Title: Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology

Authors: R. Regin; S. Suman Rajest; Bhopendra Singh; Ahmed J. Obaid; T. Shynu; S. Silvia Priscila

Addresses: Department of Computer Science and Engineering, SRM Instıtute of Science And Technology, Ramapuram, Chennai-89, Tamil Nadu, India ' Department of Research, Bharath Institute of Higher Education and Research, Chennai, 600126, Tamil Nadu, India ' Engineering and Architecture Department, Amity University, Dubai, P.O. Box 345019, UAE ' Faculty of Computer Science and Mathematics, University of Kufa, P.O Box 21, Iraq ' Department of Biomedical Engineering, Agni College of Technology, Chennai, 600130, Tamil Nadu, India ' Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai-600126, Tamil Nadu, India

Abstract: The COVID-19 epidemic is one of the deadliest viruses in recent history, but it is an infodemic that infects students and the community with false information, exacerbating the existing ailment. We attempt to identify and classify bogus news on the internet to identify false information about epidemics and coronavirus. Real comments were gathered from legitimate Twitter usernames, while phoney comments, publications, and events were obtained through realisation websites like news sources. Methods of deep learning, such as convolutional and recurrent neural networks, were combined in this process. Conventional classification techniques such as Binary Classification, K-nearest, Asymmetric Boost, and Random Forest were used up until they were exhausted in the random mutimodal deep learning (RMDL). Examining these tactics and gaining a deeper understanding of the dataset, including the information on COVID-19 that can be found on the internet, required the application of the high-frequency sub and clothing word convolutional extracted features.

Keywords: transfer learning system; machine learning; convolutional neural network; technology; self-directed learning system; COVID-19; false information; postagging phrases.

DOI: 10.1504/IJIEI.2022.129686

International Journal of Intelligent Engineering Informatics, 2022 Vol.10 No.6, pp.484 - 503

Received: 06 Jul 2022
Accepted: 15 Jan 2023
Published online: 20 Mar 2023 *

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