İZÜ Araştırma ve Akademik Performans Sistemi
DSpace@İZÜ, İstanbul Sabahattin Zaim Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Öğe Türü: Yayın , Transferable CNN-Based Data Mining Approaches for Medical Imaging: Application to Spine DXA Scans for Osteoporosis Detection(Frontiers Media SA, 2025) Naeem, Awad Bin; Osman, Onur; Alsubai, Shtwai; Çevik, Nazife; Zaidi, Abdelhamid Taieb; Seyyedabbasi, Amir; Rasheed, Jawad; Rasheed, Jawad;Introduction: Osteoporosis is the leading cause of sudden bone fractures. This is a silent and deadly disease that can affect any part of the body, such as the spine, hips, and knee bones. Aim: To measure bone mineral density, dual-energy X-ray absorptiometry (DXA) scans help radiologists and other medical professionals identify early signs of osteoporosis in the spine. Methods: A proposed 21-layer convolutional neural network (CNN) model is implemented and validated to automatically detect osteoporosis in spine DXA images. The dataset contains 174 spine DXA images, including 114 affected by osteoporosis and the rest normal or non-fractured. To improve training, the dataset is expanded using various data augmentation techniques. Results: The classification performance of the proposed model is compared with that of four popular pre-trained models: ResNet-50, Visual Geometry Group 16 (VGG-16), VGG-19, and InceptionV3. With an F1-score of 97.16%, recall of 95.41%, classification accuracy of 97.14%, and precision of 99.04%, the proposed model consistently outperforms competing approaches. Conclusion: The proposed paradigm would therefore be very valuable to radiologists and other medical professionals. The proposed approach’s capacity to detect, monitor, and diagnose osteoporosis may reduce the risk of developing the condition.Öğe Türü: Yayın , From Anxiety to Assurance: A Mixed-Methods Journey Into Service Innovation, Trust and Customer Relationships(Emerald Publishing, 2025) Bağcı, Rifgi Buğra; Tasçioglu, Mertcan; Bağcı, Rıfgı BuğraPurpose – Grounded in Resource Dependence Theory and Social Exchange Theory, this study aims to examine the relationship between service innovation, trust dimensions (competence, contractual and goodwill) and relational performance. In addition, it analyzes the mediating role of trust types and the moderating effect of relationship anxiety. The authors also analyze various configurations that lead to higher relational performance and different dimensions of trust.Design/methodology/approach – This study uses a mixed-methods approach, integrating quantitative data from 232 managers of top 500 organizations and qualitative insights from in-depth interviews with five suppliers and five customers. Quantitative data is analyzed using partial least squares structural equation modeling (PLS-SEM) and FsQCA, while qualitative data is examined through thematic analysis.Findings – PLS-SEM results indicate that competence trust is the only trust dimension mediating the service innovation-relational performance link, while relationship anxiety unexpectedly strengthens the relationship between service innovation and trust. Service innovation positively influences all trust types, which in turn enhances relational performance. FsQCA findings highlight that positive service innovation and the negation of relationship anxiety are central to trust formation and relational performance. Qualitative insights further reveal that buyers prioritize competence over goodwill and contractual trust, with long- term business to business (B2B) relationships and technical proficiency overriding the effects of relationship anxiety.Originality/value – This study advances the literature by linking suppliers’ service innovation to relational performance, addressing prior research calls and incorporating diverse outcome variables. It further demonstrates how different trust dimensions yield distinct effects in B2B relationships, refining our understanding of trust–performance relationships. In addition, it contributes by examining mediators and introducing relationship anxiety as a moderator, offering new insights into its impact on relational outcomes.