## 5 Results and Discussion  
After applying hyperparameter adjustments to the \(X_{V_{\text{concat}}}\) set, validation analyses \(EI(\mathcal{P})\) revealed that the most optimal parameters, \(\mathcal{P}^{+}\), include an \(\eta\) of \(1\times 10^{-4}\); a batch size (\(\mathcal{B}\)) of 32; a three-layer configuration (\(H_{M}\)) with 32, 64, and 128 units (U) respectively; and a total of 100 epochs (\(\mathcal{B}\)). Using the previously specified values, the test tuple (\(X_{P_{\text{concat}}},y_{P}\)) was trained using \(\mathcal{P}^{+}\) and subsequently validated with the subset (\(X_{V_{\text{concat}}},y_{V}\)). Accuracy rates of 95.14% during the training phase and 98.71% in the testing stage were recorded. The convergence of the learning process is evidenced in Figure 11.  
To assess the classification results achieved by the BERT + DNN model, Figure 12 presents the multi-class confusion matrix, along with the support (number of samples submitted) for each test sample. Figure 11: Convergence of accuracy rates throughout the training and testing stages, applying the most refined and optimal set of hyperparameters \(\mathcal{P}^{+}\). Figure 10: Multi-class confusion matrix for the labels non-infringing (\(N\)I), movies and series (\(MS\)), music (\(M\)), software (\(S\)), and books (\(B\)). Once the composite confusion matrix has been constructed, it is possible to calculate performance metrics, including precision, recall, and the \(F_{1}\)-score, whose values are referenced in Table 8.  
Although it is not possible to directly compare state-of-the-art studies in terms of their methodology, as they do not use a multi-class classification model like the one employed in this BERT + DNN project, we highlight some of the main disadvantages of these works in the following lines. For the sake of clarity and linguistic conciseness, the related works [28; 29; 30; 31; 32; 33; 35] will from now on be referred to as studies. Regarding the exploration of the Internet of Everything (IoE), studies [28; 30; 32] do not conduct a thorough search for multimedia content at risk of infringement. Study [28] narrows its focus to the detection of downloads of multimedia objects that have been previously identified and transmitted via a Local Area Network (LAN) at the application layer, identified by a protocol analyzer that targets Peer-to-Peer (P2P) ports. Meanwhile, study [32] bases its dataset on interviews that investigate the use of potentially infringing software among users of varying ages, genders, ethical beliefs, and economic statuses, without providing details on the aggregation of data over any specific transmission medium. Study [32] utilizes a pre-established database featuring logos highly susceptible to copyright infringement, depicted in images of well-known commercial brands, and conducts experiments based on this dataset, yet it does not describe the process or location within the IoE from which these data were collected. Contrary to other methods, studies [29; 31; 35] conduct selective explorations, opting for web scrapers over search engines to analyze specific sites within the Internet of Everything  
Figure 12: Confusion matrix representing classification outcomes across labels: non-infringing (\(NI\)), movies and series (\(MS\)), music (\(M\)), software (\(S\)), and books (\(B\)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F\({}_{1}\)-Score** \\ \hline \(NI\) & 99.41\% & 99.31\% & 99.36\% \\ \hline \(MS\) & 98.70\% & 99.03\% & 98.86\% \\ \hline \(M\) & 98.78\% & 98.19\% & 98.48\% \\ \hline \(S\) & 98.12\% & 97.61\% & 97.86\% \\ \hline \(B\) & 98.57\% & 99.18\% & 98.87\% \\ \hline
**Macro average** & 98.71\% & 98.87\% & 98.87\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance results of the BERT + DNN proposal obtained for classes \(NI\), \(MI\), \(M\), \(S\), and \(B\). (IoE) that are directly linked to their research objectives. For instance, Ref.