Skip to main content

Advertisement

Springer Nature Link
Log in

Scientometric analysis of ICT-assisted intelligent control systems response to COVID-19 pandemic

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

COVID-19 outbreak has caused a devastating impact on the daily lives of people, public health, and economic progress of infected countries. It has become a leading cause of substantial mortality and morbidity around the world. The emergence of new variants of virus has posed severe challenges for humanitarian society. Information and Communication Technology (ICT) has played a vital role in this pandemic and offered various promising innovations to control its dissemination. The current research study presents a scientometric analysis on the literature of ICT-assisted COVID-19 research. In this paper, ICT has been classified into six major categories; artificial intelligence and medical imaging, mobile technology, ubiquitous computing, big data analytics, social media platforms, and printing technology. It extensively examines the role of these technologies in COVID-19 by applying various empirical approaches such as co-citation analysis, publication and citation behavior analysis, participating nations, and knowledge mapping of scientific literature using visualization tool CiteSpace. Furthermore, it provides a visual approach to identify developing paths, evolution trends, research hotspots, cluster analysis, and potential future directions in medical informatics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  1. Abbas J, Wang D, Su Z, Ziapour A (2021) The role of social media in the advent of covid-19 pandemic: crisis management, mental health challenges and implications. Risk management and healthcare policy pp 1917–1932

  2. Abhiteja Konda GAMMSGDG, Prakash Abhinav, Guha S (2020) Aerosol filtration efficiency of common fabrics used in respiratory cloth masks. ACS Nano 14:6339–6347

    Article  Google Scholar 

  3. Agarwal A, Uniyal D, Toshniwal D, Deb D (2021) Dense vector embedding based approach to identify prominent disseminators from twitter data amid COVID-19 outbreak. IEEE Trans Emerg Topics Comput Intell 5(3):308–320. https://doi.org/10.1109/tetci.2021.3067661

    Article  Google Scholar 

  4. Allam Z, Jones DS (2020) On the coronavirus (COVID-19) outbreak and the smart city network: Universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare 8(1):46. https://doi.org/10.3390/healthcare8010046

    Article  Google Scholar 

  5. Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS (2021) Applications of big data analytics to control covid-19 pandemic. Sensors 21(7):2282

    Article  Google Scholar 

  6. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD (2020) How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 395(10228):931–934. https://doi.org/10.1016/s0140-6736(20)30567-5

    Article  Google Scholar 

  7. Andreadis S, Antzoulatos G, Mavropoulos T, Giannakeris P, Tzionis G, Pantelidis N, Ioannidis K, Karakostas A, Gialampoukidis I, Vrochidis S, Kompatsiaris I (2021) A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Soc Netw Media 23:100134. https://doi.org/10.1016/j.osnem.2021.100134

    Article  Google Scholar 

  8. Bennett Kleinberg MM Isabelle van der Vegt (2020) Measuring emotions in the covid-19 real world worry dataset. Comput Lang

  9. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, Rubin GJ (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 395(10227):912–920. https://doi.org/10.1016/s0140-6736(20)30460-8

    Article  Google Scholar 

  10. Chamola V, Hassija V, Gupta V, Guizani M (2020) A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5g in managing its impact. IEEE Access 8:90225–90265. https://doi.org/10.1109/access.2020.2992341

    Article  Google Scholar 

  11. Chan JFW, Yuan S, Kok KH, To KKW, Chu H, Yang J, Xing F, Liu J, Yip CCY, Poon RWS et al (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395(10223):514–523

    Article  Google Scholar 

  12. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study. Lancet 395(10223):507–513. https://doi.org/10.1016/s0140-6736(20)30211-7

    Article  Google Scholar 

  13. Chen N, Zhong Z, Pang J (2021) An exploratory study of COVID-19 information on twitter in the greater region. Big Data Cogn Compu 5(1):5. https://doi.org/10.3390/bdcc5010005

    Article  Google Scholar 

  14. Choudrie J, Patil S, Kotecha K, Matta N, Pappas I (2021) Applying and understanding an advanced, novel deep learning approach: a covid 19, text based, emotions analysis study. Inf Syst Front 23(6):1431–1465. https://doi.org/10.1007/s10796-021-10152-6

