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.
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Acknowledgements
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at king Saud University for its funding this research group No. (RGP – 1436-035).
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The authors declared that they have no conflict of interest to this work.
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Altameem, T., Amoon, M. Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks. Neural Comput & Applic 31, 1263–1273 (2019). https://doi.org/10.1007/s00521-018-3561-7
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DOI: https://doi.org/10.1007/s00521-018-3561-7