Artificial Intelligence Anomaly Detection and Root Cause Analysis
Vishal Gangadhar Puranik, T. Chithrakumar, E. Naresh, Shruti Bhargava Choubey · Auerbach Publications eBooks · 2025
Artificial intelligence (AI) anomaly detection detects data irregularities and prevents difficulties. AI may use machine learning to find anomalies like anomalous system effectiveness indicators or financial transaction trends. Analysis of anomalies entails finding and determining their causes. Advanced AI analytics discover technology faults, data integrity concerns, cybersecurity hazards, and operational inefficiencies. This proactive approach targets hidden concerns to lessen acute risks and enhance long-term outcomes. Data and feedback improve AI anomaly detection systems. Many domains need data-driven settings. Traditional radio access monitoring of the network is less accurate and efficient than anomaly detection and root cause analysis for large-scale 4G/5G heterogeneous networks. This chapter describes an outline for anomaly identification and root cause investigation utilized in field testing. It presents an anomaly detection technique with a labelled radio access network dataset. Compared to the state-of-the-art method, the algorithm is more effective with somewhat lower performance. Numerous Open Radio Access Network (O-RAN) base station sites have confirmed the autonomous network closed-loop capacity issue solution, including identifying anomalies and root cause investigation.