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An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques

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Abstract

Things or device under the Internet of Things, when connected to the internet, becomes a product, offers various applications and services for end-users. Existing methodologies offer sensor-based IoT based health-care services, to the end-users populated with lots of sensed values all at a time, making the health-care system with least robustness and inefficient due to unsystematic preview of patient’s health record. In order to address this issue, a novel semantic-based service framework is proposed in this paper which allows the end-user to subscribe for a specific physiological parameter, among all the available sensed data, making the health-care system more efficient. In the SeSem framework, Semantification rules and semantification relationship table are applied to the sensed JavaScript object notation format (JSON) data, in order to semantically separate the JSON data, into a meaningful format. To list the sensed data according to the significance of health issues, a priority is assigned to the sensors in the semantification relationship table. Hence, semantically separated data can be done along with assigned priority. Sensor-based sematic ontology is then applied to the semantically separated data, to transform the sensed data more relevant in terms of particular disease and sensor associated with it. The semantically separated sensed data are then published to the message queuing telemetry transport (MQTT) interface. Using MQTT subscribe, the end-user along with date and time, requests for a particular service, using a Semantic Similarity mapping algorithm, which compares the entire sensed data to that of requested data and responds with a particular physiological parameter request. To make the health care system deploy services in an intelligent way, deep learning algorithm Feedforward Recurrent Neural Networks are applied, which makes the prediction of sensed data based on the latest update when the end-user subscribes for a certain sensed data without specifying date and time. The proposed methodology is evaluated against IoT performance metrics which had shoed showing better results in terms of service-oriented IoT.

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Author information

Authors and Affiliations

  1. SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    R. Radhika

  2. Vellore Institute of Technology, Chennai, Tamil Nadu, India

    A. Bhuvaneswari

  3. Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India

    G. Kalpana

Authors
  1. R. Radhika
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  2. A. Bhuvaneswari
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  3. G. Kalpana
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Contributions

Conceptualization: R.R., A.B. and G.K. Methodology: R.R., A.B and G.K. Software: R.R., A.B. and G.K. Validation: R.R., A.B. and G.K. Formal analysis: R.R. Investigation: R.R., A.B. and G.K. Resources: R.R. Writing-original draft preparation: R.R. Writing-review and editing: R.R., A.B. and G.K. Supervision: R.R., A.B. and G.K.

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Correspondence to R. Radhika.

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Radhika, R., Bhuvaneswari, A. & Kalpana, G. An Intelligent Semanticification Rules Enabled User-Specific Healthcare Framework Using IoT and Deep Learning Techniques. Wireless Pers Commun 122, 2859–2883 (2022). https://doi.org/10.1007/s11277-021-09033-7

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