nach oben

New Generation Computing

Erschienen in:

04.09.2021

CASE-CF: Context Aware Smart Epidemic Control Framework

verfasst von: Harsuminder Kaur Gill, Vivek Kumar Sehgal, Anil Kumar Verma

Erschienen in: New Generation Computing | Ausgabe 3-4/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework.

Vorheriger Artikel A Deep Learning Method to Forecast COVID-19 Outbreak

Nächster Artikel Ensemble Methods for Heart Disease Prediction

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Guo, Y.R., et al.: The origin, transmission and clinical therapies on coronavirus disease (COVID-19) outbreak—an update on the status. Mil. Med. Res. (2020). https://doi.org/10.1186/s40779-020-00240-0CrossRef

Lau, H., et al.: The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China. J. Travel Med. (2020). https://doi.org/10.1093/jtm/taaa037CrossRef

WHO | Disease outbreaks. https://www.who.int/emergencies/diseases/en/. Accessed Dec 07, 2020

Sacks, J.A., et al.: Introduction of mobile health tools to support Ebola surveillance and contact tracing in Guinea. Glob. Heal. Sci. Pract. 3(4), 646–659 (2015). https://doi.org/10.9745/GHSP-D-15-00207CrossRef

Sandhu, R., Sood, S.K., Kaur, G.: An intelligent system for predicting and preventing MERS-CoV infection outbreak. J. Supercomput. 72(8), 3033–3056 (2016). https://doi.org/10.1007/s11227-015-1474-0CrossRef

Meegan, J.M.: Yellow fever. In: Handbook of Zoonoses, Second Edition, Section B: Viral Zoonoses. (2017)

Ibarra-Vega, D.: Lockdown, one, two, none, or smart. Modeling containing COVID-19 infection. A conceptual model. Sci. Total Environ. 730, 138917 (2020). https://doi.org/10.1016/j.scitotenv.2020.138917CrossRef

Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput. Surv. (2017). https://doi.org/10.1145/3057266CrossRef

Kharel, J., Reda, H.T., Shin, S.Y.: An architecture for smart health monitoring system based on fog computing. J. Commun. 12(4), 228–233 (2017). https://doi.org/10.12720/jcm.12.4.228-233CrossRef

10.

Jeong, J.S., Kim, M., Yoo, K.H.: A content oriented smart education system based on cloud computing. Int. J. Multimed. Ubiquitous Eng. 8(6), 313–328 (2013). https://doi.org/10.14257/ijmue.2013.8.6.31CrossRef

11.

Aggarwal, C.C.: Neural networks and deep learning—a textbook. Springer International Publishing, Cham (2018)CrossRef

12.

Asano, Y.: Learning OpenCV 3 computer (2012)

13.

OpenCV: OpenCV Tutorials. Web. http://docs.opencv.org/3.0.0/d9/df8/tutorial_root.html (2016). Accessed Jun 24, 2021

14.

Chollet, F.: Keras: The python deep learning library. Keras.Io (2015)

15.

Nelli, F. and Nelli, F.: Deep learning with TensorFlow. In: Python Data Analytics. (2018)

16.

Coronavirus disease-19: quarantine frame-work for travelers entering Korea. Osong Public Heal. Res. Perspect. 11(3), 133–139. (2020). https://doi.org/10.24171/j.phrp.2020.11.3.04

17.

Park, Y.J., et al.: Development and utilization of a rapid and accurate epidemic investigation support system for COVID-19. Osong Public Heal. Res. Perspect. 11(3), 118–127 (2020). https://doi.org/10.24171/j.phrp.2020.11.3.06CrossRef

18.

Won Sonn, J., Lee, J.K.: The smart city as time-space cartographer in COVID-19 control: the South Korean strategy and democratic control of surveillance technology. Eurasian Geogr. Econ. (2020). https://doi.org/10.1080/15387216.2020.1768423CrossRef

19.

Yigitcanlar, T., Butler, L., Windle, E., Desouza, K.C., Mehmood, R., Corchado, J.M.: Can building ‘artificially intelligent cities’ safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors (Switzerland) 20(10), 2988 (2020). https://doi.org/10.3390/s20102988CrossRef

20.

Sonn, J.W., Kang, M., Choi, Y.: Smart city technologies for pandemic control without lockdown. Int. J. Urban Sci. 24(2), 1–3 (2020). https://doi.org/10.1080/12265934.2020.1764207CrossRef

21.

Hong, Z., et al.: Telemedicine during the COVID-19 pandemic: experiences from Western China. J. Med. Internet Res. 22(5), e19577 (2020). https://doi.org/10.2196/19577CrossRef

22.

Sun, F., et al.: Smartphone-based multiplex 30-minute nucleic acid test of live virus from nasal swab extract. Lab Chip 20(9), 1621–1627 (2020). https://doi.org/10.1039/d0lc00304bCrossRef

23.

