Fatinnabila Kamal
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Preferred name
Fatinnabila Kamal
Official Name
Fatinnabila, Kamal
Alternative Name
Kamal, Fatinnabila
Main Affiliation
Scopus Author ID
57204171852
Researcher ID
FDL-9665-2022

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  • Publication
    Magnetohydrodynamic Stagnation-point Flow towards a Permeable Stretching/Shrinking Sheet with Slip and Heat Generation/Absorption Effects
    (Universiti Malaysia Perlis, 2023-11-10)
    Fatinnabila Kamal
    ;
    Ahmad Ashraf Abdul Halim
    ;
    Wan Mohd Khairy Adly Wan Zaimi
    This study of the magnetohydrodynamic (MHD) stagnation-point flow towards a permeable stretching/shrinking sheet in the presence of slip and heat generation/absorption effects is considered. The governing equations in the form of partial differential equations are transformed into a system of ordinary differential equations by using similarity transformation, and then solved numerically using bvp4c function in Matlab software. The variations of the numerical solutions for the skin friction coefficient and the local Nusselt number as well as velocity and temperature profiles are obtained for several values of the governing parameters. It is found that the solution is unique for the stretching case whereas dual (first and second) solutions exist for the shrinking case in certain range of parameters.
  • Publication
    UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set
    ( 2023-01-01)
    Ahmad Ashraf Abdul Halim
    ;
    Vijayasarveswari Veeraperumal
    ;
    Allan Melvin Andrew
    ;
    Mohd Najib Mohd Yasin
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Hossain K.
    ;
    Bari B.S.
    ;
    Fatinnabila Kamal
    Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.
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