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) 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 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 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 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: System and method for generating professional profile of an employee in an organization are disclosed. An organizational ontology may be built for the employee. The organizational ontology may be indicative of interrelationships between the employee, other employees and tasks performed by the employee. Further, email data associated with the employee may be stored for a complete lifecycle of the employee. The email data may be analyzed to identify insights about the employee and the professional profile may be generated based on the identified insights.

Abstract: The system and method for secure sharing of a source code is disclosed. The method comprises. The method comprises receiving a source code associated with an application, and a defect identification number associated with a defective feature in the application. The method comprises identifying a defective feature of an application and non-defective features of the application based on a defect investigation methodology. The method comprises generating a checkout ID associated with the application and further generates a secured second set of source code based on one of an encryption methodology and an obfuscating methodology. The method further comprises providing a secured source code to a third party for debugging the defective feature in the application.

Abstract: Disclosed is a system for providing source code suggestion to a user in real-time. A mining module mines source code information, pre-stored in a source code repository, to create a reference model. A code receiving module receives input lines of code from a user, via a User Interface, in real-time. A mapping module maps the one or more parameters with the metadata corresponding to each source code block stored in the reference model. A code suggestion module identifies one or more target source code blocks from the plurality of source code blocks. The code suggestion module suggests at least one target source code block, of the one or more target source code blocks, to be populated in the input lines of code thereby providing the source code suggestion to a user in real-time.

Abstract: The present disclosure relates to system(s) and method(s) for allocating human resources based on bio inspired models. The system receives primary data associated with a product under development. The primary data may comprise a set of tasks associated with the product under development. Further, the system identifies a sub-set of human resources, from a set of human resources, maintained at a human resource database, based on a resource identification algorithm. Furthermore, the system extracts secondary human resource data associated with the sub-set of human resources from the human resource database. The system further allocates one or more human resources from the sub-set of human resources to each task from the set of tasks based on a resource allocation algorithm. The resource allocation algorithm is configured to analyse the secondary human resource data and the primary data to allocate the one or more human resources.

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 present disclosure discloses system and method for evaluating a human resource in a software development environment. At first, historical performance data and profile data associated with a plurality of human resources involved in a software project is received. From such data (historical performance data and profile data), a plurality of attributes is extracted. Further, Bayesian classification technique is implemented on the plurality of attributes in order to classify the plurality of attributes, of each human resource, into a plurality of classes. The plurality of attributes is classified in such a manner that at least one attribute corresponding to at least one human resource and at least one other human resource is classified into a class and another class respectively. Further, based on the classification of each attribute associated with the human resource, a grade is assigned to the human resource.

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 is a method and system for classifying test cases. In one implementation, the method comprises creating a test step master list comprising a plurality of test case, one more test step associated with the plurality of test case, and a test step identification number associated with the one more test step. Further, the method comprises generating a sequence diagram for each of the plurality of test cases based on the test step master list. Furthermore, the method comprises classifying, by the processor, each of the plurality of test cases in to an independent test case or an asynchronous test case or a synchronous test case based on the sequence diagram.

Abstract: The present subject matter discloses system 102 and method to enable machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stake holders 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 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 enable machine learning based analytics platform.

Abstract: The system and method for secure sharing of a source code is disclosed. The method comprises. The method comprises receiving a source code associated with an application, and a defect identification number associated with a defective feature in the application. The method comprises identifying a defective feature of an application and non-defective features of the application based on a defect investigation methodology. The method comprises generating a checkout ID associated with the application and further generates a secured second set of source code based on one of an encryption methodology and an obfuscating methodology. The method further comprises providing a secured source code to a third party for debugging the defective feature in the application.

Abstract: The present disclosure relates to system(s) and method(s) for optimizing a resource allocation plan corresponding to a legacy software product. The system is configured to identify data pertaining to a set of entities, wherein each entity corresponds to a legacy software product. Further the system is configured to receive one or more user defined constraints, social analytics data, and historical data corresponding to each entity. Further the system is configured to analyze the historical data based on defined ontology to generate synthetic data corresponding to the set of entities. Further the system is configured to generate a simulation model to simulate the legacy software product support scenarios based on the ontology and the synthetic data. Further the system is configured to optimize the resource allocation plan based on the one or more factors, the one or more constraints and the social analytics data.

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.