Öğe Türü: Yayın , Cybersecurity in Smart Grids and Other Application Fields:A Review Paper(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Ali, Ahmad; Wadi, Mohammed; Elmasry, Wisam; Wadi, MohammedThis article explores various applications and advancements in the fields of energy management (EM), cybersecurity (CS), and automation across multiple sectors, including smart grids (SGs), the Internet of things (IoT), trading, e-commerce, and autonomous systems. A variety of innovative solutions and methodologies are discussed, such as enhanced impedance methods for simulation stability, decision support systems for resource allocation, and advanced algorithms for detecting cyber-physical threats. The integration of artificial intelligence (AI) and machine learning (ML) techniques is highlighted, particularly in addressing challenges such as fault tolerance, economic distribution in cyber-physical systems (CPSs), and protection coordination in complex environments. Additionally, the development of robust algorithms for real-time monitoring and control demonstrates significant potential for improving system efficiency and resilience against various types of attacks.Öğe Türü: Yayın , Simultaneous Probe of the Charm and Bottom Quark Yukawa Couplings Using ttH Events(American Physical Society, 2026) Hayrapetyan, Aram A.; Makarenko, V. V.; Tumasyan, A. R.; Atakişi, İsmail Okan; Atakişi, İsmail OkanThis article has 2,410 authors. A search for the standard model Higgs boson decaying to a charm quark-antiquark pair, (Formula presented), produced in association with a top quark-antiquark pair ((Formula presented)) is presented. The search is performed with data from proton-proton collisions at (Formula presented), corresponding to an integrated luminosity of (Formula presented). Advanced machine learning techniques are employed for jet flavor identification and event classification. The Higgs boson decay to a bottom quark-antiquark pair is measured simultaneously and the observed (Formula presented) event rate relative to the standard model expectation is (Formula presented). The observed (expected) upper limit on the product of production cross section and branching fraction (Formula presented) is 0.11 (0.13) pb at 95% confidence level, corresponding to 7.8 (8.7) times the standard model prediction. When combined with the previous search for (Formula presented) via associated production with a (Formula presented) or (Formula presented) boson, the observed (expected) 95% confidence interval on the Higgs-charm Yukawa coupling modifier, (Formula presented), is (Formula presented) (2.7), the most stringent constraint to date.Öğe Türü: Yayın , Enhancing Power Quality of PV Grid-Connected System Through Mantis Shrimp Optimization Algorithm for Optimal DC Bus Voltage Control(Nature Research, 2026) Boukhdenna, Alla Eddine; Afghoul, Hamza; Zabia, Djallal Eddine; Abdelmalek, Feriel; Nettari, Yakoub; Alharbi, Salah S.; Cezayirli, YakupThe nonlinear and intermittent nature of Photovoltaic (PV) systems introduces dynamic disturbances that negatively impact the stability of the DC bus voltage (Vdc) between PV sources and shunt active power filters (SAPFs). These fluctuations pose significant challenges to the performance of SAPFs, especially when the reference DC bus voltage (Vdc*) is constant and not adapted to the instantaneous operating conditions. In this study, a Perturb and Observe (P&O) algorithm is employed within the PV subsystem to perform Maximum Power Point Tracking (MPPT), further contributing to the time-varying behavior of Vdc. To address this problem, this paper proposes a real-time optimization strategy based on the Mantis Shrimp Optimization Algorithm (MShOA) for continuous Vdc* adjustment. This method relies on real-time Total Harmonic Distortion (THD) feedback to dynamically determine the optimal Vdc*, thereby improving harmonic mitigation and maintaining voltage stability. Simulation results demonstrate that the proposed MShOA-based approach effectively reduces THD from 3.59% to 2.85% obtained with conventional methods to 2.33% before PV injection, and maintains 4.19% after PV injection, remaining within the IEEE 519 − 92 standard limits. To confirm its superiority, a comparison with the Whale Optimization Algorithm (WOA) was performed, which achieved 2.65% before and 5.78% after PV injection. These findings validate the higher accuracy, faster convergence, and better adaptability of the proposed MShOA in ensuring robust voltage regulation and improved power quality under PV injection conditions.
