Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

This article explores the transformative integration of cloud computing, artificial intelligence, and 5G networks, focusing on predictive maintenance and personalized service delivery. The article examines how 5G infrastructure generates unprecedented volumes of data that can be leveraged for intelligent network management through AI-driven analytics. The article presents a novel framework for integrating federated learning with 5G infrastructure to preserve privacy while maintaining prediction accuracy, evaluates deep learning-based anomaly detection algorithms for fault prediction, and develops a cloud-native architecture for dynamic resource allocation. Key areas explored include theoretical frameworks for AI-driven 5G networks, predictive maintenance methodologies that employ diverse machine learning approaches, privacy-preserving AI techniques that protect sensitive user data, and personalized service delivery systems that adapt to user contexts in real time. The findings demonstrate significant improvements in operational efficiency, network reliability, service personalization, and regulatory compliance while maintaining privacy and security.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-31
DOI
https://doi.org/10.5281/zenodo.17286305
Primary Topic
Cardiac Valve Diseases and Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

Zenodo (CERN European Organization for Nuclear Research)
Cardiac Valve Diseases and Treatments
article

Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

article en

Abstract

This article explores the transformative integration of cloud computing, artificial intelligence, and 5G networks, focusing on predictive maintenance and personalized service delivery. The article examines how 5G infrastructure generates unprecedented volumes of data that can be leveraged for intelligent network management through AI-driven analytics. The article presents a novel framework for integrating federated learning with 5G infrastructure to preserve privacy while maintaining prediction accuracy, evaluates deep learning-based anomaly detection algorithms for fault prediction, and develops a cloud-native architecture for dynamic resource allocation. Key areas explored include theoretical frameworks for AI-driven 5G networks, predictive maintenance methodologies that employ diverse machine learning approaches, privacy-preserving AI techniques that protect sensitive user data, and personalized service delivery systems that adapt to user contexts in real time. The findings demonstrate significant improvements in operational efficiency, network reliability, service personalization, and regulatory compliance while maintaining privacy and security.

Zenodo (CERN European Organization for Nuclear Research)
Cochin University of Science and Technology (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 36%
Cardiac Valve Diseases and Treatments
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