Dae-Ki Cho

1.4k citations
11 papers · 886 indexed · 1 hit paper · h-index 8

Impact in

Papers in

Dae-Ki Cho

11 papers receiving 852 citations

Hit Papers

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection 2018 · 628 citations
6282018202620202023200400600

Peers

Dae-Ki Cho
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
Replace Gürsel Serpen with:
Gürsel Serpen United States
Savio Sciancalepore Qatar
Kandaraj Piamrat France
Qi Duan United States
Jialiang Lu China
Dexin Zhao China
Congrui Huang China
Donghwoon Kwon United States
Adel Binbusayyis Saudi Arabia
Lei Du China
Dae-Ki Cho relative to Gürsel Serpen United States Gürsel Serpen's profile →
Citations per field
00.5×2.8×
Gürsel Serpen · 1×
Citations per year

Countries citing papers authored by Dae-Ki Cho

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network

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.

Border = papers with Dae-Ki Cho Line = papers co-authored together Dae-Ki Cho links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Hit paper breakdown →
2018628
2 201713
3 201041
4 200916
5 200965
6 200811
7 20085
8 20084
9 200739
10 20071
11 200763

About Dae-Ki Cho

Dae-Ki Cho is a scholar working on Computer Networks and Communications, Immunology and Allergy, Hardware and Architecture, Biomedical 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), ECG Monitoring and Analysis (1 paper), Eating Disorders and Behaviors (1 paper) and Indoor and Outdoor Localization 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, Israel and South Korea. Frequent co-authors include Bo Zong, Martin Renqiang Min, Song Qi, Cristian Lumezanu, Haifeng Chen, Wei Cheng, Mário Gerla, Uichin Lee, Benjamin B. Chang and William J. Kaiser. 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.

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