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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]

In this article, we present a study on the development in the theory and application of the Z-numbers since its inception in 2011. The review covers the formalization of Z-number-based mathematical operators, the role of Z-numbers in computing with words, decision-making, and trust modeling, application of Z-numbers in real-world problems such as multisensor data fusion, dynamic controller design, safety analytics, and natural language understanding, a brief comparison with conceptually similar paradigms, and some potential areas of future investigation. The paradigm currently has at least four extensions to its definition: multidimensional Z-numbers, parametric Z-numbers, hesitant-uncertain linguistic Z-numbers, and Z*-numbers. The Z-numbers have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While this decade has seen a plethora of theoretical initiatives toward its growth, there remains a major work scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, discernment of probability–possibility distributions to map real-world situations under consideration, analysis of linguistic equivalents of Z-operator results to intuitive human responses, the endogenous arousal of belief in intelligent agents, and analysis of biases embedded in expert-belief values that are primary inputs to Z-number-based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expert-input-based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas, such as deep-sea exploration and space science, remains unexplored. [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 use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system's prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician. [ABSTRACT FROM AUTHOR]

Fuzzy cognitive maps (FCMs) play an important role in high-level reasoning but are limited in their ability to model complex systems with singularities. We are interested in systems that exhibit discontinuous behaviors as one or more of their internal node states approach a threshold. In a new approach to FCM dynamics, we define general classes of aggregation functions which “jump” to a boundary value when any input crosses a threshold, or when all inputs do. The threshold value is a context-dependent parameter which can be readily understood by subject matter experts. Aggregation functions are applied separately to positively and negatively causal antecedents to each node then combined to form the nodal state. This modeling is applied in Computing with Words (CWW) settings, in which link strengths and activation levels are elicited using vocabulary words represented by interval type-2 fuzzy membership functions. We illustrate the behaviors of these novel FCM systems in comparison with their nonsingularity versions. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system’s prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician.

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]

Depression is a common and important mental disorder that affects the quality of human life. Since people with depression are not aware of their disorder and sometimes suffer from physical symptoms such as chronic pain, refer to a physician instead of a psychologist. Hence, physician's diagnosis is not always correct in all patients. In the other words, misdiagnosis may occur by mislabeling their mental disorder as physical diseases. Delay in depression diagnosis may have irrecoverable outcomes such as suicide. Therefore, the most challenging aspect of depression diagnosis is to limit time loss and preserve accuracy. In this paper, a novel general type-2 fuzzy expert system for depression diagnosis, considering two main objectives, was developed. These objectives include accuracy of the system and diagnosis time. The proposed system might be a helpful guideline for the physician to lead patients toward psychologist by asking 15 questions from patients. The proposed general Type-2 expert system has five steps. In the first step, we generate general type-2 membership function by using zSlices method and interval agreement approach (IAA). Then fuzzy rules are extracted out of data gathered from hospital and we extend Mendel method briefly in the second step. Approximate reasoning is applied in the third step. In the fourth step, we solve a multi-objective problem to minimize time and maximize accuracy by using MOEA/D method. Accordingly, in order to minimize time, feature selection is applied. In this process, we use MIFS (Mutual Information Feature Selection) method and briefly, we extend it. In the final step, we choose an appropriate solution from achieved Pareto Front (PF). The proposed general type-2 expert system has been tested and evaluated to show its performance. This Intelligent system is able to diagnose depression accurately at a suitable time. • An expert system for diagnosis of depression is proposed. • The introduced system focuses on time and accuracy that are important in diagnosis. • The proposed diagnostic system, deals with vague linguistic variables. • Decision Support System for Physicians. [ABSTRACT FROM AUTHOR]

