Various embodiments include methods and devices for training and implementing a tree-based behavior prediction model for use in autonomous vehicle control systems. Some embodiments may include labeling real-world autonomous vehicle run data to indicate an insight of the data, selecting an insight decision tree of the tree-based behavior prediction model for training using the labeled data, training the insight decision tree using the labeled data to classify a probability of an insight associated with the insight decision tree, and updating the tree-based behavior prediction model based on training the insight decision tree. Some embodiments may include selecting an insight decision tree of a tree-based behavior prediction model configured for classifying a probability of an insight associated with the insight decision tree, executing the insight decision tree, and outputting a probability of an insight determined from executing the insight decision tree using the data.

A machine learning model training method includes: training a machine learning model using features of each sample in a training set based on an initial first weight and an initial second weight. In one iteration, the method includes determining a first sample set in which a target variable is incorrectly predicted, and a second sample set in which a target variable is correctly predicted, based on a predicted loss of each sample; and determining overall predicted loss of the first sample set based on a predicted loss and a first weight of each sample in the first sample set. The method also includes updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss; and inputting the updated second weight, the features, and the target variable of each sample to the machine learning model, and initiating a next iteration.

A method checks a plug connection, in which a first plug part is connected to a second plug part. The method determines a force-time curve of a force applied by an assembler during an assembly process of a plug connection. In addition, the method determines characteristic values of a plurality of characteristics of the force-time curve. The method also classifies the plug connection by use of a machine-learned classifier on the basis of the characteristic values of the plurality of characteristics.

A processor of an image automatic classification server may perform a method for automatically classifying images. The method includes receiving partial or entire contents of a plurality of products from an online shopping website, classifying the received contents of the plurality of products into each of the products and storing the contents classified by each of the products, extracting a plurality of product images of one product among the plurality of products form the stored contents, and automatically classifying the extracted product images of the one product into a plurality of categories to generate information for the one product. The information for the one product comprises information that classifies the plurality of product images of the one product for each of a plurality of categories to provide the classified product images to be selectable.

An information processing method in which an object of interest is classified using node group information defining a node group having modeled a scheme of classification as a tree structure and having grouped nodes possessing a same parent node, comprises: setting depth information for determining whether to perform classification for a particular node group when sequentially traversing node groups from the parent node using the node group information to classify the object of interest; and classifying the object of interest by sequentially traversing node groups from the parent node using the node group information, and providing a classification result, wherein classifying the object of interest varies a depth up to which node groups are sequentially traversed from the parent node to classify the object of interest, in accordance with setting of the depth information.

Methods, systems, and apparatuses, including computer programs for detecting pancreatic neoplasms. A method includes providing an image as an input to a first model, obtaining first output data generated by the first model based on the first model's processing of the image, the first output data representing a portion of the image that depicts a pancreas, providing the first output data as an input to a second model, obtaining second output data generated by the second model based on the second model's processing of the second input data, the second output indicating whether the depicted pancreas is normal or abnormal, providing the first output data and the second output data as an input to a third model, and obtaining third output data generated by the third model, the third output data including data indicating that the pancreas is normal or data indicating a likely location of a pancreatic neoplasm.

A method and a device for determining a presence of tumor are provided. The method includes receiving a medical image associated with a patient. The medical image includes a region of interest associated with the patient. The method includes identifying one or more blood vessels associated with the region of interest in the medical image. The method includes determining a set of characteristics associated with the one or more blood vessels using a trained machine learning model. The method also includes determining whether the one or more blood vessels are feeder vessels associated with the tumor based on the set of characteristics associated with the one or more blood vessels. The method includes detecting a tumor region in the region of interest based on the feeder vessels, when the one or more blood vessels are the feeder vessels associated with the tumor.

An Artificial Intelligence (AI) based performance prediction system predicts the performance and behavior of an entity via a complex structure made of iterative and parallel machine learning (ML) model rebuilds with real time data collection. The engine selects a best model at every level and scores the entity to help in predicting the behavior of the entity. Model selection is based on various model selection criteria. The selected model determines a propensity score that indicates a likelihood of the entity migrating from a currently categorized segment to another segment of higher or lower value. Accordingly, messages or alerts with one or more of corrective actions or system enhancements can be transmitted based on the status of the entity via various targeting channels and a post treatment analysis is carried out to find the effect of the corrective actions on the entity. The feedback from the entity in response to the implemented corrective actions or system enhancements is collected for further training the AI based model.

The disclosed embodiments describe methods, systems, and apparatuses for determining user intent. A method is disclosed comprising obtaining a session text of a user; calculating, by the processor, a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

A machine learning process for use with a pharmacy benefits management system. The machine learning process identifies a first predicted set of drug benefit claims impacted by a pricing error, reprices a sample of the first predicted set of drug benefit claims to adjust for the error, and trains a predictive model as a function of the repriced sample. Based on the trained model, the machine learning process predicts a second predicted set of drug benefit claims impacted by the error and initiates automatic repricing.