Abstract: A privacy preserving geofencing system and method are disclosed. The method includes receiving a current location data of a vehicle. The current location data is homomorphically encrypted through a homomorphic encryption technique. The method further includes randomly generating at least two secure values corresponding to the current location data of the vehicle. The method further includes combining the at least two secure values with the current location data of the vehicle. The method further includes sharing a combination of the current location data and the at least two secure values to a computing server. The method further includes computing the current location data of the vehicle obfuscated with the at least two secure values to obtain a computed result. The computed result determines if the vehicle is within a predefined geofencing region based on one or more geofencing criteria.

Abstract: The invention relates to method and system for machining a pocket on a raw material body.

Abstract: A method and system of providing assistance during an operation performed on an equipment by a smart assistance device. A first set of image frames and a second set of image frames corresponding to forward steps and reverse steps respectively performed during the operation are received from an imaging device. Parts of the equipment are detected in each of first set of image frames and tagged. The smart assistance device detects parts of the equipment present in an image frame for each of the second set of the image frames. A discrepancy is determined in the reverse steps by comparing the parts of the equipment present in the frame of the second set of the image frames with the parts tagged in the corresponding first image frame of the first set of image frames. A notification and recommendation is provided to correct the discrepancy.

Abstract: A method and system dynamically predicting driving range of vehicles is disclosed. In some embodiments, the method includes dynamically determining real-time values corresponding to in-transit parameters associated with vehicle; and determining, via trained Machine Learning (ML) model, variance in in-transit parameters when compared with pre-defined parameters. Determining the variance includes identifying overlapping subset of parameters between in-transit parameters and pre-defined parameters; identifying non-overlapping subset of parameters between in-transit parameters and pre-defined parameters; determining difference in each of the real-time values determined for overlapping subset of parameters with corresponding optimal value; and computing variance based on non-overlapping subset of parameters and difference in real-time values determined for the overlapping subset of parameters.

Abstract: This disclosure relates to system and method for preserving privacy of telematic data. The method includes receiving telematic data from a vehicle and a set of predefined policies from an insurance provider. The telematic data and the set of predefined policies are homomorphically encrypted through a multi-key homomorphic encryption technique. The method further includes analyzing the telematic data and the set of predefined policies to obtain an analysis result. The method further includes sharing the analysis result with the vehicle in an encrypted format. The method further includes partially decrypting the analysis result to obtain a partially decrypted result. The method further includes sharing the partially decrypted result to the insurance provider. The method further includes decrypting the partially decrypted result to obtain fully decrypted result in a plain-text format.

Abstract: The present disclosure relates to a system(s) and method(s) for generating a predictive model, the method comprises receiving data and extracting one or more predicator features from the data based on a feature selection methodology. In one example, the feature selection methodology comprises computing a degree connectedness for each of the plurality of features using a modified mutual information technique and a Pearson co-efficient and identifying the one or more predicator features on a comparison of degree of connectedness and a predefined threshold. Further, the method comprises identifying a data type associated with the data, and generating a predictive model to be applied on the data based on the data type and the one or more predicator features.

Abstract: A system and method for instance segmentation of densely packed objects is disclosed. The method includes receiving an input image, enhancing the input image to generate an enhanced input image, inputting the enhanced input image to a trained mask R-CNN model to generate a first segmentation map, iteratively predicting one or more missing instances of densely packed objects from the input image based on comparison of a contour map generated for the input image with a contour map generated for the first segmentation map, comparing an area of the one or more missing instances with a predefined threshold value, applying the trained masked R-CNN model on each of the one or more missing instances to generate a second segmentation map, and obtaining a refined segmentation map based on the first segmentation map and the second segmentation map.

Abstract: A method and system for validating promotional emails and product availability from E-commerce websites is disclosed. In one embodiment, the method includes retrieving a first set of images corresponding to an image strip and a second set of images corresponding to a promotional email from a database. The first set of images and the second set of images may be associated with one or more products. The method further includes calculating a similarity score between each of the first set of images and each of the second set of images using a first Computer Vision (CV) technique. The method further includes selecting one or more valid images from the second set of images based on the similarity score. The method further includes determining a stock availability status of at least one product presented in the one or more valid images from at least one website using a deep learning algorithm.

Abstract: The present disclosure relates to a system and a method for processing distributed data files. The processor executes instructions to receive a set of instructions from a primary device, wherein the set of instructions comprises verification rules, validators, primary transformers and structure query transformers; generate processed data files by processing the distributed data files. The distributed data files are processed by performing at least one of: executing one of the verification rules, the validators and the primary transformers on the distributed data files; and transforming the distributed data files by executing the structure query transformers. The execution of the structured query transformers comprises steps of generating a dependency graph based upon dependencies between the structure query transformers; and determining a sequence of execution of the structured query transformers based upon the dependency graph; and transfer the processed data files to a data warehouse.

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.