    Article  Google Scholar 

  15. Cinelli M, Quattrociocchi W, Galeazzi A, Valensise CM, Brugnoli E, Schmidt AL, Zola P, Zollo F, Scala A (2020) The COVID-19 social media infodemic. Sci Rep. https://doi.org/10.1038/s41598-020-73510-5

    Article  Google Scholar 

  16. Dbouk T, Drikakis D (2020) On coughing and airborne droplet transmission to humans. Phys Fluids 32(5):053310. https://doi.org/10.1063/5.0011960

    Article  Google Scholar 

  17. Dong Y, Yao YD (2021) IoT platform for COVID-19 prevention and control: a survey. IEEE Access 9:49929–49941. https://doi.org/10.1109/access.2021.3068276

    Article  Google Scholar 

  18. Ferrara E (2020) What types of COVID-19 conspiracies are populated by twitter bots? First Monday. https://doi.org/10.5210/fm.v25i6.10633

    Article  Google Scholar 

  19. Gallup N, Pringle AM, Oberloier S, Tanikella NG, Pearce JM (2020) Parametric nasopharyngeal swab for sampling COVID-19 and other respiratory viruses: Open source design, SLA 3-d printing and UV curing system. HardwareX 8:e00135. https://doi.org/10.1016/j.ohx.2020.e00135

    Article  Google Scholar 

  20. Gao K, Su J, Jiang Z, Zeng LL, Feng Z, Shen H, Rong P, Xu X, Qin J, Yang Y, Wang W, Hu D (2021) Dual-branch combination network (DCN): towards accurate diagnosis and lesion segmentation of COVID-19 using CT images. Medical Image Analysis 67:101836. https://doi.org/10.1016/j.media.2020.101836

    Article  Google Scholar 

  21. Ghayvat H, Awais M, Bashir A, Pandya S, Zuhair M, Rashid M, Nebhen J (2022) Ai-enabled radiologist in the loop: novel ai-based framework to augment radiologist performance for Covid-19 chest CT medical image annotation and classification from pneumonia. Neural Comput Appl 35:1–19

    Google Scholar 

  22. Gunasekeran DV, Tseng RMWW, Tham YC, Wong TY (2021) Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med. https://doi.org/10.1038/s41746-021-00412-9

    Article  Google Scholar 

  23. Haghani M, Varamini P (2021) Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature. Scientometrics 126(8):7005–7050. https://doi.org/10.1007/s11192-021-04036-4

    Article  Google Scholar 

  24. Haleem A, Javaid M (2020) Medical 4.0 and its role in healthcare during COVID-19 pandemic: a review. J Ind Integr Manag 05(04):531–545. https://doi.org/10.1142/s2424862220300045

    Article  Google Scholar 

  25. Hernández S, López-Córtes X (2023) Evaluating deep learning predictions for covid-19 from x-ray images using leave-one-out predictive densities. Neural Comput Appl 35:1–12

    Article  Google Scholar 

  26. Hinch R, Probert W, Nurtay A, Kendall M, Wymant (2020) Effective configurations of a digital contact tracing app: a report to nhsx. Retrieved July

  27. Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, Schiergens TS, Herrler G, Wu NH, Nitsche A, Müller MA, Drosten C, Pöhlmann S (2020) SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181(2):271-280.e8. https://doi.org/10.1016/j.cell.2020.02.052

    Article  Google Scholar 

  28. Hossain MS, Muhammad G, Guizani N (2020) Explainable AI and mass surveillance system-based healthcare framework to combat COVID-i9 like pandemics. IEEE Netw 34(4):126–132. https://doi.org/10.1109/mnet.011.2000458

    Article  Google Scholar 

  29. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, china. Lancet 395(10223):497–506. https://doi.org/10.1016/s0140-6736(20)30183-5

    Article  Google Scholar 

  30. Hyunghoon Cho YWY Daphne Ippolito (2020) Contact tracing mobile apps for Covid-19: privacy considerations and related trade-offs. Cryptogr Secur. https://doi.org/10.48550/arXiv.2003.11511

    Article  Google Scholar 

  31. Islam MM, Karray F, Alhajj R, Zeng J (2021) A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). IEEE Access 9:30551–30572. https://doi.org/10.1109/access.2021.3058537

