Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: tit all "Machine Learning Techniques"
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	41	Innovative Approaches to Predicting Maximum Load-Carrying Capacity of Fiber-Reinforced Polymer Reinforced Concrete Columns Using Machine Learning Techniques Enthalten in Iranian journal of science and technology / Transactions of civil engineering 21.1.2025: 1-23
	42	A Novel Approach to a Few Shot Learning Techniques Based on Thermal Error Modeling for Slant Bed CNC Lathe Machine Enthalten in International journal of precision engineering and manufacturing 20.1.2025: 1-18
	43	Automated machine learning techniques for estimating the unconfined compressive strength of soil stabilization Enthalten in International journal on interactive design and manufacturing 15.1.2025: 1-13
	44	Correlation-based network analysis combined with machine learning techniques highlights rosmarinic acid biosynthesis activation in tubers of potato plants corresponding to water recovery treatment Enthalten in Potato research 14.1.2025: 1-21
	45	UniEmbed: A Novel Approach to Detect XSS and SQL Injection Attacks Leveraging Multiple Feature Fusion with Machine Learning Techniques Enthalten in The Arabian journal for science and engineering 12.1.2025: 1-14
	46	Correction: Predicting substance use behaviors among students taking university entrance test: a cross-sectional study with machine learning techniques Enthalten in Current psychology 11.1.2025: 1
	47	Advancements of machine learning techniques in fiber-filled polymer composites: a review Enthalten in Polymer bulletin 9.1.2025: 1-31
	48	Regional Brain Analysis and Machine Learning Techniques for Classifying Familiar and Unfamiliar Faces Using EEG Enthalten in The Arabian journal for science and engineering 8.1.2025: 1-26
	49	Carbon footprint mapping of urban areas in Türkiye using hyperparameter-optimized machine learning techniques Enthalten in International journal of environmental science and technology 7.1.2025: 1-24
	50	Nonlinear modeling and machine learning techniques are needed for accurate prediction of contaminant sorption Enthalten in International journal of environmental science and technology 5.1.2025: 1-25

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