Theoretical and Empirical Analysis of Crime Data

Authors

  • Manisha Mudgal Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India https://orcid.org/0000-0001-5724-3159
  • Deepika Punj Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India
  • Anuradha Pillai Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2016

Keywords:

Crime, data mining, deep learning, KNN, RNN, Gaussian, Naïve Bayes, clustering, classification, decision tree

Abstract

Crime is one of the biggest and dominating problems in today’s world and it is not only harmful to the person involved but also to the community and government. Due to escalation in crime frequency, there is a need for a system that can detect and predict crimes. This paper describes the summary of the different methods and techniques used to identify, analyze and predict upcoming and present crimes. This paper shows, how data mining techniques can be used to detect and predict crime using association mining rule, k-means clustering, decision tree, artificial neural networks and deep learning methods are also explained. Most of the researches are currently working on forecasting the occurrence of future crime. There is a need for approaches that can work on real-time crime prediction at high speed and accuracy. In this paper, a model has been proposed that can work on real-time crime prediction by recognizing human actions. 

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Author Biographies

Manisha Mudgal, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Manisha Mudgal is a PHD scholar in Department of Computer Engineering at JC BOSE University of Science and Technology YMCA, Faridabad, India. She has done her M. Tech from M D University Haryana, India. She has successfully published 5 papers in Reputed National and International Journals. Her subjects of interest include Data Mining, Information Retrieval, and Machine Learning.

Deepika Punj, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Deepika Punj is working as Assistant Professor in Department of Computer Engineering at JC BOSE University of Science and Technology YMCA, Faridabad, India. She has done Ph.D in Computer Engineering. She is having 14 years of experience in teaching. She has published more than 25 papers in Reputed National and International Journals. Her research interests include Data Mining, Deep Learning, Machine Learning and Internet Technologies.

Anuradha Pillai, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Anuradha Pillai is an Associate Professor in the Department of Computer Engineering, JC Bose University of Science and Technology, YMCA, Faridabad, Haryana, India. She received Ph.D. in Computer Engineering from Maharishi Dayanand University, Rohtak. She published more than 60 papers in reputed international journals and successfully guided 4 PhD students. Her subjects of interest include Data Mining, Information Retrieval, Hidden web, Web Mining and Social Networks.

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Published

2021-02-18

Issue

Section

Data Science and Artificial Intelligence: Architecture, Use Cases, and Challenge