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Optimizing the performance reliability of diagnostic equipment and wearable sensors and medical devices in IOMT | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 7، دوره 16، شماره 1، فروردین 2025، صفحه 75-88 اصل مقاله (812.55 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30810.4503 | ||
| نویسندگان | ||
| Tahereh Moein؛ Mohammad Ali Keramati* ؛ Hossein Moinzad | ||
| Department of Information Technology, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| تاریخ دریافت: 11 خرداد 1402، تاریخ بازنگری: 04 تیر 1402، تاریخ پذیرش: 12 مرداد 1402 | ||
| چکیده | ||
| Today, healthcare has become an essential part of life, and in the meantime, the Internet of Things (IoT) is widely recognized as a potential solution to reduce the pressure on healthcare systems, which, by its very nature, optimizes the ability The performance reliability of diagnostic equipment, wearable sensors and medical equipment in the Internet environment has also been the focus of many recent researches. Therefore, in this research, using neural networks (LSTM), an algorithm for optimal diagnosis of medical equipment was proposed and its efficiency was evaluated. The results showed that the LSTM architecture together with the Dropout layer and the Tanh activation function showed better performance and had the lowest average absolute value of error (MAPE) as well as the root mean square error (RMSE) in determining the abnormalities of medical equipment. The accuracy of the proposed method shows 96\% and the accuracy, recall and evaluation criteria of the model are 95\% respectively. 94.5 and 97\% have been calculated, which fully shows the suitability of the proposed algorithm in predicting anomalies and, of course, its suitability for improving the assurance of the proper functioning of medical equipment and sensors. | ||
| کلیدواژه‌ها | ||
| diagnostic equipment؛ medical equipment؛ wearable sensors؛ medical IoT؛ neural networks؛ LSTM algorithm | ||
| مراجع | ||
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[1] T.A. Ahanger, U. Tariq, M. Nusir, A. Aldaej, I. Ullah, and A. Sulman, A novel IoMT-fog cloud-based healthcare system for monitoring and predicting COVID-19 outspread, J. Supercomput. 21 (2021), 1–24. [2] 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 7 (2019), 115749-115759. [3] KA. Awan, IU. Din, A. Almogren, H. Almajed, I. Mohiuddin, and M. Guizani, NeuroTrust-artificial neural network-based intelligent trust management mechanism for large-scale internet of medical things, IEEE Internet Things J. 8 (2020), no. 21, 15672–15682. [4] N. Bozbogan, M. Tekbas, and S. Gulsecen, Internet of medical things, Medical Informatics, Istanbul University Press, 2021, pp. 451–479. [5] S.H. Chang, R.D. Chiang, S.J. Wu, and W.T. Chang, A context-aware interactive M-health system for diabetics, IT Prof. 18 (2016), no. 3, 14–22. [6] M. Cui, S.S. Baek, R.G. Crespo, and R. Premalatha, Internet of things-based cloud computing platform for analyzing the physical health condition, Technol. Health Care 29 (2021), no. 6, 1233–1247. [7] L.M. Dang, M.J. Piran, D. Han, K. Min, and H. Moon, A survey on Internet of Things and cloud computing for healthcare, Electronics 8 (2019), no. 7, 768. [8] M. H. Edalat, R. Azmi, and J. Bagheri Nejad, Improving the accuracy of forecasting processes in the management of business processes by using LSTM architecture, Ind. Manag. Perspect. 