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

<p>In order to be able to better manage the production and life of urban residents, it is necessary to continuously optimize the public management model, and this paper proposes the establishment of a public management path for POI social computing. Build a computing system that can communicate with multiple computing units so that public management generation and dissemination moves to the system boundary and users can communicate, share and collaborate using a variety of methods. Taking full advantage of the complete confidence of POI point data location attributes and timely data update, puGAN model is added to improve the integrity of the collected data and distinguish the data sources by learning the differences between real and pseudo samples. Generate data discriminative classification of real unlabeled samples with unlabeled samples, adjust the distribution characteristics of the learned sample data, and improve the discriminative ability. The gradient value of the sample discriminator is calculated, and the gradient generator is updated to learn according to the data classification and finally solve the public management variance features. The analysis results show that the puGAN model can improve the accuracy of POI localization, and the training error and testing error are maintained at about 15%, which provides an important role for public management model research.</p>
</abstract>

In order to be able to better manage the production and life of urban residents, it is necessary to continuously optimize the public management model, and this paper proposes the establishment of a public management path for POI social computing. Build a computing system that can communicate with multiple computing units so that public management generation and dissemination moves to the system boundary and users can communicate, share and collaborate using a variety of methods. Taking full advantage of the complete confidence of POI point data location attributes and timely data update, puGAN model is added to improve the integrity of the collected data and distinguish the data sources by learning the differences between real and pseudo samples. Generate data discriminative classification of real unlabeled samples with unlabeled samples, adjust the distribution characteristics of the learned sample data, and improve the discriminative ability. The gradient value of the sample discriminator is calculated, and the gradient generator is updated to learn according to the data classification and finally solve the public management variance features. The analysis results show that the puGAN model can improve the accuracy of POI localization, and the training error and testing error are maintained at about 15%, which provides an important role for public management model research.

Path construction of public management model based on POI social computing

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Path construction of public management model based on POI social computing"}

In order to be able to better manage the production and life of urban residents, it is necessary to continuously optimize the public management model, and this...

Path construction of public management model based on POI social computing

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 3159 - 3174

Eingereicht: 29. März 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00028

Schlüsselwörter
POI social computing, PuGAN model, Public management, Management variance characteristics, Test error

© 2023 Shujian Wen, published by Sciendo

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

Path construction of public management model based on POI social computing

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 3159 - 3174

Eingereicht: 29. März 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00028

SchlüsselwörterPOI social computing, PuGAN model, Public management, Management variance characteristics, Test error

© 2023 Shujian Wen, published by Sciendo

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

Schlüsselwörter
POI social computing, PuGAN model, Public management, Management variance characteristics, Test error