Abstract: There is provided a volume preserving trainable artificial neural network and a system and a method for building a volume preserving trainable artificial neural network. In an aspect, an artificial neural network including: an input layer to receive input data; one or more sequentially connected hidden layers, the first hidden layer connected to the input layer, to perform operations on the input data, each hidden layer including: one or more volume-preserving rotation sublayers; one or more volume-preserving permutation sublayers; one or more volume-preserving diagonal sublayers; and an activation sublayer; and a downsizing output layer connected to the activation sublayer of the last hidden layer. In some cases, the activation sublayer includes a grouped activation function acting on a grouping of input variables to the activation sublayer.

Abstract: Exemplary embodiments provide a tool that aggregates data from multiple database sources and application programming interfaces. The tool takes the data collected from the sources and applies one or more pre-defined formulas to the data in order to execute enhanced performance calculations. The tool may receive updated data from the sources at defined intervals. Certain data may be provided in real time. The updated data and/or real time data can be used to refine the calculations and provide updated calculations.