A COMPUTATIONAL APPROACH BASED ON THE \epsilon-CONSTRAINT METHOD IN MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

Pirouz, Behzad; Khorram, Esmaile

doi:10.17654/as049060453

Cited by 15 publications

(20 citation statements)

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“…Artificial Intelligence includes a wide range of methods and algorithms that work based on machine intelligence and has many applications in various fields of science [40], including fuzzy logic theory and application [41][42][43][44][45][46], artificial intelligence techniques and sociology [47][48][49], risk assessment and hazard identification [50,51], machine learning [52][53][54][55][56][57], and meta-heuristic algorithms and clustering techniques [58][59][60][61][62]. The group method of data handling (GMDH) type of neural network is one of these algorithms that was proposed by Ivakhnenko [63].…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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“…Mathematical modeling has been used successfully for modeling many natural and human-made hazards [44][45][46][47][48]. One of the most important aspects of any optimization is the expression of the problem in the form of a mathematical model.…”

Section: Mathematical Modeling and Multivariate Linear Regression Anamentioning

confidence: 99%

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

Haghshenas

Pirouz

Haghshenas

et al. 2020

IJERPH

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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.

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“…Mathematical models have been successfully used for the analysis of many hazards [32,33]. In complex subjects, different Mathematical models can be used to solve the problem [34][35][36].…”

Section: The Statistical Analysis and Set-up Of Spss Modelmentioning

confidence: 99%

Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19

Golmohammadi

SaeidpourMasouleh

Violini

et al. 2020

Preprint

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The 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 the attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized. The 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. The 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. To test this research hypothesis, we carried on the case study of three regions in each of five countries and analyzed the correlation through F-test, and Independent-Samples T-Test. In 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 exists also 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.

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A COMPUTATIONAL APPROACH BASED ON THE \epsilon-CONSTRAINT METHOD IN MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

Cited by 15 publications

References 0 publications

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

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

Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19

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