Quantum photonics based music signal analysis with optical sensor in health monitoring using machine learning model

Smart health monitoring systems have been made possible by the internet of things (IoT). A person’s physical and emotional well-being can be tracked by these health monitoring systems. The flow of quantum light through an integrated photonic circuit ultimately determines the scalability of various photonic quantum information processing devices. Purpose of this study is to use a machine learning (ML) method to build music signal analysis coupled with an optical sensor in a health monitoring system. Quantum photonics and the optical sensor paradigm in health monitoring are used to analyse music signals. The reinforcement gradient vector Markov propagation model has been used to assess the observed data based on optical sensors (RGVMP). the experimental analysis is carried out based on various music signal based optical sensor health monitoring data in terms of training accuracy, mean average precision, F-1 score, RMSE, AUC. The suggested model’s steganography and steganalysis quantum circuits were all simulated, tested, and assessed using various audio files. The suggested method achieved 98% training accuracy, 94% mean average precision, 92% F-1 score, 73% RMSE, and 93% AUC.