Due to increase global competitive pressure, shortened product life cycles and ease of imitation, firms must continue to innovate to maintain their competitiveness. Technological innovation has become the primary basis of productivity improvements, sales volume growth, and competitiveness of firms, especially for the high-tech companies. Thus, identification and evaluation of technologies from a variety of perspectives now play important roles in the effective technological sources management. Traditionally, technological innovation studies stressed single model or variable having effects on firm productivity and performance. However, the challenge for business environment is continually changing; single model or variable is not good enough to explain the overall impact of technological innovation. The most difficult aspect of technological innovation performance measurement is the identification of appropriate metrics and approaches that provide information concerning these facets. In this study, the researcher tried to develop a technological innovation performance measurement model and determine tangible and intangible factors from the systematical perspective. That is, technological innovation in its nature is multi-dimensional and multi-criteria. Furthermore, technology innovation performance measurement can be conceptualized as multi-criteria a complex problem which involves the simultaneous consideration of multiple quantitative and qualitative requirements. In this empirical study, the researcher firstly utilizes the Delphi technique to build a hierarchical network structure model for evaluating the technological innovation performance measurement of high tech firms. Secondly, analytic network process (ANP) was applied to determine the importance weights of each dimension and criterion while exists interdependencies among criteria within the same dimension. Thirdly, Non-additive fuzzy integral method was then applied for information fusion and calculates the synthetic performance on a hierarchical network model structure for which criteria are interdependent and interactive. This study applied fuzzy measure and non-additive fuzzy integral method to derive the synthetic performance values of each dimension and firm. Through the technological innovation performance evaluation model can provide firms with an overview of their strengths and weaknesses with regards to technological innovation management. Furthermore, R&D managers and senior managers can apply this model to evaluate and determine the technological innovation capabilities of a firm to improve its technological innovation performance. Finally, this model may provide the useful information for managers and to reduce the overall technological innovation uncertainty.

In the global construction market, most construction companies are willing to undertake international projects in order to maximise their profitability by taking advantage of attractive emerging markets and minimise dependence on unfavorable domestic market conditions. In order to be awarded a contract in highly competitive global construction market, companies should excel in choosing the most attractive markets and prepare winning bids for the selected construction projects in those markets. While preparing bids, the major concern of companies is to offer an optimum price that will enable them to earn enough profits and win the contract at the same time, where profit making ability is strongly correlated with proper estimation of a risk premium that is added onto the estimated cost of the project. Due to the nature of construction works, there are lots of uncertainties associated with the project, market and country conditions. Therefore, how the profitability of the project changes with occurrence of various risk events, in other words, the sensitivity of project costs to risk events, should be estimated by bidders realistically. In this study, fuzzy set theory is used to estimate cost overrun risk in international projects at the bidding stage. The objective is to propose a methodology which can be used by bidders to quantify cost overrun risk so that a realistic risk premium may be determined. A fuzzy risk rating approach is proposed to quantify cost overrun risk rating, which takes into account of risks characterised in international construction projects. For this purpose, risk sources have been identified and a risk model is put forward by using influence diagramming method. Based on this risk model, a fuzzy risk rating algorithm has been defined and software has been developed to conduct fuzzy risk rating calculations easily. After a decision-maker inserts the necessary inputs related with project and country risk factors, the output of the software is a rating that takes into account of all factors that may affect cost overrun risk in international construction projects. The reliability of the algorithm and developed software have been tested by an application on a real construction project. The proposed methodology and decision support tool have been proved to be reliable for the estimation of cost overrun risk while giving bidding decisions in international markets.

[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinformatics, and so on. Many approaches have been proposed to address the issue of automatic generation of membership functions and rules with the corresponding subsequent adjustment of them towards more satisfactory system performance. Because one of the most important factors for building high quality of FIS is the generation of the knowledge base of it, which consists of membership functions, fuzzy rules, fuzzy logic operators and other components for fuzzy calculations. The design of FIS comes from either the experience of human experts in the corresponding field of research or input and output data observations collected from operations of systems. Therefore, it is crucial to generate high quality FIS from a highly reliable design scheme to model the desired system process best. Furthermore, due to a lack of a learning property of fuzzy systems themselves most of the suggested schemes incorporate hybridization techniques towards better performance within a fuzzy system framework. ... This systematic enhancement is required to update the FIS in order to produce flexible and robust fuzzy systems for unexpected unknown inputs from real-world environments. This thesis proposes a general framework of Adaptive T-S (Takagi-Sugeno) type Rough-Fuzzy Inference Systems (ARFIS) for a variety of practical applications in order to resolve the problems mentioned above in the context of a Rough-Fuzzy hybridization scheme. Rough set theory is employed to effectively reduce the number of attributes that pertain to input variables and obtain a minimal set of decision rules based on input and output data sets. The generated rules are examined by checking their validity to use them as T-S type fuzzy rules. Using its excellent advantages in modeling non-linear systems, the T-S type fuzzy model is chosen to perform the fuzzy inference process. A T-S type fuzzy inference system is constructed by an automatic generation of membership functions and rules by the Fuzzy C-Means (FCM) clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least-squares method and a conjugate gradient descent algorithm towards better performance within a fuzzy system framework. To show the viability of the proposed framework of ARFIS, the performance of ARFIS is compared with other existing approaches in a variety of practical applications; pattern classification, face recognition, and mobile robot navigation. The results are very satisfactory and competitive, and suggest the ARFIS is a suitable new framework for fuzzy inference systems by showing a better system performance with less number of attributes and rules in each application.

