Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

https://doi.org/10.26552/com.C.2021.2.C44-C53

  • Hanan A. R. Akkar
  • Wael A. H. Hadi
  • Ibraheem H. Al-Dosari
  • Saadi M. Saadi
  • Aseel Ismael Ali
Keywords: zigbee, wavelet transform, statistical features, wireless sensor network (wsn), classification

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.

Author Biographies

Hanan A. R. Akkar

Department of Electrical Engineering, University of Technology, Baghdad, Iraq

Wael A. H. Hadi

Department of Communications Engineering, University of Technology, Baghdad, Iraq

Ibraheem H. Al-Dosari

Department of Computer Communications Engineering, Al-Rafidain University College, Baghdad, Iraq

Saadi M. Saadi

Ministry of Education, Minister's Office, Institute of Gifted Students, Baghdad, Iraq

Aseel Ismael Ali

Ministry of Education, Minister's Office, Institute of Gifted Students, Baghdad, Iraq

Published
2021-04-01
How to Cite
Hanan A. R. Akkar, Wael A. H. Hadi, Ibraheem H. Al-Dosari, Saadi M. Saadi, & Aseel Ismael Ali. (2021). Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis. Communications - Scientific Letters of the University of Zilina, 23(2), C44-C53. https://doi.org/10.26552/com.C.2021.2.C44-C53
Section
Electrical Engineering in Transport