Title: Design and analysis on molecular level biomedical event trigger extraction using recurrent neural network-based particle swarm optimisation for COVID-19 research

Authors: R.N. Devendra Kumar; Arvind Chakrapani; Srihari Kannan

Addresses: Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India ' Karpagam College of Engineering, Coimbatore, Tamil Nadu, India ' SNS Collage of Engineering, Coimbatore, Tamil Nadu, India

Abstract: In this paper, the rich extracted feature sets are fed to the deep learning classifier that estimates the optimal extraction of lung molecule triggered events for COVID-19 infections. The feature extraction is carried out using Recurrent Neural Network (RNN) that effectively extracts the features from the rich data sets. Secondly, a Particle Swarm Optimisation (PSO) algorithm is utilised to classify the extracted features of COVID-19 infections. The rule set for feature extractor is supplied by the fuzzy logic rule set. The simulation shows that the RNN-PSO, which is the combination of two different algorithms, offers better performance than other machine learning classifiers.

Keywords: event triggers; COVID-19; lung molecules; feature extraction; classification; PSO; RNN.

DOI: 10.1504/IJCAT.2021.120459

International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.334 - 339

Received: 07 Aug 2020
Accepted: 07 Sep 2020
Published online: 21 Jan 2022 *

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