    Article  Google Scholar 

  32. Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Res Part E Logist Transp Rev 136:101922. https://doi.org/10.1016/j.tre.2020.101922

    Article  Google Scholar 

  33. Ivanov D (2020) Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals Oper Res. https://doi.org/10.1007/s10479-020-03640-6

    Article  Google Scholar 

  34. Iyengar K, Bahl S, Vaishya R, Vaish A (2020) Challenges and solutions in meeting up the urgent requirement of ventilators for COVID-19 patients. Diabetes Metab Synd Clin Res Rev 14(4):499–501. https://doi.org/10.1016/j.dsx.2020.04.048

    Article  Google Scholar 

  35. Jabra MB, Koubaa A, Benjdira B, Ammar A, Hamam H (2021) COVID-19 diagnosis in chest x-rays using deep learning and majority voting. Appl Sci 11(6):2884. https://doi.org/10.3390/app11062884

    Article  Google Scholar 

  36. Javaid M, Haleem A (2020) Exploring smart material applications for COVID-19 pandemic using 4d printing technology. J Ind Integr Manag 05(04):481–494. https://doi.org/10.1142/s2424862220500219

    Article  Google Scholar 

  37. Javaid M, Haleem A (2020) Exploring smart material applications for Covid-19 pandemic using 4d printing technology. J Ind Integr Manag 5(04):481–494

    Article  Google Scholar 

  38. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T, Wu Z, He G, Huang Y (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Mater Continua 62(3):537–551. https://doi.org/10.32604/cmc.2020.010691

    Article  Google Scholar 

  39. Joshi P, Tyagi RK, Agarwal KM (2021) Technological resources for fighting COVID-19 pandemic health issues. J Ind Integr Manag 06(02):271–285. https://doi.org/10.1142/s2424862221500196

    Article  Google Scholar 

  40. Kampf G, Todt D, Pfaender S, Steinmann E (2020) Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. J Hosp Infect 104(3):246–251. https://doi.org/10.1016/j.jhin.2020.01.022

    Article  Google Scholar 

  41. Kermany DS, Goldbaum M, Cai W (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010

    Article  Google Scholar 

  42. Khan S, Haleem A, Deshmukh SG, Javaid M (2021) Exploring the impact of COVID-19 pandemic on medical supply chain disruption. J Ind Integr Manag 06(02):235–255. https://doi.org/10.1142/s2424862221500147

    Article  Google Scholar 

  43. Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM, Sun F, Jit M, Munday JD, Davies N, Gimma A, van Zandvoort K, Gibbs H, Hellewell J, Jarvis CI, Clifford S, Quilty BJ, Bosse NI, Abbott S, Klepac P, Flasche S (2020) Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 20(5):553–558. https://doi.org/10.1016/s1473-3099(20)30144-4

    Article  Google Scholar 

  44. kumar RS, Kaliyaperumal K (2015) A scientometric analysis of mobile technology publications. Scientometrics 105(2):921–939. https://doi.org/10.1007/s11192-015-1710-7

    Article  Google Scholar 

  45. Laato S, Islam AN, Laine TH (2020) Did location-based games motivate players to socialize during COVID-19? Telem Inform 54:101458. https://doi.org/10.1016/j.tele.2020.101458

    Article  Google Scholar 

  46. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  47. Leung NHL, Chu DKW, Shiu EYC, Chan KH, McDevitt JJ, Hau BJP, Yen HL, Li Y, Ip DKM, Peiris JSM, Seto WH, Leung GM, Milton DK, Cowling BJ (2020) Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat Med 26(5):676–680. https://doi.org/10.1038/s41591-020-0843-2

    Article  Google Scholar 

  48. Li L, Zhang Q, Wang X, Zhang J, Wang T, Gao TL, Duan W, fai Tsoi KK, Wang FY (2020) Characterizing the propagation of situational information in social media during COVID-19 epidemic: a case study on weibo. IEEE Trans Comput Soc Syst 7(2):556–562. https://doi.org/10.1109/tcss.2020.2980007

    Article  Google Scholar 

  49. Li S, Wang Y, Xue J, Zhao N, Zhu T (2020) The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. Int J Environ Res Public Health 17(6):2032. https://doi.org/10.3390/ijerph17062032

