In the field of actuarial science, statistical methods have been extensively studied toestimate the risk of insurance. These methods are good at estimating the risk of typicalinsurance policies, as historical data is available. However, their performance can be pooron unique insurance policies, which require the manual assessment of an underwriter. Aclassification of insurance policies on a unique/typical scale would help insurance companiesallocate manual resources more efficiently and validate the goodness of fit of thepricing models on unique objects. The aim of this thesis is to use outlier detection methodsto identify unique non-life insurance policies. The many categorical nominal variablespresent in insurance policy data sets represent a challenge when applying outlier detectionmethods. Therefore, we also explore different ways to derive informative numericalrepresentations of categorical nominal variables. First, as a baseline, we use the principalcomponent analysis of mixed data to find a numerical representation of categorical nominalvariables and the principal component analysis to identify unique insurances. Then,we see whether better performance can be achieved using autoencoders which can capturecomplex non-linearities. In particular, we learn a numerical representation of categoricalnominal variables using the encoder layer of an autoencoder, and we use a different autoencoderto identify unique insurances. Since we are in an unsupervised setting, the twomethods are compared by performing a simulation study and using the NLS-KDD dataset. The analysis shows autoencoders are superior at identifying unique objects than principalcomponent analysis. We conclude that the ability of autoencoders to model complexnon-linearities between the variables allows for this class of methods to achieve superiorperformance.

In recent years, communities that source raw water from the Dan River experienced two severe and unprecedented outbreaks of unpleasant tastes and odors in their drinking water. During both TandO events strong 'earthy', 'musty' odors were reported, but the source was not identified. The first TandO event began in early February, 2015 and coincided with an algal bloom in the Dan River. The algal bloom was thought to be the cause, but after the bloom dissipated, odors persisted until May 2015. The second TandO in October, 2015 did not coincide with observed algal blooms. On February 2, 2014 approximately 39,000 tons of coal ash from a Duke Energy coal ash pond was spilled into the Dan River near Eden, NC. As there were no documented TandO events before the spill, there is concern the coal ash adversely impacted water quality and biological communities in the Dan River leading to the TandO events. In addition to the coal ash spill, years of industrial and agricultural activity in the Dan River area may have contributed to the TandO events. The purpose of this research was to elucidate causes of the two TandO events and provide guidance to prevent future problems. Monthly water samples were collected from August, 2016 to September, 2017 from twelve sites along the Dan and Smith Rivers. Multivariate analyses were applied to look for underlying factors, spatial or temporal trends in the data. There were no reported TandO events during the project but sensory analysis, Flavor Profile Analysis, characterized earthy/musty odors present. No temporal or spatial trends of odors were observed. Seven earthy/musty odorants commonly associated with TandO events were detected. Odor intensity was mainly driven by geosmin, but no relationship between strong odors and odorants was observed. / Master of Science / In recent years, communities that source water from the Dan River experienced two severe and unprecedented outbreaks of unpleasant tastes and odors (T&O) in their drinking water. During both odor events strong ‘earthy’, ‘musty’ odors were reported, but the source was not identified. The first event began in early February, 2015 and coincided with an algal bloom in the Dan River. The algal bloom was thought to be the cause, but after the bloom dissipated, odors persisted until May 2015. The odors returned in October, 2015 but did not coincide with an algal bloom. On February 2, 2014 approximately 39,000 tons of coal ash from a Duke Energy coal ash pond was spilled into the Dan River near Eden, NC. As no documented odor events occurred before the spill, there is concern the coal ash adversely impacted the water quality in the Dan River leading to the odor events. The purpose of this research was to elucidate causes of the two odor events and provide guidance to prevent future problems. Monthly water samples were collected from August, 2016 to September, 2017 from twelve sites along the Dan and Smith Rivers. Multivariate analyses were applied to look for important factors. There were no reported odor events during the project but sensory analysis characterized earthy/musty odors present. No temporal or spatial trends of odors were observed. Seven earthy/musty odorants commonly associated with odor events were detected.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

