Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This present study aims ay analyzing the contribuition of abolicionists from the State of Sergipe for the project of the nation s civilization, through practices of instruction and educational to naïve children. This research, delimited between the ages of 1881 to 1884, studies the educational laws of the imperial period and the aspects focused on and related to the primary instruction. It unveils the motives of the creation of an elementary teaching school and its pedagogical practices for the primary teaching to the naïve children capita of the Sergipe settlement destined to children as well as the pedagogical practices developed by the school. It identifies the profile of the Sociedade Libertadora Aracajuana Cabana do Pai Thomaz (1882) as a space for speeches, conferences, journalistic activities, delivery of emancipation letters and instruction to naïve children. It has also focused in this investiment, the role of the press and the usages made by its editors at the and of the nineteenth century. The theoreticmethodological for the study in question is supported in Roger Chartier, throught the notions of Representation an Appropriation, the concepts of Civilization by Norbert Elias, Cultura Escolar by Dominique Julia, Infância by Carlos Monarcha and the category Constructos Flutuantes by Clarice Nunes, Therefore, it treats about a study which focus on the set of pratices carried o9n by abolicionists from Sergipe for reaching goals of progress in Sergipe. / O presente estudo tem como objetivo analisar a contribuição de abolicionistas sergipanos para o projeto de civilização da nação, através das práticas de instrução e educação aos ingênuos. Esta pesquisa, delimitada entre os anos de 1881 a 1884 estuda as leis educacionais do império e os aspectos abordados no que diz respeito a instrução primária Desvenda os motivos da criação de uma escola de ensino primário e suas práticas pedagógicas para o ensino primário aos ingênuos, na Capital da Província Sergipana destinada aos ingênuos bem como, as práticas pedagógicas desenvolvidas pela escola. Identifica o perfil da Sociedade Libertadora Aracajuana Cabana do Pai Thomaz (1882) como um espaço de palestras, conferências, atividades jornalísticas, entregas de cartas de alforria e instrução aos ingênuos. Pôs em relevo também nesta investida, o papel da imprensa e os usos feitos pelos seus redatores no final do século XIX.. O referencial teórico-metodológico para o estudo em questão apóia-se em Roger Chartier, a partir das noções de Representação e Apropriação, os conceitos de Civilização de Norbert Elias, Cultura escolar de Dominique Julia, Infância de Carlos Monarcha e a categoria Constructos flutuantes de Clarice Nunes. Trata-se, portanto, de um estudo que focaliza o conjunto de práticas exercidas por abolicionistas sergipanos para alcançar metas de progresso em Sergipe.

The goal of this master’s thesis is to identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analyzed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5.

We investigate the factors that determine the performance of text-based language identification, with a particular focus on the 11 official languages of South Africa. Our study uses n-gram statistics as features for classification. In particular, we compare support vector machines, Naïve Bayesian and difference-in-frequency classifiers on different amounts of input text and various values of n, for different amounts of training data. For a fixed value of n the support vector machines generally outperforms the other classifiers, but the simpler classifiers are able to handle larger values of n. The additional computational complexity of training the support vector machine classifier may not be justified in light of importance of using a large value of n, except possibly for small sizes of the input window when limited training data is available. We find that it is more difficult to discriminate languages within language families then those across families. The accuracy on small input strings is low due to this reason, but for input strings of 100 characters or more there is only a slight confusion within families and accuracies as high as 99.4% are achieved. For the smallest input strings studied here, which consist of 15 characters, the best accuracy achieved is only 83%, but when the languages in different families are grouped together, this corresponds to a usable 95.1% accuracy. The relationship between the amount of training data and the accuracy achieved is found to depend on the window size – for the largest window (300 characters) about 400 000 characters are sufficient to achieve close-to-optimal accuracy, whereas improvements in accuracy are found even beyond 1.6 million characters of training data. Finally, we show that the confusions between the different languages in our set can be used to derive informative graphical representations of the relationships between the languages. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

