Yurdumuzda çok sayıda diyabet hastası bulunmaktadır. Bu hastalar diğer tüketiciler gibi tüm gün boyunca yoğun şekilde akıllı telefon ve mobil cihaz kullanmaktadır. Dolayısıyla, diyabet hastalarının kullandıkları mobil cihazlara özel bir yazılımla bu hastaların günlük yaşamlarını kolaylaştırmak mümkün görünmektedir. Bu çalışmada da diyabet hastalarına fiziksel veya tıbbi bir müdahalede bulunmadan, daha konforlu bir yaşam sürmelerine yardımcı olacak mobil bulut bilişim teknolojisine dayanan bir yazılım önerilmektedir. Hastaların diyabeti ilgilendiren günlük aktivitelerinin takibini kolaylaştıran özellikleriyle, geliştirilen çözümün hastaların iyileşme süreçlerine de yardımcı olacağına inanılmaktadır. Önerilen sistem birçok özellik yanında kullanıcılara özel, hem uyarıcı hem de hatırlatıcı mesajlar gönderebilmektedir. Benzer çözümlerle kıyaslandığında, önerilen yazılımın daha çok özelliği bir arada bulundurduğu ve iki yeni özellik içerdiği görülmektedir. Bir sonraki aşamada, çözümün yabancı dillere uyarlanması ve doktor gözetiminde gerçek denekler üzerinde teste tabi tutulması planlanmaktadır.

Federated learning is a collaborative machine learning paradigm where multiple parties jointly train a predictive model while keeping their data. On the other hand, multi-label learning deals with classification tasks where instances may simultaneously belong to multiple classes. This study introduces the concept of Federated Multi-Label Learning (FMLL), combining these two important approaches. The proposed approach leverages federated learning principles to address multi-label classification tasks. Specifically, it adopts the Binary Relevance (BR) strategy to handle the multi-label nature of the data and employs the Reduced-Error Pruning Tree (REPTree) as the base classifier. The effectiveness of the FMLL method was demonstrated by experiments carried out on three diverse datasets within the context of animal science: Amphibians, Anuran-Calls-(MFCCs), and HackerEarth-Adopt-A-Buddy. The accuracy rates achieved across these animal datasets were 73.24%, 94.50%, and 86.12%, respectively. Compared to state-of-the-art methods, FMLL exhibited remarkable improvements (above 10%) in average accuracy, precision, recall, and F-score metrics.

Image processing has been employed in a variety of fields since the advent of image processing techniques. One of these fields is textiles. The existence of any defect in a fabric is one of the most important factors affecting the quality of the fabric. There are many types of fabric defects that can occur for various reasons. It is critical to figure out what caused the defect and fix it so that it does not occur again. Automation of fabric defect detection has recently attracted a great deal of interest in view of the development in artificial intelligence technology to be able to discover defects with a high degree of success and to limit the harm to the manufacturer. This study focuses on analyzing different feature extraction methods and different classifiers and discussing the advantages/disadvantages of the combinations and, unlike other studies, using feature fusion for feature extraction. Different cases have been created that handle fabric datasets from different angles and apply different methods of feature extraction (convolution neural network, minimum relevance and maximum redundancy) and classification (ensemble learning (EL), k-nearest neighbor, support vector machine (SVM)) for separating defected and un-defected patterned and un-patterned fabrics. ResNet18 is the convolution neural network-based model with the highest performance in feature extraction, while EL and the SVM allow us to achieve close and highly successful results in classification. When feature fusion is used, ResNet18 & GoogLeNet & SVM is the most successful combination compared to the others (94.66%).