Statistical techniques for detecting and validating phonesthemes

05 Mar

In retail industries, digitalized CCTV and POS data of most vulnerable transactions to fraud are used.Detection and prevention of fraud is not an easy task, so special techniques like data analysis techniques are used for detecting and preventing fraud.The talk begins by setting the context: fraud is defined and its breadth outlined; figures are given showing how significant fraud is; and different areas of fraud are examined, including health care fraud, banking fraud, and scientific fraud.The particular data analytic challenges of banking fraud are described and illustrated in detail.Further researches need to be done in order to validate the use of the “Three Pictures Test: Past, Present, and Future” as a validated technique with children experiencing disasters.The Centre for Research on Epidemiology of Disasters (CRED) [1] defines disaster as “a situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance; an unforeseen and often sudden event that causes great damage, destruction and human suffering.” Disasters can be caused by nature or by men’s action.

Further research is needed to examine additional factors that may influence PHN–client–risk–intervention–outcome patterns, and to test these methods with other data sets. AU - Kim, Era AU - Votova, Brian AU - Pieczkiewicz, David S.These include the fact that the classes are highly unbalanced (with typically no more than 1 in a 1000 transactions being fraudulent), that class labels may often be incorrect, that there will typically be delays in discovering the true labels, that the transaction arrival times are random, that the data are dynamic, and, perhaps most challenging of all, that the distributions are reactive, changing in response to the implementation of fraud detection systems.The role of mechanistic and empirical models in tackling these problems is described.Finally, the method is applied to Physio Net/Computing in Cardiology Challenge 2008 database.In this stage, the achieved accuracy is about 91.0 %, which shows marginal improvement in the area of TWA quantification.