With the combination of computer-assisted image analysis and ultrasonographic imaging technology, it has been possible to study and increase the knowledge in different areas of medicine. Studies of ovarian development in female mammals using ultrasonography have shown a relationship between the day in the estrous cycle and the main structures of the ovary.<p>Ultrasound images of bovine ovaries were used to determine whether ultrasound-detected features can automatically determine the phase in the estrous cycle based on a single day's ultrasound examination of the ovaries. Five ultrasound-detected features of the bovine ovaries were used to determine the phase in the estrous cycle: (1) size of the dominant follicle; (2) size of the first subordinate follicle; (3) size of the second subordinate follicle; (4) size of the corpus luteum and (5) number of subordinate follicles with size ≥ 2mm. The collection of ultrasound images used for this study was formed by a group of 45 pairs of ovaries (left and right) which were imaged on day 3, day = 10 and day ≥ 17 of the estrous cycle corresponding to the metestrus, diestrus and proestrus phases respectively.<p>Four different experiments were performed to test the hypothesis. For experiments 1, 2 and 3 the bovine ovaries were classified into three different classes: day 3 of wave 1 (D3W1), day 1 of wave 2 (D1W2) and day 17 or higher (D≥17) that were related to the follicular development of the ovary and the estrous cycle phases as: metestrus, diestrus and proestrus respectively. For experiment 4 the bovine ovaries were classified into four classes: D3W1, D6W1, D1W2 and D≥17. The additional class (D6W1: day 6 of wave 1) was incorporated to represent the early-diestrus phase in the estrous cycle.<p>Two classifiers were implemented for all experiments and their performances compared: a decision tree classifier and a naive Bayes classifier. The decision tree classifier had the best performance with a classification rate of 100% for experiments 1, 2 and 3, giving a rather simple decision tree which used only two features to make a classification: size of the dominant follicle and size of the corpus luteum, suggesting these are key features in distinguishing between phases in the estrous cycle giving the most relevant information. The naive Bayes had a classification rate of 86.36% for experiment 1, 95.55% for experiment 2 and 90% for experiment 3. The results of this study supported the hypothesis that by using ultrasound detected features of bovine ovaries we can determine automatically the stage in the estrous cycle based on a single day's examination.

With the combination of computer-assisted image analysis and ultrasonographic imaging technology, it has been possible to study and increase the knowledge in different areas of medicine. Studies of ovarian development in female mammals using ultrasonography have shown a relationship between the day in the estrous cycle and the main structures of the ovary.<p>Ultrasound images of bovine ovaries were used to determine whether ultrasound-detected features can automatically determine the phase in the estrous cycle based on a single day's ultrasound examination of the ovaries. Five ultrasound-detected features of the bovine ovaries were used to determine the phase in the estrous cycle: (1) size of the dominant follicle; (2) size of the first subordinate follicle; (3) size of the second subordinate follicle; (4) size of the corpus luteum and (5) number of subordinate follicles with size ≥ 2mm. The collection of ultrasound images used for this study was formed by a group of 45 pairs of ovaries (left and right) which were imaged on day 3, day = 10 and day ≥ 17 of the estrous cycle corresponding to the metestrus, diestrus and proestrus phases respectively.<p>Four different experiments were performed to test the hypothesis. For experiments 1, 2 and 3 the bovine ovaries were classified into three different classes: day 3 of wave 1 (D3W1), day 1 of wave 2 (D1W2) and day 17 or higher (D≥17) that were related to the follicular development of the ovary and the estrous cycle phases as: metestrus, diestrus and proestrus respectively. For experiment 4 the bovine ovaries were classified into four classes: D3W1, D6W1, D1W2 and D≥17. The additional class (D6W1: day 6 of wave 1) was incorporated to represent the early-diestrus phase in the estrous cycle.<p>Two classifiers were implemented for all experiments and their performances compared: a decision tree classifier and a naive Bayes classifier. The decision tree classifier had the best performance with a classification rate of 100% for experiments 1, 2 and 3, giving a rather simple decision tree which used only two features to make a classification: size of the dominant follicle and size of the corpus luteum, suggesting these are key features in distinguishing between phases in the estrous cycle giving the most relevant information. The naive Bayes had a classification rate of 86.36% for experiment 1, 95.55% for experiment 2 and 90% for experiment 3. The results of this study supported the hypothesis that by using ultrasound detected features of bovine ovaries we can determine automatically the stage in the estrous cycle based on a single day's examination.

Microarray technology has enriched the study of gene expression in such a way that scientists are now able to measure the expression levels of thousands of genes in a single experiment. Microarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification, interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This thesis aims on a comparative study of state-of-the-art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k- nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t- statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used for this study. Different experiments have been applied to compare the performance of the classification methods with and without performing feature selection. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.

In current particle physics field, the progressive detection technologies are used. The pixel detectors are one of them. These detectors are divided into small subdetectors (pixels), which allow viewing exact tracks of the detected particles. This thesis defines criteria for mathematical description of the shape of the particle tracks of different kinds (e-, γ, p, α, μ) and compares several methods used for a classification -neural networks, decision trees and others. The Pixa software was implemented to process the data measured by pixel detectors. This software implements the characteristics and classification methods and creates statistical and other physical results.

Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-09-12T19:09:12Z No. of bitstreams: 1 JOÃO NAILSON DE CASTRO SILVA – DISSERTAÇÃO (PPGRN) 2017.pdf: 6066847 bytes, checksum: 06d6d215ba35522ddc64fb5c435d43f4 (MD5) / Made available in DSpace on 2018-09-12T19:09:12Z (GMT). No. of bitstreams: 1 JOÃO NAILSON DE CASTRO SILVA – DISSERTAÇÃO (PPGRN) 2017.pdf: 6066847 bytes, checksum: 06d6d215ba35522ddc64fb5c435d43f4 (MD5) Previous issue date: 2017-08-25 / Capes / A Caatinga é um bioma único que só ocorre na região do Semiárido do Brasil (SAB). Este bioma se diferencia principalmente pela capacidade de desenvolver mecanismos de adaptação aos baixos índices pluviométricos da região, além de apresentar alta resiliência nesse ambiente de elevada pressão antrópica. Tendo em vista que a cobertura vegetal exerce um papel muito importante no equilíbrio ambiental, as técnicas de sensoriamento remoto têm sido muito utilizadas para extrair informações biofísicas da vegetação. Este trabalho, teve como objetivo estabelecer uma metodologia que incorpore a capacidade de caracterização temporal do índice de vegetação à técnica de classificação de imagens para melhorar a identificação das classes de cobertura da Terra na bacia do Rio Sucuru, no Cariri paraibano. Neste sentido, busca-se uma classificação de referência dos padrões de cobertura da terra a partir de imagens de SR, para um período específico, no qual, seja possível uma validação em campo. Além disso, é realizada uma avaliação de uma série temporal de um índice de vegetação para melhoria da classificação realizada. Nessa pesquisa foi adotado parte da metodologia proposta por Chaves et al. (2008), para classificar os padrões de cobertura do solo e uma série temporal de EVI, processadas com 88 imagens selecionadas dos sensores ETM+ e OLI/TIRS, da série Landsat, para o período entre outubro de 2014 a setembro de 2016. Os resultados evidenciaram que analisar a cobertura vegetal utilizando apenas um único momento não retrata fidedignamente os padrões de cobertura do solo, visto que nesse ambiente semiárido as respostas que a vegetação apresenta diante da presença ou ausência de chuva são muito rápidas. Nesse sentido, os resultados mostram que uma análise espaço-temporal, utilizando um índice de vegetação, pode estabelecer uma melhor distinção das categorias atribuídas em uma classificação de padrões de cobertura do solo, possibilitando uma melhor percepção do comportamento da vegetação para um período de 24 meses observado. / The Caatinga is a unique biome that only occurs in the semi-arid region of Brazil. This biome is distinguished, mainly, by the capacity to develop mechanisms of adaptation to the low rainfall rates of the region. Plus, it also has a high resilience level upon high anthropic pressure. Considering that land cover plays a very important role in environmental balance, remote sensing techniques have been widely used to extract biophysical information from vegetation. The objective of this work is to establish a methodology that incorporates a time series vegetation index characterization to the image classification technique for improving the land cover classification in the Sucuru River basin in Cariri, Paraíba. In this sense, a Land Cover classification is acquired from SR images, for a specific data. For this data, there is a ground truth validation. In addition, an evaluation of a vegetation index time series is performed to improve the classification. In this research was adopted part of the methodology proposed by Chaves et al. (2008), to classify land cover patterns and a time series of EVI, processed with 88 images selected from the ETM + and OLI / TIRS sensors of the Landsat series for the period between October 2014 to September 2016. The results evidence that analyzing the land cover for a single time stamp could not reliably portray the land cover patterns since we often have fast changes before and after a rainfall event in this semi-arid environment. In this sense, the results show that a spatiotemporal analysis, using a vegetation index, can establish a better distinction of the categories assigned to a classification of land cover patterns, allowing a better perception of vegetation behavior for a period of 24 Months observed.

The aim of the doctoral thesis is to screen possibilities of multivariate classification methods used for the prediction of a financial distress of agricultural enterprises. Application of the thesis was based on a definition of an enterprise threatened by financial distress defined according to relevant literature review. The reliability of current classification models was verified first as a part of the solution process. The ability of each indicator and their combinations in terms of reliability classification were assessed as well. The main part consisted in the construction of models using classification methods (linear and quadratic discriminant analysis and robust variants, the methods of nearest neighbours and prototypes, logistic regression, probit regression, multilayer perceptron networks, classification trees and forests).

The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.

