Survival study on deep learning techniques for IoT enabled smart healthcare system

被引:9
作者
Munnangi, Ashok Kumar [1 ]
UdhayaKumar, Satheeshwaran [2 ]
Ravi, Vinayakumar [3 ]
Sekaran, Ramesh [4 ]
Kannan, Suthendran [5 ]
机构
[1] Velagapudi Ramakrishna Siddhartha Engn Coll Autono, Dept Informat Technol, Vijayawada, Andhra Pradesh, India
[2] Pragati Engn Coll, Dept Elect & Commun Engn, Surampalem, Andhra Pradesh, India
[3] Prince Mohammad Bin Fahd Univ, Ctr Artificial Intelligence, Khobar, Saudi Arabia
[4] Jain Univ, Deemed be Univ, Dept Comp Sci & Engn, Bangalore, Karnataka, India
[5] Kalasalingam Acad Res & Educ, Dept Informat Technol, Krishnankoil, India
关键词
Artificial Intelligence; Internet of things; Deep learning; Smart Healthcare; Health Monitoring; PREDICTION; DIAGNOSIS; FRAMEWORK; MACHINE;
D O I
10.1007/s12553-023-00736-4
中图分类号
R-058 [];
学科分类号
摘要
PurposeThe paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare.MethodsA deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced.ResultsExperimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%.ConclusionMAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.
引用
收藏
页码:215 / 228
页数:14
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