K. S. Nathan

491 citations
37 papers · 319 · h-index 11

Impact in

Papers in

K. S. Nathan

35 papers receiving 248 citations

Peers

K. S. Nathan
Comparison fields: 5 of 65
  • Human-Computer Interaction 49
  • Computer Vision and Pattern Recognition 161
  • Signal Processing 58
  • Artificial Intelligence 122
  • Media Technology 19
Replace Marie-Luce Viaud with:
Marie-Luce Viaud France
Jahanzeb Sherwani United States
Olivier Galibert France
Saurabh Panjwani United States
Alan McCabe United Kingdom
Víctor Soto United States
Claudia Peersman United Kingdom
A.E. Cawkell Russia
Sonia Sánchez-Cuadrado Spain
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Citations per field
00.5×6.3×
Marie-Luce Viaud · 1×
Citations per year

Countries citing papers authored by K. S. Nathan

Since Specialization
Citations

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

Fields of papers citing papers by K. S. Nathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by K. S. Nathan. 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 K. S. Nathan. The network helps show where K. S. Nathan may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200248
2
Islam in Southeast Asia: Political, Social and Strategic Challenges for the 21st Century
200535
3 199335
4 200222
5 199421
6 199419
7 200218
8 199115
9
Speckle noise reduction of 1-look SAR imagery
198711
10 200210
11 200610
12 20028
13 20028
14 20027
15 19896
16 20025
17 19905
18 20024
19 20064
20 19963

About K. S. Nathan

K. S. Nathan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Political Science and International Relations, Sociology and Political Science and Signal Processing, having authored 37 papers that have together received 319 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (11 papers), Speech Recognition and Synthesis (10 papers), Asian Studies and History (6 papers), Natural Language Processing Techniques (5 papers), Speech and Audio Processing (5 papers), Southeast Asian Sociopolitical Studies (3 papers), Socioeconomic Development in Asia (3 papers) and Hand Gesture Recognition Systems (3 papers). The work is most often cited by research in Human-Computer Interaction (49 citations), Computer Vision and Pattern Recognition (161 citations), Signal Processing (58 citations), Artificial Intelligence (122 citations) and Media Technology (19 citations). K. S. Nathan has collaborated with scholars based in United States, Malaysia and Singapore. Frequent co-authors include J. Subrahmonia, H.F. Silverman, Mohammad Hāshim Kamali, D. Nahamoo, J.R. Bellegarda, Homayoon Beigi, Gregory J. Clary, Hiroshi Maruyama, Michael Perrone and Andrew Senior. Their work appears in journals such as Asian Survey, ESMO Open, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Speech and Audio Processing and Contemporary Southeast Asia.

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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