Summary of Machine Learning For Raman Spectroscopy-based Cyber-marine Fish Biochemical Composition Analysis, by Yun Zhou et al.
Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis
by Yun Zhou, Gang Chen, Bing Xue, Mengjie Zhang, Jeremy S. Rooney, Kirill Lagutin, Andrew MacKenzie, Keith C. Gordon, Daniel P. Killeen
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new design of Convolutional Neural Networks (CNNs) to predict water, protein, and lipids yield in fish using Raman spectroscopy. Machine learning regression models are trained to associate Raman spectra with biochemical reference data for rapid detection of biochemical compositions in fish. The authors investigate different regression models and demonstrate that their CNN can outperform state-of-the-art CNN models and traditional machine learning models on a small Raman spectroscopic dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computers (called Convolutional Neural Networks) to quickly identify what’s inside fish, like water, protein, or fat. This helps the seafood industry use these valuable resources more efficiently. The researchers tried different computer programs and showed that their new design can do a better job than others on a small set of data. |
Keywords
* Artificial intelligence * Cnn * Machine learning * Regression
