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Ergebnis der Suche nach: "Machine Learning"
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16951 - 16960 von 17073

	16951	Utilizing contemporary machine learning techniques for determining soilcrete properties Enthalten in Earth science informatics Bd. 18, 14.1.2025, Nr. 1, date:1.2025: 1-30
	16952	Utilizing decision tree machine learning model to map dental students’ preferred learning styles with suitable instructional strategies Enthalten in BMC medical education Bd. 24, 11.1.2024, Nr. 1, date:12.2024: 1-13
	16953	Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes Enthalten in Scientific reports Bd. 14, 22.11.2024, Nr. 1, date:12.2024: 1-15
	16954	Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes Enthalten in Scientific reports Bd. 15, 2.1.2025, Nr. 1, date:12.2025: 1-18
	16955	Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) Enthalten in BMC psychiatry Bd. 25, 18.3.2025, Nr. 1, date:12.2025: 1-12
	16956	Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study Enthalten in Discover oncology Bd. 15, 19.9.2024, Nr. 1, date:12.2024: 1-16
	16957	Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks Enthalten in BMC medical informatics and decision making Bd. 22, 9.11.2022, Nr. 1, date:12.2022: 1-16
	16958	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
	16959	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
	16960	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

16951 - 16960 von 17073

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