Aishah Mohd Noor
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Preferred name
Aishah Mohd Noor
Official Name
Aishah, Mohd Noor
Alternative Name
Noor, Aishah Mohd
Aishah, M. N.
Noor, A. M.
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Scopus Author ID
57211319810
Researcher ID
FPN-0307-2022

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  • Publication
    Classification of White Blood Cells Based on Surf Feature
    ( 2021-01-01)
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Aishah Mohd Noor
    ;
    Ahmad Nasrul Norali
    Conventional blood analysis using blood smear image were performed manually by experts in hematology is tedious and highly depending on the level of experience. Currently, computer-assist technology is developed to reduce the time-consuming process and improved accuracy. As an example, various image processing techniques used to quantify such as white blood cells (WBCs) morphological conditions or classification in the blood smear image, which assist experts in developing confidence decision making in the analysis of cells conditions linked to the specific diseases. However, the WBCs shape features are arbitrary than the red blood cells (RBCs) because of the maturation state, cell orientations or positions, cell color variations, and the quality of the image captured influences the performance of classification accuracy. Therefore, we proposed a scale and rotation invariance feature for WBCs classification using speed up robust feature (SURF). SURF is suitable to be applied in identifying objects even though the orientation, scale, and position are varying, such as WBCs in microscopic blood smear images. We analyzed the classification performances using a support vector machine (SVM) and an artificial neural network (ANN) of WBCs types in the microscopic image based on the cell nucleus. The results show that the purposed SURF feature method has an excellent performance of accuracy for both methods and suitable to be utilized for the application of cell types classification.
      1  37
  • Publication
    Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes
    ( 2020-06-11)
    Anas Mohd Noor
    ;
    Haniza Yazid
    ;
    Zulkarnay Zakaria
    ;
    Aishah Mohd Noor
    Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.
      1  29
  • Publication
    Teaching statistics with excel: a hands-on approach for engineering students to promote thinking skills
    (AIP Publishing, 2024)
    Aishah Mohd Noor
    ;
    Siti Aisyah Zakaria
    ;
    Safwati Ibrahim
    Statistics education has become increasingly important in today's data-driven world, as the ability to analyze and interpret data is critical in many disciplines. However, introductory statistics courses traditionally emphasize rote calculations and procedural knowledge, which can result in passive learning and disengagement from students who may not see the relevance of statistics to their engineering field. To address these challenges, this paper proposes using Excel worksheets as student learning materials in an introductory statistics course to shift from traditional to experiential learning. Excel worksheets provide a hands-on approach to learning that gives students the experience of the actual process of doing statistics. The Excel worksheet facilitates quick and accurate calculations, allows more time for students to interpret statistical results, and encourages active learning. The Excel worksheet allows for real-world data analysis and what-if analyses, making abstract concepts more accessible. In addition, the Excel worksheets are designed to promote 21st-century thinking and collaboration skills, which are increasingly important in today's workforce. This paper presents several examples of Excel worksheet designs for teaching descriptive statistics, developed using the framework of substitution, augmentation, modification, and redefinition (SAMR) model. Excel worksheets promote deep learning and facilitate students' understanding of statistical ideas, concepts, and methods through learning by doing. The paper concludes that Excel worksheets offer a valuable tool for teaching introductory statistics to engineering students, enhancing their thinking skills, and preparing them for the data-driven demands of their field.