The semantic segmentation of remote sensing (RS) image is a hot research field.With the development of deep learning, the semantic segmentation based on a full convolution neural network greatly improves the segmentation accuracy.The Mullein amount of information on the RS image is very large, but the sample size is extremely uneven.Therefore, even
One of the deadliest and riskiest natural disasters is an earthquake.They often occur without a warning or any further alert.Therefore there was a need for its prognosis as it is extremely important Anti Theft System for mankind as well as the environment.In this project, the successful application of machine learning techniques have been used for
Abstract Background Marfan syndrome (MFS) is a heritable connective tissue disorder caused by mutations in the fibrillin-1 gene.This syndrome constitutes a significant identifiable subtype of aortic aneurysmal disease, accounting for over 5% of ascending and thoracic aortic aneurysms.Results We used spotted membrane DNA macroarrays to identify gene