Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment
-
Published:2021
Issue:6
Volume:18
Page:8727-8757
-
ISSN:1551-0018
-
Container-title:Mathematical Biosciences and Engineering
-
language:
-
Short-container-title:MBE
Author:
Tripathi Gaurav, ,Singh Kuldeep,Vishwakarma Dinesh Kumar, ,
Abstract
<abstract>
<p>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.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modelling and Simulation,General Medicine
Reference67 articles.
1. D. Singh, G. Tripathi, A. J. Jara, A survey of Internet-of-Things: Future vision, architecture, challenges and services, in 2014 IEEE world forum on Internet of Things (WF-IoT), 2014. 2. C. Guy, Wireless sensor networks, in Sixth international symposium on instrumentation and control technology: Signal analysis, measurement theory, photoelectronic technology, and artificial intelligence, (2006), 635711. 3. N. S. Kumar, B. Vuayalakshmi, R. J. Prarthana, A. Shankar, IOT based smart garbage alert system using Arduino UNO, in IEEE Region 10 International Conference TENCON, 1028-1034. 4. M. S. Munir, I. S. Bajwa, A. Ashraf, W. Anwar, R. Rashid, Intelligent and Smart Irrigation System Using Edge Computing and IoT, Complexity, 2021. 5. R. Rajavel, S. K. Ravichandran, K. Harimoorthy, P. Nagappan, K. R. Gobichettipalayam, IoT-based smart healthcare video surveillance system using edge computing, J. Ambient Intell. Humaniz Comput., 3 (2021), 1-13
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|