Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Author: search.filters.author.Ertugrul, SeyitSubject: search.filters.subject.Machine Learning

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  • 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, 2024-05-15) 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.
  • Conference Object
    Customer Segmentation and Churn Prediction via Customer Metrics
    (IEEE, 2022-05-15) Bozkan, Tunahan; Cakar, Tuna; Sayar, Alperen; Ertugrul, Seyit
    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.