Machine learning technology is an important and involved field of computing. So when we learned that UCLA (University of California, Los Angeles) researchers combined this technology with 3D printing in a new project, it would be very exciting.
It is understood that the researchers have developed a 3D printed prototype detector that includes a sensor that uses machine learning techniques to continuously adjust itself. A new and more effective method for detecting small substances has been developed, which can detect cancer biomarkers, viruses, proteins and the like. This can improve the diagnosis and treatment of serious infections and diseases.
In medical research, plasmonic sensing has been used for many years to collect information on submicroscopic material composition. However, the use of plasma sensing outside the laboratory environment is greatly limited due to the large number of instruments involved and the high cost. Now, the UCLA team led by Professor Aydogan Ozcan has developed a mobile and inexpensive plasma reader prototype that is more accurate than traditional sensor designs.
In the plasma sensing process, the prototype uses machine learning techniques to determine what type of light source should be used. Be aware that there are thousands of different types of LEDs available on the sensor. In a particular situation, machine learning can more effectively and accurately determine which four LEDs are best suited. Therefore, the sensor can easily adjust itself to the biological target that it will capture.
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