Survey data preprocessing for optimal modelling through ANNs applied to management environments

Texeira Quirós, Joaquín; Texeira Justino, Maria do Rosário; Gonçalves, António José; Godinho Antunes, Marina; Ribeiro Mucharreira, Pedro

Publication

Survey data preprocessing for optimal modelling through ANNs applied to management environments

2024-09-06Journal article

datacite.subject.sdg	04:Educação de Qualidade
dc.contributor.author	Texeira Quirós, Joaquín
dc.contributor.author	Texeira Justino, Maria do Rosário
dc.contributor.author	Gonçalves, António José
dc.contributor.author	Godinho Antunes, Marina
dc.contributor.author	Ribeiro Mucharreira, Pedro
dc.date.accessioned	2025-05-08T15:02:30Z
dc.date.available	2025-05-08T15:02:30Z
dc.date.issued	2024-09-06
dc.description.abstract	Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.	eng
dc.description.sponsorship	The publication of this paper was partially funded by the Autonomous University of Lisbon.
dc.identifier.citation	Texeira-Quirós, J., Justino, M.R.T.F., Gonçalves, A.J., Antunes, M.G., Mucharreira, P.R. (2024). Survey data preprocessing for optimal modelling through ANNs applied to management environments. Journal of Infrastructure, Policy and Development, 8(9), 7108. https://doi.org/10.24294/jipd.v8i9.7108
dc.identifier.doi	10.24294/jipd.v8i9.7108
dc.identifier.issn	2572-7931
dc.identifier.issn	2572-7923
dc.identifier.uri	http://hdl.handle.net/10400.21/21850
dc.language.iso	eng
dc.peerreviewed	yes
dc.publisher	EnPress Publisher
dc.relation.ispartof	Journal of Infrastructure, Policy and Development
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Survey
dc.subject	Data
dc.subject	Processing
dc.subject	Modelling
dc.subject	Neural networks
dc.subject	ANN
dc.title	Survey data preprocessing for optimal modelling through ANNs applied to management environments	eng
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.issue	9
oaire.citation.title	Journal of Infrastructure, Policy and Development
oaire.citation.volume	8
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	Texeira Quirós
person.givenName	Joaquín
person.identifier	3507951
person.identifier.ciencia-id	601F-79BA-3BCB
person.identifier.orcid	0000-0002-7693-9888
relation.isAuthorOfPublication	767a5266-c68a-46ae-9444-0b7f2d5fbd9a
relation.isAuthorOfPublication.latestForDiscovery	767a5266-c68a-46ae-9444-0b7f2d5fbd9a