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: Existing machine learning systems require historical data to perform analytics to detect faults in a machine and are unable to detect new types of faults/changes occurring in real time. These systems further fail to identify operation changes due to sensor drift and forget past events that have occurred. Present application provides systems and methods for identifying and classifying sensor drifts and diverse varying operational conditions from continually received sensor data using continual training of variational autoencoders (VAE) following drift specific characteristics, wherein sensor drift is compensated based on identified changes in sensors and degradation in machine(s).

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.