Comparison Between a Knowledge-Based System for COVID-19 using Compressed Internet of Things Data: A Review

Authors

  • Hindreen Rashid Abdulqader Department of Information Technology, Technical College of Informatics-Akre, Duhok Polytechnic University, Kurdistan, Iraq
  • Mayyadah Ramiz Mahmood Department of Computer Science, University of Zakho, Kurdistan, Iraq

DOI:

https://doi.org/10.25007/ajnu.v11n4a1385

Abstract

The world is now experiencing a pneumonia outbreak caused by a novel coronavirus. The huge volume of medical literature on coronavirus has useful information that may assist medical research communities in addressing specific challenges. Health care professionals may improve their policies by quickly reviewing and getting specific data regarding coronavirus from various published research and the larger struggle against infectious disease. It has developed a technique for extracting actionable knowledge that automatically gathers pertinent data from sections and paragraphs related to a particular topic. There are continuous efforts to construct intelligent systems capable of automatically extracting useful information from many unstructured texts. In this paper, the comparison between many papers based on IoT and the knowledge base for solving the problem of Covid-19 has been conducted.

Downloads

Download data is not yet available.

References

S. N. Chaudhari, S. P. Mene, R. M. Bora, and K. N. Somavanshi, “ROLE OF INTERNET OF THINGS (IOT) IN PANDEMIC COVID-19 CONDITION,” vol. 10, no. 6, p. 6, 2020.

H. R. Banjar, H. Alkhatabi, N. Alganmi, and G. I. Almouhana, “Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia,” Int. J. Environ. Res. Public. Health, vol. 17, no. 21, p. 8066, Nov. 2020, doi: 10.3390/ijerph17218066.

L. Xu, X. Zhou, Y. Tao, L. Liu, X. Yu, and N. Kumar, “Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using Improved CNN,” IEEE Trans. Ind. Inform., pp. 1–1, 2021, doi: 10.1109/TII.2021.3082907.

X. He et al., “Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans,” ArXiv210105442 Cs Eess, Feb. 2021, Accessed: Nov. 22, 2021. [Online]. Available: http://arxiv.org/abs/2101.05442

M.-L. Rusu, “Efficient Communication in the Period of Coronavirus Pandemic (COVID-19),” Int. Conf. Knowl.-BASED Organ., vol. 26, no. 2, pp. 325–332, Jun. 2020, doi: 10.2478/kbo-2020-0099.

B. M. Nema, Y. Makki Mohialden, N. Mahmood Hussien, and N. Ali Hussein, “COVID-19 knowledge-based system for diagnosis in Iraq using IoT environment,” Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 1, p. 328, Jan. 2021, doi: 10.11591/ijeecs.v21.i1.pp328-337.

M. Tawalbeh, M. Quwaider, and L. A. Tawalbeh, “Authorization Model for IoT Healthcare Systems: Case Study,” in 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, Apr. 2020, pp. 337–342. doi: 10.1109/ICICS49469.2020.239527.

M. Buheji and A. Rahman Buheji, “Designing Intelligent System for Stratification of COVID-19 Asymptomatic Patients,” Am. J. Med. Med. Sci., vol. 10, no. 4, pp. 246–257, Aug. 2020, doi: 10.5923/j.ajmms.20201004.17.

M. N. Mohammed, H. Syamsudin, S. Al-Zubaidi, and E. Yusuf, “NOVEL COVID-19 DETECTION AND DIAGNOSIS SYSTEM USING IOT BASED SMART HELMET,” vol. 24, no. 7, p. 9, 2020.

S. P. Amaraweera and M. N. Halgamuge, “Internet of Things in the Healthcare Sector: Overview of Security and Privacy Issues,” in Security, Privacy and Trust in the IoT Environment, Z. Mahmood, Ed. Cham: Springer International Publishing, 2019, pp. 153–179. doi: 10.1007/978-3-030-18075-1_8.

V. Singh, H. Chandna, A. Kumar, S. Kumar, N. Upadhyay, and K. Utkarsh, “IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects,” EAI Endorsed Trans. Internet Things, vol. 6, no. 21, p. 163997, Aug. 2020, doi: 10.4108/eai.13-7-2018.163997.

