Jin-Seon Lee
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Information Systems top 10%
- Co-authors
- Il-Seok OhByung-Ro MoonChing Y. SuenKyung Hwan KimHyesung KimHyeri LeeJong-Woo ChoiJunheon Yoon
- Topics
- Neural Networks and Applications (3 papers)Semiconductor materials and devices (3 papers)Handwritten Text Recognition Techniques (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceJapanese Journal of Applied PhysicsJournal of Nanoscience and Nanotechnology
- Partner nations
- South KoreaCanadaUnited States
In The Last Decade
Jin-Seon Lee
8 papers receiving 701 citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Artificial Intelligence 469
- Computer Vision and Pattern Recognition 227
- Computational Theory and Mathematics 123
- Molecular Biology 106
- Information Systems 66
Countries citing papers authored by Jin-Seon Lee
This map shows the geographic impact of Jin-Seon Lee'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 Jin-Seon Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin-Seon Lee more than expected).
Fields of papers citing papers by Jin-Seon Lee
This network shows the impact of papers produced by Jin-Seon Lee. 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 Jin-Seon Lee. The network helps show where Jin-Seon Lee may publish in the future.
Co-authorship network of co-authors of Jin-Seon Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Jin-Seon Lee. A scholar is included among the top collaborators of Jin-Seon Lee 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 Jin-Seon Lee. Jin-Seon Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 5 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 화학사고 초기대응자를 위한 검지관식 탐지장비의 반응성 연구 | 0 |
| 6 | 1 | |
| 7 | Hybrid genetic algorithms for feature selectionbreakdown → | 679 |
| 8 | 1 | |
| 9 | 66 | |
| 10 | RECONSTRUCTION OF 3-D TERRAIN DATA FROM CONTOUR MAP | 2 |
About Jin-Seon Lee
Jin-Seon Lee is a scholar working on Chemical Health and Safety, Computer Vision and Pattern Recognition and Statistics, Probability and Uncertainty, having authored 10 papers that have together received 760 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Semiconductor materials and devices (3 papers) and Handwritten Text Recognition Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (469 citations), Computer Vision and Pattern Recognition (227 citations) and Computational Theory and Mathematics (123 citations). Jin-Seon Lee has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Il-Seok Oh, Byung-Ro Moon, Ching Y. Suen, Kyung Hwan Kim, Hyesung Kim, Hyeri Lee, Jong-Woo Choi, Junheon Yoon, Won Seok Lee and Jong‐Kwon Im. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Japanese Journal of Applied Physics and Journal of Nanoscience and Nanotechnology.
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.