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\begin{document}

\title{Intelligent Ubiquitous Computing and Advanced Learning Systems for
Biomedical Engineering}



\author[1]{Chinmay Chakraborty}%
\author[2]{Mohammad Khosravi (GE)}%
\author[3]{Lalit Garg}%
\author[4]{M Shamim  Kaiser}%
\author[5]{Xingwang Li (GE)}%
\author[6]{Houbing Song}%
\affil[1]{Birla Institute of Technology}%
\affil[2]{Persian Gulf University}%
\affil[3]{University of Malta}%
\affil[4]{ Institute of Information Technology, Jahangirnagar University}%
\affil[5]{Henan Polytechnic University}%
\affil[6]{Embry-Riddle Aeronautical University}%


\vspace{-1em}



  
  \date{March 14, 2025}


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\begin{abstract}
The health monitoring for disease diagnosis and prognosis in a desired
smart medical structure is realized by interpreting the health data. The
advances in sensor technologies and biomedical data acquisition tools
have led to the new era of big data, where different sensors collect
massive medical data every day. This special issue explores the latest
development in emerging technologies of biomedical engineering,
including big medical data, artificial intelligence, cloud/fog
computing, federated learning, ubiquitous computing and communication,
internet of things, wireless technologies, and, security and privacy.
The biological wearable sensors can enhance the decision-making and
early disease diagnosis processes by intelligently investigating and
collecting large amounts of biomedical data (i.e., big health data).
Hence, there is a need for scalable advanced learning, and intelligent
algorithms that lead to reliable and interoperable solutions to make
effective decisions in emergency medicine technologies. The optimization
algorithms can be used in order to acquire the sensor data from multiple
sources for fast and accurate health monitoring.%
\end{abstract}%



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