IJFANS International Journal of Food and Nutritional Sciences

ISSN PRINT 2319 1775 Online 2320-7876

Optimizing Air Quality Management with an Energy-Efficient Deep Learning Soft Sensor

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Vijay Kumar Burugari
» doi: 10.48047/ijfans/v10/si1/27

Abstract

This paper presents a novel approach for monitoring air quality by utilizing Cryptogams, a bio-indicator that can accurately reflect pollution levels. We introduce an advanced and energy-efficient deformable active contour model designed to track the growth of transplanted Cryptogams across various pollution sites. Our study focuses on monitoring the vegetative development of Cryptogams over a span of two weeks, showcasing the effectiveness of our proposed energy-efficient contour tracing model in precise tracking, resulting in more reliable pollution monitoring.

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