Abstract: This disclosure relates to method and system for optimizing memory requirement for training an artificial neural network (ANN) model employed for natural language processing (NLP). In one embodiment, the method may include receiving a plurality of training parameters and a plurality of model parameters, selecting a set of model parameters from among the plurality of model parameters for training the ANN model based on a characteristic and an architecture of the ANN model, masking the set of model parameters in one or more layers of the ANN model based on a set of pre-defined rules to generate a set of masked model parameters, determining an amount of memory required for training the ANN model based on the set of masked model parameters, and providing the set of masked model parameters for training the ANN model when the amount of memory required is less than a determined threshold.

Abstract: This disclosure relates to method and system for detecting and tracking at least one object in a rotational environment. The method includes receiving a set of first features based on first data and a set of second features based on second data, detecting at least one object based on the set of first features using a Convolutional Neural Network (CNN) based predictive model, determining a set of first parameters for the at least one object, detecting the at least one object based on the set of second features using the CNN based predictive model, determining a set of second parameters for the at least one object, and tracking the at least one object based on the set of first parameters and the set of second parameters. It should be noted that the first data and the second data sequentially belong to an input dataset that includes images or video frames.

Abstract: The present invention discloses a method and a system for managing cache memory. The system comprising a processor is configured to receive datasets from one or more applications, segregate the received datasets into one or more data blocks, identify a checkpoint from previously created checkpoints stored in a virtual cache corresponding to the one or more data blocks, wherein the checkpoints are previously created based on frequency of repetition of each of the one or more data blocks and association between the each of the one or more data blocks, recall a sequence of previously stored data blocks from main memory based on the identified checkpoint, and send the sequence of previously stored data blocks to the one or more applications for execution, thereby managing cache memory.

Abstract: The disclosure relates to an enhanced data security system and method thereof. In some embodiments, the method includes receiving the transactional credential dataset from a user application. The transactional credential dataset is provided by a user to the user application. The method further includes storing the transactional credential dataset in nodes of a graphical embedding storage model. The nodes further store historical credential datasets of the user. Further, the method includes determining a correlation among the historical credential datasets using an artificial neural network (ANN) model and detecting a pattern of the transactional credential dataset based on the correlation. The ANN model is trained based on credential datasets provided by users stored in the nodes of the graphical embedding storage model.

Abstract: A method and system for executing processes in an operating system is disclosed. The method may include assigning a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute. The method may further include determining content patterns associated with each of the plurality of processes using a deep learning network. The method may include assigning a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern. The method may further include generating a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process. The method may include executing each process within a cluster from the set of clusters based on execution of a single process within the cluster.

Abstract: A method and system for automating repetitive task on a user interface is disclosed. The method includes identifying a plurality of Document Object Model (DOM) elements from a repetitive pattern. The method further includes identifying a set of dynamic DOM elements from the plurality of DOM elements. The method further includes determining a path and a path position within the application code of the repetitive pattern for each of the set of dynamic DOM elements. The method further includes training an Artificial Intelligence (AI) model to identify the repetitive pattern and to identify the corresponding path and the path position within the application code component, for each of the set of dynamic DOM elements. The method further includes automatically executing a code for each of the set of dynamic DOM elements based on the associated path and path position.

Abstract: A method and authentication system for alleviating authenticity of sensors in biometric authentication is disclosed. The authentication system receives fingerprint data from a plurality of sensing devices configured in the authentication system and extract one or more attributes associated with the fingerprint data. An expanded value for each of the one or more attributes is determined based on one or more predefined techniques. The authentication system (101) trains a model associated with authentication of fingerprints using the one or more attributes and corresponding expanded value. Further, one or more errors associated with the fingerprint data are predicted based on fingerprint data received over a period of time in real-time and the model is retrained based on the predicted one or more errors to alleviate authenticity of sensors in biometric authentication.

Abstract: A system and method for auto resolution of errors during compilation of data segments is disclosed. The method includes the steps of detecting one or more errors to be resolved associated with a data segment, determining an error type for each of the one or more errors from predefined error types based on neuron activations and a code logic derived from the data segment, arranging the one or more errors in a sequence of resolving, predicting a solution for each of the one or more errors, resolving the one or more errors sequentially for generating an error free data segment, and optimizing the error free data segment. The method further includes generating a pseudo code corresponding to the error free data segment and converting the pseudo code into at least one of the plurality of native languages.

Abstract: A method and system for securing data transmission in communication networks is disclosed. The method includes the steps of allocating a sequence ID (SQID) to each of a plurality of packets. The SQID is embedded in an Internet Protocol (IP) header of an associated packet from the plurality of packets. The method further includes grouping the plurality of packets into at least one cluster based on at least one of a distance amongst at least one IP attribute associated with destination address of each of the plurality of packets and variance in IP attributes associated with destination address of each of the plurality of packets. The method includes transmitting each of the at least one cluster to an associated destination address. Each cluster in the at least one cluster includes a set of packets from the plurality of packets and at least a domain-name is same in destination address.

Abstract: A system and method for auto resolution of errors during compilation of data segments is disclosed. The method includes the steps of detecting one or more errors to be resolved associated with a data segment, determining an error type for each of the one or more errors from predefined error types based on neuron activations and a code logic derived from the data segment, arranging the one or more errors in a sequence of resolving, predicting a solution for each of the one or more errors, resolving the one or more errors sequentially for generating an error free data segment, and optimizing the error free data segment. The method further includes generating a pseudo code corresponding to the error free data segment and converting the pseudo code into at least one of the plurality of native languages.

