Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach - Manoharan, Hariprasath; Selvarajan, Shitharth; Yafoz, Ayman; Alterazi, Hassan A.; Uddin, Mueen; Chen, Chin-Ling; Wu, Chih-Ming (Frontiers in Public Health, 2022)
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 behaviour 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. The deep learning toolbox in MATLAB is also used to test the performance of DCS, and the results are simulated using an interface system. Furthermore, when compared to firefly algorithms, the test results show that traditional systems are effective for an average of 79 per cent of the time.