World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
8093
World Ranking
5698
National Ranking
755

Min Han 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 Min Han 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: 275 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

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

Min Han 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 Min Han 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Min Han is affiliated with Dalian University of Technology in China and has contributed extensively to research in computer science and engineering. Their work focuses predominantly on artificial intelligence, computer vision and pattern recognition, control and systems engineering, signal processing, and electrical and electronic engineering.

The main topics that Min Han covers through their research include:

  • Neural networks and applications
  • Neural networks and reservoir computing
  • Time series analysis and forecasting
  • Machine fault diagnosis techniques
  • Fault detection and control systems
  • Remote-sensing image classification
  • Machine learning and extreme learning machines (ELM)

Min Han has published in various scientific venues, with frequent appearances in:

  • SSRN Electronic Journal
  • IEEE Transactions on Neural Networks and Learning Systems
  • Measurement
  • Engineering Applications of Artificial Intelligence
  • International Journal of Remote Sensing

Several recent papers authored or co-authored by Min Han include:

  • "Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "Advancing green extraction of bioactive compounds using deep eutectic solvent-based ultrasound-assisted matrix solid-phase dispersion: Application to UHPLC-PAD analysis of alkaloids and organic acids in Coptidis rhizoma," 2024, Talanta
  • "A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation," 2020, Neurocomputing (co-authored by Nannan Lu)
  • "A hybrid prognostic strategy with unscented particle filter and optimized multiple kernel relevance vector machine for lithium-ion battery," 2020, Measurement (co-authored by Xiaofei Sun)
  • "Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization," 2020, IEEE Transactions on Cybernetics (co-authored by Xiaodong Na)

Frequent collaborators in Min Han's research include:

  • Weijie Ren
  • Chengkun Zhang
  • Xinghan Xu
  • Nannan Lu
  • Xiaodong Na

Best Publications

  • Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance

    Zhouhua Peng;Dan Wang;Tieshan Li;Min Han

  • Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

    Decai Li;Min Han;Jun Wang

  • Support Vector Echo-State Machine for Chaotic Time-Series Prediction

    Zhiwei Shi;Min Han

  • Prediction of chaotic time series based on the recurrent predictor neural network

    Min Han;Jianhui Xi;Shiguo Xu;Fu-Liang Yin

  • Recurrent Broad Learning Systems for Time Series Prediction

    Meiling Xu;Min Han;C. L. Philip Chen;Tie Qiu

  • Online sequential extreme learning machine with kernels for nonstationary time series prediction

    Xinying Wang;Min Han

  • A Review on Intelligence Dehazing and Color Restoration for Underwater Images

    Min Han;Zhiyu Lyu;Tie Qiu;Meiling Xu

  • BitTableFI: An efficient mining frequent itemsets algorithm

    Jie Dong;Min Han

  • Noise Smoothing for Nonlinear Time Series Using Wavelet Soft Threshold

    Min Han;Yuhua Liu;Jianhui Xi;Wei Guo

  • Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

    Jianchao Fan;Min Han;Jun Wang

  • Data-driven based fault prognosis for industrial systems: a concise overview

    Kai Zhong;Min Han;Bing Han

  • Adaptive Elastic Echo State Network for Multivariate Time Series Prediction

    Unknown

  • Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction

    Min Han;Shoubo Feng;C. L. Philip Chen;Meiling Xu

  • Generalized Single-Hidden Layer Feedforward Networks for Regression Problems

    Ning Wang;Meng Joo Er;Min Han

  • Backpropagating Constraints-Based Trajectory Tracking Control of a Quadrotor With Constrained Actuator Dynamics and Complex Unknowns

    Ning Wang;Shun-Feng Su;Min Han;Wen-Hua Chen

  • Laplacian Echo State Network for Multivariate Time Series Prediction

    Min Han;Meiling Xu

  • Parsimonious extreme learning machine using recursive orthogonal least squares.

    Ning Wang;Meng Joo Er;Min Han

  • Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview

    Min Han;Kai Zhong;Tie Qiu;Bing Han

  • Analysis and modeling of multivariate chaotic time series based on neural network

    M. Han;Y. Wang

  • Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder

    Fei Lv;Min Han;Tie Qiu

  • A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control

    Min Han;Jianchao Fan;Jun Wang

  • Magnetic Induction Tomography

    Yuyan Xue;Min Han

Frequent Co-Authors

Tie Qiu
Tie Qiu Tianjin University
Jun Wang
Jun Wang City University of Hong Kong
Ning Wang
Ning Wang Dalian Maritime University
Meng Joo Er
Meng Joo Er Dalian Maritime University
C. L. Philip Chen
C. L. Philip Chen South China University of Technology
Dan Wang
Dan Wang Dalian Maritime University
Tieshan Li
Tieshan Li University of Electronic Science and Technology of China
Zhouhua Peng
Zhouhua Peng Dalian Maritime University
Dongbin Zhao
Dongbin Zhao Chinese Academy of Sciences
Wen-Hua Chen
Wen-Hua Chen Loughborough University

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