Deep Neural Networks for Advanced Medication Security in IoT-Enabled Smart Robotic Dispensing Cabinets

Ensuring advanced medication security is of the utmost importance in smart robotic dispensing cabinets enabled by the Internet of Things (IoT). Strong pharmaceutical security is of the utmost importance in smart robotic dispensing cabinets that the IoT enables. To strengthen security measures in these systems, this research presents a new method that uses deep neural networks (DNNs). The proposed system improves the identification of potential medication mistakes and unauthorized access by using DNNs' anomaly detection and pattern recognition capabilities. A thorough security layer is created by incorporating DNN models into the IoT design of smart cabinets to address drug administration and dispensation weaknesses. Discovering security breaches or procedural problems entails training DNNs on large datasets to detect typical operating patterns and abnormalities. It shows that the strategy significantly improves security procedures and operational dependability in real-world conditions via simulations and validation experiments, proving its usefulness. Providing a strong answer to the problems of improving the effectiveness of drug administration in healthcare settings based on the IoT helps move healthcare technology forward. The technology improves pharmaceutical security and lays the groundwork for responsive and adaptable healthcare infrastructures. This study will make improving the dependability and safety of healthcare delivery systems possible.