Doktora Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800
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Doctoral Thesis A reliable and secure communication design for underwater sensor networks concerning energy efficiency(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) UYAN, Osman Gökhan; Güngör, Vehbi ÇağrıUnderwater Acoustic Sensor Networks (UASNs) recently attract scientists because of its wide range of applications and emerging technology. A design challenge in UASN's is the limited network lifetime and poor reliability caused by limited battery supply of sensors and harsh channel conditions in underwater environment. Moreover, sensors might transmit sensitive data that must be disguised against eavesdropping attacks. To maintain a reliability level, packet-duplication and multi-path routing method are suggested, which renders eavesdropping attacks easier. For data security, cryptographic encryption is the most acclaimed method. However, encryption needs extra computations, which consume extra energy and cause a decrease in the network lifetime. As a countermeasure along with encryption against silent listening, fragmenting data and transmitting in pieces over different paths has been proposed. To address these challenges, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption, and data fragmentation on network lifetime. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict the energy consumptions of underwater nodes as supplementary methods to optimization models. Performance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.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 Uzaktan Kontrollü İkincil Emisyon İyonizasyon Kalorimetri Modülleri ile Yüksek Enerjili Kozmik ve Gama Radyasyon Ölçümü(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Paran, Nejdet; Tekgün, Burak; Tıraş, EmrahThe demand for precise, robust, and reliable radiation-resistant particle detectors and ionization calorimeters intensifies, due to the escalating luminosity and unprecedented radiation conditions at particle colliders and accelerators. Secondary Emission (SE) Ionization Calorimetry is a novel technology designed to measure the energy of electromagnetic and hadronic particles, particularly in extreme radiation conditions. In this study, we have tested and investigated the development and radiation test of the novel SE modules. The modules were developed by modifying the conventional Hamamatsu single anode R7761 Photomultiplier Tubes. Three different voltage conditions for the same module were developed and the new modules were tested by using cosmic, gamma (Co-60) and neutron (AmBe) radiation sources. The results show that all three modes have good sensitivity to electromagnetic showers, and they are suitable for harsh radiation environments. This study also shows that SE module is a promising technology shedding light on future radiation-resistant nuclear and high-energy detectors. Here, we discuss the technical design, test characteristics and cosmic and particle interaction results of the newly developed SE modules. Since such detector systems are either in a high radiation area or in a closed room/box, remote mode changes allow us to continue the experimental process without interruption. By adding these signals to the interface where the modes are controlled, we can instantaneously observe the modes' effects.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 Makine Öğrenmesi Tabanlı Ağ Anomali Tespiti(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Hacılar, Hilal; Güngör, Burcu; Güngör, Vehbi ÇağrıIntelligent technologies have led to a significant rise in internet users and applications. However, this rise in internet usage has also brought serious security challenges. Organizations rely on Network Intrusion Detection systems (NIDS) to protect sensitive data from unauthorized access and theft. To enhance the capabilities of IDS, Machine Learning (ML) and Deep Learning (DL) techniques have been increasingly integrated into these systems. In this context, anomaly-based network intrusion detection surpasses other detection mechanisms significantly in several instances. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances, feature selection and extraction methods, hyperparameter optimization, and classification performance for different types of network intrusions: wired, wireless, and Software Defined Networking (SDN). Additionally, existing methods may achieve high accuracy; they may suffer from high training times, low detection rate (DR), and computational complexity. By combining metaheuristics and neural networks, it is possible to solve complex optimization problems that are challenging to solve using conventional methods. To address these challenges, this thesis study first evaluates different network intrusion datasets, such as wired, wireless, and SDN, together, considering class imbalance, feature selection, and hyperparameter optimization tasks. Secondly, it proposes a novel hybrid approach combining Deep Autoencoder (DAE) and Artificial Neural Network (ANN) models trained by a parallel Artificial Bee Colony (ABC) algorithm with Bayesian hyperparameter optimization.Doctoral Thesis Koloidal Nanomalzemelerin Akıllı Kendinden Dizilimi ile Nanofotonik Mimarilerin Tasarımı ve Uygulaması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şenel, Zeynep; Erdem, TalhaDNA-driven self-assembly techniques offer precise control over the positioning of colloidal nanoparticles through specific Watson–Crick interactions, and its reversibility via controlling the temperature of medium. This thesis explores an alternative strategy to control DNA-functionalized nanoparticles' binding/unbinding process by leveraging laser radiation, inducing localized heating within the nanoparticles to facilitate disassociation. First, we demonstrate the active manipulation of the optical properties of DNA-assembled gold nanoparticle networks via external optical excitation. Specifically, irradiation with a green hand-held laser yields a substantial ∼30% increase in total transmittance, accompanied by a transition from opaque to transparent states observable in optical microscopy images. The reversibility of this process is demonstrated by the restoration of the nanoparticle network post-irradiation cessation, underscoring the efficacy of optical excitation in tailoring both the structure and optical characteristics of DNA-mediated nanoparticle assemblies. Second, we introduce a method to tailor DNA-driven self-assembly of semiconductor nanoparticles on glass by applying an external optical field. A green laser directs the assembly of DNA-functionalized red-emitting quantum dots (QDs) on DNA-functionalized glass, leaving uncoated spots owing to localized heating. This effect becomes prominent after three hours of radiation using a laser with an irradiance of 57.1 W/cm2. Experiments with different lasers and nanoparticle types confirm the role of laser-induced heating in preventing QD-glass bonding via DNA-DNA interaction. Secondary coating of previously uncoated spots with DNA-functionalized green-emitting QDs and dye-functionalized DNAs indicates a successful hierarchical self-assembly. Our findings highlight the potential of light-assisted DNA-driven self-assembly for diverse nanoparticle architectures, promising applications in optoelectronics and nanophotonics. Keywords: Programmable self-assembly, DNA-driven self-assembly, localized heating, colloidal nanoparticles, DNA conjugation.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 El Protezleri için EEG ve EMG Sinyalleriyle Algı Kestirimi ve Tork Kontrolü(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Karakullukcu, Nedime; Yılmaz, BülentUpper extremity prostheses vary based on patients' articulation levels and movement methods. They can be cosmetic, operate mechanically with shoulder movement, or be controlled by myoelectronic and electroencephalography (EEG) signals. However, unnatural prosthesis control burdens users mentally. This thesis seeks to enhance bionic hand prosthesis control using EEG and electromyography (EMG) signals, coupled with users' visual weight perception, aiming to reduce physical and mental discomfort associated with mechanical prostheses. The prototype hand's preconditioning evaluates objects' weight visually, aiming to reduce shoulder force and mental load while holding an object. EEG and EMG signals from subjects were processed for real-time implementation. In the first stage, a study focused on operating the prosthesis using the motor intention waves of prosthesis users, and the machine learning approaches' classification success (detection of the intention to activate the prosthesis) was examined using EEG data from 30 healthy participants. The second stage recorded EEG and EMG signals from 31 participants during reaching, lifting, and placing an object, employing various classifications for object weight. In the real-time classification of multi-channel EEG signals from 20 healthy individuals using Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches, the system aimed to control the stiffness of the wrist part of the prosthesis. Consequently, the system could detect the weight of the object perceived by the user while using the prosthesis, allowing for the preconditioning of the prosthesis based on this weight when the user wants to hold and move the object.
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