There is still no effective means to analyze in depth and utilize domestic mass data about agricultural product quality safety tests in china now. The neural network algorithm, the classification regression tree algor...There is still no effective means to analyze in depth and utilize domestic mass data about agricultural product quality safety tests in china now. The neural network algorithm, the classification regression tree algorithm, the Bayesian network algorithm were selected according to the principle of selecting combination model and were used to build models respectively and then combined, innovatively establishing a combination model which has relatively high precision, strong robustness and better explanatory to predict the results of perishable food transportation meta-morphism monitoring. The relative optimal prediction model of the perishable food transportation metamorphism monitoring system could be got. The relative perfect prediction model can guide the actual sampling work about food quality and safety by prognosticating the occurrence of unqualified food to select the typical and effective samples for test, thus improving the efficiency and effectiveness of sampling work effectively, so as to avoid deteriorated perishable food’s approaching the market to ensure the quality and safety of perishable food transportation. A solid protective wall was built in the protection of general perishable food consumers’ health.展开更多
The G20 countries are the locomotives of economic growth,representing 64%of the global population and including 4.7 billion inhabitants.As a monetary and market value index,real gross domestic product(GDP)is affected ...The G20 countries are the locomotives of economic growth,representing 64%of the global population and including 4.7 billion inhabitants.As a monetary and market value index,real gross domestic product(GDP)is affected by several factors and reflects the economic development of countries.This study aimed to reveal the hidden economic patterns of G20 countries,study the complexity of related economic factors,and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019(COVID-19)pandemic recession(2019–2020).In this respect,this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita,industrial production,government spending,COVID-19 cases/population,patient recovery,COVID-19 death cases,number of hospital beds/1000 people,and percentage of the vaccinated population to identify clusters for G20 countries.The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development.The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries,splitting them into three clusters by sharing different measurements and patterns(harmonies and variances across G20 countries).A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors.Similarly,the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession.Variables such as GDP per capita and patient recovery of COVID-19 cases with values of$12,012 and 82.8%,respectively,were the most significant factors for clustering the G20 countries,with a correlation coefficient(R2)of 91.8%.The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced.展开更多
文摘There is still no effective means to analyze in depth and utilize domestic mass data about agricultural product quality safety tests in china now. The neural network algorithm, the classification regression tree algorithm, the Bayesian network algorithm were selected according to the principle of selecting combination model and were used to build models respectively and then combined, innovatively establishing a combination model which has relatively high precision, strong robustness and better explanatory to predict the results of perishable food transportation meta-morphism monitoring. The relative optimal prediction model of the perishable food transportation metamorphism monitoring system could be got. The relative perfect prediction model can guide the actual sampling work about food quality and safety by prognosticating the occurrence of unqualified food to select the typical and effective samples for test, thus improving the efficiency and effectiveness of sampling work effectively, so as to avoid deteriorated perishable food’s approaching the market to ensure the quality and safety of perishable food transportation. A solid protective wall was built in the protection of general perishable food consumers’ health.
基金funded by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia and King Abdulaziz University,DSR,Jeddah,Saudi Arabia under the Project Number(IFPHI-107-135-2020).
文摘The G20 countries are the locomotives of economic growth,representing 64%of the global population and including 4.7 billion inhabitants.As a monetary and market value index,real gross domestic product(GDP)is affected by several factors and reflects the economic development of countries.This study aimed to reveal the hidden economic patterns of G20 countries,study the complexity of related economic factors,and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019(COVID-19)pandemic recession(2019–2020).In this respect,this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita,industrial production,government spending,COVID-19 cases/population,patient recovery,COVID-19 death cases,number of hospital beds/1000 people,and percentage of the vaccinated population to identify clusters for G20 countries.The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development.The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries,splitting them into three clusters by sharing different measurements and patterns(harmonies and variances across G20 countries).A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors.Similarly,the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession.Variables such as GDP per capita and patient recovery of COVID-19 cases with values of$12,012 and 82.8%,respectively,were the most significant factors for clustering the G20 countries,with a correlation coefficient(R2)of 91.8%.The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced.
