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RETRACTED ARTICLE: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

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This article was retracted on 31 August 2023

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Abstract

With the introduction of wearable network technologies, smart healthcare is gradually being realized. Real-time health care monitoring system should be safe, effective, and patient-centered. This paper aims to analyze the opinions and emotions of medical and health products, and to design and develop monitoring systems by crawling multiple websites and reviews in a variety of formats. Data preprocessing is used to preprocess the quantized image, and finally, it is recommended in combination with the analysis results. This system combines a large amount of products’ comment data, combined with MATLAB and other tools to achieve the visualization of big data analysis, by the use of the maximum entropy algorithm for comprehensive analysis and comparison. Support vector machine (SVM) algorithm is used to analyze and compare various parts of the medical image in detail. It is suitable for current medical applications. It can provide advice for consumers to purchase drugs and achieve real-time health monitoring objectives.

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Funding

This work was financially supported by the 13th Five-Year plan for the development of philosophy and Social Sciences in Guangzhou (No. 2018GZYB36), the Science Foundation of Guangdong Provincial Communications Department (No. 2015-02-064), the National Natural Science Foundation of China (No. 61402185), and the Natural Science Foundation of Guangdong Province (No. 2015A030313382).

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Authors and Affiliations

  1. Department of Information Engineering, Guangzhou Huashang Vocational College, Guangzhou, 511300, China

    Chao Wang & Weike Lai

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  1. Chao Wang
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  2. Weike Lai
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Correspondence to Chao Wang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00779-023-01748-7

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Wang, C., Lai, W. RETRACTED ARTICLE: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry. Pers Ubiquit Comput 25, 485–493 (2021). https://doi.org/10.1007/s00779-019-01247-8

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  • DOI: https://doi.org/10.1007/s00779-019-01247-8

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