Highlights • Proposes a novel approach for heart monitoring through perceptual computing (HMT Per-C). • HMT Per-C is capable to assess the medical condition of heart failure patients. • It recommends the medical attention needed for a patient by processing his/her linguistic feedbacks. • In HMT Per C, patient feedbacks are words which are modelled using interval type-2 fuzzy sets. • HMT Per-C is made freely available on www.sau.ac.in/∼cilab/ for general use. Abstract Developments in medical science have provided new ways in which care can be taken of people suffering from the risk of heart failure at reduced medical expenses, such as through wearable sensors. These are more efficient than traditional health monitoring methods such as in-person visits to medical practitioners, clinics, etc. Unfortunately, wearable sensors can measure quantitative parameters such as blood pressure and heart rate but not qualitative ones such as ease of respiration, pain, etc. The values of qualitative parameters are generally expressed by a sick person in the form of 'words'. In real life scenarios, medical experts suggest plausible medical tests/treatment to patients using their experience based on his/her feedback in terms of 'words'. In this paper, we propose a new approach, called heart monitoring through perceptual computing (HMT Per-C), that assesses the medical condition of a person (under the risk of heart failure) by processing user feedback in terms of 'words' and generates recommendations about the medical attention needed to be given to him/her. HMT Per-C is based on the technique of perceptual computing, which is a computing with words (CWW) technique that models 'words' using interval type-2 fuzzy sets. We have also compared the recommendations generated by perceptual computing with those generated by other CWW approaches viz., extension principle, symbolic method and 2-tuple. We have found that the extension principle, symbolic method and 2-tuple failed to give accurate results in 8%, 44% and 28% cases, respectively. Therefore, we believe that our proposed approach, HMT Per-C, is better, more user-friendly and close to real life scenarios. An outcome of the present work is the ready to use mobile app, "HMT Per-C", that complements the data obtained from the devices like the oximeter but does not replace them. It can be downloaded freely from http://sau.ac.in/∼cilab/. [ABSTRACT FROM AUTHOR]

Present day portable devices such as laptops, smartphones, etc., offer their users fastest processors, advanced operating systems, and numerous applications. However, a large section of the users are critical to the available battery capacity and its lifetime. This is because performance of the battery and its lifetime as perceived by the users are quite subjective in nature. It depends directly on user satisfactions, which are usually expressed in terms of words. So, in this paper, we propose a user-satisfaction-aware energy management approach, called “perceptual computer power management approach (Per-C PMA),” based on the technique of perceptual computing. At the heart of our technique is the perceptual computer that processes the linguistic input of the users to aid in the selection of a suitable processor frequency, which plays a significant role in the overall energy consumption of the systems. The Per-C PMA minimizes the energy consumption, while still keeping the user satisfied with the perceived system performance. The Per-C PMA achieves 1) reductions of 42.26% and 10.84% in power consumption, and 2) improvements in the overall satisfaction ratings of 16% and 10%, when compared to other existing power-saving schemes such as ON-DEMAND and human and application-driven frequency scaling for processor power efficiency, respectively. Per-C PMA is the first such application of Per-C on any hardware platform. It is implemented as Ubuntu scripts for end users and can be downloaded from: http://sau.ac.in/∼cilab/. We have also provided the MATLAB files so that interested researchers can use it in their research. For the ease of the users, the Ubuntu scripts and the MATLAB codes are given in the graphical user interface mode; a demo video on how to use the software is also provided on the webpage. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates new possibilities applied to enrichSWOT analysis mechanism using elements of artificial intelligence, and, especially, the computing with words paradigm. This approach is novel due to the originality of the encoding of input words that describe the situation under the investigation in a new functional organization of the SWOT engines, and the originality of the method used for decoding and aggregation of numerical outputs into a verbal form. Promising results of the experimental simulation of the prototype of SWOT+CWW tool are delivered as well. [ABSTRACT FROM AUTHOR]

Decision making is a common process in human activities. Every person or organization needs to make decisions besides dealing with uncertainty and vagueness associated with human cognition. The theory of fuzzy logic provides a mathematical base to model the uncertainities. Hesitant fuzzy linguistic term set (HFLTS) creates an appropriate method to deal with uncertainty in decision making. Managerial decision making generally implies that decision making process conducts multiple and conflicting criteria. Multi criteria decision analysis (MCDA) is a widely applied decision making method. Outranking methods are one type of MCDA methods which facilitate the decision making process through comparing binary relations in order to rank the alternatives. Elimination et Choix Traduisant la Réalité (ELECTRE), means elimination and choice that translates reality, is an outranking method. In this paper, an extended version of ELECTRE I method using HFLTS is proposed. Finally, a real case problem is provided to illustrate the HFLTS-ELECTRE I method.