Abstract: A method and data summarization system for dynamically generating summarised content for visual and contextual text data, is disclosed. The method includes classifying plurality of dataset related to one or more domains based on datatype associated with each dataset. The datatype comprises text, numeric and visual data. Upon classification, one or more usable tokens are determined from the text data using a predefined token learning model. Further, one or more graphical parameters are determined from the visual data by using a pre-trained graphical model. Thereafter, based on the one or more usable tokens and the one or more graphical parameters, a summarized content is generated for the plurality of dataset.

Abstract: A method and device for determining operation of an autonomous device is disclosed. The method includes receiving pixel data and sound data associated with an environment at an instance of time, wherein the pixel data is received from least an image sensor associated with the autonomous device, and wherein the sound data is received from at least four sound sensors placed in a quadrilateral configuration on the autonomous device. Each quadrant of the pixel data is associated with each of the at least four sound sensors. The sound data received is mapped the to the matrix to identify one or more pixels in the matrix corresponding to the sound data based on a difference in amplitude between a first sound sensor of the at least four sound sensors recording maximum sound amplitude with a plurality of second sound sensors of the at least four sound sensors.

Abstract: This disclosure relates to method and system for annotating tokens for natural language processing (NLP). In one embodiment, the method may include segmenting a plurality of corpus based on each of a plurality of instances, deriving a plurality of entities for each of the plurality of instances based on at least one of a machine learning technique or a deep learning technique, determining a word vector for each of the plurality of entities associated with each of the plurality of instances, and labelling a plurality of tokens for each of the plurality of instances. It should be noted that the plurality of tokens associated with the plurality of entities may be identified based on a frequency of each of the plurality of entities.

Abstract: The method of present disclosure relates to neural chip and optimizing operation of a neural chip. The method includes sensing current values of physical parameters indicating an environment. Sensed current values are stored in a memory unit. The memory unit also stores previously sensed values of physical parameters. The current values and the previously sensed values are compared by the neural chip. Based on the comparison, one or more actions are applied using the previously sensed values, for completing the task, if the current values and the previously sensed values are matched. In case there is no matching, the neural chip uses the current valises for applying the one or more actions. The neural chip learns from applying of the actions and updates itself accordingly.

Abstract: A method and system for automating repetitive task on a user interface is disclosed. The method includes identifying a plurality of Document Object Model (DOM) elements from a repetitive pattern. The method further includes identifying a set of dynamic DOM elements from the plurality of DOM elements. The method further includes determining a path and a path position within the application code of the repetitive pattern for each of the set of dynamic DOM elements. The method further includes training an Artificial Intelligence (AI) model to identify the repetitive pattern and to identify the corresponding path and the path position within the application code component, for each of the set of dynamic DOM elements. The method further includes automatically executing a code for each of the set of dynamic DOM elements based on the associated path and path position.

Abstract: This disclosure relates to method and system for detecting and tracking at least one object in a rotational environment. The method includes receiving a set of first features based on first data and a set of second features based on second data, detecting at least one object based on the set of first features using a Convolutional Neural Network (CNN) based predictive model, determining a set of first parameters for the at least one object, detecting the at least one object based on the set of second features using the CNN based predictive model, determining a set of second parameters for the at least one object, and tracking the at least one object based on the set of first parameters and the set of second parameters. It should be noted that the first data and the second data sequentially belong to an input dataset that includes images or video frames.

Abstract: A method and system for securing data transmission in communication networks is disclosed. The method includes the steps of allocating a sequence ID (SQID) to each of a plurality of packets. The SQID is embedded in an Internet Protocol (IP) header of an associated packet from the plurality of packets. The method further includes grouping the plurality of packets into at least one cluster based on at least one of a distance amongst at least one IP attribute associated with destination address of each of the plurality of packets and variance in IP attributes associated with destination address of each of the plurality of packets. The method includes transmitting each of the at least one cluster to an associated destination address. Each cluster in the at least one cluster includes a set of packets from the plurality of packets and at least a domain-name is same in destination address.

Abstract: A method and data summarization system for dynamically generating summarised content for visual and contextual text data, is disclosed. The method includes classifying plurality of dataset related to one or more domains based on datatype associated with each dataset. The datatype comprises text, numeric and visual data. Upon classification, one or more usable tokens are determined from the text data using a predefined token learning model. Further, one or more graphical parameters are determined from the visual data by using a pre-trained graphical model. Thereafter, based on the one or more usable tokens and the one or more graphical parameters, a summarized content is generated for the plurality of dataset.

Abstract: The disclosure relates to an enhanced data security system and method thereof. In some embodiments, the method includes receiving the transactional credential dataset from a user application. The transactional credential dataset is provided by a user to the user application. The method further includes storing the transactional credential dataset in nodes of a graphical embedding storage model. The nodes further store historical credential datasets of the user. Further, the method includes determining a correlation among the historical credential datasets using an artificial neural network (ANN) model and detecting a pattern of the transactional credential dataset based on the correlation. The ANN model is trained based on credential datasets provided by users stored in the nodes of the graphical embedding storage model.

Abstract: Disclosed herein is method and system for managing storage space complexity in a storage unit. In an embodiment, operational parameters related to memory operations and storage parameters related to memory blocks of the storage unit are analyzed to estimate storage capacity of each of the memory blocks. Subsequently, the memory blocks are clustered into plurality of clusters based on the storage capacity. Further, one or more of the plurality of clusters are selected for performing future memory operations based on ranking of the plurality of clusters. In some embodiments, the present disclosure helps in dynamically managing storage space complexity in the storage unit and optimizes the storage space utilization. Also, the present disclosure automatically handles storage volumes, thereby reducing latency in memory backup operations and reducing amount of buffer/cache memory required.