Dae-Ki Cho
Impact in
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- Network Security and Intrusion Detection
- Opportunistic and Delay-Tolerant Networks
- Caching and Content Delivery
- Mobile Ad Hoc Networks
- Signal Processing top 5%
- Time Series Analysis and Forecasting
Papers in
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- Opportunistic and Delay-Tolerant Networks 5
- Bluetooth and Wireless Communication Technologies 4
- Peer-to-Peer Network Technologies 2
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- Wireless Body Area Networks 3
- Advanced Chemical Sensor Technologies 1
- Co-authors
- Bo Zong (1 shared paper)Haifeng Chen (1 shared paper)Martin Renqiang Min (1 shared paper)Song Qi (1 shared paper)Wei Cheng (1 shared paper)Cristian Lumezanu (1 shared paper)Mário Gerla (9 shared papers)Uichin Lee (4 shared papers)
- Journals
- Pervasive and Mobile Computing (1 paper)IEEE Transactions on Vehicular Technology (1 paper)PubMed (1 paper)
- Partner nations
- United StatesSouth KoreaIsrael
In The Last Decade
Dae-Ki Cho
11 papers receiving 852 citations
Dae-Ki Cho's Hit Papers
Peers
Comparison fields: 5 of 79
- Computer Networks and Communications 487
- Signal Processing 199
- Artificial Intelligence 565
- Computer Vision and Pattern Recognition 124
- Control and Systems Engineering 93
Countries citing papers authored by Dae-Ki Cho
This map shows the geographic impact of Dae-Ki Cho'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 Dae-Ki Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dae-Ki Cho more than expected).
Fields of papers citing papers by Dae-Ki Cho
This network shows the impact of papers produced by Dae-Ki Cho. 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 Dae-Ki Cho. The network helps show where Dae-Ki Cho may publish in the future.
Co-authors
The 24 scholars most cited alongside Dae-Ki Cho, 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 | Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection Hit paper breakdown → | 2018 | 628 |
| 2 | 2009 | 65 | |
| 3 | 2007 | 63 | |
| 4 | 2010 | 41 | |
| 5 | 2007 | 39 | |
| 6 | 2009 | 16 | |
| 7 | 2017 | 13 | |
| 8 | 2008 | 11 | |
| 9 | 2008 | 5 | |
| 10 | 2008 | 4 | |
| 11 | 2007 | 1 |
About Dae-Ki Cho
Dae-Ki Cho is a scholar working on Computer Networks and Communications, Biomedical Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Dermatology, having authored 11 papers that have together received 886 indexed citations. Recurring topics across this work include Opportunistic and Delay-Tolerant Networks (5 papers), Bluetooth and Wireless Communication Technologies (4 papers), Wireless Body Area Networks (3 papers), Peer-to-Peer Network Technologies (2 papers), Context-Aware Activity Recognition Systems (2 papers), Vehicular Ad Hoc Networks (VANETs) (1 paper), ECG Monitoring and Analysis (1 paper) and Advanced Chemical Sensor Technologies (1 paper). The work is most often cited by research in Computer Networks and Communications (487 citations), Signal Processing (199 citations), Artificial Intelligence (565 citations), Computer Vision and Pattern Recognition (124 citations) and Control and Systems Engineering (93 citations). Dae-Ki Cho has collaborated with scholars based in United States, South Korea and Israel. Frequent co-authors include Bo Zong, Haifeng Chen, Martin Renqiang Min, Song Qi, Wei Cheng, Cristian Lumezanu, Mário Gerla, Uichin Lee, Benjamin B. Chang and Min Mun. Their work appears in journals such as Pervasive and Mobile Computing, IEEE Transactions on Vehicular Technology and PubMed.
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.