Yazar "Zaidan, B.B." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions
    (Academic Press, 2025) Talal, Mohammed; Garfan, Salem; Qays, Rami; Pamucar, Dragan; Delen, Dursun; Pedrycz, Witold; Alamleh, Amneh; Alamoodi, Abdullah; Zaidan, B.B.; Simic, Vladimir
    The fifth-generation (5G) network is considered a game-changing technology that promises advanced connectivity for businesses and growth opportunities. To gain a comprehensive understanding of this research domain, it is essential to scrutinize past research to investigate 5G-radio access network (RAN) architecture components and their interaction with computing tasks. This systematic literature review focuses on articles related to the past decade, specifically on machine learning models integrated with 5G-RAN architecture. The review disregards service types like the Internet of Medical Things, Internet of Things, and others provided by 5G-RAN. The review utilizes major databases such as IEEE Xplore, ScienceDirect, and Web of Science to locate highly cited peer-reviewed studies among 785 articles. After implementing a two-phase article filtration process, 143 articles are categorized into review articles (15/143) and learning-based development articles (128/143) based on the type of machine learning used in development. Motivational topics are highlighted, and recommendations are provided to facilitate and expedite the development of 5G-RAN. This review offers a learning-based mapping, delineating the current state of 5G-RAN architectures (e.g., O-RAN, C-RAN, HCRAN, and F-RAN, among others) in terms of computing capabilities and resource availability. Additionally, the article identifies the current concepts of ML prediction (categorical vs. value) that are implemented and discusses areas for future enhancements regarding the goal of network intelligence. © 2024 Elsevier Ltd
  • Küçük Resim
    Öğe
    Evaluation of industry 4.0 adoption strategies in small and medium enterprises: A Circular-Fermatean fuzzy decision-making approach
    (Elsevier ltd, 2025) Abu-Lail, Dareen; Mourad, Nahia; Qahtan, Sarah; Zaidan, A.A.; Alsattar, Hassan A.; Zaidan, B.B.; Pamucar, Dragan; Deveci, Muhammet; Pedrycz, Witold; Delen, Dursun
    The evaluation of Industry 4.0 (I4.0) technology adoption strategies (I4.0AS) in Small and Medium Enterprises (SMEs) from I4.0 technologies and technology-organization-environment (TOE) ecosystem perspectives poses a significant challenge due to three primary concerns: the importance of criteria, data variability for each individual I4.0AS, and uncertainty in expert opinions. This complexity arises from the consideration of diverse criteria groups for I4.0 deployment in the SMEs sector with a focus on the TOE context, which is linked to a second criteria group characterized by uncertain evaluative data. Although research in this area has increased, a comprehensive assessment methodology tailored to the unique needs of SMEs remains elusive. Addressing this gap is crucial to provide SME decision-makers with detailed insights that enhance their strategic choices. In our study, we introduce a holistic evaluation of I4.0ASs, emphasizing the performance dynamics within the TOE framework. Central to our assessment methodology is the integration of advanced Circular-Fermatean Fuzzy sets (C-FFS), designed to capture uncertain evaluative data. This three-phased methodology formulates the Circular-Fermatean Fuzzy Sets-Fuzzy-weighted zero-inconsistency (C-FFS–FWZIC) approach and evaluates I4.0ASs from both the I4.0 technology and TOE-ecosystem perspectives. Through a rigorous examination of 37 distinct I4.0ASs based on 30 I4.0 technology perspective criteria and 114 TOE-ecosystem perspective criteria, our study illuminates their efficacy across these dual perspectives. The results indicate that I4.0AS23 ranked first according to the I4.0 technologies perspective but 26th according to the TOE-ecosystem perspective, while I4.0AS1 ranked first according to the TOE-ecosystem perspective and 15th according to the I4.0 technologies perspective. I4.0AS25 yielded consistent results, scoring 6th place in both perspectives. Additionally, the resilience and versatility of our methodology are validated through an in-depth sensitivity and comparative analysis, reinforcing its potential as a valuable tool for future industry applications.