Browsing by Author "I. O. Muniru"

Now showing 1 - 4 of 4
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    APPLICATION OF SUPPORT VECTOR REGRESSION MODELLING FOR THE PREDICTION OF IMPACT ATTENUATION OF 3D PRINTED HIP PROTECTORS
    (2023) S. A. Yahaya; I. O. Muniru; S. Saminu; M. O. Ibitoye; T. M. Ajibola; L. J. Jilantikiri; Z. M. Ripin; M.I.Z. Ridzwan
    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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    Automated Identification of Heart Arrhythmias through HRV Analysis and Machine Learning
    (NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, 2024) S. K. Lawal; I. O. Muniru; S. A. Yahaya; M. O Ibitoye
    Sudden cardiac death and arrhythmia are responsible for about 15-20% of cardiovascular disease incidences. Conventionally, the prediction and diagnosis of cardiovascular disorders (CVDs) have been mainly through the evaluation of ECG patterns by cardiologists. To improve the accuracy of and automate this process, and facilitate early detection, Heart Rate Variability (HRV) analysis has been promoted as a diagnostic and predictive tool for CVDs. In the present study, a machine learning model capable of detecting the presence of arrhythmia, using HRV indices obtained from ECG signals was built. Unlike similar works in the literature, this study deployed the developed model on Raspberry Pi with Streamlit software. Two ECG datasets from the Physionet database, one with arrhythmia patients (48 half-hour recordings) and another with healthy individuals (18 24-hour recordings), were employed. An ensemble of seven different machine learning models was used on the two sets of datasets to classify ECG recordings into Arrhythmia and Normal Sinus Rhythm (NSR). The best models were able to predict the presence of Arrhythmia in a 3-minute recording with an accuracy of 95.96%, and in a 10-minute recording with an accuracy of 96.20%. These performance measures were calculated using test dataset. The Random Forest models also had the highest precision, AUC, (Area under the Curve) recall, and F1 scores compared to the other models tested. The highest performing model (i.e., Random Forest Model) was then deployed onto a Raspberry Pi with Streamlit as the software interface for usability. This was done to facilitate a smooth user experience for faster and seamless diagnoses for cardiologists
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    DESIGN OF A LEUKEMIA DETECTION SYSTEM USING DIGITAL BLOOD SMEAR IMAGES
    (Faculty of Engineering and Technology, University of Ilorin, Nigeria, 2023) S. Saminu; I. O. Muniru; S. A. Yahaya; A. J. Oladimeji; M. T. Ajibola; M. O. Ibitoye; Y. K. Ahmed; L. J. Jilantikiri
    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.
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    INTRODUCTION TO ENGINEERING DISCIPLINES (ABE 206 VOLUME II Second Edition)
    (2019) M. O. Ibitoye; I. O. Muniru; L. J. Jilantikiri; Y. K. Ahmed; T. M. Ajibola
    Introduction to biomedical engineering profession; Definition of biomedical engineering. Specializations/Options in biomedical engineering, Use of various equipment in biomedical engineering for different operations/processes. Prospects and job opportunities in biomedical engineering as a profession; Relevant regulatory bodies and societies in biomedical engineering. The role of biomedical engineers in advancement of humanity.