摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道研究称，NewsRx记者源于中华人民共和国广东的报道，“本研究旨在建立一种基于深度学习引导的表面增强方法。”拉曼光谱(SERS)技术实现阴道洁净度快速准确分类水平。我们提出了一种变分自编码器(VAE)的方法来提高光谱质量，并结合长短期记忆(LSTM)神经网络深度学习算法分析SERS谱阴道分泌物产生的
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating in Guangdong, Peo ple’s Republic of China, by NewsRx journalists, research stated,“In this study, it is aimed to establish a novel method based on a deep-learning-guided surface -enhancedRaman spectroscopy (SERS) technique to achieve rapid and accurate clas sification of vaginal cleanlinesslevels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled witha deep learning algorit hm long short-term memory (LSTM) neural network to analyze SERS spectraproduced by vaginal secretions.”
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