Network Traffic Prediction Using Radial Kernelized-Tversky Indexes-Based Multilayer Classifier  

作　　者：M.Govindarajan V.Chandrasekaran S.Anitha 

机构地区：[1]Department of Computer Applications,Velalar College of Engineering and Technology,Erode,638012,India [2]Department of Electronics and Communication Engineering,Velalar College of Engineering and Technology,Erode,638012,India [3]Department of Information Technology,Kongu Engineering College,Perundurai,India

出　　处：《Computer Systems Science & Engineering》2022年第3期851-863,共13页计算机系统科学与工程（英文）

摘　　要：Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

关 键 词：Cellular network traffic prediction connectionist Tversky multilayer deep structure learning attribute selection classification radial kernelized long short-term memory 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]