Abstract: The present disclosure relates to system(s) and method(s) for generating an Omni-channel support platform. The method comprises integrating a multi-channel support system with a blockchain framework. Further, the method comprises generating an Omni-channel support platform based on the integration. The Omni-channel support platform comprises an Omni-channel support block for a user from a set of users. The Omni-channel support block comprises a support ledger and a support smart contract for the user. The Omni-channel support block further comprises capturing transaction data associated with the user from the multiple support channels. Further, the Omni-channel support block comprises recommending one or more resolutions to each user upon based on a support request.

Abstract: The disclosure relates to method and system recommending tuning of parameters to generate a data analytics model. The method includes identifying at a data pre-processing stage a pre-processing subset from an associated set of predefined pre-processing methods for a predefined objective. The method includes identifying at a feature selection stage a feature subset from an associated set of predefined feature selection methods for the predefined objective. The method includes identifying at a model training stage a training subset from an associated set of predefined model training methods for the predefined objective. The method further includes generating a plurality of data analytics tuples and selecting a data analytics tuple from the plurality of data analytics tuples. An output result of the data analytics tuple includes highest ranked results for the predefined objective.

Abstract: The invention relates to method and system for providing visual explanations for image analytics decisions. The method includes extracting a set of local features from each of a plurality of image instances using a deep learning (DL) model; determining a feature list by aggregating the set of local features from each of the plurality of image instances; generating a two-dimensional (2D) pixel map based on the feature list; superimposing the 2D pixel map of aggregated features on each of the plurality of image instances; and providing a visual explanation for an image analytics decision on one or more of the plurality of image instances based on superimposition.

Abstract: A method for automatically generating codes to operate Machine Learning (ML) models disclosed. In some embodiments, the method includes operating, for each of a plurality of stages associated with ML model operations, at least one ML model. To operate each of the at least one ML model, the method may include selecting, for each of the at least one ML model at each of the plurality of stages, a predefined technique associated with a current stage; generating, for each of the at least one ML model at each of the plurality of stages, a code based on the selected predefined technique; generating, for each of the at least one ML model at each of the plurality of stages, a dependency configuration and an environment configuration for the generated code; and generating, for each of the at least one ML model, a pipeline configuration for execution of associated codes.

Abstract: This disclosure relates to a privacy preserving geofencing system and method thereof. The method includes receiving a current location data of a vehicle. The current location data may 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 may determine if the vehicle is within a predefined geofencing region based on one or more geofencing criteria.

Abstract: The present invention relates to a stable pharmaceutical composition of tetrofosmin or pharmaceutically acceptable salts thereof. It also relates to a lyophilized non-radioactive kit which upon reconstitution with 99mTc-pertechnetate solution gives a stable 99mTc-tetrofosmin radiopharmaceutical composition. It also provides process for the preparation of said radiopharmaceutical compositions and their use in diagnostic imaging procedures. The compositions provide desirable technical attributes such as stability, high radiochemical purity (RCP) and desired bio-distribution.

Abstract: The present invention relates to a stable pharmaceutical composition of tetrofosmin or pharmaceutically acceptable salts thereof. It also relates to a lyophilized non-radioactive kit which upon reconstitution with 99mTc-pertechnetate solution gives a stable 99mTc-tetrofosmin radiopharmaceutical composition. It also provides process for the preparation of said radiopharmaceutical compositions and their use in diagnostic imaging procedures. The compositions provide desirable technical attributes such as stability, high radiochemical purity (RCP) and desired bio-distribution.

Abstract: The present disclosure relates to system(s) and method(s) for tuning an analytical model. The system builds a global analytical model based on modelling data received from a user. Further, the system analyses a target eco-system to identify a set of target eco-system parameters. The system further selects a sub-set of model parameters, corresponding to the set of target eco-system parameters, from a set of model parameters. Further, the system generates a local analytical model based on updating the global analytical model, based on the sub-set of model parameters and one or more PMML wrappers. The system further deploys the local analytical model at each node, from a set of nodes, associated with the target eco-system. Further, the system gathers test results from each node based on executing the local analytical model. The system further tunes the sub-set of model parameters associated with the local analytical model using federated learning algorithms.

Abstract: The described technology is generally directed towards proactive content placement for low latency mobile access. Digital content requested by a mobile device can be sent to network nodes proactively, so that the network nodes have the digital content before it is requested by the mobile device. Mobile device travel predictions can be made to predict future locations of the mobile device. The future locations can be used to determine network nodes for proactive digital content delivery. The digital content for delivery to a network node can also be predicted based on current digital content in use at the mobile device and estimated arrival times of the mobile device into service areas of next network nodes.

Abstract: Systems and methods for data security using a blockchain ledger. The system receives request associated with a product from a user. The system further obtains data associated with the product upon receiving the request. Further, the system analyses the data to using predefined parameters identify valid data and invalid data. Upon identification, the system uploads the valid data in the blockchain ledger. Further, the valid data may be displayed to the user through a channel, associated with the user, in the blockchain ledger, thereby providing the data security.

