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A Thorough Analysis of Machine Learning and Deep Learning Methods for Crime Data Analysis

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Inventive Computation and Information Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 173))

Abstract

The analysts belonging to the police forces are obliged for exposing the complexities found in data, to help the operational staff in nabbing the criminals and guiding strategies of crime prevention. But, this task is made extremely complicated due to the innumerous crimes, which take place and the knowledge levels of recent day offenders. Crime is one of the omnipresent and worrying aspects concerning society, and preventing it is an important task. Examination of crime is a systematic means of detection as well as an examination of crime patterns and trends. The data work involving includes two important aspects, analysis of crime and prediction of perpetrator identity. Analysis of crime has a significant role to play in these two steps. Analysis of the crime data can be of massive help in the prediction and resolution of crimes from a futuristic perspective. To avert this issue in the police field, the crime rate must be predicted with the help of AI (machine learning) approaches and deep learning techniques. The objective of this review is to examine the AI approaches and deep learning methods for prediction of crime rate that yield superior accuracy, and this review article also explores the suitability of data approaches in the attempts made toward crime prediction with specific predominance to the dataset. This review evaluates the advantages and drawbacks faced by crime data analysis. The article provides extensive guidance to the evaluation of model parameters to performance in terms of prediction of crime rate by carrying out comparisons ranging from deep learning to machine learning algorithms.

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Author information

Authors and Affiliations

  1. Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India

    J. Jeyaboopathiraja

  2. Professor and Head, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India

    G. Maria Priscilla

Authors
  1. J. Jeyaboopathiraja
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  2. G. Maria Priscilla
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Corresponding author

Correspondence to J. Jeyaboopathiraja .

Editor information

Editors and Affiliations

  1. RVS Technical Campus, Coimbatore, India

    S. Smys

  2. “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Computer Science Department, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

  4. Czech Technical University, Praha, Czech Republic

    Pavel Lafata

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Jeyaboopathiraja, J., Maria Priscilla, G. (2021). A Thorough Analysis of Machine Learning and Deep Learning Methods for Crime Data Analysis. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 173. Springer, Singapore. https://doi.org/10.1007/978-981-33-4305-4_58

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