Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling

Patan, R; Kallam, S; Gandomi, AH; Hanne, T; Ramachandran, M

Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling

Patan, R Kallam, S Gandomi, AH Hanne, T Ramachandran, M

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Journal of the Operational Research Society, 2022, 73, (10), pp. 2204-2215
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Patan, R
dc.contributor.author	Kallam, S
dc.contributor.author	Gandomi, AH
dc.contributor.author	Hanne, T
dc.contributor.author	Ramachandran, M
dc.date.accessioned	2023-03-28T04:12:27Z
dc.date.available	2023-03-28T04:12:27Z
dc.date.issued	2022-01-01
dc.identifier.citation	Journal of the Operational Research Society, 2022, 73, (10), pp. 2204-2215
dc.identifier.issn	0160-5682
dc.identifier.issn	1476-9360
dc.identifier.uri	http://hdl.handle.net/10453/168714
dc.description.abstract	Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Journal of the Operational Research Society
dc.relation.isbasedon	10.1080/01605682.2021.1960908
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Operations Research
dc.title	Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling
dc.type	Journal Article
utslib.citation.volume	73
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-28T04:12:25Z
pubs.issue	10
pubs.publication-status	Published online
pubs.volume	73
utslib.citation.issue	10

Abstract:

Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.

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http://hdl.handle.net/10453/168714