Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Evaluating Electrophysiological Responses Due To Identity Judgments(Ieee, 2024) Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study was conducted to explore how the brain processes decisions about identity, employing event-related potentials (ERPs) as a measure. The aim was to ascertain if the EEG/ERP technique could be used to monitor the cognitive processing of identity judgments as they happen. The investigation focused on comparing two groups of statements: those that used the concept of 'same' and those that used 'different'. The researchers hypothesized that there would be notable differences in the ERPs, particularly around the 400-millisecond mark, correlating with the reaction time disparities observed behaviorally. The ERP data revealed that the 'different' statements generated a unique N400 response when contrasted with the 'same' statements, implying that the participants' cognitive responses to these two types of judgments were not the same.Conference Object Predicting Credit Repayment Capacity With Machine Learning Models(Ieee, 2024) Filiz, Gozde; Çakar, Tuna; Yaslidag, Nihal; Sayar, Alperen; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study examines the transformation in the financial services sector, particularly in banking, driven by the rapid development of technology and the widespread use of big data, and its impact on credit prediction processes. The developed credit prediction model aims to more accurately predict customers' credit repayment capacities. In pursuit of this goal, demographic and financial data along with credit histories of customers have been utilized to employ data preprocessing techniques and test various classification algorithms. Findings indicate that models developed with XGBoost and CATBoost algorithms exhibit the highest performance, while the effective use of feature engineering techniques is revealed to enhance the model's accuracy and reliability. The research highlights the potential for financial institutions to gain a competitive advantage in risk management and customer relationship management by leveraging machine learning models.Conference Object Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning(Ieee, 2024) Çakar, Tuna; Girisken, Yener; Drias, Yassine; Filiz, Gozde; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.Conference Object Citation - Scopus: 1Physical Activity Monitoring With Smartwatch Technology in Adolescents and Obtaining Big Data: Preliminary Findings(Ieee, 2024) Filiz, Gozde; Çakar, Tuna; Ayaz, Nuray Aktay; Yekdaneh, Asena; Albayrak, Asya; Bozkan, Tunahan; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study assesses the potential of smartwatch technology in monitoring adolescents' physical activity and health parameters. It focuses on the role of physical activity in preventing chronic diseases and improving quality of life. The primary aim of the project is to perform statistical analysis of the large data sets collected from both healthy adolescents and those with chronic rheumatic diseases, and to develop a machine learning-based classification model to distinguish between these two groups. This analysis highlights the issue of physical inactivity observed during the Covid-19 pandemic, while showcasing the capacity of technology to offer solutions. The study aims to evaluate the collected data in a way that forms the basis for personalized activity plans for adolescents, demonstrating how wearable technology and big data can be effectively used in health services and to promote physical activity.Article Ön Eğitimli Dil Modelleriyle Duygu Analizi(İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü, 2023) Demir, Şeniz; Demir, Şeniz; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityDuygu analizi, çeşitli platformlarda bir konu hakkında düşünce, duygu ya da tutumu irdelemek, analiz etmek ve yorumlamak amacıyla kullanılan yöntemlerden biridir. Farklı konulardaki metinlerin öznel içeriklerine göre sınıflandırılabildiği duygu analizinde makine öğrenmesi ve derin öğrenme modellerinden sıklıkla faydalanılmaktadır.Bu çalışmada, önceden eğitilmiş dil modellerinden yararlanılarak Covid-19 tweet metinleri üzerinde duygu analizi yapılmıştır. Naive Bayes sınıflandırıcıya ek olarak BERT, RoBERTa ve BERTweet dil modelleri kullanılarak farklı sınıflandırıcılar eğitilmiş ve tweet veri kümesi üzerinde elde edilen sonuçlar kıyaslanmıştır. Bildiride aktarılan çalışmanın ileride bu alanda yürütülecek araştırmalara bir zemin oluşturacağı öngörülmektedir.Conference Object Citation - Scopus: 6High-Performance Real-Time Data Processing: Managing Data Using Debezium, Postgres, Kafka, and Redis(IEEE, 2023) Çakar, Tuna; Çakar, Tuna; Arslan, Şuayip; Sayar, Alperen; Akçay, Ahmet; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis research focuses on monitoring