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    The electricity price prediction of victoria city based on various regression algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Örenç, Sedat; Acar, Emrullah; Özerdem, Mehmet Siraç
    Precise electricity price prediction is extremely important for all markets especially for families' life conditions because the more demand the more electricity price increases, therefore it is vital to keep the balance between demand and supply. It is crucial to know how much electricity is needed for the future as it has a remarkable impact on economic circumstances. This article proposes four productive methods in order to forecast high-precision results. In the regression algorithms, it is used several methods which are called decision tree regressions, random forest regression, gradient boosting regression, and linear regression algorithms. The dataset is divided into three parts. Training, validation, and test are split into %70, %10, and %20 respectively. The empirical and efficient results show that these methods can be used and reduce errors. The article demonstrates that a novel forecasting model can be designed for the future.
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    Utilizing the ensemble of deep learning approaches to identify monkeypox disease
    (Dicle Üniversitesi Mühendislik Fakültesi, 2022) Örenç, Sedat; Acar, Emrullah; Özerdem, Mehmet Siraç
    Recently, the monkeypox disease spreads to many countries rapidly and it becomes a serious health problem. There are several symptoms that decrease the quality of the life. These symptoms must be overcome to detect monkeypox disease in earlier stages. Therefore, it is crucial to decrease the spread rate with the quick determination of the disease. In this study, it is aimed to identify monkeypox disease from images datasets obtained from Kaggle by using Convolutional Neural Network models. These models are named EfficientNetB3, ResNet50, and InceptionV3 respectively. According to the results of the three models, resNet50 is the best model when they compare aspects of performance. The accuracy of resNet50 is %94,00 therefore it has highest accuracy value. There are four parameters to evaluate the performance of the models. They are called as precision, recall, F1-score, and accuracy. These models demonstrate that monkeypox can be classified with high precision. Therefore these models can be used for the future of the work.