Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems

Detalhes bibliográficos
Autor(a) principal: Cunha, Bruno
Data de Publicação: 2016
Outros Autores: Madureira, Ana Maria, Pereira, João Paulo, Pereira, Ivo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/10003
Resumo: The ability to adjust itself to users’ profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning and training of user modelling techniques combined with existing system components. Based on the architecture for user modelling on Intelligent and Adaptive Scheduling Systems, this paper presents an analysis of using the mentioned architecture to characterize user’s behaviours and a case study comparing the employment of different user classifiers. Bayesian and Artificial Neural Networks were selected as the elements of the computational study and this paper presents a description on how to prepare them to deal with user information.
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spelling Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling SystemsUser ModellingHuman-Computer InteractionMachine LearningScalable IntelligenceScheduling SystemsThe ability to adjust itself to users’ profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning and training of user modelling techniques combined with existing system components. Based on the architecture for user modelling on Intelligent and Adaptive Scheduling Systems, this paper presents an analysis of using the mentioned architecture to characterize user’s behaviours and a case study comparing the employment of different user classifiers. Bayesian and Artificial Neural Networks were selected as the elements of the computational study and this paper presents a description on how to prepare them to deal with user information.Institute of Electrical and Electronics EngineersRepositório Científico do Instituto Politécnico do PortoCunha, BrunoMadureira, Ana MariaPereira, João PauloPereira, Ivo20162117-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/10003eng10.1109/SSCI.2016.7849997metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:51:34Zoai:recipp.ipp.pt:10400.22/10003Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:30:32.108565Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
title Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
spellingShingle Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
Cunha, Bruno
User Modelling
Human-Computer Interaction
Machine Learning
Scalable Intelligence
Scheduling Systems
title_short Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
title_full Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
title_fullStr Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
title_full_unstemmed Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
title_sort Evaluating the Effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems
author Cunha, Bruno
author_facet Cunha, Bruno
Madureira, Ana Maria
Pereira, João Paulo
Pereira, Ivo
author_role author
author2 Madureira, Ana Maria
Pereira, João Paulo
Pereira, Ivo
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Cunha, Bruno
Madureira, Ana Maria
Pereira, João Paulo
Pereira, Ivo
dc.subject.por.fl_str_mv User Modelling
Human-Computer Interaction
Machine Learning
Scalable Intelligence
Scheduling Systems
topic User Modelling
Human-Computer Interaction
Machine Learning
Scalable Intelligence
Scheduling Systems
description The ability to adjust itself to users’ profile is imperative in modern system, given that many people interact with a lot of information in different ways. The creation of adaptive systems is a complex domain that requires very specific methods and the integration of several intelligent techniques, from an intelligent systems development perspective. Designing an adaptive system requires planning and training of user modelling techniques combined with existing system components. Based on the architecture for user modelling on Intelligent and Adaptive Scheduling Systems, this paper presents an analysis of using the mentioned architecture to characterize user’s behaviours and a case study comparing the employment of different user classifiers. Bayesian and Artificial Neural Networks were selected as the elements of the computational study and this paper presents a description on how to prepare them to deal with user information.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-01-01T00:00:00Z
2117-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/10003
url http://hdl.handle.net/10400.22/10003
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1109/SSCI.2016.7849997
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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