An optimal asthma disease detection technique for voice signal using hybrid machine learning technique-Reference-Cited by-同舟云学术

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An optimal asthma disease detection technique for voice signal using hybrid machine learning technique

Published:2022-02-10 Issue:11 Volume:34 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Asim Iqbal Mohammed¹^ORCID,Devarajan Krishnamoorthy¹,Ahmed Syed Musthak²

Affiliation:

1. Department of Electronics and Communication Engineering Annamalai University Chidambaram India

2. Department of Electronics and Communication Engineering SR University Warangal India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6856

Reference28 articles.

1. An acoustic model of the respiratory tract

2. HolmesMS Le MennM D'ArcyS et al.Automatic identification and accurate temporal detection of inhalations in asthma inhaler recordings. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; August 2012:2595‐2598.

3. HolmesMS D'ArcyS CostelloRW ReillyRB. An acoustic method of automatically evaluating patient inhaler technique. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; July 2013:1322‐1325.

4. San ChunK NathanV VatanparvarK. Towards passive assessment of pulmonary function from natural speech recorded using a mobile phone. Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom); IEEE; March 2020:1‐10.

5. MayorgaP DruzgalskiC GonzálezOH ZazuetaA CriolloMA.Expanded quantitative models for assessment of respiratory diseases and monitoring. Proceedings of the 2011 Pan American Health Care Exchanges. IEEE; March 2011:317‐322.

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1. Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds;Neural Computing and Applications;2024-06-02

2. Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review;Sensors;2024-02-10

3. Analysis on Exposition of Speech Type Video Using SSD and CNN Techniques for Face Detection;Proceedings of Data Analytics and Management;2023

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