World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
6801
World Ranking
6614
National Ranking
55

Sungzoon Cho publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Sungzoon Cho sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 203 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Sungzoon Cho D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Sungzoon Cho sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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