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@article{182696,
author = {mohammed juned shaikh shabbir and Amol Prataprao Bhatkar and Bhimrao Shriram Lankeshwar and Sagar Shrikrishna Dharamkar and Rahul Mahesh Bhutada},
title = {AI-Powered Network Management: A Comprehensive Framework for Intelligent Infrastructure Optimization},
journal = {International Journal of Innovative Research in Technology},
year = {2025},
volume = {12},
number = {2},
pages = {2803-2810},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=182696},
abstract = {Innovative techniques to network management are required as a result of the exponential development in both the complexity of networks and the volume of data flow. The purpose of this study is to propose a comprehensive framework for artificial intelligence-powered network management. This framework incorporates machine learning algorithms, predictive analytics, and autonomous decision-making capabilities. Significant increases in network performance, fault detection accuracy, and resource utilization efficiency are demonstrated by the solution that we have presented. Through thorough simulation and testing in the real world, we were able to achieve an accuracy of 89% in failure prediction, a decrease of 34% in network downtime, and an improvement of 28% in bandwidth usage. The framework contains deep learning models for the recognition of traffic patterns, reinforcement learning for the dynamic allocation of resources, and natural language processing for the intelligent study of logs.},
keywords = {Network Management, Artificial Intelligence, Machine Learning, Network Optimization, Predictive Analytics, Autonomous Systems},
month = {July},
}
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