Collaborative Automated Machine Learning (AutoML) Process Framework

Bibliographic Details
Main Author: Chami, Johnas Camillius
Publication Date: 2024
Other Authors: Santos, Vítor
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/176456
Summary: Chami, J. C. ., & Santos, V. . (2024). Collaborative automated machine learning (AutoML) process framework. Edelweiss Applied Science and Technology, 8(6), 7675–7685. https://doi.org/10.55214/25768484.v8i6.3676
id RCAP_ef9989a65c2d06e9186eb619045cae61
oai_identifier_str oai:run.unl.pt:10362/176456
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Collaborative Automated Machine Learning (AutoML) Process FrameworkAutomated machine learning (AutoML)Collaborative frameworkData-driven transformationDesign science researchDigital transformationSmall and medium enterprises (SMEs)GeneralSDG 8 - Decent Work and Economic GrowthSDG 9 - Industry, Innovation, and InfrastructureSDG 10 - Reduced InequalitiesSDG 17 - Partnerships for the GoalsChami, J. C. ., & Santos, V. . (2024). Collaborative automated machine learning (AutoML) process framework. Edelweiss Applied Science and Technology, 8(6), 7675–7685. https://doi.org/10.55214/25768484.v8i6.3676In the face of rapid technological advancements and digital disruption, Small and Medium Enterprises (SMEs) grapple with integrating data-driven practices essential for competitiveness and growth. Unlike large corporations, SMEs often lack the resources and technical expertise to implement sophisticated data analytics and machine learning solutions. This study addresses the identified gap by developing a Collaborative Automated Machine Learning (AutoML) Process Framework tailored to the unique needs of SMEs. Leveraging Design Science Research methodology, the research conceptualizes, designs, and validates an accessible AutoML tool that automates complex machine learning processes while fostering collaboration among stakeholders. The framework aims to democratize advanced analytics, enabling SMEs to harness domain knowledge and drive data-driven decision-making without extensive data science expertise. The findings demonstrate that the proposed collaborative AutoML framework significantly enhances SMEs' operational efficiency, decision-making capabilities, and competitive edge, thereby contributing to their digital transformation and broader economic growth. This research not only bridges the existing gap in AutoML applications for SMEs but also aligns with sustainable development goals by promoting inclusive innovation and economic resilience.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNChami, Johnas CamilliusSantos, Vítor2024-12-18T17:19:49Z2024-12-142024-12-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/176456eng2576-8484PURE: 102429715https://doi.org/10.55214/25768484.v8i6.3676info: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:RCAAP2025-01-27T01:36:34Zoai:run.unl.pt:10362/176456Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:19:46.569704Repositórios 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 Collaborative Automated Machine Learning (AutoML) Process Framework
title Collaborative Automated Machine Learning (AutoML) Process Framework
spellingShingle Collaborative Automated Machine Learning (AutoML) Process Framework
Chami, Johnas Camillius
Automated machine learning (AutoML)
Collaborative framework
Data-driven transformation
Design science research
Digital transformation
Small and medium enterprises (SMEs)
General
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
SDG 10 - Reduced Inequalities
SDG 17 - Partnerships for the Goals
title_short Collaborative Automated Machine Learning (AutoML) Process Framework
title_full Collaborative Automated Machine Learning (AutoML) Process Framework
title_fullStr Collaborative Automated Machine Learning (AutoML) Process Framework
title_full_unstemmed Collaborative Automated Machine Learning (AutoML) Process Framework
title_sort Collaborative Automated Machine Learning (AutoML) Process Framework
author Chami, Johnas Camillius
author_facet Chami, Johnas Camillius
Santos, Vítor
author_role author
author2 Santos, Vítor
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Chami, Johnas Camillius
Santos, Vítor
dc.subject.por.fl_str_mv Automated machine learning (AutoML)
Collaborative framework
Data-driven transformation
Design science research
Digital transformation
Small and medium enterprises (SMEs)
General
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
SDG 10 - Reduced Inequalities
SDG 17 - Partnerships for the Goals
topic Automated machine learning (AutoML)
Collaborative framework
Data-driven transformation
Design science research
Digital transformation
Small and medium enterprises (SMEs)
General
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
SDG 10 - Reduced Inequalities
SDG 17 - Partnerships for the Goals
description Chami, J. C. ., & Santos, V. . (2024). Collaborative automated machine learning (AutoML) process framework. Edelweiss Applied Science and Technology, 8(6), 7675–7685. https://doi.org/10.55214/25768484.v8i6.3676
publishDate 2024
dc.date.none.fl_str_mv 2024-12-18T17:19:49Z
2024-12-14
2024-12-14T00:00:00Z
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/10362/176456
url http://hdl.handle.net/10362/176456
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2576-8484
PURE: 102429715
https://doi.org/10.55214/25768484.v8i6.3676
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
application/pdf
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_ 1833598022634700800

Similar Items