Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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15711 - 15720 von 15845

	15711	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
	15712	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
	15713	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
	15714	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
	15715	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
	15716	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
	15717	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
	15718	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
	15719	Using wearable sensors and machine learning to assess upper limb function in Huntington’s disease Enthalten in Communications medicine Bd. 5, 25.2.2025, Nr. 1, date:12.2025: 1-9
	15720	Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator Enthalten in Scientific reports Bd. 15, 9.1.2025, Nr. 1, date:12.2025: 1-11