A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A feature set for performing classification of datasets such as speech transcripts by a machine learning classifier model is constructed using identification of features of interest through classification of subsequences of the dataset. An anchor comprising a class-differentiating token is identified, and subsequences of different lengths comprising the anchors and surrounding tokens are generated. The subsequence length producing a best performing classifier is selected. A feature set is then generated using transcript-level aggregates of token-level features for tokens in the dataset within that subsequence lengths length. The feature set may be added to a previously defined feature set for the dataset.

A composite novelty method approach to deceptive problems where a secondary objective is available to diversify the search is described. In such cases, composite objectives focus the search on the most useful tradeoffs and allow escaping deceptive areas. Novelty-based selection increases exploration in the focus area, leading to better solutions, faster and more consistently and it can be combined with other fitness-based methods.

A method is provided for detecting a higher-level action from one or more trajectories of real states. The trajectories are based on an experts' action demonstration. The method trains predictors to predict future states. Each predictor has a different duration of the higher-level action to be detected. The method predicts, using the predictors, the future states using past ones of the real states in the one or more trajectories as inputs for the predictors. The method determines if a match exists between any of the future states relative to a real future state with a corresponding same duration from the one or more trajectories. The method outputs a pair that includes the matching one of the future states as a prediction input and the real future state with the corresponding same duration from the one or more trajectories as the higher-level action corresponding thereto, responsive to the match existing.

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

Disclosed in some examples are methods, systems and machine-readable mediums which allow for more secure authentication attempts by implementing authentication systems with credentials that include interspersed noise symbols in positions determined by the user. These systems secure against eavesdroppers such as shoulder-surfers or man-in-the middle attacks as it is difficult for an eavesdropper to separate the noise symbols from legitimate credential symbols.

Systems, methods and computer-readable storage media utilized to prepare datasets for geo experiments. One method includes receiving one or more input parameters. The method further includes extracting, from the data, training data. The method further includes calculating a difference in input data and a difference in response data of the training data. The method further includes determining a first plurality of geographic pairs. The method further includes extracting, from the data, evaluation data. The method further includes separating each geographic pair of the first plurality of geographic pairs into a treatment region or a control region for a plurality of simulations of a plurality of different simulation subsets for each of a plurality of different subsets of geographic pairs. The method further includes calculating a plurality of uncertainty estimates. The method further includes selecting a first subset of geographic pairs and providing the selected subset of geographic pairs.

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.

Embodiments for implementing enhanced ensemble model diversity and learning by a processor. One or more data sets may be created by combining one or more clusters of data points of a minority class with selected data points of a majority class. One or more ensemble models may be created from the one or more data sets using a supervised machine learning operation. An occurrence of an event may be predicted using the one or more ensemble models.

A processing apparatus (10) includes a dividing means (110). The dividing means (110) divides data into a plurality of pieces of partial data by degree of importance, based on a content of the data. Then, first partial data and second partial data having a degree of importance higher than that of the first partial data are held separately from each other. The data are, for example, sensor data or camera data constituted of a plurality of values. For example, the second partial data are encrypted.