    Article  Google Scholar 

  50. Liu Y, Ning Z, Chen Y, Guo M, Liu Y, Gali NK, Sun L, Duan Y, Cai J, Westerdahl D, Liu X, Xu K, fai Ho K, Kan H, Fu Q, Lan K (2020) Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature 582(7813):557–560. https://doi.org/10.1038/s41586-020-2271-3

    Article  Google Scholar 

  51. Liu YL, Yuan WJ, Zhu SH (2021) The state of social science research on COVID-19. Scientometrics 127(1):369–383. https://doi.org/10.1007/s11192-021-04206-4

    Article  Google Scholar 

  52. Munz T, Väth D, Kuznecov P, Vu NT, Weiskopf D (2022) Visualization-based improvement of neural machine translation. Comput Graph 103:45–60. https://doi.org/10.1016/j.cag.2021.12.003

    Article  Google Scholar 

  53. Nasajpour M, Pouriyeh S, Parizi RM, Dorodchi M, Valero M, Arabnia HR (2020) Internet of things for current COVID-19 and future pandemics: an exploratory study. J Healthc Inf Res 4(4):325–364. https://doi.org/10.1007/s41666-020-00080-6

    Article  Google Scholar 

  54. Nayak SR, Nayak DR, Sinha U, Arora V, Pachori RB (2021) Application of deep learning techniques for detection of COVID-19 cases using chest x-ray images: a comprehensive study. Biomed Signal Process Control 64:102365. https://doi.org/10.1016/j.bspc.2020.102365

    Article  Google Scholar 

  55. Neelam S, Sood SK (2021) A scientometric review of global research on smart disaster management. IEEE Trans Eng Manag 68(1):317–329. https://doi.org/10.1109/tem.2020.2972288

    Article  Google Scholar 

  56. Olaf Ronneberger TB Philipp Fischer (2015) U-net: Convolutional networks for biomedical image segmentation. Comput Vision Pattern Recognit

  57. Patel P, Gohil P (2021) Role of additive manufacturing in medical application COVID-19 scenario: India case study. J Manuf Syst 60:811–822. https://doi.org/10.1016/j.jmsy.2020.11.006

    Article  Google Scholar 

  58. Rahimi I, Chen F, Gandomi AH (2021) A review on COVID-19 forecasting models. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05626-8

    Article  Google Scholar 

  59. Rahmi R, Joho H, Shirai T (2018) An analysis of natural disaster-related information-seeking behavior using temporal stages. J Assoc Inf Sci Technol 70(7):715–728. https://doi.org/10.1002/asi.24155

    Article  Google Scholar 

  60. Ramesh Raskar RBKVJGPVSKANRGABDGCKSKRBDSBBARKVPFMBACRDKJKLGNVPSPYRASGSJW Isabel Schunemann (2020) Apps gone rogue: Maintaining personal privacy in an epidemic. Cryptography and Security

  61. Rawat KS, Sood SK (2020) Emerging trends and global scope of big data analytics: a scientometric analysis. Quality & Quantity 55(4):1371–1396. https://doi.org/10.1007/s11135-020-01061-y

    Article  Google Scholar 

  62. Rehman A, Iqbal MA, Xing H, Ahmed I (2021) COVID-19 detection empowered with machine learning and deep learning techniques: a systematic review. Appl Sci 11(8):3414. https://doi.org/10.3390/app11083414

    Article  Google Scholar 

  63. Rehouma R, Buchert M, Chen YPP (2021) Machine learning for medical imaging-based COVID-19 detection and diagnosis. Int J Intell Syst 36(9):5085–5115. https://doi.org/10.1002/int.22504

    Article  Google Scholar 

  64. Sahoo S, Pandey S (2020) Evaluating research performance of coronavirus and Covid-19 pandemic using scientometric indicators. Online Inf Rev 44(7):1443–1461. https://doi.org/10.1108/oir-06-2020-0252

    Article  Google Scholar 

  65. Sareen S, Sood SK, Gupta SK (2017) Secure internet of things-based cloud framework to control zika virus outbreak. Int J Technol Assess Health Care 33(1):11–18

