Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Multimedia Systems
Erschienen in: Multimedia Systems 4/2022

13.07.2021 | Special Issue Paper

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

verfasst von: Het Shah, Saiyam Shah, Sudeep Tanwar, Rajesh Gupta, Neeraj Kumar

Erschienen in: Multimedia Systems | Ausgabe 4/2022

Einloggen

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

search-config
loading …

Abstract

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
Vorheriger Artikel Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images
Nächster Artikel Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

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 "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

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
Literatur
1.
Ahmada, A., Garhwal, S., Ray, S.K., Kumar, G., Malebary, S.J., Barukab, O.M.O.: The number of confirmed cases of COVID-19 by using machine learning: methods and challenges. arXiv preprint arXiv:​2006.​09184 (2020)
2.
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., Xia, L.: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296, E32–E40 (2020)CrossRef
3.
Alafif, T., Tehame, A.M., Bajaba, S., Barnawi, A., Zia, S.: Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions. Int. J. Environ. Res. Public Health 18(3), 1117 (2021)CrossRef
4.
Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., Alhyari, S.: COVID-19 prediction and detection using deep learning. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 12, 168–181 (2020)
5.
Aleta, A., Martin-Corral, D., y Piontti, A.P., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N.E., Halloran, M.E., Longini Jr, I.M., Merler, S., et al.: Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic. medRxiv (2020). https://​doi.​org/​10.​1101/​2020.​05.​06.​20092841
6.
Allam, Z., Dey, G., Jones, D.S.: Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in china and will influence future urban health policy internationally. AI 1(2), 156–165 (2020)CrossRef
7.
Allam, Z., Jones, D.S.: 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. In: Healthcare, vol. 8, p. 46. Multidisciplinary Digital Publishing Institute (2020)
8.
Apostolopoulos, I.D., Mpesiana, T.A.: COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43, 635–640 (2020)CrossRef
9.
Ardabili, S.F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A.R., Reuter, U., Rabczuk, T., Atkinson, P.M.: COVID-19 outbreak prediction with machine learning. Algorithm 13, 249 (2020)MathSciNetCrossRef
10.
Ariyaratnam, P.: Postponement of scheduled examinations. Bull. R. Coll. Surg. Engl. 102(5), 175–176 (2020)CrossRef
11.
Ayyoubzadeh, S.M., Ayyoubzadeh, S.M., Zahedi, H., Ahmadi, M., Kalhori, S.R.N.: Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveill. 6(2), e18828 (2020)CrossRef
12.
Bai, X., Fang, C., Zhou, Y., Bai, S., Liu, Z., Xia, L., Chen, Q., Xu, Y., Xia, T., Gong, S., et al.: Predicting COVID-19 malignant progression with AI techniques (2020)
13.
Battineni, G., Chintalapudi, N., Amenta, F.: Ai chatbot design during an epidemic like the novel coronavirus. In: Healthcare, vol. 8, pp. 154. Multidisciplinary Digital Publishing Institute (2020)
14.
Beck, B.R., Shin, B., Choi, Y., Park, S., Kang, K.: Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-COV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784–790 (2020)
15.
Bengio, Y., Janda, R., Yu, Y.W., Ippolito, D., Jarvie, M., Pilat, D., Struck, B., Krastev, S., Sharma, A.: The need for privacy with public digital contact tracing during the COVID-19 pandemic. Lancet Digit. Health 2, e342–e344 (2020)CrossRef
