Recently Added Theses
View AllDijital sürdürülebilirlik perspektifinden tekstil üretiminde bir BT yatırım değerlendirme çerçevesi
The textile industry, one of Türkiye's and the world's leading and most significant industries, is undergoing an intensive digitalization process driven by the need to enhance competitiveness through operational efficiency, social compliance, and sustainability requirements. This paper presents a comprehensive assessment framework to evaluate IT (Information Technologies) investments in textile production from a digital sustainability perspective. The framework structured around eight key criteria: technological infrastructure, cost, data security, strategic fit, organizational culture, management support, environmental impact, incentives and policies. Each main criterion is further broken down into sub-criteria to facilitate a systematic and detailed analysis. The evaluation applied on nine IT investment alternatives: Internet of Things (IoT), Digital Twin(DT), Enterprise Resource Planning ERP) Systems, Image Processing (IP), Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Smart Logistics and Inventory Management (SL/IM), Block-chain (BC), and Augmented Reality and Virtual Reality Applications (AR/VR) across eight criteria and twenty-two sub-criteria. To address the uncertainties inherent in decision-making processes, Spherical Fuzzy Logic integrated with AHP and TOPSIS are employed. This hybrid approach combines expert opinions and industry insights, providing a robust and flexible prioritization process under complex, real-world conditions. Preliminary findings indicate that the proposed framework not only helps decision- makers in selecting the most suitable IT solutions for digital transformation but also supports sustainable development in the textile industry by balancing economic, environmental and social objectives. Although the primary focus is on Türkiye's textile industry, the adaptable nature of the framework supports its application in a variety of industries and international environments. Future research directions and potential limitations of the study are discussed, paving the way for further improvements and empirical validation.
Airbnb fiyatlarının, konum ve fotoğraf verileri kullanarak tahminlenmesi
Until recently, temporary accommodation needs were met through businesses such as hotels. However, in recent years, housing rentals through Airbnb have become increasingly common. This model has several distinguishing features. First, the physical characteristics of rental properties (such as the number of rooms and available amenities) show significant variations, both compared to traditional accommodations and among themselves. Second, properties available for rent via Airbnb can be found in almost any location. Finally, the services offered vary compared to hotels. These differences necessitate different approaches to predicting prices. Identifying the factors that influence pricing accurately is crucial for profitability. This study, based on Airbnb listings in Istanbul, analyzes which factors influence property prices and to what extent, as well as determining the most successful price prediction method. In the research, in addition to basic information commonly included in Airbnb listings, factors like the integration of the location with public transportation, the extent to which daily needs can be met, and the analysis of listing photos were also considered. Various machine learning techniques were applied for price prediction, and the impact of each factor on pricing was calculated. The results indicate that while the physical characteristics of a property play a significant role, location-based information is also crucial. Proximity to public transportation stops, cultural activities, and educational facilities were identified as key factors. It was also determined that analyzing the listing photo using a large language model did not provide a meaningful contribution to price prediction.
Web erişilebilirliği değerlendirmesine PA11Y tabanlı bir yaklaşım
In today's world, the impact of digital technologies on social life is increasing, and access to information is predominantly facilitated through digital platforms. This development necessitates that publicly available websites be accessible to all individuals. Ensuring equal digital rights for people with disabilities is not only an ethical responsibility but also a legal obligation. Website accessibility evaluation processes are generally carried out using automated testing tools. However, existing tools have significant limitations, particularly in analyzing dynamically generated content. They often produce false positives and fail to provide comprehensive assessments. In this thesis, an accessible evaluation system has been developed based on the open-source tool Pa11y, offering Turkish language support and visual feedback mechanisms. The system features a Node.js-based backend and a React.js-based frontend, enabling users to visually detect and interactively correct accessibility issues such as insufficient color contrast, missing alternative texts, and unlabelled form elements. With proxy support, the system overcomes CORS restrictions and can analyze both static and dynamic content. Furthermore, it includes features such as saving analysis history, performing comparative evaluations, and exporting reports in PDF/JSON formats. Applied tests and user feedback indicate that the system produces more accurate and consistent results compared to existing tools. In particular, it successfully analyzes dynamic content that popular tools like WAVE fail to process properly. The system's Turkish localization and visual feedback features significantly enhance user experience. This study represents a concrete contribution to improving the accessibility of digital services in Turkey and lays the groundwork for future developments in compliance with WCAG 2.2 and beyond.
