Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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43391 - 43400 von 43633

	43391	Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) Enthalten in Scientific reports Bd. 15, 24.1.2025, Nr. 1, date:12.2025: 1-13
	43392	Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm Enthalten in Scientific reports Bd. 15, 16.7.2025, Nr. 1, date:12.2025: 1-13
	43393	Utilizing machine learning to predict participant response to follow-up health surveys in the Millennium Cohort Study Enthalten in Scientific reports Bd. 14, 28.10.2024, Nr. 1, date:12.2024: 1-12
	43394	Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction Enthalten in Scientific reports Bd. 14, 23.5.2024, Nr. 1, date:12.2024: 1-13
	43395	Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods Enthalten in Scientific reports Bd. 14, 5.3.2024, Nr. 1, date:12.2024: 1-9
	43396	Utilizing reinforcement learning for de novo drug design Enthalten in Machine learning Bd. 113, 8.4.2024, Nr. 7, date:7.2024: 4811-4843
	43397	Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand Enthalten in Scientific reports Bd. 15, 11.3.2025, Nr. 1, date:12.2025: 1-12
	43398	Utilizing social media and machine learning for personality and emotion recognition using PERS Enthalten in Neural computing & applications Bd. 35, 5.9.2023, Nr. 33, date:11.2023: 23927-23941
	43399	Utilizing Supervised Machine Learning and Technical Indicators for Quantitative Trading to Improve Stock Market Trading Decisions Enthalten in SN Computer Science Bd. 6, 26.5.2025, Nr. 5, date:6.2025: 1-17
	43400	Utilizing the concept of risk in calibrating the vulnerability of coastal-alluvial aquifers based on machine learning methods Enthalten in International journal of environmental science and technology Bd. 22, 5.9.2025, Nr. 16, date:12.2025: 16747-16762

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