Aspects of the disclosure relate to OCR. A computing platform may train, using historical images, a CNN and a RNN to perform OCR/identify characters in context. The computing platform may receive an image of a document, and may input the image into the CNN, which may cause the CNN to output OCR information for the image and a confidence score. Based on identifying that the confidence score exceeds a confidence threshold, the computing platform may store the OCR information to enable subsequent access of a digital version of the document. Based on identifying that the confidence score does not exceed the confidence threshold, the computing platform may: 1) input the OCR information into the first RNN, which may cause the first RNN to output contextual OCR information for the image, and 2) store the contextual OCR information to enable subsequent access of the digital version of the document.

A system and method for predicting rain fade for a rain zone using a deep learning system including a computer processor. The method may include: training a Neural Network (NN) by importing into the NN a training set of image information and beacon information, wherein the image information includes image datasets including of a cloud view of an Area of Interest (AoI), a geolocation and a timestamp, and the beacon information includes beacon datasets including a beacon strength, a current rain fade state, a geolocation and a timestamp; pre-processing to homogenize and to extract spatially and temporally matching data for the AoI from a live image information and a live beacon information; and forecasting a rain fade based on the data in a near-future. In the method, the geolocation of one or more of the beacon datasets is located within the AoI, and the periodicity of the live beacon information and the live image information is less than or equal to five (5) minutes.

A computer-implemented method for classifying video data with improved accuracy includes obtaining, by a computing system comprising one or more computing devices, video data comprising a plurality of video frames; extracting, by the computing system, a plurality of video tokens from the video data, the plurality of video tokens comprising a representation of spatiotemporal information in the video data; providing, by the computing system, the plurality of video tokens as input to a video understanding model, the video understanding model comprising a video transformer encoder model; and receiving, by the computing system, a classification output from the video understanding model.

To provide a method for predicting the c-axis length of a lithium compound crystal structure, a method for building a learning model for predicting a c-axis length, and a system for predicting a crystal structure having the maximum c-axis length. A method for predicting the c-axis length of a crystal structure of a lithium compound containing cobalt, nickel, and manganese includes preparing a descriptor including n values (n is an integer greater than or equal to 0) obtained by converting a crystal structure of the lithium compound in which manganese at any one or more of n sites is substituted by a metal atom among crystal structures of the lithium compound into binary data and a characteristic value of the metal atom; inputting the descriptor into a learned learning model; and outputting a predicted value of c-axis length of an optimized crystal structure and a descriptor corresponding to the optimized crystal structure as an output value of the learning model.

According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

Search elements are extracted from requirement definitions of a requirement management tool for managing a project. The search elements may be extracted using natural language processing. The search elements are used to identify source code from source code repositories. Machine learning correlates the requirement definitions to source code subject matter. The extracted source code is confirmed by a stakeholder of the requirement management tool.

A method of power control for mobile devices, comprising providing a power management application in a first mobile device of a plurality of mobile devices, and wirelessly sharing power among the plurality of mobile devices according to at least one of the user-selectable power sharing templates. The power management application may include a plurality of user-selectable power sharing templates that define how the plurality of mobile devices will share power between themselves. The power management application may recommend one of the plurality of power sharing templates for activities by a user of the first mobile device.

A method includes obtaining data representative of a state or condition of an evaluation target. The method also includes providing first input based on the data to a trained classifier to generate a first result. The method further includes providing second input based on the data to an adaptive neuro-fuzzy inference system to generate a second result. The method also includes assigning a classification to the state or condition of the evaluation target based on the first result and the second result.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

A learning method, executed by a computer, according to one embodiment includes an input procedure for receiving a series data set set X={X.sub.d}.sub.d.sub.∈.sub.D composed of series data sets X.sub.d for learning in a task d∈D when a task set is set as D, a sampling procedure for sampling the task d from the task set D and then sampling a first subset from a series data set X.sub.d corresponding to the task d and a second subset from a set obtained by excluding the first subset from the series data set X.sub.d, a generation procedure for generating a task vector representing characteristics of the first subset using parameters of a first neural network, a prediction procedure for calculating, from the task vector and series data included in the second subset, a predicted value of each value included in the series data using parameters of a second neural network, and a learning procedure for updating learning target parameters including the parameters of the first neural network and the parameters of the second neural network using an error between each value included in the series data and the predicted value corresponding to each value.