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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.
In recent years, the interest in biometric identification systems has increased, due to the worry about security matters in the world. Face recognition technique is one type of biometric identification technology and is considered the most widely employed .it represents one of the most significant pattern recognition technologies that use characteristics included in facial images or videos to detect the identity of individuals. Recently, it has become more important as it is used in many security applications. In recent years, Most of the traditional facial identification algorithms have faced limitations in identification and verification accuracy. As a result, this work presents a sophisticated system for face identification adopting a novel algorithm of deep learning: You Only Look Once version 8 (YOLOv8). This algorithm can detect the face identity of different individuals with different positions with high accuracy. The face identification technique consists of four main phases which are the image acquisition phase, preprocessing phase, features extraction phase, and classification phase. The YOLOv8 model has been trained for head pose images (HPI) datasets consisting of 1700 images divided as training, validation, and test images of 1190, 255, and 255, respectively. The experimental results show proposed algorithm achieved a significant improvement in face identification accuracy of 99% of mean average precision in which outperforms many state-of-the-art face identification techniques.