Abstract: A computing device, includes one or more processors, configured to receive first data corresponding to a first radiofrequency signal received from a first wireless communication device, and second data corresponding to a second radiofrequency signal received from a second wireless communication device; select either the first wireless communication device or the second wireless communication device as a repeater device based on the first data or the second data according to one or more repeater selection criteria; instruct a transceiver to send a third radiofrequency signal to the repeater device, wherein the third radiofrequency signal comprises third data and an instruction for the repeater device to repeat the third data to the third wireless communication device.

Abstract: In an example embodiment, a random forest machine learning algorithm is used to create and/or identify rules to apply to an individual entity in a computer system that has a plurality of entities, each with a number of rules. More precisely, rule predicates are used as features of a random forest model built to predict a particular outcome (e.g., a transaction that is fraudulent). Hyperparameters of the random forest model are varied and iterated. A classifier is used to calculate feature importance for all features in the training data. Feature importance may be calculated using permutation feature importance. The N “most important” features are then found from this set. The N “most important” features are then used to find rules above a certain precision and recall rate. These rules may then be backtested and the best rules can be used to generate additional rules.

Abstract: An embodiment of the present invention provides a system and method for medical image processing. The proposed system includes a grid computing framework adapted for receiving patient data including one or more patient-scan images from an end-user application, and for scheduling image processing tasks to a plurality of nodes of a grid computing network. Each of the nodes includes a central processing unit and at least one of the nodes includes programmable graphics processing unit hardware. The proposed system further includes a second framework for image processing using graphics processing unit that is operative on each node of the network. The second framework operative on any node is adapted to execute the image processing task scheduled to that node based upon the availability of graphics processing unit hardware in that node.