İstanbul Galata Üniversitesi Kurumsal Akademik Arşivi
DSpace@Galata, İstanbul Galata Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
The Mediating Role of Climate Change Mitigation Behaviors in the Effect of Environmental Values on Green Purchasing Behavior within the Framework of Sustainable Development
(Walter de Gruyter GmbH, 2025) Yilmaz, Gonca
Global environmental challenges, including the depletion of natural resources, pollution, and population growth, have significantly impacted modern lifestyles. The environmental and socioeconomic dimensions of this reality are represented by climate change, one of the major threats facing the planet. People's environmental values and the green behaviors they exhibit based on these values are crucial in mitigating significant environmental problems, such as climate change. In this context, raising environmental awareness and motivating individuals to contribute to sustainable development and the circular economy particularly environmental protection can serve as an effective starting point. In line with this idea, data were collected from 236 participants in Istanbul in 2024 using the convenience sampling method. The data collected through the survey technique in the study were analyzed using the SPSS program and PROCESS, a macro developed for SPSS. In addition, confirmatory factor analysis and path analysis were performed with the Python programming language, and fit index was also presented. The research findings reveal a significant relationship between environmental values and green purchasing behavior. The mediating role of climate change mitigation behavior was also found.
Deep Learning in neuroimaging for neurodegenerative diseases: State-of-the art, Challenges, and Opportunities
(Elsevier B.V., 2025) Akan, Taymaz; Alp, Sait; Ledbetter, Christina Raye; Tafti, Ahmad P.; Arevalo, Octavio; Bhuiyan, Mohammad Alfrad Nobel
Neuroimaging is commonly used to diagnose neurodegenerative diseases (NDDs), providing crucial insights into brain changes before clinical symptoms manifest. Deep learning (DL) for neuroimaging can improve early diagnosis and disease monitoring. Clinical implementation of DL faces challenges in accurately representing realworld data. Recent models, particularly those focused on diagnostic categorization, have achieved high accuracy, but their applicability to patients is limited. Conflicting inferences have been reported, with findings from small cohorts generalizing conclusions without considering inter-scanner, intra- and inter-site variations. A theoretically feasible method involves gathering a comprehensive dataset that encompasses all patient demographics, but this presents practical challenges including harmonization, data incompleteness, class imbalance, and substantial costs. Existing research has also mostly focused on common NDDs like Alzheimer's Disease (AD) and Parkinson's Disease (PD). This contribution expands the literature by looking at a wider range of NDDs, exploring the latest advancements in applying deep learning algorithms to neuroimaging analysis for the diagnosis and monitoring of NDDs, including AD, Frontotemporal Dementia (FTD), Lewy Body Dementia, PD, Huntington's Disease, Amyotrophic Lateral Sclerosis, and Multiple Sclerosis. We emphasize how these approaches are handling spatial/temporal information available in brain volume imaging data. We conclude by discussing the challenges associated with the use of voxel-based, patch-based, ROI-based, and slice-based approaches in brain volume imaging. These challenges are further compounded by issues such as inter-site and inter-scanner variability, class imbalances in medical datasets, and the scarcity of accurately annotated data, all of which impact the performance and generalizability of deep learning models.
Adapting the metaverse perception scale for Iranian nursing students: translation and psychometric assessment
(Springer, 2025) Aghabarary, Maryam; Yıldırım, Tuğba Öztürk; Norouzinia, Roohangiz
The integration of metaverse technologies into healthcare education is expand ing globally. However, there is a lack of culturally validated instruments to assess
students’ perceptions within the Iranian context. This study aimed to evaluate the
psychometric properties of the Persian version of the Metaverse Perception Scale
among Iranian nursing students. A cross-sectional psychometric study was con ducted with 436 nursing students. The translated scale underwent Exploratory Fac tor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Internal consistency
was assessed using Cronbach’s alpha. Convergent and discriminant validity were
evaluated through Composite Reliability (CR), Average Variance Extracted (AVE),
inter-construct correlations, √AVE, MSV and ASV. Factor analysis revealed a four factor structure—Education, Technology, Lifestyle, and Challenges—which ex plained 67.08% of the total variance. CFA results indicated that the refined 20-item
version of the questionnaire had a good overall model fit (χ²/df=1.647; CFI=0.958;
RMSEA=0.054). All CR values and AVE values were above 0.70 and 0.50, re spectively, supporting convergent validity. For all factors, √AVE values exceeded
inter-construct correlations, and both MSV and ASV were lower than AVE, sup porting discriminant validity. The scale demonstrated strong internal consistency
(Cronbach’s alpha=0.917−0.822). The Persian version of the Metaverse Perception
Scale (P-MPS) is a valid and reliable scale for assessing nursing students’ percep tions of metaverse technologies in educational settings. This scale shows strong
potential for application in both researc
Smart Contracts, Blockchain, and Health Policies: Past, Present, and Future
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Kurt, Kenan Kaan; Timurtaş, Meral; Ozaydin, Fatih; Türkeli, Serkan
The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems.
Machine learning–assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters
(Springer Science and Business Media Deutschland GmbH, 2025) Tanyildizi-Kokkulunk, Handan; Alcin, Goksel; Cavdar, Iffet; Akyel, Resit; Ciftci-Kusbeci, Tuba; Caliskan, Gonul
Accurate differentiation between non-cancerous, benign, and malignant lung cancer remains a diagnostic challenge due to overlapping clinical and imaging characteristics. This study proposes a multimodal machine learning (ML) framework integrating positron emission tomography/computed tomography (PET/CT) anatomic-metabolic parameters, sarcopenia markers, and inflammatory biomarkers to enhance classification performance in lung cancer. A retrospective dataset of 222 patients was analyzed, including demographic variables, functional and morphometric sarcopenia indices, hematological inflammation markers, and PET/CT derived parameters such as maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG). Five ML algorithms—Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Extreme Gradient Boosting, and Random Forest—were evaluated using standardized performance metrics. Synthetic Minority Oversampling Technique was applied to balance class distributions. Feature importance analysis was conducted using the optimal model, and classification was repeated using the top 15 features. Among the models, Random Forest demonstrated superior predictive performance with a test accuracy of 96%, precision, recall, and F1-score of 0.96, and an average AUC of 0.99. Feature importance analysis revealed SUVmax, SUVmean, total lesion glycolysis, and skeletal muscle index as leading predictors. A secondary classification using only the top 15 features yielded even higher test accuracy (97%). These findings underscore the potential of integrating metabolic imaging, physical function, and biochemical inflammation markers in a non-invasive ML-based diagnostic pipeline. The proposed framework demonstrates high accuracy and generalizability and may serve as an effective clinical decision support tool in early lung cancer diagnosis and risk stratification.