Pesquisas na área de Química Medicinal têm direcionado esforços na busca por métodos que acelerem o processo de descoberta de novos medicamentos. Dentre as diversas etapas relacionadas ao longo do processo de descoberta de substâncias bioativas está a análise das relações entre a estrutura química e a atividade biológica de compostos. Neste processo, os pesquisadores da área de Química Medicinal analisam conjuntos de dados que são caracterizados pela alta dimensionalidade e baixo número de observações. Dentro desse contexto, o presente trabalho apresenta uma abordagem computacional que visa contribuir para a análise de dados químicos e, consequentemente, a descoberta de novos medicamentos para o tratamento de doenças crônicas. As abordagens de análise exploratória de dados, utilizadas neste trabalho, combinam técnicas de redução de dimensionalidade e de agrupamento para detecção de estruturas naturais que reflitam a atividade biológica dos compostos analisados. Dentre as diversas técnicas existentes para a redução de dimensionalidade, são discutidas o escore de Fisher, a análise de componentes principais e a análise de componentes principais esparsas. Quanto aos algoritmos de aprendizado, são avaliados o k-médias, fuzzy c-médias e modelo de misturas ICA aperfeiçoado. No desenvolvimento deste trabalho foram utilizados quatro conjuntos de dados, contendo informações de substâncias bioativas, sendo que dois conjuntos foram relacionados ao tratamento da diabetes mellitus e da síndrome metabólica, o terceiro conjunto relacionado a doenças cardiovasculares e o último conjunto apresenta substâncias que podem ser utilizadas no tratamento do câncer. Nos experimentos realizados, os resultados alcançados sugerem a utilização das técnicas de redução de dimensionalidade juntamente com os algoritmos não supervisionados para a tarefa de agrupamento dos dados químicos, uma vez que nesses experimentos foi possível descrever níveis de atividade biológica dos compostos estudados. Portanto, é possível concluir que as técnicas de redução de dimensionalidade e de agrupamento podem possivelmente ser utilizadas como guias no processo de descoberta e desenvolvimento de novos compostos na área de Química Medicinal. / Researches in Medicinal Chemistry\'s area have focused on the search of methods that accelerate the process of drug discovery. Among several steps related to the process of discovery of bioactive substances there is the analysis of the relationships between chemical structure and biological activity of compounds. In this process, researchers of medicinal chemistry analyze data sets that are characterized by high dimensionality and small number of observations. Within this context, this work presents a computational approach that aims to contribute to the analysis of chemical data and, consequently, the discovery of new drugs for the treatment of chronic diseases. Approaches used in exploratory data analysis, employed in this work, combine techniques of dimensionality reduction and clustering for detecting natural structures that reflect the biological activity of the analyzed compounds. Among several existing techniques for dimensionality reduction, we have focused the Fisher\'s score, principal component analysis and sparse principal component analysis. For the clustering procedure, this study evaluated k-means, fuzzy c-means and enhanced ICA mixture model. In order to perform experiments, we used four data sets, containing information of bioactive substances. Two sets are related to the treatment of diabetes mellitus and metabolic syndrome, the third set is related to cardiovascular disease and the latter set has substances that can be used in cancer treatment. In the experiments, the obtained results suggest the use of dimensionality reduction techniques along with clustering algorithms for the task of clustering chemical data, since from these experiments, it was possible to describe different levels of biological activity of the studied compounds. Therefore, we conclude that the techniques of dimensionality reduction and clustering can be used as guides in the process of discovery and development of new compounds in the field of Medicinal Chemistry

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-04T11:46:54Z No. of bitstreams: 1 Allan de Barcelos Silva_.pdf: 2298557 bytes, checksum: dc876b1dd44e7a7095219195e809bb88 (MD5) / Made available in DSpace on 2018-04-04T11:46:55Z (GMT). No. of bitstreams: 1 Allan de Barcelos Silva_.pdf: 2298557 bytes, checksum: dc876b1dd44e7a7095219195e809bb88 (MD5) Previous issue date: 2017-12-14 / Nenhuma / Na área de Processamento de Linguagem Natural, a avaliação da similaridade semântica textual é considerada como um elemento importante para a construção de recursos em diversas frentes de trabalho, tais como a recuperação de informações, a classificação de textos, o agrupamento de documentos, as aplicações de tradução, a interação através de diálogos, entre outras. A literatura da área descreve