Sungzoon Cho

4.3k total citations
129 papers, 2.6k citations indexed

About

Sungzoon Cho is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering. According to data from OpenAlex, Sungzoon Cho has authored 129 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Artificial Intelligence, 31 papers in Computer Vision and Pattern Recognition and 22 papers in Industrial and Manufacturing Engineering. Recurrent topics in Sungzoon Cho's work include Face and Expression Recognition (21 papers), Neural Networks and Applications (20 papers) and Industrial Vision Systems and Defect Detection (19 papers). Sungzoon Cho is often cited by papers focused on Face and Expression Recognition (21 papers), Neural Networks and Applications (20 papers) and Industrial Vision Systems and Defect Detection (19 papers). Sungzoon Cho collaborates with scholars based in South Korea, United States and United Kingdom. Sungzoon Cho's co-authors include Pilsung Kang, Seokho Kang, Hyoungjoo Lee, Dongil Kim, Eunji Kim, Hyunjung Shin, Douglas L. MacLachlan, Hyung-Il Kim, Bok Baik and Min Seong Jang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Energy and Buildings.

In The Last Decade

Sungzoon Cho

123 papers receiving 2.5k citations

Peers

Sungzoon Cho

IME

CVPR

Iztok Fister Slovenia

Thomas A. Runkler Germany

Beatrice Lazzerini Italy

Lakhmi C. Jain Australia

Paolo Nesi Italy

Sebti Foufou France

Bernardete Ribeiro Portugal

Emilio Corchado Spain

Ke Li China

Shokri Z. Selim Saudi Arabia

Iztok Fister Slovenia

Sungzoon Cho

1.9k ×1.9

287 ×0.5

IME

379 ×0.8

587 ×1.3

CVPR

201 ×0.5

Citations per year, relative to Sungzoon Cho Sungzoon Cho (= 1×) peers Iztok Fister

Countries citing papers authored by Sungzoon Cho

Since Specialization

Citations

This map shows the geographic impact of Sungzoon 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 Sungzoon Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungzoon Cho more than expected).

Fields of papers citing papers by Sungzoon Cho

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sungzoon 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 Sungzoon Cho. The network helps show where Sungzoon Cho may publish in the future.

Co-authorship network of co-authors of Sungzoon Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Sungzoon Cho. A scholar is included among the top collaborators of Sungzoon Cho 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 Sungzoon Cho. Sungzoon Cho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Cho, Sungzoon, et al.. (2025). Human-guided collective LLM intelligence for strategic planning via two-stage information retrieval. Information Processing & Management. 63(1). 104288–104288. 1 indexed citations

Cho, Sungzoon, et al.. (2023). Stock price momentum modeling using social media data. Expert Systems with Applications. 237. 121589–121589. 5 indexed citations

Cho, Sungzoon, et al.. (2023). General-use unsupervised keyword extraction model for keyword analysis. Expert Systems with Applications. 233. 120889–120889. 7 indexed citations

Cho, Sungzoon, et al.. (2023). Hot topic detection in central bankers’ speeches. Expert Systems with Applications. 230. 120563–120563. 4 indexed citations

Kim, Eunji, et al.. (2023). A sensor data mining process for identifying root causes associated with low yield in semiconductor manufacturing. Data Technologies and Applications. 57(3). 397–417. 3 indexed citations

Cho, Sungzoon, et al.. (2020). User segmentation via interpretable user representation and relative similarity-based segmentation method. Multimedia Systems. 27(1). 61–72. 4 indexed citations

Cho, Sungzoon, et al.. (2019). Determining user needs through abnormality detection and heterogeneous embedding of usage sequence. Electronic Commerce Research. 21(2). 245–261. 7 indexed citations

Cho, Sungzoon, et al.. (2019). Document representation based on probabilistic word clustering in customer-voice classification. Pattern Analysis and Applications. 22(1). 221–232. 4 indexed citations

Cho, Sungzoon, et al.. (2019). Extraction of Product Evaluation Factors with a Convolutional Neural Network and Transfer Learning. Neural Processing Letters. 50(1). 149–164. 10 indexed citations

10.

Kim, Eunji, et al.. (2019). Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing. 32(3). 302–309. 69 indexed citations

11.

Cho, Sungzoon, et al.. (2019). Design of Semantic-Based Colorization of Graphical User Interface Through Conditional Generative Adversarial Nets. International Journal of Human-Computer Interaction. 36(8). 699–708. 9 indexed citations

12.

Cho, Sungzoon, et al.. (2019). Abnormal Usage Sequence Detection for Identification of User Needs via Recurrent Neural Network Semantic Variational Autoencoder. International Journal of Human-Computer Interaction. 36(7). 631–640. 1 indexed citations

13.

Kim, Dongil & Sungzoon Cho. (2010). Pattern Selection for Support Vector Regression using Expected Margin. 대한산업공학회 춘계공동학술대회 논문집. 312–331.

14.

Lee, Hyoungjoo & Sungzoon Cho. (2008). Retraining a Novelty Detector with Impostor Patterns for Keystroke Dynamics-based Authentication ⋆. 1 indexed citations

15.

Kang, Pilsung & Sungzoon Cho. (2008). EUS SVMs: Ensemble of UnderSampled SVMs for Data Imbalance Problems. 7 indexed citations

16.

Cho, Sungzoon, et al.. (2002). An Up-Trend Detection Using an Auto-Associative Neural Network. 대한산업공학회 춘계공동학술대회 논문집. 1049–1053. 1 indexed citations

17.

Lee, Hyoungjoo, et al.. (2002). Analysis of Novelty Detection Properties of Autoassociative MLP. Journal of Korean Institute of Industrial Engineers. 28(2). 147–161. 2 indexed citations

18.

Reggia, James A., et al.. (2000). Cortical inhibition as explained by the competitive distribution hypothesis. Intellect Books. 31–62.

19.

Cho, Sungzoon, et al.. (1998). Rock Porosity Prediction Using Multilayer Perceptrons. International Conference on Neural Information Processing. 1016–1019. 1 indexed citations

20.

Oh, Jong–Hoon, Chulan Kwon, & Sungzoon Cho. (1995). Neural networks : the statistical mechanics perspective : proceedings of the CTP-PBSRI Joint Workshop on Theoretical Physics, POSTECH, Pohang, Korea, 2-4 February 95. WORLD SCIENTIFIC eBooks. 5 indexed citations

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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