S. Venkatramaphanikumar
Impact in
- Analytical Chemistry top 10%
- Spectroscopy and Chemometric Analyses
- Plant Science top 10%
- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
- Plant Disease Management Techniques
Papers in
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- Face and Expression Recognition 3
- Face recognition and analysis 3
- Advanced Neural Network Applications 3
- Digital Imaging for Blood Diseases 2
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- Sentiment Analysis and Opinion Mining 5
S. Venkatramaphanikumar
19 papers receiving 390 citations
S. Venkatramaphanikumar's Hit Papers
Peers
Comparison fields: 5 of 78
- Analytical Chemistry 60
- Plant Science 186
- Health Information Management 18
- Neurology 32
- Health Informatics 4
Countries citing papers authored by S. Venkatramaphanikumar
This map shows the geographic impact of S. Venkatramaphanikumar'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 S. Venkatramaphanikumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Venkatramaphanikumar more than expected).
Fields of papers citing papers by S. Venkatramaphanikumar
This network shows the impact of papers produced by S. Venkatramaphanikumar. 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 S. Venkatramaphanikumar. The network helps show where S. Venkatramaphanikumar may publish in the future.
Co-authors
The 2 scholars most cited alongside S. Venkatramaphanikumar, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Transfer learning-based deep ensemble neural network for plant leaf disease detection Hit paper breakdown → | 2021 | 183 |
| 2 | 2019 | 76 | |
| 3 | 2024 | 29 | |
| 4 | 2022 | 28 | |
| 5 | 2019 | 16 | |
| 6 | 2023 | 13 | |
| 7 | 2020 | 10 | |
| 8 | 2020 | 10 | |
| 9 | 2014 | 8 | |
| 10 | 2022 | 7 | |
| 11 | 2021 | 6 | |
| 12 | 2020 | 6 | |
| 13 | 2020 | 3 | |
| 14 | 2020 | 2 | |
| 15 | 2016 | 2 | |
| 16 | 2016 | 2 | |
| 17 | 2020 | 2 | |
| 18 | 2024 | 1 | |
| 19 | 2021 | 1 | |
| 20 | 2025 | 0 |
About S. Venkatramaphanikumar
S. Venkatramaphanikumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Signal Processing and Health Information Management, having authored 21 papers that have together received 405 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (5 papers), Smart Agriculture and AI (5 papers), Face and Expression Recognition (3 papers), Face recognition and analysis (3 papers), Biometric Identification and Security (3 papers), Advanced Neural Network Applications (3 papers), Digital Imaging for Blood Diseases (2 papers) and Brain Tumor Detection and Classification (2 papers). The work is most often cited by research in Analytical Chemistry (60 citations), Plant Science (186 citations), Health Information Management (18 citations), Neurology (32 citations) and Health Informatics (4 citations). S. Venkatramaphanikumar has collaborated with scholars based in India and United States. Frequent co-authors include Venkata Krishna Kishore Kolli and Debnath Bhattacharyya. Their work appears in journals such as Journal of Ambient Intelligence and Humanized Computing, Applied Intelligence, The Visual Computer, Multimedia Tools and Applications and Heliyon.
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.