Cutting-edge optimized multi-source data fusion for trusted execution and management of blockchain transactions on the internet of medical things (IoMT) with machine learning.

ABSTRACT: A modernized healthcare environment is possibly fuelled by the collaborative nature of Blockchain Technology (BT) and the Internet of Medical Things (IoMT), which guarantees efficient management, scalability, ledger optimization, and security. However, the huge volume of data created and the heterogeneous potential of IoMT devices make it difficult to achieve trustworthy execution as well as effective transaction management and organization. Using cutting-edge Machine Learning (ML) algorithms, like Lightweight Artificial Neural Networks (LANNs) with BT, this paper proposes an optimised multi-source data fusion framework that improves data integrity, real-time decision-making, efficiency, and scalability in an IoMT application environment. In order to assure accuracy and relevance, the data

SUBMITTER: Khan AA 

PROVIDER: S-EPMC12660863 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature