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Dijital 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.
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.
Afet yönetiminde tahmin ve optimizasyon uygulamaları
This study considers the preparedness and response phases of a disaster management problem. One of the biggest problems for disaster management is that accurate predictions for the number of death and injured people are not available. Researchers in disaster management usually use data from the historical disasters as if they were the true numbers of death and injured people. However, in a future disaster, the numbers of injured and death people will be different with probability one. Consequently, recent studies focus on predicting these numbers applying different machine learning techniques such as neural networks, support vector machine, support vector regression, Gaussian process, and multiple linear regression. In this study, we use Gaussian processes to predict the numbers of injured people. This prediction method uses only two types of explanatory variables, namely, population density and the number of undamaged, slightly and moderately damaged buildings. We use the data resulting from the two earthquakes occurred on 6 February 2023 in the southeastern region of Turkey, and we collect the data from different sources. We compare our prediction method with three other benchmark prediction methods. None of the methods provide very accurate predictions. However, Gaussian processes have the advantage of estimating the variance of the predictor in addition to the predictor, and the predictor has normal distribution. We further use these predictors and their variances to solve a chance-constrained optimization problem to find shelter locations. We use randomly generated data for the optimization problem. Further research has to be done to provide: i- machine learning method with better prediction capacity; ii- solution of the shelter location problem with real data; iii- solution procedure to solve a large-scale optimization problem for shelter location problem.xii Keywords : Machine learning, Gaussian process, Chance constraints, Shelter location problem, Disaster management
Nesnelerin interneti bazlı uç bulut sistemleri için konteyner taşıma yaklaşımı
The increasing demand for edge computing and the rise of cloud systems have led to the need for efficient resource management and workload distribution across edge and central cloud environments. Containerization has emerged as a popular solution for application deployment and management due to its lightweight and portable nature. However, migrating containers between edge and cloud environments horizontally and vertically poses challenges in terms of network latency, resource constraints, and service availability. This research project aims to develop a container migration approach specifically tailored for edge-cloud systems. The objective is to design and implement a mechanism that enables seamless and efficient container migration between edge devices and cloud infrastructure. By leveraging the advantages of both edge and cloud computing, it will be possible to allocate resources dynamically based on workload demands and optimize the overall system performance. Key aspects of this project include investigating the challenges and requirements of container migration in edge-cloud systems, designing an efficient migration mechanism that considers network latency, resource availability, and service continuity, evaluating the performance and reliability of the migration approach under various scenarios and workloads, analyzing the cost-effectiveness and resource utilization of the proposed solution compared to traditional deployment models, and developing strategies for load balancing, fault tolerance, and workload optimization to enhance the overall system efficiency. This project aims to provide insights into the feasibility and benefits of container migration in edge-cloud systems. The findings will contribute to the development of more efficient resource management strategies and enable the seamless deployment and migration of containers across edge and cloud environments.
Yaygın ortamlar için olay tabanlı dinamik sensör yapılandırılması
In recent years, the use of Internet of Things (IoT) devices has grown rapidly, leading to the proliferation of IoT networks. As IoT adoption expands, the diversity of devices in use has also increased, resulting in significant heterogeneity across various dimensions, such as communication protocols and data transmission formats. While several methods have been proposed to address these challenges, the majority of existing research focuses on urban IoT applications. In contrast, rural environments typically exhibit greater device heterogeneity due to the wide range of environmental and operational conditions. Equipment selection in such settings is influenced not only by harsh weather and potentially hazardous environments, but also by factors such as energy efficiency and communication capabilities. These constraints limit the available options, making heterogeneity unavoidable. In this study, we propose a middleware solution designed for a real-time, heterogeneous IoT weather sensor network. The system delivers collected data to researchers, administrators, and end users. The middleware is carefully developed with attention to both semantic and syntactic differences among data sources and is integrated with an interface that facilitates access for various stakeholders.
