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Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Due to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well.

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.

Orientador: Sandra Regina Monteiro Masalskiene Roveda / Resumo: A nova perspectiva rumo à conservação do meio ambiente como fato categórico de subsistência planetária tem colocado a sustentabilidade em primeiro plano como o grande desafio da universidade, responsável e equipada para a formação daqueles que terão o poder decisório sobre as questões relacionadas a um futuro viável. Este estudo se refere à sustentabilidade na universidade por meio do que é percebido pelos diversos atores que nela transitam. Teve como objetivo desvendar, em algumas instituições do setor público e do setor privado, quais as impressões que professores/coordenadores, alunos e funcionários possuem sobre as atuações da instituição em seu campus, os projetos e pesquisas voltados à sustentabilidade elaborados pela equipe docente e os aprendizados efetivos na formação dos novos profissionais, que deverão atuar nas diversas áreas de atividades em nossa sociedade. Para poder medir essas impressões, foram aplicados questionários e desenvolvido um modelo fuzzy com um índice associado, que apresenta o nível de sustentabilidade de uma Instituição de Ensino Superior – IES. Isso nos leva a concluir que os sistemas de inferência fuzzy são capazes de fazer uma avaliação do que pode ser percebido pela comunidade universitária sobre a sustentabilidade de sua instituição. / Doutor

Accurate and timely diagnosis of faults is essential for the reliability and security of power grid operation and maintenance. The emergence of big data has enabled the incorporation of a vast amount of information in order to create custom fault datasets and improve the diagnostic capabilities of existing frameworks. Intelligent systems have been successful in incorporating big data to improve diagnostic performance using computational intelligence and machine learning based on fault datasets. Among these systems are fuzzy inference systems with the ability to tackle the ambiguities and uncertainties of a variety of input data such as climate data. This makes these systems a good choice for extracting knowledge from energy big data. In this thesis, qualitative climate information is used to construct a fault dataset. A fuzzy inference system is designed whose parameters are optimized using a single layer artificial neural network. This fault diagnosis framework maps the relationship between fault variables in the fault dataset and fault types in real-time to improve the accuracy and cost efficiency of the framework. / Graduate

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A gestão de fornecedores é uma atividade crítica para a gestão do desempenho de empresas inseridas em redes produtivas. O modelo de segmentação ou portfólio de compras é definido como um processo de separação de fornecedores em grupos em função de diferentes necessidades e características, requerendo diferentes tipos de relacionamentos. Esta técnica tem recebido uma atenção cada vez maior no meio acadêmico e empresarial devido a sua estrutura simples e eficaz na organização de diferentes estratégias de suprimentos. Diferentes propostas de modelos têm sido apresentadas, mas todas com grande presença de variáveis qualitativas. Embora a teoria da lógica fuzzy tenha se demosnstrada adequada no tratamento deste tipo de variável, que tem forte presença de incerteza e imprecisão na coleta e tratamento dos dados, não há na literatura uma investigação de como um sistema de inferência fuzzy poderia ser utilizado em um modelo de portfólio de compras. Desta forma, este trabalho tem por objetivo a proposição de um sistema de inferência fuzzy para auxiliar no processo de tomada de decisão na avaliação e desenvolvimento de fornecedores baseado em modelos de portfolio. Busca-se contribuir com o conhecimento das pesquisas cujo tema envolve a segmentação, avaliação e desenvolvimento de fornecedores. Os procedimentos de pesquisa utilizados no trabalho podem ser agrupados em três partes: pesquisa bibliográfica, desenvolvimento do modelo quantitativo axiomático descritivo e ilustração por meio de duas aplicações práticas. A construção do modelo proposto evidenciou a grande flexibilidade do sistema de inferência, que possibilita modificações nas variáveis e nas bases de regras de acordo com os objetivos estratégicos de suprimentos. As duas aplicações práticas mostraram que os resultados das avaliações e as diretrizes para melhoria foram coerentes com as percepções dos especialistas em gestão de fornecedores das duas empresas. Os sistemas de inferência fuzzy demostraram ser uma alternativa adequada no tratamento das variáveis, em grande parte qualitativas, que compõem as dimensões das matrizes de item comprado e de relacionamento com os fornecedores. / Purchasing and buyer-supplier relationship management have become very critical activities to the performance management of organizations and supply chains. The segmentation or purchase portfolio model is defined as a process of suppliers separation in groups as a function of different needs and characteristics, requiring different kinds of relationships to create value in their exchanges. This approach has received increasing attention in the academic and business due to its simple structure and effective in organizing different suppliers approaches. Different proposals have been presented and all have great use of qualitative variables. Although the theory of fuzzy logic has been developed to the treatment of this type of variable, which has a strong presence of uncertainty and imprecision in the data collection and processing, it couldn´t be found any research exploring how a fuzzy inference system could be used in purchase portfolio models. Thus, this thesis aims to propose a fuzzy inference system to aid the decision making process in the supplier assessment and development based on portfolio models. This research intends to contribute to the segmentation, evaluation and supplier development knowledge. The research procedures used in this study can be grouped into three parts: literature review, development of axiomatic descriptive quantitative model and illustration through two practical applications. The construction of the proposed model showed the great inference system flexibility allows changes in the variables and the rule bases in accordance with the supply strategic objectives. The evaluations results and improvement guidelines in two pratical applicatins were consistent with the supply manager perceptions from both companies. The fuzzy inference systems have shown to be a suitable alternative in the treatment of the variables that make up the dimensions of the purchased itens and supplier relationships matrices.

