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ONDL:一个优化的Neutrosophic深度学习模型,用于对废物进行可持续性分类
Nour Eldeen Mahmoud Khalifa1, Mohamed Hamed N Taha1, Heba M Khalil2
1Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.
一个优化的神经学深度学习 (ONDL) 模型有效地使用计算机视觉对废物进行分类. 这种人工智能方法提高了废物管理,为一个更绿色的星球.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 可持续性对于一个更绿色的星球至关重要,有效的废物分类和管理起着至关重要的作用.
- 计算机算法和深度学习为废物管理挑战提供了先进的解决方案.
研究的目的:
- 为准确的废物物体分类提出一个优化中性学深度学习 (ONDL) 模型.
- 评估ONDL模型在两个不同的废物数据集 (DSWM1和DSWM2) 上的性能.
主要方法:
- 在ONDL模型使用深度转移学习 (DTL) 基于Alexnet,结合真 (T) 中性化域转换.
- 灰狼优化 (GWO) 在ONDL架构中用于高效的图像特征选择.
- 对比分析包括测试各种DTL模型 (Alexnet,Googlenet,Resnet18) 和中性学域 (T,I,F).
主要成果:
- 与其他测试模型相比,ONDL模型显示出更高的效率.
- 在DSWM1 (2类) 上,ONDL实现了0.9189的测试精度 (TA),0.9177的精度 (P),0.9176的回忆 (R) 和0.9177的F1得分.
- 在DSWM2 (3类) 上,ONDL实现TA为0.8532,P为0.7728,R为0.7944,F1得分为0.7835,显示出具有竞争力的结果.
结论:
- 在废物分类方面,ONDL模型是一种有效的深度学习方法.
- 拟议的模型实现了具有竞争力的绩效指标,有助于改善废物管理的可持续性.
- 这项研究强调了中性学深度学习在应对环境挑战方面的潜力.
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