Abstract: A method includes mapping a first subset of integers to a plurality of transaction sequences, mapping a second subset of integers to a plurality of audit score sheets and quantitatively analyzing each respective one of the mapped transaction sequences relative to each respective one of the mapped audit score sheets to determine an objective score for each one of the transaction sequences and each one of the audit score sheets. The objective score for each one of the mapped transaction sequences is a function of an estimated tax liability and likelihood of being audited associated with that mapped transaction sequence. The objective score for each one of the mapped audit score sheets is a function of an estimated effectiveness associated with that audit score sheet. The method further includes generating a new population of effective transaction sequences and/or effective auditing policies based on the objective scores.

Abstract: A system and method for providing a likelihood of auditing a tax return is disclosed. A population of potential solutions of transaction sequences and/or audit score sheets is initialized. Each potential solution in the population is evaluated and assigned an objective score. A subset of the population is selected, preferably a subset with an objective score meeting a predefined criteria. The elected subset is varied using Darwinian concepts, and the previous population is replaced with the new, varied population.

Abstract: A system is provided for multi-methodology, multi-user, self-optimizing Machine Learning as a Service for that automates and optimizes the model training process. The system uses a large-scale distributed architecture and is compatible with cloud services. The system uses a hybrid optimization technique to select between multiple machine learning approaches for a given dataset. The system can also use datasets to transferring knowledge of how one modeling methodology has previously worked over to a new problem.

Abstract: Computer-implemented methods and systems are disclosed for performing wind resource assessments for potential wind farm sites using Gaussian copula correlation models.

Abstract: Methods and apparatus include presenting an initial set of names to a user. The user selects a set of names from those presented. An Interactive Evolutionary Algorithm (IEA) extracts features of each selected name from a database of names and features to form a feature set. The IEA forms a set of match features that are chosen from the feature set according to a priority function and/or weighting of the features, either of which may vary in succeeding iterations. The IEA searches the database to obtain a candidate set of names, where each name has features matching the match features. One or more names is chosen from the candidate set and added into a presentation set of names. The IEA may repeat the formation of the match features, candidate set, and selection of one or more names from the candidate set until the new presentation set is complete.

Abstract: Methods and apparatus include presenting an initial set of names to a user. The user selects a set of names from those presented. An Interactive Evolutionary Algorithm (IEA) extracts features of each selected name from a database of names and features to form a feature set. The IEA forms a set of match features that are chosen from the feature set according to a priority function and/or weighting of the features, either of which may vary in succeeding iterations. The IEA searches the database to obtain a candidate set of names, where each name has features matching the match features. One or more names is chosen from the candidate set and added into a presentation set of names. The IEA may repeat the formation of the match features, candidate set, and selection of one or more names from the candidate set until the new presentation set is complete.