Browsing by Author "Cakar, Tuna"

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Customer Segmentation and Churn Prediction via Customer Metrics
(IEEE, 2022) Bozkan, Tunahan; Cakar, Tuna; Sayar, Alperen; Ertugrul, Seyit
In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data-driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) was carried out. It was estimated by the XGBoost model with an F1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customers who will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.
Havayolu Reklamlarında İzleyicilerin Duygu Ölçümü: Microsoft Azure Face API ile Yüz Kodlama Uygulaması
(2025) Cakar, Tuna; Zengin, Burhanettin; Akpur, Akın
Amaç: Havayolu işletmeleri, müşterilerini uçuşlara çekmek ve marka imajlarını güçlendirmek için sık sık reklam kampanyaları düzenlerler. Ancak, reklam kampanyalarının etkinliği geleneksel yöntemlerle ölçüldüğünde sınırlı kalabilmektedir. Bu çalışmanın amacı yüz kodlama teknolojisinin havayolu reklamlarında duygu ölçüm performansının değerlendirilmesidir. Yöntem: Bu çalışma deneysel bir tasarıma sahip olup laboratuvar ortamında 40 denek ile yapılmıştır. Microsoft Azure Face API uygulamasından elde edilen sayısal duygu verileri istatistik paket program aracılığı ile analiz edilmiştir. Bulgular: Araştırmada final sahnesi olan reklamlar anlamlı şekilde final sahnesi olmayan reklama göre daha fazla mutluluk duygusu oluşturduğu tespit edilmiştir. Sonuç: Bu çalışma kapsamında kullanılan yüz kodlama yazılımının elde edilen çıktılar çerçevesinde ölçümlemede başarılı olduğu görülmüştür.
Citation - Scopus: 2
AI-Driven Digital Soil Mapping: Leveraging Generative AI, Ensemble Learning and Geospatial Technologies for Data-Scarce Regions
(Springer Science and Business Media Deutschland GmbH, 2025) Drias, Yassine; Drias, Habiba; Belkadi, Widad Hassina; Cakar, Tuna; Abdelhamid, Zakaria; Bensemmane, Riad Yacine
This study presents a methodology for generating highresolution digital soil maps by integrating artificial intelligence (AI) with geospatial technologies. The research begins with comprehensive data collection and the extraction of relevant soil properties with the help of generative AI. To improve predictive accuracy, ensemble learning algorithms were employed due to their ability to capture complex relationships within soil characteristics. Additionally, a structured preprocessing pipeline was developed to refine and standardize the collected soil data, ensuring its suitability for modeling. The model's performance was evaluated using spatial cross-validation techniques to identify the most effective predictive approach. To validate the proposed methodology, experiments were conducted in northern Algeria, a region characterized by diverse landscapes ranging from arid zones to fertile plains. The results demonstrate the potential of AI-driven approaches to enhance soil mapping, particularly in regions where high-quality and up-to-date soil data are limited. This study underscores the efficacy of AI and geospatial technologies in advancing precision agriculture and land management.
Financial Inputs Prediction with Machine Learning and Covariance Matrix Applications
(Institute of Electrical and Electronics Engineers Inc., 2025) Benli, Harun; Gunes, Peri; Ulkgun, Mert; Cakar, Tuna
Reliable estimation of the time-varying covariance matrix of asset returns is indispensable for portfolio construction, risk budgeting, and automated advisory services. Conventional estimators-rolling-window sample covariances, EWMA filters, and GARCH families-remain anchored to historical prices and therefore adapt slowly when market conditions pivot. To overcome this latency, we propose an end-to-end, machine-learning-driven framework that forecasts future covariances directly from high-frequency equity data, largely decoupling risk estimation from past observations. Our pipeline ingests heterogeneous stock feeds through a real-time API, applies error-minimising imputation (forward/backward fill, spline, VAR, wavelet, and co-kriging), and standardises returns via empirically selected scaling schemes. The processed features are then fed to a model zoo comprising linear and penalised regressions, tree ensembles (Random Forest, XGBoost, LightGBM, CatBoost), and kernel-based Support Vector Regression. Weekly walk-forward evaluation on a universe of Turkish insurance equities shows that LightGBM and SVR cut out-of-sample covariance prediction error by up to 35 % versus classical benchmarks. We embed the predicted matrices into five allocation engines-Markowitz mean-variance, Black-Litterman, minimum-variance, Risk Parity, and CVaR optimisation-demonstrating that Risk Parity delivers the most consistent variance reduction across 15 stock pairs, while Black-Litterman excels for idiosyncratic combinations such as ANSGR-AKGRT. A granular analysis reveals that day-to-day sign changes in returns create structural breaks that generic regressors miss; augmenting the architecture with a volatility-state classifier and regime-specific learners markedly sharpens turning-point detection. Beyond statistical gains, the framework is production-ready: it is fully implemented in Python, runs on cloud notebooks, and plugs into robo-advisory dashboards. The study thus bridges academic advances in covariance prediction with operational portfolio management, paving the way for broader cross-sector deployment and future research on deep sequential models, transaction-cost awareness, and multi-asset scalability. © 2025 Elsevier B.V., All rights reserved.