Soonja Yeom
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Artificial Intelligence
- Computer Science Applications top 5%
- Experimental and Cognitive Psychology
- Co-authors
- Soo-Hyung KimDavid HerbertShuxiang XuHyung-Jeong YangIn Seop NaGuee-Sang LeeSi FanSumbal Maqsood
- Topics
- Online Learning and Analytics (7 papers)User Authentication and Security Systems (5 papers)Face recognition and analysis (4 papers)
- Cited by
- Computer Science ApplicationsComputer Vision and Pattern RecognitionHuman-Computer Interaction
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsIEEE Access
- Partner nations
- AustraliaSouth KoreaFrance
In The Last Decade
Soonja Yeom
29 papers receiving 294 citations
Peers
Comparison fields: 5 of 68
- Computer Vision and Pattern Recognition 100
- Information Systems 88
- Artificial Intelligence 63
- Computer Science Applications 63
- Experimental and Cognitive Psychology 54
Countries citing papers authored by Soonja Yeom
This map shows the geographic impact of Soonja Yeom'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 Soonja Yeom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soonja Yeom more than expected).
Fields of papers citing papers by Soonja Yeom
This network shows the impact of papers produced by Soonja Yeom. 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 Soonja Yeom. The network helps show where Soonja Yeom may publish in the future.
Co-authorship network of co-authors of Soonja Yeom
This figure shows the co-authorship network connecting the top 25 collaborators of Soonja Yeom. A scholar is included among the top collaborators of Soonja Yeom 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 Soonja Yeom. Soonja Yeom is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 7 | |
| 8 | 8 | |
| 9 | 2 | |
| 10 | 25 | |
| 11 | 15 | |
| 12 | 29 | |
| 13 | 5 | |
| 14 | 18 | |
| 15 | 4 | |
| 16 | 0 | |
| 17 | Exploring the potential of learning analytics to measure student participation and engagement: Researchers' experiences from an exploratory study | 1 |
| 18 | Facial expression recognition using a multi-level convolutional neural network | 7 |
| 19 | User Acceptance of a Haptic Interface for Learning Anatomy | 5 |
| 20 | 19 |
About Soonja Yeom
Soonja Yeom is a scholar working on Computer Science Applications, Information Systems and General Dentistry, having authored 38 papers that have together received 315 indexed citations. Recurring topics across this work include Online Learning and Analytics (7 papers), User Authentication and Security Systems (5 papers) and Face recognition and analysis (4 papers). The work is most often cited by research in Computer Science Applications (63 citations), Computer Vision and Pattern Recognition (100 citations) and Human-Computer Interaction (26 citations). Soonja Yeom has collaborated with scholars based in Australia, South Korea and France. Frequent co-authors include Soo-Hyung Kim, David Herbert, Shuxiang Xu, Hyung-Jeong Yang, In Seop Na, Guee-Sang Lee, Si Fan, Sumbal Maqsood, Atul Sajjanhar and Yu-Chen Wei. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.
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.