J. Samuel Manoharan
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Topics
- Image and Signal Denoising Methods (5 papers)IoT-based Smart Home Systems (4 papers)Energy Efficient Wireless Sensor Networks (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionHealth Information ManagementArtificial Intelligence
- Partner nations
- IndiaUnited StatesEritrea
In The Last Decade
J. Samuel Manoharan
33 papers receiving 521 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 214
- Computer Vision and Pattern Recognition 205
- Radiology, Nuclear Medicine and Imaging 127
- Computer Networks and Communications 82
- Information Systems 73
Countries citing papers authored by J. Samuel Manoharan
This map shows the geographic impact of J. Samuel Manoharan'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 J. Samuel Manoharan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Samuel Manoharan more than expected).
Fields of papers citing papers by J. Samuel Manoharan
This network shows the impact of papers produced by J. Samuel Manoharan. 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 J. Samuel Manoharan. The network helps show where J. Samuel Manoharan may publish in the future.
Co-authorship network of co-authors of J. Samuel Manoharan
This figure shows the co-authorship network connecting the top 25 collaborators of J. Samuel Manoharan. A scholar is included among the top collaborators of J. Samuel Manoharan 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 J. Samuel Manoharan. J. Samuel Manoharan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 12 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 9 | |
| 7 | 0 | |
| 8 | A Spatio-Frequency Domain Anisotropic Filtering for Contrast Enhancement of Histopathological Images | 4 |
| 9 | 11 | |
| 10 | 12 | |
| 11 | 9 | |
| 12 | 4 | |
| 13 | 46 | |
| 14 | 1 | |
| 15 | 6 | |
| 16 | A Hybrid Transform for Robustness Enhancement of Watermarking of Medical Images | 4 |
| 17 | 1 | |
| 18 | A Survey on Texture Analysis of Mammogram for the Detection of Breast Cancer | 1 |
| 19 | A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer | 52 |
| 20 | 4 |
About J. Samuel Manoharan
J. Samuel Manoharan is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 35 papers that have together received 607 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (5 papers), IoT-based Smart Home Systems (4 papers) and Energy Efficient Wireless Sensor Networks (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (205 citations), Health Information Management (39 citations) and Artificial Intelligence (214 citations). J. Samuel Manoharan has collaborated with scholars based in India, United States and Eritrea. Frequent co-authors include Sathish, D. Narain Ponraj, Poongodi Manoharan, K. Suganya and I. A. Chidambaram. Their work appears in journals such as Scientific Reports, Ain Shams Engineering Journal and Wireless Personal Communications.
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.