Mudanya University Institutional Academic Archive System
Mudanya University's Dspace system is a platform that digitally stores and opens academic studies. Academic content such as articles, presentations, theses, books, and reports are included here. Dspace@Mudanya provides easy access, making it a valuable resource for researchers and students. It serves as a digital archive for Mudanya University's academic outputs, facilitates access to scientific information and supports its sharing. For more information and assistance, please contact us.
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Recent Submissions
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Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network
(Elsevier, 2025-01) Gülmez, Burak; 222298
Stock 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.
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A novel hybrid MCDM framework combining TOPSIS, PROMETHEE II, and VIKOR for peach drying method selection
(Elsevier, 2024-11) Gülmez, Burak; 222298
The 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.
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Optimal Smart Agriculture Technologies and Solutions in the Future of Farming
(Ataturk University, 2024-12) Çakmakçı, Muhammet Fatih; Günay, Faruk Baturalp; 410449
Precision agriculture technologies have been developed and are still being developed to increase the efficiency of agricultural processes, optimize resource utilization, and support environmental sustainability. One of the most important ways to solve food shortages is the use of modern technology and the integration of artificial intelligence in agriculture to increase productivity. Smart farming uses technologies such as optical, mechanical, and electrochemical sensors; air flow and location tracking; drones; satellite imagery; artificial intelligence; and the Internet of Things to monitor, analyze and manage farm practices. Smart agricultural technologies are utilized in a wide range of areas, including pest management, weed control, plant monitoring, storage management, irrigation management, disease management and control, weather forecasting and monitoring, yield estimation, soil composition analysis, and agricultural machinery management. By utilizing realtime data and intelligent decision-making systems, smart agriculture aims to increase productivity, reduce resource waste, improve sustainability, and address the challenges posed by a growing global population. Another goal of precision agriculture technology is to automate data collection and analysis processes, enabling farmers to make more informed decisions while reducing the cost of agricultural inputs and increasing productivity. The use of digital technologies in agriculture and livestock is rapidly increasing. Smart monitoring systems enhance agricultural efficiency, whereas digital technologies improve productivity, sustainability, and effectiveness. Smart greenhouses, irrigation, and fertilization systems support agricultural sustainability by monitoring environmental and plant parameters. In this study, the opportunities, benefits, future trends, and effects of the use of precision agriculture technologies on sustainable agriculture and food production are discussed.
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The Future of Farming: Leveraging AI, Machine Learning, and Smart Systems for Optimal Agricultural Practices
(ÇOMU Publication, 2024-10) Çakmakçı, Muhammet Fatih; Günay, Faruk Baturalp; 410449
In smart agriculture (SA), key applications of intelligent technologies include pest management, weed control, monitoring agricultural products, storage management, disease management and control, weather forecasting and monitoring, irrigation management, yield prediction, soil composition and management, and machinery management. Managing the agricultural production supply chain, measuring soil variability, improving agricultural production and management, reducing resource usage, monitoring water consumption, enhancing agricultural processes, identifying agricultural risks and hazards, and optimizing decision-making are crucial application areas of agricultural technologies.The use of digital technologies in agriculture and livestock is rapidly increasing. Optical, mechanical, electrochemical sensors, air flow, and location tracking technologies provide early warnings for diseases and pests, optimizing harvest processes. Smart monitoring systems enhance agricultural efficiency, while digital technologies improve productivity, sustainability, and effectiveness. Smart greenhouses, irrigation, and fertilization systems support agricultural sustainability by monitoring environmental and plant parameters.In livestock management, environmental and body sensors improve animal health and living conditions. Machine learning algorithms are effective in detecting mating behaviors and diseases in livestock. Precision livestock systems monitor health and welfare parameters, increasing productivity and protecting animal health. Artificial intelligence (AI) and machine learning (ML) applications are effective in analyzing soil data, plant phenotyping, and carbon stock estimation. Smart irrigation systems contribute to water conservation and increased efficiency. Additionally, smart harvesting systems help achieve sustainable production with lower costs and increased productivity. These technologies enhance the sustainability of agriculture and livestock by strengthening the capacity to manage productivity and environmental impacts. This article will discuss the topics that mentioned above.
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Güneydoğu Asya Uluslar Birliği (ASEAN) Ekonomilerinde Finansal Gelişme ve Doğrudan Yabancı Yatırımların Ekonomik Büyüme Üzerindeki Etkisi
(Muhasebe ve Finansman Öğretim Üyeleri Bilim ve Araştırma Derneği, 2025-04) Canbay, Şerif; 278780
Bu çalışma, Güneydoğu Asya Uluslar Birliği'nin Kurucu Beşlisi’ni oluşturan Endonezya, Malezya, Filipinler, Singapur ve Tayland’da doğrudan yabancı yatırımlar ve finansal gelişmişliğin ekonomik büyüme üzerindeki nedensel ilişkisini incelemektedir. 1980-2021 dönemi için panel bootstrap nedensellik testi kullanılarak yapılan analiz, doğrudan yabancı yatırımlar ve finansal gelişmişlik değişkenlerinin ekonomik büyüme üzerindeki etkilerinin ülkeden ülkeye değiştiğini göstermektedir. Elde edilen bulgulara göre, Endonezya’da doğrudan yabancı yatırımlar büyümeyi pozitif yönde etkilerken, finansal gelişmişlik ekonomik büyüme üzerinde negatif bir etkiye sahiptir. Malezya’da benzer şekilde doğrudan yabancı yatırımlar büyümeyi teşvik ederken, finansal gelişmişliğin büyüme üzerindeki etkisi negatif yönlüdür. Filipinler’de finansal gelişmişlik büyümeyi desteklerken, doğrudan yabancı yatırımların büyümeye olumsuz etkisi gözlemlenmiştir. Singapur ve Tayland’da hem doğrudan yabancı yatırımlar hem de finansal gelişmişlik büyüme üzerinde olumsuz etkiye sahiptir.
