## 2 Materials and Methods
### Convolutional Neural Network Experimental Setup  
A 1D-CNN architecture for the SRT dataset is composed of an input layer, convolutional (CNN) layer, rectified linear unit (ReLU) activation function, pooling layer, fully connected (FC) layer or often called the dense layer, dropout layer, flatten layer, and a fully connected output layer. The process from the input layer to the output layer is called forward propagation, where the input is transferred and processed for feature extraction and prediction is generated. Meanwhile, updating model parameters, such as kernels, weights, and biases, from the output layer back to the input layer is called backward propagation or backpropagation. The forward and backward propagations were applied in the training phase to obtain the model parameters' values to reduce the loss function [43]. The pseudocode of the 1D-CNN modelling process is shown in Algorithm 1 and the detailed sequence of the 1D-CNN architecture after the configuration of the hyperparameters is presented in Figure 5.  
The input layer holds the SRT dataset before it is fed into the hidden layers. The hidden layers are the intermediate layers between the input and output layers, where the processing level occurs [44]. The number of layers is empirically optimized during the training and validation process. The hidden layers shown in Figure 5 consist of convolution, ReLU, pooling, dense, dropout, and flatten layers. The CNN layer generates a feature map when each neuron's output is computed by performing the dot product of input values with the weight of filters called kernels [44]. The filter is convolved with the features in the input layer, and it adopts a one-dimensional structure where it will extract the features in sequence according to its size and stride. The stride refers to the number of columns that should be shifted at each step, while the kernel size specifies the number of columns that the filter should extract at a single time [45]. The output feature map will then undergo nonlinear transformation using the rectified linear unit (ReLU) activation function. The ReLU activation function allows the network to learn the complex relationship of the SRT dataset and normalizes each neuron's output into a small range between 0 and 1. It is commonly used for deep learning models to achieve better performance results [46] without changing the function's output shape. The outputs from the activation function are scaled down in the pooling layer where the downsampling operation, such as the average or maximum function, takes place [44]. The dimensionality of the features is reduced depending on the value of the pool size and stride to give the optimal network structure and enhance feature robustness [47]. In this architecture, the downsampling method used is max pooling, where the pool window slides across the input feature maps from the CNN layer by the stride value and takes the maximum amount as the output. Next, the pooling layer's output is fed into three dense layers where each layer passes through the ReLU activation function to provide stable convergence for the model. The number of neurons in the dense layer refers to the number of units that perform a linear transformation of the inputs with weights and biases [48]. The dense layer helps to interpret the learned features that have gone through feature extraction in the CNN and pooling layer before predicting the output. The forward propagation from the input layer to the input of the neuron where the convolution, activation function, and downsampling operation take place are demonstrated in (4)-(6).