T. Xu and I. Darwazeh, “Non-Orthogonal Narrowband Internet of Things: A Design for Saving Bandwidth and Doubling the Number of Connected Devices,” IEEE Internet Things J., vol. 5, no. 3, pp. 2120–2129, Jun. 2018, doi: 10.1109/JIOT.2018.2825098.

E. Partalidou, E. Spyromitros-Xioufis, S. Doropoulos, S. Vologiannidis, and K. Diamantaras, “Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy,” in IEEE/WIC/ACM International Conference on Web Intelligence, Thessaloniki Greece, Oct. 2019, pp. 337–341. doi: 10.1145/3350546.3352543.

Z. Li, H. Huang, and S. Misra, “Compressed Sensing via Dictionary Learning and Approximate Message Passing for Multimedia Internet of Things,” IEEE Internet Things J., vol. 4, no. 2, pp. 505–512, Apr. 2017, doi: 10.1109/JIOT.2016.2583465.

Y. Qin, Q. Z. Sheng, N. J. G. Falkner, S. Dustdar, H. Wang, and A. V. Vasilakos, “When Things Matter: A Data-Centric View of the Internet of Things,” ArXiv14072704 Cs, Jul. 2014, Accessed: Nov. 30, 2021. [Online]. Available: http://arxiv.org/abs/1407.2704

D. Cacovean, I. Ioana, and G. Nitulescu, “IoT System in Diagnosis of Covid-19 Patients,” Inform. Econ., vol. 24, no. 2/2020, pp. 75–89, Jun. 2020, doi: 10.24818/issn14531305/24.2.2020.07.

N. N. Thilakrathne, W. D. M. Priyashan, R. Samarasinghe, and M. K. Kagita, “Internet of Things for Managing Global Pandemics: Lessons from COVID-19 Pandemic,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, Jul. 2021, pp. 1–8. doi: 10.1109/ICCCNT51525.2021.9579932.

S. L. Keoh, S. S. Kumar, and H. Tschofenig, “Securing the Internet of Things: A Standardization Perspective,” IEEE Internet Things J., vol. 1, no. 3, pp. 265–275, Jun. 2014, doi: 10.1109/JIOT.2014.2323395.

L. Y. Mano et al., “Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition,” Comput. Commun., vol. 89–90, pp. 178–190, Sep. 2016, doi: 10.1016/j.comcom.2016.03.010.

R. Balajee, H. Mohapatra, and K. Venkatesh, “A comparative study on efficient cloud security, services, simulators, load balancing, resource scheduling and storage mechanisms,” 2021, vol. 1070, no. 1, p. 012053.

A. Hijikata et al., “Knowledge‐based structural models of SARS‐CoV‐2 proteins and their complexes with potential drugs,” FEBS Lett., vol. 594, no. 12, pp. 1960–1973, Jun. 2020, doi: 10.1002/1873-3468.13806.

G. Loseto et al., “Knowledge-Based Decision Support in Healthcare via Near Field Communication,” Sensors, vol. 20, no. 17, p. 4923, Aug. 2020, doi: 10.3390/s20174923.

H. Amoozad Mahdiraji, M. Sedigh, S. H. Razavi Hajiagha, J. A. Garza-Reyes, V. Jafari-Sadeghi, and L.-P. Dana, “A novel time, cost, quality and risk tradeoff model with a knowledge-based hesitant fuzzy information: An R&D project application,” Technol. Forecast. Soc. Change, vol. 172, p. 121068, Nov. 2021, doi: 10.1016/j.techfore.2021.121068.

A. Kumar, V. Jain, and A. Yadav, “A New Approach for Security in Cloud Data Storage for IOT Applications Using Hybrid Cryptography Technique,” in 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, Uttar Pradesh, India, Feb. 2020, pp. 514–517. doi: 10.1109/PARC49193.2020.236666.

H. Jeong, J. A. Rogers, and S. Xu, “Continuous on-body sensing for the COVID-19 pandemic: Gaps and opportunities,” Sci. Adv., vol. 6, no. 36, p. eabd4794, Sep. 2020, doi: 10.1126/sciadv.abd4794.

