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Several practical problems incorporate uncertainty and erroneous information within its framework of definition. Many distinguished models have already been developed to manage such unreliability, like probabilistic models, and so forth. However, to revitalize the problems where uncertainty is not probabilistic, or the information is vaguely accessible, other tools like fuzzy logic and fuzzy linguistic-based approach have emerged. The efficacy of a linguistic-based approach pertains to a problem of decision making involving qualitative information where precise numerical modeling appears to be inappropriate. The 2-tuple linguistic model formalized by Herrera and Martínez in year 2000, has upgraded several linguistic processes for solving complex decision-making issues. Its successful application in different fields has impelled several researchers to work in its extension. This study aims to offer a systematic literature review on a 2-tuple linguistic model as well as discuss its extensions. The manuscript lays out a brief review of 188 articles published between 2000 and 2019. Based on the 188 selected articles, the following questions can be justified: (1) What is the fuzzy linguistic approach? (2) What is the linguistic model based on the 2-tuples and its foundation? (3) Why the 2-tuple model preferred over other existing classical linguistic models? (4) What are the extensions of the 2-tuple model? Moreover, (5) What are the major applications of a 2-tuple model witnessed in varied disciplines, specifically decision analysis? Therefore, seeking answers to these questions indicate current trends in research. Further, it provides the direction for scholars to work in this field and overcome the challenges associated with the prevailing 2-tuple model. [ABSTRACT FROM AUTHOR]

Having enhancement of automation of sensor, huge information such as the big data are examined and has become yet another worldview for huge scope of data handling. The is a requirement of continuous investigation for getting to and handling huge information in a quick manner. High health care costs and large infected population costs alongside the expansion of Information and correspondence innovation, prompted the improvement of the frameworks of health are monitored. The research article shows in the improvement of a novel cloud-based framework of a human service where the Wireless Body Area Networks (WBAN) gives out the total the information to an individual server. This server includes Fuzzy Brain Storm Optimization (FBSO) and Fuzzy Inference System (FIS) which is a distributed real-time computation system. This is used for processing large volumes of high-velocity data. It also is constant calculation framework and the Fuzzy induction framework. The proposed framework is said to be the Improved framework using Fuzzy Brain Storm Optimization (FBSO) and Fuzzy Inference System (FIS) for healthcare (IFFFH) are facilitated on a private cloud, along these lines security and versatility are guarantee. The stream examination is performed on the physiological information, where removal of non-basic information is done and the basic information are put away and a warning is sent to a doctor or the guardians of the person who is under monitorisation. Subsequently constant investigation along with the help of the cloud to improve the adequacy of the framework of the healthcare system (HcS) and personal satisfaction through the help of medical assistance. This can be used for future reference and by expanding the qualities in the dataset, in this manner expanding the quantity of records for preparing the fuzzy framework, the precision can be expanded. [ABSTRACT FROM AUTHOR]

The enhanced interval approach (EIA) is one of the most important approaches for constructing interval type-2 fuzzy set (IT2 FS) from data intervals that are collected from a survey. However, the uniform distribution used in EIA is rough. And the shape (Left-shoulder, Right-shoulder or Interior) of the fuzzy set (FS) affects the value of the membership function (MF) of the final IT2 FS a lot. To guarantee that the final IT2 FSs are consistent with fuzzistics characteristic of the original data and improve robustness, this paper proposes a normal distribution associated with free parameter (FP) as the supplement of uniform distribution in the data part of EIA. Furthermore, a general frame for encoding words from data intervals, called general interval approach (GIA), is built. GIA includes a data part, fuzzy set (FS) part and footprint of uncertainty (FOU) part. The data part maps data intervals to probability distributions, in which normal distribution with FP and uniform distribution are included. The FS part encodes the probability distributions produced by the data part to fuzzy MFs. Gaussian MF is discussed, and the parameter transformation table is obtained. In the FOU part, the FOU of IT2 FS is built by collecting the obtained T1 FSs. The way to construct a Gaussian FOU is, for the first time, proposed in this paper. The validity of GIA is verified by experiments. Compared with EIA, the IT2 FSs built by GIA can keep the statistic characteristic of the original data intervals in the greatest degree and improve the robustness owing to the FP. [ABSTRACT FROM AUTHOR]