Combinative-distance-based assessment approach for the evaluation of artificial intelligence cloud platforms using probabilistic linguistic hesitant fuzzy sets
Affiliations: [a] Faculty of Commerce, University of Finance and Marketing, Ho Chi Minh City, Vietnam
| [b] Faculty of Business Administration, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
Correspondence:
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Corresponding author. V.P. Nguyen, Faculty of Business Administration, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. E-mail: [email protected].
Abstract: With the rapid expansion of artificial intelligence (AI) and machine learning, the evaluation of AI cloud platforms has become a critical research topic. Given the availability of many platforms, selecting the best AI cloud services that can satisfy the requirements and budget of an organization is crucial. Several solutions, each with its advantages and disadvantages, are available. In this study, a combinative-distance-based assessment approach was proposed in probabilistic linguistic hesitant fuzzy sets (PLHFSs) to accommodate the multiple characteristics of group decision-making. The original data were normalized using a standardized process that integrated numerous methodologies. Furthermore, under PLHFSs, the statistical variance approach was used to generate the weighted objective of the vector of assessment criteria. Finally, an AI cloud platform evaluation and comparison analysis case study was used to validate the feasibility of this method.
