  Recurrent Neural Networks are classes of Artificial Neural Networks that
establish connections between different nodes form a directed or undirected
graph for temporal dynamical analysis. In this research, the laser induced
breakdown spectroscopy (LIBS) technique is used for quantitative analysis of
aluminum alloys by different Recurrent Neural Network (RNN) architecture. The
fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed
to generate the LIBS plasma for the prediction of constituent concentrations of
the aluminum standard samples. Here, Recurrent Neural Networks based on
different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit
(GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent
Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are
utilized for concentration prediction. Then a comparison is performed among
prediction by classical machine learning methods of support vector regressor
(SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient
Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression,
and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine
learning tools based on Convolutional Recurrent Networks had the best
efficiencies in prediction of the most of the elements among other multivariate
methods.
