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Go to Editorial ManagerThe utilisation of fake news as a tool of soft power is regarded as a strategic component of contemporary warfare, posing an escalating threat to societal and political stability as well as public trust. Deep Learning models achieve superior performance; yet, they require substantial computational resources and possess interpretative restrictions, which hinder their practical application in real-time scenarios and impose constraints on resource configurations. This study evaluates a traditional machine-learning framework based on linear and ensemble classifiers, including Linear Support Vector Classification (LinearSVC), Logistic Regression, Multinomial Naïve Bayes, and Random Forest. The framework employs a fixed, explicitly specified TF–IDF vectorization configuration and a consistent preprocessing pipeline. The system was assessed using the WELFake dataset, which contains over 70,000 labelled news stories, employing accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The findings demonstrate the superiority of LinearSVC, attaining the maximum accuracy of 95.65%, providing balanced performance metrics, and surpassing all other models. This study contributes by providing an interpretable, scalable, and domain-agnostic solution through the development of a practical fake news detection system designed to combat digital disinformation in real-world contexts.
Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.