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, MuratThis 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.Conference Object Sentiment Analysis for Arabic Using Deep Learning(Springer Science and Business Media Deutschland GmbH, 2026) al-Hamadani, S.A.S.; Sever, H.With the explosive growth of digital communication, understanding sentiment in online content has become increasingly critical for a wide range of applications, from customer feedback analysis to social media monitoring. However, sentiment analysis for Arabic presents unique challenges due to the language's rich morphology, diverse dialects, and complex syntactic structures. These challenges are further amplified in multimodal settings, where the fusion of textual, visual, and auditory cues is required to capture the full spectrum of human emotion. To address these issues, this paper introduces a new framework for Arabic Multimodal Sentiment Analysis (AMSA), combining multi-level deep learning approaches across text, audio, and visual modalities. Our approach utilizes state-of-the-art transformer-based architecturees, including Multimodal Transformer (MulT) and Early Fusion models, to tackle both linguistic complexity and multimodal alignment. Specifically, we leverage DeBERTa for extracting rich textual features, ViT (Vision Transformer) for visual cues, and Whisper for capturing nuanced audio signals, creating robust and contextualized representations. Experimental results on a curated Arabic multimodal dataset demonstrate the effectiveness of this approach, with our proposed MulT model achieving an F1 score of 72.73%, reflecting a substantial improvement of 13.98% in F1 score and 14.6% in accuracy over existing baselines. These findings highlight the power of cross-modal attention mechanisms and early fusion strategies in accurately capturing subtle sentiments across multiple modalities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Conference Object Citation - Scopus: 1Intelligent and Energy-Aware Task Scheduling in Cloud Systems(Springer Science and Business Media Deutschland GmbH, 2025) Böke, K.N.; Qadri, S.S.S.M.; Kabarcik, A.The rapid advancement of information technologies has significantly reshaped industrial operations and daily life, leading to a growing demand for responsive and scalable digital services. Among the technologies addressing this growing need, cloud computing has emerged as a foundational infrastructure for delivering on-demand computing resources over the internet. However, its increasing adoption presents complex challenges such as managing dynamic workloads and minimizing virtual machine (VM) usage costs. Therefore, cloud service providers aim to optimize performance and reduce the operational costs of VMs by integrating intelligent scheduling algorithms. In response to this need, the present study explores the use of algorithms, particularly focusing on machine learning driven approaches, to enhance the sustainability and efficiency of cloud systems. Specifically, the study investigates the effectiveness of reinforcement learning through Q-learning for optimizing task scheduling against the traditional Round Robin (RR) scheduling algorithm. The primary objective is to evaluate their performance in minimizing VM usage costs within dynamic and continuously evolving cloud environments. Experimental results indicate that in reducing costs, Q-learning outperforms RR with a 33.14% improvement, demonstrating its superior adaptability and cost efficiency under varying conditions. These insights highlight the potential of reinforcement learning to enable intelligent and cost-aware scheduling strategies in modern cloud computing systems. © 2025 Elsevier B.V., All rights reserved.Conference Object Design and Implementation of a Custom ERP Framework for a Drilling Equipment Manufacturer(Springer Science and Business Media Deutschland GmbH, 2025) Torunoğlu, D.; Erkoç, E.C.; Abay, Z.E.; Qadri, S.S.S.M.; Gök, E.C.; Karataş, D.; Güçlüer, G.This study presents the design and implementation of a web-based Enterprise Resource Planning (ERP) system tailored for a small-to-medium-sized enterprise (SME) operating in the manufacturing sector. With a focus on GEO Sondaj Makine İmalat LTD. ŞTİ, the system was developed to digitize and streamline core operational workflows, including sales order processing, production scheduling, inventory management, procurement, and coordination between customers and suppliers. Built using the Django web framework, the ERP platform provides modular functionality with real-time data integration across departments. Unlike generic ERP packages, this custom-built solution mirrors the company’s actual business processes and addresses typical challenges faced by SMEs, such as limited IT infrastructure, absence of digital records, and resistance to organizational change. The internally developed modules led to enhanced traceability, operational efficiency, and data-driven decision-making. The system also includes a simulation module to support production visualization and planning, although advanced features like bottleneck identification and dynamic queue tracking remain under development. The findings demonstrate that a cost-effective, scalable ERP system can be successfully deployed in resource-constrained environments when grounded in business-specific needs. The system was evaluated based on internal testing, interdepartmental workflow validation, and observed improvements in operational efficiency and traceability. This project offers a practical reference for other SMEs