16.
Bennett, J.: Smart AI CT scan based coronavirus2019/COVID19 detector (2020)
17.
18.
Biswas, S., Sharma, L.K., Ranjan, R., Banerjee, J.S.: Go-COVID: an interactive cross-platform based dashboard for real-time tracking of COVID-19 using data analytics
19.
Brennen, J.S., Simon, F.M., Howard, P.N., Nielsen, R.K.: Types, Sources, and Claims of COVID-19 Misinformation. Reuters Institute, Oxford (2020)
20.
Broniec, W., An, S., Rugaber, S., Goel, A.K.: Using vera to explain the impact of social distancing on the spread of COVID-19. Preprint at arXiv:​2003.​13762 (2020)
21.
22.
Bullock, J., Pham, K.H., Lam, C.S.N., Luengo-Oroz, M., et al.: Mapping the landscape of artificial intelligence applications against COVID-19. Preprint at arXiv:​2003.​11336 (2020)
23.
Cabore, J., Karamagi, H., Kipruto, H., Asamani, J., Droti, B., Seydi, A., Titi-Ofei, R., Impouma, B., Yoti, Z., Talisuna, A.O., et al.: A predictive model of the effects of sustained community transmission of SARS-COV-2 infection across the countries of the who African region. Available at SSRN 3569842,(2020)
24.
Chagla, Z.: The bnt162b2 (biontech/pfizer) vaccine had 95% efficacy against COVID-19 7 days after the 2nd dose. Ann. Intern. Med. 174(2), JC15 (2021)CrossRef
25.
Chamola, V., Hassija, V., Gupta, V., Guizani, M.: 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 (2020)CrossRef
26.
Chang, S.L., Harding, N., Zachreson, C., Cliff, O.M., Prokopenko, M.: Modelling transmission and control of the COVID-19 pandemic in australia. Preprint at arXiv:​2003.​10218 (2020)
27.
Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 40–52. Springer (2018)
28.
Chenthamarakshan, V., Das, P., Hoffman, S.C., Strobelt, H., Padhi, I., Wai, K.: Cogmol: Target-specific and selective drug design for COVID-19 using deep generative models. Preprint at arXiv:​2004.​01215 (2020)
29.
Chowdhury, M.E., Rahman, T., Khandakar, A., Al-Madeed, S., Zughaier, S.M., Hassen, H., Islam, M.T., et al.: An early warning tool for predicting mortality risk of COVID-19 patients using machine learning. Preprint at arXiv:​2007.​15559 (2020)
30.
Cirillo, N.: Reported orofacial adverse effects of COVID-19 vaccines: the knowns and the unknowns. J. Oral Pathol. Med. 50, 424–427 (2021)CrossRef
31.
Cleve, M.: What the lightning-fast quest for COVID vaccines means for other diseases. Nature 589 (2021)
32.
Cohen, J.P., Dao, L., Morrison, P., Roth, K., Bengio, Y., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Ghassemi, M., Li, H., et al.: Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Preprint at arXiv:​2005.​11856 (2020)
33.
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: COVID-19 image data collection: prospective predictions are the future. Preprint at arXiv:​2006.​11988 (2020)
34.
35.
36.
37.
38.
del Rio-Chanona, R.M., Mealy, P., Pichler, A., Lafond, F., Farmer, D.: Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective. Preprint at arXiv:​2004.​06759 (2020)
39.
Depoux, A., Martin, S., Karafillakis, E., Preet, R., Wilder-Smith, A., Larson, H.: The pandemic of social media panic travels faster than the COVID-19 outbreak (2020)
40.
41.
42.
Ferguson, N.M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., et al.: Report 9 - Impact of non-pharmaceutical interventions (NPIS) to reduce COVID-19 mortality and healthcare demand. (2020)
43.
Fernandes, N.: Economic effects of coronavirus outbreak (COVID-19) on the world economy. Available at SSRN 3557504 (2020)
44.
Galanakis, C.M.: The food systems in the era of the coronavirus (COVID-19) pandemic crisis. Foods 9(4), 523 (2020)CrossRef
45.
