Certain Data Dimension Reduction Techniques for application with ANN based MCS for Study of High Energy Shower
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.
[1] R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable
at http://www.srl.caltech.edu/personnel/dick/cos-encyc.html,
1996.
[2] R. Engel: "Theory and Phenomenology of Extensive Air
Showers", Forschungszentrum Karlsruhe, Germany- available at
http://moriond.in2p3.fr/J05/trans/sunday/engel1.pdf, 2005.
[3] D. Hanna, "Application of Neural Nets to Extensive Air-Showers",
Proceedings of the 22nd International Cosmic Ray Conference, IUPAP,
Volume 4, p: 500, 1991.
[4] 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.
[5] 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.
[6] 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.
[7] P. Sommers: "Extensive Air Showers and Measurement Techniques",
Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12,
2004.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New
Delhi, 2003.
[18] S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed.,
Pearson Education, New Delhi, 2003.
[19] L. I. Smith: A tutorial on Principal Components Analysis: February 26,
2002.
[20] S. Fiori: "An Experimental Comparison of Three PCA Neural Networks",
Neural Processing Letters, vol. 11, pp. 209-218, 2000.
[21] H. Hoffmann: "Unsupervised Learning of Visuomotor Associations:
Dissertation", Universitt Bielefeld, Technische Fakultt, Logos Verlag
Berlin, ISBN: 3-8325-0858-9, Germany, 2005.
[22] Kernel principal component analysis: Wikipedia, the free encyclopedia
/en.wikipedia.org/wiki/Kernelprincipalcomponentanalysis
[23] T. Takiguchi, Y. Ariki: "PCA-Based Speech Enhancement for Distorted
Speech Recognition", Journal of Multimedia, vol. 2, no. 5, 2007.
[24] 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.
[25] 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.
[26] 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
[1] R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable
at http://www.srl.caltech.edu/personnel/dick/cos-encyc.html,
1996.
[2] R. Engel: "Theory and Phenomenology of Extensive Air
Showers", Forschungszentrum Karlsruhe, Germany- available at
http://moriond.in2p3.fr/J05/trans/sunday/engel1.pdf, 2005.
[3] D. Hanna, "Application of Neural Nets to Extensive Air-Showers",
Proceedings of the 22nd International Cosmic Ray Conference, IUPAP,
Volume 4, p: 500, 1991.
[4] 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.
[5] 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.
[6] 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.
[7] P. Sommers: "Extensive Air Showers and Measurement Techniques",
Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12,
2004.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New
Delhi, 2003.
[18] S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed.,
Pearson Education, New Delhi, 2003.
[19] L. I. Smith: A tutorial on Principal Components Analysis: February 26,
2002.
[20] S. Fiori: "An Experimental Comparison of Three PCA Neural Networks",
Neural Processing Letters, vol. 11, pp. 209-218, 2000.
[21] H. Hoffmann: "Unsupervised Learning of Visuomotor Associations:
Dissertation", Universitt Bielefeld, Technische Fakultt, Logos Verlag
Berlin, ISBN: 3-8325-0858-9, Germany, 2005.
[22] Kernel principal component analysis: Wikipedia, the free encyclopedia
/en.wikipedia.org/wiki/Kernelprincipalcomponentanalysis
[23] T. Takiguchi, Y. Ariki: "PCA-Based Speech Enhancement for Distorted
Speech Recognition", Journal of Multimedia, vol. 2, no. 5, 2007.
[24] 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.
[25] 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.
[26] 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
@article{"International Journal of Engineering, Mathematical and Physical Sciences:61241", author = "Gitanjali Devi and Kandarpa Kumar Sarma and Pranayee Datta and Anjana Kakoti Mahanta", title = "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.", keywords = "EAS, Shower, Core, ANN, Location.", volume = "4", number = "12", pages = "1488-8", }
