Prediction of Telecommunication Network Outage Time Using Multilayer Perceptron Modelling Approach

Oduro-Gyimah, Francis Kwabena; Boateng, Kwame Osei; Adu, Prince Boahen; Kester, Quist-Aphetsi

doi:10.1109/iccma53594.2021.00025

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The general structure of MLP is depicted in Figure 5. The related formula is designated by [24], [25], [33].…”

Section: ) Base Learner Element Selectionmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri¹,

Pierre²

2023

IEEE Access

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Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK-EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK-EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

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“…The general structure of MLP is depicted in Figure 5. The related formula is designated by [24], [25], [33].…”

Section: ) Base Learner Element Selectionmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri¹,

Pierre²

2023

IEEE Access

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“…The performance of network availability has historically been measured by two key factors used by ISPs: network availability and dependability. Network dependability refers to a device or system's ability to perform its function without error when necessary, whereas network availability refers to a network's capability to respond to requests made by users of the network [11].…”

Section: *Author For Correspondencementioning

confidence: 99%

Modeling downtime severity of telecommunication networks using discrete time Markov Chains

2023

IJATEE

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“…A study by [26] proposed a traffic prediction approach for 5G networks by utilizing neural networks to predict network traffic patterns, thereby enabling proactive optimization of network resources. [27] proposed the use of a multilayer feed forward neural network, also known as a Multilayer Perceptron (MLP), for modelling the network outage time of network elements or systems. An MLP network was trained on 150 samples of daily network outage time data obtained from the network operating centre.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Towards zero downtime: Using machine learning to predict network failure in 5G and beyond

Emmanuel Basikolo,

Thomas Basikolo

2023

ITU J-FET

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A stable network is very important to both network providers and their customers, as it increases reliability, improves security and helps customers and companies save costs. When network outages occur, they result in significant downtime and financial losses for organizations and network users. Traditional methods of detecting and troubleshooting network failures are often reactive and time-consuming, whereby network administrators rely on traditional methods such as reactive monitoring and manual troubleshooting. These methods are often not effective in detecting and preventing network failures. In this paper, we propose a machine learning-based approach to predict network failures and minimize downtime. Network performance observability data from a 5G core network testbed based on Cloud-native Network Functions (CNFs) is used to train several supervised learning models, including random forest, gradient boosting regressor, conventional support vector regressor and proposed support vector regressor, to predict network failures. Our experiments and analysis show that the proposed model Support Vector Regressor (SVR) produced better results as compared to other models. In a very short amount of time (ten seconds), the proposed SVR model is capable of predicting whether a network failure event will occur or not within the next ten minutes, with an f1-score of more than 0.9. Our results indicate that machine learning-based approaches can significantly enhance the detection and prediction of network failures, leading to zero downtime and improved network performance.

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Prediction of Telecommunication Network Outage Time Using Multilayer Perceptron Modelling Approach

Cited by 7 publications

References 8 publications

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Modeling downtime severity of telecommunication networks using discrete time Markov Chains

Towards zero downtime: Using machine learning to predict network failure in 5G and beyond

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