Predictive Modeling of Biodegradable Material Degradation Using Deep Learning with Improved Regulatory and Liability-Aware Approach

There is a significant rise in the growth of the adoption of biodegradable materials across industries. Industries including packaging, healthcare, and consumer goods require accurate prediction of their degradation behavior to support environmental sustainability and to ensure regulatory compliance. Here, we propose a deep-learning-based framework that helps with the prediction of the decomposition rates and the associated environmental impact of biodegradable materials under diverse physicochemical conditions. We train the neural network on historical data on the material performance, environmental exposure, and microbiological interactions, and the model shows its generalization capacity for life cycle estimation. The model has also been associated with an attention layer that monitors the compliance with regulatory frameworks that govern material safety, quality, and consumer transparency. Standards and frameworks (including the ISO and ISI standards) are integrated into this layer to ensure adherence to product liability guidelines. Along with this, domain-specific regulations have also been used to fine-tune the predictive outputs, respecting the permissible limits on product labeling, shelf life, and environmental claims. This approach shows better predictive results and demonstrates compliance with the legal context, and the model evaluation ensures compliance and verification of the predictive results on the materials' life. Here, we explore a multidisciplinary approach that makes use of the learning abilities of the ML algorithms and align the model performance with ethical frameworks to ensure trust and monitoring, making an intelligent system for sustainable material design to improve sustainability and reusability following the regulations.