Abstract: A method and device for predicting errors in a computing system are disclosed. The error prediction method includes: receiving log data generated by the computing system during operation of the computing system; tokenizing the log data into tokens; inputting the tokens to a discriminator model which generates scores of the respective tokens, each score corresponding to a probability that the corresponding token is an anomaly token; determining an anomaly score based on the scores; and determining a likelihood of future occurrence of an error in the computing system based on the anomaly score.

Abstract: An apparatus includes a processor configured to execute instructions, and a memory storing the instructions, which when executed by the processor configure the processor to generate system error prediction data using an error prediction neural network provided with one of a plurality of log data sequences generated by pre-processing a plurality of log data pieces of component log data of a system. The system error prediction data comprises information of a plurality of system errors occurring at a plurality of respective timepoints.

Abstract: An apparatus for measuring blood components, includes: an impedance sensor configured to measure a bio-impedance of a user; and a processor configured to measure, by using a multiple-output artificial neural network (ANN) learning model, a concentration of a basic blood component and a concentration of at least one auxiliary blood component associated with the basic blood component, based on user metadata and the measured bio-impedance.

Abstract: An apparatus is provided. The apparatus includes a memory, a second type spectrum measurer, and a processor. The memory stores an individualized blood sugar estimation model. The second type spectrum measurer measures second type spectrum data for a skin of a user. The processor calculates blood sugar of the user based on the measured second type spectrum data and the individualized blood sugar model. The blood sugar model is generated based on blood sugar profile data of the user calculated based on a first type spectrum-blood sugar profile relationship model and training second type spectrum data for the skin of the user.

Abstract: An apparatus for estimating lipid concentration is provided. According to an example embodiment, the apparatus may include a training data collector configured to collect, as training data, a reference lipid concentration measured through blood samples of a plurality of users for a predetermined time period and sensor data obtained through light signals detected from the plurality of users for the predetermined time period and a processor configured to perform preprocessing including a moving average and data augmentation on the obtained sensor data, select a valid variable relevant to a change in lipid concentration based on the preprocessed sensor data and the reference lipid concentration, and generate a lipid concentration prediction model based on the selected valid variable.

Abstract: An apparatus is provided. The apparatus includes a memory, a second type spectrum measurer, and a processor. The memory stores an individualized blood sugar estimation model. The second type spectrum measurer measures second type spectrum data for a skin of a user. The processor calculates blood sugar of the user based on the measured second type spectrum data and the individualized blood sugar model. The blood sugar model is generated based on blood sugar profile data of the user calculated based on a first type spectrum-blood sugar profile relationship model and training second type spectrum data for the skin of the user.

Abstract: An apparatus and a method for determining blood sugar based on heterogeneous spectrums are provided. The blood sugar model generation apparatus includes a data obtainer configured to obtain blood sugar profile data of a user based on a first type spectrum-blood sugar profile relationship model, a second type spectrum measurer configured to measure training second type spectrum data for the skin of the user, and a processor configured to generate an individualized blood sugar model based on the obtained blood sugar profile data and the measured training second type spectrum data.

Abstract: An apparatus and a method for determining blood sugar based on heterogeneous spectrums are provided. The blood sugar model generation apparatus includes a data obtainer configured to obtain blood sugar profile data of a user based on a first type spectrum-blood sugar profile relationship model, a second type spectrum measurer configured to measure training second type spectrum data for the skin of the user, and a processor configured to generate an individualized blood sugar model based on the obtained blood sugar profile data and the measured training second type spectrum data.