Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

Multi Intelligent Fuzzy Integration (Mifi) Support Vector Machines for the Mammogram Classification

Breast cancer is the most important problem across the globe in which the 80% of the women are suffering without knowing the causes and effects of the cancer cells. Mammogram Image is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Micro calcification in mammogram images. The new technique MIFI-SVM has been proposed which integrates the GLCM features along with the Fuzzy Support Vector Machines. ROI Segmentation using Saliency maps has been used for the proposed algorithm and feature is extracted using GLCM and fed to Fuzzy Support Vector Machines The MIAS datasets has been used for testing the proposed algorithm and accuracy, sensitivity has been measured which proves to be better when compared to other Multi-level SVM’s, C-SVM and Neural Networks.