It is aimed to facilitate obtaining of a large number of pieces of data for learning that are necessary to obtain a good-quality learning result.

A feature value of a first dataset is compared with feature values of a predetermined number of second datasets. A determination as to whether or not each of the predetermined number of second datasets is a dataset usable together with the first dataset is made on the basis of the result of the comparison. For example, the determination is made referring to lacking data information associated with the first dataset. For example, information regarding a second dataset having been determined to be the dataset usable together with the first dataset is presented.

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

An apparatus, methods and systems for monitoring network-sensitive data collection related to an industrial production process are disclosed. The system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, a data storage circuit structured to store a plurality of collector routes wherein the data collector receives collected data utilizing a selected collector route, a data analysis circuit structured to determine a data collection quality parameter and a state value of the industrial production process, a pattern recognition circuit structured to determine an identified pattern in response to at least a portion of the collected data and at least one of the state value or the data collection quality parameter, and an analysis response circuit structured to adjust one of the collector routes or the industrial production process in response to the identified pattern.

A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor, includes capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment is applied to the target object; processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of a treatment action in the vicinity of the target object; and providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.

A method for training a learning system to identify components of time-based data streams includes processing, by a machine vision component in communication with a learning system, a video file to detect at least one object in the video file. The method includes generating by the machine vision component an output including data relating to the at least one object and the video file. The method includes analyzing, by a learning system, the output. The method includes identifying, by the learning system, an unidentified object in the processed video file.

Systems and methods for data collection in an industrial environment can include a data collector to route analog signals from a plurality of analog sensor inputs to a plurality of output channels of in accordance with a first routing scheme and a controller configured to adjust the routing scheme to a second routing scheme. The first routing scheme may include providing at least two of the plurality of analog sensor inputs at one of the plurality of output channels and the second routing scheme may include providing at least one of the at least two of the plurality of analog sensor inputs to a different one of the plurality of output channels.

An embodiment provides a system and method for sample labeling. During operation, the system obtains a plurality of historical loss assessment images and obtains a plurality of candidate samples from the plurality of loss assessment images. A respective candidate sample comprises an image of a candidate damage area detected in a corresponding historical loss assessment image. The system clusters the plurality of candidate samples into a plurality of class clusters. For a respective class cluster, the system determines a center candidate sample set corresponding to a class cluster center of the respective class cluster, receives a manual labeling result associated with candidate samples in the determined center candidate sample set, and performs, according to the manual labeling result, damage classification labeling on other unlabeled candidate samples in the respective class cluster to obtain a plurality of labeled samples.

A gamified application is provided for users to feed animated virtual characters with images of real-world food items. The images fed to the virtual characters are to be uploaded to a data store in a cloud environment, for use in training a custom machine learning model. A server in the cloud environment receives a photo of a food item fed to a virtual character in an augmented reality environment in the gamified application executing on a user device, invokes the custom machine learning model to generate classification information for the photo, sends the classification information to the user device for verification by a user, and stores the verified information to the data store used for periodically training the machine learning model. Over time, the data store would include a large volume of food images with label data verified by a large number of users.

A self-organizing data marketplace includes a plurality of data collectors and a corresponding plurality of industrial environments, wherein each of the plurality of data collectors is structured to collect detection values from at least one sensor of the corresponding industrial environment, a data storage structured to store a data pool comprising at least a portion of the detection values, a data marketplace structured to self-organize the data pool, and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to the user in response to the user data request.

The technology disclosed processes input data through a neural network and produces an alternative representation of the input data. The input data includes per-cycle image data for each of one or more sequencing cycles of a sequencing run. The per-cycle image data depicts intensity emissions of one or more analytes and their surrounding background captured at a respective sequencing cycle. The technology disclosed processes the alternative representation through an output layer and producing an output and base calls one or more of the analytes at one or more of the sequencing cycles based on the output.