Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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16291 - 16300 von 16541

	16291	Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device Enthalten in BMC medical informatics and decision making Bd. 22, 6.7.2022, Nr. 1, date:12.2022: 1-12
	16292	Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T Enthalten in Scientific reports Bd. 13, 15.5.2023, Nr. 1, date:12.2023: 1-17
	16293	USE OF MULTIVARIATE MACHINE LEARNING ANALYSIS TECHNIQUES FOR FLOOD RISK PREVENTION Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLII-3/W4, 2018: 549-554
	16294	Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models Enthalten in Journal of cheminformatics Bd. 3, 7.2.2011, Nr. 1, date:12.2011: 1-12
	16295	Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa Enthalten in BMC infectious diseases Bd. 23, 19.7.2023, Nr. 1, date:12.2023: 1-13
	16296	Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study Enthalten in Systematic Reviews Bd. 12, 15.4.2023, Nr. 1, date:12.2023: 1-14
	16297	User training for machine learning controlled upper limb prostheses: a serious game approach Enthalten in Journal of neuroEngineering and rehabilitation Bd. 18, 12.2.2021, Nr. 1, date:12.2021: 1-15
	16298	Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling Enthalten in BMC medical research methodology Bd. 22, 23.11.2022, Nr. 1, date:12.2022: 1-10
	16299	Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma Enthalten in BMC cancer Bd. 21, 9.8.2021, Nr. 1, date:12.2021: 1-11
	16300	Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis Enthalten in BMC nephrology Bd. 14, 23.7.2013, Nr. 1, date:12.2013: 1-9