Abstract: A machine learning (Ml)-based computing method for forecasting financial transactions is disclosed. The ML-based computing method includes receiving inputs from users; generating cash flow data including historical cash flow data and real-time cash flow data, based on the inputs received from the users; determining a growth factor in a week and month level based on the historical cash flow data and the forecast period; determining average cash flow data in a week and month level based on the historical cash flow data, the forecast period, a lookback period received from the users, and the growth factor; generating forecast cash flow data in at least one of a week, month, quarter, and year level for the forecast period using a ML model; and providing an output of the forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices.

Abstract: Systems and methods include acquisition of an asynchronous message from a message producer, the asynchronous message associated with a message consumer, determination that the asynchronous message matches a stored message, identification, in response to determining that the asynchronous message matches a stored message, of a stored error message associated with the stored message, and return of a return message based on the stored error message to the message producer.

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.

Abstract: This disclosure provides methods, apparatuses, and computer-readable mediums for managing network devices in a telecommunication network using dynamically modifiable graph data schema. The method may be executed by one or more processors and may include creating an entity model specification file; validating a syntax of the entity model specification file based on parsing the entity model specification file; based on the syntax being valid, validating the entity model specification file, wherein validating the entity model specification file comprises converting the entity model specification file into a graph data schema; and initiating the graph data schema associated with the entity model specification file, wherein the initiating comprises changing the graph data schema to a ready state.

Abstract: Embodiment herein provides a method for classifier based constraint management. The method comprises creating a constraint graph database comprising a plurality of constraint classes associated with a plurality of Application program interfaces (APIs); receiving a request to perform at least one operation associated with at least one entity; determining at least one constraint class from the plurality of constraint classes based on the information about the at least one entity to perform the at least one operation; invoking the at least one API from the plurality of APIs based on the at least one constraint class to access the plurality of constraints associated with the at least one constraint class; and performing the at least one operation associated with the at least one entity based on the plurality of constraints associated with the at least one constraint class and the information about the at least one entity.

Abstract: Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.

Abstract: Embodiment herein provides a method for managing uniqueness constraints associated with entities in a graph database. The method includes receiving a constraint specification of an entity from a constraint management system and receiving a configure operation for maintaining uniqueness requirement in the constraint management system. The method also includes determining a constraint key based on inputs received for maintaining the uniqueness requirement in the constraint management system; and creating a composite unique constraint based on the constraint key. The composite unique constraint comprises properties of the constraint key and a constraint vertex indicating a class of constraints available in the inputs received for maintaining uniqueness requirement. The method also includes storing the composite unique constraint in the constraint specification received from the constraint management system to validate the uniqueness before performing an operation on the entity.

Abstract: Embodiment herein provides a method for managing relationship constraints associated with entities in a graph database. The method includes receiving, by a relationship constraint management server, a request to perform operation associated with entity and generating, by the relationship constraint management server, an entity configuration file based on the received information of the entity. The request comprises information of entity and, the entity configuration file includes a relationship constraint from a plurality of relationship constraints to be applied while performing the operation. The method includes retrieving, by the relationship constraint management server, metadata corresponding to relationship constraints from a constraint database based on the entity configuration file, and performing, by the relationship constraint management server, the operation associated with the entity based on the metadata corresponding to the relationship constraints and the information of the entity.

Abstract: Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.

Abstract: Embodiment herein provides a method for classifier based constraint management. The method comprises creating a constraint graph database comprising a plurality of constraint classes associated with a plurality of Application program interfaces (APIs); receiving a request to perform at least one operation associated with at least one entity; determining at least one constraint class from the plurality of constraint classes based on the information about the at least one entity to perform the at least one operation; invoking the at least one API from the plurality of APIs based on the at least one constraint class to access the plurality of constraints associated with the at least one constraint class; and performing the at least one operation associated with the at least one entity based on the plurality of constraints associated with the at least one constraint class and the information about the at least one entity.

