Katalog der Deutschen Nationalbibliothek

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

17771 - 17780 von 17976

	17771	USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 163-170. 8 S.
	17772	Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks Enthalten in Journal of Big Data Bd. 7, 24.8.2020, Nr. 1, date:12.2020: 1-16
	17773	Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data Enthalten in Scientific reports Bd. 14, 4.3.2024, Nr. 1, date:12.2024: 1-11
	17774	Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials Enthalten in Theoretical and applied genetics Bd. 137, 23.7.2024, Nr. 8, date:8.2024: 1-13
	17775	Using machine learning to detect the differential usage of novel gene isoforms Enthalten in BMC bioinformatics Bd. 23, 18.1.2022, Nr. 1, date:12.2022: 1-16
	17776	Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar Enthalten in BMC oral health Bd. 23, 20.9.2023, Nr. 1, date:12.2023: 1-9
	17777	Using machine learning to determine the correlation between physiological and environmental parameters and the induction of acute mountain sickness Enthalten in BMC bioinformatics Bd. 22, 31.5.2022, Nr. 5, date:11.2021: 1-14
	17778	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
	17779	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
	17780	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