Doktora Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800
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Doctoral Thesis Su Altı Dalgıç Pompa Uygulamaları için Doğrudan Yol Vermeli Relüktans Motorunun Sistematik Olarak Tasarım Optimizasyonu ve Gerçeklemesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Tekgün, Didem; Alan, İrfanConsidering the electric drive systems constitute roughly 40% of global energy production, improving electric machine efficiencies provides important nationwide and global scale advantages. Among the electric motors used in the industry, a major portion of them are pump motors used for pumping underground waters and petroleum products. Especially the motors for submersible pump applications run at very low-efficiency levels because of the motor design issues and wrong selection of motor-pump configurations. Due to the features like robustness, low cost, and line start capability, induction machines (IM) are generally the first choice for pump applications. However, IMs work with low efficiency, especially at low and medium power levels. Line start synchronous reluctance machines (LS-SynRM) come to the scene as a reasonable alternative by having the line start capability and not having rare earth permanent magnets as well. The working principle of these machines is a combination of a reluctance machine and an IM. In LS-SynRM, a rotor cage is inserted in the rotor for the machine to start with the line voltage, but the rotor copper losses become zero when the machine operates at synchronous speed. Moreover, SynRMs have higher power and torque density. In this thesis study, it is aimed to reduce the overall cost of the submersible water pump system by designing and optimizing a LS-SynRM as a submersible pump motor with higher efficiency compared to conventional IMs. Increasing the efficiency of the pump motor used in industry will improve the overall system performance. Accordingly, it lowers energy and maintenance costs, and easy process control will be achieved. This way, while reducing energy consumption nationwide significantly, not only the natural resources will be protected, but also huge amounts of money will be saved.Doctoral Thesis Hastalık Tahmini ve Biyobelirteçlerin Tespiti için Makine Öğrenim Modellerinin Tasarımı ve Geliştirilmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Temiz, Mustafa; Güngör, Burcu; Yousef, MalikIn medical science, the prediction of diseases and the identification of biomarkers play an important role in the diagnosis and treatment of various health conditions. The recent proliferation of data mining techniques has accelerated the development of disease prediction systems. In particular, machine learning methods are an effective way to analyze medical data and identify patterns to predict the likelihood of the disease development. Machine learning methods also help to identify biomarkers. Recently, the increasing incidence and mortality rates of inflammatory bowel disease, colorectal cancer and type 2 diabetes have drawn researchers' attention to these research areas. The aim of this thesis is to reduce the number of features and improve the prediction performance of machine learning based on complex biological datasets with a large number of disease-related features, as well as to identify potential biomarkers. In this thesis, three different studies are presented. The first study predicts eleven different cancer subgroups using miRNA data and biological domain knowledge and identifies potential biomarkers for these diseases. The second study predicts three different diseases using metagenomic data and biological domain knowledge and identifies potential biomarkers. The third study uses metagenomic data related to colorectal cancer to conduct global and population-based comprehensive experiments with traditional feature selection methods to identify potential biomarkers. This thesis presents a promising avenue for early disease detection, facilitating expedited treatment protocols, improving human survival rates, and potentially alleviating economic burdens within these critical research domains.Doctoral Thesis Merkezi Olmayan Elektronik Sağlık Kaydı Yönetim Sistemi ve Makine Öğrenmesi Yöntemleri ile Hastalık Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dedetürk, Beyhan Adanur; Güngör, BurcuElectronic health records (EHRs) are vital to the advancement of healthcare and can help detect and prevent diseases early. However, EHR sharing faces challenges such as managing large data volumes, ensuring data privacy, security, and interoperability. This thesis aims to develop and analyze a blockchain-based EHR sharing system for disease prediction mechanism integration using SysML. The AguHyper platform, built by merging the InterPlanetary File System (IPFS) with Hyperledger Fabric, ensures the immutability of health records by storing hash values in the blockchain and encrypted records in IPFS. The system architecture and implementation configurations, including CouchDB and the Raft consensus mechanism, are thoroughly examined. The study also presents a novel hybrid approach called CSA-DE-LR, which integrates Differential Evolution (DE) and Clonal Selection Algorithm (CSA) with Logistic Regression (LR) to improve LR weights for precise categorization of cardiovascular diseases. The integration of the AguHyper with the CSA-DE-LR is explained in detail. At the end of our performance evaluations, we concluded that the AguHyper model has the potential to speed up the process of collecting and sharing data, and it offers an efficient platform for the participants.Doctoral Thesis Derin Öğrenme Tabanlı Kompozit Malzemelerin Ultrasonik Tomografi Görüntülerinden Kusurların Tespiti ve Sınıflandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Gülşen, Abdulkadir; Güngör, Burcu; Kolukısa, BurakThis thesis introduces novel methodologies for enhancing defect classification and characterization in advanced composite materials by leveraging state-of-the-art machine learning (ML), deep learning (DL), and federated learning (FL) techniques within ultrasonic and acoustic emission (AE) inspection environments. First, a new ultrasonic dataset (UNDT), comprising 1,150 images