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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

40831 - 40840 von 40990

	40831	Visit-to-visit blood pressure variability as a prognostic marker in peritoneal dialysis: insights from machine learning-based risk models Enthalten in Hypertension research Bd. 48, 4.7.2025, Nr. 9, date:9.2025: 2471-2472
	40832	Visual acuity prediction on real-life patient data using a machine learning based multistage system Enthalten in Scientific reports Bd. 14, 6.3.2024, Nr. 1, date:12.2024: 1-18
	40833	Visual tools for teaching machine learning in K-12: A ten-year systematic mapping Enthalten in Education and information technologies Bd. 26, 1.5.2021, Nr. 5, date:9.2021: 5733-5778
	40834	Visualization obesity risk prediction system based on machine learning Enthalten in Scientific reports Bd. 14, 28.9.2024, Nr. 1, date:12.2024: 1-9
	40835	Visualization of hydrocarbon chain length and degree of saturation of fatty acids in mouse livers by combining near-infrared hyperspectral imaging and machine learning Enthalten in Scientific reports Bd. 13, 23.11.2023, Nr. 1, date:12.2023: 1-10
	40836	Visualization system of Hlai ethnic village landscape design based on machine learning Enthalten in Soft computing Bd. 27, 20.4.2023, Nr. 14, date:7.2023: 10001-10011
	40837	Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning Enthalten in BMC medical informatics and decision making Bd. 25, 3.6.2025, Nr. 1, date:12.2025: 1-15
	40838	ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification Enthalten in Neural computing & applications Bd. 37, 20.1.2025, Nr. 9, date:3.2025: 6583-6600
	40839	VLSI realization of hybrid fast fourier transform using reconfigurable booth multiplier Enthalten in International journal of information technology Bd. 16, 13.7.2024, Nr. 7, date:10.2024: 4323-4333
	40840	VNIR–SWIR reflectance spectroscopy as a nondestructive technique for compositional determination of archaeological talc samples with a machine learning approach Enthalten in Archaeological and anthropological sciences Bd. 16, 4.6.2024, Nr. 7, date:7.2024: 1-15