Thepchai Supnithi
- Artificial Intelligence top 5%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Education
- Computer Networks and Communications
- Co-authors
- Marut BuranarachHitoshi IsaharaMitsuru IkedaKiyotaka UchimotoVan‐Nam HuynhYasuo TanYuto LimTeerayut Horanont
- Topics
- Natural Language Processing Techniques (42 papers)Topic Modeling (36 papers)Semantic Web and Ontologies (26 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessHeliyon
In The Last Decade
Thepchai Supnithi
83 papers receiving 496 citations
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 277
- Information Systems 100
- Computer Vision and Pattern Recognition 78
- Education 52
- Computer Networks and Communications 48
Countries citing papers authored by Thepchai Supnithi
This map shows the geographic impact of Thepchai Supnithi'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 Thepchai Supnithi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thepchai Supnithi more than expected).
Fields of papers citing papers by Thepchai Supnithi
This network shows the impact of papers produced by Thepchai Supnithi. 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 Thepchai Supnithi. The network helps show where Thepchai Supnithi may publish in the future.
Co-authorship network of co-authors of Thepchai Supnithi
This figure shows the co-authorship network connecting the top 25 collaborators of Thepchai Supnithi. A scholar is included among the top collaborators of Thepchai Supnithi 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 Thepchai Supnithi. Thepchai Supnithi 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | Effectiveness of E-Learning Design and Affecting Variables in Thai Public Schools. | 24 |
| 11 | 10 | |
| 12 | 1 | |
| 13 | Joint learning of constituency and dependency grammars by decomposed cross-lingual induction | 1 |
| 14 | 0 | |
| 15 | 1 | |
| 16 | AutoTagTCG : A Framework for Automatic Thai CG Tagging. | 2 |
| 17 | 1 | |
| 18 | Memory-Inductive Categorial Grammar: An Approach to Gap Resolution in Analytic-Language Translation | 5 |
| 19 | Speech-to-Speech Translation Activities in Thailand | 2 |
| 20 | 85 |
About Thepchai Supnithi
Thepchai Supnithi is a scholar working on Artificial Intelligence, Computer Science Applications and Information Systems, having authored 100 papers that have together received 540 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (42 papers), Topic Modeling (36 papers) and Semantic Web and Ontologies (26 papers). The work is most often cited by research in Artificial Intelligence (277 citations), Computer Science Applications (36 citations) and Information Systems (100 citations). Thepchai Supnithi has collaborated with scholars based in Thailand, Japan and Myanmar. Frequent co-authors include Marut Buranarach, Hitoshi Isahara, Mitsuru Ikeda, Kiyotaka Uchimoto, Van‐Nam Huynh, Yasuo Tan, Yuto Lim, Teerayut Horanont, Youji Kohda and Chutiporn Anutariya. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Heliyon.
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.