A systematic review on AI/ML approaches against COVID-19 outbreak

Scoping studies: towards a methodological framework

Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742 · see citing works

Untitled work

(2016) · Cited by 11,679 · see citing works

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works

Re-epithelialization and immune cell behaviour in an ex vivo human skin model

Rakita, A., Nikolić, N., Mildner, M., Matiasek, J., & Elbe‐Bürger, A. (2020) · Scientific Reports · doi:10.1038/s41598-019-56847-4 · Cited by 8,234 · see citing works

Untitled work

Yang (2021) · doi:10.1038/s41598-020-79139-8 · Cited by 5,035 · see citing works

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works

Systematic Literature Reviews in Software Engineering—A Systematic Literature Review

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537 · see citing works

Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand

Ferguson, N. M., Laydon, D. J., Nedjati-Gilani, G. L., Imai, N., Ainslie, K. E. C., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z. M., Cuomo-Dannenburg, G., et al. (2020) · Spiral (Imperial College London) · doi:10.25561/77482 · Cited by 3,453 · see citing works

Systematic Mapping Studies in Software Engineering

Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008) · Electronic Workshops in Computing · doi:10.14236/ewic/ease2008.8 · Cited by 3,239 · cited 1× · see citing works

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,151 · see citing works

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

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. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070 · see citing works

The effect of human mobility and control measures on the COVID-19 epidemic in China

Kraemer, M. U. G., Yang, C.-H., Gutiérrez, B., Wu, C., Klein, B., Pigott, D. M., open COVID-19 data working group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,950 · see citing works

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski, A. J., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30144-4 · Cited by 2,675 · see citing works

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works

Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review

Adhikari, S. P., Sha, M., Wu, Y.-J., Mao, Y.-P., Ye, R.-X., Wang, Q.-Z., Sun, C., Sylvia, S., Rozelle, S., Raat, H., et al. (2020) · Infectious Diseases of Poverty · doi:10.1186/s40249-020-00646-x · Cited by 2,410 · see citing works

Lessons from Applying the Systematic Literature Review Process within the Software Engineering Domain

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007) · Journal of Systems and Software · doi:10.1016/j.jss.2006.07.009 · Cited by 2,394 · see citing works

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338 · see citing works

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054 · see citing works

An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China.

Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010 · see citing works

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927 · see citing works

Can AI help in screening viral and COVID-19 pneumonia?

Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913 · see citing works

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669 · see citing works

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,668 · see citing works

Artificial Intelligence (AI) applications for COVID-19 pandemic

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,502 · see citing works

Effect of non-pharmaceutical interventions to contain COVID-19 in China

Lai, S., Ruktanonchai, N., Zhou, L., Prosper, O., Luo, W. P., Floyd, J., Wesolowski, A., Santillana, M., Zhang, C., Du, X., et al. (2020) · Nature · doi:10.1038/s41586-020-2293-x · Cited by 1,357 · see citing works

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Khan, A. I., Shah, J. L., & Bhat, M. M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105581 · Cited by 1,312 · see citing works

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209 · see citing works

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint

Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,181 · see citing works

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01829-7 · Cited by 1,142 · see citing works

Drug treatment options for the 2019-new coronavirus (2019-nCoV)

Lu, H. (2020) · BioScience Trends · doi:10.5582/bst.2020.01020 · Cited by 1,140 · see citing works

Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

Mei, X., Lee, H.-C., Diao, K.-Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0931-3 · Cited by 1,084 · see citing works

Time series forecasting of COVID-19 transmission in Canada using LSTM networks

Chimmula, V. K. R., & Zhang, L. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109864 · Cited by 1,082 · see citing works

Serial interval of novel coronavirus (COVID-19) infections

Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.02.060 · Cited by 1,064 · see citing works

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

Minaee, S., Kafieh, R., Milan Sonka, A., Yazdani, S., & Soufi, G. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101794 · Cited by 1,026 · see citing works

COVID-19 Image Data Collection: Prospective Predictions are the Future

Cohen, J., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2020-48g7 · Cited by 1,026 · see citing works

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Randhawa, G. S., Soltysiak, M. P. M., El Roz, H., de Souza, C. P. E., Hill, K. A., & Kari, L. (2020) · PLOS ONE · doi:10.1371/journal.pone.0232391 · Cited by 1,023 · see citing works

Applications of digital technology in COVID-19 pandemic planning and response

Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30142-4 · Cited by 992 · see citing works

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 960 · see citing works

Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel

Bogoch, I. I., Watts, A., Thomas-Bachli, A. L., Huber, C., Kraemer, M. U. G., & Khan, K. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa008 · Cited by 899 · see citing works

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103795 · Cited by 848 · see citing works

High Temperature and High Humidity Reduce the Transmission of COVID-19

Wang, J., Tang, K., Feng, K., Lin, X., Lv, W., Chen, K., & Wang, F. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-043863 · Cited by 822 · see citing works

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.025 · Cited by 815 · see citing works

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

Uçar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 768 · see citing works

Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

Chen, E., Lerman, K., & Ferrara, E. (2020) · JMIR Public Health and Surveillance · doi:10.2196/19273 · Cited by 762 · see citing works

Application of the ARIMA model on the COVID-2019 epidemic dataset

Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020) · Data in Brief · doi:10.1016/j.dib.2020.105340 · Cited by 752 · see citing works

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Shahid, F., Zameer, A., & Muneeb, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110212 · Cited by 744 · see citing works

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.010 · Cited by 710 · see citing works

Covid-19: a remote assessment in primary care

Greenhalgh, T., Koh, G. C. H., & Car, J. (2020) · BMJ · doi:10.1136/bmj.m1182 · Cited by 705 · see citing works

A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2

Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695 · see citing works

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100412 · Cited by 691 · see citing works

High Temperature and High Humidity Reduce the Transmission of COVID-19Preprint

Wang, J., Tang, K., Feng, K., & Lv, W. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3551767 · Cited by 681 · see citing works

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76282-0 · Cited by 674 · see citing works

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menéndez, R., & Singh, V. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109944 · Cited by 645 · see citing works

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image AnalysisPreprint

Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 638 · see citing works

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623 · see citing works

AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app

Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621 · see citing works

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1788642 · Cited by 612 · see citing works

Artificial intelligence and machine learning to fight COVID-19

Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020) · Physiological Genomics · doi:10.1152/physiolgenomics.00029.2020 · Cited by 609 · see citing works

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18685-1 · Cited by 591 · see citing works

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103805 · Cited by 574 · see citing works

Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis

Fang, Y., Nie, Y., & Penny, M. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25750 · Cited by 562 · see citing works

Candidate drugs against SARS-CoV-2 and COVID-19

McKee, D. L., Sternberg, A., Stange, U., Laufer, S., & Naujokat, C. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.104859 · Cited by 562 · see citing works

Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak

Wells, C. R., Sah, P., Moghadas, S. M., Pandey, A., Shoukat, A., Wang, Y., Wang, Z., Meyers, L. A., Singer, B. H., & Galvani, A. P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002616117 · Cited by 551 · see citing works

Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China

Liu, J., Zhou, J., Yao, J., Zhang, X., Li, L., Xu, X., He, X., Wang, B., Fu, S., Niu, T., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138513 · Cited by 541 · see citing works

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

Wang, B., Jin, S., Yan, Q., Xu, H., Luo, C. W., Wei, L., Zhao, W. Z., Hou, X., Ma, W., Xu, Z., et al. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106897 · Cited by 539 · see citing works

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538 · see citing works

Deep learning based detection and analysis of COVID-19 on chest X-ray images

Jain, R., Gupta, M., Taneja, S., & Hemanth, D. J. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01902-1 · Cited by 523 · see citing works

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S. K., & Jo, O. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00357 · Cited by 520 · see citing works

Mapping the landscape of Artificial Intelligence applications against COVID-19

Aylett-Bullock, J., Luccioni, A. S., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. A. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12162 · Cited by 517 · see citing works

Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing

Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100222 · Cited by 497 · see citing works

The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe

Martínez‐Sancho, E., Slámová, L., Morganti, S., Grefen, C., Carvalho, B., Dauphin, B., Rellstab, C., Gugerli, F., Opgenoorth, L., Heer, K., et al. (2020) · Scientific Data · doi:10.1038/s41597-019-0340-y · Cited by 496 · see citing works

Lung Infection Quantification of COVID-19 in CT Images with Deep LearningPreprint

Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04655 · Cited by 489 · see citing works

Deep convolutional neural networks for diabetic retinopathy detection by image classification

Wan, S., Liang, Y., & Zhang⋆, Y. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.07.042 · Cited by 487 · see citing works

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & Coelho, L. D. S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109853 · Cited by 475 · see citing works

Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms

Maghdid, H. S., Asaad, A., Ghafoor, K. Z., Sadiq, A. S., Mirjalili, S., & Khan, M. K. K. (2021) · Multimodal Image Exploitation and Learning · doi:10.1117/12.2588672 · Cited by 465 · see citing works

Deep Transfer Learning Based Classification Model for COVID-19 Disease

Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., Singh, S., & Shukla, P. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.05.003 · Cited by 462 · see citing works

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

Jiang, X., Coffee, M. P., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., et al. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.010691 · Cited by 450 · see citing works

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3412865 · Cited by 447 · see citing works

COVID-19 Outbreak Prediction with Machine Learning

Ardabili, S., Mosavi, A. H., Ghamisi, P., Filip, F., Várkonyi-Kóczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020) · Algorithms · doi:10.3390/a13100249 · Cited by 427 · see citing works

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng, B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104284 · Cited by 423 · see citing works

A mathematical model for COVID-19 transmission by using the Caputo fractional derivative

Tuan, N. H., Mohammadi, H., & Rezapour, S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110107 · Cited by 417 · see citing works

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

Chakraborty, T., & Ghosh, I. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109850 · Cited by 415 · see citing works

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413 · see citing works

COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from RadiographsPreprint

Farooq, M. A., & Hafeez, A. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14395 · Cited by 409 · see citing works

COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread

McCall, B. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30054-6 · Cited by 396 · see citing works

Epidemiological data from the COVID-19 outbreak, real-time case information

Xu, B., Gutierrez, B., Mekaru, S., Sewalk, K., Goodwin, L., Loskill, A., Cohn, E. L., Hswen, Y., Hill, S. C., Cobo, M. M., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-0448-0 · Cited by 396 · see citing works

COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection

Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020) · Europe PMC (PubMed Central) · Cited by 389 · see citing works

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Chatterjee, K., Chatterjee, K., Kumar, A., & Shankar, S. (2020) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2020.03.022 · Cited by 386 · see citing works

Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification

Shi, F., Xia, L., Shan, F., Song, B., Wu, D., Wei, Y., Yuan, H., Jiang, H., He, Y., Gao, Y., et al. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abe838 · Cited by 379 · see citing works

Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine

Sethy, P. K., Behera, S. K., Ratha, P. K., & Biswas, P. (2020) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2020.5.4.052 · Cited by 375 · see citing works

Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui, K. E., & Chawki, Y. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1767212 · Cited by 363 · see citing works

Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique

Altan, A., & Karasu, S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110071 · Cited by 358 · see citing works

Comparison of deep learning approaches to predict COVID-19 infection

Alakuş, T. B., & Türkoğlu, İ. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110120 · Cited by 357 · see citing works

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

Das, N. N., Kumar, N., Kaur, M., Kumar, V., & Singh, D. (2020) · IRBM · doi:10.1016/j.irbm.2020.07.001 · Cited by 351 · see citing works

A review of modern technologies for tackling COVID-19 pandemic

Kumar, A., Gupta, P. K., & Srivastava, A. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.05.008 · Cited by 347 · see citing works

Randomized controlled trial of a 12-week digital care program in improving low back pain

Shebib, R., Bailey, J. F., Smittenaar, P., Perez, D. A., Mecklenburg, G., & Hunter, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-018-0076-7 · Cited by 341 · see citing works

Moroccan Medicinal plants as inhibitors against SARS-CoV-2 main protease: Computational investigations

Aanouz, I., Belhassan, A., Khatabi, K. E., Lakhlifi, T., El-ldrissi, M., & Bouachrıne, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1758790 · Cited by 341 · see citing works

Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics

Wang, P., Zheng, X., Li, J., & Zhu, B. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110058 · Cited by 337 · see citing works

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases

Apostolopoulos, I. D., Aznaouridis, S. I., & Tzani, M. A. (2020) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00529-4 · Cited by 333 · see citing works

Coswara — A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

Sharma, N. K., Krishnan, P., Kumar, R. V., Ramoji, S., Chetupalli, S. R., Nirmala, R., Ghosh, P., & Ganapathy, S. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2768 · Cited by 332 · see citing works

Risk for COVID-19 infection and death among Latinos in the United States: examining heterogeneity in transmission dynamics

Rodríguez-Díaz, C. E., Guilamo‐Ramos, V., Mena, L. A., Hall, E., Honermann, B., Crowley, J. S., Baral, S., Prado, G. J., Marzán‐Rodríguez, M., Beyrer, C., et al. (2020) · Annals of Epidemiology · doi:10.1016/j.annepidem.2020.07.007 · Cited by 331 · see citing works

Machine learning based approaches for detecting COVID-19 using clinical text data

Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N., & Din, M. M. U. (2020) · International Journal of Information Technology · doi:10.1007/s41870-020-00495-9 · Cited by 330 · see citing works

