Scopus İndeksli Yayınlar Koleksiyonu

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

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  • Article
    The Impact Of Cloud Computing And Artificial Intelligence Integration On Service Transformation İn Academic Libraries
    (University and Research Librarians Association (UNAK), 2025) Celik, Murat
    This study examines the effects of cloud computing and artificial intelligence (AI) technologies, which are the leading components of information age technologies, on transforming academic libraries from traditional service understanding to modern information centers. The findings obtained through the literature review method show that the infrastructural flexibility, efficiency, and scalability brought by cloud computing and the automation, personalization, and accessibility opportunities provided by AI lead to significant transformations in library services. In addition, the studies revealed that integrating cloud computing and artificial intelligence provides significant gains, such as accelerating access to information, improving user experience, and increasing operational efficiency. However, in addition to infrastructural deficiencies and financial constraints, integration processes also bring about various challenges, such as data security, ethical concerns, legal regulations, personnel competencies, and institutional readiness. The study analyzes the current status of these technologies in libraries, their potential application areas, and future effects. It offers strategic recommendations for information professionals and institution managers in this transformation process. As a result of the study aims to shed light on the future service models of academic libraries by offering suggestions such as continuous education, inter-institutional collaborations, strategic planning and the establishment of ethical policies to overcome these difficulties. © 2025 University and Research Librarians Association (UNAK). All rights reserved.
  • Article
    Citation - Scopus: 43
    Synergizing Ai and Business: Maximizing Innovation, Creativity, Decision Precision, and Operational Efficiency in High-Tech Enterprises
    (Elsevier B.V., 2024) Ali, M.; Khan, T.I.; Khattak, M.N.; ŞENER, İ.
    The study was conducted on 125 US based high-tech firms from software engineering, hardware production, biotechnology, and telecommunications. Senior-level executives, including CEOs, board members, and CTOs, provided insights through structured questionnaires. Key findings indicate that AI adoption significantly enhances organizational capabilities in terms of employees’ innovation, creativity, and experimentation. Moreover, AI adaptation positively impacts decision making thus yielding more accurate and timely valuable decisions. These findings contribute to both theoretical understanding and managerial practice by guiding strategic investments in AI technologies, fostering innovation, and advocating for ethical AI deployment practices. Future study should examine longitudinal impacts across industries and regions to optimize benefits and minimize risks in digital transformation efforts. It should also integrate qualitative methods for deeper insights and appropriate AI governance systems. © 2024
  • Article
    Citation - Scopus: 20
    Evolutionary Mathematical Science, Fractional Modeling and Artificial Intelligence of Nonlinear Dynamics in Complex Systems
    (Akif AKGUL, 2022) Karaca, Yeliz; Baleanu, Dumitru
    Complex problems in nonlinear dynamics foreground the critical support of artificial phenomena so that each domain of complex systems can generate applicable answers and solutions to the pressing challenges. This sort of view is capable of serving the needs of different aspects of complexity by minimizing the problems of complexity whose solutions are based on advanced mathematical foundations and analogous algorithmic models consisting of numerous applied aspects of complexity. Evolutionary processes, nonlinearity and all the other dimensions of complexity lie at the pedestal of time, reveal time and occur within time. In the ever-evolving landscape and variations, with causality breaking down, the idea of complexity can be stated to be a part of unifying and revolutionary scientific framework to expound complex systems whose behavior is perplexing to predict and control with the ultimate goal of attaining a global understanding related to many branches of possible states as well as high-dimensional manifolds, while at the same time keeping abreast with actuality along the evolutionary and historical path, which itself, has also been through different critical points on the manifold. In view of these, we put forth the features of complexity of varying phenomena, properties of evolution and adaptation, memory effects, nonlinear dynamic system qualities, the importance of chaos theory and applications of related aspects in this study. In addition, processes of fractional dynamics, differentiation and systems in complex systems as well as the dynamical processes and dynamical systems of fractional order with respect to natural and artificial phenomena are discussed in terms of their mathematical modeling. Fractional calculus and fractional-order calculus approach to provide novel models with fractional-order calculus as employed in machine learning algorithms to be able to attain optimized solutions are also set forth besides the justification of the need to develop analytical and numerical methods. Subsequently, algorithmic complexity and its goal towards ensuring a more effective handling of efficient algorithms in computational sciences is stated with regard to the classification of computational problems. We further point out the neural networks, as descriptive models, for providing the means to gather, store and use experiential knowledge as well as Artificial Neural Networks (ANNs) in relation to their employment for handling experimental data in different complex domains. Furthermore, the importance of generating applicable solutions to problems for various engineering areas, medicine, biology, mathematical science, applied disciplines and data science, among many others, is discussed in detail along with an emphasis on power of predictability, relying on mathematical sciences, with Artificial Intelligence (AI) and machine learning being at the pedestal and intersection with different fields which are characterized by complex, chaotic, nonlinear, dynamic and transient components to validate the significance of optimized approaches both in real systems and in related realms.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Towards an Earthquake-Resistant Architectural Design With the Image Classification Method
    (Taylor & Francis Ltd, 2024) Akan, Asli Er; Bingol, Kaan; Ormecioglu, Hilal Tugba; Er, Arzu; Ormecioglu, Tevfik Oguz; Er Akan, Aslı
    Architectural design is an interdisciplinary process which involves multiple stages that are interconnected. In this process, it is common for major decisions to be changed during the final stage, the analysis of the structural system. After making substantial corrections, the architect has to revisit the early stages, the preliminary project. This back-and-forth process can result in significant losses in time and cost. The proposed Irregularity Control Assistant (IC-Assistant) aims to provide architects with feedback on the conformity of structural system decisions to the irregularities defined in the Turkish Building Earthquake Code (TBEC-2018), using image processing methods at the early stages of the design process. The IC-Assistant was preliminarily created to evaluate the torsional irregularity of plan organization using deep learning methods. In this study, the results of the IC-Assistant were verified by structural analysis with the Prota-Structure program. The novelty of this study is the use of the image-classification method in earthquake-resistant architectural design. Up to this point, the method has been mainly used in facial recognition systems. This method minimizes time, human error, and cost losses and includes awareness of load bearing and earthquake resistance as inputs in the early stages of architectural design.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 19
    Artificial Intelligence Applications in Earthquake Resistant Architectural Design: Determination of Irregular Structural Systems With Deep Learning and Imageai Method
    (Gazi Univ, Fac Engineering Architecture, 2020) Bingol, Kaan; Akan, Asli Er; Ormecioglu, Hilal Tugba; Er, Arzu
    Although the architectural design process is carried out with the collaboration of experts who are experienced in many different areas from the main preferences to the detailing stage, the major decisions such as plan organization, mass design etc. are taken by the architect. Computer Aided Design (CAD) programs are generally effective after the major decisions of the design are taken. For this reason, it is common for the main decisions, taken during the design process, to be changed during the analysis of the structural system. In order to prevent this, in the early stages of architectural design, earthquake system awareness and structural system design should be included as an design input; as, the failure of the structural system which did not considered well in the architectural design phase leads to unexpected revisions in the implementation project phase and thus leads to serious losses in both time and cost. The aim of this study is to create an Irregularity Control Assistant (IC Assitant) that can provide architects general information about the appropriateness of structural system decisions to earthquake regulations in the early stages of design process by using the deep learning and image processing methods. In this way, correct decisions will be made in the early stages of the design and unexpected revisions that may occur during the implementation project phase will be prevented.