S. Karpagavalli
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Radiology, Nuclear Medicine and Imaging
- Topics
- Cutaneous Melanoma Detection and Management (3 papers)Handwritten Text Recognition Techniques (3 papers)Digital Imaging for Blood Diseases (2 papers)
- Journals
- Multimedia Tools and ApplicationsIndian Journal of Science and TechnologyAsian Journal of Chemistry
- Partner nations
- IndiaUnited States
In The Last Decade
S. Karpagavalli
23 papers receiving 268 citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 133
- Signal Processing 58
- Computer Vision and Pattern Recognition 53
- Information Systems 31
- Radiology, Nuclear Medicine and Imaging 20
Countries citing papers authored by S. Karpagavalli
This map shows the geographic impact of S. Karpagavalli'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. Karpagavalli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Karpagavalli more than expected).
Fields of papers citing papers by S. Karpagavalli
This network shows the impact of papers produced by S. Karpagavalli. 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. Karpagavalli. The network helps show where S. Karpagavalli may publish in the future.
Co-authorship network of co-authors of S. Karpagavalli
This figure shows the co-authorship network connecting the top 25 collaborators of S. Karpagavalli. A scholar is included among the top collaborators of S. Karpagavalli 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 S. Karpagavalli. S. Karpagavalli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | An Evolutionary Optimization of Positional-Aware Dual-Attention and Topology-Fusion Generative Adversarial Network for Plant Leaf Disease detection | 1 |
| 4 | A Deep Learning Approach for Real-Time Defect classification in Skin disease | 0 |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 14 | |
| 12 | 36 | |
| 13 | 13 | |
| 14 | 5 | |
| 15 | Electrocardiogram Beat Classification using Probabilistic Neural Network | 11 |
| 16 | 1 | |
| 17 | Breast Cancer Classification using SupportVector Machine and Genetic Programming | 12 |
| 18 | A Novel Approach for Password Strength Analysis through Support Vector Machine | 3 |
| 19 | 5 | |
| 20 | 10 |
About S. Karpagavalli
S. Karpagavalli is a scholar working on Computer Vision and Pattern Recognition, General Social Sciences and Polymers and Plastics, having authored 26 papers that have together received 284 indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (3 papers), Handwritten Text Recognition Techniques (3 papers) and Digital Imaging for Blood Diseases (2 papers). The work is most often cited by research in Signal Processing (58 citations), Artificial Intelligence (133 citations) and Computer Vision and Pattern Recognition (53 citations). S. Karpagavalli has collaborated with scholars based in India and United States. Frequent co-authors include E. Chandra, P. Asha, Suresh Perumal, P. Revathi and A. Suganthi. Their work appears in journals such as Multimedia Tools and Applications, Indian Journal of Science and Technology and Asian Journal of Chemistry.
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.