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Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
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Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
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Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
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Machine Learning Algorithms Performance Analysis for VLSI IC Design
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Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
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Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
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DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
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Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
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Machine Learning Algorithms Performance Analysis for VLSI IC Design
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Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
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Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
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Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
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Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
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Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 1 | Issue - 1 | september 2019
Published
September, 2019
The rapid advancements in the telecommunication networks, has led to the day by day progress in the data communication leading to the inclusion of many devices that causes complexities in managing and the maintaining of the networks. The outgrowing number of new network devices makes the traditional telecommunication networks incompatible to their flexible operation and the management. So the trending software defined networking can be opted for the provision of more convenient service providing a seamless communication, but the SDN's lags in the self-adaptability and the efficient usage of the resources as it uses the concept of the traditional networks so the paper proposes an modified method of software defined networking based on the deep learning to enhance the performance, of the telecommunication networks. Further the evaluation of the telecommunication network routing with the improvised SDN, on the packet loss rate and the average delay shows that the proposed method is compatible for the seamless information provision of the nowadays telecommunication networks.
KeywordsTelecommunication network Software defined Networking Deep learning seamless communication packet loss rate and average delay
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