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We introduce a new approach to high-order accuracy for the numerical solution of diffusion problems by solving the equations in differential form using a reconstruction technique. The approach has the advantages of simplicity and economy. It results in several new high-order methods including a simplified version of discontinuous Galerkin (DG). It also(More)
Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on(More)
Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden(More)
OBJECTIVE Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. SUBJECTS AND METHODS Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the(More)
DNA microarray is a multiplex technology used in molecular biology and biomedicine. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, of which the result should be analyzed by computational methods. Analyzing microarray data using intelligent computing methods has attracted many researchers in(More)
An effective training algorithm called extreme learning machine (ELM) has recently proposed for single hidden layer feedforward neural networks (SLFNs). It randomly chooses the input weights and hidden layer biases, and analytically determines the output weights by a simple matrix-inversion operation. This algorithm can achieve good performance at extremely(More)
Neural networks have been massively used in regression problems due to their ability to approximate complex nonlinear mappings directly from input patterns. However, collected data for training networks often include outliers which affect final results. This paper presents an approach for training single hidden-layer feedforward neural networks (SLFNs)(More)