AUTHOR=Yan Wenjing , Zhang Zesheng , Zhang Qingchuan , Zhang Ganggang , Hua Qiaozhi , Li Qiao 

TITLE=Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.847252

DOI=10.3389/fpubh.2022.847252

ISSN=2296-2565

ABSTRACT=Agriculture is a public healthcare industry for human beings at any time, and smart management of it is of great significance. As substantial technical advance which relies on long-term effort, reasonably scheduling temporal distributions of agricultural products also acts as an aspect for smart public healthcare. The most intuitive factor for distribution of agricultural productions is the dynamic prices. Forecasting price fluctuation in advance can optimize distribution of agricultural products and pave ways to smart public healthcare. This work introduces a typical deep learning model named graph neural network for this purpose, and proposes deep data analysis-based agricultural products management for smart public healthcare (named as GNN-APM for short). Highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of agricultural products market. Simulative results reveal that the designed GNN-APM functions well.