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Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. (1402). ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks. نشریه هنرهای کاربردی, 3(3), 23-34. doi: 10.22075/mseee.2025.34976.1172
Reza Moammer Yami; Ali Akbar Khazaei; Saeed Rahati Quchani. "ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks". نشریه هنرهای کاربردی, 3, 3, 1402, 23-34. doi: 10.22075/mseee.2025.34976.1172
Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. (1402). 'ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks', نشریه هنرهای کاربردی, 3(3), pp. 23-34. doi: 10.22075/mseee.2025.34976.1172
Moammer Yami, Reza, Khazaei, Ali Akbar, Rahati Quchani, Saeed. ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks. نشریه هنرهای کاربردی, 1402; 3(3): 23-34. doi: 10.22075/mseee.2025.34976.1172

ML and MCDM for Abnormal Cell Detection in 5G & B5G Networks

Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 3 - شماره پیاپی 13، بهمن 2023، صفحه 23-34    اصل مقاله (598.04 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/mseee.2025.34976.1172
نویسندگان
Reza Moammer Yami* 1؛ Ali Akbar Khazaei1؛ Saeed Rahati Quchani2
1Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
2Department of Biomedical Engineering, Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
تاریخ دریافت: 17 مرداد 1403،  تاریخ پذیرش: 10 بهمن 1403 
چکیده
Self-organizing communication networks are a vital pillar in 5G and B5G technology, which operate automatically without human intervention in self-healing, self-configuring, and self-optimizing. Self-healing in these networks predicts and resolves network problems and improves performance with the following three methods in the research conducted: rule-based, algorithmic, and machine-learning approaches. This research used the TOPSIS technique as a multi-criteria decision-making method to rank and score cells after data preprocessing. Then, based on the rank of each cell, it is divided into two classes: normal and abnormal. Then, with three algorithms, decision tree, New Bayes and Random Fars, normal and abnormal cell prediction was performed independently. In the last step, using the combined method of maximum voting, the algorithm was completed and the results showed an improvement in the parameters Precisio=0.939, Recall=0.962, F-Measure=0.968, Accuracy=94.0717.
کلیدواژه‌ها
Self-Organizing Networks؛ Simple Bayes؛ Decision Tree Random Forest؛ 5G/B5G Networks؛ Topsis
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