Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A system, method and apparatus for detecting facial expressions according to EMG signals.

A system, method and apparatus for detecting facial expressions according to EMG signals.

The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The platform includes a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources. At least one of the distributed edge energy resources is operationally independent of the energy grid.

A system, method and apparatus for detecting facial expressions according to EMG signals.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

A computer system obtains, using a camera, a first plurality of images of a canopy an agricultural plot. For each respective fruit of a plurality of fruit growing in the agricultural plot, the computer system identifies a first respective image in the first plurality of images that comprises the respective fruit. The first respective image has a corresponding first camera location. The computer system also identifies a second respective image in the first plurality of images that comprises the respective fruit. The second respective image has a corresponding second camera location. The computer system uses at least i) the first and second respective images and ii) a distance between the first and second camera locations to determine a corresponding size of the respective fruit.

An anomaly detection method of objects in a digital image is provided, wherein the image of the object is encoded and decoded by an autoencoder, then a pixel-wise difference is calculated between the input image of the object, and the reconstructed image of the object. Pixels whose pixel-wise difference is above a threshold are considered as dissimilar pixels, and the presence of clusters of dissimilar pixels is tested. A cluster of dissimilar pixel is considered as representing an anomaly.