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Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biome...

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Autores principales: Manoharan, Hariprasath, Selvarajan, Shitharth, Yafoz, Ayman, Alterazi, Hassan A., Uddin, Mueen, Chen, Chin-Ling, Wu, Chih-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168426/
https://www.ncbi.nlm.nih.gov/pubmed/35677767
http://dx.doi.org/10.3389/fpubh.2022.909628
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author Manoharan, Hariprasath
Selvarajan, Shitharth
Yafoz, Ayman
Alterazi, Hassan A.
Uddin, Mueen
Chen, Chin-Ling
Wu, Chih-Ming
author_facet Manoharan, Hariprasath
Selvarajan, Shitharth
Yafoz, Ayman
Alterazi, Hassan A.
Uddin, Mueen
Chen, Chin-Ling
Wu, Chih-Ming
author_sort Manoharan, Hariprasath
collection PubMed
description The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.
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spelling pubmed-91684262022-06-07 Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach Manoharan, Hariprasath Selvarajan, Shitharth Yafoz, Ayman Alterazi, Hassan A. Uddin, Mueen Chen, Chin-Ling Wu, Chih-Ming Front Public Health Public Health The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods. Frontiers Media S.A. 2022-05-23 /pmc/articles/PMC9168426/ /pubmed/35677767 http://dx.doi.org/10.3389/fpubh.2022.909628 Text en Copyright © 2022 Manoharan, Selvarajan, Yafoz, Alterazi, Uddin, Chen and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Manoharan, Hariprasath
Selvarajan, Shitharth
Yafoz, Ayman
Alterazi, Hassan A.
Uddin, Mueen
Chen, Chin-Ling
Wu, Chih-Ming
Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title_full Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title_fullStr Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title_full_unstemmed Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title_short Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach
title_sort deep conviction systems for biomedical applications using intuiting procedures with cross point approach
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168426/
https://www.ncbi.nlm.nih.gov/pubmed/35677767
http://dx.doi.org/10.3389/fpubh.2022.909628
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