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24231 - 24240 von 24365

	24231	Using machine learning algorithms to guide rehabilitation planning for home care clients Enthalten in BMC medical informatics and decision making Bd. 7, 20.12.2007, Nr. 1, date:12.2007: 1-13
	24232	Using machine learning algorithms to identify genes essential for cell survival Enthalten in BMC bioinformatics Bd. 18, 3.10.2017, Nr. 11, date:10.2017: 19-29
	24233	Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients Enthalten in Scientific reports Bd. 15, 15.7.2025, Nr. 1, date:12.2025: 1-16
	24234	Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service Enthalten in The international journal of advanced manufacturing technology Bd. 128, 23.8.2023, Nr. 9-10, date:10.2023: 3857-3883
	24235	Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors Enthalten in BMC medical informatics and decision making Bd. 24, 22.7.2024, Nr. 1, date:12.2024: 1-8
	24236	Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data Enthalten in Journal of Big Data Bd. 9, 29.4.2022, Nr. 1, date:12.2022: 1-20
	24237	Using metaheuristic algorithms to optimize a mixed model-based ground-motion prediction model and associated variance components Enthalten in Journal of seismology Bd. 26, 4.5.2022, Nr. 3, date:6.2022: 483-498
	24238	Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization Enthalten in Annals of operations research Bd. 240, 22.9.2015, Nr. 1, date:5.2016: 217-250
	24239	Using multi-objective evolutionary algorithms to predict the parameters that determine membrane resonance in a biophysical model of bursting neurons Enthalten in BMC neuroscience Bd. 15, 21.7.2014, Nr. 1, date:7.2014: 1-2
	24240	Using multivariate adaptive regression splines and extremely randomized trees algorithms to predict dust events frequency around an international wetland and prioritize its drivers Enthalten in Environmental monitoring and assessment Bd. 193, 22.6.2021, Nr. 7, date:7.2021: 1-21

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