## 3 Methodology
### Supervised ML
#### 3.4.2 Deep Learning  
D-ANN represents a class of SML models inspired by the structure of the human brain. Unlike classical SML models, which often rely on manually designed features, D-ANNs automatically learn hierarchical feature representations from raw data, making them particularly powerful for complex high-dimensional datasets. D-ANNs consist of multiple layers of interconnected nodes (neurons), including an input layer, hidden layers, and an output layer. Each neuron computes a weighted sum of its inputs, applies an activation function (e.g., ReLU or sigmoid), and passes the result to the next layer [22]. The output of the network is determined by  
\[\text{y}=f(W_{L}\cdot\sigma(W_{L-1}\cdot\sigma(\cdots\sigma(W_{1}\cdot x+b_{1 })+b_{L-1}))+b_{L}) \tag{15}\]  
where \(x\) is the input, \(W_{l}\) and \(b_{l}\) are the weights and biases for layer \(l\), \(\sigma\) is the activation function, and \(f\) is the output layer activation. Heuristically designed, the least complex model that provided diminishing returns was two fully connected inner layers of 100 nodes each, with the SoftMax function utilized for activation. Table 3 contrasts the characteristics of D-ANN with the statistical SML models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **D-ANN SML** & **Statistical SML** \\ \hline \multirow{4}{*}{Data Features} & Automatically learns features directly from & \multirow{2}{*}{Rely on engineered features and have \\  & high-dimensional and large-scale data, & \\  & enabling them to capture nonlinear & \\  & relationships in the data. & \\ \hline \multirow{2}{*}{Performance} & Achieves state-of-the-art results, particularly & \multirow{2}{*}{Less prone to overfitting from pattern \\  & in image, speech, and text processing.} & \\ \hline \multirow{2}{*}{Computational Cost} & Requires significant computational resources & \multirow{2}{*}{Computationally efficient and faster to train.} \\  & such as special purpose processors to support & \\  & their longer training time. & \\ \hline \multirow{2}{*}{Scalability} & Excel in scenarios with abundant data, & \multirow{2}{*}{Typically perform well with smaller and \\  & leveraging their capacity to model & \multirow{2}{*}{cleaner datasets.} \\  & complex relationships. & \\ \hline \multirow{3}{*}{Interpretability} & Often described as “black boxes” because & \multirow{3}{*}{Simpler models like decision trees and linear \\  & their predictions are more difficult & \multirow{3}{*}{models provide insights into their \\  & to interpret.} & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of deep-learning and statistical ML models. This study applied D-ANN to the labeled dataset and compared its performance with nine classical SML methods. The analysis evaluated their classification accuracy, computational cost, and robustness in filtering relevant documents. To evaluate the performance of the SML models, the workflow used k-fold cross-validation to produce three key evaluation metrics: area under the curve (AUC), classification accuracy (CA), and F1-score. \(K\)-fold cross-validation is a robust validation technique that splits the dataset into \(k\) equally sized \"folds\" or subsets. The procedure trained the models on \(k-1\) folds and tested them on the remaining fold. The procedure repeated the process \(k\) times, with each fold serving as the test set once. The final performance metrics were their average across all \(k\) tieraions, ensuring a reliable and unbiased evaluation by mitigating the effects of data partitioning. Iteratively adjusting the number of folds revealed that three folds provided the best results across all models. AUC measures the area under the receiver operating characteristic (ROC) curve, which plots the TP rate (sensitivity) against the FP rate (1-specificity) at various threshold settings [23]. AUC values range from zero to one, with higher values indicating better discriminatory ability of the model to distinguish between relevant and irrelevant documents.