solid waste classification reinforcement learning boosted is a M.Tech project topic for Civil Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
solid waste classification reinforcement learning boosted Project Details
| Abstract |
Automated solid waste classification is a key part of modern recycling systems and helps reduce environmental pollution. This research framework looks at using reinforcement learning (RL) algorithms to improve how accurately different types of solid waste are sorted. Because environmental engineering often works with small and unbalanced datasets, we apply advanced data‑augmentation techniques to enlarge the training set and help the model generalize better. Systematic hyperparameter optimization is also used to fine‑tune the RL agent’s decision policies, giving strong feature extraction and classification even when conditions change. The method provides a clear way to evaluate how deep Q‑networks or policy‑gradient methods perform with image‑based waste data. By measuring precision, recall,
and convergence rate, we set up a strict evaluation protocol for intelligent waste‑sorting systems. This framework can serve as a technical guide for adding adaptive machine‑learning models to waste‑management infrastructure, showing how to balance computational efficiency with classification accuracy.
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| Reference Paper |
A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization. |
| Domain |
Civil Engineering |
| Sub-Domain |
Environmental & Water Resources / Wastewater & Solid Waste / Solid Waste Management |
| PDF Download |
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