Siti Nurul Aqmariah Mohd Kanafiah
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Preferred name
Siti Nurul Aqmariah Mohd Kanafiah
Official Name
Siti Nurul Aqmariah, Mohd Kanafiah
Alternative Name
Kanafiah, S. N.Aqmariah
Kanafiah, S. N.A.M.
Kanafiah, Siti Nurul Aqmariah Mohd
Aqmariah Kanafiah, S. N.
Kanafiah, S. N. A. M
Main Affiliation
Scopus Author ID
55987982900
Researcher ID
HTR-1815-2023

Research Output

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  • Publication
    Shape Recognition of GPR Images using Hough Transform and PCA plus LDA
    ( 2022-01-01)
    Hasimah Ali
    ;
    Norfatihah Bahari
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Amran T.S.T.
    ;
    Jusman Y.
    Ground penetrating radar (GPR) is a nondestructive test used for shallow subsurface investigation such as land mine detection, mapping and locating buried utilities. In practical applications, GPR images could be noisy due to system noise, the heterogeneity of the medium, and mutual wave interactions. Hence, it is a complex task to recognize the hyperbolic pattern from GPR B-scan images. Thus, this project proposes combined shape recognition of buried objects using Hough Transform (HT) and PCA plus LDA in GPR images. The use of HT is justified because it has the property of transforming global curve detection into efficient peak detection in the Hough parameter space. Whereas PCA plus LDA tries to maximize between-class scatter while minimizing within-class scatter. In this framework, the preprocessed GPR images were extracted using HT. The extracted HT features were subjected to PCA plus LDA to map them from high into lower dimensional features. Then, the reduced PCA+LDA features were used as input to the k-NN classifier to recognize four geometrical shapes cubic, disc, and spherical of the buried objects. Based on the results obtained, the average recognition rate of reduced HT features using PCA plus LDA was achieved 85.30% thus shows a promising result.
      1
  • Publication
    Fuzzy Logic Cascaded Current Control of DC Motor Variable Speed Drive using dSPACE
    ( 2023-01-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Ni L.P.
    ;
    Leong Jenn Hwai , Jenn
    ;
    Syahrul Ashikin Azmi
    ;
    Kamarulzaman Kamarudin
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    Two-wheel e-scooter falls under low power segment for Battery Electric Vehicle (BEV) and has gain more popularity in urban commuting. Most entry level e-scooter is still powered by DC motor due to low cost and ease of control. However basic open-loop DC Motor control employed through throttling is plugged with limited efficiency, precision, and range of speed control. Closed-loop control enables real time adjustment according to preset speed which becomes handy during auto cruising. To ensure good dynamic response, improved robustness and stable wide speed control range, a good control scheme for the motor is essential. In this project, a variable speed control scheme, namely fuzzy logic cascaded current control system was designed using MATLAB Simulink, comprising speed control loop and a current control loop 185 W Separately Excited Brushed DC Motor. The proposed control system was tested on hardware using dSPACE DS1104 platform. The system's output speed is obtained using an incremental encoder, while the output current is measured with a current sensor. Subsequently, the control system's stability, robustness, and dynamic performance were evaluated by driving the system on 120 W electrical load at varying speed. The system performance has proved superior to closed-loop by 70% on low speed ripple reduction and is on par with PI cascaded current control scheme.
      1
  • Publication
    Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Jusman Y.
    ;
    Hasimah Ali
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global Statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.
      2  39
  • Publication
    Recognition of different utility pipes size of ground penetrating radar images at different penetration depth
    ( 2024-02-08)
    Nasri M.I.S.
    ;
    Hasimah Ali
    ;
    Zaidi A.F.A.
    ;
    Shukor S.A.A.
    ;
    Ahmad M.R.
    ;
    Amran T.S.T.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Othman S.M.
    ;
    Elshaikh M.
    Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.
      22  4
  • Publication
    Rebar Path Mapping using Ground Penetrating Radar
    ( 2023-01-01)
    Basri N.A.B.
    ;
    Hasimah Ali
    ;
    Ahmad M.R.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    Ground penetrating radar (GPR) is a non-destructive device that helps to determine the position and direction of underground utilities such as rebar while preventing any inaccurate excavation process. The direction of buried rebar is usually mapped using the X-Y grid scanning method, which requires a lot of manpower and time to complete. Therefore, this paper investigated the ability of parallel scanning of B-scan to imitate the result of C-scan produced by X-Y grid scanning. Parallel scanning has been emphasised to reduce the time consumption of the data acquisition process while delivering a quality output. To develop a rebar path mapping, a data processing step has been implemented on the B-scan data for seven parallel lines that correspond to the x-axis. Next, Kirchhoff migration has been applied along with stacking and interpolation techniques to map a two-dimensional (2-D) image of the buried rebar. The obtained result was then compared with the grid scanning data of C-scan to evaluate the correlation between them. The performance of the mapped rebar path using parallel B-scan data was evaluated based on the ability of the data to give an accurate depth calculation of the buried rebar. Ultimately, the results show that this proposed method for using parallel B-scan to do mapping is verified.
      1
  • Publication
    The Classification System uses a Support Vector Machine and a Decision Tree Method Based on X-Ray Images for Spinal Abnormalities
    ( 2023-01-01)
    Anna Nur Nazilah C.
    ;
    Hasimah Ali
    ;
    Jusman Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Siddik I.R.
    ;
    Yusof M.I.
    A prevalent form of disorder that effects the vertebrae is a spinal disorder. X-ray technology is frequently used by medical professionals to detect abnormalities in the human body that are not visible to the unaided eye. Spinal ailment. Using the Hu moment invariant and machine learning, this study develops a system capable of feature extraction and spinal anomaly classification. The Hu Moment Invariant technique is used to derive seven moments (features) that describe an object. A support vector machine (SVM) identifies the optimal hyperplane in the input space that separates two classes. A decision tree (DT) is a technique for predicting the future by constructing a classification or regression model in the shape of a tree. Using the DT -Fine classification model derived from the Hu-Moment extraction results, the system can classify the newly developed research data in 1 minute with an accuracy of 88.2 % (highest) and 0.885782 seconds of feature extraction.
      5  24