GCRITICPA: A CRITIC and Grey Relational Analysis Based Service Ranking Approach for Cloud Service Selection

Gireesha, Obulaporam; Somu, Nivethitha; Ramani, Gauthama Raman ManiIyer; Boopathy, Akshya Kaveri; Sankaran, Shankar Sriram Vathula

doi:10.1007/978-981-13-3582-2_1

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…However, many psychological studies prove that the observed behavior of human beings does not conform to the expected utility curve. Human judgments are usually of individual preferences [35] due to outdated ideas of decision-makers or preference for a specific standard [36]. In addition, decision-makers may not fully recognize the issues under consideration.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Connection Cloud Model Coupled with Improved CRITIC Method for Evaluation of Eutrophic Water

Yan

Wang

et al. 2022

Mathematical Problems in Engineering

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The degree of eutrophication in the water environment is deepening. For the appropriate treatment of eutrophication, it is essential to evaluate it accurately. However, the evaluation of eutrophication has not been well solved because it is full of uncertainty. Herein, a multidimensional connection cloud model, combined with the improved CRITIC (Criteria Importance Through Inter-criteria Correlation) method, was put forward here to assess water eutrophication and depict the randomness, ambiguity, and interaction of evaluation factors. First, an improved CRITIC was adopted to determine indicator weight so that the correlation among different indicators and more information were depicted. Secondly, a multidimensional connection cloud was simulated to characterize fuzzy indicators and ambiguous classification boundary values according to classification criteria. Next, the connection degree was calculated relative to the evaluation standard. The eutrophication grade was specified under the “maximum connection degree” principle. At last, the effectiveness and practicality of the model proposed here were affirmed by two cases and comparisons with supplementary methods. The results suggest that the proposed model can avoid shortcomings of the original CRITIC method and cloud model, and make the assessment result more realistic.

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Section: Introductionmentioning

confidence: 99%