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Ergebnis der Suche nach: "Machine Learning"
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34221 - 34230 von 37110

	34221	Prediction of the Axial Bearing Compressive Capacities of CFST Columns Based on Machine Learning Methods Enthalten in International journal of steel structures Bd. 24, 27.1.2024, Nr. 1, date:2.2024: 81-94
	34222	Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques Enthalten in Scientific reports Bd. 15, 4.2.2025, Nr. 1, date:12.2025: 1-18
	34223	Prediction of the axial compression capacity of stub CFST columns using machine learning techniques Enthalten in Scientific reports Bd. 14, 5.2.2024, Nr. 1, date:12.2024: 1-14
	34224	Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning Enthalten in JOM Bd. 71, 30.7.2019, Nr. 10, date:10.2019: 3433-3442
	34225	Prediction of the Composition of the Wide Light Hydrocarbon Fraction by Methods of Machine Learning in Pipeline Transportation Enthalten in Optoelectronics, instrumentation and data processing Bd. 58, 8.7.2022, Nr. 1, date:2.2022: 85-90
	34226	Prediction of the Consolidation Coefficient of Soft Soil Based on Machine Learning Models Enthalten in Soil mechanics and foundation engineering Bd. 61, 30.7.2024, Nr. 3, date:7.2024: 223-229
	34227	Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques Enthalten in Neural computing & applications Bd. 33, 20.7.2021, Nr. 24, date:12.2021: 17131-17145
	34228	Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning Enthalten in npj computational materials Bd. 10, 17.9.2024, Nr. 1, date:12.2024: 1-10
	34229	Prediction of the development of acute kidney injury following cardiac surgery by machine learning Enthalten in Critical care Bd. 24, 31.7.2020, Nr. 1, date:12.2020: 1-13
	34230	Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning Enthalten in Materials science Bd. 54, 4.12.2018, Nr. 3, date:11.2018: 333-338

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