Browsing by Author "Mahmoudi, M.R."

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Citation - Scopus: 25
Factor Analysis Approach To Classify Covid-19 Datasets in Several Regions
(Elsevier B.V., 2021) Baleanu, D.; Band, S.S.; Mosavi, A.; Mahmoudi, M.R.
The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships. © 2021 The Authors
Citation - Scopus: 76
Fuzzy Clustering Method To Compare the Spread Rate of Covid-19 in the High Risks Countries
(Elsevier Ltd, 2020) Baleanu, D.; Mansor, Z.; Tuan, B.A.; Pho, K.-H.; Mahmoudi, M.R.
The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries. © 2020 Elsevier Ltd
Citation - Scopus: 47
On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models With Symmetric Errors
(MDPI AG, 2019) Mahmoudi, M.R.; Baleanu, D.; Maleki, M.; Ji-jun, P.
In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets. © 2019 by the authors.