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Cracks in buildings are a source of concern as they may indicate structural problems. Cracks in buildings are one of the major problems with concrete structures, as they affect the appearance of the building, compromise the integrity of the masonry, jeopardize building safety, and reduce the durability of the structure. Cracks in buildings can be a cause for concern and may indicate a potential structural problem that could jeopardize the safety and stability of the building. Understanding the root causes of these cracks is crucial to determining appropriate preventive measures and repair methods. In this study, crack and slope control in buildings was performed with an image processing-based measurement system developed using CNN deep learning algorithms. A dataset of 294 photographs was used for the study. The data were subjected to image processing, preprocessing, and detection with CNN. The accuracy rate is 0.9716 for the VGG-16 architecture and 0.9513 for the Inception-V3 architecture. It was concluded that there is no significant difference between the accuracy rates of the architectures. All vertical and horizontal cracks with a thickness of 2 mm and a continuity of 4 cm were detected. Thus, crack control in buildings can be done quickly and reliably, and building safety will be ensured. [ABSTRACT FROM AUTHOR]

The physical properties of fruit and vegetable products play an important role in their quality classification. With the existing manual methods, disease, pesticide and quality status of agricultural products are checked during the control stages. Manual sorting and classification of products requires expertise and the process is time consuming and labor intensive. Today, with the developing technology, the processing and marketing of products can be realized in optimum time and efficiency with the software techniques used in the fields of agriculture and food. In this study, date palm fruit, which has an important share in the fruit and vegetable market, is considered. The use of image processing and artificial intelligence techniques in classifying the quality of date fruits makes the sales process more consistent and efficient. Within the scope of the study, four different artificial intelligence techniques were used with a uniquely prepared dataset. The dataset consists of three different classes: good, bad and medium quality dates. The dataset specially prepared for the study was trained with MobileNetV2, ResNetV2-50, DenseNet- 201 and Inception-v3 deep learning models. Among the four models used in the study, MobileNetV2 model was determined as the most successful model with 95% accuracy rate. [ABSTRACT FROM AUTHOR]

In this paper, the approaches developed for content management and automatic content control of the job postings on the İşin Olsun website, which is a blue-collar job search and finding platform, will be explained. For this purpose, experiments were carried out with supervised machine learning models and Bert transformer architecture methods, and the results were observed. At the end of the study, a system was developed in which the contents of the job texts are automatically classified using Bert for sequence classification, and this system was integrated into the job search platform. Starting from labeling the job texts in two different classes as appropriate or unsuitable content by the controllers, from the preparation stages of the dataset to the integration of the classification model into the system, the studies are summarized in this paper. [ABSTRACT FROM AUTHOR]