M Rahul
Impact in
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
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- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
Papers in
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- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 2
- Human Pose and Action Recognition 1
- Video Surveillance and Tracking Methods 1
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- 3D Shape Modeling and Analysis 2
- Co-authors
- Jogendra Nath Kundu (6 shared papers)R. Venkatesh Babu (6 shared papers)Aditya Ganeshan (2 shared papers)Himanshu Buckchash (1 shared paper)Priyanka Mandikal (1 shared paper)Jay Patravali (1 shared paper)G. Shobha (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)ACM International Conference on Multimedia (ACM MM) (1 paper)IEEE Winter Conference on Applications of Computer Vision (2 papers)Global Journal of Computer Science and Technology (1 paper)International Conference on Machine Learning and Computing (ICMLC) (1 paper)
- Partner nations
- India
In The Last Decade
M Rahul
8 papers receiving 303 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 209
- Computer Vision and Pattern Recognition 62
- Control and Systems Engineering 29
- Media Technology 7
- Health Informatics 1
Countries citing papers authored by M Rahul
This map shows the geographic impact of M Rahul'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 M Rahul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Rahul more than expected).
Fields of papers citing papers by M Rahul
This network shows the impact of papers produced by M Rahul. 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 M Rahul. The network helps show where M Rahul may publish in the future.
Co-authors
The 7 scholars most cited alongside M Rahul, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 170 | |
| 2 | 2020 | 69 | |
| 3 | 2020 | 19 | |
| 4 | Review on Motion Capture Technology | 2018 | 17 |
| 5 | 2019 | 17 | |
| 6 | 2020 | 8 | |
| 7 | 2017 | 7 | |
| 8 | 2018 | 3 |
About M Rahul
M Rahul is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Control and Systems Engineering, Artificial Intelligence and Nuclear Energy and Engineering, having authored 8 papers that have together received 310 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Human Motion and Animation (2 papers), 3D Shape Modeling and Analysis (2 papers), Advanced Neural Network Applications (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Human Pose and Action Recognition (1 paper) and Video Surveillance and Tracking Methods (1 paper). The work is most often cited by research in Artificial Intelligence (209 citations), Computer Vision and Pattern Recognition (62 citations), Control and Systems Engineering (29 citations), Media Technology (7 citations) and Health Informatics (1 citation). M Rahul has collaborated with scholars based in India. Frequent co-authors include Jogendra Nath Kundu, R. Venkatesh Babu, Aditya Ganeshan, Himanshu Buckchash, Priyanka Mandikal, Jay Patravali and G. Shobha. Their work appears in journals such as Lecture notes in computer science, ACM International Conference on Multimedia (ACM MM), IEEE Winter Conference on Applications of Computer Vision, Global Journal of Computer Science and Technology and International Conference on Machine Learning and Computing (ICMLC).
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.