EVALUATION OF MACHINE LEARNING ALGORITHMS FOR FILTERING AND ISOLATING SPAMMED MESSAGES

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dc.contributor.authorAzeez, Nureni Ayofe
dc.contributor.authorAdio, Esther
dc.contributor.authorYekinni, Adewale
dc.contributor.authorOnyema, Juliet
dc.date.accessioned2020-09-09T14:39:56Z
dc.date.available2020-09-09T14:39:56Z
dc.date.issued2020-04-13
dc.description.abstractThe use of mobile application is rising on daily basis as they offer a wide range of services and at the same time elevating the fee of those services. Short Message Service (SMS) is considered one of the most commonly deployed messaging systems. However, this deployment has led to a spike in attacks on mobile devices such as SMS Spam. In this article, Artificial Intelligence (AI) method, which discovers and filters unsolicited spam messages was adopted. The machine learning algorithms used are Logistic Regression, Decision Trees, Gaussian Naïve Bayes; Multilayer perceptron (MLP) and Support Vector Machine (SVM). After experimentation, it was observed that MLP Classifier produced the best results with 93.1% true positive rate.en_US
dc.identifier.issn2315 – 8239
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/8810
dc.language.isoenen_US
dc.publisherFUTA JOURNAL OF RESEARCH IN SCIENCEen_US
dc.subjectSpammed message; mobile application; artificial intelligence; attacks; SMSen_US
dc.titleEVALUATION OF MACHINE LEARNING ALGORITHMS FOR FILTERING AND ISOLATING SPAMMED MESSAGESen_US
dc.typeArticleen_US
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