Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

被引:9
作者
Majeed, Abdul [1 ]
Lee, Sungchang [2 ]
机构
[1] Gachon Univ, Dept Comp Engn, Seongnam 13120, South Korea
[2] Korea Aerosp Univ, Sch Informat & Elect Engn, Goyang 10540, South Korea
基金
新加坡国家研究基金会;
关键词
COVID-19; machine learning; high-performance computing; person-specific data; healthcare; Internet of Medical Things; infectious diseases; NEURAL-NETWORK; DIAGNOSIS; FIGHT; MODEL;
D O I
10.3390/asi4030040
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease's spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19's arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] The feasibility of using machine learning to predict COVID-19 cases
    Chen, Shan
    Ding, Yuanzhao
    [J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2025, 196
  • [42] ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images
    Goel, Garvit
    Gondhalekar, Atharva
    Qi, Jingyuan
    Zhang, Zhicheng
    Cao, Guohua
    Feng, Wu
    [J]. 50TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING, 2021,
  • [43] Machine learning with multimodal data for COVID-19
    Chen, Weijie
    Sa, Rui C.
    Bai, Yuntong
    Napel, Sandy
    Gevaert, Olivier
    Lauderdale, Diane S.
    Giger, Maryellen L.
    [J]. HELIYON, 2023, 9 (07)
  • [44] Automated Machine Learning for COVID-19 Forecasting
    Tetteroo, Jaco
    Baratchi, Mitra
    Hoos, Holger H.
    [J]. IEEE ACCESS, 2022, 10 : 94718 - 94737
  • [45] Harnessing Deep Learning for Omics in an Era of COVID-19
    Jahanyar, Bahareh
    Tabatabaee, Hamid
    Rowhanimanesh, Alireza
    [J]. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 2023, 27 (04) : 141 - 152
  • [46] Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19
    Tarik, Ahajjam
    Aissa, Haidar
    Yousef, Farhaoui
    [J]. 12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 : 835 - 840
  • [47] A proficient approach for face detection and recognition using machine learning and high-performance computing
    Singh, Astha
    Prakash, Shiv
    Kumar, Ankit
    Kumar, Divya
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2022, 34 (03)
  • [48] Using deep (machine) learning to forecast US inflation in the COVID-19 era
    Stoneman, David
    Duca, John V.
    [J]. JOURNAL OF FORECASTING, 2024, 43 (04) : 894 - 902
  • [49] A review about COVID-19 in the MENA region: environmental concerns and machine learning applications
    Hicham Meskher
    Samir Brahim Belhaouari
    Amrit Kumar Thakur
    Ravishankar Sathyamurthy
    Punit Singh
    Issam Khelfaoui
    Rahman Saidur
    [J]. Environmental Science and Pollution Research, 2022, 29 : 82709 - 82728
  • [50] A review about COVID-19 in the MENA region: environmental concerns and machine learning applications
    Meskher, Hicham
    Belhaouari, Samir Brahim
    Thakur, Amrit Kumar
    Sathyamurthy, Ravishankar
    Singh, Punit
    Khelfaoui, Issam
    Saidur, Rahman
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (55) : 82709 - 82728