O tema deste estudo abrange duas áreas do conhecimento: a Medicina e a Ciência da Computação. Consiste na aplicação do processo de descoberta de conhecimento em base de Dados (KDD - Knowledge Discovery in Databases), a um banco de dados real na área médica denominado Registro Desire. O Registro Desire é o registro mais longevo da cardiologia intervencionista mundial, unicêntrico e acompanha por mais de 13 anos 5.614 pacientes revascularizados unicamente pelo implante de stents farmacológicos. O objetivo é criar por meio desta técnica um modelo que seja descritivo e classifique os pacientes quanto ao risco de ocorrência de eventos cardíacos adversos maiores e indesejáveis, e avaliar objetivamente seu desempenho. Posteriormente, apresentar as regras extraídas deste modelo aos usuários para avaliar o grau de novidade e de concordância do seu conteúdo com o conhecimento dos especialistas. Foram criados modelos simbólicos de classificação pelas técnicas da árvore de decisão e regras de classificação utilizando para a etapa de mineração de dados os algoritmos C4.5, Ripper e CN2, em que o atributo-classe foi a ocorrência ou não do evento cardíaco adverso. Por se tratar de uma classificação binária, os modelos foram avaliados objetivamente pelas métricas associadas à matriz de confusão como acurácia, sensibilidade, área sob a curva ROC e outras. O algoritmo de mineração processa automaticamente todos os atributos de cada paciente exaustivamente para identificar aqueles fortemente associados com o atributo-classe (evento cardíaco) e que irão compor as regras. Foram extraídas as principais regras destes modelos de modo indireto, por meio da árvore de decisão ou diretamente pela regra de classificação, que apresentaram as variáveis mais influentes e preditoras segundo o algoritmo de mineração. Os modelos permitiram entender melhor o domínio de aplicação, relacionando a influência de detalhes da rotina e as situações associadas ao procedimento médico. Pelo modelo, foi possível analisar as probabilidades da ocorrência e da não ocorrência de eventos em diversas situações. Os modelos induzidos seguiram uma lógica de interpretação dos dados e dos fatos com a participação do especialista do domínio. Foram geradas 32 regras das quais três foram rejeitadas, 20 foram regras esperadas e sem novidade, e 9 foram consideradas regras não tão esperadas, mas que tiveram grau de concordância maior ou igual a 50%, o que as tornam candidatas à investigação para avaliar sua eventual importância. Tais modelos podem ser atualizados ao aplicar novamente o algoritmo de mineração ao banco com os dados mais recentes. O potencial dos modelos simbólicos e interpretáveis é grande na Medicina quando aliado à experiência do profissional, contribuindo para a Medicina baseada em evidência. / The main subject of this study comprehends two areas of knowledge, the Medical and Computer Science areas. Its purpose is to apply the Knowledge Discovery Database-KDD to the DESIRE Registry, an actual Database in Medical area. The DESIRE Registry is the oldest world\'s registry in interventional cardiology, is unicentric, which has been following up 5.614 resvascularized patients for more then 13 years, solely with pharmacological stent implants. The goal is to create a model using this technique that is meaningful to classify patients as the risk of major adverse cardiac events (MACE) and objectively evaluate their performance. Later present rules drawn from this model to the users to assess the degree of novelty and compliance of their content with the knowledge of experts. Symbolic classification models were created using decision tree model, and classification rules using for data mining step the C4.5 algorithms, Ripper and CN2 where the class attribute is the presence or absence of a MACE. As the classification is binary, the models where objectively evaluated by metrics associated to the Confusion Matrix, such as accuracy, sensitivity, area under the ROC curve among others. The data mining algorithm automatically processes the attributes of each patient, who are thoroughly tested in order to identify the most predictive to the class attribute (MACE), whom the rules will be based on. Indirectly, using decision tree, or directly, using the classification rules, the main rules of these models were extracted to show the more predictable and influential variables according to the mining algorithm. The models allowed better understand the application range, creating a link between the influence of the routine details and situations related to the medical procedures. The model made possible to analyse the probability of occurrence or not of events in different situations. The induction of the models followed an interpretation of the data and facts with the participation of the domain expert. Were generated 32 rules of which only three were rejected, 20 of them were expected rules and without novelty and 9 were considered rules not as expected but with a degree of agreement higher or equal 50%, which became candidates for an investigation to assess their possible importance. These models can be easily updated by reapplying the mining process to the database with the most recent data. There is a great potential of the interpretable symbolic models when they are associated with professional background, contributing to evidence-based medicine.