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327 · see citing works

Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) DetectionPreprint

Ghoshal, B., & Tucker, A. (2020) · Brunel University Research Archive (BURA) (Brunel University London) · doi:10.48550/arxiv.2003.10769 · Cited by 327 · see citing works

Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning

Asnaoui, K. E., Chawki, Y., & Idri, A. (2021) · Studies in big data · doi:10.1007/978-3-030-74575-2_14 · Cited by 321 · see citing works

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour, M., Cömert, Z., & Polat, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106580 · Cited by 316 · see citing works

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Zivkovic, M. Z., Bačanin, N., Venkatachalam, K., Nayyar, A., Djordjevic, A., Strumberger, I., & Al-Turjman, F. M. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102669 · Cited by 316 · see citing works

Predicting COVID-19 in China Using Hybrid AI Model

Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., et al. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2990162 · Cited by 314 · see citing works

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200715 · Cited by 302 · see citing works

A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, E., Tutubalina, E. V., & Chowell, G. (2021) · Epidemiologia · doi:10.3390/epidemiologia2030024 · Cited by 296 · see citing works

Age-structured impact of social distancing on the COVID-19 epidemic in IndiaPreprint

Singh, R., & Adhikari, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12055 · Cited by 286 · see citing works

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Ma, J., Wang, Y., An, X., Ge, C., Yu, Z., Chen, J., Zhu, Q., Dong, G., He, J., He, Z., et al. (2020) · Medical Physics · doi:10.1002/mp.14676 · Cited by 285 · see citing works

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Albahri, A. S., Hamid, R. A., Alwan, J. K., Al-qays, Z. T., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alamoodi, A. H., Khlaf, J. M., Almahdi, E. M. A., et al. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01582-x · Cited by 284 · see citing works

A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study

Maghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. (2020) · 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri49571.2020.00033 · Cited by 279 · see citing works

Utilization of machine-learning models to accurately predict the risk for critical COVID-19

Assaf, D., Gutman, Y., Neuman, Y., Segal, G., Amit, S., Gefen-Halevi, S., Shilo, N., Epstein, A., Mor-Cohen, R., Biber, A., et al. (2020) · Internal and Emergency Medicine · doi:10.1007/s11739-020-02475-0 · Cited by 275 · see citing works

COVID-19 open source data sets: a comprehensive survey

Shuja, J., Alanazi, E., Alasmary, W., & Alashaikh, A. S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01862-6 · Cited by 273 · see citing works

Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries

The Global Dynamic Interventions Strategies for COVID-19 Collaborative Group, Chowdhury, R., Heng, K., Shawon, M. S. R., Goh, G., Okonofua, D., Ochoa‐Rosales, C., González-Jaramillo, V., Bhuiya, A., Reidpath, D. D., et al. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00649-w · Cited by 271 · see citing works

Artificial intelligence in medical imaging of the liver

Zhou, L., Wang, J., Yu, S.-Y., Wu, G.-G., Wei, Q., Deng, Y., Wu, X., Cui, X., & Dietrich, C. F. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i6.672 · Cited by 263 · see citing works

Evaluating the mental health impacts of the COVID-19 pandemic: perceived risk of COVID-19 infection and childhood trauma predict adult depressive symptoms in urban South Africa

(2020) · doi:10.1017/s0033291720003414 · Cited by 261 · see citing works

Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM)

Battineni, G., Chintalapudi, N., & Amenta, F. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100200 · Cited by 257 · see citing works

Human–machine partnership with artificial intelligence for chest radiograph diagnosis

Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., Rajpurkar, P., Amrhein, T., Gupta, R., Halabi, S., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0189-7 · Cited by 254 · see citing works

COVID-19 Epidemic Analysis using Machine Learning and Deep Learning AlgorithmsPreprint

Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020) · medRxiv · doi:10.1101/2020.04.08.20057679 · Cited by 253 · see citing works

Open access epidemiological data from the COVID-19 outbreak

Xu, B., Kraemer, M. U. G., Xu, B., Gutierrez, B., Mekaru, S. R., Sewalk, K. C., Loskill, A., Wang, L., Cohn, E. L., Hill, S., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30119-5 · Cited by 251 · see citing works

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Pirouz, B., Haghshenas, S. S., Haghshenas, S. S., & Piro, P. (2020) · Sustainability · doi:10.3390/su12062427 · Cited by 243 · see citing works

Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study

Yue, H., Yu, Q., Liu, C., Huang, Y., Jiang, Z., Shao, C., Zhang, H., Ma, B., Wang, Y., Xie, G., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-3026 · Cited by 241 · see citing works

COVID-19 CT Lung and Infection Segmentation Dataset

Ma, J., Ge, C., Wang, Y., et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3757476 · Cited by 237 · see citing works

COVID-19 CT Lung and Infection Segmentation Dataset

Ma, J., Cheng, G., Wang, Y., An, X., Gao, J., Yu, Z., Zhang, M., Liu, X., Xueyuan, D., Cao, S., et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3757475 · Cited by 225 · see citing works

Artificial Intelligence for infectious disease Big Data Analytics

Wong, Z. S.-Y., Zhou, J., & Zhang, Q. (2018) · Infection Disease & Health · doi:10.1016/j.idh.2018.10.002 · Cited by 223 · see citing works

Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak.

Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Viedma, E. H. (2020) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.02.002 · Cited by 222 · see citing works

Use of artificial intelligence in infectious diseases

Agrebi, S., & Larbi, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00018-5 · Cited by 216 · see citing works

Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification

Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., & Wang, T. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8301998 · Cited by 205 · see citing works

A new non-invasive index for prognosis evaluation in patients with aortic stenosis

Sim, H. W., Ngiam, J. N., Zhong, L., Tan, B. Y., Low, L. Y., Djohan, A. H., Boey, E., Kong, W. K. F., Tan, R., & Poh, K. K. (2020) · Scientific Reports · doi:10.1038/s41598-020-63777-z · Cited by 205 · see citing works

Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19)

Anzai, A., Kobayashi, T., Linton, N. M., Kinoshita, R., Hayashi, K., Suzuki, A., Yang, Y., Jung, S.-M., Miyama, T., Akhmetzhanov, A. R., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020601 · Cited by 200 · see citing works

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in ChinaPreprint

Lai, S., Ruktanonchai, N., Zhou, L., Prosper, O., Luo, W., Floyd, J., Wesolowski, A., Santillana, M., Zhang, C., Du, X.-J., et al. (2020) · medRxiv · doi:10.1101/2020.03.03.20029843 · Cited by 200 · see citing works

COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

Türkoğlu, M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01888-w · Cited by 199 · see citing works

COVID-19 diagnosis prediction in emergency care patients: a machine learning approachPreprint

Batista, A. F. D. M., Miraglia, J. L., Donato, T. H. R., & Filho, A. D. P. C. (2020) · medRxiv · doi:10.1101/2020.04.04.20052092 · Cited by 198 · see citing works

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

Yeşilkanat, C. M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110210 · Cited by 197 · see citing works

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

Swapnarekha, H., Behera, H. S., Nayak, J., & Naik, B. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109947 · Cited by 194 · see citing works

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Mohammed, M. A., Abdulkareem, K. H., Al‐Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al‐Mhiqani, M. N., Alhakami, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2995597 · Cited by 193 · see citing works

Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?

