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 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: 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 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 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 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.

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: System and method for analyzing timesheet data to derive serendipitous insights are disclosed. Timesheet data for a plurality of employees working on multiple projects may be stored. The timesheet data may be analyzed to generate project insights associated with project tasks, for each of the plurality of employees. Priority scores for each of the project tasks may be calculated based on the project insights. Further, a dependency factor of the project task with respect to an outcome of the project may be evaluated.

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 are 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: The present disclosure discloses an adaptive wearable smart fabric. The adaptive wearable smart fabric may comprise sensors being accelerometer sensor, load sensor and pulse sensor. The accelerometer sensor and load sensor are adapted to sense the posture data and movement data of the user of an adaptive wearable smart fabric. The sensors are coupled with microcontroller that captures the sensed data and determines the posture based on analytic model. The microcontroller may be further coupled with PID controller and air pump which may inflate and deflate the air diaphragm placed within the fabric. The inflation and deflation of air diaphragm is dynamically controlled to provide comfort to the user of an adaptive wearable smart fabric.

Abstract: Disclosed is a method and system for providing a context aware suggestion. In one aspect, the method comprises generating one or more hash indexes associated with a primary user based on primary user data and generating a hash matrix associated to the primary user based on the primary user data, one or more secondary users associated to the primary user data, and the one or more hash indexes. The method further comprises, developing a master list based on the one or more hash indexes and creating a primary user persona associated to the primary user based on the master list and organization data. The method furthermore comprise providing a context aware suggestion to the primary user in response to a text input from the primary user, wherein the context aware suggestion is based on the primary user persona and the hash matrix.

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: Processing SMSs to provide useful, contextual and consolidated (where meaningful) information to a user. The method and user device relate to wireless communication using a user device, and more particularly to the user device processing information present in a SMS (Short Messaging Service) received by a user to present consolidated information to the user. Context of similar messages is derived where user has not specified using text processing techniques.