G. Aghila

1.1k citations
62 papers · 678 · h-index 15

Impact in

Papers in

G. Aghila

58 papers receiving 610 citations

Peers

G. Aghila
Comparison fields: 5 of 76
  • Signal Processing 229
  • Information Systems 281
  • Computer Networks and Communications 253
  • Artificial Intelligence 300
  • Computer Vision and Pattern Recognition 163
Replace Chaofeng Sha with:
Chaofeng Sha China
Seung-won Hwang South Korea
Charlie Kaufman United States
Stephen M. Matyas United States
Markulf Kohlweiss United Kingdom
Olga Ohrimenko United States
Zhaonian Zou China
Frances Perry United States
Rudolf Bayer Germany
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Citations per field
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Citations per year

Countries citing papers authored by G. Aghila

Since Specialization
Citations

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

Fields of papers citing papers by G. Aghila

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside G. Aghila, 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 G. Aghila Line = papers co-authored together G. Aghila links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201796
2 201256
3 201239
4 200437
5 201829
6 200928
7 201627
8 201026
9 201325
10 200622
11
A Survey of Naïve Bayes Machine Learning approach in Text Document Classification
201018
12 201916
13 201815
14 201114
15 201514
16 201013
17 200713
18 201013
19 201012
20 201610

About G. Aghila

G. Aghila is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Computer Vision and Pattern Recognition, having authored 62 papers that have together received 678 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (14 papers), Service-Oriented Architecture and Web Services (9 papers), Advanced Steganography and Watermarking Techniques (8 papers), Spam and Phishing Detection (7 papers), Advanced Database Systems and Queries (7 papers), Network Security and Intrusion Detection (7 papers), Data Management and Algorithms (6 papers) and Web Data Mining and Analysis (6 papers). The work is most often cited by research in Signal Processing (229 citations), Information Systems (281 citations), Computer Networks and Communications (253 citations), Artificial Intelligence (300 citations) and Computer Vision and Pattern Recognition (163 citations). G. Aghila has collaborated with scholars based in India, United States and South Korea. Frequent co-authors include K. S. Kuppusamy, Ajit Kumar, K. Saruladha, V. Prasanna Venkatesan, V. Natarajan, R. Anitha, V. Uma, P. Shanthi Bala, Amit Kumar Tyagi and T. V. Geetha. Their work appears in journals such as Journal of Chemical Information and Modeling, Journal of King Saud University - Computer and Information Sciences, Arabian Journal for Science and Engineering, SoftwareX and IEEE Transactions on Sustainable Computing.

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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