Citation

Lai, Jie Yan (2019) Prediction on network equipment failure using big data analytics. Masters thesis, Multimedia University.

Full text not available from this repository.

Abstract

Unexpected network outage specifically due to network element downtime has been one of the widely shared phenomenon in the Malaysia Telecommunication industry. This has effected customers’ satisfaction and network services which ultimately lead to huge losses to the telecommunication company. For this very reason, a series of machine learning algorithms are proposed to analyse the equipment failure patterns to predict future failures. In this project, the researcher focus has been placed on deploying a probabilistic machine learning algorithm – Naïve Bayes (NB), incorporated with hierarchical time series feature extraction to improve the network experience of users situated at the Multi-Service Access Node (MSAN) and Fibre-to-the-home (FTTH) network. The goal is to study the probability of the network equipment failure and address the early symptoms of malfunction using the connection session logs and user authentication data.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: QA76.9.B45 L35 2019
Uncontrolled Keywords:	Big data
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	21 Aug 2024 08:13
Last Modified:	21 Aug 2024 08:13
URII:	http://shdl.mmu.edu.my/id/eprint/12848

Downloads

Edit (login required)