Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Department: search.filters.department.Çankaya UniversityPublisher: search.filters.publisher.Institute of Electrical and Electronics Engineers Inc.

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Now showing 1 - 10 of 69
  • 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, Gul
  • Conference 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 Berke
  • Conference 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 Eylul
  • Conference 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, Gul
  • Conference 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, Doga
  • Conference 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, Bengisu
  • Conference 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, Ires
  • Article
    A User-Centric Domain-Adaptive Quality Model for Benchmarking Generative AI Systems
    (Institute of Electrical and Electronics Engineers Inc., 2026) Esirik, Buse Erol; Gokalp, Ebru
    Generative 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.
  • Article
    Solution Approaches for the Dynamic Naval Air Defense Planning Problem
    (Institute of Electrical and Electronics Engineers Inc., 2026) Arslan, C.; Karasakal, O.; Kirca, Ö.
    The naval air defense planning (NADP) problem entails the defense of a naval fleet against aerial threats. This complex and dynamic problem requires real-time decision-making and adaptation to evolving warfare environment. While our previous work addressed the static NADP problem by proposing a mathematical model and heuristic solutions for sensor allocation, engagement scheduling, and ship routing, this study extends to the dynamic NADP problem. Unlike the static version, which assumes complete knowledge of future threats, the dynamic NADP problem requires continuous updates and real-time adjustments to decisions as new threats emerge and situational parameters change. We present modifications in the mathematical formulation, which is based on a mixed-integer nonlinear programming (MINLP) model, alongside a comprehensive simulation structure. We employ heuristic solution approaches that utilize a combination of a genetic algorithm, construction of an engagement graph to solve the shortest path problem, and dynamic programming (DP) techniques. Computational experiments are conducted to evaluate the effectiveness of these methods in addressing the dynamic NADP problem. The study also explores machine learning models for threat prioritization, offering innovative solutions to the challenges posed by dynamic naval air defense scenarios. © 2013 IEEE.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Optical Wireless Communication in Atmosphere and Underwater: Statistical Models, Improvement Techniques, and Recent Applications
    (Institute of Electrical and Electronics Engineers Inc., 2026) Ata, Y.; Al-Sallami, F.M.; Gökçe, M.C.; Vegni, A.M.; Rajbhandari, S.; Baykal, Y.
    Optical Wireless Communication Systems (OWCSs) are becoming more popular each day, especially after numerous mobile applications are being employed within the concept of Internet of Things (IoT). OWCSs are largely used in both terrestrial and non-terrestrial environments, like underwater, air, and space scenarios. Due to the large applicability of OWCS, it represents one of the main candidate technologies for the future 6G wireless communication systems. Naturally, this market trend forces the system designers to reach the best performance in their designs, as well as optimize the cost. In this survey paper, we intend to provide information to the researchers working in this field on the statistical models adopted in OWCS, the methods and techniques used to improve their performances, mainly in outdoor environment like air, space, and underwater. In this respect, the background on theoretical aspects of OWCS, together with their benefits, limitations and challenges are presented. Performance improvement techniques employed in OWCSs, such as power increase, partial coherence, beamforming, aperture averaging, spatial diversity, and intelligent reflecting surfaces, are also introduced. Finally, we discuss the open challenges that researchers are still facing, together with future directions on next steps for a large-scale adoption of OWCS. © 1998-2012 IEEE.