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed.

觀光旅遊業已成為二十一世紀的明星產業，根據觀光局統計2015年來臺觀光旅客已達到1,043萬人次，觀光外匯收入更達到4,528億元。觀光旅遊業的迅速發展，不僅可藉由吸引外來觀光客增加外匯收入、創造就業機會，政府亦能以創新思維，推動整合性政策及各縣市行銷策略來振興經濟，藉此提升國民的生活品質。若能找出臺灣觀光發展特色並永續經營，必讓臺灣成為新的區域中心點、成為亞太新觀光中心。 基於上述研究動機，本研究主要探討2014年來臺觀光旅客所參與活動的特性。以交通部觀光局所提供之問卷，進行資料整理並使用決策樹分析，找出來臺旅客所參與各項活動之特徵，進而瞭解來臺旅客旅遊動機、消費情形及休憩滿意度，以供政府及民間相關單位研擬國際觀光宣傳與行銷策略、提昇國內觀光服務品質與國際旅遊觀光競爭力之參考，並持續提升臺灣觀光品質形象，更努力建構質量並進的觀光環境，希望能奠定觀光產業從量變到質變的基礎，達到擴大觀光服務輸出的目的。 / Tourism has become a major industry in Taiwan in the 21st century. According to the Tourism Bureau, Taiwan received over 10 million international visitors in 2015, which generates over 4.5 billion New Taiwan dollars in revenue. With the industry fast booming, tourism revenue is increased and new jobs are created. The government is thus able to boost the economy through innovation in all comprehensive policies and collaboration between cities and counties on marketing strategies, which in turn raises the living standards of Taiwanese citizens. If the industry is developed efficiently and sustainably, Taiwan has the potential to be the next focal point of Asian-Pacific tourism. With the information mentioned above in mind, this study aims to analyze international visitors to Taiwan and activities they engaged in in the year 2014. Based on surveys provided by the Tourism Bureau, it utilizes decision tree analysis to identify the characteristics of visitors and their activities. It further explores their purpose of visit, spending during and overall satisfaction with their stay. In doing so, it could make a positive contribution when the government and tourism-related industries intend to devise future marketing strategies, improve service performance, and build a global image to attract more tourists. All in all, more emphasis should be laid on quality than quantity in order for the tourism industry to expand efficiently and sustainably.

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

This master's degree thesis focuses on predictive analytics. This type of analysis uses historical data and predictive models to predict future phenomenon. The main goal of this thesis is to describe predictive analytics and its process from theoretical as well as practical point of view. Secondary goal is to implement project of predictive analytics in an important insurance company operating in the Czech market and to improve the current state of detection of fraudulent insurance claims. Thesis is divided into theoretical and practical part. The process of predictive analytics and selected types of predictive models are described in the theoretical part of the thesis. Practical part describes the implementation of predictive analytics in a company. First described are techniques of data organization used in datamart development. Predictive models are then implemented based on the data from the prepared datamart. Thesis includes examples and problems with their solutions. The main contribution of this thesis is the detailed description of the project implementation. The field of the predictive analytics is better understandable thanks to the level of detail. Another contribution of successfully implemented predictive analytics is the improvement of the detection of fraudulent insurance claims.

The aim of the thesis Determinants of car prices is to create econometric model for price predictions of new and used cars. The prediction is based on the data provided by website of Slovak retailer of new and used cars. The model should detect statistically significant variables and determine their impact on final price. In the first part of this study, there is theoretical description of automobile industry and factors influencing price of car. The second part is devoted on developing the predictive model, suitable transformation of explanatory variables, interpretation of results and the car price classification in form of decision tree.

