Abstract: One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction.

Abstract: A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.

Abstract: A health-monitoring method, system, and computer program product include operating at least one sensor of a health-monitoring device having a plurality of sensors, detecting a health condition event that requires operation of an additional sensor of the plurality of sensors to monitor the health condition event, activating the additional sensor of the health-monitoring device, and deactivating the additional sensor when the health condition event that requires the second sensor is no longer detected by the detecting.

Abstract: A computer-implemented method includes receiving an input, from a user, in the form of a destination to be navigated to; calculating a route to the destination based on the input; recognizing at least one object on a route taken by the user; processing data from the received input, the calculated route, and the recognized at least one object; and providing an output to the user based on the recognized at least one object, the output being based on natural language processing.

Abstract: A health-monitoring method, system, and computer program product include operating at least one sensor of a health-monitoring device having a plurality of sensors, detecting a health condition event that requires operation of an additional sensor of the plurality of sensors to monitor the health condition event, activating the additional sensor of the health-monitoring device, and deactivating the additional sensor when the health condition event that requires the second sensor is no longer detected by the detecting.

Abstract: A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.

Abstract: A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Abstract: One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction.

Abstract: A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Abstract: A computer-implemented method includes receiving an input, from a user, in the form of a destination to be navigated to; calculating a route to the destination based on the input; recognizing at least one object on a route taken by the user; processing data from the received input, the calculated route, and the recognized at least one object; and providing an output to the user based on the recognized at least one object, the output being based on natural language processing.

Abstract: Automatic scaling is performed on a floating point implementation of a DNN to perform scaling to a fixed point implementation. The DNN includes multiple layers in an order from a starting to an ending layer. The automatic scaling includes determining a scaling factor for each of multiple ones of the layers during training of the DNN. The scaling factor converts floating point numbers used for calculations in a layer into integer numbers to be used in the calculations. A scaling factor is determined for a selected layer, which is at a position in the order, based on scaling factors used in layers in the order prior to the position of the selected layer. The automatic scaling includes outputting the scaling factors for the multiple layers to be used for implementing the fixed point implementation of the DNN that uses integer calculations instead of floating point calculations.

Abstract: To evaluate impact to a brand on social media, a computer is used to crawl through social media postings of social media services to select postings relating to one or more sponsored events. The selected postings are analyzed by computer to quantify keywords relating to particular brands. Images posted within the selected postings are also analyzed, using a pattern matching algorithm, to quantify depictions of the one or more brands within the images.