Abstract: A computer-implemented method, a computer program product, and a computer system for retraining a model in case of a drift in machine learning. A computer detects a drift in machine learning. A computer identifies in a database features and a response of a machine learning model. A computer determines a time window of the drift. A computer extracts, from the database, data of the features and the response in the time window. A computer determines whether extracted data is sufficient for retraining the machine learning model. A computer, in response to determining that the extracted data is not sufficient for retraining the machine learning model, interpolates one or more of the features for a predetermined future time horizon. A computer interpolates a response corresponding to one or more interpolated features. A computer retrains the machine learning model, using the one or more interpolated features and an interpolated response corresponding thereto.

Abstract: The present disclosure relates to a method comprising at training system iteratively training a machine learning algorithm using current training data. The current training data comprises a local dataset of a current task and a replay dataset and may be updated for a next iteration as follows. A training dataset may be received. If the training dataset is not s shared dataset and its task is different from the current task: information representing the local dataset may be shared with other training systems, the local dataset may be added to the replay dataset, and the received training dataset may be used as the local dataset for a next iteration. In case the task is the current task: the received training dataset may be added to the local dataset. If the training dataset is a shared dataset, the received training dataset may be added to the replay dataset.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to training a learning model based on determined drift. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a selection component that can select an ensemble of deep learning regressors, and an identification component that can identify drift among the ensemble. An analysis component can analyze uncertainty samplings from the ensemble to determine a time instant when drift occurred. A training component can train one or more deep learning models, such as of the deep learning regressors, based upon the identified drift.

Abstract: Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Abstract: Various embodiments are provided for managing performance of a data processing system in a computing environment using one or more processors in a computing system. A drift may be dynamically detected in one or more machine learning models generating a plurality of predictions and deployed in a computing system. A plurality of metrics and data may be collected of the one or more machine learning models based on the drift. One or more additional machine learning models may be trained based of the drift and the plurality of metrics and data.

Abstract: Embodiments are disclosed for a method. The method includes generating statistical models of circadian rhythms based on circadian rhythm data generated by mobile computing devices of occupants of a building having a building automation system. The method also includes identifying room occupants of a room disposed within the building. Additionally, the method includes determining ambient settings for an ambient system operated by the building automation system based on a subset of the statistical models, wherein the subset corresponds to the identified room occupants. The method further includes determining a trade-off ambient setting based on the ambient settings.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

Abstract: A computer-implemented method of generating a machine learning model pipeline (“pipeline”) for a task, where the pipeline includes a machine learning model and at least one feature. A machine learning task including a data set and a set of first tags related to the task are received from a user. It is determined whether a database stores a first machine learning model pipeline correlated in the database with a second tag matching at least one first tag received from the user. Upon determining that the database stores the first machine learning model pipeline, the first machine learning model pipeline is retrieved, the retrieved first machine learning model pipeline is run, and the machine learning task is responded to. Pipelines may also be created based on stored pipelines correlated with a tag related to a tag in the task, or from received feature generator(s) and models.

Abstract: Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.

Abstract: Embodiments are disclosed for a method. The method includes generating statistical models of circadian rhythms based on circadian rhythm data generated by mobile computing devices of occupants of a building having a building automation system. The method also includes identifying room occupants of a room disposed within the building. Additionally, the method includes determining ambient settings for an ambient system operated by the building automation system based on a subset of the statistical models, wherein the subset corresponds to the identified room occupants. The method further includes determining a trade-off ambient setting based on the ambient settings.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

Abstract: The protection of water providing entities from loss due to environmental events is disclosed including receiving structured and unstructured data related to a water resource, generating time series for a plurality of index items, generating training data and testing data based on the time series for the plurality of index items, determining correlations between the time series of the index items using the generated training data, determining a classification boundary as a strike level based on the determined correlation, generating a protection strategy based on the strike level, applying the protection strategy to the testing data to generate a resulting data time series, and presenting the resulting data time series via a display of a computing device to at least one user of the computing device.

Abstract: Embodiments for estimating ice formation and depth by a processor. Ice formation and ice depth of a selected region of water may be cognitively forecasted using a prediction model such that the prediction model identifies similar characteristics between the selected region of water and one or more alternative regions of water by analyzing content of one or more data sources associated with an ontology of concepts representing a domain knowledge related to the selected region of water and the one or more alternative regions.

Abstract: Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Abstract: Embodiments for optimizing seating space in a group seating arrangement by a processor. One or more seating preferences and constraints from a user may be received. An optimized seating arrangement in the group seating arrangement, having one or more adjustable seats, may be determined according to the one or more seating preferences and constraints. A user is enabled to select the optimized seating arrangement via a graphical user interface (GUI) such that the one or more adjustable seats in the group seating arrangement are dynamically adjusted according to the optimized seating arrangement and user selection.

Abstract: A method and system for generating a map identifying the size and location of anomalous crop health patterns of a geographic area. Predictive crop health forecasting based historical crop health images generates expected crop health images. Statistical parametric mapping is used to model differences in the expected crop health images and current crop health images to generate a statistical parametric map. Regions of anomalous crop health based on the modeled differences are identified in the statistical parametric map. The number of the identified anomalous crop health regions and the size of each of the identified anomalous crop health regions are determined. The statistical significance of the size and number of the anomalous crop health regions relative to the expected crop health is quantified. A map of anomalous crop health patterns delineates the anomalous crop health regions and the statistical significance of the size and number of anomalous crop health regions.

Abstract: A sensor data fusion system includes a processor coupled to a plurality of sensors. The system is initialized by providing access to a data store storing at least one time series of sensor data; a semantic store storing semantic data including system variables, and relations between the system variables; and a mapping therebetween. A registration of a set of one or more variables of interest for which appropriate data is not available is obtained. An initially empty inference model is extended with the set of variables, to obtain an extended model. A request to observe a given one of the set of variables at a given timestamp is obtained. Responsive thereto, time series data for the set of registered variables is retrieved. The extended model is run with the retrieved data to obtain an estimate of the given one of the variables at the given timestamp.