Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

Dhruva, S; Krishankumar, R; Zavadskas, EK; Ravichandran, KS; Gandomi, AH

Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

Dhruva, S Krishankumar, R Zavadskas, EK Ravichandran, KS Gandomi, AH

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Publisher:: Vilnius University Press
Publication Type:: Journal Article
Citation:: Informatica, 2023, pp. 1-34
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Dhruva, S
dc.contributor.author	Krishankumar, R
dc.contributor.author	Zavadskas, EK
dc.contributor.author	Ravichandran, KS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-01-24T04:35:34Z
dc.date.available	2024-01-24T04:35:34Z
dc.date.issued	2023
dc.identifier.citation	Informatica, 2023, pp. 1-34
dc.identifier.issn	0868-4952
dc.identifier.issn	1822-8844
dc.identifier.uri	http://hdl.handle.net/10453/174902
dc.description.abstract	<jats:p>Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.</jats:p>
dc.language	en
dc.publisher	Vilnius University Press
dc.relation.ispartof	Informatica
dc.relation.isbasedon	10.15388/23-infor537
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-24T04:35:32Z
pubs.publication-status	Published

Abstract:

Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.

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http://hdl.handle.net/10453/174902