Distributed infrastructures (e.g., water networks and electric Grids) are difficult to manage due to their scale, lack of accessibility, complexity, ageing and uncertainties in knowledge of their structure. In addition they are subject to loads that can be highly variable and unpredictable and to accidental events such as component failure, leakage and malicious tampering. To support in-field operations and central management of these infrastructures, the availability of consistent and up-to-date knowledge about the current state of the network and how it would respond to planned interventions is argued to be highly desirable. However, at present, large-scale infrastructures are “data rich but knowledge poor”. Data, algorithms and tools for network analysis are improving but there is a need to integrate them to support more directly engineering operations. Current ICT solutions are mainly based on specialized, monolithic and heavyweight software packages that restrict the dissemination of dynamic information and its appropriate and timely presentation particularly to field engineers who operate in a resource constrained and less reliable environments. This thesis proposes a solution to these problems by recognizing that current monolithic ICT solutions for infrastructure management seek to meet the requirements of different human roles and operating environments (defined in this work as field and central sides). It proposes an architectural approach to providing dynamic, predictive, user-centric, device and platform independent access to consistent and up-to-date knowledge. This architecture integrates the components required to implement the functionalities of data gathering, data storage, simulation modelling, and information visualization and analysis. These components are tightly coupled in current implementations of software for analysing the behaviour of networks. The architectural approach, by contrast, requires they be kept as separate as possible and interact only when required using common and standard protocols. The thesis particularly concentrates on engineering practices in clean water distribution networks but the methods are applicable to other structural networks, for example, the electricity Grid. A prototype implementation is provided that establishes a dynamic hydraulic simulation model and enables the model to be queried via remote access in a device and platform independent manner.This thesis provides an extensive evaluation comparing the architecture driven approach with current approaches, to substantiate the above claims. This evaluation is conducted by the use of benchmarks that are currently published and accepted in the water engineering community. To facilitate this evaluation, a working prototype of the whole architecture has been developed and is made available under an open source licence.

The aim of this thesis is to analyse the current situation in the marketability of the products of the particular company, which operates in the territory of Albania in the production of ceramic bricks. After that their possible application to current markets as well as considering the possibility of expansion to other Balkan states. The thesis further explains the reasons leading the company to consider the expansion.In the thesis there has been evaluated the current economic situation of the company, a survey of the business environment about countries being considered for expansion has been made as well as the research of the competitors in the given area. An important part of this thesis is the selection of the forms of entry into the selected foreign markets by using selected methods and tools of managerial decision making.

Em anos recentes, muitos algoritmos bio-inspirados têm surgido para resolver problemas de classificação. Em confirmação a isso, a revista Nature, em 2002, publicou um artigo que já apontava para o ano de 2003 o uso comercial de Sistemas Imunológicos Artificiais para detecção de fraude em instituições financeiras por uma empresa britânica. Apesar disso, não observamos, a luz de nosso conhecimento, nenhuma publicação científica com resultados promissores desde então. Nosso trabalho tratou de aplicar Sistemas Imunológicos Artificiais (AIS) para detecção de fraude em cartões de crédito. Comparamos AIS com os métodos de Árvore de Decisão (DT), Redes Neurais (NN), Redes Bayesianas (BN) e Naive Bayes (NB). Para uma comparação mais justa entre os métodos, busca exaustiva e algoritmo genético (GA) foram utilizados para selecionar um conjunto paramétrico otimizado, no sentido de minimizar o custo de fraude na base de dados de cartões de crédito cedida por um emissor de cartões de crédito brasileiro. Em adição à essa otimização, fizemos também uma análise e busca por parâmetros mais robustos via multi-resolução, estes parâmetros são apresentados neste trabalho. Especificidades de bases de fraude como desbalanceamento de dados e o diferente custo entre falso positivo e negativo foram levadas em conta. Todas as execuções foram realizadas no Weka, um software público e Open Source, e sempre foram utilizadas bases de teste para validação dos classificadores. Os resultados obtidos são consistentes com Maes et al. que mostra que BN são melhores que NN e, embora NN seja um dos métodos mais utilizados hoje, para nossa base de dados e nossas implementações, encontra-se entre os piores métodos. Apesar do resultado pobre usando parâmetros default, AIS obteve o melhor resultado com os parâmetros otimizados pelo GA, o que levou DT e AIS a apresentarem os melhores e mais robustos resultados entre todos os métodos testados. / In 2002, January the 31st, the famous journal Nature, with a strong impact in the scientific environment, published some news about immune based systems. Among the different considered applications, we can find detection of fraudulent financial transactions. One can find there the possibility of a commercial use of such system as close as 2003, in a British company. In spite of that, we do not know of any scientific publication that uses Artificial Immune Systems in financial fraud detection. This work reports results very satisfactory on the application of Artificial Immune Systems (AIS) to credit card fraud detection. In fact, scientific financial fraud detection publications are quite rare, as point out Phua et al. [PLSG05], in particular for credit card transactions. Phua et al. points out the fact that no public database of financial fraud transactions is available for public tests as the main cause of such a small number of publications. Two of the most important publications in this subject that report results about their implementations are the prized Maes (2000), that compares Neural Networks and Bayesian Networks in credit card fraud detection, with a favored result for Bayesian Networks and Stolfo et al. (1997), that proposed the method AdaCost. This thesis joins both these works and publishes results in credit card fraud detection. Moreover, in spite the non availability of Maes data and implementations, we reproduce the results of their and amplify the set of comparisons in such a way to compare the methods Neural Networks, Bayesian Networks, and also Artificial Immune Systems, Decision Trees, and even the simple Naïve Bayes. We reproduce in certain way the results of Stolfo et al. (1997) when we verify that the usage of a cost sensitive meta-heuristics, in fact generalized from the generalization done from the AdaBoost to the AdaCost, applied to several tested methods substantially improves it performance for all methods, but Naive Bayes. Our analysis took into account the skewed nature of the dataset, as well as the need of a parametric adjustment, sometimes through the usage of genetic algorithms, in order to obtain the best results from each compared method.

