Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for enhancing a deterministic identity graph with probabilistic data. An example embodiment operates by identifying a node for a location indicated by an identity graph. Receiving user device information based on an indication that a user device is within proximity to the location. Generating a node for the user device on the identity graph based on the indication of the user device satisfying an association threshold. Generating an edge between the node for the location and the node for the user device based on a weighted value for an attribute of the user information. Mapping an identifier for the user device to an identifier of the location based on a distance of the edge and causing a content item to be sent to the user device based on the identifier mapping.

Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.