Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 31, 07 March 2024
Open
Access | Article
The disease prevention and rescue system based on Lorentz-RR analysis technology for public welfare organizations
* Author to whom correspondence should be addressed.
Abstract
Health concerns have become a significant focus in people’s daily lives. Currently, with the increasing demand for monitoring human health, some social organizations need to strengthen health detection technology, and various related technologies are emerging. The aim of this study is to develop a social organization disease prevention and assistance system that integrates the advantages and resources of social organizations, social service institutions, social workers, caregivers, service recipients, and their families. The main system is designed based on a front-end and back-end separation architecture, using core technology: the Lorenz-RR scatterplot classification algorithm to achieve the selection of classification algorithms, the development of AlexNet algorithm, and the optimization of dataset expansion algorithm, Thus, the improvement of the Lorenz-RR scatterplot classification algorithm of the AlexNet model is achieved, and the development of a social organization disease prevention and assistance system is completed. It has high accuracy and sensitivity in the analysis of relevant indicators of service objects, and its application value is significant, with widespread promotion significance.
Keywords
social organization, Lorenz-RR, AlexNet algorithm, health
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-317-3
- ISBN (Online)
- 978-1-83558-318-0
- Published Date
- 07 March 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/31/20241144
- Copyright
- 07 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited