International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 2 (February 2022), Pages: 152-159

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 Original Research Paper

 Title: Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

 Author(s): Naif K. Al-Shammari 1, *, Husam B. Almansour 2, Syed Muzamil Basha 3, Syed Thouheed Ahmed 4

 Affiliation(s):

 1Department of Mechanical Engineering, College of Engineering, University of Ha’il, Ha’il, Saudi Arabia
 2Department of Health Management, College of Public Health and Health Informatics, University of Ha’il, Ha’il, Saudi Arabia
 3School of Computer Science and Engineering, REVA University, Bangaluru, India
 4School of Computing and Information Technology, REVA University, Bangaluru, India

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-5100-267X

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.02.017

 Abstract:

The development of robotic partners to take care of daily human life has been expanded recently. Mobile robots have spread their presence within the public environment to assist people in a variety of problematic activities. Mobile Robots are developed with the underlying artificial intelligence technology. Adequate training is provided to the mobile robots under the classifications of supervised learning. The interaction of robots is very important to practice everything that is told to the robotic systems from domestic robots to high-risk work environments that threaten the health of the spinal cord, which focuses on robotic support during the COVID-19 epidemic. In the present research work, a mobile agent is trained using Computerized Tomography (CT) scan reports and X-rays under VGG-16 processing standards for classifying covid and non-covid patients. A hybrid model is designed using Deep Learning Network (DNN) and Convolutional Neural Network (CNN). CNN is trained using images collected using a camera and thermal camera with RGB values ranging from 0 to 255. The advantage of the proposed model in training the mobile agent is making use of CT scan and X-ray images and providing recommendations to the victim about the criticality of being affected by covid. In addition to that, the Machine Learning Algorithm like Decision Tree and Random Forest is constructed and achieved a classification accuracy of 95%. The proposed technique has efficiently provided a reliable recommendation system based on ReLu activation. The other evaluation parameters used to estimate the performance of the proposed model are precision, recall, F1-score. The proposed model achieves 0.84 Precision over the inception technique with 0.79 precision. The reason behind the improvement of accuracy in the present work is the filter used to extract the features. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: COVID-19, Convolutional neural networks, Accuracy, Precision, Recall, F1-score, COVID-19 recommendation

 Article History: Received 6 June 2021, Received in revised form 24 December 2021, Accepted 24 December 2021

 Acknowledgment 

This research has been funded by Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number COVID-1909.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Al-Shammari NK, Almansour HB, and Basha SM et al. (2022). Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification. International Journal of Advanced and Applied Sciences, 9(2): 152-159

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1  

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