing Detection 3
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- Robot Manipulation and Learning 2
- Co-authors
- Abdullah Y. MuaadJ. V. Bibal BenifaMugahed A. Al–antariK. HemachandranM. Turki-Hadj AlouaneAreej AlasiryAnil Audumbar PiseShahid Mohammad Ganie
- Cited by
- Computer Vision and Pattern RecognitionHealth Information ManagementArtificial Intelligence
- Partner nations
- IndiaSouth KoreaSaudi Arabia
In The Last Decade
Channabasava Chola
17 papers receiving 206 citations
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 84
- Health Information Management 14
- Artificial Intelligence 96
- Health Informatics 4
- Radiology, Nuclear Medicine and Imaging 40
Countries citing papers authored by Channabasava Chola
This map shows the geographic impact of Channabasava Chola'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 Channabasava Chola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Channabasava Chola more than expected).
Fields of papers citing papers by Channabasava Chola
This network shows the impact of papers produced by Channabasava Chola. 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 Channabasava Chola. The network helps show where Channabasava Chola may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Channabasava Chola, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 10 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 16 | |
| 8 | 2023 | 44 | |
| 9 | 2023 | 7 | |
| 10 | 2022 | 9 | |
| 11 | 2022 | 2 | |
| 12 | 2022 | 31 | |
| 13 | 2022 | 5 | |
| 14 | 2022 | 3 | |
| 15 | 2022 | 23 | |
| 16 | 2022 | 21 | |
| 17 | 2021 | 11 | |
| 18 | 2021 | 12 | |
| 19 | Detection of misogyny from Arabic Levantine Twitter tweets using machine learning techniques | 2021 | 1 |
| 20 | 2021 | 24 |
About Channabasava Chola
Channabasava Chola is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Health Information Management, having authored 20 papers that have together received 221 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (4 papers), Spam and Phishing Detection (3 papers), AI in cancer detection (3 papers), Text and Document Classification Technologies (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Digital Imaging for Blood Diseases (2 papers), Advanced Text Analysis Techniques (2 papers) and Robot Manipulation and Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (84 citations), Health Information Management (14 citations) and Artificial Intelligence (96 citations). Channabasava Chola has collaborated with scholars based in India, South Korea and Saudi Arabia. Frequent co-authors include Abdullah Y. Muaad, J. V. Bibal Benifa, Mugahed A. Al–antari, K. Hemachandran, M. Turki-Hadj Alouane, Areej Alasiry, Anil Audumbar Pise, Shahid Mohammad Ganie, Mehrez Marzougui and Md Belal Bin Heyat. Their work appears in journals such as Sensors, BioMed Research International and Applied Sciences.
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.