Composite Artificial Intelligence

4Machine Learning-Driven Optimization for Composite AI in Wireless Body Area Networks (WBAN)

Krishna Kumar M.^1*, Pricilla Mary S.², James Nesaratnam R.¹ and Sharon Geege A.²

¹Department of Electronics and Communication, Grace College of Engineering, Mullakkadu, Thoothukudi, India

²Department of Electronics and Communication, National Engineering College, Kovilpatti, India

Abstract

Nowadays, Wireless Body Area Networks (WBANs) are vital in healthcare— thanks to their ability to track patients’ health status and handle medical data. Many have started to pay close attention to how Composite artificial intelligence (AI) is improving reliability, fighting against data theft, and boosting energy consumption in WBAN systems these days. This chapter investigates the helpfulness of machine learning-powered optimization techniques in overcoming challenges in WBANs, such as interference, changing channel fading, and few available sources of power. A combination of many AI models, including ML, DL, RL, and expert systems, in Composite AI allows this method to be adaptable for resolving these problems. The discussion sets out different ways to boost the performance of antennas, improve channel estimates, and enable flexible network arrangements. Besides, the role of models such as CNNs and RNNs in reducing signal loss and improving how efficiently data can be transmitted in healthcare is evaluated. Besides common AI methods, federated learning and neuro-symbolic AI are gaining fans because ...