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Digital Twins for Heart Classification Theory: Practices and Advancements Using Machine Learning
Abstract
The technique referred to as “digital twins” is becoming more widely used. This study uses keyword co-occurrence network (KCN) analysis to look at how digital twin research has evolved. The authors analyse data from 9639 peer-reviewed publications that were released in the years 2000–2023. Two distinct groups may be formed from the findings. In the first part, they look at how trends and the ways that terms are linked have changed over time. Concepts related to sense technology are linked to six different uses of the technology in the second part. This study shows that different kinds of research are quickly being done on digital twins. A lot of attention is also paid to tools that work with point clouds and real-time data. There is a change towards distributed processing, which puts data safety first, going hand in hand with the rise of joint learning and edge computing. According to the results of this study, digital twins have grown into more complicated systems that can make predictions by using better tracking technology.
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