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Ergebnis der Suche nach: tit all "Machine Learning Techniques"
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1671 - 1680 von 1692

	1671	Using machine learning techniques to predict antimicrobial resistance in stone disease patients Enthalten in World journal of urology Bd. 40, 26.5.2022, Nr. 7, date:7.2022: 1731-1736
	1672	Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks Enthalten in Journal of Big Data Bd. 7, 24.8.2020, Nr. 1, date:12.2020: 1-16
	1673	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
	1674	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
	1675	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
	1676	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
	1677	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
	1678	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
	1679	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
	1680	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

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