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Artificial neural networks in the development of business analytics projects
dc.contributor.author | Quintero J.B | |
dc.contributor.author | Villanueva-Valdes D | |
dc.contributor.author | Manrique-Losada B. | |
dc.date.accessioned | 2024-07-31T21:06:50Z | |
dc.date.available | 2024-07-31T21:06:50Z | |
dc.date.created | 2024 | |
dc.identifier.issn | 17567017 | |
dc.identifier.uri | http://hdl.handle.net/11407/8403 | |
dc.description | The accelerated evolution of information and communication technologies, with an ever-growing increase in their access and availability, has become the foundation for the current big data age. Business analytics (BAs) has helped different organisations leverage the large volumes of information available today. In fact, artificial neural networks (ANNs) provide deep data mining facilities to organisations for identifying patterns, predict probable future states, and fully benefit from predictions/forecasts. This article describes three ANNs application scenarios for the development of BA projects, by using network learning for: 1) executing accounting processes; 2) time series forecasts; 3) regression-based predictions. We validate scenarios by implementing an application-case using actual data, thus demonstrating the full extent of the capabilities of this technique. The main findings exhibit the expressive power of the programming languages used in data analytics, the wide range of tools/techniques available, and the impact these factors may have on the BA development projects. © 2024 Inderscience Enterprises Ltd.. All rights reserved. | |
dc.language.iso | eng | |
dc.publisher | Inderscience Publishers | |
dc.relation.isversionof | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183874752&doi=10.1504%2fIJIDS.2024.136283&partnerID=40&md5=cd40caa1ba514b68a7ad639b4598430a | |
dc.source | International Journal of Information and Decision Sciences | |
dc.source | Int. J. Inf. Decis. Sci. | |
dc.source | Scopus | |
dc.subject | Aactivity-based costing | eng |
dc.subject | ANNs | eng |
dc.subject | Artificial neural networks | eng |
dc.subject | Big data | eng |
dc.subject | Business analytics | eng |
dc.subject | Data analytics | eng |
dc.subject | Decision making | eng |
dc.subject | Deep data mining | eng |
dc.subject | Network learning process | eng |
dc.subject | Regression-based prediction | eng |
dc.subject | Supervised learning | eng |
dc.subject | Time series forecast | eng |
dc.title | Artificial neural networks in the development of business analytics projects | eng |
dc.type | article | |
dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | |
dc.publisher.program | Ingeniería de Sistemas | spa |
dc.type.spa | Artículo | |
dc.identifier.doi | 10.1504/IJIDS.2024.136283 | |
dc.relation.citationvolume | 16 | |
dc.relation.citationissue | 1 | |
dc.relation.citationstartpage | 46 | |
dc.relation.citationendpage | 72 | |
dc.publisher.faculty | Facultad de Ingenierías | spa |
dc.affiliation | Quintero, J.B., Faculty of engineering, EAFIT University, Medellín, Colombia | |
dc.affiliation | Villanueva-Valdes, D., Faculty of engineering, University of Medellín, Medellín, Colombia | |
dc.affiliation | Manrique-Losada, B., Faculty of engineering, University of Medellín, Medellín, Colombia | |
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dc.type.version | info:eu-repo/semantics/publishedVersion | |
dc.identifier.reponame | reponame:Repositorio Institucional Universidad de Medellín | |
dc.identifier.repourl | repourl:https://repository.udem.edu.co/ | |
dc.identifier.instname | instname:Universidad de Medellín |
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