Artificial intelligence for COVID-19: A Short Article
DOI:
https://doi.org/10.24203/ajpnms.v10i1.6961Keywords:
COVID-19, Artificial intelligence, China, pandemic, Machine learning, Coronavirus diseaseAbstract
The COVID-19 virus, which began at the end of 2019 in China and spread rapidly and turned into a significant epidemic worldwide, has posed a severe threat to public health. Affected persons may develop an asymptomatic or mild illness or may experience severe consequences, up to acute respiratory failure requiring the support of healthcare workers. In this article, the authors decided to highlight artificial intelligence techniques by identifying the most influential platforms and applications that have been used to track and control the spread of the COVID-19 pandemic. Fifteen tools utilised in the United States, Canada, South Korea, China, Turkey, Iraq, Germany, India, and Netherlands are organised in one table. This article found the significance of artificial intelligence and its ability to combat and control epidemics.
References
Chakraborty I. and Maity P., “COVID-19 outbreak: Migration, effects on society, global environment and prevention,” Science of The Total Environment, vol.728, pp:138882, August 2020. https://doi.org/10.1016/j.scitotenv.2020.138882
Baloch S., Baloch M. A., Zheng T.,and Pei X., “The Coronavirus Disease 2019 (COVID-19) Pandemic”, Tohoku Journal of Experimental Medicine, vol.250, no.4, pp:271-278, April 2020. https://doi.org/10.1620/tjem.250.271
Mijwil M. M., Alsaadi A. S, and Aggarwal K., “Differences and Similarities Between Coronaviruses: A Comparative Review,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.9, no.4, pp:49-61, September 2021. https://doi.org/10.24203/ajpnms.v9i4.6696
Acter T., Uddin N., Das J., Akhter A., Choudhury T. R., and Kim S., “Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency,” Science of The Total Environment, vol.730, pp:138996, August 2020. https://doi.org/10.1016/j.scitotenv.2020.138996
Contini C., Nuzzo M., Barp N., Bonazza A., Giorgio R., Tognon M., et al., “The novel zoonotic COVID-19 pandemic: An expected global health concern,” Journal of Infection in Developing Countries, vol.14, no.3, pp:254-264, March 2020. https://doi.org/10.3855/jidc.12671
Ndwandwe D. and Wiysonge C. S., “COVID-19 vaccines,” Current Opinion in Immunology, vol.71, pp:111-116, August 2021. https://doi.org/10.1016/j.coi.2021.07.003
Mijwil M. M., Al-Mistarehi AH., and Mutur, D. S., “The Practices of Artificial Intelligence Techniques and Their Worth in the Confrontation of COVID-19 Pandemic: A Literature Review," Mobile and Forensics, vol.4, no.1, pp:11-30, March 2022. http://dx.doi.org/10.12928/mf.v4i1.5691
Glatter K. A. and Finkelman P., “History of the Plague: An Ancient Pandemic for the Age of COVID-19,” The American Journal of Medicine, vol.134, no.2, pp: 176-181, February 2021. https://doi.org/10.1016/j.amjmed.2020.08.019
Mijwil M. M., Al-Mistarehi AH., Zahran D. J., Alomari S., and Doshi R., “Spanish Flu (Great Influenza) 1918: The Tale of The Most deadly Pandemic in History,” Asian Journal of Applied Sciences, vol.10, no.2, pp: In press, April 2022.
Monod M., Blenkinsop A., Xi X., Hebert D., Bershan S., Tietze S., et al., “Age groups that sustain resurging COVID-19 epidemics in the United States,” Science, vol. 371, no. 6536, pp:1-13, February 2021. https://doi.org/10.1126/science.abe8372
Aggarwal K., Mijwil M. M., Al-Mistarehi AH., Alomari, S., Gök M., Alaabdin A. M., and Abdulrhman S. H., “Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning,” Iraqi Journal for Computer Science and Mathematics, vol.3, no.1, pp:115-123, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.013
Madani Y., Erritali M., and Bouikhalene B., “Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets,” Results in Physics, vol.25, pp:104266, June 2021. https://doi.org/10.1016/j.rinp.2021.104266
Mijwil M. M., Salem I. E, and Abttan R. A. “Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets,” Asian Journal of Computer and Information Systems, vol.9, no.5, pp:29-34, November 2021. https://doi.org/10.24203/ajcis.v9i4.6765
Afshar P., Heidarian S., Enshaei N., Naderkhani F., Rafiee M. J., Oikonomou A., et al., “COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning,” Scientific Data, vol. 8, no.121, pp:1-8, April 2021. https://doi.org/10.1038/s41597-021-00900-3
