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Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks
(Elsevier, 2018-06)
Machine learning, and more specifically artificial neural networks (ANN), are powerful and flexible numerical tools that can lead to significant improvements in many materials modelling techniques. This paper provides a ...
Optimization of the Bacillus thuringiensis var. kurstaki HD-1 δ-endotoxins production by using experimental mixture design and artificial neural networks
(Elsevier Science Sa, 2007-07)
An experimental mixture design coupled with data analysis by means of both response surface methodology (RSM) and artificial neural networks (ANNs) followed by multiple response optimization through a desirability function, ...
Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web
(USFQ PRESS, departamento editorial de la Universidad San Francisco de Quito USFQ, 2020)
Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
(EMaTTech Journals, 2019-07)
Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing ...
Stock market index prediction using artificial neural network
(Universidad ESAN. ESAN EdicionesPE, 2016-12-01)
In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been ...
Stock market index prediction using artificial neural network
(Universidad ESAN. ESAN EdicionesPE, 2016-12-01)
In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been ...
Successful combination of computationally inexpensive GIAO 13C NMR calculations and artificial neural network pattern recognition: a new strategy for simple and rapid detection of structural misassignments
(Royal Society of Chemistry, 2013-07)
GIAO NMR chemical shift calculations coupled with trained artificial neural networks (ANNs) have been shown to provide a powerful strategy for simple, rapid and reliable identification of structural misassignments of organic ...