Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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41761 - 41770 von 42063

	41761	Using the risk of spatial extrapolation by machine-learning models to assess the reliability of model predictions for conservation Enthalten in Landscape ecology Bd. 38, 10.4.2023, Nr. 6, date:6.2023: 1363-1372
	41762	Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii Enthalten in Respiratory research Bd. 25, 3.1.2024, Nr. 1, date:12.2024: 1-11
	41763	Using topological data analysis and machine learning to predict customer churn Enthalten in Journal of Big Data Bd. 11, 7.11.2024, Nr. 1, date:12.2024: 1-31
	41764	Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra Enthalten in BMC bioinformatics Bd. 24, 9.1.2023, Nr. 1, date:12.2023: 1-15
	41765	Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity Enthalten in BMC cardiovascular disorders Bd. 25, 27.10.2025, Nr. 1, date:12.2025: 1-13
	41766	Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis Enthalten in Archives of gynecology and obstetrics Bd. 309, 3.2.2024, Nr. 3, date:3.2024: 1053-1063
	41767	Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum Enthalten in Scientific reports Bd. 15, 19.2.2025, Nr. 1, date:12.2025: 1-14
	41768	Using weak signals to predict spontaneous breathing trial success: a machine learning approach Enthalten in Intensive Care Medicine Experimental Bd. 13, 18.3.2025, Nr. 1, date:12.2025: 1-14
	41769	Using wearable sensors and machine learning to assess upper limb function in Huntington’s disease Enthalten in Communications medicine Bd. 5, 25.2.2025, Nr. 1, date:12.2025: 1-9
	41770	Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator Enthalten in Scientific reports Bd. 15, 9.1.2025, Nr. 1, date:12.2025: 1-11

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