@article{(Open Science Index):https://publications.waset.org/pdf/1674,
	  title     = {Prediction of Location of High Energy Shower Cores using Artificial Neural Networks},
	  author    = {Gitanjali Devi and  Kandarpa Kumar Sarma and  Pranayee Datta and  Anjana Kakoti Mahanta},
	  country	= {},
	  institution	= {},
	  abstract     = {Artificial Neural Network (ANN)s can be modeled for
High Energy Particle analysis with special emphasis on shower core
location. The work describes the use of an ANN based system which
has been configured to predict locations of cores of showers in the
range 1010.5 to 1020.5 eV. The system receives density values as
inputs and generates coordinates of shower events recorded for values
captured by 20 core positions and 80 detectors in an area of 100
meters. Twenty ANNs are trained for the purpose and the positions
of shower events optimized by using cooperative ANN learning. The
results derived with variations of input upto 50% show success rates
in the range of 90s.},
	    journal   = {International Journal of Physical and Mathematical Sciences},
	  volume    = {5},
	  number    = {10},
	  year      = {2011},
	  pages     = {1634 - 1640},
	  ee        = {https://publications.waset.org/pdf/1674},
	  url   	= {https://publications.waset.org/vol/58},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 58, 2011},