An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination

A majority of gastrointestinal infectious diseases are caused by food contamination, and prediction of morbidity can be very useful for etiological factor controlling and medical resource utilization. However, an accurate prediction is often very difficult not only because there are various types of...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 226; S. 16 - 22
Hauptverfasser:	Song, Qin, Zheng, Yu-Jun, Xue, Yu, Sheng, Wei-Guo, Zhao, Mei-Rong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 22.02.2017
Schlagworte:	Deep denoising autoencoder (DDAE) Evolutionary algorithms (EAs) Gastrointestinal infections Morbidity prediction Neural networks Deep denoising autoencoder (DDAE) Evolutionary algorithms (EAs) Neural networks Morbidity prediction Gastrointestinal infections
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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