World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
6801
World Ranking
6613
National Ranking
55

Overview

Sungzoon Cho is affiliated with Seoul National University in South Korea, specializing in computer science with a research portfolio emphasizing artificial intelligence. Their work spans several subfields including industrial and manufacturing engineering, computer vision and pattern recognition, molecular biology, and management science and operations research.

The scientist's research concentrates on multiple advanced topics, chiefly:

  • Advanced Text Analysis Techniques
  • Topic Modeling
  • Industrial Vision Systems and Defect Detection
  • Biomedical Text Mining and Ontologies
  • Stock Market Forecasting Methods
  • Text and Document Classification Technologies
  • Advancements in Photolithography Techniques

Sungzoon Cho has contributed extensively to the academic community, with publications appearing prominently in venues such as:

  • Expert Systems with Applications
  • arXiv (Cornell University)
  • Journal of Biomedical Informatics
  • IEEE Access
  • SSRN Electronic Journal

Some notable papers authored by or involving Sungzoon Cho include:

  • "Active Learning of Convolutional Neural Network for Cost-Effective Wafer Map Pattern Classification", 2020, IEEE Transactions on Semiconductor Manufacturing
  • "Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling", 2020, Expert Systems with Applications
  • "Exposing Fake Faces Through Deep Neural Networks Combining Content and Trace Feature Extractors", 2021, IEEE Access
  • "MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection", 2023, arXiv (Cornell University)
  • "Artificial intelligence-based predictions of movie audiences on opening Saturday", 2020, International Journal of Forecasting

The scientist frequently collaborates with a consistent group of co-authors, including:

  • Hye Jin Lee
  • Jaewoong Shim
  • Seokho Kang
  • Jinwon An
  • Hunsik Shin

Best Publications

  • EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems

    Pilsung Kang;Sungzoon Cho

  • Web-Based Keystroke Dynamics Identity Verification Using Neural Network

    Sungzoon Cho;Chigeun Han;Dae Hee Han;Hyung-Il Kim

  • Keystroke dynamics identity verification-its problems and practical solutions

    Enzhe Yu;Sungzoon Cho

  • Bag-of-concepts

    Han Kyul Kim;Hyunjoong Kim;Sungzoon Cho

  • System and method for performing user authentication based on user behavior patterns

    Sungzoon Cho;Min Jang

  • Keystroke dynamics-based authentication for mobile devices

    Seong-Seob Hwang;Sungzoon Cho;Sunghoon Park

  • A virtual metrology system for semiconductor manufacturing

    Pilsung Kang;Hyoung-joo Lee;Sungzoon Cho;Dongil Kim

  • Apparatus for authenticating an individual based on a typing pattern by using a neural network system

    Sung-Zoon Cho;Dae-Hee Han

  • GA-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification

    Enzhe Yu;Sungzoon Cho

  • Virtual metrology for run-to-run control in semiconductor manufacturing

    Pilsung Kang;Dongil Kim;Hyoung-joo Lee;Seungyong Doh

  • Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

    Dongil Kim;Pilsung Kang;Sungzoon Cho;Hyoung-joo Lee

  • Improvement of Kittler and Illingworth's minimum error thresholding

    Sungzoon Cho;Robert Haralick;Seungku Yi

  • Response modeling with support vector machines

    HyunJung Shin;Sungzoon Cho

  • Response models based on bagging neural networks

    Kyoungnam Ha;Sungzoon Cho;Sungzoon Cho;Douglas MacLachlan

  • Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning

    Yeonkook J. Kim;Bok Baik;Sungzoon Cho

  • Continual retraining of keystroke dynamics based authenticator

    Pilsung Kang;Seong-seob Hwang;Sungzoon Cho

  • Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning

    Eunji Kim;Jehyuk Lee;Hunsik Shin;Hoseong Yang

  • Keystroke dynamics-based user authentication using long and free text strings from various input devices

    Pilsung Kang;Sungzoon Cho

  • Semi-supervised support vector regression based on self-training with label uncertainty

    Pilsung Kang;Dongil Kim;Sungzoon Cho

  • Neighborhood Property--Based Pattern Selection for Support Vector Machines

    Hyunjung Shin;Sungzoon Cho

Frequent Co-Authors

James A. Reggia
James A. Reggia University of Maryland, College Park
Hyun-Joong Kim
Hyun-Joong Kim Seoul National University
Patrick C. M. Wong
Patrick C. M. Wong Chinese University of Hong Kong
Granger G. Sutton
Granger G. Sutton J. Craig Venter Institute
Byung-Gook Park
Byung-Gook Park Seoul National University
Seung-Yeop Kwak
Seung-Yeop Kwak Seoul National University
Yu Kyeong Kim
Yu Kyeong Kim Seoul National University
Chen-Fu Chien
Chen-Fu Chien National Tsing Hua University
Robert M. Haralick
Robert M. Haralick City University of New York
Changhee Lee
Changhee Lee Seoul National University

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