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
Perspektifte Fotografik Görüntü; Görsel Algı ve Mesafe Algısının Teorik Analizine Yönelik Model Önerisi
(Anadolu Üniversitesi, 2025) Dişkaya, Feyza Nur; Altuncu, Damla; KARABETÇA, ALİYE RAHŞAN
Bu çalışma, perspektifin sadece teknik bir beceri olmanın ötesinde, insanın görsel algısının karmaşık doğasını yansıtan fotografik bir görüntü olduğunu vurgulamaktadır. Perspektif, mekanın algılanışındaki bakış açısını ifade etmek için kullanılmaktadır. Gerçek hayatta mekandaki nesneler arasındaki mesafe algısı farklı açılardan boyut, konum vb. faktörler bakımından farklılık gösterebilmekte ve yanılsama oluşturmaktadır. Bu yanılsamalar, farklı kamera açılarından elde edilen çoklu üç boyutlu görüntüler ile perspektifin ve görsel algının dinamiklerini göstermek için bu çalışmada kullanılmıştır. Sabit iki nesne arasındaki mutlak derinliğin sabit kalmasına rağmen göreceli derinliğin değiştiğini ve bunun mutlak derinliğin farklı olarak algılanmasına sebebiyet verdiği ileri sürülmektedir. Bu hipotezin analizi üzerinden görsel algı ve mesafe algısının teorik analizine yönelik model önerisinde bulunulmuştur. Bu çalışma, iç mekânın perspektif yapısının ve bakış açısının görsel algı üzerindeki etkilerini incelerken, bu dinamikleri aydınlatmayı hedeflemektedir. Ayrıca, perspektifin sadece görsel bir temsil olmadığını, aynı zamanda gözlemcinin mekânsal algısını ve nesnelere yönelik algısal yaklaşımını da şekillendirdiğini ortaya koymaktadır.
Views of Parents of Children With Neurodevelopmental Disabilities on Their Children's Social Media Use and Media Parenting
(Taylor & Francis Ltd., 2025) KILIÇ, FİDAN GÜNEŞ GÜRGÖR; Paftalı, Ayşe Tunç; Yıldız, Gizem
The aim of this study is to investigate the views of parents of children with neurodevelopmental disabilities regarding their children's use of social media, and their communication and approaches (media parenting) in teaching technologies to their children. A total of 319 parents participated in the study in which descriptive and relational survey models were used. According to the findings of the study, the participants' views on their children's social media use were negative regardless of all variables. On the other hand, the participants' media parenting levels were found to be high. No significant relationship was found between the participants' media parenting level and their views on social media use. The results showed that parents did not see social media as beneficial for their children in any circumstances, that they were very concerned about the use of social media, and that they exhibited a controlling and restrictive approach in media parenting.
Development a Software for Detecting Burn Severity Using Convolutional Neural Network-Based Approach
(Yıldız Technical University Press, 2025) BULUT, CANAN; Kolca, Dilek; Tarlak, Fatih
Burns are a significant cause of injury and can result in severe physiological reactions, metabolic disturbances, scarring, organ failure, and even death if not properly managed. Traditional clinical methods for assessing burn severity can be challenging due to various factors. In the event of a burn incident, an AI-based application can quickly analyse large amounts of data, expedite repetitive tasks like burn severity assessment, reduce subjective human errors, provide a more objective evaluation of burn severity, become more accessible in areas lacking expert medical personnel or during emergencies, and offer information-based treatment options. To address this issue, this study proposed a Deep Convolutional Neural Network (DCNN) approach to detect the severity of burn injury using real-time images of skin burns. Deep learning (DL) algorithms, namely GoogleNet, ResNet-50, and Inception-v3, were employed to train the images in Matlab software. In addition, almost 25% of the images were reserved for external validation. The developed interface achieved an accuracy rate of 90.22% in assessing burn severity based on visual data from actual cases. Consequently, by harnessing intelligent technologies, the suggested DCNN-based method can assist healthcare professionals in assessing the extent of burn injuries and delivering timely and suitable treatments. This, in turn, significantly mitigates the adverse outcomes associated with burns.
Prediction of Dark Cutting Carcasses in Cattle Using Machine Learning Algorithms With Stockperson Actions and Animal Behaviors at Abattoir: A Study in Türkiye
(Elsevier Science Ltd., 2025) Özdemir, Seyfi; ÖZDEMİR, GONCA NUR; Ekiz, Bülent
The aim was to investigate the relationship between stockperson actions, animal behaviors at the abattoir, and the occurrence of dark cutting in cattle using various machine learning (ML) algorithms. Season, age, sex, breed, carcass bruising score, carcass weight, and various transportation-related variables were also considered as covariates and potential predictors of dark cutting. Data was collected from 648 cattle, including Holstein, Brown Swiss, and Simmental breeds. The percentage of dark cutting carcasses was 6.64 %. The synthetic minority oversampling technique (SMOTE) was used to transform unbalanced dataset into balanced one. ML was applied with four different models, defined based on the inclusion of covariates, stockperson actions, and animal behaviors as predictors. The highest accuracy value (0.97) was obtained with Boosting algorithm. In all algorithms, the highest accuracy values were achieved with models that included stockperson actions as predictors. Age, prod use and beating at slaughter corridor, and lairage type were most important features influencing dark cutting according to Boosting algorithms. In conclusion, the classification of normal and dark cutting carcasses can be achieved with a satisfactory accuracy using the Boosting and Random Forest algorithms with the model including stockperson actions, animal behaviors and various covariates. However, this study reflects local cattle handling practices in T & uuml;rkiye; further studies are needed to explore cattle handling practices in other countries.
Terörizmin Anatomisi: Psikolojik, Sosyal ve İdeolojik Bir Sentez
(İstanbul Kültür Üniversitesi, 2025) HAVLE, NEDİM