Guma, AliSadıkoğlu, EmreAbdelhak, Hatim2023-07-172023-07-172023Ali, G., Sadıkoğlu, E., & Abdelhak, H. (2023). Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning. Wasit Journal of Computer and Mathematics Science, 2(2), 18-25. https://doi.org/10.31185/wjcm.151https://doi.org/10.31185/wjcm.151https://nru.uncst.go.ug/handle/123456789/9068Advanced Driver Assistance Systems (ADAS) are a fundamental part of various vehicles, and the automatic classification of traffic signs is a crucial component. A traffic image is classified based on its recognizable features. Traffic signs are designed with specific shapes and colours, along with text and symbols that are highly contrasted with their surroundings. This paper proposes a hybrid approach for classifying traffic signs by combining SIFT with SVM for training and classification. There are four phases to the proposed work: pre-processing, feature extraction, training, and classification. A real traffic sign image is used for classification in the proposed framework, and MATLAB is used to implement the frameworkenClassificationMachine LearningDriver Assistance SystemArticle