The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

<p>The first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN).</p><p>The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories.</p><p>This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories.</p><p>The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment.</p><p>The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.</p>

The first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN). The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories. This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories. The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment. The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech. However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power.  This greatly limits the usable bandwidth when using complex channel sensing methods.  Therefore, this thesis focuses on energy detection for channel sensing. Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices.  The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification. / Master of Science

Les adultes peuvent éprouver des difficultés à discriminer des phonèmes d’une langue seconde (L2) qui ne servent pas à distinguer des items lexicaux dans leur langue maternelle (L1). Le Feature Model (FM) de Brown (1998) propose que les adultes peuvent réussir à créer des nouvelles catégories de sons seulement si celles-ci peuvent être construites à partir de traits distinctifs existant dans la L1 des auditeurs. Cette hypothèse a été testée sur plusieurs contrastes consonantiques dans différentes langues; cependant, il semble que les traits qui s’appliquent sur les voyelles n’aient jamais été examinés dans cette perspective et encore moins les traits qui opèrent à la fois dans les systèmes vocalique et consonantique et qui peuvent avoir un statut distinctif ou non-distinctif. Le principal objectif de la présente étude était de tester la validité du FM concernant le contraste vocalique oral-nasal du portugais brésilien (PB). La perception naïve du contraste /i/-/ĩ/ par des locuteurs du français, de l’anglais, de l’espagnol caribéen et de l’espagnol conservateur a été examinée, étant donné que ces quatre langues diffèrent en ce qui a trait au statut de la nasalité. De plus, la perception du contraste non-naïf /e/-/ẽ/ a été inclus afin de comparer les performances dans la perception naïve et non-naïve. Les résultats obtenus pour la discrimination naïve de /i/-/ĩ/ a permis de tirer les conclusions suivantes pour la première exposition à un contraste non natif : (1) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 peut être redéployé au sein du système vocalique, (2) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 ne peut pas être redéployé à travers les systèmes (consonne à voyelle) et (3) le trait [nasal] qui opère de façon non-distinctive dans la grammaire d’une certaine L1 peut être ou ne pas être redéployé au statut distinctif. En dernier lieu, la discrimination non-naïve de /e/-/ẽ/ a été réussie par tous les groupes, suggérant que les trois types de redéploiement s’avèrent possibles avec plus d’expérience dans la L2. / Adults may experience difficulties discriminating phonemes of a second language (L2) that do not serve to distinguish lexical items in their native language (L1). Brown’s (1998) Feature Model (FM) advances that adults may be able to create new sound categories only if these can be built from contrastive features existing in their L1. This hypothesis has been tested on various consonant contrasts in a number of languages; however, it appears that features applying on vowels have never been examined from this perspective and neither have features that operate both in the vowel and the consonant systems and that may have a contrastive or a non-contrastive status. The main purpose of the present study was to test the validity of the FM with respect to the oral-nasal vowel contrast of Brazilian Portuguese. The naïve perception of the contrast /i/-/ĩ/ by French, English, Caribbean Spanish, and conservative Spanish speakers was examined, given that these four languages differ with respect to the status of nasality. Moreover, the perception of the non-naïve contrast /e/-/ẽ/ was included to compare naïve and non-naïve perception performances. The obtained data for the naïve discrimination of /i/-/ĩ/ allowed to draw the following conclusions for the first exposure to a non-native contrast: (1) the feature [nasal] operating contrastively in the grammar of a given L1 can be redeployed within the vowel system, (2) the feature [nasal] operating contrastively in the grammar of a given L1 may not be redeployed across systems (consonant to vowel), and (3) the feature [nasal] operating non-contrastively in the grammar of a given L1 might or might not be redeployed to contrastive status. Lastly, the non-naïve perception of /e/-/ẽ/ was successful for all groups, suggesting that the three types of redeployment are possible with more experience in the L2.

