Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment

Healthcare systems constitute a significant portion of smart cities infrastructure. The aim of smart healthcare is two folds. The internal healthcare system has a sole focus on monitoring vital parameters of patients. The external systems provide proactive health care measures by the surveillance me...

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Autores principales: Gaurav Tripathi, Kuldeep Singh, Dinesh Kumar Vishwakarma
Formato: article
Lenguaje:EN
Publicado: AIMS Press 2021
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Acceso en línea:https://doaj.org/article/f9a1dd05239443b29fd92f226f5857ff
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spelling oai:doaj.org-article:f9a1dd05239443b29fd92f226f5857ff2021-11-29T02:33:54ZApplied convolutional neural network framework for tagging healthcare systems in crowd protest environment10.3934/mbe.20214311551-0018https://doaj.org/article/f9a1dd05239443b29fd92f226f5857ff2021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021431?viewType=HTMLhttps://doaj.org/toc/1551-0018Healthcare systems constitute a significant portion of smart cities infrastructure. The aim of smart healthcare is two folds. The internal healthcare system has a sole focus on monitoring vital parameters of patients. The external systems provide proactive health care measures by the surveillance mechanism. This system utilizes the surveillance mechanism giving impetus to healthcare tagging requirements on the general public. The work exclusively deals with the mass gatherings and crowded places scenarios. Crowd gatherings and public places management is a vital challenge in any smart city environment. Protests and dissent are commonly observed crowd behavior. This behavior has the inherent capacity to transform into violent behavior. The paper explores a novel and deep learning-based method to provide an Internet of Things (IoT) environment-based decision support system for tagging healthcare systems for the people who are injured in crowd protests and violence. The proposed system is intelligent enough to classify protests into normal, medium and severe protest categories. The level of the protests is directly tagged to the nearest healthcare systems and generates the need for specialist healthcare professionals. The proposed system is an optimized solution for the people who are either participating in protests or stranded in such a protest environment. The proposed solution allows complete tagging of specialist healthcare professionals for all types of emergency response in specialized crowd gatherings. Experimental results are encouraging and have shown the proposed system has a fairly promising accuracy of more than eight one percent in classifying protest attributes and more than ninety percent accuracy for differentiating protests and violent actions. The numerical results are motivating enough for and it can be extended beyond proof of the concept into real time external surveillance and healthcare tagging.Gaurav TripathiKuldeep SinghDinesh Kumar VishwakarmaAIMS Pressarticlehealthcareinternet of thingsdeep learningconvolutional neural networkprotestviolence detectionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8727-8757 (2021)
institution DOAJ
collection DOAJ
language EN
topic healthcare
internet of things
deep learning
convolutional neural network
protest
violence detection
Biotechnology
TP248.13-248.65
Mathematics
QA1-939
spellingShingle healthcare
internet of things
deep learning
convolutional neural network
protest
violence detection
Biotechnology
TP248.13-248.65
Mathematics
QA1-939
Gaurav Tripathi
Kuldeep Singh
Dinesh Kumar Vishwakarma
Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
description Healthcare systems constitute a significant portion of smart cities infrastructure. The aim of smart healthcare is two folds. The internal healthcare system has a sole focus on monitoring vital parameters of patients. The external systems provide proactive health care measures by the surveillance mechanism. This system utilizes the surveillance mechanism giving impetus to healthcare tagging requirements on the general public. The work exclusively deals with the mass gatherings and crowded places scenarios. Crowd gatherings and public places management is a vital challenge in any smart city environment. Protests and dissent are commonly observed crowd behavior. This behavior has the inherent capacity to transform into violent behavior. The paper explores a novel and deep learning-based method to provide an Internet of Things (IoT) environment-based decision support system for tagging healthcare systems for the people who are injured in crowd protests and violence. The proposed system is intelligent enough to classify protests into normal, medium and severe protest categories. The level of the protests is directly tagged to the nearest healthcare systems and generates the need for specialist healthcare professionals. The proposed system is an optimized solution for the people who are either participating in protests or stranded in such a protest environment. The proposed solution allows complete tagging of specialist healthcare professionals for all types of emergency response in specialized crowd gatherings. Experimental results are encouraging and have shown the proposed system has a fairly promising accuracy of more than eight one percent in classifying protest attributes and more than ninety percent accuracy for differentiating protests and violent actions. The numerical results are motivating enough for and it can be extended beyond proof of the concept into real time external surveillance and healthcare tagging.
format article
author Gaurav Tripathi
Kuldeep Singh
Dinesh Kumar Vishwakarma
author_facet Gaurav Tripathi
Kuldeep Singh
Dinesh Kumar Vishwakarma
author_sort Gaurav Tripathi
title Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
title_short Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
title_full Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
title_fullStr Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
title_full_unstemmed Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
title_sort applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
publisher AIMS Press
publishDate 2021
url https://doaj.org/article/f9a1dd05239443b29fd92f226f5857ff
work_keys_str_mv AT gauravtripathi appliedconvolutionalneuralnetworkframeworkfortagginghealthcaresystemsincrowdprotestenvironment
AT kuldeepsingh appliedconvolutionalneuralnetworkframeworkfortagginghealthcaresystemsincrowdprotestenvironment
AT dineshkumarvishwakarma appliedconvolutionalneuralnetworkframeworkfortagginghealthcaresystemsincrowdprotestenvironment
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