Abstract: Methods, software programs, and systems for behavioral analysis of computer applications, and, more particularly, for the prediction of application behavior within and across heterogeneous environments, using machine learning techniques, are disclosed. A method according to certain of these embodiments includes retrieving merged configuration/utilization data and generating predicted application behavior information. The merged configuration/utilization data includes at least a portion of application configuration information and at least a portion of environment configuration information. The application configuration information is information regarding a configuration of an application. The environment configuration information is information regarding a configuration of an environment in which the application is executed. The predicted application behavior information is generated using a machine learning model, which receives the merged configuration/utilization data as one or more inputs.

Abstract: A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.

Abstract: A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.

Abstract: An apparatus in one example has: a first communication network optimized for high-speed packet data services; a first database operatively coupled to the first communication network; a second communication network for providing both voice and slow data-rate data services; a second database operatively coupled to the second communication network; and an operations platform operatively coupled to the first and second databases. The first communication network optimized for high speed packet data will generate in the first database a record which will have at least: mobile ID type, mobile ID, connection ending time, cell, sector, carrier information; and last signal strength value. The second communication network providing both voice and low speed packet data will generate, in the second data base, a record that will have at least mobile ID type, call start time, call length, mobile ID (ESN/MEID), call type (data or voice), cell, sector, carrier information; and call disposition.

Abstract: An apparatus in one example has: a telecommunications network having a plurality of mobile switching centers each of the mobile switching centers having a respective visitor location register; a selected at least one mobile switching center of the plurality of mobile switching centers; periodic time intervals; and a respective set of recordings of numbers of current entries in the respective visitor location registers measured at the same designated time of the selected at least one mobile switching center existing at an end of each of the time intervals.