Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5799

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Department: search.filters.department.AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim DalıPublisher: search.filters.publisher.Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü

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Now showing 1 - 10 of 30
  • Master Thesis
    Enhancing breast cancer detection with a hybrid machine learning approach
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Etcil, Mustafa; Güngör, Burcu; Güngör, V. Cagri
    According to the World Health Organization (WHO), breast cancer is one of the most prevalent illnesses, with 7.8 million instances recorded in the previous five years. As such, it poses a serious threat to world health. This alarming statistic underscores the urgent necessity for enhanced diagnostic methods. Against this backdrop, the current study proposes a novel diagnostic model, the CSA-PSO-LR classifier, which innovatively combines the clonal selection algorithm (CSA) with particle swarm optimization (PSO) to refine the logistic regression model training process for breast cancer detection. This research employs two extensively recognized datasets: the Wisconsin Diagnostic Breast Cancer (WDBC) and the Wisconsin Breast Cancer Database (WBCD), putting into practice a strict evaluation procedure that assesses performance using Bayesian hyperparameter optimization and 10-fold cross-validation. Furthermore, the study introduces CPU parallelization strategies to significantly curtail the model training time. Comparative analyses against machine learning algorithms, encompassing decision trees, extreme gradient boosting, k-nearest neighbors, logistic regression, random forests, and support vector machines, demonstrate the CSA-PSO-LR classifier's superior performance in detection accuracy and F1-measure. This investigation contributes a groundbreaking approach to the early detection of breast cancer, potentially facilitating more effective treatment plans and enhancing patient survival prospects.
  • Master Thesis
    İnsan Bağırsak Mikrobiyotasından Hastalık Biyobelirteçlerinin Tespiti için Makine Öğrenmesi Temelli Sistem Geliştirilmesi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Koçak, Ayşegül; Güngör, Burcu; Yousef, Malik
    The human gut microbiota consists of a diverse ecosystem of organisms, encompasses billions of species. Recently developed next-generation sequencing methods have enabled researchers to examine the microbiota in greater detail, leading to new insights into its functions and dysfunctions. This study aims to identify metagenomic biomarkers (Microorganism-Enzyme Pairs) for colorectal cancer (CRC). The tool that we used allows for the analysis of microorganisms and enzymes within the gut microbiota. It achieves this by initially clustering enzymes based on their correlations with species and subsequently utilizing these clustering results to evaluate the ability of groups to differentiate between patient and healthy cohorts. By integrating species and enzymes, it is possible to identify pathogen microorganisms and enzyme clusters, that have the potential to distinguish cases (individuals with CRC) from controls (healthy individuals). The identified enzyme clusters and associated species could potentially act as biomarkers for colorectal cancer (CRC), enabling early diagnosis and more effective treatment. This approach holds promise for further exploration of the gut microbiota and its importance in human health and illness. Keywords: Bioinformatics, Machine Learning, Colorectal Cancer Diagnosis
  • Master Thesis
    Tree-net: Biyomedikal Görüntü Segmentasyonu için Tree-net: Darboğaz Özellik Süpervizyonu Kullanılan Yapay Sinir Ağı Modeli
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Demirci, Orhan; Yılmaz, Bülent
    In this thesis, we introduce Tree-NET, a novel approach for medical image segmentation utilizing bottleneck feature supervision. This method enhances traditional segmentation algorithms by keeping supervision between bottleneck features of the network. The primary goal is to improve the model's ability to learn discriminative and robust features while simultaneously reducing computational costs. Bottleneck feature supervision involves compressing the input and label data using Autoencoders and then supervising the bottleneck features with a segmentation network named 'Bridge-Net,' which can be any segmentation model of choice. We applied Tree-NET to two critical medical image segmentation tasks: skin lesion segmentation and polyp segmentation. Our experiments demonstrate significant improvements in segmentation accuracy and efficiency. For instance, the U-NET backboned Tree-NET uses only 154.43 MB for executing and storing the model, which is almost 3.5 times smaller than the original U-Net while having a close number of trainable parameters. In skin lesion segmentation, Tree-NET achieved dice, Intersection-over-Union (IoU), and accuracy scores of 0.893, 0.751, and 0.977 respectively. For polyp segmentation, the scores were 0.856, 0.795, and 0.923 for dice, IoU, and accuracy respectively. Compared to traditional segmentation models, the empirical results show that Tree-NET achieves higher accuracy with reduced training time and computational cost, thus representing a significant advancement in medical image analysis by providing more reliable and efficient tools for clinical applications.
