使用红狐优化和混合型DenseNet-BiLSTM模型进行最佳的固体废物管理
P M Beulah Devamalar1, K Kalaiselvi2, M Jenath Sathikbasha3
1Department of Computer Science and Engineering, Prathyusha Engineering College, Tiruvallur, Chennai, India. director@prathyusha.edu.in.
Environmental monitoring and assessment
|September 27, 2023
概括
本研究引入基于Dense Net-BiLSTM的自动化红狐 (DNBiLSTM-RF) 方法,用于精确的城市固体废物分类. 这种新的方法达到98.9%的准确性,改善了废物管理和环境健康.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 城市固体废物积累对环境和健康构成风险,特别是在发展中国家.
- 手动分离废物是低效的,可能导致不当的处置.
- 先进的智能废物管理系统对于更清洁的环境至关重要.
研究的目的:
- 开发一种新型的自动化系统,以高效准确地对城市固体废物进行分类.
- 为了简化各种废物材料的机械分离过程.
- 通过智能分类来提高废物管理系统的性能.
主要方法:
- 基于DenseNet-BiLSTM的红狐 (DNBiLSTM-RF) 方法被提议用于自动化废物分类.
- 来自德黑兰废物管理组织的废物数据进行了预处理,包括使用四分区间 (IQR) 过方法去除异常值.
- 使用红狐 (RF) 优化算法优化了DenseNet-BiLSTM模型的超参数.
主要成果:
- DNBiLSTM-RF分类器准确地将城市废物分为六类:木材,织品,食品残留物,,纸张和塑料.
- 提出的方法实现了大约98.9%的高分类准确性.
- 使用RMSE,MAE,R-squared,精度,回忆和F-measure等指标来评估性能.
结论:
- DNBiLSTM-RF方法在城市固体废物分类方面展示了显著的可行性和精度.
- 这种自动化系统为人工分离提供了优质的替代方案,提高了废物管理的效率.
- 该研究强调了人工智能驱动的解决方案在解决固体废物带来的环境挑战方面的潜力.
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