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
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.
Empowering Education Through Virtual Reality-Driven Course and Examination System
(IEEE, 2025) YILDIZ, MEHMET SEDAT; MUSTAFAOĞLU, BERKAY; ELMASRY, WİSAM
This paper presents the development of "Virtual Academy", a Virtual Reality (VR)-based application designed to ensure uninterrupted access to education, particularly in scenarios where physical attendance in classrooms is challenging. The platform enables students to log in via personal credentials, granting access to assigned courses within an immersive virtual classroom. Teachers and students interact in real-time, with educators leveraging audiovisual tools to enhance the learning experience. The system also supports lesson recording for future reference, evaluates the performance of the students via designated exams, and facilitates a seamless transition to remote learning. By harnessing modern VR technology, Virtual Academy enhances educational continuity and offers an interactive and engaging alternative to traditional learning environments.
UTA Method for Sustainable Decisions in Ceramic Tile Production
(Springer Science and Business Media Deutschland GmbH, 2025) KAÇAR, YAREN; BİÇER, SENA İREM; FİLİZ, AYBERK; DURMUŞ, EMİNE ŞEYMA; KUŞ, DUYGU; GERGİN, ZEYNEP
Efforts to minimize environmental damage have gained global importance due to critical issues such as climate change and the depletion of natural resources. In this context, manufacturing industries have increasingly prioritized environmental protection goals alongside profitability when planning their operations. With growing customer awareness and regulatory pressures, such as EU directives and governmental regulations, a focus on sustainability has become both a competitive advantage and a necessity. Consequently, companies are adopting sustainability-oriented approaches, including environmentally friendly material selection from raw materials to packaging. This trend toward sustainability-oriented production planning is also evident in the ceramic industry, the focus of this study. The main raw materials for ceramic products—clay, quartz, and feldspar—are extracted from mines, but their supply is becoming increasingly difficult and costly due to dwindling natural resources. As a result, sustainability-driven competition in the sector has prompted companies to seek more sustainable alternatives for these natural materials. This study aims to assist a manufacturing company in the ceramic sector with its sustainability-focused raw material selection process. The study's objective is to identify production recipes that minimize costs while using sustainable raw materials for floor tile production. The Utility Additive (UTA) multi-criteria decision-making (MCDM) method is applied to evaluate alternative recipes. These recipes incorporate environmentally friendly raw materials, such as recycled products and wastewater. Twelve criteria are initially identified and categorized under sustainability, cost, and manufacturability to guide the decision-making process. The UTA method is then used to determine the order of preference for five different recipes, and two scenarios are evaluated based on changes in the weights of the decision criteria.
Music Generation Using RNN-LSTM with Self-Attention Mechanism
(IEEE, 2025) ABDELALIM, MAHMOUD; BASHAR, MOHAMMAD; NEMER, HAZEM; ELMASRY, WİSAM
Music generation using artificial intelligence is a rapidly evolving domain that bridges the gap between creativity and computational intelligence, offering promising applications in entertainment, education, and therapy. In this paper, a Recurrent Neural Network (RNN) model with Long Short-Term Memory (LSTM) networks for music generation was employed, utilizing the Pretty Midi library. Features were extracted from MIDI files in the dataset and fed these notes into a model composed of three LSTM layers. To prevent overfitting, dropout layers were incorporated. The model was trained on a diverse set of MIDI files, allowing it to capture various musical styles and patterns. The trained model demonstrated high accuracy in music generation, producing coherent and stylistically consistent pieces. Experimental results show that the LSTM + Self-Attention model outperformed baseline RNN, LSTM, and BiLSTM models, achieving the lowest validation loss (0.47), confirming its effectiveness for the complex task of music generation.