Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Year: 2022

Type: article

Abstract: Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.

Source: Mathematical Problems in Engineering

DOI: 10.1155/2022/7176111

Authors: Tingting Hou, Zamira Madina

Institutions: Zhengzhou Health Vocational College, Ala-Too International University

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $2,550