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So far, Google Trend data have been used for influenza surveillance in many European and American countries; however, there are few attempts to apply the low-cost surveillance method in Asian developing countries. To investigate the correlation between the search trends and the influenza activity in Asia, we examined the Google query data of four Asian(More)
The 2009 influenza pandemic teaches us how fast the influenza virus could spread globally within a short period of time. To address the challenge of timely global influenza surveillance, this paper presents a spatial-temporal method that incorporates heterogeneous data collected from the Internet to detect influenza epidemics in real time. Specifically, the(More)
—Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data. However, implementing a deep neural network in embedded systems is a challenging task, because a typical deep neural network, such as a Deep Belief Network using 128×128 images as input, could exhaust Giga bytes of memory and result in(More)
  • Xichuan Zhou, Shengli Li, Fang Tang, Shengdong Hu, Zhi Lin, Lei Zhang
  • 2017
Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an(More)
The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the(More)
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