As machine learning has progressed over the years, several industries adopted this technology to innovate and simplify business processes. Many industries, such as healthcare, retail, manufacturing, defense and education, have introduced artificial intelligence and machine learning to improve customer experiences.

Machine learning has worked wonders for microscopic computing. It has reduced processing time from months to seconds.

A nano-class bioelectric characterization team from the Catalan Institute of Biotechnology, led by Professor Gabriel Gomila, has analyzed a specific type of cell using a different type of microscopy called scanning electro force volumetric microscopy. This technique has been developed in recent years to create maps of an electrical physical property called the dielectric constant.

Scientists have chosen this technique to shorten the microscopic data processing time. To increase efficiency, they use machine learning algorithms instead of traditional calculation methods, which took months to deliver accurate results earlier. The machine learning algorithm is able to dielectrically construct a composition map in a few seconds. It works through deep neural networks that mimic the functions of the human brain.

Scientists have proven their findings by analyzing them with various cellular composition data, such as the lipid nature of the cell membrane, nucleic acids in the nucleus, and others. This recent development opens up unprecedented opportunities to study large numbers of cells in a short period of time.

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