Affiliation:
1. School of Humanities and Social Science , Heilongjiang Bayi Agricultural University , Daqing 163000 , China
2. Daqing Children’s Rescue and Protection Center , Daqing 163000 , China
Abstract
Abstract
To optimize the data clustering effect of public health emergencies, an application research on social governance ability under public health emergencies based on artificial intelligence is proposed. First, the firefly optimization algorithm is used to collect the information data of the social governance ability of public health emergencies, establish a unified format, and save it. Then, artificial intelligence technology is used to mine the correlation of clustering data, and on this basis, a learning model integrating global structure information and local structure information is established. Finally, the social governance model under public health emergencies is established. The experimental results show that the design method has high clustering accuracy, regularization cross index, and adjusted rand index (ARI) index. This shows that the design method can improve the social governance ability of data fusion clustering and improve the social governance ability.
Subject
Artificial Intelligence,Information Systems,Software
Reference22 articles.
1. Zhou S, Zhu XF, Xue L. The enabling effect of artificial intelligence in the management of public health emergencies: A case study of global epidemic prevention and control of COVID-19. Chin Public Adm. 2020;10:35–43.
2. Xu XC, Ji CJ. Challenges and countermeasures of social governance in smart society. Acad Explor. 2019;7:40–7.
3. Collins AB, Ndoye CD, Arene-Morley D, Marshall B. Addressing co-occurring public health emergencies: The importance of naloxone distribution in the era of COVID-19. Int J Drug Policy. 2020;83(5):102872.
4. Liang Z, Yu Z, Song Q. Platform governance in the context of artificial intelligence application: Core issues, transition challenges and system construction. Comp Econ Soc Syst. 2020;3:67–75.
5. Onan A, Korukoğlu S. A feature selection model based on genetic rank aggregation for text sentiment classification. J Inf Sci. 2017;43(1):25–38.
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