Fuzzy Linguistic Research Articles

Multi-Attribute Decision-Making Based on Prioritized Aggregation Operator under Hesitant Intuitionistic Fuzzy Linguistic Environment

A hesitant intuitionistic fuzzy linguistic set (HIFLS) that integrates both qualitative and quantitative evaluations is an extension of the linguistic set, intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS) and hesitant intuitionistic fuzzy set (HIFS). It can describe the qualitative evaluation information given by the decision-makers (DMs) and reflect their uncertainty. In this article, we defined some new operational laws and comparative method for HIFLSs. Then, based on these operations, we propose two prioritized aggregation (PA) operators for HIFLSs: prioritized weighted averaging operator for HIFLSs (HIFLPWA) and prioritized weighted geometric operator for HIFLSs (HIFLPWG). Based on these aggregation operators, an approach for multi-attribute decision-making (MADM) is developed under the environment of HIFLSs. Finally, a practical example is given to show the practicality and effectiveness of the developed approach by comparing with the other representative methods.

Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment.

As meteorological disaster systems are large complex systems, disaster reduction programs must be based on risk analysis. Consequently, judgment by an expert based on his or her experience (also known as qualitative evaluation) is an important link in meteorological disaster risk assessment. In some complex and non-procedural meteorological disaster risk assessments, a hesitant fuzzy linguistic preference relation (HFLPR) is often used to deal with a situation in which experts may be hesitant while providing preference information of a pairwise comparison of alternatives, that is, the degree of preference of one alternative over another. This study explores hesitation from the perspective of statistical distributions, and obtains an optimal ranking of an HFLPR based on chance-restricted programming, which provides a new approach for hesitant fuzzy optimisation of decision-making in meteorological disaster risk assessments.

A NEW MODEL FOR FUZZY PERSONALIZED ROUTE PLANNING USING FUZZY LINGUISTIC PREFERENCE RELATION

Abstract. This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user’s ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user’s preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user’s preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.

Manufacturing Process Innovation-Oriented Knowledge Evaluation Using MCDM and Fuzzy Linguistic Computing in an Open Innovation Environment

In today’s complex, constantly evolving and innovation-supporting manufacturing systems, knowledge plays a vital role in sustainable manufacturing process planning and problem-solving, especially in the case of Computer-Aided Process Innovation (CAPI). To obtain formalized and promising process innovation knowledge under the open innovation paradigm, it is necessary to evaluate candidate knowledge and encourage improvement suggestions based on actual innovation situations. This paper proposes a process innovation-oriented knowledge evaluation approach using Multi-Criteria Decision-Making (MCDM) and fuzzy linguistic computing. Firstly, a comprehensive hierarchy evaluation index system for process innovation knowledge is designed. Secondly, by combining an analytic hierarchy process with fuzzy linguistic computing, a comprehensive criteria weighting determination method is applied to effectively aggregate the evaluation of criteria weights for each criterion and corresponding sub-criteria. Furthermore, fuzzy linguistic evaluations of performance ratings for each criterion and corresponding sub-criteria are calculated. Thus, a process innovation knowledge comprehensive value can be determined. Finally, an illustrative example of knowledge capture, evaluation and knowledge-inspired process problem solving for micro-turbine machining is presented to demonstrate the applicability of the proposed approach. It is expected that our model would lay the foundation for knowledge-driven CAPI in sustainable manufacturing.

Fuzzy Linguistic Summarization with Genetic Algorithm: An Application with Operational and Financial Healthcare Data

It is now well recognized that knowledge extracted from rich healthcare data play a vital role for delivery, management and planning of healthcare services. So far, however, there is not much study done on the domain of operational and financial healthcare data since, up to now, a great deal of works are dedicated to clinical/medical healthcare data for the purposes of diagnosis and treatment of diseases. In this paper, an attempt is made, by applying fuzzy linguistic summarization, for the first time to discover knowledge from operational and financial healthcare data. Fuzzy linguistic summarization, in its simplest term, provides natural language based summaries from a dataset in a human consistent way along with a degree of truth attached to each summary. While basically valuable, its benefit can be increased by only generating summaries with a degree of truth above than an indicated threshold value. A genetic algorithm is developed within this context in order to eliminate less promising and useless linguistic summaries. We assess the proposed approach experimentally on a real data and evaluate the generated summaries to gain actionable insights from them.

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

A Gower Plot-Based Approach to Ascertain and Adjust the Ordinal and Additive Inconsistencies for Fuzzy Linguistic Preference Relations

This paper studies the ordinal and additive consistency issues of fuzzy linguistic preference relations (FLPRs). First, a graphic method of Gower plot is proposed to ascertain ordinal and additive inconsistencies for FLPRs. Then, two optimization models are established to adjust the ordinal and additive inconsistencies at the same time. Furthermore, an approach based on Gower plot is developed to help the decision maker to improve the consistency and rank the alternatives which respects the views of decision makers with greatest degree. Finally, numerical examples and comparisons with the existing methods are furnished to show the effectiveness and advantages of the proposed method.

