Mohd Shuhanaz Zanar Azalan
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Preferred name
Mohd Shuhanaz Zanar Azalan
Official Name
Mohd Shuhanaz , Zanar Azalan
Alternative Name
Zanar Azalan, M. S.
Azalan, Mohd Shuhanaz Zanar
Azalan, M. S. Z.
Azalan, M. S. Zanar
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Scopus Author ID
36469819700

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  • Publication
    Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique
    ( 2023-01-01)
    Mohd Shuhanaz Zanar Azalan
    ;
    Esian T.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Amran T.S.T.
    Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.
  • Publication
    Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique
    ( 2023-01-01)
    Mohd Shuhanaz Zanar Azalan
    ;
    Esian T.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Amran T.S.T.
    Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.
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