Enhancing Network Fault Detection with Precision Predictive AI
DOI:
https://doi.org/10.47941/ijce.2257Keywords:
Predictive AI, Network Fault Management, Machine Learning, Anomaly Detection, Time Series Analysis, Proactive Maintenance, Fault Prediction, Operational Efficiency, Data Preprocessing, Real-time MonitoringAbstract
Traditional methods for managing and predicting faults must be revised in today's complex network landscape. Predictive Artificial Intelligence (AI) offers a proactive solution, using advanced algorithms and machine learning to analyze vast data, detect patterns, and prevent issues before they escalate. This approach significantly enhances network reliability, reduces downtime, improves operational efficiency, and has transformative potential in network management. This white paper explores this potential, providing real-world examples and integration strategies. We also discuss its benefits and challenges, highlighting its promise for ensuring stable and resilient network operations.
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References
IIoT and Advanced Condition Monitoring: The Future of Industrial Excellence.
Capella Solutions Aspire Systems - blog
Capella Solutions Aspire Systems - blog
What Are The Benefits Of Remote Patient Monitoring - Go Roboted.
https://goroboted.com/what-are-the-benefits-of-remote-patient-monitoring/
What is a Network Management System (NMS)? | CellularNews.
https://cellularnews.com/definitions/what-is-a-network-management-system-nms/
Mastering Automation: Machine Learning Transforms DevOps.
https://nettyfy.com/mastering-automation-machine-learning-transforms-devops/
Deloitte United States | Grace Technologies | Home Page
Gartner. (2023). Predictive Analytics and AI: Enhancing Network Operations and Efficiency. Gartner. Retrieved from https://www.gartner.com
Data Science Techniques for Predicting Market Trends and.
https://technoticia.com/data-science-techniques-for-predicting-market-trends-and-consumer-behavior/
Tiffin, P. A., & Paton, L. W. (2018). Artificial or intelligent? Machine learning and medical
Selection: Possibilities and risks. https://doi.org/10.15694/mep.2018.0000256.1
Prins, K., & Bhuse, V. (2018). Forced Vacation: A Rogue Switch Detection Technique. European
Conference on Cyber Warfare and Security, (), 390-399,XIV.
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Copyright (c) 2024 Deepthi Kallahakalu Vijay Dev
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