A Study of Hedges in Chinese Foreign Ministry Spokespersons’ Speeches at Regular Press Conference

As a tool effective in yielding special semantic and pragmatic effects through promoting linguistic fuzziness, hedges are widely applied to various discourses, and thus hold tight a high position in the fuzzy linguistics research. Based on Chinese foreign ministry spokesperson’s speeches at regular press conference, the present study intends to analyze the hedges in qualitative and quantitative methods at both lexical and non-lexical levels, for the purpose of discovering the features of the usage of hedges in Chinese foreign ministry spokesperson’s speeches at regular press conference and figuring out how spokesperson’s speeches achieve his political meaning through hedges and the corresponding reason, hoping to help the public and foreign media comprehend Chinese foreign policy better.

A New Intuitionistic Fuzzy Linguistic Hybrid Aggregation Operator and Its Application for Linguistic Group Decision Making

The linguistic aggregation operator is an important decision-making model that is proving effective for dealing with the input data that takes the form of uncertain information. In this paper, considering the principal component of the intuitionistic fuzzy linguistic variables, we develop a new intuitionistic fuzzy linguistic hybrid aggregation (NIFLHA) operator to solve group decision-making problems under the situation with intuitionistic fuzzy linguistic information. Then, we study some of its main properties by utilizing some operational laws of intuitionistic fuzzy linguistic variables and the different families of the NIFLHA operator. Moreover, the multiperson NIFLHA (MP-NIFLHA) operator is introduced to evaluate the opinions of experts. Finally, an illustrative example about a multiperson decision-making problem is developed to reveal the applicability and the availability of the raised operator.

Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

Boosting collaborative or participatory consumption is a priority for the European Commission. It is in line with the provisions of the Europe 2020 Strategy, which proposes that consumption of goods and services should take place in accordance with smart, sustainable and inclusive growth. These have motivated us to develop an online community for collaborative consumption centered in the Senegalese community that travels by car from Europe to Africa named Teranga Go!. Carpooling relationships are based on the sense of a real existing community, social experiences among users, and connection through technology, where confidence is the key concept. To help creating values of confidence, trust and safety among the members of the Teranga Go! community, we have implemented an intelligent decision support system in the platform based on computing with words. The participants of a carpooling experience act as experts that assess the driver aptitudes and determine, together with the history of the driver, a linguistic value for the driver's karma which represents the collective opinion of people that have traveled with the driver. The karma is a public label attached to the site user profiles. A Multi-Expert Multi-Criteria Decision Making model is applied using Hesitant Fuzzy Linguistic Terms to represent the expert opinions.

Synthesis of Algorithms for Recognition of Vulnerabilities in Web Resources Using Signatures of Fuzzy Linguistic Features

The paper formalizes the problem of fuzzy recognition of vulnerabilities in web resources that is set by a complex reference description in the form of signatures of intervals of the values of fuzzy linguistic features. The reference description and the mathematics of the theory of validation of complex statistical hypotheses were applied to synthesize multi-criteria object recognition algorithms using the minimax decision rule and the Bayesian maximum of a posteriori probability and maximum likelihood criteria.

Pendekatan Fuzzy FMEA dalam Analisis Faktor Risiko Kecelakaan Kerja

<p><em>This </em><em>study</em><em> </em><em>aims to</em><em> </em><em>identify and </em><em>analyze </em><em>the </em><em>risk factors </em><em>of</em><em> work accidents</em><em>. </em><em>Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic </em><em>approach are applied</em><em>. </em><em>The information obtained from the workers is expressed using fuzzy linguistics terms, and a FMEA method is proposed to determine the risk priority of failure modes. </em><em>The results </em><em>indicate</em><em> that </em><em>injuries caused when struck by an object are the highest</em><em> risk factor </em><em>of work accident (</em><em>FRPN </em><em>=</em><em> 886</em><em>). Some work improvements are suggested to reduce or eliminate the work risks.</em><em></em></p><p><em>Keywords</em><em>: Risk factor</em><em>s</em><em>, work accident, FMEA, Fuzzy </em></p>

Hesitant Fuzzy Linguistic Maclaurin Symmetric Mean Operators and their Applications to Multi-Criteria Decision-Making Problem

Hesitant Fuzzy Linguistic Maclaurin Symmetric Mean Operators and their Applications to Multi-Criteria Decision-Making Problem

An Approach to the Evaluation of the Quality of Accounting Information Based on Relative Entropy in Fuzzy Linguistic Environments

There is a risk when company stakeholders make decisions using accounting information with varied qualities in the same way. In order to evaluate the accounting information quality, this paper proposed an approach to the evaluation of the quality of accounting information based on relative entropy in fuzzy linguistic environments. Firstly, the accounting information quality evaluation criteria are constructed not only from the quality of the accounting information content but also from the accounting information generation environment. Considering that the rating values with respect to the criteria are in linguistic forms with different granularities, the method to deal with the linguistic rating values is given. In the method, the linguistic terms are modeled with the 2-tuple linguistic model. Relative entropy is used to calculate the information consistency, which is used to derive the weight of experts and criteria. Finally, the example is given to illustrate the feasibility and practicability of the proposed method.