aplicações e técnicas voltadas, em grande parte, para a língua inglesa. Além disso, observa-se o uso prioritário de recursos probabilísticos, enquanto os aspectos linguísticos são utilizados de forma incipiente. Trabalhos na área destacam que a linguística possui um papel fundamental na avaliação de similaridade semântica textual, justamente por ampliar o potencial dos métodos exclusivamente probabilísticos e evitar algumas de suas falhas, que em boa medida são resultado da falta de tratamento mais aprofundado de aspectos da língua. Este contexto é potencializado no tratamento de frases curtas, que consistem no maior campo de utilização das técnicas de similaridade semântica textual, pois este tipo de sentença é composto por um conjunto reduzido de informações, diminuindo assim a capacidade de tratamento probabilístico eficiente. Logo, considera-se vital a identificação e aplicação de recursos a partir do estudo mais aprofundado da língua para melhor compreensão dos aspectos que definem a similaridade entre sentenças. O presente trabalho apresenta uma abordagem para avaliação da similaridade semântica textual em frases curtas no idioma português brasileiro. O principal diferencial apresentado é o uso de uma abordagem híbrida, na qual tanto os recursos de representação distribuída como os aspectos léxicos e linguísticos são utilizados. Para a consolidação do estudo, foi definida uma metodologia que permite a análise de diversas combinações de recursos, possibilitando a avaliação dos ganhos que são introduzidos com a ampliação de aspectos linguísticos e também através de sua combinação com o conhecimento gerado por outras técnicas. A abordagem proposta foi avaliada com relação a conjuntos de dados conhecidos na literatura (evento PROPOR 2016) e obteve bons resultados. / One of the areas of Natural language processing (NLP), the task of assessing the Semantic Textual Similarity (STS) is one of the challenges in NLP and comes playing an increasingly important role in related applications. The STS is a fundamental part of techniques and approaches in several areas, such as information retrieval, text classification, document clustering, applications in the areas of translation, check for duplicates and others. The literature describes the experimentation with almost exclusive application in the English language, in addition to the priority use of probabilistic resources, exploring the linguistic ones in an incipient way. Since the linguistic plays a fundamental role in the analysis of semantic textual similarity between short sentences, because exclusively probabilistic works fails in some way (e.g. identification of far or close related sentences, anaphora) due to lack of understanding of the language. This fact stems from the few non-linguistic information in short sentences. Therefore, it is vital to identify and apply linguistic resources for better understand what make two or more sentences similar or not. The current work presents a hybrid approach, in which are used both of distributed, lexical and linguistic aspects for an evaluation of semantic textual similarity between short sentences in Brazilian Portuguese. We evaluated proposed approach with well-known and respected datasets in the literature (PROPOR 2016) and obtained good results.

Pesquisas na área de Química Medicinal têm direcionado esforços na busca por métodos que acelerem o processo de descoberta de novos medicamentos. Dentre as diversas etapas relacionadas ao longo do processo de descoberta de substâncias bioativas está a análise das relações entre a estrutura química e a atividade biológica de compostos. Neste processo, os pesquisadores da área de Química Medicinal analisam conjuntos de dados que são caracterizados pela alta dimensionalidade e baixo número de observações. Dentro desse contexto, o presente trabalho apresenta uma abordagem computacional que visa contribuir para a análise de dados químicos e, consequentemente, a descoberta de novos medicamentos para o tratamento de doenças crônicas. As abordagens de análise exploratória de dados, utilizadas neste trabalho, combinam técnicas de redução de dimensionalidade e de agrupamento para detecção de estruturas naturais que reflitam a atividade biológica dos compostos analisados. Dentre as diversas técnicas existentes para a redução de dimensionalidade, são discutidas o escore de Fisher, a análise de componentes principais e a análise de componentes principais esparsas. Quanto aos algoritmos de aprendizado, são avaliados o k-médias, fuzzy c-médias e modelo de misturas ICA aperfeiçoado. No desenvolvimento deste trabalho foram utilizados quatro conjuntos de dados, contendo informações de substâncias bioativas, sendo que dois conjuntos foram relacionados ao tratamento da diabetes mellitus e da síndrome metabólica, o terceiro conjunto relacionado a doenças cardiovasculares e o último conjunto apresenta substâncias que podem ser utilizadas no tratamento do câncer. Nos experimentos realizados, os resultados alcançados sugerem a utilização das técnicas de redução de dimensionalidade juntamente