Garbage classification using convolutional neural networks (CNNs)

Al Mahmud Al Mamun, Rasel Hossain, Mst. Mahfuza Sharmin, E. Kabir, Md. Ashik Iqbal
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Abstract

Proper garbage classification is essential for effective waste management and environmental sustainability. This research paper presents a comprehensive study of garbage classification using Convolutional Neural Networks (CNNs). The objective is to develop an accurate and automated garbage classification system leveraging the power of deep learning. The proposed CNN model achieves an impressive accuracy of 98.45%, demonstrating its efficacy in classifying different waste categories. The research encompasses data collection, preprocessing, model architecture, training methodology, and evaluation. The results indicate the potential of CNNs in revolutionizing waste management practices and paving the way for a more sustainable future.
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基于卷积神经网络的垃圾分类
正确的垃圾分类对有效的废物管理和环境的可持续性至关重要。本文提出了一种基于卷积神经网络(cnn)的垃圾分类方法。目标是利用深度学习的力量开发一个准确和自动化的垃圾分类系统。本文提出的CNN模型达到了令人印象深刻的98.45%的准确率,证明了其对不同垃圾类别进行分类的有效性。研究内容包括数据收集、预处理、模型架构、训练方法和评估。研究结果表明,cnn在彻底改变废物管理实践和为更可持续的未来铺平道路方面具有潜力。
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