Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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  • Conference Object
    Analyzing Consumer Behavior: the Impact of Retro Music in Advertisements on a Chocolate Brand and Consumer Engagement
    (IEEE, 2023-10-11) Girişken, Yener; Soyaltın, Tuğçe Ezgi; Filiz, Gözde; Çakar, Tuna; Türkyılmaz, Ceyda Aysuna
    This study presents research utilizing binary classification models to analyze consumer behaviors such as chocolate consumption and retro music ad viewing. Retro music, with its potential to evoke nostalgic feelings in consumers, is used in advertisements, which can have a significant impact on brand perception and consumer engagement. Firstly, a model focusing on chocolate consumption was developed and tested. The model yields significant outcomes. Secondly, a model based on retro music ad viewing status was developed and tested. Significant potential findings were obtained. This study emphasizes the applicability of effective classification models that can be used to understand and predict consumer behaviors, yielding significant outcomes.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods
    (IEEE, 2023-07-05) Erözden, Ozan; Şahin, Türkay; Akyürek, Güçlü; Filiz, Gözde; Çakar, Tuna
    Altruistic 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.
  • Conference Object
    Citation - Scopus: 2
    Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models
    (IEEE, 2023-07-05) Şahin, Türkay; Filiz, Gözde; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin; Gebizlioǧlu Özvural, Özden
    This 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.