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应用机器学习方法对清真肉认证原则,挑战和前景的应用:一篇综述

Abdul Mustapha¹, Iskandar Ishak^1,2, Nor Nadiha Mohd Zaki^1,3

¹Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia.

|July 8, 2024

概括

机器学习 (ML) 为清真肉的认证提供了先进的解决方案,克服了传统方法的局限性. 机器学习技术为消费者保护提供了更快,更准确和更具成本效益的身份验证.

科学领域:

食品科学食品科学食品科学
计算生物学计算生物学
伊斯兰研究是伊斯兰研究.

背景情况:

肉类改对消费者来说是一个重大挑战,特别是有关哈拉尔饮食法.
传统的哈拉尔肉类认证方法在准确性,灵敏性和可扩展性方面存在局限性.
越来越多的消费者意识需要可靠和高效的肉类认证技术.

研究的目的:

批判性地评估机器学习 (ML) 在哈拉尔肉类认证中的应用.
将ML方法与传统方法的有效性进行比较.
探索 ML 在这个领域的原则,挑战,成功和未来前景.

主要方法:

审查和分析现有的关于机器学习算法应用到肉类认证的文献.
基于速度,准确性,成本和可扩展性的ML技术与传统身份验证方法的比较.
识别特定的ML算法及其在检测哈拉尔肉的真实性方面的性能.

主要成果:

机器学习技术比传统方法具有显著的优势,提供更高的速度,灵活性,自动化和成本效益.
几种ML方法在验证哈拉尔肉的真实性方面表现出很高的准确性,尽管一些方法仍在开发中.
ML提供了一个可扩展和敏感的解决方案,以解决肉类改问题.

结论:

机器学习为准确认证清真肉提供了一种有希望和有效的方法.
采用ML可以显著提高消费者对肉类供应链的信任和安全.
需要进一步的研究和开发,才能充分发挥ML在哈拉尔肉类认证中的潜力.

关键词:

奸淫的行为奸淫的行为验证身份验证验证身份验证哈拉尔肉类是合法的肉类.机器学习机器学习被监督的人监督的人没有监督的无人驾驶.

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