Skip Nav Destination
ASME Press Select Proceedings
Proceedings of the International Conference on Technology Management and InnovationAvailable to Purchase
By
ISBN:
9780791859612
No. of Pages:
612
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
70 The Research on the Audit Misstatement Risk of Assessment Methodology Based on Rough Neural Network Available to Purchase
By
Xun Yang
Xun Yang
Business College
Central South University
Department of Accounting Hunan Business College Changsha
, China
; [email protected]
Search for other works by this author on:
Page Count:
4
-
Published:2010
Citation
Yang, X. "The Research on the Audit Misstatement Risk of Assessment Methodology Based on Rough Neural Network." Proceedings of the International Conference on Technology Management and Innovation. Ed. Xie, H. ASME Press, 2010.
Download citation file:
This paper sets out the basic principle of rough neural networks and presents a method for assessing risk through the detailed analysis to audit misstatement. At last, an example is introduced to validate its effectiveness and thus provides a new way for identifying and warning audit risk.
I. Introduction
II. Factors Affecting the Audit Misstatement Risk
III. The Methodology Based on Rougu Neural Rough Neural Network
IV. The Example of Application
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
A Rapid Tool for Risk-Based Post-Cyclonic Integrity Assessment of Flexible Risers
Asset Integrity Management of Critical Infrastructure
Evaluating Systems for Oxygen Service Through the Use of Quantitative Fault Tree Analysis
Flammability and Sensitivity of Materials in Oxygen-Enriched Atmospheres: Fourth Volume
A Suggested Strategy to Increase Employee Use of Protective Clothing
Performance of Protective Clothing: Fourth Volume
Potential Application of Influence Diagram as a Risk Assessment Tool in Brownfields Sites
Superfund Risk Assessment in Soil Contamination Studies: Third Volume
Related Articles
Enhanced Hybrid Model to Predict the Surface Roughness of Honed Cylinder Bore
J. Tribol (January,2022)
Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks
J. Comput. Inf. Sci. Eng (March,2014)
Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models
J. Manuf. Sci. Eng (October,2013)
