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- W3133819332 abstract "The telecom networks generate multitudes and large sets of data related to networks, applications, users, network operations and real time call processing (Call Detail Record (CDR)). This large data set has the capability to give valuable business insights - for example, real-time user quality of service, network issues, call drop issues, customer satisfaction index, customer churn, network capacity forecast and many more revenue impacting insights. As even setting up of more towers for better coverage would also directly affect the health of habitants around. In this paper, the overall condition of call drops has been reviewed and possible ways to minimize the spectacles of network call drops. Applied Linear Regression algorithm which is used type of predictive analysis. Three major uses for regression analysis Determining the strength of predictors, Forecasting an effect and Trend forecasting. This paper gives to telecom service providers to improve their networks and minimize the network call drops with security. Deliver quality of services to their subscribers using the advanced technologies with accurate algorithms." @default.
- W3133819332 created "2021-03-15" @default.
- W3133819332 creator A5050384317 @default.
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- W3133819332 date "2020-05-25" @default.
- W3133819332 modified "2023-09-27" @default.
- W3133819332 title "To Optimize Network Call Drops and Enhances Real Time (CDR) Data Handling with Security" @default.
- W3133819332 cites W2065199439 @default.
- W3133819332 doi "https://doi.org/10.51767/jc1111" @default.
- W3133819332 hasPublicationYear "2020" @default.
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