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Ergebnis der Suche nach: "Machine Learning"
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38961 - 38970 von 39229

	38961	Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis Enthalten in Abdominal radiology Bd. 46, 31.8.2021, Nr. 12, date:12.2021: 5647-5658
	38962	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
	38963	Utilization of artificial intelligence and machine learning in chemistry education: a critical review Enthalten in Discover education Bd. 3, 10.7.2024, Nr. 1, date:12.2024: 1-15
	38964	Utilization of fuzzy logic (FL) and whale optimization algorithm (WOA) for optimization of load scheduling in energy management systems Enthalten in International journal of information technology Bd. 17, 4.6.2025, Nr. 6, date:7.2025: 3717-3722
	38965	Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling Enthalten in International journal of information technology Bd. 17, 12.5.2025, Nr. 6, date:7.2025: 3389-3396
	38966	Utilization of machine learning algorithm in the prediction of rehospitalization during one-year post traumatic spinal cord injury Enthalten in Spinal cord Bd. 63, 15.2.2025, Nr. 4, date:4.2025: 214-221
	38967	Utilization of machine learning algorithms for forecasting the likelihood of liquefaction potential in geotechnical layers of Tabriz City Enthalten in Bulletin of engineering geology and the environment Bd. 84, 15.5.2025, Nr. 6, date:6.2025: 1-30
	38968	Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications Enthalten in Scientific reports Bd. 15, 14.3.2025, Nr. 1, date:12.2025: 1-14
	38969	Utilization of machine learning for dengue case screening Enthalten in BMC public health Bd. 24, 11.6.2024, Nr. 1, date:12.2024: 1-9
	38970	Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps Enthalten in Journal of petroleum exploration and production technology Bd. 14, 22.2.2024, Nr. 5, date:5.2024: 1205-1233