Soft Computing based Machine Learning Techniques for Optical Communication Networks

Massive amounts of extremely far-off data are now being sourced from the telecommunications networks. Additionally, this information may be acquired via network business traces, network logs, warnings, signal quality indicators, drug addicts' behavioral data, and other sources. Advanced tools are required to collect useful data.. Machine learning is one of these fantastic technologies (ML), which is considered as one of the most promising tactical answers for analyzing network data and automation. The recent increase in network difficulty contributes to the abandoning of ML techniques in the context of optical communication networks. In this study high-level overview of networking and ML to optic dispatches are analyzed. This article, outlines the problem, review the relevant literature, and provide experimenters and analysts interested in this field an introduction to machine learning. By providing new possible exploration guidelines to encourage more advancement in this field, we infer the research. Even if recently reasonable investigation documents have appeared, machine learning's application to optical networks is still in its infancy.