Show simple item record

dc.contributor.authorBarasa, Peter Wawire
dc.contributor.authorWechuli, Alice Nambiro
dc.contributor.authorBarasa, Samuel Wafula
dc.date.accessioned2019-04-03T06:35:01Z
dc.date.available2019-04-03T06:35:01Z
dc.date.issued2018-06-12
dc.identifier.isbn978-9966-59-011-5
dc.identifier.urihttp://erepository.kibu.ac.ke/handle/123456789/586
dc.description.abstractIn this paper, the authors focus on using machine learning to fight corruption in governments. Corruption has been rampant in public and private sectors though prosecuting the cases in court has been wanting due to lack of clear evidence that convicts those involved. Since government operations have been digitized, machine learning algorithms can be used to scout corruption evidence which can be used to prosecute those involved in corruption. The research will adopt a positivism research philosophy and Inductive research approach. Desktop research design will be used. This paper seeks to come up with an integrated machine learning system that will connect to all the digitized government systems to curb corruption in governments.en_US
dc.language.isoenen_US
dc.publisherKIBUen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectMachine Learningen_US
dc.subjectCorruptionen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectIntegrated Systemen_US
dc.titleIntegrated machine learning system for curbing corruptionen_US
dc.typeWorking Paperen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States