An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques View Full Text


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Article Info

DATE

2022-06-14

AUTHORS

Aditya Gupta, Amritpal Singh

ABSTRACT

Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT–fog–cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptom-related data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work. More... »

PAGES

1-31

References to SciGraph publications

  • 2021-02-22. Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system in NEURAL COMPUTING AND APPLICATIONS
  • 2021-01-04. Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes in JOURNAL OF MEDICAL SYSTEMS
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  • 2020-05-16. A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment in SOFT COMPUTING
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  • 2019-02-22. A healthcare monitoring system using random forest and internet of things (IoT) in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2018-11-21. Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2018-01-21. Dimensionality Reduction Using PCA and SVD in Big Data: A Comparative Case Study in FUTURE INTERNET TECHNOLOGIES AND TRENDS
  • 2018-03-10. Medical cyber-physical systems: A survey in JOURNAL OF MEDICAL SYSTEMS
  • 2021-03-28. Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system in MULTIMEDIA SYSTEMS
  • 2021-05-23. Combinatorial auction based multi-task resource allocation in fog environment using blockchain and smart contracts in PEER-TO-PEER NETWORKING AND APPLICATIONS
  • 2021-06-07. Industry 4.0 and its Implementation: a Review in INFORMATION SYSTEMS FRONTIERS
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    http://scigraph.springernature.com/pub.10.1007/s00354-022-00175-1

    DOI

    http://dx.doi.org/10.1007/s00354-022-00175-1

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35730007


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