Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61787
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dc.contributor.authorde Oliveira, Caterine Silvaen
dc.contributor.authorSanin, Cesaren
dc.contributor.authorSzczerbicki, Edwarden
dc.date.accessioned2024-07-24T06:35:02Z-
dc.date.available2024-07-24T06:35:02Z-
dc.date.issued2020-
dc.identifier.citationProcedia Computer Science, v.176, p. 3093-3102en
dc.identifier.issn1877-0509en
dc.identifier.urihttps://hdl.handle.net/1959.11/61787-
dc.description.abstract<p>Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.</p>en
dc.languageenen
dc.publisherElsevier BVen
dc.relation.ispartofProcedia Computer Scienceen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleHuman Feedback and Knowledge Discovery: Towards Cognitive Systems Optimizationen
dc.typeConference Publicationen
dc.relation.conferenceKES 2020: 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES) Conferenceen
dc.identifier.doi10.1016/j.procs.2020.09.179en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameCaterine Silvaen
local.contributor.firstnameCesaren
local.contributor.firstnameEdwarden
local.profile.schoolSchool of Science and Technologyen
local.profile.emailcmaldon3@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference16th to 18th of September, 2020en
local.conference.placeVirtual Conferenceen
local.publisher.placeThe Netherlandsen
local.format.startpage3093en
local.format.endpage3102en
local.peerreviewedYesen
local.identifier.volume176en
local.title.subtitleTowards Cognitive Systems Optimizationen
local.access.fulltextYesen
local.contributor.lastnamede Oliveiraen
local.contributor.lastnameSaninen
local.contributor.lastnameSzczerbickien
dc.identifier.staffune-id:cmaldon3en
local.profile.orcid0000-0001-8515-417Xen
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61787en
local.date.onlineversion2020-10-02-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleHuman Feedback and Knowledge Discoveryen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsKES 2020: 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES) Conference, Virtual Conference, 16th to 18th of September, 2020en
local.search.authorde Oliveira, Caterine Silvaen
local.search.authorSanin, Cesaren
local.search.authorSzczerbicki, Edwarden
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7en
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2020en
local.year.published2020en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-05en
Appears in Collections:Conference Publication
School of Science and Technology
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