Distance Prediction for Commercial Serial Crime Cases Using Time Delay Neural Networks

Anahita Ghazvini,Mohammed Ariff,Siti Norul,Zainal Abidin,Md Nawawi

doi:10.14569/ijacsa.2015.061101

Anahita Ghazvini, Mohammed Ariff + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2015.061101

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Abstract

The prediction of the next serial criminal time is important in the field of criminology for preventing the recurring actions of serial criminals. In the associated dynamic systems, one of the main sources of instability and poor performances is the time delay, which is commonly predicted based on nonlinear methods. The aim of this study is to introduce a dynamic neural network model by using nonlinear autoregressive time series with exogenous (external) input (NARX) and Back Propagation Through Time (BPTT), which is verified intensively with MATLAB to predict and model the crime times for the next distance of serial cases. Recurrent neural networks have been extensively used for modeling of nonlinear dynamic systems. There are different types of recurrent neural networks such as Time Delay Neural Networks (TDNN), layer recurrent networks, NARX, and BPTT. The NARX model for the two cases of input- output modeling of dynamic systems and time series prediction draw more attention. In this study, a comparison of two models of NARX and BPTT used for the prediction of the next serial criminal time illustrates that the NARX model exhibits better performance for the prediction of serial cases than the BPTT model. Our future work aims to improve the NARX model by combining objective functions.

Highlights

Crime is a long-standing concern of everyday people, governments, and researchers, and an education on crime and criminals has become a requirement for many careers
To predict the time series data, the NARX and Back Propagation Through Time (BPTT) methods were used to create a model for Distance Crime Prediction for serial cases using Time Delay Neural Networks
The two models were tested for modeling distance crime prediction of serial cases by using time delay neural networks for repetitions of three and four serial cases after removing the outliers

Summary

Introduction

Crime is a long-standing concern of everyday people, governments, and researchers, and an education on crime and criminals has become a requirement for many careers. Criminology, the study of crime, is a complicated field that examines and combines all the connections and disagreements of law, social science and psychology to solve criminal cases. Improvement of the understanding of these rules for the development of suitable algorithms for identifying risky parts is the continuing focus of computational criminology [1]. According to computational criminology research, the crime time can be predicted based on previous data that has been interconnected to serial criminals. In this study, an appropriate improved method for the future values of time series is selected to create modeling distance crime predictions for serial cases using a time delay neural network, which could help the police to reduce their workload in the investigation of serial criminals

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