R.S. Latha
- Plant Science
- Computer Vision and Pattern Recognition top 10%
- Analytical Chemistry top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Co-authors
- R.C. SugantheG. R. SreekanthM. Kalaiselvi GeethaS. KarthikeyanS. GowthamN. ShanthiK. SangeethaB P Pradeep Kumar
- Topics
- Smart Agriculture and AI (11 papers)Spectroscopy and Chemometric Analyses (5 papers)Leaf Properties and Growth Measurement (4 papers)
- Journals
- Indian Journal of Clinical BiochemistryAdvances in Electrical and Computer EngineeringJournal of Medical Imaging and Health Informatics
- Partner nations
- IndiaUnited States
In The Last Decade
R.S. Latha
26 papers receiving 227 citations
Peers
Comparison fields: 5 of 75
- Plant Science 91
- Computer Vision and Pattern Recognition 71
- Analytical Chemistry 47
- Artificial Intelligence 46
- Computer Networks and Communications 36
Countries citing papers authored by R.S. Latha
This map shows the geographic impact of R.S. Latha'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 R.S. Latha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R.S. Latha more than expected).
Fields of papers citing papers by R.S. Latha
This network shows the impact of papers produced by R.S. Latha. 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 R.S. Latha. The network helps show where R.S. Latha may publish in the future.
Co-authorship network of co-authors of R.S. Latha
This figure shows the co-authorship network connecting the top 25 collaborators of R.S. Latha. A scholar is included among the top collaborators of R.S. Latha 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 R.S. Latha. R.S. Latha 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 | 1 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 5 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | Automated Detection and Classification of Diabetic Retinopathy and Diabetic Macular Edema in Retinal Fundus Images Using Deep Learning Approach | 4 |
| 11 | A Cnn Model Based Approach For Offline HandwrittenTamil Text Recognition System | 5 |
| 12 | Deep Learning based Automatic Detection of Intestinal Hemorrhage Using Wireless Capsule Endoscopy Images | 6 |
| 13 | 39 | |
| 14 | 18 | |
| 15 | 55 | |
| 16 | 29 | |
| 17 | 22 | |
| 18 | 2 | |
| 19 | 2 | |
| 20 | 2 |
About R.S. Latha
R.S. Latha is a scholar working on Health Information Management, Analytical Chemistry and Computer Vision and Pattern Recognition, having authored 33 papers that have together received 292 indexed citations. Recurring topics across this work include Smart Agriculture and AI (11 papers), Spectroscopy and Chemometric Analyses (5 papers) and Leaf Properties and Growth Measurement (4 papers). The work is most often cited by research in Analytical Chemistry (47 citations), Computer Vision and Pattern Recognition (71 citations) and Neurology (24 citations). R.S. Latha has collaborated with scholars based in India and United States. Frequent co-authors include R.C. Suganthe, G. R. Sreekanth, M. Kalaiselvi Geetha, S. Karthikeyan, S. Gowtham, N. Shanthi, K. Sangeetha, B P Pradeep Kumar, S. Anand and R. Manikandan. Their work appears in journals such as Indian Journal of Clinical Biochemistry, Advances in Electrical and Computer Engineering and Journal of Medical Imaging and Health Informatics.
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.