## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **Models** & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** & **F7** & **F8** & **F9** & **F10** \\ \hline \multirow{4}{*}{Accuracy (\%)} & Cu-DNNGRU & 98.61 & 98.89 & 99.14 & 99.12 & 99.14 & 99.86 & 99.68 & 99.71 & 99.85 & 99.92 \\  & Cu-LSTMNN & 98.89 & 97.43 & 96.61 & 97.36 & 97.79 & 98.90 & 99.15 & 98.81 & 98.62 & 97.90 \\  & Cu-BLSTM & 98.96 & 98.15 & 97.62 & 97.26 & 97.61 & 97.25 & 97.31 & 97.83 & 97.29 & 98.10 \\  & Cu-GRU & 98.68 & 97.68 & 97.89 & 98.65 & 98.51 & 98.25 & 98.36 & 98.12 & 98.64 & 98.69 \\ \hline \multirow{4}{*}{Recall (\%)} & Cu-DNNGRU & 99.65 & 99.15 & 99.34 & 98.72 & 98.69 & 98.94 & 98.20 & 98.31 & 98.86 & 99.12 \\  & Cu-LSTMDN & 99.15 & 98.69 & 98.45 & 98.94 & 98.78 & 98.65 & 98.15 & 98.14 & 97.56 & 97.37 \\  & Cu-BLSTM & 98.26 & 98.26 & 98.14 & 97.19 & 97.36 & 97.53 & 97.49 & 97.83 & 97.96 & 98.32 \\  & Cu-GRU & 98.52 & 98.34 & 98.91 & 98.79 & 98.86 & 99.35 & 98.65 & 98.61 & 98.69 & 98.54 \\ \hline \multirow{4}{*}{F-score (\%)} & Cu-DNNGRU & 98.99 & 99.41 & 99.34 & 99.11 & 99.11 & 98.89 & 99.14 & 99.29 & 99.24 & 99.81 \\  & Cu-LSTMDN & 98.32 & 98.16 & 97.93 & 97.85 & 98.19 & 98.99 & 99.36 & 99.57 & 99.57 & 99.61 \\  & Cu-BLSTM & 97.65 & 97.86 & 97.95 & 97.81 & 97.73 & 97.49 & 97.72 & 99.19 & 98.64 & 97.53 \\  & Cu-GRU & 98.54 & 98.84 & 98.17 & 98.25 & 99.65 & 99.58 & 99.61 & 98.96 & 98.21 & 98.49 \\ \hline \multirow{4}{*}{Precision (\%)} & Cu-DNNGRU & 98.24 & 99.64 & 99.12 & 99.36 & 99.52 & 99.41 & 99.43 & 99.08 & 98.41 & 98.71 \\  & Cu-LSTMDN & 98.65 & 97.56 & 97.35 & 97.56 & 97.51 & 97.46 & 97.1 & 97.25 & 97.36 & 98.42 \\ \cline{1-1}  & Cu-BLSTM & 98.65 & 98.67 & 98.46 & 98.62 & 97.51 & 97.59 & 97.43 & 97.35 & 97.16 & 97.35 \\ \cline{1-1}  & Cu-GRU & 99.15 & 98.46 & 97.69 & 97.54 & 98.53 & 98.15 & 97.56 & 98.36 & 98.13 & 98.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cross-validation results. Figure 5: TPR, TNC, and MCC Analysis. considerably less as compared to other benchmarked technologies. The low value of FPR declares the superiority of the proposed framework. The next crucial performance parameter is FNR. Cu-DNNGRU projects an FNR of 0.00183%, which is less than the FNR that other benchmarked technologies achieve. On a comparison at FDR, Cu-DNNGRU exhibits substantial performance with a value of 0.00200%. Cu-DNNGRU again illustrates a dominance over other competitive technologies with a FOR discount of 0.00419%. Furthermore, a confusion matrix summarizes the performance of a DL-based classification algorithm. Calculating a confusion matrix can provide a more accurate perspective of the categorization model's accomplishments. The proposed Cu-DNNGRU is evaluated in terms of confusion metrics as well, where it illustrates distinguished strengths over Cu-LSTMNN, Cu-BLSTM, and Cu-GRU, as shown in Figure 7.  
The operating performance of the DL classifiers can also be measured using the Receiver Operating Characteristics (ROC) Curve. An algorithm is used to determine the best possible threshold for a specific classification algorithm to increase the number of accurate results while minimizing false positives. TPR and FPR trade-offs can be determined by  
Figure 6: FPR, FNR, FDR, and FOR analysis. Figure 7: Confusion matrix analysis. employing several measurements of probability thresholds, such as ROC Curves. The proposed Cu-DNNGRU is extensively evaluated along with Cu-LSTMNN, Cu-BLSTM, and Cu-GRU. Figure 8 provides pictorial evidence regarding the superiority of the proposed framework. Moreover, the training time is a crucial metric for assessing a system's overall performance since it measures how long it takes for a plan to acquire the intrinsic sustainability of its absolute features.