Abstract
In recent years, the OPIOID abuse in the United States has caused a serious crisis in the country, that is, the opioid crisis. In order to solve this crisis to launch targeted research. First, the data provided by NFLIS is preprocessed, then the data is classified, and then, with the aid of principal component analysis, a mathematical model is established to carry out the data visualization, and the quantity scatter map of various kinds of drugs with time is obtained. Furthermore, the trend chart of broken line is obtained by processing the concrete data. The results show that the drug explosion will happen when the threshold of the total number of drugs is greater than 0.8 × 106. The model can effectively provide prevention advice.
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References
Liu, H., Zhang, C.: Constructing enterprise financial crisis early warning model by principal component analysis. J. Central Univ. Finan. Econ. 28(4), 70–75 (2004)
Xu, J., Cui, N., Mu, R.: Drift model identification methods for inertial platform based on Elman network. Syst. Eng. Electron. 32(7), 1497–1500 (2010)
Mark, C., Gauna, M.: Evaluating the risk of coal bursts in underground coal mines. Int. J. Min. Sci. Technol. 26(1), 47–52 (2016)
Zhang, T., Guo, K., Xu, X., Wu, J.: Based on Elman prediction of fighter air combat trajectory of neural network. Flight Mech. 36(05), 86–91 (2018)
Jing, L., Pan, Y.: Based on Elman Research on power load forecasting of network. Comput. Knowl. Technol. 9(16), 3871–3874 (2013)
Xi, H., Zhang, X.: Identification of coaxial helicopter dynamic system with Elman neural networks. J. Beijing Univ. Aeronaut. Astronaut. 34(7), 861–864 (2008)
Sharaf, R., Noureldin, A., Osman, A., El-Sheimy, N.: Online INS/GPS integration with a radial basis function neural network. IEEE Aerosp. Electron. Syst. Mag. 20(3), 8–14 (2005)
Li, H., Bian, C., Yuan, S.: Adaptive control of a class of nonlinear systems based on neural networks. Control Decis. 14(supplement), 511–515 (1999)
Liu, R., Fang, G.: Short-term load forecasting with comprehensive weather factors based on improved Elman neural network. Power Syst. Prot. Control 40(22), 113–137 (2012)
Chen, Y., Mao, Y., Chen, P., et al.: Short-term power load Forecasting based on ensemble empirical mode decomposition-sample entropy and Elman neural network. Proc. CSU-EPSA 28(3), 59–64 (2016)
Acknowledgements
This research was supported in part by the Project of National Natural Science Foundation of China under Grant 61672006, the Key Project of Natural Science Research in Anhui under Grants KJ2017A340, KJ2019A0533 and KJ2019A0535, the innovation team from Fuyang Normal University under Grants XDHXTD201703 and XDHXTD201709, and the Building of Brand Specialty Projects of Fuyang Normal University under Grant 2019PPZY01.
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Pang, K., Zhang, Y., Tao, Y., Tang, J., Wang, X. (2021). Research on Drug Tracing and Prediction Based on Elman Neural Network. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_59
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DOI: https://doi.org/10.1007/978-3-030-53980-1_59
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