ArticleFrontiers in public health2022
Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A model study of teaching method reform of computer laboratory course integrating internet of things technology.PloS one · 2024Article
- Regulatory mechanism of vulnerability disclosure behavior considering security crowd-testing: An evolutionary game analysis.PloS one · 2024Article
- A Compact High-Isolation Four-Element MIMO Antenna with Asymptote-Shaped Structure.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.Journal of healthcare engineering · 2023Review
- Federated learning based futuristic biomedical big-data analysis and standardization.PloS one · 2023Article
- Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- LBSS: A Lightweight Blockchain-Based Security Scheme for IoT-Enabled Healthcare Environment.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
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Abstract
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.
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