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Fang-Shii Ning

Dados Biográficos

ID8670331
NOMEFang-Shii Ning
PRENOMESFang-Shii
SOBRENOMENing
ASSINATURANING F
AFILIAÇÕESNational Chengchi University
ORCID0000-0003-2155-3061
VERIFICADOSim
TOTAL DE OBRAS1
TOTAL DE CITAÇÕES0
OBRAS COMO AUTOR1
OBRAS COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2023
ANO MAIS RECENTE DE PUBLICAÇÃO2023
ÍNDICE H0
  • PM2.5 Concentration Prediction Model

    Open Access•Mei-Hsin Chen, Yao-Chung Chen et al.•ARTICLE•International Journal of…•2023

    Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 w…

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  • PM2.5 Concentration Prediction Model

    Open Access•Mei-Hsin Chen, Yao-Chung Chen et al.•ARTICLE•International Journal of…•2023

    Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 w…

Air Quality and Health Impacts (1 obra) · Air Quality Monitoring and Forecasting (1 obra) · Artificial Intelligence (1 obra) · Computer Science (1 obra) · Convolutional neural network (1 obra) · Data mining (1 obra) · Deep learning (1 obra) · Ensemble forecasting (1 obra) · Ensemble learning (1 obra) · Machine learning (1 obra)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae