تقييم أداء شبكات WBAN باستخدام تقنيات الذكاء الاصطناعي

Abstract

The study aims to evaluate the performance of wireless body area networks (WBANs) using machine learning models such as linear regression, decision tree, and random forest. In this research, we focused on applying advanced techniques such as dynamic rerouting and data compression to improve the performance of these networks.

The analysis in the study revolves around the production performance and data transfer   efficiency of WBANs, in addition to measuring the time delay in data transmission and the bit error rate based on approved evaluation measures such as the coefficient of determination, mean square error, and mean absolute error. The impact of these advanced technologies on the data transfer process and WBAN performance was analyzed and measured. It was concluded that the use of these technologies plays a major role in enhancing the efficiency of WBANs and improving wireless. The results highlight the importance of these innovations in providing effective communication environments to improve the provision of remote healthcare and patient care in general. The PyCharm program was worked on.