Teachers are expected to respond quickly and accurately to any diabetes incident that may occur to children in the school setting. Access to diabetes information is crucial for student safety, health, academic achievement, and social competence. This paper describes a technique to provide Web-based diabetes information using computer audio and video to enrich a text-based training experience. Two groups of teachers were presented with diabetes training material via either paper or a Web-based computer system. Both groups were then evaluated for diabetes knowledge and satisfaction. Subjects using the Web-based system had significantly (t = 2.22; p < 0.033) higher knowledge scores (72.5% versus 66.4% correct) and were significantly (t = 3.9; p < 0.001) more satisfied with the training session (4.2 versus 3.1 on a five-point scale) than subjects who used paper documents traditionally used for teacher training. With the advantages in learning and the reduced cost of a Web-based system, diabetes distance education is a viable and desirable alternative to paper-based diabetes education.
Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.
Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to collecting fisheries data, such as fish size measurement, catch estimation, regulatory compliance, species recognition, and population counting. Measuring fish size accurately requires reliable image segmentation. Major challenges that can easily affect the segmentation include blurring of image areas due to water drops on the camera lens and parts of a fish body being out of the camera view. In this paper, we address each of these issues with an innovative and effective contour-based segmentation and a missing shape recovery method from an arbitrary initial segmentation. The refinement is processed from the coarse level to the fine level. At the coarse level, we align the entire fish contour of the initial segmentation with trained representative contours by using iteratively reweighted least squares (IRLS). At finer levels, we iteratively refine contour segments to represent poorly segmented or missing shape parts. This method addresses the problems listed above and generates promising results with highly robust segmentation performance and length measurement.
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