    Article  Google Scholar 

  66. Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y (2021) Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med Phys 48(4):1633–1645. https://doi.org/10.1002/mp.14609

    Article  Google Scholar 

  67. Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annual Rev Biomed Eng 19(1):221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442

    Article  Google Scholar 

  68. Shen DLFMLADHXZHRBKACSZCTH Yang; Guo (2021) Robots under covid-19 pandemic: a comprehensive survey. IEEE Access

  69. Shuja J, Alanazi E, Alasmary W, Alashaikh A (2020) COVID-19 open source data sets: a comprehensive survey. Appl Intell 51(3):1296–1325. https://doi.org/10.1007/s10489-020-01862-6

    Article  Google Scholar 

  70. Singh RP, Javaid M, Haleem A, Vaishya R, Bahl S (2020) Significance of health information technology (HIT) in context to COVID-19 pandemic: potential roles and challenges. J Ind Integr Manag 05(04):427–440. https://doi.org/10.1142/s2424862220500232

    Article  Google Scholar 

  71. Sood S, Rawat K, Sharma G (2022) 3-d printing technologies from infancy to recent times: a scientometric review. IEEE Trans Eng Manag. https://doi.org/10.1109/tem.2021.3134128

    Article  Google Scholar 

  72. Sood SK, Rawat KS (2021) A fog assisted intelligent framework based on cyber physical system for safe evacuation in panic situations. Comput Commun 178:297–306. https://doi.org/10.1016/j.comcom.2021.08.022

    Article  Google Scholar 

  73. Sood SK, Sandhu R (2015) Matrix based proactive resource provisioning in mobile cloud environment. Simul Model Pract Theory 50:83–95

    Article  Google Scholar 

  74. Sood SK, Rawat KS, Kumar D (2022) Analytical mapping of information and communication technology in emerging infectious diseases using citespace. Telemat Inform 69:101796

    Article  Google Scholar 

  75. Sood SK, Rawat KS, Kumar D (2022) A visual review of artificial intelligence and industry 4.0 in healthcare. Comput Electric Eng 101:107948

    Article  Google Scholar 

  76. Sood SK, Rawat KS, Kumar D (2023) Emerging trends of ICT in airborne disease prevention. ACM Trans Internet Technol 22(4):1–18

    Article  Google Scholar 

  77. Tan L, Yu K, Bashir AK, Cheng X, Ming F, Zhao L, Zhou X (2021) Toward real-time and efficient cardiovascular monitoring for covid-19 patients by 5g-enabled wearable medical devices: A deep learning approach. Neural Computing and Applications pp 1–14

  78. Tareq MS, Rahman T, Hossain M, Dorrington P (2021) Additive manufacturing and the COVID-19 challenges: an in-depth study. J Manuf Syst 60:787–798. https://doi.org/10.1016/j.jmsy.2020.12.021

    Article  Google Scholar 

  79. Tianshi Li JJSBYABLLBJIH Camille Cobb (2021) What makes people install a covid-19 contact-tracing app? understanding the influence of app design and individual difference on contact-tracing app adoption intention. Human-Computer Interaction https://arxiv.org/abs/2012.12415

  80. Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):459–461. https://doi.org/10.1038/s41591-020-0824-5

    Article  Google Scholar 

  81. Trancossi M, Carli C, Cannistraro G, Pascoa J, Sharma S (2021) Could thermodynamics and heat and mass transfer research produce a fundamental step advance toward and significant reduction of SARS-COV-2 spread? Int J Heat Mass Trans 170:120983. https://doi.org/10.1016/j.ijheatmasstransfer.2021.120983

    Article  Google Scholar 

  82. Vaishya R, Javaid M, Khan IH, Vaish A, Iyengar KP (2021) Significant role of modern technologies for COVID-19 pandemic. J Ind Integr Manag 06(02):147–159. https://doi.org/10.1142/s242486222150010x

    Article  Google Scholar 

  83. Vargo D, Zhu L, Benwell B, Yan Z (2020) Digital technology use during COVID -19 pandemic: a rapid review. Human Behavior Emerg Technol 3(1):13–24. https://doi.org/10.1002/hbe2.242

    Article  Google Scholar 

  84. Ventola CL (2014) Medical applications for 3d printing: Current and projected uses. P & T : a peer-reviewed journal for formulary management

