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A Dynamic Perceptual Detector Module-Related Telemonitoring for the Intertubes of Health Services

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Artificial Intelligence for Smart Healthcare

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The Network of Effective Treatment (IoMT) model is used in various clinical trials and other healthcare operations. It is based on a novel set of ultraprecise tiny sensing devices and communication infrastructures, which give unmatched data collecting and ongoing vital sign capabilities. To fully realize the technology’s promise, however, some progress is required. First and foremost, the edge-computing paradigm must be considered. A definite amount of manufacturing in the vicinity of the detector must be permitted to enhance versatility and usability, dependability, and the IoMT terminals’ availability. Second, newer, more techniques for efficient information analysis, such as those that are specially adapted intelligence and deep learning, must be used. IoMT node designers and programmers must overcome severe optimization issues to conduct moderately complicated computing operations on flexible and wearable computing with low-power consumption devices with restricted power and battery lives to achieve these aims. This work investigates the creation of an examination of intellectual data methods for computer systems with limited resources by dynamically controlling the vehicle’s hardware and software configuration and adapting it to the necessary operating mode at runtime. A low-power microcontroller and a neural network model is employed to categorize cardiac data to test this strategy. It is observed that adjusting the node architecture to the strain at execution can be reduced to 50% in power utilization using the MIT-BIH Arrhythmia set as a testbed. A quantized neural network could diagnose arrhythmia disorders with a 98 percent accuracy rate.

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Author information

Author notes

  1. S. Suman Rajest

    Present address: Department of Research, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Vaibhav Rupapara

  2. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    Regin Rajan

  3. Department of Electronics and Communication, Vins Christian College of Engineering, Nagercoil, Tamil Nadu, India

    R. Steffi

  4. Department of Biomedical Engineering, Agni College of Technology, Chennai, Tamil Nadu, India

    T. Shynu

  5. Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    G. Jerusha Angelene Christabel

Authors
  1. Vaibhav Rupapara
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  2. S. Suman Rajest
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  3. Regin Rajan
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  4. R. Steffi
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  5. T. Shynu
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  6. G. Jerusha Angelene Christabel
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Jamia Hamdard, New Delhi, India

    Parul Agarwal

  2. Delhi Skill and Entrepreneurship University, Dwarka, Delhi, India

    Kavita Khanna

  3. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt

    Ahmed A Elngar

  4. Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq

    Ahmed J. Obaid

  5. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

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Rupapara, V., Rajest, S.S., Rajan, R., Steffi, R., Shynu, T., Christabel, G.J.A. (2023). A Dynamic Perceptual Detector Module-Related Telemonitoring for the Intertubes of Health Services. In: Agarwal, P., Khanna, K., Elngar, A.A., Obaid, A.J., Polkowski, Z. (eds) Artificial Intelligence for Smart Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-23602-0_15

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  • DOI: https://doi.org/10.1007/978-3-031-23602-0_15

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