Investigation of Three Machine Learning Models for the Detection of Emails Spam
| المصدر: | مجلة شمال إفريقيا للنشر العلمي |
|---|---|
| الناشر: | الأكاديمية الأفريقية للدراسات المتقدمة |
| المؤلف الرئيسي: | Almabrouk, Abdulsalam Ashour (Author) |
| مؤلفين آخرين: | Abraheem, Salih Mousay (Co-Author), Ali, Muftah Emtir (Co-Author), Mansour, Mohamed Saleh (Co-Author) |
| المجلد/العدد: | مج1, ع1 |
| محكمة: | نعم |
| الدولة: |
ليبيا |
| التاريخ الميلادي: |
2023
|
| الشهر: | مارس |
| الصفحات: | 13 - 23 |
| ISSN: |
2959-4820 |
| رقم MD: | 1378356 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch, HumanIndex |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Machine Learning | K-Nearest Neighbor (KNN) | Support Vector Machine
|
| رابط المحتوى: |
عدد مرات التحميل
5
| المستخلص: |
Recently, machine learning has been applied to different major areas such as text classification, machine translation, and spam detection. The great performance of machine learning algorithms in several fields provided humans with opportunities to tackle some of their hard jobs to be handled by machine learning systems. These tasks seem effortless for machines and need less time as the number of texts or spam that need to be classified is huge. Hence, in his paper, we propose three different machine-learning models for the task of email spam detection. The three models are trained and validated on a public spam dataset. Experimentally, the three models performed differently, and it was seen that the Naïve Bayes outperformed the other machine learning algorithms in terms of accuracy and other evaluation metrics. |
|---|---|
| ISSN: |
2959-4820 |
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