As técnicas computacionais de aprendizagem de máquina para classificação ou categorização de dados estão sendo cada vez mais utilizadas no contexto de extração de informações ou padrões em bases de dados volumosas em variadas áreas de aplicação. Em paralelo, a aplicação destes métodos computacionais para identificação de padrões, bem como a classificação de dados relacionados ao consumo dos bens de informação é considerada uma tarefa complexa, visto que tais padrões de decisão do consumo estão relacionados com as preferências dos indivíduos e dependem de uma composição de características individuais, variáveis culturais, econômicas e sociais segregadas e agrupadas, além de ser um tópico pouco explorado no mercado brasileiro. Neste contexto, este trabalho realizou o estudo experimental a partir da aplicação do processo de Descoberta do conhecimento (KDD), o que inclui as etapas de seleção e Mineração de Dados, para um problema de classificação binária, indivíduos brasileiros que consomem e não consomem um bem de informação, filmes em salas de cinema, a partir dos dados obtidos na Pesquisa de Orçamento Familiar (POF) 2008-2009, pelo Instituto Brasileiro de Geografia e Estatística (IBGE). O estudo experimental resultou em uma análise comparativa da aplicação de duas técnicas de aprendizagem de máquina para classificação de dados, baseadas em aprendizado supervisionado, sendo estas Naïve Bayes (NB) e Support Vector Machine (SVM). Inicialmente, a revisão sistemática realizada com o objetivo de identificar estudos relacionados a aplicação de técnicas computacionais de aprendizado de máquina para classificação e identificação de padrões de consumo indica que a utilização destas técnicas neste contexto não é um tópico de pesquisa maduro e desenvolvido, visto que não foi abordado em nenhum dos trabalhos estudados. Os resultados obtidos a partir da análise comparativa realizada entre os algoritmos sugerem que a escolha dos algoritmos de aprendizagem de máquina para Classificação de Dados está diretamente relacionada a fatores como: (i) importância das classes para o problema a ser estudado; (ii) balanceamento entre as classes; (iii) universo de atributos a serem considerados em relação a quantidade e grau de importância destes para o classificador. Adicionalmente, os atributos selecionados pelo algoritmo de seleção de variáveis Information Gain sugerem que a decisão de consumo de cultura, mais especificamente do bem de informação, filmes em cinema, está fortemente relacionada a aspectos dos indivíduos relacionados a renda, nível de educação, bem como suas preferências por bens culturais / Machine learning techniques for data classification or categorization are increasingly being used for extracting information or patterns from volumous databases in various application areas. Simultaneously, the application of these computational methods to identify patterns, as well as data classification related to the consumption of information goods is considered a complex task, since such decision consumption paterns are related to the preferences of individuals and depend on a composition of individual characteristics, cultural, economic and social variables segregated and grouped, as well as being not a topic explored in the Brazilian market. In this context, this study performed an experimental study of application of the Knowledge Discovery (KDD) process, which includes data selection and data mining steps, for a binary classification problem, Brazilian individuals who consume and do not consume a information good, film at theaters in Brazil, from the microdata obtained from the Brazilian Household Budget Survey (POF), 2008-2009, performed by the Brazilian Institute of Geography and Statistics (IBGE). The experimental study resulted in a comparative analysis of the application of two machine-learning techniques for data classification, based on supervised learning, such as Naïve Bayes (NB) and Support Vector Machine (SVM). Initially, a systematic review with the objective of identifying studies related to the application of computational techniques of machine learning to classification and identification of consumption patterns indicates that the use of these techniques in this context is not a mature and developed research topic, since was not studied in any of the papers analyzed. The results obtained from the comparative analysis performed between the algorithms suggest that the choice of the machine learning algorithms for data classification is directly related to factors such as: (i) importance of the classes for the problem to be studied; (ii) balancing between classes; (iii) universe of attributes to be considered in relation to the quantity and degree of importance of these to the classifiers. In addition, the attributes selected by the Information Gain variable selection algorithm suggest that the decision to consume culture, more specifically information good, film at theaters, is directly related to aspects of individuals regarding income, educational level, as well as preferences for cultural goods

