## 3 Proposed Methodology
### Classification of MANN-AM (Modified Artificial Neural Network with Attention Mechanism)
From Figure 5, it is observed that the value of AUC for the proposed MANN-AM classifier

Figure 4: Feature importance of the proposed model. Figure 5: AUC curve (proposed system, Naive Bayes, K-Nearest Neighbour and Random Forest). has been given as 0.99961, which is closer to 1. For the KNN classifier, the value of AUC has been given as 0.64831. For the Naive Bayes classifier, the value of AUC has been given as 0.77063. For the Random Forest classifier, the value of AUC has been given as 0.72489. Hence, it is revealed that the proposed model has better efficiency in accordance with classification. The confusion matrix of the proposed system, KNN, NB and RF, is shown in Figures 6-9, respectively. From Figure 6, it is clear that the proposed method has a majority of correct estimates of UTI, which is represented in the diagonal portion (0, 0 and 1, 1). Though it estimates a few wrong predictions, which are represented in black color, when comparing, the proposed method has more accurate estimates than the wrong ones. Hence, the proposed method is more efficient. From Figure 7, it is clear that the KNN classifier has 11,406 true positives, 1257 true negatives, 2063 false negatives and 1352 false positives. The performance analysis of the Naive Bayes classifier has been shown in Figure 10 by using the AUC curve. Figure 8: Confusion matrix for the Naive Bayes classifier. Figure 10: Firefly Algorithm feature selection. Figure 2:0 Confusion matrix for the Random Forest classifier. From Figure 8, it is clear that the Naive Bayes classifier has a majority of correct estimates of UTI, which are 7058 true positives, 5605 true negatives, 55 false negatives and 3360 false positives. From Figure 9, it is clear that the Random Forest classifier has 11,825 true positives, 838 true negatives, 1653 false negatives and 1762 false positives. The Modified Flashing Behaviour-based Firefly Algorithm using a Gaussian Map has been shown in Figure 2:1

During the initial iterations, the proposed model attained a higher fitness value. When the number of iterations increased, the proposed model learned to select features more efficiently and steadily, which is shown in Figure 2:2

### Performance Analysis of Classifiers

The proposed model Modified ANN-AM has been compared internally with KNN, Naive Bayes and Random Forest classifiers to show that the proposed model has improved efficiency among other classifiers. The performance analysis of Modified ANN-AM, NB, KNN and RF is shown in Table 1 and Figure 2:3

The proposed model has been analysed by using six performance metrics, and the values are given in Figure 2:4 The proposed model has an accuracy of 0.99, recall of 0.99, precision of 1, f1-score of 0.99, specificity of 0.99 and sensitivity of 1. The Naive Bayes has an accuracy of 0.65, recall of 0.77, precision of 0.68, F1-score of 0.63, specificity of 0.98 and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** \\ \hline
**Modified ANN** & 0.99 & 1 & 0.99 & 0.99 & 1 & 0.99 \\
**with AM** & & & & & & \\ \hline
**Naive Bayes** & 0.65 & 0.68 & 0.77 & 0.63 & 0.55 & 0.98 \\
**Classifier** & 0.79 & 0.68 & 0.65 & 0.66 & 0.9 & 0.39 \\ \hline
**Random Forest** & 0.85 & 0.78 & 0.72 & 0.75 & 0.93 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance analysis of Modified ANN-AM, NB, KNN and RF. Figure 11: Performance analysis of Modified ANN-AM, NB, KNN and RF. sensitivity of 0.55. The KNN classifier has an accuracy of 0.79, recall of 0.65, precision of 0.68, f1-score of 0.66, specificity of 0.39 and sensitivity of 0.9. The Random Forest classifier has an accuracy of 0.85, recall of 0.72, precision of 0.78, f1-score of 0.75, specificity of 0.51 and sensitivity of 0.93. From Table 1 and Figure 11, it is clear that the proposed model of Modified ANN with AM is more efficient and has better values in all performance metrics. Hence, it can be concluded that the proposed model shows an enhanced performance in UTI detection.