首页|Study Results from College of Engineering and Technology Provide New Insights into Machine Learning (Design and Implementation of Tilted Fbg for Concurrent Temperature and Humidity Measurement Using Machine Learning)
Study Results from College of Engineering and Technology Provide New Insights into Machine Learning (Design and Implementation of Tilted Fbg for Concurrent Temperature and Humidity Measurement Using Machine Learning)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting out of Chengalpattu, India, by NewsRx editors, research stated, “Tilted Fiber Bragg Grating (TFBG) coated with humidity and temperature responsive material is proposed for simultaneous measurement of environmental parameters. Different types of materials are analyzed to optimize the coating material on the grating structure for the measurement of humidity and temperature simultaneously.” Financial supporters for this research include SRM Institute of Science and Technology, Kattankulathur, India, Institution of Engineers (India). Our news journalists obtained a quote from the research from the College of Engineering and Technology, “The optimized coating material is used to fabricate the sensor and experimentally investigated under different humidity and temperature conditions. To further enhance the performance, machine learning algorithms such as Gaussian Progress Regression, Random Forest, K-Nearest Neighbor, AdaBoost, Gradient Boosting algorithm were trained with the spectrum data to estimate the environmental parameters simultaneously.”
ChengalpattuIndiaAsiaCyborgsEmerging TechnologiesMachine LearningCollege of Engineering and Technology
NETL
NSTL
Elsevier
