Yazar "Kashkool, Hawraa Jaafar Murad" seçeneğine göre listele

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    Enhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques
    (Springer Science and Business Media Deutschland GmbH, 2024) Kashkool, Hawraa Jaafar Murad; Farhan, Hameed Mutlag; Naseri, Raghda Awad Shaban; Kurnaz, Sefer
    Facial recognition, as a paradigmatic instance of biometric identification, has witnessed escalating utilization across diverse domains, encompassing security, surveillance, human-computer interaction, and personalized user experiences. The fundamental premise underlying this technology resides in its capacity to extract discriminative features from facial images and subsequently classify them accurately. However, the precision and efficiency of such systems remain subject to an array of intricate challenges, necessitating innovative solutions. The overarching aim of this thesis is to enhance the performance and efficacy of facial recognition systems through the seamless integration of Convolutional Neural Networks (CNNs) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machines (SVMs) for classification. This holistic approach seeks to optimize the accuracy, efficiency, and robustness of facial recognition, thereby contributing to the advancement of computer vision and biometric identification technologies.
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    Machine learning algorithms for URLs classification
    (Institute of Electrical and Electronics Engineers Inc., 2022) Al Zirjawi, Sabah Salam Khaduair; Kashkool, Hawraa Jaafar Murad; Ibrahim, Abdullahi Abdu; Al, Mona Idan Ali
    Phishing is a technique used to collect sensitive data from a user (password or credit card information) for future misuse by posing as a trustworthy source. It often takes advantage of the user's gullibility in ways that the user will not detect at first look, and in the worst-case scenario, the attacker maintains the user's data without the user's awareness. Typically, the URL is the first and simplest piece of information we know about a website. As a result, it is logical to design algorithms for distinguishing harmful from benign URLs. Additionally, accessing and downloading the website's material may be time-consuming and involves the danger of downloading potentially hazardous information. Machine Learning techniques are used to train a model on a collection of URLs specified as a set of characteristics and then predict and categorize the URLs as benign or dangerous. This technology enables us to identify and avoid possibly dangerous URLs shortly.