ANOMALY DIAGNOSIS IN WIRELESS BODY AREA NETWORKS
| dc.contributor.author | Chenih, Cheyma | |
| dc.contributor.author | Lakhneche, Fatima Lina | |
| dc.contributor.author | Bahache, Mohamed: supervisor | |
| dc.date.accessioned | 2024-07-03T09:28:42Z | |
| dc.date.available | 2024-07-03T09:28:42Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | The primary focus of this work is on fault detection in Wireless Body Area Networks (WBANs). It emphasizes the critical importance of ensuring that information and signals from WBAN devices are accurately and reliably transmitted, particularly in the context of health monitoring. Additionally, this work aims to address the challenges related to fault detection in WBANs by conducting a comparative analysis of various machine learning algorithms (ML) and statistics. | |
| dc.identifier.uri | https://depot.univ-msila.dz/handle/123456789/43115 | |
| dc.language.iso | en | |
| dc.publisher | UNIVERSITY OF MOHAMED BOUDIAF – MSILA, FACULTY OF MATHEMATICS AND COMPUTER SCIENCE, DEPARTMENT OF COMPUTER SCIENCE | |
| dc.subject | Wireless Body Area Networks | |
| dc.subject | fault detection | |
| dc.subject | machine learning | |
| dc.subject | algorithms | |
| dc.subject | statistics | |
| dc.title | ANOMALY DIAGNOSIS IN WIRELESS BODY AREA NETWORKS | |
| dc.type | Thesis |