A nonlinear classifier using an evolution of Cellular Automata

Ponkaew, Jetsada; Wongthanavasu, Sartra; Lursinsap, Chidchanok

doi:10.1109/ispacs.2011.6146058

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Ponkaew et al discussed [17] a non-linear classifier generalized multiple attractor cellular automata (GMACA) using an evolution of cellular automata. It is the special class of cellular automata, especially used in genetic algorithm for efficient implementation.…”

Section: G Generalized Multiple Attractor Cellular Automatamentioning

confidence: 99%

An Analytical Study of Cellular Automata and its Applications in Cryptography

Kumaresan¹,

Gopalan²

2017

IJCNIS

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Abstract-Security and confidentiality are the major concerns in information technology enabled services wherein data security, user authentication, industrial security and message authentication have a great deal of access to the world anywhere, anytime. The implication is: there is a need for efficient methods to secure digital data across different platforms. The concept of cellular automata finds application in the design of efficient methods to secure digital information. It is a recent field of research and its recognition has been on the rise with its high parallel structure and ability to design complex dynamic systems. In this paper, we study the basic concepts of different types of cellular automata and also discuss its applications in cryptography with various examples.

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Section: G Generalized Multiple Attractor Cellular Automatamentioning

confidence: 99%

An Analytical Study of Cellular Automata and its Applications in Cryptography

Kumaresan¹,

Gopalan²

2017

IJCNIS

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“…An MACA with  attractors can be viewed as a pattern classifier. It classifies a given set of patterns into  distinct classes [13,14]. The following lemma shows properties of MACA.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Generation of Pattern Classifiers Based on Linear Nongroup CA

Choi

Cho

Kim

2015

Journal of Korea Multimedia Society

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Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al.[1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

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