<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we propose a deep learning restoration method for fine art painting images based on a contextual encoder and select a convolutional self-encoder-based image restoration model for workflow analysis, including forward inference and backward network propagation. We design and develop a software system for predicting the grades of college art and painting students and apply the neural network prediction model to this system to realize the grade prediction of college art students. The accuracy of the GDPN model is 0.922, the precision is 0.904, and the recall is 0.966, which can consider both the temporal and overall information in the click behavior and achieve a better prediction effect.</p>
</abstract>

In this paper, we propose a deep learning restoration method for fine art painting images based on a contextual encoder and select a convolutional self-encoder-based image restoration model for workflow analysis, including forward inference and backward network propagation. We design and develop a software system for predicting the grades of college art and painting students and apply the neural network prediction model to this system to realize the grade prediction of college art students. The accuracy of the GDPN model is 0.922, the precision is 0.904, and the recall is 0.966, which can consider both the temporal and overall information in the click behavior and achieve a better prediction effect.

A study on the prediction of development strategies of art education in universities based on deep learning model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, we propose a deep learning restoration method for fine art painting images based on a contextual encoder and select a convolutional...

A study on the prediction of development strategies of art education in universities based on deep learning model

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 03 gen 2023

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00858

Parole chiave
Deep learning model, Painting images, Contextual encoder, Image restoration network, Performance prediction

© 2023 Xiaohong Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the prediction of development strategies of art education in universities based on deep learning model

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 03 gen 2023

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00858

Parole chiaveDeep learning model, Painting images, Contextual encoder, Image restoration network, Performance prediction

© 2023 Xiaohong Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Deep learning model, Painting images, Contextual encoder, Image restoration network, Performance prediction