A New Hybrid Model Achieves Near-Perfect Accuracy in Smart Waste Classification
Researchers have developed a novel machine learning framework, HGB-CBSNet, designed to tackle the challenge of solid waste classification in IoT-enabled smart cities. The model combines advanced image pre-processing techniques with a pyramid-dilated DenseNet for feature extraction, an improved coot bird search algorithm for optimal feature selection, and a hybrid classifier using LightGBM and XGBoost. Tested on the public TrashNet dataset, this approach achieved a remarkable classification accuracy of 99.72%, significantly outperforming existing methods while also reducing computational complexity. This represents a major step forward in using artificial intelligence and data science for efficient, real-time urban management.
Why it might matter to you: For professionals focused on data science and machine learning, this study demonstrates a highly effective integration of feature engineering, dimensionality reduction, and ensemble learning within a practical IoT application. It provides a concrete blueprint for building high-accuracy predictive models that are also optimized for computational efficiency, a critical consideration for scalable cloud computing and MLOps pipelines. The work underscores how advanced data analysis and model deployment can directly address pressing real-world issues in smart infrastructure and environmental sustainability.
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