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
Investigation of Pre-Service Science Teachers’ Social Media Usage Preferences: An Integrated Structural Equation Modelling and Artificial Neural Network Approach in the Context of Creativity and the Big Five Personality Traits
(Sciendo, 2025) Doğru, M. Said; FATİH, YÜZBAŞIOĞLU
Today, social media is widely used for many purposes, from socialization to communication and education to commerce. This study examines the social media use of pre-service science teachers in the context of creativity and the Big Five Personality Traits Theory. For this study, the participants were pre-service science teachers selected to represent the target group for examining the research objectives. Data from science teachers included in the study group were collected through an online questionnaire using purposive sampling, allowing for a focused examination of specific characteristics within this group. The data obtained from the questionnaires were analyzed and interpreted using an integrated SEM-ANN method. The findings obtained as a result of the study revealed that creativity and personality traits in the Big Five Personality Traits Theory affect the social media use of pre-service science teachers.
Cloud-Based VR Cybertherapy for Dynamic Exposure Therapy
(IEEE, 2025) KURTULUŞ, FETTAH; AKBULUT, AKHAN; Güngör, Feray; Tarakcı, Ela; Çatal, Çağatay
The use of virtual reality (VR) technology in the rehabilitation domain offers opportunities such as conducting potentially dangerous exposure experiments safely in a clinical environment. Although current state-of-the-art VR headsets offer very high resolutions that provide experiences close to real life, therapy systems are generally developed for specific phobias. The majority of VR-based small animal phobia studies are limited to exposure to spiders, insects, rats, and snakes, and there is no generic system available for the treatment of various small animal phobias. In this paper, we designed and implemented a cloud-based cybertherapy system for treating patients with small animal phobias that supports exposure to multiple species. Therapists can download specific assets for any type of small animal species from the cloud repository, enhancing the therapy systems based on the needs of the patients. The proposed exposure therapy system allows therapists to control the small animal assets in the game engine and adjust the therapy sessions according to the patient's condition. The results demonstrate the system's effectiveness in offering a flexible, adaptive, and scalable solution for treating small animal phobias across different patient scenarios. As an extension of this work, we started to develop a recommendation engine that uses labeled therapy data to personalize treatment protocols, further enhancing patient care.
Test-Retest Reliability and Concurrent Validity of the One-Minute Sit to Stand Test in Children and Adolescents Who are Overweight or Obese
(Taylor & Francis Group LLC Philadelphia, 2025) Benkhalifa, Nesrine; CİLBİR, ÇİĞDEM EMİRZA; Ercan, Oya; Aslan, Goksen Kuran
Aims: To assess test-retest reliability and concurrent validity of the 1-min sit-to-stand test (1-minSTST) in children and adolescents who are overweight or obese. Methods: Thirty-nine overweight and obese children and adolescents were included. The 1-minSTST was administered twice with a one-hour break. Concurrent validity was evaluated by assessing correlations between 1-minSTST repetitions and six-minute walk test (6MWT) distances. The cardiorespiratory measures (blood pressure, heart rate, oxygen saturation, respiratory rate, dyspnea, and perceived fatigue) were recorded before and after each test. Results: Test-retest reliability was excellent (ICC: 0.90, 95% confidence interval 0.90–0.97). There was no relationship between scores on the 1-minSTST and 6MWT (r = –0.06, p = 0.71). No statistically significant correlation was found between scores on each test and change in cardiorespiratory responses, except for respiratory rate (r = 0.43, p = 0.006). Change in cardiorespiratory responses was similar when performing each test (p > 0.05). Conclusion: While the 1-minSTST seems promising, it is not significantly related to the 6MWT, indicating they may assess different dimensions of fitness in this population. Further investigations are needed to determine the clinical implications of 1-minSTST outcomes in pediatric population.
Deep Learning-Based Classification of Software Bugs Using Code Context and AST Features
(IEEE, 2025) Gökcen, Alpaslan; ARŞIK, ARDA; AKBULUT, AKHAN; Çatal, Çağatay
A software component plagued with bugs is likely to experience both functional and non-functional difficulties, such as usability, performance, and security issues. These bugs can range from improper layouts to system crashes and security vulnerabilities. In this research, we developed a deep learning-based model to classify software bugs based on preceding and corrected code statements. Preprocessing includes extracting features from the Abstract Syntax Tree (AST) by traversing the tree to capture node types and relationships, then encoding the AST into a numerical vector representation for classification. Using AST and padding methods, features are extracted from code statements for use in a Convolutional Neural Network (CNN) model. We proposed a CNN-based model with AST-based feature extraction for large-scale software bug classification and evaluated on 153,652 bug samples from 1,000 GitHub projects. Compared to traditional datasets, this large dataset presents challenges in building accurate prediction models. This model is particularly useful in continuous integration (CI) pipelines, where it can automatically detect problematic code during the build process, helping to identify bugs faster and reduce manual review.
Structural, Optical, and Dielectric Studies on Transition Metals Doping (Cu, Ni) in MgFe2o4 Ferrites
(Elsevier Ltd., 2025) Dhahri, Siwar; Omri, Aref; Dhaou, Mohamed Houcine; Dhahri, Essebti; Graça, Manuel P.F.; Brito, Anna B.; FAUSTO, RUI; Pina, João; Costa, Benilde F.O.
Spinel ferrites are increasingly researched for their tunable optical and electrical properties, with applications in electronic devices and microwave absorption. This study investigates Mg0.5Cu0.3Ni0.2Fe2O4 synthesized via sol-gel auto-combustion, yielding a single-phase cubic spinel structure (Fd-3m) with crystallite sizes of ∼1.1 μm. XRD and Raman spectroscopy confirmed the phase purity and identified five active modes (A1g, Eg, 3T2g). Optical analysis revealed a direct bandgap (Eg = 2.335 eV), low Urbach energy (Eu = 0.2386 eV), and strong UV–Vis absorption, indicating high crystallinity and suitability for optoelectronics. Electrical measurements demonstrated semiconducting behavior with an activation energy (Ea) of 0.34 eV, governed by the Correlated Barrier Hopping (CBH) model. Dielectric studies showed high permittivity (ε' > 103 at low frequencies) and significantly reduced loss (tan δ < 0.1 at 106 Hz), confirming efficacy in high-frequency energy transmission/storage. The synergistic Cu/Ni doping enables tailored structural, optical, and dielectric properties, positioning this material as a promising candidate for high-frequency electronics, energy storage systems, and solar cells.