A deep learning model to predict and classify crime rate using tweets

Dass, Pranav, Gupta, Vedika, Dhingra, Shreya, Arora, Rohan, Katariya, Piyush and Kumar, Adarsh (2024) A deep learning model to predict and classify crime rate using tweets. Journal of Discrete Mathematical Sciences and Cryptography, 27 (8). pp. 2255-2271. ISSN 0972-0529

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Abstract

Crime is a detrimental socioeconomic issue that impacts individuals globally. Crime significantly affects a nation’s standard of living, financial well-being, and standing. Over the past few years, there has been a significant increase in the crime rate. Law enforcement must implement proactive measures to mitigate crime rates. Enhanced technologies and innovative methods are required to bolster crime analytics and safeguard communities. Precise and up-to-date crime predictions can reduce crime rates, but they provide a complex problem for scientists due to the multiple factors that drive crime occurrences. This study employs a range of visualisation techniques and machine learning algorithms to predict the spread of crime across a vast geographical area. The datasets were evaluated and presented in the initial phase, based on their relevance. Subsequently, machine learning algorithms were employed to extract insights from extensive datasets and uncover concealed correlations within the data. This information was subsequently utilised to identify and analyse crime patterns, providing crime analysts with valuable tools for crime prediction. Consequently, this approach proves advantageous in the realm of crime prevention. Every day, a substantial amount of criminal activities are committed. The information in this instance includes both the date and the crime rate for the relevant years. The crime rate in this project is determined by employing various distinct criminal classifications. Utilising historical data, we employ the RNN, LSTM, and GRU algorithms to forecast the future percentage of the crime rate. The programme receives the date as an input and produces the crime rate percentage for that specific year.

Item Type: Article
Keywords: Crime | Machine learning | Deep learning | NCRB | Metropolitan cities | Twitter
Subjects: Social Sciences and humanities > Decision Sciences > Information Systems and Management
Social Sciences and humanities > Social Sciences > Social Sciences (General)
Social Sciences and humanities > Social Sciences > Law and Legal Studies
JGU School/Centre: Jindal Global Business School
Depositing User: Dharmveer Modi
Date Deposited: 15 Jan 2025 09:41
Last Modified: 15 Jan 2025 09:41
Official URL: https://doi.org/10.47974/JDMSC-1822
URI: https://pure.jgu.edu.in/id/eprint/9013

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