Article: Design of a novel big data analytical model for processing the unstructured data contents Journal: International Journal of Services Operations and Informatics (IJSOI) 2020 Vol.10 No.4 pp.269 - 287 Abstract: The advancement of exact wellbeing health condition prediction has been a key research subject starting late. In any case, current health condition expectation strategies are not exact enough, which has transformed into the bottleneck for accomplishing the full force of condition-based upkeep (CBM). Neural framework based systems have been seen as a promising class of strategies for health condition expectation. In this paper, we propose a neural system expectation model called artificial neural network based particle swarm optimisation (ANN-PSO) algorithm. Artificial neural network (ANN) classifier is utilised to identify the health condition. Moreover, particle swarm optimisation (PSO) algorithm is coordinated with ANN for giving better outcome by decreasing the errors. The outcomes show the capacity of the ANN-PSO based methodology for satisfactory health condition prediction results. This proposed approach is tested results using medical based big data in Hadoop framework. The proposed work is implemented in the working stage of JAVA. Inderscience Publishers - linking academia, business and industry through research

Title: Design of a novel big data analytical model for processing the unstructured data contents

Authors: Harika Vanam; R. Jeberson Retna Raj

Addresses: Department of Computer Science, Sathyabama Institute of Science and Technology (Deemed to be University), Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai –600 119, Tamil Nadu, India ' Department of Computer Science, Sathyabama Institute of Science and Technology (Deemed to be University), Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai –600 119, Tamil Nadu, India

Abstract: The advancement of exact wellbeing health condition prediction has been a key research subject starting late. In any case, current health condition expectation strategies are not exact enough, which has transformed into the bottleneck for accomplishing the full force of condition-based upkeep (CBM). Neural framework based systems have been seen as a promising class of strategies for health condition expectation. In this paper, we propose a neural system expectation model called artificial neural network based particle swarm optimisation (ANN-PSO) algorithm. Artificial neural network (ANN) classifier is utilised to identify the health condition. Moreover, particle swarm optimisation (PSO) algorithm is coordinated with ANN for giving better outcome by decreasing the errors. The outcomes show the capacity of the ANN-PSO based methodology for satisfactory health condition prediction results. This proposed approach is tested results using medical based big data in Hadoop framework. The proposed work is implemented in the working stage of JAVA.

Keywords: ANN-PSO; artificial neural network based particle swarm optimisation; health condition; GOA; Grasshopper optimsation algorithm; error; classifier; accuracy.

DOI: 10.1504/IJSOI.2020.111296

International Journal of Services Operations and Informatics, 2020 Vol.10 No.4, pp.269 - 287

Received: 27 Dec 2019
Accepted: 07 Jan 2020
Published online: 18 Nov 2020 *

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Title: Design of a novel big data analytical model for processing the unstructured data contents

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