seeking to modernize their operations through digital integration. © 2025 Elsevier B.V., All rights reserved.Article Cognitive and Social Antecedents of Brand Loyalty in Over-The Streaming Services(Science Publishing Corporation Inc., 2025) Ôzsaçmaci, B.This study examines cognitive (brand image, perceived quality, brand identity) and social (eWOM) antecedents of brand loyalty in Over-the-Top (OTT) streaming using a cross-sectional survey (N=418) and covariance-based SEM. All four antecedents significantly predict loyalty, with brand image exerting the strongest effect, followed by eWOM, then perceived quality and brand identity. Group comparisons show that evaluations of perceived quality, but not eWOM, identity, image, or loyalty, differ by usage frequency and subscription breadth. The findings substantiate a dual cognitive–social route to loyalty, and suggest that, in content-rich, multi-homing markets, quality functions as a hygiene factor while brand image carries the differentiating signal. We discuss managerial implications for sequencing investments (image, eWOM, quality thresholds) and outline finance-relevant links to CLV, LTV: CAC, and revenue stability. © 2025 Elsevier B.V., All rights reserved.Article On the Determination of the Quadratic Pencil of the Sturm-Liouville Operator With an Impulse(Pleiades Publishing Ltd, 2025) Khalili, Y.; Baleanu, D.In this work, an inverse problem for the quadratic pencil of the Sturm-Liouville operator with an impulse in the finite interval is considered. It is shown that some information on eigenfunctions at some internal point \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\in\left(\frac{1}{2},1\right)$$\end{document} and parts of two spectra uniquely determine the potential functions and all parameters in the boundary conditions. Moreover we prove that the potential functions on the whole interval and the parameters in the boundary conditions can be established from one spectrum and the potentials prescribed on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(\frac{1}{2},1\right)$$\end{document}.Article Ecg Signal Denoising With Scilab(Ismail Saritas, 2023) Ahmad, I.; Özaydın, Selma; Ozaydin, S.; Çankaya Meslek YüksekokuluThis paper presents a study on de-noising electrocardiogram (ECG) signals using Scilab, an open-source software package known for its signal processing capabilities. ECG signals are often contaminated by various noise sources, which can reduce the accurate diagnosis and monitoring of heart health. In this work, digital signal processing methods such as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters are used to effectively suppress noise while preserving the essential features of the ECG waveform. We explore main noise sources that commonly affect ECG recordings, such as baseline wandering noise, power-line interference, and muscle artifacts, and discuss their respective challenges. The de-noising methods has been extensively evaluated and demonstrated its ability to improve signal quality and diagnostic accuracy by eliminating noise artifacts. The results highlight Scilab's potential for de-noising ECG signals and its importance in improving patient care and biomedical signal processing applications. The efficacy of the de-noising methods is thoroughly evaluated through comparative analyses with other commonly used de-noising approaches. Experimental results demonstrate its superiority in preserving the QRS complex while efficiently eliminating noise artifacts, leading to more accurate and reliable diagnostic information. In conclusion, this paper presents a comprehensive study on de-noising ECG signals using Scilab, offering a valuable contribution to the field of biomedical signal processing. Researchers and practitioners in the domain of ECG signal processing can benefit from the insights and techniques presented herein to advance their studies and further applications. © 2023, Ismail Saritas. All rights reserved.Book Part Citation - Scopus: 8Advanced Topics in Fractional Differential Equations a Fixed Point Approach(Springer Nature, 2023) Benchohra, M.; Karapınar, Erdal; Karapınar, E.; Lazreg, J.E.; Salim, A.; MatematikArticle Citation - WoS: 11Citation - Scopus: 11Best Proximity Results on Condensing Operators Via Measure of Noncompactness With Application To Integral Equations(Chiang Mai Univ, Fac Science, 2020) Gabeleh, Moosa; Karapınar, Erdal; Asadi, Mehdi; Karapinar, Erdal; MatematikWe prove the best proximity point results for condensing operators on C-class of functions, by using a concept of measure of noncompactness. The results are applied to show the existence of a solution for certain integral equations. We express also an illsutrative examples to indicate the validity of the observed results.Article Citation - Scopus: 1Analysis of Fractional Fokker-Planck Equation With Caputo and Caputo-Fabrizio Derivatives(Univ Craiova, 2021) Cetinkaya, Suleyman; Baleanu, Dumitru; Demir, Ali; Baleanu, Dumitru; MatematikThis research focus on the determination of the numerical solution for the mathematical model of Fokker-Planck equations utilizing a new method, in which Sumudu transformation and homotopy analysis method (SHAM) are used together. By SHAM analytical series solution of any mathematical model including fractional derivative can be obtained. By this method, we constructed the solution of fractional Fokker-Planck equations in Caputo and Caputo-Fabrizio senses. The results show that this method is advantageous and applicable to form the series resolution of the fractional mathematical models.