Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Fast robot voice interface through optimum-path forest

Voice-based user interfaces have been actively pursued aiming to help individuals with motor impairments, providing natural interfaces to communicate with machines. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for voice-based robot interface, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster. Experiments were conducted against Support Vector Machines, Neural Networks and a Bayesian classifier to show the OPF robustness. The proposed architecture provides high accuracy rates allied with low computational times. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica

This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer.

The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach

Background: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal's life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality.Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism.Conclusions: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle. © 2013 Mokry et al.; licensee BioMed Central Ltd.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Estimativa da radiação solar ultravioleta em Botucatu/SP Brasil utilizando técnicas de aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Construção e análise da rede integrada de interações entre genes humanos envolvida com a regulação da trnsição G1/S do ciclo celular plea adesão à matriz extracelular

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Analisando a determinação sexual de vertebrados com base em redes de interação entre proteínas

Pós-graduação em Ciências Biológicas (Genética) - IBB

Conversão grafema-fone para um sistema de reconhecimento de voz com suporte a grandes vocabulários para o português brasileiro

O processamento de voz tornou-se uma tecnologia cada vez mais baseada na modelagem automática de vasta quantidade de dados. Desta forma, o sucesso das pesquisas nesta área está diretamente ligado a existência de corpora de domínio público e outros recursos específicos, tal como um dicionário fonético. No Brasil, ao contrário do que acontece para a língua inglesa, por exemplo, não existe atualmente em domínio público um sistema de Reconhecimento Automático de Voz (RAV) para o Português Brasileiro com suporte a grandes vocabulários. Frente a este cenário, o trabalho tem como principal objetivo discutir esforços dentro da iniciativa FalaBrasil [1], criada pelo Laboratório de Processamento de Sinais (LaPS) da UFPA, apresentando pesquisas e softwares na área de RAV para o Português do Brasil. Mais especificamente, o presente trabalho discute a implementação de um sistema de reconhecimento de voz com suporte a grandes vocabulários para o Português do Brasil, utilizando a ferramenta HTK baseada em modelo oculto de Markov (HMM) e a criação de um módulo de conversão grafema-fone, utilizando técnicas de aprendizado de máquina.