An augmented fuzzy decision support system to analyse compatible cosmetic face masks for various complexions

Author:

Brainy Joseph Raj Vikilal Joice1,Narayanamoorthy Samayan1,Kalaiselvan Samayan2,Saraswathy Ranganathan3,Ahmadian Ali45,Senu Norazak6,Jeon Jeonghwan7ORCID

Affiliation:

1. Department of Mathematics Bharathiar University Coimbatore India

2. Department of Social Work Sri Ramakrishna Mission Vidyalaya College of Arts and Science, SMRV Coimbatore India

3. Department of Radiology Karpagam Medical College and Hospital Coimbatore India

4. Decisions Lab Mediterranea University of Reggio Calabria Reggio Calabria Italy

5. Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey

6. Institute for Mathematical Research Universiti Putra Malaysia Serdang Malaysia

7. Department of Industrial & Systems Engineering/Engineering Research Institute (ERI) Gyeongsang National University JinJu Republic of Korea

Abstract

AbstractBeauty face masks (BFM) are becoming increasingly popular among both men and women since they provide quick refreshment and nurture the skin. Given the wide range of skin types and the chemicals used in their formulation, it can be difficult to find a product that not only complements the skin type but is also free of potentially harmful ingredients that could endanger the consumer's health. When dealing with ambiguous situations, the multi‐attribute decision making (MADM) approach combined with fuzzy set theory is more effective. Type‐2 fuzzy sets (T2FS) provide greater flexibility in dealing with uncertainty in real‐world issues since they are characterised by a main and secondary membership function. In this research, we present the innovative idea of type‐2 linear diophantine fuzzy set (T2LDFS) as an intriguing tool for capturing expert reluctance about an issue. For analysing the discussed problem, a hybrid fuzzy VIKOR enhanced with the proposed fuzzy logic is suggested. A sensitivity and comparative analysis is carried out to establish the validity of the recommended approach.

Funder

National Research Foundation of Korea

Ministry of Science and ICT, South Korea

Publisher

Wiley

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