A method includes receiving a data set including medical information of respective patients, respective types of a clinical procedure performed on the patients, and respective survival rates of the patients. A survival tree graph is generated by maximizing a cost function of differences in the survival rates between the types of the clinical treatment procedure. A type of the clinical procedure is selected for a given patient, based on the survival tree graph.

To automatically evaluate the reasonable price of real estate according to the housing data, the present invention discloses a novel intelligent property evaluation system. The system includes the following components: a housing data input system, a pre-processing filter, a feature extractor, a housing price trainer, and a housing price predictor, wherein the housing price predictor further includes a regression model generator and a decision integrator. The pre-processing filter is used to filter unreasonable samples from housing data and integrate synonymous features. The feature extractor is used to choose required variables of housing price model. The housing price trainer generates housing price model which is trained by a great amount of housing data. The housing price predictor then generates a prediction by the trained model. Furthermore, to maintain the accuracy of prediction under the social evolution, the housing price predictor could be regularly or irregularly updated by a rolling-based method.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.

A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features. The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.

The present invention relates to a system with active learning and execution of user's preference functionalities for use in a garment. The present system includes a sensor module, an optional user input panel and/or interface, a printed circuit board, a power source and an output. In an event that a user of the present system voluntarily changes the output setting during the operation of the system, the system performs an active learning action to execute the output setting initiated by the user over a passive learning action triggered by a change in sensor data with respect to the changing environment. In other event, the present system performs passive learning action with respect to the changing environment and also any comparative data from similar user of a particular instance. The present invention also relates to a power management unit and how to use the same in a garment.

A database comprising data associated with one or more routes is maintained. The data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes. In response to receiving a query from a given computing device, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool are predicted. One or more routes for the given user to traverse are selected based at least in part on the predicted amounts of physical exertion. One or more contextual factors of the given user are estimated to at least one of optimize and prioritize the selected one or more routes for the given user based on analyzing user data.

A method for use in a computing device, comprising: receiving an invite for a communications session; obtaining context information associated with the invite; generating a signature for the invite based on the context information; generating an attendance score for the invite by evaluating a neural network based on the signature for the invite, the attendance score being an estimate of a degree of importance of attending the communications session; generating a response to the invite based on the attendance score, the response indicating whether a user of the computing device accepts or rejects the invite; and transmitting the response to a sender of the invite.

Improved mechanisms for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, atmospheric liquid water content, or precipitable water and other atmospheric-based phenomena, for example presence and intensity of fog, based upon these sensor readings, predicting future precipitation and atmospheric-based phenomena, and estimating effects of the atmospheric-based phenomena on visibility, for example by calculating runway visible range (RVR) estimates and forecasts based on the atmospheric-based phenomena.

Techniques are described herein for enabling autonomous agents to generate conclusive answers. An example of a conclusive answer is text that addresses concerns of a user who is interacting with an autonomous agent. For example, an autonomous agent interacts with a user device, answering user utterances, for example questions or concerns. Based on the interactions, the autonomous agent determines that a conclusive answer is appropriate. The autonomous agent formulates the conclusive answer, which addresses multiple user utterances. The conclusive answer provided to the user device.