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Driver drowsiness detection is an important issue to prevent traffic accidents. 40% of severe traffic accidents are due to drowsiness. Various methods are used for driver drowsiness detection. One of the driver drowsiness detection method is driver drowsiness detection based on the analysis of signals such as EEG and ECG. Another driver drowsiness detection method is driver drowsiness detection based on vehicle-driver interaction. The last driver drowsiness detection method used in the study is driver drowsiness detection from images. This method is more advantageous than the other two methods in terms of cost and usability because no driver intervention required. Classical image processing techniques and deep learning algorithms are used for driver drowsiness detection from images. Recent driver drowsiness detection studies are based on deep learning models. In addition, the model to be developed will need to be able to work on mobile devices in order to ensure widespread use. In the study, Convolutional Neural Networks were used for driver drowsiness detection on mobile devices. In order to increase the success rate of the model, the pre-trained model was reused with the transfer learning technique. The model developed for the training consists of 14 layers and 1,236,217 parameters. The dataset consists of two categories, open-eye and closed-eye images. The developed model achieved 95.65% accuracy, 95.86% precision, 94.32% recall and 95.17% f1 score which achieved better results than previous studies. A dataset of 2425 images was used to train the model. [ABSTRACT FROM AUTHOR]

Bees are one of the oldest living species in the world, having a major impact on the development of living species. The continuity of plants at the bottom of the food chain is directly related to the pollination of bees. Bees are a global insurance because of this characteristic. For this reason, it is very important to check the health status of bees. Depending on the technology developed nowadays, it is possible to control the health status of bees remotely with real-time image processing applications. In the study, feature extraction methods, which are the strengths of deep learning, were executed from two different arms and aggressive changes in images were detected. In the classification process; Instead of Softmax classifier based on probability calculation, multi-layer feedback artificial neural network (MLFB-ANN) model has been used. The success of the designed system has also been compared with the Softmax classifier. As a result of experimental studies, 93.07% success rate can be achieved with Softmax classifier for six different bee diseases on the same data set, while 95.04% success rate has been obtained with the developed system. In this study, a hybrid method based on deep learning methods was proposed for the classification of bee diseases and successful results were obtained. [ABSTRACT FROM AUTHOR]

Although the architectural design process is carried out with the collaboration of experts who are experienced in many different areas from the main preferences to the detailing stage, the major decisions such as plan organization, mass design etc. are taken by the architect. Computer Aided Design (CAD) programs are generally effective after the major decisions of the design are taken. For this reason, it is common for the main decisions, taken during the design process, to be changed during the analysis of the structural system. In order to prevent this, in the early stages of architectural design, earthquake system awareness and structural system design should be included as an design input; as, the failure of the structural system which did not considered well in the architectural design phase leads to unexpected revisions in the implementation project phase and thus leads to serious losses in both time and cost. The aim of this study is to create an Irregularity Control Assistant (IC Assitant) that can provide architects general information about the appropriateness of structural system decisions to earthquake regulations in the early stages of design process by using the deep learning and image processing methods. In this way, correct decisions will be made in the early stages of the design and unexpected revisions that may occur during the implementation project phase will be prevented. [ABSTRACT FROM AUTHOR]

Image processing applications in real-time systems have become a popular topic in recent years. Deep learning methods, one of the sub-branches of artificial intelligence, and image processing algorithms used in the field of object detection from images can be used together. In this way, applications are developed in many areas such as autonomous cars, autonomous unmanned aerial vehicles, assist robot technologies, assistant technologies for disabled and elderly individuals. This study aims to detect the tactile paving surfaces with deep learning methods in order to design an assistive technology system that can be used by visually impaired individuals, autonomous vehicles and robots. Contrary to traditional image processing algorithms, deep learning methods and image processing algorithms are used together in this study. The YOLO-V3 model, which is one of the best methods of object detection, is combined with the DenseNet model to create the YOLOV3-Dense model. YOLO-V2, YOLO-V3 and YOLOV3Dense models were trained on the Marmara Tactile Paving Surface (MDPY) dataset, which was created by the researchers and included 4580 images and their performances were compared with each other on the test dataset. It was observed that YOLOV3-Dense model is better than other models in detecting tactile paving surface with 89% F1-score, 92% mean average Precision(mAP) and 81% IoU values. [ABSTRACT FROM AUTHOR]