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: Araştırmacı , Köymen, ErdemÖğr. Gör.Öğe Türü: Yayın , I. Uluslararası II. Ulusal Sağlık Bilimleri Kongresi Bildiri Kitabı: Dijital Sağlık Çağında Kapsayıcılık ve Erişilebilirlik: Herkes İçin Sürdürülebilir Çözümler(İstanbul Sabahattin Zaim Üniversitesi, 2025) Bahçecik, Ayşe Nefise; Özer, Zülfünaz; Sevim, Kaan; Bahçecik, Ayşe Nefise; Özer, Zülfünaz; Sevim, KaanÖnsöz'den: Dijital sağlık çağında kapsayıcılık ve erişilebilirlik, sağlık bilimlerinin gündemlerinden biri haline gelmiştir. Teknolojinin sağlık hizmetlerine entegrasyonu, bireylerin ve toplumların sağlıklı bir geleceğe erişimlerini kolaylaştırırken, aynı zamanda eşit ve sürdürülebilir çözümler geliştirme zorunluluğunu da beraberinde getirmektedir. Bu kapsamda, akademisyenler ve sağlık profesyonelleri olarak bir araya gelerek bilgi ve deneyimlerimi paylaşmak, güncel gelişmeleri tartışmak ve geleceğe yönelik yenilikçi yaklaşımlar ortaya koymak büyük önem taşımaktadır. Ulusal ve uluslararası düzeyde alanın önde gelen uzmanlarının bilgi ve deneyimlerini paylaşıldığı kongremizde, bilimsel sunumlar, yarışmalar, paneller ve tartışma oturumlarıyla dijital sağlık çağında herkese ulaşabilir ve sürdürülebilir sağlık hizmetleri sunma yollarını ele alınmıştır. Kabul edilen bildiriler yüzyüze ve online oturumlarda sunulmuş ve elektronik ortamda özet/tam metin olarak yayınlanmıştır. Kongremizde sunulan bildiriler hakemkonumundaki akademisyenler tarafından değerlendirilmiş ve en başarılı bulunan üç bildiriye (birinci, ikinci ve üçüncü olarak) ödül verilmiştir.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.
