The present invention discloses a method for recognizing a seawater polluted area based on a high-resolution remote sensing image and a device and belongs to the field of digital image processing. According to the method, firstly, automatic sea and land classification is performed on a remote sensing image by using a supervised learning algorithm, a classification result may reach a higher precision level by processized iterative clustering, and meanwhile, compared with an existing analysis and classification method for a sea and land boundary, the algorithm is less in calculation; and then, a chlorophyll-associated normalized difference vegetation index, a brightness-associated normalized difference water shadow index, a segmentation-based image interpretation thought and a human visual saliency based mechanism in remote sensing interpretation are combined by virtue of a chlorophyll concentration difference of a seawater polluted area and surrounding seawater and a brightness difference of pollutant shadows, and the seawater polluted area is extracted by threshold segmentation, an area where the water quality is good and a heavily polluted area are respectively extracted, and then, a pollution transition area is further extracted. The method disclosed by the present invention provides convenience and an accurate reference for prevention and control of marine pollution.

Provided is a system, method, and computer program product for analyzing spatio-temporal medical images using an artificial neural network. The method includes capturing a series of medical images of a patient, the series of medical images comprising visual movement of at least one entity, tracking time-varying spatial data associated with the at least one entity based on the visual movement, generating spatio-temporal data by correlating the time-varying spatial data with the series of medical images, and analyzing the series of medical images based on an artificial neural network comprising a plurality of layers, one or more layers of the plurality of layers each combining features from at least three different scales, at least one layer of the plurality of layers of the artificial neural network configured to learn spatio-temporal relationships based on the spatio-temporal data.

A system and method for automatically detecting misinformation is disclosed. The misinformation detection system is implemented using a cross-stitch based semi-supervised end-to-end neural attention model which is configured to leverage the large amount of unlabeled data that is available. In one embodiment, the model can at least partially generalize and identify emerging misinformation as it learns from an array of relevant external knowledge. Embodiments of the proposed system rely on heterogeneous information such as a social media post's text content, user details, and activity around the post, as well as external knowledge from the web, to identify whether the content includes misinformation. The results of the model are produced via an attention mechanism.

A computer system trains a machine learning model. A vector representation is generated for each document in a collection of documents. The documents are clustered based on the vector representations of the documents to produce a plurality of clusters. A training set is produced by selecting one or more documents from each cluster, wherein the selected documents represent a sample of the collection of documents to train the machine learning model. The machine learning model is trained by applying the training set to the machine learning model. Embodiments of the present invention further include a method and program product for training a machine learning model in substantially the same manner described above.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive utility evaluation for a utility measure value given a defined demographic profile. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive utility evaluation for a utility measure value given a defined demographic profile by utilizing mappings across risk domains and evaluation domains to generate predicted utility scores based at least in part on utility distribution measures that are determined based at least in part on risk distribution measures, for example based at least in part on relationships between a predicted utility score and a distribution of predicted utility scores for a plurality of utility discretization categories.

This disclosure describes one or more implementations of a panoptic segmentation system that generates panoptic segmented digital images that classify both known and unknown instances of digital images. For example, the panoptic segmentation system builds and utilizes a panoptic segmentation neural network to discover, cluster, and segment new unknown object subclasses for previously unknown object instances. In addition, the panoptic segmentation system can determine additional unknown object instances from additional digital images. Moreover, in some implementations, the panoptic segmentation system utilizes the newly generated unknown object subclasses to refine and tune the panoptic segmentation neural network to improve the detection of unknown object instances in input digital images.

A method may include obtaining a dataset including one or more data points. The method may include separating the dataset into one or more partitions based on a target number of subjects and a dimensionality of the data points included in the dataset. The method may include obtaining one or more weight vectors, each respective weight vector corresponding to a respective subject. The method may include selecting a first partition of the plurality of partitions to remove from the dataset based on respective relationships between a first weighted centroid of the dataset and first partition weights corresponding to each of the partitions. The method may include obtaining a first subset of the dataset by removing the data points associated with the selected first partition from the dataset. The method may include training a machine learning model based on the first subset of the dataset.

The present disclosure relates to methods and systems to generate a modified feature map specific to a client. A template feature map may be modified based on usage data associated with a client. The template feature map may represent a visual representation of a plurality of features provided by an operator, each feature associated with a plurality of instructions to be processed for the client. The usage data may be compared with each feature to determine whether any feature is associated and/or utilized by the client. Based on determining whether the usage data indicates that any feature is associated and/or utilized by the client, the template feature map may be modified to perform an action to the template feature map indicating that a feature is associated and/or utilized by the client. A modified template feature map may be generated that is specific for a client.

Disclosed is a method for inspecting a food product, the method includes: receiving image data representing the food product captured with an X-ray imaging unit; performing a texture analysis to image data for generating a first set of detections; performing a pattern analysis to at least part of the image data, the pattern analysis performed with a machine-learning component trained to identify objects with predefined pattern, for generating a second set of detections; generating an indication of an outcome of an inspection of the food product in accordance with a combination of the generated first set of detections and the second set of detections. Also disclosed is an apparatus and a computer program product.

Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.