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This paper compares two alternative methods for measuring multidimensional poverty. This question has become extremely important in recent years, both in the scientific literature and in social policy. We propose to use latent class analysis to evaluate poverty in Spain. We make use of the “fuzzy set” approach, and compare the results achieved from these two methodologies.

This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).

The paper introduces a new interpretation of multidimensional fuzzy implication (MFI) to represent the author's knowledge about the training data set. It also considers the notion of a fuzzy pattern vector (FPV) to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one‐dimensional fuzzy implication. For the estimation of Ri floating point representation of GA is used. Thus, a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non‐fuzzy features of a test pattern can be classified.

The performance of the proposed scheme is tested on synthetic data. Subsequently, the paper uses the proposed scheme for the vowel classification problem of an Indian language. In all these case studies the recognition score of the proposed method is very good. Finally, a benchmark of performance is established by considering Multilayer Perceptron (MLP), Support Vector Machine (SVM) and the proposed method. The Abalone, Hosse colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. The benchmark study also establishes the superiority of the proposed method.

This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV. A set of fuzzy relations which is the core of the pattern classifier, is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed. This new approach to pattern classification avoids the curse of high dimensionality of feature vector. It can provide multiple classifications under overlapped classes.

The multidimensional nature of well-being is now widely recognized. However, multidimensional poverty measurement is still an expanding field of research and a consensus about the “best” composite indicator has not yet emerged. In this chapter, we provide an empirical analysis using three existing methodologies: Bourguignon and Chakravarty (2003), Alkire and Foster (2007), and Lemmi (2005); Chiappero Martinetti (2000). We present an empirical study of the convergence and divergence of poverty profiles for children in Uruguay considering the following dimensions: nutritional status, child educational achievement, housing condition, and household income. Our data gather information of 1,185 children attending public schools in Montevideo and the surrounding metropolitan area, and were specially gathered to carry out a multidimensional analysis of poverty.

Our results indicate that the three families of indexes yield very different cardinalizations of poverty. At the same time, the correlation coefficients among the three groups of measures for the generalized headcount ratio also highlight important differences in the children labeled as “more deprived.” For the generalized severity and intensity indexes the correlation coefficients increase significantly suggesting a high level of concordance among the three measures, particularly among the Bourguignon and Chakravarty methodology and the Alkire and Foster one.

The study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.

The problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.

An appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.

The authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.

The tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.

This work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.

This study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

The purpose of this paper is to present a multidimensional evaluation of brand equity performance incorporating dimensions adopted from the balance scorecard (BSC) approach to business performance.

In this study, text mining is used for automatic extraction of valuable information from textual data such as the financial reports of firms. Instead of expert opinions, linguistic scales built upon outcomes of text mining are used as inputs for decision-making. The proposed model combines fuzzy DEMATEL (FDEMATEL), fuzzy ANP (FANP), fuzzy TOPSIS (FTOPSIS) and fuzzy VIKOR (FVIKOR) methods for weighting criteria and ranking alternatives.

Using data from five privatized firms in Turkey, the study’s findings demonstrate that the customer is the most important dimension of brand equity performance evaluation. Cash flow and brand loyalty are identified as the most important criteria in the measurement of brand equity performance.

Findings highlight the importance of firms taking action to increase consumer perceptions, attitudes and behaviors in the privatization processes. For this purpose, privatized firms need to understand the expectations of customers to increase customer satisfaction and loyalty and therefore improve brand equity.

The paper contributes to literature in several important ways. First, by adopting the BSC approach, it proposes a holistic and a multidimensional model for measuring brand equity performance. Second, the study offers a novel methodology using a hybrid multi-criteria decision-making model designed for the fuzzy environment. Third, the study uses the knowledge extraction tool of text mining in the fuzzy decision-making process. Finally, the study evaluates the brand equity performance of privatized firms in an emerging country context.

The purpose of this paper is to use the capability approach to analyse the wellbeing of rural households in Nigeria and determine the factors that influence the wellbeing status reported.

The paper analyses multidimensional wellbeing of the households in the capability space using data in seven dimensions obtained from the Nigeria Core Welfare Indices Survey of 2006. The wellbeing status of households was derived using the fuzzy set approach, while a logistic regression was used to isolate the factors that determine wellbeing.

The results of the fuzzy set analysis showed that overall rural households in Nigeria have a low mean wellbeing status at 0.27. Capability to attain a desired state of wellbeing is highest with respect to asset ownership and lowest with respect to security. The logistic analysis shows that the predicted probability of attaining the mean capability wellbeing increases for male headed households, increasing educational level and age of the head, household size, and public service occupation.

The paper showed that the capability to attain desired levels of wellbeing increases for dimensions which are key variables in making policies for human capital development, with direct implications for improving wellbeing.

This paper attempts to bridge the knowledge gap in the empirical literature of wellbeing studies and specifically in the use of the capability approach and its application in the Nigerian wellbeing context.

In this paper, we present a brief study on various paradigms to tackle complexity or in other words manage uncertainty in the context of understanding science, society and nature. Fuzzy real numbers, fuzzy logic, possibility theory, probability theory, Dempster‐Shafer theory, artificial neural nets, neuro‐fuzzy, fractals and multifractals, etc. are some of the paradigms to help us to understand complex systems. We present a very detailed discussion on the mathematical theory of fuzzy dynamical system (FDS), which is the most fundamental theory from the point of view of evolution of any fuzzy system. We have made considerable extension of FDS in this paper, which has great practical value in studying some of the very complex systems in society and nature. The theories of fuzzy controllers, fuzzy pattern recognition and fuzzy computer vision are but some of the most prominent subclasses of FDS. We enunciate the concept of fuzzy differential inclusion (not equation) and fuzzy attractor. We attempt to present this theoretical framework to give an interpretation of cyclogenesis in atmospheric cybernetics as a case study. We also have presented a Dempster‐Shafer's evidence theoretic analysis and a classical probability theoretic analysis (from general system theoretic outlook) of carcinogenesis as other interesting case studies of bio‐cybernetics.

This paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.

After a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.

The utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.

This study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.

Looking at the Jewish population in Israel in 1995 this paper compares three multidimensional approaches to poverty measurement and checks to what extent they identify the same households as poor. Logit regressions are then estimated to understand which variables have an impact on poverty. Finally, the so-called Shapley decomposition is introduced to estimate the exact marginal impact of these determinants of poverty.

Of particular interest to this study was the combined effect of the generation to which the head of the household belongs and his/her place of birth. It turns out that the ethnic origin has a significant impact on multidimensional poverty in Israel insofar as being a head of household born in Asia or Africa, whatever the generation to which one belongs, increases, ceteris paribus, the probability of being poor.

This paper aims to apply a multidimensional approach to assessing the financial well-being of European countries.

Financial well-being is a very complex phenomenon to measure because it is composed of different dimensions. Therefore, this paper uses a multidimensional and fuzzy methodology to assess financial well-being in Europe. The financial well-being fuzzy indicator was calculated using European Quality of Life Survey data.

Financial well-being is heterogeneous across European countries. This evidence is confirmed both at the level of overall financial well-being and at the level of sub-indices. The degree of financial well-being is not directly related to wealth as traditionally measured (i.e. GDP), but shows some correspondence with socio-economic characteristics of the population and with governance and cultural elements of a country.

Understanding financial well-being could help financial institutions to transition from a one-size-fits-all approach to a more tailored approach when they provide financial services and could help policy makers to consider financial well-being when they decide how and where to allocate public spending.

To the best of authors’ knowledge, this study is the first to employ a fuzzy methodology for the analysis of financial well-being in Europe.