A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction
The rapid growth of electrical and electronic equipment waste (e-waste) presents a major environmental challenge. Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. Given the high...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014586 |
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