Welcome to IKU Academic Digital Archive System
OpenAccess@IKU is Istanbul Kultur University's Academic Digital Archive System, established in June 2014 to digitally store and provide open access to academic and artistic outputs in line with international standards and intellectual property rights. The system includes various outputs such as articles, presentations, theses, books, book chapters, reports, encyclopedias, and works of art produced by the university's faculty members and students.
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Recent Submissions
Fall Risk and Avoidance Behavior Due to Fear of Falling in Elderly Nursing Home Residents
(Rzeszow University Press, 2025) Türen, Sevda; ÖZÇALIK, CENNET KARA; YILDIZ, GÜLİŞAN; TEKİR, MERYEM İREM
Introduction and aim. Falls in the elderly affect their daily activities, causing a decrease in their quality of life and may even lead to death. This study aims to examine the risk of falling and the relationship between fear of falling and avoidance behaviors in elderly nursing home residents. Material and methods. Data were obtained using the “Fall Risk Assessment Scale (FRAS)” and the “Fear of Falling Avoidance Behavior Questionnaire (FFABQ)”. Results. The average age of the participants was 70.70±5.23 years. Total mean scores of FRAS and FFABQ were significantly higher in participants who could partially meet their daily needs on their own, had chronic diseases, used continuous medication, had problems with walking or balance, had vision or hearing problems, used walking aids, had fear of falling, and had experienced falls in the last three months. It was found that their average was significantly higher. It was determined that there was a strong and significant positive relationship between the FRAS and FFABQ total score averages. Conclusion. It was determined that elderly residents of nursing homes have a high risk of falling and that increased risk is associated with an increase in avoidance behaviors due to fear of falling.
Enhancing Privacy in Smart Grids and IOTs Systems by Using Federated Learning: Case Study
(IEEE, 2025) Ali, Ahmad; Drlik, Martin; Wadi, Mohammed; ELMASRY, WİSAM
To enhance privacy in smart grids (SGs) and internet of thing (IoT) systems, a Federated Learning (FL) framework is proposed for practical application. By leveraging the idea of decentralizing model training and keeping raw data local, the framework addresses the privacy and security challenges associated with data collection on centralized servers. The framework achieves high accuracy (98.2% on MNIST, 85.6% on CIFAR-10) while resisting poisoning attacks and scaling efficiently by integrating differential privacy and secure aggregation. A case study on energy demand forecasting confirms its real-world applicability. The results demonstrate the potential of FL for scalable, privacy-preserving data analysis in IoT and SGs, with future work focused on integrating other privacy-enhancing technologies such as blockchain (BC).
Structural, Docking, Molecular Dynamics and Antibacterial Analysis of Thymopentin: A Potent Anti-Cancer, -COVID-19 And Viral
(Chemical Society of Ethiopia, 2025) Er, Alev; Çelik, Sefa; Çakır, Elif; Özel, Ayşen E.; AKYÜZ, SEVİM
To gain deeper insights into the biological activity of thymopentin, its structural, anticancer, antiviral and antimicrobial properties were systematically investigated. Conformational preferences of thymopentin were investigated through conformational analysis and the lowest energy conformation was optimized using density functional theory (DFT) method, Becke three Lee–Yang–Parr (B3LYP) functional and 6-31G(d,p) basis set. Vibrational wavenumbers of the optimized structure were computed and compared with the experimental results. To elucidate the potential of thymopentin as anti-COVID-19 and anticancer agents, molecular docking simulations were performed. Thymopentin was docked into DNA (PDB ID: 1BNA), SARSCoV-2 main protease (PDB ID: 6M03) and EGFR receptor complex (PDB ID: 4HJO). Additionally, following molecular docking analyses, top-scoring ligand-receptor complexes of thymopentin with SARS-CoV-2 enzyme (6M03) and EGFR (4HJO) were subjected to 50 ns all-atom molecular dynamics (MD) simulations to examine the ligand-receptor interactions in greater detail. Moreover, the antimicrobial potency of thymopentin against the most prevailing human pathogenic microorganisms was also investigated. The strongest antibacterial activity was observed against “Listeria monocytogenes”, a pathogenic bacterium capable of causing listeriosis, a serious infection that can potentially lead to death. This study revealed the anticancer, anti-Covid 19 and antimicrobial activities of the thymopentin molecule, demonstrating its multifunctional bioactivity.
Gender-Based Digital Divide and Discrimination: A Time Use Analysis in Türkiye
(CRC Press, 2025) ŞAHANOĞULLARI, NAZLI; BİLGİN, DERYA
Access to information and communication technologies (ICTs) offers significant advantages globally. The Internet, enabling real-time interaction, is a key part of ICT. Improved access helps reduce inequalities in digital technology access between countries or groups. Examining the digital divide through gender highlights the disadvantages women face in accessing and using ICTs, potentially leading to digital discrimination. This not only contradicts the goal of ending discrimination against women but also hinders their participation in political, economic, and social spheres. In Türkiye, women have lower computer and Internet usage rates compared to men. Teaching digital skills to women could enhance their competitiveness in the labor market. This study investigates the gender-based digital divide and digital discrimination in Turkey through the Time Use Survey conducted by the Turkish Statistical Institute (Turkstat). By analyzing data on ‘computer use and programming’, ‘information acquisition via computer’, ‘communication via computer’, and ‘computer and video games’, the research explores daily time usage patterns to highlight the differences between men and women in accessing and utilizing digital technology, categorized by age groups, income levels, and education levels. Results show that, almost in all computer-related activities analyzed, men spend more time than women. 2026 selection and editorial matter, Muharrem Kılıç and Sezer Bozkuş Kahyaoğlu; individual chapters, the contributors.
Sentiment Analysis Dataset and Web Application for Turkish Tweets
(IEEE, 2025) ELMASRY, WİSAM
Today, Twitter (X ) is one of the most essential and popular social networking sites. It is very important to analyze the sentiments of the tweets posted on this platform to understand people and understand their opinions on any topic. Thus, you can determine what people are thinking and talking about on a topic you can choose, such as a brand, business, economy, or education. In this study, a dataset is created with Turkish tweets collected using the Twitter API. Then, techniques such as Word2Vec and Bag of Words (BoW) are used to clean this dataset and use it more comfortably. Afterward, this cleaned dataset is classified as Positive, Negative, and Neutral using classification methods such as Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBClassifier. Finally, a simple website has been created using JavaScript for users to use this application efficiently.