This dissertation explored the way in which bumblebees' visual system helps them discover their first flower. Previous studies found bees have unlearned preferences for parts of a flower, such as its colour and shape. The first study pitted two variables against each other: pattern type: sunburst or bull's eye, versus the location of the pattern: shapes appeared peripherally or centrally. We observed free-flying bees in a flight cage using Radio-Frequency Identification (RFID) tracking. The results show two distinct behavioural preferences: Pattern type predicts landing: bees prefer radial over concentric patterns, regardless of whether the radial pattern is on the perimeter or near the centre of the flower. Pattern location predicts exploration: bees were more likely to explore the inside of artificial flowers if the shapes were displayed near the centre of the flower, regardless of whether the pattern was radial or concentric. As part of the second component, we implemented a mathematical model aimed at explaining how bees come to prefer radial patterns, leafy backgrounds and symmetry. The model was based on unsupervised neural networks used to describe cognitive mechanisms. The results captured with the results of multiple behavioural experiments. The model suggests that bees choose computationally "cheaper" stimuli, those that contain less information. The third study tested the computational load hypothesis generated by the artificial neural networks. Visual properties of symmetry, and spatial frequency were tested. Studying free-flying bees in a flight cage using motion-sensitive video recordings, we found that bees preferred 4-axis symmetrical patterns in both low and high frequency displays.

A presente dissertação analisa o conteúdo dos diários e cartas de Virginia Woolf para avaliar se um algoritmo de classificação de texto poderia identificar um padrão escrito relacionado aos dois meses anteriores ao suicídio de Virginia Woolf. Este é um estudo de classificação de texto. Comparamos 46 entradas de textos dos dois meses anteriores ao suicídio de Virginia Woolf com 54 textos selecionados aleatoriamente do trabalho de Virginia Woolf durante outro período de sua vida. O texto de cartas e dos diários foi incluído, enquanto livros, romances, histórias curtas e fragmentos de artigos foram excluídos. Os dados foram analisados usando um algoritmo de aprendizagem mecânica Naïve-Bayes. O modelo mostrou uma acurácia de 80,45%, sensibilidade de 69% e especificidade de 91%. A estatística Kappa foi de 0,6, o que significa um bom acordo, e o valor P do modelo foi de 0,003. A Área Sob a curva ROC foi 0,80. O presente estudo foi o primeiro a analisar a viabilidade de um modelo de machine learning, juntamente com dados de texto, a fim de identificar padrões escritos associados ao comportamento suicida nos diários e cartas de um romancista. Nossa assinatura de texto foi capaz de identificar o período de dois meses antes do suicídio com uma alta precisão / The present study analyzes the content of Virginia Woolf’s diaries and letters to assess whether a text classification algorithm could identify written pattern related to the two months previous to Virginia Woolf’s suicide. This is a text classification study. We compared 46 texts entries from the two months previous to Virginia Woolf’s suicide with 54 texts randomly selected from Virginia Woolf’s work during other period of her life. Letters and diaries were included, while books, novels, short stories, and article fragments were excluded. The data was analyzed by using a Naïve-Bayes machine-learning algorithm. The model showed a balanced accuracy of 80.45%, sensitivity of 69%, and specificity of 91%. The Kappa statistic was 0.6, which means a good agreement, and the p value of the model was 0.003. The Area Under the ROC curve was 0.80. The present study was the first to analyze the feasibility of a machine learning model coupled with text data in order to identify written patterns associated with suicidal behavior in the diaries and letters of a novelist. Our text signature was able to identify the period of two months preceding suicide with a high accuracy.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22833号 / 医博第4672号 / 新制||医||1047(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 竹内 理, 教授 濵﨑 洋子, 教授 椛島 健治 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The quality of a wine is important for the consumers as well as the wine industry. The traditional (expert) way of measuring wine quality is time-consuming. Nowadays, machine learning models are important tools to replace human tasks. In this case, there are several features to predict the wine quality but the entire features will not be relevant for better prediction. So, our thesis work is focusing on what wine features are important to get the promising result. For the purposeof classification model and evaluation of the relevant features, we used three algorithms namely support vector machine (SVM), naïve Bayes (NB), and artificial neural network (ANN). In this study, we used two wine quality datasets red wine and white wine. To evaluate the feature importance we used the Pearson coefficient correlation and performance measurement matrices such as accuracy, recall, precision, and f1 score for comparison of the machine learning algorithm. A grid search algorithm was applied to improve the model accuracy. Finally, we achieved the artificial neural network (ANN) algorithm has better prediction results than the Support Vector Machine (SVM) algorithm and the Naïve Bayes (NB) algorithm for both red wine and white wine datasets.

