A deep learning model to predict and classify crime rate using tweets
Pranav Dasspdasscs@shyamlal.du.ac.inDepartment of Computer ScienceShyam Lal CollegeUniversity of DelhiDelhi, 110032, IndiaView full profile → , *Vedika GuptaCorresponding authorvgupta2@jgu.edu.inJindal Global Business SchoolO. P. Jindal Global UniversitySonipat, Haryana, 131001, IndiaView full profile → , Shreya Dhingrashreyadhingra54@gmail.comDepartment of Computer Science EngineeringBharati Vidyapeeth’s College of EngineeringNew Delhi, 110063, IndiaView full profile → , Rohan Arorarohanaroraaashi@gmail.comDepartment of Computer Science EngineeringBharati Vidyapeeth’s College of EngineeringNew Delhi, 110063, IndiaView full profile → , Piyush Katariyapiyushkatariya95@gmail.comDepartment of Computer Science EngineeringBharati Vidyapeeth’s College of EngineeringNew Delhi, 110063, IndiaView full profile → , Adarsh Kumaradarsh12k@gmail.comDepartment of Computer Science EngineeringBharati Vidyapeeth’s College of EngineeringNew Delhi, 110063, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 09 Mar 2023
- Published Online:
- 18 Dec 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1822
- Pages:
- 2255–2271
Abstract
Keywords
Subject Classifications
References
[1] S. Aravindan, E. Anusuya, and M. Ashok Kumar, “GUI based prediction of crime rate using machine learning approach,” (2020).
[2] B. Wang, P. Yin, A. L. Bertozzi, P. J. Brantingham, S. J. Osher, and J. Xin, “Deep learning for real-time crime forecasting and its ternarization,” Chinese Annals of Mathematics, Series B, vol. 40, no. 6, pp. 949-966 (2019).
[3] C. Tabedzki, A. Thirumalaiswamy, P. van Vliet, S. Agarwal, and S. Sun, “Yo home to Bel-Air: Predicting crime on the streets of Philadelphia,” University of Pennsylvania, CIS, 520 (2018).
[4] S. R. Bandekar and C. Vijayalakshmi, “Design and analysis of machine learning algorithms for the reduction of crime rates in India,” Procedia Computer Science, vol. 172, pp. 122-127 (2020).
[5] S. Hossain, A. Abtahee, I. Kashem, M. M. Hoque, and I. H. Sarker, “Crime prediction using spatio-temporal data,” in Computing Science, Communication and Security: First International Conference, COMS2 2020, Gujarat, India, Mar. 26–27, 2020, Revised Selected Papers 1, pp. 277-289, Springer Singapore (2020).
[6] S. Kim, P. Joshi, P. S. Kalsi, and P. Taheri, “Crime analysis through machine learning,” in Proc. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 415-420, IEEE, Nov. (2018).
[7] A. Bharati and R. A. K. Sarvanaguru, “Crime prediction and analysis using machine learning,” Int. Res. J. Eng. Technol., vol. 5, no. 9, pp. 1037-1042 (2018).
[8] H.-W. Kang and H.-B. Kang, “Prediction of crime occurrence from multi-modal data using deep learning,” PLoS One, vol. 12, no. 4, p. e0176244 (2017).
[9] P. Stalidis, T. Semertzidis, and P. Daras, “Examining deep learning architectures for crime classification and prediction,” arXiv preprint arXiv:1812.00602 (2018).
[10] J. J. Nolan III, J. McDevitt, S. Cronin, and A. Farrell, “Learning to see hate crimes: A framework for understanding and clarifying ambiguities in bias crime classification,” Criminal Justice Studies, vol. 17, no. 1, pp. 91-105 (2004).
[11] S. Chainey, L. Tompson, and S. Uhlig, “The utility of hotspot mapping for predicting spatial patterns of crime,” Security Journal, vol. 21, no. 1, pp. 4-28 (2008).
[12] D. Williamson, S. McLafferty, P. McGuire, T. Ross, J. Mollenkopf, V. Goldsmith, and S. Quinn, Tools in the spatial analysis of crime. Mapping and analysing crime data, A. Hirschfield and K. Bowers, Eds. London and New York: Taylor & Francis, vol. 1, p. 187 (2001).
[13] K. Kianmehr and R. Alhajj, “Effectiveness of support vector machine for crime hot-spots prediction,” Appl. Artif. Intell., vol. 22, no. 5, pp. 433-458 (2008).
[14] C. H. Yu, M. W. Ward, M. Morabito, and W. Ding, “Crime forecasting using data mining techniques,” in Proc. 2011 IEEE 11th Int. Conf. Data Mining Workshops, pp. 779-786, IEEE, Dec. (2011).
[15] J. Xu, P. N. Tan, J. Zhou, and L. Luo, “Online multi-task learning framework for ensemble forecasting,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 6, pp. 1268-1280 (2017).
[16] C. H. Yu, W. Ding, M. Morabito, and P. Chen, “Hierarchical spatio-temporal pattern discovery and predictive modeling,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 4, pp. 979-993 (2015).
[17] A. R. Pathak, M. Pandey, and S. Rautaray, “Empirical evaluation of deep learning models for sentiment analysis,” J. Stat. Manag. Syst., vol. 22, no. 4, pp. 741-752 (2019).
[18] A. Jain and A. Ghosh, “Novel insights into data mining to improve the specificity of pharmacovigilance and prevent adverse drug reactions in psychiatric patients,” Asia Pacific J. Health Manag., vol. 16, no. 3, pp. 130-136 (2021).
[19] N. Jain, V. Gupta, U. Tariq, and D. J. Hemanth, “Fast violence recognition in video surveillance by integrating object detection and Conv-LSTM,” Int. J. Artif. Intell. Tools (2022), doi: 10.1142/S0218213023400183.
[20] V. Gupta, N. Jain, H. Garg, S. Jhunthra, S. Mohan, A. H. Omar, and A. Ahmadian, “Predicting attributes-based movie success through ensemble machine learning,” Multimedia Tools Appl., pp. 1-30 (2022).




