استخدام خوارزميات التعلم الآلي لتحسين دقة كشف الرسائل النصية المزعجة

Abstract

The rapid technological advancement and the increasing reliance on electronic media in all aspects of life have necessitated the protection of users from the negative impacts of unwanted SMS messages. In this research, machine learning algorithms were employed to distinguish between non-spam and spam messages. The results obtained using Logistic Regression, Support Vector Machine (SVM), and Random Forest algorithms are presented. The evaluation included accuracy, recall, and F1-score metrics, where the Random Forest algorithm demonstrated the best performance in terms of both accuracy and recall when classifying spam and ham messages. The study confirms that the use of the Random Forest algorithm is more effective for SMS classification, as it yielded precise and efficient results in differentiating between desired and undesired messages.