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FogLearn: Leveraging Fog-Based Machine Learning for Smart System Big Data Analytics
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Author(s): Rabindra K. Barik (Kalinga Institute of Industrial Technology, India), Rojalina Priyadarshini (Kalinga Institute of Industrial Technology, India), Harishchandra Dubey (The University of Texas at Dallas, USA), Vinay Kumar (Visvesvaraya National Institute of Technology, India)and Kunal Mankodiya (University of Rhode Island, USA)
Copyright: 2019
Pages: 17
Source title:
Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8054-6.ch052
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Abstract
Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.
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