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Soft Computing

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22-03-2015 | Methodologies and Application

A maximum spanning tree-based dynamic fuzzy supervised neural network architecture for classification of murder cases

Authors: M. M. Janeela Theresa, V. Joseph Raj

Published in: Soft Computing | Issue 6/2016

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Abstract

In this paper, a hybrid algorithm based on maximum spanning tree and dynamic fuzzy neural network is proposed for classification of murder cases. The proposed classification model of criminal law is useful for judges, lawyers or other people who want to determine the guilt and deliver judgment in their cases. The model is trained and tested for sufficient number of court decisions. The experimental results show that the proposed maximum spanning tree-based dynamic fuzzy supervised neural network algorithm overcomes the problem of slow convergence and large computation caused by artificial neural network and fuzzy neural network algorithms. Comparative studies were carried out for a number of different networks and configurations and reported. Simulations are presented to illustrate the performance of the proposed algorithm.

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Title: A maximum spanning tree-based dynamic fuzzy supervised neural network architecture for classification of murder cases
Authors: M. M. Janeela Theresa
V. Joseph Raj
Publication date: 22-03-2015
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 6/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1645-1

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