Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Analyzing Customer Churn: a Comparative Study of Machine Learning Models on Pay-Tv Subscribers in Turkey(IEEE, 2023) Obalı, Emir; Çakar, Tuna; Karani Yılmaz, Veysel; Kara, Erkan; Meşe, Yasemin Kürtcü; Çakar, Tuna; Yıldız, Ayşenur; Hataş, Tuğce Aydın; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityUnderstanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.Conference Object Fault Detection Model Using Measurement Data in Fiber Optic Internet Lines(IEEE, 2023) Çakar, Tuna; Çakar, Tuna; Battal, Eray; Özkan, Gözde; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, a model has been developed to predict potential faults in advance based on performance metrics of various fiber-optic internet lines, as well as alarm (fault data) and performance measurement values from the 5 hours prior to the occurrence of the alarm. Performance metrics that vary over time have been analyzed in a time-series format based on alarm numbers, and anomaly detection methods have been used to label the data for any potential patterns that may occur in the performance metrics specific to the alarm. The labeled data was then fed into a classification model to create a model that enables to detect possible patterns in the relevant performance values for the specific fault type. The best performing model was Random Forest Classifier with accuracy and F1 scores of 0.89 and 0.84 respectively.Conference Object Citation - Scopus: 3Grafraud: Fraud Detection Using Graph Databases and Neural Networks(IEEE, 2023) Raina, Ajeet Singh; Çakar, Tuna; Ertuğrul, Seyit; Arslan, Şuayip; Sayar, Alperen; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle Databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle Database to construct and deploy real-time systems that can efficiently detect fraudulent activities.Article Citation - WoS: 4Citation - Scopus: 8Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception(Frontiers Media SA, 2023) Çakar, Tuna; Filiz, Gözde; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityPolitical neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behaviorConference Object Spine Posture Detection for Office Workers With Hybrid Machine Learning(IEEE, 2023) Öke, Deniz; Çakar, Tuna; Yıldız, Ahmet; Mise, Pelin; Terzibaşıoğlu, Aynur Metin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study aims to detect bad spine posture using an al-ternative approach that doesn't rely on deep learning or excessive energy. The goal is to improve accuracy and effectiveness without disrupting workflow. A custom dataset was created, numerical inferences were made from posture values, and a hybrid approach using Light Gradient Boosting achieved a 96 % success rate.Conference Object Citation - Scopus: 3Segmentation for Factoring Customers: Using Unsupervised Machine Learning Algorithms(IEEE, 2023) Yalçuva, Berat; Akçay, Ahmet; Çakar, Tuna; Çakar, Tuna; Sayar, Alperen; Ayyıldız, Nur Seher; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityNowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown in real life segmentation problem by using the domestic factoring company’s customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixture Modeling methods, Fuzzy c- Means were used as well as the most popular K-Means algorithm. When the clustering results were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering could not produce expected results. It was observed that the best results were obtained with the K-Means and Fuzzy c - Means algorithms.Conference Object Model for Estimating the Probability of a Customer To Have a Transaction(IEEE, 2022) Sayar Alperen; Çakar, Tuna; Ertugrul Seyit; Bozkan Tunahan; Sayar, Alperen; Cakar, Tuna; Ertugrul, Seyit; Bozkan, Tunaban; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, it is aimed to estimate the probability of a customer who comes to the institution for the first time to make a transaction in the next 3 months, using data-driven machine learning models, in order to provide financing to the seller company by assigning the receivables arising from the sale of goods and services in a company actively operating in the factoring sector. Accordingly, it was aimed to directly contribute to the transaction volume on a business basis by acting and taking action with more effective, efficient and correct approaches by finding high-potential and low-potential customers. In this context, provided by KKB (Credit Registration Bureau); The data set to he used in machine learning models was created with feature engineering and exploratory data analysis, using the Risk, Mersis, GIB information of the prospective customers and the historical information of the customers, check issuers, customer representatives and branches kept in the database. Since the leads coming to the institution are in two different types of organizations (Individual and Legal), two different forecasting models were applied. Multiple classification models were tried, and the highest F1-Score of 86% for private companies was obtained with the Random Forest model, and the highest F1- Score for commercial companies was obtained with the Random Forest model with 82%. © 2022 IEEE.Conference Object Citation - Scopus: 3Emg-Based Bci for Picar Mobilization(IEEE, 2022) Yilmaz, Yasin; Arslan, Şefik Şuayb; Çakar, Tuna; Sayar, Alperen; Çakar, Tuna; Arslan, Şefik Şuayb; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, the main scope was to develop a brain-computer interface (BCI) with the use of PiCar and EEG/ERP devices. Thus, it is aimed to facilitate the lives of people with certain diseases and disabilities. The ultimate goal of this project has been to direct and control a BCI-based PiCar concerning the signals captured via the EEG/ERP device. With the EEG headset, the EMG signals of the gestures (facial expressions) of the participant were captured. With the collected data, filtering and other preprocessing methods were applied to have noise-free signals. In the preprocessing, the detrending method was used to clean the data set which showed a constantly increasing trend, to a certain range, and zero trends. The denoising (Wavelet Denoising) and outlier detection/elimination methods (OneClassSVM) were used for noise elimination. The SMOTE oversampling method was used for data augmentation. Welch's method was used to get band powers from the signals. With the use of augmented data, several machine learning algorithms were applied such as Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, Random forest Classifier, Gradient Boosting Classifier, Multinomial Naive Bayes, Decision tree, K-Nearest Neighbor, and voting classifier. The developed models were used to predict the direction that is passed as an input to PiCar's API. After that, PiCar was controlled concerning the predicted direction with HTTP GET requests. In this project, the OpenBCI headset and the Brainflow library for EEG/EMG signal obtaining and processing were used. Also, the Tkinter library was used for the Graphical user interface and Django for establishing a server on PiCar's brain which is RaspberryPi. © 2022 IEEE.