# Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
## 4 Discussion
### Neural Network Classifier
Balanced accuracy is a metric that computes the mean of sensitivity and specificity. As both indicators are at their maximum, the balanced accuracy attains 1.0000, signifying that the model is entirely balanced and precise across all classes. The AUC (Area Under the Curve), which assesses the classifier's capacity to distinguish between classes at various threshold levels, is also at its maximum, signifying optimal classification efficacy. Collectively, these indications demonstrate that the neural network model operates at optimal efficiency, accurately categorizing each surface type without mistakes, and hence assuring excellent sensitivity, specificity, and accuracy across all classes. This indicates that the model has strong generalization capabilities and exhibits high accuracy across all investigated surface types. The accuracy of prediction on the test set is equal to 100%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Basalt Cube** & **Belgian Cobblestone** & **Concrete Cube** & **Cobblestone** & **Thump** & **Pothole** & **Grater** \\ \hline Sensitivity & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Specificity & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Pos Pred Value & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Neg Pred Value & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ Prevalence & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 \\ Detection Rate & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 \\ Detection Prevalence & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 & 0.1429 \\ Balanced Accuracy & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ AUC & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \end{tabular}
\end{table} 
Table 4: Characteristics for classifier based on neural network. Figure 11 presents the Receiver Operating Characteristics curves for each pavement type estimated for the test set. The ROC curve illustrates the correlation between sensitivity and 1-specificity (the false positive rate) across different classifier thresholds. The illustration illustrates all seven surface types: basalt payer, Belgian payer, concrete tile, stone payer, mud, pit, and grate. Each class ought to possess a distinct curve; nevertheless, this graph illustrates that all the curves converge at the top left corner. The findings indicate that all surface classes possess an AUC (Area Under the Curve) value of 1.0000, representing the optimal performance metric of the classifier. The model can flawlessly differentiate between all positive and negative instances across all classes, exhibiting no erroneous positive or negative predictions. The ROC curve's position on the top and left axes indicates that the classifier attains 100% sensitivity (accurately recognizing all positive instances) and 100% specificity (without misclassifying any negative cases), without any trade-offs. This outcome demonstrates that the neural network is exceptionally exact and proficient at identifying all seven surfaces without mistakes. The ROC curve and results indicate that the model is optimal for the surface classification job, as it can flawlessly differentiate all surface types based on their accelerometer data.