Sangwon Chae
Impact in
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
-
- Artificial Intelligence in Healthcare
Papers in
-
- Air Quality Monitoring and Forecasting 2
- Hydrological Forecasting Using AI 1
- Co-authors
- Dong-Hyun Lee (5 shared papers)Sungjun Kwon (4 shared papers)Sungwon Kang (3 shared papers)Mingyu Kim (1 shared paper)Suyoung Jang (1 shared paper)Hong‐Yeop Song (1 shared paper)
- Journals
- Scientific Reports (2 papers)International Journal of Environmental Research and Public Health (1 paper)Technological Forecasting and Social Change (1 paper)Journal of the Institute of Electronics Engineers of Korea (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- South Korea
In The Last Decade
Sangwon Chae
6 papers receiving 359 citations
Sangwon Chae's Hit Papers
Peers
Comparison fields: 5 of 82
- Modeling and Simulation 58
- Health Information Management 54
- Health Informatics 13
- Environmental Engineering 110
- Health, Toxicology and Mutagenesis 73
Countries citing papers authored by Sangwon Chae
This map shows the geographic impact of Sangwon Chae'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 Sangwon Chae with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sangwon Chae more than expected).
Fields of papers citing papers by Sangwon Chae
This network shows the impact of papers produced by Sangwon Chae. 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 Sangwon Chae. The network helps show where Sangwon Chae may publish in the future.
Co-authors
The 6 scholars most cited alongside Sangwon Chae, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Predicting Infectious Disease Using Deep Learning and Big Data Hit paper breakdown → | 2018 | 238 |
| 2 | 2021 | 95 | |
| 3 | 2022 | 37 | |
| 4 | High-speed Integer Operations in the Fuzzy Consequent Part and the Defuzzification Stage for Intelligent Systems | 2006 | 2 |
| 5 | 2020 | 1 | |
| 6 | 2022 | 1 | |
| 7 | 2025 | 0 |
About Sangwon Chae
Sangwon Chae is a scholar working on Environmental Engineering, Computer Networks and Communications, Automotive Engineering, Health, Toxicology and Mutagenesis and Epidemiology, having authored 7 papers that have together received 374 indexed citations. Recurring topics across this work include Air Quality Monitoring and Forecasting (2 papers), Hydrological Forecasting Using AI (1 paper), Vehicle emissions and performance (1 paper), Fuzzy Logic and Control Systems (1 paper), Computational and Text Analysis Methods (1 paper), Air Quality and Health Impacts (1 paper), Data-Driven Disease Surveillance (1 paper) and Water Quality Monitoring Technologies (1 paper). The work is most often cited by research in Modeling and Simulation (58 citations), Health Information Management (54 citations), Health Informatics (13 citations), Environmental Engineering (110 citations) and Health, Toxicology and Mutagenesis (73 citations). Sangwon Chae has collaborated with scholars based in South Korea. Frequent co-authors include Dong-Hyun Lee, Sungjun Kwon, Sungwon Kang, Mingyu Kim, Suyoung Jang and Hong‐Yeop Song. Their work appears in journals such as Scientific Reports, International Journal of Environmental Research and Public Health, Technological Forecasting and Social Change, Journal of the Institute of Electronics Engineers of Korea and SSRN Electronic Journal.
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.