Abstract: The present invention provides a method and a machine-learning system for predicting pipeline corrosion. The pipeline corrosion prediction according to the method and system comprises steps of i) generating a predictive model (100) based on a neural network algorithm; ii) applying a supervised machine learning technique for training the predictive model (100) from step i); and iii) applying the predictive model (100) from step ii) to other sets of the input data in order to predict a depth of metal loss rate (122). The predictive model (100) includes multiple modules relevant to a water condensation rate module, a flow regime module, a corrosion rate module, and an operating data module. Each module is integrated into a concatenate layer (114) and then processing through hidden layers (116), a long short-term memory layer (118) and hidden layers (120) respectively, in order to generate the depth of metal loss rate with high accuracy.

Abstract: An approach is provided for providing driving assistant services to a user before, during, and after the user starts traveling. Specifically, a personal travel pattern associated with a device, the user of a device, or a combination thereof is processed to determine at least one prediction of a time that the device, the user, or a combination thereof will travel to at least one or more travel paths, one or more places of interest, or a combination thereof. The travel information associated with the at least one of the one or more travel paths, the one or more places of interest, or a combination thereof is presented to the user prior to the time predicted. The travel information is also processed to cause, at least in part, a generation of a recommendation of at least one alternate travel path, at least one alternate place of interest, or a combination thereof.