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Prediction of Loan Decisions With Optical Neuroimaging (fnirs) and Machine Learning
(IEEE, 2023-07-05) Girişken, Yener; Son Turan, Semen; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Son, Semen; Giriken, Yener
The successful applications of neuroscientific methods and artificial learning approaches have increased in applied fields such as economics, marketing, and finance in the last decade. In this study, a prediction model was developed using the output of optical neuroimaging (fNIRS) measurements from the prefrontal brain regions while 40 participants made decisions for 35 credit offers. The aim was to predict participants' responses to credit offers using artificial learning methods based on four metrics obtained over time from the optical neuroimaging system. The findings of the study indicate that the first 6 seconds (prior to the response entry) are particularly critical. While the performance rate in the developed prediction models is found to be higher, especially in tree-based algorithms, this paper includes a performance comparison of 5 models specifically.
Citation - WoS: 35
Covid-19 Sonrası Yükseköğretim
(Deomed, 2020-07-27) Erkut, Erhan
Higher education is one of the most severely impacted sectors by Covid-19. Almost all schools worldwide had to stop face-to-face education and approximately 2 billions students were forced to get their education online. This pandemic demonstrated that the Turkish higher education system was not well-prepared for a crisis of this proportion, nor was it ready for effective online teaching. High-quality online education is a fact of life now, and administrators, as well as faculty members have an important task on hand. In this opinion piece, 1 offered some ideas about improving the quality of online education, and discussed other impacts of this pandemic on higher education, along with some warnings. I believe this pandemic offers an opportunity for the outdated higher education system worldwide and the universities (and countries) that adopt quickly will be the winners.
Türkiye'de Yükseköğrenim Finansmanının Özelleştirilmesi
(Sosyoekonomi Society, 2018-01-31) Son-Turan, Semen
This study aims at developing a model for the privatization of higher education finance inTurkey. While the primary target is the NEET (Not in Education, Employment, or Training)population, it offers a broad range of potential solutions involving asset securitization for highereducation financing. Data is driven from secondary sources. The paper presents an interdisciplinaryapproach for privatizing higher education finance involving the labor market, higher educationinstitutions and the Turkish capital market and constitutes a unique contribution to the Turkish highereducation finance literature.

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