In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.

This paper introduces Matrix-Valued Neural Coordinated Federated Deep Extreme Machine Learning, a novel approach for enhancing health prediction and intrusion detection on the Internet of Healthcare Things (IoHT). By leveraging Machine Learning (ML), blockchain, and Intrusion Detection Systems (IDS), this technique ensures the security of medical data while enabling predictive health analytics. The IoHT, characterized by its vast network of interconnected devices, poses significant challenges in security and confidentiality. However, the integration of proposed technique empowers healthcare systems to proactively address these concerns while enhancing patient outcomes and reducing healthcare costs. Smart healthcare, enabled by ML and blockchain, is revolutionizing healthcare 5.0. Healthcare systems may employ IoHTs’ intelligent and interactive characteristics using proposed approach. Despite its benefits, medical data aggregation poses security, ownership, and regulatory compliance challenges. Federated Learning (FL) is a key technique for distributed learning that protects data. The proposed architecture has several unique benefits like (1) it provides a thorough examination of the incorporation of blockchain technology with FL for healthcare 5.0, (2) it takes the lead in creating a robust healthcare monitoring system that utilizes blockchain technology and IDS to identify and prevent harmful actions, (3) the development of crucial blockchain elements by means of mutual neuronal synchronization of Artificial Neural Networks (ANNs) showcases pioneering progress in safeguarding medical data, (4) the framework underwent a thorough empirical assessment and outperformed existing methods in accurately predicting intrusion detection, achieving an impressive efficiency rate of 96.18%. This development represents a major step forward in improving security and predictive abilities within the IoHT ecosystem, offering the potential for revolutionary advancements in healthcare delivery and patient care.