  • Master Thesis
    Ultra Geniş Bantlı Vivaldi Antenlerin Tasarımı ve Performans İyileştirmesi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Güzelkara, İzzet; Kılıç, Veli Tayfun
    Ultra-wideband technology has become a trending topic in the academic community since 2002 due to the release of the spectral mask by Federal Communications Commission, allowing the use of 3.6-10.1 GHz band for commercial and industrial applications. Being one of the fundamental components of ultra-wideband systems, ultra-wideband antennas are an important research area. In this research, Vivaldi antennas for ultra-wideband communications and several performance enhancement techniques for the antennas were studied. Antennas were designed and simulated using a commercially available three-dimensional electromagnetic simulation tool. First, a simple design of a Vivaldi antenna with a rectangular microstrip feed was obtained. The initial design has a -10 dB impedance bandwidth between 3.1 and 13.6 GHz and an average realized gain of 2.75 dBi. A method based on the alignment of the microstrip feed was described for adjusting the bandwidth of the initial design. Then, using several performance enhancement techniques such as implementation of corrugations and a parasitic patch, the antenna design was improved. Thanks to the applied methods, an antenna design with -10 dB impedance bandwidth extending from 1.33 to 10.1 GHz and an average realized gain of 6 dBi was achieved. Findings of this thesis study show that Vivaldi antennas having specific structures designed with the applied techniques are a promising solution for ultra-wideband communication systems, especially where antennas with directive radiation patterns are desired.
  • Master Thesis
    Meme Kanseri Histopatoloji Görüntülerinde Evrişimsel Sinir Ağları Kullanarak Tümör Tespiti
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şahbaz, Zeki; Aksebzeci, Bekir Hakan
    Breast cancer is one of the most common cancer types among women worldwide. Early detection significantly increases the chances of survival and effective treatment, making advancements in diagnostic methodologies crucial. This study aims to improve the detection of tumor cells in breast cancer histopathology images using deep learning and image processing techniques. Significant modifications have been made to the hyperparameters, including the tumor bounding box size, batch size, optimization algorithms, learning rate, and weight decay. These changes focus on determining the best parameters of the Faster R-CNN model. A comprehensive analysis of different parameters was conducted using the Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) dataset. The analysis identified the best settings for model performance, shows by improvements in precision, recall, and F-score. Our research contributes to the field of medical image analysis by identifying critical factors that affect the accuracy of tumor detection, contributing to the development of more accurate diagnostic tools.
  • Master Thesis
    Makine Öğrenimi Algoritmalarını Kullanarak Ağ Trafiğini Analiz Etme ve Ağ Tehditlerini Tespit Etme
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Küçükkoç, Abdurrahman; Aydın, Zafer
    As information technologies progress, the possibilities of access to information increase and therefore it becomes difficult to ensure the security of information. Today, with the use of information systems in all areas of life, network threats have also increased. The increase in individual access to and use of the internet has also brought network threats. In addition, the latest developments in information technologies, developing global communication networks, the internet of things aiming to connect all objects with networks, cloud technologies, the spread of mobile internet and the renewal of devices have brought network threats and uncertainties. Network threats increase the security vulnerabilities in the information and communication systems of individuals and organisations day by day. This situation causes systems to malfunction, economic damage and cyber security to be jeopardised. In order to contribute to individuals, institutions and organisations, our thesis aims to protect information systems against network threats, to ensure data confidentiality, integrity and accessibility, to detect network threats in advance and to take measures against these threats. We believe that by analysing heterogeneous network traffic, which includes most network attacks on the Internet, and using machine learning algorithms, we will reach a result close to reality in the detection of network threats. In line with this result, we will be able to take precautions against network threats in information systems and structures
  • Master Thesis
    Koroner Arter Hastalığının Makine Öğrenimi Yaklaşımları ile Teşhisi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Halıcı, İkram; Güngör, Vehbi Çağrı
    The World Health Organization states that Coronary Artery Disease (CAD) ranks as a primary cause of recorded fatalities. CAD occurs as a result of the blockage of coronary artery vessels, which are located on the surface of the heart and supply the blood that the heart needs. Diagnosing the disease using traditional methods is challenging and requires costly tests. In recent years, the use of machine learning-based methods has increased as an alternative diagnostic approach. However, existing studies in the literature suffer from low detection rates and long training times. Therefore, there is still a need for reliable and low-cost diagnostic methods. In this thesis, a new model, CSA-PSO-ANN, is proposed for the diagnosis of coronary artery disease. The aim is to reduce the training time of the machine learning model and achieve a higher accuracy in diagnosing the disease. Experiments have been conducted on two publicly available datasets. Parallelization, feature selection, and hyperparameter optimization have been performed to shorten the model's training time. The performance of the model has been compared with well-known machine-learning algorithms and previous studies. The experiments showed that the proposed model effectively diagnoses the disease and outperforms other methods in terms of accuracy and F1 score performance metrics.
