참고문헌
- 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.
- Ivan, N., Ahishakiye, E., Omulo, E.O. and Taremwa, D. ,, 2017. Crime Prediction Using Decision Tree (J48) Classification Algorithm.
- Zhang, Weishan, et al. Dynamic fusion-based federated learning for COVID-19 detection. IEEE Internet of Things Journal (2021).
- 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).
- Yang, Qiang, et al. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019): 1-19.
- Abdul Salam, M., Taha, S. and Ramadan, M.,2021. COVID-19 detection using federated machine learning. Plos one, 16(6), p.e0252573. https://doi.org/10.1371/journal.pone.0252573
- Li, Tian, et al. Federated learning: Challenges, methods, and future directions IEEE Signal Processing Magazine 37.3 (2020): 50-60. https://doi.org/10.1109/msp.2020.2975749
- 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.
- 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. https://doi.org/10.3390/socsci10010004
- 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. https://doi.org/10.1109/access.2020.3028420
- Wheeler, A.P. and Steenbeek, W., 2021. Mapping the risk terrain forcrime using machine learning. Journal of Quantitative Criminology, 37(2), pp.445-480. https://doi.org/10.1007/s10940-020-09457-7
- 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.
- 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.
- 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.
- 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).
- 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).
- Hajela, G., Chawla, M. and Rasool, A., 2020. A clustering based hotspot identification approach for crime prediction. Procedia Computer Science, 167, pp.1462-1470. https://doi.org/10.1016/j.procs.2020.03.357
- 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. https://doi.org/10.1007/s41666-020-00082-4
- 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).
- Zhang, Weishan, et al. "Dynamic fusion-based federated learning for COVID-19 detection." IEEE Internet of Things Journal (2021).