In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne
nature of this disease has made it highly contagious which has led to a great number of people being
infected very fast. This requires a new method of testing that is faster and more precise. Machine
Learning has allowed us to develop sophisticated self-learning models that can learn from data being
fed and decide on entirely new options. In the past we have used different Machine Learning algorithm
to make models on different biomedical dataset to detect various kind of acute or chronic diseases.
Here we have developed a model that successfully detects severe cases of Novel corona virus affected
person with great precision.
A general procedure to compare molecules and materials powers insightful representations of energy landscapes and precise machine-learning predictions of properties.
Abstract
Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.
Detection of Novel Corona Virus Using Machine
Learning and Image Recognition
