The DOI matches its Crossref record; 149 of the 182 references it cites were found in a registry or an index.
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
Open accessPublished version of a preprint
Abstract
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
(2016) · Cited by 11,679
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K. W., Bleicker, T., Brünink, S., Schneider, J. A., Schmidt, M. L., et al. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.3.2000045 · Cited by 7,266
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al‐Jabir, A., Iosifidis, C., & Agha, R. (2020) · International Journal of Surgery · doi:10.1016/j.ijsu.2020.02.034 · Cited by 6,269
Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., & Hsueh, P.-R. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105924 · Cited by 6,068
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020) · Science · doi:10.1126/science.aba9757 · Cited by 3,694
Flagged first, then by citations
33 of the 182 references couldn’t be matched to a known paper.
Research works printed but not linked: 19
- 2020, Almost One Zettabyte of Mobile Data Traffic in 2022-Cisco
- 10.1109/ACCESS.2020.2971576 · DOI · Paper page
- 2020, arXiv Announces New COVID-19 Quick Search
- 2020, White House Announces New Partnership to Unleash U S Supercomputing Resources to Fight COVID-19
- 2020, Cisco Annual Internet Report (2018–2023)
- offermanns, 2008, IC50 Values, 611
- 2020, Seoul Introduces the COVID-19 AI Monitoring Call System
- 2020, How Next-Generation Information Technologies Tackled COVID-19 in China
- 10.2139/ssrn.3551338 · DOI
- 2020, How DAMO Academy’s AI System Detects Coronavirus Cases
- 2020, Mipasa Project and ibm Blockchain Team on Open Data Platform to Support Covid-19 Response
- rees, 2020, AI and Cloud Computing Used to Develop COVID-19 Vaccine
- 2020, Coronavirus (COVID-19)
- zheng, 2020, Deep learning-based detection for COVID-19 from chest CT using weak label, medRxiv
- 2020, COVID-19 Open Research Dataset Challenge (CORD-19) An AI Challenge With AI2 CZI MSR Georgetown NIH & the White House
- 2020, IBM Releases Novel AI-Powered Technologies to Help Health and Research Community Accelerate the Discovery of Medical Insights and Treatments for COVID-19
- li, 2020, High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method, BioRxiv
- banerjee, 2020, Energetics based epitope screening in SARS CoV-2 (COVID 19) spike glycoprotein by immuno-informatic analysis aiming to a suitable vaccine development, BioRxiv
- li, 2020, FEP-based screening prompts drug repositioning against COVID-19, BioRxiv
Cited by
Sorted by citations
Items 201–300 of 413
Artificial intelligence and machine learning responses to COVID-19 related inquiries
Zaeri, N. (2023) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2024.2321846 · Cited by 6
The COVID-19 Pandemic Prediction in the US Based on Machine Learning
Zou, W. (2020) · International Conference on Public Health and Data Science (ICPHDS) · doi:10.1109/icphds51617.2020.00062 · Cited by 6
AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review
Deng, H., & Li, X. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.612914 · Cited by 6
Ftl‐CoV19: A Transfer Learning Approach to Detect COVID‐19
Singh, T., Saurabh, P., Bisen, D., Kane, L., Pathak, M., Sinha, G. R., & Gupta, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1953992 · Cited by 6
Resolving Covid-19 with Blockchain and AI
Singh, S., & Verma, S. B. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31454 · Cited by 9
A Novel Pandemic Tracking Map: From Theory to Implementation
Gouissem, A., Abualsaud, K., Yaacoub, E. E., Khattab, T. M., & Guizani, M. (2021) · IEEE Access · doi:10.1109/access.2021.3067824 · Cited by 6
An online COVID-19 self-assessment framework supported by IoMT technology
Nsaif, M., Mahdi, B. A., Al‐Mayouf, Y. R. B., Mahdi, O. A., Aljaaf, A. J., & Khan, S. (2021) · Journal of Intelligent Systems · doi:10.1515/jisys-2021-0048 · Cited by 6
Tongkachok, K., Ali, B. M., Ganguly, M., Kumar, S., Malathi, M., & Subramanian, M. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-0108-9_15 · Cited by 6
Role of Computational Intelligence Against COVID-19
Kaur, S. P., & Hasija, Y. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-8534-0_2 · Cited by 6
Selvakumar, K., & Lokesh, S. (2021) · Soft Computing · doi:10.1007/s00500-021-05643-2 · Cited by 6
RetractedCould amantadine possibly interfere with COVID-19 vaccines based on LNP-mRNA platform ?