The topic of students’ application of conceptual knowledge in physics is constantly being researched. It is a common occurrence that students are able to solve numerical problems without understanding the concepts involved. The primary focus of this dissertation is to investigate the extent to which a group of first year physics students are able to identify and use the correct physics concepts when solving problems set in different contexts. Furthermore, this study aims to identify underlying factors giving way to students not applying appropriate physics concepts. A questionnaire was designed in test-format in which all the problems dealt with two objects whose movement had to be compared to each other. The physical quantities describing or influencing the objects’ movement differed in each consecutive problem; whilst the nature of the concept under consideration remained the same. The problems were set in various contexts namely: i. Formal conceptual questions, some with numeric values; ii. Questions set in every day context with/without numeric values; iii. Questions on vertical upward, vertical downward and horizontal motion. The questionnaire was distributed to 481 students in the first-year physics course in 2014 at the Potchefstroom Campus of the North West University. It was expected that the percentage of correct answers would reveal discrepancies in the responses to contextual, numeric and formal conceptual questions. The outcome of the statistical analysis confirmed this expectation. In addition, it seemed that only a few students were able to correctly identify the appropriate variables when considering vertical and horizontal movement while the majority of the students did not apply the same physics principle in isomorphic vertical upward and vertical downward problems. It appears that the context in which the question was posed determined whether the problem was seen as an item that would require “physics reasoning” or as a setting where physics reasoning did not apply. The results revealed students inability to relate physics concepts to appropriate mathematical equations. Two important results from this work are: (1) the presentation of a questionnaire that can be implemented to investigate various aspects regarding the contexts of physics problems; and (2) expanding the concept of context to include the direction of movement as a separate context. / MSc (Natural Science Education), North-West University, Potchefstroom Campus, 2015

The topic of students’ application of conceptual knowledge in physics is constantly being researched. It is a common occurrence that students are able to solve numerical problems without understanding the concepts involved. The primary focus of this dissertation is to investigate the extent to which a group of first year physics students are able to identify and use the correct physics concepts when solving problems set in different contexts. Furthermore, this study aims to identify underlying factors giving way to students not applying appropriate physics concepts. A questionnaire was designed in test-format in which all the problems dealt with two objects whose movement had to be compared to each other. The physical quantities describing or influencing the objects’ movement differed in each consecutive problem; whilst the nature of the concept under consideration remained the same. The problems were set in various contexts namely: i. Formal conceptual questions, some with numeric values; ii. Questions set in every day context with/without numeric values; iii. Questions on vertical upward, vertical downward and horizontal motion. The questionnaire was distributed to 481 students in the first-year physics course in 2014 at the Potchefstroom Campus of the North West University. It was expected that the percentage of correct answers would reveal discrepancies in the responses to contextual, numeric and formal conceptual questions. The outcome of the statistical analysis confirmed this expectation. In addition, it seemed that only a few students were able to correctly identify the appropriate variables when considering vertical and horizontal movement while the majority of the students did not apply the same physics principle in isomorphic vertical upward and vertical downward problems. It appears that the context in which the question was posed determined whether the problem was seen as an item that would require “physics reasoning” or as a setting where physics reasoning did not apply. The results revealed students inability to relate physics concepts to appropriate mathematical equations. Two important results from this work are: (1) the presentation of a questionnaire that can be implemented to investigate various aspects regarding the contexts of physics problems; and (2) expanding the concept of context to include the direction of movement as a separate context. / MSc (Natural Science Education), North-West University, Potchefstroom Campus, 2015

The modern Arabic term for national homeland, waṭan, derives its sense from the related yet semantically different usage of this term in classical Arabic, particularly in classical Arabic poetry. In modern usage, waṭan refers to a politically defined, visually memorialized territory whose expanse is cognized abstractly rather than through personal experience. The modern waṭan is the geopolitical locus of national identity. The classical notion of waṭan, however, is rarely given much geographical content, although it usually designates a relatively localized area on the scale of a neighborhood, town, or village. More important than geographical content is the subjective meaning of the waṭan, in the sense of its essential place in the psyche of an individual. The waṭan (also mawṭin, awṭān), both in poetry and other types of classical writing, is strongly associated with the childhood/youth and primary love attachments of the speaker. This sense of waṭan is thus temporally defined as much as spatially, and as such can be seen as an archetypal instance of the Bakhtinian chronotope, one intrinsically associated with nostalgia and estrangement. The waṭan, as the site of the classical self’s former plenitude, is by definition lost or transfigured and unrecoverable, becoming an attachment that must be relinquished for the sake of virtue and glory. This paper argues that the bivalency of the classical waṭan chronotope, recoverable through analysis of poetic and literary texts, allows us to understand the space and time of the self in classical Arabic literature and how this self differs from that presupposed by modern ideals of patriotism.

