Abstract: Aspects of the present disclosure relate generally to machine inspection of part production and, more particularly, to systems and methods of automated Al inspection of customized part production. For example, a computer-implemented method includes receiving, by a processor, design information for a custom part; extracting, by the processor, feature information of the custom part from the design information; receiving, by the processor, images of the custom part in production from a recording in near real time; and verifying, by the processor, using machine learning that features in the images of the custom part in production are in compliance with the feature information of the custom part.

Abstract: A computer-implemented method, according to one embodiment, includes identifying, in a reference video of a production process of a product, a discrete and non-overlapping set of first tasks. The first tasks define at least a first sub-process. The method further includes analyzing a live video of the production process to identify frames of the live video that include second tasks that define a second sub-process, and analyzing the frames of the live video for determining whether a match exists between the first tasks and the second tasks. In response to a determination that the match does not exist, an alert that a deviation is present in the production process depicted in the live video is output.

Abstract: The technique relates to a system and method for handling uncertainty in supplier selection using robust optimization. The technique involves receiving the historical data of one or more suppliers with respect to a plurality of factors from a database for selecting at least one of the one or more suppliers. After receiving the historical data, a convex hull is generated for each of the suppliers to determine a relationship between the pluralities of factors. Then adding equations which are more related to uncertainty values associated with at least one of the plurality of factors in the convex hull for each of the one or more suppliers. Thereafter, using robust optimization to determine an optimal performance level of each of the one or more suppliers with respect to the plurality of factors based on the fetched uncertainty value. Finally, selecting the suppliers based on the optimal performance level.

Abstract: The disclosure describes a method for prediction of functional dependencies between various factors involved in a payment given by an insurance service provider to a medical service provider for medical treatment of a patient comprising various steps like receiving input information wherein the input information comprises the basic factors involved in computation of the payment given by the insurance service provider to the medical service provider for the medical treatment of the patient. The received input information is then sorted based on some user selected basic factor and the sorted information is processed using data mining techniques to obtain the various functional dependencies between the basic factors. The determined functional dependencies are then displayed accordingly.

Abstract: Described are methods, systems, and computer-readable storage media for decision making through convex hull optimization. A plurality of key performance indicators (KPIs) are received. A convex hull encompassing the plurality of KPIs is generated. Based at least in part on the generated convex hull and on at least one KPI satisfying a condition, an operating range of one or more other KPIs is determined. Moreover, specific values at which each of the other KPIs may be maintained in order to reach a defined objective are determined.