Soft Computing Methods for Big Data Problems

Bibliographic Details
Main Author: Hasan, Shafaatunnur
Publication Date: 2014
Other Authors: Shamsuddin, Siti Mariyam, Lopes, Noel
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10314/2414
Summary: Link para aquisição - http://link.springer.com/chapter/10.1007/978-981-287-134-3_15
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Soft Computing Methods for Big Data ProblemsGPGPUSoft computingBig dataSOMMBPBiomedical classification problemsLink para aquisição - http://link.springer.com/chapter/10.1007/978-981-287-134-3_15Generally, big data computing deals with massive and high-dimensional data such as DNA microarray data, financial data, medical imagery, satellite imagery, and hyperspectral imagery. Therefore, big data computing needs advanced technologies or methods to solve the issues of computational time to extract valuable information without information loss. In this context, generally, machine learning (ML) algorithms have been considered to learn and find useful and valuable information from large value of data. However, ML algorithms such as neural networks are computationally expensive, and typically, the central processing unit (CPU) is unable to cope with these requirements. Thus, we need a high-performance computer to execute faster solutions such graphics processing unit (GPU). GPUs provide remarkable performance gains compared to CPUs. The GPU is relatively inexpensive with affordable price, availability, and scalability. Since 2006, NVIDIA provides simplification of the GPU programming model with the Compute Unified Device Architecture (CUDA), which supports for accessible programming interfaces and industry-standard languages, such as C and C++. Since then, general-purpose graphics processing unit (GPGPU) using ML algorithms are applied on various applications, including signal and image pattern classification in biomedical area. The importance of fast analysis of detecting cancer or non-cancer becomes the motivation of this study. Accordingly, we proposed soft computing methods, self-organizing map (SOM) and multiple back-propagation (MBP) for big data, particularly on biomedical classification problems. Big data such as gene expression datasets are executed on high-performance computer and Fermi architecture graphics hardware. Based on the experiment, MBP and SOM with GPU-Tesla generate faster computing times than high-performance computer with feasible results in terms of speed and classification performance.Springer2016-07-05T14:17:21Z2016-07-052014-12-12T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10314/2414http://hdl.handle.net/10314/2414eng978-981-287-133-6Hasan, ShafaatunnurShamsuddin, Siti MariyamLopes, Noelinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-01-05T02:57:46Zoai:bdigital.ipg.pt:10314/2414Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:23:13.447324Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Soft Computing Methods for Big Data Problems
title Soft Computing Methods for Big Data Problems
spellingShingle Soft Computing Methods for Big Data Problems
Hasan, Shafaatunnur
GPGPU
Soft computing
Big data
SOM
MBP
Biomedical classification problems
title_short Soft Computing Methods for Big Data Problems
title_full Soft Computing Methods for Big Data Problems
title_fullStr Soft Computing Methods for Big Data Problems
title_full_unstemmed Soft Computing Methods for Big Data Problems
title_sort Soft Computing Methods for Big Data Problems
author Hasan, Shafaatunnur
author_facet Hasan, Shafaatunnur
Shamsuddin, Siti Mariyam
Lopes, Noel
author_role author
author2 Shamsuddin, Siti Mariyam
Lopes, Noel
author2_role author
author
dc.contributor.author.fl_str_mv Hasan, Shafaatunnur
Shamsuddin, Siti Mariyam
Lopes, Noel
dc.subject.por.fl_str_mv GPGPU
Soft computing
Big data
SOM
MBP
Biomedical classification problems
topic GPGPU
Soft computing
Big data
SOM
MBP
Biomedical classification problems
description Link para aquisição - http://link.springer.com/chapter/10.1007/978-981-287-134-3_15
publishDate 2014
dc.date.none.fl_str_mv 2014-12-12T00:00:00Z
2016-07-05T14:17:21Z
2016-07-05
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10314/2414
http://hdl.handle.net/10314/2414
url http://hdl.handle.net/10314/2414
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-981-287-133-6
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833598063165308928

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