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First, the Active Basis algorithm was used to detect the fragment of object contour and to obtain the initial object contour by using fragment connection based on morphology.
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A radial basis neural network algorithm was used to test all six models.
To better consider the thermodynamic model in the alignment, the Smith Waterman algorithm was used as the basis of a variety of implementations, including RiSearch [ 28] and TargetRNA [ 29].
In the high-resolution image where nuclei were abnormally bright, a region growing algorithm was used to identify collagen on the basis of its connectivity.
A hierarchical clustering algorithm was used to group genes on the basis of similarity in the pattern with which their expression varied over all samples [ 8].
A serial testing algorithm was used.
Moreover, we discover the performance of reconstructing sparse zero-one signals by sparse binary matrix, which is used in the proposed MA-Greedy algorithm, is better than that by Gaussian random matrix when Basis Pursuit (BP) algorithm is used for signal recovery.
Traditional models based on multiple linear regression and curve estimation regression analyses, and artificial neural network (ANN) type of multi-layer perception on the basis of back-propagation training algorithm were used for predicting the SOC.
We compared the predictive abilities of EANNs in which the following kinds of training algorithms were used: linear, radial basis function, general regression (GR), and multilayer perceptron.
Algorithms were used to predict the next heel contact on the basis of the preceding steps.
Three different algorithms were used.
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