Máquina de vetores-suporte intervalar

The Support Vector Machines (SVM) has attracted increasing attention in machine learning area, particularly on classification and patterns recognition. However, in some cases it is not easy to determinate accurately the class which given pattern belongs. This thesis involves the construction of a intervalar pattern classifier using SVM in association with intervalar theory, in order to model the separation of a pattern set between distinct classes with precision, aiming to obtain an optimized separation capable to treat imprecisions contained in the initial data and generated during the computational processing. The SVM is a linear machine. In order to allow it to solve real-world problems (usually nonlinear problems), it is necessary to treat the pattern set, know as input set, transforming from nonlinear nature to linear problem. The kernel machines are responsible to do this mapping. To create the intervalar extension of SVM, both for linear and nonlinear problems, it was necessary define intervalar kernel and the Mercer s theorem (which caracterize a kernel function) to intervalar function

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Origem física das curvas sigmoidais respiratórias pressão-volume: recrutamento alveolar e elasticidade não-linear

An important unsolved problem in medical science concerns the physical origin of the sigmoidal shape of pressure volume curves of healthy (and some unhealthy) lungs. Such difficulties are expected because the lung, which is the most important structure in the respiratory system, is extremely complex. Its rheological properties are unknown and seem to depend on phenomena occurring from the alveolar scale up to the thoracic scale. Conventional wisdom holds that linear response, i.e., Hooke s law, together with alveolar overdistention, play a dominant role in respiration, but such assumptions cannot explainthe crucial empirical sigmoidal shape of the curves. In this doctorate thesis, we propose an alternative theory to solve this problem, based on the alveolar recruitment together with the nonlinear elasticity of the alveoli. This theory suggests that recruitment may be the predominant factor shaping these curves in the entire range of pressures normally employed in experiments. The proposed model correctly predicts the observed sigmoidal pressure volume curves, allowing us to discuss adequately the importance of this result, as well as its implications for medical practice

Influência do jet lag social em marcadores circadianos de atividade - repouso e cardíaco em estudantes de medicina

Studies reveal that in recent decades a decrease in sleep duration has occurred. Social commitments, such as work and school are often not aligned to the "biological time" of individuals. Added to this, there is a reduced force of zeitgeber caused by less exposure to daylight and larger exposure to evenings. This causes a chronic sleep debt that is offset in a free days. Indeed, a restriction and extent of sleep called "social Jet lag" occurs weekly. Sleep deprivation has been associated to obesity, cancer, and cardiovascular risk. It is suggested that the autonomic nervous system is a pathway that connects sleep problems to cardiovascular diseases. However, beyond the evidence demonstrated by studies using models of acute and controlled sleep deprivation, studies are needed to investigate the effects of chronic sleep deprivation as it occurs in the social jet lag. The aim of this study was to investigate the influence of social jet lag in circadian rest-activity markers and heart function in medical students. It is a cross-sectional, observational study conducted in the Laboratory of Neurobiology and Biological Rhythmicity (LNRB) at the Department of Physiology UFRN. Participated in the survey medical students enrolled in the 1st semester of their course at UFRN. Instruments for data collection: Munich Chronotype Questionnaire, Morningness Eveningness Questionnaire of Horne and Östberg, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Actimeter; Heart rate monitor. Analysed were descriptive variables of sleep, nonparametric (IV60, IS60, L5 and M10) and cardiac indexes of time domain, frequency (LF, HF LF / HF) and nonlinear (SD1, SD2, SD1 / SD2). Descriptive, comparative and correlative statistical analysis was performed with SPSS software version 20. 41 students participated in the study, 48.8% (20) females and 51.2% (21) males, 19.63 ± 2.07 years. The social jet lag had an average of 02: 39h ± 00:55h, 82.9% (34) with social jet lag ≥ 1h and there was a negative correlation with the Munich chronotype score indicating greater sleep deprivation in subjects prone to eveningness. Poor sleep quality was detected in 90.2% (37) (X2 = 26.56, p <0.001) and 56.1% (23) excessive daytime sleepiness (X2 = 0.61, p = 0.435). Significant differences were observed in the values of LFnu, HFnu and LF / HF between the groups of social jet lag <2h and ≥ 2h and correlation of the social jet lag with LFnu (rs = 0.354, p = 0.023), HFnu (rs = - 0.354 , p = 0.023) and LF / HF (r = 0.355, p = 0.023). There was also a negative association between IV60 and indexes in the time domain and non-linear. It is suggested that chronic sleep deprivation may be associated with increased sympathetic activation promoting greater cardiovascular risk.