## 3 Intelligent Cluster-Based Routing Approach
### Cluster Formation
An expanded array of features bolstered the system's performance metrics. Accuracy, as a performance metric, was calculated by determining the ratio of correct predictions the model made to the total number of predictions. Equation (2) outlines the computational formula employed for the accuracy assessment of the GBNN model. The accuracy precision and recall values were computed using parameters such as true positive (TP), true negative (TN), false positive (FP), and false negative (FN). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}. \tag{2}\]

Precision was computed by ascertaining the number of accurate positive predictions made by the model and dividing this by the total count of positive predictions. Equation (3) delineates the formula used for computing precision in the context of the ANN model. \[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{ False Positives}}. \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline
0 & 0.97 & 0.99 & 0.98 & 0.97 \\
1 & 0.98 & 0.98 & 0.98 & 0.98 \\ Average & 0.975 & 0.985 & 0.98 & 0.975 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANN Model parameter results over classification classed 0, 1 and average. Figure 9: Input layer, hidden layer, and output layer of the NN model. Recall was determined by quantifying the number of correct positive predictions generated by the model and dividing this figure by the total count of positive instances within the dataset. Equation (4) elucidates the mathematical formula utilized for calculating recall within the framework of the ANN model. \[\text{F1}-\text{Score}=\frac{(\text{TP})}{(\text{TP}+\text{FN})}. \tag{4}\]

The F1 score is a metric that assesses the equilibrium between precision and recall. This metric was computed as the harmonic mean of both precision and recall, as demonstrated by Equation (5), which provided the formula for calculating the F1 score within the context of the ANN model. \[\text{Precision}=\frac{2\times(\text{Precision}\times\text{Recall})}{(\text{ Precision}+\text{Recall})}. \tag{5}\]

### Model Accuracy

Building upon the performance evaluation of the proposed system concerning the BHT dataset, we compiled a comprehensive set of outcomes to facilitate the effective analysis and prediction of system performance metrics. Our training efforts demonstrate that the training prediction of the system has undergone significant enhancements, enabling valuable performance comparisons across various datasets. Figure 10 vividly presents the model's accuracy, further detailed in Table 4. Model accuracy, as assessed using Equation (2), offers insights into the system's performance. In addition to the system's precision, the model demonstrates a noteworthy reduction in the system metric, signifying remarkable accuracy levels. The epoch values that underpin the model's accuracy and the subsequent improved results are of particular significance for research and analytical purposes. A meticulous validation test was conducted using the carefully selected BHT model dataset, encompassing 100 epochs, to yield the model's results. The graphical depiction, with blue lines tracing the model's loss during the training and its testing accuracies, provides valuable insights. The proposed ANN model was methodically trained to assess its performance meticulously. The training phase was conducted on 70% of the dataset, while the testing phase encompassed 30% across all classification categories. The training and testing model results were quantified using Equations (2)-(4). The percentages denoting accuracy, precision, recall, and F1 score, as displayed in Table 3, underscore the elevated accuracy levels that persistently showcase

Figure 10: Model accuracy of the proposed BHT model. the model's efficacy and the functions that govern its performance. Figure 11 shows the model accuracy comparison with other techniques.