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A Survey on Analysis of Crime Detection Techniques Using Machine Learning

Ashish Kumar1 , Kaptan Singh2 , Amit Saxena3

Section:Survey Paper, Product Type: Journal Paper
Volume-10 , Issue-2 , Page no. 35-40, Feb-2022

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v10i2.3540

Online published on Feb 28, 2022

Copyright © Ashish Kumar, Kaptan Singh, Amit Saxena . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ashish Kumar, Kaptan Singh, Amit Saxena, “A Survey on Analysis of Crime Detection Techniques Using Machine Learning,” International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.35-40, 2022.

MLA Style Citation: Ashish Kumar, Kaptan Singh, Amit Saxena "A Survey on Analysis of Crime Detection Techniques Using Machine Learning." International Journal of Computer Sciences and Engineering 10.2 (2022): 35-40.

APA Style Citation: Ashish Kumar, Kaptan Singh, Amit Saxena, (2022). A Survey on Analysis of Crime Detection Techniques Using Machine Learning. International Journal of Computer Sciences and Engineering, 10(2), 35-40.

BibTex Style Citation:
@article{Kumar_2022,
author = {Ashish Kumar, Kaptan Singh, Amit Saxena},
title = {A Survey on Analysis of Crime Detection Techniques Using Machine Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2022},
volume = {10},
Issue = {2},
month = {2},
year = {2022},
issn = {2347-2693},
pages = {35-40},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5444},
doi = {https://doi.org/10.26438/ijcse/v10i2.3540}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v10i2.3540}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5444
TI - A Survey on Analysis of Crime Detection Techniques Using Machine Learning
T2 - International Journal of Computer Sciences and Engineering
AU - Ashish Kumar, Kaptan Singh, Amit Saxena
PY - 2022
DA - 2022/02/28
PB - IJCSE, Indore, INDIA
SP - 35-40
IS - 2
VL - 10
SN - 2347-2693
ER -

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Abstract

Finding the patterns from the huge collection of datasets is considered as one of the primary application of machine learning. Machine learning has already proved itself in transportation field and can be used in various other fields such as manufacturing, healthcare, investigation of crimes etc. Great advancement in technologies and societies has led to advancement in crimes and also the damage caused by them. It becomes even more difficult to prevent when the population in any area is concentrated and changes are rapid. That’s why in many cities various crime prevention measures have been adopted as a part of smart city development. However, crimes can happen anywhere the need only is to determine the pattern of their occurrences which in turn can reduce the crime percentage. In order to provide society a better living crime investigation or analysis is considered as important application of machine learning. In this paper a survey has been done on analysis of crime and their prediction using machine learning techniques.

