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: 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 present disclosure relates to system(s) and method(s) for data security using a blockchain ledger. The system (102) receives request associated with a product from a user. The system (102) further obtains data associated with the product upon receiving the request. Further, the system (102) analyses the data to using predefined parameters identify valid data and invalid data. Upon identification, the system (102) uploads the valid data in the blockchain ledger (108). Further, the valid data may be displayed to the user through a channel (110), associated with the user, in the blockchain ledger (108), thereby providing the data security.

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: Disclosed is a system to perform parallel processing on a distributed dataset. A receiving module, for receiving a dataset along with a set of functions. A partitioning module, for partitioning the dataset into a set of distributed datasets. A distributing module, for distributing the set of distributed datasets amongst a set of computing nodes. A determining module, for determining an applicability of the function on the distributed dataset. An executing module, for executing one or more functions applicable on the distributed dataset. A generating module, for generating processed data for the distributed dataset based upon the executing of the one or more functions.

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: Disclosed are systems and methods for optimizing power consumption of devices. The system includes monitoring module, generating module, matching module, determining module, and identifying module. The monitoring module monitors a device including program code which further includes power consuming functions. The generating module generates plurality of power patterns corresponding to the power consuming functions. The matching module matches the plurality of power patterns with pre-stored plurality of power patterns to identify one or more power patterns having maximum peak value. The determining module determines occurrence of the one or more power patterns for predefined time interval. The identifying module identifies a power consuming function corresponding to a power pattern based on the occurrence. The generating module generates recommendation for the power consuming function by suggesting changes in a code section of the power consuming function.

Abstract: Disclosed are systems and methods for optimizing power consumption of devices. The system includes monitoring module, generating module, matching module, determining module, and identifying module. The monitoring module monitors a device including program code which further includes power consuming functions. The generating module generates plurality of power patterns corresponding to the power consuming functions. The matching module matches the plurality of power patterns with pre-stored plurality of power patterns to identify one or more power patterns having maximum peak value. The determining module determines occurrence of the one or more power patterns for predefined time interval. The identifying module identifies a power consuming function corresponding to a power pattern based on the occurrence. The generating module generates recommendation for the power consuming function by suggesting changes in a code section of the power consuming function.