M. E. Paramasivam

24 papers receiving 311 citations

Peers

M. E. Paramasivam
Comparison fields: 5 of 90
  • Developmental and Educational Psychology 72
  • Statistics and Probability 25
  • Industrial and Manufacturing Engineering 29
  • Genetics 65
  • Computer Vision and Pattern Recognition 44
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Countries citing papers authored by M. E. Paramasivam

Since Specialization
Citations

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

Fields of papers citing papers by M. E. Paramasivam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside M. E. Paramasivam, 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. E. Paramasivam Line = papers co-authored together M. E. Paramasivam links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006115
2 200547
3 201034
4 200121
5 199121
6 202219
7 200817
8 201016
9
SYNTHESIS OF POROUS SILICON NANOSTRUCTURES FOR PHOTOLUMINESCENT DEVICES
200113
10 201313
11 20129
12 20165
13 20125
14
Data analytics and resource planning using deep learning for overcoming challenges of COVID-19
20214
15 20174
16 20043
17 20213
18 20103
19 20082
20 20122

About M. E. Paramasivam

M. E. Paramasivam is a scholar working on Computer Vision and Pattern Recognition, Civil and Structural Engineering, Media Technology, Materials Chemistry and Industrial and Manufacturing Engineering, having authored 27 papers that have together received 362 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (4 papers), Corrosion Behavior and Inhibition (3 papers), Smart Agriculture and AI (3 papers), Conducting polymers and applications (2 papers), Image Processing Techniques and Applications (2 papers), Spectroscopy and Chemometric Analyses (2 papers), Vehicle License Plate Recognition (2 papers) and Concrete Corrosion and Durability (2 papers). The work is most often cited by research in Developmental and Educational Psychology (72 citations), Statistics and Probability (25 citations), Industrial and Manufacturing Engineering (29 citations), Genetics (65 citations) and Computer Vision and Pattern Recognition (44 citations). M. E. Paramasivam has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include R. S. Sabeenian, S. Venkatakrishna Iyer, Juha Kere, Joseph J. LoTurco, Albert M. Galaburda, Glenn D. Rosen, Nina Kaminen‐Ahola, Ankur Thomas, Jilin Bai and Y. Wang. Their work appears in journals such as Journal of Applied Electrochemistry, Neuroscience, IEEE Transactions on Multimedia, Journal of Testing and Evaluation and Journal of Cardiovascular Pharmacology.

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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