Teeradaj Racharak
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Neurology
- Radiology, Nuclear Medicine and Imaging
- Biomedical Engineering
- Co-authors
- Le-Minh NguyenYizhi PanZichang XuJianhua MaBoontawee SuntisrivarapornSatoshi TojoShuang WangMatthew N. Dailey
- Topics
- Topic Modeling (9 papers)Semantic Web and Ontologies (9 papers)Natural Language Processing Techniques (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessKnowledge-Based Systems
In The Last Decade
Teeradaj Racharak
28 papers receiving 165 citations
Hit Papers
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 76
- Computer Vision and Pattern Recognition 64
- Neurology 32
- Radiology, Nuclear Medicine and Imaging 28
- Biomedical Engineering 15
Countries citing papers authored by Teeradaj Racharak
This map shows the geographic impact of Teeradaj Racharak'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 Teeradaj Racharak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Teeradaj Racharak more than expected).
Fields of papers citing papers by Teeradaj Racharak
This network shows the impact of papers produced by Teeradaj Racharak. 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 Teeradaj Racharak. The network helps show where Teeradaj Racharak may publish in the future.
Co-authorship network of co-authors of Teeradaj Racharak
This figure shows the co-authorship network connecting the top 25 collaborators of Teeradaj Racharak. A scholar is included among the top collaborators of Teeradaj Racharak 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 Teeradaj Racharak. Teeradaj Racharak 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 | 0 | |
| 4 | 5 | |
| 5 | 20 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentationbreakdown → | 88 |
| 9 | 2 | |
| 10 | 0 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 0 | |
| 15 | 7 | |
| 16 | 5 | |
| 17 | 0 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 1 |
About Teeradaj Racharak
Teeradaj Racharak is a scholar working on Human-Computer Interaction, Artificial Intelligence and Signal Processing, having authored 37 papers that have together received 169 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Semantic Web and Ontologies (9 papers) and Natural Language Processing Techniques (7 papers). The work is most often cited by research in Neurology (32 citations), Health Informatics (5 citations) and Computer Vision and Pattern Recognition (64 citations). Teeradaj Racharak has collaborated with scholars based in Japan, Thailand and China. Frequent co-authors include Le-Minh Nguyen, Yizhi Pan, Zichang Xu, Jianhua Ma, Boontawee Suntisrivaraporn, Satoshi Tojo, Shuang Wang, Matthew N. Dailey, Chutiporn Anutariya and Frédéric Andrès. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Knowledge-Based 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.