Saliency-based bit plane detection for network applications

Kaljahi, Maryam Asadzadeh; Shivakumara, Palaiahnakote; Hakak, Saqib; Idris, Mohd Yamani Idna; Anisi, Mohammad Hossein; Rajan, Dinesh

doi:10.1007/s11042-020-08741-9

Cited by 2 publications

(1 citation statement)

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“…However, it is fails to address the prediction accuracy, false-positive and prediction time. In [22][23][24][25] techniques are used traffic flow prediction for vehicles networks and network applications. However, they are used pattern for predicting traffic using deep learning architectures can capture these nonlinear spatio-temporal effects.…”

Section: Related Workmentioning

confidence: 99%

ECMCRR-MPDNL for Cellular Network Traffic Prediction With Big Data

et al. 2020

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Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily basis. Processing such volume of data is a complex task as well as the challenging one. This big data is applied in the cellular network for traffic prediction. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In order to improve the traffic prediction accuracy with minimum time, Expected Conditional Maximization Clustering and Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL) technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of data over the spatial and temporal aspects of cellular networks. Then the collected data are trained with multiple layers such as one input layer, two hidden layers, and one output layer. The activation function is used at the output layer to predict the network traffic based on the similarity value with higher accuracy. These predictors are evaluated using real network traces. Finally, the error rate is calculated for minimizing the prediction error. Experimental evaluation is carried out using a big dataset with different metrics such as prediction accuracy, false-positive and prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique improves on an average the 98% of performance of network traffic prediction with higher accuracy and 20 % minimum time as well as the false-positive rate as compared to the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%