A system for generating a contention scheme includes a computing device, the computing device configured to obtain a solvency signature as a function of a solvency entity, determine a solvency grouping as a function of the solvency signature, identify a null element as a function of the solvency grouping, wherein identifying the null element further comprises receiving a regulation element as a function of a regulation database, and identifying the null element as a function of the regulation element and the solvency grouping, produce a weighted vector as a function of the null element, and generate a contention scheme as a function of the weighted vector.

A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.

Methods for improving the prediction accuracy for an ensemble machine learning model are described. In some instances, the methods comprise: (i) receiving data characterizing levels of trust in one or more machine learning models that form the ensemble machine learning model; (ii) calculating a prediction error estimate for each of the one or more machine learning models based on a trust score for that machine learning model and relative weights calculated for the data points in a training data set used to train that machine learning model; (iii) calculating a normalized weight for each of the one or more machine learning models using the prediction error estimate calculated for each; and (iv) adjusting an output prediction equation for the ensemble machine learning model, where the adjustment is based, at least in part, on the normalized weights calculated in for each of the one or more machine learning models.

Methods and In an aspect a method of generating an instantaneous quote of any part without toolpathing, the method includes receiving, using a computing device, a geometric model of a part, constructing, using the computing device, at least a rotation-invariant feature as a function of the geometric model, predicting, using the computing device, a manufacturing time as a function of the at least a rotation-invariant feature and a manufacturing time machine learning model, selecting, using the computing device, a stock as a function of the at least a rotation-invariant and a stock selection machine learning model feature, and estimating, using the computing device, a quote as a function of the manufacturing time and the stock.

Techniques for determining anomalous user behavior in connection with an online application are disclosed. In one embodiment, a method is disclosed comprising obtaining user behavior data in connection with a user of an application, generating feature data using the obtained user behavior data, obtaining one or more user behavior anomaly predictions from one or more anomaly prediction models trained to output a user behavior anomaly prediction in response to the feature data. Each user behavior anomaly prediction indicates a probability that the user behavior is anomalous. A user behavior anomaly determination is made using the user behavior anomaly prediction(s).

Systems, devices, and methods discussed herein are directed to generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees. A supported decision tree can be generated from the various decision chains (e.g., a sequence of elements comprising a premise and a decision connected by rhetorical relationships), where the nodes of the decision tree are identified from the plurality of decision chains and ordered based on a set of predefined priority rules. A lattice may include nodes that individually correspond to a respective supported decision tree. Nodes of the lattice may be identified for an input query. The passages corresponding to those nodes may be obtained and an answer for the query may be generated from the obtained passages using machine reading comprehension techniques. The generated answer may be provided in response to the query.

An approach is provided for accessing traffic impact caused by individual driving behaviors. For example, the approach involves receiving, by one or more processors, sensor data collected from one or more sensors of a vehicle traveling on a road network. The approach also involves processing, by the processors, the sensor data to determine one or more driving behaviors associated with the vehicle. The approach further involves computing, by the processors, a traffic impact index based on the one or more driving behaviors and at least one contextual parameter associated with the vehicle, the road network, a driver of the vehicle, or a combination thereof. The traffic impact index represents an estimated impact of the vehicle on a traffic flow within at least a portion of the road network. The approach further involves providing, by the processors, the traffic impact index as an output.

A method of training a machine learning model to determine an item margin is provided. The method includes monitoring a first value for a first item having attributes and monitoring a first value for a second type of item having attributes where an attribute of the first attributes is the same as an attribute of the second attributes. The method also includes determining a first margin based on the first values. The first attributes, the second attributes, and the first margin are input as training data for the machine learning model where the machine learning model is trained with the training data. The monitoring operations for the first item and the second item are repeated to obtain a second value for the first and second items. Furthermore, the trained machine learning model is applied to the second values to determine a second margin.