Fedorowski, J. (2021) · Archives of Medical Science · doi:10.5114/aoms/134716 · Cited by 6
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., & Zomaya, A. Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3120998 · Cited by 5
Self-citationArtificial Intelligence for All: Challenges and Harnessing Opportunities in AI Democratization
de Castro, K. V. G., Siwady, J. J. A., Castillo, E., Alonzo, A., Cardona, M., & Perdomo, M. E. (2024) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant63295.2024.00030 · Cited by 5
Chen, G., He, H., Lv, Q., Zhao, L., & Chen, C. Y. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00663 · Cited by 5
An Overview of Medical Electronic Hardware Security and Emerging Solutions
Taheri, S., & Asadizanjani, N. (2022) · Electronics · doi:10.3390/electronics11040610 · Cited by 5
Funding Sources for Medical Entrepreneurship in the Digital Age (Middle East Perspective)
El-Chaarani, H., Raimi, L., & El-Abiad, Z. (2023) · Springer eBooks · doi:10.1007/978-981-19-6696-5_7 · Cited by 5
Retracted: A Novel Covid Prevention Method Using Deep Learning
Kaviya, V., Vedhashree, D., & Rani, T. P. (2020) · International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) · doi:10.1109/icpects49113.2020.9337018 · Cited by 5
RetractedMulti-Lane Capsule Network Architecture for Detection of COVID-19
Sridhar, S., & Sanagavarapu, S. (2021) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem51511.2021.9445363 · Cited by 5
Özcan, A. N. Ş., & Aslan, K. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210806123720 · Cited by 5
Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model
Das, M., Ghosh, A. K., & Ghosh, S. K. (2021) · SN Computer Science · doi:10.1007/s42979-021-00845-9 · Cited by 5
Recent artificial intelligence methods and coronaviruses
Rehman, S. U., Shafqat, F., & Niaz, K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-95047-3.00009-5 · Cited by 5
Kafi, D. K., & Ayyash, A. N. (2023) · Applied Nanoscience · doi:10.1007/s13204-022-02735-9 · Cited by 5
Analisa Splitting Criteria Pada Decision Tree dan Random Forest untuk Klasifikasi Evaluasi Kendaraan
Nugroho, A. (2022) · JSITIK Jurnal Sistem Informasi dan Teknologi Informasi Komputer · doi:10.53624/jsitik.v1i1.154 · Cited by 5
Enhancing Epidemiological Surveillance Systems Using Dynamic Modeling: A Scoping Review
Obe, O. O., Sarumi, O. A., & Adebayo, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96302-6_48 · Cited by 5
A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
Makhanov, N., Tu, N. A., & Wong, K. (2022) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp54360.2022.00029 · Cited by 5
A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 Pandemic
Salameh, W. A., Surakhi, O., & Khanafseh, M. (2024) · WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE · doi:10.37394/23208.2024.21.21 · Cited by 5
AI-Driven Data Integration to Transform Epidemiology
Mittal, S., Singh, P. K., Gochhait, S., & Kumar, S. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-1243-8.ch003 · Cited by 4
Liu, Y., Xu, X., & Li, S. (2025) · Scientometrics · doi:10.1007/s11192-025-05236-y · Cited by 5
Shankar, S. P., Supriya, M. S., Varadam, D., Kumar, M., Gupta, H., & Saha, R. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-5925-6.ch003 · Cited by 4
Covid-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Saranya, S., & Bobby, S. (2023) · doi:10.46632/daai/3/2/13 · Cited by 4
Fake News Detection in the Medical Field Using Machine Learning Techniques
Murugesan, S., & Pachamuthu, K. (2022) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.120608 · Cited by 4
Shrivastav, L. K., & Kumar, R. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10367-7 · Cited by 4
Towards intelligent supervision of operating rooms using stencil-based character recognition
Hernández‐Aceituno, J., Pérez, J. A. M., González-Cava, J. M., & Reboso, J. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107071 · Cited by 5
Artificial Intelligence Applications for Workplace Safety
Rybak, N., & Hassall, M. E. (2024) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch085 · Cited by 4
Exploring the Intersection of Artificial Intelligence and Fine Arts: A Data-Driven Approach
Chen, X. (2024) · doi:10.1145/3695080.3695117 · Cited by 4
Designing a Framework to Control ?the Spread of Covid-19 by Utilizing ?Cellular System
