Fine particulate matter () has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new, 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/, 2014), our technique improves the accuracy of concentration level predictions that are being reported in Japan.
This article outlines the challenge to understand how to integrate people into a new generation of cyber-physical-human systems (CPHSs) and proposes a human service capability description model to help.
SUMMARYMobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.