com os algoritmos não supervisionados para a tarefa de agrupamento dos dados químicos, uma vez que nesses experimentos foi possível descrever níveis de atividade biológica dos compostos estudados. Portanto, é possível concluir que as técnicas de redução de dimensionalidade e de agrupamento podem possivelmente ser utilizadas como guias no processo de descoberta e desenvolvimento de novos compostos na área de Química Medicinal. / Researches in Medicinal Chemistry\'s area have focused on the search of methods that accelerate the process of drug discovery. Among several steps related to the process of discovery of bioactive substances there is the analysis of the relationships between chemical structure and biological activity of compounds. In this process, researchers of medicinal chemistry analyze data sets that are characterized by high dimensionality and small number of observations. Within this context, this work presents a computational approach that aims to contribute to the analysis of chemical data and, consequently, the discovery of new drugs for the treatment of chronic diseases. Approaches used in exploratory data analysis, employed in this work, combine techniques of dimensionality reduction and clustering for detecting natural structures that reflect the biological activity of the analyzed compounds. Among several existing techniques for dimensionality reduction, we have focused the Fisher\'s score, principal component analysis and sparse principal component analysis. For the clustering procedure, this study evaluated k-means, fuzzy c-means and enhanced ICA mixture model. In order to perform experiments, we used four data sets, containing information of bioactive substances. Two sets are related to the treatment of diabetes mellitus and metabolic syndrome, the third set is related to cardiovascular disease and the latter set has substances that can be used in cancer treatment. In the experiments, the obtained results suggest the use of dimensionality reduction techniques along with clustering algorithms for the task of clustering chemical data, since from these experiments, it was possible to describe different levels of biological activity of the studied compounds. Therefore, we conclude that the techniques of dimensionality reduction and clustering can be used as guides in the process of discovery and development of new compounds in the field of Medicinal Chemistry

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-14T18:15:08Z No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) / Made available in DSpace on 2017-08-14T18:15:08Z (GMT). No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) Previous issue date: 2008-02-25 / Female breast cancer is the major cause of death in western countries. Efforts in Computer Vision have been made in order to add improve the diagnostic accuracy by radiologists. In this work, we present a methodology that uses independent component analysis (ICA) along with support vector machine (SVM) and linear discriminant analysis (LDA) to distinguish between mass or non-mass and benign or malign tissues from mammograms. As a result, it was found that: LDA reaches 90,11% of accuracy to discriminante between mass or non-mass and 95,38% to discriminate between benign or malignant tissues in DDSM database and in mini-MIAS database we obtained 85% to discriminate between mass or non-mass and 92% of accuracy to discriminate between benign or malignant tissues; SVM reaches 99,55% of accuracy to discriminate between mass or non-mass and the same percentage to discriminate between benign or malignat tissues in DDSM database whereas, and in MIAS database it was obtained 98% to discriminate between mass or non-mass and 100% to discriminate between benign or malignant tissues. / Câncer de mama feminino é o câncer que mais causa morte nos países ocidentais. Esforços em processamento de imagens foram feitos para melhorar a precisão dos diagnósticos por radiologistas. Neste trabalho, nós apresentamos uma metodologia que usa análise de componentes independentes (ICA) junto com análise discriminante linear (LDA) e máquina de vetor de suporte (SVM) para distinguir as imagens entre nódulos ou não-nódulos e os tecidos em benignos ou malignos. Como resultado, obteve-se com LDA 90,11% de acurácia na discriminação entre nódulo ou não-nódulo e 95,38% na discriminação de tecidos benignos ou malignos na base de dados DDSM. Na base de dados mini- MIAS, obteve-se 85% e 92% na discriminação entre nódulos ou não-nódulos e tecidos benignos ou malignos respectivamente. Com SVM, alcançou-se uma taxa de até 99,55% na discriminação de nódulos ou não-nódulos e a mesma porcentagem na discriminação entre tecidos benignos ou malignos na base de dados DDSM enquanto que na base de dados mini-MIAS, obteve-se 98% e até 100% na discriminação de nódulos ou não-nódulos e tecidos benignos ou malignos, respectivamente.

In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Monitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control. / O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.