Semantic Risk Analysis Based On Single Valued Neutrosophic Sets
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Semantic Risk Analysis Based on Single-Valued Neutrosophic Sets
Fuzzy risk analysis is diffusely applied in risk assessment of components by the semantic model. Due to the fuzzy characteristic in the process of fuzzy risk analysis, analysis parameters are imprecise and vague. Therefore, the determination of the risk of failure is challenging part of fuzzy risk analysis with existing methods. Hence, in this paper, a semantic risk analysis method based on the technique for order performance by similarity to ideal solution (TOPSIS) under a single-valued neutrosophic set (SVNS) is presented. First, a ve-member linguistic term set is introduced and these linguistic terms are expressed in terms of the generalized trapezoidal fuzzy numbers. Then, the linguistic term is transformed into the SVNS and generated SVNS is fused by single-valued neutrosophic prioritized weighted average (SVNPWA) operator. On this basis, the TOPSIS approach is used to obtain the nal rank to ascertain future risk. Finally, a fuzzy risk analysis example is conducted to illustrate the effectiveness of the proposed method. Further, the out-performance of the proposed method is illustrated in comparisons to the existing methods.
A Further Study on Multiperiod Health Diagnostics Methodology under a Single-Valued Neutrosophic Set
Employing the concept and function of tangency with similarity measures and counterpart distances for reliable medical consultations has been extensively studied in the past decades and results in lots of isomorphic measures for application. We compared the majority of such isomorphic measures proposed by various researchers and classified them into (a) maximum norm and (b) one-norm categories. Moreover, we found that previous researchers used monotonic functions to transform an identity function and resulted in complicated expressions. In this study, we provide a theoretical foundation to explain the isomorphic nature of a newer measure proposed by the following research paper against its studied existing one in deriving the same pattern recognition results. Specifically, this study initially proposes two similarity measures using maximum norm, arithmetic mean, and aggregation operators and followed by a detailed discussion on their mathematical characteristics. Subsequently, a simplified version of such measures is presented for easy application. This study completely covers two previous methods to point out that the complex approaches used were unnecessary. The findings will help physicians, patients, and their family members to obtain a proper medical diagnosis during multiple examinations.
A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data.