Doktora Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800
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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 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 Nesnelerin İnterneti Tabanlı Araç Tipi Sınıflandırma ve Ağ Anomalisi Tespiti için Makine Öğrenmesi Yaklaşımları(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Kolukısa, Burak; Güngör, Vehbi ÇağrıThis thesis presents innovative approaches in the realms of Intelligent Transportation Systems (ITS) and Network Intrusion Detection Systems (NIDS) within the Internet of Things (IoT). Leveraging IoT technologies, a low-cost, battery-operated 3-D magnetic sensor has been developed for ITS to enable the classification of vehicle categories. The research presents machine learning and deep learning models that are improved by using oversampling, feature selection and extraction methods, hyperparameter optimization, and converting signals into 2-D images. New methods have been proposed for vehicle type classification to boost classification performance and achieve an accuracy of up to 92.92%. Additionally, the increasing reliance on IoT devices for such applications introduces significant cybersecurity risks. To mitigate these vulnerabilities, a novel logistic regression model trained with a parallel artificial bee colony (LR-ABC) algorithm has been proposed for network anomaly detection. This model incorporates hyperparameter optimization to enhance detection capabilities, showcasing superior performance on popular benchmark NIDS datasets with accuracies of 88.25% and 90.11%. Overall, this research contributes to the advancement of IoT and IoT cybersecurity by offering robust, scalable, and efficient solutions. These innovations not only enhance vehicle type classification and network security in the IoT era but also pave the way for future IoT infrastructure development in an increasingly connected digital landscape.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 Trafik Yoğunluğu Tahmini için Derin Öğrenme Modelleri(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Çini, Nevin; Aydın, ZaferIn the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long- term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short term memory (LSTM) network, and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Numerical results show that our proposed model performs better than individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression (LR), decision tree regression (DTR), k-nearest-neighbors regression (KNNR) and other ensemble models such as random-forest-regression(RFR).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 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.