Katalog der Deutschen Nationalbibliothek

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

16401 - 16410 von 16541

	16401	Using published pathway figures in enrichment analysis and machine learning Enthalten in BMC genomics Bd. 24, 25.11.2023, Nr. 1, date:12.2023: 1-13
	16402	Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content Enthalten in Scientific reports Bd. 14, 15.4.2024, Nr. 1, date:12.2024: 1-14
	16403	Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning Enthalten in Scientific reports Bd. 13, 18.12.2023, Nr. 1, date:12.2023: 1-12
	16404	Using symbolic machine learning to assess and model substance transport and decay in water distribution networks Enthalten in Scientific reports Bd. 14, 8.2.2024, Nr. 1, date:12.2024: 1-14
	16405	Using the grouping function of machine learning algorithm to reduce the influence of information avoidance tendency during reading behavior Enthalten in Smart Learning Environments Bd. 10, 27.11.2023, Nr. 1, date:12.2023: 1-16
	16406	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
	16407	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
	16408	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
	16409	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
	16410	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