Yazar "Farhan, Hameed Mutlag" seçeneğine göre listele

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    A Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification
    (Taylor and Francis Ltd., 2025) Athab, Naama Methab; Ibrahim, Abdullahi Abdu; Naseri, Raghda Awad Shaban; Farhan, Hameed Mutlag
    A deadly disease with poor prognosis procedure available at present is the pancreatic tumor. Efficient detection is done using a Computer-Aided Diagnosis (CAD) system. The early detection of pancreatic tumors can enhance the survival rate. However, no sufficient works are dedicated to detect pancreatic tumors at its beginning stages. Hence, an advanced deep learning-oriented segmentation process to assist in the detection of pancreatic tumor is developed in this work. The necessary CT and MRI images are gathered from the utilization of IoT-based devices. Once the input image is gathered, the segmentation is carried out. An Adaptive TransResUnet (ATResUNet) is utilized for the segmentation procedure. The variables in the ATResUNet are tuned with the help of Improved African Vultures Optimization Algorithm (IAVOA). The segmented image is further considered to crop the Region of Interest (ROI). The cropped ROI is finally given as input to the suggested Atrous Spatial Pyramid Pooling-based Multi-scale EfficientNet with Attention Mechanism (ASPP-MENetAM) model. The detection of the pancreatic tumor is carried out using the ASPP-MENetAM framework. The detection outcome from the implemented ASPP-MENetAM is then compared with the results from other conventional pancreatic tumor detection models to assess the efficacy of the implemented detection system.
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    An enhanced attention and dilated convolution-based ensemble model for network intrusion detection system against adversarial evasion attacks
    (Springer, 2025) Awad, Omer Fawzi; Çevik, Mesut; Farhan, Hameed Mutlag
    Network Intrusion Detection System (NIDS) is a system for recognizing suspicious activities in the network traffic. Numerous machines learning and deep learning-aided IDSs have been implemented in the past, however, most of these techniques face challenges based on class imbalance issues and high false positive rates. Other primary problems of the conventional techniques are their vulnerability to adversarial attacks and also there is no analysis done on how NIDS sustain their performance over various attacks. Moreover, recent studies have demonstrated that while handling the attackers in real-time, the deep learning-based IDS shows slight variations in accuracy. To defend against adversarial evasion attacks, an enhanced deep learning-based NIDS model is designed in this work. For this purpose, at first, the required data is collected from available websites. From the collected data, effective features are extracted to improve the accuracy of the process. To select the optimal features, this work employed the Improved Cheetah Optimizer (ICO) that eliminates the unwanted features efficiently. Further, an Attention and Dilated Convolution based Ensemble Network (ADCEN) is implemented to detect the intrusions from the optimal features. The Deep Temporal Convolutional Neural Network (DTCN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) models are integrated to develop the ADCEN. The outcomes from each technique are considered for the fuzzy ranking mechanism to generate the final detected outcome. Thus, recognized intrusion is attained as the outcome and to demonstrate how well the recommended deep learning-based NIDS defends against adversarial evasion assaults, experiments are conducted against conventional models. The accuracy and the FPR values of the recommended model are 95 and 4.9 when considering the first dataset which is superior to the conventional techniques. Thus, the findings indicated that the implemented NIDS against adversarial evasion attacks attained more effective solutions than the baseline approaches.
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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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    Improving Oil Pipeline Surveillance with a Novel 3D Drone Simulation Using Dynamically Constrained Accumulative Membership Fuzzy Logic Algorithm (DCAMFL) for Crack Detection
    (Springer Science and Business Media B.V., 2025) Muhi, Omar Saber; Farhan, Hameed Mutlag; Kurnaz, Sefer
    Cracks in oil pipelines pose significant risks to the environment, public safety, and the overall integrity of the infrastructure. In this paper, we propose a novel approach for crack detection in oil pipes using a combination of 3D drone simulation, convolutional neural network (CNN) feature extraction, and the dynamically constrained accumulative membership fuzzy logic algorithm (DCAMFL). The algorithm leverages the strengths of CNNs in extracting discriminative features from images and the DCAMFL’s ability to handle uncertainties and overlapping linguistic variables. We evaluated the proposed algorithm on a comprehensive dataset containing images of cracked oil pipes, achieving remarkable results. The precision, recall, and F1-score for crack detection were found to be 96.5%, 97.3%, and 95.6%, respectively. These high-performance metrics demonstrate the algorithm’s accuracy and reliability in identifying and classifying cracks. Our findings highlight the effectiveness of integrating advanced simulation techniques, deep learning, and fuzzy logic for crack detection in oil pipelines. The proposed algorithm holds promise for enhancing pipeline surveillance, improving safety measures, and extending the lifespan of oil infrastructure. Future work involves expanding the dataset, fine-tuning the CNN architecture, and validating the algorithm on large-scale pipelines to further enhance its performance and applicability.
