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Ergebnis der Suche nach: tit all "Machine Learning Techniques"
im Bestand: Gesamter Bestand

1721 - 1730 von 1741

	1721	Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data Enthalten in Scientific reports Bd. 14, 4.3.2024, Nr. 1, date:12.2024: 1-11
	1722	Using Machine Learning Techniques to Test the Load Capacity Curve Hypothesis: A Classifier-Lasso Application on Global Panel Data Enthalten in Biophysical economics and sustainability Bd. 10, 19.2.2025, Nr. 1, date:6.2025: 1-18
	1723	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
	1724	Utilizing contemporary machine learning techniques for determining soilcrete properties Enthalten in Earth science informatics Bd. 18, 14.1.2025, Nr. 1, date:1.2025: 1-30
	1725	Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques Enthalten in BMC bioinformatics Bd. 25, 27.3.2024, Nr. 1, date:12.2024: 1-17
	1726	VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques Enthalten in Journal of biosciences Bd. 45, 27.8.2020, Nr. 1, date:12.2020: 1-9
	1727	Various optimized machine learning techniques to predict agricultural commodity prices Enthalten in Neural computing & applications Bd. 36, 16.4.2024, Nr. 19, date:7.2024: 11439-11459
	1728	Vehicle-specific power-based emission modeling using unsupervised machine learning techniques Enthalten in Environmental science and pollution research Bd. 32, 23.6.2025, Nr. 27, date:6.2025: 16120-16141
	1729	Versatile time-window sliding machine learning techniques for stock market forecasting Enthalten in Artificial intelligence review Bd. 57, 16.7.2024, Nr. 8, date:8.2024: 1-20
	1730	Vibration analysis for fault detection in wind turbines using machine learning techniques Enthalten in Advances in computational intelligence Bd. 2, 10.1.2022, Nr. 1, date:2.2022: 1-12