USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE

Nihal İnce; Sevil Şentürk

doi:10.18038/estubtda.1258361

EN TR

USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE

Abstract

Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries.

Keywords

Intuitionistic fuzzy sets, Fuzzy C-means clustering algorithm, COVID-19, Statistical analysis

USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE

Abstract

Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries.

Keywords

Intuitionistic fuzzy sets, Fuzzy C-means clustering algorithm, COVID-19, Statistical analysis

References

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Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Nihal İnce ^*
0000-0001-6684-5848
Türkiye

Sevil Şentürk
0000-0002-9503-7388
Türkiye

Publication Date

March 29, 2023

Submission Date

March 7, 2023

Acceptance Date

March 23, 2023

Published in Issue

Year 2023 Volume: 24 Number: 1

DOI

https://doi.org/10.18038/estubtda.1258361

IZ

https://izlik.org/JA82FS39FZ

APA

İnce, N., & Şentürk, S. (2023). USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 24(1), 71-85. https://doi.org/10.18038/estubtda.1258361

AMA

1.İnce N, Şentürk S. USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE. Estuscience - Se. 2023;24(1):71-85. doi:10.18038/estubtda.1258361

Chicago

İnce, Nihal, and Sevil Şentürk. 2023. “USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 (1): 71-85. https://doi.org/10.18038/estubtda.1258361.

EndNote

İnce N, Şentürk S (March 1, 2023) USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 1 71–85.

IEEE

[1]N. İnce and S. Şentürk, “USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE”, Estuscience - Se, vol. 24, no. 1, pp. 71–85, Mar. 2023, doi: 10.18038/estubtda.1258361.

ISNAD

İnce, Nihal - Şentürk, Sevil. “USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24/1 (March 1, 2023): 71-85. https://doi.org/10.18038/estubtda.1258361.

JAMA

1.İnce N, Şentürk S. USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE. Estuscience - Se. 2023;24:71–85.

MLA

İnce, Nihal, and Sevil Şentürk. “USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 24, no. 1, Mar. 2023, pp. 71-85, doi:10.18038/estubtda.1258361.

Vancouver

1.Nihal İnce, Sevil Şentürk. USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE. Estuscience - Se. 2023 Mar. 1;24(1):71-85. doi:10.18038/estubtda.1258361

USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE

Abstract

Keywords

USING INTUITIONISTIC FUZZY C-MEANS CLUSTERING ALGORITHMS TO MODEL COVID-19 CASES FOR COUNTRIES IN THE WORLDWIDE

Abstract

Keywords

References

Details

Primary Language

Subjects

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Authors

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Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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