Abstract: A transaction classification model is trained using historical transaction and account data to predict classifications of transactions. When the model is deployed, a transaction review device receives transaction data and account data for an account. The transaction classification model is applied to the transaction data and the account data to generate predicted classifications for at least some of the transactions identified in the account data. A tax liability is estimated based on the predicted classifications and the tax liability estimate is provided for display at the transaction review device.

Abstract: A computer-implemented method of recommending information sources is provided. The method comprising collecting raw user log data of a browser user and transforming the raw user log data to remove data that is specific to the user, wherein the transformed user log data comprises only general pages visited. A number of key page mappings are appended to the transformed user log data. A machine learning model determines a user interaction function according to the transformed user log data and generates a number of web page recommendations, wherein each web page recommendation has a respective probability of engagement based on the user interaction function. The web page recommendations are then displayed to the user on an interface.

Abstract: A method, apparatus, system, and computer program code for identifying at-risk items. Raw account data is collected for a set of accounts, each account comprising a set of subscriptions to a set of items. The raw account data is transformed to generate a first subset of account data that comprises only accounts having subscriptions that have been modified, and a second subset of account data that comprises only accounts having subscriptions that are unmodified. An interaction function it is determined, by a machine learning model, according to the first subset of account data. A number of at-risk items is determined, by the machine learning model. Each at-risk item has a respective probability of modification based on the interaction function. The at-risk items are displayed on a graphical user interface.

Abstract: Pressure-inducing devices and pressure transducers can be used to detect and quantify liquid pools in hydrocarbon fluid pipelines. Pressure fluctuations can be detected by a pressure transducer, where the pressure fluctuations are the response of a pressure-inducing device outputting a pressure signal in a pipe carrying hydrocarbons. Variation in a pipe diameter caused by pooling or deposition can be estimated using an inverse model. The pooling or depositions can be classified by applying a machine-learning model to the pressure fluctuations. The variation in pipe diameter can be converted to an equivalent liquid volume for pooling locations. A pooling or deposition location and volume can be output and used for determining an action on the pipe to remove the pooling or deposition.

Abstract: This disclosure relates to a method and system for monitoring health and predicting failure of an electro-mechanical machine. In an embodiment, the method may include receiving a plurality of operational parameters with respect to the electro-mechanical machine and determining a set of features and a set of events, based on the plurality of operational parameters. The method may further include detecting one or more fault signatures associated the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, or the set of events. The method may further include determining at least one of a time to the possible failure and a remaining useful life of the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, the set of events, or the one or more fault signature, by using a hybrid machine learning model.

Abstract: A computer-implemented method of recommending information sources is provided. The method comprising collecting raw user log data of a browser user and transforming the raw user log data to remove data that is specific to the user, wherein the transformed user log data comprises only general pages visited. A number of key page mappings are appended to the transformed user log data. A machine learning model determines a user interaction function according to the transformed user log data and generates a number of web page recommendations, wherein each web page recommendation has a respective probability of engagement based on the user interaction function. The web page recommendations are then displayed to the user on an interface.

Abstract: This disclosure relates to a method and system for monitoring health and predicting failure of an electro-mechanical machine. In an embodiment, the method may include receiving a plurality of operational parameters with respect to the electro-mechanical machine and determining a set of features and a set of events, based on the plurality of operational parameters. The method may further include detecting one or more fault signatures associated the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, or the set of events. The method may further include determining at least one of a time to the possible failure and a remaining useful life of the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, the set of events, or the one or more fault signature, by using a hybrid machine learning model.

Abstract: Pressure-inducing devices and pressure transducers can be used to detect and quantify liquid pools in hydrocarbon fluid pipelines. Pressure fluctuations can be detected by a pressure transducer, where the pressure fluctuations are the response of a pressure-inducing device outputting a pressure signal in a pipe carrying hydrocarbons. Variation in a pipe diameter caused by pooling or deposition can be estimated using an inverse model. The pooling or depositions can be classified by applying a machine-learning model to the pressure fluctuations. The variation in pipe diameter can be converted to an equivalent liquid volume for pooling locations. A pooling or deposition location and volume can be output and used for determining an action on the pipe to remove the pooling or deposition.