Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5799
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Master Thesis Optimal Isı Yalıtımı için Yapı Malzemelerinin Özelliklerinin Belirlenmesi ve Yapı Malzemesi Seçimi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Kılıçarslan, Mustafa Özgür; Kara, GökmenThe escalating urgency of climate change demands innovative approaches to energy conservation, particularly in the realm of building construction, known for substantial energy consumption and greenhouse gas emissions. This research delves into transformative strategies for enhancing energy efficiency in office buildings, with a concentrated analysis of the implementation of advanced building materials and state-of-the-art construction methodologies. Utilizing OpenStudio, a cutting-edge energy modeling software tool from the U.S. Department of Energy's National Renewable Energy Laboratory, this study quantitatively evaluates the energy-conserving potential of various avant-garde materials and construction techniques. The investigation is anchored around a case study of an office building in Ankara, Turkey, serving as a representative model for exploring diverse scenarios. These scenarios encompass the integration of high-performance framing, airtight construction, materials with superior thermal resistance properties, and advanced glazing systems. The research meticulously assesses each scenario with the aim of delineating the configurations that most significantly reduce energy consumption. The results reveal that specific combinations of advanced techniques and materials can lead to substantial reductions in energy use, thereby contributing profoundly to global efforts in mitigating climate change impacts. The conclusion emphasizes the necessity for widespread adoption and standardization of these energy-efficient practices in the construction industry, proposing them as pivotal contributors to the broader environmental sustainability movement.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. CagriAccording 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, MalikThe 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 DiagnosisMaster 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, BülentIn 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 Mesleki Bilgi Transferinin Bir Aracı Olarak Türk Mimarlık Dergileri: 1960-1980(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Katırcıoğlu, Nida; Tozoğlu, Ahmet ErdemIn this thesis, the role of architectural periodicals published in Turkey in the context of knowledge transfer between 1960 and 1980 is analyzed with textual analysis. One of the functions of architectural periodicals, transferring the changes and developments in the professional field in the world to their readers, has been investigated. The contents of three architectural periodicals that transfer architectural knowledge have been categorized by examining their archives.Master Thesis Ultra Geniş Bantlı Vivaldi Antenlerin Tasarımı ve Performans İyileştirmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Güzelkara, İzzet; Kılıç, Veli TayfunUltra-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 RNA Etkileşimlerinin İn Silico Analizi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; Demirci, Müşerref Duygu SaçarMany supervised machine learning models have been developed for the classification and identification of non-coding RNA (ncRNA) sequences. These models play a significant role in the diagnosis and treatment of various diseases. During such analyses, positive learning datasets typically consist of known ncRNA examples, some of which may even be confirmed with strong experimental evidence. However, there is no database of validated negative sequences for ncRNA classes or standardized methodologies for generating high quality negative samples. To overcome this challenge, a new method for generating negative data called the NeRNA (Negative RNA) method has been developed in this study. NeRNA generates negative sequences using known ncRNA sequences and their octal representations, similar with frame shift mutations found in biology but without base deletions or insertions. In this thesis, the NeRNA method was tested separately with four different ncRNA datasets, including microRNA (miRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). Additionally, a species-specific case study was conducted to demonstrate and compare the performance of the study's miRNA predictions. The results of 1000-fold cross-validation on machine learning algorithms such as Decision Trees, Naive Bayes, Random Forest classifiers, and deep learning algorithms like Multilayer Perceptrons, Convolutional Neural Networks, and Simple Feedforward Neural Networks showed that models developed using datasets generated by NeRNA exhibited significantly high prediction performance. NeRNA has been published as an easy-to-use, updatable, and modifiable KNIME workflow, along with example datasets and required extensions that can be downloaded and utilized. NeRNA is designed specifically as a powerful tool for RNA sequence data analysis.Master Thesis Tasarlanmış Mikroorganizmalar ile Katma Değeri Yüksek Karotenoidlerin