1D sequence homologous alignment tool, like FastA (FAST-ALL) [8] or BLAST (Basic Local Alignment Search Tool) [1], has been widely used in bioinformatics field and perform elegant and fast searching for the sequences ...
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1D sequence homologous alignment tool, like FastA (FAST-ALL) [8] or BLAST (Basic Local Alignment Search Tool) [1], has been widely used in bioinformatics field and perform elegant and fast searching for the sequences developed from the same kinds of species. In other word, it can classify through determining the homologous similarity which is not totally similar in sequences of protein sequences, structure or nucleotide sequences. An approach is proposed in this paper called AA-FAST (abbreviation for Acoustics Alphabet-FAST) which takes advantage of alignment tool and significant sequence encoding method. In this experiment, it could not only determine 4 fish species with similar size and shape but also the motion of them with identical alignment matrix. Besides, it shows that the position containing higher similarity encoding sequence fragment is related to the position of specific fish species and the acoustic features of specific fish species. Other purpose of this paper is to demonstrate how a bioinformatics tool could be applied to the acoustic field.
Most research concerning the influence of network structure on phenomena taking place on the network focus on relationships between global statistics of the network structure and characteristic properties of those phe...
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ISBN:
(纸本)9780769543758
Most research concerning the influence of network structure on phenomena taking place on the network focus on relationships between global statistics of the network structure and characteristic properties of those phenomena, even though local structure has a significant effect on the dynamics of some phenomena. In the present paper, we propose a new analysis method for phenomena on networks based on a categorization of nodes. First, local statistics such as the average path length and the clustering coefficient for a node are calculated and assigned to the respective node. Then, the nodes are categorized using the self-organizingmap (SOM) algorithm. Characteristic properties of the phenomena of interest are visualized for each category of nodes. The validity of our method is demonstrated using the results of two simulation models.
In this paper, we present and compare three clustering approaches which group fingerprints according to its minutiae point's locations. The best technique for grouping fingerprints is based on the ART] clustering ...
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ISBN:
(纸本)9781424433964
In this paper, we present and compare three clustering approaches which group fingerprints according to its minutiae point's locations. The best technique for grouping fingerprints is based on the ART] clustering algorithm. We compare the quality of clustering of ARTI based clustering with K-Mean clustering technique and selforganizing Neural Network (SOM) [I] clustering algorithm in terms of intracluster distances. Our results show that the average intracluster distance of the clusters formed by SOM and K-Means algorithm varies from 83.36 to 127.372 and 33.925 to 58.17 respectively while the average intra-cluster distance of clusters formed by ART] based clustering technique varies from 4.55 to 13.06, which indicates the clusters formed by ART] Clustering approach are much compact and isolated as compare to selforganizingmap and K-Means based clustering approaches.
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