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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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    Optimal feature tuning model by variants of convolutional neural network with LSTM for driver distract detection in IoT platform
    (SPRINGER LONDON, 2025) Farhan, Hameed Mutlag; Kurnaz Türkben, Ayça; Naseri, Raghda Awad Shaban
    Nowadays, traffic accidents are caused due to the distracted behaviors of drivers that have been noticed with the emergence of smartphones. Due to distracted drivers, more accidents have been reported in recent years. Therefore, there is a need to recognize whether the driver is in a distracted driving state, so essential alerts can be given to the driver to avoid possible safety risks. For supporting safe driving, several approaches for identifying distraction have been suggested based on specific gaze behavior and driving contexts. Thus, in this paper, a new Internet of Things (IoT)-assisted driver distraction detection model is suggested. Initially, the images from IoT devices are gathered for feature tuning. The set of convolutional neural network (CNN) methods like ResNet, LeNet, VGG 16, AlexNet GoogleNet, Inception-ResNet, DenseNet, Xception, and mobilenet are used, in which the best model is selected using Self Adaptive Grass Fibrous Root Optimization (SA-GFRO) algorithm. The optimal feature tuning CNN model processes the input images for obtaining the optimal features. These optimal features are fed into the long short-term memory (LSTM) for getting the classified distraction behaviors of the drivers. From the validation of the outcomes, the accuracy of the proposed technique is 95.89%. Accordingly, the accuracy of the existing techniques like SMO-LSTM, PSO-LSTM, JA-LSTM, and GFRO-LSTM is attained as 92.62%, 91.08%, 90.99%, and 89.87%, respectively, for dataset 1. Thus, the suggested model achieves better classification accuracy while detecting distracted behaviors of drivers and this model can support the drivers to continue with safe driving habits.
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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.
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    SCEN-SCADA Security: An Enhanced Osprey Optimization-Based Cyber Attack Detection Model in Supervisory Control and Data Acquisition System Using Serial Cascaded Ensemble Network
    (John Wiley and Sons Ltd, 2025) Alzubaidi, Fatimah Yaseen Hashim; Kurnaz, Sefer; Naseri, Raghda Awad Shaban; Farhan, Hameed Mutlag
    A significant role of Supervisory Control and Data Acquisition (SCADA) systems is to support the operation of the energy system, where Information and Communication Technology (ICT) is utilized to interconnect devices, and this increases the system complexity. The interconnection of SCADA systems increases complexity and the potential for cybersecurity vulnerabilities. In addition, the SCADA networks with legacy devices are affected by inherent cybersecurity deliberation that has provided severe cybersecurity vulnerable points. With the adoption of local-area networks and Internet Protocol (IP)-driven proprietary, malicious or unauthorized user accesses the information from outside sources, and hence, the SCADA systems are weakened by the elaborate attacks. SCADA systems need to deliberate the Denial of Service (DoS) and catastrophic failure and maloperation, which may subsequently compromise the safety and stability of the operations in the power system. Therefore, the pertinent priority in SCADA is to strengthen cybersecurity to guarantee reliable operation, and also, the system stability is governed concerning communications integrity. The smart grid features are used in the conventional machine learning approaches for identifying cyber attacks. Hence, implementing an efficient and accurate cyber attack detection approach with less computational overhead is still a crucial research problem in SCADA. So, a novel and secure model for cyber attack detection in the SCADA system using advanced deep learning techniques together with the heuristic algorithm is executed in this research work. The SCADA data are collected from various power grids. The features from these data are optimally selected and fused with the optimal weights to obtain the weighted optimal features. The weighted optimal feature selection is done using the Enhanced Osprey Optimization Algorithm (EOOA). These optimally selected weighted features are given to the Serial Cascaded Ensemble Network (SCEN) to obtain the final detection output. The developed SCEN is made with the cascading of Autoencoder, Dilated Bidirectional Long Short Term Memory (Bi-LSTM), and Bayesian classifier. The parameters in the SCEN are tuned using the executed IOOA. The final detection of the presence or absence of a cyber attack is evaluated by this SCEN. The performance and the efficiency of the developed framework are confirmed and contrasted by conducting various experiments.