[pt] Com a crescente quantidade de dados sendo gerados e coletados, torna-se mais comum cenários em que se dispõe de dados rotulados em larga escala, mas com recursos computacionais limitados, de modo que não seja possível treinar modelos preditivos utilizando todas as amostras disponíveis. Diante dessa realidade, adotamos o paradigma de Machine Teaching como uma alternativa para obter modelos eficazes utilizando um subconjunto representativo dos dados disponíveis. Inicialmente, consideramos um problema central da área de Machine Teaching que consiste em encontrar o menor conjunto de amostras necessário para obter uma dada hipótese alvo h(asterisco). Adotamos o modelo de ensino black-box learner introduzido em (DASGUPTA et al., 2019), em que o ensino é feito interativamente sem qualquer conhecimento sobre o algoritmo do learner e sua classe de hipóteses, exceto que ela contém a hipótese alvo h(asterisco). Refinamos alguns resultados existentes para esse modelo e estudamos variantes dele. Em particular, estendemos um resultado de (DASGUPTA et al., 2019) para o cenário mais realista em que h(asterisco) pode não estar contido na classe de hipóteses do learner e, portanto, o objetivo do teacher é fazer o learner convergir para a melhor aproximação disponível de h(asterisco). Também consideramos o cenário com black-box learners não adversários e mostramos que podemos obter melhores resultados para o tipo de learner que se move para a próxima hipótese de maneira suave, preferindo hipóteses que são mais próximas da hipótese atual. Em seguida, definimos e abordamos o problema de Aprendizado com Restrição de Tempo considerando um cenário em que temos um enorme conjunto de dados e um limite de tempo para treinar um dado learner usando esse conjunto. Propomos o método TCT, um algoritmo para essa tarefa, desenvolvido com base nos princípios de Machine Teaching. Apresentamos um estudo experimental envolvendo 5 diferentes learners e 20 datasets no qual mostramos que TCT supera métodos alternativos considerados. Finalmente, provamos garantias de aproximação para uma versão simplificada do TCT. / [en] With the growing amount of data being generated and collected, it becomes increasingly common to have scenarios where there are large-scale labeled data but limited computational resources, making it impossible to train predictive models using all available samples. Faced with this reality, we adopt the Machine Teaching paradigm as an alternative to obtain effective models using a representative subset of available data. Initially, we consider a central problem of the Machine Teaching area which consists of finding the smallest set of samples necessary to obtain a given target hypothesis h(asterisk). We adopt the black-box learner teaching model introduced in (DASGUPTA et al., 2019), where teaching is done interactively without any knowledge about the learner s algorithm and its hypothesis class, except that it contains the target hypothesis h(asterisk). We refine some existing results for this model and study its variants. In particular, we extend a result from (DASGUPTA et al., 2019) to the more realistic scenario where h(asterisk) may not be contained in the learner s hypothesis class, and therefore, the teacher s objective is to make the learner converge to the best available approximation of h(asterisk). We also consider the scenario with non-adversarial black-box learners and show that we can obtain better results for the type of learner that moves to the next hypothesis smoothly, preferring hypotheses that are closer to the current hypothesis. Next, we address the Time-Constrained Learning problem, considering a scenario where we have a huge dataset and a time limit to train a given learner using this dataset. We propose the TCT method, an algorithm for this task, developed based on Machine Teaching principles. We present an experimental study involving 5 different learners and 20 datasets in which we show that TCT outperforms alternative methods considered. Finally, we prove approximation guarantees for a simplified version of TCT.

We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.

Dans un monde où tous les appareils électro-ménagers se connectent et deviennent intelligents, il est apparu pour des industriels français le besoin de créer des fours de cuisson innovants capables de suivre l’état de cuisson à cœur de poissons et de viandes sans capteur au contact. Cette thèse se place dans ce contexte et se divise en deux grandes parties. La première est une phase de sélection d’attributs parmi un ensemble de mesures issues de capteurs spécifiques de laboratoire afin de permettre d’appliquer un algorithme de classification supervisée sur trois états de cuisson. Une méthode de sélection basée sur la logique floue a notamment été appliquée pour réduire grandement le nombre de variable à surveiller. La seconde partie concerne la phase de suivi de cuisson en ligne par plusieurs méthodes. Les techniques employées sont une approche par classification sur dix états à cœur, la résolution d’équation de la chaleur discrétisée, ainsi que le développement d’un capteur logiciel basé sur des réseaux de neurones artificiels synthétisés à partir d’expériences de cuisson, pour réaliser la reconstruction du signal de la température au cœur des aliments à partir de mesures disponibles en ligne. Ces algorithmes ont été implantés sur microcontrôleur équipant une version prototype d’un nouveau four afin d’être testés et validés dans le cas d’utilisations réelles. / In a world where all personal devices become smart and connected, some French industrials created a project to make ovens able detecting the cooking state of fish and meat without contact sensor. This thesis takes place in this context and is divided in two major parts. The first one is a feature selection phase to be able to classify food in three states: under baked, well baked and over baked. The point of this selection method, based on fuzzy logic is to strongly reduce the number of features got from laboratory specific sensors. The second part concerns on-line monitoring of the food cooking state by several methods. These technics are: classification algorithm into ten bake states, the use of a discrete version of the heat equation and the development of a soft sensor based on an artificial neural network model build from cooking experiments to infer the temperature inside the food from available on-line measurements. These algorithms have been implemented on microcontroller equipping a prototype version of a new oven in order to be tested and validated on real use cases.