ArticleIEEE access : practical innovations, open solutions2021
ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Global Communication Practices and Their Impact on Patient Caregivers' Satisfaction in the Surgical Waiting Area: A Scoping Review.Healthcare (Basel, Switzerland) · 2025Review
- Deployable machine learning-based decision support system for tracheostomy in acute burn patients.Burns & trauma · 2025Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
- The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis.Expert systems · 2022Article
- Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control.Artificial intelligence in medicine · 2022Article
- Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.Sensors (Basel, Switzerland) · 2022Article
- QoS-Aware Smart Phone-Based User Tracking Application to Prevent Outbreak of COVID-19 in Saudi Arabia.Computational intelligence and neuroscience · 2022Article
- A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.
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Registered trials
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