Abstract: In an example embodiment, machine learning is utilized to automatically identify pain points of an entity. More particularly, a random forest search algorithm receives inputs from a number of different outside-in data engines. These inputs are segregated in terms of key performance indexes (KPIs), which is then fed to a random forest decision tree. The random forest search algorithm calculates a p-value for each KPI. This p-value may then be used to identify the most relevant KPIs (such as by ranking the KPIs by their respective p-values). Rules are utilized not to identify the pain points themselves, but instead to define sorting preferences (refine the ranking). Based on a combination of the p-values and the evaluation of the rules, the top-N pain points may be identified.

Abstract: In an example embodiment, machine learning is utilized to automatically identify pain points of an entity. More particularly, a random forest search algorithm receives inputs from a number of different outside-in data engines. These inputs are segregated in terms of key performance indexes (KPIs), which is then fed to a random forest decision tree. The random forest search algorithm calculates a p-value for each KPI. This p-value may then be used to identify the most relevant KPIs (such as by ranking the KPIs by their respective p-values). Rules are utilized not to identify the pain points themselves, but instead to define sorting preferences (refine the ranking). Based on a combination of the p-values and the evaluation of the rules, the top-N pain points may be identified.

Abstract: Embodiments allow rapid prediction of the impact of media data upon brand worth. One cloud service crawls service providers (e.g., TWITTER, FACEBOOK, blogging services) and provides sentiment analysis of internet feeds. Another cloud service may have pre-populated knowledge of an internal organization chart, in order to focus upon feeds relating to employees. Yet another machine learning (ML) service may predict an impact of the media data upon brand worth. Data models of the ML service can consider factors such as: a source of the information, a particular publisher sharing the news, a time since the news was published, and/or a specific individual associated with the news. An output identifier could be a severity index, the sentiment (e.g., positive or negative), financial impact trends, the time to react, and others. Following testing of the data model and the training data, embodiments may predict the impact of a future media communication.

Abstract: A gesture recognition apparatus includes a sensor unit configured to be worn by a user on a user's body part. The sensor unit includes one or more sensors, a processor unit, the processor unit and the sensor unit are arranged in communication with each other, the processor unit receiving acoustic data from the one or more sensors, wherein the acoustic data corresponds to a gesture performed by a user, and the processor unit is configured to process the acoustic data received from the one or more sensors to determine a gesture performed by the user.

Abstract: Various embodiments of systems, computer program products, and methods to provide centralized taxation evaluation are described herein. In an aspect, a request is received to determine taxation information of a user corresponding to a time interval. Baseline data corresponding to the taxation information is automatically obtained from sources associated with the time interval and carried forward taxation information. Further, the taxation information including liability owed by the user and/or refund owed to the user based on the obtained baseline data, carried forward taxation information and corresponding taxation rules is determined. The determined taxation information is provided to the user for taxation evaluation using a monitor widget displayed on a display device of a computing system.

Abstract: A gesture recognition apparatus includes a sensor unit configured to be worn by a user on a user's body part. The sensor unit includes one or more sensors, a processor unit, the processor unit and the sensor unit are arranged in communication with each other, the processor unit receiving acoustic data from the one or more sensors, wherein the acoustic data corresponds to a gesture performed by a user, and the processor unit is configured to process the acoustic data received from the one or more sensors to determine a gesture performed by the user.