Khamron Sunat

1.2k citations
78 papers · 796 · h-index 16

Impact in

Papers in

Khamron Sunat

70 papers receiving 770 citations

Peers

Khamron Sunat
Comparison fields: 5 of 106
  • Artificial Intelligence 477
  • Computer Vision and Pattern Recognition 207
  • Computational Theory and Mathematics 83
  • Health Information Management 22
  • Media Technology 45
Replace Rania M. Ghoniem with:
Rania M. Ghoniem Saudi Arabia
Khaled H. Almotairi Saudi Arabia
Yassine Meraihi Algeria
Waleed M. Mohamed Egypt
B. R. Rajakumar India
Hassan Shaban Egypt
Priyanka Anand India
Erkan Tanyıldızı Türkiye
Samah Alshathri Saudi Arabia
Jaffar Atwan Jordan
Khamron Sunat relative to Rania M. Ghoniem Saudi Arabia Rania M. Ghoniem's profile →
Citations per field
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Rania M. Ghoniem · 1×
Citations per year

Countries citing papers authored by Khamron Sunat

Since Specialization
Citations

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

Fields of papers citing papers by Khamron Sunat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Khamron Sunat, 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 Khamron Sunat Line = papers co-authored together Khamron Sunat links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012119
2 201464
3 202353
4 202446
5 201844
6 202141
7 201733
8 201631
9 201725
10 201223
11 201721
12 202221
13 201319
14 202016
15 201415
16 201915
17 200912
18 201910
19 201310
20 201410

About Khamron Sunat

Khamron Sunat is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Control and Systems Engineering and Automotive Engineering, having authored 78 papers that have together received 796 indexed citations. Recurring topics across this work include Machine Learning and ELM (20 papers), Neural Networks and Applications (18 papers), Metaheuristic Optimization Algorithms Research (18 papers), Face and Expression Recognition (8 papers), Advanced Image and Video Retrieval Techniques (8 papers), Evolutionary Algorithms and Applications (6 papers), Energy Load and Power Forecasting (5 papers) and Advanced Multi-Objective Optimization Algorithms (5 papers). The work is most often cited by research in Artificial Intelligence (477 citations), Computer Vision and Pattern Recognition (207 citations), Computational Theory and Mathematics (83 citations), Health Information Management (22 citations) and Media Technology (45 citations). Khamron Sunat has collaborated with scholars based in Thailand, Indonesia and China. Frequent co-authors include Sirapat Chiewchanwattana, Chidchanok Lursinsap, W. Kiranon, Noppadol Khaehintung, Phaophak Sirisuk, Alfian Ma’arif, Sanun Srisuk, Chakchai So–In, S. Mitatha and Chee‐Hung Henry Chu. Their work appears in journals such as IEEE Access, Expert Systems with Applications, Microwave and Optical Technology Letters, Neurocomputing and IEEE Transactions on Neural Networks and Learning Systems.

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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