Psikoloji Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1938

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Browsing Psikoloji Bölümü Koleksiyonu by Department "Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü"

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    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
    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.
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    Citation - Scopus: 1
    Modeling Consumer Creditworthiness Via Psychometric Scale and Machine Learning
    (IEEE, 2022) Çakar, Tuna; Ertugrul, Seyit; Sayar, Alperen; Sahin, Türkay; Bozkan, Tunahan
    Although the predictive power of economic metrics to detect the creditworthiness of the customers is high, there is a rising interest in the integration of cognitive, psychological, behavioral, alternative, and demographic data into credit risk systems and processing the data through modern methods. The primary motivation for the rising interest is increased customer classification accuracy. In this research, customer creditworthiness was modeled through data consisting of personality, money attitudes, impulsivity, self-esteem, self-control, and material values and processed through artificial intelligence. The obtained findings have been evaluated as a reference point for the following research. © 2022 IEEE.