Yazar "Farhan, Hameed Mutlag" seçeneğine göre listele

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    Data mining and analysis for predicting electrical energy consumption
    (Institute of Electrical and Electronics Engineers Inc., 2022) Almohammed, Mustafa; Farhan, Hameed Mutlag; Naseri, Raghad Awad Sababan; Türkben, Ayça Kurnaz
    In this research, data mining techniques that included the deep learning with different scenario is presented for predicting electrical energy consumption data. Energy of the features are implemented and calculated from the electrical energy consumption data. Different scenario with neuron numbers for artificial neural network are investigated and analyzed. Also the results are compared with other k-fold numbers of the data. The results of proposed method shows that the proposed methods has high accuracy and high performance.
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    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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    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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    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.
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    Optic disc segmentation in human retina images with meta heuristic optimization
    (Institute of Electrical and Electronics Engineers Inc., 2022) Khalef, Dhirgham Atia; Türkben, Ayça Kurnaz; Farhan, Hameed Mutlag; Naseri, Raghda Awad Shaban
    The primary objective of this paper is to use the Whale Optimization Algorithm (WOA). This technique is a new approach for computerized detection of the optic disc positioned in retinal fundus pictures. The optic disc illumination is more genuine in retinal pictures. In this precept, all whales travel to the optic disc region, and accordingly, the area of the optical disc in the picture is determined and discovered with the usage of an algorithm (WOA). discuss the algorithm for enhancing the whale, in addition to the previous remedy of images, and a diagram illustrating the software of the algorithm on retinal pics. The simulation effects will drastically examine inside the datasets DiaRetDB1 STARE, and pressure. The consequences of the present-day observation will examine with different representative research, and through its results, it's going to examine.
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    Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach
    (Elsevier Ltd, 2023) Naseri, Raghda Awad Shaban; Kurnaz, Ayça; Farhan, Hameed Mutlag
    The growth of the “Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a “Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2.