## 2 Materials and Methods
### Convolutional Neural Network Experimental Setup
\[x_{k}^{l}=b_{k}^{l}+\sum_{i=1}^{N_{l-1}}\left(s_{i}^{l-1},w_{ik}^{l-1}\right) \tag{4}\]  
\[y_{k}^{l}=f\left(x_{k}^{l}\right) \tag{5}\]  
\[s_{k}^{l}=\ y_{k}^{l}\downarrow SS \tag{6}\]  
In (4)-(6), \(x_{k}^{l}\) is the input in the CNN layer; \(b_{k}^{l}\) is the bias of the \(k^{th}\) neuron in the CNN layer; \(s_{i}^{l-1}\) is the output of the \(i^{th}\) neuron in the input layer; \(w_{ik}^{l-1}\) is the kernel from the \(i^{th}\) neuron in the input layer to the \(k^{th}\) neuron in the CNN layer; \(f\left(x_{k}^{l}\right)\) represents the ReLU activation function applied to the input in the CNN layer; \(y_{k}^{l}\) is the output of the convolution operation; \(s_{k}^{l}\) is the output of the \(k^{th}\) neuron at the pooling layer; and \(\downarrow SS\) refers to the downsampling operation with the factor, \(SS\). Next, the forward propagation from the pooling layer to the input of the neuron in the dense layer is formulated in (7) and (8). \[x_{i}^{l+1}=b_{i}^{l+1}+\sum_{i=1}^{N_{l}}\left(s_{k}^{l},w_{ki}^{l}\right) \tag{7}\]  
\[y_{i}^{l+1}=f\left(x_{i}^{l+1}\right) \tag{8}\]  
In (7) and (8); \(x_{i}^{l+1}\) is the input in dense layer; \(b_{i}^{l+1}\) is the bias of the \(i^{th}\) neuron in the dense layer; \(w_{ki}^{l}\) is the weight from the \(k^{th}\) neuron in the pooling layer to the \(i^{th}\) neuron in the dense layer; and \(y_{i}^{l+1}\) is the output of the dense layer after applying the ReLU activation function. The dropout layer is added after the third dense layer to reduce overfitting due to the large number of model parameters that the network has. Dropout is a technique that drops specific nodes randomly according to the dropout rate and creates a network ensemble [48]. All the connections from and to the dropped nodes need to be removed as well. The use of dropout works nicely to improve regularization error and boost the testing set performance [44]. Lastly, the output from the dropout layer turns into a single vector in  
Figure 5: The detailed block of the 1D-CNN architecture for the SRT dataset after the configuration of the hyperparameters. the flatten layer and passes through a dense layer before the output layer, which follows the format that can be used to generate the final prediction. The flatten layer is needed to convert the output from the dropout layer, which has a 3D output shape into a 1D output shape with a single long vector without changing the output values. The output layer can only take the input with a single vector from the previous layer. Hyperparameters in the Convolutional Neural Network  
Before implementing the 1D-CNN model on the SRT dataset, specific parameter values needed to be configured. There are two types of parameters that are used to train 1D-CNN model to make predictions. The internal parameters, which are learned automatically during the backpropagation process, are called model parameters. The model parameters are present only in the CNN, dense and output layers where the weights of filters or kernels, weights of neurons, and biases are learned during the training of 1D-CNN. The external parameters that determine the structure of 1D-CNN and how it is trained is called hyperparameters. Hyperparameter tuning is based on a manual trial-and-error process. The list of hyperparameters and the range of values are shown in Table 3. Tuning of the hyperparameters leads to the development of six different models for each dataset, as shown in Table 4.  
All the list of hyperparameters is briefly explained previously except for the epoch, batch size, and optimizer. An epoch refers to the entire training dataset that passes through the forward and backward propagation through the network at one time, while the batch size refers to the number of samples (rows of data) in a single iteration before the updating of model parameters [25].