A skills learning method for a student gathers objective data relating to the student in response to various stimuli, and produces a predicted feedback units as a function of the objective data using a machine learning-base classifier. The method can include training a neural network using objective data of student interactions and associated subjective assessments of a skill of each objective data. The method includes receiving a new dataset with objective data of a new student and an associated subjective assessment of a skill of the first student represented by the new objective data. A predicted assessment of the skill of the new objective data is calculated by inputting the new objective data into the neural network. The method can include updating the neural network by combining the initial dataset and the new dataset and recompiling the neural network to fit the model dataset based on a learning algorithm.

A system for managing innovation challenges includes a computing apparatus having a processor and a memory with computer readable program code, wherein the processor under control of the computer readable program code is configured to implement, a content management system that operates to automatically generate an innovation challenge for students based on a common template that includes student eligibility requirements, a user management system that operates to collect user information, obtains parental or guardian consent, and determines individual student eligibility for specific innovation challenges, a submission management system that operates to automate ingestion, display, storage, and judging of challenge entry submissions, and a dashboard display that operates to manage innovation challenge participation.

Embodiments of the present disclosure provide a method and apparatus for recommending a test question, and an intelligent device. The method includes: acquiring a plurality of skill entities of a post; calculating, according to the data of the post, a weight value of each of the plurality of skill entities; and acquiring, according to the weight value of each skill entity, a recommended test question of the post from a question bank.

A fleet vehicle management system includes an edge device disposed in a vehicle and a cloud device. A processing module of the edge device acquires around-view image and determines at least one traffic indication accordingly. The nearing event warning module of the edge device sends out a nearing event alert to a driver via an output module. The after event detection module of the edge device acquires GPS informations in a first time interval and a second time interval respectively to determine whether the vehicle meets an after event criterion. A violation event is triggered if the after event criterion is met. A database of the cloud device stores the nearing event alert, a driving violation result and a driving record information. The driver behavior analysis module generates an information for evaluating a driving score based on the nearing event, the violation event and the driving record information.

Systems and methods to connect care seekers with care providers for caretaking needs are disclosed. Exemplary implementations may: store seeker profiles for care seekers and/or provider profiles for care providers; present a user interface configured to facilitate generation of individual provider profiles; receive care seeker-defined characteristics of potential care providers; generate new individual provider profiles for individual potential care providers; and/or perform other operations.

Methods for optimizing the routing of customer communications include receiving a customer communication; identifying a customer associated with the customer communication; accessing a profile of the identified customer to determine customer data; receiving customer metric scores for a plurality of customer metrics; identifying available agents; accessing a profile of each available agent to determine agent data; predicting interaction outcome metric values for a plurality of customer metrics based on the customer data and the agent data; calculating, in real-time, an aggregate agent-customer pairing score for each available agent; selecting a responding agent from the available agents with the highest aggregate agent-customer pairing score; and providing a routing recommendation to a communication distributor to route the customer communication to the responding agent with the highest aggregate agent-customer pairing score.

An information handling system may receive a request from a user for expert assistance associated with a gaming application. The information handling system may determine a recommendation of one or more experts from a plurality of available experts based, at least in part, on the gaming application and one or more gaming characteristics of each of the plurality of available experts. The information handling system may initiate a gaming assistance session between the user and at least one of the determined experts.

A processor on an industrial vehicle is programmed to identify a metric that characterizes an event associated with an operator of the industrial vehicle, where the metric has a performance parameter to evaluate against the event, and a behavior modification action that defines a desired operator behavior in response to the event. The processor is further operatively programmed to monitor for the event, and record event data that characterizes a response of the vehicle operator to the event. In response to detecting an event, the processor evaluates the recorded event data against the performance parameter to determine whether the vehicle operator demonstrated appropriate behavior for the event, computes a vehicle operator score based upon the evaluation, and performs the behavior modification action where the operator has not been inculcated to respond to the event.

Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

A computerized method for providing a sentiment score by evaluating expressions of participants during a video meeting is provided herein. The computerized method comprising: a Sentiment Analysis (SA) module. The SA module is: (i) retrieving one or more recordings of a video meeting from the database of video meeting recordings of each participant in the video meeting and associating the one or more recordings with a participant; (ii) dividing each retrieved recording into segments; (iii) processing the segments in a Facial Expression Recognition (FER) system to associate each segment with a timestamped sequence of expressions for each participant in the video meeting; and (iv) processing each segment in an Artificial Neural Network (ANN) having a dense layer, by applying a prebuilt and pretrained deep learning model, to yield a sentiment score for each statement for each participant.