Graph computing over micro and macro views includes expanding, with a processor at run-time, a set of nodes to include a node generated in response to received data corresponding to an event query. A first inference of an inference ensemble is determined by traversing a base graph whose nodes are associated with a discriminant power that exceeds a predetermined entity threshold. A second inference of the inference ensemble is determined by traversing a micro-view graph whose nodes are selected based on a number of references that exceeds a predetermined reference threshold. A third inference of the inference ensemble is determined by traversing a macro-view graph having one or more committee nodes and computing for each committee node a macro-node vote and generating a response to the event query based on the inference ensemble.

A computing system according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system to determine a plurality of implant size predictions with associated confidence levels based on one or more patient or surgical parameters, wherein each of the implant size predictions identifies a confidence level that a prospective implant of a corresponding size will fit a patient, determine whether a combined confidence level determined based on a subset of the plurality of associated confidence levels is at least a threshold value, and recommend, in response to a determination that the combined confidence level is not at least the threshold value, incorporation of at least one of an additional implant size prediction of the plurality of implant size predictions in the subset or digital templating data to improve an accuracy of an implant size estimation.

An apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for adaptively generating an initial electronic bid value for a new digital content object.

Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for adaptively generating an initial electronic bid value for a new digital content object.

Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.