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

38471 - 38480 von 38864
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Artikel 38471 Using machine learning algorithms to enhance IoT system security
Enthalten in Scientific reports Bd. 14, 27.5.2024, Nr. 1, date:12.2024: 1-19
Online Ressource
Artikel 38472 Using machine learning algorithms to guide rehabilitation planning for home care clients
Enthalten in BMC medical informatics and decision making Bd. 7, 20.12.2007, Nr. 1, date:12.2007: 1-13
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Artikel 38473 Using machine learning algorithms to identify genes essential for cell survival
Enthalten in BMC bioinformatics Bd. 18, 3.10.2017, Nr. 11, date:10.2017: 19-29
Online Ressource
Artikel 38474 Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Enthalten in npj computational materials Bd. 4, 16.7.2018, Nr. 1, date:12.2018: 1-10
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Artikel 38475 Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service
Enthalten in The international journal of advanced manufacturing technology Bd. 128, 23.8.2023, Nr. 9-10, date:10.2023: 3857-3883
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Artikel 38476 Using machine learning and DeepLabCut in animal behavior
Enthalten in Acta ethologica Bd. 25, 16.7.2022, Nr. 3, date:10.2022: 125-133
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Artikel 38477 Using Machine Learning and Multi-Element Analysis to Evaluate the Authenticity of Organic and Conventional Vegetables
Enthalten in Food analytical methods Bd. 12, 25.7.2019, Nr. 11, date:11.2019: 2542-2554
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Artikel 38478 Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department
Enthalten in BMC emergency medicine Bd. 24, 18.12.2024, Nr. 1, date:12.2024: 1-18
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Artikel 38479 Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation
Enthalten in Scientific reports Bd. 13, 8.11.2023, Nr. 1, date:12.2023: 1-17
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Artikel 38480 Using machine learning approach to investigate the impact of missing sensors on fault classification of multistage gear box
Enthalten in Associação Brasileira de Engenharia e Ciências Mecânicas: Journal of the Brazilian Society of Mechanical Sciences and Engineering Bd. 47, 22.2.2025, Nr. 3, date:3.2025: 1-14
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