The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the ’TrashNet Waste’ database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models’ trash classification performance and provided specific viewpoints. The results explain each technique’s pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method’s excellent performance suggests its seamless integration into practical waste management solutions.

中文翻译：

---

可持续环境的智能垃圾分类：深度学习和迁移学习的混合模型


由于工业发展的加快和智慧城市化的进步，垃圾处理、分类和监测的重要性急剧增加。自过去几十年以来，深度学习技术在废物管理研究中的应用日益增长。废物再利用和回收过程的效率取决于其将资源恢复到原始状态的能力，从而最大限度地减少污染并促进生态可持续框架。选择最佳的深度学习方法来分类和预测废物既具有挑战性又耗时。本文提出使用双向长短期记忆 (Bi-LSTM) 和基于 CNN 的迁移学习进行智能垃圾分类，以提高环境可持续性。有机垃圾和可回收垃圾分开。为了简化垃圾分类，混合模型结合了基于 TL 的 CNN 和 Bi-LSTM。这项研究使用“TrashNet Waste”数据库，使用多种 CNN 计算方法（包括 VGG-19、ResNet-34、AlexNet、ResNet-50 和 VGG-16）广泛检查了建议的技术。主要发现表明，我们的混合模型优于现有模型。我们的分类准确率为 96.78%，比最佳模型高 5.27%。我们的模型还将错误分类减少了 7.25%，证明了其可靠性。这项综合检查检查了计算机模型的垃圾分类性能并提供了具体的观点。结果解释了每种技术的优缺点，并显示了它们在现实环境中的有用性。废物分类采用混合模型，既实用又复杂。该模型的有效性和巧妙性改善了可持续的环境实践。 该方法的优异性能表明它可以无缝集成到实际的废物管理解决方案中。