Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms
Confronted by an increasingly competitive environment and chaotic economic conditions, businesses are faced with the need to accept greater risk.Businesses do not become insolvent overnight, rather creditors, investors and the financial community will receive either direct or indirect indications th...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2002
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/12329/1/01033085.pdf |
| _version_ | 1825802989725548544 |
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| author | Ab. Aziz, Azizi Siraj, Fadzilah Zakaria, Azizi |
| author_facet | Ab. Aziz, Azizi Siraj, Fadzilah Zakaria, Azizi |
| author_sort | Ab. Aziz, Azizi |
| collection | UUM |
| description | Confronted by an increasingly competitive environment and chaotic economic conditions, businesses are faced with the need to accept greater risk.Businesses do not become insolvent overnight, rather creditors, investors and the financial community will receive either direct or indirect indications that a company is experiencing financial distress.Thus, this paper analyzed the ability of AVICENA to classify business insolvency performance events.Neural networks (multilayer perceptron-backpropagation) serves as a classifier mechanism while a priori algorithms (auto association rules) support the decision made by the neural networks, in which rules are generated.The conventional model for predicting business performance, the Altman-Z scores model, is used for performance comparison. |
| first_indexed | 2024-07-04T05:49:36Z |
| format | Conference or Workshop Item |
| id | uum-12329 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:49:36Z |
| publishDate | 2002 |
| record_format | eprints |
| spelling | uum-123292014-10-23T02:08:18Z https://repo.uum.edu.my/id/eprint/12329/ Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms Ab. Aziz, Azizi Siraj, Fadzilah Zakaria, Azizi QA76 Computer software Confronted by an increasingly competitive environment and chaotic economic conditions, businesses are faced with the need to accept greater risk.Businesses do not become insolvent overnight, rather creditors, investors and the financial community will receive either direct or indirect indications that a company is experiencing financial distress.Thus, this paper analyzed the ability of AVICENA to classify business insolvency performance events.Neural networks (multilayer perceptron-backpropagation) serves as a classifier mechanism while a priori algorithms (auto association rules) support the decision made by the neural networks, in which rules are generated.The conventional model for predicting business performance, the Altman-Z scores model, is used for performance comparison. 2002 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/12329/1/01033085.pdf Ab. Aziz, Azizi and Siraj, Fadzilah and Zakaria, Azizi (2002) Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms. In: Student Conference on Research and Development (SCOReD 2002), 2002. http://dx.doi.org/10.1109/SCORED.2002.1033085 doi:10.1109/SCORED.2002.1033085 doi:10.1109/SCORED.2002.1033085 |
| spellingShingle | QA76 Computer software Ab. Aziz, Azizi Siraj, Fadzilah Zakaria, Azizi Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title | Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title_full | Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title_fullStr | Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title_full_unstemmed | Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title_short | Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms |
| title_sort | development of an adaptive business insolvency classifier prototype avicena using hybrid intelligent algorithms |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/12329/1/01033085.pdf |
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