Rahman, M.S., Peeri, N.C., Shrestha, N., Zaki, R., Haque, U., Hamid, S.H.A.: Defending against the Novel Coronavirus (COVID-19) Outbreak: How Can the Internet of Things (IoT) help to save the World? Heal. Policy Technol. (2020). https://doi.org/10.1016/j.hlpt.2020.04.005CrossRef

24.

Podpora, M., Gardecki, A., Beniak, R., Klin, B., Vicario, J.L., Kawala-Sterniuk, A.: Human interaction smart subsystem—extending speech-based human-robot interaction systems with an implementation of external smart sensors. Sensors (Switzerland) 20(8), 2376 (2020). https://doi.org/10.3390/s20082376CrossRef

25.

Kummitha, R.K.R.: Smart technologies for fighting pandemics: the techno- and human- driven approaches in controlling the virus transmission. Govern. Inf. Quart. 37(3), 101481 (2020). https://doi.org/10.1016/j.giq.2020.101481. (Elsevier Ltd)CrossRef

26.

Yasaka, T.M., Lehrich, B.M., Sahyouni, R.: Peer-to-peer contact tracing: development of a privacy-preserving smartphone app. JMIR mHealth uHealth 8(4), e18936 (2020). https://doi.org/10.2196/18936CrossRef

27.

Armbruster, B., Brandeau, M.L.: Contact tracing to control infectious disease: when enough is enough. Health Care Manag. Sci. 10(4), 341–355 (2007). https://doi.org/10.1007/s10729-007-9027-6CrossRef

28.

Abeler, J., Bäcker, M., Buermeyer, U., Zillessen, H.: Covid-19 contact tracing and data protection can go together. JMIR Mhealth Uhealth 8(4), e19359 (2020). https://doi.org/10.2196/19359. (JMIR Publications)CrossRef

29.

Sujath, R., Chatterjee, J.M., Hassanien, A.E.: A machine learning forecasting model for COVID-19 pandemic in India. Stoch. Environ. Res. Risk Assess. (2020). https://doi.org/10.1007/s00477-020-01827-8CrossRef

30.

Zhang, Y., Yu, X., Sun, H., Tick, G.R., Wei, W., Jin, B.: Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19. Chaos Solit. Fractals 138, 109959 (2020). https://doi.org/10.1016/j.chaos.2020.109959MathSciNetCrossRef

31.

Parbat, D., Chakraborty, M.: A python based support vector regression model for prediction of COVID19 cases in India. Chaos Solit. Fractals 138, 109942 (2020). https://doi.org/10.1016/j.chaos.2020.109942CrossRef

32.

Zhang, X., Ma, R., Wang, L.: Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solit. Fractals (2020). https://doi.org/10.1016/j.chaos.2020.109829CrossRef

33.

Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of COVID-19 Pandemic using machine learning and cloud computing. Internet Things 11, 100222 (2020). https://doi.org/10.1016/j.iot.2020.100222CrossRef

34.

Melin, P., Monica, J.C., Sanchez, D., Castillo, O.: Analysis of spatial spread relationships of coronavirus (COVID-19) Pandemic in the world using self organizing maps. Chaos Solit. Fractals 138, 109917 (2020). https://doi.org/10.1016/j.chaos.2020.109917CrossRef

35.

Salgotra, R., Gandomi, M., Gandomi, A.H.: Time series analysis and forecast of the COVID-19 Pandemic in India using genetic programming. Chaos Solit. Fractals (2020). https://doi.org/10.1016/j.chaos.2020.109945MathSciNetCrossRef

36.

Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Rajendra Acharya, U.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. (2020). https://doi.org/10.1016/j.compbiomed.2020.103792CrossRef

37.

Rosebrock, A.: OpenCV People counter—PyImageSearch. https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/ (2018). Accessed Jun 03, 2020

38.

GitHub - CSSEGISandData/COVID-19: novel coronavirus (COVID-19) cases, provided by JHU CSSE. https://github.com/CSSEGISandData/COVID-19. Accessed Jun 04, 2020

39.

Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20(5), 533–534 (2020). https://doi.org/10.1016/S1473-3099(20)30120-1. (Lancet Publishing Group)CrossRef

40.

Schwarz, G.: Estimating the dimension of a model. Ann. Stat. (1978). https://doi.org/10.1214/aos/1176344136MathSciNetCrossRefMATH

41.

Covid19India: Coronavirus Outbreak in India—covid19india.org. https://www.covid19india.org/. (2020) Accessed Feb 01, 2021

42.

Tan, P.N., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining, First Edition (2014)

Titel: CASE-CF: Context Aware Smart Epidemic Control Framework
verfasst von: Harsuminder Kaur Gill
Vivek Kumar Sehgal
Anil Kumar Verma
Publikationsdatum: 04.09.2021
Verlag: Ohmsha
Erschienen in: New Generation Computing / Ausgabe 3-4/2021
Print ISSN: 0288-3635
Elektronische ISSN: 1882-7055
DOI: https://doi.org/10.1007/s00354-021-00135-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3-4/2021

Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data

Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

Ensemble Methods for Heart Disease Prediction

Semi-supervised Ensemble Learning for Efficient Cancer Sample Classification from miRNA Gene Expression Data

Premium Partner