Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Trigueiros, D. & Sam, C. (2016). A software application to streamline and enhance the detection of fraud in published financial statements of companies. International Journal on Advances in Software. 9 (1-2), 95-106
Exportar Referência (IEEE) 
D. M. Trigueiros and C. Sam,  "A software application to streamline and enhance the detection of fraud in published financial statements of companies", in Int. Journal on Advances in Software, vol. 9, no. 1-2, pp. 95-106, 2016
Exportar BibTeX 
@article{trigueiros2016_1775764912445,
	author = "Trigueiros, D. and Sam, C.",
	title = "A software application to streamline and enhance the detection of fraud in published financial statements of companies",
	journal = "International Journal on Advances in Software",
	year = "2016",
	volume = "9",
	number = "1-2",
	pages = "95-106",
	url = "http://www.iariajournals.org/software/"
}
Exportar RIS 
TY  - JOUR
TI  - A software application to streamline and enhance the detection of fraud in published financial statements of companies
T2  - International Journal on Advances in Software
VL  - 9
IS  - 1-2
AU  - Trigueiros, D.
AU  - Sam, C.
PY  - 2016
SP  - 95-106
SN  - 1942-2628
UR  - http://www.iariajournals.org/software/
AB  - Considerable effort has been devoted to the development of software to support the detection of fraud in published financial statements of companies. Until the present date, however, the applied use of such research has been extremely limited due to the “black box” character of the existing solutions and the cumbersome input task they require. The application described in this paper solves both problems while significantly improving performance. It is based on Webmining and on the use of three Multilayer Perceptron where a modified learning method leads to the formation of meaningful internal representations. Such representations are then input to a features’ map where trajectories towards or away from fraud and other financial attributes are identified. The result is a Web-based, self-explanatory, financial statements’ fraud detection solution.
ER  -