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      Retracted: Research on Improving the Executive Ability of University Administrators Based on Deep Learning

      retraction
      Computational and Mathematical Methods in Medicine
      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          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1].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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          Research on Improving the Executive Ability of University Administrators Based on Deep Learning

          Over the years, experts have focused their research on ways to increase the executive capacity of university administrators. This is because only by improving the quality of execution of college and university administrative personnel can they actively execute various policies and measures, fully exploit their subjective initiative, and ensure the educational reform of colleges and universities. Increasing the executive capacity of administrative staff can help colleges and universities manage more effectively. Therefore, in the development process of higher education institutions, it is necessary to strengthen the execution of administrative staff, especially the need to adhere to the problem as the basic orientation. Take scientific and practical steps to strengthen administrative personnel's executive ability in light of current issues with administrative management personnel's executive power, and establish the groundwork for ensuring the quality of management work. Combining deep learning, this paper proposes a path to improve the executive power of college administrators based on deep learning. To begin, familiarize yourself with the deep noise reduction autoencoder model and support vector regression (SVR) theory and build the DDAE-SVR deep neural network (DNN) model. Then, input a small-scale feature index sample data set and a large-scale short-term traffic flow data set for experiments; then, assess the model's parameters to achieve the optimal model. Finally, use performance indicators such as MSE and MAPE to compare with other shallow models to verify the effectiveness and advantages of the DDAE-SVR DNN model in the execution improvement path output of university administrators and large-scale data sets.
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            Author and article information

            Contributors
            Journal
            Comput Math Methods Med
            Comput Math Methods Med
            cmmm
            Computational and Mathematical Methods in Medicine
            Hindawi
            1748-670X
            1748-6718
            2023
            18 October 2023
            18 October 2023
            : 2023
            : 9803786
            Affiliations
            Article
            10.1155/2023/9803786
            10599849
            37885867
            667a1b52-1845-4931-a9ca-81437a45f3af
            Copyright © 2023 Computational and Mathematical Methods in Medicine.

            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
            : 17 October 2023
            : 17 October 2023
            Categories
            Retraction

            Applied mathematics
            Applied mathematics

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