Computing with Words based Question Answering (CWQA) system provides a foundation to develop futuristic search engines where more of reasoning and less of pattern matching and statistical methods are used for information retrieval. In order to perform successful reasoning, these systems should analyze the semantic of the query and the related information in the Knowledge Base. The concept of Computing with Words (CW) which is a kind of perception based reasoning where manipulation of perceptions using fuzzy set theory and fuzzy logic play a key role in recognition, decision and execution processes can be utilized for this purpose. Two concepts that were introduced by Computing with Words are the Generalized Constraint Language (GCL) and the Generalized Theory of Uncertainty (GTU) . In GCL propositions, i.e. perceptions in natural language, are denoted using generalized constraints. The Generalized Theory of Uncertainty (GTU) uses GCL to express proposition drawn from natural language as a generalized constraint. The GCL plays a fundamental role in GTU by serving as a precisiation language for propositions, commands and questions in natural language. In GTU, deduction rules are used to propagate generalized constraints to accomplish reasoning under uncertainty. In the previous work a CW-based QA-system methodology was introduced which uses a knowledge tree data structure, called as a Constraint Propagation Tree (CPT) that utilizes the concepts briefed above. The realization of Constraint Propagation Tree, the first phase, and partial implementation of constraint propagation and node combination, the second phase, is the main goal of this work.

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.

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

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

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.

As we all know the classical set theory has a deep-rooted influence in the traditional mathematics. According to the two-valued logic, an element can belong to a set or cannot. In the former case, the element’s membership degree will be assigned to one, whereas in the latter case it takes the zero value. With other words, a feeling of imprecision or fuzziness in the two-valued logic does not exist. With the rapid development of science and technology, more and more scientists have gradually come to realize the vital importance of the multi-valued logic. Thus, in 1965, Professor Lotfi A. Zadeh from Berkeley University put forward the concept of a fuzzy set. In less than 60 years, people became more and more familiar with fuzzy set theory. The theory of fuzzy sets has been turned to be a favor applied to many fields. The study aims to apply some classical and extensional methods of fuzzy set theory in life expectancy and treatment prognoses for cancer patients. The research is based on real-life problems encountered in clinical works by physicians. From the introductory items of the fuzzy set theory to the medical applications, a collection of detailed analysis of fuzzy set theory and its extensions are presented in the thesis. Concretely speaking, the Mamdani fuzzy control systems and the Sugeno controller have been applied to predict the survival length of gastric cancer patients. In order to keep the gastric cancer patients, already examined, away from the unnecessary suffering from surgical operation, the fuzzy c-means clustering analysis has been adopted to investigate the possibilities for operation contra to nonoperation. Furthermore, the approach of point set approximation has been adopted to estimate the operation possibilities against to nonoperation for an arbitrary gastric cancer patient. In addition, in the domain of multi-expert decision-making, the probabilistic model, the model of 2-tuple linguistic representations and the hesitant fuzzy linguistic term sets (HFLTS) have been utilized to select the most consensual treatment scheme(s) for two separate prostate cancer patients. The obtained results have supplied the physicians with reliable and helpful information. Therefore, the research work can be seen as the mathematical complements to the physicians’ queries.

The increased demand for biofuels is expected to put additional strain on the available agricultural resources while at the same time causing environmental degradation. Hence, new energy policies need to be formulated and implemented in order to meet global energy needs while reducing the impact of biofuels farming and production. This research focuses on proving a decision support system which can aid the formulation of policies for the sustainable biofuel production. The system seeks to address policy formulation that requires reconciliation of the qualitative aspects of decision making (such as stakeholder’s viewpoints) with quantitative data, which often may be imprecise. To allow this, based on: Fuzzy logic and Multi Criteria Decision Making (MCDM) in the form of Analytical Hierarchy Process (AHP). Using these concepts, three software functionalities, “Options vs. Fuzzy Criteria Matrix”, “Analytical Hierarchy Process” and “Fuzzy AHP” were developed. These were added within the framework of pre-existing base software, Compendium (developed by the Open University, UK). A number of case study based models have been investigated using the software. These models made use of data from the Philippines and India in order to pinpoint suitable land and crop options for these countries. The models based on AHP and Fuzzy AHP were very successful in identifying suitable crop options for India by capturing both the stakeholder viewpoints and quantitative data. The software functionalities are very effective in scenario planning and selection of policies that would be beneficial in achieving a desired future scenario. The models further revealed that the newly developed software correctly identified many of the important issues in a consistent manner.