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27121 - 27130 von 32660

	27121	Measuring the Hubble constant with cosmic chronometers: a machine learning approach Enthalten in The European physical journal / C / Particles and fields Bd. 83, 29.6.2023, Nr. 6, date:6.2023: 1-13
	27122	Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction Enthalten in Financial innovation Bd. 8, 12.7.2022, Nr. 1, date:12.2022: 1-35
	27123	Measuring the prediction difficulty of individual cases in a dataset using machine learning Enthalten in Scientific reports Bd. 14, 7.5.2024, Nr. 1, date:12.2024: 1-15
	27124	Mechanical characterization of marl soil treated by cement and lignosulfonate under freeze–thaw cycles: experimental studies and machine-learning modeling Enthalten in Bulletin of engineering geology and the environment Bd. 82, 5.5.2023, Nr. 6, date:6.2023: 1-26
	27125	Mechanical degradations of Fe–C alloys induced by stress corrosion in supercritical CO2 environments: a study based on molecular dynamics simulation and machine learning Enthalten in Journal of materials science Bd. 59, 24.9.2024, Nr. 37, date:10.2024: 17609-17624
	27126	Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate Enthalten in Discover sustainability Bd. 6, 7.6.2025, Nr. 1, date:12.2025: 1-12
	27127	Mechanism design for public projects via three machine learning based approaches Enthalten in Autonomous agents and multi-agent systems Bd. 38, 20.4.2024, Nr. 1, date:6.2024: 1-30
	27128	Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring Enthalten in Journal of petroleum exploration and production technology Bd. 14, 14.8.2024, Nr. 10, date:10.2024: 2577-2616
	27129	Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations Enthalten in Nature Communications Bd. 14, 2.10.2023, Nr. 1, date:12.2023: 1-9
	27130	Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences Enthalten in Fire technology Bd. 57, 2.1.2021, Nr. 6, date:11.2021: 2741-2784

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