World's Best Scientists 2026 revealed!
Yuan-Hai Shao

Yuan-Hai Shao

D-Index & Metrics

Computer Science

D-Index
35
Citations
4982
World Ranking
11733
National Ranking
1452

Yuan-Hai Shao 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 Yuan-Hai Shao 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: 174 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Yuan-Hai Shao 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 Yuan-Hai Shao 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Yuan-Hai Shao is affiliated with Hainan University in China, with a research focus primarily in the fields of Computer Science and Engineering. Their work has notably contributed to subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing, and Control and Systems Engineering.

Their research spans several main topics, including:

  • Face and Expression Recognition
  • Sparse and Compressive Sensing Techniques
  • Machine Learning and ELM
  • Anomaly Detection Techniques and Applications
  • Blind Source Separation Techniques
  • Advanced Algorithms and Applications
  • Remote-Sensing Image Classification

Yuan-Hai Shao's recent academic contributions include:

  • Comprehensive review on twin support vector machines, 2022, published in Annals of Operations Research
  • Support Vector Machine Classifier via Soft-Margin Loss, 2021, published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Smooth pinball loss nonparallel support vector machine for robust classification, 2020, published in Applied Soft Computing
  • Fast generalized ramp loss support vector machine for pattern classification, 2023, published in Pattern Recognition
  • Robust support vector regression with generic quadratic nonconvex ε-insensitive loss, 2020, published in Applied Mathematical Modelling

The venues in which Yuan-Hai Shao frequently publishes reflect their research relevance, including:

  • arXiv (Cornell University)
  • Applied Soft Computing
  • Information Sciences
  • IEEE Access
  • Pattern Recognition

Collaboration forms an integral part of Yuan-Hai Shao's work, with frequent coauthors including:

  • Chun-Na Li
  • Wei-Jie Chen
  • Lan Bai
  • Zhen Wang
  • Yanru Guo

Best Publications

  • Improvements on Twin Support Vector Machines

    Yuan-Hai Shao;Chun-Hua Zhang;Xiao-Bo Wang;Nai-Yang Deng

  • Comprehensive Review On Twin Support Vector Machines

    Mohammad Tanveer;T. Rajani;Reshma Rastogi;Yuan-Hai Shao

  • Nonparallel hyperplane support vector machine for binary classification problems

    Yuan-Hai Shao;Wei-Jie Chen;Nai-Yang Deng

  • An efficient weighted Lagrangian twin support vector machine for imbalanced data classification

    Yuan-Hai Shao;Wei-Jie Chen;Jing-Jing Zhang;Zhen Wang

  • Least squares recursive projection twin support vector machine for classification

    Yuan-Hai Shao;Nai-Yang Deng;Zhi-Min Yang

  • MLTSVM: A novel twin support vector machine to multi-label learning

    Wei-Jie Chen;Yuan-Hai Shao;Chun-Na Li;Nai-Yang Deng

  • An ε-twin support vector machine for regression

    Yuan-Hai Shao;Chun-Hua Zhang;Zhi-Min Yang;Ling Jing

  • Support Vector Machine Classifier via $L_{0/1}$ Soft-Margin Loss

    Huajun Wang;Yuanhai Shao;Shenglong Zhou;Ce Zhang

  • Twin Support Vector Machine for Clustering

    Zhen Wang;Yuan-Hai Shao;Lan Bai;Nai-Yang Deng

  • A regularization for the projection twin support vector machine

    Yuan-Hai Shao;Zhen Wang;Wei-Jie Chen;Nai-Yang Deng

  • A coordinate descent margin based-twin support vector machine for classification

    Yuan-Hai Shao;Nai-Yang Deng

  • Robust L1-norm two-dimensional linear discriminant analysis

    Chun-Na Li;Yuan-Hai Shao;Nai-Yang Deng

  • Weighted linear loss twin support vector machine for large-scale classification

    Yuan-Hai Shao;Wei-Jie Chen;Zhen Wang;Chun-Na Li

  • Multiple birth support vector machine for multi-class classification

    Zhi-Xia Yang;Yuan-Hai Shao;Xiang-Sun Zhang

  • Laplacian smooth twin support vector machine for semi-supervised classification

    Wei-Jie Chen;Yuan-Hai Shao;Ning Hong

  • Improved Generalized Eigenvalue Proximal Support Vector Machine

    Yuan-Hai Shao;Nai-Yang Deng;Wei-Jie Chen;Zhen Wang

  • A GA-based model selection for smooth twin parametric-margin support vector machine

    Zhen Wang;Yuan-Hai Shao;Tie-Ru Wu

  • Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers

    Chun-Na Li;Yuan-Hai Shao;Wotao Yin;Ming-Zeng Liu

  • Robust L1-norm non-parallel proximal support vector machine

    Chun-Na Li;Yuan-Hai Shao;Nai-Yang Deng

  • Laplacian least squares twin support vector machine for semi-supervised classification

    Wei-Jie Chen;Yuan-Hai Shao;Nai-Yang Deng;Zhi-Lin Feng

  • Probabilistic outputs for twin support vector machines

    Yuan-Hai Shao;Nai-Yang Deng;Zhi-Min Yang;Wei-Jie Chen

Frequent Co-Authors

Nai-Yang Deng
Nai-Yang Deng China Agricultural University
Wotao Yin
Wotao Yin Alibaba Group (China)
Yingjie Tian
Yingjie Tian University of Chinese Academy of Sciences

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you're exploring your options beyond traditional on-campus Computer Science programs, there are a variety of flexible online degrees to consider. 1 year associate degree programs online offer an accelerated path into tech careers or further study, perfect for those eager to enter the workforce quickly.

Affordability is another key consideration. Many students find that the cheapest online degrees make higher education more accessible, allowing them to manage costs while earning a respected qualification from accredited institutions.

If your academic record isn't perfect, don't worry. There are excellent college with low gpa admission requirements, ensuring everyone has an opportunity, regardless of their high school or previous college performance.

Computer Science pairs well with other fields too. For students interested in environmental impact, earning an environmental science degree opens up new career pathways at the intersection of technology and sustainability.

Best Scientists Citing Yuan-Hai Shao

Trending Scientists

Recently Published Articles