The present invention discloses an indoor Electronic Traveling Aid (ETA) system for blind and visually impaired (BVI) people. The system comprises a headband, intuitive tactile display with myographic (EMG) feedback, controller, and server-based methods corresponding to three operation modalities. In 1.sup.st modality, sighted users mark routes, map navigational directions, and create semantic comments for BVIs. This information of routes is continuously collected and estimated in ETA servers. In the 2.sup.nd modality, BVIs choose the routes from servers, thereby, are supplied with real-time navigational guidance. Also, an EMG interface is used, where the user's facial muscles are enabled is to send commands to the ETA system. In the 3.sup.rd modality, BVIs receive real-time audio guidance in complex or unforeseen situations: ETA provides a crowd-assisted interface and real-time sensory (e.g., video) data, where crowd-assistants analyze the situation and help the BVI to navigate.

A method for analyzing a viewing direction of an electronic component includes inputting a package type and a file image of an electronic component, with the file image having at least one engineering drawing image, and the at least one engineering drawing image being a view of the electronic component in at least one viewing direction; querying and acquiring a viewing direction detection model meeting the package type from a database, with the database storing respective viewing direction detection models of different package types of electronic components; inputting the file image into the viewing direction detection model of the package type to identify the viewing direction of the at least one engineering drawing image; and outputting the viewing direction of the at least one engineering drawing image of the electronic component.

In various examples, machine learning of encoding parameter values for a network is performed using a video encoder. Feedback associated with streaming video encoded by a video encoder over a network may be applied to an MLM(s). Using such feedback, the MLM(s) may predict a value(s) of an encoding parameter(s). The video encoder may then use the value to encode subsequent video data for the streaming. By using the video encoder in training, the MLM(s) may learn based on actual encoded parameter values of the video encoder. The MLM(s) may be trained via reinforcement learning based on video encoded by the video encoder. A rewards metric(s) may be used to train the MLM(s) using data generated or applied to the physical network in which the MLM(s) is to be deployed and/or a simulation thereof. Penalty metric(s) (e.g., the quantity of dropped frames) may also be used to train the MLM(s).

Present invention discloses method and system for generating knowledge graph and sub-graph clusters to perform a root cause analysis. Method comprising extracting at least one of objects, data entities, links between the objects and the data entities, or relationships between the objects and the data entities from input content. Thereafter, method comprising generating a knowledge graph from the extracted data and sub-graphs from the knowledge graph using an unsupervised ML technique and extracting graph data structure information for each sub-graph. Subsequently, method comprising generating root cause model based on the sub-graphs and the graph data structure information and generating at least one sub-graph cluster and corresponding probabilistic graphical model using the root cause model and the knowledge graph. Generated Knowledge graph, root cause model and at least one sub-graph cluster and corresponding probabilistic graphical model are used to determine a root cause for an issue from an issue content.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

Some embodiments of the present application include obtaining datasets including a plurality of features and computing a correlation score between each of the features. Based on the correlation scores, the features may be clustered together such that each cluster includes features that are correlated with one another, and features included in different feature clusters lack correlation with one another. A machine learning model may be selected based on a set of input features for the model and the plurality of clusters such that each input feature is included in one of the feature clusters and no feature cluster includes more than one of the input features. Datasets may then be selected based on the set of input features, which may be used to generate training data for training the machine learning model.

In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.

Methods and systems for artificial intelligence (AI)-assisted document annotation and training of machine learning-based models for document data extraction are described. The methods and systems described herein take advantage of a continuous machine learning approach to create document processing pipelines that provide accurate and efficient data extraction from documents that include structured text, semi-structured text, unstructured text, or any combination thereof.