Abstract: Embodiment herein provides a method for managing uniqueness constraints associated with entities in a graph database. The method includes receiving a constraint specification of an entity from a constraint management system and receiving a configure operation for maintaining uniqueness requirement in the constraint management system. The method also includes determining a constraint key based on inputs received for maintaining the uniqueness requirement in the constraint management system; and creating a composite unique constraint based on the constraint key. The composite unique constraint comprises properties of the constraint key and a constraint vertex indicating a class of constraints available in the inputs received for maintaining uniqueness requirement. The method also includes storing the composite unique constraint in the constraint specification received from the constraint management system to validate the uniqueness before performing an operation on the entity.

Abstract: This disclosure provides methods, apparatuses, and computer-readable mediums for managing network devices in a telecommunication network using a graph data schema may be provided. the method may include retrieving, from a database, an entity model specification associated with a network device being deployed; determining whether a base model associated with the entity model specification is supported; adding one or more vertex node labels in the graph data schema based on the name, the type of network device, and the version in the entity model specification; adding one or more characteristic nodes in the graph data schema based on one or more characteristics; adding one or more edges in the graph data schema on the one or more relationships in the entity model specification; and based on no error being generated, activating the graph data schema to manage the network device being deployed in the telecommunication network.

Abstract: This disclosure provides methods, apparatuses, and computer-readable mediums for managing network devices in a telecommunication network using dynamically modifiable graph data schema. The method may be executed by one or more processors and may include creating an entity model specification file; validating a syntax of the entity model specification file based on parsing the entity model specification file; based on the syntax being valid, validating the entity model specification file, wherein validating the entity model specification file comprises converting the entity model specification file into a graph data schema; and initiating the graph data schema associated with the entity model specification file, wherein the initiating comprises changing the graph data schema to a ready state.

Abstract: A system and method for rendering AI-based haptic feedback and recommendations in a VR environment is provided. The system detects an active VR session on a VR device that renders immersive content associated with a VR environment that includes a digital avatar and a virtual object. The system acquires the immersive content based on the detection and determines physical attributes of the virtual object, scene information associated with the VR environment, and an activity of the digital avatar based on a first network-based analysis, a second network-based analysis, a third neural network-based analysis, respectively, of the content. The system further detects an interaction between the digital avatar and the virtual object in a duration of the activity, and generates a feedback signal based on the interaction, the scene information, and the physical attributes. The system further controls a haptic device based on the feedback signal to generate a haptic feedback.

Abstract: An electronic device and method for chronic pulmonary disease prediction from audio input based on short-winded breath determination using artificial intelligence is disclosed. The electronic device receives an audio input associated with a user. The electronic device applies an Artificial Intelligence (AI) model to detect a short-winded breath duration that corresponds to a time duration between an end of a first spoken word and a start of a second spoken word succeeding the first spoken word. The electronic device detects a speaking pattern. The electronic device applies a Recurrent neural network (RNN) model to reconstruct a set of short-winded breath audio samples. The electronic device generates an audio sample dataset and a set of audio features. The electronic device applies a modular neural network model on the generated audio sample dataset and on the generated set of audio features to determine a set of chronic obstructive pulmonary disease (COPD) metrics.

Abstract: Detection of interaction events in recorded audio streams is disclosed, including: detecting an interaction event within a recorded audio stream; analyzing text before and after the interaction event in the recorded audio stream to determine a causer of the interaction event; and determining an action to be performed in response to the interaction event based at least in part on the causer of the interaction event.

Abstract: Devices and techniques are generally described for generating customized invoice data. In some examples, first data describing invoice settings for a first entity may be received during a registration process. In various examples, a plurality of invoice customizations may be determined based on a similarity between the first data and historical invoice settings of other entities. In some examples, the customized invoice data comprising the plurality of invoice customizations may be generated. In at least some examples, the customized invoice data may be stored in non-transitory computer-readable memory.

Abstract: The present disclosure relates to methods of making of one or more bioengineered ferritin proteins having a preselected ratio of H-subunits (ferritin heavy chain (FTH or H)) to L-subunits (ferritin light chain (FTL or L)). The disclosure further relates to cDNAs, vectors, and host cells for forming the bioengineered ferritin proteins of the present disclosure.

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.