A Comprehensive Study on Applications of AI Based Tools and Techniques for COVID-19

Authors

  • Dr. A. P. Nirmala  Associate Professor, Department of MCA, New Horizon College of Engineering, Bangalore, Karnataka, India
  • Paramita Chandra  Department of MCA, New Horizon College of Engineering, Bangalore, Karnataka, India

Keywords:

Artificial Intelligence, COVID-19, Machine Learning, Deep Learning.

Abstract

In recent times, the application of Artificial Intelligence (AI) is highly appreciated and in practice and demand.AI manages machine intelligence and builds the chance of achievement and exactness rate. It has an incredible effect in different fields like medical image processing or in analysis of data. The new technologies like Internet of Things, Text mining, Natural language processing, and their contribution towards computational biology and medicine is highly effective in this field. AI is utilized for diagnosis tasks in medical care as it is a troublesome assignment for people without the assistance of clever machines. Subsequently, AI is applied in battling against COVID 19 pandemic. This paper presents an extensive study of tools involving AI, machine learning and deep learning methods used to fight against the pandemic COVID 19 and likewise layout the data sets that are available identified with COVID 19. It likewise features the best in class of AI applications in handling the episode and spurs specialists soon.

References

  1. Adedoyin Ahmed Hussain, OunsBouachir, Fadi Al-Turjman And MoayadAloqaily, “AI Techniques for COVID-19”, A. A. Hussain et al.: AI Techniques for COVID-19, 128778, VOLUME 8, 2020.
  2. Leslie Mertz, “AI-Driven COVID-19 Tools to Interpret, Quantify Lung Images”, IEEE Pulse, Digital Object Identifier 10.1109/MPULS.2020.3008354, Date of current version: 17 August 2020.
  3. Mohammad(Behdad) Jamshidi, Ali Lalbakhsh, JakubTalla, FarimahHadjilooeiPedramLalbakhsh, MortezaJamshidi, Luigi La Spada, MirhamedMirmozafari, MojganDehghani, AsalSabet, SaeedRoshani, SobhanRoshani, NimaBayat-Makou, BahareMohamadzade, Zahra Malek, AlirezaJamshidi, Sarah Kiani, HamedHashemi-Dezaki, And WahabMohyuddin, “Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment”, M. Jamshidi et al.: AI and COVID-19: Deep Learning Approaches for Diagnosis and Treatment, 109582 VOLUME 8, 2020.
  4. Larry J. Kricka1, Sergei Polevikov, Jason Y. Park, Paolo Fortina, Sergio Bernardini, Daniel Satchkov, ValentinKolesov, Maxim Grishkov, “Artificial Intelligence-powered search tools and resources in the fight against COVID-19”, Artificial Intelligence-powered search tools and resources in the fight against COVID-19, eJIFCC2020Vol31No2pp106-116.
  5. Xueyan Mei, Hao-Chih Lee, Kai-yueDiao, Mingqian Huang, Bin Lin, Chenyu Liu, ZongyuXie, Yixuan Ma, Philip M. Robson, Michael Chung, Adam Bernheim, Venkatesh Mani, Claudia Calcagno, Kunwei Li, Shaolin Li, Hong Shan, JianLv, Tongtong Zhao, Junli Xia, Qihua Long, Sharon Steinberger, Adam Jacobi, Timothy Deyer, Marta Luksza, Fang Liu, Brent P. Little, Zahi A. Fayad and Yang Yang, “Artificial intelligence–enabled rapid diagnosis of patients with COVID-19”, Nature Medicine | VOL 26 | August 2020 | 1224–1228 | www.nature.com/naturemedicine.
  6. ThanhThi Nguyen, “Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions”, Nguyen, T. T. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. Preprint, DOI:10.13140/RG.2.2.36491.23846/1.
  7. Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, DinggangShen, “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19”, DOI 10.1109/RBME.2020.2987975, IEEE Reviews in Biomedical Engineering.
  8. AgamBansal, RanaPrathapPadappayil, ChandanGarg, Anjali Singal, Mohak Gupta and Allan Klein, “Utility of Artificial Intelligence Amidst the COVID 19 Pandemic:A Review”, Springer, Journal of Medical Systems (2020) 44: 156, Published online: 1 August 2020.
  9. Jiaying Liu, Xiangjie Kong, Feng Xia, XiaomeiBai, Lei Wang, Qing Qing And Ivan Lee, “Artificial Intelligence in the 21st Century”, J. Liu et al.: AI in the 21st Century, 34404 VOLUME 6, 2018.
  10. JunaidShuja, EisaAlanazi, WaleedAlasmary, AbdulazizAlashaikh, “Covid-19 Datasets: A Survey And Future Challenges”, medRxiv preprint doi: https://doi.org/10.1101/2020.05.19.20107532, May 26, 2020.
  11. YoussoufaMohamadou, AminouHalidou, PascalinTiamKapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19”, A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction, Applied Intelligence https://doi.org/10.1007/s10489-020-01770-9, Springer, Published online: 06 July 2020.
  12. Lin Li, Lixin Qin, ZeguoXu, Youbing Yin, Xin Wang, Bin Kong, JunjieBai,Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, QizhongXu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia, “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy”, Radiology: Volume 296: Number 2—August 2020.
  13. Quoc-Viet Pham, Dinh C. Nguyen, Thien Huynh-The, Won-Joo Hwang And Pubudu N. Pathirana, “Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts”, Q.-V. Pham et al.: Artificial Intelligence and Big Data for Coronavirus Pandemic, VOLUME 4, 2020.