10 (2019), no. 3, 97–91. [9] F. Firouzi, B. Farahani, M. Ibrahim, and K. Chakrabarty, Keynote Paper: from EDA to IoT eHealth: Promises, Challenges, and Solutions, IEEE Trans. Comput.-Aided Design. Integr. Circ. Syst. 37 (2018), 2965–2978. [10] A. Gatouillat, Y. Badr, B. Massot, and E. Sejdi´c, Internet of medical things: A review of recent contributions dealing with cyber-physical systems in medicine, IEEE Internet Things J. 5 (2018), no. 5, 3810–3822. [11] P. Gope and T. Hwang, BSN-care: A secure IoT-based modern healthcare system using body sensor network, IEEE Sensors J. 16 (2016), 1368–1376. [12] S. Hochreiter and H. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), no. 8, 1735–1780. [13] G.J. Joyia, R.M. Liaqat, A. Farooq, and S. Rehman, Internet of medical things (IOMT): Applications, benefits and future challenges in healthcare domain, J. Commun. 12 (2017), no. 4, 240–247. [14] S. Li, LD. Xu, and S. Zhao, 5G internet of things: A survey, J. Ind. Inf. Integr. 10 (2018), 1–9. [15] H. Magsi, A.H. Sodhro, F.A. Chachar, S.A.K. Abro, G.H. Sodhro, and S. Pirbhulal, Evolution of 5G in Internet of medical things, Int. Conf. Comput. Math. Engin. Technol. (iCoMET), Sukkur, 2018, pp. 1–7. [16] M. Papaioannou, M. Karageorgou, G. Mantas, V. Sucasas, I. Essop, J. Rodriguez, and D. Lymberopoulos, A survey on security threats and countermeasures in internet of medical things (IoMT), Trans. Emerg. Telecommun. Technol. 33 (2022), no. 6, e4049. [17] E. Perrier, Positive Disruption: Healthcare Ageing and Participation in the Age of Technology, Sydney, NSW, Australia: The McKell Institute, 2015. [18] M. Pham, Y. Mengistu, H. Do, and W. Sheng, Delivering home healthcare through a cloud-based smart home environment (CoSHE), Future Generat Comput Syst. 81 (2018), 129–140. [19] Y.A. Qadri, A. Nauman, Y.B. Zikria, A.V. Vasilakos, and S.W. Kim, The future of healthcare internet of things: A survey of emerging technologies, IEEE Commun. Surveys Tutor. 22 (2020), 1121–1167. [20] A. Rhayem, MBA. Mhiri, K. Drira, S. Tazi, and F. Gargouri, A semantic-enabled and context-aware monitoring system for the internet of medical things, Expert Syst. 38 (2021), no. 2, e12629. [21] D. Rothman, Artificial Intelligence By Example, Packt, Birmingham, 2018. [22] O. Salem, A. Guerassimov, and A. Mehaoua, Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models, IEEE. Med. 8 (2015), 34–48. [23] F. Sarhaddi, I. Azimi, S. Labbaf, H. Niela-Vilen, N. Dutt, A. Axelin, P. Liljeberg, and A.M. Rahmani, Long-term IoMT-based maternal monitoring: system design and evaluation, Sensors (Basel). 21 (2021), no. 7, 2281. [24] P. Sethi and S. Sarangi, Internet of things: architectures, protocols, and applications, J. Electric. Comput. Eng. 2017 (2017), 1–25. [25] K. Greff, R.K. Srivastava, J. Koutn´ık, B.R. Steunebrink, and J. Schmidhuber, LSTM: A Search Space Odyssey, IEEE Trans. Neural Networks Learn. Syst. 28 (2017), no. 10, 2222–2232. [26] A.F. Subahi, Edge-based IoT medical record system: requirements, recommendations and conceptual design, IEEE Access 7 (2019), 94150–94159. [27] S. Tahir, S.T. Bakhsh, M. Abulkhair, and M.O. Alassafi, An energy-efficient fog-to-cloud Internet of Medical Things architecture, Int. J. Distrib. Sensor Networks 15 (2019), 155014771985197. [28] G. Thamilarasu, A. Odesile, and A. Hoang, An intrusion detection system for Internet of medical things, IEEE Access 8 (2020), 181560–181576. [29] Y. Yin, Y. Zeng, X. Chen, and X. Fan, The Internet of Things in healthcare: An overview, J. Ind. Inf. Integr. 1 (2016), 3–13. | ||
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