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Real-Time HealthCare Recommendation System for Social Media Platforms | IEEE Journals & Magazine | IEEE Xplore

Real-Time HealthCare Recommendation System for Social Media Platforms


The Graphical Abstract shows the results of processing and developing a specialised controlled learning neural networking model to manage concurrent requests of different...

Abstract:

Biomedical services play a vital role in calibration and validation of services with the influence of social media platform and big-data processing toolkits such as “Spar...Show More

Abstract:

Biomedical services play a vital role in calibration and validation of services with the influence of social media platform and big-data processing toolkits such as “Spark”, the processing and recommendation system can achieve a higher order of reliability and validation. In this research article, a novel framework is proposed to provide a recommendation of biomedical services from various social media platforms datasets. The framework is supported by a distributed processing technique under spark framework principle to segregate and process datasets based on SQL injection queries. The proposed technique’s distributed datasets are trained and validated under a distributed storage. The Spark-bits are processed with a recommendation system for healthcare services. The technique deploys a fuzzy neural principle in cross-reference identification of recommended framework with supportive decision making. The categorization and adapting distributed programming and storage using Spark extracts relevance in recommended properties. These qualities support decision-making using Controlled Learning based Neural Networking model (CLNN). The proposed technique has provided a higher value of reliability in a real-time recommendation environment.
The Graphical Abstract shows the results of processing and developing a specialised controlled learning neural networking model to manage concurrent requests of different...
Published in: IEEE Access ( Volume: 12)
Page(s): 74161 - 74168
Date of Publication: 25 April 2024
Electronic ISSN: 2169-3536

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