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http://dx.doi.org/10.22937/IJCSNS.2022.22.4.16

Time Series Crime Prediction Using a Federated Machine Learning Model  

Salam, Mustafa Abdul (Artificial Intelligence Dept., Faculty of Computers and Artificial Intelligence, Benha University)
Taha, Sanaa (Information Technology Dept., Faculty of Computers and Artificial Intelligence, Cairo University)
Ramadan, Mohamed (Computer Science Dept., Faculty of Computers and Information, Egyptian E-Learning University)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.4, 2022 , pp. 119-130 More about this Journal
Abstract
Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.
Keywords
Federated Learning (FL); Deep Learning; Tensor- Flow Federated (TFF); Keras; Data Privacy; Long Short-Term Memory (LSTM);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Ramasubbareddy, S., Srinivas, T.A.S., Govinda, K. and Manivannan, S.S., 2020. Crime prediction system. Innovations in Computer Science and Engineering, pp.127-134.
2 Reier Forradellas, R.F., N'anez Alonso, S.L., Jorge-Vazquez, J. and Rodriguez, M.L., 2021. Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction. Social Sciences, 10(1), p.4.   DOI
3 Zhang, X., Liu, L., Xiao, L. and Ji, J., 2020. Comparison of machine learning algorithms for predicting crime hotspots. IEEE Access, 8, pp.181302-181310.   DOI
4 Bappee, F.K., Junior, A.S. and Matwin, S., 2018, May. Predicting crime using spatial features. In Canadian Conference on Artificial Intelligence (pp. 367-373). Springer, Cham.
5 Prabakaran, S. and Mitra, S., 2018, April. Survey of analysis of crime detection techniques using data mining and machine learning. In Journal of Physics: Conference Series (Vol. 1000, No. 1, p. 012046). OP Publishing.
6 Chun, S.A., Avinash Paturu, V., Yuan, S., Pathak, R., Atluri, V. and R. Adam, N., 2019, June. Crime prediction model using deep neural networks. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 512-514).
7 Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J. and Wang, F., 2021.Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5(1), pp.1-19.   DOI
8 Abdul Salam, M., Taha, S. and Ramadan, M.,2021. COVID-19 detection using federated machine learning. Plos one, 16(6), p.e0252573.   DOI
9 Nguyen, T.T., Hatua, A. and Sung, A.H., 2017. Building a learning machine classifier with inadequate data for crime prediction. Journal of Advances in Information Technology Vol, 8(2).
10 Li, Q., He, B. and Song, D., 2021. Model-Contrastive Federated Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10713-10722).
11 Wheeler, A.P. and Steenbeek, W., 2021. Mapping the risk terrain forcrime using machine learning. Journal of Quantitative Criminology, 37(2), pp.445-480.   DOI
12 Ivan, N., Ahishakiye, E., Omulo, E.O. and Taremwa, D. ,, 2017. Crime Prediction Using Decision Tree (J48) Classification Algorithm.
13 Yang, Qiang, et al. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019): 1-19.
14 Kim, S., Joshi, P., Kalsi, P.S. and Taheri, P., 2018, November. Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEM-CON) (pp. 415-420). IEEE.
15 Hajela, G., Chawla, M. and Rasool, A., 2020. A clustering based hotspot identification approach for crime prediction. Procedia Computer Science, 167, pp.1462-1470.   DOI
16 Li, Tian, et al. Federated learning: Challenges, methods, and future directions IEEE Signal Processing Magazine 37.3 (2020): 50-60.   DOI
17 Kim, S., Joshi, P., Kalsi, P.S. and Taheri, P. , 2018, November. Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 415-420). IEEE.
18 Zhang, Weishan, et al. Dynamic fusion-based federated learning for COVID-19 detection. IEEE Internet of Things Journal (2021).
19 Zhang, Weishan, et al. "Dynamic fusion-based federated learning for COVID-19 detection." IEEE Internet of Things Journal (2021).
20 Lian, Xiangru, et al. Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent. arXiv preprint arXiv:1705.09056 (2017).