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- W2770146469 abstract "With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category." @default.
- W2770146469 created "2017-12-04" @default.
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- W2770146469 date "2017-11-20" @default.
- W2770146469 modified "2023-10-18" @default.
- W2770146469 title "Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities" @default.
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- W2770146469 doi "https://doi.org/10.3390/jsan6040026" @default.
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