Background: Celiac disease arises from gluten consumption and shares symptoms with other conditions, leading to delayed diagnoses. Untreated celiac disease heightens the risk of autoimmune disorders, neurological issues, and certain cancers like lymphoma while also impacting skin health due to intestinal disruptions. This study uses facial photos to distinguish individuals with celiac disease from those without. Surprisingly, there is a lack of research involving transfer learning for this purpose despite its benefits such as faster training, enhanced performance, and reduced overfitting. While numerous studies exist on endoscopic intestinal photo classification and a few have explored the link between facial morphology measurements and celiac disease, none have concentrated on diagnosing celiac disease through facial photo classification. Methods: This study sought to apply transfer learning techniques with VGG16 to address a gap in research by identifying distinct facial features that differentiate patients with celiac disease from healthy individuals. A dataset containing a total of 200 facial images of adult individuals with and without celiac condition was utilized. Half of the dataset had a ratio of 70% females to 30% males with celiac condition, and the rest had a ratio of 60% females to 40% males without celiac condition. Among those with celiac condition, 28 were newly diagnosed and 72 had been previously diagnosed, with 25 not adhering to a gluten-free diet and 47 partially adhering to such a diet. Results: Utilizing transfer learning, the model achieved a 73% accuracy in classifying the facial images of the patients during testing, with corresponding precision, recall, and F1 score values of 0.54, 0.56, and 0.52, respectively. The training process involved 50,178 parameters, showcasing the model’s efficacy in diagnostic image analysis. Conclusions: The model correctly classified approximately three-quarters of the test images. While this is a reasonable level of accuracy, it also suggests that there is room for improvement as the dataset contains images that are inherently difficult to classify even for humans. Increasing the proportion of newly diagnosed patients in the dataset and expanding the dataset size could notably improve the model’s efficacy. Despite being the first study in this field, further refinement holds promise for the development of a diagnostic tool for celiac disease using transfer learning in medical image analysis, addressing the lack of prior studies in this area.

Educational games have been widely used to teach children about cyber security. This systematic literature review reveals evidence of positive learning outcomes, after analysing 91 such games reported in 68 papers published between 2010 and 2024. However, critical gaps have also been identified regarding the design processes and the methodological rigour, including lack of systematic design, misalignment between proposed and achieved learning outcomes, rare use of control groups, limited discussions on ethical considerations, and underutilisation of emerging technologies. We recommend multiple future research directions, e.g., a hybrid approach to game design and evaluation that combines bottom-up and top-down approaches.

Arap yazısına bağlı olarak doğan güzel yazma sanatı Hat Sanatı Türk-İslam sanatı ve medeniyeti hakkında birçok bilgi içermektedir. Bu eserlerdeki mesajların anlaşılması yerli halkın kendi tarihini anlaması açısından önemlidir. Bunun yanında turistlerin yoğun ilgi gösterdiği tarihi mekanlar da bu eserler ile donatılmıştır. Bu eserlerdeki yazılar ile gezilen mekanların önemi bilgileri daha iyi anlaşılacaktır. Son zamanlarda yapıların özellikle camilerin girişlerinde yapı içerisindeki hat eserlerinin anlamlarının yazılı olduğu levhaların yerleştirilmeye başlanması bu eserlerin anlaşılmaya olan ihtiyacını göstermektedir. Türkiye’de ve Dünya’daki milyonlarca eserin anlamlandırılması manuel bir işlemle çok zordur. Bu sebeple bu işlemin otomatikleştirilmesi adına bu çalışmada başlangıç adımı atılmıştır. Hat metinlerinin karmaşık görüntüden yazıya çevrilmesindeki aşamalardan olan yazıların stillerinin belirlenmesi ve yazıdaki harflerin tanınması problemleri Yapay Zeka algoritmaları ile incelenmiştir.

Psychological resilience is increasingly conceptualized as a multidimensional construct encompassing identity, emotional, cognitive, behavioural, and social domains. Using data from 620 Iranian adults (aged 18–64 years; 52% female), collected through an online self-report survey, this study applied unsupervised machine-learning techniques combining a deep autoencoder for dimensionality reduction with a Gaussian Mixture Model (GMM) for latent clustering-to examine psychological resilience profiles in pre-conflict Iran. Thirty-seven standardized psychological subscales were aggregated into five theoretically grounded dimensions: Self-Identity and Meaning, Emotional Regulation, Cognitive Flexibility, Coping and Growth, and Social Support and Connectedness. Unsupervised analysis identified four latent archetypes-Fragile Striver, Reactive Idealist, Hidden Reactor, and Stable Withdrawer-that reflected nonlinear configurations of resilience capacities across generational and gender groups. However, because the research employed a cross-sectional and self-report design, findings illustrate associative rather than causal relationships, and representativeness is limited to online participants. These contextual and demographic influences suggest that resilience is embedded within Iran’s evolving social environment. Despite these limitations, the study demonstrates the potential of AI-based latent profiling to clarify the multidimensional nature of resilience within culturally demanding contexts.

Enhancing Pediatric Congenital Heart Disease Outcomes: The Role of Machine Learning Models and AI-Driven Methodologies - Cam & Sakura Medical Journal

With the improvements in technology, computers are being used for assistance in learning and teaching in different fields. Therefore, Computer Assisted Learning is used for our study to teach operating computer numerical controlled lathe machine in computer integrated manufacturing lab. Our education is planned in major phases: traditional teaching and computer assisted learning. Before the education students took a questionnaire with different subscales to determine the level of readiness for learning. And after the education they took a test on operating the lathe machine to measure the success of the education. Then the students were asked to design a project. This education is a comprehensive one and it is not always possible to make all students use the lathe machine since there can be some issues on safety, time management and waste of products. So with this study it is intended to determine the effect of readiness for learning and academic success on the students and give this education accordingly. A dichotomous classification problem is constructed with the subscales of readiness for learning questionnaire and academic success as features and lathe test result as class labels. Consequently, a model that can determine whether a student would be successful in learning to operate the lathe machine with the subscales of the readiness for learning questionnaire (motivation, health – nutrition, learning, planned working, efficient reading, writing, listening, note taking, attending a course, preparation and attending an exam) and GPA is constructed with Support Vector Machines and leave-one-out cross validation. Keywords: