Abstract: A computer-implemented method for technologically improving a computer-implemented machine-learning model, the method comprising receiving, by a model, at least a first data record; generating a first score representing a first likelihood that the first data record is associated with a first classification, in response to feedback received from one or more data sources communicating with at least one computing system on which the model is implemented; generating a second score to represent a second likelihood that the first data record is associated with the first classification, in response to the first score being higher than a threshold value.

Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Abstract: A routing protocol for a multicasting network, such as an ad hoc network, employs alternate tree or path computation algorithms that continually compute backup trees or paths that can be employed to replace failed trees or paths. The sets of alternate multicast trees or paths are preferably pre-calculated before a first tree or path fails to minimize delay in replacing a failed tree or path. Preferably, the algorithms are designed to compute the alternate multicast trees or paths in such a manner that they are maximally independent of the original set of trees and paths to minimize correlation between the original trees or paths and the replacement trees or paths and to possibly increase the useful time of the calculated trees. This helps insure that the replacement trees or paths will not be likely themselves to fail soon after failure of the original trees or paths.

Abstract: A routing protocol for a multicasting network, such as an ad hoc network, employs alternate tree or path computation algorithms that continually compute backup trees or paths that can be employed to replace failed trees or paths. The sets of alternate multicast trees or paths are preferably pre-calculated before a first tree or path fails to minimize delay in replacing a failed tree or path. Preferably, the algorithms are designed to compute the alternate multicast trees or paths in such a manner that they are maximally independent of the original set of trees and paths to minimize correlation between the original trees or paths and the replacement trees or paths and to possibly increase the useful time of the calculated trees. This helps insure that the replacement trees or paths will not be likely themselves to fail soon after failure of the original trees or paths.