World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8238
World Ranking
7203
National Ranking
954

Ke Yan 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 Ke Yan 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: 207 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Ke Yan 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 Ke Yan 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Ke Yan is affiliated with Alibaba Group (China) in China and has contributed extensively to the fields of computer science and engineering. Their research spans multiple subfields including artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, radiology, nuclear medicine and imaging, and control and systems engineering.

Their main topics of work cover various interdisciplinary areas, particularly focusing on:

  • Energy load and power forecasting
  • Building energy and comfort optimization
  • Solar radiation and photovoltaics
  • Radiomics and machine learning in medical imaging
  • Metaheuristic optimization algorithms research
  • Domain adaptation and few-shot learning
  • Air quality monitoring and forecasting

Ke Yan has published multiple recent papers in well-regarded venues. These include:

  • "Generative adversarial network for fault detection diagnosis of chillers," 2020, Building and Environment
  • "Highly accurate energy consumption forecasting model based on parallel LSTM neural networks," 2021, Advanced Engineering Informatics
  • "A hybrid deep learning technology for PM2.5 air quality forecasting," 2021, Environmental Science and Pollution Research
  • "Chiller Fault Diagnosis Based on VAE-Enabled Generative Adversarial Networks," 2020, IEEE Transactions on Automation Science and Engineering
  • "Multi-Task Learning Model Based on Multi-Scale CNN and LSTM for Sentiment Classification," 2020, IEEE Access

Their frequent coauthors include Le Lü, Dakai Jin, Jia Guo, Xiaokang Zhou, and Yuji Sato. These collaborations suggest a strong network of researchers working in related fields.

Ke Yan's publications are often found in venues such as:

  • arXiv (Cornell University)
  • Building and Environment
  • Sensors
  • Advanced Engineering Informatics
  • Energy and Buildings

The scientist's research integrates advanced machine learning techniques with applications in energy systems and environmental monitoring. Key methodologies employed include generative adversarial networks, long short-term memory networks (LSTM), convolutional neural networks (CNN), and variational autoencoders (VAE).

This combination of research topics and technological approaches highlights a multidisciplinary expertise at the intersection of computer science and engineering dedicated to practical challenges in forecasting, diagnostics, and optimization within energy and environmental systems.

Best Publications

  • IrisNet: an architecture for a worldwide sensor Web

    P.B. Gibbons;B. Karp;Y. Ke;S. Nath

  • Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism

    Hangxia Zhou;Yujin Zhang;Lingfan Yang;Qian Liu

  • Robust Sparse Linear Discriminant Analysis

    Jie Wen;Xiaozhao Fang;Jinrong Cui;Lunke Fei

  • A hybrid feature selection algorithm for gene expression data classification

    Huijuan Lu;Junying Chen;Ke Yan;Qun Jin

  • Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization.

    Ke Yan;Lu Kou;David Zhang

  • Computer vision for music identification

    Yan Ke;D. Hoiem;R. Sukthankar

  • Adaptive Graph Completion Based Incomplete Multi-View Clustering

    Jie Wen;Ke Yan;Zheng Zhang;Yong Xu

  • A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

    Ke Yan;Wei Li;Zhiwei Ji;Meng Qi

  • Generative adversarial network for fault detection diagnosis of chillers

    Ke Yan;Adrian Chong;Yuchang Mo

  • Generative adversarial network for fault detection diagnosis of chillers

    Ke Yan;Adrian Chong;Yuchang Mo

  • Low-Rank Preserving Projection Via Graph Regularized Reconstruction

    Jie Wen;Na Han;Xiaozhao Fang;Lunke Fei

  • Multi-step short-term power consumption forecasting with a hybrid deep learning strategy

    Ke Yan;Xudong Wang;Yang Du;Ning Jin

  • ARX model based fault detection and diagnosis for chillers using support vector machines

    Ke Yan;Wen Shen;Timothy Mulumba;Afshin Afshari

  • Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis.

    Ke Yan;Lulu Ma;Yuting Dai;Wen Shen

  • Semi-supervised learning for early detection and diagnosis of various air handling unit faults

    Ke Yan;Chaowen Zhong;Zhiwei Ji;Jing Huang

  • Unsupervised learning for fault detection and diagnosis of air handling units

    Ke Yan;Jing Huang;Wen Shen;Zhiwei Ji

  • Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM

    Ke Yan;Zhiwei Ji;Wen Shen

  • Robust model-based fault diagnosis for air handling units

    Timothy Mulumba;Afshin Afshari;Ke Yan;Wen Shen

  • Highly accurate energy consumption forecasting model based on parallel LSTM neural networks

    Ning Jin;Fan Yang;Yuchang Mo;Yongkang Zeng

  • Chiller Fault Diagnosis Based on VAE-Enabled Generative Adversarial Networks

    Ke Yan;Jianye Su;Jing Huang;Yuchang Mo

  • Mathematical and Computational Modeling in Complex Biological Systems

    Zhiwei Ji;Ke Yan;Wenyang Li;Haigen Hu

  • Multi-Task Learning Model Based on Multi-Scale CNN and LSTM for Sentiment Classification

    Ning Jin;Jiaxian Wu;Xiang Ma;Ke Yan

  • DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks.

    Bin Liu;Chen-Chen Li;Ke Yan

  • Multivariate Air Quality Forecasting With Nested Long Short Term Memory Neural Network

    Ning Jin;Yongkang Zeng;Ke Yan;Zhiwei Ji

  • Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units

    Ke Yan;Zhiwei Ji;Huijuan Lu;Jing Huang

Frequent Co-Authors

Yong Xu
Yong Xu Harbin Institute of Technology
Bin Liu
Bin Liu National University of Singapore
Lunke Fei
Lunke Fei Guangdong University of Technology
Yu Xue
Yu Xue Nanjing University of Information Science and Technology
Cristina V. Lopes
Cristina V. Lopes University of California, Irvine
Lin Jiang
Lin Jiang University of Liverpool
Weidong Xiao
Weidong Xiao University of Sydney
Huiqing Wen
Huiqing Wen Xi’an Jiaotong-Liverpool University

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