Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Cellular automata (CA) are parallel computational models that comprise of a grid of cells. CA is mainly used for modeling complex systems in various fields, where the geometric structure of the lattices is different. In the absence of a CA model to accommodate different types of lattices in CA, an angle-based CA model is proposed to accommodate various lattices. In the proposed model, the neighborhood structure in a two dimensional cellular automata (2D-CA) is viewed as a star graph. The vertices of the proposed graph are determined by a parameter, angle ( θ ) . Based on the angle ( θ ) , the neighborhood of the CA, which is treated as the vertices of the graph, varies. So this model is suitable for the representation of different types of two dimensional lattices such as square lattice, rectangular lattice, hexagonal lattice, etc. in CA. A mathematical model is formulated for representing CA rules which suit for different types of symmetric lattices. The star graph representation helps to find out the internal symmetries exists in CA rules. Classification of CA rules based on the symmetry exists in the rules, which generates symmetric patterns are discussed in this work.

Computer Science

School of Computer Science and Engineering

Chennai Campus

Fulltext

Symmetry

Classification of Two Dimensional Cellular Automata Rules for Symmetric Pattern Generation

Computer aided diagnosis (CAD) is an advancing technology in medical imaging. CAD acts as an additional computing power for doctors to interpret the medical images which leads to a more accurate diagnosis of the disease.CAD system increases the chances of detection of brain lesions by assisting the physicians in decreasing the observational oversight in the early stage of diseases.This paper focuses on the development of a cellular automata based model to find the anomaly prone areas in human brains.Because of the bilateral symmetric nature of human brain, a symmetry based cellular automata model is proposed.An algorithm is designed based on the proposed model to detect the anomaly prone areas in brain images. The proposed model can be a standalone model or it can be incorporated to a sophisticated computer aided diagnosis system. By incorporating asymmetry information into a computer aided diagnosis system, enhances its performance in identifying the anomalies exists in bilaterally symmetrical brain images.

Indonesian Journal of Electrical Engineering and Computer Science

A symmetry based anomaly detection in brain using cellular automata for computer aided diagnosis

Urban cities are like complex systems. In order to test various policies urban models are used. A cellular automaton is used to simulate complex systems. The data used for urban planning is huge and complex because it is based on various factors like the growth in population, economy etc. So an accurate classifier is required. This paper proposes a cellular automata model, called Scalable Cellular Automata-based Classifier (SCAC), which has the capability to deal with non-conforming patterns in the binary features. It is developed on a Decision Support Elementary Cellular Automata (DS-ECA). The classifying capability of DS-ECA is accurate since it can describe very complicated decision rule in high dimension problems with less complexity. SCAC has double rule vectors and a scalable algorithm with decision function, the structure of which has two layers; the first layer is employed to evolve an input pattern into feature space using Apache Spark and the other interprets the patterns in feature space as binary answer through the decision function which is also scaled in a cluster. The algorithm proposed is a Cloud based Cluster computing model which is based on Cellular Automata. © IAEME Publication.