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Artificial neural networks in the development of business analytics projects

dc.contributor.authorQuintero J.B
dc.contributor.authorVillanueva-Valdes D
dc.contributor.authorManrique-Losada B.
dc.date.accessioned2024-07-31T21:06:50Z
dc.date.available2024-07-31T21:06:50Z
dc.date.created2024
dc.identifier.issn17567017
dc.identifier.urihttp://hdl.handle.net/11407/8403
dc.descriptionThe 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.isoeng
dc.publisherInderscience Publishers
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85183874752&doi=10.1504%2fIJIDS.2024.136283&partnerID=40&md5=cd40caa1ba514b68a7ad639b4598430a
dc.sourceInternational Journal of Information and Decision Sciences
dc.sourceInt. J. Inf. Decis. Sci.
dc.sourceScopus
dc.subjectAactivity-based costingeng
dc.subjectANNseng
dc.subjectArtificial neural networkseng
dc.subjectBig dataeng
dc.subjectBusiness analyticseng
dc.subjectData analyticseng
dc.subjectDecision makingeng
dc.subjectDeep data miningeng
dc.subjectNetwork learning processeng
dc.subjectRegression-based predictioneng
dc.subjectSupervised learningeng
dc.subjectTime series forecasteng
dc.titleArtificial neural networks in the development of business analytics projectseng
dc.typearticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemasspa
dc.type.spaArtículo
dc.identifier.doi10.1504/IJIDS.2024.136283
dc.relation.citationvolume16
dc.relation.citationissue1
dc.relation.citationstartpage46
dc.relation.citationendpage72
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationQuintero, J.B., Faculty of engineering, EAFIT University, Medellín, Colombia
dc.affiliationVillanueva-Valdes, D., Faculty of engineering, University of Medellín, Medellín, Colombia
dc.affiliationManrique-Losada, B., Faculty of engineering, University of Medellín, Medellín, Colombia
dc.relation.referencesAzevedo, A., Santos, M.F., KDD, SEMMA and CRISP-DM: a parallel overview (2008) IADIS 2008: Proc. Int’l Assoc. Development of the Information Soc., European Conf. Data Mining, pp. 182-185. , Amsterdam, The Netherlands
dc.relation.referencesBone, R., Assaad, M., Cardot, H., A new boosting algorithm for improved time-series forecasting with recurrent neural networks (2008) Information Fusion, 9 (1), pp. 45-55
dc.relation.referencesChapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., (2008) CRISP-DM 1.0 – Step-by-Step Data Mining Guide, , https://www.the-modeling-agency.com/crisp-dm.pdf, Technical Report CRISPMWP-1104, SPSS Inc., USA [online] (accessed 28 February 2020)
dc.relation.referencesCokins, G., (2001) Activity-Based Cost Management: An Executive’s Guide, , John Wiley & Sons, USA
dc.relation.referencesConstangioara, A., Bodog, S., Laszlo, F., Petrica, D., Forecasting in business (2009) Journal of Electrical and Electronics engineering, 2 (211), pp. 211-214
dc.relation.referencesDong, X., Qian, L., Huang, L., Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach (2017) BigComp 2017: Proceedings of the IEEE International Conference on Big Data and Smart Computing, pp. 119-125. , Jeju, Korea
dc.relation.referencesFayyad, U.M., Data mining and knowledge discovery: making sense out of data (1996) IEEE Expert: Intelligent Systems and Their Applications, 11 (5), pp. 20-25
dc.relation.referencesFranses, P.H., (1998) Time Series Models for Business and Economic Forecasting, , Cambridge University Press, London
dc.relation.references(2020) Gartner IT Glossary, , https://www.gartner.com/it-glossary/business-analytics, Gartner, Inc. Technical Report, Gartner, Inc., Stamford [online] (accessed 28 February 2020)
dc.relation.referencesHamilton, J.D., (1994) Time Series Analysis, , 2nd ed., Princeton University Press, New Jersey
dc.relation.referencesJames, M., Tom, M., Groeneveld, P., Kibardin, V., Physical mapping of neural networks on a wafer-scale deep learning accelerator (2020) ISPD 2020: Proceedings of the International Symposium on Physical Design, pp. 145-149. , Taipei, Taiwan
dc.relation.referencesJara, G.N., Castañeda, J., Gómez, L.F., Sistema de costeo basado en actividades como herramienta del presupuesto inteligente para el distrito capital, Santa Fé de Bogotá – Colombia-Secretaria de Hacienda Distrital Bogotá DC (2003) Contaduría Universidad de Antioquia, 1 (43), pp. 179-204
dc.relation.references(2020), http://www.sas.com/en_us/software/analytics/enterpriseminer.html, [online] (accessed 20 February 2020)
dc.relation.referencesShmueli, G., Patel, N., Bruce, P., (2016) Data Mining for Business Analytics, , John Wiley & Songs, New Jersey
dc.relation.referencesSiami-Namini, S., Tavakoli, N., Namin, A.S., The performance of LSTM and BiLSTM (2019) Big Data 2019: Proceedings IEEE International Conference on Big Data, Forecasting Time Series, pp. 3285-3292. , Los Angeles, California, USA
dc.relation.referencesVan Veen, F., (2020) Neural Network Zoo Prequel: Cells and Layers, , https://www.asimovinstitute.org/author/fjodorvanveen/, [online] (accessed 28 February 2020)
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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