Ebner N. and Iacovidou E., “The challenges of Covid-19 pandemic on improving plastic waste recycling rates,” Sustainable Production and Consumption, vol.28, pp:726-735, October 2021. https://doi.org/10.1016/j.spc.2021.07.001
Heffron R. J., Körner M., Schöpf M., Wagner J., and Weibelzahl M., “The role of flexibility in the light of the COVID-19 pandemic and beyond: Contributing to a sustainable and resilient energy future in Europe,” Renewable and Sustainable Energy Reviews, vol.140, pp:110743, April 2021. https://doi.org/10.1016/j.rser.2021.110743
Mijwil M. M., Al-Mistarehi AH., and Aggarwal K., “The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review,” Asian Journal of Applied Sciences, vol.9, no.5, pp:343-352, November 2021. https://doi.org/10.24203/ajas.v9i5.6753
Szylovec A., Umbelino-Walker I., Cain B. N., Ng H. T., Flahault A., and Rozanova L., “Brazil’s Actions and Reactions in the Fight against COVID-19 from January to March 2020,” International Journal of Environmental Research and Public Health, vol.18, no.2, pp:1-17, January 2021. https://doi.org/10.3390/ijerph18020555
Mijwil M. M. and Al-Zubaidi, E. A., “Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks,” Iraqi Journal of Science, vol.62, no.8, pp: 2740-2747, August 2021. https://doi.org/10.24996/ijs.2021.62.8.27
Saiful Islam S., Ferdous Z., Islam U. S., Mosaddek A. S. M., Potenza M. N., and Pardhan S., “Treatment, Persistent Symptoms, and Depression in People Infected with COVID-19 in Bangladesh,” International Journal of Environmental Research and Public Health, vol.18, no.4, pp:1-16, February 2021. https://doi.org/10.3390/ijerph18041453
Peghin M., Palese A., Venturini M., Martino M., Gerussi V., Graziano E., et al., “Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients,” Clinical Microbiology and Infection, vol.27, no.10, pp:1507-1513, October 2021. https://doi.org/10.1016/j.cmi.2021.05.033
Ademoğlu E., İslam M. M., Aksel G., and Eroğlu S. E, “A Rare Presentation of Covid-19: Case Series with Conjunctivitis and Ocular Involvement”, Kırıkkale Üniversitesi Tıp Fakültesi Dergisi, vol.22, no.3, pp: 483-487, 2020 (Turkish language). https://doi.org/10.24938/kutfd.789772
Pham Q., Nguyen D. C., Huynh-Th T., Hwang W., and Pathirana P. N., “Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts,” IEEE Access, vol.8, pp:130820 - 130839, July 2020. https://doi.org/10.1109/ACCESS.2020.3009328
Abir S. M. A. A., Islam S. N. Anwar A., Mahmood A. N., and Oo A. M. T., “Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress,” IoT, vol.1, no.2, pp:1-23, December 2020. https://doi.org/10.3390/iot1020028
Zhou Y., Wang F., Tang J., Nussinov R., and Cheng F., “Artificial intelligence in COVID-19 drug repurposing,” The Lancet Digital Health, vol.2, no.12, pp:e667-e676, December 2020. https://doi.org/10.1016/S2589-7500(20)30192-8
Mijwil M. M., Mutar D. S., filali Y., Aggarwal K., and Al-Shahwani H., “Comparison Between Expert Systems, Machine Learning, and Big Data: An Overview,” Asian Journal of Applied Sciences, vol.10, no.1, pp:83-88, March 2022. https://doi.org/10.24203/ajas.v10i1.6930
Cui M. and Zhang D. Y., “Artificial intelligence and computational pathology,” Laboratory Investigation, vol. 101, pp:412-422, January 2021. https://doi.org/10.1038/s41374-020-00514-0
Rammo F. M. and Al-Hamdani M. N., “Detecting The Speaker Language Using CNN Deep Learning Algorithm,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.1, pp:43-52, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.005
Qamar Q., Bajao N., Suwarno I., and Jokhio F. A., “Survey on Generative Adversarial Behavior in Artificial Neural Tasks,” Iraqi Journal For Computer Science and Mathematics, vol. 3, no. 2, pp: 83-94, March 2022. https://doi.org/10.52866/ijcsm.2022.02.01.009
Abttan R. A., Tawafan A. H., and Ismael S. J., “Economic dispatch by optimization techniques,” International Journal of Electrical and Computer Engineering, vol.12, no.3, pp:2228-2241, June 2022. https://doi.org/10.11591/ijece.v12i3.pp2228-2241
Mijwil M. M. and Abttan R. A., “Artificial Intelligence: A Survey on Evolution and Future Trends,” Asian Journal of Applied Sciences, vol.9, no.2, pp:87-93, April 2021. https://doi.org/10.24203/ajas.v9i2.6589
Mijwil M. M., “Malware Detection in Android OS Using Machine Learning Techniques,” Data Science and Applications, vol.3, no.2, pp:5-9, December 2020.
