护理学 | SCI期刊专刊信息1条

Deep learning approaches with the benefits of enhancing efficiency and improving accuracy has been widely used in both of academia and industry. From a safety science in transportation point of view, the deep learning approaches bring about both opportunities and challenges for transportation applications. On the one hand, deep learning approaches can help interested parties to better protect safety in dangerous traffic situations, improving the state-of-the-art of safety solutions. On the other hand, deep learning approaches including perception, neural network, machine learning, knowledge representation and so on, has been making revolutions in various areas, such as autonomous vehicles, robotic manipulators, image analysis, computer vision, natural language processing, time-series analysis, and target online advertisement. This has made deep learning approaches a promising tool to be utilized in modelling and optimization of chemical processes. For example, deep learning has been shown to be effective in reconstructing missing information of damaged image. Deep neural networks have also been widely used to restore information in ill-posed situation.