Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Language: search.filters.language.trSubject: search.filters.subject.Machine Learning

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Now showing 1 - 6 of 6
  • 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 University
    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.
  • 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 University
    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.
  • Conference Object
    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; Çakar, Tuna; Son Turan, Semen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna; 02.02. Department of Computer Engineering; 04.03. Department of Business Administration; 02. Faculty of Engineering; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF University
    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.
  • Conference Object
    Predicting Animal Behaviours: Physical and Behavioural Classification Of Dog Walking Levels
    (IEEE, 2022) Ozen, Guris; Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    Methods of predicting canine behaviour is an area covered by canine behaviour experts. This study aims to predict the behaviour of dogs during walking based on available information about dogs. In this data-driven project based on up-to-date company data, the problem of predicting dog behaviour was addressed in two different ways. First, it is aimed to create a supervised classification model. Within the scope of this study, improvements were made to various classification algorithms. The results were analyzed in different axes. Secondly, it is aimed to create a new parameter that predicts dog walking difficulties by formulating the parameters.
  • Conference Object
    Citation - Scopus: 1
    Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods
    (IEEE, 2022) Koksal, Mehmet Yigit; Çakar, Tuna; Demircioğlu, Esin Tuna; Girisken, Yener; Tuna, Esin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    The fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, near infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether people like a stimulus or not, using machine learning various algorithms. A comparison was made between feature extraction methods, which was the main focus while developing the model.
  • Conference Object
    Customer Segmentation and Churn Prediction via Customer Metrics
    (IEEE, 2022) Bozkan, Tunahan; Çakar, Tuna; Sayar, Alperen; Ertugrul, Seyit; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data-driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) was carried out. It was estimated by the XGBoost model with an F1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customers who will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.