A. R. Watson, R. Wah, and R. Thamman, “The Value of Remote Monitoring for the COVID-19 Pandemic,” Telemed. E-Health, vol. 26, no. 9, pp. 1110–1112, Sep. 2020, doi: 10.1089/tmj.2020.0134.

R. Mumtaz et al., “Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective,” Electronics, vol. 10, no. 2, p. 184, Jan. 2021, doi: 10.3390/electronics10020184.

V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact,” IEEE Access, vol. 8, pp. 90225–90265, 2020, doi: 10.1109/ACCESS.2020.2992341.

A. Azizy, M. Fayaz, and M. Agirbasli, “Do Not Forget Afghanistan in Times of COVID-19: Telemedicine and the Internet of Things to Strengthen Planetary Health Systems,” OMICS J. Integr. Biol., vol. 24, no. 6, pp. 311–313, Jun. 2020, doi: 10.1089/omi.2020.0053.

D. H. Haneberg, “Interorganizational learning between knowledge-based entrepreneurial ventures responding to COVID-19,” Learn. Organ., vol. 28, no. 2, pp. 137–152, May 2021, doi: 10.1108/TLO-05-2020-0101.

S. Kumar, R. D. Raut, and B. E. Narkhede, “A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers,” Int. J. Healthc. Manag., vol. 13, no. 4, pp. 337–345, Oct. 2020, doi: 10.1080/20479700.2020.1810453.

A. Ghimire, S. Thapa, A. K. Jha, A. Kumar, A. Kumar, and S. Adhikari, “AI and IoT Solutions for Tackling COVID-19 Pandemic,” in 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, Nov. 2020, pp. 1083–1092. doi: 10.1109/ICECA49313.2020.9297454.

F. Ahmed, M. S. Hossain, R. U. Islam, and K. Andersson, “An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty,” Appl. Sci., vol. 11, no. 13, p. 5810, Jun. 2021, doi: 10.3390/app11135810.

R. P. Singh, M. Javaid, A. Haleem, R. Vaishya, and S. Bahl, “Significance of Health Information Technology (HIT) in Context to COVID-19 Pandemic: Potential Roles and Challenges,” J. Ind. Integr. Manag., vol. 05, no. 04, pp. 427–440, Dec. 2020, doi: 10.1142/S2424862220500232.

J. L. Wilson, A. Hensley, A. Culp-Roche, D. Hampton, F. Hardin-Fanning, and A. Thaxton-Wiggins, “Transitioning to Teaching Online During the COVID-19 Pandemic,” SAGE Open Nurs., vol. 7, p. 237796082110261, Jan. 2021, doi: 10.1177/23779608211026137.

N. Ye, Y. Zhu, R. Wang, R. Malekian, and L. Qiao-min, “An Efficient Authentication and Access Control Scheme for Perception Layer of Internet of Things,” Appl. Math. Inf. Sci., vol. 8, no. 4, pp. 1617–1624, Jul. 2014, doi: 10.12785/amis/080416.

O. N. Toxirjonovich, A. E. Zaylobiddinovich, and A. M. V. O’g’li, “An Overview Of Anomaly Detection Systems In Cloud Networks And An Overview Of Security Measures In Cloud Storage,” vol. 03, no. 02, p. 18, 2021.

S. Li, L. D. Xu, and X. Wang, “Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things,” IEEE Trans. Ind. Inform., vol. 9, no. 4, pp. 2177–2186, Nov. 2013, doi: 10.1109/TII.2012.2189222.

N. Siegfried, T. Rosenthal, and A. Benlian, “Blockchain and the Industrial Internet of Things: A requirement taxonomy and systematic fit analysis,” J. Enterp. Inf. Manag., vol. ahead-of-print, no. ahead-of-print, Jan. 2020, doi: 10.1108/JEIM-06-2018-0140.

Y. Zhang, Y. Sun, R. Jin, K. Lin, and W. Liu, “High-Performance Isolation Computing Technology for Smart IoT Healthcare in Cloud Environments,” IEEE Internet Things J., vol. 8, no. 23, pp. 16872–16879, Dec. 2021, doi: 10.1109/JIOT.2021.3051742.

V. Roblek, M. Pejić Bach, M. Meško, and A. Bertoncelj, “The impact of social media to value added in knowledge‐based industries,” Kybernetes, vol. 42, no. 4, pp. 554–568, Apr. 2013, doi: 10.1108/K-01-2013-0014.

