DSpace Arşivi :: Ana Sayfa

İZÜ Araştırma ve Akademik Performans Sistemi

DSpace@İZÜ, İstanbul Sabahattin Zaim Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.

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Integrating AI Technologies in Interest-Free Finance: Advancing Sector Capabilities through Innovative Model Proposals
(University of Turin, 2025) Lokce, Ahmet; Yumuşak, İbrahim Güran; Yumuşak, İbrahim Güran
This study explores the intersection of artificial intelligence (AI) technologies and interest-free finance, delving into the transformative potential of financial technology advancements in this sector. The aim establishes a theoretical framework fora financial interaction model grounded in machine learning, a subset of AI technologies. This framework underpins the development of novel digital contract methods adhering to the principles of interest-free finance. Central to this investigation is conceptualising and evaluating the "Benefit Sharing Model," which utilizes machine-learning techniques. This model serves as the foundation for eight distinct digital contract proposals, offering innovative solutions for the operational challengesin the interest-free finance sector. These digital models facilitate various financial interactions, such as deposit collection and financing processes, for users within the interest-free financial system. A significant study component involves a comparative analysis of these smart contract proposals, envisioned as blockchain-based, smart, interest-free financial contracts, against existing models in the field. This comparison demonstrates the technical feasibility and applicability of these proposals andhighlights their uniqueness and potential advantages. This research contributes to the diversification and expansion of interest-free financial technology applications by introducing smart contract models and exploring their practical implications. It underscores the possibilities for broadening the scope and enhancing the growth of the interest-free finance sector, marking a significant step towards integrating cutting-edge AI technologies into ethical and interest-free financial practices.
The Impact of Stock Market-Based Financial Development on Economic Growth: Evidence from Turkey
(Bandırma Onyedi Eylül Üniversitesi, 2025) Yıldırım, Durmuş Çağrı; Yumuşak, İbrahim Güran; Gürçek, Dilan; Yumuşak, İbrahim Güran
his study investigates the impact of stock market-based financial development on economicgrowth in the case of Türkiye. Financial development is considered a fundamental component ofeconomic progress, playing a crucial role in understanding the relationship between financial marketsand economic expansion. The study utilizes quarterly data from January 1999 to January 2023 andapplies the Autoregressive Distributed Lag (ARDL) cointegration methodology. The findings suggestthat financial development, alongside consumption and net exports, has a positive influence oneconomic growth. However, investment has no significant effect on the growth rate, whereas governmentexpenditure has a negative impact. Jel Codes: D53, O11, C32.
Nagayev, Ruslan
Doç. Dr.
Fueling the bottom line: Decoding the effects of oil on banking performance in net oil-importing economies
(Elsevier, 2024) Çıkıryel, Burak; Savaşan, Fatih; Nagayev, Ruslan; Görmüş, Şakir; Nagayev, Ruslan
Banks play a pivotal role in the financial sector, assuming critical functions such as facilitating the monetary policy transmission mechanism and acting as intermediaries between savers and borrowers. Meanwhile, oil represents a fundamental input for economic activities, and its inherent volatility serves as a significant catalyst for economic instability. Given the critical roles of banking institutions and oil in the economy, their relationship garners attention from various stakeholders. The growing body of literature has examined the nexus between oil and banking performance. However, existing research has predominantly concentrated on either oil-exporting jurisdictions or country-specific analyses. Hence, the present study endeavors to bridge this gap in the litera-ture by investigating the intricate dynamics between oil and banking performance, specifically in net oilimporting countries. The dynamic panel method is employed. The findings indicate that oil has direct and in-direct effects on the profitability of banks operating through transmission channels.
Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms
(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2024) Zontul, Metin; Ersan, Ziya Gökalp; Yelmen, İlkay; Çevik, Taner; Anka, Ferzat; Gesoğlu, Kevser;
In the realm of wireless communications, the Global Positioning System (GPS), integral toGlobal Navigation Satellite Systems (GNSS), finds extensive applications ranging fromvehicle navigation to military operations, aircraft tracking, and Geographic InformationSystems (GIS). The reliability of GPS is often compromised by errors particularly prevalentin dense and structurally complex environments, where signal attenuation by environmentalobstacles like mountains and buildings is common. These challenges necessitate thedeployment of high-cost, precision GPS receivers capable of enhanced signal tracking andacquisition. This study investigates the reduction of GPS positioning errors by implementinga machine learning framework, utilizing a dataset from vehicle tracking devices equippedwith Novatel and Ublox technologies. Ten machine learning prediction algorithms wereevaluated, focusing on techniques that introduce randomness for stability, employ proximityfor predictions, incorporate regularization to prevent overfitting, and leverage both singleand ensemble methods to refine analyses. Among the evaluated algorithms, the Extra Treesalgorithm was distinguished by its superior performance, achieving a coefficient ofdetermination (R²) of 99.6%, with the lowest error rates compared to its counterparts. Theerrors were quantified as Root Mean Square Error (RMSE) at 1.01E-4, Mean Absolute Error(MAE) at 4.14E-5, and Mean Square Error (MSE) at 1.03E+0 for normalized data. Acomparative assessment across ten scenarios demonstrated that the machine learning-enhanced approach deviated by approximately 6.8 meters on average, markedly improvingaccuracy over traditional GPS methods and reducing positional deviations to a scale ofmeters. This advance represents a significant stride towards minimizing GPS inaccuraciesin complex environments, providing a robust framework for enhancing navigationalprecision in critical applications.