An Intelligent Smart Ranked Feature Construction Analysis based on Multi-dimensional Data Streams - V. , Sellam; K. , Sudharsan; K.H. , Ajit Baskar; K.H. , Ajay Baskar (International Journal of Psychosocial Rehabilitation, 2020)
To determine the consequence of force, load, acceleration, deformation, and displacement in structures in a construction through the live images taken in the construction site and improving the quality of the images using the Bayesian Sequential Algorithm (BSP) in construction analysis. Generally the construction site should need heavy monitoring done by the engineers for analyzing the consequence of force, load, acceleration, deformation, and displacement in structures. The data streams that we are using in our proposed system always have the ability to adapt to the changes caused by the stream, where the memory footprint and execution efficiency are decreased. The problem statement is that live images that are used for effective analysis are not so clear, and we are not able to find out the accurate datasets in structures. So we are using a Bayesian sequential algorithm to improve the quality of the image. Based on this abstract model, we introduce the Bayesian Sequential Algorithm. Observations on both low- and high-dimensional data streams endorse our proposed algorithm.