La présente thèse envisage de cerner les comportements des Camerounais sur les routes.En l’occurrence, nous voulons savoir comment les croyances fatalistes, les croyances decontrôle, les croyances au contrôle divin, les croyances et valeurs culturelles, l’explicationnaïve des accidents et la perception du risque peuvent affecter les comportements des usagerset agents du trafic routier au Cameroun. Cinq études sont réalisées dans le cadre de notrethèse. La première étude examine 522 procès-verbaux d’accidents réels et montre qu’unegrande majorité des accidents surviennent dans de bonnes conditions de conduite. De plus, lesconducteurs se rejettent mutuellement la faute, mais s’accordent avec les gendarmes pour direque le comportement des conducteurs est la première cause des accidents de la route auCameroun. Une deuxième étude évalue la variation des comportements en fonction des explicationscausales et des croyances, sur un échantillon de 525 participants. On observe que lesparticipants présentent davantage des comportements sécuritaires lorsqu’ils expliquent lesaccidents par le comportement des conducteurs, mais leurs comportements tendent à êtremoins sécuritaires lorsqu’ils expliquent ceux-ci par des causes externes non contrôlables. Lesexplications causales tendent à être externes aux conducteurs lorsque les participants sontfatalistes, croient qu’ils peuvent affronter les situations de trafic dangereuses sans en êtreinquiété, croient que Dieu contrôle les situations dangereuses auxquelles ils peuvent faire faceou lorsqu’ils adhèrent fortement aux croyances et valeurs culturelles. En particulier, lescroyances et valeurs culturelles qui sont supposées protéger la vie, les croyances fatalistes etles croyances au contrôle divin se distinguent par leur capacité à favoriser l’explication desaccidents par des forces invisibles et à induire des comportements insécuritaires. Enfin, le rôlemédiateur des explications causales se révèlent pour toutes les croyances.A partir d’une quasi-expérimentation réalisée auprès de 444 participants, l’étude 3analyse la variation des explications causales et de l’attitude vis-à-vis des mesures deprévention, en fonction de la pertinence situationnelle, de la pertinence personnelle et de lagravité de l’accident. On observe que les participants ont tendance à fournir des explicationscausales défensives d’autant plus que la situation accidentelle leur est pertinente, qu’ilss’identifient à la victime et que l’accident est grave. De plus ils ont une préférence pour desmesures de prévention orientées vers les conducteurs lorsqu’ils expliquent les accidents par lecomportement de ces derniers. L’étude 4 montre une tendance à adopter des comportements moins sécuritaires lorsque les participants sous-estiment le risque routier. En outre, ces derniers ont tendance à sousestimer le risque lorsqu’ils sont fatalistes alors qu’ils ont davantage peur du risque lorsqu’ils sont attachés à leur identité culturelle. Enfin, les participants ont tendance à se croire capables d’affronter le risque routier sans en être inquiété lorsqu’ils croient que Dieu contrôle lessituations dangereuses ou lorsqu’ils croient aux pratiques culturelles supposées protéger lavie. Dans l’étude 5, on montre que les participants ont tendance à adopter descomportements davantage sécuritaires lorsqu’ils ont une perception élevée du risque etexpliquent les accidents par des causes contrôlables. Par contre, ils se montrent plutôtimprudents sur les routes lorsqu’ils ont une perception élevée du risque et croient que lesaccidents sont causés par des forces invisibles. Les résultats vont dans le sens des travaux antérieurs et sont discutés en rapport avec les connaissances théoriques. Enfin, des suggestions encouragent une prévention fondée sur les croyances de la population cible. / This thesis intends to examine Cameroonians behaviors on the roads. Our objective is toknow how fatalistic beliefs, control beliefs, divine control beliefs, cultural beliefs and values,naive explanation of accidents and risk perception can affect road users and traffic agents’behaviors. Five studies are carried out within the framework of our thesis. The first study examined 522 actual accidents reports and shows that a large majority of accidents occur in good driving conditions. In addition, drivers accused each other of wrongdoing, but agree with the gendarmes that, drivers’ behavior is the main cause of trafic accidents in Cameroon. A second study evaluates the variability of behaviors according to the causal explanations and beliefs, on a sample of 525 participants. It is shown that, participants adopt safer behaviors when they explain accidents by drivers’ behavior, but their behavior tend to be less safe when they explain accidents by external and uncontrollable causes. Causal explanations tend to be external to drivers when participants are fatalists, believe they can face dangerous traffic situations without being worried, believe that God is in control of dangerous situations that they may faced or when they adhere strongly to cultural beliefs and values. In particular, cultural beliefs and values that are supposed to protect the life, fatalistic beliefs, divine control beliefs tend to promote the explanation of accidents in terms ofinvisible forces and induce unsafe behaviors. Finally, the mediating role of causalexplanations is confirmed on all the beliefs. From a quasi-experiment conducted with 444 participants, the third study analyzes the variation of the causal explanations and attitude towards prevention measures, according to the situational relevance, personal relevance, and the severity of the accident. It is shown that participants tend to provide defensive causal explanations especially when the accident situation is relevant to them, they identify themselves to the victim and when the accident is serious. In addition they prefer drivers-oriented preventive measures when they explainaccidents by the drivers’ behaviors. Study 4 shows a tendency to adopt unsafe behaviors when participants underestimate traffic risk. Moreover, they tend to underestimate the traffic risk when they are fatalistic, but they fear risk when they are attached to their cultural identity. Finally, participants tend to believe that they can face traffic risk without being worried when they believe that God controls dangerous situations, or when they believe on cultural practices intended to protect life. In Study 5, we show that participants tend to adopt safer behaviors when they feartraffic risk and explain accidents by controllable causes. They are rather careless on the roadswhen they fear risk but believe that accidents are caused by invisible forces. The results are consistent with previous studies and are discussed in relation to the theoretical knowledge. Finally, suggestions encourage preventive measures based on the beliefs of the target population.

