Abstract: A method for securing metaverse premises using Non-Fungible Tokens (NFTs), is disclosed. In some embodiments, the method includes receiving an access request corresponding to at least one of a plurality of premises, from an avatar of a user. The method further includes retrieving the metadata associated with the avatar of the user based on the NFT, upon receiving the access request. The method further includes validating the access request received from the avatar based on the metadata. The method to validate further includes scanning the metadata associated with the avatar to verify information associated with the avatar based on the NFT. The method further includes providing an access of the at least one of the plurality of premises to the avatar upon validating the access request.

Abstract: The disclosure relates to method and system for intelligently managing time and attendance of a user in an establishment. The method includes recording, by a custom wearable device, a set of evaluation parameters associated with the user; receiving, by an intelligent Access Point Network (IAPN), the set of evaluation parameters from the custom wearable device; computing, by a distance calculator, a distance of the custom wearable device from the IAPN; determining, by an intelligent monitoring subsystem, a valid usage of the custom wearable device based on the set of evaluation parameters using a first trained machine learning model; determining one of a valid presence or a valid movement of the custom wearable device based on the set of evaluation parameters and the distance using a second trained machine learning model; and generating an alert in response to determination of at least one the valid usage or the valid presence.

Abstract: The invention relates to method and system for extracting and classifying manufacturing features from a three-dimensional (3D) model of a product. The method includes generating graph corresponding to product based on 3D model of product. The graph includes nodes corresponding to faces of the product and links corresponding to edges of product. The graph generation includes determining adjacency attribute matrix from the 3D model. The method further includes assigning scores to each of links; determining a cumulative score for each of links; extracting sub-graphs from graph by discarding one or more links from links; extracting node parameters and edge parameters from 3D model of product; determining node feature vector based on node parameters and edge feature vector based on edge parameters; and determining a type of manufacturing feature based on corresponding node feature vector and edge feature vector using a Graph Neural Network (GNN) model.

Abstract: The present disclosure relates to system(s) and method(s) for deploying a data analytics model in a target environment. The system records a set of data pre-processing stages, associated with the data analytics model. The set of data pre-processing stages may comprise receiving raw data, executing a set of ETL functions on the raw data, and executing a set of algorithms on the raw data. Further, the system generates the data analytics model based on the set of algorithms. Furthermore, the system generates a scoring engine workflow, associated with the data analytics model, based on the set of data pre-processing stages. The scoring engine workflow comprises one or more ETL functions and one or more algorithms. Further, the system deploys the data analytics model and the scoring engine workflow in the target environment. The scoring engine workflow enables pre-processing of production data in the target environment.

Abstract: The present disclosure relates to system(s) and method(s) for deploying a data analytics model in a target environment. The system records a set of data pre-processing stages, associated with the data analytics model. The set of data pre-processing stages may comprise receiving raw data, executing a set of ETL functions on the raw data, and executing a set of algorithms on the raw data. Further, the system generates the data analytics model based on the set of algorithms. Furthermore, the system generates a scoring engine workflow, associated with the data analytics model, based on the set of data pre-processing stages. The scoring engine workflow comprises one or more ETL functions and one or more algorithms. Further, the system deploys the data analytics model and the scoring engine workflow in the target environment. The scoring engine workflow enables pre-processing of production data in the target environment.

Abstract: The present disclosure relates to system(s) and method(s) for logging tasks and and transmitting alerts to a user. The method may comprise receiving raw data, associated with a set of activities corresponding to a user. The raw data may be received from a set of data sources. The method may further comprise generating a conversation thread associated with a target activity from the set of activities. The method may further comprise identifying at least one abnormal event associated with the conversation thread, based on analysis of the conversation thread using one or more data analysis algorithms. The method may further comprise transmitting one or more alerts to a user or one or more other users associated with the conversation thread. The one or more alerts may be generated based on the abnormal event corresponding to the conversation thread.