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Updated: Jan 16, 2026

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Deep Neural Networks for Image-Based Dietary Assessment
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配方相似性网络:一种新的算法,可以从书中提取相关信息
Michele Bellingeri1,2, Axel Bidon-Chanal Badia3, Marta Vila Rigat4
1Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, via G.P. Usberti, 7/a, Parma, 43124, Italy. michele.bellingeri@unipr.it.
Scientific reports
|October 2, 2025
概括
网络科学揭示了加泰罗尼亚美食食谱中的独特结构. 高级美食网络更加专业,识别关键的传统和现代菜.
科学领域:
- 计算式美食学是一种计算式的美食学.
- 网络科学 网络科学
- 图形理论 图形理论
背景情况:
- 通过数据分析了解传统至关重要.
- 配方相似性网络提供了对成分关系和演变的见解.
研究的目的:
- 分析加泰罗尼亚菜谱网络的结构性质.
- 使用网络科学来比较传统和高级美食网络.
- 开发用于配方网络分析的新方法.
主要方法:
- 基于共享的成分构建的配方相似性网络.
- 引入了一个新的特设相似性测量方法,并探索了链接重量规范化.
- 应用网络分析和社区检测算法.
主要成果:
- 配方网络高度相互联系,传统和高级美食之间的结构差异很大.
- 高级美食网络展示了更专业的食谱.
- 识别了定义传统的关键食谱,如"Allioli"和"Becada con brioche de su salmis". 这两种食谱都非常重要.
结论:
- 网络科学为分析结构提供了一个强大的框架.
- 方法选择对网络分析结果产生重大影响.
- 这项研究为人工智能驱动的食谱个性化和美食创新奠定了基础.
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