A. Lenin Fred
Impact in
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- Medical Image Segmentation Techniques
- Image and Signal Denoising Methods
- Advanced Data Compression Techniques
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- Brain Tumor Detection and Classification
Papers in
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- Image and Signal Denoising Methods 8
- Medical Image Segmentation Techniques 6
- Face and Expression Recognition 5
- Advanced Data Compression Techniques 5
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- Advanced Image Fusion Techniques 4
- Co-authors
- S. N. Kumar (11 shared papers)Parasuraman Padmanabhan (4 shared papers)Balázs Gulyás (4 shared papers)Sundramurthy Kumar (2 shared papers)Govindaraju Archunan (1 shared paper)Subramanian Tamil Selvan (1 shared paper)M. Sivakumar (1 shared paper)Sujatha Krishnamoorthy (1 shared paper)
In The Last Decade
A. Lenin Fred
30 papers receiving 381 citations
Peers
Comparison fields: 5 of 90
- Computer Vision and Pattern Recognition 121
- Neurology 34
- Media Technology 24
- Cognitive Neuroscience 50
- Cardiology and Cardiovascular Medicine 45
Countries citing papers authored by A. Lenin Fred
This map shows the geographic impact of A. Lenin Fred'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 A. Lenin Fred with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Lenin Fred more than expected).
Fields of papers citing papers by A. Lenin Fred
This network shows the impact of papers produced by A. Lenin Fred. 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 A. Lenin Fred. The network helps show where A. Lenin Fred may publish in the future.
Co-authors
The 16 scholars most cited alongside A. Lenin Fred, 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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 99 | |
| 2 | 2019 | 54 | |
| 3 | 2022 | 52 | |
| 4 | 2018 | 33 | |
| 5 | 2019 | 22 | |
| 6 | 2018 | 17 | |
| 7 | 2007 | 17 | |
| 8 | 2017 | 16 | |
| 9 | 2017 | 9 | |
| 10 | 2018 | 9 | |
| 11 | 2019 | 9 | |
| 12 | 2016 | 9 | |
| 13 | 2006 | 8 | |
| 14 | 2017 | 6 | |
| 15 | 2018 | 5 | |
| 16 | 2018 | 5 | |
| 17 | 2018 | 4 | |
| 18 | 2019 | 4 | |
| 19 | 2019 | 3 | |
| 20 | 2014 | 3 |
About A. Lenin Fred
A. Lenin Fred is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Signal Processing, Artificial Intelligence and Biomedical Engineering, having authored 31 papers that have together received 398 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (8 papers), Medical Image Segmentation Techniques (6 papers), Face and Expression Recognition (5 papers), Advanced Data Compression Techniques (5 papers), Biometric Identification and Security (4 papers), Advanced Image Fusion Techniques (4 papers), Brain Tumor Detection and Classification (3 papers) and EEG and Brain-Computer Interfaces (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (121 citations), Neurology (34 citations), Media Technology (24 citations), Cognitive Neuroscience (50 citations) and Cardiology and Cardiovascular Medicine (45 citations). A. Lenin Fred has collaborated with scholars based in India, Singapore and Slovenia. Frequent co-authors include S. N. Kumar, Parasuraman Padmanabhan, Balázs Gulyás, Sundramurthy Kumar, Govindaraju Archunan, Subramanian Tamil Selvan, M. Sivakumar, Sujatha Krishnamoorthy, Vimalan Vijayaragavan and Sayantan Ghosh. Their work appears in journals such as Multimedia Tools and Applications, International Journal of Nanomedicine, Journal of Digital Imaging, Measurement and Journal of Medical Systems.
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.