Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network

09/09/2020
by   Liangrui Pan, et al.
0

This paper proposes a new framework based on a wavelet transform and deep neural network for identifying noisy Raman spectrum since, in practice, it is relatively difficult to classify the spectrum under baseline noise and additive white Gaussian noise environments. The framework consists of two main engines. Wavelet transform is proposed as the framework front-end for transforming 1-D noise Raman spectrum to two-dimensional data. This two-dimensional data will be fed to the framework back-end which is a classifier. The optimum classifier is chosen by implementing several traditional machine learning (ML) and deep learning (DL) algorithms, and then we investigated their classification accuracy and robustness performances. The four MLs we choose included a Naive Bayes (NB), a Support Vector Machine (SVM), a Random Forest (RF) and a K-Nearest Neighbor (KNN) where a deep convolution neural network (DCNN) was chosen for a DL classifier. Noise-free, Gaussian noise, baseline noise, and mixed-noise Raman spectrums were applied to train and validate the ML and DCNN models. The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectrums (10-30 dB noise power). Based on the simulation, the accuracy of the DCNN classifier is 9 classifier, 3.5 classifier, and 0.5 the mixed noise scenarios, the framework with DCNN back-end showed superior performance than the other ML back-ends. The DCNN back-end achieved 90 accuracy at 3 dB SNR while NB, SVM, RF, and K-NN back-ends required 27 dB, 22 dB, 27 dB, and 23 dB SNR, respectively. In addition, in the low-noise test data set, the F-measure score of the DCNN back-end exceeded 99.1 F-measure scores of the other ML engines were below 98.7

READ FULL TEXT

Authors

page 1

page 4

page 6

page 7

page 8

page 11

09/09/2020

Noise Reduction Technique for Raman Spectrum using Deep Learning Network

In a normal indoor environment, Raman spectrum encounters noise often co...
04/17/2018

A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder

The Centers for Disease Control and Prevention (CDC) coordinates a labor...
06/23/2018

Disease Classification in Metagenomics with 2D Embeddings and Deep Learning

Deep learning (DL) techniques have shown unprecedented success when appl...
10/29/2020

Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network

With noisy environment caused by fluoresence and additive white noise as...
12/07/2021

A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications

An ML model based on precomputed per-channel SCI is proposed. Due to its...
09/09/2020

Deep learning for gravitational-wave data analysis: A resampling white-box approach

In this work, we apply Convolutional Neural Networks (CNNs) to detect gr...
12/16/2020

Optimized Random Forest Model for Botnet Detection Based on DNS Queries

The Domain Name System (DNS) protocol plays a major role in today's Inte...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.