Abstract
This book chapter describes our project “COROBOT,” the background theme of which revolves around developing a cost-effective Robot and associated kit(s) to provide a “contactless” alternative to regular services to patients diagnosed with COVID-19. Algorithmic deep learning can help in Tele-diagnosis and can provide the passivation to the Corona warriors from the medical field from the virus, which is the current demand. Since Telemedicine requires signal transmission through wireless media, this chapter also addresses parameters that mainly cause deteriorations of the medical data, which may result in the wrong diagnosis in the absence of the computational intelligence provided by deep learning approach.
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References
Y. Ahir, S.V. Rathkanthiwar, Y. Kale, A review on teleconsultation and telediagnosis using IoT module, in 2019 International Conference on Communication and Signal Processing (ICCSP), (IEEE, Chennai, 2019), pp. 0153–0156. https://doi.org/10.1109/ICCSP.2019.8698073
Y.S. Kale, D.S.V. Rathkanthiwar, Smart healthcare system using low cost IoT module. Int. J. Futur. Gener. Commun. Netw. 13(3), 9 (2020)
A.S. Rapatwar, S.V. Rathkanthiwar, Design of channel selection filter for wireless receiver, in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), (IEEE, 2015), pp. 809–812. https://doi.org/10.1109/ECS.2015.7125024
S. Rathkanthiwar, C.G. Dethe, K.D. Kulat, A neural approach for overall improvement of OFDM system, in International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), (IET, Chennai, 2011), pp. 764–769. https://doi.org/10.1049/cp.2011.0466
S. Rathkanthiwar, C. Dethe, K. Kulat, Performance evaluation of a novel WLAN receiver performance in highly dispersive nonlinear environment, in 2012 2nd International Conference on Power, Control and Embedded Systems, (Allahabad, India, 2013, April 29). https://doi.org/10.1109/ICPCES.2012.6508087
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Rathkanthiwar, S. (2021). Self-Organized Deep Learning: A Novel Step to Fight Against Severe Acute Respiratory Syndrome. In: Bhatia, S., Dubey, A.K., Chhikara, R., Chaudhary, P., Kumar, A. (eds) Intelligent Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-67051-1_12
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DOI: https://doi.org/10.1007/978-3-030-67051-1_12
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