Abstract: In one aspect of the invention, there is a computer-implemented method including: determining, by a processor set, for each of a plurality of covariates in subject data on a plurality of subjects, a statistical bias measure; selecting, by a processor set, one of the covariates based on the statistical bias measure; dividing, by a processor set, the subjects into two or more groups, based on the selected covariate, such that each of the groups has an enhanced uniformity in a probability of being assigned a same treatment in a hypothetical randomized controlled trial; recursively repeating, by the processor set, for each respective group of the groups, the applying a division of the subjects into two or more groups, until reaching a selected stopping criterion for the respective group; and deriving, by the processor set, observational results of one or more input factors of one or more of the groups.

Abstract: Methods and systems for operating a machine learning system are described. In an example, a device can receive an image and assign a set of pixels in the image as a digital representation of a livestock. The device can further train a machine learning model using the digital representation. The device can further run the machine learning model to generate prediction data relating to the livestock. The device can further generate output data relating to at least one activity among a livestock value chain. The at least one activity can correspond to a process to generate a commodity based on the livestock.

Abstract: In an aspect, a decision platform that optimizes honey value chain can be provided. The decision platform may receive images of a geographic region including catchment areas, run a first machine learning model with the images as input to identify resources in the catchment areas, run a second machine learning model with the identified resources to predict pollen and nectar concentration in the catchment areas, run a third machine learning model with at least the predicted pollen and nectar concentration to predict honey yield in each of the catchment areas, and determine placement of a swarm to at least one of the catchment areas. The decision platform may also control an unmanned aerial vehicle to guide the swarm to at least one of the catchment areas.

Abstract: Methods and systems for operating a machine learning system are described. In an example, a device can receive an image and assign a set of pixels in the image as a digital representation of a livestock. The device can further train a machine learning model using the digital representation. The device can further run the machine learning model to generate prediction data relating to the livestock. The device can further generate output data relating to at least one activity among a livestock value chain. The at least one activity can correspond to a process to generate a commodity based on the livestock.

Abstract: In an aspect, a decision platform that optimizes honey value chain can be provided. The decision platform may receive images of a geographic region including catchment areas, run a first machine learning model with the images as input to identify resources in the catchment areas, run a second machine learning model with the identified resources to predict pollen and nectar concentration in the catchment areas, run a third machine learning model with at least the predicted pollen and nectar concentration to predict honey yield in each of the catchment areas, and determine placement of a swarm to at least one of the catchment areas. The decision platform may also control an unmanned aerial vehicle to guide the swarm to at least one of the catchment areas.

Abstract: A Parkinson's disease treatment method, system, and computer program product, including administering a game to a user, monitoring a Parkinson's disease sign and a Parkinson's disease symptom based on a result of the user playing the game, adjusting treatment of the user depending on the Parkinson's sign and the Parkinson's symptom, and suggesting a tailored rehabilitation exercise for the user based on the treatment.