Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations.

No abstract

No abstract

The World Health Organization (WHO) reports that hand hygiene education is important for health workers. However, handwashing methods in hospitals require specialized handwashing knowledge. In this paper, we present a new quantitative handwashing skills assessment method based on the Convolutional Neural Network (CNN). An image capture box with blacklights was created and photos were taken before and after handwashing after the application of fluorescent paint. The photos after handwashing were labeled by specialists in each part. The total number of photos after handwashing labeled by specialists was 585. After that, 9-fold cross-validation was performed on the photos after handwashing to calculate the discrimination accuracy (F-value). As a result, the F-value was 81.26 ± 6.18% in the handwashing image evaluation system using CNN.

No abstract