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Search: "[ keyword: Particulate Matter ]" (2)
Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes
Dong Jun Min, Hyerim Kim, Sangkyun Lee The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 251-259,
Jun.
2024
https://doi.org/10.3745/TKIPS.2024.13.6.251
Keywords: Particulate Matter, PM2.5, Time Series Forecast, Deep Learning, Ensemble
https://doi.org/10.3745/TKIPS.2024.13.6.251
Keywords: Particulate Matter, PM2.5, Time Series Forecast, Deep Learning, Ensemble
Hourly Prediction of Particulate Matter (PM2.5) Concentration Using Time Series Data and Random Forest
Deukwoo Lee, Soowon Lee KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 4, pp. 129-136,
Apr.
2020
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
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Vol. 13, 2024
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Vol. 12, 2023
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Vol. 11, 2022
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Vol. 10, 2021
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Vol. 9, 2020
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Vol. 2, 2013
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Vol. 1, 2012
Old Journals
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All publications of KTSDE is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.
KTSDE is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).
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