Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Poverty is the worst form of human deprivation. The literature on poverty has gone through advances, since the traditional way of measuring poverty through monetary income does not capture all forms of deprivation suffered by people. The advancement of the concept of poverty is to include other important dimensions of people's lives; from the one-dimensional approach to the multidimensional approach. This dissertation, based on Capability Approach of Sen (1981, 1988, 2000), aims to measure multidimensional poverty for Brazilian cities in 2010. Using data from the Census (IBGE), which involved the choice of 16 indicators, five types of indices were constructed: the first four for each of the dimensions (housing conditions, income, access to knowledge and education and health and sanitary conditions), and the last one for the aggregated IFP, through Fuzzy Set Theory that allowed to approach poverty as a complex phenomenon and to generate the relative index of poverty. The results indicate that there is greater poverty in terms of health and sanitary conditions. However, the dimensions of access to knowledge and education and housing conditions also had weight in the multidimensional poverty index. The income dimension is one of less deprivation among cities, which emphasizes the importance of addressing and measuring poverty multidimensionally. The indicators with the greatest deprivations and that deserve greater attention on the part of the public managers are microcomputer with access to internet, washing machine, schooling and the type of sanitary sewage. The characteristics of poverty in the dimensions studied were similar and showed that the regions and states have similar poverty profiles, indicating that the North and Northeast of the country are the regions with the highest number of cities in the situation of very high and high poverty. / A pobreza é a pior forma de privação humana. A literatura sobre a pobreza passou por avanços, pois a forma tradicional de medir a pobreza via renda monetária, não captura todas as formas de privação sofridas pelas pessoas. O avanço do conceito de pobreza é no sentido de incluir outras dimensões importantes sobre a vida das pessoas; passando da abordagem unidimensional para a abordagem multidimensional. Esta dissertação, com base na Abordagem das Capacitações de Sen (1981, 1988, 2000) tem por objetivo medir a pobreza multidimensional para os municípios brasileiros no ano de 2010. Utilizando-se dados do Censo Demográfico (IBGE), que envolveu a escolha de 16 indicadores, foram construídos cinco tipos de índices: os quatro primeiros para cada uma das dimensões (condições de moradia, renda, acesso ao conhecimento e educação e saúde e condições sanitárias), e o último para o IFP agregado, através da Teoria dos Conjuntos Fuzzy que permitiu abordar a pobreza como um fenômeno complexo e gerar o índice relativo de pobreza. Os resultados encontrados indicam que existe maior pobreza na dimensão saúde e condições sanitárias. Entretanto, as dimensões acesso ao conhecimento e educação e condições de moradia também tiveram peso no índice de pobreza multidimensional. A dimensão renda é a de menor privação entre os municípios, o que enfatiza a importância de abordar e mensurar a pobreza multidimensionalmente. Os indicadores com as maiores privações e que merecem maior atenção por parte dos gestores públicos são microcomputador com acesso a internet, máquina de lavar, escolaridade e o tipo de esgotamento sanitário. As características da pobreza nas dimensões estudadas foram parecidas e mostraram que as regiões e estados possuem perfis de pobreza semelhantes, ao indicar que o Norte e Nordeste do país são as regiões que possuem o maior número de municípios na situação de pobreza muito alta e alta.

