Programa de Pós-graduação em Ciência da Computação em Rede
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Navegando Programa de Pós-graduação em Ciência da Computação em Rede por Por Orientador "Soares, Anderson da Silva"
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Item Reconhecimento de padrões por processos adaptativos de compressão(Universidade Federal de Goiás, 2020-03-02) Bailão, Adriano Soares de Oliveira; Delbem, Alexandre Cláudio Botazzo; http://lattes.cnpq.br/1201079310363734; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Silva, Nadia Felix Felipe da; Duque, Cláudio Gottschalg; Costa, Ronaldo Martins da; Monaco, Francisco JoséData compression is a process widely used by the industry in the storage and transport of information and is applied to a variety of domains such as text, image, audio and video. The compression processes are a set of mathematical operations that aim to represent each sample of data in compressed form, or with a smaller size. Pattern recognition techniques can use compression properties and metrics to design machine learning models from adaptive algorithms that represent samples in compressed form. An advantage of adaptive compression models, is that they have dimensionality reduction techniques resulting from the compression properties. This thesis proposes a general unsupervised learning model (for different problem domains and different types of data), which combines adaptive compression strategies in two phases: granulation, responsible for the perception and representation of the knowledge necessary to solve a problem generalization, and the codification phase, responsible for structuring the reasoning of the model, based on the representation and organization of the problem objects. The reasoning expressed by the model denotes the ability to generalize data objects in the general context. Generic methods, based on compactors (without loss of information), lack generalization capacity for some types of data objects, and in this thesis, lossy compression techniques are also used, in order to circumvent the problem and increase the capacity of generalization of the model. Results demonstrate that the use of techniques and metrics based on adaptive compression produce a good approximation of the original data samples in data sources with high dimensionality. Tests point to good machine learning models with good generalization capabilities derived from the approach based on the reduction of dimensionality offered by adaptive compression processes.Item Classificação de tecidos epiteliais tumorais empregando imagens hiperespectrais e infravermelho de ondas curtas(Universidade Federal de Goiás, 2021-08-04) Lucena, Daniel Vitor de; Coelho, Clarimar José; http://lattes.cnpq.br/1350166605717268; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Coelho, Clarimar José; Wastowski, Isabela Jubé; Laureano, Gustavo Teodoro; Soares, Fabrízzio Alphonsus Alves de Melo NunesHyperspectral Imaging (HSI) is a new concept of disease diagnosis by image analysis. Although there are many approaches for HSI image analysis, the classification of spatial informations to tumor classification is still limited. In this thesis is proposed the building of a new method of analysis and classification of present objects in HSI based on techniques of machine learning to understand the molecular vibrational behavior of healthy and tumoral human epithelial tissue by means of short-wave infrared (SWIR) spectroscopy. In the experimental study is analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. It can be concluded that HSI-SWIR can be used to build new methods for tumor classification.Item Variable selection in multivariate calibration considering non-decomposability assumption and building blocks hypothesis(Universidade Federal de Goiás, 2018-12-06) Paula, Lauro Cássio Martins de; Coelho, Clarimar José; http://lattes.cnpq.br/1350166605717268; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Coelho, Clarimar José; Camilo Junior, Celso Gonçalves; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Oliveira, Anselmo Elcana deThe procedure used to select a subset of suitable features in a given data set consists in variable selection, which is important when the dataset contains large number of variables and many of them are redundant. Multivariate calibration combines variable selection with statistical techniques to build mathematical models which relate the data to a given property of interest in order to predict this property by selecting informative variables. In this context, variable selection techniques have been widely applied to the solution of several optimization problems. For instance, Genetic Algorithms (GAs) are easy to implement and consist in a population-based model that uses selection and recombination operators to generate new solutions. However, usually in multivariate calibration the dataset present a considerable correlation degree among variables and this provides an evidence about the problem not being properly decomposed. Moreover, some studies in literature have claimed genetic operators used by GAs can cause the building blocks (BBs) disruption of viable solutions. Therefore, this work aims to claim that selecting variables in multivariate calibration is a non-completely decomposable problem (hypothesis 1) as well as that recombination operators affects the non-decomposability assumption (hypothesis 2). Additionally, we are proposing two heuristics, one local search-based operator and two versions of an Epistasis-based Feature Selection Algorithm (EbFSA) to improve model prediction performance and avoid BBs disruption. Based on the performed inquiry and experimental results, we are able to endorse the viability of our hypotheses and demonstrate EbFSA can overcome some traditional algorithms.