Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, 224122, Uttar pradesh, India.
Department of of Computer Science and Engineering, Mahamaya Colege of Agriculture Engineering and Technology, Ambedkar Nagar, 224122, Uttar pradesh, India.
Environ Monit Assess. 2024 Feb 17;196(3):279. doi: 10.1007/s10661-024-12410-x.
Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution - a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.
有效的废物管理对于人类福祉和环境健康至关重要，因为忽视适当的处理方法可能会导致财务损失和自然资源枯竭。考虑到快速的城市化和人口增长，开发自动化、创新的废物分类模型变得势在必行。为了满足这一需求，我们的论文提出了一种新颖而强大的解决方案——一种智能废物分类模型，该模型利用混合深度学习模型（优化密集网络-121 + SVM），使用 TrashNet 数据集对废物进行分类。我们提出的方法使用了经过优化以实现卓越性能的先进深度学习模型 DenseNet-121，从扩展的 TrashNet 数据集中提取有意义的特征。然后，这些特征被输入到支持向量机（SVM）中进行精确分类。采用数据增强技术进一步提高了分类精度，同时降低了过拟合的风险，尤其是在处理有限的 TrashNet 数据时。我们对这种混合深度学习模型进行实验评估的结果非常有希望，准确率高达 99.84%。这一准确率超过了类似的现有模型，证实了我们的方法在为可持续和更清洁的未来彻底改变废物分类方面的有效性和潜力。
