An assessment of neural network algorithms that could aid SME survival

Walcott, Terry H. and Palmer-Brown, Dominic and Williams, Godfried and Mouratidis, Haralambos and Lee, Sin Wee (2007) ‘An assessment of neural network algorithms that could aid SME survival’, Proceedings of Advances in Computing and Technology. (AC&T) The School of Computing and Technology 2nd Annual Conference. University of East London, pp. 120-127.

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Abstract

Artificial Neural Networks (ANNs) have been used in a wide variety of application sectors from credit card fraud detection to transportation. Over the last two decades many algorithms have been applied in the areas of classification, association, prediction and filtering of data. Such systems would allow managers of smaller businesses to determine the significance of large volumes of data quickly and just as importantly data can be classified and ordered based on the importance of the data to a predefined task for smaller businesses. Mantagno et al (2002) proposed using neural networks for identifying organisational improvement strategies. Even though Mantagno’s research focused on large organisations we believe it is imperative that smaller businesses adopt a similar approach as it could well be the deciding factor between business decline and business survival. Choy et al (2003) supports this approach in arguing that there has to be a technological searching strategy to support businesses and effective management. Although it is now possible to find many neural network models being incorporated into business applications it is still uncommon in the smaller business sector. This paper is a review of neural network algorithms applicable to SMEs.

Item Type: Conference or Event Item (Paper)
Additional Information: Citation: Walcott, T., Palmer-Brown, D., Williams, G., Mouratidis, H., Lee, S.W. (2007) ‘An assessment of neural network algorithms that could aid SME survival’ Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 2nd Annual Conference, University of East London, pp.120-127.
Divisions: Schools > Architecture Computing and Engineering, School of
Depositing User: Mr Stephen Grace
Date Deposited: 19 Jul 2010 15:52
Last Modified: 27 Sep 2012 11:58
URI: http://hdl.handle.net/10552/875

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