Social Group Behavior Analysis Model Integrating Multitask Learning and Convolutional Neural Network

Chuan Zhou,Gaoming Zhang,Qirui Zhang,Suying Gui,Xudong Wang,Jianqing Wei,Hasan Khattak

doi:10.1155/2021/5540201

Abstract

Social group behavior analysis has always been the key research direction of sociologists and psychologists. With the rapid development of the Internet of Things and the proposal of deep learning theory, convolutional neural networks are also used in social group research. In the process of social development, group incidents continue to increase, and there are more and more studies on social group behavior analysis. Although the research content and research methods are also richer, the research that combines the Internet of Things, convolutional neural network, and group behavior is more. This article will specifically propose a social group behavior analysis model that combines multitask learning and convolutional neural networks. This paper deeply learns the research of convolutional neural network and group behavior-related theories, makes full use of the advantages of convolutional neural network algorithm and multitask learning mode, and builds a social group behavior analysis model based on multitask learning and convolutional neural network. The experimental results on different data sets are analyzed. The results show that the accuracy rate of the experimental algorithm of convolutional neural network is as high as 95.10%, and it is better than other algorithms in time complexity, which is very suitable for social group behavior analysis.

Highlights

In recent years, with the development of the economy, the adjustment of economic structure, and the acceleration of urbanization, various social collective actions have increased, and their development has become more and more complex, which has had a huge impact on the construction of today’s society and moral culture
This paper mainly studies the social group behavior analysis model that combines multitask learning and convolutional neural network and consults a large number of references and in-depth study of convolutional neural network, multitask learning, and group theory-related content
This paper constructs a social group behavior analysis model and a dynamic adaptive pooling model based on convolutional neural networks and makes full use of the advantages of convolutional neural network algorithms to analyze social group behaviors

Summary

Introduction

With the development of the economy, the adjustment of economic structure, and the acceleration of urbanization, various social collective actions have increased, and their development has become more and more complex, which has had a huge impact on the construction of today’s society and moral culture. Its biggest characteristic is parallel transfer learning, which is different from the traditional progressive and procedural learning methods. Multitask learning is called parallel migration learning because information is shared between tasks and is transferred between different tasks. It can realize the sharing and transmission of information between tasks. Convolutional neural network has the same characteristics as it; that is, it can classify the network data information by translation. It has the representation learning ability of artificial intelligence. It can be called “translation invariant artificial neural network.”

Methods

Results

Conclusion