AI Based Ontology-Driven Information Retrieval for Healthcare Information System

Abstract

Over the years, advancements in the work have been incorporated towards professor relationships and data collection techniques to model a wide variety of human activities and behaviors. Most sensor data come from smart devices such as cleaners and cleaners that provide the ability to manipulate and extract data from monitoring data for monitoring and healthcare. Due to the high popularity and use of smart devices as respondents, performance recognition systems are more accurate and easier to use. Identifying smooth designs is a difficult task in contrasting environments and scattering tube data. This work presents a knowledge-based model based on job descriptions that reflects the work of the worker. The knowledge model is based on two new approaches: to consider a functional degree scheme between measuring sensor energy and functional consciousness to model controversial sensor data and establish the relationship between them. Low efficiency (easy work) and high level (weak). In this article we make a case why ontology can contribute to blockchain design. To support this issue, we make an analysis translate the tonnage ontology and some of its representations into the smart contracts that enable it implement traceability restrictions on the original traceability feature and Ethereum blocking platform.