DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks.
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| Title: | DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks. |
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| Authors: | Jayasutha, D.1 (AUTHOR) Jayasuthaphd@gmail.com, Hemamalini, V.2 (AUTHOR), Sangeetha, S.3 (AUTHOR), Yeruva, Ajay Reddy4 (AUTHOR) |
| Source: | International Journal of Communication Systems. 11/25/2024, Vol. 37 Issue 17, p1-17. 17p. |
| Subjects: | Body sensor networks, Generative adversarial networks, Wireless sensor networks, Optimization algorithms, Volcanic eruptions, Deep learning |
| Abstract: | Summary: Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 180348098 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jayasutha%2C+D%2E%22">Jayasutha, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Jayasuthaphd@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Hemamalini%2C+V%2E%22">Hemamalini, V.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sangeetha%2C+S%2E%22">Sangeetha, S.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yeruva%2C+Ajay+Reddy%22">Yeruva, Ajay Reddy</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Communication+Systems%22">International Journal of Communication Systems</searchLink>. 11/25/2024, Vol. 37 Issue 17, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Body+sensor+networks%22">Body sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Volcanic+eruptions%22">Volcanic eruptions</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/dac.5934 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Body sensor networks Type: general – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Wireless sensor networks Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Volcanic eruptions Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jayasutha, D. – PersonEntity: Name: NameFull: Hemamalini, V. – PersonEntity: Name: NameFull: Sangeetha, S. – PersonEntity: Name: NameFull: Yeruva, Ajay Reddy IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 11 Text: 11/25/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10745351 Numbering: – Type: volume Value: 37 – Type: issue Value: 17 Titles: – TitleFull: International Journal of Communication Systems Type: main |
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