Mijwil M. M. and Salem I. E., “Credit Card Fraud Detection in Payment Using Machine Learning Classifiers,” Asian Journal of Computer and Information Systems, vol.8, no.4, pp:50-53, December 2020. https://doi.org/10.24203/ajcis.v8i4.6449
Dong S., WangP., and Abbas K., “A survey on deep learning and its applications,” Computer Science Review, vol.40, pp:100379, May 2021. https://doi.org/10.1016/j.cosrev.2021.100379
Al-Zubaidi E. A., Mijwil M. M., and Alsaadi, A. S., “Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification,” Journal of Southwest Jiaotong University, vol.54, no.4, pp.1-6, Augusts 2019. https://doi.org/10.35741/issn.0258-2724.54.4.4
Guo M., Xu T., Liu J., Liu Z., Jiang P., Mu T., et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media, pp:1-38, March 2022. https://doi.org/10.1007/s41095-022-0271-y
Faieq, A. K., and Mijwil, M. M., “Prediction of Heart Diseases Utilising Support Vector Machine and Artificial Neural Network,” Indonesian Journal of Electrical Engineering and Computer Science, vol.26, no.1, pp:374-380, April 2022 .http://doi.org/10.11591/ijeecs.v26.i1.pp374-380
Dilsizian S. E. and Siegel E. L., “Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment,” Current Cardiology Reports, vol.16, no.441, December 2013. https://doi.org/10.1007/s11886-013-0441-8
Aggarwal K., Bhamrah M. S., and Ryait H. S., “Detection of cirrhosis through ultrasound imaging by intensity difference technique,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 80, pp:1-10, September 2019. https://doi.org/10.1186/s13640-019-0482-z
Salem I. E., Mijwil M. M., Abdulqader A. W., and Ismaeel M. M., “Flight-Schedule using Dijkstra's Algorithm with Comparison of Routes Finding,” International Journal of Electrical and Computer Engineering, vol.12, no.2, pp:1675-1682, April 2022. http://doi.org/10.11591/ijece.v12i2.pp1675-1682
Ameen N., Tarhini A., Reppel A., and Anand A., “Customer experiences in the age of artificial intelligence,” Computers in Human Behavior, vol.114, pp:106548, January 2021. https://doi.org/10.1016/j.chb.2020.106548
Kaplan A. and Haenlein M., “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Business Horizons, vol.61, no.1, pp:15-25, February 2019. https://doi.org/10.1016/j.bushor.2018.08.004
Mijwil M. M. and Abttan R. A., “Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm,” Asian Journal of Applied Sciences, vol.9, no.1, pp:45-52, February 2021. https://doi.org/10.24203/ajas.v9i1.6503
Amann J., Blasimme A., Vayena E., Frey D., and Madai V. I., “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC Medical Informatics and Decision Making, vol. 20, no.310, pp:1-9, November 2020. https://doi.org/10.1186/s12911-020-01332-6
Secinaro S., Calandra D., Secinaro A., Muthurangu V., and Biancone P., “The role of artificial intelligence in healthcare: a structured literature review,” BMC Medical Informatics and Decision Making, vol. 21, no.125, pp:1-23, April 2021. https://doi.org/10.1186/s12911-021-01488-9
Loh E., “Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health,” BMJ leader, pp:1-5 June 2018. https://doi.org/10.1136/leader-2018-000071
Dias R. and Torkamani A., “Artificial intelligence in clinical and genomic diagnostics,” Genome Medicine, vol. 11, no.70, pp:1-12, November 2019. https://doi.org/10.1186/s13073-019-0689-8
Dheeba J., Singh N. A., and Selvi S. T., Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach, Journal of Biomedical Informatics, vol.49, pp:45-52, June 2014. https://doi.org/10.1016/j.jbi.2014.01.010
Mijwil M. M., “Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans,” Iraqi Journal of Science, vol.62, no.6., pp: 2099-2109, July 2021. https://doi.org/10.24996/ijs.2021.62.6.35
Kassania S. H., Kassanib P. H., Wesolowskic M. J., Schneidera K. A., and Detersa R., “Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach,” Biocybernetics and Biomedical Engineering, vol.41, no.3, pp:867-879, September 2021. https://doi.org/10.1016/j.bbe.2021.05.013
Saha P., Sadi M. S., and Islam M., “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Informatics in Medicine Unlocked, vol.22, pp:100505, 2021. https://doi.org/10.1016/j.imu.2020.100505
Elaziz M. A., Hosny K. M., Salah A., Darwish M. M., Lu S., Sahlol A. T.,” New machine learning method for image-based diagnosis of COVID-19,” Plos One, pp:1-18, June 2020. https://doi.org/10.1371/journal.pone.0235187
Yang J. W., Yang L., Luo R. G., and Xu J. F., “Corticosteroid administration for viral pneumonia: COVID-19 and beyond,” Clinical Microbiology and Infection, vol.26, no.9, pp:1171-1177, September 2020. https://doi.org/10.1016/j.cmi.2020.06.020
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