Access the full text.
Sign up today, get DeepDyve free for 14 days.
PB Hill (2003)
The Japanese mafia: Yakuza, law, and the state
R Maesschalck, D Jouan-Rimbaud, DL Massart (2000)
The Mahalanobis distanceChemometr Intell Lab Syst, 50
L Farmer (2014)
Criminal law as a security projectCriminol Crim Justice, 14
Jae-Hyun Seo, Daeseon Choi (2016)
Feature selection for chargeback fraud detection based on machine learning algorithmsInt J Appl Eng Res, 11
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.
Neural Computing and Applications – Springer Journals
Published: May 29, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.