IoT-Enabled Deep Reinforcement Learning for Adaptive Waste Management in Hospital Environments
DOI:
https://doi.org/10.33333/lajc.vol13n2.06Palabras clave:
Biomedical Waste Management, Internet of Things (IoT), Deep Reinforcement Learning (DRL), Healthcare Systems, Intelligent Optimization, Sustainability, Hospital SafetyResumen
The healthcare industry produces huge amounts of biomedical waste on a daily basis, which invariably poses threats to society. Biomedical waste ejected from hospitals on a daily basis has to be properly handled and treated using appropriate techniques to avoid risks to hospital staff and society. Even though there are different models in the market that are specifically used for disposing of biomedical waste at cheaper costs and ensuring that it is properly treated and recycled without posing risks to society and medical professionals working in institutions. The proposed work makes use of deep reinforcement learning techniques and Internet of Things for biomedical waste management setups in institutions and proposes an innovative setup that has better capabilities and superior to other models because it ensures that biomedical waste is properly treated and recycled at cheaper costs while posing no risks to society and medical professionals working in institutions at all times.
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Derechos de autor 2026 Muhammad Masood Usmani, Rimsha Rafiq, Makki Riaz Khan
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