Abstract: In a process of inferential analysis and reporting of contextual complaints data, a social media message is processed to formulate an input data for a neural network, The input data is processed to chunk and identify one or more entities of interest to an enterprise, wherein the enterprise includes one or more line of business (LOB). The identified entities of interest are processed by a plurality of neural nodes in the neural network to identify a specific set of LOB(s) that are impacted by the social media message. With the entities of interest as input to the neural network, corresponding to a plurality of parameters associated with an enterprise, output from the individual neural nodes is dynamically determined. The output from the plurality of neural nodes is collated to generate a report with action items specific to the set of LOB(s) to take pre-emptive actions in the enterprise.

Abstract: In a process of inferential analysis and reporting of contextual complaints data, a social media message is processed to formulate an input data for a neural network, The input data is processed to chunk and identify one or more entities of interest to an enterprise, wherein the enterprise includes one or more line of business (LOB). The identified entities of interest are processed by a plurality of neural nodes in the neural network to identify a specific set of LOB(s) that are impacted by the social media message. With the entities of interest as input to the neural network, corresponding to a plurality of parameters associated with an enterprise, output from the individual neural nodes is dynamically determined. The output from the plurality of neural nodes is collated to generate a report with action items specific to the set of LOB(s) to take pre-emptive actions in the enterprise.

Abstract: A method includes generating a nominal dose distribution based on an image and clinical goals. The method further includes generating a setup error dose distribution based on range and setup uncertainties. The method further includes generating a dose distribution for a parameter of an internal organ. The method further includes optimizing a planned dose distribution of an intensity modulated proton therapy plan by minimizing a total objective value including the nominal dose distribution, the setup error dose distribution dose distribution, and the dose distribution for the internal organ. The method further includes generating a final dose distribution for the intensity modulated proton therapy plan based on beam parameters of the optimized planned dose distribution. The method further includes controlling a proton therapy apparatus configured to deliver proton therapy based on the intensity modulated proton therapy plan with the optimized planned dose distribution.

Abstract: A method includes generating a nominal dose distribution based on an image and clinical goals. The method further includes generating a setup error dose distribution based on range and setup uncertainties. The method further includes generating a dose distribution for a parameter of an internal organ. The method further includes optimizing a planned dose distribution of an intensity modulated proton therapy plan by minimizing a total objective value including the nominal dose distribution, the setup error dose distribution dose distribution, and the dose distribution for the internal organ. The method further includes generating a final dose distribution for the intensity modulated proton therapy plan based on beam parameters of the optimized planned dose distribution. The method further includes controlling a proton therapy apparatus configured to deliver proton therapy based on the intensity modulated proton therapy plan with the optimized planned dose distribution.