Big DNA datasets analysis under push down automata

Kamal, MS; Trivdedi, MC; Alam, JB; Dey, N; Ashour, AS; Shi, F; Tavares, JMRS

Big DNA datasets analysis under push down automata

Kamal, MS Trivdedi, MC Alam, JB Dey, N Ashour, AS Shi, F Tavares, JMRS

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Publisher:: IOS Press
Publication Type:: Journal Article
Citation:: Journal of Intelligent and Fuzzy Systems, 2018, 35, (Preprint), pp. 1555-1565
Issue Date:: 2018-08-26

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Field	Value	Language
dc.contributor.author	Kamal, MS
dc.contributor.author	Trivdedi, MC
dc.contributor.author	Alam, JB
dc.contributor.author	Dey, N
dc.contributor.author	Ashour, AS
dc.contributor.author	Shi, F
dc.contributor.author	Tavares, JMRS
dc.date.accessioned	2023-01-03T04:18:41Z
dc.date.available	2023-01-03T04:18:41Z
dc.date.issued	2018-08-26
dc.identifier.citation	Journal of Intelligent and Fuzzy Systems, 2018, 35, (Preprint), pp. 1555-1565
dc.identifier.issn	1064-1246
dc.identifier.issn	1875-8967
dc.identifier.uri	http://hdl.handle.net/10453/164644
dc.description.abstract	Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically.
dc.language	en
dc.publisher	IOS Press
dc.relation.ispartof	Journal of Intelligent and Fuzzy Systems
dc.relation.isbasedon	10.3233/jifs-169695
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Big DNA datasets analysis under push down automata
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-01-03T04:18:40Z
pubs.issue	Preprint
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	Preprint

Abstract:

Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically.

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http://hdl.handle.net/10453/164644