A gestão de fornecedores é uma atividade crítica para a gestão do desempenho de empresas inseridas em redes produtivas. O modelo de segmentação ou portfólio de compras é definido como um processo de separação de fornecedores em grupos em função de diferentes necessidades e características, requerendo diferentes tipos de relacionamentos. Esta técnica tem recebido uma atenção cada vez maior no meio acadêmico e empresarial devido a sua estrutura simples e eficaz na organização de diferentes estratégias de suprimentos. Diferentes propostas de modelos têm sido apresentadas, mas todas com grande presença de variáveis qualitativas. Embora a teoria da lógica fuzzy tenha se demosnstrada adequada no tratamento deste tipo de variável, que tem forte presença de incerteza e imprecisão na coleta e tratamento dos dados, não há na literatura uma investigação de como um sistema de inferência fuzzy poderia ser utilizado em um modelo de portfólio de compras. Desta forma, este trabalho tem por objetivo a proposição de um sistema de inferência fuzzy para auxiliar no processo de tomada de decisão na avaliação e desenvolvimento de fornecedores baseado em modelos de portfolio. Busca-se contribuir com o conhecimento das pesquisas cujo tema envolve a segmentação, avaliação e desenvolvimento de fornecedores. Os procedimentos de pesquisa utilizados no trabalho podem ser agrupados em três partes: pesquisa bibliográfica, desenvolvimento do modelo quantitativo axiomático descritivo e ilustração por meio de duas aplicações práticas. A construção do modelo proposto evidenciou a grande flexibilidade do sistema de inferência, que possibilita modificações nas variáveis e nas bases de regras de acordo com os objetivos estratégicos de suprimentos. As duas aplicações práticas mostraram que os resultados das avaliações e as diretrizes para melhoria foram coerentes com as percepções dos especialistas em gestão de fornecedores das duas empresas. Os sistemas de inferência fuzzy demostraram ser uma alternativa adequada no tratamento das variáveis, em grande parte qualitativas, que compõem as dimensões das matrizes de item comprado e de relacionamento com os fornecedores. / Purchasing and buyer-supplier relationship management have become very critical activities to the performance management of organizations and supply chains. The segmentation or purchase portfolio model is defined as a process of suppliers separation in groups as a function of different needs and characteristics, requiring different kinds of relationships to create value in their exchanges. This approach has received increasing attention in the academic and business due to its simple structure and effective in organizing different suppliers approaches. Different proposals have been presented and all have great use of qualitative variables. Although the theory of fuzzy logic has been developed to the treatment of this type of variable, which has a strong presence of uncertainty and imprecision in the data collection and processing, it couldn´t be found any research exploring how a fuzzy inference system could be used in purchase portfolio models. Thus, this thesis aims to propose a fuzzy inference system to aid the decision making process in the supplier assessment and development based on portfolio models. This research intends to contribute to the segmentation, evaluation and supplier development knowledge. The research procedures used in this study can be grouped into three parts: literature review, development of axiomatic descriptive quantitative model and illustration through two practical applications. The construction of the proposed model showed the great inference system flexibility allows changes in the variables and the rule bases in accordance with the supply strategic objectives. The evaluations results and improvement guidelines in two pratical applicatins were consistent with the supply manager perceptions from both companies. The fuzzy inference systems have shown to be a suitable alternative in the treatment of the variables that make up the dimensions of the purchased itens and supplier relationships matrices.