Browsing by Author "Suleiman Abimbola, Yahaya"

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    APPLICATION OF SUPPORT VECTOR REGRESSION MODELLING FOR THE PREDICTION OF IMPACT ATTENUATION OF 3D PRINTED HIP PROTECTORS
    (Faculty of Engineering and Technology, University of Ilorin, 2023-06) Suleiman Abimbola, Yahaya; Muniru, I.O.; Saminu, Sani; Ibitoye, M.O.; Ajibola, T.M.; Jilantikiri, L. J.; Ripin, Z.M.; Ridzwan, M.I.Z.
    3D printed thermoplastic polyurethanes of different shore hardness were used to make hip protectors for the prevention of osteoporotic hip fracture, which was then tested. The result was used to develop a support vector regression model to estimate the effect of the protector shore hardness, shell thickness, and infill density on the impact attenuation capacity at different energy levels. The results from the model show that the impact attenuation ability of a hip protector is significantly dependent on the infill density of the hip protector and its shore hardness. Excellent agreement was found between the model results and test results.
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    DESIGN OF A LEUKEMIA DETECTION SYSTEM USING DIGITAL BLOOD SMEAR IMAGES
    (Faculty of Engineering and Technology, University of Ilorin, 2023-06) Saminu, Sani; Muniru, I.O.; Suleiman Abimbola, Yahaya; Oladimeji, A. J.; Ajibola, T.M.; Ibitoye, M.O.; Ahmed, Yusuf Kola; Jilantikiri, L. J.
    Leukaemia is a fatal blood cancer that occurs due to the formation of abnormal and excessive increases in white blood cells in the bone marrow or blood. The traditional approaches used to diagnose the disease involve the manual analysis of blood sample images obtained from a microscope. This approach is tedious, slow, timeconsuming, and prone to errors. Therefore, automatic detection of leukaemia based on the counting of the two blood cells is paramount for diagnosis and increasing the patient’s survival rate. This paper presents a system that can detect each of the two blood cells needed through image processing, segmentation, and classification. The detection, classification, and counts are only limited to two of the cells present in the digital blood smear which are the white blood cells (WBCs) and red blood cells (RBCs). The model was evaluated with a collection of confirmed cases and normal cases to test its effectiveness in predicting the presence of Leukaemia by computing the ratio of WBC to RBC. The suggested model exhibits good performance results and can be utilized to make a reliable computer-aided diagnosis detection of leukaemia cancer.