61 resultados para Training and pruning

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Background & Study Aim: Physical activity has been an important factor to increase bone mineral density (BMD) and, consequently, to prevent and treat osteoporosis. The study aimed the effects of adapted Judo training on BMD in postmenopausal women, during pharmacological treatment. Material & Methods: Eighteen female volunteers participated in this study. They were separated into two groups: Adapted Judo training (AJT) (n=11; 52.2±5.3 years) and control group (CG) (n=7; 53.8±4.4 years). Lunar GE Dual Energy X-Ray Absorptiometry (DXA) measured BMD at lumbar L2-L4, femoral neck and trochanter sites. The training period for AJT was two years, comprised 12 mesocycles with different intensities. ANOVA compared 2 groups in 3 moments of testing and Scheffé Test allowed multiple comparisons between groups for the L2-L4 and femoral neck sites, but at trochanter was Fisher LSD. Results: ANOVA showed significant differences in the AJT group (F(2, 32)=15.187, p=0.000023). Scheffé Test showed significant increase on lumbar BMD after one year of AJT (Δ%=+8.9%, p=0.000017) and after two years this improvement stand still (p=0.33). The CG after one year presented significant decrease in BMD of femoral neck (Δ%=-6.9%, p=0.03) and trochanter (Δ%=-3.7%, p=0.0084). However, the CG recovered the loss of BMD of femoral neck (Δ%=+7.6%, p=0.02) and trochanter (Δ%=+3.8%, p=0.0079) after two years of study. Conclusions: Drug therapy, without the physical activity practice, can aid the maintenance of BMD. AJT may be considered as an efficient physical activity for postmenopausal women with low BMD in pharmacological treatment. © ARCHIVES OF BUDO | SCIENCE OF MARTIAL ARTS.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Essential arterial hypertension is the most common risk factor for cardiovascular morbidity and mortality. Regular exercise is a well-established intervention for the prevention and treatment of hypertension. Continuous moderate-intensity exercise training (CMT) that can be sustained for 30 min or more has been traditionally recommended for hypertension prevention and treatment. On the other hand, several studies have shown that high-intensity interval training (HIT), which consists of several bouts of high-intensity exercise (~85% to 95% of HRMAX and/or VO2MAX lasting 1 to 4 min interspersed with intervals of rest or active recovery, is superior to CMT for improving cardiorespiratory fitness, endothelial function and its markers, insulin sensitivity, markers of sympathetic activity and arterial stiffness in hypertensive and normotensive at high familial risk for hypertension subjects. This compelling evidence suggesting larger beneficial effects of HIT for several factors involved in the pathophysiology of hypertension raises the hypothesis that HIT may be more effective for preventing and controlling hypertension.

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In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.