FRDA : Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved..
Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:896 |
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| Enthalten in: |
The Science of the total environment - 896(2023) vom: 20. Okt., Seite 165340 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yan, Xinyu [VerfasserIn] |
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| Links: |
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| Themen: |
Data augmentation |
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| Anmerkungen: |
Date Revised 19.09.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.scitotenv.2023.165340 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM359152880 |
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| 520 | |a Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Data augmentation | |
| 650 | 4 | |a Data pre-processing | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a FTIR | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Microplastic identification | |
| 700 | 1 | |a Cao, Zhi |e verfasserin |4 aut | |
| 700 | 1 | |a Murphy, Alan |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Yuhang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xinwu |e verfasserin |4 aut | |
| 700 | 1 | |a Qiao, Yuansong |e verfasserin |4 aut | |
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