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Computational Model of Artificial Neural Networks and its Applications in Data Mining

Authors :
J. Abah
E. S. Alu
A. E. Chahari
Source :
Arid Zone Journal of Engineering, Technology and Environment; Vol. 16 No. 2 (2020); 243-254, ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT; Vol. 16 No. 2 (2020); 243-254, Arid Zone Journal of Engineering, Technology and Environment, Vol 16, Iss 2, Pp 243-254 (2020)
Publication Year :
2023
Publisher :
University of Maiduguri, Nigeria, 2023.

Abstract

Data remain a very important ingredient required by any organization to make informed decision as it affects operations. Companies have been collecting data from various sources over the decades bringing about a very large volume of data warehouse. Unfortunately, most organizations build databases which are redundant and never used for any meaningful thing. While few companies use the data collected in their databases when taking strategic decisions others barely do same. However, for an organization to immensely derive benefits from the massive data warehouse, there is the need for an effective and efficient means of analysing the data with a view to extracting meaningful knowledge that is sufficient to achieve organizational goal. To achieve this, Artificial Neural Network (ANN) technique through the concept known as data mining is presented. The paper reviewed artificial neural network technique for data mining, examines the computational model behind this technique and analysed its use and application as a predicting or forecasting tool. Results shows that ANN’ has capability in data management, analysis and able to provide desirable knowledge for management decision making processes. It is therefore recommended that data mining tools like ANN and others be applied to organization’s databases which hitherto have not been minned in order to provide management with intelligence for decision making.

Details

Language :
English
ISSN :
15962644, 25455818, and 15962490
Database :
OpenAIRE
Journal :
Arid Zone Journal of Engineering, Technology and Environment
Accession number :
edsair.dedup.wf.001..c89d231ffff942189d10928ba59e848a