Postnikov, E. B. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109841 · Cited by 190 · see citing works

Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique

Özkaya, U., Öztürk, Ş., & Barstuğan, M. (2020) · Studies in big data · doi:10.1007/978-3-030-55258-9_17 · Cited by 189 · see citing works

Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning

Kadioglu, O., Saeed, M. E. M., Greten, H. J., & Efferth, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104359 · Cited by 189 · see citing works

Development of a prognostic model for mortality in COVID-19 infection using machine learning

Booth, A. L., Abels, E. A., & McCaffrey, P. (2020) · Modern Pathology · doi:10.1038/s41379-020-00700-x · Cited by 182 · see citing works

Worldviews, Research Methods, and their Relationship to Validity in Empirical Software Engineering Research

Petersen, K., & Gencel, Ç. (2013) · Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA) · doi:10.1109/iwsm-mensura.2013.22 · Cited by 179 · see citing works

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein, J., & Jeong, I. C. (2016) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13218 · Cited by 176 · see citing works

COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images

Shibly, K. H., Dey, S. K., Islam, M. T.-U., & Rahman, M. M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100405 · Cited by 176 · see citing works

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)Preprint

Born, J., Brändle, G., Cossio, M., Disdier, M., Goulet, J., Roulin, J., & Wiedemann, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12084 · Cited by 174 · see citing works

Measuring Emotions in the COVID-19 Real World Worry Dataset

Kleinberg, B., van der Vegt, I., & Mozes, M. (2020) · NLPCOVID19 · doi:10.48550/arxiv.2004.04225 · Cited by 170 · see citing works

Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach

Rahman, M. S., Hoque, M. N., Islam, M. R., Akter, S., Alam, A. R.-U., Siddique, M. A., Saha, O., Rahaman, M. M., Sultana, M., Crandall, K. A., et al. (2020) · PeerJ · doi:10.7717/peerj.9572 · Cited by 166 · see citing works

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020) · Applied Sciences · doi:10.3390/app10186448 · Cited by 166 · see citing works

A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients

Ahamad, M. M., Aktar, S., Rashed-Al-Mahfuz, M., Uddin, S., Lió, P., Xu, H., Summers, M. A., Quinn, J. M. W., & Moni, M. A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113661 · Cited by 165 · see citing works

Tracking COVID-19 using online search

Lampos, V., Majumder, M. S., Yom-Tov, E., Edelstein, M., Moura, S., Hamada, Y., Rangaka, M. X., McKendry, R. A., & Cox, I. J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00384-w · Cited by 164 · see citing works

Demystification of AI-driven medical image interpretation: past, present and future

Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019) · European Radiology · doi:10.1007/s00330-018-5674-x · Cited by 161 · see citing works

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research DirectionsPreprint

Nguyen, T. T. (2020) · doi:10.36227/techrxiv.12743933 · Cited by 161 · see citing works

Analysis on novel coronavirus (COVID-19) using machine learning methods

Yadav, M., Perumal, M., & Srinivas, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110050 · Cited by 160 · see citing works

Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector MachinePreprint

Mahdy, L. N., Ezzat, K. A., Elmousalami, H. H., Aboul-Ella, H., & Hassanien, A. E. (2020) · medRxiv · doi:10.1101/2020.03.30.20047787 · Cited by 158 · see citing works

Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19

Sun, L., Song, F., Shi, N., Liu, F., Li, S., Li, P., Zhang, W., Jiang, X., Zhang, Y., Sun, L., et al. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104431 · Cited by 153 · see citing works

DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images

Karim, M. R., Döhmen, T., Cochez, M., Beyan, O., Rebholz‐Schuhmann, D., & Decker, S. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313304 · Cited by 152 · see citing works

Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features

Perumal, V., Narayanan, V., & Rajasekar, S. J. S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01831-z · Cited by 149 · see citing works

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C. B., Salvatore, C., Schiaffino, S., Polidori, A., Gandola, D. G., Messa, C., & Sardanelli, F. (2021) · European Radiology Experimental · doi:10.1186/s41747-020-00203-z · Cited by 145 · see citing works

Using twitter and web news mining to predict COVID-19 outbreak

Rahmanian, V., & Jahanbin, K. (2020) · Asian Pacific Journal of Tropical Medicine · doi:10.4103/1995-7645.279651 · Cited by 141 · see citing works

Bayesian analysis for emerging infectious diseases

Jewell, C., Kypraios, T., Neal, P. J., & Roberts, G. O. (2009) · Bayesian Analysis · doi:10.1214/09-ba417 · Cited by 140 · see citing works

Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter studyPreprint

Qi, X., Jiang, Z., Yu, Q., et al. (2020) · medRxiv · doi:10.1101/2020.02.29.20029603 · Cited by 135 · see citing works

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

Yao, H., Zhang, N., Zhang, R., Duan, M., Xie, T., Pan, J., Peng, E., Huang, J., Zhang, Y., Xu, X., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00683 · Cited by 134 · see citing works

Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms

Ray, D., Salvatore, M., Bhattacharyya, R., Wang, L., Mohammed, S., Purkayastha, S., Halder, A., Rix, A., Barker, D., Kleinsasser, M., et al. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.60e08ed5 · Cited by 134 · see citing works

Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource

Sarker, A., Lakamana, S., Hogg-Bremer, W., Xie, A., Al-Garadi, M. A., & Yang, Y. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa116 · Cited by 133 · see citing works

Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT ImagesPreprint

Tang, Z., Zhao, W., Xie, X., Zheng, Z., Shi, F., Liu, J., & Shen, D. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11988 · Cited by 132 · see citing works

Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020)

Ghosal, S., Sengupta, S., Majumder, M., & Sinha, B. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.03.017 · Cited by 126 · see citing works

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach

Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A. T. F., Schoevers, R. A., van Hemert, A. M., Penninx, B. W. J. H., & Schmaal, L. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0289-1 · Cited by 125 · see citing works

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

Levy, J., Álvarez, D., Rosenberg, A. A., Alexandrovich, A., del Campo, F., & Behar, J. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00373-5 · Cited by 125 · see citing works

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan ImagesPreprint

Rajinikanth, V., Dey, N., Raj, A. N. J., Hassanien, A. E., Santosh, K., & Raja, N. S. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03431 · Cited by 123 · see citing works