Companies often process invoices manually, therefore automation could reduce manual labor. The aim of this thesis is to evaluate which OCR-engine, Tesseract or OCRopus, performs best at interpreting invoices. This thesis also evaluates if it is possible to use machine learning to automatically process invoices based on previously stored data. By interpreting invoices with the OCR-engines, it results in the output text having few spelling errors. However, the invoice structure is lost, making it impossible to interpret the corresponding fields. If Naïve Bayes is chosen as the algorithm for machine learning, the prototype can correctly classify recurring invoice lines after a set of data has been processed. The conclusion is, neither of the two OCR-engines can interpret the invoices to plain text making it understandable. Machine learning with Naïve Bayes works on invoices if there is enough previously processed data. The findings in this thesis concludes that machine learning and OCR can be utilized to automatize manual labor. / Företag behandlar oftast fakturor manuellt och en automatisering skulle kunna minska fysiskt arbete. Målet med examensarbetet var att undersöka vilken av OCR-läsarna, Tesseract och OCRopus som fungerar bäst på att tolka en inskannad faktura. Även undersöka om det är möjligt med maskininlärning att automatiskt behandla fakturor utifrån tidigare sparad data. Genom att tolka text med hjälp av OCR-läsarna visade resultaten att den producerade texten blev språkligt korrekt, men att strukturen i fakturan inte behölls vilket gjorde det svårt att tolka vilka fält som hör ihop. Naïve Bayes valdes som algoritm till maskininlärningen och resultatet blev en prototyp som korrekt kunde klassificera återkommande fakturarader, efter att en mängd träningsdata var behandlad. Slutsatsen är att ingen av OCR-läsarna kunde tolka fakturor så att resultatet kunde användas vidare, och att maskininlärning med Naïve Bayes fungerar på fakturor om tillräckligt med tidigare behandlad data finns. Utfallet av examensarbetet är att maskininlärning och OCR kan användas för att automatisera fysiskt arbete.

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

Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2016-03-03T20:22:51Z No. of bitstreams: 2 Dissertação - Thamer Horbylon Nascimento - 2015.pdf: 3404276 bytes, checksum: 8b96b87a1ad94259e867e586b0cdde9b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-03-04T11:20:16Z (GMT) No. of bitstreams: 2 Dissertação - Thamer Horbylon Nascimento - 2015.pdf: 3404276 bytes, checksum: 8b96b87a1ad94259e867e586b0cdde9b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-03-04T11:20:16Z (GMT). No. of bitstreams: 2 Dissertação - Thamer Horbylon Nascimento - 2015.pdf: 3404276 bytes, checksum: 8b96b87a1ad94259e867e586b0cdde9b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-10-13 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / This paper proposes a method for gesture-based text input to be used in devices with touch screens. The steps required for this are: recognizing gestures, identify actions needed to enter letters and recognize letters with up to two interactions. To the recognition of gestures, we used the incremental recognition algorithm, which works so that it is not necessary to finish a gesture for this to be recognized, that is, works with continuous gesture recognition. Furthermore, a template is used as a reference for the recognition of gestures. The algorithm identifies the likelihood of the gesture being a running the template. As the incremental recognition algorithm did not have curves in templates using the reduced equation of the circle, was created a template with curves and straight lines were also added in it. The template created served to create a database for entering the letters of the alphabet from A to Z, using user gestures. This base was used for training of a Naïve Bayes classifier, which identifies the probability of inserting a letter entered by the user based on the gestures. Three experiments were conducted to test the method developed. It was found that most users entered a letter using up to two interactions when inserted the five vowels and the five most frequent consonants. When inserting the five vowels and five consonants less frequent, users were also able to enter the letters with up to two interactions. Thus, there is evidence that the method to solve the problem which has been proposed as a solution. / Este trabalho propõe um método para entrada de texto baseado em gestos para ser usado em dispositivos com telas sensíveis ao toque. Os passos necessários para isso são: reconhecer gestos, identificar gestos necessários para inserir letras e reconhecer letras com até duas interações. Para fazer o reconhecimento dos gestos, foi utilizado o algoritmo de reconhecimento incremental, o qual trabalha de forma que não seja necessário terminar um gesto para que este seja reconhecido, ou seja, trabalha com reconhecimento contínuo de gestos. Além disso, um template é utilizado como referência para o reconhecimento dos gestos, assim, o algoritmo identifica a probabilidade de o gesto em execução ser um do template. Como o algoritmo de reconhecimento incremental não possuía curvas em seus templates, utilizando a equação reduzida da circunferência, foi criado um template com curvas e foram também adicionadas retas nele. O template criado serviu para a criação de uma base de dados para a inserção das letras do alfabeto de A a Z, utilizando gestos de usuários. Essa base foi utilizada para treinamento de um classificador Naïve Bayes, que identifica a probabilidade de inserção de uma letra baseado nos gestos inseridos pelo usuário. Foram realizados três experimentos para testar o método desenvolvido. Verificouse que a maioria dos usuários inseriu uma letra utilizando até duas interações, quando inseriram as cinco vogais e as cinco consoantes mais frequentes. Ao inserir as cinco vogais e cinco consoantes menos frequentes, os usuários também conseguiram inserir as letras com até duas interações. Assim, há evidências de que o método resolva o problema para qual foi proposto como solução.