  • Master Thesis
    Yarı-Tek-Aşamalı Dört-Anahtarlı Alçaltıcı-Yükseltici Evirici için Pürüzsüz Mod Geçiş Tekniğinin İncelenmesi ve Geliştirilmesi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Keskinkılıç, Ebubekir; Keskinkılıç, Ebubekir; Tekgün, Burak
    In recent decades, given the world's inevitable energy scarcity, increasing energy demand and green energy concerns, high efficiency energy conversion has become more important and attractive than ever, and researchers have directed their interest to energy-efficient converters. Inverters are a commonly utilized type of converter, which can be classified into two categories: single and two-stage inverters. Considering the inherent drawbacks of traditional inverters, a quasi-single-stage inverter (QSSI) has emerged. The QSSI uses a DC-DC converter to shape the rectified version of the desired AC waveform in the first stage and, in the second stage, it switches only once to alternate the polarity. It stands forward in terms of efficiency, control simplicity, and system stability. Among QSSI, a non-inverting buck-boost converter has drawn attention due to its capability to perform both step-up and down modes and its bidirectional power transfer feature. In the first stage of the QSS non-inverting buck-boost converter; smooth transitions between the buck and boost modes and efficient conversion cannot be achieved by the traditional two-mode control method when the output voltage level is close to the input voltage level due to various limitations, non-idealities, and disturbances. Many methods have been applied and studied in the literature to minimize or eliminate the effects of the region which is called the 'dead zone'. In this thesis study, further efficiency and THD improvement for the QSSI is targeted by employing a four-mode control method. The study incorporates a comparative study of the dead zone effects on inverter systems, which have not been previously documented in the literature. Moreover, it places a priority on optimizing efficiency and minimizing distortion in various applications—ranging from motor control and solar energy systems to grid-tied wind turbines and switched-mode power supplies—by comparing existing methods with open-loop voltage control. In conclusion, the theoretical results are verified with experimental studies.
  • Master Thesis
    Anahtarlamalı Relüktans Motorlarında Tork Dalgalanmasının Azaltılması için Uyarlanabilir Çevrimiçi Tork Paylaşım Fonksiyonu Geliştirilmesi
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Genç, Ufuk; Tekgün, Burak
    Electrical machines play a crucial role in modern society by transforming electrical energy into mechanical energy and vice versa. These machines include various types of motors and generators, which are used in a wide range of applications such as electric vehicles, industrial automation, and renewable energy systems. One of the popular electrical machines is the switched reluctance machine (SRM), which is known for its high reliability and efficiency. The key advantages of the SRM include its simple structure, robustness, and low cost. The SRM does not require a permanent magnet or an excitation winding, making it an attractive option for high-volume, low-cost applications. Despite its advantages, the SRM also has some disadvantages that need to be considered. One of the main drawbacks of the SRM is being susceptible to torque ripple, which can result in vibration and noise. In order to overcome these disadvantages, advanced control methods have been developed for the SRM. One such control method is the torque sharing function, which distributes the load among the phases of the motor. This results in improved torque characteristics and reduced torque ripple. However, this control method also has some disadvantages, such as increased complexity and the need for more advanced sensors and controllers. Additionally, the torque sharing function may result in reduced efficiency, especially at high speeds. The purpose of this thesis study is to improve the torque ripple performance of SRM for a wide speed range through the proposed control approach. In conclusion, minimizing the torque ripple is a critical aspect of the operation of SRMs, and a range of control strategies and techniques can be used to achieve this goal. By reducing the torque ripple, SRMs can deliver improved efficiency, performance, and reliability, making them even more attractive for a wide range of applications.
  • Master Thesis
    Bilgisayar Algoritmalarının GPU ile Hızlandırılması
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yalçın, Salih; Alkan, Gülay Yalçın
    Travelling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it involves in many computing problems such as networks, genome analysis, logistic etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem-solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from host device would reduce the performance in execution time. In this study, we present a methodology for compressing data to represent cities in the TSP so that we include more cities in GPU memory. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and show that our implementation presents 29% performance improvement compared to the state-of-the-art GPU implementation.