İşbirlikçi filtreleme performansının çok boyutlu analizi: Metrikler, topolojiler ve algoritmalar
Recommendation systems play a crucial role in digital platforms by offering relevant items to users based on their interaction histories. Although considerable progress has been made in developing new algorithms, evaluating these systems remains challenging due to the overreliance on accuracy metrics and limited understanding of how dataset characteristics influence model performance. This thesis proposes a comprehensive evaluation framework for personalized collaborative filtering models (PCFMs) through a systematic analysis of 13 recommendation algorithms across 10 real-world bipartite datasets, using 12 performance metrics spanning accuracy, diversity, and fairness. Our experimental design is structured around four research questions, examining performance variations across evaluation dimensions, the coherence among metrics, model sensitivity to metric types, and the impact of graph topological features on performance. The results show that graph-based models achieve more balanced outcomes across all three evaluation aspects. Additionally, while accuracy metrics exhibit strong internal correlations, diversity and fairness metrics display more complex and model-dependent relationships. Moreover, topological features such as sparsity and L3 similarity significantly affect performance, though their influence varies by metric and model type. Overall, this study demonstrates that reliable evaluation of recommendation systems requires a multidimensional perspective, offering insights that are essential for both academic benchmarking and real-world deployment.
Elektrik sinyallerinden makine öğrenmesi ile cihaz kategorizasyonu
The growing need for energy management and sustainability necessitates innovative solutions in the analysis of electrical signals and device recognition. This study presents a novel approach for categorizing devices based on their unique energy consumption patterns using electrical signals. A TinyML-based system has been developed to enable resource-efficient and scalable device recognition on embedded platforms. The research addresses critical challenges in device recognition systems, such as signal noise, overlapping energy profiles, and real-time processing constraints. By leveraging advanced analytical techniques, the proposed system improves the accuracy and efficiency of device categorization. Additionally, it contributes to the broader adoption of TinyML in energy monitoring systems, providing significant benefits in terms of energy efficiency, fault detection, and sustainability. This study bridges the gap between academic innovation and practical application, supporting the development of smart home technologies, industrial automation, and intelligent energy management systems. The findings demonstrate the potential of integrating TinyML into energy monitoring frameworks, paving the way for more sustainable and user-friendly solutions.
Beyin bilgisayar arayüzlerinin kullanıcıda sebebiyet verdiği irade sakatlığı halleri ve sözleşmeye etkisi
Çalışmanın birinci bölümünde; beyin bilgisayar arayüzü teknolojisine ilişkin teorik bilgilerin ardından, nöro haklar kuramı ile beyin bilgisayar arayüzlerinin doğurduğu güvenlik ve etik sorunları ele alınmıştır. Bu bölümde, çalışmanın ilerideki bölümlerinde yapılan analizlerin teknik altyapısı gösterilmeye ve çalışmada risk olarak değerlendirilen hususların varsayımsal değil, gerçek birer risk olduğu ortaya konulmaya çalışılmıştır. Çalışmanın ikinci bölümünde, sözleşmenin kurucu unsuru olarak irade beyanı ve arayüz vasıtasıyla iletilen iradelerin niteliği ele alınmıştır. Arayüzün irade ileten bir araç olarak kullanılması konusundaki rolü tespit edilmeye çalışılmış, ardından sözleşmenin geçerlilik şartlarının incelemesine geçilmiştir. Çalışmanın üçüncü bölümünde, ikinci bölümde ele alınan irade sakatlıkları ve aşırı yararlanma halinin sözleşme üzerindeki etkisi incelenmiştir. Bu çerçevede irade sakatlığı ve aşırı yararlanma hali etkisi altında kurulan sözleşmenin geçerliliği ile bu hallerin tarafların tazminat yükümlülüklerine etkisi ele alınmıştır.