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Computation 2018, 6(4), 62; https://doi.org/10.3390/computation6040062

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

1
School of Computer Science and Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India
2
School of Computer Application, KIIT Deemed to be University, Bhubaneswar 751024, India
3
Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX 75080, USA
*
Authors to whom correspondence should be addressed.
Received: 13 October 2018 / Revised: 13 November 2018 / Accepted: 26 November 2018 / Published: 4 December 2018
(This article belongs to the Section Computational Engineering)
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Abstract

The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments. View Full-Text
Keywords: fog computing; deep learning; deep neural network; stress prediction; diabetes mellitus; hypertension attack; smart health; connected health fog computing; deep learning; deep neural network; stress prediction; diabetes mellitus; hypertension attack; smart health; connected health
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Priyadarshini, R.; Barik, R.K.; Dubey, H. DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks. Computation 2018, 6, 62.

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