The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.
Route design is an indispensable skill for ships and navigators. However, in actual work, most of the current route design still lies in manually reading port information and other graphic materials, and then revising the sailing plan in conjunction with the weather forecast. On the one hand, this method is not accurate enough, and the graphics and text data are not sufficiently real-time. On the other hand, this method has the disadvantages of being cumbersome and labor-intensive. In response to the above problems, this article combines the actual data set of ICOAD, applies the neural network model, and uses the A* algorithm to plan the route, which provides a reference for the application of artificial intelligence to ship route planning.
With the development of economy and society, science and technology are changing with each passing day; especially the application of computer technology is the most typical and extensive. Computer technology is applied to all levels of people’s production and life, which brings great convenience to people’s production and life, and also promotes the improvement of social informatization. The development of computer application has become an inevitable trend of society, but there are also some problems in its development. In this paper, the current situation and problems of computer application are analyzed, and the future development trend of computer application is discussed.
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