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7.883 Call For Paper

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Author(s):

Teja Shree C V | Nathiya M | Ashish Christin C
Keywords:

Machine Learning, Intelligent Triage, IT Automation, Fault Prediction, System Monitoring
Abstract:
Modern Office IT Environments Are Highly Complex And Often Experience Unexpected System Failures That Impact Productivity And Increase Operational Costs. Traditional IT Support Systems Follow A Reactive Approach, Where Issues Are Addressed Only After Failures Occur, Leading To Delays And Inefficiencies. This Project Proposes A Dynamic IT Support System With Intelligent Triage And Operational Memory That Leverages Machine Learning Techniques To Proactively Detect And Classify System Failures. The System Continuously Monitors Key Performance Metrics Such As CPU Usage, Memory Utilization, Disk Activity, Network Latency, And Application Response Time. Using Predictive Models, The System Identifies Potential Failures Before They Occur And Classifies Them Into Categories Such As Hardware, Software, Network, Or Security Issues. Based On The Prediction, Automated Support Tickets Are Generated To Assist IT Personnel In Taking Preventive Action. The Proposed System Improves Fault Management Efficiency, Reduces Downtime, And Enhances System Reliability By Shifting From Reactive To Proactive IT Support.