|
6261 |
Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms
Enthalten in BMC medical informatics and decision making Bd. 23, 5.5.2023, Nr. 1, date:12.2023: 1-11
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6262 |
Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment
Enthalten in European transport research review Bd. 16, 21.11.2024, Nr. 1, date:12.2024: 1-13
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6263 |
Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database
Enthalten in Scientific reports Bd. 14, 15.5.2024, Nr. 1, date:12.2024: 1-9
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6264 |
Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms
Enthalten in BMC medical informatics and decision making Bd. 19, 5.11.2019, Nr. 1, date:12.2019: 1-10
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6265 |
Predicting stroke severity of patients using interpretable machine learning algorithms
Enthalten in European journal of medical research Bd. 29, 14.11.2024, Nr. 1, date:12.2024: 1-23
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6266 |
Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS)
Enthalten in Biomed Central (London): BMC Research Notes Bd. 17, 24.10.2024, Nr. 1, date:12.2024: 1-10
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6267 |
Predicting the availability of power line communication nodes using semi-supervised learning algorithms
Enthalten in Scientific reports Bd. 15, 21.5.2025, Nr. 1, date:12.2025: 1-11
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6268 |
Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
Enthalten in Building simulation Bd. 17, 16.3.2024, Nr. 5, date:5.2024: 839-855
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6269 |
Predicting the compressive strength of ultra-high-performance concrete using a decision tree machine learning model enhanced by the integration of two optimization meta-heuristic algorithms
Enthalten in Journal of engineering and applied science Bd. 71, 14.2.2024, Nr. 1, date:12.2024: 1-17
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6270 |
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
Enthalten in Financial innovation Bd. 5, 15.6.2019, Nr. 1, date:12.2019: 1-20
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