109 results about "Learning analytics" patented technology

Systems and methods for automating data science machine learning using analytical workflows are disclosed that provide for user interaction and iterative analysis including automated suggestions based on at least one analysis of a dataset.

An experiential digitalized multi-screen seamless cross-media interactive opening teaching laboratory is integrated in testing, researching and analyzing. Experiment and data analysis are performed in a real teaching environment; under support of the multi-screen interactive technology, the laboratory comprises a laboratory functional partition, an operation support system, a data working system, an experiment information acquisition system and an audio and video input and output device; a screen jilting function among multiple mobile terminals is realized; the data working system comprises a server, a database, education resource cloud, a U-teaching system, a learning analysis and evaluation system, a mobile device, a cross-screen management module, a recording and broadcasting system and an Internet; learning space for cross-media interactive learning is provided, technologies of holographic imaging, multi-screen interaction, learning analysis and the like are integrated, and seamless fusion of the physical space and the virtual space is realized; seamless fusion of supporting technologies from formal learning to informal learning, multiple learning modes, cross-terminal, cross-media and the like is realized, and good learning experience is provided for learners.

A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface.

A mechanism for facilitating personalized learning. A computer receives graded response data including grades that have been assigned to answers provided by learners in response to a set of questions. Output data is computed based on the graded response data using a latent factor model. The output data includes at least: an association matrix that defines a set of K concepts implicit in the set of questions, wherein K is smaller than the number of questions, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the questions; and a learner knowledge matrix including, for each learner and each of the K concepts, an extent of the learner's knowledge of the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the learner knowledge matrix.

The invention discloses a method for positioning a region of interest based on a convolutional neural network significance graph. The method comprises the following five steps of marking a sample, training a deep convolutional neural network model until the convergence is realized, extracting a significance graph, generating a positioning atlas for a focus, and positioning the focus. According to the technical scheme of the invention, based on the combination of a qualitative-grade calibration sample, a convergent deep convolutional neural network model and a significance graph, the intelligent learn and analysis based on medical image big data can be realized. In this way, the region of interest having a particular attribute in a medical image can be positioned. The method reduces the workload and the cost of calibration samples. Meanwhile, the method is capable of efficiently and accurately positioning the focus position, so as to facilitate doctors to make diagnosis and treatment on medical images.

To address problems that video imaging systems and platforms face when analyzing image and video content for detection and feature extraction, a solution is provided in which accumulating significant amounts of data suitable for training and learning analytics is leveraged to improve over time, the classifiers used to perform the detection and feature extraction, by employing a larger search space and generate additional and more complex classifiers through distributed processing. A distributed learning platform is therefore provided, which is configured for operating on large scale data, in a true big data paradigm. The learning platform is operable to empirically estimate a set of optimal feature vectors and a set of discriminant functions using a parallelizable learning algorithm. A method of adding new data into a database utilized by such a learning platform is also provided. The method comprises identifying an unrepresented sample space; determining new data samples associated with the unrepresented sample space; and adding the new data samples to the database.

Embodiments of the present invention provide a system for estimating a location of a gas leak, based on machine learning from forward gas concentration data provided by an analog or scale model including a gas source. The system improves significantly over previous systems by providing high quality, physically accurate forward modeling data inexpensively. During operation, the system configures an aerosol source at a first location to emit a gaseous aerosol. The system then configures a laser source to illuminate the aerosol with a laser sheet. The system may then obtain an image of a reflection of the laser sheet from the aerosol. The system may then analyze the image to quantify a three-dimensional concentration distribution of the aerosol. The system may then estimate, based on solving an inverse problem and an observed second gas concentration, a second location of a second gas source.

System, method, and computer program product for researching reviews written online to assess the performance and functionality of digital media consumer products bought online or not (e.g. eBooks, movies, TV shows, music, DVD's, etc.). The system extracts reviews from multiple online sources comprising: online “stores”, professional articles, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each product's reviews, comprising: a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. performance, price, etc.). Additionally, the user may view a separate review detail page per product that provides further summaries, such as a short list of other products that the same reviewer gave a very positive review for the features. The user is then able to purchase the product via a link.

The invention discloses a system based on expert technical and the financial analysis of nonlinear systems and methods. The system and method use of a powerful database system as a background, through the use of expert knowledge base, analysis of nonlinear self-learning algorithm, national industry standards trinity value of the diagnostic methods intelligent enterprise management, enterprise three financial statements (income statement, balance sheet, cash-flow) data for capital and solvency, and financial risk warning, decision-making financial forecasts, management and optimization, investment decision-making, decision-making financing, liquidity management, and other aspects of the comprehensive analysis, and automatically generate detailed corporate financial analysis report. This invention changed the financial management of enterprises rely entirely on professional advice and financial analysis manual analysis of the characteristics of the system and methodology adopted for enterprises to provide professional, efficient financial analysis services, thus greatly enhance the enterprise's financial analysis of the quality and efficiency , and to avoid the man-made factors on the financial analysis of the results of the adverse impact.

Method and devices for a medication adherence platform including machine-learning analytics platform, and real-time pharmaceutical and consumer product fulfillment platform are provided. A device can comprise a sensor for sensing a medicine container or medicine, a database for storing patient related data, a computer readable medium for storing a patient treatment calendar, causing a patient's electronic device to transmit an alert based upon an event logged onto the patient treatment calendar determine medication adherence, storing data in the database, transmitting treatment-based information to the patient's device, and establishing an electronic communication channel between the patient and a healthcare professional.

The disclosure describes a sensor system that provides end users with intelligent sensing capabilities, and embodies both crowd sourcing and machine learning together. Further, a sporadic crowd assessment is used to ensure continued sensor accuracy when the system is relying on machine learning analysis. This sensor approach requires minimal and non-permanent sensor installation by utilizing any device with a camera as a sensor host, and provides human-centered and actionable sensor output.

The present invention provides a system and method for analyzing sensor data related to an irrigation system. According to a preferred embodiment, the system includes algorithms for analyzing real-time, near real-time and historical data acquired from sensors in communication with a mechanized irrigation machine. Further, the algorithms of the present invention system may analyze collected sensor data to determine if an event has occurred or is predicted to occur. Further, the algorithms of the present invention may provide commands to an irrigation machine and notifications to users. According to further aspects of the present invention, the algorithms of the present invention may preferably apply machine learning and other data analysis tools to detect maintenance patterns, geographic trends, environmental trends, and to provide predictive analysis for future events.

A system, method, and computer program product for researching online reviews to assess the performance and functionality of digital media consumer products bought online or not (e.g. eBooks, movies, TV shows, music, DVD's, etc.). The system extracts reviews from multiple online sources, including online “stores”, professional articles, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each product's reviews, wherein each summary includes a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. performance, price, etc.). Additionally, the user may view a separate review detail page per product that provides further summaries, such as a short list of other products that the same reviewer gave a very positive review for the features. The user is then able to purchase the product via a link.

This invention concerns methods and systems for monitoring one or more students' use of computers, measuring, analyzing, and summarizing in statistical and pedagogical ways learning quality, on-task versus off-taskness, making diagnostic and prescriptive recommendations, and displaying relevant and configurable results in real time and historically in meaningful ways to students, teachers, and various educationalists and managers.

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

The invention discloses a linkage control early warning method of accidental occurrence of elderly people living alone. The method comprises the steps: obtaining use data of intelligent household equipment, carrying out learning analysis according to the use data, and obtaining a user portrait; detecting real-time use data of smart home equipment of a house where the elderly people living alone islocated in real time, and triggering an early warning signal when the real-time use data does not accord with threshold comparison of a user portrait; after the early warning signal is triggered, searching through a community video file stored in a community management system to obtain an activity track of the elderly people living alone; judging time of the activity track; and when the related activity track is determined not to be queried within a preset time threshold before and after occurrence time of the early warning signal, determining that the elderly people living alone is in distress, sending preset distress information to a user terminal of a guardian to which the elderly people living alone belongs, and sending the preset distress information to a property management system so that property management personnel can timely go to the house for rescue.

The invention relates to a method for constructing a dermatoglyph classification prediction model by introducing a ResNet deep learning network. The method comprises steps of using an intelligent terminal device for fully collecting a sample dermatoglyph original image and sequentially carrying out normalization, Wiener filtering denoising, Sobel operator algorithm sharpening, binarization algorithm processing and OPTA pixel skeletonization processing; repairing and enhancing by adopting a GAN generative adversarial network model algorithm, and manually labeling each sample dermatoglyph image;and finally, establishing a dermatoglyph classification prediction model, optimizing a loss function, iteratively training the model, and verifying to obtain a dermatoglyph classification model. According to the method, a CNN-based ResNet deep learning network is introduced to construct a dermatoglyph classification prediction model; when the constructed model is applied, different dermatoglyph feature images can be learned and analyzed from the aspects of multiple dimensions and multiple features, more features are extracted from dermatoglyph image information, and high accuracy is achievedin dermatoglyph recognition and classification.

Methods and devices for accelerating web page rendering include processing web pages and gathering web page element information, performing machine learning analysis on the gathered web page element information to identify patterns in layout independence correlated to web page element information, and training a classifier to predict sub-tree independence based on element information in a web page script. The predicted sub-tree independence may be used to concurrently process portions of a web page to be rendered to reduce the time required to render the page. Sub-trees may be conditionally independent, in which case, the conditionally independent sub-trees may be made independent by speculating data to render the sub-trees independent, or by performing a task to obtain the certain information to render the sub-tree independent.

The present invention relates to the field of automated evaluation, assessment and analysis. Disclosed is a method to create and store assignments, automatically analyze student accuracy and ability, and store analysis on individual students for an extended amount of time. This method can reduce the time and effort exerted by teachers in the creation and grading of assignments and more effectively analyzes student progress than any known software. An assessment software, “TEACH-TECH,” combined with a web-based database, “Cloud,” in the novel manner disclosed provides a means for substantially increasing the effectiveness of the educational system.

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.