Dual‐grained representation for hand, foot, and mouth disease prediction within public health cyber‐physical systems
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[1] Yiming Yang,et al. Deep Learning for Epidemiological Predictions , 2018, SIGIR.
[2] Xiaomin Song,et al. RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series , 2018, AAAI.
[3] Jin-Feng Wang,et al. Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors. , 2018, The Science of the total environment.
[4] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[5] Tom Solomon,et al. Bmc Infectious Diseases Identification and Validation of Clinical Predictors for the Risk of Neurological Involvement in Children with Hand, Foot, and Mouth Disease in Sarawak , 2022 .
[6] Yong Wang,et al. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China. , 2019, The Science of the total environment.
[7] Cristina C. R. Sady,et al. Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease , 2016, Comput. Biol. Medicine.
[8] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[9] Borja Bordel,et al. Cyber-physical systems: Extending pervasive sensing from control theory to the Internet of Things , 2017, Pervasive Mob. Comput..
[10] Jingjing Ren,et al. Epidemiological features of and changes in incidence of infectious diseases in China in the first decade after the SARS outbreak: an observational trend study , 2017, The Lancet Infectious Diseases.
[11] Zhen Zhu,et al. Surveillance, epidemiology, and pathogen spectrum of hand, foot, and mouth disease in mainland of China from 2008 to 2017 , 2019, Biosafety and Health.
[12] Hung-yi Lee,et al. Temporal pattern attention for multivariate time series forecasting , 2018, Machine Learning.
[13] L. Tian,et al. Acute effects of air pollution on the incidence of hand, foot, and mouth disease in Wuhan, China , 2020 .
[14] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[15] Junzhong Gu,et al. Diarrhoea outpatient visits prediction based on time series decomposition and multi-local predictor fusion , 2015, Knowl. Based Syst..
[16] Jinju Wu,et al. Effects of relative humidity on childhood hand, foot, and mouth disease reinfection in Hefei, China. , 2018, The Science of the total environment.
[17] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[18] Junzhong Gu,et al. Comparative study among three different artificial neural networks to infectious diarrhea forecasting , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[19] Xuerui Tan,et al. The application of meteorological data and search index data in improving the prediction of HFMD: A study of two cities in Guangdong Province, China. , 2019, The Science of the total environment.
[20] Xiaoli Li,et al. Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN , 2020, Softw. Pract. Exp..
[21] Lawrence C McCandless,et al. Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city. , 2019, Environmental pollution.
[22] Sanyam Shukla,et al. Dynamic selection of normalization techniques using data complexity measures , 2018, Expert Syst. Appl..
[23] Deniz Erdogmus,et al. The Future of Human-in-the-Loop Cyber-Physical Systems , 2013, Computer.
[24] Borja Bordel,et al. Cyber-Physical Systems for Environment and People Monitoring in Large Facilities: A Study Case in Public Health , 2019, ICITS.
[25] S. Rajaram,et al. A survey on forecasting of time series data , 2016, 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16).
[26] Wadii Boulila,et al. Standalone noise and anomaly detection in wireless sensor networks: A novel time‐series and adaptive Bayesian‐network‐based approach , 2020, Softw. Pract. Exp..
[27] Yang Yang,et al. Using Baidu Search Index to Predict Dengue Outbreak in China , 2016, Scientific Reports.
[28] Liang He,et al. User identification for enhancing IP-TV recommendation , 2016, Knowl. Based Syst..
[29] Guokun Lai,et al. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.
[30] P. Perron,et al. Estimating and testing linear models with multiple structural changes , 1995 .
[31] Ting Luo,et al. TDDF: HFMD Outpatients Prediction Based on Time Series Decomposition and Heterogenous Data Fusion in Xiamen, China , 2019, ADMA.
[32] Junlong Zhou,et al. Security-Critical Energy-Aware Task Scheduling for Heterogeneous Real-Time MPSoCs in IoT , 2020, IEEE Transactions on Services Computing.
[33] Min Chi,et al. ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling , 2019, IJCAI.
[34] Y. Hao,et al. Interactions between climate factors and air pollution on daily HFMD cases: A time series study in Guangdong, China. , 2019, The Science of the total environment.
[35] Jinyan Wang,et al. Modelling the effects of contaminated environments on HFMD infections in mainland China , 2016, Biosyst..
[36] Bo Zhang,et al. Stochastic Volatility Models with ARMA Innovations an Application to G7 Inflation Forecasts , 2018, International Journal of Forecasting.
[37] Garrison W. Cottrell,et al. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.