Abstract: The system identifies false positives about a user and closes resulting alerts before the alert wastefully consume system resources. Machine learning techniques including clustering and multi-labeling classification are used to effectively categorize prior text-based notes to efficiently identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis provide a means for condensing large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures inefficiently/ineffectively organized past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features that directly improve model performance for alert triaging as a whole.

Abstract: The system identifies false positives about a user and closes resulting alerts before the alert wastefully consume system resources. Machine learning techniques including clustering and multi-labeling classification are used to effectively categorize prior text-based notes to efficiently identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis provide a means for condensing large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures inefficiently/ineffectively organized past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features that directly improve model performance for alert triaging as a whole.

Abstract: An composition comprising: a homopolycarbonate; a poly(carbonate-siloxane) having a siloxane content of 30-70 wt %, preferably 35-65 wt %, based on the total weight of the poly(carbonate-siloxane) present in an amount effective to provide 2-10 wt % total siloxane, based on the total weight of the composition; an antimicrobial agent, wherein the antimicrobial agent is a silver zinc zeolite powder, wherein the antimicrobial agent is present in amount effective to provide up to 1000 parts per million of elemental silver based on the total weight of the composition and optionally, an additive composition.

Abstract: A structural health monitoring system comprises a first set of sensors operable for coupling to a structure positioned under ground, the first set of sensors further configured to detect an impact upon the structure while the first set of sensors is positioned under the ground; a second set of sensors operable to be positioned on or proximate to a surface of the ground, the second set of sensors further configured to detect an audible event occurring at a distance from the second set of sensors and the structure; and a computer readable memory storing one or more audio signatures that may correspond to the audible event.

Abstract: A structural health monitoring system comprises a first set of sensors operable for coupling to a structure positioned under ground, the first set of sensors further configured to detect an impact upon the structure while the first set of sensors is positioned under the ground; a second set of sensors operable to be positioned on or proximate to a surface of the ground, the second set of sensors further configured to detect an audible event occurring at a distance from the second set of sensors and the structure; and a computer readable memory storing one or more audio signatures that may correspond to the audible event.