Browsing by Author "Erdem, Z."

Now showing 1 - 4 of 4

Citation - Scopus: 3
A Comparative Study on Denoising From Facial Images Using Convolutional Autoencoder
(Gazi Universitesi, 2023) Darici, M.B.; Erdem, Z.
Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise. © 2023, Gazi Universitesi. All rights reserved.
Citation - Scopus: 0
Forecasting the Short-Term Electricity in Steel Manufacturing for Purchase Accuracy on Day-Ahead Market
(Institute of Electrical and Electronics Engineers Inc., 2022) Koca, A.; Erdem, Z.; Aydin, M.N.
Forecasting electricity consumption in the most accurate way is crucial for purchase on the day-ahead market in steel manufacturing. This study is aimed to predict short-term electricity consumption regarding the day-ahead market purchase by employing important features of electricity consumption time-series data. We utilize Random Forest (RF), Gradient-Boosted Trees (GBT), and Generalized Linear Models (GLM), as they are appropriate for the given problem and widely used regression algorithms for prediction purposes. This study leverages the regression algorithms in the Apache Spark Machine Learning library. The performance of the prediction models is evaluated based on the standard deviation of the residuals (RMSE) and the proportion of variance explained (R-squared). We additionally discuss the distribution of prediction errors of the models. Experiments show that the RF model outperforms the GBT and GLM. It is considered that the results can contribute to accurate forecasting of short-term electricity demand for purchasing on the day-ahead. © 2022 IEEE.
Citation - Scopus: 1
Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case
(Institute of Electrical and Electronics Engineers Inc., 2022) Ecevit, M.I.; Ecevit, Mert İlhan; Erdem, Z.; Dag, H.
In the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry. © 2022 IEEE.
Towards Better Energy Efficiency Through Coil-Based Electricity Consumption Forecasting in Steel Manufacturing
(Institute of Electrical and Electronics Engineers Inc., 2024) Koca, A.; Erdem, Z.; Dag, H.
Forecasting electricity consumption with the possibly-highest accuracy is crucial for cost optimization, operational efficiency, competitiveness, contract negotiation, and achieving the global goals of sustainable development in steel manufacturing. This study focuses on identifying the most appropriate prediction algorithm for coil-based electricity consumption and the most effective implementation purposes in a steel company. Random Forest, Gradient-Boosted Trees, and Deep Neural Networks are preferred because they are suitable for the given problem and widely used for forecasting. The performance of the prediction models is evaluated based on the root mean squared error (RMSE) and the coefficient of determination (R-squared). Experiments show that the Random Forest model outperforms the Gradient-Boosted Trees and Deep Neural Network models. The results will provide benefits for many different purposes. Firstly, during contract negotiations, it will enable us to gain a competitive advantage when purchasing electricity in the day-ahead market. Secondly, in the production scheduling phase, the ones with the highest electricity consumption will be produced during the hours when there is the least demand at the most affordable prices. Finally, when prioritizing sales orders, the use of the existing capacity for orders with lower energy intensity or a higher profit margin will be ensured. © 2024 IEEE.