Gupta, M., Abdelsalam, M., Mittal, S.: Enabling and enforcing social distancing measures using smart city and its infrastructures: a COVID-19 use case. Preprint at arXiv:​2004.​09246 (2020)
46.
47.
Gupta, R., Shukla, A., Tanwar, S.: Aayush: a smart contract-based telesurgery system for healthcare 4.0. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6 (2020)
48.
49.
50.
51.
Gupta, R., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M.S., Sadoun, B.: Habits: Blockchain-based telesurgery framework for healthcare 4.0. In: 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5 (2019). https://​doi.​org/​10.​1109/​CITS.​2019.​8862127
52.
Haleem, A., Javaid, M., Vaishya, R.: Effects of COVID 19 pandemic in daily life. Curr. Med. Res. Pract. 10(2), 78–79 (2020)
53.
Hamzah, F.B., Lau, C., Nazri, H., Ligot, D., Lee, G., Tan, C., et al.: Coronatracker: worldwide COVID-19 outbreak data analysis and prediction. Bull. World Health Organ 1, 32 (2020)
54.
Hao, Y., Xu, T., Hu, H., Wang, P., Bai, Y.: Prediction and analysis of corona virus disease 2019. PLoS One 15(10), e0239960 (2020)CrossRef
55.
Hassanat, A.B., Abbadi, M.A., Altarawneh, G.A., Alhasanat, A.A.: Solving the problem of the k parameter in the knn classifier using an ensemble learning approach. Preprint at arXiv:​1409.​0919 (2014)
56.
Hathaliya, J., Sharma, P., Tanwar, S., Gupta, R.: Blockchain-based remote patient monitoring in healthcare 4.0. In: 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp. 87–91 (2019). 10.1109/IACC48062.2019.8971593
57.
He, T., Heidemeyer, M., Ban, F., Cherkasov, A., Ester, M.: Simboost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J. Cheminform. 9(1), 1–14 (2017)CrossRef
58.
Hemdan, E.E.D., Shouman, M.A., Karar, M.E.: Covidx-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Preprint at arXiv:​2003.​11055 (2020)
59.
Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., Seidl, P., Vall, A., Widrich, M., Hochreiter, S., et al.: Large-scale ligand-based virtual screening for SARS-COV-2 inhibitors using deep neural networks. Available at SSRN 3561442 (2020)
60.
61.
62.
63.
64.
Huet, F., Prieur, C., Schurtz, G., Gerbaud, E., Manzo-Silberman, S., Vanzetto, G., Elbaz, M., Tea, V., Mercier, G., Lattuca, B., et al.: One train may hide another: acute cardiovascular diseases could be neglected because of the COVID-19 pandemic. Arch. Cardiovasc. Dis. 113, 3303–307 (2020)CrossRef
65.
Hussain, A.A., Bouachir, O., Al-Turjman, F., Aloqaily, M.: Ai techniques for COVID-19. IEEE Access 8, 128776–128795 (2020)CrossRef
66.
Ienca, M., Vayena, E.: On the responsible use of digital data to tackle the COVID-19 pandemic. Nat. Med. 26(4), 463–464 (2020)CrossRef
67.
Imran, A., Posokhova, I., Qureshi, H.N., Masood, U., Riaz, S., Ali, K., John, C.N., Nabeel, M.: Ai4covid-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Preprint at arXiv:​2004.​01275 (2020)
68.
Inn, T.L.: Smart city technologies take on COVID-19. World Health vol. 841. (2020)
69.
Izquierdo, J.L., Ancochea, J., Soriano, J.B.: Clinical characteristics and prognostic factors for ICU admission of patients with COVID-19 using machine learning and natural language processing. medRxiv (2020)
70.
Jahanbin, K., Rahmanian, V.: Using twitter and web news mining to predict COVID-19 outbreak. Asian Pac. J. Trop. Med. 13, 378 (2020)CrossRef
71.
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., La Spada, L., Mirmozafari, M., Dehghani, M., et al.: Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access 8, 109581–109595 (2020)CrossRef
72.
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., Vaish, A.: Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 14, 419–422 (2020)CrossRef