Ahmed, A. M. (2020) · Kurdistan Journal of Applied Research · doi:10.24017/covid.16 · Cited by 4
Digital Technology for predicting, preventing, and controlling COVID-19
Sengar, D. (2020) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset2072136 · Cited by 5
Dash, D., Agarwal, A. K., Agarwal, K., & Misra, G. (2021) · Journal of Information Technology and Digital World · doi:10.36548/jitdw.2021.1.002 · Cited by 4
A Machine Learning-Based Tool for Exploring COVID-19 Scientific Literature
Allaoui, M., Aissa, N. E. H. S. B., Belghith, A. B., & Kherfi, M. L. (2021) · International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) · doi:10.1109/icrami52622.2021.9585958 · Cited by 4
COVID-19 Impacts in the New Technological Era: Cross-Border Privacy Issues With Emphasis on AI
Rizou, S., Alexandropoulou-Egyptiadou, E., & Psannis, K. E. (2022) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2022.3203962 · Cited by 5
Three-Step Master Data Creation Method from Big Data: Scraping, Semi-Structuring, and Extraction
Kudo, T., Yamamoto, T., & Watanabe, T. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.070 · Cited by 4
Patra, R., Das, N. C., Bhattacharya, M., Shit, P. K., Patra, B. C., & Mukherjee, S. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-68936-0_3 · Cited by 4
Mahdi, A. Y., & Yuhaniz, S. S. (2021) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v9i2.1945 · Cited by 4
A Review of Computational Intelligence Technologies for Tackling Covid-19 Pandemic
Rana, A., & Malik, S. (2021) · Internet of things · doi:10.1007/978-3-030-75220-0_11 · Cited by 4
AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study
Zaeri, N. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.99442 · Cited by 3
A Technical Review on Machine Learning-Based Prediction on COVID-19 Diagnosis
Kejriwal, S., & Rajagopalan, N. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-3571-8_50 · Cited by 4
AI for Sustainable Development: MENA Strategies
Pasumarti, S. S., & Hajj, C. E. (2025) · Springer eBooks · doi:10.1007/978-3-031-75589-7_4 · Cited by 3
استخدام البيانات الضخمة والذكاء الاصطناعي في مواجهة جائحة فيروس كورونا المستجد
Arishee, J., & Al-Ghamdi, F. B. S. (2020) · ARAB JOURNAL FOR SECURITY STUDIES · doi:10.26735/gycx5740 · Cited by 3
Nafea, I. T. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-70713-2_44 · Cited by 3
Achieving Ambient Intelligence in Addressing the COVID-19 Pandemic Using Fog Computing-Driven IoT
Rawlley, O., & Gupta, S. R. (2021) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-7998-8367-8.ch003 · Cited by 3
Artificial Intelligence Effect on Marketing Mix
Djermani, F., & Hajimia, H. (2021) · Jurnal Aplikasi Manajemen Ekonomi dan Bisnis · doi:10.51263/jameb.v6i1.132 · Cited by 3
Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic
de Sá, Â. A. R., Carvalho, J. D., & Naves, E. L. M. (2021) · AI & Society · doi:10.1007/s00146-021-01315-9 · Cited by 3
Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning
Safi, S. K., Aliyu, S., Ibrahim, K. S., & Sanusi, O. I. (2022) · International Journal of Energy Economics and Policy · doi:10.32479/ijeep.13200 · Cited by 3
Gunturu, L. N., Dornadula, G., & Nimbagal, R. N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90531-2.00009-6 · Cited by 3
Ahmed, S. T., Basha, S. M., Muthukumaran, V., Mathivanan, S. K., Mallik, S., Alsubaie, N. M., & Alqahtani, M. S. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01100-8 · Cited by 3
IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic
Almujally, N. A., Aljrees, T., Umer, M., Saidani, O., Hanif, D., Abuzinadah, N. E., Alnowaiser, K., & Ashraf, I. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1493 · Cited by 3
Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors
Mukhlif, F., Ithnin, N., Alroobaea, R., Algarni, S. A., Alghamdi, W. Y., & Hashem, M. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038757 · Cited by 3
The Role of AI in Healthcare Policy Development and Management
Yadav, S., Mane, P., Swarnkar, V. K., Rawandale, S. A., Patil, A. S., Katke, K. S., & Bhat, N. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489810 · Cited by 4
Chouit, E. M., Rachdi, M., Bellafkih, M., & Raouyane, B. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-3807-0.ch005 · Cited by 3
Artificial intelligence-based solutions for COVID-19
