Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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16531 - 16540 von 16685

	16531	Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty Enthalten in Scientific reports Bd. 13, 7.2.2023, Nr. 1, date:12.2023: 1-11
	16532	Using machine learning to simultaneously quantify multiple cognitive components of episodic memory Enthalten in Nature Communications Bd. 16, 24.3.2025, Nr. 1, date:12.2025: 1-14
	16533	Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo Enthalten in BMC bioinformatics Bd. 11, 11.2.2010, Nr. 1, date:12.2010: 1-13
	16534	Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community Enthalten in Schizophrenia Bd. 10, 5.10.2024, Nr. 1, date:12.2024: 1-8
	16535	Using machine learning tools for protein database biocuration assistance Enthalten in Scientific reports Bd. 8, 5.7.2018, Nr. 1, date:12.2018: 1-10
	16536	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
	16537	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
	16538	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
	16539	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
	16540	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