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    An In-Depth Case Study of Volkswagen's AI Integration
    (CEUR-WS, 2024) Erdoğan, Ali Mert; Hiziroglu, Ourania Areta; Hiziroglu, Abdulkadir
    As Artificial Intelligence (AI) technologies have become increasingly integral to business operations and many firms aspire to generate business value with that, understanding the factors that facilitate or hinder successful implementation is crucial for organizations across industries. Using Volkswagen Group (VW) as a case study, the goal of this study is to comprehensively examine the AI implementations in a holistic manner, including enablers and inhibitors, utilization in terms of automation and augmentation, process-level impacts, and broader firm-level outcomes. This work not only contributes to the understanding of AI adoption within a major automotive player, but also serves as a resource for organizations by navigating through the complexities of AI implementation, offering practical insights and lessons learned from the case. © 2023 Copyright for this paper by its authors.
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    Comparing the Performance of Ensemble Methods in Predicting Emergency Department Admissions Using Machine Learning Techniques
    (İzmir Bakırçay Üniversitesi, 2024) Yapıcı, Murat Emre; Hızıroğlu, A. Kadir; Erdoğan, Ali Mert
    Healthcare data collection, storage, retrieval, and analysis are enabled by various technologies and tools in health information systems. These systems include health information exchanges, telemedicine platforms, clinical decision support systems, and electronic health records. They aim to improve patient outcomes, provider communication, and healthcare workflows. Machine learning is being used in emergency rooms to address challenges such as increasing patient volume, limited resources, and the need for quick decisions. Machine learning algorithms can assist in triage and risk stratification by identifying patients requiring urgent care and predicting the severity of their condition. By analyzing various patient data sources, machine learning can detect patterns and indicators that human clinicians may miss, enabling early intervention and potentially saving lives. However, there is a lack of comparative evaluation of ensemble methods used in analysis. Therefore, this study aims to thoroughly examine and analyze various ensemble methods to understand their efficacy and performance, contributing valuable insights to researchers and practitioners.
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    Comparison of machine learning algorithms for improved admission prediction of the emergency department patients
    (İzmir Bakırçay Üniversitesi Lisansüstü Eğitim Enstitüsü, 2023) Erdoğan, Ali Mert; Hızıroğlu, Abdulkadir
    Bu çalışma acil servise başvuran hastaların yatışının tahmin edilmesi için farklı makine öğrenmesi yöntemlerinin karşılaştırılmasını amaçlamaktadır. Çalışmada Lojistik Regresyon, Yapay Sinir Ağları ve RUSBoost Ağaçlar (Random Under-Sampling Boosted Trees) makine öğrenmesi yöntemleri kullanılarak geliştirilen altı farklı modele yönelik tahmin performansları incelenmektedir. Her bir algoritma ile, hasta yatışını ilgilendiren bağımlı değişkenin dengesiz ve dengeli olarak dağıldığı farklı eğitim setleri kullanılarak iki ayrı model eğitilmiştir. Modeller çeşitli performans metrikleri kullanılarak karşılaştırılmıştır. Yapay Sinir Ağları ve dengeli veriseti kullanılarak eğitilen model, 0,88 doğruluk oranı, 0,84 hassasiyet, 0,90 özgüllük, 0,81 F1 skoru ve 0,94 AUC ile en başarılı tahmin performansını göstermiştir.