## 4 Results and Discussion
### Performance Evaluation  
Here, the performance analysis of the proposed framework is executed to prove the model's supremacy. The activity coverage analysis of the proposed SDQL is presented in Table 1.  
Table 1 demonstrates the evaluation of the proposed SDQL and existing techniques like QL, Reinforcement Learning (RL), State-Action-Reward-State-Action (SARSA), and Temporal Difference Learning (TDL) regarding Activity Coverage (AC). The proposed SDQL employs the SDER to categorize the update or error that improves the model performance. The proposed SDQL achieved an AC of 91.83%, whereas traditional works retained a limited AC of 82.14%. The proposed SDQL outperformed the conventional models. Then, the performance analysis of the proposed SDQL centered on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) is graphically represented in Figure 3a,b. Based on MSE and MAPE, the performance validation of the proposed work and existing techniques is shown in Figure 3. The proposed SDQL obtained an MSE and MAPE of 1.018% and 0.305%, respectively. Furthermore, the traditional models acquired an average MSE and MAPE of 6.173% and 0.675%, respectively. The proposed system is proved as a less error-prone model. Next, the proposed SDQL's Processing Time (PT) is analyzed with the existing works in Figure 4. \begin{table}
\begin{tabular}{c c} \hline
**Technique** & **Activity Coverage (\%)** \\ \hline Proposed SDQL & 91.83 \\ \hline QL & 86.45 \\ \hline RL & 83.29 \\ \hline SARSA & 81.78 \\ \hline TDL & 77.05 \\ \hline \end{tabular}
\end{table} 
Table 1: Activity coverage analysis. Figure 3: Performance analysis according to (**a**) MSE and (**b**) MAPE. Figure 4 compares the PT of the proposed SDQL and conventional models. The proposed SDQL takes a minimum PT of 21,456 ms. However, existing systems attained the PT in approximately 36,470 ms. From Figure 4, it is clear that the proposed framework achieved low time complexity. Similarly, the Test Case Length (TCL) vs. Correctly Classified (CC) analysis of the proposed SDQL is described in Figure 5.  
Figure 5 validates the proposed SDQL regarding Test Case Length (TCL) vs. CC. The proposed SDQL obtained a CC of 92.45% when the TCL is 20. Likewise, the traditional algorithms retained an average CC of 88.26% when the TCL is 20. The analysis results justified that the proposed model has better superiority than the prevailing works. The effectiveness of the proposed GDP-LSTM is proved and centered on in the analysis of performance metrics in Figure 6a,b. Figure 4: Processing time evaluation. Figure 5: Test case length vs. correctly classified percentage. The above figures illustrate the performance analysis of the GDP-LSTM. Figure 6 compares the proposed GDP-LSTM and existing classifiers like LSTM, GRU, Bidirectional LSTM (Bi-LSTM), and Recurrent Neural Network (RNN). The proposed GDP-LSTM introduces the global decayed learning rate and PAF to enhance the classification results of GT. The proposed GDP-LSTM achieved an accuracy, precision, F-measure, sensitivity, specificity, and recall of 98.89%, 98.98%, 99.21%, 98.97%, 98.84%, and 98.77%, respectively. The proposed GDP-LSTM retained FPR and FNR of 5.933% and 6.167%, respectively. Furthermore, the existing models reached an average accuracy, precision, F-measure, sensitivity, specificity, and recall of 94.63%, 95.05%, 94.29%, 94.04%, 95.14%, and 94.26%, respectively. The traditional models obtained an FPR and FNR of 8.651% and 9.099%, respectively. Figure 6  
Figure 6: Performance assessment with respect to (**a**) accuracy, precision, F-measure, sensitivity, specificity, and recall; (**b**) False Positive Rate (FPR) and False Negative Rate (FNR). depicts the proposed model's reliability. In Table 2, the clustering time analysis of the proposed SKGC and the existing works are stated. The Clustering Time (CT) evaluation of the proposed SKGC and prevailing approaches is presented in Table 2. The proposed SKGC incorporates the GK mapping function to attain effective clustering.