探索用于光谱分析的生成人工智能和数据增强技术
Aaron R Flanagan1, Dhairya Dalal2, Frank G Glavin1
1School of Computer Science, University of Galway, Galway City, Co. Galway H91 FYH2, Ireland.
这篇评论将化学测量和生成人工智能 (AI) 联系在一起,为光谱学研究人员提供了人工智能和数据增强的实用指南. 它简化了复杂的AI概念,以便在科学研究中更容易实施.
科学领域:
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 生成型人工智能 (AI) 方法复杂且难以实施.
- 现有的数据增强技术更简单,但可能与先进的AI性能不匹配.
- 化学测量和生成AI之间存在一个知识差距,用于光谱应用.
研究的目的:
- 为了弥合化学测量和生成AI之间的知识差距.
- 为新的光谱学研究人员提供起点.
- 在光谱学中审查最先进的生成人工智能和数据增强技术.
主要方法:
- 收集并对104篇同行评审期刊和论文进行了分类.
- 描述了光谱学中流行的预处理技术.
- 审查了最先进的生成人工智能和数据增强方法.
主要成果:
- 提供了对人工智能和数据增强方法的直观解释.
- 突出了各种技术的优缺点.
- 包括图形和实际应用示例.
结论:
- 生成型人工智能和数据增强对现代光谱学至关重要.
- 这篇评论为研究人员简化了复杂的AI概念.
- 这项工作作为在光谱学中实施先进AI的基础资源.
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