Clustering and Classification based real time analysis of health monitoring and risk assessment in Wireless Body Sensor Networks

• Autorzy: Alameen Abdalla , Gupta Ashu

Identyfikatory

DOI

10.1515/bams-2019-0016

• Języki publikacji: EN

Abstrakty

EN

Wireless body sensor networks (WBSNs) play a vital role in monitoring the health conditions of patients and are a low-cost solution for dealing with several healthcare applications. However, processing a large amount of data and making feasible decisions in emergency cases are the major challenges attributed to WBSNs. Thus, this paper addresses these challenges by designing a deep learning approach for health risk assessment by proposing fractional cat based salp swarm algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid harmony search algorithm and particle swarm optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the deep belief network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating fractional cat swarm optimization (FCSO) and salp swarm algorithm (SSA) for initiating the classification. The proposed FCSSA-based DBN shows better performance using metrics, namely accuracy, energy, and throughput with values 94.604, 0.145, and 0.058, respectively.

Słowa kluczowe

EN

classification; deep belief network; fractional cat swarm optimization; harmony search algorithm; health risk assessment; particle swarm optimization (PSO); wireless body network sensor

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 4
• Strony: art. no. 20190016
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Alameen Abdalla

• Prince Sattam Bin Abdulaziz University, Department of Computer Science, College of Arts and Sciences, Wadi Al-Dawasir, Al Kharj, Saudi Arabia

autor

Gupta Ashu

• Prince Sattam Bin Abdulaziz University, Department of Computer Science, College of Arts and Sciences, Wadi Al-Dawasir, Al Kharj, Saudi Arabia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).