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    Öğe
    Data mining techniques for extraction and analysis of covid-19 data
    (Institute of Electrical and Electronics Engineers Inc., 2022) Al-Obadi, Mohamed Ghanim; Farhan, Hameed Mutlag; Naseri, Raghda Awad Shaban; Türkben, Ayça Kurnaz; Mustafa, Ahmed Khalid; Al-Aloosi, Ahmed Raad
    Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals.
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    Öğe
    Face recognition system using local binary pattern with binary dragonfly algorithm to feature selection
    (Institute of Electrical and Electronics Engineers Inc., 2022) Al-Aloosi, Ahmed Raad; Farhan, Hameed Mutlag; Naseri, Raghda Awad Shaban; Türkben, Ayça Kurnaz; Mustafa, Ahmed Khalid; Al-Obadi, Mohammed G.F.
    In this paper, the local binary pattern is used to feature extraction, and the Binary Dragonfly Algorithm (BDA) is exploited to find the optimum features. This is a new methodology for face recognition systems. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized a local binary pattern for extraction of the important feature of the face that prepares the information and afterward, we utilized BDA to find the best features from feature data. We will execute and assess the proposed strategy on ORL and other datasets with MATLAB 2021a.