In recent years, the necessity of personal working in traffic control is increasing because the numbers of vehicles in traffic is increasing. To deal with this problem, computer based automatic control systems are being developed. One of these systems is automatic vehicle license plate recognition system. In this work, the automatic vehicle license plate recognition system based on artificial neural networks is presented. In this system, 259 vehicle pictures were used. These vehicle pictures were taken from the CCD camera and then the license plate region dimensioned by 220×50 pixels is determined from this picture by using image processing algorithms. The characters including letters and numbers placing in the license plate were located and determined by using Canny edge detection operator and the blob coloring method. The blob coloring method was applied to the ROI for separation of the characters. In the last phase of this work, the character features were extracted by using average absolute deviation formula. The digitized characters were then classified by using feed forward back propagated multi layered perceptron neural networks. The correct classification rates were given in last section.

This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs. Two different test processes were used in this study. In the first test application, a data set containing five fertile eggs was used. In the second, testing was carried out on the data set containing 18 fertile eggs. For evaluating this study, we used AP, one of the most important metrics for evaluating object detection and segmentation models in computer vision. When the results obtained were examined, the optimum threshold value (IoU) value was determined as 0.7. According to the IoU of 0.7, it was observed that all fertile eggs in the incubator were determined correctly on the third day of both test periods. Considering the methods used and the ease of the designed system, it can be said that a very successful system has been designed according to the studies in the literature. In order to increase the segmentation performance, it is necessary to carry out an experimental study to improve the camera and lighting setup prepared for taking the images.

The aim of this study is to demonstrate effectiveness of mobile game called SOS Table in the context of the subject “Tenses in English” within the framework of mobile learning tools acceptance, learning language motivation and mobile learning attitude of the students. The target group of the research consists of preparatory class students of a school of foreign languages in a state university in Turkey. The research was carried out with mixed method research. The quantitative paradigm-based section of this study was designed with a single-group pre-test and post-test model. In this model, three different scales were applied to the participants. The participants used the SOS Table mobile game developed by the researchers for 8 weeks. After the applications, semi-structured interviews were conducted with seven participants. The quantitative data of the study were analyzed with paired sampled t-Test and the qualitative data were subjected to thematic content analysis. The results of the study indicated that the mobile application named SOS Table increased both the mobile learning tools acceptance of the participants and motivation in English, and mediated the positive attitude development for mobile learning in English. The qualitative data obtained also supported these findings.

The number of animals worldwide is increasing day by day to meet the increasing animal protein needs. Depending on the increase in animal production, yield amount which can be obtained from per unit area can be increased by increasing the number of animals. In dairy cattle farms, it is necessary to group the animals according to their body condition score (BCS) and to care and feed the animals at certain times. Under normal conditions, these processes should be conducted by animal caregivers or experts coming to the enterprise. BCS ratings conducted by experts on farms based on visual examination may give unreliable results and may include misinterpretations. Therefore, technology-supported systems are required. In this study, the prediction of BCS, which is the most important indicator of proper feeding of dairy cattle, is aimed. In addition, by adapting the designed system to simple, fast and user-friendly mobile software, it will be possible to provide tests in enterprise environments in a shorter time. In the design of the system, deep learning models, which have been used frequently in recent years in computer science, have been used. The CNN model, which was trained with a 94.69% success rate through these data, has been converted into a mobile-friendly format for real-time tests. It is aimed to make real-time tests and provide easy access for dairy producers with the help of the designed mobile software. In order to increase the success of the CNN architecture, pre-trained networks have been utilized. In the study, VGG19 pre-trained network, whose success rate has been proved in the previous studies conducted in the literature, was used in model design. The 78.0% performance results obtained from the study indicate that pre-trained CNN architectures based on deep learning are successful for the real-time BCS classification problem.

Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision–recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance.