Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach

Frontiers in Public Health

article

Hariprasath Manoharan¹  Shitharth Selvarajan²  Ayman Yafoz³  Hassan A. Alterazi³  Mueen Uddin⁴  Chin-Ling Chen⁵  Chih-Ming Wu⁸ 

[1] Department of Electronics and Communication Engineering, Panimalar Institute of Technology;Department of Computer Science & Engineering, Kebri Dehar University;Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University;School of Digital Science, University Brunei Darussalam;School of Information Engineering, Changchun Sci-Tech University;Department of Computer Science and Information Engineering, Chaoyang University of Technology;School of Computer and Information Engineering, Xiamen University of Technology;School of Civil Engineering and Architecture, Xiamen University of Technology

关键词: biomedical signals;    deep learning;    Fourier filters;    sensors;    cross points;   

DOI  :  10.3389/fpubh.2022.909628

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.

【 授权许可】

CC BY