Self-tuning flowcharts: a priority-based approach to optimize diagnostic flowcharts

Bekaroo, Girish ORCID: https://orcid.org/0000-0003-1753-4300 and Warren, Paul (2016) Self-tuning flowcharts: a priority-based approach to optimize diagnostic flowcharts. 2016 IEEE Proceedings International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies. In: 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech), 03-06 Aug 2016, Balaclava, Mauritius. e-ISBN 9781509007066, pbk-ISBN 9781509007073. (doi:10.1109/emergitech.2016.7737352)

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Abstract

Flowcharts have been used in problem diagnosis for a long time because of their effectiveness during process representation. However, with time, diagnostic flowcharts can become unmanageably complex and incomprehensible, thus leading to longer decision paths. A lengthy decision path also implies a time consuming diagnosis process while at the same time being boring to end users utilizing systems containing diagnostic flowcharts. This study investigates the extent to which diagnostic flowcharts can be made dynamic so as to optimize the decision making process without reducing the number of nodes. In this endeavor, the Dynamic Flowchart Parser Algorithm has been proposed using a priority-based approach to optimize diagnostic flowcharts within a diagnostic tool named Self Tuning Flowcharts.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 29779
Notes on copyright: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Girish Bekaroo
Date Deposited: 12 May 2020 08:54
Last Modified: 16 May 2020 19:49
URI: https://eprints.mdx.ac.uk/id/eprint/29779

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