Published November 30, 2020 | Version v1
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Designing Time Series Crime Prediction Model using Long Short-Term Memory Recurrent Neural Network

  • 1. Department of Computer Science and Engineering, Adama Science and Technology University, Adama, Ethiopia
  • 2. Department of Information Systems, Wolkite University, Wolkite, Ethiopia
  • 3. Department of Software Engineering, Wolkite University, Wolkite, Ethiopia.
  • 1. Publisher

Description

Crime influences people in many ways. Prior studies have shown the relationship between time and crime incidence behavior. This research attempts to determine and examine the relationship between time, crime incidences types and locations by using one of the neural network models for time series data that is, Long Short-Term Memory network. The collected data is pre-processed, analyzed and tested using Long Short-Term Memory recurrent neural network model. R-square score is also used to test the accuracy. The study results show that applying Long Short-Term Memory Recurrent Neural Network (LSTM RNN) enables to come up with more accurate prediction about crime incidence occurrence with respect to time. Predicting crimes accurately helps to improve crime prevention and decision and advance the justice system.

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Journal article: 2277-3878 (ISSN)

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ISSN
2277-3878
Retrieval Number
100.1/ijrte.D5025119420