Provided are a system for recommending related data based on similarity, and a method thereof, the system including: a data collection device; an event extraction device; a data cleansing device; an event vector generation device; an artificial intelligence learning device; and a similar data recommendation device. The present disclosure is directed to providing a system for recommending related data based on similarity and a method thereof, wherein unstructured open data on a webpage is collected to automatically generate an event label for determining a similarity relation, and an artificial intelligence (AI)-based model is trained to group and recommend semantically similar related data, thereby effectively helping users including data scientists who want to see meaningful results through open data.

Example systems perform complex optimization tasks with improved efficiency via neural meta-optimization of experts. In particular, provided is a machine learning framework in which a meta-optimization neural network can learn to fuse a collection of experts to provide a predicted solution. Specifically, the meta-optimization neural network can learn to predict the output of a complex optimization process which optimizes over outputs from the collection of experts to produce an optimized output. In such fashion, the meta-optimization neural network can, after training, be used in place of the complex optimization process to produce a synthesized solution from the experts, leading to orders of magnitude faster and computationally more efficient prediction or problem solution.

A system for generating a contention scheme includes a computing device, the computing device configured to obtain a solvency signature as a function of a solvency entity, determine a solvency grouping as a function of the solvency signature, identify a null element as a function of the solvency grouping, wherein identifying the null element further comprises receiving a regulation element as a function of a regulation database, and identifying the null element as a function of the regulation element and the solvency grouping, produce a weighted vector as a function of the null element, and generate a contention scheme as a function of the weighted vector.

In a method for determining a fat-reduced MR image, a first MR image is provided having, apart from the other tissue constituents, MR signals from only one of the two fat constituents, the first MR image is applied to a trained ANN, which was trained by first MR training data as the input data, the training data including, apart from the other tissue constituents, MR signals from only the one of the two fat constituents, and using second MR training data as a base knowledge, the second MR training data including, apart from the other tissue constituents, no MR signals from the two fat constituents; and an MR output image is determined from the trained ANN, to which the first MR image was applied, as a fat-reduced MR image, wherein the fat-reduced MR image includes, apart from the other tissue constituents, no MR signals from the two fat constituents.

A computer-implemented method for generating a control signal by locating at least one instrument by way of a combination of machine learning systems on the basis of digital images is described. In this case, the method includes determining parameter values of a movement context by using the at least two digital images and determining an influence parameter value which controls an influence of one of the digital images and the parameter values of the movement context on the input data which are used within a first trained machine learning system, which has a first learning model, for generating the control signal.

This disclosure describes systems and methods for training a neural network with a training data set including data items labeled at different granularities. During training, each item within the training data set can be fed through the neural network. For items with labels of a higher granularity, weights of the network can be adjusted based on a comparison between the output of the network and the label of the item. For items with labels of a lower granularity, an output of the network can be fed through a conversion function that convers the output from the higher granularity to the lower granularity. The weights of the network can then be adjusted based on a comparison between the converted output and the label of the item.

A system, method, and computer program product for implementing deep learning software model modification is provided. The method includes monitoring operational performance of a software model. An expected confidence level associated with the operational performance is first determined and it is determined that an inference associated with the expected confidence level is below a selected range of inferences associated with assigning new feature data as candidate video data. A candidate sequence comprising video data associated with the candidate video data is received and a similarity between frames of the candidate sequence is determined. A frame comprising a highest similarity with respect to segments of candidate video data is selected and it is detected that the frame is not associated with additional frames stored within a full cache structure. The software model is retrained such that the operational performance is modified.

An approach is provided for accessing traffic impact caused by individual driving behaviors. For example, the approach involves receiving, by one or more processors, sensor data collected from one or more sensors of a vehicle traveling on a road network. The approach also involves processing, by the processors, the sensor data to determine one or more driving behaviors associated with the vehicle. The approach further involves computing, by the processors, a traffic impact index based on the one or more driving behaviors and at least one contextual parameter associated with the vehicle, the road network, a driver of the vehicle, or a combination thereof. The traffic impact index represents an estimated impact of the vehicle on a traffic flow within at least a portion of the road network. The approach further involves providing, by the processors, the traffic impact index as an output.

A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

A method is provided for generating a visualization for explaining a behavior of a machine learning (ML) model. In the method, an image is input to the ML model for an inference operation. The input image has an increased resolution compared to an image resolution the ML model was intended to receive as an input. A resolution of a plurality of resolution-independent convolutional layers of the neural network are adjusted because of the increased resolution of the input image. A resolution-independent convolutional layer of the neural network is selected. The selected resolution-independent convolutional layer is used to generate a plurality of activation maps. The plurality of activation maps is used in a visualization method to show what features of the image were important for the ML model to derive an inference conclusion. The method may be implemented in a computer program having instructions executable by a processor.