# Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
## 4 Discussion
### Neural Network Classifier
Simultaneously, the validation accuracy first ascends at a gradual pace; nevertheless, after 75 epochs, it experiences a significant surge, ultimately approaching 100%. This indicates that the model accurately classifies the training data throughout the learning phase and effectively generalizes it to the validation data, which is crucial for ensuring optimal performance with novel data. The reduction in cross-entropy and the improvement in categorical accuracy demonstrate that the model has successfully acquired the ability to categorize the data, maintaining consistent performance within both the training and validation datasets. This indicates that the model is well calibrated since there are no apparent indications of overfitting or inability to handle fresh data. In addition, validation was performed on the test set. Table 3 below shows the confusion matrix and the results of the metrics when recognizing the classifier on the test set. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Basalt Cube** & **Belgian Cobblestone** & **Concrete Cube** & **Cobblestone** & **Thump** & **Pothole** & **Grater** \\ \hline basalt cube & 20 & 0 & 0 & 0 & 0 & 0 \\ Belgian cobblestone & 0 & 20 & 0 & 0 & 0 & 0 \\ concrete cube & 0 & 0 & 20 & 0 & 0 & 0 \\ cobblestone & 0 & 0 & 0 & 20 & 0 & 0 \\ thump & 0 & 0 & 0 & 20 & 0 & 0 \\ pothole & 0 & 0 & 0 & 0 & 20 & 0 \\ grater & 0 & 0 & 0 & 0 & 0 & 0 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for classifier based on neural network. Figure 10: Cross-entropy and accuracy during the neural network learning process. The provided table indicates that each surface class--basal cube, Belgian cobblestone, concrete cube, cobblestone, thud, pothole, and grater--was accurately categorized after 20 trials, with no instances of misclassification. The findings indicate that the neural network operates with high efficiency and precision. Every surface type was categorized with complete accuracy since each category yielded accurate predictions without any mistakes (20 true positives for all categories and 0 false positives or negatives). This indicates that the classifier successfully differentiated each surface type without any ambiguity between classes, which is an optimal outcome for a classification problem. The increased accuracy indicates that the model effectively processed and identified the distinct characteristics of the surface texture captured throughout the trial. This indicates that the network generalized enough since there were no misclassified instances during validation. The model clearly identified the variations among pavements, including basalt pavement, Belgian pavement, and grating, each possessing unique vibration profiles, and accurately predicted the surface categorization. The confusion matrix findings indicate that the neural network was efficiently used for the classification job, achieving a high accuracy rate and accurately identifying various surface types without mistakes. Table 4 presents many performance metrics for the artificial neural network classifier to assess the model's efficacy across distinct surface classifications. The indicated metrics, including sensitivity, specificity, positive and negative predictive values, balanced accuracy, and Area Under the Curve, all possess a value of 1.0000, signifying that the classifier achieved optimal accuracy across all surface categories. The sensitivity and specificity are both 1.0000, indicating that the model accurately identified both positive (true positive) and negative (true negative) examples without mistake. The neural network successfully classified all surfaces, accurately identifying every positive sample without any false positives. The positive/negative predicted value is 1.0000, indicating that all predictions provided by the network were accurate. In other terms, each instance the model categorized a surface as a specific class was accurate, and each instance it classed as negative (not belonging to any class) was likewise accurate.