Research on the Evaluation Model for Network Pollution Status Based on Fuzzy Linguistic Assessments and the Scatter Degree Method

Research on the Evaluation Model for Network Pollution Status Based on Fuzzy Linguistic Assessments and the Scatter Degree Method

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

A Fuzzy Linguistic Extended LibQUAL+ Model to Assess Service Quality in Academic Libraries

LibQUAL+ model is the best-known method for the quality evaluation of library services, but it has two major drawbacks. First, to measure the quality, it is devised on a cardinal scale: the service levels range from 1 to 9. However, the standard representation of the concepts used by humans for communication is the natural language and, hence, users should express their judgments by using words instead of numbers. Second, it considers that all users’ opinions are equally important. Nevertheless, users do not play an equal role in assessing the service quality, i.e., the opinion given by some users should be more relevant than the opinion provided by others. To solve these drawbacks, we present an extended LibQUAL+ model representing the users’ perceptions by using a fuzzy linguistic modeling and taking into account that users’ opinions on the library services are not equally important.

Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

The significance of addressing Big Data applications is beyond all doubt. The current ability of extracting interesting knowledge from large volumes of information provides great advantages to both corporations and academia. Therefore, researchers and practitioners must deal with the problem of scalability so that Machine Learning and Data Mining algorithms can address Big Data properly. With this end, the MapRe-duce programming framework is by far the most widely used mechanism to implement fault-tolerant distributed applications. This novel framework implies the design of a divide-and-conquer mechanism in which local models are learned separately in one stage (Map tasks) whereas a second stage (Reduce) is devoted to aggregate all sub-models into a single solution. In this paper, we focus on the analysis of the behavior of Linguistic Fuzzy Rule Based Classification Systems when embedded into a MapRe-duce working procedure. By retrieving different information regarding the rules learned throughout the MapReduce process, we will be able to identify some of the capabilities of this particular paradigm that allowed them to provide a good performance when addressing Big Data problems. In summary, we will show that linguistic fuzzy classifiers are a robust approach in case of scalability requirements.

Integrating Principal Component Analysis, Fuzzy Linguistic Reasoning and Taguchi Philosophy for Quality-Productivity Optimization

The present work demonstrates a multi-response optimization problem for selection of optimal cutting parameters (optimal process environment) for machining (turning) of nylon 6; by using Principal Component Analysis (PCA) followed by fuzzy linguistic reasoning in combination with Taguchi’s robust design technique. In this study, three controllable process parameters: cutting speed, feed, and depth of cut have been considered for obtaining desired Material Removal Rate (MRR) of the process and favorable multiple surface roughness features for the machined product; based on L9 orthogonal array experimental design. The study has been aimed to search an appropriate process environment for simultaneous optimization of quality-productivity favorably. Various surface roughness parameters of statistical importance (of the machined product) have been considered as product quality characteristics whereas; MRR has been treated as productivity measure for the said machining process. To avoid assumptions, limitations, uncertainties and imprecision in application of existing multi-response optimization techniques; Principal Component Analysis (PCA) has been proposed to convert correlated responses into uncorrelated quality indices (called individual principal components); next, a fuzzy inference system (FIS) has been proposed for meaningful and feasible aggregation of individual principal components into an equivalent single quality index, thereby, converting such a multi-objective optimization problem into an equivalent single objective optimization situation. A Multi-Performance Characteristic Index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method. The study exhibits application feasibility of the proposed approach with satisfactory result of confirmatory test.

A Hesitant Fuzzy Linguistic Multicriteria Decision-Making Method with Interactive Criteria and Its Application to Renewable Energy Projects Selection

A variety of multicriteria decision-making (MCDM) methods for renewable energy projects evaluation have been proposed, of which the premise of using these methods is to assume that the criteria are independent of each other. However, it may be difficult or costly to build independent criteria set in some cases because renewable energy planning is to pursue a balance of economic, social, and environmental goals, which makes the existence of interaction among criteria be of great possibility. In this paper, we consider a highly ambiguous decision situation, where the experts are allowed to give the evaluations in the form of hesitant fuzzy linguistic terms set (HFLTS). We build a hesitant fuzzy linguistic decision-making model handling the interaction among criteria from the perspective of distance measure and apply it to renewable energy projects selection. The proposed method can consider more fuzzy factors and deal with the interaction among criteria more approximately. It can reduce the decision pressure and improve the decision-making efficiency because the decision makers are allowed to express their preference in form of HFLTS and a decision criteria set of which the criteria are independent of each other is not necessary.