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      Retracted: Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

      retraction
      Journal of Environmental and Public Health
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

          Xiaohe Xie (2022)
          There are some problems in the creation process of public health visual art works, such as low accuracy and poor content quality. In order to further improve the accuracy and quality of artistic creation, based on artificial intelligence technology, a neural network model and an error backpropagation algorithm are used to analyze artistic creation, so as to obtain the corresponding optimization model. This model can analyze the application of public health visual art creation concepts in the Venice Biennale, so as to obtain the corresponding model calculation results under different indicators. Finally, data comparison is used to verify the accuracy of the model. Relevant studies show that activation values have different trends in connection weights under the action of different factors. The fluctuation of the curve is obvious in the initial state, but when the factor is high, the corresponding connection weight tends to be stable gradually. The range of learning rate in the initial state is relatively small, and the connection weights show a V-shaped change at higher factors. It can be seen from the curve corresponding to the ω learning method that the increase in learning speed will lead to further compression of calculation time, and the curve shows a trend of fluctuation. The corresponding learning error decreases gradually with the increase in factors, which indicates that higher factors will promote the accuracy of connection weights. Artificial intelligence-based visual models of art can calculate public health. It can be seen from the calculation results that the curve corresponding to artistic connotation has the largest range of variation and the highest influence on the model. The theme, form, and style of art all show a linear trend of improvement with the increase in time, while the content of art shows a downward trend. This research can provide support for the application of artificial intelligence theory in the field of public health.
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            Author and article information

            Contributors
            Journal
            J Environ Public Health
            J Environ Public Health
            jeph
            Journal of Environmental and Public Health
            Hindawi
            1687-9805
            1687-9813
            2023
            28 June 2023
            28 June 2023
            : 2023
            : 9792650
            Affiliations
            Article
            10.1155/2023/9792650
            10322553
            ae87d7d3-c26e-4591-a483-addf52350e4d
            Copyright © 2023 Journal of Environmental and Public Health.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 27 June 2023
            : 27 June 2023
            Categories
            Retraction

            Public health
            Public health

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