It is rarely possible to use an optimal classifier. Often the classifier used for a specific problem is an approximation of the optimal classifier. Methods are presented for evaluating the performance of an approximation in the model class of Bayesian Networks. Specifically for the approximation of class conditional independence a bound for the performance is sharpened. The class conditional independence approximation is connected to the minimum description length principle (MDL), which is connected to Jeffreys’ prior through commonly used assumptions. One algorithm for unsupervised classification is presented and compared against other unsupervised classifiers on three data sets. / <p>Report code: LiU-TEK-LIC 2006:11.</p>

This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective, are still in their infant stages, and have not yet reached a consensus. Therefore, it is essential to develop valid and reliable methods for effective ORA and evaluations. The main contribution of this thesis is to apply Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. Further, a new ORA framework and measurement metrics are proposed with six factors: frequency of triggering event, effectiveness of avoidance barriers, frequency of undesirable operational state, effectiveness of recovery barriers before the risk outcome, approximate cost for Undesirable Operational State (UOS) occurrence, and severity of the risk outcome. The study results were reported based on surveys conducted with Nigerian senior banking officers and banking customers. The study revealed that the framework and assessment tools gave good predictions for risk learning and inference in such systems. Thus, results obtained can be considered promising and useful for both E-banking system adopters and future researchers in this area.

Natural Language Processing (NLP) is a branch in data science that marries artificial intelligence with linguistics. Essentially, it tries to program computers to understand human language, both spoken and written. Over the past decade, researchers have applied novel algorithms to gain a better understanding of human sentiment. While no easy feat, incredible improvements have allowed organizations, politicians, governments, and other institutions to capture the attitudes and opinions of the public. It has been particularly constructive for companies who want to check the pulse of a new product or see what the positive or negative sentiments are for their services. NLP has even become useful in boosting sales performance and improving training. Over the years, there have been countless studies on sales performance, both from a psychological perspective, where characteristics of salespersons are explored, and from a data science/AI (Artificial Intelligence) perspective, where text is analyzed to predict sales forecasting (Pai &amp; Liu, 2018) and coach sales agents using AI trainers (Luo et al., 2021). However, few studies have discussed how NLP models can help characterize sales performance using actual sales transcripts. Thus, there is a need to explore to what extent NLP models can inform B2B businesses of the characteristics embodied within their salesforce. This study aims to fill that literature gap. Through a partnership with a medium-sized tech company based out of California, USA, this study conducted an experiment to try and answer to what extent can we characterize sales performance based on real-life sales communication? And in what ways can conversational data inform the sales team at a California-based mid-sized tech company about how top performers communicate with customers? In total, over 5000 sentences containing over 110 000 words were collected and analyzed using two separate rule-based sentiment analysis techniques: TextBlob developed by Steven Loria (2013) and Valence Aware Dictionary and sEntiment Reasoner (VADER) developed by CJ Hutto and Eric Gilbert (2014). A Naïve Bayes classifier was then adopted to test and train each sentiment output from the two rule-based techniques. While both models obtained high accuracy, above 90%, it was concluded that an oversampled VADER approach yields the highest results. Additionally, VADER also tends to classify positive and negative sentences more correctly than TextBlob, when manually reviewing the output, hence making it a better model for the used dataset.