M. Ramkumar

558 citations
39 papers · 303 · h-index 10

Impact in

Papers in

M. Ramkumar

32 papers receiving 279 citations

Peers

M. Ramkumar
Comparison fields: 5 of 81
  • Computer Vision and Pattern Recognition 127
  • Health Information Management 18
  • Information Systems 46
  • Artificial Intelligence 61
  • Signal Processing 17
Replace Yogesh Kumar with:
Yogesh Kumar India
Nidhi Sindhwani India
Ramanathan Lakshmanan India
Milan Tripathi Nepal
G. Charlyn Pushpa Latha India
P. Dayananda India
K. Gayathri India
Ranjan Walia India
T. Manikandan India
Hani Mahdi Egypt
M. Ramkumar relative to Yogesh Kumar India Yogesh Kumar's profile →
Citations per field
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Yogesh Kumar · 1×
Citations per year

Countries citing papers authored by M. Ramkumar

Since Specialization
Citations

This map shows the geographic impact of M. Ramkumar'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. Ramkumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Ramkumar more than expected).

Fields of papers citing papers by M. Ramkumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Ramkumar. 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. Ramkumar. The network helps show where M. Ramkumar may publish in the future.

Co-authors

The 25 scholars most cited alongside M. Ramkumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with M. Ramkumar Line = papers co-authored together M. Ramkumar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202254
2 202236
3 199933
4 200223
5 202222
6 202019
7 200312
8 202210
9 201210
10 20219
11 20029
12 20228
13 20218
14 20196
15 19975
16 20035
17 20025
18
Achieving Efficient and Secure Data Acquisition for Cloud-Supported Internet of Things in Grid Connected Solar, Wind and Battery Systems
20185
19 20244
20 20153

About M. Ramkumar

M. Ramkumar is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications, Artificial Intelligence, Electrical and Electronic Engineering and Molecular Biology, having authored 39 papers that have together received 303 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (6 papers), Chaos-based Image/Signal Encryption (6 papers), IoT and Edge/Fog Computing (5 papers), Gene expression and cancer classification (4 papers), Gaze Tracking and Assistive Technology (3 papers), Digital Media Forensic Detection (3 papers), Advanced Data Compression Techniques (3 papers) and Spectroscopy and Chemometric Analyses (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (127 citations), Health Information Management (18 citations), Information Systems (46 citations), Artificial Intelligence (61 citations) and Signal Processing (17 citations). M. Ramkumar has collaborated with scholars based in India, United States and Hong Kong. Frequent co-authors include Ali N. Akansu, C. Venkatesan, A. Aydın Alatan, N. Yuvaraj, R.G. Vidhya, J. Surendiran, G. V. Anand, Siddhartha Choubey, K. Vengatesan and Apurv Verma. Their work appears in journals such as Signal Processing, Materials Today Proceedings, ECS Transactions, Circuits Systems and Signal Processing and Journal of Computational and Theoretical Nanoscience.

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.

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