Made available in DSpace on 2015-03-03T15:47:46Z (GMT). No. of bitstreams: 1 AndersonPC_DISSERT.pdf: 1402040 bytes, checksum: 960b15bc1392a94fb7ba8ba980e3a0b4 (MD5) Previous issue date: 2012-12-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Atualmente, h? diferentes defini??es de implica??es fuzzy aceitas na literatura. Do ponto de vista te?rico, esta falta de consenso demonstra que h? discord?ncias sobre o real significado de "implica??o l?gica" nos contextos Booleano e fuzzy. Do ponto de vista pr?tico, isso gera d?vidas a respeito de quais "operadores de implica??o" os engenheiros de software devem considerar para implementar um Sistema Baseado em Regras Fuzzy (SBRF). Uma escolha ruim destes operadores pode implicar em SBRF's com menor acur?cia e menos apropriados aos seus dom?nios de aplica??o. Uma forma de contornar esta situa??o e conhecer melhor os conectivos l?gicos fuzzy. Para isso se faz necess?rio saber quais propriedades tais conectivos podem satisfazer. Portanto, a m de corroborar com o significado de implica??o fuzzy e corroborar com a implementa??o de SBRF's mais apropriados, v?rias leis Booleanas t?m sido generalizadas e estudadas como equa??es ou inequa??es nas l?gicas fuzzy. Tais generaliza??es s?o chamadas de leis Boolean-like e elas n?o s?o comumente v?lidas em qualquer sem?ntica fuzzy. Neste cen?rio, esta disserta??o apresenta uma investiga??o sobre as condi??es suficientes e necess?rias nas quais tr?s leis Booleanlike ?like ? y ? I(x, y), I(x, I(y, x)) = 1 e I(x, I(y, z)) = I(I(x, y), I(x, z)) ?? se mant?m v?lidas no contexto fuzzy, considerando seis classes de implica??es fuzzy e implica??es geradas por automorfismos. Al?m disso, ainda no intuito de implementar SBRF's mais apropriados, propomos uma extens?o para os mesmos / There are more than one acceptable fuzzy implication definitions in the current literature dealing with this subject. From a theoretical point of view, this fact demonstrates a lack of consensus regarding logical implication meanings in Boolean and fuzzy contexts. From a practical point of view, this raises questions about the implication operators" that software engineers must consider to implement a Fuzzy Rule Based System (FRBS). A poor choice of these operators generates less appropriate FRBSs with respect to1 their application domain. In order to have a better understanding of logical connectives, it is necessary to know the properties that they can satisfy. Therefore, aiming to corroborate with fuzzy implication meaning and contribute to implementing more appropriate FRBSs to their domain, several Boolean laws have been generalized and studied as equations or inequations in fuzzy logics. Those generalizations are called Booleanlike laws and a lot of them do not remain valid in any fuzzy semantics. Within this context, this dissertation presents the investigation of sucient and necessary conditions under which three Boolean-like laws | y I(x; y), I(x; I(y; x)) = 1 and I(x; I(y; z)) = I(I(x; y); I(x; z)) | hold for six known classes of fuzzy implications and for implications generated by automorphisms. Moreover, an extension to FRBSs is proposed

Orientador: Akebo Yamakami / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T21:49:58Z (GMT). No. of bitstreams: 1 Kawamura_Jorge_M.pdf: 1810800 bytes, checksum: 1fe99baac150732c9cf14dacf6188caf (MD5) Previous issue date: 2007 / Resumo: Este trabalho tem como objetivo a utilização de métodos de inteligência artificial para diagnosticar câncer do esôfago. Este estudo concentrou-se na utilização dos conceitos de sistemas fuzzy. O emprego de sistemas fuzzy ou sistemas difusos para a área de saúde foi motivado pela deficiência de sistemas inteligentes nesta área e pela simplicidade na sua utilização. O sistema fuzzy apresenta características como a existência de uma região duvidosa (ou região vaga) na análise das informações e seu método de interpretação é mais próximo à linguagem do ser humano. Os modelos de inferência utilizados foram o método de Mamdani e o método Sugeno. São analisadas as vantagens e desvantagens de cada método. A partir das características do câncer do esôfago e dos conceitos de sistemas fuzzy foi desenvolvido um sistema para diagnóstico de câncer do esôfago / Abstract: The aim of this work is to use the artificial intelligent methods to diagnose esophagus cancer. The artificial intelligence theme has many areas, so this study concentrated in fuzzy system concepts. The lack of intelligent system in health's area motivated this study and fuzzy theory was chosen by its simplicity. This type of system has characteristics like existence of a doubt region in the information analysis and its interpretation's methods is closer to human language. The inference models used are Mamdani and Sugeno models. The advantages and disadvantages are checked too. From esophagus cancer characteristics and fuzzy system concepts, a system to diagnose esophagus cancer was built / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Orientadores: Akebo Yamakami, Jose Luis Verdegay Galdeano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-14T06:44:19Z (GMT). No. of bitstreams: 1 Silva_RicardoCoelho_D.pdf: 1144878 bytes, checksum: 38379443fb6892fd6eda74c55c3b99dc (MD5) Previous issue date: 2009 / Resumo: O objetivo deste trabalho é buscar, estudar e estabelecer as condições de otimali-dade para resolver problemas de programação multi-objetivo irrestritos e restritos em um ambiente impreciso. Essas imprecisões estão presentes nos problemas da vida real e existem muitas formas de tratá-las, mas nesse trabalho será usado a teoria de conjuntos nebulosos. Utilizando como base a otimização nebulosa, foram desenvolvidas duas abordagens para resolver problemas multi-objetivo nebulosos. A primeira abordagem transforma um problema nebuloso em um problema clássico paramétrico com um número maior de funções objetivo, a qual é chamada de paramétrica. A segunda abordagem, chamada de possibilística, usa a teoria de possibilidade como um índice de comparação entre números nebulosos com a finalidade de garantir condições de otimalidade em um ambiente nebuloso. Alguns exemplos numéricos são resolvidos usando um algoritmo genético chamado NSGA-II elitista, com algumas modificações para a comparação de números nebulosos, e depois feita uma análise dos resultados encontrados por ambos os enfoques. / Abstract: The main goal of this work is to search, study and present the optimality conditions to solve the unconstraint and constraint multiobjetive programming problems in imprecise environment. These imprécisions can be found in the real-world optimization problems and there are utmost ways for dealing with them, but in this work will be used the theory of fuzzy sets. Using as a basis the fuzzy optimization, two approaches were developed to solve fuzzy multiobjective problems. The first approach transforms a fuzzy problem into a parametric classic multiobjective programming problem with many more objective functions, which is called parametric approach. The second one, called possibilistic, uses the possibility theory as a comparison index between two fuzzy numbers in order to ensure optimality conditions in a fuzzy environment. Some numerical examples are solved by using a genetic algorithm called elitist NSGA-II with some modifications to compare fuzzy numbers, and then the results obtained with both approaches are analysed. / Doutorado / Automação / Doutor em Engenharia Elétrica

