World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

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
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
Yang Zhao
Yang Zhao Beijing Institute of Technology
Huiqing Wen
Huiqing Wen Xi’an Jiaotong-Liverpool University

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