Browsing by Author "Aslan, E."

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Citation - Scopus: 0
Comparison and Optimization of Machine Learning Methods for Fault Detection in District Heating and Cooling Systems
(Polska Akademia Nauk, 2025) Aslan, Emrah; Aslan, E.; Özüpak, Y.; Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
In this study, the methods used for the detection of sub-station pollution failures in district heating and cooling (DHC) systems are analyzed. In the study, high, medium, and low-level pollution situations are considered and machine learning methods are applied for the detection of these failures. Random forest, decision tree, logistic regression, and CatBoost regression algorithms are compared within the scope of the analysis. The models are trained to perform fault detection at different pollution levels. To improve the model performance, hyperparameter optimization was performed with random search optimization, and the most appropriate values were selected. The results show that the CatBoost regression algorithm provides the highest accuracy and overall performance compared to other methods. The CatBoost model stood out with an accuracy of 0.9832 and a superior performance. These findings reveal that CatBoost-based approaches provide an effective solution in situations requiring high accuracy, such as contamination detection in DHC systems. The study makes an important contribution as a reliable fault detection solution in industrial applications. © 2025 The Author(s).
Citation - Scopus: 0
Development of Malaria Diagnosis With Convolutional Neural Network Architectures: a Cnn-Based Software for Accurate Cell Image Analysis
(Galileo Institute of Technology and Education of the Amazon (ITEGAM), 2025) Aslan, Emrah; Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü
This study emphasizes that early diagnosis and treatment of malaria is critical in reducing health problems and mortality from the disease, especially in developing countries where the disease is prevalent. Malaria is a potentially fatal disease transmitted to humans by mosquitoes infected by a blood parasite called Plasmodium. The traditional method of diagnosis relies on experts examining red blood cells under a microscope and is inefficient as it is dependent on expert knowledge and experience. Nowadays, machine learning methods that provide high accuracy are increasingly used in disease detection. In this paper, a Convolutional Neural Network (CNN) architecture is proposed to distinguish between parasitized and non-parasitized cells. In addition, the performance of the proposed CNN architecture is compared to pre-trained CNN models such as VGG-19 and EfficientNetB3. The studies were carried out using the Malaria Dataset supplied by the National Institute of Health (NIH), and our proposed architecture was shown to function with 99.12% accuracy. The results of the study reveal that it is effective in improving the accuracy of cell images containing Plasmodium. In addition, a software that predicts whether cell images are noisy or not has been developed. © 2025 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM).