Katalog der Deutschen Nationalbibliothek

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

35651 - 35660 von 35971

	35651	Using machine learning to determine the time of exposure to infection by a respiratory pathogen Enthalten in Scientific reports Bd. 13, 1.4.2023, Nr. 1, date:12.2023: 1-10
	35652	Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study Enthalten in BMC medical informatics and decision making Bd. 23, 11.6.2023, Nr. 1, date:12.2023: 1-11
	35653	Using machine learning to develop a five-item short form of the children’s depression inventory Enthalten in BMC public health Bd. 24, 23.4.2024, Nr. 1, date:12.2024: 1-11
	35654	Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases Enthalten in Scientific reports Bd. 14, 19.11.2024, Nr. 1, date:12.2024: 1-11
	35655	Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions Enthalten in World journal of surgery Bd. 46, 26.10.2021, Nr. 2, date:2.2022: 339-346
	35656	Using machine learning to estimate the incidence rate of intimate partner violence Enthalten in Scientific reports Bd. 13, 4.4.2023, Nr. 1, date:12.2023: 1-9
	35657	Using machine learning to examine the relationship between asthma and absenteeism Enthalten in Environmental monitoring and assessment Bd. 191, 28.6.2019, Nr. 2, date:6.2019: 1-9
	35658	Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis Enthalten in Journal of eating disorders Bd. 10, 10.2.2022, Nr. 1, date:12.2022: 1-11
	35659	Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic Enthalten in Service business Bd. 17, 16.5.2023, Nr. 2, date:6.2023: 449-476
	35660	Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand Enthalten in Scientific reports Bd. 15, 25.2.2025, Nr. 1, date:12.2025: 1-10