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Developing deep neural networks

Xem 1-5 trên 5 kết quả Developing deep neural networks
  • Electricity demand is increasing, transmission line development can not keep up with it. This puts the power system in a full load state which puts the power system operating near the boundary of stability. This paper applies deep neural networks to predict power system dynamic stability.

    pdf10p viling 11-10-2024 2 1   Download

  • In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.

    pdf8p viling 11-10-2024 1 1   Download

  • The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

    pdf7p viengfa 28-10-2024 2 2   Download

  • This thesis develops a flexible customer behavior analysis system, including essential head pose estimation or F-formation modules. This system will be evaluated in an actual retail store. Further, after studying the system, realizing the mentioned problems of the head pose problem, we also propose a process to collect the head pose dataset and multi-task deep neural network model, fusing face detection and head pose estimation to yield face position and head pose at the same time.

    pdf72p khanhchi0912 12-04-2024 8 2   Download

  • Research Aims: The thesis aims to develop deep neural networks for analyzing security data. These techniques improve the accuracy of machine learning-based models applied in NAD. Therefore, the thesis attempts to address the above challenging problems in NAD using models and techniques in deep neural networks. Specifically, the following problems are studied.

    pdf128p armyofthedead 23-06-2021 21 3   Download

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