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Certain Data Dimension Reduction Techniques for application with ANN based MCS for Study of High Energy Shower
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078761Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1301
 R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable at http://www.srl.caltech.edu/personnel/dick/cos-encyc.html, 1996.
 R. Engel: "Theory and Phenomenology of Extensive Air Showers", Forschungszentrum Karlsruhe, Germany- available at http://moriond.in2p3.fr/J05/trans/sunday/engel1.pdf, 2005.
 D. Hanna, "Application of Neural Nets to Extensive Air-Showers", Proceedings of the 22nd International Cosmic Ray Conference, IUPAP, Volume 4, p: 500, 1991.
 J. C. Perrett and J T P M van Stekelenborg, "The applications of neural networks in the core location analysis of extensive air showers", Bartol Research Institute, University of Delaware, 217 Sharp Laboratory, Newark, DE 19716, USA J. Phys. G: Nucl. Pert. Phys. 17, pp:1291-1302, 1991.
 H. J. Mayer, "A neural network algorithm for core location analysis at large extended air shower arrays", Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 317, Issues 1-2, pp. 339-345, June, 1992.
 T. Wibig, "The Artificial Neural Networks as a tool for analysis of the individual Extensive Air Showers data", Preprint submitted to Elsevier Preprint (1 February 2008), available at http://arxiv.org/abs/hepph/ 9608227v1.
 P. Sommers: "Extensive Air Showers and Measurement Techniques", Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12, 2004.
 A. Chilingarian, G. Gharagyozyan, S. Ghazaryan, G. Hovsepyan, E. Mamidjanyan, L. Melkumyan, V. Romakhin, A. Vardanyan and S. Sokhoyan: "Study of extensive air showers and primary energy spectra by MAKET-ANI detector on mountain Aragats", Elsavier Journal of Astroparticle Physics, vol. 28, pp. 5871, 2007.
 G. Devi, K. K. Sarma, P. Datta and A. K. Mahanta: "Prediction of Location of High Energy Shower Cores using Artificial Neural Networks", International Journal of Engineering and Applied Sciences Volume 7, No.1, pp.-20 - 26, 2011.
 G. Devi, K. K. Sarma, P. Datta and A. K. Mahanta: "ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location", International Journal of Information and Mathematical Sciences, Vol 6, No. 3, pp. 176- 185, 2010.
 L. Chen and M. S. Kamel: "Design of Multiple Classifier Systems for Time Series Data ", Spinger Lecture Notes in Computer Science, 2005, vol. 3541, pp. 216-225, 2005.
 M. T. El-Melegy and S. M. Ahmed: "Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification", Spinger Studies in Fuzziness and Soft Computing, vol. 210, pp. 65-94, 2007.
 Z. Suraj, N. E. Gayar, P. Delimata: "A Rough Set Approach to Multiple Classifier Systems", Fundamenta Informaticae, vol. 72, no. 1-3, pp. 393-406, 2006.
 F. Roli: "Mini Tutorial on Multiple Classifier Systems", University of Cagliari, Dept. of Electrical and Electronics Eng., Italy, School on the Analysis of Patterns, 2009.
 M. Sharma and R. Mammone: "Speech recognition using sub-word neural tree network models and multiple classifier fusion", CAIP Center, Rutgers University, Piscataway, NJ, USA, 1995.
 M. Sharma and R. Mammone: "Artificial neural network fusion: Application to Arabic words recognition", Proceedings of ESANN - 2005- European Symposium on Artificial Neural Networks Bruges, (pp. 151 - 156), Belgium, 27-29 April, 2005.
 B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New Delhi, 2003.
 S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed., Pearson Education, New Delhi, 2003.
 L. I. Smith: A tutorial on Principal Components Analysis: February 26, 2002.
 S. Fiori: "An Experimental Comparison of Three PCA Neural Networks", Neural Processing Letters, vol. 11, pp. 209-218, 2000.
 H. Hoffmann: "Unsupervised Learning of Visuomotor Associations: Dissertation", Universitt Bielefeld, Technische Fakultt, Logos Verlag Berlin, ISBN: 3-8325-0858-9, Germany, 2005.
 Kernel principal component analysis: Wikipedia, the free encyclopedia /en.wikipedia.org/wiki/Kernelprincipalcomponentanalysis
 T. Takiguchi, Y. Ariki: "PCA-Based Speech Enhancement for Distorted Speech Recognition", Journal of Multimedia, vol. 2, no. 5, 2007.
 I. Rosca, L. State and C. L. Cocianu: "Investigations on the Potential of PCA Based Neural Implementation Attempts in Solving Specific Tasks in Image Processing", 9th WSEAS Int. Conf. on Mathematics and Computer in Business and Economics (MCBE -08), Bucharest, Romania, pp. 116- 124, June 24-26, 2008.
 A. Hyvrinen and E. Oja: "Independent Component Analysis- Algorithms and Applications: Neural Networks", Research Centre, Helsinki University of Technology, Finland, 13 (4-5), pp. 411-430, 2000.
 J. H. Lee, H. Y. Jung, T. W. Lee, S. Y. Lee: "Speech Feature Extraction Using Independent Component Analysis", In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing ( ICASSP -00), Istanbul, Turkey, vol.3, pp. 1631 - 1634, 2000