<![CDATA[Email spam detection is a critical challenge in maintaining the security and efficiency of digital communication. This research proposes and evaluates an optimized pipeline for email spam detection by integrating Bidirectional Encoder Representations from Transformers (BERT) for feature extraction, Mutual Information (MI) for feature selection to reduce dimensionality, and a dense neural network for classification. The Lingspam dataset, consisting of 2893 emails (2412 ham and 481 spam), was used in the experiments with an 80% training and 20% testing data split. Text features were extracted using BERT (bert-base-uncased), resulting in a 768-dimensional embedding, which was then reduced to the 200 most relevant features using MI. A dense neural network model with a 256-128-64-32-1 neuron architecture was trained using the Adam optimizer, binary cross-entropy loss function, and techniques such as early stopping and class weights to handle class imbalance. Evaluation results on the test data demonstrated very high performance, achieving an accuracy of 99.14%, precision of 0.9596, recall of 0.9896, F1-score of 0.9744, and ROC-AUC of 0.9995. This approach indicates that the combination of BERT-MI with a dense network can achieve accuracy comparable to more complex methods, but with the potential for a simpler and more efficient architecture.]]>
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