73.
74.
75.
76.
Ke, Y.Y., Peng, T.T., Yeh, T.K., Huang, W.Z., Chang, S.E., Wu, S.H., Hung, H.C., Hsu, T.A., Lee, S.J., Song, J.S., et al.: Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed. J. 43, 355–362 (2020)CrossRef
77.
Khunti, K., de Bono, A., Browne, I., Greenhalgh, T., Hanif, W., Majeed, A., Patel, K., Razaq, M.A., Smeeth, L.: Risk reduction framework for NHS staff at risk of COVID-19 infection. Workforce 20(17), 44 (2020)
78.
Kim, J., Cha, Y., Kolitz, S., Funt, J., Escalante Chong, R., Barrett, S., Zeskind, B., Kusko, R., Kaufman, H., et al.: Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19). J. Transl. Med. 18(1), 1–9 (2020)CrossRef
79.
Kumar, R., Khan, A.A., Zhang, S., Wang, W., Abuidris, Y., Amin, W., Kumar, J.: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. Preprint at arXiv:​2007.​06537 (2020)
80.
Kumari, A., Vekaria, D., Gupta, R., Tanwar, S.: Redills: Deep learning-based secure data analytic framework for smart grid systems. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6 (2020)
81.
Li, X., Li, C., Zhu, D.: Covid-mobilexpert: on-device COVID-19 screening using snapshots of chest X-ray. Preprint at arXiv:​2004.​03042 (2020)
82.
Lin, Z., Xu, P., Winata, G.I., Siddique, F.B., Liu, Z., Shin, J., Fung, P.: Caire: an end-to-end empathetic chatbot. In: AAAI, pp. 13622–13623 (2020)
83.
Liu, D., Clemente, L., Poirier, C., Ding, X., Chinazzi, M., Davis, J.T., Vespignani, A., Santillana, M.: A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using internet searches, news alerts, and estimates from mechanistic models. Preprint at arXiv:​2004.​04019 (2020)
84.
Long, J.B., Ehrenfeld, J.M.: The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus (2020)
85.
Magar, R., Yadav, P., Farimani, A.B.: Potential neutralizing antibodies discovered for novel corona virus using machine learning. Preprint at arXiv:​2003.​08447 (2020)
86.
Magdon-Ismail, M.: Machine learning the phenomenology of COVID-19 from early infection dynamics. Preprint at arXiv:​2003.​07602 (2020)
87.
Maghdid, H.S., Ghafoor, K.Z.: A smartphone enabled approach to manage COVID-19 lockdown and economic crisis. Preprint at arXiv:​2004.​12240 (2020)
88.
Maghdid, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., Rabie, K.: A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study. Preprint at arXiv:​2003.​07434 (2020)
89.
Marbouh, D., Abbasi, T., Maasmi, F., Omar, I.A., Debe, M.S., Salah, K., Jayaraman, R., Ellahham, S.: Blockchain for COVID-19: review, opportunities, and a trusted tracking system. Arab. J. Sci. Eng. 45, 1–17 (2020)
90.
McCall, B.: COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit. Health 2(4), e166–e167 (2020)CrossRef
91.
92.
Michael, N.: Artificial Intelligence a Guide to Intelligent Systems. Pearson education, London (2005)
93.
Miner, A.S., Laranjo, L., Kocaballi, A.B.: Chatbots in the fight against the COVID-19 pandemic. NPJ Digit. Med. 3(1), 1–4 (2020)CrossRef
94.
Miralles-Pechuán, L., Jiménez, F., Ponce, H., Martínez-Villaseñor, L.: A deep q-learning/genetic algorithms based novel methodology for optimizing COVID-19 pandemic government actions. Preprint at arXiv:​2005.​07656 (2020)
95.
96.
97.
Moghadas, S.M., Shoukat, A., Fitzpatrick, M.C., Wells, C.R., Sah, P., Pandey, A., Sachs, J.D., Wang, Z., Meyers, L.A., Singer, B.H., et al.: Projecting hospital utilization during the COVID-19 outbreaks in the united states. Proc. Natl. Acad. Sci. 117(16), 9122–9126 (2020)CrossRef
98.
Moon, S.: Effects of COVID-19 on the entertainment industry. IDOSR J. Exp. Sci. 5(1), 8–12 (2020)
99.
100.
