基于多目标白优化和深度学习的智能和可持续废物分类模型
Gehad Ismail Sayed1,2, Mohamed Abd Elfattah3,4, Ashraf Darwish5,4
1School of Computer Science, Canadian International College (CIC), Cairo, Egypt. gehad_sayed@cic-cairo.com.
Environmental science and pollution research international
|April 18, 2024
概括
本研究介绍了一个智能废物分类模型,使用深度学习和多目标白优化 (MBWO) 进行智能城市废物管理. 这种先进的模型显著提高了废物识别的准确性,促进了可持续的资源回收利用.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 可持续发展需要有效的资源回收,特别是在面临废物产生增加的城市地区.
- 传统的废物管理方法不足以减轻废物对环境造成的破坏.
- 智能技术为自动化废物管理提供先进的解决方案.
研究的目的:
- 提出一个智能废物分类模型,以加强废物材料的识别.
- 提高智能城市废物分类的准确性和效率.
- 为更有效的废物管理战略和可持续实践做出贡献.
主要方法:
- 使用InceptionV3深度学习架构进行废物分类.
- 采用多目标白优化 (MBWO) 进行超参数调整 (放弃率,学习率,批量大小).
- 作为MBWO内部的目标函数,集成的灵敏性和特异性用于优化.
主要成果:
- 拟议的模型在TrashNet数据集上实现了高性能.
- 获得了97.75%的精度,99.55%的特异性,97.58%的F1得分和98.88%的灵敏度.
- 在废物分类任务中超越现有的最先进模型.
结论:
- 智能废物分类模型有效地提高了废物材料的识别.
- 整合MBWO显著提高了模型的准确性和效率.
- 该模型支持更有效的废物管理,并促进智慧城市的可持续实践.
更多相关视频
相关概念视频
Classification of Systems-I
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Aggregates Classification
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...


