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

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Alameen A. , Gupta A.

tom Vol. 15, no. 4

art. no. 20190016

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.

Optimizing the harmony search algorithm for combined heat and power economic dispatch in american english

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Benayed F. Z. , Abdelhakem-Koridak L. , Bouadi A. , Rahli M.

tom Vol. 103, nr 1

14--20

Achieving optimal utilization of multiple combined heat and power (CHP) systems is a complex problem that requires powerful methods for resolution. This paper presents a harmony search (HS) algorithm to address the economic dispatch issue in CHP (CHPED ). The recently developed metaheuristic HS algorithm has been successfully employed in a wide range of optimization problems. The method is demonstrated through a test case from existing literature and a new one proposed by the authors. Numerical results indicate that the proposed algorithm can identify superior solutions compared to traditional methods, and that the Harmony Search algorithm can be effectively applied to CHPED-related problems.