Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies

dc.contributor.authorMumtaz, Zahid
dc.contributor.authorWhiteford, Peter
dc.date.accessioned2023-05-04T01:59:26Z
dc.date.issued2021
dc.date.updated2022-02-13T07:17:16Z
dc.description.abstractMachine learning has been increasingly used for making informed public policy decisions, however, its application in the area of social protection in developing societies has been largely overlooked. We have employed unsupervised machine learning K-means clustering technique for exploring a big data that comprised of 88 attributes and 570 instances for better targeting of households that are in urgent need of welfare from the government. The clusters formed showed common patterns relating to insecurities in terms of loss of income and property, unemployment, disasters and disease etc. faced by households in each cluster. We found that households falling in rural areas jurisdictions face severe insecurities compared to other localities and are in urgent need of social protection interventions. We concluded that by employing K-means clustering unsupervised machine learning approach big data (even if it is limited) can be explored effectively for better targeting of social protection interventions for both developing and smart societies. The unsupervised machine learning technique presented in this study is an efficient approach because it can be used by societies that are facing data constraints and can achieve optimal results for increasing the welfare of poor by using the said approach.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1383-469Xen_AU
dc.identifier.urihttp://hdl.handle.net/1885/289862
dc.language.isoen_AUen_AU
dc.publisherBaltzer Science Publishers B.V.en_AU
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2021en_AU
dc.sourceMobile Networks and Applicationsen_AU
dc.subjectArtificial intelligenceen_AU
dc.subjectMachine learningen_AU
dc.subjectK-means clusteringen_AU
dc.subjectBig dataen_AU
dc.subjectSocial protectionen_AU
dc.subjectSmart and developing societiesen_AU
dc.titleMachine Learning Based Approach for Sustainable Social Protection Policies in Developing Societiesen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.lastpage173en_AU
local.bibliographicCitation.startpage159en_AU
local.contributor.affiliationMumtaz, Zahid, OTH Other Departments, ANUen_AU
local.contributor.affiliationWhiteford, Peter, College of Asia and the Pacific, ANUen_AU
local.contributor.authoremailu5709581@anu.edu.auen_AU
local.contributor.authoruidMumtaz, Zahid, u5709581en_AU
local.contributor.authoruidWhiteford, Peter, u1503628en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor440708 - Public administrationen_AU
local.identifier.absfor440709 - Public policyen_AU
local.identifier.absfor440705 - Gender, policy and administrationen_AU
local.identifier.ariespublicationa383154xPUB17352en_AU
local.identifier.citationvolume26en_AU
local.identifier.doi10.1007/s11036-020-01696-zen_AU
local.identifier.scopusID2-s2.0-85099108796
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://link.springer.com/en_AU
local.type.statusPublished Versionen_AU

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