专家知识诱导:访问专家大脑中的大数据
Jacobo Robledo1,2,3,4, Aaron I Plex Sulá1,2,3,4, Lauren G Jaworski1,2,3,4
1Plant Pathology Department, University of Florida, Gainesville, FL, U.S.A.
Phytopathology
|September 15, 2025
概括
专家知识的提取使得宝贵的见解可用于植物健康挑战. 这种系统的方法综合了专家知识,为全球植物健康和疾病管理做出关键决策提供了信息.
科学领域:
- 植物病理学 植物病理学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 专家知识仍然在很大程度上无法访问数字系统,阻碍了植物病理学的及时决策.
- 客观数据往往是不完整的,以应对紧急,不确定的或未来的植物健康挑战,如新出现的疾病.
研究的目的:
- 探索专家知识获取对植物健康挑战的有效性.
- 强调其在为基于专家的决策提供信息和与大数据流集成方面的作用.
- 概述未来在全球植物卫生领域扩大专家知识获取规模的机会.
主要方法:
- 系统的方法来访问和综合主题专家的见解.
- 将专家知识视为大数据,与遥感,众包报告和数字监控集成.
- 捕获输出作为可扩展数据集 (文本,表格,音频,视频) 用于人工智能支持的合成和贝叶斯分析.
主要成果:
- 专家知识提取为解决复杂的植物病理问题和知识差距提供了有价值的数据.
- 现实世界的实施提供了提取,结构化和解释专家衍生数据的教训.
- 与大数据和人工智能的整合增强了理解不确定性的推断和透明度.
结论:
- 专家知识的提取对于及时,基于专家的植物健康决策至关重要,特别是在不完整的数据的情况下.
- 它可以通过与人工智能和大数据流的整合来扩展和加强.
- 这种方法支持全球植物健康倡议,使专家的见解变得可行.
关键词:
贝叶斯的更新是贝叶斯的更新.人工智能是一种人工智能.大数据就是大数据.决策支持提供了决策支持.流行病建模的流行病建模专家引发的诱惑专家的意见 专家的意见未来的场景 未来的场景存在的知识差距和知识缺口.自然语言处理自然语言处理.之前的知识 之前的知识更多相关视频
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