There is a significant need to identify approaches for classifying chemical sensor array data with high success rates that would enhance sensor detection capabilities. The present study attempts to fill this need by investigating six machine learning methods to classify a dataset collected using a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 classes containing 136 samples is used to build the classifiers, and a dataset of 4 classes with 56 samples is used for testing. The results generated with the six different methods are compared and discussed. The RF, CART, and KNN are found to have success rates greater than 90%, and to outperform the other methods.

This research is motivated by a number of open questions in the behavioural finance literature. Firstly, if investors do not learn in a rational Bayesian manner but rather suffer from biases set out in the naïve reinforcement hypothesis, rationality assumptions in individual preference models may not hold. I use a unique longitudinal dataset comprising in excess of 1.5 million fixed-odds financial bets, where bettors perform identical, consecutive decisions which mimic financial choices made in a laboratory, but the use of their own funds departs from the artificiality of an experiment. I present evidence of unwarranted overconfidence generated by reinforcement learning in both real and simulated markets. Secondly, Kahneman and Tversky (1979) state that losses loom larger than gains. I examine whether the disposition to avoid losses is driving behaviour in the losing domain in the dataset and conclude that there is little evidence of loss aversion. I differentiate between betting on Financial Markets, in which agents may perceive an internal locus of control, and betting on the simulated market, where results are uncorrelated and in which the emotions of regret and disappointment may not loom as large. Finally, Odean (1998) provides evidence that investors readily realise paper gains by selling their winning stocks, yet hold on to their losing stocks too long. This loss aversion is consistent with Kahneman and Tversky (1979) prospect theory, however, how long would the investor hold on to a stock that is losing value on a day-to-day basis? Conversely, would an investor rush to sell a stock that has yielded positive returns in each month during the past year? I test the interaction between learning and loss aversion in a financial betting experiment in which two treatment groups are subjected to consecutive gains or losses.

Automation of document handling in the construction industries could save large amounts of time, effort and money and classifying a document is an important step in that automation. In the field of machine learning, lots of research have been done on perfecting the algorithms and techniques, but there are many areas where those techniques could be used that has not yet been studied. In this study I looked at how effectively the machine learning algorithm multinomial Naïve-Bayes would be able to classify 1427 documents split up into 19 different categories from a construction project. The experiment achieved an accuracy of 92.7% and the paper discusses some of the ways that accuracy can be improved. However, data extraction proved to be a bottleneck and only 66% of the original documents could be used for testing the classifier.

L'objectif des travaux présentés dans cette thèse est de poser les fondements d'une formalisation qui permettrait, d'une part la description d'une représentation numérique de terrain dans un langage intelligible à destination d'observateurs localisés au sol et, d'autre part, de faciliter les échanges entre des communautés scientifiques disposant de corpus sémantiques différents.<br />Nous cherchons à décrire une silhouette - i.e., une coupe de terrain ou la séparation terre/ciel à l'horizon - selon la perception que peut en avoir un observateur. Nous introduisons un modèle, fondé sur une approche qualitative, qui consiste à décrire une silhouette par une séquence de symboles signifiants. L'utilisateur ayant la possibilité de définir ses propres catégories, le modèle possède la capacité de s'adapter à différents contextes.<br />L'originalité de notre modèle repose dans la méthode de détection des formes significatives. Nous nous ecartons volontairement des méthodes numériques généralement utilisées dans les systèmes de détection ou de reconnaissance de forme. Nous considérons que la perception d'une forme particulière de relief est contingente de la perception de saillances, définies ici comme des points qualitativement remarquables. La description d'une silhouette inclut les relations topologiques qui relient les formes de relief détectées. Afin de tenir compte de l'imprécision des frontières des formes de relief, nous proposons une extension de la méthode 9-intersection. Les relations méréologiques, quant à elles, nous sont utiles pour dériver des représentations à différents niveaux d'abstraction.<br />Nous avons implanté le modèle en Java. Le prototype réealisé permet de définir des catégories, d'analyser des silhouettes, de déterminer les relations topologiques qui lient les formes détectées et d'obtenir une description à différents niveaux d'abstraction.<br />Cette thèse a bénéficié du soutien financier de la Région Bretagne.

Ovarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).<p>Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.<p>A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired <i>in vivo</i> were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. <p>The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.<p>The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm.

Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time. The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter. Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.