Orientador: Akebo Yamakami / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T23:52:00Z (GMT). No. of bitstreams: 1 Carvalho_MarciaBragade_M.pdf: 2154346 bytes, checksum: caafd847980349294a73d2ad38d6414c (MD5) Previous issue date: 2007 / Resumo: Dentre as várias técnicas heurísticas e exatas existentes para a resolução de problemas combinatórios, os algoritmos populacionais de otimização por colônia de formigas e genéticos têm se destacado devido à sua boa performance. Em especial os algoritmos de colônia de formigas são considerados atualmente como uma das técnicas mais bem sucedidas para a resolução de vários problemas combinatórios, dentre eles o problema do caixeiro viajante. Neste trabalho é apresentado um algoritmo híbrido que trabalha com as meta-heurísticas de sistema de colônia de formigas e genético conjuntamente aplicados no problema do caixeiro viajante simétrico. Além disso, apresentamos uma proposta para o algoritmo de formigas quando temos incertezas associadas aos parâmetros do problema. Os resultados obtidos com as metodologias propostas apresentam resultados satisfatórios para todas as instâncias utilizadas / Abstract: Amongst the several existing heuristical and accurate techniques for the resolution of combinatorial problems, the population algorithms ant colony optimization and genetic have been detached due to their good performance. In special the ant colony algorithms are considered currently as one of the techniques most succeeded for the resolution of some combinatorial problems, amongst them the travelling salesman problem. In this work is presented a hybrid algorithm which works with the ant colony system and genetic metaheuristics jointly applied in the symmetric travelling salesman problem. Moreover, we presented a proposal for the ant algorithm when we have uncertainties associated to problem parameters. The results gotten with the methodology proposals present resulted satisfactory for all the used instances / Mestrado / Automação / Mestre em Engenharia Elétrica

Attractiveness matters in business markets, because firms do not dedicate resources equally to all partners. Instead they invest more resources in partners with higher relational attractiveness. Firms need to become attractive in order to gain access to more resources or to be able to work with more skilled or reputable partners. This dissertation studies the construct of relational attractiveness of the customer (RAC), defined as the attractiveness of a business relationship with a particular customer in the eyes of the supplier. The research also investigates corporate online references (COR), because gaining powerful referrals is one of the driving forces behind creating attractiveness in business markets. The study is a three-stage research project drawing on an empirical investigation comprising two focus groups, 79 interviews, a survey of 107 suppliers and online referral data from 1002 companies. These studies investigate the conditions and configurations leading to high or low relational attractiveness, and the motivational conditions and structure of a specific corporate online referral network. Bearing in mind that attractiveness exists in the eyes of the beholder, Study I resolves the previously unclarified problem of how attractiveness can be achieved in different ways. Social Exchange Theory helps to identify conditions of RAC: Trust, Dependency, Financial, Non-Financial Rewards and Costs. In Study II conditions of Trust and Dependency are further developed into Relational Fit and the Comparison Level of Alternatives that address the mutuality and network perspectives of relationship development. The time perspective is introduced to the configurational analysis of RAC through the Maturity condition. As it is revealed in Study I and II, Nonfinancial Rewards are important in creating attractiveness and one of their essential forms is referrals that are addressed in more detail in Study III. This PhD research takes a configurational approach to attractiveness and explores different causal recipes in order to reach the same outcome. In order to investigate the relational complexity of attractiveness, fuzzy set Qualitative Comparative Analysis (fsQCA) is applied throughout the three studies combined with some other methods, such as content analysis and Social Network Analysis (SNA). QCA is a data analytic strategy that combines within-case analysis and formalised cross-case studies in order to identify multiple configurations leading to the same outcome. Hence, QCA deals more efficiently with the equifinality of complex business problems compared with traditional data analysis methods. Equifinality means that there are various ways in the causal system of achieving the desired outcome. QCA is sufficient in handling methodological challenges such as multi-causality (an outcome of interest rarely has a single cause), interrelatedness (causes are usually not independent of one another) and asymmetry (a specific cause may have different effects on the outcome depending on the context). By challenging existing knowledge, the results show that there is no one best way to achieve relational attractiveness. It is achievable even if Trust and Financial Rewards are not present. Very high RAC was typically achieved in less mature relationships. During the initiation of referral relationships in the case of COR, the expected increase in the initiators` attractiveness in the eyes of potential future partners also plays a vital role. The generalizability of the findings has some limitations, especially regarding the qualitative study where the results are appropriate to falsify some theories (for example, the primary importance of Financial Rewards) but their impact is more related to theoretical development than to statistical generalizability.

