Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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32881 - 32890 von 33359

	32881	Use of optimized machine learning tool for predicting compressive strength of concrete Enthalten in Asian journal of civil engineering Bd. 26, 29.7.2025, Nr. 11, date:11.2025: 4875-4895
	32882	Use of proper orthogonal decomposition and machine learning for efficient blood flow prediction in cerebral saccular aneurysms Enthalten in Scientific reports Bd. 15, 2.9.2025, Nr. 1, date:12.2025: 1-15
	32883	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
	32884	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
	32885	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
	32886	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
	32887	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
	32888	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
	32889	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
	32890	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

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