Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part I)

  • Chinmay Chakraborty Birla Institute of Technology, Mesra, Jharkhand, India
  • Sayonara Barbosa Federal University of Santa Catarina, Brazil
  • Lalit Garg University of Malta, Msida, Malta

Abstract

This special issue introduces intelligent healthcare emerging technologies that incorporate big medical data, artificial intelligence, cloud/fog scientific computing, federated learning, bio-inspired computation, the internet of medical things, security and privacy, semantic databases, etc. The health monitoring and diagnosis for the target structure of interest are achieved by interpreting collected data. The advances in sensor technologies and data acquisition tools have led to a new era of big data, where different sensors collect massive amounts of medical data. This special issue offers valuable perceptions to researchers and engineers on designing intelligent healthcare technologies and improving patient information delivery care remotely. Sensors can enhance the decision-making process and early disease diagnosis by intelligently investigating and collecting large amounts of healthcare data (i.e., big data). Hence, there is a need for scalable machine learning, deep learning, and intelligent algorithms that lead to more interoperable  solutions and make effective decisions in emerging sensor technologies. More specifically, the proposed special issue intends to study the impact of integrating artificial intelligence, advanced learning and scientific computing systems, and the internet of medical things ideas on secured health data processing and analytics.

Keywords

References

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Published
Apr 21, 2023
How to Cite
CHAKRABORTY, Chinmay; BARBOSA, Sayonara; GARG, Lalit. Preface to Special Issue on Scientific Computing and Learning Analytics for Smart Healthcare Systems (Part I). Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 2, p. 107–109, apr. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/1286>. Date accessed: 23 dec. 2024.
Section
Articles