The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion. INDEX TERMS Major emergencies, internet public opinion, Early warning index system, COVID-19, CRITIC, GA-BP neural network.

No abstract

The increasing growth in the application of global computing and pervasive systems has necessitated careful consideration of security issues.In particular, there has been a growth in the use of electronic communities, in which there exist many relationships between different entities. Such relationships require establishing trust between entities and a great deal of effort has been expended in developing accurate and reliable models of trust in such multi-client environments. Many of these models are complex and not necessarily guaranteed to give accurate trust predictions. In this paper we present a review of some of these models before proposing a simple, lightweight model for trust. The proposed model does not require the estimation of a large parameter set, nor make great assumptions about the parameters that affect trust.

No abstract

No abstract