<![CDATA[Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.]]>
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