## 6 Results and Discussion  
The experimental results are presented in this section. Firstly, we present the evaluation of the first stage of the proposed method, followed by the results of the second stage, and finally, an overall analysis of the method's performance. Table 3 presents the results after the first stage (see Section 4.1), while Tables 4 and 5 present the second stage of the proposed method. The rows in Tables 3-5 correspond to the pre-trained model employed: DenseNet121, EfficientNetB0, or MobileNetV2 (see Section 3.1). The columns are \"epoch\", the number of epochs performed in the proposed approach (see Algorithm 3), and the metrics used to evaluate the built models \"recall\", \"specificity\", and \"AUC\" (see Section 5.3). Additionally, the tables include the number of physical violence (\"_PV_\") and non-physical violence (\"_NPV_\") scenes considered by the classifier as ambiguous or hard to classify in a respective epoch (see Section 4.3). Finally, the \"size\" column indicates the number of images in the training dataset (\(\|DB\|\)), including those added to \(DB\), because they were considered ambiguous. Figures 3-5 show representative scenes where the proposed approach has uncertainty about the correct label in two contexts: uncertainty when the model labeled the image as an image with physical violence (Figures 3a, 4a and 5a), and uncertainty when the model labeled the image as an image with non-physical violence (Figures 3b, 4b and 5b). These figures are intended to understand what scenes are confusing or ambiguous for the model and could be misclassified. Table 3 presents the results of the initial model built with the AIRTLab dataset (dataset base, \(DB\)) in epoch 0, followed by subsequent epochs (epochs 1, 2,..., 5) where the model is retrained using ambiguous images to enhance its robustness. Retraining the model with ambiguous scenes generally improves classifier performance compared to the initial model or epoch 0 (Figure 3, shows some samples of images identified as ambiguous by the neural network model). Additionally, it is observed that the number of ambiguous samples decreases as the number of epochs increases. Moreover, it is noteworthy that, overall, specificity values are lower than the recall values. The latter indicates that the classifier correctly identifies more images as physical violence (_PV_) than non-physical violence (\(NPV\)). However, it is notable that a more significant number of ambiguous samples are selected by the model in the class \(PV\) than in the class \(NPV\). Also, note in Table 3 in the column \"size\" that increasing the number of epochs implies an increase in the size of the dataset base (\(DB\)). This is because the ambiguous samples are added to the training dataset in each iteration to robust the model performance, as explained in Section 4. In Figure 3a,b, examples of images (from the AIRTLab dataset) selected by the classifier as ambiguous or hard to classify are shown to clarify the ambiguous concept. The left and right columns display physical violence and non-physical violence scenes, respectively; it is very unclear whether physical violence exists in these images, which means the proposed threshold works properly. The results of the second stage are presented in Tables 4 and 5. In this stage, the last model obtained in epoch 5 of the first stage is tested with an unknown dataset, simulating an online system akin to a camera surveillance system. It is worth noting that while the primary objective of this work is to demonstrate the effectiveness of the proposed approach (as outlined in Section 4), it does not aim to implement a camera surveillance system for real-time operation. Instead, the focus is on evaluating the performance of the proposed method under simulated real-world conditions. Table 4 presents the experimental results obtained after testing the model with the RVLS/Pexels dataset, which contains images markedly different from those used in the first stage (see Figure 2) or those employed to build the model.