Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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35531 - 35540 von 35807

	35531	Using machine learning tools to predict outcomes for emergency department intensive care unit patients Enthalten in Scientific reports Bd. 10, 1.12.2020, Nr. 1, date:12.2020: 1-10
	35532	Using machine learning with case studies to identify practices that reduce greenhouse gas emissions across Australian grain production regions Enthalten in Agronomy for sustainable development Bd. 43, 20.3.2023, Nr. 2, date:4.2023: 1-11
	35533	Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening Enthalten in npj vaccines Bd. 9, 28.8.2024, Nr. 1, date:12.2024: 1-14
	35534	Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis Enthalten in Malaria journal Bd. 18, 30.5.2019, Nr. 1, date:12.2019: 1-9
	35535	Using music to soothe our patients, measuring hair cortisol, and machine learning in diabetes: Prof. Batrinos would have been very happy! Enthalten in Hormones Bd. 22, 10.11.2023, Nr. 4, date:12.2023: 533-535
	35536	Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data Enthalten in Frontiers in zoology Bd. 19, 22.2.2022, Nr. 1, date:12.2022: 1-15
	35537	Using natural language processing and machine learning to identify breast cancer local recurrence Enthalten in BMC bioinformatics Bd. 19, 28.12.2018, Nr. 17, date:12.2018: 65-74
	35538	Using of Machine Learning Capabilities to Predict Double Phosphate Structures for Biomedical Applications Enthalten in Journal of surface investigation Bd. 18, 15.7.2024, Nr. 3, date:6.2024: 633-640
	35539	USING OPEN DATA CUBE ON THE CLOUD TO INVESTIGATE FOOD SECURITY BY MEANS OF CROPLAND CHANGES IN DJIBOUTI Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLIII-B3-2022, 2022: 1039-1044. 6 S.
	35540	Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms Enthalten in Water resources management Bd. 36, 17.1.2022, Nr. 2, date:1.2022: 699-716