Endüstri Mühendisliği Bölümü Koleksiyonu
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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.
- ItemXAKUR Endeksinde Yer Alan Hisselerin Ortak Hareketlerinin Birliktelik Kural Analizi ile Belirlenmesi(Sosyoekonomi Derneği, 2025-01) Sayıl, Gizel Busem; Korhan, Emrah; 372791With the public offerings of many companies, there has been a significant increase in the number of new investors joining the stock exchange. In this process, the limits of the impact of brokerage houses on the markets have expanded. This study aims to determine whether the companies in the BIST brokerage firms index (XAKUR) move together with the IPO index (XHARZ), Borsa Istanbul 100 Index (BIST100), Volatility Index (VIX) and different macroeconomic variables with association rule analysis. Daily data from January 2018 to June 2023 found that brokerage house stocks do not move together with each other and with XHARZ, while CPI has a tight upward relationship with different variables.
- ItemEnhancing municipal solid waste management efficiency through clustering: a case study(Taylor and Francis, 2024-11) Çil, Sedat; Karaer, Feza; Salihoğlu, N. Kamil; Tabansız Göç, Gülveren; Çavdur, Fatih; 409903This study leverages real-time datasets generated through IoT technology and smart city applications to enhance solid waste management in Yalova Province, Turkey. By integrating these datasets with the municipality’s Geographic Information System (GIS) using the ITRF/96 3 UTM X Y Coordinate System, a dynamic waste collection framework was established. The K-Means clustering algorithm was employed to determine the optimal waste container placement, considering capacities of 550, 800, 1,000, and 3,000 liters and walking distances of 50–100 ms. Results indicated that 1,000 and 3,000-liter containers with a 100-m walking distance maximized collection efficiency. Replacing 484 traditional containers with 105 units of 3,000 liters reduced total routes by 34%, transport costs by 42.2%, and CO2 emissions by 33.5%. The study underscores the importance of integrating GIS and IoT technologies for real-time waste management, aligning with the UN’s Sustainable Development Goals (SDG 11 and SDG 13). By combining data-driven decision-making with urban sustainability practices, it offers a replicable model for municipalities seeking to reduce costs and environmental impacts in waste collection.
- ItemÖğrenci-danışman atama problemindeki farklı problem kurgularının öğrenci ve danışman memnuniyet düzeylerine etkisinin incelenmesi(Gazi Üniversitesi, 2024-09) Tabansız Göç, Gülveren; Sebatlı Sağlam, Aslı; Çavdur, Fatih; 409903; 372024Hem lisansüstü çalışmaların etkili bir şekilde yürütülebilmesi hem de lisansüstü programların genel başarısı için uygun öğrenci-danışman atamalarının yapılması kritik öneme sahiptir. Bu çalışmada, Öğrenci-Danışman Atama (ÖDA) problemi için öğrenci ve danışman tercihlerine ek olarak; (i) danışman kapasitelerinin dikkate alınmadığı (ii) danışman kapasiteleri olarak bireysel danışman taleplerinin ve (iii) danışman kapasiteleri olarak ortalama iş yükünün dikkate alındığı durum olmak üzere üç farklı problem kurgusu ele alınmıştır. Her bir problem kurgusunun çözümü için matematiksel programlama modelleri ve bir sezgisel algoritma önerilmiştir. Önerilen yaklaşımın geçerliliğin test edilmesi amacıyla küçük ve büyük ölçekte olmak üzere iki farklı veri kümesi üretilmiştir. Bu veri kümeleri kullanılarak önerilen matematiksel programlar ve sezgisel algoritma, öğrencilerin ve danışmanların tercihlerinin farklı ağırlıklandırma stratejileri için çözülmüştür. Farklı problem kurguları ve atama stratejileri için elde edilen öğrenci-danışman atamaları, öğrencilerin ve danışmanların memnuniyet düzeyleri cinsinden tanımlanan performans ölçütlerine göre analiz edilmiştir. Buna ek olarak, matematiksel programlama modeli ve sezgisel algoritmanın sonuçları kıyaslanmıştır. Bu çalışmanın öne çıkan unsuru, ÖDA problemi için farklı problem kurguları ve atama stratejilerinin ele alınması ve bu durumlar için elde edilen sonuçların, karar vericilerin farklı bakış açılarını yansıtacak şekilde kapsamlı olarak analiz edilmesidir.
- ItemComparative analysis of different drying methods on strawberry aroma compounds via multi-criteria decision-making techniques(MDPI, 2025-01) Cengiz, Nurten; Abdulvahitoğlu, Aslı; Abdulvahitoğlu, Adnan; 382420Food and food safety, as one of the basic issues of human life, has made it necessary to store foods for a long time with the increasing population. One of the oldest and most common methods of extending the shelf life of food products is the drying process. The drying process contributes to the higher quality of foods in terms of physical, chemical, and microbial properties by ensuring that beneficial contents such as vitamins, minerals, and aroma compounds are better preserved. The aroma values of foods, which consist of taste and smell components, gain importance. In foods, the taste is determined by permanent components, while smell is determined by volatile components. The loss of volatile aroma compounds in the strawberry drying process negatively affects product quality. Small changes in aroma compounds can lead to significant differences in product taste. Therefore, strawberry aroma is a critical factor for consumer appeal and commercial success. In this study, the effects of drying methods on the aroma compounds of strawberry fruit were compared with Multi Criteria Decision Making (MCDM) techniques. In this study, PSI-based MCDM techniques were used to make the most appropriate choice among strawberry drying methods. The values of 23 distinct aroma compounds obtained with different drying methods applied to strawberry fruit were analyzed with 7 different MCDM techniques. The calculations gave similar results and these results were combined with the Borda rule. Accordingly, the drying methods with the highest scores were determined as freeze drying.
