Katalog der Deutschen Nationalbibliothek

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

43301 - 43310 von 43746

	43301	Using Jupyter Notebooks for re-training machine learning models Enthalten in Journal of cheminformatics Bd. 14, 13.8.2022, Nr. 1, date:12.2022: 1-9
	43302	Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer Enthalten in Scientific reports Bd. 13, 6.11.2023, Nr. 1, date:12.2023: 1-10
	43303	Using Machine Learning Algorithms and Honeypot Systems to Detect Adversarial Attacks on Intrusion Detection Systems Enthalten in Automatic control and computer sciences Bd. 58, 14.3.2025, Nr. 8, date:12.2024: 1226-1233
	43304	Using Machine Learning Algorithms for Water Segmentation in Gas Diffusion Layers of Polymer Electrolyte Fuel Cells Enthalten in Transport in porous media Bd. 144, 4.8.2022, Nr. 3, date:9.2022: 715-737
	43305	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
	43306	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
	43307	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
	43308	Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients Enthalten in Scientific reports Bd. 15, 15.7.2025, Nr. 1, date:12.2025: 1-16
	43309	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
	43310	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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