Aspects relate to apparatuses and methods for rating the quality of a posting. An exemplary apparatus includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to acquire a plurality of inputs from at least a posting, classify the posting to a posting category as a function of the plurality of inputs, calculate a quality metric as a function of the posting category and the plurality of inputs, wherein the quality metric reflects a level of completeness regarding the arrangement of inputs in a posting, and generate, as a function of the quality metric, a ordering of the posting, wherein the order relates to a probable level of focus a user may use to fill the posting.

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.

The present disclosure presents systems and methods for assessing hospital acquired infection reduction strategies. One such method comprises analyzing, by a computing device, a risk of hospital acquired infections, using supervised learning to generate fuzzy set membership rules; assessing resilience based on observed hospital acquired infection risk moderation performance level across a continuum of fuzzy membership sets; and inferring, by the computing device, a performance of a hospital in hospital acquired infection risk factor prevention employing the fuzzy membership set rules. Other systems and methods are also provided.

To provide a precipitation particle classification apparatus for obtaining a proper classification result of precipitation particles based on information from a plurality of radar devices. The precipitation particle classification apparatus includes a data processing part, a fuzzy processing part, a coordinate conversion part, an interpolation part, and a classification part. The data processing part acquires polarization parameters obtained by reflection on the precipitation particles from each of the plurality of radar devices which are arranged at different positions and have a part of a scanning area overlapping with each other. The fuzzy processing part obtains a polar coordinate distribution evaluation value indicating the distribution in polar coordinates of an evaluation value indicating the degree of attribution to each type of precipitation particles from polarization parameters by using a fuzzy inference. The coordinate conversion part converts the polar coordinate distribution evaluation value into the Cartesian coordinate distribution evaluation value. The interpolation part integrates the Cartesian coordinate distribution evaluation values whose positions on the coordinates are substantially equal among the Cartesian coordinate distribution evaluation values obtained for each of the plurality of radar devices to obtain a composite evaluation value. The classification part classifies precipitation particle species based on the composite evaluation value.

The invention concerns a software based system for computer-aided design (CAD) that includes user interface tailoring and methods for continuously evaluating the learning progress of the user and increase work productivity by searching for the patterns in the user input to predict the goal of user actions and propose next action to reach the goal in optimal way. Components of the presented invention relate to collection of the different user input including at least eye tracking and user focus and attention related features; analyzing continuously user's behavior to evaluate user learning progress and work productivity related to the computer-aided design tool; monitoring user interface components that are used by the user; searching for the patterns in user behavior; tailoring user interface controls to maximize a work productivity at the same time increasing user's qualification profile. The core of the invention comprises gaze tracking as an input component for better user activity and performance tracking, component for features extraction fusion of different types user input, continuously monitored users qualification profile and two classifiers making decision on user interface complexity level and a set of most relevant graphical user interface controls for the next user action.

In systems for interpreting the predictions of a machine learning model with the help of a surrogate model, feature vectors of inputs to the machine learning model can be grouped based on locality sensitive hashes or other hashes that reflect similarity between the feature vectors in matching hash values. For a given prediction to be interpreted and the corresponding input feature vector, a suitable training dataset for the surrogate model can then be obtained at low computational cost by hashing the input feature vector and retrieving stored feature vectors with matching hash values, along with their respective predictions.

A system and methods relate to, inter alia, determining a prediction confidence level associated with machine identification of production data based on a machine learning model. The system and methods further relate to routing the production data to at least one of a human analyzer device associated with the human analyzer or a prediction engine of the server based on the prediction confidence level for identification of the data. The machine learning model of the system and methods may be configured to be modifiable in response to feedback from at least one of the human analyzer device or the prediction engine.

A system for classifying resources to niche models includes a computing device configured to receive a plurality of resource data corresponding to a plurality of resources, generate a plurality of resource models, generating a resource model corresponding to the resource as a function of the plurality of resource data and the merit quantitative field, compute a niche model having a plurality of niche data and an output quantitative field, combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources by classifying the output quantitative field to at least a selected merit quantitative field of the resource model and a niche datum of the plurality of niche data to at least a datum of the plurality of resource data, and provide an indication of the at least a selected resource model to a client device of the niche model.

Methods, systems, and non-transitory computer readable storage media are disclosed for using reject inference to generate synthetic data for modifying lead scoring models. For example, the disclosed system identifies an original dataset corresponding to an output of a lead scoring model that generates scores for a plurality of prospects to indicate a likelihood of success of prospects of the plurality of prospects. In one or more embodiments, the disclosed system selects a reject inference model by performing simulations on historical prospect data associated with the original dataset. Additionally, the disclosed system uses the selected reject inference model to generate an imputed dataset by generating synthetic outcome data representing simulated outcomes of rejected prospects in the original dataset. The disclosed system then uses the imputed dataset to modify the lead scoring model by modifying at least one parameter of the lead scoring model using the synthetic outcome data.

A system that incorporates teachings of the present disclosure may include, for example, a synthesis engine having a controller, and a storage medium for storing instructions to be executed by the controller. The instructions, when executed by the controller, can cause the controller to retrieve collected information associated with a behavior of an individual, synthesize from the information a measure of a mood of the individual to interact with others, and transmit the measure to a system associated with the individual to manage requests between the individual and the entity. The measure of the mood of the individual can indicate an availability of the individual and a receptiveness of the individual to accept a request to interact with an entity. The measure can be described by a plurality of dimensions. Other embodiments are disclosed.