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Recurrent Neural Networks are classes of Artificial Neural Networks thatestablish connections between different nodes form a directed or undirectedgraph for temporal dynamical analysis. In this research, the laser inducedbreakdown spectroscopy (LIBS) technique is used for quantitative analysis ofaluminum alloys by different Recurrent Neural Network (RNN) architecture. Thefundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employedto generate the LIBS plasma for the prediction of constituent concentrations ofthe aluminum standard samples. Here, Recurrent Neural Networks based ondifferent networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit(GRU), Simple Recurrent Neural Network (Simple RNN), and as well as RecurrentConvolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU areutilized for concentration prediction. Then a comparison is performed amongprediction by classical machine learning methods of support vector regressor(SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, GradientBoosting Regression (GBR), Random Forest Regression (RFR), Linear Regression,and k-Nearest Neighbor (KNN) algorithm. Results showed that the machinelearning tools based on Convolutional Recurrent Networks had the bestefficiencies in prediction of the most of the elements among other multivariatemethods.

A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy