Uncertainty representation of ocean fronts based on fuzzy-rough set theory

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

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A Fuzzy Rough Set Theory based Feature Selection for Phishing Attack Classification

Journal of Advanced Research in Dynamical and Control Systems ◽

10.5373/jardcs/v12sp4/20201492 ◽

2020 ◽

Vol 12 (SP4) ◽

pp. 298-306

Author(s):

Illakiya T.

Keyword(s):

Feature Selection ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Fuzzy Rough Set ◽

Selection For

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An Improved Power Transformer Diagnosis System for Incipient Fault Based on Fuzzy Rough Set Theory

2006 International Conference on Power System Technology ◽

10.1109/icpst.2006.321559 ◽

2006 ◽

Author(s):

Xiong Hao ◽

Li Weiguo ◽

Chang Guanghui ◽

Guo Huimin

Keyword(s):

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Power Transformer ◽

Fuzzy Rough Set ◽

Diagnosis System ◽

Incipient Fault

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Knowledge discovery based on fuzzy rough set theory by inclusion degrees for space load forecasting

2009 International Conference on Sustainable Power Generation and Supply ◽

10.1109/supergen.2009.5347968 ◽

2009 ◽

Author(s):

Weiguo Li ◽

Hong Li ◽

Haoqing Xiong ◽

Guoyi Xu ◽

Jiangfeng Zou ◽

...

Keyword(s):

Knowledge Discovery ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Load Forecasting ◽

Fuzzy Rough Set ◽

Inclusion Degrees

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IDENTIFYING KEY FACTORS FOR ADOPTING ARTIFICIAL INTELLIGENCE-ENABLED AUDITING TECHNIQUES BY JOINT UTILIZATION OF FUZZY-ROUGH SET THEORY AND MRDM TECHNIQUE

Technological and Economic Development of Economy ◽

10.3846/tede.2020.13181 ◽

2020 ◽

Vol 0 (0) ◽

pp. 1-34

Author(s):

Kuang-Hua Hu ◽

Fu-Hsiang Chen ◽

Ming-Fu Hsu ◽

Gwo-Hshiung Tzeng

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Set Theory ◽

Rough Set ◽

Audit Quality ◽

Rough Set Theory ◽

Decision Makers ◽

Fuzzy Rough Set ◽

Rule Based ◽

Cpa Firms

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

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