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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

Author

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  • Behrouz Pirouz

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Sina Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Sami Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Patrizia Piro

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

Abstract

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.

Suggested Citation

  • Behrouz Pirouz & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "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 Intelligenc," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2427-:d:334646
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    References listed on IDEAS

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    1. Behrouz Pirouz & Natale Arcuri & Behzad Pirouz & Stefania Anna Palermo & Michele Turco & Mario Maiolo, 2020. "Development of an Assessment Method for Evaluation of Sustainable Factories," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    2. Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1099-1113, July.
    3. Marina Andrijevic & Jesus Crespo Cuaresma & Raya Muttarak & Carl-Friedrich Schleussner, 2020. "Governance in socioeconomic pathways and its role for future adaptive capacity," Nature Sustainability, Nature, vol. 3(1), pages 35-41, January.
    4. Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
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    Cited by:

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    3. Sharov, Konstantin S., 2020. "Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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