@article{(Open Science Index):https://publications.waset.org/pdf/12209,
	  title     = {Certain Data Dimension Reduction Techniques for application with ANN based MCS for Study of High Energy Shower},
	  author    = {Gitanjali Devi and  Kandarpa Kumar Sarma and  Pranayee Datta and  Anjana Kakoti Mahanta},
	  country	= {},
	  institution	= {},
	  abstract     = {Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.},
	    journal   = {International Journal of Physical and Mathematical Sciences},
	  volume    = {4},
	  number    = {12},
	  year      = {2010},
	  pages     = {1481 - 1488},
	  ee        = {https://publications.waset.org/pdf/12209},
	  url   	= {https://publications.waset.org/vol/48},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 48, 2010},
	}