Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

Sistema inteligente para diagnóstico de patologias na laringe utilizando máquinas de vetor de suporte

The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Análise da máquina de indução trifásica através das grandezas de Buchholz-Goodhue aplicando os vetores espaciais instantâneos nas condições de desequilíbrio e distorção conforme a IEEE 1459-2000

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Implementação em hardware de um sistema inteligente para detecção de plantas daninhas em plantações de soja utilizando máquinas de vetores de suporte e redes neurais artificiais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Interface para classificação de dados por máquinas de vetores de suporte

In this project the Pattern Recognition Problem is approached with the Support Vector Machines (SVM) technique, a binary method of classification that provides the best solution separating the data in the better way with a hiperplan and an extension of the input space dimension, as a Machine Learning solution. The system aims to classify two classes of pixels chosen by the user in the interface in the interest selection phase and in the background selection phase, generating all the data to be used in the LibSVM library, a library that implements the SVM, illustrating the library operation in a casual way. The data provided by the interface is organized in three types, RGB (Red, Green and Blue color system), texture (calculated) or RGB + texture. At last the project showed successful results, where the classification of the image pixels was showed as been from one of the two classes, from the interest selection area or from the background selection area. The simplest user view of results classification is the RGB type of data arrange, because it’s the most concrete way of data acquisition

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

O mundo, e suas máquinas: um estudo sobre propagação temática em A Máquina do Mundo, de Carlos Drummond de Andrade

Este estudo parte de preocupações filosóficas, teóricas e analíticas para promover exercícios de leitura e de interpretação do poema A Máquina do Mundo (1951), de Carlos Drummond de Andrade. Estabelecendo pela via ensaística a noção de ontologia da propagação, busca localizar a necessidade da ontologia no fundamento pré-paradigmático da ciência. Incorporando referências da Fenomenologia e da Hermenêutica, tenta assumir seus pressupostos e implicações para a constituição do método em Teoria da Literatura e para a definição dos campos básicos para a analítica da existência lógica, empírica e pragmática: os campos que representam as instâncias ontológicas do real, do simbólico e do imaginário. Da assunção fenomenológica e hermenêutica, passa-se a considerações sobre categorias pertencentes ao jargão literário, escolhidas por sua relação com o artefato literário em questão e correspondendo ao âmbito das três instâncias ontológicas: discute-se a Poesia como sendo um fenômeno constituinte, a Literatura como uma manifestação instituinte e o Poema como uma manifestação restituinte do signo literário A Máquina do Mundo. Em seguida, considera afetações e interferências de algumas correntes sociológicas, formalistas e antropológicas, buscando participar do diálogo sobre a possibilidade de aceitação da prática literária como uma prática de valor cognitivo, e não apenas ideológico e estético. Em seu terceiro momento, o estudo busca aplicar os pressupostos ontológicos e epistêmicos para estabelecer o limite dos espaços comparativos tornados possíveis ao poema A Máquina do Mundo e buscando o pano de fundo histórico e historiográfico do século XX a partir de suas possibilidades de relação com a obra de Drummond e segundo a orientação dos vetores dados no poema: a consolidação do veio crepuscular na tópica canônica do Novecentos brasileiro; a implicação da postura ideológica de Drummond na recepção crítica à sua obra; e, por fim, a localização posterior dos topoi cosmológicos e existenciais. Em seu último ensaio, refere-se à questão cosmológica e sua afetação sobre a postura existencial do Caminhante. Recuperam-se pontos de semelhança e de diferença entre as poéticas encontradas em Os Lusíadas, de Luís de Camões e em A Divina Comédia, de Dante Alighieri, para por fim remeter à identificação de uma continuidade temática entre o poema de Drummond e aquele que se costuma designar como pensamento originário na tradição da Filosofia ocidental. A seguir, buscam-se referências para alguns dos elementos formais do poema A Máquina do Mundo segundo sua origem no contexto da Literatura Ocidental. Consideram-se alguns traços característicos dos três seres representados (A Máquina, o Mundo, o Caminhante) e, encerrando-se a tese, procede-se a um exercício de leitura centrado especificamente no poema

JFloat: uma biblioteca de ponto flutuante para a linguagem Java com suporte a arredondamento direcionado

This work presents JFLoat, a software implementation of IEEE-754 standard for binary floating point arithmetic. JFloat was built to provide some features not implemented in Java, specifically directed rounding support. That feature is important for Java-XSC, a project developed in this Department. Also, Java programs should have same portability when using floating point operations, mainly because IEEE-754 specifies that programs should have exactly same behavior on every configuration. However, it was noted that programs using Java native floating point types may be machine and operating system dependent. Also, JFloat is a possible solution to that problem

Detecção e recuperação de falhas para a máquina de redução de grafos PEWS-AM

Web services are software units that allow access to one or more resources, supporting the deployment of business processes in the Web. They use well-defined interfaces, using web standard protocols, making possible the communication between entities implemented on different platforms. Due to these features, Web services can be integrated as services compositions to form more robust loose coupling applications. Web services are subject to failures, unwanted situations that may compromise the business process partially or completely. Failures can occur both in the design of compositions as in the execution of compositions. As a result, it is essential to create mechanisms to make the implementation of service compositions more robust and to treat failures. Specifically, we propose the support for fault recovery in service compositions described in PEWS language and executed on PEWS-AM, an graph reduction machine. To support recovery failure on PEWS-AM, we extend the PEWS language specification and adapted the rules of translation and reduction of graphs for this machine. These contributions were made both in the model of abstract machine as at the implementation level