Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

Crime activities prediction using hybridization of firefly optimization technique and fuzzy... In the developing technology, crime reduction is one of the major and complex processes due to the various techniques and minimum amount of crime-related data. The traditional method is difficult to identify the crime activities with effective manner due to the minimum data. So, this paper introduces the novel big data and soft computing techniques for recognizing the crime activities with effective manner. Initially, the crime activities-related data have been collected from the various resources present in the big data. From the collected data, the inconsistent data and missing values are eliminated by applying the incremental mean normalization method. After that, the similar crime data have been clustered with the help of the fireflies-based fuzzy cognitive map neural networks which help to predict the crime activity-related features with effective manner. Finally, the prediction process is done by using the enhanced associative neural networks approach. The efficiency of the system is evaluated with the help of the experimental results and discussions in terms of the precision, recall, accuracy. Keywords Crime analysis  Firefly algorithm  Neural networks  Fuzzy clustering 1 Introduction police analyze the criminals involved in those criminal activities. From their analyses, 38,000 peoples are unoffi- Crime activities http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks

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Publisher
Springer London
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3561-7
Publisher site
See Article on Publisher Site

Abstract

In the developing technology, crime reduction is one of the major and complex processes due to the various techniques and minimum amount of crime-related data. The traditional method is difficult to identify the crime activities with effective manner due to the minimum data. So, this paper introduces the novel big data and soft computing techniques for recognizing the crime activities with effective manner. Initially, the crime activities-related data have been collected from the various resources present in the big data. From the collected data, the inconsistent data and missing values are eliminated by applying the incremental mean normalization method. After that, the similar crime data have been clustered with the help of the fireflies-based fuzzy cognitive map neural networks which help to predict the crime activity-related features with effective manner. Finally, the prediction process is done by using the enhanced associative neural networks approach. The efficiency of the system is evaluated with the help of the experimental results and discussions in terms of the precision, recall, accuracy. Keywords Crime analysis  Firefly algorithm  Neural networks  Fuzzy clustering 1 Introduction police analyze the criminals involved in those criminal activities. From their analyses, 38,000 peoples are unoffi- Crime activities

Journal

Neural Computing and ApplicationsSpringer Journals

Published: May 29, 2018

References

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