This page has only limited features, please log in for full access.
Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.
Behrouz Pirouz; Aldo Ferrante; Behzad Pirouz; Patrizia Piro. Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems. ISPRS International Journal of Geo-Information 2021, 10, 424 .
AMA StyleBehrouz Pirouz, Aldo Ferrante, Behzad Pirouz, Patrizia Piro. Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems. ISPRS International Journal of Geo-Information. 2021; 10 (6):424.
Chicago/Turabian StyleBehrouz Pirouz; Aldo Ferrante; Behzad Pirouz; Patrizia Piro. 2021. "Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems." ISPRS International Journal of Geo-Information 10, no. 6: 424.
The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.
Behzad Pirouz; Hana Javadi Nejad; Galileo Violini; Behrouz Pirouz. The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19. Information 2020, 11, 454 .
AMA StyleBehzad Pirouz, Hana Javadi Nejad, Galileo Violini, Behrouz Pirouz. The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19. Information. 2020; 11 (9):454.
Chicago/Turabian StyleBehzad Pirouz; Hana Javadi Nejad; Galileo Violini; Behrouz Pirouz. 2020. "The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19." Information 11, no. 9: 454.
Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.
Sina Shaffiee Haghshenas; Behrouz Pirouz; Behzad Pirouz; Patrizia Piro; Kyoung-Sae Na; Seo-Eun Cho; Zong Woo Geem. Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications. International Journal of Environmental Research and Public Health 2020, 17, 3730 .
AMA StyleSina Shaffiee Haghshenas, Behrouz Pirouz, Behzad Pirouz, Patrizia Piro, Kyoung-Sae Na, Seo-Eun Cho, Zong Woo Geem. Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications. International Journal of Environmental Research and Public Health. 2020; 17 (10):3730.
Chicago/Turabian StyleSina Shaffiee Haghshenas; Behrouz Pirouz; Behzad Pirouz; Patrizia Piro; Kyoung-Sae Na; Seo-Eun Cho; Zong Woo Geem. 2020. "Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications." International Journal of Environmental Research and Public Health 17, no. 10: 3730.
Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.
Behrouz Pirouz; Sina Shaffiee Haghshenas; Behzad Pirouz; Patrizia Piro. Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development. International Journal of Environmental Research and Public Health 2020, 17, 2801 .
AMA StyleBehrouz Pirouz, Sina Shaffiee Haghshenas, Behzad Pirouz, Patrizia Piro. Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development. International Journal of Environmental Research and Public Health. 2020; 17 (8):2801.
Chicago/Turabian StyleBehrouz Pirouz; Sina Shaffiee Haghshenas; Behzad Pirouz; Patrizia Piro. 2020. "Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development." International Journal of Environmental Research and Public Health 17, no. 8: 2801.
AIMSThe main purpose of this study is to investigate the correlation between the average daily temperature and the rate of coronavirus epidemic growth in the infected regions.BACKGROUNDThe rapid outbreak of the new Coronavirus (COVID-19) pandemic and the spread of the virus worldwide, especially in the Northern Hemisphere, have prompted various investigations about the impact of environmental factors on the rate of development of this epidemic. Different studies have called attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized.OBJECTIVEThe main hypothesis object of our research is that between regions exhibiting a significant difference in the mean daily temperature, a significant difference is also observed in the average cumulative daily rate of confirmed cases and that this does not happen if there is no significant difference in mean daily temperature.METHODThe research hypothesis was investigated through statistical analysis. The F-test was used to test whether there is significant equality of variances for each pair of case studies, and then, by the T- Test, the existence of a significant difference was investigated. In all statistical tests, the confidence level of 95% is considered. In order to minimize the impact on the results of factors like the policy of the government or cultural differences among countries (food, exercise, weight, etc.), three case studies within five countries, namely Iran, Italy, Germany, Spain, and United States were compared separately.RESULTThis statistical analysis shows that there is a correlation between the average temperature and the epidemic rate, and this is especially evident when differences in average daily temperature are significantly larger, as it happens for Bandar Abbas in Iran, Milan in Italy, Santa Cruz in Spain, and Los Angeles in the US. Besides, the analysis of the average air temperatures shows that the epidemic rates of COVID-19 were higher in the case studies with a lower average temperature. Instead, when no significant differences exist in the average daily temperature of two cities in the same country, there is no significant difference in the average cumulative daily rate of confirmed cases.CONCLUSIONIn all five selected countries, we found that when there is a significant difference in the daily mean temperature between two regions of a country, a significant difference also exists in the average cumulative daily rate of confirmed cases. Conversely, if there are no significant differences in the mean daily temperature of two regions in the same country, no significant difference is observed in the average cumulative daily rate of confirmed cases for these regions. In conclusion, the results of this study support the research hypothesis and confirm the effectiveness of the proposed method for analysis of the epidemic rates.
Behzad Pirouz; Amirsina Golmohammadi; Hasti Saeidpour Masouleh; Galileo Violini; Behrouz Pirouz. Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19. 2020, 1 .
AMA StyleBehzad Pirouz, Amirsina Golmohammadi, Hasti Saeidpour Masouleh, Galileo Violini, Behrouz Pirouz. Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19. . 2020; ():1.
Chicago/Turabian StyleBehzad Pirouz; Amirsina Golmohammadi; Hasti Saeidpour Masouleh; Galileo Violini; Behrouz Pirouz. 2020. "Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19." , no. : 1.
The role of the industrial sector in total greenhouse gas (GHG) emissions and resource consumption is well-known, and many industrial activities may have a negative environmental impact. The solution to decreasing the negative effects cannot be effective without the consideration of sustainable development. There are several methods for sustainability evaluation, such as tools based on products, processes, or plants besides supply chain or life cycle analysis, and there are different rating systems suggesting 80, 140, or more indicators for assessment. The critical point is the limits such as required techniques and budget in using all indicators for all factories in the beginning. Moreover, the weight of each indicator might change based on the selected alternative that it is not a fixed value and could change in a new case study. In this regard, to determine the impact and weight of different indicators in sustainable factories, a multi-layer Triangular Fuzzy Analytic Hierarchy Process (TFAHP) approach was developed, and the application of the method was described and verified. The defined layers are six; for each layer, the pairwise comparison matrix was developed, and the total aggregated score concerning the sustainability goal for each alternative was calculated that shows the Relative Importance Coefficient (RIC). The method is formulated in a way that allows adding the new indicators in all layers as the verification shows, and thus, there are no limits for using any green rating systems. Therefore, the presented approach by TFAHP would provide an additional tool toward the sustainable development of factories.
Behrouz Pirouz; Natale Arcuri; Behzad Pirouz; Stefania Anna Palermo; Michele Turco; Mario Maiolo. Development of an Assessment Method for Evaluation of Sustainable Factories. Sustainability 2020, 12, 1841 .
AMA StyleBehrouz Pirouz, Natale Arcuri, Behzad Pirouz, Stefania Anna Palermo, Michele Turco, Mario Maiolo. Development of an Assessment Method for Evaluation of Sustainable Factories. Sustainability. 2020; 12 (5):1841.
Chicago/Turabian StyleBehrouz Pirouz; Natale Arcuri; Behzad Pirouz; Stefania Anna Palermo; Michele Turco; Mario Maiolo. 2020. "Development of an Assessment Method for Evaluation of Sustainable Factories." Sustainability 12, no. 5: 1841.