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Ergebnis der Suche nach: "Machine Learning"
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19981 - 19990 von 20149

	19981	Using topological data analysis and machine learning to predict customer churn Enthalten in Journal of Big Data Bd. 11, 7.11.2024, Nr. 1, date:12.2024: 1-31
	19982	Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra Enthalten in BMC bioinformatics Bd. 24, 9.1.2023, Nr. 1, date:12.2023: 1-15
	19983	Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity Enthalten in BMC cardiovascular disorders Bd. 25, 27.10.2025, Nr. 1, date:12.2025: 1-13
	19984	Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum Enthalten in Scientific reports Bd. 15, 19.2.2025, Nr. 1, date:12.2025: 1-14
	19985	Using weak signals to predict spontaneous breathing trial success: a machine learning approach Enthalten in Intensive Care Medicine Experimental Bd. 13, 18.3.2025, Nr. 1, date:12.2025: 1-14
	19986	Using wearable sensors and machine learning to assess upper limb function in Huntington’s disease Enthalten in Communications medicine Bd. 5, 25.2.2025, Nr. 1, date:12.2025: 1-9
	19987	Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator Enthalten in Scientific reports Bd. 15, 9.1.2025, Nr. 1, date:12.2025: 1-11
	19988	Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach Enthalten in The journal of prevention of Alzheimer's disease Bd. 10, 26.1.2023, Nr. 2, date:4.2023: 223-235
	19989	Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma Enthalten in European radiology Bd. 32, 25.8.2022, Nr. 10, date:10.2022: 7237-7247
	19990	Utilization of an automated machine learning approach for the detection of granular corneal dystrophy via slit lamp photographs Enthalten in BMC ophthalmology Bd. 25, 22.11.2025, Nr. 1, date:12.2025: 1-8