and transferring logs of operations performed on a relational database, specifically PostgreSQL, in real-time using an event-driven approach. The logs generated from database operations are transferred using Apache Kafka, an open-source message queuing system, and Debezium running on Kafka, to Redis, a non-relational (No-SQL) key-value database. Time-consuming query operations and read operations are performed on Redis, which operates on memory (in-memory), instead of on the primary database, PostgreSQL. This approach has significantly improved query execution performance, data processing time, and backend service performance. The study showcases the practical application of an event-driven approach using Debezium, Kafka, Redis, and relational databases for real-time data processing and querying.Conference Object Noise Effect on Forecasting(IEEE, 2023) Tuncer, Suat; Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe lack of regulation and liquidity in crypto money markets causes higher volatility compared to other financial markets. This situation increases the noise in price change. The high noise and random walk create a problem that cannot be explained by traditional stochastic financial methods. For this reason, a multi-layered deep learning model with an additive attention layer, which uses a single observation in 10-day sequences, was used in this study. Different transformations are used to reduce the noise of the closing values. As a result of the comparisons made between different approaches, it has been revealed that exponential moving averages, to be used as the value to predict, give better results than other conversions and estimation of the original price, since they explain the price better than simple moving averages and reduce the noise of the original price.Conference Object Transaction Volume Estimation in Financial Markets With Lstm(IEEE, 2023) Bozkan, Tunahan; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Akçay, Ahmet; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, it was aimed to determine the transaction volume that will be encountered in the future (hourly) in the factoring sector, and then to take financial and operational action early. For the study, the LSTM model, which is a kind of recurrent neural network (RNN) that can capture long and short-term dependencies, was applied by using data-driven approaches to estimate the check amounts of hourly transactions. As a result of the results, it was aimed to increase the operational efficiency in a broad scope by allowing the factoring company to determine the loan amounts to be obtained from banks in the most optimal way, and then to take early action within the scope of both the workforce and business management of the financial resource allocation management process and operational activities. MAPE score was used as a measure of error in the time series analysis model. MAPE scores were found as %5.05 for 30 days, %4.18 for 10 days, %3.47 for 5 days, %3.09 for 3 days and %1.83 for 1 day. According to the MAPE scores calculated for different days, the enterprise will be able to decide on the loan to be drawn from banks both in terms of time and amount, and the necessary action will be taken.Conference Object Citation - Scopus: 2Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models(IEEE, 2023) Şahin, Türkay; Çakar, Tuna; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin; Gebizlioǧlu Özvural, Özden; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study aims to evaluate individuals with limited access to banking services and enhance credit scoring models with alternative data sources. A psychometric-based credit scoring model was developed and tested. Despite limited data, significant potential findings were obtained. However, clarification of the distinction between credit payment intention and ability and validation of the results with more data are necessary.Conference Object Citation - WoS: 2Citation - Scopus: 2Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods(IEEE, 2023) Erözden, Ozan; Akyürek, Güçlü; Çakar, Tuna; Erözden, Ozan; Çakar, Tuna; 02.02. Department of Computer Engineering; 05. Faculty of Law; 02. Faculty of Engineering; 01. MEF UniversityAltruistic punishment (third-party punishment) is important in terms of maintaining social norms and promoting prosocial behavior. This study examined data obtained using the near infrared spectroscopy (fNIRS) method to predict altruistic punishment decisions. It was found that specific neural activity patterns were significantly related to decisions regarding the punishment of the perpetrator. This research contributes to the development of social decision-making models and helps advance our understanding of the cognitive and neural processes involved in third-party punishments.
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