Peerapon Vateekul
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Media Technology top 2%
- Environmental Engineering top 10%
- Ocean Engineering top 5%
- Co-authors
- Kulsawasd JitkajornwanichSiam LawawirojwongPanu SrestasathiernTeerapong PanboonyuenMiroslav KubátKanoksri SarinnapakornRoongroj BhidayasiriRungsun Rerknimitr
- Topics
- Text and Document Classification Technologies (18 papers)Topic Modeling (14 papers)Natural Language Processing Techniques (9 papers)
- Journals
- Scientific ReportsIEEE AccessSensors
- Partner nations
- ThailandUnited StatesJapan
In The Last Decade
Peerapon Vateekul
99 papers receiving 980 citations
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 359
- Computer Vision and Pattern Recognition 194
- Media Technology 148
- Environmental Engineering 116
- Ocean Engineering 108
Countries citing papers authored by Peerapon Vateekul
This map shows the geographic impact of Peerapon Vateekul'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 Peerapon Vateekul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peerapon Vateekul more than expected).
Fields of papers citing papers by Peerapon Vateekul
This network shows the impact of papers produced by Peerapon Vateekul. 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 Peerapon Vateekul. The network helps show where Peerapon Vateekul may publish in the future.
Co-authorship network of co-authors of Peerapon Vateekul
This figure shows the co-authorship network connecting the top 25 collaborators of Peerapon Vateekul. A scholar is included among the top collaborators of Peerapon Vateekul based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peerapon Vateekul. Peerapon Vateekul is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 4 | |
| 8 | 6 | |
| 9 | 7 | |
| 10 | 14 | |
| 11 | 12 | |
| 12 | 17 | |
| 13 | 19 | |
| 14 | 22 | |
| 15 | 61 | |
| 16 | 6 | |
| 17 | 9 | |
| 18 | 8 | |
| 19 | 8 | |
| 20 | 11 |
About Peerapon Vateekul
Peerapon Vateekul is a scholar working on Artificial Intelligence, Signal Processing and Health Informatics, having authored 105 papers that have together received 1.0k indexed citations. Recurring topics across this work include Text and Document Classification Technologies (18 papers), Topic Modeling (14 papers) and Natural Language Processing Techniques (9 papers). The work is most often cited by research in Media Technology (148 citations), Artificial Intelligence (359 citations) and Computer Vision and Pattern Recognition (194 citations). Peerapon Vateekul has collaborated with scholars based in Thailand, United States and Japan. Frequent co-authors include Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, Teerapong Panboonyuen, Miroslav Kubát, Kanoksri Sarinnapakorn, Roongroj Bhidayasiri, Rungsun Rerknimitr, Rapat Pittayanon and Piyawat Lertvittayakumjorn. Their work appears in journals such as Scientific Reports, IEEE Access and Sensors.
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.