2018
DOI: 10.14257/ijgdc.2018.11.8.07
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Heterogeneous Network Access for Seamless Data Transmission in Remote Healthcare

Abstract: In Remote Health care application, the availability of reliable communication channel is a major challenge for successful transmission of the medical data from one point to other. Heterogeneous network functions across multiple radio technologies including Wireless Body Area Networks (WBANs). However, there is always a need of a model where the best suited network satisfying the need of transmitting the critical data is available to the user from a set of network. The paper presents an efficient and seamless N… Show more

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Cited by 6 publications
(2 citation statements)
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“…In this paper, pretrained CNN models have been used through transfer learning approach since the dataset of X-ray images for COVID-19 cases is quite limited and also, training of fresh models is a long time-consuming process. The weights of the model to be used are learnable from the knowledge stored in weights of pre-trained models [23][24][25][26][27].…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, pretrained CNN models have been used through transfer learning approach since the dataset of X-ray images for COVID-19 cases is quite limited and also, training of fresh models is a long time-consuming process. The weights of the model to be used are learnable from the knowledge stored in weights of pre-trained models [23][24][25][26][27].…”
Section: Methodsmentioning
confidence: 99%
“…For instance in Van et al (2017), authors proposed an effective handover approach based on improved TOPSIS method integrated with content-centric networking that allows seamless connectivity with QoS guarantees. Yadav et al (2018) presented a context aware network selection strategy based on MADM method that allows seamless connectivity for the transmission of a patient's physiological data to the clinicians with reduction in unnecessary switching. Zhong et al (2020) presented a cross-layer architecture based on cognitive cycle and a cognitive MADM approach that considers network parameters along with the user's QoE to select optimal network.…”
Section: Introductionmentioning
confidence: 99%