This dissertation addresses the validation of the hypothesis there is a general sense that violence and security perception influence the use of sustainable travel modes. The research characterizes the issue of security perception among University of São Paulo (Brazil) users and identifies the way the sense of security and violence occurrences are related to the travel mode choice. An online survey on security perception and the way its participants access the campus was conducted. The target relationships were explored by Decision Tree (DT) algorithms. An initial exploratory analysis revealed occurrences of violence and reports of insecurity perception were strongly correlated on streets around the campus. The time analysis of violence distribution presented the incidents concentrated at night and during the week. The study also showed that security perception variation according to gender and travel mode choice is less sensitive to security perception than to the occurrence of violence, or type of affiliation to the university. Finally, DT algorithms explored the relation of spatially treated variables (i.e. route length to the university, density of violence occurrences and insecurity reports on the route) to mode choice. The results also showed that distance to the campus was relevant to the mode choice only in routes not strongly considered unsafe. In routes of higher insecurity perception, the share of nonmotorized modes was more expressive and the largest participation of sustainable modes was on routes with high incidence of violence. Since it is counterintuitive to assume numerous walking trips are a consequence of violence, the opposite was considered as a possible explanation to those results. The present study reinforces the need for increased surveillance in regions with high participation of non-motorized modes, for preventing users from shifting to motorized modes. / Esta dissertação busca comprovar a hipótese de que a violência e a percepção de segurança influenciam o uso de modos de transporte sustentáveis. A pesquisa caracteriza a questão da percepção de segurança entre os usuários da Universidade de São Paulo (Brasil), em São Carlos, e identifica como o sentimento de segurança pessoal e a violência estão relacionados com a escolha do modo de viagem. Foi realizada uma pesquisa on-line sobre a percepção de segurança dos usuários da universidade e a forma como eles acessam o campus. As interações foram exploradas por algoritmos de Árvore de Decisão (AD). Uma análise exploratória inicial mostrou que ocorrências de violência e relatos de insegurança estavam fortemente correlacionados nos trechos de via ao redor do campus. A análise temporal da distribuição da violência apresentou os incidentes concentrados à noite e durante os dias de semana. Além disso, a pesquisa mostrou que a percepção de segurança variou de acordo com o gênero e a escolha modal é menos sensível à percepção de segurança do que a ocorrência de violência, ou vinculação com a universidade. Por fim, os algoritmos de AD foram executados para explorar a relação das variáveis tratadas espacialmente (ou seja, o comprimento da rota até o campus, além da densidade de ocorrências e relatos de insegurança na rota) com a escolha modal. O último resultado obtido na análise foi que a distância até a universidade era relevante para a escolha modal apenas em rotas onde não há numerosos relatos de insegurança. A participação dos modos não motorizados foi mais expressiva nas rotas com maior percepção de insegurança, e em rotas com alta incidência de violência. Como não é razoável supor que mais viagens a pé são uma consequência dos roubos e sim o oposto, o estudo reforça a importância de aumentar a segurança nas regiões de alta incidência de viagens não motorizadas, de forma a não incentivar a migração destes usuários para modos motorizados.