Crime Prediction using Machine Learning
DOI:
https://doi.org/10.4108/eetiot.5123Keywords:
Crime prediction, Linear regression, Visualization, Geographic mapping, Crime analysis, Random Forest Classifier, Machine LearningAbstract
The process of researching crime patterns and trends in order to find underlying issues and potential solutions to crime prevention is known as crime analysis. This includes using statistical analysis, geographic mapping, and other approaches of type and scope of crime in their areas. Crime analysis can also entail the creation of predictive models that use previous data to anticipate future crime tendencies. Law enforcement authorities can more efficiently allocate resources and target initiatives to reduce crime and increase public safety by evaluating crime data and finding trends. For prediction, this data was fed into algorithms such as Linear Regression and Random Forest. Using data from 2001 to 2016, crime-type projections are made for each state as well as all states in India. Simple visualisation charts are used to represent these predictions. One critical feature of these algorithms is identifying the trend-changing year in order to boost the accuracy of the predictions. The main aim is to predict crime cases from 2017 to 2020 by using the dataset from 2001 to 2016.
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Zakir Hussain, K, Durairaj, M, Farzana, G.R.J.: Criminal behaviour analysis by using data mining techniques,30-31 March 2012, Nagapattinam, India, Proceedings of the International Conference On Advances In Engineering Science And Management, IEEE, 2012 pp. 1 – 8.
Kavitha, M, Roobini, S, Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems, Principles, Challenges and Applications, Machine Learning and Artificial Intelligence in Healthcare Systems. 2023; 25-56. DOI: https://doi.org/10.1201/9781003265436-2
Sathya, R, Ananthi S, Vaidehi K, A Hybrid Location-dependent Ultra Convolutional Neural Network-based Vehicle Number Plate Recognition Approach for Intelligent Transportation Systems, Concurrency and Computation: Practice and Experience, 2023; 35:1-25. DOI: https://doi.org/10.1002/cpe.7615
Tayebi, M.A, Gla, U, Brantingham, P. L. Learning where to inspect: location learning for crime prediction, 27-29 May 2015, Baltimore, MD, USA, Proceedings of the IEEE International Conference on Intelligence and Security Informatics (ISI), IEEE, 2015, pp. 25-30. DOI: https://doi.org/10.1109/ISI.2015.7165934
Akash, S, Prabaharan Poornachandran, Vijay Krishna Menon, Soman, K.P. Cybersecurity and Secure Information Systems, Springer Cham, 2019, Chapter number:12, A Detailed Investigation and Analysis of Deep Learning Architectures and Visualization Techniques for Malware Family Identification, Cybersecurity and Secure Information Systems, pp. 24-46. DOI: https://doi.org/10.1007/978-3-030-16837-7_12
Rajesh Kanna, P, Santhi, P, Hybrid Intrusion Detection using Map Reduce based Black Widow Optimized Convolutional Long Short-Term Memory Neural Networks, Expert Systems with Applications, 2022, Vol. 194:(116545). DOI: https://doi.org/10.1016/j.eswa.2022.116545
Rajesh Kanna, P, Santhi, P, Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial–Temporal Features, 2021, Knowledge-Based Systems Vol. 226:(107132). DOI: https://doi.org/10.1016/j.knosys.2021.107132
Sathyadevan, S., Gangadharan, S.: Crime analysis and prediction using data mining, Date of conference: 19-20 August 2014, Location of conference: Guntur, India, Proceedings of International Conference on Networks & Soft Computing, IEEE, 2016, pp. 406-412.
Nath, S. V.: Crime pattern detection using data mining, 18-22 December 2006, Hong Kong, China, Proceedings of the International Conference in Web intelligence and intelligent agent technology, IEEE, 2007, pp. 41-44.
Zhao, X, Tang, J.: Exploring Transfer Learning for Crime Prediction, 18-21 November 2017, New Orleans, LA, USA, Proceedings of the International Conference on Data Mining Workshops, IEEE, 2017, pp. 1158-1159. DOI: https://doi.org/10.1109/ICDMW.2017.165
Priyatharishini, M, Nirmala Devi, M.: A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability, Measurement, 2022, Vol. 194(111055). DOI: https://doi.org/10.1016/j.measurement.2022.111055
Shamsuddin, N. H. M., Ali, N. A., Alwee, R.: An overview on crime prediction method, 23-24 May 2017, Johor, Malaysia, 2017 6th ICT International Student Project Conference, IEEE, 2017, pp. 1-5. DOI: https://doi.org/10.1109/ICT-ISPC.2017.8075335
Sivaranjani, S, Sivakumari, S, Aasha, M.: Crime prediction and forecasting in Tamil Nadu using clustering approaches, 21-22 October 2016, Kollam, India, Proceedings of the International Conference on Emerging Technological Trends, IEEE, 2016, pp. 1-6. DOI: https://doi.org/10.1109/ICETT.2016.7873764
Kansara, Chirag, E.: Crime mitigation at Twitter using Big Data analytics and risk modelling, 23-25 December 2016, Jaipur, India, Proceedings of the IEEE International Conference on Recent Advances and Innovations in Engineering, IEEE, 2017, pp. 1-8. DOI: https://doi.org/10.1109/ICRAIE.2016.7939484
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