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

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

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
• 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