  14. Linda Wang, ZhongQiu Lin1, and Alexander Wong, “COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images”, arXiv:2003.09871v4, eess.IV], 11 May 2020.
  15. FurqanRustam, Aijaz Ahmad Reshi, ArifMehmood, SaleemUllah, Byung-Won On, WaqarAslam, And Gyu Sang Choi, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”, F. Rustam et al.: COVID-19 Future Forecasting Using Supervised ML Models, 101490 VOLUME 8, 2020.
  16. Michael J. Horry, SubrataChakraborty, Manoranjan Paul, AnwaarUlhaq, BiswajeetPradhan, ManasSaha, And NageshShukla, “COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data”, M. J. Horry et al.: COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data, VOLUME 8, 2020 149809, August 25, 2020.
  17. RachnaSethi and Monica Mehrotra, DhaarnaSethi, “Deep Learning based Diagnosis Recommendation for COVID-19 using Chest X-Rays Images”, Proceedings of the Second International Conference on Inventive Research in Computing Applications (ICIRCA-2020) IEEE Xplore Part Number: CFP20N67-ART; ISBN: 978-1-7281-5374-2.
  18. MoutazAlazab, AlbaraAwajan, AbdelwadoodMesleh, Ajith Abraham, VanshJatana, Salah Alhyari, “COVID-19 Prediction and Detection Using Deep Learning”, International Journal of Computer Information Systems and Industrial Management Applications ISSN 2150-7988 Volume 12 (2020) pp. 168-181, Published: 29 May, 2020.
  19. Asmaa Abbas, Mohammed M., Abdelsamea and Mohamed Medhat Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network”, medRxiv preprint doi: https://doi.org/10.1101/2020.03.30.20047456.this version posted May 18, 2020.
  20. Yuexiang Li, Dong Wei, Jiawei Chen, Shilei Cao, Hongyu Zhou, Yanchun Zhu, Jianrong Wu, LanLan,Wenbo Sun, TianyiQian, Kai Ma, HaiboXu, YefengZheng, “Efficient and Effective Training of COVID-19 Classification Networks with Self-supervised Dual-track Learning to Rank”, JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015, DOI 10.1109/JBHI.2020.3018181, IEEE Journal of Biomedical and Health Informatics.
  21. Rajesh Kumar, Abdullah Aman Khan, Sinmin Zhang, WenYong Wang, YousifAbuidris, Waqas Amin , and Jay Kumar, “Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging”, arXiv:2007.06537v1, eess.IV], 10 July 2020.
  22. Ramjeet Singh Yadav, “Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India”, Int. j. inf. Tecnol, Springer, Published online: 26 May 2020.
  23. L. J. Muhammad, Md. Milon Islam, Sani Sharif Usman and Safal Islam Ayon, “Predictive Data Mining Models for Novel Coronavirus (COVID?19) Infected Patients’ Recovery”, SN Computer Science (2020) 1:206, Springer, Published online: 21 June 2020.
  24. Rahul Kumar, RidhiArora, VipulBansal, Vinodh J Sahayasheela, HimanshuBuckchash, Javed Imran, Narayanan Narayanan, Ganesh N Pandian and Balasubramanian Raman, “Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers”, medRxiv preprint April 17, 2020.
  25. Bayley King, SiddharthBarve, Andrew Ford and Rashmi Jha, “Unsupervised Clustering of COVID-19 Chest X-Ray Images with a Self-Organizing Feature Map”, IEEE Xplore, September 23,2020.
  26. Shaoping Hu , Yuan Gao , ZhangmingNiu , Yinghui Jiang , Lao Li , Xianglu Xiao, Minhao Wang , EvandroFei Fang , Wade Menpes-Smith , Jun Xia , Hui Ye , and GuangYang, “Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images”,.
  27. Asmaa Abbas, Mohammed M. Abdelsamea and Mohamed Medhat Gaber, “4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection”, medRxiv preprint doi: https://doi.org/10.1101/2020.06.22.20137547, June 23, 2020.
  28. Joseph Bullock, Alexandra Luccioni, Katherine Hoffmann Pham, Cynthia Sin Nga Lam and Miguel Luengo-Oroz, “Mapping the landscape of Artificial Intelligence applications against COVID-19”, arXiv:2003.11336v2 cs.CY] 24 April 2020.
  29. Dr. A. P. Nirmala, Paramita Chandra, “An analysis on Applications of Machine Learning Algorithms to predict COVID-19”, International Journal of Future Generation Communication and Networking, Vol. 13, No. 4, (2020), pp. 2461–2470, ISSN: 2233-7857 IJFGCN, Published on 1 December 2020.
  30. Bowen Wang, Yanjing Sun, Trung Q. Duong, Long D. Nguyen and LajosHanzo, “Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach”, B. Wang et al.: Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT, 115656 VOLUME 8, 2020, July 2, 2020..
  31. Yanfang Ye, ShifuHou, Yujie Fan, YiyueQian, Yiming Zhang, Shiyu Sun, QianPeng and Kenneth Laparo, “α-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19”, arXiv:2003.12232v1 cs.SI] 27 March 2020.
  32. Sheryl L. Chang, Nathan Harding, Cameron Zachreson, Oliver M. Cliff1 and Mikhail Prokopenko, “Modelling transmission and control of the COVID-19 pandemic in Australia”, arXiv:2003.10218v3 q-bio.PE], 3 May 2020.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2021-03-27

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. A. P. Nirmala, Paramita Chandra "A Comprehensive Study on Applications of AI Based Tools and Techniques for COVID-19" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 1, pp.23-29, March-April-2021.