Evidence map›Paper›PMID 35978903›Full record

ArticleComputational intelligence and neuroscience2022

Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence.

Chaomiao Chen

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Chaomiao ChenCollege of Fine Art and Design, Quanzhou Normal University, Quanzhou 362000, Fujian, China.ORCID https://orcid.org/0000-0001-5489-5629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China's digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China's excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set.

Indexed as

Artificial IntelligenceInternetAlgorithmsNeural Networks, ComputerTechnology

Identifiers

PMID35978903
PMCPMC9377854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.