World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8985
World Ranking
7901
National Ranking
249

Dianhui Wang 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 Dianhui Wang 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: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

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

Dianhui Wang 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 Dianhui Wang 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Dianhui Wang is affiliated with La Trobe University in Australia and has an extensive research portfolio primarily in the fields of Computer Science and Engineering. Their scholarly work spans topics such as Artificial Intelligence, Control and Systems Engineering, and Computer Vision and Pattern Recognition, with additional contributions in Electrical and Electronic Engineering and Mechanical Engineering.

Their main areas of study include:

  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Dianhui Wang's research heavily focuses on Machine Learning and Extreme Learning Machines (ELM), Neural Networks and Applications, Fault Detection and Control Systems, Face and Expression Recognition, Fuzzy Logic and Control Systems, Advanced Neural Network Applications, and Advanced Graph Neural Networks.

The main topics of their work encompass:

  • Machine Learning and ELM
  • Neural Networks and Applications
  • Fault Detection and Control Systems
  • Face and Expression Recognition
  • Fuzzy Logic and Control Systems
  • Advanced Neural Network Applications
  • Advanced Graph Neural Networks

The scientist has published research in frequently appearing venues such as:

  • Information Sciences
  • Neural Computing and Applications
  • IEEE Transactions on Fuzzy Systems
  • IEEE Transactions on Industrial Informatics
  • arXiv (Cornell University)

Among significant recent papers authored or co-authored by Dianhui Wang are:

  • "Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing" (2021), IEEE Transactions on Cybernetics
  • "Fuzzy Stochastic Configuration Networks for Nonlinear System Modeling" (2023), IEEE Transactions on Fuzzy Systems
  • "Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks" (2020), Neural Computing and Applications
  • "Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis" (2023), IEEE Transactions on Industrial Informatics
  • "Stochastic configuration network ensembles with selective base models" (2021), Neural Networks

Frequent co-authors working with Dianhui Wang include:

  • Gang Dang
  • Yongxuan Chen
  • Aijun Yan
  • Pengxin Tian
  • Matthew J. Felicetti

Their contributions mainly address advanced methodologies in neural networks, stochastic configuration networks, and fuzzy systems, with applications ranging from system modeling and data stream analysis to network representation learning.

Best Publications

  • Extreme learning machines: a survey

    Guang-Bin Huang;Dian Hui Wang;Yuan Lan

  • Stochastic Configuration Networks: Fundamentals and Algorithms

    Dianhui Wang;Ming Li

  • Randomness in neural networks: an overview

    Simone Scardapane;Dianhui Wang

  • Fast decorrelated neural network ensembles with random weights

    Monther Alhamdoosh;Dianhui Wang

  • Insights into randomized algorithms for neural networks: Practical issues and common pitfalls

    Ming Li;Dianhui Wang

  • A Self-Adaptive RBF Neural Network Classifier for Transformer Fault Analysis

    Ke Meng;Zhao Yang Dong;Dian Hui Wang;Kit Po Wong

  • Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System

    Yan Xu;Rui Zhang;Junhua Zhao;Zhao Yang Dong

  • Distributed learning for Random Vector Functional-Link networks

    Simone Scardapane;Dianhui Wang;Massimo Panella;Aurelio Uncini

  • Robust stochastic configuration networks with kernel density estimation for uncertain data regression

    Dianhui Wang;Ming Li

  • Protein sequence classification using extreme learning machine

    Dianhui Wang;Guang-Bin Huang

  • Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics

    Dianhui Wang;Caihao Cui

  • A robust adaptive neural networks controller for maritime dynamic positioning system

    Jialu Du;Yang Yang;Dianhui Wang;Chen Guo

  • A decentralized training algorithm for Echo State Networks in distributed big data applications

    Simone Scardapane;Dianhui Wang;Massimo Panella

  • New Stability Criteria of Delayed Load Frequency Control Systems via Infinite-Series-Based Inequality

    Feisheng Yang;Jing He;Dianhui Wang

  • Deep Stochastic Configuration Networks with Universal Approximation Property

    Dianhui Wang;Ming Li

  • Global Convergence of Online BP Training With Dynamic Learning Rate

    Rui Zhang;Zong-Ben Xu;Guang-Bin Huang;Dianhui Wang

  • 2-D Stochastic Configuration Networks for Image Data Analytics

    Ming Li;Dianhui Wang

  • Adaptive robust control of oxygen excess ratio for PEMFC system based on type-2 fuzzy logic system

    H. K. Zhang;Y. F. Wang;Dianhui Wang;Dianhui Wang;Y. L. Wang

  • Flame Image-Based Burning State Recognition for Sintering Process of Rotary Kiln Using Heterogeneous Features and Fuzzy Integral

    Weitao Li;Dianhui Wang;Tianyou Chai

  • Evolutionary extreme learning machine ensembles with size control

    Dianhui Wang;Monther Alhamdoosh

  • A new robust training algorithm for a class of single-hidden layer feedforward neural networks

    Zhihong Man;Kevin Lee;Dianhui Wang;Zhenwei Cao

  • Letters: A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome

    Joon Shik Lim;Dianhui Wang;Yong-Soo Kim;Sudhir Gupta

Frequent Co-Authors

Tharam S. Dillon
Tharam S. Dillon La Trobe University
Tianyou Chai
Tianyou Chai Northeastern University
Zhihong Man
Zhihong Man Swinburne University of Technology
Elizabeth Chang
Elizabeth Chang Griffith University
Zhenwei Cao
Zhenwei Cao Swinburne University of Technology
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Mahardhika Pratama
Mahardhika Pratama University of South Australia
Witold Pedrycz
Witold Pedrycz University of Alberta
Zhao Yang Dong
Zhao Yang Dong City University of Hong Kong
Junhua Zhao
Junhua Zhao Chinese University of Hong Kong, Shenzhen

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Related Online Degrees & Career Pathways

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Exploring these pathways can broaden both your skills and career prospects, making your educational experience more flexible and versatile.

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