Abstract
Prescription drug abuse is one of the fastest growing public health problems in the USA. This work develops a broad learning method for Drug Abuse Detection (DAD). In this paper, we propose a new broad learning-based method named ILSTM, short for Improved Long Short-Term Memory, to study the data fusion and prediction from heterogeneous data sources for DAD. The algorithm utilizes the broad learning framework to handle data fusion broadly and information mining deeply simultaneously. Moreover, the effectiveness and prevalence of Holt-Winter inspire our work in the temporal property for DAD.
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References
Wang, F., Qu, Y., Zheng, L., Lu, C.-T., Yu, P.S.: Deep and broad learning on content-aware POI recommendation. In: CIC 2017, USA, pp. 369–378 (2017)
Zhang, J., Xia, C., Zhang, C., Cui, L., Fu, Y., Yu, P.S.: BL-MNE: emerging heterogeneous social network embedding through broad learning with aligned autoencoder. In: ICDM 2017, USA, pp. 605–614 (2017)
Cao, B., Mao, M., Viidu, S., Yu, P.S.: HitFraud: a broad learning approach for collective fraud detection in heterogeneous information networks. In: ICDM 2017, USA, pp. 769–774 (2017)
Hu, H., et al.: Deep learning model for classifying drug abuse risk behavior in tweets. In: ICHI 2018, USA, pp. 386–387 (2018)
Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2009)
Wu, Y., Wu, X.: Using loglinear model for discrimination discovery and prevention. In: DSAA 2016, Canada, pp. 110–119 (2016)
Keith Norambuena, B., Lettura, E.F., Villegas, C.M.: Sentiment analysis and opinion mining applied to scientific paper reviews. Intell. Data Anal. 23(1), 191–214 (2019)
Chary, M., Genes, N., McKenzie, A., Manini, A.F.: Leveraging social networks for toxicovigilance. J. Med. Toxicol. 9(2), 184–191 (2013)
Balsamo, D., Bajardi, P., et al.: Firsthand opiates abuse on social media: monitoring geospatial patterns of interest through a digital cohort. In: WWW 2019, USA, pp. 1–7 (2019)
Han, X., Xu, L., Qiao, F.: CNN-BiLSTM-CRF model for term extraction in Chinese corpus. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 267–274. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_25
Wang, S., Wu, B., Wang, B., Tong, X.: Complaint classification using hybrid-attention GRU neural network. In: Yang, Q., Zhou, Z.-H., Gong, Z., Zhang, M.-L., Huang, S.-J. (eds.) PAKDD 2019. LNCS (LNAI), vol. 11439, pp. 251–262. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16148-4_20
Raikwar, A.R., et al.: Long-Term and short-term traffic forecasting using holt-winters method: a comparability approach with comparable data in multiple seasons. IJSE 8(2), 38–50 (2017)
Acknowledgements
This work is supported by the Initial Scientific Research Fund of Introduced Talents in Anhui Polytechnic University (No. 2017YQQ015), Pre-research Project of National Natural Science Foundation of China (No. 2019yyzr03) and National Natural Science Foundation of China Youth Fund (No. 61300170).
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Kong, C., Liu, J., Li, H., Liu, Y., Zhu, H., Liu, T. (2019). Drug Abuse Detection via Broad Learning. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_49
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DOI: https://doi.org/10.1007/978-3-030-30952-7_49
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