TY - JOUR
T1 - Artificial neural networks in the development of business analytics projects
AU - Quintero, Juan Bernardo
AU - Villanueva-Valdes, David
AU - Manrique-Losada, Bell
N1 - Publisher Copyright:
© 2024 Inderscience Enterprises Ltd.. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - activity-based costing
KW - ANNs
KW - artificial neural networks
KW - big data
KW - business analytics
KW - data analytics
KW - decision making
KW - deep data mining
KW - network learning process
KW - regression-based prediction
KW - supervised learning
KW - time series forecast
UR - http://www.scopus.com/inward/record.url?scp=85183874752&partnerID=8YFLogxK
U2 - 10.1504/IJIDS.2024.136283
DO - 10.1504/IJIDS.2024.136283
M3 - Artículo
AN - SCOPUS:85183874752
SN - 1756-7017
VL - 16
SP - 46
EP - 72
JO - International Journal of Information and Decision Sciences
JF - International Journal of Information and Decision Sciences
IS - 1
ER -