Key-Words / Index Term

Machine Learning, Crime prediction, pattern extraction, Decision tree, KNN, SVM

References

[1] S Prabakaran and Shilpa Mitra, “Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning” , J. Phys.: Conf.,2018.
[2] Syed Ahsan Shabbir and Kanna Dasan R, “An Effective Fraud Detection System Using Mining Technique” International Journal of Scientific And Research Publications 3(5), 2013.
[3] Abhinav Srivastava, Amlan Kundu, Shamik Sural and Arun K. Majumdar, “Credit Card Fraud Detection Using Hidden Markov Model” ,IEEE Transactions On Dependable And Secure Computing 5, 2008.
[4] Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick, “Credit Card Fraud Detection Using Bayesian And Neural Network” ,Researchgate.Net/Publication/254198382, 1993.
[5] Chao Yangt, Shiyuan Chet, Xueting Cao, Yeqing Sun, Ajith Abraham, “A Rough-Fuzzy C-Means Using Information Entropy For Discretized Violent Crimes Data” 13th International Conference On Hybrid Intelligent Systems, 2013.
[6] Saleha Farheen, Monika Raghuwanshi, "Performance of Machine Learning Techniques in the Prevention of Financial Frauds", International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.27-29, 2021.
[7] Chao Yang, Hongbo Liu, Yeqing Sun, Ajith Abraham, “Multi-Knowledge Extraction From Violent Crime Datasets Using Swarm Rough Algorithm”, 12th International Conference On Hybrid Intelligent Systems (His), 2012.
[8] Jieling Jin, Yuanchang Deng, “A Comparative Study On Traffic Violation Level Prediction Using Different Models” , 4th International Conference On Transportation Information And Safety (Ictis), 2017.
[9] Anshu Sharma, Shilpa Sharma, “An Intelligent Analysis Of Web Crime Data Using Data Mining”, International Journal Of Engineering And Innovative Technology (Ijeit) 2(3), 2012.
[10] K K Sindhu and B B Meshram, “Digital Forensics And Cybercrime Data Mining”, Journal Of Information Security, 3, 196-201, 2012.
[11] Sachin Kumar and Durga Toshniwal, “A Data Mining Approach To Characterize Road Accident Locations” , Journal Of Modern Transportation 24(1) pp.6272 , 2016.
[12] Sachin Kumar and Durga Toshniwa, “A Data Mining Framework To Analyze Road Accident Data” Journal Of Big Data, 2015.
[13] Neetu Singh, Tripti Ajariya, Shailesh Raghuvanshi, Neha Singh, "Crime Analysis and Prediction Model Using Data Mining and Machine Learning Techniques: Comparative Analysis", International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.97-104, 2021.
[14] A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, F. Pianesi, and A. Pentland, "Once upon a crime: towards crime prediction from demographics and mobile data," Proc. of the 16th Intl. Conf. on Multimodal Interaction, pp. 427-434, 2014.
[15] R. Iqbal, M. A. A. Murad, A. Mustapha, P. H. Shariat Panahy, and N. Khanahmadliravi, "An experimental study of classification algorithms for crime prediction," Indian J. of Sci. and Technol., vol. 6, no. 3, pp. 4219- 4225, Mar. 2013.
[16] T. Beshah and S. Hill, "Mining road traffic accident data to improve safety: role of road-related factors on accident severity in Ethiopia," Proc. of Artificial Intell. for Develop. (AID 2010), pp. 14-19, 2010.
[17] H. Chen, W. Chung, J. J. Xu, G. Wang, Y. Qin, and M. Chau, "Crime data mining: a general framework and some examples," IEEE Computer, vol. 37, no. 4, pp. 50-56, Apr. 2004.
[18] T. Beshah and S. Hill, "Mining road traffic accident data to improve safety: role of road-related factors on accident severity in Ethiopia," Proc. of Artificial Intell. for Develop. (AID 2010), pp. 14-19, 2010.
[19] M. Al Boni and M. S. Gerber, "Area-specific crime prediction models," 15th IEEE Intl. Conf. on Mach. Learn. and Appl., Anaheim, CA, USA, Dec. 2016.
[20] Q. Zhang, P. Yuan, Q. Zhou, and Z. Yang, "Mixed spatial-temporal characteristics based crime hot spots prediction," IEEE 20th Intl. Conf. on Comput. Supported Cooperative Work in Des. (CSCWD), Nanchang, China, May 2016.
[21] N. Mahmud, K. Ibn Zinnah, Y. Ar Rahman, and N. Ahmed, "CRIMECAST: a crime prediction and strategy direction service," IEEE 19th Intl. Conf. on Comput. and Inform. Technol., Dhaka, Bangladesh, Dec. 2016.
[22] Y. L. Lin, L. C. Yu, and T. Y. Chen, "Using machine learning to assist crime prevention," IEEE 6th Intl. Congr. on Advanced Appl. Inform. (IIAIAAI), Hamamatsu, Japan, Jul. 2017.
[23] F. K. Bappee, A. S. Júnior, and S. Matwin, "Predicting crime using spatial features," Can. AI 2018: Advances in Artificial Intel.-Lecture Notes in Comput. Sci., vol. 10832, pp. 367-373, Springer, Mar. 2018.
[24] H. W. Kang, H. B. Kang, "Prediction of crime occurrence from multimodal data using deep learning," PLoS ONE, vol. 12, no. 4, Apr. 2017.
[25] V. Grover, R. Adderley, and M. Bramer, "Review of current crime prediction techniques," Intl. Conf. on Innovative Techn. and Appl. of Artificial Intel., pp. 233-237, Springer, London, 2007.
[26] X. Zhao and J. Tang, "Exploring transfer learning for crime prediction," IEEE Intl. Conf. on Data Mining Workshop (ICDMW), New Orleans, LA, USA, Nov. 2017.
[27] R. Marchant, S. Haan, G. Clancey, and S. Cripps, "Applying machine learning to criminology: semi parametric spatial demographic Bayesian regression," Security Inform., vol. 7, no. 1, Dec. 2018.
[28] L. McClendon and N. Meghanathan, "Using machine learning algorithms to analyze crime data," Mach. Learn. and Appl.: an Intl. J. (MLAIJ), vol.2, no.1, Mar. 2015.
[29] S. Prabakaran and S. Mitra, "Survey of analysis of crime detection techniques using data mining and machine learning," Nat. Conf. on Math. Techn. and its Appl. (NCMTA 2018), IOP J. of Physics: Conf. Series, vol. 1000, 2018.
[30] E. Cesario, C. Catlett, and D. Talia, "Forecasting crimes using autoregressive models," IEEE 14th Intl. Conf. on Dependable, Auton. and Secure Comput., Auckland, New Zealand, Aug. 2016.
[31] M. A. Tayebi, U. Glässer, and P. L. Brantingham, "Learning where to inspect: location learning for crime prediction," IEEE Intl. Conf. on Intel. and Security Inform. (ISI), Baltimore, MD, USA, May 2015.
[32] L. G. A. Alves, H. V. Ribeiro, and F. A. Rodrigues, "Crime prediction through urban metrics and statistical learning," Physica A, vol. 505, pp. 435-443, 2018.
[33] N. Baloian, E. Bassaletti, M. Fernández, O. Figueroa, P. Fuentes. R. Manasevich, M. Orchard. S. Peñafiel, J. A. Pino, and M. Vergara, "Crime prediction using patterns and context," IEEE 21st Intl. Conf. on Comput. Supported Cooperative Work in Des. (CSCWD), Wellington, New Zealand, Apr. 2017.
[34] M. V. Barnadas, Machine learning applied to crime prediction, Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, Sep. 2016.