FACILITATING IDENTIFICATION OF ERROR IMAGE LABEL

    公开(公告)号:US20250037432A1

    公开(公告)日:2025-01-30

    申请号:US18358274

    申请日:2023-07-25

    Abstract: A method, computer system, and program product facilitate identification of error image labels in training data. The method comprises: evenly dividing a training dataset into N subsets, where the training dataset includes M data items each comprising a pair of image and its original image label; training a prediction model to label images by respectively using each of the N subsets as training data to generate N respective trained prediction models; respectively using each of the N trained prediction models trained by using one of the N subsets as training data to label the images in other N−1 subsets of the N subsets to generate N−1 prediction labels for each of the M images in the training dataset. For each image in the M data items, whether the original image label of the image is a potential error image label is based on the N−1 prediction labels of the image.

    Data migration in a distributed file system

    公开(公告)号:US12135695B2

    公开(公告)日:2024-11-05

    申请号:US17662274

    申请日:2022-05-06

    Abstract: In an approach, a processor obtains a configuration file of a distributed file system federation, the configuration file comprising a list of a plurality of subclusters within the distributed file system federation and migration trigger factors for the plurality of subclusters. A processor determines a list of one or more source subclusters and a list of to-be-migrated directories in the one or more source subclusters based on a scanning result of the plurality of subclusters and the migration trigger factors in the configuration file. A processor generates a migration plan to migrate the to-be-migrated directories from the one or more source subclusters to one or more target subclusters in the distributed file system federation.

    Balanced optimization within a broker cluster

    公开(公告)号:US11848847B1

    公开(公告)日:2023-12-19

    申请号:US17963340

    申请日:2022-10-11

    CPC classification number: H04L43/0876 H04L67/10015

    Abstract: An example operation may include one or more of monitoring a plurality of brokers within a cluster to identify current workload attributes of the plurality of brokers, determining a health value of a lead broker within the cluster via execution of a machine learning model on current workload attributes of the lead broker, determining to modify resources assigned to the lead broker based on the determined health value of the lead broker, executing an optimization algorithm on the current workload attributes of the plurality of brokers within the cluster to determine an optimum task distribution, and reallocating tasks amongst the lead broker and the one or more other brokers within the cluster based on the optimum task distribution.

    COGNITIVE ROUTE PLANNING USING METRIC-BASED COMBINATORIAL EVALUATION TECHNIQUES

    公开(公告)号:US20230259872A1

    公开(公告)日:2023-08-17

    申请号:US17671263

    申请日:2022-02-14

    CPC classification number: G06Q10/08355

    Abstract: An embodiment includes parsing geographical data into a path graph having a plurality of nodes and edges, and identifying first and second subsets of the nodes as source nodes and destination nodes, respectively. The embodiment generates path data for a candidate delivery route from a source node to a destination node and along an edge between the source and destination nodes. The embodiment processes the path data using first and second evaluation techniques based on respective metrics. The embodiment compares evaluation values from the evaluation techniques to evaluation values associated with another candidate delivery route, and selects the candidate delivery route as a finalized delivery route based on the comparison results. The embodiment then generates a route plan that includes the finalized delivery route.

    COMPUTER-ASSISTED TOPIC GUIDANCE IN DOCUMENT WRITING

    公开(公告)号:US20230090993A1

    公开(公告)日:2023-03-23

    申请号:US17448552

    申请日:2021-09-23

    Abstract: A method, computer program product and computer system to provide topic guide during document drafting is provided. A processor retrieves at least one section of text from a document. A processor receives a target topic for the document. A processor extracts at least one local topic from the at least one section of text. A processor generates a semantic network comprising the at least one local topic and the target topic. A processor determines a deviation value for the at least one local topic based on a distance between the at least one local topic and the target topic in the semantic network. A processor, in response to the deviation value exceeding a threshold value, alerts a user that the at least one section of text from the document is off-topic from the target topic.

    CONTINUOUS PRODUCTION PROCESS OPTIMIZATION USING MACHINE LEARNING

    公开(公告)号:US20250053144A1

    公开(公告)日:2025-02-13

    申请号:US18231657

    申请日:2023-08-08

    Abstract: One embodiment of the invention provides a computer-implemented method for optimization of a continuous production process. The method comprises receiving input data comprising a plurality of datasets each including one or more variables relating to a production equipment involved in the continuous production process. The method further comprises generating different prediction models based on the input data. Each of the prediction models is configured to output a target prediction relating to the production equipment. The method further comprises generating an objective optimization model based on each target prediction output from each of the prediction models. The objective optimization model comprises a deep neural network. The method further comprises generating a loss function corresponding to the objective optimization model, and optimizing weights for parameters of the prediction models using backpropagation of the deep neural network and the loss function, resulting in optimized weights for the parameters of the prediction models.

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