Network public opinion is a double-edged sword. If we do not make good use of the network’s public opinion, it will result in immeasurable losses to individuals and the country. Therefore, we provide a dynamic model of infectious diseases to analyze and solve the problem of the spread of network public opinion. To deal with the problem, we have established four models based on the SEIR model, calculated the disease-free equilibrium points corresponding to the four models, and then figured out the basic reproduction number of the four models according to the disease-free equilibrium points. Based on the basic reproduction number, reasonable suggestions and references are given to solve this problem.
With the in-depth development of intelligent transportation, traffic sign recognition has attracted widespread attention as an essential part of intelligent transportation. This paper studies several machine learning methods for traffic sign recognition. Through comparative analysis, it is found that Convolutional Neural Network (CNN) is superior to Support Vector Machine (SVM) and K Nearest Neighbor (KNN) methods in recognizing traffic signs. And adding Gaussian noise to the image data for enhancement can further improve the accuracy of applying a Convolutional Neural Network to identify traffic signs. The accuracy of applying a Convolutional Neural Network to identify traffic signs is 99.2%. After adding Gaussian noise with a mean of 0 and a standard deviation of 1 to the image set, the accuracy of applying a Convolutional Neural Network to identify traffic signs was increased to 99.6%. We also compared the CNN-based traffic signs recognition experiment in this paper with the experiments of two other scholars. Our experiment has higher accuracy in a particular data range and environment.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.