UUM Repository | Universiti Utara Malaysian Institutional Repository
FAQs | Feedback | Search Tips | Sitemap

Development of an adaptive business insolvency clasifier prototype (AVICENA) using hybrid intelligent


Ab. Aziz, Azizi and Siraj, Fadzilah and Zakaria, Azizi (2002) Development of an adaptive business insolvency clasifier prototype (AVICENA) using hybrid intelligent. In: 2002 Student Conference on Research and Development Proceedings (SCOReD2002) , 16-17 July 2002, Shah Alam. IEEE Computer Society, pp. 173-176. ISBN 0780375653

[img] PDF
Restricted to Repository staff only

Download (259kB)

Abstract

Confronted by an increasingly competitive environment and chaotic economy conditions. Businesses are facing with the need to accept greater risk. Businesses do not become insolvent overnight,rather many times creditors, investors and the financial community will receive either direct or indirect indrcations that a company is experiencing financial distress. Thus, this paper analyzed the ability of AVICENA in classifying business insolvency performance events. Neural networks (Multi layer Perceptron - Backpropagation) setves as a classifier mechanism while Apriori algorithms (Auto Association Rules) supports the decision made by the neural networks, in which rules are generated The conventional model in predicting business performances, called as Altman- Z Scores model is used for performance comparison.

Item Type: Book Section
Additional Information: The conference was jointly organized by Faculty of Electrical Engineering UiTM and IEEE Malaysian Section
Uncontrolled Keywords: Neural Networks, Business Insolvency, Apriori Algorithms, Discriminant Analysis, Financial Ratios
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 12 Nov 2010 02:54
Last Modified: 21 Feb 2011 01:47
URI: http://repo.uum.edu.my/id/eprint/1531

Actions (login required)

View Item View Item