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27071 |
Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms
Enthalten in Communications medicine Bd. 2, 24.9.2022, Nr. 1, date:12.2022: 1-8
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27072 |
Short-term prediction of celestial pole offsets with interpretable machine learning
Enthalten in Earth, planets and space Bd. 76, 29.1.2024, Nr. 1, date:12.2024: 1-15
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27073 |
Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning
Enthalten in BMC medical informatics and decision making Bd. 23, 24.7.2023, Nr. 1, date:12.2023: 1-10
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27074 |
Short-term salinity prediction for coastal areas of the Vietnamese Mekong Delta using various machine learning algorithms: a case study in Soc Trang Province
Enthalten in Applied water science Bd. 15, 19.3.2025, Nr. 4, date:4.2025: 1-25
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27075 |
Short-term solar eruptive activity prediction models based on machine learning approaches: A review
Enthalten in Science China / Earth sciences Bd. 67, 24.9.2024, Nr. 12, date:12.2024: 3727-3764
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27076 |
Short-term solar radiation forecasting using machine learning models under different sky conditions: evaluations and comparisons
Enthalten in Environmental science and pollution research Bd. 31, 30.11.2023, Nr. 1, date:1.2024: 966-981
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27077 |
Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods
Enthalten in Sustainable Water Resources Management Bd. 2, 17.12.2015, Nr. 1, date:3.2016: 87-101
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27078 |
Short-term stock trends prediction based on sentiment analysis and machine learning
Enthalten in Soft computing Bd. 26, 27.1.2022, Nr. 5, date:3.2022: 2209-2224
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27079 |
Short-term streamflow modeling using data-intelligence evolutionary machine learning models
Enthalten in Scientific reports Bd. 13, 24.8.2023, Nr. 1, date:12.2023: 1-23
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27080 |
Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model
Enthalten in Smart water Bd. 5, 29.5.2020, Nr. 1, date:12.2020: 1-18
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