Abstract: The described technology is generally directed towards proactive content placement for low latency mobile access. Digital content requested by a mobile device can be sent to network nodes proactively, so that the network nodes have the digital content before it is requested by the mobile device. Mobile device travel predictions can be made to predict future locations of the mobile device. The future locations can be used to determine network nodes for proactive digital content delivery. The digital content for delivery to a network node can also be predicted based on current digital content in use at the mobile device and estimated arrival times of the mobile device into service areas of next network nodes.

Abstract: The present disclosure relates to system(s) and method(s) to generate alerts while monitoring a machine learning model in real time. The system is configured to receive, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model. The system is further configured to receive, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model. Further, the system is configured to compare the first output parameter with the second output parameter and the first input parameter with the second input parameter to generate tuning and rebuilding alerts.

Abstract: The invention relates to method and system for providing visual explanations for image analytics decisions. The method includes extracting a set of local features from each of a plurality of image instances using a deep learning (DL) model; determining a feature list by aggregating the set of local features from each of the plurality of image instances; generating a two-dimensional (2D) pixel map based on the feature list; superimposing the 2D pixel map of aggregated features on each of the plurality of image instances; and providing a visual explanation for an image analytics decision on one or more of the plurality of image instances based on superimposition.

Abstract: The disclosure relates to method and system recommending tuning of parameters to generate a data analytics model. The method includes identifying at a data pre-processing stage a pre-processing subset from an associated set of predefined pre-processing methods for a predefined objective. The method includes identifying at a feature selection stage a feature subset from an associated set of predefined feature selection methods for the predefined objective. The method includes identifying at a model training stage a training subset from an associated set of predefined model training methods for the predefined objective. The method further includes generating a plurality of data analytics tuples and selecting a data analytics tuple from the plurality of data analytics tuples. An output result of the data analytics tuple includes highest ranked results for the predefined objective.

Abstract: Techniques for secure mobile device recognition are disclosed. An IOT edge device determines, based on a network message received at the IOT edge device, that a mobile device is not recognized. The IOT edge device transmits a token request to the mobile device. In response, the IOT edge device receives an encrypted token from the mobile device. The IOT edge device transmits the encrypted token to a server. The server is configured to determine an identifier corresponding with the mobile device, based on the encrypted token. A recognition task is initiated for the mobile device, based on the determined identifier.

Abstract: The present subject matter discloses a system and method to enable a machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stakeholders to generate and manage a model for predictive analysis. The method may further comprise enabling a business user to define the business problem, and generate models to perform predictive analysis. The method may further comprise deploying the model, in a distributed environment, over a target platform. The method may further comprise monitoring the model to identify at least one error in the model and re-training the model for performing predictive analysis based on the at least one error, thereby enabling the machine learning based analytics platform.

Abstract: The described technology is generally directed towards proactive content placement for low latency mobile access. Digital content requested by a mobile device can be sent to network nodes proactively, so that the network nodes have the digital content before it is requested by the mobile device. Mobile device travel predictions can be made to predict future locations of the mobile device. The future locations can be used to determine network nodes for proactive digital content delivery. The digital content for delivery to a network node can also be predicted based on current digital content in use at the mobile device and estimated arrival times of the mobile device into service areas of next network nodes.

Abstract: The present disclosure relates to system(s) and method(s) for generating an Omni-channel support platform. The method comprises integrating a multi-channel support system with a blockchain framework. Further, the method comprises generating an Omni-channel support platform based on the integration. The Omni-channel support platform comprises an Omni-channel support block for a user from a set of users. The Omni-channel support block comprises a support ledger and a support smart contract for the user. The Omni-channel support block further comprises capturing transaction data associated with the user from the multiple support channels. Further, the Omni-channel support block comprises recommending one or more resolutions to each user upon based on a support request.

Abstract: The embodiments herein relate to operational data analysis (ODA) and, more particularly to automate operational data analysis and generate the analysis report for various products using a web-based multi-tenant product intelligence framework. The system allows the user to configure a data collection process, define schema structure, select a data storage for storing the collected data, select or create a data formatting algorithm, and generate a data report to perform the ODA process. Based on the ODA report, appropriate decisions can be taken by an organization.

Abstract: Techniques for secure mobile device recognition are disclosed. An IOT edge device determines, based on a network message received at the IOT edge device, that a mobile device is not recognized. The IOT edge device transmits a token request to the mobile device. In response, the IOT edge device receives an encrypted token from the mobile device. The IOT edge device transmits the encrypted token to a server. The server is configured to determine an identifier corresponding with the mobile device, based on the encrypted token. A recognition task is initiated for the mobile device, based on the determined identifier.