Systems and methods for recording information at a granular level; checking and verifying that data is used and processed is consistent with an entity’s internal policies and/or external regulations; and producing reports to authorized users (e.g., individuals and organizations) with information are provided. The system and methods capture required data in an immutable fashion so that users outside of an entity (e.g., public, third parties) can check and audit that internal policies and other regulatory policies and frameworks are followed.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by dynamically parameterized machine learning frameworks, such as a dynamically parameterized machine learning framework comprising an encoder machine learning model that is configured to generate dynamically generated parameters for a target machine learning model of the dynamically parameterized machine learning framework.

Disclosed embodiments relate to generating training data for a machine learning model. Techniques can include accessing a machine learning model from a machine learning model repository and identifying a data set associated with the machine learning model. The identified data set is utilized to generate a set of data augmentation operators. The data augmentation operators applied on a selected sequence of tokens associated with the machine learning model to generate sequences of tokens. A subset of sequences of tokens are selected and stored in a training data repository. The stored sequences of tokens are provided to the machine learning model as training data.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops.

Embodiments are directed to a system, computer program product, and method for dynamic object mining. A received file is segmented, re-formatted, and organized into buffers, while maintaining the order of the received file. Multiple buffers are processed in parallel for object mining. A listener is encoded into the buffers to support asynchronous processing, and more specifically ordering of mined objects. An output file of the mined objected or associated frames is created. The file is populated with a sequential ordering of the objects that follows the order of the received file.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.

Example systems and methods provide dynamic generation and display of workstation assignments in a pharmacy information system. A plurality of electronic workstation displays are in network communication with a workstation assignment engine configured to generate workstation assignments for employees assigned to the workstations displays and store a schedule record. Based on various triggers, workstations identified in the workstation assignments display the workstation assignments on a graphical user interface portion of the workstation displays. Dynamically generated changes to the workstation assignments in the schedule record may be generated and displayed in response to real-time change in coverage events.

An electronic device using an artificial neural network model including an attention mechanism, according to various embodiments, can comprise: a memory configured to store information including a plurality of recurrent neural network (RNN) layers; and at least one processor connected with the memory and configured to set, as a first key and a value, at least one first hidden representation acquired through at least one layer among the plurality of RNN layers, set, as a second key, at least one second hidden representation acquired through at least one second layer among the plurality of RNN layers, and acquire an attention included in an attention structure at least on the basis of data on the first key, data on the second key, or data on the value.

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including: obtaining an estimation value of a third variable by subtracting a second output value of a second parametric model to which a second variable is input from a first output value of a first parametric model to which a first variable is input; performing first parameter update of updating first parameters of the first parametric model and second parameters of the second parametric model such that independence between the second variable and the estimation value of the third variable is maximized; and updating the first parameters and third parameters of a third parametric model in the first parameter update, such that a third output value of the third parametric model is approximated to the first variable, the third parametric model being input with the first output value.

In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, determining a source platform code for migration from a source platform to a target platform and determining one or more attributes of the source platform code. The method also includes determining, using a machine learning (ML) model, one or more existing templates based on the one or more attributes of the source platform code, and recommending the one or more existing templates for use in generating a template for migration of the source platform code to the target platform. The template for the source platform code is configured to convert the source platform code to a target platform code suitable for the target platform. The one or more existing template can then be used to generate a template for migrating the source platform code to a target platform code suitable for the target platform.