Forecasting the Effects of Real-Time Indoor PM2.5 on Peak Expiratory Flow Rates (PEFR) of Asthmatic Children in Korea: A Deep Learning Approach

Author:

Woo JiyoungORCID,Lee Ji-Hyun,Kim Yeonjin,Rudasingwa GuillaumeORCID,Lim Dae Hyun,Kim Sungroul

Funder

Korea Environmental Industry and Technology Institute, Ministry of Environment, South Korea, through the Environmental Health Research Center

Basic Science Research Program through the National Research Foundation of Korea

Ministry of Education

Soonchunhyang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

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1. Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India;ACM Journal on Computing and Sustainable Societies;2024-08-02

2. Prediction of the number of asthma patients using environmental factors based on deep learning algorithms;Respiratory Research;2023-12-01

3. Smart indoor air quality monitoring system: implementation and analysis;2023 24th International Conference on Computational Problems of Electrical Engineering (CPEE);2023-09-10

4. Federated Learning for Improved Air Pollution Prediction: A Combined LSTM-SVR Approach;2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON);2023-08-05

5. The association between air pollutants and hospitalizations for asthma: an evidence from Chengdu, China;Annals of Translational Medicine;2023-01

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