Enhancing Network Evaluation Through Machine Learning Techniques

Author Details

This work emphasises how machine learning methods can be applied to improve network assessment procedures in a variety of fields. Networks are the foundation of many essential modern-day operations, such as information sharing, transportation, and communication. The intricacies and dynamics present in contemporary network settings are frequently beyond the scope of traditional methods of network evaluation. In order to overcome these obstacles, machine learning has gained popularity among academics and industry professionals as a potent tool for pattern recognition, prediction, and analysis of massive amounts of network data. The use of machine learning methods to improve network efficiency, security, dependability, and performance is examined in this study. It goes into detail on how machine learning techniques can be used to prioritise important applications, optimise network setups, identify anomalies and security concerns, and anticipate network problems before they happen. Through real-world examples and case studies, this paper illustrates the benefits and implications of using machine learning for network evaluation. Additionally, it examines the challenges and considerations associated with integrating machine learning into network evaluation processes, including data quality, model interpretability, and scalability. By leveraging advanced computational techniques and interdisciplinary approaches, organizations can gain deeper insights into network behaviour, address emerging challenges, and design more robust and adaptive network infrastructures for the future.

Keywords

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Singh (2003); Enhancing Network Evaluation Through Machine Learning Techniques,IPEM JOURNAL OF COMPUTER APPLICATION & RESEARCH, 8(1), 48-51