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Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks

2023Journal article Open access
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dc.contributor.authorChaves, Aníbal
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-16T14:20:38Z
dc.date.available2024-02-16T14:20:38Z
dc.date.issued2023
dc.description.abstractThrough the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have the inherent capabilities of humans, such as pattern recognition in images and signals. However, conventional methods are based on deterministic models, which cannot express the epistemic uncertainty of their predictions. The alternative consists of probabilistic models, although these are considerably more difficult to develop. To address the problems related to the development of probabilistic networks and the choice of network architecture, this article proposes the development of an application that allows the user to choose the desired architecture with the trained model for the given data. This application, named “Graphical User Interface for Probabilistic Neural Networks”, allows the user to develop or to use a standard convolutional neural network for the provided data, with networks already adapted to implement a probabilistic model. Contrary to the existing models for generic use, which are deterministic and already pre-trained on databases to be used in transfer learning, the approach followed in this work creates the network layer by layer, with training performed on the provided data, originating a specific model for the data in question.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationChaves, A.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks. Signals 2023, 4, 297–314. https:// doi.org/10.3390/signals4020016pt_PT
dc.identifier.doi10.3390/signals4020016pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5559
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectGraphical interfacept_PT
dc.subjectProbabilistic convolutional neural networkpt_PT
dc.subjectNo-code development platformpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleGraphical User Interface for the Development of Probabilistic Convolutional Neural Networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.endPage314pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage297pt_PT
oaire.citation.titleSignalspt_PT
oaire.citation.volume4pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameChaves
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameAníbal
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-1034-4419
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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