Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images

P. Kiran Sree; Sssn Usha Devi N.

doi:doi:10.11648/j.mlr.20160101.12

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Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images

P. Kiran Sree, Sssn Usha Devi N.

Published in Machine Learning Research (Volume 1, Issue 1)

Received: 27 November 2016 Accepted: 17 December 2016 Published: 16 January 2017

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Abstract

This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.

Published in	Machine Learning Research (Volume 1, Issue 1)
DOI	10.11648/j.mlr.20160101.12
Page(s)	15-18
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Cellular Automata (CA), Active Learning (DL), Non Linear CA

References

[1]	Dr P. KiranSree & DrInampudi Ramesh Babu et al, Investigating an Artificial Immune System to Strengthen the Protein Structure Prediction and Protein Coding Region Identification using Cellular Automata Classifier. International Journal of Bioinformatics Research and Applications, Vol 5, Number 6, pp 647-662, ISSN: 1744-5493. (2009) (Inderscience Journals, UK) Listed & Recognized in US National Library of Medicine National Institutes of Health. National Center for Biotechnology Information (Government of USA)PMID: 19887338 [PubMed-indexed for MEDLINE] H Index (Citation Index): 08 (SCImago, www.scimagojr.com) (Nine Years Old Journal).
[2]	Dr P. KiranSree & DrInampudi Ramesh Babu et al, Identification of Promoter Region in Genomic DNA Using Cellular Automata Based Text Clustering. The International Arab Journal of Information Technology (IAJIT), Volume 7, No 1, 2010, pp 75-78. ISSN: 1683-3198H Index (Citation Index): 05 (SCImago, www.scimagojr.com)(Eleven Years Old Journal)( SCI Indexed Journal).
[3]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, A Fast Multiple Attractor Cellular Automata with Modified Clonal Classifier for Coding Region Prediction in Human Genome, Journal of Bioinformatics and Intelligent Control, Vol. 3, 2014, pp 1-6. DOI: 10.1166/jbic. 2014. 1077 (American Scientific Publications, USA).
[4]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, A Fast Multiple Attractor Cellular Automata with Modified Clonal Classifier Promoter Region Prediction in Eukaryotes. Journal of Bioinformatics and Intelligent Control, Vol. 3, 1–6, 2014. DOI: 10.1166/jbic. 2014. 1077 (American Scientific Publications, USA).
[5]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, 5. MACA-MCC-DA: A Fast MACA with Modified Clonal Classifier Promoter Region Prediction in Drosophila and Arabidopsis. European Journal of Biotechnology and Bioscience, 1 (6), 2014, pp 22-26, Impact Factor: 1.74.
[6]	Dr P. KiranSree & DrInampudi Ramesh Babu et al, Cellular Automata in Splice Site Prediction. European Journal of Biotechnology and Bioscience, 1 (6), 2014, pp 36-39, Impact Factor: 1.74.
[7]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, AIX-MACA-Y Multiple Attractor Cellular Automata Based Clonal Classifier for Promoter and Protein Coding Region Prediction. Journal of Bioinformatics and Intelligent Control 3, no. 1 (2014): 23-30. DOI: 10.1166/jbic. 2014. 1071, (American Scientific Publications, USA).
[8]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, PSMACA: An Automated Protein Structure Prediction Using MACA (Multiple Attractor Cellular Automata). Journal of Bioinformatics and Intelligent Control 2, no. 3 (2013): 211-215. DOI:10.1166/jbic. 2013. 1052 (American Scientific Publications, USA).
[9]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, An extensive report on Cellular Automata based Artificial Immune System for strengthening Automated Protein Prediction. Advances in Biomedical Engineering Research (ABER) Volume 1 Issue 3, September 2013, pp 45-51. Science Publications (USA).
[10]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, A Novel Protein Coding Region Identifying Tool using Cellular Automata Classifier with Trust-Region Method and Parallel Scan Algorithm (NPCRITCACA). International Journal of Biotechnology & Biochemistry (IJBB) Volume 4, 177-189 Number 2 (December 2008). (Eight Years Old Journal) Listed in Indian Science Abstracts, ISSN: 0019-6339, Volume 45, Number 22, November 2009.
[11]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, HMACA: Towards proposing Cellular Automata based tool for protein coding, promoter region identification and protein structure prediction. International Journal of Research in Computer Applications & Information Technology, Volume 1 Number 1, pp 26-31, 2013.
[12]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, PRMACA: A Promoter Region identification using Multiple Attractor Cellular Automata (MACA) in the proceedings CT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I Advances in Intelligent Systems and Computing Volume 248, 2014, pp 393-399 (Springer-AISC series).
[13]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, Towards Proposing an Artificial Immune System for strengthening PSMACA: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata proceedings ofInternational Conference on Advances in electrical, electronics, mechanical and Computer Science (ICAEEMCS)-2013, ISBN: 978-93-81693-66-04 on September 2nd 2013, Hyderabad.
[14]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics in Review of Bioinformatics and Biometrics (RBB) Volume 2 Issue 3, September 2013, pp70-76.
[15]	Dr P. KiranSree&DrInampudi Ramesh Babu et al, Protein coding region Identification, in proceedings of 2nd International Conference on Proteomics Bioinformatics, July 2-4, 2012 Embassy Suites Las Vegas, USA “,( Special Issue of Journal of Proteomics & Bioinformatics. (USA), Volume 5 Issue 6 – 123, ISSN: 0974-276X, H Index (Citation Index): 06 (SCImago, www.scimagojr.com)Impact Factor: 2.2, (Five Years Old Journal).

