A cooperative neural network approach for enhancing data traffic prediction

Abdulkarim, Salihu Aish; Lawal, Isa Abdullahi

doi:10.3906/elk-1704-126

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…However, the performance of time and space consumptions in traffic prediction was not estimated. A cooperative neural network approach was designed in Abdulkarim et al [15] to enhance the prediction accuracy of mobile data traffic. However, the approach failed to use a suitable technique for reducing space complexity when considering big data sets.…”

Section: Related Researchmentioning

confidence: 99%

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

Govindarajan¹,

Chandrasekaran²,

Pillai³

2022

Computer Systems Science and Engineering

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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.

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Section: Related Researchmentioning

confidence: 99%

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

Govindarajan¹,

Chandrasekaran²,

Pillai³

2022

Computer Systems Science and Engineering

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“…An ANN-based model depends upon an adequate number of hidden layers of neural networks for the prediction of air passenger flow. Despite its predictive capability, the black-box nature of an ANN model makes it less transparent and provides no insight into the modelled phenomena (Abdulkarim & Lawal, 2017). Thus, the above discussion highlights the three main limitations of the existing forecasting methods.…”

Section: Introductionmentioning

confidence: 99%

Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data.

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“…The proposed model can predict the network traffic more accurately. Abdulkarim and Lawal (2017) proposed a new prediction model based on a cooperative neural network strategy in order to improve the prediction accuracy of mobile data in UMTS-based mobile data networks. The proposed method produced superior results in comparison with the results obtained on the same problems from the traditional method.…”

Section: Introductionmentioning

confidence: 99%