Pawar, M. V., Pawar, A. M., Bhapkar, H. R., Anuradha, J., Bachate, R. P., Sharma, A. K., Bhoyar, S., & Shardoor, N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90769-9.00004-9 · Cited by 3
Utilizing Link Prediction Approach to Predict Express-Related Counterfeit Cigarette Crime Cases
Qiao, L.-C., Wang, J., Gao, B.-H., Yang, X., Feng, W., Zhang, Y.-X., Liu, W., & Xu, R.-Y. (2021) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct52962.2021.9657960 · Cited by 3
The New Technologies in the Pandemic Era
Nascimento, E. G. S., Furtado, A. N., Badaró, R. J. D. S., & Knop, L. (2020) · Journal of Bioengineering, Technologies and Health · doi:10.34178/jbth.v3i2.122 · Cited by 4
Firdose, S., Kumar, S. S., & Meegama, R. G. N. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i1.pp596-604 · Cited by 3
The Enigmatic COVID-19 Vulnerabilities and the Invaluable Artificial Intelligence (AI)
Lainjo, B. (2021) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s321751 · Cited by 3
Approach to the management of COVID-19 patients: When home care can represent the best practice
Tricarico, G., & Travagli, V. (2022) · International Journal of Risk & Safety in Medicine · doi:10.3233/jrs-210064 · Cited by 3
Ravishankar, K., & Jothikumar, C. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac154 · Cited by 3
Imani, S., Vahed, M., Satodia, S., & Vahed, M. (2023) · SoftwareX · doi:10.1016/j.softx.2023.101350 · Cited by 3
Najjar, R., Hossain, M. Z., Ahmed, K. A., & Hasan, M. R. (2024) · medRxiv · doi:10.1101/2024.03.17.24304450 · Cited by 3
The Role of Artificial Intelligence in Mitigating Unknown-Unknown Risks
Beshwari, F., Beshwari, M., & Beshwari, A. (2020) · International Journal of Research Publications · doi:10.47119/ijrp1006411120201516 · Cited by 2
Gaie, C., & Mueck, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90769-9.00025-6 · Cited by 2
Tello-Mijares, S., & Woo, F. (2022) · Tomography · doi:10.3390/tomography8030134 · Cited by 2
Indhumathi, K., & Kumar, K. S. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823453 · Cited by 5
CNN Models Using Chest X-Ray Images for COVID-19 Detection: A Survey
Mellal, N., & Zaidi, S. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370621 · Cited by 2
Leveraging Big Data for Effective Healthcare Management with Artificial Intelligence
Dubey, K., Kasar, C. B., Thulluri, G., Yesugad, A., Karishma, M., & Padhi, D. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489731 · Cited by 2
A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study
Azam, M. M. B., Anwaar, F., Khan, A., Anwar, M., Ghani, H. A., Eisa, T. A. E., & Abdelmaboud, A. (2024) · Egyptian Informatics Journal · doi:10.1016/j.eij.2024.100508 · Cited by 2
A Comprehensive Survey on Federated Learning and its Applications in Health Care
Pacheco, S. A. B. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730687 · Cited by 2
Data Analysis and Detection of Coronavirus Disease using Convolution Neural Network
Porwal, P., Thirunavukkarasu, K. S., Sinha, A. N., & Singh, A. S. (2020) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn51052.2020.9362763 · Cited by 2
Valamede, L. S., & Akkari, A. C. S. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-3-030-75680-2_3 · Cited by 2
Artificial Intelligence (AI) and Big Data Analytics for the COVID-19 Pandemic
Pandit, P., Krishnamurthy, K. N., & Bakshi, B. (2021) · Springer eBooks · doi:10.1007/978-3-030-79753-9_1 · Cited by 2
Artificial Intelligence and Its Importance in Post-COVID-19 China
Zhang, Y., Hua, J., Adu-Gyamfi, B., & Shaw, R. (2021) · Disaster risk reduction · doi:10.1007/978-981-16-6959-0_8 · Cited by 2
A Systematic Review of Fuzzy Logic Applications for the COVID-19 Pandemic
Çakıt, E. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-9172-7.ch004 · Cited by 2
Kaur, A., Elarabawy, M. M., Abd‐Elnaby, M., Vijayakumar, V., & Sharma, S. (2022) · Security and Communication Networks · doi:10.1155/2022/6272480 · Cited by 2
RetractedPlataforma de Monitoramento Inteligente da COVID-19
Beuren, A. T., de Freitas, H. J. T., Naves, T. F., Flórez, H. A. R., Sepúlveda, G. P. L., Soares, A. D. S., & Soares, A. S. (2022) · Research Society and Development · doi:10.33448/rsd-v11i14.36659 · Cited by 2
IoT and Fuzzy Logic Applied to Smart Public Hall for COVID-19 Monitoring System
