## 3 Materials and Methods
### Performance Evaluation Metrics  
In the experimental results, classification models are evaluated with accuracy (ACC), area under curve (AUC), and [PERSON]'s kappa (KE) metrics. A confusion matrix is used to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **nIn, nOut** & **Total Trainable Parameters** & **Parameters Shape** \\ \hline LSTM layer & 3485, 32 & 450.304 & W: [3485, 128], RW: [32; 128], b: [1; 128] \\ BiLSTM layer & 32, 16 & 3.184 & W: [32; 64], RW: [16; 67], b: [1; 64] \\ Dense layer 1 & 16, 8 & 136 & W: [16, 8], b: [1, 8] \\ Dense layer 2 & 8, 4 & 36 & W: [8, 4], b: [1, 4] \\ Output layer & 4, 2 & 10 & W: [4, 2], b: [1, 2] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RNN-Based Deep Learning (RNNBDL) neural network model. Figure 1: The LSTM architecture [39]. calculate several useful metrics like ACC and AUC. A confusion matrix created from a two-class classification problem can be seen in Table 3, in which four possible actual-to-estimated class mappings are represented by TP for true positive, FP for false positive, FN for false negative, and TN for true negative values. ACC is a widely-used metric to determine the ability of the classifier for class distinction. It is defined as the percentage of the samples correctly classified by the algorithm [37]. ACC is calculated by the formula given in Equation (7). \[ACC=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{7}\]  
The receiver operating characteristic (ROC) curve, or simply the ROC curve, enables a model to differentiate, facilitating the comparison of various models in terms of their effectiveness [37]. The most widely-used measurement is the area under curve (AUC) that remains under ROC curve. The performance of a classifier gets better as the AUC becomes larger. The possible values of AUC vary between 0.5 and 1.0. The AUC is calculated by the formula given in Equation (8). \[AUC=\frac{1}{2}\bigg{(}\frac{TP}{(TP+FN)}+\frac{TN}{(TN+FP)}\bigg{)} \tag{8}\]  
[PERSON]'s kappa (KE) is a metric used to determine whether the performance of a classifier depends on chance. KE ranges between \(-1\) and \(1\). As the KE value of the classifier approaches 1, the ACC value is deemed to be more realistic. KE is calculated by the formula given in Equation (6) [37]. In the Equation (9), po refers to \"total agreement probability\" and pc refers to \"agreement probability\" depending on chance. \[KE=\frac{p_{o}-p_{c}}{1-p_{c}} \tag{9}\]  
Probability of detection (PD): As referred to by the term 'Recall', it is calculated by the ratios of negative values. Additionally, it is also calculated based on the division of the \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Batch size & 100 \\ LSTM Activation Function & ActivationReLU \\ LSTM Get Activation Function & Activation Sigmoid \\ BiLSTM Activation Function & ActivationReLU \\ BiLSTM Get Activation Function & Activation Sigmoid \\ Dense Layer Activation Function & ActivationReLU \\ Seed & 1 \\ Optimization Algorithm & ADAM \\ Learning rate & 0.001 \\ Epsilon & 1.0 \(\times\) 10\({}^{-8}\) \\ \hline \end{tabular}
\end{table} 
Table 2: The parameter settings for the empirical results. \begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{2}{c}{**Estimated Class**} \\ \hline  & & C1 (+) & C2 (\(-\)) \\ Actual Class & C1 (+) \(\sum\) Positive & TP & FP \\  & C2 (\(-\)) \(\sum\) Negative & FP & TN \\ \hline \end{tabular}
\end{table} 
Table 3: A confusion matrix for a two-class problem. true negative value by the sum of the false negative and true negative values. The PD is calculated by the formula given in Equation (10). \[PD=\frac{TN}{(FN+TN)} \tag{10}\]  
Probability of false (PF): The computation process involves dividing the positive values by one another. It is calculated by dividing the false positive value by the sum of the true positive and false positive values. The PF is calculated by the formula given in Equation (11).