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One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...
When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always...
Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: APriori and ZigZag. Execution time clearly shows the advantage...
This paper presents a Neural Network (NN) based scheme for sizing and classification of defects in Steam Generator (SG) tubes of ferromagnetic material from both measured and simulated Remote Field Eddy Current Testing (RFECT) signals. A novel 2D-3D hybrid database approach of edge FEM method is applied for the rapid computation of RFECT signals due to local defects that is necessary for NN training...
Information retrieve is one of the most important operations in computer information systems. This paper presents a kind of fuzzy information retrieve method based on soft computing (SCFIR for short). SCFIR adopts fuzzy clustering analysis and artificial neural networks to organize databases in systems so as to increase the efficiency of fuzzy retrieve. At the same time, SCFIR realizes the understanding...
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