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Ergebnis der Suche nach: "Machine Learning"
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38871 - 38880 von 39100

	38871	Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems Enthalten in Scientific reports Bd. 14, 4.11.2024, Nr. 1, date:12.2024: 1-17
	38872	Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy Enthalten in Supportive care in cancer Bd. 33, 16.12.2024, Nr. 1, date:1.2025: 1-14
	38873	Utilizing machine learning approaches within concrete technology offers an intelligent perspective towards sustainability in the construction industry: a comprehensive review Enthalten in Multiscale and multidisciplinary modeling, experiments and design Bd. 8, 26.10.2024, Nr. 1, date:1.2025: 1-24
	38874	Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea Enthalten in Aerosol and Air Quality Research Bd. 24, 17.5.2024, Nr. 7, date:7.2024: 1-11
	38875	Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study Enthalten in BMC chemistry Bd. 18, 20.12.2024, Nr. 1, date:12.2024: 1-16
	38876	Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department Enthalten in BMC medical research methodology Bd. 21, 17.4.2021, Nr. 1, date:12.2021: 1-13
	38877	Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification Enthalten in Scientific reports Bd. 14, 20.2.2024, Nr. 1, date:12.2024: 1-29
	38878	Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines Enthalten in Annals of surgical oncology Bd. 27, 6.11.2019, Nr. 3, date:3.2020: 866-874
	38879	Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters Enthalten in Frontiers of environmental science & engineering Bd. 19, 18.11.2024, Nr. 2, date:2.2025: 1-16
	38880	Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration Enthalten in Water resources management Bd. 38, 7.2.2024, Nr. 6, date:4.2024: 1921-1942

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