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Shamsi, S. R. F., Babaei, S., Khosravi, R., Etemadi, M., Ghanbarian, G., Farhadi, A., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.06.058 · Cited by 122 · see citing works

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionPreprint

Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J. W. H., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12338 · Cited by 122 · see citing works

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Liang, R., Lu, Y., Qu, X., Su, Q., Li, C., Xia, S., Liu, Y., Zhang, Q., Cao, X., Chen, Q., et al. (2019) · Transboundary and Emerging Diseases · doi:10.1111/tbed.13424 · Cited by 118 · see citing works

Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries

Singh, S., Parmar, K. S., Makkhan, S. J. S., Kaur, J. K., Peshoria, S., & Kumar, J. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110086 · Cited by 118 · see citing works

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Sharma, A. B., Rani, S. S., & Gupta, D. K. (2020) · International Journal of Biomedical Imaging · doi:10.1155/2020/8889023 · Cited by 117 · see citing works

Analisis Mutu Data Time Series Covid-19: Studi kasus di Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

Pangestu, D. S., & Hidayat, Y. (2020) · Cited by 116 · see citing works

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools

Prachař, M., Justesen, S. F. L., Steen-Jensen, D. B., Thorgrimsen, S., Jurgons, E., Winther, O., & Bagger, F. O. (2020) · Scientific Reports · doi:10.1038/s41598-020-77466-4 · Cited by 115 · see citing works

An Early Study on Intelligent Analysis of Speech Under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

Han, J., Qian, K., Song, M., Yang, Z., Ren, Z. X., Liu, S., Liu, J., Zheng, H., Ji, W., Koike, T., et al. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2223 · Cited by 113 · see citing works

Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemicPreprint

Jagodnik, K. M., Ray, F., Giorgi, F. M., & Lachmann, A. (2020) · medRxiv · doi:10.1101/2020.03.14.20036178 · Cited by 110 · see citing works

Clinical Predictive Models for COVID-19: Systematic Study

Schwab, P., Schütte, A. D., Dietz, B., & Bauer, S. (2020) · Journal of Medical Internet Research · doi:10.2196/21439 · Cited by 110 · see citing works

A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Voulodimos, A. S., Protopapadakis, E. E., Katsamenis, I., Doulamis, A. D., & Doulamis, N. D. (2021) · Sensors · doi:10.3390/s21062215 · Cited by 108 · see citing works

Understanding the perception of COVID-19 policies by mining a multilanguage Twitter datasetPreprint

Lopez, C. E., Vasu, M., & Gallemore, C. T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.10359 · Cited by 106 · see citing works

Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning ApproachesPreprint

Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B. A., Terentiev, V. A., Bezrukov, D., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., Orekhov, P., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11829102.v1 · Cited by 106 · see citing works

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B

Chen, Y., Luo, Y., Huang, W., Hu, D., Zheng, R., Cong, S., Meng, F.-K., Yang, H., Lin, H.-J., Sun, Y., et al. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.07.012 · Cited by 105 · see citing works

An outbreak of intestinal schistosomiasis, alongside increasing urogenital schistosomiasis prevalence, in primary school children on the shoreline of Lake Malawi, Mangochi District, Malawi

Kayuni, S. A., O’Ferrall, A. M., Baxter, H., Hesketh, J., Mainga, B., Lally, D., Alharbi, M., LaCourse, E. J., Juziwelo, L. T., Musaya, J., et al. (2020) · Infectious Diseases of Poverty · doi:10.1186/s40249-020-00736-w · Cited by 104 · see citing works

Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds

Faezipour, M., & Abuzneid, A. (2020) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2020.0114 · Cited by 100 · see citing works

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Heldt, F. S., Vizcaychipi, M. P., Peacock, S., Cinelli, M., McLachlan, L., Andreotti, F., Jovanović, S., Dürichen, R., Lipunova, N., Fletcher, R. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83784-y · Cited by 97 · see citing works

Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Zhu, J., Ge, P., Jiang, C., Zhang, Y., Li, X., Zhao, Z., Zhang, L., & Duong, T. Q. (2020) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.12205 · Cited by 96 · see citing works

Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Misra, S., Jeon, S., Lee, S., Managuli, R., Jang, I.-S., & Kim, C. (2020) · Electronics · doi:10.3390/electronics9091388 · Cited by 95 · see citing works

Weight convergence analysis of DV-hop localization algorithm with GA

Cai, X., Wang, P., Cui, Z., Zhang, W., & Chen, J. (2020) · Soft Computing · doi:10.1007/s00500-020-05088-z · Cited by 94 · see citing works

COVID-19 Vaccines: A Race Against Time in the Middle of Death and Devastation!

Khuroo, M. S., Khuroo, M. S., Khuroo, M. S., Sofi, A. A., & Khuroo, N. S. (2020) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2020.06.003 · Cited by 92 · see citing works

A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Farid, A. A., Selim, G. I., & Khater, H. A. A. (2020) · International Journal of Scientific and Engineering Research · doi:10.14299/ijser.2020.03.02 · Cited by 89 · see citing works

Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning

Anno, S., Hara, T., Kai, H., Lee, M., Chang, Y., Oyoshi, K., Mizukami, Y., & Tadono, T. (2019) · Geospatial health · doi:10.4081/gh.2019.771 · Cited by 87 · see citing works

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study

Yang, S., Jiang, L., Cao, Z., Wang, L., Cao, J., Feng, R., Zhang, Z., Xue, X., Shi, Y., & Shan, F. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.03.132 · Cited by 87 · see citing works

Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Kavadi, D. P., Patan, R., Manikandan, R., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110056 · Cited by 85 · see citing works

Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, ItalyPreprint

Castiglioni, I., Ippolito, D., Interlenghi, M., et al. (2020) · medRxiv · doi:10.1101/2020.04.08.20040907 · Cited by 84 · see citing works

Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

Wahbeh, A., Nasralah, T., Al-Ramahi, M. A., & El-Gayar, O. (2020) · JMIR Public Health and Surveillance · doi:10.2196/19276 · Cited by 84 · see citing works

Bayesian inference of COVID-19 spreading rates in South Africa

Mbuvha, R., & Marwala, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237126 · Cited by 83 · see citing works

How to explain AI systems to end users: a systematic literature review and research agenda

Laato, S., Tiainen, M., Najmul Islam, A. K. M., & Mäntymäki, M. (2022) · Internet Research · doi:10.1108/intr-08-2021-0600 · Cited by 155 · see citing works

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Zhang, L., Guo, W., & Lv, C. (2023) · Science in One Health · doi:10.1016/j.soh.2023.100061 · Cited by 112 · see citing works

Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey

Ijaz, A., Nabeel, M., Masood, U., Mahmood, T., Hashmi, M. S., Posokhova, I., Rizwan, A., & Imran, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100832 · Cited by 97 · see citing works

Systematic reviews of machine learning in healthcare: a literature review

Kolasa, K., Admassu, B. Y., Hołownia-Voloskova, M. E., Kędzior, K. J., Poirrier, J., & Perni, S. (2023) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2023.2279107 · Cited by 87 · see citing works

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions

Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works

TEKNIK AVAILABLE LIGHT DALAM FOTOGRAFI FASHION UNTUK MENINGKATKAN DAYA TARIK VISUAL PRODUK HIJAB NIBRAS HOUSE MITRA CIKANGKUNG RENGASDENGKLOK.NPM:216020047

Muhammad Arief Azhar, H. R. (2026) · Universitas Pasundan institutional repositories & scientific journals (Universitas Pasundan) · Cited by 68 · see citing works

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Majeed, A., & Hwang, S. O. (2021) · Symmetry · doi:10.3390/sym14010016 · Cited by 62 · see citing works

Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

Liu, J., Dong, X., Zhao, H., & Tian, Y. (2022) · Processes · doi:10.3390/pr10040749 · Cited by 54 · see citing works

IoT-enabled technologies for controlling COVID-19 Spread: A scientometric analysis using CiteSpace

Kumar, D., Sood, S. K., & Rawat, K. S. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100863 · Cited by 53 · see citing works

Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model

Morais, L. R. D. A., & Gomes, G. S. D. S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109315 · Cited by 52 · see citing works

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

Ghosh, A., Larrondo-Petrie, M. M., & Pavlović, M. D. (2023) · Information · doi:10.3390/info14120665 · Cited by 48 · see citing works

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

Paul, S. G., Saha, A., Biswas, A. A., Zulfiker, M. S., Arefin, M. S., Rahman, M. M., & Reza, A. W. (2022) · Array · doi:10.1016/j.array.2022.100271 · Cited by 38 · see citing works

Biosensors for healthcare: an artificial intelligence approach

Davé, S., Dave, A., Radhakrishnan, S., Das, J., & Dave, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-88464-8.00008-7 · Cited by 39 · see citing works

How does AI use drive individual digital resilience? a conservation of resources (COR) theory perspective

Hu, Q., Lu, Y., Pan, Z., & Wang, B. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2137698 · Cited by 31 · see citing works

Defect analysis of 3D printed object using transfer learning approaches

Ahsan, M. M., Raman, S., Liu, Y., & Siddique, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124293 · Cited by 26 · see citing works

Korean dental hygiene students’ perceptions and attitudes toward artificial intelligence: An online survey

Jeong, H., Han, S., Kim, K., Park, I., Choi, Y.-Y., & Jeon, K. J. (2023) · Journal of Dental Education · doi:10.1002/jdd.13189 · Cited by 24 · see citing works

Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico

Dong, B., Khan, L. R., Smith, M., Trevino, J., Zhao, B., Hamer, G. L., López-Lemus, U. A., Ángulo-Molina, A., Lubinda, J., Nguyen, U., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00192-7 · Cited by 22 · see citing works

Importance analysis of decision making factors based on fuzzy decision trees

Zaitseva, E., Rabčan, J., Levashenko, V., & Kvaššay, M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.109988 · Cited by 20 · see citing works

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods

Tariq, M. U., & Ismail, S. B. (2024) · Osong Public Health and Research Perspectives · doi:10.24171/j.phrp.2023.0287 · Cited by 20 · see citing works

Applications of machine learning approaches to combat COVID-19: A survey

Tiwari, S. M., Doğan, O., Jabbar, M. A., Shandilya, S. K., Ortiz, F., Bajpai, S., & Banerjee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99878-9.00014-5 · Cited by 19 · see citing works

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

Botz, J., Wang, D., Lambert, N., Wagner, N., Génin, M., Thommes, E. W., Madan, S., Coudeville, L., & Fröhlich, H. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.994949 · Cited by 17 · see citing works

Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning

Yuan, M., Zhang, Z., Li, G., He, X., Huang, Z., Li, Z., & Du, H. (2024) · Agriculture · doi:10.3390/agriculture14081245 · Cited by 16 · see citing works

An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics

Tahmasbi, F., Toni, E., Javanmard, Z., Kheradbin, N., Nasiri, S., & Sadoughi, F. (2025) · Archives of Public Health · doi:10.1186/s13690-025-01590-8 · Cited by 16 · see citing works

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

Dabbagh, R., Jamal, A. A., Masud, J. H. B., Titi, M. A., Amer, Y. S., Khayat, A., Alhazmi, T. S., Hneiny, L., Baothman, F. A., Alkubeyyer, M., et al. (2023) · Cureus · doi:10.7759/cureus.38373 · Cited by 13 · see citing works

A clustering and graph deep learning-based framework for COVID-19 drug repurposing

Bansal, C., Deepa, P. R., Agarwal, V., & Chandra, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123560 · Cited by 16 · see citing works

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Shen, J. P., Ghatti, S., Levkov, N. R., Shen, H., Sen, T., Rheuban, K. S., Enfield, K., Facteau, N. R., Engel, G., & Dowdell, K. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1034732 · Cited by 13 · see citing works

An Intelligent stacking Ensemble-Based Machine Learning Model for Heart abnormality

Vijayakumar, J., Kumar, H. S., Kalyanasundaram, P., Markkandeyan, S., & Sengottaiyan, N. (2022) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses55317.2022.9914250 · Cited by 12 · see citing works

A systematic literature review of time series methods applied to epidemic prediction

Bamana, A. B., Kamalabad, M. S., & Oberski, D. L. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101571 · Cited by 12 · see citing works

A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data

Farhang‐Sardroodi, S., Ghaemi, M. S., Craig, M., Ooi, H. K., & Heffernan, J. M. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022272 · Cited by 11 · see citing works

Innovative Livestock: A Survey of Artificial Intelligence Techniques in Livestock Farming Management

Mijwil, M. M., Adelaja, O. A., Badr, A., Ali, G., Buruga, B. A., & Pudasaini, P. (2023) · Wasit Journal of Computer and Mathematics Science · doi:10.31185/wjcms.206 · Cited by 11 · see citing works

Artificial Intelligence and Machine Learning in Tropical Disease Management

Ogwu, M. C., & Izah, S. C. (2025) · Health information science · doi:10.1007/978-3-031-82622-1_7 · Cited by 8 · see citing works

Machine learning predicts pulmonary Long Covid sequelae using clinical data

Cordelli, E., Soda, P., Citter, S., Schiavon, E., Salvatore, C., Fazzini, D., Clementi, G., Cellina, M. I., Cozzi, A., Bortolotto, C., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02745-3 · Cited by 8 · see citing works

How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan)

