Skip to main content
SpringerLink
Log in

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

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

All the data’s available in the manuscript.

References

  • Al-Ashwal, N.H., Al Soufy, K.A., Hamza, M.E., Swillam, M.A.: Deep learning for optical sensor applications: a review. Sensors 23(14), 6486 (2023)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Baduge, S.K., Thilakarathna, S., Perera, J.S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., Mendis, P.: Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Constr. 141, 104440 (2022)

    Article  Google Scholar 

  • Cuțitoi, A.C.: Remote patient monitoring systems, wearable internet of medical things sensor devices, and deep learning-based computer vision algorithms in COVID-19 screening, detection, diagnosis, and treatment. Am. J. Med. Res. 9(1), 129–144 (2022)

    Article  Google Scholar 

  • Dang, L.M., Min, K., Wang, H., Piran, M.J., Lee, C.H., Moon, H.: Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 108, 107561 (2020)

    Article  Google Scholar 

  • Dhanalakshmi, S., Nandini, P., Rakshit, S., Rawat, P., Narayanamoorthi, R., Kumar, R., Senthil, R.: Fiber Bragg grating sensor-based temperature monitoring of solar photovoltaic panels using machine learning algorithms. Opt. Fiber Technol. 69, 102831 (2022)

    Article  CAS  Google Scholar 

  • Goyal, S., Waghmare, J.M., Ranjanikar, M.A.: Heart disease classification models from optical device-based electrocardiogram signals using machine learning algorithms. Optik 271, 170176 (2022)

    Article  ADS  Google Scholar 

  • Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.M.A.: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things 7, 100059 (2019)

    Article  Google Scholar 

  • Huang, B., Sheng, X., Cao, J., Jia, H., Gao, W., Gu, S., Wang, X., Chu, P.K., Lou, S.: Machine-learning-assisted omnidirectional bending sensor based on a cascaded asymmetric dual-core PCF sensor. Opt. Lett. 48(19), 4929–4932 (2023)

    Article  ADS  CAS  PubMed  Google Scholar 

  • Jayawickrema, U.M.N., Herath, H.M.C.M., Hettiarachchi, N.K., Sooriyaarachchi, H.P., Epaarachchi, J.A.: Fibre-optic sensor and deep learning-based structural health monitoring systems for civil structures: a review. Measurement 199, 111543 (2022)

    Article  Google Scholar 

  • Li, W., Chai, Y., Khan, F., Jan, S.R.U., Verma, S., Menon, V.G., Kavita, F., Li, X.: A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mob. Netw. Appl. 26, 234–252 (2021)

    Article  Google Scholar 

  • Nagarajan, S.M., Deverajan, G.G., Chatterjee, P., Alnumay, W., Ghosh, U.: Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities. Sustain. Cities Soc. 71, 102945 (2021)

    Article  Google Scholar 

  • Palmero, C., Torres, M.I., Esposito, A., Escalera, S.: Guest editorial: special issue on computer vision and machine learning for healthcare applications. Pattern Anal. Appl. 25(3), 489–492 (2022)

    Article  Google Scholar 

  • Sharma, S., Dudeja, R.K., Aujla, G.S., Bali, R.S., Kumar, N.: DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients. Neural Comput. Appl. (2020). https://doi.org/10.1007/s00521-020-05327-2

    Article  Google Scholar 

  • Wang, Q., Lyu, W., Cheng, Z., Yu, C.: Noninvasive measurement of vital signs with the optical fiber sensor based on deep learning. J. Lightwave Technol. 41(13), 4452–4462 (2023a)

    Article  ADS  Google Scholar 

  • Wang, Q., Lyu, W., Zhou, J., Yu, C.: Sleep condition detection and assessment with optical fiber interferometer based on machine learning. Iscience 26(7), 107244 (2023b)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Zvarikova, K., Horak, J., Bradley, P.: Machine and deep learning algorithms, computer vision technologies, and Internet of Things based healthcare monitoring systems in COVID-19 prevention, testing, detection, and treatment. Am. J. Med. Res. 9(1), 145–160 (2022)

    Article  Google Scholar 

Download references

Funding

This work was sponsored in part by Shanxi Provincial Education Science Planning Fund Project (PJ-21041).

Author information

Authors and Affiliations

  1. Surveying and Mapping Engineering Department, Shanxi Conservancy Technical Institute, Yun Cheng, 044000, China

    Yanhua Zhang & Baiyong Wang

Authors
  1. Yanhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baiyong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ: Conceived and design the analysis. Writing—Original draft preparation. Collecting the Data, Contributed data and analysis stools; BW: Performed and analysis, Performed and analysis. Wrote the Paper. Editing and Figure Design.

Corresponding author

Correspondence to Baiyong Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Wang, B. Revolutionizing healthcare mapping with quantum remote sensing based data analysis using deep learning model. Opt Quant Electron 56, 285 (2024). https://doi.org/10.1007/s11082-023-06068-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11082-023-06068-x

Keywords

Search

Navigation