Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis
Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis
Author(s): Hanan A. R. Akkar, Wael A. H. Hadi, Ibraheem H. Al-Dosari, Saadi M. Saadi, Aseel Ismael AliSubject(s): Methodology and research technology, ICT Information and Communications Technologies
Published by: Žilinská univerzita v Žilině
Keywords: zigbee; wavelet transform; statistical features; wireless sensor network (wsn); classification;
Summary/Abstract: The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.
Journal: Komunikácie - vedecké listy Žilinskej univerzity v Žiline
- Issue Year: 23/2021
- Issue No: 2
- Page Range: 44-53
- Page Count: 10
- Language: English
