Yazar "Al-azzawi, Athar" seçeneğine göre listele

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    Öğe
    Covid-19 X-ray image classification using SVM based on Local Binary Pattern
    (IEEE, 2021) Al-jumaili, Saif; Al-azzawi, Athar; Duru, Adil Deniz
    Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.
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    Öğe
    Investigate Schizophrenia Classification Based on EEG Electrode Reduction Using Machine Learning Techniques
    (2025) Al-azzawi, Athar; Uçan, Osman Nuri
    Schizophrenia is a mental disorder condition that causes patients to become distracted from reality. Over time, the patient loses his cognitive and social abilities to communicate with the outside world. Due to machine learning's strong ability to analyze complicated brain data, it has become an increasingly important tool in recent years. This study considers the brain's neurologic signals in the resting state in two scenarios to classify schizophrenia disease by electroencephalography (EEG). The performed scenarios were to investigate the impact of selecting electrodes randomly (5 electrodes and 8 electrodes) and comparing it with applying the principal component analysis (PCA), utilizing four algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy Entropy (LogEn), and Shannon Entropy (ShnEn). We used publicly available datasets with 19 EEG channels consisting of two classes, which are schizophrenia and health control, using a one-second epoch window size. We applied a band-pass filter to decompose the EEG signals into five sub-bands. Also, the L2-normalization method has been applied to the derived features, which positively impacted the outcomes. The features were applied to three classifiers named K-nearest neighbor (KNN), support vector machine (SVM), and quadratic discriminant analysis (QDA). From all the scenarios, the five-electrode with random selection showed remarkable results of 99% using the SVM classifier in all evaluation metrics with LogEn+ Bandpass features.