Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives image data showing a portion of a delivery location. The computer system determines an artificial intelligence (AI) model associated with the delivery location. The computer system inputs the image data to the AI model. The computer system receives an indication of whether the portion corresponds to a correct drop-off location and causes a presentation about the indication to be provided at a device.

Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives image data showing a portion of a delivery location. The computer system determines an artificial intelligence (AI) model associated with the delivery location. The computer system inputs the image data to the AI model. The computer system receives an indication of whether the portion corresponds to a correct drop-off location and causes a presentation about the indication to be provided at a device.

An approach for a non-factoid question answering framework across tasks and domains may be provided. The approach may include training a multi-task joint learning model in a general domain. The approach may also include initializing the multi-task joint learning model in a specific target domain. The approach may include tuning the joint learning model in the target domain. The approach may include determining which task of the multiple tasks is more difficult for the multi-task joint learning model to learn. The approach may also include dynamically adjusting the weights of the multi-task joint learning model, allowing the model to concentrate on learning the more difficult learning task.

A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.

A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.

Systems, computer program products, and methods are described herein for machine learning process/model building using fuzzying techniques. The present invention is configured to determine one or more process components associated with a workflow process of an application; retrieve data from each of the one or more process components; initiate a data fuzzying engine to introduce one or more adversarial noise components on the data retrieved from each of the one or more process components; receive one or more instances of exposure for the application from each of the one or more process components in response to introducing adversarial noise on the data retrieved from each of the one or more process components; and automatically initiate one or more mitigation actions in response to receiving the one or more instances of exposure for the application from each of the one or more process components.

A system for training a computational neural network to recognise objects and/or actions from images, the system comprising: a training unit, comprising: an input interface configured to receive: a plurality of images captured from one or more cameras, each image having an associated timestamp indicating the time the image was captured; a data stream containing a plurality of timestamps each associated with an object and/or action; the data stream being generated by a system in an operative field of view of the one or more cameras; an image identification unit configured to identify from the plurality of images a set of images that each have a timestamp that correlates to a timestamp associated with an object and/or action from the data stream; a data-labelling unit configured to determine, for each image of the set of images, an image label that indicates the probability the image depicts: (i) an object of each of a set of one or more specified object classes; and/or (ii) a specified human action in dependence on the correlation between the timestamp for the image and the timestamp associated with the object and/or action from the data stream; and an output interface configured to output the image labels for use in training a computational neural network to identify from images objects of the object classes and/or the specified actions.

Provided is a method and processing unit for computer-implemented analysis of a classification model which is adapted to map, as a prediction, a number of input instances, each of them having a number n of features, into a number of probabilities of output classes, as a classification decision, according to a predetermined function, and which is adapted to determine a relevance value for each feature resulting in a saliency map. The disclosure includes the step of identifying an effect of each feature on the prediction of the instance by determining, for each feature, a relevance information representing a contextual information for all features of the instance omitting the considered feature. Then, the relevance value for each feature is determined. Finally, the plurality of relevance values for the features of the instance is evaluated to identify the effect of each feature on the prediction of the instance.