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A Proposed Architecture for Predicting Breast Cancer using Fog Computing

by Laila A. Abd-Elmegid
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 26
Year of Publication: 2019
Authors: Laila A. Abd-Elmegid
10.5120/cae2019652815

Laila A. Abd-Elmegid . A Proposed Architecture for Predicting Breast Cancer using Fog Computing. Communications on Applied Electronics. 7, 26 ( Feb 2019), 32-35. DOI=10.5120/cae2019652815

@article{ 10.5120/cae2019652815,
author = { Laila A. Abd-Elmegid },
title = { A Proposed Architecture for Predicting Breast Cancer using Fog Computing },
journal = { Communications on Applied Electronics },
issue_date = { Feb 2019 },
volume = { 7 },
number = { 26 },
month = { Feb },
year = { 2019 },
issn = { 2394-4714 },
pages = { 32-35 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number26/848-2019652815/ },
doi = { 10.5120/cae2019652815 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:03:53.992741+05:30
%A Laila A. Abd-Elmegid
%T A Proposed Architecture for Predicting Breast Cancer using Fog Computing
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 26
%P 32-35
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new emerged concept, known as fog computing, aims to local processing and storage of large amounts of data at its sources. Such concept differs from the centralized style of processing and storage used with the cloud computing concept. Fog computing technology improves both the performance and the efficiency. The main cause of advantages gained from fog computing is the reduced amount of transferred data to the cloud need to be analyzed, processed, and stored. little research has been dedicated to study how to efficiently apply big data analytics locally where data has been collected and stored. This study proposes a fog computing-based architecture for prediction of the breast cancer prognosis. The proposed architecture uses the BCOAP model for the prediction purpose. It addresses the problem of real time processing of large amounts of data without overwhelming the data center at the cloud. It distinguishes between the processes to be performed on the cloud and the others to be performed on the fog. Initial prototype of the proposed architecture has highlighted its efficiency and how it increases the potentials of the applied BCOAP model.

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Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer BCOAP Classification Cloud computing Fog computing.

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