Publication Date: 2021/05/01
Abstract: An optimum machine learning model that used cell tower statistics such as usage, customer count, etc., was instructed to project the kind of congestion that might occur. The accuracy of the model was appreciable and proper measures were taken to make it robust. As per further analysis carried out with respect to all possible algorithms like linear regression, support vector machine and neural network its found that the major factors causing congestions were byte usage and subscribers. Hence, vendors should look for beefing up their hardware’s to serve more subscribers at the same time with increased byte rate. Also, in case of congestion, they can come up with a scheme to prioritize network traffic i.e., giving critical bytes usage like communication more priority over less critical bytes usage.
Keywords: Linear Regression, Machine Learning Model Support Vector Machine ,Neural Network, Congestion.
DOI: No DOI Available
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT21APR262.pdf
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