Bu çalışmada 5mm dilimindeki patatesin mikrodalga bantlı kurutucuda mikrodalga gücünün (1500W ve 2100W) ve bant hızının (0,175, 0,210 ve 0,245 m/dak) kurutma zamanı, renk değişimi ve enerji tüketimine etkisi araştırıldı. Elde edilen sonuçlara göre mikrodalga gücünün artması ve bant hızının azalması ile enerji tüketimi azalmıştır. Dokuz adet kurutma modelinin uygunluğunu belirlemek için deneysel ve teorik nem oranları dikkate alınarak korelasyon katsayısı (r), standart hata (e_s) ve (χ²) hesaplandı. Tüm kurutma şartları için Page modelin en uygun model olduğu belirlenmiştir. Efektif difüzyon katsayısı 2,958.10^-7 ve 3,587.10^-6 m²/s aralığında hesaplanmıştır. Kurutma süresince en az enerji 0,175m/dak ve 2100W gücünde belirlenmiştir.

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Nowadays, in parallel with the rapid increase in industrialization and human population, a significant increase in all types of waste, especially domestic, industrial, and agricultural waste, can be observed. In this study, microwave drying, one of the disposal methods for agricultural waste, such as prina and hazelnut shell, was performed. To reduce the time, energy, and cost spent on drying processes, two recently prominent machine learning prediction methods (Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)) were applied. In this study, our aim is to model the disposal of waste using artificial intelligence techniques, especially considering the importance of environmental pollution in today’s context. Microwave power values of 120, 350, and 460 W were used for 100 g of hazelnut shell, and 90 W, 360 W, and 600 W were used for 7 mm thickness of prina. Both ANN and ANFIS approaches were applied to a dataset obtained from the calculation of moisture content and drying rate values. It was observed that the ANFIS and ANN models were applicable for predicting moisture levels, but not applicable for predicting drying rates. When the coefficient of determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for moisture level are examined both for ANN and ANFIS models’ predictions, it is seen that the R² value is between 0.981340 and 0.999999, the RMSE value is between 0.000012 and 0.015010 and the MAPE value is between 0.034268 and 23.833481.

Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.

Sanayi üretim endeksi, bir ülkedeki sanayi sektörünün durumunu göstermektedir. Sanayi sektörü gayrisafi yurt içi hasılanın belirlenmesinde önemli bir öncüldür. Sanayi üretimi ülke temelinde ekonomiyi doğrudan etkilemektedir. Sanayi üretim endeksinin gelecekteki hareketlerini önceden tahmin edebilmek, büyüme göstergelerinden biri olduğundan dolayı önem arz etmektedir. Yapılmış olan bu çalışmada sanayi üretim endeksi bileşenlerinden olan elektrik üretim endeksinin tahmin edilmesi için önceki yıllardaki veriler kullanılarak bulanık mantık yöntemleri arasında yer alan Uyarlamalı Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS) modelleri üretilmiştir. ANFIS modelleri için çeşitlendirme üyelik fonksiyonu ve üyelik fonksiyon sayısını değiştirerek elde edilmiştir. ANFIS’de yapılan tahminler bu modeller üzerinde gerçekleştirilmiştir. ANFIS modelleri incelendiğinde zaman, maden ve imalat üretim endeksleri girdilerinden oluşan sistemin sadece zaman veya sadece imalat-maden üretim endekslerine dayalı tahmin yapan sistemden daha iyi sonuç verdiği de görülmüştür.