This paper focuses on an approach, called the HM Approach (HMA), to determine (for the first time) a normal interval type-2 fuzzy set model for a word that uses interval data about a word that are collected either from a group of subjects or from one subject. The HMA has two parts: 1) Data part, which is the same as the Data Part of the enhanced interval approach (EIA) [44], and 2) Fuzzy Set Part, which is very different from the second part of the EIA, the most notable difference being that in the HMA, the common overlap of subject data intervals is interpreted to indicate agreement by all of the subjects for that overlap, and therefore, a membership grade of 1 is assigned to the common overlap. Another difference between the HMA and EIA is the way in which data intervals are collectively classified into either a Left-shoulder, Interior, or Right-shoulder footprint of uncertainty. The HMA does this more simply than does the EIA, and requires fewer probability assumptions about the intervals than does the EIA. [ABSTRACT FROM PUBLISHER]

Until now, routes evaluation for unmanned aerial vehicle still faces a variety of difficulties, which is due to the fact that during route evaluating, subjective judgments, quantitative data, and random information need to be considered simultaneously. In this paper, by formulating route evaluation as a multi-criteria decision making problem including uncertainties, an integrated route evaluation approach based on type-2 fuzzy sets is proposed. Firstly, a systemic evaluation framework that incorporates models for scoring evaluation criteria is proposed. Specifically, a survivability model incorporating dynamics and uncertainties in battlefield is developed, including some special features, such as calculating the probability of detecting, tracking, and destroying an unmanned aerial vehicle, and modeling the location of pop-up threats as a Markov chain. Then, type-2 fuzzy sets are introduced to represent linguistic values, managing linguistic uncertainty effectively and making the evaluation process realistic and reliable. Finally, the architecture of perceptual computer is extended, and the computing with words engine by means of linguistic weighted average method is adopted to obtain the overall score of each route, enabling both random and fuzzy uncertainties existing universally in the data to be effectively managed in a unified format. The proposed method has the advantages of diverse inputs such as numbers, probability distributions and words. All these can be aggregated to a final decision. Furthermore, it provides a useful tool to handle route evaluation problem in a highly reliable and intelligent manner, and it can be applied to solve multi-criteria decision making problems in many disciplines. Experimental results demonstrate the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This work proposes an effective synergy of the Intervals׳ Number k-nearest neighbor (INknn) classifier, that is a granular extension of the conventional knn classifier in the metric lattice of Intervals׳ Numbers (INs), with the gravitational search algorithm (GSA) for stochastic search and optimization. Hence, the gsaINknn classifier emerges whose effectiveness is demonstrated here on 12 benchmark classification datasets. The experimental results show that the gsaINknn classifier compares favorably with alternative classifiers from the literature. The far-reaching potential of the gsaINknn classifier in computing with words is also delineated. [ABSTRACT FROM AUTHOR]

We introduce two new classes of single-parameter aggregation functions based upon the Tsallis q-exponential (QE) function of nonextensive statistical mechanics. These aggregation functions (denoted QE aggregation) facilitate simple modeling of the common human reasoning trait of “threshold” inference, where either 1) at least one input must exceed a threshold in order to achieve a nonzero aggregation output; or 2) if any one of the inputs exceeds a different threshold, the aggregation output takes its maximum value. We illustrate the thresholding behavior of these functions on interval type-2 fuzzy inputs using an example known in the literature as the Investment Judgment Advisor. We believe that the new QE class of aggregation operators will prove useful in extending the range of options available for the design of perceptual computing systems. [ABSTRACT FROM PUBLISHER]

Computing with words, CWW, is considered in the context of natural language functioning, unifying language with thinking. Previous attempts at modeling natural languages as well as thinking processes in artificial intelligence have met with computational complexity. To overcome computational complexity we use dynamic logic (DL), an extension of fuzzy logic describing fuzzy to crisp transitions. We suggest a possible architecture motivated by mathematical and neural considerations. We discuss the reasons why CWW has to be modeled jointly with thinking and propose an architecture consistent with brain neural structure and with a wealth of psychological knowledge. The proposed architecture implies the existence of relationships between languages and cultures. We discuss these implications for further evolution of English and Chinese cultures, and for cultural effects of interactions between natural languages and CWW. [ABSTRACT FROM AUTHOR]

