A system and method for processing loans includes a machine learning model associated with processing loans. The machine learning model may be configured based on an objective function associated with loan processing. One or more weights of the objective function may be updated to account for changes in one or more business conditions. The machine learning model may be configured based on the updates to the one or more weights.

The invention proposes an automatic driving “machine consciousness” model, which is composed by the human's safety driving rules. Establish the dynamic fuzzy event probability measure relation, or fuzzy relation, or probability relation of the automatic driving vehicle and the surrounding passing vehicle. The decision result of “machine consciousness” of automatic driving vehicle is realized by complicated logic operation and using the antagonistic result of logic operation in both positive and negative directions. The implementation result is that it can make the decision-making result of automatic driving vehicle close to the result of human's biological consciousness, which can improve the safety of automatic driving vehicle, reduce the development cost and reduce the distance of road test.

Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Machine data reflecting operation of a monitored system is ingested and made available for search by a data intake and query system (DIQS). Monitoring includes obtaining a subset of ordered events that are assigned to a task. In a graphical user interface on a display, a chart for the task is displayed. The chart includes an event identifier for each event of the subset of the ordered events, a confidence level value related to each event identifier of each event of the subset of ordered events, the confidence level value indicating the confidence level that the event is in the task. The chart further includes a time reference value identifying a time of each event.

In some embodiments, a self-monitoring intravenous catheter system is provided. An alarm controller is provided that receives signals representing a pH value, an oxygen saturation value, and a pressure value in proximity to the distal end of the catheter. By performing a fuzzy logic analysis of the values, the alarm controller is able to detect that the catheter is about to fail or has failed, and can cause alerts to be presented. In some embodiments, an intravenous catheter is provided that has a pH sensor and an oximeter disposed at a distal end of the catheter to obtain the pH value and oxygen saturation values analyzed by the alarm controller. Embodiments of the catheter and self-monitoring intravenous catheter system may be particularly useful in treating neonates, who are sensitive to catheter failure and are not capable of detecting the signs of failure themselves.

A system for application selection using behavioral propensities includes a computing device configured to identify a negative behavioral propensity associated with a human subject, generate, using category training data including a plurality of applications and a plurality of correlated categories, and using a classification algorithm, an application category classifier, wherein the application category classifier inputs applications and outputs categories of the applications, receive an application to be loaded to a device operated by the human subject, identify, using the application category classifier, a category of the application, wherein identifying the category includes determining, a plurality of application data elements, classifying each application data element of the plurality of application data elements to an application data object of a plurality of application data objects using an object classifier, and inputting the plurality of objects to the application category classifier, and determine an effect of the category on the negative behavioral propensity.

Provided is a remote sensing image feature discretization method based on rough-fuzzy model, comprising the following steps: listing the digital number in each band and category of a selected sample in remote sensing images, and building an image information decision table based on the digital number and category; initializing the class center of each category and the membership degree of a sample example relative to the class center; updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining the final value of the class center of each category and the final value of the membership degree; building a rough-fuzzy set, computing the mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating the discretization results based on the mean approximation accuracy and a genetic algorithm, and selecting an optimal discretization solution.

In an approach for reasoning with real-valued propositional logic, a processor receives a set of propositional logic formulae, a set of intervals representing upper and lower bounds on truth values of a set of atomic propositions in the set of propositional logic formulae, and a query. A processor generates a logical neural network based on the set of propositional logic formulae and the set of intervals representing upper and lower bounds on truth values. A processor generates a credal network with a same structure of the logical neural network. A processor runs probabilistic inference on the credal network to compute a conditional probability based on the query. A processor outputs the conditional probability as an answer to the query.

A cart robot analyzing a hand motion of a user is disclosed. The cart robot comprises a code input interface obtaining a product identification code, a sensor detecting a hand motion of a user, a weight measuring device measuring a weight of one or more products contained in a product loading space, and a controller. When there is no change in weight of the loaded products, the cart robot updates a shopping list with respect to the products loaded in the product loading space in real time by analyzing the hand motion of the user. Therefore, the convenience of the user is enhanced.

A method for recommending a drilling target of a new well based on cognitive computing is provided, including: establishing a reservoir geological model; acquiring a dynamic parameter and a static parameter; establishing multiple fuzzy rules bases; inputting the dynamic and static parameters into the fuzzy rules base to obtain aggregated output fuzzy sets of membership values; defuzzifying the fuzzy set of the membership values to obtain crisp values of the fuzzy variables; inputting the crisp values into the fuzzy rules base to obtain a aggregated output fuzzy set of DA membership values of drilling attractiveness DA as a fuzzy variable; defuzzifying the DA to obtain a score of the DA; establishing a drilling attractiveness region with a radius R by taking each grid as a center; calculating region drilling attractiveness RDA score of the region; and determining a region with a highest score as the location of the new well.