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Ergebnis der Suche nach: "Machine Learning"
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17051 - 17060 von 17193

	17051	Using the grouping function of machine learning algorithm to reduce the influence of information avoidance tendency during reading behavior Enthalten in Smart Learning Environments Bd. 10, 27.11.2023, Nr. 1, date:12.2023: 1-16
	17052	Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii Enthalten in Respiratory research Bd. 25, 3.1.2024, Nr. 1, date:12.2024: 1-11
	17053	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
	17054	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
	17055	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
	17056	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
	17057	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
	17058	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
	17059	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
	17060	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

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