Abstract: A computer-implemented method including: receiving a first data set including a plurality of values for a plurality of features; removing one or more features and associated values from the first data set to generate a second data set; determining feature importance of at least a subset of the features of the second data set using multiple-evaluation criteria; and performing an action using at least one feature of the second data set and the determined feature importance.

Abstract: A computer-implemented method including: receiving a first data set including a plurality of values for a plurality of features; identifying at least a first feature of the first data set that is non-redundant and at least a second feature of the first data set that is redundant; identifying one or more clusters of features in the plurality of features of the first data set, a first cluster of the one or more clusters including at least the first feature and the second feature; and controlling a display to display the first feature and one or more redundant features from the first cluster, the displayed one or more redundant features from the first cluster including the second feature.

Abstract: A baseline visualization is parsed to identify visual elements in the baseline visualization. A user interface allows a user to configure a lens overlay that associates an overlay shape, with corresponding data obtained through an API, from an external source. The data and shape in the lens overlay are also associated with a visual element in the baseline visualization. The lens overlay is separate from the baseline visualization. When the baseline visualization is displayed, the lens overlay, is displayed concurrently with the baseline visualization so that shapes in the lens overlay are displayed at a configured location and position relative to visual elements in the baseline visualization.

Abstract: A method is provided of forming a neural network for detecting surface defects in aircraft engine components. The method includes: providing (i) a pre-trained deep learning network and (ii) a learning machine network; providing a set of pixelated training images of aircraft engine components exhibiting examples of different classes of surface defect; training the trainable weights of the learning machine network on the set of training images.

Abstract: A method is provided of forming a neural network for detecting surface defects in aircraft engine components. The method includes: providing (i) a pre-trained deep learning network and (ii) a learning machine network; providing a set of pixelated training images of aircraft engine components exhibiting examples of different classes of surface defect; training the trainable weights of the learning machine network on the set of training images.