#Artifical by janaki raman in pdf portable

We report the development of a label-free, simple, and high efficiency breast cancer detection platform with multimodal biomarker analytic algorithms on a portable 785 nm Raman setup with an endoscopic Raman-lensed fiber optic probe. proposed a method based on a single-layer multiplekernel-based convolutional neural network (SLMK-CNN) as an analytical tool for By optimizing the network structure, Won Bum et al. The proposed network consists of two preprocessing layers (noise reduction and baseline correction layer) and a fully connected classifier. CNN was achieved to distinguish the Raman spectra of human and animal blood.

The cooperation between different network layers makes the model more specific and sensitive. Compared with traditional perception, Convolutional Neural Network (CNN) extracts low-level features from the input, following with local perception, parameter sharing, and spatial self-sampling. In addition to simple feedforward neural networks, various neural networks have also been applied to analyze Raman spectra mixtures. One-dimensional Raman spectrum is passed through the input layer, local perception and feature extraction are done in the hidden layer, and judgments are made from the output layer. Workflow of a simple feedforward neural network. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail. This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. This article summarizes the work of Raman spectroscopy in identifying the composition of substances as well as provides detailed reviews on the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. However, the development of Raman spectroscopy, machine learning, and deep learning techniques have paved the way for achieving efficient analytical tools capable of identifying mixture components, making an apparent breakthrough in the identification of mixtures beyond the traditional chemical analysis methods. In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task.

Artifical by janaki raman in pdf