Blockchain Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network

Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in medical field. BC technology has emerged as promising solution to revolutionize field by addressing challenges related efficiency, data security, interoperability. A BC-aided smart healthcare system leverages immutable decentralized nature of construct secured transparent ecosystem manage processes data. It secure optimize processes, interoperability, efficiency The existing is exposed security attacks on can be necessary real-time detection device utilizing cyber-physical (CPS) with significant way. This article designs novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. presented JSO-DPDCNN technique exploits concept BC-enabled transmission DL-based diagnosis model for moneypox disease monitoring. To accomplish this, JSO-DPCNN uses Ethereum-based public privacy images. In addition, applies feature extraction module DPCNN, which extracts suitable set features input Moreover, multiplicative long short-term memory (MLSTM) approach was used process. Lastly, JSO employed parameter tuning MLSTM model. simulation result executed benchmark dataset. comprehensive outcomes highlighted outcome terms different measures.