| Autor: |
Meti, Subhash, Razauddin, S., Nallakumar, R., Mansingh, P. M. Benson, Sameen, Aws Zuhair, Pandey, Sakshi, Bhatt, Sanjeev Kumar, Jayabalan, Bhuvana |
| Zdroj: |
International Journal of Information Technology; December 2024, Vol. 16 Issue: 8 p5317-5323, 7p |
| Abstrakt: |
In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
Full text from SpringerLink