Revolutionizing healthcare mapping with quantum remote sensing based data analysis using deep learning model.

The promise of digital technology to greatly improve the efficiency of sorting and processing facilities of the future has not yet been fully realised. Improved sensor-based material flow characterization methods may pave the way for new sensor applications including adaptive plant management, increased sensor-based sorting, and more far-reaching data utilisations throughout the value chain. Using quantum remote sensors, this research proposes a novel deep learning model-based technique for evaluating healthcare data. In this scenario, healthcare data from quantum far-field sensors is collected and analysed using fuzzy K clustering-based kernel convolutional transfer Bayesian neural networks. Experimental evaluations of various detected signal data are analysed in terms of accuracy, precision, recall, and root-mean-squared error. In addition, we demonstrate that the proposed approach has reasonable computation speeds, meeting the requirements of real-time node processing on smartphones and a wearable sensor platform. [ABSTRACT FROM AUTHOR]