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

Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts...

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Autores principales: Yan, Wenjing, Zhang, Zesheng, Zhang, Qingchuan, Zhang, Ganggang, Hua, Qiaozhi, Li, Qiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021602/
https://www.ncbi.nlm.nih.gov/pubmed/35462816
http://dx.doi.org/10.3389/fpubh.2022.847252
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author Yan, Wenjing
Zhang, Zesheng
Zhang, Qingchuan
Zhang, Ganggang
Hua, Qiaozhi
Li, Qiao
author_facet Yan, Wenjing
Zhang, Zesheng
Zhang, Qingchuan
Zhang, Ganggang
Hua, Qiaozhi
Li, Qiao
author_sort Yan, Wenjing
collection PubMed
description Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The 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 the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.
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spelling pubmed-90216022022-04-22 Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare Yan, Wenjing Zhang, Zesheng Zhang, Qingchuan Zhang, Ganggang Hua, Qiaozhi Li, Qiao Front Public Health Public Health Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The 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 the agricultural products market. Simulative results reveal that the designed GNN-APM functions well. Frontiers Media S.A. 2022-04-07 /pmc/articles/PMC9021602/ /pubmed/35462816 http://dx.doi.org/10.3389/fpubh.2022.847252 Text en Copyright © 2022 Yan, Zhang, Zhang, Zhang, Hua and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Yan, Wenjing
Zhang, Zesheng
Zhang, Qingchuan
Zhang, Ganggang
Hua, Qiaozhi
Li, Qiao
Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title_full Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title_fullStr Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title_full_unstemmed Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title_short Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare
title_sort deep data analysis-based agricultural products management for smart public healthcare
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021602/
https://www.ncbi.nlm.nih.gov/pubmed/35462816
http://dx.doi.org/10.3389/fpubh.2022.847252
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