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    Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2021) Karamollaoğlu, Hamdullah; Yücedağ, İbrahim; Doğru, İbrahim Alper
    Customer churn analysis is the process of predicting customers who tend to cancel the service (subscription) they receive for various reasons, especially in sectors such as telecommunications, finance and insurance, and determining the necessary operational steps to prevent this cancellation. The study used two separate datasets from kaggle.com to identify customers who tend to unsubscribe in the telecommunications industry. The analysis process was carried out by applying machine learning methods such as Logistic Regression, K-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Multi-Layer Sensors and Naive Bayes methods on the relevant datasets. It was seen that the most successful method in the customer loss analysis performed on both datasets was the Random Forest method. © 2021 IEEE
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    An in-depth analysis of Android malware using hybrid techniques
    (Elsevier Sci Ltd, 2018) Kabakuş, Abdullah Talha; Doğru, İbrahim Alper
    Android malware is widespread despite the effort provided by Google in order to prevent it from the official application market, Play Store. Two techniques namely static and dynamic analysis are commonly used to detect malicious applications in Android ecosystem. Both of these techniques have their own advantages and disadvantages. In this paper, we propose a novel hybrid Android malware analysis approach namely mad4a which uses the advantages of both static and dynamic analysis techniques. The aim of this study is revealing some unknown characteristics of Android malware through the used various analysis techniques. As the result of static and dynamic analysis on the widely used Android application datasets, digital investigators are informed about some underestimated characteristics of Android malware. (c) 2018 Elsevier Ltd. All rights reserved.
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    Risk Assessment for Electricity Generation Management Process with SWARA Based Fuzzy TOPSIS Method
    (Gazi Univ, 2022) Karamollaoğlu, Hamdullah; Yücedağ, İbrahim; Doğru, İbrahim Alper
    In the successful maintenance of electricity generation management processes in power generation plants, it is of great importance to determine the risks that may arise during the operation of the relevant processes, take measures to minimize these risks, and take the necessary actions. In this study, common risks in the electricity generation management process in HEPPs were identified and these risks were rated by experts (decision-makers) within each power plant itself. Since this rating is made by the experts of each power plant, the impact and probability values of the same risk may differ, and accordingly, different risk levels may arise for the same risk. In the study, the SWARA method was used to compare the risk levels of common risks in the electricity generation process in different power plants and calculate the final weight values of the related risks. As a result of the measures determined for each risk in the electricity generation management processes in the power plants and the actions taken for these measures, it was determined whether the relevant risks were reduced to acceptable levels by looking at the results of the internal audits. In the internal audits, the performance of HEPPs in eliminating the related risks is evaluated with fuzzy expressions separately for each risk. The risk weight values obtained by the SWARA method and the fuzzy expressions obtained as a result of the risk assessment were analyzed with the Fuzzy TOPSIS method, and the performance values of the power plants in eliminating the risks were calculated, then the performance ranking was made in the light of these values.