## 4 Results  
Several performance metrics were used to scrutinize the performance of the DCNN models, and these metrics are discussed in this section. The Keras library was used to create the deep learning models, and tensor flow served as the backend. The DCNNs were trained on the collected real-time data to classify them into three categories, namely non- over-volume goods carriers, over-volume goods carriers, and non-goods-carriers. On the basis of major criteria such as ROC curves, AUC scores, confusion matrixes, and test accuracies, the effectiveness of the deep learning models was examined. The AUC represents the level or measure of separability, and the ROC (receiver operating characteristic) is a probability curve. The model performs better when predicting the intended classes when the AUC is larger. The FPR and TPR are displayed against each other on the ROC curve, with the FPR on the \(x\)-axis and the TPR on the \(y\)-axis. TPR (true positive rate)/recall/sensitivity is the proportion of actual positives that are accurately classified. The TPR is calculated using Equation (1):  
\[\text{TPR}=\text{TP}/(\text{TP}+\text{FN}) \tag{1}\]  
where TP = true positives; FN = false negatives; and FPR is the proportion of negative cases incorrectly identified as positive cases in the data. FPR is calculated using Equation (2):  
\[\text{FPR}=\text{FP}/(\text{FP}+\text{TN}) \tag{2}\]  
where FP = false positives; TN = true negatives; A confusion matrix can be defined as an M \(\times\) M matrix that is used to assess the effectiveness of a classification model, where M defines the number of target classes. The matrix compares between the actual labels and the labels that are predicted by the neural network. A model's accuracy is calculated by dividing the number of classifications that are correctly predicted by the total number of predictions. The computational cost is essentially a measure of the amount of resources used by the neural network in training or inference, which is useful in determining how much time or computing power will be required to train or operate a NN. The number of operations required to execute a single instance of a particular model is expressed in floating-point operations per second (FLOPS). This will aid in describing the model's computational costs. The effectiveness of the training procedure for several deep learning models is examined based on the accuracy and loss. All these models have undergone training over 30 epochs, and the results are shown in Table 4. From Table 4, the loss and epoch graphs are almost inversely proportional to each other, which suggests that the models implemented for the mentioned dataset are learning towards convergence. The loss decreases from epoch 0 to epoch 10, and later it remains primarily constant. This explains that maximum model learning is achieved between epochs 0 and 10. \"Accuracy vs. epochs\" shows the same result as \"loss vs. epochs\", where the model increases its accuracy to the maximum between epochs 0 and 10. It can be observed that the validation accuracy is stagnant for the rest of the epochs, and to avoid overfitting the model, in this work the learning rate is reduced at \(10^{-4}\) by a factor of 2 after two continuous epochs with no improvement in the validation accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Loss** & **Accuracy** \\ \hline MobileNet V2 & & (train_loss \& val_loss) vs epoch \\ \hline VGG19 & (train_loss \& val_loss) vs epoch \\ \hline ResNet & (train_loss \& val_loss) vs epoch \\ \hline EfficientNet & (train_loss \& val_loss) vs epoch \\ \hline EfficientNet & (train_loss \& val_loss) vs epoch \\ \hline EfficientNet & (train_loss \& val_loss) vs epoch \\ \hline EfficientNet & (train_loss \& val_loss) vs epoch \\ \hline EfficientNet & (train_loss \& val_loss) vs epoch \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance analysis of the training process for deep learning models with real-time dataset collection.