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
YouTube Video Comments Sentiment Analysis Using Custom NLP Model
(IEEE, 2025) ERCİKAN, YAVUZ SELİM; ELMASRY, WİSAM
This paper presents a sentiment analysis platform designed to process user comments on YouTube videos. Leveraging an LSTM-based neural network trained on large-scale datasets such as Sentiment140 and a 3-million-row Twitter sentiment dataset, the platform categorizes comments into positive, negative, or neutral sentiments. It integrates modern web technologies like React.js for the front-end, Flask for the back-end, Firebase for user authentication, and the YouTube Data API for comment retrieval. Additionally, OpenAI's language models are employed to provide advanced contextual analysis, extracting key themes and emotional trends from the comments. The system visualizes sentiment distributions using dynamic charts, offering insights valuable for content creators and researchers. This work demonstrates the effective combination of deep learning and scalable web technologies to build a user-friendly sentiment analysis solution.
APAYDIN, TUĞÇE
Dr. Öğr. Üyesi
Forecasting Inbound Logistics for Express Cargo Transportation: A Case Study of Turkey
(Springer Science and Business Media Deutschland GmbH, 2025) Budur, Buse; Demircioğlu, Helin Öykü; Şimşek, Berna; Konyalıoğlu, Aziz Kemal; APAYDIN, TUĞÇE; Özcan, Tuncay
Within the domain of inbound logistics, the express air cargo transportation sector has become an essential component of global trade. As the disparity between actual demand and forecasted demand in express cargo transportation widens, the potential for resource wastage correspondingly increases due to the unpredictability of volume and weight. Therefore, this study aims to forecast the daily quantity and weight of incoming cargo, categorized by type, within the context of inbound logistics. Utilizing a case study of express cargo transportation in Turkey, we employ both Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to compare the forecasting performance of the LSTM approach. In the LSTM model, the maximum epoch, batch size, number of neurons and optimizer parameters are adjusted using grid search to reduce the prediction error. This forecasting capability enables businesses to better prepare for sudden fluctuations in incoming shipments and provides a methodological and analytical framework that influences daily operations. Additionally, we seek to contribute to the existing literature on operational planning by developing a model capable of generating daily forecasts, as opposed to traditional forecasting models that operate on different temporal scales. The numerical results indicate that the improved LSTM model outperforms the SARIMA model for all data sets.
Molecular Docking and Molecular Dynamics Analyses of Pazopanib with VEGF Receptors
(Istanbul University Press, 2025) Er, Alev; Önem, Zeynep Cicek; Çelik, Sefa; Özel, Ayşen; AKYÜZ, SEVİM
Objective: The antineoplastic agent Pazopanib is effective for treating renal cell cancer and soft tissue sarcoma. The aim of this study was to elucidate the anticancer mechanism of Pazopanib by exploring its molecular interactions with vascular endothelial growth factor receptors (VEGFRs). For this purpose, the most stable structure was determined, and molecular docking and molecular dynamics calculations of Pazopanib with VEGFR1 and VEGFR2 receptors were performed. Materials and Methods: Conformational analysis of Pazopanib was performed using VegaZZ software. Pazopanib was docked to the active sites of the VEGFR1 and VEGR2 receptors (PDB IDs: 3HNG; 3VHE) using Autodock Vina software. The molecular dynamics (MD) simulations were carried out using the YASARA v22.9.24 program with the AMBER14 force field. The anticancer, antibacterial, antifungal, and antiviral activities of the compounds were predicted using PaccMann, AntiBac-Pred, AntiFun-Pred, and AntiVir-Pred. Results: The molecular docking analysis of the Pazopanib molecule with the VEGFR1 and VEGFR2 receptors revealed a strong binding affinity of the investigated molecule towards the targets. The MD simulations, performed for Pazopanib-VEGFR1 and Pazopanib-VEGFR2 complexes showed that each docking complex and intermolecular interactions were stable throughout the simulations. Conclusion: Molecular docking simulations revealed a strong binding affinity of Pazopanib towards VEGFR1 (-8.6 kcal/mol) and VEGFR2 (-9.9 kcal/mol), indicating its efficacy in cancer treatment. During the 40-ns MD simulation of the Pazopanib-3hng and Pazopanib-3vhe complexes, we validated the stability of Pazopanib in the active sites of the receptors. The predicted anticancer, antibacterial, antifungal, and antiviral activities of Pazopanib revealed its versatile bioactivity.
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.