Construction of interval type-2 fuzzy set models is the first step in the perceptual computer, which is an implementation of computing with words. The interval approach (IA) has, so far, been the only systematic method to construct such models from data intervals that are collected from a survey. However, as pointed out in this paper, it has some limitations, and its performance can be further improved. This paper proposes an enhanced interval approach (EIA) and demonstrates its performance on data that are collected from a web survey. The data part of the EIA has more strict and reasonable tests than the IA, and the fuzzy set part of the EIA has an improved procedure to compute the lower membership function. We also perform a convergence analysis to answer two important questions: 1) Does the output interval type-2 fuzzy set from the EIA converge to a stable model as increasingly more data intervals are collected, and 2) if it converges, then how many data intervals are needed before the resulting interval type-2 fuzzy set is sufficiently similar to the model obtained from infinitely many data intervals? We show that the EIA converges in a mean-square sense, and generally, 30 data intervals seem to be a good compromise between cost and accuracy. [ABSTRACT FROM AUTHOR]

Abstract: As part of human resource management policies and practices, construction firms need to define competency requirements for project staff, and recruit the necessary team for completion of project assignments. Traditionally, potential candidates are interviewed and the most qualified are selected. Applicable methodologies that could take various candidate competencies and inherent uncertainties of human evaluation into consideration and then pinpoint the most qualified person with a high degree of reliability would be beneficial. In the last decade, computing with words (CWW) has been the center of attention of many researchers for its intrinsic capability of dealing with linguistic, vague, interdependent, and imprecise information under uncertain environments. This paper presents a CWW approach, based on the specific architecture of Perceptual Computer (Per-C) and the Linguistic Weighted Average (LWA), for competency based selection of human resources in construction firms. First, human resources are classified into two types of main personnel: project manager and engineer. Then, a hierarchical criteria structure for competency based evaluation of each main personnel category is established upon the available literature and survey. Finally, the perceptual computer approach is utilized to develop a practical model for competency based selection of personnel in construction companies. We believe that the proposed approach provides a useful tool to handle personnel selection problem in a more reliable and intelligent manner. [Copyright &y& Elsevier]

We point out some relevant issues that are related to the computing-with-words (CWW) paradigm and argue for an urgent need for a new, nontraditional look at the area, since the traditional approach has resulted in very valuable theoretical research results. However, there is no proper exposure and recognition in other areas to which CWW belongs and can really contribute, notably natural-language processing (NLP), in general, and naturallanguage understanding (NLU) and natural-language generation (NLG), in particular. First,we present crucial elements ofCWW, in particular Zadeh's protoforms, and indicate their power and stress a need to develop new tools to handle more modalities. We argue that CWW also has a high implementation potential and present our approach to linguistic data(base) summaries,which is a very intuitive and human-consistent natural-language-based knowledge discovery tool. Special emphasis is on the use of Zadeh's protoform (prototypical form) as a general form of a linguistic data summary. We present an extension of our interactive approach, which is based on fuzzy logic and fuzzy database queries, to implement such linguistic summaries. In the main part of the paper, we discuss a close relation between linguistic summarization in the sense considered and some basic ideas and solutions in NLG, thus analyzing possible common elements and an opportunity to use developed tools, as well as some inherent differences and difficulties. Notably, we indicate a close relation of linguistic summaries that are considered to be some type of an extended template-based, and even a simple phrase-based, NLG system and emphasize a possibility to use software that is available in these areas. An important conclusion is also an urgent need to develop new protoforms, thus going beyond the classical ones of Zadeh. For illustration, we present an implementation for a sales database in a computer retailer, thereby showing the power of linguistic summaries, as well as an urgent need for new types of protoforms. Although we use linguistic summaries throughout, our discussion is also valid for CWW in general. We hope that this paper--which presents our personal view and perspective that result fromour long-time involvement in both theoretical work in broadly perceived CWW and real-world implementations--will trigger a discussion and research efforts to help find a way out of a strange situation in which, on one hand, one can clearly see that CWW is related to words (language) and computing and, hence, should be part of broadly perceived mainstream computational linguistics, which lack tools to handle imprecision. These tools can be provided by CWW. Yet, CWW is practically unknown to these communities and is not mentioned or cited, and--reciprocally--even the top people in CWW do not refer to the results that are obtained in these areas. We hope that our paper, for the benefit of both the areas, will help bridge this gap that results from a wrong and dangerous fragmentation of science. [ABSTRACT FROM AUTHOR]