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....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.展开更多
The surface of the cotton fabric was modified using a direct current(DC)air plasma treatment and hence enhances its hydrophilicity.The Box-Behnken approach(design expert software)was used to optimise the input process...The surface of the cotton fabric was modified using a direct current(DC)air plasma treatment and hence enhances its hydrophilicity.The Box-Behnken approach(design expert software)was used to optimise the input process parameters.The sample prepared under optimized condition is subjected to ATR-FTIR and Field Emission Scanning Electron Microscopy(FESEM)studies in order to determine the changes in hydrogen bond energies(EH),Total Crystallinity Index(TCI),Hydrogen Bond Intensity(HBI),Lateral Order Index(LOI),functionalization,lattice parameters(a,b,c&β),degree of crystallinity(in%)and surface etching.The ageing of this sample has been studied by comparing the values of carboxyl content and AC-C/AC-O-C ratio calculated using data extracted from ATR-FTIR spectra of the sample recorded periodically for one month.展开更多
文摘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.
文摘The surface of the cotton fabric was modified using a direct current(DC)air plasma treatment and hence enhances its hydrophilicity.The Box-Behnken approach(design expert software)was used to optimise the input process parameters.The sample prepared under optimized condition is subjected to ATR-FTIR and Field Emission Scanning Electron Microscopy(FESEM)studies in order to determine the changes in hydrogen bond energies(EH),Total Crystallinity Index(TCI),Hydrogen Bond Intensity(HBI),Lateral Order Index(LOI),functionalization,lattice parameters(a,b,c&β),degree of crystallinity(in%)and surface etching.The ageing of this sample has been studied by comparing the values of carboxyl content and AC-C/AC-O-C ratio calculated using data extracted from ATR-FTIR spectra of the sample recorded periodically for one month.
