Faculty of Information and Communication Engineering, Anna University, Chennai, India.
Department of ECE, Rathinam Technical Campus, India.
Scanning. 2022 Jun 6;2022:1200860. doi: 10.1155/2022/1200860. eCollection 2022.
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms' capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space (). The Kullback-Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.
生物学和矿物学中的高光谱显微镜，无监督深度学习神经网络去噪 SRS 照片：高光谱分辨率增强和去噪，一个高光谱图像就足以教授无监督方法。使用 -means 聚类生成锂矿石样本的直观化学物质图。现在许多研究人员对生物信号感兴趣。不确定性限制了算法对这些信号进行进一步评估的能力。即使人工智能系统可以解答难题，但它们仍然存在局限性。当机器学习效率低下时，会使用深度学习。深度学习在现代人工智能中至关重要。监督学习需要大量标记数据集。深度学习需要大量标记数据集。参数选择可以防止过拟合或欠拟合。无监督学习用于克服上述挑战（由聚类算法执行）。为此，使用了两种处理过程：（1）利用非线性深度学习网络将数据转换为潜在特征空间（）。使用 Kullback-Leibler 散度测试目标函数的收敛性。本文探讨了一种使用深度学习和有效的无监督学习对高光谱微观图像进行研究的新方法。
