Comparative Analysis of Similarity Check Mechanism for Motif Extraction

In this work, a comparative analysis of the similarity check mechanism used in the most effective algorithm for mining simple motifs GEMS (Gene Enrichment Motif Searching) and that used in a popular multi-objective genetic algorithm, MOGAMOD (Multi-Objective Genetic Algorithm for Motif Discovery) was done. In our previous work, we had reported the implementation of GEMS on suffix tree –Suffix Tree Gene Enrichment Motif Searching (STGEMS) and shown the linear asymptotic runtime achieved. Here, we attempt to empirically proof the high sensitivity of the resulting algorithm, STGEMS in mining motifs from challenging sequences like we have in Plasmodium falciparum. The results obtained validates the high sensitivity of the similarity check mechanism employed in GEMS and also shows that a careful deployment of this mechanism in the multi-objective genetic algorithm, improved the sensiti

HUMAN FACE RECOGNITION USING A HYBRID LEARNING RBF NEURAL NETWORK WITH PSEUDO ZERNIKE MOMENT INVARIANT

In this paper a new method for the recognition of human face in 2-Dimentional digital images using new hybrid learning algorithm (HLA) for radial basis function neural network as classifier and pseudo Zernike moment invariant (PZMI) as a face feature is proposed