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In the current era of computation, machine learning is the most commonly used technique to find out a pattern of highly complex datasets. The present paper shows some existing applications, such as stock data mining, undergraduate admission, and breast lesion detection, where different supervised machine learning algorithms are used to classify various patterns. A performance analysis, in terms of accuracy, precision, sensitivity, and specificity is given for all three applications. It is observed that a support vector machine (SVM) is the commonly used supervised learning method that shows good performance in terms of performance metrics. A comparative analysis of SVM classifiers on the above-mentioned applications is shown in the paper.

Nilpotent cellular automata have the simplest possible dynamics: all initial configurations lead in bounded time into the unique fixed point of the system. We investigate nilpotency in the setup of one-dimensional non-uniform cellular automata (NUCA) where different cells may use different local rules. There are infinitely many cells in NUCA but only a finite number of different local rules. Changing the distribution of the local rules in the system may drastically change the dynamics. We prove that if the available local rules are such that every periodic distribution of the rules leads to nilpotent behavior then so do also all eventually periodic distributions. However, in some cases there may be non-periodic distributions that are not nilpotent even if all periodic distributions are nilpotent. We demonstrate such a possibility using aperiodic Wang tile sets. We also investigate temporally periodic points in NUCA. In contrast to classical uniform cellular automata, there are NUCA—even reversible equicontinuous ones—that do not have any temporally periodic points. We prove the undecidability of this property: there is no algorithm to determine if a NUCA with a given finite distribution of local rules has a periodic point.

This paper studies chaotic behavior of 1-dimensional non-uniform cellular automata (CAs). The chaotic system is unpredictable in the long run. To understand the source of unpredictability, we add an information to a cell and check how it flows to its neighbors. That is, once the state of a cell is changed, the probability to which the change affects its neighbors, is calculated. The probabilistic value decides whether one cell communicates with another cell. Depending on the communication among cells, a binary relation over the set of cells is defined in this paper which is known as communication relation. It is shown that the relation should be an equivalence relation to make a cellular automaton (CA) chaotic. The equivalence relation among the cells form an equivalence class which is named as communication class. Another important property of a chaotic CA is transitivity of the CA. Transitivity of a CA does not allow disjoint subsets of a configuration space. Based on the communication class and transitivity property of a CA, we newly define the chaos in CA. Sometimes one cell may not be able to communicate with another cell due to blocking word. The presence of blocking word in a CA makes the CA non-transitive. This paper shows a method to find out such blocking words. Finally, a parametrization technique is developed in this work based on the transitivity, communication class and blocking words. Depending on the parameter value of a non-uniform CA, one can predict whether the behavior of the CA is chaotic.

This work reports a unification technique that can form a universal platform to emulate any cellular automata (CAs). The paper shows that a number of non-uniform CAs can be mapped into one uniform CA. To show the effectiveness of the unification method, three well known applications, such as pseudo random pattern generation, cryptography and pattern classification are chosen where three different CAs are used for the corresponding hardware implementations. Finally, the hardware is proposed to show that all these existing applications, that use different platforms, can now be implemented in a universal platform.

This paper presents a study of chaos in one-dimensional cellular automata (CAs). The communication of information from one part of the system to another has been taken into consideration in this study. This communication is formalized as a binary relation over the set of cells. It is shown that this relation is an equivalence relation and all the cells form a single equivalence class when the cellular automaton (CA) is chaotic. However, the communication between two cells is sometimes blocked in some CAs by a subconfiguration which appears in between the cells during evolution. This blocking of communication by a subconfiguration has been analyzed in this paper with the help of de Bruijn graph. We identify two types of blocking — full and partial. Finally a parameter has been developed for the CAs. We show that the proposed parameter performs better than the existing parameters.