Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
Browse
85 results
Search Results
Conference Object Unveiling the Landscape of Requirements Engineering: Insights from Text Mining(Institute of Electrical and Electronics Engineers Inc., 2026) Uguz, Sezer; Nazlioglu, Selma; Tokdemir, GulConference Object Transformative Technologies in Neurosurgery: A Systematic Literature Review(Institute of Electrical and Electronics Engineers Inc., 2026) Serpil, Mete; Atas, Sezer; Ozcan, Muhammed Yusuf; Çetinkaya, Alperen BerkeConference Object Publicly Available Datasets for Smart and Precision Agriculture: A Systematic Review(Institute of Electrical and Electronics Engineers Inc., 2026) Arabaci, Hatice Elif; Keskin, Mustafa Berk; Kahraman, Arda; Bozdag, Oyku EylulConference Object GenAI-Assisted Software Development: Is It Killing the Creativity(Institute of Electrical and Electronics Engineers Inc., 2026) Yildiz, Hayri; Sinav, Alper; Peker, Volkan; Tokdemir, GulConference Object Enhanced Object Detection for Vehicle Safety through Multi-Sensor Fusion(Institute of Electrical and Electronics Engineers Inc., 2025) Erkan, Beyza Nur; Aydin, Elif; Akpinar, Atacan; Ozbay, Berke; Tik, DogaConference Object Deep Learning-Based Object Detection for Vehicular Safety: A Comparative Study on COCO, KITTI, and Merged Datasets(Institute of Electrical and Electronics Engineers Inc., 2025) Aydin, Elif; Yildirim, BengisuConference Object Comparison of SMO and MRAS-Based Speed Estimation Methods for High-Speed PMSM Drives(Institute of Electrical and Electronics Engineers Inc., 2025) Eser, Secil; Iskender, IresArticle A User-Centric Domain-Adaptive Quality Model for Benchmarking Generative AI Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Esirik, Buse Erol; Gokalp, EbruGenerative AI (GenAI) systems operate across diverse application domains where quality priorities shift dynamically in response to user expectations and contextual requirements. This variability calls for a comprehensive quality model that enables stakeholder-driven weight recalibration to support product evaluation and selection. However, existing approaches do not simultaneously account for GenAIspecific attributes, user-centric quality priorities, and domain-adaptive evaluation mechanisms. To bridge this gap, this study proposes the User-Centric Generative AI Quality Model (UC-GAIQM), a domainadaptive framework in which Analytic Hierarchy Process (AHP) weights can be recalibrated to reflect quality priorities across different application scenarios and user profiles. The proposed model was developed through a mixed-methods, three-phase research design. In the first phase, a Systematic Literature Review (SLR) and Multivocal Literature Review (MLR) established the theoretical foundation. In the second phase, a quantitative survey of active GenAI users (n = 111) validated eight quality dimensions through exploratory and confirmatory factor analysis (alpha = 0.94, KMO = 0.88, CFI = 0.943). In the third phase, a three-round expert-driven Delphi study confirmed the structural validity of the model (Kendall's W = 0.84), and an AHP study demonstrated the weight recalibration mechanism. UC-GAIQM comprises eight quality dimensions and thirty sub-dimensions aligned with key ISO/IEC standards, the NIST AI Risk Management Framework, and the EU AI Act. The results demonstrate that the proposed model facilitates dynamic, context-sensitive evaluation of GenAI products by enabling quality priority adaptation across application domains.Conference Object Web Service-Based Turkish Automatic Speech Recognition Platform(Institute of Electrical and Electronics Engineers Inc., 2020) Polat, Huseyin; Sever, Hayri; Oyucu, SaadinConference Object Small and Unbalanced Data Set Problem in Classification(Institute of Electrical and Electronics Engineers Inc., 2019) Akcapinar Sezer, Ebru; Sever, Hayri; Par, Oznur Esra