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APA Style

P. Kiran Sree, Sssn Usha Devi N. (2017). Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Machine Learning Research, 1(1), 15-18. https://doi.org/10.11648/j.mlr.20160101.12

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ACS Style

P. Kiran Sree; Sssn Usha Devi N. Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Mach. Learn. Res. 2017, 1(1), 15-18. doi: 10.11648/j.mlr.20160101.12

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AMA Style

P. Kiran Sree, Sssn Usha Devi N. Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Mach Learn Res. 2017;1(1):15-18. doi: 10.11648/j.mlr.20160101.12

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@article{10.11648/j.mlr.20160101.12,
  author = {P. Kiran Sree and Sssn Usha Devi N.},
  title = {Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images},
  journal = {Machine Learning Research},
  volume = {1},
  number = {1},
  pages = {15-18},
  doi = {10.11648/j.mlr.20160101.12},
  url = {https://doi.org/10.11648/j.mlr.20160101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20160101.12},
  abstract = {This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.},
 year = {2017}
}

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TY  - JOUR
T1  - Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images
AU  - P. Kiran Sree
AU  - Sssn Usha Devi N.
Y1  - 2017/01/16
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20160101.12
DO  - 10.11648/j.mlr.20160101.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 15
EP  - 18
PB  - Science Publishing Group
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AB  - This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.
VL  - 1
IS  - 1
ER  -

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Author Information

P. Kiran Sree

Dept of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India
Sssn Usha Devi N.

Dept of Computer Science and Engineering, University College of Engineering, Jawaharlal Nehru Technological University, Kakinada, India

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APA Style

P. Kiran Sree, Sssn Usha Devi N. (2017). Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Machine Learning Research, 1(1), 15-18. https://doi.org/10.11648/j.mlr.20160101.12

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ACS Style

P. Kiran Sree; Sssn Usha Devi N. Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Mach. Learn. Res. 2017, 1(1), 15-18. doi: 10.11648/j.mlr.20160101.12

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AMA Style

P. Kiran Sree, Sssn Usha Devi N. Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images. Mach Learn Res. 2017;1(1):15-18. doi: 10.11648/j.mlr.20160101.12

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@article{10.11648/j.mlr.20160101.12,
  author = {P. Kiran Sree and Sssn Usha Devi N.},
  title = {Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images},
  journal = {Machine Learning Research},
  volume = {1},
  number = {1},
  pages = {15-18},
  doi = {10.11648/j.mlr.20160101.12},
  url = {https://doi.org/10.11648/j.mlr.20160101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20160101.12},
  abstract = {This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images
AU  - P. Kiran Sree
AU  - Sssn Usha Devi N.
Y1  - 2017/01/16
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20160101.12
DO  - 10.11648/j.mlr.20160101.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 15
EP  - 18
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20160101.12
AB  - This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.
VL  - 1
IS  - 1
ER  -

Copy | Download