Medication prescriptions constitute an important type of clinical intervention. Medication adherence is the degree to which a patient consumes their medication as agreed upon with a prescriber. Despite many years of research, medication non-adherence continues to be a problem of note, partially due to its multi-faceted in nature. Numerous interventions have attempted to improve adherence but none have emerged as definitive. A significant sub-problem is the lack of consensus regarding definitions and measurement of adherence. Several recent reviews indicate that discrepancies in definitions, measurement techniques, and study methodologies make it impossible to draw strong conclusions via meta-analyses of the literature. Technological interventions aimed at improving adherence have been the subject of ongoing research. Due to the increasing prevalence of the Internet of Things, technology can be used to provide a continuous stream of data regarding a patient's behaviour. To date, several researchers have proposed interventions that leverage data from the Internet of Things, however none have established an acceptable means of analyzing and acting upon this wealth of data. This thesis introduces a computational definition for adherence that can be used to support continued development of technological adherence interventions. A central part of the proposed definition is a formal language for specifying prescriptions that uses fuzzy set theory to accommodate imprecise concepts commonly found in natural language medication prescriptions. A prescription specified in this language can be transformed into an evaluation function which can be used to score the adherence of a given medication taking behaviour. Additionally, the evaluator function is applied to the problem of scheduling medication administrations. A compiler for the proposed language was implemented and had its breadth of expression and clinical accuracy evaluated. The results indicate that the proposed computational definition of adherence is acceptable as a proof of concept and merits further works. / Graduate

This Master's thesis deals with design of models for rating of M. K. R. plus suppliers of steel wires using fuzzy logic. The decision-making models are created in the MS Excel and in the MATLAB software. This thesis includes acknowledgement with theory, which will be used in the practical part for design of individual models. Current and potential suppliers of the company will be evaluated using the created models and their benefit to company will be assessed.

A huge amount of data is generated daily by digital technologies such as social media, web logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the archiving and storage of larger and larger datasets, many of which are multi-modal, or contain different types of data which contribute to the problem that is now known as “Big Data”. In the area of Big Data, volume, variety and velocity problems remain difficult to solve. The work presented in this thesis focuses on the variety aspect of Big Data. For example, data can come in various and mixed formats for the same feature(attribute) or different features and can be identified mainly by one of the following data types: real-valued, crisp and linguistic values. The increasing variety and ambiguity of such data are particularly challenging to process and to build accurate machine learning models. Therefore, data heterogeneity requires new methods of analysis and modelling techniques to enable useful information extraction and the modelling of achievable tasks. In this thesis, new approaches are proposed for handling heterogeneous Big Data. these include two techniques for filtering heterogeneous data objects are proposed. The two techniques called Two-Dimensional Similarity Space(2DSS) for data described by numeric and categorical features, and Three-Dimensional Similarity Space(3DSS) for real-valued, crisp and linguistic data are proposed for filtering such data. Both filtering techniques are used in this research to reduce the noise from the initial dataset and make the dataset more homogeneous. Furthermore, a new similarity measure based on intuitionistic fuzzy set theory is proposed. The proposed measure is used to handle the heterogeneity and ambiguity within crisp and linguistic data. In addition, new combine similarity models are proposed which allow for a comparison between the heterogeneous data objects represented by a combination of crisp and linguistic values. Diverse examples are used to illustrate and discuss the efficiency of the proposed similarity models. The thesis also presents modification of the k-Nearest Neighbour classifier, called k-Nearest Neighbour Weighted Average (k-NNWA), to classify the heterogeneous dataset described by real-valued, crisp and linguistic data. Finally, the thesis also introduces a novel classification model, called FCCM (Filter Combined Classification Model), for heterogeneous data classification. The proposed model combines the advantages of the 3DSS and k-NNWA classifier and outperforms the latter algorithm. All the proposed models and techniques have been applied to weather datasets and evaluated using accuracy, Fscore and ROC area measures. The experiments revealed that the proposed filtering techniques are an efficient approach for removing noise from heterogeneous data and improving the performance of classification models. Moreover, the experiments showed that the proposed similarity measure for intuitionistic fuzzy data is capable of handling the fuzziness of heterogeneous data and the intuitionistic fuzzy set theory offers some promise in solving some Big Data problems by handling the uncertainties, and the heterogeneity of the data.

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