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
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
ÇAVUŞOĞLU, AYSEL
Dr. Öğr. Üyesi
The Impact of Epilepsy Education on Knowledge, Self-Management, and Stigma in Individuals With Epilepsy
(Lippincott Williams & Wilkins, 2025) Kaya, İrem İlgezdi; ÇAVUŞOĞLU, AYSEL; Elmalı, Ayşe Deniz; Bebek, Nerses
Background: Epilepsy should be approached in a multidimensional manner, considering its biological, psychological, and social aspects. The aim of this study is to examine the impact of epilepsy education on people with epilepsy regarding knowledge level, self-management, and stigma. METHODS: An online survey, including an epilepsy information form, epilepsy self-management scale, and stigma scale was sent to registered patients in our epilepsy clinic. After the survey, patients were invited to a 1.5-hour epilepsy education program, conducted by 2 instructors on different days, followed by a question-and-answer session. Participants were retested posttraining. RESULTS: Of 265 patients who filled out the pretraining survey, 69 (26%) attended the education program. Those who participated were generally more knowledgeable at the baseline. University graduates and those using the internet as a source of information were more inclined to attend, whereas unmarried individuals attended less. The participant age was 39.1 years (9.2 years), with 61% female, 65% having a university degree, and 61% actively working. Seizure types included focal (45%), generalized (22%), and both (33%), with 70% experiencing less than 1 seizure per month. Posteducation, participants answered more knowledge questions correctly ( P < .001, before: 37.0 [6.0], after: 40.7 [6.1]). Awareness about swimming risks increased in the self-management scale, along with the tendency to carry informative cards, join support groups, and educate relatives. There was no change in the stigma scale. CONCLUSION: Epilepsy education has a positive impact on raising awareness about the disease and promoting self-management in people with epilepsy. The fight against stigma needs to involve broader segments of society.