Abstract: Techniques for predictive disease identification using simulations improved via machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases; generating disease contraction statistics based on the outputs of the at least one machine learning model; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.

Abstract: A system and method for predictive disease identification via simulations improved using machine learning. A method includes applying a plurality of machine learning models including a plurality of first machine learning models and a second machine learning model to features extracted from data including animal characteristics data of at least one animal, wherein the second machine learning model is a combiner model, wherein outputs of the plurality of machine learning models include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types; running a plurality of disease contraction simulations based on the plurality of disease predictor values; and generating at least one display element based on results of the plurality of disease contraction simulations.

Abstract: A system and method for predictive disease identification via simulations improved using machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types; running a plurality of disease contraction simulations based on the plurality of disease predictor values; generating disease contraction statistics based on results of the plurality of disease contraction simulations; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.

Abstract: A method which includes obtaining a first sequence of images of a user attempting to reproduce a sequence of poses of a reference user and determining correct performance of the user. For each body part of a plurality of body parts of the user, a spatial orientation of the body part in a referential independent of an orientation of any other body part of the user is compared to the spatial orientation of the reference user. If the comparison does not meet a matching criterion for at least one body part of the user, data representative of a mismatch between the orientation of the body part of the user and the spatial orientation of the reference user is output, thereby enabling the user to correct his pose using a feedback pointing to the body part to be corrected.

Abstract: A method which comprises, by a processor and memory circuitry: obtaining a first sequence of images of a user, determining correct performance of a pose of the user in the first sequence of images, including based on at least one image, for each body part of a plurality of body parts of the user, comparing at least one of spatial orientation and position of the body part, or of at least one body node thereof, with at least one of desired spatial orientation and position, if the comparison does not meet a matching criterion for at least one body part of the user, or of at least one body node thereof, outputting data representative of a mismatch between the at least one body part of the user, or of the at least one body node thereof, and the at least one of desired spatial orientation and position.

Abstract: Method and system for classification in imbalanced datasets within a supervised classification framework. Bootstrap methodology is modified according to sampling weights and adaptive target set size principle, to induce weak classifiers from the bootstrap samples in an iterative procedure that results in a set of weak classifiers. A weighted combination scheme is used to adaptively combine the weak classifiers to a strong classifier that achieves good performance for all classes (reflected as high values for metrics such as G-mean and F-score) as well as good overall accuracy.

Abstract: A method, system and computer program product, for identifying anomalies in a monitored scene, the method comprising: receiving into a spiking neural network sensor readings from a capture device monitoring a scene; and outputting an indication to a change in the scene, wherein the spiking neural network comprises a multiplicity of layers, each of the multiplicity of layers comprising a neuron per substantially each pixel in a sensor capturing the monitored scene, and wherein one or more of the layers comprises a memory-like unit for comparing states occurring at a time difference.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: Method and system for classification in imbalanced datasets within a supervised classification framework. Bootstrap methodology is modified according to k-Nearest Neighbor sampling weights and adaptive target set size principle, to induce weak classifiers from the bootstrap samples in an iterative procedure that results in a set of weak classifiers. A weighted combination scheme is used to adaptively combine the weak classifiers to a strong classifier that achieves good performance for all classes (reflected as high values for metrics such as G-mean and F-score) as well as good overall accuracy.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: A system and method to perform multisensory data fusion in a distributed sensor environment for object identification classification. Embodiments of the invention are sensor-agnostic and capable handling a large number of sensors of different types via a gateway which transmits sensor measurements to a fusion engine according to predefined rules. A relation exploiter allows combining sensor measurements with information on object relationships from a knowledge base. Also included in the knowledge base is a travel model for objects, along with a graph generator to enable forecasting of object locations for further correlation of sensor data in object identification. Multiple task managers allow multiple fusion tasks to be performed in parallel for flexibility and scalability of the system.

Abstract: A computer implemented method (200), computer program product, and computer system for updating a data structure reflecting a spatio-temporal phenomenon in a physical area, wherein the spatio-temporal phenomenon is estimated at any location of the physical area by a Gaussian Process with a mean function and a covariance function, the method (200) comprising: storing (210) a data set adapted to represent fixed locations of the physical area, wherein the data set has a mean vector and a covariance matrix according to the Gaussian Process, and wherein the data structure includes the mean vector and the covariance matrix; receiving (222, 224) sensor measurement data of the spatio-temporal phenomenon from at least one sensor node out of a plurality of sensor nodes located at specific arbitrary locations of the physical area; and merging (230, 232, 234, 236, 238) the specific arbitrary locations and the received measurement data into the data structure by using exact recursive Bayesian regression.