World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
10763
World Ranking
10985
National Ranking
146

Kezhi Mao 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 Kezhi Mao 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: 154 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Kezhi Mao 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 Kezhi Mao 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Kezhi Mao is affiliated with Nanyang Technological University in Singapore and has contributed extensively to the field of Computer Science, with a focus on Artificial Intelligence and related subfields. Their research encompasses areas such as Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Mechanical Engineering, and Infectious Diseases.

The scientist has published over a hundred works, with notable activity in Artificial Intelligence and Computer Vision specifically. Their main research topics include Human Pose and Action Recognition, Topic Modeling, Domain Adaptation and Few-Shot Learning, Natural Language Processing Techniques, Video Surveillance and Tracking Methods, Advanced Text Analysis Techniques, and Anomaly Detection Techniques and Applications.

Frequent collaborators of Kezhi Mao include the following researchers:

  • Yuecong Xu
  • Jianfei Yang
  • Haozhi Cao
  • Jianxiong Yin
  • Simon See

The scientist has published in various venues, with multiple contributions to:

  • arXiv (Cornell University)
  • Neurocomputing
  • Expert Systems with Applications
  • IEEE Journal of Emerging and Selected Topics in Power Electronics
  • SSRN Electronic Journal

Selected recent papers authored by or coauthored with Kezhi Mao include:

  • "Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter With Minimized Current Stress" (2021), IEEE Journal of Emerging and Selected Topics in Power Electronics
  • "Particle swarm optimization with state-based adaptive velocity limit strategy" (2021), Neurocomputing
  • "Improving convolutional neural network for text classification by recursive data pruning" (2020), Neurocomputing
  • "Aligning Correlation Information for Domain Adaptation in Action Recognition" (2022), IEEE Transactions on Neural Networks and Learning Systems
  • "Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full ZVS Range and Optimal Efficiency" (2023), IEEE Journal of Emerging and Selected Topics in Power Electronics

Best Publications

  • Deep learning and its applications to machine health monitoring

    Rui Zhao;Ruqiang Yan;Zhenghua Chen;Kezhi Mao

  • Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

    Rui Zhao;Dongzhe Wang;Ruqiang Yan;Kezhi Mao

  • Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

    Rui Zhao;Ruqiang Yan;Jinjiang Wang;Kezhi Mao

  • 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

  • Probabilistic neural-network structure determination for pattern classification

    K.Z. Mao;K.-C. Tan;W. Ser

  • Can threshold networks be trained directly

    Guang-Bin Huang;Qin-Yu Zhu;K.Z. Mao;Chee-Kheong Siew

  • Orthogonal forward selection and backward elimination algorithms for feature subset selection

    K.Z. Mao

  • Automatic detection of cyberbullying on social networks based on bullying features

    Rui Zhao;Anna Zhou;Kezhi Mao

  • Fuzzy Bag-of-Words Model for Document Representation

    Rui Zhao;Kezhi Mao

  • Machine health monitoring with LSTM networks

    Rui Zhao;Jinjiang Wang;Ruqiang Yan;Kezhi Mao

  • Supervised learning-based cell image segmentation for P53 immunohistochemistry

    K.Z. Mao;Peng Zhao;Puay-Hoon Tan

  • Feature subset selection for support vector machines through discriminative function pruning analysis

    K.Z. Mao

  • RBF neural network center selection based on Fisher ratio class separability measure

    K.Z. Mao

  • Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

    Feng Yang;K. Z. Mao

  • Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts

    Pengfei Li;Kezhi Mao

  • Deep Learning and Its Applications to Machine Health Monitoring: A Survey

    Rui Zhao;Ruqiang Yan;Zhenghua Chen;Kezhi Mao

  • Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder

    Rui Zhao;Kezhi Mao

  • Algorithms for minimal model structure detection in nonlinear dynamic system identification

    K. Z. Mao;S. A. Billings

  • Neuron selection for RBF neural network classifier based on data structure preserving criterion

    K.Z. Mao;Guang-Bin Huang

  • Identifying critical variables of principal components for unsupervised feature selection

    K.Z. Mao

Frequent Co-Authors

Jianfei Yang
Jianfei Yang Nanyang Technological University
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Ruqiang Yan
Ruqiang Yan Xi'an Jiaotong University
Tianyou Chai
Tianyou Chai Northeastern University
Hong Zhang
Hong Zhang University of Alberta
Puay Hoon Tan
Puay Hoon Tan Duke NUS Graduate Medical School
Amaury Lendasse
Amaury Lendasse University of Houston
Kar-Ann Toh
Kar-Ann Toh Yonsei University
Peng Wang
Peng Wang Nanyang Technological University
Robert X. Gao
Robert X. Gao Case Western Reserve University

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