from 60 distinct composite materials, is introduced. Applying transfer learning methods to both the UNDT and a publicly available dataset demonstrates the efficacy of advanced neural architectures—such as DenseNet121 and VGG19—achieving accuracy rates up to 98.8% and 98.6%, respectively. Next, the scope is extended to AE-based health monitoring by introducing an ensemble feature selection methodology to identify features strongly correlated with damage modes. By selecting amplitude and peak frequency for labeling and subsequently applying unsupervised clustering, the analysis confirms that both traditional AE features (e.g., counts and energy) and less commonly employed features (e.g., partial powers) correlate with distinct defect types. Finally, a novel FL framework is introduced to address the scarcity of publicly available, real-world ultrasonic datasets. This decentralized approach preserves data privacy while maintaining performance levels comparable to centralized methods, ensuring scalability and confidentiality in diverse data environments. Overall, these contributions significantly advance the field of NDT, offering robust defect classification and characterization. In doing so, the findings not only improve the accuracy and reliability of material integrity assessments but also lay a durable foundation for more secure, collaborative, and efficient NDT systems.Doctoral Thesis MRG Taramalarında Alzheimer Hastalığının Zaman Dağılımlı Sınıflandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dündar, Mehmet Sait; Yılmaz, BülentThis thesis presents a comprehensive framework for studying Alzheimer's Disease (AD) progression by focusing on the classification of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) individuals using advanced machine learning models that analyze changes in brain volumetrics over time through MRI scans. In the first phase of the research, MR images from the Alzheimer's Disease Neuroimaging Initiative database were utilized, which included sequences of 3-4 scans taken annually from 22 CN, 18 AD, and 20 MCI subjects. Key volumetric parameters such as cortical thickness and intracranial volumes were extracted using the CAT12 toolbox in SPM software. A novel classification method based on the rate of volumetric changes over time was employed, effectively capturing the progressive nature of neurological changes. This approach achieved accuracies of 82.5% in distinguishing AD from CN, 71% in differentiating MCI from AD, and 69% in separating MCI from CN, alongside a 55% accuracy in a three-way classification using random forest and support vector machines. Building on these initial insights, the second phase of the study significantly advanced the methodology by integrating a pre-trained 3D ResNet 101 CNN algorithm for initial spatial categorization of MRI scans, followed by the use of Long Short-Term Memory (LSTM) networks. These LSTMs processed the same sequences of 3-4 annual scans for each patient, enhancing the model's ability to analyze and interpret the temporal progression of volumetric changes. This sophisticated approach led to marked improvements in classification accuracy: 96.7% in differentiating AD from CN, 87.5% in distinguishing AD from MCI, and 86.4% in separating MCI from CN. The study effectively demonstrates a significant enhancement in capturing the temporal dynamics of AD progression.Doctoral Thesis Genetik ve Enfeksiyon Hastalıklarının Tespiti için Makine Öğrenmesi Yöntemleri(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Işık, Yunus Emre; Aydın, ZaferCompletion of the whole human genome in the 2003 has led to various advances in many fields, particularly in biology, genetics, health sciences, treatment, and pharmacology. In the following years, spread of faster and cheaper sequencing technologies has enabled us to extract and analyze genetic profiles of individuals digitally. Consequently, individual-specific forecasting and personalized treatment and precision medicine-, what once seemed like science fiction, have become more and more real. In both approaches, one of the crucial steps is identifying the presence of diseases using individual-specific genetic data. This thesis aims to comprehensively and comparatively evaluate the predictive performance of machine learning methods for Behçet's disease and respiratory infections. Additionally, feature selection methods were employed to identify the genetic factors (such as SNPs and genes) associated with disease presence for both diseases. Furthermore, the usability of selected features depending on biological pathway-driven active subnetworks listed in the literature was analyzed for the prediction of Behçet's disease. For the respiratory infection prediction problem, on the other hand, the prediction performance of features calculated by single-sample gene set enrichment analysis (ssGSEA) was evaluated using different machine learning methods. As the data types used in both experiments were different (genome-wide association studies data, gene expression profiles), the performance of machine learning approaches on different data types was also observed. It is hoped that the findings of both experiments will contribute to future machine learning based disease prediction studies.Doctoral Thesis Görüntü Tanıma Tabanlı Gökyüzü Kamerası Entegrasyonunu Kullanarak Sezgisel Vektörize Öğrenme Yöntemine Dayalı PV Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yavuz, Levent; Önen, AhmetIn order to ensure the continuity of demand and production balance, the use of renewable energy resources (RES) by countries will increase in the near future. Solar power generation is important for the integration of renewable energy into the power grid, but it can cause problems in power systems due to the uncertain and intermittent nature of solar power. Deep learning methods provide promising results in solar energy prediction, but the performance of these models depends on the initial weights assigned to the network. In this thesis, a novel weight initialization method, the Heuristic Vectorised Learning method, which takes into account certain characteristics of solar generation data has been proposed. This method aims to achieve better accuracy