Abstract: The disclosed computer-implemented method includes normalizing tabular data corresponding to a query target, and inputting the normalized tabular data into a shallow neural network corresponding to a fully-connected three-layer model comprising an input layer, a hidden layer, and an output layer. Normalizing the tabular data may replace feature detection for the shallow neural network. The method may further include predicting a plurality of classifications for the query target, wherein a plurality of nodes of the output layer respectively correspond to the plurality of classifications. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.

Abstract: An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.