Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: A method and system for measuring interest in a product. A processor operating on a server receives an identifier associated with a mobile computing device. The processor determines a location of the mobile computing device within a marketing environment and associates the location of the mobile computing device with a location of a product. The processor obtains interaction data indicative of an engagement of the mobile computing device with the product and determines a measure of interest in the product using the interaction data. Alternatively, data indicating an interaction of the mobile computing device with a product are received by the processor. The product location, which is known, is used to determine the location of the mobile computing device.

Abstract: Disclosed is a method for scheduling work orders where a user receives a request to schedule a work order, and then a labor amount value is assigned to the work order request. The work order is assigned a labor amount requirement, and the labor amount requirement is compared to a labor amount capacity for the requested days. Additionally the scheduler attempts to verify any essential information. Finally the work order is assigned an actionable for dispatch status.

Abstract: A system and method for a dispatching program and for its implementation, involves logging into a system, selecting or creating a work unit, and assigning work orders to the selected unit. After the work orders have been assigned the assignments are transmitted to each work crew.