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欧亚-CNN:增强注意力-CNN提供可解释的AI,用于水果和蔬菜的分类.
Zeshan Aslam Khan1, Muhammad Waqar1, Khalid Mehmood Cheema2
1International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC.
Heliyon
|December 19, 2024
概括
本研究引入了一种增强的注意力-CNN (EA-CNN) 模型,用于使用可解释的AI准确地分类水果和蔬菜. 该EA-CNN模型在Fruit-360数据集上实现了高精度和效率,提供可解释的预测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 水果和蔬菜的错误分类导致零售业的财务损失.
- 现有的CNN分类模型复杂,计算成本昂贵,缺乏可解释性.
- 通常使用有限的数据集和类,阻碍现实世界的应用.
研究的目的:
- 为准确和高效的水果和蔬菜分类提出一个可解释的AI (XAI) 驱动的增强注意力-CNN (EA-CNN).
- 与现有模型相比,提高分类准确性和降低计算成本.
- 为实际应用提供可解释的预测.
主要方法:
- 开发了一个增强的注意力-CNN (EA-CNN) 模型,结合了新的聚合技术和注意力机制.
- 使用全面的Fruit-360基准数据集 (141个类) 进行培训和验证.
- 使用XAI方法进行可解释的预测分析.
主要成果:
- EA-CNN模型在Fruit-360数据集上实现了98.1%的准确性,与基线模型相比,代次数更少.
- 与现有方法相比,证明了更高的准确性和更低的计算成本.
- 在"水果识别"数据集上验证了模型的概括能力,达到96%的准确性.
结论:
- 拟议的EA-CNN在实际应用中为水果和蔬菜分类提供了有效和可靠的解决方案.
- 欧亚-CNN提供准确,高效和可解释的分类结果.
- 该模型在不同数据集中表现出强大的概括性,稳定性,可扩展性和适应性.
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