in solar forecasting by combining a statistical approach with a method based on deep learning. The method was compared with other commonly used methods such as Xavier, LeCun, He and Random, and it was seen that the proposed method performed better. Overall, the proposed weight initialization method provides significant benefits for solar forecasting applications in the context of renewable energy integration into the power grid. So, to reach higher accuracy, monitoring the sky is the best option for intra-day forecasts. Therefore, a hybrid model was created for photovoltaic generation prediction by using it together with environmental sensor data. The proposed method and panel shading model achieve higher accuracy values at the Abdullah Gül University campus in Kayseri. The proposed system provides a reliable PV energy forecast for the intraday energy markets.Doctoral Thesis Histopatoloji Görüntülerinden Bilgisayar Destekli Kanser Tespiti(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Taşdemir, Sena Büşra Yengeç; Yılmaz, Bülent; Aydın, ZaferDetecting colon adenomatous polyps early is crucial for reducing colon cancer risk. This thesis investigated various deep learning approaches for computer-aided diagnosis of colon polyps on histopathology images using deep learning. The thesis addressed key challenges in polyp classification, including differentiating adenomatous polyps from non-adenomatous tissues and multi-class classification of polyp types. Initially, a histopathology image dataset is collected and refined from Kayseri City Hospital. The first study used stain normalization algorithms and an ensemble framework for binary classification, achieving 95% accuracy on the custom dataset and 91.1% and 90% on UnitoPatho and EBHI datasets, respectively. The second study implemented a tailored version of the supervised contrastive learning model for multi-class classification, outperforming state-of-the-art deep learning models with accuracies of 87.1% on custom dataset and 70.3% on UnitoPatho dataset. The third study proposed a self-supervised contrastive learning approach for utilizing all data in cases of limited labeled images. This approach achieved better performance than transfer learning with ImageNet pre-trained models. In conclusion, this PhD thesis investigated deep learning approaches for computer-aided diagnosis of colon polyps on histopathology images, demonstrating high accuracy in binary and multi-class classification, outperforming state-of-the-art models. These findings contribute to improving colon polyp classification accuracy and efficiency, ultimately facilitating the early detection and prevention of colon cancer.Doctoral Thesis Biyoçipler için Mikro Biyomalzemelerin ve Hücrelerin Görüntü İşleme Yöntemleri ile Otomatik Olarak Sayılması ve Analizi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Çelebi, Fatma; İçöz, KutayQuantification of tumor cells is essential for early cancer detection and progression tracking. Multiple techniques have been devised to detect tumor cells. In addition to conventional laboratory instruments, several biochip-based techniques have been devised for this purpose. Our biochip design incorporates micron-sized immunomagnetic beads and micropad arrays, necessitating automated detection and quantification not only of cells but also of the micropads and immunomagnetic beads. The primary function of the biochip is to simultaneously acquire target cells with distinct antigens. As a readout technique for the biochip, this study devised a digital image processing-based method for quantifying leukemia cells, immunomagnetic beads, and micropads. Images were acquired on the chip using bright-field microscopy with image objectives of 20X and 40X. Conventional image processing methods, machine learning methods, and deep learning methods were used to analyze the images. To quantify targets in the images captured by a bright-field microscope, color- and size-based object recognition and machine learning-based methods were first implemented. Secondly, color- and size-based object detection and object segmentation methods were implemented to detect structures in bright-field optical microscope images acquired from the biochip. Third, segmentation of the minimal residual disease (MRD) using deep learning. Implemented biochip images comprised of leukemic cells, immunomagnetic beads, and micropads. Moreover, mesenchymal stem cells (MSCs) are stem cells with the capacity for multilineage differentiation and self-renewal. Estimating the proportion of senescent cells is therefore essential for clinical applications of MSCs. In this study, a self-supervised learning (SSL)-based method for segmenting and quantifying the density of cellular senescence was implemented, which can perform well despite the small size of the labeled dataset.Doctoral Thesis Makine Öğrenmesi Yöntemleriyle Antimikrobiyal Peptit Aktivite Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Söylemez, Ümmü Gülsüm; Güngör, BurcuAntimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this thesis, using the multiple properties of the peptides we aimed to develop machine learning approaches that can predict the antimicrobial activities of the peptides. We have created two datasets for the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately. In our first study, ten different physico-chemical properties of the peptides were calculated, and used as features of the peptides. Following the data exploration and data preprocessing steps, a variety of classification models were build with 100-fold Monte Carlo Cross-Validation; and the performance of these models were evaluated. In the second study, we proposed a novel method called AMP-GSM. The method was tested for three datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. In the last study, using motif matching score with the models of activity against Gram-positive and Gram-negative bacteria, we created novel peptides and predicted the target selectivity of these peptides. The studies presented in this thesis advance the field of computational research by making it easier to predict the possible antimicrobial effects of peptides and to design AMPs in wet laboratory environments.