GADGET - Online Gambling Addiction Detection
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/110872 |
Resumo: | This chapter describes an automatic tool that highlights potential pathological online gamblers using machine learning time series clustering algorithms. The project relies on data related to Portuguese gamblers.A theoretical overview of the two different clustering algorithms considered in this project is presented. We also provide some implementation details and the changes that we have introduced to make the algorithm executable in a parallel and efficient manner. Finally, the results obtained by the two different techniques are presented, highlighting especially the clusters of pathological gamblers obtained. This work represents a starting point for a possible future system where the individuals at risk are notified of their dangerous condition, preventing possible future gambling disorders or risky behavior. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
spelling |
GADGET - Online Gambling Addiction DetectionTime series clustering of Portuguese online gamblersMachine LearningTime SeriesClusteringGamblingAddictionThis chapter describes an automatic tool that highlights potential pathological online gamblers using machine learning time series clustering algorithms. The project relies on data related to Portuguese gamblers.A theoretical overview of the two different clustering algorithms considered in this project is presented. We also provide some implementation details and the changes that we have introduced to make the algorithm executable in a parallel and efficient manner. Finally, the results obtained by the two different techniques are presented, highlighting especially the clusters of pathological gamblers obtained. This work represents a starting point for a possible future system where the individuals at risk are notified of their dangerous condition, preventing possible future gambling disorders or risky behavior.Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa. NOVA Information Management School (NOVA IMS)RUNCastelli, MauroFallacara, EnricoManzoni, Luca2022-12-31T01:31:05Z2021-012021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttp://hdl.handle.net/10362/110872eng978-972-8093-19-8info: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:RCAAP2024-03-11T04:54:46Zoai:run.unl.pt:10362/110872Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:41:45.034169Repositório 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 |
GADGET - Online Gambling Addiction Detection Time series clustering of Portuguese online gamblers |
title |
GADGET - Online Gambling Addiction Detection |
spellingShingle |
GADGET - Online Gambling Addiction Detection Castelli, Mauro Machine Learning Time Series Clustering Gambling Addiction |
title_short |
GADGET - Online Gambling Addiction Detection |
title_full |
GADGET - Online Gambling Addiction Detection |
title_fullStr |
GADGET - Online Gambling Addiction Detection |
title_full_unstemmed |
GADGET - Online Gambling Addiction Detection |
title_sort |
GADGET - Online Gambling Addiction Detection |
author |
Castelli, Mauro |
author_facet |
Castelli, Mauro Fallacara, Enrico Manzoni, Luca |
author_role |
author |
author2 |
Fallacara, Enrico Manzoni, Luca |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RUN |
dc.contributor.author.fl_str_mv |
Castelli, Mauro Fallacara, Enrico Manzoni, Luca |
dc.subject.por.fl_str_mv |
Machine Learning Time Series Clustering Gambling Addiction |
topic |
Machine Learning Time Series Clustering Gambling Addiction |
description |
This chapter describes an automatic tool that highlights potential pathological online gamblers using machine learning time series clustering algorithms. The project relies on data related to Portuguese gamblers.A theoretical overview of the two different clustering algorithms considered in this project is presented. We also provide some implementation details and the changes that we have introduced to make the algorithm executable in a parallel and efficient manner. Finally, the results obtained by the two different techniques are presented, highlighting especially the clusters of pathological gamblers obtained. This work represents a starting point for a possible future system where the individuals at risk are notified of their dangerous condition, preventing possible future gambling disorders or risky behavior. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01 2021-01-01T00:00:00Z 2022-12-31T01:31:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/110872 |
url |
http://hdl.handle.net/10362/110872 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
978-972-8093-19-8 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa. NOVA Information Management School (NOVA IMS) |
publisher.none.fl_str_mv |
Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa. NOVA Information Management School (NOVA IMS) |
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 |
repository.mail.fl_str_mv |
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1799138030364131328 |