Hyoju Nam
Impact in
- Information Systems top 2%
- Data Mining Algorithms and Applications
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- Rough Sets and Fuzzy Logic
Papers in
-
- Data Mining Algorithms and Applications 10
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- Imbalanced Data Classification Techniques 4
- Data Stream Mining Techniques 3
- Algorithms and Data Compression 1
- Co-authors
- Unil Yun (10 shared papers)Eunchul Yoon (6 shared papers)Jerry Chun‐Wei Lin (5 shared papers)Yoonji Baek (6 shared papers)Heonho Kim (5 shared papers)Gangin Lee (2 shared papers)Bay Vo (4 shared papers)Witold Pedrycz (3 shared papers)
- Journals
- Future Generation Computer Systems (2 papers)Knowledge-Based Systems (2 papers)Engineering Applications of Artificial Intelligence (1 paper)IEEE Access (1 paper)Information Sciences (1 paper)
- Partner nations
- South KoreaVietnamNorway
In The Last Decade
Hyoju Nam
10 papers receiving 328 citations
Peers
Comparison fields: 5 of 21
- Information Systems 303
- Computational Theory and Mathematics 194
- Signal Processing 89
- Artificial Intelligence 206
- Marketing 19
Countries citing papers authored by Hyoju Nam
This map shows the geographic impact of Hyoju Nam'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 Hyoju Nam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyoju Nam more than expected).
Fields of papers citing papers by Hyoju Nam
This network shows the impact of papers produced by Hyoju Nam. 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 Hyoju Nam. The network helps show where Hyoju Nam may publish in the future.
Co-authors
The 15 scholars most cited alongside Hyoju Nam, 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 | 2019 | 54 | |
| 2 | 2019 | 47 | |
| 3 | 2019 | 40 | |
| 4 | 2020 | 37 | |
| 5 | 2021 | 35 | |
| 6 | 2020 | 34 | |
| 7 | 2020 | 33 | |
| 8 | 2020 | 22 | |
| 9 | 2024 | 14 | |
| 10 | 2021 | 13 |
About Hyoju Nam
Hyoju Nam is a scholar working on Information Systems, Artificial Intelligence, Signal Processing, Computational Theory and Mathematics and Computer Networks and Communications, having authored 10 papers that have together received 329 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (10 papers), Data Management and Algorithms (5 papers), Rough Sets and Fuzzy Logic (5 papers), Imbalanced Data Classification Techniques (4 papers), Data Stream Mining Techniques (3 papers), Advanced Database Systems and Queries (1 paper), Time Series Analysis and Forecasting (1 paper) and Algorithms and Data Compression (1 paper). The work is most often cited by research in Information Systems (303 citations), Computational Theory and Mathematics (194 citations), Signal Processing (89 citations), Artificial Intelligence (206 citations) and Marketing (19 citations). Hyoju Nam has collaborated with scholars based in South Korea, Vietnam and Norway. Frequent co-authors include Unil Yun, Eunchul Yoon, Jerry Chun‐Wei Lin, Yoonji Baek, Heonho Kim, Gangin Lee, Bay Vo, Witold Pedrycz, Bay Vo and Hyunsoo Kim. Their work appears in journals such as Future Generation Computer Systems, Knowledge-Based Systems, Engineering Applications of Artificial Intelligence, IEEE Access and Information Sciences.
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.