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Ergebnis der Suche nach: "Machine Learning"
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33951 - 33960 von 37207

	33951	Predicting the stereoselectivity of chemical reactions by composite machine learning method Enthalten in Scientific reports Bd. 14, 27.5.2024, Nr. 1, date:12.2024: 1-12
	33952	Predicting the strength of alkali-activated masonry blocks using machine learning models: geopolymer mortar with quarry waste, rice husk ash, and eggshell ash Enthalten in Journal of building pathology and rehabilitation Bd. 10, 28.1.2025, Nr. 1, date:6.2025: 1-25
	33953	Predicting the success of startups using a machine learning approach Enthalten in Journal of innovation and entrepreneurship Bd. 13, 28.10.2024, Nr. 1, date:12.2024: 1-27
	33954	Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP Enthalten in BMC genomics Bd. 23, 7.1.2022, Nr. 1, date:12.2022: 1-18
	33955	Predicting the time to get back to work using statistical models and machine learning approaches Enthalten in BMC medical research methodology Bd. 24, 29.11.2024, Nr. 1, date:12.2024: 1-8
	33956	Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology Enthalten in The international journal of advanced manufacturing technology Bd. 116, 30.6.2021, Nr. 3-4, date:9.2021: 1247-1257
	33957	Predicting the value of football players: machine learning techniques and sensitivity analysis based on FIFA and real-world statistical datasets Enthalten in Applied intelligence Bd. 55, 4.1.2025, Nr. 4, date:2.2025: 1-26
	33958	Predicting the Viscosity of CaF2-Based Slag and Reverse Design of Electroslag Systems Using Explainable Machine Learning Enthalten in Metallurgical and materials transactions / B / Process metallurgy and materials processing science Bd. 56, 14.4.2025, Nr. 3, date:6.2025: 3125-3139
	33959	Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability Enthalten in Scientific reports Bd. 15, 8.3.2025, Nr. 1, date:12.2025: 1-22
	33960	Predicting thermal conductivity of granite subjected to high temperature using machine learning techniques Enthalten in Environmental earth sciences Bd. 84, 15.4.2025, Nr. 8, date:4.2025: 1-18

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