Abstract: Novel tools and techniques for are provided for machine learning control of automatic kick detection and blowout prevention. A system includes one or more blowout preventers (BOP), one or more sensors, a neural network bank comprising one or more neural networks, and a machine learning (ML) controller coupled to the one or more BOPs. The ML controller includes a processor, and non-transitory computer readable media comprising instructions executable by the processor to obtain operational data associated with a local well, generate one or more feature vectors based on the operational data, and generate one or more respective kick scores. In a fully automatic operational mode, the ML controller may issue a position command based on the kick score, and in a semi-automatic operational mode, determine the position command recommended to be issued.

Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.

Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.

Abstract: A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.