Katalog der Deutschen Nationalbibliothek

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

35911 - 35920 von 36330

	35911	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
	35912	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
	35913	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
	35914	Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization? Enthalten in European radiology Bd. 33, 26.5.2023, Nr. 7, date:7.2023: 5058-5059
	35915	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
	35916	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
	35917	User structural information in priority-based ranking for top-N recommendation Enthalten in Advances in computational intelligence Bd. 3, 6.1.2023, Nr. 1, date:2.2023: 1-16
	35918	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
	35919	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
	35920	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