Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Method for classifying a noisy Raman spectrum based on a wavelet
  transform and a deep neural network   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% higher than the NB
classifier, 3.5% higher than the RF classifier, 1% higher than the KNN
classifier, and 0.5% higher than the SVM classifier. In terms of robustness to
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% while the
F-measure scores of the other ML engines were below 98.7%.

8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas