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A Machine-Learning-Blockchain-Based Authentication Using Smart Contracts for an IoHT System.

Rajkumar GaurShiva PrakashSanjay KumarKumar AbhishekMounira MsahliAbdul Wahid
Published in: Sensors (Basel, Switzerland) (2022)
Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning-blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.
Keyphrases
  • machine learning
  • big data
  • electronic health record
  • healthcare
  • artificial intelligence
  • deep learning
  • data analysis
  • social media
  • global health
  • replacement therapy
  • protein protein