## 4 Results and Discussion
### Evaluating Results  
The implementation of the algorithm, training and testing of the visual data is carried out by a MATLAB R2020a environment in Window10, a 64-bit operating system with the hardware configuration of the intel(R) Core(TM) i7-7700 CPU @ 3.60 GHz, RAM 8 gigabyte. The training progress of the activity recognition system is showing in Figure 6. The number of epochs for the training process is selected as 30, and each epoch consists of 15 iterations. The training and validation error is updated after every epoch. The loss of information during the training is known as training loss, whereas the loss of a validation set tested by the previously trained neural network is known as validation loss. The loss of training and validation decreases with the increase in training and validation accuracy. The training and validation accuracy increases with the increase in the number of epochs, and the training loss is lower than the validation loss, as shown in Figure 7. Hence, it is clear that the system is reading the sequenced visual feature to classify the activities of the excavator. The validation accuracy progress throughout the training can be visualized through Figure 7. It can be observed that the validation accuracy increased after each iteration. Similarly, with the increase in the training/validation accuracy, the training loss reduces. The work presented is this study is novel in terms of the integrated application of CNN-BiLSTM for earthwork construction applications. A general approach to evaluate the performance of overall systems can be performed through a Receiver Operating Characteristic (ROC) Curve. A ROC curve demonstrates the true positive rate (TPR, or sensitivity) versus the false positive rate (FPR, or 1-specificity) for different classification scores. Each point on the ROC curve is a pair value of a TPR and TNR. A TPR in one class is a TNR of another class such as the plot between TPRs, and TNR is the same as the graph between 1-TPR and 1-TNR. By reading the plot at any point, the TPR and TNR can be calculated. The ROC curve has been shown in Figure 8 for each activity of the excavator. The behavior of the training performance can be visualized in it. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Known**} & \multirow{2}{*}{**Dumping**} & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Heuling**} & \multirow{2}{*}{**Return**} \\  & & & & \\ \hline Dumping & TN & TN & FP & TN \\ Excavation & TN & TN & FP & TN \\ Hailing & FN & FN & TP & FN \\ Swing & TN & TN & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for activity “hauling”. Figure 7: Training progress, and loss of CNN-LSTM network. The validation dataset is part of the training set and takes part in the model building process. However, the validation data is different from the test dataset as well as the training dataset. The validation dataset evaluates the training performance by parameter selection and avoiding overfitting. A good validation accuracy with a good testing accuracy verified the correctness of the model training process. The validation accuracy of the model is calculated after completing the last iteration, as shown in Table 4. Twenty (20)% of the training dataset is used as a validation process, and the video clip is tested by the trained model to test and tune the model. All the clips are classified well in the respected classes. Only three (3) clips were misclassified: one of a swing falling into the dumping category, and two clips of excavations falling into the category of hauling and dumping. The validation precision of the 4 activities, dumping, excavation, hauling, and swinging, is 86.7%, 100%, 94.4%, and 100%, respectively. The overall precision, recall, miss rate and accuracy of the activity recognition system were 95.04%, 95.28%, 4.96%, and 95.2%. The result shows that the second activity, \"excavation,\" misclassified the most among other activities.