Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks

K. Lavanya; K. Vimala Devi; M. Subramaniam

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Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks

机译：Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks

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The typical applications of the Internet of things (IoT) in medical video transmissionhave reached its new dimension for better analysis and diagnosis. Asthe IoT-based services may have its peak number of end-users, achieving thequality, errorless medical video transmission in high traffic networks remainsto be the darker side of the research. However, the above problem in an IoTnetwork was overcome by the integration of most intelligent machine learningand deep learning algorithms as a predictive model for the detection of medicalcontents under the network properties. But these algorithms also need itstuning in terms of achieving high-quality video transmission without sacrificingthe performance. With all these drawbacks, this paper proposes the newhybrid predictive model LEAN (Long Effective Adaptive Networks), whichworks on the principle of integrating the time predictive LSTM (Long ShortTerm memory) with boosting machine learning algorithms. These algorithmsare modeled for an efficient medical video transmission based on the novelprinciple of visual saliency content clustering along with the network-centricLEAN predictive models. Sufficient experimentations such as QoS evaluationsalong with the proposed models are conducted on real-time testbeds, whichconsist of Raspberry Pi 3 Model B+ interconnected with a cloud environment.Moreover, the proposed models are compared with the other existing models,such as multi-layer perceptrons and deep LSTM networks, in which theproposed predictive model demonstrates the better performance than thetraditional models.

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《International journal of communication systems》 |2022年第16期|e4833.1-e4833.18|共18页
作者
K. Lavanya; K. Vimala Devi; M. Subramaniam;
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作者单位

Department of Electronics &Communication, Velammal EngineeringCollege, Chennai, India;

School of Computer Science andEngineering, Vellore Institute ofTechnology, Vellore, India;

Department of Computer Science andEngineering, S. A. Engineering College,Chennai, India;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
deep learning; IoT; LEAN; LSTM; machine learning; multilayer Perceptron's; support vector machines; visual saliency clustering;

Quality of experience (QOE) content aware hybrid lean predictive models for medical video transmission over Internet of things (IOT) networks

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