Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

Abstract: An anomaly detection method for detecting an anomaly in an in-vehicle network of an in-vehicle network system including a plurality of electronic control units that transmit and receive messages via the network includes: generating image data of a reception interval between a plurality of messages included in a message sequence in a predetermined period out of a message sequence received from the in-vehicle network, or image data of a transition of a sensor value of the plurality of messages; classifying the image data using a trained CNN according to whether an attack message has been inserted in the predetermined period; and when the attack message has been inserted in the predetermined period, outputting a detection result indicating that an insertion attack which is an insertion of the attack message has been made in the predetermined period.