  85. Verde GGAAMAMKNSG L; De Pietro (2021) Exploring the use of artificial intelligence techniques to detect the presence of coronavirus covid-19 through speech and voice analysis. IEEE Access

  86. Verma S, Dhanak M, Frankenfield J (2020) Visualizing the effectiveness of face masks in obstructing respiratory jets. Phys Fluids 32(6):061708. https://doi.org/10.1063/5.0016018

    Article  Google Scholar 

  87. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151. https://doi.org/10.1126/science.aap9559

    Article  Google Scholar 

  88. Wang L, Lin ZQ, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Sci Rep. https://doi.org/10.1038/s41598-020-76550-z

    Article  Google Scholar 

  89. Wu JT, Leung K, Leung GM (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in wuhan, china: a modelling study. Lancet 395(10225):689–697. https://doi.org/10.1016/s0140-6736(20)30260-9

    Article  Google Scholar 

  90. Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in china. JAMA 323(13):1239. https://doi.org/10.1001/jama.2020.2648

    Article  Google Scholar 

  91. Xin B, Keng ZW, Yao CZ, Han HZ, Long LQ, Qi WJ (2021) How green finance sparks sustainability: Using big data analysis and visualization software to unite future economic and social value potential. In: 2021 2nd International conference on internet and E-business, ACM, https://doi.org/10.1145/3471988.3472013

  92. Xu LD, Xu EL, Li L (2018) Industry 4.0: state of the art and future trends. Int J Prod Res 56(8):2941–2962. https://doi.org/10.1080/00207543.2018.1444806

    Article  MathSciNet  Google Scholar 

  93. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J, Lang G, Li Y, Zhao H, Liu J, Xu K, Ruan L, Sheng J, Qiu Y, Wu W, Liang T, Li L (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10):1122–1129. https://doi.org/10.1016/j.eng.2020.04.010

    Article  Google Scholar 

  94. Yildirim E, Cicioğlu M, Çalhan A (2022) Real-time internet of medical things framework for early detection of Covid-19. Neural Comput Appl 34(22):20365–20378

    Article  Google Scholar 

  95. Zender-Świercz E, Telejko M, Galiszewska B (2021) Influence of masks protecting against SARS-CoV-2 on thermal comfort. Energies 14(11):3315. https://doi.org/10.3390/en14113315

    Article  Google Scholar 

  96. Zhao S, Li Z, Chen Y, Zhao W, Xie X, Liu J, Zhao D, Li Y (2021) SCOAT-net: A novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recogn 119:108109. https://doi.org/10.1016/j.patcog.2021.108109

    Article  Google Scholar 

  97. Zhi S, Liu Y, Li X, Guo Y (2018) Toward real-time 3d object recognition: a lightweight volumetric CNN framework using multitask learning. Comput Grap 71:199–207. https://doi.org/10.1016/j.cag.2017.10.007

    Article  Google Scholar 

  98. Zhou C, Xiu H, Wang Y, Yu X (2021) Characterizing the dissemination of misinformation on social media in health emergencies: an empirical study based on COVID-19. Inform Process Manag 58(4):102554. https://doi.org/10.1016/j.ipm.2021.102554

    Article  Google Scholar 

  99. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W (2020) A novel coronavirus from patients with pneumonia in china. N Engl J Med 382(8):727–733. https://doi.org/10.1056/nejmoa2001017

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Kurukshetra, Haryana, India

    Sandeep Kumar Sood

  2. Central University of Himachal Pradesh, Dharamshala, India

    Keshav Singh Rawat & Dheeraj Kumar

  3. Central University of Haryana, Mahendergarh, India

    Keshav Singh Rawat

Authors
  1. Sandeep Kumar Sood
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keshav Singh Rawat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dheeraj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dheeraj Kumar.

Ethics declarations

Conflict of Interest

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sood, S.K., Rawat, K.S. & Kumar, D. Scientometric analysis of ICT-assisted intelligent control systems response to COVID-19 pandemic. Neural Comput & Applic 35, 18829–18849 (2023). https://doi.org/10.1007/s00521-023-08788-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08788-3

Keywords

Search

Navigation