## 4 Performance Validation
Figure 5: TACC and VACC outcome of RHSODL-AMD system on 80:20 of TRS/TSS. Figure 6: TLS and VLS outcome of RHSODL-AMD system on 80:20 of TRS/TSS. The TACC and VACC of the RHSODL-AMD approach on 70:30 of TRS/TSS have been represented in Figure 7. The figure inferred that the RHSODL-AMD system has exhibited a higher performance with maximum values of TACC and VACC. It is observable that the RHSODL-AMD method has gained higher TACC outcomes. The TLS and VLS of the RHSODL-AMD approach on 70:30 of TRS/TSS are exhibited in Figure 8. The figure implied that the RHSODL-AMD algorithm has revealed an improved performance with minimum values of TLS and VLS. It is noticeable that the RHSODL-AMD system has resulted in reduced VLS outcomes. Figure 9 reveals the classifier outcomes of the RHSODL-AMD system under 80:20 and 70:30 of TRS/TSS. Figure 9a,b determines the PR investigation of the RHSODL-AMD method on 80:20 of TRS/TSS. The figures describes that the RHSODL-AMD methodology has gained maximal PR performance in several classes. Finally, Figure 9c,d exemplifies the ROC study of the RHSODL-AMD approach on 70:30 of TRS/TSS. The figure represents that the RHSODL-AMD technique has proficient results with higher ROC values under various classes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy\({}_{\text{bal}}\)** & **Precision** & **Recall** & **F-Score** & **MCC** \\ \hline \multicolumn{6}{c}{Training Phase (70\%)} \\ \hline Benign & 98.94 & 98.78 & 98.94 & 98.86 & 98.07 \\ Malware & 99.15 & 99.26 & 99.15 & 99.21 & 98.07 \\ Average & 99.05 & 99.02 & 99.05 & 99.03 & 98.07 \\ \hline \multicolumn{6}{c}{Testing Phase (30\%)} \\ \hline Benign & 97.99 & 98.76 & 97.99 & 98.37 & 97.27 \\ Malware & 99.16 & 98.63 & 99.16 & 98.89 & 97.27 \\ Average & 98.57 & 98.69 & 98.57 & 98.63 & 97.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Android malware detection outcome of RHSODL-AMD system at 70:30 of TRS/TSS. Figure 7: TACC and VACC outcome of RHSODL-AMD system on 70:30 of TRS/TSS. To demonstrate the better performance of the RHSODL-AMD technique for Android malware classification, a detailed comparison study is represented in Table 4[29]. The simulation values inferred that the J48, RF, MLP, and AdaBoost-M1 models reported poor classification performances. At the same time, the DBN, LSTM, Decision Table, NB, and SMO models attained moderately closer results. Although the logistic model has reached near optimal outcomes, the RHSODL-AMD technique outperformed the existing models with a maximum \(accu_{y}\) of 99.05%. To ensure the computation time (CT) analysis of the RHSODL-AMD technique, a brief comparison study is made in Figure 10. The experimental results indicate that the MLP model has shown a poor performance with increased CT value. Next, the decision table and SMO models reported slightly reduced CT values. Although the remaining models exhibited closer CT values, the RHSODL-AMD technique outperformed the existing models with a minimal CT of 0.06s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \(Accu_{y}\) & \(Prec_{n}\) & \(Reca_{1}\) & \(F_{Score}\) \\ \hline RHSODL-AMD & 99.05 & 99.02 & 99.05 & 99.03 \\ DBN Model & 96.81 & 97.46 & 96.82 & 97.99 \\ LSTM Model & 96.37 & 95.45 & 95.91 & 97.20 \\ J48 Model & 94.85 & 94.26 & 94.92 & 94.09 \\ RF Model & 94.93 & 94.38 & 94.92 & 94.69 \\ DecisionTable & 96.40 & 96.17 & 95.95 & 97.45 \\ Model & 96.64 & 97.42 & 96.74 & 97.72 \\ NB Model & 95.25 & 94.66 & 95.27 & 95.49 \\ SMO Model & 97.05 & 97.48 & 96.92 & 98.26 \\ Logistic Model & 98.17 & 98.27 & 97.47 & 98.46 \\ AdaBoost-M1 & 95.88 & 95.18 & 95.42 & 96.82 \\ model & 96.41 & 96.89 & 95.95 & 97.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of the RHSODL-AMD algorithm with other approaches [29]. Figure 8: TLS and VLS outcome of RHSODL-AMD system on 70:30 of TRS/TSS. The effectual performance of the RHSODL-AMD technique is due to the effectual selection of features and hyperparameters.