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Erschienen in:
01.03.2024
Revolutionizing healthcare mapping with quantum remote sensing based data analysis using deep learning model
Erschienen in: Optical and Quantum Electronics | Ausgabe 3/2024
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Abstract
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.