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks. Preprint at arXiv:​2003.​10849 (2020)
101.
102.
Naudé, W.: Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. 35, 761–765 (2020)CrossRef
103.
Nelson, B.: Too little or too much? missing the goldilocks zone of hospital capacity during COVID-19. BMJ 369 (2020)
104.
Newton, P.N., Bond, K.C., Adeyeye, M., Antignac, M., Ashenef, A., Awab, G.R., Bannenberg, W.J., Bower, J., Breman, J., Brock, A., et al.: COVID-19 and risks to the supply and quality of tests, drugs, and vaccines. Lancet Glob. Health 8(6), e754–e755 (2020)CrossRef
105.
Nguyen, C.T., Saputra, Y.M., Van Huynh, N., Nguyen, N.T., Khoa, T.V., Tuan, B.M., Nguyen, D.N., Hoang, D.T., Vu, T.X., Dutkiewicz, E., et al.: Enabling and emerging technologies for social distancing: a comprehensive survey. Preprint at arXiv:​2005.​02816 (2020)
106.
Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A.: Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: a survey. Preprint at arXiv:​2106.​14631 (2020)
107.
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., Agha, R.: The socio-economic implications of the coronavirus and COVID-19 pandemic: a review. Int. J. Surg. (2020)
108.
Ordun, C., Purushotham, S., Raff, E.: Exploratory analysis of COVID-19 tweets using topic modeling, umap, and digraphs. Preprint at arXiv:​2005.​03082 (2020)
109.
Organization, W.H., et al.: Mental health and psychosocial considerations during the COVID-19 outbreak, 18 March 2020. World Health Organization, Tech. rep (2020)
110.
111.
Ozili, P.K., Arun, T.: Spillover of COVID-19: impact on the global economy. Available at SSRN 3562570 (2020)
112.
Öztürk, H., Özgür, A., Ozkirimli, E.: Deepdta: deep drug-target binding affinity prediction. Bioinformatics 34(17), i821–i829 (2018)CrossRef
113.
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)CrossRef
114.
Pahikkala, T., Airola, A., Pietilä, S., Shakyawar, S., Szwajda, A., Tang, J., Aittokallio, T.: Toward more realistic drug–target interaction predictions. Brief. Bioinform. 16(2), 325–337 (2015)CrossRef
115.
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., Alazab, M.: Facial sentiment analysis using AI techniques: state-of-the-art, taxonomies, and challenges. IEEE Access 8, 90495–90519 (2020)CrossRef
116.
117.
118.
Paul, M., Haque, S.M., Chakraborty, S.: Human detection in surveillance videos and its applications: a review. EURASIP J. Adv. Signal Process. 2013(1), 176 (2013)CrossRef
119.
Peng, C.Y.J., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96(1), 3–14 (2002)CrossRef
120.
121.
Prakash, K.B., Imambi, S.S., Ismail, M., Kumar, T.P., Pawan, Y.: Analysis, prediction and evaluation of COVID-19 datasets using machine learning algorithms. Int. J. 8(5) (2020)
122.
Preethika, T., Vaishnavi, P., Agnishwar, J., Padmanathan, K., Umashankar, S., Annapoorani, S., Subash, M., Aruloli, K.: Artificial intelligence and drones to combat COVID-19 (2020)
123.
Prieto Tejedor, J., Corchado Rodríguez, J.M., et al.: Blockchain and AI to flatten the curve (2020)
124.
Punn, N.S., Sonbhadra, S.K., Agarwal, S.: Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned yolo v3 and deepsort techniques. Preprint at arXiv:​2005.​01385 (2020)
125.
Qi, X., Jiang, Z., Yu, Q., Shao, C., Zhang, H., Yue, H., Ma, B., Wang, Y., Liu, C., Meng, X., et al.: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-COV-2 infection: a multicenter study. medRxiv (2020). https://​doi.​org/​10.​1101/​2020.​02.​29.​20029603
126.
Randhawa, G.S., Soltysiak, M.P., El Roz, H., de Souza, C.P., Hill, K.A., Kari, L.: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One 15(4), e0232391 (2020)CrossRef
