Communications - Scientific Letters of the University of Zilina 2021, 23(2):C44-C53 | DOI: 10.26552/com.C.2021.2.C44-C53

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

Hanan A. R. Akkar ORCID...1, Wael A. H. Hadi ORCID...2, Ibraheem H. Al-Dosari ORCID...3, Saadi M. Saadi4, Aseel Ismael Ali ORCID...4
1 Department of Electrical Engineering, University of Technology, Baghdad, Iraq
2 Department of Communications Engineering, University of Technology, Baghdad, Iraq
3 Department of Computer Communications Engineering, Al-Rafidain University College, Baghdad, Iraq
4 Ministry of Education, Minister's Office, Institute of Gifted Students, Baghdad, Iraq

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.

Keywords: zigbee; wavelet transform; statistical features; wireless sensor network (wsn); classification

Received: February 29, 2020; Accepted: August 26, 2020; Prepublished online: February 8, 2021; Published: April 1, 2021  Show citation

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Akkar, H.A.R., Hadi, W.A.H., Al-Dosari, I.H., Saadi, S.M., & Ali, A.I. (2021). Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis. Communications - Scientific Letters of the University of Zilina, 23(2), C44-53. doi: 10.26552/com.C.2021.2.C44-C53
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