Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://dss2016.inn....arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
https://dss2016.inn.ac/DSS-201...
Article
Data sources: UnpayWall
https://doi.org/10.1109/fas-w....
Article . 2016 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object
Data sources: DBLP
https://dx.doi.org/10.1109/fas...
Other literature type
Data sources: Microsoft Academic Graph
 View all 2 versions
Claim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Logistic Regression Multinomial for Arrhythmia Detection

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Other literature type 01 Sep 2016Publisher:IEEEJournal:2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W)
Authors: Omar Behadada; Marcello Trovati; Mohammed Amine Chikh; Nik Bessis; Yannis Korkontzelos;

doi: 10.1109/fas-w.2016.39

Logistic Regression Multinomial for Arrhythmia Detection

Abstract

In this paper, we introduce a method based on logistics Regression multi-class as a classifier to provide a powerful and accurate insight into cardiac arrhythmia. As suggested by our evaluation, this provide a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilization of big data in the medical sector.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average