Vehicle Classification using Machine Learning Techniques

1b3f069f-b1ca-49c1-af0d-c58993ba38f420250110051815839wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH2415-15211991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372110202511020251310.37394/232018.2025.13https://wseas.com/journals/cr/2025.phpVehicle Classification using Machine Learning TechniquesNada FuadKhattabFaculty of IT, Department of Computer Science, Zarqa University, Zarqa, JORDANRaedAlazaidahFaculty of IT, Department of and AI, Zarqa University, Zarqa, JORDANAla’aAl-ShaikhFaculty of IT Department of Cyber Security Zarqa University, Zarqa, JORDANNidalYoussefFaculty of IT, Department of Computer Science, Zarqa University, Zarqa, JORDANNawafAlshdaifatFaculty of IT, Applied Science Private University, Amman, JORDANMohmmadDmourFaculty of IT, Department of Computer Science, Zarqa University, Zarqa, JORDANDuring the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in vehicle classification. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model, best learning strategy, and the best feature selection method. Hence, four different vehicle datasets have been used to train seventeen classification models and five well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, Precision, and Recall. The results reveal that RandomForest and LMT are the best classifiers when it comes to handling vehicle datasets respectively. 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