## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & **Recall** & **Specificity** & **AUC** & **PV** & **NPV** & **Size** \\ \hline \multirow{8}{*}{DenseNet121} & 0 & 0.417 & 0.470 & 0.444 & 276 & 171 & 4393 \\ \cline{2-8}  & 1 & 0.781 & 0.671 & 0.726 & 333 & 261 & 4987 \\  & 2 & 0.805 & 0.767 & 0.786 & 484 & 342 & 5813 \\  & 3 & 0.717 & 0.917 & 0.817 & 148 & 440 & 6401 \\  & 4 & 0.914 & 0.701 & 0.807 & 568 & 89 & 7058 \\  & 5 & 0.689 & 0.933 & 0.811 & 86 & 363 & 7507 \\ \cline{2-8}  & 0 & 0.585 & 0.565 & 0.575 & 298 & 347 & 3444 \\ \cline{2-8}  & 1 & 0.839 & 0.876 & 0.857 & 157 & 117 & 3718 \\  & 2 & 0.907 & 0.917 & 0.912 & 162 & 105 & 3985 \\ \cline{2-8}  & 3 & 0.876 & 0.962 & 0.919 & 74 & 216 & 4275 \\  & 4 & 0.951 & 0.871 & 0.911 & 293 & 45 & 4613 \\  & 5 & 0.846 & 0.983 & 0.914 & 34 & 251 & 4898 \\ \cline{2-8}  & 0 & 0.564 & 0.593 & 0.579 & 356 & 261 & 3473 \\ \cline{2-8}  & 1 & 0.750 & 0.850 & 0.800 & 278 & 206 & 3957 \\  & 2 & 0.805 & 0.930 & 0.867 & 221 & 231 & 4409 \\ MobileNetV2 & 3 & 0.818 & 0.906 & 0.862 & 205 & 328 & 4942 \\  & 4 & 0.924 & 0.953 & 0.938 & 122 & 100 & 5164 \\  & 5 & 0.932 & 0.874 & 0.903 & 338 & 181 & 5683 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results values on recall, specificity, and AUC metrics corresponding to the SCVD dataset using our proposed active learning approach. These results were obtained in the second stage (Section 4.2). actual classes (it does not consider them as ambiguous), but it is wrong. Figure 6 presents some images misclassified by the neural network model, which notices the challenge of identifying physical violence in images obtained from video across several environments. In addition, there is not a clear trend in terms of accuracy or recall in Tables 3-5 because sometimes these values decrease or increase after a new training or in the next iteration. This behavior is related to the new samples included in each new iteration in the training dataset. Thus, the learning process has different conditions in each iteration, which is reflected in the experimental results of specificity and recall. Finally, Table 6 compares the performance of the initial and final models. The initial model for the AIRTLab column was built solely using the AIRTLab dataset and ambiguous samples from AIRTLab; the initial model for RVLS/Pexels was constructed with the AIRTLab dataset and ambiguous samples from the RVLS/Pexels dataset; and the initial model for SCVD was built with the AIRTLab dataset and ambiguous samples from the RVLS/Pexels and SCVD datasets. The final model is trained with AIRTLab along with ambiguous images from RVLS/Pexels and SCVD, representing the last model of the second stage. Observe that the initial and final models are the same in the SCVD dataset (last column in Table 6). Each model (initial and final) was tested with the original datasets: AIRTLab, RVLS/Pexels, and SCVD. Table 6 shows that the final model is highly robust for the AIRTLab test dataset; also, for most models, the AUC values increase or only a minimal decrease is observed. However, when the models are tested with the RVLS/Pexels dataset, the performance is affected, with AUC values reduced to 0.836 (for the MobileNetV2 model), while the other models are less affected. This reduction in performance indicates that the model's ability to classify new images from different environments is diminished, but it still retains acceptable performance on previously learned tasks. Figure 6: Examples of images misclassified by the model; it does not identify them as ambiguous or hard to classify but assigns them a wrong label. The first row displays non-physical violence and the second row physical violence scenes. The experimental results on the RVLS/Pexels dataset significantly differ from the other datasets because it represents a sudden environmental change. The reasonable response of the model to that dataset indicates the proposed approach's ability to adapt to changing scenarios.