Optimal Call Failure Rates Modelling with Joint Support Vector Machine and Discrete Wavelet Transform

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Isabona Joseph 1,* Agbotiname Lucky Imoize 2,3 Stephen Ojo 4 Ikechi Risi 5

1. Federal University Lokoja, Department of Physics, Lokoja, Kogi State, Nigeria

2. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria

3. Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany

4. Department of Electrical and Computer Engineering, Anderson University, Anderson, SC 29621, USA

5. River State University Port Harcourt Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2022.04.04

Received: 5 Feb. 2022 / Revised: 18 Mar. 2022 / Accepted: 25 Apr. 2022 / Published: 8 Aug. 2022

Index Terms

Wavelet transform, Service quality, Failure rate, Failure modeling, Support Vector Machine.


Failure modeling is an essential component of reliability engineering. Enhanced failure rate modeling techniques are vital to the effective development of predictive and analytical methodologies, demonstration of the engineering procedure, allocation of procedures, design, and control of procedures. However, failure rate modeling has not been given adequate treatment in the literature. The need to investigate failure rate modeling leveraging cutting-edge techniques cannot be overemphasized. This paper proposed and applied a joint support vector regression (SVR) and wavelet transform (WT) approach termed (WT-SVR) to training and learning the call failures rate in wireless system networks. The wavelet transform has been accomplished using the wavelet compression sensing technique. In this technique, the standardized call failure rate data first go through a wavelet filtering transformation matrix. This is followed by separating and outputting the transformed filtered components in the compression phase. Finally, the transformed filtered output components were trained and evaluated using the SVR based on statistical learning theory. The resultant outcome revealed that the proposed WT-SVR learning method is by far better than using only the SVR method for call rate prognostic analysis. As a case in point, the WT-SVR attained STD values of 0.12, 0.21, 2.32, 0.22, 0.90, 0.81 and 0.34 on call failure data estimation compared to the basic SVR that attained higher STD values of 0.45, 0.98, 0.99, 0.46, 1.44, 2.32 and 3.22, respectively.

Cite This Paper

Isabona Joseph, Agbotiname Lucky Imoize, Stephen Ojo, Ikechi Risi, "Optimal Call Failure Rates Modelling with Joint Support Vector Machine and Discrete Wavelet Transform", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.4, pp. 46-57, 2022. DOI:10.5815/ijigsp.2022.04.04


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