A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment

Lilhore, Umesh Kumar; Simaiya, Sarita; Dalal, Surjeet; Damaševičius, Robertas

doi:10.1007/s11042-023-16677-z

Cited by 17 publications

(2 citation statements)

References 42 publications

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“…Lilhore et al [19] discuss a study on improving waste management in the context of increasing industrialization and smart city development. The work emphasizes the importance of waste collection, classification, and planning, particularly for recycling processes that aim to minimize pollution and promote sustainability.…”

Section: Modelsmentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

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Section: Modelsmentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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“…Several studies have demonstrated the effectiveness of transfer learning for waste classification. Lilhore et al achieved a 95.45% accuracy for two waste categories using a hybrid CNN-LSTM model with transfer learning, highlighting its potential for efficient classification [15]. Similarly, Wulansari et al employed transfer learning for medical waste classification with an impressive 99.40% accuracy [16], showcasing its adaptability to diverse waste types.…”

Section: Related Workmentioning

confidence: 99%

Managing Household Waste Through Transfer Learning

Kunwar

2024

Ind. Domest. Waste Manag.

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As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification.

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Boosting Accuracy and Transparency in Biometric Finger-Vein Recognition: CNN with Adaptive Interpretable Recognition

Khatri,

Sharma

2024

Emerging Trends in Mechatronics

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A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment

Cited by 17 publications

References 42 publications

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Managing Household Waste Through Transfer Learning

Boosting Accuracy and Transparency in Biometric Finger-Vein Recognition: CNN with Adaptive Interpretable Recognition

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