A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services

Chowdhury, Morshed, Abawajy, Jemal, Kelarev, Andrei and Jelinek, Herbert F. 2020, A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services, IEEE transactions on cloud computing, vol. 8, no. 2, pp. 473-483, doi: 10.1109/TCC.2016.2567389.

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Title A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services
Author(s) Chowdhury, MorshedORCID iD for Chowdhury, Morshed orcid.org/0000-0002-2866-4955
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Kelarev, Andrei
Jelinek, Herbert F.
Journal name IEEE transactions on cloud computing
Volume number 8
Issue number 2
Start page 473
End page 483
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2020-04
ISSN 2168-7161
Keyword(s) cardiac autonomic neuropathy
cloud services
distributed ensembles
Summary This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.
Language eng
DOI 10.1109/TCC.2016.2567389
Indigenous content off
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083442

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