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Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory.
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Using soft sets as the granulation structures, Feng et al. [8] defined soft approximation spaces, soft rough approximations and soft rough sets, which are generalizations of Pawlak's rough set model based on soft sets.
Using soft sets as the granulation structures, Feng et al. [21] initiated soft approximation spaces and soft rough sets, which generalize Pawlak's rough sets based on soft sets.
Saikia et al. [9] have extended the method in [6] using intuitionistic fuzzy soft set theory.
In this paper, we apply fuzzy soft set theory through well-known Sanchez's approach for medical diagnosis using fuzzy arithmetic operations and exhibit the technique with a hypothetical case study.
We apply fuzzy soft set theory to develop a technique through Sanchez's method to diagnose which patient is suffering from what disease.
In this study we aim to discuss how soft set theory can be used for developing knowledge-based system in medicine and devise a prediction system named soft expert system (SES) by using the PSA, PV and age data of patients based on fuzzy sets and soft sets and calculate the patients prostate cancer risk.
The soft set theory [34, 35] is used as a general mathematical tool for dealing with uncertainty.
This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory.
The new approach to soft mappings enables us to obtain the natural first fixed-point results in the soft set theory.
Molodtsov's soft set theory was originally proposed as a general mathematical tool for dealing with uncertainty.
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