Higher education provides a common educational pattern to all students in colleges and universities. However, under the general law of higher education, the teaching and education of a certain category or subject has its special regularity. Besides, students in colleges and universities have different demands of education. To address the challenges in higher education, it is very important to carry out higher education reform in colleges and universities. Personalized education has been considered to be a new educational model that is a result of the individual desire of students and the development of society. Traditional methodologies of teaching in colleges and universities cannot fulfill the implementation of personalized training. Hence, it is very urgent to develop new methodologies for personalized training. Among the methodologies of realization of personalized training, artificial intelligence is one of the most important methodologies. We exploit artificial intelligence for personalize education reform. First, we analyze the information of students before entering the colleges and universities. Then, we propose a method to extract modeling features of the student information. Second, we propose a method to build a personalized training model based on artificial intelligence. Third, we propose a method to predict the development track of students based on the personalized training model. On the basis of above designs, we design personalized training for students in colleges and universities. Furthermore, we implement our design by using artificial intelligence. Besides, our design can be applied to the career planning or related areas.

No abstract

No abstract

No abstract

No abstract

When public health emergencies occur, relevant information containing different topics, sentiments, and emotions spread rapidly on social media. From the cognitive and emotional dimensions, this paper explores the relationship between information attributes and information dissemination behavior. At the same time, the moderating role of the media factor (user influence) and the time factor (life cycle) in information attributes and information transmission is also discussed. The results confirm differences in the spread of posts under different topic types, sentiment types, and emotion types on social media. At the same time, the study also found that posts published by users with a high number of followers and users of a media type are more likely to spread on social media. In addition, the study also found that posts with different information attributes are easier to spread on social media during the outbreak and recurrence periods. The driving effect of life cycles is more obvious, especially for topics of prayer and fact, negative sentiment, emotions of fear, and anger. Relevant findings have specific contributions to the information governance of public opinion, the development of social media theory, and the maintenance of network order, which can further weaken the negative impact of information epidemic in the occurrence of public health emergencies, maintain normal social order, and thus create favorable conditions for the further promotion of global recovery.

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

No abstract