Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service
CSE Department, Thapar University.
Department of Computer Science and Information Systems, BITS Pilani.
CSE Department, Thapar University.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
Show others and affiliations
2017 (English)In: IEEE International Conference on Communications, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7996965Conference paper, Published paper (Refereed)
Abstract [en]

With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. article id 7996965
Series
IEEE International Conference on Communications, ISSN 1550-3607
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-65490DOI: 10.1109/ICC.2017.7996965ISI: 000424872104001Scopus ID: 2-s2.0-85028297243ISBN: 9781467389990 (electronic)OAI: oai:DiVA.org:ltu-65490DiVA, id: diva2:1138403
Conference
2017 IEEE International Conference on Communications, ICC 2017, Paris, France, 21-25 May 2017
Available from: 2017-09-05 Created: 2017-09-05 Last updated: 2018-03-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Vasilakos, Athanasios

Search in DiVA

By author/editor
Vasilakos, Athanasios
By organisation
Computer Science
Media and Communication Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 43 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf