Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization.

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Optimization of Modular Neural Networks with the LVQ Algorithm for Classification of Arrhythmias Using Particle Swarm Optimization

Studies in Computational Intelligence - Recent Advances on Hybrid Approaches for Designing Intelligent Systems ◽

10.1007/978-3-319-05170-3_21 ◽

2014 ◽

pp. 307-314 ◽

Cited By ~ 3

Author(s):

Jonathan Amezcua ◽

Patricia Melin

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Swarm Optimization ◽

Modular Neural Networks

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Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds

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Microarray spot partitioning by autonoumsly organising maps thorugh contour model

Optimization of Modular Neural Networks with the LVQ Algorithm for Classification of Arrhythmias Using Particle Swarm Optimization

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