Browsing by Author "Filiz, Gozde"

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İnternet Trafik Hızının Tahmininde Derin Öğrenme ve Ağaç Tabanlı Modellerin Karşılaştırılması
(Institute of Electrical and Electronics Engineers Inc., 2025) Filiz, Gozde; Altıntaş, Suat; Yıldız, Ayşenur; Kara, Erkan; Drias, Yassine; Çakar, Tuna
This study addresses the prediction of internet traffic speed using time-dependent data from an internet service provider through different modeling approaches. On an anonymized dataset, the performance of the moving average method, various deep learning models (N-BEATS, N-HITS, TimesNet, TSMixer, LSTM), and the XGBoost regression model enhanced with feature engineering was compared. Time series cross-validation and random hyperparameter search were used for model training. According to the results, the XGBoost model achieved the highest accuracy with 98.7% explained variance (R2), while among the deep learning models, N-BEATS and N-HITS achieved the best performance with R2 values around 90%. The findings indicate that tree-based methods supported by carefully selected features can offer higher accuracy and computational efficiency compared to complex deep learning models in internet traffic forecasting. © 2025 Elsevier B.V., All rights reserved.
Predicting Credit Repayment Capacity With Machine Learning Models
(Ieee, 2024) Filiz, Gozde; Bodur, Tolga; Yaslidag, Nihal; Sayar, Alperen; Çakar, Tuna
This 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.
Citation - Scopus: 1
Physical Activity Monitoring With Smartwatch Technology in Adolescents and Obtaining Big Data: Preliminary Findings
(Ieee, 2024) Filiz, Gozde; Arman, Nilay; Ayaz, Nuray Aktay; Yekdaneh, Asena; Albayrak, Asya; Bozkan, Tunahan; Çakar, Tuna
This 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.
Reliability Study of Psychometric Tests in a Credit Scoring Model
(Ieee, 2024) Nicat, Sahin; Filiz, Gozde; Ozvural, Ozden Gebizlioglu; Çakar, Tuna
This study investigates the effectiveness and reliability of using psychometric tests in the credit decision-making processes within the finance sector. Psychometric tests, by measuring individuals' cognitive and psychological traits, hold the potential to broaden access to credit and identify high credit risk. However, after the literature review, it was seen that there was a need for more studies on the reliability and validity of these tests in finance. This study is designed to measure the test-retest reliability of a machine learning model and its inputs that utilize psychometric test results. Within the scope of the research, 115 participants were re-subjected to the same psychometric tests after an average of 6 months. Findings showed that psychometric tests and the machine learning model were generally consistent over time. This work has the potential to fill the gaps in the literature regarding the use of psychometric tests in the finance sector and lays a foundation for future research.
Citation - Scopus: 1
Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making
(Ieee, 2024) Filiz, Gozde; Son, Semen; Sayar, Alperen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna
Functional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research.
Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning
(Ieee, 2024) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, Yassine
This 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.