Yu-Seop Kim
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
Papers in
-
- Topic Modeling 21
- Natural Language Processing Techniques 20
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- Biosensors and Analytical Detection 21
- Microfluidic and Capillary Electrophoresis Applications 9
- Co-authors
- Chan-Young Park (80 shared papers)Jong-Dae Kim (73 shared papers)Hye-Jeong Song (57 shared papers)Kui Hong (1 shared paper)Young‐Bum Kim (5 shared papers)W. Nick Street (1 shared paper)Filippo Menczer (1 shared paper)Taek Jin Kang (1 shared paper)
- Journals
- Applied Sciences (12 papers)BioMedical Engineering OnLine (8 papers)Sensors (6 papers)Expert Systems with Applications (4 papers)Sensors and Materials (19 papers)
- Partner nations
- South KoreaUnited States
In The Last Decade
Yu-Seop Kim
109 papers receiving 569 citations
Peers
Comparison fields: 5 of 129
- Health Informatics 8
- Artificial Intelligence 154
- Oral Surgery 24
- Biomedical Engineering 130
- Biophysics 17
Countries citing papers authored by Yu-Seop Kim
This map shows the geographic impact of Yu-Seop Kim'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 Yu-Seop Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Seop Kim more than expected).
Fields of papers citing papers by Yu-Seop Kim
This network shows the impact of papers produced by Yu-Seop Kim. 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 Yu-Seop Kim. The network helps show where Yu-Seop Kim may publish in the future.
Co-authors
The 25 scholars most cited alongside Yu-Seop Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 119 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 80 | |
| 2 | 2000 | 52 | |
| 3 | 2018 | 28 | |
| 4 | 2005 | 28 | |
| 5 | 2022 | 27 | |
| 6 | 2005 | 21 | |
| 7 | 2017 | 18 | |
| 8 | 2007 | 15 | |
| 9 | 2015 | 13 | |
| 10 | 2007 | 12 | |
| 11 | 2015 | 12 | |
| 12 | 2014 | 12 | |
| 13 | 2020 | 12 | |
| 14 | 2018 | 11 | |
| 15 | 2019 | 9 | |
| 16 | 2017 | 9 | |
| 17 | 2015 | 9 | |
| 18 | 2003 | 8 | |
| 19 | 2021 | 8 | |
| 20 | 2018 | 8 |
About Yu-Seop Kim
Yu-Seop Kim is a scholar working on Artificial Intelligence, Biomedical Engineering, Molecular Biology, Computer Vision and Pattern Recognition and Information Systems, having authored 119 papers that have together received 611 indexed citations. Recurring topics across this work include Biosensors and Analytical Detection (21 papers), Topic Modeling (21 papers), Natural Language Processing Techniques (20 papers), Advanced Biosensing Techniques and Applications (11 papers), Microfluidic and Capillary Electrophoresis Applications (9 papers), Molecular Biology Techniques and Applications (9 papers), Language Development and Disorders (6 papers) and Image Processing Techniques and Applications (5 papers). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (154 citations), Oral Surgery (24 citations), Biomedical Engineering (130 citations) and Biophysics (17 citations). Yu-Seop Kim has collaborated with scholars based in South Korea and United States. Frequent co-authors include Chan-Young Park, Jong-Dae Kim, Hye-Jeong Song, Kui Hong, Young‐Bum Kim, W. Nick Street, Filippo Menczer, Taek Jin Kang, Yejin Jang and Chulho Kim. Their work appears in journals such as Applied Sciences, BioMedical Engineering OnLine, Sensors, Expert Systems with Applications and Sensors and Materials.
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.