Thermal characterisation of insulation panels based on vegetable typha domengensis and starch

Human Action Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample some measures of a body's tangential acceleration and speed using inertial sensors and exercise them only to learn model skills of incorrectly categorizing behavior into the relevant categories. In detecting human activities, the use of detectors in personal and portable devices has increased to better understand and anticipate human behavior. Many specialists are working toward developing a classification that can distinguish between a user's behavior and uncooked data while utilizing as few reserves as possible. A Long-term Recurrent Convolutional Network (LRCN) is proposed as a comprehensive human action recognition system based on deep neural networks in this paper.

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Thermal characterisation of insulation panels based on vegetable typha domengensis and starch

Cited by 3 publications

References 23 publications

Fiber extraction and characterization of Typha Domingensis Pers for the development of bio-composites: a renewable approach to minimize plastic usage in the field of agriculture

Fiber extraction and characterization of Typha Domingensis Pers for the development of bio-composites: a renewable approach to minimize plastic usage in the field of agriculture

Acoustical, thermal, and mechanical performance of Typha Latifolia fiber panels: Experimental evaluation and modeling for sustainable building applications

Human Action Recognition using Long Short-Term Memory and Convolutional Neural Network Model

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