Skip to main content

Research Repository

Advanced Search

An optimized machine learning and big data approach to crime detection.

Palanivinayagam, Ashokkumar; Gopal, Siva Shankar; Bhattacharya, Sweta; Anumbe, Noble; Ibeke, Ebuka; Biamba, Cresantus

Authors

Ashokkumar Palanivinayagam

Siva Shankar Gopal

Sweta Bhattacharya

Noble Anumbe

Cresantus Biamba



Abstract

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

Citation

PALANIVINAYAGAM, A., GOPAL, S.S., BHATTACHARYA, S., ANUMBE, N., IBEKE, E. and BIAMBA, C. 2021. An optimized machine learning and big data approach to crime detection. Wireless communications and mobile computing [online], 2021, article ID 5291528. Available from: https://doi.org/10.1155/2021/5291528

Journal Article Type Article
Acceptance Date Oct 10, 2021
Online Publication Date Nov 13, 2021
Publication Date Dec 31, 2021
Deposit Date Nov 15, 2021
Publicly Available Date Nov 15, 2021
Journal Wireless Communications and Mobile Computing
Print ISSN 1530-8669
Electronic ISSN 1530-8677
Publisher Hindawi Publishing Corporation
Peer Reviewed Peer Reviewed
Volume 2021
Article Number 5291528
DOI https://doi.org/10.1155/2021/5291528
Keywords Electrical and electronic engineering; Computer networks and communications; Information systems; Crime detection; Machine learning
Public URL https://rgu-repository.worktribe.com/output/1529112

Files

PALANIVINAYAGAM 2021 An optimized machine learning (VOR) (1.1 Mb)
PDF

Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
Copyright © 2021 Ashokkumar Palanivinayagam et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work
is properly cited.





You might also like



Downloadable Citations