Abstract: Devices with radiating antennas are subject to various regulations designed to limit the absorption of radiofrequency energy in a human body part in close proximity to a radiating antenna. Various conventional strategies are available for management of transmissions to comply with these regulations; however, each of these conventional strategies has drawbacks, such as a negative effect on the wireless link. Various strategies are disclosed herein to comply with the requisite regulations while maintaining the wireless link. In addition, strategies are presented to select wireless link rates, taking into account limitations that may be in place to satisfy the necessary regulations.

Abstract: A computerized-method for building ensemble of supervised and unsupervised Machine Learning (ML) models for fraud-predictions, for a client having an extremely-imbalanced-dataset, is provided herein.

Abstract: A computerized-method for maintaining ethical Artificial-Intelligence by generating a representative-training-sample-dataset for a fraud-detection Machine-Learning (ML) model, by: (i) operating a representative-dataset-preparation module to generate a representative-training-sample-dataset by operating balanced-sampling on randomly-selected preconfigured-number of financial-transactions. The balanced-sampling may be operated by applying a configurable-rule on at least two values of a parameter of non-sensitive PII parameters of each financial-transaction by a low-frequency value; (ii) training the fraud-detection ML model on the representative-training-sample-dataset; and (iii) deploying the trained fraud-detection ML model in a finance-system in test-environment, and operating the trained fraud-detection ML model on a stream-of-financial-transactions to predict a risk-score for each financial-transaction.

Abstract: A machine learning (ML) system configured to detect fraud in tenant data systems. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform ML modeling operations which include receiving a first data set, determining that the first data set meets or exceeds a low fraud tenant threshold, segmenting the first tenant data system based on the first data set, determining first features of a first ML model, determining a first explanation of a first feature importance of each of the first features, comparing the first tenant data system to a second tenant data system based on at least the first explanation and a second explanation, ranking at least the first features and the second features, and performing a feature selection.

Abstract: A system and method is provided for automatically creating machine learned fraud detection models. Data received from a plurality of devices can be used to train a model for each of the plurality of entities. Each of the models can be trained using recursive model stacking and each model can output a corresponding score. A second model can be trained for each of the plurality of entities based on the first model and a corresponding output score of the first model. The second model can also be trained using recursive model stacking.

Abstract: A system is provided for estimating quantile values for fraud assessments. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include capturing one or more first data values for a quantile value profile associated with an entity, wherein the quantile value profile includes one of real values or a first plurality of quantile marker values calculated from the real values, accessing the quantile value profile for the entity, determining a first number of the one or more first data values, and based on the first number of the one or more first data values and the one of the real values or the first plurality of quantile marker values in the quantile value profile, performing one of a first merge operation, a second merge operation, or a third merge operation.

Abstract: A method and system for building and implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model. A computer processor may iteratively simulate a backpropagation algorithm by executing a sequence of optimization steps. At each optimization step a position of a loss function may be determined that may be closer than a previously determined position of the loss function to a local minimum. A computer processor may compute and store after each iteration a detachment of the loss function, learning rate, and optimal learning rate. A computer processor may train a machine learning connective model to model the optimal learning rates of the simulated backpropagation algorithm. The meta-ML optimization engine may be implemented for a NN or ML connective model by generating a modified backpropagation algorithm in which algorithmic features of gradient descent may be replaced by the meta-ML optimization engine.

Abstract: An artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.

Abstract: A method and system for generating content title recommendations for content titles associated with a content page is disclosed. The method and system collects user activity data representing user engagement levels relating to multiple content webpages, wherein each content page is associated with a content title. A title replacement candidate is identified in view of the collected user activity data, wherein the title replacement candidate includes a plurality of title components. The title replacement candidate is compared to one or more high user engagement value titles. Based on the comparison, one or more high user engagement title component recommendations are identified which correspond to one or more of the title components of the title replacement candidate.