Biyosentezi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Arslansoy, Nuriye; Fidan, ÖzkanCarotenoids are pigment molecules that play an important role in coloring plants, algae, and other organisms. These molecules exhibit various biological activities such as anticancer, antiviral and antioxidant activities. They have a huge market size and are mainly used in the food, feed, and cosmetic industries. The current supply chain for carotenoids is mostly relied on the extraction from plants and/or chemical synthesis for certain carotenoids. However, these strategies have various bottlenecks and disadvantages such as being affected by climate change, more difficult and costly extraction processes, and environmental issues. These can be overcome with microbial biosynthesis, which not only addresses the previous problems but also provides advantages of producing in a short time and scale-up for industrial production. In this research, we aimed to biosynthesize the high value-added carotenoids by engineered microorganisms. The genome of a native producer of zeaxanthin diglucoside, identified as endophytic Pseudomonas sp. 102515, was first edited by CRISPR-Cas9 to knock out zeaxanthin glucosyltransferase (CrtX), lycopene β-cyclase (CrtY) and beta-carotene hydroxylase (CrtZ). This led to ΔcrtX, ΔcrtY and ΔcrtZ mutant strains of Pseudomonas sp. 102515. On the other hand, overexpression plasmids carrying crtW, CaZEP and CaZEP-CaCCSm40 genes were constructed and transformed to ΔcrtX mutant to synthesize astaxanthin, violaxanthin and capsanthin/capsorubin. HPLC analysis of extracts from mutant strains and overexpression strains revealed that all the engineered strains produced the corresponding carotenoids such as zeaxanthin, β-carotene, and lycopene. Thus, this study paved the way for the biosynthesis of valuable carotenoids in the engineered endophytic bacteria.Master Thesis İstatistiksel Ön Puanlama Bileşeni ile Gruplama Puanlama Modellemesi (GSM) Yaklaşımın Geliştirilmesi: Yüksek Boyutlu Transkriptomik Veri Analizi için Bir Vaka Çalışması(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2024) Khokhar, Maham; Güngör, BurcuRapid advancements in transcriptomic technologies have significantly increased the volume of data available for analysis, which presents challenges in terms of efficiency and computational demand. This thesis introduces a Pre-Scoring component to the Grouping-Scoring-Modeling (G-S-M) framework to address inefficiencies caused by the excessive number of gene groups generated by traditional GSM. By selectively prioritizing gene groups based on their statistical significance, this innovation aims to reduce the computational demands associated with scoring these groups using machine learning models, thereby streamlining the analysis process. Assessed across nine diverse Gene Expression datasets, the Pre-Scoring G-S-M framework not only maintained accuracy comparable to the traditional approach but did so with significantly fewer genes. This refinement conserves resources while maintaining the robustness and reliability of the data analysis, crucial for advancing research in personalized medicine and therapeutic strategies. The findings suggest that the modified G-S-M framework serves as a valuable tool in bioinformatics, offering a more efficient approach to handling large-scale genomic datasets. Future work will focus on adapting this enhanced framework to incorporate diverse types of omics knowledge, such as proteomics and metabolomics, further optimizing its performance to broaden its applicability in both clinical and research settingsMaster Thesis Tasarım ve Sergileme Arasındaki Diyaloğu Keşfetmek: İtalyan Kurumlarında Çağdaş Sanat Sergilerinin Mimari Entegrasyonu(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Ünal, Cansu; Asıliskender, BurakMuseums are dynamic spaces where architecture and exhibition design interact in significant ways, influencing each other and contributing to a cohesive cultural experience. The relationship between contemporary art exhibitions and museum architecture is crucial in shaping the narrative and the display of art. By exploring the relationship between contemporary art exhibitions and the architecture of museum buildings, a comprehensive analysis is offered of how these elements engage in a dynamic dialogue. Through a detailed comparative analysis of four landmark institutions—Fondazione Prada in Milan, Strozzi Palace in Florence, MAXXI in Rome, and the Venice Biennale's Arsenale—the study investigates the transformative role of architectural innovation in redefining historical structures for contemporary cultural purposes. The research underscores the significance of adaptive reuse in the evolution of museum spaces, examining how the architectural characteristics and spatial configurations of each building influence curatorial strategies and display of art. Focusing on the interplay between museum architecture and the thematic content of art exhibitions, the study highlights how museums serve as active participants in cultural discourse. This research provides insights into the ways contemporary art exhibitions and museum buildings collaboratively construct a hierarchical narrative space, enhancing the aesthetic, cultural, and educational value of art.