T. S. Arulananth

410 citations
29 papers · 197 · h-index 7

Impact in

Papers in

T. S. Arulananth

23 papers receiving 190 citations

Peers

T. S. Arulananth
Comparison fields: 5 of 70
  • Computer Networks and Communications 54
  • Computer Vision and Pattern Recognition 42
  • Human-Computer Interaction 7
  • Signal Processing 12
  • Information Systems 24
Replace Finney Daniel Shadrach with:
Finney Daniel Shadrach India
A. Suresh Kumar India
Rashid Nasimov Uzbekistan
K. Rajkumar India
Christophe de Vaulx France
Yutaka Yanagisawa Japan
M. A. El-Dosuky Egypt
Fahad Alqurashi Saudi Arabia
Laiq Hasan Pakistan
C. V. Ravikumar India
T. S. Arulananth relative to Finney Daniel Shadrach India Finney Daniel Shadrach's profile →
Citations per field
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Citations per year

Countries citing papers authored by T. S. Arulananth

Since Specialization
Citations

This map shows the geographic impact of T. S. Arulananth'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 T. S. Arulananth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. S. Arulananth more than expected).

Fields of papers citing papers by T. S. Arulananth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202136
2 202030
3 202125
4 202422
5 201719
6 202215
7 202314
8 20236
9 20175
10 20233
11 20203
12 20232
13 20242
14 20232
15 20242
16 20242
17 20252
18 20212
19 20251
20 20231

About T. S. Arulananth

T. S. Arulananth is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computer Networks and Communications, Media Technology and Information Systems, having authored 29 papers that have together received 197 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), IoT-based Smart Home Systems (3 papers), Video Surveillance and Tracking Methods (3 papers), Advanced Image Fusion Techniques (2 papers), Blockchain Technology Applications and Security (2 papers), Image and Object Detection Techniques (2 papers), Remote-Sensing Image Classification (2 papers) and IoT and GPS-based Vehicle Safety Systems (2 papers). The work is most often cited by research in Computer Networks and Communications (54 citations), Computer Vision and Pattern Recognition (42 citations), Human-Computer Interaction (7 citations), Signal Processing (12 citations) and Information Systems (24 citations). T. S. Arulananth has collaborated with scholars based in India, Ethiopia and Slovakia. Frequent co-authors include M. Baskar, V. Anbarasu, Koppula Srinivas Rao, S. M. Udhaya Sankar, A. Suresh, Aruru Sai Kumar, P. Chinnasamy, C. Karthikeyan, J. Ramkumar and Ajmeera Kiran. Their work appears in journals such as PLoS ONE, Pattern Recognition Letters, Neural Processing Letters, Journal of Ambient Intelligence and Humanized Computing and Arabian Journal for Science and Engineering.

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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