Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare
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I. Al-Barazanchi et al., “Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System,” Comput. Intell. Neurosci., vol. 2022, pp. 1–14, Sep. 2022, doi: 10.1155/2022/9879259.
G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.
K. Sivaraman, R. M. V. Krishnan, B. Sundarraj, and S. Sri Gowthem, “Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9 Special Issue 3, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.
A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “A decentralized privacy-preserving healthcare blockchain for IoT,” Sensors (Switzerland), vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.
F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.
S. Kumar and M. Singh, “Big data analytics for healthcare industry: Impact, applications, and tools,” Big Data Min. Anal., vol. 2, no. 1, pp. 48–57, 2019, doi: 10.26599/BDMA.2018.9020031.
L. M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, “Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges,” IEEE Access, vol. 7, pp. 6473–6492, 2019, doi: 10.1109/ACCESS.2018.2887076.
B. P. L. Lau et al., “A survey of data fusion in smart city applications,” Inf. Fusion, vol. 52, no. January, pp. 357–374, 2019, doi: 10.1016/j.inffus.2019.05.004.
Y. Wu et al., “Large scale incremental learning,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 374–382, 2019, doi: 10.1109/CVPR.2019.00046.
A. Mosavi, S. Shamshirband, E. Salwana, K. wing Chau, and J. H. M. Tah, “Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning,” Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 482–492, 2019, doi: 10.1080/19942060.2019.1613448.
V. Palanisamy and R. Thirunavukarasu, “Implications of big data analytics in developing healthcare frameworks – A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 31, no. 4, pp. 415–425, 2019, doi: 10.1016/j.jksuci.2017.12.007.
J. Sadowski, “When data is capital: Datafication, accumulation, and extraction,” Big Data Soc., vol. 6, no. 1, pp. 1–12, 2019, doi: 10.1177/2053951718820549.
J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, “Comparing a traditional approach for financial brand communication analysis with a big data analytics technique,” IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301.
D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4679–4690, 2019, doi: 10.1109/TITS.2019.2924883.
S. Schulz, M. Becker, M. R. Groseclose, S. Schadt, and C. Hopf, “Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development,” Curr. Opin. Biotechnol., vol. 55, pp. 51–59, 2019, doi: 10.1016/j.copbio.2018.08.003.
C. Shang and F. You, “Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era,” Engineering, vol. 5, no. 6, pp. 1010–1016, 2019, doi: 10.1016/j.eng.2019.01.019.
Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, “Clinical big data and deep learning: Applications, challenges, and future outlooks,” Big Data Min. Anal., vol. 2, no. 4, pp. 288–305, 2019, doi: 10.26599/BDMA.2019.9020007.
M. Huang, W. Liu, T. Wang, H. Song, X. Li, and A.Liu,"A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks," IEEE Access , vol .7 , pp .23816-23833 ,2019 ,doi :10 .1109/ACCESS .2019 .2899402.
G.Xu,Y.Shi,X.Sun,andW.Shen,"Internetofthingsinmarineenvironmentmonitoring:A review," Sensors (Switzerland), vol .19 ,no .7 ,pp .1-21 ,2019 ,doi :10 .3390/s19071711
M. Aqib, R. Mehmood, A. Alzahrani, I. Katib, A. Albeshri, and S. M. Altowaijri, "Smarter traffic prediction using big data, in-memory computing, deep learning and gpus," vol. 19, no. 9, 2019. DOI: 10.1109/MIS.2019.2947728.
S. Leonelli and N. Tempini, "Data Journeys in the Sciences," 2020. DOI: 10.7551/mitpr
DOI: http://dx.doi.org/10.21533/pen.v11i3.3604
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Copyright (c) 2023 Azmi Shawkat Abdulbaqi, Hussein Ali Hussein Al Naffakh, Ahmed Dheyaa Radhi, Jamal Fadhil Tawfeq, Poh Soon JosephNg
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ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License