"AN ANALYSIS OF MACHINE LEARNING-BASED TECHNIQUES FOR DETECTING UREA".

Abstract

Sensitive sensing of Urea is one of the major requirements in health sectors such as medical diagnostics, environmental and agricultural studies. Other standard methods used in the detection of urea, despite being reliable, may require expensive reagents as well as long procedure. Intelligent and automatized systems demonstrated potential improvement in the speed, detection sensitivity, and precision with the emergence of machine learning (ML). The paper evaluates the different techniques applied in the detection of urea which are based on the use of machine learning, the data-driven methods that have been adopted in detecting urea in the recent past and how they performed. The challenges affecting the study are also mentioned, and possible changes in future research are proposed.