Nyakuri, J. P., Harerimana, F., Karanguza, F., Ghadi, N., D’Amour, M. J., Uwimana, J. L., Habiyaremye, T., Ndayisenga, J. C., & Bizimana, J. (2023) · American Journal of Computing and Engineering · doi:10.47672/ajce.1358 · Cited by 2
Impacto da indústria 4.0 na geração de inovação em tecnologias médicas: uma abordagem patentária
Goes, A. B. B., Munhoz, I. P., & Akkari, A. C. S. (2020) · Revista Tecnologia e Sociedade · doi:10.3895/rts.v16n45.11551 · Cited by 2
Efficient Diagnosis COVID-19 using Gabor and Transfer Learning
Ketfi, M., Belahcene, M., & Bourennane, S. (2021) · International Conference on Cyber Management and Engineering (CyMaEn) · doi:10.1109/cymaen50288.2021.9497304 · Cited by 2
Machine Learning in the Context of COVID-19 Pandemic Data Analysis
Hrabia, A., Kozák, J. T., & Juszczuk, P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88113-9_29 · Cited by 2
COVID-19: The Dynamics of Quality of Life (QOL) and Artificial Intelligence (AI)
Lainjo, B. (2021) · African Journal of Inter/Multidisciplinary Studies · doi:10.51415/ajims.v3i1.908 · Cited by 2
Babu, R. M. H., Shebana, M., Harish, R. M., Kanimozhi, V., & Kumar, K. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90769-9.00001-3 · Cited by 3
The economic impact of covid-19 and the role of AI
Nayak, C. B., Nanda, P. K., Tripathy, S., Swain, S. C., Das, C. K., & Sahu, R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90054-6.00002-7 · Cited by 2
Pandemic-driven innovations contribute to the development of information-based medicine
Kalina, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821318-6.00019-0 · Cited by 2
Onwubiko, E. C. (2022) · Advances in library and information science (ALIS) book series · doi:10.4018/978-1-6684-5964-5.ch003 · Cited by 2
The Role of Big Data Analytics in Digital Health for COVID-19 Prevention and Control in Asia
Azmi, N. A., Noor, N. M., Shukri, M. I. M., Mahmud, A., & Manaf, R. A. (2022) · Malaysian Journal of Medicine and Health Sciences · doi:10.47836//mjmhs18.4.24 · Cited by 2
Emerging technologies for COVID-19, diagnosis, prevention, and management
Uğur, A. R., & Özdemir, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91794-0.00019-6 · Cited by 2
Genomic Characterization of Emerging SARS-CoV-2: A Systematic Review
Sharma, S., Rani, R., & Kalra, N. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230228115423 · Cited by 2
Darwish, D. A. M. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch020 · Cited by 2
Sridhar, P., Ramasamy, J., Kumar, R., Ramanathan, R., Nayak, R., & Tholkapiyan, M. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10180-1 · Cited by 2
Tracking and Analysis of Corona Disease Using Intelligent Data Analysis
Amulya, M. P., Niranjanamurthy, M., Yogish, H. K., & Ravikumar, G. K. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-1574-0_16 · Cited by 2
Nanobioengineering: A promising approach for early detection of COVID-19
Gill, A. A. S., Nate, Z., Chauhan, R., Faya, M. A., Karpoormath, R. V., & Omolo, C. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90280-9.00004-3 · Cited by 2
Body Sensor Networks as Emerging Trends of Technology in Health Care System: Challenges and Future
Lakshmi, N. J., & Jabalia, N. (2021) · Internet of things · doi:10.1007/978-3-030-66633-0_6 · Cited by 2
Metadata
- Authors
- Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N.
- Affiliations
- Pusan National University (KR)
- Deakin University (AU)
- Corresponding author
- Hwang, Won-Joo
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 130820-130839
- ISSN
- 2169-3536 (ISSN-L)
- PMC
- PMC8545324
- OpenAlex
- W3019283372
- Cited by
- 413
- References
- 182
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 41 · 2023: 72 · 2022: 84 · 2021: 125 · 2020: 56
- FWCI
- 41.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation of Korea
- Ministry of Science and ICT (MSIT), Korea Government (NRF-2019R1C1C1006143, NRF-2019R1I1A3A01060518)
- Institute of Information & Communications Technology Planning & Evaluation
- Korea Government (MSIT) through the Artificial Intelligence Convergence Research Center, Pusan National University (2020-0-01450)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- COVID-19 pandemic, artificial intelligence applications, big data, pandemic response, epidemic control, public health communication, epidemic forecasting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