Tedarik zincirlerinde kamçı etkisinin kapsamlı empirik analizi
The Bullwhip Effect (BWE), where demand variability amplifies as orders move upstream, remains a key supply chain challenge. Despite extensive theoretical study, industry-level empirical analysis is limited. Many assumptions about BWE's impact on inventory, production, and labor remain untested across sectors. This dissertation fills this gap by analyzing industry data to identify BWE drivers, assess its effects during disruptions like COVID-19, and propose advanced forecasting techniques. The study first examines BWE's impact across industries using indicators like inventory, backorders, job losses, and capacity utilization. Findings show that industries experience BWE differently. While inventory smoothing is thought to reduce BWE, empirical evidence is inconsistent. Similarly, backordering does not always amplify fluctuations, whereas job loss rates correlate more strongly with BWE, underscoring the need for sector-specific strategies. Next, the study evaluates importation's role in BWE. While extended lead times are often blamed, results show that factors like information distortion and supply chain rigidity are more influential. Strengthening collaboration and real-time data sharing proves more effective in mitigating BWE than nearshoring or reshoring. Finally, the research explores advanced forecasting methods. Machine learning models such as Long Short-Term Memory (LSTM) networks and LightGBM significantly enhance forecast accuracy and supply chain stability. The findings highlight the crucial role of AI-driven forecasting and digitalization in managing modern supply chains.
Sürdürülebilir ulaşım sistemleri için değerlendirme yaklaşımları ve uygulamaları
Sustainable Transportation is one of the key factors for societies aiming to build a future based on Sustainable Development. Both the United Nations and the European Union are focusing on projects, plans and studies on Sustainable Transportation to achieve the Sustainability goal by 2030. The main reason is that, as a result of extensive research, it has been determined that Transportation contributes approximately 30% of the total harmful gas emissions released into the world. This study, based on a wide literature review, observed that one of the most important methodologies in evaluating the sustainability of any sector is Multi-Criteria Decision Making (MCDM). Similarly, in evaluating sustainable transportation systems, MCDM techniques have become frequently used to make comparisons between alternatives. It has also been observed that Fuzzy Set Theory is used together with MCDM methods to eliminate uncertainty. Decomposed fuzzy sets, newly introduced to the literature and derived from intuitionistic fuzzy sets, are based on taking expert evaluations from different perspectives in uncertain situations. This approach enables experts to express their opinions by asking the same topic in different ways. By using functional and dysfunctional questions from optimistic and pessimistic perspectives, decision makers are helped to reflect their knowledge, experience and personal views more clearly. Decomposed fuzzy sets also aim to provide the best solution under uncertainty by considering inconsistent or indecisive responses. In this thesis, decomposed fuzzy sets are integrated with q-Rung Orthopair fuzzy number set and expanded with EDAS and MAIRCA methods, named as DqROF EDAS and DqROF MAIRCA. These methods are explained with case studies in aviation. Finally, q-Rung Orthopair sets were compared with fuzzy-based approaches, and the new methods provided successful results.
Hava kargo taşımacılığında kapasite ataması ve dinamik fiyatlandırma
The air cargo industry is of significant importance to the global economy, employing a distinctive business model that makes it the most preferred mode of transportation, particularly for the rapid delivery of high-value goods. Airlines are constrained by limited cargo capacity, which presents a significant challenge for the industry. Maximizing revenue by selling capacity to the most profitable customers at the most advantageous price is an important issue that directly affects profitability. This study addresses the issue of capacity allocation, demand forecasting and dynamic pricing for air cargo firms. It is divided into three steps: the first is the allocation of capacity to allotment sales, the second involves the development of a demand-forecasting model that considers capacity excluding pre-sales, while the third stage entails the creation of a dynamic pricing model using the aforementioned forecast as an input. The unit revenue recommendations obtained from the dynamic pricing model were integrated to feed the capacity allocation model, which is the first stage of the study. Air cargo companies have limited capacity and wish to utilize it optimally. It is possible to pre-sell capacity through agreements or to make it available for future demand. The proportion of total capacity allocated to deals is crucial for more efficient capacity utilization. In the first stage of the problem, a model is proposed for the optimum allocation of capacity. The capacity solution to the problem is formulated with CVaR and ANN models, and the outputs of the models are then compared. The model that yields the optimal result is selected. Subsequently, a demand forecasting model is constructed, incorporating capacity as input. Regression analysis, time series methods, and ANNs were employed for demand forecasting. The values produced by the model were compared with the actual values, and the model that gave the best result was included in the solution. Due to the multitude of variables influencing the price in the air cargo sector and the intricate structure of the sector, it is exceedingly challenging and complex to determine the price dynamically in air cargo. To circumvent the difficulties inherent in this complexity, in addition to a comprehensive literature review, the opinions of industry experts were sought and the model inputs were determined. The problem solution was completed by employing SARSA algorithm for dynamic pricing. The study is concluded by evaluating the solutions and making suggestions for future studies.