Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

16601 - 16610 von 16806

	16601	Using machine learning methods to predict electric vehicles penetration in the automotive market Enthalten in Scientific reports Bd. 13, 23.5.2023, Nr. 1, date:12.2023: 1-16
	16602	Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU Enthalten in BMC medical informatics and decision making Bd. 20, 2.10.2020, Nr. 1, date:12.2020: 1-10
	16603	Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction Enthalten in Scientific reports Bd. 13, 22.12.2023, Nr. 1, date:12.2023: 1-13
	16604	Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis Enthalten in BMC pediatrics Bd. 25, 24.5.2025, Nr. 1, date:12.2025: 1-12
	16605	Using machine learning models to improve stroke risk level classification methods of China national stroke screening Enthalten in BMC medical informatics and decision making Bd. 19, 10.12.2019, Nr. 1, date:12.2019: 1-7
	16606	Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia Enthalten in BMC pulmonary medicine Bd. 24, 1.7.2024, Nr. 1, date:12.2024: 1-9
	16607	Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance Enthalten in Scientific reports Bd. 15, 9.5.2025, Nr. 1, date:12.2025: 1-11
	16608	Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis Enthalten in BMC medical informatics and decision making Bd. 20, 29.9.2020, Nr. 1, date:12.2020: 1-13
	16609	Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients Enthalten in Scientific reports Bd. 13, 8.2.2023, Nr. 1, date:12.2023: 1-11
	16610	Using machine learning prediction models for quality control: a case study from the automotive industry Enthalten in Computational management science Bd. 20, 16.3.2023, Nr. 1, date:12.2023: 1-28