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.

Systems and methods include receiving a functional integrated circuit design and generating a plurality of place and route (PnR) layouts based on the received functional integrated circuit design and one or more integrated circuit floorplans may be generated. One or more fuzzy logic rules may be applied to analyze attributes associated with each of the generated PnR layouts, and a PnR layout of the plurality of PnR layouts having an area utilization complying with the one or more fuzzy logic rules may be generated.

Embodiments for an intelligent directing service in an Internet of Things (IoT) computing environment by a processor. One or more objects may be identified within a defined region relative to an entity. At least a portion of an extremity of the entity may be directed to select or avoid the one or more objects according to one or more internet of things (IoT) devices.

Examples of methods for media classification are described herein. In some examples, a method includes analyzing text associated with media using a first machine learning model to produce a first result. In some examples, the method includes analyzing numerical metadata associated with the media using a second machine learning model to produce a second result. In some examples, the method includes inputting the first result and the second result to a third machine learning model to determine a classification of the media.

A system and method for processing loans includes loan approval decision module that receives input from a loan applicant and collects external data including credit bureau data, bank transaction data, and social media data. The system also includes a machine learning module having a pre-processing subsystem, an automated feature engineering subsystem and a feature statistical assessment subsystem. A business objective determination module and an adverse notice notification module is also provided. The business objective determination module includes a weight optimization company valuation maximization model. A set of models is developed using the machine learning module to predict performance of the borrower based on the business objective determination.

A computer-implemented method, system and computer program product for analyzing test result failures using artificial intelligence models. A first machine learning model is trained to differentiate between a bug failure and a test failure within the test failures based on the failure attributes and historical failures. The failure type for each failed test in test failure groups is then determined using the first machine learning model. The failed tests in the test failure groups are then clustered into a set of clusters according to the failure attributes and the determined failure type for each failed test. A root cause failure for each cluster is identified based on the set of clusters and the failure attributes. The root cause of an unclassified failure is predicted using a second machine learning model trained to predict a root cause of the unclassified failure based on identifying the root cause failure for each cluster.

In an aspect, an apparatus for entity data aggregation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to generate a web harvester. A web harvester is configured to extract entity data from an external database as a function of an extraction criterion. At least a processor is configured to classify extracted entity data to an entity data category. At least a processor is configured to aggregate extracted entity data into an entity profile as a function of an entity data category. At least a processor is configured to generate an entity search index as a function of aggregation of entity data.

A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.

Technologies for a neural belief reasoner model generative models that specifies belief functions are described. Aspects include receiving, by a device operatively coupled to a processor, a request for a belief function, and processing, by the device, the request for the belief function in the generative model based on trained probability parameters and a minimization function to determine a generalized belief function defined by fuzzy sets. Data corresponding to the generalized belief function is output, e.g., as a belief value and plausibility value.

Methods, apparatus, systems and articles of manufacture to classify a first file are disclosed herein. Example apparatus include a feature hash generator to generate respective sets of one or more feature hashes for respective features of the first file. The number of the one or more feature hashes to be generated is based on an ability of the feature to distinguish the first file from a second file. The apparatus also includes a bit setter to set respective bits of a first fuzzy hash value based on respective ones of the one or more feature hashes, a classifier to assign the first file to a class associated with a second file based on a similarity between the first fuzzy hash value and a second fuzzy hash value for a second file.