A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

In order to provide a learning data generating apparatus that is able to efficiently restrain erroneous detections, the learning data generating apparatus includes a data acquisition unit configured to acquire learning data including teacher data, and a generation unit configured to generate generated learning data based on the learning data and a generating condition, wherein the generation unit converts teacher data of a positive instance into teacher data of a negative instance according to a preset rule when generating the generated learning data.

A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a locating detection network and a judgment network; training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image; training the locating detection network by using a sample image of a product containing different defect types to obtain a locating detection network capable of locating a position of each type of defect in the sample image; inputting an acquired product image into the defect detection framework, inputting a classification result and a detection result obtained into the judgment network to judge whether the product has a defect, and detecting a defect type and a defect position when the product has a defect.

An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.

Examples relate to a hybrid model for case complexity classification. In an example, a set of fields corresponding to a case is received and the set of fields are inputted to a hybrid model comprising a set of rules and a predictive model. The predictive model includes a predictive machine learning model trained using a historical set of cases that are specific to a technical domain of the case. A case complexity classification for the case is determined via the hybrid model and based on analysis of the set of fields using the set of rules and the predictive model. The case complexity classification is utilized to route the case for processing.

A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

A method, system, apparatus and computer program product are disclosed for enhancing operable functionality of xAPI statements by sequentially comparing a plurality of authorized xAPI statements against a plurality of trigger rules with a pattern matching process which evaluates matching xAPI data properties and logical and/or comparative operator requirements from each trigger rule against each authorized xAPI statement to identify matching trigger rules which are evaluated against one or more required trigger rules associated with a first trigger to automatically generate one or more outbound REST API calls to communicate with a third-party application upon detecting that the all the required trigger rules associated with the first trigger are matched by an actor.

A method for determining a driver score for a first driver of a first vehicle. The method comprises receiving, from a database server, first maintenance data, first booking data, and first vehicle data associated with a plurality of vehicles, and first driver behavioral data for a plurality of drivers. The method includes obtaining a plurality of features and a plurality of feature values based on the first maintenance data, the first booking data, the first vehicle data, and the first driver behavioral data. The method further includes segregating the plurality of feature values into a plurality of clusters. The method includes training a classifier to determine the driver score. The method further includes receiving from the database server, a first dataset associated with the first vehicle and the first driver. The method includes determining the driver score for the first driver based on an output of the trained classifier.

A computer system for mapping products to taxability categories includes one or more processors configured to execute, in a run-time inference phase, an artificial intelligence model, a taxability category mapping engine, and a taxability category driver record association engine. The artificial intelligence model is configured to receive product text including a product name and product description associated with a product catalog, and output a predicted tax category for a product associated with the product catalog. The taxability category mapping engine is configured to link a taxability driver to the product. The taxability category driver record association engine is configured to create a taxability category mapping drivers record including the taxability driver that is linked to the product. The predicted tax category output from the artificial intelligence model and the taxability category mapping drivers record are stored in a product taxability record.