127.
Ranney, M.L., Griffeth, V., Jha, A.K.: Critical supply shortages-the need for ventilators and personal protective equipment during the COVID-19 pandemic. N. Engl. J. Med. 382(18), e41 (2020)CrossRef
128.
Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., Stebbing, J.: Baricitinib as potential treatment for 2019-ncov acute respiratory disease. Lancet (London, England) 395(10223), e30 (2020)CrossRef
129.
Roberts, M., Andreasen, V., Lloyd, A., Pellis, L.: Nine challenges for deterministic epidemic models. Epidemics 10, 49–53 (2015)CrossRef
130.
Rodsawang, C., Thongkliang, P., Intawong, T., Sonong, A., Thitiwatthana, Y., Chottanapund, S.: Designing a competent chatbot to counter the COVID-19 pandemic and empower risk communication in an emergency response system. OSIR J. 13(2) (2020)
131.
Rothan, H.A., Byrareddy, S.N.: The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 109, 102433 (2020)CrossRef
132.
Ruchansky, N., Seo, S., Liu, Y.: Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806 (2017)
133.
Sathiyabhama, B., Revathi, T., Basker, N., Vinothkumar, R., et al.: Tracing of vehicle region and number plate detection using deep learning. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1–4. IEEE (2020)
134.
Sengar, V.S., Das, S.: Applying AI in times of COVID-19
135.
Senior, A.W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A.W., Bridgland, A., et al.: Improved protein structure prediction using potentials from deep learning. Nature 577(7792), 706–710 (2020)CrossRef
136.
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shi, Y.: Lung infection quantification of COVID-19 in CT images with deep learning. Preprint at arXiv:​2003.​04655 (2020)
137.
138.
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., Shen, D.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE reviews in biomedical engineering (2020)
139.
Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., Jiang, H., Gao, Y., Sui, H., Shen, D.: Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. Preprint at arXiv:​2003.​09860 (2020)
140.
Shibly, K.H., Dey, S.K., Islam, M.T.U., Rahman, M.M.: COVID faster R-CNN: a novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images. Inform. Med. Unlocked 20, 100405 (2020)CrossRef
141.
142.
143.
Singhal, N., Prakash, S.: A fight against COVID-19: Major it trends. Available at SSRN 3601504 (2020)
144.
Song, Y., Huang, J., Zhou, D., Zha, H., Giles, C.L.: Iknn: Informative k-nearest neighbor pattern classification. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 248–264. Springer (2007)
145.
Soures, N., Chambers, D., Carmichael, Z., Daram, A., Shah, D.P., Clark, K., Potter, L., Kudithipudi, D.: Sirnet: understanding social distancing measures with hybrid neural network model for COVID-19 infectious spread. Preprint at arXiv:​2004.​10376 (2020)
146.
Srivastava, A., Singh, S.K., Tanwar, S., Tyagi, S.: Suitability of big data analytics in Indian banking sector to increase revenue and profitability. In: 2017 3rd International Conference on Advances in Computing, Communication Automation (ICACCA) (Fall), pp. 1–6 (2017). https://​doi.​org/​10.​1109/​ICACCAF.​2017.​8344732
147.
Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., Richardson, P.: COVID-19: combining antiviral and anti-inflammatory treatments. Lancet Infect. Dis. 20(4), 400–402 (2020)CrossRef
148.
Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep recurrent neural network for intrusion detection in sdn-based networks. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 202–206. IEEE (2018)
149.
