Integrating AI-Based Predictive Analytics in Network Monitoring for Real-Time Fault Detection: A Comprehensive Analysis of Modern Network Infrastructure Management

@article{1709319,
      author = {Sullivan Afanna Ezike},
      title = {Integrating AI-Based Predictive Analytics in Network Monitoring for Real-Time Fault Detection: A Comprehensive Analysis of Modern Network Infrastructure Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {12},
      pages = {307-318},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709319.pdf},
      abstract = {The exponential growth of network infrastructure complexity in the United States has necessitated the evolution from traditional reactive network monitoring approaches to proactive, AI-driven predictive analytics systems. This paper examines the integration of artificial intelligence and machine learning algorithms in network monitoring frameworks to enable real-time fault detection and prevention. Through comprehensive analysis of current implementations across major U.S. telecommunications providers and enterprise networks, this study demonstrates that AI-based predictive analytics can reduce network downtime by up to 78% and improve fault detection accuracy to 94.3%. The research presents a systematic evaluation of various machine learning algorithms, their effectiveness in different network environments, and provides actionable recommendations for implementation strategies in diverse organizational contexts.},
      keywords = {Network Monitoring, Predictive Analytics, Artificial Intelligence, Fault Detection, Machine Learning, Network Infrastructure},
      month = {June},
  }