Abstract: A method including generating sequence data based on measured values of respective one or more process factors of each of a plurality of processes of a semiconductor fabrication process for a wafer within the semiconductor fabrication process, generating a temporary quality index of the wafer using a second neural network connected to a first neural network that is provided the sequence data, training the first neural network and the second neural network based on a loss between the temporary quality index and a set actual quality index of the wafer, selecting a control process from among the plurality of processes using at least one of the trained first neural network and/or the trained second neural network, and selecting a control factor from among multiple process factors of the selected process using the trained second neural network.

Abstract: Provided is a method for diagnosing a disease risk based on a complex biomarker network. More particularly, provided is a method for predicting or diagnosing a disease risk by constructing a complex disease relation network from biomarkers extracted from a liquid biological specimen and automatically extracting a disease marker from the complex disease relation network. It was confirmed that the method for predicting or diagnosing a disease risk using the biomarkers extracted from the liquid biological specimen developed according to the present invention applies an improved network analysis and learning method as compared to conventional methods, and thus enables disease-related diagnosis which shows high sensitivity and specificity even when only a few biomarkers are applied, which indicates that the method of the present invention shows superior extraction performance, compared to the methods using conventional variational dropout-based biomarker extraction.

Abstract: Disclosed is a reinforcement learning-based resource allocation method for a wireless backhaul network, which is performed by a resource allocation apparatus. The method includes estimating locations of a plurality of base stations on the basis of channel state information (CSI) measured by the plurality of base stations; and allocating resources of the wireless backhaul network to the plurality of base stations using a reinforcement learning neural network having the locations as an input.

Abstract: Provided is a method for diagnosing a disease risk based on complex genetic information network analysis. In the method for diagnosing a disease risk based on complex genetic information network analysis according to the present invention, it is possible to deduce a stable correlation with a disease from a small number of genetic information combination by introducing an optimization method or learning method, and it is possible to provide a genetic information correlation based on a network model. A diagnosis technology satisfying accuracy and economical efficiency enough to be commercially used in an actual medical field by using the correlation between the genetic information and the disease deduced in the present invention will be secured. Further, the biomarker deduced in the present invention will be commercially used in manufacturing a medical device including a diagnosis chip and terminal and in disease diagnosis service.

Abstract: Disclosed is a reinforcement learning-based resource allocation method for a wireless backhaul network, which is performed by a resource allocation apparatus. The method includes estimating locations of a plurality of base stations on the basis of channel state information (CSI) measured by the plurality of base stations; and allocating resources of the wireless backhaul network to the plurality of base stations using a reinforcement learning neural network having the locations as an input.