Atashbar, T. (2023) · IMF Working Paper · doi:10.5089/9798400252242.001 · Cited by 7 · see citing works

Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period

Mano, L. Y., Torres, A. M., Morales, A. G., Cruz, C. C. P., Cardoso, F. H., Alves, S. H., de Faria, C. O., Lanzillotti, R. S., Cerceau, R., da Costa, R. M. E. M., et al. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00236-3 · Cited by 7 · see citing works

Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review

Burnazovic, E., Yee, A. V., Levy, J., Gore, G. C., & Rahimi, S. A. (2023) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2023.105129 · Cited by 7 · see citing works

Classification of ECG Signals Using Machine Learning Techniques

Losada, D. F. S., Gómez, J. J. S., & Vela, J. A. Z. (2024) · Academic Journal of Interdisciplinary Studies · doi:10.36941/ajis-2024-0067 · Cited by 7 · see citing works

Within Host Dynamics of SARS-CoV-2 in Humans: Modeling Immune Responses and Antiviral Treatments

Ghosh, I. (2021) · SN Computer Science · doi:10.1007/s42979-021-00919-8 · Cited by 6 · see citing works

Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method

Zhang, H., Gao, H., & Лю, П. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00928-x · Cited by 6 · see citing works

Influence of augmentation on the performance of double ResNet-based model for chest X-rays classification

Kłoska, A., Tarczewska, M., Giełczyk, A., Kloska, S. M., & Michalski, A. (2023) · Polish Journal of Radiology · doi:10.5114/pjr.2023.126717 · Cited by 6 · see citing works

Unsupervised natural language processing in the identification of patients with suspected COVID-19 infection

da Silva, R. P., Pollettini, J. T., & Pazin‐Filho, A. (2023) · Cadernos de Saúde Pública · doi:10.1590/0102-311xen243722 · Cited by 6 · see citing works

ENTREPRENEURSHIP: SCALABILITY STRATEGIES IN ENTREPRENEURIAL VENTURES: A COMPREHENSIVE LITERATURE REVIEW

Ikwue, U., Eyo-Udo, N. L., Onunka, O., Ekwezia, A. V., Nwankwo, E. E., & Daraojimba, C. (2023) · Agriculture Extension in Developing Countries · doi:10.26480/aedc.02.2023.78.88 · Cited by 5 · see citing works

Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements

Martsenyuk, V., Dimitrov, G. P., Rančić, D., Ľuptáková, I. D., Jovančević, I., Bernaś, M., Kłos-Witkowska, A., Gancarczyk, T., Kostadinova, I. S., Mihaylova, E., et al. (2024) · Applied Sciences · doi:10.3390/app14104107 · Cited by 11 · see citing works

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort

Ülgen, A., Çetin, Ş., Çetin, M., Şıvgın, H., & Li, W. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107681 · Cited by 6 · see citing works

Impact of covid-19 in lung cancer detection using image processing techniques, artificial intelligence and machine learning approaches

Singh, K., Chauhan, U., & Varshney, L. (2023) · Smart Science · doi:10.1080/23080477.2023.2246285 · Cited by 5 · see citing works

Spreading of Infections on Network Models: Percolation Clusters and Random Trees

Roman, H. E., & Croccolo, F. (2021) · Mathematics · doi:10.3390/math9233054 · Cited by 5 · see citing works

Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

Zhang, G., Li, H., He, R., & Lü, P. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00595-4 · Cited by 5 · see citing works

A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review

Ganesh, P., & Kim, S. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.135645 · Cited by 5 · see citing works

Processamento de linguagem natural não supervisionado na identificação de pacientes suspeitos de infecção por COVID-19

da Silva, R. P., Pollettini, J. T., & Pazin‐Filho, A. (2023) · Cadernos de Saúde Pública · doi:10.1590/0102-311xpt243722 · Cited by 5 · see citing works

Systematic Reviews of Machine Learning in Healthcare: A Literature ReviewPreprint

Kolasa, K., Admassu, B. Y., Hołownia-Voloskova, M. E., Kędzior, K. J., Poirrier, J., & Perni, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3035548/v1 · Cited by 5 · see citing works

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

Ovcharenko, E. A., Кутихин, А. Г., Gruzdeva, O. V., Kuzmina, A. A., Александровна, С. Т., Брусина, Е. Б., Владимировна, К. С., Евгеньевна, Б. Т., Kuzmenko, S., Osyaev, N. Y., et al. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10020039 · Cited by 4 · see citing works

A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR

Shi, X.-L., Wei, F.-F., & Chen, W. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00908-1 · Cited by 4 · see citing works

ANALYSIS OF CARDIAC ANOMALIES BY SELECTION AND EXTRACTION OF FEATURES USING MACHINE LEARNING METHODS

Das, S., Pradhan, S., Mishra, S. K., Pradhant, S., & Pattnaik, P. K. (2022) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2022/v13i6/221306116 · Cited by 4 · see citing works

Data-set class-balancing and the Convolutional Vision Transformer

Escobar-Ortiz, A. F., Amézquita, M. A., Galindo-Sánchez, J. S., Pardo-Cabrera, J., Hurtado-López, J., Ramírez-Moreno, D. F., Sua, L. F., & Fernández‐Trujillo, L. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106190 · Cited by 3 · see citing works

Soft Computational Techniques to Discover Unique and Precise Knowledge from Big Data

Basavesha, D., Bharathi, S., & Pareek, P. K. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-91305-2_24 · Cited by 2 · see citing works

Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example

Zheng, L., Chen, Y., Jiang, S., Song, J., & Zheng, J. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1008292 · Cited by 2 · see citing works

ITAQ: Image Tag Recommendation Framework for Aquatic Species Integrating Semantic Intelligence via Knowledge Graphs

Hariharan, S., Deepak, G., Ortiz, F., & Panchal, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47745-4_11 · Cited by 2 · see citing works

Artificial Intelligence (AI) and the Future of Mankind

Dargham, J. A., Moung, E. G., Chin, R. K. Y., Mamat, M. B., & Wong, T. H. (2024) · Internet of things · doi:10.1007/978-981-97-1432-2_5 · Cited by 4 · see citing works

Extending and Testing Protection Motivation Theory in the Context of COVID-19 Contact-Tracing Technology: A Comparison of South Korea and the United States

Hong, S. J., & Cho, H. (2025) · Health Communication · doi:10.1080/10410236.2024.2447107 · Cited by 1 · see citing works

Benchmark Analysis of Time Series Models for Malaria Trends in the Adamawa Region (Cameroon)

Bamana, A. B., lamou, Y. S. B., & Abdoulaye, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-88226-5_5 · Cited by 1 · see citing works

Emerging Paradigms and Predictive Models in Eco-Epidemiology for Disease Control in Human and Wildlife Populations

