Welcome to IKU Institutional Repository
OpenAccess@IKU is Istanbul Kultur University's Institutional Repository , 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.
Recent Submissions
Item type:Publication, Access status: Open Access , A Comparative Analysis of CNN Architectures, Fusion Strategies, and Explainable AI for Fine-Grained Macrofungi Classification(MDPI, 2025) Sevindik, Mustafa; KORKMAZ, ARAS FAHRETTİN; Ekinci, Fatih; Kumru, Eda; Altındal, Ömer Burak; Aydin, Alperen; Güzel, Mehmet Serdar; Akata, IlgazThis study was motivated by the persistent difficulty of accurately identifying morphologically similar macrofungi species, which remains a significant challenge in fungal taxonomy and biodiversity monitoring. This study presents a deep learning framework for the automated classification of seven morphologically similar coprinoid macrofungi species. A curated dataset of 1692 high-resolution images was used to evaluate ten state-of-the-art convolutional neural networks (CNNs) and three novel fusion models. The Dual Path Network (DPN) achieved the highest performance as a single model with 89.35% accuracy, a 0.8764 Matthews Correlation Coefficient (MCC), and a 0.9886 Area Under the Curve (AUC). The feature-level fusion of Xception and DPN yielded competitive results, reaching 88.89% accuracy and 0.8803 MCC, demonstrating the synergistic potential of combining architectures. In contrast, lighter models like LCNet and MixNet showed lower performance, achieving only 72.05% accuracy. Explainable AI (XAI) techniques, including Grad-CAM and Integrated Gradients, confirmed that high-performing models focused accurately on discriminative morphological structures such as caps and gills. The results underscore the efficacy of deep learning, particularly deeper architectures and strategic fusion models, in overcoming the challenges of fine-grained visual classification in mycology. This work provides a robust, interpretable computational tool for automated fungal identification, with significant implications for biodiversity research and taxonomic studies.Item type:Publication, Access status: Open Access , Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi(MDPI, 2025) KORKMAZ, ARAS FAHRETTİN; Ekinci, Fatih; Kumru, Eda; Altas, Sehmus; Gunes, Seyit Kaan; Yalcin, Ahmet Tunahan; Guzel, Mehmet Serdar; Akata, IlgazAccurate identification of wild edible macrofungi is essential for biodiversity conservation, food safety, and ecological sustainability, yet remains challenging due to the morphological similarity between edible and toxic species. In this study, a curated dataset of 24 wild edible macrofungi species was analyzed using six state-of-the-art convolutional neural networks (CNNs) and four ensemble configurations, benchmarked across eight evaluation metrics. Among individual models, EfficientNetB0 achieved the highest performance (95.55% accuracy), whereas MobileNetV3-L underperformed (90.55%). Pairwise ensembles yielded inconsistent improvements, highlighting the importance of architectural complementarity. Notably, the proposed Combination Model, integrating EfficientNetB0, ResNet50, and RegNetY through a hierarchical voting strategy, achieved the best results with 97.36% accuracy, 0.9996 AUC, and 0.9725 MCC, surpassing all other models. To enhance interpretability, explainable AI (XAI) methods Grad-CAM, Eigen-CAM, and LIME were employed, consistently revealing biologically meaningful regions and transforming the framework into a transparent decision-support tool. These findings establish a robust and scalable paradigm for fine-grained fungal classification, demonstrating that carefully engineered ensemble learning combined with XAI not only advances mycological research but also paves the way for broader applications in plant recognition, spore analysis, and large-scale vegetation monitoring from satellite imagery.Item type:Publication, Access status: Restricted , A Hybrid Deep Reinforcement and Machine Learning-Based Intrusion Detection System for Dynamic XSS Attacks(John Wiley and Sons Ltd., 2025) Kara, Mustafa; Okur, Fatma Betul; DURMUŞKAYA, MUHAMMED ERSİN; Kabasakaloglu, Murat Utku; Okutan Kara, AyseWeb-based systems are vulnerable to continuously evolving or self-updating attacks such as Cross-Site Scripting (XSS). Traditional Intrusion Detection Systems (IDS) provide limited protection against this threat through signature-based and anomaly-based methods. In this study, Machine Learning (ML) methods are used in conjunction with Deep Reinforcement Learning (DRL) techniques. In the proposed approach, ML methods are utilized to rapidly detect known attacks, while DRL provides adaptive learning against more general and unknown threats. These two components are trained independently and then make decisions through a weighted combination during the prediction phase. The aim is to address the shortcomings of current IDS systems in defending against dynamic XSS attacks. Experimental results show that, in real-time IDS environments, combining Random Forest with Word2Vec ensures detection within 10 ms, maintains an F1 score of about 0.99, and keeps computational cost minimal. In contrast, for offline or SOC-based setups where longer training and adaptive learning are acceptable, the DDQN-Word2Vec combination proves most effective. Overall, the proposed hybrid system delivers scalable, real-time protection against dynamic and zero-day web threats.Item type:Publication, Access status: Open Access , Promoting Healthy Eating and Activity in School Children: A Quasi-Experimental Study(Oxford University Press, 2025) Peker, S.; Akbulut, M; Ceviker, R.; BALTACI, PELİN CİN; Tanriover, O.Item type:Publication, Access status: Restricted , Inequalities of the Turán-Type for the Le Roy Type's Mittag-Leffler Function(John Wiley and Sons Ltd, 2025) Mert Coskun, Oya; ÇETİNKAYA, ASENA; Altinkaya, SahseneThis paper presents the necessary and sufficient conditions for monotonicity of the Mittag-Leffler function of the Le Roy type (abbr. MLR-functions), taking into account its special place in the theory of analytic functions. Mehrez and Sitnik studied the monotonicity of the ratio of sections on the series of Mittag-Leffler function (MLF) and developed some Tur & aacute;n-type inequalities. These inequalities have a wide range of applications in understanding the analytical properties of functions. The proposed work is a study for MLR-functions. The primary objective of this study is the development of the Tur & aacute;n-type inequalities and subsequent validation of these inequalities on specific intervals. In addition, the log-convex property of this function is analyzed, and the theoretical significance of this property is elaborated.