An Optimized Machine Learning and Big Data Approach to Crime Detection

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 em...

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Autores principales:	Ashokkumar Palanivinayagam, Siva Shankar Gopal, Sweta Bhattacharya, Noble Anumbe, Ebuka Ibeke, Cresantus Biamba
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/8dbe6dfa966c49139714bab91b1a8c74
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id	oai:doaj.org-article:8dbe6dfa966c49139714bab91b1a8c74
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spelling	oai:doaj.org-article:8dbe6dfa966c49139714bab91b1a8c742021-11-22T01:11:38ZAn Optimized Machine Learning and Big Data Approach to Crime Detection1530-867710.1155/2021/5291528https://doaj.org/article/8dbe6dfa966c49139714bab91b1a8c742021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5291528https://doaj.org/toc/1530-8677Crime 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.Ashokkumar PalanivinayagamSiva Shankar GopalSweta BhattacharyaNoble AnumbeEbuka IbekeCresantus BiambaHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Ashokkumar Palanivinayagam Siva Shankar Gopal Sweta Bhattacharya Noble Anumbe Ebuka Ibeke Cresantus Biamba An Optimized Machine Learning and Big Data Approach to Crime Detection
description	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.
format	article
author	Ashokkumar Palanivinayagam Siva Shankar Gopal Sweta Bhattacharya Noble Anumbe Ebuka Ibeke Cresantus Biamba
author_facet	Ashokkumar Palanivinayagam Siva Shankar Gopal Sweta Bhattacharya Noble Anumbe Ebuka Ibeke Cresantus Biamba
author_sort	Ashokkumar Palanivinayagam
title	An Optimized Machine Learning and Big Data Approach to Crime Detection
title_short	An Optimized Machine Learning and Big Data Approach to Crime Detection
title_full	An Optimized Machine Learning and Big Data Approach to Crime Detection
title_fullStr	An Optimized Machine Learning and Big Data Approach to Crime Detection
title_full_unstemmed	An Optimized Machine Learning and Big Data Approach to Crime Detection
title_sort	optimized machine learning and big data approach to crime detection
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/8dbe6dfa966c49139714bab91b1a8c74
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An Optimized Machine Learning and Big Data Approach to Crime Detection

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