Gupta, J., Pal, S., Jain, S., Bharti, D., Gurung, P., & Sharma, S. (2025) · Springer eBooks · doi:10.1007/978-981-95-0329-2_16 · Cited by 1 · see citing works

Listening for COVID: Noninvasive Detection From Cough and Breath Sounds

Mallick, D., Aggarwal, A., Ng, E. Y.-K., & Arora, V. (2025) · IEEE Pulse · doi:10.1109/mpuls.2025.3618438 · Cited by 1 · see citing works

Advancing decision-making: A comprehensive review of intelligent systems, applications, and challenges

Al-Khamees, H. A. A., Smadi, A. A., Alsmadi, M. K., ALKannad, A. A., Abugabah, A., Almusfar, L. A., & AlThani, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200631 · Cited by 1 · see citing works

Defect localization using region of interest and histogram-based enhancement approaches in fused filament fabrication additive manufacturing

Ahsan, M. M., Liu, Y.-T., Raman, S., & Siddique, Z. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.102174 · Cited by 1 · see citing works

Predictive Analytics on Covid-19 Prediction using ResNets

Vadivel, S., & Jayakarthik, R. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9754134 · Cited by 1 · see citing works

Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model

Adeoye, E. A., Rozenfeld, Y., Beam, J., Boudreau, K., Cox, E. J., & Scanlan, J. M. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02549-5 · Cited by 1 · see citing works

DESI: Diversification of E-Commerce Recommendations Using Semantic Intelligence

Deepak, G., & Anavkar, H. V. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-55486-5_11 · Cited by 1 · see citing works

Forecasting the Unseen: the Role of Artificial Intelligence in Pandemic

Sachdeva, R., Kumar, A., Behl, B., & Singh, H. P. (2024) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs62700.2024.10840877 · Cited by 1 · see citing works

Machine Learning Based Inventory Management for Medical Stores: Predicting Seasonal and Regular Drug Demand

Keerthika, K., Kavana, N., & Ruchitha, R. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115775 · Cited by 1 · see citing works

An Analysis of COVID-19 in Europe Based on Fractal Dimension and Meteorological DataPreprint

Păcurar, C. M., Păcurar, V. D., & Păun, M. (2021) · Preprints.org · doi:10.20944/preprints202109.0425.v1 · Cited by 1 · see citing works

Analysis of web data classification methods based on semantic similarity measure

Ramesh, K., & Mohanasundram, R. (2022) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2022.2080614

ARTIFICIAL INTELLIGENCE IN THE HEALTH SECTOR

(2022) · International Journal of Biology Pharmacy and Allied Sciences · doi:10.31032/ijbpas/2022/11.9.6407

Semantic Web and Internet of Things in e‐Health for Covid‐19

(2022) · doi:10.1002/9781394171460.ch11

REVIEW OF AI/ML TECHNIQUES ON COVID-19

Kumar, S., & Bhattacharjee, S. (2022) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2022.v07i05.020

AI, Epidemiology and Public Health in the Covid Pandemic

Lefèvre, T., Colineaux, H., Morgand, C., Tournois, L., & Delpierre, C. (2022) · Springer eBooks · doi:10.1007/978-3-031-08506-2_13

A New Machine Learning Framework for Detecting COVID-19 From Clinical Data on Lung And Heart Function

Alzahrani, A. A., Bataineh, B., Alshammari, H. H., Kabrah, S. M., & Baghdadi, M. A. (2022) · National Computing Colleges Conference (NCCC) · doi:10.1109/nccc57165.2022.10067357

Short Analysis of the Impact of COVID-19 Ontologies

Groppe, S., Tiwari, S. M., Khorashadizadeh, H., Groppe, J., Groth, T., Benamara, F., & Sahri, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-7126-6_17

A Machine Learning Approach to Detect Infected People to Coronavirus Based on Raman Spectroscopy Data

Aligholipour, O., & Sadaghiyanfam, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40395-8_17

Enhanced COVID-19 pneumonia diagnosis through optimized transformers and chest X-rays: post-pandemic insights

Escobar-Ortiz, A. F. (2023) · Encuentro Internacional de Educación en Ingeniería · doi:10.26507/paper.3216

Unraveling COVID-19 Progression: A Comprehensive Com-parison of Advanced Deep Learning Methods for Precise Pre-dictions

Tariq, M. U., & Ismail, S. B. (2023) · International Journal of Advanced Science and Computer Applications · doi:10.47679/ijasca.v3i1.37

COVID-19 in the Era of Artificial Intelligence: Existing Technologies and A Strategic Model for Mitigating Future Pandemics

Alshammari, B. M. (2024) · Journal of Computer Science · doi:10.3844/jcssp.2024.465.486

PixNet for early diagnosis of COVID-19 using CT images

Pravin, S. C., Rohith, G., Kiruthika, V., Saranya, J. D., Latha, B., Vigneshwar, K. R., Krishna, S. V., Nambirajan, H., & Sumitra, Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19221-9

Large Language Models for Text Classification: A New Era of Accuracy and Efficiency

Santos, D. P. D., de Mendonça, F. L. L., da Costa, J. P. C. L., Serrano, A. L. M., Torres, J. A. S., & da Silva, D. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-90341-0_1

A Comprehensive Approach for Predicting Kawasaki Disease for Early Risk Prognosis and Diagnosis

Chauhan, R., Verma, P., Kaur, H., & Alankar, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4071-3_24

Zoonotic diseases and their implications

Singh, S., & Singh, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27461-9.00007-x

Variant-Specific Diagnosis of COVID-19

Bhattacharjee, M., Kumar, S., & Ghosh, M. K. (2025) · Medical virology · doi:10.1007/978-981-95-1426-7_8

Evaluating Ensemble of LLMs for Agricultural Yield Prediction

Maidugu, S. A., Usman, U. M., Grema, M., Tiwari, S., & Mishra, M. K. (2026) · International Conference on Sustainable Engineering and Technology Innovations (ICSETI) · doi:10.1109/icseti67678.2026.11636922

A Survey on the Use of AI and ML for Fighting the COVID-19 PandemicPreprint

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07449 · Cited by 7 · see citing works

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3062771 · Cited by 99 · see citing works

Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review

Cheng, Y., Cheng, R. L., Xu, T., Tan, X., & Bai, Y. (2025) · Bioengineering · doi:10.3390/bioengineering12050514 · Cited by 21 · see citing works

Applications of machine learning approaches to combat COVID-19: A survey

Tiwari, S. M., Doğan, O., Jabbar, M. A., Shandilya, S. K., Ortiz, F., Bajpai, S., & Banerjee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99878-9.00014-5 · Cited by 19 · see citing works

REVIEW OF AI/ML TECHNIQUES ON COVID-19

Kumar, S., & Bhattacharjee, S. (2022) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2022.v07i05.020