Geon Heo
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Privacy-Preserving Technologies in Data
- Data Stream Mining Techniques
- Adversarial Robustness in Machine Learning
Papers in
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- Machine Learning and Data Classification 2
- Data Stream Mining Techniques 1
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- Human Pose and Action Recognition 1
- Co-authors
- Yuji Roh (2 shared papers)Steven Euijong Whang (3 shared papers)Hyungjun Lim (1 shared paper)Minjeong Kim (1 shared paper)Doory Kim (1 shared paper)Won‐Sang Ra (1 shared paper)
- Journals
- Biosensors and Bioelectronics (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Proceedings of the VLDB Endowment (1 paper)Journal of Institute of Control Robotics and Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- South KoreaCanada
In The Last Decade
Geon Heo
6 papers receiving 505 citations
Hit Papers
Peers
Comparison fields: 5 of 129
- Health Informatics 13
- Artificial Intelligence 226
- Management Information Systems 35
- Computer Science Applications 21
- Computer Vision and Pattern Recognition 75
Countries citing papers authored by Geon Heo
This map shows the geographic impact of Geon Heo'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 Geon Heo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geon Heo more than expected).
Fields of papers citing papers by Geon Heo
This network shows the impact of papers produced by Geon Heo. 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 Geon Heo. The network helps show where Geon Heo may publish in the future.
Co-authors
The 6 scholars most cited alongside Geon Heo, 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 | A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective Hit paper breakdown → | 2019 | 510 |
| 2 | 2024 | 11 | |
| 3 | 2020 | 8 | |
| 4 | 2015 | 3 | |
| 5 | 2023 | 1 | |
| 6 | 2017 | 1 |
About Geon Heo
Geon Heo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Molecular Biology and Structural Biology, having authored 6 papers that have together received 534 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Data Stream Mining Techniques (1 paper), Indoor and Outdoor Localization Technologies (1 paper), Robotics and Sensor-Based Localization (1 paper), Human Pose and Action Recognition (1 paper), Advanced Fluorescence Microscopy Techniques (1 paper), Smart Grid Security and Resilience (1 paper) and Extracellular vesicles in disease (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (226 citations), Management Information Systems (35 citations), Computer Science Applications (21 citations) and Computer Vision and Pattern Recognition (75 citations). Geon Heo has collaborated with scholars based in South Korea and Canada. Frequent co-authors include Yuji Roh, Steven Euijong Whang, Hyungjun Lim, Minjeong Kim, Doory Kim and Won‐Sang Ra. Their work appears in journals such as Biosensors and Bioelectronics, IEEE Transactions on Knowledge and Data Engineering, Proceedings of the VLDB Endowment, Journal of Institute of Control Robotics and Systems and Proceedings of the AAAI Conference on Artificial Intelligence.
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.