Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Browsing Bilgisayar Mühendisliği Bölümü Koleksiyonu by Journal "2023 31st Signal Processing and Communications Applications Conference (SIU)"

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    Citation - Scopus: 2
    Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models
    (IEEE, 2023) Şahin, Türkay; Filiz, Gözde; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin
    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.
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    Citation - WoS: 2
    Citation - Scopus: 2
    Classification of Altruistic Punishment Decisions by Optical Neuroimaging and Machine Learning Methods
    (IEEE, 2023) 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.
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    Neural Correlates of Identity Judgments in the Prefrontal Cortex: an Optical Brain Imaging (fnirs) Study
    (IEEE, 2023) Çakar, Tuna; Hohenberger, Annette
    The prefrontal cortex (PFC) plays a crucial role in human reasoning and decision-making. Studies using neuroimaging techniques have shown that situations involving conflicts lead to increased activity in both the prefrontal cortex and the anterior cingulate cortex (ACC). This research specifically investigates the activity in the prefrontal cortex when individuals assess statements related to identity. The results obtained through optical brain imaging (fNIRS) indicate that participants experience greater conflict when evaluating propositions they strongly disagree with, compared to propositions they strongly agree with. Furthermore, responses that are indeterminate lead to higher activation levels in prefrontal regions. Additionally, the analysis of the participants' reaction times reveals significant differences associated with the content of their responses.
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    Noise Effect on Forecasting
    (IEEE, 2023) Tuncer, Suat; Kayan, Ersan; Çakar, Tuna
    The lack of regulation and liquidity in crypto money markets causes higher volatility compared to other financial markets. This situation increases the noise in price change. The high noise and random walk create a problem that cannot be explained by traditional stochastic financial methods. For this reason, a multi-layered deep learning model with an additive attention layer, which uses a single observation in 10-day sequences, was used in this study. Different transformations are used to reduce the noise of the closing values. As a result of the comparisons made between different approaches, it has been revealed that exponential moving averages, to be used as the value to predict, give better results than other conversions and estimation of the original price, since they explain the price better than simple moving averages and reduce the noise of the original price.
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    Transaction Volume Estimation in Financial Markets With Lstm
    (IEEE, 2023) Bozkan, Tunahan; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Akçay, Ahmet
    In this study, it was aimed to determine the transaction volume that will be encountered in the future (hourly) in the factoring sector, and then to take financial and operational action early. For the study, the LSTM model, which is a kind of recurrent neural network (RNN) that can capture long and short-term dependencies, was applied by using data-driven approaches to estimate the check amounts of hourly transactions. As a result of the results, it was aimed to increase the operational efficiency in a broad scope by allowing the factoring company to determine the loan amounts to be obtained from banks in the most optimal way, and then to take early action within the scope of both the workforce and business management of the financial resource allocation management process and operational activities. MAPE score was used as a measure of error in the time series analysis model. MAPE scores were found as %5.05 for 30 days, %4.18 for 10 days, %3.47 for 5 days, %3.09 for 3 days and %1.83 for 1 day. According to the MAPE scores calculated for different days, the enterprise will be able to decide on the loan to be drawn from banks both in terms of time and amount, and the necessary action will be taken.