Analysis of support vector machine and random forest models for predicting the scalability of a broadband network

Authors

  • Gabriel James
    Department of Computing, Topfaith University, Nigeria
  • Anietie Ekong
    Department of Computer Science, Akwa Ibom State University, Nigeria
  • Etimbuk Abraham
    Department of Electrical Electronics Engineering, Topfaith University, Nigeria
  • Enobong Oduobuk
    Department of Physics, Topfaith University, Nigeria
  • Peace Okafor
    Department of State Service, Bayelsa State Command, Bayelsa State, Nigeria

Keywords:

Broadband Networks, Machine Learning, Network performance metrics, Random forest, Support vector machine

Abstract

This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network’s ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Forest (RF) and Support Vector Machine (SVM) algorithms to predict scalability. A large dataset of 40,000 data points was collected, focusing on six key metrics: Response Time, Bandwidth, Latency, Error Rate, Throughput, and Number of Users Connected. The data was preprocessed and divided into training and testing sets (80:20 ratio). Both RF and SVM algorithms were trained on the dataset, and a comparative analysis was conducted to determine which algorithm performed better. The results showed that the RF model outperformed the SVM model, achieving an accuracy of 95.0% compared to 91.0%. The RF model also exhibited higher precision, recall, and AUC scores. Feature importance analysis revealed that Response Time and Throughput were the most significant factors in determining network scalability. The study demonstrated the effectiveness of the RF model in predicting broadband network scalability, with a lower loss value (0.0133 for training and 0.0160 for validation) compared to the SVM model. This approach will help network operators and administrators predict and improve network scalability, ensuring reliable and fast internet connectivity. The study contributes to the development of machine learning-based solutions for broadband network performance evaluation and optimization.

Dimensions

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Published

2024-07-27

How to Cite

Analysis of support vector machine and random forest models for predicting the scalability of a broadband network. (2024). Journal of the Nigerian Society of Physical Sciences, 6(3), 2093. https://doi.org/10.46481/jnsps.2024.2093

Issue

Volume 6, Issue 3, August 2024

Section

Computer Science
Copyright & Licensing

Copyright (c) 2024 Gabriel James, Anietie Ekong, Etimbuk Abraham, Enobong Oduobuk, Peace Okafor

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Analysis of support vector machine and random forest models for predicting the scalability of a broadband network. (2024). Journal of the Nigerian Society of Physical Sciences, 6(3), 2093. https://doi.org/10.46481/jnsps.2024.2093

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