Katalog der Deutschen Nationalbibliothek

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

331 - 340 von 1773

	331	Potential of Machine learning techniques compared to MIKE-SHE model for drain flow predictions in tile-drained agricultural areas of Denmark Enthalten in EGUsphere 31.08.2023: 1-29. 29 S.
	332	Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review Enthalten in Theoretical and applied climatology 28.8.2023: 1-44
	333	Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques Enthalten in Advanced energy & sustainability research 23.08.2023. 20 S.
	334	Cancer detection and segmentation using machine learning and deep learning techniques: a review Enthalten in Multimedia tools and applications 22.8.2023: 1-35
	335	Technical Note: Accurate, reliable and high resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using machine learning techniques Enthalten in EGUsphere 18.08.2023: 1-27. 27 S.
	336	Optimizing Shear Capacity Prediction of Steel Beams with Machine Learning Techniques Enthalten in The Arabian journal for science and engineering 17.8.2023: 1-25
	337	Quantifying the impact of different material properties on Barkhausen noise generation from machined steel using machine learning techniques Enthalten in International journal on interactive design and manufacturing 9.8.2023: 1-13
	338	Correction: Forecasting heating and cooling loads in residential buildings using machine learning: a comparative study of techniques and influential indicators Enthalten in Asian journal of civil engineering 8.8.2023: 1-3
	339	The use of machine learning techniques for assessing the potential of organizational resilience Enthalten in Central European journal of operations research 7.8.2023: 1-26
	340	Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques Enthalten in Wind energy science Bd. 8, 2023, Nr. 4: 621-637. 17 S.