Tang, Z., Zhao, W., Xie, X., Zhong, Z., Shi, F., Liu, J., Shen, D.: Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. Preprint at arXiv:​2003.​11988 (2020)
150.
151.
152.
Tanwar, S., Vora, J., Kanriya, S., Tyagi, S., Kumar, N., Sharma, V., You, I.: Human arthritis analysis in fog computing environment using Bayesian network classifier and thread protocol. IEEE Consum. Electron. Mag. 9, 88–94 (2018)CrossRef
153.
Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y.: Digital technology and COVID-19. Nat. Med. 26(4), 459–461 (2020)CrossRef
154.
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)CrossRef
155.
Uyanık, G.K., Güler, N.: A study on multiple linear regression analysis. Procedia Soc. Behav. Sci. 106, 234–240 (2013)CrossRef
156.
Vaishya, R., Javaid, M., Khan, I.H., Haleem, A.: Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. 14, 337–339 (2020)CrossRef
157.
Viniavskyi, O., Dobko, M., Dobosevych, O.: Weakly-supervised segmentation for disease localization in chest X-ray images. arXiv preprint arXiv:​2007.​00748 (2020)
158.
159.
Wang, Y., Hu, M., Li, Q., Zhang, X.P., Zhai, G., Yao, N.: Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. Preprint at arXiv:​2002.​05534 (2020)
160.
Wuest, T., Kusiak, A., Dai, T., Tayur, S.R.: Impact of COVID-19 on manufacturing and supply networks-the case for ai-inspired digital transformation. Available at SSRN 3593540 (2020)
161.
Wynants, L., Van Calster, B., Bonten, M.M., Collins, G.S., Debray, T.P., De Vos, M., Haller, M.C., Heinze, G., Moons, K.G., Riley, R.D., et al.: Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 369 (2020)
162.
163.
164.
165.
Yan, L., Zhang, H.T., Xiao, Y., Wang, M., Sun, C., Liang, J., Li, S., Zhang, M., Guo, Y., Xiao, Y., et al.: Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. medRxiv (2020)
166.
Yang, G.Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Goldberg, K., Ikuta, K., Jacobstein, N., et al.: Combating COVID-19-the role of robotics in managing public health and infectious diseases (2020)
167.
Ye, Y., Hou, S., Fan, Y., Qian, Y., Zhang, Y., Sun, S., Peng, Q., Laparo, K.: \(\alpha\)-satellite: an AI-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19. Preprint at arXiv:​2003.​12232 (2020)
168.
Zeng, Z., Chen, P.J., Lew, A.A.: From high-touch to high-tech: COVID-19 drives robotics adoption. Tour. Geogr. 23, 724–734 (2020)CrossRef
169.
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 18, 1423–1433 (2020)CrossRef
170.
171.
Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018)CrossRef
172.
173.
Zuo, F., Wang, J., Gao, J., Ozbay, K., Ban, X.J., Shen, Y., Yang, H., Iyer, S.: An interactive data visualization and analytics tool to evaluate mobility and sociability trends during COVID-19. Preprint at arXiv:​2006.​14882 (2020)
Metadaten
Titel
Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends
verfasst von
Het Shah
Saiyam Shah
Sudeep Tanwar
Rajesh Gupta
Neeraj Kumar
Publikationsdatum
13.07.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 4/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-021-00818-1

Weitere Artikel der Ausgabe 4/2022

Multimedia Systems 4/2022 Zur Ausgabe

Special Issue Paper

Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images

Special Issue Paper

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Special Issue Paper

Intelligent multimodal medical image fusion with deep guided filtering

Special Issue Paper

Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities

Editorial

Special issue deep learning for multimedia healthcare

Special Issue Paper

Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein

Neuer Inhalt

    Bildnachweise