ml-and vikor for anomaly detection and cell ranking in 5g/b5g

This study introduces a hybrid framework that integrates supervised machine learning (ml) algorithms with the vikor (vlsekriterijumska optimizacija i kompromisno resenje) multi-criteria decision-making (mcdm) technique to advance anomaly detection and cell ranking in next-generation networks. the proposed model addresses critical challenges in heterogeneous network environments, including data imbalance and fault prioritization. three ml algorithms—naïve bayes, decision tree, and random forest—were evaluated, with random forest achieving the highest accuracy (93.658%). however, the decision tree algorithm demonstrated optimal efficiency, balancing high accuracy (92.688%) with the fastest execution time (0.04 seconds), rendering it particularly suitable for real-time applications. the incorporation of vikor enhanced the framework by enabling fault prioritization based on severity and impact, improving detection of minority fault classes, and supporting multi-criteria resource management. this hybrid approach resulted in improved system accuracy, flexibility, and scalability, ultimately contributing to reduced operational response times and enhanced network reliability. the findings validate the efficacy of combining ml with mcdm for intelligent fault management and cell ranking in complex network ecosystems