Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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32911 - 32920 von 33403

	32911	Use of machine learning models for prediction of compressive strength of concrete produced with waste materials Enthalten in Innovative infrastructure solutions Bd. 10, 12.9.2025, Nr. 10, date:10.2025: 1-29
	32912	Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population Enthalten in BMC pulmonary medicine Bd. 22, 29.8.2022, Nr. 1, date:12.2022: 1-11
	32913	Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study Enthalten in European journal of medical research Bd. 29, 3.1.2024, Nr. 1, date:12.2024: 1-9
	32914	Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa Enthalten in BMC medical research methodology Bd. 21, 31.7.2021, Nr. 1, date:12.2021: 1-11
	32915	Use of machine learning to analyse routinely collected intensive care unit data: a systematic review Enthalten in Critical care Bd. 23, 22.8.2019, Nr. 1, date:12.2019: 1-11
	32916	Use of machine learning to assess factors affecting progression, retention, and graduation in first-year health professions students in Qatar: a longitudinal study Enthalten in BMC medical education Bd. 23, 30.11.2023, Nr. 1, date:12.2023: 1-11
	32917	Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales Enthalten in Scientific reports Bd. 10, 2.11.2020, Nr. 1, date:12.2020: 1-10
	32918	Use of machine learning to diagnose breast cancer from raw electrical impedance tomography data Enthalten in Biomedical engineering Bd. 58, 17.9.2024, Nr. 3, date:9.2024: 208-212
	32919	Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit Enthalten in Scientific reports Bd. 9, 11.3.2019, Nr. 1, date:12.2019: 1-18
	32920	Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device Enthalten in BMC medical informatics and decision making Bd. 22, 6.7.2022, Nr. 1, date:12.2022: 1-12

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