Comparative Analysis on Machine Learning and One-Dimensional Convolutional Neural Network to Predict Surface Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS) established on machine learning (ML) techniques have been used in intelligence investigation, food safety, biological recognition, and material study. However, ML techniques typically require additional preprocessing or attribute engineering, and conducting...

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Bibliographic Details
Published in2023 3rd International Conference on Computing and Information Technology (ICCIT) pp. 216 - 221
Main Authors Jamil, Nasrin Nadher, Khairi Kareem, Aythem
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.09.2023
Subjects
Convolutional Neural Network
Convolutional neural networks
Decision Tree
Feature extraction
Machine Learning
Naïve Bayes
Predictive models
Raman scattering
Safety
Support Vector Machine
Support vector machines
Training
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Summary:Surface-enhanced Raman spectroscopy (SERS) established on machine learning (ML) techniques have been used in intelligence investigation, food safety, biological recognition, and material study. However, ML techniques typically require additional preprocessing or attribute engineering, and conducting large data employing these techniques is problematic. Deep Learning (DL) techniques involve minimal input data by analyzing every probable attribute established in the network. Therefore, this study employed ML techniques. These are K-Nearest Neighbor (KNN), Naïve Bayes (NB) Support Vector Machine (SVM), and Decision Trees (DT). Then, the result is compared with a one-dimensional convolutional neural network structure (1D-CNN). In this research, we utilize Rhodamine 6G (R6G) as an objective molecule. The experimental results demonstrate the effectiveness of the 1D-CNN offered.
DOI:10.1109/ICCIT58132.2023.10273940