Mühendislik, Mimarlık ve Tasarım Fakültesi
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- ItemA Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture(Springer, 2024-09) Gülmez, Burak; 222298This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.
- ItemA novel hybrid MCDM framework combining TOPSIS, PROMETHEE II, and VIKOR for peach drying method selection(Elsevier, 2024-11) Gülmez, Burak; 222298The selection of optimal drying technologies for peach processing presents a complex decision-making challenge due to multiple conflicting criteria. This study introduces a novel hybrid multi-criteria decision-making (MCDM) framework combining TOPSIS, VIKOR, and PROMETHEE II methods to evaluate eight drying technologies. The evaluation was conducted across twelve criteria, encompassing product quality, operational efficiency, economic factors, and environmental impact. Data were collected from five industry experts through structured matrices. The results demonstrate that vacuum drying emerged as the optimal technology, maintaining the top position in 75 % of sensitivity scenarios. Freeze drying and heat pump drying consistently ranked among the top three alternatives across all methods. The correlation analysis revealed strong agreement between VIKOR and PROMETHEE II rankings (0.857), while TOPSIS provided complementary insights. Sensitivity analysis identified energy consumption, investment cost, and nutritional retention as the most critical factors influencing technology selection. The findings indicate that advanced drying technologies significantly outperform traditional methods in terms of overall performance. This research provides a comprehensive framework for evidence-based decision-making in food processing technology selection and establishes quantitative benchmarks for future technology evaluations in the fruit drying industry.
- ItemA Symmetry-Based Spherical Fuzzy MCDM Approach for the Strategic Assessment of Alternative Fuels Toward Sustainable Energy Policies(MDPI, 2025-06) Abdulvahitoğlu, Adnan; 382420Alternative fuels obtained from renewable sources, providing low greenhouse gas emissions and high energy efficiency, offer significant advantages in terms of sustainability. In addition, the wide applicability of these fuel types in sectors such as housing, transportation, and industry creates significant opportunities in terms of reducing dependence on fossil fuels. Alternative fuels should be evaluated not only according to their environmental contributions but also based on multi-dimensional criteria such as economic cost, technical suitability, sustainability level, fuel properties, infrastructure requirements, and social acceptance. In this context, a comparative analysis of alternative fuel types in terms of various basic parameters is no longer optional, but a necessity. These parameters generally include symmetrical relationships such as balanced trade-offs between economic and environmental dimensions or mutual effects between technical and social criteria. However, they also show variability and uncertainty depending on the fuel type. Therefore, Spherical Fuzzy Multi-Criteria Decision Making (SF-MCDM) methods, which can effectively represent symmetry in membership and hesitation degrees, have been used to achieve more realistic and reliable results in uncertain decision environments. The proposed model provides a systematic and flexible evaluation structure that helps decision makers determine the most appropriate alternative fuel options and contributes to the formation of sustainable energy policies.
- PublicationMathematical model to upcycle end-of-roll leftover fabrics in apparel manufacturing(Sage, 2024-06) İşeri, Ali; Kızılaslan, Recep; 135066This study addresses the problem of end-of-roll leftover fabrics originating after the production of baby/child apparel. The ineffective management of these leftovers results in excess inventory, occupies storage space, and imposes economic and environmental loads. To address this challenge, a novel mathematical modeling approach is proposed. The model maximizes the upcycling of leftovers by incorporating these into the manufacturing of garments while adhering to marketing, production, and ordering constraints. This model also introduces the feasibility of ordering new fabrics with a penalty, as defined by the decision makers, to increase utilization. The model was tested using actual end-of-roll leftover data. The upcycling utilization of leftovers was calculated to be between 57% and 87%. Notably, at an upcycling rate of 58%, 96% of the utilized fabrics were sourced from leftovers. The case study results validate the model efficacy and provide insights into leftover-fabric management.
- ItemSelecting facility location of gendarmerie search and rescue (GSR) units; an analysis of efficiency in disaster response(Elsevier, 2024-10) Abdulvahitoğlu, Adnan; Vural, Danişment; Macit, İrfan; 382420Disasters, referred to as events that result in physical, economic, and social losses for individuals and disrupt the daily activities of human communities, necessitate ongoing preparedness due to their unpredictable nature. Swift response during and after a disaster is crucial for preserving human life. Hence, it is imperative to initiate planning immediately following a disaster to ensure readiness for various tasks. Given these factors, search and rescue units must carefully select a base location that enables them to promptly reach affected areas. Disasters exhibit unique characteristics across different regions of Türkiye. While some regions are prone to earthquakes, others face the risks of landslides, avalanches, or floods. Consequently, the required measures for disaster management vary from region to region. Nevertheless, when the term “disaster” is mentioned in Türkiye, earthquakes often come to mind due to their frequent occurrence and significant impact. The Gendarmerie Search and Rescue (GSR) units have been actively responding to these earthquakes, renowned for their exemplary institutional discipline and working methods. This study aims to examine the operations and deployment locations of GSR units, which play a crucial role in mitigating the impact of frequent earthquakes in Türkiye, utilizing a SWOT analysis. Additionally, a Multi-Criteria Decision Making-based mathematical model will be employed to optimize task activities and to select the most suitable facility locations for GSR units. The use of mathematical modeling in this context ensures that GSR units are strategically positioned to maximize their operational effectiveness and minimize response times. The results will be evaluated through sensitivity analysis.
- ItemStock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network(Elsevier, 2025-01) Gülmez, Burak; 222298Stock price prediction remains a complex challenge in financial markets. This study introduces a novel Long Short-Term Memory (LSTM) model optimized by Sand Cat Swarm Optimization (SCSO) for stock price prediction. The research evaluates multiple algorithms including ANN, LSTM variants, Auto-ARIMA, Gradient Boosted Trees, DeepAR, N-BEATS, N-HITS, and the proposed LSTM-SCSO using DAX index data from 2018 to 2023. Model performance was assessed through Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and out-of-sample R2 metrics. Statistical significance was validated using Model Confidence Set analysis with 5000 bootstrap replications. Results demonstrate LSTM-SCSO's superior performance across all evaluation metrics. The model achieved an annualized return of 66.25% compared to the DAX index's 47.45%, with a Sharpe ratio of 2.9091. The integration of technical indicators and macroeconomic variables enhanced the model's predictive capabilities. These findings establish LSTM-SCSO as an effective tool for stock price prediction, offering practical value for investment decision-making.
