Development and Application of Big Data-Driven Intelligent Evaluation System for Physical and Aesthetic Education

Abstract

Big data driven intelligence evaluation system for physical and aesthetic education are used to leverage vast amounts of data to provide more objective, nuanced, and personalized assessments of student performance and progress. This paper proposes a hybrid approach for development of big data driven intelligence evaluation system for physical and aesthetic education. The proposed hybrid approach is the combined performance of both the Dual attention graph convolution network (DAGCN) and Weibull time to event Recurrent Neural Network (WRNN).Commonly it is named as WRNN-DAGCN technique. The major objective of the proposed approach is to provide for development of big data driven intelligence evaluation system for physical and aesthetic education. WRNN is design to enhancing the efficiency of physical training. Enhance the physical training from the WRNN by using the DAGCN. By then, the MATLAB/Simulink working platform has the suggested model implemented, and the present processes are used to calculate the execution. The proposed method shows better results in all existing methods. The accuracy level of proposed BDEPA-WRNN-DAGCN approach is 98% that is higher than the other existing methods. From the result, it is conclude that the proposed approach based error rate is less compared to existing techniques.