Abstract: Video analytics are used to evaluate interactions between humans and animals and identify possible occurrences of animal abuse in an objective manner. The video analytics system processes successive video frames to identify objects of interest (e.g., humans, animals, tools/weapons, etc.), creates mathematical models of such objects (e.g., essentially stick-figure models), analyzes movements of such objects (e.g., the speed and/or directional motion of an object or portion of an object such as an arm or leg), determines mathematically and objectively whether or not the analyzed movements meet predetermined criteria for possible abuse (e.g., wherein the predetermined criteria can be mathematical models defining ethical and unethical movements), and outputs relevant information via a user interface (e.g., a list of possible abuse instances identifying the time and probability of possible abuse, from which the user can select an instance in order to view the corresponding video for human analysis).

Abstract: A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

Abstract: A method comprising: obtaining information about training data comprising first inputs and first outputs, comprising a first representation of a first distribution of the first inputs and first performance data indicative of a measure of performance of a trained machine learning (ML) model on the first inputs; obtaining information about new data comprising second inputs and second outputs, comprising a second representation of a second distribution of the second inputs and second performance data indicative of the measure of performance of the trained ML model on the second inputs; determining, using the first representation, the second representation, the first performance data, and the second performance data, whether to update the trained ML model or to generate a supplemental ML model; and when it is determined to update the trained ML model or to generate the supplemental ML model, updating the trained ML model or generating the supplemental ML model.

Abstract: A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

Abstract: A system is proposed for automated detection and segmentation of lung cancer from registered pairs of thoracic Computerized Tomography (CT) and Positron Emission Tomography (PET) scans. The system segments the lungs from the CT data and uses this as a volumetric constraint that is applied on the PET data set. Cancer candidates are segmented from the PET data set from within the image regions identified as lungs. Weak signal candidates are rejected. Strong signal candidates are back projected into the CT set and reconstructed to correct for segmentation errors due to the poor resolution of the PET data. Reconstructed candidates are classified as cancer or not using a Convolutional Neural Network (CNN) algorithm. Those retained are 3D segments that are then attributed and reported. Attributes include size, shape, location, density, sparseness and proximity to any other pre-identified anatomical feature.

Abstract: A system and method, after performing the comparison, identifies at least one a match of the target feature from the imaging source with a library of target features. The system and method receive at least one numerical representation from a diagnostic source of a target feature. The system and method compares the at least one numerical representation of the target feature with a library of numerical representations. The system and method, after performing the comparison, identifies at least one match of the target feature from the imaging source with the library.