Nik Ahmad Akram

Work place: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor



Research Interests: Real-Time Computing


Nik Akram received M.Eng in Electronic and Computer Engineering from the University of Nottingham in 2010 and currently pursuing his PhD degree at the same university. His current research interests focus on developing and applying unsupervised AI technique in NDT domain and real time system. He currently works on unsupervised NDT for oil and gas pipeline system.

Author Articles
Reducing Support Vector Machine Classification Error by Implementing Kalman Filter

By Muhsin Hassan Dino Isa Rajprasad Rajkumar Nik Ahmad Akram Roselina Arelhi

DOI:, Pub. Date: 8 Aug. 2013

The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.

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