Abstract: Embodiments relate to a system, computer program product, and method for smart storage units using Internet of things (IoT) technology, and, more specifically, for substantially end-to-end autonomous management of consumable commodities. The system includes a robot including one or more external lighting devices and a storage unit comprising a cap and a container portion, the cap is coupled to the container portion and is communicatively coupled to the robot. The cap includes one or more external energy collection devices configured to receive light energy from the one or more external lighting devices. The cap also includes a first processing device electrically coupled to the one or more external energy collection devices. The first processing device is configured to collect data associated with an inventory of the contents of the container portion and transmit the inventory data to the robot.

Abstract: The classifier of an artificial intelligence model is trained by mapping output values in a final run of a training phase, measuring dense data portion and sparse data portion of a data plot produced by the training phase by identifying outputs near edges of the data plot and extracting the curve patterns as linear functions in the dense areas of the output; obtaining a fractal function base set of patterns from the linear functions provided by the dense areas of the output; applying the fractal function base set of patterns from the dense areas of the output to the sparse data portion of the data plot; and training the artificial intelligence model using the data plot including the dense areas of the output and the sparse data portion of the data plot that has been fit to the data curve using the fractal function base set of patterns.

Abstract: Classifier accuracy is increased in machine learning applications by training a machine learning (ML) model including a classifier across classes by determining weighted input points for a contributing set to store the values for true positive and true negative predications. In a following step, input data is provided to the classifier of the machine learning model at runtime, and a classification output is determined from the classifier. For the classification output, values for input during runtime are compared with a sample of inputs stored for training the machine learning model to determine the distance in spread for the classification output. A class is determined from the classification output having a smallest distance and spread. The method can further determine if the class with the smallest distance and spread is a true positive or true negative by comparing the class with the smallest distance and spread with the classification output.

Abstract: A computer implemented method for analysis and tracking perishable items for selection of perishable items using IOT devices that includes registering a consumer to a system that assists the selection of perishable items using IOT devices based upon consumer preferences, in which the system collects data from the user, wherein the user can revoke permission to the system at any time; and monitoring harvesting of a plurality of perishable item types using a plurality of internet of things (IOT) devices. The method may further include recording a consumer profile on the system that assists in the selection of perishable items from the consumer including consumer preferences for freshness of perishable items. The method may further include matching perishable items having freshness data from monitoring the harvest to the consumer based upon consumer preferences from the consumer profile.

Abstract: Embodiments relate to a system, computer program product, and method for smart storage units using Internet of things (IoT) technology, and, more specifically, for substantially end-to-end autonomous management of consumable commodities. The system includes a robot including one or more external lighting devices and a storage unit comprising a cap and a container portion, the cap is coupled to the container portion and is communicatively coupled to the robot. The cap includes one or more external energy collection devices configured to receive light energy from the one or more external lighting devices. The cap also includes a first processing device electrically coupled to the one or more external energy collection devices. The first processing device is configured to collect data associated with an inventory of the contents of the container portion and transmit the inventory data to the robot.

Abstract: A computer implemented method for analysis and tracking perishable items for selection of perishable items using IOT devices that includes registering a consumer to a system that assists the selection of perishable items using IOT devices based upon consumer preferences, in which the system collects data from the user, wherein the user can revoke permission to the system at any time; and monitoring harvesting of a plurality of perishable item types using a plurality of internet of things (IOT) devices. The method may further include recording a consumer profile on the system that assists in the selection of perishable items from the consumer including consumer preferences for freshness of perishable items. The method may further include matching perishable items having freshness data from monitoring the harvest to the consumer based upon consumer preferences from the consumer profile.

Abstract: A lubricant formulation of carbamoylethyl katira and a microwave assisted process for the preparation of the lubricant formulation of carbamoylethyl katira. The lubricant formulation of carbamoylethyl katira having antibacterial activity useful for eye lubricant based formulations, treatment of dry eye disease syndrome, etc.

Abstract: A lubricant formulation of carbamoylethyl katira and a microwave assisted process for the preparation of the lubricant formulation of carbamoylethyl katira. The lubricant formulation of carbamoylethyl katira having antibacterial activity useful for eye lubricant based formulations, treatment of dry eye disease syndrome, etc.