Abstract: An inspection system includes a ground penetrating radar (GPR) sensor configured to provide at least one probe signal to a portion of a structure and to receive at least one return signal resulting from the corresponding at least one probe signal. A processor is programmed to: receive the at least one return signal as scan data from the GPR sensor; generate maximum amplitude trace normalized data based on the scan data; generate temporal signal gain data based on the scan data; generate a power spectral density image for each of the scan data, the maximum amplitude trace normalized data, and the temporal signal gain data; and analyze, using a machine learning processor, the power spectral density images to identify an anomaly of interest, wherein the machine learning processor is trained to detect an anomaly of interest based on a training set of power spectral density images.

Abstract: An organizational fraud detection (OFD) system and method for flagging one or more transactions as a potential fraudulent activity, in an organization is disclosed. The OFD system comprises: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: receive a suspected transaction for investigation, classify the suspected transaction into one or more groups of fraudulent activity; select, based on the classification, a set of investigation rules for investigating the suspected transaction; determine, based on data selection rules, the data associated with the suspected transaction; ascertain an accuracy score and an impact score associated with the suspected transaction; and classify the suspected transaction as a potential fraudulent activity on at least one of the accuracy score and the impact score exceeding a pre-defined threshold.

Abstract: A method, non-transitory computer readable medium and network analyzer device that generates a relationship health score for a plurality of entities based on relationship utility scores and relationship strength scores generated for each pair of one of a plurality of connects and one of a plurality of connectors having a relationship. Next, connect dependence scores are generated for each of the connects. A total monetary value of the network is then generated based on a potential monetary value of the network and a realized monetary value of the network. The potential and realized monetary values are respectively generated based on potential and executed interactions between the connectors and the connects. A total value of the network is then generated based on the relationship health scores, the connect dependence scores, and the total monetary value of the network. The total value of the network is output to an information consumer device.