H. A. Massett et al., “AccrualNet: Addressing Low Accrual Via a Knowledge-Based, Community of Practice Platform,” J. Oncol. Pract., vol. 7, no. 6, pp. e32–e39, Nov. 2011, doi: 10.1200/JOP.2011.000272.

V. Özdemir and N. Hekim, “Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, ‘The Internet of Things’ and Next-Generation Technology Policy,” OMICS J. Integr. Biol., vol. 22, no. 1, pp. 65–76, Jan. 2018, doi: 10.1089/omi.2017.0194.

M. Khurshid Khan and D. Wibisono, “A hybrid knowledge‐based performance measurement system,” Bus. Process Manag. J., vol. 14, no. 2, pp. 129–146, Apr. 2008, doi: 10.1108/14637150810864899.

[45] C. A. Borcoși, “KNOWLEDGE MANAGEMENT, USEFUL SYSTEM FOR AN ECONOMY IN THE COVID-19 PANDEMIC,” no. 1, p. 7.

W. J. Gordon et al., “Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients,” Appl. Clin. Inform., vol. 11, no. 05, pp. 792–801, Oct. 2020, doi: 10.1055/s-0040-1721039.

C. Massaroni, A. Nicolò, E. Schena, and M. Sacchetti, “Remote Respiratory Monitoring in the Time of COVID-19,” Front. Physiol., vol. 11, p. 635, May 2020, doi: 10.3389/fphys.2020.00635.

R. K. N.V., A. M., B. E., S. J. P. J., K. A., and P. S., “Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors,” Int. J. Pervasive Comput. Commun., vol. ahead-of-print, no. ahead-of-print, Sep. 2020, doi: 10.1108/IJPCC-08-2020-0107.

T. Annis et al., “Rapid implementation of a COVID-19 remote patient monitoring program,” J. Am. Med. Inform. Assoc., vol. 27, no. 8, pp. 1326–1330, Aug. 2020, doi: 10.1093/jamia/ocaa097.

M. Martínez-García et al., “Monitoring of COVID-19 patients via telemedicine with telemonitoring,” Rev. Clínica Esp. Engl. Ed., vol. 220, no. 8, pp. 472–479, Nov. 2020, doi: 10.1016/j.rceng.2020.07.001.

S. Shrestha, J. B. Tuladhar, and N. Thapa, “Knowledge, Practices and Anxiety related to Corona Virus Disease-19 (COVID -19) among Nursing Students in Nepal,” vol. 9, no. 1, p. 7, 2021.

Z. K. Shinwari, M. Qaiser, M. Q. Nasar, and A. Ali, “Indigenous knowledge based herbal medicine for Corona treatment,” Pak. J. Bot., vol. 52, no. 4, Aug. 2020, doi: 10.30848/PJB2020-4(13).

M. Masum et al., “Actionable Knowledge Extraction Framework for COVID-19,” in 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, Dec. 2020, pp. 4036–4041. doi: 10.1109/BigData50022.2020.9378398.

M. R. Mufid, A. Basofi, S. Mawaddah, K. Khotimah, and N. Fuad, “Risk Diagnosis and Mitigation System of COVID-19 Using Expert System and Web Scraping,” in 2020 International Electronics Symposium (IES), Surabaya, Indonesia, Sep. 2020, pp. 577–583. doi: 10.1109/IES50839.2020.9231619.

A. S. Bhagavathula, W. A. Aldhaleei, J. Rahmani, M. A. Mahabadi, and D. K. Bandari, “Knowledge and Perceptions of COVID-19 Among Health Care Workers: Cross-Sectional Study,” JMIR Public Health Surveill., vol. 6, no. 2, p. e19160, Apr. 2020, doi: 10.2196/19160.

Published

2022-11-03

How to Cite

Rashid Abdulqader, H., & Ramiz Mahmood, M. (2022). Comparison Between a Knowledge-Based System for COVID-19 using Compressed Internet of Things Data: A Review . Academic Journal of Nawroz University, 11(4), 129–138. https://doi.org/10.25007/ajnu.v11n4a1385

Issue

Section

Review Articles