## 5 Results and Discussion
Additionally, it is established that Word2 Vec cannot recognize the order of words across multiple sentences, which limits understanding [102]. Moreover, study [35] proposes extracting sentiment polarity as a feature. This can lead to oversimplification since a user's text may carry multiple meanings depending on the emotional context, potentially misinterpreting the underlying sentiment [103]. Fur thermore, these features lack context, as polarity does not provide semantic information, only interpretation. Only study [31] and the current project employ BERT, which, as discussed throughout the manuscript, reduces the need for extensive feature engineering, preserves the latent context of sentences, and ensures that words maintain their meaning regardless of their position and frequency in the text. **Third comparative finding**: In contrast to the previously mentioned research, the methodology presented here (BERT + DNN) harnesses BERT's pre-trained weights to consolidate, abstract, and more accurately reflect the text embedded within websites. BERT has been meticulously engineered to address the myriad shortcomings prevalent in NLP models, issues that have been reiterated in this section: the lack of semantic depth, the intricacies of composition and connections, the challenges posed by high dimensionality and data dispersion [28; 29], contextual rigidity [33], and premises inclined to introduce ambiguity [35]. In the cutting-edge realm of Artificial Intelligence and Machine Learning (AI/ML), assessing the effectiveness of various models is critical. Study [28] initially presents RBC, which excels with small and structured datasets. Yet, its rigidity in the face of complex rules diminishes its applicability for the synthesis of textual and numerical features, which is a distinctive feature of this research (BERT + DNN), and restricts its ability to grasp nonlinear dynamics [104]. While study [28] stops short of detailing the RBC algorithm's mathematical underpinnings, the prevailing literature suggests that its linear orientation significantly undermines its utility with nonlinear data types, such as textual content, often leading to error-prone models with subpar performance. Study [30] acknowledges the intricacy of ACO, a formidable challenge for amalgamating features in multidimensional contexts, necessitating a thorough search methodology to pinpoint websites likely in breach of copyright laws [105]. Moreover, the study [31] endorses LR, an algorithm attuned to predictive tasks involving less complex datasets. However, the process of integrating diverse data characteristics (BERT + DNN) calls for significant regularization trials, potentially detracting from its efficacy in identifying multimedia content within digital lexicons. Correspondingly, study [32] employs CNNs, renowned for their proficiency in object detection, yet their performance can be erratic within the heterogeneous IoE. A recalibration of the DOM could potentially refine the detection of illicit multimedia material. Finally, study [35] introduces fundamental algorithms like AdaBoost, XG-Boost, RF, SVM, and MLP. Despite the unique challenges each algorithm faces in pinpointing infringing content, they all share the common requirement for meticulous hyperparameter optimization [106]. AdaBoost risks overfitting without proper classifier baseline selection, XG-Boost may misinterpret features with outlier data, Random Forest necessitates exacting adjustments of its estimators, SVM demands intricate kernel and regularization parameter selection, and MLP grapples with learning rules that could erode the integrity of weight values. **Fourth comparative finding**: The BERT + DNN architecture is more versatile and streamlined, effectively managing the fusion of textual and numerical features. It exhibits a classification potential that surpasses the performance metrics of other state-of-the-art studies (precision = 98.71%; recall = 98.67%, and F1-score = 98.67%) in detecting infringing multimedia content, outperforming most the state-of-the-art works.