Ahmad Firdaus Ahmad Zaidi
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Preferred name
Ahmad Firdaus Ahmad Zaidi
Official Name
Ahmad Firdaus, Ahmad Zaidi
Alternative Name
Ahmad Zaidi, Ahmad Firdaus
Ahmad Firdaus, A. Z.
Zaidi, Ahmad Firdaus Bin Ahmad
Firdaus, A. Z.Ahmad
Main Affiliation
Scopus Author ID
55992689600
Researcher ID
S-8233-2019

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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.
      2  38
  • 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.
      1