A Deep Learning based Feature Entity Relationship Extraction Method for Telemedicine Sensing Big Data
نویسندگان
چکیده
Abstract To solve the problem of inaccurate entity extraction caused by low application efficiency and big data noise in telemedicine sensing data, a deep learning-based method for relationship is proposed. By analyzing distribution structure medical fuzzy function shape calculated seed set transformed inverse Shearlet transform. Combined with learning technology, GMM-GAN enhancement model built, interactive features are obtained, association rules matched one one, noiseless extracted time sequence, feature items highest similarity obtained used as constraint to complete data. The experimental results show that relations more than 70%, which can handle overly long complex sentences information text; shortest volatility low.
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ژورنال
عنوان ژورنال: Mobile Networks and Applications
سال: 2022
ISSN: ['1383-469X', '1572-8153']
DOI: https://doi.org/10.1007/s11036-022-02024-3