World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
77
Citations
66145
World Ranking
1227
National Ranking
24

Guang-Bin Huang 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 Guang-Bin Huang 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: 236 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Guang-Bin Huang 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 Guang-Bin Huang 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: 77 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Guang-Bin Huang is affiliated with Nanyang Technological University in Singapore. Their research spans multiple fields within computer science and engineering, with a particular focus on machine learning and extreme learning machines (ELM).

The main fields of study Guang-Bin Huang contributes to include:

  • Computer Science
  • Engineering

Within these broad areas, their work addresses several subfields of study:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Biomedical Engineering
  • Molecular Biology

Key topics covered by Guang-Bin Huang's research encompass:

  • Machine Learning and ELM
  • Domain Adaptation and Few-Shot Learning
  • Face and Expression Recognition
  • Neural Networks and Applications
  • Video Surveillance and Tracking Methods
  • Obstructive Sleep Apnea Research
  • Advanced Neural Network Applications

The following recent papers highlight their publication activity with details on publication year and venue:

  • Learning local discriminative representations via extreme learning machine for machine fault diagnosis (2020), published in Neurocomputing
  • Deep and wide feature based extreme learning machine for image classification (2020), published in Neurocomputing
  • Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment (2020), published in arXiv (Cornell University)
  • R-ELMNet: Regularized extreme learning machine network (2020), published in Neural Networks
  • NOx Measurements in Vehicle Exhaust Using Advanced Deep ELM Networks (2020), published in IEEE Transactions on Instrumentation and Measurement

Guang-Bin Huang frequently collaborates with a set of coauthors, including:

  • Yue Li
  • Yijie Zeng
  • Yuanyuan Qing
  • Dongshun Cui
  • Qi Cao

The predominant venues for their publications are:

  • Neurocomputing
  • Neural Networks
  • arXiv (Cornell University)
  • IEEE Transactions on Instrumentation and Measurement
  • Sleep And Breathing

In addition to journal publications, Guang-Bin Huang has contributed to book publications. Notably, they were involved in a book published by Frontiers Media titled Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms (2023).

Best Publications

  • Extreme learning machine: Theory and applications

    Guang-Bin Huang;Qin-Yu Zhu;Chee Kheong Siew

  • Extreme Learning Machine for Regression and Multiclass Classification

    Guang-Bin Huang;Hongming Zhou;Xiaojian Ding;Rui Zhang

  • Extreme learning machine: a new learning scheme of feedforward neural networks

    Guang-Bin Huang;Qin-Yu Zhu;Chee-Kheong Siew

  • Universal approximation using incremental constructive feedforward networks with random hidden nodes

    Guang-Bin Huang;Lei Chen;Chee-Kheong Siew

  • A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks

    Nan-Ying Liang;Guang-Bin Huang;P. Saratchandran;N. Sundararajan

  • Extreme learning machines: a survey

    Guang-Bin Huang;Dian Hui Wang;Yuan Lan

  • Trends in extreme learning machines

    Gao Huang;Guang-Bin Huang;Shiji Song;Keyou You

  • Letters: Convex incremental extreme learning machine

    Guang-Bin Huang;Lei Chen

  • Extreme Learning Machine for Multilayer Perceptron

    Jiexiong Tang;Chenwei Deng;Guang-Bin Huang

  • An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

    Guang-Bin Huang

  • Rapid and brief communication: Evolutionary extreme learning machine

    Qin-Yu Zhu;A. K. Qin;P. N. Suganthan;Guang-Bin Huang

  • Enhanced random search based incremental extreme learning machine

    Guang-Bin Huang;Lei Chen

  • Optimization method based extreme learning machine for classification

    Guang-Bin Huang;Xiaojian Ding;Hongming Zhou

  • Learning capability and storage capacity of two-hidden-layer feedforward networks

    Guang-Bin Huang

  • A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation

    Guang-Bin Huang;P. Saratchandran;N. Sundararajan

  • Weighted extreme learning machine for imbalance learning

    Weiwei Zong;Guang-Bin Huang;Yiqiang Chen

  • Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning

    Guorui Feng;Guang-Bin Huang;Qingping Lin

  • Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

    Guang-Bin Huang;H.A. Babri

  • Representational learning with ELMs for big data

    Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou;Guang-Bin Huang;Chi Man Vong

  • Extreme Learning Machine

    Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou

Frequent Co-Authors

Zhiping Lin
Zhiping Lin Nanyang Technological University
Narasimhan Sundararajan
Narasimhan Sundararajan Nanyang Technological University
Yeng Chai Soh
Yeng Chai Soh Nanyang Technological University
Wee Ser
Wee Ser Nanyang Technological University
Erik Cambria
Erik Cambria Nanyang Technological University
Dianhui Wang
Dianhui Wang La Trobe University
Zongben Xu
Zongben Xu Xi'an Jiaotong University
Amaury Lendasse
Amaury Lendasse University of Houston
Soujanya Poria
Soujanya Poria Nanyang Technological University
Kezhi Mao
Kezhi Mao Nanyang Technological University

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