Zhijin Wang
Scholar

Zhijin Wang

Google Scholar ID: rKUpBpMAAAAJ
Jimei University
AI in health/healthcareInfectious disease predictionTime series forecasting
Citations & Impact
All-time
Citations
691
 
H-index
14
 
i10-index
21
 
Publications
20
 
Co-authors
0
 
Resume (English only)
Academic Achievements
  • Published multiple journal articles covering topics such as time series forecasting, disease prediction, and energy forecasting. Some of the papers include:
  • - Explainable time-varying directional representations for photovoltaic power generation forecasting
  • - Sparse dynamic graph learning for district heat load forecasting
  • - Carbon futures price forecasting based on feature selection
  • - A new feature selection method based on importance measures for crude oil return forecasting
  • - Temporal collaborative attention for wind power forecasting
  • - Enhanced transfer learning with data augmentation
  • - Crosstalk between computational medicine and neuroscience in healthcare
  • - Oriented Transformer for Infectious Disease Case Prediction
  • - Explainable district heat load forecasting with active deep learning
  • - A multivariate time series graph neural network for district heat load forecasting
  • - A low-complexity evolutionary algorithm for wind farm layout optimization
  • - Laplacian Lp norm least squares twin support vector machine
  • - HFMD Cases Prediction using Transfer One-step-ahead Learning
  • - Dual-grained directional representation for infectious disease case prediction
  • - A Multi-view Multi-omics Model for Cancer Drug Response Prediction
  • - A multi-view time series model for share turnover prediction
  • - COVID-19 cases prediction in multiple areas via shapelet learning
  • - Parallel XPath query based on cost optimization
  • - Protein Secondary Structure Prediction With a Reductive Deep Learning Method
  • - Dual-grained representation for hand, foot, and mouth disease
Research Experience
  • Held a position at the College of Computer Engineering, Jimei University from July 2016 to present; project founder and lead programmer of the popular open-source time series forecasting library pyFAST.
Education
  • No specific educational background information provided.
Background
  • Research interests include: AI in health/healthcare (developing predictive models for infectious diseases and personalized medicine), time series forecasting (creating advanced models for multivariate, graph-based, and sparse time series forecasting), and recommendation systems (enhancing user experience through personalized recommendations). Affiliated with the College of Computer Engineering, Jimei University.
Miscellany
  • Contributions to pyFAST are welcome! Please submit issues or pull requests on GitHub.
Co-authors
0 total
Co-authors: 0 (list not available)