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Because it only takes into account the difference of each endpoint, the method cannot describe the correlation between two dynamic sequences.
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Little information exists concerning the interactions between these two dynamic processes in trees.
The optimal cost of a dynamic time warping (DTW) algorithm is frequently used for measuring similarity between two spectral sequences.
To measure the similarity between two activity sequences, the one-dimensional Sequential Alignment Method (SAM) is used to compute the Levenshtein distance between two sequences of activities by applying a dynamic programming algorithm [23, 24].
In this paper, we apply a dynamic time warping (DTW) algorithm [ 21] to align the rows of matrices, which is a technique to find an optimal alignment between two given sequences.
The fitness function used adapted the Needleman-Wunsch alignment algorithm (a dynamic programming algorithm used to efficiently find similar regions between two sequences of DNA, RNA or protein. It can be used to efficiently compare code fragments) to increase the similarity between the two sets of code, allowing the derived refactoring sequences to remove code smells.
The Smith-Waterman method is a well-established dynamic programming algorithm for finding the optimal local alignment between two sequences using a substitution matrix and a gap-scoring system.
Dynamic range also refers to limit values of similarity between two sequences that can be interpreted to distinguish each other.
Sanders et al. [ 61] examined 21 patients between 1 and 22 months following osteochondral transplantation using a series of five dynamic contrast-enhanced sequences at 1-min intervals post contrast.
More work on dynamic sequences.
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